> self.estrm, \"Problem sending file data: \" + str(ex)\n finally:\n if fd: fd.close()\n # self.ostrm.close()","def send(self, request, filepath):\n gzip = False\n if 'gzip' in request.headers[hdrs.ACCEPT_ENCODING]:\n gzip_path = filepath.with_name(filepath.name + '.gz')\n\n if gzip_path.is_file():\n filepath = gzip_path\n gzip = True\n\n st = filepath.stat()\n\n modsince = request.if_modified_since\n if modsince is not None and st.st_mtime <= modsince.timestamp():\n raise HTTPNotModified()\n\n ct, encoding = mimetypes.guess_type(str(filepath))\n if not ct:\n ct = 'application/octet-stream'\n\n resp = self._response_factory()\n resp.content_type = ct\n if encoding:\n resp.headers[hdrs.CONTENT_ENCODING] = encoding\n if gzip:\n resp.headers[hdrs.VARY] = hdrs.ACCEPT_ENCODING\n resp.last_modified = st.st_mtime\n\n # CACHE HACK\n if not request.app[KEY_DEVELOPMENT]:\n cache_time = 31 * 86400 # = 1 month\n resp.headers[hdrs.CACHE_CONTROL] = \"public, max-age={}\".format(\n cache_time)\n\n file_size = st.st_size\n\n resp.content_length = file_size\n with filepath.open('rb') as f:\n yield from self._sendfile(request, resp, f, file_size)\n\n return resp","def file(service, ip, file_name, file_path=None, user=None):\n\n timestamp = format_time(get_time())\n coordinates = get_coordinates(ip)\n\n if not file_path:\n file_path = PLACEHOLDER_STRING\n if not user:\n user = PLACEHOLDER_STRING\n\n values = defaultdict(lambda: PLACEHOLDER_STRING,\n {'event_type': 'file',\n '@timestamp': timestamp,\n 'service': service,\n 'ip': ip,\n 'user': user,\n 'filename': file_name,\n 'honeypotID': ID})\n \n if coordinates:\n values['coordinates'] = '{:.4f},{:.4f}'.format(coordinates[0], coordinates[1])\n\n if Config.use_broker:\n BrokerEndpoint.BrokerEndpoint.sendLogs(json.dumps(values))\n if file_path:\n BrokerEndpoint.BrokerEndpoint.sendFile(file_path)\n\n if coordinates:\n values['lat'] = '{:.4f}'.format(coordinates[0])\n values['lon'] = '{:.4f}'.format(coordinates[1])\n\n message = '{@timestamp} - [FILE] - {service}, {ip}, Lat: {lat}, Lon: {lon}, {filename}, {user}'.format_map(values)\n _log_alert(message)","def upload_file_to_shock(self,\n console, # DEBUG\n shock_service_url = None,\n filePath = None,\n ssl_verify = True,\n token = None):\n self.log(console,\"UPLOADING FILE \"+filePath+\" TO SHOCK\")\n\n if token is None:\n raise Exception(\"Authentication token required!\")\n\n #build the header\n header = dict()\n header[\"Authorization\"] = \"Oauth {0}\".format(token)\n if filePath is None:\n raise Exception(\"No file given for upload to SHOCK!\")\n\n dataFile = open(os.path.abspath(filePath), 'rb')\n m = MultipartEncoder(fields={'upload': (os.path.split(filePath)[-1], dataFile)})\n header['Content-Type'] = m.content_type\n\n #logger.info(\"Sending {0} to {1}\".format(filePath,shock_service_url))\n try:\n response = requests.post(shock_service_url + \"/node\", headers=header, data=m, allow_redirects=True, verify=ssl_verify)\n dataFile.close()\n except:\n dataFile.close()\n raise\n if not response.ok:\n response.raise_for_status()\n result = response.json()\n if result['error']:\n raise Exception(result['error'][0])\n else:\n return result[\"data\"]","def upload_file_to_shock(self,\n console, # DEBUG\n shock_service_url = None,\n filePath = None,\n ssl_verify = True,\n token = None):\n self.log(console,\"UPLOADING FILE \"+filePath+\" TO SHOCK\")\n\n if token is None:\n raise Exception(\"Authentication token required!\")\n\n #build the header\n header = dict()\n header[\"Authorization\"] = \"Oauth {0}\".format(token)\n if filePath is None:\n raise Exception(\"No file given for upload to SHOCK!\")\n\n dataFile = open(os.path.abspath(filePath), 'rb')\n m = MultipartEncoder(fields={'upload': (os.path.split(filePath)[-1], dataFile)})\n header['Content-Type'] = m.content_type\n\n #logger.info(\"Sending {0} to {1}\".format(filePath,shock_service_url))\n try:\n response = requests.post(shock_service_url + \"/node\", headers=header, data=m, allow_redirects=True, verify=ssl_verify)\n dataFile.close()\n except:\n dataFile.close()\n raise\n if not response.ok:\n response.raise_for_status()\n result = response.json()\n if result['error']:\n raise Exception(result['error'][0])\n else:\n return result[\"data\"]","def upload_file(self, f):\n with FilesOpener(f) as files:\n response = self.session.post(\n 'https://{}/upload'.format(self.domain),\n files=files\n ).json()\n\n if isinstance(response, list):\n error = response[0].get('error')\n else:\n error = response.get('error')\n\n if error:\n if isinstance(error, str) and error.startswith('FLOOD_WAIT_'):\n retry_after = int(error.rsplit('_',1)[-1])\n raise RetryAfterError(retry_after)\n else:\n raise TelegraphException(error)\n\n return response","def copy_file_to_server(self, file_path):\n raise NotImplementedError","def process(self):\n\n requested_file_path = self._get_requested_file_path()\n\n if os.path.exists(requested_file_path):\n self._respond_with_status_line(OK)\n self._respond_with_headers(file_path=requested_file_path,\n requested_headers=('Date',\n 'Content-Length',\n 'Content-Type',\n 'Server'))\n else:\n self._respond_with_status_line(NOT_FOUND)\n self._respond_with_headers(requested_headers=('Server',))","def raw_convert_file():\n try:\n str_out = io.BytesIO()\n str_out.write(process_text(request.data.decode('utf-8')).encode('utf-8'))\n str_out.seek(0)\n\n return send_file(\n str_out,\n attachment_filename='result.txt',\n as_attachment=True,\n mimetype='text/plain'\n )\n except:\n return make_response('', 400)","def send_file(\n self,\n *,\n channels: str | Sequence[str] | None = None,\n file: str | Path | None = None,\n content: str | None = None,\n filename: str | None = None,\n filetype: str | None = None,\n initial_comment: str | None = None,\n title: str | None = None,\n ) -> SlackResponse:\n if not exactly_one(file, content):\n raise ValueError(\"Either `file` or `content` must be provided, not both.\")\n elif file:\n file = Path(file)\n with open(file, \"rb\") as fp:\n if not filename:\n filename = file.name\n return self.client.files_upload(\n file=fp,\n filename=filename,\n filetype=filetype,\n initial_comment=initial_comment,\n title=title,\n channels=channels,\n )\n\n return self.client.files_upload(\n content=content,\n filename=filename,\n filetype=filetype,\n initial_comment=initial_comment,\n title=title,\n channels=channels,\n )","def send_file(ip, port):\n server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n server.bind((ip, port))\n server.listen(5)\n while True:\n client_socket, client_address = server.accept()\n data = client_socket.recv(1024)\n while data == '':\n data = client_socket.recv(1024)\n data = data[:-1]\n f = open(data, 'rb')\n l = f.read(1024)\n while (l):\n client_socket.send(l)\n l = f.read(1024)\n f.close()\n client_socket.close()","def send_file(self, file_name: str) -> None:\n\n with open(file_name, 'rb') as f:\n message = {'type': 'DATA', 'data': None}\n read_size = 16 * 60\n while True:\n if self.ppos != 0:\n f.seek(self.ppos)\n self.ppos = 0\n\n if self.count == 20:\n self.state = STATE_ROTATION\n\n #Generate DH client private and public keys\n bytes_public_key,p,g,y = self.crypto.dh_client()\n message = {'type':'DH_PARAMETERS','parameters':{'p':p,'g':g,'public_key':str(bytes_public_key,'ISO-8859-1')}}\n self.count=0\n self.ppos=f.tell()\n self._send(message)\n break\n\n self.count += 1\n \n data = f.read(16 * 60)\n message['data'] = base64.b64encode(data).decode()\n #logger.debug(\"Data: {} read size {}\".format(data,f.tell()))\n secure_message = self.encrypt_message(message)\n \n self._send(secure_message)\n self.send_mac()\n \n if len(data) != read_size:\n self.end = True\n break\n \n # When it ends create MAC\n if self.end:\n self._send(self.encrypt_message({'type': 'CLOSE'}))\n self.send_mac()\n logger.info(\"File transfer finished. Closing transport\")\n self.transport.close()","def post(cls, flow_name: str):\n data = file_schema.load(request.files) # {\"file\": FileStorage}\n try:\n file_path = uploads.save_file(data[\"file\"], folder=flow_name)\n basename = uploads.get_basename(file_path)\n return {\"message\": gettext(\"file_uploaded\").format(basename)}, 200\n \n except UploadNotAllowed:\n extension = uploads.get_extension(data[\"file\"])\n return {\"message\": gettext(\"file_illegal_extension\").format(extension)}, 400","async def send_file(self, l_file: str, r_dest: str) -> None:\n # pause logic\n if not self.running.is_set():\n self.add_to_output(\"Paused...\")\n await self.running.wait()\n\n # get/create ssh connection to miner\n conn = await self.get_connection(\"root\", \"admin\")\n # send file over scp\n await asyncssh.scp(l_file, (conn, r_dest))\n self.add_to_output(f\"File sent...\")","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def send_text_file(file_name):\n file_dot_text = file_name + '.txt'\n return app.send_static_file(file_dot_text)","def push_file_to_server(cnc_bot, filename, content, encryption_key=None):\r\n c = content\r\n if encryption_key is not None:\r\n c = rc4.encrypt(c, encryption_key, salt_length=0) # encrypt content via rc4\r\n cfg = {'filename': filename, 'content': c}\r\n cnc_bot.host_orders(cPickle.dumps(cfg)) # upload a serialized dict\r","def _upload_file(cluster_connection, girder_client, file, path):\n\n r = requests.get(\n '%s/file/%s/download' % (girder_client.urlBase, file['_id']),\n headers={'Girder-Token': girder_client.token}, stream=True)\n check_status(r)\n cluster_connection.put(r.raw, os.path.join(path, file['name']))","def do_POST(self):\r\n content_length = int(self.headers['Content-Length'])\r\n body = self.rfile.read(content_length)\r\n\r\n response = BytesIO()\r\n try:\r\n res = webServer.handle_post_msg(body)\r\n print(res)\r\n self.send_response(200)\r\n except Exception as e:\r\n print(e)\r\n res = str(e)\r\n self.send_response(500)\r\n self.end_headers()\r\n response.write(res.encode())\r\n self.wfile.write(response.getvalue())","def post_file(self, method, params, headers=None):\n\n post_params = {'merge': params['merge']}\n file_data = open(params['full_file_path'], 'rb').read() \n files = {'file': (params['file_name'], file_data)} \n r = requests.post(self.endpoint + method, files=files, params=post_params,\n auth=(self.session.auth[0], self.session.auth[1]), headers=headers)\n if 400 <= r.status_code < 600:\n r.reason = r.text\n r.raise_for_status()\n return r","def upload(self, requests, file):\n # Set the source and dest paths\n dest_url = self.base_url + '/upload'\n source_path = os.path.join(self.data_dir, file)\n\n # Get the sha256 hash of the file\n with open(source_path, 'rb') as afile:\n beforeDigest = hashlib.sha256(afile.read()).hexdigest()\n\n print(\"Generated a hash of the temp file: \" + beforeDigest)\n\n # Upload the file and time it\n with open(source_path, 'rb') as f:\n startTime = time.time()\n r = requests.post(dest_url, files={'file': (file, f)}, max_price=5)\n endTime = time.time()\n uploadElapsedTime = endTime - startTime\n\n print(\"Uploaded the file. Elapsed time: \" + str(uploadElapsedTime))\n\n # Verify the upload was successful\n if r.json()['success'] is not True:\n return {'success': False}\n\n retVal = {\n 'success': True,\n 'time': uploadElapsedTime,\n 'digest': beforeDigest,\n 'upload_filename': r.json()['filename']\n }\n\n return retVal","def uploaded_file(image):\n return send_from_directory(app.config['UPLOAD_FOLDER'], image)","async def stream_file(self, file: Union[str, io.BufferedReader], **kwargs) -> None:\n await self.relay(\"stream_file\")(file, **kwargs)","def do_POST(self):\n real_url = (\"https://stream.watsonplatform.net/text-to-speech/api\" +\n self.path)\n # pull out the body to send\n content_len = int(self.headers.get('content-length', 0))\n post_body = self.rfile.read(content_len)\n\n # make a content addressable local storage path based on\n # string args (which is voice & audio type) and body, which is\n # the text to be sent. This should be a good unique seed.\n hash_seed = self.path.encode('utf-8') + post_body\n fname = hashlib.sha256(hash_seed).hexdigest() + \".wav\"\n cache = self.server.cache\n fullfile = join(cache, fname)\n if not exists(fullfile):\n # make the real request\n resp = requests.post(real_url, headers=self.headers,\n data=post_body)\n\n # TODO(sdague): intermediate debugging, keep a copy around.\n with open(fullfile, 'wb') as f:\n f.write(resp.content)\n print(\"Wrote cached content to %s\" % fullfile)\n\n # Process request back to client\n self.send_response(resp.status_code)\n for header in resp.headers:\n # Requests has now decoded the response so some\n # characteristics of this may no longer be relevant. Don't\n # send them.\n if header not in (\"connection\", \"content-length\",\n \"keep-alive\"):\n self.send_header(header, resp.headers[header])\n\n self.end_headers()\n # The client expects chunked encoding, which is just a way to\n # stream data as it shows up with per line content length. We\n # already have *all* the data, so we can send a single line\n # which has it all with the right length.\n content = resp.content\n self.wfile.write(('%X\\r\\n' % len(content)).encode('utf-8'))\n self.wfile.write(content)\n self.wfile.write(b'\\r\\n')\n\n else:\n print(\"Reading from cached content\")\n self.send_response(200)\n for k, v in self.fake_headers().items():\n self.send_header(k, v)\n self.end_headers()\n with open(fullfile, 'rb') as f:\n content = f.read()\n self.wfile.write(('%X\\r\\n' % len(content)).encode('utf-8'))\n self.wfile.write(content)\n self.wfile.write(b'\\r\\n')\n\n # Then we need to send the null to signal to the client that\n # we're done and it should close out shop.\n self.wfile.write(b'0\\r\\n\\r\\n')\n\n if self.server.play_sound:\n print(\"Playing audio.\")\n watson_tts_proxy_client.play_file(fullfile)","async def write_file(self, directory: str, name: str, file: bytes):\n pass","def put_file(cls, filegen, signed_url):\n headers = {\"Content-Type\": \"application/octet-stream\"}\n try:\n resp = requests.put(signed_url, headers=headers, data=filegen)\n except Exception as err:\n raise exceptions.FilePutError(err)\n else:\n return resp","def __PostFile(self, url, fileName, prefix):\n CRLF = '\\r\\n'\n\n f = open(fileName, \"rb\")\n content = f.read()\n boundary = \"-------------------------------\"+ \\\n \"\".join([ random.choice('0123456789') for x in range(28) ])\n\n output = []\n output.append(\"--\"+boundary)\n output.append('Content-Disposition: form-data; name=\"'+prefix+ \\\n '\"; filename=\"avatar.png\"')\n output.append('Content-Type: '+mimetypes.guess_type(fileName)[0] \\\n or 'application/octet-stream')\n output.append(\"\")\n output.append(content)\n output.append(\"--\"+boundary+\"--\")\n output.append(\"\")\n\n encoded = CRLF.join(output)\n\n conn = self.__GetConnection()\n headers = self.__MakeHeaders(True)\n\n conn.putrequest(\"POST\", url)\n for (k,v) in headers.iteritems():\n conn.putheader(k, v)\n\n conn.putheader(\"Content-Type\", \"multipart/form-data; boundary=\" + \\\n boundary)\n conn.putheader(\"Content-Length\", str(len(encoded)))\n\n conn.endheaders()\n conn.send(encoded)\n response = conn.getresponse()\n self.__CheckResponse(response)","def upload_file(\n self, bucket_id: uplink.Path, filename: uplink.Path, file: uplink.Body\n ):\n pass","def uploaded(self, filename):\n return send_from_directory(self.upload_path, filename)","def handle(self): \n \n data = self.request[0].strip()\n self.socket = self.request[1]\n\n #split off first word of file, assume is filename\n filename,sep,data = data.partition(\" \")\n\n #assume is requesting file\n if not data:\n self.sendfile(filename)\n #assume we have to save the file since data was sent\n else:\n self.savefile(filename,data)\n\n return True","def upload(upload_url: str, file_path: str) -> None:\n with open(file_path, 'r') as data:\n try:\n r = requests.put(\n upload_url,\n data=data,\n headers={\"Content-Type\": \"application/octet-stream\"},\n )\n r.raise_for_status()\n except requests.exceptions.HTTPError as err:\n print('Error uploading file')\n raise err","def send_file(cls, filename, status_code=200, content_type=None,\n stream=None, max_age=None, compressed=False,\n file_extension=''):\n if content_type is None:\n ext = filename.split('.')[-1]\n if ext in Response.types_map:\n content_type = Response.types_map[ext]\n else:\n content_type = 'application/octet-stream'\n headers = {'Content-Type': content_type}\n\n if max_age is None:\n max_age = cls.default_send_file_max_age\n if max_age is not None:\n headers['Cache-Control'] = 'max-age={}'.format(max_age)\n\n if compressed:\n headers['Content-Encoding'] = compressed \\\n if isinstance(compressed, str) else 'gzip'\n\n f = stream or open(filename + file_extension, 'rb')\n return cls(body=f, status_code=status_code, headers=headers)","def upload(self):\n\n # Try to retrieve the file from the request\n file = None\n try:\n file = request.files[\"file\"]\n except KeyError as e:\n logging.warning(f\"File was not found in request: {e}.\")\n flash(\"No file given.\", \"error\")\n return redirect(request.url)\n except AttributeError as e:\n logging.warning(f\"Error: Request did not contain any files: {e}.\")\n flash(\"No file given.\", \"error\")\n return redirect(request.url)\n\n # Check if file was correctly uploaded\n if not file or len(file.filename) == 0:\n flash(\"No file selected for upload.\", \"message\")\n return redirect(request.url)\n\n \"\"\" Check if file has correct extension. Allowed extensions depend on\n the connector. To make the code more readable, group connectors\n with the same allowed file extensions together like this:\n if connector in ['someconnector', 'someotherconnector']:\n extensions = [...] \"\"\"\n\n if self.connector in [\"overtime\"]:\n allowed_extensions = OVERTIME_SUPPORTED_EXTENSIONS\n else:\n allowed_extensions = []\n\n if not allowed_file(file, allowed_extensions=allowed_extensions):\n flash(\"File extension not allowed.\", \"warning\")\n return redirect(request.url)\n\n \"\"\" File seems uploaded correctly and has correct extension.\n Generate a new record ID to keep track of the uploaded file.\n \"\"\"\n rec_id = generate_temp_record_id()\n\n # Save file to disk\n path = store_temp_file(file, record_id=rec_id)\n\n if not path:\n flash(\"Error saving file!\", \"error\")\n return redirect(request.url)\n\n \"\"\" If everything ended successfully, send the user to the\n confirmation page so he can review his changes \"\"\"\n\n return redirect(url_for(f\"{self.endpoint}.upload_confirm\", rec_id=rec_id))","async def test_upload_file(self):\n rsps = respx.put(f'{PROVISIONING_API_URL}/users/current/provisioning-profiles/id/servers.dat') \\\n .mock(return_value=Response(204))\n with mock.patch('__main__.open', new=mock.mock_open(read_data='test')) as file:\n file.return_value = json.dumps('test').encode()\n await provisioning_client.upload_provisioning_profile_file('id', 'servers.dat', file())\n assert rsps.calls[0].request.url == \\\n f'{PROVISIONING_API_URL}/users/current/provisioning-profiles/id/servers.dat'\n assert rsps.calls[0].request.headers['auth-token'] == 'header.payload.sign'","def test_post_file(self):\n self._register_uri(httpretty.POST)\n with open(self.test_file, 'rb') as in_file:\n response = self.client.post(self.test_endpoint,\n files={\"file\": in_file})\n self.assertEqual(response, self.test_data)\n body = str(self._last_request().body)\n self.assertIn(\"Content-Disposition: form-data; \"+\n \"name=\\\"file\\\"; filename=\\\"test_file.txt\\\"\", body)\n self.assertIn(\"Test File\", str(body))","def upload(self, file_path):\n LOG.debug((\"Uploading file to mattermost: %s\", file_path))\n filename = os.path.basename(file_path)\n post_data = {'channel_id': self.channel_id,\n 'client_ids': filename}\n file_data = {'files': (filename, open(file_path, 'rb'))}\n url = '%s/api/v4/files' % self.server_url\n response = self._request(self._session.post, url, data=post_data,\n files=file_data)\n LOG.debug(\"Received: %s - %s\", response.status_code, response.text)\n\n if response.status_code != 201:\n if response.status_code == 400:\n msg = \"Type of the uploaded file doesn't match its file \" \\\n \" extension or uploaded file is an image that \" \\\n \"exceeds the maximum dimensions\"\n elif response.status_code == 401:\n msg = \"User is not logged in\"\n elif response.status_code == 403:\n msg = \"User does not have permission to upload file to \" \\\n \"the provided team/channel\"\n elif response.status_code == 413:\n msg = \"Uploaded file is too large\"\n elif response.status_code == 500:\n msg = \"File storage is disabled\"\n else:\n msg = response\n raise RuntimeError(\"Server unhappy with request, reports: %s\"\n % msg)\n\n file_id = response.json()[\"file_infos\"][0][\"id\"]\n return file_id"],"string":"[\n \"def send_file(self, file_path) -> object:\\n try:\\n self.payload = {'file': open(file_path, 'rb')}\\n except FileNotFoundError as fl_er:\\n print(fl_er)\\n exit(1)\\n try:\\n return requests.post(url = self.__webhooks, files = self.payload)\\n except OverflowError as err:\\n print('Size Overflow Error', err)\\n exit(1)\",\n \"def send_file():\\n data = ARGS.data\\n filename = ARGS.file\\n outstream = \\\"POST||\\\" + filename + \\\"||\\\" + data\\n CLIENT_SOCKET.send(outstream.encode())\",\n \"def sendRequest(event, context):\\n file = event\\n print(f\\\"Processing file: {file['name']}.\\\")\\n\\n filename = file['name']\\n\\n url = 'http://34.123.136.112:5000'\\n myobj = {'filename': filename}\\n\\n x = requests.post(url, data = myobj)\\n\\n print(x.text)\",\n \"def sendFile(self, path):\\n\\t\\t# Either guess the mime type or just send it as a binary file.\\n\\t\\tif self.guessMime:\\n\\t\\t\\tmimeType = mimetypes.guess_type(path)\\n\\t\\telse:\\n\\t\\t\\tmimeType = \\\"application/octet-stream\\\"\\n\\n\\t\\t# Send the headers.\\n\\t\\tself.send_response(200)\\n\\t\\tself.send_header(\\\"Content-type\\\", mimeType)\\n\\t\\tself.send_header(\\\"Content-Length\\\", os.path.getsize(path))\\n\\t\\tself.end_headers()\\n\\t\\t\\n\\t\\tf = open(path, 'rb')\\n\\t\\t# self.wfile is the output stream.\\n\\t\\tshutil.copyfileobj(f, self.wfile)\\n\\t\\tf.close()\",\n \"def sendFile(self, fullfilename):\\n raise NotImplementedError(\\\"Implement this method in child class\\\")\",\n \"def send_file(cobj, dest, port, fname, hash, handler):\\n pass\",\n \"def send_file(self, model):\\n\\n fh = get_pdb_file(model)\\n file_size = fh.tell()\\n fh.seek(0)\\n\\n # Send request\\n request = self.SENDFILE + model + ' ' + str(file_size)\\n logging.debug(\\\"request {request}\\\")\\n self.send(request)\\n\\n # Wait for ACK\\n srv_resp = self.recieve()\\n\\n if srv_resp != self.RESP_OK:\\n logging.debug(\\\"response {srv_resp}\\\")\\n raise Exception('Server refuse to accept file')\\n\\n\\n self.send(fh.read(), encode=False)\\n fh.close()\\n\\n srv_resp = self.recieve()\\n if srv_resp != \\\"OK\\\":\\n logging.debug(\\\"response {srv_resp}\\\")\\n raise Exception('Fail after sending file')\",\n \"def send_file(self, src: PathLike, dest: PathLike, force: bool = False):\",\n \"def _send_file(self, file_path: Path, stream_id: int) -> None:\\n file_size = file_path.stat().st_size\\n content_type, content_encoding = mimetypes.guess_type(str(file_path))\\n response_headers = [\\n (':status', '200'),\\n ('content-length', str(file_size)),\\n ('server', self._server_name)\\n ]\\n if content_type:\\n response_headers.append(('content-type', content_type))\\n if content_encoding:\\n response_headers.append(('content-encoding', content_encoding))\\n\\n self._connection.send_headers(stream_id, response_headers)\\n self._sock.sendall(self._connection.data_to_send())\\n\\n with file_path.open(mode='rb', buffering=0) as f:\\n self._send_file_data(f, stream_id)\",\n \"def post(self):\\n filename = str(time.time())\\n filepath = os.path.join(\\n os.path.join(current_app.config['UPLOAD_FOLDER'], filename))\\n with open(filepath, 'bw') as uploadfile:\\n chunk_size = 1024\\n while True:\\n chunk = request.stream.read(chunk_size)\\n if len(chunk) == 0:\\n break\\n uploadfile.write(chunk)\\n current_app.logger.info('file %s upload successfully', filename)\\n return {'timestamp': filename}, http.HTTPStatus.CREATED\",\n \"def send_file(self, file_path) -> object:\\n method = 'sendDocument?' + 'chat_id=' + str(self.__chat_id_response())\\n try:\\n files = {'document': open(file_path, 'rb')}\\n return requests.post(self.api_url + method, files = files)\\n except FileNotFoundError as fn_err:\\n print(fn_err)\\n sys.exit(1)\\n except TimeoutError as tm_err:\\n print(tm_err)\\n sys.exit(1)\",\n \"def send_file_message(self, filename):\\n data = self._readFile(filename)\\n self.print_debug_message(data)\\n self.socket.send(data)\",\n \"def request_file(sender_ip, sender_port, file_name):\\n # create a new TCP socket.\\n h = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n h.connect((sender_ip, sender_port))\\n request = file_name + \\\"\\\\n\\\"\\n h.send(request.encode())\\n f = open(file_name, 'wb')\\n l = h.recv(1024)\\n while (l):\\n f.write(l)\\n l = h.recv(1024)\\n f.close()\\n h.close()\",\n \"def send_data(self, fp, dest: PathLike, force: bool = False):\",\n \"def send_file(self, filename, BUFF_LENGTH):\\n out_file = open(filename,\\\"rb\\\")\\n file_bytes = out_file.read(1024) \\n while file_bytes != b'':\\n self.client.send(file_bytes)\\n file_bytes = out_file.read(1024) # read next block from file\\n self.client.send(b'')\",\n \"async def send_file(self, file):\\n with open(file, \\\"rb\\\") as file_bytes: # Opening file as readable in bytes\\n print(f\\\"Send: {file_bytes!r}\\\")\\n\\n total_bytes = 0\\n while True:\\n chunk = file_bytes.read(1024)\\n total_bytes += len(chunk)\\n\\n if not chunk: # Error with write_eof(). Need a way to finish\\n print(\\\"Draining...\\\")\\n\\n check = \\\"end\\\"\\n self.writer.write(check.encode())\\n # Maybe writing at the end an empty list could work\\n\\n await self.writer.drain()\\n break\\n\\n self.writer.write(chunk)\\n print(f\\\"Sent: {total_bytes!r} bytes\\\")\",\n \"def send_json_file(self, endpoint, filename):\\n headers = {'Content-Type': 'application/json',\\n 'Accept': 'application/json'}\\n\\n headers.update(self.authorization())\\n with open(filename) as json_data:\\n response = requests.post(endpoint, data=json_data, headers=headers)\\n return response\",\n \"def upload_file():\\n try:\\n if 'file' not in request.files:\\n flash(\\\"No file selected or file is too big (max size is %sB)\\\" % app.config['MAX_CONTENT_LENGTH'])\\n return redirect(url_for('index'))\\n\\n file = request.files['file']\\n str_in = file.read()\\n file.close()\\n\\n str_out = io.BytesIO()\\n str_out.write(process_text(str_in.decode('utf-8')).encode('utf-8'))\\n str_out.seek(0)\\n\\n return send_file(\\n str_out,\\n attachment_filename=file.filename,\\n as_attachment=True,\\n mimetype='text/plain'\\n )\\n except RuntimeError:\\n flash('Something bad happened')\\n return redirect(url_for('index'))\",\n \"def uploaded_file(filename):\\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)\",\n \"def uploaded_file(filename):\\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)\",\n \"def uploaded_file(filename):\\n return send_from_directory(app.config['UPLOAD_FOLDER'], filename)\",\n \"def upload_file(self, f):\\n return self._telegraph.upload_file(f)\",\n \"def _send_json_file(endpoint, filename, custom_headers=None):\\n headers = {'Content-Type': 'application/json',\\n 'Accept': 'application/json'}\\n if custom_headers is not None:\\n headers.update(custom_headers)\\n with open(filename) as json_data:\\n response = requests.post(endpoint, data=json_data, headers=headers)\\n return response\",\n \"def uploaded_file(filename):\\n return send_from_directory(app.config['UPLOAD_FOLDER'],\\n filename)\",\n \"def upload(self, filename, file_path):\\n return\",\n \"async def send_file(self, filename, content_type=None, content_encoding=None, max_age=2592000, buf_size=128):\\n try:\\n # Get file size\\n stat = os.stat(filename)\\n slen = str(stat[6])\\n self.add_header('Content-Length', slen)\\n # Find content type\\n if content_type:\\n self.add_header('Content-Type', content_type)\\n # Add content-encoding, if any\\n if content_encoding:\\n self.add_header('Content-Encoding', content_encoding)\\n # Since this is static content is totally make sense\\n # to tell browser to cache it, however, you can always\\n # override it by setting max_age to zero\\n self.add_header('Cache-Control', 'max-age={}, public'.format(max_age))\\n with open(filename) as f:\\n await self._send_headers()\\n gc.collect()\\n buf = bytearray(min(stat[6], buf_size))\\n while True:\\n size = f.readinto(buf)\\n if size == 0:\\n break\\n await self.send(buf, sz=size)\\n except OSError as e:\\n # special handling for ENOENT / EACCESS\\n if e.args[0] in (errno.ENOENT, errno.EACCES):\\n raise HTTPException(404)\\n else:\\n raise\",\n \"def do_GET(self):\\n self.send_response(200)\\n self.send_header(\\\"Content-type\\\", self.mimetype)\\n self.end_headers()\\n\\n with open(filename, \\\"rb\\\") as file_:\\n self.wfile.write(file_.read())\\n file_.close()\",\n \"def download_file():\\n\\n if 'POST' == request.method:\\n file_id = request.form['file_id']\\n else:\\n file_id = request.args.get('file_id')\\n\\n # 1 ==> example_1.tgz\\n file_path = file_manager.get_file_path_from_id(file_id)\\n print \\\"serving file: \\\" + file_path\\n return send_file(file_path, as_attachment=True)\",\n \"def upload_file(self, file_path, file_name, output_path):\",\n \"def send_final_request(self):\\n with open(self.output_path, \\\"r\\\") as text_file:\\n data = json.load(text_file)\\n print self.request_handler.send(data)\",\n \"def send_file(self, file_name):\\n\\n try:\\n dataPort = self.client_socket.recv(1024).decode()\\n print(\\\"[Control] Data port is {}\\\".format(dataPort))\\n\\n # Connect to the data connection\\n dataConnection = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n dataConnection.connect((self.client_ip, int(dataPort)))\\n\\n file_size = os.path.getsize(file_name)\\n self.client_socket.sendall(\\n \\\"Exists,{}\\\".format(file_size).encode('utf-8'))\\n\\n while True:\\n recv_data = self.client_socket.recv(1024)\\n request = recv_data.decode('utf-8').strip().split(\\\",\\\")\\n\\n if request[0] == \\\"Ready\\\":\\n print(\\\"Sending file {} to client {}\\\".format(\\n file_name, self.client_ip))\\n\\n with open(file_name, \\\"rb\\\") as file:\\n dataConnection.sendfile(file)\\n elif request[0] == \\\"Received\\\":\\n if int(request[1]) == file_size:\\n self.client_socket.sendall(\\\"Success\\\".encode('utf-8'))\\n print(\\\"{} successfully downloaded to client {}\\\".format(\\n file_name, self.client_ip))\\n break\\n else:\\n print(\\\"Something went wrong trying to download to client {}:{}. Try again\\\".format(\\n self.client_ip, self.client_port))\\n break\\n else:\\n print(\\\"Something went wrong trying to download to client {}:{}. Try again\\\".format(\\n self.client_ip, self.client_port))\\n break\\n except IOError:\\n print(\\\"File {} does not exist on server\\\".format(file_name))\\n self.client_socket.sendall(\\\"Failed\\\".encode('utf-8'))\",\n \"def serve_upload(conn, ssn_key, file_name, client_name):\\n # get signal to begin upload\\n request = aes.decrypt(ssn_key, conn.recv(1024))\\n if request != SIG_START:\\n conn.sendall(aes.encrypt(ssn_key, SIG_BAD))\\n return print(\\\"Bob: something went wrong with file transfer\\\")\\n response = aes.encrypt(ssn_key, SIG_GOOD)\\n conn.sendall(response)\\n print(\\\"Bob: beginning transfer for {}...\\\".format(file_name))\\n\\n # get file contents from client\\n contents = list()\\n completed_upload = False\\n response = aes.encrypt(ssn_key, SIG_GOOD)\\n while not completed_upload:\\n request = aes.decrypt(ssn_key, conn.recv(1024))\\n if request == SIG_END:\\n completed_upload = True\\n print(\\\"Bob: completed transfer for {}\\\".format(file_name))\\n else:\\n contents.append(request)\\n conn.sendall(response)\\n\\n # save file to server folder\\n file_path = \\\"{}/{}\\\".format(client_name, file_name)\\n os.makedirs(os.path.dirname(file_path), exist_ok=True)\\n with open(file_path, \\\"w\\\") as outputStream:\\n outputStream.write(''.join(contents))\\n print(\\\"Bob: file saved in {}\\\".format(file_path))\",\n \"def send_file(file_name, path, ftp, callback):\\n file_src = path + file_name\\n if not os.path.isfile(file_src):\\n log(\\\"Uploading file: '{}'. File not found. Skipping.\\\".format(file_src))\\n return\\n\\n with open(file_src, \\\"rb\\\") as f:\\n callback(\\\"Uploading file: {}. Status: {}\\\\n\\\".format(file_name, str(ftp.storbinary(\\\"STOR \\\" + file_name, f))))\",\n \"def file_request(self):\\n magic_number = int.to_bytes(int(0x497E), 2, byteorder='big') \\n type_bytes = int.to_bytes(1, 1, byteorder='big')\\n filename_in_bytes = bytes(self.file_name, 'utf-8') #ENCODE FILENAME IN BYTES\\n try:\\n filename_len = int.to_bytes(len(filename_in_bytes), 2, byteorder='big')\\n except OverflowError:\\n self.socket.close()\\n sys.exit(\\\"ERROR: Filename is too long, Stack Overflow\\\") \\n \\n message_to_send = bytearray(magic_number+type_bytes+filename_len+filename_in_bytes)\\n self.socket.send(message_to_send)\\n \\n #WAIT FOR FILE RESPONSE FROM SERVER\\n try:\\n self.socket.settimeout(1)\\n while True: #LOOP TO RECIEVE DATA IN CHUNKS <= 4096 BYTES\\n data = self.socket.recv(4096)\\n if data:\\n self.data += data\\n else:\\n break\\n\\n self.socket.settimeout(None)\\n self.socket.close()\\n\\n except socket.timeout:\\n self.socket.close()\\n sys.exit(\\\"ERROR: Connection timed out\\\")\",\n \"async def upload_file(self):\\n logger.debug(\\\"uploading %s\\\", self.tgzfile)\\n with aiohttp.MultipartWriter(\\\"form-data\\\") as mpwriter:\\n with open(self.tgzfile, \\\"rb\\\") as file_handle:\\n part = mpwriter.append(file_handle)\\n part.set_content_disposition(\\n \\\"form-data\\\", name=\\\"file\\\", filename=\\\"inventory.gz\\\"\\n )\\n part.headers[aiohttp.hdrs.CONTENT_TYPE] = self.UPLOAD_CONTENT_TYPE\\n\\n headers = {}\\n # TODO : Use mTLS certs not userid/password\\n auth = aiohttp.BasicAuth(\\n self.config[\\\"AUTH\\\"][\\\"username\\\"], self.config[\\\"AUTH\\\"][\\\"password\\\"]\\n )\\n headers[\\\"Authorization\\\"] = auth.encode()\\n async with aiohttp.ClientSession(headers=headers) as session:\\n async with session.post(\\n self.upload_url, ssl=self.ssl_context, data=mpwriter\\n ) as response:\\n logger.debug(\\\"Status: %s\\\", response.status)\\n logger.debug(\\n \\\"Content-type: %s\\\", response.headers[\\\"Content-Type\\\"]\\n )\\n\\n return await response.text()\",\n \"def transfer(file_obj):\",\n \"def peer_server_upload(self, conn, data_received):\\n try:\\n file_size = os.path.getsize(SHARED_DIR+'/'+data_received['file_name'])\\n f = open(SHARED_DIR+'/'+data_received['file_name'], 'rb')\\n #print \\\"Hosting File: %s for download\\\" % data_received\\n for chunk_start, chunk_size in self.get_chunks(file_size):\\n file_chunk = f.read(chunk_size)\\n conn.sendall(file_chunk)\\n '''\\n while True:\\n data = f.readline()\\n if data:\\n conn.sendall(data)\\n else:\\n break\\n '''\\n f.close()\\n conn.sendall('')\\n conn.close()\\n except Exception as e:\\n print \\\"File Upload Error, %s\\\" % e\",\n \"def send_file_upload_request(self, view_name, filename, expected_response_code=200):\\n url = reverse(view_name)\\n\\n client = Client()\\n with open(filename) as file_upload:\\n response = client.post(path=url, data={'file': file_upload})\\n\\n self.assertEqual(expected_response_code, response.status_code)\\n\\n return response\",\n \"def post_file(self, file_, api=None):\\n api = api or self.api\\n url = utils.join_url(self.path)\\n files = {'data': file_}\\n new_attributes = api.post(url, {}, {}, files)\\n # self.error = None\\n self.merge(new_attributes)\\n return self.success()\",\n \"def upload_file(self, filename: str) -> SlackResponse:\\n try:\\n # Send Slack Message\\n response = self.client.files_upload(\\n file=filename,\\n )\\n return response\\n except SlackApiError as e:\\n logging.error(f\\\"Error uploading file: {e}\\\")\",\n \"def send_file_contents(self):\\n self.send_comm.send_nolimit(self.ply_dict)\\n self.send_comm.send_eof()\",\n \"def download_file(filename):\\n return send_from_directory('uploads', filename, as_attachment=True)\",\n \"def run(self):\\n self.socket.connect()\\n with open('src/inputs/output.file', 'rb') as f:\\n self.sent_bytes = f.read()\\n self.socket.send(self.sent_bytes)\\n self.socket.disconnect()\\n self.socket.close()\",\n \"def upload_file(self):\\n request = copy.deepcopy(self.request_template)\\n data = json.dumps(request)\\n curr_file = {\\n 'request': data,\\n 'file': open(self.file_path, 'rb')\\n }\\n print(\\\"Sending Upload request of av for file {}\\\".format(self.file_name))\\n try:\\n response = requests.post(url=self.url + \\\"upload\\\", files=curr_file, verify=False)\\n except Exception as E:\\n print(\\\"Upload file failed. file: {} , failure: {}\\\".format(self.file_name, E))\\n raise\\n response_j = response.json()\\n print(\\\"av Upload response status for file {} : {}\\\".format(self.file_name,\\n response_j[\\\"response\\\"][0][\\\"status\\\"][\\\"label\\\"]))\\n return response_j\",\n \"def upload_file(self, file_name, content):\\n return self.files.upload(file_name, content)\",\n \"def serve_download(conn, ssn_key, file_name, client_name):\\n # read file contents\\n file_path = \\\"{}/{}\\\".format(client_name, file_name)\\n contents = None\\n with open(file_path, \\\"r\\\") as fileStream:\\n buffer = fileStream.read()\\n contents = [buffer[0+i:16+i] for i in range(0, len(buffer), 16)]\\n # get signal to begin download\\n request = aes.decrypt(ssn_key, conn.recv(1024))\\n if request != SIG_START:\\n conn.sendall(aes.encrypt(ssn_key, SIG_BAD))\\n return print(\\\"Bob: something went wrong with file transfer\\\")\\n print(\\\"Bob: beginning transfer for {}...\\\".format(file_name))\\n # upload file contents to client\\n for i, content in enumerate(contents):\\n response = aes.encrypt(ssn_key, content)\\n conn.sendall(response)\\n if aes.decrypt(ssn_key, conn.recv(1024)) != SIG_GOOD:\\n return print(\\\"Bob: something went wrong with file transfer, exiting...\\\")\\n print(\\\"Bob: transferring file... ({}/{})\\\".format(i+1, len(contents)))\\n # send signal that transfer is complete\\n request = aes.encrypt(ssn_key, SIG_END)\\n conn.sendall(request)\\n if aes.decrypt(ssn_key, conn.recv(1024)) != SIG_GOOD:\\n return print(\\\"Bob: something went wrong with file transfer, exiting...\\\")\\n print(\\\"Bob: successful upload for {}\\\".format(file_name))\",\n \"def uploaded_file(filename):\\n return send_from_directory('/static/images/uploads/', filename)\",\n \"def send_file(filename, host, port):\\r\\n filesize = os.path.getsize(filename)\\r\\n s = socket.socket()\\r\\n print(\\\"[+] Connecting to %s:%s\\\" %(host, port))\\r\\n s.connect((host, port))\\r\\n print(\\\"[+] Connected.\\\")\\r\\n msg = str(filename+SEPARATOR+str(filesize)).encode()\\r\\n s.send(msg)\\r\\n progress = tqdm.tqdm(range(filesize), \\\"Sending \\\"+filename, unit=\\\"B\\\", unit_scale=True, unit_divisor=50)\\r\\n with open(filename, \\\"rb\\\") as f:\\r\\n for _ in progress:\\r\\n bytes_read = f.read(BUFFER_SIZE)\\r\\n if not bytes_read:\\r\\n break\\r\\n s.sendall(bytes_read)\\r\\n progress.update(len(bytes_read))\\r\\n s.close()\",\n \"def api_upload():\\n return make_response(file_manager.save_uploaded_file(), 200)\",\n \"def node_file_upload(ctx, file, filename):\\n filepath = click.format_filename(file)\\n filename = filename if filename else filepath\\n\\n with open(file, \\\"r\\\") as f:\\n content = f.read()\\n\\n try:\\n ctx.obj['node'].create_file(filename, content=content)\\n except TimeoutError as e:\\n logger.error('Error: %s' % e)\\n exit(1)\",\n \"def post(self):\\n request_data = request.get_json(force=True)\\n current_path = self.get_current_path()\\n file_name = request_data.get('file_name')\\n\\n if not file_name:\\n abort(400, message=\\\"File name must not be empty!\\\")\\n\\n full_path = os.path.join(current_path, file_name)\\n\\n if os.path.exists(full_path):\\n abort(400, message=\\\"File already exists!\\\")\\n\\n if not self.is_allowed(full_path):\\n abort(403, message=\\\"You are not allowed to this path\\\")\\n\\n with open(full_path, 'w+') as fp:\\n pass\\n\\n return {\\\"message\\\": \\\"OK\\\"}\",\n \"def deliverFile(self, filename=None, docroot=None):\\n if not docroot:\\n docroot = self.docroot\\n if not docroot or not os.path.isdir(docroot):\\n if not filename: filename = \\\"index.html\\\"\\n return self.sendNotFound(filename)\\n\\n if not filename:\\n filename = \\\"index.htm\\\"\\n if not os.path.exists(os.path.join(docroot, filename)):\\n filename = \\\"index.html\\\"\\n filename = filename.strip(os.sep)\\n\\n path = os.path.join(docroot, filename)\\n if not os.path.exists(path) or not os.path.isfile(path):\\n print >> self.estrm, \\\"vaologin: doc not found: \\\" + path\\n return self.sendNotFound(filename)\\n\\n defbufsz = 1024000\\n try:\\n bufsz = int(self._getConfigParam(\\\"bufsize\\\", defbufsz))\\n except TypeError, ex:\\n bufsz = defbufsz\\n\\n ext = os.path.splitext(filename)[1]\\n if ext and ext.startswith('.'):\\n ext = ext[1:]\\n mimetype = self.getMimeTypeFor(ext)\\n self._sendheader()\\n self.ostrm.write(\\\"Content-Type: %s\\\\r\\\\n\\\\r\\\\n\\\" % mimetype)\\n\\n fd = None\\n try:\\n fd = open(path, 'rb')\\n data = fd.read(bufsz)\\n while data:\\n self.ostrm.write(data)\\n data = fd.read(bufsz)\\n\\n except Exception, ex:\\n print >> self.estrm, \\\"Problem sending file data: \\\" + str(ex)\\n finally:\\n if fd: fd.close()\\n # self.ostrm.close()\",\n \"def send(self, request, filepath):\\n gzip = False\\n if 'gzip' in request.headers[hdrs.ACCEPT_ENCODING]:\\n gzip_path = filepath.with_name(filepath.name + '.gz')\\n\\n if gzip_path.is_file():\\n filepath = gzip_path\\n gzip = True\\n\\n st = filepath.stat()\\n\\n modsince = request.if_modified_since\\n if modsince is not None and st.st_mtime <= modsince.timestamp():\\n raise HTTPNotModified()\\n\\n ct, encoding = mimetypes.guess_type(str(filepath))\\n if not ct:\\n ct = 'application/octet-stream'\\n\\n resp = self._response_factory()\\n resp.content_type = ct\\n if encoding:\\n resp.headers[hdrs.CONTENT_ENCODING] = encoding\\n if gzip:\\n resp.headers[hdrs.VARY] = hdrs.ACCEPT_ENCODING\\n resp.last_modified = st.st_mtime\\n\\n # CACHE HACK\\n if not request.app[KEY_DEVELOPMENT]:\\n cache_time = 31 * 86400 # = 1 month\\n resp.headers[hdrs.CACHE_CONTROL] = \\\"public, max-age={}\\\".format(\\n cache_time)\\n\\n file_size = st.st_size\\n\\n resp.content_length = file_size\\n with filepath.open('rb') as f:\\n yield from self._sendfile(request, resp, f, file_size)\\n\\n return resp\",\n \"def file(service, ip, file_name, file_path=None, user=None):\\n\\n timestamp = format_time(get_time())\\n coordinates = get_coordinates(ip)\\n\\n if not file_path:\\n file_path = PLACEHOLDER_STRING\\n if not user:\\n user = PLACEHOLDER_STRING\\n\\n values = defaultdict(lambda: PLACEHOLDER_STRING,\\n {'event_type': 'file',\\n '@timestamp': timestamp,\\n 'service': service,\\n 'ip': ip,\\n 'user': user,\\n 'filename': file_name,\\n 'honeypotID': ID})\\n \\n if coordinates:\\n values['coordinates'] = '{:.4f},{:.4f}'.format(coordinates[0], coordinates[1])\\n\\n if Config.use_broker:\\n BrokerEndpoint.BrokerEndpoint.sendLogs(json.dumps(values))\\n if file_path:\\n BrokerEndpoint.BrokerEndpoint.sendFile(file_path)\\n\\n if coordinates:\\n values['lat'] = '{:.4f}'.format(coordinates[0])\\n values['lon'] = '{:.4f}'.format(coordinates[1])\\n\\n message = '{@timestamp} - [FILE] - {service}, {ip}, Lat: {lat}, Lon: {lon}, {filename}, {user}'.format_map(values)\\n _log_alert(message)\",\n \"def upload_file_to_shock(self,\\n console, # DEBUG\\n shock_service_url = None,\\n filePath = None,\\n ssl_verify = True,\\n token = None):\\n self.log(console,\\\"UPLOADING FILE \\\"+filePath+\\\" TO SHOCK\\\")\\n\\n if token is None:\\n raise Exception(\\\"Authentication token required!\\\")\\n\\n #build the header\\n header = dict()\\n header[\\\"Authorization\\\"] = \\\"Oauth {0}\\\".format(token)\\n if filePath is None:\\n raise Exception(\\\"No file given for upload to SHOCK!\\\")\\n\\n dataFile = open(os.path.abspath(filePath), 'rb')\\n m = MultipartEncoder(fields={'upload': (os.path.split(filePath)[-1], dataFile)})\\n header['Content-Type'] = m.content_type\\n\\n #logger.info(\\\"Sending {0} to {1}\\\".format(filePath,shock_service_url))\\n try:\\n response = requests.post(shock_service_url + \\\"/node\\\", headers=header, data=m, allow_redirects=True, verify=ssl_verify)\\n dataFile.close()\\n except:\\n dataFile.close()\\n raise\\n if not response.ok:\\n response.raise_for_status()\\n result = response.json()\\n if result['error']:\\n raise Exception(result['error'][0])\\n else:\\n return result[\\\"data\\\"]\",\n \"def upload_file_to_shock(self,\\n console, # DEBUG\\n shock_service_url = None,\\n filePath = None,\\n ssl_verify = True,\\n token = None):\\n self.log(console,\\\"UPLOADING FILE \\\"+filePath+\\\" TO SHOCK\\\")\\n\\n if token is None:\\n raise Exception(\\\"Authentication token required!\\\")\\n\\n #build the header\\n header = dict()\\n header[\\\"Authorization\\\"] = \\\"Oauth {0}\\\".format(token)\\n if filePath is None:\\n raise Exception(\\\"No file given for upload to SHOCK!\\\")\\n\\n dataFile = open(os.path.abspath(filePath), 'rb')\\n m = MultipartEncoder(fields={'upload': (os.path.split(filePath)[-1], dataFile)})\\n header['Content-Type'] = m.content_type\\n\\n #logger.info(\\\"Sending {0} to {1}\\\".format(filePath,shock_service_url))\\n try:\\n response = requests.post(shock_service_url + \\\"/node\\\", headers=header, data=m, allow_redirects=True, verify=ssl_verify)\\n dataFile.close()\\n except:\\n dataFile.close()\\n raise\\n if not response.ok:\\n response.raise_for_status()\\n result = response.json()\\n if result['error']:\\n raise Exception(result['error'][0])\\n else:\\n return result[\\\"data\\\"]\",\n \"def upload_file(self, f):\\n with FilesOpener(f) as files:\\n response = self.session.post(\\n 'https://{}/upload'.format(self.domain),\\n files=files\\n ).json()\\n\\n if isinstance(response, list):\\n error = response[0].get('error')\\n else:\\n error = response.get('error')\\n\\n if error:\\n if isinstance(error, str) and error.startswith('FLOOD_WAIT_'):\\n retry_after = int(error.rsplit('_',1)[-1])\\n raise RetryAfterError(retry_after)\\n else:\\n raise TelegraphException(error)\\n\\n return response\",\n \"def copy_file_to_server(self, file_path):\\n raise NotImplementedError\",\n \"def process(self):\\n\\n requested_file_path = self._get_requested_file_path()\\n\\n if os.path.exists(requested_file_path):\\n self._respond_with_status_line(OK)\\n self._respond_with_headers(file_path=requested_file_path,\\n requested_headers=('Date',\\n 'Content-Length',\\n 'Content-Type',\\n 'Server'))\\n else:\\n self._respond_with_status_line(NOT_FOUND)\\n self._respond_with_headers(requested_headers=('Server',))\",\n \"def raw_convert_file():\\n try:\\n str_out = io.BytesIO()\\n str_out.write(process_text(request.data.decode('utf-8')).encode('utf-8'))\\n str_out.seek(0)\\n\\n return send_file(\\n str_out,\\n attachment_filename='result.txt',\\n as_attachment=True,\\n mimetype='text/plain'\\n )\\n except:\\n return make_response('', 400)\",\n \"def send_file(\\n self,\\n *,\\n channels: str | Sequence[str] | None = None,\\n file: str | Path | None = None,\\n content: str | None = None,\\n filename: str | None = None,\\n filetype: str | None = None,\\n initial_comment: str | None = None,\\n title: str | None = None,\\n ) -> SlackResponse:\\n if not exactly_one(file, content):\\n raise ValueError(\\\"Either `file` or `content` must be provided, not both.\\\")\\n elif file:\\n file = Path(file)\\n with open(file, \\\"rb\\\") as fp:\\n if not filename:\\n filename = file.name\\n return self.client.files_upload(\\n file=fp,\\n filename=filename,\\n filetype=filetype,\\n initial_comment=initial_comment,\\n title=title,\\n channels=channels,\\n )\\n\\n return self.client.files_upload(\\n content=content,\\n filename=filename,\\n filetype=filetype,\\n initial_comment=initial_comment,\\n title=title,\\n channels=channels,\\n )\",\n \"def send_file(ip, port):\\n server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n server.bind((ip, port))\\n server.listen(5)\\n while True:\\n client_socket, client_address = server.accept()\\n data = client_socket.recv(1024)\\n while data == '':\\n data = client_socket.recv(1024)\\n data = data[:-1]\\n f = open(data, 'rb')\\n l = f.read(1024)\\n while (l):\\n client_socket.send(l)\\n l = f.read(1024)\\n f.close()\\n client_socket.close()\",\n \"def send_file(self, file_name: str) -> None:\\n\\n with open(file_name, 'rb') as f:\\n message = {'type': 'DATA', 'data': None}\\n read_size = 16 * 60\\n while True:\\n if self.ppos != 0:\\n f.seek(self.ppos)\\n self.ppos = 0\\n\\n if self.count == 20:\\n self.state = STATE_ROTATION\\n\\n #Generate DH client private and public keys\\n bytes_public_key,p,g,y = self.crypto.dh_client()\\n message = {'type':'DH_PARAMETERS','parameters':{'p':p,'g':g,'public_key':str(bytes_public_key,'ISO-8859-1')}}\\n self.count=0\\n self.ppos=f.tell()\\n self._send(message)\\n break\\n\\n self.count += 1\\n \\n data = f.read(16 * 60)\\n message['data'] = base64.b64encode(data).decode()\\n #logger.debug(\\\"Data: {} read size {}\\\".format(data,f.tell()))\\n secure_message = self.encrypt_message(message)\\n \\n self._send(secure_message)\\n self.send_mac()\\n \\n if len(data) != read_size:\\n self.end = True\\n break\\n \\n # When it ends create MAC\\n if self.end:\\n self._send(self.encrypt_message({'type': 'CLOSE'}))\\n self.send_mac()\\n logger.info(\\\"File transfer finished. Closing transport\\\")\\n self.transport.close()\",\n \"def post(cls, flow_name: str):\\n data = file_schema.load(request.files) # {\\\"file\\\": FileStorage}\\n try:\\n file_path = uploads.save_file(data[\\\"file\\\"], folder=flow_name)\\n basename = uploads.get_basename(file_path)\\n return {\\\"message\\\": gettext(\\\"file_uploaded\\\").format(basename)}, 200\\n \\n except UploadNotAllowed:\\n extension = uploads.get_extension(data[\\\"file\\\"])\\n return {\\\"message\\\": gettext(\\\"file_illegal_extension\\\").format(extension)}, 400\",\n \"async def send_file(self, l_file: str, r_dest: str) -> None:\\n # pause logic\\n if not self.running.is_set():\\n self.add_to_output(\\\"Paused...\\\")\\n await self.running.wait()\\n\\n # get/create ssh connection to miner\\n conn = await self.get_connection(\\\"root\\\", \\\"admin\\\")\\n # send file over scp\\n await asyncssh.scp(l_file, (conn, r_dest))\\n self.add_to_output(f\\\"File sent...\\\")\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def send_text_file(file_name):\\n file_dot_text = file_name + '.txt'\\n return app.send_static_file(file_dot_text)\",\n \"def push_file_to_server(cnc_bot, filename, content, encryption_key=None):\\r\\n c = content\\r\\n if encryption_key is not None:\\r\\n c = rc4.encrypt(c, encryption_key, salt_length=0) # encrypt content via rc4\\r\\n cfg = {'filename': filename, 'content': c}\\r\\n cnc_bot.host_orders(cPickle.dumps(cfg)) # upload a serialized dict\\r\",\n \"def _upload_file(cluster_connection, girder_client, file, path):\\n\\n r = requests.get(\\n '%s/file/%s/download' % (girder_client.urlBase, file['_id']),\\n headers={'Girder-Token': girder_client.token}, stream=True)\\n check_status(r)\\n cluster_connection.put(r.raw, os.path.join(path, file['name']))\",\n \"def do_POST(self):\\r\\n content_length = int(self.headers['Content-Length'])\\r\\n body = self.rfile.read(content_length)\\r\\n\\r\\n response = BytesIO()\\r\\n try:\\r\\n res = webServer.handle_post_msg(body)\\r\\n print(res)\\r\\n self.send_response(200)\\r\\n except Exception as e:\\r\\n print(e)\\r\\n res = str(e)\\r\\n self.send_response(500)\\r\\n self.end_headers()\\r\\n response.write(res.encode())\\r\\n self.wfile.write(response.getvalue())\",\n \"def post_file(self, method, params, headers=None):\\n\\n post_params = {'merge': params['merge']}\\n file_data = open(params['full_file_path'], 'rb').read() \\n files = {'file': (params['file_name'], file_data)} \\n r = requests.post(self.endpoint + method, files=files, params=post_params,\\n auth=(self.session.auth[0], self.session.auth[1]), headers=headers)\\n if 400 <= r.status_code < 600:\\n r.reason = r.text\\n r.raise_for_status()\\n return r\",\n \"def upload(self, requests, file):\\n # Set the source and dest paths\\n dest_url = self.base_url + '/upload'\\n source_path = os.path.join(self.data_dir, file)\\n\\n # Get the sha256 hash of the file\\n with open(source_path, 'rb') as afile:\\n beforeDigest = hashlib.sha256(afile.read()).hexdigest()\\n\\n print(\\\"Generated a hash of the temp file: \\\" + beforeDigest)\\n\\n # Upload the file and time it\\n with open(source_path, 'rb') as f:\\n startTime = time.time()\\n r = requests.post(dest_url, files={'file': (file, f)}, max_price=5)\\n endTime = time.time()\\n uploadElapsedTime = endTime - startTime\\n\\n print(\\\"Uploaded the file. Elapsed time: \\\" + str(uploadElapsedTime))\\n\\n # Verify the upload was successful\\n if r.json()['success'] is not True:\\n return {'success': False}\\n\\n retVal = {\\n 'success': True,\\n 'time': uploadElapsedTime,\\n 'digest': beforeDigest,\\n 'upload_filename': r.json()['filename']\\n }\\n\\n return retVal\",\n \"def uploaded_file(image):\\n return send_from_directory(app.config['UPLOAD_FOLDER'], image)\",\n \"async def stream_file(self, file: Union[str, io.BufferedReader], **kwargs) -> None:\\n await self.relay(\\\"stream_file\\\")(file, **kwargs)\",\n \"def do_POST(self):\\n real_url = (\\\"https://stream.watsonplatform.net/text-to-speech/api\\\" +\\n self.path)\\n # pull out the body to send\\n content_len = int(self.headers.get('content-length', 0))\\n post_body = self.rfile.read(content_len)\\n\\n # make a content addressable local storage path based on\\n # string args (which is voice & audio type) and body, which is\\n # the text to be sent. This should be a good unique seed.\\n hash_seed = self.path.encode('utf-8') + post_body\\n fname = hashlib.sha256(hash_seed).hexdigest() + \\\".wav\\\"\\n cache = self.server.cache\\n fullfile = join(cache, fname)\\n if not exists(fullfile):\\n # make the real request\\n resp = requests.post(real_url, headers=self.headers,\\n data=post_body)\\n\\n # TODO(sdague): intermediate debugging, keep a copy around.\\n with open(fullfile, 'wb') as f:\\n f.write(resp.content)\\n print(\\\"Wrote cached content to %s\\\" % fullfile)\\n\\n # Process request back to client\\n self.send_response(resp.status_code)\\n for header in resp.headers:\\n # Requests has now decoded the response so some\\n # characteristics of this may no longer be relevant. Don't\\n # send them.\\n if header not in (\\\"connection\\\", \\\"content-length\\\",\\n \\\"keep-alive\\\"):\\n self.send_header(header, resp.headers[header])\\n\\n self.end_headers()\\n # The client expects chunked encoding, which is just a way to\\n # stream data as it shows up with per line content length. We\\n # already have *all* the data, so we can send a single line\\n # which has it all with the right length.\\n content = resp.content\\n self.wfile.write(('%X\\\\r\\\\n' % len(content)).encode('utf-8'))\\n self.wfile.write(content)\\n self.wfile.write(b'\\\\r\\\\n')\\n\\n else:\\n print(\\\"Reading from cached content\\\")\\n self.send_response(200)\\n for k, v in self.fake_headers().items():\\n self.send_header(k, v)\\n self.end_headers()\\n with open(fullfile, 'rb') as f:\\n content = f.read()\\n self.wfile.write(('%X\\\\r\\\\n' % len(content)).encode('utf-8'))\\n self.wfile.write(content)\\n self.wfile.write(b'\\\\r\\\\n')\\n\\n # Then we need to send the null to signal to the client that\\n # we're done and it should close out shop.\\n self.wfile.write(b'0\\\\r\\\\n\\\\r\\\\n')\\n\\n if self.server.play_sound:\\n print(\\\"Playing audio.\\\")\\n watson_tts_proxy_client.play_file(fullfile)\",\n \"async def write_file(self, directory: str, name: str, file: bytes):\\n pass\",\n \"def put_file(cls, filegen, signed_url):\\n headers = {\\\"Content-Type\\\": \\\"application/octet-stream\\\"}\\n try:\\n resp = requests.put(signed_url, headers=headers, data=filegen)\\n except Exception as err:\\n raise exceptions.FilePutError(err)\\n else:\\n return resp\",\n \"def __PostFile(self, url, fileName, prefix):\\n CRLF = '\\\\r\\\\n'\\n\\n f = open(fileName, \\\"rb\\\")\\n content = f.read()\\n boundary = \\\"-------------------------------\\\"+ \\\\\\n \\\"\\\".join([ random.choice('0123456789') for x in range(28) ])\\n\\n output = []\\n output.append(\\\"--\\\"+boundary)\\n output.append('Content-Disposition: form-data; name=\\\"'+prefix+ \\\\\\n '\\\"; filename=\\\"avatar.png\\\"')\\n output.append('Content-Type: '+mimetypes.guess_type(fileName)[0] \\\\\\n or 'application/octet-stream')\\n output.append(\\\"\\\")\\n output.append(content)\\n output.append(\\\"--\\\"+boundary+\\\"--\\\")\\n output.append(\\\"\\\")\\n\\n encoded = CRLF.join(output)\\n\\n conn = self.__GetConnection()\\n headers = self.__MakeHeaders(True)\\n\\n conn.putrequest(\\\"POST\\\", url)\\n for (k,v) in headers.iteritems():\\n conn.putheader(k, v)\\n\\n conn.putheader(\\\"Content-Type\\\", \\\"multipart/form-data; boundary=\\\" + \\\\\\n boundary)\\n conn.putheader(\\\"Content-Length\\\", str(len(encoded)))\\n\\n conn.endheaders()\\n conn.send(encoded)\\n response = conn.getresponse()\\n self.__CheckResponse(response)\",\n \"def upload_file(\\n self, bucket_id: uplink.Path, filename: uplink.Path, file: uplink.Body\\n ):\\n pass\",\n \"def uploaded(self, filename):\\n return send_from_directory(self.upload_path, filename)\",\n \"def handle(self): \\n \\n data = self.request[0].strip()\\n self.socket = self.request[1]\\n\\n #split off first word of file, assume is filename\\n filename,sep,data = data.partition(\\\" \\\")\\n\\n #assume is requesting file\\n if not data:\\n self.sendfile(filename)\\n #assume we have to save the file since data was sent\\n else:\\n self.savefile(filename,data)\\n\\n return True\",\n \"def upload(upload_url: str, file_path: str) -> None:\\n with open(file_path, 'r') as data:\\n try:\\n r = requests.put(\\n upload_url,\\n data=data,\\n headers={\\\"Content-Type\\\": \\\"application/octet-stream\\\"},\\n )\\n r.raise_for_status()\\n except requests.exceptions.HTTPError as err:\\n print('Error uploading file')\\n raise err\",\n \"def send_file(cls, filename, status_code=200, content_type=None,\\n stream=None, max_age=None, compressed=False,\\n file_extension=''):\\n if content_type is None:\\n ext = filename.split('.')[-1]\\n if ext in Response.types_map:\\n content_type = Response.types_map[ext]\\n else:\\n content_type = 'application/octet-stream'\\n headers = {'Content-Type': content_type}\\n\\n if max_age is None:\\n max_age = cls.default_send_file_max_age\\n if max_age is not None:\\n headers['Cache-Control'] = 'max-age={}'.format(max_age)\\n\\n if compressed:\\n headers['Content-Encoding'] = compressed \\\\\\n if isinstance(compressed, str) else 'gzip'\\n\\n f = stream or open(filename + file_extension, 'rb')\\n return cls(body=f, status_code=status_code, headers=headers)\",\n \"def upload(self):\\n\\n # Try to retrieve the file from the request\\n file = None\\n try:\\n file = request.files[\\\"file\\\"]\\n except KeyError as e:\\n logging.warning(f\\\"File was not found in request: {e}.\\\")\\n flash(\\\"No file given.\\\", \\\"error\\\")\\n return redirect(request.url)\\n except AttributeError as e:\\n logging.warning(f\\\"Error: Request did not contain any files: {e}.\\\")\\n flash(\\\"No file given.\\\", \\\"error\\\")\\n return redirect(request.url)\\n\\n # Check if file was correctly uploaded\\n if not file or len(file.filename) == 0:\\n flash(\\\"No file selected for upload.\\\", \\\"message\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" Check if file has correct extension. Allowed extensions depend on\\n the connector. To make the code more readable, group connectors\\n with the same allowed file extensions together like this:\\n if connector in ['someconnector', 'someotherconnector']:\\n extensions = [...] \\\"\\\"\\\"\\n\\n if self.connector in [\\\"overtime\\\"]:\\n allowed_extensions = OVERTIME_SUPPORTED_EXTENSIONS\\n else:\\n allowed_extensions = []\\n\\n if not allowed_file(file, allowed_extensions=allowed_extensions):\\n flash(\\\"File extension not allowed.\\\", \\\"warning\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" File seems uploaded correctly and has correct extension.\\n Generate a new record ID to keep track of the uploaded file.\\n \\\"\\\"\\\"\\n rec_id = generate_temp_record_id()\\n\\n # Save file to disk\\n path = store_temp_file(file, record_id=rec_id)\\n\\n if not path:\\n flash(\\\"Error saving file!\\\", \\\"error\\\")\\n return redirect(request.url)\\n\\n \\\"\\\"\\\" If everything ended successfully, send the user to the\\n confirmation page so he can review his changes \\\"\\\"\\\"\\n\\n return redirect(url_for(f\\\"{self.endpoint}.upload_confirm\\\", rec_id=rec_id))\",\n \"async def test_upload_file(self):\\n rsps = respx.put(f'{PROVISIONING_API_URL}/users/current/provisioning-profiles/id/servers.dat') \\\\\\n .mock(return_value=Response(204))\\n with mock.patch('__main__.open', new=mock.mock_open(read_data='test')) as file:\\n file.return_value = json.dumps('test').encode()\\n await provisioning_client.upload_provisioning_profile_file('id', 'servers.dat', file())\\n assert rsps.calls[0].request.url == \\\\\\n f'{PROVISIONING_API_URL}/users/current/provisioning-profiles/id/servers.dat'\\n assert rsps.calls[0].request.headers['auth-token'] == 'header.payload.sign'\",\n \"def test_post_file(self):\\n self._register_uri(httpretty.POST)\\n with open(self.test_file, 'rb') as in_file:\\n response = self.client.post(self.test_endpoint,\\n files={\\\"file\\\": in_file})\\n self.assertEqual(response, self.test_data)\\n body = str(self._last_request().body)\\n self.assertIn(\\\"Content-Disposition: form-data; \\\"+\\n \\\"name=\\\\\\\"file\\\\\\\"; filename=\\\\\\\"test_file.txt\\\\\\\"\\\", body)\\n self.assertIn(\\\"Test File\\\", str(body))\",\n \"def upload(self, file_path):\\n LOG.debug((\\\"Uploading file to mattermost: %s\\\", file_path))\\n filename = os.path.basename(file_path)\\n post_data = {'channel_id': self.channel_id,\\n 'client_ids': filename}\\n file_data = {'files': (filename, open(file_path, 'rb'))}\\n url = '%s/api/v4/files' % self.server_url\\n response = self._request(self._session.post, url, data=post_data,\\n files=file_data)\\n LOG.debug(\\\"Received: %s - %s\\\", response.status_code, response.text)\\n\\n if response.status_code != 201:\\n if response.status_code == 400:\\n msg = \\\"Type of the uploaded file doesn't match its file \\\" \\\\\\n \\\" extension or uploaded file is an image that \\\" \\\\\\n \\\"exceeds the maximum dimensions\\\"\\n elif response.status_code == 401:\\n msg = \\\"User is not logged in\\\"\\n elif response.status_code == 403:\\n msg = \\\"User does not have permission to upload file to \\\" \\\\\\n \\\"the provided team/channel\\\"\\n elif response.status_code == 413:\\n msg = \\\"Uploaded file is too large\\\"\\n elif response.status_code == 500:\\n msg = \\\"File storage is disabled\\\"\\n else:\\n msg = response\\n raise RuntimeError(\\\"Server unhappy with request, reports: %s\\\"\\n % msg)\\n\\n file_id = response.json()[\\\"file_infos\\\"][0][\\\"id\\\"]\\n return file_id\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.76177657","0.7581571","0.741669","0.7285191","0.71710384","0.7149347","0.7077242","0.7058303","0.69783664","0.6898794","0.68531007","0.68401706","0.6838323","0.6822907","0.6806711","0.6760568","0.6707573","0.6676297","0.66599524","0.66599524","0.66599524","0.6649976","0.65780103","0.6571915","0.65685016","0.6567458","0.6487562","0.64730483","0.64712733","0.64691633","0.6455308","0.64428407","0.64081025","0.63599366","0.6355251","0.6354631","0.6336293","0.6329762","0.62821466","0.62780374","0.626207","0.6252137","0.6249389","0.6215291","0.6206116","0.61953104","0.61928105","0.617919","0.61777","0.61727846","0.6166854","0.6162743","0.61612666","0.61590767","0.613132","0.613132","0.61312085","0.6127511","0.61238307","0.6123541","0.61103076","0.6093423","0.60850084","0.60670376","0.60618854","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.6061565","0.606034","0.60413355","0.6038096","0.6035927","0.6035647","0.6034441","0.60334337","0.6030039","0.60189694","0.6012654","0.60022694","0.5995704","0.599066","0.598466","0.5984529","0.59822255","0.5976207","0.5973693","0.5957209","0.59431404"],"string":"[\n \"0.76177657\",\n \"0.7581571\",\n \"0.741669\",\n \"0.7285191\",\n \"0.71710384\",\n \"0.7149347\",\n \"0.7077242\",\n \"0.7058303\",\n \"0.69783664\",\n \"0.6898794\",\n \"0.68531007\",\n \"0.68401706\",\n \"0.6838323\",\n \"0.6822907\",\n \"0.6806711\",\n \"0.6760568\",\n \"0.6707573\",\n \"0.6676297\",\n \"0.66599524\",\n \"0.66599524\",\n \"0.66599524\",\n \"0.6649976\",\n \"0.65780103\",\n \"0.6571915\",\n \"0.65685016\",\n \"0.6567458\",\n \"0.6487562\",\n \"0.64730483\",\n \"0.64712733\",\n \"0.64691633\",\n \"0.6455308\",\n \"0.64428407\",\n \"0.64081025\",\n \"0.63599366\",\n \"0.6355251\",\n \"0.6354631\",\n \"0.6336293\",\n \"0.6329762\",\n \"0.62821466\",\n \"0.62780374\",\n \"0.626207\",\n \"0.6252137\",\n \"0.6249389\",\n \"0.6215291\",\n \"0.6206116\",\n \"0.61953104\",\n \"0.61928105\",\n \"0.617919\",\n \"0.61777\",\n \"0.61727846\",\n \"0.6166854\",\n \"0.6162743\",\n \"0.61612666\",\n \"0.61590767\",\n \"0.613132\",\n \"0.613132\",\n \"0.61312085\",\n \"0.6127511\",\n \"0.61238307\",\n \"0.6123541\",\n \"0.61103076\",\n \"0.6093423\",\n \"0.60850084\",\n \"0.60670376\",\n \"0.60618854\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.6061565\",\n \"0.606034\",\n \"0.60413355\",\n \"0.6038096\",\n \"0.6035927\",\n \"0.6035647\",\n \"0.6034441\",\n \"0.60334337\",\n \"0.6030039\",\n \"0.60189694\",\n \"0.6012654\",\n \"0.60022694\",\n \"0.5995704\",\n \"0.599066\",\n \"0.598466\",\n \"0.5984529\",\n \"0.59822255\",\n \"0.5976207\",\n \"0.5973693\",\n \"0.5957209\",\n \"0.59431404\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77068,"cells":{"query":{"kind":"string","value":"Checks that the endpoint is ready to receive incoming messages."},"document":{"kind":"string","value":"def _is_ready(self):\n current_wait_time = 0\n start_time = time.time()\n while current_wait_time < self.max_wait_time_ready:\n try:\n response = requests.get(os.path.join(self.url, \"ready\"), timeout=1)\n if response.status_code == 200:\n break\n except KeyboardInterrupt:\n raise KeyboardInterrupt\n except:\n current_wait_time = time.time() - start_time\n if current_wait_time >= self.max_wait_time_ready:\n raise TimeoutError(\"Interrupting execution\\n'/ready' endpoint is not ready \" +\n \"for maximum allowed {:d} seconds!\".format(self.max_wait_time_ready))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _is_ready(self):\n current_wait_time = 0\n start_time = time.time()\n while current_wait_time < self.max_wait_time_ready:\n try:\n response = requests.get(os.path.join(self.url, \"ready\"))\n if response.status_code == 200:\n break\n except KeyboardInterrupt:\n raise KeyboardInterrupt\n except:\n time.sleep(1)\n current_wait_time = time.time() - start_time\n if current_wait_time >= self.max_wait_time_ready:\n raise TimeoutError(\"Interrupting execution\\n'/ready' endpoint is not ready \" +\n \"for maximum allowed {:d} seconds!\".format(self.max_wait_time_ready))","def is_ready(self, addr: int, /) -> bool:","def msg_ready(self):\n if self._in_queue.qsize() == 0:\n return False\n else:\n return True","def msg_ready(self):\n if self._in_queue.qsize() == 0:\n return False\n else:\n return True","def ready(self):\n if self.socket is None or self._is_connected is False:\n return False\n\n try:\n # Use a timeout of 0 so we get an \"instant\" result\n ready, _, _ = select.select([self.socket], [], [], 0)\n except (socket.error, socket.timeout, ValueError):\n # Evt17: Transport connection closed\n self.event_queue.put('Evt17')\n return False\n\n return bool(ready)","def ready(self):\n\n if not self.running:\n return False\n\n try:\n response = requests.get(\n 'http://{}:{}/v1/kv/health'.format(\n self.running_host,\n self.running_port\n )\n )\n except requests.ConnectionError:\n return False\n\n if response.status_code == 404:\n return True\n elif response.status_code == 500:\n return False\n else:\n return False","def check_for_incoming_info(self):\n\n if self.test_message_response:\n self.parse_incoming_message(self.test_message_response)\n return True\n\n POLL_ONLY_TIMEOUT_VALUE = 0\n got_at_least_one = False\n while (True):\n readables, writables, errors = select.select([self.socket_datastream], [], [], POLL_ONLY_TIMEOUT_VALUE)\n if not self.socket_datastream in readables:\n return got_at_least_one\n got_at_least_one = True\n data, remote_ip_port = self.socket_datastream.recvfrom(MAX_EXPECTED_MSG_SIZE)\n if remote_ip_port != self.ip_port_arduino_datastream:\n errorhandler.loginfo(\"Msg from unexpected source {}\".format(remote_ip_port))\n else:\n errorhandler.logdebug(\"msg received:{}\".format(data.hex()))\n self.parse_incoming_message(data)","def _IsReady(self):\n if self.ip_address is None:\n self._GetIpAddress()\n if self.ip_address is not None:\n url = 'http://%s' % (self.ip_address)\n r = requests.get(url)\n if r.status_code == 200:\n return True\n return False","def is_ready(self) -> bool:\n pass","def connection_ready(self) -> bool:\n return self._connection_ready","def sock_avail(self):\n return self.channel.recv_ready()","def _availability_message_received(self, msg: ReceiveMessage) -> None:\n self._available = msg.payload == \"online\"\n self.async_write_ha_state()","def __check_for_messages(self):\n\n # Wait for at least poll_interval sec\n polling_interval = self.conf.messaging_server.polling_interval\n time.sleep(polling_interval)\n if self.conf.messaging_server.debug:\n LOG.debug(\"Topic {}: Checking for new messages\".format(\n self.target.topic))\n self._do()\n return True","def new_messages(self):\n ready, _, _ = select([self.socket], [], [], 0.0)\n return self.socket in ready","def on_endpoint_connected(self):\n\n self.endpoint_stream.read_until_close(self.on_endpoint_final, self.on_endpoint_data)\n self.endpoint_stream.set_close_callback(self.on_endpoint_closed)\n\n logger.info('Connection succeeded')","def _is_running(self, _):\n if self._shutdown_event.is_set():\n raise RequestProcessingError(\n \"Unable to process message - currently shutting down\"\n )","def available(self):\n from pyhs3 import STATE_LISTENING\n return self._connection.api.state == STATE_LISTENING","def ready(self):\n\n if not self.running:\n return False\n\n try:\n response = requests.get(\n 'http://{}:{}'.format(\n self.running_host,\n self.running_port\n )\n )\n except requests.ConnectionError:\n return False\n\n if response.status_code == 404:\n return True\n elif response.status_code == 500:\n return False\n else:\n return False","def ready(self):\n return self._channel.recv_ready()","def availability_message_received(msg):\n payload = msg.payload\n\n if payload == \"online\":\n self._available = True\n elif payload == \"offline\":\n self._available = False\n else:\n _LOGGER.info(f\"Invalid payload received for {self.name}\")\n return\n\n self.async_write_ha_state()","def is_ready() -> bool:\n return True","def peer_ready():\n global mtu, status\n print(\"RECEIVED REQUEST /peer/ready\")\n status = \"NOT_INITIALIZED\"\n\n from_server_queue.put(\"INITIALIZE\")\n return jsonify({\"server_mtu\": mtu, \"server_status\": status})","def running(self):\n\n return can_connect_to(APIConsumer.host, APIConsumer.port)","def check_availability(self):\n\t\tif not self.connection_is_usable:\n\t\t\treturn False\n\t\twith self.client_lock:\n\t\t\tif self.stream is None:\n\t\t\t\treturn False\n\t\t\tif self.last_ping is None or self.last_ping.age() >= self.ping_max_age:\n\t\t\t\tself.last_ping = SendPing(self, self.ping_timeout)\n\t\t\tlast_ping = self.last_ping\n\t\treturn last_ping.answered(self.ping_timeout)","def poll_create(event, context):\n endpoint_name = get_endpoint_name(event)\n logger.info('Polling for update of endpoint: %s', endpoint_name)\n return is_endpoint_ready(endpoint_name)","def has_event(self):\n return self.ser.inWaiting()","def _wait_ready(self):\n command = self._recv_from_client()\n while command != \"READY\":\n command = self._client.recv_from_client()","def is_setup(self):\n return self._market_data_sock_info.ready.is_set() and \\\n self._orders_sock_info.ready.is_set()","def poll(self):\r\n if self.channel.is_available():\r\n self.serve()\r\n return True\r\n else:\r\n return False","def check(self):\n if self.backend.poll():\n raise RuntimeError('Backend process died.')\n\n if self.esp.poll():\n raise RuntimeError('ESP process died.')","def isReady(self):\n return self._lowLevelIsReady()","def DataAvailable(self) -> bool:","def ServerIsReady( self ):\n return self.ServerIsHealthy()","async def is_server_ready(self, headers: dict[str, t.Any] = ...) -> bool:","def issuer_liveness_check():\n global app_config\n\n if app_config[\"running\"]:\n # return True until we get a shutdown request\n return True\n\n # return True until the work queue is cleared\n return tob_connection_active()","def _check_publishers_connection(self):\n rate = rospy.Rate(10) # 1hz\n while self._vel_pub.get_num_connections() == 0 and not rospy.is_shutdown():\n rospy.logdebug(\"No susbribers to _vel_pub yet so we wait and try again\")\n try:\n rate.sleep()\n except rospy.ROSInterruptException:\n # This is to avoid error when world is rested, time when backwards.\n pass\n rospy.logdebug(\"_vel_pub Publisher Connected\")\n\n rospy.logdebug(\"All Publishers READY\")","async def _track_and_propagate_available_endpoints(self) -> None:\n async for ev in self._endpoint.stream(EventBusConnected):\n self._available_endpoints = self._available_endpoints + (ev.connection_config,)\n self.logger.debug(\"New EventBus Endpoint connected %s\", ev.connection_config.name)\n # Broadcast available endpoints to all connected endpoints, giving them\n # a chance to cross connect\n await self._endpoint.broadcast(AvailableEndpointsUpdated(self._available_endpoints))\n self.logger.debug(\"Connected EventBus Endpoints %s\", self._available_endpoints)","def ready(self):\n if not self.running:\n return False\n\n try:\n response = requests.get(\n 'http://{host}:{port}/'.format(\n host=self.running_host,\n port=self.running_port\n )\n )\n except requests.ConnectionError:\n return False\n\n if response.status_code == 200:\n return True\n elif response.status_code >= 500:\n return False\n else:\n self.logger.warning('Unexpected error code from {}: {}'.format(self.image, response.status_code))\n return True","def check_publishers_connection(self):\n rate = rospy.Rate(10) # 10hz\n while (self._cart_velocity_publisher.get_num_connections() == 0 and not rospy.is_shutdown()):\n rospy.logdebug(\"No susbribers to _cart_velocity_publisher yet so we wait and try again\")\n try:\n rate.sleep()\n except rospy.ROSInterruptException:\n # This is to avoid error when world is rested, time when backwards.\n pass\n rospy.logdebug(\"_base_pub Publisher Connected\")\n\n rospy.logdebug(\"All Publishers READY\")","def isReady(self):\n return self._state in self._ReadyStates","def _check_ws(self) -> None:\n if self.websocket is False:\n raise UninitializedWebsocket('Websocket is not initialized, if you are not using a context manager '\n 'you have to await start_ws() coroutine manually')","def check_heartbeat(self):\n return True","def status_check(self):\n try:\n client = self.connect()\n client.sys.is_initialized() # make an actual network connection\n return True\n except:\n return False","def ready(self):\n if not self.running:\n return False\n\n try:\n rs = Redis(host=self.running_host, port=self.running_port)\n rs.client_list()\n return True\n except ConnectionError:\n return False\n except Exception as error:\n self.logger.error('Unexpected error: {}'.format(error))","def check_connection(self):\n pass","def ready(self):\n return True","def is_ready(cls):\n\n return False","def wait_until_ready(self):\n while not self.is_ready():\n time.sleep(0.01)","def is_polling_done(self):\n if self.message_request_more:\n return False\n \n if self.message_cache:\n return False\n \n return True","def _IsReady(self):\n return self._GetPod()['status']['phase'] != 'Pending'","def verify_connection(self):\n return self.device.verify_connection()","def check_in(self):\n etree = self._encapsulate_request(self._generate_ping())\n self.zmq_scheduler_request_queue.put_nowait(etree)","def test_check_connection(self):\n self.assertIsNotNone(app.check_connection())","def ready(self):\n if self._wait_auth:\n return False\n return True","def control_wait_for_ready(self) -> None:\n self.__logger.debug('Eva.control_wait_for_ready called')\n return self.__http_client.control_wait_for_ready()","def do_ready(self) -> bool:\n logger.info('Device ' + self.name + ' is ready.')\n return False","def check_connect_ready(self):\r\n need_no_account = not self.client.secret.know_secret()\r\n need_no_depth = not self.config.get_bool(\"gox\", \"load_fulldepth\")\r\n need_no_history = not self.config.get_bool(\"gox\", \"load_history\")\r\n need_no_depth = need_no_depth or FORCE_NO_FULLDEPTH\r\n need_no_history = need_no_history or FORCE_NO_HISTORY\r\n ready_account = \\\r\n self.ready_idkey and self.ready_info and self.orderbook.ready_owns\r\n if ready_account or need_no_account:\r\n if self.orderbook.ready_depth or need_no_depth:\r\n if self.history.ready_history or need_no_history:\r\n if self._was_disconnected:\r\n self.signal_ready(self, None)\r\n self._was_disconnected = False","def is_ready(self):\n return self._is_ready()","def watch_for_heartbeat_messages(self):\n while True:\n message = self.socket_manager.get_heartbeat_message()\n self.ensure_sender_is_known(message)\n if message.direction == \"0\":\n self.respond_to_heartbeat_message(message)\n elif message.direction == \"1\":\n self.handle_heartbeat_response(message)","def should_poll(self):\n return self.notifier.socket is not None","def poll(self):\n while self.running and reactor._started and not reactor._stopped:\n self.check_response_queue()\n sleep(0.5)","def __check_ping(self):\n if not self.communications.ping():\n self.communications.ping(True)","def _ready(cls):\n sync_call(cls.ready)","def _check_live(self):\n # pylint: disable=protected-access\n if self._shutdown.is_set():\n raise ValueError(\n \"The handler has already been shutdown. Please use ServiceBusClient to \"\n \"create a new instance.\"\n )\n # The following client validation is for two purposes in a session receiver:\n # 1. self._session._lock_lost is set when a session receiver encounters a connection error,\n # once there's a connection error, we don't retry on the session entity and simply raise SessionlockLostError.\n # 2. self._session._lock_expired is a hot fix as client validation for session lock expiration.\n # Because currently uamqp doesn't have the ability to detect remote session lock lost.\n # Usually the service would send a detach frame once a session lock gets expired, however, in the edge case\n # when we drain messages in a queue and try to settle messages after lock expiration,\n # we are not able to receive the detach frame by calling uamqp connection.work(),\n # Eventually this should be a fix in the uamqp library.\n # see issue: https://github.com/Azure/azure-uamqp-python/issues/183\n try:\n if self._session and (\n self._session._lock_lost or self._session._lock_expired\n ):\n raise SessionLockLostError(error=self._session.auto_renew_error)\n except AttributeError:\n pass","def check_up(self):\n\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(BusController.PULSE_DELAY * 0.8)\n output = True\n start_time = time.time()\n try:\n s.connect((self.__address[0], BusController.HEART_BEAT_PORT))\n data = \"Check\".encode()\n s.send(data)\n data = s.recv(1024).decode()\n if int(data) != int(self.__id):\n print(f\"bus had the wrong ID\\nwas supposed to be {self.__id}, but received {data}\")\n output = False\n # listen for an answer\n except Exception as e:\n print(f\"exception in check_up (heart_beat) look:{e}\")\n print(f\"something went wrong, couldn't establish connection with {self.__address}\")\n output = False\n s.close()\n return output","def serverExplicitReady (self):\n self.server_ready.set()","def ready(self, all_ok, message):\n if self.status != WAIT_PROCESS:\n logging.error(\"Connection status switched to {} when\"\n \"processing requests. Server bug?\".format(self.status))\n all_ok = False\n\n if not all_ok:\n self.close()\n self._server.wake_up()\n return\n self.len = 0\n if len(message) == 0:\n # it was a oneway request, do not write answer\n self.message = b''\n self._set_status(WAIT_LEN)\n else:\n self.message = message\n self._set_status(SEND_ANSWER)\n self._server.wake_up()","def is_msg_inited(self):\n pass","async def wait_until_ready(self):\n await self._ready.wait()","def is_ready(self):\n if self.producer is None:\n return False\n return True","def has_an_incomming_message(self):\n return self.pipe_start.poll(1)","def _check(self):\n\t\tif not self._raven:\n\t\t\traise NoDeviceFoundException","def connection_ready(self, active: bool):\n self._connection_ready = active","def is_ready(self) -> bool:\n return self._ready.is_set()","def is_connected(self) -> bool:\n try:\n # When MSG_PEEK is used the data is treated as unread\n # and the next recv shall still return this data\n data = self.socket.recv(self.BUFFER_SIZE, socket.MSG_PEEK)\n if len(data) == 0:\n return False\n return True\n except ConnectionResetError:\n return False","def ready(self):\n return not self._searchthread.is_alive()","def ready(cls):\n pass","def is_ready(cls):\n\n return SUB_NOTIFY_READY","def is_ready(self):\n try:\n self._data\n except ValidationError as ve:\n log.error(f\"{self.endpoint} relation data not yet valid. ({ve}\")\n return False\n if self._data is None:\n log.error(f\"{self.endpoint} relation data not yet available.\")\n return False\n return all(\n field is not None\n for field in [\n self._data.auth_url,\n self._data.username,\n self._data.password,\n self._data.user_domain_name,\n self._data.project_domain_name,\n self._data.project_name,\n ]\n )","def isConnected(self):\n return self.transport is not None and self.started","async def test_connection(self):\n await self.webhook_connection.connect()\n assert self.webhook_connection.is_connected is True","def check_event_status(self):\n pass","def _check_queue(self):\n self._process_incoming_queue_messages()\n self._root.after(200, self._check_queue)","def test_readiness_endpoint(self):\n url = f'{BASE_URL}/ready'\n response = requests.get(url)\n response_json = response.json()\n assert response.status_code == 503\n assert response_json['status'] == 503","def ready(self):\n return self.dead or self._exc_info is not None","def is_ready(self):\n return self._is_ready","def available(self) -> bool:\n\n if CORE_STATUS_STATE in self.tahoma_device.active_states:\n return bool(\n self.tahoma_device.active_states.get(CORE_STATUS_STATE) == \"available\"\n )\n\n if CORE_SENSOR_DEFECT_STATE in self.tahoma_device.active_states:\n return (\n self.tahoma_device.active_states.get(CORE_SENSOR_DEFECT_STATE) != \"dead\"\n )\n\n # A RTS power socket doesn't have a feedback channel,\n # so we must assume the socket is available.\n return True","async def check_connection_status(self):\n while True:\n if not self.connected:\n self.log.error(\"Lost connection to spa, attempting reconnect.\")\n await self.connect()\n await asyncio.sleep(10)\n continue\n if (self.lastupd + 5 * self.sleep_time) < time.time():\n self.log.error(\"Spa stopped responding, requesting panel config.\")\n await self.send_panel_req(0, 1)\n await asyncio.sleep(self.sleep_time)","def is_ready(self):\n return self.__is_ready","def is_connected(self):\n return self.socket is not None and self.socket.connected and super(WebsocketTransport, self).is_connected()","def connectionInitialized(self):\n log.msg('Connection Initialized')\n self.send(AvailablePresence())\n self.xmlstream.addObserver(\"/iq[@type='result']\", self.handleRequest)\n self.xmlstream.addObserver(\"/message\", self._onMessage)","def check_ready(self):\r\n print \"Checking ready\"\r\n\t\tif self.game.trough.is_full():\r\n print \"Ready\"\r\n\t\t\tself.ready()\r\n\t\t\treturn True\r\n\t\tprint \"Not Ready\"\r\n\t\treturn False","def check_connection(self):\n return False","def is_ready(self, calculate = True, assert_r = True):\r\n\r\n if not self.is_open(): return False\r\n if calculate: self._calculate()\r\n if not self.end_headers: return False\r\n if not self.end_stream: return False\r\n if assert_r: self.assert_ready()\r\n return True","def isListening(self):\n if not self.proxy:\n self.proxy = self.session.service(\"ALExpressiveListening\")\n return self.proxy.isListening()","def _check_endpoints_last_communication(self):\n while self._thread_running:\n endpoints = EndpointService.get_all_endpoints()\n for endpoint in endpoints:\n if datetime.now() - endpoint.lastCommunication > timedelta(minutes=TOKEN_EXPIRE_TIME):\n EventService.add_event(Event(\"0\", \"Lost Endpoint At \" +\n str(endpoint[EndpointKeys.LAST_COMMUNICATION_KEY]),\n \"Report\", \"IDLE\", endpoint[EndpointKeys.HOSTNAME_KEY],\n endpoint[EndpointKeys.IP_ADDRESS_KEY]))\n EndpointService.delete_endpoint(endpoint.id)\n sleep(SLEEP_TIME)","def on_request_complete(self) -> Union[socket.socket, bool]:\n pass # pragma: no cover","def _check_for_life_signs(self):\n self._lock.acquire()\n if not self._running.is_set():\n return False\n try:\n if self._writes_since_check == 0:\n self.send_heartbeat()\n if self._reads_since_check == 0:\n self._threshold += 1\n if self._threshold >= 2:\n self._running.set()\n message = (\n 'Connection dead, no heartbeat or data received in >= '\n '%ds' % (\n self._interval * 2\n )\n )\n why = AMQPConnectionError(message)\n if self._exceptions is None:\n raise why\n self._exceptions.append(why)\n return False\n else:\n self._threshold = 0\n finally:\n self._reads_since_check = 0\n self._writes_since_check = 0\n self._lock.release()\n if self._timer:\n self._start_new_timer()\n return True","def has_message_available(self):\n return not self.feedback_log.empty()","def verify_state(self):\n if self.state == CONNECTING:\n self.state = OPEN\n\n self.conn.on_open(self.conn_info)"],"string":"[\n \"def _is_ready(self):\\n current_wait_time = 0\\n start_time = time.time()\\n while current_wait_time < self.max_wait_time_ready:\\n try:\\n response = requests.get(os.path.join(self.url, \\\"ready\\\"))\\n if response.status_code == 200:\\n break\\n except KeyboardInterrupt:\\n raise KeyboardInterrupt\\n except:\\n time.sleep(1)\\n current_wait_time = time.time() - start_time\\n if current_wait_time >= self.max_wait_time_ready:\\n raise TimeoutError(\\\"Interrupting execution\\\\n'/ready' endpoint is not ready \\\" +\\n \\\"for maximum allowed {:d} seconds!\\\".format(self.max_wait_time_ready))\",\n \"def is_ready(self, addr: int, /) -> bool:\",\n \"def msg_ready(self):\\n if self._in_queue.qsize() == 0:\\n return False\\n else:\\n return True\",\n \"def msg_ready(self):\\n if self._in_queue.qsize() == 0:\\n return False\\n else:\\n return True\",\n \"def ready(self):\\n if self.socket is None or self._is_connected is False:\\n return False\\n\\n try:\\n # Use a timeout of 0 so we get an \\\"instant\\\" result\\n ready, _, _ = select.select([self.socket], [], [], 0)\\n except (socket.error, socket.timeout, ValueError):\\n # Evt17: Transport connection closed\\n self.event_queue.put('Evt17')\\n return False\\n\\n return bool(ready)\",\n \"def ready(self):\\n\\n if not self.running:\\n return False\\n\\n try:\\n response = requests.get(\\n 'http://{}:{}/v1/kv/health'.format(\\n self.running_host,\\n self.running_port\\n )\\n )\\n except requests.ConnectionError:\\n return False\\n\\n if response.status_code == 404:\\n return True\\n elif response.status_code == 500:\\n return False\\n else:\\n return False\",\n \"def check_for_incoming_info(self):\\n\\n if self.test_message_response:\\n self.parse_incoming_message(self.test_message_response)\\n return True\\n\\n POLL_ONLY_TIMEOUT_VALUE = 0\\n got_at_least_one = False\\n while (True):\\n readables, writables, errors = select.select([self.socket_datastream], [], [], POLL_ONLY_TIMEOUT_VALUE)\\n if not self.socket_datastream in readables:\\n return got_at_least_one\\n got_at_least_one = True\\n data, remote_ip_port = self.socket_datastream.recvfrom(MAX_EXPECTED_MSG_SIZE)\\n if remote_ip_port != self.ip_port_arduino_datastream:\\n errorhandler.loginfo(\\\"Msg from unexpected source {}\\\".format(remote_ip_port))\\n else:\\n errorhandler.logdebug(\\\"msg received:{}\\\".format(data.hex()))\\n self.parse_incoming_message(data)\",\n \"def _IsReady(self):\\n if self.ip_address is None:\\n self._GetIpAddress()\\n if self.ip_address is not None:\\n url = 'http://%s' % (self.ip_address)\\n r = requests.get(url)\\n if r.status_code == 200:\\n return True\\n return False\",\n \"def is_ready(self) -> bool:\\n pass\",\n \"def connection_ready(self) -> bool:\\n return self._connection_ready\",\n \"def sock_avail(self):\\n return self.channel.recv_ready()\",\n \"def _availability_message_received(self, msg: ReceiveMessage) -> None:\\n self._available = msg.payload == \\\"online\\\"\\n self.async_write_ha_state()\",\n \"def __check_for_messages(self):\\n\\n # Wait for at least poll_interval sec\\n polling_interval = self.conf.messaging_server.polling_interval\\n time.sleep(polling_interval)\\n if self.conf.messaging_server.debug:\\n LOG.debug(\\\"Topic {}: Checking for new messages\\\".format(\\n self.target.topic))\\n self._do()\\n return True\",\n \"def new_messages(self):\\n ready, _, _ = select([self.socket], [], [], 0.0)\\n return self.socket in ready\",\n \"def on_endpoint_connected(self):\\n\\n self.endpoint_stream.read_until_close(self.on_endpoint_final, self.on_endpoint_data)\\n self.endpoint_stream.set_close_callback(self.on_endpoint_closed)\\n\\n logger.info('Connection succeeded')\",\n \"def _is_running(self, _):\\n if self._shutdown_event.is_set():\\n raise RequestProcessingError(\\n \\\"Unable to process message - currently shutting down\\\"\\n )\",\n \"def available(self):\\n from pyhs3 import STATE_LISTENING\\n return self._connection.api.state == STATE_LISTENING\",\n \"def ready(self):\\n\\n if not self.running:\\n return False\\n\\n try:\\n response = requests.get(\\n 'http://{}:{}'.format(\\n self.running_host,\\n self.running_port\\n )\\n )\\n except requests.ConnectionError:\\n return False\\n\\n if response.status_code == 404:\\n return True\\n elif response.status_code == 500:\\n return False\\n else:\\n return False\",\n \"def ready(self):\\n return self._channel.recv_ready()\",\n \"def availability_message_received(msg):\\n payload = msg.payload\\n\\n if payload == \\\"online\\\":\\n self._available = True\\n elif payload == \\\"offline\\\":\\n self._available = False\\n else:\\n _LOGGER.info(f\\\"Invalid payload received for {self.name}\\\")\\n return\\n\\n self.async_write_ha_state()\",\n \"def is_ready() -> bool:\\n return True\",\n \"def peer_ready():\\n global mtu, status\\n print(\\\"RECEIVED REQUEST /peer/ready\\\")\\n status = \\\"NOT_INITIALIZED\\\"\\n\\n from_server_queue.put(\\\"INITIALIZE\\\")\\n return jsonify({\\\"server_mtu\\\": mtu, \\\"server_status\\\": status})\",\n \"def running(self):\\n\\n return can_connect_to(APIConsumer.host, APIConsumer.port)\",\n \"def check_availability(self):\\n\\t\\tif not self.connection_is_usable:\\n\\t\\t\\treturn False\\n\\t\\twith self.client_lock:\\n\\t\\t\\tif self.stream is None:\\n\\t\\t\\t\\treturn False\\n\\t\\t\\tif self.last_ping is None or self.last_ping.age() >= self.ping_max_age:\\n\\t\\t\\t\\tself.last_ping = SendPing(self, self.ping_timeout)\\n\\t\\t\\tlast_ping = self.last_ping\\n\\t\\treturn last_ping.answered(self.ping_timeout)\",\n \"def poll_create(event, context):\\n endpoint_name = get_endpoint_name(event)\\n logger.info('Polling for update of endpoint: %s', endpoint_name)\\n return is_endpoint_ready(endpoint_name)\",\n \"def has_event(self):\\n return self.ser.inWaiting()\",\n \"def _wait_ready(self):\\n command = self._recv_from_client()\\n while command != \\\"READY\\\":\\n command = self._client.recv_from_client()\",\n \"def is_setup(self):\\n return self._market_data_sock_info.ready.is_set() and \\\\\\n self._orders_sock_info.ready.is_set()\",\n \"def poll(self):\\r\\n if self.channel.is_available():\\r\\n self.serve()\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def check(self):\\n if self.backend.poll():\\n raise RuntimeError('Backend process died.')\\n\\n if self.esp.poll():\\n raise RuntimeError('ESP process died.')\",\n \"def isReady(self):\\n return self._lowLevelIsReady()\",\n \"def DataAvailable(self) -> bool:\",\n \"def ServerIsReady( self ):\\n return self.ServerIsHealthy()\",\n \"async def is_server_ready(self, headers: dict[str, t.Any] = ...) -> bool:\",\n \"def issuer_liveness_check():\\n global app_config\\n\\n if app_config[\\\"running\\\"]:\\n # return True until we get a shutdown request\\n return True\\n\\n # return True until the work queue is cleared\\n return tob_connection_active()\",\n \"def _check_publishers_connection(self):\\n rate = rospy.Rate(10) # 1hz\\n while self._vel_pub.get_num_connections() == 0 and not rospy.is_shutdown():\\n rospy.logdebug(\\\"No susbribers to _vel_pub yet so we wait and try again\\\")\\n try:\\n rate.sleep()\\n except rospy.ROSInterruptException:\\n # This is to avoid error when world is rested, time when backwards.\\n pass\\n rospy.logdebug(\\\"_vel_pub Publisher Connected\\\")\\n\\n rospy.logdebug(\\\"All Publishers READY\\\")\",\n \"async def _track_and_propagate_available_endpoints(self) -> None:\\n async for ev in self._endpoint.stream(EventBusConnected):\\n self._available_endpoints = self._available_endpoints + (ev.connection_config,)\\n self.logger.debug(\\\"New EventBus Endpoint connected %s\\\", ev.connection_config.name)\\n # Broadcast available endpoints to all connected endpoints, giving them\\n # a chance to cross connect\\n await self._endpoint.broadcast(AvailableEndpointsUpdated(self._available_endpoints))\\n self.logger.debug(\\\"Connected EventBus Endpoints %s\\\", self._available_endpoints)\",\n \"def ready(self):\\n if not self.running:\\n return False\\n\\n try:\\n response = requests.get(\\n 'http://{host}:{port}/'.format(\\n host=self.running_host,\\n port=self.running_port\\n )\\n )\\n except requests.ConnectionError:\\n return False\\n\\n if response.status_code == 200:\\n return True\\n elif response.status_code >= 500:\\n return False\\n else:\\n self.logger.warning('Unexpected error code from {}: {}'.format(self.image, response.status_code))\\n return True\",\n \"def check_publishers_connection(self):\\n rate = rospy.Rate(10) # 10hz\\n while (self._cart_velocity_publisher.get_num_connections() == 0 and not rospy.is_shutdown()):\\n rospy.logdebug(\\\"No susbribers to _cart_velocity_publisher yet so we wait and try again\\\")\\n try:\\n rate.sleep()\\n except rospy.ROSInterruptException:\\n # This is to avoid error when world is rested, time when backwards.\\n pass\\n rospy.logdebug(\\\"_base_pub Publisher Connected\\\")\\n\\n rospy.logdebug(\\\"All Publishers READY\\\")\",\n \"def isReady(self):\\n return self._state in self._ReadyStates\",\n \"def _check_ws(self) -> None:\\n if self.websocket is False:\\n raise UninitializedWebsocket('Websocket is not initialized, if you are not using a context manager '\\n 'you have to await start_ws() coroutine manually')\",\n \"def check_heartbeat(self):\\n return True\",\n \"def status_check(self):\\n try:\\n client = self.connect()\\n client.sys.is_initialized() # make an actual network connection\\n return True\\n except:\\n return False\",\n \"def ready(self):\\n if not self.running:\\n return False\\n\\n try:\\n rs = Redis(host=self.running_host, port=self.running_port)\\n rs.client_list()\\n return True\\n except ConnectionError:\\n return False\\n except Exception as error:\\n self.logger.error('Unexpected error: {}'.format(error))\",\n \"def check_connection(self):\\n pass\",\n \"def ready(self):\\n return True\",\n \"def is_ready(cls):\\n\\n return False\",\n \"def wait_until_ready(self):\\n while not self.is_ready():\\n time.sleep(0.01)\",\n \"def is_polling_done(self):\\n if self.message_request_more:\\n return False\\n \\n if self.message_cache:\\n return False\\n \\n return True\",\n \"def _IsReady(self):\\n return self._GetPod()['status']['phase'] != 'Pending'\",\n \"def verify_connection(self):\\n return self.device.verify_connection()\",\n \"def check_in(self):\\n etree = self._encapsulate_request(self._generate_ping())\\n self.zmq_scheduler_request_queue.put_nowait(etree)\",\n \"def test_check_connection(self):\\n self.assertIsNotNone(app.check_connection())\",\n \"def ready(self):\\n if self._wait_auth:\\n return False\\n return True\",\n \"def control_wait_for_ready(self) -> None:\\n self.__logger.debug('Eva.control_wait_for_ready called')\\n return self.__http_client.control_wait_for_ready()\",\n \"def do_ready(self) -> bool:\\n logger.info('Device ' + self.name + ' is ready.')\\n return False\",\n \"def check_connect_ready(self):\\r\\n need_no_account = not self.client.secret.know_secret()\\r\\n need_no_depth = not self.config.get_bool(\\\"gox\\\", \\\"load_fulldepth\\\")\\r\\n need_no_history = not self.config.get_bool(\\\"gox\\\", \\\"load_history\\\")\\r\\n need_no_depth = need_no_depth or FORCE_NO_FULLDEPTH\\r\\n need_no_history = need_no_history or FORCE_NO_HISTORY\\r\\n ready_account = \\\\\\r\\n self.ready_idkey and self.ready_info and self.orderbook.ready_owns\\r\\n if ready_account or need_no_account:\\r\\n if self.orderbook.ready_depth or need_no_depth:\\r\\n if self.history.ready_history or need_no_history:\\r\\n if self._was_disconnected:\\r\\n self.signal_ready(self, None)\\r\\n self._was_disconnected = False\",\n \"def is_ready(self):\\n return self._is_ready()\",\n \"def watch_for_heartbeat_messages(self):\\n while True:\\n message = self.socket_manager.get_heartbeat_message()\\n self.ensure_sender_is_known(message)\\n if message.direction == \\\"0\\\":\\n self.respond_to_heartbeat_message(message)\\n elif message.direction == \\\"1\\\":\\n self.handle_heartbeat_response(message)\",\n \"def should_poll(self):\\n return self.notifier.socket is not None\",\n \"def poll(self):\\n while self.running and reactor._started and not reactor._stopped:\\n self.check_response_queue()\\n sleep(0.5)\",\n \"def __check_ping(self):\\n if not self.communications.ping():\\n self.communications.ping(True)\",\n \"def _ready(cls):\\n sync_call(cls.ready)\",\n \"def _check_live(self):\\n # pylint: disable=protected-access\\n if self._shutdown.is_set():\\n raise ValueError(\\n \\\"The handler has already been shutdown. Please use ServiceBusClient to \\\"\\n \\\"create a new instance.\\\"\\n )\\n # The following client validation is for two purposes in a session receiver:\\n # 1. self._session._lock_lost is set when a session receiver encounters a connection error,\\n # once there's a connection error, we don't retry on the session entity and simply raise SessionlockLostError.\\n # 2. self._session._lock_expired is a hot fix as client validation for session lock expiration.\\n # Because currently uamqp doesn't have the ability to detect remote session lock lost.\\n # Usually the service would send a detach frame once a session lock gets expired, however, in the edge case\\n # when we drain messages in a queue and try to settle messages after lock expiration,\\n # we are not able to receive the detach frame by calling uamqp connection.work(),\\n # Eventually this should be a fix in the uamqp library.\\n # see issue: https://github.com/Azure/azure-uamqp-python/issues/183\\n try:\\n if self._session and (\\n self._session._lock_lost or self._session._lock_expired\\n ):\\n raise SessionLockLostError(error=self._session.auto_renew_error)\\n except AttributeError:\\n pass\",\n \"def check_up(self):\\n\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.settimeout(BusController.PULSE_DELAY * 0.8)\\n output = True\\n start_time = time.time()\\n try:\\n s.connect((self.__address[0], BusController.HEART_BEAT_PORT))\\n data = \\\"Check\\\".encode()\\n s.send(data)\\n data = s.recv(1024).decode()\\n if int(data) != int(self.__id):\\n print(f\\\"bus had the wrong ID\\\\nwas supposed to be {self.__id}, but received {data}\\\")\\n output = False\\n # listen for an answer\\n except Exception as e:\\n print(f\\\"exception in check_up (heart_beat) look:{e}\\\")\\n print(f\\\"something went wrong, couldn't establish connection with {self.__address}\\\")\\n output = False\\n s.close()\\n return output\",\n \"def serverExplicitReady (self):\\n self.server_ready.set()\",\n \"def ready(self, all_ok, message):\\n if self.status != WAIT_PROCESS:\\n logging.error(\\\"Connection status switched to {} when\\\"\\n \\\"processing requests. Server bug?\\\".format(self.status))\\n all_ok = False\\n\\n if not all_ok:\\n self.close()\\n self._server.wake_up()\\n return\\n self.len = 0\\n if len(message) == 0:\\n # it was a oneway request, do not write answer\\n self.message = b''\\n self._set_status(WAIT_LEN)\\n else:\\n self.message = message\\n self._set_status(SEND_ANSWER)\\n self._server.wake_up()\",\n \"def is_msg_inited(self):\\n pass\",\n \"async def wait_until_ready(self):\\n await self._ready.wait()\",\n \"def is_ready(self):\\n if self.producer is None:\\n return False\\n return True\",\n \"def has_an_incomming_message(self):\\n return self.pipe_start.poll(1)\",\n \"def _check(self):\\n\\t\\tif not self._raven:\\n\\t\\t\\traise NoDeviceFoundException\",\n \"def connection_ready(self, active: bool):\\n self._connection_ready = active\",\n \"def is_ready(self) -> bool:\\n return self._ready.is_set()\",\n \"def is_connected(self) -> bool:\\n try:\\n # When MSG_PEEK is used the data is treated as unread\\n # and the next recv shall still return this data\\n data = self.socket.recv(self.BUFFER_SIZE, socket.MSG_PEEK)\\n if len(data) == 0:\\n return False\\n return True\\n except ConnectionResetError:\\n return False\",\n \"def ready(self):\\n return not self._searchthread.is_alive()\",\n \"def ready(cls):\\n pass\",\n \"def is_ready(cls):\\n\\n return SUB_NOTIFY_READY\",\n \"def is_ready(self):\\n try:\\n self._data\\n except ValidationError as ve:\\n log.error(f\\\"{self.endpoint} relation data not yet valid. ({ve}\\\")\\n return False\\n if self._data is None:\\n log.error(f\\\"{self.endpoint} relation data not yet available.\\\")\\n return False\\n return all(\\n field is not None\\n for field in [\\n self._data.auth_url,\\n self._data.username,\\n self._data.password,\\n self._data.user_domain_name,\\n self._data.project_domain_name,\\n self._data.project_name,\\n ]\\n )\",\n \"def isConnected(self):\\n return self.transport is not None and self.started\",\n \"async def test_connection(self):\\n await self.webhook_connection.connect()\\n assert self.webhook_connection.is_connected is True\",\n \"def check_event_status(self):\\n pass\",\n \"def _check_queue(self):\\n self._process_incoming_queue_messages()\\n self._root.after(200, self._check_queue)\",\n \"def test_readiness_endpoint(self):\\n url = f'{BASE_URL}/ready'\\n response = requests.get(url)\\n response_json = response.json()\\n assert response.status_code == 503\\n assert response_json['status'] == 503\",\n \"def ready(self):\\n return self.dead or self._exc_info is not None\",\n \"def is_ready(self):\\n return self._is_ready\",\n \"def available(self) -> bool:\\n\\n if CORE_STATUS_STATE in self.tahoma_device.active_states:\\n return bool(\\n self.tahoma_device.active_states.get(CORE_STATUS_STATE) == \\\"available\\\"\\n )\\n\\n if CORE_SENSOR_DEFECT_STATE in self.tahoma_device.active_states:\\n return (\\n self.tahoma_device.active_states.get(CORE_SENSOR_DEFECT_STATE) != \\\"dead\\\"\\n )\\n\\n # A RTS power socket doesn't have a feedback channel,\\n # so we must assume the socket is available.\\n return True\",\n \"async def check_connection_status(self):\\n while True:\\n if not self.connected:\\n self.log.error(\\\"Lost connection to spa, attempting reconnect.\\\")\\n await self.connect()\\n await asyncio.sleep(10)\\n continue\\n if (self.lastupd + 5 * self.sleep_time) < time.time():\\n self.log.error(\\\"Spa stopped responding, requesting panel config.\\\")\\n await self.send_panel_req(0, 1)\\n await asyncio.sleep(self.sleep_time)\",\n \"def is_ready(self):\\n return self.__is_ready\",\n \"def is_connected(self):\\n return self.socket is not None and self.socket.connected and super(WebsocketTransport, self).is_connected()\",\n \"def connectionInitialized(self):\\n log.msg('Connection Initialized')\\n self.send(AvailablePresence())\\n self.xmlstream.addObserver(\\\"/iq[@type='result']\\\", self.handleRequest)\\n self.xmlstream.addObserver(\\\"/message\\\", self._onMessage)\",\n \"def check_ready(self):\\r\\n print \\\"Checking ready\\\"\\r\\n\\t\\tif self.game.trough.is_full():\\r\\n print \\\"Ready\\\"\\r\\n\\t\\t\\tself.ready()\\r\\n\\t\\t\\treturn True\\r\\n\\t\\tprint \\\"Not Ready\\\"\\r\\n\\t\\treturn False\",\n \"def check_connection(self):\\n return False\",\n \"def is_ready(self, calculate = True, assert_r = True):\\r\\n\\r\\n if not self.is_open(): return False\\r\\n if calculate: self._calculate()\\r\\n if not self.end_headers: return False\\r\\n if not self.end_stream: return False\\r\\n if assert_r: self.assert_ready()\\r\\n return True\",\n \"def isListening(self):\\n if not self.proxy:\\n self.proxy = self.session.service(\\\"ALExpressiveListening\\\")\\n return self.proxy.isListening()\",\n \"def _check_endpoints_last_communication(self):\\n while self._thread_running:\\n endpoints = EndpointService.get_all_endpoints()\\n for endpoint in endpoints:\\n if datetime.now() - endpoint.lastCommunication > timedelta(minutes=TOKEN_EXPIRE_TIME):\\n EventService.add_event(Event(\\\"0\\\", \\\"Lost Endpoint At \\\" +\\n str(endpoint[EndpointKeys.LAST_COMMUNICATION_KEY]),\\n \\\"Report\\\", \\\"IDLE\\\", endpoint[EndpointKeys.HOSTNAME_KEY],\\n endpoint[EndpointKeys.IP_ADDRESS_KEY]))\\n EndpointService.delete_endpoint(endpoint.id)\\n sleep(SLEEP_TIME)\",\n \"def on_request_complete(self) -> Union[socket.socket, bool]:\\n pass # pragma: no cover\",\n \"def _check_for_life_signs(self):\\n self._lock.acquire()\\n if not self._running.is_set():\\n return False\\n try:\\n if self._writes_since_check == 0:\\n self.send_heartbeat()\\n if self._reads_since_check == 0:\\n self._threshold += 1\\n if self._threshold >= 2:\\n self._running.set()\\n message = (\\n 'Connection dead, no heartbeat or data received in >= '\\n '%ds' % (\\n self._interval * 2\\n )\\n )\\n why = AMQPConnectionError(message)\\n if self._exceptions is None:\\n raise why\\n self._exceptions.append(why)\\n return False\\n else:\\n self._threshold = 0\\n finally:\\n self._reads_since_check = 0\\n self._writes_since_check = 0\\n self._lock.release()\\n if self._timer:\\n self._start_new_timer()\\n return True\",\n \"def has_message_available(self):\\n return not self.feedback_log.empty()\",\n \"def verify_state(self):\\n if self.state == CONNECTING:\\n self.state = OPEN\\n\\n self.conn.on_open(self.conn_info)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69959426","0.65242666","0.6468929","0.6468929","0.6450049","0.637877","0.635823","0.63148576","0.6309489","0.6287994","0.62630296","0.6251497","0.62316585","0.6223134","0.6218407","0.6216059","0.6203177","0.6190777","0.6145414","0.61205196","0.6097501","0.6089998","0.6074302","0.6050107","0.6044621","0.6037907","0.6035147","0.6031078","0.6006711","0.59870124","0.5979693","0.59600437","0.5953754","0.59533226","0.59353596","0.5917118","0.5912357","0.59045464","0.5896007","0.5877564","0.5853759","0.58439416","0.5841587","0.5827007","0.58255905","0.5824483","0.5818285","0.5817004","0.5814516","0.5810749","0.5801971","0.5798608","0.5785363","0.577616","0.5765711","0.57637274","0.5760567","0.57510626","0.57368696","0.5729322","0.571634","0.5713905","0.5713388","0.5712401","0.5697549","0.56967926","0.5696222","0.5695811","0.56832117","0.5682617","0.56811804","0.5679922","0.56786144","0.56779003","0.56767094","0.5673051","0.56712073","0.56656206","0.5661832","0.566027","0.56593513","0.56586766","0.5657822","0.56547725","0.5649923","0.5645293","0.56373584","0.56320083","0.5618305","0.5616652","0.5609783","0.5603296","0.56016755","0.56013614","0.5594981","0.5590812","0.5586301","0.5580167","0.55794156","0.55772555"],"string":"[\n \"0.69959426\",\n \"0.65242666\",\n \"0.6468929\",\n \"0.6468929\",\n \"0.6450049\",\n \"0.637877\",\n \"0.635823\",\n \"0.63148576\",\n \"0.6309489\",\n \"0.6287994\",\n \"0.62630296\",\n \"0.6251497\",\n \"0.62316585\",\n \"0.6223134\",\n \"0.6218407\",\n \"0.6216059\",\n \"0.6203177\",\n \"0.6190777\",\n \"0.6145414\",\n \"0.61205196\",\n \"0.6097501\",\n \"0.6089998\",\n \"0.6074302\",\n \"0.6050107\",\n \"0.6044621\",\n \"0.6037907\",\n \"0.6035147\",\n \"0.6031078\",\n \"0.6006711\",\n \"0.59870124\",\n \"0.5979693\",\n \"0.59600437\",\n \"0.5953754\",\n \"0.59533226\",\n \"0.59353596\",\n \"0.5917118\",\n \"0.5912357\",\n \"0.59045464\",\n \"0.5896007\",\n \"0.5877564\",\n \"0.5853759\",\n \"0.58439416\",\n \"0.5841587\",\n \"0.5827007\",\n \"0.58255905\",\n \"0.5824483\",\n \"0.5818285\",\n \"0.5817004\",\n \"0.5814516\",\n \"0.5810749\",\n \"0.5801971\",\n \"0.5798608\",\n \"0.5785363\",\n \"0.577616\",\n \"0.5765711\",\n \"0.57637274\",\n \"0.5760567\",\n \"0.57510626\",\n \"0.57368696\",\n \"0.5729322\",\n \"0.571634\",\n \"0.5713905\",\n \"0.5713388\",\n \"0.5712401\",\n \"0.5697549\",\n \"0.56967926\",\n \"0.5696222\",\n \"0.5695811\",\n \"0.56832117\",\n \"0.5682617\",\n \"0.56811804\",\n \"0.5679922\",\n \"0.56786144\",\n \"0.56779003\",\n \"0.56767094\",\n \"0.5673051\",\n \"0.56712073\",\n \"0.56656206\",\n \"0.5661832\",\n \"0.566027\",\n \"0.56593513\",\n \"0.56586766\",\n \"0.5657822\",\n \"0.56547725\",\n \"0.5649923\",\n \"0.5645293\",\n \"0.56373584\",\n \"0.56320083\",\n \"0.5618305\",\n \"0.5616652\",\n \"0.5609783\",\n \"0.5603296\",\n \"0.56016755\",\n \"0.56013614\",\n \"0.5594981\",\n \"0.5590812\",\n \"0.5586301\",\n \"0.5580167\",\n \"0.55794156\",\n \"0.55772555\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69550943"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":77069,"cells":{"query":{"kind":"string","value":"Queries the endpoint with all the files specified in 'in_dir' folder. This method goes over the list of files, sends every of them to the specified endpoint and put all the results into one DataFrame. Each column of the dataframe corresponds to the firstlevel key in the response json. If the json dict is nested then the values of the cells would be dicts themselves. In case of failure in one of the requests the corresponding line would contain all NaN."},"document":{"kind":"string","value":"def query(self, n_jobs=1) -> str:\n\n def get_one_answer(file):\n try:\n ans = ask_endpoint(file, os.path.join(self.url, \"predict\"))\n except KeyboardInterrupt:\n raise KeyboardInterrupt\n except:\n ans = {}\n return json.dumps(ans)\n\n # send each file to the endpoint\n # note that in case of many files parallel queries might speed up the processing drastically\n answers = Parallel(n_jobs=n_jobs)(delayed(get_one_answer)(file) for file in tqdm(self.filelist))\n\n # put all answers to the dataframe\n answers = pd.DataFrame(answers, columns=[\"answer\"])\n answers[\"answer\"] = answers[\"answer\"].apply(lambda x: json.loads(x))\n answers[\"path\"] = self.filelist\n\n # create report folder\n os.makedirs(self._report_path, exist_ok=False)\n # save raw answers\n answers.to_csv(os.path.join(self._report_path, \"raw_answers.csv\"), index=False)\n\n # parse answers\n parsed_answers = pd.DataFrame(columns=[\"path\",\n \"row_number\",\n \"prediction\"])\n for _, row in answers.iterrows():\n for _num, _pred in enumerate(row[\"answer\"][\"predictions\"]):\n parsed_answers.loc[len(parsed_answers)] = [row[\"path\"], int(_num), _pred]\n # save parsed answers\n parsed_answers = parsed_answers.sort_values(by=[\"path\", \"row_number\"]).reset_index(drop=True)\n parsed_answers.to_csv(os.path.join(self._report_path, \"parsed_answers.csv\"), index=False)\n\n return self._report_path"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_data(directory):\n dictlist = []\n cols = ['title', 'text', 'authors', 'num_images', 'domain', 'url']\n\n folders = glob.glob(directory + '/*')\n for index, subdir in enumerate(folders):\n\n file_path = glob.glob(subdir + '/*')\n\n #check if glob returned a valid file path (non-empty list)\n if len(file_path) == 1:\n file = open(file_path[0]).read()\n jsondata = json.loads(file)\n dictlist.append(scaledict(jsondata))\n return pd.DataFrame(dictlist, columns=cols)","def query(root_directory, request):\n assert isinstance(request, DataRequest)\n\n # Initialize data objects\n data_map = {}\n\n request_file_names = list_requested_files(request)\n existing_files = ls_json(root_directory)\n\n # Load request files\n print(\"Loading %d files in total.\" % (len(request_file_names)))\n for (count, file_name) in enumerate(request_file_names):\n print(\"File %d: %s\" % (count + 1, file_name))\n # File does not exist.\n if file_name not in existing_files:\n print(\"File does not exist\\n\")\n continue\n\n # Open file and parse json\n json_data = load_file(root_directory + file_name)\n if json_data is None:\n raise RuntimeError()\n\n # Extract and add data\n parse_stats = \\\n parse_stations(json_data, data_map, request.region)\n\n log_parse_stats(parse_stats)\n\n utils.add_alias(data_map)\n\n response = DataResponse()\n response.data_map = data_map\n return response","def index_bulk_from_dir(self, dir_data_in, ignore_already_imported=True):\r\n if ignore_already_imported:\r\n already_imported_ids = self.already_imported_ids\r\n else:\r\n already_imported_ids = set()\r\n return self.index_bulk_from_files(\r\n helpers.get_files_from_dir(dir_data_in, already_imported_ids=already_imported_ids))","def search_load_toa5df(api_token, base_url, search_params, biggish_data=False,\n keep_files=False, multiple_delim=False,\n dst_folder='./raw_data'):\n # search records\n records = search(api_token, base_url, search_params)\n\n # use 'biggish data' mode\n if biggish_data:\n # set and create download folder if it does not exist\n dst_folder = Path(dst_folder)\n if not dst_folder.is_dir():\n os.makedirs(dst_folder)\n\n # display number of files beeing downloaded\n print(f'Downloading {len(records)} files:')\n\n # build download url for each file\n for record in tqdm.tqdm(records):\n download_url = f\"{record['url']}?auth_token={api_token}\"\n\n # check if file exists, if not downloads\n file_path = dst_folder / record['filename']\n if not file_path.is_file():\n urllib.request.urlretrieve(download_url, file_path)\n\n # create empty dataframe to store final data\n df_all = pd.DataFrame()\n\n # loop through all downloaded files\n for i in list(dst_folder.glob('*.dat')):\n\n # read data into dataframe discarding undesired header columns\n if multiple_delim:\n df = pd.read_csv(i, skiprows=[0, 2, 3], na_values='NAN',\n sep='\\\\t|,|;', engine='python')\n df.columns = [i.replace('\"', \"\") for i in df.columns]\n df['TIMESTAMP'] = df['TIMESTAMP'].str.replace('\"', '')\n else:\n df = pd.read_csv(i, skiprows=[0, 2, 3], na_values='NAN')\n\n # generate datetimeindex\n df = df.set_index('TIMESTAMP')\n df.index = pd.to_datetime(df.index)\n\n # optimize memory usage\n # first get names of float, integer and object columns\n float_cols = df.select_dtypes(include=['float64']).columns\n integer_cols = df.select_dtypes(include=['int64']).columns\n object_cols = df.select_dtypes(include=['object']).columns\n # the assign dtype that uses least memory for each column\n df[integer_cols] = df[integer_cols].apply(\n pd.to_numeric, downcast='integer')\n df[float_cols] = df[float_cols].apply(\n pd.to_numeric, downcast='float')\n # converting objects to category is only more memory efficient if\n # less tha 50% of values are unique\n for col in object_cols:\n num_unique_values = len(df[col].unique())\n num_total_values = len(df[col])\n if num_unique_values / num_total_values < 0.5:\n df[col] = df[col].astype('category')\n\n # append data\n df_all = pd.concat([df_all, df], sort=False)\n\n # delete dst_folder if wanted\n if not keep_files:\n shutil.rmtree(dst_folder)\n\n else:\n # print number of records found\n print(f'Loading {len(records)} files:')\n\n # create empty dataframe to save data in\n df_all = pd.DataFrame()\n\n # loop through all records and generate progressbar\n for record in tqdm.tqdm(records):\n # build download url for each file\n download_url = f\"{record['url']}?auth_token={api_token}\"\n # get data\n req = urllib.request.urlopen(download_url)\n data = req.read()\n\n # read data into dataframe discarding undesired header columns\n if multiple_delim:\n df = pd.read_csv(io.StringIO(data.decode('utf-8')),\n skiprows=[0, 2, 3], na_values='NAN',\n sep='\\\\t|,|;', engine='python')\n df.columns = [i.replace('\"', \"\") for i in df.columns]\n df['TIMESTAMP'] = df['TIMESTAMP'].str.replace('\"', '')\n else:\n df = pd.read_csv(io.StringIO(data.decode('utf-8')),\n skiprows=[0, 2, 3], na_values='NAN')\n\n # generate datetimeindex\n df = df.set_index('TIMESTAMP')\n df.index = pd.to_datetime(df.index)\n\n # infer data types of all other columns\n df = df.infer_objects()\n\n # append data\n df_all = pd.concat([df_all, df], sort=False)\n\n # if from_date provided sort and trim data\n if 'from_date' in search_params:\n df_all = df_all.sort_index()[search_params['from_date']:]\n # if to_date provided sort and trim data\n if 'to_date' in search_params:\n df_all = df_all.sort_index()[:search_params['to_date']]\n\n return df_all.drop_duplicates()","def test_list_files(self, mock_get):\n list_data = {\n 'has_next': False,\n 'next_offset': None,\n 'limit': 100,\n 'offset': 0,\n 'file_count_in_result': 3,\n 'total_file_count': 3,\n 'secure_file_summaries': [\n {\n 'sdbox_id': '244cfc0d-4beb-8189-5056-194f18ead6f4',\n 'path': 'studio54/test.py',\n 'size_in_bytes': 1323,\n 'name': 'test.py',\n 'created_by': 'tester@studio54.com',\n 'created_ts': '1974-11-17T00:02:30Z',\n 'last_updated_by': 'tester@studio54.com',\n 'last_updated_ts': '1974-11-17T00:02:30Z'\n },\n {\n 'sdbox_id': '244cfc0d-4beb-8189-5056-194f18ead6f4',\n 'path': 'studio54/test.gif',\n 'size_in_bytes': 686,\n 'name': 'test.gif',\n 'created_by': 'tester@studio54.com',\n 'created_ts': '1974-11-17T00:02:30Z',\n 'last_updated_by': 'tester@studio54.com',\n 'last_updated_ts': '1974-11-17T00:02:30Z'\n },\n {\n 'sdbox_id': '244cfc0d-4beb-8189-5056-194f18ead6f4',\n 'path': 'studio54/5621.gif',\n 'size_in_bytes': 686,\n 'name': '5621.gif',\n 'created_by': 'tester@studio54.com',\n 'created_ts': '1974-11-17T00:02:30Z',\n 'last_updated_by': 'tester@studio54.com',\n 'last_updated_ts': '1974-11-17T00:02:30Z'\n }\n ]\n }\n\n payload = {'limit': '100', 'offset': '0'}\n mock_resp = self._mock_response(content=json.dumps(list_data))\n mock_get.return_value = mock_resp\n\n keys = self.client.list_files('fake/path')\n\n assert_equals(keys['limit'], 100)\n assert_equals(keys['offset'], 0)\n assert_equals(keys, list_data)\n assert_in('X-Cerberus-Client', self.client.HEADERS)\n mock_get.assert_called_with(\n self.cerberus_url + '/v1/secure-files/fake/path/',\n params=payload, headers=self.client.HEADERS\n )","def collect_results( results_dir = \"experiments\" ) :\n #%%\n import pandas as pd\n exps_fn = os.listdir( results_dir )\n dics = []\n for fname in exps_fn :\n with open( results_dir + \"/\" + fname, \"rt\", encoding=\"utf8\" ) as f_out :\n dics.append( json.load( f_out ) )\n\n results_df = pd.DataFrame( dics )\n #%%\n return results_df","def make_df_from_json(json_files, out_file):\n table = [[\"name\", \n \"cik\", \n \"city\",\n \"state\",\n \"street1\",\n \"street2\",\n \"zip_code\",\n \"year_of_incorp\", \n \"min_inv\", \n \"tot_off\", \n \"tot_sold\", \n \"tot_rem\", \n \"ind_group_type\", \n \"has_non_accred\", \n \"num_non_accred\", \n \"tot_num_inv\"\n ]] \n\n for json_dict in json_files:\n\n with open(json_dict, \"rb\") as f:\n data = json.load(f)\n print(json_dict)\n\n for i, key in enumerate(data):\n # if i % 1000 == 0:\n # print(i)\n entry = data[key] \n if entry == {}:\n #print(\"missing entry {0}\".format(i))\n continue\n row = []\n\n primary_issuer = entry[\"Primary Issuer\"]\n cik = primary_issuer[\"cik\"]\n name = primary_issuer[\"entity_name\"]\n phone = primary_issuer[\"phone\"]\n year_of_incorp = primary_issuer[\"year_of_incorp\"]\n address = primary_issuer[\"address\"]\n city = address[\"city\"]\n state = address[\"state\"]\n street1 = address[\"street1\"]\n street2 = address[\"street2\"]\n zip_code = address[\"zip_code\"]\n\n secondary_issuers = entry[\"Secondary Issuers\"]\n related_people = entry[\"Related People\"]\n \n offering_data = entry[\"Offering Data\"]\n min_inv = offering_data[\"min_investment_accepted\"]\n tot_off = offering_data[\"total_offering_amount\"]\n tot_sold = offering_data[\"total_amount_sold\"]\n tot_rem = offering_data[\"total_remaining\"]\n ind_group_type = offering_data[\"ind_group_type\"]\n has_non_accred = offering_data[\"has_non_accred\"]\n num_non_accred = offering_data[\"num_non_accred\"]\n tot_num_inv = offering_data[\"tot_num_inv\"] \n\n row = [name, \n cik, \n city,\n state,\n street1,\n street2,\n zip_code,\n year_of_incorp,\n min_inv,\n tot_off,\n tot_sold,\n tot_rem,\n ind_group_type,\n has_non_accred,\n num_non_accred,\n tot_num_inv\n ]\n\n table.append(row)\n\n df = pd.DataFrame(table)\n df.to_csv(out_file)\n\n return 0","def list_files(self, path: str, tenant: str, root: str) -> None:\n self.listing_dir = True\n current_page = 0\n pagination_value = 100\n disable_metadata = self.get_query_argument(\"disable_metadata\", None)\n try:\n current_page = int(self.get_query_argument(\"page\"))\n pagination_value = int(self.get_query_argument(\"per_page\"))\n except HTTPError as e:\n pass # use default value\n except ValueError:\n raise ClientError(\"next values must be integers\")\n if current_page < 0:\n raise ClientError(\"next values are natural numbers\")\n if pagination_value > 50000:\n raise ClientError(\"per_page cannot exceed 1000\")\n\n # don't list symlinked directories\n try:\n any_path_islink(path, opts=options)\n except Exception as e:\n self.write({\"files\": [], \"page\": None})\n return\n\n # arbitrary order\n # if not returning what you want\n # then try next page\n dir_map = os.scandir(path)\n paginate = False\n files = []\n start_at = (current_page * pagination_value) - 1\n stop_at = start_at + pagination_value\n # only materialise the necessary entries\n for num, entry in enumerate(dir_map):\n if num <= start_at:\n continue\n elif num <= stop_at and num >= start_at:\n files.append(entry)\n elif num == stop_at + 1:\n paginate = True\n break # there is more\n if len(files) == 0:\n self.write({\"files\": [], \"page\": None})\n else:\n if paginate and not current_page:\n next_page = 1\n elif paginate:\n next_page = int(current_page) + 1\n else:\n next_page = None\n baseuri = self._base_uri()\n nextref = f\"{baseuri}?page={next_page}\" if next_page else None\n if self.export_max and len(files) > self.export_max:\n raise ClientError(\n f\"number of files exceed configured maximum: {self.export_max}\"\n )\n names = []\n times = []\n exportable = []\n reasons = []\n sizes = []\n mimes = []\n owners = []\n etags = []\n mtimes = []\n if not self.group_config[\"enabled\"]:\n default_owner = options.default_file_owner.replace(\n options.tenant_string_pattern, tenant\n )\n for file in files:\n filepath = file.path\n size, mime_type, latest = self.get_file_metadata(filepath)\n status = self.enforce_export_policy(\n self.export_policy, filepath, tenant, size, mime_type\n )\n reason = None if status else \"not allowed\"\n path_stat = file.stat(follow_symlinks=False)\n etag = self.mtime_to_digest(latest)\n date_time = str(datetime.datetime.fromtimestamp(latest).isoformat())\n if self.has_posix_ownership:\n try:\n owner = pwd.getpwuid(path_stat.st_uid).pw_name\n except KeyError:\n try:\n default_owner_id = pwd.getpwnam(default_owner).pw_uid\n group_id = path_stat.st_gid\n os.chown(file.path, default_owner_id, group_id)\n owner = default_owner\n except Exception:\n logging.error(\n f\"could not reset owner of {filepath} to {default_owner}\"\n )\n owner = \"nobody\"\n else:\n owner = options.api_user\n names.append(os.path.basename(filepath))\n times.append(date_time)\n exportable.append(status)\n reasons.append(reason)\n sizes.append(size)\n mimes.append(mime_type)\n owners.append(owner)\n etags.append(etag)\n mtimes.append(latest)\n else: # then it is the TSD import dir\n group_memberships = self.claims.get(\"groups\")\n if root:\n for file in files:\n if file.name in group_memberships:\n names.append(os.path.basename(file.path))\n times.append(None)\n exportable.append(False)\n reasons.append(None)\n sizes.append(None)\n mimes.append(None)\n owners.append(None)\n else:\n for file in files:\n if not disable_metadata:\n path_stat = file.stat()\n latest = path_stat.st_mtime\n etag = self.mtime_to_digest(latest)\n date_time = str(\n datetime.datetime.fromtimestamp(latest).isoformat()\n )\n size, mime_type, mtime = self.get_file_metadata(file.path)\n else:\n date_time = None\n etag = None\n size, mime_type, mtime = None, None, None\n names.append(file.name)\n times.append(date_time)\n exportable.append(False)\n reasons.append(None)\n sizes.append(size)\n mimes.append(mime_type)\n owners.append(None)\n etags.append(etag)\n mtimes.append(mtime)\n file_info = []\n for f, t, e, r, s, m, o, g, d in zip(\n names, times, exportable, reasons, sizes, mimes, owners, etags, mtimes\n ):\n href = f\"{baseuri}/{url_escape(f)}\"\n file_info.append(\n {\n \"filename\": f,\n \"size\": s,\n \"modified_date\": t,\n \"href\": href,\n \"exportable\": e,\n \"reason\": r,\n \"mime-type\": m,\n \"owner\": o,\n \"etag\": g,\n \"mtime\": d,\n }\n )\n logging.info(f\"{self.requestor} listed {path}\")\n self.write({\"files\": file_info, \"page\": nextref})","def do_GET(self):\n self.send_response(200)\n self.end_headers()\n self.wfile.write(json.dumps(sample_files_list_response).encode())","def test_files(self, location):\n for filename in os.listdir(location):\n with open(location + '/' + filename) as json_file:\n data = json.load(json_file)\n self.test_data(data)","def only_in_one_to_json(\n self,\n path_dir_1,\n list_in_1,\n path_dir_2,\n list_in_2):\n only_in_one_json = []\n for fl in list_in_1:\n file_path_1 = os.path.join(path_dir_1, fl)\n file_size_1 = os.path.getsize(file_path_1)\n type_ = self.get_type(file_path_1)\n if (type_ == self.dir):\n file_size_1 = None\n only_in_one_json.append(self.cmp_files_to_json(\n file_path_1,\n type_,\n True,\n False,\n file_size_1,\n None,\n False,\n None))\n\n for fl in list_in_2:\n file_path_2 = os.path.join(path_dir_2, fl)\n file_size_2 = os.path.getsize(file_path_2)\n type_ = self.get_type(file_path_2)\n if (type_ == self.dir):\n file_size_2 = None\n\n only_in_one_json.append(self.cmp_files_to_json(\n file_path_2,\n type_,\n False,\n True,\n None,\n file_size_2,\n False,\n None))\n\n return only_in_one_json","def format_streamflows(data_dir):\n\n search_expr = data_dir + \"/*.json\"\n\n df = pd.DataFrame()\n\n for json_file in glob.glob(search_expr):\n\n with open(json_file, 'r') as fn:\n data = json.load(fn)\n\n try:\n data = json2dataframe(data)\n\n new_df = data[0]\n new_df = new_df.drop(columns=['dateTime', 'qualifiers'])\n new_df = new_df.rename(columns={'value': data[1]})\n df = pd.concat([df, new_df], axis=1)\n\n except IndexError, e:\n print 'Error:', e\n continue\n\n return df","def execute(self, req):\n\t\tmyPath = req[\"url\"].replace(self.settings[\"ns\"][\"local\"], \"\", 1).split(\"/\")\n\t\tfile = myPath.pop(0)\n\t\tcurrentDir = getcwd()\n\t\tservice = self.basedir + file\n\t\turi = req[\"url\"]\n\t\tqueryPath = \"%s/queries/\" % service\n\t\ttemplatePath = \"%s/\" % service\n\t\ttemplateName = self.mime.getExtension(req[\"request\"].accept_mimetypes.best)\n\t\ttry:\n\t\t\tonlyfiles = [f for f in listdir(queryPath) if isfile(join(queryPath, f))]\n\t\texcept OSError:\n\t\t\tprint \"Warning: Can't find path %s for queries.\" % templatePath\n\t\t\tonlyfiles = []\n\t\tqueries = {}\n\t\tfirst={}\n\t\tfor root, dirs, files in walk(queryPath):\n\t\t\tfor filename in files:\n\t\t\t\ttry:\n\t\t\t\t\tcurrentEndpoint = \"local\"\n\t\t\t\t\t_aux = root.rstrip(\"/\").split(\"/\").pop()\n\t\t\t\t\tif _aux != \"queries\":\n\t\t\t\t\t\tcurrentEndpoint = _aux\n\t\t\t\t\tif not filename.endswith(\".query\"):\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tsqlQuery = self.env.get_template(\"%s/%s\" % (root, filename))\n\t\t\t\t\trenderedSqlQuery = sqlQuery.render(queries=queries, first=first, uri=uri, session=session, flod=self.flod, args=myPath)\n\t\t\t\t\tif re.match(\"^\\s*select\", renderedSqlQuery, flags=re.IGNORECASE) is None:\n\t\t\t\t\t\treturn {\"content\": \"Not a valid SQL Select query\", \"status\": 500}\n\t\t\t\t\tresults = self.sqlserver.query(renderedSqlQuery, currentEndpoint)\n\t\t\t\t\t_name = filename.replace(\".query\", \"\")\n\t\t\t\t\tqueries[_name] = []\n\t\t\t\t\tif results is not None:\n\t\t\t\t\t\tqueries[_name] = results\n\n\t\t\t\texcept Exception, ex:\n\t\t\t\t\tprint sys.exc_info()\n\t\t\t\t\tprint ex\n\t\t\t\t\treturn {\"content\": \"A problem with the SQL endpoint occurred\", \"status\": 500}\n\t\tchdir(currentDir)\n\t\ttry:\n\t\t\tif templateName == \"json\" and not isfile( \"%s%s.template\" % (templatePath, templateName)):\n\t\t\t\tout = json.dumps(queries)\n\t\t\telse:\n\t\t\t\tcontent = self.env.get_template(\"%s%s.template\" % (templatePath, templateName))\n\t\t\t\tout = content.render(queries=queries, uri=uri, session=session, flod=self.flod, args=myPath)\n\t\texcept Exception:\n\t\t\tprint sys.exc_info()\n\t\t\treturn {\"content\": \"Rendering problems\" , \"status\": 500}\n\t\treturn {\"content\": out, \"mimetype\": \"text/html\"}","def whole_data(arg_file_names_list):\r\n\r\n whole_data_list = []\r\n # loop which iterates through the directory and executes function vat_return_reader for each file\r\n for file_name in arg_file_names_list:\r\n whole_data_list.append(vat_return_reader(file_name))\r\n # creates data frame from list\r\n df = pd.DataFrame(whole_data_list)\r\n # adds total row \r\n df = pd.concat([df,pd.DataFrame(df.sum(axis=0),columns=['Total']).T])\r\n return df","def fetch_data(data_dir):\n check_dir(data_dir)\n\n data = list()\n for data_file in os.listdir(data_dir):\n data_file_path = normalize_path([data_dir, data_file])\n data.append(read_data_file(data_file_path))\n\n data = pd.concat(data, sort=True)\n data.sort_values(by=\"datetime\", inplace=True)\n return data.reset_index(drop=True)","def on_get(self, req, resp):\n resp.set_header('Content-Type', 'text/json')\n tif_paths = encode.get_files_in_directory(DIARIES_TO_ENCODE_DIR, \".tif\")\n zip_paths = encode.get_files_in_directory(DIARIES_TO_ENCODE_DIR, \".zip\")\n diaries_paths = tif_paths + zip_paths\n def extract_file_name(path): return os.path.basename(path)\n resp.body = json.dumps({\"diaries\": list(map(extract_file_name, diaries_paths)),\n \"diaries_paths\": diaries_paths})","def fetch_data(files, folder):\n if not os.path.exists(folder):\n _log(\"Creating new folder %s\" % (folder))\n os.makedirs(folder)\n\n all_skip = True\n for f in files:\n url, md5 = files[f]\n fullpath = pjoin(folder, f)\n if os.path.exists(fullpath) and (_get_file_md5(fullpath) == md5):\n continue\n all_skip = False\n _log('Downloading \"%s\" to %s' % (f, folder))\n _get_file_data(fullpath, url)\n if _get_file_md5(fullpath) != md5:\n msg = \"\"\"The downloaded file, %s, does not have the expected md5\nchecksum of \"%s\". This could mean that that something is wrong with the file or\nthat the upstream file has been updated. You can try downloading the file again\nor updating to the newest version of dipy.\"\"\" % (fullpath, md5)\n msg = textwrap.fill(msg)\n raise FetcherError(msg)\n\n if all_skip:\n _log(\"All files already in %s.\" % (folder))\n else:\n _log(\"Files successfully downloaded to %s\" % (folder))","def process_data(cur, conn, filepath, func):\n\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def request(\n self,\n method: str,\n endpoint: str,\n json: Any = None,\n files: Optional[dict[str, Any]] = None,\n retryCount: int = 5,\n ) -> Any:\n pass","def collect_files(config: Config,\n files: Optional[List[str]] = None,\n method: Optional[str] = None) -> DFs:\n filenames = files if files else config.get('args.inputs', [])\n if method is None:\n method = config.get('collect.method', 'csv')\n frames: Dict[str, List[DF]] = {}\n for filename in filenames:\n dfs: DFs = FILE_READERS[method](config, filename, method)\n postprocess_file(config, dfs)\n for k, frame in dfs.items():\n if k not in frames:\n frames[k] = []\n frames[k].append(frame)\n dfs = {}\n for k, v in frames.items():\n dfs[k] = pd.concat(v, ignore_index=True)\n postprocess_collected(config, dfs)\n return dfs","def ingest_files(is_authenticated, is_authorized):\n if not is_authorized:\n return render_template(\"unauthorized_user.html\"), 401\n\n data_json = request.get_json()\n payload_data = {}\n payload_data['egnyte_uploaded_files'] = data_json.get('egnyte_uploaded_files')\n for field in payload_data:\n response_msg = check_field_validations(payload_data, field)\n if response_msg:\n return Response(json.dumps(response_msg), status=400, mimetype='application/json')\n try:\n thread = threading.Thread(target=corpus_indexer.index_based_on_trigger, args=payload_data['egnyte_uploaded_files'])\n thread.start()\n resp = Response(json.dumps({'status':'Ingest Files started'}), status=201, mimetype='application/json')\n except Exception as e:\n logger.error(\"ingest_files api is failed with error %s\" % str(e))\n return render_template(\"internal_server_error.html\"), 500\n return resp","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def process_data(cur, conn, filepath, func):\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{} files processed.'.format(num_files))","def process_data(cur, conn, filepath, func):\n \n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n \n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n \n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def get_files(filedir):\n inputfiles = sorted(glob.glob(os.path.normcase(filedir+\"/\")+\"*.json\"))\n return inputfiles","def read_json(self, bucket, key, with_prefix=False, file_extension=None, **kwargs):\n if with_prefix:\n files = self.get_prefix_object(bucket=bucket,\n key=key,\n file_extension=file_extension)\n\n df = pd.concat([pd.read_json(self.get_file_buffer(bucket=bucket, key=key), **kwargs) for key in files])\n\n else:\n df = pd.read_json(self.get_file_buffer(bucket=bucket, key=key), **kwargs)\n\n return df","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for file in files:\n all_files.append(os.path.abspath(file))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def create_dataframe_from_dir(directory):\n\n if not os.path.exists(directory):\n return pd.DataFrame()\n\n file_list = os.listdir(directory)\n\n file_list.sort()\n\n df_list = []\n for filename in file_list:\n\n if filename.startswith(\"_\") or (not filename.endswith(\".csv\")):\n continue\n\n # Assert that the file is named correctly\n _, start_date, end_date = check_filename_convention(filename)\n\n df = pd.read_csv(os.path.join(directory, filename))\n df = df.assign(SourceFile=filename)\n\n # In January 2020, MS changed the date format used in the usage\n # export files from US to UK. This happen between 24/01/2020 -\n # 28/01/2020. The following if statement is to deal with this\n # change.\n if start_date is None or end_date is None:\n continue\n\n if start_date > datetime.datetime(2020, 1, 24, 0, 0):\n try:\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\n df[CONST_COL_NAME_DATE], format=\"%d/%m/%Y\"\n )\n except Exception:\n try:\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\n df[CONST_COL_NAME_DATE], format=\"%m/%d/%Y\"\n )\n except Exception:\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\n df[CONST_COL_NAME_DATE], format=\"%Y-%m-%d\"\n )\n else:\n try:\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\n df[CONST_COL_NAME_DATE], format=\"%m/%d/%Y\"\n )\n except Exception:\n try:\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\n df[CONST_COL_NAME_DATE], format=\"%d/%m/%Y\"\n )\n except Exception:\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\n df[CONST_COL_NAME_DATE], format=\"%Y-%m-%d\"\n )\n\n # Check if data comes from EduHub\n if CONST_COL_NAME_HANDOUTNAME in df.columns:\n\n # Renaming HandoutName to SubscriptionName\n df = df.rename(\n columns={CONST_COL_NAME_HANDOUTNAME: CONST_COL_NAME_SNAME}\n )\n\n # Dropping columns CourseName,LabName\n df = df.drop(\n columns=[CONST_COL_NAME_LABNAME, CONST_COL_NAME_COURSENAME]\n )\n\n df_list.append(df)\n\n if len(df_list) == 0:\n return pd.DataFrame()\n\n total_df = pd.concat(df_list, axis=0, ignore_index=True)\n\n return total_df","def files(self, start=None, limit=None):\r\n params = base.get_params(None, locals())\r\n url = '{0}/files'.format(self.get_url())\r\n return http.Request('GET', url, params), parsers.parse_json","def files(self, start=None, limit=None):\r\n params = base.get_params(None, locals())\r\n url = '{0}/files'.format(self.get_url())\r\n return http.Request('GET', url, params), parsers.parse_json","def files(self, start=None, limit=None):\r\n params = base.get_params(None, locals())\r\n url = '{0}/files'.format(self.get_url())\r\n return http.Request('GET', url, params), parsers.parse_json","def file_df_from_files(raw_files: list, file_folder: str) -> pd.DataFrame:\n raw_files.sort()\n sizes = [\n round(get_size(os.path.join(file_folder, _)) / 1024 ** 3, 2)\n for _ in raw_files\n ]\n created = [\n datetime.datetime.fromtimestamp(os.path.getctime(os.path.join(file_folder, _)))\n for _ in raw_files\n ]\n file_df = pd.DataFrame(\n list(zip(range(1, len(raw_files) + 1), raw_files, created, sizes)),\n columns=[\"#\", \"Filename\", \"Creation date\", \"Size (GB)\"],\n )\n file_df[\"Shortname\"] = [os.path.splitext(_)[0] for _ in raw_files]\n\n return file_df","def get_raw_files(raw_dir, data_source):\n raw_files = {\n \"inputs\": [],\n \"targets\": [],\n } # keys\n for d in data_source:\n if d[\"url\"].endswith(\"/\"): # the url is a dir and not a compressed file\n input_file = download_from_url(raw_dir, d[\"url\"] + d[\"input\"])\n target_file = download_from_url(raw_dir, d[\"url\"] + d[\"target\"])\n else:\n input_file, target_file = download_and_extract(\n raw_dir, d[\"url\"], d[\"input\"], d[\"target\"])\n raw_files[\"inputs\"].append(input_file)\n raw_files[\"targets\"].append(target_file)\n return raw_files","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def process_data(cur, conn, filepath, func):\r\n\r\n # get all files matching extension from directory\r\n all_files = []\r\n for root, dirs, files in os.walk(filepath):\r\n files = glob.glob(os.path.join(root,'*.json'))\r\n for f in files :\r\n all_files.append(os.path.abspath(f))\r\n\r\n # get total number of files found\r\n num_files = len(all_files)\r\n print('{} files found in {}'.format(num_files, filepath))\r\n\r\n # iterate over files and process\r\n try:\r\n for i, datafile in enumerate(all_files, 1):\r\n func(cur, datafile)\r\n conn.commit()\r\n print('{}/{} files processed.'.format(i, num_files))\r\n except Exception as e:\r\n print(e)","def process_data(cur, conn, filepath, func):\n\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def checkMissingFiles(inDir, jsonUrl):\n\n file_list = []\n remote = False\n try:\n file_list = os.listdir(inDir)\n except OSError:\n remote = True\n file_list = eos_ls(inDir)\n\n if file_list == []:\n print \"Directory does not exist or is empty!\"\n return []\n\n total_expected = 0\n missing_files = []\n suspicious_files = []\n recovered_files = []\n\n print 'Found %d files in input directory' % len(file_list)\n print 20*'-'\n\n jsonFile = open(jsonUrl,'r')\n procList = json.load(jsonFile,encoding = 'utf-8').items()\n\n for proc in procList:\n for desc in proc[1]:\n data = desc['data']\n isData = desc.get('isdata',False)\n mctruthmode = desc.get('mctruthmode')\n for d in data:\n dtag = d.get('dtag','')\n split = d.get('split',1)\n\n for segment in range(0,split):\n eventsFile = dtag\n if split > 1:\n eventsFile = dtag + '_' + str(segment)\n if mctruthmode:\n eventsFile += '_filt%d' % mctruthmode\n filename = eventsFile+'.root'\n\n sys.stdout.write('... checking %s' % filename)\n sys.stdout.flush()\n\n total_expected += 1\n\n if not filename in file_list:\n missing_files.append(filename)\n sys.stdout.write('\\033[91m MISSING \\033[0m \\n')\n # sys.stdout.flush()\n continue\n\n rootFileUrl = os.path.join(inDir, filename)\n if remote:\n rootFileUrl = ('root://eoscms//eos/cms/store' +\n rootFileUrl.split('store',1)[1])\n\n recovered, suspicious = False, False\n tfile = TFile.Open(rootFileUrl)\n try:\n if tfile.TestBit(TFile.kRecovered):\n recovered = True\n if tfile.IsZombie():\n suspicious = True\n tfile.Close()\n except AttributeError, ReferenceError:\n suspicious = True\n\n if recovered:\n sys.stdout.write('\\033[93m Recovered \\033[0m \\n')\n recovered_files.append(filename)\n if suspicious:\n sys.stdout.write('\\033[93m Failed to open \\033[0m \\n')\n suspicious_files.append(filename)\n\n sys.stdout.write('\\033[92m OK \\033[0m \\n')\n sys.stdout.flush()\n\n print 20*'-'\n if len(missing_files):\n print \"Missing the following files:\"\n print \"(%d out of %d expected)\"% (len(missing_files), total_expected)\n for filename in missing_files:\n print filename\n else:\n print \"NO MISSING FILES!\"\n print 20*'-'\n if len(suspicious_files):\n print \"Failed to open the following files:\"\n print \"(%d out of %d expected)\"% (len(suspicious_files), total_expected)\n for filename in suspicious_files:\n print filename\n print 20*'-'\n if len(recovered_files):\n print \"The following files are recovered:\"\n print \"(%d out of %d expected)\"% (len(recovered_files), total_expected)\n for filename in recovered_files:\n print filename\n print 20*'-'\n\n return missing_files+suspicious_files+recovered_files","def ingest_httpdir(self, url=None, dest=None):\n\n if url is None or dest is None:\n raise Exception(\"URL and destination path are required\")\n\n app.check_traversal_okay(self)\n\n # Get directory\n try:\n res = requests.get(url)\n res.raise_for_status()\n dir_info = res.json()\n\n parsed = urlparse(url)\n dirname = parsed.path.split('/')[-2]\n new_folder_path = dest + dirname + '/'\n logger.info(\"DIRNAME \"+new_folder_path)\n res = get_client().mkdir(new_folder_path)\n if not res.ok():\n raise IOError(str(res))\n logger.info(\"DIRECTORY INGESTED: \"+new_folder_path)\n\n file_ingests = []\n folder_ingests = []\n for f in dir_info:\n if 'file' == f['type']:\n s = ingest_httpfile.s(str(url)+f['name'], new_folder_path, metadata=f)\n file_ingests.append(s)\n elif 'directory' == f['type']:\n s = ingest_httpdir.s(url=str(url)+f['name']+'/', dest=new_folder_path)\n folder_ingests.append(s)\n file_job = group(file_ingests)\n file_job.apply_async()\n # result.join() # wait for files to ingest in parallel\n # file_count += result.completed_count()\n group(folder_ingests).apply_async()\n # for file_c, folder_c in folder_res.get():\n # file_count += file_c\n # folder_count += folder_c\n # return (file_count, folder_count)\n except IOError as e:\n raise self.retry(exc=e)","def _read_files(self, input_dir: str, schema: StructType,\n post_read: Optional[Callable[[DataFrame], DataFrame]] = None) -> DataFrame:\n df = self.spark.read.option(\"multiline\", \"true\").schema(schema).json(input_dir)\n if post_read:\n df = post_read(df)\n return df","def process_files_json():\n # chdir into beep root\n pwd = os.getcwd()\n os.chdir(os.environ.get(\"BEEP_ROOT\", \"/\"))\n\n meta_list = list(filter(lambda x: '_Metadata.csv' in x, os.listdir(SRC_DIR)))\n file_list = list(filter(lambda x: '.csv' in x if x not in meta_list else None, os.listdir(SRC_DIR)))\n all_list = list(filter(lambda x: '.csv' in x, os.listdir(SRC_DIR)))\n\n all_list = sorted(all_list)\n dumpfn(all_list, \"all_files.json\")\n\n [file_id, mapdf] = init_map(PROJECT_NAME, DEST_DIR)\n\n new_file_index = file_id\n\n for filename in tqdm(sorted(file_list)):\n # If the file has already been renamed another entry should not be made\n if mapdf['filename'].str.contains(filename).sum() > 0:\n continue\n old_file = os.path.join(SRC_DIR, filename)\n new_path = os.path.join(DEST_DIR, PROJECT_NAME)\n shutil.copy(old_file, new_path) # copy main data file\n shutil.copy(old_file.replace(\".csv\", '_Metadata.csv'), new_path) # copy meta data file\n\n if PROJECT_NAME == 'FastCharge':\n [date, channel_no, strname, protocol] = get_parameters_fastcharge(filename, SRC_DIR)\n elif PROJECT_NAME == 'ClosedLoopOED':\n [date, channel_no, strname, protocol] = get_parameters_oed(filename, SRC_DIR)\n else:\n raise ValueError(\"Unsupported PROJECT_NAME: {}\".format(PROJECT_NAME))\n\n df_dup = mapdf.set_index(['protocol', 'date'])\n if (protocol, date) in df_dup.index:\n row = mapdf[(mapdf['protocol'] == protocol) & (mapdf['date'] == date)]\n file_id = row['fid'].iloc[0]\n protocol = row['protocol'].iloc[0]\n date = row['date'].iloc[0]\n strname = row['strname'].iloc[0]\n else:\n file_id = new_file_index\n new_file_index = new_file_index + 1\n\n new_name = \"{}_{}_{}\".format(PROJECT_NAME, f'{file_id:06}', channel_no)\n new_file = os.path.join(DEST_DIR, PROJECT_NAME, \"{}.csv\".format(new_name))\n\n new_row = pd.DataFrame([[file_id, protocol, channel_no, date, strname,\n os.path.abspath(old_file),\n os.path.abspath(new_file)]],\n columns=METADATA_COLUMN_NAMES)\n mapdf = mapdf.append(new_row)\n\n os.rename(os.path.join(DEST_DIR, PROJECT_NAME, filename), new_file)\n os.rename(os.path.join(DEST_DIR, PROJECT_NAME, filename).replace(\".csv\", \"_Metadata.csv\"),\n new_file.replace(\".csv\", \"_Metadata.csv\"))\n\n mapdf.to_csv(os.path.join(DEST_DIR, PROJECT_NAME, PROJECT_NAME + \"map.csv\"), index=False)\n mapdf = mapdf.reset_index(drop=True)\n os.chdir(pwd)\n return json.dumps(mapdf.to_dict(\"list\"))","def folder_to_df(path):\n summary_df = pd.DataFrame(columns=[\"file_name\", \"invoice_nr\", \"address\", \"contract\", \"base_charge\"])\n \n for file in Path(path).glob(\"*.pdf\"):\n print(file)\n try: \n summary_df = summary_df.append({\n \"file_name\": pdf_to_test(file)[0],\n \"invoice_nr\": pdf_to_test(file)[1],\n \"address\": pdf_to_test(file)[2],\n \"contract\": pdf_to_test(file)[3],\n \"base_charge\": pdf_to_test(file)[4]}, \n ignore_index = True)\n except:\n summary_df = summary_df.append({\n \"file_name\": file.name,\n \"invoice_nr\": \"Could not read malformed PDF file\",\n \"address\": \"Could not read malformed PDF file\",\n \"contract\": \"Could not read malformed PDF file\",\n \"base_charge\": \"Could not read malformed PDF file\"}, \n ignore_index = True)\n return summary_df","def search_files_folders(request,**kwargs):\n name = request.data['name']\n files_and_folders = FileSystem.objects.filter(name__icontains=name, status=\"CREATED\")\n\n response_list = []\n dictionary = {}\n for fsobject in files_and_folders :\n fs_object = convert_fsobject_to_fstypeobject(fsobject)\n if fs_object.path in dictionary.keys() :\n for fs_object in FileSystem.objects.filter(path=fs_object.path,status='CREATED').order_by('-creation_datetime')[:2] :\n try :\n response = requests.get(fs_object.location)\n if response.status_code == 200 :\n break\n except requests.ConnectionError :\n pass\n print\"final object location\", fs_object.location\n dictionary[fs_object.path]=fs_object\n else :\n dictionary[fs_object.path]=fs_object\n\n for fs_object in dictionary.values():\n fs_object.creation_datetime = convert_datetime_to_string(fs_object.creation_datetime)\n data = FileSystemSerializer(fs_object).data\n response_list.append(data)\n\n\n return Response(data=response_list,status=status.HTTP_200_OK)","def parse_directory(dpath):\n print(\"Starting to parse\")\n print(dpath)\n selected_files = sel_files(dpath)\n print(\"Number of selected files\", len(selected_files))\n\n # iterave over selected files and build dataframe\n empties = 0\n idx = pd.DataFrame(columns=list(PATH_REGEX.keys()))\n for fpath in selected_files:\n m = FNAME_REGEX.search(fpath)\n if m is None:\n print(\"ERROR:\", fpath, FNAME_REGEX.pattern)\n continue\n row_head = {k: m.group(k) for k in PATH_REGEX.keys()}\n for i, (webpage_id, lengths) in enumerate(it_webpages(fpath)):\n if len(lengths) == 0:\n empties += 1\n continue\n row_head['fname'] = os.path.basename(fpath)\n row_head['class_label'] = webpage_id\n row_head['lengths'] = lengths\n idx = idx.append(row_head, ignore_index=True)\n print(i, 'sites in', fpath)\n print(\"Empty traces:\", empties)\n\n # fix some naming issues:\n idx['inst'] = idx.inst.fillna(0)\n idx['date'] = pd.to_datetime(idx.date.str.replace('-18', '-2018'),\n format='%d-%m-%Y')\n #idx['dev'] = idx.dev.replace('browse', 'desktop')\n #idx.loc[idx.sites == 'desktop', ['dev', 'sites']] = ['desktop', None]\n return idx","def getfiles_from_dir(self,dir):\n assert not os.path.isdir(dir),\"Invalid dir format\"+str(dir)\n print(\"-----Read Dir :\",dir)\n self.files=glob.glob(os.path.join(dir,\"./*.tif\"))","def ask_endpoint(file, endpoint) -> dict:\n\n # define headers with the content type\n headers = {\n \"Content-Type\": \"application/json\",\n \"Accept\": \"application/json\",\n }\n\n data = None\n if isinstance(file, str):\n data = pd.read_csv(file).to_dict(orient=\"records\")\n elif isinstance(file, list):\n data = file\n else:\n raise TypeError(\"'file' should be either file path or loaded list of jsons!\")\n\n # send request to the endpoint\n response = requests.post(endpoint,\n json={\"data\": data},\n headers=headers)\n\n # check response status and parse the body\n if response.status_code == 200:\n response = response.json()\n else:\n response = {}\n\n return response","def list_files():\n try:\n return jsonify(os.listdir(env(\"FILES_DIRECTORY\"))), 200\n except:\n return {\"msg\": \"Não há arquivos a serem listados.\"}, 404","def process_data(cur, conn, filepath: str, func: Callable) -> None:\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, \"*.json\"))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print(\"{} files found in {}\".format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print(\"{}/{} files processed.\".format(i, num_files))","def batch(infolder, outfile): # type: (str, str) -> None\n\n if not os.path.isdir(infolder):\n return\n\n results = []\n\n for filename in os.listdir(infolder):\n print('Processing ' + filename)\n curresults = []\n if filename.endswith('.txt'):\n with open(os.path.join(infolder, filename), 'r') as curfile:\n curdata = curfile.read() + '\\n'\n curresults = processClauseText(curdata, 'text')\n elif filename.endswith('.pdf'):\n with open(os.path.join(infolder, filename), 'rb') as curfile:\n curdata = base64.b64encode(curfile.read()).decode()\n curresults = processClauseText(curdata, 'pdf')\n elif filename.endswith('.docx'):\n with open(os.path.join(infolder, filename), 'rb') as curfile:\n curdata = base64.b64encode(curfile.read()).decode()\n curresults = processClauseText(curdata, 'word')\n if len(curresults) > 0:\n for result in curresults:\n result['filename'] = filename\n results.extend(curresults)\n\n if outfile is not None:\n with open(outfile, 'w') as outfile:\n json.dump(results, outfile, indent=2)","def files(self):\r\n url = '{0}/files'.format(self.get_url())\r\n\r\n return http.Request('GET', url), parsers.parse_json","def get_files_folders(request,**kwargs):\n import requests\n from mainserver.constants import FORWARD_TO_SERVERS\n\n path = request.data['path']\n\n\n print (\"path \", path)\n\n\n try :\n # print FileSystem.objects.filter(path=path,type='FOLDER',status='CREATED').order_by('-creation_datetime').count()\n fsobject = FileSystem.objects.filter(path=path,type='FOLDER',status='CREATED').order_by('-creation_datetime')[0]\n print fsobject\n except :\n print \"illegal file object query access\"\n return Response(status=status.HTTP_417_EXPECTATION_FAILED)\n\n\n fsobjects_list = FileSystem.objects.filter(parent=fsobject,status='CREATED').order_by('-creation_datetime')\n\n fs_object = FileSystem.objects.all().order_by('-creation_datetime')[:][0]\n\n sync_with_servers(tail=None, datetime=convert_datetime_to_string(fs_object.creation_datetime))\n\n response_list = []\n dictionary = {}\n\n print (\"sync complete\")\n\n for fsobject in fsobjects_list :\n print fsobject\n fs_object = convert_fsobject_to_fstypeobject(fsobject)\n if fs_object.path not in dictionary.keys() :\n for fs_object in FileSystem.objects.filter(path=fs_object.path,status='CREATED').order_by('-creation_datetime')[:2] :\n try :\n response = requests.get(fs_object.location)\n if response.status_code == 200 :\n break\n except requests.ConnectionError :\n pass\n print\"final object lopcation\", fs_object.location\n dictionary[fs_object.path]=fs_object\n\n for fs_object in dictionary.values():\n fs_object.creation_datetime = convert_datetime_to_string(fs_object.creation_datetime)\n data = FileSystemSerializer(fs_object).data\n response_list.append(data)\n\n # print response_list\n\n data = {\"current_dir\" : path}\n\n data ['files_folders'] = response_list\n\n\n print data\n\n return Response(data=data,status=status.HTTP_200_OK)","def dir_to_h5df(walk_dir, N):\n\n h5_file_list = list_h5(walk_dir)\n\n if (N == 'all') or (N > len(h5_file_list)):\n files_to_convert = h5_file_list\n else:\n files_to_convert = h5_file_list[:N]\n\n # Convert list of files names to list of dictionaries\n\n h5_df_list = []\n\n for filename in files_to_convert:\n f = h5py.File(filename, 'r')\n h5_df = pd.DataFrame(multi_indexer(h5_to_dict(f)))\n h5_df_list.append(h5_df)\n\n h5df = pd.concat(h5_df_list, ignore_index=True)\n\n return h5df","def _download_inputs(inputs: Dict[str, str]) -> Dict[str, Path]:\n in_paths = dict()\n for name, endpoint in inputs.items():\n target_dir = _BASE_PATH / name\n target_dir.mkdir()\n target_file = target_dir / 'data.json'\n r = requests.get('{}/data.json'.format(endpoint))\n with target_file.open('wb') as f:\n f.write(r.content)\n in_paths[name] = target_file\n return in_paths","def get_files_df(pxd_identifier, filetypes, pattern):\r\n check_pxd_id(pxd_identifier)\r\n\r\n get_files_url = \"https://www.ebi.ac.uk:443/pride/ws/archive/file/list/project\"\r\n\r\n # Get dataframe with file info through PRIDE Archive REST API\r\n url = \"{}/{}\".format(get_files_url, pxd_identifier)\r\n response = pd.DataFrame(json.loads(requests.get(url).content.decode('utf-8'))['list'])\r\n response['fileExtension'] = response['fileName'].str.split('.').apply(lambda x: x[-1])\r\n\r\n # Set regex pattern\r\n if pattern:\r\n response = response[response['fileName'].str.contains(pattern)]\r\n\r\n # Set extensions\r\n extensions = response['fileExtension'].unique()\r\n #print(\"Found files with extensions: \\t\\t{}\".format(extensions))\r\n if filetypes:\r\n extensions = filetypes\r\n response = response[response['fileExtension'].str.lower().isin([x.lower() for x in filetypes])]\r\n\r\n #print(\"Downloading files with extensions: \\t{}\".format(extensions))\r\n\r\n return response","def directory_to_json(self, path, list_in):\n directory_json = {\"base_path\": path, \"files\": list_in}\n return directory_json","def read_json_to_df(start_dt, end_dt, cache):\r\n # Establish files daterange\r\n collect_sdt = start_dt\r\n if (end_dt.hour == 0) & (end_dt.minute == 0):\r\n collect_edt = end_dt\r\n else:\r\n collect_edt = end_dt + timedelta(days=1)\r\n\r\n date_str_list = [datetime.strftime(i,\"%Y-%m-%d\") for i in pd.date_range(collect_sdt, collect_edt)]\r\n\r\n # Find only a subset of JSON files in the given daterange\r\n JSON_subset = [glob.glob(os.path.join(cache, \"NEMED_PS_DAILY_{}*.json\".format(i))) for i in date_str_list]\r\n JSON_subset = [item for sublist in JSON_subset for item in sublist]\r\n\r\n print(\"Reading selected {} JSON files to pandas, of cached files\".format(len(JSON_subset)))\r\n logger.info(\"Loading Cached Price Setter Files...\")\r\n\r\n all_df = []\r\n for file in tqdm(JSON_subset):\r\n with open(file, 'r') as f:\r\n data = json.loads(f.read())\r\n df_nested_list = pd.json_normalize(data)\r\n df_nested_list['@PeriodID'] = pd.to_datetime(df_nested_list['@PeriodID'], format=\"%Y-%m-%d %H:%M:%S\").dt.tz_localize(None)\r\n df_nested_list = df_nested_list[(df_nested_list['@Market'] == 'Energy') &\r\n (df_nested_list['@DispatchedMarket'] == 'ENOF')]\r\n all_df += [df_nested_list]\r\n\r\n all_df = pd.concat(all_df)\r\n all_df.columns = all_df.columns.str.strip('@')\r\n all_df.drop(['Market','DispatchedMarket'], axis=1, inplace=True)\r\n all_df = all_df.astype({'RegionID': str, 'Price': float, 'Unit': str, 'BandNo': int, \\\r\n 'Increase': float, 'RRNBandPrice': float, 'BandCost': float})\r\n all_df = all_df[all_df['PeriodID'].between(start_dt, end_dt, inclusive=\"right\")].sort_values(['PeriodID','RegionID'])\r\n return all_df.reset_index(drop=True)","def parse(self, response):\n json_response = json.loads(response.text)\n fetched_results = len(json_response['results'])\n\n if not hasattr(self, 'processed_results'):\n self.processed_results = 0\n self.processed_results += fetched_results\n if fetched_results == 0:\n logging.info('No more files. Total files queried {}'.format(\n self.processed_results))\n return\n\n for result in json_response['results']:\n document = self._create_document(result['columns'])\n if document['language'] != u'ENG':\n info = 'Case {} {} not in ENG, original language {}'.format(\n document['original_id'], document['name'].encode('utf-8'),\n result['columns']['languageisocode'])\n logging.info(info)\n continue\n doc_url = self.base_document_url.format(document['original_id'])\n new_request = scrapy.Request(doc_url, callback=self.parse_document)\n new_request.meta['document'] = document\n yield new_request\n\n if self.processed_results < int(getattr(self, 'limit', DEFAULT_LIMIT)):\n next_url = self.base_main_url.format(\n start_index=self.processed_results, length=RESULT_PER_PAGE)\n yield scrapy.Request(next_url, callback=self.parse)\n else:\n logging.info('Limit reached. Total files obtained {}'.format(\n self.processed_results))","def get_files(input_dir):\n file_rep = { \"tars\" : [] }\n \n files = os.listdir(input_dir)\n \n the_file, the_date = find_bootstrap(files)\n \n #add index file in file_rep\n file_rep['index'] = the_file\n file_rep['date'] = the_date\n \n pattern = \"ncep_forecast_%s_(?P<name>\\S+).tar\" % (the_date)\n \n the_re = re.compile(pattern)\n\n for the_file in files:\n matched = the_re.match(the_file)\n if matched:\n print(\"matched %s\" % (matched.group(\"name\")))\n file_rep['tars'].append(the_file)\n \n return file_rep","def json_data(self):\n return_data = []\n\n # Loop over num files\n for ifile in range(len(self.data)):\n\n # Loop over steps\n file_return_data = {}\n for istep in range(len(self.data[ifile])):\n file_return_data[istep] = {}\n for key in self.data[ifile][istep]:\n file_return_data[istep][key] = self.data[ifile][istep][key].tolist()\n\n return_data.append(file_return_data)\n\n return return_data","def test_get_all(client: FlaskClient):\n response1 = util.upload_file(\n client, DEFAULT_USER, get_example_file(ExampleFileType.Txt)\n )\n response2 = util.upload_file(\n client, DEFAULT_USER, get_example_file(ExampleFileType.Jpg)\n )\n response3 = util.upload_file(\n client, DEFAULT_USER, get_example_file(ExampleFileType.Png)\n )\n\n # Now retrieve them\n response_get = util.get_all_files(client, DEFAULT_USER)\n assert response_get.status == \"200 OK\"\n assert len(response_get.json) == 3\n assert response1.json in response_get.json\n assert response2.json in response_get.json\n assert response3.json in response_get.json","def read_query_files(**kwargs):\n # Read query files contents and write to query_list\n query_list = {\"query_group\": \"\", \"queries\": []}\n query_group = kwargs[\"queries_dir\"].split(\"/\")[-1]\n query_list.update(query_group=query_group)\n logging.debug(\"Queries dir: \" + kwargs[\"queries_dir\"])\n try:\n for query_filename in sorted(os.listdir(kwargs[\"queries_dir\"])):\n logging.debug(\"Validating query filename: \" + query_filename)\n if validate_query_file(query_filename=query_filename):\n with open(\n kwargs[\"queries_dir\"] + \"/\" + query_filename, \"r\"\n ) as query_filepath:\n logging.debug(\n \"Reading query with filename: \" + query_filename\n )\n query_mapdql = query_filepath.read().replace(\"\\n\", \" \")\n query_mapdql = query_mapdql.replace(\n \"##TAB##\", kwargs[\"source_table\"]\n )\n query_list[\"queries\"].append(\n {\"name\": query_filename, \"mapdql\": query_mapdql}\n )\n logging.info(\"Read all query files\")\n return query_list\n except FileNotFoundError:\n logging.exception(\"Could not find queries directory.\")\n return False","def file_loader(self):\n\n for folder in self.config[\"data_folders\"]:\n f = os.path.join(folder, self.data_file)\n yield jsonlist.load_file(f)","def list_all_files(self, branch_name) -> dict:\n try:\n files_response = self.repo.get_contents(self.repo_folder,\n ref=branch_name)\n files_list = []\n for file in files_response:\n files_list.append(file.raw_data.get('path'))\n return make_success_response(200, files_list)\n except GithubException as github_exc:\n return make_error_response(github_exc.status, github_exc.data)","def test_samples_request(self):\n req = Request()\n for name, data in sample_data.items():\n resp = req.get(fromfile=self._filepath(name))\n\n df = resp.write()\n assert df.equals(data), \\\n '\\n'.join(map(str, [name, df.index, data.index,\n getattr(df, 'columns', ''),\n getattr(data, 'columns', '')]))","def index_mock_files(self,mfiles):\n results = []\n for i in range(len(mfiles['file_name'])):\n print(\"Submitting {} to indexd at {}.\".format(mfiles['file_name'][i],mfiles['object_id'][i]))\n res = self.create_record(\n did=mfiles['object_id'][i],\n hashes={'md5':mfiles['md5sum'][i]},\n size=mfiles['file_size'][i],\n urls=mfiles['storage_urls'][i],\n file_name=mfiles['file_name'][i],\n acl=mfiles['acl'][i],\n authz=mfiles['authz'][i])\n results.append(res)\n return results","def createFileListHTTP(self, server, directory):\r\n conn = httplib.HTTPConnection(server)\r\n conn.request(\"GET\",directory)\r\n r1 = conn.getresponse()\r\n '''if r1.status==200:\r\n print \"status200 received ok\"\r\n else:\r\n print \"oh no = status=%d %s\" \\\r\n % (r1.status,r1.reason)'''\r\n\r\n data = r1.read()\r\n parser = parseHTMLDirectoryListing()\r\n parser.feed(data)\r\n continents = parser.getDirListing()\r\n '''print continents'''\r\n\r\n for continent in continents:\r\n '''print \"Downloading file list for\", continent'''\r\n conn.request(\"GET\",\"%s/%s\" % \\\r\n (self.directory,continent))\r\n r1 = conn.getresponse()\r\n '''if r1.status==200:\r\n print \"status200 received ok\"\r\n else:\r\n print \"oh no = status=%d %s\" \\\r\n % (r1.status,r1.reason)'''\r\n data = r1.read()\r\n parser = parseHTMLDirectoryListing()\r\n parser.feed(data)\r\n files = parser.getDirListing()\r\n\r\n for filename in files:\r\n self.filelist[self.parseFilename(filename)] = (\r\n continent, filename)\r\n\r\n '''print self.filelist'''\r\n # Add meta info\r\n self.filelist[\"server\"] = self.server\r\n self.filelist[\"directory\"] = self.directory\r\n with open(self.filelist_file , 'wb') as output:\r\n pickle.dump(self.filelist, output)","def process_multiple_files(self, filepaths, email_col='EMAIL',min_size=100, threshold=0.05):\r\n new_paths = []\r\n for f in filepaths:\r\n df = pd.read_csv(f)\r\n df = self.pre_process_frame(df, col=email_col)\r\n orig_size = df.index.size\r\n FLAG = True\r\n if orig_size < min_size:\r\n pass\r\n else:\r\n print(\"Cleaning {}\".format(f))\r\n \r\n try: # Try to first do an easy match with results directly from the database. (Very fast compared to API calls)\r\n self.count_matching_emails(df, col=email_col, verify_integrity=True, thresh=threshold)\r\n except Exception as e:\r\n \r\n print(\"{}\\n Calling missing emails from remote server.\".format(e))\r\n df = self.deep_clean_frame(df,dealno=0,clean_col=email_col) # The long way - calling the API.\r\n \r\n try:\r\n self.deep_processing_rerun(dealno=0,thresh=0.05,max_tries=5) # Handling records stuck in processing.\r\n count = self.count_matching_emails(df, col=email_col, verify_integrity=True, thresh=threshold)\r\n print(\"Successfully matched {} records\".format(count))\r\n except Exception as e:\r\n \r\n FLAG = False # Stop this from finalizing...too many records stuck in processing/not in database...somethings wrong.\r\n print(\"Failed to reprocess some records for {}\\n Error: {}\".format(f,e))\r\n \r\n if FLAG:\r\n df = self.suppress_email_frame(df, col=email_col, clean_type=1)\r\n new_path = os.path.splitext(f) + \"ListWised.csv\"\r\n df.to_csv(new_path, index=False)\r\n new_paths.append(new_path)\r\n \r\n self.deep_processing_rerun_all() # Wraps up making one last try at rerunning any emails stuck in processing (for next time).\r\n return new_paths","def load_all(self, dir_path):\n # each file name corresponds to another date\n input_paths = [os.path.join(dir_path, f) for f in os.listdir(dir_path)\n if os.path.isfile(os.path.join(dir_path, f)) and f.endswith('.csv')]\n\n if self.n_proc > 1:\n # Load in parallel\n _n_proc = min(self.n_proc, len(input_paths)) # no more than file_names needed here\n logger.info(\"Loading {} datasets files using {} parallel processes ...\".format(len(input_paths), _n_proc))\n with Pool(processes=_n_proc) as pool:\n all_df = pd.concat(pool.map(HDD_data.load_single, input_paths))\n else: # read 1 file at a time\n all_df = pd.concat(HDD_data.load_single(path) for path in input_paths)\n\n return all_df","def fetch_rows(links_input_file, args):\n root_url = args.root_url\n skip = args.skip\n\n for i, row in enumerate(csv.DictReader(links_input_file)):\n if i < skip:\n continue\n\n if i % 100 == 0 and i > 0:\n sys.stderr.write('Row {}\\n'.format(i + 1))\n\n url, original_base_path = extract_base_path(row)\n real_base_path = test_base_path(original_base_path, args)\n search_url = root_url + '/api/search.json'\n search_result = request_search_result(link=real_base_path, search_url=search_url)\n\n yield format_result(url, real_base_path, search_result)","def sina_weibo_emotion4(root):\n start = time.time()\n task_path = assert_dirs(root, 'chinese_reviews_sina_weibo_emotion4')\n url_json = 'https://raw.githubusercontent.com/Hourout/datasets/master/nlp/chinese_reviews_sina_weibo_emotion4/chinese_reviews_sina_weibo_emotion4.json'\n url_txt = ['https://raw.githubusercontent.com/Hourout/datasets/master/nlp/chinese_reviews_sina_weibo_emotion4/chinese_reviews_sina_weibo_emotion4_01.txt',\n 'https://raw.githubusercontent.com/Hourout/datasets/master/nlp/chinese_reviews_sina_weibo_emotion4/chinese_reviews_sina_weibo_emotion4_02.txt',\n 'https://raw.githubusercontent.com/Hourout/datasets/master/nlp/chinese_reviews_sina_weibo_emotion4/chinese_reviews_sina_weibo_emotion4_03.txt',]\n rq.json(url_json, path_join(task_path, 'chinese_reviews_sina_weibo_emotion4.json'))\n data = pd.DataFrame()\n for url in url_txt:\n s = requests.get(url).content\n data = pd.concat([data, pd.read_csv(io.StringIO(s.decode('utf-8')))])\n data.to_csv(path_join(task_path, 'chinese_reviews_sina_weibo_emotion4.txt'), index=False)\n print('chinese_reviews_sina_weibo_emotion4 dataset download completed, run time %d min %.2f sec' %divmod((time.time()-start), 60))\n return task_path","def get_data_files(main_directory):\n print('************************************')\n print('Log data list')\n print('************************************')\n log_files_list = globlin(main_directory + '/*/*.json' , recursive=True)\n song_files_list = globlin(main_directory + '/*/*/*/*/*.json', recursive=True)\n print(log_files_list)\n print('************************************')\n print('Song data list')\n print('************************************')\n print(song_files_list)\n return log_files_list, song_files_list","def query(self, n_jobs=1) -> str:\n\n def get_one_answer(file):\n return json.dumps(ask_endpoint(file, os.path.join(self.url, \"take_exam\")))\n\n # send each file to the endpoint\n query_start_time = time.time()\n answers = Parallel(n_jobs=n_jobs)(delayed(get_one_answer)(file) for file in tqdm(self.filelist))\n query_end_time = time.time()\n query_time = query_end_time - query_start_time\n\n # put all answers to the dataframe\n answers = pd.DataFrame(answers, columns=[\"prediction\"])\n answers[\"prediction\"] = answers[\"prediction\"].apply(lambda x: json.loads(x))\n answers[\"path\"] = self.filelist\n\n # create report folder\n os.makedirs(self._report_path, exist_ok=False)\n # save raw answers\n answers.to_csv(os.path.join(self._report_path, \"raw_answers.csv\"), index=False)\n # parse answers\n parsed_answers = pd.DataFrame(columns=[\"path\",\n \"id\",\n \"prediction\"])\n for _, row in answers.iterrows():\n for k, v in row[\"prediction\"][\"answers\"].items():\n parsed_answers.loc[len(parsed_answers)] = [row[\"path\"], int(k), v]\n # save parsed answers\n parsed_answers = parsed_answers.sort_values(by=[\"path\", \"id\"]).reset_index(drop=True)\n parsed_answers.to_csv(os.path.join(self._report_path, \"parsed_answers.csv\"), index=False)\n # save statistics\n stats = {\n \"readiness_time\": self._readiness_time,\n \"query_total_files\": len(self.filelist),\n \"query_total_time\": query_time,\n \"query_n_jobs\": n_jobs,\n \"query_mean_latency\": query_time / len(self.filelist) * n_jobs,\n \"query_rps\": len(self.filelist) / query_time\n }\n with open(os.path.join(self._report_path, \"stats.json\"), \"w\") as f:\n json.dump(stats, f)\n\n return self._report_path","def index_all_files(self, root_dir):\n pass","def create_df(files_list=my_files):\n\n all_records = list()\n\n for file in files_list:\n all_records += zr_parser(path.join(my_dir, file))\n\n return pd.DataFrame(all_records)","def api_files():\n files = FileWrapper.get_files(g.user.id)\n return jsonify([f.serialize() for f in files])","def bulk_gzipped_jsonline_files_to_dfs(glob_or_list, nproc=20):\n import multiprocessing as mp\n from tqdm import tqdm\n\n if isinstance(glob_or_list, str):\n from glob import glob\n glob_or_list = glob(glob_or_list)\n\n if len(glob_or_list) == 0:\n return []\n\n pool = mp.Pool(min(nproc, len(glob_or_list)))\n dfs = []\n for df in tqdm(pool.imap_unordered(gzipped_jsonline_file_to_df, glob_or_list),\n total=len(glob_or_list)):\n dfs.append(df)\n\n pool.close()\n pool.join()\n\n return dfs","def get_indexd(self, limit=1000, page=0, format=\"JSON\", uploader=None, args=None):\n if format in [\"JSON\", \"TSV\"]:\n dc_regex = re.compile(r\"https:\\/\\/(.+)\\/?$\")\n dc = dc_regex.match(self._endpoint).groups()[0]\n dc = dc.strip('/')\n else:\n print(\"\\n\\n'{}' != a valid output format. Please provide a format of either 'JSON' or 'TSV'.\\n\\n\".format(format))\n\n stats_url = \"{}/index/_stats\".format(self._endpoint)\n try:\n response = requests.get(stats_url).text\n stats = json.loads(response)\n print(\"Stats for '{}': {}\".format(self._endpoint, stats))\n except Exception as e:\n print(\"\\tUnable to parse indexd response as JSON!\\n\\t\\t{} {}\".format(type(e), e))\n\n print(\"Getting all records in indexd (limit: {}, starting at page: {})\".format(limit, page))\n\n all_records = []\n\n done = False\n while done == False:\n\n records = self.query_indexd(limit=limit, page=page, uploader=uploader, args=args)\n all_records.extend(records)\n\n if len(records) != limit:\n print(\n \"\\tLength of returned records ({}) does not equal limit ({}).\".format(\n len(records), limit\n )\n )\n if len(records) == 0:\n done = True\n\n print(\n \"\\tPage {}: {} records ({} total)\".format(\n page, len(records), len(all_records)\n )\n )\n page += 1\n\n print(\n \"\\t\\tScript finished. Total records retrieved: {}\".format(len(all_records))\n )\n now = datetime.datetime.now()\n date = \"{}-{}-{}_{}.{}\".format(now.year, now.month, now.day, now.minute, now.second)\n\n if format == \"JSON\":\n outname = \"{}_indexd_records_{}.json\".format(dc,date)\n with open(outname, \"w\") as output:\n output.write(json.dumps(all_records))\n\n if format == \"TSV\":\n outname = \"{}_indexd_records_{}.tsv\".format(dc,date)\n all_records = pd.DataFrame(all_records)\n all_records['md5sum'] = [hashes.get('md5') for hashes in all_records.hashes]\n all_records.to_csv(outname,sep='\\t',index=False)\n\n return all_records","def iterfiles(dir, prefix):\n files = (f for f in os.listdir(dir) if f.startswith(prefix))\n for filename in sorted(files):\n filename = os.path.join(dir, filename)\n with open(filename) as fd:\n data = json.load(fd)\n yield data\n os.remove(filename)","def download_data(files: page_iterator.HTTPIterator, folder: str) -> None:\n logging.info('File download Started... Wait for the job to complete.')\n\n # create folder locally if not exists\n if not os.path.exists(folder): os.makedirs(folder)\n\n for file in files:\n logging.info('GCS File: {}'.format(file.name))\n destination_uri = '{}/{}'.format(folder, file.name.split('/')[-1])\n file.download_to_filename(destination_uri if destination_uri.endswith('.csv') else destination_uri + '.csv')\n logging.info('Exported {} to {}'.format(file.name, destination_uri))\n\n return None","def render_multi_json_per_folder(history_dir, nb_json_per_folder):\n jsons_to_render = []\n\n # Finding files to render\n subfolders = [os.path.join(history_dir, path)\n for path in os.listdir(history_dir)\n if os.path.isdir(os.path.join(history_dir, path))]\n for folder in subfolders:\n json_games = sorted([os.path.join(folder, json_filename)\n for json_filename in os.listdir(folder)\n if json_filename[-5:] == '.json'])\n json_games = json_games[:nb_json_per_folder]\n for json_path in json_games:\n jsons_to_render += [json_path]\n\n # Running over multiple processes\n nb_cores = multiprocessing.cpu_count()\n with multiprocessing.Pool(nb_cores) as pool:\n pool.map(render_json, jsons_to_render)","def process_files(exp_folders):\n pool = mp.Pool()\n results = pool.imap_unordered(read_and_serialize, exp_folders)\n\n stat = []\n for res in results:\n print(res)\n stat.append(res)\n\n pool.close()\n pool.join()","def readAllDatForCurrency(data_dir, currencyCode):\n datatestnames = list(filter(lambda a : (currencyCode in a), listdir(data_dir)))\n dfs = [readDAT(data_dir + name) for name in datatestnames]\n allDf = pd.concat(dfs)\n allDf = allDf.sort_values(by=['datetime'], ascending=True).reset_index(drop=True).set_index('datetime')\n return allDf","def download_data(base_url,\n lista_anni,\n lista_inquinanti):\n \n # Inizializziamo la lista dei df ognuno dei quali corrisponde ad un inquinante\n df_template = pd.DataFrame(columns=['jd','h','1','2','3','4','5','6','7','8','9','10','11','13','14','15','16','38','39','40',\n '41','45','47','48','49','55','56','57','60','83','84','85','86','87','Anno','Inquinante'])\n lista_df = [df_template]\n\t\n\t# Per ogni inquinante\n for chimico in lista_inquinanti:\n \t# Per ogni anno\n for anno in lista_anni:\n print('Retrieving {} for year {} from {}'.format(chimico, anno, compose_url(base_url, anno, chimico)))\n \n # Esegui la richiesta\n r = requests.get(compose_url(base_url, anno, chimico))\n\n # Crea il rispettivo dataframe\n df = write_response(r)\n print('{} rows'.format(len(df)))\n\t\t\t\n\t\t\t# Prendi la linea che corrisponde all'header del df\n columns_ = df.iloc[0].index[0]\n \n \"\"\" Individua i nomi delle colonne splittando la stringa che li contiene tutti\n ed escludendo lestringhe vuote ottenute tramite lo split\"\"\"\n clean_columns = [item.strip()\\\n for item in columns_.split(' ')\\\n if len(item)!=0]\n \n # aggiungo le colonne Anno e Inquinante\n columns = clean_columns + ['Anno', 'Inquinante']\n\t\t\t\n list_rows = []\n # Per ogni linea del df\n for line_idx in range(1, len(df)):\n \t\n # Come nel caso precedente splitto la linea per ottenere le diverse celle\n line = df.iloc[line_idx].values[0].strip().split(' ')\n \n # Quindi ottengo la lista delle celle della riga i-th\n raw_line = [item for item in line if len(item)!=0] \n \n # Aggiungiamo le colonne anno e inquinante\n list_rows += [raw_line + [anno, chimico]]\n\t\t\t\n\t\t\t# Definiamo il nuovo dataset \n df_idx = pd.DataFrame(list_rows, columns=columns)\n \n # Creiamo aggiungiamo alla lista di df da concatenare quello appena creato \n lista_df += [df_idx]\n\n\t# Facciamo la union dei df tenendo conto che le colonne possono essere diverse (concat con pandas)\n df_final = pd.concat(lista_df, ignore_index=False)\n\n # sostituisco i NaN e -999.0 con un valore vuoto\n df_final = df_final.fillna('')\n df_final = df_final.replace(to_replace='-999.0', value='')\n \n return df_final","def file_upload_to_obj():\n\n temp = []\n file_content = pd.read_excel(INPUT_FILE_NAME).fillna(0).to_dict('records')\n sorted_content = sorted(file_content, key=itemgetter(\n 'filedbentity.file_extension'))\n for item in file_content:\n\n raw_date = item.get('filedbentity.file_date')\n if raw_date:\n temp_date = raw_date.strftime('%Y-%m-%d')\n raw_date = datetime.strptime(temp_date, \"%Y-%m-%d\").date()\n else:\n raw_date = datetime.now().date()\n\n raw_status = item.get('dbentity.status')\n if raw_status == 'Archive':\n raw_status = 'Archived'\n \n obj = {\n 'path': item.get('EBS path'),\n 'display_name': item.get('dbentity.display_name'),\n 'status': raw_status,\n 'source': item.get('dbentity.source'),\n 'topic_edam_id': item.get('topic edam_id').upper().replace('TOPIC', 'EDAM').strip(),\n 'data_edam_id': item.get('data edam_id').upper().replace('DATA', 'EDAM').strip(),\n 'format_edam_id': item.get('format edam_id').upper().replace('FORMAT', 'EDAM').strip(),\n 'file_extension': item.get('filedbentity.file_extension'),\n 'file_date': raw_date,\n 'is_public': (item.get('filedbentity.is_public') == '1'),\n 'is_in_spell': item.get('filedbentity.is_in_spell'),\n 'is_in_browser': (item.get('filedbentity.is_in_browser') == '1'),\n 'readme_name': item.get('readme name'),\n 'description': item.get('filedbentity.description'),\n 'pmids': item.get('pmids (|)'),\n 'keywords': item.get('keywords (|)')\n }\n temp.append(obj)\n\n if len(temp) > 0:\n return temp\n return None","def index_bulk_from_files(self, files):\r\n\r\n docs = self._mailextractor.extract_jsons(files) # Generator-Iterable\r\n actions = self.convert_docstrs_to_bulk_actions(docs) # Generator-Iterable\r\n\r\n self._cur_print = 0\r\n actions_for_chunk = self.print_chunk_progress(actions) # Generator-Iterable\r\n (cnt_success, errors_index) = es_helpers.bulk(\r\n self._es, actions_for_chunk, chunk_size=constants.ES_BULK_CHUNK_SIZE)\r\n\r\n cnt_total = self._mailextractor.cnt_total\r\n errors_convert = self._mailextractor.errors_convert\r\n cnt_error = len(errors_convert) + len(errors_index)\r\n return Summary(cnt_total=cnt_total, cnt_success=cnt_success, cnt_error=cnt_error,\r\n errors_convert=errors_convert, errors_index=errors_index)","def read_json(self):\n list_filepath_bsn_test = []\n\n try:\n for json_file in tqdm(self.file_path_list, desc='Loading in json files'):\n with open(json_file) as f:\n data = json.load(f)\n\n # Get out: filepath, _bsn, text, offsets and main text\n list_filepath_bsn_test.append([data['filepath'], data['fields']['_belanghebbende_bsn'], data['text'],\n data['offsets']['main_text']])\n except:\n print(\"Faulty json file: ,\", json_file)\n\n # Make it into a data_frame\n self.data = pd.DataFrame(list_filepath_bsn_test)\n headers = ['path', 'bsn', 'text', 'offsets']\n self.data.columns = headers","def getFile(self, irodsdir, inlocaldir, filenames):\n utilities.log.debug('Begin subdirectory GET from iRODS at {}'.format(irodsdir))\n options = {kw.FORCE_FLAG_KW: ''}\n num=0\n with self._open_connection() as session:\n for fn in filenames: # Irods filenames are OBJECT. Get string name using .name\n localfile=\"{0}/{1}\".format(inlocaldir,fn)\n irodsfile=\"{0}/{1}\".format(irodsdir,fn)\n #print('Get iRODS file {} to local file {}'.format(irodsfile, localfile))\n session.data_objects.get(irodsfile, localfile, **options )\n num+=1\n utilities.log.debug('Get iRODS file {} to local file {}'.format(irodsfile, localfile))\n return num","def collect_data(folder):\n folder = pathlib.Path(folder)\n cases = []\n for case_folder in folder.iterdir():\n print(f'start collecting data for location {case_folder.name}')\n for tr_folder in case_folder.iterdir():\n case = calculate_values(tr_folder)\n cases.append(case)\n \n df = pd.DataFrame(cases)\n print(folder.parent.joinpath(f'{folder.stem}.csv'))\n df.to_csv(folder.parent.joinpath(f'{folder.stem}.csv'), index=False)","def summarize_models(folder):\r\n filepaths = sorted([n for n in glob(folder+r'/*') if '.json' in n])\r\n # create dictionary of results by looping over each file\r\n d = {}\r\n for fi, f in enumerate(filepaths):\r\n # load dictionary from json file\r\n print('processing model {}/{}'.format(fi+1, len(filepaths)))\r\n with open(f, 'r') as fp:\r\n data = json.load(fp)\r\n # extract label and trial number from filename\r\n L = os.path.split(f)[1].split('.')[0].split('__trial_')[0]\r\n t = int(os.path.split(f)[1].split('.')[0].split('__trial_')[1])\r\n # create new dictionary for each model\r\n if L not in d:\r\n d[L] = {\r\n 'img': None,\r\n 'sim': {},\r\n 'avg_err': [],\r\n 'bub_num': [],\r\n 'df': pd.DataFrame()}\r\n # populate dictionary with model information\r\n d[L]['img'] = data['img']\r\n d[L]['sim'][t] = data['sim']\r\n d[L]['df']['rad_trial_'+str(t)] = pd.Series(data['rad'])\r\n d[L]['bub_num'].append(len(pd.Series(data['rad']).dropna()))\r\n d[L]['df']['x_trial_'+str(t)] = pd.Series(np.array(data['cent'])[:, 0])\r\n d[L]['df']['y_trial_'+str(t)] = pd.Series(np.array(data['cent'])[:, 1])\r\n d[L]['avg_err'].append(data['tot_err_percent'])\r\n return d","def download_files_from_minio(bucket_name, file_prefix, ls_files_to_download):\n minio_client = get_minio_client()\n ls_minio_files = list(minio_client.list_objects(bucket_name, file_prefix))\n ls_downloaded_files = []\n local_object_dir = tempfile.mkdtemp()\n for minio_object_path in ls_files_to_download:\n local_object_name = minio_object_path.replace(file_prefix, \"\")\n local_object_path = os.path.join(local_object_dir,local_object_name) \n get_file_fromMinio(bucket_name, minio_object_path, local_object_path)\n ls_downloaded_files.append(local_object_path)\n return ls_downloaded_files\n\n\n def process_columns(string_to_process, replace_string='_'):\n \"\"\"\n Process a column name by :\n - processing special aplphabetical characters\n - replacing special characters by value set in 'replace_string' \n #Parameters\n :param string_to_process (str): String to process (column name)\n :param replace_string (str, default : '_'): Value to set for replaced characters\n (ex : \"/\" -> \"\", where \"\" is the replace_string)\n #Return\n return out (str): Processed string\n \"\"\"\n BIGQUERY_COLUMN_NAME_RE = \"[^0-9a-zA-Z_]+\"\n # Get encoding of column\n str_bytes = str.encode(string_to_process)\n encoding = 'utf-8'\n # Process any special alphabetical character\n temp_out = unicodedata\\\n .normalize('NFKD', string_to_process)\\\n .encode('ASCII', 'ignore')\\\n .decode(encoding)\n \n out = re.sub(BIGQUERY_COLUMN_NAME_RE, replace_string, temp_out)\n out = out.lower()\n return out","def read_files_to_df(clicks_file_list, users_file_list, max_workers):\n\n with max_workers as pool:\n df_clicks = pool.map(process_file_pd, clicks_file_list)\n df_clicks = pd.concat(df_clicks, ignore_index=True, sort=False)\n df_users = pool.map(process_file_pd, users_file_list)\n df_users = pd.concat(df_users, ignore_index=True, sort=False)\n\n return df_clicks, df_users","def loadFiles(root=\"data/TAIWAN_RAW_DATA/ADHD\"):\n\tdata_rt = [] # realtime.csv\n\tdata_trial = [] # trialdata.csv\n\tdata_id = [] # caseid/subjectid\n\tRealTime = \"A2RealTime_\"\n\tTrialData = \"A2TrialData_\"\n\tfolder_list = os.listdir(root) # list of subfolders in the root\n\tfor folders in folder_list:\n\t\tfolders_path = os.path.join(root,folders)\n\t\tif folders.find(\"pass\") != -1:\n\t\t\tcontinue\n\t\t\t\n\t\ttry:\n\t\t\tdata_rt.append(pd.read_csv(os.path.join\n\t\t\t\t\t\t\t\t (folders_path,\n\t\t\t\t\t\t\t\t RealTime+folders[3:]+\".csv\")))\n\t\t\tdata_trial.append(pd.read_csv(os.path.join\n\t\t\t\t\t\t\t\t\t (folders_path,\n\t\t\t\t\t\t\t\t\t TrialData+folders[3:]+\".csv\")))\n\t\t\tdata_id.append(int(folders.split('_')[1]))\n\t\texcept:\n\t\t\tprint(os.path.join(folders_path,TrialData+folders[3:]+\".csv\"))\n\t\t\t\n\treturn data_rt,data_trial,data_id,folder_list","def _api_scrape(json_inp, ndx):\n\n try:\n headers = json_inp['resultSets'][ndx]['headers']\n values = json_inp['resultSets'][ndx]['rowSet']\n except KeyError:\n # This is so ugly but this is what you get when your data comes out\n # in not a standard format\n try:\n headers = json_inp['resultSet'][ndx]['headers']\n values = json_inp['resultSet'][ndx]['rowSet']\n except KeyError:\n # Added for results that only include one set (ex. LeagueLeaders)\n headers = json_inp['resultSet']['headers']\n values = json_inp['resultSet']['rowSet']\n return DataFrame(values, columns=headers)","def process_data(*args, **kwargs):\n\n filepath = kwargs[\"filepath\"]\n func = kwargs[\"func\"]\n \n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(datafile)\n print('{}/{} files processed.'.format(i, num_files))","def load_all_experiments_from_dir(data_list, single_object_files=False, obj_ids=None):\n all_experiments = {}\n for i, batch_element in enumerate(data_list):\n if single_object_files:\n experiment = load_data_from_list_of_paths_single_object_files(batch_element, obj_ids)\n else:\n experiment = load_data_from_list_of_paths(batch_element)\n\n all_experiments[i] = experiment\n return all_experiments","def get_weather_as_df(keys):\n s3 = boto3.client('s3')\n bucket_name = 'trailreportdata'\n files = b''\n for key in keys:\n response = s3.get_object(Bucket=bucket_name, Key=key)\n body = response['Body']\n csv = body.read()\n files += csv\n f = BytesIO(files)\n csv = pd.read_csv(f)\n return csv","def testListFiles(self):\n testCollection = CouchCollection(database=self.testInit.couchDbName,\n url=self.testInit.couchUrl,\n name=\"Thunderstruck\")\n testFileset = CouchFileset(database=self.testInit.couchDbName,\n url=self.testInit.couchUrl,\n name=\"TestFileset\")\n testCollection.addFileset(testFileset)\n\n testFiles = {}\n for i in range(5):\n lfn = makeUUID()\n testFile = File(lfn=lfn, size=random.randint(1024, 4096),\n events=random.randint(1024, 4096))\n testFiles[lfn] = testFile\n testFileset.add([testFile])\n\n for file in testFileset.listFiles():\n self.assertTrue(file[\"lfn\"] in testFiles,\n \"Error: File missing.\")\n self.assertEqual(file[\"events\"], testFiles[file[\"lfn\"]][\"events\"],\n \"Error: Wrong number of events.\")\n self.assertEqual(file[\"size\"], testFiles[file[\"lfn\"]][\"size\"],\n \"Error: Wrong file size.\")\n return","def integrate_data(dataPath):\n data_set = []\n\n # List all json files\n data_files = glob.glob(dataPath + \"*.json\")\n\n # Concat json files into a list of dataframes\n for json_file in data_files:\n data = pd.read_json(json_file, orient='columns', lines=True)\n data_set.append(data)\n\n # Integrate dataframes into one dataframe\n data_sef_df = pd.concat(data_set)\n\n # Select product ids\n products = pd.DataFrame(data_sef_df[\"products\"].tolist())\n \n # Convert column names from int to string \n products.columns = products.columns.astype(str)\n\n return data_sef_df, products","def request_large_data(path, params):\r\n start = 0\r\n result = []\r\n while True:\r\n process = __split_path(path)\r\n if process['status'] == 'success':\r\n conn = process['conn']\r\n info = MetaInfo()\r\n header = {'vendor_key': info.vendor_id}\r\n # check 2.x and 3.x differences in using urllib\r\n params['start'] = str(start)\r\n try:\r\n conn.request(\"GET\", process['req_path'] + \"?\" +\r\n urllib.urlencode(params), headers=header)\r\n except AttributeError:\r\n conn.request(\"GET\", process['req_path'] + \"?\" +\r\n urllib.parse.urlencode(params), headers=header)\r\n resp = conn.getresponse()\r\n\r\n if resp.status != 200:\r\n print(\"Code: \" + str(resp.status))\r\n resp_obj = HTTPConnect(resp.status, resp.read()).json()\r\n print(resp_obj)\r\n break\r\n resp_obj_read = resp.read()\r\n resp_obj = HTTPConnect(resp.status, resp_obj_read).json()\r\n if type(resp_obj) is str:\r\n try:\r\n resp_obj = json.loads(resp_obj)\r\n except:\r\n # return error msg\r\n print(\"parse json str failed\")\r\n # print(resp_obj)\r\n break\r\n elif type(resp_obj) is dict:\r\n try:\r\n resp_obj = json.loads(json.dumps(resp_obj))\r\n except:\r\n print(\"parse dict failed\")\r\n print(resp_obj)\r\n break\r\n else:\r\n print(\"result not str\")\r\n print(resp_obj)\r\n break\r\n\r\n total = int(resp_obj['total'])\r\n next_start = int(resp_obj['next_start'])\r\n\r\n result.extend(resp_obj['data'])\r\n print(\"Finish extracting: \" + str(start+1) + \" to \" + str(next_start-1) + \" , remaining: \"\r\n + str(total - next_start))\r\n start = next_start\r\n conn.close()\r\n if start == total:\r\n break\r\n return result"],"string":"[\n \"def get_data(directory):\\n dictlist = []\\n cols = ['title', 'text', 'authors', 'num_images', 'domain', 'url']\\n\\n folders = glob.glob(directory + '/*')\\n for index, subdir in enumerate(folders):\\n\\n file_path = glob.glob(subdir + '/*')\\n\\n #check if glob returned a valid file path (non-empty list)\\n if len(file_path) == 1:\\n file = open(file_path[0]).read()\\n jsondata = json.loads(file)\\n dictlist.append(scaledict(jsondata))\\n return pd.DataFrame(dictlist, columns=cols)\",\n \"def query(root_directory, request):\\n assert isinstance(request, DataRequest)\\n\\n # Initialize data objects\\n data_map = {}\\n\\n request_file_names = list_requested_files(request)\\n existing_files = ls_json(root_directory)\\n\\n # Load request files\\n print(\\\"Loading %d files in total.\\\" % (len(request_file_names)))\\n for (count, file_name) in enumerate(request_file_names):\\n print(\\\"File %d: %s\\\" % (count + 1, file_name))\\n # File does not exist.\\n if file_name not in existing_files:\\n print(\\\"File does not exist\\\\n\\\")\\n continue\\n\\n # Open file and parse json\\n json_data = load_file(root_directory + file_name)\\n if json_data is None:\\n raise RuntimeError()\\n\\n # Extract and add data\\n parse_stats = \\\\\\n parse_stations(json_data, data_map, request.region)\\n\\n log_parse_stats(parse_stats)\\n\\n utils.add_alias(data_map)\\n\\n response = DataResponse()\\n response.data_map = data_map\\n return response\",\n \"def index_bulk_from_dir(self, dir_data_in, ignore_already_imported=True):\\r\\n if ignore_already_imported:\\r\\n already_imported_ids = self.already_imported_ids\\r\\n else:\\r\\n already_imported_ids = set()\\r\\n return self.index_bulk_from_files(\\r\\n helpers.get_files_from_dir(dir_data_in, already_imported_ids=already_imported_ids))\",\n \"def search_load_toa5df(api_token, base_url, search_params, biggish_data=False,\\n keep_files=False, multiple_delim=False,\\n dst_folder='./raw_data'):\\n # search records\\n records = search(api_token, base_url, search_params)\\n\\n # use 'biggish data' mode\\n if biggish_data:\\n # set and create download folder if it does not exist\\n dst_folder = Path(dst_folder)\\n if not dst_folder.is_dir():\\n os.makedirs(dst_folder)\\n\\n # display number of files beeing downloaded\\n print(f'Downloading {len(records)} files:')\\n\\n # build download url for each file\\n for record in tqdm.tqdm(records):\\n download_url = f\\\"{record['url']}?auth_token={api_token}\\\"\\n\\n # check if file exists, if not downloads\\n file_path = dst_folder / record['filename']\\n if not file_path.is_file():\\n urllib.request.urlretrieve(download_url, file_path)\\n\\n # create empty dataframe to store final data\\n df_all = pd.DataFrame()\\n\\n # loop through all downloaded files\\n for i in list(dst_folder.glob('*.dat')):\\n\\n # read data into dataframe discarding undesired header columns\\n if multiple_delim:\\n df = pd.read_csv(i, skiprows=[0, 2, 3], na_values='NAN',\\n sep='\\\\\\\\t|,|;', engine='python')\\n df.columns = [i.replace('\\\"', \\\"\\\") for i in df.columns]\\n df['TIMESTAMP'] = df['TIMESTAMP'].str.replace('\\\"', '')\\n else:\\n df = pd.read_csv(i, skiprows=[0, 2, 3], na_values='NAN')\\n\\n # generate datetimeindex\\n df = df.set_index('TIMESTAMP')\\n df.index = pd.to_datetime(df.index)\\n\\n # optimize memory usage\\n # first get names of float, integer and object columns\\n float_cols = df.select_dtypes(include=['float64']).columns\\n integer_cols = df.select_dtypes(include=['int64']).columns\\n object_cols = df.select_dtypes(include=['object']).columns\\n # the assign dtype that uses least memory for each column\\n df[integer_cols] = df[integer_cols].apply(\\n pd.to_numeric, downcast='integer')\\n df[float_cols] = df[float_cols].apply(\\n pd.to_numeric, downcast='float')\\n # converting objects to category is only more memory efficient if\\n # less tha 50% of values are unique\\n for col in object_cols:\\n num_unique_values = len(df[col].unique())\\n num_total_values = len(df[col])\\n if num_unique_values / num_total_values < 0.5:\\n df[col] = df[col].astype('category')\\n\\n # append data\\n df_all = pd.concat([df_all, df], sort=False)\\n\\n # delete dst_folder if wanted\\n if not keep_files:\\n shutil.rmtree(dst_folder)\\n\\n else:\\n # print number of records found\\n print(f'Loading {len(records)} files:')\\n\\n # create empty dataframe to save data in\\n df_all = pd.DataFrame()\\n\\n # loop through all records and generate progressbar\\n for record in tqdm.tqdm(records):\\n # build download url for each file\\n download_url = f\\\"{record['url']}?auth_token={api_token}\\\"\\n # get data\\n req = urllib.request.urlopen(download_url)\\n data = req.read()\\n\\n # read data into dataframe discarding undesired header columns\\n if multiple_delim:\\n df = pd.read_csv(io.StringIO(data.decode('utf-8')),\\n skiprows=[0, 2, 3], na_values='NAN',\\n sep='\\\\\\\\t|,|;', engine='python')\\n df.columns = [i.replace('\\\"', \\\"\\\") for i in df.columns]\\n df['TIMESTAMP'] = df['TIMESTAMP'].str.replace('\\\"', '')\\n else:\\n df = pd.read_csv(io.StringIO(data.decode('utf-8')),\\n skiprows=[0, 2, 3], na_values='NAN')\\n\\n # generate datetimeindex\\n df = df.set_index('TIMESTAMP')\\n df.index = pd.to_datetime(df.index)\\n\\n # infer data types of all other columns\\n df = df.infer_objects()\\n\\n # append data\\n df_all = pd.concat([df_all, df], sort=False)\\n\\n # if from_date provided sort and trim data\\n if 'from_date' in search_params:\\n df_all = df_all.sort_index()[search_params['from_date']:]\\n # if to_date provided sort and trim data\\n if 'to_date' in search_params:\\n df_all = df_all.sort_index()[:search_params['to_date']]\\n\\n return df_all.drop_duplicates()\",\n \"def test_list_files(self, mock_get):\\n list_data = {\\n 'has_next': False,\\n 'next_offset': None,\\n 'limit': 100,\\n 'offset': 0,\\n 'file_count_in_result': 3,\\n 'total_file_count': 3,\\n 'secure_file_summaries': [\\n {\\n 'sdbox_id': '244cfc0d-4beb-8189-5056-194f18ead6f4',\\n 'path': 'studio54/test.py',\\n 'size_in_bytes': 1323,\\n 'name': 'test.py',\\n 'created_by': 'tester@studio54.com',\\n 'created_ts': '1974-11-17T00:02:30Z',\\n 'last_updated_by': 'tester@studio54.com',\\n 'last_updated_ts': '1974-11-17T00:02:30Z'\\n },\\n {\\n 'sdbox_id': '244cfc0d-4beb-8189-5056-194f18ead6f4',\\n 'path': 'studio54/test.gif',\\n 'size_in_bytes': 686,\\n 'name': 'test.gif',\\n 'created_by': 'tester@studio54.com',\\n 'created_ts': '1974-11-17T00:02:30Z',\\n 'last_updated_by': 'tester@studio54.com',\\n 'last_updated_ts': '1974-11-17T00:02:30Z'\\n },\\n {\\n 'sdbox_id': '244cfc0d-4beb-8189-5056-194f18ead6f4',\\n 'path': 'studio54/5621.gif',\\n 'size_in_bytes': 686,\\n 'name': '5621.gif',\\n 'created_by': 'tester@studio54.com',\\n 'created_ts': '1974-11-17T00:02:30Z',\\n 'last_updated_by': 'tester@studio54.com',\\n 'last_updated_ts': '1974-11-17T00:02:30Z'\\n }\\n ]\\n }\\n\\n payload = {'limit': '100', 'offset': '0'}\\n mock_resp = self._mock_response(content=json.dumps(list_data))\\n mock_get.return_value = mock_resp\\n\\n keys = self.client.list_files('fake/path')\\n\\n assert_equals(keys['limit'], 100)\\n assert_equals(keys['offset'], 0)\\n assert_equals(keys, list_data)\\n assert_in('X-Cerberus-Client', self.client.HEADERS)\\n mock_get.assert_called_with(\\n self.cerberus_url + '/v1/secure-files/fake/path/',\\n params=payload, headers=self.client.HEADERS\\n )\",\n \"def collect_results( results_dir = \\\"experiments\\\" ) :\\n #%%\\n import pandas as pd\\n exps_fn = os.listdir( results_dir )\\n dics = []\\n for fname in exps_fn :\\n with open( results_dir + \\\"/\\\" + fname, \\\"rt\\\", encoding=\\\"utf8\\\" ) as f_out :\\n dics.append( json.load( f_out ) )\\n\\n results_df = pd.DataFrame( dics )\\n #%%\\n return results_df\",\n \"def make_df_from_json(json_files, out_file):\\n table = [[\\\"name\\\", \\n \\\"cik\\\", \\n \\\"city\\\",\\n \\\"state\\\",\\n \\\"street1\\\",\\n \\\"street2\\\",\\n \\\"zip_code\\\",\\n \\\"year_of_incorp\\\", \\n \\\"min_inv\\\", \\n \\\"tot_off\\\", \\n \\\"tot_sold\\\", \\n \\\"tot_rem\\\", \\n \\\"ind_group_type\\\", \\n \\\"has_non_accred\\\", \\n \\\"num_non_accred\\\", \\n \\\"tot_num_inv\\\"\\n ]] \\n\\n for json_dict in json_files:\\n\\n with open(json_dict, \\\"rb\\\") as f:\\n data = json.load(f)\\n print(json_dict)\\n\\n for i, key in enumerate(data):\\n # if i % 1000 == 0:\\n # print(i)\\n entry = data[key] \\n if entry == {}:\\n #print(\\\"missing entry {0}\\\".format(i))\\n continue\\n row = []\\n\\n primary_issuer = entry[\\\"Primary Issuer\\\"]\\n cik = primary_issuer[\\\"cik\\\"]\\n name = primary_issuer[\\\"entity_name\\\"]\\n phone = primary_issuer[\\\"phone\\\"]\\n year_of_incorp = primary_issuer[\\\"year_of_incorp\\\"]\\n address = primary_issuer[\\\"address\\\"]\\n city = address[\\\"city\\\"]\\n state = address[\\\"state\\\"]\\n street1 = address[\\\"street1\\\"]\\n street2 = address[\\\"street2\\\"]\\n zip_code = address[\\\"zip_code\\\"]\\n\\n secondary_issuers = entry[\\\"Secondary Issuers\\\"]\\n related_people = entry[\\\"Related People\\\"]\\n \\n offering_data = entry[\\\"Offering Data\\\"]\\n min_inv = offering_data[\\\"min_investment_accepted\\\"]\\n tot_off = offering_data[\\\"total_offering_amount\\\"]\\n tot_sold = offering_data[\\\"total_amount_sold\\\"]\\n tot_rem = offering_data[\\\"total_remaining\\\"]\\n ind_group_type = offering_data[\\\"ind_group_type\\\"]\\n has_non_accred = offering_data[\\\"has_non_accred\\\"]\\n num_non_accred = offering_data[\\\"num_non_accred\\\"]\\n tot_num_inv = offering_data[\\\"tot_num_inv\\\"] \\n\\n row = [name, \\n cik, \\n city,\\n state,\\n street1,\\n street2,\\n zip_code,\\n year_of_incorp,\\n min_inv,\\n tot_off,\\n tot_sold,\\n tot_rem,\\n ind_group_type,\\n has_non_accred,\\n num_non_accred,\\n tot_num_inv\\n ]\\n\\n table.append(row)\\n\\n df = pd.DataFrame(table)\\n df.to_csv(out_file)\\n\\n return 0\",\n \"def list_files(self, path: str, tenant: str, root: str) -> None:\\n self.listing_dir = True\\n current_page = 0\\n pagination_value = 100\\n disable_metadata = self.get_query_argument(\\\"disable_metadata\\\", None)\\n try:\\n current_page = int(self.get_query_argument(\\\"page\\\"))\\n pagination_value = int(self.get_query_argument(\\\"per_page\\\"))\\n except HTTPError as e:\\n pass # use default value\\n except ValueError:\\n raise ClientError(\\\"next values must be integers\\\")\\n if current_page < 0:\\n raise ClientError(\\\"next values are natural numbers\\\")\\n if pagination_value > 50000:\\n raise ClientError(\\\"per_page cannot exceed 1000\\\")\\n\\n # don't list symlinked directories\\n try:\\n any_path_islink(path, opts=options)\\n except Exception as e:\\n self.write({\\\"files\\\": [], \\\"page\\\": None})\\n return\\n\\n # arbitrary order\\n # if not returning what you want\\n # then try next page\\n dir_map = os.scandir(path)\\n paginate = False\\n files = []\\n start_at = (current_page * pagination_value) - 1\\n stop_at = start_at + pagination_value\\n # only materialise the necessary entries\\n for num, entry in enumerate(dir_map):\\n if num <= start_at:\\n continue\\n elif num <= stop_at and num >= start_at:\\n files.append(entry)\\n elif num == stop_at + 1:\\n paginate = True\\n break # there is more\\n if len(files) == 0:\\n self.write({\\\"files\\\": [], \\\"page\\\": None})\\n else:\\n if paginate and not current_page:\\n next_page = 1\\n elif paginate:\\n next_page = int(current_page) + 1\\n else:\\n next_page = None\\n baseuri = self._base_uri()\\n nextref = f\\\"{baseuri}?page={next_page}\\\" if next_page else None\\n if self.export_max and len(files) > self.export_max:\\n raise ClientError(\\n f\\\"number of files exceed configured maximum: {self.export_max}\\\"\\n )\\n names = []\\n times = []\\n exportable = []\\n reasons = []\\n sizes = []\\n mimes = []\\n owners = []\\n etags = []\\n mtimes = []\\n if not self.group_config[\\\"enabled\\\"]:\\n default_owner = options.default_file_owner.replace(\\n options.tenant_string_pattern, tenant\\n )\\n for file in files:\\n filepath = file.path\\n size, mime_type, latest = self.get_file_metadata(filepath)\\n status = self.enforce_export_policy(\\n self.export_policy, filepath, tenant, size, mime_type\\n )\\n reason = None if status else \\\"not allowed\\\"\\n path_stat = file.stat(follow_symlinks=False)\\n etag = self.mtime_to_digest(latest)\\n date_time = str(datetime.datetime.fromtimestamp(latest).isoformat())\\n if self.has_posix_ownership:\\n try:\\n owner = pwd.getpwuid(path_stat.st_uid).pw_name\\n except KeyError:\\n try:\\n default_owner_id = pwd.getpwnam(default_owner).pw_uid\\n group_id = path_stat.st_gid\\n os.chown(file.path, default_owner_id, group_id)\\n owner = default_owner\\n except Exception:\\n logging.error(\\n f\\\"could not reset owner of {filepath} to {default_owner}\\\"\\n )\\n owner = \\\"nobody\\\"\\n else:\\n owner = options.api_user\\n names.append(os.path.basename(filepath))\\n times.append(date_time)\\n exportable.append(status)\\n reasons.append(reason)\\n sizes.append(size)\\n mimes.append(mime_type)\\n owners.append(owner)\\n etags.append(etag)\\n mtimes.append(latest)\\n else: # then it is the TSD import dir\\n group_memberships = self.claims.get(\\\"groups\\\")\\n if root:\\n for file in files:\\n if file.name in group_memberships:\\n names.append(os.path.basename(file.path))\\n times.append(None)\\n exportable.append(False)\\n reasons.append(None)\\n sizes.append(None)\\n mimes.append(None)\\n owners.append(None)\\n else:\\n for file in files:\\n if not disable_metadata:\\n path_stat = file.stat()\\n latest = path_stat.st_mtime\\n etag = self.mtime_to_digest(latest)\\n date_time = str(\\n datetime.datetime.fromtimestamp(latest).isoformat()\\n )\\n size, mime_type, mtime = self.get_file_metadata(file.path)\\n else:\\n date_time = None\\n etag = None\\n size, mime_type, mtime = None, None, None\\n names.append(file.name)\\n times.append(date_time)\\n exportable.append(False)\\n reasons.append(None)\\n sizes.append(size)\\n mimes.append(mime_type)\\n owners.append(None)\\n etags.append(etag)\\n mtimes.append(mtime)\\n file_info = []\\n for f, t, e, r, s, m, o, g, d in zip(\\n names, times, exportable, reasons, sizes, mimes, owners, etags, mtimes\\n ):\\n href = f\\\"{baseuri}/{url_escape(f)}\\\"\\n file_info.append(\\n {\\n \\\"filename\\\": f,\\n \\\"size\\\": s,\\n \\\"modified_date\\\": t,\\n \\\"href\\\": href,\\n \\\"exportable\\\": e,\\n \\\"reason\\\": r,\\n \\\"mime-type\\\": m,\\n \\\"owner\\\": o,\\n \\\"etag\\\": g,\\n \\\"mtime\\\": d,\\n }\\n )\\n logging.info(f\\\"{self.requestor} listed {path}\\\")\\n self.write({\\\"files\\\": file_info, \\\"page\\\": nextref})\",\n \"def do_GET(self):\\n self.send_response(200)\\n self.end_headers()\\n self.wfile.write(json.dumps(sample_files_list_response).encode())\",\n \"def test_files(self, location):\\n for filename in os.listdir(location):\\n with open(location + '/' + filename) as json_file:\\n data = json.load(json_file)\\n self.test_data(data)\",\n \"def only_in_one_to_json(\\n self,\\n path_dir_1,\\n list_in_1,\\n path_dir_2,\\n list_in_2):\\n only_in_one_json = []\\n for fl in list_in_1:\\n file_path_1 = os.path.join(path_dir_1, fl)\\n file_size_1 = os.path.getsize(file_path_1)\\n type_ = self.get_type(file_path_1)\\n if (type_ == self.dir):\\n file_size_1 = None\\n only_in_one_json.append(self.cmp_files_to_json(\\n file_path_1,\\n type_,\\n True,\\n False,\\n file_size_1,\\n None,\\n False,\\n None))\\n\\n for fl in list_in_2:\\n file_path_2 = os.path.join(path_dir_2, fl)\\n file_size_2 = os.path.getsize(file_path_2)\\n type_ = self.get_type(file_path_2)\\n if (type_ == self.dir):\\n file_size_2 = None\\n\\n only_in_one_json.append(self.cmp_files_to_json(\\n file_path_2,\\n type_,\\n False,\\n True,\\n None,\\n file_size_2,\\n False,\\n None))\\n\\n return only_in_one_json\",\n \"def format_streamflows(data_dir):\\n\\n search_expr = data_dir + \\\"/*.json\\\"\\n\\n df = pd.DataFrame()\\n\\n for json_file in glob.glob(search_expr):\\n\\n with open(json_file, 'r') as fn:\\n data = json.load(fn)\\n\\n try:\\n data = json2dataframe(data)\\n\\n new_df = data[0]\\n new_df = new_df.drop(columns=['dateTime', 'qualifiers'])\\n new_df = new_df.rename(columns={'value': data[1]})\\n df = pd.concat([df, new_df], axis=1)\\n\\n except IndexError, e:\\n print 'Error:', e\\n continue\\n\\n return df\",\n \"def execute(self, req):\\n\\t\\tmyPath = req[\\\"url\\\"].replace(self.settings[\\\"ns\\\"][\\\"local\\\"], \\\"\\\", 1).split(\\\"/\\\")\\n\\t\\tfile = myPath.pop(0)\\n\\t\\tcurrentDir = getcwd()\\n\\t\\tservice = self.basedir + file\\n\\t\\turi = req[\\\"url\\\"]\\n\\t\\tqueryPath = \\\"%s/queries/\\\" % service\\n\\t\\ttemplatePath = \\\"%s/\\\" % service\\n\\t\\ttemplateName = self.mime.getExtension(req[\\\"request\\\"].accept_mimetypes.best)\\n\\t\\ttry:\\n\\t\\t\\tonlyfiles = [f for f in listdir(queryPath) if isfile(join(queryPath, f))]\\n\\t\\texcept OSError:\\n\\t\\t\\tprint \\\"Warning: Can't find path %s for queries.\\\" % templatePath\\n\\t\\t\\tonlyfiles = []\\n\\t\\tqueries = {}\\n\\t\\tfirst={}\\n\\t\\tfor root, dirs, files in walk(queryPath):\\n\\t\\t\\tfor filename in files:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tcurrentEndpoint = \\\"local\\\"\\n\\t\\t\\t\\t\\t_aux = root.rstrip(\\\"/\\\").split(\\\"/\\\").pop()\\n\\t\\t\\t\\t\\tif _aux != \\\"queries\\\":\\n\\t\\t\\t\\t\\t\\tcurrentEndpoint = _aux\\n\\t\\t\\t\\t\\tif not filename.endswith(\\\".query\\\"):\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tsqlQuery = self.env.get_template(\\\"%s/%s\\\" % (root, filename))\\n\\t\\t\\t\\t\\trenderedSqlQuery = sqlQuery.render(queries=queries, first=first, uri=uri, session=session, flod=self.flod, args=myPath)\\n\\t\\t\\t\\t\\tif re.match(\\\"^\\\\s*select\\\", renderedSqlQuery, flags=re.IGNORECASE) is None:\\n\\t\\t\\t\\t\\t\\treturn {\\\"content\\\": \\\"Not a valid SQL Select query\\\", \\\"status\\\": 500}\\n\\t\\t\\t\\t\\tresults = self.sqlserver.query(renderedSqlQuery, currentEndpoint)\\n\\t\\t\\t\\t\\t_name = filename.replace(\\\".query\\\", \\\"\\\")\\n\\t\\t\\t\\t\\tqueries[_name] = []\\n\\t\\t\\t\\t\\tif results is not None:\\n\\t\\t\\t\\t\\t\\tqueries[_name] = results\\n\\n\\t\\t\\t\\texcept Exception, ex:\\n\\t\\t\\t\\t\\tprint sys.exc_info()\\n\\t\\t\\t\\t\\tprint ex\\n\\t\\t\\t\\t\\treturn {\\\"content\\\": \\\"A problem with the SQL endpoint occurred\\\", \\\"status\\\": 500}\\n\\t\\tchdir(currentDir)\\n\\t\\ttry:\\n\\t\\t\\tif templateName == \\\"json\\\" and not isfile( \\\"%s%s.template\\\" % (templatePath, templateName)):\\n\\t\\t\\t\\tout = json.dumps(queries)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcontent = self.env.get_template(\\\"%s%s.template\\\" % (templatePath, templateName))\\n\\t\\t\\t\\tout = content.render(queries=queries, uri=uri, session=session, flod=self.flod, args=myPath)\\n\\t\\texcept Exception:\\n\\t\\t\\tprint sys.exc_info()\\n\\t\\t\\treturn {\\\"content\\\": \\\"Rendering problems\\\" , \\\"status\\\": 500}\\n\\t\\treturn {\\\"content\\\": out, \\\"mimetype\\\": \\\"text/html\\\"}\",\n \"def whole_data(arg_file_names_list):\\r\\n\\r\\n whole_data_list = []\\r\\n # loop which iterates through the directory and executes function vat_return_reader for each file\\r\\n for file_name in arg_file_names_list:\\r\\n whole_data_list.append(vat_return_reader(file_name))\\r\\n # creates data frame from list\\r\\n df = pd.DataFrame(whole_data_list)\\r\\n # adds total row \\r\\n df = pd.concat([df,pd.DataFrame(df.sum(axis=0),columns=['Total']).T])\\r\\n return df\",\n \"def fetch_data(data_dir):\\n check_dir(data_dir)\\n\\n data = list()\\n for data_file in os.listdir(data_dir):\\n data_file_path = normalize_path([data_dir, data_file])\\n data.append(read_data_file(data_file_path))\\n\\n data = pd.concat(data, sort=True)\\n data.sort_values(by=\\\"datetime\\\", inplace=True)\\n return data.reset_index(drop=True)\",\n \"def on_get(self, req, resp):\\n resp.set_header('Content-Type', 'text/json')\\n tif_paths = encode.get_files_in_directory(DIARIES_TO_ENCODE_DIR, \\\".tif\\\")\\n zip_paths = encode.get_files_in_directory(DIARIES_TO_ENCODE_DIR, \\\".zip\\\")\\n diaries_paths = tif_paths + zip_paths\\n def extract_file_name(path): return os.path.basename(path)\\n resp.body = json.dumps({\\\"diaries\\\": list(map(extract_file_name, diaries_paths)),\\n \\\"diaries_paths\\\": diaries_paths})\",\n \"def fetch_data(files, folder):\\n if not os.path.exists(folder):\\n _log(\\\"Creating new folder %s\\\" % (folder))\\n os.makedirs(folder)\\n\\n all_skip = True\\n for f in files:\\n url, md5 = files[f]\\n fullpath = pjoin(folder, f)\\n if os.path.exists(fullpath) and (_get_file_md5(fullpath) == md5):\\n continue\\n all_skip = False\\n _log('Downloading \\\"%s\\\" to %s' % (f, folder))\\n _get_file_data(fullpath, url)\\n if _get_file_md5(fullpath) != md5:\\n msg = \\\"\\\"\\\"The downloaded file, %s, does not have the expected md5\\nchecksum of \\\"%s\\\". This could mean that that something is wrong with the file or\\nthat the upstream file has been updated. You can try downloading the file again\\nor updating to the newest version of dipy.\\\"\\\"\\\" % (fullpath, md5)\\n msg = textwrap.fill(msg)\\n raise FetcherError(msg)\\n\\n if all_skip:\\n _log(\\\"All files already in %s.\\\" % (folder))\\n else:\\n _log(\\\"Files successfully downloaded to %s\\\" % (folder))\",\n \"def process_data(cur, conn, filepath, func):\\n\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def request(\\n self,\\n method: str,\\n endpoint: str,\\n json: Any = None,\\n files: Optional[dict[str, Any]] = None,\\n retryCount: int = 5,\\n ) -> Any:\\n pass\",\n \"def collect_files(config: Config,\\n files: Optional[List[str]] = None,\\n method: Optional[str] = None) -> DFs:\\n filenames = files if files else config.get('args.inputs', [])\\n if method is None:\\n method = config.get('collect.method', 'csv')\\n frames: Dict[str, List[DF]] = {}\\n for filename in filenames:\\n dfs: DFs = FILE_READERS[method](config, filename, method)\\n postprocess_file(config, dfs)\\n for k, frame in dfs.items():\\n if k not in frames:\\n frames[k] = []\\n frames[k].append(frame)\\n dfs = {}\\n for k, v in frames.items():\\n dfs[k] = pd.concat(v, ignore_index=True)\\n postprocess_collected(config, dfs)\\n return dfs\",\n \"def ingest_files(is_authenticated, is_authorized):\\n if not is_authorized:\\n return render_template(\\\"unauthorized_user.html\\\"), 401\\n\\n data_json = request.get_json()\\n payload_data = {}\\n payload_data['egnyte_uploaded_files'] = data_json.get('egnyte_uploaded_files')\\n for field in payload_data:\\n response_msg = check_field_validations(payload_data, field)\\n if response_msg:\\n return Response(json.dumps(response_msg), status=400, mimetype='application/json')\\n try:\\n thread = threading.Thread(target=corpus_indexer.index_based_on_trigger, args=payload_data['egnyte_uploaded_files'])\\n thread.start()\\n resp = Response(json.dumps({'status':'Ingest Files started'}), status=201, mimetype='application/json')\\n except Exception as e:\\n logger.error(\\\"ingest_files api is failed with error %s\\\" % str(e))\\n return render_template(\\\"internal_server_error.html\\\"), 500\\n return resp\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def process_data(cur, conn, filepath, func):\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{} files processed.'.format(num_files))\",\n \"def process_data(cur, conn, filepath, func):\\n \\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n \\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n \\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def get_files(filedir):\\n inputfiles = sorted(glob.glob(os.path.normcase(filedir+\\\"/\\\")+\\\"*.json\\\"))\\n return inputfiles\",\n \"def read_json(self, bucket, key, with_prefix=False, file_extension=None, **kwargs):\\n if with_prefix:\\n files = self.get_prefix_object(bucket=bucket,\\n key=key,\\n file_extension=file_extension)\\n\\n df = pd.concat([pd.read_json(self.get_file_buffer(bucket=bucket, key=key), **kwargs) for key in files])\\n\\n else:\\n df = pd.read_json(self.get_file_buffer(bucket=bucket, key=key), **kwargs)\\n\\n return df\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for file in files:\\n all_files.append(os.path.abspath(file))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def create_dataframe_from_dir(directory):\\n\\n if not os.path.exists(directory):\\n return pd.DataFrame()\\n\\n file_list = os.listdir(directory)\\n\\n file_list.sort()\\n\\n df_list = []\\n for filename in file_list:\\n\\n if filename.startswith(\\\"_\\\") or (not filename.endswith(\\\".csv\\\")):\\n continue\\n\\n # Assert that the file is named correctly\\n _, start_date, end_date = check_filename_convention(filename)\\n\\n df = pd.read_csv(os.path.join(directory, filename))\\n df = df.assign(SourceFile=filename)\\n\\n # In January 2020, MS changed the date format used in the usage\\n # export files from US to UK. This happen between 24/01/2020 -\\n # 28/01/2020. The following if statement is to deal with this\\n # change.\\n if start_date is None or end_date is None:\\n continue\\n\\n if start_date > datetime.datetime(2020, 1, 24, 0, 0):\\n try:\\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\\n df[CONST_COL_NAME_DATE], format=\\\"%d/%m/%Y\\\"\\n )\\n except Exception:\\n try:\\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\\n df[CONST_COL_NAME_DATE], format=\\\"%m/%d/%Y\\\"\\n )\\n except Exception:\\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\\n df[CONST_COL_NAME_DATE], format=\\\"%Y-%m-%d\\\"\\n )\\n else:\\n try:\\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\\n df[CONST_COL_NAME_DATE], format=\\\"%m/%d/%Y\\\"\\n )\\n except Exception:\\n try:\\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\\n df[CONST_COL_NAME_DATE], format=\\\"%d/%m/%Y\\\"\\n )\\n except Exception:\\n df[CONST_COL_NAME_DATE] = pd.to_datetime(\\n df[CONST_COL_NAME_DATE], format=\\\"%Y-%m-%d\\\"\\n )\\n\\n # Check if data comes from EduHub\\n if CONST_COL_NAME_HANDOUTNAME in df.columns:\\n\\n # Renaming HandoutName to SubscriptionName\\n df = df.rename(\\n columns={CONST_COL_NAME_HANDOUTNAME: CONST_COL_NAME_SNAME}\\n )\\n\\n # Dropping columns CourseName,LabName\\n df = df.drop(\\n columns=[CONST_COL_NAME_LABNAME, CONST_COL_NAME_COURSENAME]\\n )\\n\\n df_list.append(df)\\n\\n if len(df_list) == 0:\\n return pd.DataFrame()\\n\\n total_df = pd.concat(df_list, axis=0, ignore_index=True)\\n\\n return total_df\",\n \"def files(self, start=None, limit=None):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/files'.format(self.get_url())\\r\\n return http.Request('GET', url, params), parsers.parse_json\",\n \"def files(self, start=None, limit=None):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/files'.format(self.get_url())\\r\\n return http.Request('GET', url, params), parsers.parse_json\",\n \"def files(self, start=None, limit=None):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/files'.format(self.get_url())\\r\\n return http.Request('GET', url, params), parsers.parse_json\",\n \"def file_df_from_files(raw_files: list, file_folder: str) -> pd.DataFrame:\\n raw_files.sort()\\n sizes = [\\n round(get_size(os.path.join(file_folder, _)) / 1024 ** 3, 2)\\n for _ in raw_files\\n ]\\n created = [\\n datetime.datetime.fromtimestamp(os.path.getctime(os.path.join(file_folder, _)))\\n for _ in raw_files\\n ]\\n file_df = pd.DataFrame(\\n list(zip(range(1, len(raw_files) + 1), raw_files, created, sizes)),\\n columns=[\\\"#\\\", \\\"Filename\\\", \\\"Creation date\\\", \\\"Size (GB)\\\"],\\n )\\n file_df[\\\"Shortname\\\"] = [os.path.splitext(_)[0] for _ in raw_files]\\n\\n return file_df\",\n \"def get_raw_files(raw_dir, data_source):\\n raw_files = {\\n \\\"inputs\\\": [],\\n \\\"targets\\\": [],\\n } # keys\\n for d in data_source:\\n if d[\\\"url\\\"].endswith(\\\"/\\\"): # the url is a dir and not a compressed file\\n input_file = download_from_url(raw_dir, d[\\\"url\\\"] + d[\\\"input\\\"])\\n target_file = download_from_url(raw_dir, d[\\\"url\\\"] + d[\\\"target\\\"])\\n else:\\n input_file, target_file = download_and_extract(\\n raw_dir, d[\\\"url\\\"], d[\\\"input\\\"], d[\\\"target\\\"])\\n raw_files[\\\"inputs\\\"].append(input_file)\\n raw_files[\\\"targets\\\"].append(target_file)\\n return raw_files\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def process_data(cur, conn, filepath, func):\\r\\n\\r\\n # get all files matching extension from directory\\r\\n all_files = []\\r\\n for root, dirs, files in os.walk(filepath):\\r\\n files = glob.glob(os.path.join(root,'*.json'))\\r\\n for f in files :\\r\\n all_files.append(os.path.abspath(f))\\r\\n\\r\\n # get total number of files found\\r\\n num_files = len(all_files)\\r\\n print('{} files found in {}'.format(num_files, filepath))\\r\\n\\r\\n # iterate over files and process\\r\\n try:\\r\\n for i, datafile in enumerate(all_files, 1):\\r\\n func(cur, datafile)\\r\\n conn.commit()\\r\\n print('{}/{} files processed.'.format(i, num_files))\\r\\n except Exception as e:\\r\\n print(e)\",\n \"def process_data(cur, conn, filepath, func):\\n\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def checkMissingFiles(inDir, jsonUrl):\\n\\n file_list = []\\n remote = False\\n try:\\n file_list = os.listdir(inDir)\\n except OSError:\\n remote = True\\n file_list = eos_ls(inDir)\\n\\n if file_list == []:\\n print \\\"Directory does not exist or is empty!\\\"\\n return []\\n\\n total_expected = 0\\n missing_files = []\\n suspicious_files = []\\n recovered_files = []\\n\\n print 'Found %d files in input directory' % len(file_list)\\n print 20*'-'\\n\\n jsonFile = open(jsonUrl,'r')\\n procList = json.load(jsonFile,encoding = 'utf-8').items()\\n\\n for proc in procList:\\n for desc in proc[1]:\\n data = desc['data']\\n isData = desc.get('isdata',False)\\n mctruthmode = desc.get('mctruthmode')\\n for d in data:\\n dtag = d.get('dtag','')\\n split = d.get('split',1)\\n\\n for segment in range(0,split):\\n eventsFile = dtag\\n if split > 1:\\n eventsFile = dtag + '_' + str(segment)\\n if mctruthmode:\\n eventsFile += '_filt%d' % mctruthmode\\n filename = eventsFile+'.root'\\n\\n sys.stdout.write('... checking %s' % filename)\\n sys.stdout.flush()\\n\\n total_expected += 1\\n\\n if not filename in file_list:\\n missing_files.append(filename)\\n sys.stdout.write('\\\\033[91m MISSING \\\\033[0m \\\\n')\\n # sys.stdout.flush()\\n continue\\n\\n rootFileUrl = os.path.join(inDir, filename)\\n if remote:\\n rootFileUrl = ('root://eoscms//eos/cms/store' +\\n rootFileUrl.split('store',1)[1])\\n\\n recovered, suspicious = False, False\\n tfile = TFile.Open(rootFileUrl)\\n try:\\n if tfile.TestBit(TFile.kRecovered):\\n recovered = True\\n if tfile.IsZombie():\\n suspicious = True\\n tfile.Close()\\n except AttributeError, ReferenceError:\\n suspicious = True\\n\\n if recovered:\\n sys.stdout.write('\\\\033[93m Recovered \\\\033[0m \\\\n')\\n recovered_files.append(filename)\\n if suspicious:\\n sys.stdout.write('\\\\033[93m Failed to open \\\\033[0m \\\\n')\\n suspicious_files.append(filename)\\n\\n sys.stdout.write('\\\\033[92m OK \\\\033[0m \\\\n')\\n sys.stdout.flush()\\n\\n print 20*'-'\\n if len(missing_files):\\n print \\\"Missing the following files:\\\"\\n print \\\"(%d out of %d expected)\\\"% (len(missing_files), total_expected)\\n for filename in missing_files:\\n print filename\\n else:\\n print \\\"NO MISSING FILES!\\\"\\n print 20*'-'\\n if len(suspicious_files):\\n print \\\"Failed to open the following files:\\\"\\n print \\\"(%d out of %d expected)\\\"% (len(suspicious_files), total_expected)\\n for filename in suspicious_files:\\n print filename\\n print 20*'-'\\n if len(recovered_files):\\n print \\\"The following files are recovered:\\\"\\n print \\\"(%d out of %d expected)\\\"% (len(recovered_files), total_expected)\\n for filename in recovered_files:\\n print filename\\n print 20*'-'\\n\\n return missing_files+suspicious_files+recovered_files\",\n \"def ingest_httpdir(self, url=None, dest=None):\\n\\n if url is None or dest is None:\\n raise Exception(\\\"URL and destination path are required\\\")\\n\\n app.check_traversal_okay(self)\\n\\n # Get directory\\n try:\\n res = requests.get(url)\\n res.raise_for_status()\\n dir_info = res.json()\\n\\n parsed = urlparse(url)\\n dirname = parsed.path.split('/')[-2]\\n new_folder_path = dest + dirname + '/'\\n logger.info(\\\"DIRNAME \\\"+new_folder_path)\\n res = get_client().mkdir(new_folder_path)\\n if not res.ok():\\n raise IOError(str(res))\\n logger.info(\\\"DIRECTORY INGESTED: \\\"+new_folder_path)\\n\\n file_ingests = []\\n folder_ingests = []\\n for f in dir_info:\\n if 'file' == f['type']:\\n s = ingest_httpfile.s(str(url)+f['name'], new_folder_path, metadata=f)\\n file_ingests.append(s)\\n elif 'directory' == f['type']:\\n s = ingest_httpdir.s(url=str(url)+f['name']+'/', dest=new_folder_path)\\n folder_ingests.append(s)\\n file_job = group(file_ingests)\\n file_job.apply_async()\\n # result.join() # wait for files to ingest in parallel\\n # file_count += result.completed_count()\\n group(folder_ingests).apply_async()\\n # for file_c, folder_c in folder_res.get():\\n # file_count += file_c\\n # folder_count += folder_c\\n # return (file_count, folder_count)\\n except IOError as e:\\n raise self.retry(exc=e)\",\n \"def _read_files(self, input_dir: str, schema: StructType,\\n post_read: Optional[Callable[[DataFrame], DataFrame]] = None) -> DataFrame:\\n df = self.spark.read.option(\\\"multiline\\\", \\\"true\\\").schema(schema).json(input_dir)\\n if post_read:\\n df = post_read(df)\\n return df\",\n \"def process_files_json():\\n # chdir into beep root\\n pwd = os.getcwd()\\n os.chdir(os.environ.get(\\\"BEEP_ROOT\\\", \\\"/\\\"))\\n\\n meta_list = list(filter(lambda x: '_Metadata.csv' in x, os.listdir(SRC_DIR)))\\n file_list = list(filter(lambda x: '.csv' in x if x not in meta_list else None, os.listdir(SRC_DIR)))\\n all_list = list(filter(lambda x: '.csv' in x, os.listdir(SRC_DIR)))\\n\\n all_list = sorted(all_list)\\n dumpfn(all_list, \\\"all_files.json\\\")\\n\\n [file_id, mapdf] = init_map(PROJECT_NAME, DEST_DIR)\\n\\n new_file_index = file_id\\n\\n for filename in tqdm(sorted(file_list)):\\n # If the file has already been renamed another entry should not be made\\n if mapdf['filename'].str.contains(filename).sum() > 0:\\n continue\\n old_file = os.path.join(SRC_DIR, filename)\\n new_path = os.path.join(DEST_DIR, PROJECT_NAME)\\n shutil.copy(old_file, new_path) # copy main data file\\n shutil.copy(old_file.replace(\\\".csv\\\", '_Metadata.csv'), new_path) # copy meta data file\\n\\n if PROJECT_NAME == 'FastCharge':\\n [date, channel_no, strname, protocol] = get_parameters_fastcharge(filename, SRC_DIR)\\n elif PROJECT_NAME == 'ClosedLoopOED':\\n [date, channel_no, strname, protocol] = get_parameters_oed(filename, SRC_DIR)\\n else:\\n raise ValueError(\\\"Unsupported PROJECT_NAME: {}\\\".format(PROJECT_NAME))\\n\\n df_dup = mapdf.set_index(['protocol', 'date'])\\n if (protocol, date) in df_dup.index:\\n row = mapdf[(mapdf['protocol'] == protocol) & (mapdf['date'] == date)]\\n file_id = row['fid'].iloc[0]\\n protocol = row['protocol'].iloc[0]\\n date = row['date'].iloc[0]\\n strname = row['strname'].iloc[0]\\n else:\\n file_id = new_file_index\\n new_file_index = new_file_index + 1\\n\\n new_name = \\\"{}_{}_{}\\\".format(PROJECT_NAME, f'{file_id:06}', channel_no)\\n new_file = os.path.join(DEST_DIR, PROJECT_NAME, \\\"{}.csv\\\".format(new_name))\\n\\n new_row = pd.DataFrame([[file_id, protocol, channel_no, date, strname,\\n os.path.abspath(old_file),\\n os.path.abspath(new_file)]],\\n columns=METADATA_COLUMN_NAMES)\\n mapdf = mapdf.append(new_row)\\n\\n os.rename(os.path.join(DEST_DIR, PROJECT_NAME, filename), new_file)\\n os.rename(os.path.join(DEST_DIR, PROJECT_NAME, filename).replace(\\\".csv\\\", \\\"_Metadata.csv\\\"),\\n new_file.replace(\\\".csv\\\", \\\"_Metadata.csv\\\"))\\n\\n mapdf.to_csv(os.path.join(DEST_DIR, PROJECT_NAME, PROJECT_NAME + \\\"map.csv\\\"), index=False)\\n mapdf = mapdf.reset_index(drop=True)\\n os.chdir(pwd)\\n return json.dumps(mapdf.to_dict(\\\"list\\\"))\",\n \"def folder_to_df(path):\\n summary_df = pd.DataFrame(columns=[\\\"file_name\\\", \\\"invoice_nr\\\", \\\"address\\\", \\\"contract\\\", \\\"base_charge\\\"])\\n \\n for file in Path(path).glob(\\\"*.pdf\\\"):\\n print(file)\\n try: \\n summary_df = summary_df.append({\\n \\\"file_name\\\": pdf_to_test(file)[0],\\n \\\"invoice_nr\\\": pdf_to_test(file)[1],\\n \\\"address\\\": pdf_to_test(file)[2],\\n \\\"contract\\\": pdf_to_test(file)[3],\\n \\\"base_charge\\\": pdf_to_test(file)[4]}, \\n ignore_index = True)\\n except:\\n summary_df = summary_df.append({\\n \\\"file_name\\\": file.name,\\n \\\"invoice_nr\\\": \\\"Could not read malformed PDF file\\\",\\n \\\"address\\\": \\\"Could not read malformed PDF file\\\",\\n \\\"contract\\\": \\\"Could not read malformed PDF file\\\",\\n \\\"base_charge\\\": \\\"Could not read malformed PDF file\\\"}, \\n ignore_index = True)\\n return summary_df\",\n \"def search_files_folders(request,**kwargs):\\n name = request.data['name']\\n files_and_folders = FileSystem.objects.filter(name__icontains=name, status=\\\"CREATED\\\")\\n\\n response_list = []\\n dictionary = {}\\n for fsobject in files_and_folders :\\n fs_object = convert_fsobject_to_fstypeobject(fsobject)\\n if fs_object.path in dictionary.keys() :\\n for fs_object in FileSystem.objects.filter(path=fs_object.path,status='CREATED').order_by('-creation_datetime')[:2] :\\n try :\\n response = requests.get(fs_object.location)\\n if response.status_code == 200 :\\n break\\n except requests.ConnectionError :\\n pass\\n print\\\"final object location\\\", fs_object.location\\n dictionary[fs_object.path]=fs_object\\n else :\\n dictionary[fs_object.path]=fs_object\\n\\n for fs_object in dictionary.values():\\n fs_object.creation_datetime = convert_datetime_to_string(fs_object.creation_datetime)\\n data = FileSystemSerializer(fs_object).data\\n response_list.append(data)\\n\\n\\n return Response(data=response_list,status=status.HTTP_200_OK)\",\n \"def parse_directory(dpath):\\n print(\\\"Starting to parse\\\")\\n print(dpath)\\n selected_files = sel_files(dpath)\\n print(\\\"Number of selected files\\\", len(selected_files))\\n\\n # iterave over selected files and build dataframe\\n empties = 0\\n idx = pd.DataFrame(columns=list(PATH_REGEX.keys()))\\n for fpath in selected_files:\\n m = FNAME_REGEX.search(fpath)\\n if m is None:\\n print(\\\"ERROR:\\\", fpath, FNAME_REGEX.pattern)\\n continue\\n row_head = {k: m.group(k) for k in PATH_REGEX.keys()}\\n for i, (webpage_id, lengths) in enumerate(it_webpages(fpath)):\\n if len(lengths) == 0:\\n empties += 1\\n continue\\n row_head['fname'] = os.path.basename(fpath)\\n row_head['class_label'] = webpage_id\\n row_head['lengths'] = lengths\\n idx = idx.append(row_head, ignore_index=True)\\n print(i, 'sites in', fpath)\\n print(\\\"Empty traces:\\\", empties)\\n\\n # fix some naming issues:\\n idx['inst'] = idx.inst.fillna(0)\\n idx['date'] = pd.to_datetime(idx.date.str.replace('-18', '-2018'),\\n format='%d-%m-%Y')\\n #idx['dev'] = idx.dev.replace('browse', 'desktop')\\n #idx.loc[idx.sites == 'desktop', ['dev', 'sites']] = ['desktop', None]\\n return idx\",\n \"def getfiles_from_dir(self,dir):\\n assert not os.path.isdir(dir),\\\"Invalid dir format\\\"+str(dir)\\n print(\\\"-----Read Dir :\\\",dir)\\n self.files=glob.glob(os.path.join(dir,\\\"./*.tif\\\"))\",\n \"def ask_endpoint(file, endpoint) -> dict:\\n\\n # define headers with the content type\\n headers = {\\n \\\"Content-Type\\\": \\\"application/json\\\",\\n \\\"Accept\\\": \\\"application/json\\\",\\n }\\n\\n data = None\\n if isinstance(file, str):\\n data = pd.read_csv(file).to_dict(orient=\\\"records\\\")\\n elif isinstance(file, list):\\n data = file\\n else:\\n raise TypeError(\\\"'file' should be either file path or loaded list of jsons!\\\")\\n\\n # send request to the endpoint\\n response = requests.post(endpoint,\\n json={\\\"data\\\": data},\\n headers=headers)\\n\\n # check response status and parse the body\\n if response.status_code == 200:\\n response = response.json()\\n else:\\n response = {}\\n\\n return response\",\n \"def list_files():\\n try:\\n return jsonify(os.listdir(env(\\\"FILES_DIRECTORY\\\"))), 200\\n except:\\n return {\\\"msg\\\": \\\"Não há arquivos a serem listados.\\\"}, 404\",\n \"def process_data(cur, conn, filepath: str, func: Callable) -> None:\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, \\\"*.json\\\"))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print(\\\"{} files found in {}\\\".format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print(\\\"{}/{} files processed.\\\".format(i, num_files))\",\n \"def batch(infolder, outfile): # type: (str, str) -> None\\n\\n if not os.path.isdir(infolder):\\n return\\n\\n results = []\\n\\n for filename in os.listdir(infolder):\\n print('Processing ' + filename)\\n curresults = []\\n if filename.endswith('.txt'):\\n with open(os.path.join(infolder, filename), 'r') as curfile:\\n curdata = curfile.read() + '\\\\n'\\n curresults = processClauseText(curdata, 'text')\\n elif filename.endswith('.pdf'):\\n with open(os.path.join(infolder, filename), 'rb') as curfile:\\n curdata = base64.b64encode(curfile.read()).decode()\\n curresults = processClauseText(curdata, 'pdf')\\n elif filename.endswith('.docx'):\\n with open(os.path.join(infolder, filename), 'rb') as curfile:\\n curdata = base64.b64encode(curfile.read()).decode()\\n curresults = processClauseText(curdata, 'word')\\n if len(curresults) > 0:\\n for result in curresults:\\n result['filename'] = filename\\n results.extend(curresults)\\n\\n if outfile is not None:\\n with open(outfile, 'w') as outfile:\\n json.dump(results, outfile, indent=2)\",\n \"def files(self):\\r\\n url = '{0}/files'.format(self.get_url())\\r\\n\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def get_files_folders(request,**kwargs):\\n import requests\\n from mainserver.constants import FORWARD_TO_SERVERS\\n\\n path = request.data['path']\\n\\n\\n print (\\\"path \\\", path)\\n\\n\\n try :\\n # print FileSystem.objects.filter(path=path,type='FOLDER',status='CREATED').order_by('-creation_datetime').count()\\n fsobject = FileSystem.objects.filter(path=path,type='FOLDER',status='CREATED').order_by('-creation_datetime')[0]\\n print fsobject\\n except :\\n print \\\"illegal file object query access\\\"\\n return Response(status=status.HTTP_417_EXPECTATION_FAILED)\\n\\n\\n fsobjects_list = FileSystem.objects.filter(parent=fsobject,status='CREATED').order_by('-creation_datetime')\\n\\n fs_object = FileSystem.objects.all().order_by('-creation_datetime')[:][0]\\n\\n sync_with_servers(tail=None, datetime=convert_datetime_to_string(fs_object.creation_datetime))\\n\\n response_list = []\\n dictionary = {}\\n\\n print (\\\"sync complete\\\")\\n\\n for fsobject in fsobjects_list :\\n print fsobject\\n fs_object = convert_fsobject_to_fstypeobject(fsobject)\\n if fs_object.path not in dictionary.keys() :\\n for fs_object in FileSystem.objects.filter(path=fs_object.path,status='CREATED').order_by('-creation_datetime')[:2] :\\n try :\\n response = requests.get(fs_object.location)\\n if response.status_code == 200 :\\n break\\n except requests.ConnectionError :\\n pass\\n print\\\"final object lopcation\\\", fs_object.location\\n dictionary[fs_object.path]=fs_object\\n\\n for fs_object in dictionary.values():\\n fs_object.creation_datetime = convert_datetime_to_string(fs_object.creation_datetime)\\n data = FileSystemSerializer(fs_object).data\\n response_list.append(data)\\n\\n # print response_list\\n\\n data = {\\\"current_dir\\\" : path}\\n\\n data ['files_folders'] = response_list\\n\\n\\n print data\\n\\n return Response(data=data,status=status.HTTP_200_OK)\",\n \"def dir_to_h5df(walk_dir, N):\\n\\n h5_file_list = list_h5(walk_dir)\\n\\n if (N == 'all') or (N > len(h5_file_list)):\\n files_to_convert = h5_file_list\\n else:\\n files_to_convert = h5_file_list[:N]\\n\\n # Convert list of files names to list of dictionaries\\n\\n h5_df_list = []\\n\\n for filename in files_to_convert:\\n f = h5py.File(filename, 'r')\\n h5_df = pd.DataFrame(multi_indexer(h5_to_dict(f)))\\n h5_df_list.append(h5_df)\\n\\n h5df = pd.concat(h5_df_list, ignore_index=True)\\n\\n return h5df\",\n \"def _download_inputs(inputs: Dict[str, str]) -> Dict[str, Path]:\\n in_paths = dict()\\n for name, endpoint in inputs.items():\\n target_dir = _BASE_PATH / name\\n target_dir.mkdir()\\n target_file = target_dir / 'data.json'\\n r = requests.get('{}/data.json'.format(endpoint))\\n with target_file.open('wb') as f:\\n f.write(r.content)\\n in_paths[name] = target_file\\n return in_paths\",\n \"def get_files_df(pxd_identifier, filetypes, pattern):\\r\\n check_pxd_id(pxd_identifier)\\r\\n\\r\\n get_files_url = \\\"https://www.ebi.ac.uk:443/pride/ws/archive/file/list/project\\\"\\r\\n\\r\\n # Get dataframe with file info through PRIDE Archive REST API\\r\\n url = \\\"{}/{}\\\".format(get_files_url, pxd_identifier)\\r\\n response = pd.DataFrame(json.loads(requests.get(url).content.decode('utf-8'))['list'])\\r\\n response['fileExtension'] = response['fileName'].str.split('.').apply(lambda x: x[-1])\\r\\n\\r\\n # Set regex pattern\\r\\n if pattern:\\r\\n response = response[response['fileName'].str.contains(pattern)]\\r\\n\\r\\n # Set extensions\\r\\n extensions = response['fileExtension'].unique()\\r\\n #print(\\\"Found files with extensions: \\\\t\\\\t{}\\\".format(extensions))\\r\\n if filetypes:\\r\\n extensions = filetypes\\r\\n response = response[response['fileExtension'].str.lower().isin([x.lower() for x in filetypes])]\\r\\n\\r\\n #print(\\\"Downloading files with extensions: \\\\t{}\\\".format(extensions))\\r\\n\\r\\n return response\",\n \"def directory_to_json(self, path, list_in):\\n directory_json = {\\\"base_path\\\": path, \\\"files\\\": list_in}\\n return directory_json\",\n \"def read_json_to_df(start_dt, end_dt, cache):\\r\\n # Establish files daterange\\r\\n collect_sdt = start_dt\\r\\n if (end_dt.hour == 0) & (end_dt.minute == 0):\\r\\n collect_edt = end_dt\\r\\n else:\\r\\n collect_edt = end_dt + timedelta(days=1)\\r\\n\\r\\n date_str_list = [datetime.strftime(i,\\\"%Y-%m-%d\\\") for i in pd.date_range(collect_sdt, collect_edt)]\\r\\n\\r\\n # Find only a subset of JSON files in the given daterange\\r\\n JSON_subset = [glob.glob(os.path.join(cache, \\\"NEMED_PS_DAILY_{}*.json\\\".format(i))) for i in date_str_list]\\r\\n JSON_subset = [item for sublist in JSON_subset for item in sublist]\\r\\n\\r\\n print(\\\"Reading selected {} JSON files to pandas, of cached files\\\".format(len(JSON_subset)))\\r\\n logger.info(\\\"Loading Cached Price Setter Files...\\\")\\r\\n\\r\\n all_df = []\\r\\n for file in tqdm(JSON_subset):\\r\\n with open(file, 'r') as f:\\r\\n data = json.loads(f.read())\\r\\n df_nested_list = pd.json_normalize(data)\\r\\n df_nested_list['@PeriodID'] = pd.to_datetime(df_nested_list['@PeriodID'], format=\\\"%Y-%m-%d %H:%M:%S\\\").dt.tz_localize(None)\\r\\n df_nested_list = df_nested_list[(df_nested_list['@Market'] == 'Energy') &\\r\\n (df_nested_list['@DispatchedMarket'] == 'ENOF')]\\r\\n all_df += [df_nested_list]\\r\\n\\r\\n all_df = pd.concat(all_df)\\r\\n all_df.columns = all_df.columns.str.strip('@')\\r\\n all_df.drop(['Market','DispatchedMarket'], axis=1, inplace=True)\\r\\n all_df = all_df.astype({'RegionID': str, 'Price': float, 'Unit': str, 'BandNo': int, \\\\\\r\\n 'Increase': float, 'RRNBandPrice': float, 'BandCost': float})\\r\\n all_df = all_df[all_df['PeriodID'].between(start_dt, end_dt, inclusive=\\\"right\\\")].sort_values(['PeriodID','RegionID'])\\r\\n return all_df.reset_index(drop=True)\",\n \"def parse(self, response):\\n json_response = json.loads(response.text)\\n fetched_results = len(json_response['results'])\\n\\n if not hasattr(self, 'processed_results'):\\n self.processed_results = 0\\n self.processed_results += fetched_results\\n if fetched_results == 0:\\n logging.info('No more files. Total files queried {}'.format(\\n self.processed_results))\\n return\\n\\n for result in json_response['results']:\\n document = self._create_document(result['columns'])\\n if document['language'] != u'ENG':\\n info = 'Case {} {} not in ENG, original language {}'.format(\\n document['original_id'], document['name'].encode('utf-8'),\\n result['columns']['languageisocode'])\\n logging.info(info)\\n continue\\n doc_url = self.base_document_url.format(document['original_id'])\\n new_request = scrapy.Request(doc_url, callback=self.parse_document)\\n new_request.meta['document'] = document\\n yield new_request\\n\\n if self.processed_results < int(getattr(self, 'limit', DEFAULT_LIMIT)):\\n next_url = self.base_main_url.format(\\n start_index=self.processed_results, length=RESULT_PER_PAGE)\\n yield scrapy.Request(next_url, callback=self.parse)\\n else:\\n logging.info('Limit reached. Total files obtained {}'.format(\\n self.processed_results))\",\n \"def get_files(input_dir):\\n file_rep = { \\\"tars\\\" : [] }\\n \\n files = os.listdir(input_dir)\\n \\n the_file, the_date = find_bootstrap(files)\\n \\n #add index file in file_rep\\n file_rep['index'] = the_file\\n file_rep['date'] = the_date\\n \\n pattern = \\\"ncep_forecast_%s_(?P<name>\\\\S+).tar\\\" % (the_date)\\n \\n the_re = re.compile(pattern)\\n\\n for the_file in files:\\n matched = the_re.match(the_file)\\n if matched:\\n print(\\\"matched %s\\\" % (matched.group(\\\"name\\\")))\\n file_rep['tars'].append(the_file)\\n \\n return file_rep\",\n \"def json_data(self):\\n return_data = []\\n\\n # Loop over num files\\n for ifile in range(len(self.data)):\\n\\n # Loop over steps\\n file_return_data = {}\\n for istep in range(len(self.data[ifile])):\\n file_return_data[istep] = {}\\n for key in self.data[ifile][istep]:\\n file_return_data[istep][key] = self.data[ifile][istep][key].tolist()\\n\\n return_data.append(file_return_data)\\n\\n return return_data\",\n \"def test_get_all(client: FlaskClient):\\n response1 = util.upload_file(\\n client, DEFAULT_USER, get_example_file(ExampleFileType.Txt)\\n )\\n response2 = util.upload_file(\\n client, DEFAULT_USER, get_example_file(ExampleFileType.Jpg)\\n )\\n response3 = util.upload_file(\\n client, DEFAULT_USER, get_example_file(ExampleFileType.Png)\\n )\\n\\n # Now retrieve them\\n response_get = util.get_all_files(client, DEFAULT_USER)\\n assert response_get.status == \\\"200 OK\\\"\\n assert len(response_get.json) == 3\\n assert response1.json in response_get.json\\n assert response2.json in response_get.json\\n assert response3.json in response_get.json\",\n \"def read_query_files(**kwargs):\\n # Read query files contents and write to query_list\\n query_list = {\\\"query_group\\\": \\\"\\\", \\\"queries\\\": []}\\n query_group = kwargs[\\\"queries_dir\\\"].split(\\\"/\\\")[-1]\\n query_list.update(query_group=query_group)\\n logging.debug(\\\"Queries dir: \\\" + kwargs[\\\"queries_dir\\\"])\\n try:\\n for query_filename in sorted(os.listdir(kwargs[\\\"queries_dir\\\"])):\\n logging.debug(\\\"Validating query filename: \\\" + query_filename)\\n if validate_query_file(query_filename=query_filename):\\n with open(\\n kwargs[\\\"queries_dir\\\"] + \\\"/\\\" + query_filename, \\\"r\\\"\\n ) as query_filepath:\\n logging.debug(\\n \\\"Reading query with filename: \\\" + query_filename\\n )\\n query_mapdql = query_filepath.read().replace(\\\"\\\\n\\\", \\\" \\\")\\n query_mapdql = query_mapdql.replace(\\n \\\"##TAB##\\\", kwargs[\\\"source_table\\\"]\\n )\\n query_list[\\\"queries\\\"].append(\\n {\\\"name\\\": query_filename, \\\"mapdql\\\": query_mapdql}\\n )\\n logging.info(\\\"Read all query files\\\")\\n return query_list\\n except FileNotFoundError:\\n logging.exception(\\\"Could not find queries directory.\\\")\\n return False\",\n \"def file_loader(self):\\n\\n for folder in self.config[\\\"data_folders\\\"]:\\n f = os.path.join(folder, self.data_file)\\n yield jsonlist.load_file(f)\",\n \"def list_all_files(self, branch_name) -> dict:\\n try:\\n files_response = self.repo.get_contents(self.repo_folder,\\n ref=branch_name)\\n files_list = []\\n for file in files_response:\\n files_list.append(file.raw_data.get('path'))\\n return make_success_response(200, files_list)\\n except GithubException as github_exc:\\n return make_error_response(github_exc.status, github_exc.data)\",\n \"def test_samples_request(self):\\n req = Request()\\n for name, data in sample_data.items():\\n resp = req.get(fromfile=self._filepath(name))\\n\\n df = resp.write()\\n assert df.equals(data), \\\\\\n '\\\\n'.join(map(str, [name, df.index, data.index,\\n getattr(df, 'columns', ''),\\n getattr(data, 'columns', '')]))\",\n \"def index_mock_files(self,mfiles):\\n results = []\\n for i in range(len(mfiles['file_name'])):\\n print(\\\"Submitting {} to indexd at {}.\\\".format(mfiles['file_name'][i],mfiles['object_id'][i]))\\n res = self.create_record(\\n did=mfiles['object_id'][i],\\n hashes={'md5':mfiles['md5sum'][i]},\\n size=mfiles['file_size'][i],\\n urls=mfiles['storage_urls'][i],\\n file_name=mfiles['file_name'][i],\\n acl=mfiles['acl'][i],\\n authz=mfiles['authz'][i])\\n results.append(res)\\n return results\",\n \"def createFileListHTTP(self, server, directory):\\r\\n conn = httplib.HTTPConnection(server)\\r\\n conn.request(\\\"GET\\\",directory)\\r\\n r1 = conn.getresponse()\\r\\n '''if r1.status==200:\\r\\n print \\\"status200 received ok\\\"\\r\\n else:\\r\\n print \\\"oh no = status=%d %s\\\" \\\\\\r\\n % (r1.status,r1.reason)'''\\r\\n\\r\\n data = r1.read()\\r\\n parser = parseHTMLDirectoryListing()\\r\\n parser.feed(data)\\r\\n continents = parser.getDirListing()\\r\\n '''print continents'''\\r\\n\\r\\n for continent in continents:\\r\\n '''print \\\"Downloading file list for\\\", continent'''\\r\\n conn.request(\\\"GET\\\",\\\"%s/%s\\\" % \\\\\\r\\n (self.directory,continent))\\r\\n r1 = conn.getresponse()\\r\\n '''if r1.status==200:\\r\\n print \\\"status200 received ok\\\"\\r\\n else:\\r\\n print \\\"oh no = status=%d %s\\\" \\\\\\r\\n % (r1.status,r1.reason)'''\\r\\n data = r1.read()\\r\\n parser = parseHTMLDirectoryListing()\\r\\n parser.feed(data)\\r\\n files = parser.getDirListing()\\r\\n\\r\\n for filename in files:\\r\\n self.filelist[self.parseFilename(filename)] = (\\r\\n continent, filename)\\r\\n\\r\\n '''print self.filelist'''\\r\\n # Add meta info\\r\\n self.filelist[\\\"server\\\"] = self.server\\r\\n self.filelist[\\\"directory\\\"] = self.directory\\r\\n with open(self.filelist_file , 'wb') as output:\\r\\n pickle.dump(self.filelist, output)\",\n \"def process_multiple_files(self, filepaths, email_col='EMAIL',min_size=100, threshold=0.05):\\r\\n new_paths = []\\r\\n for f in filepaths:\\r\\n df = pd.read_csv(f)\\r\\n df = self.pre_process_frame(df, col=email_col)\\r\\n orig_size = df.index.size\\r\\n FLAG = True\\r\\n if orig_size < min_size:\\r\\n pass\\r\\n else:\\r\\n print(\\\"Cleaning {}\\\".format(f))\\r\\n \\r\\n try: # Try to first do an easy match with results directly from the database. (Very fast compared to API calls)\\r\\n self.count_matching_emails(df, col=email_col, verify_integrity=True, thresh=threshold)\\r\\n except Exception as e:\\r\\n \\r\\n print(\\\"{}\\\\n Calling missing emails from remote server.\\\".format(e))\\r\\n df = self.deep_clean_frame(df,dealno=0,clean_col=email_col) # The long way - calling the API.\\r\\n \\r\\n try:\\r\\n self.deep_processing_rerun(dealno=0,thresh=0.05,max_tries=5) # Handling records stuck in processing.\\r\\n count = self.count_matching_emails(df, col=email_col, verify_integrity=True, thresh=threshold)\\r\\n print(\\\"Successfully matched {} records\\\".format(count))\\r\\n except Exception as e:\\r\\n \\r\\n FLAG = False # Stop this from finalizing...too many records stuck in processing/not in database...somethings wrong.\\r\\n print(\\\"Failed to reprocess some records for {}\\\\n Error: {}\\\".format(f,e))\\r\\n \\r\\n if FLAG:\\r\\n df = self.suppress_email_frame(df, col=email_col, clean_type=1)\\r\\n new_path = os.path.splitext(f) + \\\"ListWised.csv\\\"\\r\\n df.to_csv(new_path, index=False)\\r\\n new_paths.append(new_path)\\r\\n \\r\\n self.deep_processing_rerun_all() # Wraps up making one last try at rerunning any emails stuck in processing (for next time).\\r\\n return new_paths\",\n \"def load_all(self, dir_path):\\n # each file name corresponds to another date\\n input_paths = [os.path.join(dir_path, f) for f in os.listdir(dir_path)\\n if os.path.isfile(os.path.join(dir_path, f)) and f.endswith('.csv')]\\n\\n if self.n_proc > 1:\\n # Load in parallel\\n _n_proc = min(self.n_proc, len(input_paths)) # no more than file_names needed here\\n logger.info(\\\"Loading {} datasets files using {} parallel processes ...\\\".format(len(input_paths), _n_proc))\\n with Pool(processes=_n_proc) as pool:\\n all_df = pd.concat(pool.map(HDD_data.load_single, input_paths))\\n else: # read 1 file at a time\\n all_df = pd.concat(HDD_data.load_single(path) for path in input_paths)\\n\\n return all_df\",\n \"def fetch_rows(links_input_file, args):\\n root_url = args.root_url\\n skip = args.skip\\n\\n for i, row in enumerate(csv.DictReader(links_input_file)):\\n if i < skip:\\n continue\\n\\n if i % 100 == 0 and i > 0:\\n sys.stderr.write('Row {}\\\\n'.format(i + 1))\\n\\n url, original_base_path = extract_base_path(row)\\n real_base_path = test_base_path(original_base_path, args)\\n search_url = root_url + '/api/search.json'\\n search_result = request_search_result(link=real_base_path, search_url=search_url)\\n\\n yield format_result(url, real_base_path, search_result)\",\n \"def sina_weibo_emotion4(root):\\n start = time.time()\\n task_path = assert_dirs(root, 'chinese_reviews_sina_weibo_emotion4')\\n url_json = 'https://raw.githubusercontent.com/Hourout/datasets/master/nlp/chinese_reviews_sina_weibo_emotion4/chinese_reviews_sina_weibo_emotion4.json'\\n url_txt = ['https://raw.githubusercontent.com/Hourout/datasets/master/nlp/chinese_reviews_sina_weibo_emotion4/chinese_reviews_sina_weibo_emotion4_01.txt',\\n 'https://raw.githubusercontent.com/Hourout/datasets/master/nlp/chinese_reviews_sina_weibo_emotion4/chinese_reviews_sina_weibo_emotion4_02.txt',\\n 'https://raw.githubusercontent.com/Hourout/datasets/master/nlp/chinese_reviews_sina_weibo_emotion4/chinese_reviews_sina_weibo_emotion4_03.txt',]\\n rq.json(url_json, path_join(task_path, 'chinese_reviews_sina_weibo_emotion4.json'))\\n data = pd.DataFrame()\\n for url in url_txt:\\n s = requests.get(url).content\\n data = pd.concat([data, pd.read_csv(io.StringIO(s.decode('utf-8')))])\\n data.to_csv(path_join(task_path, 'chinese_reviews_sina_weibo_emotion4.txt'), index=False)\\n print('chinese_reviews_sina_weibo_emotion4 dataset download completed, run time %d min %.2f sec' %divmod((time.time()-start), 60))\\n return task_path\",\n \"def get_data_files(main_directory):\\n print('************************************')\\n print('Log data list')\\n print('************************************')\\n log_files_list = globlin(main_directory + '/*/*.json' , recursive=True)\\n song_files_list = globlin(main_directory + '/*/*/*/*/*.json', recursive=True)\\n print(log_files_list)\\n print('************************************')\\n print('Song data list')\\n print('************************************')\\n print(song_files_list)\\n return log_files_list, song_files_list\",\n \"def query(self, n_jobs=1) -> str:\\n\\n def get_one_answer(file):\\n return json.dumps(ask_endpoint(file, os.path.join(self.url, \\\"take_exam\\\")))\\n\\n # send each file to the endpoint\\n query_start_time = time.time()\\n answers = Parallel(n_jobs=n_jobs)(delayed(get_one_answer)(file) for file in tqdm(self.filelist))\\n query_end_time = time.time()\\n query_time = query_end_time - query_start_time\\n\\n # put all answers to the dataframe\\n answers = pd.DataFrame(answers, columns=[\\\"prediction\\\"])\\n answers[\\\"prediction\\\"] = answers[\\\"prediction\\\"].apply(lambda x: json.loads(x))\\n answers[\\\"path\\\"] = self.filelist\\n\\n # create report folder\\n os.makedirs(self._report_path, exist_ok=False)\\n # save raw answers\\n answers.to_csv(os.path.join(self._report_path, \\\"raw_answers.csv\\\"), index=False)\\n # parse answers\\n parsed_answers = pd.DataFrame(columns=[\\\"path\\\",\\n \\\"id\\\",\\n \\\"prediction\\\"])\\n for _, row in answers.iterrows():\\n for k, v in row[\\\"prediction\\\"][\\\"answers\\\"].items():\\n parsed_answers.loc[len(parsed_answers)] = [row[\\\"path\\\"], int(k), v]\\n # save parsed answers\\n parsed_answers = parsed_answers.sort_values(by=[\\\"path\\\", \\\"id\\\"]).reset_index(drop=True)\\n parsed_answers.to_csv(os.path.join(self._report_path, \\\"parsed_answers.csv\\\"), index=False)\\n # save statistics\\n stats = {\\n \\\"readiness_time\\\": self._readiness_time,\\n \\\"query_total_files\\\": len(self.filelist),\\n \\\"query_total_time\\\": query_time,\\n \\\"query_n_jobs\\\": n_jobs,\\n \\\"query_mean_latency\\\": query_time / len(self.filelist) * n_jobs,\\n \\\"query_rps\\\": len(self.filelist) / query_time\\n }\\n with open(os.path.join(self._report_path, \\\"stats.json\\\"), \\\"w\\\") as f:\\n json.dump(stats, f)\\n\\n return self._report_path\",\n \"def index_all_files(self, root_dir):\\n pass\",\n \"def create_df(files_list=my_files):\\n\\n all_records = list()\\n\\n for file in files_list:\\n all_records += zr_parser(path.join(my_dir, file))\\n\\n return pd.DataFrame(all_records)\",\n \"def api_files():\\n files = FileWrapper.get_files(g.user.id)\\n return jsonify([f.serialize() for f in files])\",\n \"def bulk_gzipped_jsonline_files_to_dfs(glob_or_list, nproc=20):\\n import multiprocessing as mp\\n from tqdm import tqdm\\n\\n if isinstance(glob_or_list, str):\\n from glob import glob\\n glob_or_list = glob(glob_or_list)\\n\\n if len(glob_or_list) == 0:\\n return []\\n\\n pool = mp.Pool(min(nproc, len(glob_or_list)))\\n dfs = []\\n for df in tqdm(pool.imap_unordered(gzipped_jsonline_file_to_df, glob_or_list),\\n total=len(glob_or_list)):\\n dfs.append(df)\\n\\n pool.close()\\n pool.join()\\n\\n return dfs\",\n \"def get_indexd(self, limit=1000, page=0, format=\\\"JSON\\\", uploader=None, args=None):\\n if format in [\\\"JSON\\\", \\\"TSV\\\"]:\\n dc_regex = re.compile(r\\\"https:\\\\/\\\\/(.+)\\\\/?$\\\")\\n dc = dc_regex.match(self._endpoint).groups()[0]\\n dc = dc.strip('/')\\n else:\\n print(\\\"\\\\n\\\\n'{}' != a valid output format. Please provide a format of either 'JSON' or 'TSV'.\\\\n\\\\n\\\".format(format))\\n\\n stats_url = \\\"{}/index/_stats\\\".format(self._endpoint)\\n try:\\n response = requests.get(stats_url).text\\n stats = json.loads(response)\\n print(\\\"Stats for '{}': {}\\\".format(self._endpoint, stats))\\n except Exception as e:\\n print(\\\"\\\\tUnable to parse indexd response as JSON!\\\\n\\\\t\\\\t{} {}\\\".format(type(e), e))\\n\\n print(\\\"Getting all records in indexd (limit: {}, starting at page: {})\\\".format(limit, page))\\n\\n all_records = []\\n\\n done = False\\n while done == False:\\n\\n records = self.query_indexd(limit=limit, page=page, uploader=uploader, args=args)\\n all_records.extend(records)\\n\\n if len(records) != limit:\\n print(\\n \\\"\\\\tLength of returned records ({}) does not equal limit ({}).\\\".format(\\n len(records), limit\\n )\\n )\\n if len(records) == 0:\\n done = True\\n\\n print(\\n \\\"\\\\tPage {}: {} records ({} total)\\\".format(\\n page, len(records), len(all_records)\\n )\\n )\\n page += 1\\n\\n print(\\n \\\"\\\\t\\\\tScript finished. Total records retrieved: {}\\\".format(len(all_records))\\n )\\n now = datetime.datetime.now()\\n date = \\\"{}-{}-{}_{}.{}\\\".format(now.year, now.month, now.day, now.minute, now.second)\\n\\n if format == \\\"JSON\\\":\\n outname = \\\"{}_indexd_records_{}.json\\\".format(dc,date)\\n with open(outname, \\\"w\\\") as output:\\n output.write(json.dumps(all_records))\\n\\n if format == \\\"TSV\\\":\\n outname = \\\"{}_indexd_records_{}.tsv\\\".format(dc,date)\\n all_records = pd.DataFrame(all_records)\\n all_records['md5sum'] = [hashes.get('md5') for hashes in all_records.hashes]\\n all_records.to_csv(outname,sep='\\\\t',index=False)\\n\\n return all_records\",\n \"def iterfiles(dir, prefix):\\n files = (f for f in os.listdir(dir) if f.startswith(prefix))\\n for filename in sorted(files):\\n filename = os.path.join(dir, filename)\\n with open(filename) as fd:\\n data = json.load(fd)\\n yield data\\n os.remove(filename)\",\n \"def download_data(files: page_iterator.HTTPIterator, folder: str) -> None:\\n logging.info('File download Started... Wait for the job to complete.')\\n\\n # create folder locally if not exists\\n if not os.path.exists(folder): os.makedirs(folder)\\n\\n for file in files:\\n logging.info('GCS File: {}'.format(file.name))\\n destination_uri = '{}/{}'.format(folder, file.name.split('/')[-1])\\n file.download_to_filename(destination_uri if destination_uri.endswith('.csv') else destination_uri + '.csv')\\n logging.info('Exported {} to {}'.format(file.name, destination_uri))\\n\\n return None\",\n \"def render_multi_json_per_folder(history_dir, nb_json_per_folder):\\n jsons_to_render = []\\n\\n # Finding files to render\\n subfolders = [os.path.join(history_dir, path)\\n for path in os.listdir(history_dir)\\n if os.path.isdir(os.path.join(history_dir, path))]\\n for folder in subfolders:\\n json_games = sorted([os.path.join(folder, json_filename)\\n for json_filename in os.listdir(folder)\\n if json_filename[-5:] == '.json'])\\n json_games = json_games[:nb_json_per_folder]\\n for json_path in json_games:\\n jsons_to_render += [json_path]\\n\\n # Running over multiple processes\\n nb_cores = multiprocessing.cpu_count()\\n with multiprocessing.Pool(nb_cores) as pool:\\n pool.map(render_json, jsons_to_render)\",\n \"def process_files(exp_folders):\\n pool = mp.Pool()\\n results = pool.imap_unordered(read_and_serialize, exp_folders)\\n\\n stat = []\\n for res in results:\\n print(res)\\n stat.append(res)\\n\\n pool.close()\\n pool.join()\",\n \"def readAllDatForCurrency(data_dir, currencyCode):\\n datatestnames = list(filter(lambda a : (currencyCode in a), listdir(data_dir)))\\n dfs = [readDAT(data_dir + name) for name in datatestnames]\\n allDf = pd.concat(dfs)\\n allDf = allDf.sort_values(by=['datetime'], ascending=True).reset_index(drop=True).set_index('datetime')\\n return allDf\",\n \"def download_data(base_url,\\n lista_anni,\\n lista_inquinanti):\\n \\n # Inizializziamo la lista dei df ognuno dei quali corrisponde ad un inquinante\\n df_template = pd.DataFrame(columns=['jd','h','1','2','3','4','5','6','7','8','9','10','11','13','14','15','16','38','39','40',\\n '41','45','47','48','49','55','56','57','60','83','84','85','86','87','Anno','Inquinante'])\\n lista_df = [df_template]\\n\\t\\n\\t# Per ogni inquinante\\n for chimico in lista_inquinanti:\\n \\t# Per ogni anno\\n for anno in lista_anni:\\n print('Retrieving {} for year {} from {}'.format(chimico, anno, compose_url(base_url, anno, chimico)))\\n \\n # Esegui la richiesta\\n r = requests.get(compose_url(base_url, anno, chimico))\\n\\n # Crea il rispettivo dataframe\\n df = write_response(r)\\n print('{} rows'.format(len(df)))\\n\\t\\t\\t\\n\\t\\t\\t# Prendi la linea che corrisponde all'header del df\\n columns_ = df.iloc[0].index[0]\\n \\n \\\"\\\"\\\" Individua i nomi delle colonne splittando la stringa che li contiene tutti\\n ed escludendo lestringhe vuote ottenute tramite lo split\\\"\\\"\\\"\\n clean_columns = [item.strip()\\\\\\n for item in columns_.split(' ')\\\\\\n if len(item)!=0]\\n \\n # aggiungo le colonne Anno e Inquinante\\n columns = clean_columns + ['Anno', 'Inquinante']\\n\\t\\t\\t\\n list_rows = []\\n # Per ogni linea del df\\n for line_idx in range(1, len(df)):\\n \\t\\n # Come nel caso precedente splitto la linea per ottenere le diverse celle\\n line = df.iloc[line_idx].values[0].strip().split(' ')\\n \\n # Quindi ottengo la lista delle celle della riga i-th\\n raw_line = [item for item in line if len(item)!=0] \\n \\n # Aggiungiamo le colonne anno e inquinante\\n list_rows += [raw_line + [anno, chimico]]\\n\\t\\t\\t\\n\\t\\t\\t# Definiamo il nuovo dataset \\n df_idx = pd.DataFrame(list_rows, columns=columns)\\n \\n # Creiamo aggiungiamo alla lista di df da concatenare quello appena creato \\n lista_df += [df_idx]\\n\\n\\t# Facciamo la union dei df tenendo conto che le colonne possono essere diverse (concat con pandas)\\n df_final = pd.concat(lista_df, ignore_index=False)\\n\\n # sostituisco i NaN e -999.0 con un valore vuoto\\n df_final = df_final.fillna('')\\n df_final = df_final.replace(to_replace='-999.0', value='')\\n \\n return df_final\",\n \"def file_upload_to_obj():\\n\\n temp = []\\n file_content = pd.read_excel(INPUT_FILE_NAME).fillna(0).to_dict('records')\\n sorted_content = sorted(file_content, key=itemgetter(\\n 'filedbentity.file_extension'))\\n for item in file_content:\\n\\n raw_date = item.get('filedbentity.file_date')\\n if raw_date:\\n temp_date = raw_date.strftime('%Y-%m-%d')\\n raw_date = datetime.strptime(temp_date, \\\"%Y-%m-%d\\\").date()\\n else:\\n raw_date = datetime.now().date()\\n\\n raw_status = item.get('dbentity.status')\\n if raw_status == 'Archive':\\n raw_status = 'Archived'\\n \\n obj = {\\n 'path': item.get('EBS path'),\\n 'display_name': item.get('dbentity.display_name'),\\n 'status': raw_status,\\n 'source': item.get('dbentity.source'),\\n 'topic_edam_id': item.get('topic edam_id').upper().replace('TOPIC', 'EDAM').strip(),\\n 'data_edam_id': item.get('data edam_id').upper().replace('DATA', 'EDAM').strip(),\\n 'format_edam_id': item.get('format edam_id').upper().replace('FORMAT', 'EDAM').strip(),\\n 'file_extension': item.get('filedbentity.file_extension'),\\n 'file_date': raw_date,\\n 'is_public': (item.get('filedbentity.is_public') == '1'),\\n 'is_in_spell': item.get('filedbentity.is_in_spell'),\\n 'is_in_browser': (item.get('filedbentity.is_in_browser') == '1'),\\n 'readme_name': item.get('readme name'),\\n 'description': item.get('filedbentity.description'),\\n 'pmids': item.get('pmids (|)'),\\n 'keywords': item.get('keywords (|)')\\n }\\n temp.append(obj)\\n\\n if len(temp) > 0:\\n return temp\\n return None\",\n \"def index_bulk_from_files(self, files):\\r\\n\\r\\n docs = self._mailextractor.extract_jsons(files) # Generator-Iterable\\r\\n actions = self.convert_docstrs_to_bulk_actions(docs) # Generator-Iterable\\r\\n\\r\\n self._cur_print = 0\\r\\n actions_for_chunk = self.print_chunk_progress(actions) # Generator-Iterable\\r\\n (cnt_success, errors_index) = es_helpers.bulk(\\r\\n self._es, actions_for_chunk, chunk_size=constants.ES_BULK_CHUNK_SIZE)\\r\\n\\r\\n cnt_total = self._mailextractor.cnt_total\\r\\n errors_convert = self._mailextractor.errors_convert\\r\\n cnt_error = len(errors_convert) + len(errors_index)\\r\\n return Summary(cnt_total=cnt_total, cnt_success=cnt_success, cnt_error=cnt_error,\\r\\n errors_convert=errors_convert, errors_index=errors_index)\",\n \"def read_json(self):\\n list_filepath_bsn_test = []\\n\\n try:\\n for json_file in tqdm(self.file_path_list, desc='Loading in json files'):\\n with open(json_file) as f:\\n data = json.load(f)\\n\\n # Get out: filepath, _bsn, text, offsets and main text\\n list_filepath_bsn_test.append([data['filepath'], data['fields']['_belanghebbende_bsn'], data['text'],\\n data['offsets']['main_text']])\\n except:\\n print(\\\"Faulty json file: ,\\\", json_file)\\n\\n # Make it into a data_frame\\n self.data = pd.DataFrame(list_filepath_bsn_test)\\n headers = ['path', 'bsn', 'text', 'offsets']\\n self.data.columns = headers\",\n \"def getFile(self, irodsdir, inlocaldir, filenames):\\n utilities.log.debug('Begin subdirectory GET from iRODS at {}'.format(irodsdir))\\n options = {kw.FORCE_FLAG_KW: ''}\\n num=0\\n with self._open_connection() as session:\\n for fn in filenames: # Irods filenames are OBJECT. Get string name using .name\\n localfile=\\\"{0}/{1}\\\".format(inlocaldir,fn)\\n irodsfile=\\\"{0}/{1}\\\".format(irodsdir,fn)\\n #print('Get iRODS file {} to local file {}'.format(irodsfile, localfile))\\n session.data_objects.get(irodsfile, localfile, **options )\\n num+=1\\n utilities.log.debug('Get iRODS file {} to local file {}'.format(irodsfile, localfile))\\n return num\",\n \"def collect_data(folder):\\n folder = pathlib.Path(folder)\\n cases = []\\n for case_folder in folder.iterdir():\\n print(f'start collecting data for location {case_folder.name}')\\n for tr_folder in case_folder.iterdir():\\n case = calculate_values(tr_folder)\\n cases.append(case)\\n \\n df = pd.DataFrame(cases)\\n print(folder.parent.joinpath(f'{folder.stem}.csv'))\\n df.to_csv(folder.parent.joinpath(f'{folder.stem}.csv'), index=False)\",\n \"def summarize_models(folder):\\r\\n filepaths = sorted([n for n in glob(folder+r'/*') if '.json' in n])\\r\\n # create dictionary of results by looping over each file\\r\\n d = {}\\r\\n for fi, f in enumerate(filepaths):\\r\\n # load dictionary from json file\\r\\n print('processing model {}/{}'.format(fi+1, len(filepaths)))\\r\\n with open(f, 'r') as fp:\\r\\n data = json.load(fp)\\r\\n # extract label and trial number from filename\\r\\n L = os.path.split(f)[1].split('.')[0].split('__trial_')[0]\\r\\n t = int(os.path.split(f)[1].split('.')[0].split('__trial_')[1])\\r\\n # create new dictionary for each model\\r\\n if L not in d:\\r\\n d[L] = {\\r\\n 'img': None,\\r\\n 'sim': {},\\r\\n 'avg_err': [],\\r\\n 'bub_num': [],\\r\\n 'df': pd.DataFrame()}\\r\\n # populate dictionary with model information\\r\\n d[L]['img'] = data['img']\\r\\n d[L]['sim'][t] = data['sim']\\r\\n d[L]['df']['rad_trial_'+str(t)] = pd.Series(data['rad'])\\r\\n d[L]['bub_num'].append(len(pd.Series(data['rad']).dropna()))\\r\\n d[L]['df']['x_trial_'+str(t)] = pd.Series(np.array(data['cent'])[:, 0])\\r\\n d[L]['df']['y_trial_'+str(t)] = pd.Series(np.array(data['cent'])[:, 1])\\r\\n d[L]['avg_err'].append(data['tot_err_percent'])\\r\\n return d\",\n \"def download_files_from_minio(bucket_name, file_prefix, ls_files_to_download):\\n minio_client = get_minio_client()\\n ls_minio_files = list(minio_client.list_objects(bucket_name, file_prefix))\\n ls_downloaded_files = []\\n local_object_dir = tempfile.mkdtemp()\\n for minio_object_path in ls_files_to_download:\\n local_object_name = minio_object_path.replace(file_prefix, \\\"\\\")\\n local_object_path = os.path.join(local_object_dir,local_object_name) \\n get_file_fromMinio(bucket_name, minio_object_path, local_object_path)\\n ls_downloaded_files.append(local_object_path)\\n return ls_downloaded_files\\n\\n\\n def process_columns(string_to_process, replace_string='_'):\\n \\\"\\\"\\\"\\n Process a column name by :\\n - processing special aplphabetical characters\\n - replacing special characters by value set in 'replace_string' \\n #Parameters\\n :param string_to_process (str): String to process (column name)\\n :param replace_string (str, default : '_'): Value to set for replaced characters\\n (ex : \\\"/\\\" -> \\\"\\\", where \\\"\\\" is the replace_string)\\n #Return\\n return out (str): Processed string\\n \\\"\\\"\\\"\\n BIGQUERY_COLUMN_NAME_RE = \\\"[^0-9a-zA-Z_]+\\\"\\n # Get encoding of column\\n str_bytes = str.encode(string_to_process)\\n encoding = 'utf-8'\\n # Process any special alphabetical character\\n temp_out = unicodedata\\\\\\n .normalize('NFKD', string_to_process)\\\\\\n .encode('ASCII', 'ignore')\\\\\\n .decode(encoding)\\n \\n out = re.sub(BIGQUERY_COLUMN_NAME_RE, replace_string, temp_out)\\n out = out.lower()\\n return out\",\n \"def read_files_to_df(clicks_file_list, users_file_list, max_workers):\\n\\n with max_workers as pool:\\n df_clicks = pool.map(process_file_pd, clicks_file_list)\\n df_clicks = pd.concat(df_clicks, ignore_index=True, sort=False)\\n df_users = pool.map(process_file_pd, users_file_list)\\n df_users = pd.concat(df_users, ignore_index=True, sort=False)\\n\\n return df_clicks, df_users\",\n \"def loadFiles(root=\\\"data/TAIWAN_RAW_DATA/ADHD\\\"):\\n\\tdata_rt = [] # realtime.csv\\n\\tdata_trial = [] # trialdata.csv\\n\\tdata_id = [] # caseid/subjectid\\n\\tRealTime = \\\"A2RealTime_\\\"\\n\\tTrialData = \\\"A2TrialData_\\\"\\n\\tfolder_list = os.listdir(root) # list of subfolders in the root\\n\\tfor folders in folder_list:\\n\\t\\tfolders_path = os.path.join(root,folders)\\n\\t\\tif folders.find(\\\"pass\\\") != -1:\\n\\t\\t\\tcontinue\\n\\t\\t\\t\\n\\t\\ttry:\\n\\t\\t\\tdata_rt.append(pd.read_csv(os.path.join\\n\\t\\t\\t\\t\\t\\t\\t\\t (folders_path,\\n\\t\\t\\t\\t\\t\\t\\t\\t RealTime+folders[3:]+\\\".csv\\\")))\\n\\t\\t\\tdata_trial.append(pd.read_csv(os.path.join\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t (folders_path,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t TrialData+folders[3:]+\\\".csv\\\")))\\n\\t\\t\\tdata_id.append(int(folders.split('_')[1]))\\n\\t\\texcept:\\n\\t\\t\\tprint(os.path.join(folders_path,TrialData+folders[3:]+\\\".csv\\\"))\\n\\t\\t\\t\\n\\treturn data_rt,data_trial,data_id,folder_list\",\n \"def _api_scrape(json_inp, ndx):\\n\\n try:\\n headers = json_inp['resultSets'][ndx]['headers']\\n values = json_inp['resultSets'][ndx]['rowSet']\\n except KeyError:\\n # This is so ugly but this is what you get when your data comes out\\n # in not a standard format\\n try:\\n headers = json_inp['resultSet'][ndx]['headers']\\n values = json_inp['resultSet'][ndx]['rowSet']\\n except KeyError:\\n # Added for results that only include one set (ex. LeagueLeaders)\\n headers = json_inp['resultSet']['headers']\\n values = json_inp['resultSet']['rowSet']\\n return DataFrame(values, columns=headers)\",\n \"def process_data(*args, **kwargs):\\n\\n filepath = kwargs[\\\"filepath\\\"]\\n func = kwargs[\\\"func\\\"]\\n \\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(datafile)\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def load_all_experiments_from_dir(data_list, single_object_files=False, obj_ids=None):\\n all_experiments = {}\\n for i, batch_element in enumerate(data_list):\\n if single_object_files:\\n experiment = load_data_from_list_of_paths_single_object_files(batch_element, obj_ids)\\n else:\\n experiment = load_data_from_list_of_paths(batch_element)\\n\\n all_experiments[i] = experiment\\n return all_experiments\",\n \"def get_weather_as_df(keys):\\n s3 = boto3.client('s3')\\n bucket_name = 'trailreportdata'\\n files = b''\\n for key in keys:\\n response = s3.get_object(Bucket=bucket_name, Key=key)\\n body = response['Body']\\n csv = body.read()\\n files += csv\\n f = BytesIO(files)\\n csv = pd.read_csv(f)\\n return csv\",\n \"def testListFiles(self):\\n testCollection = CouchCollection(database=self.testInit.couchDbName,\\n url=self.testInit.couchUrl,\\n name=\\\"Thunderstruck\\\")\\n testFileset = CouchFileset(database=self.testInit.couchDbName,\\n url=self.testInit.couchUrl,\\n name=\\\"TestFileset\\\")\\n testCollection.addFileset(testFileset)\\n\\n testFiles = {}\\n for i in range(5):\\n lfn = makeUUID()\\n testFile = File(lfn=lfn, size=random.randint(1024, 4096),\\n events=random.randint(1024, 4096))\\n testFiles[lfn] = testFile\\n testFileset.add([testFile])\\n\\n for file in testFileset.listFiles():\\n self.assertTrue(file[\\\"lfn\\\"] in testFiles,\\n \\\"Error: File missing.\\\")\\n self.assertEqual(file[\\\"events\\\"], testFiles[file[\\\"lfn\\\"]][\\\"events\\\"],\\n \\\"Error: Wrong number of events.\\\")\\n self.assertEqual(file[\\\"size\\\"], testFiles[file[\\\"lfn\\\"]][\\\"size\\\"],\\n \\\"Error: Wrong file size.\\\")\\n return\",\n \"def integrate_data(dataPath):\\n data_set = []\\n\\n # List all json files\\n data_files = glob.glob(dataPath + \\\"*.json\\\")\\n\\n # Concat json files into a list of dataframes\\n for json_file in data_files:\\n data = pd.read_json(json_file, orient='columns', lines=True)\\n data_set.append(data)\\n\\n # Integrate dataframes into one dataframe\\n data_sef_df = pd.concat(data_set)\\n\\n # Select product ids\\n products = pd.DataFrame(data_sef_df[\\\"products\\\"].tolist())\\n \\n # Convert column names from int to string \\n products.columns = products.columns.astype(str)\\n\\n return data_sef_df, products\",\n \"def request_large_data(path, params):\\r\\n start = 0\\r\\n result = []\\r\\n while True:\\r\\n process = __split_path(path)\\r\\n if process['status'] == 'success':\\r\\n conn = process['conn']\\r\\n info = MetaInfo()\\r\\n header = {'vendor_key': info.vendor_id}\\r\\n # check 2.x and 3.x differences in using urllib\\r\\n params['start'] = str(start)\\r\\n try:\\r\\n conn.request(\\\"GET\\\", process['req_path'] + \\\"?\\\" +\\r\\n urllib.urlencode(params), headers=header)\\r\\n except AttributeError:\\r\\n conn.request(\\\"GET\\\", process['req_path'] + \\\"?\\\" +\\r\\n urllib.parse.urlencode(params), headers=header)\\r\\n resp = conn.getresponse()\\r\\n\\r\\n if resp.status != 200:\\r\\n print(\\\"Code: \\\" + str(resp.status))\\r\\n resp_obj = HTTPConnect(resp.status, resp.read()).json()\\r\\n print(resp_obj)\\r\\n break\\r\\n resp_obj_read = resp.read()\\r\\n resp_obj = HTTPConnect(resp.status, resp_obj_read).json()\\r\\n if type(resp_obj) is str:\\r\\n try:\\r\\n resp_obj = json.loads(resp_obj)\\r\\n except:\\r\\n # return error msg\\r\\n print(\\\"parse json str failed\\\")\\r\\n # print(resp_obj)\\r\\n break\\r\\n elif type(resp_obj) is dict:\\r\\n try:\\r\\n resp_obj = json.loads(json.dumps(resp_obj))\\r\\n except:\\r\\n print(\\\"parse dict failed\\\")\\r\\n print(resp_obj)\\r\\n break\\r\\n else:\\r\\n print(\\\"result not str\\\")\\r\\n print(resp_obj)\\r\\n break\\r\\n\\r\\n total = int(resp_obj['total'])\\r\\n next_start = int(resp_obj['next_start'])\\r\\n\\r\\n result.extend(resp_obj['data'])\\r\\n print(\\\"Finish extracting: \\\" + str(start+1) + \\\" to \\\" + str(next_start-1) + \\\" , remaining: \\\"\\r\\n + str(total - next_start))\\r\\n start = next_start\\r\\n conn.close()\\r\\n if start == total:\\r\\n break\\r\\n return result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58557445","0.5841202","0.57185346","0.5678337","0.5617907","0.5616196","0.5553351","0.55467594","0.550163","0.5499053","0.54974854","0.54938287","0.54541194","0.5434235","0.5411787","0.5370609","0.5369148","0.53324836","0.5332005","0.53307456","0.53236085","0.53193825","0.5313311","0.5287711","0.52841836","0.52635014","0.52367586","0.52367586","0.5228345","0.5226857","0.5221529","0.5221529","0.5221529","0.5211004","0.520846","0.52081895","0.52010506","0.51842767","0.51812184","0.51745504","0.5172158","0.5166983","0.51463646","0.51335585","0.51334614","0.51323545","0.5114764","0.51074046","0.5104524","0.5090686","0.50858855","0.508486","0.50663835","0.50626355","0.5060319","0.50542486","0.5046176","0.50434285","0.50340074","0.503132","0.50234914","0.5020307","0.5003449","0.5002847","0.49979818","0.49910367","0.4986401","0.49812242","0.49809214","0.4975374","0.4974124","0.49705","0.49698332","0.49479908","0.49390867","0.49248436","0.49113804","0.49056596","0.49023303","0.48983797","0.48978624","0.48950726","0.48943862","0.4878469","0.48708266","0.48706073","0.48701724","0.48692906","0.48674294","0.4860727","0.48562214","0.48509866","0.48429143","0.483559","0.48282373","0.482582","0.4818538","0.48139092","0.481319","0.48124605"],"string":"[\n \"0.58557445\",\n \"0.5841202\",\n \"0.57185346\",\n \"0.5678337\",\n \"0.5617907\",\n \"0.5616196\",\n \"0.5553351\",\n \"0.55467594\",\n \"0.550163\",\n \"0.5499053\",\n \"0.54974854\",\n \"0.54938287\",\n \"0.54541194\",\n \"0.5434235\",\n \"0.5411787\",\n \"0.5370609\",\n \"0.5369148\",\n \"0.53324836\",\n \"0.5332005\",\n \"0.53307456\",\n \"0.53236085\",\n \"0.53193825\",\n \"0.5313311\",\n \"0.5287711\",\n \"0.52841836\",\n \"0.52635014\",\n \"0.52367586\",\n \"0.52367586\",\n \"0.5228345\",\n \"0.5226857\",\n \"0.5221529\",\n \"0.5221529\",\n \"0.5221529\",\n \"0.5211004\",\n \"0.520846\",\n \"0.52081895\",\n \"0.52010506\",\n \"0.51842767\",\n \"0.51812184\",\n \"0.51745504\",\n \"0.5172158\",\n \"0.5166983\",\n \"0.51463646\",\n \"0.51335585\",\n \"0.51334614\",\n \"0.51323545\",\n \"0.5114764\",\n \"0.51074046\",\n \"0.5104524\",\n \"0.5090686\",\n \"0.50858855\",\n \"0.508486\",\n \"0.50663835\",\n \"0.50626355\",\n \"0.5060319\",\n \"0.50542486\",\n \"0.5046176\",\n \"0.50434285\",\n \"0.50340074\",\n \"0.503132\",\n \"0.50234914\",\n \"0.5020307\",\n \"0.5003449\",\n \"0.5002847\",\n \"0.49979818\",\n \"0.49910367\",\n \"0.4986401\",\n \"0.49812242\",\n \"0.49809214\",\n \"0.4975374\",\n \"0.4974124\",\n \"0.49705\",\n \"0.49698332\",\n \"0.49479908\",\n \"0.49390867\",\n \"0.49248436\",\n \"0.49113804\",\n \"0.49056596\",\n \"0.49023303\",\n \"0.48983797\",\n \"0.48978624\",\n \"0.48950726\",\n \"0.48943862\",\n \"0.4878469\",\n \"0.48708266\",\n \"0.48706073\",\n \"0.48701724\",\n \"0.48692906\",\n \"0.48674294\",\n \"0.4860727\",\n \"0.48562214\",\n \"0.48509866\",\n \"0.48429143\",\n \"0.483559\",\n \"0.48282373\",\n \"0.482582\",\n \"0.4818538\",\n \"0.48139092\",\n \"0.481319\",\n \"0.48124605\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.52002525"},"document_rank":{"kind":"string","value":"37"}}},{"rowIdx":77070,"cells":{"query":{"kind":"string","value":"Tracks a position through the duration of the loaded video"},"document":{"kind":"string","value":"def track(self, frame, init_pos, frame_ctr=1):\n distance_list = []\n self.tracker.init(frame, tuple(init_pos))\n init_center_pos = int((init_pos[3] / 2) + init_pos[1])\n\n if self.debug_mode:\n # Created window to display video\n cv2.namedWindow('Barbell_Tracker', cv2.WINDOW_NORMAL)\n\n while(self.video.isOpened()):\n \"\"\"\n Based off: https://pythonprogramming.net/haar-cascade-object-detection-python-opencv-tutorial/\n Using tracker instead of constant detection\n \"\"\"\n # Read a frame\n retval, frame = self.video.read()\n if not retval:\n break\n frame_ctr += 1\n\n # Update the tracker\n retval, current_pos = self.tracker.update(frame)\n if not retval:\n raise Exception('Could not update tracker')\n\n # Find center of current position\n current_center_pos = int((current_pos[3] / 2) + current_pos[1])\n\n # Log distance moved\n distance_moved = init_center_pos - current_center_pos\n distance_list.append(distance_moved)\n logging.info('Frame {}. Distance moved: {}'.format(frame_ctr, distance_moved))\n\n if self.debug_mode:\n # Draw inital position\n cv2.rectangle(frame, (int(init_pos[0]), int(init_pos[1])),\n (int(init_pos[0] + init_pos[2]), int(init_pos[1] + init_pos[3])),\n (0, 255, 0), 1)\n # Draw rectangle over tracker position\n cv2.rectangle(frame, (int(current_pos[0]), int(current_pos[1])),\n (int(current_pos[0] + current_pos[2]), int(current_pos[1] + current_pos[3])),\n (0, 0, 255), 2)\n # Display distance moved\n cv2.putText(frame, 'Distance moved: {}'.format(distance_moved),\n (100, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)\n # Show frame\n cv2.imshow('Barbell_Tracker', frame)\n # Exit if q pressed\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n # Stop video when finished\n self.video.release()\n return distance_list"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def seek(self,event):\r\n if self.app.controlLock.locked():\r\n return\r\n self.app.controlLock.acquire()\r\n x = event.x\r\n scalex,_ = self.getScale()\r\n scalex_secs = [scalex[0]/self.samplerate,scalex[1]/self.samplerate]# Get x scale in seconds\r\n seekTo = (x/self.w) * (scalex_secs[1]-scalex_secs[0]) + scalex_secs[0]# Transform pixel coordinates to represented time\r\n self.app.videoPlayer.pause()\r\n self.app.videoPlayer.seek(seekTo-self.app.dataOffset)\r\n self.app.videoPlayer.pause()# Restart audio to sync\r\n self.update(self.app.videoPlayer.startTimestamp)\r\n self.draw()\r\n self.app.videoPlayer.play()\r\n self.app.controlLock.release()","def seek(self,t):\r\n if (t > self.vid_len) or (t < 0):# If seeking to beyond end of video\r\n frame = ImageTk.PhotoImage(Image.fromarray(np.array([[0]*self.w]*self.h)))# Set frame to a black image of same proportions\r\n self.player.config(image=frame)\r\n self.player.image = frame\r\n self.root.update_idletasks()\r\n self.startTimestamp = time.time() - t\r\n if self.hasAudio:\r\n mixer.music.play(start=t,loops=0)\r\n self.updateDataplayers()\r\n # If already playing, skip calling the stream method, or if no video data loaded\r\n if self.isPlaying() or self.isEmpty():\r\n return\r\n self.state = VideoPlayer.State.PLAYING\r\n self.root.after(0,self.stream)","def track(self, label, debug=False):\n prev_speed = 0\n count_frames_speed_0 = 0\n mv = MoveRobot()\n # loop over frames from the video stream\n while True:\n # grab the current frame, then handle if we are using a\n # VideoStream or VideoCapture object\n \"\"\"\n frame = self.vs.read()\n frame = frame[1] if not self.webCam else frame\n \"\"\"\n if count_frames_speed_0 >= 10:\n break\n frame, boundary_lines = self.video_stream.read()\n # check to see if we have reached the end of the stream\n if frame is None:\n break\n # resize the frame (so we can process it faster) and grab the\n # frame dimensions\n frame = imutils.resize(frame, width=500) if not self.webCam else frame\n (H, W) = frame.shape[:2]\n\n # Start tracking the given label\n if self.initBB is None and label is not None:\n self.initBB = label\n # start OpenCV object tracker using the supplied bounding box\n # coordinates, then start the FPS throughput estimator as well\n self.tracker.init(frame, self.initBB)\n self.fps = FPS().start()\n label = None\n\n # check to see if we are currently tracking an object\n if self.initBB is not None:\n # grab the new bounding box coordinates of the object\n (success, box) = self.tracker.update(frame)\n # check to see if the tracking was a success\n if success:\n (x, y, w, h) = [int(v) for v in box]\n\n # Get the spine boundary lines\n label_rectangle = Rectangle(x, y, w, h)\n left_spine_bound, right_spine_bound = findSpineBoundaries(label_rectangle, boundary_lines)\n\n # Plot the lines\n if debug and left_spine_bound:\n left_spine_bound.plotOnImage(frame, thickness=2)\n if debug and right_spine_bound:\n right_spine_bound.plotOnImage(frame, thickness=2)\n\n distance_to_middle = 0\n\n # If both spine bounds are in frame and found\n if (right_spine_bound is not None) and (left_spine_bound is not None):\n\n # Adjust the position of the robot\n # Find a point on the spine boundaries which is in the middle of the frame height\n left_spine_coordinate = left_spine_bound.calculateXgivenY(H/2)\n right_spine_coordinate = right_spine_bound.calculateXgivenY(H/2)\n\n # Find a point on the middle of the spine\n spine_midpoint = left_spine_coordinate + (right_spine_coordinate - left_spine_coordinate) / 2\n\n # Distance from the point on the middle of the spine to the middle of the frame\n # Range 100 if spine is on the very left of the frame to -100 on the right\n distance_to_middle = int(( (W/2 - spine_midpoint) * 100 ) / (W/2))\n\n # If only one spine bound is found\n if (right_spine_bound is None) and (left_spine_bound is not None):\n\n # Distance from the point on the middle of the spine to the middle of the frame\n # Range 100 if spine is on the very left of the frame to -100 on the right\n left_spine_coordinate = left_spine_bound.calculateXgivenY(H/2)\n distance_to_middle = int(( (W/2 - left_spine_coordinate) * 100 ) / (W/2))\n\n if (right_spine_bound is not None) and (left_spine_bound is None):\n\n # Distance from the point on the middle of the spine to the middle of the frame\n # Range 100 if spine is on the very left of the frame to -100 on the right\n right_spine_coordinate = right_spine_bound.calculateXgivenY(H/2)\n distance_to_middle = int(( (W/2 - right_spine_coordinate) * 100 ) / (W/2))\n\n if (right_spine_bound is not None) or (left_spine_bound is not None):\n if abs(distance_to_middle < 20):\n abs_speed = 0.005\n else:\n abs_speed = 0.001\n if abs(distance_to_middle) < 5:\n speed = 0\n count_frames_speed_0 += 1\n elif distance_to_middle < 0:\n speed = abs_speed\n count_frames_speed_0 = 0\n else:\n speed = -abs_speed\n count_frames_speed_0 = 0\n if speed != prev_speed:\n print(\"Moving with speed \" + str(speed) + \" !\")\n Thread(target=mv.setSpeed, args=(speed,)).start()\n prev_speed = speed\n # Draw the rectangle around the label\n if debug:\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\n #else:\n #return success\n\n # update the FPS counter\n self.fps.update()\n self.fps.stop()\n print(\"FPS\", \"{:.2f}\".format(self.fps.fps()))\n # initialize the set of information we'll be displaying on\n # the frame\n info = [\n (\"Tracker\", self.trackerType),\n (\"Success\", \"Yes\" if success else \"No\"),\n (\"FPS\", \"{:.2f}\".format(self.fps.fps())),\n ]\n # loop over the info tuples and draw them on our frame\n if debug:\n for (i, (k, v)) in enumerate(info):\n text = \"{}: {}\".format(k, v)\n cv2.putText(frame, text, (10, H - ((i * 20) + 20)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)\n\n # show the output frame\n if debug:\n cv2.imshow(\"Frame\", frame)\n key = cv2.waitKey(1) & 0xFF\n # if the `q` key was pressed, break from the loop\n if key == ord(\"q\"):\n break\n mv.shutDown()\n # if we are using a webcam, release the pointer\n if self.webCam:\n #self.vs.stop()\n self.video_stream.stop()\n # otherwise, release the file pointer\n else:\n self.vs.release()\n # close all windows\n cv2.destroyAllWindows()\n return True","def update(self):\n self.frame = self.video_stream.read()","def arm_video(self, nframes, timeout=30):\n pass","def duration():\r\n elapsed_time, duration = video_time()\r\n return duration","def analyze_video(vidNum_iter, config, pointInds_toUse, pts_spaced, session): # function needed for multiprocessing\n\n optic = config['Optic']\n\n numVids = session['num_vids']\n path_vid_allFiles = session['videos']\n lk_names = [key for key in optic.keys() if 'lk_' in key]\n lk_params = {k.split('lk_')[1]: (tuple(optic[k]) if type(optic[k]) is list else optic[k]) \\\n for k in lk_names}\n\n vid = imageio.get_reader(path_vid_allFiles[vidNum_iter], 'ffmpeg')\n # metadata = vid.get_meta_data()\n\n path_vid = path_vid_allFiles[vidNum_iter] # get path of the current vid\n video = cv2.VideoCapture(path_vid) # open the video object with openCV\n numFrames = int(video.get(\n cv2.CAP_PROP_FRAME_COUNT)) # get frame count of this vid GENERALLY INACCURATE. OFF BY AROUND -25 frames\n\n frameToSet = 0\n frame = vid.get_data(\n frameToSet) # Get a single frame to use as the first 'previous frame' in calculating optic flow\n new_frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n old_frame = new_frame_gray\n\n displacements_tmp = np.zeros((pts_spaced.shape[0], 2, np.uint64(numFrames + (numVids * 1000)))) * np.nan\n\n print(' ', end='', flush=True)\n text = \"progresser #{}\".format(vidNum_iter)\n print(f'\\n Calculating displacement field: video # {vidNum_iter + 1}/{numVids}')\n\n for iter_frame, new_frame in enumerate(tqdm(vid, total=numFrames, desc=text, position=vidNum_iter)):\n new_frame_gray = cv2.cvtColor(new_frame, cv2.COLOR_BGR2GRAY) # convert to grayscale\n\n ##calculate optical flow\n pointInds_new, status, error = cv2.calcOpticalFlowPyrLK(old_frame, new_frame_gray, pointInds_toUse, None,\n **lk_params) # Calculate displacement distance between STATIC/ANCHORED points and the calculated new points. Also note the excluded 'NextPts' parameter. Could be used for fancier tracking\n\n ## Calculate displacement and place into variable 'displacements' (changes in size every iter)\n if iter_frame == 0:\n displacements_tmp[:, :, iter_frame] = np.zeros((pts_spaced.shape[0], 2))\n else:\n displacements_tmp[:, :, iter_frame] = np.single(np.squeeze((\n pointInds_new - pointInds_toUse))) # this is the important variable. Simply the difference in the estimate\n\n old_frame = new_frame_gray # make current frame the 'old_frame' for the next iteration\n\n return displacements_tmp","def position(self, video_display_name=None):\r\n selector = self.get_element_selector(video_display_name, CSS_CLASS_NAMES['video_time'])\r\n current_seek_position = self.q(css=selector).text[0]\r\n return current_seek_position.split('/')[0].strip()","def media_position(self):\n return (\n self._table.active_track_total_time\n - self._table.active_track_remaining_time\n ).total_seconds()","def video_playing(self, paused, title, total_time, position):\n self.device.responses['playing'] = PlayingResponse(\n paused=paused, title=title,\n total_time=total_time, position=position,\n mediakind=3)","def read_video_info_cv2(vid_fp: str):\n cap = cv2.VideoCapture(vid_fp)\n total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\n total_seconds = int(total_frames / cap.get(cv2.CAP_PROP_FPS))\n cap.release()\n return timedelta(seconds=total_seconds)\n # cap.set(cv2.CAP_PROP_POS_AVI_RATIO, 1)\n # total_ms = cap.get(cv2.CAP_PROP_POS_MSEC)\n # return timedelta(milliseconds=total_ms)","def delace(self):\n if not self.isdelaced:\n newframes = []\n self.frame_dt /= 2\n for i, frame in enumerate(self.frames):\n newframes.append(\n Frame(\n frame.data[1::2, :],\n self.starttime\n + datetime.timedelta(seconds=self.frame_dt * (2 * i)),\n )\n )\n newframes.append(\n Frame(\n frame.data[0::2, :],\n self.starttime\n + datetime.timedelta(seconds=self.frame_dt * (2 * i + 1)),\n )\n )\n self.isdelaced = True\n self.frames = newframes\n self.length = len(newframes)\n else:\n print(\"Video already deinterlaced.\")","def process_video(self):\n if os.path.isfile(self.source):\n self.cap = cv2.VideoCapture(self.source)\n else:\n try:\n file_name = \"input.mp4\"\n self.source = self.source.replace('open', 'uc')\n print( \"\\nDownloading video file from drive link to %s\\n\"%file_name)\n gdown.download(self.source, file_name, quiet=False)\n print( \"%s downloaded!\\n\"%file_name )\n self.cap = cv2.VideoCapture(file_name)\n except Exception:\n raise RuntimeError(\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\n\")\n\n\n assert self.cap.isOpened(), \"Failed to open %s\" % self.source\n\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\n return","def wheelEvent(self,event):\n\n steps = event.angleDelta().y() // 120\n vector = steps and steps // abs(steps) # 0, 1, or -1\n for step in range(1, abs(steps) + 1):\n if self.mediaPlayer.duration() != 0:\n # If there a video is open.\n self.set_position(self.mediaPlayer.position() + 200*vector)\n else:\n # If no video is open, but a csv is open, I set a limit of the fast forward.\n if self.xValues != []:\n if self.mediaPlayer.position() + 200*vector < max(self.xValues):\n self.set_position(self.mediaPlayer.position() + 200*vector)\n elif max(self.xValues) - self.mediaPlayer.position() <= 200:\n self.set_position(max(self.xValues))","def example_video(self, **kwargs):\n kwargs.setdefault(\"title\", \"dummy\")\n kwargs.setdefault(\"paused\", True)\n kwargs.setdefault(\"position\", 3)\n kwargs.setdefault(\"total_time\", 123)\n self.video_playing(**kwargs)","def __init__(self, videoPath=\"\", cacheString=\"-cache\", faceDetector=None):\n self.videoPath = videoPath\n self.cacheString = cacheString\n self.video = None \n self.currentFrame = []\n self.currentFrameNumber = 0\n self.frameCount = 0 #total frame number\n self.fps = 25\n self.ret = True #if the video is over\n self.isPlaying = False\n self.ifDrawAxis = False\n self.ifDrawSquare = False\n self.cacheData = {}\n \n if videoPath != \"\":\n self.load()","def track_video(frames):\n first_frame = next(frames)\n lkt = lktrack.LKTracker(first_frame)\n lkt.detect_points()\n yield lkt.draw()\n\n for im in frames:\n lkt.step(im)\n lkt.track_points()\n yield lkt.draw()","def getPoslbFFmpeg(self, event):\n # if self.measurement_w:\n\n if hasattr(self, \"measurement_w\") and self.measurement_w is not None and self.measurement_w.isVisible():\n x = event.pos().x()\n y = event.pos().y()\n\n # distance\n if self.measurement_w.rbDistance.isChecked():\n if event.button() == 1: # left\n self.draw_point(x, y, \"lime\")\n self.memx, self.memy = x, y\n\n if event.button() == 2 and self.memx != -1 and self.memy != -1:\n self.draw_point(x, y, \"lime\")\n self.draw_line(self.memx, self.memy, x, y, \"lime\")\n\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\"line\", self.memx, self.memy, x, y])\n else:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\"line\", self.memx, self.memy, x, y]]\n\n d = ((x - self.memx) ** 2 + (y - self.memy) ** 2) ** 0.5\n try:\n d = d / float(self.measurement_w.lePx.text()) * float(self.measurement_w.leRef.text())\n except:\n QMessageBox.critical(self, programName,\n \"Check reference and pixel values! Values must be numeric.\",\n QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton)\n\n self.measurement_w.pte.appendPlainText(\"Time: {}\\tFrame: {}\\tDistance: {}\".format(self.getLaps(),\n self.FFmpegGlobalFrame,\n round(d, 1)))\n self.measurement_w.flagSaved = False\n self.memx, self.memy = -1, -1\n\n # angle 1st clic -> vertex\n if self.measurement_w.rbAngle.isChecked():\n if event.button() == 1: # left for vertex\n self.draw_point(x, y, \"lime\")\n self.memPoints = [(x, y)]\n\n if event.button() == 2 and len(self.memPoints):\n self.draw_point(x, y, \"lime\")\n self.draw_line(self.memPoints[0][0], self.memPoints[0][1], x, y, \"lime\")\n\n self.memPoints.append((x, y))\n\n if len(self.memPoints) == 3:\n self.measurement_w.pte.appendPlainText(\"Time: {}\\tFrame: {}\\tAngle: {}\".format(self.getLaps(),\n self.FFmpegGlobalFrame,\n round(angle(\n self.memPoints[\n 0],\n self.memPoints[\n 1],\n self.memPoints[\n 2]),\n 1)\n ))\n self.measurement_w.flagSaved = False\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\"angle\", self.memPoints])\n else:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\"angle\", self.memPoints]]\n\n self.memPoints = []\n\n # Area\n if self.measurement_w.rbArea.isChecked():\n if event.button() == 1: # left\n self.draw_point(x, y, \"lime\")\n if len(self.memPoints):\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], x, y, \"lime\")\n self.memPoints.append((x, y))\n\n if event.button() == 2 and len(self.memPoints) >= 2:\n self.draw_point(x, y, \"lime\")\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], x, y, \"lime\")\n self.memPoints.append((x, y))\n # close polygon\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], self.memPoints[0][0],\n self.memPoints[0][1], \"lime\")\n a = polygon_area(self.memPoints)\n\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\"polygon\", self.memPoints])\n else:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\"polygon\", self.memPoints]]\n try:\n a = a / (float(self.measurement_w.lePx.text()) ** 2) * float(\n self.measurement_w.leRef.text()) ** 2\n except:\n QMessageBox.critical(self, programName,\n \"Check reference and pixel values! Values must be numeric.\",\n QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton)\n\n self.measurement_w.pte.appendPlainText(\"Time: {}\\tFrame: {}\\tArea: {}\".format(self.getLaps(),\n self.FFmpegGlobalFrame,\n round(a, 1)))\n\n self.memPoints = []","def num_frames(self):\n return len(self.video)","def play_video(self):\n cv.namedWindow('Planes', cv.WINDOW_NORMAL)\n cv.resizeWindow('Planes', self.__yuv_video.frame_width, self.__yuv_video.frame_height)\n\n inter_frame_delay = int(1000 / self.__yuv_video.frame_rate)\n\n while True:\n (ret, frame) = self.__yuv_video.get_frame()\n\n if not ret:\n break\n\n cv.imshow('Planes', frame)\n cv.waitKey(0)\n\n self.__yuv_video.join()","def moveFrame(self, num, callback=None): \n if(self.isLoaded() == False):\n return\n self.isPlaying = False\n self.currentFrameNumber = int(self.video.get(cv2.CAP_PROP_POS_FRAMES))\n self.setFrame(self.currentFrameNumber + num - 1, callback)","def on_update_playback(self, event):\n try:\n offset = self.mplayer.GetTimePos()\n except:\n return\n print offset\n mod_off = str(offset)[-1]\n if mod_off == '0':\n print \"mod_off\"\n offset = int(offset)\n self.playbackSlider.SetValue(offset)\n secsPlayed = time.strftime('%M:%S', time.gmtime(offset))\n self.trackCounter.SetLabel(secsPlayed)","def seek(self, position):\n if self.player:\n value = position / self._length\n self.player.set_position(value)","def track_motion(self, video=None, set_roi=False):\r\n \r\n if video is None:\r\n video = self.video_buffer\r\n if set_roi:\r\n roi = self.get_roi(video=video)\r\n \r\n video_track = video.copy()\r\n motion_tracker = []\r\n # Generate different colors for tracking display \r\n color = np.random.randint(0,255,(100,3))\r\n \r\n # params for ShiTomasi corner detection\r\n feature_params = dict( maxCorners = 100,\r\n qualityLevel = 0.3,\r\n minDistance = 5,\r\n blockSize = 7 )\r\n # Parameters for lucas kanade optical flow\r\n lk_params = dict( winSize = (15,15),\r\n maxLevel = 8,\r\n criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))\r\n\r\n old_gray = cv2.cvtColor(video[0], cv2.COLOR_BGR2GRAY)\r\n # Create mask for drawing\r\n mask = np.zeros_like(video[0])\r\n # Mask to dectate the features to track\r\n features_mask = np.zeros_like(old_gray)\r\n features_mask[roi['x1']: roi['x2'], roi['y1']: roi['y2']] = old_gray[roi['x1']: roi['x2'], roi['y1']: roi['y2']]\r\n # Find corners in first frame\r\n p0 = cv2.goodFeaturesToTrack(features_mask, mask = None, **feature_params)\r\n \r\n for idx in range(1, video.shape[0]):\r\n new_gray = cv2.cvtColor(video[idx], cv2.COLOR_BGR2GRAY)\r\n # calculate optical flow\r\n p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, new_gray, p0, None, **lk_params)\r\n \r\n # Get good points\r\n good_old = p0[st==1]\r\n good_new = p1[st==1]\r\n motion_tracker.append(good_new)\r\n for i, (old, new) in enumerate(zip(good_old, good_new)):\r\n (ox, oy) = old.reval()\r\n (nx, ny) = new.ravel()\r\n mask = cv2.circle(mask, (nx, ny), 5, color[i].tolist(), -1)\r\n frame = cv2.add(video[idx], mask)\r\n video_track[idx] = frame\r\n \r\n cv2.imshow('frame', frame)\r\n if cv2.waitKey(30) & 0xFF==27:\r\n break\r\n # Updat old frames and points before checking next frame\r\n \r\n old_gray = new_gray.copy()\r\n p0 = p1.resapr(-1,1,2)\r\n cv2.destroyAllWindows()\r\n \r\n return video_track, motion_tracker","def elapsed_time():\r\n elapsed_time, duration = video_time()\r\n return elapsed_time","def continue_video(self):\n\n print(\"continue_video needs implementation\")","def set_position(self, posicion):\n\n if self.duracion < posicion:\n return\n\n if self.duracion == 0 or posicion == 0:\n return\n\n posicion = self.duracion * posicion / 100\n\n # http://pygstdocs.berlios.de/pygst-reference/gst-constants.html\n #self.player.set_state(gst.STATE_PAUSED)\n # http://nullege.com/codes/show/src@d@b@dbr-HEAD@trunk@src@reproductor.py/72/gst.SEEK_TYPE_SET\n #self.player.seek(\n # 1.0,\n # gst.FORMAT_TIME,\n # gst.SEEK_FLAG_FLUSH,\n # gst.SEEK_TYPE_SET,\n # posicion,\n # gst.SEEK_TYPE_SET,\n # self.duracion)\n\n # http://nullege.com/codes/show/src@c@o@congabonga-HEAD@congaplayer@congalib@engines@gstplay.py/104/gst.SEEK_FLAG_ACCURATE\n event = gst.event_new_seek(\n 1.0, gst.FORMAT_TIME,\n gst.SEEK_FLAG_FLUSH | gst.SEEK_FLAG_ACCURATE,\n gst.SEEK_TYPE_SET, posicion * 1000000000,\n gst.SEEK_TYPE_NONE, self.duracion * 1000000000)\n\n self.send_event(event)\n #self.player.set_state(gst.STATE_PLAYING)","def play_video(self, cap_frame):\n # Plus 35 to synchronize video and recording\n self.cap.set(cv2.CAP_PROP_POS_FRAMES, cap_frame+30)\n _, frame = self.cap.read()\n cv2.imshow('frame', frame)\n cv2.waitKey(1)","def frame_forward(self):\n if self.playMode == FFMPEG:\n self.ffmpegTimerOut()","def run(input_video_file, output_video_file):\n print(\"Debut de la transformation du format de la video\")\n #récupération de la vidéo\n video = cv2.VideoCapture(input_video_file)\n #fps de la vidéo\n fps = video.get(cv2.CAP_PROP_FPS)\n #largeur des images de la vidéo\n width_video = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\n #hauteur des images de la vidéo\n height_video = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n #nombre d'images dans la vidéo\n frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))\n #durée de la vidéo\n duration = frame_count/fps\n #nouvelle durée de la vidéo (on arrondi)\n new_duration = math.floor(duration)\n #nouveau fps de la vidéo\n new_fps = float(round(fps))\n #appliquer le nouveau fps\n video.set(cv2.CAP_PROP_FPS,new_fps)\n #appliquer la nouvelle durée\n print(new_duration)\n print(new_fps)\n print(new_duration*new_fps)\n new_frame_count = new_duration*new_fps\n video.set(cv2.CAP_PROP_FRAME_COUNT,new_duration*new_fps)\n #déffinition du format de la vidéo en sortie\n video_out = cv2.VideoWriter(output_video_file,0x7634706d,new_fps,(width_video,height_video),True)\n \n count = 0\n #ouverture de la vidéo\n while(video.isOpened()):\n #lecture image par image\n ret, frame = video.read()\n if ret==True:\n\n #ecriture de l'image dans la vidéo en sortie\n video_out.write(frame)\n count = count + 1\n \n if (count > (new_frame_count-1)):\n # Libérer la vidéo\n video.release()\n break\n else:\n break\n\n print(\"fin de la transformation\")\n #fermer les vidéos\n video.release()\n video_out.release()","def _capture_video(video_duration=0., record=False):\n global _shutdown\n if cap is not None:\n skip = False\n t = time.time()\n frames = []\n frame_size = (640, 480) # default frame size\n countdown = 1 if record else min(3, int(video_duration)) # number of seconds to countdown\n margin = 0.1 # time margin (in seconds)\n time_left = video_duration - time.time() + t\n\n while time_left > 0:\n if not record and time_left > 0.5 and abs(countdown - time_left) <= margin:\n _play_audio(COUNTDOWN_SOUND, no_audio)\n countdown -= 1\n\n ret, frame = cap.read()\n frames.append(frame.copy())\n frame = cv2.flip(frame, 1) # horizontal flip for video-preview\n frame_size = (frame.shape[1], frame.shape[0])\n\n if record:\n message = f\"Recording video {str(index + 1)}\"\n else:\n message = f\"Get into position {str(index + 1)}\"\n\n # Recording prompt\n cv2.putText(frame, message, (100, 100), FONT, 3, (255, 255, 255),\n 4, cv2.LINE_AA)\n # Recording timer\n cv2.putText(frame, f\" {str(int(time_left) + 1)}\",\n (200, 250), FONT, 10, (255, 255, 255),\n 6, cv2.LINE_AA)\n cv2.imshow('frame', frame)\n\n # Get key-press to skip current video or terminate script\n key = cv2.waitKey(1)\n if key & 0xFF == ord('s'): # Press `S` to skip the current video prompt\n if record:\n print(f'\\t[PROMPT]\\tSkipping video {index + 1} of {num_videos}.')\n cv2.destroyAllWindows()\n skip = True\n break\n elif key == 27: # Press `ESC` to exit the script\n print('\\t[PROMPT]\\tShutting down video-recording and releasing resources.')\n _play_audio(EXIT_SOUND, no_audio)\n cv2.destroyAllWindows()\n _shutdown = True\n break\n\n time_left = video_duration - time.time() + t\n\n if not _shutdown:\n _play_audio(DONE_SOUND, no_audio)\n\n calculated_fps = round(len(frames) / video_duration)\n fps = 16 if calculated_fps <= 16 else calculated_fps\n\n if record and not skip and not _shutdown:\n out = cv2.VideoWriter(os.path.join(path_out, file), 0x7634706d, fps, frame_size)\n for frame in frames:\n out.write(frame)\n out.release()\n\n cv2.destroyAllWindows()","async def async_media_seek(self, position):\n if not self._slave_mode:\n _LOGGER.debug(\"Seek. Device: %s, DUR: %s POS: %\", self.name, self._duration, position)\n if self._duration > 0 and position >= 0 and position <= self._duration:\n value = await self.async_call_linkplay_httpapi(\"setPlayerCmd:seek:{0}\".format(str(position)), None)\n self._position_updated_at = utcnow()\n self._idletime_updated_at = self._position_updated_at\n if value != \"OK\":\n _LOGGER.warning(\"Failed to seek. Device: %s, Got response: %s\", self.entity_id, value)\n else:\n await self._master.async_media_seek(position)","def media_position(self):\n if self._media_playback_trackable():\n self._media_position_updated_at = utcnow()\n return self.coordinator.data.nowplaying[self.zone.SourceID].CurrProgress\n return None","def update(self):\n if self.mudpi.is_prepared:\n if self.duration > self.delay.total_seconds():\n if self.record_video:\n self.capture_recording(duration=self.record_duration)\n else:\n self.capture_image()\n self.reset_duration()","def get_video_pts(self):\n statrec = self.decoded_stats\n if statrec.last_pts is None:\n return None\n return statrec.last_pts + time.time() - statrec.last_pts_changed","def process_video(input_file, output_file):\n # video = VideoFileClip(input_file).subclip(40,44) # from 38s to 46s\n video = VideoFileClip(input_file)\n annotated_video = video.fl_image(process_pipeline)\n annotated_video.write_videofile(output_file, audio=False)","def process_video(input_file, output_file):\n # video = VideoFileClip(input_file).subclip(40,44) # from 38s to 46s\n video = VideoFileClip(input_file)\n annotated_video = video.fl_image(process_pipeline)\n annotated_video.write_videofile(output_file, audio=False)","def play_video_file(fname : str):\n cap = cv2.VideoCapture(fname)\n fps = cap.get(5)\n font = cv2.FONT_HERSHEY_SIMPLEX\n fontScale = 1\n fontColor = (0, 0, 0)\n lineType = 2\n\n myvideo = []\n while cap.isOpened():\n ret, frame = cap.read()\n\n if ret is True:\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n cv2.putText(gray, 'Time: ' + str(round(cap.get(0) / 1000, 2)),\n (10, 30),\n font,\n fontScale,\n fontColor,\n lineType)\n cv2.putText(gray, 'Frame: ' + str(int(cap.get(1))),\n (10, 70),\n font,\n fontScale,\n fontColor,\n lineType)\n myvideo.append(gray)\n #cv2.imshow('frame', gray)\n #cv2.waitKey(10)\n #if cv2.waitKey(delay=2) & 0xFF == ord('q'):\n # break\n else:\n break\n\n cap.release()\n\n if fps < 60:\n for frame in myvideo:\n cv2.imshow('frame', frame)\n cv2.waitKey(10)\n else:\n for ind, frame in enumerate(myvideo):\n if ind % 3 == 0:\n cv2.imshow('frame', frame)\n cv2.waitKey(10)\n else:\n continue\n cv2.destroyAllWindows()","def load(self):\n if self.ref == None:\n self.ref = av.open(self.abspath)\n self.n_audio_streams = len([s for s in self.ref.streams if s.type == 'audio'])\n self.n_video_streams = len([s for s in self.ref.streams if s.type == 'video'])\n if self.n_audio_streams > 0:\n self.media_types += ['audio']\n if self.n_video_streams > 0:\n self.media_types += ['video']\n self.duration = self.ref.duration / self.AV_TIME_BASE","def run(self):\n self.track_len = []\n # debug\n while self._segment_index <= self._segment_cnt:\n if self._segment_index < 0: # Uncomment this block to debug specific segment\n self._segment_index += 1\n continue\n # run association\n print \"[Tracking]\\tSegment index:\\t{} Total segment num:\\t{}\".format(self._segment_index, self._segment_cnt)\n start = cv2.getTickCount()\n \n self._run_segment()\n print \"[Tracking]\\tSegment start:\\t{} Segment end\\t{}\".format(self._segment_start_fid,\n self._segment_end_fid)\n # dump into file\n \"\"\"\n seg_name = 'segment_{}.track'.format(self._segment_index)\n seg_file = os.path.join(self._segment_dir, seg_name)\n self._segments_path.append(seg_file)\n Track.dump_to_track_file(self._high_level_tracks, save_name=seg_file)\n print \"Track contains {} high level tracks\".format(len(self._high_level_tracks))\n \"\"\"\n self._segment_index += 1\n end = cv2.getTickCount()\n print \"[Tracking]\\tTime:\\t{} seconds\".format(float(end - start) / cv2.getTickFrequency())\n if P['debug']:\n pos_feature_num = self.pos_arr.shape[0]\n neg_feature_num = self.neg_arr.shape[0]\n pos_arr = np.hstack((self.pos_arr, np.ones(shape=(pos_feature_num, 1))))\n neg_arr = np.hstack((self.neg_arr, np.zeros(shape=(neg_feature_num, 1))))\n np.savetxt(os.path.join(\"../feature_classifier/\", \"{}_pos_feature.txt\".format(self._video_name)), pos_arr)\n np.savetxt(os.path.join(\"../feature_classifier/\", \"{}_neg_feature.txt\".format(self._video_name)), neg_arr)\n\n final_track_save_file = os.path.join(self._save_dir, self._video_name + \"_final_merged.track\")\n mot_track_save_file = os.path.join(self._save_dir, self._video_name + \".txt\")\n Track.dump_to_track_file_no_feature(self._final_tracks, final_track_save_file, self._calib_w, self._calib_h)\n Track.dump_track_with_mot_format(self._final_tracks, mot_track_save_file,)\n print(\"there are {} tracklet in final merged track\".format(len(self._final_tracks)))","def yolo_test_video(self):\n # Open the input video, blocking call\n inputVideo = cv2.VideoCapture(self.inputFile)\n\t\t\n # Get infomration about the input video\n codec = int(inputVideo.get(cv2.CAP_PROP_FOURCC))\n fps = int(inputVideo.get(cv2.CAP_PROP_FPS))\n frameWidth = int(inputVideo.get(cv2.CAP_PROP_FRAME_WIDTH))\n frameHeight = int(inputVideo.get(cv2.CAP_PROP_FRAME_HEIGHT))\n\n # Open the output stream\n outputVideo = cv2.VideoWriter(self.outputFile,\n codec,\n fps,\n (frameWidth,frameHeight))\n frameIndex = inputVideo.get(cv2.CAP_PROP_POS_FRAMES)\n totalFrames = inputVideo.get(cv2.CAP_PROP_FRAME_COUNT)\n \t \n\tavgGrabTime = 0\n\tavgYoloTime = 0\n\tavgWriteTime = 0\n \n # For each frame in the video\n while True:\n \n startTime = time.time()\n \n # Calculate the time it takes to grab a frame\n startGrabTime = time.time()\n grabbed, frame = inputVideo.read()\n endGrabTime = time.time() \n\t avgGrabTime+=(endGrabTime-startGrabTime)\n\t \n\n if grabbed:\n\t\t\n # Calculate the time it takes to run YOLO pipeline \n\t\tstartYoloTime = time.time()\n annotatedFrame, predictedObjects = self.detect_from_image(frame)\n\t\tendYoloTime = time.time()\n\t\tavgYoloTime+= ( endYoloTime - startYoloTime)\n\n frameIndex = inputVideo.get(cv2.CAP_PROP_POS_FRAMES)\n \t\n\t\tcurrentTime = time.time()\n\t\telapsedTime = currentTime - startTime\n\t\tcurrentFPS = (1)/elapsedTime \n\t\t \t\n #cv2.rectangle(annotatedFrame, (0, 0), (30, 30), (0,0,0), -1)\n cv2.putText(\n annotatedFrame, 'FPS' + ': %.2f' % currentFPS,\n (15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n (255, 255, 255), 2\n )\n\t\t\n # Calculate the time it takes to write an annotated frame to video\n\t\tstartWriteTime = time.time()\n outputVideo.write(annotatedFrame)\n\t\tendWriteTime = time.time()\n\t\tavgWriteTime +=(endWriteTime - startWriteTime)\n\t\n else:\n inputVideo.set(cv2.CAP_PROP_POS_FRAMES, frameIndex-1)\n cv2.waitKey(100)\n\n if frameIndex==totalFrames:\n break\n\t\t\n inputVideo.release()\n outputVideo.release()\n cv2.destroyAllWindows()\n \n avgGrabTime/=totalFrames\n avgYoloTime/=totalFrames\n avgWriteTime/=totalFrames\n\n if self.verbose:\n print ('Average time for extracting compressed video frame : %.3f' %avgGrabTime)\n print ('Average time for YOLO object detection : %.3f' %avgYoloTime )\n print ('Average time for writing frame to video : %.3f' %avgWriteTime)","def __init__(self):\n self.active = True # Camera activation control\n self.stream = cv2.VideoCapture(0) # Open video stream\n while not self.stream.isOpened():\n pass\n _,self.image = self.stream.read()# Save the first frame\n cv2.waitKey(10)\n self.frame = self.image[196:304,:546,:]# Cropped frame\n self.diff_frame = self.frame\n# self.reference_frame = copy.deepcopy(self.frame)\n# self.abs_diff_frame = copy.deepcopy(self.frame)\n self.reference_frame = self.frame\n self.abs_diff_frame = self.frame\n self.frame_count = 1 # Used for framerate estimation\n self.frame_rate = 0\n self.tic = time()","def play_video(self, video_path, period):\n self.video_playing = True\n self.state_time_remain = period\n abs_path = QFileInfo(video_path).absoluteFilePath()\n QMetaObject.invokeMethod(self.video_player, \"setMedia\", Qt.QueuedConnection,\n Q_ARG(QMediaContent, QMediaContent(QUrl.fromLocalFile(abs_path))))","def stream_frames(video_capture):","def segment_intervals(filename, basedir, start_sec=0, end_sec=0, seconds_between_frame_grabs=10):\n # Get video id\n video_id = re.findall('v\\d+', filename)[0]\n\n # Open file handle\n vid = imageio.get_reader(filename, 'ffmpeg')\n\n # Get metadata\n meta = vid.get_meta_data()\n fps = int(meta['fps'])\n nframes = meta['nframes']\n frames_to_get = np.arange(start_sec, end_sec, seconds_between_frame_grabs) * fps\n\n # Check frames\n for i in frames_to_get:\n try:\n img = vid.get_data(i)\n except:\n raise\n\n # Downlsample full image\n downsampled = downsample_image(img)\n\n # Player status\n h, w, c = img.shape\n factor = 0.23\n y1 = int(h * 0.04)\n x1 = int(w * (0.5 - factor))\n y2 = int(h * 0.0645)\n x2 = int(w * (0.5 + factor))\n pl_status = crop_rect(img, x1, y1, x2, y2)\n\n # Block out center of player status\n h, w, c = pl_status.shape\n factor = .12\n x1 = int(w * (0.5 - factor))\n x2 = int(w * (0.5 + factor))\n pl_status = blackout_middle(pl_status, x1, x2)\n\n # Write full frame and header frame\n imageio.imwrite(os.path.join(basedir, 'full_{}_s{}.png'.format(video_id, int(i/fps))), img)\n imageio.imwrite(os.path.join(basedir, 'pl_status_{}_s{}.png'.format(video_id, int(i/fps))), pl_status)\n imageio.imwrite(os.path.join(basedir, 'downsampled_{}_s{}.png'.format(video_id, int(i/fps))), downsampled)\n\n vid.close()\n\n return True","def play_random_video(self):\n\n print(\"play_random_video needs implementation\")","def test_video_player(self):\r\n\r\n # Navigate to a video\r\n self.course_info_page.visit()\r\n self.tab_nav.go_to_tab('Courseware')\r\n\r\n # The video should start off paused\r\n # Since the video hasn't loaded yet, it's elapsed time is 0\r\n self.assertFalse(self.video.is_playing)\r\n self.assertEqual(self.video.elapsed_time, 0)\r\n\r\n # Play the video\r\n self.video.play()\r\n\r\n # Now we should be playing\r\n self.assertTrue(self.video.is_playing)\r\n\r\n # Commented the below EmptyPromise, will move to its page once this test is working and stable\r\n # Also there is should be no Promise check in any test as this should be done in Page Object\r\n # Wait for the video to load the duration\r\n # EmptyPromise(\r\n # lambda: self.video.duration > 0,\r\n # 'video has duration', timeout=20\r\n # ).fulfill()\r\n\r\n # Pause the video\r\n self.video.pause()\r\n\r\n # Expect that the elapsed time and duration are reasonable\r\n # Again, we can't expect the video to actually play because of\r\n # latency through the ssh tunnel\r\n self.assertGreaterEqual(self.video.elapsed_time, 0)\r\n self.assertGreaterEqual(self.video.duration, self.video.elapsed_time)","def analyze_livevideo(self, camera=0, ofile=None, droprate=2, cameraid=None, location=None, publish=True):\n # Use Camera#0 by default otherwise use the open specified\n cameradev = 0\n if camera:\n cameradev = camera\n\n vcamera = cv2.VideoCapture(cameradev)\n vstarttime = self.epoch()\n livefeed = True\n fcount = 0\n\n while livefeed:\n ret, frame = vcamera.read()\n oframe = frame.copy()\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\n # print(\"FrameNum: {}\".format(fcount))\n if fcount % droprate == 0:\n print(\" Analyzing Frame: {}\".format(fcount))\n faceinfo = self.analyze_frame(iframe=frame, label=True, publish=True)\n cv2.imshow('Agender', faceinfo['frame'])\n if publish:\n finfo = {}\n finfo['cameraid'] = cameraid\n finfo['location'] = location\n finfo['agender'] = faceinfo['dbdata']\n finfo['epoch'] = self.epoch()\n dtstr = str(datetime.datetime.now()).split('.')[0]\n dtdstr = dtstr.split(\" \")\n finfo['datetime'] = {'date': dtdstr[0], 'time': dtdstr[1]}\n if len(faceinfo['dbdata']):\n #print(json.dumps(finfo))\n self.publishstats(stats=finfo)\n\n fcount += 1\n if cv2.waitKey(5) == 27: # ESC key press\n livefeed = False","def running_video(self, video_source):\n self.video_controller.set_button_enable()\n self.cap = cv2.VideoCapture(video_source)\n self.next_frame_slot()","def readVideo(self):\n vid = cv2.VideoCapture(self.fname)\n imgstack = []\n # grab = True\n grab, img = vid.read()\n while grab:\n imgstack.append(\n Frame(\n cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\n self.starttime\n + datetime.timedelta(seconds=self.frame_dt * self.length),\n )\n )\n self.length += 1\n grab, img = vid.read()\n self.frames = imgstack","def displacements_monothread(config, pointInds_toUse, pointInds_tracked, pointInds_tracked_tuple, displacements,\n pts_spaced, color_tuples, session):\n\n ## Main loop to pull out displacements in each video\n ind_concat = 0\n fps = 0\n tic_fps = time.time()\n tic_all = time.time()\n\n optic = config['Optic']\n video = config['Video']\n\n vidNums_toUse = optic['vidNums_toUse']\n showVideo_pref = optic['showVideo_pref']\n fps_counterPeriod = video['fps_counterPeriod']\n printFPS_pref = video['printFPS_pref']\n remote = config['General']['remote']\n save_vid = video['save_demo']\n\n Fs = video['Fs']\n vid_width = video['width']\n vid_height = video['height']\n test_len = video['demo_len']\n save_pathFull = str(Path(config['Paths']['viz']) / 'optic_test.avi')\n\n numVids = session['num_vids']\n path_vid_allFiles = session['videos']\n lk_names = [key for key in optic.keys() if 'lk_' in key]\n lk_params = {k.split('lk_')[1]: (tuple(optic[k]) if type(optic[k]) is list else optic[k]) \\\n for k in lk_names}\n\n # Define the codec and create VideoWriter object\n if showVideo_pref and (save_vid or remote):\n fourcc = cv2.VideoWriter_fourcc(*'MJPG')\n print(f'saving to file {save_pathFull}')\n out = cv2.VideoWriter(save_pathFull, fourcc, Fs, (np.int64(vid_width), np.int64(vid_height)))\n else:\n out = None\n vid_lens = []\n for vidNum_iter in vidNums_toUse:\n vid = imageio.get_reader(path_vid_allFiles[vidNum_iter], 'ffmpeg')\n # metadata = vid.get_meta_data()\n\n path_vid = path_vid_allFiles[vidNum_iter] # get path of the current vid\n video = cv2.VideoCapture(path_vid) # open the video object with openCV\n numFrames_rough = int(video.get(\n cv2.CAP_PROP_FRAME_COUNT)) # get frame count of this vid GENERALLY INACCURATE. OFF BY AROUND -25 frames\n\n frameToSet = 0\n frame = vid.get_data(0) # Get a single frame to use as the first 'previous frame' in calculating optic flow\n new_frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n old_frame = new_frame_gray\n\n print(f'\\n Calculating displacement field: video # {vidNum_iter + 1}/{numVids}')\n # while True:\n for iter_frame, new_frame in enumerate(tqdm(vid, total=numFrames_rough)):\n new_frame_gray = cv2.cvtColor(new_frame, cv2.COLOR_BGR2GRAY) # convert to grayscale\n\n ##calculate optical flow\n pointInds_new, status, error = cv2.calcOpticalFlowPyrLK(old_frame, new_frame_gray, pointInds_toUse, None,\n **lk_params) # Calculate displacement distance between STATIC/ANCHORED points and the calculated new points. Also note the excluded 'NextPts' parameter. Could be used for fancier tracking\n\n ## Calculate displacement and place into variable 'displacements' (changes in size every iter) \n if iter_frame == 0:\n displacements[:, :, ind_concat] = np.zeros((pts_spaced.shape[0], 2))\n else:\n displacements[:, :, ind_concat] = np.single(np.squeeze((\n pointInds_new - pointInds_toUse))) # this is the important variable. Simply the difference in the estimate\n\n old_frame = new_frame_gray # make current frame the 'old_frame' for the next iteration\n\n ## below is just for visualization. Nothing calculated is maintained\n if showVideo_pref:\n pointInds_tracked = pointInds_tracked + (\n pointInds_new - pointInds_toUse) # calculate integrated position\n pointInds_tracked = pointInds_tracked - (\n pointInds_tracked - pointInds_toUse) * 0.01 # multiplied constant is the relaxation term. this is just for display purposes. Relaxation term chosen during cleanup will be real\n pointInds = [pointInds_tracked, pointInds_tracked_tuple]\n counters = [iter_frame, vidNum_iter, ind_concat, fps]\n if (remote and iter_frame < test_len) or not remote:\n videos.visualize_progress(config, session, new_frame, pointInds, color_tuples, counters, out)\n\n if (save_vid or remote) and iter_frame == test_len:\n out.release()\n\n k = cv2.waitKey(1) & 0xff\n if k == 27: break\n\n ind_concat = ind_concat + 1\n\n if ind_concat % fps_counterPeriod == 0:\n elapsed = time.time() - tic_fps\n fps = fps_counterPeriod / elapsed\n if printFPS_pref:\n print(fps)\n tic_fps = time.time()\n vid_lens.append(iter_frame+1)\n ## Calculate how long calculation took\n elapsed = time.time() - tic_all\n helpers.print_time('video time elapsed:', elapsed)\n print(f'Capture rate: {round(ind_concat / elapsed, 3)} fps')\n\n numFrames_total = ind_concat - 1\n cv2.destroyAllWindows()\n\n displacements = displacements[:, :, ~np.isnan(displacements[0, 0, :])]\n\n return displacements, numFrames_total, vid_lens","def video_times():\n p = parse_cmdline(get_parser=get_parser_times)\n log.setup_main_handler(\n mods=(\"fogtools\", \"typhon\", \"fogpy\", \"sattools\", \"fcitools\", \"satpy\",\n \"pyresample\"),\n level=logging.DEBUG)\n vis.show_video_abi_glm_times(\n start_date=p.start_time,\n end_date=p.end_time,\n img_out=p.filename_pattern_image,\n vid_out=p.filename_pattern_video,\n out_dir=p.outdir,\n sector=p.sector,\n area=p.area)\n print(\"Files written to:\", p.outdir)","def load_video(self):\r\n if self.file_name=='':\r\n Tk().withdraw()\r\n self.file_name = askopenfilename()\r\n cap = cv2.VideoCapture(self.file_name)\r\n self.length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\r\n self.width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\r\n self.heigth = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\n self.fps = int(round(cap.get(cv2.CAP_PROP_FPS)))\r\n \r\n video_buffer = []#np.ndarray(shape=(self.length, self.heigth, self.width, 3), dtype=np.uint8)\r\n for i in tqdm(range(self.length), desc='Loading video from: {}'.format(self.file_name)):\r\n ret, frame = cap.read()\r\n if not ret:\r\n break\r\n video_buffer.append(frame)\r\n #assert(i==self.length-1)\r\n video_buffer = np.array(video_buffer, dtype=np.uint8)\r\n self.video_buffer = video_buffer\r\n cap.release()\r\n self.ix = self.width-1\r\n self.iy = self.heigth-1 \r\n self.roi = dict(x1=self.x, y1=self.y, x2=self.ix, y2=self.iy)\r\n return video_buffer","def track_ball_1(video):\n return track_ball_generic(video)","def update(self, time, frame, face_position = None):\n\t\tself.face_position = face_position or self.face_position","def play(self): \n if(self.isLoaded() == False):\n return\n self.isPlaying = True\n #if the video is end, we restart\n if(self.ret == False):\n self.video.set(1, 0)","def seek(self, seek_value, video_display_name=None):\r\n seek_time = self._parse_time_str(seek_value)\r\n seek_selector = self.get_element_selector(video_display_name, ' .video')\r\n js_code = \"$('{seek_selector}').data('video-player-state').videoPlayer.onSlideSeek({{time: {seek_time}}})\".format(\r\n seek_selector=seek_selector, seek_time=seek_time)\r\n self.browser.execute_script(js_code)","def video_duration(self):\n # type: () -> int\n return self._video_duration","def __init__(self, record_video=True, video_name='video.avi', lower_color=(20, 80, 20), upper_color=(30, 255, 255)):\n self.video = cv2.VideoCapture(0)\n\n # We need to check if camera \n # is opened previously or not \n if not self.video.isOpened():\n print(\"Error reading video file\")\n\n # We need to set resolutions.\n # so, convert them from float to integer. \n self.frame_width = int(self.video.get(3))\n self.frame_height = int(self.video.get(4))\n self.fps = self.video.get(cv2.CAP_PROP_FPS)\n self.size = (self.frame_width, self.frame_height)\n # Below VideoWriter object will create \n # a frame of above defined The output \n # is stored in file with the name stored in self.video_name.\n self.record_video = record_video\n if self.record_video:\n self.video_result = cv2.VideoWriter(video_name, cv2.VideoWriter_fourcc(*'MJPG'), self.fps, self.size)\n\n # define the lower and upper boundaries of the colored\n # ball in the HSV color space\n self.lower_color = lower_color\n self.upper_color = upper_color\n self.x = 0\n self.y = 0\n self.is_ball_visible = False\n self.radius = 10","def __loadVideo(self):\n # Check if movie file exists ...\n #\n if not(os.path.isfile(self.fNameVideo)):\n return stm.StimErrC.videoFileNotFound\n\n try: \n # Load video\n #\n self.video = mpe.VideoFileClip(self.fNameVideo)\n \n except IOError: \n return stm.StimErrC.invalidVideoFormat\n \n # Retrieve video description\n #\n self.dxFr = self.video.size[0]\n self.dyFr = self.video.size[1]\n self.nFr = self.video.duration *self.video.fps\n self.fps = self.video.fps\n ssp.Log.write(\"DEBUG\", \"stim_video: {0}x{1} pixel, {2} frames, {3} fps\"\n .format(self.dxFr, self.dyFr, self.nFr, self.fps))\n \n if self.isTestOnly:\n # Return here if the video was only loaded to test if it is ok\n #\n self.video = None\n return stm.StimErrC.ok\n \n # Load movie frames (note that frames is a generator!)\n #\n self.frames = self.video.iter_frames() \n self.isReady = True\n return stm.StimErrC.ok","def track_ball_2(video):\n return track_ball_generic(video)","def jump_in_video(self, jump_time):\n self.pause_video()\n current_time = self.driver.find_element_by_xpath('//UIAApplication[1]/UIAWindow[1]/UIAStaticText[1]')\n current_time_text = current_time.text\n total_time = self.driver.find_element_by_xpath('//UIAApplication[1]/UIAWindow[1]/UIAStaticText[2]')\n total_time_text = total_time.text\n\n # total_time = minutes*60 + seconds\n total_time_text = float(total_time_text[-5:-3])*60 + float(total_time_text[-2:])\n\n seek_pct = jump_time / total_time_text\n\n seek_bar = self._find_element(class_name='UIASlider')\n seek_bar_size = seek_bar.size['width'] - current_time.size['width'] - total_time.size['width']\n seek_bar_percentage = float(float(str(seek_bar.get_attribute(\"value\"))[:-1]) / 100)\n\n # width * seek_pct is how far over in the bar to tap\n seek_bar_end_x = seek_bar.location['x'] + (seek_bar.size['width'] * seek_pct) - total_time.size['width']\n\n # this is just the vertical middle of the seek bar\n seek_bar_end_y = seek_bar.location['y'] + seek_bar.size['height']/2\n\n seek_bar_start_x = seek_bar.location['x'] + current_time.size['width'] + (seek_bar_size * seek_bar_percentage)\n\n while seek_bar_start_x < seek_bar_end_x:\n self.swipe(seek_bar_start_x, seek_bar_end_y, seek_bar_end_x, seek_bar_end_y, 2000)\n seek_bar_start_x += 15\n print seek_bar_start_x\n\n self.unpause_video()","def track_ball_3(video):\n return track_ball_generic(video)","def update(self):\n print('VIDEO: Video Stream started')\n while True:\n if self.stopped:\n return\n (self.grabbed, self.frame) = self.stream.read()","def analyze_movie(\n video_path, aspect_ratio=0, palette_size=32, frames=-1, step=1, show_frames=False, show_last_frame=False, color_format='hex'\n):\n\n # Parse video frame-by-frame\n vidcap = cv2.VideoCapture(video_path)\n success, image = vidcap.read()\n pil_img = None\n count = 0\n while success and frames == -1 or count < frames:\n if count % step == 0:\n # Convert to PIL image\n img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n pil_img = Image.fromarray(img)\n\n # Crop frame to remove border\n if aspect_ratio != 0:\n width, height = pil_img.size\n left = 0\n right = width\n content_height = 1/aspect_ratio * width\n border = (height - content_height) * 0.5\n top = border\n bottom = border + content_height\n pil_img = pil_img.crop((left, top, right, bottom))\n\n # Get primary color\n main_color = get_primary_color(\n pil_img, palette_size, show_img=show_frames)\n\n if color_format == 'hex':\n main_color = rgbToHex(main_color)\n \n print(main_color)\n\n # Attempt to read next frame\n success, image = vidcap.read()\n count += 1\n\n if show_last_frame:\n pil_img.show()","def test_read_video_in_range_from_memory(self, test_video, config, num_frames):\n full_path, video_tensor = _get_video_tensor(VIDEO_DIR, test_video)\n # video related\n width, height, min_dimension, max_dimension = 0, 0, 0, 0\n video_start_pts, video_end_pts = 0, -1\n video_timebase_num, video_timebase_den = 0, 1\n # audio related\n samples, channels = 0, 0\n audio_start_pts, audio_end_pts = 0, -1\n audio_timebase_num, audio_timebase_den = 0, 1\n # pass 1: decode all frames using new decoder\n tv_result = torch.ops.video_reader.read_video_from_memory(\n video_tensor,\n SEEK_FRAME_MARGIN,\n 0, # getPtsOnly\n 1, # readVideoStream\n width,\n height,\n min_dimension,\n max_dimension,\n video_start_pts,\n video_end_pts,\n video_timebase_num,\n video_timebase_den,\n 1, # readAudioStream\n samples,\n channels,\n audio_start_pts,\n audio_end_pts,\n audio_timebase_num,\n audio_timebase_den,\n )\n (\n vframes,\n vframe_pts,\n vtimebase,\n vfps,\n vduration,\n aframes,\n aframe_pts,\n atimebase,\n asample_rate,\n aduration,\n ) = tv_result\n assert abs(config.video_fps - vfps.item()) < 0.01\n\n start_pts_ind_max = vframe_pts.size(0) - num_frames\n if start_pts_ind_max <= 0:\n return\n # randomly pick start pts\n start_pts_ind = randint(0, start_pts_ind_max)\n end_pts_ind = start_pts_ind + num_frames - 1\n video_start_pts = vframe_pts[start_pts_ind]\n video_end_pts = vframe_pts[end_pts_ind]\n\n video_timebase_num, video_timebase_den = vtimebase[0], vtimebase[1]\n if len(atimebase) > 0:\n # when audio stream is available\n audio_timebase_num, audio_timebase_den = atimebase[0], atimebase[1]\n audio_start_pts = _pts_convert(\n video_start_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(audio_timebase_num.item(), audio_timebase_den.item()),\n math.floor,\n )\n audio_end_pts = _pts_convert(\n video_end_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(audio_timebase_num.item(), audio_timebase_den.item()),\n math.ceil,\n )\n\n # pass 2: decode frames in the randomly generated range\n tv_result = torch.ops.video_reader.read_video_from_memory(\n video_tensor,\n SEEK_FRAME_MARGIN,\n 0, # getPtsOnly\n 1, # readVideoStream\n width,\n height,\n min_dimension,\n max_dimension,\n video_start_pts,\n video_end_pts,\n video_timebase_num,\n video_timebase_den,\n 1, # readAudioStream\n samples,\n channels,\n audio_start_pts,\n audio_end_pts,\n audio_timebase_num,\n audio_timebase_den,\n )\n\n # pass 3: decode frames in range using PyAv\n video_timebase_av, audio_timebase_av = _get_timebase_by_av_module(full_path)\n\n video_start_pts_av = _pts_convert(\n video_start_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(video_timebase_av.numerator, video_timebase_av.denominator),\n math.floor,\n )\n video_end_pts_av = _pts_convert(\n video_end_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(video_timebase_av.numerator, video_timebase_av.denominator),\n math.ceil,\n )\n if audio_timebase_av:\n audio_start_pts = _pts_convert(\n video_start_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(audio_timebase_av.numerator, audio_timebase_av.denominator),\n math.floor,\n )\n audio_end_pts = _pts_convert(\n video_end_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(audio_timebase_av.numerator, audio_timebase_av.denominator),\n math.ceil,\n )\n\n pyav_result = _decode_frames_by_av_module(\n full_path,\n video_start_pts_av,\n video_end_pts_av,\n audio_start_pts,\n audio_end_pts,\n )\n\n assert tv_result[0].size(0) == num_frames\n if pyav_result.vframes.size(0) == num_frames:\n # if PyAv decodes a different number of video frames, skip\n # comparing the decoding results between Torchvision video reader\n # and PyAv\n self.compare_decoding_result(tv_result, pyav_result, config)","def video_thread():\n global last_frame\n # Creating stream capture object\n cap = cv2.VideoCapture('udp://' + drone.tello_ip + ':11111')\n\n while(True):\n _, last_frame = cap.read()\n cap.release()","def init(self, parent):\n super(_VideoComponentEditor, self).init(parent)\n\n self.playTimer = QTimer(self.control)\n self.playTimer.timeout.connect(self.update)\n # self.control.connect(self.playTimer, SIGNAL('timeout()'), self.update)\n print(\"fffffffffff\", self.value.fps)\n if self.value.fps:\n self.playTimer.setInterval(1000 / float(self.value.fps))\n self.playTimer.start()\n\n self.value.on_trait_change(self.stop, \"closed_event\")\n\n self.value.on_trait_change(self._update_fps, \"fps\")\n self.sync_value(\"stop_timer\", \"stop_timer\", mode=\"from\")\n # self._prev_time = time.time()\n self._alive = True\n # QTimer.singleShot(self._get_interval(), lambda: self.update(-1))","def play(self, frame):\n cv2.imshow(\"view\", frame)\n return 0","def run(self):\n i = 0\n t = time.time()\n while True:\n i = i + 1\n ret, frame = self.stream.read()\n if (i == 20):\n self.fps = 20/(time.time() - t)\n t = time.time()\n i = 0\n #If a frame is None need to re-init it: \n # - close a stream;\n # - reopen it;\n # - read frame again\n if frame is None:\n self.stream.release()\n self.stream = cv2.VideoCapture(self.url)\n ret, frame = self.stream.read()\n text = time.strftime('%Y-%m-%d %H:%M:%S')\n if (self.fps > 0):\n text = text + ' FPS: ' + str(round(self.fps))\n self.frame = cv2.putText(frame, text, (10, int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)) - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\n self.frameID = uuid.uuid4()","def target_position(self, time):\n pass","def target_position(self, time):\n pass","def hsVideo_sliderMoved(self):\n\n if self.pj[OBSERVATIONS][self.observationId][TYPE] in [MEDIA]:\n\n if self.playerType == VLC and self.playMode == VLC:\n sliderPos = self.hsVideo.value() / (slider_maximum - 1)\n videoPosition = sliderPos * self.mediaplayer.get_length()\n self.mediaplayer.set_time(int(videoPosition))\n # second video together\n if self.simultaneousMedia:\n # synchronize 2nd player\n self.mediaplayer2.set_time(int(self.mediaplayer.get_time()\n - self.pj[OBSERVATIONS][self.observationId][\n TIME_OFFSET_SECOND_PLAYER] * 1000))\n self.timer_out(scrollSlider=False)\n self.timer_spectro_out()","def showVideo( oVideo, oPathXY=np.array([]) ):\n global oVideo_t, iFrame, oPathXY_t\n fig = plt.figure()\n # prikazi prvi okvir\n iFrame = 0\n oPathXY_t = oPathXY\n oVideo_t = oVideo\n print(oVideo.shape)\n im = plt.imshow(oVideo[...,iFrame], cmap=plt.get_cmap('Greys_r'))\n # definiraj funkcijo za osvezevanje prikaza\n def updatefig(*args):\n global oVideo_t, iFrame, oPathXY_t\n iFrame = ( iFrame + 1 ) % oVideo_t.shape[-1]\n im.set_array( oVideo_t[...,iFrame] ) \n if iFrame < oPathXY.shape[0]:\n plt.plot( oPathXY[iFrame,0], oPathXY[iFrame,1], 'xr' ,markersize=3 ) \n return im,\n # prikazi animacijo poti\n ani = animation.FuncAnimation(fig, updatefig, interval=25, blit=True)\n plt.show()","def testVideoTrackType(self):\n\n trackLine = _buildTrackLine(0, 'video', {'hello': 'goodbye'})\n\n trackID, trackType, trackDict = tools._trackInfo(trackLine)\n\n self.assertEqual(\n 'video',\n trackType,\n )","def play_video(self,\r\n video=None,\r\n window_name=None,\r\n mode=MANUAL_MODE,\r\n mouse_callback_func=None):\r\n if mouse_callback_func==None:\r\n mouse_callback_func = self.dummy_mouse_call_back_func\r\n zoom = 1\r\n if video is None:\r\n video = self.video_buffer\r\n if window_name==None:\r\n window_name = 'Video Player'\r\n cv2.namedWindow(window_name)\r\n cv2.setMouseCallback(window_name, mouse_callback_func)\r\n idx=0\r\n self.current_frame = video[idx]\r\n self.img = self.current_frame.copy()\r\n while True:\r\n cv2.imshow(window_name, self.img)\r\n k = cv2.waitKey(30) & 0xFF\r\n if mode==MANUAL_MODE:\r\n if k==ord('f'):\r\n idx = min(idx+1, len(video)-1)\r\n self.current_frame = cv2.resize(video[idx], None, fx=zoom, fy=zoom, \\\r\n interpolation=cv2.INTER_AREA)\r\n self.img = self.current_frame.copy()\r\n if k==ord('b'):\r\n idx = max(0, idx-1)\r\n self.current_frame = cv2.resize(video[idx], None, fx=zoom, fy=zoom, \\\r\n interpolation=cv2.INTER_AREA)\r\n self.img = self.current_frame.copy()\r\n if k==ord('i'):\r\n zoom *= 1.33\r\n self.current_frame = cv2.resize(video[idx], None, fx=zoom, fy=zoom, \\\r\n interpolation=cv2.INTER_AREA)\r\n self.img = self.current_frame.copy()\r\n if k==ord('o'):\r\n zoom *= 0.75\r\n self.current_frame = cv2.resize(video[idx], None, fx=zoom, fy=zoom, \\\r\n interpolation=cv2.INTER_AREA)\r\n self.img = self.current_frame.copy()\r\n\r\n if k==27:\r\n break\r\n cv2.destroyAllWindows()\r\n self.x /=zoom\r\n self.ix/=zoom\r\n self.y /=zoom\r\n self.iy/=zoom\r\n \r\n self.poly = (np.asarray(self.poly)/zoom).astype(int)","def capture_video(self):\n while self.capturing:\n nparray = self.source.get_frame()\n self.frame_buffer.put(Frame(nparray, self.frame))\n self.frame += 1\n print \"Stopping Capture\"","def play_video(self):\n raise NotImplementedError(\n \"This method needs to be implemented by a derived class\"\n )","def track(self, offset: int):","def detect_with_tracking(self, video_path, k_frames, per_frames = 1, offset = None):\n\n font = cv2.FONT_HERSHEY_SIMPLEX\n \n current_face_id = 0\n current_frame = 0\n \n face_trackers = {}\n confidence = {}\n\n info = []\n \n cap = cv2.VideoCapture(video_path)\n \n if not cap.isOpened():\n raise Exception(\"Video file does not exist or is invalid\")\n\n if offset:\n cap.set(cv2.CAP_PROP_POS_MSEC, offset)\n \n while cap.isOpened() :\n ret, frame = cap.read()\n if ret:\n if cap.get(cv2.CAP_PROP_POS_FRAMES) % per_frames == 0:\n face_ids_to_delete = []\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n for fid in face_trackers.keys():\n tracking_quality = face_trackers[ fid ].update( frame )\n\n if tracking_quality < 7:\n face_ids_to_delete.append( fid )\n\n for fid in face_ids_to_delete:\n face_trackers.pop(fid)\n\n if (cap.get(cv2.CAP_PROP_POS_FRAMES) % k_frames)==0 or cap.get(cv2.CAP_PROP_POS_FRAMES) == 1:\n faces_info = self.detect_faces_from_image(frame,\n desired_width=224, desired_height=224) \n if faces_info:\n for element in faces_info:\n bbox = element[0][0]\n confidence[ current_face_id ] = round(element[5], 3)\n x = bbox.left()\n y = bbox.top()\n width = bbox.width()\n height = bbox.height()\n\n x_center = x + 0.5 * width\n y_center = y + 0.5 * height\n\n matched_fid = None\n\n for fid in face_trackers.keys():\n tracked_position = face_trackers[fid].get_position()\n\n t_x = int(tracked_position.left())\n t_y = int(tracked_position.top())\n t_w = int(tracked_position.width())\n t_h = int(tracked_position.height())\n\n t_x_center = t_x + 0.5 * t_w\n t_y_center = t_y + 0.5 * t_h\n\n if ( ( t_x <= x_center <= (t_x + t_w)) and \n ( t_y <= y_center <= (t_y + t_h)) and \n ( x <= t_x_center <= (x + width)) and \n ( y <= t_y_center <= (y + height))):\n matched_fid = fid\n\n if matched_fid is None:\n\n tracker = dlib.correlation_tracker()\n tracker.start_track(frame,\n dlib.rectangle( x,\n y,\n x+width,\n y+height))\n\n face_trackers[ current_face_id ] = tracker\n current_face_id += 1\n\n for fid in face_trackers.keys():\n t_x, t_y, t_w, t_h, label, decision_value = self._process_tracked_face(fid, face_trackers, frame)\n t_bbox = dlib.rectangle(t_x, t_y, t_x+t_w, t_y+t_h)\n info.append([\n cap.get(cv2.CAP_PROP_POS_FRAMES), fid, t_bbox, (t_w, t_h), label,\n decision_value, confidence[fid]\n ])\n\n\n else: \n break\n cap.release()\n track_res = pd.DataFrame.from_records(info, columns = ['frame', 'faceid', 'bb', 'size','label', 'decision', 'conf'])\n info = _smooth_labels(track_res)\n \n return info","def __init__(self, video_w, video_h, video_fps, tracking_result):\n self.font = cv2.FONT_HERSHEY_SIMPLEX\n self.fontScale = 1\n self.thickness = 2\n self.thicknessUpdate = 3\n self.color = (238, 221, 192) # A surfrider color\n self.icons = get_icons()\n self.classes_to_icons = {'bottles':self.icons[0], 'fragments':self.icons[1], 'others':self.icons[2]}\n self.video_w = video_w\n self.video_h = video_h\n self.video_fps = video_fps\n self.tracking_result = tracking_result\n self.detection_image_size = (1024, 768)\n self.frames_to_boxes_dict = None\n self.frames_to_update_hud = None","def process_video(data_info, name, mode, is_training=True):\r\n data = Action_Dataset(name, mode, [data_info])\r\n if is_training:\r\n clip_seq, label_seq = data.next_batch(1, _CLIP_SIZE)\r\n else:\r\n clip_seq, label_seq = data.next_batch(\r\n 1, _EACH_VIDEO_TEST_SIZE+1, shuffle=False, data_augment=False)\r\n clip_seq = 2*(clip_seq/255) - 1\r\n clip_seq = np.array(clip_seq, dtype='float32')\r\n return clip_seq, label_seq","def _track_finished(self, *_args):\n if not self.loop:\n self.stop()\n else:\n self.seek(0.)\n self.player.play()","def run(self):\n for index, playlist_line in enumerate(self.playlist):\n gv.logger.info(\"New video\")\n self.index = index\n self.video_command = playlist_line.strip(\" \")\n gv.logger.info(self.video_command)\n if gv.api_dm.probe_status == \"killed\":\n gv.logger.info(\"Exiting playlist\")\n break\n self.launch_video_process()\n gv.logger.info(\"Finished video\")\n self.finish_playlist()","def stabilization(videopath,\n smoothing_radius=50,\n fixed_area=[0, -1, 0, -1],\n stab_points=200):\n\n # Read original video\n # Extract directory and videoname for saving purposes\n capture = cv2.VideoCapture(videopath)\n directory, videoname = os.path.split(videopath)\n videoname = os.path.splitext(videoname)[0]\n\n # Get number of frames\n # Get width and height of video stream\n # Get frames per second (fps)\n n_frames = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))\n w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))\n h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\n fps = capture.get(cv2.CAP_PROP_FPS)\n\n # Set up output video\n out = cv2.VideoWriter(os.path.join(directory, str(videoname)+'_stabilized.mp4'),\n -1,\n fps,\n (w, h))\n\n # Read first frame\n # Convert frame to grayscale and extract part of image\n _, prev = capture.read()\n xmin, xmax, ymin, ymax = fixed_area\n prev_gray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)[ymin:ymax, xmin:xmax]\n\n # Pre-define transformation-store array\n transforms = np.zeros((n_frames-1, 3), np.float32)\n\n # Filling in transformation array per frameset\n with tqdm(total=2*(n_frames-2), ncols=50) as pbar:\n for i in range(n_frames-2):\n # Detect feature points in previous frame\n prev_pts = cv2.goodFeaturesToTrack(prev_gray,\n maxCorners=stab_points,\n qualityLevel=0.1,\n minDistance=100,\n blockSize=10)\n # Read next frame\n # If not success: break loop\n success, curr = capture.read()\n if not success:\n break\n\n # Convert to grayscale and extract part of image\n # Calculate optical flow (i.e. track feature points)\n # Sanity check\n curr_gray = cv2.cvtColor(curr, cv2.COLOR_BGR2GRAY)[ymin:ymax,\n xmin:xmax]\n curr_pts, status, err = cv2.calcOpticalFlowPyrLK(prev_gray,\n curr_gray,\n prev_pts,\n None)\n assert prev_pts.shape == curr_pts.shape\n\n # Filter only valid points\n idx = np.where(status == 1)[0]\n prev_pts = prev_pts[idx]\n curr_pts = curr_pts[idx]\n\n # Find transformation matrix\n m = cv2.estimateAffinePartial2D(prev_pts, curr_pts)[0]\n\n # Extract translation\n # Extract rotation angle\n dx = m[0, 2]\n dy = m[1, 2]\n da = np.arctan2(m[1, 0], m[0, 0])\n\n # Store transformation\n transforms[i] = [dx, dy, da]\n\n # Move to next frame\n prev_gray = curr_gray\n\n pbar.update(1)\n\n # Compute trajectory using cumulative sum of transformations\n # Create variable to store smoothed trajectory\n # Calculate diference in smoothed_trajectory and trajectory\n trajectory = np.cumsum(transforms, axis=0)\n smoothed_trajectory = smooth(trajectory, smoothing_radius)\n difference = smoothed_trajectory - trajectory\n\n # Calculate newer transformation array\n transform_smooth = transforms + difference\n\n # Reset stream to first frame\n capture.set(cv2.CAP_PROP_POS_FRAMES, 0)\n\n # Apply transformations to video\n for i in range(n_frames-2):\n # Read next frame\n success, frame = capture.read()\n if not success:\n break\n\n # Extract transformations from the new transformation array\n dx = transform_smooth[i, 0]\n dy = transform_smooth[i, 1]\n da = transform_smooth[i, 2]\n\n # Reconstruct transformation matrix accordingly to new values\n m = np.zeros((2, 3), np.float32)\n m[0, 0] = np.cos(da)\n m[0, 1] = -np.sin(da)\n m[1, 0] = np.sin(da)\n m[1, 1] = np.cos(da)\n m[0, 2] = dx\n m[1, 2] = dy\n\n # Apply affine wrapping to the given frame\n # Fix border artifacts\n frame_stabilized = cv2.warpAffine(frame, m, (w, h))\n frame_stabilized = fixBorder(frame_stabilized)\n\n # Save new frame\n out.write(frame_stabilized)\n\n pbar.update(1)\n\n # Release original and stabilized video\n capture.release()\n out.release()","def process_Video(cap, out):\r\n count = 0\r\n\r\n while(cap.isOpened()):\r\n ret, frame = cap.read()\r\n\r\n if(ret == True):\r\n if(count % __speed__ == 0):\r\n out.write(frame)\r\n\r\n if(cv.waitKey(1) & 0xFF == ord('q')):\r\n break\r\n\r\n count += 1\r\n else:\r\n break","def update(self,startTime):\r\n if self.updateLock.locked():\r\n return\r\n with self.updateLock:\r\n # Calculate elapsed time\r\n now = time.time()\r\n elapsedTime = now-startTime+self.app.dataOffset\r\n if self.app.videoPlayer.state == VideoPlayer.State.PLAYING:\r\n self.progress = elapsedTime\r\n elif self.app.videoPlayer.state == VideoPlayer.State.PAUSED or self.app.videoPlayer.state == VideoPlayer.State.STOPPED:\r\n self.progress = self.app.videoPlayer.progress+self.app.dataOffset\r\n\r\n scalex,_ = self.getScale()\r\n\r\n # Move the scrubber to the right place\r\n timespan = 1/self.samplerate * (scalex[1]-scalex[0])# Time represented by canvas width, in seconds\r\n scalex_secs = [scalex[0]/self.samplerate,scalex[1]/self.samplerate]# Start and end of plot, in seconds\r\n x = ((elapsedTime-scalex_secs[0])/(scalex_secs[1]-scalex_secs[0]))*self.w\r\n self.drawScrubber()\r\n\r\n self.scaleAroundX(x)","def parse():\n all_players = list(FACE_IMAGE_LOCATIONS.keys())\n face_encodings = VideoParser.__load_faces_encodings(all_players)\n player_occurrences = VideoParser.__get_player_occurrences(all_players, face_encodings)\n VideoParser.__save_parsed_video(player_occurrences)","def detect_markers_in_video(source_path, source_type=\"auto\", decoder_pipeline=None, pipeline_factory=None,\n tag_pixel_diameter=30.0, timestamps=None,\n start_timestamp=None, fps=3.0, cam_id=0,\n verbose=False, n_frames=None, progress=tqdm.auto.tqdm,\n calculate_confidences=True, confidence_filter=None,\n use_parallel_jobs=False, clahe=False):\n if source_type == \"auto\":\n if isinstance(source_path, str):\n if source_path.endswith(\"jpg\") or source_path.endswith(\"png\"):\n source_type = \"image\"\n else:\n source_type = \"video\"\n elif isinstance(source_path, list):\n source_type = \"image\"\n calculate_confidences = calculate_confidences or confidence_filter is not None\n scale = 30.0 / tag_pixel_diameter\n if decoder_pipeline is None and pipeline_factory is None:\n decoder_pipeline = get_default_pipeline()\n\n if timestamps is None:\n def generate_timestamps(start_from):\n def gen():\n i = 0.0\n while True:\n yield start_from + i * 1.0 / fps\n i += 1.0\n yield from gen()\n timestamps = generate_timestamps(start_timestamp or 0.0)\n else:\n timestamps = list(timestamps) # Evaluate generators.\n ts = timestamps[0]\n if not isinstance(ts, (np.floating, float)):\n if not ts.tzinfo or int(ts.utcoffset().total_seconds()) != 0:\n raise ValueError(\"timestamps argument must be iterable of floats or UTC datetime objects with timezones.\")\n timestamps = [t.timestamp() for t in timestamps]\n import skimage.transform\n import pipeline as bb_pipeline\n \n interrupted = False\n def interruptable_frame_generator(gen):\n nonlocal interrupted\n for g in gen:\n yield g\n if interrupted:\n print(\"Stopping early...\")\n break\n \n def preprocess_image(im):\n if clahe:\n im = skimage.exposure.equalize_adapthist(im, kernel_size=3 * tag_pixel_diameter)\n im = (skimage.transform.rescale(im, scale, order=1, multichannel=False, anti_aliasing=False, mode='constant') * 255).astype(np.uint8)\n return im\n\n def get_frames_from_images():\n import skimage.io\n for idx, (path, ts) in enumerate(zip(interruptable_frame_generator(source_path), timestamps)):\n if type(path) is str:\n im = skimage.io.imread(path, as_gray=True) \n else:\n im = path\n im = preprocess_image(im)\n yield idx, ts, im\n\n if n_frames is not None and idx >= n_frames - 1:\n break\n\n def get_frames_from_video():\n frames_generator = bb_pipeline.io.raw_frames_generator(source_path, format=None)\n \n for idx, (im, ts) in enumerate(zip(interruptable_frame_generator(frames_generator), timestamps)):\n assert im is not None\n # Skip broken videos.\n if im.shape[0] <= 0:\n print(\"Warning: Could not read frame {} of file {}. The video is corrupt.\".format(idx, source_path))\n break\n # Skip broken frames because clahe would fail on constant input.\n if im.min() == im.max():\n print(\"Warning: Frame {} of file {} is empty.\".format(idx, source_path))\n continue\n \n assert im.shape[0] > 0\n assert im.shape[1] > 0\n\n im = preprocess_image(im)\n\n yield idx, ts, im\n \n if n_frames is not None and idx >= n_frames - 1:\n break\n \n def get_detections_from_frame(idx, ts, im, thread_context=None, **kwargs):\n nonlocal decoder_pipeline\n if decoder_pipeline is not None:\n pipeline_results = decoder_pipeline([im])\n else:\n if \"pipeline\" not in thread_context:\n thread_context[\"pipeline\"] = pipeline_factory()\n pipeline_results = thread_context[\"pipeline\"]([im])\n \n #confident_ids = [r for c, r in zip(confidences, decoded_ids) if c >= threshold]\n #decimal_ids = set([ids.BeesbookID.from_bb_binary(i).as_ferwar() for i in confident_ids])\n\n if verbose:\n import pipeline.objects\n from pipeline.stages.visualization import ResultCrownVisualizer\n import matplotlib.pyplot as plt\n crowns = pipeline_results[pipeline.objects.CrownOverlay]\n frame = ResultCrownVisualizer.add_overlay(im.astype(np.float64) / 255, crowns)\n fig, ax = plt.subplots(figsize=(20, 10))\n plt.imshow(frame)\n plt.axis(\"off\")\n plt.show()\n \n frame_id = bb_pipeline.io.unique_id()\n required_results = pipeline_results[PipelineResult] \n n_detections = required_results.orientations.shape[0]\n decoded_ids = [list(r) for r in list(required_results.ids)]\n\n if n_detections > 0:\n frame_data = {\n \"localizerSaliency\": required_results.tag_saliencies.flatten(),\n \"beeID\": decoded_ids,\n \"xpos\": required_results.tag_positions[:, 1] / scale,\n \"ypos\": required_results.tag_positions[:, 0] / scale,\n \"camID\": [cam_id] * n_detections, \n \"zrotation\": required_results.orientations[:, 0],\n \"timestamp\": [ts] * n_detections,\n \"frameIdx\": [idx] * n_detections,\n \"frameId\": frame_id,\n \"detection_index\": range(n_detections),\n \"detection_type\": \"TaggedBee\"\n }\n \n frame_data = pd.DataFrame(frame_data)\n\n if calculate_confidences:\n confidences = np.array([np.product(np.abs(0.5 - np.array(r)) * 2) for r in decoded_ids])\n frame_data[\"confidence\"] = confidences\n if confidence_filter is not None:\n frame_data = frame_data[frame_data.confidence >= confidence_filter]\n\n else:\n frame_data = None\n\n n_bees = required_results.bee_positions.shape[0]\n\n if n_bees > 0:\n bee_data = {\n \"localizerSaliency\": required_results.bee_saliencies.flatten(),\n \"beeID\": [np.nan] * n_bees,\n \"xpos\": required_results.bee_positions[:, 1] / scale,\n \"ypos\": required_results.bee_positions[:, 0] / scale,\n \"camID\": [cam_id] * n_bees, \n \"zrotation\": [np.nan] * n_bees,\n \"timestamp\": [ts] * n_bees,\n \"frameIdx\": [idx] * n_bees,\n \"frameId\": frame_id,\n \"detection_index\": range(n_bees),\n \"detection_type\": required_results.bee_types\n }\n\n if calculate_confidences:\n bee_data[\"confidence\"] = [np.nan] * n_bees\n\n bee_data = pd.DataFrame(bee_data)\n\n if frame_data is not None:\n frame_data = pd.concat((frame_data, bee_data))\n else:\n frame_data = bee_data\n\n return idx, frame_id, ts, frame_data\n \n \n progress_bar = None\n if progress is not None:\n progress_bar = progress(total=n_frames)\n \n def save_frame_data(idx, frame_id, ts, frame_data, **kwargs):\n nonlocal frame_info\n nonlocal video_dataframe\n frame_info.append((idx, frame_id, ts))\n if frame_data is not None:\n video_dataframe.append(frame_data)\n if progress is not None:\n progress_bar.update()\n \n source = get_frames_from_video\n if source_type == \"image\":\n source = get_frames_from_images\n if isinstance(source_path, str):\n source_path = [source_path]\n \n if use_parallel_jobs:\n from ..utils.processing import ParallelPipeline\n\n thread_context_factory = None\n n_pipeline_jobs = 1\n if not decoder_pipeline:\n # The thread context is used to provide each thread with a unique pipeline object.\n class Ctx():\n def __enter__(self):\n return dict()\n def __exit__(self, *args):\n pass\n \n n_pipeline_jobs = 4\n thread_context_factory = Ctx\n\n jobs = ParallelPipeline([source, get_detections_from_frame, save_frame_data],\n n_thread_map={0: 1, 1: n_pipeline_jobs},\n thread_context_factory=thread_context_factory)\n else:\n def sequential_jobs():\n for im in source():\n if im is not None:\n detections = get_detections_from_frame(*im)\n save_frame_data(*detections)\n jobs = sequential_jobs\n\n frame_info = []\n video_dataframe = []\n try:\n jobs()\n except KeyboardInterrupt:\n interrupted = True\n frame_info = list(sorted(frame_info))\n if len(video_dataframe) > 0:\n video_dataframe = pd.concat(video_dataframe)\n video_dataframe.sort_values(\"frameIdx\", inplace=True)\n # Enfore frame ID datatype to be unsigned which may have gotten lost when concatenating the data frames.\n video_dataframe.frameId = video_dataframe.frameId.astype(np.uint64)\n else:\n video_dataframe = None\n \n return frame_info, video_dataframe","def getPosition(self, time: float, view: Optional[Str] = ...) -> CVec3:\n ...","def run_tracker(p):\n # load model\n net = torch.load(os.path.join(p.net_base_path, p.net))\n net = net.to(device)\n\n # evaluation mode\n net.eval()\n\n # load sequence\n img_list, target_position, target_size = load_sequence(p.seq_base_path, p.video)\n\n # first frame\n img_uint8 = cv2.imread(img_list[0])\n img_uint8 = cv2.cvtColor(img_uint8, cv2.COLOR_BGR2RGB)\n img_double = np.double(img_uint8) # uint8 to float\n\n # compute avg for padding\n avg_chans = np.mean(img_double, axis=(0, 1))\n\n wc_z = target_size[1] + p.context_amount * sum(target_size)\n hc_z = target_size[0] + p.context_amount * sum(target_size)\n s_z = np.sqrt(wc_z * hc_z)\n scale_z = p.examplar_size / s_z\n\n # crop examplar z in the first frame\n z_crop = get_subwindow_tracking(img_double, target_position, p.examplar_size, round(s_z), avg_chans)\n\n z_crop = np.uint8(z_crop) # you need to convert it to uint8\n # convert image to tensor\n z_crop_tensor = 255.0 * F.to_tensor(z_crop).unsqueeze(0)\n\n d_search = (p.instance_size - p.examplar_size) / 2\n pad = d_search / scale_z\n s_x = s_z + 2 * pad\n # arbitrary scale saturation\n min_s_x = p.scale_min * s_x\n max_s_x = p.scale_max * s_x\n\n # generate cosine window\n if p.windowing == 'cosine':\n window = np.outer(np.hanning(p.score_size * p.response_UP), np.hanning(p.score_size * p.response_UP))\n elif p.windowing == 'uniform':\n window = np.ones((p.score_size * p.response_UP, p.score_size * p.response_UP))\n window = window / sum(sum(window))\n\n # pyramid scale search\n scales = p.scale_step**np.linspace(-np.ceil(p.num_scale/2), np.ceil(p.num_scale/2), p.num_scale)\n\n # extract feature for examplar z\n z_features = net.feat_extraction(Variable(z_crop_tensor).to(device))\n z_features = z_features.repeat(p.num_scale, 1, 1, 1)\n\n # do tracking\n bboxes = np.zeros((len(img_list), 4), dtype=np.double) # save tracking result\n start_time = datetime.datetime.now()\n for i in range(0, len(img_list)):\n if i > 0:\n # do detection\n # currently, we only consider RGB images for tracking\n img_uint8 = cv2.imread(img_list[i])\n img_uint8 = cv2.cvtColor(img_uint8, cv2.COLOR_BGR2RGB)\n img_double = np.double(img_uint8) # uint8 to float\n\n scaled_instance = s_x * scales\n scaled_target = np.zeros((2, scales.size), dtype = np.double)\n scaled_target[0, :] = target_size[0] * scales\n scaled_target[1, :] = target_size[1] * scales\n\n # extract scaled crops for search region x at previous target position\n x_crops = make_scale_pyramid(img_double, target_position, scaled_instance, p.instance_size, avg_chans, p)\n\n # get features of search regions\n x_crops_tensor = torch.FloatTensor(x_crops.shape[3], x_crops.shape[2], x_crops.shape[1], x_crops.shape[0])\n # response_map = SiameseNet.get_response_map(z_features, x_crops)\n for k in range(x_crops.shape[3]):\n tmp_x_crop = x_crops[:, :, :, k]\n tmp_x_crop = np.uint8(tmp_x_crop)\n # numpy array to tensor\n x_crops_tensor[k, :, :, :] = 255.0 * F.to_tensor(tmp_x_crop).unsqueeze(0)\n\n # get features of search regions\n x_features = net.feat_extraction(Variable(x_crops_tensor).to(device))\n\n # evaluate the offline-trained network for exemplar x features\n target_position, new_scale = tracker_eval(net, round(s_x), z_features, x_features, target_position, window, p)\n\n # scale damping and saturation\n s_x = max(min_s_x, min(max_s_x, (1 - p.scale_LR) * s_x + p.scale_LR * scaled_instance[int(new_scale)]))\n target_size = (1 - p.scale_LR) * target_size + p.scale_LR * np.array([scaled_target[0, int(new_scale)], scaled_target[1, int(new_scale)]])\n\n rect_position = np.array([target_position[1]-target_size[1]/2, target_position[0]-target_size[0]/2, target_size[1], target_size[0]])\n\n if p.visualization:\n visualize_tracking_result(img_uint8, rect_position, 1)\n\n # output bbox in the original frame coordinates\n o_target_position = target_position\n o_target_size = target_size\n bboxes[i,:] = np.array([o_target_position[1]-o_target_size[1]/2, o_target_position[0]-o_target_size[0]/2, o_target_size[1], o_target_size[0]])\n\n end_time = datetime.datetime.now()\n fps = len(img_list)/max(1.0, (end_time-start_time).seconds)\n\n return bboxes, fps","def get_video_position_in_show(self, video_id, **kwargs):\n kwargs['video_id'] = video_id\n return self.get('video_position_in_show', **kwargs)","def display_video_stream(self):\n _, frame = self.capture.read()\n\n if frame is None:\n return\n\n # hand rectangle dimensions\n x0 = self.video_size.height() - int(self.handrect_y + self.handrect_height / 2.)\n x1 = self.video_size.height() - int(self.handrect_y - self.handrect_height / 2.)\n y0 = int(self.handrect_x - self.handrect_width / 2.)\n y1 = int(self.handrect_x + self.handrect_width / 2.)\n cv2_p0 = (y0, x0)\n cv2_p1 = (y1, x1)\n self.video_size.width()\n\n frame, count_defects = find_gesture(frame, hand_p0=cv2_p1, hand_p1=cv2_p0,\n invert=self.checkBox_invert.isChecked())\n\n if count_defects == Gestures.ONE.value:\n cv2.putText(frame, \"closed fist\", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)\n elif count_defects == 2:\n cv2.putText(frame, \"Two fingers\", (5, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, 2)\n\n elif count_defects == 3:\n cv2.putText(frame, \"three fingers\", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)\n elif count_defects == 4:\n cv2.putText(frame, \"four fingers\", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)\n else:\n cv2.putText(frame, \"five fingers\", (50, 50), \\\n cv2.FONT_HERSHEY_SIMPLEX, 2, 2)\n\n if count_defects != self.current_gesture or count_defects == Gestures.ONE.value:\n # update progress time\n self.gesture_time = time()\n self.progressBar.setValue(0)\n else:\n self.current_gesture = count_defects\n\n dt = time() - self.gesture_time\n\n if dt < self.maxdt:\n # update progress bar\n self.progressBar.setValue(int(100*dt/self.maxdt))\n else:\n # trigger action\n cbox = self.comboboxes[max(min(4, count_defects-2), 0)]\n func_idx = cbox.currentIndex()\n callfunc = cbox.itemData(func_idx)\n callfunc()\n self.labelStatus.setText(\"{}, {}\".format(count_defects, cbox.itemText(func_idx)))\n\n # reset progress time\n self.gesture_time = time()\n\n self.current_gesture = count_defects\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n image = QImage(frame, frame.shape[1], frame.shape[0],\n frame.strides[0], QImage.Format_RGB888)\n self.image_label.setPixmap(QPixmap.fromImage(image))","def read(self, ifDrawAxis=None, ifDrawSquare=None):\n if(self.isLoaded() == False):\n return\n self.ret, frame = self.video.read()\n if (self.ret == True):\n \n # Draw frame\n self.currentFrame = frame\n self.currentFrameNumber = int(self.video.get(cv2.CAP_PROP_POS_FRAMES))\n \n else: # If the video is end\n self.isPlaying = False\n \n return self.frame(ifDrawAxis, ifDrawSquare)","def change_track_play_position(value):\n\n global track_last_slided_pos\n\n cancel_update_play_time_loop()\n track_last_slided_pos = value\n\n # If the track is playing, send slider value to update_play_time()\n if track_status.get() == \"(Playing)\":\n update_play_time(value)\n # else just update track position Label\n else:\n # Convert to format minutes:seconds\n current_time_formated = strftime(\n '%M:%S', gmtime(float(track_pos.get())))\n track_pos_label.configure(text=current_time_formated)","def videotime_end(self):\n # type: () -> int\n return self._videotime_end","def __handle(self):\n\n if not self.entrada.closed:\n # Control por tiempo\n #self.entrada.write(\"%s 0\\n\" % (\"get_time_length\"))\n #self.entrada.flush()\n #duracion = self.salida.readline()\n #if \"ANS_LENGTH\" in duracion:\n # duracion = float(duracion.split(\"=\")[1]) # en Segundos\n # print \"dur\", duracion\n\n #self.entrada.write(\"%s 0\\n\" % (\"get_time_pos\"))\n #self.entrada.flush()\n #posicion = self.salida.readline()\n #if \"ANS_TIME_POSITION\" in posicion:\n # posicion = float(posicion.split(\"=\")[1]) # en Segundos\n # print \"pos\", posicion\n\n self.entrada.write(\"%s 0\\n\" % (\"get_property percent_pos\"))\n self.entrada.flush()\n linea = self.salida.readline()\n\n if linea:\n if \"ANS_percent_pos\" in linea:\n # Información sobre el porcentaje Reproducido\n # hasta el momento. Ejemplo:\n # ANS_percent_pos=0\n\n self.__get_progress_in_mplayer(linea)\n self.get_volumen()\n\n elif \"Video: no video\" in linea or \\\n \"Audio only file format detected\" in linea:\n # Cuando no hay video en la fuente. Ejemplo:\n # Audio only file format detected.\n # Video: no video\n\n if self.video_in_stream:\n self.video_in_stream = False\n self.emit(\"video\", False)\n\n elif \"Movie-Aspect\" in linea:\n # Información sobre el aspecto del video. Ejemplo:\n # Movie-Aspect is 1.78:1 - prescaling to\n # correct movie aspect.\n\n if not self.video_in_stream:\n self.video_in_stream = True\n self.emit(\"video\", True)\n\n elif \"Cache\" in linea:\n # Información Sobre Carga de caché. Ejemplo:\n # Cache fill: 6.25% (65536 bytes)\n # self.get_progress_cache_in_mplayer(linea)\n\n pass\n\n elif \"Starting playback\" in linea:\n # Cuando comienza la Reproducción. Ejemplo:\n # Starting playback...\n\n self.estado = \"playing\"\n self.emit(\"estado\", \"playing\")\n\n elif \"AO:\" in linea:\n # Información Sobre Audio en la pista. Ejemplo:\n # AO: [pulse] 44100Hz 2ch s16le (2 bytes per sample)\n\n pass\n\n elif \"VO:\" in linea:\n # Información sobre Video en la pista. Ejemplo:\n # VO: [xv] 428x240 => 428x240 Planar YV12\n\n pass\n\n elif \"Resolving\" in linea:\n # Información sobre Resolución de Streamings. Ejemplo:\n # Resolving radio1.oceanofm.com for AF_INET6...\n\n pass\n\n elif \"Connecting\" in linea:\n # Información sobre Conexión a un Streaming. Ejemplo:\n # Connecting to server\n # main-office.rautemusik.fm[87.230.101.9]: 80...\n\n pass\n\n elif \"Name\" in linea:\n # El nombre de una streaming de Radio. Ejemplo:\n # Name : #MUSIK.MAIN - WWW.RAUTEMUSIK.FM - 24H\n # TOP 40 POP HITS 80S 90S DANCE HOUSE ROCK RNB AND MORE!\n\n pass\n\n elif \"Playing\" in linea:\n # La Pista que se está reproduciendo. Ejemplo:\n # Playing /media/4E432D364BC64012/E - Videos/Tylor\n # swift/Back to December-Taylor Swift Lyrics.\n\n pass\n\n elif \"Genre\" in linea or \\\n \"Website\" in linea or \"Bitrate\" in linea:\n # Información Sobre un Streaming de Radio. Ejemplo:\n # Genre : Pop Rock Top 40 RnB 80s\n # Website: http://www.RauteMusik.FM/\n # Bitrate: 128kbit/s\n\n pass\n\n elif \"Opening\" in linea or \\\n \"AUDIO\" in linea or \"Selected\" in linea:\n # Información sobre Codecs Utilizados. Ejemplo:\n # Opening video decoder: [ffmpeg] FFmpeg's\n # libavcodec codec family\n # Selected video codec: [ffh264] vfm: ffmpeg (FFmpeg H.264)\n # Opening audio decoder: [ffmpeg]\n # FFmpeg/libavcodec audio decoders\n # AUDIO: 44100 Hz, 2 ch, s16le, 98.6 kbit/6.99%\n # (ratio: 12323->176400)\n # Selected audio codec: [ffaac] afm: ffmpeg\n # (FFmpeg AAC (MPEG-2/MPEG-4 Audio))\n\n pass\n\n else:\n # Información Diversa. Ejemplo:\"\n # Failed to open /dev/rtc: Permission denied\n # (it should be readable by the us\"\n # Unsupported PixelFormat 61\"\n # Unsupported PixelFormat 53\"\n # Unsupported PixelFormat 81\"\n # eo (h264), -vid 0\"\n # [lavf] stream 1: audio (aac), -aid 0\"\n # VIDEO: [H264] 640x480 0bpp 30.000 fps\n # 218.3 kbps (26.6 kbyte/s)\"\n # Clip info:\"\n # starttime: 0\"\n # totalduration: 226\"\n # totaldatarate: 338\"\n # bytelength: 9570679\"\n # canseekontime: true\"\n # sourcedata: BC8280065HH1341966475963833\"\n # purl: \"\n # pmsg: \"\n # Load subtitles in /media/4E432D364BC64012/E -\n # Videos/Tylor swift/\"\n\n # libavcodec version 53.35.0 (external)\"\n # Mismatching header version 53.32.2\"\n\n # A: 0.0 V: 0.0 A-V: 0.014 ct: 0.000 0/\n # 0 ??% ??% ??,?% 0 0 90%\"\n # A: 123.2 V: 123.2 A-V: -0.000 ct: 0.033 0/\n # 0 2% 1% 0.4% 0 0 50%\"\n\n # Cuando no se puede Acceder a un Streaming. Ejemplo:\"\n # Failed to get value of property 'percent_pos'.\"\n # Failed to get value of property\n # percent_ANS_ERFailed to get value of\"\n # prANS_ERROR=PROPERTY_UNAFailed\n # toANS_ERROR=PROPERTY_UNAVAILABLE\"\n # ANS_ERROR=PROPERTY_UNAVAILABLE\"\n\n pass\n\n return True","def get_frame(self, camera: int = 0) -> Tuple[float, np.ndarray]:\n result = self.video.read()\n if result[0]:\n return result\n else: # If we reach the end of the video, go back to the beginning.\n self.video.set(cv2.CAP_PROP_POS_FRAMES, 0)\n return self.video.read()","def video_config(FILENAME):\n\n\tcap = cv2.VideoCapture(FILENAME)\n\twhile not cap.isOpened():\n\t cap = cv2.VideoCapture(FILENAME)\n\t cv2.waitKey(1000)\n\t print \"Wait for the header\"\n\tpos_frame = cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES)\n\t\n\treturn cap, pos_frame","def AnnotateVideo(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\n raise NotImplementedError()"],"string":"[\n \"def seek(self,event):\\r\\n if self.app.controlLock.locked():\\r\\n return\\r\\n self.app.controlLock.acquire()\\r\\n x = event.x\\r\\n scalex,_ = self.getScale()\\r\\n scalex_secs = [scalex[0]/self.samplerate,scalex[1]/self.samplerate]# Get x scale in seconds\\r\\n seekTo = (x/self.w) * (scalex_secs[1]-scalex_secs[0]) + scalex_secs[0]# Transform pixel coordinates to represented time\\r\\n self.app.videoPlayer.pause()\\r\\n self.app.videoPlayer.seek(seekTo-self.app.dataOffset)\\r\\n self.app.videoPlayer.pause()# Restart audio to sync\\r\\n self.update(self.app.videoPlayer.startTimestamp)\\r\\n self.draw()\\r\\n self.app.videoPlayer.play()\\r\\n self.app.controlLock.release()\",\n \"def seek(self,t):\\r\\n if (t > self.vid_len) or (t < 0):# If seeking to beyond end of video\\r\\n frame = ImageTk.PhotoImage(Image.fromarray(np.array([[0]*self.w]*self.h)))# Set frame to a black image of same proportions\\r\\n self.player.config(image=frame)\\r\\n self.player.image = frame\\r\\n self.root.update_idletasks()\\r\\n self.startTimestamp = time.time() - t\\r\\n if self.hasAudio:\\r\\n mixer.music.play(start=t,loops=0)\\r\\n self.updateDataplayers()\\r\\n # If already playing, skip calling the stream method, or if no video data loaded\\r\\n if self.isPlaying() or self.isEmpty():\\r\\n return\\r\\n self.state = VideoPlayer.State.PLAYING\\r\\n self.root.after(0,self.stream)\",\n \"def track(self, label, debug=False):\\n prev_speed = 0\\n count_frames_speed_0 = 0\\n mv = MoveRobot()\\n # loop over frames from the video stream\\n while True:\\n # grab the current frame, then handle if we are using a\\n # VideoStream or VideoCapture object\\n \\\"\\\"\\\"\\n frame = self.vs.read()\\n frame = frame[1] if not self.webCam else frame\\n \\\"\\\"\\\"\\n if count_frames_speed_0 >= 10:\\n break\\n frame, boundary_lines = self.video_stream.read()\\n # check to see if we have reached the end of the stream\\n if frame is None:\\n break\\n # resize the frame (so we can process it faster) and grab the\\n # frame dimensions\\n frame = imutils.resize(frame, width=500) if not self.webCam else frame\\n (H, W) = frame.shape[:2]\\n\\n # Start tracking the given label\\n if self.initBB is None and label is not None:\\n self.initBB = label\\n # start OpenCV object tracker using the supplied bounding box\\n # coordinates, then start the FPS throughput estimator as well\\n self.tracker.init(frame, self.initBB)\\n self.fps = FPS().start()\\n label = None\\n\\n # check to see if we are currently tracking an object\\n if self.initBB is not None:\\n # grab the new bounding box coordinates of the object\\n (success, box) = self.tracker.update(frame)\\n # check to see if the tracking was a success\\n if success:\\n (x, y, w, h) = [int(v) for v in box]\\n\\n # Get the spine boundary lines\\n label_rectangle = Rectangle(x, y, w, h)\\n left_spine_bound, right_spine_bound = findSpineBoundaries(label_rectangle, boundary_lines)\\n\\n # Plot the lines\\n if debug and left_spine_bound:\\n left_spine_bound.plotOnImage(frame, thickness=2)\\n if debug and right_spine_bound:\\n right_spine_bound.plotOnImage(frame, thickness=2)\\n\\n distance_to_middle = 0\\n\\n # If both spine bounds are in frame and found\\n if (right_spine_bound is not None) and (left_spine_bound is not None):\\n\\n # Adjust the position of the robot\\n # Find a point on the spine boundaries which is in the middle of the frame height\\n left_spine_coordinate = left_spine_bound.calculateXgivenY(H/2)\\n right_spine_coordinate = right_spine_bound.calculateXgivenY(H/2)\\n\\n # Find a point on the middle of the spine\\n spine_midpoint = left_spine_coordinate + (right_spine_coordinate - left_spine_coordinate) / 2\\n\\n # Distance from the point on the middle of the spine to the middle of the frame\\n # Range 100 if spine is on the very left of the frame to -100 on the right\\n distance_to_middle = int(( (W/2 - spine_midpoint) * 100 ) / (W/2))\\n\\n # If only one spine bound is found\\n if (right_spine_bound is None) and (left_spine_bound is not None):\\n\\n # Distance from the point on the middle of the spine to the middle of the frame\\n # Range 100 if spine is on the very left of the frame to -100 on the right\\n left_spine_coordinate = left_spine_bound.calculateXgivenY(H/2)\\n distance_to_middle = int(( (W/2 - left_spine_coordinate) * 100 ) / (W/2))\\n\\n if (right_spine_bound is not None) and (left_spine_bound is None):\\n\\n # Distance from the point on the middle of the spine to the middle of the frame\\n # Range 100 if spine is on the very left of the frame to -100 on the right\\n right_spine_coordinate = right_spine_bound.calculateXgivenY(H/2)\\n distance_to_middle = int(( (W/2 - right_spine_coordinate) * 100 ) / (W/2))\\n\\n if (right_spine_bound is not None) or (left_spine_bound is not None):\\n if abs(distance_to_middle < 20):\\n abs_speed = 0.005\\n else:\\n abs_speed = 0.001\\n if abs(distance_to_middle) < 5:\\n speed = 0\\n count_frames_speed_0 += 1\\n elif distance_to_middle < 0:\\n speed = abs_speed\\n count_frames_speed_0 = 0\\n else:\\n speed = -abs_speed\\n count_frames_speed_0 = 0\\n if speed != prev_speed:\\n print(\\\"Moving with speed \\\" + str(speed) + \\\" !\\\")\\n Thread(target=mv.setSpeed, args=(speed,)).start()\\n prev_speed = speed\\n # Draw the rectangle around the label\\n if debug:\\n cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\\n #else:\\n #return success\\n\\n # update the FPS counter\\n self.fps.update()\\n self.fps.stop()\\n print(\\\"FPS\\\", \\\"{:.2f}\\\".format(self.fps.fps()))\\n # initialize the set of information we'll be displaying on\\n # the frame\\n info = [\\n (\\\"Tracker\\\", self.trackerType),\\n (\\\"Success\\\", \\\"Yes\\\" if success else \\\"No\\\"),\\n (\\\"FPS\\\", \\\"{:.2f}\\\".format(self.fps.fps())),\\n ]\\n # loop over the info tuples and draw them on our frame\\n if debug:\\n for (i, (k, v)) in enumerate(info):\\n text = \\\"{}: {}\\\".format(k, v)\\n cv2.putText(frame, text, (10, H - ((i * 20) + 20)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)\\n\\n # show the output frame\\n if debug:\\n cv2.imshow(\\\"Frame\\\", frame)\\n key = cv2.waitKey(1) & 0xFF\\n # if the `q` key was pressed, break from the loop\\n if key == ord(\\\"q\\\"):\\n break\\n mv.shutDown()\\n # if we are using a webcam, release the pointer\\n if self.webCam:\\n #self.vs.stop()\\n self.video_stream.stop()\\n # otherwise, release the file pointer\\n else:\\n self.vs.release()\\n # close all windows\\n cv2.destroyAllWindows()\\n return True\",\n \"def update(self):\\n self.frame = self.video_stream.read()\",\n \"def arm_video(self, nframes, timeout=30):\\n pass\",\n \"def duration():\\r\\n elapsed_time, duration = video_time()\\r\\n return duration\",\n \"def analyze_video(vidNum_iter, config, pointInds_toUse, pts_spaced, session): # function needed for multiprocessing\\n\\n optic = config['Optic']\\n\\n numVids = session['num_vids']\\n path_vid_allFiles = session['videos']\\n lk_names = [key for key in optic.keys() if 'lk_' in key]\\n lk_params = {k.split('lk_')[1]: (tuple(optic[k]) if type(optic[k]) is list else optic[k]) \\\\\\n for k in lk_names}\\n\\n vid = imageio.get_reader(path_vid_allFiles[vidNum_iter], 'ffmpeg')\\n # metadata = vid.get_meta_data()\\n\\n path_vid = path_vid_allFiles[vidNum_iter] # get path of the current vid\\n video = cv2.VideoCapture(path_vid) # open the video object with openCV\\n numFrames = int(video.get(\\n cv2.CAP_PROP_FRAME_COUNT)) # get frame count of this vid GENERALLY INACCURATE. OFF BY AROUND -25 frames\\n\\n frameToSet = 0\\n frame = vid.get_data(\\n frameToSet) # Get a single frame to use as the first 'previous frame' in calculating optic flow\\n new_frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n old_frame = new_frame_gray\\n\\n displacements_tmp = np.zeros((pts_spaced.shape[0], 2, np.uint64(numFrames + (numVids * 1000)))) * np.nan\\n\\n print(' ', end='', flush=True)\\n text = \\\"progresser #{}\\\".format(vidNum_iter)\\n print(f'\\\\n Calculating displacement field: video # {vidNum_iter + 1}/{numVids}')\\n\\n for iter_frame, new_frame in enumerate(tqdm(vid, total=numFrames, desc=text, position=vidNum_iter)):\\n new_frame_gray = cv2.cvtColor(new_frame, cv2.COLOR_BGR2GRAY) # convert to grayscale\\n\\n ##calculate optical flow\\n pointInds_new, status, error = cv2.calcOpticalFlowPyrLK(old_frame, new_frame_gray, pointInds_toUse, None,\\n **lk_params) # Calculate displacement distance between STATIC/ANCHORED points and the calculated new points. Also note the excluded 'NextPts' parameter. Could be used for fancier tracking\\n\\n ## Calculate displacement and place into variable 'displacements' (changes in size every iter)\\n if iter_frame == 0:\\n displacements_tmp[:, :, iter_frame] = np.zeros((pts_spaced.shape[0], 2))\\n else:\\n displacements_tmp[:, :, iter_frame] = np.single(np.squeeze((\\n pointInds_new - pointInds_toUse))) # this is the important variable. Simply the difference in the estimate\\n\\n old_frame = new_frame_gray # make current frame the 'old_frame' for the next iteration\\n\\n return displacements_tmp\",\n \"def position(self, video_display_name=None):\\r\\n selector = self.get_element_selector(video_display_name, CSS_CLASS_NAMES['video_time'])\\r\\n current_seek_position = self.q(css=selector).text[0]\\r\\n return current_seek_position.split('/')[0].strip()\",\n \"def media_position(self):\\n return (\\n self._table.active_track_total_time\\n - self._table.active_track_remaining_time\\n ).total_seconds()\",\n \"def video_playing(self, paused, title, total_time, position):\\n self.device.responses['playing'] = PlayingResponse(\\n paused=paused, title=title,\\n total_time=total_time, position=position,\\n mediakind=3)\",\n \"def read_video_info_cv2(vid_fp: str):\\n cap = cv2.VideoCapture(vid_fp)\\n total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\\n total_seconds = int(total_frames / cap.get(cv2.CAP_PROP_FPS))\\n cap.release()\\n return timedelta(seconds=total_seconds)\\n # cap.set(cv2.CAP_PROP_POS_AVI_RATIO, 1)\\n # total_ms = cap.get(cv2.CAP_PROP_POS_MSEC)\\n # return timedelta(milliseconds=total_ms)\",\n \"def delace(self):\\n if not self.isdelaced:\\n newframes = []\\n self.frame_dt /= 2\\n for i, frame in enumerate(self.frames):\\n newframes.append(\\n Frame(\\n frame.data[1::2, :],\\n self.starttime\\n + datetime.timedelta(seconds=self.frame_dt * (2 * i)),\\n )\\n )\\n newframes.append(\\n Frame(\\n frame.data[0::2, :],\\n self.starttime\\n + datetime.timedelta(seconds=self.frame_dt * (2 * i + 1)),\\n )\\n )\\n self.isdelaced = True\\n self.frames = newframes\\n self.length = len(newframes)\\n else:\\n print(\\\"Video already deinterlaced.\\\")\",\n \"def process_video(self):\\n if os.path.isfile(self.source):\\n self.cap = cv2.VideoCapture(self.source)\\n else:\\n try:\\n file_name = \\\"input.mp4\\\"\\n self.source = self.source.replace('open', 'uc')\\n print( \\\"\\\\nDownloading video file from drive link to %s\\\\n\\\"%file_name)\\n gdown.download(self.source, file_name, quiet=False)\\n print( \\\"%s downloaded!\\\\n\\\"%file_name )\\n self.cap = cv2.VideoCapture(file_name)\\n except Exception:\\n raise RuntimeError(\\\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\\\n\\\")\\n\\n\\n assert self.cap.isOpened(), \\\"Failed to open %s\\\" % self.source\\n\\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \\n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n return\",\n \"def wheelEvent(self,event):\\n\\n steps = event.angleDelta().y() // 120\\n vector = steps and steps // abs(steps) # 0, 1, or -1\\n for step in range(1, abs(steps) + 1):\\n if self.mediaPlayer.duration() != 0:\\n # If there a video is open.\\n self.set_position(self.mediaPlayer.position() + 200*vector)\\n else:\\n # If no video is open, but a csv is open, I set a limit of the fast forward.\\n if self.xValues != []:\\n if self.mediaPlayer.position() + 200*vector < max(self.xValues):\\n self.set_position(self.mediaPlayer.position() + 200*vector)\\n elif max(self.xValues) - self.mediaPlayer.position() <= 200:\\n self.set_position(max(self.xValues))\",\n \"def example_video(self, **kwargs):\\n kwargs.setdefault(\\\"title\\\", \\\"dummy\\\")\\n kwargs.setdefault(\\\"paused\\\", True)\\n kwargs.setdefault(\\\"position\\\", 3)\\n kwargs.setdefault(\\\"total_time\\\", 123)\\n self.video_playing(**kwargs)\",\n \"def __init__(self, videoPath=\\\"\\\", cacheString=\\\"-cache\\\", faceDetector=None):\\n self.videoPath = videoPath\\n self.cacheString = cacheString\\n self.video = None \\n self.currentFrame = []\\n self.currentFrameNumber = 0\\n self.frameCount = 0 #total frame number\\n self.fps = 25\\n self.ret = True #if the video is over\\n self.isPlaying = False\\n self.ifDrawAxis = False\\n self.ifDrawSquare = False\\n self.cacheData = {}\\n \\n if videoPath != \\\"\\\":\\n self.load()\",\n \"def track_video(frames):\\n first_frame = next(frames)\\n lkt = lktrack.LKTracker(first_frame)\\n lkt.detect_points()\\n yield lkt.draw()\\n\\n for im in frames:\\n lkt.step(im)\\n lkt.track_points()\\n yield lkt.draw()\",\n \"def getPoslbFFmpeg(self, event):\\n # if self.measurement_w:\\n\\n if hasattr(self, \\\"measurement_w\\\") and self.measurement_w is not None and self.measurement_w.isVisible():\\n x = event.pos().x()\\n y = event.pos().y()\\n\\n # distance\\n if self.measurement_w.rbDistance.isChecked():\\n if event.button() == 1: # left\\n self.draw_point(x, y, \\\"lime\\\")\\n self.memx, self.memy = x, y\\n\\n if event.button() == 2 and self.memx != -1 and self.memy != -1:\\n self.draw_point(x, y, \\\"lime\\\")\\n self.draw_line(self.memx, self.memy, x, y, \\\"lime\\\")\\n\\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\\\"line\\\", self.memx, self.memy, x, y])\\n else:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\\\"line\\\", self.memx, self.memy, x, y]]\\n\\n d = ((x - self.memx) ** 2 + (y - self.memy) ** 2) ** 0.5\\n try:\\n d = d / float(self.measurement_w.lePx.text()) * float(self.measurement_w.leRef.text())\\n except:\\n QMessageBox.critical(self, programName,\\n \\\"Check reference and pixel values! Values must be numeric.\\\",\\n QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton)\\n\\n self.measurement_w.pte.appendPlainText(\\\"Time: {}\\\\tFrame: {}\\\\tDistance: {}\\\".format(self.getLaps(),\\n self.FFmpegGlobalFrame,\\n round(d, 1)))\\n self.measurement_w.flagSaved = False\\n self.memx, self.memy = -1, -1\\n\\n # angle 1st clic -> vertex\\n if self.measurement_w.rbAngle.isChecked():\\n if event.button() == 1: # left for vertex\\n self.draw_point(x, y, \\\"lime\\\")\\n self.memPoints = [(x, y)]\\n\\n if event.button() == 2 and len(self.memPoints):\\n self.draw_point(x, y, \\\"lime\\\")\\n self.draw_line(self.memPoints[0][0], self.memPoints[0][1], x, y, \\\"lime\\\")\\n\\n self.memPoints.append((x, y))\\n\\n if len(self.memPoints) == 3:\\n self.measurement_w.pte.appendPlainText(\\\"Time: {}\\\\tFrame: {}\\\\tAngle: {}\\\".format(self.getLaps(),\\n self.FFmpegGlobalFrame,\\n round(angle(\\n self.memPoints[\\n 0],\\n self.memPoints[\\n 1],\\n self.memPoints[\\n 2]),\\n 1)\\n ))\\n self.measurement_w.flagSaved = False\\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\\\"angle\\\", self.memPoints])\\n else:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\\\"angle\\\", self.memPoints]]\\n\\n self.memPoints = []\\n\\n # Area\\n if self.measurement_w.rbArea.isChecked():\\n if event.button() == 1: # left\\n self.draw_point(x, y, \\\"lime\\\")\\n if len(self.memPoints):\\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], x, y, \\\"lime\\\")\\n self.memPoints.append((x, y))\\n\\n if event.button() == 2 and len(self.memPoints) >= 2:\\n self.draw_point(x, y, \\\"lime\\\")\\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], x, y, \\\"lime\\\")\\n self.memPoints.append((x, y))\\n # close polygon\\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], self.memPoints[0][0],\\n self.memPoints[0][1], \\\"lime\\\")\\n a = polygon_area(self.memPoints)\\n\\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\\\"polygon\\\", self.memPoints])\\n else:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\\\"polygon\\\", self.memPoints]]\\n try:\\n a = a / (float(self.measurement_w.lePx.text()) ** 2) * float(\\n self.measurement_w.leRef.text()) ** 2\\n except:\\n QMessageBox.critical(self, programName,\\n \\\"Check reference and pixel values! Values must be numeric.\\\",\\n QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton)\\n\\n self.measurement_w.pte.appendPlainText(\\\"Time: {}\\\\tFrame: {}\\\\tArea: {}\\\".format(self.getLaps(),\\n self.FFmpegGlobalFrame,\\n round(a, 1)))\\n\\n self.memPoints = []\",\n \"def num_frames(self):\\n return len(self.video)\",\n \"def play_video(self):\\n cv.namedWindow('Planes', cv.WINDOW_NORMAL)\\n cv.resizeWindow('Planes', self.__yuv_video.frame_width, self.__yuv_video.frame_height)\\n\\n inter_frame_delay = int(1000 / self.__yuv_video.frame_rate)\\n\\n while True:\\n (ret, frame) = self.__yuv_video.get_frame()\\n\\n if not ret:\\n break\\n\\n cv.imshow('Planes', frame)\\n cv.waitKey(0)\\n\\n self.__yuv_video.join()\",\n \"def moveFrame(self, num, callback=None): \\n if(self.isLoaded() == False):\\n return\\n self.isPlaying = False\\n self.currentFrameNumber = int(self.video.get(cv2.CAP_PROP_POS_FRAMES))\\n self.setFrame(self.currentFrameNumber + num - 1, callback)\",\n \"def on_update_playback(self, event):\\n try:\\n offset = self.mplayer.GetTimePos()\\n except:\\n return\\n print offset\\n mod_off = str(offset)[-1]\\n if mod_off == '0':\\n print \\\"mod_off\\\"\\n offset = int(offset)\\n self.playbackSlider.SetValue(offset)\\n secsPlayed = time.strftime('%M:%S', time.gmtime(offset))\\n self.trackCounter.SetLabel(secsPlayed)\",\n \"def seek(self, position):\\n if self.player:\\n value = position / self._length\\n self.player.set_position(value)\",\n \"def track_motion(self, video=None, set_roi=False):\\r\\n \\r\\n if video is None:\\r\\n video = self.video_buffer\\r\\n if set_roi:\\r\\n roi = self.get_roi(video=video)\\r\\n \\r\\n video_track = video.copy()\\r\\n motion_tracker = []\\r\\n # Generate different colors for tracking display \\r\\n color = np.random.randint(0,255,(100,3))\\r\\n \\r\\n # params for ShiTomasi corner detection\\r\\n feature_params = dict( maxCorners = 100,\\r\\n qualityLevel = 0.3,\\r\\n minDistance = 5,\\r\\n blockSize = 7 )\\r\\n # Parameters for lucas kanade optical flow\\r\\n lk_params = dict( winSize = (15,15),\\r\\n maxLevel = 8,\\r\\n criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))\\r\\n\\r\\n old_gray = cv2.cvtColor(video[0], cv2.COLOR_BGR2GRAY)\\r\\n # Create mask for drawing\\r\\n mask = np.zeros_like(video[0])\\r\\n # Mask to dectate the features to track\\r\\n features_mask = np.zeros_like(old_gray)\\r\\n features_mask[roi['x1']: roi['x2'], roi['y1']: roi['y2']] = old_gray[roi['x1']: roi['x2'], roi['y1']: roi['y2']]\\r\\n # Find corners in first frame\\r\\n p0 = cv2.goodFeaturesToTrack(features_mask, mask = None, **feature_params)\\r\\n \\r\\n for idx in range(1, video.shape[0]):\\r\\n new_gray = cv2.cvtColor(video[idx], cv2.COLOR_BGR2GRAY)\\r\\n # calculate optical flow\\r\\n p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, new_gray, p0, None, **lk_params)\\r\\n \\r\\n # Get good points\\r\\n good_old = p0[st==1]\\r\\n good_new = p1[st==1]\\r\\n motion_tracker.append(good_new)\\r\\n for i, (old, new) in enumerate(zip(good_old, good_new)):\\r\\n (ox, oy) = old.reval()\\r\\n (nx, ny) = new.ravel()\\r\\n mask = cv2.circle(mask, (nx, ny), 5, color[i].tolist(), -1)\\r\\n frame = cv2.add(video[idx], mask)\\r\\n video_track[idx] = frame\\r\\n \\r\\n cv2.imshow('frame', frame)\\r\\n if cv2.waitKey(30) & 0xFF==27:\\r\\n break\\r\\n # Updat old frames and points before checking next frame\\r\\n \\r\\n old_gray = new_gray.copy()\\r\\n p0 = p1.resapr(-1,1,2)\\r\\n cv2.destroyAllWindows()\\r\\n \\r\\n return video_track, motion_tracker\",\n \"def elapsed_time():\\r\\n elapsed_time, duration = video_time()\\r\\n return elapsed_time\",\n \"def continue_video(self):\\n\\n print(\\\"continue_video needs implementation\\\")\",\n \"def set_position(self, posicion):\\n\\n if self.duracion < posicion:\\n return\\n\\n if self.duracion == 0 or posicion == 0:\\n return\\n\\n posicion = self.duracion * posicion / 100\\n\\n # http://pygstdocs.berlios.de/pygst-reference/gst-constants.html\\n #self.player.set_state(gst.STATE_PAUSED)\\n # http://nullege.com/codes/show/src@d@b@dbr-HEAD@trunk@src@reproductor.py/72/gst.SEEK_TYPE_SET\\n #self.player.seek(\\n # 1.0,\\n # gst.FORMAT_TIME,\\n # gst.SEEK_FLAG_FLUSH,\\n # gst.SEEK_TYPE_SET,\\n # posicion,\\n # gst.SEEK_TYPE_SET,\\n # self.duracion)\\n\\n # http://nullege.com/codes/show/src@c@o@congabonga-HEAD@congaplayer@congalib@engines@gstplay.py/104/gst.SEEK_FLAG_ACCURATE\\n event = gst.event_new_seek(\\n 1.0, gst.FORMAT_TIME,\\n gst.SEEK_FLAG_FLUSH | gst.SEEK_FLAG_ACCURATE,\\n gst.SEEK_TYPE_SET, posicion * 1000000000,\\n gst.SEEK_TYPE_NONE, self.duracion * 1000000000)\\n\\n self.send_event(event)\\n #self.player.set_state(gst.STATE_PLAYING)\",\n \"def play_video(self, cap_frame):\\n # Plus 35 to synchronize video and recording\\n self.cap.set(cv2.CAP_PROP_POS_FRAMES, cap_frame+30)\\n _, frame = self.cap.read()\\n cv2.imshow('frame', frame)\\n cv2.waitKey(1)\",\n \"def frame_forward(self):\\n if self.playMode == FFMPEG:\\n self.ffmpegTimerOut()\",\n \"def run(input_video_file, output_video_file):\\n print(\\\"Debut de la transformation du format de la video\\\")\\n #récupération de la vidéo\\n video = cv2.VideoCapture(input_video_file)\\n #fps de la vidéo\\n fps = video.get(cv2.CAP_PROP_FPS)\\n #largeur des images de la vidéo\\n width_video = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\\n #hauteur des images de la vidéo\\n height_video = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n #nombre d'images dans la vidéo\\n frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))\\n #durée de la vidéo\\n duration = frame_count/fps\\n #nouvelle durée de la vidéo (on arrondi)\\n new_duration = math.floor(duration)\\n #nouveau fps de la vidéo\\n new_fps = float(round(fps))\\n #appliquer le nouveau fps\\n video.set(cv2.CAP_PROP_FPS,new_fps)\\n #appliquer la nouvelle durée\\n print(new_duration)\\n print(new_fps)\\n print(new_duration*new_fps)\\n new_frame_count = new_duration*new_fps\\n video.set(cv2.CAP_PROP_FRAME_COUNT,new_duration*new_fps)\\n #déffinition du format de la vidéo en sortie\\n video_out = cv2.VideoWriter(output_video_file,0x7634706d,new_fps,(width_video,height_video),True)\\n \\n count = 0\\n #ouverture de la vidéo\\n while(video.isOpened()):\\n #lecture image par image\\n ret, frame = video.read()\\n if ret==True:\\n\\n #ecriture de l'image dans la vidéo en sortie\\n video_out.write(frame)\\n count = count + 1\\n \\n if (count > (new_frame_count-1)):\\n # Libérer la vidéo\\n video.release()\\n break\\n else:\\n break\\n\\n print(\\\"fin de la transformation\\\")\\n #fermer les vidéos\\n video.release()\\n video_out.release()\",\n \"def _capture_video(video_duration=0., record=False):\\n global _shutdown\\n if cap is not None:\\n skip = False\\n t = time.time()\\n frames = []\\n frame_size = (640, 480) # default frame size\\n countdown = 1 if record else min(3, int(video_duration)) # number of seconds to countdown\\n margin = 0.1 # time margin (in seconds)\\n time_left = video_duration - time.time() + t\\n\\n while time_left > 0:\\n if not record and time_left > 0.5 and abs(countdown - time_left) <= margin:\\n _play_audio(COUNTDOWN_SOUND, no_audio)\\n countdown -= 1\\n\\n ret, frame = cap.read()\\n frames.append(frame.copy())\\n frame = cv2.flip(frame, 1) # horizontal flip for video-preview\\n frame_size = (frame.shape[1], frame.shape[0])\\n\\n if record:\\n message = f\\\"Recording video {str(index + 1)}\\\"\\n else:\\n message = f\\\"Get into position {str(index + 1)}\\\"\\n\\n # Recording prompt\\n cv2.putText(frame, message, (100, 100), FONT, 3, (255, 255, 255),\\n 4, cv2.LINE_AA)\\n # Recording timer\\n cv2.putText(frame, f\\\" {str(int(time_left) + 1)}\\\",\\n (200, 250), FONT, 10, (255, 255, 255),\\n 6, cv2.LINE_AA)\\n cv2.imshow('frame', frame)\\n\\n # Get key-press to skip current video or terminate script\\n key = cv2.waitKey(1)\\n if key & 0xFF == ord('s'): # Press `S` to skip the current video prompt\\n if record:\\n print(f'\\\\t[PROMPT]\\\\tSkipping video {index + 1} of {num_videos}.')\\n cv2.destroyAllWindows()\\n skip = True\\n break\\n elif key == 27: # Press `ESC` to exit the script\\n print('\\\\t[PROMPT]\\\\tShutting down video-recording and releasing resources.')\\n _play_audio(EXIT_SOUND, no_audio)\\n cv2.destroyAllWindows()\\n _shutdown = True\\n break\\n\\n time_left = video_duration - time.time() + t\\n\\n if not _shutdown:\\n _play_audio(DONE_SOUND, no_audio)\\n\\n calculated_fps = round(len(frames) / video_duration)\\n fps = 16 if calculated_fps <= 16 else calculated_fps\\n\\n if record and not skip and not _shutdown:\\n out = cv2.VideoWriter(os.path.join(path_out, file), 0x7634706d, fps, frame_size)\\n for frame in frames:\\n out.write(frame)\\n out.release()\\n\\n cv2.destroyAllWindows()\",\n \"async def async_media_seek(self, position):\\n if not self._slave_mode:\\n _LOGGER.debug(\\\"Seek. Device: %s, DUR: %s POS: %\\\", self.name, self._duration, position)\\n if self._duration > 0 and position >= 0 and position <= self._duration:\\n value = await self.async_call_linkplay_httpapi(\\\"setPlayerCmd:seek:{0}\\\".format(str(position)), None)\\n self._position_updated_at = utcnow()\\n self._idletime_updated_at = self._position_updated_at\\n if value != \\\"OK\\\":\\n _LOGGER.warning(\\\"Failed to seek. Device: %s, Got response: %s\\\", self.entity_id, value)\\n else:\\n await self._master.async_media_seek(position)\",\n \"def media_position(self):\\n if self._media_playback_trackable():\\n self._media_position_updated_at = utcnow()\\n return self.coordinator.data.nowplaying[self.zone.SourceID].CurrProgress\\n return None\",\n \"def update(self):\\n if self.mudpi.is_prepared:\\n if self.duration > self.delay.total_seconds():\\n if self.record_video:\\n self.capture_recording(duration=self.record_duration)\\n else:\\n self.capture_image()\\n self.reset_duration()\",\n \"def get_video_pts(self):\\n statrec = self.decoded_stats\\n if statrec.last_pts is None:\\n return None\\n return statrec.last_pts + time.time() - statrec.last_pts_changed\",\n \"def process_video(input_file, output_file):\\n # video = VideoFileClip(input_file).subclip(40,44) # from 38s to 46s\\n video = VideoFileClip(input_file)\\n annotated_video = video.fl_image(process_pipeline)\\n annotated_video.write_videofile(output_file, audio=False)\",\n \"def process_video(input_file, output_file):\\n # video = VideoFileClip(input_file).subclip(40,44) # from 38s to 46s\\n video = VideoFileClip(input_file)\\n annotated_video = video.fl_image(process_pipeline)\\n annotated_video.write_videofile(output_file, audio=False)\",\n \"def play_video_file(fname : str):\\n cap = cv2.VideoCapture(fname)\\n fps = cap.get(5)\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n fontScale = 1\\n fontColor = (0, 0, 0)\\n lineType = 2\\n\\n myvideo = []\\n while cap.isOpened():\\n ret, frame = cap.read()\\n\\n if ret is True:\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n cv2.putText(gray, 'Time: ' + str(round(cap.get(0) / 1000, 2)),\\n (10, 30),\\n font,\\n fontScale,\\n fontColor,\\n lineType)\\n cv2.putText(gray, 'Frame: ' + str(int(cap.get(1))),\\n (10, 70),\\n font,\\n fontScale,\\n fontColor,\\n lineType)\\n myvideo.append(gray)\\n #cv2.imshow('frame', gray)\\n #cv2.waitKey(10)\\n #if cv2.waitKey(delay=2) & 0xFF == ord('q'):\\n # break\\n else:\\n break\\n\\n cap.release()\\n\\n if fps < 60:\\n for frame in myvideo:\\n cv2.imshow('frame', frame)\\n cv2.waitKey(10)\\n else:\\n for ind, frame in enumerate(myvideo):\\n if ind % 3 == 0:\\n cv2.imshow('frame', frame)\\n cv2.waitKey(10)\\n else:\\n continue\\n cv2.destroyAllWindows()\",\n \"def load(self):\\n if self.ref == None:\\n self.ref = av.open(self.abspath)\\n self.n_audio_streams = len([s for s in self.ref.streams if s.type == 'audio'])\\n self.n_video_streams = len([s for s in self.ref.streams if s.type == 'video'])\\n if self.n_audio_streams > 0:\\n self.media_types += ['audio']\\n if self.n_video_streams > 0:\\n self.media_types += ['video']\\n self.duration = self.ref.duration / self.AV_TIME_BASE\",\n \"def run(self):\\n self.track_len = []\\n # debug\\n while self._segment_index <= self._segment_cnt:\\n if self._segment_index < 0: # Uncomment this block to debug specific segment\\n self._segment_index += 1\\n continue\\n # run association\\n print \\\"[Tracking]\\\\tSegment index:\\\\t{} Total segment num:\\\\t{}\\\".format(self._segment_index, self._segment_cnt)\\n start = cv2.getTickCount()\\n \\n self._run_segment()\\n print \\\"[Tracking]\\\\tSegment start:\\\\t{} Segment end\\\\t{}\\\".format(self._segment_start_fid,\\n self._segment_end_fid)\\n # dump into file\\n \\\"\\\"\\\"\\n seg_name = 'segment_{}.track'.format(self._segment_index)\\n seg_file = os.path.join(self._segment_dir, seg_name)\\n self._segments_path.append(seg_file)\\n Track.dump_to_track_file(self._high_level_tracks, save_name=seg_file)\\n print \\\"Track contains {} high level tracks\\\".format(len(self._high_level_tracks))\\n \\\"\\\"\\\"\\n self._segment_index += 1\\n end = cv2.getTickCount()\\n print \\\"[Tracking]\\\\tTime:\\\\t{} seconds\\\".format(float(end - start) / cv2.getTickFrequency())\\n if P['debug']:\\n pos_feature_num = self.pos_arr.shape[0]\\n neg_feature_num = self.neg_arr.shape[0]\\n pos_arr = np.hstack((self.pos_arr, np.ones(shape=(pos_feature_num, 1))))\\n neg_arr = np.hstack((self.neg_arr, np.zeros(shape=(neg_feature_num, 1))))\\n np.savetxt(os.path.join(\\\"../feature_classifier/\\\", \\\"{}_pos_feature.txt\\\".format(self._video_name)), pos_arr)\\n np.savetxt(os.path.join(\\\"../feature_classifier/\\\", \\\"{}_neg_feature.txt\\\".format(self._video_name)), neg_arr)\\n\\n final_track_save_file = os.path.join(self._save_dir, self._video_name + \\\"_final_merged.track\\\")\\n mot_track_save_file = os.path.join(self._save_dir, self._video_name + \\\".txt\\\")\\n Track.dump_to_track_file_no_feature(self._final_tracks, final_track_save_file, self._calib_w, self._calib_h)\\n Track.dump_track_with_mot_format(self._final_tracks, mot_track_save_file,)\\n print(\\\"there are {} tracklet in final merged track\\\".format(len(self._final_tracks)))\",\n \"def yolo_test_video(self):\\n # Open the input video, blocking call\\n inputVideo = cv2.VideoCapture(self.inputFile)\\n\\t\\t\\n # Get infomration about the input video\\n codec = int(inputVideo.get(cv2.CAP_PROP_FOURCC))\\n fps = int(inputVideo.get(cv2.CAP_PROP_FPS))\\n frameWidth = int(inputVideo.get(cv2.CAP_PROP_FRAME_WIDTH))\\n frameHeight = int(inputVideo.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n\\n # Open the output stream\\n outputVideo = cv2.VideoWriter(self.outputFile,\\n codec,\\n fps,\\n (frameWidth,frameHeight))\\n frameIndex = inputVideo.get(cv2.CAP_PROP_POS_FRAMES)\\n totalFrames = inputVideo.get(cv2.CAP_PROP_FRAME_COUNT)\\n \\t \\n\\tavgGrabTime = 0\\n\\tavgYoloTime = 0\\n\\tavgWriteTime = 0\\n \\n # For each frame in the video\\n while True:\\n \\n startTime = time.time()\\n \\n # Calculate the time it takes to grab a frame\\n startGrabTime = time.time()\\n grabbed, frame = inputVideo.read()\\n endGrabTime = time.time() \\n\\t avgGrabTime+=(endGrabTime-startGrabTime)\\n\\t \\n\\n if grabbed:\\n\\t\\t\\n # Calculate the time it takes to run YOLO pipeline \\n\\t\\tstartYoloTime = time.time()\\n annotatedFrame, predictedObjects = self.detect_from_image(frame)\\n\\t\\tendYoloTime = time.time()\\n\\t\\tavgYoloTime+= ( endYoloTime - startYoloTime)\\n\\n frameIndex = inputVideo.get(cv2.CAP_PROP_POS_FRAMES)\\n \\t\\n\\t\\tcurrentTime = time.time()\\n\\t\\telapsedTime = currentTime - startTime\\n\\t\\tcurrentFPS = (1)/elapsedTime \\n\\t\\t \\t\\n #cv2.rectangle(annotatedFrame, (0, 0), (30, 30), (0,0,0), -1)\\n cv2.putText(\\n annotatedFrame, 'FPS' + ': %.2f' % currentFPS,\\n (15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\\n (255, 255, 255), 2\\n )\\n\\t\\t\\n # Calculate the time it takes to write an annotated frame to video\\n\\t\\tstartWriteTime = time.time()\\n outputVideo.write(annotatedFrame)\\n\\t\\tendWriteTime = time.time()\\n\\t\\tavgWriteTime +=(endWriteTime - startWriteTime)\\n\\t\\n else:\\n inputVideo.set(cv2.CAP_PROP_POS_FRAMES, frameIndex-1)\\n cv2.waitKey(100)\\n\\n if frameIndex==totalFrames:\\n break\\n\\t\\t\\n inputVideo.release()\\n outputVideo.release()\\n cv2.destroyAllWindows()\\n \\n avgGrabTime/=totalFrames\\n avgYoloTime/=totalFrames\\n avgWriteTime/=totalFrames\\n\\n if self.verbose:\\n print ('Average time for extracting compressed video frame : %.3f' %avgGrabTime)\\n print ('Average time for YOLO object detection : %.3f' %avgYoloTime )\\n print ('Average time for writing frame to video : %.3f' %avgWriteTime)\",\n \"def __init__(self):\\n self.active = True # Camera activation control\\n self.stream = cv2.VideoCapture(0) # Open video stream\\n while not self.stream.isOpened():\\n pass\\n _,self.image = self.stream.read()# Save the first frame\\n cv2.waitKey(10)\\n self.frame = self.image[196:304,:546,:]# Cropped frame\\n self.diff_frame = self.frame\\n# self.reference_frame = copy.deepcopy(self.frame)\\n# self.abs_diff_frame = copy.deepcopy(self.frame)\\n self.reference_frame = self.frame\\n self.abs_diff_frame = self.frame\\n self.frame_count = 1 # Used for framerate estimation\\n self.frame_rate = 0\\n self.tic = time()\",\n \"def play_video(self, video_path, period):\\n self.video_playing = True\\n self.state_time_remain = period\\n abs_path = QFileInfo(video_path).absoluteFilePath()\\n QMetaObject.invokeMethod(self.video_player, \\\"setMedia\\\", Qt.QueuedConnection,\\n Q_ARG(QMediaContent, QMediaContent(QUrl.fromLocalFile(abs_path))))\",\n \"def stream_frames(video_capture):\",\n \"def segment_intervals(filename, basedir, start_sec=0, end_sec=0, seconds_between_frame_grabs=10):\\n # Get video id\\n video_id = re.findall('v\\\\d+', filename)[0]\\n\\n # Open file handle\\n vid = imageio.get_reader(filename, 'ffmpeg')\\n\\n # Get metadata\\n meta = vid.get_meta_data()\\n fps = int(meta['fps'])\\n nframes = meta['nframes']\\n frames_to_get = np.arange(start_sec, end_sec, seconds_between_frame_grabs) * fps\\n\\n # Check frames\\n for i in frames_to_get:\\n try:\\n img = vid.get_data(i)\\n except:\\n raise\\n\\n # Downlsample full image\\n downsampled = downsample_image(img)\\n\\n # Player status\\n h, w, c = img.shape\\n factor = 0.23\\n y1 = int(h * 0.04)\\n x1 = int(w * (0.5 - factor))\\n y2 = int(h * 0.0645)\\n x2 = int(w * (0.5 + factor))\\n pl_status = crop_rect(img, x1, y1, x2, y2)\\n\\n # Block out center of player status\\n h, w, c = pl_status.shape\\n factor = .12\\n x1 = int(w * (0.5 - factor))\\n x2 = int(w * (0.5 + factor))\\n pl_status = blackout_middle(pl_status, x1, x2)\\n\\n # Write full frame and header frame\\n imageio.imwrite(os.path.join(basedir, 'full_{}_s{}.png'.format(video_id, int(i/fps))), img)\\n imageio.imwrite(os.path.join(basedir, 'pl_status_{}_s{}.png'.format(video_id, int(i/fps))), pl_status)\\n imageio.imwrite(os.path.join(basedir, 'downsampled_{}_s{}.png'.format(video_id, int(i/fps))), downsampled)\\n\\n vid.close()\\n\\n return True\",\n \"def play_random_video(self):\\n\\n print(\\\"play_random_video needs implementation\\\")\",\n \"def test_video_player(self):\\r\\n\\r\\n # Navigate to a video\\r\\n self.course_info_page.visit()\\r\\n self.tab_nav.go_to_tab('Courseware')\\r\\n\\r\\n # The video should start off paused\\r\\n # Since the video hasn't loaded yet, it's elapsed time is 0\\r\\n self.assertFalse(self.video.is_playing)\\r\\n self.assertEqual(self.video.elapsed_time, 0)\\r\\n\\r\\n # Play the video\\r\\n self.video.play()\\r\\n\\r\\n # Now we should be playing\\r\\n self.assertTrue(self.video.is_playing)\\r\\n\\r\\n # Commented the below EmptyPromise, will move to its page once this test is working and stable\\r\\n # Also there is should be no Promise check in any test as this should be done in Page Object\\r\\n # Wait for the video to load the duration\\r\\n # EmptyPromise(\\r\\n # lambda: self.video.duration > 0,\\r\\n # 'video has duration', timeout=20\\r\\n # ).fulfill()\\r\\n\\r\\n # Pause the video\\r\\n self.video.pause()\\r\\n\\r\\n # Expect that the elapsed time and duration are reasonable\\r\\n # Again, we can't expect the video to actually play because of\\r\\n # latency through the ssh tunnel\\r\\n self.assertGreaterEqual(self.video.elapsed_time, 0)\\r\\n self.assertGreaterEqual(self.video.duration, self.video.elapsed_time)\",\n \"def analyze_livevideo(self, camera=0, ofile=None, droprate=2, cameraid=None, location=None, publish=True):\\n # Use Camera#0 by default otherwise use the open specified\\n cameradev = 0\\n if camera:\\n cameradev = camera\\n\\n vcamera = cv2.VideoCapture(cameradev)\\n vstarttime = self.epoch()\\n livefeed = True\\n fcount = 0\\n\\n while livefeed:\\n ret, frame = vcamera.read()\\n oframe = frame.copy()\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n\\n # print(\\\"FrameNum: {}\\\".format(fcount))\\n if fcount % droprate == 0:\\n print(\\\" Analyzing Frame: {}\\\".format(fcount))\\n faceinfo = self.analyze_frame(iframe=frame, label=True, publish=True)\\n cv2.imshow('Agender', faceinfo['frame'])\\n if publish:\\n finfo = {}\\n finfo['cameraid'] = cameraid\\n finfo['location'] = location\\n finfo['agender'] = faceinfo['dbdata']\\n finfo['epoch'] = self.epoch()\\n dtstr = str(datetime.datetime.now()).split('.')[0]\\n dtdstr = dtstr.split(\\\" \\\")\\n finfo['datetime'] = {'date': dtdstr[0], 'time': dtdstr[1]}\\n if len(faceinfo['dbdata']):\\n #print(json.dumps(finfo))\\n self.publishstats(stats=finfo)\\n\\n fcount += 1\\n if cv2.waitKey(5) == 27: # ESC key press\\n livefeed = False\",\n \"def running_video(self, video_source):\\n self.video_controller.set_button_enable()\\n self.cap = cv2.VideoCapture(video_source)\\n self.next_frame_slot()\",\n \"def readVideo(self):\\n vid = cv2.VideoCapture(self.fname)\\n imgstack = []\\n # grab = True\\n grab, img = vid.read()\\n while grab:\\n imgstack.append(\\n Frame(\\n cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\\n self.starttime\\n + datetime.timedelta(seconds=self.frame_dt * self.length),\\n )\\n )\\n self.length += 1\\n grab, img = vid.read()\\n self.frames = imgstack\",\n \"def displacements_monothread(config, pointInds_toUse, pointInds_tracked, pointInds_tracked_tuple, displacements,\\n pts_spaced, color_tuples, session):\\n\\n ## Main loop to pull out displacements in each video\\n ind_concat = 0\\n fps = 0\\n tic_fps = time.time()\\n tic_all = time.time()\\n\\n optic = config['Optic']\\n video = config['Video']\\n\\n vidNums_toUse = optic['vidNums_toUse']\\n showVideo_pref = optic['showVideo_pref']\\n fps_counterPeriod = video['fps_counterPeriod']\\n printFPS_pref = video['printFPS_pref']\\n remote = config['General']['remote']\\n save_vid = video['save_demo']\\n\\n Fs = video['Fs']\\n vid_width = video['width']\\n vid_height = video['height']\\n test_len = video['demo_len']\\n save_pathFull = str(Path(config['Paths']['viz']) / 'optic_test.avi')\\n\\n numVids = session['num_vids']\\n path_vid_allFiles = session['videos']\\n lk_names = [key for key in optic.keys() if 'lk_' in key]\\n lk_params = {k.split('lk_')[1]: (tuple(optic[k]) if type(optic[k]) is list else optic[k]) \\\\\\n for k in lk_names}\\n\\n # Define the codec and create VideoWriter object\\n if showVideo_pref and (save_vid or remote):\\n fourcc = cv2.VideoWriter_fourcc(*'MJPG')\\n print(f'saving to file {save_pathFull}')\\n out = cv2.VideoWriter(save_pathFull, fourcc, Fs, (np.int64(vid_width), np.int64(vid_height)))\\n else:\\n out = None\\n vid_lens = []\\n for vidNum_iter in vidNums_toUse:\\n vid = imageio.get_reader(path_vid_allFiles[vidNum_iter], 'ffmpeg')\\n # metadata = vid.get_meta_data()\\n\\n path_vid = path_vid_allFiles[vidNum_iter] # get path of the current vid\\n video = cv2.VideoCapture(path_vid) # open the video object with openCV\\n numFrames_rough = int(video.get(\\n cv2.CAP_PROP_FRAME_COUNT)) # get frame count of this vid GENERALLY INACCURATE. OFF BY AROUND -25 frames\\n\\n frameToSet = 0\\n frame = vid.get_data(0) # Get a single frame to use as the first 'previous frame' in calculating optic flow\\n new_frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n old_frame = new_frame_gray\\n\\n print(f'\\\\n Calculating displacement field: video # {vidNum_iter + 1}/{numVids}')\\n # while True:\\n for iter_frame, new_frame in enumerate(tqdm(vid, total=numFrames_rough)):\\n new_frame_gray = cv2.cvtColor(new_frame, cv2.COLOR_BGR2GRAY) # convert to grayscale\\n\\n ##calculate optical flow\\n pointInds_new, status, error = cv2.calcOpticalFlowPyrLK(old_frame, new_frame_gray, pointInds_toUse, None,\\n **lk_params) # Calculate displacement distance between STATIC/ANCHORED points and the calculated new points. Also note the excluded 'NextPts' parameter. Could be used for fancier tracking\\n\\n ## Calculate displacement and place into variable 'displacements' (changes in size every iter) \\n if iter_frame == 0:\\n displacements[:, :, ind_concat] = np.zeros((pts_spaced.shape[0], 2))\\n else:\\n displacements[:, :, ind_concat] = np.single(np.squeeze((\\n pointInds_new - pointInds_toUse))) # this is the important variable. Simply the difference in the estimate\\n\\n old_frame = new_frame_gray # make current frame the 'old_frame' for the next iteration\\n\\n ## below is just for visualization. Nothing calculated is maintained\\n if showVideo_pref:\\n pointInds_tracked = pointInds_tracked + (\\n pointInds_new - pointInds_toUse) # calculate integrated position\\n pointInds_tracked = pointInds_tracked - (\\n pointInds_tracked - pointInds_toUse) * 0.01 # multiplied constant is the relaxation term. this is just for display purposes. Relaxation term chosen during cleanup will be real\\n pointInds = [pointInds_tracked, pointInds_tracked_tuple]\\n counters = [iter_frame, vidNum_iter, ind_concat, fps]\\n if (remote and iter_frame < test_len) or not remote:\\n videos.visualize_progress(config, session, new_frame, pointInds, color_tuples, counters, out)\\n\\n if (save_vid or remote) and iter_frame == test_len:\\n out.release()\\n\\n k = cv2.waitKey(1) & 0xff\\n if k == 27: break\\n\\n ind_concat = ind_concat + 1\\n\\n if ind_concat % fps_counterPeriod == 0:\\n elapsed = time.time() - tic_fps\\n fps = fps_counterPeriod / elapsed\\n if printFPS_pref:\\n print(fps)\\n tic_fps = time.time()\\n vid_lens.append(iter_frame+1)\\n ## Calculate how long calculation took\\n elapsed = time.time() - tic_all\\n helpers.print_time('video time elapsed:', elapsed)\\n print(f'Capture rate: {round(ind_concat / elapsed, 3)} fps')\\n\\n numFrames_total = ind_concat - 1\\n cv2.destroyAllWindows()\\n\\n displacements = displacements[:, :, ~np.isnan(displacements[0, 0, :])]\\n\\n return displacements, numFrames_total, vid_lens\",\n \"def video_times():\\n p = parse_cmdline(get_parser=get_parser_times)\\n log.setup_main_handler(\\n mods=(\\\"fogtools\\\", \\\"typhon\\\", \\\"fogpy\\\", \\\"sattools\\\", \\\"fcitools\\\", \\\"satpy\\\",\\n \\\"pyresample\\\"),\\n level=logging.DEBUG)\\n vis.show_video_abi_glm_times(\\n start_date=p.start_time,\\n end_date=p.end_time,\\n img_out=p.filename_pattern_image,\\n vid_out=p.filename_pattern_video,\\n out_dir=p.outdir,\\n sector=p.sector,\\n area=p.area)\\n print(\\\"Files written to:\\\", p.outdir)\",\n \"def load_video(self):\\r\\n if self.file_name=='':\\r\\n Tk().withdraw()\\r\\n self.file_name = askopenfilename()\\r\\n cap = cv2.VideoCapture(self.file_name)\\r\\n self.length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\r\\n self.width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\r\\n self.heigth = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\r\\n self.fps = int(round(cap.get(cv2.CAP_PROP_FPS)))\\r\\n \\r\\n video_buffer = []#np.ndarray(shape=(self.length, self.heigth, self.width, 3), dtype=np.uint8)\\r\\n for i in tqdm(range(self.length), desc='Loading video from: {}'.format(self.file_name)):\\r\\n ret, frame = cap.read()\\r\\n if not ret:\\r\\n break\\r\\n video_buffer.append(frame)\\r\\n #assert(i==self.length-1)\\r\\n video_buffer = np.array(video_buffer, dtype=np.uint8)\\r\\n self.video_buffer = video_buffer\\r\\n cap.release()\\r\\n self.ix = self.width-1\\r\\n self.iy = self.heigth-1 \\r\\n self.roi = dict(x1=self.x, y1=self.y, x2=self.ix, y2=self.iy)\\r\\n return video_buffer\",\n \"def track_ball_1(video):\\n return track_ball_generic(video)\",\n \"def update(self, time, frame, face_position = None):\\n\\t\\tself.face_position = face_position or self.face_position\",\n \"def play(self): \\n if(self.isLoaded() == False):\\n return\\n self.isPlaying = True\\n #if the video is end, we restart\\n if(self.ret == False):\\n self.video.set(1, 0)\",\n \"def seek(self, seek_value, video_display_name=None):\\r\\n seek_time = self._parse_time_str(seek_value)\\r\\n seek_selector = self.get_element_selector(video_display_name, ' .video')\\r\\n js_code = \\\"$('{seek_selector}').data('video-player-state').videoPlayer.onSlideSeek({{time: {seek_time}}})\\\".format(\\r\\n seek_selector=seek_selector, seek_time=seek_time)\\r\\n self.browser.execute_script(js_code)\",\n \"def video_duration(self):\\n # type: () -> int\\n return self._video_duration\",\n \"def __init__(self, record_video=True, video_name='video.avi', lower_color=(20, 80, 20), upper_color=(30, 255, 255)):\\n self.video = cv2.VideoCapture(0)\\n\\n # We need to check if camera \\n # is opened previously or not \\n if not self.video.isOpened():\\n print(\\\"Error reading video file\\\")\\n\\n # We need to set resolutions.\\n # so, convert them from float to integer. \\n self.frame_width = int(self.video.get(3))\\n self.frame_height = int(self.video.get(4))\\n self.fps = self.video.get(cv2.CAP_PROP_FPS)\\n self.size = (self.frame_width, self.frame_height)\\n # Below VideoWriter object will create \\n # a frame of above defined The output \\n # is stored in file with the name stored in self.video_name.\\n self.record_video = record_video\\n if self.record_video:\\n self.video_result = cv2.VideoWriter(video_name, cv2.VideoWriter_fourcc(*'MJPG'), self.fps, self.size)\\n\\n # define the lower and upper boundaries of the colored\\n # ball in the HSV color space\\n self.lower_color = lower_color\\n self.upper_color = upper_color\\n self.x = 0\\n self.y = 0\\n self.is_ball_visible = False\\n self.radius = 10\",\n \"def __loadVideo(self):\\n # Check if movie file exists ...\\n #\\n if not(os.path.isfile(self.fNameVideo)):\\n return stm.StimErrC.videoFileNotFound\\n\\n try: \\n # Load video\\n #\\n self.video = mpe.VideoFileClip(self.fNameVideo)\\n \\n except IOError: \\n return stm.StimErrC.invalidVideoFormat\\n \\n # Retrieve video description\\n #\\n self.dxFr = self.video.size[0]\\n self.dyFr = self.video.size[1]\\n self.nFr = self.video.duration *self.video.fps\\n self.fps = self.video.fps\\n ssp.Log.write(\\\"DEBUG\\\", \\\"stim_video: {0}x{1} pixel, {2} frames, {3} fps\\\"\\n .format(self.dxFr, self.dyFr, self.nFr, self.fps))\\n \\n if self.isTestOnly:\\n # Return here if the video was only loaded to test if it is ok\\n #\\n self.video = None\\n return stm.StimErrC.ok\\n \\n # Load movie frames (note that frames is a generator!)\\n #\\n self.frames = self.video.iter_frames() \\n self.isReady = True\\n return stm.StimErrC.ok\",\n \"def track_ball_2(video):\\n return track_ball_generic(video)\",\n \"def jump_in_video(self, jump_time):\\n self.pause_video()\\n current_time = self.driver.find_element_by_xpath('//UIAApplication[1]/UIAWindow[1]/UIAStaticText[1]')\\n current_time_text = current_time.text\\n total_time = self.driver.find_element_by_xpath('//UIAApplication[1]/UIAWindow[1]/UIAStaticText[2]')\\n total_time_text = total_time.text\\n\\n # total_time = minutes*60 + seconds\\n total_time_text = float(total_time_text[-5:-3])*60 + float(total_time_text[-2:])\\n\\n seek_pct = jump_time / total_time_text\\n\\n seek_bar = self._find_element(class_name='UIASlider')\\n seek_bar_size = seek_bar.size['width'] - current_time.size['width'] - total_time.size['width']\\n seek_bar_percentage = float(float(str(seek_bar.get_attribute(\\\"value\\\"))[:-1]) / 100)\\n\\n # width * seek_pct is how far over in the bar to tap\\n seek_bar_end_x = seek_bar.location['x'] + (seek_bar.size['width'] * seek_pct) - total_time.size['width']\\n\\n # this is just the vertical middle of the seek bar\\n seek_bar_end_y = seek_bar.location['y'] + seek_bar.size['height']/2\\n\\n seek_bar_start_x = seek_bar.location['x'] + current_time.size['width'] + (seek_bar_size * seek_bar_percentage)\\n\\n while seek_bar_start_x < seek_bar_end_x:\\n self.swipe(seek_bar_start_x, seek_bar_end_y, seek_bar_end_x, seek_bar_end_y, 2000)\\n seek_bar_start_x += 15\\n print seek_bar_start_x\\n\\n self.unpause_video()\",\n \"def track_ball_3(video):\\n return track_ball_generic(video)\",\n \"def update(self):\\n print('VIDEO: Video Stream started')\\n while True:\\n if self.stopped:\\n return\\n (self.grabbed, self.frame) = self.stream.read()\",\n \"def analyze_movie(\\n video_path, aspect_ratio=0, palette_size=32, frames=-1, step=1, show_frames=False, show_last_frame=False, color_format='hex'\\n):\\n\\n # Parse video frame-by-frame\\n vidcap = cv2.VideoCapture(video_path)\\n success, image = vidcap.read()\\n pil_img = None\\n count = 0\\n while success and frames == -1 or count < frames:\\n if count % step == 0:\\n # Convert to PIL image\\n img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n pil_img = Image.fromarray(img)\\n\\n # Crop frame to remove border\\n if aspect_ratio != 0:\\n width, height = pil_img.size\\n left = 0\\n right = width\\n content_height = 1/aspect_ratio * width\\n border = (height - content_height) * 0.5\\n top = border\\n bottom = border + content_height\\n pil_img = pil_img.crop((left, top, right, bottom))\\n\\n # Get primary color\\n main_color = get_primary_color(\\n pil_img, palette_size, show_img=show_frames)\\n\\n if color_format == 'hex':\\n main_color = rgbToHex(main_color)\\n \\n print(main_color)\\n\\n # Attempt to read next frame\\n success, image = vidcap.read()\\n count += 1\\n\\n if show_last_frame:\\n pil_img.show()\",\n \"def test_read_video_in_range_from_memory(self, test_video, config, num_frames):\\n full_path, video_tensor = _get_video_tensor(VIDEO_DIR, test_video)\\n # video related\\n width, height, min_dimension, max_dimension = 0, 0, 0, 0\\n video_start_pts, video_end_pts = 0, -1\\n video_timebase_num, video_timebase_den = 0, 1\\n # audio related\\n samples, channels = 0, 0\\n audio_start_pts, audio_end_pts = 0, -1\\n audio_timebase_num, audio_timebase_den = 0, 1\\n # pass 1: decode all frames using new decoder\\n tv_result = torch.ops.video_reader.read_video_from_memory(\\n video_tensor,\\n SEEK_FRAME_MARGIN,\\n 0, # getPtsOnly\\n 1, # readVideoStream\\n width,\\n height,\\n min_dimension,\\n max_dimension,\\n video_start_pts,\\n video_end_pts,\\n video_timebase_num,\\n video_timebase_den,\\n 1, # readAudioStream\\n samples,\\n channels,\\n audio_start_pts,\\n audio_end_pts,\\n audio_timebase_num,\\n audio_timebase_den,\\n )\\n (\\n vframes,\\n vframe_pts,\\n vtimebase,\\n vfps,\\n vduration,\\n aframes,\\n aframe_pts,\\n atimebase,\\n asample_rate,\\n aduration,\\n ) = tv_result\\n assert abs(config.video_fps - vfps.item()) < 0.01\\n\\n start_pts_ind_max = vframe_pts.size(0) - num_frames\\n if start_pts_ind_max <= 0:\\n return\\n # randomly pick start pts\\n start_pts_ind = randint(0, start_pts_ind_max)\\n end_pts_ind = start_pts_ind + num_frames - 1\\n video_start_pts = vframe_pts[start_pts_ind]\\n video_end_pts = vframe_pts[end_pts_ind]\\n\\n video_timebase_num, video_timebase_den = vtimebase[0], vtimebase[1]\\n if len(atimebase) > 0:\\n # when audio stream is available\\n audio_timebase_num, audio_timebase_den = atimebase[0], atimebase[1]\\n audio_start_pts = _pts_convert(\\n video_start_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(audio_timebase_num.item(), audio_timebase_den.item()),\\n math.floor,\\n )\\n audio_end_pts = _pts_convert(\\n video_end_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(audio_timebase_num.item(), audio_timebase_den.item()),\\n math.ceil,\\n )\\n\\n # pass 2: decode frames in the randomly generated range\\n tv_result = torch.ops.video_reader.read_video_from_memory(\\n video_tensor,\\n SEEK_FRAME_MARGIN,\\n 0, # getPtsOnly\\n 1, # readVideoStream\\n width,\\n height,\\n min_dimension,\\n max_dimension,\\n video_start_pts,\\n video_end_pts,\\n video_timebase_num,\\n video_timebase_den,\\n 1, # readAudioStream\\n samples,\\n channels,\\n audio_start_pts,\\n audio_end_pts,\\n audio_timebase_num,\\n audio_timebase_den,\\n )\\n\\n # pass 3: decode frames in range using PyAv\\n video_timebase_av, audio_timebase_av = _get_timebase_by_av_module(full_path)\\n\\n video_start_pts_av = _pts_convert(\\n video_start_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(video_timebase_av.numerator, video_timebase_av.denominator),\\n math.floor,\\n )\\n video_end_pts_av = _pts_convert(\\n video_end_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(video_timebase_av.numerator, video_timebase_av.denominator),\\n math.ceil,\\n )\\n if audio_timebase_av:\\n audio_start_pts = _pts_convert(\\n video_start_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(audio_timebase_av.numerator, audio_timebase_av.denominator),\\n math.floor,\\n )\\n audio_end_pts = _pts_convert(\\n video_end_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(audio_timebase_av.numerator, audio_timebase_av.denominator),\\n math.ceil,\\n )\\n\\n pyav_result = _decode_frames_by_av_module(\\n full_path,\\n video_start_pts_av,\\n video_end_pts_av,\\n audio_start_pts,\\n audio_end_pts,\\n )\\n\\n assert tv_result[0].size(0) == num_frames\\n if pyav_result.vframes.size(0) == num_frames:\\n # if PyAv decodes a different number of video frames, skip\\n # comparing the decoding results between Torchvision video reader\\n # and PyAv\\n self.compare_decoding_result(tv_result, pyav_result, config)\",\n \"def video_thread():\\n global last_frame\\n # Creating stream capture object\\n cap = cv2.VideoCapture('udp://' + drone.tello_ip + ':11111')\\n\\n while(True):\\n _, last_frame = cap.read()\\n cap.release()\",\n \"def init(self, parent):\\n super(_VideoComponentEditor, self).init(parent)\\n\\n self.playTimer = QTimer(self.control)\\n self.playTimer.timeout.connect(self.update)\\n # self.control.connect(self.playTimer, SIGNAL('timeout()'), self.update)\\n print(\\\"fffffffffff\\\", self.value.fps)\\n if self.value.fps:\\n self.playTimer.setInterval(1000 / float(self.value.fps))\\n self.playTimer.start()\\n\\n self.value.on_trait_change(self.stop, \\\"closed_event\\\")\\n\\n self.value.on_trait_change(self._update_fps, \\\"fps\\\")\\n self.sync_value(\\\"stop_timer\\\", \\\"stop_timer\\\", mode=\\\"from\\\")\\n # self._prev_time = time.time()\\n self._alive = True\\n # QTimer.singleShot(self._get_interval(), lambda: self.update(-1))\",\n \"def play(self, frame):\\n cv2.imshow(\\\"view\\\", frame)\\n return 0\",\n \"def run(self):\\n i = 0\\n t = time.time()\\n while True:\\n i = i + 1\\n ret, frame = self.stream.read()\\n if (i == 20):\\n self.fps = 20/(time.time() - t)\\n t = time.time()\\n i = 0\\n #If a frame is None need to re-init it: \\n # - close a stream;\\n # - reopen it;\\n # - read frame again\\n if frame is None:\\n self.stream.release()\\n self.stream = cv2.VideoCapture(self.url)\\n ret, frame = self.stream.read()\\n text = time.strftime('%Y-%m-%d %H:%M:%S')\\n if (self.fps > 0):\\n text = text + ' FPS: ' + str(round(self.fps))\\n self.frame = cv2.putText(frame, text, (10, int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)) - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\\n self.frameID = uuid.uuid4()\",\n \"def target_position(self, time):\\n pass\",\n \"def target_position(self, time):\\n pass\",\n \"def hsVideo_sliderMoved(self):\\n\\n if self.pj[OBSERVATIONS][self.observationId][TYPE] in [MEDIA]:\\n\\n if self.playerType == VLC and self.playMode == VLC:\\n sliderPos = self.hsVideo.value() / (slider_maximum - 1)\\n videoPosition = sliderPos * self.mediaplayer.get_length()\\n self.mediaplayer.set_time(int(videoPosition))\\n # second video together\\n if self.simultaneousMedia:\\n # synchronize 2nd player\\n self.mediaplayer2.set_time(int(self.mediaplayer.get_time()\\n - self.pj[OBSERVATIONS][self.observationId][\\n TIME_OFFSET_SECOND_PLAYER] * 1000))\\n self.timer_out(scrollSlider=False)\\n self.timer_spectro_out()\",\n \"def showVideo( oVideo, oPathXY=np.array([]) ):\\n global oVideo_t, iFrame, oPathXY_t\\n fig = plt.figure()\\n # prikazi prvi okvir\\n iFrame = 0\\n oPathXY_t = oPathXY\\n oVideo_t = oVideo\\n print(oVideo.shape)\\n im = plt.imshow(oVideo[...,iFrame], cmap=plt.get_cmap('Greys_r'))\\n # definiraj funkcijo za osvezevanje prikaza\\n def updatefig(*args):\\n global oVideo_t, iFrame, oPathXY_t\\n iFrame = ( iFrame + 1 ) % oVideo_t.shape[-1]\\n im.set_array( oVideo_t[...,iFrame] ) \\n if iFrame < oPathXY.shape[0]:\\n plt.plot( oPathXY[iFrame,0], oPathXY[iFrame,1], 'xr' ,markersize=3 ) \\n return im,\\n # prikazi animacijo poti\\n ani = animation.FuncAnimation(fig, updatefig, interval=25, blit=True)\\n plt.show()\",\n \"def testVideoTrackType(self):\\n\\n trackLine = _buildTrackLine(0, 'video', {'hello': 'goodbye'})\\n\\n trackID, trackType, trackDict = tools._trackInfo(trackLine)\\n\\n self.assertEqual(\\n 'video',\\n trackType,\\n )\",\n \"def play_video(self,\\r\\n video=None,\\r\\n window_name=None,\\r\\n mode=MANUAL_MODE,\\r\\n mouse_callback_func=None):\\r\\n if mouse_callback_func==None:\\r\\n mouse_callback_func = self.dummy_mouse_call_back_func\\r\\n zoom = 1\\r\\n if video is None:\\r\\n video = self.video_buffer\\r\\n if window_name==None:\\r\\n window_name = 'Video Player'\\r\\n cv2.namedWindow(window_name)\\r\\n cv2.setMouseCallback(window_name, mouse_callback_func)\\r\\n idx=0\\r\\n self.current_frame = video[idx]\\r\\n self.img = self.current_frame.copy()\\r\\n while True:\\r\\n cv2.imshow(window_name, self.img)\\r\\n k = cv2.waitKey(30) & 0xFF\\r\\n if mode==MANUAL_MODE:\\r\\n if k==ord('f'):\\r\\n idx = min(idx+1, len(video)-1)\\r\\n self.current_frame = cv2.resize(video[idx], None, fx=zoom, fy=zoom, \\\\\\r\\n interpolation=cv2.INTER_AREA)\\r\\n self.img = self.current_frame.copy()\\r\\n if k==ord('b'):\\r\\n idx = max(0, idx-1)\\r\\n self.current_frame = cv2.resize(video[idx], None, fx=zoom, fy=zoom, \\\\\\r\\n interpolation=cv2.INTER_AREA)\\r\\n self.img = self.current_frame.copy()\\r\\n if k==ord('i'):\\r\\n zoom *= 1.33\\r\\n self.current_frame = cv2.resize(video[idx], None, fx=zoom, fy=zoom, \\\\\\r\\n interpolation=cv2.INTER_AREA)\\r\\n self.img = self.current_frame.copy()\\r\\n if k==ord('o'):\\r\\n zoom *= 0.75\\r\\n self.current_frame = cv2.resize(video[idx], None, fx=zoom, fy=zoom, \\\\\\r\\n interpolation=cv2.INTER_AREA)\\r\\n self.img = self.current_frame.copy()\\r\\n\\r\\n if k==27:\\r\\n break\\r\\n cv2.destroyAllWindows()\\r\\n self.x /=zoom\\r\\n self.ix/=zoom\\r\\n self.y /=zoom\\r\\n self.iy/=zoom\\r\\n \\r\\n self.poly = (np.asarray(self.poly)/zoom).astype(int)\",\n \"def capture_video(self):\\n while self.capturing:\\n nparray = self.source.get_frame()\\n self.frame_buffer.put(Frame(nparray, self.frame))\\n self.frame += 1\\n print \\\"Stopping Capture\\\"\",\n \"def play_video(self):\\n raise NotImplementedError(\\n \\\"This method needs to be implemented by a derived class\\\"\\n )\",\n \"def track(self, offset: int):\",\n \"def detect_with_tracking(self, video_path, k_frames, per_frames = 1, offset = None):\\n\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n \\n current_face_id = 0\\n current_frame = 0\\n \\n face_trackers = {}\\n confidence = {}\\n\\n info = []\\n \\n cap = cv2.VideoCapture(video_path)\\n \\n if not cap.isOpened():\\n raise Exception(\\\"Video file does not exist or is invalid\\\")\\n\\n if offset:\\n cap.set(cv2.CAP_PROP_POS_MSEC, offset)\\n \\n while cap.isOpened() :\\n ret, frame = cap.read()\\n if ret:\\n if cap.get(cv2.CAP_PROP_POS_FRAMES) % per_frames == 0:\\n face_ids_to_delete = []\\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\\n for fid in face_trackers.keys():\\n tracking_quality = face_trackers[ fid ].update( frame )\\n\\n if tracking_quality < 7:\\n face_ids_to_delete.append( fid )\\n\\n for fid in face_ids_to_delete:\\n face_trackers.pop(fid)\\n\\n if (cap.get(cv2.CAP_PROP_POS_FRAMES) % k_frames)==0 or cap.get(cv2.CAP_PROP_POS_FRAMES) == 1:\\n faces_info = self.detect_faces_from_image(frame,\\n desired_width=224, desired_height=224) \\n if faces_info:\\n for element in faces_info:\\n bbox = element[0][0]\\n confidence[ current_face_id ] = round(element[5], 3)\\n x = bbox.left()\\n y = bbox.top()\\n width = bbox.width()\\n height = bbox.height()\\n\\n x_center = x + 0.5 * width\\n y_center = y + 0.5 * height\\n\\n matched_fid = None\\n\\n for fid in face_trackers.keys():\\n tracked_position = face_trackers[fid].get_position()\\n\\n t_x = int(tracked_position.left())\\n t_y = int(tracked_position.top())\\n t_w = int(tracked_position.width())\\n t_h = int(tracked_position.height())\\n\\n t_x_center = t_x + 0.5 * t_w\\n t_y_center = t_y + 0.5 * t_h\\n\\n if ( ( t_x <= x_center <= (t_x + t_w)) and \\n ( t_y <= y_center <= (t_y + t_h)) and \\n ( x <= t_x_center <= (x + width)) and \\n ( y <= t_y_center <= (y + height))):\\n matched_fid = fid\\n\\n if matched_fid is None:\\n\\n tracker = dlib.correlation_tracker()\\n tracker.start_track(frame,\\n dlib.rectangle( x,\\n y,\\n x+width,\\n y+height))\\n\\n face_trackers[ current_face_id ] = tracker\\n current_face_id += 1\\n\\n for fid in face_trackers.keys():\\n t_x, t_y, t_w, t_h, label, decision_value = self._process_tracked_face(fid, face_trackers, frame)\\n t_bbox = dlib.rectangle(t_x, t_y, t_x+t_w, t_y+t_h)\\n info.append([\\n cap.get(cv2.CAP_PROP_POS_FRAMES), fid, t_bbox, (t_w, t_h), label,\\n decision_value, confidence[fid]\\n ])\\n\\n\\n else: \\n break\\n cap.release()\\n track_res = pd.DataFrame.from_records(info, columns = ['frame', 'faceid', 'bb', 'size','label', 'decision', 'conf'])\\n info = _smooth_labels(track_res)\\n \\n return info\",\n \"def __init__(self, video_w, video_h, video_fps, tracking_result):\\n self.font = cv2.FONT_HERSHEY_SIMPLEX\\n self.fontScale = 1\\n self.thickness = 2\\n self.thicknessUpdate = 3\\n self.color = (238, 221, 192) # A surfrider color\\n self.icons = get_icons()\\n self.classes_to_icons = {'bottles':self.icons[0], 'fragments':self.icons[1], 'others':self.icons[2]}\\n self.video_w = video_w\\n self.video_h = video_h\\n self.video_fps = video_fps\\n self.tracking_result = tracking_result\\n self.detection_image_size = (1024, 768)\\n self.frames_to_boxes_dict = None\\n self.frames_to_update_hud = None\",\n \"def process_video(data_info, name, mode, is_training=True):\\r\\n data = Action_Dataset(name, mode, [data_info])\\r\\n if is_training:\\r\\n clip_seq, label_seq = data.next_batch(1, _CLIP_SIZE)\\r\\n else:\\r\\n clip_seq, label_seq = data.next_batch(\\r\\n 1, _EACH_VIDEO_TEST_SIZE+1, shuffle=False, data_augment=False)\\r\\n clip_seq = 2*(clip_seq/255) - 1\\r\\n clip_seq = np.array(clip_seq, dtype='float32')\\r\\n return clip_seq, label_seq\",\n \"def _track_finished(self, *_args):\\n if not self.loop:\\n self.stop()\\n else:\\n self.seek(0.)\\n self.player.play()\",\n \"def run(self):\\n for index, playlist_line in enumerate(self.playlist):\\n gv.logger.info(\\\"New video\\\")\\n self.index = index\\n self.video_command = playlist_line.strip(\\\" \\\")\\n gv.logger.info(self.video_command)\\n if gv.api_dm.probe_status == \\\"killed\\\":\\n gv.logger.info(\\\"Exiting playlist\\\")\\n break\\n self.launch_video_process()\\n gv.logger.info(\\\"Finished video\\\")\\n self.finish_playlist()\",\n \"def stabilization(videopath,\\n smoothing_radius=50,\\n fixed_area=[0, -1, 0, -1],\\n stab_points=200):\\n\\n # Read original video\\n # Extract directory and videoname for saving purposes\\n capture = cv2.VideoCapture(videopath)\\n directory, videoname = os.path.split(videopath)\\n videoname = os.path.splitext(videoname)[0]\\n\\n # Get number of frames\\n # Get width and height of video stream\\n # Get frames per second (fps)\\n n_frames = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))\\n w = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))\\n h = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n fps = capture.get(cv2.CAP_PROP_FPS)\\n\\n # Set up output video\\n out = cv2.VideoWriter(os.path.join(directory, str(videoname)+'_stabilized.mp4'),\\n -1,\\n fps,\\n (w, h))\\n\\n # Read first frame\\n # Convert frame to grayscale and extract part of image\\n _, prev = capture.read()\\n xmin, xmax, ymin, ymax = fixed_area\\n prev_gray = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)[ymin:ymax, xmin:xmax]\\n\\n # Pre-define transformation-store array\\n transforms = np.zeros((n_frames-1, 3), np.float32)\\n\\n # Filling in transformation array per frameset\\n with tqdm(total=2*(n_frames-2), ncols=50) as pbar:\\n for i in range(n_frames-2):\\n # Detect feature points in previous frame\\n prev_pts = cv2.goodFeaturesToTrack(prev_gray,\\n maxCorners=stab_points,\\n qualityLevel=0.1,\\n minDistance=100,\\n blockSize=10)\\n # Read next frame\\n # If not success: break loop\\n success, curr = capture.read()\\n if not success:\\n break\\n\\n # Convert to grayscale and extract part of image\\n # Calculate optical flow (i.e. track feature points)\\n # Sanity check\\n curr_gray = cv2.cvtColor(curr, cv2.COLOR_BGR2GRAY)[ymin:ymax,\\n xmin:xmax]\\n curr_pts, status, err = cv2.calcOpticalFlowPyrLK(prev_gray,\\n curr_gray,\\n prev_pts,\\n None)\\n assert prev_pts.shape == curr_pts.shape\\n\\n # Filter only valid points\\n idx = np.where(status == 1)[0]\\n prev_pts = prev_pts[idx]\\n curr_pts = curr_pts[idx]\\n\\n # Find transformation matrix\\n m = cv2.estimateAffinePartial2D(prev_pts, curr_pts)[0]\\n\\n # Extract translation\\n # Extract rotation angle\\n dx = m[0, 2]\\n dy = m[1, 2]\\n da = np.arctan2(m[1, 0], m[0, 0])\\n\\n # Store transformation\\n transforms[i] = [dx, dy, da]\\n\\n # Move to next frame\\n prev_gray = curr_gray\\n\\n pbar.update(1)\\n\\n # Compute trajectory using cumulative sum of transformations\\n # Create variable to store smoothed trajectory\\n # Calculate diference in smoothed_trajectory and trajectory\\n trajectory = np.cumsum(transforms, axis=0)\\n smoothed_trajectory = smooth(trajectory, smoothing_radius)\\n difference = smoothed_trajectory - trajectory\\n\\n # Calculate newer transformation array\\n transform_smooth = transforms + difference\\n\\n # Reset stream to first frame\\n capture.set(cv2.CAP_PROP_POS_FRAMES, 0)\\n\\n # Apply transformations to video\\n for i in range(n_frames-2):\\n # Read next frame\\n success, frame = capture.read()\\n if not success:\\n break\\n\\n # Extract transformations from the new transformation array\\n dx = transform_smooth[i, 0]\\n dy = transform_smooth[i, 1]\\n da = transform_smooth[i, 2]\\n\\n # Reconstruct transformation matrix accordingly to new values\\n m = np.zeros((2, 3), np.float32)\\n m[0, 0] = np.cos(da)\\n m[0, 1] = -np.sin(da)\\n m[1, 0] = np.sin(da)\\n m[1, 1] = np.cos(da)\\n m[0, 2] = dx\\n m[1, 2] = dy\\n\\n # Apply affine wrapping to the given frame\\n # Fix border artifacts\\n frame_stabilized = cv2.warpAffine(frame, m, (w, h))\\n frame_stabilized = fixBorder(frame_stabilized)\\n\\n # Save new frame\\n out.write(frame_stabilized)\\n\\n pbar.update(1)\\n\\n # Release original and stabilized video\\n capture.release()\\n out.release()\",\n \"def process_Video(cap, out):\\r\\n count = 0\\r\\n\\r\\n while(cap.isOpened()):\\r\\n ret, frame = cap.read()\\r\\n\\r\\n if(ret == True):\\r\\n if(count % __speed__ == 0):\\r\\n out.write(frame)\\r\\n\\r\\n if(cv.waitKey(1) & 0xFF == ord('q')):\\r\\n break\\r\\n\\r\\n count += 1\\r\\n else:\\r\\n break\",\n \"def update(self,startTime):\\r\\n if self.updateLock.locked():\\r\\n return\\r\\n with self.updateLock:\\r\\n # Calculate elapsed time\\r\\n now = time.time()\\r\\n elapsedTime = now-startTime+self.app.dataOffset\\r\\n if self.app.videoPlayer.state == VideoPlayer.State.PLAYING:\\r\\n self.progress = elapsedTime\\r\\n elif self.app.videoPlayer.state == VideoPlayer.State.PAUSED or self.app.videoPlayer.state == VideoPlayer.State.STOPPED:\\r\\n self.progress = self.app.videoPlayer.progress+self.app.dataOffset\\r\\n\\r\\n scalex,_ = self.getScale()\\r\\n\\r\\n # Move the scrubber to the right place\\r\\n timespan = 1/self.samplerate * (scalex[1]-scalex[0])# Time represented by canvas width, in seconds\\r\\n scalex_secs = [scalex[0]/self.samplerate,scalex[1]/self.samplerate]# Start and end of plot, in seconds\\r\\n x = ((elapsedTime-scalex_secs[0])/(scalex_secs[1]-scalex_secs[0]))*self.w\\r\\n self.drawScrubber()\\r\\n\\r\\n self.scaleAroundX(x)\",\n \"def parse():\\n all_players = list(FACE_IMAGE_LOCATIONS.keys())\\n face_encodings = VideoParser.__load_faces_encodings(all_players)\\n player_occurrences = VideoParser.__get_player_occurrences(all_players, face_encodings)\\n VideoParser.__save_parsed_video(player_occurrences)\",\n \"def detect_markers_in_video(source_path, source_type=\\\"auto\\\", decoder_pipeline=None, pipeline_factory=None,\\n tag_pixel_diameter=30.0, timestamps=None,\\n start_timestamp=None, fps=3.0, cam_id=0,\\n verbose=False, n_frames=None, progress=tqdm.auto.tqdm,\\n calculate_confidences=True, confidence_filter=None,\\n use_parallel_jobs=False, clahe=False):\\n if source_type == \\\"auto\\\":\\n if isinstance(source_path, str):\\n if source_path.endswith(\\\"jpg\\\") or source_path.endswith(\\\"png\\\"):\\n source_type = \\\"image\\\"\\n else:\\n source_type = \\\"video\\\"\\n elif isinstance(source_path, list):\\n source_type = \\\"image\\\"\\n calculate_confidences = calculate_confidences or confidence_filter is not None\\n scale = 30.0 / tag_pixel_diameter\\n if decoder_pipeline is None and pipeline_factory is None:\\n decoder_pipeline = get_default_pipeline()\\n\\n if timestamps is None:\\n def generate_timestamps(start_from):\\n def gen():\\n i = 0.0\\n while True:\\n yield start_from + i * 1.0 / fps\\n i += 1.0\\n yield from gen()\\n timestamps = generate_timestamps(start_timestamp or 0.0)\\n else:\\n timestamps = list(timestamps) # Evaluate generators.\\n ts = timestamps[0]\\n if not isinstance(ts, (np.floating, float)):\\n if not ts.tzinfo or int(ts.utcoffset().total_seconds()) != 0:\\n raise ValueError(\\\"timestamps argument must be iterable of floats or UTC datetime objects with timezones.\\\")\\n timestamps = [t.timestamp() for t in timestamps]\\n import skimage.transform\\n import pipeline as bb_pipeline\\n \\n interrupted = False\\n def interruptable_frame_generator(gen):\\n nonlocal interrupted\\n for g in gen:\\n yield g\\n if interrupted:\\n print(\\\"Stopping early...\\\")\\n break\\n \\n def preprocess_image(im):\\n if clahe:\\n im = skimage.exposure.equalize_adapthist(im, kernel_size=3 * tag_pixel_diameter)\\n im = (skimage.transform.rescale(im, scale, order=1, multichannel=False, anti_aliasing=False, mode='constant') * 255).astype(np.uint8)\\n return im\\n\\n def get_frames_from_images():\\n import skimage.io\\n for idx, (path, ts) in enumerate(zip(interruptable_frame_generator(source_path), timestamps)):\\n if type(path) is str:\\n im = skimage.io.imread(path, as_gray=True) \\n else:\\n im = path\\n im = preprocess_image(im)\\n yield idx, ts, im\\n\\n if n_frames is not None and idx >= n_frames - 1:\\n break\\n\\n def get_frames_from_video():\\n frames_generator = bb_pipeline.io.raw_frames_generator(source_path, format=None)\\n \\n for idx, (im, ts) in enumerate(zip(interruptable_frame_generator(frames_generator), timestamps)):\\n assert im is not None\\n # Skip broken videos.\\n if im.shape[0] <= 0:\\n print(\\\"Warning: Could not read frame {} of file {}. The video is corrupt.\\\".format(idx, source_path))\\n break\\n # Skip broken frames because clahe would fail on constant input.\\n if im.min() == im.max():\\n print(\\\"Warning: Frame {} of file {} is empty.\\\".format(idx, source_path))\\n continue\\n \\n assert im.shape[0] > 0\\n assert im.shape[1] > 0\\n\\n im = preprocess_image(im)\\n\\n yield idx, ts, im\\n \\n if n_frames is not None and idx >= n_frames - 1:\\n break\\n \\n def get_detections_from_frame(idx, ts, im, thread_context=None, **kwargs):\\n nonlocal decoder_pipeline\\n if decoder_pipeline is not None:\\n pipeline_results = decoder_pipeline([im])\\n else:\\n if \\\"pipeline\\\" not in thread_context:\\n thread_context[\\\"pipeline\\\"] = pipeline_factory()\\n pipeline_results = thread_context[\\\"pipeline\\\"]([im])\\n \\n #confident_ids = [r for c, r in zip(confidences, decoded_ids) if c >= threshold]\\n #decimal_ids = set([ids.BeesbookID.from_bb_binary(i).as_ferwar() for i in confident_ids])\\n\\n if verbose:\\n import pipeline.objects\\n from pipeline.stages.visualization import ResultCrownVisualizer\\n import matplotlib.pyplot as plt\\n crowns = pipeline_results[pipeline.objects.CrownOverlay]\\n frame = ResultCrownVisualizer.add_overlay(im.astype(np.float64) / 255, crowns)\\n fig, ax = plt.subplots(figsize=(20, 10))\\n plt.imshow(frame)\\n plt.axis(\\\"off\\\")\\n plt.show()\\n \\n frame_id = bb_pipeline.io.unique_id()\\n required_results = pipeline_results[PipelineResult] \\n n_detections = required_results.orientations.shape[0]\\n decoded_ids = [list(r) for r in list(required_results.ids)]\\n\\n if n_detections > 0:\\n frame_data = {\\n \\\"localizerSaliency\\\": required_results.tag_saliencies.flatten(),\\n \\\"beeID\\\": decoded_ids,\\n \\\"xpos\\\": required_results.tag_positions[:, 1] / scale,\\n \\\"ypos\\\": required_results.tag_positions[:, 0] / scale,\\n \\\"camID\\\": [cam_id] * n_detections, \\n \\\"zrotation\\\": required_results.orientations[:, 0],\\n \\\"timestamp\\\": [ts] * n_detections,\\n \\\"frameIdx\\\": [idx] * n_detections,\\n \\\"frameId\\\": frame_id,\\n \\\"detection_index\\\": range(n_detections),\\n \\\"detection_type\\\": \\\"TaggedBee\\\"\\n }\\n \\n frame_data = pd.DataFrame(frame_data)\\n\\n if calculate_confidences:\\n confidences = np.array([np.product(np.abs(0.5 - np.array(r)) * 2) for r in decoded_ids])\\n frame_data[\\\"confidence\\\"] = confidences\\n if confidence_filter is not None:\\n frame_data = frame_data[frame_data.confidence >= confidence_filter]\\n\\n else:\\n frame_data = None\\n\\n n_bees = required_results.bee_positions.shape[0]\\n\\n if n_bees > 0:\\n bee_data = {\\n \\\"localizerSaliency\\\": required_results.bee_saliencies.flatten(),\\n \\\"beeID\\\": [np.nan] * n_bees,\\n \\\"xpos\\\": required_results.bee_positions[:, 1] / scale,\\n \\\"ypos\\\": required_results.bee_positions[:, 0] / scale,\\n \\\"camID\\\": [cam_id] * n_bees, \\n \\\"zrotation\\\": [np.nan] * n_bees,\\n \\\"timestamp\\\": [ts] * n_bees,\\n \\\"frameIdx\\\": [idx] * n_bees,\\n \\\"frameId\\\": frame_id,\\n \\\"detection_index\\\": range(n_bees),\\n \\\"detection_type\\\": required_results.bee_types\\n }\\n\\n if calculate_confidences:\\n bee_data[\\\"confidence\\\"] = [np.nan] * n_bees\\n\\n bee_data = pd.DataFrame(bee_data)\\n\\n if frame_data is not None:\\n frame_data = pd.concat((frame_data, bee_data))\\n else:\\n frame_data = bee_data\\n\\n return idx, frame_id, ts, frame_data\\n \\n \\n progress_bar = None\\n if progress is not None:\\n progress_bar = progress(total=n_frames)\\n \\n def save_frame_data(idx, frame_id, ts, frame_data, **kwargs):\\n nonlocal frame_info\\n nonlocal video_dataframe\\n frame_info.append((idx, frame_id, ts))\\n if frame_data is not None:\\n video_dataframe.append(frame_data)\\n if progress is not None:\\n progress_bar.update()\\n \\n source = get_frames_from_video\\n if source_type == \\\"image\\\":\\n source = get_frames_from_images\\n if isinstance(source_path, str):\\n source_path = [source_path]\\n \\n if use_parallel_jobs:\\n from ..utils.processing import ParallelPipeline\\n\\n thread_context_factory = None\\n n_pipeline_jobs = 1\\n if not decoder_pipeline:\\n # The thread context is used to provide each thread with a unique pipeline object.\\n class Ctx():\\n def __enter__(self):\\n return dict()\\n def __exit__(self, *args):\\n pass\\n \\n n_pipeline_jobs = 4\\n thread_context_factory = Ctx\\n\\n jobs = ParallelPipeline([source, get_detections_from_frame, save_frame_data],\\n n_thread_map={0: 1, 1: n_pipeline_jobs},\\n thread_context_factory=thread_context_factory)\\n else:\\n def sequential_jobs():\\n for im in source():\\n if im is not None:\\n detections = get_detections_from_frame(*im)\\n save_frame_data(*detections)\\n jobs = sequential_jobs\\n\\n frame_info = []\\n video_dataframe = []\\n try:\\n jobs()\\n except KeyboardInterrupt:\\n interrupted = True\\n frame_info = list(sorted(frame_info))\\n if len(video_dataframe) > 0:\\n video_dataframe = pd.concat(video_dataframe)\\n video_dataframe.sort_values(\\\"frameIdx\\\", inplace=True)\\n # Enfore frame ID datatype to be unsigned which may have gotten lost when concatenating the data frames.\\n video_dataframe.frameId = video_dataframe.frameId.astype(np.uint64)\\n else:\\n video_dataframe = None\\n \\n return frame_info, video_dataframe\",\n \"def getPosition(self, time: float, view: Optional[Str] = ...) -> CVec3:\\n ...\",\n \"def run_tracker(p):\\n # load model\\n net = torch.load(os.path.join(p.net_base_path, p.net))\\n net = net.to(device)\\n\\n # evaluation mode\\n net.eval()\\n\\n # load sequence\\n img_list, target_position, target_size = load_sequence(p.seq_base_path, p.video)\\n\\n # first frame\\n img_uint8 = cv2.imread(img_list[0])\\n img_uint8 = cv2.cvtColor(img_uint8, cv2.COLOR_BGR2RGB)\\n img_double = np.double(img_uint8) # uint8 to float\\n\\n # compute avg for padding\\n avg_chans = np.mean(img_double, axis=(0, 1))\\n\\n wc_z = target_size[1] + p.context_amount * sum(target_size)\\n hc_z = target_size[0] + p.context_amount * sum(target_size)\\n s_z = np.sqrt(wc_z * hc_z)\\n scale_z = p.examplar_size / s_z\\n\\n # crop examplar z in the first frame\\n z_crop = get_subwindow_tracking(img_double, target_position, p.examplar_size, round(s_z), avg_chans)\\n\\n z_crop = np.uint8(z_crop) # you need to convert it to uint8\\n # convert image to tensor\\n z_crop_tensor = 255.0 * F.to_tensor(z_crop).unsqueeze(0)\\n\\n d_search = (p.instance_size - p.examplar_size) / 2\\n pad = d_search / scale_z\\n s_x = s_z + 2 * pad\\n # arbitrary scale saturation\\n min_s_x = p.scale_min * s_x\\n max_s_x = p.scale_max * s_x\\n\\n # generate cosine window\\n if p.windowing == 'cosine':\\n window = np.outer(np.hanning(p.score_size * p.response_UP), np.hanning(p.score_size * p.response_UP))\\n elif p.windowing == 'uniform':\\n window = np.ones((p.score_size * p.response_UP, p.score_size * p.response_UP))\\n window = window / sum(sum(window))\\n\\n # pyramid scale search\\n scales = p.scale_step**np.linspace(-np.ceil(p.num_scale/2), np.ceil(p.num_scale/2), p.num_scale)\\n\\n # extract feature for examplar z\\n z_features = net.feat_extraction(Variable(z_crop_tensor).to(device))\\n z_features = z_features.repeat(p.num_scale, 1, 1, 1)\\n\\n # do tracking\\n bboxes = np.zeros((len(img_list), 4), dtype=np.double) # save tracking result\\n start_time = datetime.datetime.now()\\n for i in range(0, len(img_list)):\\n if i > 0:\\n # do detection\\n # currently, we only consider RGB images for tracking\\n img_uint8 = cv2.imread(img_list[i])\\n img_uint8 = cv2.cvtColor(img_uint8, cv2.COLOR_BGR2RGB)\\n img_double = np.double(img_uint8) # uint8 to float\\n\\n scaled_instance = s_x * scales\\n scaled_target = np.zeros((2, scales.size), dtype = np.double)\\n scaled_target[0, :] = target_size[0] * scales\\n scaled_target[1, :] = target_size[1] * scales\\n\\n # extract scaled crops for search region x at previous target position\\n x_crops = make_scale_pyramid(img_double, target_position, scaled_instance, p.instance_size, avg_chans, p)\\n\\n # get features of search regions\\n x_crops_tensor = torch.FloatTensor(x_crops.shape[3], x_crops.shape[2], x_crops.shape[1], x_crops.shape[0])\\n # response_map = SiameseNet.get_response_map(z_features, x_crops)\\n for k in range(x_crops.shape[3]):\\n tmp_x_crop = x_crops[:, :, :, k]\\n tmp_x_crop = np.uint8(tmp_x_crop)\\n # numpy array to tensor\\n x_crops_tensor[k, :, :, :] = 255.0 * F.to_tensor(tmp_x_crop).unsqueeze(0)\\n\\n # get features of search regions\\n x_features = net.feat_extraction(Variable(x_crops_tensor).to(device))\\n\\n # evaluate the offline-trained network for exemplar x features\\n target_position, new_scale = tracker_eval(net, round(s_x), z_features, x_features, target_position, window, p)\\n\\n # scale damping and saturation\\n s_x = max(min_s_x, min(max_s_x, (1 - p.scale_LR) * s_x + p.scale_LR * scaled_instance[int(new_scale)]))\\n target_size = (1 - p.scale_LR) * target_size + p.scale_LR * np.array([scaled_target[0, int(new_scale)], scaled_target[1, int(new_scale)]])\\n\\n rect_position = np.array([target_position[1]-target_size[1]/2, target_position[0]-target_size[0]/2, target_size[1], target_size[0]])\\n\\n if p.visualization:\\n visualize_tracking_result(img_uint8, rect_position, 1)\\n\\n # output bbox in the original frame coordinates\\n o_target_position = target_position\\n o_target_size = target_size\\n bboxes[i,:] = np.array([o_target_position[1]-o_target_size[1]/2, o_target_position[0]-o_target_size[0]/2, o_target_size[1], o_target_size[0]])\\n\\n end_time = datetime.datetime.now()\\n fps = len(img_list)/max(1.0, (end_time-start_time).seconds)\\n\\n return bboxes, fps\",\n \"def get_video_position_in_show(self, video_id, **kwargs):\\n kwargs['video_id'] = video_id\\n return self.get('video_position_in_show', **kwargs)\",\n \"def display_video_stream(self):\\n _, frame = self.capture.read()\\n\\n if frame is None:\\n return\\n\\n # hand rectangle dimensions\\n x0 = self.video_size.height() - int(self.handrect_y + self.handrect_height / 2.)\\n x1 = self.video_size.height() - int(self.handrect_y - self.handrect_height / 2.)\\n y0 = int(self.handrect_x - self.handrect_width / 2.)\\n y1 = int(self.handrect_x + self.handrect_width / 2.)\\n cv2_p0 = (y0, x0)\\n cv2_p1 = (y1, x1)\\n self.video_size.width()\\n\\n frame, count_defects = find_gesture(frame, hand_p0=cv2_p1, hand_p1=cv2_p0,\\n invert=self.checkBox_invert.isChecked())\\n\\n if count_defects == Gestures.ONE.value:\\n cv2.putText(frame, \\\"closed fist\\\", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)\\n elif count_defects == 2:\\n cv2.putText(frame, \\\"Two fingers\\\", (5, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, 2)\\n\\n elif count_defects == 3:\\n cv2.putText(frame, \\\"three fingers\\\", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)\\n elif count_defects == 4:\\n cv2.putText(frame, \\\"four fingers\\\", (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)\\n else:\\n cv2.putText(frame, \\\"five fingers\\\", (50, 50), \\\\\\n cv2.FONT_HERSHEY_SIMPLEX, 2, 2)\\n\\n if count_defects != self.current_gesture or count_defects == Gestures.ONE.value:\\n # update progress time\\n self.gesture_time = time()\\n self.progressBar.setValue(0)\\n else:\\n self.current_gesture = count_defects\\n\\n dt = time() - self.gesture_time\\n\\n if dt < self.maxdt:\\n # update progress bar\\n self.progressBar.setValue(int(100*dt/self.maxdt))\\n else:\\n # trigger action\\n cbox = self.comboboxes[max(min(4, count_defects-2), 0)]\\n func_idx = cbox.currentIndex()\\n callfunc = cbox.itemData(func_idx)\\n callfunc()\\n self.labelStatus.setText(\\\"{}, {}\\\".format(count_defects, cbox.itemText(func_idx)))\\n\\n # reset progress time\\n self.gesture_time = time()\\n\\n self.current_gesture = count_defects\\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\\n image = QImage(frame, frame.shape[1], frame.shape[0],\\n frame.strides[0], QImage.Format_RGB888)\\n self.image_label.setPixmap(QPixmap.fromImage(image))\",\n \"def read(self, ifDrawAxis=None, ifDrawSquare=None):\\n if(self.isLoaded() == False):\\n return\\n self.ret, frame = self.video.read()\\n if (self.ret == True):\\n \\n # Draw frame\\n self.currentFrame = frame\\n self.currentFrameNumber = int(self.video.get(cv2.CAP_PROP_POS_FRAMES))\\n \\n else: # If the video is end\\n self.isPlaying = False\\n \\n return self.frame(ifDrawAxis, ifDrawSquare)\",\n \"def change_track_play_position(value):\\n\\n global track_last_slided_pos\\n\\n cancel_update_play_time_loop()\\n track_last_slided_pos = value\\n\\n # If the track is playing, send slider value to update_play_time()\\n if track_status.get() == \\\"(Playing)\\\":\\n update_play_time(value)\\n # else just update track position Label\\n else:\\n # Convert to format minutes:seconds\\n current_time_formated = strftime(\\n '%M:%S', gmtime(float(track_pos.get())))\\n track_pos_label.configure(text=current_time_formated)\",\n \"def videotime_end(self):\\n # type: () -> int\\n return self._videotime_end\",\n \"def __handle(self):\\n\\n if not self.entrada.closed:\\n # Control por tiempo\\n #self.entrada.write(\\\"%s 0\\\\n\\\" % (\\\"get_time_length\\\"))\\n #self.entrada.flush()\\n #duracion = self.salida.readline()\\n #if \\\"ANS_LENGTH\\\" in duracion:\\n # duracion = float(duracion.split(\\\"=\\\")[1]) # en Segundos\\n # print \\\"dur\\\", duracion\\n\\n #self.entrada.write(\\\"%s 0\\\\n\\\" % (\\\"get_time_pos\\\"))\\n #self.entrada.flush()\\n #posicion = self.salida.readline()\\n #if \\\"ANS_TIME_POSITION\\\" in posicion:\\n # posicion = float(posicion.split(\\\"=\\\")[1]) # en Segundos\\n # print \\\"pos\\\", posicion\\n\\n self.entrada.write(\\\"%s 0\\\\n\\\" % (\\\"get_property percent_pos\\\"))\\n self.entrada.flush()\\n linea = self.salida.readline()\\n\\n if linea:\\n if \\\"ANS_percent_pos\\\" in linea:\\n # Información sobre el porcentaje Reproducido\\n # hasta el momento. Ejemplo:\\n # ANS_percent_pos=0\\n\\n self.__get_progress_in_mplayer(linea)\\n self.get_volumen()\\n\\n elif \\\"Video: no video\\\" in linea or \\\\\\n \\\"Audio only file format detected\\\" in linea:\\n # Cuando no hay video en la fuente. Ejemplo:\\n # Audio only file format detected.\\n # Video: no video\\n\\n if self.video_in_stream:\\n self.video_in_stream = False\\n self.emit(\\\"video\\\", False)\\n\\n elif \\\"Movie-Aspect\\\" in linea:\\n # Información sobre el aspecto del video. Ejemplo:\\n # Movie-Aspect is 1.78:1 - prescaling to\\n # correct movie aspect.\\n\\n if not self.video_in_stream:\\n self.video_in_stream = True\\n self.emit(\\\"video\\\", True)\\n\\n elif \\\"Cache\\\" in linea:\\n # Información Sobre Carga de caché. Ejemplo:\\n # Cache fill: 6.25% (65536 bytes)\\n # self.get_progress_cache_in_mplayer(linea)\\n\\n pass\\n\\n elif \\\"Starting playback\\\" in linea:\\n # Cuando comienza la Reproducción. Ejemplo:\\n # Starting playback...\\n\\n self.estado = \\\"playing\\\"\\n self.emit(\\\"estado\\\", \\\"playing\\\")\\n\\n elif \\\"AO:\\\" in linea:\\n # Información Sobre Audio en la pista. Ejemplo:\\n # AO: [pulse] 44100Hz 2ch s16le (2 bytes per sample)\\n\\n pass\\n\\n elif \\\"VO:\\\" in linea:\\n # Información sobre Video en la pista. Ejemplo:\\n # VO: [xv] 428x240 => 428x240 Planar YV12\\n\\n pass\\n\\n elif \\\"Resolving\\\" in linea:\\n # Información sobre Resolución de Streamings. Ejemplo:\\n # Resolving radio1.oceanofm.com for AF_INET6...\\n\\n pass\\n\\n elif \\\"Connecting\\\" in linea:\\n # Información sobre Conexión a un Streaming. Ejemplo:\\n # Connecting to server\\n # main-office.rautemusik.fm[87.230.101.9]: 80...\\n\\n pass\\n\\n elif \\\"Name\\\" in linea:\\n # El nombre de una streaming de Radio. Ejemplo:\\n # Name : #MUSIK.MAIN - WWW.RAUTEMUSIK.FM - 24H\\n # TOP 40 POP HITS 80S 90S DANCE HOUSE ROCK RNB AND MORE!\\n\\n pass\\n\\n elif \\\"Playing\\\" in linea:\\n # La Pista que se está reproduciendo. Ejemplo:\\n # Playing /media/4E432D364BC64012/E - Videos/Tylor\\n # swift/Back to December-Taylor Swift Lyrics.\\n\\n pass\\n\\n elif \\\"Genre\\\" in linea or \\\\\\n \\\"Website\\\" in linea or \\\"Bitrate\\\" in linea:\\n # Información Sobre un Streaming de Radio. Ejemplo:\\n # Genre : Pop Rock Top 40 RnB 80s\\n # Website: http://www.RauteMusik.FM/\\n # Bitrate: 128kbit/s\\n\\n pass\\n\\n elif \\\"Opening\\\" in linea or \\\\\\n \\\"AUDIO\\\" in linea or \\\"Selected\\\" in linea:\\n # Información sobre Codecs Utilizados. Ejemplo:\\n # Opening video decoder: [ffmpeg] FFmpeg's\\n # libavcodec codec family\\n # Selected video codec: [ffh264] vfm: ffmpeg (FFmpeg H.264)\\n # Opening audio decoder: [ffmpeg]\\n # FFmpeg/libavcodec audio decoders\\n # AUDIO: 44100 Hz, 2 ch, s16le, 98.6 kbit/6.99%\\n # (ratio: 12323->176400)\\n # Selected audio codec: [ffaac] afm: ffmpeg\\n # (FFmpeg AAC (MPEG-2/MPEG-4 Audio))\\n\\n pass\\n\\n else:\\n # Información Diversa. Ejemplo:\\\"\\n # Failed to open /dev/rtc: Permission denied\\n # (it should be readable by the us\\\"\\n # Unsupported PixelFormat 61\\\"\\n # Unsupported PixelFormat 53\\\"\\n # Unsupported PixelFormat 81\\\"\\n # eo (h264), -vid 0\\\"\\n # [lavf] stream 1: audio (aac), -aid 0\\\"\\n # VIDEO: [H264] 640x480 0bpp 30.000 fps\\n # 218.3 kbps (26.6 kbyte/s)\\\"\\n # Clip info:\\\"\\n # starttime: 0\\\"\\n # totalduration: 226\\\"\\n # totaldatarate: 338\\\"\\n # bytelength: 9570679\\\"\\n # canseekontime: true\\\"\\n # sourcedata: BC8280065HH1341966475963833\\\"\\n # purl: \\\"\\n # pmsg: \\\"\\n # Load subtitles in /media/4E432D364BC64012/E -\\n # Videos/Tylor swift/\\\"\\n\\n # libavcodec version 53.35.0 (external)\\\"\\n # Mismatching header version 53.32.2\\\"\\n\\n # A: 0.0 V: 0.0 A-V: 0.014 ct: 0.000 0/\\n # 0 ??% ??% ??,?% 0 0 90%\\\"\\n # A: 123.2 V: 123.2 A-V: -0.000 ct: 0.033 0/\\n # 0 2% 1% 0.4% 0 0 50%\\\"\\n\\n # Cuando no se puede Acceder a un Streaming. Ejemplo:\\\"\\n # Failed to get value of property 'percent_pos'.\\\"\\n # Failed to get value of property\\n # percent_ANS_ERFailed to get value of\\\"\\n # prANS_ERROR=PROPERTY_UNAFailed\\n # toANS_ERROR=PROPERTY_UNAVAILABLE\\\"\\n # ANS_ERROR=PROPERTY_UNAVAILABLE\\\"\\n\\n pass\\n\\n return True\",\n \"def get_frame(self, camera: int = 0) -> Tuple[float, np.ndarray]:\\n result = self.video.read()\\n if result[0]:\\n return result\\n else: # If we reach the end of the video, go back to the beginning.\\n self.video.set(cv2.CAP_PROP_POS_FRAMES, 0)\\n return self.video.read()\",\n \"def video_config(FILENAME):\\n\\n\\tcap = cv2.VideoCapture(FILENAME)\\n\\twhile not cap.isOpened():\\n\\t cap = cv2.VideoCapture(FILENAME)\\n\\t cv2.waitKey(1000)\\n\\t print \\\"Wait for the header\\\"\\n\\tpos_frame = cap.get(cv2.cv.CV_CAP_PROP_POS_FRAMES)\\n\\t\\n\\treturn cap, pos_frame\",\n \"def AnnotateVideo(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\\n raise NotImplementedError()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6350717","0.599357","0.59822786","0.59489673","0.5899728","0.58592576","0.5791302","0.5753723","0.570756","0.5707165","0.5696297","0.5657556","0.56508243","0.56491834","0.5642732","0.562807","0.5619208","0.56147677","0.5613509","0.56129265","0.55933154","0.55625314","0.55245596","0.5514461","0.5497987","0.54972553","0.5491488","0.54790217","0.54782176","0.547745","0.54749537","0.54662114","0.546192","0.5447032","0.542652","0.54242307","0.54242307","0.54209","0.54122734","0.54067177","0.5404212","0.53945726","0.5393117","0.53874385","0.53853154","0.5378585","0.5377319","0.5346498","0.5337498","0.5328054","0.53243065","0.5322081","0.5312552","0.53064287","0.5302847","0.5299848","0.5285781","0.52792364","0.52679956","0.5266724","0.5253951","0.5252757","0.5245133","0.52396196","0.52369195","0.5232339","0.52304244","0.52230227","0.5222903","0.5220355","0.52118075","0.52118075","0.5206794","0.5178201","0.5166747","0.51618814","0.51616246","0.51577157","0.5156954","0.5155033","0.5149922","0.51410735","0.51410234","0.5117255","0.50997376","0.5087769","0.5081953","0.5078893","0.5078179","0.50771224","0.50721776","0.50715995","0.5069445","0.50690395","0.50683326","0.50562143","0.5049873","0.5049595","0.504826","0.50481546"],"string":"[\n \"0.6350717\",\n \"0.599357\",\n \"0.59822786\",\n \"0.59489673\",\n \"0.5899728\",\n \"0.58592576\",\n \"0.5791302\",\n \"0.5753723\",\n \"0.570756\",\n \"0.5707165\",\n \"0.5696297\",\n \"0.5657556\",\n \"0.56508243\",\n \"0.56491834\",\n \"0.5642732\",\n \"0.562807\",\n \"0.5619208\",\n \"0.56147677\",\n \"0.5613509\",\n \"0.56129265\",\n \"0.55933154\",\n \"0.55625314\",\n \"0.55245596\",\n \"0.5514461\",\n \"0.5497987\",\n \"0.54972553\",\n \"0.5491488\",\n \"0.54790217\",\n \"0.54782176\",\n \"0.547745\",\n \"0.54749537\",\n \"0.54662114\",\n \"0.546192\",\n \"0.5447032\",\n \"0.542652\",\n \"0.54242307\",\n \"0.54242307\",\n \"0.54209\",\n \"0.54122734\",\n \"0.54067177\",\n \"0.5404212\",\n \"0.53945726\",\n \"0.5393117\",\n \"0.53874385\",\n \"0.53853154\",\n \"0.5378585\",\n \"0.5377319\",\n \"0.5346498\",\n \"0.5337498\",\n \"0.5328054\",\n \"0.53243065\",\n \"0.5322081\",\n \"0.5312552\",\n \"0.53064287\",\n \"0.5302847\",\n \"0.5299848\",\n \"0.5285781\",\n \"0.52792364\",\n \"0.52679956\",\n \"0.5266724\",\n \"0.5253951\",\n \"0.5252757\",\n \"0.5245133\",\n \"0.52396196\",\n \"0.52369195\",\n \"0.5232339\",\n \"0.52304244\",\n \"0.52230227\",\n \"0.5222903\",\n \"0.5220355\",\n \"0.52118075\",\n \"0.52118075\",\n \"0.5206794\",\n \"0.5178201\",\n \"0.5166747\",\n \"0.51618814\",\n \"0.51616246\",\n \"0.51577157\",\n \"0.5156954\",\n \"0.5155033\",\n \"0.5149922\",\n \"0.51410735\",\n \"0.51410234\",\n \"0.5117255\",\n \"0.50997376\",\n \"0.5087769\",\n \"0.5081953\",\n \"0.5078893\",\n \"0.5078179\",\n \"0.50771224\",\n \"0.50721776\",\n \"0.50715995\",\n \"0.5069445\",\n \"0.50690395\",\n \"0.50683326\",\n \"0.50562143\",\n \"0.5049873\",\n \"0.5049595\",\n \"0.504826\",\n \"0.50481546\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5986019"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":77071,"cells":{"query":{"kind":"string","value":"Detects a barbell's position in a frame"},"document":{"kind":"string","value":"def detect_barbell_pos(self, classifier):\n self._check_vid_open()\n retval, frame = self.video.read()\n if not retval:\n raise IOError('Could not read frame from video')\n\n barbell_pos = classifier.detectMultiScale(frame, 1.1, 25)\n if not barbell_pos.any():\n raise Exception('Classifier detection failed')\n\n for p in barbell_pos:\n circles = self.test_hough_circle(frame[p[1]:p[1]+p[3], p[0]:p[0]+p[2]])\n if circles is not None:\n # If one circle detected, track current position\n if len(circles[0]) == 1:\n # Extract diameter of circle to find cm_multiplier\n pixel_diameter = circles[0, :][0][2] * 2\n cm_multiplier = self._get_cm_per_pixel(pixel_diameter)\n logging.info('CM Multiplier: {}'.format(cm_multiplier))\n return frame, p, cm_multiplier\n\n # If no circles found, return random object\n return frame, barbell_pos[random.randint(0, len(barbell_pos)-1)], None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def buff_region(self, x, y):\n if x>120 and x<220 and y>275 and y<375:\n return 1\n elif x>580 and x<680 and y>125 and y<225:\n return 2\n return 0","def track(self, frame):\n mask = self.get_arrow_mask(frame)\n label = skimage.measure.label(mask)\n props = skimage.measure.regionprops(label)\n\n arrow = max(props, key=lambda prop: prop.area)\n yc, xc = arrow.centroid\n self.position_list.append((xc, yc))\n y0, x0, y1, x1 = arrow.bbox\n self.bbox = (x0, y0, x1, y1)","def position_is_bomb(bombs, position):\n for bomb in bombs:\n if position == bomb.position:\n return True\n return False","def on_fruit(self):\r\n if self.grid_pos in self.app.fruit:\r\n if int(self.pix_pos.x+TOP_BOTTOM_BUFFER//2) % self.app.cell_width == 0:\r\n if self.direction == vec(1, 0) or self.direction == vec(-1, 0):\r\n return True\r\n # in the x-direction \r\n\r\n if int(self.pix_pos.y+TOP_BOTTOM_BUFFER//2) % self.app.cell_height == 0:\r\n if self.direction == vec(0, 1) or self.direction == vec(0, -1):\r\n return True\r\n # in the y-direction\r\n\r\n return False","def bout_detect(raw_motion_data):\n import numpy as np\n window_length = 50 # Most bouts last much lesser than 20 frames,\n # so this is a really safe window to search for bouts.\n nonzero_indices = np.flatnonzero(raw_motion_data)\n all_bout_indices = [] # A list of all the indices that belong to all bouts.\n bout_indices = [] # A list of tuples of bout indices.\n bouts = [] # A list of bouts, stored as tuples.\n # Run through the list of nonzero indices and look for the first zero value\n # after it.\n do_not_append = False\n for i in nonzero_indices:\n if i not in all_bout_indices:\n bout_start = i # Bout starting point\n try:\n first_zero_after_start = np.flatnonzero(\n raw_motion_data[bout_start:bout_start+window_length+1] == 0)[0]\n except IndexError:\n try:\n first_zero_after_start = np.flatnonzero(\n raw_motion_data[bout_start:len(raw_motion_data)] == 0)[0]\n except IndexError:\n first_zero_after_start = len(raw_motion_data)\n do_not_append = True\n if not do_not_append:\n # Adjusted to start and end at 0\n bout = tuple(raw_motion_data[bout_start-1:bout_start+first_zero_after_start+1])\n bout_inds = tuple(range(bout_start-1, bout_start+first_zero_after_start+1))\n bout_indices.append(bout_inds)\n bouts.append(bout)\n for j in range(bout_start, bout_start+first_zero_after_start):\n all_bout_indices.append(j)\n\n # Filter out the bouts list to remove tuples which are smaller than 5 entries long.\n \"\"\"This is not really needed, because the fish can make some tiny movements\n that need to be detected and counted as bouts. In any case, the decision for this\n can be made much later. It is not really important to figure this out right now.\"\"\"\n # bouts[:] = [tup for tup in bouts if len(tup) > 5]\n # bout_indices[:] = [x for x in bout_indices if len(x) > 5]\n return bouts, bout_indices","def ball_detector(out_q_x, out_q_y):\n global ball_before\n global ball_count\n global new_ball\n global bounce\n global frame\n global crop_img\n\n green_color_hsv_min = cv.Scalar(43, 54, 84)\n green_color_hsv_max = cv.Scalar(78, 179, 256)\n orange_color_hsv_min = cv.Scalar(0, 105, 200)\n orange_color_hsv_max = cv.Scalar(11, 256, 256)\n blue_color_hsv_min = cv.Scalar(103, 90, 87)\n blue_color_hsv_max = cv.Scalar(112, 256, 181)\n yellow_color_hsv_min = cv.Scalar(17, 70, 163)\n yellow_color_hsv_max = cv.Scalar(27, 256, 256)\n ball_number = 0\n\n while True:\n ret, frame = cap.read()\n if ret:\n crop_img = frame[SCREEN_Y_TOP:SCREEN_Y_BOT, SCREEN_X_TOP:SCREEN_X_BOT]\n hsv_image = cv2.cvtColor(crop_img, cv2.COLOR_BGR2HSV)\n hsv_blue = cv2.inRange(hsv_image,\n blue_color_hsv_min,\n blue_color_hsv_max\n )\n hsv_orange = cv2.inRange(hsv_image,\n orange_color_hsv_min,\n orange_color_hsv_max\n )\n hsv_green = cv2.inRange(hsv_image,\n green_color_hsv_min,\n green_color_hsv_max\n )\n hsv_yellow = cv2.inRange(hsv_image,\n yellow_color_hsv_min,\n yellow_color_hsv_max\n )\n green_yellow_hsv = cv2.bitwise_or(hsv_green, hsv_yellow)\n green_yellow_blue_hsv = cv2.bitwise_or(green_yellow_hsv, hsv_blue)\n green_yellow_blue_orange_hsv = cv2.bitwise_or(green_yellow_blue_hsv,\n hsv_orange)\n\n str_el = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\n open_morphed = cv2.morphologyEx(green_yellow_blue_orange_hsv,\n cv2.MORPH_OPEN,\n str_el\n )\n morphed = cv2.morphologyEx(open_morphed, cv2.MORPH_CLOSE, str_el)\n\n hsv_blur = cv2.GaussianBlur(morphed, (11, 11), 4, 4)\n circles = cv2.HoughCircles(hsv_blur, cv.CV_HOUGH_GRADIENT, 2, 720 / 9,\n param1=75, param2=45, minRadius=5,\n maxRadius=70)\n ball_count = 0\n\n if circles is not None:\n circles = np.uint16(np.around(circles))\n ball_count = len(circles)\n for i in circles[0, :]:\n\n # create ball object if ball's number is increase\n if is_ball_increase(ball_before, ball_count) and i[2] > 16:\n ball_number += 1\n print \"new ball\"\n balls.append(Ball(i[0], i[1], i[2], ball_number))\n\n # update ball position in ball object\n pos_list = []\n for b in balls:\n pos_list.append([(b.center[0], b.center[1]), b])\n\n if len(pos_list) == 0:\n pass\n elif len(pos_list) == 1:\n pos_list[0][1].update(i[0], i[1], i[2])\n if (0 <= i[0] <= SCREEN_X_BOT) and (0 <= i[1] <= SCREEN_Y_BOT):\n pos_list[0][1].check_size(out_q_x, out_q_y)\n\n pos_list[0][1].show()\n\n else:\n close = closest((i[0], i[1]), pos_list)\n close[1].update(i[0], i[1], i[2])\n if (0 <= i[0] <= SCREEN_X_BOT) and (0 <= i[1] <= SCREEN_Y_BOT):\n close[1].check_size(out_q_x, out_q_y)\n print pos_list\n close[1].show()\n\n if is_ball_decrease(ball_before, ball_count):\n for b in balls:\n if b.bounced is True:\n b.destroy()\n cv2.rectangle(frame, (SCREEN_X_TOP, SCREEN_Y_TOP), (SCREEN_X_BOT, SCREEN_Y_BOT), (0, 255, 0), 3)\n cv2.imshow('crop', crop_img)\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n ball_before = ball_count\n print \"Exiting Background Thread: Ball detector\"","def tick(self, iteration):\n frame = self.cam.get_current_fram()\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n # on recupaire la zone qui on les zone de couleur\n mask = cv2.inRange(hsv, self.greenLower, self.greenUpper)\n # on fait une erosion et dilation pour supprimer les petit pixel blanc qui peut être pris pour la balle\n mask = cv2.erode(mask, None, iterations=iteration)\n mask = cv2.dilate(mask, None, iterations=iteration)\n\n cv2.imshow(\"mask\", mask)\n\n # Recherche de contour pour savoir ou est la balle\n cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\n center = None\n if len(cnts) > 0:\n # on charche le contour le plus grand (celui de la balle)\n c = max(cnts, key=cv2.contourArea)\n ((x, y), radius) = cv2.minEnclosingCircle(c)\n moment_cont = cv2.moments(c)\n # on recupaire le centre de la balle\n center = (int(moment_cont[\"m10\"] / moment_cont[\"m00\"]), int(moment_cont[\"m01\"] / moment_cont[\"m00\"]))\n # on controle la taille du rayon de la balle\n if radius > 30:\n # Puis on dessin la boulle\n cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2)\n cv2.circle(frame, center, 5, (0, 0, 255), -1)\n return center, frame","def belt(image):\n\n # Belt Detector\n x, y = circular_detector(image, 70, 80)\n\n return x, y","def is_ball(self, blob, im):\n size=self.cam.info.get_pixel_size(blob[0], im)*len(blob)\n return size>15 and size<45","def at_pos(self, pos):\n return pos-self.deadband < self.wm() < pos+self.deadband","def bomb(self, index):\n\n coords = self.canvas.coords(self.squares[index])\n x, y = coords[0] + 10, coords[1] + 10\n tag = self.hit[index]\n\n # Count moves for player (used for scoring)\n if self.players.winCondition == 1:\n self.players.moves[self.playerNumber][0] += 1\n\n # Hit\n if tag != 0:\n self.tracker[tag] -= 1\n\n # Count moves for player (used in scoring)\n if self.players.winCondition == 1:\n self.players.moves[self.playerNumber].append(\\\n self.players.moves[self.playerNumber][0])\n self.players.moves[self.playerNumber][0] = 0\n\n # Ship was sunk\n if self.tracker[tag] == 0:\n text = []\n tagname = 'tag%s' % tag\n\n # Bonus points equal to the size of ship\n # awarded for sinking entire ship\n if self.players.winCondition == 0:\n self.players.score[self.playerNumber] += \\\n self.counter_copy[tag]\n\n # Show bombed location with black & orange flashing bar\n for i in range(5):\n text.append(self.canvas.create_text(\\\n x, y, text='O', fill='red'))\n self.canvas.addtag_withtag('text', text[-1])\n self.canvas.tag_raise(tagname, 'square')\n for i in range(3): # Flashing bar\n self.canvas.itemconfig(tagname, {'fill': 'black'})\n self.canvas.update()\n self.myframe.after(100)\n self.canvas.itemconfig(tagname, {'fill': 'orange'})\n self.canvas.update()\n self.myframe.after(100)\n\n self.hit[index] = 5\n self.players.message[not self.playerNumber] = \\\n '%s,\\nYour ship of size %s was sunk by enemy' % \\\n (self.players.usernames[not self.playerNumber], \\\n self.counter_copy[tag])\n self.players.endOfTurn(self.tracker)\n return\n\n # Hit, but not sunk. Player gets only 1 point\n if self.players.winCondition == 0:\n self.players.score[self.playerNumber] += 1\n\n # Show hit location with flashing black & red circle\n text = []\n for i in range(3):\n del text[:]\n for i in range(5): # flash black circle\n text.append(self.canvas.create_text(\\\n x, y, text='O', fill='black'))\n self.canvas.addtag_withtag('text', text[-1])\n self.canvas.update()\n self.myframe.after(100)\n del text[:]\n for i in range(5): # flash red circle\n text.append(self.canvas.create_text(\\\n x, y, text='O', fill='red'))\n self.canvas.addtag_withtag('text', text[-1])\n self.canvas.update()\n self.myframe.after(100)\n\n # Complete miss. Draw 'X'\n else:\n for i in range(5):\n text = self.canvas.create_text(x, y, text='X', fill='yellow')\n self.canvas.addtag_withtag('text', text)\n self.canvas.update()\n self.myframe.after(250)\n self.hit[index] = 5\n self.players.endOfTurn(self.tracker)","def detect_bolt_collision(self,bolt):\n\n # [x,y]\n top_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\n top_right_bolt = [bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\n bottom_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\n bottom_right_bolt =[bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\n\n if self.contains((top_left_bolt[0],top_left_bolt[1])):\n return True\n if self.contains((top_right_bolt[0],top_right_bolt[1])):\n return True\n if self.contains((bottom_left_bolt[0],bottom_left_bolt[1])):\n return True\n if self.contains((bottom_right_bolt[0],bottom_right_bolt[1])):\n return True\n\n return False","def detect_bolt_collision(self,bolt):\n\n # [x,y]\n top_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\n top_right_bolt = [bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\n bottom_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\n bottom_right_bolt =[bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\n\n if self.contains((top_left_bolt[0],top_left_bolt[1])):\n return True\n if self.contains((top_right_bolt[0],top_right_bolt[1])):\n return True\n if self.contains((bottom_left_bolt[0],bottom_left_bolt[1])):\n return True\n if self.contains((bottom_right_bolt[0],bottom_right_bolt[1])):\n return True\n\n return False","def checkball(self):\r\n for ball in self.overlapping_sprites:\r\n ball.bottom=self.top\r\n if math.fabs(ball.x-self.x)<math.fabs(ball.x-self.left) and math.fabs(ball.x-self.x)<math.fabs(ball.x-self.right):\r\n ball.vertbounce()\r\n if math.fabs(ball.x-self.left)<math.fabs(ball.x-self.x) and math.fabs(ball.x-self.left)<math.fabs(ball.x-self.right):\r\n ball.leftbounce()\r\n if math.fabs(ball.x-self.right)<math.fabs(ball.x-self.left) and math.fabs(ball.x-self.right)<math.fabs(ball.x-self.x):\r\n ball.rightbounce()\r\n self.points.value+=10\r\n if self.points.value==500:\r\n ball.ballchange()\r\n elif self.points.value==2000:\r\n ball.ballchange()\r\n elif self.points.value==4500:\r\n ball.ballchange()\r\n elif self.points.value==10000:\r\n ball.ballchange()","def get_aa_pos_on_screen(self,position,frame):\n position=position*3+float(frame)-1\n x,y=self.get_base_pos_on_screen(position)\n y=y+20.0+float(frame)*15.0\n return x,y","def opponentBoarderPosition(self, gameState):\n if self.red:\n i = self.midWidth\n else:\n i = self.midWidth - 1\n boudaries = [(i,j) for j in range(self.height)]\n validPositions = []\n for i in boudaries:\n if not gameState.hasWall(i[0],i[1]):\n validPositions.append(i)\n return validPositions","def rectangleSignal(flankHeight = 30, flankWidth = 10, robotHeight = -90):\n rectanglePos = [\n [flankHeight/2,-2.5*flankWidth, robotHeight,0,0,0,'mov'],\n\n [-flankHeight/2,-2.5*flankWidth, robotHeight,0,0,0,'lin'],\n [-flankHeight/2,-1.5*flankWidth, robotHeight,0,0,0,'lin'],\n\n [flankHeight/2,-1.5*flankWidth, robotHeight,0,0,0,'lin'],\n [flankHeight/2,-0.5*flankWidth, robotHeight,0,0,0,'lin'],\n\n [-flankHeight/2,-0.5*flankWidth, robotHeight,0,0,0,'lin'],\n [-flankHeight/2,0.5*flankWidth, robotHeight,0,0,0,'lin'],\n\n [flankHeight/2,0.5*flankWidth, robotHeight,0,0,0,'lin'],\n [flankHeight/2,1.5*flankWidth, robotHeight,0,0,0,'lin'],\n\n [-flankHeight/2,1.5*flankWidth, robotHeight,0,0,0,'lin'],\n [-flankHeight/2,2.5*flankWidth, robotHeight,0,0,0,'lin'],\n\n [flankHeight/2,2.5*flankWidth, robotHeight,0,0,0,'mov']\n ]\n\n rectanglePos.append([0,0,-127,0,0,0,'mov'])\n return rectanglePos","def find_ball(self):\n ret, frame = self.video.read()\n\n if ret:\n\n # ------- detect object --------\n # resize the frame, blur it, and convert it to the HSV\n # color space\n blurred = cv2.GaussianBlur(frame, (11, 11), 0)\n hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)\n # construct a mask for the color \"green\", then perform\n # a series of dilations and erosions to remove any small\n # blobs left in the mask\n mask = cv2.inRange(hsv, self.lower_color, self.upper_color)\n mask = cv2.erode(mask, None, iterations=2)\n mask = cv2.dilate(mask, None, iterations=2)\n # find contours in the mask and initialize the current\n # (x, y) center of the ball\n cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n cnts = imutils.grab_contours(cnts)\n center = None\n # only proceed if at least one contour was found\n if len(cnts) > 0:\n # find the largest contour in the mask, then use\n # it to compute the minimum enclosing circle and\n # centroid\n c = max(cnts, key=cv2.contourArea)\n ((self.x, self.y), radius) = cv2.minEnclosingCircle(c)\n M = cv2.moments(c)\n center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n # only proceed if the radius meets a minimum size\n if radius > self.radius:\n # draw the circle and centroid on the frame,\n # then update the list of tracked points\n cv2.circle(frame, (int(self.x), int(self.y)), int(radius), (0, 255, 255), 2)\n cv2.circle(frame, center, 5, (0, 0, 255), -1)\n\n self.is_ball_visible = len(cnts) > 0 and radius > self.radius\n # ------------------------------ \n\n # add timestamp\n ts = datetime.datetime.now().strftime(\"%A %d %B %Y %I:%M:%S%p\")\n cv2.putText(frame, ts, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)\n cv2.putText(frame, \"x: {}, y: {}, object: {}\".format(round(self.x, 2), round(self.y, 2),\n len(cnts) > 0 and radius > 10),\n (10, frame.shape[0] - 50),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)\n # Write the frame into the \n # file 'filename.avi'\n cv2.imwrite(\"test.jpg\", frame)\n if self.record_video:\n self.video_result.write(frame)\n\n else:\n print('no frame found')","def in_bomb_range(self, game_state: dict):\n is_in_bomb_range = False\n agent_position = game_state['self'][3]\n agent_position = list(agent_position)\n\n for bomb in game_state['bombs']:\n if agent_position == list(bomb[0]):\n is_in_bomb_range = True\n\n for i in range(3):\n agent_search = copy.copy(agent_position)\n if agent_position[0] - i - 1 >= 0:\n agent_search[0] = agent_position[0] - i - 1\n if agent_search == list(bomb[0]):\n is_in_bomb_range = True\n\n for i in range(3):\n agent_search = copy.copy(agent_position)\n if agent_position[0] + i + 1 <= 16:\n agent_search[0] = agent_position[0] + i + 1\n if agent_search == list(bomb[0]):\n is_in_bomb_range = True\n\n for i in range(3):\n agent_search = copy.copy(agent_position)\n if agent_position[1] - i - 1 >= 0:\n agent_search[1] = agent_position[1] - i - 1\n if agent_search == list(bomb[0]):\n is_in_bomb_range = True\n\n for i in range(3):\n agent_search = copy.copy(agent_position)\n if agent_position[1] + i + 1 <= 16:\n agent_search[1] = agent_position[1] + i + 1\n if agent_search == list(bomb[0]):\n is_in_bomb_range = True\n\n # check if a stone wall is between the agent an the bomb\n if is_in_bomb_range:\n if agent_position[0] == list(bomb[0])[0]:\n if agent_position[1] < list(bomb[0])[1]:\n for i in range(agent_position[1], list(bomb[0])[1]):\n if game_state['field'][agent_position[0]][i] == -1:\n is_in_bomb_range = False\n else:\n for i in range(list(bomb[0])[1], agent_position[1]):\n if game_state['field'][agent_position[0]][i] == -1:\n is_in_bomb_range = False\n elif agent_position[1] == list(bomb[0])[1]:\n if agent_position[0] < list(bomb[0])[0]:\n for i in range(agent_position[0], list(bomb[0])[0]):\n if game_state['field'][i][agent_position[1]] == -1:\n is_in_bomb_range = False\n else:\n for i in range(list(bomb[0])[0], agent_position[0]):\n if game_state['field'][i][agent_position[1]] == -1:\n is_in_bomb_range = False\n\n return is_in_bomb_range","def bounds(self, pos):","def locate_board_callback(self, data):\n self.home_count = 0\n self.is_home = True\n while self.home_count < self.home_count_thresh or not self.board_position:\n try:\n for d in self.tag_vals.detections:\n if d.id == (0,):\n self.board_position = self.tag_vals.detections[0].pose.pose.pose\n self.board_present = True\n except:\n pass\n\n self.is_home = False\n\n return self.board_position","def createBouncingPixel():\n height = width = 16\n empty = numpy.zeros((height, width), dtype=numpy.int32)\n x = 0\n y = 0\n x_diff = 1\n y_diff = 1\n frames = []\n for i in xrange(61): # Comes back to starting position in 60 steps\n copy = numpy.array(empty)\n if x + x_diff not in range(0, width):\n x_diff *= -1\n if y + y_diff not in range(0, height):\n y_diff *= -1\n copy[y, x] = 1\n print copy\n x += x_diff\n if i % 2:\n y += y_diff\n frames.append(copy.tolist())\n writeFrames(frames, 'actual', 'bouncing_pixel')","def chkBD(pos, m):\n cur = False\n for x in [-1, 1]:\n if(pos[0] + x >= 0 and pos[0] + x < m['r_len']):\n cur = cur or m['ver'][pos[0]+x][pos[1]] == 2\n if(pos[1] + x >= 0 and pos[1] + x < m['c_len']):\n cur = cur or m['hor'][pos[0]][pos[1]+x] == 2\n\n return cur","def boarderPosition(self, gameState):\n if gameState.isOnRedTeam(self.index):\n i = self.midWidth - 1\n else:\n i = self.midWidth + 1\n boudaries = [(i,j) for j in range(self.height)]\n validPositions = []\n for i in boudaries:\n if not gameState.hasWall(i[0],i[1]):\n validPositions.append(i)\n return validPositions","def boatLocation(self):\n if self.operator is None:\n return 0\n else:\n if self.operator[0] == 's':\n return 1\n elif self.operator[0] == 'b':\n return 0\n else:\n return -1","def get_pos(self, frame):\n frame = self.perspective_shift(frame)\n \n puck_mask = self.color_mask(frame, self.color_green, thresh=15)\n striker_mask = self.color_mask(frame, self.color_orange, thresh=25, blur=5)\n \n puck_loc, _ = self.find_centroids(puck_mask)\n striker_locs, _ = self.find_centroids(striker_mask, 2)\n \n p_pos = self.abs_to_meter(puck_loc[0])\n # cases: (pos,pos), (pos,None), (None,None)\n if striker_locs[0] is not None:\n pos_1 = self.abs_to_meter(striker_locs[0])\n pos_2 = self.abs_to_meter(striker_locs[1])\n s1_pos = pos_1 if pos_1[1]<0 else pos_2\n s2_pos = pos_2 if pos_1[1]<0 else pos_1\n else:\n s1_pos, s2_pos = None, None \n \n return [p_pos, s1_pos, s2_pos]","def check_bounce(self):\n if self.ball.center.x < 0 and self.ball.velocity.dx < 0:\n self.ball.bounce_horizontal()\n\n if self.ball.center.y < 0 and self.ball.velocity.dy < 0:\n self.ball.bounce_vertical()\n\n if self.ball.center.y > SCREEN_HEIGHT and self.ball.velocity.dy > 0:\n self.ball.bounce_vertical()","def motion_b(input_line, cur, count):\n # \"b\" is just \"e\" on inverted data (e.g. \"olleH\" instead of \"Hello\").\n pos_inv = motion_e(input_line[::-1], len(input_line) - cur - 1, count)[0]\n pos = len(input_line) - pos_inv - 1\n return pos, True, False","def beam_positions(closepack=False):\n \n x_pos, y_pos = [], []\n\n x=0\n for j in range(0,6,1):\n x += 0.1\n y=0\n for k in range(0,6,2):\n y += 0.2\n x_pos.append(x+(0.05 if closepack else 0))\n y_pos.append(y)\n y += 0.2\n x_pos.append(x)\n y_pos.append(y)\n\n return x_pos, y_pos","def _bounce(self):\n right = self.surface.get_width() - self.size\n left = self.size\n top = self.size\n bottom = self.surface.get_height() - self.size\n if self.pos.x > right: # right border\n self.pos.x = right\n self.direction = self.direction.elementwise() * pygame.Vector2(-1.0, 1.0)\n elif self.pos.x < left: # left border\n self.pos.x = left\n self.direction = self.direction.elementwise() * pygame.Vector2(-1.0, 1.0)\n if self.pos.y > bottom: # bottom border\n self.pos.y = bottom\n self.direction = self.direction.elementwise() * pygame.Vector2(1.0, -1.0)\n elif self.pos.y < top: # top border\n self.pos.y = top\n self.direction = self.direction.elementwise() * pygame.Vector2(1.0, -1.0)","def update(self):\n self.bpos_x += 3","def track_ball_4(video):\n\n result = []\n frames = readFrames(video)\n\n i = 0\n while i < len(frames):\n frame = frames[i] # get the new frame\n\n # find edges of the ball in the frame\n # parameters filter out background\n edges = cv2.Canny(frame, 700, 800)\n\n # find contours in the edge frame\n contours, _ = cv2.findContours(edges, cv2.RETR_TREE,\n cv2.CHAIN_APPROX_SIMPLE)\n\n # sort the contours in reverse order by width to eliminate small\n # contours caused by noise.\n sort_cnt = sorted(contours, key=lambda x: cv2.boundingRect(x)[2],\n reverse=True)\n\n # get the parameters of the bounding box for the largest width contour\n x, y, w, h = cv2.boundingRect(sort_cnt[0])\n # append to result list\n result.append((x, y, x + w, y + h))\n\n if VISUALIZE:\n orig_frame = frames[i]\n cv2.rectangle(orig_frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\n cv2.imshow(\"ball\", orig_frame)\n cv2.waitKey(30)\n\n i += 1\n\n return result","def adjust_thresholding(self, pos_frame, which='animal'):\n\n cv2.namedWindow('Adjust Thresholding')\n if which == 'animal':\n cv2.createTrackbar('H_Low', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"min_animal\"][0],\n 255,\n self.nothing)\n cv2.createTrackbar('H_High', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"max_animal\"][0],\n 255,\n self.nothing)\n cv2.createTrackbar('S_Low', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"min_animal\"][1],\n 255,\n self.nothing)\n cv2.createTrackbar('S_High', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"max_animal\"][1],\n 255,\n self.nothing)\n cv2.createTrackbar('V_Low', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"min_animal\"][2],\n 255,\n self.nothing)\n cv2.createTrackbar('V_High', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"max_animal\"][2],\n 255,\n self.nothing)\n elif which == 'material':\n cv2.createTrackbar('H_Low', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"min_material\"][0],\n 255,\n self.nothing)\n cv2.createTrackbar('H_High', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"max_material\"][0],\n 255,\n self.nothing)\n cv2.createTrackbar('S_Low', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"min_material\"][1],\n 255,\n self.nothing)\n cv2.createTrackbar('S_High', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"max_material\"][1],\n 255,\n self.nothing)\n cv2.createTrackbar('V_Low', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"min_material\"][2],\n 255,\n self.nothing)\n cv2.createTrackbar('V_High', 'Adjust Thresholding',\n self._args[\"tracking\"][\"thresholding\"][\"max_material\"][2],\n 255,\n self.nothing)\n else:\n utils.print_color_message(\"[INFO] Select 'animal' or 'material' to preview the default thresholding values\",\n \"darkgreen\")\n cv2.createTrackbar('H_Low', 'Adjust Thresholding', 0, 255, self.nothing)\n cv2.createTrackbar('H_High', 'Adjust Thresholding', 255, 255, self.nothing)\n cv2.createTrackbar('S_Low', 'Adjust Thresholding', 0, 255, self.nothing)\n cv2.createTrackbar('S_High', 'Adjust Thresholding', 255, 255, self.nothing)\n cv2.createTrackbar('V_Low', 'Adjust Thresholding', 0, 255, self.nothing)\n cv2.createTrackbar('V_High', 'Adjust Thresholding', 255, 255, self.nothing)\n\n test_frame = self._color_capture.get_frame(pos_frame)\n test_frame_cropped = test_frame[self.up_left_y:self.low_right_y, self.up_left_x:self.low_right_x]\n test_frame_cropped_hsv = cv2.cvtColor(test_frame_cropped, cv2.COLOR_BGR2HSV)\n test_frame_blurred = cv2.blur(test_frame_cropped_hsv, (5, 5))\n\n while True:\n h_l = cv2.getTrackbarPos('H_Low', 'Adjust Thresholding')\n h_h = cv2.getTrackbarPos('H_High', 'Adjust Thresholding')\n s_l = cv2.getTrackbarPos('S_Low', 'Adjust Thresholding')\n s_h = cv2.getTrackbarPos('S_High', 'Adjust Thresholding')\n v_l = cv2.getTrackbarPos('V_Low', 'Adjust Thresholding')\n v_h = cv2.getTrackbarPos('V_High', 'Adjust Thresholding')\n test_mask_mouse = cv2.inRange(test_frame_blurred, (h_l, s_l, v_l), (h_h, s_h, v_h))\n overlay = cv2.bitwise_and(test_frame_cropped_hsv, test_frame_cropped_hsv, mask=test_mask_mouse)\n cv2.imshow('Adjust Thresholding', overlay)\n key = cv2.waitKey(10) & 0xFF\n if key == ord(\"q\"):\n break\n cv2.destroyAllWindows()\n for i in range(1, 5):\n cv2.waitKey(1)","def detect_boundary(self, x, l_old):\n pass","def check_boundary(self):\n\n\t\tif self.Bubble_initial_pos[0] <= self.Bubble_radius or self.Bubble_initial_pos[0] >= self.tk_pic.width - self.Bubble_radius:\n\t\t\tself.Bubble_vel[0] = -self.Bubble_vel[0]","def bomb_vector(self):\n\n\t\tif self.b_offset == 0:\n\t\t\top = sin\n\t\telse:\n\t\t\top = cos\n\n\t\tself.y -= self.speed\n\t\tself.rect.y = self.y\n\t\t# MMMMMMMMMMMMMMMMMMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAATHS\n\t\tself.x = int((self.g_settings.screen_height/2) + self.amplitude*op(self.frequency*((float(self.y)/self.g_settings.screen_width)*(2*pi) + (self.speed*time()))))\n\t\tif self.b_offset == 0:\n\t\t\tself.rect.x = self.x + self.position_x - 16\n\t\telif self.b_offset == 1:\n\t\t\tself.rect.x = self.x + self.position_x + 16\n\t\tself.screen.blit(self.image, self.rect)","def draw_color_bar(frame):\n depth = 35\n cv2.rectangle(frame, (0,0), (72,depth - 1), (0,0,0), 2) # white/eraser\n cv2.rectangle(frame, (72,0), (138,depth), (0,0,0), -1) # black\n cv2.rectangle(frame, (138,0), (204,depth), (122,78,32), -1) # brown\n cv2.rectangle(frame, (204,0), (270,depth), (242,0,255), -1) # violet/purple\n cv2.rectangle(frame, (270,0), (336,depth), (0,0,255), -1) # blue\n cv2.rectangle(frame, (336,0), (402,depth), (63,255,0), -1) # green\n cv2.rectangle(frame, (402,0), (468,depth), (255,250,0), -1) # yellow\n cv2.rectangle(frame, (468,0), (534,depth), (255,174,0), -1) # orange\n cv2.rectangle(frame, (534,0), (600,depth), (255,0,0), -1) # red","def check_borders(self):\n # Go Homer!\n # https://en.wikipedia.org/wiki/Torus#Flat_torus\n if self._posn.x < 0:\n self._posn.x += self._win_w\n elif self._posn.x > self._win_w:\n self._posn.x -= self._win_w\n if self._posn.y < 0:\n self._posn.y += self._win_h\n elif self._posn.y > self._win_h:\n self._posn.y -= self._win_h","def _get_loc_top_left_placement_b(button_number):\r\n if button_number == 0:\r\n return 35 / 2, 7 / 2 # placement button that starts the game\r\n\r\n elif button_number == 1:\r\n return 35 / 2, 13 / 2 # placement button that brings program back to main menu\r","def get_bbox(frame_data, camera_image_data):\n object_decodification = {0: 'unkown', 1: 'vehicle', 2: 'pedestrian', 3: 'sign', 4: 'cyclist'}\n bbox = {}\n valid_bb_data = True\n if not frame_data.camera_labels:\n valid_bb_data = False\n # Draw the camera labels.\n for camera_labels in frame_data.camera_labels:\n # Ignore camera labels from other views (i.e. I want Front but it also gives left, right, front left, ...)\n if camera_labels.name != camera_image_data.name:\n continue\n # Iterate over the individual labels\n for label in camera_labels.labels:\n if label.detection_difficulty_level == 0:\n difficulty = \"easy\"\n elif label.detection_difficulty_level == 2:\n difficulty = \"hard\"\n\n if label.tracking_difficulty_level == 0:\n tracking_level = \"easy\"\n elif label.tracking_difficulty_level == 2:\n tracking_level = 'hard'\n\n object_class = object_decodification[label.type]\n # I'm not saving the other labels so that it matches my CARLA dataset\n if object_class not in bbox and (object_class == \"vehicle\" or object_class == \"pedestrian\"):\n bbox[object_class] = []\n \n if (object_class == \"vehicle\" or object_class == \"pedestrian\"):\n # Get BB\n xmin = int(label.box.center_x - 0.5 * label.box.length)\n ymin = int(label.box.center_y - 0.5 * label.box.width)\n xmax = int(xmin + label.box.length)\n ymax = int(ymin + label.box.width)\n bbox[object_class].append([xmin, ymin, xmax, ymax, difficulty, tracking_level])\n return bbox, valid_bb_data","def _CheckBoundaries(self, frameSize):\n if (frameSize[0] + self.frame_position[0] > self.display_size[0]):\n return (0, self.frame_position[1])\n elif (frameSize[1] + self.frame_position[1] > self.display_size[1]):\n return (self.frame_position[0], 0)\n else:\n return self.frame_position","def return_to_center(): #ignore this for now, use move_to_position_(0,0)\n current_pos = '\\xAA\\xBB\\xCC\\xDD'\n #run command until back to center (0,0)\n while True: #change the byte locations\n current_pos = to_center()\n print(current_pos)\n time.sleep(0.2) #check timing\n if((current_pos[1] == 0) and (current_pos[1] == 0)):\n break\n print('At center')","def intercept_ball(self):\n\t\t\t##Parameters\n\t\t\tdistance_annulation = 15\n\t\t\tvitesse_danulation = 0\n\t\t\tcoefficient_prediction = 10\n\t\t\t\n\t\t\tif self.get_distance_to_ball() <= distance_annulation or self.b_speed < vitesse_danulation:\n\t\t\t\treturn self.PB\n\t\t\t\n\t\t\telse:\n\t\t\t\tpoint_interception = self.PB + coefficient_prediction * self.ball.vitesse\n\t\t\t\treturn point_interception","def find_bbox_coord(point_x, point_y):\r\n is_good_rect = True\r\n bottom_x, bottom_y = [], []\r\n top_x, top_y = [], []\r\n if len(point_x) < 4:\r\n is_good_rect = False\r\n if len(point_x) == 4:\r\n out_of_repeats_x = []\r\n out_of_repeats_y = []\r\n delta = 10**(-6)\r\n for j in range(len(point_x)): # add delta for the reason of not mess in equal angles\r\n out_of_repeats_x.append(point_x[j] + delta*j)\r\n out_of_repeats_y.append(point_y[j] + delta*j)\r\n point_x, point_y = out_of_repeats_x, out_of_repeats_y\r\n \r\n quadrate_width = ((point_x[1] - point_x[0])**2+(point_y[1] - point_y[0])**2)**0.5\r\n quadrate_height = ((point_x[1] - point_x[2])**2+(point_y[1] - point_y[2])**2)**0.5\r\n aspect_ratio = quadrate_width / quadrate_height\r\n if aspect_ratio > 0.7 and aspect_ratio < 1.3:\r\n is_good_rect = False \r\n ###Aprint('Квадрат. Закрашиваем')\r\n elif quadrate_width * quadrate_height < 100:\r\n is_good_rect = False \r\n ###Aprint('Квадрат. Закрашиваем')\r\n else:\r\n ###Aprint('Прямоугольник')\r\n edge_x, edge_y = point_x, point_y\r\n bottom_x, bottom_y, top_x, top_y, is_good_rect = top_bottom_dots(point_x, point_y, edge_x, edge_y)\r\n \r\n elif len(point_x) > 4:\r\n ###Aprint('Многоугольник')\r\n out_of_repeats_x = []\r\n out_of_repeats_y = []\r\n delta = 10**(-4)\r\n for j in range(len(point_x)): # add delta for the reason of not mess in equal angles\r\n out_of_repeats_x.append(point_x[j] + delta*j)\r\n out_of_repeats_y.append(point_y[j] + delta*j)\r\n point_x, point_y = out_of_repeats_x, out_of_repeats_y\r\n \r\n edge_x, edge_y = find_4_dots(point_x, point_y)\r\n\r\n bottom_x, bottom_y, top_x, top_y, is_good_rect = top_bottom_dots(point_x, point_y, edge_x, edge_y)\r\n \r\n if is_good_rect:\r\n \r\n bottom_edge_x, bottom_edge_y = [], []\r\n for i in bottom_x:\r\n if i in edge_x:\r\n index = bottom_x.index(i)\r\n bottom_edge_x.append(bottom_x[index])\r\n bottom_edge_y.append(bottom_y[index])\r\n bottom_edge_x, bottom_edge_y = zip(*sorted(zip(bottom_edge_x, bottom_edge_y)))\r\n bottom_lowest_point = [bottom_edge_x[0], bottom_edge_y[0]]\r\n\r\n top_edge_x, top_edge_y = [], []\r\n for i in top_x:\r\n if i in edge_x:\r\n index = top_x.index(i)\r\n top_edge_x.append(top_x[index])\r\n top_edge_y.append(top_y[index])\r\n top_edge_x, top_edge_y = zip(*sorted(zip(top_edge_x, top_edge_y)))\r\n top_lowest_point = [top_edge_x[0], top_edge_y[0]]\r\n\r\n bottom_x, bottom_y = Euclidian_distance_sorting(bottom_x, bottom_y, bottom_lowest_point)\r\n top_x, top_y = Euclidian_distance_sorting(top_x, top_y, top_lowest_point)\r\n else:\r\n bottom_x, bottom_y, top_x, top_y = [], [], [], []\r\n \r\n return is_good_rect, bottom_x, bottom_y, top_x, top_y","def detect_alien_bolt_collision(self,bolt):\n\n # [x,y]\n top_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\n top_right_bolt = [bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\n bottom_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\n bottom_right_bolt =[bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\n\n if self.contains((top_left_bolt[0],top_left_bolt[1])):\n return True\n if self.contains((top_right_bolt[0],top_right_bolt[1])):\n return True\n if self.contains((bottom_left_bolt[0],bottom_left_bolt[1])):\n return True\n if self.contains((bottom_right_bolt[0],bottom_right_bolt[1])):\n return True\n\n return False","def frame(self, frame):\n if self.vertical:\n cell = ((frame-1)//self.rows)+1\n row = frame-(cell-1)*self.rows\n else:\n row = ((frame-1)//self.cells)+1\n cell = frame-(row-1)*self.cells\n\n return cell, row","def detect_collision(self, pos):\n for rect in self.stock:\n if rect.collidepoint((pos)):\n return ['S0', 0]\n for rect in self.waste:\n if rect.collidepoint((pos)):\n return ['W0', 0]\n for i, rect in enumerate(self.foundations):\n if rect.collidepoint((pos)):\n return ['F'+str(i), 0]\n for rect in self.tableaus:\n if rect[0].collidepoint((pos)):\n return rect[1]\n\n return [0,0]","def myBarbieHome(pos):\n bulldozer(pos)\n ground(pos)\n mc.postToChat(\"Ground done !\")\n\n pos.z += 5\n makeTheHouse(pos)\n mc.postToChat(\"House done !\")\n\n theRoof(pos)\n mc.postToChat(\"Roof done !\")\n\n makeTheDeco(pos, flowers = wFlower_Cyan)\n mc.postToChat(\"ALL Work done !\")","def mover_bm_arriba(self):\n self.nueva_posicion_posible_parte_superior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0],\n self.casilla[1] - 1],\n [self.vertice_1[0] ,self.vertice_1[1] - self.velocidad],\n [self.vertice_1[0], self.vertice_1[1] - 5 -5])\n self.nueva_posicion_posible_parte_inferior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0] + 1,\n self.casilla[1] - 1],\n [self.vertice_2[0] ,self.vertice_2[1] - self.velocidad],\n [self.vertice_1[0],self.vertice_1[1]])\n self.y -= self.velocidad * (self.y >= 15) *(self.nueva_posicion_posible_parte_inferior[0] != 1) * (self.nueva_posicion_posible_parte_superior[0] != 1)\n self.posicion = [self.posicion[0],self.y]\n self.casilla = [self.casilla[0],self.casilla[1] - self.nueva_posicion_posible_parte_superior[1] * (self.nueva_posicion_posible_parte_inferior[0] != 1) * (self.nueva_posicion_posible_parte_superior[0] != 1)]\n self.redefinir_vertices()","def detect(self, frame, foreground_mask):\n pass","def hit_object(self):\n obj1 = self.window.get_object_at(self.ball.x, self.ball.y)\n obj2 = self.window.get_object_at(self.ball.x, self.ball.y + BALL_RADIUS*2)\n obj3 = self.window.get_object_at(self.ball.x + BALL_RADIUS*2, self.ball.y)\n obj4 = self.window.get_object_at(self.ball.x + BALL_RADIUS*2, self.ball.y + BALL_RADIUS*2)\n if obj1 is not None and obj1.width == BRICK_WIDTH:\n self.window.remove(obj1)\n self.num_bricks -= 1\n self.__dy = -self.__dy\n elif obj2 is not None and obj2.width == BRICK_WIDTH:\n self.window.remove(obj2)\n self.num_bricks -= 1\n self.__dy = -self.__dy\n elif obj2 is not None and obj2.width == PADDLE_WIDTH:\n self.__dy = -self.__dy\n elif obj3 is not None and obj3.width == BRICK_WIDTH:\n self.window.remove(obj3)\n self.num_bricks -= 1\n self.__dy = -self.__dy\n elif obj4 is not None and obj4.width == BRICK_WIDTH:\n self.window.remove(obj4)\n self.num_bricks -= 1\n self.__dy = -self.__dy\n elif obj4 is not None and obj4.width == PADDLE_WIDTH:\n self.__dy = -self.__dy","def detect_collision():\n # with the top and bottom of screen\n if ball.ycor() > GAME_Y_BARRIER or ball.ycor() < -GAME_Y_BARRIER:\n ball.bounce_y()\n # with the paddles\n if ball.distance(paddle_right) < 50 and ball.xcor() > GAME_X_BARRIER \\\n or ball.distance(paddle_left) < 50 and ball.xcor() < -GAME_X_BARRIER:\n ball.bounce_x()","def inSmallBlindPosition(self):\n return len(self.in_game) > 0 and ((self.dealer + 1) % len(self.in_game)) == self.position","def _slice_ball(self, ball_speed_x, platform_speed_x):\n # If the platform doesn't move, bounce normally.\n if platform_speed_x == 0:\n return 7 if ball_speed_x > 0 else -7\n # If the platform moves at the same direction as the ball moving,\n # speed up the ball.\n elif ball_speed_x * platform_speed_x > 0:\n return 10 if ball_speed_x > 0 else -10\n # If the platform moves at the opposite direction against the ball moving,\n # hit the ball back.\n else:\n return -7 if ball_speed_x > 0 else 7","def imageframe(sf, identifier, loc=\".\"):\n from matplotlib.patches import Circle\n\n # Computing center and radius\n blurred = blur(sf.data)\n bin_img = threshold(blurred, sf.calcthresh(blurred))\n cont_found, big_cont = find_largest_contour(bin_img, blurred)\n if cont_found:\n _, radius = cv2.minEnclosingCircle(big_cont)\n center = (sf.c, sf.r)\n sig_r = RADIUS_SIGNAL\n bg_r1, bg_r2 = RADIUS_BGINNER, RADIUS_BGOUTER\n # -----------------------\n # Temporary and just for display and testing\n fig, ax = plt.subplots()\n ax.imshow(\n sf.data,\n cmap=\"gray\",\n extent=[\n sf.c - sf.radius,\n sf.c + sf.radius,\n sf.r + sf.radius,\n sf.r - sf.radius,\n ],\n )\n p = Circle(center, radius + sig_r, fill=False, ec=\"green\", lw=2)\n p2 = Circle(center, radius + bg_r1, fill=False, ec=\"cyan\", lw=2)\n p3 = Circle(center, radius + bg_r2, fill=False, ec=\"cyan\", lw=2)\n ax.add_patch(p)\n ax.add_patch(p2)\n ax.add_patch(p3)\n fig.suptitle(f\"Frame {identifier}\", size=12, weight=\"bold\")\n ax.set_title(sf.time.strftime(\"%Y%m%d %H:%M:%S.%f %Z\"))\n # plt.imshow(signal | background) # Shows masks\n if os.path.exists(loc):\n fig.savefig(f\"{loc}/Frame{identifier}.png\")\n else:\n os.makedirs(loc)\n fig.savefig(f\"{loc}/Frame{identifier}.png\")\n #plt.close(fig)\n # -----------------------","def _get_bounding_box(self, frame, bounding_offset):\n\n # Try to find board if the boundingbox is not set\n center, ellipse, mask = self.board.detect(frame)\n\n # Should not be None\n if center is None:\n print(\"skipping frame\")\n return None\n if ellipse is None:\n print(\"skipping frame\")\n return None\n if mask is None:\n print(\"skipping frame\")\n return None\n\n self.point_mask = mask\n # cv2.imshow(\"mask\", mask)\n\n x_offset = (ellipse[1][0] / 2)\n x_center = ellipse[0][0]\n\n y_offset = ellipse[1][1] / 2\n y_center = ellipse[0][1]\n\n minx = max(0, x_center - x_offset - bounding_offset)\n maxx = min(self.width, x_center + x_offset + bounding_offset)\n miny = max(0, y_center - y_offset - bounding_offset)\n maxy = min(self.height, y_center + y_offset + bounding_offset)\n return ((int(minx), int(miny)), (int(maxx), int(maxy)))","def edges(self):\r\n # appearing on other side of the window #\r\n \r\n # if boid gets out of the margins, it shows on the other side of the window\r\n \r\n if self.position.x > self.width:\r\n self.position.x = 0\r\n elif self.position.x < 0:\r\n self.position.x = self.width\r\n\r\n if self.position.y > self.height:\r\n self.position.y = 0\r\n elif self.position.y < 0:\r\n self.position.y = self.height\r\n \r\n # bouncing from the window #\r\n \r\n # if self.position.x > self.width or self.position.x < 0:\r\n # self.velocity.x = -self.velocity.x\r\n # if self.position.y > self.height or self.position.y < 0: \r\n # self.velocity.y = -self.velocity.y\r","def brickCollision(self, ball):\n for brick in self._bricks:\n if brick.contains(ball.right,ball.bottom) and brick in self._bricks:\n self.breaking.play()\n self._bricks.remove(brick)\n #print 'bottom right'\n ball.verticalBounce()\n if brick.contains(ball.left,ball.bottom) and brick in self._bricks:\n self.breaking.play()\n self._bricks.remove(brick)\n #print 'bottom left'\n ball.verticalBounce()\n if brick.contains(ball.right,ball.top) and brick in self._bricks:\n self.breaking.play()\n self._bricks.remove(brick)\n #print 'top right'\n ball.verticalBounce()\n if brick.contains(ball.left,ball.top) and brick in self._bricks:\n self.breaking.play()\n self._bricks.remove(brick)\n #print 'top left'\n ball.verticalBounce()","def mover_bm_abajo(self):\n self.nueva_posicion_posible_parte_superior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0],\n self.casilla[1] + 1],\n [self.vertice_3[0], self.vertice_3[1] + self.velocidad],\n [self.vertice_1[0], self.vertice_1[1]+ 5])\n self.nueva_posicion_posible_parte_inferior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0] + 1,\n self.casilla[1] + 1],\n [self.vertice_4[0],self.vertice_4[1] + self.velocidad],\n self.vertice_1)\n if self.nueva_posicion_posible_parte_superior[0] != 1 and self.nueva_posicion_posible_parte_inferior[0] != 1:\n self.y += self.velocidad * (self.y <= 655)\n self.posicion = [self.posicion[0],self.y]\n self.casilla = [self.casilla[0], self.casilla[1] + self.nueva_posicion_posible_parte_superior[1]]\n self.redefinir_vertices()","def CheckEdgeDrop(window, docks, pt):\r\n\r\n screenPt = window.ClientToScreen(pt)\r\n clientSize = window.GetClientSize()\r\n frameRect = GetInternalFrameRect(window, docks)\r\n\r\n if screenPt.y >= frameRect.GetTop() and screenPt.y < frameRect.GetBottom():\r\n if pt.x < auiLayerInsertOffset and pt.x > auiLayerInsertOffset - auiLayerInsertPixels:\r\n return wx.LEFT\r\n \r\n if pt.x >= clientSize.x - auiLayerInsertOffset and \\\r\n pt.x < clientSize.x - auiLayerInsertOffset + auiLayerInsertPixels:\r\n return wx.RIGHT\r\n \r\n if screenPt.x >= frameRect.GetLeft() and screenPt.x < frameRect.GetRight():\r\n if pt.y < auiLayerInsertOffset and pt.y > auiLayerInsertOffset - auiLayerInsertPixels:\r\n return wx.TOP\r\n \r\n if pt.y >= clientSize.y - auiLayerInsertOffset and \\\r\n pt.y < clientSize.y - auiLayerInsertOffset + auiLayerInsertPixels:\r\n return wx.BOTTOM\r\n\r\n return -1","def bead_position_pix(im, selem):\n # The x, y coordinates of pixels are nonzero values in selem\n y, x = np.nonzero(selem)\n x = x - selem.shape[1] // 2\n y = y - selem.shape[0] // 2\n\n # Find the center of the bead to pixel accuracy\n peak_flat_ind = np.argmax(im)\n peak_j = peak_flat_ind % im.shape[0]\n peak_i = (peak_flat_ind - peak_j) // im.shape[1]\n\n # Define local neighborhood\n irange = (peak_i - selem.shape[0] // 2, peak_i + selem.shape[0] // 2 + 1)\n jrange = (peak_j - selem.shape[1] // 2, peak_j + selem.shape[1] // 2 + 1)\n\n # Get values of the image in local neighborhood\n z = im[irange[0]:irange[1], jrange[0]:jrange[1]][selem.astype(np.bool)]\n\n # Fit Gaussian\n a, j_subpix, i_subpix, sigma = fit_gaussian(x, y, z)\n\n # Return x-y position\n return np.array([peak_i + i_subpix, peak_j + j_subpix])","def put_bomb(self):\n s = self\n if s.bombs == 0:\n return\n\n block = s.physics.blocks[s.stype][0]\n xinf = block.inf.x - block.inf.x % s.physics.len_blocks\n yinf = block.inf.y - block.inf.y % s.physics.len_blocks\n\n length = s.physics.len_blocks\n new_bomb = Rectangle(Vector(xinf, yinf), Vector(xinf + length, yinf + length))\n\n bombs = list()\n if 'bomb' in s.physics.blocks:\n bombs = s.physics.blocks['bomb']\n\n for bomb in bombs:\n if bomb.overlap(new_bomb):\n return\n\n bomb = Bomb(s, s.pjs, s.bombradius, s.fps, Vector(xinf, yinf))\n s.last_bomb = bomb\n s.pjs.add_bomb(bomb)\n s.bombs -= 1","def _find_b0(self, x_bar, y_bar):\n self.model['b0'] = y_bar - self.model['b1'] * x_bar","def update_fruit_position(self):\n fruit_drawn = False\n while not fruit_drawn:\n self.set_fruit_y(randint(1, self.BOARD_HEIGHT - 2))\n self.set_fruit_x(randint(1, self.BOARD_WIDTH - 2))\n\n if self.window.inch(self.get_fruit_y(), self.get_fruit_x()) == ord(\" \"):\n fruit_drawn = True\n\n else:\n self.set_fruit_y(randint(1, self.BOARD_HEIGHT - 2))\n self.set_fruit_x(randint(1, self.BOARD_WIDTH - 2))","def _calc_ball_scan(self):\n # Default scan to false.\n self._scan_line = [False] * Stella.FRAME_WIDTH\n\n if self._enabled:\n for x in range(self._x_min, self._x_max):\n self._scan_line[x % Stella.FRAME_WIDTH] = True","def detect(frame: numpy.ndarray) -> bool:\n color = frame[:20, 1100:1150].mean(axis=(0, 1))\n return numpy.linalg.norm(color - BG_COLOR) < 5","def detectBall():\n\t\n\tglobal np_arr\n\timage_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) # OpenCV >= 3.0:\n\n\tblackLower = (0, 0, 0) \n\tblackUpper = (5,50,50)\n\tredLower = (0, 50, 50)\n\tredUpper = (5, 255, 255)\n\tyellowLower = (25, 50, 50) \n\tyellowUpper = (35, 255, 255)\n\tgreenLower = (50, 50, 50) \n\tgreenUpper = (70, 255, 255)\n\tblueLower = (100, 50, 50) \n\tblueUpper = (130, 255, 255)\n\tmagentaLower = (125, 50, 50) \n\tmagentaUpper = (150, 255, 255)\n\n blurred = cv2.GaussianBlur(image_np, (11, 11), 0)\n hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)\n\n\tmask_blk = cv2.inRange(hsv, blackLower, blackUpper)\n mask_blk = cv2.erode(mask_blk, None, iterations=2)\n mask_blk = cv2.dilate(mask_blk, None, iterations=2)\n\n\tmask_r = cv2.inRange(hsv, redLower, redUpper)\n mask_r = cv2.erode(mask_r, None, iterations=2)\n mask_r = cv2.dilate(mask_r, None, iterations=2)\n\n\tmask_y = cv2.inRange(hsv, yellowLower, yellowUpper)\n mask_y = cv2.erode(mask_y, None, iterations=2)\n mask_y = cv2.dilate(mask_y, None, iterations=2)\n\n\tmask_g = cv2.inRange(hsv, greenLower, greenUpper)\n mask_g = cv2.erode(mask_g, None, iterations=2)\n mask_g = cv2.dilate(mask_g, None, iterations=2)\n\n mask_blu = cv2.inRange(hsv, blueLower, blueUpper)\n mask_blu = cv2.erode(mask_blu, None, iterations=2)\n mask_blu = cv2.dilate(mask_blu, None, iterations=2)\n\n\tmask_m = cv2.inRange(hsv, magentaLower, magentaUpper)\n mask_m = cv2.erode(mask_m, None, iterations=2)\n mask_m = cv2.dilate(mask_m, None, iterations=2)\n #cv2.imshow('mask', mask)\n\n cnts_blk = cv2.findContours(mask_blk.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n\tcnts_r = cv2.findContours(mask_r.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n\tcnts_y = cv2.findContours(mask_y.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n\tcnts_g = cv2.findContours(mask_g.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n\tcnts_blu = cv2.findContours(mask_blu.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n\tcnts_m = cv2.findContours(mask_m.copy(), cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_SIMPLE)\n\n cnts_blk = imutils.grab_contours(cnts_blk)\n\tcnts_r = imutils.grab_contours(cnts_r)\n\tcnts_y = imutils.grab_contours(cnts_y)\n\tcnts_g = imutils.grab_contours(cnts_g)\n\tcnts_blu = imutils.grab_contours(cnts_blu)\n\tcnts_m = imutils.grab_contours(cnts_m)\n\n center = None\n\tc = 0\n\tradius = 0\n\n\tglobal black_ball, red_ball, yellow_ball, green_ball, blue_ball, magenta_ball\n\n # only proceed if at least one contour was found\n if len(cnts_blk) > 0 and black_ball.detected_flag != True:\n c = max(cnts_blk, key=cv2.contourArea)\n\t ((x, y), radius) = cv2.minEnclosingCircle(c)\n\t M = cv2.moments(c)\n\t center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\n\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\n\t black_ball = track(radius, center, black_ball)\n\t pub_blackBall.publish(black_ball)\n\t print (\"Black ball detected.\\n\")\n\n\telif len(cnts_r) > 0 and red_ball.detected_flag != True:\n c = max(cnts_r, key=cv2.contourArea)\n\t ((x, y), radius) = cv2.minEnclosingCircle(c)\n\t M = cv2.moments(c)\n\t center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\n\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\n\t red_ball = track(radius, center, red_ball)\n\t pub_redBall.publish(red_ball)\n\t print (\"Red ball detected.\\n\")\n\t \n\telif len(cnts_y) > 0 and yellow_ball.detected_flag != True:\n c = max(cnts_y, key=cv2.contourArea)\n\t ((x, y), radius) = cv2.minEnclosingCircle(c)\n\t M = cv2.moments(c)\n\t center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\n\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\n\t yellow_ball = track(radius, center, yellow_ball)\n\t pub_yellowBall.publish(yellow_ball)\n\t print (\"Yellow ball detected.\\n\")\n\n\telif len(cnts_g) > 0 and green_ball.detected_flag != True:\n c = max(cnts_g, key=cv2.contourArea)\n\t ((x, y), radius) = cv2.minEnclosingCircle(c)\n\t M = cv2.moments(c)\n\t center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\n\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\n\t green_ball = track(radius, center, green_ball)\n\t pub_greenBall.publish(green_ball)\n\t print (\"Green ball detected.\\n\")\n\n\telif len(cnts_blu) > 0 and blue_ball.detected_flag != True:\n c = max(cnts_blu, key=cv2.contourArea)\n\t ((x, y), radius) = cv2.minEnclosingCircle(c)\n\t M = cv2.moments(c)\n\t center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\n\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\n\t blue_ball = track(radius, center, blue_ball)\n\t pub_blueBall.publish(blue_ball)\n\t print (\"Blue ball detected.\\n\")\n\n\telif len(cnts_m) > 0 and magenta_ball.detected_flag != True:\n c = max(cnts_m, key=cv2.contourArea)\n\t ((x, y), radius) = cv2.minEnclosingCircle(c)\n\t M = cv2.moments(c)\n\t center = (int(M[\"m10\"] / M[\"m00\"]), int(M[\"m01\"] / M[\"m00\"]))\n\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\n\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\n\t magenta_ball = track(radius, center, magenta_ball)\n\t pub_magentaBall.publish(magenta_ball)\n\t print (\"Magenta ball detected.\\n\")\n\n\tcv2.imshow('window', image_np)\n\tcv2.waitKey(2)\n\tc = 0","def track_ball_generic(video):\n result = []\n frames = readFrames(video)\n\n # get background with background estimator method\n background = findBackground(frames)\n # Setup background subtractor object with parameters\n subtractor = cv2.BackgroundSubtractorMOG(30, 10, 0.7, 0)\n # Feed estimated background as first input to subtractor\n subtractor.apply(background)\n\n # Iterate over every frame in video\n i = 0\n while i < len(frames):\n frame = frames[i] # get the new frame\n\n # apply background subtraction to frame\n frame = subtractor.apply(frame)\n\n # find contours in the frame\n contours, _ = cv2.findContours(frame, cv2.RETR_TREE,\n cv2.CHAIN_APPROX_SIMPLE)\n\n # sort the contours in reverse order by width to eliminate small\n # contours caused by noise.\n sort_cnt = sorted(contours, key=lambda x: cv2.boundingRect(x)[2],\n reverse=True)\n\n # get the parameters of the bounding box for the largest width contour\n x, y, w, h = cv2.boundingRect(sort_cnt[0])\n # append to result list\n result.append((x, y, x + w, y + h))\n\n if VISUALIZE:\n orig_frame = frames[i]\n cv2.rectangle(orig_frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\n cv2.imshow('frame', orig_frame)\n cv2.waitKey(30)\n\n i += 1\n\n return result","def getPoslbFFmpeg(self, event):\n # if self.measurement_w:\n\n if hasattr(self, \"measurement_w\") and self.measurement_w is not None and self.measurement_w.isVisible():\n x = event.pos().x()\n y = event.pos().y()\n\n # distance\n if self.measurement_w.rbDistance.isChecked():\n if event.button() == 1: # left\n self.draw_point(x, y, \"lime\")\n self.memx, self.memy = x, y\n\n if event.button() == 2 and self.memx != -1 and self.memy != -1:\n self.draw_point(x, y, \"lime\")\n self.draw_line(self.memx, self.memy, x, y, \"lime\")\n\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\"line\", self.memx, self.memy, x, y])\n else:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\"line\", self.memx, self.memy, x, y]]\n\n d = ((x - self.memx) ** 2 + (y - self.memy) ** 2) ** 0.5\n try:\n d = d / float(self.measurement_w.lePx.text()) * float(self.measurement_w.leRef.text())\n except:\n QMessageBox.critical(self, programName,\n \"Check reference and pixel values! Values must be numeric.\",\n QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton)\n\n self.measurement_w.pte.appendPlainText(\"Time: {}\\tFrame: {}\\tDistance: {}\".format(self.getLaps(),\n self.FFmpegGlobalFrame,\n round(d, 1)))\n self.measurement_w.flagSaved = False\n self.memx, self.memy = -1, -1\n\n # angle 1st clic -> vertex\n if self.measurement_w.rbAngle.isChecked():\n if event.button() == 1: # left for vertex\n self.draw_point(x, y, \"lime\")\n self.memPoints = [(x, y)]\n\n if event.button() == 2 and len(self.memPoints):\n self.draw_point(x, y, \"lime\")\n self.draw_line(self.memPoints[0][0], self.memPoints[0][1], x, y, \"lime\")\n\n self.memPoints.append((x, y))\n\n if len(self.memPoints) == 3:\n self.measurement_w.pte.appendPlainText(\"Time: {}\\tFrame: {}\\tAngle: {}\".format(self.getLaps(),\n self.FFmpegGlobalFrame,\n round(angle(\n self.memPoints[\n 0],\n self.memPoints[\n 1],\n self.memPoints[\n 2]),\n 1)\n ))\n self.measurement_w.flagSaved = False\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\"angle\", self.memPoints])\n else:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\"angle\", self.memPoints]]\n\n self.memPoints = []\n\n # Area\n if self.measurement_w.rbArea.isChecked():\n if event.button() == 1: # left\n self.draw_point(x, y, \"lime\")\n if len(self.memPoints):\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], x, y, \"lime\")\n self.memPoints.append((x, y))\n\n if event.button() == 2 and len(self.memPoints) >= 2:\n self.draw_point(x, y, \"lime\")\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], x, y, \"lime\")\n self.memPoints.append((x, y))\n # close polygon\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], self.memPoints[0][0],\n self.memPoints[0][1], \"lime\")\n a = polygon_area(self.memPoints)\n\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\"polygon\", self.memPoints])\n else:\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\"polygon\", self.memPoints]]\n try:\n a = a / (float(self.measurement_w.lePx.text()) ** 2) * float(\n self.measurement_w.leRef.text()) ** 2\n except:\n QMessageBox.critical(self, programName,\n \"Check reference and pixel values! Values must be numeric.\",\n QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton)\n\n self.measurement_w.pte.appendPlainText(\"Time: {}\\tFrame: {}\\tArea: {}\".format(self.getLaps(),\n self.FFmpegGlobalFrame,\n round(a, 1)))\n\n self.memPoints = []","def get_face_rect_dlib(frame):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n gray = clahe.apply(gray)\n rects = detector(gray, 0)\n return ((rects[0].top(), rects[0].left()), (rects[0].bottom(), rects[0].right())) if len(rects) > 0 else None","def frame_analysis_green(fW, fH, frame):\n # Compute the ratio to go from pixel coordinate to grid coordinate\n cam_grid_ratio = (gw / fW, gh / fH)\n\n # Compute the green mask needed to find thymio\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n mask = cv2.inRange(hsv, low_green, up_green)\n\n # Find to contours of the square in order to compute the center of it\n contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]\n areas = [cv2.contourArea(c) for c in contours]\n\n # If we don't find the square, return impossible value for the followin code\n # to know no measurement were possible\n if len(areas) < 1:\n\n # Display the resulting frame\n x2, y2 = (-1, -1)\n xf, yf = (-1, -1)\n\n else:\n\n # Find the largest moving object in the image\n max_index = np.argmax(areas)\n cnt = contours[max_index]\n x, y, w, h = cv2.boundingRect(cnt)\n\n # Find the center of the green square\n xf = x + int(w / 2)\n yf = y + int(h / 2)\n\n # Change from pixel coordinate to grid coordinate\n x2 = xf * cam_grid_ratio[0]\n y2 = gh - yf * cam_grid_ratio[1]\n\n frame = frame[:, :, ::-1]\n\n return x2, y2, xf, yf, frame","def pos_perframe(self):\n return self.__pos_perframe","def Bas():\r\n X1, Y1, X2, Y2 = canvas.coords(boule)\r\n canvas.coords(boule,X1,Y1+20,X2,Y2+20)","def bee_at(self, point):\n \"\"\" Responsible for transformations \"\"\"\n pos, com, success = self.perception\n \n point = point.copy()\n \n if self.phase == 2:\n point -= self.position\n elif self.phase > 2:\n point = self.transform(point - self.position)\n\n return any(map(lambda x: array_equal(point,x),pos))","def update(self, balls):\n\n # Find closest ball\n closest_ball = [self.settings.WINDOW_WIDTH/2, self.settings.WINDOW_HEIGHT/2]\n\n # Scan through all the list of balls to find the closest to the enemy bar\n for ball in balls:\n if self.topSide:\n if ball.rect.y < closest_ball[1]:\n closest_ball = ball.rect\n elif not self.topSide:\n if ball.rect.y > closest_ball[1]:\n closest_ball = ball.rect\n\n # Calculation for if the bar is on top\n if self.rect.centerx >= closest_ball[0]:\n self.rect.x -= self.velocity.x\n elif self.rect.centerx <= closest_ball[0]:\n self.rect.x += self.velocity.x\n\n # Checks if the enemy bar gone out of bound\n self.check_boundaries()","def get_frame_root_pos(self, frame):\n root_pos = frame[:self.POS_SIZE].copy()\n return root_pos","def get_ball_position_and_state(self):\n return (self.x, self.y, self.is_ball_visible)","def frame_analysis_yellow(fW, fH, frame):\n\n # Compute the ratio to go from pixel coordinate to grid coordinate\n cam_grid_ratio = (gw / fW, gh / fH)\n\n # Compute the yellow mask in order to find the uper square on the thymio\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n mask = cv2.inRange(hsv, low_yellow, up_yellow)\n\n # Find to contours of the square in order to compute the center of it\n contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]\n areas = [cv2.contourArea(c) for c in contours]\n\n # If we don't find the square, return impossible value for the followin code\n # to know no measurement were possible\n if len(areas) < 1:\n\n # Display the resulting frame\n x2, y2 = (-1, -1)\n xf, yf = (-1, -1)\n\n else:\n\n # Find the largest moving object in the image\n max_index = np.argmax(areas)\n cnt = contours[max_index]\n x, y, w, h = cv2.boundingRect(cnt)\n\n # Change from pixel to grid coordinate\n xf = x + int(w / 2)\n yf = y + int(h / 2)\n\n x2 = xf * cam_grid_ratio[0]\n y2 = gh - yf * cam_grid_ratio[1]\n\n frame = frame[:, :, ::-1]\n\n return x2, y2, xf, yf, frame","def matched_brand_positions(self):\n collector = self._results.collector\n \"\"\"@type: ok.query.whoosh_contrib.find_brands.FindBrandsQuery.BrandsCollector\"\"\"\n brand = self.current_match['brand']\n pos = collector.brands_found[brand][1]\n return pos","def check_terminal(self,bump,DLightBump, AnalogBump, IR):\r\n terminal = False\r\n # signal returned from distance to obstacle /terminal 50 mm,5cm\r\n # by measurement, small obstacle (height = height of light bumper) in 2cm: signal 120 ~300\r\n # within 1cm >400\r\n # if big obstacle: (like a wall) at 2cm: 1300~1600\r\n # d_obs = 140\r\n d_obs = 500.0\r\n threshold = d_obs/self.max_strength\r\n obstacles = []\r\n Infra_Omi, Infra_L, Infra_R =IR\r\n\r\n L, FL, CL, CR, FR, R = AnalogBump\r\n prob_obs =np.array([L, FL, CL, CR, FR, R]).astype(float)\r\n strength = prob_obs/self.max_strength # maximum signal strength light bumper can receive\r\n for i in range(len(strength)):\r\n strength[i] = 1 if strength[i] >=threshold else 0\r\n\r\n cnt = strength.sum()\r\n if Infra_Omi!=0 or Infra_L!=0 or Infra_R!= 0:\r\n terminal =True\r\n x = int(self.Motion.x +d_obs)\r\n y = int(self.Motion.y)\r\n s = (x, y)\r\n obstacles.append(s)\r\n\r\n if bump != 0 or cnt >=1:\r\n terminal=True\r\n # stop immediately\r\n self.Roomba.Move(0,0)\r\n #-------------determine position of obstacles-------------\r\n l_bump = 1 if bump&2 !=0 else 0\r\n r_bump = 1 if bump& 1 !=0 else 0\r\n # Assume Left , right bumpers are at -45 degree, 45 degree\r\n # Then find the average degree of object:0, -45, 45 degree\r\n b_avg_angle = 45*(r_bump -l_bump)\r\n prob_obs /= (prob_obs.sum()+1.0)\r\n # average angles of obstacle detected by light bumper\r\n # [-90, -60,-30,30,60,90] are heading angles of 6 analog light bumper\r\n lb_avg_agl = np.dot(prob_obs,[-90, -60,-30,30,60,90])\r\n\r\n # if there are 2 obstacles\r\n if np.abs(lb_avg_agl - b_avg_angle)>=60 or (np.sign(lb_avg_agl) !=np.sign(b_avg_angle)):\r\n th = self.Motion.theta + lb_avg_agl\r\n x = self.Motion.x + d_obs * math.cos(th)\r\n y = self.Motion.y + d_obs * math.sin(th)\r\n x = int(x)\r\n y = int(y)\r\n s= (x,y)\r\n if obstacles.count(s) == 0:\r\n obstacles.append(s)\r\n\r\n th = self.Motion.theta + b_avg_angle\r\n x = self.Motion.x + d_obs * math.cos(th)\r\n y = self.Motion.y + d_obs * math.sin(th)\r\n x = int(x)\r\n y = int(y)\r\n s = (x,y)\r\n if obstacles.count(s) ==0:\r\n obstacles.append(s)\r\n\r\n else:\r\n # if there is 1 obstacle\r\n alg = (b_avg_angle+lb_avg_agl)/2.0\r\n th= self.Motion.theta+ alg\r\n x = self.Motion.x + d_obs * math.cos(th)\r\n y = self.Motion.y + d_obs * math.sin(th)\r\n x = int(x)\r\n y = int(y)\r\n s = (x,y)\r\n if obstacles.count(s) == 0:\r\n obstacles.append(s)\r\n\r\n # check if the obstacle is one of other agents\r\n for k in self.global_trans.keys():\r\n # Format: self.global_trans={id: (degree, [a,st,s_t+1])}\r\n states = self.global_trans[k][1]\r\n st = self.get_gridState(states[1])\r\n st1 = self.get_gridState(states[2])\r\n # if obstacles are other agents, remove them\r\n for o in obstacles:\r\n grid_o = self.get_gridState((o[0],o[1],th))\r\n if (grid_o[0],grid_o[1]) == (st[0],st[1]) or (grid_o[0],grid_o[1]) == (st1[0],st1[1]):\r\n obstacles.remove(o)\r\n return terminal, obstacles","def framewise_inside_rectangle_roi(\n bp_location: np.ndarray, roi_coords: np.ndarray\n ) -> np.ndarray:\n results = np.full((bp_location.shape[0]), 0)\n within_x_idx = np.argwhere(\n (bp_location[:, 0] <= roi_coords[1][0])\n & (bp_location[:, 0] >= roi_coords[0][0])\n ).flatten()\n within_y_idx = np.argwhere(\n (bp_location[:, 1] <= roi_coords[1][1])\n & (bp_location[:, 1] >= roi_coords[0][1])\n ).flatten()\n for i in prange(within_x_idx.shape[0]):\n match = np.argwhere(within_y_idx == within_x_idx[i])\n if match.shape[0] > 0:\n results[within_x_idx[i]] = 1\n return results","def touching_border(self, bb: list, quickness: int = 1) -> str:\r\n \r\n # Make the coordinates easier to handle\r\n x1, y1, x2, y2 = bb[0], bb[1], bb[2], bb[3]\r\n\r\n # Check which way the box should be expanded\r\n # The first two conditions check if the box should\r\n # be expanded diagonally downwards.\r\n # These just speed up the box sizing\r\n \r\n ## Check if there is a green pixel on the bottom right of the box\r\n if not (x2 + quickness >= len(self.bin_pic[0])) and \\\r\n not (y2 + quickness >= len(self.bin_pic)):\r\n if self.bin_pic[y2][x2]:\r\n return 'BR'\r\n\r\n ## Check if there is a green pixel on the bottom right of the box\r\n if not (x1 - quickness < 0) and not (y2 + quickness >= len(self.bin_pic)):\r\n if self.bin_pic[y2][x1]:\r\n return 'BL'\r\n\r\n # The next four check the sides of the box\r\n ## Check if there is a green pixel on the right side of the box\r\n if not (x2 + quickness >= len(self.bin_pic[0])):\r\n for i in range(y2 - y1 + 1):\r\n if self.bin_pic[y1 + i][x2]:\r\n return 'RIGHT'\r\n \r\n ## Check if there is a green pixel on the bottom side of the box\r\n if not (y2 + quickness >= len(self.bin_pic)):\r\n for i in range(x2 - x1 + 1):\r\n if self.bin_pic[y2][x1 + i]:\r\n return 'BOTTOM'\r\n\r\n ## Check if there is a green pixel on the left side of the box\r\n if not (x1 - quickness < 0):\r\n for i in range(y2 - y1 + 1):\r\n if self.bin_pic[y1 + i][x1 - quickness]:\r\n return 'LEFT'\r\n\r\n ## Check if there is a green pixel on the top side of the box\r\n if not (y1 - quickness < 0):\r\n for i in range(x2 - x1 + 1):\r\n if self.bin_pic[y1 - quickness][x1 + i]:\r\n return 'TOP'\r\n\r\n # If all the green pixels are found and the box is on the edge\r\n if (x2 + quickness >= len(self.bin_pic[0])) or \\\r\n (y2 + quickness >= len(self.bin_pic)) or \\\r\n (y1 - quickness < 0) or \\\r\n (x1 - quickness < 0):\r\n return 'EDGE'\r\n\r\n # If there are no green pixels touching the box, return 'NONE'\r\n return 'NONE'","def on_collision(self):","def frame(self):","def detectBorders(self, points):\n lane1 = []; lane2 = []\n self.leftLane = [None for _ in range(int(np.floor(self.BIRDVIEW_HEIGHT / self.slideThickness)))]\n self.rightLane = [None for _ in range(int(np.floor(self.BIRDVIEW_HEIGHT / self.slideThickness)))]\n\n pointMap = np.zeros((points.shape[0], 20))\n prePoint = np.zeros((points.shape[0], 20))\n postPoint = np.zeros((points.shape[0], 20))\n\n dis = 10\n max1 = -1; max2 = -1\n\n ##\n ## /!\\ UNSAFE LOOP, TODO: FIX\n ##\n for i in range(points.shape[0]):\n for j in range(len(points[i])):\n pointMap[i][j] = 1\n prePoint[i][j] = -1\n postPoint[i][j] = -1\n\n for i in reversed(range(points.shape[0] - 2)):\n\n for j in range(len(points[i])):\n\n err = 320\n for m in range(1, min(points.shape[0] - 1 - i, 5)):\n check = False ## TODO: why unused ?\n\n for k in range(len(points[i + 1])):\n\n (x_m, y_m) = points[i + m][k].pt\n (x, y) = points[i][j].pt\n\n if (abs(x_m - x) < dis and abs(y_m - y) < err):\n err = abs(x_m - x)\n\n pointMap[i][j] = pointMap[i + m][k] + 1\n prePoint[i][j] = k\n postPoint[i + m][k] = j\n check = True\n\n break ## breaks out of the m loop. Why is it not conditioned by check ? TODO: ???\n\n if (pointMap[i][j] > max1):\n max1 = pointMap[i][j]\n posMax = cv2.KeyPoint(i, j, _size=0)\n \n else:\n posMax = None\n\n for i in range(points.shape[0]):\n for j in range(len(points[i])):\n if posMax:\n if (pointMap[i][j] > max2 and (i != posMax.pt[0] or j != posMax.pt[1]) and postPoint[i][j] == -1): #FIXME \"local variable 'posMax' referenced before assignment\" possible\n max2 = pointMap[i][j]\n posMax2 = cv2.KeyPoint(i, j, _size=0)\n\n\n\n if max1 == -1:\n return\n\n # DEFINES LANE 1 POINTS\n while (max1 >= 1):\n (x,y) = points[int(posMax.pt[0])][int(posMax.pt[1])].pt\n lane1.append(\n [x,y]\n )\n if (max1 == 1):\n break\n\n posMax = cv2.KeyPoint(\n posMax.pt[0]+1,\n prePoint[int(posMax.pt[0])][int(posMax.pt[1])],\n _size=0\n )\n\n max1 -= 1\n\n # DEFINES LANE 2 POINTS\n while (max2 >= 1):\n (x,y) = points[int(posMax2.pt[0])][int(posMax2.pt[1])].pt\n lane2.append(\n [x, y]\n )\n if (max2 == 1):\n break\n\n posMax2 = cv2.KeyPoint(\n posMax2.pt[0]+1,\n prePoint[int(posMax2.pt[0])][int(posMax2.pt[1])],\n _size=0\n )\n\n max2-= 1\n\n subLane1 = np.array(lane1[0:5])\n subLane2 = np.array(lane2[0:5])\n\n # checking if sublane has an empty value\n\n line1 = cv2.fitLine(subLane1, 2, 0, 0.01, 0.01)\n line2 = cv2.fitLine(subLane2, 2, 0, 0.01, 0.01)\n\n try:\n lane1X = (self.BIRDVIEW_WIDTH - line1[3]) * line1[0] / line1[1] + line1[2]\n except:\n lane1X = 0\n\n try:\n lane2X = (self.BIRDVIEW_WIDTH - line2[3]) * line2[0] / line2[1] + line2[2]\n except:\n lane2X = 0\n \n if (lane1X < lane2X):\n for i in range(len(lane1)):\n self.leftLane[int(np.floor(lane1[i][1] / self.slideThickness ))] = lane1[i]\n\n for i in range(len(lane2)):\n self.rightLane[int(np.floor(lane2[i][1] / self.slideThickness ))] = lane2[i]\n\n else:\n\n for i in range(len(lane1)):\n self.rightLane[int(np.floor(lane1[i][1] / self.slideThickness ))] = lane1[i]\n\n for i in range(len(lane2)):\n self.leftLane[int(np.floor(lane2[i][1] / self.slideThickness ))] = lane2[i]","def int_21H_B(self):\r\n\r\n char_present = self.GUI.is_key_pressed()\r\n\r\n if char_present:\r\n self.registers['AX'].move_into(255, 0, is_int=True)\r\n else:\r\n self.registers['AX'].move_into(0, 0, is_int=True)","def Haut():\r\n X1, Y1, X2, Y2 = canvas.coords(boule)\r\n canvas.coords(boule,X1,Y1-20,X2,Y2-20)","def boundary(self):\n if self.pos.x < 0:\n self.pos.x = 0\n if self.pos.x > WIDTH - 48:\n self.pos.x = WIDTH - 48\n if self.pos.y < 0:\n self.pos.y = 0\n if self.pos.y > HEIGHT - 48:\n self.pos.y = HEIGHT - 48\n\n self.rect.topleft = self.pos","def boundingBox(self):\n pmodel = (glm.vec3(1, -self.y_sign, 0)\n * self.model.pos * self.transform.scale)\n x, y, _ = self.transform.pos + pmodel\n y += -self.y_sign * self.font.table['ascent'] * self.transform.scale[1]\n return x, y, self.pixwidth(), self.pixheight()","def mover_bm_izquierda(self):\n self.nueva_posicion_posible_parte_superior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0] - 1,\n self.casilla[1]],\n [self.vertice_1[0] - self.velocidad,self.vertice_1[1]], \n [self.vertice_1[0] - 5 - 5, self.vertice_1[1]])\n self.nueva_posicion_posible_parte_inferior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0] - 1,\n self.casilla[1] + 1],\n [self.vertice_3[0] - self.velocidad,self.vertice_3[1]],\n [self.vertice_3[0] - 5,self.vertice_3[1]]) \n if self.nueva_posicion_posible_parte_superior[0] != 1 and self.nueva_posicion_posible_parte_inferior[0] != 1:\n self.x -= self.velocidad * (self.x >= 15)\n self.posicion = [self.x,self.posicion[1]]\n self.casilla = [self.casilla[0] - self.nueva_posicion_posible_parte_superior[1] *(self.nueva_posicion_posible_parte_inferior[0] != 1) * (self.nueva_posicion_posible_parte_superior[0] != 1), self.casilla[1]]\n self.redefinir_vertices()","def get_frame_bbox(annotations_dir, seq):\n root_dir = \"/home/sdb/wangshentao/myspace/thesis/data/VisDrone2019-MOT-test-dev/\"\n bbox = []\n frame_id = []\n trace_bbox = []\n trace_frame_id = []\n annotations_dir = root_dir + 'annotations/'\n gt_txt = os.path.join(annotations_dir, seq)\n gt = np.loadtxt(gt_txt, dtype=np.float64, delimiter=',')\n tid_last = gt[0][1]\n for fid, tid, x, y, w, h, mark, label, _, _ in gt:\n if int(mark) == 0 or int(label) == 0 or int(label) == 11:\n continue\n if tid == tid_last:\n trace_bbox.append([x, y, w, h])\n trace_frame_id.append(fid)\n else:\n tid_last = tid\n if len(trace_bbox) > 1:\n bbox.append(np.array(trace_bbox))\n frame_id.append(np.array(trace_frame_id))\n trace_bbox = [[x, y, w, h]]\n trace_frame_id = [fid]\n\n return bbox, frame_id","def update(self, frame):\n self.__update_state(frame)\n\n if self.strategy == \"mean\":\n ax = np.mean(np.ravel(self.flow[...,0]))\n ay = np.mean(np.ravel(self.flow[...,1]))\n else:\n ax = statistics.median(np.ravel(self.flow[...,0]))\n ay = statistics.median(np.ravel(self.flow[...,1]))\n\n P = 1\n new_x = int(self.bbox[0] + self.bbox[2] * ax * P)\n new_y = int(self.bbox[1] + self.bbox[3] * ay * P)\n self.bbox = (new_x, new_y, self.bbox[2], self.bbox[3])\n return True, self.bbox","def position(self):\r\n pass","def check_boundaries(self):\n # Checks if the enemy bar has gone of the net\n if self.rect.left <= self.settings.WINDOW_WIDTH / 2:\n self.rect.left = self.settings.WINDOW_WIDTH / 2\n self.isMovingUp = False\n\n # Checks if the enemy bar has gone out of bound to the right\n if self.rect.right >= self.settings.WINDOW_WIDTH:\n self.rect.right = self.settings.WINDOW_WIDTH\n self.isMovingUp = True","def seek_beacon(self):\n\n beacon_sensor = ev3.BeaconSeeker(channel=1)\n forward_speed = 300\n turn_speed = 100\n\n while not self.touch_sensor.is_pressed:\n current_heading = beacon_sensor.heading\n current_distance = beacon_sensor.distance\n if current_distance == -128:\n print(\"IR Remote not found. Distance is -128\")\n self.stop()\n else:\n if math.fabs(current_heading) < 2:\n print(\"On the right heading. Distance: \", current_distance)\n if current_distance == 0:\n self.right_forward(True, forward_speed)\n self.left_forward(True, forward_speed)\n time.sleep(0.4)\n self.stop()\n return True\n elif current_distance > 0:\n print(\"Beacon is in front of the robot.\")\n self.right_forward(True, forward_speed)\n self.left_forward(True, forward_speed)\n elif math.fabs(current_heading) > 2 and math.fabs(\n current_heading) < 10:\n if current_heading < 0:\n print(\"Beacon is on the left.\")\n self.right_forward(True, turn_speed)\n self.left_backward(True, turn_speed)\n else:\n print(\"Beacon is on the right.\")\n self.right_backward(True, turn_speed)\n self.left_forward(True, turn_speed)\n elif math.fabs(current_heading) > 10:\n self.stop()\n print('Heading too far off.')\n time.sleep(0.01)\n\n print(\"Abandon ship!\")\n self.stop()\n return False","def expect_bell(self, index: int, bell: Bell) -> None:\n if self._user_assigned_bell(bell):\n self._rhythm.expect_bell(\n bell,\n self._row_number,\n index,\n self.stroke\n )","def generate_bottonDown(self):\n posx = self.posx + self.sizex - 2*self.sizebotx \n posy = self.posy + self.sizeboty \n return Boton(posx, posy, self.sizebotx, self.sizeboty, color=(124, 117, 221), color_h=(95, 90, 173), text_color=(0,0,0), press_color = (36, 26, 117), font_size=5)","def on_bar(self, bar: BarData):\n self.handle_bar(bar)\n self.bg5.update_bar(bar)\n # self.bg15.update_bar(bar)","def blockDetector(self, frame):\n self.detectBlocksInDepthImage()\n self.block_pos = np.zeros((50,3))\n self.block_num = 0\n pixal_arm = np.zeros(2)\n if self.kinectCalibrated == True:\n # get current arm position\n real_arm_x, real_arm_y,_,_ = self.rexarm.get_wrist_pose()\n real_arm_x *= -1000\n real_arm_y *= 1000\n real_arm = np.array(([real_arm_x],[real_arm_y],[1]))\n \n # normalize arm_line vector\n arm_length = np.sqrt(real_arm_x**2 + real_arm_y**2)\n l = np.array([real_arm_x,real_arm_y])\n\n # find center of block_1_height and put them into block_position\n for cnt in self.block_contours:\n M = cv2.moments(cnt)\n if M['m00']>0:\n cx = int(M['m10']/M['m00'])\n cy = int(M['m01']/M['m00'])\n c = np.array([[cx],[cy]])\n # Eliminate Arm itself\n real_x = self.real_coord[cx][cy][0]\n real_y = self.real_coord[cx][cy][1]\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\n \n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\n\n \n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\n # Check if its in our ROI\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\n # points\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\n self.block_pos[self.block_num][2] = 1\n # orientation\n rect = cv2.minAreaRect(cnt)\n self.block_ori[self.block_num][0] = -rect[2]\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n # detect color\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\n # draw contours\n cv2.drawContours(self.currentDetectFrame,[box],0,(30,145,86),3)\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(30,145,86),-1)\n self.block_num += 1\n\n # find centers of 2 blocks\n for cnt in self.block_contours_2:\n M = cv2.moments(cnt)\n if M['m00']>0:\n cx = int(M['m10']/M['m00'])\n cy = int(M['m01']/M['m00'])\n c = np.array([[cx],[cy]])\n # Eliminate Arm itself\n real_x = self.real_coord[cx][cy][0]\n real_y = self.real_coord[cx][cy][1]\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\n \n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\n \n \n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\n # Check if its in our ROI\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\n # points\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\n self.block_pos[self.block_num][2] = 2\n # orientation\n rect = cv2.minAreaRect(cnt)\n self.block_ori[self.block_num][0] = -rect[2]\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n # detect color\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\n # draw contours\n cv2.drawContours(self.currentDetectFrame,[box],0,(30,87,137),3)\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(30,87,137),-1)\n self.block_num += 1 \n\n # find centers of 3 blocks\n for cnt in self.block_contours_3:\n M = cv2.moments(cnt)\n if M['m00']>0:\n cx = int(M['m10']/M['m00'])\n cy = int(M['m01']/M['m00'])\n c = np.array([[cx],[cy]])\n # Eliminate Arm itself\n real_x = self.real_coord[cx][cy][0]\n real_y = self.real_coord[cx][cy][1]\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\n \n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\n \n \n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\n # Check if its in our ROI\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\n # points\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\n self.block_pos[self.block_num][2] = 3\n # orientation\n rect = cv2.minAreaRect(cnt)\n self.block_ori[self.block_num][0] = -rect[2]\n box = cv2.boxPoints(rect)\n box = np.int0(box)\n # detect color\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\n # draw contours\n cv2.drawContours(self.currentDetectFrame,[box],0,(204,6,6),3)\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(204,6,6),-1)\n self.block_num += 1 \n self.block_pos[self.block_num:50] = 0\n self.block_ori[self.block_num:50] = 0\n\n return frame","def attack_barbarian(self):\n Utils.sleep()\n Utils.wait_and_click(f\"barbarian_{self.config.barbarian_level['BarbarianLevel']}\", similarity=0.85, wait=20)\n Utils.wait_and_click('barbarian_attack')\n Utils.wait_and_click('attack_new_troops', wait=120)\n Utils.wait_and_click('attack_march')"],"string":"[\n \"def buff_region(self, x, y):\\n if x>120 and x<220 and y>275 and y<375:\\n return 1\\n elif x>580 and x<680 and y>125 and y<225:\\n return 2\\n return 0\",\n \"def track(self, frame):\\n mask = self.get_arrow_mask(frame)\\n label = skimage.measure.label(mask)\\n props = skimage.measure.regionprops(label)\\n\\n arrow = max(props, key=lambda prop: prop.area)\\n yc, xc = arrow.centroid\\n self.position_list.append((xc, yc))\\n y0, x0, y1, x1 = arrow.bbox\\n self.bbox = (x0, y0, x1, y1)\",\n \"def position_is_bomb(bombs, position):\\n for bomb in bombs:\\n if position == bomb.position:\\n return True\\n return False\",\n \"def on_fruit(self):\\r\\n if self.grid_pos in self.app.fruit:\\r\\n if int(self.pix_pos.x+TOP_BOTTOM_BUFFER//2) % self.app.cell_width == 0:\\r\\n if self.direction == vec(1, 0) or self.direction == vec(-1, 0):\\r\\n return True\\r\\n # in the x-direction \\r\\n\\r\\n if int(self.pix_pos.y+TOP_BOTTOM_BUFFER//2) % self.app.cell_height == 0:\\r\\n if self.direction == vec(0, 1) or self.direction == vec(0, -1):\\r\\n return True\\r\\n # in the y-direction\\r\\n\\r\\n return False\",\n \"def bout_detect(raw_motion_data):\\n import numpy as np\\n window_length = 50 # Most bouts last much lesser than 20 frames,\\n # so this is a really safe window to search for bouts.\\n nonzero_indices = np.flatnonzero(raw_motion_data)\\n all_bout_indices = [] # A list of all the indices that belong to all bouts.\\n bout_indices = [] # A list of tuples of bout indices.\\n bouts = [] # A list of bouts, stored as tuples.\\n # Run through the list of nonzero indices and look for the first zero value\\n # after it.\\n do_not_append = False\\n for i in nonzero_indices:\\n if i not in all_bout_indices:\\n bout_start = i # Bout starting point\\n try:\\n first_zero_after_start = np.flatnonzero(\\n raw_motion_data[bout_start:bout_start+window_length+1] == 0)[0]\\n except IndexError:\\n try:\\n first_zero_after_start = np.flatnonzero(\\n raw_motion_data[bout_start:len(raw_motion_data)] == 0)[0]\\n except IndexError:\\n first_zero_after_start = len(raw_motion_data)\\n do_not_append = True\\n if not do_not_append:\\n # Adjusted to start and end at 0\\n bout = tuple(raw_motion_data[bout_start-1:bout_start+first_zero_after_start+1])\\n bout_inds = tuple(range(bout_start-1, bout_start+first_zero_after_start+1))\\n bout_indices.append(bout_inds)\\n bouts.append(bout)\\n for j in range(bout_start, bout_start+first_zero_after_start):\\n all_bout_indices.append(j)\\n\\n # Filter out the bouts list to remove tuples which are smaller than 5 entries long.\\n \\\"\\\"\\\"This is not really needed, because the fish can make some tiny movements\\n that need to be detected and counted as bouts. In any case, the decision for this\\n can be made much later. It is not really important to figure this out right now.\\\"\\\"\\\"\\n # bouts[:] = [tup for tup in bouts if len(tup) > 5]\\n # bout_indices[:] = [x for x in bout_indices if len(x) > 5]\\n return bouts, bout_indices\",\n \"def ball_detector(out_q_x, out_q_y):\\n global ball_before\\n global ball_count\\n global new_ball\\n global bounce\\n global frame\\n global crop_img\\n\\n green_color_hsv_min = cv.Scalar(43, 54, 84)\\n green_color_hsv_max = cv.Scalar(78, 179, 256)\\n orange_color_hsv_min = cv.Scalar(0, 105, 200)\\n orange_color_hsv_max = cv.Scalar(11, 256, 256)\\n blue_color_hsv_min = cv.Scalar(103, 90, 87)\\n blue_color_hsv_max = cv.Scalar(112, 256, 181)\\n yellow_color_hsv_min = cv.Scalar(17, 70, 163)\\n yellow_color_hsv_max = cv.Scalar(27, 256, 256)\\n ball_number = 0\\n\\n while True:\\n ret, frame = cap.read()\\n if ret:\\n crop_img = frame[SCREEN_Y_TOP:SCREEN_Y_BOT, SCREEN_X_TOP:SCREEN_X_BOT]\\n hsv_image = cv2.cvtColor(crop_img, cv2.COLOR_BGR2HSV)\\n hsv_blue = cv2.inRange(hsv_image,\\n blue_color_hsv_min,\\n blue_color_hsv_max\\n )\\n hsv_orange = cv2.inRange(hsv_image,\\n orange_color_hsv_min,\\n orange_color_hsv_max\\n )\\n hsv_green = cv2.inRange(hsv_image,\\n green_color_hsv_min,\\n green_color_hsv_max\\n )\\n hsv_yellow = cv2.inRange(hsv_image,\\n yellow_color_hsv_min,\\n yellow_color_hsv_max\\n )\\n green_yellow_hsv = cv2.bitwise_or(hsv_green, hsv_yellow)\\n green_yellow_blue_hsv = cv2.bitwise_or(green_yellow_hsv, hsv_blue)\\n green_yellow_blue_orange_hsv = cv2.bitwise_or(green_yellow_blue_hsv,\\n hsv_orange)\\n\\n str_el = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))\\n open_morphed = cv2.morphologyEx(green_yellow_blue_orange_hsv,\\n cv2.MORPH_OPEN,\\n str_el\\n )\\n morphed = cv2.morphologyEx(open_morphed, cv2.MORPH_CLOSE, str_el)\\n\\n hsv_blur = cv2.GaussianBlur(morphed, (11, 11), 4, 4)\\n circles = cv2.HoughCircles(hsv_blur, cv.CV_HOUGH_GRADIENT, 2, 720 / 9,\\n param1=75, param2=45, minRadius=5,\\n maxRadius=70)\\n ball_count = 0\\n\\n if circles is not None:\\n circles = np.uint16(np.around(circles))\\n ball_count = len(circles)\\n for i in circles[0, :]:\\n\\n # create ball object if ball's number is increase\\n if is_ball_increase(ball_before, ball_count) and i[2] > 16:\\n ball_number += 1\\n print \\\"new ball\\\"\\n balls.append(Ball(i[0], i[1], i[2], ball_number))\\n\\n # update ball position in ball object\\n pos_list = []\\n for b in balls:\\n pos_list.append([(b.center[0], b.center[1]), b])\\n\\n if len(pos_list) == 0:\\n pass\\n elif len(pos_list) == 1:\\n pos_list[0][1].update(i[0], i[1], i[2])\\n if (0 <= i[0] <= SCREEN_X_BOT) and (0 <= i[1] <= SCREEN_Y_BOT):\\n pos_list[0][1].check_size(out_q_x, out_q_y)\\n\\n pos_list[0][1].show()\\n\\n else:\\n close = closest((i[0], i[1]), pos_list)\\n close[1].update(i[0], i[1], i[2])\\n if (0 <= i[0] <= SCREEN_X_BOT) and (0 <= i[1] <= SCREEN_Y_BOT):\\n close[1].check_size(out_q_x, out_q_y)\\n print pos_list\\n close[1].show()\\n\\n if is_ball_decrease(ball_before, ball_count):\\n for b in balls:\\n if b.bounced is True:\\n b.destroy()\\n cv2.rectangle(frame, (SCREEN_X_TOP, SCREEN_Y_TOP), (SCREEN_X_BOT, SCREEN_Y_BOT), (0, 255, 0), 3)\\n cv2.imshow('crop', crop_img)\\n\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\n break\\n ball_before = ball_count\\n print \\\"Exiting Background Thread: Ball detector\\\"\",\n \"def tick(self, iteration):\\n frame = self.cam.get_current_fram()\\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\\n # on recupaire la zone qui on les zone de couleur\\n mask = cv2.inRange(hsv, self.greenLower, self.greenUpper)\\n # on fait une erosion et dilation pour supprimer les petit pixel blanc qui peut être pris pour la balle\\n mask = cv2.erode(mask, None, iterations=iteration)\\n mask = cv2.dilate(mask, None, iterations=iteration)\\n\\n cv2.imshow(\\\"mask\\\", mask)\\n\\n # Recherche de contour pour savoir ou est la balle\\n cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\\n center = None\\n if len(cnts) > 0:\\n # on charche le contour le plus grand (celui de la balle)\\n c = max(cnts, key=cv2.contourArea)\\n ((x, y), radius) = cv2.minEnclosingCircle(c)\\n moment_cont = cv2.moments(c)\\n # on recupaire le centre de la balle\\n center = (int(moment_cont[\\\"m10\\\"] / moment_cont[\\\"m00\\\"]), int(moment_cont[\\\"m01\\\"] / moment_cont[\\\"m00\\\"]))\\n # on controle la taille du rayon de la balle\\n if radius > 30:\\n # Puis on dessin la boulle\\n cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2)\\n cv2.circle(frame, center, 5, (0, 0, 255), -1)\\n return center, frame\",\n \"def belt(image):\\n\\n # Belt Detector\\n x, y = circular_detector(image, 70, 80)\\n\\n return x, y\",\n \"def is_ball(self, blob, im):\\n size=self.cam.info.get_pixel_size(blob[0], im)*len(blob)\\n return size>15 and size<45\",\n \"def at_pos(self, pos):\\n return pos-self.deadband < self.wm() < pos+self.deadband\",\n \"def bomb(self, index):\\n\\n coords = self.canvas.coords(self.squares[index])\\n x, y = coords[0] + 10, coords[1] + 10\\n tag = self.hit[index]\\n\\n # Count moves for player (used for scoring)\\n if self.players.winCondition == 1:\\n self.players.moves[self.playerNumber][0] += 1\\n\\n # Hit\\n if tag != 0:\\n self.tracker[tag] -= 1\\n\\n # Count moves for player (used in scoring)\\n if self.players.winCondition == 1:\\n self.players.moves[self.playerNumber].append(\\\\\\n self.players.moves[self.playerNumber][0])\\n self.players.moves[self.playerNumber][0] = 0\\n\\n # Ship was sunk\\n if self.tracker[tag] == 0:\\n text = []\\n tagname = 'tag%s' % tag\\n\\n # Bonus points equal to the size of ship\\n # awarded for sinking entire ship\\n if self.players.winCondition == 0:\\n self.players.score[self.playerNumber] += \\\\\\n self.counter_copy[tag]\\n\\n # Show bombed location with black & orange flashing bar\\n for i in range(5):\\n text.append(self.canvas.create_text(\\\\\\n x, y, text='O', fill='red'))\\n self.canvas.addtag_withtag('text', text[-1])\\n self.canvas.tag_raise(tagname, 'square')\\n for i in range(3): # Flashing bar\\n self.canvas.itemconfig(tagname, {'fill': 'black'})\\n self.canvas.update()\\n self.myframe.after(100)\\n self.canvas.itemconfig(tagname, {'fill': 'orange'})\\n self.canvas.update()\\n self.myframe.after(100)\\n\\n self.hit[index] = 5\\n self.players.message[not self.playerNumber] = \\\\\\n '%s,\\\\nYour ship of size %s was sunk by enemy' % \\\\\\n (self.players.usernames[not self.playerNumber], \\\\\\n self.counter_copy[tag])\\n self.players.endOfTurn(self.tracker)\\n return\\n\\n # Hit, but not sunk. Player gets only 1 point\\n if self.players.winCondition == 0:\\n self.players.score[self.playerNumber] += 1\\n\\n # Show hit location with flashing black & red circle\\n text = []\\n for i in range(3):\\n del text[:]\\n for i in range(5): # flash black circle\\n text.append(self.canvas.create_text(\\\\\\n x, y, text='O', fill='black'))\\n self.canvas.addtag_withtag('text', text[-1])\\n self.canvas.update()\\n self.myframe.after(100)\\n del text[:]\\n for i in range(5): # flash red circle\\n text.append(self.canvas.create_text(\\\\\\n x, y, text='O', fill='red'))\\n self.canvas.addtag_withtag('text', text[-1])\\n self.canvas.update()\\n self.myframe.after(100)\\n\\n # Complete miss. Draw 'X'\\n else:\\n for i in range(5):\\n text = self.canvas.create_text(x, y, text='X', fill='yellow')\\n self.canvas.addtag_withtag('text', text)\\n self.canvas.update()\\n self.myframe.after(250)\\n self.hit[index] = 5\\n self.players.endOfTurn(self.tracker)\",\n \"def detect_bolt_collision(self,bolt):\\n\\n # [x,y]\\n top_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\\n top_right_bolt = [bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\\n bottom_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\\n bottom_right_bolt =[bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\\n\\n if self.contains((top_left_bolt[0],top_left_bolt[1])):\\n return True\\n if self.contains((top_right_bolt[0],top_right_bolt[1])):\\n return True\\n if self.contains((bottom_left_bolt[0],bottom_left_bolt[1])):\\n return True\\n if self.contains((bottom_right_bolt[0],bottom_right_bolt[1])):\\n return True\\n\\n return False\",\n \"def detect_bolt_collision(self,bolt):\\n\\n # [x,y]\\n top_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\\n top_right_bolt = [bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\\n bottom_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\\n bottom_right_bolt =[bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\\n\\n if self.contains((top_left_bolt[0],top_left_bolt[1])):\\n return True\\n if self.contains((top_right_bolt[0],top_right_bolt[1])):\\n return True\\n if self.contains((bottom_left_bolt[0],bottom_left_bolt[1])):\\n return True\\n if self.contains((bottom_right_bolt[0],bottom_right_bolt[1])):\\n return True\\n\\n return False\",\n \"def checkball(self):\\r\\n for ball in self.overlapping_sprites:\\r\\n ball.bottom=self.top\\r\\n if math.fabs(ball.x-self.x)<math.fabs(ball.x-self.left) and math.fabs(ball.x-self.x)<math.fabs(ball.x-self.right):\\r\\n ball.vertbounce()\\r\\n if math.fabs(ball.x-self.left)<math.fabs(ball.x-self.x) and math.fabs(ball.x-self.left)<math.fabs(ball.x-self.right):\\r\\n ball.leftbounce()\\r\\n if math.fabs(ball.x-self.right)<math.fabs(ball.x-self.left) and math.fabs(ball.x-self.right)<math.fabs(ball.x-self.x):\\r\\n ball.rightbounce()\\r\\n self.points.value+=10\\r\\n if self.points.value==500:\\r\\n ball.ballchange()\\r\\n elif self.points.value==2000:\\r\\n ball.ballchange()\\r\\n elif self.points.value==4500:\\r\\n ball.ballchange()\\r\\n elif self.points.value==10000:\\r\\n ball.ballchange()\",\n \"def get_aa_pos_on_screen(self,position,frame):\\n position=position*3+float(frame)-1\\n x,y=self.get_base_pos_on_screen(position)\\n y=y+20.0+float(frame)*15.0\\n return x,y\",\n \"def opponentBoarderPosition(self, gameState):\\n if self.red:\\n i = self.midWidth\\n else:\\n i = self.midWidth - 1\\n boudaries = [(i,j) for j in range(self.height)]\\n validPositions = []\\n for i in boudaries:\\n if not gameState.hasWall(i[0],i[1]):\\n validPositions.append(i)\\n return validPositions\",\n \"def rectangleSignal(flankHeight = 30, flankWidth = 10, robotHeight = -90):\\n rectanglePos = [\\n [flankHeight/2,-2.5*flankWidth, robotHeight,0,0,0,'mov'],\\n\\n [-flankHeight/2,-2.5*flankWidth, robotHeight,0,0,0,'lin'],\\n [-flankHeight/2,-1.5*flankWidth, robotHeight,0,0,0,'lin'],\\n\\n [flankHeight/2,-1.5*flankWidth, robotHeight,0,0,0,'lin'],\\n [flankHeight/2,-0.5*flankWidth, robotHeight,0,0,0,'lin'],\\n\\n [-flankHeight/2,-0.5*flankWidth, robotHeight,0,0,0,'lin'],\\n [-flankHeight/2,0.5*flankWidth, robotHeight,0,0,0,'lin'],\\n\\n [flankHeight/2,0.5*flankWidth, robotHeight,0,0,0,'lin'],\\n [flankHeight/2,1.5*flankWidth, robotHeight,0,0,0,'lin'],\\n\\n [-flankHeight/2,1.5*flankWidth, robotHeight,0,0,0,'lin'],\\n [-flankHeight/2,2.5*flankWidth, robotHeight,0,0,0,'lin'],\\n\\n [flankHeight/2,2.5*flankWidth, robotHeight,0,0,0,'mov']\\n ]\\n\\n rectanglePos.append([0,0,-127,0,0,0,'mov'])\\n return rectanglePos\",\n \"def find_ball(self):\\n ret, frame = self.video.read()\\n\\n if ret:\\n\\n # ------- detect object --------\\n # resize the frame, blur it, and convert it to the HSV\\n # color space\\n blurred = cv2.GaussianBlur(frame, (11, 11), 0)\\n hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)\\n # construct a mask for the color \\\"green\\\", then perform\\n # a series of dilations and erosions to remove any small\\n # blobs left in the mask\\n mask = cv2.inRange(hsv, self.lower_color, self.upper_color)\\n mask = cv2.erode(mask, None, iterations=2)\\n mask = cv2.dilate(mask, None, iterations=2)\\n # find contours in the mask and initialize the current\\n # (x, y) center of the ball\\n cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\\n cnts = imutils.grab_contours(cnts)\\n center = None\\n # only proceed if at least one contour was found\\n if len(cnts) > 0:\\n # find the largest contour in the mask, then use\\n # it to compute the minimum enclosing circle and\\n # centroid\\n c = max(cnts, key=cv2.contourArea)\\n ((self.x, self.y), radius) = cv2.minEnclosingCircle(c)\\n M = cv2.moments(c)\\n center = (int(M[\\\"m10\\\"] / M[\\\"m00\\\"]), int(M[\\\"m01\\\"] / M[\\\"m00\\\"]))\\n # only proceed if the radius meets a minimum size\\n if radius > self.radius:\\n # draw the circle and centroid on the frame,\\n # then update the list of tracked points\\n cv2.circle(frame, (int(self.x), int(self.y)), int(radius), (0, 255, 255), 2)\\n cv2.circle(frame, center, 5, (0, 0, 255), -1)\\n\\n self.is_ball_visible = len(cnts) > 0 and radius > self.radius\\n # ------------------------------ \\n\\n # add timestamp\\n ts = datetime.datetime.now().strftime(\\\"%A %d %B %Y %I:%M:%S%p\\\")\\n cv2.putText(frame, ts, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)\\n cv2.putText(frame, \\\"x: {}, y: {}, object: {}\\\".format(round(self.x, 2), round(self.y, 2),\\n len(cnts) > 0 and radius > 10),\\n (10, frame.shape[0] - 50),\\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)\\n # Write the frame into the \\n # file 'filename.avi'\\n cv2.imwrite(\\\"test.jpg\\\", frame)\\n if self.record_video:\\n self.video_result.write(frame)\\n\\n else:\\n print('no frame found')\",\n \"def in_bomb_range(self, game_state: dict):\\n is_in_bomb_range = False\\n agent_position = game_state['self'][3]\\n agent_position = list(agent_position)\\n\\n for bomb in game_state['bombs']:\\n if agent_position == list(bomb[0]):\\n is_in_bomb_range = True\\n\\n for i in range(3):\\n agent_search = copy.copy(agent_position)\\n if agent_position[0] - i - 1 >= 0:\\n agent_search[0] = agent_position[0] - i - 1\\n if agent_search == list(bomb[0]):\\n is_in_bomb_range = True\\n\\n for i in range(3):\\n agent_search = copy.copy(agent_position)\\n if agent_position[0] + i + 1 <= 16:\\n agent_search[0] = agent_position[0] + i + 1\\n if agent_search == list(bomb[0]):\\n is_in_bomb_range = True\\n\\n for i in range(3):\\n agent_search = copy.copy(agent_position)\\n if agent_position[1] - i - 1 >= 0:\\n agent_search[1] = agent_position[1] - i - 1\\n if agent_search == list(bomb[0]):\\n is_in_bomb_range = True\\n\\n for i in range(3):\\n agent_search = copy.copy(agent_position)\\n if agent_position[1] + i + 1 <= 16:\\n agent_search[1] = agent_position[1] + i + 1\\n if agent_search == list(bomb[0]):\\n is_in_bomb_range = True\\n\\n # check if a stone wall is between the agent an the bomb\\n if is_in_bomb_range:\\n if agent_position[0] == list(bomb[0])[0]:\\n if agent_position[1] < list(bomb[0])[1]:\\n for i in range(agent_position[1], list(bomb[0])[1]):\\n if game_state['field'][agent_position[0]][i] == -1:\\n is_in_bomb_range = False\\n else:\\n for i in range(list(bomb[0])[1], agent_position[1]):\\n if game_state['field'][agent_position[0]][i] == -1:\\n is_in_bomb_range = False\\n elif agent_position[1] == list(bomb[0])[1]:\\n if agent_position[0] < list(bomb[0])[0]:\\n for i in range(agent_position[0], list(bomb[0])[0]):\\n if game_state['field'][i][agent_position[1]] == -1:\\n is_in_bomb_range = False\\n else:\\n for i in range(list(bomb[0])[0], agent_position[0]):\\n if game_state['field'][i][agent_position[1]] == -1:\\n is_in_bomb_range = False\\n\\n return is_in_bomb_range\",\n \"def bounds(self, pos):\",\n \"def locate_board_callback(self, data):\\n self.home_count = 0\\n self.is_home = True\\n while self.home_count < self.home_count_thresh or not self.board_position:\\n try:\\n for d in self.tag_vals.detections:\\n if d.id == (0,):\\n self.board_position = self.tag_vals.detections[0].pose.pose.pose\\n self.board_present = True\\n except:\\n pass\\n\\n self.is_home = False\\n\\n return self.board_position\",\n \"def createBouncingPixel():\\n height = width = 16\\n empty = numpy.zeros((height, width), dtype=numpy.int32)\\n x = 0\\n y = 0\\n x_diff = 1\\n y_diff = 1\\n frames = []\\n for i in xrange(61): # Comes back to starting position in 60 steps\\n copy = numpy.array(empty)\\n if x + x_diff not in range(0, width):\\n x_diff *= -1\\n if y + y_diff not in range(0, height):\\n y_diff *= -1\\n copy[y, x] = 1\\n print copy\\n x += x_diff\\n if i % 2:\\n y += y_diff\\n frames.append(copy.tolist())\\n writeFrames(frames, 'actual', 'bouncing_pixel')\",\n \"def chkBD(pos, m):\\n cur = False\\n for x in [-1, 1]:\\n if(pos[0] + x >= 0 and pos[0] + x < m['r_len']):\\n cur = cur or m['ver'][pos[0]+x][pos[1]] == 2\\n if(pos[1] + x >= 0 and pos[1] + x < m['c_len']):\\n cur = cur or m['hor'][pos[0]][pos[1]+x] == 2\\n\\n return cur\",\n \"def boarderPosition(self, gameState):\\n if gameState.isOnRedTeam(self.index):\\n i = self.midWidth - 1\\n else:\\n i = self.midWidth + 1\\n boudaries = [(i,j) for j in range(self.height)]\\n validPositions = []\\n for i in boudaries:\\n if not gameState.hasWall(i[0],i[1]):\\n validPositions.append(i)\\n return validPositions\",\n \"def boatLocation(self):\\n if self.operator is None:\\n return 0\\n else:\\n if self.operator[0] == 's':\\n return 1\\n elif self.operator[0] == 'b':\\n return 0\\n else:\\n return -1\",\n \"def get_pos(self, frame):\\n frame = self.perspective_shift(frame)\\n \\n puck_mask = self.color_mask(frame, self.color_green, thresh=15)\\n striker_mask = self.color_mask(frame, self.color_orange, thresh=25, blur=5)\\n \\n puck_loc, _ = self.find_centroids(puck_mask)\\n striker_locs, _ = self.find_centroids(striker_mask, 2)\\n \\n p_pos = self.abs_to_meter(puck_loc[0])\\n # cases: (pos,pos), (pos,None), (None,None)\\n if striker_locs[0] is not None:\\n pos_1 = self.abs_to_meter(striker_locs[0])\\n pos_2 = self.abs_to_meter(striker_locs[1])\\n s1_pos = pos_1 if pos_1[1]<0 else pos_2\\n s2_pos = pos_2 if pos_1[1]<0 else pos_1\\n else:\\n s1_pos, s2_pos = None, None \\n \\n return [p_pos, s1_pos, s2_pos]\",\n \"def check_bounce(self):\\n if self.ball.center.x < 0 and self.ball.velocity.dx < 0:\\n self.ball.bounce_horizontal()\\n\\n if self.ball.center.y < 0 and self.ball.velocity.dy < 0:\\n self.ball.bounce_vertical()\\n\\n if self.ball.center.y > SCREEN_HEIGHT and self.ball.velocity.dy > 0:\\n self.ball.bounce_vertical()\",\n \"def motion_b(input_line, cur, count):\\n # \\\"b\\\" is just \\\"e\\\" on inverted data (e.g. \\\"olleH\\\" instead of \\\"Hello\\\").\\n pos_inv = motion_e(input_line[::-1], len(input_line) - cur - 1, count)[0]\\n pos = len(input_line) - pos_inv - 1\\n return pos, True, False\",\n \"def beam_positions(closepack=False):\\n \\n x_pos, y_pos = [], []\\n\\n x=0\\n for j in range(0,6,1):\\n x += 0.1\\n y=0\\n for k in range(0,6,2):\\n y += 0.2\\n x_pos.append(x+(0.05 if closepack else 0))\\n y_pos.append(y)\\n y += 0.2\\n x_pos.append(x)\\n y_pos.append(y)\\n\\n return x_pos, y_pos\",\n \"def _bounce(self):\\n right = self.surface.get_width() - self.size\\n left = self.size\\n top = self.size\\n bottom = self.surface.get_height() - self.size\\n if self.pos.x > right: # right border\\n self.pos.x = right\\n self.direction = self.direction.elementwise() * pygame.Vector2(-1.0, 1.0)\\n elif self.pos.x < left: # left border\\n self.pos.x = left\\n self.direction = self.direction.elementwise() * pygame.Vector2(-1.0, 1.0)\\n if self.pos.y > bottom: # bottom border\\n self.pos.y = bottom\\n self.direction = self.direction.elementwise() * pygame.Vector2(1.0, -1.0)\\n elif self.pos.y < top: # top border\\n self.pos.y = top\\n self.direction = self.direction.elementwise() * pygame.Vector2(1.0, -1.0)\",\n \"def update(self):\\n self.bpos_x += 3\",\n \"def track_ball_4(video):\\n\\n result = []\\n frames = readFrames(video)\\n\\n i = 0\\n while i < len(frames):\\n frame = frames[i] # get the new frame\\n\\n # find edges of the ball in the frame\\n # parameters filter out background\\n edges = cv2.Canny(frame, 700, 800)\\n\\n # find contours in the edge frame\\n contours, _ = cv2.findContours(edges, cv2.RETR_TREE,\\n cv2.CHAIN_APPROX_SIMPLE)\\n\\n # sort the contours in reverse order by width to eliminate small\\n # contours caused by noise.\\n sort_cnt = sorted(contours, key=lambda x: cv2.boundingRect(x)[2],\\n reverse=True)\\n\\n # get the parameters of the bounding box for the largest width contour\\n x, y, w, h = cv2.boundingRect(sort_cnt[0])\\n # append to result list\\n result.append((x, y, x + w, y + h))\\n\\n if VISUALIZE:\\n orig_frame = frames[i]\\n cv2.rectangle(orig_frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\\n cv2.imshow(\\\"ball\\\", orig_frame)\\n cv2.waitKey(30)\\n\\n i += 1\\n\\n return result\",\n \"def adjust_thresholding(self, pos_frame, which='animal'):\\n\\n cv2.namedWindow('Adjust Thresholding')\\n if which == 'animal':\\n cv2.createTrackbar('H_Low', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"min_animal\\\"][0],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('H_High', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"max_animal\\\"][0],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('S_Low', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"min_animal\\\"][1],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('S_High', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"max_animal\\\"][1],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('V_Low', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"min_animal\\\"][2],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('V_High', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"max_animal\\\"][2],\\n 255,\\n self.nothing)\\n elif which == 'material':\\n cv2.createTrackbar('H_Low', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"min_material\\\"][0],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('H_High', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"max_material\\\"][0],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('S_Low', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"min_material\\\"][1],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('S_High', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"max_material\\\"][1],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('V_Low', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"min_material\\\"][2],\\n 255,\\n self.nothing)\\n cv2.createTrackbar('V_High', 'Adjust Thresholding',\\n self._args[\\\"tracking\\\"][\\\"thresholding\\\"][\\\"max_material\\\"][2],\\n 255,\\n self.nothing)\\n else:\\n utils.print_color_message(\\\"[INFO] Select 'animal' or 'material' to preview the default thresholding values\\\",\\n \\\"darkgreen\\\")\\n cv2.createTrackbar('H_Low', 'Adjust Thresholding', 0, 255, self.nothing)\\n cv2.createTrackbar('H_High', 'Adjust Thresholding', 255, 255, self.nothing)\\n cv2.createTrackbar('S_Low', 'Adjust Thresholding', 0, 255, self.nothing)\\n cv2.createTrackbar('S_High', 'Adjust Thresholding', 255, 255, self.nothing)\\n cv2.createTrackbar('V_Low', 'Adjust Thresholding', 0, 255, self.nothing)\\n cv2.createTrackbar('V_High', 'Adjust Thresholding', 255, 255, self.nothing)\\n\\n test_frame = self._color_capture.get_frame(pos_frame)\\n test_frame_cropped = test_frame[self.up_left_y:self.low_right_y, self.up_left_x:self.low_right_x]\\n test_frame_cropped_hsv = cv2.cvtColor(test_frame_cropped, cv2.COLOR_BGR2HSV)\\n test_frame_blurred = cv2.blur(test_frame_cropped_hsv, (5, 5))\\n\\n while True:\\n h_l = cv2.getTrackbarPos('H_Low', 'Adjust Thresholding')\\n h_h = cv2.getTrackbarPos('H_High', 'Adjust Thresholding')\\n s_l = cv2.getTrackbarPos('S_Low', 'Adjust Thresholding')\\n s_h = cv2.getTrackbarPos('S_High', 'Adjust Thresholding')\\n v_l = cv2.getTrackbarPos('V_Low', 'Adjust Thresholding')\\n v_h = cv2.getTrackbarPos('V_High', 'Adjust Thresholding')\\n test_mask_mouse = cv2.inRange(test_frame_blurred, (h_l, s_l, v_l), (h_h, s_h, v_h))\\n overlay = cv2.bitwise_and(test_frame_cropped_hsv, test_frame_cropped_hsv, mask=test_mask_mouse)\\n cv2.imshow('Adjust Thresholding', overlay)\\n key = cv2.waitKey(10) & 0xFF\\n if key == ord(\\\"q\\\"):\\n break\\n cv2.destroyAllWindows()\\n for i in range(1, 5):\\n cv2.waitKey(1)\",\n \"def detect_boundary(self, x, l_old):\\n pass\",\n \"def check_boundary(self):\\n\\n\\t\\tif self.Bubble_initial_pos[0] <= self.Bubble_radius or self.Bubble_initial_pos[0] >= self.tk_pic.width - self.Bubble_radius:\\n\\t\\t\\tself.Bubble_vel[0] = -self.Bubble_vel[0]\",\n \"def bomb_vector(self):\\n\\n\\t\\tif self.b_offset == 0:\\n\\t\\t\\top = sin\\n\\t\\telse:\\n\\t\\t\\top = cos\\n\\n\\t\\tself.y -= self.speed\\n\\t\\tself.rect.y = self.y\\n\\t\\t# MMMMMMMMMMMMMMMMMMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAATHS\\n\\t\\tself.x = int((self.g_settings.screen_height/2) + self.amplitude*op(self.frequency*((float(self.y)/self.g_settings.screen_width)*(2*pi) + (self.speed*time()))))\\n\\t\\tif self.b_offset == 0:\\n\\t\\t\\tself.rect.x = self.x + self.position_x - 16\\n\\t\\telif self.b_offset == 1:\\n\\t\\t\\tself.rect.x = self.x + self.position_x + 16\\n\\t\\tself.screen.blit(self.image, self.rect)\",\n \"def draw_color_bar(frame):\\n depth = 35\\n cv2.rectangle(frame, (0,0), (72,depth - 1), (0,0,0), 2) # white/eraser\\n cv2.rectangle(frame, (72,0), (138,depth), (0,0,0), -1) # black\\n cv2.rectangle(frame, (138,0), (204,depth), (122,78,32), -1) # brown\\n cv2.rectangle(frame, (204,0), (270,depth), (242,0,255), -1) # violet/purple\\n cv2.rectangle(frame, (270,0), (336,depth), (0,0,255), -1) # blue\\n cv2.rectangle(frame, (336,0), (402,depth), (63,255,0), -1) # green\\n cv2.rectangle(frame, (402,0), (468,depth), (255,250,0), -1) # yellow\\n cv2.rectangle(frame, (468,0), (534,depth), (255,174,0), -1) # orange\\n cv2.rectangle(frame, (534,0), (600,depth), (255,0,0), -1) # red\",\n \"def check_borders(self):\\n # Go Homer!\\n # https://en.wikipedia.org/wiki/Torus#Flat_torus\\n if self._posn.x < 0:\\n self._posn.x += self._win_w\\n elif self._posn.x > self._win_w:\\n self._posn.x -= self._win_w\\n if self._posn.y < 0:\\n self._posn.y += self._win_h\\n elif self._posn.y > self._win_h:\\n self._posn.y -= self._win_h\",\n \"def _get_loc_top_left_placement_b(button_number):\\r\\n if button_number == 0:\\r\\n return 35 / 2, 7 / 2 # placement button that starts the game\\r\\n\\r\\n elif button_number == 1:\\r\\n return 35 / 2, 13 / 2 # placement button that brings program back to main menu\\r\",\n \"def get_bbox(frame_data, camera_image_data):\\n object_decodification = {0: 'unkown', 1: 'vehicle', 2: 'pedestrian', 3: 'sign', 4: 'cyclist'}\\n bbox = {}\\n valid_bb_data = True\\n if not frame_data.camera_labels:\\n valid_bb_data = False\\n # Draw the camera labels.\\n for camera_labels in frame_data.camera_labels:\\n # Ignore camera labels from other views (i.e. I want Front but it also gives left, right, front left, ...)\\n if camera_labels.name != camera_image_data.name:\\n continue\\n # Iterate over the individual labels\\n for label in camera_labels.labels:\\n if label.detection_difficulty_level == 0:\\n difficulty = \\\"easy\\\"\\n elif label.detection_difficulty_level == 2:\\n difficulty = \\\"hard\\\"\\n\\n if label.tracking_difficulty_level == 0:\\n tracking_level = \\\"easy\\\"\\n elif label.tracking_difficulty_level == 2:\\n tracking_level = 'hard'\\n\\n object_class = object_decodification[label.type]\\n # I'm not saving the other labels so that it matches my CARLA dataset\\n if object_class not in bbox and (object_class == \\\"vehicle\\\" or object_class == \\\"pedestrian\\\"):\\n bbox[object_class] = []\\n \\n if (object_class == \\\"vehicle\\\" or object_class == \\\"pedestrian\\\"):\\n # Get BB\\n xmin = int(label.box.center_x - 0.5 * label.box.length)\\n ymin = int(label.box.center_y - 0.5 * label.box.width)\\n xmax = int(xmin + label.box.length)\\n ymax = int(ymin + label.box.width)\\n bbox[object_class].append([xmin, ymin, xmax, ymax, difficulty, tracking_level])\\n return bbox, valid_bb_data\",\n \"def _CheckBoundaries(self, frameSize):\\n if (frameSize[0] + self.frame_position[0] > self.display_size[0]):\\n return (0, self.frame_position[1])\\n elif (frameSize[1] + self.frame_position[1] > self.display_size[1]):\\n return (self.frame_position[0], 0)\\n else:\\n return self.frame_position\",\n \"def return_to_center(): #ignore this for now, use move_to_position_(0,0)\\n current_pos = '\\\\xAA\\\\xBB\\\\xCC\\\\xDD'\\n #run command until back to center (0,0)\\n while True: #change the byte locations\\n current_pos = to_center()\\n print(current_pos)\\n time.sleep(0.2) #check timing\\n if((current_pos[1] == 0) and (current_pos[1] == 0)):\\n break\\n print('At center')\",\n \"def intercept_ball(self):\\n\\t\\t\\t##Parameters\\n\\t\\t\\tdistance_annulation = 15\\n\\t\\t\\tvitesse_danulation = 0\\n\\t\\t\\tcoefficient_prediction = 10\\n\\t\\t\\t\\n\\t\\t\\tif self.get_distance_to_ball() <= distance_annulation or self.b_speed < vitesse_danulation:\\n\\t\\t\\t\\treturn self.PB\\n\\t\\t\\t\\n\\t\\t\\telse:\\n\\t\\t\\t\\tpoint_interception = self.PB + coefficient_prediction * self.ball.vitesse\\n\\t\\t\\t\\treturn point_interception\",\n \"def find_bbox_coord(point_x, point_y):\\r\\n is_good_rect = True\\r\\n bottom_x, bottom_y = [], []\\r\\n top_x, top_y = [], []\\r\\n if len(point_x) < 4:\\r\\n is_good_rect = False\\r\\n if len(point_x) == 4:\\r\\n out_of_repeats_x = []\\r\\n out_of_repeats_y = []\\r\\n delta = 10**(-6)\\r\\n for j in range(len(point_x)): # add delta for the reason of not mess in equal angles\\r\\n out_of_repeats_x.append(point_x[j] + delta*j)\\r\\n out_of_repeats_y.append(point_y[j] + delta*j)\\r\\n point_x, point_y = out_of_repeats_x, out_of_repeats_y\\r\\n \\r\\n quadrate_width = ((point_x[1] - point_x[0])**2+(point_y[1] - point_y[0])**2)**0.5\\r\\n quadrate_height = ((point_x[1] - point_x[2])**2+(point_y[1] - point_y[2])**2)**0.5\\r\\n aspect_ratio = quadrate_width / quadrate_height\\r\\n if aspect_ratio > 0.7 and aspect_ratio < 1.3:\\r\\n is_good_rect = False \\r\\n ###Aprint('Квадрат. Закрашиваем')\\r\\n elif quadrate_width * quadrate_height < 100:\\r\\n is_good_rect = False \\r\\n ###Aprint('Квадрат. Закрашиваем')\\r\\n else:\\r\\n ###Aprint('Прямоугольник')\\r\\n edge_x, edge_y = point_x, point_y\\r\\n bottom_x, bottom_y, top_x, top_y, is_good_rect = top_bottom_dots(point_x, point_y, edge_x, edge_y)\\r\\n \\r\\n elif len(point_x) > 4:\\r\\n ###Aprint('Многоугольник')\\r\\n out_of_repeats_x = []\\r\\n out_of_repeats_y = []\\r\\n delta = 10**(-4)\\r\\n for j in range(len(point_x)): # add delta for the reason of not mess in equal angles\\r\\n out_of_repeats_x.append(point_x[j] + delta*j)\\r\\n out_of_repeats_y.append(point_y[j] + delta*j)\\r\\n point_x, point_y = out_of_repeats_x, out_of_repeats_y\\r\\n \\r\\n edge_x, edge_y = find_4_dots(point_x, point_y)\\r\\n\\r\\n bottom_x, bottom_y, top_x, top_y, is_good_rect = top_bottom_dots(point_x, point_y, edge_x, edge_y)\\r\\n \\r\\n if is_good_rect:\\r\\n \\r\\n bottom_edge_x, bottom_edge_y = [], []\\r\\n for i in bottom_x:\\r\\n if i in edge_x:\\r\\n index = bottom_x.index(i)\\r\\n bottom_edge_x.append(bottom_x[index])\\r\\n bottom_edge_y.append(bottom_y[index])\\r\\n bottom_edge_x, bottom_edge_y = zip(*sorted(zip(bottom_edge_x, bottom_edge_y)))\\r\\n bottom_lowest_point = [bottom_edge_x[0], bottom_edge_y[0]]\\r\\n\\r\\n top_edge_x, top_edge_y = [], []\\r\\n for i in top_x:\\r\\n if i in edge_x:\\r\\n index = top_x.index(i)\\r\\n top_edge_x.append(top_x[index])\\r\\n top_edge_y.append(top_y[index])\\r\\n top_edge_x, top_edge_y = zip(*sorted(zip(top_edge_x, top_edge_y)))\\r\\n top_lowest_point = [top_edge_x[0], top_edge_y[0]]\\r\\n\\r\\n bottom_x, bottom_y = Euclidian_distance_sorting(bottom_x, bottom_y, bottom_lowest_point)\\r\\n top_x, top_y = Euclidian_distance_sorting(top_x, top_y, top_lowest_point)\\r\\n else:\\r\\n bottom_x, bottom_y, top_x, top_y = [], [], [], []\\r\\n \\r\\n return is_good_rect, bottom_x, bottom_y, top_x, top_y\",\n \"def detect_alien_bolt_collision(self,bolt):\\n\\n # [x,y]\\n top_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\\n top_right_bolt = [bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() + BOLT_HEIGHT/2]\\n bottom_left_bolt = [bolt.get_bolt_x() - BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\\n bottom_right_bolt =[bolt.get_bolt_x() + BOLT_WIDTH/2 , bolt.get_bolt_y() - BOLT_HEIGHT/2]\\n\\n if self.contains((top_left_bolt[0],top_left_bolt[1])):\\n return True\\n if self.contains((top_right_bolt[0],top_right_bolt[1])):\\n return True\\n if self.contains((bottom_left_bolt[0],bottom_left_bolt[1])):\\n return True\\n if self.contains((bottom_right_bolt[0],bottom_right_bolt[1])):\\n return True\\n\\n return False\",\n \"def frame(self, frame):\\n if self.vertical:\\n cell = ((frame-1)//self.rows)+1\\n row = frame-(cell-1)*self.rows\\n else:\\n row = ((frame-1)//self.cells)+1\\n cell = frame-(row-1)*self.cells\\n\\n return cell, row\",\n \"def detect_collision(self, pos):\\n for rect in self.stock:\\n if rect.collidepoint((pos)):\\n return ['S0', 0]\\n for rect in self.waste:\\n if rect.collidepoint((pos)):\\n return ['W0', 0]\\n for i, rect in enumerate(self.foundations):\\n if rect.collidepoint((pos)):\\n return ['F'+str(i), 0]\\n for rect in self.tableaus:\\n if rect[0].collidepoint((pos)):\\n return rect[1]\\n\\n return [0,0]\",\n \"def myBarbieHome(pos):\\n bulldozer(pos)\\n ground(pos)\\n mc.postToChat(\\\"Ground done !\\\")\\n\\n pos.z += 5\\n makeTheHouse(pos)\\n mc.postToChat(\\\"House done !\\\")\\n\\n theRoof(pos)\\n mc.postToChat(\\\"Roof done !\\\")\\n\\n makeTheDeco(pos, flowers = wFlower_Cyan)\\n mc.postToChat(\\\"ALL Work done !\\\")\",\n \"def mover_bm_arriba(self):\\n self.nueva_posicion_posible_parte_superior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0],\\n self.casilla[1] - 1],\\n [self.vertice_1[0] ,self.vertice_1[1] - self.velocidad],\\n [self.vertice_1[0], self.vertice_1[1] - 5 -5])\\n self.nueva_posicion_posible_parte_inferior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0] + 1,\\n self.casilla[1] - 1],\\n [self.vertice_2[0] ,self.vertice_2[1] - self.velocidad],\\n [self.vertice_1[0],self.vertice_1[1]])\\n self.y -= self.velocidad * (self.y >= 15) *(self.nueva_posicion_posible_parte_inferior[0] != 1) * (self.nueva_posicion_posible_parte_superior[0] != 1)\\n self.posicion = [self.posicion[0],self.y]\\n self.casilla = [self.casilla[0],self.casilla[1] - self.nueva_posicion_posible_parte_superior[1] * (self.nueva_posicion_posible_parte_inferior[0] != 1) * (self.nueva_posicion_posible_parte_superior[0] != 1)]\\n self.redefinir_vertices()\",\n \"def detect(self, frame, foreground_mask):\\n pass\",\n \"def hit_object(self):\\n obj1 = self.window.get_object_at(self.ball.x, self.ball.y)\\n obj2 = self.window.get_object_at(self.ball.x, self.ball.y + BALL_RADIUS*2)\\n obj3 = self.window.get_object_at(self.ball.x + BALL_RADIUS*2, self.ball.y)\\n obj4 = self.window.get_object_at(self.ball.x + BALL_RADIUS*2, self.ball.y + BALL_RADIUS*2)\\n if obj1 is not None and obj1.width == BRICK_WIDTH:\\n self.window.remove(obj1)\\n self.num_bricks -= 1\\n self.__dy = -self.__dy\\n elif obj2 is not None and obj2.width == BRICK_WIDTH:\\n self.window.remove(obj2)\\n self.num_bricks -= 1\\n self.__dy = -self.__dy\\n elif obj2 is not None and obj2.width == PADDLE_WIDTH:\\n self.__dy = -self.__dy\\n elif obj3 is not None and obj3.width == BRICK_WIDTH:\\n self.window.remove(obj3)\\n self.num_bricks -= 1\\n self.__dy = -self.__dy\\n elif obj4 is not None and obj4.width == BRICK_WIDTH:\\n self.window.remove(obj4)\\n self.num_bricks -= 1\\n self.__dy = -self.__dy\\n elif obj4 is not None and obj4.width == PADDLE_WIDTH:\\n self.__dy = -self.__dy\",\n \"def detect_collision():\\n # with the top and bottom of screen\\n if ball.ycor() > GAME_Y_BARRIER or ball.ycor() < -GAME_Y_BARRIER:\\n ball.bounce_y()\\n # with the paddles\\n if ball.distance(paddle_right) < 50 and ball.xcor() > GAME_X_BARRIER \\\\\\n or ball.distance(paddle_left) < 50 and ball.xcor() < -GAME_X_BARRIER:\\n ball.bounce_x()\",\n \"def inSmallBlindPosition(self):\\n return len(self.in_game) > 0 and ((self.dealer + 1) % len(self.in_game)) == self.position\",\n \"def _slice_ball(self, ball_speed_x, platform_speed_x):\\n # If the platform doesn't move, bounce normally.\\n if platform_speed_x == 0:\\n return 7 if ball_speed_x > 0 else -7\\n # If the platform moves at the same direction as the ball moving,\\n # speed up the ball.\\n elif ball_speed_x * platform_speed_x > 0:\\n return 10 if ball_speed_x > 0 else -10\\n # If the platform moves at the opposite direction against the ball moving,\\n # hit the ball back.\\n else:\\n return -7 if ball_speed_x > 0 else 7\",\n \"def imageframe(sf, identifier, loc=\\\".\\\"):\\n from matplotlib.patches import Circle\\n\\n # Computing center and radius\\n blurred = blur(sf.data)\\n bin_img = threshold(blurred, sf.calcthresh(blurred))\\n cont_found, big_cont = find_largest_contour(bin_img, blurred)\\n if cont_found:\\n _, radius = cv2.minEnclosingCircle(big_cont)\\n center = (sf.c, sf.r)\\n sig_r = RADIUS_SIGNAL\\n bg_r1, bg_r2 = RADIUS_BGINNER, RADIUS_BGOUTER\\n # -----------------------\\n # Temporary and just for display and testing\\n fig, ax = plt.subplots()\\n ax.imshow(\\n sf.data,\\n cmap=\\\"gray\\\",\\n extent=[\\n sf.c - sf.radius,\\n sf.c + sf.radius,\\n sf.r + sf.radius,\\n sf.r - sf.radius,\\n ],\\n )\\n p = Circle(center, radius + sig_r, fill=False, ec=\\\"green\\\", lw=2)\\n p2 = Circle(center, radius + bg_r1, fill=False, ec=\\\"cyan\\\", lw=2)\\n p3 = Circle(center, radius + bg_r2, fill=False, ec=\\\"cyan\\\", lw=2)\\n ax.add_patch(p)\\n ax.add_patch(p2)\\n ax.add_patch(p3)\\n fig.suptitle(f\\\"Frame {identifier}\\\", size=12, weight=\\\"bold\\\")\\n ax.set_title(sf.time.strftime(\\\"%Y%m%d %H:%M:%S.%f %Z\\\"))\\n # plt.imshow(signal | background) # Shows masks\\n if os.path.exists(loc):\\n fig.savefig(f\\\"{loc}/Frame{identifier}.png\\\")\\n else:\\n os.makedirs(loc)\\n fig.savefig(f\\\"{loc}/Frame{identifier}.png\\\")\\n #plt.close(fig)\\n # -----------------------\",\n \"def _get_bounding_box(self, frame, bounding_offset):\\n\\n # Try to find board if the boundingbox is not set\\n center, ellipse, mask = self.board.detect(frame)\\n\\n # Should not be None\\n if center is None:\\n print(\\\"skipping frame\\\")\\n return None\\n if ellipse is None:\\n print(\\\"skipping frame\\\")\\n return None\\n if mask is None:\\n print(\\\"skipping frame\\\")\\n return None\\n\\n self.point_mask = mask\\n # cv2.imshow(\\\"mask\\\", mask)\\n\\n x_offset = (ellipse[1][0] / 2)\\n x_center = ellipse[0][0]\\n\\n y_offset = ellipse[1][1] / 2\\n y_center = ellipse[0][1]\\n\\n minx = max(0, x_center - x_offset - bounding_offset)\\n maxx = min(self.width, x_center + x_offset + bounding_offset)\\n miny = max(0, y_center - y_offset - bounding_offset)\\n maxy = min(self.height, y_center + y_offset + bounding_offset)\\n return ((int(minx), int(miny)), (int(maxx), int(maxy)))\",\n \"def edges(self):\\r\\n # appearing on other side of the window #\\r\\n \\r\\n # if boid gets out of the margins, it shows on the other side of the window\\r\\n \\r\\n if self.position.x > self.width:\\r\\n self.position.x = 0\\r\\n elif self.position.x < 0:\\r\\n self.position.x = self.width\\r\\n\\r\\n if self.position.y > self.height:\\r\\n self.position.y = 0\\r\\n elif self.position.y < 0:\\r\\n self.position.y = self.height\\r\\n \\r\\n # bouncing from the window #\\r\\n \\r\\n # if self.position.x > self.width or self.position.x < 0:\\r\\n # self.velocity.x = -self.velocity.x\\r\\n # if self.position.y > self.height or self.position.y < 0: \\r\\n # self.velocity.y = -self.velocity.y\\r\",\n \"def brickCollision(self, ball):\\n for brick in self._bricks:\\n if brick.contains(ball.right,ball.bottom) and brick in self._bricks:\\n self.breaking.play()\\n self._bricks.remove(brick)\\n #print 'bottom right'\\n ball.verticalBounce()\\n if brick.contains(ball.left,ball.bottom) and brick in self._bricks:\\n self.breaking.play()\\n self._bricks.remove(brick)\\n #print 'bottom left'\\n ball.verticalBounce()\\n if brick.contains(ball.right,ball.top) and brick in self._bricks:\\n self.breaking.play()\\n self._bricks.remove(brick)\\n #print 'top right'\\n ball.verticalBounce()\\n if brick.contains(ball.left,ball.top) and brick in self._bricks:\\n self.breaking.play()\\n self._bricks.remove(brick)\\n #print 'top left'\\n ball.verticalBounce()\",\n \"def mover_bm_abajo(self):\\n self.nueva_posicion_posible_parte_superior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0],\\n self.casilla[1] + 1],\\n [self.vertice_3[0], self.vertice_3[1] + self.velocidad],\\n [self.vertice_1[0], self.vertice_1[1]+ 5])\\n self.nueva_posicion_posible_parte_inferior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0] + 1,\\n self.casilla[1] + 1],\\n [self.vertice_4[0],self.vertice_4[1] + self.velocidad],\\n self.vertice_1)\\n if self.nueva_posicion_posible_parte_superior[0] != 1 and self.nueva_posicion_posible_parte_inferior[0] != 1:\\n self.y += self.velocidad * (self.y <= 655)\\n self.posicion = [self.posicion[0],self.y]\\n self.casilla = [self.casilla[0], self.casilla[1] + self.nueva_posicion_posible_parte_superior[1]]\\n self.redefinir_vertices()\",\n \"def CheckEdgeDrop(window, docks, pt):\\r\\n\\r\\n screenPt = window.ClientToScreen(pt)\\r\\n clientSize = window.GetClientSize()\\r\\n frameRect = GetInternalFrameRect(window, docks)\\r\\n\\r\\n if screenPt.y >= frameRect.GetTop() and screenPt.y < frameRect.GetBottom():\\r\\n if pt.x < auiLayerInsertOffset and pt.x > auiLayerInsertOffset - auiLayerInsertPixels:\\r\\n return wx.LEFT\\r\\n \\r\\n if pt.x >= clientSize.x - auiLayerInsertOffset and \\\\\\r\\n pt.x < clientSize.x - auiLayerInsertOffset + auiLayerInsertPixels:\\r\\n return wx.RIGHT\\r\\n \\r\\n if screenPt.x >= frameRect.GetLeft() and screenPt.x < frameRect.GetRight():\\r\\n if pt.y < auiLayerInsertOffset and pt.y > auiLayerInsertOffset - auiLayerInsertPixels:\\r\\n return wx.TOP\\r\\n \\r\\n if pt.y >= clientSize.y - auiLayerInsertOffset and \\\\\\r\\n pt.y < clientSize.y - auiLayerInsertOffset + auiLayerInsertPixels:\\r\\n return wx.BOTTOM\\r\\n\\r\\n return -1\",\n \"def bead_position_pix(im, selem):\\n # The x, y coordinates of pixels are nonzero values in selem\\n y, x = np.nonzero(selem)\\n x = x - selem.shape[1] // 2\\n y = y - selem.shape[0] // 2\\n\\n # Find the center of the bead to pixel accuracy\\n peak_flat_ind = np.argmax(im)\\n peak_j = peak_flat_ind % im.shape[0]\\n peak_i = (peak_flat_ind - peak_j) // im.shape[1]\\n\\n # Define local neighborhood\\n irange = (peak_i - selem.shape[0] // 2, peak_i + selem.shape[0] // 2 + 1)\\n jrange = (peak_j - selem.shape[1] // 2, peak_j + selem.shape[1] // 2 + 1)\\n\\n # Get values of the image in local neighborhood\\n z = im[irange[0]:irange[1], jrange[0]:jrange[1]][selem.astype(np.bool)]\\n\\n # Fit Gaussian\\n a, j_subpix, i_subpix, sigma = fit_gaussian(x, y, z)\\n\\n # Return x-y position\\n return np.array([peak_i + i_subpix, peak_j + j_subpix])\",\n \"def put_bomb(self):\\n s = self\\n if s.bombs == 0:\\n return\\n\\n block = s.physics.blocks[s.stype][0]\\n xinf = block.inf.x - block.inf.x % s.physics.len_blocks\\n yinf = block.inf.y - block.inf.y % s.physics.len_blocks\\n\\n length = s.physics.len_blocks\\n new_bomb = Rectangle(Vector(xinf, yinf), Vector(xinf + length, yinf + length))\\n\\n bombs = list()\\n if 'bomb' in s.physics.blocks:\\n bombs = s.physics.blocks['bomb']\\n\\n for bomb in bombs:\\n if bomb.overlap(new_bomb):\\n return\\n\\n bomb = Bomb(s, s.pjs, s.bombradius, s.fps, Vector(xinf, yinf))\\n s.last_bomb = bomb\\n s.pjs.add_bomb(bomb)\\n s.bombs -= 1\",\n \"def _find_b0(self, x_bar, y_bar):\\n self.model['b0'] = y_bar - self.model['b1'] * x_bar\",\n \"def update_fruit_position(self):\\n fruit_drawn = False\\n while not fruit_drawn:\\n self.set_fruit_y(randint(1, self.BOARD_HEIGHT - 2))\\n self.set_fruit_x(randint(1, self.BOARD_WIDTH - 2))\\n\\n if self.window.inch(self.get_fruit_y(), self.get_fruit_x()) == ord(\\\" \\\"):\\n fruit_drawn = True\\n\\n else:\\n self.set_fruit_y(randint(1, self.BOARD_HEIGHT - 2))\\n self.set_fruit_x(randint(1, self.BOARD_WIDTH - 2))\",\n \"def _calc_ball_scan(self):\\n # Default scan to false.\\n self._scan_line = [False] * Stella.FRAME_WIDTH\\n\\n if self._enabled:\\n for x in range(self._x_min, self._x_max):\\n self._scan_line[x % Stella.FRAME_WIDTH] = True\",\n \"def detect(frame: numpy.ndarray) -> bool:\\n color = frame[:20, 1100:1150].mean(axis=(0, 1))\\n return numpy.linalg.norm(color - BG_COLOR) < 5\",\n \"def detectBall():\\n\\t\\n\\tglobal np_arr\\n\\timage_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) # OpenCV >= 3.0:\\n\\n\\tblackLower = (0, 0, 0) \\n\\tblackUpper = (5,50,50)\\n\\tredLower = (0, 50, 50)\\n\\tredUpper = (5, 255, 255)\\n\\tyellowLower = (25, 50, 50) \\n\\tyellowUpper = (35, 255, 255)\\n\\tgreenLower = (50, 50, 50) \\n\\tgreenUpper = (70, 255, 255)\\n\\tblueLower = (100, 50, 50) \\n\\tblueUpper = (130, 255, 255)\\n\\tmagentaLower = (125, 50, 50) \\n\\tmagentaUpper = (150, 255, 255)\\n\\n blurred = cv2.GaussianBlur(image_np, (11, 11), 0)\\n hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)\\n\\n\\tmask_blk = cv2.inRange(hsv, blackLower, blackUpper)\\n mask_blk = cv2.erode(mask_blk, None, iterations=2)\\n mask_blk = cv2.dilate(mask_blk, None, iterations=2)\\n\\n\\tmask_r = cv2.inRange(hsv, redLower, redUpper)\\n mask_r = cv2.erode(mask_r, None, iterations=2)\\n mask_r = cv2.dilate(mask_r, None, iterations=2)\\n\\n\\tmask_y = cv2.inRange(hsv, yellowLower, yellowUpper)\\n mask_y = cv2.erode(mask_y, None, iterations=2)\\n mask_y = cv2.dilate(mask_y, None, iterations=2)\\n\\n\\tmask_g = cv2.inRange(hsv, greenLower, greenUpper)\\n mask_g = cv2.erode(mask_g, None, iterations=2)\\n mask_g = cv2.dilate(mask_g, None, iterations=2)\\n\\n mask_blu = cv2.inRange(hsv, blueLower, blueUpper)\\n mask_blu = cv2.erode(mask_blu, None, iterations=2)\\n mask_blu = cv2.dilate(mask_blu, None, iterations=2)\\n\\n\\tmask_m = cv2.inRange(hsv, magentaLower, magentaUpper)\\n mask_m = cv2.erode(mask_m, None, iterations=2)\\n mask_m = cv2.dilate(mask_m, None, iterations=2)\\n #cv2.imshow('mask', mask)\\n\\n cnts_blk = cv2.findContours(mask_blk.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n\\tcnts_r = cv2.findContours(mask_r.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n\\tcnts_y = cv2.findContours(mask_y.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n\\tcnts_g = cv2.findContours(mask_g.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n\\tcnts_blu = cv2.findContours(mask_blu.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n\\tcnts_m = cv2.findContours(mask_m.copy(), cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_SIMPLE)\\n\\n cnts_blk = imutils.grab_contours(cnts_blk)\\n\\tcnts_r = imutils.grab_contours(cnts_r)\\n\\tcnts_y = imutils.grab_contours(cnts_y)\\n\\tcnts_g = imutils.grab_contours(cnts_g)\\n\\tcnts_blu = imutils.grab_contours(cnts_blu)\\n\\tcnts_m = imutils.grab_contours(cnts_m)\\n\\n center = None\\n\\tc = 0\\n\\tradius = 0\\n\\n\\tglobal black_ball, red_ball, yellow_ball, green_ball, blue_ball, magenta_ball\\n\\n # only proceed if at least one contour was found\\n if len(cnts_blk) > 0 and black_ball.detected_flag != True:\\n c = max(cnts_blk, key=cv2.contourArea)\\n\\t ((x, y), radius) = cv2.minEnclosingCircle(c)\\n\\t M = cv2.moments(c)\\n\\t center = (int(M[\\\"m10\\\"] / M[\\\"m00\\\"]), int(M[\\\"m01\\\"] / M[\\\"m00\\\"]))\\n\\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\\n\\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\\n\\t black_ball = track(radius, center, black_ball)\\n\\t pub_blackBall.publish(black_ball)\\n\\t print (\\\"Black ball detected.\\\\n\\\")\\n\\n\\telif len(cnts_r) > 0 and red_ball.detected_flag != True:\\n c = max(cnts_r, key=cv2.contourArea)\\n\\t ((x, y), radius) = cv2.minEnclosingCircle(c)\\n\\t M = cv2.moments(c)\\n\\t center = (int(M[\\\"m10\\\"] / M[\\\"m00\\\"]), int(M[\\\"m01\\\"] / M[\\\"m00\\\"]))\\n\\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\\n\\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\\n\\t red_ball = track(radius, center, red_ball)\\n\\t pub_redBall.publish(red_ball)\\n\\t print (\\\"Red ball detected.\\\\n\\\")\\n\\t \\n\\telif len(cnts_y) > 0 and yellow_ball.detected_flag != True:\\n c = max(cnts_y, key=cv2.contourArea)\\n\\t ((x, y), radius) = cv2.minEnclosingCircle(c)\\n\\t M = cv2.moments(c)\\n\\t center = (int(M[\\\"m10\\\"] / M[\\\"m00\\\"]), int(M[\\\"m01\\\"] / M[\\\"m00\\\"]))\\n\\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\\n\\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\\n\\t yellow_ball = track(radius, center, yellow_ball)\\n\\t pub_yellowBall.publish(yellow_ball)\\n\\t print (\\\"Yellow ball detected.\\\\n\\\")\\n\\n\\telif len(cnts_g) > 0 and green_ball.detected_flag != True:\\n c = max(cnts_g, key=cv2.contourArea)\\n\\t ((x, y), radius) = cv2.minEnclosingCircle(c)\\n\\t M = cv2.moments(c)\\n\\t center = (int(M[\\\"m10\\\"] / M[\\\"m00\\\"]), int(M[\\\"m01\\\"] / M[\\\"m00\\\"]))\\n\\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\\n\\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\\n\\t green_ball = track(radius, center, green_ball)\\n\\t pub_greenBall.publish(green_ball)\\n\\t print (\\\"Green ball detected.\\\\n\\\")\\n\\n\\telif len(cnts_blu) > 0 and blue_ball.detected_flag != True:\\n c = max(cnts_blu, key=cv2.contourArea)\\n\\t ((x, y), radius) = cv2.minEnclosingCircle(c)\\n\\t M = cv2.moments(c)\\n\\t center = (int(M[\\\"m10\\\"] / M[\\\"m00\\\"]), int(M[\\\"m01\\\"] / M[\\\"m00\\\"]))\\n\\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\\n\\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\\n\\t blue_ball = track(radius, center, blue_ball)\\n\\t pub_blueBall.publish(blue_ball)\\n\\t print (\\\"Blue ball detected.\\\\n\\\")\\n\\n\\telif len(cnts_m) > 0 and magenta_ball.detected_flag != True:\\n c = max(cnts_m, key=cv2.contourArea)\\n\\t ((x, y), radius) = cv2.minEnclosingCircle(c)\\n\\t M = cv2.moments(c)\\n\\t center = (int(M[\\\"m10\\\"] / M[\\\"m00\\\"]), int(M[\\\"m01\\\"] / M[\\\"m00\\\"]))\\n\\t cv2.circle(image_np, (int(x), int(y)), int(radius), (0, 255, 255), 2)\\n\\t cv2.circle(image_np, center, 5, (0, 0, 255), -1)\\n\\t magenta_ball = track(radius, center, magenta_ball)\\n\\t pub_magentaBall.publish(magenta_ball)\\n\\t print (\\\"Magenta ball detected.\\\\n\\\")\\n\\n\\tcv2.imshow('window', image_np)\\n\\tcv2.waitKey(2)\\n\\tc = 0\",\n \"def track_ball_generic(video):\\n result = []\\n frames = readFrames(video)\\n\\n # get background with background estimator method\\n background = findBackground(frames)\\n # Setup background subtractor object with parameters\\n subtractor = cv2.BackgroundSubtractorMOG(30, 10, 0.7, 0)\\n # Feed estimated background as first input to subtractor\\n subtractor.apply(background)\\n\\n # Iterate over every frame in video\\n i = 0\\n while i < len(frames):\\n frame = frames[i] # get the new frame\\n\\n # apply background subtraction to frame\\n frame = subtractor.apply(frame)\\n\\n # find contours in the frame\\n contours, _ = cv2.findContours(frame, cv2.RETR_TREE,\\n cv2.CHAIN_APPROX_SIMPLE)\\n\\n # sort the contours in reverse order by width to eliminate small\\n # contours caused by noise.\\n sort_cnt = sorted(contours, key=lambda x: cv2.boundingRect(x)[2],\\n reverse=True)\\n\\n # get the parameters of the bounding box for the largest width contour\\n x, y, w, h = cv2.boundingRect(sort_cnt[0])\\n # append to result list\\n result.append((x, y, x + w, y + h))\\n\\n if VISUALIZE:\\n orig_frame = frames[i]\\n cv2.rectangle(orig_frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\\n cv2.imshow('frame', orig_frame)\\n cv2.waitKey(30)\\n\\n i += 1\\n\\n return result\",\n \"def getPoslbFFmpeg(self, event):\\n # if self.measurement_w:\\n\\n if hasattr(self, \\\"measurement_w\\\") and self.measurement_w is not None and self.measurement_w.isVisible():\\n x = event.pos().x()\\n y = event.pos().y()\\n\\n # distance\\n if self.measurement_w.rbDistance.isChecked():\\n if event.button() == 1: # left\\n self.draw_point(x, y, \\\"lime\\\")\\n self.memx, self.memy = x, y\\n\\n if event.button() == 2 and self.memx != -1 and self.memy != -1:\\n self.draw_point(x, y, \\\"lime\\\")\\n self.draw_line(self.memx, self.memy, x, y, \\\"lime\\\")\\n\\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\\\"line\\\", self.memx, self.memy, x, y])\\n else:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\\\"line\\\", self.memx, self.memy, x, y]]\\n\\n d = ((x - self.memx) ** 2 + (y - self.memy) ** 2) ** 0.5\\n try:\\n d = d / float(self.measurement_w.lePx.text()) * float(self.measurement_w.leRef.text())\\n except:\\n QMessageBox.critical(self, programName,\\n \\\"Check reference and pixel values! Values must be numeric.\\\",\\n QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton)\\n\\n self.measurement_w.pte.appendPlainText(\\\"Time: {}\\\\tFrame: {}\\\\tDistance: {}\\\".format(self.getLaps(),\\n self.FFmpegGlobalFrame,\\n round(d, 1)))\\n self.measurement_w.flagSaved = False\\n self.memx, self.memy = -1, -1\\n\\n # angle 1st clic -> vertex\\n if self.measurement_w.rbAngle.isChecked():\\n if event.button() == 1: # left for vertex\\n self.draw_point(x, y, \\\"lime\\\")\\n self.memPoints = [(x, y)]\\n\\n if event.button() == 2 and len(self.memPoints):\\n self.draw_point(x, y, \\\"lime\\\")\\n self.draw_line(self.memPoints[0][0], self.memPoints[0][1], x, y, \\\"lime\\\")\\n\\n self.memPoints.append((x, y))\\n\\n if len(self.memPoints) == 3:\\n self.measurement_w.pte.appendPlainText(\\\"Time: {}\\\\tFrame: {}\\\\tAngle: {}\\\".format(self.getLaps(),\\n self.FFmpegGlobalFrame,\\n round(angle(\\n self.memPoints[\\n 0],\\n self.memPoints[\\n 1],\\n self.memPoints[\\n 2]),\\n 1)\\n ))\\n self.measurement_w.flagSaved = False\\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\\\"angle\\\", self.memPoints])\\n else:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\\\"angle\\\", self.memPoints]]\\n\\n self.memPoints = []\\n\\n # Area\\n if self.measurement_w.rbArea.isChecked():\\n if event.button() == 1: # left\\n self.draw_point(x, y, \\\"lime\\\")\\n if len(self.memPoints):\\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], x, y, \\\"lime\\\")\\n self.memPoints.append((x, y))\\n\\n if event.button() == 2 and len(self.memPoints) >= 2:\\n self.draw_point(x, y, \\\"lime\\\")\\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], x, y, \\\"lime\\\")\\n self.memPoints.append((x, y))\\n # close polygon\\n self.draw_line(self.memPoints[-1][0], self.memPoints[-1][1], self.memPoints[0][0],\\n self.memPoints[0][1], \\\"lime\\\")\\n a = polygon_area(self.memPoints)\\n\\n if self.FFmpegGlobalFrame in self.measurement_w.draw_mem:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame].append([\\\"polygon\\\", self.memPoints])\\n else:\\n self.measurement_w.draw_mem[self.FFmpegGlobalFrame] = [[\\\"polygon\\\", self.memPoints]]\\n try:\\n a = a / (float(self.measurement_w.lePx.text()) ** 2) * float(\\n self.measurement_w.leRef.text()) ** 2\\n except:\\n QMessageBox.critical(self, programName,\\n \\\"Check reference and pixel values! Values must be numeric.\\\",\\n QMessageBox.Ok | QMessageBox.Default, QMessageBox.NoButton)\\n\\n self.measurement_w.pte.appendPlainText(\\\"Time: {}\\\\tFrame: {}\\\\tArea: {}\\\".format(self.getLaps(),\\n self.FFmpegGlobalFrame,\\n round(a, 1)))\\n\\n self.memPoints = []\",\n \"def get_face_rect_dlib(frame):\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n gray = clahe.apply(gray)\\n rects = detector(gray, 0)\\n return ((rects[0].top(), rects[0].left()), (rects[0].bottom(), rects[0].right())) if len(rects) > 0 else None\",\n \"def frame_analysis_green(fW, fH, frame):\\n # Compute the ratio to go from pixel coordinate to grid coordinate\\n cam_grid_ratio = (gw / fW, gh / fH)\\n\\n # Compute the green mask needed to find thymio\\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\\n mask = cv2.inRange(hsv, low_green, up_green)\\n\\n # Find to contours of the square in order to compute the center of it\\n contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]\\n areas = [cv2.contourArea(c) for c in contours]\\n\\n # If we don't find the square, return impossible value for the followin code\\n # to know no measurement were possible\\n if len(areas) < 1:\\n\\n # Display the resulting frame\\n x2, y2 = (-1, -1)\\n xf, yf = (-1, -1)\\n\\n else:\\n\\n # Find the largest moving object in the image\\n max_index = np.argmax(areas)\\n cnt = contours[max_index]\\n x, y, w, h = cv2.boundingRect(cnt)\\n\\n # Find the center of the green square\\n xf = x + int(w / 2)\\n yf = y + int(h / 2)\\n\\n # Change from pixel coordinate to grid coordinate\\n x2 = xf * cam_grid_ratio[0]\\n y2 = gh - yf * cam_grid_ratio[1]\\n\\n frame = frame[:, :, ::-1]\\n\\n return x2, y2, xf, yf, frame\",\n \"def pos_perframe(self):\\n return self.__pos_perframe\",\n \"def Bas():\\r\\n X1, Y1, X2, Y2 = canvas.coords(boule)\\r\\n canvas.coords(boule,X1,Y1+20,X2,Y2+20)\",\n \"def bee_at(self, point):\\n \\\"\\\"\\\" Responsible for transformations \\\"\\\"\\\"\\n pos, com, success = self.perception\\n \\n point = point.copy()\\n \\n if self.phase == 2:\\n point -= self.position\\n elif self.phase > 2:\\n point = self.transform(point - self.position)\\n\\n return any(map(lambda x: array_equal(point,x),pos))\",\n \"def update(self, balls):\\n\\n # Find closest ball\\n closest_ball = [self.settings.WINDOW_WIDTH/2, self.settings.WINDOW_HEIGHT/2]\\n\\n # Scan through all the list of balls to find the closest to the enemy bar\\n for ball in balls:\\n if self.topSide:\\n if ball.rect.y < closest_ball[1]:\\n closest_ball = ball.rect\\n elif not self.topSide:\\n if ball.rect.y > closest_ball[1]:\\n closest_ball = ball.rect\\n\\n # Calculation for if the bar is on top\\n if self.rect.centerx >= closest_ball[0]:\\n self.rect.x -= self.velocity.x\\n elif self.rect.centerx <= closest_ball[0]:\\n self.rect.x += self.velocity.x\\n\\n # Checks if the enemy bar gone out of bound\\n self.check_boundaries()\",\n \"def get_frame_root_pos(self, frame):\\n root_pos = frame[:self.POS_SIZE].copy()\\n return root_pos\",\n \"def get_ball_position_and_state(self):\\n return (self.x, self.y, self.is_ball_visible)\",\n \"def frame_analysis_yellow(fW, fH, frame):\\n\\n # Compute the ratio to go from pixel coordinate to grid coordinate\\n cam_grid_ratio = (gw / fW, gh / fH)\\n\\n # Compute the yellow mask in order to find the uper square on the thymio\\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\\n mask = cv2.inRange(hsv, low_yellow, up_yellow)\\n\\n # Find to contours of the square in order to compute the center of it\\n contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)[-2:]\\n areas = [cv2.contourArea(c) for c in contours]\\n\\n # If we don't find the square, return impossible value for the followin code\\n # to know no measurement were possible\\n if len(areas) < 1:\\n\\n # Display the resulting frame\\n x2, y2 = (-1, -1)\\n xf, yf = (-1, -1)\\n\\n else:\\n\\n # Find the largest moving object in the image\\n max_index = np.argmax(areas)\\n cnt = contours[max_index]\\n x, y, w, h = cv2.boundingRect(cnt)\\n\\n # Change from pixel to grid coordinate\\n xf = x + int(w / 2)\\n yf = y + int(h / 2)\\n\\n x2 = xf * cam_grid_ratio[0]\\n y2 = gh - yf * cam_grid_ratio[1]\\n\\n frame = frame[:, :, ::-1]\\n\\n return x2, y2, xf, yf, frame\",\n \"def matched_brand_positions(self):\\n collector = self._results.collector\\n \\\"\\\"\\\"@type: ok.query.whoosh_contrib.find_brands.FindBrandsQuery.BrandsCollector\\\"\\\"\\\"\\n brand = self.current_match['brand']\\n pos = collector.brands_found[brand][1]\\n return pos\",\n \"def check_terminal(self,bump,DLightBump, AnalogBump, IR):\\r\\n terminal = False\\r\\n # signal returned from distance to obstacle /terminal 50 mm,5cm\\r\\n # by measurement, small obstacle (height = height of light bumper) in 2cm: signal 120 ~300\\r\\n # within 1cm >400\\r\\n # if big obstacle: (like a wall) at 2cm: 1300~1600\\r\\n # d_obs = 140\\r\\n d_obs = 500.0\\r\\n threshold = d_obs/self.max_strength\\r\\n obstacles = []\\r\\n Infra_Omi, Infra_L, Infra_R =IR\\r\\n\\r\\n L, FL, CL, CR, FR, R = AnalogBump\\r\\n prob_obs =np.array([L, FL, CL, CR, FR, R]).astype(float)\\r\\n strength = prob_obs/self.max_strength # maximum signal strength light bumper can receive\\r\\n for i in range(len(strength)):\\r\\n strength[i] = 1 if strength[i] >=threshold else 0\\r\\n\\r\\n cnt = strength.sum()\\r\\n if Infra_Omi!=0 or Infra_L!=0 or Infra_R!= 0:\\r\\n terminal =True\\r\\n x = int(self.Motion.x +d_obs)\\r\\n y = int(self.Motion.y)\\r\\n s = (x, y)\\r\\n obstacles.append(s)\\r\\n\\r\\n if bump != 0 or cnt >=1:\\r\\n terminal=True\\r\\n # stop immediately\\r\\n self.Roomba.Move(0,0)\\r\\n #-------------determine position of obstacles-------------\\r\\n l_bump = 1 if bump&2 !=0 else 0\\r\\n r_bump = 1 if bump& 1 !=0 else 0\\r\\n # Assume Left , right bumpers are at -45 degree, 45 degree\\r\\n # Then find the average degree of object:0, -45, 45 degree\\r\\n b_avg_angle = 45*(r_bump -l_bump)\\r\\n prob_obs /= (prob_obs.sum()+1.0)\\r\\n # average angles of obstacle detected by light bumper\\r\\n # [-90, -60,-30,30,60,90] are heading angles of 6 analog light bumper\\r\\n lb_avg_agl = np.dot(prob_obs,[-90, -60,-30,30,60,90])\\r\\n\\r\\n # if there are 2 obstacles\\r\\n if np.abs(lb_avg_agl - b_avg_angle)>=60 or (np.sign(lb_avg_agl) !=np.sign(b_avg_angle)):\\r\\n th = self.Motion.theta + lb_avg_agl\\r\\n x = self.Motion.x + d_obs * math.cos(th)\\r\\n y = self.Motion.y + d_obs * math.sin(th)\\r\\n x = int(x)\\r\\n y = int(y)\\r\\n s= (x,y)\\r\\n if obstacles.count(s) == 0:\\r\\n obstacles.append(s)\\r\\n\\r\\n th = self.Motion.theta + b_avg_angle\\r\\n x = self.Motion.x + d_obs * math.cos(th)\\r\\n y = self.Motion.y + d_obs * math.sin(th)\\r\\n x = int(x)\\r\\n y = int(y)\\r\\n s = (x,y)\\r\\n if obstacles.count(s) ==0:\\r\\n obstacles.append(s)\\r\\n\\r\\n else:\\r\\n # if there is 1 obstacle\\r\\n alg = (b_avg_angle+lb_avg_agl)/2.0\\r\\n th= self.Motion.theta+ alg\\r\\n x = self.Motion.x + d_obs * math.cos(th)\\r\\n y = self.Motion.y + d_obs * math.sin(th)\\r\\n x = int(x)\\r\\n y = int(y)\\r\\n s = (x,y)\\r\\n if obstacles.count(s) == 0:\\r\\n obstacles.append(s)\\r\\n\\r\\n # check if the obstacle is one of other agents\\r\\n for k in self.global_trans.keys():\\r\\n # Format: self.global_trans={id: (degree, [a,st,s_t+1])}\\r\\n states = self.global_trans[k][1]\\r\\n st = self.get_gridState(states[1])\\r\\n st1 = self.get_gridState(states[2])\\r\\n # if obstacles are other agents, remove them\\r\\n for o in obstacles:\\r\\n grid_o = self.get_gridState((o[0],o[1],th))\\r\\n if (grid_o[0],grid_o[1]) == (st[0],st[1]) or (grid_o[0],grid_o[1]) == (st1[0],st1[1]):\\r\\n obstacles.remove(o)\\r\\n return terminal, obstacles\",\n \"def framewise_inside_rectangle_roi(\\n bp_location: np.ndarray, roi_coords: np.ndarray\\n ) -> np.ndarray:\\n results = np.full((bp_location.shape[0]), 0)\\n within_x_idx = np.argwhere(\\n (bp_location[:, 0] <= roi_coords[1][0])\\n & (bp_location[:, 0] >= roi_coords[0][0])\\n ).flatten()\\n within_y_idx = np.argwhere(\\n (bp_location[:, 1] <= roi_coords[1][1])\\n & (bp_location[:, 1] >= roi_coords[0][1])\\n ).flatten()\\n for i in prange(within_x_idx.shape[0]):\\n match = np.argwhere(within_y_idx == within_x_idx[i])\\n if match.shape[0] > 0:\\n results[within_x_idx[i]] = 1\\n return results\",\n \"def touching_border(self, bb: list, quickness: int = 1) -> str:\\r\\n \\r\\n # Make the coordinates easier to handle\\r\\n x1, y1, x2, y2 = bb[0], bb[1], bb[2], bb[3]\\r\\n\\r\\n # Check which way the box should be expanded\\r\\n # The first two conditions check if the box should\\r\\n # be expanded diagonally downwards.\\r\\n # These just speed up the box sizing\\r\\n \\r\\n ## Check if there is a green pixel on the bottom right of the box\\r\\n if not (x2 + quickness >= len(self.bin_pic[0])) and \\\\\\r\\n not (y2 + quickness >= len(self.bin_pic)):\\r\\n if self.bin_pic[y2][x2]:\\r\\n return 'BR'\\r\\n\\r\\n ## Check if there is a green pixel on the bottom right of the box\\r\\n if not (x1 - quickness < 0) and not (y2 + quickness >= len(self.bin_pic)):\\r\\n if self.bin_pic[y2][x1]:\\r\\n return 'BL'\\r\\n\\r\\n # The next four check the sides of the box\\r\\n ## Check if there is a green pixel on the right side of the box\\r\\n if not (x2 + quickness >= len(self.bin_pic[0])):\\r\\n for i in range(y2 - y1 + 1):\\r\\n if self.bin_pic[y1 + i][x2]:\\r\\n return 'RIGHT'\\r\\n \\r\\n ## Check if there is a green pixel on the bottom side of the box\\r\\n if not (y2 + quickness >= len(self.bin_pic)):\\r\\n for i in range(x2 - x1 + 1):\\r\\n if self.bin_pic[y2][x1 + i]:\\r\\n return 'BOTTOM'\\r\\n\\r\\n ## Check if there is a green pixel on the left side of the box\\r\\n if not (x1 - quickness < 0):\\r\\n for i in range(y2 - y1 + 1):\\r\\n if self.bin_pic[y1 + i][x1 - quickness]:\\r\\n return 'LEFT'\\r\\n\\r\\n ## Check if there is a green pixel on the top side of the box\\r\\n if not (y1 - quickness < 0):\\r\\n for i in range(x2 - x1 + 1):\\r\\n if self.bin_pic[y1 - quickness][x1 + i]:\\r\\n return 'TOP'\\r\\n\\r\\n # If all the green pixels are found and the box is on the edge\\r\\n if (x2 + quickness >= len(self.bin_pic[0])) or \\\\\\r\\n (y2 + quickness >= len(self.bin_pic)) or \\\\\\r\\n (y1 - quickness < 0) or \\\\\\r\\n (x1 - quickness < 0):\\r\\n return 'EDGE'\\r\\n\\r\\n # If there are no green pixels touching the box, return 'NONE'\\r\\n return 'NONE'\",\n \"def on_collision(self):\",\n \"def frame(self):\",\n \"def detectBorders(self, points):\\n lane1 = []; lane2 = []\\n self.leftLane = [None for _ in range(int(np.floor(self.BIRDVIEW_HEIGHT / self.slideThickness)))]\\n self.rightLane = [None for _ in range(int(np.floor(self.BIRDVIEW_HEIGHT / self.slideThickness)))]\\n\\n pointMap = np.zeros((points.shape[0], 20))\\n prePoint = np.zeros((points.shape[0], 20))\\n postPoint = np.zeros((points.shape[0], 20))\\n\\n dis = 10\\n max1 = -1; max2 = -1\\n\\n ##\\n ## /!\\\\ UNSAFE LOOP, TODO: FIX\\n ##\\n for i in range(points.shape[0]):\\n for j in range(len(points[i])):\\n pointMap[i][j] = 1\\n prePoint[i][j] = -1\\n postPoint[i][j] = -1\\n\\n for i in reversed(range(points.shape[0] - 2)):\\n\\n for j in range(len(points[i])):\\n\\n err = 320\\n for m in range(1, min(points.shape[0] - 1 - i, 5)):\\n check = False ## TODO: why unused ?\\n\\n for k in range(len(points[i + 1])):\\n\\n (x_m, y_m) = points[i + m][k].pt\\n (x, y) = points[i][j].pt\\n\\n if (abs(x_m - x) < dis and abs(y_m - y) < err):\\n err = abs(x_m - x)\\n\\n pointMap[i][j] = pointMap[i + m][k] + 1\\n prePoint[i][j] = k\\n postPoint[i + m][k] = j\\n check = True\\n\\n break ## breaks out of the m loop. Why is it not conditioned by check ? TODO: ???\\n\\n if (pointMap[i][j] > max1):\\n max1 = pointMap[i][j]\\n posMax = cv2.KeyPoint(i, j, _size=0)\\n \\n else:\\n posMax = None\\n\\n for i in range(points.shape[0]):\\n for j in range(len(points[i])):\\n if posMax:\\n if (pointMap[i][j] > max2 and (i != posMax.pt[0] or j != posMax.pt[1]) and postPoint[i][j] == -1): #FIXME \\\"local variable 'posMax' referenced before assignment\\\" possible\\n max2 = pointMap[i][j]\\n posMax2 = cv2.KeyPoint(i, j, _size=0)\\n\\n\\n\\n if max1 == -1:\\n return\\n\\n # DEFINES LANE 1 POINTS\\n while (max1 >= 1):\\n (x,y) = points[int(posMax.pt[0])][int(posMax.pt[1])].pt\\n lane1.append(\\n [x,y]\\n )\\n if (max1 == 1):\\n break\\n\\n posMax = cv2.KeyPoint(\\n posMax.pt[0]+1,\\n prePoint[int(posMax.pt[0])][int(posMax.pt[1])],\\n _size=0\\n )\\n\\n max1 -= 1\\n\\n # DEFINES LANE 2 POINTS\\n while (max2 >= 1):\\n (x,y) = points[int(posMax2.pt[0])][int(posMax2.pt[1])].pt\\n lane2.append(\\n [x, y]\\n )\\n if (max2 == 1):\\n break\\n\\n posMax2 = cv2.KeyPoint(\\n posMax2.pt[0]+1,\\n prePoint[int(posMax2.pt[0])][int(posMax2.pt[1])],\\n _size=0\\n )\\n\\n max2-= 1\\n\\n subLane1 = np.array(lane1[0:5])\\n subLane2 = np.array(lane2[0:5])\\n\\n # checking if sublane has an empty value\\n\\n line1 = cv2.fitLine(subLane1, 2, 0, 0.01, 0.01)\\n line2 = cv2.fitLine(subLane2, 2, 0, 0.01, 0.01)\\n\\n try:\\n lane1X = (self.BIRDVIEW_WIDTH - line1[3]) * line1[0] / line1[1] + line1[2]\\n except:\\n lane1X = 0\\n\\n try:\\n lane2X = (self.BIRDVIEW_WIDTH - line2[3]) * line2[0] / line2[1] + line2[2]\\n except:\\n lane2X = 0\\n \\n if (lane1X < lane2X):\\n for i in range(len(lane1)):\\n self.leftLane[int(np.floor(lane1[i][1] / self.slideThickness ))] = lane1[i]\\n\\n for i in range(len(lane2)):\\n self.rightLane[int(np.floor(lane2[i][1] / self.slideThickness ))] = lane2[i]\\n\\n else:\\n\\n for i in range(len(lane1)):\\n self.rightLane[int(np.floor(lane1[i][1] / self.slideThickness ))] = lane1[i]\\n\\n for i in range(len(lane2)):\\n self.leftLane[int(np.floor(lane2[i][1] / self.slideThickness ))] = lane2[i]\",\n \"def int_21H_B(self):\\r\\n\\r\\n char_present = self.GUI.is_key_pressed()\\r\\n\\r\\n if char_present:\\r\\n self.registers['AX'].move_into(255, 0, is_int=True)\\r\\n else:\\r\\n self.registers['AX'].move_into(0, 0, is_int=True)\",\n \"def Haut():\\r\\n X1, Y1, X2, Y2 = canvas.coords(boule)\\r\\n canvas.coords(boule,X1,Y1-20,X2,Y2-20)\",\n \"def boundary(self):\\n if self.pos.x < 0:\\n self.pos.x = 0\\n if self.pos.x > WIDTH - 48:\\n self.pos.x = WIDTH - 48\\n if self.pos.y < 0:\\n self.pos.y = 0\\n if self.pos.y > HEIGHT - 48:\\n self.pos.y = HEIGHT - 48\\n\\n self.rect.topleft = self.pos\",\n \"def boundingBox(self):\\n pmodel = (glm.vec3(1, -self.y_sign, 0)\\n * self.model.pos * self.transform.scale)\\n x, y, _ = self.transform.pos + pmodel\\n y += -self.y_sign * self.font.table['ascent'] * self.transform.scale[1]\\n return x, y, self.pixwidth(), self.pixheight()\",\n \"def mover_bm_izquierda(self):\\n self.nueva_posicion_posible_parte_superior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0] - 1,\\n self.casilla[1]],\\n [self.vertice_1[0] - self.velocidad,self.vertice_1[1]], \\n [self.vertice_1[0] - 5 - 5, self.vertice_1[1]])\\n self.nueva_posicion_posible_parte_inferior = self.mapa.consultar_casilla_por_movimiento([self.casilla[0] - 1,\\n self.casilla[1] + 1],\\n [self.vertice_3[0] - self.velocidad,self.vertice_3[1]],\\n [self.vertice_3[0] - 5,self.vertice_3[1]]) \\n if self.nueva_posicion_posible_parte_superior[0] != 1 and self.nueva_posicion_posible_parte_inferior[0] != 1:\\n self.x -= self.velocidad * (self.x >= 15)\\n self.posicion = [self.x,self.posicion[1]]\\n self.casilla = [self.casilla[0] - self.nueva_posicion_posible_parte_superior[1] *(self.nueva_posicion_posible_parte_inferior[0] != 1) * (self.nueva_posicion_posible_parte_superior[0] != 1), self.casilla[1]]\\n self.redefinir_vertices()\",\n \"def get_frame_bbox(annotations_dir, seq):\\n root_dir = \\\"/home/sdb/wangshentao/myspace/thesis/data/VisDrone2019-MOT-test-dev/\\\"\\n bbox = []\\n frame_id = []\\n trace_bbox = []\\n trace_frame_id = []\\n annotations_dir = root_dir + 'annotations/'\\n gt_txt = os.path.join(annotations_dir, seq)\\n gt = np.loadtxt(gt_txt, dtype=np.float64, delimiter=',')\\n tid_last = gt[0][1]\\n for fid, tid, x, y, w, h, mark, label, _, _ in gt:\\n if int(mark) == 0 or int(label) == 0 or int(label) == 11:\\n continue\\n if tid == tid_last:\\n trace_bbox.append([x, y, w, h])\\n trace_frame_id.append(fid)\\n else:\\n tid_last = tid\\n if len(trace_bbox) > 1:\\n bbox.append(np.array(trace_bbox))\\n frame_id.append(np.array(trace_frame_id))\\n trace_bbox = [[x, y, w, h]]\\n trace_frame_id = [fid]\\n\\n return bbox, frame_id\",\n \"def update(self, frame):\\n self.__update_state(frame)\\n\\n if self.strategy == \\\"mean\\\":\\n ax = np.mean(np.ravel(self.flow[...,0]))\\n ay = np.mean(np.ravel(self.flow[...,1]))\\n else:\\n ax = statistics.median(np.ravel(self.flow[...,0]))\\n ay = statistics.median(np.ravel(self.flow[...,1]))\\n\\n P = 1\\n new_x = int(self.bbox[0] + self.bbox[2] * ax * P)\\n new_y = int(self.bbox[1] + self.bbox[3] * ay * P)\\n self.bbox = (new_x, new_y, self.bbox[2], self.bbox[3])\\n return True, self.bbox\",\n \"def position(self):\\r\\n pass\",\n \"def check_boundaries(self):\\n # Checks if the enemy bar has gone of the net\\n if self.rect.left <= self.settings.WINDOW_WIDTH / 2:\\n self.rect.left = self.settings.WINDOW_WIDTH / 2\\n self.isMovingUp = False\\n\\n # Checks if the enemy bar has gone out of bound to the right\\n if self.rect.right >= self.settings.WINDOW_WIDTH:\\n self.rect.right = self.settings.WINDOW_WIDTH\\n self.isMovingUp = True\",\n \"def seek_beacon(self):\\n\\n beacon_sensor = ev3.BeaconSeeker(channel=1)\\n forward_speed = 300\\n turn_speed = 100\\n\\n while not self.touch_sensor.is_pressed:\\n current_heading = beacon_sensor.heading\\n current_distance = beacon_sensor.distance\\n if current_distance == -128:\\n print(\\\"IR Remote not found. Distance is -128\\\")\\n self.stop()\\n else:\\n if math.fabs(current_heading) < 2:\\n print(\\\"On the right heading. Distance: \\\", current_distance)\\n if current_distance == 0:\\n self.right_forward(True, forward_speed)\\n self.left_forward(True, forward_speed)\\n time.sleep(0.4)\\n self.stop()\\n return True\\n elif current_distance > 0:\\n print(\\\"Beacon is in front of the robot.\\\")\\n self.right_forward(True, forward_speed)\\n self.left_forward(True, forward_speed)\\n elif math.fabs(current_heading) > 2 and math.fabs(\\n current_heading) < 10:\\n if current_heading < 0:\\n print(\\\"Beacon is on the left.\\\")\\n self.right_forward(True, turn_speed)\\n self.left_backward(True, turn_speed)\\n else:\\n print(\\\"Beacon is on the right.\\\")\\n self.right_backward(True, turn_speed)\\n self.left_forward(True, turn_speed)\\n elif math.fabs(current_heading) > 10:\\n self.stop()\\n print('Heading too far off.')\\n time.sleep(0.01)\\n\\n print(\\\"Abandon ship!\\\")\\n self.stop()\\n return False\",\n \"def expect_bell(self, index: int, bell: Bell) -> None:\\n if self._user_assigned_bell(bell):\\n self._rhythm.expect_bell(\\n bell,\\n self._row_number,\\n index,\\n self.stroke\\n )\",\n \"def generate_bottonDown(self):\\n posx = self.posx + self.sizex - 2*self.sizebotx \\n posy = self.posy + self.sizeboty \\n return Boton(posx, posy, self.sizebotx, self.sizeboty, color=(124, 117, 221), color_h=(95, 90, 173), text_color=(0,0,0), press_color = (36, 26, 117), font_size=5)\",\n \"def on_bar(self, bar: BarData):\\n self.handle_bar(bar)\\n self.bg5.update_bar(bar)\\n # self.bg15.update_bar(bar)\",\n \"def blockDetector(self, frame):\\n self.detectBlocksInDepthImage()\\n self.block_pos = np.zeros((50,3))\\n self.block_num = 0\\n pixal_arm = np.zeros(2)\\n if self.kinectCalibrated == True:\\n # get current arm position\\n real_arm_x, real_arm_y,_,_ = self.rexarm.get_wrist_pose()\\n real_arm_x *= -1000\\n real_arm_y *= 1000\\n real_arm = np.array(([real_arm_x],[real_arm_y],[1]))\\n \\n # normalize arm_line vector\\n arm_length = np.sqrt(real_arm_x**2 + real_arm_y**2)\\n l = np.array([real_arm_x,real_arm_y])\\n\\n # find center of block_1_height and put them into block_position\\n for cnt in self.block_contours:\\n M = cv2.moments(cnt)\\n if M['m00']>0:\\n cx = int(M['m10']/M['m00'])\\n cy = int(M['m01']/M['m00'])\\n c = np.array([[cx],[cy]])\\n # Eliminate Arm itself\\n real_x = self.real_coord[cx][cy][0]\\n real_y = self.real_coord[cx][cy][1]\\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\\n \\n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\\n\\n \\n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\\n # Check if its in our ROI\\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\\n # points\\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\\n self.block_pos[self.block_num][2] = 1\\n # orientation\\n rect = cv2.minAreaRect(cnt)\\n self.block_ori[self.block_num][0] = -rect[2]\\n box = cv2.boxPoints(rect)\\n box = np.int0(box)\\n # detect color\\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\\n # draw contours\\n cv2.drawContours(self.currentDetectFrame,[box],0,(30,145,86),3)\\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(30,145,86),-1)\\n self.block_num += 1\\n\\n # find centers of 2 blocks\\n for cnt in self.block_contours_2:\\n M = cv2.moments(cnt)\\n if M['m00']>0:\\n cx = int(M['m10']/M['m00'])\\n cy = int(M['m01']/M['m00'])\\n c = np.array([[cx],[cy]])\\n # Eliminate Arm itself\\n real_x = self.real_coord[cx][cy][0]\\n real_y = self.real_coord[cx][cy][1]\\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\\n \\n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\\n \\n \\n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\\n # Check if its in our ROI\\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\\n # points\\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\\n self.block_pos[self.block_num][2] = 2\\n # orientation\\n rect = cv2.minAreaRect(cnt)\\n self.block_ori[self.block_num][0] = -rect[2]\\n box = cv2.boxPoints(rect)\\n box = np.int0(box)\\n # detect color\\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\\n # draw contours\\n cv2.drawContours(self.currentDetectFrame,[box],0,(30,87,137),3)\\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(30,87,137),-1)\\n self.block_num += 1 \\n\\n # find centers of 3 blocks\\n for cnt in self.block_contours_3:\\n M = cv2.moments(cnt)\\n if M['m00']>0:\\n cx = int(M['m10']/M['m00'])\\n cy = int(M['m01']/M['m00'])\\n c = np.array([[cx],[cy]])\\n # Eliminate Arm itself\\n real_x = self.real_coord[cx][cy][0]\\n real_y = self.real_coord[cx][cy][1]\\n d = np.linalg.norm(np.cross(l, np.array([real_x,real_y])))/np.linalg.norm(l)\\n \\n d_to_arm = np.sqrt((real_x-real_arm_x)**2+(real_y-real_arm_y)**2)\\n d_to_ori = np.sqrt(real_x**2 + real_y**2)\\n \\n \\n if d > 2 and not(d_to_ori<arm_length and d_to_arm<arm_length):\\n # Check if its in our ROI\\n if self.real_coord[cx][cy][0]>self.real_points[0][0] and self.real_coord[cx][cy][0]<self.real_points[2][0]:\\n if self.real_coord[cx][cy][1]<self.real_points[0][1] and self.real_coord[cx][cy][1]>self.real_points[2][1]:\\n # points\\n self.block_pos[self.block_num][0] = self.real_coord[cx][cy][0]\\n self.block_pos[self.block_num][1] = self.real_coord[cx][cy][1]\\n self.block_pos[self.block_num][2] = 3\\n # orientation\\n rect = cv2.minAreaRect(cnt)\\n self.block_ori[self.block_num][0] = -rect[2]\\n box = cv2.boxPoints(rect)\\n box = np.int0(box)\\n # detect color\\n self.block_color[self.block_num] = self.colorDetector(cx,cy)\\n # draw contours\\n cv2.drawContours(self.currentDetectFrame,[box],0,(204,6,6),3)\\n cv2.circle(self.currentDetectFrame,(cx,cy),5,(204,6,6),-1)\\n self.block_num += 1 \\n self.block_pos[self.block_num:50] = 0\\n self.block_ori[self.block_num:50] = 0\\n\\n return frame\",\n \"def attack_barbarian(self):\\n Utils.sleep()\\n Utils.wait_and_click(f\\\"barbarian_{self.config.barbarian_level['BarbarianLevel']}\\\", similarity=0.85, wait=20)\\n Utils.wait_and_click('barbarian_attack')\\n Utils.wait_and_click('attack_new_troops', wait=120)\\n Utils.wait_and_click('attack_march')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60581046","0.59345615","0.57604194","0.5743953","0.5729966","0.571696","0.570723","0.5651073","0.5620212","0.5571446","0.55702275","0.5536488","0.5536488","0.5512448","0.54974544","0.54932207","0.5444628","0.5442565","0.5442322","0.5441738","0.5436839","0.5410785","0.540617","0.5387526","0.5360575","0.53486913","0.5348089","0.53457594","0.5340788","0.53405005","0.5320513","0.5307961","0.5297892","0.52926755","0.52769434","0.5274121","0.5273968","0.52695465","0.5257754","0.52499235","0.52430445","0.5237165","0.52367216","0.5235246","0.5227707","0.52265626","0.5224505","0.5222807","0.5206335","0.5204632","0.5204487","0.51889306","0.516919","0.51676166","0.51629984","0.5160675","0.51541585","0.514893","0.5148567","0.51406527","0.5133565","0.5132466","0.5132448","0.5130651","0.51295537","0.51264507","0.5112739","0.5108045","0.5106779","0.5103473","0.5103011","0.5087908","0.5071125","0.50639606","0.50603175","0.50586146","0.50549144","0.50539976","0.5048175","0.50344265","0.5031136","0.50262135","0.50247073","0.5023507","0.5012405","0.501087","0.50071657","0.50060564","0.49991617","0.4998656","0.4991566","0.49897522","0.49839932","0.49757275","0.49727505","0.49710977","0.49697876","0.49680048","0.4965692","0.49579188"],"string":"[\n \"0.60581046\",\n \"0.59345615\",\n \"0.57604194\",\n \"0.5743953\",\n \"0.5729966\",\n \"0.571696\",\n \"0.570723\",\n \"0.5651073\",\n \"0.5620212\",\n \"0.5571446\",\n \"0.55702275\",\n \"0.5536488\",\n \"0.5536488\",\n \"0.5512448\",\n \"0.54974544\",\n \"0.54932207\",\n \"0.5444628\",\n \"0.5442565\",\n \"0.5442322\",\n \"0.5441738\",\n \"0.5436839\",\n \"0.5410785\",\n \"0.540617\",\n \"0.5387526\",\n \"0.5360575\",\n \"0.53486913\",\n \"0.5348089\",\n \"0.53457594\",\n \"0.5340788\",\n \"0.53405005\",\n \"0.5320513\",\n \"0.5307961\",\n \"0.5297892\",\n \"0.52926755\",\n \"0.52769434\",\n \"0.5274121\",\n \"0.5273968\",\n \"0.52695465\",\n \"0.5257754\",\n \"0.52499235\",\n \"0.52430445\",\n \"0.5237165\",\n \"0.52367216\",\n \"0.5235246\",\n \"0.5227707\",\n \"0.52265626\",\n \"0.5224505\",\n \"0.5222807\",\n \"0.5206335\",\n \"0.5204632\",\n \"0.5204487\",\n \"0.51889306\",\n \"0.516919\",\n \"0.51676166\",\n \"0.51629984\",\n \"0.5160675\",\n \"0.51541585\",\n \"0.514893\",\n \"0.5148567\",\n \"0.51406527\",\n \"0.5133565\",\n \"0.5132466\",\n \"0.5132448\",\n \"0.5130651\",\n \"0.51295537\",\n \"0.51264507\",\n \"0.5112739\",\n \"0.5108045\",\n \"0.5106779\",\n \"0.5103473\",\n \"0.5103011\",\n \"0.5087908\",\n \"0.5071125\",\n \"0.50639606\",\n \"0.50603175\",\n \"0.50586146\",\n \"0.50549144\",\n \"0.50539976\",\n \"0.5048175\",\n \"0.50344265\",\n \"0.5031136\",\n \"0.50262135\",\n \"0.50247073\",\n \"0.5023507\",\n \"0.5012405\",\n \"0.501087\",\n \"0.50071657\",\n \"0.50060564\",\n \"0.49991617\",\n \"0.4998656\",\n \"0.4991566\",\n \"0.49897522\",\n \"0.49839932\",\n \"0.49757275\",\n \"0.49727505\",\n \"0.49710977\",\n \"0.49697876\",\n \"0.49680048\",\n \"0.4965692\",\n \"0.49579188\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71624565"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77072,"cells":{"query":{"kind":"string","value":"Converts pixels in a barbell's diameter to centimeters"},"document":{"kind":"string","value":"def _get_cm_per_pixel(self, diameter):\n CM_PER_INCH = 2.54\n pixels_per_inch = 2 / diameter\n cm_per_pixel = CM_PER_INCH * pixels_per_inch\n return cm_per_pixel"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __conv_inch(length_mm):\n return length_mm / 25.4","def get_diameter(self) -> float:\r\n \r\n return (self.box[3] - self.box[1] + self.box[2] - self.box[0]) / 2","def bin_edges_to_centres(edges):\r\n if edges.ndim == 1:\r\n steps = (edges[1:] - edges[:-1]) / 2\r\n return edges[:-1] + steps\r\n else:\r\n steps = (edges[1:, 1:] - edges[:-1, :-1]) / 2\r\n centres = edges[:-1, :-1] + steps\r\n return centres","def cm2inch(size):\n return size / 2.54","def cm2inch(size):\n return size / 2.54","def cm2inch(size):\n return size / 2.54","def pixels_to_inches(self, px: float) -> float:\r\n \r\n return (px / self.ratio[0]) * self.ratio[1]","def howell_center(self,postage_stamp):\n xpixels = np.arange(postage_stamp.shape[1])\n ypixels = np.arange(postage_stamp.shape[0])\n I = np.sum(postage_stamp, axis=0)\n J = np.sum(postage_stamp, axis=1)\n\n Isub = I-np.sum(I)/I.size\n Isub[Isub<0] = 0\n Jsub = J-np.sum(J)/J.size\n Jsub[Jsub<0] = 0\n xc = np.sum(Isub*xpixels)/np.sum(Isub)\n yc = np.sum(Jsub*ypixels)/np.sum(Jsub)\n return xc, yc","def barycentre (liste_objets):\r\n x = 0\r\n y = 0\r\n summass = 0\r\n for i in liste_objets:\r\n x += i.mass * i.posx\r\n y += i.mass * i.posy\r\n summass += i.mass\r\n x /= summass\r\n y /= summass\r\n return x,y,summass","def calculate_barycenter(self, node):\n bary = 0.0\n for n in node.neighbors:\n bary += n.slot\n bary /= len(node.neighbors)\n node.barycenter = bary","def distance_in_cm_from_pixels(pixels):\n return K_INCH_PIXELS / pixels * CM_PER_INCH","def pixel_size_arcsec(self):\n from astropy import units\n if type(self.pixel_size_deg) is not units.quantity.Quantity:\n return [ps.to(\"arcsec\") for ps in self.pixel_size_deg]\n return self.pixel_size_deg.to(\"arcsec\")","def centspacing(x):\n\n dx = x.copy()\n\n x = x.values\n\n xpad = np.r_[2*x[0]-x[1], x, 2* x[-1] - x[-2]]\n\n dx.values = xpad[2:] - xpad[:-2]\n\n return dx","def center_of_bees(self):\n pos, com, success = self.perception\n return around(sum(pos)/(len(pos) + 1))","def circle_center(self):\n return self.container.width / 2, self.container.height / 2","def calculate_cohesion(boids: List[b.Boid]) -> Tuple[float, float]:\n center_x = 0\n center_y = 0\n for boid in boids:\n center_x += boid.x\n center_y += boid.y\n return center_x / len(boids), center_y / len(boids)","def equivalentDiameter(cnt):\n\treturn np.sqrt(4 * (cv2.contourArea(cnt)) / np.pi)","def bin_centers(radial_bins):\n\n outer = radial_bins[1:]\n inner = radial_bins[:-1]\n return 0.5 * (outer + inner)","def cone_volume(radius: number, height: number) -> number:\n return 1/3*(pi*radius*radius*height)","def barycenter(self):\n _value = (sum((v[0] for v in self.objects.values())),sum((v[1] for v in self.objects.values())))\n if self.objects:\n _value = (_value[0]/len(self.objects), _value[1]/len(self.objects))\n self.bc=_value\n return _value","def convert_ha_to_km2(nb):\n return nb / 100","def center_of_charge(self):\n ret = [0.0, 0.0, 0.0]\n total_c = 0.0\n\n for at in range(self.natom()):\n c = self.charge(at)\n ret = add(ret, scale(self.xyz(at), c))\n total_c += c\n\n ret = scale(ret, 1.0 / total_c)\n return ret","def center_of_mass(mask):\n M = cv2.moments(mask)\n # Usando a expressão do centróide definida em: https://en.wikipedia.org/wiki/Image_moment\n if M[\"m00\"] == 0:\n M[\"m00\"] = 1\n cX = int(M[\"m10\"] / M[\"m00\"])\n cY = int(M[\"m01\"] / M[\"m00\"])\n return [int(cX), int(cY)]","def center_of_mass(im_binary, x_offset=0, y_offset=0):\n n = np.sum(im_binary)\n\n x = np.arange(im_binary.shape[1]) + x_offset\n y = np.arange(im_binary.shape[0]) + y_offset\n xv, yv = np.meshgrid(x, y)\n cx = np.sum(xv[im_binary]) / n\n cy = np.sum(yv[im_binary]) / n\n\n return cx, cy","def get_centers_from_bins(bins):\n return 0.5 * (bins[:-1] + bins[1:])","def pixelConverter(pixels):\n intensity = int((sum(pixels[:3]) / 3) * (pixels[-1] / 100))\n return intensity","def box2cs(box):\r\n input_size = (256, 256)\r\n\r\n x, y, w, h = box[:4]\r\n aspect_ratio = input_size[0] / input_size[1]\r\n center = np.array([x + w * 0.5, y + h * 0.5], dtype=np.float32)\r\n\r\n if w > aspect_ratio * h:\r\n h = w * 1.0 / aspect_ratio\r\n elif w < aspect_ratio * h:\r\n w = h * aspect_ratio\r\n\r\n # pixel std is 200.0\r\n scale = np.array([w / 200.0, h / 200.0], dtype=np.float32)\r\n\r\n scale = scale * 1.25\r\n\r\n return center, scale","def centre_of_mass(image, black_blob=False):\r\n image = image.copy()\r\n shape = image.shape\r\n if black_blob:\r\n image = 255-image\r\n centre = np.array([0, 0]).astype(float)\r\n\r\n #------------------------------START YOUR CODE-----------------------------#\r\n s = np.sum(image)\r\n indices = np.mgrid[0:image.shape[0],0:image.shape[1]]\r\n ys = np.sum(indices[0]*image)\r\n xs = np.sum(indices[1]*image)\r\n\r\n # Equivalent, but slower\r\n #xs = 0.0\r\n #ys = 0.0\r\n #s = 0.0 \r\n #for y in range(shape[0]):\r\n # for x in range(shape[1]):\r\n # p = image[y, x]\r\n # xs += x*p\r\n # ys += y*p\r\n # s += p\r\n\r\n centre = np.array([ ys/s, xs/s ])\r\n #-------------------------------END YOUR CODE------------------------------#\r\n return centre.astype(int)","def center(self):\n return np.sum(self.bbox, 0) / 2","def centres_of_mass_2D(image):\n centroids = []\n bords = []\n areas = []\n radius = []\n \n for info in measure.regionprops(image, ['Centroid', 'BoundingBox', 'Area', 'equivalent_diameter', 'Label']): \n \n # Skip wrong regions\n index = np.where(image==info['Label'])\n if index[0].size==0 & index[1].size==0:\n continue\n \n # TODO: change this value\n if info['Area'] > image.shape[0] / 4.:\n \n \n centre = info['Centroid']\n D = info['equivalent_diameter']\n \n #min_row, min_col, max_row, max_col = info['BoundingBox']\n #a1 = int((max_row - min_row) / 2.)\n #a2 = int((max_col - min_col) / 2.)\n \n #box_cent = (a1 + min_row, a2 + min_col)\n \n radius.append(round(D / 2.0, 3))\n centroids.append( (round(centre[0], 3),round(centre[1], 3)) )\n #bords.append(box_cent)\n\n return [centroids, radius]","def hz2cents(pitchInHz, tonic=261.626):\n cents = 1200*np.log2(1.0*pitchInHz/tonic)\n return cents","def center_size(boxes):\n concat = P.Concat(1)\n return concat(((boxes[:, 2:] + boxes[:, :2])/2, # cx, cy\n boxes[:, 2:] - boxes[:, :2])) # w, h","def buldge_disk_ratio(kwargs_buldge_disk):\n kwargs_bd = copy.deepcopy(kwargs_buldge_disk)\n kwargs_bd['center_x'] = 0\n kwargs_bd['center_y'] = 0\n deltaPix = 0.05\n numPix = 200\n x_grid, y_grid = util.make_grid(numPix, deltaPix)\n from lenstronomy.LightModel.Profiles.sersic import BuldgeDisk\n bd_class = BuldgeDisk()\n light_grid = bd_class.function(x_grid, y_grid, **kwargs_bd)\n light_tot = np.sum(light_grid)\n kwargs_bd['I0_d'] = 0\n light_grid = bd_class.function(x_grid, y_grid, **kwargs_bd)\n light_buldge = np.sum(light_grid)\n return light_tot, light_buldge","def ci(x, y, z):\n\n return (x * 16 + z) * CHUNK_HEIGHT + y","def cm2inch(x: Union[float, Sequence[float], NDArray]) -> Sequence[float]:\n return list(np.array(x) / 2.54)","def center(width, height):\n return width/2, height/2","def center_size(boxes):\n return torch.cat([(boxes[:, :2] + boxes[:, 2:])/2, # cx, cy\n boxes[:, :2] - boxes[:, 2:]], 1) # w, h","def hern_bulge_mass(r,b):\n rb = r/b\n return ((rb*rb)/(2*(1+rb)**2.))","def compute_center(self, mole_object):\r\n if mole_object.plugin_type == \"PyMOL\":\r\n sel = PymolPlugin.PymolPlugin().get_model('all')\r\n cnt = len(sel.atom)\r\n\r\n else:\r\n sel = ChimeraPlugin.ChimeraPlugin().select()\r\n cnt = len(ChimeraPlugin.ChimeraPlugin().current_atoms())\r\n\r\n cent_x = 0\r\n cent_y = 0\r\n cent_z = 0\r\n\r\n if cnt == 0:\r\n return 0, 0, 0\r\n\r\n if mole_object.plugin_type == \"PyMOL\":\r\n\r\n for a in sel.atom:\r\n cent_x += a.coord[0]\r\n cent_y += a.coord[1]\r\n cent_z += a.coord[2]\r\n\r\n else:\r\n\r\n for a in ChimeraPlugin.ChimeraPlugin().current_atoms():\r\n cent_x += a.coord()[0]\r\n cent_y += a.coord()[1]\r\n cent_z += a.coord()[2]\r\n\r\n cent_x /= cnt\r\n cent_y /= cnt\r\n cent_z /= cnt\r\n\r\n self.point_x.component('entryfield').setentry(cent_x)\r\n self.point_y.component('entryfield').setentry(cent_y)\r\n self.point_z.component('entryfield').setentry(cent_z)\r\n\r\n self.show_crisscross(mole_object)","def bBoxMiddle(bBox):\n x0, y0 = bBox[0]\n x1, y1 = bBox[1]\n return (x0 + x1) / 2 , (y0 + y1) /2","def _calculate_binary_center(_binary_label):\n _pixel_inds = np.indices(np.shape(_binary_label)).astype(np.uint16)\n _coord = []\n for _l in (_pixel_inds * _binary_label):\n _coord.append(np.mean(_l[_l>0]))\n return np.array(_coord)","def calc_carbon_herb(height, diameter = 1, age = 1):\n \"\"\"This includes habits: perennial, annual, bulb, climber, biennial\\\n annual/biennial, perennial climber, annual/perennial, corm, annual climber\"\"\"\n \n #convert to imperial\n height /= 3.281 #feet\n diameter /= 2.54 #inches\n \n #print(height, diameter)\n \n #calculate green weight of herb: (above-ground weight) * 1.2\n green_weight = ( diameter**2 * height) * 1.2\n \n #dry weight: average tree is 72.5 dry matter \n dry_weight = 0.725 * green_weight\n \n #weight of carbon: 50% of tree dry weight\n c_weight = 0.5 * dry_weight\n \n #weight of CO2 sequestered\n co2_weight = 3.67 * c_weight\n \n return co2_weight/2.205/1 #convert from lbs to kg, divide by age","def cutout(self, centre, radius):","def fahren_conv(self, c):\n if c == 0:\n return 32\n else:\n return c * (9.0 / 5.0) + 32","def cm(feet = 0, inches = 0):\n inches_to_cm = inches * 2.54 # first convert inches to centimeters\n feet_to_cm = feet * 12 * 2.54 # second convert feet to centimeters\n return inches_to_cm + feet_to_cm # return the combined value","def inches(n):\n return n * 25.4","def dist2cax(self) -> float:\n # TODO: see about using line and built-in dist to point.\n center_fit = np.poly1d(self.fit)\n if self.orientation == Orientation.UP_DOWN:\n length = self.image.shape[0]\n else:\n length = self.image.shape[1]\n x_data = np.arange(length)\n y_data = center_fit(x_data)\n idx = int(round(len(x_data) / 2))\n if self.orientation == Orientation.UP_DOWN:\n axis = \"x\"\n p1 = Point(y_data[idx], x_data[idx])\n else:\n axis = \"y\"\n p1 = Point(x_data[idx], y_data[idx])\n return (getattr(self.image.center, axis) - getattr(p1, axis)) / self.image.dpmm","def NumberOfPixels(radius, bin_size):\n\n return int((radius/math.sqrt(2.0))*2.0 / bin_size)","def diameter(self):\n return self.radius * 2","def diameter(self):\n return self.radius * 2","def diameter(self):\n return 2 * self.radius","def bin_edges_to_center(edges):\n df = np.diff(edges)\n if isinstance(df[0], datetime.timedelta) and sys.version_info[0:2]<=(3,2):\n return edges[:-1] + df//2\n else:\n return edges[:-1] + df/2","def getRangeInches(self) -> float:\n ...","def circ(diam):\n return(pi * diam)","def find_diameter(self):\n try:\n #Get the contours of the gear\n contours, hierarchy = cv.findContours(self.frame, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)\n \n #Get the leftmost and the rightmost point of the gear\n leftmost = (sys.maxsize, sys.maxsize)\n rightmost = (sys.maxsize*-1, sys.maxsize*-1)\n frame_area = self.frame.shape[0] * self.frame.shape[1]\n new_contours = []\n for c in contours:\n c_area = cv.contourArea(c)\n area_percentage = (c_area/frame_area) * 100\n if area_percentage > .01:\n new_contours.append(c)\n\n c_leftmost = tuple(c[c[:, :, 0].argmin()][0])\n if leftmost[0] > c_leftmost[0]:\n leftmost = c_leftmost\n\n c_rightmost = tuple(c[c[:, :, 0].argmax()][0])\n if rightmost[0] < c_rightmost[0]:\n rightmost = c_rightmost\n\n cv.drawContours(self.result, new_contours, -1, (255, 0, 0), 3)\n\n cv.circle(self.result, leftmost, 4, [0, 0, 255], -1)\n cv.circle(self.result, rightmost, 4, [0, 0, 255], -1)\n\n #Get the distance between the two points\n distance = math.sqrt((leftmost[0] - rightmost[0])**2 + (leftmost[1] - rightmost[1])**2)\n\n #Convert the distance, which is in pixels to inches by using the background width as a point of reference\n self.diameter = (distance/self.frame.shape[1]) * self.background_width\n self.diameter = round(self.diameter, 2)\n \n except:\n cv.putText(self.result, 'Error', (10, 50), cv.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 2, cv.LINE_AA)","def _d_converter(self):\n units = {'um':1e-6, 'mm':1e-3, 'inch':2.54e-2, 'in':2.54e-2,\\\n 'micron':1e-6, 'mil':2.54e-5, 'm':1.0}\n for i in self.stack:\n i.thickness = i.thickness*units[i.units]\n return","def belt(image):\n\n # Belt Detector\n x, y = circular_detector(image, 70, 80)\n\n return x, y","def bbox_center(bbox):\n y, x, h, w = bbox\n return int(y + h/2), int(x + w/2)","def centerOfMass(data):\r\n dd = []\r\n for d in data:\r\n dd.append(d.coordinate)\r\n\r\n data = dd\r\n data = np.array(data)\r\n n = len(data)\r\n x = sum(data[:,0])\r\n y = sum(data[:,1])\r\n z = sum(data[:,2])\r\n x/=n\r\n y/=n\r\n z/=n\r\n return x,y,z,n","def get_cents_from_a(self) -> float:\n return self._cents","def total_climb(self):\n climb = 0\n for segment in self.data:\n segment_climb = 0\n\n last_height = None\n\n for point in segment:\n current_height = point[\"ele\"]\n\n # in case data is missing skip point !\n if current_height is None:\n continue\n\n # the first valid element is processed, get distance\n if last_height is not None and current_height > last_height:\n segment_climb += (current_height - last_height)\n\n last_height = current_height\n\n climb += segment_climb\n\n return climb","def center(self):\n return self._lower + 0.5 * (self._upper - self._lower)","def get_radius(size):\n return (size * 10) - 5","def cellvolumn(self, a, c):\n return 0.866*(a**2)*c","def fifteen():\r\n\r\n currentcell = 1.0\r\n cellpaths = 2.0\r\n \r\n while currentcell < 20.0:\r\n currentcell += 1.0\r\n cellpaths = cellpaths * (4.0 - 2.0/currentcell)\r\n \r\n return cellpaths","def beamarea_pix(self):\n beamsigma1 = self.header['BMAJ'] / self.wcs.wcs.cdelt[0]\n beamsigma2 = self.header['BMIN'] / self.wcs.wcs.cdelt[0]\n return (np.pi * beamsigma1 * beamsigma2) / (4 * np.log(2))","def x(self):\n return np.sum(self.bbox, 0)[0] / 2","def find_centroid_cell(self):\n\n x_min, y_min = self.find_min()\n x_max, y_max = self.find_max()\n x_centroid = int((x_max+x_min)/2)\n y_centroid = int((y_max+y_min)/2)\n centroide = x_centroid, y_centroid\n return centroide","def _size_pixels(self, renderer):\n return renderer.points_to_pixels(self.size)","def annulus_centres(self) -> Quantity:\n return self._ann_centres","def cy(y):\n return pic_height + ch(y - global_min_y)","def center(self):\n return (self.upper_right + self.lower_left) * 0.5","def computeA(diameter):\n radius = diameter / 2.0\n return np.pi * (radius**2)","def perimeter(self):\n\t\treturn self.height * 4","def bead_size(self):\n if \"diameter_conv\" in self._per_bead_data.columns:\n d_name = \"diameter_conv\"\n dim = \"Converted\"\n else:\n d_name = \"diameter\"\n dim = \"Pixels\"\n b_std = self._per_bead_data[d_name].std()\n b_mean = self._per_bead_data[d_name].mean()\n x_left = b_mean - (5 * b_std)\n x_right = b_mean + (5 * b_std)\n self._per_bead_data[d_name].plot(\n kind=\"hist\",\n bins=100,\n color=\"lightgray\",\n title=\"Beads Diameter (%s) Mean: %0.2f SD: %0.2f\" % (dim, b_mean, b_std),\n ).set_xlim(left=x_left, right=x_right)\n self._per_bead_data[d_name].plot(\n kind=\"kde\", secondary_y=True, color=\"black\", alpha=0.7\n ).set_ylim(bottom=0)\n if self._savefig is True:\n plt.savefig((self.report_folder + \"bead_size.png\"), dpi=self.dpi)","def length_to_pixels(length, boxsize, num_pixels):\n pixels = int((length / boxsize) * num_pixels)\n\n return pixels","def circumference(self):\n return self.width + self.height","def dist_from_center_to(x):\n pass","def find_center(self) -> tuple:\r\n \r\n # Add up all the x values of pixels in the plant\r\n # Then divide by total pixels in the plant\r\n avg_x = sum([i[0] for i in self.cluster]) / len(self.cluster)\r\n\r\n # Add up all the y values of pixels in the plant\r\n # Then divide by total pixels in the plant\r\n avg_y = sum([i[1] for i in self.cluster]) / len(self.cluster)\r\n\r\n self.center = (int(round(avg_x)), int(round(avg_y)))\r\n \r\n # return the results in a tuple of integers\r\n return self.center","def center_of_flux(self, LF, n_pix):\n # Loops over all pixels:\n LF_copy = copy(LF) # Copy to avoid overwriting\n flux_max = zeros(n_pix)\n x_max = zeros(n_pix)\n y_max = zeros(n_pix)\n pixel = zeros(n_pix)\n for j in range(n_pix):\n flux_max[j] = np.max(LF_copy) # Maximum value for array\n max_dex = np.where(LF_copy == flux_max[j]) # Find row, column for min value\n x_max[j] = max_dex[0][0] # max for x coordinate\n y_max[j] = max_dex[1][0] # max for y coordinate\n pixel[j] = j\n # Min pixel is et to max in order to find the next min:\n LF_copy[int(y_max[j]), int(x_max[j])] = 0 \n\n # Flux center is found:\n flux = sum(pixel)\n cen_x = 1/flux*dot(pixel,x_max) # Flux-center in x\n cen_y = 1/flux*dot(pixel,y_max) # Flux-center in y\n return cen_x, cen_y","def compute_delta_position(centroid, pixelscale=6.628, rotation=0.):\n rotation = rotation * np.pi/180.0\n cent = ((+centroid[0]*np.cos(rotation) + centroid[1]*np.sin(rotation)) * pixelscale * u.arcsec,\n (-centroid[0]*np.sin(rotation) + centroid[1]*np.cos(rotation)) * pixelscale * u.arcsec)\n return cent","def _rad_center(self):\n return ((self.rad_hi + self.rad_lo) / 2).to(\"deg\")","def get_hp_bar_color(total, value):\n\t# 256 values per color, so red-to-yellow and yellow-to-green make 512 - but yellow gets counted twice, so it's really 511\n\tnum_colors = 511\n\tc = scale(value, total, num_colors)\n\treturn (min(num_colors - c, 255), min(c, 255), 0)","def test_height_feet_only(self):\n result = height_to_cm(\"6'\")\n self.assertEqual(result, 183)","def center(self):\n return np.array([0,0,1/self.C+self.pos()])","def cellsize_2d(self):\t\r\n return self.dx * self.dy","def get_meters_to_pixels_coefficient():\n return 1/get_pixels_to_meters_coefficient()","def _get_column_size(self) -> int:\n return self.BARCODE_SIZE[0] // self.BARCODE_COLUMN_NUMBER","def chunk(self):\n return int((self.center_x + config.SPRITE_PIXEL_SIZE / 2) // 320)","def perimeter(cnt):\n\treturn cv2.arcLength(cnt, True)","def to_inch(self):\r\n if self.units != 'inch':\r\n self.units = 'inch'\r\n for statement in self.statements:\r\n statement.to_inch()\r\n for tool in iter(self.tools.values()):\r\n tool.to_inch()\r\n for primitive in self.primitives:\r\n primitive.to_inch()\r\n for hit in self.hits:\r\n hit.to_inch()","def Hexagon(image):\n return x, y","def circle_circumference(a):\n return (2*a*math.pi)","def centrx(self, e: int) -> float:\n result = self._read_inline(f\"centrx({e})\")\n return result","def cent(self, value):\n self._cent = value","def circleArea(radius):\n return math.pi * radius * radius","def normalize(self):\n norm_value = 60 if self.currency == \"btc\" else 18\n self.barHeightsNormalized = [heights / norm_value for heights in self.barHeights]","def find_cuts(width, height, isOdd):\n\n if isOdd:\n return width//2, height//2\n\n if width % 4 == 0:\n return width//2, height//2\n return (width-2)//2, (height-2)//2","def center(self):\n return [self.position[i]+self.radius for i in range(2)]","def conToCelc(faren):\n return (faren - 32)*(5/9)"],"string":"[\n \"def __conv_inch(length_mm):\\n return length_mm / 25.4\",\n \"def get_diameter(self) -> float:\\r\\n \\r\\n return (self.box[3] - self.box[1] + self.box[2] - self.box[0]) / 2\",\n \"def bin_edges_to_centres(edges):\\r\\n if edges.ndim == 1:\\r\\n steps = (edges[1:] - edges[:-1]) / 2\\r\\n return edges[:-1] + steps\\r\\n else:\\r\\n steps = (edges[1:, 1:] - edges[:-1, :-1]) / 2\\r\\n centres = edges[:-1, :-1] + steps\\r\\n return centres\",\n \"def cm2inch(size):\\n return size / 2.54\",\n \"def cm2inch(size):\\n return size / 2.54\",\n \"def cm2inch(size):\\n return size / 2.54\",\n \"def pixels_to_inches(self, px: float) -> float:\\r\\n \\r\\n return (px / self.ratio[0]) * self.ratio[1]\",\n \"def howell_center(self,postage_stamp):\\n xpixels = np.arange(postage_stamp.shape[1])\\n ypixels = np.arange(postage_stamp.shape[0])\\n I = np.sum(postage_stamp, axis=0)\\n J = np.sum(postage_stamp, axis=1)\\n\\n Isub = I-np.sum(I)/I.size\\n Isub[Isub<0] = 0\\n Jsub = J-np.sum(J)/J.size\\n Jsub[Jsub<0] = 0\\n xc = np.sum(Isub*xpixels)/np.sum(Isub)\\n yc = np.sum(Jsub*ypixels)/np.sum(Jsub)\\n return xc, yc\",\n \"def barycentre (liste_objets):\\r\\n x = 0\\r\\n y = 0\\r\\n summass = 0\\r\\n for i in liste_objets:\\r\\n x += i.mass * i.posx\\r\\n y += i.mass * i.posy\\r\\n summass += i.mass\\r\\n x /= summass\\r\\n y /= summass\\r\\n return x,y,summass\",\n \"def calculate_barycenter(self, node):\\n bary = 0.0\\n for n in node.neighbors:\\n bary += n.slot\\n bary /= len(node.neighbors)\\n node.barycenter = bary\",\n \"def distance_in_cm_from_pixels(pixels):\\n return K_INCH_PIXELS / pixels * CM_PER_INCH\",\n \"def pixel_size_arcsec(self):\\n from astropy import units\\n if type(self.pixel_size_deg) is not units.quantity.Quantity:\\n return [ps.to(\\\"arcsec\\\") for ps in self.pixel_size_deg]\\n return self.pixel_size_deg.to(\\\"arcsec\\\")\",\n \"def centspacing(x):\\n\\n dx = x.copy()\\n\\n x = x.values\\n\\n xpad = np.r_[2*x[0]-x[1], x, 2* x[-1] - x[-2]]\\n\\n dx.values = xpad[2:] - xpad[:-2]\\n\\n return dx\",\n \"def center_of_bees(self):\\n pos, com, success = self.perception\\n return around(sum(pos)/(len(pos) + 1))\",\n \"def circle_center(self):\\n return self.container.width / 2, self.container.height / 2\",\n \"def calculate_cohesion(boids: List[b.Boid]) -> Tuple[float, float]:\\n center_x = 0\\n center_y = 0\\n for boid in boids:\\n center_x += boid.x\\n center_y += boid.y\\n return center_x / len(boids), center_y / len(boids)\",\n \"def equivalentDiameter(cnt):\\n\\treturn np.sqrt(4 * (cv2.contourArea(cnt)) / np.pi)\",\n \"def bin_centers(radial_bins):\\n\\n outer = radial_bins[1:]\\n inner = radial_bins[:-1]\\n return 0.5 * (outer + inner)\",\n \"def cone_volume(radius: number, height: number) -> number:\\n return 1/3*(pi*radius*radius*height)\",\n \"def barycenter(self):\\n _value = (sum((v[0] for v in self.objects.values())),sum((v[1] for v in self.objects.values())))\\n if self.objects:\\n _value = (_value[0]/len(self.objects), _value[1]/len(self.objects))\\n self.bc=_value\\n return _value\",\n \"def convert_ha_to_km2(nb):\\n return nb / 100\",\n \"def center_of_charge(self):\\n ret = [0.0, 0.0, 0.0]\\n total_c = 0.0\\n\\n for at in range(self.natom()):\\n c = self.charge(at)\\n ret = add(ret, scale(self.xyz(at), c))\\n total_c += c\\n\\n ret = scale(ret, 1.0 / total_c)\\n return ret\",\n \"def center_of_mass(mask):\\n M = cv2.moments(mask)\\n # Usando a expressão do centróide definida em: https://en.wikipedia.org/wiki/Image_moment\\n if M[\\\"m00\\\"] == 0:\\n M[\\\"m00\\\"] = 1\\n cX = int(M[\\\"m10\\\"] / M[\\\"m00\\\"])\\n cY = int(M[\\\"m01\\\"] / M[\\\"m00\\\"])\\n return [int(cX), int(cY)]\",\n \"def center_of_mass(im_binary, x_offset=0, y_offset=0):\\n n = np.sum(im_binary)\\n\\n x = np.arange(im_binary.shape[1]) + x_offset\\n y = np.arange(im_binary.shape[0]) + y_offset\\n xv, yv = np.meshgrid(x, y)\\n cx = np.sum(xv[im_binary]) / n\\n cy = np.sum(yv[im_binary]) / n\\n\\n return cx, cy\",\n \"def get_centers_from_bins(bins):\\n return 0.5 * (bins[:-1] + bins[1:])\",\n \"def pixelConverter(pixels):\\n intensity = int((sum(pixels[:3]) / 3) * (pixels[-1] / 100))\\n return intensity\",\n \"def box2cs(box):\\r\\n input_size = (256, 256)\\r\\n\\r\\n x, y, w, h = box[:4]\\r\\n aspect_ratio = input_size[0] / input_size[1]\\r\\n center = np.array([x + w * 0.5, y + h * 0.5], dtype=np.float32)\\r\\n\\r\\n if w > aspect_ratio * h:\\r\\n h = w * 1.0 / aspect_ratio\\r\\n elif w < aspect_ratio * h:\\r\\n w = h * aspect_ratio\\r\\n\\r\\n # pixel std is 200.0\\r\\n scale = np.array([w / 200.0, h / 200.0], dtype=np.float32)\\r\\n\\r\\n scale = scale * 1.25\\r\\n\\r\\n return center, scale\",\n \"def centre_of_mass(image, black_blob=False):\\r\\n image = image.copy()\\r\\n shape = image.shape\\r\\n if black_blob:\\r\\n image = 255-image\\r\\n centre = np.array([0, 0]).astype(float)\\r\\n\\r\\n #------------------------------START YOUR CODE-----------------------------#\\r\\n s = np.sum(image)\\r\\n indices = np.mgrid[0:image.shape[0],0:image.shape[1]]\\r\\n ys = np.sum(indices[0]*image)\\r\\n xs = np.sum(indices[1]*image)\\r\\n\\r\\n # Equivalent, but slower\\r\\n #xs = 0.0\\r\\n #ys = 0.0\\r\\n #s = 0.0 \\r\\n #for y in range(shape[0]):\\r\\n # for x in range(shape[1]):\\r\\n # p = image[y, x]\\r\\n # xs += x*p\\r\\n # ys += y*p\\r\\n # s += p\\r\\n\\r\\n centre = np.array([ ys/s, xs/s ])\\r\\n #-------------------------------END YOUR CODE------------------------------#\\r\\n return centre.astype(int)\",\n \"def center(self):\\n return np.sum(self.bbox, 0) / 2\",\n \"def centres_of_mass_2D(image):\\n centroids = []\\n bords = []\\n areas = []\\n radius = []\\n \\n for info in measure.regionprops(image, ['Centroid', 'BoundingBox', 'Area', 'equivalent_diameter', 'Label']): \\n \\n # Skip wrong regions\\n index = np.where(image==info['Label'])\\n if index[0].size==0 & index[1].size==0:\\n continue\\n \\n # TODO: change this value\\n if info['Area'] > image.shape[0] / 4.:\\n \\n \\n centre = info['Centroid']\\n D = info['equivalent_diameter']\\n \\n #min_row, min_col, max_row, max_col = info['BoundingBox']\\n #a1 = int((max_row - min_row) / 2.)\\n #a2 = int((max_col - min_col) / 2.)\\n \\n #box_cent = (a1 + min_row, a2 + min_col)\\n \\n radius.append(round(D / 2.0, 3))\\n centroids.append( (round(centre[0], 3),round(centre[1], 3)) )\\n #bords.append(box_cent)\\n\\n return [centroids, radius]\",\n \"def hz2cents(pitchInHz, tonic=261.626):\\n cents = 1200*np.log2(1.0*pitchInHz/tonic)\\n return cents\",\n \"def center_size(boxes):\\n concat = P.Concat(1)\\n return concat(((boxes[:, 2:] + boxes[:, :2])/2, # cx, cy\\n boxes[:, 2:] - boxes[:, :2])) # w, h\",\n \"def buldge_disk_ratio(kwargs_buldge_disk):\\n kwargs_bd = copy.deepcopy(kwargs_buldge_disk)\\n kwargs_bd['center_x'] = 0\\n kwargs_bd['center_y'] = 0\\n deltaPix = 0.05\\n numPix = 200\\n x_grid, y_grid = util.make_grid(numPix, deltaPix)\\n from lenstronomy.LightModel.Profiles.sersic import BuldgeDisk\\n bd_class = BuldgeDisk()\\n light_grid = bd_class.function(x_grid, y_grid, **kwargs_bd)\\n light_tot = np.sum(light_grid)\\n kwargs_bd['I0_d'] = 0\\n light_grid = bd_class.function(x_grid, y_grid, **kwargs_bd)\\n light_buldge = np.sum(light_grid)\\n return light_tot, light_buldge\",\n \"def ci(x, y, z):\\n\\n return (x * 16 + z) * CHUNK_HEIGHT + y\",\n \"def cm2inch(x: Union[float, Sequence[float], NDArray]) -> Sequence[float]:\\n return list(np.array(x) / 2.54)\",\n \"def center(width, height):\\n return width/2, height/2\",\n \"def center_size(boxes):\\n return torch.cat([(boxes[:, :2] + boxes[:, 2:])/2, # cx, cy\\n boxes[:, :2] - boxes[:, 2:]], 1) # w, h\",\n \"def hern_bulge_mass(r,b):\\n rb = r/b\\n return ((rb*rb)/(2*(1+rb)**2.))\",\n \"def compute_center(self, mole_object):\\r\\n if mole_object.plugin_type == \\\"PyMOL\\\":\\r\\n sel = PymolPlugin.PymolPlugin().get_model('all')\\r\\n cnt = len(sel.atom)\\r\\n\\r\\n else:\\r\\n sel = ChimeraPlugin.ChimeraPlugin().select()\\r\\n cnt = len(ChimeraPlugin.ChimeraPlugin().current_atoms())\\r\\n\\r\\n cent_x = 0\\r\\n cent_y = 0\\r\\n cent_z = 0\\r\\n\\r\\n if cnt == 0:\\r\\n return 0, 0, 0\\r\\n\\r\\n if mole_object.plugin_type == \\\"PyMOL\\\":\\r\\n\\r\\n for a in sel.atom:\\r\\n cent_x += a.coord[0]\\r\\n cent_y += a.coord[1]\\r\\n cent_z += a.coord[2]\\r\\n\\r\\n else:\\r\\n\\r\\n for a in ChimeraPlugin.ChimeraPlugin().current_atoms():\\r\\n cent_x += a.coord()[0]\\r\\n cent_y += a.coord()[1]\\r\\n cent_z += a.coord()[2]\\r\\n\\r\\n cent_x /= cnt\\r\\n cent_y /= cnt\\r\\n cent_z /= cnt\\r\\n\\r\\n self.point_x.component('entryfield').setentry(cent_x)\\r\\n self.point_y.component('entryfield').setentry(cent_y)\\r\\n self.point_z.component('entryfield').setentry(cent_z)\\r\\n\\r\\n self.show_crisscross(mole_object)\",\n \"def bBoxMiddle(bBox):\\n x0, y0 = bBox[0]\\n x1, y1 = bBox[1]\\n return (x0 + x1) / 2 , (y0 + y1) /2\",\n \"def _calculate_binary_center(_binary_label):\\n _pixel_inds = np.indices(np.shape(_binary_label)).astype(np.uint16)\\n _coord = []\\n for _l in (_pixel_inds * _binary_label):\\n _coord.append(np.mean(_l[_l>0]))\\n return np.array(_coord)\",\n \"def calc_carbon_herb(height, diameter = 1, age = 1):\\n \\\"\\\"\\\"This includes habits: perennial, annual, bulb, climber, biennial\\\\\\n annual/biennial, perennial climber, annual/perennial, corm, annual climber\\\"\\\"\\\"\\n \\n #convert to imperial\\n height /= 3.281 #feet\\n diameter /= 2.54 #inches\\n \\n #print(height, diameter)\\n \\n #calculate green weight of herb: (above-ground weight) * 1.2\\n green_weight = ( diameter**2 * height) * 1.2\\n \\n #dry weight: average tree is 72.5 dry matter \\n dry_weight = 0.725 * green_weight\\n \\n #weight of carbon: 50% of tree dry weight\\n c_weight = 0.5 * dry_weight\\n \\n #weight of CO2 sequestered\\n co2_weight = 3.67 * c_weight\\n \\n return co2_weight/2.205/1 #convert from lbs to kg, divide by age\",\n \"def cutout(self, centre, radius):\",\n \"def fahren_conv(self, c):\\n if c == 0:\\n return 32\\n else:\\n return c * (9.0 / 5.0) + 32\",\n \"def cm(feet = 0, inches = 0):\\n inches_to_cm = inches * 2.54 # first convert inches to centimeters\\n feet_to_cm = feet * 12 * 2.54 # second convert feet to centimeters\\n return inches_to_cm + feet_to_cm # return the combined value\",\n \"def inches(n):\\n return n * 25.4\",\n \"def dist2cax(self) -> float:\\n # TODO: see about using line and built-in dist to point.\\n center_fit = np.poly1d(self.fit)\\n if self.orientation == Orientation.UP_DOWN:\\n length = self.image.shape[0]\\n else:\\n length = self.image.shape[1]\\n x_data = np.arange(length)\\n y_data = center_fit(x_data)\\n idx = int(round(len(x_data) / 2))\\n if self.orientation == Orientation.UP_DOWN:\\n axis = \\\"x\\\"\\n p1 = Point(y_data[idx], x_data[idx])\\n else:\\n axis = \\\"y\\\"\\n p1 = Point(x_data[idx], y_data[idx])\\n return (getattr(self.image.center, axis) - getattr(p1, axis)) / self.image.dpmm\",\n \"def NumberOfPixels(radius, bin_size):\\n\\n return int((radius/math.sqrt(2.0))*2.0 / bin_size)\",\n \"def diameter(self):\\n return self.radius * 2\",\n \"def diameter(self):\\n return self.radius * 2\",\n \"def diameter(self):\\n return 2 * self.radius\",\n \"def bin_edges_to_center(edges):\\n df = np.diff(edges)\\n if isinstance(df[0], datetime.timedelta) and sys.version_info[0:2]<=(3,2):\\n return edges[:-1] + df//2\\n else:\\n return edges[:-1] + df/2\",\n \"def getRangeInches(self) -> float:\\n ...\",\n \"def circ(diam):\\n return(pi * diam)\",\n \"def find_diameter(self):\\n try:\\n #Get the contours of the gear\\n contours, hierarchy = cv.findContours(self.frame, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)\\n \\n #Get the leftmost and the rightmost point of the gear\\n leftmost = (sys.maxsize, sys.maxsize)\\n rightmost = (sys.maxsize*-1, sys.maxsize*-1)\\n frame_area = self.frame.shape[0] * self.frame.shape[1]\\n new_contours = []\\n for c in contours:\\n c_area = cv.contourArea(c)\\n area_percentage = (c_area/frame_area) * 100\\n if area_percentage > .01:\\n new_contours.append(c)\\n\\n c_leftmost = tuple(c[c[:, :, 0].argmin()][0])\\n if leftmost[0] > c_leftmost[0]:\\n leftmost = c_leftmost\\n\\n c_rightmost = tuple(c[c[:, :, 0].argmax()][0])\\n if rightmost[0] < c_rightmost[0]:\\n rightmost = c_rightmost\\n\\n cv.drawContours(self.result, new_contours, -1, (255, 0, 0), 3)\\n\\n cv.circle(self.result, leftmost, 4, [0, 0, 255], -1)\\n cv.circle(self.result, rightmost, 4, [0, 0, 255], -1)\\n\\n #Get the distance between the two points\\n distance = math.sqrt((leftmost[0] - rightmost[0])**2 + (leftmost[1] - rightmost[1])**2)\\n\\n #Convert the distance, which is in pixels to inches by using the background width as a point of reference\\n self.diameter = (distance/self.frame.shape[1]) * self.background_width\\n self.diameter = round(self.diameter, 2)\\n \\n except:\\n cv.putText(self.result, 'Error', (10, 50), cv.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 2, cv.LINE_AA)\",\n \"def _d_converter(self):\\n units = {'um':1e-6, 'mm':1e-3, 'inch':2.54e-2, 'in':2.54e-2,\\\\\\n 'micron':1e-6, 'mil':2.54e-5, 'm':1.0}\\n for i in self.stack:\\n i.thickness = i.thickness*units[i.units]\\n return\",\n \"def belt(image):\\n\\n # Belt Detector\\n x, y = circular_detector(image, 70, 80)\\n\\n return x, y\",\n \"def bbox_center(bbox):\\n y, x, h, w = bbox\\n return int(y + h/2), int(x + w/2)\",\n \"def centerOfMass(data):\\r\\n dd = []\\r\\n for d in data:\\r\\n dd.append(d.coordinate)\\r\\n\\r\\n data = dd\\r\\n data = np.array(data)\\r\\n n = len(data)\\r\\n x = sum(data[:,0])\\r\\n y = sum(data[:,1])\\r\\n z = sum(data[:,2])\\r\\n x/=n\\r\\n y/=n\\r\\n z/=n\\r\\n return x,y,z,n\",\n \"def get_cents_from_a(self) -> float:\\n return self._cents\",\n \"def total_climb(self):\\n climb = 0\\n for segment in self.data:\\n segment_climb = 0\\n\\n last_height = None\\n\\n for point in segment:\\n current_height = point[\\\"ele\\\"]\\n\\n # in case data is missing skip point !\\n if current_height is None:\\n continue\\n\\n # the first valid element is processed, get distance\\n if last_height is not None and current_height > last_height:\\n segment_climb += (current_height - last_height)\\n\\n last_height = current_height\\n\\n climb += segment_climb\\n\\n return climb\",\n \"def center(self):\\n return self._lower + 0.5 * (self._upper - self._lower)\",\n \"def get_radius(size):\\n return (size * 10) - 5\",\n \"def cellvolumn(self, a, c):\\n return 0.866*(a**2)*c\",\n \"def fifteen():\\r\\n\\r\\n currentcell = 1.0\\r\\n cellpaths = 2.0\\r\\n \\r\\n while currentcell < 20.0:\\r\\n currentcell += 1.0\\r\\n cellpaths = cellpaths * (4.0 - 2.0/currentcell)\\r\\n \\r\\n return cellpaths\",\n \"def beamarea_pix(self):\\n beamsigma1 = self.header['BMAJ'] / self.wcs.wcs.cdelt[0]\\n beamsigma2 = self.header['BMIN'] / self.wcs.wcs.cdelt[0]\\n return (np.pi * beamsigma1 * beamsigma2) / (4 * np.log(2))\",\n \"def x(self):\\n return np.sum(self.bbox, 0)[0] / 2\",\n \"def find_centroid_cell(self):\\n\\n x_min, y_min = self.find_min()\\n x_max, y_max = self.find_max()\\n x_centroid = int((x_max+x_min)/2)\\n y_centroid = int((y_max+y_min)/2)\\n centroide = x_centroid, y_centroid\\n return centroide\",\n \"def _size_pixels(self, renderer):\\n return renderer.points_to_pixels(self.size)\",\n \"def annulus_centres(self) -> Quantity:\\n return self._ann_centres\",\n \"def cy(y):\\n return pic_height + ch(y - global_min_y)\",\n \"def center(self):\\n return (self.upper_right + self.lower_left) * 0.5\",\n \"def computeA(diameter):\\n radius = diameter / 2.0\\n return np.pi * (radius**2)\",\n \"def perimeter(self):\\n\\t\\treturn self.height * 4\",\n \"def bead_size(self):\\n if \\\"diameter_conv\\\" in self._per_bead_data.columns:\\n d_name = \\\"diameter_conv\\\"\\n dim = \\\"Converted\\\"\\n else:\\n d_name = \\\"diameter\\\"\\n dim = \\\"Pixels\\\"\\n b_std = self._per_bead_data[d_name].std()\\n b_mean = self._per_bead_data[d_name].mean()\\n x_left = b_mean - (5 * b_std)\\n x_right = b_mean + (5 * b_std)\\n self._per_bead_data[d_name].plot(\\n kind=\\\"hist\\\",\\n bins=100,\\n color=\\\"lightgray\\\",\\n title=\\\"Beads Diameter (%s) Mean: %0.2f SD: %0.2f\\\" % (dim, b_mean, b_std),\\n ).set_xlim(left=x_left, right=x_right)\\n self._per_bead_data[d_name].plot(\\n kind=\\\"kde\\\", secondary_y=True, color=\\\"black\\\", alpha=0.7\\n ).set_ylim(bottom=0)\\n if self._savefig is True:\\n plt.savefig((self.report_folder + \\\"bead_size.png\\\"), dpi=self.dpi)\",\n \"def length_to_pixels(length, boxsize, num_pixels):\\n pixels = int((length / boxsize) * num_pixels)\\n\\n return pixels\",\n \"def circumference(self):\\n return self.width + self.height\",\n \"def dist_from_center_to(x):\\n pass\",\n \"def find_center(self) -> tuple:\\r\\n \\r\\n # Add up all the x values of pixels in the plant\\r\\n # Then divide by total pixels in the plant\\r\\n avg_x = sum([i[0] for i in self.cluster]) / len(self.cluster)\\r\\n\\r\\n # Add up all the y values of pixels in the plant\\r\\n # Then divide by total pixels in the plant\\r\\n avg_y = sum([i[1] for i in self.cluster]) / len(self.cluster)\\r\\n\\r\\n self.center = (int(round(avg_x)), int(round(avg_y)))\\r\\n \\r\\n # return the results in a tuple of integers\\r\\n return self.center\",\n \"def center_of_flux(self, LF, n_pix):\\n # Loops over all pixels:\\n LF_copy = copy(LF) # Copy to avoid overwriting\\n flux_max = zeros(n_pix)\\n x_max = zeros(n_pix)\\n y_max = zeros(n_pix)\\n pixel = zeros(n_pix)\\n for j in range(n_pix):\\n flux_max[j] = np.max(LF_copy) # Maximum value for array\\n max_dex = np.where(LF_copy == flux_max[j]) # Find row, column for min value\\n x_max[j] = max_dex[0][0] # max for x coordinate\\n y_max[j] = max_dex[1][0] # max for y coordinate\\n pixel[j] = j\\n # Min pixel is et to max in order to find the next min:\\n LF_copy[int(y_max[j]), int(x_max[j])] = 0 \\n\\n # Flux center is found:\\n flux = sum(pixel)\\n cen_x = 1/flux*dot(pixel,x_max) # Flux-center in x\\n cen_y = 1/flux*dot(pixel,y_max) # Flux-center in y\\n return cen_x, cen_y\",\n \"def compute_delta_position(centroid, pixelscale=6.628, rotation=0.):\\n rotation = rotation * np.pi/180.0\\n cent = ((+centroid[0]*np.cos(rotation) + centroid[1]*np.sin(rotation)) * pixelscale * u.arcsec,\\n (-centroid[0]*np.sin(rotation) + centroid[1]*np.cos(rotation)) * pixelscale * u.arcsec)\\n return cent\",\n \"def _rad_center(self):\\n return ((self.rad_hi + self.rad_lo) / 2).to(\\\"deg\\\")\",\n \"def get_hp_bar_color(total, value):\\n\\t# 256 values per color, so red-to-yellow and yellow-to-green make 512 - but yellow gets counted twice, so it's really 511\\n\\tnum_colors = 511\\n\\tc = scale(value, total, num_colors)\\n\\treturn (min(num_colors - c, 255), min(c, 255), 0)\",\n \"def test_height_feet_only(self):\\n result = height_to_cm(\\\"6'\\\")\\n self.assertEqual(result, 183)\",\n \"def center(self):\\n return np.array([0,0,1/self.C+self.pos()])\",\n \"def cellsize_2d(self):\\t\\r\\n return self.dx * self.dy\",\n \"def get_meters_to_pixels_coefficient():\\n return 1/get_pixels_to_meters_coefficient()\",\n \"def _get_column_size(self) -> int:\\n return self.BARCODE_SIZE[0] // self.BARCODE_COLUMN_NUMBER\",\n \"def chunk(self):\\n return int((self.center_x + config.SPRITE_PIXEL_SIZE / 2) // 320)\",\n \"def perimeter(cnt):\\n\\treturn cv2.arcLength(cnt, True)\",\n \"def to_inch(self):\\r\\n if self.units != 'inch':\\r\\n self.units = 'inch'\\r\\n for statement in self.statements:\\r\\n statement.to_inch()\\r\\n for tool in iter(self.tools.values()):\\r\\n tool.to_inch()\\r\\n for primitive in self.primitives:\\r\\n primitive.to_inch()\\r\\n for hit in self.hits:\\r\\n hit.to_inch()\",\n \"def Hexagon(image):\\n return x, y\",\n \"def circle_circumference(a):\\n return (2*a*math.pi)\",\n \"def centrx(self, e: int) -> float:\\n result = self._read_inline(f\\\"centrx({e})\\\")\\n return result\",\n \"def cent(self, value):\\n self._cent = value\",\n \"def circleArea(radius):\\n return math.pi * radius * radius\",\n \"def normalize(self):\\n norm_value = 60 if self.currency == \\\"btc\\\" else 18\\n self.barHeightsNormalized = [heights / norm_value for heights in self.barHeights]\",\n \"def find_cuts(width, height, isOdd):\\n\\n if isOdd:\\n return width//2, height//2\\n\\n if width % 4 == 0:\\n return width//2, height//2\\n return (width-2)//2, (height-2)//2\",\n \"def center(self):\\n return [self.position[i]+self.radius for i in range(2)]\",\n \"def conToCelc(faren):\\n return (faren - 32)*(5/9)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5748787","0.57177395","0.5671113","0.5644989","0.5644989","0.5644989","0.56154454","0.5552707","0.54947394","0.5494285","0.5480687","0.5467712","0.5456314","0.54467314","0.5419826","0.5408947","0.5400958","0.5398737","0.53467226","0.53363895","0.5334988","0.533294","0.53133744","0.53131926","0.53071624","0.5284064","0.5261995","0.52441335","0.5230999","0.52065665","0.5205179","0.52048993","0.5193549","0.5141597","0.5132347","0.51301646","0.51268595","0.5114783","0.50907564","0.5080208","0.5076487","0.50764376","0.50736886","0.5071775","0.5064141","0.50589114","0.50578696","0.5055627","0.5055227","0.5055227","0.50450045","0.5043579","0.5029665","0.50197554","0.50092095","0.5006192","0.4998836","0.4995608","0.49875727","0.49875528","0.49784464","0.49765596","0.4975459","0.497338","0.4965854","0.495351","0.49506792","0.49470952","0.4945824","0.49437237","0.4941318","0.49371243","0.49275118","0.49252155","0.491523","0.49106607","0.49088413","0.4906423","0.48993078","0.48991266","0.4897722","0.4889861","0.48835093","0.4881586","0.4877034","0.4864208","0.48641518","0.48636663","0.48633897","0.4861213","0.48584673","0.4856158","0.4855786","0.48539674","0.48523274","0.48494735","0.48465434","0.4844685","0.48408335","0.48399237"],"string":"[\n \"0.5748787\",\n \"0.57177395\",\n \"0.5671113\",\n \"0.5644989\",\n \"0.5644989\",\n \"0.5644989\",\n \"0.56154454\",\n \"0.5552707\",\n \"0.54947394\",\n \"0.5494285\",\n \"0.5480687\",\n \"0.5467712\",\n \"0.5456314\",\n \"0.54467314\",\n \"0.5419826\",\n \"0.5408947\",\n \"0.5400958\",\n \"0.5398737\",\n \"0.53467226\",\n \"0.53363895\",\n \"0.5334988\",\n \"0.533294\",\n \"0.53133744\",\n \"0.53131926\",\n \"0.53071624\",\n \"0.5284064\",\n \"0.5261995\",\n \"0.52441335\",\n \"0.5230999\",\n \"0.52065665\",\n \"0.5205179\",\n \"0.52048993\",\n \"0.5193549\",\n \"0.5141597\",\n \"0.5132347\",\n \"0.51301646\",\n \"0.51268595\",\n \"0.5114783\",\n \"0.50907564\",\n \"0.5080208\",\n \"0.5076487\",\n \"0.50764376\",\n \"0.50736886\",\n \"0.5071775\",\n \"0.5064141\",\n \"0.50589114\",\n \"0.50578696\",\n \"0.5055627\",\n \"0.5055227\",\n \"0.5055227\",\n \"0.50450045\",\n \"0.5043579\",\n \"0.5029665\",\n \"0.50197554\",\n \"0.50092095\",\n \"0.5006192\",\n \"0.4998836\",\n \"0.4995608\",\n \"0.49875727\",\n \"0.49875528\",\n \"0.49784464\",\n \"0.49765596\",\n \"0.4975459\",\n \"0.497338\",\n \"0.4965854\",\n \"0.495351\",\n \"0.49506792\",\n \"0.49470952\",\n \"0.4945824\",\n \"0.49437237\",\n \"0.4941318\",\n \"0.49371243\",\n \"0.49275118\",\n \"0.49252155\",\n \"0.491523\",\n \"0.49106607\",\n \"0.49088413\",\n \"0.4906423\",\n \"0.48993078\",\n \"0.48991266\",\n \"0.4897722\",\n \"0.4889861\",\n \"0.48835093\",\n \"0.4881586\",\n \"0.4877034\",\n \"0.4864208\",\n \"0.48641518\",\n \"0.48636663\",\n \"0.48633897\",\n \"0.4861213\",\n \"0.48584673\",\n \"0.4856158\",\n \"0.4855786\",\n \"0.48539674\",\n \"0.48523274\",\n \"0.48494735\",\n \"0.48465434\",\n \"0.4844685\",\n \"0.48408335\",\n \"0.48399237\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6118975"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77073,"cells":{"query":{"kind":"string","value":"Gets Frames Per Second of loaded video"},"document":{"kind":"string","value":"def get_video_fps(self):\n fps = self.video.get(cv2.CAP_PROP_FPS)\n logging.info('Video FPS: {}'.format(fps))\n return fps"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_fps(self):\n return self._num_frames / (datetime.now() - self._start).total_seconds()","def get_video_frame_rate(filename):\n clip = VideoFileClip(filename)\n frame_rate = clip.fps\n clip.close()\n return frame_rate","def duration():\r\n elapsed_time, duration = video_time()\r\n return duration","def fps(self):\n\t\treturn float(len(self.buf)) / (self.buf[-1][0] - self.buf[0][0])","def get_frame_rate(video):\n\n video = cv2.VideoCapture(video)\n\n # Find OpenCV version\n (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')\n\n # With webcam get(CV_CAP_PROP_FPS) does not work.\n # Let's see for ourselves.\n\n if int(major_ver) < 3:\n fps = video.get(cv2.cv.CV_CAP_PROP_FPS)\n logger.info(\"Frames per second using video.get(cv2.cv.CV_CAP_PROP_FPS): {0}\".format(fps))\n else:\n fps = video.get(cv2.CAP_PROP_FPS)\n logger.info(\"Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}\".format(fps))\n\n cv2.destroyAllWindows()\n video.release()\n return fps","def read_fps(vid_name):\n\n video = cv2.VideoCapture(vid_name)\n fps = video.get(cv2.CAP_PROP_FPS)\n video.release()\n\n return fps","def read_video_info_cv2(vid_fp: str):\n cap = cv2.VideoCapture(vid_fp)\n total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\n total_seconds = int(total_frames / cap.get(cv2.CAP_PROP_FPS))\n cap.release()\n return timedelta(seconds=total_seconds)\n # cap.set(cv2.CAP_PROP_POS_AVI_RATIO, 1)\n # total_ms = cap.get(cv2.CAP_PROP_POS_MSEC)\n # return timedelta(milliseconds=total_ms)","def get_fps(self):\n # Take difference.\n interframe_intervals = np.diff(self.data[\"t\"])\n\n # Inter-frame interval in milliseconds.\n mean_interval = np.mean(interframe_intervals)\n fps = round(1 / (mean_interval / 1000))\n\n return int(fps)","def get_fps(self):\n return self.fps","def getFrameRate(self):\n if not self.proxy:\n self.proxy = self.session.service(\"ALVideoRecorder\")\n return self.proxy.getFrameRate()","def get_fps(video, use_opencv=False):\n\n if use_opencv:\n video_cap = cv2.VideoCapture(video)\n fps = video_cap.get(cv2.CAP_PROP_FPS)\n video_cap.release()\n return fps\n else:\n return convert_to_float(\n utils.exec_shell_command(f\"{SHELL_CMD_GET_FPS} '{video}'\", silent=True)[0]\n )","def num_frames(self):\n return len(self.video)","def getTotalFramesVid(srcVideoPath):\n cap = cv2.VideoCapture(srcVideoPath)\n # if the videoCapture object is not opened then exit without traceback\n if not cap.isOpened():\n print(\"Error reading the video file !!\")\n return 0\n\n tot_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\n cap.release()\n return tot_frames","def get_fps(self):\n return self._document.GetFps()","def get_video_property(video_path):\n\tvideocap = cv2.VideoCapture(video_path)\n\tframe_count = videocap.get(cv2.CAP_PROP_FRAME_COUNT)\n\tfps = videocap.get(cv2.CAP_PROP_FPS)\n\theight = videocap.get(cv2.CAP_PROP_FRAME_HEIGHT)\n\twidth = videocap.get(cv2.CAP_PROP_FRAME_WIDTH)\n\treturn frame_count, fps, width, height","def get_fps(self):\n \n return self.fps, self.average_fps","def get_video_frame_rate(video_filename):\n if not os.path.exists(video_filename):\n raise ValueError(\"%s does not exist\" % video_filename)\n \n probe = ffmpeg.probe(video_filename)\n assert len(probe['streams']) == 1\n \n # Seems to be two ways of coding, not sure which is better\n avg_frame_rate = probe['streams'][0]['avg_frame_rate']\n r_frame_rate = probe['streams'][0]['r_frame_rate']\n assert avg_frame_rate == r_frame_rate\n \n # Convert fraction to number\n num, den = avg_frame_rate.split('/')\n frame_rate = float(num) / float(den)\n \n return frame_rate","def get_fps(vid_path, prefix):\n _, name = os.path.split(vid_path)\n i1 = name.find(prefix) + len(prefix)\n i2 = name[i1:].find('_')\n fps = int(name[i1:i1+i2])\n\n return fps","def elapsed_time():\r\n elapsed_time, duration = video_time()\r\n return elapsed_time","def get_fps(self):\n raise NotImplementedError(\"get_fps is not implemented\")","def get_duration(self) -> int:\n return int( (self._frame_count / self._fps) * 1000 )","def get_fps(clock):\n if video_mode:\n return \"30\" # Video FPS will be 30\n else:\n return str(int(round(clock.get_fps(), 0)))","def video_duration(self):\n # type: () -> int\n return self._video_duration","def gif_fps(self):\n return self._gif_fps","def get_frame_duration(self):\n return self._frame_duration","def realFrameNumber(self, callback=None):\n count = 0\n theoreticalFrameNumber = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))\n if theoreticalFrameNumber > 30000:\n return theoreticalFrameNumber\n while(True):\n # Capture frame-by-frame\n ret, frame = self.video.read()\n if not ret:\n break\n if callback != None:\n callback(0.1 + (count / theoreticalFrameNumber) * 0.75, \"Calculating the number of frame\")\n count += 1\n return count","def get_framerate(self):\n return self._framerate","def bspb_frameCounter():\n curTime = int(pm.currentTime())\n maxTime = int(pm.playbackOptions(q=True, maxTime=True))\n return '{0} / {1}'.format(curTime, maxTime)","def video(path):\n cap = cv2.VideoCapture(path)\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n return frame_count, cap","def get_fps(self):\n if len(self.times) >= 2:\n dif = np.diff(self.times)\n fps = 1. / dif.min()\n # if the FPS crosses 500, do not update it\n if fps <= 500:\n self.fps = fps\n return self.fps\n else:\n return 0.","def get_video_params(video_filename):\n \n width, height = get_video_aspect(video_filename)\n frame_rate = get_video_frame_rate(video_filename)\n return width, height, frame_rate","def get_frame(self, camera: int = 0) -> Tuple[float, np.ndarray]:\n return self.video.read()","def get_duration(self):\n frame_dur = self.get_frame_duration()\n num_frames = self.get_num_frames()\n motion_dur = frame_dur * (num_frames - 1)\n return motion_dur","def getVideoFrames(videoFilePath, startFrameNumber=-1, endFrameNumber=-1):\n frames=[]\n vidcap = cv2.VideoCapture(videoFilePath)\n fps=vidcap.get(cv2.CAP_PROP_FPS)\n totalFrame=vidcap.get(cv2.CAP_PROP_FRAME_COUNT)\n if startFrameNumber==-1:\n startFrameNumber = 0\n if endFrameNumber == -1:\n endFrameNumber = totalFrame - 1\n success,image = vidcap.read()\n count = 0\n success = True\n while success:\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n image = color.rgb2yiq(image).astype(np.float32)\n \n if count<startFrameNumber:\n success,image = vidcap.read()\n count+=1\n continue\n elif count>=endFrameNumber:\n break\n else: \n frames.append(image)\n success,image = vidcap.read()\n count += 1\n frames=np.array(frames)\n \n return fps, frames","def frame_duration(self):\n return self.samples_per_frame / self.input_data_sample_rate","def calculate_fps(self):\n time_difference = self.time_array[-1] - self.time_array[0]\n time_difference_in_seconds = time_difference.to_sec()\n if time_difference_in_seconds == 0:\n pass\n self.fps = self.buffer_size / time_difference_in_seconds\n rospy.loginfo(\"[EulerianMotionMagnification] Estimated FPS: \" + str(self.fps) + \" (Measured timespan: \" + str(time_difference_in_seconds) + \"s)\")\n rospy.loginfo(\"[EulerianMotionMagnification] Video array length: \" + str(len(self.video_array)))","def readVideo(self):\n vid = cv2.VideoCapture(self.fname)\n imgstack = []\n # grab = True\n grab, img = vid.read()\n while grab:\n imgstack.append(\n Frame(\n cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\n self.starttime\n + datetime.timedelta(seconds=self.frame_dt * self.length),\n )\n )\n self.length += 1\n grab, img = vid.read()\n self.frames = imgstack","def update_frame(self):\r\n while not self.stopped:\r\n if not self.grabbed or not self.cap.isOpened():\r\n self.stop()\r\n else:\r\n self.grabbed, self.frame = self.cap.read()\r\n try:\r\n if self.grabbed:\r\n #self.New_Frame_Time = time.time()\r\n #self.FPS = 1/(self.New_Frame_Time-self.Old_Frame_Time)\r\n #self.Old_Frame_Time = self.New_Frame_Time\r\n self.FrameCount += 1\r\n else:\r\n print(f'Grabbed status is: {self.grabbed}')\r\n #self.Old_Frame_Time = time.time()\r\n except ZeroDivisionError:\r\n print(\"Division by zero error when finding video feed fps\")\r\n self.FPS = 0\r\n self.Old_Frame_Time = time.time()","def get_video_aspect(video_filename):\n if not os.path.exists(video_filename):\n raise ValueError(\"%s does not exist\" % video_filename)\n \n probe = ffmpeg.probe(video_filename)\n assert len(probe['streams']) == 1\n width = probe['streams'][0]['width']\n height = probe['streams'][0]['height']\n \n return width, height","def video_to_frames(video_filename,output_dir):\n cap = cv2.VideoCapture(video_filename)\n video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1\n vid_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n vid_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n vid_fps = int(cap.get(cv2.CAP_PROP_FPS))\n print(\"vid_res=%d x %d, fps=%d\\n\" % (vid_width, vid_height,vid_fps))\n crop_width=int(vid_width/128)*128\n crop_height=int(vid_height/128)*128\n grab_step=int(vid_fps/2)\n if cap.isOpened() and video_length > 0:\n count = 0\n frame_id=0\n success, image = cap.read()\n while success and frame_id <= 9999:\n if count%grab_step==0:\n crop_img = image[0:crop_width, 0:crop_height]\n resized_img = cv2.resize(crop_img, (128, 128)) \n cv2.imwrite(output_dir+\"/frame%05d.jpg\" % frame_id, resized_img)\n frame_id+=1\n success, image = cap.read()\n count += 1\n return 0","def read_video(video_filename, width=224, height=224, rot=None):\n cap = cv2.VideoCapture(video_filename)\n fps = cap.get(cv2.CAP_PROP_FPS)\n\n n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n pbar = tqdm(total=n_frames, desc=f\"Getting frames from {video_filename} ...\")\n\n frames = []\n if cap.isOpened():\n while True:\n success, frame_bgr = cap.read()\n if not success:\n break\n frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)\n frame_rgb = cv2.resize(frame_rgb, (width, height))\n if rot:\n frame_rgb = cv2.rotate(frame_rgb, rot)\n frames.append(frame_rgb)\n\n pbar.update()\n pbar.close()\n frames = np.asarray(frames)\n return frames, fps","def get_frames(self):\n video_getter = Thread(target=self.streamer)\n video_getter.daemon = True\n video_getter.start()","def get_frame(self, camera: int = 0) -> Tuple[float, np.ndarray]:\n result = self.video.read()\n if result[0]:\n return result\n else: # If we reach the end of the video, go back to the beginning.\n self.video.set(cv2.CAP_PROP_POS_FRAMES, 0)\n return self.video.read()","def calcFrameRate(self):\n\n tot = 0\n count = 0\n for session in self.sessions:\n for sample in session.samples:\n if not sample.isLoading:\n tot += sample.fps\n count += 1\n if count:\n self.avgFps = tot / count\n self.lowFps = (self.avgFps < 10)\n self.highFps = (self.avgFps > 25)","def get_frame_time(self, f):\n return f * self.get_frame_duration()","def get_fps(self):\n #return int(self._root.knob('fps').getValue())\n return None","def framerate(self):\n return self.config.get('framerate', 15)","def get_video_info(filename: str) -> Dict:\n cap = cv2.VideoCapture(filename)\n if cap.isOpened():\n fps = int(round(cap.get(cv2.CAP_PROP_FPS)))\n width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n duration = frame_count / fps\n cap.release()\n video_info = {\n 'fps': fps,\n 'width': width,\n 'height': height,\n 'frame_count': frame_count,\n 'duration': duration\n }\n return video_info\n else:\n raise ValueError(f'OpenCV failed to open {filename}.')","def frame_rate(self):\n return self._frame_rate","def get_video_average(video_path):\n vidcap = cv2.VideoCapture(video_path)\n\n width = int(vidcap.get(cv2.CAP_PROP_FRAME_WIDTH))\n height = int(vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n num_color_channels = 3\n\n avg_frame = np.zeros((height, width, num_color_channels), dtype=np.float64)\n frames = 0\n\n while True:\n success, img = vidcap.read()\n if not success:\n break\n avg_frame += img\n frames += 1\n\n avg_frame = avg_frame / frames\n ####avg_frame = cv2.cvtColor(avg_frame, cv2.COLOR_BGR2RGB)\n avg_frame = avg_frame.astype(np.uint8)\n cv2.imwrite(\"average_frame.png\", avg_frame)","def obtenerVideo(camara):\n val, frame = camara.read()\n return val, frame","def get_frame_time(self):\n return self.get_timings().frame_time","def get_fps(self):\n if not self.count_fps:\n logging.error(\"No FPSCounter set\")\n return None\n return self.fps.get_fps()","def get_fps(self):\n if not self.count_fps:\n logging.error(\"No FPSCounter set\")\n return None\n return self.fps.get_fps()","def video_time():\r\n # The full time has the form \"0:32 / 3:14\"\r\n full_time = world.css_text('div.vidtime')\r\n\r\n # Split the time at the \" / \", to get [\"0:32\", \"3:14\"]\r\n elapsed_str, duration_str = full_time.split(' / ')\r\n\r\n # Convert each string to seconds\r\n return (parse_time_str(elapsed_str), parse_time_str(duration_str))","def frameTimes(self):\n sr = self.sampleRate\n offset = self.activeOffset\n stride = self.activeStride\n nf = self.numFrames\n t = np.arange(nf) * (stride[0] / sr) + (offset / sr)\n return t","def video_bitrate(self):\n assert self.has_video\n\n return self.__video_bitrate","def parse_video(cap, base_path, step, size):\n # Get nb of fps\n fps = cap.get(cv2.CAP_PROP_FPS)\n\n # Compute how many images to skip to match the step\n nb_skip = int(step / 1000 * fps)\n index = 1\n count = -1\n while True:\n count += 1\n # Get next image\n success, frame = cap.read()\n\n # If no image to read anymore\n if not success:\n break\n\n # Skip images\n if count % nb_skip != 0:\n continue\n\n # Save current image\n suffix = '-{}.jpg'.format('0' + str(index) if index <= 9 else str(index))\n # Compute new height to keep aspect ratio\n if size is None:\n pass\n elif len(size) == 1:\n if frame.shape[0] >= frame.shape[1]:\n aspect_ratio = frame.shape[0] / frame.shape[1]\n new_h = int(size[0])\n new_w = int(size[0] / aspect_ratio)\n else:\n aspect_ratio = frame.shape[1] / frame.shape[0]\n new_w = int(size[0])\n new_h = int(size[0] / aspect_ratio)\n frame = cv2.resize(frame, (new_w, new_h))\n # Resize with width and height given\n elif len(size) == 2:\n frame = cv2.resize(frame, tuple(size))\n dump_image(frame, base_path + suffix)\n\n # Append index\n index += 1\n\n return","def run(self):\n i = 0\n t = time.time()\n while True:\n i = i + 1\n ret, frame = self.stream.read()\n if (i == 20):\n self.fps = 20/(time.time() - t)\n t = time.time()\n i = 0\n #If a frame is None need to re-init it: \n # - close a stream;\n # - reopen it;\n # - read frame again\n if frame is None:\n self.stream.release()\n self.stream = cv2.VideoCapture(self.url)\n ret, frame = self.stream.read()\n text = time.strftime('%Y-%m-%d %H:%M:%S')\n if (self.fps > 0):\n text = text + ' FPS: ' + str(round(self.fps))\n self.frame = cv2.putText(frame, text, (10, int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)) - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\n self.frameID = uuid.uuid4()","def get_video_pts(self):\n statrec = self.decoded_stats\n if statrec.last_pts is None:\n return None\n return statrec.last_pts + time.time() - statrec.last_pts_changed","def readFrame(self):\n\t\tsuccess, self.frameImage = self.vidcap.read()\n\t\treturn success, self.frameImage","def get_mpeg_info(videos_dir, filename):\n logger.info(\"Getting info from %s/%s\" % (videos_dir, filename))\n if not os.path.exists(videos_dir):\n raise Exception(\"%s dir does not exist!\" % videos_dir)\n path = os.path.join(videos_dir, filename)\n if not os.path.exists(path):\n raise Exception(\"%s does not exist!\" % path)\n\n p = subprocess.Popen([FFMPEG, \"-i\", filename], cwd=videos_dir,\n stderr=subprocess.STDOUT, stdout=subprocess.PIPE)\n rc = p.wait()\n\n out = p.stdout.read()\n pattern = r'Video: mpeg2video \\(Main\\), (?P<vdata>.*?)\\n'\n m = re.search(pattern, out)\n\n if not m:\n raise Exception(\"Failed to search mpeg info: '%s'\" % out)\n\n vdata = m.groups()[0]\n mdata = vdata.split(\", \")\n logger.info(mdata)\n\n resolution = mdata[1].split(\" \")[0]\n (width, height) = resolution.split(\"x\")\n width = int(width)\n height = int(height)\n logger.info(\"%dx%d\" % (width, height))\n\n bitrate = mdata[2].split(\" \")[0] # kb/s\n\n fps = float(mdata[3].split(\" \")[0])\n\n return {\n \"width\": width,\n \"height\": height,\n \"bitrate\": bitrate, # kb/s\n \"fps\": fps,\n }","def get_Video_Dimentions(video):\r\n width = int(video.get(cv.CAP_PROP_FRAME_WIDTH))\r\n height = int(video.get(cv.CAP_PROP_FRAME_HEIGHT))\r\n return width, height","def get_total_frames(self) -> int:\n return self.num_frames","def next_frame(self):\n # Tick will limit framerate and return delta in millis\n delta = self.clock.tick(self.framerate)\n\n # Convert delta to seconds\n delta /= 1000.0\n\n # Limit worst-case delta to make debugging easier\n delta = min(delta, FrameTimer.MAX_FRAME_TIME)\n\n return delta","def find_video_period(clip, fps=None, start_time=0.3):\n\n def frame(t):\n return clip.get_frame(t).flatten()\n\n timings = np.arange(start_time, clip.duration, 1 / fps)[1:]\n ref = frame(0)\n corrs = [np.corrcoef(ref, frame(t))[0, 1] for t in timings]\n return timings[np.argmax(corrs)]","def video_bitrate(self):\n # type: () -> int\n return self._video_bitrate","def get_session_videodata(videos):\n # Get first frame of first video for future processing and number of frames in each video\n videos_data = {'Frame rate': [], 'Number frames': []}\n for idx, videofile in enumerate(videos):\n cap = cv2.VideoCapture(videofile)\n videos_data['Frame rate'].append(cap.get(cv2.CAP_PROP_FPS))\n videos_data['Number frames'].append(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))\n videos_data['Cumu. Num Frames'] = np.cumsum(videos_data['Number frames'])\n return videos_data","def stream_frames(video_capture):","def frame_size(self):\n size = None\n if self.is_video():\n width = self.__dict__['width']\n height = self.__dict__['height']\n if width and height:\n try:\n size = (int(width), int(height))\n except ValueError:\n raise FFProbeError(\"None integer size %s:%s\" % (width, height))\n\n return size","def video_playback_width(self):\n # type: () -> int\n return self._video_playback_width","def readLevelTimer(self, fps=60):\n frames_elapsed = self.readLevelTimerValue()\n return frames_elapsed / fps","def ms_to_frames(time, fps):\n return int(round(float(time) / 1000 * fps))","def get_frames(self):\n if not self.video:\n return []\n # We cannot validate shape on construction as that happens inside graph\n # mode as we construct from a tf.data.Dataset, so we validate here.\n self.video[0].validate_shape_and_dtype()\n return self.video","def update(self):\n self.frame = self.video_stream.read()","def process_video(file_path):\n video = cv2.VideoCapture(file_path)\n\n video_sum = 0\n frame_cnt = 0\n\n previous_frame_digits = set()\n\n while True:\n return_value, frame = video.read()\n\n # skip same frames to improve performance\n frame_cnt += 1\n if frame_cnt % 2 != 0:\n continue\n\n if not return_value:\n break\n\n frame_results = find_sum_of_digits_which_crossed_line(frame, previous_frame_digits)\n video_sum += frame_results.sum\n\n previous_frame_digits = frame_results.digits\n\n cv2.imshow('frame', frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n video.release()\n cv2.destroyAllWindows()\n\n return video_sum","def __loadVideo(self):\n # Check if movie file exists ...\n #\n if not(os.path.isfile(self.fNameVideo)):\n return stm.StimErrC.videoFileNotFound\n\n try: \n # Load video\n #\n self.video = mpe.VideoFileClip(self.fNameVideo)\n \n except IOError: \n return stm.StimErrC.invalidVideoFormat\n \n # Retrieve video description\n #\n self.dxFr = self.video.size[0]\n self.dyFr = self.video.size[1]\n self.nFr = self.video.duration *self.video.fps\n self.fps = self.video.fps\n ssp.Log.write(\"DEBUG\", \"stim_video: {0}x{1} pixel, {2} frames, {3} fps\"\n .format(self.dxFr, self.dyFr, self.nFr, self.fps))\n \n if self.isTestOnly:\n # Return here if the video was only loaded to test if it is ok\n #\n self.video = None\n return stm.StimErrC.ok\n \n # Load movie frames (note that frames is a generator!)\n #\n self.frames = self.video.iter_frames() \n self.isReady = True\n return stm.StimErrC.ok","def get_frame_rate(filename):\n cmd = ('ffprobe -v 0 -of flat=s=_ -select_streams v:0 -show_entries '\n 'stream=avg_frame_rate -of default=nokey=1:noprint_wrappers=1 ' +\n filename).split()\n pid = subprocess.run(cmd, stdout=subprocess.PIPE,\n universal_newlines=True)\n if pid.returncode != 0:\n return None\n\n frame_rate_exp = pid.stdout.rstrip()\n try:\n frame_rate = float(frame_rate_exp)\n except:\n if frame_rate_exp == 'N/A':\n frame_rate = 0.\n else:\n numerator, denominator = map(float, frame_rate_exp.split('/'))\n if denominator == 0:\n frame_rate = 0\n else:\n frame_rate = numerator / denominator\n return frame_rate","def duration(self):\n return self.sound.nframes","def get_frame(self):\n if self.vid.isOpened():\n ret, frame = self.vid.read()\n if ret:\n return (ret, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n else:\n return (ret, None)\n else:\n return (ret, None)","def video_playback_height(self):\n # type: () -> int\n return self._video_playback_height","def read(self):\n\n # ret, image = self.video.read()\n (self.grabbed, self.frame) = self.cap.read()\n image = self.frame\n\n if image is not None:\n \"\"\"Update FPS, and incode received frame. \"\"\"\n self.fps.update()\n # TODO: add self.fps.fps() to image, if flagged raised.\n\n # We are using Motion JPEG, but OpenCV defaults to cap raw images,\n # so we must encode it into JPEG in order to correctly display the\n # video stream.\n\n # display a piece of text to the frame (so we can benchmark\n # fairly against the fast method)\n self.fps.stop()\n cv2.putText(image, \"FPS (simple): {:.2f}\".format(self.fps.fps()), (10, 30),\n cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)\n self.frame = image.copy()\n\n ret, jpeg = cv2.imencode('.jpg', image)\n return jpeg.tobytes()\n else:\n self.logger.debug(\"in 'get_frame', video.read not success\")","def readFrames(video):\n frames = []\n while True:\n _, frame = video.read()\n\n if frame is None:\n break\n else:\n frames.append(frame)\n video.release()\n return frames","def resolution(self):\n assert self.has_video\n\n return self.__resolution","def get_duration(filename):\n cmd = ('ffprobe -v 0 -of flat=s=_ -select_streams v:0 -show_entries '\n 'stream=duration -of default=nokey=1:noprint_wrappers=1 ' +\n filename).split()\n pid = subprocess.run(cmd, universal_newlines=True,\n stdout=subprocess.PIPE)\n if pid.returncode != 0:\n return None\n\n duration_exp = pid.stdout.rstrip()\n try:\n duration = float(duration_exp)\n except:\n duration = 0.\n return duration","def load_vid_frame(self, ind, camname, preload=True, extension=\".mp4\"):\n fname = (\n str(self._N_VIDEO_FRAMES * int(np.floor(ind / self._N_VIDEO_FRAMES)))\n + extension\n )\n frame_num = int(ind % self._N_VIDEO_FRAMES)\n keyname = os.path.join(camname, fname)\n if preload:\n return self.vidreaders[camname][keyname].get_data(frame_num)\n else:\n thisvid_name = self.vidreaders[camname][keyname]\n abname = thisvid_name.split(\"/\")[-1]\n if abname == self.currvideo_name[camname]:\n vid = self.currvideo[camname]\n else:\n vid = imageio.get_reader(thisvid_name)\n print(\"Loading new video: {} for {}\".format(abname, camname))\n self.currvideo_name[camname] = abname\n # close current vid\n # Without a sleep here, ffmpeg can hang on video close\n time.sleep(0.25)\n if self.currvideo[camname] is not None:\n self.currvideo[camname].close()\n self.currvideo[camname] = vid\n\n im = vid.get_data(frame_num)\n\n return im","def find_average_duration(video: dict):\n global num_videos\n global total_duration\n\n if duration := video.get('duration'):\n with data_lock:\n num_videos += 1\n total_duration += (duration/1000)\n show_progress()","def frame_capture(path, dest):\n \n # Path to video file \n cap = cv2.VideoCapture(path)\n \n # Total number of frames\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n \n # Used as counter variable \n count = 0\n \n # checks whether frames were extracted \n success = 1\n \n while success: \n \n # cap object calls read \n # function extract frames \n success, image = cap.read()\n \n if image is not None:\n image = image_preprocess(image)\n # Saves the frames with frame-count \n cv2.imwrite(f\"{dest}/frame{count}.jpg\", image)\n else:\n break\n \n count += 1\n percentage = int(count/frame_count) * 100\n return f'{count}/{frame_count} ({percentage})% of frames extracted'","def get_video_statistics(self):\n return self._video_statistics","def getFPS(self):\n # load it each time, since this setting is not limited to a single user\n projectSettingsDB = self.loadProjectSettings()\n try:\n fpsValue = projectSettingsDB[\"FPS\"]\n return fpsValue\n except KeyError:\n msg = \"Database Error while reading projectSettings.json\"\n logger.error(msg)\n return None","def load_video(self, filename):\n \n reader = imageio.get_reader(filename, 'ffmpeg')\n \n return np.array(list(reader), dtype=np.float32)","def process_video(self):\n if os.path.isfile(self.source):\n self.cap = cv2.VideoCapture(self.source)\n else:\n try:\n file_name = \"input.mp4\"\n self.source = self.source.replace('open', 'uc')\n print( \"\\nDownloading video file from drive link to %s\\n\"%file_name)\n gdown.download(self.source, file_name, quiet=False)\n print( \"%s downloaded!\\n\"%file_name )\n self.cap = cv2.VideoCapture(file_name)\n except Exception:\n raise RuntimeError(\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\n\")\n\n\n assert self.cap.isOpened(), \"Failed to open %s\" % self.source\n\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\n return","def getFrames():\n\t\tfor cam in Camera.CAMERAS: cam.getFrame()","def get(self, flag: int):\n if flag == cv2.CAP_PROP_FRAME_COUNT:\n return self.length","def get_frame(self, camera: int = 0) -> Tuple[float, np.ndarray]:\n frame_time, self.frame = self.sources[camera].grabFrameNoTimeout(\n image=self.frame\n )\n return frame_time, self.frame","def _load_video(self, video):\n\t\timport cv2\n\t\tcap = cv2.VideoCapture(video)\n\t\tassert cap.get(cv2.CAP_PROP_FRAME_WIDTH) >= 100, 'width must be at least 100 pixels'\n\t\tassert cap.get(cv2.CAP_PROP_FRAME_HEIGHT) >= 100, 'height must be at least 100 pixels'\n\t\tn = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n\t\tbuf = np.empty((n, int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), 3), np.dtype('uint8'))\n\t\ti, ret = 0, True\n\t\twhile (i < n and ret):\n\t\t\tret, frame = cap.read()\n\t\t\tframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\t\t\tbuf[i] = frame\n\t\t\ti += 1\n\t\tcap.release()\n\t\treturn np.moveaxis(buf, -1, 1)","def get_frame_rate(gif=''):\n\n # identify -format \"Frame %s: %Tcs\\n\" ~/test.gif - frame life in centiseconds - how very odd.\n frame_rate_str = subprocess.check_output([\n 'identify',\n '-format',\n '%T\\n',\n gif\n ])\n # If we failed to get a frame lifetime from imagemagick...\n if len(frame_rate_str) <= 0:\n frame_rate = str(FRAME_RATE)\n else:\n # Get gif's lifetime...\n frame_rate_list = frame_rate_str.split('\\n')\n frame_rate_list = [int(n) for n in frame_rate_list if n]\n lifetime = float(sum(frame_rate_list)) / 100\n\n # print \"gif lifetime is \" + str(lifetime) + \"s\"\n # Determine average frames per second\n frame_rate = str(len(frame_rate_list) / lifetime)\n if float(frame_rate) <= 0.0:\n frame_rate = FRAME_RATE\n\n return frame_rate","def play_video_file(fname : str):\n cap = cv2.VideoCapture(fname)\n fps = cap.get(5)\n font = cv2.FONT_HERSHEY_SIMPLEX\n fontScale = 1\n fontColor = (0, 0, 0)\n lineType = 2\n\n myvideo = []\n while cap.isOpened():\n ret, frame = cap.read()\n\n if ret is True:\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n cv2.putText(gray, 'Time: ' + str(round(cap.get(0) / 1000, 2)),\n (10, 30),\n font,\n fontScale,\n fontColor,\n lineType)\n cv2.putText(gray, 'Frame: ' + str(int(cap.get(1))),\n (10, 70),\n font,\n fontScale,\n fontColor,\n lineType)\n myvideo.append(gray)\n #cv2.imshow('frame', gray)\n #cv2.waitKey(10)\n #if cv2.waitKey(delay=2) & 0xFF == ord('q'):\n # break\n else:\n break\n\n cap.release()\n\n if fps < 60:\n for frame in myvideo:\n cv2.imshow('frame', frame)\n cv2.waitKey(10)\n else:\n for ind, frame in enumerate(myvideo):\n if ind % 3 == 0:\n cv2.imshow('frame', frame)\n cv2.waitKey(10)\n else:\n continue\n cv2.destroyAllWindows()","def analyze_movie(\n video_path, aspect_ratio=0, palette_size=32, frames=-1, step=1, show_frames=False, show_last_frame=False, color_format='hex'\n):\n\n # Parse video frame-by-frame\n vidcap = cv2.VideoCapture(video_path)\n success, image = vidcap.read()\n pil_img = None\n count = 0\n while success and frames == -1 or count < frames:\n if count % step == 0:\n # Convert to PIL image\n img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n pil_img = Image.fromarray(img)\n\n # Crop frame to remove border\n if aspect_ratio != 0:\n width, height = pil_img.size\n left = 0\n right = width\n content_height = 1/aspect_ratio * width\n border = (height - content_height) * 0.5\n top = border\n bottom = border + content_height\n pil_img = pil_img.crop((left, top, right, bottom))\n\n # Get primary color\n main_color = get_primary_color(\n pil_img, palette_size, show_img=show_frames)\n\n if color_format == 'hex':\n main_color = rgbToHex(main_color)\n \n print(main_color)\n\n # Attempt to read next frame\n success, image = vidcap.read()\n count += 1\n\n if show_last_frame:\n pil_img.show()","def FrameCount(self):\r\n\t\treturn self._get_attribute('frameCount')"],"string":"[\n \"def get_fps(self):\\n return self._num_frames / (datetime.now() - self._start).total_seconds()\",\n \"def get_video_frame_rate(filename):\\n clip = VideoFileClip(filename)\\n frame_rate = clip.fps\\n clip.close()\\n return frame_rate\",\n \"def duration():\\r\\n elapsed_time, duration = video_time()\\r\\n return duration\",\n \"def fps(self):\\n\\t\\treturn float(len(self.buf)) / (self.buf[-1][0] - self.buf[0][0])\",\n \"def get_frame_rate(video):\\n\\n video = cv2.VideoCapture(video)\\n\\n # Find OpenCV version\\n (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')\\n\\n # With webcam get(CV_CAP_PROP_FPS) does not work.\\n # Let's see for ourselves.\\n\\n if int(major_ver) < 3:\\n fps = video.get(cv2.cv.CV_CAP_PROP_FPS)\\n logger.info(\\\"Frames per second using video.get(cv2.cv.CV_CAP_PROP_FPS): {0}\\\".format(fps))\\n else:\\n fps = video.get(cv2.CAP_PROP_FPS)\\n logger.info(\\\"Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}\\\".format(fps))\\n\\n cv2.destroyAllWindows()\\n video.release()\\n return fps\",\n \"def read_fps(vid_name):\\n\\n video = cv2.VideoCapture(vid_name)\\n fps = video.get(cv2.CAP_PROP_FPS)\\n video.release()\\n\\n return fps\",\n \"def read_video_info_cv2(vid_fp: str):\\n cap = cv2.VideoCapture(vid_fp)\\n total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\\n total_seconds = int(total_frames / cap.get(cv2.CAP_PROP_FPS))\\n cap.release()\\n return timedelta(seconds=total_seconds)\\n # cap.set(cv2.CAP_PROP_POS_AVI_RATIO, 1)\\n # total_ms = cap.get(cv2.CAP_PROP_POS_MSEC)\\n # return timedelta(milliseconds=total_ms)\",\n \"def get_fps(self):\\n # Take difference.\\n interframe_intervals = np.diff(self.data[\\\"t\\\"])\\n\\n # Inter-frame interval in milliseconds.\\n mean_interval = np.mean(interframe_intervals)\\n fps = round(1 / (mean_interval / 1000))\\n\\n return int(fps)\",\n \"def get_fps(self):\\n return self.fps\",\n \"def getFrameRate(self):\\n if not self.proxy:\\n self.proxy = self.session.service(\\\"ALVideoRecorder\\\")\\n return self.proxy.getFrameRate()\",\n \"def get_fps(video, use_opencv=False):\\n\\n if use_opencv:\\n video_cap = cv2.VideoCapture(video)\\n fps = video_cap.get(cv2.CAP_PROP_FPS)\\n video_cap.release()\\n return fps\\n else:\\n return convert_to_float(\\n utils.exec_shell_command(f\\\"{SHELL_CMD_GET_FPS} '{video}'\\\", silent=True)[0]\\n )\",\n \"def num_frames(self):\\n return len(self.video)\",\n \"def getTotalFramesVid(srcVideoPath):\\n cap = cv2.VideoCapture(srcVideoPath)\\n # if the videoCapture object is not opened then exit without traceback\\n if not cap.isOpened():\\n print(\\\"Error reading the video file !!\\\")\\n return 0\\n\\n tot_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\\n cap.release()\\n return tot_frames\",\n \"def get_fps(self):\\n return self._document.GetFps()\",\n \"def get_video_property(video_path):\\n\\tvideocap = cv2.VideoCapture(video_path)\\n\\tframe_count = videocap.get(cv2.CAP_PROP_FRAME_COUNT)\\n\\tfps = videocap.get(cv2.CAP_PROP_FPS)\\n\\theight = videocap.get(cv2.CAP_PROP_FRAME_HEIGHT)\\n\\twidth = videocap.get(cv2.CAP_PROP_FRAME_WIDTH)\\n\\treturn frame_count, fps, width, height\",\n \"def get_fps(self):\\n \\n return self.fps, self.average_fps\",\n \"def get_video_frame_rate(video_filename):\\n if not os.path.exists(video_filename):\\n raise ValueError(\\\"%s does not exist\\\" % video_filename)\\n \\n probe = ffmpeg.probe(video_filename)\\n assert len(probe['streams']) == 1\\n \\n # Seems to be two ways of coding, not sure which is better\\n avg_frame_rate = probe['streams'][0]['avg_frame_rate']\\n r_frame_rate = probe['streams'][0]['r_frame_rate']\\n assert avg_frame_rate == r_frame_rate\\n \\n # Convert fraction to number\\n num, den = avg_frame_rate.split('/')\\n frame_rate = float(num) / float(den)\\n \\n return frame_rate\",\n \"def get_fps(vid_path, prefix):\\n _, name = os.path.split(vid_path)\\n i1 = name.find(prefix) + len(prefix)\\n i2 = name[i1:].find('_')\\n fps = int(name[i1:i1+i2])\\n\\n return fps\",\n \"def elapsed_time():\\r\\n elapsed_time, duration = video_time()\\r\\n return elapsed_time\",\n \"def get_fps(self):\\n raise NotImplementedError(\\\"get_fps is not implemented\\\")\",\n \"def get_duration(self) -> int:\\n return int( (self._frame_count / self._fps) * 1000 )\",\n \"def get_fps(clock):\\n if video_mode:\\n return \\\"30\\\" # Video FPS will be 30\\n else:\\n return str(int(round(clock.get_fps(), 0)))\",\n \"def video_duration(self):\\n # type: () -> int\\n return self._video_duration\",\n \"def gif_fps(self):\\n return self._gif_fps\",\n \"def get_frame_duration(self):\\n return self._frame_duration\",\n \"def realFrameNumber(self, callback=None):\\n count = 0\\n theoreticalFrameNumber = int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))\\n if theoreticalFrameNumber > 30000:\\n return theoreticalFrameNumber\\n while(True):\\n # Capture frame-by-frame\\n ret, frame = self.video.read()\\n if not ret:\\n break\\n if callback != None:\\n callback(0.1 + (count / theoreticalFrameNumber) * 0.75, \\\"Calculating the number of frame\\\")\\n count += 1\\n return count\",\n \"def get_framerate(self):\\n return self._framerate\",\n \"def bspb_frameCounter():\\n curTime = int(pm.currentTime())\\n maxTime = int(pm.playbackOptions(q=True, maxTime=True))\\n return '{0} / {1}'.format(curTime, maxTime)\",\n \"def video(path):\\n cap = cv2.VideoCapture(path)\\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n return frame_count, cap\",\n \"def get_fps(self):\\n if len(self.times) >= 2:\\n dif = np.diff(self.times)\\n fps = 1. / dif.min()\\n # if the FPS crosses 500, do not update it\\n if fps <= 500:\\n self.fps = fps\\n return self.fps\\n else:\\n return 0.\",\n \"def get_video_params(video_filename):\\n \\n width, height = get_video_aspect(video_filename)\\n frame_rate = get_video_frame_rate(video_filename)\\n return width, height, frame_rate\",\n \"def get_frame(self, camera: int = 0) -> Tuple[float, np.ndarray]:\\n return self.video.read()\",\n \"def get_duration(self):\\n frame_dur = self.get_frame_duration()\\n num_frames = self.get_num_frames()\\n motion_dur = frame_dur * (num_frames - 1)\\n return motion_dur\",\n \"def getVideoFrames(videoFilePath, startFrameNumber=-1, endFrameNumber=-1):\\n frames=[]\\n vidcap = cv2.VideoCapture(videoFilePath)\\n fps=vidcap.get(cv2.CAP_PROP_FPS)\\n totalFrame=vidcap.get(cv2.CAP_PROP_FRAME_COUNT)\\n if startFrameNumber==-1:\\n startFrameNumber = 0\\n if endFrameNumber == -1:\\n endFrameNumber = totalFrame - 1\\n success,image = vidcap.read()\\n count = 0\\n success = True\\n while success:\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n image = color.rgb2yiq(image).astype(np.float32)\\n \\n if count<startFrameNumber:\\n success,image = vidcap.read()\\n count+=1\\n continue\\n elif count>=endFrameNumber:\\n break\\n else: \\n frames.append(image)\\n success,image = vidcap.read()\\n count += 1\\n frames=np.array(frames)\\n \\n return fps, frames\",\n \"def frame_duration(self):\\n return self.samples_per_frame / self.input_data_sample_rate\",\n \"def calculate_fps(self):\\n time_difference = self.time_array[-1] - self.time_array[0]\\n time_difference_in_seconds = time_difference.to_sec()\\n if time_difference_in_seconds == 0:\\n pass\\n self.fps = self.buffer_size / time_difference_in_seconds\\n rospy.loginfo(\\\"[EulerianMotionMagnification] Estimated FPS: \\\" + str(self.fps) + \\\" (Measured timespan: \\\" + str(time_difference_in_seconds) + \\\"s)\\\")\\n rospy.loginfo(\\\"[EulerianMotionMagnification] Video array length: \\\" + str(len(self.video_array)))\",\n \"def readVideo(self):\\n vid = cv2.VideoCapture(self.fname)\\n imgstack = []\\n # grab = True\\n grab, img = vid.read()\\n while grab:\\n imgstack.append(\\n Frame(\\n cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\\n self.starttime\\n + datetime.timedelta(seconds=self.frame_dt * self.length),\\n )\\n )\\n self.length += 1\\n grab, img = vid.read()\\n self.frames = imgstack\",\n \"def update_frame(self):\\r\\n while not self.stopped:\\r\\n if not self.grabbed or not self.cap.isOpened():\\r\\n self.stop()\\r\\n else:\\r\\n self.grabbed, self.frame = self.cap.read()\\r\\n try:\\r\\n if self.grabbed:\\r\\n #self.New_Frame_Time = time.time()\\r\\n #self.FPS = 1/(self.New_Frame_Time-self.Old_Frame_Time)\\r\\n #self.Old_Frame_Time = self.New_Frame_Time\\r\\n self.FrameCount += 1\\r\\n else:\\r\\n print(f'Grabbed status is: {self.grabbed}')\\r\\n #self.Old_Frame_Time = time.time()\\r\\n except ZeroDivisionError:\\r\\n print(\\\"Division by zero error when finding video feed fps\\\")\\r\\n self.FPS = 0\\r\\n self.Old_Frame_Time = time.time()\",\n \"def get_video_aspect(video_filename):\\n if not os.path.exists(video_filename):\\n raise ValueError(\\\"%s does not exist\\\" % video_filename)\\n \\n probe = ffmpeg.probe(video_filename)\\n assert len(probe['streams']) == 1\\n width = probe['streams'][0]['width']\\n height = probe['streams'][0]['height']\\n \\n return width, height\",\n \"def video_to_frames(video_filename,output_dir):\\n cap = cv2.VideoCapture(video_filename)\\n video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1\\n vid_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\n vid_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n vid_fps = int(cap.get(cv2.CAP_PROP_FPS))\\n print(\\\"vid_res=%d x %d, fps=%d\\\\n\\\" % (vid_width, vid_height,vid_fps))\\n crop_width=int(vid_width/128)*128\\n crop_height=int(vid_height/128)*128\\n grab_step=int(vid_fps/2)\\n if cap.isOpened() and video_length > 0:\\n count = 0\\n frame_id=0\\n success, image = cap.read()\\n while success and frame_id <= 9999:\\n if count%grab_step==0:\\n crop_img = image[0:crop_width, 0:crop_height]\\n resized_img = cv2.resize(crop_img, (128, 128)) \\n cv2.imwrite(output_dir+\\\"/frame%05d.jpg\\\" % frame_id, resized_img)\\n frame_id+=1\\n success, image = cap.read()\\n count += 1\\n return 0\",\n \"def read_video(video_filename, width=224, height=224, rot=None):\\n cap = cv2.VideoCapture(video_filename)\\n fps = cap.get(cv2.CAP_PROP_FPS)\\n\\n n_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n pbar = tqdm(total=n_frames, desc=f\\\"Getting frames from {video_filename} ...\\\")\\n\\n frames = []\\n if cap.isOpened():\\n while True:\\n success, frame_bgr = cap.read()\\n if not success:\\n break\\n frame_rgb = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)\\n frame_rgb = cv2.resize(frame_rgb, (width, height))\\n if rot:\\n frame_rgb = cv2.rotate(frame_rgb, rot)\\n frames.append(frame_rgb)\\n\\n pbar.update()\\n pbar.close()\\n frames = np.asarray(frames)\\n return frames, fps\",\n \"def get_frames(self):\\n video_getter = Thread(target=self.streamer)\\n video_getter.daemon = True\\n video_getter.start()\",\n \"def get_frame(self, camera: int = 0) -> Tuple[float, np.ndarray]:\\n result = self.video.read()\\n if result[0]:\\n return result\\n else: # If we reach the end of the video, go back to the beginning.\\n self.video.set(cv2.CAP_PROP_POS_FRAMES, 0)\\n return self.video.read()\",\n \"def calcFrameRate(self):\\n\\n tot = 0\\n count = 0\\n for session in self.sessions:\\n for sample in session.samples:\\n if not sample.isLoading:\\n tot += sample.fps\\n count += 1\\n if count:\\n self.avgFps = tot / count\\n self.lowFps = (self.avgFps < 10)\\n self.highFps = (self.avgFps > 25)\",\n \"def get_frame_time(self, f):\\n return f * self.get_frame_duration()\",\n \"def get_fps(self):\\n #return int(self._root.knob('fps').getValue())\\n return None\",\n \"def framerate(self):\\n return self.config.get('framerate', 15)\",\n \"def get_video_info(filename: str) -> Dict:\\n cap = cv2.VideoCapture(filename)\\n if cap.isOpened():\\n fps = int(round(cap.get(cv2.CAP_PROP_FPS)))\\n width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\n height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n duration = frame_count / fps\\n cap.release()\\n video_info = {\\n 'fps': fps,\\n 'width': width,\\n 'height': height,\\n 'frame_count': frame_count,\\n 'duration': duration\\n }\\n return video_info\\n else:\\n raise ValueError(f'OpenCV failed to open {filename}.')\",\n \"def frame_rate(self):\\n return self._frame_rate\",\n \"def get_video_average(video_path):\\n vidcap = cv2.VideoCapture(video_path)\\n\\n width = int(vidcap.get(cv2.CAP_PROP_FRAME_WIDTH))\\n height = int(vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n num_color_channels = 3\\n\\n avg_frame = np.zeros((height, width, num_color_channels), dtype=np.float64)\\n frames = 0\\n\\n while True:\\n success, img = vidcap.read()\\n if not success:\\n break\\n avg_frame += img\\n frames += 1\\n\\n avg_frame = avg_frame / frames\\n ####avg_frame = cv2.cvtColor(avg_frame, cv2.COLOR_BGR2RGB)\\n avg_frame = avg_frame.astype(np.uint8)\\n cv2.imwrite(\\\"average_frame.png\\\", avg_frame)\",\n \"def obtenerVideo(camara):\\n val, frame = camara.read()\\n return val, frame\",\n \"def get_frame_time(self):\\n return self.get_timings().frame_time\",\n \"def get_fps(self):\\n if not self.count_fps:\\n logging.error(\\\"No FPSCounter set\\\")\\n return None\\n return self.fps.get_fps()\",\n \"def get_fps(self):\\n if not self.count_fps:\\n logging.error(\\\"No FPSCounter set\\\")\\n return None\\n return self.fps.get_fps()\",\n \"def video_time():\\r\\n # The full time has the form \\\"0:32 / 3:14\\\"\\r\\n full_time = world.css_text('div.vidtime')\\r\\n\\r\\n # Split the time at the \\\" / \\\", to get [\\\"0:32\\\", \\\"3:14\\\"]\\r\\n elapsed_str, duration_str = full_time.split(' / ')\\r\\n\\r\\n # Convert each string to seconds\\r\\n return (parse_time_str(elapsed_str), parse_time_str(duration_str))\",\n \"def frameTimes(self):\\n sr = self.sampleRate\\n offset = self.activeOffset\\n stride = self.activeStride\\n nf = self.numFrames\\n t = np.arange(nf) * (stride[0] / sr) + (offset / sr)\\n return t\",\n \"def video_bitrate(self):\\n assert self.has_video\\n\\n return self.__video_bitrate\",\n \"def parse_video(cap, base_path, step, size):\\n # Get nb of fps\\n fps = cap.get(cv2.CAP_PROP_FPS)\\n\\n # Compute how many images to skip to match the step\\n nb_skip = int(step / 1000 * fps)\\n index = 1\\n count = -1\\n while True:\\n count += 1\\n # Get next image\\n success, frame = cap.read()\\n\\n # If no image to read anymore\\n if not success:\\n break\\n\\n # Skip images\\n if count % nb_skip != 0:\\n continue\\n\\n # Save current image\\n suffix = '-{}.jpg'.format('0' + str(index) if index <= 9 else str(index))\\n # Compute new height to keep aspect ratio\\n if size is None:\\n pass\\n elif len(size) == 1:\\n if frame.shape[0] >= frame.shape[1]:\\n aspect_ratio = frame.shape[0] / frame.shape[1]\\n new_h = int(size[0])\\n new_w = int(size[0] / aspect_ratio)\\n else:\\n aspect_ratio = frame.shape[1] / frame.shape[0]\\n new_w = int(size[0])\\n new_h = int(size[0] / aspect_ratio)\\n frame = cv2.resize(frame, (new_w, new_h))\\n # Resize with width and height given\\n elif len(size) == 2:\\n frame = cv2.resize(frame, tuple(size))\\n dump_image(frame, base_path + suffix)\\n\\n # Append index\\n index += 1\\n\\n return\",\n \"def run(self):\\n i = 0\\n t = time.time()\\n while True:\\n i = i + 1\\n ret, frame = self.stream.read()\\n if (i == 20):\\n self.fps = 20/(time.time() - t)\\n t = time.time()\\n i = 0\\n #If a frame is None need to re-init it: \\n # - close a stream;\\n # - reopen it;\\n # - read frame again\\n if frame is None:\\n self.stream.release()\\n self.stream = cv2.VideoCapture(self.url)\\n ret, frame = self.stream.read()\\n text = time.strftime('%Y-%m-%d %H:%M:%S')\\n if (self.fps > 0):\\n text = text + ' FPS: ' + str(round(self.fps))\\n self.frame = cv2.putText(frame, text, (10, int(self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)) - 20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 2)\\n self.frameID = uuid.uuid4()\",\n \"def get_video_pts(self):\\n statrec = self.decoded_stats\\n if statrec.last_pts is None:\\n return None\\n return statrec.last_pts + time.time() - statrec.last_pts_changed\",\n \"def readFrame(self):\\n\\t\\tsuccess, self.frameImage = self.vidcap.read()\\n\\t\\treturn success, self.frameImage\",\n \"def get_mpeg_info(videos_dir, filename):\\n logger.info(\\\"Getting info from %s/%s\\\" % (videos_dir, filename))\\n if not os.path.exists(videos_dir):\\n raise Exception(\\\"%s dir does not exist!\\\" % videos_dir)\\n path = os.path.join(videos_dir, filename)\\n if not os.path.exists(path):\\n raise Exception(\\\"%s does not exist!\\\" % path)\\n\\n p = subprocess.Popen([FFMPEG, \\\"-i\\\", filename], cwd=videos_dir,\\n stderr=subprocess.STDOUT, stdout=subprocess.PIPE)\\n rc = p.wait()\\n\\n out = p.stdout.read()\\n pattern = r'Video: mpeg2video \\\\(Main\\\\), (?P<vdata>.*?)\\\\n'\\n m = re.search(pattern, out)\\n\\n if not m:\\n raise Exception(\\\"Failed to search mpeg info: '%s'\\\" % out)\\n\\n vdata = m.groups()[0]\\n mdata = vdata.split(\\\", \\\")\\n logger.info(mdata)\\n\\n resolution = mdata[1].split(\\\" \\\")[0]\\n (width, height) = resolution.split(\\\"x\\\")\\n width = int(width)\\n height = int(height)\\n logger.info(\\\"%dx%d\\\" % (width, height))\\n\\n bitrate = mdata[2].split(\\\" \\\")[0] # kb/s\\n\\n fps = float(mdata[3].split(\\\" \\\")[0])\\n\\n return {\\n \\\"width\\\": width,\\n \\\"height\\\": height,\\n \\\"bitrate\\\": bitrate, # kb/s\\n \\\"fps\\\": fps,\\n }\",\n \"def get_Video_Dimentions(video):\\r\\n width = int(video.get(cv.CAP_PROP_FRAME_WIDTH))\\r\\n height = int(video.get(cv.CAP_PROP_FRAME_HEIGHT))\\r\\n return width, height\",\n \"def get_total_frames(self) -> int:\\n return self.num_frames\",\n \"def next_frame(self):\\n # Tick will limit framerate and return delta in millis\\n delta = self.clock.tick(self.framerate)\\n\\n # Convert delta to seconds\\n delta /= 1000.0\\n\\n # Limit worst-case delta to make debugging easier\\n delta = min(delta, FrameTimer.MAX_FRAME_TIME)\\n\\n return delta\",\n \"def find_video_period(clip, fps=None, start_time=0.3):\\n\\n def frame(t):\\n return clip.get_frame(t).flatten()\\n\\n timings = np.arange(start_time, clip.duration, 1 / fps)[1:]\\n ref = frame(0)\\n corrs = [np.corrcoef(ref, frame(t))[0, 1] for t in timings]\\n return timings[np.argmax(corrs)]\",\n \"def video_bitrate(self):\\n # type: () -> int\\n return self._video_bitrate\",\n \"def get_session_videodata(videos):\\n # Get first frame of first video for future processing and number of frames in each video\\n videos_data = {'Frame rate': [], 'Number frames': []}\\n for idx, videofile in enumerate(videos):\\n cap = cv2.VideoCapture(videofile)\\n videos_data['Frame rate'].append(cap.get(cv2.CAP_PROP_FPS))\\n videos_data['Number frames'].append(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))\\n videos_data['Cumu. Num Frames'] = np.cumsum(videos_data['Number frames'])\\n return videos_data\",\n \"def stream_frames(video_capture):\",\n \"def frame_size(self):\\n size = None\\n if self.is_video():\\n width = self.__dict__['width']\\n height = self.__dict__['height']\\n if width and height:\\n try:\\n size = (int(width), int(height))\\n except ValueError:\\n raise FFProbeError(\\\"None integer size %s:%s\\\" % (width, height))\\n\\n return size\",\n \"def video_playback_width(self):\\n # type: () -> int\\n return self._video_playback_width\",\n \"def readLevelTimer(self, fps=60):\\n frames_elapsed = self.readLevelTimerValue()\\n return frames_elapsed / fps\",\n \"def ms_to_frames(time, fps):\\n return int(round(float(time) / 1000 * fps))\",\n \"def get_frames(self):\\n if not self.video:\\n return []\\n # We cannot validate shape on construction as that happens inside graph\\n # mode as we construct from a tf.data.Dataset, so we validate here.\\n self.video[0].validate_shape_and_dtype()\\n return self.video\",\n \"def update(self):\\n self.frame = self.video_stream.read()\",\n \"def process_video(file_path):\\n video = cv2.VideoCapture(file_path)\\n\\n video_sum = 0\\n frame_cnt = 0\\n\\n previous_frame_digits = set()\\n\\n while True:\\n return_value, frame = video.read()\\n\\n # skip same frames to improve performance\\n frame_cnt += 1\\n if frame_cnt % 2 != 0:\\n continue\\n\\n if not return_value:\\n break\\n\\n frame_results = find_sum_of_digits_which_crossed_line(frame, previous_frame_digits)\\n video_sum += frame_results.sum\\n\\n previous_frame_digits = frame_results.digits\\n\\n cv2.imshow('frame', frame)\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\n break\\n\\n video.release()\\n cv2.destroyAllWindows()\\n\\n return video_sum\",\n \"def __loadVideo(self):\\n # Check if movie file exists ...\\n #\\n if not(os.path.isfile(self.fNameVideo)):\\n return stm.StimErrC.videoFileNotFound\\n\\n try: \\n # Load video\\n #\\n self.video = mpe.VideoFileClip(self.fNameVideo)\\n \\n except IOError: \\n return stm.StimErrC.invalidVideoFormat\\n \\n # Retrieve video description\\n #\\n self.dxFr = self.video.size[0]\\n self.dyFr = self.video.size[1]\\n self.nFr = self.video.duration *self.video.fps\\n self.fps = self.video.fps\\n ssp.Log.write(\\\"DEBUG\\\", \\\"stim_video: {0}x{1} pixel, {2} frames, {3} fps\\\"\\n .format(self.dxFr, self.dyFr, self.nFr, self.fps))\\n \\n if self.isTestOnly:\\n # Return here if the video was only loaded to test if it is ok\\n #\\n self.video = None\\n return stm.StimErrC.ok\\n \\n # Load movie frames (note that frames is a generator!)\\n #\\n self.frames = self.video.iter_frames() \\n self.isReady = True\\n return stm.StimErrC.ok\",\n \"def get_frame_rate(filename):\\n cmd = ('ffprobe -v 0 -of flat=s=_ -select_streams v:0 -show_entries '\\n 'stream=avg_frame_rate -of default=nokey=1:noprint_wrappers=1 ' +\\n filename).split()\\n pid = subprocess.run(cmd, stdout=subprocess.PIPE,\\n universal_newlines=True)\\n if pid.returncode != 0:\\n return None\\n\\n frame_rate_exp = pid.stdout.rstrip()\\n try:\\n frame_rate = float(frame_rate_exp)\\n except:\\n if frame_rate_exp == 'N/A':\\n frame_rate = 0.\\n else:\\n numerator, denominator = map(float, frame_rate_exp.split('/'))\\n if denominator == 0:\\n frame_rate = 0\\n else:\\n frame_rate = numerator / denominator\\n return frame_rate\",\n \"def duration(self):\\n return self.sound.nframes\",\n \"def get_frame(self):\\n if self.vid.isOpened():\\n ret, frame = self.vid.read()\\n if ret:\\n return (ret, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\\n else:\\n return (ret, None)\\n else:\\n return (ret, None)\",\n \"def video_playback_height(self):\\n # type: () -> int\\n return self._video_playback_height\",\n \"def read(self):\\n\\n # ret, image = self.video.read()\\n (self.grabbed, self.frame) = self.cap.read()\\n image = self.frame\\n\\n if image is not None:\\n \\\"\\\"\\\"Update FPS, and incode received frame. \\\"\\\"\\\"\\n self.fps.update()\\n # TODO: add self.fps.fps() to image, if flagged raised.\\n\\n # We are using Motion JPEG, but OpenCV defaults to cap raw images,\\n # so we must encode it into JPEG in order to correctly display the\\n # video stream.\\n\\n # display a piece of text to the frame (so we can benchmark\\n # fairly against the fast method)\\n self.fps.stop()\\n cv2.putText(image, \\\"FPS (simple): {:.2f}\\\".format(self.fps.fps()), (10, 30),\\n cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)\\n self.frame = image.copy()\\n\\n ret, jpeg = cv2.imencode('.jpg', image)\\n return jpeg.tobytes()\\n else:\\n self.logger.debug(\\\"in 'get_frame', video.read not success\\\")\",\n \"def readFrames(video):\\n frames = []\\n while True:\\n _, frame = video.read()\\n\\n if frame is None:\\n break\\n else:\\n frames.append(frame)\\n video.release()\\n return frames\",\n \"def resolution(self):\\n assert self.has_video\\n\\n return self.__resolution\",\n \"def get_duration(filename):\\n cmd = ('ffprobe -v 0 -of flat=s=_ -select_streams v:0 -show_entries '\\n 'stream=duration -of default=nokey=1:noprint_wrappers=1 ' +\\n filename).split()\\n pid = subprocess.run(cmd, universal_newlines=True,\\n stdout=subprocess.PIPE)\\n if pid.returncode != 0:\\n return None\\n\\n duration_exp = pid.stdout.rstrip()\\n try:\\n duration = float(duration_exp)\\n except:\\n duration = 0.\\n return duration\",\n \"def load_vid_frame(self, ind, camname, preload=True, extension=\\\".mp4\\\"):\\n fname = (\\n str(self._N_VIDEO_FRAMES * int(np.floor(ind / self._N_VIDEO_FRAMES)))\\n + extension\\n )\\n frame_num = int(ind % self._N_VIDEO_FRAMES)\\n keyname = os.path.join(camname, fname)\\n if preload:\\n return self.vidreaders[camname][keyname].get_data(frame_num)\\n else:\\n thisvid_name = self.vidreaders[camname][keyname]\\n abname = thisvid_name.split(\\\"/\\\")[-1]\\n if abname == self.currvideo_name[camname]:\\n vid = self.currvideo[camname]\\n else:\\n vid = imageio.get_reader(thisvid_name)\\n print(\\\"Loading new video: {} for {}\\\".format(abname, camname))\\n self.currvideo_name[camname] = abname\\n # close current vid\\n # Without a sleep here, ffmpeg can hang on video close\\n time.sleep(0.25)\\n if self.currvideo[camname] is not None:\\n self.currvideo[camname].close()\\n self.currvideo[camname] = vid\\n\\n im = vid.get_data(frame_num)\\n\\n return im\",\n \"def find_average_duration(video: dict):\\n global num_videos\\n global total_duration\\n\\n if duration := video.get('duration'):\\n with data_lock:\\n num_videos += 1\\n total_duration += (duration/1000)\\n show_progress()\",\n \"def frame_capture(path, dest):\\n \\n # Path to video file \\n cap = cv2.VideoCapture(path)\\n \\n # Total number of frames\\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n \\n # Used as counter variable \\n count = 0\\n \\n # checks whether frames were extracted \\n success = 1\\n \\n while success: \\n \\n # cap object calls read \\n # function extract frames \\n success, image = cap.read()\\n \\n if image is not None:\\n image = image_preprocess(image)\\n # Saves the frames with frame-count \\n cv2.imwrite(f\\\"{dest}/frame{count}.jpg\\\", image)\\n else:\\n break\\n \\n count += 1\\n percentage = int(count/frame_count) * 100\\n return f'{count}/{frame_count} ({percentage})% of frames extracted'\",\n \"def get_video_statistics(self):\\n return self._video_statistics\",\n \"def getFPS(self):\\n # load it each time, since this setting is not limited to a single user\\n projectSettingsDB = self.loadProjectSettings()\\n try:\\n fpsValue = projectSettingsDB[\\\"FPS\\\"]\\n return fpsValue\\n except KeyError:\\n msg = \\\"Database Error while reading projectSettings.json\\\"\\n logger.error(msg)\\n return None\",\n \"def load_video(self, filename):\\n \\n reader = imageio.get_reader(filename, 'ffmpeg')\\n \\n return np.array(list(reader), dtype=np.float32)\",\n \"def process_video(self):\\n if os.path.isfile(self.source):\\n self.cap = cv2.VideoCapture(self.source)\\n else:\\n try:\\n file_name = \\\"input.mp4\\\"\\n self.source = self.source.replace('open', 'uc')\\n print( \\\"\\\\nDownloading video file from drive link to %s\\\\n\\\"%file_name)\\n gdown.download(self.source, file_name, quiet=False)\\n print( \\\"%s downloaded!\\\\n\\\"%file_name )\\n self.cap = cv2.VideoCapture(file_name)\\n except Exception:\\n raise RuntimeError(\\\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\\\n\\\")\\n\\n\\n assert self.cap.isOpened(), \\\"Failed to open %s\\\" % self.source\\n\\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \\n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n return\",\n \"def getFrames():\\n\\t\\tfor cam in Camera.CAMERAS: cam.getFrame()\",\n \"def get(self, flag: int):\\n if flag == cv2.CAP_PROP_FRAME_COUNT:\\n return self.length\",\n \"def get_frame(self, camera: int = 0) -> Tuple[float, np.ndarray]:\\n frame_time, self.frame = self.sources[camera].grabFrameNoTimeout(\\n image=self.frame\\n )\\n return frame_time, self.frame\",\n \"def _load_video(self, video):\\n\\t\\timport cv2\\n\\t\\tcap = cv2.VideoCapture(video)\\n\\t\\tassert cap.get(cv2.CAP_PROP_FRAME_WIDTH) >= 100, 'width must be at least 100 pixels'\\n\\t\\tassert cap.get(cv2.CAP_PROP_FRAME_HEIGHT) >= 100, 'height must be at least 100 pixels'\\n\\t\\tn = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n\\t\\tbuf = np.empty((n, int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), 3), np.dtype('uint8'))\\n\\t\\ti, ret = 0, True\\n\\t\\twhile (i < n and ret):\\n\\t\\t\\tret, frame = cap.read()\\n\\t\\t\\tframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\\n\\t\\t\\tbuf[i] = frame\\n\\t\\t\\ti += 1\\n\\t\\tcap.release()\\n\\t\\treturn np.moveaxis(buf, -1, 1)\",\n \"def get_frame_rate(gif=''):\\n\\n # identify -format \\\"Frame %s: %Tcs\\\\n\\\" ~/test.gif - frame life in centiseconds - how very odd.\\n frame_rate_str = subprocess.check_output([\\n 'identify',\\n '-format',\\n '%T\\\\n',\\n gif\\n ])\\n # If we failed to get a frame lifetime from imagemagick...\\n if len(frame_rate_str) <= 0:\\n frame_rate = str(FRAME_RATE)\\n else:\\n # Get gif's lifetime...\\n frame_rate_list = frame_rate_str.split('\\\\n')\\n frame_rate_list = [int(n) for n in frame_rate_list if n]\\n lifetime = float(sum(frame_rate_list)) / 100\\n\\n # print \\\"gif lifetime is \\\" + str(lifetime) + \\\"s\\\"\\n # Determine average frames per second\\n frame_rate = str(len(frame_rate_list) / lifetime)\\n if float(frame_rate) <= 0.0:\\n frame_rate = FRAME_RATE\\n\\n return frame_rate\",\n \"def play_video_file(fname : str):\\n cap = cv2.VideoCapture(fname)\\n fps = cap.get(5)\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n fontScale = 1\\n fontColor = (0, 0, 0)\\n lineType = 2\\n\\n myvideo = []\\n while cap.isOpened():\\n ret, frame = cap.read()\\n\\n if ret is True:\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n cv2.putText(gray, 'Time: ' + str(round(cap.get(0) / 1000, 2)),\\n (10, 30),\\n font,\\n fontScale,\\n fontColor,\\n lineType)\\n cv2.putText(gray, 'Frame: ' + str(int(cap.get(1))),\\n (10, 70),\\n font,\\n fontScale,\\n fontColor,\\n lineType)\\n myvideo.append(gray)\\n #cv2.imshow('frame', gray)\\n #cv2.waitKey(10)\\n #if cv2.waitKey(delay=2) & 0xFF == ord('q'):\\n # break\\n else:\\n break\\n\\n cap.release()\\n\\n if fps < 60:\\n for frame in myvideo:\\n cv2.imshow('frame', frame)\\n cv2.waitKey(10)\\n else:\\n for ind, frame in enumerate(myvideo):\\n if ind % 3 == 0:\\n cv2.imshow('frame', frame)\\n cv2.waitKey(10)\\n else:\\n continue\\n cv2.destroyAllWindows()\",\n \"def analyze_movie(\\n video_path, aspect_ratio=0, palette_size=32, frames=-1, step=1, show_frames=False, show_last_frame=False, color_format='hex'\\n):\\n\\n # Parse video frame-by-frame\\n vidcap = cv2.VideoCapture(video_path)\\n success, image = vidcap.read()\\n pil_img = None\\n count = 0\\n while success and frames == -1 or count < frames:\\n if count % step == 0:\\n # Convert to PIL image\\n img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\n pil_img = Image.fromarray(img)\\n\\n # Crop frame to remove border\\n if aspect_ratio != 0:\\n width, height = pil_img.size\\n left = 0\\n right = width\\n content_height = 1/aspect_ratio * width\\n border = (height - content_height) * 0.5\\n top = border\\n bottom = border + content_height\\n pil_img = pil_img.crop((left, top, right, bottom))\\n\\n # Get primary color\\n main_color = get_primary_color(\\n pil_img, palette_size, show_img=show_frames)\\n\\n if color_format == 'hex':\\n main_color = rgbToHex(main_color)\\n \\n print(main_color)\\n\\n # Attempt to read next frame\\n success, image = vidcap.read()\\n count += 1\\n\\n if show_last_frame:\\n pil_img.show()\",\n \"def FrameCount(self):\\r\\n\\t\\treturn self._get_attribute('frameCount')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74567306","0.7410015","0.7227333","0.71586215","0.7142597","0.7126073","0.71154517","0.71066225","0.70365375","0.70240074","0.7009666","0.6964138","0.68664753","0.6860731","0.6837427","0.67995274","0.6797968","0.6792723","0.67588943","0.6752216","0.67057985","0.6678188","0.6614478","0.6575193","0.65506804","0.65410525","0.6538884","0.64852893","0.6483586","0.6443816","0.64350706","0.64284253","0.64004403","0.639097","0.6377706","0.6377014","0.6338431","0.6323209","0.629142","0.6287438","0.627445","0.61892337","0.6186461","0.6185945","0.6167481","0.6156221","0.60946864","0.6089855","0.6084448","0.6070923","0.6065313","0.6065234","0.60610974","0.60610974","0.60588217","0.60293","0.6015719","0.59973","0.5996158","0.599588","0.5995133","0.59603864","0.59465444","0.593369","0.5925024","0.59115803","0.591105","0.59105873","0.59003687","0.5900214","0.5898519","0.5889795","0.5889466","0.5887891","0.5884198","0.5882832","0.5880626","0.58708084","0.5861828","0.5861315","0.58539855","0.5852601","0.5840874","0.5826123","0.5823806","0.582079","0.58189076","0.5818167","0.5814093","0.578985","0.5786491","0.57844484","0.57796556","0.57756376","0.5772988","0.5772315","0.5744656","0.5744345","0.5739623","0.5739287"],"string":"[\n \"0.74567306\",\n \"0.7410015\",\n \"0.7227333\",\n \"0.71586215\",\n \"0.7142597\",\n \"0.7126073\",\n \"0.71154517\",\n \"0.71066225\",\n \"0.70365375\",\n \"0.70240074\",\n \"0.7009666\",\n \"0.6964138\",\n \"0.68664753\",\n \"0.6860731\",\n \"0.6837427\",\n \"0.67995274\",\n \"0.6797968\",\n \"0.6792723\",\n \"0.67588943\",\n \"0.6752216\",\n \"0.67057985\",\n \"0.6678188\",\n \"0.6614478\",\n \"0.6575193\",\n \"0.65506804\",\n \"0.65410525\",\n \"0.6538884\",\n \"0.64852893\",\n \"0.6483586\",\n \"0.6443816\",\n \"0.64350706\",\n \"0.64284253\",\n \"0.64004403\",\n \"0.639097\",\n \"0.6377706\",\n \"0.6377014\",\n \"0.6338431\",\n \"0.6323209\",\n \"0.629142\",\n \"0.6287438\",\n \"0.627445\",\n \"0.61892337\",\n \"0.6186461\",\n \"0.6185945\",\n \"0.6167481\",\n \"0.6156221\",\n \"0.60946864\",\n \"0.6089855\",\n \"0.6084448\",\n \"0.6070923\",\n \"0.6065313\",\n \"0.6065234\",\n \"0.60610974\",\n \"0.60610974\",\n \"0.60588217\",\n \"0.60293\",\n \"0.6015719\",\n \"0.59973\",\n \"0.5996158\",\n \"0.599588\",\n \"0.5995133\",\n \"0.59603864\",\n \"0.59465444\",\n \"0.593369\",\n \"0.5925024\",\n \"0.59115803\",\n \"0.591105\",\n \"0.59105873\",\n \"0.59003687\",\n \"0.5900214\",\n \"0.5898519\",\n \"0.5889795\",\n \"0.5889466\",\n \"0.5887891\",\n \"0.5884198\",\n \"0.5882832\",\n \"0.5880626\",\n \"0.58708084\",\n \"0.5861828\",\n \"0.5861315\",\n \"0.58539855\",\n \"0.5852601\",\n \"0.5840874\",\n \"0.5826123\",\n \"0.5823806\",\n \"0.582079\",\n \"0.58189076\",\n \"0.5818167\",\n \"0.5814093\",\n \"0.578985\",\n \"0.5786491\",\n \"0.57844484\",\n \"0.57796556\",\n \"0.57756376\",\n \"0.5772988\",\n \"0.5772315\",\n \"0.5744656\",\n \"0.5744345\",\n \"0.5739623\",\n \"0.5739287\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7280317"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":77074,"cells":{"query":{"kind":"string","value":"Loads a video. Returns 1 on success"},"document":{"kind":"string","value":"def load_video(self, vid_path):\n self.video = cv2.VideoCapture(vid_path)\n self._check_vid_open()\n logging.info('Loaded video at: {}'.format(vid_path))\n return 1"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __loadVideo(self):\n # Check if movie file exists ...\n #\n if not(os.path.isfile(self.fNameVideo)):\n return stm.StimErrC.videoFileNotFound\n\n try: \n # Load video\n #\n self.video = mpe.VideoFileClip(self.fNameVideo)\n \n except IOError: \n return stm.StimErrC.invalidVideoFormat\n \n # Retrieve video description\n #\n self.dxFr = self.video.size[0]\n self.dyFr = self.video.size[1]\n self.nFr = self.video.duration *self.video.fps\n self.fps = self.video.fps\n ssp.Log.write(\"DEBUG\", \"stim_video: {0}x{1} pixel, {2} frames, {3} fps\"\n .format(self.dxFr, self.dyFr, self.nFr, self.fps))\n \n if self.isTestOnly:\n # Return here if the video was only loaded to test if it is ok\n #\n self.video = None\n return stm.StimErrC.ok\n \n # Load movie frames (note that frames is a generator!)\n #\n self.frames = self.video.iter_frames() \n self.isReady = True\n return stm.StimErrC.ok","def _load_video(self, video):\n\t\timport cv2\n\t\tcap = cv2.VideoCapture(video)\n\t\tassert cap.get(cv2.CAP_PROP_FRAME_WIDTH) >= 100, 'width must be at least 100 pixels'\n\t\tassert cap.get(cv2.CAP_PROP_FRAME_HEIGHT) >= 100, 'height must be at least 100 pixels'\n\t\tn = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n\t\tbuf = np.empty((n, int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), 3), np.dtype('uint8'))\n\t\ti, ret = 0, True\n\t\twhile (i < n and ret):\n\t\t\tret, frame = cap.read()\n\t\t\tframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n\t\t\tbuf[i] = frame\n\t\t\ti += 1\n\t\tcap.release()\n\t\treturn np.moveaxis(buf, -1, 1)","def onclick_load_video(self):\n video_source = select_file(\n \"Select Video Files\",\n \"../\",\n \"Video Files (*.mp4 *.avi *.mpg *.gif *.mov)\")\n if video_source:\n param_name = select_file(\n \"Select Parameter\", \"../\", \"Parameter Files (*.json)\")\n if param_name:\n self.moildev = Moildev(param_name)\n self.running_video(video_source)","def loadVideo(self,path,loadAudio=False):\r\n self.videoPlayer.loadVideo(path,loadAudio=loadAudio)\r\n self.videoPath = path","def loadVideo(self,path,loadAudio=True):\r\n self.aud_path = \"\"\r\n # Get cv2 video capture object\r\n self.vid_path = path\r\n self.vid = cv2.VideoCapture(self.vid_path)\r\n self.delay = int(1000/self.vid.get(cv2.CAP_PROP_FPS))\r\n self.vid_len = int(self.vid.get(cv2.CAP_PROP_FRAME_COUNT))/self.vid.get(cv2.CAP_PROP_FPS)\r\n self.state = VideoPlayer.State.STOPPED\r\n self.hasAudio = True# If no audio in video, ignore audio\r\n if loadAudio:\r\n self.loadAudio(self.vid_path)\r\n else:\r\n self.loadCachedAudio()","def load_vid_frame(self, ind, camname, preload=True, extension=\".mp4\"):\n fname = (\n str(self._N_VIDEO_FRAMES * int(np.floor(ind / self._N_VIDEO_FRAMES)))\n + extension\n )\n frame_num = int(ind % self._N_VIDEO_FRAMES)\n keyname = os.path.join(camname, fname)\n if preload:\n return self.vidreaders[camname][keyname].get_data(frame_num)\n else:\n thisvid_name = self.vidreaders[camname][keyname]\n abname = thisvid_name.split(\"/\")[-1]\n if abname == self.currvideo_name[camname]:\n vid = self.currvideo[camname]\n else:\n vid = imageio.get_reader(thisvid_name)\n print(\"Loading new video: {} for {}\".format(abname, camname))\n self.currvideo_name[camname] = abname\n # close current vid\n # Without a sleep here, ffmpeg can hang on video close\n time.sleep(0.25)\n if self.currvideo[camname] is not None:\n self.currvideo[camname].close()\n self.currvideo[camname] = vid\n\n im = vid.get_data(frame_num)\n\n return im","def load_video(self):\r\n if self.file_name=='':\r\n Tk().withdraw()\r\n self.file_name = askopenfilename()\r\n cap = cv2.VideoCapture(self.file_name)\r\n self.length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\r\n self.width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\r\n self.heigth = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\n self.fps = int(round(cap.get(cv2.CAP_PROP_FPS)))\r\n \r\n video_buffer = []#np.ndarray(shape=(self.length, self.heigth, self.width, 3), dtype=np.uint8)\r\n for i in tqdm(range(self.length), desc='Loading video from: {}'.format(self.file_name)):\r\n ret, frame = cap.read()\r\n if not ret:\r\n break\r\n video_buffer.append(frame)\r\n #assert(i==self.length-1)\r\n video_buffer = np.array(video_buffer, dtype=np.uint8)\r\n self.video_buffer = video_buffer\r\n cap.release()\r\n self.ix = self.width-1\r\n self.iy = self.heigth-1 \r\n self.roi = dict(x1=self.x, y1=self.y, x2=self.ix, y2=self.iy)\r\n return video_buffer","def openVideo(self):\n fname = self.openFile(self.user[\"Video\"])\n if fname != \"\":\n error_opening_video = False\n\n cam = cv2.VideoCapture(fname)\n logging.info(\"Opening video Check: {0}\".format(fname))\n\n currentframe = 0\n ret, frame = cam.read()\n\n if ret is False:\n error_opening_video = True\n # Release all space and windows once done\n cam.release()\n cv2.destroyAllWindows()\n\n if error_opening_video:\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"Error on opening the video: {0}\".format(fname))\n msg.setWindowTitle(\"Information\")\n msg.setStandardButtons(QMessageBox.Ok)\n msg.exec_()\n else:\n self.user[\"Video\"] = fname\n name = self.splitPath(fname)[-1]\n self.vid_name = name.split(\".\")[0]\n self.ui.l_vid.setText(\"Load: \" + self.vid_name)\n self.checkFiles()","def load(self):\n if self.ref == None:\n self.ref = av.open(self.abspath)\n self.n_audio_streams = len([s for s in self.ref.streams if s.type == 'audio'])\n self.n_video_streams = len([s for s in self.ref.streams if s.type == 'video'])\n if self.n_audio_streams > 0:\n self.media_types += ['audio']\n if self.n_video_streams > 0:\n self.media_types += ['video']\n self.duration = self.ref.duration / self.AV_TIME_BASE","def init_video(self):\n\n assert self.container is None\n\n retry = 3\n while self.container is None and 0 < retry:\n retry -= 1\n try:\n self.container = av.open(self.tello.get_video_stream())\n except av.AVError as ave:\n print(ave)\n print('retry...')\n\n\n assert self.container is not None","def get_video(self, video_id):\n uri = 'videos/' + video_id\n return self.make_request(uri)","def load_video_from_path(path: Union[Path, str]) -> videoType:\n\n if isinstance(path, str):\n path = Path(path)\n video_path = path.resolve()\n if video_path.exists():\n print(f\"Video loaded from {video_path}\")\n vid: videoType = cv2.VideoCapture(str(video_path))\n else:\n sys.exit(\"Error: Video file not found...\")\n\n return vid","def play_video(file, name):\n url = u\"http://smooth.ruv.cache.is/opid/{0}\".format(file)\n r = requests.head(url)\n\n if r.status_code != 200:\n url = u\"http://smooth.ruv.cache.is/lokad/{0}\".format(file)\n\n player.play(url, name)","def video():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def test_probe_video_from_file(self, test_video, config):\n full_path = os.path.join(VIDEO_DIR, test_video)\n probe_result = torch.ops.video_reader.probe_video_from_file(full_path)\n self.check_probe_result(probe_result, config)","def process_video(self):\n if os.path.isfile(self.source):\n self.cap = cv2.VideoCapture(self.source)\n else:\n try:\n file_name = \"input.mp4\"\n self.source = self.source.replace('open', 'uc')\n print( \"\\nDownloading video file from drive link to %s\\n\"%file_name)\n gdown.download(self.source, file_name, quiet=False)\n print( \"%s downloaded!\\n\"%file_name )\n self.cap = cv2.VideoCapture(file_name)\n except Exception:\n raise RuntimeError(\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\n\")\n\n\n assert self.cap.isOpened(), \"Failed to open %s\" % self.source\n\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\n return","def load(self, uri):\n\n self.stop()\n self.uri = uri\n if os.path.exists(self.uri):\n uri = \"%s%s%s\" % (\"\\\"\", self.uri, \"\\\"\")\n\n cache_pantalla = \"%s -cache %i -wid %i\" % (\n MPLAYER, 1024, self.ventana_id)\n\n estructura = \"%s -slave -idle -nolirc\" % (cache_pantalla)\n estructura = \"%s -rtc -nomouseinput\" % estructura\n estructura = \"%s -noconsolecontrols -nojoystick\" % estructura\n\n self.mplayer = subprocess.Popen(\n estructura, shell=True, stdin=subprocess.PIPE,\n stdout=open(STDOUT, \"w+b\"), stderr=open(STDOUT, \"r+b\"),\n universal_newlines=True)\n\n self.entrada = self.mplayer.stdin\n self.salida = open(STDOUT, \"r\")\n self.entrada.write(\"loadfile %s 0\\n\" % uri)\n self.entrada.flush()\n self.video_in_stream = False\n\n self.__new_handle(True)","def obtenerVideo(camara):\n val, frame = camara.read()\n return val, frame","def test_task_video_download(url_to_video: str, empty_video_resource: VideoResource):\n download_video(url_to_video, empty_video_resource.id)\n empty_video_resource.refresh_from_db()\n video_instance = empty_video_resource.videos.filter(primary=True).first()\n\n assert empty_video_resource.videos.all()\n assert video_instance.extension == 'mp4'\n assert video_instance.primary\n for item in video_instance.video.open():\n assert item","async def get_video(videoid):\n\theaders = {\n\t\t'Client-ID': config['twitch_clientid'],\n\t\t'Authorization': f\"Bearer {get_token()}\",\n\t}\n\tdata = await common.http.request_coro(\"https://api.twitch.tv/helix/videos\", {\"id\": videoid.lstrip('v')}, headers=headers)\n\treturn json.loads(data)[\"data\"][0]","def load(self, fileName=None, cacheString=None, callback=None):\n if fileName != None:\n self.videoPath = fileName\n if cacheString != None:\n self.cacheString = cacheString\n \n if not os.path.exists(self.videoPath):\n raise ValueError(\"ERROR : %s don't exist\" % (self.videoPath))\n \n if callback != None:\n callback(0.1, \"Load the video with OpenCV\")\n \n self.video = cv2.VideoCapture(self.videoPath)\n \n if(not self.video.isOpened()):\n self.video = None\n raise ValueError(\"ERROR : Can't load %s\" % (self.videoPath))\n \n self.fps = int(self.video.get(cv2.CAP_PROP_FPS))\n self.frameCount = self.realFrameNumber(callback)\n \n if callback != None:\n callback(0.85, \"Load or create the cache file\")\n \n # we load the cache\n self.cachePath = self.videoPath + self.cacheString + \".json\"\n self.loadCacheFile() #we load or create the cache file\n \n # we draw the first frame\n self.setFrame(0)","def load_video(self):\n self.video_file = tkFileDialog.askopenfilename()\n self.video_parser = VideoFileParser(self.video_file)\n\n self.video_entries = self.video_parser.entries\n\n for index, entry in enumerate(self.video_entries):\n self.video_box.insert(index, entry.word)","def play(self): \n if(self.isLoaded() == False):\n return\n self.isPlaying = True\n #if the video is end, we restart\n if(self.ret == False):\n self.video.set(1, 0)","def importVideo(self):\n path, filter = QtGui.QFileDialog.getOpenFileNames(self, str(self.tr(\"Import a video\")), \"/home/cecilia/\", self.tr(\"Video files (*.avi *.mov)\"))\n videoPath = path[0].encode(\"utf-8\")\n self._app.importVideo(videoPath)","def load_stream(filename, folder):\n cap = cv2.VideoCapture(os.path.join(folder, filename))\n while True:\n ret, frame = cap.read()\n cv2.imshow(\"img\", frame)\n if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n cap.release()\n cv2.destroyAllWindows()\n break","def load_frame_from_video(path: str, frame_index: int) -> np.ndarray:\n vid = load_video(path)\n img = vid[frame_index]\n return img","def load(self, uri):\n if self.p:\n self.p.stop()\n self.p = self.vlc.media_player_new(uri)\n Player._finished = False\n e = self.p.event_manager()\n e.event_attach(vlc.EventType.MediaPlayerEndReached, self.__end_reached, None)\n if (not '://' in uri or uri.startswith('file://')) and os.stat(uri).st_size < 100:\n self._finished = True\n else:\n self.p.play()","def _read_file(self):\n extension = self.path.split('.')[-1]\n if extension!='avi':\n raise Exception(\"Invalid Format\")\n\n return cv2.VideoCapture(self.path)","def play_video(path):\r\n #logger.info(\"######: {}, log: {}########\".format('rk8', path))\r\n #original=https://api.hotstar.com/h/v1/play?contentId=1000238814\r\n #path=\"https://api.hotstar.com/h/v2/play/in/contents/1000238814\"\r\n # Create a playable item with a path to play.\r\n data = make_request(path)\r\n #logger.info(\"######: {}, log: {}########\".format('rk3', path))\r\n if not data:\r\n return\r\n\r\n def get_subtitle(url):\r\n #\r\n # https://hses.akamaized.net/videos/hotstarint/hostages/1260003409/1558430241469/\r\n # 265b9dab22d4e9a033e6df6f89639f17/master.m3u8?hdnea=st=1560107863~exp=1560111463~acl=\r\n # /*~hmac=2f6fb393159ed5fa1b12bbf12e954eb377cfa0fc852d4ff5eb24446233237620\r\n #\r\n # https://hses.akamaized.net/videos/hotstarint/hostages/1260003409/1558430241469/\r\n # 5d0f83c3ccbf4501cf952bdfc8c0d785/subtitle/lang_en/sub-0.vtt\r\n #\r\n _url = urlparse(url)\r\n values = _url._asdict()\r\n values['query'] = ''\r\n values['path'] = '{}/subtitle/lang_en/sub-0.vtt'.format(\"/\".join(values['path'].split('/')[:-1]))\r\n\r\n subtitle_url = ParseResult(**values).geturl()\r\n # subtitle_file = kodiutils.download_url_content_to_temp(subtitle_url, '{}-{}.srt'.format(\r\n # Zee5Plugin.safe_string(item['title']),\r\n # subtitle_lang,\r\n # ))\r\n\r\n return subtitle_url\r\n\r\n #logger.info(\"######: {}, log: {}########\".format('rk6', data))\r\n #item = data['body']['results']['item']\r\n item=data['body']['results']['playBackSets'][0]\r\n path = item['playbackUrl']\r\n licenseURL = item.get('licenseUrl')\r\n subtitle = get_subtitle(path)\r\n\r\n logger.info('Playing video URL: {}, licenseURL: {}, subtitle: {}'.format(path, licenseURL, subtitle))\r\n\r\n play_item = xbmcgui.ListItem(path=path)\r\n if licenseURL:\r\n play_item.setProperty('inputstreamaddon', 'inputstream.adaptive')\r\n play_item.setProperty('inputstream.adaptive.manifest_type', 'hls')\r\n play_item.setMimeType('application/dash+xml')\r\n play_item.setContentLookup(False)\r\n\r\n play_item.setSubtitles([get_subtitle(path)])\r\n\r\n # Pass the item to the Kodi player.\r\n xbmcplugin.setResolvedUrl(_handle, True, listitem=play_item)","def test_read_video_from_file(self, test_video, config):\n # video related\n width, height, min_dimension, max_dimension = 0, 0, 0, 0\n video_start_pts, video_end_pts = 0, -1\n video_timebase_num, video_timebase_den = 0, 1\n # audio related\n samples, channels = 0, 0\n audio_start_pts, audio_end_pts = 0, -1\n audio_timebase_num, audio_timebase_den = 0, 1\n\n full_path = os.path.join(VIDEO_DIR, test_video)\n\n # pass 1: decode all frames using new decoder\n tv_result = torch.ops.video_reader.read_video_from_file(\n full_path,\n SEEK_FRAME_MARGIN,\n 0, # getPtsOnly\n 1, # readVideoStream\n width,\n height,\n min_dimension,\n max_dimension,\n video_start_pts,\n video_end_pts,\n video_timebase_num,\n video_timebase_den,\n 1, # readAudioStream\n samples,\n channels,\n audio_start_pts,\n audio_end_pts,\n audio_timebase_num,\n audio_timebase_den,\n )\n # pass 2: decode all frames using av\n pyav_result = _decode_frames_by_av_module(full_path)\n # check results from TorchVision decoder\n self.check_separate_decoding_result(tv_result, config)\n # compare decoding results\n self.compare_decoding_result(tv_result, pyav_result, config)","def load_video(video_path, dim):\n videogen = skvideo.io.vreader(video_path)\n vid_data = []\n for frame in videogen:\n try:\n vid_data.append(scipy.misc.imresize(frame, dim))\n except:\n print len(vid_data)\n return np.array(vid_data)","def video1():\n return Response(gen_frames(1),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def load(self, _fName, _testOnly=False): \n tempDir = os.path.dirname(_fName)\n if len(tempDir) > 0:\n tempDir += \"\\\\\"\n self.fNameVideo = _fName\n self.fExtVideo = os.path.splitext(_fName)[1].lower()\n self.isTestOnly = _testOnly\n \n if self.fExtVideo in glo.QDSpy_vidAllowedVideoExts:\n return self.__loadVideo()\n else:\n return stm.StimErrC.invalidVideoFormat","def isLoaded(self):\n if self.video == None:\n return False\n return True","def load_one_frame(self):\n\n if self.loaded_frames >= self.n_frames: return False\n\n frame = cv2.imread(os.path.join(self.path, self.files[self.loaded_frames]))\n\n self.height = frame.shape[0]\n self.width = frame.shape[1]\n self.aspect_ratio = float(self.width)/self.height\n\n ## Calculate total video size\n total_size = self.n_frames * float(frame.shape[0]*frame.shape[1]*frame.shape[2]*8) / (1024)**3\n x = 2.0/math.sqrt(total_size)\n\n frame = cv2.resize(frame, (min(int(self.aspect_ratio*1080), int(x * frame.shape[1])), min(1080, int(x * frame.shape[0]))), interpolation=cv2.INTER_AREA)\n frame = cv2.flip(frame, 0)\n\n self.frames.append(frame)\n\n texture = Texture.create(size=(frame.shape[1], frame.shape[0]), colorfmt='bgr')\n texture.blit_buffer(frame.tostring(), bufferfmt=\"ubyte\", colorfmt=\"bgr\")\n\n self.textures.append(texture)\n\n self.loaded_frames += 1\n\n return True","def watch_movie():\r\n if os.path.isfile('files/final_movie.mp4'): # if the file exists\r\n with open('files/final_movie.mp4', 'rb') as f:\r\n video_data = f.read()\r\n st.video(video_data)\r\n else: # if the file doesn't exist, let the user know\r\n st.header(\"You haven't created a movie yet!\")","def get_video(lesson_id, video_id):\n url = '{0}?cat={1}&video={2}'.format(BASE_URL, lesson_id, video_id)\n page = requests.get(url, verify=False)\n soup = BeautifulSoup(page.content)\n return soup.find('iframe')['src'].split('/')[-1]","def play_random_video(self):\n num_videos = len(self._video_library.get_all_videos())\n videos = self._video_library.get_all_videos()\n random_index = randint(0, num_videos-1)\n self.play_video(videos[random_index].video_id)\n # print(\"play_random_video needs implementation\")","def play_random_video(self):\n\n print(\"play_random_video needs implementation\")","def open_video(self):\n\n \n self.filename_temp, _ = QFileDialog.getOpenFileName(self, \"Open Video\")\n\n if self.filename_temp != '':\n if self.filename_temp[-3:] == \"mp4\" or self.filename_temp[-3:] == \"wav\" or self.filename_temp[-3:] == \"wmv\" or self.filename_temp[-3:] == \"mov\":\n self.mediaPlayer.setMedia(QMediaContent(QUrl.fromLocalFile(self.filename_temp)))\n self.playBtn.setEnabled(True)\n self.saveBtn.setEnabled(True)\n self.videoOpened = True\n self.clear_annotation()\n self.filename = self.filename_temp\n\n\n else:\n message = QMessageBox()\n message.setWindowTitle(\"Fail\")\n message.setText(\"Please choose a file with one of the following extensions:\\nmp4, wav, mov or wmv.\")\n x = message.exec_() # this will show our messagebox¨¨\n\n\n elif self.filename_temp == '' and self.videoOpened:\n self.filename = self.filename\n elif self.filename_temp == '' and not self.videoOpened:\n self.filename = None","def video0():\n return Response(gen_frames(0),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def readVideo(self):\n vid = cv2.VideoCapture(self.fname)\n imgstack = []\n # grab = True\n grab, img = vid.read()\n while grab:\n imgstack.append(\n Frame(\n cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\n self.starttime\n + datetime.timedelta(seconds=self.frame_dt * self.length),\n )\n )\n self.length += 1\n grab, img = vid.read()\n self.frames = imgstack","def load_video(self, filename):\n \n reader = imageio.get_reader(filename, 'ffmpeg')\n \n return np.array(list(reader), dtype=np.float32)","def play_video(self, video_path, period):\n self.video_playing = True\n self.state_time_remain = period\n abs_path = QFileInfo(video_path).absoluteFilePath()\n QMetaObject.invokeMethod(self.video_player, \"setMedia\", Qt.QueuedConnection,\n Q_ARG(QMediaContent, QMediaContent(QUrl.fromLocalFile(abs_path))))","def play(self, path=None):\n if path is None:\n path = self.download()\n\n # Clear the users out buffer before playing the video\n os.system('cls' if os.name == 'nt' else 'clear')\n\n # Better colors for display\n utils.term.bgcolor = 'white'\n count = 0\n\n vid = cv2.VideoCapture(path)\n\n while(vid.isOpened()):\n # Read the frame\n ret, frame = vid.read()\n count += 1\n\n # Convert to work with PIL and fabulous\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n image = Image.fromarray(frame)\n\n display = fi.Image(\"demo.png\")\n display.img = image.convert(\"RGBA\")\n display.resize()\n print str(display)[:-1]\n\n # Clear the stdout buffer after MAX_FRAME number frames\n if count % self.MAX_BUF_SIZE == 0:\n os.system('cls' if os.name == 'nt' else 'clear')","def play_video(path):\n # Create a playable item with a path to play.\n play_item = xbmcgui.ListItem(path=path)\n vid_url = play_item.getfilename()\n if 'WatchOnlineMovies' not in vid_url:\n stream_url = resolve_url(vid_url)\n if stream_url:\n play_item.setPath(stream_url)\n # Pass the item to the Kodi player.\n xbmcplugin.setResolvedUrl(_handle, True, listitem=play_item)","def _load_player(self, filename):\n self._unload_player()\n\n Logger.info(\"VLCPlayer: Loading player\")\n SoundVLCPlayer.player = player = self.instance.media_player_new()\n media = player.set_mrl(filename)\n player.event_manager().event_attach(\n EventType.MediaPlayerEndReached, self._track_finished)\n media.parse() # Determine duration\n self._length = media.get_duration() / 1000.0\n media.release()","async def igvideo(self, ctx, url):\n response = requests.get(url.replace(\"`\", \"\"), headers={\"Accept-Encoding\": \"utf-8\"})\n tree = html.fromstring(response.content)\n results = tree.xpath('//meta[@content]')\n sources = []\n for result in results:\n try:\n if result.attrib['property'] == \"og:video\":\n sources.append(result.attrib['content'])\n except KeyError:\n pass\n if sources:\n await ctx.send(sources[0])\n self.logger.info(misolog.format_log(ctx, f\"Success\"))\n else:\n await ctx.send(\"Found nothing, sorry!\")\n self.logger.warning(misolog.format_log(ctx, f\"Found nothing\"))","def video():\n return render_template('video.html')","def video2():\n return Response(gen_frames(2),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def load(self, media):\n path = self.check_media(media)\n if path is False:\n self._log(\"warning\", \"Unknown media {0} => aborting\".format(media))\n #return False prevent continue to play or play last media, force send unknow file to load to vlc\n # self.stdin_queue.put_nowait()\n self._direct_stdin_writer(\"load {0}\".format(path))","def video(path):\n cap = cv2.VideoCapture(path)\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n return frame_count, cap","def video_feed(self):\r\n model.video.link(self.link)\r\n age_net, gender_net = model.video.caffe_models()\r\n return Response(model.video.video_detector(age_net, gender_net),mimetype='multipart/x-mixed-replace; boundary=frame')","def download_vid(item):\n vid_name, vid_id = item\n vid = Video(vid_name, vid_id, resolution='224p')\n vid.download()","def play_random_video(self):\n if self._current_video:\n self.stop_video()\n videos = [v for v in self._video_library.get_all_videos() if v.flag is None]\n if not videos:\n print(\"No videos available\")\n return\n self.play_video(choice(videos).video_id)","def play_video(filename):\n if (not path.exists(filename)):\n print(f\"The video file: {filename} doesn't exist\")\n return\n \n video = cv2.VideoCapture(filename)\n\n # Check if camera opened successfully\n if (video.isOpened() == False):\n print(f\"Error opening video stream or file: {filename}\")\n return\n\n frame_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\n frame_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\n fps = int(video.get(cv2.CAP_PROP_FPS))\n\n # Read until video is completed\n video_title = f'Video {filename}, {frame_width}x{frame_height}, fps = {fps}'\n while (video.isOpened()):\n ret, frame = video.read()\n if ret == True:\n # Display the resulting frame\n cv2.imshow(video_title, frame)\n cv2.waitKey(1000 // fps)\n else:\n break\n\n # When everything done, release the video capture object\n video.release()\n\n # Closes all the frames\n cv2.waitKey(0)\n cv2.destroyAllWindows()","def __init__(self, videoPath=\"\", cacheString=\"-cache\", faceDetector=None):\n self.videoPath = videoPath\n self.cacheString = cacheString\n self.video = None \n self.currentFrame = []\n self.currentFrameNumber = 0\n self.frameCount = 0 #total frame number\n self.fps = 25\n self.ret = True #if the video is over\n self.isPlaying = False\n self.ifDrawAxis = False\n self.ifDrawSquare = False\n self.cacheData = {}\n \n if videoPath != \"\":\n self.load()","def get_video_by_id():\n sample_id = demisto.getArg('id')\n r = req('GET', SUB_API + 'samples/' + sample_id + '/video.webm')\n ec = {'ThreatGrid.Sample.Id': sample_id}\n demisto.results([\n {\n 'Type': entryTypes['note'],\n 'EntryContext': ec,\n 'HumanReadable': '### ThreatGrid Sample Run Video File -\\n'\n + 'Your sample run video file download request has been completed successfully for '\n + sample_id,\n 'Contents': ec,\n 'ContentsFormat': formats['json']\n },\n fileResult(sample_id + '.webm', r.content)\n ])","def start(self):\n if not path.exists(self.dir):\n mkdir(self.dir)\n\n try:\n yt = YouTube(self.link)\n ys = yt.streams\n if self.aud == True:\n ysf = ys.get_audio_only()\n elif self.aud == False:\n ysf = ys.get_by_resolution(resolution=self.res)\n ysf.download(self.dir)\n except AttributeError:\n raise Exception(f\"Resolution [{self.res}] is not available for this video\")\n except excep.RegexMatchError:\n raise Exception(\"Invalid link!\")\n except excep.VideoPrivate:\n raise Exception(\"This video is private! Try with a public one\")\n except excep.PytubeError:\n raise Exception(\"Error occured!\")\n except excep.VideoPrivate:\n raise Exception(\"This video is private! Try with a public one\")\n except excep.VideoUnavailable:\n raise Exception(\"This video is unvailable!\")\n except excep.ExtractError:\n raise Exception(\"Error in extractiong the video!\")\n except Exception:\n raise Exception(\"Error occured. Make sure of your connection ;)\")","def show_video(path: str): \n video_path = sorted(glob(path + \"/*.mp4\"))[-1]\n video = io.open(video_path, 'r+b').read()\n encoded = base64.b64encode(video)\n\n return HTML(data='''<video alt=\"test\" controls>\n <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" /> </video>'''\n .format(encoded.decode('ascii')))","def loadVideoFrame(self):\n self.VideoFrame = cv2.cvtColor(\n cv2.imread(script_path + \"/data/rgb_image.png\", cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)","def test_read_video_from_memory(self, test_video, config):\n # video related\n width, height, min_dimension, max_dimension = 0, 0, 0, 0\n video_start_pts, video_end_pts = 0, -1\n video_timebase_num, video_timebase_den = 0, 1\n # audio related\n samples, channels = 0, 0\n audio_start_pts, audio_end_pts = 0, -1\n audio_timebase_num, audio_timebase_den = 0, 1\n\n full_path, video_tensor = _get_video_tensor(VIDEO_DIR, test_video)\n\n # pass 1: decode all frames using cpp decoder\n tv_result = torch.ops.video_reader.read_video_from_memory(\n video_tensor,\n SEEK_FRAME_MARGIN,\n 0, # getPtsOnly\n 1, # readVideoStream\n width,\n height,\n min_dimension,\n max_dimension,\n video_start_pts,\n video_end_pts,\n video_timebase_num,\n video_timebase_den,\n 1, # readAudioStream\n samples,\n channels,\n audio_start_pts,\n audio_end_pts,\n audio_timebase_num,\n audio_timebase_den,\n )\n # pass 2: decode all frames using av\n pyav_result = _decode_frames_by_av_module(full_path)\n\n self.check_separate_decoding_result(tv_result, config)\n self.compare_decoding_result(tv_result, pyav_result, config)","def test_read_video_in_range_from_memory(self, test_video, config, num_frames):\n full_path, video_tensor = _get_video_tensor(VIDEO_DIR, test_video)\n # video related\n width, height, min_dimension, max_dimension = 0, 0, 0, 0\n video_start_pts, video_end_pts = 0, -1\n video_timebase_num, video_timebase_den = 0, 1\n # audio related\n samples, channels = 0, 0\n audio_start_pts, audio_end_pts = 0, -1\n audio_timebase_num, audio_timebase_den = 0, 1\n # pass 1: decode all frames using new decoder\n tv_result = torch.ops.video_reader.read_video_from_memory(\n video_tensor,\n SEEK_FRAME_MARGIN,\n 0, # getPtsOnly\n 1, # readVideoStream\n width,\n height,\n min_dimension,\n max_dimension,\n video_start_pts,\n video_end_pts,\n video_timebase_num,\n video_timebase_den,\n 1, # readAudioStream\n samples,\n channels,\n audio_start_pts,\n audio_end_pts,\n audio_timebase_num,\n audio_timebase_den,\n )\n (\n vframes,\n vframe_pts,\n vtimebase,\n vfps,\n vduration,\n aframes,\n aframe_pts,\n atimebase,\n asample_rate,\n aduration,\n ) = tv_result\n assert abs(config.video_fps - vfps.item()) < 0.01\n\n start_pts_ind_max = vframe_pts.size(0) - num_frames\n if start_pts_ind_max <= 0:\n return\n # randomly pick start pts\n start_pts_ind = randint(0, start_pts_ind_max)\n end_pts_ind = start_pts_ind + num_frames - 1\n video_start_pts = vframe_pts[start_pts_ind]\n video_end_pts = vframe_pts[end_pts_ind]\n\n video_timebase_num, video_timebase_den = vtimebase[0], vtimebase[1]\n if len(atimebase) > 0:\n # when audio stream is available\n audio_timebase_num, audio_timebase_den = atimebase[0], atimebase[1]\n audio_start_pts = _pts_convert(\n video_start_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(audio_timebase_num.item(), audio_timebase_den.item()),\n math.floor,\n )\n audio_end_pts = _pts_convert(\n video_end_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(audio_timebase_num.item(), audio_timebase_den.item()),\n math.ceil,\n )\n\n # pass 2: decode frames in the randomly generated range\n tv_result = torch.ops.video_reader.read_video_from_memory(\n video_tensor,\n SEEK_FRAME_MARGIN,\n 0, # getPtsOnly\n 1, # readVideoStream\n width,\n height,\n min_dimension,\n max_dimension,\n video_start_pts,\n video_end_pts,\n video_timebase_num,\n video_timebase_den,\n 1, # readAudioStream\n samples,\n channels,\n audio_start_pts,\n audio_end_pts,\n audio_timebase_num,\n audio_timebase_den,\n )\n\n # pass 3: decode frames in range using PyAv\n video_timebase_av, audio_timebase_av = _get_timebase_by_av_module(full_path)\n\n video_start_pts_av = _pts_convert(\n video_start_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(video_timebase_av.numerator, video_timebase_av.denominator),\n math.floor,\n )\n video_end_pts_av = _pts_convert(\n video_end_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(video_timebase_av.numerator, video_timebase_av.denominator),\n math.ceil,\n )\n if audio_timebase_av:\n audio_start_pts = _pts_convert(\n video_start_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(audio_timebase_av.numerator, audio_timebase_av.denominator),\n math.floor,\n )\n audio_end_pts = _pts_convert(\n video_end_pts.item(),\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\n Fraction(audio_timebase_av.numerator, audio_timebase_av.denominator),\n math.ceil,\n )\n\n pyav_result = _decode_frames_by_av_module(\n full_path,\n video_start_pts_av,\n video_end_pts_av,\n audio_start_pts,\n audio_end_pts,\n )\n\n assert tv_result[0].size(0) == num_frames\n if pyav_result.vframes.size(0) == num_frames:\n # if PyAv decodes a different number of video frames, skip\n # comparing the decoding results between Torchvision video reader\n # and PyAv\n self.compare_decoding_result(tv_result, pyav_result, config)","def video_handle_for_demo():\n frame = cv2.imread(\"vision.png\")\n\n return frame","def run(self, vid_input_path='project_video.mp4'):\n vid_output_path = self.g.output_movie_path + vid_input_path\n print('Finding lanes for:', vid_input_path) \n\n # Load the Video\n # video_clip = VideoFileClip(video_filename).subclip(10, 11)\n clip1 = VideoFileClip(vid_input_path)\n\n # Feed the video, clip by clip into the pipeline.\n test_clip = clip1.fl_image(self.__image_pipeline) \n test_clip.write_videofile(vid_output_path, audio=False)\n\n return True","def videoFeed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()), mimetype='multipart/x-mixed-replace; boundary=frame')","def play_video(self):\n raise NotImplementedError(\n \"This method needs to be implemented by a derived class\"\n )","def play_video(self, video_id):\n video = self._video_library.get_video(video_id)\n if video is None:\n print(\"Cannot play video: Video does not exist\")\n return\n if video.flag is not None:\n print(f\"Cannot play video: Video is currently flagged (reason: {video.flag})\")\n return\n if self._current_video:\n self.stop_video()\n print(f\"Playing video: {video.title}\")\n self._current_video = video","def read(self, ifDrawAxis=None, ifDrawSquare=None):\n if(self.isLoaded() == False):\n return\n self.ret, frame = self.video.read()\n if (self.ret == True):\n \n # Draw frame\n self.currentFrame = frame\n self.currentFrameNumber = int(self.video.get(cv2.CAP_PROP_POS_FRAMES))\n \n else: # If the video is end\n self.isPlaying = False\n \n return self.frame(ifDrawAxis, ifDrawSquare)","def ServeVideo(request):\n cv=None\n data=request.data\n if data['course_id']=='1':\n cv=CourseOneVideo.objects.get(video_id=data['video_id'])\n elif data['course_id']=='2':\n cv=CourseTwoVideo.objects.get(video_id=1)\n video_title=cv.video_title\n video_url=cv.video_url\n return Response(data={'video_title':video_title, 'video_url':video_url}, status=status.HTTP_200_OK )","def play_video(video_path):\n key_esc = 27\n videoCapture = cv2.VideoCapture(video_path)\n\n success, img = videoCapture.read()\n # Loop until there are no more frames.\n while success:\n cv2.imshow('video', img)\n if 0xFF & cv2.waitKey(5) == key_esc:\n break\n success, img = videoCapture.read()\n\n cv2.destroyAllWindows()","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def video_feed():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def get_video(self, video_id):\n return self._videos.get(video_id, None)","def download(self, url=None):\n if url is None:\n if self.results is None:\n raise ValueError(\"Please specify a valid url.\")\n else:\n url = self.results[0]\n try:\n meta = pafy.new(url)\n except Exception:\n raise IOError(\"Video not available for download.\")\n\n vid = meta.getbest()\n path = vid.download()\n self.videos.append(path)\n return path","def load_video(cih_file):\n cih = get_cih(cih_file)\n mraw_file = path.splitext(cih_file)[0] + '.mraw'\n N = cih['Total Frame']\n h = cih['Image Height']\n w = cih['Image Width']\n bit = cih['Color Bit']\n images = load_images(mraw_file, h, w, N, bit, roll_axis=False)\n return images, cih","def test_video_player(self):\r\n\r\n # Navigate to a video\r\n self.course_info_page.visit()\r\n self.tab_nav.go_to_tab('Courseware')\r\n\r\n # The video should start off paused\r\n # Since the video hasn't loaded yet, it's elapsed time is 0\r\n self.assertFalse(self.video.is_playing)\r\n self.assertEqual(self.video.elapsed_time, 0)\r\n\r\n # Play the video\r\n self.video.play()\r\n\r\n # Now we should be playing\r\n self.assertTrue(self.video.is_playing)\r\n\r\n # Commented the below EmptyPromise, will move to its page once this test is working and stable\r\n # Also there is should be no Promise check in any test as this should be done in Page Object\r\n # Wait for the video to load the duration\r\n # EmptyPromise(\r\n # lambda: self.video.duration > 0,\r\n # 'video has duration', timeout=20\r\n # ).fulfill()\r\n\r\n # Pause the video\r\n self.video.pause()\r\n\r\n # Expect that the elapsed time and duration are reasonable\r\n # Again, we can't expect the video to actually play because of\r\n # latency through the ssh tunnel\r\n self.assertGreaterEqual(self.video.elapsed_time, 0)\r\n self.assertGreaterEqual(self.video.duration, self.video.elapsed_time)","def play_video(self, video_id):\n video_info = self._video_library.get_all_videos()\n for i in video_info:\n if video_id == i.video_id:\n print(f\"Playing video: {i.title}\")\n def search(list, platform):\n for i in range(len(list)):\n if list[i] == platform:\n return False\n return True\n video_id_list = []\n for i in video_info:\n video_id_list.append(i.video_id)\n if search(video_id_list, video_id):\n print(\"Cannot play video: Video does not exist\")","def play_random_video(self):\n video = random.choice(self._video_library.get_all_videos())\n self.play_video(video.video_id)","def play_random_video(self):\n if self.video_id != None:\n print(f\"Stopping video: {self.video_id}\")\n video_info = self._video_library.get_all_videos()\n a_list = []\n for i in video_info:\n a_list.append(video_info.title)\n a = random.randint(0, len(a_list)-1)\n print(f\"Playing video: {a_list[a]}\")","def get_video(self, timeout=None):\n self._event.wait(timeout)\n return self._video","def load_videos(self):\n logging.debug(\"Loading videos data...\")\n\n # loading videos\n data=requests.get(self.__URL_VIDEOS)\n self.__dataframe_videos=pd.DataFrame(data.json())\n # XXX transposing as the API returns a pre index list of videos\n self.__dataframe_videos = self.__dataframe_videos.transpose()\n if self.__save_raw_data_to_csv:\n logging.debug(\"Saving raw data to CSV [%s...\" % self.__RAW_DATA_FILENAME)\n self.__dataframe_videos.to_csv(self.__RAW_DATA_FILENAME, encoding='utf-8', sep=',', index=False)\n self.__dataframe_videos['video_contents'] = self.__dataframe_videos[['video_title', 'video_desc']].\\\n apply(lambda x: \" \".join(x), axis=1)\n\n logging.debug(\"Informative videos data loaded! n=%s\" % self.__dataframe_videos.shape[0])\n\n return self.__dataframe_videos","def OnPlay(self):\r\n # check if there is a file to play, otherwise open a\r\n # Tk.FileDialog to select a file\r\n print(\"1-1\")\r\n\r\n\r\n self.Media = self.Instance.media_new(self.youtube_url)\r\n self.player.set_media(self.Media)\r\n\r\n # set the window id where to render VLC's video output\r\n if platform.system() == 'Windows':\r\n print(\"1-3\")\r\n self.player.set_hwnd(self.GetHandle())\r\n else:\r\n print(\"1-4\")\r\n self.player.set_xwindow(self.GetHandle()) # this line messes up windows\r\n # FIXME: this should be made cross-platform\r\n\r\n # Try to launch the media, if this fails display an error message\r\n if self.player.play() == -1:\r\n print(\"1-6\")\r\n self.errorDialog(\"Unable to play.\")","def _check_vid_open(self):\n if not self.video.isOpened():\n raise IOError('Video is not open')\n return 1","def load_parsed_video(season: int, episode: int) -> Union[ParsedVideo, None]:\n file_path = VideoParser.__get_parsed_video_file_path(season, episode)\n if not os.path.isfile(file_path):\n return None\n\n with open(file_path, 'rb') as file:\n video_parsing = pickle.load(file)\n\n parsed_video = ParsedVideo(*video_parsing)\n player_occurrences = {player: sorted(occurrences) for player, occurrences in parsed_video.player_occurrences.items()}\n return ParsedVideo(player_occurrences, parsed_video.alive_players, parsed_video.frame_skip)","def main_func_video(param_list: list = None) -> bool:\r\n # index of param\r\n # noinspection PyPep8Naming\r\n PORT_RAW_PICT = 0\r\n\r\n # check if param OK\r\n if len(param_list) != 1:\r\n log_error_to_console(\"GET FRAME VIDEO MAIN FUNCTION PARAM NOK\", str(len(param_list)))\r\n return False\r\n else:\r\n port_image = get_port_from_wave(name=param_list[PORT_RAW_PICT])\r\n\r\n try:\r\n # noinspection PyUnresolvedReferences\r\n success, port_image.arr[:] = global_var_handler.VIDEO.read()\r\n if success is True:\r\n port_image.set_valid()\r\n except BaseException as error:\r\n is_error()\r\n # noinspection PyUnresolvedReferences\r\n log_error_to_console('RAW PICTURE NOK TO READ: ' + str(global_var_handler.VIDEO.__str__()), str(error))\r\n port_image.set_invalid()\r\n pass\r\n\r\n # noinspection PyUnresolvedReferences\r\n log_to_file(str(global_var_handler.FRAME))\r\n # noinspection PyUnresolvedReferences\r\n log_to_file(global_var_handler.STR_L0_SIZE)\r\n\r\n return True","def test_probe_video_from_memory(self, test_video, config):\n _, video_tensor = _get_video_tensor(VIDEO_DIR, test_video)\n probe_result = torch.ops.video_reader.probe_video_from_memory(video_tensor)\n self.check_probe_result(probe_result, config)"],"string":"[\n \"def __loadVideo(self):\\n # Check if movie file exists ...\\n #\\n if not(os.path.isfile(self.fNameVideo)):\\n return stm.StimErrC.videoFileNotFound\\n\\n try: \\n # Load video\\n #\\n self.video = mpe.VideoFileClip(self.fNameVideo)\\n \\n except IOError: \\n return stm.StimErrC.invalidVideoFormat\\n \\n # Retrieve video description\\n #\\n self.dxFr = self.video.size[0]\\n self.dyFr = self.video.size[1]\\n self.nFr = self.video.duration *self.video.fps\\n self.fps = self.video.fps\\n ssp.Log.write(\\\"DEBUG\\\", \\\"stim_video: {0}x{1} pixel, {2} frames, {3} fps\\\"\\n .format(self.dxFr, self.dyFr, self.nFr, self.fps))\\n \\n if self.isTestOnly:\\n # Return here if the video was only loaded to test if it is ok\\n #\\n self.video = None\\n return stm.StimErrC.ok\\n \\n # Load movie frames (note that frames is a generator!)\\n #\\n self.frames = self.video.iter_frames() \\n self.isReady = True\\n return stm.StimErrC.ok\",\n \"def _load_video(self, video):\\n\\t\\timport cv2\\n\\t\\tcap = cv2.VideoCapture(video)\\n\\t\\tassert cap.get(cv2.CAP_PROP_FRAME_WIDTH) >= 100, 'width must be at least 100 pixels'\\n\\t\\tassert cap.get(cv2.CAP_PROP_FRAME_HEIGHT) >= 100, 'height must be at least 100 pixels'\\n\\t\\tn = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n\\t\\tbuf = np.empty((n, int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), 3), np.dtype('uint8'))\\n\\t\\ti, ret = 0, True\\n\\t\\twhile (i < n and ret):\\n\\t\\t\\tret, frame = cap.read()\\n\\t\\t\\tframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\\n\\t\\t\\tbuf[i] = frame\\n\\t\\t\\ti += 1\\n\\t\\tcap.release()\\n\\t\\treturn np.moveaxis(buf, -1, 1)\",\n \"def onclick_load_video(self):\\n video_source = select_file(\\n \\\"Select Video Files\\\",\\n \\\"../\\\",\\n \\\"Video Files (*.mp4 *.avi *.mpg *.gif *.mov)\\\")\\n if video_source:\\n param_name = select_file(\\n \\\"Select Parameter\\\", \\\"../\\\", \\\"Parameter Files (*.json)\\\")\\n if param_name:\\n self.moildev = Moildev(param_name)\\n self.running_video(video_source)\",\n \"def loadVideo(self,path,loadAudio=False):\\r\\n self.videoPlayer.loadVideo(path,loadAudio=loadAudio)\\r\\n self.videoPath = path\",\n \"def loadVideo(self,path,loadAudio=True):\\r\\n self.aud_path = \\\"\\\"\\r\\n # Get cv2 video capture object\\r\\n self.vid_path = path\\r\\n self.vid = cv2.VideoCapture(self.vid_path)\\r\\n self.delay = int(1000/self.vid.get(cv2.CAP_PROP_FPS))\\r\\n self.vid_len = int(self.vid.get(cv2.CAP_PROP_FRAME_COUNT))/self.vid.get(cv2.CAP_PROP_FPS)\\r\\n self.state = VideoPlayer.State.STOPPED\\r\\n self.hasAudio = True# If no audio in video, ignore audio\\r\\n if loadAudio:\\r\\n self.loadAudio(self.vid_path)\\r\\n else:\\r\\n self.loadCachedAudio()\",\n \"def load_vid_frame(self, ind, camname, preload=True, extension=\\\".mp4\\\"):\\n fname = (\\n str(self._N_VIDEO_FRAMES * int(np.floor(ind / self._N_VIDEO_FRAMES)))\\n + extension\\n )\\n frame_num = int(ind % self._N_VIDEO_FRAMES)\\n keyname = os.path.join(camname, fname)\\n if preload:\\n return self.vidreaders[camname][keyname].get_data(frame_num)\\n else:\\n thisvid_name = self.vidreaders[camname][keyname]\\n abname = thisvid_name.split(\\\"/\\\")[-1]\\n if abname == self.currvideo_name[camname]:\\n vid = self.currvideo[camname]\\n else:\\n vid = imageio.get_reader(thisvid_name)\\n print(\\\"Loading new video: {} for {}\\\".format(abname, camname))\\n self.currvideo_name[camname] = abname\\n # close current vid\\n # Without a sleep here, ffmpeg can hang on video close\\n time.sleep(0.25)\\n if self.currvideo[camname] is not None:\\n self.currvideo[camname].close()\\n self.currvideo[camname] = vid\\n\\n im = vid.get_data(frame_num)\\n\\n return im\",\n \"def load_video(self):\\r\\n if self.file_name=='':\\r\\n Tk().withdraw()\\r\\n self.file_name = askopenfilename()\\r\\n cap = cv2.VideoCapture(self.file_name)\\r\\n self.length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\r\\n self.width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\r\\n self.heigth = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\r\\n self.fps = int(round(cap.get(cv2.CAP_PROP_FPS)))\\r\\n \\r\\n video_buffer = []#np.ndarray(shape=(self.length, self.heigth, self.width, 3), dtype=np.uint8)\\r\\n for i in tqdm(range(self.length), desc='Loading video from: {}'.format(self.file_name)):\\r\\n ret, frame = cap.read()\\r\\n if not ret:\\r\\n break\\r\\n video_buffer.append(frame)\\r\\n #assert(i==self.length-1)\\r\\n video_buffer = np.array(video_buffer, dtype=np.uint8)\\r\\n self.video_buffer = video_buffer\\r\\n cap.release()\\r\\n self.ix = self.width-1\\r\\n self.iy = self.heigth-1 \\r\\n self.roi = dict(x1=self.x, y1=self.y, x2=self.ix, y2=self.iy)\\r\\n return video_buffer\",\n \"def openVideo(self):\\n fname = self.openFile(self.user[\\\"Video\\\"])\\n if fname != \\\"\\\":\\n error_opening_video = False\\n\\n cam = cv2.VideoCapture(fname)\\n logging.info(\\\"Opening video Check: {0}\\\".format(fname))\\n\\n currentframe = 0\\n ret, frame = cam.read()\\n\\n if ret is False:\\n error_opening_video = True\\n # Release all space and windows once done\\n cam.release()\\n cv2.destroyAllWindows()\\n\\n if error_opening_video:\\n msg = QMessageBox()\\n msg.setIcon(QMessageBox.Information)\\n msg.setText(\\\"Error on opening the video: {0}\\\".format(fname))\\n msg.setWindowTitle(\\\"Information\\\")\\n msg.setStandardButtons(QMessageBox.Ok)\\n msg.exec_()\\n else:\\n self.user[\\\"Video\\\"] = fname\\n name = self.splitPath(fname)[-1]\\n self.vid_name = name.split(\\\".\\\")[0]\\n self.ui.l_vid.setText(\\\"Load: \\\" + self.vid_name)\\n self.checkFiles()\",\n \"def load(self):\\n if self.ref == None:\\n self.ref = av.open(self.abspath)\\n self.n_audio_streams = len([s for s in self.ref.streams if s.type == 'audio'])\\n self.n_video_streams = len([s for s in self.ref.streams if s.type == 'video'])\\n if self.n_audio_streams > 0:\\n self.media_types += ['audio']\\n if self.n_video_streams > 0:\\n self.media_types += ['video']\\n self.duration = self.ref.duration / self.AV_TIME_BASE\",\n \"def init_video(self):\\n\\n assert self.container is None\\n\\n retry = 3\\n while self.container is None and 0 < retry:\\n retry -= 1\\n try:\\n self.container = av.open(self.tello.get_video_stream())\\n except av.AVError as ave:\\n print(ave)\\n print('retry...')\\n\\n\\n assert self.container is not None\",\n \"def get_video(self, video_id):\\n uri = 'videos/' + video_id\\n return self.make_request(uri)\",\n \"def load_video_from_path(path: Union[Path, str]) -> videoType:\\n\\n if isinstance(path, str):\\n path = Path(path)\\n video_path = path.resolve()\\n if video_path.exists():\\n print(f\\\"Video loaded from {video_path}\\\")\\n vid: videoType = cv2.VideoCapture(str(video_path))\\n else:\\n sys.exit(\\\"Error: Video file not found...\\\")\\n\\n return vid\",\n \"def play_video(file, name):\\n url = u\\\"http://smooth.ruv.cache.is/opid/{0}\\\".format(file)\\n r = requests.head(url)\\n\\n if r.status_code != 200:\\n url = u\\\"http://smooth.ruv.cache.is/lokad/{0}\\\".format(file)\\n\\n player.play(url, name)\",\n \"def video():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def test_probe_video_from_file(self, test_video, config):\\n full_path = os.path.join(VIDEO_DIR, test_video)\\n probe_result = torch.ops.video_reader.probe_video_from_file(full_path)\\n self.check_probe_result(probe_result, config)\",\n \"def process_video(self):\\n if os.path.isfile(self.source):\\n self.cap = cv2.VideoCapture(self.source)\\n else:\\n try:\\n file_name = \\\"input.mp4\\\"\\n self.source = self.source.replace('open', 'uc')\\n print( \\\"\\\\nDownloading video file from drive link to %s\\\\n\\\"%file_name)\\n gdown.download(self.source, file_name, quiet=False)\\n print( \\\"%s downloaded!\\\\n\\\"%file_name )\\n self.cap = cv2.VideoCapture(file_name)\\n except Exception:\\n raise RuntimeError(\\\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\\\n\\\")\\n\\n\\n assert self.cap.isOpened(), \\\"Failed to open %s\\\" % self.source\\n\\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \\n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n return\",\n \"def load(self, uri):\\n\\n self.stop()\\n self.uri = uri\\n if os.path.exists(self.uri):\\n uri = \\\"%s%s%s\\\" % (\\\"\\\\\\\"\\\", self.uri, \\\"\\\\\\\"\\\")\\n\\n cache_pantalla = \\\"%s -cache %i -wid %i\\\" % (\\n MPLAYER, 1024, self.ventana_id)\\n\\n estructura = \\\"%s -slave -idle -nolirc\\\" % (cache_pantalla)\\n estructura = \\\"%s -rtc -nomouseinput\\\" % estructura\\n estructura = \\\"%s -noconsolecontrols -nojoystick\\\" % estructura\\n\\n self.mplayer = subprocess.Popen(\\n estructura, shell=True, stdin=subprocess.PIPE,\\n stdout=open(STDOUT, \\\"w+b\\\"), stderr=open(STDOUT, \\\"r+b\\\"),\\n universal_newlines=True)\\n\\n self.entrada = self.mplayer.stdin\\n self.salida = open(STDOUT, \\\"r\\\")\\n self.entrada.write(\\\"loadfile %s 0\\\\n\\\" % uri)\\n self.entrada.flush()\\n self.video_in_stream = False\\n\\n self.__new_handle(True)\",\n \"def obtenerVideo(camara):\\n val, frame = camara.read()\\n return val, frame\",\n \"def test_task_video_download(url_to_video: str, empty_video_resource: VideoResource):\\n download_video(url_to_video, empty_video_resource.id)\\n empty_video_resource.refresh_from_db()\\n video_instance = empty_video_resource.videos.filter(primary=True).first()\\n\\n assert empty_video_resource.videos.all()\\n assert video_instance.extension == 'mp4'\\n assert video_instance.primary\\n for item in video_instance.video.open():\\n assert item\",\n \"async def get_video(videoid):\\n\\theaders = {\\n\\t\\t'Client-ID': config['twitch_clientid'],\\n\\t\\t'Authorization': f\\\"Bearer {get_token()}\\\",\\n\\t}\\n\\tdata = await common.http.request_coro(\\\"https://api.twitch.tv/helix/videos\\\", {\\\"id\\\": videoid.lstrip('v')}, headers=headers)\\n\\treturn json.loads(data)[\\\"data\\\"][0]\",\n \"def load(self, fileName=None, cacheString=None, callback=None):\\n if fileName != None:\\n self.videoPath = fileName\\n if cacheString != None:\\n self.cacheString = cacheString\\n \\n if not os.path.exists(self.videoPath):\\n raise ValueError(\\\"ERROR : %s don't exist\\\" % (self.videoPath))\\n \\n if callback != None:\\n callback(0.1, \\\"Load the video with OpenCV\\\")\\n \\n self.video = cv2.VideoCapture(self.videoPath)\\n \\n if(not self.video.isOpened()):\\n self.video = None\\n raise ValueError(\\\"ERROR : Can't load %s\\\" % (self.videoPath))\\n \\n self.fps = int(self.video.get(cv2.CAP_PROP_FPS))\\n self.frameCount = self.realFrameNumber(callback)\\n \\n if callback != None:\\n callback(0.85, \\\"Load or create the cache file\\\")\\n \\n # we load the cache\\n self.cachePath = self.videoPath + self.cacheString + \\\".json\\\"\\n self.loadCacheFile() #we load or create the cache file\\n \\n # we draw the first frame\\n self.setFrame(0)\",\n \"def load_video(self):\\n self.video_file = tkFileDialog.askopenfilename()\\n self.video_parser = VideoFileParser(self.video_file)\\n\\n self.video_entries = self.video_parser.entries\\n\\n for index, entry in enumerate(self.video_entries):\\n self.video_box.insert(index, entry.word)\",\n \"def play(self): \\n if(self.isLoaded() == False):\\n return\\n self.isPlaying = True\\n #if the video is end, we restart\\n if(self.ret == False):\\n self.video.set(1, 0)\",\n \"def importVideo(self):\\n path, filter = QtGui.QFileDialog.getOpenFileNames(self, str(self.tr(\\\"Import a video\\\")), \\\"/home/cecilia/\\\", self.tr(\\\"Video files (*.avi *.mov)\\\"))\\n videoPath = path[0].encode(\\\"utf-8\\\")\\n self._app.importVideo(videoPath)\",\n \"def load_stream(filename, folder):\\n cap = cv2.VideoCapture(os.path.join(folder, filename))\\n while True:\\n ret, frame = cap.read()\\n cv2.imshow(\\\"img\\\", frame)\\n if cv2.waitKey(1) & 0xFF == ord(\\\"q\\\"):\\n cap.release()\\n cv2.destroyAllWindows()\\n break\",\n \"def load_frame_from_video(path: str, frame_index: int) -> np.ndarray:\\n vid = load_video(path)\\n img = vid[frame_index]\\n return img\",\n \"def load(self, uri):\\n if self.p:\\n self.p.stop()\\n self.p = self.vlc.media_player_new(uri)\\n Player._finished = False\\n e = self.p.event_manager()\\n e.event_attach(vlc.EventType.MediaPlayerEndReached, self.__end_reached, None)\\n if (not '://' in uri or uri.startswith('file://')) and os.stat(uri).st_size < 100:\\n self._finished = True\\n else:\\n self.p.play()\",\n \"def _read_file(self):\\n extension = self.path.split('.')[-1]\\n if extension!='avi':\\n raise Exception(\\\"Invalid Format\\\")\\n\\n return cv2.VideoCapture(self.path)\",\n \"def play_video(path):\\r\\n #logger.info(\\\"######: {}, log: {}########\\\".format('rk8', path))\\r\\n #original=https://api.hotstar.com/h/v1/play?contentId=1000238814\\r\\n #path=\\\"https://api.hotstar.com/h/v2/play/in/contents/1000238814\\\"\\r\\n # Create a playable item with a path to play.\\r\\n data = make_request(path)\\r\\n #logger.info(\\\"######: {}, log: {}########\\\".format('rk3', path))\\r\\n if not data:\\r\\n return\\r\\n\\r\\n def get_subtitle(url):\\r\\n #\\r\\n # https://hses.akamaized.net/videos/hotstarint/hostages/1260003409/1558430241469/\\r\\n # 265b9dab22d4e9a033e6df6f89639f17/master.m3u8?hdnea=st=1560107863~exp=1560111463~acl=\\r\\n # /*~hmac=2f6fb393159ed5fa1b12bbf12e954eb377cfa0fc852d4ff5eb24446233237620\\r\\n #\\r\\n # https://hses.akamaized.net/videos/hotstarint/hostages/1260003409/1558430241469/\\r\\n # 5d0f83c3ccbf4501cf952bdfc8c0d785/subtitle/lang_en/sub-0.vtt\\r\\n #\\r\\n _url = urlparse(url)\\r\\n values = _url._asdict()\\r\\n values['query'] = ''\\r\\n values['path'] = '{}/subtitle/lang_en/sub-0.vtt'.format(\\\"/\\\".join(values['path'].split('/')[:-1]))\\r\\n\\r\\n subtitle_url = ParseResult(**values).geturl()\\r\\n # subtitle_file = kodiutils.download_url_content_to_temp(subtitle_url, '{}-{}.srt'.format(\\r\\n # Zee5Plugin.safe_string(item['title']),\\r\\n # subtitle_lang,\\r\\n # ))\\r\\n\\r\\n return subtitle_url\\r\\n\\r\\n #logger.info(\\\"######: {}, log: {}########\\\".format('rk6', data))\\r\\n #item = data['body']['results']['item']\\r\\n item=data['body']['results']['playBackSets'][0]\\r\\n path = item['playbackUrl']\\r\\n licenseURL = item.get('licenseUrl')\\r\\n subtitle = get_subtitle(path)\\r\\n\\r\\n logger.info('Playing video URL: {}, licenseURL: {}, subtitle: {}'.format(path, licenseURL, subtitle))\\r\\n\\r\\n play_item = xbmcgui.ListItem(path=path)\\r\\n if licenseURL:\\r\\n play_item.setProperty('inputstreamaddon', 'inputstream.adaptive')\\r\\n play_item.setProperty('inputstream.adaptive.manifest_type', 'hls')\\r\\n play_item.setMimeType('application/dash+xml')\\r\\n play_item.setContentLookup(False)\\r\\n\\r\\n play_item.setSubtitles([get_subtitle(path)])\\r\\n\\r\\n # Pass the item to the Kodi player.\\r\\n xbmcplugin.setResolvedUrl(_handle, True, listitem=play_item)\",\n \"def test_read_video_from_file(self, test_video, config):\\n # video related\\n width, height, min_dimension, max_dimension = 0, 0, 0, 0\\n video_start_pts, video_end_pts = 0, -1\\n video_timebase_num, video_timebase_den = 0, 1\\n # audio related\\n samples, channels = 0, 0\\n audio_start_pts, audio_end_pts = 0, -1\\n audio_timebase_num, audio_timebase_den = 0, 1\\n\\n full_path = os.path.join(VIDEO_DIR, test_video)\\n\\n # pass 1: decode all frames using new decoder\\n tv_result = torch.ops.video_reader.read_video_from_file(\\n full_path,\\n SEEK_FRAME_MARGIN,\\n 0, # getPtsOnly\\n 1, # readVideoStream\\n width,\\n height,\\n min_dimension,\\n max_dimension,\\n video_start_pts,\\n video_end_pts,\\n video_timebase_num,\\n video_timebase_den,\\n 1, # readAudioStream\\n samples,\\n channels,\\n audio_start_pts,\\n audio_end_pts,\\n audio_timebase_num,\\n audio_timebase_den,\\n )\\n # pass 2: decode all frames using av\\n pyav_result = _decode_frames_by_av_module(full_path)\\n # check results from TorchVision decoder\\n self.check_separate_decoding_result(tv_result, config)\\n # compare decoding results\\n self.compare_decoding_result(tv_result, pyav_result, config)\",\n \"def load_video(video_path, dim):\\n videogen = skvideo.io.vreader(video_path)\\n vid_data = []\\n for frame in videogen:\\n try:\\n vid_data.append(scipy.misc.imresize(frame, dim))\\n except:\\n print len(vid_data)\\n return np.array(vid_data)\",\n \"def video1():\\n return Response(gen_frames(1),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def load(self, _fName, _testOnly=False): \\n tempDir = os.path.dirname(_fName)\\n if len(tempDir) > 0:\\n tempDir += \\\"\\\\\\\\\\\"\\n self.fNameVideo = _fName\\n self.fExtVideo = os.path.splitext(_fName)[1].lower()\\n self.isTestOnly = _testOnly\\n \\n if self.fExtVideo in glo.QDSpy_vidAllowedVideoExts:\\n return self.__loadVideo()\\n else:\\n return stm.StimErrC.invalidVideoFormat\",\n \"def isLoaded(self):\\n if self.video == None:\\n return False\\n return True\",\n \"def load_one_frame(self):\\n\\n if self.loaded_frames >= self.n_frames: return False\\n\\n frame = cv2.imread(os.path.join(self.path, self.files[self.loaded_frames]))\\n\\n self.height = frame.shape[0]\\n self.width = frame.shape[1]\\n self.aspect_ratio = float(self.width)/self.height\\n\\n ## Calculate total video size\\n total_size = self.n_frames * float(frame.shape[0]*frame.shape[1]*frame.shape[2]*8) / (1024)**3\\n x = 2.0/math.sqrt(total_size)\\n\\n frame = cv2.resize(frame, (min(int(self.aspect_ratio*1080), int(x * frame.shape[1])), min(1080, int(x * frame.shape[0]))), interpolation=cv2.INTER_AREA)\\n frame = cv2.flip(frame, 0)\\n\\n self.frames.append(frame)\\n\\n texture = Texture.create(size=(frame.shape[1], frame.shape[0]), colorfmt='bgr')\\n texture.blit_buffer(frame.tostring(), bufferfmt=\\\"ubyte\\\", colorfmt=\\\"bgr\\\")\\n\\n self.textures.append(texture)\\n\\n self.loaded_frames += 1\\n\\n return True\",\n \"def watch_movie():\\r\\n if os.path.isfile('files/final_movie.mp4'): # if the file exists\\r\\n with open('files/final_movie.mp4', 'rb') as f:\\r\\n video_data = f.read()\\r\\n st.video(video_data)\\r\\n else: # if the file doesn't exist, let the user know\\r\\n st.header(\\\"You haven't created a movie yet!\\\")\",\n \"def get_video(lesson_id, video_id):\\n url = '{0}?cat={1}&video={2}'.format(BASE_URL, lesson_id, video_id)\\n page = requests.get(url, verify=False)\\n soup = BeautifulSoup(page.content)\\n return soup.find('iframe')['src'].split('/')[-1]\",\n \"def play_random_video(self):\\n num_videos = len(self._video_library.get_all_videos())\\n videos = self._video_library.get_all_videos()\\n random_index = randint(0, num_videos-1)\\n self.play_video(videos[random_index].video_id)\\n # print(\\\"play_random_video needs implementation\\\")\",\n \"def play_random_video(self):\\n\\n print(\\\"play_random_video needs implementation\\\")\",\n \"def open_video(self):\\n\\n \\n self.filename_temp, _ = QFileDialog.getOpenFileName(self, \\\"Open Video\\\")\\n\\n if self.filename_temp != '':\\n if self.filename_temp[-3:] == \\\"mp4\\\" or self.filename_temp[-3:] == \\\"wav\\\" or self.filename_temp[-3:] == \\\"wmv\\\" or self.filename_temp[-3:] == \\\"mov\\\":\\n self.mediaPlayer.setMedia(QMediaContent(QUrl.fromLocalFile(self.filename_temp)))\\n self.playBtn.setEnabled(True)\\n self.saveBtn.setEnabled(True)\\n self.videoOpened = True\\n self.clear_annotation()\\n self.filename = self.filename_temp\\n\\n\\n else:\\n message = QMessageBox()\\n message.setWindowTitle(\\\"Fail\\\")\\n message.setText(\\\"Please choose a file with one of the following extensions:\\\\nmp4, wav, mov or wmv.\\\")\\n x = message.exec_() # this will show our messagebox¨¨\\n\\n\\n elif self.filename_temp == '' and self.videoOpened:\\n self.filename = self.filename\\n elif self.filename_temp == '' and not self.videoOpened:\\n self.filename = None\",\n \"def video0():\\n return Response(gen_frames(0),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def readVideo(self):\\n vid = cv2.VideoCapture(self.fname)\\n imgstack = []\\n # grab = True\\n grab, img = vid.read()\\n while grab:\\n imgstack.append(\\n Frame(\\n cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\\n self.starttime\\n + datetime.timedelta(seconds=self.frame_dt * self.length),\\n )\\n )\\n self.length += 1\\n grab, img = vid.read()\\n self.frames = imgstack\",\n \"def load_video(self, filename):\\n \\n reader = imageio.get_reader(filename, 'ffmpeg')\\n \\n return np.array(list(reader), dtype=np.float32)\",\n \"def play_video(self, video_path, period):\\n self.video_playing = True\\n self.state_time_remain = period\\n abs_path = QFileInfo(video_path).absoluteFilePath()\\n QMetaObject.invokeMethod(self.video_player, \\\"setMedia\\\", Qt.QueuedConnection,\\n Q_ARG(QMediaContent, QMediaContent(QUrl.fromLocalFile(abs_path))))\",\n \"def play(self, path=None):\\n if path is None:\\n path = self.download()\\n\\n # Clear the users out buffer before playing the video\\n os.system('cls' if os.name == 'nt' else 'clear')\\n\\n # Better colors for display\\n utils.term.bgcolor = 'white'\\n count = 0\\n\\n vid = cv2.VideoCapture(path)\\n\\n while(vid.isOpened()):\\n # Read the frame\\n ret, frame = vid.read()\\n count += 1\\n\\n # Convert to work with PIL and fabulous\\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\\n image = Image.fromarray(frame)\\n\\n display = fi.Image(\\\"demo.png\\\")\\n display.img = image.convert(\\\"RGBA\\\")\\n display.resize()\\n print str(display)[:-1]\\n\\n # Clear the stdout buffer after MAX_FRAME number frames\\n if count % self.MAX_BUF_SIZE == 0:\\n os.system('cls' if os.name == 'nt' else 'clear')\",\n \"def play_video(path):\\n # Create a playable item with a path to play.\\n play_item = xbmcgui.ListItem(path=path)\\n vid_url = play_item.getfilename()\\n if 'WatchOnlineMovies' not in vid_url:\\n stream_url = resolve_url(vid_url)\\n if stream_url:\\n play_item.setPath(stream_url)\\n # Pass the item to the Kodi player.\\n xbmcplugin.setResolvedUrl(_handle, True, listitem=play_item)\",\n \"def _load_player(self, filename):\\n self._unload_player()\\n\\n Logger.info(\\\"VLCPlayer: Loading player\\\")\\n SoundVLCPlayer.player = player = self.instance.media_player_new()\\n media = player.set_mrl(filename)\\n player.event_manager().event_attach(\\n EventType.MediaPlayerEndReached, self._track_finished)\\n media.parse() # Determine duration\\n self._length = media.get_duration() / 1000.0\\n media.release()\",\n \"async def igvideo(self, ctx, url):\\n response = requests.get(url.replace(\\\"`\\\", \\\"\\\"), headers={\\\"Accept-Encoding\\\": \\\"utf-8\\\"})\\n tree = html.fromstring(response.content)\\n results = tree.xpath('//meta[@content]')\\n sources = []\\n for result in results:\\n try:\\n if result.attrib['property'] == \\\"og:video\\\":\\n sources.append(result.attrib['content'])\\n except KeyError:\\n pass\\n if sources:\\n await ctx.send(sources[0])\\n self.logger.info(misolog.format_log(ctx, f\\\"Success\\\"))\\n else:\\n await ctx.send(\\\"Found nothing, sorry!\\\")\\n self.logger.warning(misolog.format_log(ctx, f\\\"Found nothing\\\"))\",\n \"def video():\\n return render_template('video.html')\",\n \"def video2():\\n return Response(gen_frames(2),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def load(self, media):\\n path = self.check_media(media)\\n if path is False:\\n self._log(\\\"warning\\\", \\\"Unknown media {0} => aborting\\\".format(media))\\n #return False prevent continue to play or play last media, force send unknow file to load to vlc\\n # self.stdin_queue.put_nowait()\\n self._direct_stdin_writer(\\\"load {0}\\\".format(path))\",\n \"def video(path):\\n cap = cv2.VideoCapture(path)\\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n return frame_count, cap\",\n \"def video_feed(self):\\r\\n model.video.link(self.link)\\r\\n age_net, gender_net = model.video.caffe_models()\\r\\n return Response(model.video.video_detector(age_net, gender_net),mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def download_vid(item):\\n vid_name, vid_id = item\\n vid = Video(vid_name, vid_id, resolution='224p')\\n vid.download()\",\n \"def play_random_video(self):\\n if self._current_video:\\n self.stop_video()\\n videos = [v for v in self._video_library.get_all_videos() if v.flag is None]\\n if not videos:\\n print(\\\"No videos available\\\")\\n return\\n self.play_video(choice(videos).video_id)\",\n \"def play_video(filename):\\n if (not path.exists(filename)):\\n print(f\\\"The video file: {filename} doesn't exist\\\")\\n return\\n \\n video = cv2.VideoCapture(filename)\\n\\n # Check if camera opened successfully\\n if (video.isOpened() == False):\\n print(f\\\"Error opening video stream or file: {filename}\\\")\\n return\\n\\n frame_width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))\\n frame_height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n fps = int(video.get(cv2.CAP_PROP_FPS))\\n\\n # Read until video is completed\\n video_title = f'Video {filename}, {frame_width}x{frame_height}, fps = {fps}'\\n while (video.isOpened()):\\n ret, frame = video.read()\\n if ret == True:\\n # Display the resulting frame\\n cv2.imshow(video_title, frame)\\n cv2.waitKey(1000 // fps)\\n else:\\n break\\n\\n # When everything done, release the video capture object\\n video.release()\\n\\n # Closes all the frames\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\",\n \"def __init__(self, videoPath=\\\"\\\", cacheString=\\\"-cache\\\", faceDetector=None):\\n self.videoPath = videoPath\\n self.cacheString = cacheString\\n self.video = None \\n self.currentFrame = []\\n self.currentFrameNumber = 0\\n self.frameCount = 0 #total frame number\\n self.fps = 25\\n self.ret = True #if the video is over\\n self.isPlaying = False\\n self.ifDrawAxis = False\\n self.ifDrawSquare = False\\n self.cacheData = {}\\n \\n if videoPath != \\\"\\\":\\n self.load()\",\n \"def get_video_by_id():\\n sample_id = demisto.getArg('id')\\n r = req('GET', SUB_API + 'samples/' + sample_id + '/video.webm')\\n ec = {'ThreatGrid.Sample.Id': sample_id}\\n demisto.results([\\n {\\n 'Type': entryTypes['note'],\\n 'EntryContext': ec,\\n 'HumanReadable': '### ThreatGrid Sample Run Video File -\\\\n'\\n + 'Your sample run video file download request has been completed successfully for '\\n + sample_id,\\n 'Contents': ec,\\n 'ContentsFormat': formats['json']\\n },\\n fileResult(sample_id + '.webm', r.content)\\n ])\",\n \"def start(self):\\n if not path.exists(self.dir):\\n mkdir(self.dir)\\n\\n try:\\n yt = YouTube(self.link)\\n ys = yt.streams\\n if self.aud == True:\\n ysf = ys.get_audio_only()\\n elif self.aud == False:\\n ysf = ys.get_by_resolution(resolution=self.res)\\n ysf.download(self.dir)\\n except AttributeError:\\n raise Exception(f\\\"Resolution [{self.res}] is not available for this video\\\")\\n except excep.RegexMatchError:\\n raise Exception(\\\"Invalid link!\\\")\\n except excep.VideoPrivate:\\n raise Exception(\\\"This video is private! Try with a public one\\\")\\n except excep.PytubeError:\\n raise Exception(\\\"Error occured!\\\")\\n except excep.VideoPrivate:\\n raise Exception(\\\"This video is private! Try with a public one\\\")\\n except excep.VideoUnavailable:\\n raise Exception(\\\"This video is unvailable!\\\")\\n except excep.ExtractError:\\n raise Exception(\\\"Error in extractiong the video!\\\")\\n except Exception:\\n raise Exception(\\\"Error occured. Make sure of your connection ;)\\\")\",\n \"def show_video(path: str): \\n video_path = sorted(glob(path + \\\"/*.mp4\\\"))[-1]\\n video = io.open(video_path, 'r+b').read()\\n encoded = base64.b64encode(video)\\n\\n return HTML(data='''<video alt=\\\"test\\\" controls>\\n <source src=\\\"data:video/mp4;base64,{0}\\\" type=\\\"video/mp4\\\" /> </video>'''\\n .format(encoded.decode('ascii')))\",\n \"def loadVideoFrame(self):\\n self.VideoFrame = cv2.cvtColor(\\n cv2.imread(script_path + \\\"/data/rgb_image.png\\\", cv2.IMREAD_UNCHANGED), cv2.COLOR_BGR2RGB)\",\n \"def test_read_video_from_memory(self, test_video, config):\\n # video related\\n width, height, min_dimension, max_dimension = 0, 0, 0, 0\\n video_start_pts, video_end_pts = 0, -1\\n video_timebase_num, video_timebase_den = 0, 1\\n # audio related\\n samples, channels = 0, 0\\n audio_start_pts, audio_end_pts = 0, -1\\n audio_timebase_num, audio_timebase_den = 0, 1\\n\\n full_path, video_tensor = _get_video_tensor(VIDEO_DIR, test_video)\\n\\n # pass 1: decode all frames using cpp decoder\\n tv_result = torch.ops.video_reader.read_video_from_memory(\\n video_tensor,\\n SEEK_FRAME_MARGIN,\\n 0, # getPtsOnly\\n 1, # readVideoStream\\n width,\\n height,\\n min_dimension,\\n max_dimension,\\n video_start_pts,\\n video_end_pts,\\n video_timebase_num,\\n video_timebase_den,\\n 1, # readAudioStream\\n samples,\\n channels,\\n audio_start_pts,\\n audio_end_pts,\\n audio_timebase_num,\\n audio_timebase_den,\\n )\\n # pass 2: decode all frames using av\\n pyav_result = _decode_frames_by_av_module(full_path)\\n\\n self.check_separate_decoding_result(tv_result, config)\\n self.compare_decoding_result(tv_result, pyav_result, config)\",\n \"def test_read_video_in_range_from_memory(self, test_video, config, num_frames):\\n full_path, video_tensor = _get_video_tensor(VIDEO_DIR, test_video)\\n # video related\\n width, height, min_dimension, max_dimension = 0, 0, 0, 0\\n video_start_pts, video_end_pts = 0, -1\\n video_timebase_num, video_timebase_den = 0, 1\\n # audio related\\n samples, channels = 0, 0\\n audio_start_pts, audio_end_pts = 0, -1\\n audio_timebase_num, audio_timebase_den = 0, 1\\n # pass 1: decode all frames using new decoder\\n tv_result = torch.ops.video_reader.read_video_from_memory(\\n video_tensor,\\n SEEK_FRAME_MARGIN,\\n 0, # getPtsOnly\\n 1, # readVideoStream\\n width,\\n height,\\n min_dimension,\\n max_dimension,\\n video_start_pts,\\n video_end_pts,\\n video_timebase_num,\\n video_timebase_den,\\n 1, # readAudioStream\\n samples,\\n channels,\\n audio_start_pts,\\n audio_end_pts,\\n audio_timebase_num,\\n audio_timebase_den,\\n )\\n (\\n vframes,\\n vframe_pts,\\n vtimebase,\\n vfps,\\n vduration,\\n aframes,\\n aframe_pts,\\n atimebase,\\n asample_rate,\\n aduration,\\n ) = tv_result\\n assert abs(config.video_fps - vfps.item()) < 0.01\\n\\n start_pts_ind_max = vframe_pts.size(0) - num_frames\\n if start_pts_ind_max <= 0:\\n return\\n # randomly pick start pts\\n start_pts_ind = randint(0, start_pts_ind_max)\\n end_pts_ind = start_pts_ind + num_frames - 1\\n video_start_pts = vframe_pts[start_pts_ind]\\n video_end_pts = vframe_pts[end_pts_ind]\\n\\n video_timebase_num, video_timebase_den = vtimebase[0], vtimebase[1]\\n if len(atimebase) > 0:\\n # when audio stream is available\\n audio_timebase_num, audio_timebase_den = atimebase[0], atimebase[1]\\n audio_start_pts = _pts_convert(\\n video_start_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(audio_timebase_num.item(), audio_timebase_den.item()),\\n math.floor,\\n )\\n audio_end_pts = _pts_convert(\\n video_end_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(audio_timebase_num.item(), audio_timebase_den.item()),\\n math.ceil,\\n )\\n\\n # pass 2: decode frames in the randomly generated range\\n tv_result = torch.ops.video_reader.read_video_from_memory(\\n video_tensor,\\n SEEK_FRAME_MARGIN,\\n 0, # getPtsOnly\\n 1, # readVideoStream\\n width,\\n height,\\n min_dimension,\\n max_dimension,\\n video_start_pts,\\n video_end_pts,\\n video_timebase_num,\\n video_timebase_den,\\n 1, # readAudioStream\\n samples,\\n channels,\\n audio_start_pts,\\n audio_end_pts,\\n audio_timebase_num,\\n audio_timebase_den,\\n )\\n\\n # pass 3: decode frames in range using PyAv\\n video_timebase_av, audio_timebase_av = _get_timebase_by_av_module(full_path)\\n\\n video_start_pts_av = _pts_convert(\\n video_start_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(video_timebase_av.numerator, video_timebase_av.denominator),\\n math.floor,\\n )\\n video_end_pts_av = _pts_convert(\\n video_end_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(video_timebase_av.numerator, video_timebase_av.denominator),\\n math.ceil,\\n )\\n if audio_timebase_av:\\n audio_start_pts = _pts_convert(\\n video_start_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(audio_timebase_av.numerator, audio_timebase_av.denominator),\\n math.floor,\\n )\\n audio_end_pts = _pts_convert(\\n video_end_pts.item(),\\n Fraction(video_timebase_num.item(), video_timebase_den.item()),\\n Fraction(audio_timebase_av.numerator, audio_timebase_av.denominator),\\n math.ceil,\\n )\\n\\n pyav_result = _decode_frames_by_av_module(\\n full_path,\\n video_start_pts_av,\\n video_end_pts_av,\\n audio_start_pts,\\n audio_end_pts,\\n )\\n\\n assert tv_result[0].size(0) == num_frames\\n if pyav_result.vframes.size(0) == num_frames:\\n # if PyAv decodes a different number of video frames, skip\\n # comparing the decoding results between Torchvision video reader\\n # and PyAv\\n self.compare_decoding_result(tv_result, pyav_result, config)\",\n \"def video_handle_for_demo():\\n frame = cv2.imread(\\\"vision.png\\\")\\n\\n return frame\",\n \"def run(self, vid_input_path='project_video.mp4'):\\n vid_output_path = self.g.output_movie_path + vid_input_path\\n print('Finding lanes for:', vid_input_path) \\n\\n # Load the Video\\n # video_clip = VideoFileClip(video_filename).subclip(10, 11)\\n clip1 = VideoFileClip(vid_input_path)\\n\\n # Feed the video, clip by clip into the pipeline.\\n test_clip = clip1.fl_image(self.__image_pipeline) \\n test_clip.write_videofile(vid_output_path, audio=False)\\n\\n return True\",\n \"def videoFeed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()), mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def play_video(self):\\n raise NotImplementedError(\\n \\\"This method needs to be implemented by a derived class\\\"\\n )\",\n \"def play_video(self, video_id):\\n video = self._video_library.get_video(video_id)\\n if video is None:\\n print(\\\"Cannot play video: Video does not exist\\\")\\n return\\n if video.flag is not None:\\n print(f\\\"Cannot play video: Video is currently flagged (reason: {video.flag})\\\")\\n return\\n if self._current_video:\\n self.stop_video()\\n print(f\\\"Playing video: {video.title}\\\")\\n self._current_video = video\",\n \"def read(self, ifDrawAxis=None, ifDrawSquare=None):\\n if(self.isLoaded() == False):\\n return\\n self.ret, frame = self.video.read()\\n if (self.ret == True):\\n \\n # Draw frame\\n self.currentFrame = frame\\n self.currentFrameNumber = int(self.video.get(cv2.CAP_PROP_POS_FRAMES))\\n \\n else: # If the video is end\\n self.isPlaying = False\\n \\n return self.frame(ifDrawAxis, ifDrawSquare)\",\n \"def ServeVideo(request):\\n cv=None\\n data=request.data\\n if data['course_id']=='1':\\n cv=CourseOneVideo.objects.get(video_id=data['video_id'])\\n elif data['course_id']=='2':\\n cv=CourseTwoVideo.objects.get(video_id=1)\\n video_title=cv.video_title\\n video_url=cv.video_url\\n return Response(data={'video_title':video_title, 'video_url':video_url}, status=status.HTTP_200_OK )\",\n \"def play_video(video_path):\\n key_esc = 27\\n videoCapture = cv2.VideoCapture(video_path)\\n\\n success, img = videoCapture.read()\\n # Loop until there are no more frames.\\n while success:\\n cv2.imshow('video', img)\\n if 0xFF & cv2.waitKey(5) == key_esc:\\n break\\n success, img = videoCapture.read()\\n\\n cv2.destroyAllWindows()\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def video_feed():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def get_video(self, video_id):\\n return self._videos.get(video_id, None)\",\n \"def download(self, url=None):\\n if url is None:\\n if self.results is None:\\n raise ValueError(\\\"Please specify a valid url.\\\")\\n else:\\n url = self.results[0]\\n try:\\n meta = pafy.new(url)\\n except Exception:\\n raise IOError(\\\"Video not available for download.\\\")\\n\\n vid = meta.getbest()\\n path = vid.download()\\n self.videos.append(path)\\n return path\",\n \"def load_video(cih_file):\\n cih = get_cih(cih_file)\\n mraw_file = path.splitext(cih_file)[0] + '.mraw'\\n N = cih['Total Frame']\\n h = cih['Image Height']\\n w = cih['Image Width']\\n bit = cih['Color Bit']\\n images = load_images(mraw_file, h, w, N, bit, roll_axis=False)\\n return images, cih\",\n \"def test_video_player(self):\\r\\n\\r\\n # Navigate to a video\\r\\n self.course_info_page.visit()\\r\\n self.tab_nav.go_to_tab('Courseware')\\r\\n\\r\\n # The video should start off paused\\r\\n # Since the video hasn't loaded yet, it's elapsed time is 0\\r\\n self.assertFalse(self.video.is_playing)\\r\\n self.assertEqual(self.video.elapsed_time, 0)\\r\\n\\r\\n # Play the video\\r\\n self.video.play()\\r\\n\\r\\n # Now we should be playing\\r\\n self.assertTrue(self.video.is_playing)\\r\\n\\r\\n # Commented the below EmptyPromise, will move to its page once this test is working and stable\\r\\n # Also there is should be no Promise check in any test as this should be done in Page Object\\r\\n # Wait for the video to load the duration\\r\\n # EmptyPromise(\\r\\n # lambda: self.video.duration > 0,\\r\\n # 'video has duration', timeout=20\\r\\n # ).fulfill()\\r\\n\\r\\n # Pause the video\\r\\n self.video.pause()\\r\\n\\r\\n # Expect that the elapsed time and duration are reasonable\\r\\n # Again, we can't expect the video to actually play because of\\r\\n # latency through the ssh tunnel\\r\\n self.assertGreaterEqual(self.video.elapsed_time, 0)\\r\\n self.assertGreaterEqual(self.video.duration, self.video.elapsed_time)\",\n \"def play_video(self, video_id):\\n video_info = self._video_library.get_all_videos()\\n for i in video_info:\\n if video_id == i.video_id:\\n print(f\\\"Playing video: {i.title}\\\")\\n def search(list, platform):\\n for i in range(len(list)):\\n if list[i] == platform:\\n return False\\n return True\\n video_id_list = []\\n for i in video_info:\\n video_id_list.append(i.video_id)\\n if search(video_id_list, video_id):\\n print(\\\"Cannot play video: Video does not exist\\\")\",\n \"def play_random_video(self):\\n video = random.choice(self._video_library.get_all_videos())\\n self.play_video(video.video_id)\",\n \"def play_random_video(self):\\n if self.video_id != None:\\n print(f\\\"Stopping video: {self.video_id}\\\")\\n video_info = self._video_library.get_all_videos()\\n a_list = []\\n for i in video_info:\\n a_list.append(video_info.title)\\n a = random.randint(0, len(a_list)-1)\\n print(f\\\"Playing video: {a_list[a]}\\\")\",\n \"def get_video(self, timeout=None):\\n self._event.wait(timeout)\\n return self._video\",\n \"def load_videos(self):\\n logging.debug(\\\"Loading videos data...\\\")\\n\\n # loading videos\\n data=requests.get(self.__URL_VIDEOS)\\n self.__dataframe_videos=pd.DataFrame(data.json())\\n # XXX transposing as the API returns a pre index list of videos\\n self.__dataframe_videos = self.__dataframe_videos.transpose()\\n if self.__save_raw_data_to_csv:\\n logging.debug(\\\"Saving raw data to CSV [%s...\\\" % self.__RAW_DATA_FILENAME)\\n self.__dataframe_videos.to_csv(self.__RAW_DATA_FILENAME, encoding='utf-8', sep=',', index=False)\\n self.__dataframe_videos['video_contents'] = self.__dataframe_videos[['video_title', 'video_desc']].\\\\\\n apply(lambda x: \\\" \\\".join(x), axis=1)\\n\\n logging.debug(\\\"Informative videos data loaded! n=%s\\\" % self.__dataframe_videos.shape[0])\\n\\n return self.__dataframe_videos\",\n \"def OnPlay(self):\\r\\n # check if there is a file to play, otherwise open a\\r\\n # Tk.FileDialog to select a file\\r\\n print(\\\"1-1\\\")\\r\\n\\r\\n\\r\\n self.Media = self.Instance.media_new(self.youtube_url)\\r\\n self.player.set_media(self.Media)\\r\\n\\r\\n # set the window id where to render VLC's video output\\r\\n if platform.system() == 'Windows':\\r\\n print(\\\"1-3\\\")\\r\\n self.player.set_hwnd(self.GetHandle())\\r\\n else:\\r\\n print(\\\"1-4\\\")\\r\\n self.player.set_xwindow(self.GetHandle()) # this line messes up windows\\r\\n # FIXME: this should be made cross-platform\\r\\n\\r\\n # Try to launch the media, if this fails display an error message\\r\\n if self.player.play() == -1:\\r\\n print(\\\"1-6\\\")\\r\\n self.errorDialog(\\\"Unable to play.\\\")\",\n \"def _check_vid_open(self):\\n if not self.video.isOpened():\\n raise IOError('Video is not open')\\n return 1\",\n \"def load_parsed_video(season: int, episode: int) -> Union[ParsedVideo, None]:\\n file_path = VideoParser.__get_parsed_video_file_path(season, episode)\\n if not os.path.isfile(file_path):\\n return None\\n\\n with open(file_path, 'rb') as file:\\n video_parsing = pickle.load(file)\\n\\n parsed_video = ParsedVideo(*video_parsing)\\n player_occurrences = {player: sorted(occurrences) for player, occurrences in parsed_video.player_occurrences.items()}\\n return ParsedVideo(player_occurrences, parsed_video.alive_players, parsed_video.frame_skip)\",\n \"def main_func_video(param_list: list = None) -> bool:\\r\\n # index of param\\r\\n # noinspection PyPep8Naming\\r\\n PORT_RAW_PICT = 0\\r\\n\\r\\n # check if param OK\\r\\n if len(param_list) != 1:\\r\\n log_error_to_console(\\\"GET FRAME VIDEO MAIN FUNCTION PARAM NOK\\\", str(len(param_list)))\\r\\n return False\\r\\n else:\\r\\n port_image = get_port_from_wave(name=param_list[PORT_RAW_PICT])\\r\\n\\r\\n try:\\r\\n # noinspection PyUnresolvedReferences\\r\\n success, port_image.arr[:] = global_var_handler.VIDEO.read()\\r\\n if success is True:\\r\\n port_image.set_valid()\\r\\n except BaseException as error:\\r\\n is_error()\\r\\n # noinspection PyUnresolvedReferences\\r\\n log_error_to_console('RAW PICTURE NOK TO READ: ' + str(global_var_handler.VIDEO.__str__()), str(error))\\r\\n port_image.set_invalid()\\r\\n pass\\r\\n\\r\\n # noinspection PyUnresolvedReferences\\r\\n log_to_file(str(global_var_handler.FRAME))\\r\\n # noinspection PyUnresolvedReferences\\r\\n log_to_file(global_var_handler.STR_L0_SIZE)\\r\\n\\r\\n return True\",\n \"def test_probe_video_from_memory(self, test_video, config):\\n _, video_tensor = _get_video_tensor(VIDEO_DIR, test_video)\\n probe_result = torch.ops.video_reader.probe_video_from_memory(video_tensor)\\n self.check_probe_result(probe_result, config)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7726525","0.70660406","0.69042337","0.68983585","0.6861589","0.6831531","0.67470115","0.6746388","0.65393686","0.64091796","0.6401286","0.6394237","0.634081","0.62345356","0.621533","0.6213619","0.6195335","0.6193793","0.6168615","0.6168126","0.61294425","0.6112704","0.6074151","0.6017877","0.600059","0.59913176","0.5989264","0.59744394","0.594029","0.593568","0.59288853","0.59111583","0.5893526","0.5886841","0.5884161","0.5875336","0.5874087","0.58621407","0.58545697","0.5849702","0.58283305","0.5813985","0.58038664","0.57851297","0.5782727","0.5781198","0.5772928","0.5764932","0.57385236","0.5736138","0.573348","0.5720753","0.5711087","0.5711045","0.57100636","0.5705285","0.5702056","0.5685711","0.5678864","0.5676602","0.5671288","0.56486505","0.56319726","0.5630818","0.56204194","0.5611773","0.5610054","0.5608906","0.5607732","0.5602008","0.55996174","0.5588129","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55752426","0.55747205","0.5573816","0.55727977","0.55697256","0.5569664","0.5568552","0.5547841","0.55387956","0.5535315","0.5534248","0.5526536","0.55090696","0.55051315","0.5500855"],"string":"[\n \"0.7726525\",\n \"0.70660406\",\n \"0.69042337\",\n \"0.68983585\",\n \"0.6861589\",\n \"0.6831531\",\n \"0.67470115\",\n \"0.6746388\",\n \"0.65393686\",\n \"0.64091796\",\n \"0.6401286\",\n \"0.6394237\",\n \"0.634081\",\n \"0.62345356\",\n \"0.621533\",\n \"0.6213619\",\n \"0.6195335\",\n \"0.6193793\",\n \"0.6168615\",\n \"0.6168126\",\n \"0.61294425\",\n \"0.6112704\",\n \"0.6074151\",\n \"0.6017877\",\n \"0.600059\",\n \"0.59913176\",\n \"0.5989264\",\n \"0.59744394\",\n \"0.594029\",\n \"0.593568\",\n \"0.59288853\",\n \"0.59111583\",\n \"0.5893526\",\n \"0.5886841\",\n \"0.5884161\",\n \"0.5875336\",\n \"0.5874087\",\n \"0.58621407\",\n \"0.58545697\",\n \"0.5849702\",\n \"0.58283305\",\n \"0.5813985\",\n \"0.58038664\",\n \"0.57851297\",\n \"0.5782727\",\n \"0.5781198\",\n \"0.5772928\",\n \"0.5764932\",\n \"0.57385236\",\n \"0.5736138\",\n \"0.573348\",\n \"0.5720753\",\n \"0.5711087\",\n \"0.5711045\",\n \"0.57100636\",\n \"0.5705285\",\n \"0.5702056\",\n \"0.5685711\",\n \"0.5678864\",\n \"0.5676602\",\n \"0.5671288\",\n \"0.56486505\",\n \"0.56319726\",\n \"0.5630818\",\n \"0.56204194\",\n \"0.5611773\",\n \"0.5610054\",\n \"0.5608906\",\n \"0.5607732\",\n \"0.5602008\",\n \"0.55996174\",\n \"0.5588129\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55752426\",\n \"0.55747205\",\n \"0.5573816\",\n \"0.55727977\",\n \"0.55697256\",\n \"0.5569664\",\n \"0.5568552\",\n \"0.5547841\",\n \"0.55387956\",\n \"0.5535315\",\n \"0.5534248\",\n \"0.5526536\",\n \"0.55090696\",\n \"0.55051315\",\n \"0.5500855\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7348447"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":77075,"cells":{"query":{"kind":"string","value":"Raises an error if a video is not opened. Else return 1"},"document":{"kind":"string","value":"def _check_vid_open(self):\n if not self.video.isOpened():\n raise IOError('Video is not open')\n return 1"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def videostart_failed(self):\n # type: () -> bool\n return self._videostart_failed","def check(self):\n #\n # *****************\n # *****************\n # TODO: Check really if video is valid\n # *****************\n # *****************\n return True","def openVideo(self):\n fname = self.openFile(self.user[\"Video\"])\n if fname != \"\":\n error_opening_video = False\n\n cam = cv2.VideoCapture(fname)\n logging.info(\"Opening video Check: {0}\".format(fname))\n\n currentframe = 0\n ret, frame = cam.read()\n\n if ret is False:\n error_opening_video = True\n # Release all space and windows once done\n cam.release()\n cv2.destroyAllWindows()\n\n if error_opening_video:\n msg = QMessageBox()\n msg.setIcon(QMessageBox.Information)\n msg.setText(\"Error on opening the video: {0}\".format(fname))\n msg.setWindowTitle(\"Information\")\n msg.setStandardButtons(QMessageBox.Ok)\n msg.exec_()\n else:\n self.user[\"Video\"] = fname\n name = self.splitPath(fname)[-1]\n self.vid_name = name.split(\".\")[0]\n self.ui.l_vid.setText(\"Load: \" + self.vid_name)\n self.checkFiles()","def test_error(self):\n with build_video(self.user) as video:\n video = self._send(video, None)\n eq_(video.state, 'error')","def open_video(self):\n\n \n self.filename_temp, _ = QFileDialog.getOpenFileName(self, \"Open Video\")\n\n if self.filename_temp != '':\n if self.filename_temp[-3:] == \"mp4\" or self.filename_temp[-3:] == \"wav\" or self.filename_temp[-3:] == \"wmv\" or self.filename_temp[-3:] == \"mov\":\n self.mediaPlayer.setMedia(QMediaContent(QUrl.fromLocalFile(self.filename_temp)))\n self.playBtn.setEnabled(True)\n self.saveBtn.setEnabled(True)\n self.videoOpened = True\n self.clear_annotation()\n self.filename = self.filename_temp\n\n\n else:\n message = QMessageBox()\n message.setWindowTitle(\"Fail\")\n message.setText(\"Please choose a file with one of the following extensions:\\nmp4, wav, mov or wmv.\")\n x = message.exec_() # this will show our messagebox¨¨\n\n\n elif self.filename_temp == '' and self.videoOpened:\n self.filename = self.filename\n elif self.filename_temp == '' and not self.videoOpened:\n self.filename = None","def __check_video(self):\n stream_status = self.communications.get_video()\n if self.__video_status[\"last_video_streaming\"] != stream_status: # Set initial video status\n self.__video_status[\"last_video_streaming\"] = stream_status\n self.__live_video_stream(stream_status)","def is_valid(video):\n return video.length != -1","def has_video(self):\n return self.__video_format is not None","def isVideoFolder():","def verify_network_stream(self, link):\n\n cap = cv2.VideoCapture(link)\n if not cap.isOpened():\n print('CAMERA is already opened')\n return False\n cap.release()\n return True","def __check_for_video_file(self):\n formats = ('avi', 'mpg', 'mpeg', 'mp4')\n if os.path.splitext(self.args.input.name)[-1] in (\".%s\" % ext for ext in formats):\n # we got a valid (at least according to extension) file\n pass\n else:\n logging.critical(\"Input is not a video file. Only supports %s\" % \", \".join(formats))\n sys.exit(10)","def check_media(self, media):\n return AbstractVLC.check_media(self, os.path.join(settings.get(\"path\", \"relative\", \"video\"), media))","def video_no_found(error):\n return {'message': 'video does not exist'}, 404","def watch_movie():\r\n if os.path.isfile('files/final_movie.mp4'): # if the file exists\r\n with open('files/final_movie.mp4', 'rb') as f:\r\n video_data = f.read()\r\n st.video(video_data)\r\n else: # if the file doesn't exist, let the user know\r\n st.header(\"You haven't created a movie yet!\")","def test_success(self):\n with build_video(self.user, shortlink='') as video:\n eq_(video.shortlink, '')\n video = self._send(video, 'asdf')\n eq_(video.shortlink, 'asdf')\n eq_(video.state, 'pending')","def init_video(self):\n\n assert self.container is None\n\n retry = 3\n while self.container is None and 0 < retry:\n retry -= 1\n try:\n self.container = av.open(self.tello.get_video_stream())\n except av.AVError as ave:\n print(ave)\n print('retry...')\n\n\n assert self.container is not None","def process_video(self):\n if os.path.isfile(self.source):\n self.cap = cv2.VideoCapture(self.source)\n else:\n try:\n file_name = \"input.mp4\"\n self.source = self.source.replace('open', 'uc')\n print( \"\\nDownloading video file from drive link to %s\\n\"%file_name)\n gdown.download(self.source, file_name, quiet=False)\n print( \"%s downloaded!\\n\"%file_name )\n self.cap = cv2.VideoCapture(file_name)\n except Exception:\n raise RuntimeError(\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\n\")\n\n\n assert self.cap.isOpened(), \"Failed to open %s\" % self.source\n\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\n return","def isLoaded(self):\n if self.video == None:\n return False\n return True","def load_video(self, vid_path):\n self.video = cv2.VideoCapture(vid_path)\n self._check_vid_open()\n logging.info('Loaded video at: {}'.format(vid_path))\n return 1","def main_func_video(param_list: list = None) -> bool:\r\n # index of param\r\n # noinspection PyPep8Naming\r\n PORT_RAW_PICT = 0\r\n\r\n # check if param OK\r\n if len(param_list) != 1:\r\n log_error_to_console(\"GET FRAME VIDEO MAIN FUNCTION PARAM NOK\", str(len(param_list)))\r\n return False\r\n else:\r\n port_image = get_port_from_wave(name=param_list[PORT_RAW_PICT])\r\n\r\n try:\r\n # noinspection PyUnresolvedReferences\r\n success, port_image.arr[:] = global_var_handler.VIDEO.read()\r\n if success is True:\r\n port_image.set_valid()\r\n except BaseException as error:\r\n is_error()\r\n # noinspection PyUnresolvedReferences\r\n log_error_to_console('RAW PICTURE NOK TO READ: ' + str(global_var_handler.VIDEO.__str__()), str(error))\r\n port_image.set_invalid()\r\n pass\r\n\r\n # noinspection PyUnresolvedReferences\r\n log_to_file(str(global_var_handler.FRAME))\r\n # noinspection PyUnresolvedReferences\r\n log_to_file(global_var_handler.STR_L0_SIZE)\r\n\r\n return True","def check_video_format(movie_file, desired_format='.mp4', original_format='.avi'):\n\n if not os.path.isfile(movie_file+original_format):\n print 'Error. avi file does not exist:'+movie_file+'.avi'\n if not os.path.isfile(movie_file+desired_format):\n cmd = ['ffmpeg']\n cmd += ['-i', movie_file+original_format]\n cmd += [movie_file+desired_format]\n cmd_string = ''.join([\"%s \" % el for el in cmd])\n #print '-->Running: ', cmd_string\n p = subprocess.Popen(cmd, shell=False)\n p.wait()","def video_is_available(url):\r\n vid_unavail_regex = re.compile(r'(Sorry about that\\.)')\r\n response = requests.get(url)\r\n match = vid_unavail_regex.search(response.text)\r\n\r\n if match is None:\r\n return True\r\n else:\r\n return False","def is_video(mine=None, file=None):\n if file:\n mine = get_file_mine(file)\n print(mine)\n\n if mine:\n return mine.find('video') != -1\n\n return False","def continue_video(self):\n\n print(\"continue_video needs implementation\")","def check_camera(self):\n # -- 2. Read the video stream\n if not self.cap.isOpened:\n rospy.logerr(\"[FACE] Error opening video capture\")\n if not self.face_cascade.load(self.face_cascade_name):\n rospy.logerr(\"[FACE] Error loading face cascade\")\n if not self.eyes_cascade.load(self.eyes_cascade_name):\n rospy.logerr(\"[FACE] Error loading eye cascade\")","def is_video(self):\n val = False\n if self.__dict__['codec_type']:\n if self.__dict__['codec_type'] == 'video':\n val = True\n return val","def start(self):\n if not path.exists(self.dir):\n mkdir(self.dir)\n\n try:\n yt = YouTube(self.link)\n ys = yt.streams\n if self.aud == True:\n ysf = ys.get_audio_only()\n elif self.aud == False:\n ysf = ys.get_by_resolution(resolution=self.res)\n ysf.download(self.dir)\n except AttributeError:\n raise Exception(f\"Resolution [{self.res}] is not available for this video\")\n except excep.RegexMatchError:\n raise Exception(\"Invalid link!\")\n except excep.VideoPrivate:\n raise Exception(\"This video is private! Try with a public one\")\n except excep.PytubeError:\n raise Exception(\"Error occured!\")\n except excep.VideoPrivate:\n raise Exception(\"This video is private! Try with a public one\")\n except excep.VideoUnavailable:\n raise Exception(\"This video is unvailable!\")\n except excep.ExtractError:\n raise Exception(\"Error in extractiong the video!\")\n except Exception:\n raise Exception(\"Error occured. Make sure of your connection ;)\")","def __loadVideo(self):\n # Check if movie file exists ...\n #\n if not(os.path.isfile(self.fNameVideo)):\n return stm.StimErrC.videoFileNotFound\n\n try: \n # Load video\n #\n self.video = mpe.VideoFileClip(self.fNameVideo)\n \n except IOError: \n return stm.StimErrC.invalidVideoFormat\n \n # Retrieve video description\n #\n self.dxFr = self.video.size[0]\n self.dyFr = self.video.size[1]\n self.nFr = self.video.duration *self.video.fps\n self.fps = self.video.fps\n ssp.Log.write(\"DEBUG\", \"stim_video: {0}x{1} pixel, {2} frames, {3} fps\"\n .format(self.dxFr, self.dyFr, self.nFr, self.fps))\n \n if self.isTestOnly:\n # Return here if the video was only loaded to test if it is ok\n #\n self.video = None\n return stm.StimErrC.ok\n \n # Load movie frames (note that frames is a generator!)\n #\n self.frames = self.video.iter_frames() \n self.isReady = True\n return stm.StimErrC.ok","def allow_video(self, video_id):\n print(\"allow_video needs implementation\")","def allow_video(self, video_id):\n print(\"allow_video needs implementation\")","def allow_video(self, video_id):\n print(\"allow_video needs implementation\")","def allow_video(self, video_id):\n print(\"allow_video needs implementation\")","def num_frames(self):\n return len(self.video)","def available(self):\n return self.ffmpeg.is_running","def can_open_stream(self):\n return True","def flag_video(self, video_id, flag_reason=\"\"):\n print(\"flag_video needs implementation\")","def flag_video(self, video_id, flag_reason=\"\"):\n print(\"flag_video needs implementation\")","def get_video_count(self):\n done = self.cur.execute(\"SELECT video_ID FROM videos\")\n return done","def is_available(self, video_ID): # WORKS\n done = self.cur.execute(\"SELECT video_ID FROM videos WHERE video_ID = \\\"{}\\\"\".format(video_ID))\n if done == 1:\n return True\n else:\n return False","def test_can_initialize_files_and_get_frame(self):\n video_controller = webcam.VideoController(webcam.CaptureDeviceType.FILES)\n # simply test that it returns something. it's hard to actually test what it returns\n self.assertIsNotNone(video_controller.get_current_frame())","def main_func_video_camera(param_list: list = None) -> bool:\r\n # index of param\r\n # noinspection PyPep8Naming\r\n PORT_RAW_PICT = 0\r\n\r\n # check if param OK\r\n if len(param_list) != 1:\r\n log_error_to_console(\"GET FRAME VIDEO CAPTURE MAIN FUNCTION PARAM NOK\", str(len(param_list)))\r\n return False\r\n else:\r\n port_image = get_port_from_wave(name=param_list[PORT_RAW_PICT])\r\n\r\n try:\r\n # noinspection PyUnresolvedReferences\r\n success, port_image.arr[:] = global_var_handler.VIDEO.read()\r\n if success is True:\r\n port_image.set_valid()\r\n except BaseException as error:\r\n is_error()\r\n # noinspection PyUnresolvedReferences\r\n log_error_to_console('RAW PICTURE NOK TO READ: ' + str(global_var_handler.VIDEO.__str__()), str(error))\r\n port_image.set_invalid()\r\n pass\r\n\r\n # noinspection PyUnresolvedReferences\r\n log_to_file(str(global_var_handler.FRAME))\r\n # noinspection PyUnresolvedReferences\r\n log_to_file(global_var_handler.STR_L0_SIZE)\r\n\r\n return True","def open(self):\n file = askopenfilename(\n initialdir=self.initial_directory,\n filetypes=(\n (\"Audio Video Interleave\", \"*.avi\"),\n (\"Matroska\", \"*.mkv\"),(\"MPEG-4 AVC\",\"*.mp4\"),\n )\n )\n if isinstance(file, tuple):\n return\n if os.path.isfile(file):\n self.play_film(file)","def checkCamera(self):\n cameraFound = False\n print(\"[INFO]: Searching for camera...\")\n try:\n for camera in glob.glob(\"/dev/video?\"):\n if camera == \"/dev/video2\":\n cameraIndex = 2\n cameraFound = True\n print(\"[INFO]: Using index 2 for the camera.\")\n return cameraIndex, cameraFound\n elif camera == \"/dev/video1\":\n cameraIndex = 1\n cameraFound = True\n print(\"[INFO]: Using index 1 for the camera.\")\n return cameraIndex, cameraFound\n elif camera == \"/dev/video0\":\n cameraIndex = 0\n cameraFound = True\n print(\"[INFO]: Using index 0 for the camera\")\n return cameraIndex, cameraFound\n else:\n print(\"[ERROR]: No camera found.\")\n cameraFound = False\n cameraIndex = 0\n return cameraIndex, cameraFound\n except(TypeError):\n print(\"[ERROR]: Camera is probably not connected.\")","def _check_scene_open(self):\n return self._engine.current_file_path() is not None","def video(path):\n cap = cv2.VideoCapture(path)\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n return frame_count, cap","def OnPlay(self):\r\n # check if there is a file to play, otherwise open a\r\n # Tk.FileDialog to select a file\r\n print(\"1-1\")\r\n\r\n\r\n self.Media = self.Instance.media_new(self.youtube_url)\r\n self.player.set_media(self.Media)\r\n\r\n # set the window id where to render VLC's video output\r\n if platform.system() == 'Windows':\r\n print(\"1-3\")\r\n self.player.set_hwnd(self.GetHandle())\r\n else:\r\n print(\"1-4\")\r\n self.player.set_xwindow(self.GetHandle()) # this line messes up windows\r\n # FIXME: this should be made cross-platform\r\n\r\n # Try to launch the media, if this fails display an error message\r\n if self.player.play() == -1:\r\n print(\"1-6\")\r\n self.errorDialog(\"Unable to play.\")","def isfailure(self):\n\n return self.proc.returncode != 0","def isMediaPlayer(self):\n if self.context.isFile():\n return True\n return None","def play(self): \n if(self.isLoaded() == False):\n return\n self.isPlaying = True\n #if the video is end, we restart\n if(self.ret == False):\n self.video.set(1, 0)","def get_vmedia_status(self):\n\n try:\n sushy_system = self._get_sushy_system()\n vmedia_status = sushy_system.vmedia\n except sushy.exceptions.SushyError as e:\n msg = (self._('The vmedia is not found. Error '\n '%(error)s') %\n {'error': str(e)})\n LOG.debug(msg)\n raise exception.SDFlexError(msg)\n return vmedia_status","def stop_video(self):\n global value\n if value>0:\n value=0\n print(f\"Stopping video: {name}\")\n else:\n print(\"Cannot stop video: No video is currently playing\")","def test_video_player(self):\r\n\r\n # Navigate to a video\r\n self.course_info_page.visit()\r\n self.tab_nav.go_to_tab('Courseware')\r\n\r\n # The video should start off paused\r\n # Since the video hasn't loaded yet, it's elapsed time is 0\r\n self.assertFalse(self.video.is_playing)\r\n self.assertEqual(self.video.elapsed_time, 0)\r\n\r\n # Play the video\r\n self.video.play()\r\n\r\n # Now we should be playing\r\n self.assertTrue(self.video.is_playing)\r\n\r\n # Commented the below EmptyPromise, will move to its page once this test is working and stable\r\n # Also there is should be no Promise check in any test as this should be done in Page Object\r\n # Wait for the video to load the duration\r\n # EmptyPromise(\r\n # lambda: self.video.duration > 0,\r\n # 'video has duration', timeout=20\r\n # ).fulfill()\r\n\r\n # Pause the video\r\n self.video.pause()\r\n\r\n # Expect that the elapsed time and duration are reasonable\r\n # Again, we can't expect the video to actually play because of\r\n # latency through the ssh tunnel\r\n self.assertGreaterEqual(self.video.elapsed_time, 0)\r\n self.assertGreaterEqual(self.video.duration, self.video.elapsed_time)","def is_open(self):\n return self.stream.is_open()","def GetStatusOfVideo(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def isopen(self):\n return _image.image_isopen(self)","def stop_video(self):\n stopped_video = self.video_id\n if stopped_video == None:\n print(\"Cannot stop video: No video is currently playing\")\n else:\n print(f\"Stopping video: {stopped_video}\")","def continue_video(self):\n video_playing = self._video_library.get_video(self.now_playing_videoid)\n if self.pause:\n self.pause = False\n print(f\"Continuing video: {video_playing.title}\")\n elif not video_playing:\n print(f\"Cannot continue video: No video is currently playing\")\n else:\n print(\"Cannot continue video: Video is not paused\")\n # print(\"continue_video needs implementation\")","def is_valid(self, value) -> 'True | str':\n err_str = super().is_valid()\n if isinstance(err_str, str):\n return err_str\n try:\n cv2.VideoCapture(value)\n except Exception as e:\n return str(e)\n return True","def is_video(self):\n if self.settings.background_image is None:\n return False\n\n filename, _ = b64decode_file(self.settings.background_image)\n self.mimetype, _ = mimetypes.guess_type(filename)\n return 'video' in self.mimetype","def continue_video(self):\n global value\n if value==2:\n global name\n value=1\n print(f\"Continuing video: {name}\")\n elif value==1:\n\n\n print(f\"Cannot continue video: Video is not paused\")","def open_video(self, video_display_name):\r\n self.course_nav.go_to_sequential(video_display_name)\r\n self.video.wait_for_video_player_render()","def feed_connection_check(stream, formatted_date, recording_log_msg):\n stream_url = \"http://\" + fetch_ros_master_ip() + \":8080/stream?topic=/cv_camera/image_raw\"\n error_state = 0\n connection_check = os.system('ffprobe -select_streams v -i ' + stream_url)\n if connection_check == 0:\n threading.Thread(target = start_ffmpeg_record, args = (stream, stream_url, formatted_date)).start()\n print(recording_log_msg)\n return jsonify(recording_log_msg=recording_log_msg, error_state=error_state)\n\n else:\n recording_log_msg = 'failed to connect to video feed of ' + stream\n error_state = 1\n print(recording_log_msg)\n return jsonify(recording_log_msg=recording_log_msg, error_state=error_state)","def test_task_video_download(url_to_video: str, empty_video_resource: VideoResource):\n download_video(url_to_video, empty_video_resource.id)\n empty_video_resource.refresh_from_db()\n video_instance = empty_video_resource.videos.filter(primary=True).first()\n\n assert empty_video_resource.videos.all()\n assert video_instance.extension == 'mp4'\n assert video_instance.primary\n for item in video_instance.video.open():\n assert item","def play_video(self, video_id):\n video_info = self._video_library.get_all_videos()\n for i in video_info:\n if video_id == i.video_id:\n print(f\"Playing video: {i.title}\")\n def search(list, platform):\n for i in range(len(list)):\n if list[i] == platform:\n return False\n return True\n video_id_list = []\n for i in video_info:\n video_id_list.append(i.video_id)\n if search(video_id_list, video_id):\n print(\"Cannot play video: Video does not exist\")","def getTotalFramesVid(srcVideoPath):\n cap = cv2.VideoCapture(srcVideoPath)\n # if the videoCapture object is not opened then exit without traceback\n if not cap.isOpened():\n print(\"Error reading the video file !!\")\n return 0\n\n tot_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\n cap.release()\n return tot_frames","def start(self):\n if hasattr(self, 'process'):\n err = \"video '{}' Frames Extraction has already \" \\\n \"started.\".format(self.video_file)\n print err\n raise Exception(err)\n\n process_number = subprocess.Popen(self.start_frames, stdout=subprocess.PIPE)\n process_number.wait()\n return process_number","def get_video(self):\n if self.parsing_template.video and self.parsing_template.video in self.headline.url:\n return True\n return False","def continue_video(self):\n if self.is_playing() is not None:\n print(\"Cannot continue video: Video is not paused\")\n elif self.is_paused() is not None:\n print(\"Continuing video: {}\".format(self.is_paused()._title))\n self.is_paused()._status = 1\n else:\n print(\"Cannot continue video: No video is currently playing\")","def videostart_failed_reason(self):\n # type: () -> AnalyticsVideoStartFailedReason\n return self._videostart_failed_reason","def check_streamer(params) -> None:\n try:\n with open(params['pid'], \"r\") as f:\n pid = f.read()\n if os.path.isdir(\"/proc/\" + str(pid)):\n print(\"Streamer is running\")\n else:\n print(\"Streamer is NOT running\")\n except Exception as e:\n print(\"Streamer is NOT running\")","def checkCamera(self):\n #how to check if cam exits\n #https://stackoverflow.com/questions/48049886/how-to-correctly-check-if-a-camera-is-available\n if \"has_cam\" in self.store.keys():\n return self.store[\"has_cam\"]\n\n cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)\n\n if cap is None or not cap.isOpened():\n return False\n #Close the cam\n cap.release()\n cv2.destroyAllWindows()\n return True","def state(self) -> MediaPlayerState | None:\n if self.is_volume_muted:\n return MediaPlayerState.IDLE\n return STREAM_STATUS.get(self._group.stream_status)","def is_alive(self):\n conn = HTTPConnection(self.browser.host, self.browser.port)\n conn.request(\"HEAD\", \"/invalid\")\n res = conn.getresponse()\n return res.status == 404","def test_api_instructor_start_non_live_video(self):\n video = factories.VideoFactory(\n id=\"27a23f52-3379-46a2-94fa-697b59cfe3c7\",\n upload_state=random.choice([s[0] for s in STATE_CHOICES]),\n )\n jwt_token = AccessToken()\n jwt_token.payload[\"resource_id\"] = str(video.id)\n jwt_token.payload[\"roles\"] = [random.choice([\"instructor\", \"administrator\"])]\n jwt_token.payload[\"permissions\"] = {\"can_update\": True}\n\n # start a live video,\n with mock.patch.object(api, \"start_live_channel\"):\n response = self.client.post(\n f\"/api/videos/{video.id}/start-live/\",\n HTTP_AUTHORIZATION=f\"Bearer {jwt_token}\",\n )\n self.assertEqual(response.status_code, 400)","def validate(base_path: Path,\n max_size: int) -> None:\n valid = invalid = 0\n for path, is_valid in validate_videos(base_path, max_size):\n print(colorama.Fore.GREEN if is_valid else colorama.Fore.RED,\n \"Processing\", end='', sep='')\n\n if is_valid:\n valid += 1\n print(f\"{get_info(path, short=True)} is valid\".rjust(50, '.'))\n else:\n invalid += 1\n print(f\"{get_info(path, short=True)} is invalid\".rjust(50, '.'))\n\n print(colorama.Fore.GREEN, \"=\" * 60, colorama.Fore.RESET, sep='')\n print(f\"Total files count: {len(os.listdir(base_path))}\")\n if valid == invalid == 0:\n print(f\"No video found\")\n return\n\n print(f\"Total videos count: {valid + invalid}\\n\")\n if invalid == 0:\n print(f\"All {valid} videos are valid\")\n else:\n print(f\"Total valid videos: {valid}\")\n print(f\"Total invalid videos: {invalid}\")","def continue_video(self):\n if self.current_video is None:\n print(\"Cannot continue video: No video is currently playing\")\n elif not self.current_paused:\n print(\"Cannot continue video: Video is not paused\")\n elif self.current_paused:\n print(\"Continuing video:\", self.current_video.title)","def playstatus_error(self, updater, exception: Exception) -> None:\n if updater == self.main_instance:\n self.listener.playstatus_error(updater, exception)","def test_camera_id() -> None: # pragma: no cover\n cap = cv2.VideoCapture(1)\n if (cap.isOpened() == False):\n print(\"Error opening video stream or file\")\n ret, frame = cap.read()\n cv2.imshow('frame', frame)\n cv2.waitKey(0)\n cap.release()","def is_open(self) -> bool:\n pass","def is_video_rendered(self, mode, video_display_name=None):\r\n selector = self.get_element_selector(video_display_name, VIDEO_MODES[mode])\r\n\r\n def _is_element_present():\r\n \"\"\"\r\n Check if a web element is present in DOM.\r\n\r\n Returns:\r\n tuple: (is_satisfied, result)`, where `is_satisfied` is a boolean indicating whether the promise was\r\n satisfied, and `result` is a value to return from the fulfilled `Promise`.\r\n\r\n \"\"\"\r\n is_present = self.q(css=selector).present\r\n return is_present, is_present\r\n\r\n return Promise(_is_element_present, 'Video Rendering Failed in {0} mode.'.format(mode)).fulfill()","def assert_video_status(self, url, edx_video_id, status):\n response = self.client.get_json(url)\n self.assertEqual(response.status_code, 200)\n videos = json.loads(response.content.decode('utf-8'))[\"videos\"]\n for video in videos:\n if video['edx_video_id'] == edx_video_id:\n return self.assertEqual(video['status'], status)\n\n # Test should fail if video not found\n self.assertEqual(True, False, 'Invalid edx_video_id')","def assertUsable (self):\n if self.usableChecked:\n return self.usableError\n try:\n # check if parent encoder is runnable\n aviErr = AviH264.assertUsable ()\n if aviErr != None:\n self.usableChecked = True\n self.usableError = aviErr\n return self.usableError\n\n # check if mencoder is runnable\n result = subprocess.run ([_MP4H264Encoder._getMP4BoxRunnable ()], capture_output = True)\n if result.returncode != 0:\n if not 'MP4Box' in (str(result.stdout) + ' ' + str(result.stderr)):\n self.usableError = \"Can not execute MP4Box. Is inside PATH variable?\"\n else:\n self.usableError = None\n self.usableChecked = True\n return self.usableError\n except:\n self.usableError = \"Can not execute \" + _MP4H264Encoder._getMP4BoxRunnable () + \". Is inside PATH variable?\"\n self.usableChecked = True\n return self.usableError","def test_api_video_update_live_state_unknown_video(self):\n data = {\n \"logGroupName\": \"/aws/lambda/dev-test-marsha-medialive\",\n \"state\": \"running\",\n }\n signature = generate_hash(\"shared secret\", json.dumps(data).encode(\"utf-8\"))\n response = self.client.patch(\n \"/api/videos/9087c52d-cb87-4fd0-9b57-d9f28a0c69cb/update-live-state/\",\n data,\n content_type=\"application/json\",\n HTTP_X_MARSHA_SIGNATURE=signature,\n )\n\n self.assertEqual(response.status_code, 404)","def play_video(self, video_id):\n\n if self._video_library.get_video(video_id) is None:\n print(\"Cannot play video: Video does not exist\")\n elif self._video_library.get_video(video_id).flagged:\n print(f\"Cannot play video: Video is currently flagged (reason: {self._video_library.get_video(video_id).flag_reason})\")\n elif self.current_video is not None:\n # If different must stop\n print(\"Stopping video:\", self.current_video.title)\n self.current_video = self._video_library.get_video(video_id)\n self.current_paused = False\n print(\"Playing video:\", self.current_video.title)\n elif self.current_video is None:\n # If nothing currently playing\n self.current_video = self._video_library.get_video(video_id)\n self.current_paused = False\n print(\"Playing video:\", self.current_video.title)","def has_parsed_video(season: int, episode: int) -> bool:\n file_path = VideoParser.__get_parsed_video_file_path(season, episode)\n return os.path.isfile(file_path)","def streamer(self):\n retry = 3\n print ('start streamer!')\n while self.container is None and 0 < retry:\n if not self.collect_frames:\n break\n #print (type(self.container))\n retry -= 1\n try:\n self.container = av.open(self.drone.get_video_stream())\n print('success')\n except av.AVError as ave:\n print(ave)\n print('retry...')","def onclick_load_video(self):\n video_source = select_file(\n \"Select Video Files\",\n \"../\",\n \"Video Files (*.mp4 *.avi *.mpg *.gif *.mov)\")\n if video_source:\n param_name = select_file(\n \"Select Parameter\", \"../\", \"Parameter Files (*.json)\")\n if param_name:\n self.moildev = Moildev(param_name)\n self.running_video(video_source)","def check_playable(url):\r\n import urllib\r\n import requests\r\n try:\r\n headers = url.rsplit('|', 1)[1]\r\n except:\r\n headers = ''\r\n headers = urllib.quote_plus(headers).replace('%3D', '=') if ' ' in headers else headers\r\n headers = dict(urlparse.parse_qsl(headers))\r\n\r\n result = None\r\n try:\r\n if url.startswith('http') and '.m3u8' in url:\r\n result = requests.head(url.split('|')[0], headers=headers, timeout=5)\r\n if result is None:\r\n return None\r\n\r\n elif url.startswith('http'):\r\n result = requests.head(url.split('|')[0], headers=headers, timeout=5)\r\n if result is None:\r\n return None\r\n except:\r\n pass\r\n\r\n return result","def open(self):\n self._dev_obj = cv2.VideoCapture(self._dev_num)\n if not self._dev_obj.isOpened():\n LOG.error(\"Fail to open the camera, number: %d\", self._dev_num)\n return False\n\n self._dev_obj.set(cv2.CAP_PROP_FRAME_WIDTH, self._width)\n self._dev_obj.set(cv2.CAP_PROP_FRAME_HEIGHT, self._height)\n self._dev_obj.set(cv2.CAP_PROP_FPS, self._fps)\n\n LOG.info(\"Frame width: %d\", self._width)\n LOG.info(\"Frame height: %d\", self._height)\n\n return True","def record_video(self):\n return self.config.get('record_video', False)","def get_frame(self):\n if self.vid.isOpened():\n ret, frame = self.vid.read()\n if ret:\n return (ret, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\n else:\n return (ret, None)\n else:\n return (ret, None)","def canPlay(self):\r\n return self.__canPlay","def _navigate_to_courseware_video_no_render(self):\r\n self._navigate_to_courseware_video()\r\n self.video.wait_for_video_class()","def valid_video_file(file):\r\n return file.lower().endswith(('.ogg', '.webm'))","def _next_frame(self):\n ret, self.frame = self.capture.read()\n if not ret:\n self.logger.warning('Failed to read frame')\n if self.show_video:\n cv2.imshow('frame', self.frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n exit(0)\n return ret","def check_media(self, media):\n path = os.path.join(settings.get_path(\"media\"), media)\n if os.path.exists(path):\n return path\n else:\n return False","def download_video(video_stream):\n global file_size\n file_size = size_in_mb(video_stream.filesize)\n home_dir = os.environ['HOME']\n path = f'{home_dir}/Downloads/Video'\n print('-'*60)\n print(f'Filename:\\t{video_stream.title}')\n print(f'Location:\\t{path}')\n print(f'Size:\\t\\t{file_size} MB\\n')\n\n filename = video_stream.title + '_video.mp4'\n filename = filename.replace('/', ' ')\n filename = filename.replace('\\\\', ' ')\n\n if os.path.exists(os.path.join(path, filename)):\n print(\"The file has been already downloaded.\")\n sys.exit()\n \n video_stream.download(path, filename)","def media_status_changed(self, state):\n if state == self.MediaStatus.LoadedMedia.value:\n QMetaObject.invokeMethod(self.video_player, \"play\", Qt.QueuedConnection)\n self.timer.start(self.timer_interval)\n elif state == self.MediaStatus.EndOfMedia.value:\n if self.state_time_remain > self.timer_interval / 1000:\n QMetaObject.invokeMethod(self.video_player, \"play\", Qt.QueuedConnection)","def check_channel_request(self, kind, chanid):\n if kind == 'session':\n return paramiko.OPEN_SUCCEEDED\n return paramiko.OPEN_FAILED_ADMINISTRATIVELY_PROHIBITED","def get_video_num(self, username):\n done = self.cur.execute(\"SELECT video_ID FROM videos where uploader = \\\"{}\\\"\".format(username))\n return done"],"string":"[\n \"def videostart_failed(self):\\n # type: () -> bool\\n return self._videostart_failed\",\n \"def check(self):\\n #\\n # *****************\\n # *****************\\n # TODO: Check really if video is valid\\n # *****************\\n # *****************\\n return True\",\n \"def openVideo(self):\\n fname = self.openFile(self.user[\\\"Video\\\"])\\n if fname != \\\"\\\":\\n error_opening_video = False\\n\\n cam = cv2.VideoCapture(fname)\\n logging.info(\\\"Opening video Check: {0}\\\".format(fname))\\n\\n currentframe = 0\\n ret, frame = cam.read()\\n\\n if ret is False:\\n error_opening_video = True\\n # Release all space and windows once done\\n cam.release()\\n cv2.destroyAllWindows()\\n\\n if error_opening_video:\\n msg = QMessageBox()\\n msg.setIcon(QMessageBox.Information)\\n msg.setText(\\\"Error on opening the video: {0}\\\".format(fname))\\n msg.setWindowTitle(\\\"Information\\\")\\n msg.setStandardButtons(QMessageBox.Ok)\\n msg.exec_()\\n else:\\n self.user[\\\"Video\\\"] = fname\\n name = self.splitPath(fname)[-1]\\n self.vid_name = name.split(\\\".\\\")[0]\\n self.ui.l_vid.setText(\\\"Load: \\\" + self.vid_name)\\n self.checkFiles()\",\n \"def test_error(self):\\n with build_video(self.user) as video:\\n video = self._send(video, None)\\n eq_(video.state, 'error')\",\n \"def open_video(self):\\n\\n \\n self.filename_temp, _ = QFileDialog.getOpenFileName(self, \\\"Open Video\\\")\\n\\n if self.filename_temp != '':\\n if self.filename_temp[-3:] == \\\"mp4\\\" or self.filename_temp[-3:] == \\\"wav\\\" or self.filename_temp[-3:] == \\\"wmv\\\" or self.filename_temp[-3:] == \\\"mov\\\":\\n self.mediaPlayer.setMedia(QMediaContent(QUrl.fromLocalFile(self.filename_temp)))\\n self.playBtn.setEnabled(True)\\n self.saveBtn.setEnabled(True)\\n self.videoOpened = True\\n self.clear_annotation()\\n self.filename = self.filename_temp\\n\\n\\n else:\\n message = QMessageBox()\\n message.setWindowTitle(\\\"Fail\\\")\\n message.setText(\\\"Please choose a file with one of the following extensions:\\\\nmp4, wav, mov or wmv.\\\")\\n x = message.exec_() # this will show our messagebox¨¨\\n\\n\\n elif self.filename_temp == '' and self.videoOpened:\\n self.filename = self.filename\\n elif self.filename_temp == '' and not self.videoOpened:\\n self.filename = None\",\n \"def __check_video(self):\\n stream_status = self.communications.get_video()\\n if self.__video_status[\\\"last_video_streaming\\\"] != stream_status: # Set initial video status\\n self.__video_status[\\\"last_video_streaming\\\"] = stream_status\\n self.__live_video_stream(stream_status)\",\n \"def is_valid(video):\\n return video.length != -1\",\n \"def has_video(self):\\n return self.__video_format is not None\",\n \"def isVideoFolder():\",\n \"def verify_network_stream(self, link):\\n\\n cap = cv2.VideoCapture(link)\\n if not cap.isOpened():\\n print('CAMERA is already opened')\\n return False\\n cap.release()\\n return True\",\n \"def __check_for_video_file(self):\\n formats = ('avi', 'mpg', 'mpeg', 'mp4')\\n if os.path.splitext(self.args.input.name)[-1] in (\\\".%s\\\" % ext for ext in formats):\\n # we got a valid (at least according to extension) file\\n pass\\n else:\\n logging.critical(\\\"Input is not a video file. Only supports %s\\\" % \\\", \\\".join(formats))\\n sys.exit(10)\",\n \"def check_media(self, media):\\n return AbstractVLC.check_media(self, os.path.join(settings.get(\\\"path\\\", \\\"relative\\\", \\\"video\\\"), media))\",\n \"def video_no_found(error):\\n return {'message': 'video does not exist'}, 404\",\n \"def watch_movie():\\r\\n if os.path.isfile('files/final_movie.mp4'): # if the file exists\\r\\n with open('files/final_movie.mp4', 'rb') as f:\\r\\n video_data = f.read()\\r\\n st.video(video_data)\\r\\n else: # if the file doesn't exist, let the user know\\r\\n st.header(\\\"You haven't created a movie yet!\\\")\",\n \"def test_success(self):\\n with build_video(self.user, shortlink='') as video:\\n eq_(video.shortlink, '')\\n video = self._send(video, 'asdf')\\n eq_(video.shortlink, 'asdf')\\n eq_(video.state, 'pending')\",\n \"def init_video(self):\\n\\n assert self.container is None\\n\\n retry = 3\\n while self.container is None and 0 < retry:\\n retry -= 1\\n try:\\n self.container = av.open(self.tello.get_video_stream())\\n except av.AVError as ave:\\n print(ave)\\n print('retry...')\\n\\n\\n assert self.container is not None\",\n \"def process_video(self):\\n if os.path.isfile(self.source):\\n self.cap = cv2.VideoCapture(self.source)\\n else:\\n try:\\n file_name = \\\"input.mp4\\\"\\n self.source = self.source.replace('open', 'uc')\\n print( \\\"\\\\nDownloading video file from drive link to %s\\\\n\\\"%file_name)\\n gdown.download(self.source, file_name, quiet=False)\\n print( \\\"%s downloaded!\\\\n\\\"%file_name )\\n self.cap = cv2.VideoCapture(file_name)\\n except Exception:\\n raise RuntimeError(\\\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\\\n\\\")\\n\\n\\n assert self.cap.isOpened(), \\\"Failed to open %s\\\" % self.source\\n\\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \\n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n return\",\n \"def isLoaded(self):\\n if self.video == None:\\n return False\\n return True\",\n \"def load_video(self, vid_path):\\n self.video = cv2.VideoCapture(vid_path)\\n self._check_vid_open()\\n logging.info('Loaded video at: {}'.format(vid_path))\\n return 1\",\n \"def main_func_video(param_list: list = None) -> bool:\\r\\n # index of param\\r\\n # noinspection PyPep8Naming\\r\\n PORT_RAW_PICT = 0\\r\\n\\r\\n # check if param OK\\r\\n if len(param_list) != 1:\\r\\n log_error_to_console(\\\"GET FRAME VIDEO MAIN FUNCTION PARAM NOK\\\", str(len(param_list)))\\r\\n return False\\r\\n else:\\r\\n port_image = get_port_from_wave(name=param_list[PORT_RAW_PICT])\\r\\n\\r\\n try:\\r\\n # noinspection PyUnresolvedReferences\\r\\n success, port_image.arr[:] = global_var_handler.VIDEO.read()\\r\\n if success is True:\\r\\n port_image.set_valid()\\r\\n except BaseException as error:\\r\\n is_error()\\r\\n # noinspection PyUnresolvedReferences\\r\\n log_error_to_console('RAW PICTURE NOK TO READ: ' + str(global_var_handler.VIDEO.__str__()), str(error))\\r\\n port_image.set_invalid()\\r\\n pass\\r\\n\\r\\n # noinspection PyUnresolvedReferences\\r\\n log_to_file(str(global_var_handler.FRAME))\\r\\n # noinspection PyUnresolvedReferences\\r\\n log_to_file(global_var_handler.STR_L0_SIZE)\\r\\n\\r\\n return True\",\n \"def check_video_format(movie_file, desired_format='.mp4', original_format='.avi'):\\n\\n if not os.path.isfile(movie_file+original_format):\\n print 'Error. avi file does not exist:'+movie_file+'.avi'\\n if not os.path.isfile(movie_file+desired_format):\\n cmd = ['ffmpeg']\\n cmd += ['-i', movie_file+original_format]\\n cmd += [movie_file+desired_format]\\n cmd_string = ''.join([\\\"%s \\\" % el for el in cmd])\\n #print '-->Running: ', cmd_string\\n p = subprocess.Popen(cmd, shell=False)\\n p.wait()\",\n \"def video_is_available(url):\\r\\n vid_unavail_regex = re.compile(r'(Sorry about that\\\\.)')\\r\\n response = requests.get(url)\\r\\n match = vid_unavail_regex.search(response.text)\\r\\n\\r\\n if match is None:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def is_video(mine=None, file=None):\\n if file:\\n mine = get_file_mine(file)\\n print(mine)\\n\\n if mine:\\n return mine.find('video') != -1\\n\\n return False\",\n \"def continue_video(self):\\n\\n print(\\\"continue_video needs implementation\\\")\",\n \"def check_camera(self):\\n # -- 2. Read the video stream\\n if not self.cap.isOpened:\\n rospy.logerr(\\\"[FACE] Error opening video capture\\\")\\n if not self.face_cascade.load(self.face_cascade_name):\\n rospy.logerr(\\\"[FACE] Error loading face cascade\\\")\\n if not self.eyes_cascade.load(self.eyes_cascade_name):\\n rospy.logerr(\\\"[FACE] Error loading eye cascade\\\")\",\n \"def is_video(self):\\n val = False\\n if self.__dict__['codec_type']:\\n if self.__dict__['codec_type'] == 'video':\\n val = True\\n return val\",\n \"def start(self):\\n if not path.exists(self.dir):\\n mkdir(self.dir)\\n\\n try:\\n yt = YouTube(self.link)\\n ys = yt.streams\\n if self.aud == True:\\n ysf = ys.get_audio_only()\\n elif self.aud == False:\\n ysf = ys.get_by_resolution(resolution=self.res)\\n ysf.download(self.dir)\\n except AttributeError:\\n raise Exception(f\\\"Resolution [{self.res}] is not available for this video\\\")\\n except excep.RegexMatchError:\\n raise Exception(\\\"Invalid link!\\\")\\n except excep.VideoPrivate:\\n raise Exception(\\\"This video is private! Try with a public one\\\")\\n except excep.PytubeError:\\n raise Exception(\\\"Error occured!\\\")\\n except excep.VideoPrivate:\\n raise Exception(\\\"This video is private! Try with a public one\\\")\\n except excep.VideoUnavailable:\\n raise Exception(\\\"This video is unvailable!\\\")\\n except excep.ExtractError:\\n raise Exception(\\\"Error in extractiong the video!\\\")\\n except Exception:\\n raise Exception(\\\"Error occured. Make sure of your connection ;)\\\")\",\n \"def __loadVideo(self):\\n # Check if movie file exists ...\\n #\\n if not(os.path.isfile(self.fNameVideo)):\\n return stm.StimErrC.videoFileNotFound\\n\\n try: \\n # Load video\\n #\\n self.video = mpe.VideoFileClip(self.fNameVideo)\\n \\n except IOError: \\n return stm.StimErrC.invalidVideoFormat\\n \\n # Retrieve video description\\n #\\n self.dxFr = self.video.size[0]\\n self.dyFr = self.video.size[1]\\n self.nFr = self.video.duration *self.video.fps\\n self.fps = self.video.fps\\n ssp.Log.write(\\\"DEBUG\\\", \\\"stim_video: {0}x{1} pixel, {2} frames, {3} fps\\\"\\n .format(self.dxFr, self.dyFr, self.nFr, self.fps))\\n \\n if self.isTestOnly:\\n # Return here if the video was only loaded to test if it is ok\\n #\\n self.video = None\\n return stm.StimErrC.ok\\n \\n # Load movie frames (note that frames is a generator!)\\n #\\n self.frames = self.video.iter_frames() \\n self.isReady = True\\n return stm.StimErrC.ok\",\n \"def allow_video(self, video_id):\\n print(\\\"allow_video needs implementation\\\")\",\n \"def allow_video(self, video_id):\\n print(\\\"allow_video needs implementation\\\")\",\n \"def allow_video(self, video_id):\\n print(\\\"allow_video needs implementation\\\")\",\n \"def allow_video(self, video_id):\\n print(\\\"allow_video needs implementation\\\")\",\n \"def num_frames(self):\\n return len(self.video)\",\n \"def available(self):\\n return self.ffmpeg.is_running\",\n \"def can_open_stream(self):\\n return True\",\n \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\n print(\\\"flag_video needs implementation\\\")\",\n \"def flag_video(self, video_id, flag_reason=\\\"\\\"):\\n print(\\\"flag_video needs implementation\\\")\",\n \"def get_video_count(self):\\n done = self.cur.execute(\\\"SELECT video_ID FROM videos\\\")\\n return done\",\n \"def is_available(self, video_ID): # WORKS\\n done = self.cur.execute(\\\"SELECT video_ID FROM videos WHERE video_ID = \\\\\\\"{}\\\\\\\"\\\".format(video_ID))\\n if done == 1:\\n return True\\n else:\\n return False\",\n \"def test_can_initialize_files_and_get_frame(self):\\n video_controller = webcam.VideoController(webcam.CaptureDeviceType.FILES)\\n # simply test that it returns something. it's hard to actually test what it returns\\n self.assertIsNotNone(video_controller.get_current_frame())\",\n \"def main_func_video_camera(param_list: list = None) -> bool:\\r\\n # index of param\\r\\n # noinspection PyPep8Naming\\r\\n PORT_RAW_PICT = 0\\r\\n\\r\\n # check if param OK\\r\\n if len(param_list) != 1:\\r\\n log_error_to_console(\\\"GET FRAME VIDEO CAPTURE MAIN FUNCTION PARAM NOK\\\", str(len(param_list)))\\r\\n return False\\r\\n else:\\r\\n port_image = get_port_from_wave(name=param_list[PORT_RAW_PICT])\\r\\n\\r\\n try:\\r\\n # noinspection PyUnresolvedReferences\\r\\n success, port_image.arr[:] = global_var_handler.VIDEO.read()\\r\\n if success is True:\\r\\n port_image.set_valid()\\r\\n except BaseException as error:\\r\\n is_error()\\r\\n # noinspection PyUnresolvedReferences\\r\\n log_error_to_console('RAW PICTURE NOK TO READ: ' + str(global_var_handler.VIDEO.__str__()), str(error))\\r\\n port_image.set_invalid()\\r\\n pass\\r\\n\\r\\n # noinspection PyUnresolvedReferences\\r\\n log_to_file(str(global_var_handler.FRAME))\\r\\n # noinspection PyUnresolvedReferences\\r\\n log_to_file(global_var_handler.STR_L0_SIZE)\\r\\n\\r\\n return True\",\n \"def open(self):\\n file = askopenfilename(\\n initialdir=self.initial_directory,\\n filetypes=(\\n (\\\"Audio Video Interleave\\\", \\\"*.avi\\\"),\\n (\\\"Matroska\\\", \\\"*.mkv\\\"),(\\\"MPEG-4 AVC\\\",\\\"*.mp4\\\"),\\n )\\n )\\n if isinstance(file, tuple):\\n return\\n if os.path.isfile(file):\\n self.play_film(file)\",\n \"def checkCamera(self):\\n cameraFound = False\\n print(\\\"[INFO]: Searching for camera...\\\")\\n try:\\n for camera in glob.glob(\\\"/dev/video?\\\"):\\n if camera == \\\"/dev/video2\\\":\\n cameraIndex = 2\\n cameraFound = True\\n print(\\\"[INFO]: Using index 2 for the camera.\\\")\\n return cameraIndex, cameraFound\\n elif camera == \\\"/dev/video1\\\":\\n cameraIndex = 1\\n cameraFound = True\\n print(\\\"[INFO]: Using index 1 for the camera.\\\")\\n return cameraIndex, cameraFound\\n elif camera == \\\"/dev/video0\\\":\\n cameraIndex = 0\\n cameraFound = True\\n print(\\\"[INFO]: Using index 0 for the camera\\\")\\n return cameraIndex, cameraFound\\n else:\\n print(\\\"[ERROR]: No camera found.\\\")\\n cameraFound = False\\n cameraIndex = 0\\n return cameraIndex, cameraFound\\n except(TypeError):\\n print(\\\"[ERROR]: Camera is probably not connected.\\\")\",\n \"def _check_scene_open(self):\\n return self._engine.current_file_path() is not None\",\n \"def video(path):\\n cap = cv2.VideoCapture(path)\\n frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n return frame_count, cap\",\n \"def OnPlay(self):\\r\\n # check if there is a file to play, otherwise open a\\r\\n # Tk.FileDialog to select a file\\r\\n print(\\\"1-1\\\")\\r\\n\\r\\n\\r\\n self.Media = self.Instance.media_new(self.youtube_url)\\r\\n self.player.set_media(self.Media)\\r\\n\\r\\n # set the window id where to render VLC's video output\\r\\n if platform.system() == 'Windows':\\r\\n print(\\\"1-3\\\")\\r\\n self.player.set_hwnd(self.GetHandle())\\r\\n else:\\r\\n print(\\\"1-4\\\")\\r\\n self.player.set_xwindow(self.GetHandle()) # this line messes up windows\\r\\n # FIXME: this should be made cross-platform\\r\\n\\r\\n # Try to launch the media, if this fails display an error message\\r\\n if self.player.play() == -1:\\r\\n print(\\\"1-6\\\")\\r\\n self.errorDialog(\\\"Unable to play.\\\")\",\n \"def isfailure(self):\\n\\n return self.proc.returncode != 0\",\n \"def isMediaPlayer(self):\\n if self.context.isFile():\\n return True\\n return None\",\n \"def play(self): \\n if(self.isLoaded() == False):\\n return\\n self.isPlaying = True\\n #if the video is end, we restart\\n if(self.ret == False):\\n self.video.set(1, 0)\",\n \"def get_vmedia_status(self):\\n\\n try:\\n sushy_system = self._get_sushy_system()\\n vmedia_status = sushy_system.vmedia\\n except sushy.exceptions.SushyError as e:\\n msg = (self._('The vmedia is not found. Error '\\n '%(error)s') %\\n {'error': str(e)})\\n LOG.debug(msg)\\n raise exception.SDFlexError(msg)\\n return vmedia_status\",\n \"def stop_video(self):\\n global value\\n if value>0:\\n value=0\\n print(f\\\"Stopping video: {name}\\\")\\n else:\\n print(\\\"Cannot stop video: No video is currently playing\\\")\",\n \"def test_video_player(self):\\r\\n\\r\\n # Navigate to a video\\r\\n self.course_info_page.visit()\\r\\n self.tab_nav.go_to_tab('Courseware')\\r\\n\\r\\n # The video should start off paused\\r\\n # Since the video hasn't loaded yet, it's elapsed time is 0\\r\\n self.assertFalse(self.video.is_playing)\\r\\n self.assertEqual(self.video.elapsed_time, 0)\\r\\n\\r\\n # Play the video\\r\\n self.video.play()\\r\\n\\r\\n # Now we should be playing\\r\\n self.assertTrue(self.video.is_playing)\\r\\n\\r\\n # Commented the below EmptyPromise, will move to its page once this test is working and stable\\r\\n # Also there is should be no Promise check in any test as this should be done in Page Object\\r\\n # Wait for the video to load the duration\\r\\n # EmptyPromise(\\r\\n # lambda: self.video.duration > 0,\\r\\n # 'video has duration', timeout=20\\r\\n # ).fulfill()\\r\\n\\r\\n # Pause the video\\r\\n self.video.pause()\\r\\n\\r\\n # Expect that the elapsed time and duration are reasonable\\r\\n # Again, we can't expect the video to actually play because of\\r\\n # latency through the ssh tunnel\\r\\n self.assertGreaterEqual(self.video.elapsed_time, 0)\\r\\n self.assertGreaterEqual(self.video.duration, self.video.elapsed_time)\",\n \"def is_open(self):\\n return self.stream.is_open()\",\n \"def GetStatusOfVideo(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def isopen(self):\\n return _image.image_isopen(self)\",\n \"def stop_video(self):\\n stopped_video = self.video_id\\n if stopped_video == None:\\n print(\\\"Cannot stop video: No video is currently playing\\\")\\n else:\\n print(f\\\"Stopping video: {stopped_video}\\\")\",\n \"def continue_video(self):\\n video_playing = self._video_library.get_video(self.now_playing_videoid)\\n if self.pause:\\n self.pause = False\\n print(f\\\"Continuing video: {video_playing.title}\\\")\\n elif not video_playing:\\n print(f\\\"Cannot continue video: No video is currently playing\\\")\\n else:\\n print(\\\"Cannot continue video: Video is not paused\\\")\\n # print(\\\"continue_video needs implementation\\\")\",\n \"def is_valid(self, value) -> 'True | str':\\n err_str = super().is_valid()\\n if isinstance(err_str, str):\\n return err_str\\n try:\\n cv2.VideoCapture(value)\\n except Exception as e:\\n return str(e)\\n return True\",\n \"def is_video(self):\\n if self.settings.background_image is None:\\n return False\\n\\n filename, _ = b64decode_file(self.settings.background_image)\\n self.mimetype, _ = mimetypes.guess_type(filename)\\n return 'video' in self.mimetype\",\n \"def continue_video(self):\\n global value\\n if value==2:\\n global name\\n value=1\\n print(f\\\"Continuing video: {name}\\\")\\n elif value==1:\\n\\n\\n print(f\\\"Cannot continue video: Video is not paused\\\")\",\n \"def open_video(self, video_display_name):\\r\\n self.course_nav.go_to_sequential(video_display_name)\\r\\n self.video.wait_for_video_player_render()\",\n \"def feed_connection_check(stream, formatted_date, recording_log_msg):\\n stream_url = \\\"http://\\\" + fetch_ros_master_ip() + \\\":8080/stream?topic=/cv_camera/image_raw\\\"\\n error_state = 0\\n connection_check = os.system('ffprobe -select_streams v -i ' + stream_url)\\n if connection_check == 0:\\n threading.Thread(target = start_ffmpeg_record, args = (stream, stream_url, formatted_date)).start()\\n print(recording_log_msg)\\n return jsonify(recording_log_msg=recording_log_msg, error_state=error_state)\\n\\n else:\\n recording_log_msg = 'failed to connect to video feed of ' + stream\\n error_state = 1\\n print(recording_log_msg)\\n return jsonify(recording_log_msg=recording_log_msg, error_state=error_state)\",\n \"def test_task_video_download(url_to_video: str, empty_video_resource: VideoResource):\\n download_video(url_to_video, empty_video_resource.id)\\n empty_video_resource.refresh_from_db()\\n video_instance = empty_video_resource.videos.filter(primary=True).first()\\n\\n assert empty_video_resource.videos.all()\\n assert video_instance.extension == 'mp4'\\n assert video_instance.primary\\n for item in video_instance.video.open():\\n assert item\",\n \"def play_video(self, video_id):\\n video_info = self._video_library.get_all_videos()\\n for i in video_info:\\n if video_id == i.video_id:\\n print(f\\\"Playing video: {i.title}\\\")\\n def search(list, platform):\\n for i in range(len(list)):\\n if list[i] == platform:\\n return False\\n return True\\n video_id_list = []\\n for i in video_info:\\n video_id_list.append(i.video_id)\\n if search(video_id_list, video_id):\\n print(\\\"Cannot play video: Video does not exist\\\")\",\n \"def getTotalFramesVid(srcVideoPath):\\n cap = cv2.VideoCapture(srcVideoPath)\\n # if the videoCapture object is not opened then exit without traceback\\n if not cap.isOpened():\\n print(\\\"Error reading the video file !!\\\")\\n return 0\\n\\n tot_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)\\n cap.release()\\n return tot_frames\",\n \"def start(self):\\n if hasattr(self, 'process'):\\n err = \\\"video '{}' Frames Extraction has already \\\" \\\\\\n \\\"started.\\\".format(self.video_file)\\n print err\\n raise Exception(err)\\n\\n process_number = subprocess.Popen(self.start_frames, stdout=subprocess.PIPE)\\n process_number.wait()\\n return process_number\",\n \"def get_video(self):\\n if self.parsing_template.video and self.parsing_template.video in self.headline.url:\\n return True\\n return False\",\n \"def continue_video(self):\\n if self.is_playing() is not None:\\n print(\\\"Cannot continue video: Video is not paused\\\")\\n elif self.is_paused() is not None:\\n print(\\\"Continuing video: {}\\\".format(self.is_paused()._title))\\n self.is_paused()._status = 1\\n else:\\n print(\\\"Cannot continue video: No video is currently playing\\\")\",\n \"def videostart_failed_reason(self):\\n # type: () -> AnalyticsVideoStartFailedReason\\n return self._videostart_failed_reason\",\n \"def check_streamer(params) -> None:\\n try:\\n with open(params['pid'], \\\"r\\\") as f:\\n pid = f.read()\\n if os.path.isdir(\\\"/proc/\\\" + str(pid)):\\n print(\\\"Streamer is running\\\")\\n else:\\n print(\\\"Streamer is NOT running\\\")\\n except Exception as e:\\n print(\\\"Streamer is NOT running\\\")\",\n \"def checkCamera(self):\\n #how to check if cam exits\\n #https://stackoverflow.com/questions/48049886/how-to-correctly-check-if-a-camera-is-available\\n if \\\"has_cam\\\" in self.store.keys():\\n return self.store[\\\"has_cam\\\"]\\n\\n cap = cv2.VideoCapture(0, cv2.CAP_DSHOW)\\n\\n if cap is None or not cap.isOpened():\\n return False\\n #Close the cam\\n cap.release()\\n cv2.destroyAllWindows()\\n return True\",\n \"def state(self) -> MediaPlayerState | None:\\n if self.is_volume_muted:\\n return MediaPlayerState.IDLE\\n return STREAM_STATUS.get(self._group.stream_status)\",\n \"def is_alive(self):\\n conn = HTTPConnection(self.browser.host, self.browser.port)\\n conn.request(\\\"HEAD\\\", \\\"/invalid\\\")\\n res = conn.getresponse()\\n return res.status == 404\",\n \"def test_api_instructor_start_non_live_video(self):\\n video = factories.VideoFactory(\\n id=\\\"27a23f52-3379-46a2-94fa-697b59cfe3c7\\\",\\n upload_state=random.choice([s[0] for s in STATE_CHOICES]),\\n )\\n jwt_token = AccessToken()\\n jwt_token.payload[\\\"resource_id\\\"] = str(video.id)\\n jwt_token.payload[\\\"roles\\\"] = [random.choice([\\\"instructor\\\", \\\"administrator\\\"])]\\n jwt_token.payload[\\\"permissions\\\"] = {\\\"can_update\\\": True}\\n\\n # start a live video,\\n with mock.patch.object(api, \\\"start_live_channel\\\"):\\n response = self.client.post(\\n f\\\"/api/videos/{video.id}/start-live/\\\",\\n HTTP_AUTHORIZATION=f\\\"Bearer {jwt_token}\\\",\\n )\\n self.assertEqual(response.status_code, 400)\",\n \"def validate(base_path: Path,\\n max_size: int) -> None:\\n valid = invalid = 0\\n for path, is_valid in validate_videos(base_path, max_size):\\n print(colorama.Fore.GREEN if is_valid else colorama.Fore.RED,\\n \\\"Processing\\\", end='', sep='')\\n\\n if is_valid:\\n valid += 1\\n print(f\\\"{get_info(path, short=True)} is valid\\\".rjust(50, '.'))\\n else:\\n invalid += 1\\n print(f\\\"{get_info(path, short=True)} is invalid\\\".rjust(50, '.'))\\n\\n print(colorama.Fore.GREEN, \\\"=\\\" * 60, colorama.Fore.RESET, sep='')\\n print(f\\\"Total files count: {len(os.listdir(base_path))}\\\")\\n if valid == invalid == 0:\\n print(f\\\"No video found\\\")\\n return\\n\\n print(f\\\"Total videos count: {valid + invalid}\\\\n\\\")\\n if invalid == 0:\\n print(f\\\"All {valid} videos are valid\\\")\\n else:\\n print(f\\\"Total valid videos: {valid}\\\")\\n print(f\\\"Total invalid videos: {invalid}\\\")\",\n \"def continue_video(self):\\n if self.current_video is None:\\n print(\\\"Cannot continue video: No video is currently playing\\\")\\n elif not self.current_paused:\\n print(\\\"Cannot continue video: Video is not paused\\\")\\n elif self.current_paused:\\n print(\\\"Continuing video:\\\", self.current_video.title)\",\n \"def playstatus_error(self, updater, exception: Exception) -> None:\\n if updater == self.main_instance:\\n self.listener.playstatus_error(updater, exception)\",\n \"def test_camera_id() -> None: # pragma: no cover\\n cap = cv2.VideoCapture(1)\\n if (cap.isOpened() == False):\\n print(\\\"Error opening video stream or file\\\")\\n ret, frame = cap.read()\\n cv2.imshow('frame', frame)\\n cv2.waitKey(0)\\n cap.release()\",\n \"def is_open(self) -> bool:\\n pass\",\n \"def is_video_rendered(self, mode, video_display_name=None):\\r\\n selector = self.get_element_selector(video_display_name, VIDEO_MODES[mode])\\r\\n\\r\\n def _is_element_present():\\r\\n \\\"\\\"\\\"\\r\\n Check if a web element is present in DOM.\\r\\n\\r\\n Returns:\\r\\n tuple: (is_satisfied, result)`, where `is_satisfied` is a boolean indicating whether the promise was\\r\\n satisfied, and `result` is a value to return from the fulfilled `Promise`.\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n is_present = self.q(css=selector).present\\r\\n return is_present, is_present\\r\\n\\r\\n return Promise(_is_element_present, 'Video Rendering Failed in {0} mode.'.format(mode)).fulfill()\",\n \"def assert_video_status(self, url, edx_video_id, status):\\n response = self.client.get_json(url)\\n self.assertEqual(response.status_code, 200)\\n videos = json.loads(response.content.decode('utf-8'))[\\\"videos\\\"]\\n for video in videos:\\n if video['edx_video_id'] == edx_video_id:\\n return self.assertEqual(video['status'], status)\\n\\n # Test should fail if video not found\\n self.assertEqual(True, False, 'Invalid edx_video_id')\",\n \"def assertUsable (self):\\n if self.usableChecked:\\n return self.usableError\\n try:\\n # check if parent encoder is runnable\\n aviErr = AviH264.assertUsable ()\\n if aviErr != None:\\n self.usableChecked = True\\n self.usableError = aviErr\\n return self.usableError\\n\\n # check if mencoder is runnable\\n result = subprocess.run ([_MP4H264Encoder._getMP4BoxRunnable ()], capture_output = True)\\n if result.returncode != 0:\\n if not 'MP4Box' in (str(result.stdout) + ' ' + str(result.stderr)):\\n self.usableError = \\\"Can not execute MP4Box. Is inside PATH variable?\\\"\\n else:\\n self.usableError = None\\n self.usableChecked = True\\n return self.usableError\\n except:\\n self.usableError = \\\"Can not execute \\\" + _MP4H264Encoder._getMP4BoxRunnable () + \\\". Is inside PATH variable?\\\"\\n self.usableChecked = True\\n return self.usableError\",\n \"def test_api_video_update_live_state_unknown_video(self):\\n data = {\\n \\\"logGroupName\\\": \\\"/aws/lambda/dev-test-marsha-medialive\\\",\\n \\\"state\\\": \\\"running\\\",\\n }\\n signature = generate_hash(\\\"shared secret\\\", json.dumps(data).encode(\\\"utf-8\\\"))\\n response = self.client.patch(\\n \\\"/api/videos/9087c52d-cb87-4fd0-9b57-d9f28a0c69cb/update-live-state/\\\",\\n data,\\n content_type=\\\"application/json\\\",\\n HTTP_X_MARSHA_SIGNATURE=signature,\\n )\\n\\n self.assertEqual(response.status_code, 404)\",\n \"def play_video(self, video_id):\\n\\n if self._video_library.get_video(video_id) is None:\\n print(\\\"Cannot play video: Video does not exist\\\")\\n elif self._video_library.get_video(video_id).flagged:\\n print(f\\\"Cannot play video: Video is currently flagged (reason: {self._video_library.get_video(video_id).flag_reason})\\\")\\n elif self.current_video is not None:\\n # If different must stop\\n print(\\\"Stopping video:\\\", self.current_video.title)\\n self.current_video = self._video_library.get_video(video_id)\\n self.current_paused = False\\n print(\\\"Playing video:\\\", self.current_video.title)\\n elif self.current_video is None:\\n # If nothing currently playing\\n self.current_video = self._video_library.get_video(video_id)\\n self.current_paused = False\\n print(\\\"Playing video:\\\", self.current_video.title)\",\n \"def has_parsed_video(season: int, episode: int) -> bool:\\n file_path = VideoParser.__get_parsed_video_file_path(season, episode)\\n return os.path.isfile(file_path)\",\n \"def streamer(self):\\n retry = 3\\n print ('start streamer!')\\n while self.container is None and 0 < retry:\\n if not self.collect_frames:\\n break\\n #print (type(self.container))\\n retry -= 1\\n try:\\n self.container = av.open(self.drone.get_video_stream())\\n print('success')\\n except av.AVError as ave:\\n print(ave)\\n print('retry...')\",\n \"def onclick_load_video(self):\\n video_source = select_file(\\n \\\"Select Video Files\\\",\\n \\\"../\\\",\\n \\\"Video Files (*.mp4 *.avi *.mpg *.gif *.mov)\\\")\\n if video_source:\\n param_name = select_file(\\n \\\"Select Parameter\\\", \\\"../\\\", \\\"Parameter Files (*.json)\\\")\\n if param_name:\\n self.moildev = Moildev(param_name)\\n self.running_video(video_source)\",\n \"def check_playable(url):\\r\\n import urllib\\r\\n import requests\\r\\n try:\\r\\n headers = url.rsplit('|', 1)[1]\\r\\n except:\\r\\n headers = ''\\r\\n headers = urllib.quote_plus(headers).replace('%3D', '=') if ' ' in headers else headers\\r\\n headers = dict(urlparse.parse_qsl(headers))\\r\\n\\r\\n result = None\\r\\n try:\\r\\n if url.startswith('http') and '.m3u8' in url:\\r\\n result = requests.head(url.split('|')[0], headers=headers, timeout=5)\\r\\n if result is None:\\r\\n return None\\r\\n\\r\\n elif url.startswith('http'):\\r\\n result = requests.head(url.split('|')[0], headers=headers, timeout=5)\\r\\n if result is None:\\r\\n return None\\r\\n except:\\r\\n pass\\r\\n\\r\\n return result\",\n \"def open(self):\\n self._dev_obj = cv2.VideoCapture(self._dev_num)\\n if not self._dev_obj.isOpened():\\n LOG.error(\\\"Fail to open the camera, number: %d\\\", self._dev_num)\\n return False\\n\\n self._dev_obj.set(cv2.CAP_PROP_FRAME_WIDTH, self._width)\\n self._dev_obj.set(cv2.CAP_PROP_FRAME_HEIGHT, self._height)\\n self._dev_obj.set(cv2.CAP_PROP_FPS, self._fps)\\n\\n LOG.info(\\\"Frame width: %d\\\", self._width)\\n LOG.info(\\\"Frame height: %d\\\", self._height)\\n\\n return True\",\n \"def record_video(self):\\n return self.config.get('record_video', False)\",\n \"def get_frame(self):\\n if self.vid.isOpened():\\n ret, frame = self.vid.read()\\n if ret:\\n return (ret, cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))\\n else:\\n return (ret, None)\\n else:\\n return (ret, None)\",\n \"def canPlay(self):\\r\\n return self.__canPlay\",\n \"def _navigate_to_courseware_video_no_render(self):\\r\\n self._navigate_to_courseware_video()\\r\\n self.video.wait_for_video_class()\",\n \"def valid_video_file(file):\\r\\n return file.lower().endswith(('.ogg', '.webm'))\",\n \"def _next_frame(self):\\n ret, self.frame = self.capture.read()\\n if not ret:\\n self.logger.warning('Failed to read frame')\\n if self.show_video:\\n cv2.imshow('frame', self.frame)\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\n exit(0)\\n return ret\",\n \"def check_media(self, media):\\n path = os.path.join(settings.get_path(\\\"media\\\"), media)\\n if os.path.exists(path):\\n return path\\n else:\\n return False\",\n \"def download_video(video_stream):\\n global file_size\\n file_size = size_in_mb(video_stream.filesize)\\n home_dir = os.environ['HOME']\\n path = f'{home_dir}/Downloads/Video'\\n print('-'*60)\\n print(f'Filename:\\\\t{video_stream.title}')\\n print(f'Location:\\\\t{path}')\\n print(f'Size:\\\\t\\\\t{file_size} MB\\\\n')\\n\\n filename = video_stream.title + '_video.mp4'\\n filename = filename.replace('/', ' ')\\n filename = filename.replace('\\\\\\\\', ' ')\\n\\n if os.path.exists(os.path.join(path, filename)):\\n print(\\\"The file has been already downloaded.\\\")\\n sys.exit()\\n \\n video_stream.download(path, filename)\",\n \"def media_status_changed(self, state):\\n if state == self.MediaStatus.LoadedMedia.value:\\n QMetaObject.invokeMethod(self.video_player, \\\"play\\\", Qt.QueuedConnection)\\n self.timer.start(self.timer_interval)\\n elif state == self.MediaStatus.EndOfMedia.value:\\n if self.state_time_remain > self.timer_interval / 1000:\\n QMetaObject.invokeMethod(self.video_player, \\\"play\\\", Qt.QueuedConnection)\",\n \"def check_channel_request(self, kind, chanid):\\n if kind == 'session':\\n return paramiko.OPEN_SUCCEEDED\\n return paramiko.OPEN_FAILED_ADMINISTRATIVELY_PROHIBITED\",\n \"def get_video_num(self, username):\\n done = self.cur.execute(\\\"SELECT video_ID FROM videos where uploader = \\\\\\\"{}\\\\\\\"\\\".format(username))\\n return done\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6829495","0.6795602","0.67080617","0.6603272","0.6496463","0.64905244","0.6287083","0.62592316","0.62456816","0.612378","0.61099005","0.6035323","0.6004216","0.6001032","0.5871629","0.5861267","0.582632","0.57916695","0.5785687","0.5746918","0.5741277","0.57069945","0.5684202","0.565927","0.56403726","0.56326735","0.5605589","0.55905366","0.55527884","0.55527884","0.55527884","0.55527884","0.55496955","0.5547594","0.5530156","0.5520271","0.5520271","0.55110085","0.5510678","0.548916","0.54885674","0.54631406","0.5423123","0.54190874","0.54137474","0.54045355","0.54020685","0.53888","0.5381776","0.5376824","0.53647286","0.53604776","0.5345737","0.5339247","0.53321904","0.5316889","0.5313406","0.530925","0.53027266","0.53004134","0.5298221","0.5263713","0.5261194","0.52595323","0.52441025","0.52330244","0.52273643","0.5219922","0.5211849","0.51861066","0.5183675","0.5182984","0.51745564","0.5174431","0.517269","0.5163181","0.5162385","0.51584095","0.5153166","0.51524436","0.5152327","0.5149301","0.5142185","0.5134578","0.5132987","0.5128042","0.51162976","0.5115923","0.5108854","0.5106964","0.5095269","0.50908947","0.5089681","0.5076851","0.507093","0.5068158","0.5063663","0.50595427","0.50567454","0.5056395"],"string":"[\n \"0.6829495\",\n \"0.6795602\",\n \"0.67080617\",\n \"0.6603272\",\n \"0.6496463\",\n \"0.64905244\",\n \"0.6287083\",\n \"0.62592316\",\n \"0.62456816\",\n \"0.612378\",\n \"0.61099005\",\n \"0.6035323\",\n \"0.6004216\",\n \"0.6001032\",\n \"0.5871629\",\n \"0.5861267\",\n \"0.582632\",\n \"0.57916695\",\n \"0.5785687\",\n \"0.5746918\",\n \"0.5741277\",\n \"0.57069945\",\n \"0.5684202\",\n \"0.565927\",\n \"0.56403726\",\n \"0.56326735\",\n \"0.5605589\",\n \"0.55905366\",\n \"0.55527884\",\n \"0.55527884\",\n \"0.55527884\",\n \"0.55527884\",\n \"0.55496955\",\n \"0.5547594\",\n \"0.5530156\",\n \"0.5520271\",\n \"0.5520271\",\n \"0.55110085\",\n \"0.5510678\",\n \"0.548916\",\n \"0.54885674\",\n \"0.54631406\",\n \"0.5423123\",\n \"0.54190874\",\n \"0.54137474\",\n \"0.54045355\",\n \"0.54020685\",\n \"0.53888\",\n \"0.5381776\",\n \"0.5376824\",\n \"0.53647286\",\n \"0.53604776\",\n \"0.5345737\",\n \"0.5339247\",\n \"0.53321904\",\n \"0.5316889\",\n \"0.5313406\",\n \"0.530925\",\n \"0.53027266\",\n \"0.53004134\",\n \"0.5298221\",\n \"0.5263713\",\n \"0.5261194\",\n \"0.52595323\",\n \"0.52441025\",\n \"0.52330244\",\n \"0.52273643\",\n \"0.5219922\",\n \"0.5211849\",\n \"0.51861066\",\n \"0.5183675\",\n \"0.5182984\",\n \"0.51745564\",\n \"0.5174431\",\n \"0.517269\",\n \"0.5163181\",\n \"0.5162385\",\n \"0.51584095\",\n \"0.5153166\",\n \"0.51524436\",\n \"0.5152327\",\n \"0.5149301\",\n \"0.5142185\",\n \"0.5134578\",\n \"0.5132987\",\n \"0.5128042\",\n \"0.51162976\",\n \"0.5115923\",\n \"0.5108854\",\n \"0.5106964\",\n \"0.5095269\",\n \"0.50908947\",\n \"0.5089681\",\n \"0.5076851\",\n \"0.507093\",\n \"0.5068158\",\n \"0.5063663\",\n \"0.50595427\",\n \"0.50567454\",\n \"0.5056395\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8533003"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77076,"cells":{"query":{"kind":"string","value":"Create a \"bounding box\" that is bounded on the left and the right by using the gradients and below by using the ambient estimate."},"document":{"kind":"string","value":"def remove_dc_from_spad_edge(spad, ambient, grad_th=1e3, n_std=1.):\n # Detect edges:\n assert len(spad.shape) == 1\n edges = np.abs(np.diff(spad)) > grad_th\n first = np.nonzero(edges)[0][1] + 1 # Want the right side of the first edge\n last = np.nonzero(edges)[0][-1] # Want the left side of the second edge\n below = ambient + n_std*np.sqrt(ambient)\n # Walk first and last backward and forward until we encounter a value below the threshold\n while first >= 0 and spad[first] > below:\n first -= 1\n while last < len(spad) and spad[last] > below:\n last += 1\n spad[:first] = 0.\n spad[last+1:] = 0.\n return np.clip(spad - ambient, a_min=0., a_max=None)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def paint_box(top=0, bottom=DISPLAY_HEIGHT-1, left=0, right=DISPLAY_WIDTH-1):\n def filter_box(col, row):\n \"\"\"For a given pixel position, turn it on if it's with the bounds\n \"\"\"\n # remember rows count from 0 at the top!\n correct_vertical = top <= row <= bottom\n correct_horizontal = left <= col <= right\n return correct_vertical and correct_horizontal\n light(MAX_BRIGHTNESS, filter_box)","def calc_bounding_box(self):\n self.BB = self.geos.abs_el(0).BB\n for geo in self.geos.abs_iter():\n self.BB = self.BB.joinBB(geo.BB)","def create_bounds(self):\n # Bounds should be created for\n x0, y0 = (0, 0)\n x1 = self.game_area_size[0]\n y1 = self.game_area_size[1]\n space = self.space\n\n borders = [\n phy.Segment(space.static_body, phy.Vec2d(x0, y0), phy.Vec2d(x1, y0), 0),\n phy.Segment(space.static_body, phy.Vec2d(x0, y0), phy.Vec2d(x0, y1), 0),\n phy.Segment(space.static_body, phy.Vec2d(x1, y0), phy.Vec2d(x1, y1), 0),\n phy.Segment(space.static_body, phy.Vec2d(x0, y1), phy.Vec2d(x1, y1), 0),\n ]\n for b in borders:\n b.elasticity = 0.5\n self.space.add(borders)","def bbox_giou(boxes1, boxes2):\n\n # obtain boxes where properties are (x_min, y_min, x_max, y_max)\n boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5, boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis = -1)\n boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5, boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis = -1)\n\n # obtain boxes where properties are (x_min, y_min, x_max, y_max)\n boxes1 = tf.concat([tf.minimum(boxes1[..., :2], boxes1[..., 2:]),\n tf.maximum(boxes1[..., :2], boxes1[..., 2:])], axis = -1)\n boxes2 = tf.concat([tf.minimum(boxes2[..., :2], boxes2[..., 2:]),\n tf.maximum(boxes2[..., :2], boxes2[..., 2:])], axis = -1)\n\n # obtain area of from the 2 boxes\n boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])\n boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])\n\n # obtain maximum coordinates amongst 2 box at top left corner\n left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])\n\n # obtain minimum coordinates amongst 2 box at bottom right corner\n right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])\n\n # obtain a positive intersection \n inter_section = tf.maximum(right_down - left_up, 0.0)\n\n # obtain intersection area \n inter_area = inter_section[..., 0] * inter_section[..., 1]\n\n # obtain union area \n union_area = boxes1_area + boxes2_area - inter_area\n\n # calculate the iou value between the two bounding boxes\n iou = inter_area / union_area\n\n # calculate the coordinates of the upper left corner and the lower right corner of the smallest closed convex \n # surface\n enclose_left_up = tf.minimum(boxes1[..., :2], boxes2[..., :2])\n enclose_right_down = tf.maximum(boxes1[..., 2:], boxes2[..., 2:])\n enclose = tf.maximum(enclose_right_down - enclose_left_up, 0.0)\n\n # calculate the area of the smallest closed convex surface C\n enclose_area = enclose[..., 0] * enclose[..., 1]\n\n # calculate the GIoU value according to the GioU formula \n giou = iou - 1.0 * (enclose_area - union_area) / enclose_area\n\n return giou","def sweptAABB(b1, b2):\n\tnormalX = 0;\n\tnormalY = 0;\n\n\t\"\"\"\n\tfloat xInvEntry, yInvEntry;\n\tfloat xInvExit, yInvExit;\n\n\t// find the distance between the objects on the near and far sides for both x and y\n\tif (b1.vx > 0.0f) {\n\t\txInvEntry = b2.x - (b1.x + b1.w);\n\t\txInvExit = (b2.x + b2.w) - b1.x;\n\t} else {\n\t\txInvEntry = (b2.x + b2.w) - b1.x;\n\t\txInvExit = b2.x - (b1.x + b1.w);\n\t}\n\n\tif (b1.vy > 0.0f) {\n\t\tyInvEntry = b2.y - (b1.y + b1.h);\n\t\tyInvExit = (b2.y + b2.h) - b1.y;\n\t} else {\n\t\tyInvEntry = (b2.y + b2.h) - b1.y;\n\t\tyInvExit = b2.y - (b1.y + b1.h);\n\t}\n\t\"\"\"\n\n\txInvEntry = 0;\n\tyInvEntry = 0;\n\txInvExit = 0;\n\tyInvExit = 0;\n\n\tif (b1.vx > 0):\n\t\txInvEntry = b2.x - (b1.x + b1.w);\n\t\txInvExit = (b2.x + b2.w) - b1.x;\n\telse:\n\t\txInvEntry = (b2.x + b2.w) - b1.x;\n\t\txInvExit = b2.x - (b1.x + b1.w);\n\t\n\n\tif (b1.vy > 0):\n\t\tyInvEntry = b2.y - (b1.y + b1.h);\n\t\tyInvExit = (b2.y + b2.h) - b1.y;\n\telse:\n\t\tyInvEntry = (b2.y + b2.h) - b1.y;\n\t\tyInvExit = b2.y - (b1.y + b1.h);\n\n\t\"\"\"\n\t// find time of collision and time of leaving for each axis (if statement is to prevent divide by zero)\n\tfloat xEntry, yEntry;\n\tfloat xExit, yExit;\n\n\tif (b1.vx == 0.0f) {\n\t\txEntry = -std::numeric_limits<float>::infinity();\n\t\txExit = std::numeric_limits<float>::infinity();\n\t} else {\n\t\txEntry = xInvEntry / b1.vx;\n\t\txExit = xInvExit / b1.vx;\n\t}\n\n\tif (b1.vy == 0.0f) {\n\t\tyEntry = -std::numeric_limits<float>::infinity();\n\t\tyExit = std::numeric_limits<float>::infinity();\n\t} else {\n\t\tyEntry = yInvEntry / b1.vy;\n\t\tyExit = yInvExit / b1.vy;\n\t}\n\t\"\"\"\n\txEntry = 0;\n\tyEntry = 0;\n\txExit = 0;\n\tyExit = 0;\n\n\tif (b1.vx == 0):\n\t\txEntry = -float(\"inf\");\n\t\txExit = float(\"inf\");\n\telse:\n\t\txEntry = xInvEntry / b1.vx;\n\t\txExit = xInvExit / b1.vx;\n\n\tif (b1.vy == 0):\n\t\tyEntry = -float(\"inf\");\n\t\tyExit = float(\"inf\");\n\telse:\n\t\tyEntry = yInvEntry / b1.vy;\n\t\tyExit = yInvExit / b1.vy;\n\t\n\tif (yEntry > 1):\n\t\tyEntry = -float(\"inf\");\n\tif (xEntry > 1):\n\t\txEntry = -float(\"inf\");\n\n\n\t\"\"\"\n\t// find the earliest/latest times of collision\n\tfloat entryTime = std::max(xEntry, yEntry);\n\tfloat exitTime = std::min(xExit, yExit);\n\t\"\"\"\n\n\tentryTime = max(xEntry, yEntry);\n\texitTime = min(xExit, yExit);\n\n\t\"\"\"\n\t// if there was no collision\n\tif (entryTime > exitTime || xEntry < 0.0f && yEntry < 0.0f || xEntry > 1.0f || yEntry > 1.0f) {\n\t\tnormalX = 0.0f;\n\t\tnormalY = 0.0f;\n\t\treturn 1.0f;\n\t} else {\n\t\t// if there was a collision\n\t\t// calculate normal of collided surface\n\t\tif (xEntry > yEntry) {\n\t\t\tif (xInvEntry < 0.0f) {\n\t\t\t\tnormalX = 1.0f;\n\t\t\t\tnormalY = 0.0f;\n\t\t\t} else {\n\t\t\t\tnormalX = -1.0f;\n\t\t\t\tnormalY = 0.0f;\n\t\t\t}\n\t\t} else {\n\t\t\tif (yInvEntry < 0.0f) {\n\t\t\t\tnormalX = 0.0f;\n\t\t\t\tnormalY = 1.0f;\n\t\t\t} else {\n\t\t\t\tnormalX = 0.0f;\n\t\t\t\tnormalY = -1.0f;\n\t\t\t}\n\t\t}\n\n\t\t// return the time of collision\n\t\treturn entryTime;\n\t}\n\t\"\"\"\n\n\t\"\"\"\n\tif (entryTime > exitTime)\n\t\treturn 1.0f; // This check was correct.\n\tif (entryX < 0.0f && entryY < 0.0f)\n\t\treturn 1.0f;\n\tif (entryX < 0.0f) {\n\t\t// Check that the bounding box started overlapped or not.\n\t\tif (s.max.x < t.min.x || s.min.x > t.max.x)\n\t\t\treturn 1.0f;\n\t}\n\tif (entryY < 0.0f) {\n\t\t// Check that the bounding box started overlapped or not.\n\t\tif (s.max.y < t.min.y || s.min.y > t.max.y)\n\t\t\treturn 1.0f;\n\t}\"\"\"\n\n\n\t# if (entryTime > exitTime or xEntry < 0 and yEntry < 0 or xEntry > 1 or yEntry > 1):\n\t# \tnormalX = 0;\n\t# \tnormalY = 0;\n\t# \treturn (1, (normalX, normalY));\n\t# else:\n\n\tnormalX = 0;\n\tnormalY = 0;\n\n\tif (entryTime > exitTime):\n\t\treturn (1, (0, 0));\n\t\n\tif (xEntry < 0 and yEntry < 0):\n\t\treturn (1, (0, 0));\n\n\tif (xEntry < 0):\n\t\tif (b1.x + b1.w < b2.x or b1.x > b2.x + b2.w):\n\t\t\treturn (1, (0, 0));\n\n\tif (yEntry < 0):\n\t\tif (b1.y + b1.h < b2.y or b1.y > b2.y + b2.h):\n\t\t\treturn (1, (0, 0));\n\n\tif (xEntry > yEntry):\n\t\tif (xInvEntry < 0):\n\t\t\tnormalX = 1;\n\t\t\tnormalY = 0;\n\t\telse:\n\t\t\tnormalX = -1;\n\t\t\tnormalY = 0;\n\telse:\n\t\tif (yInvEntry < 0):\n\t\t\tnormalX = 0;\n\t\t\tnormalY = 1;\n\t\telse:\n\t\t\tnormalX = 0;\n\t\t\tnormalY = -1;\n\n\treturn (entryTime, (normalX, normalY));","def bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","def apply_boundary_conditions(self):\n E = self.__mesh.get_edge_list()\n\n # Top and bottom wall Dirichlet bcs (boundary_id = 21)\n \n e21_iterator = self.__mesh.edge_iterator(21)\n\n self.edge_center_value[e21_iterator[0]:e21_iterator[1]+1] = 0.0 \n \n # Left Dirichlet bc (boundary_id = 2)\n \n e2_iterator = self.__mesh.edge_iterator(2)\n\n b = np.sin(np.pi*self.y_e[e2_iterator[0]:e2_iterator[1]+1])\n\n self.edge_center_value[e2_iterator[0]:e2_iterator[1]+1] \\\n = b\n \n # Right Neumann bc (Zero flux, boundary_id = 3)\n \n e3_iterator = self.__mesh.edge_iterator(3)\n \n for i in range(e3_iterator[0], e3_iterator[1]+1):\n LC = E[i].get_straddling_cells()\n n = LC.get_global_cell_number() - 1\n self.edge_center_value[i] = self.cell_centroid_value[n]","def boundaries_and_initialize():\n greenLower = (29, 86, 6) # define the lower and upper boundaries of the \"green\"\n greenUpper = (64, 255, 255)\n pts = [((200,300),(255,255,255), 0)]\n blanks = []\n linecolor = (0,0,0)\n counter = 1\n radius = 11\n return greenLower, greenUpper, pts, linecolor, counter, blanks, radius","def _boundRect(self):\n self.upperleft = list(map(min, zip(*self.addresstamp)))\n self.bottomright = list(map(max, zip(*self.addresstamp)))\n self.upperright = [self.bottomright[0], self.upperleft[1]]\n self.bottomleft = [self.upperleft[0], self.bottomright[1]]\n (self.width, self.height) = (self.upperright[0] - self.bottomleft[0], self.bottomleft[1] - self.upperright[1])\n assert self.width >= 0\n assert self.height >= 0\n self.center = [self.upperleft[0] + self.width / float(2), self.upperleft[1] + self.height / float(2)]\n self.corners = [self.upperright, self.bottomleft, self.upperleft, self.bottomright]","def draw_bounding_box(self):\n # Gets the bounding box\n xmin, ymin, xmax, ymax = self.get_bounding_box()\n\n # Gets the actual coordinates\n width = xmax - xmin\n height = ymax - ymin\n center_x = xmin + (width)/2\n center_y = ymin + (height)/2\n\n arcade.draw_rectangle_outline(center_x, center_y, width, height, (255, 0, 0))","def _boundRect(self):\n addresstamp = reduce(lambda x, y: x + y, [v.addresstamp for v in self.footprints])\n self.upperleft = list(map(min, zip(*addresstamp)))\n self.bottomright = list(map(max, zip(*addresstamp)))\n self.upperright = [self.bottomright[0], self.upperleft[1]]\n self.bottomleft = [self.upperleft[0], self.bottomright[1]]\n (self.width, self.height) = (self.upperright[0] - self.bottomleft[0], self.bottomleft[1] - self.upperright[1])\n assert self.width >= 0\n assert self.height >= 0\n self.center = [self.upperleft[0] + self.width / float(2), self.upperleft[1] + self.height / float(2)]\n self.corners = [self.upperright, self.bottomleft, self.upperleft, self.bottomright]","def bounds(self):\n frame_ = self.to_frame().total_bounds.flatten().tolist()\n return BBox(\n left=frame_[0], bottom=frame_[1], right=frame_[2], top=frame_[3]\n )","def box_refinement(box, gt_box):\n box = box.astype(np.float32)\n gt_box = gt_box.astype(np.float32)\n\n height = box[:, 2] - box[:, 0]\n width = box[:, 3] - box[:, 1]\n center_y = box[:, 0] + 0.5 * height\n center_x = box[:, 1] + 0.5 * width\n\n gt_height = gt_box[:, 2] - gt_box[:, 0]\n gt_width = gt_box[:, 3] - gt_box[:, 1]\n gt_center_y = gt_box[:, 0] + 0.5 * gt_height\n gt_center_x = gt_box[:, 1] + 0.5 * gt_width\n\n dy = (gt_center_y - center_y) / height\n dx = (gt_center_x - center_x) / width\n dh = np.log(gt_height / height)\n dw = np.log(gt_width / width)\n\n return np.stack([dy, dx, dh, dw], axis=1)","def expanded_boundaries(self):\n width = self._points[0][3][0] - self._points[0][1][0]\n height = self._points[0][3][1] - self._points[0][1][1]\n factor = np.multiply((width, height), Window.BORDER)\n return (\n np.subtract(self._points[0][1], factor),\n np.add(self._points[0][3], factor))","def create_ring(self):\n\t\tself.north_coords = numpy.add(self.center, self.north)\n\t\tself.northeast_coords = numpy.add(self.center, self.northeast)\n\t\tself.east_coords = numpy.add(self.center, self.east)\n\t\tself.southeast_coords = numpy.add(self.center, self.southeast)\n\t\tself.south_coords = numpy.add(self.center, self.south)\n\t\tself.southwest_coords = numpy.add(self.center, self.southwest)\n\t\tself.west_coords = numpy.add(self.center, self.west)\n\t\tself.northwest_coords = numpy.add(self.center, self.northwest)","def bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","def box_refinement_graph(box, gt_box):\n box = tf.cast(box, tf.float32)\n gt_box = tf.cast(gt_box, tf.float32)\n\n height = box[:, 2] - box[:, 0]\n width = box[:, 3] - box[:, 1]\n center_y = box[:, 0] + 0.5 * height\n center_x = box[:, 1] + 0.5 * width\n\n gt_height = gt_box[:, 2] - gt_box[:, 0]\n gt_width = gt_box[:, 3] - gt_box[:, 1]\n gt_center_y = gt_box[:, 0] + 0.5 * gt_height\n gt_center_x = gt_box[:, 1] + 0.5 * gt_width\n\n dy = (gt_center_y - center_y) / height\n dx = (gt_center_x - center_x) / width\n dh = tf.math.log(gt_height / height)\n dw = tf.math.log(gt_width / width)\n\n result = tf.stack([dy, dx, dh, dw], axis=1)\n return result","def _trailing_edge(top, bottom, config):\n\n def rectangle(top_left,\n bottom_left,\n left_line,\n horizontal_discretisation,\n wake_progression = 1):\n \"\"\"\n :param top_left: top left point of rectangle (existing point)\n :param bottom_left: bottom left point of rectangle (existing point)\n :param left_line: left line of rectangle (existing line)\n :param horizontal_discretisation: horizontal discretisation.\n :param wake_progression: progression on horizontal line from trailing edge aft-wards.\n :return: block data structure representing a rectangle in the trailing edge wake.\n \"\"\"\n x_dim = 2 * config['grid']['regular']['thickness']\n top_right = Point((x_dim,\n top_left.coordinates[1],\n 0))\n bottom_right = Point((x_dim,\n bottom_left.coordinates[1],\n 0))\n\n transfinite_num = int((x_dim - bottom_left.coordinates[0]) / horizontal_discretisation)\n\n if transfinite_num < 2:\n transfinite_num = 2\n\n top_line = Line(top_left,\n top_right,\n transfinite = transfinite_num,\n progression = wake_progression)\n bottom_line = Line(bottom_left,\n bottom_right,\n transfinite = transfinite_num,\n progression = wake_progression)\n\n if wake_progression is None:\n x = []\n for i in range(0, 4):\n x.append(bottom['points']['aerofoil'][i].coordinates[0])\n d = np.mean(np.diff(np.array(x)))\n top_line.progression_from_width(d)\n bottom_line.progression_from_width(d)\n\n right_line = Line(bottom_right,\n top_right,\n transfinite = left_line.transfinite,\n progression = left_line.progression)\n\n loop = Loop([left_line.id,\n top_line.id,\n -right_line.id,\n -bottom_line.id])\n\n surface = Surface([loop.id], transfinite = True)\n\n return {'points': {'all': [top_right, bottom_right]},\n 'curves': {'all': [top_line, right_line, bottom_line]},\n 'loops': {'all': [loop]},\n 'surfaces': {'all': [surface]}}\n\n te_line = Line(top['points']['aerofoil'][-1],\n bottom['points']['aerofoil'][0])\n te_line.transfinite_from_grid_size(config['grid']['regular']['boundary_layer']['initial_thickness'])\n\n top_rectangle = rectangle(top_left = top['points']['boundary_layer'][-1],\n bottom_left = top['points']['aerofoil'][-1],\n left_line = top['curves']['normals'][-1],\n horizontal_discretisation = config['grid']['regular']['wake']['width'],\n wake_progression = config['grid']['regular']['wake']['progression'])\n\n bottom_rectangle = rectangle(top_left = bottom['points']['boundary_layer'][0],\n bottom_left = bottom['points']['aerofoil'][0],\n left_line = bottom['curves']['normals'][0],\n horizontal_discretisation = config['grid']['regular']['wake']['width'],\n wake_progression = config['grid']['regular']['wake']['progression'])\n\n center_right_line = Line(top_rectangle['points']['all'][1],\n bottom_rectangle['points']['all'][1])\n center_right_line.transfinite_from_grid_size(config['grid']['regular']['boundary_layer']['initial_thickness'])\n\n center_loop = Loop([te_line.id,\n - top_rectangle['curves']['all'][2].id,\n - center_right_line.id,\n bottom_rectangle['curves']['all'][2].id])\n center_surface = Surface([center_loop.id], transfinite = True)\n\n r = top_rectangle\n for key in ['points', 'curves', 'loops', 'surfaces']:\n r[key]['all'] += bottom_rectangle[key]['all']\n r['curves']['trailing_edge'] = [te_line]\n r['curves']['all'] += [center_right_line]\n r['loops']['all'] += [center_loop]\n r['surfaces']['all'] += [center_surface]\n\n return r","def draw_boundary() -> None:\n # Upper edge\n print(rpipes.terminal.move_xy(0, 0), WBorder.HORIZONTAL * (rpipes.terminal.width - 1))\n\n # Left and Right edges\n for row in range(rpipes.terminal.height - 2):\n print(\n WBorder.VERTICAL,\n rpipes.terminal.move_right(rpipes.terminal.width - 4),\n WBorder.VERTICAL,\n )\n\n # Bottom edge\n print(\n rpipes.terminal.move_xy(0, rpipes.terminal.height - 2),\n WBorder.HORIZONTAL * (rpipes.terminal.width - 1),\n )\n\n # Top left corner\n print(rpipes.terminal.move_xy(0, 0) + WBorder.DOWN_AND_RIGHT)\n\n # Top right corner\n print(rpipes.terminal.move_xy(rpipes.terminal.width - 1, 0) + WBorder.DOWN_AND_LEFT)\n\n # Bottom left corner\n print(rpipes.terminal.move_xy(0, rpipes.terminal.height - 2) + WBorder.UP_AND_RIGHT)\n\n # Bottom right corner\n print(\n rpipes.terminal.move_xy(rpipes.terminal.width - 1, rpipes.terminal.height - 2)\n + WBorder.UP_AND_LEFT\n )","def drawBoundingBoxes(ax, x, y, z, w, l, h, r, col='b', linewidth=2):\n # Do this, because we have center point\n l = l / 2.0\n w = w / 2.0\n\n # Calculate corner locations with rotation\n x1 = x + (w * math.cos(r) + l * math.sin(r))\n y1 = y + (-w * math.sin(r) + l * math.cos(r))\n x2 = x + (-w * math.cos(r) + l * math.sin(r))\n y2 = y + (+w * math.sin(r) + l * math.cos(r))\n x3 = x + (-w * math.cos(r) - l * math.sin(r))\n y3 = y + (w * math.sin(r) - l * math.cos(r))\n x4 = x + (w * math.cos(r) - l * math.sin(r))\n y4 = y + (-w * math.sin(r) - l * math.cos(r))\n\n # Bottom rectangle\n ax.plot3D([x3, x4], [y3, y4], [z, z], col, linewidth=2, alpha=0.8)\n ax.plot3D([x2, x1], [y2, y1], [z, z], col, linewidth=2, alpha=0.8)\n ax.plot3D([x3, x2], [y3, y2], [z, z], col, linewidth=2, alpha=0.8)\n ax.plot3D([x4, x1], [y4, y1], [z, z], col, linewidth=2, alpha=0.8)\n\n # Top rectangle\n ax.plot3D([x3, x4], [y3, y4], [z+h, z+h], col, linewidth=2, alpha=0.8)\n ax.plot3D([x2, x1], [y2, y1], [z+h, z+h], col, linewidth=2, alpha=0.8)\n ax.plot3D([x3, x2], [y3, y2], [z+h, z+h], col, linewidth=2, alpha=0.8)\n ax.plot3D([x4, x1], [y4, y1], [z+h, z+h], col, linewidth=2, alpha=0.8)\n\n # Vertical lines\n ax.plot3D([x1, x1], [y1, y1], [z, z+h], col, linewidth=2, alpha=0.8)\n ax.plot3D([x2, x2], [y2, y2], [z, z+h], col, linewidth=2, alpha=0.8)\n ax.plot3D([x3, x3], [y3, y3], [z, z+h], col, linewidth=2, alpha=0.8)\n ax.plot3D([x4, x4], [y4, y4], [z, z+h], col, linewidth=2, alpha=0.8)","def boundary(self): # -> BaseGeometry:\n ...","def bounds(self) -> Box:\n raise NotImplementedError()","def canvas_bounds(self) -> utils.BoxRegion:","def get_bound(box_list):\n box_xyxy_list = []\n for box in box_list:\n box_xyxy = xywh2xyxy(box)\n box_xyxy_list.append(box_xyxy)\n\n box_xyxy_list = np.array(box_xyxy_list)\n x1max, y1max, x2max, y2max = np.amax(box_xyxy_list, axis=0)\n x1min, y1min, x2min, y2min = np.amin(box_xyxy_list, axis=0)\n\n boundbox = xyxy2xywh([x1min, y1min, x2max, y2max])\n return boundbox","def set_bounding_box(self, top_left_y: int, top_left_x: int, bottom_right_y: int, bottom_right_x: int) -> None:\n self.top_left_y = top_left_y\n self.top_left_x = top_left_x\n self.bottom_right_y = bottom_right_y\n self.bottom_right_x = bottom_right_x","def overlay_bounds(over: 'Entity') -> Tuple[Vec, Vec]:\n origin = Vec.from_str(over['origin'])\n mat = Matrix.from_angle(Angle.from_str(over['angles']))\n return Vec.bbox(\n (origin + Vec.from_str(over['uv' + str(x)]) @ mat)\n for x in\n range(4)\n )","def create_from_bounds(self, lbs, ubs):\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)","def compute_bb(self):\n all_shapes = list(self.parts.values()) + list(self.edges.values())\n bbox_vertices = cascaded_union(all_shapes).envelope.exterior.coords.xy\n min_x = min(bbox_vertices[0])\n max_x = max(bbox_vertices[0])\n min_y = min(bbox_vertices[1])\n max_y = max(bbox_vertices[1])\n return [min_x, max_x,min_y, max_y]","def clip(self, B):\n # /* Left */\n if ((self.x >= B.x) and (self.x < (B.x + B.w))):\n x = self.x\n elif ((B.x >= self.x) and (B.x < (self.x + self.w))):\n x = B.x\n else:\n return Rect(self.x, self.y, 0, 0)\n\n # /* Right */\n if (((self.x + self.w) > B.x) and ((self.x + self.w) <= (B.x + B.w))):\n w = (self.x + self.w) - x\n elif (((B.x + B.w) > self.x) and ((B.x + B.w) <= (self.x + self.w))):\n w = (B.x + B.w) - x\n else:\n return Rect(self.x, self.y, 0, 0)\n\n # /* Top */\n if ((self.y >= B.y) and (self.y < (B.y + B.h))):\n y = self.y\n elif ((B.y >= self.y) and (B.y < (self.y + self.h))):\n y = B.y\n else:\n return Rect(self.x, self.y, 0, 0)\n\n # /* Bottom */\n if (((self.y + self.h) > B.y) and ((self.y + self.h) <= (B.y + B.h))):\n h = (self.y + self.h) - y\n elif (((B.y + B.h) > self.y) and ((B.y + B.h) <= (self.y + self.h))):\n h = (B.y + B.h) - y\n else:\n return Rect(self.x, self.y, 0, 0)\n\n return Rect(x, y, w, h)","def get_bounding_box(self):\n lon, lat = self.coordinates\n\n ll = (np.min(lon),np.min(lat))\n ul = (np.min(lon),np.max(lat))\n ur = (np.max(lon),np.max(lat))\n lr = (np.max(lon),np.min(lat))\n\n return (ll, ul, ur, lr)","def gradientRect( window, left_colour, right_colour, target_rect ):\n colour_rect = pygame.Surface( ( 2, 2 ) ) # tiny! 2x2 bitmap\n pygame.draw.line( colour_rect, left_colour, ( 0,0 ), ( 0,1 ) ) # left colour line\n pygame.draw.line( colour_rect, right_colour, ( 1,0 ), ( 1,1 ) ) # right colour line\n colour_rect = pygame.transform.smoothscale( colour_rect, ( target_rect.width, target_rect.height ) ) # stretch!\n window.blit( colour_rect, target_rect ) # paint it","def compute_bounding_box(homography, w, h):\n\n # todo in what step to do int??\n # todo should i do -1? if so why?\n\n\n all_corners = np.array([[0, 0], [w, 0], [0, h], [w, h]])\n all_corners = apply_homography(all_corners,homography)\n top_left = [np.min(all_corners[:,0]),np.min(all_corners[:,1])]\n bottom_right = [np.max(all_corners[:,0]),np.max(all_corners[:,1])]\n return np.array([top_left,bottom_right]).astype(np.int)","def bounding_box(vertices, (height, width), extend=5):\n x_min = min(x for x, y in vertices) - extend\n x_max = max(x for x, y in vertices) + extend\n y_min = min(y for x, y in vertices) - extend\n y_max = max(y for x, y in vertices) + extend\n \n return max(x_min, 0), min(x_max, width), max(y_min, 0), min(y_max, height)","def define_grid():\n grid_left = np.array([[-13.1000000000000, -35.5000000000000, -48.3000000000000, -60, -16.9000000000000,\n -34.8000000000000, -67.5000000000000, -46.1000000000000, -59.8000000000000,\n -14.2000000000000, -28.3000000000000, -42.3000000000000, -67.6000000000000,\n -50.5000000000000, -14.6000000000000, -60.9000000000000, -31.6000000000000,\n -5.10000000000000, -65.6000000000000, -41.8000000000000, -55.1000000000000,\n -22.7000000000000, -5.80000000000000, -49.2000000000000, -34.5000000000000,\n -61.5500000000000, -63.6000000000000, -40.4000000000000, -48.7000000000000,\n -21.8000000000000, -58.2000000000000, -7, -36.3000000000000, -48.1000000000000,\n -56.8000000000000, -7.30000000000000, -22.2000000000000, -36.8000000000000,\n -46.8000000000000],\n [-67.7000000000000, -60, -55.1000000000000, -51.8000000000000, -51.6000000000000,\n -49.3000000000000, -47.1000000000000, -43.7000000000000, -39.6000000000000,\n -39.1000000000000, -31.2000000000000, -30.7000000000000, -30.1000000000000,\n -24.4000000000000, -22.7000000000000, -18.7000000000000, -16.9000000000000,\n -12.6000000000000, -10.8000000000000, -10.2000000000000, -4.01000000000000, 1.20000000000000,\n 2.80000000000000, 3.70000000000000, 3.90000000000000, 6.20000000000000, 8.30000000000000,\n 11.8000000000000, 14.5000000000000, 16, 18.2000000000000, 18.4000000000000, 19.9000000000000,\n 24.6000000000000, 28.5200000000000, 33.8000000000000, 35, 35.4000000000000,\n 35.6000000000000],\n [69.1000000000000, 66, 58.2000000000000, 48, 78, 71.7000000000000, 31, 61.1000000000000,\n 53.3000000000000, 81.1000000000000, 76, 70.2000000000000, 41.2000000000000, 64.4000000000000,\n 80.2000000000000, 50.9000000000000, 75.2000000000000, 77.3000000000000, 37.8000000000000, 67,\n 53.2000000000000, 72, 74.8000000000000, 54.7000000000000, 66.5000000000000, 35.9000000000000,\n 25.7000000000000, 60.7000000000000, 50.5000000000000, 68.9000000000000, 27.3000000000000,\n 70.3000000000000, 59.6000000000000, 44, 20.8000000000000, 61.7000000000000, 57.2000000000000,\n 47, 36]])\n stn_left = np.array([[-14.6, -13.2, -11.7, -9.10, -11.7, -13.2, -7.90, -10],\n [-15.1, -15.1, -15.1, -12.6, -12.6, -12.6, -9.40, -10.1],\n [-5.40, -7.20, -8.70, -8.70, -7.50, -5.10, -10.3, -7.80]])\n grid_right = np.copy(grid_left)\n grid_right[0, :] = grid_right[0, :] * -1\n stn_right = np.copy(stn_left)\n stn_right[0, :] = stn_right[0, :] * -1\n\n return grid_left, grid_right, stn_left, stn_right","def compute_bb(self) -> List[float]:\n all_shapes = list(self.parts.values())\n bbox_vertices = unary_union(all_shapes).envelope.exterior.coords.xy\n min_x = min(bbox_vertices[0])\n max_x = max(bbox_vertices[0])\n min_y = min(bbox_vertices[1])\n max_y = max(bbox_vertices[1])\n return [min_x, max_x, min_y, max_y]","def uv_bounds(self):\n umin, umax, vmin, vmax = breptools_UVBounds(self.topods_shape())\n bounds = Box(np.array([umin, vmin]))\n bounds.encompass_point(np.array([umax, vmax]))\n return bounds","def compute_bounding_box(homography, w, h):\n corners = np.array([[0,0],\n [0,h],\n [w,0],\n [w,h]])\n t_corners = apply_homography(corners, homography)\n return np.array([t_corners.min(axis=0),t_corners.max(axis=0)],\n dtype= np.int)\n #[top-left, btm-right]","def get_boundingbox(face, width, height, scale=1.3, minsize=None):\n x1 = face.left()\n y1 = face.top()\n x2 = face.right()\n y2 = face.bottom()\n size_bb = int(max(x2 - x1, y2 - y1) * scale)\n if minsize:\n if size_bb < minsize:\n size_bb = minsize\n center_x, center_y = (x1 + x2) // 2, (y1 + y2) // 2\n\n # Check for out of bounds, x-y top left corner\n x1 = max(int(center_x - size_bb // 2), 0)\n y1 = max(int(center_y - size_bb // 2), 0)\n # Check for too big bb size for given x, y\n size_bb = min(width - x1, size_bb)\n size_bb = min(height - y1, size_bb)\n\n return x1, y1, size_bb","def get_aabb(self):\n c, s = np.cos(self.eangles), np.sin(self.eangles)\n r = np.array([[c, s], [s, c]])\n return rect(self.get_com(), np.abs(np.dot(r, self.halfexts)), 0)","def CalcBoundingBox(*args, **kwargs):\n return _gdi_.DC_CalcBoundingBox(*args, **kwargs)","def create_box(self, a, b, c):\n proj_to_xy = lambda x: x[:2]\n get_angle = lambda x,y: (x @ y) / (np.linalg.norm(x) * np.linalg.norm(y))\n\n ab = proj_to_xy(b) - proj_to_xy(a)\n ac = proj_to_xy(c) - proj_to_xy(a)\n bc = proj_to_xy(c) - proj_to_xy(b)\n\n ab_ac = np.abs(get_angle(ab, ac))\n ab_bc = np.abs(get_angle(ab, bc))\n\n x1, y1, z1 = a\n x2, y2, z2 = b\n x3, y3, z3 = c\n\n z = (z1 + z2)/2\n\n down = np.array([0., 0., z - z3])\n\n if ab_ac < ab_bc: # 3. point is bottom-left\n back = np.array([ac[0], ac[1], 0])\n else: # 3. point is bottom-right\n back = np.array([bc[0], bc[1], 0])\n\n tfl = np.array([x1, y1, z])\n tfr = np.array([x2, y2, z])\n\n tbl = tfl + back\n tbr = tfr + back\n\n bfl = tfl - down\n bfr = tfr - down\n\n bbl = bfl + back\n bbr = bfr + back\n\n return np.array([\n tfl, tfr,\n tbl, tbr,\n bfl, bfr,\n bbl, bbr\n ])","def compute_bounding_box(homography, w, h):\n top_left_x, top_left_y = apply_homography(np.array([[0, 0]]), homography).transpose()\n top_right_x, top_right_y = apply_homography(np.array([[w, 0]]), homography).transpose()\n bottem_left_x, bottem_left_y = apply_homography(np.array([[0, h]]), homography).transpose()\n bottem_right_x, bottem_right_y = apply_homography(np.array([[w, h]]), homography).transpose()\n\n min_x = min([top_left_x, top_right_x, bottem_left_x, bottem_right_x])[0]\n max_x = max([top_left_x, top_right_x, bottem_left_x, bottem_right_x])[0]\n min_y = min([top_left_y, top_right_y, bottem_left_y, bottem_right_y])[0]\n max_y = max([top_left_y, top_right_y, bottem_left_y, bottem_right_y])[0]\n return np.array([[min_x, min_y], [max_x, max_y]]).astype(int)","def intersection_with_anchors(bbox):\n int_ymin = tf.maximum(ymin, bbox[0])\n int_xmin = tf.maximum(xmin, bbox[1])\n int_ymax = tf.minimum(ymax, bbox[2])\n int_xmax = tf.minimum(xmax, bbox[3])\n h = tf.maximum(int_ymax - int_ymin, 0.)\n w = tf.maximum(int_xmax - int_xmin, 0.)\n inter_vol = h * w\n scores = tf.div(inter_vol, vol_anchors)\n return scores","def defineRectangleLayout(self):\n\n #--- Define a 2-D mesh ---\n # Divide x- and y-axis\n self.xPoints = self.frange(0,self.Lx,self.h)\n self.yPoints = self.frange(0,self.Ly,self.h)\n\n # Position (xy-coordinate) of boundary points\n boundary_xyCoord = [(0,j) for j in self.yPoints] + [(self.Lx,j) for j in self.yPoints] + [(i,0) for i in self.xPoints[1:len(self.xPoints)-1]] + [(i,self.Ly) for i in self.xPoints[1:len(self.xPoints)-1]] \n\n # Define the dictionary containing boundary points\n for k in boundary_xyCoord:\n x = k[0]\n y = k[1]\n xLabel = self.xPoints.index(x)\n yLabel = self.yPoints.index(y)\n self.boundaryPoints[(xLabel,yLabel)] = meshPoint(type = 'boundary',x = x, y = y, xLabel = xLabel, yLabel = yLabel) \n \n # Position (xy-coordinate) of internal mesh points\n internal_xyCoord = [(i,j) for i in self.xPoints[1:len(self.xPoints)-1] for j in self.yPoints[1:len(self.yPoints)-1]] \n\n # Define the dictionary containing internal points\n for k in internal_xyCoord:\n x = k[0]\n y = k[1]\n xLabel = self.xPoints.index(x)\n yLabel = self.yPoints.index(y)\n self.internalPoints[(xLabel,yLabel)] = meshPoint(type = 'internal',x = x, y = y, xLabel = xLabel, yLabel = yLabel) \n\n # Now that we have assigned the labels we can define fE, fW, fN and fS\n self.fCalc()","def draw_lrtb_rectangle_filled(left, right, top, bottom, color):\n if left > right:\n raise AttributeError(\"Left coordinate {} must be less than or equal \"\n \"to the right coordinate {}.\".format(left, right))\n\n if bottom > top:\n raise AttributeError(\"Bottom coordinate {} must be less than or equal \"\n \"to the top coordinate {}\".format(bottom, top))\n\n center_x = (left + right) / 2\n center_y = (top + bottom) / 2\n width = right - left\n height = top - bottom\n draw_rectangle_filled(center_x, center_y, width, height, color)","def boundingBox(self):\n minx, miny, maxx, maxy = self.substrates.bounds\n return pcbnew.BOX2I(\n pcbnew.VECTOR2I(int(minx), int(miny)),\n pcbnew.VECTOR2I(int(maxx - minx), int(maxy - miny)))","def boundary(self):\n answer = self.zero()\n for k, v in self.items():\n for idx, cube in enumerate(k):\n acc_dim = sum((cube_l.dimension for cube_l in k[:idx]))\n for i in range(cube.dimension):\n for epsilon in (0, 1):\n new_cube = cube.face(i, epsilon)\n new_k = k[:idx] + (new_cube,) + k[idx + 1:]\n sign_exp = (acc_dim + i + epsilon) % 2\n answer += answer.create({new_k: v * (-1)**sign_exp})\n return answer","def GetBounds(self, vtkAMRBox, , , p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\n ...","def get_bounding_box(self):\n if len(self.elements) == 0:\n return None\n if not (self._bb_valid and\n all(ref._bb_valid for ref in self.get_dependencies(True))):\n bb = numpy.array(((1e300, 1e300), (-1e300, -1e300)))\n all_polygons = []\n for element in self.elements:\n if isinstance(element, PolygonSet):\n all_polygons.extend(element.polygons)\n elif isinstance(element, CellReference) or isinstance(\n element, CellArray):\n element_bb = element.get_bounding_box()\n if element_bb is not None:\n bb[0, 0] = min(bb[0, 0], element_bb[0, 0])\n bb[0, 1] = min(bb[0, 1], element_bb[0, 1])\n bb[1, 0] = max(bb[1, 0], element_bb[1, 0])\n bb[1, 1] = max(bb[1, 1], element_bb[1, 1])\n if len(all_polygons) > 0:\n all_points = numpy.concatenate(all_polygons).transpose()\n bb[0, 0] = min(bb[0, 0], all_points[0].min())\n bb[0, 1] = min(bb[0, 1], all_points[1].min())\n bb[1, 0] = max(bb[1, 0], all_points[0].max())\n bb[1, 1] = max(bb[1, 1], all_points[1].max())\n self._bb_valid = True\n _bounding_boxes[self] = bb\n return _bounding_boxes[self]","def _create_main_shape(self):\n\n a, b = gc( self.size/2,\n self._ZERO_DEGREES - self.angle,\n self._180_DEGREES + self.angle)\n self.wafer_points = zip(a,b)\n self.wafer_polygon = gdspy.Polygon(self.wafer_points, self.WAFER_LAYER)\n self.cell.add(self.wafer_polygon)","def get_final_bounding_box(boxes, nms_idx, width: int, height: int):\n x1 = np.inf\n y1 = np.inf\n x2 = -np.inf\n y2 = -np.inf\n\n bx = [boxes[i] for i in nms_idx]\n for box in bx:\n xmin = np.min(box[[0, 2]])\n xmax = np.max(box[[0, 2]])\n ymin = np.min(box[[1, 3]])\n ymax = np.max(box[[1, 3]])\n\n x1 = np.min([xmin, x1])\n y1 = np.min([ymin, y1])\n x2 = np.max([xmax, x2])\n y2 = np.max([ymax, y2])\n return x1, y1, x2, y2","def _hold_bounds(self):\n adc_channel = self.graph_renderer.channels[0]\n if self.sx2 > adc_channel.size():\n self.anchored = True\n\n if self.anchored:\n # anchor right side of the window to the last graph sample. so the graph always animates, grows out from\n # the right side of the window. (anchor sx2 to adc_channel.size())\n dx = self.sx2 - adc_channel.size()\n dxw = self.wsx2 - adc_channel.size()\n self.sx1 -= dx\n self.sx2 -= dx\n self.wsx1 -= dxw\n self.wsx2 -= dxw\n\n # eliminate integer overflow problems. only allow indices smaller than a 32bit integer value. and then divide\n # it by four just to be sure.. maybe it's not necessary, but maybe there are some other tricks used in the\n # graph rendering..\n bound = 0xffffffff / 4\n # hmm. this allows only 12 days of data with ~960Hz. time to go 64bit?\n self.sx1 = max(self.sx1, -bound)\n self.sy1 = max(self.sy1, -bound)\n self.sx1 = min(self.sx1, bound)\n self.sy1 = min(self.sy1, bound)\n self.sx2 = max(self.sx2, -bound)\n self.sy2 = max(self.sy2, -bound)\n self.sx2 = min(self.sx2, bound)\n self.sy2 = min(self.sy2, bound)\n self.wsx1 = max(self.wsx1, -bound)\n self.wsy1 = max(self.wsy1, -bound)\n self.wsx1 = min(self.wsx1, bound)\n self.wsy1 = min(self.wsy1, bound)\n self.wsx2 = max(self.wsx2, -bound)\n self.wsy2 = max(self.wsy2, -bound)\n self.wsx2 = min(self.wsx2, bound)\n self.wsy2 = min(self.wsy2, bound)\n\n # limit horizontal zoom to 2 samples. can't zoom in anymore if less than one sample stays on screen.\n # don't have time to implement and test line segment cutting, if one sample is outside the window, and another\n # is inside.\n if self.wsx2 - self.wsx1 < 2.:\n self.wsx2 = self.wsx1 + 2.\n if self.sx2 - self.sx1 < 2.:\n self.sx2 = self.sx1 + 2.\n\n #\n # limit vertical movement and vertical zoom\n #\n\n val_min = adc_channel.value_min\n val_max = adc_channel.value_max\n\n # allow offset of this percent/100 of the screen\n overlap = .30\n\n # top of the screen has smaller sample values than bottom of the screen. inverted graph.\n # sy1 is top pixel, sy2 bottom. bottom-left coordinat is (0, 0)\n if self.sy1 < self.sy2:\n val_top = val_min + (self.wsy1 - self.wsy2) * overlap\n val_bottom = val_max - (self.wsy1 - self.wsy2) * overlap\n if self.wsy1 < val_top:\n self.wsy2 -= self.wsy1 - val_top\n self.wsy1 = val_top\n if self.wsy2 > val_bottom:\n self.wsy1 += val_bottom - self.wsy2\n self.wsy2 = val_bottom\n if self.wsy1 < val_top:\n self.wsy1 = val_top\n if self.wsy2 > val_bottom:\n self.wsy2 = val_bottom\n else:\n val_bottom = val_min - (self.wsy1 - self.wsy2) * overlap\n val_top = val_max + (self.wsy1 - self.wsy2) * overlap\n if self.wsy1 > val_top:\n self.wsy2 -= self.wsy1 - val_top\n self.wsy1 = val_top\n if self.wsy2 < val_bottom:\n self.wsy1 += val_bottom - self.wsy2\n self.wsy2 = val_bottom\n if self.wsy1 > val_top:\n self.wsy1 = val_top\n if self.wsy2 < val_bottom:\n self.wsy2 = val_bottom","def boundingRect(self):\n return QRectF(-self.innerRectangleSize + self.edge_size,\n -self.innerRectangleSize + self.edge_size,\n (self.innerRectangleSize * 2) - (self.edge_size * 2),\n (self.innerRectangleSize * 2) - (self.edge_size * 2)).normalized()","def feet_of_ribbon(d):\n a, b = shortest_two_sides(d)\n return a+a+b+b + d.l * d.w * d.h","def bounding_box(self):\n latlon00 = self.ij_to_latlon(-1,-1)\n latlon01 = self.ij_to_latlon(-1,self.domain_size[1]+1)\n latlon11 = self.ij_to_latlon(self.domain_size[0]+1,self.domain_size[1]+1)\n latlon10 = self.ij_to_latlon(self.domain_size[0]+1,-1)\n return (latlon00,latlon01,latlon11,latlon10)","def border_gate(self,\n bottom_cutoff: float = 0.01,\n top_cutoff: float = 0.99):\n if self.y is None:\n raise GateError('For a border filter gate a value for `y` must be given')\n pos_pop = self.data.copy()\n lt_x, lt_y = pos_pop[self.x].quantile(bottom_cutoff), pos_pop[self.y].quantile(bottom_cutoff)\n tt_x, tt_y = pos_pop[self.x].quantile(top_cutoff), pos_pop[self.y].quantile(top_cutoff)\n return self.rect_gate(x_min=lt_x, x_max=tt_x, y_min=lt_y, y_max=tt_y)","def get_bounds():\n return [0.00], [1.00]","def box_IoU(b1, b2):\n with tf.name_scope('BB1'):\n \"\"\"Calculate 2 corners: {left bottom, right top} and area of this box\"\"\"\n b1 = tf.expand_dims(b1, -2) # shape= (None, 13, 13, 3, 1, 4)\n b1_xy = b1[..., :2] # x,y shape=(None, 13, 13, 3, 1, 2)\n b1_wh = b1[..., 2:4] # w,h shape=(None, 13, 13, 3, 1, 2)\n b1_wh_half = b1_wh / 2. # w/2, h/2 shape= (None, 13, 13, 3, 1, 2)\n b1_mins = b1_xy - b1_wh_half # x,y: left bottom corner of BB\n b1_maxes = b1_xy + b1_wh_half # x,y: right top corner of BB\n b1_area = b1_wh[..., 0] * b1_wh[..., 1] # w1 * h1 (None, 13, 13, 3, 1)\n\n with tf.name_scope('BB2'):\n \"\"\"Calculate 2 corners: {left bottom, right top} and area of this box\"\"\"\n # b2 = tf.expand_dims(b2, -2) # shape= (None, 13, 13, 3, 1, 4)\n b2 = tf.expand_dims(b2, 0) # shape= (1, None, 13, 13, 3, 4) # TODO 0?\n b2_xy = b2[..., :2] # x,y shape=(None, 13, 13, 3, 1, 2)\n b2_wh = b2[..., 2:4] # w,h shape=(None, 13, 13, 3, 1, 2)\n b2_wh_half = b2_wh / 2. # w/2, h/2 shape=(None, 13, 13, 3, 1, 2)\n b2_mins = b2_xy - b2_wh_half # x,y: left bottom corner of BB\n b2_maxes = b2_xy + b2_wh_half # x,y: right top corner of BB\n b2_area = b2_wh[..., 0] * b2_wh[..., 1] # w2 * h2\n\n with tf.name_scope('Intersection'):\n \"\"\"Calculate 2 corners: {left bottom, right top} based on BB1, BB2 and area of this box\"\"\"\n # intersect_mins = tf.maximum(b1_mins, b2_mins, name='left_bottom') # (None, 13, 13, 3, 1, 2)\n intersect_mins = K.maximum(b1_mins, b2_mins) # (None, 13, 13, 3, 1, 2)\n # intersect_maxes = tf.minimum(b1_maxes, b2_maxes, name='right_top') #\n intersect_maxes = K.minimum(b1_maxes, b2_maxes)\n # intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.) # (None, 13, 13, 3, 1, 2), 2: w,h\n intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)\n intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] # intersection: wi * hi (None, 13, 13, 3, 1)\n\n IoU = tf.divide(intersect_area, (b1_area + b2_area - intersect_area), name='divise-IoU') # (None, 13, 13, 3, 1)\n\n return IoU","def covering_box(boxes):\n x_min = np.amin([b.x for b in boxes])\n x_max = np.amax([b.x + b.width for b in boxes])\n y_min = np.amin([b.y for b in boxes])\n y_max = np.amax([b.y + b.height for b in boxes])\n cover = Box(x_min, y_min, x_max - x_min, y_max - y_min)\n return cover","def apply_deviations(self, dev_x, dev_y, dev_w, dev_h):\n\n return BoundingBox.from_cx_cy_w_h(\n (dev_x * self.width) + self.center_x,\n (dev_y * self.height) + self.center_y,\n math.exp(dev_w) * self.width,\n math.exp(dev_h) * self.height\n )","def create_geofence(self):\n\t\tring = ogr.Geometry(ogr.wkbLinearRing)\n\t\tring.AddPoint(*self.north_coords)\n\t\tring.AddPoint(*self.northeast_coords)\n\t\tring.AddPoint(*self.east_coords)\n\t\tring.AddPoint(*self.southeast_coords)\n\t\tring.AddPoint(*self.south_coords)\n\t\tring.AddPoint(*self.southwest_coords)\n\t\tring.AddPoint(*self.west_coords)\n\t\tring.AddPoint(*self.northwest_coords)\n\t\tring.AddPoint(*self.north_coords)\n\t\tself.polygon.AddGeometry(ring)","def get_bounds(self):\r\n left, bottom, front = 10000, 10000, 10000\r\n right, top, back = -10000, -10000, -10000\r\n for b in self.buf:\r\n for v in b.vertices:\r\n if v[0] < left:\r\n left = v[0]\r\n if v[0] > right:\r\n right = v[0]\r\n if v[1] < bottom:\r\n bottom = v[1]\r\n if v[1] > top:\r\n top = v[1]\r\n if v[2] < front:\r\n front = v[2]\r\n if v[2] > back:\r\n back = v[2]\r\n\r\n return (left, bottom, front, right, top, back)","def _find_bboxes_on_rect_edge(bboxes, left, bottom, right, top):\n bboxes_left = _find_bboxes_in_rect(bboxes, left, bottom, left, top)\n bboxes_right = _find_bboxes_in_rect(bboxes, right, bottom, right, top)\n bboxes_top = _find_bboxes_in_rect(bboxes, left, top, right, top)\n bboxes_bottom = _find_bboxes_in_rect(bboxes, left, bottom, right, bottom)\n result = bboxes_left | bboxes_right | bboxes_top | bboxes_bottom\n return result","def bottomLeftCorner(self):\n self._updateExtents()\n return (self._mMinX,self._mMaxY)","def bounds_gdf(gdf: gpd.GeoDataFrame) -> box:\n if gdf.empty:\n return Polygon()\n gdf_bounds = gdf.total_bounds\n gdf_bounds_box = box(*gdf_bounds.tolist())\n return gdf_bounds_box","def make_highlight_region(ax, limits, axis, **kwargs):\n xmin, xmax = 0, 0\n ymin, ymax = 0, 0\n if axis == 'x':\n xmin, xmax = limits\n ymin, ymax = ax.get_ylim()\n else:\n ymin, ymax = limits\n xmin, xmax = ax.get_xlim()\n if not kwargs:\n kwargs = dict(color='red', alpha=0.2)\n patch = patches.Rectangle((xmin, ymin), xmax-xmin, ymax-ymin, **kwargs)\n ax.add_patch(patch)\n return patch","def test_bounding_rectangle(self, world):\n positions = [(0, 2), (2, 0), (3, 1), (2, 3)]\n for pos in positions:\n world.set_cell(pos)\n assert world.min_pos() == (0, 0) and world.max_pos() == (3, 3)","def db_eval_boundary(foreground_mask,gt_mask,bound_th=0.008):\n assert np.atleast_3d(foreground_mask).shape[2] == 1\n\n bound_pix = bound_th if bound_th >= 1 else \\\n np.ceil(bound_th*np.linalg.norm(foreground_mask.shape))\n\n # Get the pixel boundaries of both masks\n fg_boundary = seg2bmap(foreground_mask);\n gt_boundary = seg2bmap(gt_mask);\n\n from skimage.morphology import binary_dilation,disk\n\n fg_dil = binary_dilation(fg_boundary,disk(bound_pix))\n gt_dil = binary_dilation(gt_boundary,disk(bound_pix))\n\n # Get the intersection\n gt_match = gt_boundary * fg_dil\n fg_match = fg_boundary * gt_dil\n\n # Area of the intersection\n n_fg = np.sum(fg_boundary)\n n_gt = np.sum(gt_boundary)\n\n #% Compute precision and recall\n if n_fg == 0 and n_gt > 0:\n precision = 1\n recall = 0\n elif n_fg > 0 and n_gt == 0:\n precision = 0\n recall = 1\n elif n_fg == 0 and n_gt == 0:\n precision = 1\n recall = 1\n else:\n precision = np.sum(fg_match)/float(n_fg)\n recall = np.sum(gt_match)/float(n_gt)\n\n # Compute F measure\n if precision + recall == 0:\n F = 0\n else:\n F = 2*precision*recall/(precision+recall);\n\n return F","def fitRectangle(self):\n \n #TODO MAKE SOMETHING MORE GENERIC!!\n \n fA, (fXg, fYg) = self.getArea_and_CenterOfMass()\n \n x1,y1, x2,y2 = self.getBoundingBox()\n #build a rectangle with same \"width\" as the polygon... is-it good enough??\n w = x2 - x1\n \n #but this width should not lead to go out of the bounding box!\n fW = min(w, (x2-fXg)*2, (fXg-x1)*2)\n \n #same area\n fH = fA / fW\n \n x1,y1, x2,y2 = [ int(round(v)) for v in [ fXg - fW/2.0, fYg - fH/2\n , fXg + fW/2.0, fYg + fH/2 ]]\n \n return x1,y1, x2,y2","def __clip_bbox(min_y, min_x, max_y, max_x):\n min_y = tf.clip_by_value(min_y, 0.0, 1.0)\n min_x = tf.clip_by_value(min_x, 0.0, 1.0)\n max_y = tf.clip_by_value(max_y, 0.0, 1.0)\n max_x = tf.clip_by_value(max_x, 0.0, 1.0)\n return min_y, min_x, max_y, max_x","def bounding_box(alpha):\n assert alpha.ndim == 2\n\n # Take the bounding box of the support, with a certain threshold.\n #print(\"Using alpha\", self.use_alpha, \"support\", self.support)\n supp_axs = [alpha.max(axis=1-i) for i in range(2)]\n\n th = 0.5 \n # Check first and last value of that threshold\n bb = [np.where(supp_axs[i] > th)[0][[0,-1]] for i in range(2)]\n\n # This bb looks like [(x0, x1), (y0, y1)], when we want it as (x0, y0, x1, y1)\n #psize = self.settings['subsample_size']\n #ret = (bb[0][0]/psize[0], bb[1][0]/psize[1], bb[0][1]/psize[0], bb[1][1]/psize[1])\n\n return (bb[0][0], bb[1][0], bb[0][1], bb[1][1])","def get_raw_bounds(self) -> [Vector, Vector]:\n\t\tverts = np.array([v.co for mesh in self._meshes for v in mesh.data.vertices])\n\t\tbbox_min = Vector([*np.min(verts, axis=0)])\n\t\tbbox_max = Vector([*np.max(verts, axis=0)])\n\t\treturn bbox_min, bbox_max","def draw_bounding_box(objects,color):\n\n for i in range(len(objects)):\n x, y, w, h, d = objects[i].get_attributes()\n print(x, y, w, h, d)\n corr = get_correction(d, a, hfov, x)\n cv2.rectangle(color, (x-corr, y), (x+w-corr, y+h), (0, 255, 0), 4)\n\n try:\n real_x, real_y = get_dimensions(d, w, h, hfov, vfov, 640, 480)\n real_x = round(real_x, 3)\n real_y = round(real_y, 3)\n\n except:\n real_x, real_y = 'ERROR'\n\n cv2.putText(color, 'depth = ' + str(d) + 'm', (30, i*60 + 30) ,\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)\n cv2.putText(color, 'width = ' + str(real_x)+ 'm', (30, i*60 + 45) ,\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)\n cv2.putText(color, 'height = ' + str(real_y)+ 'm', (30, i*60 + 60) ,\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)\n\n if(i < len(objects)-1):\n ## distance between left and right object\n distance = round(distance_between_objects(objects[i], objects[i+1], hfov, 640), 3)\n if distance > l:\n textcolor = (0, 255, 0)\n else:\n textcolor = (0, 0, 255)\n\n cv2.putText(color, 'distance between objects = ' + str(distance) + 'm',\n (320, i*60 + 70) , cv2.FONT_HERSHEY_SIMPLEX, 0.5, textcolor, 1)","def bbox(\n bbox: Tuple[Coordinate, Coordinate] = ((-1.0, -1.0), (3.0, 4.0)),\n layer: Tuple[int, int] = (1, 0),\n top: float = 0,\n bottom: float = 0,\n left: float = 0,\n right: float = 0,\n) -> gf.Component:\n D = gf.Component()\n (xmin, ymin), (xmax, ymax) = bbox\n points = [\n [xmin - left, ymin - bottom],\n [xmax + right, ymin - bottom],\n [xmax + right, ymax + top],\n [xmin - left, ymax + top],\n ]\n D.add_polygon(points, layer=layer)\n return D","def make_prism(\n self,\n p1: Vec,\n p2: Vec,\n mat: str = 'tools/toolsnodraw',\n ) -> PrismFace:\n b_min = Vec(p1)\n b_max = Vec(p1)\n b_min.min(p2)\n b_max.max(p2)\n\n # Sanity check - all dimensions must be different, otherwise we'll\n # have an invalid zero-thick brush.\n if b_min.x == b_max.x or b_min.y == b_max.y or b_min.z == b_max.z:\n raise ValueError(f\"Zero volume brush requested! ({b_min}, {b_max})\")\n\n f_bottom = Side(\n self,\n [ # -z side\n Vec(b_min.x, b_min.y, b_min.z),\n Vec(b_max.x, b_min.y, b_min.z),\n Vec(b_max.x, b_max.y, b_min.z),\n ],\n mat=mat,\n uaxis=UVAxis(1, 0, 0),\n vaxis=UVAxis(0, -1, 0),\n )\n\n f_top = Side(\n self,\n [ # +z side\n Vec(b_min.x, b_max.y, b_max.z),\n Vec(b_max.x, b_max.y, b_max.z),\n Vec(b_max.x, b_min.y, b_max.z),\n ],\n mat=mat,\n uaxis=UVAxis(1, 0, 0),\n vaxis=UVAxis(0, -1, 0),\n )\n\n f_west = Side(\n self,\n [ # -x side\n Vec(b_min.x, b_max.y, b_max.z),\n Vec(b_min.x, b_min.y, b_max.z),\n Vec(b_min.x, b_min.y, b_min.z),\n ],\n mat=mat,\n uaxis=UVAxis(0, 1, 0),\n vaxis=UVAxis(0, 0, -1),\n )\n\n f_east = Side(\n self,\n planes=[ # +x side\n Vec(b_max.x, b_max.y, b_min.z),\n Vec(b_max.x, b_min.y, b_min.z),\n Vec(b_max.x, b_min.y, b_max.z),\n ],\n mat=mat,\n uaxis=UVAxis(0, 1, 0),\n vaxis=UVAxis(0, 0, -1),\n )\n\n f_south = Side(\n self,\n [ # -y side\n Vec(b_max.x, b_min.y, b_min.z),\n Vec(b_min.x, b_min.y, b_min.z),\n Vec(b_min.x, b_min.y, b_max.z),\n ],\n mat=mat,\n uaxis=UVAxis(1, 0, 0),\n vaxis=UVAxis(0, 0, -1),\n )\n\n f_north = Side(\n self,\n [ # +y side\n Vec(b_min.x, b_max.y, b_min.z),\n Vec(b_max.x, b_max.y, b_min.z),\n Vec(b_max.x, b_max.y, b_max.z),\n ],\n mat=mat,\n uaxis=UVAxis(1, 0, 0),\n vaxis=UVAxis(0, 0, -1),\n )\n\n solid = Solid(\n self,\n sides=[\n f_bottom,\n f_top,\n f_north,\n f_south,\n f_east,\n f_west,\n ],\n )\n return PrismFace(\n solid=solid,\n top=f_top,\n bottom=f_bottom,\n north=f_north,\n south=f_south,\n east=f_east,\n west=f_west,\n )","def fill_bounds(x, upper, lower, axis, c = 'k', alpha = 0.2):\n axis.fill_between(\n x, \n upper[:, 0], \n lower[:, 0], \n facecolor = c,\n alpha = alpha\n )","def _initialise_aabb(self):\n # calculate the bounding box for all tetraherdon in the mesh\n # find the min/max extents for xyz\n tetra_bb = np.zeros((self.elements.shape[0], 19, 3))\n minx = np.min(self.nodes[self.elements[:, :4], 0], axis=1)\n maxx = np.max(self.nodes[self.elements[:, :4], 0], axis=1)\n miny = np.min(self.nodes[self.elements[:, :4], 1], axis=1)\n maxy = np.max(self.nodes[self.elements[:, :4], 1], axis=1)\n minz = np.min(self.nodes[self.elements[:, :4], 2], axis=1)\n maxz = np.max(self.nodes[self.elements[:, :4], 2], axis=1)\n cell_indexes = self.aabb_grid.global_index_to_cell_index(\n np.arange(self.aabb_grid.n_elements)\n )\n corners = self.aabb_grid.cell_corner_indexes(cell_indexes)\n positions = self.aabb_grid.node_indexes_to_position(corners)\n ## Because we known the node orders just select min/max from each\n # coordinate. Use these to check whether the tetra is in the cell\n x_boundary = positions[:, [0, 1], 0]\n y_boundary = positions[:, [0, 2], 1]\n z_boundary = positions[:, [0, 6], 2]\n a = np.logical_and(\n minx[None, :] > x_boundary[:, None, 0],\n minx[None, :] < x_boundary[:, None, 1],\n ) # min point between cell\n b = np.logical_and(\n maxx[None, :] < x_boundary[:, None, 1],\n maxx[None, :] > x_boundary[:, None, 0],\n ) # max point between cell\n c = np.logical_and(\n minx[None, :] < x_boundary[:, None, 0],\n maxx[None, :] > x_boundary[:, None, 0],\n ) # min point < than cell & max point > cell\n\n x_logic = np.logical_or(np.logical_or(a, b), c)\n\n a = np.logical_and(\n miny[None, :] > y_boundary[:, None, 0],\n miny[None, :] < y_boundary[:, None, 1],\n ) # min point between cell\n b = np.logical_and(\n maxy[None, :] < y_boundary[:, None, 1],\n maxy[None, :] > y_boundary[:, None, 0],\n ) # max point between cell\n c = np.logical_and(\n miny[None, :] < y_boundary[:, None, 0],\n maxy[None, :] > y_boundary[:, None, 0],\n ) # min point < than cell & max point > cell\n\n y_logic = np.logical_or(np.logical_or(a, b), c)\n\n a = np.logical_and(\n minz[None, :] > z_boundary[:, None, 0],\n minz[None, :] < z_boundary[:, None, 1],\n ) # min point between cell\n b = np.logical_and(\n maxz[None, :] < z_boundary[:, None, 1],\n maxz[None, :] > z_boundary[:, None, 0],\n ) # max point between cell\n c = np.logical_and(\n minz[None, :] < z_boundary[:, None, 0],\n maxz[None, :] > z_boundary[:, None, 0],\n ) # min point < than cell & max point > cell\n\n z_logic = np.logical_or(np.logical_or(a, b), c)\n logic = np.logical_and(x_logic, y_logic)\n logic = np.logical_and(logic, z_logic)\n\n self.aabb_table = csr_matrix(logic)","def DrawGradientRectangle(dc, rect, start_colour, end_colour, direction, offset=0, length=0):\r\n \r\n if direction == AUI_GRADIENT_VERTICAL:\r\n dc.GradientFillLinear(rect, start_colour, end_colour, wx.SOUTH)\r\n else:\r\n dc.GradientFillLinear(rect, start_colour, end_colour, wx.EAST)","def _merge_boundaries(self):\n optical = self._amalgamated_optical\n if bool(optical):\n optical[\"catagory\"] = OPTICAL * tf.ones_like(\n optical[\"xp\"],\n dtype=tf.int64\n )\n self._optical_count = tf.shape(\n optical[\"xp\"],\n out_type=tf.int64\n )[0]\n else:\n self._optical_count = 0\n \n stop = self._amalgamated_stop\n if bool(stop):\n stop[\"catagory\"] = STOP * tf.ones_like(\n stop[\"xp\"],\n dtype=tf.int64\n )\n self._stop_count = tf.shape(\n stop[\"xp\"],\n out_type=tf.int64\n )[0]\n else:\n self._stop_count = 0\n \n target = self._amalgamated_target\n if bool(target):\n target[\"catagory\"] = TARGET * tf.ones_like(\n target[\"xp\"],\n dtype=tf.int64\n )\n self._target_count = tf.shape(\n target[\"xp\"],\n out_type=tf.int64\n )[0]\n else:\n self._target_count = 0\n \n self._merged = amalgamate(\n [optical, stop, target], \n TRIANGLE_GEO_SIG | {\"catagory\"}\n )","def test_benzene(self):\n def draw(image: ShapeImage):\n image.add_regular_hexagon(100, start_coord=(400, 400))\n image.add_line((415, 405), (487, 364))\n image.add_line((415, 495), (487, 536))\n image.add_line((559, 409), (559, 491))\n\n self._test_shape(\n image_size=(1000, 1000),\n expected_corners=np.array([\n [[400, 400]],\n [[487, 350]],\n [[574, 400]],\n [[574, 500]],\n [[487, 550]],\n [[400, 500]]\n ]),\n drawer=draw,\n expected_edges=np.array([\n [[400, 400, 487, 350]],\n [[487, 350, 574, 400]],\n [[574, 400, 574, 500]],\n [[574, 500, 487, 550]],\n [[487, 550, 400, 500]],\n [[400, 500, 400, 400]],\n # \"Double\" bonds\n [[415, 405, 487, 364]],\n [[415, 495, 487, 536]],\n [[559, 409, 559, 491]]\n ]),\n vertex_atol=15\n )","def bounding_box(safe, priority='reference', polarization='hh', orbits='Orbits', epsg=4326):\n frames = []\n for swath in range(1, 4):\n rdr = Sentinel1()\n rdr.configure()\n rdr.safe = [os.path.abspath(safe)]\n rdr.output = priority\n rdr.orbitDir = os.path.abspath(orbits)\n rdr.auxDir = os.path.abspath(orbits)\n rdr.swathNumber = swath\n rdr.polarization = polarization\n rdr.parse()\n frames.append(rdr.product)\n\n first_burst = frames[0].bursts[0]\n sensing_start = min([x.sensingStart for x in frames])\n sensing_stop = max([x.sensingStop for x in frames])\n starting_range = min([x.startingRange for x in frames])\n far_range = max([x.farRange for x in frames])\n range_pixel_size = first_burst.rangePixelSize\n prf = 1.0 / first_burst.azimuthTimeInterval\n\n orb = Orbit()\n orb.configure()\n\n for state_vector in first_burst.orbit:\n orb.addStateVector(state_vector)\n\n for frame in frames:\n for burst in frame.bursts:\n for state_vector in burst.orbit:\n if state_vector.time < orb.minTime or state_vector.time > orb.maxTime:\n orb.addStateVector(state_vector)\n\n obj = Geogrid()\n obj.configure()\n\n obj.startingRange = starting_range\n obj.rangePixelSize = range_pixel_size\n obj.sensingStart = sensing_start\n obj.prf = prf\n obj.lookSide = -1\n obj.numberOfLines = int(np.round((sensing_stop - sensing_start).total_seconds() * prf))\n obj.numberOfSamples = int(np.round((far_range - starting_range)/range_pixel_size))\n obj.orbit = orb\n obj.epsg = epsg\n\n obj.determineBbox()\n\n lat_limits = obj._xlim\n lon_limits = obj._ylim\n\n log.info(f'Latitude limits [min, max]: {lat_limits}')\n log.info(f'Longitude limits [min, max]: {lon_limits}')\n\n return lat_limits, lon_limits","def __check_bbox_area(min_y, min_x, max_y, max_x, delta=0.05):\n height = max_y - min_y\n width = max_x - min_x\n\n def __adjust_bbox_boundaries(min_coord, max_coord):\n # Make sure max is never 0 and min is never 1.\n max_coord = tf.maximum(max_coord, 0.0 + delta)\n min_coord = tf.minimum(min_coord, 1.0 - delta)\n return min_coord, max_coord\n\n min_y, max_y = tf.cond(tf.equal(height, 0.0),\n lambda: __adjust_bbox_boundaries(min_y, max_y),\n lambda: (min_y, max_y))\n min_x, max_x = tf.cond(tf.equal(width, 0.0),\n lambda: __adjust_bbox_boundaries(min_x, max_x),\n lambda: (min_x, max_x))\n return min_y, min_x, max_y, max_x","def lineBoxIntersection(w1, w2, b, xmin, ymin, xmax, ymax):\n \n point1 = None\n point2 = None\n if w2 == 0:\n x1a = -(w2*ymin + b)*1.0/w1\n x1b = -(w2*ymax + b)*1.0/w1\n \n point1 = (x1a, ymin)\n point2 = (x1b, ymax)\n else:\n x2a = -(w1*xmin + b)*1.0/w2\n x2b = -(w1*xmax + b)*1.0/w2\n \n if w1 == 0:\n point1 = (xmin, x2a)\n point2 = (xmax, x2b)\n else:\n\n x1a = -(w2*ymin + b)*1.0/w1\n x1b = -(w2*ymax + b)*1.0/w1\n # Point 1\n if x2a < ymin:\n if xmin <= x1a and x1a <= xmax:\n # Point 1 on bottom edge\n point1 = (x1a, ymin)\n elif x2a > ymax:\n if xmin <= x1b and x1b <= xmax:\n # Point 1 on top edge\n point1 = (x1b, ymax)\n else:\n # Point 1 on left edge\n point1 = (xmin, x2a)\n \n # Point 2\n if point1 is not None:\n if x2b < ymin:\n # Point 2 on bottom edge\n point2 = (x1a, ymin)\n elif x2b > ymax:\n # Point 2 on top edge\n point2 = (x1b, ymax)\n else:\n # Point 2 on right edge\n point2 = (xmax, x2b) \n return (point1, point2)","def area_of(self, left_top, right_bottom):\n hw = np.clip(right_bottom - left_top, 0.0, None)\n return hw[..., 0] * hw[..., 1]","def rectangle_surface_area(a,b):\n return (a*b)","def bounding_box(self, integral=False):\n box_min = []\n box_max = []\n if self.n_vertices==0:\n raise ValueError('Empty polytope is not allowed')\n for i in range(0,self.ambient_dim()):\n coords = [ v[i] for v in self.Vrep_generator() ]\n max_coord = max(coords)\n min_coord = min(coords)\n if integral:\n box_max.append(ceil(max_coord))\n box_min.append(floor(min_coord))\n else:\n box_max.append(max_coord)\n box_min.append(min_coord)\n return (tuple(box_min), tuple(box_max))","def test_bounding_box_intersection(self):\n\n west_java = [105, -7, 108, -5]\n jakarta = [106.5, -6.5, 107, -6]\n\n # Test commutative law\n assert numpy.allclose(bbox_intersection(west_java, jakarta),\n bbox_intersection(jakarta, west_java))\n\n # Test inclusion\n assert numpy.allclose(bbox_intersection(west_java, jakarta), jakarta)\n\n # Ignore Bounding Boxes that are None\n assert numpy.allclose(bbox_intersection(west_java, jakarta, None),\n jakarta)\n\n # Realistic ones\n bbox1 = [94.972335, -11.009721, 141.014, 6.073612333333]\n bbox2 = [105.3, -8.5, 110.0, -5.5]\n bbox3 = [105.6, -7.8, 110.5, -5.1]\n\n ref1 = [max(bbox1[0], bbox2[0]),\n max(bbox1[1], bbox2[1]),\n min(bbox1[2], bbox2[2]),\n min(bbox1[3], bbox2[3])]\n assert numpy.allclose(bbox_intersection(bbox1, bbox2), ref1)\n assert numpy.allclose(bbox_intersection(bbox1, bbox2), bbox2)\n\n ref2 = [max(bbox3[0], bbox2[0]),\n max(bbox3[1], bbox2[1]),\n min(bbox3[2], bbox2[2]),\n min(bbox3[3], bbox2[3])]\n assert numpy.allclose(bbox_intersection(bbox3, bbox2), ref2)\n assert numpy.allclose(bbox_intersection(bbox2, bbox3), ref2)\n\n # Multiple boxes\n assert numpy.allclose(bbox_intersection(bbox1, bbox2, bbox3),\n bbox_intersection(ref1, ref2))\n\n assert numpy.allclose(bbox_intersection(bbox1, bbox2, bbox3,\n west_java, jakarta),\n jakarta)\n\n # From actual example\n b1 = [94.972335000000001, -11.009721000000001,\n 141.014002, 6.0736119999999998]\n b2 = (95.059660952000002, -10.997409961000001,\n 141.00132578099999, 5.9109226959999983)\n b3 = (94.972335000000001, -11.009721000000001,\n 141.0140016666665, 6.0736123333332639)\n\n res = bbox_intersection(b1, b2, b3)\n assert numpy.allclose(res, [95.059660952, -10.997409961,\n 141.001325781, 5.910922695999998],\n rtol=1.0e-12, atol=1.0e-12)\n\n # Empty intersection should return None\n assert bbox_intersection(bbox2, [50, 2, 53, 4]) is None\n\n # Deal with invalid boxes\n try:\n bbox_intersection(bbox1, [53, 2, 40, 4])\n except BoundingBoxError:\n pass\n else:\n msg = 'Should have raised exception'\n raise Exception(msg)\n\n try:\n bbox_intersection(bbox1, [50, 7, 53, 4])\n except BoundingBoxError:\n pass\n else:\n msg = 'Should have raised exception'\n raise Exception(msg)\n\n try:\n bbox_intersection(bbox1, 'blko ho skrle')\n except BoundingBoxError:\n pass\n else:\n msg = 'Should have raised exception'\n raise Exception(msg)\n\n try:\n bbox_intersection(bbox1)\n except VerificationError:\n pass\n else:\n msg = 'Should have raised exception'\n raise Exception(msg)\n\n try:\n bbox_intersection('')\n except VerificationError:\n pass\n else:\n msg = 'Should have raised exception'\n raise Exception(msg)\n\n try:\n bbox_intersection()\n except VerificationError:\n pass\n else:\n msg = 'Should have raised exception'\n raise Exception(msg)","def draw_border_gradient(img, center_color=(0, 0, 0, 255), \\\nouter_color=(255, 255, 255, 255), excluding_region=None):\n center = (img.size[0] / 2, img.size[1] / 2)\n # Used to calculate scalar\n max_distance = math.sqrt(center[0] ** 2 + center[1] ** 2)\n for x in range(img.size[0]):\n for y in range(img.size[1]):\n if excluding_region != None:\n if not (x < excluding_region[0] or x >= excluding_region[1] or \\\n y < excluding_region[2] or y >= excluding_region[3]):\n continue\n \n # This gets the delta between the current point and the center\n distance = math.sqrt((center[0] - x) ** 2 + (center[1] - y) ** 2)\n # We turn this into a scale from 0 to 1, with 1 meaning being on the\n # very outside, and 0 being on the center\n distance_scalar = float(distance) / max_distance\n \n r = (1 - distance_scalar) * center_color[0] + distance_scalar * \\\n outer_color[0]\n g = (1 - distance_scalar) * center_color[1] + distance_scalar * \\\n outer_color[1]\n b = (1 - distance_scalar) * center_color[2] + distance_scalar * \\\n outer_color[2]\n \n img.putpixel((x, y), (int(r), int(g), int(b), 255))","def rectangle(xcenter, ycenter, width, height):\n x1, x2 = xcenter - width, xcenter + width\n y1, y2 = ycenter - height, ycenter + height\n return Shape([Point(x1, y1), Point(x1, y2), Point(x2, y2), Point(x2, y1)])","def draw_lrtb_rectangle_outline(left, right, top, bottom, color,\n border_width=1):\n if left > right:\n raise AttributeError(\"Left coordinate must be less than or equal to \"\n \"the right coordinate\")\n\n if bottom > right:\n raise AttributeError(\"Bottom coordinate must be less than or equal to \"\n \"the top coordinate\")\n\n center_x = (left + right) / 2\n center_y = (top + bottom) / 2\n width = right - left\n height = top - bottom\n draw_rectangle_outline(center_x, center_y, width, height, color,\n border_width)","def build_background():\n layer_1 = GRect(800, 550)\n layer_1.filled = True\n layer_1.color = 'silver'\n layer_1.fill_color = 'silver'\n window.add(layer_1)\n layer_2 = GRect(800, 90)\n layer_2.filled = True\n layer_2.color = 'whitesmoke'\n layer_2.fill_color = 'whitesmoke'\n window.add(layer_2)\n layer_3 = GRect(800, 40, x=0, y=510)\n layer_3.filled = True\n layer_3.color = 'whitesmoke'\n layer_3.fill_color = 'whitesmoke'\n window.add(layer_3)","def compute_bounds(self, weights, add_coeff, dual_vars, lower_bounds, upper_bounds, store_primal=False):\n x0_coeff = -weights[0].backward(dual_vars.mus[0])\n x0 = torch.where(x0_coeff >= 0, lower_bounds[0].unsqueeze(1), upper_bounds[0].unsqueeze(1))\n bound = utils.bdot(x0, x0_coeff)\n if store_primal:\n self.bounds_primal = x0\n else:\n del x0\n del x0_coeff\n\n for lay_idx in range(1, len(weights)):\n lbs = lower_bounds[lay_idx].unsqueeze(1).clamp(None, 0)\n ubs = upper_bounds[lay_idx].unsqueeze(1).clamp(0, None)\n neg_bias = ((lbs * ubs) / (ubs - lbs))\n neg_bias.masked_fill_(ubs == lbs, 0) # cover case in which ubs & lbs coincide\n bound += utils.bdot(dual_vars.lambdas[lay_idx - 1].clamp(0, None), neg_bias)\n bound -= utils.bdot(dual_vars.mus[lay_idx - 1], weights[lay_idx - 1].get_bias())\n\n bound += utils.bdot(add_coeff, weights[-1].get_bias())\n return bound","def build_constraints_boundaries(self):\n\n # Trapezoidal and Hermite-Simpson methods can't compute\n # defects at the last node contrary to pseudospectral methods\n coll_method = self.options['tr_method'] in [\n 'trapezoidal', 'hermite-simpson']\n n_nodes = self.problem.prm['n_nodes'] - \\\n 1 if coll_method else self.problem.prm['n_nodes']\n\n # Defects lower and upper boundaries\n defects_low = np.zeros(\n self.problem.prm['n_states'] * n_nodes)\n defects_upp = np.zeros(\n self.problem.prm['n_states'] * n_nodes)\n\n # Path lower and upper boundaries\n path_low = np.hstack([self.problem.low_bnd.path]\n * (self.problem.prm['n_nodes']))\n path_upp = np.hstack([self.problem.upp_bnd.path]\n * (self.problem.prm['n_nodes']))\n\n # Events lower and upper boundaries\n event_low = self.problem.low_bnd.event\n event_upp = self.problem.upp_bnd.event\n\n # Assembly of the lower and upper boundaries vectors\n low = np.concatenate((defects_low, path_low, event_low))\n upp = np.concatenate((defects_upp, path_upp, event_upp))\n\n return low, upp","def box(self):\n b = Bnd_Box()\n brepbndlib_Add(self.topods_shape(), b)\n return geom_utils.box_to_geometry(b)","def draw_bounds():\n\n pass","def get_bounds(self):\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\n return bottom_right, upper_left","def from_bounds(cls, bounds) -> \"Box\":\n return cls(\n x=bounds[\"left\"],\n y=bounds[\"top\"],\n width=bounds[\"right\"] - bounds[\"left\"],\n height=bounds[\"bottom\"] - bounds[\"top\"],\n )","def point_label_bounds(x, y, width, height, radius, placement):\n if placement in (NE, ENE, ESE, SE):\n # to the right\n x += radius + width/2\n \n if placement in (NW, WNW, WSW, SW):\n # to the left\n x -= radius + width/2\n\n if placement in (NW, NE):\n # way up high\n y -= height/2\n\n if placement in (SW, SE):\n # way down low\n y += height/2\n\n if placement in (ENE, WNW):\n # just a little above\n y -= height/6\n\n if placement in (ESE, WSW):\n # just a little below\n y += height/6\n \n if placement in (NNE, SSE, SSW, NNW):\n _x = radius * cos(pi/4) + width/2\n _y = radius * sin(pi/4) + height/2\n \n if placement in (NNE, SSE):\n x += _x\n else:\n x -= _x\n \n if placement in (SSE, SSW):\n y += _y\n else:\n y -= _y\n \n if placement == N:\n # right on top\n y -= radius + height / 2\n \n if placement == S:\n # right on the bottom\n y += radius + height / 2\n \n x1, y1 = x - width/2, y - height/2\n x2, y2 = x + width/2, y + height/2\n \n return Polygon(((x1, y1), (x1, y2), (x2, y2), (x2, y1), (x1, y1)))","def boundingRect(self):\n if not self.source or not self.destination:\n return QRectF()\n pen_width = 1.0\n extra = (pen_width + self.arrow_size) / 2.0\n\n return QRectF(\n self.source_point, QSizeF(\n self.destination_point.x() - self.source_point.x(),\n self.destination_point.y() - self.source_point.y()\n )\n ).normalized().adjusted(\n -extra,\n -extra,\n extra,\n extra\n )","def checkBoundingBox(ontology_bounding_box):\n heigth = ontology_bounding_box.has_heigth[0]\n width = ontology_bounding_box.has_width[0]\n length = ontology_bounding_box.has_length[0]\n x = ontology_bounding_box.has_x_center[0]\n y = ontology_bounding_box.has_y_center[0]\n z = ontology_bounding_box.has_z_center[0]\n return xosc.BoundingBox(width,length,heigth,x,y,z)","def random_offset_bounds(self) -> utils.BoxRegion:\n extra_size = self.random_canvas_extra_ratio * self.canvas_bounds().size / 2\n return utils.BoxRegion(\n minimum=-extra_size,\n maximum=extra_size\n )"],"string":"[\n \"def paint_box(top=0, bottom=DISPLAY_HEIGHT-1, left=0, right=DISPLAY_WIDTH-1):\\n def filter_box(col, row):\\n \\\"\\\"\\\"For a given pixel position, turn it on if it's with the bounds\\n \\\"\\\"\\\"\\n # remember rows count from 0 at the top!\\n correct_vertical = top <= row <= bottom\\n correct_horizontal = left <= col <= right\\n return correct_vertical and correct_horizontal\\n light(MAX_BRIGHTNESS, filter_box)\",\n \"def calc_bounding_box(self):\\n self.BB = self.geos.abs_el(0).BB\\n for geo in self.geos.abs_iter():\\n self.BB = self.BB.joinBB(geo.BB)\",\n \"def create_bounds(self):\\n # Bounds should be created for\\n x0, y0 = (0, 0)\\n x1 = self.game_area_size[0]\\n y1 = self.game_area_size[1]\\n space = self.space\\n\\n borders = [\\n phy.Segment(space.static_body, phy.Vec2d(x0, y0), phy.Vec2d(x1, y0), 0),\\n phy.Segment(space.static_body, phy.Vec2d(x0, y0), phy.Vec2d(x0, y1), 0),\\n phy.Segment(space.static_body, phy.Vec2d(x1, y0), phy.Vec2d(x1, y1), 0),\\n phy.Segment(space.static_body, phy.Vec2d(x0, y1), phy.Vec2d(x1, y1), 0),\\n ]\\n for b in borders:\\n b.elasticity = 0.5\\n self.space.add(borders)\",\n \"def bbox_giou(boxes1, boxes2):\\n\\n # obtain boxes where properties are (x_min, y_min, x_max, y_max)\\n boxes1 = tf.concat([boxes1[..., :2] - boxes1[..., 2:] * 0.5, boxes1[..., :2] + boxes1[..., 2:] * 0.5], axis = -1)\\n boxes2 = tf.concat([boxes2[..., :2] - boxes2[..., 2:] * 0.5, boxes2[..., :2] + boxes2[..., 2:] * 0.5], axis = -1)\\n\\n # obtain boxes where properties are (x_min, y_min, x_max, y_max)\\n boxes1 = tf.concat([tf.minimum(boxes1[..., :2], boxes1[..., 2:]),\\n tf.maximum(boxes1[..., :2], boxes1[..., 2:])], axis = -1)\\n boxes2 = tf.concat([tf.minimum(boxes2[..., :2], boxes2[..., 2:]),\\n tf.maximum(boxes2[..., :2], boxes2[..., 2:])], axis = -1)\\n\\n # obtain area of from the 2 boxes\\n boxes1_area = (boxes1[..., 2] - boxes1[..., 0]) * (boxes1[..., 3] - boxes1[..., 1])\\n boxes2_area = (boxes2[..., 2] - boxes2[..., 0]) * (boxes2[..., 3] - boxes2[..., 1])\\n\\n # obtain maximum coordinates amongst 2 box at top left corner\\n left_up = tf.maximum(boxes1[..., :2], boxes2[..., :2])\\n\\n # obtain minimum coordinates amongst 2 box at bottom right corner\\n right_down = tf.minimum(boxes1[..., 2:], boxes2[..., 2:])\\n\\n # obtain a positive intersection \\n inter_section = tf.maximum(right_down - left_up, 0.0)\\n\\n # obtain intersection area \\n inter_area = inter_section[..., 0] * inter_section[..., 1]\\n\\n # obtain union area \\n union_area = boxes1_area + boxes2_area - inter_area\\n\\n # calculate the iou value between the two bounding boxes\\n iou = inter_area / union_area\\n\\n # calculate the coordinates of the upper left corner and the lower right corner of the smallest closed convex \\n # surface\\n enclose_left_up = tf.minimum(boxes1[..., :2], boxes2[..., :2])\\n enclose_right_down = tf.maximum(boxes1[..., 2:], boxes2[..., 2:])\\n enclose = tf.maximum(enclose_right_down - enclose_left_up, 0.0)\\n\\n # calculate the area of the smallest closed convex surface C\\n enclose_area = enclose[..., 0] * enclose[..., 1]\\n\\n # calculate the GIoU value according to the GioU formula \\n giou = iou - 1.0 * (enclose_area - union_area) / enclose_area\\n\\n return giou\",\n \"def sweptAABB(b1, b2):\\n\\tnormalX = 0;\\n\\tnormalY = 0;\\n\\n\\t\\\"\\\"\\\"\\n\\tfloat xInvEntry, yInvEntry;\\n\\tfloat xInvExit, yInvExit;\\n\\n\\t// find the distance between the objects on the near and far sides for both x and y\\n\\tif (b1.vx > 0.0f) {\\n\\t\\txInvEntry = b2.x - (b1.x + b1.w);\\n\\t\\txInvExit = (b2.x + b2.w) - b1.x;\\n\\t} else {\\n\\t\\txInvEntry = (b2.x + b2.w) - b1.x;\\n\\t\\txInvExit = b2.x - (b1.x + b1.w);\\n\\t}\\n\\n\\tif (b1.vy > 0.0f) {\\n\\t\\tyInvEntry = b2.y - (b1.y + b1.h);\\n\\t\\tyInvExit = (b2.y + b2.h) - b1.y;\\n\\t} else {\\n\\t\\tyInvEntry = (b2.y + b2.h) - b1.y;\\n\\t\\tyInvExit = b2.y - (b1.y + b1.h);\\n\\t}\\n\\t\\\"\\\"\\\"\\n\\n\\txInvEntry = 0;\\n\\tyInvEntry = 0;\\n\\txInvExit = 0;\\n\\tyInvExit = 0;\\n\\n\\tif (b1.vx > 0):\\n\\t\\txInvEntry = b2.x - (b1.x + b1.w);\\n\\t\\txInvExit = (b2.x + b2.w) - b1.x;\\n\\telse:\\n\\t\\txInvEntry = (b2.x + b2.w) - b1.x;\\n\\t\\txInvExit = b2.x - (b1.x + b1.w);\\n\\t\\n\\n\\tif (b1.vy > 0):\\n\\t\\tyInvEntry = b2.y - (b1.y + b1.h);\\n\\t\\tyInvExit = (b2.y + b2.h) - b1.y;\\n\\telse:\\n\\t\\tyInvEntry = (b2.y + b2.h) - b1.y;\\n\\t\\tyInvExit = b2.y - (b1.y + b1.h);\\n\\n\\t\\\"\\\"\\\"\\n\\t// find time of collision and time of leaving for each axis (if statement is to prevent divide by zero)\\n\\tfloat xEntry, yEntry;\\n\\tfloat xExit, yExit;\\n\\n\\tif (b1.vx == 0.0f) {\\n\\t\\txEntry = -std::numeric_limits<float>::infinity();\\n\\t\\txExit = std::numeric_limits<float>::infinity();\\n\\t} else {\\n\\t\\txEntry = xInvEntry / b1.vx;\\n\\t\\txExit = xInvExit / b1.vx;\\n\\t}\\n\\n\\tif (b1.vy == 0.0f) {\\n\\t\\tyEntry = -std::numeric_limits<float>::infinity();\\n\\t\\tyExit = std::numeric_limits<float>::infinity();\\n\\t} else {\\n\\t\\tyEntry = yInvEntry / b1.vy;\\n\\t\\tyExit = yInvExit / b1.vy;\\n\\t}\\n\\t\\\"\\\"\\\"\\n\\txEntry = 0;\\n\\tyEntry = 0;\\n\\txExit = 0;\\n\\tyExit = 0;\\n\\n\\tif (b1.vx == 0):\\n\\t\\txEntry = -float(\\\"inf\\\");\\n\\t\\txExit = float(\\\"inf\\\");\\n\\telse:\\n\\t\\txEntry = xInvEntry / b1.vx;\\n\\t\\txExit = xInvExit / b1.vx;\\n\\n\\tif (b1.vy == 0):\\n\\t\\tyEntry = -float(\\\"inf\\\");\\n\\t\\tyExit = float(\\\"inf\\\");\\n\\telse:\\n\\t\\tyEntry = yInvEntry / b1.vy;\\n\\t\\tyExit = yInvExit / b1.vy;\\n\\t\\n\\tif (yEntry > 1):\\n\\t\\tyEntry = -float(\\\"inf\\\");\\n\\tif (xEntry > 1):\\n\\t\\txEntry = -float(\\\"inf\\\");\\n\\n\\n\\t\\\"\\\"\\\"\\n\\t// find the earliest/latest times of collision\\n\\tfloat entryTime = std::max(xEntry, yEntry);\\n\\tfloat exitTime = std::min(xExit, yExit);\\n\\t\\\"\\\"\\\"\\n\\n\\tentryTime = max(xEntry, yEntry);\\n\\texitTime = min(xExit, yExit);\\n\\n\\t\\\"\\\"\\\"\\n\\t// if there was no collision\\n\\tif (entryTime > exitTime || xEntry < 0.0f && yEntry < 0.0f || xEntry > 1.0f || yEntry > 1.0f) {\\n\\t\\tnormalX = 0.0f;\\n\\t\\tnormalY = 0.0f;\\n\\t\\treturn 1.0f;\\n\\t} else {\\n\\t\\t// if there was a collision\\n\\t\\t// calculate normal of collided surface\\n\\t\\tif (xEntry > yEntry) {\\n\\t\\t\\tif (xInvEntry < 0.0f) {\\n\\t\\t\\t\\tnormalX = 1.0f;\\n\\t\\t\\t\\tnormalY = 0.0f;\\n\\t\\t\\t} else {\\n\\t\\t\\t\\tnormalX = -1.0f;\\n\\t\\t\\t\\tnormalY = 0.0f;\\n\\t\\t\\t}\\n\\t\\t} else {\\n\\t\\t\\tif (yInvEntry < 0.0f) {\\n\\t\\t\\t\\tnormalX = 0.0f;\\n\\t\\t\\t\\tnormalY = 1.0f;\\n\\t\\t\\t} else {\\n\\t\\t\\t\\tnormalX = 0.0f;\\n\\t\\t\\t\\tnormalY = -1.0f;\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\t// return the time of collision\\n\\t\\treturn entryTime;\\n\\t}\\n\\t\\\"\\\"\\\"\\n\\n\\t\\\"\\\"\\\"\\n\\tif (entryTime > exitTime)\\n\\t\\treturn 1.0f; // This check was correct.\\n\\tif (entryX < 0.0f && entryY < 0.0f)\\n\\t\\treturn 1.0f;\\n\\tif (entryX < 0.0f) {\\n\\t\\t// Check that the bounding box started overlapped or not.\\n\\t\\tif (s.max.x < t.min.x || s.min.x > t.max.x)\\n\\t\\t\\treturn 1.0f;\\n\\t}\\n\\tif (entryY < 0.0f) {\\n\\t\\t// Check that the bounding box started overlapped or not.\\n\\t\\tif (s.max.y < t.min.y || s.min.y > t.max.y)\\n\\t\\t\\treturn 1.0f;\\n\\t}\\\"\\\"\\\"\\n\\n\\n\\t# if (entryTime > exitTime or xEntry < 0 and yEntry < 0 or xEntry > 1 or yEntry > 1):\\n\\t# \\tnormalX = 0;\\n\\t# \\tnormalY = 0;\\n\\t# \\treturn (1, (normalX, normalY));\\n\\t# else:\\n\\n\\tnormalX = 0;\\n\\tnormalY = 0;\\n\\n\\tif (entryTime > exitTime):\\n\\t\\treturn (1, (0, 0));\\n\\t\\n\\tif (xEntry < 0 and yEntry < 0):\\n\\t\\treturn (1, (0, 0));\\n\\n\\tif (xEntry < 0):\\n\\t\\tif (b1.x + b1.w < b2.x or b1.x > b2.x + b2.w):\\n\\t\\t\\treturn (1, (0, 0));\\n\\n\\tif (yEntry < 0):\\n\\t\\tif (b1.y + b1.h < b2.y or b1.y > b2.y + b2.h):\\n\\t\\t\\treturn (1, (0, 0));\\n\\n\\tif (xEntry > yEntry):\\n\\t\\tif (xInvEntry < 0):\\n\\t\\t\\tnormalX = 1;\\n\\t\\t\\tnormalY = 0;\\n\\t\\telse:\\n\\t\\t\\tnormalX = -1;\\n\\t\\t\\tnormalY = 0;\\n\\telse:\\n\\t\\tif (yInvEntry < 0):\\n\\t\\t\\tnormalX = 0;\\n\\t\\t\\tnormalY = 1;\\n\\t\\telse:\\n\\t\\t\\tnormalX = 0;\\n\\t\\t\\tnormalY = -1;\\n\\n\\treturn (entryTime, (normalX, normalY));\",\n \"def bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\n \"def apply_boundary_conditions(self):\\n E = self.__mesh.get_edge_list()\\n\\n # Top and bottom wall Dirichlet bcs (boundary_id = 21)\\n \\n e21_iterator = self.__mesh.edge_iterator(21)\\n\\n self.edge_center_value[e21_iterator[0]:e21_iterator[1]+1] = 0.0 \\n \\n # Left Dirichlet bc (boundary_id = 2)\\n \\n e2_iterator = self.__mesh.edge_iterator(2)\\n\\n b = np.sin(np.pi*self.y_e[e2_iterator[0]:e2_iterator[1]+1])\\n\\n self.edge_center_value[e2_iterator[0]:e2_iterator[1]+1] \\\\\\n = b\\n \\n # Right Neumann bc (Zero flux, boundary_id = 3)\\n \\n e3_iterator = self.__mesh.edge_iterator(3)\\n \\n for i in range(e3_iterator[0], e3_iterator[1]+1):\\n LC = E[i].get_straddling_cells()\\n n = LC.get_global_cell_number() - 1\\n self.edge_center_value[i] = self.cell_centroid_value[n]\",\n \"def boundaries_and_initialize():\\n greenLower = (29, 86, 6) # define the lower and upper boundaries of the \\\"green\\\"\\n greenUpper = (64, 255, 255)\\n pts = [((200,300),(255,255,255), 0)]\\n blanks = []\\n linecolor = (0,0,0)\\n counter = 1\\n radius = 11\\n return greenLower, greenUpper, pts, linecolor, counter, blanks, radius\",\n \"def _boundRect(self):\\n self.upperleft = list(map(min, zip(*self.addresstamp)))\\n self.bottomright = list(map(max, zip(*self.addresstamp)))\\n self.upperright = [self.bottomright[0], self.upperleft[1]]\\n self.bottomleft = [self.upperleft[0], self.bottomright[1]]\\n (self.width, self.height) = (self.upperright[0] - self.bottomleft[0], self.bottomleft[1] - self.upperright[1])\\n assert self.width >= 0\\n assert self.height >= 0\\n self.center = [self.upperleft[0] + self.width / float(2), self.upperleft[1] + self.height / float(2)]\\n self.corners = [self.upperright, self.bottomleft, self.upperleft, self.bottomright]\",\n \"def draw_bounding_box(self):\\n # Gets the bounding box\\n xmin, ymin, xmax, ymax = self.get_bounding_box()\\n\\n # Gets the actual coordinates\\n width = xmax - xmin\\n height = ymax - ymin\\n center_x = xmin + (width)/2\\n center_y = ymin + (height)/2\\n\\n arcade.draw_rectangle_outline(center_x, center_y, width, height, (255, 0, 0))\",\n \"def _boundRect(self):\\n addresstamp = reduce(lambda x, y: x + y, [v.addresstamp for v in self.footprints])\\n self.upperleft = list(map(min, zip(*addresstamp)))\\n self.bottomright = list(map(max, zip(*addresstamp)))\\n self.upperright = [self.bottomright[0], self.upperleft[1]]\\n self.bottomleft = [self.upperleft[0], self.bottomright[1]]\\n (self.width, self.height) = (self.upperright[0] - self.bottomleft[0], self.bottomleft[1] - self.upperright[1])\\n assert self.width >= 0\\n assert self.height >= 0\\n self.center = [self.upperleft[0] + self.width / float(2), self.upperleft[1] + self.height / float(2)]\\n self.corners = [self.upperright, self.bottomleft, self.upperleft, self.bottomright]\",\n \"def bounds(self):\\n frame_ = self.to_frame().total_bounds.flatten().tolist()\\n return BBox(\\n left=frame_[0], bottom=frame_[1], right=frame_[2], top=frame_[3]\\n )\",\n \"def box_refinement(box, gt_box):\\n box = box.astype(np.float32)\\n gt_box = gt_box.astype(np.float32)\\n\\n height = box[:, 2] - box[:, 0]\\n width = box[:, 3] - box[:, 1]\\n center_y = box[:, 0] + 0.5 * height\\n center_x = box[:, 1] + 0.5 * width\\n\\n gt_height = gt_box[:, 2] - gt_box[:, 0]\\n gt_width = gt_box[:, 3] - gt_box[:, 1]\\n gt_center_y = gt_box[:, 0] + 0.5 * gt_height\\n gt_center_x = gt_box[:, 1] + 0.5 * gt_width\\n\\n dy = (gt_center_y - center_y) / height\\n dx = (gt_center_x - center_x) / width\\n dh = np.log(gt_height / height)\\n dw = np.log(gt_width / width)\\n\\n return np.stack([dy, dx, dh, dw], axis=1)\",\n \"def expanded_boundaries(self):\\n width = self._points[0][3][0] - self._points[0][1][0]\\n height = self._points[0][3][1] - self._points[0][1][1]\\n factor = np.multiply((width, height), Window.BORDER)\\n return (\\n np.subtract(self._points[0][1], factor),\\n np.add(self._points[0][3], factor))\",\n \"def create_ring(self):\\n\\t\\tself.north_coords = numpy.add(self.center, self.north)\\n\\t\\tself.northeast_coords = numpy.add(self.center, self.northeast)\\n\\t\\tself.east_coords = numpy.add(self.center, self.east)\\n\\t\\tself.southeast_coords = numpy.add(self.center, self.southeast)\\n\\t\\tself.south_coords = numpy.add(self.center, self.south)\\n\\t\\tself.southwest_coords = numpy.add(self.center, self.southwest)\\n\\t\\tself.west_coords = numpy.add(self.center, self.west)\\n\\t\\tself.northwest_coords = numpy.add(self.center, self.northwest)\",\n \"def bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\n \"def box_refinement_graph(box, gt_box):\\n box = tf.cast(box, tf.float32)\\n gt_box = tf.cast(gt_box, tf.float32)\\n\\n height = box[:, 2] - box[:, 0]\\n width = box[:, 3] - box[:, 1]\\n center_y = box[:, 0] + 0.5 * height\\n center_x = box[:, 1] + 0.5 * width\\n\\n gt_height = gt_box[:, 2] - gt_box[:, 0]\\n gt_width = gt_box[:, 3] - gt_box[:, 1]\\n gt_center_y = gt_box[:, 0] + 0.5 * gt_height\\n gt_center_x = gt_box[:, 1] + 0.5 * gt_width\\n\\n dy = (gt_center_y - center_y) / height\\n dx = (gt_center_x - center_x) / width\\n dh = tf.math.log(gt_height / height)\\n dw = tf.math.log(gt_width / width)\\n\\n result = tf.stack([dy, dx, dh, dw], axis=1)\\n return result\",\n \"def _trailing_edge(top, bottom, config):\\n\\n def rectangle(top_left,\\n bottom_left,\\n left_line,\\n horizontal_discretisation,\\n wake_progression = 1):\\n \\\"\\\"\\\"\\n :param top_left: top left point of rectangle (existing point)\\n :param bottom_left: bottom left point of rectangle (existing point)\\n :param left_line: left line of rectangle (existing line)\\n :param horizontal_discretisation: horizontal discretisation.\\n :param wake_progression: progression on horizontal line from trailing edge aft-wards.\\n :return: block data structure representing a rectangle in the trailing edge wake.\\n \\\"\\\"\\\"\\n x_dim = 2 * config['grid']['regular']['thickness']\\n top_right = Point((x_dim,\\n top_left.coordinates[1],\\n 0))\\n bottom_right = Point((x_dim,\\n bottom_left.coordinates[1],\\n 0))\\n\\n transfinite_num = int((x_dim - bottom_left.coordinates[0]) / horizontal_discretisation)\\n\\n if transfinite_num < 2:\\n transfinite_num = 2\\n\\n top_line = Line(top_left,\\n top_right,\\n transfinite = transfinite_num,\\n progression = wake_progression)\\n bottom_line = Line(bottom_left,\\n bottom_right,\\n transfinite = transfinite_num,\\n progression = wake_progression)\\n\\n if wake_progression is None:\\n x = []\\n for i in range(0, 4):\\n x.append(bottom['points']['aerofoil'][i].coordinates[0])\\n d = np.mean(np.diff(np.array(x)))\\n top_line.progression_from_width(d)\\n bottom_line.progression_from_width(d)\\n\\n right_line = Line(bottom_right,\\n top_right,\\n transfinite = left_line.transfinite,\\n progression = left_line.progression)\\n\\n loop = Loop([left_line.id,\\n top_line.id,\\n -right_line.id,\\n -bottom_line.id])\\n\\n surface = Surface([loop.id], transfinite = True)\\n\\n return {'points': {'all': [top_right, bottom_right]},\\n 'curves': {'all': [top_line, right_line, bottom_line]},\\n 'loops': {'all': [loop]},\\n 'surfaces': {'all': [surface]}}\\n\\n te_line = Line(top['points']['aerofoil'][-1],\\n bottom['points']['aerofoil'][0])\\n te_line.transfinite_from_grid_size(config['grid']['regular']['boundary_layer']['initial_thickness'])\\n\\n top_rectangle = rectangle(top_left = top['points']['boundary_layer'][-1],\\n bottom_left = top['points']['aerofoil'][-1],\\n left_line = top['curves']['normals'][-1],\\n horizontal_discretisation = config['grid']['regular']['wake']['width'],\\n wake_progression = config['grid']['regular']['wake']['progression'])\\n\\n bottom_rectangle = rectangle(top_left = bottom['points']['boundary_layer'][0],\\n bottom_left = bottom['points']['aerofoil'][0],\\n left_line = bottom['curves']['normals'][0],\\n horizontal_discretisation = config['grid']['regular']['wake']['width'],\\n wake_progression = config['grid']['regular']['wake']['progression'])\\n\\n center_right_line = Line(top_rectangle['points']['all'][1],\\n bottom_rectangle['points']['all'][1])\\n center_right_line.transfinite_from_grid_size(config['grid']['regular']['boundary_layer']['initial_thickness'])\\n\\n center_loop = Loop([te_line.id,\\n - top_rectangle['curves']['all'][2].id,\\n - center_right_line.id,\\n bottom_rectangle['curves']['all'][2].id])\\n center_surface = Surface([center_loop.id], transfinite = True)\\n\\n r = top_rectangle\\n for key in ['points', 'curves', 'loops', 'surfaces']:\\n r[key]['all'] += bottom_rectangle[key]['all']\\n r['curves']['trailing_edge'] = [te_line]\\n r['curves']['all'] += [center_right_line]\\n r['loops']['all'] += [center_loop]\\n r['surfaces']['all'] += [center_surface]\\n\\n return r\",\n \"def draw_boundary() -> None:\\n # Upper edge\\n print(rpipes.terminal.move_xy(0, 0), WBorder.HORIZONTAL * (rpipes.terminal.width - 1))\\n\\n # Left and Right edges\\n for row in range(rpipes.terminal.height - 2):\\n print(\\n WBorder.VERTICAL,\\n rpipes.terminal.move_right(rpipes.terminal.width - 4),\\n WBorder.VERTICAL,\\n )\\n\\n # Bottom edge\\n print(\\n rpipes.terminal.move_xy(0, rpipes.terminal.height - 2),\\n WBorder.HORIZONTAL * (rpipes.terminal.width - 1),\\n )\\n\\n # Top left corner\\n print(rpipes.terminal.move_xy(0, 0) + WBorder.DOWN_AND_RIGHT)\\n\\n # Top right corner\\n print(rpipes.terminal.move_xy(rpipes.terminal.width - 1, 0) + WBorder.DOWN_AND_LEFT)\\n\\n # Bottom left corner\\n print(rpipes.terminal.move_xy(0, rpipes.terminal.height - 2) + WBorder.UP_AND_RIGHT)\\n\\n # Bottom right corner\\n print(\\n rpipes.terminal.move_xy(rpipes.terminal.width - 1, rpipes.terminal.height - 2)\\n + WBorder.UP_AND_LEFT\\n )\",\n \"def drawBoundingBoxes(ax, x, y, z, w, l, h, r, col='b', linewidth=2):\\n # Do this, because we have center point\\n l = l / 2.0\\n w = w / 2.0\\n\\n # Calculate corner locations with rotation\\n x1 = x + (w * math.cos(r) + l * math.sin(r))\\n y1 = y + (-w * math.sin(r) + l * math.cos(r))\\n x2 = x + (-w * math.cos(r) + l * math.sin(r))\\n y2 = y + (+w * math.sin(r) + l * math.cos(r))\\n x3 = x + (-w * math.cos(r) - l * math.sin(r))\\n y3 = y + (w * math.sin(r) - l * math.cos(r))\\n x4 = x + (w * math.cos(r) - l * math.sin(r))\\n y4 = y + (-w * math.sin(r) - l * math.cos(r))\\n\\n # Bottom rectangle\\n ax.plot3D([x3, x4], [y3, y4], [z, z], col, linewidth=2, alpha=0.8)\\n ax.plot3D([x2, x1], [y2, y1], [z, z], col, linewidth=2, alpha=0.8)\\n ax.plot3D([x3, x2], [y3, y2], [z, z], col, linewidth=2, alpha=0.8)\\n ax.plot3D([x4, x1], [y4, y1], [z, z], col, linewidth=2, alpha=0.8)\\n\\n # Top rectangle\\n ax.plot3D([x3, x4], [y3, y4], [z+h, z+h], col, linewidth=2, alpha=0.8)\\n ax.plot3D([x2, x1], [y2, y1], [z+h, z+h], col, linewidth=2, alpha=0.8)\\n ax.plot3D([x3, x2], [y3, y2], [z+h, z+h], col, linewidth=2, alpha=0.8)\\n ax.plot3D([x4, x1], [y4, y1], [z+h, z+h], col, linewidth=2, alpha=0.8)\\n\\n # Vertical lines\\n ax.plot3D([x1, x1], [y1, y1], [z, z+h], col, linewidth=2, alpha=0.8)\\n ax.plot3D([x2, x2], [y2, y2], [z, z+h], col, linewidth=2, alpha=0.8)\\n ax.plot3D([x3, x3], [y3, y3], [z, z+h], col, linewidth=2, alpha=0.8)\\n ax.plot3D([x4, x4], [y4, y4], [z, z+h], col, linewidth=2, alpha=0.8)\",\n \"def boundary(self): # -> BaseGeometry:\\n ...\",\n \"def bounds(self) -> Box:\\n raise NotImplementedError()\",\n \"def canvas_bounds(self) -> utils.BoxRegion:\",\n \"def get_bound(box_list):\\n box_xyxy_list = []\\n for box in box_list:\\n box_xyxy = xywh2xyxy(box)\\n box_xyxy_list.append(box_xyxy)\\n\\n box_xyxy_list = np.array(box_xyxy_list)\\n x1max, y1max, x2max, y2max = np.amax(box_xyxy_list, axis=0)\\n x1min, y1min, x2min, y2min = np.amin(box_xyxy_list, axis=0)\\n\\n boundbox = xyxy2xywh([x1min, y1min, x2max, y2max])\\n return boundbox\",\n \"def set_bounding_box(self, top_left_y: int, top_left_x: int, bottom_right_y: int, bottom_right_x: int) -> None:\\n self.top_left_y = top_left_y\\n self.top_left_x = top_left_x\\n self.bottom_right_y = bottom_right_y\\n self.bottom_right_x = bottom_right_x\",\n \"def overlay_bounds(over: 'Entity') -> Tuple[Vec, Vec]:\\n origin = Vec.from_str(over['origin'])\\n mat = Matrix.from_angle(Angle.from_str(over['angles']))\\n return Vec.bbox(\\n (origin + Vec.from_str(over['uv' + str(x)]) @ mat)\\n for x in\\n range(4)\\n )\",\n \"def create_from_bounds(self, lbs, ubs):\\n self.base_vertices = (np.array([lbs])+np.array([ubs])).T/2\\n self.base_vectors = np.diag((np.array(ubs)-np.array(lbs))/2)\",\n \"def compute_bb(self):\\n all_shapes = list(self.parts.values()) + list(self.edges.values())\\n bbox_vertices = cascaded_union(all_shapes).envelope.exterior.coords.xy\\n min_x = min(bbox_vertices[0])\\n max_x = max(bbox_vertices[0])\\n min_y = min(bbox_vertices[1])\\n max_y = max(bbox_vertices[1])\\n return [min_x, max_x,min_y, max_y]\",\n \"def clip(self, B):\\n # /* Left */\\n if ((self.x >= B.x) and (self.x < (B.x + B.w))):\\n x = self.x\\n elif ((B.x >= self.x) and (B.x < (self.x + self.w))):\\n x = B.x\\n else:\\n return Rect(self.x, self.y, 0, 0)\\n\\n # /* Right */\\n if (((self.x + self.w) > B.x) and ((self.x + self.w) <= (B.x + B.w))):\\n w = (self.x + self.w) - x\\n elif (((B.x + B.w) > self.x) and ((B.x + B.w) <= (self.x + self.w))):\\n w = (B.x + B.w) - x\\n else:\\n return Rect(self.x, self.y, 0, 0)\\n\\n # /* Top */\\n if ((self.y >= B.y) and (self.y < (B.y + B.h))):\\n y = self.y\\n elif ((B.y >= self.y) and (B.y < (self.y + self.h))):\\n y = B.y\\n else:\\n return Rect(self.x, self.y, 0, 0)\\n\\n # /* Bottom */\\n if (((self.y + self.h) > B.y) and ((self.y + self.h) <= (B.y + B.h))):\\n h = (self.y + self.h) - y\\n elif (((B.y + B.h) > self.y) and ((B.y + B.h) <= (self.y + self.h))):\\n h = (B.y + B.h) - y\\n else:\\n return Rect(self.x, self.y, 0, 0)\\n\\n return Rect(x, y, w, h)\",\n \"def get_bounding_box(self):\\n lon, lat = self.coordinates\\n\\n ll = (np.min(lon),np.min(lat))\\n ul = (np.min(lon),np.max(lat))\\n ur = (np.max(lon),np.max(lat))\\n lr = (np.max(lon),np.min(lat))\\n\\n return (ll, ul, ur, lr)\",\n \"def gradientRect( window, left_colour, right_colour, target_rect ):\\n colour_rect = pygame.Surface( ( 2, 2 ) ) # tiny! 2x2 bitmap\\n pygame.draw.line( colour_rect, left_colour, ( 0,0 ), ( 0,1 ) ) # left colour line\\n pygame.draw.line( colour_rect, right_colour, ( 1,0 ), ( 1,1 ) ) # right colour line\\n colour_rect = pygame.transform.smoothscale( colour_rect, ( target_rect.width, target_rect.height ) ) # stretch!\\n window.blit( colour_rect, target_rect ) # paint it\",\n \"def compute_bounding_box(homography, w, h):\\n\\n # todo in what step to do int??\\n # todo should i do -1? if so why?\\n\\n\\n all_corners = np.array([[0, 0], [w, 0], [0, h], [w, h]])\\n all_corners = apply_homography(all_corners,homography)\\n top_left = [np.min(all_corners[:,0]),np.min(all_corners[:,1])]\\n bottom_right = [np.max(all_corners[:,0]),np.max(all_corners[:,1])]\\n return np.array([top_left,bottom_right]).astype(np.int)\",\n \"def bounding_box(vertices, (height, width), extend=5):\\n x_min = min(x for x, y in vertices) - extend\\n x_max = max(x for x, y in vertices) + extend\\n y_min = min(y for x, y in vertices) - extend\\n y_max = max(y for x, y in vertices) + extend\\n \\n return max(x_min, 0), min(x_max, width), max(y_min, 0), min(y_max, height)\",\n \"def define_grid():\\n grid_left = np.array([[-13.1000000000000, -35.5000000000000, -48.3000000000000, -60, -16.9000000000000,\\n -34.8000000000000, -67.5000000000000, -46.1000000000000, -59.8000000000000,\\n -14.2000000000000, -28.3000000000000, -42.3000000000000, -67.6000000000000,\\n -50.5000000000000, -14.6000000000000, -60.9000000000000, -31.6000000000000,\\n -5.10000000000000, -65.6000000000000, -41.8000000000000, -55.1000000000000,\\n -22.7000000000000, -5.80000000000000, -49.2000000000000, -34.5000000000000,\\n -61.5500000000000, -63.6000000000000, -40.4000000000000, -48.7000000000000,\\n -21.8000000000000, -58.2000000000000, -7, -36.3000000000000, -48.1000000000000,\\n -56.8000000000000, -7.30000000000000, -22.2000000000000, -36.8000000000000,\\n -46.8000000000000],\\n [-67.7000000000000, -60, -55.1000000000000, -51.8000000000000, -51.6000000000000,\\n -49.3000000000000, -47.1000000000000, -43.7000000000000, -39.6000000000000,\\n -39.1000000000000, -31.2000000000000, -30.7000000000000, -30.1000000000000,\\n -24.4000000000000, -22.7000000000000, -18.7000000000000, -16.9000000000000,\\n -12.6000000000000, -10.8000000000000, -10.2000000000000, -4.01000000000000, 1.20000000000000,\\n 2.80000000000000, 3.70000000000000, 3.90000000000000, 6.20000000000000, 8.30000000000000,\\n 11.8000000000000, 14.5000000000000, 16, 18.2000000000000, 18.4000000000000, 19.9000000000000,\\n 24.6000000000000, 28.5200000000000, 33.8000000000000, 35, 35.4000000000000,\\n 35.6000000000000],\\n [69.1000000000000, 66, 58.2000000000000, 48, 78, 71.7000000000000, 31, 61.1000000000000,\\n 53.3000000000000, 81.1000000000000, 76, 70.2000000000000, 41.2000000000000, 64.4000000000000,\\n 80.2000000000000, 50.9000000000000, 75.2000000000000, 77.3000000000000, 37.8000000000000, 67,\\n 53.2000000000000, 72, 74.8000000000000, 54.7000000000000, 66.5000000000000, 35.9000000000000,\\n 25.7000000000000, 60.7000000000000, 50.5000000000000, 68.9000000000000, 27.3000000000000,\\n 70.3000000000000, 59.6000000000000, 44, 20.8000000000000, 61.7000000000000, 57.2000000000000,\\n 47, 36]])\\n stn_left = np.array([[-14.6, -13.2, -11.7, -9.10, -11.7, -13.2, -7.90, -10],\\n [-15.1, -15.1, -15.1, -12.6, -12.6, -12.6, -9.40, -10.1],\\n [-5.40, -7.20, -8.70, -8.70, -7.50, -5.10, -10.3, -7.80]])\\n grid_right = np.copy(grid_left)\\n grid_right[0, :] = grid_right[0, :] * -1\\n stn_right = np.copy(stn_left)\\n stn_right[0, :] = stn_right[0, :] * -1\\n\\n return grid_left, grid_right, stn_left, stn_right\",\n \"def compute_bb(self) -> List[float]:\\n all_shapes = list(self.parts.values())\\n bbox_vertices = unary_union(all_shapes).envelope.exterior.coords.xy\\n min_x = min(bbox_vertices[0])\\n max_x = max(bbox_vertices[0])\\n min_y = min(bbox_vertices[1])\\n max_y = max(bbox_vertices[1])\\n return [min_x, max_x, min_y, max_y]\",\n \"def uv_bounds(self):\\n umin, umax, vmin, vmax = breptools_UVBounds(self.topods_shape())\\n bounds = Box(np.array([umin, vmin]))\\n bounds.encompass_point(np.array([umax, vmax]))\\n return bounds\",\n \"def compute_bounding_box(homography, w, h):\\n corners = np.array([[0,0],\\n [0,h],\\n [w,0],\\n [w,h]])\\n t_corners = apply_homography(corners, homography)\\n return np.array([t_corners.min(axis=0),t_corners.max(axis=0)],\\n dtype= np.int)\\n #[top-left, btm-right]\",\n \"def get_boundingbox(face, width, height, scale=1.3, minsize=None):\\n x1 = face.left()\\n y1 = face.top()\\n x2 = face.right()\\n y2 = face.bottom()\\n size_bb = int(max(x2 - x1, y2 - y1) * scale)\\n if minsize:\\n if size_bb < minsize:\\n size_bb = minsize\\n center_x, center_y = (x1 + x2) // 2, (y1 + y2) // 2\\n\\n # Check for out of bounds, x-y top left corner\\n x1 = max(int(center_x - size_bb // 2), 0)\\n y1 = max(int(center_y - size_bb // 2), 0)\\n # Check for too big bb size for given x, y\\n size_bb = min(width - x1, size_bb)\\n size_bb = min(height - y1, size_bb)\\n\\n return x1, y1, size_bb\",\n \"def get_aabb(self):\\n c, s = np.cos(self.eangles), np.sin(self.eangles)\\n r = np.array([[c, s], [s, c]])\\n return rect(self.get_com(), np.abs(np.dot(r, self.halfexts)), 0)\",\n \"def CalcBoundingBox(*args, **kwargs):\\n return _gdi_.DC_CalcBoundingBox(*args, **kwargs)\",\n \"def create_box(self, a, b, c):\\n proj_to_xy = lambda x: x[:2]\\n get_angle = lambda x,y: (x @ y) / (np.linalg.norm(x) * np.linalg.norm(y))\\n\\n ab = proj_to_xy(b) - proj_to_xy(a)\\n ac = proj_to_xy(c) - proj_to_xy(a)\\n bc = proj_to_xy(c) - proj_to_xy(b)\\n\\n ab_ac = np.abs(get_angle(ab, ac))\\n ab_bc = np.abs(get_angle(ab, bc))\\n\\n x1, y1, z1 = a\\n x2, y2, z2 = b\\n x3, y3, z3 = c\\n\\n z = (z1 + z2)/2\\n\\n down = np.array([0., 0., z - z3])\\n\\n if ab_ac < ab_bc: # 3. point is bottom-left\\n back = np.array([ac[0], ac[1], 0])\\n else: # 3. point is bottom-right\\n back = np.array([bc[0], bc[1], 0])\\n\\n tfl = np.array([x1, y1, z])\\n tfr = np.array([x2, y2, z])\\n\\n tbl = tfl + back\\n tbr = tfr + back\\n\\n bfl = tfl - down\\n bfr = tfr - down\\n\\n bbl = bfl + back\\n bbr = bfr + back\\n\\n return np.array([\\n tfl, tfr,\\n tbl, tbr,\\n bfl, bfr,\\n bbl, bbr\\n ])\",\n \"def compute_bounding_box(homography, w, h):\\n top_left_x, top_left_y = apply_homography(np.array([[0, 0]]), homography).transpose()\\n top_right_x, top_right_y = apply_homography(np.array([[w, 0]]), homography).transpose()\\n bottem_left_x, bottem_left_y = apply_homography(np.array([[0, h]]), homography).transpose()\\n bottem_right_x, bottem_right_y = apply_homography(np.array([[w, h]]), homography).transpose()\\n\\n min_x = min([top_left_x, top_right_x, bottem_left_x, bottem_right_x])[0]\\n max_x = max([top_left_x, top_right_x, bottem_left_x, bottem_right_x])[0]\\n min_y = min([top_left_y, top_right_y, bottem_left_y, bottem_right_y])[0]\\n max_y = max([top_left_y, top_right_y, bottem_left_y, bottem_right_y])[0]\\n return np.array([[min_x, min_y], [max_x, max_y]]).astype(int)\",\n \"def intersection_with_anchors(bbox):\\n int_ymin = tf.maximum(ymin, bbox[0])\\n int_xmin = tf.maximum(xmin, bbox[1])\\n int_ymax = tf.minimum(ymax, bbox[2])\\n int_xmax = tf.minimum(xmax, bbox[3])\\n h = tf.maximum(int_ymax - int_ymin, 0.)\\n w = tf.maximum(int_xmax - int_xmin, 0.)\\n inter_vol = h * w\\n scores = tf.div(inter_vol, vol_anchors)\\n return scores\",\n \"def defineRectangleLayout(self):\\n\\n #--- Define a 2-D mesh ---\\n # Divide x- and y-axis\\n self.xPoints = self.frange(0,self.Lx,self.h)\\n self.yPoints = self.frange(0,self.Ly,self.h)\\n\\n # Position (xy-coordinate) of boundary points\\n boundary_xyCoord = [(0,j) for j in self.yPoints] + [(self.Lx,j) for j in self.yPoints] + [(i,0) for i in self.xPoints[1:len(self.xPoints)-1]] + [(i,self.Ly) for i in self.xPoints[1:len(self.xPoints)-1]] \\n\\n # Define the dictionary containing boundary points\\n for k in boundary_xyCoord:\\n x = k[0]\\n y = k[1]\\n xLabel = self.xPoints.index(x)\\n yLabel = self.yPoints.index(y)\\n self.boundaryPoints[(xLabel,yLabel)] = meshPoint(type = 'boundary',x = x, y = y, xLabel = xLabel, yLabel = yLabel) \\n \\n # Position (xy-coordinate) of internal mesh points\\n internal_xyCoord = [(i,j) for i in self.xPoints[1:len(self.xPoints)-1] for j in self.yPoints[1:len(self.yPoints)-1]] \\n\\n # Define the dictionary containing internal points\\n for k in internal_xyCoord:\\n x = k[0]\\n y = k[1]\\n xLabel = self.xPoints.index(x)\\n yLabel = self.yPoints.index(y)\\n self.internalPoints[(xLabel,yLabel)] = meshPoint(type = 'internal',x = x, y = y, xLabel = xLabel, yLabel = yLabel) \\n\\n # Now that we have assigned the labels we can define fE, fW, fN and fS\\n self.fCalc()\",\n \"def draw_lrtb_rectangle_filled(left, right, top, bottom, color):\\n if left > right:\\n raise AttributeError(\\\"Left coordinate {} must be less than or equal \\\"\\n \\\"to the right coordinate {}.\\\".format(left, right))\\n\\n if bottom > top:\\n raise AttributeError(\\\"Bottom coordinate {} must be less than or equal \\\"\\n \\\"to the top coordinate {}\\\".format(bottom, top))\\n\\n center_x = (left + right) / 2\\n center_y = (top + bottom) / 2\\n width = right - left\\n height = top - bottom\\n draw_rectangle_filled(center_x, center_y, width, height, color)\",\n \"def boundingBox(self):\\n minx, miny, maxx, maxy = self.substrates.bounds\\n return pcbnew.BOX2I(\\n pcbnew.VECTOR2I(int(minx), int(miny)),\\n pcbnew.VECTOR2I(int(maxx - minx), int(maxy - miny)))\",\n \"def boundary(self):\\n answer = self.zero()\\n for k, v in self.items():\\n for idx, cube in enumerate(k):\\n acc_dim = sum((cube_l.dimension for cube_l in k[:idx]))\\n for i in range(cube.dimension):\\n for epsilon in (0, 1):\\n new_cube = cube.face(i, epsilon)\\n new_k = k[:idx] + (new_cube,) + k[idx + 1:]\\n sign_exp = (acc_dim + i + epsilon) % 2\\n answer += answer.create({new_k: v * (-1)**sign_exp})\\n return answer\",\n \"def GetBounds(self, vtkAMRBox, , , p_float=..., p_float=..., p_float=..., p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def get_bounding_box(self):\\n if len(self.elements) == 0:\\n return None\\n if not (self._bb_valid and\\n all(ref._bb_valid for ref in self.get_dependencies(True))):\\n bb = numpy.array(((1e300, 1e300), (-1e300, -1e300)))\\n all_polygons = []\\n for element in self.elements:\\n if isinstance(element, PolygonSet):\\n all_polygons.extend(element.polygons)\\n elif isinstance(element, CellReference) or isinstance(\\n element, CellArray):\\n element_bb = element.get_bounding_box()\\n if element_bb is not None:\\n bb[0, 0] = min(bb[0, 0], element_bb[0, 0])\\n bb[0, 1] = min(bb[0, 1], element_bb[0, 1])\\n bb[1, 0] = max(bb[1, 0], element_bb[1, 0])\\n bb[1, 1] = max(bb[1, 1], element_bb[1, 1])\\n if len(all_polygons) > 0:\\n all_points = numpy.concatenate(all_polygons).transpose()\\n bb[0, 0] = min(bb[0, 0], all_points[0].min())\\n bb[0, 1] = min(bb[0, 1], all_points[1].min())\\n bb[1, 0] = max(bb[1, 0], all_points[0].max())\\n bb[1, 1] = max(bb[1, 1], all_points[1].max())\\n self._bb_valid = True\\n _bounding_boxes[self] = bb\\n return _bounding_boxes[self]\",\n \"def _create_main_shape(self):\\n\\n a, b = gc( self.size/2,\\n self._ZERO_DEGREES - self.angle,\\n self._180_DEGREES + self.angle)\\n self.wafer_points = zip(a,b)\\n self.wafer_polygon = gdspy.Polygon(self.wafer_points, self.WAFER_LAYER)\\n self.cell.add(self.wafer_polygon)\",\n \"def get_final_bounding_box(boxes, nms_idx, width: int, height: int):\\n x1 = np.inf\\n y1 = np.inf\\n x2 = -np.inf\\n y2 = -np.inf\\n\\n bx = [boxes[i] for i in nms_idx]\\n for box in bx:\\n xmin = np.min(box[[0, 2]])\\n xmax = np.max(box[[0, 2]])\\n ymin = np.min(box[[1, 3]])\\n ymax = np.max(box[[1, 3]])\\n\\n x1 = np.min([xmin, x1])\\n y1 = np.min([ymin, y1])\\n x2 = np.max([xmax, x2])\\n y2 = np.max([ymax, y2])\\n return x1, y1, x2, y2\",\n \"def _hold_bounds(self):\\n adc_channel = self.graph_renderer.channels[0]\\n if self.sx2 > adc_channel.size():\\n self.anchored = True\\n\\n if self.anchored:\\n # anchor right side of the window to the last graph sample. so the graph always animates, grows out from\\n # the right side of the window. (anchor sx2 to adc_channel.size())\\n dx = self.sx2 - adc_channel.size()\\n dxw = self.wsx2 - adc_channel.size()\\n self.sx1 -= dx\\n self.sx2 -= dx\\n self.wsx1 -= dxw\\n self.wsx2 -= dxw\\n\\n # eliminate integer overflow problems. only allow indices smaller than a 32bit integer value. and then divide\\n # it by four just to be sure.. maybe it's not necessary, but maybe there are some other tricks used in the\\n # graph rendering..\\n bound = 0xffffffff / 4\\n # hmm. this allows only 12 days of data with ~960Hz. time to go 64bit?\\n self.sx1 = max(self.sx1, -bound)\\n self.sy1 = max(self.sy1, -bound)\\n self.sx1 = min(self.sx1, bound)\\n self.sy1 = min(self.sy1, bound)\\n self.sx2 = max(self.sx2, -bound)\\n self.sy2 = max(self.sy2, -bound)\\n self.sx2 = min(self.sx2, bound)\\n self.sy2 = min(self.sy2, bound)\\n self.wsx1 = max(self.wsx1, -bound)\\n self.wsy1 = max(self.wsy1, -bound)\\n self.wsx1 = min(self.wsx1, bound)\\n self.wsy1 = min(self.wsy1, bound)\\n self.wsx2 = max(self.wsx2, -bound)\\n self.wsy2 = max(self.wsy2, -bound)\\n self.wsx2 = min(self.wsx2, bound)\\n self.wsy2 = min(self.wsy2, bound)\\n\\n # limit horizontal zoom to 2 samples. can't zoom in anymore if less than one sample stays on screen.\\n # don't have time to implement and test line segment cutting, if one sample is outside the window, and another\\n # is inside.\\n if self.wsx2 - self.wsx1 < 2.:\\n self.wsx2 = self.wsx1 + 2.\\n if self.sx2 - self.sx1 < 2.:\\n self.sx2 = self.sx1 + 2.\\n\\n #\\n # limit vertical movement and vertical zoom\\n #\\n\\n val_min = adc_channel.value_min\\n val_max = adc_channel.value_max\\n\\n # allow offset of this percent/100 of the screen\\n overlap = .30\\n\\n # top of the screen has smaller sample values than bottom of the screen. inverted graph.\\n # sy1 is top pixel, sy2 bottom. bottom-left coordinat is (0, 0)\\n if self.sy1 < self.sy2:\\n val_top = val_min + (self.wsy1 - self.wsy2) * overlap\\n val_bottom = val_max - (self.wsy1 - self.wsy2) * overlap\\n if self.wsy1 < val_top:\\n self.wsy2 -= self.wsy1 - val_top\\n self.wsy1 = val_top\\n if self.wsy2 > val_bottom:\\n self.wsy1 += val_bottom - self.wsy2\\n self.wsy2 = val_bottom\\n if self.wsy1 < val_top:\\n self.wsy1 = val_top\\n if self.wsy2 > val_bottom:\\n self.wsy2 = val_bottom\\n else:\\n val_bottom = val_min - (self.wsy1 - self.wsy2) * overlap\\n val_top = val_max + (self.wsy1 - self.wsy2) * overlap\\n if self.wsy1 > val_top:\\n self.wsy2 -= self.wsy1 - val_top\\n self.wsy1 = val_top\\n if self.wsy2 < val_bottom:\\n self.wsy1 += val_bottom - self.wsy2\\n self.wsy2 = val_bottom\\n if self.wsy1 > val_top:\\n self.wsy1 = val_top\\n if self.wsy2 < val_bottom:\\n self.wsy2 = val_bottom\",\n \"def boundingRect(self):\\n return QRectF(-self.innerRectangleSize + self.edge_size,\\n -self.innerRectangleSize + self.edge_size,\\n (self.innerRectangleSize * 2) - (self.edge_size * 2),\\n (self.innerRectangleSize * 2) - (self.edge_size * 2)).normalized()\",\n \"def feet_of_ribbon(d):\\n a, b = shortest_two_sides(d)\\n return a+a+b+b + d.l * d.w * d.h\",\n \"def bounding_box(self):\\n latlon00 = self.ij_to_latlon(-1,-1)\\n latlon01 = self.ij_to_latlon(-1,self.domain_size[1]+1)\\n latlon11 = self.ij_to_latlon(self.domain_size[0]+1,self.domain_size[1]+1)\\n latlon10 = self.ij_to_latlon(self.domain_size[0]+1,-1)\\n return (latlon00,latlon01,latlon11,latlon10)\",\n \"def border_gate(self,\\n bottom_cutoff: float = 0.01,\\n top_cutoff: float = 0.99):\\n if self.y is None:\\n raise GateError('For a border filter gate a value for `y` must be given')\\n pos_pop = self.data.copy()\\n lt_x, lt_y = pos_pop[self.x].quantile(bottom_cutoff), pos_pop[self.y].quantile(bottom_cutoff)\\n tt_x, tt_y = pos_pop[self.x].quantile(top_cutoff), pos_pop[self.y].quantile(top_cutoff)\\n return self.rect_gate(x_min=lt_x, x_max=tt_x, y_min=lt_y, y_max=tt_y)\",\n \"def get_bounds():\\n return [0.00], [1.00]\",\n \"def box_IoU(b1, b2):\\n with tf.name_scope('BB1'):\\n \\\"\\\"\\\"Calculate 2 corners: {left bottom, right top} and area of this box\\\"\\\"\\\"\\n b1 = tf.expand_dims(b1, -2) # shape= (None, 13, 13, 3, 1, 4)\\n b1_xy = b1[..., :2] # x,y shape=(None, 13, 13, 3, 1, 2)\\n b1_wh = b1[..., 2:4] # w,h shape=(None, 13, 13, 3, 1, 2)\\n b1_wh_half = b1_wh / 2. # w/2, h/2 shape= (None, 13, 13, 3, 1, 2)\\n b1_mins = b1_xy - b1_wh_half # x,y: left bottom corner of BB\\n b1_maxes = b1_xy + b1_wh_half # x,y: right top corner of BB\\n b1_area = b1_wh[..., 0] * b1_wh[..., 1] # w1 * h1 (None, 13, 13, 3, 1)\\n\\n with tf.name_scope('BB2'):\\n \\\"\\\"\\\"Calculate 2 corners: {left bottom, right top} and area of this box\\\"\\\"\\\"\\n # b2 = tf.expand_dims(b2, -2) # shape= (None, 13, 13, 3, 1, 4)\\n b2 = tf.expand_dims(b2, 0) # shape= (1, None, 13, 13, 3, 4) # TODO 0?\\n b2_xy = b2[..., :2] # x,y shape=(None, 13, 13, 3, 1, 2)\\n b2_wh = b2[..., 2:4] # w,h shape=(None, 13, 13, 3, 1, 2)\\n b2_wh_half = b2_wh / 2. # w/2, h/2 shape=(None, 13, 13, 3, 1, 2)\\n b2_mins = b2_xy - b2_wh_half # x,y: left bottom corner of BB\\n b2_maxes = b2_xy + b2_wh_half # x,y: right top corner of BB\\n b2_area = b2_wh[..., 0] * b2_wh[..., 1] # w2 * h2\\n\\n with tf.name_scope('Intersection'):\\n \\\"\\\"\\\"Calculate 2 corners: {left bottom, right top} based on BB1, BB2 and area of this box\\\"\\\"\\\"\\n # intersect_mins = tf.maximum(b1_mins, b2_mins, name='left_bottom') # (None, 13, 13, 3, 1, 2)\\n intersect_mins = K.maximum(b1_mins, b2_mins) # (None, 13, 13, 3, 1, 2)\\n # intersect_maxes = tf.minimum(b1_maxes, b2_maxes, name='right_top') #\\n intersect_maxes = K.minimum(b1_maxes, b2_maxes)\\n # intersect_wh = tf.maximum(intersect_maxes - intersect_mins, 0.) # (None, 13, 13, 3, 1, 2), 2: w,h\\n intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)\\n intersect_area = intersect_wh[..., 0] * intersect_wh[..., 1] # intersection: wi * hi (None, 13, 13, 3, 1)\\n\\n IoU = tf.divide(intersect_area, (b1_area + b2_area - intersect_area), name='divise-IoU') # (None, 13, 13, 3, 1)\\n\\n return IoU\",\n \"def covering_box(boxes):\\n x_min = np.amin([b.x for b in boxes])\\n x_max = np.amax([b.x + b.width for b in boxes])\\n y_min = np.amin([b.y for b in boxes])\\n y_max = np.amax([b.y + b.height for b in boxes])\\n cover = Box(x_min, y_min, x_max - x_min, y_max - y_min)\\n return cover\",\n \"def apply_deviations(self, dev_x, dev_y, dev_w, dev_h):\\n\\n return BoundingBox.from_cx_cy_w_h(\\n (dev_x * self.width) + self.center_x,\\n (dev_y * self.height) + self.center_y,\\n math.exp(dev_w) * self.width,\\n math.exp(dev_h) * self.height\\n )\",\n \"def create_geofence(self):\\n\\t\\tring = ogr.Geometry(ogr.wkbLinearRing)\\n\\t\\tring.AddPoint(*self.north_coords)\\n\\t\\tring.AddPoint(*self.northeast_coords)\\n\\t\\tring.AddPoint(*self.east_coords)\\n\\t\\tring.AddPoint(*self.southeast_coords)\\n\\t\\tring.AddPoint(*self.south_coords)\\n\\t\\tring.AddPoint(*self.southwest_coords)\\n\\t\\tring.AddPoint(*self.west_coords)\\n\\t\\tring.AddPoint(*self.northwest_coords)\\n\\t\\tring.AddPoint(*self.north_coords)\\n\\t\\tself.polygon.AddGeometry(ring)\",\n \"def get_bounds(self):\\r\\n left, bottom, front = 10000, 10000, 10000\\r\\n right, top, back = -10000, -10000, -10000\\r\\n for b in self.buf:\\r\\n for v in b.vertices:\\r\\n if v[0] < left:\\r\\n left = v[0]\\r\\n if v[0] > right:\\r\\n right = v[0]\\r\\n if v[1] < bottom:\\r\\n bottom = v[1]\\r\\n if v[1] > top:\\r\\n top = v[1]\\r\\n if v[2] < front:\\r\\n front = v[2]\\r\\n if v[2] > back:\\r\\n back = v[2]\\r\\n\\r\\n return (left, bottom, front, right, top, back)\",\n \"def _find_bboxes_on_rect_edge(bboxes, left, bottom, right, top):\\n bboxes_left = _find_bboxes_in_rect(bboxes, left, bottom, left, top)\\n bboxes_right = _find_bboxes_in_rect(bboxes, right, bottom, right, top)\\n bboxes_top = _find_bboxes_in_rect(bboxes, left, top, right, top)\\n bboxes_bottom = _find_bboxes_in_rect(bboxes, left, bottom, right, bottom)\\n result = bboxes_left | bboxes_right | bboxes_top | bboxes_bottom\\n return result\",\n \"def bottomLeftCorner(self):\\n self._updateExtents()\\n return (self._mMinX,self._mMaxY)\",\n \"def bounds_gdf(gdf: gpd.GeoDataFrame) -> box:\\n if gdf.empty:\\n return Polygon()\\n gdf_bounds = gdf.total_bounds\\n gdf_bounds_box = box(*gdf_bounds.tolist())\\n return gdf_bounds_box\",\n \"def make_highlight_region(ax, limits, axis, **kwargs):\\n xmin, xmax = 0, 0\\n ymin, ymax = 0, 0\\n if axis == 'x':\\n xmin, xmax = limits\\n ymin, ymax = ax.get_ylim()\\n else:\\n ymin, ymax = limits\\n xmin, xmax = ax.get_xlim()\\n if not kwargs:\\n kwargs = dict(color='red', alpha=0.2)\\n patch = patches.Rectangle((xmin, ymin), xmax-xmin, ymax-ymin, **kwargs)\\n ax.add_patch(patch)\\n return patch\",\n \"def test_bounding_rectangle(self, world):\\n positions = [(0, 2), (2, 0), (3, 1), (2, 3)]\\n for pos in positions:\\n world.set_cell(pos)\\n assert world.min_pos() == (0, 0) and world.max_pos() == (3, 3)\",\n \"def db_eval_boundary(foreground_mask,gt_mask,bound_th=0.008):\\n assert np.atleast_3d(foreground_mask).shape[2] == 1\\n\\n bound_pix = bound_th if bound_th >= 1 else \\\\\\n np.ceil(bound_th*np.linalg.norm(foreground_mask.shape))\\n\\n # Get the pixel boundaries of both masks\\n fg_boundary = seg2bmap(foreground_mask);\\n gt_boundary = seg2bmap(gt_mask);\\n\\n from skimage.morphology import binary_dilation,disk\\n\\n fg_dil = binary_dilation(fg_boundary,disk(bound_pix))\\n gt_dil = binary_dilation(gt_boundary,disk(bound_pix))\\n\\n # Get the intersection\\n gt_match = gt_boundary * fg_dil\\n fg_match = fg_boundary * gt_dil\\n\\n # Area of the intersection\\n n_fg = np.sum(fg_boundary)\\n n_gt = np.sum(gt_boundary)\\n\\n #% Compute precision and recall\\n if n_fg == 0 and n_gt > 0:\\n precision = 1\\n recall = 0\\n elif n_fg > 0 and n_gt == 0:\\n precision = 0\\n recall = 1\\n elif n_fg == 0 and n_gt == 0:\\n precision = 1\\n recall = 1\\n else:\\n precision = np.sum(fg_match)/float(n_fg)\\n recall = np.sum(gt_match)/float(n_gt)\\n\\n # Compute F measure\\n if precision + recall == 0:\\n F = 0\\n else:\\n F = 2*precision*recall/(precision+recall);\\n\\n return F\",\n \"def fitRectangle(self):\\n \\n #TODO MAKE SOMETHING MORE GENERIC!!\\n \\n fA, (fXg, fYg) = self.getArea_and_CenterOfMass()\\n \\n x1,y1, x2,y2 = self.getBoundingBox()\\n #build a rectangle with same \\\"width\\\" as the polygon... is-it good enough??\\n w = x2 - x1\\n \\n #but this width should not lead to go out of the bounding box!\\n fW = min(w, (x2-fXg)*2, (fXg-x1)*2)\\n \\n #same area\\n fH = fA / fW\\n \\n x1,y1, x2,y2 = [ int(round(v)) for v in [ fXg - fW/2.0, fYg - fH/2\\n , fXg + fW/2.0, fYg + fH/2 ]]\\n \\n return x1,y1, x2,y2\",\n \"def __clip_bbox(min_y, min_x, max_y, max_x):\\n min_y = tf.clip_by_value(min_y, 0.0, 1.0)\\n min_x = tf.clip_by_value(min_x, 0.0, 1.0)\\n max_y = tf.clip_by_value(max_y, 0.0, 1.0)\\n max_x = tf.clip_by_value(max_x, 0.0, 1.0)\\n return min_y, min_x, max_y, max_x\",\n \"def bounding_box(alpha):\\n assert alpha.ndim == 2\\n\\n # Take the bounding box of the support, with a certain threshold.\\n #print(\\\"Using alpha\\\", self.use_alpha, \\\"support\\\", self.support)\\n supp_axs = [alpha.max(axis=1-i) for i in range(2)]\\n\\n th = 0.5 \\n # Check first and last value of that threshold\\n bb = [np.where(supp_axs[i] > th)[0][[0,-1]] for i in range(2)]\\n\\n # This bb looks like [(x0, x1), (y0, y1)], when we want it as (x0, y0, x1, y1)\\n #psize = self.settings['subsample_size']\\n #ret = (bb[0][0]/psize[0], bb[1][0]/psize[1], bb[0][1]/psize[0], bb[1][1]/psize[1])\\n\\n return (bb[0][0], bb[1][0], bb[0][1], bb[1][1])\",\n \"def get_raw_bounds(self) -> [Vector, Vector]:\\n\\t\\tverts = np.array([v.co for mesh in self._meshes for v in mesh.data.vertices])\\n\\t\\tbbox_min = Vector([*np.min(verts, axis=0)])\\n\\t\\tbbox_max = Vector([*np.max(verts, axis=0)])\\n\\t\\treturn bbox_min, bbox_max\",\n \"def draw_bounding_box(objects,color):\\n\\n for i in range(len(objects)):\\n x, y, w, h, d = objects[i].get_attributes()\\n print(x, y, w, h, d)\\n corr = get_correction(d, a, hfov, x)\\n cv2.rectangle(color, (x-corr, y), (x+w-corr, y+h), (0, 255, 0), 4)\\n\\n try:\\n real_x, real_y = get_dimensions(d, w, h, hfov, vfov, 640, 480)\\n real_x = round(real_x, 3)\\n real_y = round(real_y, 3)\\n\\n except:\\n real_x, real_y = 'ERROR'\\n\\n cv2.putText(color, 'depth = ' + str(d) + 'm', (30, i*60 + 30) ,\\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)\\n cv2.putText(color, 'width = ' + str(real_x)+ 'm', (30, i*60 + 45) ,\\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)\\n cv2.putText(color, 'height = ' + str(real_y)+ 'm', (30, i*60 + 60) ,\\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)\\n\\n if(i < len(objects)-1):\\n ## distance between left and right object\\n distance = round(distance_between_objects(objects[i], objects[i+1], hfov, 640), 3)\\n if distance > l:\\n textcolor = (0, 255, 0)\\n else:\\n textcolor = (0, 0, 255)\\n\\n cv2.putText(color, 'distance between objects = ' + str(distance) + 'm',\\n (320, i*60 + 70) , cv2.FONT_HERSHEY_SIMPLEX, 0.5, textcolor, 1)\",\n \"def bbox(\\n bbox: Tuple[Coordinate, Coordinate] = ((-1.0, -1.0), (3.0, 4.0)),\\n layer: Tuple[int, int] = (1, 0),\\n top: float = 0,\\n bottom: float = 0,\\n left: float = 0,\\n right: float = 0,\\n) -> gf.Component:\\n D = gf.Component()\\n (xmin, ymin), (xmax, ymax) = bbox\\n points = [\\n [xmin - left, ymin - bottom],\\n [xmax + right, ymin - bottom],\\n [xmax + right, ymax + top],\\n [xmin - left, ymax + top],\\n ]\\n D.add_polygon(points, layer=layer)\\n return D\",\n \"def make_prism(\\n self,\\n p1: Vec,\\n p2: Vec,\\n mat: str = 'tools/toolsnodraw',\\n ) -> PrismFace:\\n b_min = Vec(p1)\\n b_max = Vec(p1)\\n b_min.min(p2)\\n b_max.max(p2)\\n\\n # Sanity check - all dimensions must be different, otherwise we'll\\n # have an invalid zero-thick brush.\\n if b_min.x == b_max.x or b_min.y == b_max.y or b_min.z == b_max.z:\\n raise ValueError(f\\\"Zero volume brush requested! ({b_min}, {b_max})\\\")\\n\\n f_bottom = Side(\\n self,\\n [ # -z side\\n Vec(b_min.x, b_min.y, b_min.z),\\n Vec(b_max.x, b_min.y, b_min.z),\\n Vec(b_max.x, b_max.y, b_min.z),\\n ],\\n mat=mat,\\n uaxis=UVAxis(1, 0, 0),\\n vaxis=UVAxis(0, -1, 0),\\n )\\n\\n f_top = Side(\\n self,\\n [ # +z side\\n Vec(b_min.x, b_max.y, b_max.z),\\n Vec(b_max.x, b_max.y, b_max.z),\\n Vec(b_max.x, b_min.y, b_max.z),\\n ],\\n mat=mat,\\n uaxis=UVAxis(1, 0, 0),\\n vaxis=UVAxis(0, -1, 0),\\n )\\n\\n f_west = Side(\\n self,\\n [ # -x side\\n Vec(b_min.x, b_max.y, b_max.z),\\n Vec(b_min.x, b_min.y, b_max.z),\\n Vec(b_min.x, b_min.y, b_min.z),\\n ],\\n mat=mat,\\n uaxis=UVAxis(0, 1, 0),\\n vaxis=UVAxis(0, 0, -1),\\n )\\n\\n f_east = Side(\\n self,\\n planes=[ # +x side\\n Vec(b_max.x, b_max.y, b_min.z),\\n Vec(b_max.x, b_min.y, b_min.z),\\n Vec(b_max.x, b_min.y, b_max.z),\\n ],\\n mat=mat,\\n uaxis=UVAxis(0, 1, 0),\\n vaxis=UVAxis(0, 0, -1),\\n )\\n\\n f_south = Side(\\n self,\\n [ # -y side\\n Vec(b_max.x, b_min.y, b_min.z),\\n Vec(b_min.x, b_min.y, b_min.z),\\n Vec(b_min.x, b_min.y, b_max.z),\\n ],\\n mat=mat,\\n uaxis=UVAxis(1, 0, 0),\\n vaxis=UVAxis(0, 0, -1),\\n )\\n\\n f_north = Side(\\n self,\\n [ # +y side\\n Vec(b_min.x, b_max.y, b_min.z),\\n Vec(b_max.x, b_max.y, b_min.z),\\n Vec(b_max.x, b_max.y, b_max.z),\\n ],\\n mat=mat,\\n uaxis=UVAxis(1, 0, 0),\\n vaxis=UVAxis(0, 0, -1),\\n )\\n\\n solid = Solid(\\n self,\\n sides=[\\n f_bottom,\\n f_top,\\n f_north,\\n f_south,\\n f_east,\\n f_west,\\n ],\\n )\\n return PrismFace(\\n solid=solid,\\n top=f_top,\\n bottom=f_bottom,\\n north=f_north,\\n south=f_south,\\n east=f_east,\\n west=f_west,\\n )\",\n \"def fill_bounds(x, upper, lower, axis, c = 'k', alpha = 0.2):\\n axis.fill_between(\\n x, \\n upper[:, 0], \\n lower[:, 0], \\n facecolor = c,\\n alpha = alpha\\n )\",\n \"def _initialise_aabb(self):\\n # calculate the bounding box for all tetraherdon in the mesh\\n # find the min/max extents for xyz\\n tetra_bb = np.zeros((self.elements.shape[0], 19, 3))\\n minx = np.min(self.nodes[self.elements[:, :4], 0], axis=1)\\n maxx = np.max(self.nodes[self.elements[:, :4], 0], axis=1)\\n miny = np.min(self.nodes[self.elements[:, :4], 1], axis=1)\\n maxy = np.max(self.nodes[self.elements[:, :4], 1], axis=1)\\n minz = np.min(self.nodes[self.elements[:, :4], 2], axis=1)\\n maxz = np.max(self.nodes[self.elements[:, :4], 2], axis=1)\\n cell_indexes = self.aabb_grid.global_index_to_cell_index(\\n np.arange(self.aabb_grid.n_elements)\\n )\\n corners = self.aabb_grid.cell_corner_indexes(cell_indexes)\\n positions = self.aabb_grid.node_indexes_to_position(corners)\\n ## Because we known the node orders just select min/max from each\\n # coordinate. Use these to check whether the tetra is in the cell\\n x_boundary = positions[:, [0, 1], 0]\\n y_boundary = positions[:, [0, 2], 1]\\n z_boundary = positions[:, [0, 6], 2]\\n a = np.logical_and(\\n minx[None, :] > x_boundary[:, None, 0],\\n minx[None, :] < x_boundary[:, None, 1],\\n ) # min point between cell\\n b = np.logical_and(\\n maxx[None, :] < x_boundary[:, None, 1],\\n maxx[None, :] > x_boundary[:, None, 0],\\n ) # max point between cell\\n c = np.logical_and(\\n minx[None, :] < x_boundary[:, None, 0],\\n maxx[None, :] > x_boundary[:, None, 0],\\n ) # min point < than cell & max point > cell\\n\\n x_logic = np.logical_or(np.logical_or(a, b), c)\\n\\n a = np.logical_and(\\n miny[None, :] > y_boundary[:, None, 0],\\n miny[None, :] < y_boundary[:, None, 1],\\n ) # min point between cell\\n b = np.logical_and(\\n maxy[None, :] < y_boundary[:, None, 1],\\n maxy[None, :] > y_boundary[:, None, 0],\\n ) # max point between cell\\n c = np.logical_and(\\n miny[None, :] < y_boundary[:, None, 0],\\n maxy[None, :] > y_boundary[:, None, 0],\\n ) # min point < than cell & max point > cell\\n\\n y_logic = np.logical_or(np.logical_or(a, b), c)\\n\\n a = np.logical_and(\\n minz[None, :] > z_boundary[:, None, 0],\\n minz[None, :] < z_boundary[:, None, 1],\\n ) # min point between cell\\n b = np.logical_and(\\n maxz[None, :] < z_boundary[:, None, 1],\\n maxz[None, :] > z_boundary[:, None, 0],\\n ) # max point between cell\\n c = np.logical_and(\\n minz[None, :] < z_boundary[:, None, 0],\\n maxz[None, :] > z_boundary[:, None, 0],\\n ) # min point < than cell & max point > cell\\n\\n z_logic = np.logical_or(np.logical_or(a, b), c)\\n logic = np.logical_and(x_logic, y_logic)\\n logic = np.logical_and(logic, z_logic)\\n\\n self.aabb_table = csr_matrix(logic)\",\n \"def DrawGradientRectangle(dc, rect, start_colour, end_colour, direction, offset=0, length=0):\\r\\n \\r\\n if direction == AUI_GRADIENT_VERTICAL:\\r\\n dc.GradientFillLinear(rect, start_colour, end_colour, wx.SOUTH)\\r\\n else:\\r\\n dc.GradientFillLinear(rect, start_colour, end_colour, wx.EAST)\",\n \"def _merge_boundaries(self):\\n optical = self._amalgamated_optical\\n if bool(optical):\\n optical[\\\"catagory\\\"] = OPTICAL * tf.ones_like(\\n optical[\\\"xp\\\"],\\n dtype=tf.int64\\n )\\n self._optical_count = tf.shape(\\n optical[\\\"xp\\\"],\\n out_type=tf.int64\\n )[0]\\n else:\\n self._optical_count = 0\\n \\n stop = self._amalgamated_stop\\n if bool(stop):\\n stop[\\\"catagory\\\"] = STOP * tf.ones_like(\\n stop[\\\"xp\\\"],\\n dtype=tf.int64\\n )\\n self._stop_count = tf.shape(\\n stop[\\\"xp\\\"],\\n out_type=tf.int64\\n )[0]\\n else:\\n self._stop_count = 0\\n \\n target = self._amalgamated_target\\n if bool(target):\\n target[\\\"catagory\\\"] = TARGET * tf.ones_like(\\n target[\\\"xp\\\"],\\n dtype=tf.int64\\n )\\n self._target_count = tf.shape(\\n target[\\\"xp\\\"],\\n out_type=tf.int64\\n )[0]\\n else:\\n self._target_count = 0\\n \\n self._merged = amalgamate(\\n [optical, stop, target], \\n TRIANGLE_GEO_SIG | {\\\"catagory\\\"}\\n )\",\n \"def test_benzene(self):\\n def draw(image: ShapeImage):\\n image.add_regular_hexagon(100, start_coord=(400, 400))\\n image.add_line((415, 405), (487, 364))\\n image.add_line((415, 495), (487, 536))\\n image.add_line((559, 409), (559, 491))\\n\\n self._test_shape(\\n image_size=(1000, 1000),\\n expected_corners=np.array([\\n [[400, 400]],\\n [[487, 350]],\\n [[574, 400]],\\n [[574, 500]],\\n [[487, 550]],\\n [[400, 500]]\\n ]),\\n drawer=draw,\\n expected_edges=np.array([\\n [[400, 400, 487, 350]],\\n [[487, 350, 574, 400]],\\n [[574, 400, 574, 500]],\\n [[574, 500, 487, 550]],\\n [[487, 550, 400, 500]],\\n [[400, 500, 400, 400]],\\n # \\\"Double\\\" bonds\\n [[415, 405, 487, 364]],\\n [[415, 495, 487, 536]],\\n [[559, 409, 559, 491]]\\n ]),\\n vertex_atol=15\\n )\",\n \"def bounding_box(safe, priority='reference', polarization='hh', orbits='Orbits', epsg=4326):\\n frames = []\\n for swath in range(1, 4):\\n rdr = Sentinel1()\\n rdr.configure()\\n rdr.safe = [os.path.abspath(safe)]\\n rdr.output = priority\\n rdr.orbitDir = os.path.abspath(orbits)\\n rdr.auxDir = os.path.abspath(orbits)\\n rdr.swathNumber = swath\\n rdr.polarization = polarization\\n rdr.parse()\\n frames.append(rdr.product)\\n\\n first_burst = frames[0].bursts[0]\\n sensing_start = min([x.sensingStart for x in frames])\\n sensing_stop = max([x.sensingStop for x in frames])\\n starting_range = min([x.startingRange for x in frames])\\n far_range = max([x.farRange for x in frames])\\n range_pixel_size = first_burst.rangePixelSize\\n prf = 1.0 / first_burst.azimuthTimeInterval\\n\\n orb = Orbit()\\n orb.configure()\\n\\n for state_vector in first_burst.orbit:\\n orb.addStateVector(state_vector)\\n\\n for frame in frames:\\n for burst in frame.bursts:\\n for state_vector in burst.orbit:\\n if state_vector.time < orb.minTime or state_vector.time > orb.maxTime:\\n orb.addStateVector(state_vector)\\n\\n obj = Geogrid()\\n obj.configure()\\n\\n obj.startingRange = starting_range\\n obj.rangePixelSize = range_pixel_size\\n obj.sensingStart = sensing_start\\n obj.prf = prf\\n obj.lookSide = -1\\n obj.numberOfLines = int(np.round((sensing_stop - sensing_start).total_seconds() * prf))\\n obj.numberOfSamples = int(np.round((far_range - starting_range)/range_pixel_size))\\n obj.orbit = orb\\n obj.epsg = epsg\\n\\n obj.determineBbox()\\n\\n lat_limits = obj._xlim\\n lon_limits = obj._ylim\\n\\n log.info(f'Latitude limits [min, max]: {lat_limits}')\\n log.info(f'Longitude limits [min, max]: {lon_limits}')\\n\\n return lat_limits, lon_limits\",\n \"def __check_bbox_area(min_y, min_x, max_y, max_x, delta=0.05):\\n height = max_y - min_y\\n width = max_x - min_x\\n\\n def __adjust_bbox_boundaries(min_coord, max_coord):\\n # Make sure max is never 0 and min is never 1.\\n max_coord = tf.maximum(max_coord, 0.0 + delta)\\n min_coord = tf.minimum(min_coord, 1.0 - delta)\\n return min_coord, max_coord\\n\\n min_y, max_y = tf.cond(tf.equal(height, 0.0),\\n lambda: __adjust_bbox_boundaries(min_y, max_y),\\n lambda: (min_y, max_y))\\n min_x, max_x = tf.cond(tf.equal(width, 0.0),\\n lambda: __adjust_bbox_boundaries(min_x, max_x),\\n lambda: (min_x, max_x))\\n return min_y, min_x, max_y, max_x\",\n \"def lineBoxIntersection(w1, w2, b, xmin, ymin, xmax, ymax):\\n \\n point1 = None\\n point2 = None\\n if w2 == 0:\\n x1a = -(w2*ymin + b)*1.0/w1\\n x1b = -(w2*ymax + b)*1.0/w1\\n \\n point1 = (x1a, ymin)\\n point2 = (x1b, ymax)\\n else:\\n x2a = -(w1*xmin + b)*1.0/w2\\n x2b = -(w1*xmax + b)*1.0/w2\\n \\n if w1 == 0:\\n point1 = (xmin, x2a)\\n point2 = (xmax, x2b)\\n else:\\n\\n x1a = -(w2*ymin + b)*1.0/w1\\n x1b = -(w2*ymax + b)*1.0/w1\\n # Point 1\\n if x2a < ymin:\\n if xmin <= x1a and x1a <= xmax:\\n # Point 1 on bottom edge\\n point1 = (x1a, ymin)\\n elif x2a > ymax:\\n if xmin <= x1b and x1b <= xmax:\\n # Point 1 on top edge\\n point1 = (x1b, ymax)\\n else:\\n # Point 1 on left edge\\n point1 = (xmin, x2a)\\n \\n # Point 2\\n if point1 is not None:\\n if x2b < ymin:\\n # Point 2 on bottom edge\\n point2 = (x1a, ymin)\\n elif x2b > ymax:\\n # Point 2 on top edge\\n point2 = (x1b, ymax)\\n else:\\n # Point 2 on right edge\\n point2 = (xmax, x2b) \\n return (point1, point2)\",\n \"def area_of(self, left_top, right_bottom):\\n hw = np.clip(right_bottom - left_top, 0.0, None)\\n return hw[..., 0] * hw[..., 1]\",\n \"def rectangle_surface_area(a,b):\\n return (a*b)\",\n \"def bounding_box(self, integral=False):\\n box_min = []\\n box_max = []\\n if self.n_vertices==0:\\n raise ValueError('Empty polytope is not allowed')\\n for i in range(0,self.ambient_dim()):\\n coords = [ v[i] for v in self.Vrep_generator() ]\\n max_coord = max(coords)\\n min_coord = min(coords)\\n if integral:\\n box_max.append(ceil(max_coord))\\n box_min.append(floor(min_coord))\\n else:\\n box_max.append(max_coord)\\n box_min.append(min_coord)\\n return (tuple(box_min), tuple(box_max))\",\n \"def test_bounding_box_intersection(self):\\n\\n west_java = [105, -7, 108, -5]\\n jakarta = [106.5, -6.5, 107, -6]\\n\\n # Test commutative law\\n assert numpy.allclose(bbox_intersection(west_java, jakarta),\\n bbox_intersection(jakarta, west_java))\\n\\n # Test inclusion\\n assert numpy.allclose(bbox_intersection(west_java, jakarta), jakarta)\\n\\n # Ignore Bounding Boxes that are None\\n assert numpy.allclose(bbox_intersection(west_java, jakarta, None),\\n jakarta)\\n\\n # Realistic ones\\n bbox1 = [94.972335, -11.009721, 141.014, 6.073612333333]\\n bbox2 = [105.3, -8.5, 110.0, -5.5]\\n bbox3 = [105.6, -7.8, 110.5, -5.1]\\n\\n ref1 = [max(bbox1[0], bbox2[0]),\\n max(bbox1[1], bbox2[1]),\\n min(bbox1[2], bbox2[2]),\\n min(bbox1[3], bbox2[3])]\\n assert numpy.allclose(bbox_intersection(bbox1, bbox2), ref1)\\n assert numpy.allclose(bbox_intersection(bbox1, bbox2), bbox2)\\n\\n ref2 = [max(bbox3[0], bbox2[0]),\\n max(bbox3[1], bbox2[1]),\\n min(bbox3[2], bbox2[2]),\\n min(bbox3[3], bbox2[3])]\\n assert numpy.allclose(bbox_intersection(bbox3, bbox2), ref2)\\n assert numpy.allclose(bbox_intersection(bbox2, bbox3), ref2)\\n\\n # Multiple boxes\\n assert numpy.allclose(bbox_intersection(bbox1, bbox2, bbox3),\\n bbox_intersection(ref1, ref2))\\n\\n assert numpy.allclose(bbox_intersection(bbox1, bbox2, bbox3,\\n west_java, jakarta),\\n jakarta)\\n\\n # From actual example\\n b1 = [94.972335000000001, -11.009721000000001,\\n 141.014002, 6.0736119999999998]\\n b2 = (95.059660952000002, -10.997409961000001,\\n 141.00132578099999, 5.9109226959999983)\\n b3 = (94.972335000000001, -11.009721000000001,\\n 141.0140016666665, 6.0736123333332639)\\n\\n res = bbox_intersection(b1, b2, b3)\\n assert numpy.allclose(res, [95.059660952, -10.997409961,\\n 141.001325781, 5.910922695999998],\\n rtol=1.0e-12, atol=1.0e-12)\\n\\n # Empty intersection should return None\\n assert bbox_intersection(bbox2, [50, 2, 53, 4]) is None\\n\\n # Deal with invalid boxes\\n try:\\n bbox_intersection(bbox1, [53, 2, 40, 4])\\n except BoundingBoxError:\\n pass\\n else:\\n msg = 'Should have raised exception'\\n raise Exception(msg)\\n\\n try:\\n bbox_intersection(bbox1, [50, 7, 53, 4])\\n except BoundingBoxError:\\n pass\\n else:\\n msg = 'Should have raised exception'\\n raise Exception(msg)\\n\\n try:\\n bbox_intersection(bbox1, 'blko ho skrle')\\n except BoundingBoxError:\\n pass\\n else:\\n msg = 'Should have raised exception'\\n raise Exception(msg)\\n\\n try:\\n bbox_intersection(bbox1)\\n except VerificationError:\\n pass\\n else:\\n msg = 'Should have raised exception'\\n raise Exception(msg)\\n\\n try:\\n bbox_intersection('')\\n except VerificationError:\\n pass\\n else:\\n msg = 'Should have raised exception'\\n raise Exception(msg)\\n\\n try:\\n bbox_intersection()\\n except VerificationError:\\n pass\\n else:\\n msg = 'Should have raised exception'\\n raise Exception(msg)\",\n \"def draw_border_gradient(img, center_color=(0, 0, 0, 255), \\\\\\nouter_color=(255, 255, 255, 255), excluding_region=None):\\n center = (img.size[0] / 2, img.size[1] / 2)\\n # Used to calculate scalar\\n max_distance = math.sqrt(center[0] ** 2 + center[1] ** 2)\\n for x in range(img.size[0]):\\n for y in range(img.size[1]):\\n if excluding_region != None:\\n if not (x < excluding_region[0] or x >= excluding_region[1] or \\\\\\n y < excluding_region[2] or y >= excluding_region[3]):\\n continue\\n \\n # This gets the delta between the current point and the center\\n distance = math.sqrt((center[0] - x) ** 2 + (center[1] - y) ** 2)\\n # We turn this into a scale from 0 to 1, with 1 meaning being on the\\n # very outside, and 0 being on the center\\n distance_scalar = float(distance) / max_distance\\n \\n r = (1 - distance_scalar) * center_color[0] + distance_scalar * \\\\\\n outer_color[0]\\n g = (1 - distance_scalar) * center_color[1] + distance_scalar * \\\\\\n outer_color[1]\\n b = (1 - distance_scalar) * center_color[2] + distance_scalar * \\\\\\n outer_color[2]\\n \\n img.putpixel((x, y), (int(r), int(g), int(b), 255))\",\n \"def rectangle(xcenter, ycenter, width, height):\\n x1, x2 = xcenter - width, xcenter + width\\n y1, y2 = ycenter - height, ycenter + height\\n return Shape([Point(x1, y1), Point(x1, y2), Point(x2, y2), Point(x2, y1)])\",\n \"def draw_lrtb_rectangle_outline(left, right, top, bottom, color,\\n border_width=1):\\n if left > right:\\n raise AttributeError(\\\"Left coordinate must be less than or equal to \\\"\\n \\\"the right coordinate\\\")\\n\\n if bottom > right:\\n raise AttributeError(\\\"Bottom coordinate must be less than or equal to \\\"\\n \\\"the top coordinate\\\")\\n\\n center_x = (left + right) / 2\\n center_y = (top + bottom) / 2\\n width = right - left\\n height = top - bottom\\n draw_rectangle_outline(center_x, center_y, width, height, color,\\n border_width)\",\n \"def build_background():\\n layer_1 = GRect(800, 550)\\n layer_1.filled = True\\n layer_1.color = 'silver'\\n layer_1.fill_color = 'silver'\\n window.add(layer_1)\\n layer_2 = GRect(800, 90)\\n layer_2.filled = True\\n layer_2.color = 'whitesmoke'\\n layer_2.fill_color = 'whitesmoke'\\n window.add(layer_2)\\n layer_3 = GRect(800, 40, x=0, y=510)\\n layer_3.filled = True\\n layer_3.color = 'whitesmoke'\\n layer_3.fill_color = 'whitesmoke'\\n window.add(layer_3)\",\n \"def compute_bounds(self, weights, add_coeff, dual_vars, lower_bounds, upper_bounds, store_primal=False):\\n x0_coeff = -weights[0].backward(dual_vars.mus[0])\\n x0 = torch.where(x0_coeff >= 0, lower_bounds[0].unsqueeze(1), upper_bounds[0].unsqueeze(1))\\n bound = utils.bdot(x0, x0_coeff)\\n if store_primal:\\n self.bounds_primal = x0\\n else:\\n del x0\\n del x0_coeff\\n\\n for lay_idx in range(1, len(weights)):\\n lbs = lower_bounds[lay_idx].unsqueeze(1).clamp(None, 0)\\n ubs = upper_bounds[lay_idx].unsqueeze(1).clamp(0, None)\\n neg_bias = ((lbs * ubs) / (ubs - lbs))\\n neg_bias.masked_fill_(ubs == lbs, 0) # cover case in which ubs & lbs coincide\\n bound += utils.bdot(dual_vars.lambdas[lay_idx - 1].clamp(0, None), neg_bias)\\n bound -= utils.bdot(dual_vars.mus[lay_idx - 1], weights[lay_idx - 1].get_bias())\\n\\n bound += utils.bdot(add_coeff, weights[-1].get_bias())\\n return bound\",\n \"def build_constraints_boundaries(self):\\n\\n # Trapezoidal and Hermite-Simpson methods can't compute\\n # defects at the last node contrary to pseudospectral methods\\n coll_method = self.options['tr_method'] in [\\n 'trapezoidal', 'hermite-simpson']\\n n_nodes = self.problem.prm['n_nodes'] - \\\\\\n 1 if coll_method else self.problem.prm['n_nodes']\\n\\n # Defects lower and upper boundaries\\n defects_low = np.zeros(\\n self.problem.prm['n_states'] * n_nodes)\\n defects_upp = np.zeros(\\n self.problem.prm['n_states'] * n_nodes)\\n\\n # Path lower and upper boundaries\\n path_low = np.hstack([self.problem.low_bnd.path]\\n * (self.problem.prm['n_nodes']))\\n path_upp = np.hstack([self.problem.upp_bnd.path]\\n * (self.problem.prm['n_nodes']))\\n\\n # Events lower and upper boundaries\\n event_low = self.problem.low_bnd.event\\n event_upp = self.problem.upp_bnd.event\\n\\n # Assembly of the lower and upper boundaries vectors\\n low = np.concatenate((defects_low, path_low, event_low))\\n upp = np.concatenate((defects_upp, path_upp, event_upp))\\n\\n return low, upp\",\n \"def box(self):\\n b = Bnd_Box()\\n brepbndlib_Add(self.topods_shape(), b)\\n return geom_utils.box_to_geometry(b)\",\n \"def draw_bounds():\\n\\n pass\",\n \"def get_bounds(self):\\n bottom_right = np.asarray([self.coords[k][0] for k in range(self.dim)])\\n upper_left = np.asarray([self.coords[k][-1] for k in range(self.dim)])\\n return bottom_right, upper_left\",\n \"def from_bounds(cls, bounds) -> \\\"Box\\\":\\n return cls(\\n x=bounds[\\\"left\\\"],\\n y=bounds[\\\"top\\\"],\\n width=bounds[\\\"right\\\"] - bounds[\\\"left\\\"],\\n height=bounds[\\\"bottom\\\"] - bounds[\\\"top\\\"],\\n )\",\n \"def point_label_bounds(x, y, width, height, radius, placement):\\n if placement in (NE, ENE, ESE, SE):\\n # to the right\\n x += radius + width/2\\n \\n if placement in (NW, WNW, WSW, SW):\\n # to the left\\n x -= radius + width/2\\n\\n if placement in (NW, NE):\\n # way up high\\n y -= height/2\\n\\n if placement in (SW, SE):\\n # way down low\\n y += height/2\\n\\n if placement in (ENE, WNW):\\n # just a little above\\n y -= height/6\\n\\n if placement in (ESE, WSW):\\n # just a little below\\n y += height/6\\n \\n if placement in (NNE, SSE, SSW, NNW):\\n _x = radius * cos(pi/4) + width/2\\n _y = radius * sin(pi/4) + height/2\\n \\n if placement in (NNE, SSE):\\n x += _x\\n else:\\n x -= _x\\n \\n if placement in (SSE, SSW):\\n y += _y\\n else:\\n y -= _y\\n \\n if placement == N:\\n # right on top\\n y -= radius + height / 2\\n \\n if placement == S:\\n # right on the bottom\\n y += radius + height / 2\\n \\n x1, y1 = x - width/2, y - height/2\\n x2, y2 = x + width/2, y + height/2\\n \\n return Polygon(((x1, y1), (x1, y2), (x2, y2), (x2, y1), (x1, y1)))\",\n \"def boundingRect(self):\\n if not self.source or not self.destination:\\n return QRectF()\\n pen_width = 1.0\\n extra = (pen_width + self.arrow_size) / 2.0\\n\\n return QRectF(\\n self.source_point, QSizeF(\\n self.destination_point.x() - self.source_point.x(),\\n self.destination_point.y() - self.source_point.y()\\n )\\n ).normalized().adjusted(\\n -extra,\\n -extra,\\n extra,\\n extra\\n )\",\n \"def checkBoundingBox(ontology_bounding_box):\\n heigth = ontology_bounding_box.has_heigth[0]\\n width = ontology_bounding_box.has_width[0]\\n length = ontology_bounding_box.has_length[0]\\n x = ontology_bounding_box.has_x_center[0]\\n y = ontology_bounding_box.has_y_center[0]\\n z = ontology_bounding_box.has_z_center[0]\\n return xosc.BoundingBox(width,length,heigth,x,y,z)\",\n \"def random_offset_bounds(self) -> utils.BoxRegion:\\n extra_size = self.random_canvas_extra_ratio * self.canvas_bounds().size / 2\\n return utils.BoxRegion(\\n minimum=-extra_size,\\n maximum=extra_size\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5981826","0.58952916","0.5884403","0.5581767","0.55763274","0.5549178","0.5539936","0.5526793","0.5501055","0.54238534","0.541401","0.54067093","0.5399093","0.53788334","0.5367486","0.5344063","0.53401744","0.5332257","0.529883","0.52931905","0.5287063","0.528249","0.5281925","0.5276451","0.52738297","0.52668226","0.5266274","0.52655905","0.5263041","0.5255363","0.5246741","0.5236333","0.5214482","0.52144736","0.52107096","0.518487","0.5171043","0.51696396","0.51637954","0.51516235","0.514832","0.51255953","0.51145816","0.5112619","0.50949955","0.50817513","0.5081298","0.50629973","0.5060714","0.50520027","0.50491625","0.50479996","0.5046078","0.50460774","0.5040609","0.5033846","0.5033443","0.501558","0.501371","0.50123596","0.5009497","0.50036407","0.49936095","0.49819866","0.497902","0.49771282","0.49758652","0.49745798","0.49728143","0.496393","0.495716","0.4957051","0.49529848","0.49511024","0.49442714","0.49397433","0.49395442","0.4938242","0.49327165","0.49308884","0.49280894","0.49132106","0.4905019","0.49043757","0.49023727","0.49022156","0.48982668","0.48873854","0.48847952","0.48840177","0.48832917","0.48818925","0.487619","0.48734918","0.48725593","0.4872506","0.4871077","0.48626125","0.48624015","0.4860262","0.48508438"],"string":"[\n \"0.5981826\",\n \"0.58952916\",\n \"0.5884403\",\n \"0.5581767\",\n \"0.55763274\",\n \"0.5549178\",\n \"0.5539936\",\n \"0.5526793\",\n \"0.5501055\",\n \"0.54238534\",\n \"0.541401\",\n \"0.54067093\",\n \"0.5399093\",\n \"0.53788334\",\n \"0.5367486\",\n \"0.5344063\",\n \"0.53401744\",\n \"0.5332257\",\n \"0.529883\",\n \"0.52931905\",\n \"0.5287063\",\n \"0.528249\",\n \"0.5281925\",\n \"0.5276451\",\n \"0.52738297\",\n \"0.52668226\",\n \"0.5266274\",\n \"0.52655905\",\n \"0.5263041\",\n \"0.5255363\",\n \"0.5246741\",\n \"0.5236333\",\n \"0.5214482\",\n \"0.52144736\",\n \"0.52107096\",\n \"0.518487\",\n \"0.5171043\",\n \"0.51696396\",\n \"0.51637954\",\n \"0.51516235\",\n \"0.514832\",\n \"0.51255953\",\n \"0.51145816\",\n \"0.5112619\",\n \"0.50949955\",\n \"0.50817513\",\n \"0.5081298\",\n \"0.50629973\",\n \"0.5060714\",\n \"0.50520027\",\n \"0.50491625\",\n \"0.50479996\",\n \"0.5046078\",\n \"0.50460774\",\n \"0.5040609\",\n \"0.5033846\",\n \"0.5033443\",\n \"0.501558\",\n \"0.501371\",\n \"0.50123596\",\n \"0.5009497\",\n \"0.50036407\",\n \"0.49936095\",\n \"0.49819866\",\n \"0.497902\",\n \"0.49771282\",\n \"0.49758652\",\n \"0.49745798\",\n \"0.49728143\",\n \"0.496393\",\n \"0.495716\",\n \"0.4957051\",\n \"0.49529848\",\n \"0.49511024\",\n \"0.49442714\",\n \"0.49397433\",\n \"0.49395442\",\n \"0.4938242\",\n \"0.49327165\",\n \"0.49308884\",\n \"0.49280894\",\n \"0.49132106\",\n \"0.4905019\",\n \"0.49043757\",\n \"0.49023727\",\n \"0.49022156\",\n \"0.48982668\",\n \"0.48873854\",\n \"0.48847952\",\n \"0.48840177\",\n \"0.48832917\",\n \"0.48818925\",\n \"0.487619\",\n \"0.48734918\",\n \"0.48725593\",\n \"0.4872506\",\n \"0.4871077\",\n \"0.48626125\",\n \"0.48624015\",\n \"0.4860262\",\n \"0.48508438\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77077,"cells":{"query":{"kind":"string","value":"Identify if given doc_no is a supported document type. Retrieve the candidate list of materials that will be returned."},"document":{"kind":"string","value":"def return_candidates(cls, doc_no):\n # Retrieve documents based on what it is given\n if isinstance(doc_no, Doc):\n doc = doc_no\n else:\n doc = Docs.of_doc_no(doc_no)\n\n # Case StoreAuxTask\n if isinstance(doc, prod_doc.StoreAuxTask):\n if doc.status <= prod_doc.StoreAuxTask.STATUS_CONFIRMED:\n raise BadParameterError(_(\"ERR_INVALID_DOCUMENT_STATUS: %(doc_no)s\") % {\n 'doc_no': doc_no\n })\n doc.populate('parent_task')\n if doc.parent_task:\n if prod_doc.UserActiveTask.probe(operation=doc.parent_task) is not None:\n raise BadParameterError(_(\"ERR_INVALID_STATE_TASK_IN_PROGRESS: %(doc_no)s\") % {\n 'doc_no': doc_no\n })\n\n if doc.parent_task.status >= prod_doc.ProductionOrderOperation.STATUS_CONFIRMED:\n staged_components = stock_doc.InventoryContent.query_content(ref_doc=doc.parent_task,\n location=Location.factory('STAGING'))\n return [{\n \"type\": cls.TYPE_STAGED_IN_INVENTORY_CONTENT,\n \"object_id\": str(doc.object_id),\n \"doc_no\": doc.doc_no,\n \"return_list\": map(lambda a: {\n # As TaskComponent.as_json()\n 'material': str(a.material),\n 'quantity': a.quantity,\n 'uom': stock_doc.MaterialMaster.get(a.material).uom.code,\n 'revision': None,\n 'weight': a.weight,\n 'size': None\n }, staged_components)\n }]\n else:\n staged_components = doc.list_components()\n return [{\n \"type\": cls.TYPE_STAGED_FROM_CLERK_AUX_TASK,\n \"object_id\": str(doc.object_id),\n \"doc_no\": doc.doc_no,\n \"return_list\": map(lambda a: {\n # As TaskComponent.as_json()\n 'material': str(a.material),\n 'quantity': a.quantity,\n 'uom': stock_doc.MaterialMaster.get(a.material).uom.code,\n 'revision': None,\n 'weight': a.weight,\n 'size': None\n }, staged_components)\n }]\n raise BadParameterError(_(\"ERR_UNKNOWN_PARENT_TASK_FOR: %(doc_no)s\") % {\n 'doc_no': doc_no\n })\n elif isinstance(doc, prod_doc.ProductionOrder):\n return map(lambda a: {\n \"type\": cls.TYPE_WIP_FROM_PRODUCTION_ORDER,\n \"object_id\": str(a.object_id),\n \"doc_no\": a.doc_no,\n \"return_list\": [{\n 'material': 'WIP-CANDIDATE', # WIP CANDIDATE ...\n 'quantity': a.get_confirmable_quantity(),\n 'uom': 'pc',\n 'revision': doc.revision,\n 'weight': 0,\n 'size': doc.size\n }]\n }, doc.get_edge_operations())\n\n raise BadParameterError(_(\"ERR_UNSUPPORTED_DOCUMENT_TYPE: %(document_type)s\" % {\n 'document_type': str(type(doc))\n }))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def return_materials(cls, user, doc_no, mode, return_list):\n\n # Retrieve documents based on what it is given\n doc = Docs.of_doc_no(doc_no)\n\n # Validate input,\n # Extract source to compare\n current_list = []\n ref_doc = None\n from_ref_doc = None\n if mode in [cls.TYPE_STAGED_IN_INVENTORY_CONTENT, cls.TYPE_STAGED_FROM_CLERK_AUX_TASK] and isinstance(doc, prod_doc.StoreAuxTask):\n o = cls.return_candidates(doc_no)\n current_list = list(o[0]['return_list'])\n doc.populate('parent_task')\n doc.parent_task.populate('ref_doc')\n ref_doc = doc.parent_task.ref_doc\n from_ref_doc = doc.parent_task\n elif mode == cls.TYPE_WIP_FROM_PRODUCTION_ORDER and isinstance(doc, prod_doc.ProductionOrderOperation):\n doc.populate('ref_doc')\n current_list = [{\n 'material': 'WIP-CANDIDATE',\n 'quantity': doc.get_confirmable_quantity(),\n 'uom': doc.ref_doc.uom,\n 'revision': None,\n 'size': None,\n 'weight': 0,\n }]\n ref_doc = doc.ref_doc\n else:\n # Unsupported case\n raise BadParameterError(_(\"ERR_UNSUPPORTED_DOCUMENT_TYPE: %(document_type)s\") % {\n 'document_type': str(type(doc))\n })\n\n # prepare movement based on return_list\n # Sanity check\n if len(current_list) != len(return_list):\n raise BadParameterError(_(\"ERR_UNEQUAL_SIZE_OF_RETURN_LIST: %(expected_size)s != $(actual_size)s\") % {\n 'expected_size': len(current_list),\n 'actual_size': len(return_list)\n })\n\n # index list for ease of comparison\n current_list = dict(map(lambda a: (str(a['material']), a), current_list))\n return_list = dict(map(lambda a: (str(a['material']), a), return_list))\n\n movements = []\n entries = []\n lost_entries = []\n movement_type = None\n lost_movement_type = None\n # From STAGED => STORE\n # From STAGED => LOST & FOUND\n for key, value in current_list.iteritems():\n lost = value['quantity'] - return_list[key]['quantity']\n\n # NOTE: For WIP scenario\n if key == 'WIP-CANDIDATE':\n # init Goods receipt by-product for WIP to STORE\n # TODO: value for WIP to be fixed later with proper logic. now using static 543210.\n movement_type = stock_doc.InventoryMovement.GR_BP\n user.can(stock_doc.InventoryMovement.ACTION_WRITE(), movement_type, throw=True)\n\n # NOTE: Generate WIP Stock code\n wip_material = doc.ref_doc.material.create_wip(doc.task.code)\n\n # NOTE: Create material master from stock code\n stock_doc.MaterialMaster.factory(wip_material, uom=doc.ref_doc.uom, procurement_type=stock_doc.MaterialMaster.INTERNAL, author=user)\n\n current_list[key]['material'] = wip_material\n current_list[key]['uom'] = stock_doc.MaterialMaster.get(doc.ref_doc.material).uom.code\n entries.append(stock_doc.InventoryMovementEntry.factory(material=wip_material,\n quantity=return_list[key]['quantity'],\n location=Location.factory('STORE').code,\n weight=return_list[key]['weight'],\n value=543210))\n\n if lost > 0:\n # if lost is found, init Goods receipt lost and found for WIP to STORE\n lost_movement_type = stock_doc.InventoryMovement.GR_LT\n lost_entries.append(stock_doc.InventoryMovementEntry.factory(material=wip_material,\n quantity=return_list[key]['quantity'],\n location=Location.factory('LOST').code,\n weight=return_list[key]['weight'],\n value=543210))\n # NOTE: For STAGING scenario\n else:\n # init Stock transfer production to location for STAGING to STORE\n movement_type = stock_doc.InventoryMovement.ST_PL\n user.can(stock_doc.InventoryMovement.ACTION_WRITE(), movement_type, throw=True)\n entries.extend(stock_doc.InventoryMovementEntry.transfer_pair_factory(material=value['material'],\n quantity=return_list[key]['quantity'],\n from_location=Location.factory('STAGING').code,\n to_location=Location.factory('STORE').code,\n from_ref_doc=from_ref_doc))\n\n if lost > 0:\n # if lost is found, init Stock transfer lost and found for STAGING to STORE retaining ref_doc\n lost_movement_type = stock_doc.InventoryMovement.ST_LT\n lost_entries.extend(stock_doc.InventoryMovementEntry.transfer_pair_factory(material=value['material'],\n quantity=return_list[key]['quantity'],\n from_location=Location.factory('STAGING').code,\n to_location=Location.factory('LOST').code,\n from_ref_doc=from_ref_doc,\n to_ref_doc=from_ref_doc))\n if not entries:\n raise ValueError(\"Nothing to return\")\n\n movement = stock_doc.InventoryMovement.factory(movement_type, list(entries), ref_doc=ref_doc)\n movements.append(movement)\n\n if lost > 0:\n user.can(stock_doc.InventoryMovement.ACTION_WRITE(), lost_movement_type, throw='challenge')\n movement = stock_doc.InventoryMovement.factory(lost_movement_type, list(lost_entries), ref_doc=ref_doc)\n movements.append(movement)\n\n if movements:\n # validate first then touched\n map(lambda x: x.validate(user=user), movements)\n\n # NOTE: create a new pair of store & clerk with different parent depending on mode\n if mode == cls.TYPE_STAGED_FROM_CLERK_AUX_TASK:\n task_signals.task_repeat.send(cls, parent=doc.parent_task, components=[v for i, v in current_list.iteritems()])\n if mode == cls.TYPE_WIP_FROM_PRODUCTION_ORDER:\n task_signals.task_repeat.send(cls, parent=doc, components=[v for i, v in current_list.iteritems()])\n doc.ready(user)\n \n map(lambda x: x.touched(user), movements)\n\n return len(movements)","def get_required_docs(doc_type):\n doc_type = 1 if doc_type == 'registration' else 2\n return Documents.query.filter_by(type=doc_type, required=True).all()","def get_documents(doc_type):\n doc_type = 1 if doc_type == 'registration' else 2\n return Documents.query.filter_by(type=doc_type).all()","def list_available_document_types(cls):\n\n response = cls._client.get(\"automatedDocumentOptions/\")\n return from_api(response.json())","def get_material(material):\n for libn,tdict in liblist:\n if material in tdict:\n return tdict[material]\n print (material, \" not found\")\n raise KeyError","def find_material(material):\n for libn,tdict in liblist:\n if material in tdict:\n print(libn)","def _getMaterials(self):\n warnings.warn(\"This function is deprecated; call Document.getMaterialNodes() instead.\", DeprecationWarning, stacklevel = 2)\n return self.getMaterialNodes()","def find_by_doc_reg_num(cls, doc_reg_num: str):\n document = None\n if doc_reg_num:\n try:\n document = cls.query.filter(Db2Document.document_reg_id == doc_reg_num).one_or_none()\n except Exception as db_exception: # noqa: B902; return nicer error\n current_app.logger.error('Db2Document.find_by_doc_reg_num exception: ' + str(db_exception))\n raise DatabaseException(db_exception)\n if document:\n document.strip()\n return document","def documents(self, **kw):\r\n \r\n doc_reader = self.doc_reader\r\n return (doc_reader[docnum] for docnum in self.document_numbers(**kw))","def info_materials_type_id_get(type_id):\n session = info_map.Session()\n q = session.query(info_map.Material).filter(info_map.Material.type == type_id)\n\n mat = q.one_or_none() # return the only result or `None`\n\n if mat is not None:\n material_info = MaterialInfo(\n type=mat.type,\n group=mat.group_id,\n name=mat.name,\n volume=mat.volume)\n return material_info, 200\n else:\n error = Error('Type {} Not Found'.format(type_id))\n return error, 404","def document_number(self, **kw):\r\n for docnum in self.document_numbers(**kw):\r\n return docnum","def is_extendable(doc):\n # Defensive programming.\n if not isinstance(doc, str):\n rt.assert_doc('doc', doc, \"Invalid document\")\n\n doc_type = doc if isinstance(doc, str) else doc.type_key\n\n return doc_type.lower() in SUPPORTED","def documents(pmid_23982599, civic_aid6_document):\n return [pmid_23982599, civic_aid6_document]","def info_materials_polymer_get():\n materials = _material_by_group(974) # 974 == intermediate group\n return materials, 200","def get_wmis_for_oem(self, oem):\n return self.get('vehicles/GetWMIsForManufacturer/{}'.format(oem))","def info_materials_get():\n materials = _material_by_group() # empty means all groups\n return materials, 200","def test_mhr_document_type(session):\n results = type_tables.MhrDocumentType.find_all()\n assert results\n assert len(results) >= 55\n for result in results:\n assert result.document_type in type_tables.MhrDocumentTypes\n assert result.document_type_desc\n\n doc_result = type_tables.MhrDocumentType.find_by_doc_type(type_tables.MhrDocumentTypes.REG_101.value)\n assert doc_result\n assert doc_result.document_type == type_tables.MhrDocumentTypes.REG_101.value\n assert doc_result.document_type_desc == 'MANUFACTURED HOME REGISTRATION'\n assert doc_result.legacy_fee_code == 'MHR400'\n doc_result = type_tables.MhrDocumentType.find_by_doc_type('XXX')\n assert not doc_result","def get_document_info(self, document_code):\n if \".zip\" in self.filename:\n return self.zip.getinfo(document_code + '_mets.xml')\n else:\n return","def list_support_required(self):\n\t\treturn self.typemanager.has_lists","def XCAFDoc_DocumentTool_MaterialTool(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialTool(*args)","def get_document_multiple_fields(self, metadata_dict):\n\n for field in metadata_dict.keys():\n if field not in self.metadata_fields:\n raise MissingMetadataError([field])\n\n for document in self.documents:\n match = True\n for field, val in metadata_dict.items():\n if getattr(document, field, None) != val:\n match = False\n if match:\n return document\n\n raise ValueError(\"Document not found\")","def check_only_supported_materials_are_used(progress_controller=None):\n if progress_controller is None:\n progress_controller = ProgressControllerBase()\n\n non_arnold_materials = []\n all_valid_materials = []\n for renderer in VALID_MATERIALS.keys():\n all_valid_materials.extend(VALID_MATERIALS[renderer])\n\n all_materials = pm.ls(mat=1)\n progress_controller.maximum = len(all_materials)\n for material in all_materials:\n if material.name() not in [\"lambert1\", \"particleCloud1\", \"standardSurface1\"]:\n if material.type() not in all_valid_materials:\n non_arnold_materials.append(material)\n progress_controller.increment()\n\n if len(non_arnold_materials):\n pm.select(non_arnold_materials)\n progress_controller.complete()\n raise PublishError(\n \"There are non-Arnold materials in the scene:<br><br>%s<br><br>\"\n \"Please remove them!!!\"\n % \"<br>\".join(map(lambda x: x.name(), non_arnold_materials))\n )\n progress_controller.complete()","def find_by_mhr_number(cls, mhr_number: str):\n documents = None\n if mhr_number:\n try:\n documents = cls.query.filter(Db2Document.mhr_number == mhr_number).all()\n if documents:\n for doc in documents:\n doc.strip()\n except Exception as db_exception: # noqa: B902; return nicer error\n current_app.logger.error('Db2Document.find_by_mhr_number exception: ' + str(db_exception))\n raise DatabaseException(db_exception)\n\n return documents","def is_use_documents(self):\n\t\treturn bool(call_sdk_function('PrlVmCfg_IsUseDocuments', self.handle))","def IsMaterial(self, *args):\n return _XCAFDoc.XCAFDoc_MaterialTool_IsMaterial(self, *args)","def doc_topics(self, doc_number):\n doc_topic = self.gammas / self.gammas.sum(axis=1)[:, np.newaxis]\n return doc_topic[doc_number]","def info_materials_booster_get():\n materials = _material_by_group(712) # 712 == intermediate group\n return materials, 200","def info_materials_composites_get():\n materials = _material_by_group(429) # 429 == intermediate group\n return materials, 200","def has_material(obj, name):\n return name in obj.data.materials.keys()","def XCAFDoc_DocumentTool_MaterialsLabel(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialsLabel(*args)","def MaterialTool(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialTool(*args)","def test_materials_present(self):\n self.assertIsNotNone('Materials' in self.header.parameters.attrs)","def process(self, doc):\n raise multisearch.errors.FeatureNotAvailableError","def document_type(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"document_type\")","def get_makes_by_oem(self, oem, year=None):\n if year is None:\n return self.get('vehicles/GetMakesForManufacturer/{}'.format(oem))\n return self.get(\n 'vehicles/GetMakesForManufacturerAndYear/{}'.format(oem), params=dict(year=year))","def _getDocumentType(self):\n\n fileName, fileExtension = os.path.splitext(self.sourceFileName)\n documentType = None\n\n for knownType in list(TextRepresentation.KNOWNTYPES.keys()):\n if knownType == fileExtension[1:]:\n documentType = knownType\n\n if documentType is None:\n raise Exception(\"Unknown document type: %s\" % fileExtension[1:])\n \n return documentType","def choose_documents(update, context):\n sc_api = SmartCAT(SMARTCAT_API_USERNAME, SMARTCAT_API_PASSWORD)\n names_or_ids = update.message.text.lower().strip()\n documents = []\n response = sc_api.project.get(SMARTCAT_PROJECT_ID)\n if response.status_code != 200:\n update.message.reply_text(SHIT_HAPPENS + \"\\nВведи названия глав еще раз или /cancel для выхода\")\n return constants.STATE_CHOOSE_DOCUMENT\n\n project_data = json.loads(response.content.decode('utf-8'))\n if not project_data:\n update.message.reply_text(SHIT_HAPPENS + \"\\nВведи названия глав еще раз или /cancel для выхода\")\n return constants.STATE_CHOOSE_DOCUMENT\n\n logger.info('names_or_ids = {}'.format(names_or_ids))\n if names_or_ids == ALL_CHAPTERS.lower() or names_or_ids == ALL.lower():\n documents = project_data['documents']\n elif names_or_ids == ACTIVE_CHAPTERS.lower():\n for d in project_data['documents']:\n logger.info(\"{} - {}\".format(d['name'], get_document_stage(d)))\n if get_document_stage(d) < constants.CHAPTER_STATE_FINAL_EDITING:\n logger.info(\"Document added: {0} {1}\".format(d['id'], d['name']))\n documents.append(d)\n elif names_or_ids == CHAPTERS_BEING_TRANSLATED.lower():\n for d in project_data['documents']:\n if get_document_stage(d) == constants.CHAPTER_STATE_TRANSLATION:\n logger.info(\"Document added: {0} {1}\".format(d['id'], d['name']))\n documents.append(d)\n elif names_or_ids == CHAPTERS_BEING_EDITED.lower():\n for d in project_data['documents']:\n if get_document_stage(d) == constants.CHAPTER_STATE_EDITING:\n logger.info(\"Document added: {0} {1}\".format(d['id'], d['name']))\n documents.append(d)\n else:\n names_or_ids = [s.lower().strip() for s in names_or_ids.split(\"\\n\")]\n for d in project_data['documents']:\n if d['id'].lower() in names_or_ids or d['name'].lower().strip() in names_or_ids:\n logger.info(\"Document added: {0} {1}\".format(d['id'], d['name']))\n documents.append(d)\n\n if len(documents) == 0:\n update.message.reply_text(NOTHING_FOUND + \"\\nВведи названия глав еще раз или /cancel для выхода\")\n return constants.STATE_CHOOSE_DOCUMENT\n\n context.user_data['documents'] = documents\n\n if len(documents) == 1:\n reply = \"Ок, я нашел одну главу: {0} ({1})\".format(documents[0]['name'], documents[0]['id'])\n else:\n reply = \"Ок, я нашел {0} глав.\\n\".format(len(documents))\n for d in documents:\n stage_name = constants.CHAPTER_STAGE_NAMES[get_document_stage(d)]\n reply += \"- {0}: {1}\\n\".format(stage_name, d['name'])\n\n return reply","def valid_doc_types():\r\n return \", \".join(DOC_PATHS.keys())","def document_type(self, key, value):\n _doc_type = self.get(\"document_type\", {})\n\n def doc_type_mapping(val):\n if val:\n return mapping(DOCUMENT_TYPE, val)\n\n for v in force_list(value):\n val_a = doc_type_mapping(clean_val(\"a\", v, str))\n val_b = doc_type_mapping(clean_val(\"b\", v, str))\n\n if not val_a and not val_b and not _doc_type:\n raise UnexpectedValue(subfield=\"a\")\n\n if val_a and val_b and (val_a != val_b != _doc_type):\n raise ManualImportRequired(\n subfield=\"a or b - \" \"inconsistent doc type\"\n )\n if val_a:\n if _doc_type and _doc_type != val_a:\n raise ManualImportRequired(\n subfield=\"a\" \"inconsistent doc type\"\n )\n _doc_type = val_a\n if val_b:\n if _doc_type and _doc_type != val_a:\n raise ManualImportRequired(\n subfield=\"b\" \"inconsistent doc type\"\n )\n _doc_type = val_b\n return _doc_type","def test_list_media_type(self):\n\n # check if documentalist has access to list media-types\n self.login_documentalist()\n response = self.client.get('/multimedia/media-types/' )\n\n # 403 = unauthorized\n self.assertEqual(response.status_code, 403)\n\n self.client.logout()\n self.login_admin()\n\n response = self.client.get('/multimedia/media-types/')\n self.assertContains(response, \"Video\")","def get_document_complexe_tags(self, document):\n kw_extractor = yake.KeywordExtractor(lan=\"en\", n=3, top=30)\n keywords = kw_extractor.extract_keywords(document)\n\n complexe_tag = []\n for keyword in keywords:\n if keyword[-1] >= 0.1:\n break\n complexe_tag.append(keyword[0])\n return complexe_tag","def info_materials_raw_get():\n materials = _material_by_group(427) # 427 == intermediate group\n return materials, 200","def is_required_data(self, typename):\n return typename in self.required_data_products","def info_materials_intermediates_get():\n materials = _material_by_group(428) # 428 == intermediate group\n return materials, 200","def is_content_supported(self, cbas):\n\n # 1\n if self.accept_all_content:\n return SupportInfo(True, None)\n\n # 2\n if len(self.supported_content.filter(content_binding=cbas.content_binding, subtype=None)) > 0:\n return SupportInfo(True, None)\n\n # 2a (e.g., subtype = None so #3 would end up being the same check as #2)\n if not cbas.subtype: # No further checking can be done\n return SupportInfo(False, None)\n\n # 3\n if len(self.supported_content.filter(content_binding=cbas.content_binding, subtype=cbas.subtype)) > 0:\n return SupportInfo(True, None)\n\n # 4\n return SupportInfo(False, None)","def getDocuments(self):\n return self.objectValues('Multifile')","def get_documents(self, engine_name, document_ids):\n endpoint = \"engines/{}/documents\".format(engine_name)\n data = json.dumps(document_ids)\n return self.swiftype_session.request('get', endpoint, data=data)","def is_required_data(self, typename):\r\n return typename in self.required_data_products","def doc_types(self):\n return self._extract_set('doc_type')","def get_specnos(self, spectra_file):\n\n self.plate_num = spectra_file[-23:-19]\n print(\"Fitting spectra in plate %s\" % self.plate_num)\n self.spectra = np.load(spectra_file) \n self.this_plate = self.MetaData[self.MetaData['PLATE'] == int(self.plate_num)]\n\n if self.ttype == 'test':\n max_num = 10 \n self.specnos = self.this_plate[0: max_num]['SPECNO']\n elif self.ttype == 'blue':\n max_num = len(self.spectra)-1\n self.specnos = self.this_plate[(self.this_plate['CAMERAS'] == b'b1') | (self.this_plate['CAMERAS'] == b'b2')]['SPECNO']\n elif self.ttype == 'red':\n max_num = len(self.spectra)-1\n self.specnos = self.this_plate[(self.this_plate['CAMERAS'] == b'r1') | (self.this_plate['CAMERAS'] == b'r2')]['SPECNO']\n elif self.ttype == 'full':\n max_num = len(self.spectra)\n self.specnos = self.this_plate['SPECNO']\n else: \n print(\"not a valid type. Going to test\")\n max_num = 10 #len(spectra) Number of spectra in a given plate that you want to run this for. Mostly for debugging\n self.specnos = np.random.choice(self.this_plate['SPECNO'], size=max_num)\n\n self.spectra_length = len(self.specnos)","def check_document(self, document):\n if isinstance(document, dict):\n document = json.dumps(document)\n r = requests.post('/'.join([self.base_url,\n 'check']),\n data=document,\n headers={'Content-Type': 'application/json'})\n return r.json()","def GetDoc(index_wd, key, value):\n # init code:\n key = key.lower().strip()\n value = value.strip()\n docid_to_docno_path = os.path.join(index_wd, 'docid_to_docno.p')\n docno_to_data_path = os.path.join(index_wd, 'docno_to_data.p')\n # check to see what key is (docno or docid)\n\n # if docid, need to grab the docid_to_docno from disk\n # Lookup via docid:\n if key == 'docid':\n docid_to_docno = pickle.load(open(docid_to_docno_path, \"rb\"))\n value = int(value)\n if value not in docid_to_docno:\n print(\"The following docid: {} was not found.\".format(value))\n return False\n else:\n docno = docid_to_docno[value]\n #Lookup via docno:\n elif key == 'docno':\n docno = value\n else:\n print(\"The following key: {} is invalid. Pass: 'docid' or 'docno'\".format(key))\n return False\n docno_to_data = pickle.load(open(docno_to_data_path, \"rb\"))\n\n if docno not in docno_to_data:\n print(\"The following docno: {} was not found.\".format(value))\n return False\n\n doc_path = docno_to_data[docno]\n\n basepath = os.path.dirname(doc_path)\n\n if not os.path.exists(basepath):\n print(\"The basepath {} does not exist.\".format(basepath))\n return False\n\n metadata = MetaData(doc_path)\n metadata.load()\n metadata.meta_print()\n return True","def test_application_document_types_have_generation_spec(self, test_client, db_session,\n auth_headers):\n\n get_resp = test_client.get(\n f'/now-applications/application-document-types',\n headers=auth_headers['full_auth_header'])\n assert get_resp.status_code == 200\n get_data = json.loads(get_resp.data.decode())\n assert len([x for x in get_data['records'] if x['document_template']]) > 0\n assert len([x for x in get_data['records'] if x['document_template']]) == len(\n [x for x in NOWApplicationDocumentType.get_all() if x.document_template_code])","def get_custom_fields(self, doc_type):\n\t\tcustom_doclist = []\n\t\tres = webnotes.conn.sql(\"\"\"SELECT * FROM `tabCustom Field`\n\t\t\tWHERE dt = %s AND docstatus < 2\"\"\", doc_type, as_dict=1)\n\t\tfor r in res:\n\t\t\t# Cheat! Mask Custom Field as DocField\n\t\t\tcustom_field = webnotes.model.doc.Document(fielddata=r)\n\t\t\tself.mask_custom_field(custom_field, doc_type)\n\t\t\tcustom_doclist.append(custom_field)\n\n\t\treturn custom_doclist","def isSonyMscCamera(info):\n return info.manufacturer == SONY_MANUFACTURER_SHORT and info.model in SONY_MSC_MODELS","def test_team_builder_config_product_size_materials_find_one_get(self):\n pass","def get_receipt_knowledge_status(self):\n return self.get_document_status_choice(self.receipt_knowledge_status)","def get_availability(self):\n return [self.to_fname(type).is_file() for type in (\"pdf\", \"si\")]","def is_content_supported(self, cbas):\n\n # 1\n if self.accept_all_content:\n return SupportInfo(True)\n\n # 2\n if len(self.supported_content.filter(content_binding=cbas.content_binding, subtype=None)) > 0:\n return SupportInfo(True)\n\n # 2a (e.g., subtype = None so #3 would end up being the same check as #2)\n if not cbas.subtype: # No further checking can be done\n return SupportInfo(False)\n\n # 3\n if len(self.supported_content.filter(content_binding=cbas.content_binding, subtype=cbas.subtype)) > 0:\n return SupportInfo(True)\n\n # 4\n return SupportInfo(False)","def GetMaterial(self, *args):\n return _XCAFDoc.XCAFDoc_MaterialTool_GetMaterial(self, *args)","def process_rec(rec_number: int,\n incl_rec_slicer: slicer.SpecProcessor,\n excl_rec_slicer: slicer.SpecProcessor,\n rec: List[str],\n incl_col_slicer: slicer.SpecProcessor,\n excl_col_slicer: slicer.SpecProcessor) -> Optional[List[str]]:\n # minimal validation\n assert int(rec_number) >= 0\n\n # reject record if it isn't in the inclusion spec\n if not incl_rec_slicer.spec_evaluator(rec_number):\n return None\n\n # reject record if it is in the exclusion spec\n if excl_rec_slicer.spec_evaluator(rec_number):\n return None\n\n output_rec = []\n for col_number in range(len(rec)):\n if not incl_col_slicer.spec_evaluator(col_number):\n continue\n if excl_col_slicer.spec_evaluator(col_number):\n continue\n output_rec.append(rec[col_number])\n\n if output_rec:\n return output_rec\n else:\n return None # don't return empty list","def test_team_builder_config_product_size_materials_id_exists_get(self):\n pass","def DocumentType(self, default=[None]):\n return self.data.get('metadata', {}).get('document_type', default)","def getMediaInfo(self, type='dict'):\n\t\t# define the content lists for sections 'General','Video','Audio'\n\t\tcont_general = ['Count of video streams','Count of audio streams','Duration','File size']\n\t\tcont_video = ['Format','Format profile','Codec ID','Duration','Bit rate','Width','Height','Frame rate','Frame count','Stream size']\n\t\tcont_audio = ['Format','Codec ID/Info','Duration','Bit rate','Sampling rate','Samples count','Stream size']\n\t\t\n\t\t# the type parameter is used to provide the information in several formats\n\t\tif type == 'dict': # Info is provided as python dictionary\n\t\t\t# prepare dictionary\n\t\t\tresult = {}\n\t\t\t# prepare section 'General'\n\t\t\tresult['General'] = {}\n\t\t\t# fill section 'General' with its information\n\t\t\tresult = self._fill_section_dict(result, 'General',cont_general)\n\t\t\t\n\t\t\t# prepare and fill section 'Video' with its information\n\t\t\tif 'Count of video streams' in result['General'][0].keys():\n\t\t\t\tresult['Video'] = {}\n\t\t\t\tresult = self._fill_section_dict(result,'Video',cont_video, result['General'][0]['Count of video streams'])\n\t\t\telse:\n\t\t\t\tresult['Video'] = 'No stream'\n\t\t\t\n\t\t\t# prepare and fill section 'Audio' with its information\n\t\t\tif 'Count of audio streams' in result['General'][0].keys():\n\t\t\t\tresult['Audio'] = {}\n\t\t\t\tresult = self._fill_section_dict(result,'Audio',cont_audio, result['General'][0]['Count of audio streams'])\n\t\t\telse:\n\t\t\t\tresult['Audio'] = 'No stream'\n\t\t\t\n\t\t\treturn result\n\t\t\t\n\t\t\t# I'm not sure, if a section 'Text' as mentioned in the MediaInfo documentation is needed here\n\t\t\t\n\t\telif type == 'json': # Info is provided as json string\n\t\t\t# start json string\n\t\t\tresult = '{'\n\t\t\t# prepare section 'General'\n\t\t\tresult += '\\'General\\':'\n\t\t\t# fill section 'General' with its information\n\t\t\tresult = self._fill_section_json(result, 'General', cont_general)\n\t\t\t\n\t\t\t# prepare section 'Video'\n\t\t\tresult += '\\'Video\\':'\n\t\t\tif 'General_0_Count of video streams' in self.full_info.keys():\n\t\t\t\tresult = self._fill_section_json(result, 'Video', cont_video,int(self.full_info['General_0_Count of video streams']))\n\t\t\telse:\n\t\t\t\tresult += '\\'No stream\\''\n\t\t\t\t\t\t\n\t\t\t# prepare section 'Audio'\n\t\t\tresult += '\\'Audio\\''\n\t\t\tif 'General_0_Count of audio streams' in self.full_info.keys():\n\t\t\t\tresult = self._fill_section_json(result, 'Audio', cont_audio,int(self.full_info['General_0_Count of audio streams']))\n\t\t\telse:\n\t\t\t\tresult += '\\'No stream\\''\n\t\t\n\t\t\t# end result\n\t\t\tresult += '}'\n\t\treturn result","def DocumentType(self, default='article'):\n return self.data.get('document_type', [{}])","def check_if_embedded(lookup_value, lookup_type='content_id'):\n video_json = get_video_info(lookup_value, lookup_type)\n return video_json['embedded']","def is_supported_document_type(path):\n return any([str(path).lower().endswith(suffix) for suffix in _ALL_SUPPORTED_EXTENSIONS])","def query_supported_software(self):\n api_uri = self._uri_dict.get('querySupportedSoftware')\n data = {}\n r_data = self._post(api_uri, data)\n return r_data","def document_parse(self, document):\n if type(document) == dict:\n self.list_parse(document['blocks'])\n elif type(document) == list:\n self.list_parse(document[1])\n else:\n print('Incompatible Pandoc version. Process failed.')","def assertCanFind(self, document):\n ctool = self.portal.portal_catalog\n self.assertEqual(len(ctool.searchResults(UID=document.UID())), 1)","def supports(cls, filename_):\n raise BookInfoNotImplementedError('supports', cls.__name__)","async def _find(self, document: str) -> Union[List[pq], None]:\n doc = pq(document)\n data = doc(self.selector)\n if not data and self.raise_not_found:\n raise InformationError(\"No matches for '{selector}' found in document\".format(selector=self.selector))\n elif not data:\n return None\n elif len(data) > 1 and not self.accept_multiples:\n raise InformationError(\"Too many matches for '{selector}' found in document\".format(selector=self.selector))\n return data","def test_parse_library_charges_from_spec_docs(self):\n # TODO: This test is practically useless while the XML strings are hard-coded at the top of this file.\n # We should implement something like doctests for the XML snippets on the SMIRNOFF spec page.\n ForceField(xml_spec_docs_ala_library_charges_xml)\n ForceField(xml_spec_docs_tip3p_library_charges_xml)","def parse_docs_from_page(self, page_url: str, page_text: str) -> Iterable[Document]:\n # parse html response\n url = \"https://www.med.navy.mil/directives/Pages/Publications.aspx\"\n base_url = 'https://www.med.navy.mil'\n parsed_docs = []\n doc_name_list = []\n if (page_url.find(\"Publications\") != -1):\n doc_type = \"NAVMED\"\n elif (page_url.find(\"BUMEDNotes\") != -1):\n doc_type = \"BUMEDNOTE\"\n elif (page_url.find(\"BUMEDInstructions\") != -1):\n doc_type = \"BUMEDINST\"\n cac_required = ['CAC', 'PKI certificate required', 'placeholder', 'FOUO']\n page = requests.get(page_url, verify=CERTIFICATE_DIR + '/cat3.pem')\n soup = bs4.BeautifulSoup(page.content, 'html.parser')\n webpart = soup.find(id=\"onetidDoclibViewTbl0\")\n items = webpart.find_all('a')\n meta = webpart.find_all(lambda tag: tag.name == 'td' and tag.get('class') == ['ms-vb2'])\n meta_list = list(meta)\n meta_list = [remove_html_tags(str(t)) for t in meta_list]\n meta_list = [str(t).encode('ascii', 'ignore').decode('ascii') for t in meta_list]\n meta_list = [x.replace(\"\\r\\n\", \" \") for x in meta_list]\n if (doc_type == \"NAVMED\"):\n n = 3\n elif (doc_type == \"BUMEDINST\" or doc_type == \"BUMEDNOTE\"):\n n = 4\n meta_ = [meta_list[i:i + n] for i in range(0, len(meta_list), n)]\n if (doc_type == \"NAVMED\"):\n subject = webpart.find_all(lambda tag: tag.name == 'td' and tag.get('class') == ['ms-vb-title'])\n name_list = list(subject)\n name_list = [remove_html_tags(str(t)) for t in name_list]\n name_list = [str(t).encode('ascii', 'ignore').decode('ascii') for t in name_list]\n subnum = [str(t[1]).split()[:2] for t in meta_]\n title_ = [str(t[1]).split()[2:] for t in meta_]\n title = [' '.join(t) for t in title_]\n title = [str(t).replace(',', '') for t in title]\n date = [t[0] for t in meta_]\n metadata = zip(subnum, title, date, name_list)\n metadata = [list(a) for a in metadata]\n elif (doc_type == \"BUMEDINST\"):\n subject = webpart.find_all(lambda tag: tag.name == 'td' and tag.get('class') == ['ms-vb-title'])\n name_list = list(subject)\n name_list = [remove_html_tags(str(t)) for t in name_list]\n name_list = [str(t).encode('ascii', 'ignore').decode('ascii') for t in name_list]\n metadata = list(zip(name_list, meta_))\n elif (doc_type == \"BUMEDNOTE\"):\n metadata = meta_\n item_list = list(items)\n pdf_links = [link['href'] for link in item_list if link['href'].endswith(('pdf', 'html'))]\n pdf_links = [\"https://www.med.navy.mil\" + a for a in pdf_links]\n pdf_links = [str(a).replace(' ', '%20') for a in pdf_links]\n if (doc_type == \"BUMEDINST\" or doc_type == \"BUMEDNOTE\"):\n metadata = [list(ele) for ele in metadata]\n for i in range(0, len(metadata)):\n metadata[i].append(pdf_links[i])\n for item in metadata:\n if (doc_type == \"NAVMED\"):\n pdf_di = DownloadableItem(doc_type='pdf', web_url=item[4])\n if (str(item[3])[0].isdigit()):\n doc_name = \"NAVMED P-\" + str(item[3]).split()[0]\n doc_num = \"P-\" + str(item[3]).split()[0]\n if (doc_name in doc_name_list):\n number_of_times = sum(1 for s in doc_name_list if doc_name in s)\n doc_name = doc_type + \" \" + doc_num + \"-\" + str(number_of_times)\n doc_num = doc_num + \"-\" + str(number_of_times)\n else:\n doc_name = \"NAVMED \" + str(item[0][1]) + \" \" + ' '.join(str(item[3]).split()[:3])\n doc_num == str(item[0][1]) + \" \" + ' '.join(str(item[3]).split()[:3])\n if (doc_name in doc_name_list):\n number_of_times = sum(1 for s in doc_name_list if doc_name in s)\n doc_name = doc_type + \" \" + doc_num + \"-\" + str(number_of_times)\n doc_num = doc_num + \"-\" + str(number_of_times)\n doc_title = str(item[1])\n publication_date = str(item[2])\n cac_login_required = False\n crawler_used = \"navy_med_pubs\"\n source_page_url = page_url\n downloadable_items = [pdf_di]\n version_hash_fields = {\n \"item_currency\": str(item[3]).split('/')[-1], # version metadata found on pdf links\n \"pub_date\": publication_date.strip(),\n \"document_title\": doc_title.strip(),\n \"document_number\": doc_num.strip()\n }\n\n elif (doc_type == \"BUMEDINST\"):\n pdf_di = DownloadableItem(doc_type='pdf', web_url=item[2])\n doc_num = str(item[0]).split()[0]\n doc_name = doc_type + \" \" + doc_num\n doc_title = str(item[1][1])\n publication_date = str(item[1][0])\n if (str(item[2]).endswith('html')):\n cac_login_required = True\n elif (str(item[2]).endswith('pdf')):\n cac_login_required = False\n if (doc_name in doc_name_list):\n number_of_times = sum(1 for s in doc_name_list if doc_name in s)\n doc_name = doc_type + \" \" + doc_num + \"-\" + str(number_of_times)\n doc_num = doc_num + \"-\" + str(number_of_times)\n doc_name_list.append(doc_name)\n elif (doc_type == \"BUMEDNOTE\"):\n pdf_di = DownloadableItem(doc_type='pdf', web_url=item[4])\n doc_num = str(item[0]).split()[1]\n doc_name = doc_type + \" \" + doc_num\n doc_title = str(item[2])\n publication_date = str(item[1])\n cac_login_required = False\n if (doc_name in doc_name_list):\n number_of_times = sum(1 for s in doc_name_list if doc_name in s)\n doc_name = doc_type + \" \" + doc_num + \"-\" + str(number_of_times)\n doc_num = doc_num + \"-\" + str(number_of_times)\n doc_name_list.append(doc_name)\n version_hash_fields = {\n \"doc_name\": doc_name, # version metadata found on pdf links\n \"pub_date\": publication_date.strip(),\n \"document_title\": doc_title.strip(),\n \"document_number\": doc_num.strip()\n }\n version_hash_raw_data = version_hash_fields\n doc = Document(\n doc_name=re.sub(',', '', doc_name.strip()),\n doc_title=re.sub('\\\\\"', '', doc_title),\n doc_num=re.sub(',', '', doc_num.strip()),\n doc_type=re.sub(',', '', doc_type.strip()),\n publication_date=publication_date,\n cac_login_required=cac_login_required,\n crawler_used=\"navy_med_pubs\",\n source_page_url=page_url.strip(),\n version_hash_raw_data=version_hash_fields,\n downloadable_items=[pdf_di]\n )\n parsed_docs.append(doc)\n return parsed_docs","def get_supported_types(soup):\n abam = soup.find('a', href=re.compile('#bam'))\n ol = abam.parent.parent.parent.parent\n supported_types = []\n for ul in ol.find_all('ul'):\n for a in ul.find_all('a'):\n ref = a['href'].replace('#', '')\n\n # some have text like \"#bigMaf_-_Multiple_Alignments\", so split on\n # the '_'\n ref = ref.split('_')[0]\n supported_types.append(ref)\n\n # multiWig is not included in this table, but it's considered a type in the\n # divs.\n supported_types.append('multiWig')\n\n # If a setting pertains to all types, they are not all listed, rather, the\n # \"all\" type is specified.\n supported_types.append('all')\n\n return supported_types","def documentdetail(request, docid):\n\tdocument = get_object_or_404(Document, pk=docid)\n\tif not request.user.has_perm('documents.view_document', obj=document):\n\t\treturn HttpResponse(loader.render_to_string('401.html',\n\t\t\tRequestContext(request, {'error_message':\n\t\t\t\t_(\"You are not allowed to view this document.\")})), status=401)\n\ttry:\n\t\trelated = document.content_type.get_object_for_this_type(id=document.object_id)\n\texcept:\n\t\trelated = ''\n\n\treturn render_to_response(\"documents/docinfo.html\", RequestContext(request, {\n\t\t'permissions_json': json.dumps(_perms_info(document, DOCUMENT_LEV_NAMES)),\n\t\t'document': document,\n\t\t'imgtypes': imgtypes,\n\t\t'related': related\n\t}))","def load_material_library( self, url, materials, baseURL=None ):\n #( resolvedURL, os.path.abspath(filename), file, headers )\n try:\n finalURL, filename, file, headers = loader.Loader( url, baseURL )\n except IOError, err:\n if '/' in url:\n possible = url.split( '/' )[-1]\n try:\n finalURL, filename, file, headers = loader.Loader( \n possible, baseURL \n )\n except IOError, err:\n log.warn(\n \"\"\"Unable to load material library: %s\"\"\",\n url,\n )\n return False\n \n material = None\n for line in file.read().splitlines():\n if line.startswith('#'):\n continue\n values = line.split()\n if not values:\n continue\n\n if values[0] == 'newmtl':\n material = self.defaultMaterial()\n materials[values[1]] = material\n elif material is None:\n log.warn('Expected \"newmtl\" in %s', url)\n continue\n\n try:\n if values[0] == 'Kd':\n material.material.diffuseColor = map(float, values[1:])\n elif values[0] == 'Ka':\n material.material.ambientColor = map(float, values[1:])\n elif values[0] == 'Ks':\n material.material.specularColor = map(float, values[1:])\n elif values[0] == 'Ke':\n material.material.emissiveColor = map(float, values[1:])\n elif values[0] == 'Ns':\n material.material.shininess = float(values[1])\n elif values[0] == 'd':\n material.material.opacity = float(values[1])\n elif values[0] == 'map_Kd':\n if '/' in values[1]:\n img_url = [ values[1], values[1].split('/')[-1] ]\n else:\n img_url = [ values[1] ]\n img_url = [\n urllib.basejoin(baseURL, u )\n for u in img_url\n ]\n texture = basenodes.ImageTexture(url=img_url)\n material.texture = texture\n except:\n log.warn('Parse error in %s.', url)","async def document_valid(self, document):\n return web.json_response()","def document_has_numbers(db, documentId):\n document = db.documents.find_one({'_id': documentId})\n phrases = list(numeric.finditer(document['body']))\n\n return len(phrases) > 1","def read_book(url,book_num):\n\t#calls open_url function to open the url\n\tbook_contents = open_url(url)\n\tif book_contents != None:\n\t\t#calls filter data function to clean the data\n\t\tclean_data = filter_data(book_contents)\n\t\t#create dictionary for all the words in this book with 0's filling for count in all the books\n\t\tcreate_dict(clean_data)\n\t\treturn clean_data\n\telse:\n\t\treturn []","def stored_fields(self, docnum):\r\n return self.doc_reader[docnum]","def have_doc_extension(l):\r\n if \".doc\" in str(l):\r\n return 1\r\n else:\r\n return 0","def _load_document(path, app):\n start_inventor()\n document_type_enum = {\n 12289: 'UnnownDocument',\n 12290: 'PartDocument',\n 12291: 'AssemblyDocument',\n 12292: 'DrawingDocument',\n 12293: 'PresentationDocument',\n 12294: 'DesignElementDocument',\n 12295: 'ForeignModelDocument',\n 12296: 'SATFileDocument',\n 12297: 'NoDocument',\n }\n try:\n app.Documents.Open(str(path))\n document_type = document_type_enum[app.ActiveDocumentType]\n doc = win32com.client.CastTo(app.ActiveDocument, document_type)\n print(doc, document_type)\n return doc\n except:\n print('unable to load file')\n return None","def get_schema(self, schema_versions_info):\n schema = None\n version = api_version_request.APIVersionRequest(VOLUME_MICROVERSION)\n for items in schema_versions_info:\n min_version = api_version_request.APIVersionRequest(items['min'])\n max_version = api_version_request.APIVersionRequest(items['max'])\n # This is case where COMPUTE_MICROVERSION is None, which means\n # request without microversion So select base v2.1 schema.\n if version.is_null() and items['min'] is None:\n schema = items['schema']\n break\n # else select appropriate schema as per COMPUTE_MICROVERSION\n elif version.matches(min_version, max_version):\n schema = items['schema']\n break\n if schema is None:\n raise exceptions.JSONSchemaNotFound(\n version=version.get_string(),\n schema_versions_info=schema_versions_info)\n return schema","def get_lens(filenames, yesno=True):\n if yesno:\n param = \"yes_lens\"\n else:\n param = \"no_lens\"\n return filter_filenames(filenames, [param])","def select_calib_manu(calib_lst, promotion,\n error_message='Warning: \"{}\" is not a valid calib object',\n ):\n # print promotion and read input frameid list\n while(True):\n string = input(promotion)\n ref_calib_lst = []\n succ = True\n for s in string.split(','):\n s = s.strip()\n if len(s)>0 and s.isdigit():\n frameid = int(s)\n if frameid in calib_lst:\n # user input is found in calib_lst\n calib = calib_lst[frameid]\n ref_calib_lst.append(calib)\n else:\n # user input is not found in calib_lst\n print(error_message.format(s))\n succ = False\n break\n\n else:\n print(error_messag.format(s))\n succ = False\n break\n if succ:\n return ref_calib_lst\n else:\n continue","def document_to_lda_features(lda_model, document):\n topic_importances = lda_model.get_document_topics(document, minimum_probability=0)\n topic_importances = numpy.array(topic_importances)\n return topic_importances[:,1]","def _parse_data(self, data):\n return data.get('docs', [])","def get_material_set(**kw):\n mat_ids = set()\n volumes = get_volume_list()\n for v in volumes:\n d = volume_metadata( v )\n if( kw.get('with_rho') is True ):\n # rho is undefined for the void material and dagmc may return anything.\n if d['material'] == 0:\n mat_ids.add( (d['material'], 0.0) )\n else:\n mat_ids.add( (d['material'], d['rho']) )\n else:\n mat_ids.add( d['material'] )\n return mat_ids","def not_sent(self, wos_collections_allowed, code_title=None, publication_year=1800):\n\n fltr = {'sent_wos': 'False',\n 'applicable': 'True',\n 'collection': {'$in': wos_collections_allowed},\n 'publication_year': {'$gte': str(publication_year)}}\n\n if code_title:\n fltr.update({'code_title': code_title})\n logging.debug('Select documents: %s' % str(fltr))\n documents = []\n total = 0\n for document in self._articles_coll.find(fltr, {'collection':1, 'code': 1}):\n total += 1\n documents.append([document['collection'], document['code']])\n\n i = 0\n for document in documents:\n i = i + 1\n logging.debug('Selected document: %s' % str(document))\n yield [total, i, self._articles_coll.find_one({'collection': document[0], 'code': document[1]}, {'citations': 0})]","def supportedType(request, video_types):\n return request.FILES['file'].content_type in video_types.keys()","def _validate_built_in(self, tipo):\n\n self.source_type = False\n self.source_file = \"builtin\"\n return tipo in self.c_built_ins or self._match_array(tipo, self.c_built_in_array_types)","def isrequired(self, typename):\n return typename in self.required_products","def show_available_products():\n\n mongo = MongoDBConnection()\n result = {}\n\n with mongo:\n db = mongo.connection.HPNorton\n productcollection = db[\"products\"]\n for document in productcollection.find({\"quantity_available\": {\"$gt\": \"0\"}}):\n key = document['product_id']\n\n result[key] = {\n 'description': document['description'],\n 'product_type': document['product_type'],\n 'quantity_available': document['quantity_available']\n }\n\n return result","def test_create_material_multi_basic(self):\n expected_materials = [\n ['cotton', 'AAA', 'BBB', 'CCC'],\n ['cotton', 'AAA', 'BBB', 'CCC'],\n ['wool', 'AAA', 'BBB', 'CCC'],\n ]\n\n select_listings_to_edit(self.driver)\n d = self.driver\n bp = BulkPage(d)\n\n send_keys(bp.operation_input(), 'AAA,BBB ,CCC')\n click(bp.operation_apply())\n\n material_names = bp.material_names()\n assert material_names == expected_materials","def unitsDetector(self, num):\n try:\n num = int(num)\n except:\n sys.exit('Invalid input! Method only takes ints or floats.')\n \n digits = 0\n while num > 1:\n num /= 10\n digits += 1\n \n digits -= 1\n ind = digits // 3\n units = {3: 'B', 2: 'M', 1: 'K', 0: ''}[ind]\n \n return 10 ** (ind * 3), units","def has_doc() -> None:","def listFeaturableContentTypes():","def get_document(self, docid):\n raise NotImplementedError","def get_multimedia_documents(shelter_id=0, category_id=2, section=\"Documents\"):\r\n shelter = Shelter.query.filter(Shelter.id==shelter_id).first()\r\n if not current_user.is_authenticated:\r\n return 'unauthorized', 403\r\n\t\r\n if not shelter:\r\n return 'no such shelter', 400 \r\n\r\n file = request.files.get('mediafile', None)\r\n\r\n for f in request.files:\r\n if request.files[f] and request.files[f].filename == '':\r\n return 'No selected file', 400 \r\n if request.files[f] and not allowed_file(request.files[f].filename,\r\n conf.ALLOWED_EXTENSIONS_DOCUMENT):\t\r\n return 'File type not allowed', 400\r\n\t\t\r\n path = os.path.join(conf.SHELTERS_DOCUMENTS_SERVER_PATH, str(shelter.id))\r\n logging.debug('path:' + path)\r\n\t\t\t\r\n if not os.path.exists(path):\r\n os.makedirs(path)\r\n\r\n filename = secure_filename(request.files[f].filename)\r\n request.files[f].save(os.path.join(path , filename))\r\n\r\n if category_id:\r\n new_media = ShelterDocument(file_name=filename,\r\n shelter_id=shelter.id, category_id=category_id)\r\n db.session.add(new_media)\r\n db.session.commit()\r\n return str(new_media.id), 200\r\n\r\n return 'Document not uploaded', 400"],"string":"[\n \"def return_materials(cls, user, doc_no, mode, return_list):\\n\\n # Retrieve documents based on what it is given\\n doc = Docs.of_doc_no(doc_no)\\n\\n # Validate input,\\n # Extract source to compare\\n current_list = []\\n ref_doc = None\\n from_ref_doc = None\\n if mode in [cls.TYPE_STAGED_IN_INVENTORY_CONTENT, cls.TYPE_STAGED_FROM_CLERK_AUX_TASK] and isinstance(doc, prod_doc.StoreAuxTask):\\n o = cls.return_candidates(doc_no)\\n current_list = list(o[0]['return_list'])\\n doc.populate('parent_task')\\n doc.parent_task.populate('ref_doc')\\n ref_doc = doc.parent_task.ref_doc\\n from_ref_doc = doc.parent_task\\n elif mode == cls.TYPE_WIP_FROM_PRODUCTION_ORDER and isinstance(doc, prod_doc.ProductionOrderOperation):\\n doc.populate('ref_doc')\\n current_list = [{\\n 'material': 'WIP-CANDIDATE',\\n 'quantity': doc.get_confirmable_quantity(),\\n 'uom': doc.ref_doc.uom,\\n 'revision': None,\\n 'size': None,\\n 'weight': 0,\\n }]\\n ref_doc = doc.ref_doc\\n else:\\n # Unsupported case\\n raise BadParameterError(_(\\\"ERR_UNSUPPORTED_DOCUMENT_TYPE: %(document_type)s\\\") % {\\n 'document_type': str(type(doc))\\n })\\n\\n # prepare movement based on return_list\\n # Sanity check\\n if len(current_list) != len(return_list):\\n raise BadParameterError(_(\\\"ERR_UNEQUAL_SIZE_OF_RETURN_LIST: %(expected_size)s != $(actual_size)s\\\") % {\\n 'expected_size': len(current_list),\\n 'actual_size': len(return_list)\\n })\\n\\n # index list for ease of comparison\\n current_list = dict(map(lambda a: (str(a['material']), a), current_list))\\n return_list = dict(map(lambda a: (str(a['material']), a), return_list))\\n\\n movements = []\\n entries = []\\n lost_entries = []\\n movement_type = None\\n lost_movement_type = None\\n # From STAGED => STORE\\n # From STAGED => LOST & FOUND\\n for key, value in current_list.iteritems():\\n lost = value['quantity'] - return_list[key]['quantity']\\n\\n # NOTE: For WIP scenario\\n if key == 'WIP-CANDIDATE':\\n # init Goods receipt by-product for WIP to STORE\\n # TODO: value for WIP to be fixed later with proper logic. now using static 543210.\\n movement_type = stock_doc.InventoryMovement.GR_BP\\n user.can(stock_doc.InventoryMovement.ACTION_WRITE(), movement_type, throw=True)\\n\\n # NOTE: Generate WIP Stock code\\n wip_material = doc.ref_doc.material.create_wip(doc.task.code)\\n\\n # NOTE: Create material master from stock code\\n stock_doc.MaterialMaster.factory(wip_material, uom=doc.ref_doc.uom, procurement_type=stock_doc.MaterialMaster.INTERNAL, author=user)\\n\\n current_list[key]['material'] = wip_material\\n current_list[key]['uom'] = stock_doc.MaterialMaster.get(doc.ref_doc.material).uom.code\\n entries.append(stock_doc.InventoryMovementEntry.factory(material=wip_material,\\n quantity=return_list[key]['quantity'],\\n location=Location.factory('STORE').code,\\n weight=return_list[key]['weight'],\\n value=543210))\\n\\n if lost > 0:\\n # if lost is found, init Goods receipt lost and found for WIP to STORE\\n lost_movement_type = stock_doc.InventoryMovement.GR_LT\\n lost_entries.append(stock_doc.InventoryMovementEntry.factory(material=wip_material,\\n quantity=return_list[key]['quantity'],\\n location=Location.factory('LOST').code,\\n weight=return_list[key]['weight'],\\n value=543210))\\n # NOTE: For STAGING scenario\\n else:\\n # init Stock transfer production to location for STAGING to STORE\\n movement_type = stock_doc.InventoryMovement.ST_PL\\n user.can(stock_doc.InventoryMovement.ACTION_WRITE(), movement_type, throw=True)\\n entries.extend(stock_doc.InventoryMovementEntry.transfer_pair_factory(material=value['material'],\\n quantity=return_list[key]['quantity'],\\n from_location=Location.factory('STAGING').code,\\n to_location=Location.factory('STORE').code,\\n from_ref_doc=from_ref_doc))\\n\\n if lost > 0:\\n # if lost is found, init Stock transfer lost and found for STAGING to STORE retaining ref_doc\\n lost_movement_type = stock_doc.InventoryMovement.ST_LT\\n lost_entries.extend(stock_doc.InventoryMovementEntry.transfer_pair_factory(material=value['material'],\\n quantity=return_list[key]['quantity'],\\n from_location=Location.factory('STAGING').code,\\n to_location=Location.factory('LOST').code,\\n from_ref_doc=from_ref_doc,\\n to_ref_doc=from_ref_doc))\\n if not entries:\\n raise ValueError(\\\"Nothing to return\\\")\\n\\n movement = stock_doc.InventoryMovement.factory(movement_type, list(entries), ref_doc=ref_doc)\\n movements.append(movement)\\n\\n if lost > 0:\\n user.can(stock_doc.InventoryMovement.ACTION_WRITE(), lost_movement_type, throw='challenge')\\n movement = stock_doc.InventoryMovement.factory(lost_movement_type, list(lost_entries), ref_doc=ref_doc)\\n movements.append(movement)\\n\\n if movements:\\n # validate first then touched\\n map(lambda x: x.validate(user=user), movements)\\n\\n # NOTE: create a new pair of store & clerk with different parent depending on mode\\n if mode == cls.TYPE_STAGED_FROM_CLERK_AUX_TASK:\\n task_signals.task_repeat.send(cls, parent=doc.parent_task, components=[v for i, v in current_list.iteritems()])\\n if mode == cls.TYPE_WIP_FROM_PRODUCTION_ORDER:\\n task_signals.task_repeat.send(cls, parent=doc, components=[v for i, v in current_list.iteritems()])\\n doc.ready(user)\\n \\n map(lambda x: x.touched(user), movements)\\n\\n return len(movements)\",\n \"def get_required_docs(doc_type):\\n doc_type = 1 if doc_type == 'registration' else 2\\n return Documents.query.filter_by(type=doc_type, required=True).all()\",\n \"def get_documents(doc_type):\\n doc_type = 1 if doc_type == 'registration' else 2\\n return Documents.query.filter_by(type=doc_type).all()\",\n \"def list_available_document_types(cls):\\n\\n response = cls._client.get(\\\"automatedDocumentOptions/\\\")\\n return from_api(response.json())\",\n \"def get_material(material):\\n for libn,tdict in liblist:\\n if material in tdict:\\n return tdict[material]\\n print (material, \\\" not found\\\")\\n raise KeyError\",\n \"def find_material(material):\\n for libn,tdict in liblist:\\n if material in tdict:\\n print(libn)\",\n \"def _getMaterials(self):\\n warnings.warn(\\\"This function is deprecated; call Document.getMaterialNodes() instead.\\\", DeprecationWarning, stacklevel = 2)\\n return self.getMaterialNodes()\",\n \"def find_by_doc_reg_num(cls, doc_reg_num: str):\\n document = None\\n if doc_reg_num:\\n try:\\n document = cls.query.filter(Db2Document.document_reg_id == doc_reg_num).one_or_none()\\n except Exception as db_exception: # noqa: B902; return nicer error\\n current_app.logger.error('Db2Document.find_by_doc_reg_num exception: ' + str(db_exception))\\n raise DatabaseException(db_exception)\\n if document:\\n document.strip()\\n return document\",\n \"def documents(self, **kw):\\r\\n \\r\\n doc_reader = self.doc_reader\\r\\n return (doc_reader[docnum] for docnum in self.document_numbers(**kw))\",\n \"def info_materials_type_id_get(type_id):\\n session = info_map.Session()\\n q = session.query(info_map.Material).filter(info_map.Material.type == type_id)\\n\\n mat = q.one_or_none() # return the only result or `None`\\n\\n if mat is not None:\\n material_info = MaterialInfo(\\n type=mat.type,\\n group=mat.group_id,\\n name=mat.name,\\n volume=mat.volume)\\n return material_info, 200\\n else:\\n error = Error('Type {} Not Found'.format(type_id))\\n return error, 404\",\n \"def document_number(self, **kw):\\r\\n for docnum in self.document_numbers(**kw):\\r\\n return docnum\",\n \"def is_extendable(doc):\\n # Defensive programming.\\n if not isinstance(doc, str):\\n rt.assert_doc('doc', doc, \\\"Invalid document\\\")\\n\\n doc_type = doc if isinstance(doc, str) else doc.type_key\\n\\n return doc_type.lower() in SUPPORTED\",\n \"def documents(pmid_23982599, civic_aid6_document):\\n return [pmid_23982599, civic_aid6_document]\",\n \"def info_materials_polymer_get():\\n materials = _material_by_group(974) # 974 == intermediate group\\n return materials, 200\",\n \"def get_wmis_for_oem(self, oem):\\n return self.get('vehicles/GetWMIsForManufacturer/{}'.format(oem))\",\n \"def info_materials_get():\\n materials = _material_by_group() # empty means all groups\\n return materials, 200\",\n \"def test_mhr_document_type(session):\\n results = type_tables.MhrDocumentType.find_all()\\n assert results\\n assert len(results) >= 55\\n for result in results:\\n assert result.document_type in type_tables.MhrDocumentTypes\\n assert result.document_type_desc\\n\\n doc_result = type_tables.MhrDocumentType.find_by_doc_type(type_tables.MhrDocumentTypes.REG_101.value)\\n assert doc_result\\n assert doc_result.document_type == type_tables.MhrDocumentTypes.REG_101.value\\n assert doc_result.document_type_desc == 'MANUFACTURED HOME REGISTRATION'\\n assert doc_result.legacy_fee_code == 'MHR400'\\n doc_result = type_tables.MhrDocumentType.find_by_doc_type('XXX')\\n assert not doc_result\",\n \"def get_document_info(self, document_code):\\n if \\\".zip\\\" in self.filename:\\n return self.zip.getinfo(document_code + '_mets.xml')\\n else:\\n return\",\n \"def list_support_required(self):\\n\\t\\treturn self.typemanager.has_lists\",\n \"def XCAFDoc_DocumentTool_MaterialTool(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialTool(*args)\",\n \"def get_document_multiple_fields(self, metadata_dict):\\n\\n for field in metadata_dict.keys():\\n if field not in self.metadata_fields:\\n raise MissingMetadataError([field])\\n\\n for document in self.documents:\\n match = True\\n for field, val in metadata_dict.items():\\n if getattr(document, field, None) != val:\\n match = False\\n if match:\\n return document\\n\\n raise ValueError(\\\"Document not found\\\")\",\n \"def check_only_supported_materials_are_used(progress_controller=None):\\n if progress_controller is None:\\n progress_controller = ProgressControllerBase()\\n\\n non_arnold_materials = []\\n all_valid_materials = []\\n for renderer in VALID_MATERIALS.keys():\\n all_valid_materials.extend(VALID_MATERIALS[renderer])\\n\\n all_materials = pm.ls(mat=1)\\n progress_controller.maximum = len(all_materials)\\n for material in all_materials:\\n if material.name() not in [\\\"lambert1\\\", \\\"particleCloud1\\\", \\\"standardSurface1\\\"]:\\n if material.type() not in all_valid_materials:\\n non_arnold_materials.append(material)\\n progress_controller.increment()\\n\\n if len(non_arnold_materials):\\n pm.select(non_arnold_materials)\\n progress_controller.complete()\\n raise PublishError(\\n \\\"There are non-Arnold materials in the scene:<br><br>%s<br><br>\\\"\\n \\\"Please remove them!!!\\\"\\n % \\\"<br>\\\".join(map(lambda x: x.name(), non_arnold_materials))\\n )\\n progress_controller.complete()\",\n \"def find_by_mhr_number(cls, mhr_number: str):\\n documents = None\\n if mhr_number:\\n try:\\n documents = cls.query.filter(Db2Document.mhr_number == mhr_number).all()\\n if documents:\\n for doc in documents:\\n doc.strip()\\n except Exception as db_exception: # noqa: B902; return nicer error\\n current_app.logger.error('Db2Document.find_by_mhr_number exception: ' + str(db_exception))\\n raise DatabaseException(db_exception)\\n\\n return documents\",\n \"def is_use_documents(self):\\n\\t\\treturn bool(call_sdk_function('PrlVmCfg_IsUseDocuments', self.handle))\",\n \"def IsMaterial(self, *args):\\n return _XCAFDoc.XCAFDoc_MaterialTool_IsMaterial(self, *args)\",\n \"def doc_topics(self, doc_number):\\n doc_topic = self.gammas / self.gammas.sum(axis=1)[:, np.newaxis]\\n return doc_topic[doc_number]\",\n \"def info_materials_booster_get():\\n materials = _material_by_group(712) # 712 == intermediate group\\n return materials, 200\",\n \"def info_materials_composites_get():\\n materials = _material_by_group(429) # 429 == intermediate group\\n return materials, 200\",\n \"def has_material(obj, name):\\n return name in obj.data.materials.keys()\",\n \"def XCAFDoc_DocumentTool_MaterialsLabel(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialsLabel(*args)\",\n \"def MaterialTool(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialTool(*args)\",\n \"def test_materials_present(self):\\n self.assertIsNotNone('Materials' in self.header.parameters.attrs)\",\n \"def process(self, doc):\\n raise multisearch.errors.FeatureNotAvailableError\",\n \"def document_type(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"document_type\\\")\",\n \"def get_makes_by_oem(self, oem, year=None):\\n if year is None:\\n return self.get('vehicles/GetMakesForManufacturer/{}'.format(oem))\\n return self.get(\\n 'vehicles/GetMakesForManufacturerAndYear/{}'.format(oem), params=dict(year=year))\",\n \"def _getDocumentType(self):\\n\\n fileName, fileExtension = os.path.splitext(self.sourceFileName)\\n documentType = None\\n\\n for knownType in list(TextRepresentation.KNOWNTYPES.keys()):\\n if knownType == fileExtension[1:]:\\n documentType = knownType\\n\\n if documentType is None:\\n raise Exception(\\\"Unknown document type: %s\\\" % fileExtension[1:])\\n \\n return documentType\",\n \"def choose_documents(update, context):\\n sc_api = SmartCAT(SMARTCAT_API_USERNAME, SMARTCAT_API_PASSWORD)\\n names_or_ids = update.message.text.lower().strip()\\n documents = []\\n response = sc_api.project.get(SMARTCAT_PROJECT_ID)\\n if response.status_code != 200:\\n update.message.reply_text(SHIT_HAPPENS + \\\"\\\\nВведи названия глав еще раз или /cancel для выхода\\\")\\n return constants.STATE_CHOOSE_DOCUMENT\\n\\n project_data = json.loads(response.content.decode('utf-8'))\\n if not project_data:\\n update.message.reply_text(SHIT_HAPPENS + \\\"\\\\nВведи названия глав еще раз или /cancel для выхода\\\")\\n return constants.STATE_CHOOSE_DOCUMENT\\n\\n logger.info('names_or_ids = {}'.format(names_or_ids))\\n if names_or_ids == ALL_CHAPTERS.lower() or names_or_ids == ALL.lower():\\n documents = project_data['documents']\\n elif names_or_ids == ACTIVE_CHAPTERS.lower():\\n for d in project_data['documents']:\\n logger.info(\\\"{} - {}\\\".format(d['name'], get_document_stage(d)))\\n if get_document_stage(d) < constants.CHAPTER_STATE_FINAL_EDITING:\\n logger.info(\\\"Document added: {0} {1}\\\".format(d['id'], d['name']))\\n documents.append(d)\\n elif names_or_ids == CHAPTERS_BEING_TRANSLATED.lower():\\n for d in project_data['documents']:\\n if get_document_stage(d) == constants.CHAPTER_STATE_TRANSLATION:\\n logger.info(\\\"Document added: {0} {1}\\\".format(d['id'], d['name']))\\n documents.append(d)\\n elif names_or_ids == CHAPTERS_BEING_EDITED.lower():\\n for d in project_data['documents']:\\n if get_document_stage(d) == constants.CHAPTER_STATE_EDITING:\\n logger.info(\\\"Document added: {0} {1}\\\".format(d['id'], d['name']))\\n documents.append(d)\\n else:\\n names_or_ids = [s.lower().strip() for s in names_or_ids.split(\\\"\\\\n\\\")]\\n for d in project_data['documents']:\\n if d['id'].lower() in names_or_ids or d['name'].lower().strip() in names_or_ids:\\n logger.info(\\\"Document added: {0} {1}\\\".format(d['id'], d['name']))\\n documents.append(d)\\n\\n if len(documents) == 0:\\n update.message.reply_text(NOTHING_FOUND + \\\"\\\\nВведи названия глав еще раз или /cancel для выхода\\\")\\n return constants.STATE_CHOOSE_DOCUMENT\\n\\n context.user_data['documents'] = documents\\n\\n if len(documents) == 1:\\n reply = \\\"Ок, я нашел одну главу: {0} ({1})\\\".format(documents[0]['name'], documents[0]['id'])\\n else:\\n reply = \\\"Ок, я нашел {0} глав.\\\\n\\\".format(len(documents))\\n for d in documents:\\n stage_name = constants.CHAPTER_STAGE_NAMES[get_document_stage(d)]\\n reply += \\\"- {0}: {1}\\\\n\\\".format(stage_name, d['name'])\\n\\n return reply\",\n \"def valid_doc_types():\\r\\n return \\\", \\\".join(DOC_PATHS.keys())\",\n \"def document_type(self, key, value):\\n _doc_type = self.get(\\\"document_type\\\", {})\\n\\n def doc_type_mapping(val):\\n if val:\\n return mapping(DOCUMENT_TYPE, val)\\n\\n for v in force_list(value):\\n val_a = doc_type_mapping(clean_val(\\\"a\\\", v, str))\\n val_b = doc_type_mapping(clean_val(\\\"b\\\", v, str))\\n\\n if not val_a and not val_b and not _doc_type:\\n raise UnexpectedValue(subfield=\\\"a\\\")\\n\\n if val_a and val_b and (val_a != val_b != _doc_type):\\n raise ManualImportRequired(\\n subfield=\\\"a or b - \\\" \\\"inconsistent doc type\\\"\\n )\\n if val_a:\\n if _doc_type and _doc_type != val_a:\\n raise ManualImportRequired(\\n subfield=\\\"a\\\" \\\"inconsistent doc type\\\"\\n )\\n _doc_type = val_a\\n if val_b:\\n if _doc_type and _doc_type != val_a:\\n raise ManualImportRequired(\\n subfield=\\\"b\\\" \\\"inconsistent doc type\\\"\\n )\\n _doc_type = val_b\\n return _doc_type\",\n \"def test_list_media_type(self):\\n\\n # check if documentalist has access to list media-types\\n self.login_documentalist()\\n response = self.client.get('/multimedia/media-types/' )\\n\\n # 403 = unauthorized\\n self.assertEqual(response.status_code, 403)\\n\\n self.client.logout()\\n self.login_admin()\\n\\n response = self.client.get('/multimedia/media-types/')\\n self.assertContains(response, \\\"Video\\\")\",\n \"def get_document_complexe_tags(self, document):\\n kw_extractor = yake.KeywordExtractor(lan=\\\"en\\\", n=3, top=30)\\n keywords = kw_extractor.extract_keywords(document)\\n\\n complexe_tag = []\\n for keyword in keywords:\\n if keyword[-1] >= 0.1:\\n break\\n complexe_tag.append(keyword[0])\\n return complexe_tag\",\n \"def info_materials_raw_get():\\n materials = _material_by_group(427) # 427 == intermediate group\\n return materials, 200\",\n \"def is_required_data(self, typename):\\n return typename in self.required_data_products\",\n \"def info_materials_intermediates_get():\\n materials = _material_by_group(428) # 428 == intermediate group\\n return materials, 200\",\n \"def is_content_supported(self, cbas):\\n\\n # 1\\n if self.accept_all_content:\\n return SupportInfo(True, None)\\n\\n # 2\\n if len(self.supported_content.filter(content_binding=cbas.content_binding, subtype=None)) > 0:\\n return SupportInfo(True, None)\\n\\n # 2a (e.g., subtype = None so #3 would end up being the same check as #2)\\n if not cbas.subtype: # No further checking can be done\\n return SupportInfo(False, None)\\n\\n # 3\\n if len(self.supported_content.filter(content_binding=cbas.content_binding, subtype=cbas.subtype)) > 0:\\n return SupportInfo(True, None)\\n\\n # 4\\n return SupportInfo(False, None)\",\n \"def getDocuments(self):\\n return self.objectValues('Multifile')\",\n \"def get_documents(self, engine_name, document_ids):\\n endpoint = \\\"engines/{}/documents\\\".format(engine_name)\\n data = json.dumps(document_ids)\\n return self.swiftype_session.request('get', endpoint, data=data)\",\n \"def is_required_data(self, typename):\\r\\n return typename in self.required_data_products\",\n \"def doc_types(self):\\n return self._extract_set('doc_type')\",\n \"def get_specnos(self, spectra_file):\\n\\n self.plate_num = spectra_file[-23:-19]\\n print(\\\"Fitting spectra in plate %s\\\" % self.plate_num)\\n self.spectra = np.load(spectra_file) \\n self.this_plate = self.MetaData[self.MetaData['PLATE'] == int(self.plate_num)]\\n\\n if self.ttype == 'test':\\n max_num = 10 \\n self.specnos = self.this_plate[0: max_num]['SPECNO']\\n elif self.ttype == 'blue':\\n max_num = len(self.spectra)-1\\n self.specnos = self.this_plate[(self.this_plate['CAMERAS'] == b'b1') | (self.this_plate['CAMERAS'] == b'b2')]['SPECNO']\\n elif self.ttype == 'red':\\n max_num = len(self.spectra)-1\\n self.specnos = self.this_plate[(self.this_plate['CAMERAS'] == b'r1') | (self.this_plate['CAMERAS'] == b'r2')]['SPECNO']\\n elif self.ttype == 'full':\\n max_num = len(self.spectra)\\n self.specnos = self.this_plate['SPECNO']\\n else: \\n print(\\\"not a valid type. Going to test\\\")\\n max_num = 10 #len(spectra) Number of spectra in a given plate that you want to run this for. Mostly for debugging\\n self.specnos = np.random.choice(self.this_plate['SPECNO'], size=max_num)\\n\\n self.spectra_length = len(self.specnos)\",\n \"def check_document(self, document):\\n if isinstance(document, dict):\\n document = json.dumps(document)\\n r = requests.post('/'.join([self.base_url,\\n 'check']),\\n data=document,\\n headers={'Content-Type': 'application/json'})\\n return r.json()\",\n \"def GetDoc(index_wd, key, value):\\n # init code:\\n key = key.lower().strip()\\n value = value.strip()\\n docid_to_docno_path = os.path.join(index_wd, 'docid_to_docno.p')\\n docno_to_data_path = os.path.join(index_wd, 'docno_to_data.p')\\n # check to see what key is (docno or docid)\\n\\n # if docid, need to grab the docid_to_docno from disk\\n # Lookup via docid:\\n if key == 'docid':\\n docid_to_docno = pickle.load(open(docid_to_docno_path, \\\"rb\\\"))\\n value = int(value)\\n if value not in docid_to_docno:\\n print(\\\"The following docid: {} was not found.\\\".format(value))\\n return False\\n else:\\n docno = docid_to_docno[value]\\n #Lookup via docno:\\n elif key == 'docno':\\n docno = value\\n else:\\n print(\\\"The following key: {} is invalid. Pass: 'docid' or 'docno'\\\".format(key))\\n return False\\n docno_to_data = pickle.load(open(docno_to_data_path, \\\"rb\\\"))\\n\\n if docno not in docno_to_data:\\n print(\\\"The following docno: {} was not found.\\\".format(value))\\n return False\\n\\n doc_path = docno_to_data[docno]\\n\\n basepath = os.path.dirname(doc_path)\\n\\n if not os.path.exists(basepath):\\n print(\\\"The basepath {} does not exist.\\\".format(basepath))\\n return False\\n\\n metadata = MetaData(doc_path)\\n metadata.load()\\n metadata.meta_print()\\n return True\",\n \"def test_application_document_types_have_generation_spec(self, test_client, db_session,\\n auth_headers):\\n\\n get_resp = test_client.get(\\n f'/now-applications/application-document-types',\\n headers=auth_headers['full_auth_header'])\\n assert get_resp.status_code == 200\\n get_data = json.loads(get_resp.data.decode())\\n assert len([x for x in get_data['records'] if x['document_template']]) > 0\\n assert len([x for x in get_data['records'] if x['document_template']]) == len(\\n [x for x in NOWApplicationDocumentType.get_all() if x.document_template_code])\",\n \"def get_custom_fields(self, doc_type):\\n\\t\\tcustom_doclist = []\\n\\t\\tres = webnotes.conn.sql(\\\"\\\"\\\"SELECT * FROM `tabCustom Field`\\n\\t\\t\\tWHERE dt = %s AND docstatus < 2\\\"\\\"\\\", doc_type, as_dict=1)\\n\\t\\tfor r in res:\\n\\t\\t\\t# Cheat! Mask Custom Field as DocField\\n\\t\\t\\tcustom_field = webnotes.model.doc.Document(fielddata=r)\\n\\t\\t\\tself.mask_custom_field(custom_field, doc_type)\\n\\t\\t\\tcustom_doclist.append(custom_field)\\n\\n\\t\\treturn custom_doclist\",\n \"def isSonyMscCamera(info):\\n return info.manufacturer == SONY_MANUFACTURER_SHORT and info.model in SONY_MSC_MODELS\",\n \"def test_team_builder_config_product_size_materials_find_one_get(self):\\n pass\",\n \"def get_receipt_knowledge_status(self):\\n return self.get_document_status_choice(self.receipt_knowledge_status)\",\n \"def get_availability(self):\\n return [self.to_fname(type).is_file() for type in (\\\"pdf\\\", \\\"si\\\")]\",\n \"def is_content_supported(self, cbas):\\n\\n # 1\\n if self.accept_all_content:\\n return SupportInfo(True)\\n\\n # 2\\n if len(self.supported_content.filter(content_binding=cbas.content_binding, subtype=None)) > 0:\\n return SupportInfo(True)\\n\\n # 2a (e.g., subtype = None so #3 would end up being the same check as #2)\\n if not cbas.subtype: # No further checking can be done\\n return SupportInfo(False)\\n\\n # 3\\n if len(self.supported_content.filter(content_binding=cbas.content_binding, subtype=cbas.subtype)) > 0:\\n return SupportInfo(True)\\n\\n # 4\\n return SupportInfo(False)\",\n \"def GetMaterial(self, *args):\\n return _XCAFDoc.XCAFDoc_MaterialTool_GetMaterial(self, *args)\",\n \"def process_rec(rec_number: int,\\n incl_rec_slicer: slicer.SpecProcessor,\\n excl_rec_slicer: slicer.SpecProcessor,\\n rec: List[str],\\n incl_col_slicer: slicer.SpecProcessor,\\n excl_col_slicer: slicer.SpecProcessor) -> Optional[List[str]]:\\n # minimal validation\\n assert int(rec_number) >= 0\\n\\n # reject record if it isn't in the inclusion spec\\n if not incl_rec_slicer.spec_evaluator(rec_number):\\n return None\\n\\n # reject record if it is in the exclusion spec\\n if excl_rec_slicer.spec_evaluator(rec_number):\\n return None\\n\\n output_rec = []\\n for col_number in range(len(rec)):\\n if not incl_col_slicer.spec_evaluator(col_number):\\n continue\\n if excl_col_slicer.spec_evaluator(col_number):\\n continue\\n output_rec.append(rec[col_number])\\n\\n if output_rec:\\n return output_rec\\n else:\\n return None # don't return empty list\",\n \"def test_team_builder_config_product_size_materials_id_exists_get(self):\\n pass\",\n \"def DocumentType(self, default=[None]):\\n return self.data.get('metadata', {}).get('document_type', default)\",\n \"def getMediaInfo(self, type='dict'):\\n\\t\\t# define the content lists for sections 'General','Video','Audio'\\n\\t\\tcont_general = ['Count of video streams','Count of audio streams','Duration','File size']\\n\\t\\tcont_video = ['Format','Format profile','Codec ID','Duration','Bit rate','Width','Height','Frame rate','Frame count','Stream size']\\n\\t\\tcont_audio = ['Format','Codec ID/Info','Duration','Bit rate','Sampling rate','Samples count','Stream size']\\n\\t\\t\\n\\t\\t# the type parameter is used to provide the information in several formats\\n\\t\\tif type == 'dict': # Info is provided as python dictionary\\n\\t\\t\\t# prepare dictionary\\n\\t\\t\\tresult = {}\\n\\t\\t\\t# prepare section 'General'\\n\\t\\t\\tresult['General'] = {}\\n\\t\\t\\t# fill section 'General' with its information\\n\\t\\t\\tresult = self._fill_section_dict(result, 'General',cont_general)\\n\\t\\t\\t\\n\\t\\t\\t# prepare and fill section 'Video' with its information\\n\\t\\t\\tif 'Count of video streams' in result['General'][0].keys():\\n\\t\\t\\t\\tresult['Video'] = {}\\n\\t\\t\\t\\tresult = self._fill_section_dict(result,'Video',cont_video, result['General'][0]['Count of video streams'])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tresult['Video'] = 'No stream'\\n\\t\\t\\t\\n\\t\\t\\t# prepare and fill section 'Audio' with its information\\n\\t\\t\\tif 'Count of audio streams' in result['General'][0].keys():\\n\\t\\t\\t\\tresult['Audio'] = {}\\n\\t\\t\\t\\tresult = self._fill_section_dict(result,'Audio',cont_audio, result['General'][0]['Count of audio streams'])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tresult['Audio'] = 'No stream'\\n\\t\\t\\t\\n\\t\\t\\treturn result\\n\\t\\t\\t\\n\\t\\t\\t# I'm not sure, if a section 'Text' as mentioned in the MediaInfo documentation is needed here\\n\\t\\t\\t\\n\\t\\telif type == 'json': # Info is provided as json string\\n\\t\\t\\t# start json string\\n\\t\\t\\tresult = '{'\\n\\t\\t\\t# prepare section 'General'\\n\\t\\t\\tresult += '\\\\'General\\\\':'\\n\\t\\t\\t# fill section 'General' with its information\\n\\t\\t\\tresult = self._fill_section_json(result, 'General', cont_general)\\n\\t\\t\\t\\n\\t\\t\\t# prepare section 'Video'\\n\\t\\t\\tresult += '\\\\'Video\\\\':'\\n\\t\\t\\tif 'General_0_Count of video streams' in self.full_info.keys():\\n\\t\\t\\t\\tresult = self._fill_section_json(result, 'Video', cont_video,int(self.full_info['General_0_Count of video streams']))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tresult += '\\\\'No stream\\\\''\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t# prepare section 'Audio'\\n\\t\\t\\tresult += '\\\\'Audio\\\\''\\n\\t\\t\\tif 'General_0_Count of audio streams' in self.full_info.keys():\\n\\t\\t\\t\\tresult = self._fill_section_json(result, 'Audio', cont_audio,int(self.full_info['General_0_Count of audio streams']))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tresult += '\\\\'No stream\\\\''\\n\\t\\t\\n\\t\\t\\t# end result\\n\\t\\t\\tresult += '}'\\n\\t\\treturn result\",\n \"def DocumentType(self, default='article'):\\n return self.data.get('document_type', [{}])\",\n \"def check_if_embedded(lookup_value, lookup_type='content_id'):\\n video_json = get_video_info(lookup_value, lookup_type)\\n return video_json['embedded']\",\n \"def is_supported_document_type(path):\\n return any([str(path).lower().endswith(suffix) for suffix in _ALL_SUPPORTED_EXTENSIONS])\",\n \"def query_supported_software(self):\\n api_uri = self._uri_dict.get('querySupportedSoftware')\\n data = {}\\n r_data = self._post(api_uri, data)\\n return r_data\",\n \"def document_parse(self, document):\\n if type(document) == dict:\\n self.list_parse(document['blocks'])\\n elif type(document) == list:\\n self.list_parse(document[1])\\n else:\\n print('Incompatible Pandoc version. Process failed.')\",\n \"def assertCanFind(self, document):\\n ctool = self.portal.portal_catalog\\n self.assertEqual(len(ctool.searchResults(UID=document.UID())), 1)\",\n \"def supports(cls, filename_):\\n raise BookInfoNotImplementedError('supports', cls.__name__)\",\n \"async def _find(self, document: str) -> Union[List[pq], None]:\\n doc = pq(document)\\n data = doc(self.selector)\\n if not data and self.raise_not_found:\\n raise InformationError(\\\"No matches for '{selector}' found in document\\\".format(selector=self.selector))\\n elif not data:\\n return None\\n elif len(data) > 1 and not self.accept_multiples:\\n raise InformationError(\\\"Too many matches for '{selector}' found in document\\\".format(selector=self.selector))\\n return data\",\n \"def test_parse_library_charges_from_spec_docs(self):\\n # TODO: This test is practically useless while the XML strings are hard-coded at the top of this file.\\n # We should implement something like doctests for the XML snippets on the SMIRNOFF spec page.\\n ForceField(xml_spec_docs_ala_library_charges_xml)\\n ForceField(xml_spec_docs_tip3p_library_charges_xml)\",\n \"def parse_docs_from_page(self, page_url: str, page_text: str) -> Iterable[Document]:\\n # parse html response\\n url = \\\"https://www.med.navy.mil/directives/Pages/Publications.aspx\\\"\\n base_url = 'https://www.med.navy.mil'\\n parsed_docs = []\\n doc_name_list = []\\n if (page_url.find(\\\"Publications\\\") != -1):\\n doc_type = \\\"NAVMED\\\"\\n elif (page_url.find(\\\"BUMEDNotes\\\") != -1):\\n doc_type = \\\"BUMEDNOTE\\\"\\n elif (page_url.find(\\\"BUMEDInstructions\\\") != -1):\\n doc_type = \\\"BUMEDINST\\\"\\n cac_required = ['CAC', 'PKI certificate required', 'placeholder', 'FOUO']\\n page = requests.get(page_url, verify=CERTIFICATE_DIR + '/cat3.pem')\\n soup = bs4.BeautifulSoup(page.content, 'html.parser')\\n webpart = soup.find(id=\\\"onetidDoclibViewTbl0\\\")\\n items = webpart.find_all('a')\\n meta = webpart.find_all(lambda tag: tag.name == 'td' and tag.get('class') == ['ms-vb2'])\\n meta_list = list(meta)\\n meta_list = [remove_html_tags(str(t)) for t in meta_list]\\n meta_list = [str(t).encode('ascii', 'ignore').decode('ascii') for t in meta_list]\\n meta_list = [x.replace(\\\"\\\\r\\\\n\\\", \\\" \\\") for x in meta_list]\\n if (doc_type == \\\"NAVMED\\\"):\\n n = 3\\n elif (doc_type == \\\"BUMEDINST\\\" or doc_type == \\\"BUMEDNOTE\\\"):\\n n = 4\\n meta_ = [meta_list[i:i + n] for i in range(0, len(meta_list), n)]\\n if (doc_type == \\\"NAVMED\\\"):\\n subject = webpart.find_all(lambda tag: tag.name == 'td' and tag.get('class') == ['ms-vb-title'])\\n name_list = list(subject)\\n name_list = [remove_html_tags(str(t)) for t in name_list]\\n name_list = [str(t).encode('ascii', 'ignore').decode('ascii') for t in name_list]\\n subnum = [str(t[1]).split()[:2] for t in meta_]\\n title_ = [str(t[1]).split()[2:] for t in meta_]\\n title = [' '.join(t) for t in title_]\\n title = [str(t).replace(',', '') for t in title]\\n date = [t[0] for t in meta_]\\n metadata = zip(subnum, title, date, name_list)\\n metadata = [list(a) for a in metadata]\\n elif (doc_type == \\\"BUMEDINST\\\"):\\n subject = webpart.find_all(lambda tag: tag.name == 'td' and tag.get('class') == ['ms-vb-title'])\\n name_list = list(subject)\\n name_list = [remove_html_tags(str(t)) for t in name_list]\\n name_list = [str(t).encode('ascii', 'ignore').decode('ascii') for t in name_list]\\n metadata = list(zip(name_list, meta_))\\n elif (doc_type == \\\"BUMEDNOTE\\\"):\\n metadata = meta_\\n item_list = list(items)\\n pdf_links = [link['href'] for link in item_list if link['href'].endswith(('pdf', 'html'))]\\n pdf_links = [\\\"https://www.med.navy.mil\\\" + a for a in pdf_links]\\n pdf_links = [str(a).replace(' ', '%20') for a in pdf_links]\\n if (doc_type == \\\"BUMEDINST\\\" or doc_type == \\\"BUMEDNOTE\\\"):\\n metadata = [list(ele) for ele in metadata]\\n for i in range(0, len(metadata)):\\n metadata[i].append(pdf_links[i])\\n for item in metadata:\\n if (doc_type == \\\"NAVMED\\\"):\\n pdf_di = DownloadableItem(doc_type='pdf', web_url=item[4])\\n if (str(item[3])[0].isdigit()):\\n doc_name = \\\"NAVMED P-\\\" + str(item[3]).split()[0]\\n doc_num = \\\"P-\\\" + str(item[3]).split()[0]\\n if (doc_name in doc_name_list):\\n number_of_times = sum(1 for s in doc_name_list if doc_name in s)\\n doc_name = doc_type + \\\" \\\" + doc_num + \\\"-\\\" + str(number_of_times)\\n doc_num = doc_num + \\\"-\\\" + str(number_of_times)\\n else:\\n doc_name = \\\"NAVMED \\\" + str(item[0][1]) + \\\" \\\" + ' '.join(str(item[3]).split()[:3])\\n doc_num == str(item[0][1]) + \\\" \\\" + ' '.join(str(item[3]).split()[:3])\\n if (doc_name in doc_name_list):\\n number_of_times = sum(1 for s in doc_name_list if doc_name in s)\\n doc_name = doc_type + \\\" \\\" + doc_num + \\\"-\\\" + str(number_of_times)\\n doc_num = doc_num + \\\"-\\\" + str(number_of_times)\\n doc_title = str(item[1])\\n publication_date = str(item[2])\\n cac_login_required = False\\n crawler_used = \\\"navy_med_pubs\\\"\\n source_page_url = page_url\\n downloadable_items = [pdf_di]\\n version_hash_fields = {\\n \\\"item_currency\\\": str(item[3]).split('/')[-1], # version metadata found on pdf links\\n \\\"pub_date\\\": publication_date.strip(),\\n \\\"document_title\\\": doc_title.strip(),\\n \\\"document_number\\\": doc_num.strip()\\n }\\n\\n elif (doc_type == \\\"BUMEDINST\\\"):\\n pdf_di = DownloadableItem(doc_type='pdf', web_url=item[2])\\n doc_num = str(item[0]).split()[0]\\n doc_name = doc_type + \\\" \\\" + doc_num\\n doc_title = str(item[1][1])\\n publication_date = str(item[1][0])\\n if (str(item[2]).endswith('html')):\\n cac_login_required = True\\n elif (str(item[2]).endswith('pdf')):\\n cac_login_required = False\\n if (doc_name in doc_name_list):\\n number_of_times = sum(1 for s in doc_name_list if doc_name in s)\\n doc_name = doc_type + \\\" \\\" + doc_num + \\\"-\\\" + str(number_of_times)\\n doc_num = doc_num + \\\"-\\\" + str(number_of_times)\\n doc_name_list.append(doc_name)\\n elif (doc_type == \\\"BUMEDNOTE\\\"):\\n pdf_di = DownloadableItem(doc_type='pdf', web_url=item[4])\\n doc_num = str(item[0]).split()[1]\\n doc_name = doc_type + \\\" \\\" + doc_num\\n doc_title = str(item[2])\\n publication_date = str(item[1])\\n cac_login_required = False\\n if (doc_name in doc_name_list):\\n number_of_times = sum(1 for s in doc_name_list if doc_name in s)\\n doc_name = doc_type + \\\" \\\" + doc_num + \\\"-\\\" + str(number_of_times)\\n doc_num = doc_num + \\\"-\\\" + str(number_of_times)\\n doc_name_list.append(doc_name)\\n version_hash_fields = {\\n \\\"doc_name\\\": doc_name, # version metadata found on pdf links\\n \\\"pub_date\\\": publication_date.strip(),\\n \\\"document_title\\\": doc_title.strip(),\\n \\\"document_number\\\": doc_num.strip()\\n }\\n version_hash_raw_data = version_hash_fields\\n doc = Document(\\n doc_name=re.sub(',', '', doc_name.strip()),\\n doc_title=re.sub('\\\\\\\\\\\"', '', doc_title),\\n doc_num=re.sub(',', '', doc_num.strip()),\\n doc_type=re.sub(',', '', doc_type.strip()),\\n publication_date=publication_date,\\n cac_login_required=cac_login_required,\\n crawler_used=\\\"navy_med_pubs\\\",\\n source_page_url=page_url.strip(),\\n version_hash_raw_data=version_hash_fields,\\n downloadable_items=[pdf_di]\\n )\\n parsed_docs.append(doc)\\n return parsed_docs\",\n \"def get_supported_types(soup):\\n abam = soup.find('a', href=re.compile('#bam'))\\n ol = abam.parent.parent.parent.parent\\n supported_types = []\\n for ul in ol.find_all('ul'):\\n for a in ul.find_all('a'):\\n ref = a['href'].replace('#', '')\\n\\n # some have text like \\\"#bigMaf_-_Multiple_Alignments\\\", so split on\\n # the '_'\\n ref = ref.split('_')[0]\\n supported_types.append(ref)\\n\\n # multiWig is not included in this table, but it's considered a type in the\\n # divs.\\n supported_types.append('multiWig')\\n\\n # If a setting pertains to all types, they are not all listed, rather, the\\n # \\\"all\\\" type is specified.\\n supported_types.append('all')\\n\\n return supported_types\",\n \"def documentdetail(request, docid):\\n\\tdocument = get_object_or_404(Document, pk=docid)\\n\\tif not request.user.has_perm('documents.view_document', obj=document):\\n\\t\\treturn HttpResponse(loader.render_to_string('401.html',\\n\\t\\t\\tRequestContext(request, {'error_message':\\n\\t\\t\\t\\t_(\\\"You are not allowed to view this document.\\\")})), status=401)\\n\\ttry:\\n\\t\\trelated = document.content_type.get_object_for_this_type(id=document.object_id)\\n\\texcept:\\n\\t\\trelated = ''\\n\\n\\treturn render_to_response(\\\"documents/docinfo.html\\\", RequestContext(request, {\\n\\t\\t'permissions_json': json.dumps(_perms_info(document, DOCUMENT_LEV_NAMES)),\\n\\t\\t'document': document,\\n\\t\\t'imgtypes': imgtypes,\\n\\t\\t'related': related\\n\\t}))\",\n \"def load_material_library( self, url, materials, baseURL=None ):\\n #( resolvedURL, os.path.abspath(filename), file, headers )\\n try:\\n finalURL, filename, file, headers = loader.Loader( url, baseURL )\\n except IOError, err:\\n if '/' in url:\\n possible = url.split( '/' )[-1]\\n try:\\n finalURL, filename, file, headers = loader.Loader( \\n possible, baseURL \\n )\\n except IOError, err:\\n log.warn(\\n \\\"\\\"\\\"Unable to load material library: %s\\\"\\\"\\\",\\n url,\\n )\\n return False\\n \\n material = None\\n for line in file.read().splitlines():\\n if line.startswith('#'):\\n continue\\n values = line.split()\\n if not values:\\n continue\\n\\n if values[0] == 'newmtl':\\n material = self.defaultMaterial()\\n materials[values[1]] = material\\n elif material is None:\\n log.warn('Expected \\\"newmtl\\\" in %s', url)\\n continue\\n\\n try:\\n if values[0] == 'Kd':\\n material.material.diffuseColor = map(float, values[1:])\\n elif values[0] == 'Ka':\\n material.material.ambientColor = map(float, values[1:])\\n elif values[0] == 'Ks':\\n material.material.specularColor = map(float, values[1:])\\n elif values[0] == 'Ke':\\n material.material.emissiveColor = map(float, values[1:])\\n elif values[0] == 'Ns':\\n material.material.shininess = float(values[1])\\n elif values[0] == 'd':\\n material.material.opacity = float(values[1])\\n elif values[0] == 'map_Kd':\\n if '/' in values[1]:\\n img_url = [ values[1], values[1].split('/')[-1] ]\\n else:\\n img_url = [ values[1] ]\\n img_url = [\\n urllib.basejoin(baseURL, u )\\n for u in img_url\\n ]\\n texture = basenodes.ImageTexture(url=img_url)\\n material.texture = texture\\n except:\\n log.warn('Parse error in %s.', url)\",\n \"async def document_valid(self, document):\\n return web.json_response()\",\n \"def document_has_numbers(db, documentId):\\n document = db.documents.find_one({'_id': documentId})\\n phrases = list(numeric.finditer(document['body']))\\n\\n return len(phrases) > 1\",\n \"def read_book(url,book_num):\\n\\t#calls open_url function to open the url\\n\\tbook_contents = open_url(url)\\n\\tif book_contents != None:\\n\\t\\t#calls filter data function to clean the data\\n\\t\\tclean_data = filter_data(book_contents)\\n\\t\\t#create dictionary for all the words in this book with 0's filling for count in all the books\\n\\t\\tcreate_dict(clean_data)\\n\\t\\treturn clean_data\\n\\telse:\\n\\t\\treturn []\",\n \"def stored_fields(self, docnum):\\r\\n return self.doc_reader[docnum]\",\n \"def have_doc_extension(l):\\r\\n if \\\".doc\\\" in str(l):\\r\\n return 1\\r\\n else:\\r\\n return 0\",\n \"def _load_document(path, app):\\n start_inventor()\\n document_type_enum = {\\n 12289: 'UnnownDocument',\\n 12290: 'PartDocument',\\n 12291: 'AssemblyDocument',\\n 12292: 'DrawingDocument',\\n 12293: 'PresentationDocument',\\n 12294: 'DesignElementDocument',\\n 12295: 'ForeignModelDocument',\\n 12296: 'SATFileDocument',\\n 12297: 'NoDocument',\\n }\\n try:\\n app.Documents.Open(str(path))\\n document_type = document_type_enum[app.ActiveDocumentType]\\n doc = win32com.client.CastTo(app.ActiveDocument, document_type)\\n print(doc, document_type)\\n return doc\\n except:\\n print('unable to load file')\\n return None\",\n \"def get_schema(self, schema_versions_info):\\n schema = None\\n version = api_version_request.APIVersionRequest(VOLUME_MICROVERSION)\\n for items in schema_versions_info:\\n min_version = api_version_request.APIVersionRequest(items['min'])\\n max_version = api_version_request.APIVersionRequest(items['max'])\\n # This is case where COMPUTE_MICROVERSION is None, which means\\n # request without microversion So select base v2.1 schema.\\n if version.is_null() and items['min'] is None:\\n schema = items['schema']\\n break\\n # else select appropriate schema as per COMPUTE_MICROVERSION\\n elif version.matches(min_version, max_version):\\n schema = items['schema']\\n break\\n if schema is None:\\n raise exceptions.JSONSchemaNotFound(\\n version=version.get_string(),\\n schema_versions_info=schema_versions_info)\\n return schema\",\n \"def get_lens(filenames, yesno=True):\\n if yesno:\\n param = \\\"yes_lens\\\"\\n else:\\n param = \\\"no_lens\\\"\\n return filter_filenames(filenames, [param])\",\n \"def select_calib_manu(calib_lst, promotion,\\n error_message='Warning: \\\"{}\\\" is not a valid calib object',\\n ):\\n # print promotion and read input frameid list\\n while(True):\\n string = input(promotion)\\n ref_calib_lst = []\\n succ = True\\n for s in string.split(','):\\n s = s.strip()\\n if len(s)>0 and s.isdigit():\\n frameid = int(s)\\n if frameid in calib_lst:\\n # user input is found in calib_lst\\n calib = calib_lst[frameid]\\n ref_calib_lst.append(calib)\\n else:\\n # user input is not found in calib_lst\\n print(error_message.format(s))\\n succ = False\\n break\\n\\n else:\\n print(error_messag.format(s))\\n succ = False\\n break\\n if succ:\\n return ref_calib_lst\\n else:\\n continue\",\n \"def document_to_lda_features(lda_model, document):\\n topic_importances = lda_model.get_document_topics(document, minimum_probability=0)\\n topic_importances = numpy.array(topic_importances)\\n return topic_importances[:,1]\",\n \"def _parse_data(self, data):\\n return data.get('docs', [])\",\n \"def get_material_set(**kw):\\n mat_ids = set()\\n volumes = get_volume_list()\\n for v in volumes:\\n d = volume_metadata( v )\\n if( kw.get('with_rho') is True ):\\n # rho is undefined for the void material and dagmc may return anything.\\n if d['material'] == 0:\\n mat_ids.add( (d['material'], 0.0) )\\n else:\\n mat_ids.add( (d['material'], d['rho']) )\\n else:\\n mat_ids.add( d['material'] )\\n return mat_ids\",\n \"def not_sent(self, wos_collections_allowed, code_title=None, publication_year=1800):\\n\\n fltr = {'sent_wos': 'False',\\n 'applicable': 'True',\\n 'collection': {'$in': wos_collections_allowed},\\n 'publication_year': {'$gte': str(publication_year)}}\\n\\n if code_title:\\n fltr.update({'code_title': code_title})\\n logging.debug('Select documents: %s' % str(fltr))\\n documents = []\\n total = 0\\n for document in self._articles_coll.find(fltr, {'collection':1, 'code': 1}):\\n total += 1\\n documents.append([document['collection'], document['code']])\\n\\n i = 0\\n for document in documents:\\n i = i + 1\\n logging.debug('Selected document: %s' % str(document))\\n yield [total, i, self._articles_coll.find_one({'collection': document[0], 'code': document[1]}, {'citations': 0})]\",\n \"def supportedType(request, video_types):\\n return request.FILES['file'].content_type in video_types.keys()\",\n \"def _validate_built_in(self, tipo):\\n\\n self.source_type = False\\n self.source_file = \\\"builtin\\\"\\n return tipo in self.c_built_ins or self._match_array(tipo, self.c_built_in_array_types)\",\n \"def isrequired(self, typename):\\n return typename in self.required_products\",\n \"def show_available_products():\\n\\n mongo = MongoDBConnection()\\n result = {}\\n\\n with mongo:\\n db = mongo.connection.HPNorton\\n productcollection = db[\\\"products\\\"]\\n for document in productcollection.find({\\\"quantity_available\\\": {\\\"$gt\\\": \\\"0\\\"}}):\\n key = document['product_id']\\n\\n result[key] = {\\n 'description': document['description'],\\n 'product_type': document['product_type'],\\n 'quantity_available': document['quantity_available']\\n }\\n\\n return result\",\n \"def test_create_material_multi_basic(self):\\n expected_materials = [\\n ['cotton', 'AAA', 'BBB', 'CCC'],\\n ['cotton', 'AAA', 'BBB', 'CCC'],\\n ['wool', 'AAA', 'BBB', 'CCC'],\\n ]\\n\\n select_listings_to_edit(self.driver)\\n d = self.driver\\n bp = BulkPage(d)\\n\\n send_keys(bp.operation_input(), 'AAA,BBB ,CCC')\\n click(bp.operation_apply())\\n\\n material_names = bp.material_names()\\n assert material_names == expected_materials\",\n \"def unitsDetector(self, num):\\n try:\\n num = int(num)\\n except:\\n sys.exit('Invalid input! Method only takes ints or floats.')\\n \\n digits = 0\\n while num > 1:\\n num /= 10\\n digits += 1\\n \\n digits -= 1\\n ind = digits // 3\\n units = {3: 'B', 2: 'M', 1: 'K', 0: ''}[ind]\\n \\n return 10 ** (ind * 3), units\",\n \"def has_doc() -> None:\",\n \"def listFeaturableContentTypes():\",\n \"def get_document(self, docid):\\n raise NotImplementedError\",\n \"def get_multimedia_documents(shelter_id=0, category_id=2, section=\\\"Documents\\\"):\\r\\n shelter = Shelter.query.filter(Shelter.id==shelter_id).first()\\r\\n if not current_user.is_authenticated:\\r\\n return 'unauthorized', 403\\r\\n\\t\\r\\n if not shelter:\\r\\n return 'no such shelter', 400 \\r\\n\\r\\n file = request.files.get('mediafile', None)\\r\\n\\r\\n for f in request.files:\\r\\n if request.files[f] and request.files[f].filename == '':\\r\\n return 'No selected file', 400 \\r\\n if request.files[f] and not allowed_file(request.files[f].filename,\\r\\n conf.ALLOWED_EXTENSIONS_DOCUMENT):\\t\\r\\n return 'File type not allowed', 400\\r\\n\\t\\t\\r\\n path = os.path.join(conf.SHELTERS_DOCUMENTS_SERVER_PATH, str(shelter.id))\\r\\n logging.debug('path:' + path)\\r\\n\\t\\t\\t\\r\\n if not os.path.exists(path):\\r\\n os.makedirs(path)\\r\\n\\r\\n filename = secure_filename(request.files[f].filename)\\r\\n request.files[f].save(os.path.join(path , filename))\\r\\n\\r\\n if category_id:\\r\\n new_media = ShelterDocument(file_name=filename,\\r\\n shelter_id=shelter.id, category_id=category_id)\\r\\n db.session.add(new_media)\\r\\n db.session.commit()\\r\\n return str(new_media.id), 200\\r\\n\\r\\n return 'Document not uploaded', 400\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.55780417","0.514967","0.49377292","0.4870183","0.47371918","0.47266638","0.47241977","0.46641853","0.4619189","0.46141052","0.45978752","0.45675355","0.4562565","0.45569354","0.45257613","0.45226312","0.45016974","0.45015574","0.44937566","0.44864237","0.44327515","0.44271433","0.44057995","0.43983084","0.43926385","0.43907982","0.43681607","0.43476015","0.4333839","0.43179","0.43138093","0.4312801","0.42969128","0.4278637","0.42749885","0.4274776","0.42445385","0.42386866","0.4232058","0.42287794","0.42207268","0.42182776","0.42090842","0.42083058","0.41993523","0.41992733","0.4194328","0.41877592","0.41868064","0.41862854","0.41860905","0.41835538","0.41809687","0.4176414","0.417177","0.41678953","0.41642228","0.4160544","0.41528344","0.41487515","0.41400814","0.4131034","0.41309926","0.4112024","0.4109955","0.40962195","0.40925306","0.40903544","0.40890557","0.40885586","0.40759534","0.40739477","0.40737927","0.40673363","0.40571854","0.4047171","0.4041694","0.40281355","0.4015584","0.40151924","0.39989388","0.3981884","0.3981304","0.39782566","0.39752507","0.39741617","0.39611784","0.39605406","0.39591026","0.395366","0.39441466","0.39435455","0.394268","0.3938374","0.39331192","0.3932325","0.39320228","0.39285752","0.3926515","0.39249182"],"string":"[\n \"0.55780417\",\n \"0.514967\",\n \"0.49377292\",\n \"0.4870183\",\n \"0.47371918\",\n \"0.47266638\",\n \"0.47241977\",\n \"0.46641853\",\n \"0.4619189\",\n \"0.46141052\",\n \"0.45978752\",\n \"0.45675355\",\n \"0.4562565\",\n \"0.45569354\",\n \"0.45257613\",\n \"0.45226312\",\n \"0.45016974\",\n \"0.45015574\",\n \"0.44937566\",\n \"0.44864237\",\n \"0.44327515\",\n \"0.44271433\",\n \"0.44057995\",\n \"0.43983084\",\n \"0.43926385\",\n \"0.43907982\",\n \"0.43681607\",\n \"0.43476015\",\n \"0.4333839\",\n \"0.43179\",\n \"0.43138093\",\n \"0.4312801\",\n \"0.42969128\",\n \"0.4278637\",\n \"0.42749885\",\n \"0.4274776\",\n \"0.42445385\",\n \"0.42386866\",\n \"0.4232058\",\n \"0.42287794\",\n \"0.42207268\",\n \"0.42182776\",\n \"0.42090842\",\n \"0.42083058\",\n \"0.41993523\",\n \"0.41992733\",\n \"0.4194328\",\n \"0.41877592\",\n \"0.41868064\",\n \"0.41862854\",\n \"0.41860905\",\n \"0.41835538\",\n \"0.41809687\",\n \"0.4176414\",\n \"0.417177\",\n \"0.41678953\",\n \"0.41642228\",\n \"0.4160544\",\n \"0.41528344\",\n \"0.41487515\",\n \"0.41400814\",\n \"0.4131034\",\n \"0.41309926\",\n \"0.4112024\",\n \"0.4109955\",\n \"0.40962195\",\n \"0.40925306\",\n \"0.40903544\",\n \"0.40890557\",\n \"0.40885586\",\n \"0.40759534\",\n \"0.40739477\",\n \"0.40737927\",\n \"0.40673363\",\n \"0.40571854\",\n \"0.4047171\",\n \"0.4041694\",\n \"0.40281355\",\n \"0.4015584\",\n \"0.40151924\",\n \"0.39989388\",\n \"0.3981884\",\n \"0.3981304\",\n \"0.39782566\",\n \"0.39752507\",\n \"0.39741617\",\n \"0.39611784\",\n \"0.39605406\",\n \"0.39591026\",\n \"0.395366\",\n \"0.39441466\",\n \"0.39435455\",\n \"0.394268\",\n \"0.3938374\",\n \"0.39331192\",\n \"0.3932325\",\n \"0.39320228\",\n \"0.39285752\",\n \"0.3926515\",\n \"0.39249182\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5849427"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77078,"cells":{"query":{"kind":"string","value":"Returning Materials please see return_candidates for more information."},"document":{"kind":"string","value":"def return_materials(cls, user, doc_no, mode, return_list):\n\n # Retrieve documents based on what it is given\n doc = Docs.of_doc_no(doc_no)\n\n # Validate input,\n # Extract source to compare\n current_list = []\n ref_doc = None\n from_ref_doc = None\n if mode in [cls.TYPE_STAGED_IN_INVENTORY_CONTENT, cls.TYPE_STAGED_FROM_CLERK_AUX_TASK] and isinstance(doc, prod_doc.StoreAuxTask):\n o = cls.return_candidates(doc_no)\n current_list = list(o[0]['return_list'])\n doc.populate('parent_task')\n doc.parent_task.populate('ref_doc')\n ref_doc = doc.parent_task.ref_doc\n from_ref_doc = doc.parent_task\n elif mode == cls.TYPE_WIP_FROM_PRODUCTION_ORDER and isinstance(doc, prod_doc.ProductionOrderOperation):\n doc.populate('ref_doc')\n current_list = [{\n 'material': 'WIP-CANDIDATE',\n 'quantity': doc.get_confirmable_quantity(),\n 'uom': doc.ref_doc.uom,\n 'revision': None,\n 'size': None,\n 'weight': 0,\n }]\n ref_doc = doc.ref_doc\n else:\n # Unsupported case\n raise BadParameterError(_(\"ERR_UNSUPPORTED_DOCUMENT_TYPE: %(document_type)s\") % {\n 'document_type': str(type(doc))\n })\n\n # prepare movement based on return_list\n # Sanity check\n if len(current_list) != len(return_list):\n raise BadParameterError(_(\"ERR_UNEQUAL_SIZE_OF_RETURN_LIST: %(expected_size)s != $(actual_size)s\") % {\n 'expected_size': len(current_list),\n 'actual_size': len(return_list)\n })\n\n # index list for ease of comparison\n current_list = dict(map(lambda a: (str(a['material']), a), current_list))\n return_list = dict(map(lambda a: (str(a['material']), a), return_list))\n\n movements = []\n entries = []\n lost_entries = []\n movement_type = None\n lost_movement_type = None\n # From STAGED => STORE\n # From STAGED => LOST & FOUND\n for key, value in current_list.iteritems():\n lost = value['quantity'] - return_list[key]['quantity']\n\n # NOTE: For WIP scenario\n if key == 'WIP-CANDIDATE':\n # init Goods receipt by-product for WIP to STORE\n # TODO: value for WIP to be fixed later with proper logic. now using static 543210.\n movement_type = stock_doc.InventoryMovement.GR_BP\n user.can(stock_doc.InventoryMovement.ACTION_WRITE(), movement_type, throw=True)\n\n # NOTE: Generate WIP Stock code\n wip_material = doc.ref_doc.material.create_wip(doc.task.code)\n\n # NOTE: Create material master from stock code\n stock_doc.MaterialMaster.factory(wip_material, uom=doc.ref_doc.uom, procurement_type=stock_doc.MaterialMaster.INTERNAL, author=user)\n\n current_list[key]['material'] = wip_material\n current_list[key]['uom'] = stock_doc.MaterialMaster.get(doc.ref_doc.material).uom.code\n entries.append(stock_doc.InventoryMovementEntry.factory(material=wip_material,\n quantity=return_list[key]['quantity'],\n location=Location.factory('STORE').code,\n weight=return_list[key]['weight'],\n value=543210))\n\n if lost > 0:\n # if lost is found, init Goods receipt lost and found for WIP to STORE\n lost_movement_type = stock_doc.InventoryMovement.GR_LT\n lost_entries.append(stock_doc.InventoryMovementEntry.factory(material=wip_material,\n quantity=return_list[key]['quantity'],\n location=Location.factory('LOST').code,\n weight=return_list[key]['weight'],\n value=543210))\n # NOTE: For STAGING scenario\n else:\n # init Stock transfer production to location for STAGING to STORE\n movement_type = stock_doc.InventoryMovement.ST_PL\n user.can(stock_doc.InventoryMovement.ACTION_WRITE(), movement_type, throw=True)\n entries.extend(stock_doc.InventoryMovementEntry.transfer_pair_factory(material=value['material'],\n quantity=return_list[key]['quantity'],\n from_location=Location.factory('STAGING').code,\n to_location=Location.factory('STORE').code,\n from_ref_doc=from_ref_doc))\n\n if lost > 0:\n # if lost is found, init Stock transfer lost and found for STAGING to STORE retaining ref_doc\n lost_movement_type = stock_doc.InventoryMovement.ST_LT\n lost_entries.extend(stock_doc.InventoryMovementEntry.transfer_pair_factory(material=value['material'],\n quantity=return_list[key]['quantity'],\n from_location=Location.factory('STAGING').code,\n to_location=Location.factory('LOST').code,\n from_ref_doc=from_ref_doc,\n to_ref_doc=from_ref_doc))\n if not entries:\n raise ValueError(\"Nothing to return\")\n\n movement = stock_doc.InventoryMovement.factory(movement_type, list(entries), ref_doc=ref_doc)\n movements.append(movement)\n\n if lost > 0:\n user.can(stock_doc.InventoryMovement.ACTION_WRITE(), lost_movement_type, throw='challenge')\n movement = stock_doc.InventoryMovement.factory(lost_movement_type, list(lost_entries), ref_doc=ref_doc)\n movements.append(movement)\n\n if movements:\n # validate first then touched\n map(lambda x: x.validate(user=user), movements)\n\n # NOTE: create a new pair of store & clerk with different parent depending on mode\n if mode == cls.TYPE_STAGED_FROM_CLERK_AUX_TASK:\n task_signals.task_repeat.send(cls, parent=doc.parent_task, components=[v for i, v in current_list.iteritems()])\n if mode == cls.TYPE_WIP_FROM_PRODUCTION_ORDER:\n task_signals.task_repeat.send(cls, parent=doc, components=[v for i, v in current_list.iteritems()])\n doc.ready(user)\n \n map(lambda x: x.touched(user), movements)\n\n return len(movements)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def info_materials_raw_get():\n materials = _material_by_group(427) # 427 == intermediate group\n return materials, 200","def info_materials_get():\n materials = _material_by_group() # empty means all groups\n return materials, 200","def GetMaterial(self, *args):\n return _XCAFDoc.XCAFDoc_MaterialTool_GetMaterial(self, *args)","def info_materials_polymer_get():\n materials = _material_by_group(974) # 974 == intermediate group\n return materials, 200","def info_materials_booster_get():\n materials = _material_by_group(712) # 712 == intermediate group\n return materials, 200","def info_materials_intermediates_get():\n materials = _material_by_group(428) # 428 == intermediate group\n return materials, 200","def info_materials_gas_get():\n materials = _material_by_group(711) # 711 == intermediate group\n return materials, 200","def get_smeared_material(materials, void_material='', void_percent=1.0):\n smear_material = {}\n for material, materialWeightPercent in materials.items():\n void_multiplier = 1.0\n if material == 'Void':\n pass\n else:\n base_material = Material()\n base_material.set_material(material)\n\n if base_material.materialName == void_material:\n void_multiplier = void_percent\n\n for isotope, isotopeWeightPercent in base_material.weightPercent.items():\n element = str(isotope)\n if len(element) < 5:\n current_element = element[:1] + '000'\n else:\n current_element = element[:2] + '000'\n current_element = int(current_element)\n try:\n smear_material[isotope] += isotopeWeightPercent * materialWeightPercent * base_material.density \\\n * AVOGADROS_NUMBER * void_multiplier / \\\n base_material.elementDict[current_element].molecularMassDict[isotope]\n except KeyError:\n smear_material[isotope] = isotopeWeightPercent * materialWeightPercent * base_material.density \\\n * AVOGADROS_NUMBER * void_multiplier / \\\n base_material.elementDict[current_element].molecularMassDict[isotope]\n smeared_material = Material()\n smeared_material.name = \"{}\".format([val for val in materials])\n smeared_material.atomDensity = sum(smear_material.values())\n smeared_atom_percent = {}\n for k, v in smear_material.items():\n smeared_atom_percent[k] = v / smeared_material.atomDensity\n smeared_material.atomPercent = smeared_atom_percent\n return smeared_material","def info_materials_composites_get():\n materials = _material_by_group(429) # 429 == intermediate group\n return materials, 200","def get_mats_from_args(self, args):\n try:\n lhslist = args.split(\"/\")\n material = CraftingMaterialType.objects.get(name__iexact=lhslist[0])\n amt = int(lhslist[1])\n if amt < 1:\n raise ValueError(\n \"You must specify a positive value of a material to send.\"\n )\n # different errors\n except (IndexError, AttributeError, TypeError):\n self.msg(\"You must specify materials to send.\")\n except CraftingMaterialType.DoesNotExist:\n self.msg(\"That is not a valid material type.\")\n except OwnedMaterial.DoesNotExist:\n self.msg(\"You don't have any of that material.\")\n except ValueError as err:\n self.msg(err)\n # succeeded, return amount. It'll be decremented when sent off later\n else:\n return material.id, amt","def test_has_raw_materials_handles_insufficient_raw_materials(self):\n # Params\n m_raw_materials = self.selection['ingredients']\n d_raw_materials = {\n 'sugar': 0,\n 'butter': 0,\n 'caramel': 15,\n 'dark chocolate': 0,\n 'mint chocolate': 30,\n 'milk chocolate': 30,\n 'light corn syrup': 0,\n 'sweetened condensed milk': 0,\n 'vanilla extract': 0,\n 'Reese\\'s Pieces': 15,\n }\n # Returns\n return_1 = 'Machine Needs Additional: sugar\\n' \\\n 'Machine Needs Additional: butter\\n' \\\n 'Machine Needs Additional: dark chocolate\\n' \\\n 'Machine Needs Additional: light corn syrup\\n' \\\n 'Machine Needs Additional: sweetened condensed milk\\n' \\\n 'Machine Needs Additional: vanilla extract\\n'\n # Calls\n bool_1 = self.chocolate_machine.has_raw_materials(m_raw_materials, d_raw_materials)\n # Asserts\n self.assertEqual(bool_1, return_1)","def test_has_raw_materials_handles_insufficient_raw_materials(self):\n # Setup\n choice = 'dark cherry'\n selection = CUPCAKE_CHOICES[choice]\n\n # Params\n f_raw_materials = selection['ingredients']\n d_raw_materials = {\n 'sugar': 0,\n 'butter': 0,\n 'dark chocolate': 0,\n 'light corn syrup': 0,\n 'sweetened condensed milk': 0,\n 'vanilla extract': 0,\n 'bing cherries': 10,\n }\n\n # Returns\n return_1 = 'Machine Needs Additional: sugar\\n' \\\n 'Machine Needs Additional: butter\\n' \\\n 'Machine Needs Additional: dark chocolate\\n' \\\n 'Machine Needs Additional: light corn syrup\\n' \\\n 'Machine Needs Additional: sweetened condensed milk\\n' \\\n 'Machine Needs Additional: vanilla extract\\n'\n\n # Calls\n string_1 = has_raw_materials(f_raw_materials, d_raw_materials)\n\n # Asserts\n self.assertEqual(string_1, return_1)","def get_material_features(self):\n return self.material_features","def get(self, request, mat_pk:int, params:str=\"all\", target_results:int=Conf.results_num, components:int=pcaConf.components):\n \n try:\n mats_id = [x.id for x in Material.objects.all()]\n \n # Get data from database and prepocess those data.\n # Preprocess use Standardization or Normalization.\n if params == \"all\":\n mats_data = self._extract_data()\n if params == \"imp\":\n mats_data = self._extract_imp_data()\n mats_data = self._preprocess(mats_data)\n \n # Calling machine learning algorithms to finish similarity anaylys\n # and returns the final result.\n mats_data_reducted = run_kernel_pca(mats_data, components=components) # ndarray\n \n # Material instance to compare.\n mat_protocol = mats_data_reducted[mats_id.index(mat_pk)]\n \n # Calculate euclidean distance for each instance and get the best.\n results = []\n for mat_id, mat_data in zip(mats_id, mats_data_reducted):\n \n # Skip the material data to compare.\n if mat_id == mat_pk: continue\n \n mat_dist = np.sum(np.square(mat_protocol - mat_data))\n mat_dist_tuple = (mat_id, mat_dist)\n results.append(mat_dist_tuple)\n \n results.sort(key=lambda x:x[1])\n \n return JsonResponse({\n \"data_results\": results[:target_results], # Material data in json format\n \"data_returned_num\": Conf.results_num, # Returned data numbers.\n \"data_total_num\": len(mats_data_reducted), # length of materials in analysis\n \"data_reducted_dim\": len(mats_data_reducted[0]) # Reducted data dimention\n })\n except RuntimeError as e:\n print(e)\n return Http404(\"Sorry, the procedures has failed. Check backend for more info\")","def make_materials(*args, **kwargs):\n\n ret_args = []\n for parameters in args:\n if isinstance(parameters, (list, tuple)):\n ret_args.append(Material(*parameters))\n else:\n ret_args.append(Material(parameters))\n\n ret_kwargs = {}\n for name, parameters in kwargs.items():\n if isinstance(parameters, (list, tuple)):\n ret_kwargs[name] = Material(*parameters)\n else:\n ret_kwargs[name] = Material(parameters)\n\n if len(ret_kwargs) == 0:\n return ret_args\n elif len(ret_args) == 0:\n return ret_kwargs\n else:\n return ret_args, ret_kwargs","def show_materials(self):\n print('\\nThe materials with known dielectric properties are:\\n')\n pprint.pprint(mats.Electrical.props)\n# pprint.pprint(mats.Electrical.DIELECTRIC)\n print('\\nThe materials with known loss tangents are:\\n')\n pprint.pprint(mats.Electrical.props)\n# pprint.pprint(mats.Electrical.LOSS_TAN)\n return","def _getMaterials(self):\n warnings.warn(\"This function is deprecated; call Document.getMaterialNodes() instead.\", DeprecationWarning, stacklevel = 2)\n return self.getMaterialNodes()","def IsMaterial(self, *args):\n return _XCAFDoc.XCAFDoc_MaterialTool_IsMaterial(self, *args)","def get_material_info(TABLE_info):\n \"\"\"\n 1 Get info from TABLE_info.\n \"\"\"\n width = TABLE_info[0]\n height = TABLE_info[1]\n t_m = TABLE_info[2]\n\n \"\"\"\n 2 Get material info.\n \"\"\"\n z_m = 3 * t_m\n\n m_width = rs.GetInteger(\"Put the width of material\", z_m, None, None)\n\n t_sen = rs.GetReal(\"Put Int(mm): Thickness of material to cut SEN.\", t_m / 2, None, None)\n\n x_m1 = m_width\n x_m2 = height - x_m1\n x_m3 = x_m2\n x_m4 = x_m1\n\n y_m2 = m_width\n y_m3 = y_m2\n y_m1 = width - (y_m2 + y_m3)\n y_m4 = y_m1\n\n\n # material1\n m1_p0 = (x_m3, y_m3)\n m1_p1 = (x_m3, y_m3 + y_m1)\n m1_p2 = (x_m3 + x_m1, y_m3 + y_m1)\n m1_p3 = (x_m3 + x_m1, y_m3)\n m1_points = [m1_p0, m1_p1, m1_p2, m1_p3]\n\n m1_info = [x_m1, y_m1, z_m, m1_points, t_sen]\n\n # material2\n m2_p0 = (0, width - y_m2)\n m2_p1 = (0, width)\n m2_p2 = (height - x_m1, width)\n m2_p3 = (height - x_m1, width - y_m2)\n m2_points = [m2_p0, m2_p1, m2_p2, m2_p3]\n\n m2_info = [x_m2, y_m2, z_m, m2_points, t_sen]\n\n # material3\n m3_p0 = (0, 0)\n m3_p1 = (0, y_m3)\n m3_p2 = (x_m3, y_m3)\n m3_p3 = (x_m3, 0)\n m3_points = [m3_p0, m3_p1, m3_p2, m3_p3]\n\n m3_info = [x_m3, y_m3, z_m, m3_points, t_sen]\n\n # material4\n m4_p0 = (0, y_m3)\n m4_p1 = (0, y_m3 + y_m4)\n m4_p2 = (-x_m4, y_m3 + y_m4)\n m4_p3 = (-x_m4, y_m3)\n m4_points = [m4_p0, m4_p1, m4_p2, m4_p3]\n\n m4_info = [x_m4, y_m4, z_m, m4_points, t_sen]\n\n return m1_info, m2_info, m3_info, m4_info","def get_material_mapping(self):\n return {name: self.get_material(name) for name in self.parts.keys()}","def MaterialTool(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialTool(*args)","def input_materials(type_id: int, quantity: int, me: int = 0, prod_type: str = 'manufacturing', prices: bool = False) -> pd.DataFrame:\n actID = {'manufacturing': 1, 'reaction': 11}\n mats = pd.DataFrame(columns=['type_id', 'type_name', 'quantity'])\n if type_id in quantities.loc[(quantities['activityID'] == actID[prod_type]) & (quantities['productTypeID'] == type_id)].values: # item can be manufactured\n\n if prod_type == 'reaction':\n bpid = productToFormula(type_id)\n\n elif prod_type == 'manufacturing':\n bpid = productToBP(type_id)\n \n else: \n raise ValueError(\"thats not a valid manufacturing type. options are 'reaction', 'manufacturing'\")\n\n qPerRun = quantPerRun(bpid)\n runs = quantity // qPerRun\n\n if runs > 0:\n for _, row in materials.loc[(materials['activityID'] == actID[prod_type]) & (materials['typeID'] == bpid)].iterrows():\n\n if prod_type == 'reaction':\n quant = row['quantity'] * runs\n\n elif prod_type == 'manufacturing':\n quant = me_formula(row['quantity'],me) * runs\n\n mats = mats.append({'type_id': row['materialTypeID'], 'type_name': emt.typeIDToName(row['materialTypeID']), 'quantity': quant}, ignore_index=True)\n \n \n # buys the product instead of manufacturing more of it than needed\n if int(runs * qPerRun) < int(quantity):\n mats = mats.append({'type_id': type_id, 'type_name': emt.typeIDToName(type_id), 'quantity': quantity - (runs * qPerRun)},ignore_index=True)\n \n mats = mats.groupby(['type_id', 'type_name']).sum().reset_index()\n mats = mats.astype({\"type_id\": int, \"quantity\": int})\n\n if prices:\n mats = emt.add_price(mats)\n\n return mats","def materials(self):\n return MaterialManager(session=self._session)","def XCAFDoc_DocumentTool_MaterialTool(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialTool(*args)","def AddMaterial(self, *args):\n return _XCAFDoc.XCAFDoc_MaterialTool_AddMaterial(self, *args)","def GetMaterialLabels(self, *args):\n return _XCAFDoc.XCAFDoc_MaterialTool_GetMaterialLabels(self, *args)","def test_create_material_multi_basic(self):\n expected_materials = [\n ['cotton', 'AAA', 'BBB', 'CCC'],\n ['cotton', 'AAA', 'BBB', 'CCC'],\n ['wool', 'AAA', 'BBB', 'CCC'],\n ]\n\n select_listings_to_edit(self.driver)\n d = self.driver\n bp = BulkPage(d)\n\n send_keys(bp.operation_input(), 'AAA,BBB ,CCC')\n click(bp.operation_apply())\n\n material_names = bp.material_names()\n assert material_names == expected_materials","def get_items(self):\n\n to_process_mat_ids = self._find_to_process()\n\n self.logger.info(\n \"Updating all substrate calculations for {} materials\".format(\n len(to_process_mat_ids)\n )\n )\n\n for mpid in to_process_mat_ids:\n e_tensor = self.elasticity.query_one(\n criteria={self.elasticity.key: mpid},\n properties=[\"elasticity\", \"last_updated\"],\n )\n e_tensor = (\n e_tensor.get(\"elasticity\", {}).get(\"elastic_tensor\", None)\n if e_tensor\n else None\n )\n mat = self.materials.query_one(\n criteria={self.materials.key: mpid},\n properties=[\"structure\", \"deprecated\", \"material_id\", \"last_updated\"],\n )\n\n yield {\n \"structure\": mat[\"structure\"],\n \"material_id\": mat[self.materials.key],\n \"elastic_tensor\": e_tensor,\n \"deprecated\": mat[\"deprecated\"],\n \"last_updated\": max(\n mat.get(\"last_updated\"), e_tensor.get(\"last_updated\")\n ),\n }","def test_create_material(self):\n expected_materials = [\n ['cotton', 'AAA'],\n ['cotton', 'AAA'],\n ['wool', 'AAA'],\n ]\n\n select_listings_to_edit(self.driver)\n d = self.driver\n bp = BulkPage(d)\n\n send_keys(bp.operation_input(), 'AAA')\n click(bp.operation_apply())\n\n material_names = bp.material_names()\n assert material_names == expected_materials\n\n apply_class = bp.operation_apply().get_attribute('class')\n assert 'inactive' in apply_class.split(' ')","def get_player_material(board):\n # clear board to read out information\n information = str(board).replace(\"\\n\", \" \")\n\n # get black and white material\n materialPlayer_B = information.count('r') + information.count('n') + information.count('b') + information.count('q') + information.count('k') + information.count('p')\n materialPlayer_W = information.count('R') + information.count('N') + information.count('B') + information.count('Q') + information.count('K') + information.count('P')\n \n # return materials for each player\n return materialPlayer_W, materialPlayer_B","def material(self):\n return self._F_Mstr","def op_master_material(op_skills):\n # left = copy.deepcopy(owned)\n needed = defaultdict(int)\n for operator, skills in op_skills.items():\n # op_ret = defaultdict(int)\n materials = master_material(operator)\n for skill, rank in skills.items():\n if isinstance(rank, int):\n target_rank = rank\n present_rank = 1\n else:\n target_rank, present_rank = rank\n for mat_dict in materials[skill - 1][present_rank: target_rank]:\n for mat, num in mat_dict.items():\n needed[mat] += num\n return dict(needed)","def get_material_set(**kw):\n mat_ids = set()\n volumes = get_volume_list()\n for v in volumes:\n d = volume_metadata( v )\n if( kw.get('with_rho') is True ):\n # rho is undefined for the void material and dagmc may return anything.\n if d['material'] == 0:\n mat_ids.add( (d['material'], 0.0) )\n else:\n mat_ids.add( (d['material'], d['rho']) )\n else:\n mat_ids.add( d['material'] )\n return mat_ids","def get_similar_materials(self, material):\n method = \"GET\"\n sub_url = '/materials/similar/{}'.format(material)\n return self._make_request(sub_url, method=method)","def get_material(material):\n for libn,tdict in liblist:\n if material in tdict:\n return tdict[material]\n print (material, \" not found\")\n raise KeyError","def materials(cls) -> MaterialSelector:\n selector: MaterialSelector = cls._materials\n return selector","def info_materials_type_id_get(type_id):\n session = info_map.Session()\n q = session.query(info_map.Material).filter(info_map.Material.type == type_id)\n\n mat = q.one_or_none() # return the only result or `None`\n\n if mat is not None:\n material_info = MaterialInfo(\n type=mat.type,\n group=mat.group_id,\n name=mat.name,\n volume=mat.volume)\n return material_info, 200\n else:\n error = Error('Type {} Not Found'.format(type_id))\n return error, 404","def test_create_material_multi_over(self):\n expected_materials_01 = [\n ['cotton'],\n ['cotton'],\n ['wool', 'AAA', 'BBB', 'CCC'],\n ]\n\n expected_materials_02 = [\n ['cotton', '00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11'],\n ['cotton', '00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11'],\n ['wool', 'AAA', 'BBB', 'CCC', '00', '01', '02', '03', '04', '05', '06', '07', '08'],\n ]\n\n select_listings_to_edit(self.driver)\n d = self.driver\n bp = BulkPage(d)\n\n # deselect 2, 3\n bp.click_on_listings(['Second something 1235 (2)', 'Third something LG-512a (3)'])\n\n # append AAA BBB CCC materials to the 1st listing\n send_keys(bp.operation_input(), 'AAA,BBB ,CCC')\n click(bp.operation_apply())\n\n material_names = bp.material_names()\n assert material_names == expected_materials_01\n\n # append 00, 01, 02... to all listings\n bp.click_on_listings(['Second something 1235 (2)', 'Third something LG-512a (3)'])\n send_keys(bp.operation_input(), '00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13, 14, 15')\n click(bp.operation_apply())\n\n material_names = bp.material_names()\n assert material_names == expected_materials_02","def test_materials_present(self):\n self.assertIsNotNone('Materials' in self.header.parameters.attrs)","def XCAFDoc_MaterialTool_Set(*args):\n return _XCAFDoc.XCAFDoc_MaterialTool_Set(*args)","def generate_materials_dict(self):\n c = 299792458.0\n w_mat = 2 * np.pi * c / self.l_mat - self.w0\n l2_mat = (self.l_mat * 1e6) ** 2\n\n n_air = 1 + 0.05792105 * l2_mat / (238.0185 * l2_mat - 1) + 0.00167917 * l2_mat / (57.362 * l2_mat - 1)\n air_ip = interp1d(w_mat, n_air, bounds_error=False, fill_value=np.nan, kind=\"quadratic\")\n self.materials['air'] = air_ip\n\n n_fs = np.sqrt(1 + 0.6961663 * l2_mat / (l2_mat - 0.0684043 ** 2) +\n 0.4079426 * l2_mat / (l2_mat - 0.1162414 ** 2) +\n 0.8974794 * l2_mat / (l2_mat - 9.896161 ** 2))\n fs_ip = interp1d(w_mat, n_fs, bounds_error=False, fill_value=np.nan, kind=\"quadratic\")\n self.materials['fs'] = fs_ip\n\n n_mgf2 = np.sqrt(1 + 0.48755108 * l2_mat / (l2_mat - 0.04338408 ** 2) +\n 0.39875031 * l2_mat / (l2_mat - 0.09461442 ** 2) +\n 2.3120353 * l2_mat / (l2_mat - 23.793604 ** 2))\n mgf2_ip = interp1d(w_mat, n_mgf2, bounds_error=False, fill_value=np.nan, kind=\"quadratic\")\n self.materials['mgf2'] = mgf2_ip\n\n n_sapphire_o = np.sqrt(1 + 1.4313493 * l2_mat / (l2_mat - 0.0726631 ** 2) +\n 0.65054713 * l2_mat / (l2_mat - 0.1193242 ** 2) +\n 5.3414021 * l2_mat / (l2_mat - 18.028251 ** 2))\n sapphire_o_ip = interp1d(w_mat, n_sapphire_o, bounds_error=False, fill_value=np.nan, kind=\"quadratic\")\n self.materials['sapphire_o'] = sapphire_o_ip\n\n n_sapphire_e = np.sqrt(1 + 1.5039759 * l2_mat / (l2_mat - 0.0740288 ** 2) +\n 0.55069141 * l2_mat / (l2_mat - 0.1216529 ** 2) +\n 6.5927379 * l2_mat / (l2_mat - 20.072248 ** 2))\n sapphire_e_ip = interp1d(w_mat, n_sapphire_e, bounds_error=False, fill_value=np.nan, kind=\"quadratic\")\n self.materials['sapphire_e'] = sapphire_e_ip\n\n n_bbo_o = np.sqrt(2.7405 + 0.0184 / (l2_mat - 0.0179) - 0.0155 * l2_mat)\n bbo_o_ip = interp1d(w_mat, n_bbo_o, bounds_error=False, fill_value=np.nan, kind=\"quadratic\")\n self.materials['bbo_o'] = bbo_o_ip\n\n n_bbo_e = np.sqrt(2.3730 + 0.0128 / (l2_mat - 0.0156) - 0.0044 * l2_mat)\n bbo_e_ip = interp1d(w_mat, n_bbo_e, bounds_error=False, fill_value=np.nan, kind=\"quadratic\")\n self.materials['bbo_e'] = bbo_e_ip\n\n materials_files = os.listdir(self.materials_path)\n logger.info(\"Found {0:d}\".format(materials_files.__len__()))\n for mat_file in materials_files:\n logger.debug(mat_file)\n self.read_material(''.join((self.materials_path, '/', mat_file)))","def __render_material_preview(self, scene):\n\n # Don't render material thumbnails.\n (width, height) = util.get_render_resolution(scene)\n if width <= 96:\n return\n\n # Collect objects and their materials in a object -> [materials] dictionary.\n objects_materials = {}\n for obj in (obj for obj in scene.objects if obj.is_visible(scene) and not obj.hide_render):\n for mat in util.get_instance_materials(obj):\n if mat is not None:\n if obj.name not in objects_materials.keys():\n objects_materials[obj] = []\n objects_materials[obj].append(mat)\n\n # Find objects that are likely to be the preview objects.\n preview_objects = [o for o in objects_materials.keys() if o.name.startswith('preview')]\n if not preview_objects:\n return\n\n # Find the materials attached to the likely preview object.\n likely_materials = objects_materials[preview_objects[0]]\n if not likely_materials:\n return\n\n # Build the path to the output preview project.\n preview_output_dir = os.path.join(tempfile.gettempdir(), \"blenderseed\", \"material_preview\")\n preview_project_filepath = os.path.join(preview_output_dir, \"material_preview.appleseed\")\n\n # Create target directories if necessary.\n if not os.path.exists(preview_output_dir):\n try:\n os.makedirs(preview_output_dir)\n except os.error:\n self.report({\"ERROR\"}, \"The directory {0} could not be created. Check directory permissions.\".format(preview_output_dir))\n return\n\n # Copy assets from template project to output directory.\n preview_template_dir = os.path.join(os.sep.join(util.realpath(__file__).split(os.sep)[:-1]), \"mat_preview\")\n existing_files = os.listdir(preview_output_dir)\n for item in os.listdir(preview_template_dir):\n if item not in existing_files:\n copyfile(os.path.join(preview_template_dir, item), os.path.join(preview_output_dir, item))\n\n prev_mat = likely_materials[0]\n prev_type = prev_mat.preview_render_type.lower()\n\n # Export the project.\n writer = projectwriter.Writer()\n file_written = writer.export_preview(scene,\n preview_project_filepath,\n prev_mat,\n prev_type,\n width,\n height)\n if not file_written:\n print('Error while exporting. Check the console for details.')\n return\n\n # Render the project.\n self.__render_project_file(scene, preview_project_filepath)","def create_materials_from_data(textures):\n\n materials = []\n\n #Set colour to incremenet from 0 - 8\n colour_inc = 1.0 / len(textures)\n colour = 0\n\n for current_material in textures:\n mat = bpy.data.materials.new(current_material[0])\n mat.diffuse_color = (0, colour, 0,)\n mat.diffuse_shader = 'LAMBERT' \n mat.diffuse_intensity = 1.0 \n mat.specular_color = (1, 1, 1,)\n mat.specular_shader = 'COOKTORR'\n mat.specular_intensity = 0.5\n mat.alpha = 1\n mat.ambient = 1\n mat.use_shadeless = True\n\n mtex = mat.texture_slots.add()\n mtex.texture = current_material[1]\n mtex.texture_coords = 'UV'\n mtex.use_map_color_diffuse = True \n\n materials.append(mat)\n colour += colour_inc\n \n return materials","def get_openmc_material(openmoc_material):\n\n cv.check_type('openmoc_material', openmoc_material, openmoc.Material)\n\n material_id = openmoc_material.getId()\n\n # If this Material was already created, use it\n if material_id in OPENMC_MATERIALS:\n return OPENMC_MATERIALS[material_id]\n\n # Create an OpenMC Material to represent this OpenMOC Material\n name = openmoc_material.getName()\n openmc_material = openmc.Material(material_id=material_id, name=name)\n\n # Add the OpenMOC Material to the global collection of all OpenMOC Materials\n OPENMOC_MATERIALS[material_id] = openmoc_material\n\n # Add the OpenMC Material to the global collection of all OpenMC Materials\n OPENMC_MATERIALS[material_id] = openmc_material\n\n return openmc_material","def check_only_supported_materials_are_used(progress_controller=None):\n if progress_controller is None:\n progress_controller = ProgressControllerBase()\n\n non_arnold_materials = []\n all_valid_materials = []\n for renderer in VALID_MATERIALS.keys():\n all_valid_materials.extend(VALID_MATERIALS[renderer])\n\n all_materials = pm.ls(mat=1)\n progress_controller.maximum = len(all_materials)\n for material in all_materials:\n if material.name() not in [\"lambert1\", \"particleCloud1\", \"standardSurface1\"]:\n if material.type() not in all_valid_materials:\n non_arnold_materials.append(material)\n progress_controller.increment()\n\n if len(non_arnold_materials):\n pm.select(non_arnold_materials)\n progress_controller.complete()\n raise PublishError(\n \"There are non-Arnold materials in the scene:<br><br>%s<br><br>\"\n \"Please remove them!!!\"\n % \"<br>\".join(map(lambda x: x.name(), non_arnold_materials))\n )\n progress_controller.complete()","def info_materials_groups_get():\n session = info_map.Session()\n\n mat = aliased(info_map.Material)\n grp = aliased(info_map.Group)\n\n q = session.query(mat.group_id,grp.name).join(grp).distinct()\n groups = [Group(group=row.group_id,name=row.name) for row in q.all()]\n return groups, 200","def create_material(self, name, pbmr=None, emissive=None):\n new_material = self._build_material(pbmr, emissive)\n\n self.materials.append(new_material)\n\n if name:\n self.materials_map[name] = self._last_index(self.materials)\n\n return self._last_index(self.materials)","def calculate_material(board):\n\n\tmaterial_values = {\n\t\tchess.PAWN: 1,\n\t\tchess.ROOK: 5,\n\t\tchess.KNIGHT: 3,\n\t\tchess.BISHOP: 3,\n\t\tchess.QUEEN: 9,\n\t\tchess.KING: 0,\n\t}\n\tblack = 0\n\twhite = 0\n\n\tif board.is_game_over():\n\t\tif board.result() == \"1-0\":\n\t\t\treturn 999\n\t\telif board.result() == \"0-1\":\n\t\t\treturn - 999\n\t\telif board.result() == \"1/2-1/2\":\n\t\t\treturn 0\n\n\tfor piece in board.piece_map().values():\n\t\tif piece.color == chess.WHITE:\n\t\t\twhite += material_values[piece.piece_type]\n\t\telse:\n\t\t\tblack += material_values[piece.piece_type]\n\n\treturn white - black","def view_material(request, username, course_code):\n if request.method == \"GET\":\n #return JsonResponse({\"name\":username}, status=200)\n errors = []\n\n # if user log in \n try:\n user = User.objects.get(username=username)\n if ensure_login(user) == False:\n return JsonResponse({'login': 'User must login'}, status=403) \n except:\n return JsonResponse({'login': 'User must login'}, status=403)\n # try to get the course\n try:\n code = Course.objects.get(course_code = course_code)\n except:\n errors.append({\"course\":\"course not exist\"})\n \n # if errors found return errors \n if len(errors) != 0:\n return JsonResponse({'errors': errors}, status=400)\n\n # check if student join the course\n if code.students.filter(username=username).count() == 0:\n return JsonResponse({\"error\":\"student not join course\"}, status=400)\n \n else:\n # fetch matiral from database \n matrials = code.uploaded_materials.all()\n # matrials = Matrial.objects.filter(course=code).values()\n\n # list the path of matiral \n list_of_matrial = []\n for matrial in matrials:\n list_of_matrial.append({\"id\":matrial.id,\"path\" : matrial.path , \"description\":matrial.description})\n \n return JsonResponse({ \"matrials\": list_of_matrial }, status=200)\n \n else:\n return JsonResponse({'error': 'Method not Allowed'}, status=405)","def getContents(self):\n return None if (self.pot.d() == None) else CraftMagicNumbers.getMaterial(self.pot.getItem()).getNewData(int(self.pot.getData()))\n # PAIL: rename","def read_all():\n # Query the database for all the materials\n materials = Material.query.order_by(Material.family_id, Material.material_name).all()\n\n # Serialize the list of materials from our data\n material_schema = MaterialSchema(many=True, exclude=[\"family.materials\"])\n data = material_schema.dump(materials).data\n return data","def validateMaterial(material, adjust=False):\n errors = []\n\n if not material:\n errors.append(ValidateMessage(\"No material defined.\", 'WARNING', material, None, {}))\n return errors, material\n\n if isinstance(material, bpy.types.Object):\n # there are always 18 slots, regardless of whether they are filled or not\n for tex in material.texture_slots:\n if tex is not None:\n try:\n # regular diffuse color texture\n if tex.use_map_color_diffuse:\n # grab the first texture\n material.texture_slots[0].texture.image.filepath.replace('//', '')\n except (KeyError, AttributeError):\n errors.append(\n ValidateMessage(\n \"Diffuse texture incomplete/undefined.\", 'WARNING', material, None, {}\n )\n )\n try:\n # normal map\n if tex.use_map_normal:\n # grab the first texture\n material.texture_slots[0].texture.image.filepath.replace('//', '')\n except (KeyError, AttributeError):\n errors.append(\n ValidateMessage(\n \"Normal texture incomplete/undefined.\", 'WARNING', material, None, {}\n )\n )\n try:\n # displacement map\n if tex.use_map_displacement:\n # grab the first texture\n material.texture_slots[0].texture.image.filepath.replace('//', '')\n except (KeyError, AttributeError):\n errors.append(\n ValidateMessage(\n \"Displacement texture incomplete/undefined.\",\n 'WARNING',\n material,\n None,\n {},\n )\n )\n else:\n if not hasattr(material, \"name\"):\n if adjust:\n material = {'name': 'phobos_error'}\n loglevel = 'WARNING'\n else:\n loglevel = 'ERROR'\n errors.append(\n ValidateMessage(\"Material name not defined.\", 'ERROR', material, None, {})\n )\n return errors, material\n\n if 'diffuse' not in material:\n if adjust:\n material['diffuse'] = (1., 1., 1., 1.)\n loglevel = 'WARNING'\n else:\n loglevel = 'ERROR'\n errors.append(\n ValidateMessage(\"Material diffuse color not defined.\", 'ERROR', material, None, {})\n )\n elif len(material['diffuse']) != 4:\n if adjust:\n if len(material['diffuse']) == 3:\n material['diffuse'] = tuple(material['diffuse'] + [1.])\n loglevel = 'WARNING'\n else:\n loglevel = 'ERROR'\n errors.append(\n ValidateMessage(\n \"Material diffuse color definition insufficient.\", loglevel, material, None, {}\n )\n )\n\n if 'diffuse_intensity' not in material:\n errors.append(\n ValidateMessage(\n \"Material diffuse intensity not defined.\", 'WARNING', material, None, {}\n )\n )\n if adjust:\n material['diffuse_intensity'] = 1.\n return errors, material","def test_team_builder_config_product_size_materials_find_one_get(self):\n pass","def func(self):\n caller = self.caller\n\n if not self.args or not self.recipe:\n self.caller.msg(\"Usage: craft <recipe> from <ingredient>, ... [using <tool>,...]\")\n return\n\n ingredients = []\n for ingr_key in self.ingredients:\n if not ingr_key:\n continue\n obj = caller.search(ingr_key, location=self.caller)\n # since ingredients are consumed we need extra check so we don't\n # try to include characters or accounts etc.\n if not obj:\n return\n if (\n not inherits_from(obj, \"evennia.objects.models.ObjectDB\")\n or obj.sessions.all()\n or not obj.access(caller, \"craft\", default=True)\n ):\n # We don't allow to include puppeted objects nor those with the\n # 'negative' permission 'nocraft'.\n caller.msg(\n obj.attributes.get(\n \"crafting_consumable_err_msg\",\n default=f\"{obj.get_display_name(looker=caller)} can't be used for this.\",\n )\n )\n return\n ingredients.append(obj)\n\n tools = []\n for tool_key in self.tools:\n if not tool_key:\n continue\n # tools are not consumed, can also exist in the current room\n obj = caller.search(tool_key)\n if not obj:\n return None\n if not obj.access(caller, \"craft\", default=True):\n caller.msg(\n obj.attributes.get(\n \"crafting_tool_err_msg\",\n default=f\"{obj.get_display_name(looker=caller)} can't be used for this.\",\n )\n )\n return\n tools.append(obj)\n\n # perform craft and make sure result is in inventory\n # (the recipe handles all returns to caller)\n result = craft(caller, self.recipe, *(tools + ingredients))\n if result:\n for obj in result:\n obj.location = caller","def has_material(obj, name):\n return name in obj.data.materials.keys()","def SetMaterial(self, *args):\n return _XCAFDoc.XCAFDoc_MaterialTool_SetMaterial(self, *args)","def set_prodmaterials(_craftable):\n partial_resources = []\n partial_craftables = []\n _craftable.resources_list = []\n _craftable.craftables_list = []\n\n # Checking for Production Materials\n for material, quantity in _craftable.craft_materials.items():\n # Checking for Resources or Workbench Need\n if 'Minimum Bench Cost' in material or material in resources.keys():\n _craftable.resources_list.append([material, int(quantity)])\n _craftable.res_totalcost = float(_craftable.res_totalcost + resources[material].unit_price * int(quantity))\n\n # Check for sub-craftables as a need\n elif material not in resources.keys():\n _craftable.craftables_list.append([material, int(quantity)])\n\n return partial_resources, partial_craftables","def test_closest_multiple_mats(self):\n m = mats.Materials(\"mats_test.json\", NoneVisited())\n self.assertEqual( '164 G. Canis Majoris', m.closest([0, 0, 0], ['Tungsten', 'Germanium'])[1]['system'])\n self.assertEqual( '2MASS J10433563-5945136', m.closest([8000, 0, 3000], ['Tungsten', 'Germanium'])[1]['system'])","def get_openmoc_material(openmc_material):\n\n cv.check_type('openmc_material', openmc_material, openmc.Material)\n\n material_id = openmc_material.id\n\n # If this Material was already created, use it\n if material_id in OPENMOC_MATERIALS:\n return OPENMOC_MATERIALS[material_id]\n\n # Create an OpenMOC Material to represent this OpenMC Material\n name = str(openmc_material.name)\n openmoc_material = openmoc.Material(id=material_id, name=name)\n\n # Add the OpenMC Material to the global collection of all OpenMC Materials\n OPENMC_MATERIALS[material_id] = openmc_material\n\n # Add the OpenMOC Material to the global collection of all OpenMOC Materials\n OPENMOC_MATERIALS[material_id] = openmoc_material\n\n return openmoc_material","def __repr__(self):\n\n return f'<Material material_id={self.material_id} name={self.name}>'","def GetHandle(self):\n return _XCAFDoc.XCAFDoc_Material_GetHandle(self)","def read_material_data(self, material):\n material_yaml_file = glob.glob(os.path.join(material_dir, material + '.yaml'))\n\n inputs = utilities.yaml_reader(material_yaml_file, material_dir, material)\n self.name = inputs['Name']\n self.materialName = material\n self.elements = inputs['Elements']\n self.zaids = inputs['Elemental ZAIDs']\n self.weightFraction = inputs['Elemental Weight Fractions'] if 'Elemental Weight Fractions' in inputs else []\n self.enrichmentZaids = inputs['Elemental Adjustment ZAIDs'] if 'Elemental Adjustment ZAIDs' in inputs else []\n self.enrichmentIsotopes = inputs['Isotopic Adjustment ZAIDs'] if 'Isotopic Adjustment ZAIDs' in inputs else []\n self.enrichmentVector = inputs['Isotopic Weight Percents'] if 'Isotopic Weight Percents' in inputs else []\n self.isotopicAtomPercents = inputs['Isotopic Atom Percents'] if 'Isotopic Atom Percents' in inputs else []\n self.density = inputs['Density']\n self.linearCoeffExpansion = inputs['Linear Coefficient of Expansion']","def create_object_material(obj, mat_name):\n if not has_material(obj, mat_name):\n if bpy.data.materials.get(mat_name, None):\n # XXX if material with this name already exists in another object\n # append the object name to this material name\n mat_name += \".{}\".format(obj.name)\n\n mat = bpy.data.materials.new(mat_name)\n link_material(obj, mat)\n return mat\n return obj.data.materials.get(mat_name)","def test_return_molecule(self):\n\n # Ask for a few molecules.\n mol1 = mol_res_spin.return_molecule('#Ap4Aase')\n mol2 = mol_res_spin.return_molecule(selection='#RNA', pipe='orig')\n\n # Test the data of molecule 1.\n self.assertEqual(mol1.name, 'Ap4Aase')\n\n # Test the data of molecule 2.\n self.assertEqual(mol2.name, 'RNA')","def make_input_materials(self) :\n # 1 5 1 MATERIAL 1 (arbitrary line, i think) \n # 1.4493e+00 9.9000e-03 7.9000e-03 1. 0. 0. 7.9000e-03 1.\n # 3.8070e-01 1.0420e-01 1.6920e-01 0 1.5100e-02 0. 1.6920e-01 1.\n self.input_materials = \"\"\n number_mats = len(self.core.pattern)+1\n a = self.core.assemblies\n for i in range(0, number_mats-1) :\n # Row 1: description.\n self.input_materials += \" \" + str(i+1) + \" 5 1 MATERIAL \" + \\\n str(i+1) + \" (\" + \\\n a[i].model + \", \" + \\\n str(a[i].enrichment) + \" w/o, \" + \\\n str(a[i].burnup) + \" MWd/kg)\\n\" \n # Rows 2 and 3.\n D1,D2,A1,A2,F1,F2,S12 = a[i].get_constants()\n d = np.array([[D1,A1,F1,1.0,0.0,0.0,F1,1.0],[D2,A2,F2,0.0,S12,0.0,F2,1.0]])\n for j in range(0, 2) :\n for k in range(0, 8) :\n self.input_materials +='%12.4e' %(d[j,k])\n self.input_materials += '\\n'\n \n a = self.core.reflector\n # Row 1: description.\n self.input_materials += \" \" + str(number_mats) + \" 5 1 MATERIAL \" + \\\n str(number_mats) + \" (REFLECTOR) \\n\" \n # Rows 2 and 3.\n D1,D2,A1,A2,F1,F2,S12 = a.get_constants()\n d = np.array([[D1,A1,F1,1.0,0.0,0.0,F1,1.0],[D2,A2,F2,0.0,S12,0.0,F2,1.0]])\n for i in range(0, 2) :\n for j in range(0, 8) :\n self.input_materials +='%12.4e' %(d[i,j])\n self.input_materials += '\\n'\n self.input_materials += \"WHITE\\n\" + \"BLACK\\n\" + \"END\\n\"","def maturity(self, *args):\n return _ida_hexrays.Hexrays_Hooks_maturity(self, *args)","def has_raw_materials(self, m_raw_materials, d_raw_materials):\n additional_resources_needed = ''\n for m_raw_material in m_raw_materials:\n if m_raw_materials[m_raw_material] > d_raw_materials[m_raw_material]:\n additional_resources_needed += 'Machine Needs Additional: {0}\\n'.format(m_raw_material)\n if additional_resources_needed:\n return additional_resources_needed\n else:\n return True","def ReadMaterialInfoAndMakeMaterialsMolFiles(OutputPath,MaterialPathList,CutOff,Multiple,PDBCharges,MaterialInputFormat):\r\n\r\n MaterialInfoList=[]\r\n ID=0\r\n Time = time.strftime('%Y-%m-%d', time.localtime(time.time()))\r\n\r\n if os.path.exists(os.path.join(OutputPath,'Mols')): pass\r\n else: os.mkdir(os.path.join(OutputPath,'Mols'))\r\n\r\n for Range,MaterialPath in enumerate(MaterialPathList):\r\n\r\n\r\n AtomNum=0\r\n MaterialsCharge = 0.0\r\n MaterialInfo,ElementList,CellLength,CellAngle,AtomInfo,AtomInfoList,CellSize=[],[],[],[],[],[],[]\r\n\r\n MaterialName=os.path.splitext(MaterialPath.split('/')[-1])[0]\r\n MaterialInfo.append(MaterialName)\r\n MaterialNewName=re.sub('\\W','',MaterialName)\r\n\r\n if len(MaterialNewName) > 20:\r\n ID+=1\r\n MaterialNewName=MaterialNewName[:20]\r\n MaterialNewName=MaterialNewName+'_ID_'+str(ID)\r\n\r\n if MaterialInputFormat == 'pdb':\r\n with open(MaterialPath,'r') as File:\r\n with open(os.path.join(OutputPath,'Mols',MaterialNewName)+'.mol','w') as Mol:\r\n\r\n for Line in File.readlines():\r\n if Line[0:6] == 'CRYST1':\r\n CellLength.append(float('%.3f' % (float(Line[6:15].strip()))))\r\n CellLength.append(float('%.3f' % (float(Line[15:24].strip()))))\r\n CellLength.append(float('%.3f' % (float(Line[24:33].strip()))))\r\n CellAngle.append(float('%.2f' % (float(Line[33:40].strip()))))\r\n CellAngle.append(float('%.2f' % (float(Line[40:47].strip()))))\r\n CellAngle.append(float('%.2f' % (float(Line[47:54].strip()))))\r\n elif Line[0:6] == 'ATOM ':\r\n AtomNum += 1\r\n AtomInfo = []\r\n AtomInfo.append(int(Line[6:11].strip())) # serial\r\n AtomInfo.append(str(Line[12:16].strip())) # atom name\r\n AtomInfo.append(float(Line[30:38].strip())) # x\r\n AtomInfo.append(float(Line[38:46].strip())) # y\r\n AtomInfo.append(float(Line[46:54].strip())) # z\r\n\r\n if PDBCharges == True:\r\n AtomInfo.append(float(Line[54:60].strip())) # charge\r\n MaterialsCharge += float(Line[54:60].strip())\r\n else:\r\n AtomInfo.append(float('0.0')) # charge\r\n\r\n ElementList.append(str(Line[12:16].strip())) # atom name list\r\n AtomInfoList.append(AtomInfo)\r\n\r\n if abs(MaterialsCharge) < 0.001:\r\n Mol.write('# Basic Molecule Information\\n# Created by PyMSATm at %s\\n'\r\n 'Molecule_name: %s \\n\\nCoord_Info: Listed Cartesian None\\n' % (Time, MaterialNewName))\r\n else: Mol.write('# Basic Molecule Information\\n# Created by PyMSATm at %s\\n'\r\n 'Molecule_name: %s CHARGED\\n\\nCoord_Info: Listed Cartesian None\\n' % (Time, MaterialNewName))\r\n\r\n Mol.write(' %d\\n'%(AtomNum))\r\n\r\n for AtomInfo2 in AtomInfoList:\r\n Mol.write('{:<6d} {:>8} {:>8} {:>8} {:>6} {:>6} 0 0\\n'.format(AtomInfo2[0],\r\n AtomInfo2[2],AtomInfo2[3],AtomInfo2[4],AtomInfo2[1],AtomInfo2[5]))\r\n\r\n Mol.write('\\nFundcell_Info: Listed\\n%.5f %.5f %.5f\\n%.5f %.5f %.5f\\n0.00000 0.00000 0.00000\\n%.5f %.5f %.5f'%(CellLength[0],\r\n CellLength[1],CellLength[2],CellAngle[0],CellAngle[1],CellAngle[2],CellLength[0],CellLength[1],CellLength[2]))\r\n\r\n if MaterialInputFormat == 'mol':\r\n\r\n KeyOne,KeyTwo,KeyThree= False,False,False\r\n\r\n with open(MaterialPath, 'r') as File:\r\n with open(os.path.join(OutputPath, 'Mols', MaterialNewName) + '.mol', 'w') as Mol:\r\n\r\n for Line in File.readlines():\r\n if Line.strip(): # skip blank\r\n WordList = Line.strip().split()\r\n if WordList[0] == 'Coord_Info:':\r\n KeyOne = True\r\n elif WordList[0] == 'Fundcell_Info:':\r\n KeyOne = False\r\n KeyTwo = True\r\n elif len(WordList) > 1 and KeyOne == True:\r\n AtomNum += 1\r\n AtomInfo = []\r\n AtomInfo.append(int(WordList[0].strip())) # serial\r\n AtomInfo.append(str(WordList[4].strip())) # atom name\r\n AtomInfo.append(float(WordList[1].strip())) # x\r\n AtomInfo.append(float(WordList[2].strip())) # y\r\n AtomInfo.append(float(WordList[3].strip())) # z\r\n AtomInfo.append(float(WordList[5].strip())) # charge\r\n ElementList.append(str(WordList[4].strip())) # atom name list\r\n AtomInfoList.append(AtomInfo)\r\n MaterialsCharge += float(WordList[5].strip())\r\n elif len(WordList) > 1 and KeyTwo == True:\r\n KeyTwo = False\r\n KeyThree = True\r\n CellLength.append(float('%.3f' % (float(WordList[0].strip()))))\r\n CellLength.append(float('%.3f' % (float(WordList[1].strip()))))\r\n CellLength.append(float('%.3f' % (float(WordList[2].strip()))))\r\n elif len(WordList) > 1 and KeyThree == True:\r\n KeyThree = False\r\n CellAngle.append(float('%.2f' % (float(WordList[0].strip()))))\r\n CellAngle.append(float('%.2f' % (float(WordList[1].strip()))))\r\n CellAngle.append(float('%.2f' % (float(WordList[2].strip()))))\r\n\r\n if abs(MaterialsCharge) < 0.001:\r\n Mol.write('# Basic Molecule Information\\n# Created by PyMSATm at %s\\n'\r\n 'Molecule_name: %s \\n\\nCoord_Info: Listed Cartesian None\\n' % (\r\n Time, MaterialNewName))\r\n else:\r\n Mol.write('# Basic Molecule Information\\n# Created by PyMSATm at %s\\n'\r\n 'Molecule_name: %s CHARGED\\n\\nCoord_Info: Listed Cartesian None\\n' % (\r\n Time, MaterialNewName))\r\n\r\n Mol.write(' %d\\n' % (AtomNum))\r\n\r\n for AtomInfo2 in AtomInfoList:\r\n Mol.write('{:<6d} {:>8.4f} {:>8.4f} {:>8.4f} {:>8s} {:>10.6f} 0 0\\n'.format(AtomInfo2[0], AtomInfo2[2],\r\n AtomInfo2[3], AtomInfo2[4],\r\n AtomInfo2[1], AtomInfo2[5]))\r\n\r\n Mol.write(\r\n '\\nFundcell_Info: Listed\\n%.4f %.4f %.4f\\n%.4f %.4f %.4f\\n0.00000 0.00000 0.00000\\n%.4f %.4f %.4f' % (\r\n CellLength[0], CellLength[1], CellLength[2], CellAngle[0], CellAngle[1], CellAngle[2],\r\n CellLength[0], CellLength[1], CellLength[2]))\r\n\r\n if CellAngle[0] == 90.0 and CellAngle[1] == 90.0 and CellAngle[2] == 90.0:\r\n Orthogonality = True\r\n else: Orthogonality=False\r\n\r\n if Orthogonality == True:\r\n for Length in CellLength:\r\n Count = 1\r\n while Length * Count < CutOff * Multiple+1:\r\n Count += 1\r\n CellSize.append(str(Count))\r\n else: # definite the lengths of x, y, z in orthogonal directions\r\n\r\n SinA, SinB, SinC = math.sin(math.radians(CellAngle[0])), math.sin(math.radians(CellAngle[1])), math.sin(math.radians(CellAngle[2]))\r\n CosA, CosB, CosC = math.cos(math.radians(CellAngle[0])), math.cos(math.radians(CellAngle[1])), math.cos(math.radians(CellAngle[2]))\r\n CosParameterA = (CosC * CosA - CosB) / (SinC * SinA)\r\n SinParameterA = math.sqrt(1 - CosParameterA ** 2)\r\n CosParameterB = (CosA * CosB - CosC) / (SinA * SinB)\r\n SinParameterB = math.sqrt(1 - CosParameterB ** 2)\r\n CosParameterC = (CosB * CosC - CosA) / (SinB * SinC)\r\n SinParameterC = math.sqrt(1 - CosParameterC ** 2)\r\n x=CellLength[0]*SinC*SinParameterA\r\n y=CellLength[1]*SinA*SinParameterB\r\n z=CellLength[2]*SinB*SinParameterC\r\n TransLength=[x,y,z]\r\n\r\n for Length in TransLength:\r\n Count = 1\r\n while float(Length) * Count < CutOff * Multiple+1:\r\n Count += 1\r\n CellSize.append(str(Count))\r\n\r\n MaterialInfo.append(AtomNum)\r\n MaterialInfo.append(CellLength)\r\n MaterialInfo.append(CellAngle)\r\n MaterialInfo.append(CellSize)\r\n MaterialInfo.append(list(set(ElementList)))\r\n MaterialInfo.append(Orthogonality)\r\n MaterialInfo.append(MaterialNewName)\r\n MaterialInfoList.append(MaterialInfo)\r\n\r\n return MaterialInfoList","def get_items(self):\n\n self.__logger.info(\"Thermo Builder Started\")\n\n # All relevant materials that have been updated since thermo props were last calculated\n q = dict(self.query)\n q.update(self.materials.lu_filter(self.thermo))\n comps = [m[\"elements\"] for m in self.materials().find(q, {\"elements\": 1})]\n\n self.__logger.info(\"Found {} compositions with new/updated materials\".format(len(comps)))\n\n # Only yields maximal super sets: e.g. if [\"A\",\"B\"] and [\"A\"] are both in the list, will only yield [\"A\",\"B\"]\n # as this will calculate thermo props for all [\"A\"] compounds\n processed = set()\n # Start with the largest set to ensure we don\"t miss superset/subset relations\n for chemsys in sorted(comps, key=lambda x: len(x), reverse=True):\n if \"-\".join(sorted(chemsys)) not in processed:\n processed |= self.chemsys_permutations(chemsys)\n yield self.get_entries(chemsys)","def get_items(self):\n\n self.logger.info(\"Site-Descriptors Builder Started\")\n\n self.logger.info(\"Setting indexes\")\n\n # All relevant materials that have been updated since site-descriptors\n # were last calculated\n\n q = dict(self.mat_query)\n all_task_ids = list(self.materials.distinct(self.materials.key, q))\n q.update(self.materials.lu_filter(self.site_descriptors))\n new_task_ids = list(self.materials.distinct(self.materials.key, q))\n self.logger.info(\n \"Found {} entirely new materials for site-descriptors data\".format(\n len(new_task_ids)))\n for task_id in all_task_ids:\n if task_id in new_task_ids:\n any_piece = True\n\n else: # Any piece of info missing?\n data_present = self.site_descriptors.query(\n properties=[self.site_descriptors.key, \"site_descriptors\", \"statistics\"],\n criteria={self.site_descriptors.key: task_id}).limit(1)[0]\n any_piece = False\n for k, v in self.all_output_pieces.items():\n if k not in list(data_present.keys()):\n any_piece = True\n break\n else:\n any_piece = False\n for e in v:\n if e not in data_present[k]:\n any_piece = True\n break\n if not any_piece:\n for l in self.sds['csf'].feature_labels():\n for fpi in data_present['site_descriptors']['csf']:\n if l not in fpi.keys():\n any_piece = True\n break\n if any_piece:\n yield self.materials.query(\n properties=[self.materials.key, \"structure\"],\n criteria={self.materials.key: task_id}).limit(1)[0]","def test_closest_common_mats(self):\n m = mats.Materials(\"mats_test.json\", NoneVisited())\n self.assertEqual( '164 G. Canis Majoris', m.closest([0, 0, 0], ['Iron'])[1]['system'])\n self.assertEqual( '2MASS J10433563-5945136', m.closest([8000, 0, 3000], ['Iron'])[1]['system'])","def MaterialsLabel(*args):\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialsLabel(*args)","def create_material(name, diffuse, alpha):\n mat = bpy.data.materials.new(name)\n mat.diffuse_color = diffuse\n mat.diffuse_intensity = 1.0\n mat.alpha = alpha\n if alpha:\n mat.use_transparency = True\n return mat","def read_one(family_id, material_id):\n # Query the database for the material\n material = (\n Material.query.join(Family, Family.family_id == Material.family_id)\n .filter(Family.family_id == family_id)\n .filter(Material.material_id == material_id)\n .one_or_none()\n )\n\n # Was a material found?\n if material is not None:\n material_schema = MaterialSchema()\n data = material_schema.dump(material).data\n return data\n\n # Otherwise, nope, didn't find that material\n else:\n abort(404, f\"Material not found for Id: {material_id}\")","def test_return_residue(self):\n\n # Ask for a few residues.\n res1 = mol_res_spin.return_residue(':1')\n res2 = mol_res_spin.return_residue(selection=':2')\n res4 = mol_res_spin.return_residue(selection=':4', pipe='orig')\n res5 = mol_res_spin.return_residue(selection='#RNA:-5', pipe='orig')\n\n # Test the data of residue 1.\n self.assertEqual(res1.num, 1)\n self.assertEqual(res1.name, None)\n\n # Test the data of residue 2.\n self.assertEqual(res2.num, 2)\n self.assertEqual(res2.name, 'Glu')\n\n # Test the data of residue 4.\n self.assertEqual(res4.num, 4)\n self.assertEqual(res4.name, 'Pro')\n\n # Test the data of the RNA residue -5.\n self.assertEqual(res5.num, -5)\n self.assertEqual(res5.name, None)\n self.assertEqual(res5.spin[1].name, 'N5')","def result(self):","def result(self):","def get_biomaterials(self, provider_id=\"\", biomaterial_id=\"\", organism_id=\"\", biomaterial_name=\"\", analysis_id=\"\"):\n\n if analysis_id:\n res = self.session.query(self.model.biomaterial) \\\n .join(self.model.assay_biomaterial, self.model.biomaterial.biomaterial_id == self.model.assay_biomaterial.biomaterial_id) \\\n .join(self.model.assay, self.model.assay_biomaterial.assay_id == self.model.assay.assay_id) \\\n .join(self.model.acquisition, self.model.assay.assay_id == self.model.acquisition.assay_id) \\\n .join(self.model.quantification, self.model.acquisition.acquisition_id == self.model.quantification.acquisition_id) \\\n .filter(self.model.quantification.analysis_id == analysis_id)\n else:\n res = self.session.query(self.model.biomaterial)\n\n if biomaterial_id:\n res = res.filter_by(biomaterial_id=biomaterial_id)\n if provider_id:\n res = res.filter_by(biosourceprovider_id=provider_id)\n if organism_id:\n res = res.filter_by(taxon_id=organism_id)\n if biomaterial_name:\n res = res.filter_by(name=biomaterial_name)\n\n data = []\n for biomat in res:\n data.append({\n 'provider_id': biomat.biosourceprovider_id,\n 'description': biomat.description,\n 'biomaterial_id': biomat.biomaterial_id,\n 'organism_id': biomat.taxon_id,\n 'dbxref_id': biomat.dbxref_id,\n 'name': biomat.name,\n })\n return data","def GetHandle(self):\n return _XCAFDoc.XCAFDoc_MaterialTool_GetHandle(self)","def return_book_process(self):\n s1 = System()\n b1 = Books(\"1984\", \"George Orwell\", \"Harvill Secker\", \"1949\", \"0123456789123\")\n m1 = Members(\"Richard\", \"Blackmore\", \"14-04-1945\", \"Weston\")\n s1.add_resource(b1)\n s1.lending_process(b1, m1)\n s1.remove_resource(b1)\n #prove that the return of a book doen't take place if not in the ctalogue, \n #but also that in the remove_resource method should be added somthing to remeove that book from all borrowers\n #or doesn't allow the operation if not returned\n s1.return_book_process(b1, False, \"none\")\n self.assertEqual(b1.get_borrower(), m1)\n self.assertIn(b1, m1.get_borrowed_books())\n s1.add_resource(b1)\n self.assertEqual(b1.get_borrower(), None)\n self.assertNotIn(b1, m1.get_borrowed_books())","def complete_material_equivalences(self):\n for material in self.materials:\n material.geu = self\n for material_aux in self.materials:\n material.equivalent_materials.add(material_aux)","def test_JCB_VISUAL_MATERIALS( self ):\n driver = self.driver\n driver.get(self.base_url + \"/record=b5660654~S6\")\n driver.find_element_by_link_text(\"Request\").click()\n self.assertTrue( 'aeon' in driver.current_url )\n self.assertTrue( 'ReferenceNumber=b5660654' in driver.current_url )\n self.assertTrue( 'ItemTitle=Thomas%20Jefferson' in driver.current_url )\n self.assertTrue( 'ItemAuthor=&ItemPublisher' in driver.current_url )\n self.assertTrue( 'ItemPublisher=Princeton' in driver.current_url )\n self.assertTrue( 'CallNumber=VHS' in driver.current_url )\n # self.assertTrue( 'Notes=(bibnum%3A%20b5660654)' in driver.current_url )\n self.assertEqual( 'ItemInfo2=', driver.current_url[-10:] )","def generate_products(self = random.sample, name = random.choice(result), price = random.randint(5, 100), weight = random.randint(5, 100), \nflammability= random.uniform(0, 2.5)):\n return sample","def product_size_materials(self):\n return self._product_size_materials","def test_team_builder_config_product_size_materials_get(self):\n pass","def search_all_id():\n\n # Quering with MPRester to obtain corresponding material id, reduced formula and space group.\n with MPR(get_api_key()) as m:\n\n # Criteria is set as greater than -1 to search materials, which is unphysical in real world, therefore in effect\n # search all available materials.\n llist = m.query(criteria={'band_gap': {'$gt': -1}}, properties=['material_id', 'pretty_formula','spacegroup'],\n # Chunk size is set as 0 for no chunking.\n chunk_size=0)\n\n # Generate an empty dictionary for further sampling.\n space_group_dict = dict()\n\n # To loop all items in space_group_dict and to label by their space groups.\n for index in llist:\n\n # To find space group in Hall Symbol.\n space_group = index['spacegroup']['hall']\n\n # If the item space group is not in space_group_dict, then add it and create an empty value list for further\n # storing.\n if space_group not in space_group_dict:\n space_group_dict[space_group] = []\n\n # To append the material id (str) to the list(value) of corresponding space group (key).\n space_group_dict[space_group].append(index['material_id'])\n\n # To sample the space_group_dict by random_sample.\n final_list = random_sample(space_group_dict, 0.01)\n\n return final_list","def define_material(self):\n\n # Check which class should be called.\n const_eqn = self.config['material']['const_eqn']\n if isclass(const_eqn):\n mat_class = self.config['material']['const_eqn']\n elif const_eqn == 'lin_elastic':\n mat_class = materials.solid_materials.LinearIsoMaterial\n elif const_eqn == 'neo_hookean':\n mat_class = materials.solid_materials.NeoHookeMaterial\n elif const_eqn == 'demiray':\n mat_class = materials.solid_materials.DemirayMaterial\n elif const_eqn == 'fung':\n mat_class = materials.solid_materials.FungMaterial\n elif const_eqn == 'guccione':\n mat_class = materials.solid_materials.GuccioneMaterial\n elif const_eqn == 'holzapfel_ogden':\n mat_class = materials.solid_materials.HolzapfelOgdenMaterial\n elif const_eqn == 'newtonian' or const_eqn == 'stokes':\n mat_class = materials.fluids.NewtonianFluid\n else:\n raise NotImplementedError(\"Shouldn't be in here...\")\n\n # Create an instance of the material class and store\n # as member data.\n try:\n inverse = self.config['formulation']['inverse']\n except KeyError:\n inverse = False\n self._material = mat_class(inverse=inverse,\n **self.config['material'])\n\n return None","def get_one_meal():","def get_all_meals():","def materiaal(self):\n return self._materiaal.get_waarde()","def getMaterialString(self):\n matString = 'm10 1001.70c -0.080538 $Lucite (PMMA / Plexiglass) rho = 1.19 g/cc\\n'\n matString += ' 6012.70c -0.599848 8016.70c -0.319614 \\n'\n matString += 'm204 7014.70c -0.755636 $air (US S. Atm at sea level) rho = 0.001225 \\n'\n matString += ' 8016.70c -0.231475 18036.70c -3.9e-005 18038.70c -8e-006\\n'\n matString += ' 18040.70c -0.012842 \\n'\n matString += 'm5 98252.66c 1 $ Cf-252, rho =15.1 g/cc wiki \\n'\n matString += 'm406 82204.70c -0.013781 $Lead, \\n'\n matString += ' 82206.70c -0.239557 82207.70c -0.220743 82208.70c -0.525919\\n'\n matString += 'm456 1001.70c -0.143716 $Polyethylene - rho = 0.93 g/cc \\n'\n matString += ' 6000.70c -0.856284 \\n'\n matString += 'm488 14028.70c -0.009187 $Steel, Stainless 316 rho = 7.92 \\n'\n matString += ' 14029.70c -0.000482 14030.70c -0.000331 24050.70c -0.007095\\n'\n matString += ' 24052.70c -0.142291 24053.70c -0.016443 24054.70c -0.004171\\n'\n matString += ' 25055.70c -0.02 26054.70c -0.037326 26056.70c -0.601748\\n'\n matString += ' 26057.70c -0.014024 26058.70c -0.001903 28058.70c -0.080873\\n'\n matString += ' 28060.70c -0.031984 28061.70c -0.001408 28062.70c -0.004546\\n'\n matString += ' 28064.70c -0.001189 42092.70c -0.003554 42094.70c -0.002264\\n'\n matString += ' 42095.70c -0.003937 42096.70c -0.004169 42097.70c -0.002412\\n'\n matString += ' 42098.70c -0.006157 42100.70c -0.002507 \\n'\n matString += 'mt3 poly.01t \\n'\n matString += 'mt456 poly.01t \\n'\n matString += 'mt10 poly.01t \\n'\n return matString","def run(self):\n matched = ['Program Description']\n matched += sorted(self.matching_recipes())\n\n msg = 'Your Search For:'\n msg += PREFIX + PREFIX.join([\"'\" + word + \"'\" for word in self.words])\n msg += '\\nMatched These Recipes:'\n msg += PREFIX + PREFIX.join(matched)\n print(msg)\n return matched","def get_items(self):\n self.logger.info(\"Electrode Builder Started\")\n\n # All updated chemical systems that contain at least one redox active elements\n q = dict(self.query)\n q.update(self.materials.lu_filter(self.thermo))\n q.update({\"chemsys\": {\"$in\": self.redox_els}})\n\n comps = self.materials.distinct(\"chemsys\", q)\n\n self.logger.info(\n \"Found {} compositions with new/updated materials\".format(len(comps)))\n\n # Only yields maximal super sets: e.g. if [\"A\",\"B\"] and [\"A\"] are both in the list, will only yield [\"A\",\"B\"]\n # as this will calculate thermo props for all [\"A\"] compounds\n processed = set()\n\n # Start with the largest set to ensure we don\"t miss superset/subset\n # relations\n for chemsys in sorted(comps, key=lambda x: len(x.split(\"-\")), reverse=True):\n if chemsys not in processed:\n processed |= chemsys_permutations(chemsys)\n yield self.get_entries(chemsys)","def get_materials(dbpath):\n odb = openOdb(path=dbpath)\n _materials = []\n for _name in odb.materials.items():\n _materials.append(_name)\n odb.close()\n return _materials","def find_material(material):\n for libn,tdict in liblist:\n if material in tdict:\n print(libn)","def test_names_no_mats(self):\n m = mats.Materials()\n self.assertEqual([], m.names())","def material_matrix(self):\n out = Tmatrix()\n out.translate(Vector([.5, .5, .5]))\n out.scale(Vector([self.radius, self.radius, self.radius]) *\n (.5 / (self.radius + self.thickness)))\n return out","def test_delete_material(self):\n expected_materials = [\n [],\n ['cotton'],\n ['cotton'],\n ]\n\n select_listings_to_edit(self.driver, 'Delete')\n d = self.driver\n bp = BulkPage(d)\n\n send_keys(bp.operation_input(), 'wool')\n click(bp.operation_apply())\n\n material_names = bp.material_names()\n assert material_names == expected_materials","def test_team_builder_config_product_size_materials_id_product_size_material_get(self):\n pass","def info_equipment_reactors_get():\n equipment = _equipment_by_group(438) # 438 == Mobile Reactor\n return equipment, 200"],"string":"[\n \"def info_materials_raw_get():\\n materials = _material_by_group(427) # 427 == intermediate group\\n return materials, 200\",\n \"def info_materials_get():\\n materials = _material_by_group() # empty means all groups\\n return materials, 200\",\n \"def GetMaterial(self, *args):\\n return _XCAFDoc.XCAFDoc_MaterialTool_GetMaterial(self, *args)\",\n \"def info_materials_polymer_get():\\n materials = _material_by_group(974) # 974 == intermediate group\\n return materials, 200\",\n \"def info_materials_booster_get():\\n materials = _material_by_group(712) # 712 == intermediate group\\n return materials, 200\",\n \"def info_materials_intermediates_get():\\n materials = _material_by_group(428) # 428 == intermediate group\\n return materials, 200\",\n \"def info_materials_gas_get():\\n materials = _material_by_group(711) # 711 == intermediate group\\n return materials, 200\",\n \"def get_smeared_material(materials, void_material='', void_percent=1.0):\\n smear_material = {}\\n for material, materialWeightPercent in materials.items():\\n void_multiplier = 1.0\\n if material == 'Void':\\n pass\\n else:\\n base_material = Material()\\n base_material.set_material(material)\\n\\n if base_material.materialName == void_material:\\n void_multiplier = void_percent\\n\\n for isotope, isotopeWeightPercent in base_material.weightPercent.items():\\n element = str(isotope)\\n if len(element) < 5:\\n current_element = element[:1] + '000'\\n else:\\n current_element = element[:2] + '000'\\n current_element = int(current_element)\\n try:\\n smear_material[isotope] += isotopeWeightPercent * materialWeightPercent * base_material.density \\\\\\n * AVOGADROS_NUMBER * void_multiplier / \\\\\\n base_material.elementDict[current_element].molecularMassDict[isotope]\\n except KeyError:\\n smear_material[isotope] = isotopeWeightPercent * materialWeightPercent * base_material.density \\\\\\n * AVOGADROS_NUMBER * void_multiplier / \\\\\\n base_material.elementDict[current_element].molecularMassDict[isotope]\\n smeared_material = Material()\\n smeared_material.name = \\\"{}\\\".format([val for val in materials])\\n smeared_material.atomDensity = sum(smear_material.values())\\n smeared_atom_percent = {}\\n for k, v in smear_material.items():\\n smeared_atom_percent[k] = v / smeared_material.atomDensity\\n smeared_material.atomPercent = smeared_atom_percent\\n return smeared_material\",\n \"def info_materials_composites_get():\\n materials = _material_by_group(429) # 429 == intermediate group\\n return materials, 200\",\n \"def get_mats_from_args(self, args):\\n try:\\n lhslist = args.split(\\\"/\\\")\\n material = CraftingMaterialType.objects.get(name__iexact=lhslist[0])\\n amt = int(lhslist[1])\\n if amt < 1:\\n raise ValueError(\\n \\\"You must specify a positive value of a material to send.\\\"\\n )\\n # different errors\\n except (IndexError, AttributeError, TypeError):\\n self.msg(\\\"You must specify materials to send.\\\")\\n except CraftingMaterialType.DoesNotExist:\\n self.msg(\\\"That is not a valid material type.\\\")\\n except OwnedMaterial.DoesNotExist:\\n self.msg(\\\"You don't have any of that material.\\\")\\n except ValueError as err:\\n self.msg(err)\\n # succeeded, return amount. It'll be decremented when sent off later\\n else:\\n return material.id, amt\",\n \"def test_has_raw_materials_handles_insufficient_raw_materials(self):\\n # Params\\n m_raw_materials = self.selection['ingredients']\\n d_raw_materials = {\\n 'sugar': 0,\\n 'butter': 0,\\n 'caramel': 15,\\n 'dark chocolate': 0,\\n 'mint chocolate': 30,\\n 'milk chocolate': 30,\\n 'light corn syrup': 0,\\n 'sweetened condensed milk': 0,\\n 'vanilla extract': 0,\\n 'Reese\\\\'s Pieces': 15,\\n }\\n # Returns\\n return_1 = 'Machine Needs Additional: sugar\\\\n' \\\\\\n 'Machine Needs Additional: butter\\\\n' \\\\\\n 'Machine Needs Additional: dark chocolate\\\\n' \\\\\\n 'Machine Needs Additional: light corn syrup\\\\n' \\\\\\n 'Machine Needs Additional: sweetened condensed milk\\\\n' \\\\\\n 'Machine Needs Additional: vanilla extract\\\\n'\\n # Calls\\n bool_1 = self.chocolate_machine.has_raw_materials(m_raw_materials, d_raw_materials)\\n # Asserts\\n self.assertEqual(bool_1, return_1)\",\n \"def test_has_raw_materials_handles_insufficient_raw_materials(self):\\n # Setup\\n choice = 'dark cherry'\\n selection = CUPCAKE_CHOICES[choice]\\n\\n # Params\\n f_raw_materials = selection['ingredients']\\n d_raw_materials = {\\n 'sugar': 0,\\n 'butter': 0,\\n 'dark chocolate': 0,\\n 'light corn syrup': 0,\\n 'sweetened condensed milk': 0,\\n 'vanilla extract': 0,\\n 'bing cherries': 10,\\n }\\n\\n # Returns\\n return_1 = 'Machine Needs Additional: sugar\\\\n' \\\\\\n 'Machine Needs Additional: butter\\\\n' \\\\\\n 'Machine Needs Additional: dark chocolate\\\\n' \\\\\\n 'Machine Needs Additional: light corn syrup\\\\n' \\\\\\n 'Machine Needs Additional: sweetened condensed milk\\\\n' \\\\\\n 'Machine Needs Additional: vanilla extract\\\\n'\\n\\n # Calls\\n string_1 = has_raw_materials(f_raw_materials, d_raw_materials)\\n\\n # Asserts\\n self.assertEqual(string_1, return_1)\",\n \"def get_material_features(self):\\n return self.material_features\",\n \"def get(self, request, mat_pk:int, params:str=\\\"all\\\", target_results:int=Conf.results_num, components:int=pcaConf.components):\\n \\n try:\\n mats_id = [x.id for x in Material.objects.all()]\\n \\n # Get data from database and prepocess those data.\\n # Preprocess use Standardization or Normalization.\\n if params == \\\"all\\\":\\n mats_data = self._extract_data()\\n if params == \\\"imp\\\":\\n mats_data = self._extract_imp_data()\\n mats_data = self._preprocess(mats_data)\\n \\n # Calling machine learning algorithms to finish similarity anaylys\\n # and returns the final result.\\n mats_data_reducted = run_kernel_pca(mats_data, components=components) # ndarray\\n \\n # Material instance to compare.\\n mat_protocol = mats_data_reducted[mats_id.index(mat_pk)]\\n \\n # Calculate euclidean distance for each instance and get the best.\\n results = []\\n for mat_id, mat_data in zip(mats_id, mats_data_reducted):\\n \\n # Skip the material data to compare.\\n if mat_id == mat_pk: continue\\n \\n mat_dist = np.sum(np.square(mat_protocol - mat_data))\\n mat_dist_tuple = (mat_id, mat_dist)\\n results.append(mat_dist_tuple)\\n \\n results.sort(key=lambda x:x[1])\\n \\n return JsonResponse({\\n \\\"data_results\\\": results[:target_results], # Material data in json format\\n \\\"data_returned_num\\\": Conf.results_num, # Returned data numbers.\\n \\\"data_total_num\\\": len(mats_data_reducted), # length of materials in analysis\\n \\\"data_reducted_dim\\\": len(mats_data_reducted[0]) # Reducted data dimention\\n })\\n except RuntimeError as e:\\n print(e)\\n return Http404(\\\"Sorry, the procedures has failed. Check backend for more info\\\")\",\n \"def make_materials(*args, **kwargs):\\n\\n ret_args = []\\n for parameters in args:\\n if isinstance(parameters, (list, tuple)):\\n ret_args.append(Material(*parameters))\\n else:\\n ret_args.append(Material(parameters))\\n\\n ret_kwargs = {}\\n for name, parameters in kwargs.items():\\n if isinstance(parameters, (list, tuple)):\\n ret_kwargs[name] = Material(*parameters)\\n else:\\n ret_kwargs[name] = Material(parameters)\\n\\n if len(ret_kwargs) == 0:\\n return ret_args\\n elif len(ret_args) == 0:\\n return ret_kwargs\\n else:\\n return ret_args, ret_kwargs\",\n \"def show_materials(self):\\n print('\\\\nThe materials with known dielectric properties are:\\\\n')\\n pprint.pprint(mats.Electrical.props)\\n# pprint.pprint(mats.Electrical.DIELECTRIC)\\n print('\\\\nThe materials with known loss tangents are:\\\\n')\\n pprint.pprint(mats.Electrical.props)\\n# pprint.pprint(mats.Electrical.LOSS_TAN)\\n return\",\n \"def _getMaterials(self):\\n warnings.warn(\\\"This function is deprecated; call Document.getMaterialNodes() instead.\\\", DeprecationWarning, stacklevel = 2)\\n return self.getMaterialNodes()\",\n \"def IsMaterial(self, *args):\\n return _XCAFDoc.XCAFDoc_MaterialTool_IsMaterial(self, *args)\",\n \"def get_material_info(TABLE_info):\\n \\\"\\\"\\\"\\n 1 Get info from TABLE_info.\\n \\\"\\\"\\\"\\n width = TABLE_info[0]\\n height = TABLE_info[1]\\n t_m = TABLE_info[2]\\n\\n \\\"\\\"\\\"\\n 2 Get material info.\\n \\\"\\\"\\\"\\n z_m = 3 * t_m\\n\\n m_width = rs.GetInteger(\\\"Put the width of material\\\", z_m, None, None)\\n\\n t_sen = rs.GetReal(\\\"Put Int(mm): Thickness of material to cut SEN.\\\", t_m / 2, None, None)\\n\\n x_m1 = m_width\\n x_m2 = height - x_m1\\n x_m3 = x_m2\\n x_m4 = x_m1\\n\\n y_m2 = m_width\\n y_m3 = y_m2\\n y_m1 = width - (y_m2 + y_m3)\\n y_m4 = y_m1\\n\\n\\n # material1\\n m1_p0 = (x_m3, y_m3)\\n m1_p1 = (x_m3, y_m3 + y_m1)\\n m1_p2 = (x_m3 + x_m1, y_m3 + y_m1)\\n m1_p3 = (x_m3 + x_m1, y_m3)\\n m1_points = [m1_p0, m1_p1, m1_p2, m1_p3]\\n\\n m1_info = [x_m1, y_m1, z_m, m1_points, t_sen]\\n\\n # material2\\n m2_p0 = (0, width - y_m2)\\n m2_p1 = (0, width)\\n m2_p2 = (height - x_m1, width)\\n m2_p3 = (height - x_m1, width - y_m2)\\n m2_points = [m2_p0, m2_p1, m2_p2, m2_p3]\\n\\n m2_info = [x_m2, y_m2, z_m, m2_points, t_sen]\\n\\n # material3\\n m3_p0 = (0, 0)\\n m3_p1 = (0, y_m3)\\n m3_p2 = (x_m3, y_m3)\\n m3_p3 = (x_m3, 0)\\n m3_points = [m3_p0, m3_p1, m3_p2, m3_p3]\\n\\n m3_info = [x_m3, y_m3, z_m, m3_points, t_sen]\\n\\n # material4\\n m4_p0 = (0, y_m3)\\n m4_p1 = (0, y_m3 + y_m4)\\n m4_p2 = (-x_m4, y_m3 + y_m4)\\n m4_p3 = (-x_m4, y_m3)\\n m4_points = [m4_p0, m4_p1, m4_p2, m4_p3]\\n\\n m4_info = [x_m4, y_m4, z_m, m4_points, t_sen]\\n\\n return m1_info, m2_info, m3_info, m4_info\",\n \"def get_material_mapping(self):\\n return {name: self.get_material(name) for name in self.parts.keys()}\",\n \"def MaterialTool(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialTool(*args)\",\n \"def input_materials(type_id: int, quantity: int, me: int = 0, prod_type: str = 'manufacturing', prices: bool = False) -> pd.DataFrame:\\n actID = {'manufacturing': 1, 'reaction': 11}\\n mats = pd.DataFrame(columns=['type_id', 'type_name', 'quantity'])\\n if type_id in quantities.loc[(quantities['activityID'] == actID[prod_type]) & (quantities['productTypeID'] == type_id)].values: # item can be manufactured\\n\\n if prod_type == 'reaction':\\n bpid = productToFormula(type_id)\\n\\n elif prod_type == 'manufacturing':\\n bpid = productToBP(type_id)\\n \\n else: \\n raise ValueError(\\\"thats not a valid manufacturing type. options are 'reaction', 'manufacturing'\\\")\\n\\n qPerRun = quantPerRun(bpid)\\n runs = quantity // qPerRun\\n\\n if runs > 0:\\n for _, row in materials.loc[(materials['activityID'] == actID[prod_type]) & (materials['typeID'] == bpid)].iterrows():\\n\\n if prod_type == 'reaction':\\n quant = row['quantity'] * runs\\n\\n elif prod_type == 'manufacturing':\\n quant = me_formula(row['quantity'],me) * runs\\n\\n mats = mats.append({'type_id': row['materialTypeID'], 'type_name': emt.typeIDToName(row['materialTypeID']), 'quantity': quant}, ignore_index=True)\\n \\n \\n # buys the product instead of manufacturing more of it than needed\\n if int(runs * qPerRun) < int(quantity):\\n mats = mats.append({'type_id': type_id, 'type_name': emt.typeIDToName(type_id), 'quantity': quantity - (runs * qPerRun)},ignore_index=True)\\n \\n mats = mats.groupby(['type_id', 'type_name']).sum().reset_index()\\n mats = mats.astype({\\\"type_id\\\": int, \\\"quantity\\\": int})\\n\\n if prices:\\n mats = emt.add_price(mats)\\n\\n return mats\",\n \"def materials(self):\\n return MaterialManager(session=self._session)\",\n \"def XCAFDoc_DocumentTool_MaterialTool(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialTool(*args)\",\n \"def AddMaterial(self, *args):\\n return _XCAFDoc.XCAFDoc_MaterialTool_AddMaterial(self, *args)\",\n \"def GetMaterialLabels(self, *args):\\n return _XCAFDoc.XCAFDoc_MaterialTool_GetMaterialLabels(self, *args)\",\n \"def test_create_material_multi_basic(self):\\n expected_materials = [\\n ['cotton', 'AAA', 'BBB', 'CCC'],\\n ['cotton', 'AAA', 'BBB', 'CCC'],\\n ['wool', 'AAA', 'BBB', 'CCC'],\\n ]\\n\\n select_listings_to_edit(self.driver)\\n d = self.driver\\n bp = BulkPage(d)\\n\\n send_keys(bp.operation_input(), 'AAA,BBB ,CCC')\\n click(bp.operation_apply())\\n\\n material_names = bp.material_names()\\n assert material_names == expected_materials\",\n \"def get_items(self):\\n\\n to_process_mat_ids = self._find_to_process()\\n\\n self.logger.info(\\n \\\"Updating all substrate calculations for {} materials\\\".format(\\n len(to_process_mat_ids)\\n )\\n )\\n\\n for mpid in to_process_mat_ids:\\n e_tensor = self.elasticity.query_one(\\n criteria={self.elasticity.key: mpid},\\n properties=[\\\"elasticity\\\", \\\"last_updated\\\"],\\n )\\n e_tensor = (\\n e_tensor.get(\\\"elasticity\\\", {}).get(\\\"elastic_tensor\\\", None)\\n if e_tensor\\n else None\\n )\\n mat = self.materials.query_one(\\n criteria={self.materials.key: mpid},\\n properties=[\\\"structure\\\", \\\"deprecated\\\", \\\"material_id\\\", \\\"last_updated\\\"],\\n )\\n\\n yield {\\n \\\"structure\\\": mat[\\\"structure\\\"],\\n \\\"material_id\\\": mat[self.materials.key],\\n \\\"elastic_tensor\\\": e_tensor,\\n \\\"deprecated\\\": mat[\\\"deprecated\\\"],\\n \\\"last_updated\\\": max(\\n mat.get(\\\"last_updated\\\"), e_tensor.get(\\\"last_updated\\\")\\n ),\\n }\",\n \"def test_create_material(self):\\n expected_materials = [\\n ['cotton', 'AAA'],\\n ['cotton', 'AAA'],\\n ['wool', 'AAA'],\\n ]\\n\\n select_listings_to_edit(self.driver)\\n d = self.driver\\n bp = BulkPage(d)\\n\\n send_keys(bp.operation_input(), 'AAA')\\n click(bp.operation_apply())\\n\\n material_names = bp.material_names()\\n assert material_names == expected_materials\\n\\n apply_class = bp.operation_apply().get_attribute('class')\\n assert 'inactive' in apply_class.split(' ')\",\n \"def get_player_material(board):\\n # clear board to read out information\\n information = str(board).replace(\\\"\\\\n\\\", \\\" \\\")\\n\\n # get black and white material\\n materialPlayer_B = information.count('r') + information.count('n') + information.count('b') + information.count('q') + information.count('k') + information.count('p')\\n materialPlayer_W = information.count('R') + information.count('N') + information.count('B') + information.count('Q') + information.count('K') + information.count('P')\\n \\n # return materials for each player\\n return materialPlayer_W, materialPlayer_B\",\n \"def material(self):\\n return self._F_Mstr\",\n \"def op_master_material(op_skills):\\n # left = copy.deepcopy(owned)\\n needed = defaultdict(int)\\n for operator, skills in op_skills.items():\\n # op_ret = defaultdict(int)\\n materials = master_material(operator)\\n for skill, rank in skills.items():\\n if isinstance(rank, int):\\n target_rank = rank\\n present_rank = 1\\n else:\\n target_rank, present_rank = rank\\n for mat_dict in materials[skill - 1][present_rank: target_rank]:\\n for mat, num in mat_dict.items():\\n needed[mat] += num\\n return dict(needed)\",\n \"def get_material_set(**kw):\\n mat_ids = set()\\n volumes = get_volume_list()\\n for v in volumes:\\n d = volume_metadata( v )\\n if( kw.get('with_rho') is True ):\\n # rho is undefined for the void material and dagmc may return anything.\\n if d['material'] == 0:\\n mat_ids.add( (d['material'], 0.0) )\\n else:\\n mat_ids.add( (d['material'], d['rho']) )\\n else:\\n mat_ids.add( d['material'] )\\n return mat_ids\",\n \"def get_similar_materials(self, material):\\n method = \\\"GET\\\"\\n sub_url = '/materials/similar/{}'.format(material)\\n return self._make_request(sub_url, method=method)\",\n \"def get_material(material):\\n for libn,tdict in liblist:\\n if material in tdict:\\n return tdict[material]\\n print (material, \\\" not found\\\")\\n raise KeyError\",\n \"def materials(cls) -> MaterialSelector:\\n selector: MaterialSelector = cls._materials\\n return selector\",\n \"def info_materials_type_id_get(type_id):\\n session = info_map.Session()\\n q = session.query(info_map.Material).filter(info_map.Material.type == type_id)\\n\\n mat = q.one_or_none() # return the only result or `None`\\n\\n if mat is not None:\\n material_info = MaterialInfo(\\n type=mat.type,\\n group=mat.group_id,\\n name=mat.name,\\n volume=mat.volume)\\n return material_info, 200\\n else:\\n error = Error('Type {} Not Found'.format(type_id))\\n return error, 404\",\n \"def test_create_material_multi_over(self):\\n expected_materials_01 = [\\n ['cotton'],\\n ['cotton'],\\n ['wool', 'AAA', 'BBB', 'CCC'],\\n ]\\n\\n expected_materials_02 = [\\n ['cotton', '00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11'],\\n ['cotton', '00', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11'],\\n ['wool', 'AAA', 'BBB', 'CCC', '00', '01', '02', '03', '04', '05', '06', '07', '08'],\\n ]\\n\\n select_listings_to_edit(self.driver)\\n d = self.driver\\n bp = BulkPage(d)\\n\\n # deselect 2, 3\\n bp.click_on_listings(['Second something 1235 (2)', 'Third something LG-512a (3)'])\\n\\n # append AAA BBB CCC materials to the 1st listing\\n send_keys(bp.operation_input(), 'AAA,BBB ,CCC')\\n click(bp.operation_apply())\\n\\n material_names = bp.material_names()\\n assert material_names == expected_materials_01\\n\\n # append 00, 01, 02... to all listings\\n bp.click_on_listings(['Second something 1235 (2)', 'Third something LG-512a (3)'])\\n send_keys(bp.operation_input(), '00, 01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 12, 13, 14, 15')\\n click(bp.operation_apply())\\n\\n material_names = bp.material_names()\\n assert material_names == expected_materials_02\",\n \"def test_materials_present(self):\\n self.assertIsNotNone('Materials' in self.header.parameters.attrs)\",\n \"def XCAFDoc_MaterialTool_Set(*args):\\n return _XCAFDoc.XCAFDoc_MaterialTool_Set(*args)\",\n \"def generate_materials_dict(self):\\n c = 299792458.0\\n w_mat = 2 * np.pi * c / self.l_mat - self.w0\\n l2_mat = (self.l_mat * 1e6) ** 2\\n\\n n_air = 1 + 0.05792105 * l2_mat / (238.0185 * l2_mat - 1) + 0.00167917 * l2_mat / (57.362 * l2_mat - 1)\\n air_ip = interp1d(w_mat, n_air, bounds_error=False, fill_value=np.nan, kind=\\\"quadratic\\\")\\n self.materials['air'] = air_ip\\n\\n n_fs = np.sqrt(1 + 0.6961663 * l2_mat / (l2_mat - 0.0684043 ** 2) +\\n 0.4079426 * l2_mat / (l2_mat - 0.1162414 ** 2) +\\n 0.8974794 * l2_mat / (l2_mat - 9.896161 ** 2))\\n fs_ip = interp1d(w_mat, n_fs, bounds_error=False, fill_value=np.nan, kind=\\\"quadratic\\\")\\n self.materials['fs'] = fs_ip\\n\\n n_mgf2 = np.sqrt(1 + 0.48755108 * l2_mat / (l2_mat - 0.04338408 ** 2) +\\n 0.39875031 * l2_mat / (l2_mat - 0.09461442 ** 2) +\\n 2.3120353 * l2_mat / (l2_mat - 23.793604 ** 2))\\n mgf2_ip = interp1d(w_mat, n_mgf2, bounds_error=False, fill_value=np.nan, kind=\\\"quadratic\\\")\\n self.materials['mgf2'] = mgf2_ip\\n\\n n_sapphire_o = np.sqrt(1 + 1.4313493 * l2_mat / (l2_mat - 0.0726631 ** 2) +\\n 0.65054713 * l2_mat / (l2_mat - 0.1193242 ** 2) +\\n 5.3414021 * l2_mat / (l2_mat - 18.028251 ** 2))\\n sapphire_o_ip = interp1d(w_mat, n_sapphire_o, bounds_error=False, fill_value=np.nan, kind=\\\"quadratic\\\")\\n self.materials['sapphire_o'] = sapphire_o_ip\\n\\n n_sapphire_e = np.sqrt(1 + 1.5039759 * l2_mat / (l2_mat - 0.0740288 ** 2) +\\n 0.55069141 * l2_mat / (l2_mat - 0.1216529 ** 2) +\\n 6.5927379 * l2_mat / (l2_mat - 20.072248 ** 2))\\n sapphire_e_ip = interp1d(w_mat, n_sapphire_e, bounds_error=False, fill_value=np.nan, kind=\\\"quadratic\\\")\\n self.materials['sapphire_e'] = sapphire_e_ip\\n\\n n_bbo_o = np.sqrt(2.7405 + 0.0184 / (l2_mat - 0.0179) - 0.0155 * l2_mat)\\n bbo_o_ip = interp1d(w_mat, n_bbo_o, bounds_error=False, fill_value=np.nan, kind=\\\"quadratic\\\")\\n self.materials['bbo_o'] = bbo_o_ip\\n\\n n_bbo_e = np.sqrt(2.3730 + 0.0128 / (l2_mat - 0.0156) - 0.0044 * l2_mat)\\n bbo_e_ip = interp1d(w_mat, n_bbo_e, bounds_error=False, fill_value=np.nan, kind=\\\"quadratic\\\")\\n self.materials['bbo_e'] = bbo_e_ip\\n\\n materials_files = os.listdir(self.materials_path)\\n logger.info(\\\"Found {0:d}\\\".format(materials_files.__len__()))\\n for mat_file in materials_files:\\n logger.debug(mat_file)\\n self.read_material(''.join((self.materials_path, '/', mat_file)))\",\n \"def __render_material_preview(self, scene):\\n\\n # Don't render material thumbnails.\\n (width, height) = util.get_render_resolution(scene)\\n if width <= 96:\\n return\\n\\n # Collect objects and their materials in a object -> [materials] dictionary.\\n objects_materials = {}\\n for obj in (obj for obj in scene.objects if obj.is_visible(scene) and not obj.hide_render):\\n for mat in util.get_instance_materials(obj):\\n if mat is not None:\\n if obj.name not in objects_materials.keys():\\n objects_materials[obj] = []\\n objects_materials[obj].append(mat)\\n\\n # Find objects that are likely to be the preview objects.\\n preview_objects = [o for o in objects_materials.keys() if o.name.startswith('preview')]\\n if not preview_objects:\\n return\\n\\n # Find the materials attached to the likely preview object.\\n likely_materials = objects_materials[preview_objects[0]]\\n if not likely_materials:\\n return\\n\\n # Build the path to the output preview project.\\n preview_output_dir = os.path.join(tempfile.gettempdir(), \\\"blenderseed\\\", \\\"material_preview\\\")\\n preview_project_filepath = os.path.join(preview_output_dir, \\\"material_preview.appleseed\\\")\\n\\n # Create target directories if necessary.\\n if not os.path.exists(preview_output_dir):\\n try:\\n os.makedirs(preview_output_dir)\\n except os.error:\\n self.report({\\\"ERROR\\\"}, \\\"The directory {0} could not be created. Check directory permissions.\\\".format(preview_output_dir))\\n return\\n\\n # Copy assets from template project to output directory.\\n preview_template_dir = os.path.join(os.sep.join(util.realpath(__file__).split(os.sep)[:-1]), \\\"mat_preview\\\")\\n existing_files = os.listdir(preview_output_dir)\\n for item in os.listdir(preview_template_dir):\\n if item not in existing_files:\\n copyfile(os.path.join(preview_template_dir, item), os.path.join(preview_output_dir, item))\\n\\n prev_mat = likely_materials[0]\\n prev_type = prev_mat.preview_render_type.lower()\\n\\n # Export the project.\\n writer = projectwriter.Writer()\\n file_written = writer.export_preview(scene,\\n preview_project_filepath,\\n prev_mat,\\n prev_type,\\n width,\\n height)\\n if not file_written:\\n print('Error while exporting. Check the console for details.')\\n return\\n\\n # Render the project.\\n self.__render_project_file(scene, preview_project_filepath)\",\n \"def create_materials_from_data(textures):\\n\\n materials = []\\n\\n #Set colour to incremenet from 0 - 8\\n colour_inc = 1.0 / len(textures)\\n colour = 0\\n\\n for current_material in textures:\\n mat = bpy.data.materials.new(current_material[0])\\n mat.diffuse_color = (0, colour, 0,)\\n mat.diffuse_shader = 'LAMBERT' \\n mat.diffuse_intensity = 1.0 \\n mat.specular_color = (1, 1, 1,)\\n mat.specular_shader = 'COOKTORR'\\n mat.specular_intensity = 0.5\\n mat.alpha = 1\\n mat.ambient = 1\\n mat.use_shadeless = True\\n\\n mtex = mat.texture_slots.add()\\n mtex.texture = current_material[1]\\n mtex.texture_coords = 'UV'\\n mtex.use_map_color_diffuse = True \\n\\n materials.append(mat)\\n colour += colour_inc\\n \\n return materials\",\n \"def get_openmc_material(openmoc_material):\\n\\n cv.check_type('openmoc_material', openmoc_material, openmoc.Material)\\n\\n material_id = openmoc_material.getId()\\n\\n # If this Material was already created, use it\\n if material_id in OPENMC_MATERIALS:\\n return OPENMC_MATERIALS[material_id]\\n\\n # Create an OpenMC Material to represent this OpenMOC Material\\n name = openmoc_material.getName()\\n openmc_material = openmc.Material(material_id=material_id, name=name)\\n\\n # Add the OpenMOC Material to the global collection of all OpenMOC Materials\\n OPENMOC_MATERIALS[material_id] = openmoc_material\\n\\n # Add the OpenMC Material to the global collection of all OpenMC Materials\\n OPENMC_MATERIALS[material_id] = openmc_material\\n\\n return openmc_material\",\n \"def check_only_supported_materials_are_used(progress_controller=None):\\n if progress_controller is None:\\n progress_controller = ProgressControllerBase()\\n\\n non_arnold_materials = []\\n all_valid_materials = []\\n for renderer in VALID_MATERIALS.keys():\\n all_valid_materials.extend(VALID_MATERIALS[renderer])\\n\\n all_materials = pm.ls(mat=1)\\n progress_controller.maximum = len(all_materials)\\n for material in all_materials:\\n if material.name() not in [\\\"lambert1\\\", \\\"particleCloud1\\\", \\\"standardSurface1\\\"]:\\n if material.type() not in all_valid_materials:\\n non_arnold_materials.append(material)\\n progress_controller.increment()\\n\\n if len(non_arnold_materials):\\n pm.select(non_arnold_materials)\\n progress_controller.complete()\\n raise PublishError(\\n \\\"There are non-Arnold materials in the scene:<br><br>%s<br><br>\\\"\\n \\\"Please remove them!!!\\\"\\n % \\\"<br>\\\".join(map(lambda x: x.name(), non_arnold_materials))\\n )\\n progress_controller.complete()\",\n \"def info_materials_groups_get():\\n session = info_map.Session()\\n\\n mat = aliased(info_map.Material)\\n grp = aliased(info_map.Group)\\n\\n q = session.query(mat.group_id,grp.name).join(grp).distinct()\\n groups = [Group(group=row.group_id,name=row.name) for row in q.all()]\\n return groups, 200\",\n \"def create_material(self, name, pbmr=None, emissive=None):\\n new_material = self._build_material(pbmr, emissive)\\n\\n self.materials.append(new_material)\\n\\n if name:\\n self.materials_map[name] = self._last_index(self.materials)\\n\\n return self._last_index(self.materials)\",\n \"def calculate_material(board):\\n\\n\\tmaterial_values = {\\n\\t\\tchess.PAWN: 1,\\n\\t\\tchess.ROOK: 5,\\n\\t\\tchess.KNIGHT: 3,\\n\\t\\tchess.BISHOP: 3,\\n\\t\\tchess.QUEEN: 9,\\n\\t\\tchess.KING: 0,\\n\\t}\\n\\tblack = 0\\n\\twhite = 0\\n\\n\\tif board.is_game_over():\\n\\t\\tif board.result() == \\\"1-0\\\":\\n\\t\\t\\treturn 999\\n\\t\\telif board.result() == \\\"0-1\\\":\\n\\t\\t\\treturn - 999\\n\\t\\telif board.result() == \\\"1/2-1/2\\\":\\n\\t\\t\\treturn 0\\n\\n\\tfor piece in board.piece_map().values():\\n\\t\\tif piece.color == chess.WHITE:\\n\\t\\t\\twhite += material_values[piece.piece_type]\\n\\t\\telse:\\n\\t\\t\\tblack += material_values[piece.piece_type]\\n\\n\\treturn white - black\",\n \"def view_material(request, username, course_code):\\n if request.method == \\\"GET\\\":\\n #return JsonResponse({\\\"name\\\":username}, status=200)\\n errors = []\\n\\n # if user log in \\n try:\\n user = User.objects.get(username=username)\\n if ensure_login(user) == False:\\n return JsonResponse({'login': 'User must login'}, status=403) \\n except:\\n return JsonResponse({'login': 'User must login'}, status=403)\\n # try to get the course\\n try:\\n code = Course.objects.get(course_code = course_code)\\n except:\\n errors.append({\\\"course\\\":\\\"course not exist\\\"})\\n \\n # if errors found return errors \\n if len(errors) != 0:\\n return JsonResponse({'errors': errors}, status=400)\\n\\n # check if student join the course\\n if code.students.filter(username=username).count() == 0:\\n return JsonResponse({\\\"error\\\":\\\"student not join course\\\"}, status=400)\\n \\n else:\\n # fetch matiral from database \\n matrials = code.uploaded_materials.all()\\n # matrials = Matrial.objects.filter(course=code).values()\\n\\n # list the path of matiral \\n list_of_matrial = []\\n for matrial in matrials:\\n list_of_matrial.append({\\\"id\\\":matrial.id,\\\"path\\\" : matrial.path , \\\"description\\\":matrial.description})\\n \\n return JsonResponse({ \\\"matrials\\\": list_of_matrial }, status=200)\\n \\n else:\\n return JsonResponse({'error': 'Method not Allowed'}, status=405)\",\n \"def getContents(self):\\n return None if (self.pot.d() == None) else CraftMagicNumbers.getMaterial(self.pot.getItem()).getNewData(int(self.pot.getData()))\\n # PAIL: rename\",\n \"def read_all():\\n # Query the database for all the materials\\n materials = Material.query.order_by(Material.family_id, Material.material_name).all()\\n\\n # Serialize the list of materials from our data\\n material_schema = MaterialSchema(many=True, exclude=[\\\"family.materials\\\"])\\n data = material_schema.dump(materials).data\\n return data\",\n \"def validateMaterial(material, adjust=False):\\n errors = []\\n\\n if not material:\\n errors.append(ValidateMessage(\\\"No material defined.\\\", 'WARNING', material, None, {}))\\n return errors, material\\n\\n if isinstance(material, bpy.types.Object):\\n # there are always 18 slots, regardless of whether they are filled or not\\n for tex in material.texture_slots:\\n if tex is not None:\\n try:\\n # regular diffuse color texture\\n if tex.use_map_color_diffuse:\\n # grab the first texture\\n material.texture_slots[0].texture.image.filepath.replace('//', '')\\n except (KeyError, AttributeError):\\n errors.append(\\n ValidateMessage(\\n \\\"Diffuse texture incomplete/undefined.\\\", 'WARNING', material, None, {}\\n )\\n )\\n try:\\n # normal map\\n if tex.use_map_normal:\\n # grab the first texture\\n material.texture_slots[0].texture.image.filepath.replace('//', '')\\n except (KeyError, AttributeError):\\n errors.append(\\n ValidateMessage(\\n \\\"Normal texture incomplete/undefined.\\\", 'WARNING', material, None, {}\\n )\\n )\\n try:\\n # displacement map\\n if tex.use_map_displacement:\\n # grab the first texture\\n material.texture_slots[0].texture.image.filepath.replace('//', '')\\n except (KeyError, AttributeError):\\n errors.append(\\n ValidateMessage(\\n \\\"Displacement texture incomplete/undefined.\\\",\\n 'WARNING',\\n material,\\n None,\\n {},\\n )\\n )\\n else:\\n if not hasattr(material, \\\"name\\\"):\\n if adjust:\\n material = {'name': 'phobos_error'}\\n loglevel = 'WARNING'\\n else:\\n loglevel = 'ERROR'\\n errors.append(\\n ValidateMessage(\\\"Material name not defined.\\\", 'ERROR', material, None, {})\\n )\\n return errors, material\\n\\n if 'diffuse' not in material:\\n if adjust:\\n material['diffuse'] = (1., 1., 1., 1.)\\n loglevel = 'WARNING'\\n else:\\n loglevel = 'ERROR'\\n errors.append(\\n ValidateMessage(\\\"Material diffuse color not defined.\\\", 'ERROR', material, None, {})\\n )\\n elif len(material['diffuse']) != 4:\\n if adjust:\\n if len(material['diffuse']) == 3:\\n material['diffuse'] = tuple(material['diffuse'] + [1.])\\n loglevel = 'WARNING'\\n else:\\n loglevel = 'ERROR'\\n errors.append(\\n ValidateMessage(\\n \\\"Material diffuse color definition insufficient.\\\", loglevel, material, None, {}\\n )\\n )\\n\\n if 'diffuse_intensity' not in material:\\n errors.append(\\n ValidateMessage(\\n \\\"Material diffuse intensity not defined.\\\", 'WARNING', material, None, {}\\n )\\n )\\n if adjust:\\n material['diffuse_intensity'] = 1.\\n return errors, material\",\n \"def test_team_builder_config_product_size_materials_find_one_get(self):\\n pass\",\n \"def func(self):\\n caller = self.caller\\n\\n if not self.args or not self.recipe:\\n self.caller.msg(\\\"Usage: craft <recipe> from <ingredient>, ... [using <tool>,...]\\\")\\n return\\n\\n ingredients = []\\n for ingr_key in self.ingredients:\\n if not ingr_key:\\n continue\\n obj = caller.search(ingr_key, location=self.caller)\\n # since ingredients are consumed we need extra check so we don't\\n # try to include characters or accounts etc.\\n if not obj:\\n return\\n if (\\n not inherits_from(obj, \\\"evennia.objects.models.ObjectDB\\\")\\n or obj.sessions.all()\\n or not obj.access(caller, \\\"craft\\\", default=True)\\n ):\\n # We don't allow to include puppeted objects nor those with the\\n # 'negative' permission 'nocraft'.\\n caller.msg(\\n obj.attributes.get(\\n \\\"crafting_consumable_err_msg\\\",\\n default=f\\\"{obj.get_display_name(looker=caller)} can't be used for this.\\\",\\n )\\n )\\n return\\n ingredients.append(obj)\\n\\n tools = []\\n for tool_key in self.tools:\\n if not tool_key:\\n continue\\n # tools are not consumed, can also exist in the current room\\n obj = caller.search(tool_key)\\n if not obj:\\n return None\\n if not obj.access(caller, \\\"craft\\\", default=True):\\n caller.msg(\\n obj.attributes.get(\\n \\\"crafting_tool_err_msg\\\",\\n default=f\\\"{obj.get_display_name(looker=caller)} can't be used for this.\\\",\\n )\\n )\\n return\\n tools.append(obj)\\n\\n # perform craft and make sure result is in inventory\\n # (the recipe handles all returns to caller)\\n result = craft(caller, self.recipe, *(tools + ingredients))\\n if result:\\n for obj in result:\\n obj.location = caller\",\n \"def has_material(obj, name):\\n return name in obj.data.materials.keys()\",\n \"def SetMaterial(self, *args):\\n return _XCAFDoc.XCAFDoc_MaterialTool_SetMaterial(self, *args)\",\n \"def set_prodmaterials(_craftable):\\n partial_resources = []\\n partial_craftables = []\\n _craftable.resources_list = []\\n _craftable.craftables_list = []\\n\\n # Checking for Production Materials\\n for material, quantity in _craftable.craft_materials.items():\\n # Checking for Resources or Workbench Need\\n if 'Minimum Bench Cost' in material or material in resources.keys():\\n _craftable.resources_list.append([material, int(quantity)])\\n _craftable.res_totalcost = float(_craftable.res_totalcost + resources[material].unit_price * int(quantity))\\n\\n # Check for sub-craftables as a need\\n elif material not in resources.keys():\\n _craftable.craftables_list.append([material, int(quantity)])\\n\\n return partial_resources, partial_craftables\",\n \"def test_closest_multiple_mats(self):\\n m = mats.Materials(\\\"mats_test.json\\\", NoneVisited())\\n self.assertEqual( '164 G. Canis Majoris', m.closest([0, 0, 0], ['Tungsten', 'Germanium'])[1]['system'])\\n self.assertEqual( '2MASS J10433563-5945136', m.closest([8000, 0, 3000], ['Tungsten', 'Germanium'])[1]['system'])\",\n \"def get_openmoc_material(openmc_material):\\n\\n cv.check_type('openmc_material', openmc_material, openmc.Material)\\n\\n material_id = openmc_material.id\\n\\n # If this Material was already created, use it\\n if material_id in OPENMOC_MATERIALS:\\n return OPENMOC_MATERIALS[material_id]\\n\\n # Create an OpenMOC Material to represent this OpenMC Material\\n name = str(openmc_material.name)\\n openmoc_material = openmoc.Material(id=material_id, name=name)\\n\\n # Add the OpenMC Material to the global collection of all OpenMC Materials\\n OPENMC_MATERIALS[material_id] = openmc_material\\n\\n # Add the OpenMOC Material to the global collection of all OpenMOC Materials\\n OPENMOC_MATERIALS[material_id] = openmoc_material\\n\\n return openmoc_material\",\n \"def __repr__(self):\\n\\n return f'<Material material_id={self.material_id} name={self.name}>'\",\n \"def GetHandle(self):\\n return _XCAFDoc.XCAFDoc_Material_GetHandle(self)\",\n \"def read_material_data(self, material):\\n material_yaml_file = glob.glob(os.path.join(material_dir, material + '.yaml'))\\n\\n inputs = utilities.yaml_reader(material_yaml_file, material_dir, material)\\n self.name = inputs['Name']\\n self.materialName = material\\n self.elements = inputs['Elements']\\n self.zaids = inputs['Elemental ZAIDs']\\n self.weightFraction = inputs['Elemental Weight Fractions'] if 'Elemental Weight Fractions' in inputs else []\\n self.enrichmentZaids = inputs['Elemental Adjustment ZAIDs'] if 'Elemental Adjustment ZAIDs' in inputs else []\\n self.enrichmentIsotopes = inputs['Isotopic Adjustment ZAIDs'] if 'Isotopic Adjustment ZAIDs' in inputs else []\\n self.enrichmentVector = inputs['Isotopic Weight Percents'] if 'Isotopic Weight Percents' in inputs else []\\n self.isotopicAtomPercents = inputs['Isotopic Atom Percents'] if 'Isotopic Atom Percents' in inputs else []\\n self.density = inputs['Density']\\n self.linearCoeffExpansion = inputs['Linear Coefficient of Expansion']\",\n \"def create_object_material(obj, mat_name):\\n if not has_material(obj, mat_name):\\n if bpy.data.materials.get(mat_name, None):\\n # XXX if material with this name already exists in another object\\n # append the object name to this material name\\n mat_name += \\\".{}\\\".format(obj.name)\\n\\n mat = bpy.data.materials.new(mat_name)\\n link_material(obj, mat)\\n return mat\\n return obj.data.materials.get(mat_name)\",\n \"def test_return_molecule(self):\\n\\n # Ask for a few molecules.\\n mol1 = mol_res_spin.return_molecule('#Ap4Aase')\\n mol2 = mol_res_spin.return_molecule(selection='#RNA', pipe='orig')\\n\\n # Test the data of molecule 1.\\n self.assertEqual(mol1.name, 'Ap4Aase')\\n\\n # Test the data of molecule 2.\\n self.assertEqual(mol2.name, 'RNA')\",\n \"def make_input_materials(self) :\\n # 1 5 1 MATERIAL 1 (arbitrary line, i think) \\n # 1.4493e+00 9.9000e-03 7.9000e-03 1. 0. 0. 7.9000e-03 1.\\n # 3.8070e-01 1.0420e-01 1.6920e-01 0 1.5100e-02 0. 1.6920e-01 1.\\n self.input_materials = \\\"\\\"\\n number_mats = len(self.core.pattern)+1\\n a = self.core.assemblies\\n for i in range(0, number_mats-1) :\\n # Row 1: description.\\n self.input_materials += \\\" \\\" + str(i+1) + \\\" 5 1 MATERIAL \\\" + \\\\\\n str(i+1) + \\\" (\\\" + \\\\\\n a[i].model + \\\", \\\" + \\\\\\n str(a[i].enrichment) + \\\" w/o, \\\" + \\\\\\n str(a[i].burnup) + \\\" MWd/kg)\\\\n\\\" \\n # Rows 2 and 3.\\n D1,D2,A1,A2,F1,F2,S12 = a[i].get_constants()\\n d = np.array([[D1,A1,F1,1.0,0.0,0.0,F1,1.0],[D2,A2,F2,0.0,S12,0.0,F2,1.0]])\\n for j in range(0, 2) :\\n for k in range(0, 8) :\\n self.input_materials +='%12.4e' %(d[j,k])\\n self.input_materials += '\\\\n'\\n \\n a = self.core.reflector\\n # Row 1: description.\\n self.input_materials += \\\" \\\" + str(number_mats) + \\\" 5 1 MATERIAL \\\" + \\\\\\n str(number_mats) + \\\" (REFLECTOR) \\\\n\\\" \\n # Rows 2 and 3.\\n D1,D2,A1,A2,F1,F2,S12 = a.get_constants()\\n d = np.array([[D1,A1,F1,1.0,0.0,0.0,F1,1.0],[D2,A2,F2,0.0,S12,0.0,F2,1.0]])\\n for i in range(0, 2) :\\n for j in range(0, 8) :\\n self.input_materials +='%12.4e' %(d[i,j])\\n self.input_materials += '\\\\n'\\n self.input_materials += \\\"WHITE\\\\n\\\" + \\\"BLACK\\\\n\\\" + \\\"END\\\\n\\\"\",\n \"def maturity(self, *args):\\n return _ida_hexrays.Hexrays_Hooks_maturity(self, *args)\",\n \"def has_raw_materials(self, m_raw_materials, d_raw_materials):\\n additional_resources_needed = ''\\n for m_raw_material in m_raw_materials:\\n if m_raw_materials[m_raw_material] > d_raw_materials[m_raw_material]:\\n additional_resources_needed += 'Machine Needs Additional: {0}\\\\n'.format(m_raw_material)\\n if additional_resources_needed:\\n return additional_resources_needed\\n else:\\n return True\",\n \"def ReadMaterialInfoAndMakeMaterialsMolFiles(OutputPath,MaterialPathList,CutOff,Multiple,PDBCharges,MaterialInputFormat):\\r\\n\\r\\n MaterialInfoList=[]\\r\\n ID=0\\r\\n Time = time.strftime('%Y-%m-%d', time.localtime(time.time()))\\r\\n\\r\\n if os.path.exists(os.path.join(OutputPath,'Mols')): pass\\r\\n else: os.mkdir(os.path.join(OutputPath,'Mols'))\\r\\n\\r\\n for Range,MaterialPath in enumerate(MaterialPathList):\\r\\n\\r\\n\\r\\n AtomNum=0\\r\\n MaterialsCharge = 0.0\\r\\n MaterialInfo,ElementList,CellLength,CellAngle,AtomInfo,AtomInfoList,CellSize=[],[],[],[],[],[],[]\\r\\n\\r\\n MaterialName=os.path.splitext(MaterialPath.split('/')[-1])[0]\\r\\n MaterialInfo.append(MaterialName)\\r\\n MaterialNewName=re.sub('\\\\W','',MaterialName)\\r\\n\\r\\n if len(MaterialNewName) > 20:\\r\\n ID+=1\\r\\n MaterialNewName=MaterialNewName[:20]\\r\\n MaterialNewName=MaterialNewName+'_ID_'+str(ID)\\r\\n\\r\\n if MaterialInputFormat == 'pdb':\\r\\n with open(MaterialPath,'r') as File:\\r\\n with open(os.path.join(OutputPath,'Mols',MaterialNewName)+'.mol','w') as Mol:\\r\\n\\r\\n for Line in File.readlines():\\r\\n if Line[0:6] == 'CRYST1':\\r\\n CellLength.append(float('%.3f' % (float(Line[6:15].strip()))))\\r\\n CellLength.append(float('%.3f' % (float(Line[15:24].strip()))))\\r\\n CellLength.append(float('%.3f' % (float(Line[24:33].strip()))))\\r\\n CellAngle.append(float('%.2f' % (float(Line[33:40].strip()))))\\r\\n CellAngle.append(float('%.2f' % (float(Line[40:47].strip()))))\\r\\n CellAngle.append(float('%.2f' % (float(Line[47:54].strip()))))\\r\\n elif Line[0:6] == 'ATOM ':\\r\\n AtomNum += 1\\r\\n AtomInfo = []\\r\\n AtomInfo.append(int(Line[6:11].strip())) # serial\\r\\n AtomInfo.append(str(Line[12:16].strip())) # atom name\\r\\n AtomInfo.append(float(Line[30:38].strip())) # x\\r\\n AtomInfo.append(float(Line[38:46].strip())) # y\\r\\n AtomInfo.append(float(Line[46:54].strip())) # z\\r\\n\\r\\n if PDBCharges == True:\\r\\n AtomInfo.append(float(Line[54:60].strip())) # charge\\r\\n MaterialsCharge += float(Line[54:60].strip())\\r\\n else:\\r\\n AtomInfo.append(float('0.0')) # charge\\r\\n\\r\\n ElementList.append(str(Line[12:16].strip())) # atom name list\\r\\n AtomInfoList.append(AtomInfo)\\r\\n\\r\\n if abs(MaterialsCharge) < 0.001:\\r\\n Mol.write('# Basic Molecule Information\\\\n# Created by PyMSATm at %s\\\\n'\\r\\n 'Molecule_name: %s \\\\n\\\\nCoord_Info: Listed Cartesian None\\\\n' % (Time, MaterialNewName))\\r\\n else: Mol.write('# Basic Molecule Information\\\\n# Created by PyMSATm at %s\\\\n'\\r\\n 'Molecule_name: %s CHARGED\\\\n\\\\nCoord_Info: Listed Cartesian None\\\\n' % (Time, MaterialNewName))\\r\\n\\r\\n Mol.write(' %d\\\\n'%(AtomNum))\\r\\n\\r\\n for AtomInfo2 in AtomInfoList:\\r\\n Mol.write('{:<6d} {:>8} {:>8} {:>8} {:>6} {:>6} 0 0\\\\n'.format(AtomInfo2[0],\\r\\n AtomInfo2[2],AtomInfo2[3],AtomInfo2[4],AtomInfo2[1],AtomInfo2[5]))\\r\\n\\r\\n Mol.write('\\\\nFundcell_Info: Listed\\\\n%.5f %.5f %.5f\\\\n%.5f %.5f %.5f\\\\n0.00000 0.00000 0.00000\\\\n%.5f %.5f %.5f'%(CellLength[0],\\r\\n CellLength[1],CellLength[2],CellAngle[0],CellAngle[1],CellAngle[2],CellLength[0],CellLength[1],CellLength[2]))\\r\\n\\r\\n if MaterialInputFormat == 'mol':\\r\\n\\r\\n KeyOne,KeyTwo,KeyThree= False,False,False\\r\\n\\r\\n with open(MaterialPath, 'r') as File:\\r\\n with open(os.path.join(OutputPath, 'Mols', MaterialNewName) + '.mol', 'w') as Mol:\\r\\n\\r\\n for Line in File.readlines():\\r\\n if Line.strip(): # skip blank\\r\\n WordList = Line.strip().split()\\r\\n if WordList[0] == 'Coord_Info:':\\r\\n KeyOne = True\\r\\n elif WordList[0] == 'Fundcell_Info:':\\r\\n KeyOne = False\\r\\n KeyTwo = True\\r\\n elif len(WordList) > 1 and KeyOne == True:\\r\\n AtomNum += 1\\r\\n AtomInfo = []\\r\\n AtomInfo.append(int(WordList[0].strip())) # serial\\r\\n AtomInfo.append(str(WordList[4].strip())) # atom name\\r\\n AtomInfo.append(float(WordList[1].strip())) # x\\r\\n AtomInfo.append(float(WordList[2].strip())) # y\\r\\n AtomInfo.append(float(WordList[3].strip())) # z\\r\\n AtomInfo.append(float(WordList[5].strip())) # charge\\r\\n ElementList.append(str(WordList[4].strip())) # atom name list\\r\\n AtomInfoList.append(AtomInfo)\\r\\n MaterialsCharge += float(WordList[5].strip())\\r\\n elif len(WordList) > 1 and KeyTwo == True:\\r\\n KeyTwo = False\\r\\n KeyThree = True\\r\\n CellLength.append(float('%.3f' % (float(WordList[0].strip()))))\\r\\n CellLength.append(float('%.3f' % (float(WordList[1].strip()))))\\r\\n CellLength.append(float('%.3f' % (float(WordList[2].strip()))))\\r\\n elif len(WordList) > 1 and KeyThree == True:\\r\\n KeyThree = False\\r\\n CellAngle.append(float('%.2f' % (float(WordList[0].strip()))))\\r\\n CellAngle.append(float('%.2f' % (float(WordList[1].strip()))))\\r\\n CellAngle.append(float('%.2f' % (float(WordList[2].strip()))))\\r\\n\\r\\n if abs(MaterialsCharge) < 0.001:\\r\\n Mol.write('# Basic Molecule Information\\\\n# Created by PyMSATm at %s\\\\n'\\r\\n 'Molecule_name: %s \\\\n\\\\nCoord_Info: Listed Cartesian None\\\\n' % (\\r\\n Time, MaterialNewName))\\r\\n else:\\r\\n Mol.write('# Basic Molecule Information\\\\n# Created by PyMSATm at %s\\\\n'\\r\\n 'Molecule_name: %s CHARGED\\\\n\\\\nCoord_Info: Listed Cartesian None\\\\n' % (\\r\\n Time, MaterialNewName))\\r\\n\\r\\n Mol.write(' %d\\\\n' % (AtomNum))\\r\\n\\r\\n for AtomInfo2 in AtomInfoList:\\r\\n Mol.write('{:<6d} {:>8.4f} {:>8.4f} {:>8.4f} {:>8s} {:>10.6f} 0 0\\\\n'.format(AtomInfo2[0], AtomInfo2[2],\\r\\n AtomInfo2[3], AtomInfo2[4],\\r\\n AtomInfo2[1], AtomInfo2[5]))\\r\\n\\r\\n Mol.write(\\r\\n '\\\\nFundcell_Info: Listed\\\\n%.4f %.4f %.4f\\\\n%.4f %.4f %.4f\\\\n0.00000 0.00000 0.00000\\\\n%.4f %.4f %.4f' % (\\r\\n CellLength[0], CellLength[1], CellLength[2], CellAngle[0], CellAngle[1], CellAngle[2],\\r\\n CellLength[0], CellLength[1], CellLength[2]))\\r\\n\\r\\n if CellAngle[0] == 90.0 and CellAngle[1] == 90.0 and CellAngle[2] == 90.0:\\r\\n Orthogonality = True\\r\\n else: Orthogonality=False\\r\\n\\r\\n if Orthogonality == True:\\r\\n for Length in CellLength:\\r\\n Count = 1\\r\\n while Length * Count < CutOff * Multiple+1:\\r\\n Count += 1\\r\\n CellSize.append(str(Count))\\r\\n else: # definite the lengths of x, y, z in orthogonal directions\\r\\n\\r\\n SinA, SinB, SinC = math.sin(math.radians(CellAngle[0])), math.sin(math.radians(CellAngle[1])), math.sin(math.radians(CellAngle[2]))\\r\\n CosA, CosB, CosC = math.cos(math.radians(CellAngle[0])), math.cos(math.radians(CellAngle[1])), math.cos(math.radians(CellAngle[2]))\\r\\n CosParameterA = (CosC * CosA - CosB) / (SinC * SinA)\\r\\n SinParameterA = math.sqrt(1 - CosParameterA ** 2)\\r\\n CosParameterB = (CosA * CosB - CosC) / (SinA * SinB)\\r\\n SinParameterB = math.sqrt(1 - CosParameterB ** 2)\\r\\n CosParameterC = (CosB * CosC - CosA) / (SinB * SinC)\\r\\n SinParameterC = math.sqrt(1 - CosParameterC ** 2)\\r\\n x=CellLength[0]*SinC*SinParameterA\\r\\n y=CellLength[1]*SinA*SinParameterB\\r\\n z=CellLength[2]*SinB*SinParameterC\\r\\n TransLength=[x,y,z]\\r\\n\\r\\n for Length in TransLength:\\r\\n Count = 1\\r\\n while float(Length) * Count < CutOff * Multiple+1:\\r\\n Count += 1\\r\\n CellSize.append(str(Count))\\r\\n\\r\\n MaterialInfo.append(AtomNum)\\r\\n MaterialInfo.append(CellLength)\\r\\n MaterialInfo.append(CellAngle)\\r\\n MaterialInfo.append(CellSize)\\r\\n MaterialInfo.append(list(set(ElementList)))\\r\\n MaterialInfo.append(Orthogonality)\\r\\n MaterialInfo.append(MaterialNewName)\\r\\n MaterialInfoList.append(MaterialInfo)\\r\\n\\r\\n return MaterialInfoList\",\n \"def get_items(self):\\n\\n self.__logger.info(\\\"Thermo Builder Started\\\")\\n\\n # All relevant materials that have been updated since thermo props were last calculated\\n q = dict(self.query)\\n q.update(self.materials.lu_filter(self.thermo))\\n comps = [m[\\\"elements\\\"] for m in self.materials().find(q, {\\\"elements\\\": 1})]\\n\\n self.__logger.info(\\\"Found {} compositions with new/updated materials\\\".format(len(comps)))\\n\\n # Only yields maximal super sets: e.g. if [\\\"A\\\",\\\"B\\\"] and [\\\"A\\\"] are both in the list, will only yield [\\\"A\\\",\\\"B\\\"]\\n # as this will calculate thermo props for all [\\\"A\\\"] compounds\\n processed = set()\\n # Start with the largest set to ensure we don\\\"t miss superset/subset relations\\n for chemsys in sorted(comps, key=lambda x: len(x), reverse=True):\\n if \\\"-\\\".join(sorted(chemsys)) not in processed:\\n processed |= self.chemsys_permutations(chemsys)\\n yield self.get_entries(chemsys)\",\n \"def get_items(self):\\n\\n self.logger.info(\\\"Site-Descriptors Builder Started\\\")\\n\\n self.logger.info(\\\"Setting indexes\\\")\\n\\n # All relevant materials that have been updated since site-descriptors\\n # were last calculated\\n\\n q = dict(self.mat_query)\\n all_task_ids = list(self.materials.distinct(self.materials.key, q))\\n q.update(self.materials.lu_filter(self.site_descriptors))\\n new_task_ids = list(self.materials.distinct(self.materials.key, q))\\n self.logger.info(\\n \\\"Found {} entirely new materials for site-descriptors data\\\".format(\\n len(new_task_ids)))\\n for task_id in all_task_ids:\\n if task_id in new_task_ids:\\n any_piece = True\\n\\n else: # Any piece of info missing?\\n data_present = self.site_descriptors.query(\\n properties=[self.site_descriptors.key, \\\"site_descriptors\\\", \\\"statistics\\\"],\\n criteria={self.site_descriptors.key: task_id}).limit(1)[0]\\n any_piece = False\\n for k, v in self.all_output_pieces.items():\\n if k not in list(data_present.keys()):\\n any_piece = True\\n break\\n else:\\n any_piece = False\\n for e in v:\\n if e not in data_present[k]:\\n any_piece = True\\n break\\n if not any_piece:\\n for l in self.sds['csf'].feature_labels():\\n for fpi in data_present['site_descriptors']['csf']:\\n if l not in fpi.keys():\\n any_piece = True\\n break\\n if any_piece:\\n yield self.materials.query(\\n properties=[self.materials.key, \\\"structure\\\"],\\n criteria={self.materials.key: task_id}).limit(1)[0]\",\n \"def test_closest_common_mats(self):\\n m = mats.Materials(\\\"mats_test.json\\\", NoneVisited())\\n self.assertEqual( '164 G. Canis Majoris', m.closest([0, 0, 0], ['Iron'])[1]['system'])\\n self.assertEqual( '2MASS J10433563-5945136', m.closest([8000, 0, 3000], ['Iron'])[1]['system'])\",\n \"def MaterialsLabel(*args):\\n return _XCAFDoc.XCAFDoc_DocumentTool_MaterialsLabel(*args)\",\n \"def create_material(name, diffuse, alpha):\\n mat = bpy.data.materials.new(name)\\n mat.diffuse_color = diffuse\\n mat.diffuse_intensity = 1.0\\n mat.alpha = alpha\\n if alpha:\\n mat.use_transparency = True\\n return mat\",\n \"def read_one(family_id, material_id):\\n # Query the database for the material\\n material = (\\n Material.query.join(Family, Family.family_id == Material.family_id)\\n .filter(Family.family_id == family_id)\\n .filter(Material.material_id == material_id)\\n .one_or_none()\\n )\\n\\n # Was a material found?\\n if material is not None:\\n material_schema = MaterialSchema()\\n data = material_schema.dump(material).data\\n return data\\n\\n # Otherwise, nope, didn't find that material\\n else:\\n abort(404, f\\\"Material not found for Id: {material_id}\\\")\",\n \"def test_return_residue(self):\\n\\n # Ask for a few residues.\\n res1 = mol_res_spin.return_residue(':1')\\n res2 = mol_res_spin.return_residue(selection=':2')\\n res4 = mol_res_spin.return_residue(selection=':4', pipe='orig')\\n res5 = mol_res_spin.return_residue(selection='#RNA:-5', pipe='orig')\\n\\n # Test the data of residue 1.\\n self.assertEqual(res1.num, 1)\\n self.assertEqual(res1.name, None)\\n\\n # Test the data of residue 2.\\n self.assertEqual(res2.num, 2)\\n self.assertEqual(res2.name, 'Glu')\\n\\n # Test the data of residue 4.\\n self.assertEqual(res4.num, 4)\\n self.assertEqual(res4.name, 'Pro')\\n\\n # Test the data of the RNA residue -5.\\n self.assertEqual(res5.num, -5)\\n self.assertEqual(res5.name, None)\\n self.assertEqual(res5.spin[1].name, 'N5')\",\n \"def result(self):\",\n \"def result(self):\",\n \"def get_biomaterials(self, provider_id=\\\"\\\", biomaterial_id=\\\"\\\", organism_id=\\\"\\\", biomaterial_name=\\\"\\\", analysis_id=\\\"\\\"):\\n\\n if analysis_id:\\n res = self.session.query(self.model.biomaterial) \\\\\\n .join(self.model.assay_biomaterial, self.model.biomaterial.biomaterial_id == self.model.assay_biomaterial.biomaterial_id) \\\\\\n .join(self.model.assay, self.model.assay_biomaterial.assay_id == self.model.assay.assay_id) \\\\\\n .join(self.model.acquisition, self.model.assay.assay_id == self.model.acquisition.assay_id) \\\\\\n .join(self.model.quantification, self.model.acquisition.acquisition_id == self.model.quantification.acquisition_id) \\\\\\n .filter(self.model.quantification.analysis_id == analysis_id)\\n else:\\n res = self.session.query(self.model.biomaterial)\\n\\n if biomaterial_id:\\n res = res.filter_by(biomaterial_id=biomaterial_id)\\n if provider_id:\\n res = res.filter_by(biosourceprovider_id=provider_id)\\n if organism_id:\\n res = res.filter_by(taxon_id=organism_id)\\n if biomaterial_name:\\n res = res.filter_by(name=biomaterial_name)\\n\\n data = []\\n for biomat in res:\\n data.append({\\n 'provider_id': biomat.biosourceprovider_id,\\n 'description': biomat.description,\\n 'biomaterial_id': biomat.biomaterial_id,\\n 'organism_id': biomat.taxon_id,\\n 'dbxref_id': biomat.dbxref_id,\\n 'name': biomat.name,\\n })\\n return data\",\n \"def GetHandle(self):\\n return _XCAFDoc.XCAFDoc_MaterialTool_GetHandle(self)\",\n \"def return_book_process(self):\\n s1 = System()\\n b1 = Books(\\\"1984\\\", \\\"George Orwell\\\", \\\"Harvill Secker\\\", \\\"1949\\\", \\\"0123456789123\\\")\\n m1 = Members(\\\"Richard\\\", \\\"Blackmore\\\", \\\"14-04-1945\\\", \\\"Weston\\\")\\n s1.add_resource(b1)\\n s1.lending_process(b1, m1)\\n s1.remove_resource(b1)\\n #prove that the return of a book doen't take place if not in the ctalogue, \\n #but also that in the remove_resource method should be added somthing to remeove that book from all borrowers\\n #or doesn't allow the operation if not returned\\n s1.return_book_process(b1, False, \\\"none\\\")\\n self.assertEqual(b1.get_borrower(), m1)\\n self.assertIn(b1, m1.get_borrowed_books())\\n s1.add_resource(b1)\\n self.assertEqual(b1.get_borrower(), None)\\n self.assertNotIn(b1, m1.get_borrowed_books())\",\n \"def complete_material_equivalences(self):\\n for material in self.materials:\\n material.geu = self\\n for material_aux in self.materials:\\n material.equivalent_materials.add(material_aux)\",\n \"def test_JCB_VISUAL_MATERIALS( self ):\\n driver = self.driver\\n driver.get(self.base_url + \\\"/record=b5660654~S6\\\")\\n driver.find_element_by_link_text(\\\"Request\\\").click()\\n self.assertTrue( 'aeon' in driver.current_url )\\n self.assertTrue( 'ReferenceNumber=b5660654' in driver.current_url )\\n self.assertTrue( 'ItemTitle=Thomas%20Jefferson' in driver.current_url )\\n self.assertTrue( 'ItemAuthor=&ItemPublisher' in driver.current_url )\\n self.assertTrue( 'ItemPublisher=Princeton' in driver.current_url )\\n self.assertTrue( 'CallNumber=VHS' in driver.current_url )\\n # self.assertTrue( 'Notes=(bibnum%3A%20b5660654)' in driver.current_url )\\n self.assertEqual( 'ItemInfo2=', driver.current_url[-10:] )\",\n \"def generate_products(self = random.sample, name = random.choice(result), price = random.randint(5, 100), weight = random.randint(5, 100), \\nflammability= random.uniform(0, 2.5)):\\n return sample\",\n \"def product_size_materials(self):\\n return self._product_size_materials\",\n \"def test_team_builder_config_product_size_materials_get(self):\\n pass\",\n \"def search_all_id():\\n\\n # Quering with MPRester to obtain corresponding material id, reduced formula and space group.\\n with MPR(get_api_key()) as m:\\n\\n # Criteria is set as greater than -1 to search materials, which is unphysical in real world, therefore in effect\\n # search all available materials.\\n llist = m.query(criteria={'band_gap': {'$gt': -1}}, properties=['material_id', 'pretty_formula','spacegroup'],\\n # Chunk size is set as 0 for no chunking.\\n chunk_size=0)\\n\\n # Generate an empty dictionary for further sampling.\\n space_group_dict = dict()\\n\\n # To loop all items in space_group_dict and to label by their space groups.\\n for index in llist:\\n\\n # To find space group in Hall Symbol.\\n space_group = index['spacegroup']['hall']\\n\\n # If the item space group is not in space_group_dict, then add it and create an empty value list for further\\n # storing.\\n if space_group not in space_group_dict:\\n space_group_dict[space_group] = []\\n\\n # To append the material id (str) to the list(value) of corresponding space group (key).\\n space_group_dict[space_group].append(index['material_id'])\\n\\n # To sample the space_group_dict by random_sample.\\n final_list = random_sample(space_group_dict, 0.01)\\n\\n return final_list\",\n \"def define_material(self):\\n\\n # Check which class should be called.\\n const_eqn = self.config['material']['const_eqn']\\n if isclass(const_eqn):\\n mat_class = self.config['material']['const_eqn']\\n elif const_eqn == 'lin_elastic':\\n mat_class = materials.solid_materials.LinearIsoMaterial\\n elif const_eqn == 'neo_hookean':\\n mat_class = materials.solid_materials.NeoHookeMaterial\\n elif const_eqn == 'demiray':\\n mat_class = materials.solid_materials.DemirayMaterial\\n elif const_eqn == 'fung':\\n mat_class = materials.solid_materials.FungMaterial\\n elif const_eqn == 'guccione':\\n mat_class = materials.solid_materials.GuccioneMaterial\\n elif const_eqn == 'holzapfel_ogden':\\n mat_class = materials.solid_materials.HolzapfelOgdenMaterial\\n elif const_eqn == 'newtonian' or const_eqn == 'stokes':\\n mat_class = materials.fluids.NewtonianFluid\\n else:\\n raise NotImplementedError(\\\"Shouldn't be in here...\\\")\\n\\n # Create an instance of the material class and store\\n # as member data.\\n try:\\n inverse = self.config['formulation']['inverse']\\n except KeyError:\\n inverse = False\\n self._material = mat_class(inverse=inverse,\\n **self.config['material'])\\n\\n return None\",\n \"def get_one_meal():\",\n \"def get_all_meals():\",\n \"def materiaal(self):\\n return self._materiaal.get_waarde()\",\n \"def getMaterialString(self):\\n matString = 'm10 1001.70c -0.080538 $Lucite (PMMA / Plexiglass) rho = 1.19 g/cc\\\\n'\\n matString += ' 6012.70c -0.599848 8016.70c -0.319614 \\\\n'\\n matString += 'm204 7014.70c -0.755636 $air (US S. Atm at sea level) rho = 0.001225 \\\\n'\\n matString += ' 8016.70c -0.231475 18036.70c -3.9e-005 18038.70c -8e-006\\\\n'\\n matString += ' 18040.70c -0.012842 \\\\n'\\n matString += 'm5 98252.66c 1 $ Cf-252, rho =15.1 g/cc wiki \\\\n'\\n matString += 'm406 82204.70c -0.013781 $Lead, \\\\n'\\n matString += ' 82206.70c -0.239557 82207.70c -0.220743 82208.70c -0.525919\\\\n'\\n matString += 'm456 1001.70c -0.143716 $Polyethylene - rho = 0.93 g/cc \\\\n'\\n matString += ' 6000.70c -0.856284 \\\\n'\\n matString += 'm488 14028.70c -0.009187 $Steel, Stainless 316 rho = 7.92 \\\\n'\\n matString += ' 14029.70c -0.000482 14030.70c -0.000331 24050.70c -0.007095\\\\n'\\n matString += ' 24052.70c -0.142291 24053.70c -0.016443 24054.70c -0.004171\\\\n'\\n matString += ' 25055.70c -0.02 26054.70c -0.037326 26056.70c -0.601748\\\\n'\\n matString += ' 26057.70c -0.014024 26058.70c -0.001903 28058.70c -0.080873\\\\n'\\n matString += ' 28060.70c -0.031984 28061.70c -0.001408 28062.70c -0.004546\\\\n'\\n matString += ' 28064.70c -0.001189 42092.70c -0.003554 42094.70c -0.002264\\\\n'\\n matString += ' 42095.70c -0.003937 42096.70c -0.004169 42097.70c -0.002412\\\\n'\\n matString += ' 42098.70c -0.006157 42100.70c -0.002507 \\\\n'\\n matString += 'mt3 poly.01t \\\\n'\\n matString += 'mt456 poly.01t \\\\n'\\n matString += 'mt10 poly.01t \\\\n'\\n return matString\",\n \"def run(self):\\n matched = ['Program Description']\\n matched += sorted(self.matching_recipes())\\n\\n msg = 'Your Search For:'\\n msg += PREFIX + PREFIX.join([\\\"'\\\" + word + \\\"'\\\" for word in self.words])\\n msg += '\\\\nMatched These Recipes:'\\n msg += PREFIX + PREFIX.join(matched)\\n print(msg)\\n return matched\",\n \"def get_items(self):\\n self.logger.info(\\\"Electrode Builder Started\\\")\\n\\n # All updated chemical systems that contain at least one redox active elements\\n q = dict(self.query)\\n q.update(self.materials.lu_filter(self.thermo))\\n q.update({\\\"chemsys\\\": {\\\"$in\\\": self.redox_els}})\\n\\n comps = self.materials.distinct(\\\"chemsys\\\", q)\\n\\n self.logger.info(\\n \\\"Found {} compositions with new/updated materials\\\".format(len(comps)))\\n\\n # Only yields maximal super sets: e.g. if [\\\"A\\\",\\\"B\\\"] and [\\\"A\\\"] are both in the list, will only yield [\\\"A\\\",\\\"B\\\"]\\n # as this will calculate thermo props for all [\\\"A\\\"] compounds\\n processed = set()\\n\\n # Start with the largest set to ensure we don\\\"t miss superset/subset\\n # relations\\n for chemsys in sorted(comps, key=lambda x: len(x.split(\\\"-\\\")), reverse=True):\\n if chemsys not in processed:\\n processed |= chemsys_permutations(chemsys)\\n yield self.get_entries(chemsys)\",\n \"def get_materials(dbpath):\\n odb = openOdb(path=dbpath)\\n _materials = []\\n for _name in odb.materials.items():\\n _materials.append(_name)\\n odb.close()\\n return _materials\",\n \"def find_material(material):\\n for libn,tdict in liblist:\\n if material in tdict:\\n print(libn)\",\n \"def test_names_no_mats(self):\\n m = mats.Materials()\\n self.assertEqual([], m.names())\",\n \"def material_matrix(self):\\n out = Tmatrix()\\n out.translate(Vector([.5, .5, .5]))\\n out.scale(Vector([self.radius, self.radius, self.radius]) *\\n (.5 / (self.radius + self.thickness)))\\n return out\",\n \"def test_delete_material(self):\\n expected_materials = [\\n [],\\n ['cotton'],\\n ['cotton'],\\n ]\\n\\n select_listings_to_edit(self.driver, 'Delete')\\n d = self.driver\\n bp = BulkPage(d)\\n\\n send_keys(bp.operation_input(), 'wool')\\n click(bp.operation_apply())\\n\\n material_names = bp.material_names()\\n assert material_names == expected_materials\",\n \"def test_team_builder_config_product_size_materials_id_product_size_material_get(self):\\n pass\",\n \"def info_equipment_reactors_get():\\n equipment = _equipment_by_group(438) # 438 == Mobile Reactor\\n return equipment, 200\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6908583","0.6790556","0.6723287","0.6699483","0.66595334","0.6358154","0.6162048","0.611894","0.60959214","0.59961724","0.58494353","0.58435357","0.5802915","0.5775458","0.5744499","0.5669334","0.56455684","0.563473","0.562491","0.56161094","0.56081563","0.55570406","0.5556632","0.54553485","0.54445034","0.54373616","0.54256064","0.5421645","0.5370063","0.53618795","0.53474367","0.53461134","0.53354734","0.5317079","0.52608085","0.5242109","0.52088684","0.5142577","0.5127302","0.5116975","0.51155716","0.5095761","0.5090695","0.50590783","0.5057468","0.50538814","0.5043523","0.50243664","0.50144416","0.5007072","0.5005866","0.49920166","0.49716935","0.49550113","0.49548754","0.4944163","0.49395669","0.49372527","0.4933878","0.4930449","0.49302068","0.4905222","0.48682714","0.48677337","0.48572293","0.48511901","0.48448163","0.4808927","0.48057592","0.47956032","0.47865406","0.47766656","0.47740555","0.47694352","0.47579187","0.47554472","0.47554472","0.47523844","0.47266465","0.47200245","0.4718781","0.47106892","0.470761","0.47034726","0.4702613","0.46860182","0.46849197","0.4683797","0.4680382","0.4670405","0.46684435","0.4661583","0.46550813","0.4651958","0.4647163","0.46455085","0.4640827","0.463252","0.46302536","0.4616527"],"string":"[\n \"0.6908583\",\n \"0.6790556\",\n \"0.6723287\",\n \"0.6699483\",\n \"0.66595334\",\n \"0.6358154\",\n \"0.6162048\",\n \"0.611894\",\n \"0.60959214\",\n \"0.59961724\",\n \"0.58494353\",\n \"0.58435357\",\n \"0.5802915\",\n \"0.5775458\",\n \"0.5744499\",\n \"0.5669334\",\n \"0.56455684\",\n \"0.563473\",\n \"0.562491\",\n \"0.56161094\",\n \"0.56081563\",\n \"0.55570406\",\n \"0.5556632\",\n \"0.54553485\",\n \"0.54445034\",\n \"0.54373616\",\n \"0.54256064\",\n \"0.5421645\",\n \"0.5370063\",\n \"0.53618795\",\n \"0.53474367\",\n \"0.53461134\",\n \"0.53354734\",\n \"0.5317079\",\n \"0.52608085\",\n \"0.5242109\",\n \"0.52088684\",\n \"0.5142577\",\n \"0.5127302\",\n \"0.5116975\",\n \"0.51155716\",\n \"0.5095761\",\n \"0.5090695\",\n \"0.50590783\",\n \"0.5057468\",\n \"0.50538814\",\n \"0.5043523\",\n \"0.50243664\",\n \"0.50144416\",\n \"0.5007072\",\n \"0.5005866\",\n \"0.49920166\",\n \"0.49716935\",\n \"0.49550113\",\n \"0.49548754\",\n \"0.4944163\",\n \"0.49395669\",\n \"0.49372527\",\n \"0.4933878\",\n \"0.4930449\",\n \"0.49302068\",\n \"0.4905222\",\n \"0.48682714\",\n \"0.48677337\",\n \"0.48572293\",\n \"0.48511901\",\n \"0.48448163\",\n \"0.4808927\",\n \"0.48057592\",\n \"0.47956032\",\n \"0.47865406\",\n \"0.47766656\",\n \"0.47740555\",\n \"0.47694352\",\n \"0.47579187\",\n \"0.47554472\",\n \"0.47554472\",\n \"0.47523844\",\n \"0.47266465\",\n \"0.47200245\",\n \"0.4718781\",\n \"0.47106892\",\n \"0.470761\",\n \"0.47034726\",\n \"0.4702613\",\n \"0.46860182\",\n \"0.46849197\",\n \"0.4683797\",\n \"0.4680382\",\n \"0.4670405\",\n \"0.46684435\",\n \"0.4661583\",\n \"0.46550813\",\n \"0.4651958\",\n \"0.4647163\",\n \"0.46455085\",\n \"0.4640827\",\n \"0.463252\",\n \"0.46302536\",\n \"0.4616527\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66234"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":77079,"cells":{"query":{"kind":"string","value":"Replace tags in the given path template and return either Windows or Linux formatted path."},"document":{"kind":"string","value":"def get_path_from_template(path_template: str, path_type: PathType = PathType.AUTO) -> str:\r\n # automatically select path type depending on running OS\r\n if path_type == PathType.AUTO:\r\n if platform.system() == \"Windows\":\r\n path_type = PathType.WINDOWS\r\n elif platform.system() == \"Linux\":\r\n path_type = PathType.LINUX\r\n else:\r\n raise RuntimeError(\"Unknown platform\")\r\n\r\n path_template = path_template.replace(\"<USERNAME>\", get_user_name())\r\n\r\n # return correctly formatted path\r\n if path_type == PathType.WINDOWS:\r\n return str(pathlib.PureWindowsPath(path_template))\r\n elif path_type == PathType.LINUX:\r\n return str(pathlib.PurePosixPath(path_template))\r\n else:\r\n raise RuntimeError(\"Unknown platform\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_template_from_path(path: str) -> str:\r\n path = path.replace(\"\\\\\", \"/\")\r\n return path","def system_path(path):\n if is_windows(): return path.replace('/', '\\\\')\n else: return path.replace('\\\\', '/')","def replace_template_tags(lines, file_provider):\n ret = []\n for line in lines:\n m = re.search(r'{/' + path_re + '}', line)\n if m:\n span = m.span()\n template_path = m.group('path').lstrip('/')\n template_lines = file_provider(template_path)\n template_lines = replace_template_tags(template_lines, file_provider)\n ret += process_replacement_lines(line[:span[0]], line[span[1]:], template_lines)\n else:\n ret.append(line)\n return ret","def convertString(path):\n if (\"win\" in sys.platform):\n return path.replace(\"/\",\"\\\\\")\n elif (\"linux\" in sys.platform):\n return path.replace(\"\\\\\",\"/\")","def replaceVariables(template, virtroot, treename):\n if not template.startswith('${') and not template.endswith('}'): raise SyntaxError('Template format unknown.')\n template = template[2:-1]\n template = template.replace('VIRTROOT', virtroot)\n template = template.replace('TREENAME', treename)\n\n # deal with concurrent / issues by having os.path.join build the final path\n return os.path.join('/', *template.split('/'))","def path_replace(args):\n print(header(\"Substitutions Made\"))\n replacements = parse_replacements(args)\n new_parts = []\n\n for part in os.environ[\"PATH\"].split(\":\"):\n for search, replace in replacements:\n new_part = re.sub(search, replace, part)\n if part != new_part:\n print(part, \"=>\", new_part)\n\n new_parts.append(new_part)\n\n print(\"\\n\\nexport PATH={}\".format(\":\".join(new_parts)))","def determine_template_by_path(path):\n path = path.lstrip('/')\n\n path_chunks = re.split('\\/', path)\n if len(path_chunks) <= 1:\n return path\n else:\n \"\"\"\n For now be ignorant and just return the\n first entry of the list as the possible template\n name, so in fact we only have a 1 level deep structure\n \"\"\"\n return '_%s.html' % path_chunks[0]","def resolve_template(template, item):\n root_dir = config['link_root_dir'].as_filename()\n placeholders = re.findall(r'(\\%\\{?\\w+\\}?)', template)\n path = template\n for p in placeholders:\n path = path.replace(p, item[p.strip('%{}')])\n return os.path.join(root_dir, path)","def _escape_path(path):\n path = path.strip()\n return '\"{0}\"'.format(path) if _platform_windows else path.replace(\" \", \"\\ \")","def path2support(path):\n parts = path.split(os.path.sep)\n # return '{{ site.baseurl}}' + IPYTHON_STATIC_DIR + '/' + '/'.join(quote(part) for part in parts)\n return '/' + IPYTHON_STATIC_DIR + '/' + '/'.join(quote(part) for part in parts)","def resolve_path(self, path, dont_create = False):\n\t\tif not os.path.isabs(path) and not os.path.sep in path:\n\t\t\t# clean it up to avoid typos\n\t\t\tpath = path.strip().lower()\n\t\t\ttmppath = self.temp_files.get(path)\n\t\t\tif tmppath is None:\n\t\t\t\tif dont_create is False:\n\t\t\t\t\ttmppath = self.mktemp(\n\t\t\t\t\t\tsuffix = '.py',\n\t\t\t\t\t\tprefix = '_console_%s_' %(path,)\n\t\t\t\t\t)\n\t\t\t\t\tself.temp_files[path] = tmppath\n\t\t\t\telse:\n\t\t\t\t\treturn path\n\t\t\treturn tmppath\n\t\treturn path","def change_path_to_windows_style(input):\n\n try:\n new_output_path = re.sub(\"^/cygdrive/c/\", \"C:/\", input)\n except Exception as e:\n print e\n new_output_path = input\n\n return new_output_path","def make_posix_path(windows_path):\n for regex, sub in [\n (re.compile(r'\\\\'), '/'),\n (re.compile('^[Cc]:'), '/c'),\n ]:\n windows_path = regex.sub(sub, windows_path)\n return windows_path","def normalized_file_path(path: str) -> str:\n # Convert Unix path to Windows path for WSL\n if PLATFORM == \"WSL\":\n return path.replace(\"/\", \"\\\\\")\n\n return path","def path(self):\r\n quoted_tag_name = urllib.quote(self.name.encode('utf8'))\r\n if not c.default_sr:\r\n return \"/r/%s/tag/%s/\" % (c.site.name, quoted_tag_name)\r\n else:\r\n return \"/tag/%s/\" % (quoted_tag_name)","def _path(unix_path):\n return unix_path.replace(\"/\", os.path.sep)","def normalizeWindowsPath(path):\n ret = libxml2mod.xmlNormalizeWindowsPath(path)\n return ret","def _parse_vm_path(self, vm_names, vm_path):\r\n if vm_path not in ['\\\\', '']:\r\n if not vm_path.startswith('\\\\'):\r\n vm_path = '\\\\' + vm_path\r\n\r\n vm_path_list = vm_path.split('\\\\')\r\n\r\n for vm_name in vm_names:\r\n if vm_name in vm_path_list[1]:\r\n vm_path = vm_path.replace(vm_path_list[1], vm_names[vm_name])\r\n break\r\n\r\n return vm_path","def replace_vars_path(self, _path):\n old_path = _path\n _path = _path.replace(\"{userhome}\", USERHOME)\n _path = _path.replace(\"{size}\", str(self.default_icon_size))\n _path = _path.replace(\"{arch}\", ARCH)\n if self.data[\"exec_path_script\"]:\n _path = execute([absolute_path + \"paths/\" +\n self.data[\"exec_path_script\"], _path],\n verbose=True).decode(\"utf-8\").strip()\n if _path != old_path:\n log(\"new application {0} path : {1}\".format(self.data[\"name\"], _path))\n return _path","def system_path(self, path):\n return os.path.join(self.prefix, path.lstrip('/'))","def pretty_path(input_path):\n home_path = os.path.expanduser('~')\n cwd_path = os.getcwd()\n output_path = input_path.replace(home_path, '~').replace(cwd_path, './')\n return output_path","def resolve(self, filespec):\n \n parts = re.split(r\"[\\\\/]\", filespec)\n # try to substitute the first part as if it is a handle\n parts[0] = self.fhdict.get(parts[0].lower(), (parts[0],))[0]\n return os.path.sep.join(parts)","def format_path (in_path):\n return os.path.realpath(os.path.expanduser(in_path))","def prettyfypath(path):\n home = os.path.expanduser(\"~/\")\n home_n = pathnormalize(home)\n path_n = pathnormalize(path)\n if path_n.startswith(home_n):\n path = os.path.join(\"~\", os.path.relpath(path, home))\n return path","def substitute_macros(text):\n f_text = text\n for (pattern,replacement) in context.environment.items():\n replacement = replacement.replace(os.path.sep,'/')\n f_text = f_text.replace('$(%s)' % pattern.upper(), replacement)\n return f_text","def normalizeNativePath(path: unicode) -> unicode:\n ...","def win2unix(a_path, use_abs=1):\r\n if use_abs:\r\n a_path = os.path.abspath(a_path)\r\n return re.sub(r\"\\\\\", \"/\", a_path)","def win2unix(a_path, use_abs=1):\r\n if use_abs:\r\n a_path = os.path.abspath(a_path)\r\n return re.sub(r\"\\\\\", \"/\", a_path)","def format_path(s,\n path=None,\n replace_long_filename=False):\n # TODO: could possibly simplify by moving representation logic to FileNode\n replaced_path_name = False\n if path is not None:\n if s.startswith(path):\n replaced_path_name = True\n s = s[len(path)+1:]\n if replace_long_filename:\n head, tail = os.path.split(s)\n name_prefix = head.replace('/','_')\n if '/' in head and len(name_prefix) > 0:\n s = s.replace(name_prefix, '. . . ')\n if replaced_path_name:\n s = \"[DIR]/\" + s\n return \"/\\\\n\".join(s.split('/'))","def handle_path_string(text, cur_dir_subst: bool=True) -> str:\n path = strip_trailing_slash(strip_quotes(text))\n\n if path.startswith(\"~\"):\n path = path.replace(\"~\", os.environ['HOME'])\n elif not path.startswith(\"/\") and cur_dir_subst:\n path = os.environ['PWD'] + \"/\" + path\n\n return path","def path_converter(text_path):\n\n out = ''\n out_list = []\n\n for i, t in enumerate(text_path):\n \n if t == ' ':\n out_list.append('\\\\ ')\n elif t == \"(\" :\n out_list.append('\\(')\n elif t == \")\" :\n out_list.append('\\)') \n else:\n out_list.append(t)\n \n return out.join(out_list)","def native(path):\n path = _os.path.sep.join(path.split('/'))\n return _os.path.normpath(_os.path.join(root, path))","def replace_tag(tag, value, file):\r\n with open(file, \"r\") as origin:\r\n with open(file+\".replaced\", \"w\") as dest:\r\n dest.write(origin.read().replace(tag, str(value)))\r\n return file+\".replaced\"","def WindowsPath(path):\n # TODO(pamg): make this work for other drives too.\n if path.startswith('/cygdrive/c/'):\n return path.replace('/cygdrive/c/', 'C:/')\n return path","def nt_path_to_posix_path(path):\r\n path = path.replace(\"\\\\\", \"/\")\r\n parts = path.split(\":\")\r\n if len(parts) > 1:\r\n return \"/\" + parts[0].lower() + parts[1]\r\n return path","def getReplacement(tag):\n global repd\n \n # read files to load global\n # dictionary if hasn't been read before\n \n if len(repd.keys()) == 0:\n readDirectory()\n \n # tag shouldn't have <>, but be safe\n \n if tag.startswith(\"<\"):\n end = tag.find(\">\")\n tag = tag[1:end]\n \n \n if not tag in repd:\n tag = tag+'s'\n if not tag in repd:\n return \"NO-REPLACEMENT\"\n \n return random.choice(repd[tag])","def _windows_seps(path: str) -> str:\n\n if not path:\n return None\n elif os.sep != ntpath.sep:\n return path.replace(os.sep, ntpath.sep)\n else:\n return path","def split_template_path(template):\n pieces = []\n for piece in template.split('/'):\n if os.path.sep in piece \\\n or (os.path.altsep and os.path.altsep in piece) or \\\n piece == os.path.pardir:\n raise TemplateNotFound(template)\n elif piece and piece != '.':\n pieces.append(piece)\n return pieces","def htmlpath(path):\n filelocation = f\"public/pages/{path}\"\n print(filelocation)\n\n return filelocation","def find_custom_template(args):\n for arg in args:\n if os.path.isdir(arg):\n dirlist = os.listdir(arg)\n if \"custom.html\" in dirlist:\n return os.path.join(arg, \"custom.html\")\n elif \"custom.jinja\" in dirlist:\n return os.path.join(arg, \"custom.jinja\")","def destPath(file, package, type='files'):\n\treturn tmpDir(package)+'/etc/univention/templates/'+type+'/'+file","def mangle_path(path):\n # Remove assigns\n path = servers.get_file_server().manglepath( str(path) )\n # Remove parent special directories\n path = os.path.abspath( path )\n # Convert path to Nebula format (slashes instead of backslashes)\n path = servers.get_file_server().manglepath( str(path) )\n # Convert drive letter to lowercase\n if len(path) > 1:\n if path[1] == ':':\n path = path[:1].lower() + path[1:]\n return path","def sanitize_file_path(file_path, replacement_text=\"\"):\n\n return __RE_INVALID_PATH.sub(replacement_text, file_path.strip())","def sanitize_file_path_for_shell(file_path):\n file_path_sanitized = file_path.replace('\\\\', '\\\\\\\\')\n file_path_sanitized = file_path_sanitized.replace('$', '\\\\$')\n file_path_sanitized = file_path_sanitized.replace('\"', '\\\\\"')\n file_path_sanitized = file_path_sanitized.replace('`', '\\\\`')\n return file_path_sanitized","def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:\r\n path_template = get_template_from_path(path)\r\n path = get_path_from_template(path_template, path_type)\r\n return path","def test_replace_namespaced_template(self):\n pass","def escapeForPath(s):\n return s.replace(os.sep, \"\")","def _get_path(self, path, resource_path):\n for item in self.path_replace:\n if len(item) == 2:\n src, dest = item\n else:\n assert len(item) == 3\n src = item[0]\n dest = item[2 if resource_path else 1]\n if path.endswith(src):\n path = replace_end(path, src, dest)\n return path","def render(\n path_or_template: str,\n **kwargs,\n) -> str:\n if isinstance(path_or_template, Template):\n template = path_or_template\n elif path_or_template.startswith(\"<\"):\n template = Template(path_or_template)\n else:\n with open(path_or_template, \"r\") as filp:\n contents = filp.read()\n template = Template(contents)\n return template.safe_substitute(**kwargs)","def make_link(path):\r\n tryFile = path.replace('\\\\', '/')\r\n\r\n if os.path.isabs(tryFile) and os.path.isfile(tryFile):\r\n (folder, filename) = os.path.split(tryFile)\r\n (base, ext) = os.path.splitext(filename)\r\n app = get_app()\r\n\r\n editable = {'controllers': '.py', 'models': '.py', 'views': '.html'}\r\n for key in editable.keys():\r\n check_extension = folder.endswith(\"%s/%s\" % (app,key))\r\n if ext.lower() == editable[key] and check_extension:\r\n return A('\"' + tryFile + '\"',\r\n _href=URL(r=request,\r\n f='edit/%s/%s/%s' % (app, key, filename))).xml()\r\n return ''","def py38_translate_path(self, path):\n # abandon query parameters\n path = path.split('?', 1)[0]\n path = path.split('#', 1)[0]\n # Don't forget explicit trailing slash when normalizing. Issue17324\n trailing_slash = path.rstrip().endswith('/')\n try:\n path = urllib.parse.unquote(path, errors='surrogatepass')\n except UnicodeDecodeError:\n path = urllib.parse.unquote(path)\n path = posixpath.normpath(path)\n words = path.split('/')\n words = filter(None, words)\n # py38: self.directory\n # py36: os.getcwd()\n path = STATIC_DIR\n for word in words:\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\n # Ignore components that are not a simple file/directory name\n continue\n path = os.path.join(path, word)\n if trailing_slash:\n path += '/'\n return path","def tag(referencefile):\n dirpath = path.abspath(referencefile)\n\n if path.isdir(dirpath):\n dircontents = listdir(dirpath)\n else:\n dirpath = path.split(dirpath)[0]\n dircontents = listdir(dirpath)\n\n while not 'tag' in dircontents:\n dirpath = path.split(dirpath)[0]\n dircontents = listdir(dirpath)\n if len(dircontents) == 0 or path.split(dirpath)[1] == 'chemistry':\n print(\"tag file not found\")\n return None\n\n return path.join(dirpath, 'tag')","def convert_unc(host, path):\n return ''.join(['\\\\\\\\', host, '\\\\', path.replace(':', '$')])","def prettify_path(path, leading=None):\r\n leading = (leading or os.getcwd()).replace(os.altsep, os.sep)\r\n s = os.path.splitext(path.replace(os.altsep, os.sep))[0]\r\n if s.startswith(leading):\r\n s = s.replace(leading, '')\r\n return s.strip(os.sep)","def normalize_path(path, config):\n\n for alias, link in config[\"Aliases\"].items():\n if path.startswith(alias):\n return path.replace(alias, link, 1)\n\n return path","def split_template_path(template):\n pieces = []\n for piece in template.split('/'):\n if path.sep in piece \\\n or (path.altsep and path.altsep in piece) or \\\n piece == path.pardir:\n raise TemplateNotFound(template)\n elif piece and piece != '.':\n pieces.append(piece)\n return pieces","def path_to_word(grid, path):\n return ''.join([grid[p] for p in path])","def path_to_word(grid, path):\n return ''.join([grid[p] for p in path])","def resolved_path(path):\n path = os.path.abspath(path)\n elements = path_elements(path)\n result = \"\"\n for element in elements:\n segment = element\n segment_path = os.path.join(result, segment)\n if os.path.islink(segment_path):\n segment = os.readlink(segment_path)\n result = os.path.join(result, segment)\n result = os.path.normpath(result)\n return result","def _formatPath(directoryPath, filePath):\n return directoryPath + \"\\\\\" + filePath","def win_path_check(path):\n if IS_WIN:\n return path.replace(\"\\\\\", \"/\").replace(\":\", \"\\\\:\")\n return path","def format_path(path):\n if not path:\n return path\n\n path = re.sub(r'/+', '/', path)\n\n if path == '/':\n return (u\"\" if isinstance(path, unicode) else \"\")\n else:\n return '/' + path.strip('/')","def get_template_filename(template):\n config = read_config(SETTINGS_PATH)\n #String templates\n if (template in STRING_TEMPLATES):\n options = config.options(STRING_TEMPLATES_SECTION) \n for option in options:\n if (option==template):\n #Get root path for the templates\n root_path = config.get(TEMPLATES_SECTION,TEMPLATES_ROOT_PATH)\n #Get the strings path templates\n strings_path = config.get(STRING_TEMPLATES_SECTION,STRING_TEMPLATES_PATH)\n return join(root_path,strings_path),config.get(STRING_TEMPLATES_SECTION,option)","def get_source(self, environment, template):\n if \"/\" not in template:\n path = template.split(\".\")\n\n # The first part of the path must be a bundle name\n # The other parts are the hierarchy of directory after 'views'\n bundle = path[0]\n sub_hierarchy = \"/\".join(path[1:])\n path = \"bundles/\" + bundle + \"/views/\" + sub_hierarchy + \".jj2\"\n else:\n path = template\n\n path = join(self.server.user_directory, path)\n if not exists(path):\n raise TemplateNotFound(template)\n\n mtime = getmtime(path)\n with open(path, 'r', encoding=\"utf-8\") as file:\n source = file.read()\n\n return source, path, lambda: mtime == getmtime(path)","def _get_path_category(self, path: Path) -> str:\n if str(path).startswith(\"/\"):\n return \"global\"\n elif str(path).startswith(\"~\"):\n return \"local\"\n\n return \"custom\"","def _git_templates():\n search_locations = [\n '/usr/share/git-core/templates',\n '/usr/local/share/git-core/templates',\n '/usr/local/git/share/git-core/templates'\n ]\n\n for possible_location in search_locations:\n if isdir(possible_location):\n return possible_location\n\n return None","def pathify(value):\n \n value = stringify(value)\n return value.replace('$ ', '$$ ').replace(' ', '$ ').replace(':', '$:')","def shorten_path(path):\n # On Windows, the filenames are not case-sensitive\n # and the way Python displays filenames may vary.\n # To properly compare, we convert everything to lowercase\n # However, we ensure that the shortened path retains its cases\n ext = os.path.splitext(path)[1]\n\n path_lower = path.lower()\n path_lower = os.path.splitext(path_lower)[0]\n\n if path_lower.startswith(PYTHON):\n path = \"PYTHON:\" + path[len(PYTHON) : -len(ext)]\n elif path_lower.startswith(IDEAS):\n path = \"IDEAS:\" + path[len(IDEAS) : -len(ext)]\n elif path_lower.startswith(TESTS):\n path = \"TESTS:\" + path[len(TESTS) : -len(ext)]\n elif path_lower.startswith(SITE_PACKAGES):\n path = \"SITE-PACKAGES:\" + path[len(SITE_PACKAGES) : -len(ext)]\n elif path_lower.startswith(HOME):\n path = \"~\" + path[len(HOME) : -len(ext)]\n return path","def compute_opath(self, i_path: str) -> str:\n c = self.c\n for _i in range(3):\n i_path, ext = os.path.splitext(i_path)\n if not ext:\n break\n # #1373.\n base_dir = os.path.dirname(c.fileName())\n return g.finalize_join(base_dir, i_path + '.html')","def cleanUpPath(path):\n # Remove extra quotes and spaces\n cleanPath = path.strip()\n cleanPath = cleanPath.strip(\"\\\"\")\n # The normalize operation needs to happen before prepend project directory \n # variable operation. After appending the placeholder variable to the output \n # is not a standard path so the normalize operation does not work correctly.\n cleanPath = ntpath.normpath(cleanPath)\n # Append project dir\n cleanPath = ntpath.join(PROJ_DIR_STR, cleanPath)\n return cleanPath","def corrTIFPath(path, symbol, value): \n reg_expr_search = '(\\%s+)'%str(symbol)\n length_of_replace = len(re.search(reg_expr_search, path).group(0))\n str_format = '{0:%sd}'%'{0:02d}'.format(length_of_replace)\n str_replace = str_format.format(int(value))\n\n return re.sub(reg_expr_search, str_replace, path)","def clean(path):\n if os.sep == \"\\\\\":\n return path.replace(os.sep, \"\\\\\\\\\")\n return path","def _adjust_path_values(self, variable_name: str, value: str) -> str:\n if not self._file_path:\n return value\n for token in FileConfig.PATH_TOKEN:\n if token in variable_name:\n config_file_dir = os.path.dirname(self._file_path)\n resolved_path = os.path.join(config_file_dir, value)\n value = os.path.realpath(resolved_path)\n break\n return value","def copy_html_template(name, new_string=None, old_string=None,\n path=\"core/srv/html_templates\", \n tmp_path=\"core/srv/tmp\"):\n filepath = \"{}/{path}/{name}\".format(\n os.getcwd(), path=path, name=name)\n \n tmp_filepath = \"{}/{path}/{name}\".format(\n os.getcwd(), path=tmp_path, name=name)\n\n copyfile(filepath, tmp_filepath)\n\n if all([new_string, old_string]):\n with open(tmp_filepath, \"w+b\") as fout:\n with open(filepath, \"r+b\") as fin:\n for line in fin:\n fout.write(line.replace(old_string, new_string))","def Sourceify(path):\n return path","def __clean_path(self, path):\n matches = re.finditer(r'\\%\\(.*?\\)[diouxXeEfFgGcrsa]', path)\n for _, match in enumerate(matches):\n pattern = match.group()\n path = path.replace(pattern, u'')\n pos = path.find(os.path.sep*2)\n if pos>=0:\n path = path[:pos+1]\n return path","def standardize_path(path):\n path.rstrip('/')\n if not path.startswith('.*'):\n path = '/' + path\n path = re.compile('/+').sub('/', path)\n return path","def render_legacy_template(template_path: str, context: Mapping[str, str]) -> str:\n\n # will be replaced by django templates in the future\n with open(template_path, 'r', encoding='utf-8') as template:\n template_str = template.read()\n return Template(template_str).substitute(**context)","def _get_os_path(self, name=None, path=''):\n\t\t\n\t\tif self.notebook_dir:\n\t\t\tout_path =os.path.join( self.notebook_dir, path.lstrip('/'))\n\t\telse:\n\t\t\tout_path = path\n\t\t\n\t\tif name:\n\t\t\tout_path = os.path.join(out_path, name.lstrip('/'))\n\t\t\n\t\treturn out_path","def _find_relative(self, spec):\n if spec.template_rel_path is not None:\n return os.path.split(spec.template_rel_path)\n # Otherwise, determine the file name separately.\n\n locator = self.loader._make_locator()\n\n # We do not use the ternary operator for Python 2.4 support.\n if spec.template_name is not None:\n template_name = spec.template_name\n else:\n template_name = locator.make_template_name(spec)\n\n file_name = locator.make_file_name(template_name, spec.template_extension)\n\n return (spec.template_rel_directory, file_name)","def safepath(p):\n return p.replace('/', os.sep)","def cleanupFile( path, original_path):\n # check the header path\n src_path = path\n\n if not os.path.exists(src_path):\n if noUpdate:\n panic( \"file does not exist: '%s'\\n\" % path )\n sys.stderr.write( \"warning: file does not exit: %s\\n\" % path )\n return None, None\n\n if not os.path.isfile(src_path):\n if noUpdate:\n panic( \"path is not a file: '%s'\\n\" % path )\n sys.stderr.write( \"warning: not a file: %s\\n\" % path )\n return None, None\n\n if os.path.commonprefix( [ src_path, original_path ] ) != original_path:\n if noUpdate:\n panic( \"file is not in 'original' directory: %s\\n\" % path );\n sys.stderr.write( \"warning: file not in 'original' ignored: %s\\n\" % path )\n return None, None\n\n src_path = src_path[len(original_path):]\n if len(src_path) > 0 and src_path[0] == '/':\n src_path = src_path[1:]\n\n if len(src_path) == 0:\n panic( \"oops, internal error, can't extract correct relative path\\n\" )\n\n # convert into destination path, extracting architecture if needed\n # and the corresponding list of known static functions\n #\n arch = None\n re_asm_arch = re.compile( r\"asm-([\\w\\d_\\+\\.\\-]+)(/.*)\" )\n m = re_asm_arch.match(src_path)\n statics = kernel_known_generic_statics\n if m and m.group(1) != 'generic':\n dst_path = \"arch-%s/asm/%s\" % m.groups()\n arch = m.group(1)\n statics = statics.union( kernel_known_statics.get( arch, set() ) )\n else:\n dst_path = \"common/\" + src_path\n\n dst_path = os.path.normpath( kernel_cleaned_path + \"/\" + dst_path )\n\n # now, let's parse the file\n #\n blocks = cpp.BlockParser().parseFile(path)\n if not blocks:\n sys.stderr.write( \"error: can't parse '%s'\" % path )\n sys.exit(1)\n\n macros = kernel_known_macros.copy()\n if arch and arch in kernel_default_arch_macros:\n macros.update(kernel_default_arch_macros[arch])\n\n if arch and arch in kernel_arch_token_replacements:\n blocks.replaceTokens( kernel_arch_token_replacements[arch] )\n\n blocks.optimizeMacros( macros )\n blocks.optimizeIf01()\n blocks.removeVarsAndFuncs( statics )\n blocks.replaceTokens( kernel_token_replacements )\n blocks.removeComments()\n blocks.removeMacroDefines( kernel_ignored_macros )\n blocks.removeWhiteSpace()\n\n out = StringOutput()\n out.write( kernel_disclaimer )\n blocks.writeWithWarning(out, kernel_warning, 4)\n return dst_path, out.get()","def _update_template(template_path):\n template_definition = template_path\n\n # template output directory is output/templates, so need to create that location before pulling out the templates\n template_location = template_utilities.get_template_directory()\n\n # Install the template and get the path to the template directory for updating the configuration file.\n templates_path = template_utilities.install_template(template_location, template_definition)\n\n if templates_path:\n # Now need to find the templates definition of that zip file and locate it in the file system so that it can be\n settings = get_configuration()\n\n # Override the configuration details with the new template path. This should probably be handled by the\n # publishing plugin, but for now this will work\n settings.publishing.templates = str(templates_path.relative_to(get_configuration_root()))\n configuration_file_path = get_configuration_root() / 'config.yaml'\n\n dump_configuration(configuration_file_path, settings)","def platformify(self):\n actions = ['rsync -aP {0} {1}'.format(\n os.path.join(TEMPLATEDIR, self.name, 'files/'), self.builddir\n )]\n patches = glob(os.path.join(TEMPLATEDIR, self.name, \"*.patch\"))\n for patch in patches:\n actions.append('cd {0}/web && patch -p1 < {1}'.format(\n self.builddir, patch)\n )\n\n # In some cases the package updater needs to be run after we've platform-ified the\n # template, so run it a second time. Worst case it's a bit slower to build but doesn't\n # hurt anything.\n actions.extend(self.package_update_actions())\n\n return actions","def imgOutputPath(self, newpath):\n newimg = self.imagePath().replace(self.home, newpath)\n return newimg","def template_replace(template, replace_map, result):\n # Read content of source file.\n with open(template) as fp:\n lines = fp.readlines()\n # Replace placeholders.\n for key, value in list(replace_map.items()):\n for i, line in enumerate(lines):\n # Ignore VHDL comments\n if not line.strip().startswith('--'):\n lines[i] = line.replace(key, value)\n # Write content to destination file.\n with open(result, 'w') as fp:\n fp.write(''.join(lines))","def _replace_scopes(value):\n\n value = value.replace('linux-*', 'linux')\n value = value.replace('macx', 'macos')\n value = value.replace('win32', 'win')\n\n return value","def _escapePaths(self, paths):\n cmd = \"\"\n for p in paths:\n if ' ' in p:\n cmd += ' \"{0}\"'.format(p)\n else:\n cmd += ' ' + p\n return cmd","def test_build_dirhtml_from_template(temp_with_override, cli):\n # Create the book from the template\n book = temp_with_override / \"new_book\"\n _ = cli.invoke(commands.create, book.as_posix())\n build_result = cli.invoke(\n commands.build, [book.as_posix(), \"-n\", \"-W\", \"--builder\", \"dirhtml\"]\n )\n assert build_result.exit_code == 0, build_result.output\n html = book.joinpath(\"_build\", \"dirhtml\")\n assert html.joinpath(\"index.html\").exists()\n assert html.joinpath(\"intro\", \"index.html\").exists()","def systemPathToFileUrl(self, path):\n from unohelper import systemPathToFileUrl\n return systemPathToFileUrl(path)","def marker_replace_template(in_file, out_file, old, new):\n replace_file = open(in_file, \"r\")\n template_content = replace_file.read()\n result_content = template_content.replace(old, new)\n replace_file = open(out_file, \"w\")\n replace_file.write(result_content)\n replace_file.close()","def normalizePath(path):\n\tfrom os.path import normpath, sep\n\tresult = normpath(path)\n\tresult = result.replace(\"/\",sep)\n\tresult = result.replace(\"\\\\\",sep)\n\treturn adaptPath(result)","def _replaceFind(self, match):\r\n path = self._loader.findPkgPath(match.group('pkg'))\r\n return '\"{0}\"'.format(path) if ' ' in path else path","def path2str(a,b,path):\n (s1,s2) = zip(*path)\n line1 = \" \"+\"\".join([get_char(s1,a,x) for x in range(1,len(s1))])\n line2 = \" \"+\"\".join([get_char(s2,b,x) for x in range(1,len(s2))])\n return \"%s\\n%s\"%(line1,line2)","def processed_texture_path(path):\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('png', 'webp')","def presentation(self, value):\r\n realpath = os.path.expanduser(value)\r\n if self.auto_create:\r\n if not os.path.exists(realpath):\r\n os.makedirs(realpath)\r\n return realpath","def _replaceFind(self, match):\n path = self._loader.findPkgPath(match.group('pkg'))\n return '\"{0}\"'.format(path) if ' ' in path else path","def _format_environment_value(value):\n value = str(value)\n if platform.system() == \"Windows\":\n # Split on semicolons first\n components = value.split(os.pathsep)\n\n # On each component, replace anything that looks like\n # a drive letter with a unix-like drive path.\n components = [re.sub(r\"^([A-Za-z]):\\\\\",\n r\"\\\\\\1\\\\\",\n c) for c in components]\n\n return \":\".join(components).replace(\"\\\\\", \"/\")\n\n return value","def fix_filepaths(df):\n\n if platform in [\"linux\", \"linux2\", \"darwin\"]:\n pass\n else:\n raise NotImplementedError(\n \"OSes other than Linux and Mac are currently not supported.\"\n )\n return df","def _cleanup_path(path):\n return string.join(filter(None, string.split(path, '/')), '/')"],"string":"[\n \"def get_template_from_path(path: str) -> str:\\r\\n path = path.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n return path\",\n \"def system_path(path):\\n if is_windows(): return path.replace('/', '\\\\\\\\')\\n else: return path.replace('\\\\\\\\', '/')\",\n \"def replace_template_tags(lines, file_provider):\\n ret = []\\n for line in lines:\\n m = re.search(r'{/' + path_re + '}', line)\\n if m:\\n span = m.span()\\n template_path = m.group('path').lstrip('/')\\n template_lines = file_provider(template_path)\\n template_lines = replace_template_tags(template_lines, file_provider)\\n ret += process_replacement_lines(line[:span[0]], line[span[1]:], template_lines)\\n else:\\n ret.append(line)\\n return ret\",\n \"def convertString(path):\\n if (\\\"win\\\" in sys.platform):\\n return path.replace(\\\"/\\\",\\\"\\\\\\\\\\\")\\n elif (\\\"linux\\\" in sys.platform):\\n return path.replace(\\\"\\\\\\\\\\\",\\\"/\\\")\",\n \"def replaceVariables(template, virtroot, treename):\\n if not template.startswith('${') and not template.endswith('}'): raise SyntaxError('Template format unknown.')\\n template = template[2:-1]\\n template = template.replace('VIRTROOT', virtroot)\\n template = template.replace('TREENAME', treename)\\n\\n # deal with concurrent / issues by having os.path.join build the final path\\n return os.path.join('/', *template.split('/'))\",\n \"def path_replace(args):\\n print(header(\\\"Substitutions Made\\\"))\\n replacements = parse_replacements(args)\\n new_parts = []\\n\\n for part in os.environ[\\\"PATH\\\"].split(\\\":\\\"):\\n for search, replace in replacements:\\n new_part = re.sub(search, replace, part)\\n if part != new_part:\\n print(part, \\\"=>\\\", new_part)\\n\\n new_parts.append(new_part)\\n\\n print(\\\"\\\\n\\\\nexport PATH={}\\\".format(\\\":\\\".join(new_parts)))\",\n \"def determine_template_by_path(path):\\n path = path.lstrip('/')\\n\\n path_chunks = re.split('\\\\/', path)\\n if len(path_chunks) <= 1:\\n return path\\n else:\\n \\\"\\\"\\\"\\n For now be ignorant and just return the\\n first entry of the list as the possible template\\n name, so in fact we only have a 1 level deep structure\\n \\\"\\\"\\\"\\n return '_%s.html' % path_chunks[0]\",\n \"def resolve_template(template, item):\\n root_dir = config['link_root_dir'].as_filename()\\n placeholders = re.findall(r'(\\\\%\\\\{?\\\\w+\\\\}?)', template)\\n path = template\\n for p in placeholders:\\n path = path.replace(p, item[p.strip('%{}')])\\n return os.path.join(root_dir, path)\",\n \"def _escape_path(path):\\n path = path.strip()\\n return '\\\"{0}\\\"'.format(path) if _platform_windows else path.replace(\\\" \\\", \\\"\\\\ \\\")\",\n \"def path2support(path):\\n parts = path.split(os.path.sep)\\n # return '{{ site.baseurl}}' + IPYTHON_STATIC_DIR + '/' + '/'.join(quote(part) for part in parts)\\n return '/' + IPYTHON_STATIC_DIR + '/' + '/'.join(quote(part) for part in parts)\",\n \"def resolve_path(self, path, dont_create = False):\\n\\t\\tif not os.path.isabs(path) and not os.path.sep in path:\\n\\t\\t\\t# clean it up to avoid typos\\n\\t\\t\\tpath = path.strip().lower()\\n\\t\\t\\ttmppath = self.temp_files.get(path)\\n\\t\\t\\tif tmppath is None:\\n\\t\\t\\t\\tif dont_create is False:\\n\\t\\t\\t\\t\\ttmppath = self.mktemp(\\n\\t\\t\\t\\t\\t\\tsuffix = '.py',\\n\\t\\t\\t\\t\\t\\tprefix = '_console_%s_' %(path,)\\n\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\tself.temp_files[path] = tmppath\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\treturn path\\n\\t\\t\\treturn tmppath\\n\\t\\treturn path\",\n \"def change_path_to_windows_style(input):\\n\\n try:\\n new_output_path = re.sub(\\\"^/cygdrive/c/\\\", \\\"C:/\\\", input)\\n except Exception as e:\\n print e\\n new_output_path = input\\n\\n return new_output_path\",\n \"def make_posix_path(windows_path):\\n for regex, sub in [\\n (re.compile(r'\\\\\\\\'), '/'),\\n (re.compile('^[Cc]:'), '/c'),\\n ]:\\n windows_path = regex.sub(sub, windows_path)\\n return windows_path\",\n \"def normalized_file_path(path: str) -> str:\\n # Convert Unix path to Windows path for WSL\\n if PLATFORM == \\\"WSL\\\":\\n return path.replace(\\\"/\\\", \\\"\\\\\\\\\\\")\\n\\n return path\",\n \"def path(self):\\r\\n quoted_tag_name = urllib.quote(self.name.encode('utf8'))\\r\\n if not c.default_sr:\\r\\n return \\\"/r/%s/tag/%s/\\\" % (c.site.name, quoted_tag_name)\\r\\n else:\\r\\n return \\\"/tag/%s/\\\" % (quoted_tag_name)\",\n \"def _path(unix_path):\\n return unix_path.replace(\\\"/\\\", os.path.sep)\",\n \"def normalizeWindowsPath(path):\\n ret = libxml2mod.xmlNormalizeWindowsPath(path)\\n return ret\",\n \"def _parse_vm_path(self, vm_names, vm_path):\\r\\n if vm_path not in ['\\\\\\\\', '']:\\r\\n if not vm_path.startswith('\\\\\\\\'):\\r\\n vm_path = '\\\\\\\\' + vm_path\\r\\n\\r\\n vm_path_list = vm_path.split('\\\\\\\\')\\r\\n\\r\\n for vm_name in vm_names:\\r\\n if vm_name in vm_path_list[1]:\\r\\n vm_path = vm_path.replace(vm_path_list[1], vm_names[vm_name])\\r\\n break\\r\\n\\r\\n return vm_path\",\n \"def replace_vars_path(self, _path):\\n old_path = _path\\n _path = _path.replace(\\\"{userhome}\\\", USERHOME)\\n _path = _path.replace(\\\"{size}\\\", str(self.default_icon_size))\\n _path = _path.replace(\\\"{arch}\\\", ARCH)\\n if self.data[\\\"exec_path_script\\\"]:\\n _path = execute([absolute_path + \\\"paths/\\\" +\\n self.data[\\\"exec_path_script\\\"], _path],\\n verbose=True).decode(\\\"utf-8\\\").strip()\\n if _path != old_path:\\n log(\\\"new application {0} path : {1}\\\".format(self.data[\\\"name\\\"], _path))\\n return _path\",\n \"def system_path(self, path):\\n return os.path.join(self.prefix, path.lstrip('/'))\",\n \"def pretty_path(input_path):\\n home_path = os.path.expanduser('~')\\n cwd_path = os.getcwd()\\n output_path = input_path.replace(home_path, '~').replace(cwd_path, './')\\n return output_path\",\n \"def resolve(self, filespec):\\n \\n parts = re.split(r\\\"[\\\\\\\\/]\\\", filespec)\\n # try to substitute the first part as if it is a handle\\n parts[0] = self.fhdict.get(parts[0].lower(), (parts[0],))[0]\\n return os.path.sep.join(parts)\",\n \"def format_path (in_path):\\n return os.path.realpath(os.path.expanduser(in_path))\",\n \"def prettyfypath(path):\\n home = os.path.expanduser(\\\"~/\\\")\\n home_n = pathnormalize(home)\\n path_n = pathnormalize(path)\\n if path_n.startswith(home_n):\\n path = os.path.join(\\\"~\\\", os.path.relpath(path, home))\\n return path\",\n \"def substitute_macros(text):\\n f_text = text\\n for (pattern,replacement) in context.environment.items():\\n replacement = replacement.replace(os.path.sep,'/')\\n f_text = f_text.replace('$(%s)' % pattern.upper(), replacement)\\n return f_text\",\n \"def normalizeNativePath(path: unicode) -> unicode:\\n ...\",\n \"def win2unix(a_path, use_abs=1):\\r\\n if use_abs:\\r\\n a_path = os.path.abspath(a_path)\\r\\n return re.sub(r\\\"\\\\\\\\\\\", \\\"/\\\", a_path)\",\n \"def win2unix(a_path, use_abs=1):\\r\\n if use_abs:\\r\\n a_path = os.path.abspath(a_path)\\r\\n return re.sub(r\\\"\\\\\\\\\\\", \\\"/\\\", a_path)\",\n \"def format_path(s,\\n path=None,\\n replace_long_filename=False):\\n # TODO: could possibly simplify by moving representation logic to FileNode\\n replaced_path_name = False\\n if path is not None:\\n if s.startswith(path):\\n replaced_path_name = True\\n s = s[len(path)+1:]\\n if replace_long_filename:\\n head, tail = os.path.split(s)\\n name_prefix = head.replace('/','_')\\n if '/' in head and len(name_prefix) > 0:\\n s = s.replace(name_prefix, '. . . ')\\n if replaced_path_name:\\n s = \\\"[DIR]/\\\" + s\\n return \\\"/\\\\\\\\n\\\".join(s.split('/'))\",\n \"def handle_path_string(text, cur_dir_subst: bool=True) -> str:\\n path = strip_trailing_slash(strip_quotes(text))\\n\\n if path.startswith(\\\"~\\\"):\\n path = path.replace(\\\"~\\\", os.environ['HOME'])\\n elif not path.startswith(\\\"/\\\") and cur_dir_subst:\\n path = os.environ['PWD'] + \\\"/\\\" + path\\n\\n return path\",\n \"def path_converter(text_path):\\n\\n out = ''\\n out_list = []\\n\\n for i, t in enumerate(text_path):\\n \\n if t == ' ':\\n out_list.append('\\\\\\\\ ')\\n elif t == \\\"(\\\" :\\n out_list.append('\\\\(')\\n elif t == \\\")\\\" :\\n out_list.append('\\\\)') \\n else:\\n out_list.append(t)\\n \\n return out.join(out_list)\",\n \"def native(path):\\n path = _os.path.sep.join(path.split('/'))\\n return _os.path.normpath(_os.path.join(root, path))\",\n \"def replace_tag(tag, value, file):\\r\\n with open(file, \\\"r\\\") as origin:\\r\\n with open(file+\\\".replaced\\\", \\\"w\\\") as dest:\\r\\n dest.write(origin.read().replace(tag, str(value)))\\r\\n return file+\\\".replaced\\\"\",\n \"def WindowsPath(path):\\n # TODO(pamg): make this work for other drives too.\\n if path.startswith('/cygdrive/c/'):\\n return path.replace('/cygdrive/c/', 'C:/')\\n return path\",\n \"def nt_path_to_posix_path(path):\\r\\n path = path.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n parts = path.split(\\\":\\\")\\r\\n if len(parts) > 1:\\r\\n return \\\"/\\\" + parts[0].lower() + parts[1]\\r\\n return path\",\n \"def getReplacement(tag):\\n global repd\\n \\n # read files to load global\\n # dictionary if hasn't been read before\\n \\n if len(repd.keys()) == 0:\\n readDirectory()\\n \\n # tag shouldn't have <>, but be safe\\n \\n if tag.startswith(\\\"<\\\"):\\n end = tag.find(\\\">\\\")\\n tag = tag[1:end]\\n \\n \\n if not tag in repd:\\n tag = tag+'s'\\n if not tag in repd:\\n return \\\"NO-REPLACEMENT\\\"\\n \\n return random.choice(repd[tag])\",\n \"def _windows_seps(path: str) -> str:\\n\\n if not path:\\n return None\\n elif os.sep != ntpath.sep:\\n return path.replace(os.sep, ntpath.sep)\\n else:\\n return path\",\n \"def split_template_path(template):\\n pieces = []\\n for piece in template.split('/'):\\n if os.path.sep in piece \\\\\\n or (os.path.altsep and os.path.altsep in piece) or \\\\\\n piece == os.path.pardir:\\n raise TemplateNotFound(template)\\n elif piece and piece != '.':\\n pieces.append(piece)\\n return pieces\",\n \"def htmlpath(path):\\n filelocation = f\\\"public/pages/{path}\\\"\\n print(filelocation)\\n\\n return filelocation\",\n \"def find_custom_template(args):\\n for arg in args:\\n if os.path.isdir(arg):\\n dirlist = os.listdir(arg)\\n if \\\"custom.html\\\" in dirlist:\\n return os.path.join(arg, \\\"custom.html\\\")\\n elif \\\"custom.jinja\\\" in dirlist:\\n return os.path.join(arg, \\\"custom.jinja\\\")\",\n \"def destPath(file, package, type='files'):\\n\\treturn tmpDir(package)+'/etc/univention/templates/'+type+'/'+file\",\n \"def mangle_path(path):\\n # Remove assigns\\n path = servers.get_file_server().manglepath( str(path) )\\n # Remove parent special directories\\n path = os.path.abspath( path )\\n # Convert path to Nebula format (slashes instead of backslashes)\\n path = servers.get_file_server().manglepath( str(path) )\\n # Convert drive letter to lowercase\\n if len(path) > 1:\\n if path[1] == ':':\\n path = path[:1].lower() + path[1:]\\n return path\",\n \"def sanitize_file_path(file_path, replacement_text=\\\"\\\"):\\n\\n return __RE_INVALID_PATH.sub(replacement_text, file_path.strip())\",\n \"def sanitize_file_path_for_shell(file_path):\\n file_path_sanitized = file_path.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\')\\n file_path_sanitized = file_path_sanitized.replace('$', '\\\\\\\\$')\\n file_path_sanitized = file_path_sanitized.replace('\\\"', '\\\\\\\\\\\"')\\n file_path_sanitized = file_path_sanitized.replace('`', '\\\\\\\\`')\\n return file_path_sanitized\",\n \"def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:\\r\\n path_template = get_template_from_path(path)\\r\\n path = get_path_from_template(path_template, path_type)\\r\\n return path\",\n \"def test_replace_namespaced_template(self):\\n pass\",\n \"def escapeForPath(s):\\n return s.replace(os.sep, \\\"\\\")\",\n \"def _get_path(self, path, resource_path):\\n for item in self.path_replace:\\n if len(item) == 2:\\n src, dest = item\\n else:\\n assert len(item) == 3\\n src = item[0]\\n dest = item[2 if resource_path else 1]\\n if path.endswith(src):\\n path = replace_end(path, src, dest)\\n return path\",\n \"def render(\\n path_or_template: str,\\n **kwargs,\\n) -> str:\\n if isinstance(path_or_template, Template):\\n template = path_or_template\\n elif path_or_template.startswith(\\\"<\\\"):\\n template = Template(path_or_template)\\n else:\\n with open(path_or_template, \\\"r\\\") as filp:\\n contents = filp.read()\\n template = Template(contents)\\n return template.safe_substitute(**kwargs)\",\n \"def make_link(path):\\r\\n tryFile = path.replace('\\\\\\\\', '/')\\r\\n\\r\\n if os.path.isabs(tryFile) and os.path.isfile(tryFile):\\r\\n (folder, filename) = os.path.split(tryFile)\\r\\n (base, ext) = os.path.splitext(filename)\\r\\n app = get_app()\\r\\n\\r\\n editable = {'controllers': '.py', 'models': '.py', 'views': '.html'}\\r\\n for key in editable.keys():\\r\\n check_extension = folder.endswith(\\\"%s/%s\\\" % (app,key))\\r\\n if ext.lower() == editable[key] and check_extension:\\r\\n return A('\\\"' + tryFile + '\\\"',\\r\\n _href=URL(r=request,\\r\\n f='edit/%s/%s/%s' % (app, key, filename))).xml()\\r\\n return ''\",\n \"def py38_translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?', 1)[0]\\n path = path.split('#', 1)[0]\\n # Don't forget explicit trailing slash when normalizing. Issue17324\\n trailing_slash = path.rstrip().endswith('/')\\n try:\\n path = urllib.parse.unquote(path, errors='surrogatepass')\\n except UnicodeDecodeError:\\n path = urllib.parse.unquote(path)\\n path = posixpath.normpath(path)\\n words = path.split('/')\\n words = filter(None, words)\\n # py38: self.directory\\n # py36: os.getcwd()\\n path = STATIC_DIR\\n for word in words:\\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\\n # Ignore components that are not a simple file/directory name\\n continue\\n path = os.path.join(path, word)\\n if trailing_slash:\\n path += '/'\\n return path\",\n \"def tag(referencefile):\\n dirpath = path.abspath(referencefile)\\n\\n if path.isdir(dirpath):\\n dircontents = listdir(dirpath)\\n else:\\n dirpath = path.split(dirpath)[0]\\n dircontents = listdir(dirpath)\\n\\n while not 'tag' in dircontents:\\n dirpath = path.split(dirpath)[0]\\n dircontents = listdir(dirpath)\\n if len(dircontents) == 0 or path.split(dirpath)[1] == 'chemistry':\\n print(\\\"tag file not found\\\")\\n return None\\n\\n return path.join(dirpath, 'tag')\",\n \"def convert_unc(host, path):\\n return ''.join(['\\\\\\\\\\\\\\\\', host, '\\\\\\\\', path.replace(':', '$')])\",\n \"def prettify_path(path, leading=None):\\r\\n leading = (leading or os.getcwd()).replace(os.altsep, os.sep)\\r\\n s = os.path.splitext(path.replace(os.altsep, os.sep))[0]\\r\\n if s.startswith(leading):\\r\\n s = s.replace(leading, '')\\r\\n return s.strip(os.sep)\",\n \"def normalize_path(path, config):\\n\\n for alias, link in config[\\\"Aliases\\\"].items():\\n if path.startswith(alias):\\n return path.replace(alias, link, 1)\\n\\n return path\",\n \"def split_template_path(template):\\n pieces = []\\n for piece in template.split('/'):\\n if path.sep in piece \\\\\\n or (path.altsep and path.altsep in piece) or \\\\\\n piece == path.pardir:\\n raise TemplateNotFound(template)\\n elif piece and piece != '.':\\n pieces.append(piece)\\n return pieces\",\n \"def path_to_word(grid, path):\\n return ''.join([grid[p] for p in path])\",\n \"def path_to_word(grid, path):\\n return ''.join([grid[p] for p in path])\",\n \"def resolved_path(path):\\n path = os.path.abspath(path)\\n elements = path_elements(path)\\n result = \\\"\\\"\\n for element in elements:\\n segment = element\\n segment_path = os.path.join(result, segment)\\n if os.path.islink(segment_path):\\n segment = os.readlink(segment_path)\\n result = os.path.join(result, segment)\\n result = os.path.normpath(result)\\n return result\",\n \"def _formatPath(directoryPath, filePath):\\n return directoryPath + \\\"\\\\\\\\\\\" + filePath\",\n \"def win_path_check(path):\\n if IS_WIN:\\n return path.replace(\\\"\\\\\\\\\\\", \\\"/\\\").replace(\\\":\\\", \\\"\\\\\\\\:\\\")\\n return path\",\n \"def format_path(path):\\n if not path:\\n return path\\n\\n path = re.sub(r'/+', '/', path)\\n\\n if path == '/':\\n return (u\\\"\\\" if isinstance(path, unicode) else \\\"\\\")\\n else:\\n return '/' + path.strip('/')\",\n \"def get_template_filename(template):\\n config = read_config(SETTINGS_PATH)\\n #String templates\\n if (template in STRING_TEMPLATES):\\n options = config.options(STRING_TEMPLATES_SECTION) \\n for option in options:\\n if (option==template):\\n #Get root path for the templates\\n root_path = config.get(TEMPLATES_SECTION,TEMPLATES_ROOT_PATH)\\n #Get the strings path templates\\n strings_path = config.get(STRING_TEMPLATES_SECTION,STRING_TEMPLATES_PATH)\\n return join(root_path,strings_path),config.get(STRING_TEMPLATES_SECTION,option)\",\n \"def get_source(self, environment, template):\\n if \\\"/\\\" not in template:\\n path = template.split(\\\".\\\")\\n\\n # The first part of the path must be a bundle name\\n # The other parts are the hierarchy of directory after 'views'\\n bundle = path[0]\\n sub_hierarchy = \\\"/\\\".join(path[1:])\\n path = \\\"bundles/\\\" + bundle + \\\"/views/\\\" + sub_hierarchy + \\\".jj2\\\"\\n else:\\n path = template\\n\\n path = join(self.server.user_directory, path)\\n if not exists(path):\\n raise TemplateNotFound(template)\\n\\n mtime = getmtime(path)\\n with open(path, 'r', encoding=\\\"utf-8\\\") as file:\\n source = file.read()\\n\\n return source, path, lambda: mtime == getmtime(path)\",\n \"def _get_path_category(self, path: Path) -> str:\\n if str(path).startswith(\\\"/\\\"):\\n return \\\"global\\\"\\n elif str(path).startswith(\\\"~\\\"):\\n return \\\"local\\\"\\n\\n return \\\"custom\\\"\",\n \"def _git_templates():\\n search_locations = [\\n '/usr/share/git-core/templates',\\n '/usr/local/share/git-core/templates',\\n '/usr/local/git/share/git-core/templates'\\n ]\\n\\n for possible_location in search_locations:\\n if isdir(possible_location):\\n return possible_location\\n\\n return None\",\n \"def pathify(value):\\n \\n value = stringify(value)\\n return value.replace('$ ', '$$ ').replace(' ', '$ ').replace(':', '$:')\",\n \"def shorten_path(path):\\n # On Windows, the filenames are not case-sensitive\\n # and the way Python displays filenames may vary.\\n # To properly compare, we convert everything to lowercase\\n # However, we ensure that the shortened path retains its cases\\n ext = os.path.splitext(path)[1]\\n\\n path_lower = path.lower()\\n path_lower = os.path.splitext(path_lower)[0]\\n\\n if path_lower.startswith(PYTHON):\\n path = \\\"PYTHON:\\\" + path[len(PYTHON) : -len(ext)]\\n elif path_lower.startswith(IDEAS):\\n path = \\\"IDEAS:\\\" + path[len(IDEAS) : -len(ext)]\\n elif path_lower.startswith(TESTS):\\n path = \\\"TESTS:\\\" + path[len(TESTS) : -len(ext)]\\n elif path_lower.startswith(SITE_PACKAGES):\\n path = \\\"SITE-PACKAGES:\\\" + path[len(SITE_PACKAGES) : -len(ext)]\\n elif path_lower.startswith(HOME):\\n path = \\\"~\\\" + path[len(HOME) : -len(ext)]\\n return path\",\n \"def compute_opath(self, i_path: str) -> str:\\n c = self.c\\n for _i in range(3):\\n i_path, ext = os.path.splitext(i_path)\\n if not ext:\\n break\\n # #1373.\\n base_dir = os.path.dirname(c.fileName())\\n return g.finalize_join(base_dir, i_path + '.html')\",\n \"def cleanUpPath(path):\\n # Remove extra quotes and spaces\\n cleanPath = path.strip()\\n cleanPath = cleanPath.strip(\\\"\\\\\\\"\\\")\\n # The normalize operation needs to happen before prepend project directory \\n # variable operation. After appending the placeholder variable to the output \\n # is not a standard path so the normalize operation does not work correctly.\\n cleanPath = ntpath.normpath(cleanPath)\\n # Append project dir\\n cleanPath = ntpath.join(PROJ_DIR_STR, cleanPath)\\n return cleanPath\",\n \"def corrTIFPath(path, symbol, value): \\n reg_expr_search = '(\\\\%s+)'%str(symbol)\\n length_of_replace = len(re.search(reg_expr_search, path).group(0))\\n str_format = '{0:%sd}'%'{0:02d}'.format(length_of_replace)\\n str_replace = str_format.format(int(value))\\n\\n return re.sub(reg_expr_search, str_replace, path)\",\n \"def clean(path):\\n if os.sep == \\\"\\\\\\\\\\\":\\n return path.replace(os.sep, \\\"\\\\\\\\\\\\\\\\\\\")\\n return path\",\n \"def _adjust_path_values(self, variable_name: str, value: str) -> str:\\n if not self._file_path:\\n return value\\n for token in FileConfig.PATH_TOKEN:\\n if token in variable_name:\\n config_file_dir = os.path.dirname(self._file_path)\\n resolved_path = os.path.join(config_file_dir, value)\\n value = os.path.realpath(resolved_path)\\n break\\n return value\",\n \"def copy_html_template(name, new_string=None, old_string=None,\\n path=\\\"core/srv/html_templates\\\", \\n tmp_path=\\\"core/srv/tmp\\\"):\\n filepath = \\\"{}/{path}/{name}\\\".format(\\n os.getcwd(), path=path, name=name)\\n \\n tmp_filepath = \\\"{}/{path}/{name}\\\".format(\\n os.getcwd(), path=tmp_path, name=name)\\n\\n copyfile(filepath, tmp_filepath)\\n\\n if all([new_string, old_string]):\\n with open(tmp_filepath, \\\"w+b\\\") as fout:\\n with open(filepath, \\\"r+b\\\") as fin:\\n for line in fin:\\n fout.write(line.replace(old_string, new_string))\",\n \"def Sourceify(path):\\n return path\",\n \"def __clean_path(self, path):\\n matches = re.finditer(r'\\\\%\\\\(.*?\\\\)[diouxXeEfFgGcrsa]', path)\\n for _, match in enumerate(matches):\\n pattern = match.group()\\n path = path.replace(pattern, u'')\\n pos = path.find(os.path.sep*2)\\n if pos>=0:\\n path = path[:pos+1]\\n return path\",\n \"def standardize_path(path):\\n path.rstrip('/')\\n if not path.startswith('.*'):\\n path = '/' + path\\n path = re.compile('/+').sub('/', path)\\n return path\",\n \"def render_legacy_template(template_path: str, context: Mapping[str, str]) -> str:\\n\\n # will be replaced by django templates in the future\\n with open(template_path, 'r', encoding='utf-8') as template:\\n template_str = template.read()\\n return Template(template_str).substitute(**context)\",\n \"def _get_os_path(self, name=None, path=''):\\n\\t\\t\\n\\t\\tif self.notebook_dir:\\n\\t\\t\\tout_path =os.path.join( self.notebook_dir, path.lstrip('/'))\\n\\t\\telse:\\n\\t\\t\\tout_path = path\\n\\t\\t\\n\\t\\tif name:\\n\\t\\t\\tout_path = os.path.join(out_path, name.lstrip('/'))\\n\\t\\t\\n\\t\\treturn out_path\",\n \"def _find_relative(self, spec):\\n if spec.template_rel_path is not None:\\n return os.path.split(spec.template_rel_path)\\n # Otherwise, determine the file name separately.\\n\\n locator = self.loader._make_locator()\\n\\n # We do not use the ternary operator for Python 2.4 support.\\n if spec.template_name is not None:\\n template_name = spec.template_name\\n else:\\n template_name = locator.make_template_name(spec)\\n\\n file_name = locator.make_file_name(template_name, spec.template_extension)\\n\\n return (spec.template_rel_directory, file_name)\",\n \"def safepath(p):\\n return p.replace('/', os.sep)\",\n \"def cleanupFile( path, original_path):\\n # check the header path\\n src_path = path\\n\\n if not os.path.exists(src_path):\\n if noUpdate:\\n panic( \\\"file does not exist: '%s'\\\\n\\\" % path )\\n sys.stderr.write( \\\"warning: file does not exit: %s\\\\n\\\" % path )\\n return None, None\\n\\n if not os.path.isfile(src_path):\\n if noUpdate:\\n panic( \\\"path is not a file: '%s'\\\\n\\\" % path )\\n sys.stderr.write( \\\"warning: not a file: %s\\\\n\\\" % path )\\n return None, None\\n\\n if os.path.commonprefix( [ src_path, original_path ] ) != original_path:\\n if noUpdate:\\n panic( \\\"file is not in 'original' directory: %s\\\\n\\\" % path );\\n sys.stderr.write( \\\"warning: file not in 'original' ignored: %s\\\\n\\\" % path )\\n return None, None\\n\\n src_path = src_path[len(original_path):]\\n if len(src_path) > 0 and src_path[0] == '/':\\n src_path = src_path[1:]\\n\\n if len(src_path) == 0:\\n panic( \\\"oops, internal error, can't extract correct relative path\\\\n\\\" )\\n\\n # convert into destination path, extracting architecture if needed\\n # and the corresponding list of known static functions\\n #\\n arch = None\\n re_asm_arch = re.compile( r\\\"asm-([\\\\w\\\\d_\\\\+\\\\.\\\\-]+)(/.*)\\\" )\\n m = re_asm_arch.match(src_path)\\n statics = kernel_known_generic_statics\\n if m and m.group(1) != 'generic':\\n dst_path = \\\"arch-%s/asm/%s\\\" % m.groups()\\n arch = m.group(1)\\n statics = statics.union( kernel_known_statics.get( arch, set() ) )\\n else:\\n dst_path = \\\"common/\\\" + src_path\\n\\n dst_path = os.path.normpath( kernel_cleaned_path + \\\"/\\\" + dst_path )\\n\\n # now, let's parse the file\\n #\\n blocks = cpp.BlockParser().parseFile(path)\\n if not blocks:\\n sys.stderr.write( \\\"error: can't parse '%s'\\\" % path )\\n sys.exit(1)\\n\\n macros = kernel_known_macros.copy()\\n if arch and arch in kernel_default_arch_macros:\\n macros.update(kernel_default_arch_macros[arch])\\n\\n if arch and arch in kernel_arch_token_replacements:\\n blocks.replaceTokens( kernel_arch_token_replacements[arch] )\\n\\n blocks.optimizeMacros( macros )\\n blocks.optimizeIf01()\\n blocks.removeVarsAndFuncs( statics )\\n blocks.replaceTokens( kernel_token_replacements )\\n blocks.removeComments()\\n blocks.removeMacroDefines( kernel_ignored_macros )\\n blocks.removeWhiteSpace()\\n\\n out = StringOutput()\\n out.write( kernel_disclaimer )\\n blocks.writeWithWarning(out, kernel_warning, 4)\\n return dst_path, out.get()\",\n \"def _update_template(template_path):\\n template_definition = template_path\\n\\n # template output directory is output/templates, so need to create that location before pulling out the templates\\n template_location = template_utilities.get_template_directory()\\n\\n # Install the template and get the path to the template directory for updating the configuration file.\\n templates_path = template_utilities.install_template(template_location, template_definition)\\n\\n if templates_path:\\n # Now need to find the templates definition of that zip file and locate it in the file system so that it can be\\n settings = get_configuration()\\n\\n # Override the configuration details with the new template path. This should probably be handled by the\\n # publishing plugin, but for now this will work\\n settings.publishing.templates = str(templates_path.relative_to(get_configuration_root()))\\n configuration_file_path = get_configuration_root() / 'config.yaml'\\n\\n dump_configuration(configuration_file_path, settings)\",\n \"def platformify(self):\\n actions = ['rsync -aP {0} {1}'.format(\\n os.path.join(TEMPLATEDIR, self.name, 'files/'), self.builddir\\n )]\\n patches = glob(os.path.join(TEMPLATEDIR, self.name, \\\"*.patch\\\"))\\n for patch in patches:\\n actions.append('cd {0}/web && patch -p1 < {1}'.format(\\n self.builddir, patch)\\n )\\n\\n # In some cases the package updater needs to be run after we've platform-ified the\\n # template, so run it a second time. Worst case it's a bit slower to build but doesn't\\n # hurt anything.\\n actions.extend(self.package_update_actions())\\n\\n return actions\",\n \"def imgOutputPath(self, newpath):\\n newimg = self.imagePath().replace(self.home, newpath)\\n return newimg\",\n \"def template_replace(template, replace_map, result):\\n # Read content of source file.\\n with open(template) as fp:\\n lines = fp.readlines()\\n # Replace placeholders.\\n for key, value in list(replace_map.items()):\\n for i, line in enumerate(lines):\\n # Ignore VHDL comments\\n if not line.strip().startswith('--'):\\n lines[i] = line.replace(key, value)\\n # Write content to destination file.\\n with open(result, 'w') as fp:\\n fp.write(''.join(lines))\",\n \"def _replace_scopes(value):\\n\\n value = value.replace('linux-*', 'linux')\\n value = value.replace('macx', 'macos')\\n value = value.replace('win32', 'win')\\n\\n return value\",\n \"def _escapePaths(self, paths):\\n cmd = \\\"\\\"\\n for p in paths:\\n if ' ' in p:\\n cmd += ' \\\"{0}\\\"'.format(p)\\n else:\\n cmd += ' ' + p\\n return cmd\",\n \"def test_build_dirhtml_from_template(temp_with_override, cli):\\n # Create the book from the template\\n book = temp_with_override / \\\"new_book\\\"\\n _ = cli.invoke(commands.create, book.as_posix())\\n build_result = cli.invoke(\\n commands.build, [book.as_posix(), \\\"-n\\\", \\\"-W\\\", \\\"--builder\\\", \\\"dirhtml\\\"]\\n )\\n assert build_result.exit_code == 0, build_result.output\\n html = book.joinpath(\\\"_build\\\", \\\"dirhtml\\\")\\n assert html.joinpath(\\\"index.html\\\").exists()\\n assert html.joinpath(\\\"intro\\\", \\\"index.html\\\").exists()\",\n \"def systemPathToFileUrl(self, path):\\n from unohelper import systemPathToFileUrl\\n return systemPathToFileUrl(path)\",\n \"def marker_replace_template(in_file, out_file, old, new):\\n replace_file = open(in_file, \\\"r\\\")\\n template_content = replace_file.read()\\n result_content = template_content.replace(old, new)\\n replace_file = open(out_file, \\\"w\\\")\\n replace_file.write(result_content)\\n replace_file.close()\",\n \"def normalizePath(path):\\n\\tfrom os.path import normpath, sep\\n\\tresult = normpath(path)\\n\\tresult = result.replace(\\\"/\\\",sep)\\n\\tresult = result.replace(\\\"\\\\\\\\\\\",sep)\\n\\treturn adaptPath(result)\",\n \"def _replaceFind(self, match):\\r\\n path = self._loader.findPkgPath(match.group('pkg'))\\r\\n return '\\\"{0}\\\"'.format(path) if ' ' in path else path\",\n \"def path2str(a,b,path):\\n (s1,s2) = zip(*path)\\n line1 = \\\" \\\"+\\\"\\\".join([get_char(s1,a,x) for x in range(1,len(s1))])\\n line2 = \\\" \\\"+\\\"\\\".join([get_char(s2,b,x) for x in range(1,len(s2))])\\n return \\\"%s\\\\n%s\\\"%(line1,line2)\",\n \"def processed_texture_path(path):\\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('png', 'webp')\",\n \"def presentation(self, value):\\r\\n realpath = os.path.expanduser(value)\\r\\n if self.auto_create:\\r\\n if not os.path.exists(realpath):\\r\\n os.makedirs(realpath)\\r\\n return realpath\",\n \"def _replaceFind(self, match):\\n path = self._loader.findPkgPath(match.group('pkg'))\\n return '\\\"{0}\\\"'.format(path) if ' ' in path else path\",\n \"def _format_environment_value(value):\\n value = str(value)\\n if platform.system() == \\\"Windows\\\":\\n # Split on semicolons first\\n components = value.split(os.pathsep)\\n\\n # On each component, replace anything that looks like\\n # a drive letter with a unix-like drive path.\\n components = [re.sub(r\\\"^([A-Za-z]):\\\\\\\\\\\",\\n r\\\"\\\\\\\\\\\\1\\\\\\\\\\\",\\n c) for c in components]\\n\\n return \\\":\\\".join(components).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n\\n return value\",\n \"def fix_filepaths(df):\\n\\n if platform in [\\\"linux\\\", \\\"linux2\\\", \\\"darwin\\\"]:\\n pass\\n else:\\n raise NotImplementedError(\\n \\\"OSes other than Linux and Mac are currently not supported.\\\"\\n )\\n return df\",\n \"def _cleanup_path(path):\\n return string.join(filter(None, string.split(path, '/')), '/')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6149726","0.5994707","0.5957013","0.58530486","0.56230843","0.55114543","0.54932165","0.5389428","0.5368151","0.5365089","0.52960074","0.52290237","0.521562","0.5209126","0.5170885","0.51437145","0.51309836","0.5099991","0.5087957","0.50286967","0.49951643","0.4980025","0.49794263","0.49688816","0.49685395","0.49602187","0.4958042","0.4958042","0.49425352","0.49120754","0.49094373","0.4906701","0.4906226","0.49014568","0.4899516","0.4882641","0.4858793","0.48537084","0.4849555","0.482456","0.48191586","0.4809526","0.47969934","0.4788488","0.47816798","0.4766563","0.47577715","0.47560528","0.47538066","0.47500166","0.47412416","0.47374433","0.47206178","0.4707275","0.46775648","0.4669508","0.4666367","0.4666367","0.46575546","0.46557054","0.4651073","0.46498606","0.4642516","0.46405298","0.4634606","0.46321782","0.46258807","0.46213666","0.46167472","0.46107695","0.45875156","0.45855373","0.4584231","0.45779908","0.45777705","0.45737946","0.45721185","0.45672795","0.4562995","0.45589828","0.45581216","0.45561567","0.45558572","0.45469478","0.45462373","0.4545492","0.45377246","0.45353025","0.45352304","0.45293057","0.45288578","0.4519683","0.45193025","0.45167398","0.45048788","0.44963202","0.44848952","0.44839317","0.448294","0.44738102"],"string":"[\n \"0.6149726\",\n \"0.5994707\",\n \"0.5957013\",\n \"0.58530486\",\n \"0.56230843\",\n \"0.55114543\",\n \"0.54932165\",\n \"0.5389428\",\n \"0.5368151\",\n \"0.5365089\",\n \"0.52960074\",\n \"0.52290237\",\n \"0.521562\",\n \"0.5209126\",\n \"0.5170885\",\n \"0.51437145\",\n \"0.51309836\",\n \"0.5099991\",\n \"0.5087957\",\n \"0.50286967\",\n \"0.49951643\",\n \"0.4980025\",\n \"0.49794263\",\n \"0.49688816\",\n \"0.49685395\",\n \"0.49602187\",\n \"0.4958042\",\n \"0.4958042\",\n \"0.49425352\",\n \"0.49120754\",\n \"0.49094373\",\n \"0.4906701\",\n \"0.4906226\",\n \"0.49014568\",\n \"0.4899516\",\n \"0.4882641\",\n \"0.4858793\",\n \"0.48537084\",\n \"0.4849555\",\n \"0.482456\",\n \"0.48191586\",\n \"0.4809526\",\n \"0.47969934\",\n \"0.4788488\",\n \"0.47816798\",\n \"0.4766563\",\n \"0.47577715\",\n \"0.47560528\",\n \"0.47538066\",\n \"0.47500166\",\n \"0.47412416\",\n \"0.47374433\",\n \"0.47206178\",\n \"0.4707275\",\n \"0.46775648\",\n \"0.4669508\",\n \"0.4666367\",\n \"0.4666367\",\n \"0.46575546\",\n \"0.46557054\",\n \"0.4651073\",\n \"0.46498606\",\n \"0.4642516\",\n \"0.46405298\",\n \"0.4634606\",\n \"0.46321782\",\n \"0.46258807\",\n \"0.46213666\",\n \"0.46167472\",\n \"0.46107695\",\n \"0.45875156\",\n \"0.45855373\",\n \"0.4584231\",\n \"0.45779908\",\n \"0.45777705\",\n \"0.45737946\",\n \"0.45721185\",\n \"0.45672795\",\n \"0.4562995\",\n \"0.45589828\",\n \"0.45581216\",\n \"0.45561567\",\n \"0.45558572\",\n \"0.45469478\",\n \"0.45462373\",\n \"0.4545492\",\n \"0.45377246\",\n \"0.45353025\",\n \"0.45352304\",\n \"0.45293057\",\n \"0.45288578\",\n \"0.4519683\",\n \"0.45193025\",\n \"0.45167398\",\n \"0.45048788\",\n \"0.44963202\",\n \"0.44848952\",\n \"0.44839317\",\n \"0.448294\",\n \"0.44738102\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5748834"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":77080,"cells":{"query":{"kind":"string","value":"Convert a normal path back to its template representation."},"document":{"kind":"string","value":"def get_template_from_path(path: str) -> str:\r\n path = path.replace(\"\\\\\", \"/\")\r\n return path"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:\r\n path_template = get_template_from_path(path)\r\n path = get_path_from_template(path_template, path_type)\r\n return path","def convert(kls, path, configuration=None, converters=None, ignore_converters=None, joined=None):\n path_type = type(path)\n if path_type is Path:\n return path\n else:\n joined = dot_joiner(path, item_type=path_type)\n return Path(path, configuration, converters, ignore_converters, joined=joined)","def normalizePath(path):\n\tfrom os.path import normpath, sep\n\tresult = normpath(path)\n\tresult = result.replace(\"/\",sep)\n\tresult = result.replace(\"\\\\\",sep)\n\treturn adaptPath(result)","def osnorm(self):\n import os\n if os.sep=='/' and \"\\\\\" in str(self):\n return Path(os.path.normpath(str(self).replace('\\\\','/' )))\n elif os.sep=='\\\\' and \"/\" in str(self):\n return Path(os.path.normpath(str(self).replace('/','\\\\' )))\n else:\n return self.norm()","def normalizePath(path):\n if path == None or len(path) == 0 or path == '/':\n return '/'\n buff = '/' + path if path[0] != '/' else path\n return buff.replace('//', '/')","def unglom(\r\n d: T_StrAnyMapping, path: str, value: typing.Any\r\n) -> T_StrAnyMapping:\r\n try:\r\n return glom.assign(d, path, value)\r\n except KeyError:\r\n parent, child = path.rsplit(\".\", 1)\r\n return unglom(d, parent, {child: value})","def path_convert(self):\n pub_path = Exp_msg()\n for i in self.path:\n epoint = Cordi()\n (epoint.x, epoint.y) = i\n pub_path.bliss.append(epoint)\n return(pub_path)","def convert_to_path(arg: Any) -> Path:\n return Path(arg)","def _purepath_to_str(\n self, path: Union[Path, PurePath, str]\n ) -> Union[Path, PurePath, str]:\n if isinstance(path, PurePath):\n path = str(path)\n return path","def prepare_path(self,path):\n debug.info(4,\"Set path: \" + str(path))\n\n # This is marked for debug\n path.set_path()\n\n # For debugging... if the path failed to route.\n if False or path==None:\n self.write_debug_gds()\n\n # First, simplify the path for\n #debug.info(1,str(self.path)) \n contracted_path = self.contract_path(path)\n debug.info(3,\"Contracted path: \" + str(contracted_path))\n \n return contracted_path","def path_converter(text_path):\n\n out = ''\n out_list = []\n\n for i, t in enumerate(text_path):\n \n if t == ' ':\n out_list.append('\\\\ ')\n elif t == \"(\" :\n out_list.append('\\(')\n elif t == \")\" :\n out_list.append('\\)') \n else:\n out_list.append(t)\n \n return out.join(out_list)","def clean_path(path):\n return resolved_path(path)","def py38_translate_path(self, path):\n # abandon query parameters\n path = path.split('?', 1)[0]\n path = path.split('#', 1)[0]\n # Don't forget explicit trailing slash when normalizing. Issue17324\n trailing_slash = path.rstrip().endswith('/')\n try:\n path = urllib.parse.unquote(path, errors='surrogatepass')\n except UnicodeDecodeError:\n path = urllib.parse.unquote(path)\n path = posixpath.normpath(path)\n words = path.split('/')\n words = filter(None, words)\n # py38: self.directory\n # py36: os.getcwd()\n path = STATIC_DIR\n for word in words:\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\n # Ignore components that are not a simple file/directory name\n continue\n path = os.path.join(path, word)\n if trailing_slash:\n path += '/'\n return path","def canonicalPath(path_or_object):","def normalizeNativePath(path: unicode) -> unicode:\n ...","def sanatize_path(self, path):\n # Remove extra whitespace\n path = path.strip()\n\n # Remove slash from end of path\n path = path.rstrip(os.sep)\n\n return path","def find_and_normalize(value):\n if isinstance(value, dict):\n # if value is a dictionary, try to normalize the values\n return {k: normalize_dict_value(v) for k, v in value.items()}\n return normalize_path(value)","def path_to_str(path):\n if hasattr(path, '__fspath__'):\n path = as_str_any(path.__fspath__())\n return path","def save_path(self):\n return self.template.manager.render_template_txt(self.path, self.template)","def new_path(path):\n return Path(path[0], path[1], path[2])","def translate_path(self, path):\n # abandon query parameters\n path = path.split('?', 1)[0]\n path = path.split('#', 1)[0]\n # Don't forget explicit trailing slash when normalizing. Issue17324\n trailing_slash = path.rstrip().endswith('/')\n try:\n path = urllib.parse.unquote(path, errors='surrogatepass')\n except UnicodeDecodeError:\n path = urllib.parse.unquote(path)\n path = posixpath.normpath(path)\n words = path.split('/')\n words = filter(None, words)\n path = os.getcwd()\n for word in words:\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\n # Ignore components that are not a simple file/directory name\n continue\n path = os.path.join(path, word)\n if trailing_slash:\n path += '/'\n return path","def translate_path(self, path):\n # abandon query parameters\n path = path.split('?', 1)[0]\n path = path.split('#', 1)[0]\n # Don't forget explicit trailing slash when normalizing. Issue17324\n trailing_slash = path.rstrip().endswith('/')\n try:\n path = urllib.parse.unquote(path, errors='surrogatepass')\n except UnicodeDecodeError:\n path = urllib.parse.unquote(path)\n path = normpath(path)\n words = path.split('/')\n words = filter(None, words)\n path = self.serve_path # use own path here (no cwd)\n for word in words:\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\n # Ignore components that are not a simple file/directory name\n continue\n path = os.path.join(path, word)\n if trailing_slash:\n path += '/'\n return path","def convert_path_units(self, path_to_conv):\n\n converted_path = copy.deepcopy(path_to_conv) # TODO: figure out if this is the right way to do this- I have not yet understood how data should be handled here\n\n for i, coord in enumerate(converted_path):\n converted_path[i][0] = coord[0] / self.units_in_meter\n converted_path[i][1] = coord[1] / self.units_in_meter\n converted_path[i][2] = coord[2] / self.units_in_meter\n\n return converted_path","def _normalize_path(path):\n\n i = 0\n for c in path:\n if c != \"/\":\n break\n i = i + 1\n\n if i:\n return path[(i - 1) :]\n\n return path","def path_deserializer(obj: str, cls: type = PurePath, **_: Any) -> PurePath:\n path: PurePath = cls(obj)\n return path","def resolve_path(self, path):\n if path:\n if path[0] == '/':\n #zope objects case\n try: return self.unrestrictedTraverse(path)\n except: pass\n else:\n #aliss (python) objects case\n try: return self.get_aliss_object(path)\n except: pass\n #case of no path\n pass","def path_to_operation(path, verb):\n # type: (unicode, unicode) -> unicode\n character_map = {\n ord(\"{\"): None,\n ord(\"}\"): None,\n ord(\"_\"): u\"/\"\n }\n if path == u\"/\":\n operation = ROOT_OPERATION\n else:\n sanitised = path.translate(character_map)\n operation = u\"_\".join(p for p in sanitised.split(\"/\"))\n\n return \"{}_{}\".format(verb, operation)","def Transform(*args, **kwargs):\n return _gdi_.GraphicsPath_Transform(*args, **kwargs)","def normalise(self) -> \"Route\":\n pass","def translate(self, uri_path):\n _parts = [self.root] + uri_path.lstrip('/').split('/')\n fs_path = os.sep.join(_parts)\n fs_path = os.path.realpath(fs_path)\n return fs_path","def slashesToPipes(self):\n self.logger.debug(\"Convert slashes to pipes\")\n\n if self.getAttr(attr='cacheGeomPath') != \"\":\n \n # cleanup the attribute value\n value = self.getAttr(attr='cacheGeomPath').lstrip('[\"').rstrip('\"]').replace('/', '|')\n self.setAttr(attr='cacheGeomPath', value=value)","def simplifyPath(self, path):\n assert isinstance(path, str)\n stack = ['/']\n newDir = ''\n for char in path:\n if char in ['.', '/']:\n instructionFlag = True\n else:\n\n newDir += stack.pop()","def reconstruct_object(path):\n if len(path) == 0:\n return '%s'\n else:\n # The indexed query on `path` below is the means by which we recurse\n # Every iteration pushes it closer to a length of 0 and, thus, bottoming out\n return '{{%s: {recons}}}'.format(recons=reconstruct_object(path[1:]))","def convert_path(path):\n lower_path = path.replace('\\\\', '/')\n if lower_path[1] == \":\":\n path_lower = lower_path.replace(\":\", \"\")\n path_server = \"/\" + path_lower\n else:\n path_server = lower_path[1:]\n\n return path_server","def mangle_path(path):\n # Remove assigns\n path = servers.get_file_server().manglepath( str(path) )\n # Remove parent special directories\n path = os.path.abspath( path )\n # Convert path to Nebula format (slashes instead of backslashes)\n path = servers.get_file_server().manglepath( str(path) )\n # Convert drive letter to lowercase\n if len(path) > 1:\n if path[1] == ':':\n path = path[:1].lower() + path[1:]\n return path","def userPath2Path(arg, frame):\n arg = ingest(arg)\n if isabs(arg):\n return Path(arg)\n else:\n return Path(arg, frame)","def translate_path(self, path):\n # abandon query parameters\n path = path.split('?', 1)[0]\n path = path.split('#', 1)[0]\n path = posixpath.normpath(urllib.unquote(path))\n words = path.split('/')\n words = filter(None, words)\n try:\n path = self.rootpath\n except AttributeError:\n path = os.getcwd()\n for word in words:\n drive, word = os.path.splitdrive(word)\n head, word = os.path.split(word)\n if word in (os.curdir, os.pardir):\n continue\n path = os.path.join(path, word)\n return path","def flatten_path(path):\n return path.split(\"/\")[-1]","def preprocess(self, newpath):\n return dict()","def strip_path(self):\n return self.path.replace('/', '')","def standardize_path(path):\n path.rstrip('/')\n if not path.startswith('.*'):\n path = '/' + path\n path = re.compile('/+').sub('/', path)\n return path","def get_path_from_template(path_template: str, path_type: PathType = PathType.AUTO) -> str:\r\n # automatically select path type depending on running OS\r\n if path_type == PathType.AUTO:\r\n if platform.system() == \"Windows\":\r\n path_type = PathType.WINDOWS\r\n elif platform.system() == \"Linux\":\r\n path_type = PathType.LINUX\r\n else:\r\n raise RuntimeError(\"Unknown platform\")\r\n\r\n path_template = path_template.replace(\"<USERNAME>\", get_user_name())\r\n\r\n # return correctly formatted path\r\n if path_type == PathType.WINDOWS:\r\n return str(pathlib.PureWindowsPath(path_template))\r\n elif path_type == PathType.LINUX:\r\n return str(pathlib.PurePosixPath(path_template))\r\n else:\r\n raise RuntimeError(\"Unknown platform\")","def path_serializer(obj: PurePath, **_: Any) -> str:\n return obj.as_posix()","def reconstructPath(came_from, current):\n path = [current]\n while current in came_from:\n current = came_from[current]\n path.append(current)\n print(f\"path: {path}\")\n return path","def format_path(path):\n if not path:\n return path\n\n path = re.sub(r'/+', '/', path)\n\n if path == '/':\n return (u\"\" if isinstance(path, unicode) else \"\")\n else:\n return '/' + path.strip('/')","def reverse(self):\n cls = self.__class__\n rev_interfaces = tuple(reversed([i.reverse() for i in self.interfaces]))\n rev_materials = tuple(reversed(self.materials))\n rev_modes = tuple(reversed(self.modes))\n rev_path = cls(rev_interfaces, rev_materials, rev_modes, name=self.name)\n if self.rays is not None:\n rev_path.rays = self.rays.reverse()\n return rev_path","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, six.integer_types):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, six.integer_types):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, six.integer_types):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def mutate_transposition(path, board):\n path_transposed = swap(path)\n x, y = get_start(board)\n while not is_path_correct(x, y, path_transposed, board):\n path_transposed = swap(path)\n return remove_constant_points(path_transposed)","def _flip_path(path, path_lens):\n bs = path.size(0)\n max_path_len = path.size(1)\n rotate = (torch.arange(max_path_len) + path_lens[:, None]) % max_path_len\n return torch.flip(path[torch.arange(bs, dtype=torch.int64)[:, None], rotate], dims=[1])","def translate_path(self, path):\n # abandon query parameters\n path = path.split('?', 1)[0]\n path = path.split('#', 1)[0]\n # Don't forget explicit trailing slash when normalizing. Issue17324\n trailing_slash = path.rstrip().endswith('/')\n path = posixpath.normpath(urllib.unquote(path))\n words = path.split('/')\n words = filter(None, words)\n path = self.working_dir\n for word in words:\n _drive, word = os.path.splitdrive(word)\n _head, word = os.path.split(word)\n if word in (os.curdir, os.pardir):\n continue\n path = os.path.join(path, word)\n if trailing_slash:\n path += '/'\n return path","def resolved_path(path):\n path = os.path.abspath(path)\n elements = path_elements(path)\n result = \"\"\n for element in elements:\n segment = element\n segment_path = os.path.join(result, segment)\n if os.path.islink(segment_path):\n segment = os.readlink(segment_path)\n result = os.path.join(result, segment)\n result = os.path.normpath(result)\n return result","def convertGridPathToReal(pathInGrid, sx, sy, gx, gy, grid_reso = 0.1):\n\n pathInReal = (pathInGrid * grid_reso + grid_reso / 2)\n stepNum = pathInReal.shape[1]\n # Replace head and tail\n pathInReal[:, 0] = [sx, sy]\n pathInReal[:, 0] = [sx, sy]\n pathInReal[:, stepNum - 1] = [gx, gy]\n pathInReal[:, stepNum - 1] = [gx, gy]\n\n return pathInReal","def normalize_path(path, config):\n\n for alias, link in config[\"Aliases\"].items():\n if path.startswith(alias):\n return path.replace(alias, link, 1)\n\n return path","def render_path(path_to_item):\n result = \"\"\n for pth in path_to_item:\n if isinstance(pth, int):\n result += \"[{0}]\".format(pth)\n else:\n result += \"['{0}']\".format(pth)\n return result","def __fspath__(self):\n return str(self)","def unformat_file(path: str, out_dir: str):\n\n data_dir = get_data_dir()\n path = Path(path)\n out_dir = Path(out_dir)\n if not path.exists() and out_dir.exists() and out_dir.is_dir():\n return\n\n if path.is_dir():\n path.mkdir(exist_ok=True)\n for filename in path.iterdir():\n unformat_file(filename, str(out_dir))\n\n else:\n dataobj = frontmatter.load(str(path))\n\n try:\n # get relative path of object in `data` dir\n datapath = path.parent.resolve().relative_to(data_dir)\n except ValueError:\n datapath = Path()\n\n # create subdir if doesn't exist\n (out_dir / datapath).mkdir(exist_ok=True)\n new_path = out_dir / datapath / f\"{dataobj.metadata['title']}.md\"\n with new_path.open(\"w\") as f:\n f.write(dataobj.content)\n\n current_app.logger.info(\n f\"Unformatted and moved {str(path)} to {str(new_path.resolve())}\"\n )\n path.unlink()","def normalize(path):\n return os.path.normcase(os.path.realpath(path))","def transformed(self, T):\n new = Polygon2()\n new.path = self.path.transformed(Affine2D(T.A))\n return new","def _normalized_path(path):\n return os.path.abspath(os.path.expanduser(path))","def determine_template_by_path(path):\n path = path.lstrip('/')\n\n path_chunks = re.split('\\/', path)\n if len(path_chunks) <= 1:\n return path\n else:\n \"\"\"\n For now be ignorant and just return the\n first entry of the list as the possible template\n name, so in fact we only have a 1 level deep structure\n \"\"\"\n return '_%s.html' % path_chunks[0]","def Sourceify(path):\n return path","def _get_path(self, path, resource_path):\n for item in self.path_replace:\n if len(item) == 2:\n src, dest = item\n else:\n assert len(item) == 3\n src = item[0]\n dest = item[2 if resource_path else 1]\n if path.endswith(src):\n path = replace_end(path, src, dest)\n return path","def translate_path(self, path):\n\t\t# abandon query parameters\n\t\tpath = path.split('?',1)[0]\n\t\tpath = path.split('#',1)[0]\n\t\tpath = posixpath.normpath(urllib.unquote(path))\n\t\twords = path.split('/')\n\t\twords = filter(None, words)\n\t\tpath = os.getcwd() + \"/\" + self.wwwroot\n\t\tfor word in words:\n\t\t\tdrive, word = os.path.splitdrive(word)\n\t\t\thead, word = os.path.split(word)\n\t\t\tif word in (os.curdir, os.pardir): continue\n\t\t\tpath = os.path.join(path, word)\n\t\treturn path","def convertString(path):\n if (\"win\" in sys.platform):\n return path.replace(\"/\",\"\\\\\")\n elif (\"linux\" in sys.platform):\n return path.replace(\"\\\\\",\"/\")","def fixpaths(d):\n if isinstance(d, dict):\n if \"path\" in d:\n if \":\" not in d[\"path\"]:\n local_path = os.path.normpath(\n os.path.join(os.getcwd(), basedir, d[\"path\"])\n )\n d[\"location\"] = pathname2url(local_path)\n else:\n d[\"location\"] = d[\"path\"]\n del d[\"path\"]","def pathify(value):\n \n value = stringify(value)\n return value.replace('$ ', '$$ ').replace(' ', '$ ').replace(':', '$:')","def _get_as_path(self):\n return self.__as_path","def conversion_binaire(self, binary_path):\r\n if self.node_leaf():\r\n return self.name + ':' + str(binary_path) + '\\n'\r\n else:\r\n path = self.zero_son.conversion_binaire(str(binary_path) + '0')\r\n path += self.one_son.conversion_binaire(str(binary_path) + '1')\r\n #path determined by recursion\r\n return path","def clean_path(path: str) -> str:\n previous_path = \"\"\n next_path = path\n while next_path != previous_path:\n previous_path = next_path\n next_path = copy_annotations(path, next_path.replace(\"//\", \"/\"))\n while next_path.endswith(\"/\"):\n next_path = next_path[:-1]\n return next_path","def translate_path(path, workdir):\n # abandon query parameters\n path = path.split('?', 1)[0]\n path = path.split('#', 1)[0]\n path = urllib.unquote(path)\n parts = path.split('/')\n return os.path.join(workdir, *parts)","def convert(self, pt):\n # default transformation is to do nothing\n return pt","def convert(\n path_in, path_out, pathway_iri, wp_id, pathway_version, scale=100, theme=\"plain\"\n):\n if not path.exists(path_in):\n raise Exception(f\"Missing file '{path_in}'\")\n\n if path.exists(path_out):\n print(f\"File {path_out} already exists. Skipping.\")\n return True\n\n dir_in = path.dirname(path_in)\n base_in = path.basename(path_in)\n # example base_in: 'WP4542.gpml'\n [stub_in, ext_in_with_dot] = path.splitext(base_in)\n # gettting rid of the leading dot, e.g., '.gpml' to 'gpml'\n ext_in = LEADING_DOT_RE.sub(\"\", ext_in_with_dot)\n\n if ext_in != \"gpml\":\n # TODO: how about *.gpml.xml?\n raise Exception(f\"Currently only accepting *.gpml for path_in\")\n gpml_f = path_in\n\n dir_out = path.dirname(path_out)\n # example base_out: 'WP4542.svg'\n base_out = path.basename(path_out)\n [stub_out, ext_out_with_dot] = path.splitext(base_out)\n # getting rid of the leading dot, e.g., '.svg' to 'svg'\n ext_out = LEADING_DOT_RE.sub(\"\", ext_out_with_dot)\n\n tree = ET.parse(gpml_f, parser=parser)\n root = tree.getroot()\n\n if root is None:\n raise Exception(\"no root element\")\n if root.tag is None:\n raise Exception(\"no root tag\")\n\n gpml_version = re.sub(r\"{http://pathvisio.org/GPML/(\\w+)}Pathway\", r\"\\1\", root.tag)\n if ext_out != \"gpml\" and gpml_version != LATEST_GPML_VERSION:\n old_f = f\"{dir_in}/{stub_in}.{gpml_version}.gpml\"\n rename(gpml_f, old_f)\n convert(old_f, gpml_f, pathway_iri, wp_id, pathway_version, scale)\n\n # trying to get wd ids via sparql via pywikibot\n site = pywikibot.Site(\"wikidata\", \"wikidata\")\n repo = site.data_repository() # this is a DataSite object\n wd_sparql = sparql.SparqlQuery(\n endpoint=\"https://query.wikidata.org/sparql\", repo=repo\n )\n # (self, endpoint=None, entity_url=None, repo=None, 2 max_retries=None, retry_wait=None)\n\n if ext_out in [\"gpml\", \"owl\", \"pdf\", \"pwf\", \"txt\"]:\n subprocess.run(shlex.split(f\"pathvisio convert {path_in} {path_out}\"))\n elif ext_out == \"png\":\n # TODO: look at using --scale as an option (instead of an argument),\n # for both pathvisio and gpmlconverter.\n # TODO: move the setting of a default value for scale into\n # pathvisio instead of here.\n subprocess.run(shlex.split(f\"pathvisio convert {path_in} {path_out} {scale}\"))\n # Use interlacing? See https://github.com/PathVisio/pathvisio/issues/78\n # It's probably not worthwhile. If we did it, we would need to install\n # imagemagick and then run this:\n # mv \"$path_out\" \"$path_out.noninterlaced.png\"\n # convert -interlace PNG \"$path_out.noninterlaced.png\" \"$path_out\"\n elif ext_out in [\"json\", \"jsonld\"]:\n gpml2json(path_in, path_out, pathway_iri, wp_id, pathway_version, wd_sparql)\n elif ext_out in [\"svg\", \"pvjssvg\"]:\n #############################\n # SVG\n #############################\n\n json_f = f\"{dir_out}/{stub_in}.json\"\n if not path.isfile(json_f):\n gpml2json(path_in, json_f, pathway_iri, wp_id, pathway_version, wd_sparql)\n\n json2svg(json_f, path_out, pathway_iri, wp_id, pathway_version, theme)\n else:\n raise Exception(f\"Invalid output extension: '{ext_out}'\")","def resolve_template(template, item):\n root_dir = config['link_root_dir'].as_filename()\n placeholders = re.findall(r'(\\%\\{?\\w+\\}?)', template)\n path = template\n for p in placeholders:\n path = path.replace(p, item[p.strip('%{}')])\n return os.path.join(root_dir, path)","def reformat(self):\n\t\told_path = os.path.join( self.path, self.init_str )\n\t\tnew_path = os.path.join( self.path, self.reorder() )\n\t\tos.rename(old_path,new_path)","def path_to_message(self, mapdata, path):\n rospy.loginfo(\"Returning a Path message\")\n pathObj = Path()\n pathObj.header.frame_id = '/map'\n pathObj.poses = PathPlanner.path_to_poses(mapdata, path)\n return pathObj","def normalize_path(path: str) -> str:\n if '@' in path:\n split_path = path.split(':')\n split_path[1] = os.path.normpath(split_path[1])\n\n return ':'.join(split_path)\n\n return os.path.normpath(path)","def translate_path(self, path):\n # abandon query parameters\n path = path.split('?',1)[0]\n path = path.split('#',1)[0]\n # Don't forget explicit trailing slash when normalizing. Issue17324\n trailing_slash = path.rstrip().endswith('/')\n path = posixpath.normpath(urllib.unquote(path))\n words = path.split('/')\n words = filter(None, words)\n path = str(port_number)\n for word in words:\n drive, word = os.path.splitdrive(word)\n head, word = os.path.split(word)\n if word in (os.curdir, os.pardir): continue\n path = os.path.join(path, word)\n if trailing_slash:\n path += '/'\n return path","def from_path(path):\n path = to_pagina(path)\n return path[6:]","def stringyfy(path):\n try:\n # Pathlib support\n path = path.__fspath__()\n except AttributeError:\n pass\n if hasattr(path, 'name'): # passed in a file\n path = path.name\n if isinstance(path, str):\n return path\n raise ValueError(f'Cannot convert {path} to a path')","def path_filename_representation(path):\n # Strip leading / and replace / with .\n return re.sub(r\"^/(.*)$\", r\"\\1\", path).replace(\"/\", \".\")","def parse_path(path): # -> Path | UnparsedPath | ParsedPath:\n ...","def reconstruct_path(current):\r\n path = [current.coord]\r\n parent = current.parent\r\n while parent:\r\n path = [parent.coord] + path\r\n parent = parent.parent\r\n path = path[1:]\r\n return path","def convert_unc(host, path):\n return ''.join(['\\\\\\\\', host, '\\\\', path.replace(':', '$')])","def regenerate_tree(self, newpos):\n self.path = self.tree[newpos][2]\n self.tree = self.get_tree()\n self.pos = self.get_curpos()","def path(self, toNative=True):\n return self.text(toNative=toNative)","def directory_to_individual_conversion_method(self, path):\n\n\t\treturn Individual(calculation_set=VaspRelaxation(path=path, input_dictionary=copy.deepcopy(self.calculation_set_input_dictionary)))","def Reconstruct_Path(self, dir_map):\n path = ''\n first_time = True\n x = self.xFinish\n y = self.yFinish\n while not (x == self.xStart and y == self.yStart):\n j = dir_map[y][x]\n c = str((self.num_directions-j-1) % self.num_directions)\n if first_time:\n path=c+path\n first_time=False\n else:\n path = c + ','+ path\n x += self.dx[j]\n y += self.dy[j]\n return path","def _create_path(root, dict_type, path):\n for sub_path in path:\n if not isinstance(root.get(sub_path, None), dict):\n root[sub_path] = dict_type()\n\n root = root[sub_path]\n\n return root","def repair_path(dict_1):\n dup_dict = dict(dict_1)\n for k,v in dup_dict.items():\n if '\\\\' in k:\n key = k.replace('\\\\', '/')\n val = v.replace('\\\\', '/')\n del dict_1[k]\n dict_1[key] = val\n return dict_1","def orig_path(self):\n return self._path[0]","def format_path (in_path):\n return os.path.realpath(os.path.expanduser(in_path))","def _process_path(path, path_trans):\n path_points = path.transformed(path_trans).interpolated(500).vertices\n deltas = (path_points[1:] - path_points[:-1]).T\n pt_offsets = np.concatenate(([0], np.hypot(*deltas).cumsum()))\n angles = np.arctan2(deltas[-1], deltas[0])\n return path_points, pt_offsets, angles","def normalize(self: T) -> T:","def shell_path_post_op(path):\n \n def fix_path(**kwargs):\n \n # extract key_name \n name = kwargs.get('name')\n if not name:\n name = kwargs.get('key_name')\n if not name:\n name = kwargs.get('obj_path')\n if not name:\n raise ValueError('no (key)name specified') \n \n if os.path.isdir(path):\n return os.path.join(path, name)\n else: # dir may not exist yet\n dname = os.path.dirname(path)\n if dname:\n try:\n os.makedirs(dname)\n except OSError, e:\n if e.errno != errno.EEXIST:\n raise\n basename = os.path.basename(path)\n if basename: # dest is a file\n return path\n else:\n return os.path.join(path, name)\n \n return fix_path","def pathmaker(first_segment, *in_path_segments, rev=False):\n\n _path = first_segment\n\n _path = os.path.join(_path, *in_path_segments)\n if rev is True or sys.platform not in ['win32', 'linux']:\n return os.path.normpath(_path)\n return os.path.normpath(_path).replace(os.path.sep, '/')","def transform_url(result):\n import re\n result = re.sub('//', '/', result)\n result = re.sub('/', '//', result, count=1)\n return encode_url_path(result)","def routeunpack(value):\n return str(value).replace(\"!\",\"/\")","def _path_to_str(var):\n if not isinstance(var, (Path, str)):\n raise ValueError(\"All path parameters must be either strings or \"\n \"pathlib.Path objects. Found type %s.\" % type(var))\n else:\n return str(var)"],"string":"[\n \"def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:\\r\\n path_template = get_template_from_path(path)\\r\\n path = get_path_from_template(path_template, path_type)\\r\\n return path\",\n \"def convert(kls, path, configuration=None, converters=None, ignore_converters=None, joined=None):\\n path_type = type(path)\\n if path_type is Path:\\n return path\\n else:\\n joined = dot_joiner(path, item_type=path_type)\\n return Path(path, configuration, converters, ignore_converters, joined=joined)\",\n \"def normalizePath(path):\\n\\tfrom os.path import normpath, sep\\n\\tresult = normpath(path)\\n\\tresult = result.replace(\\\"/\\\",sep)\\n\\tresult = result.replace(\\\"\\\\\\\\\\\",sep)\\n\\treturn adaptPath(result)\",\n \"def osnorm(self):\\n import os\\n if os.sep=='/' and \\\"\\\\\\\\\\\" in str(self):\\n return Path(os.path.normpath(str(self).replace('\\\\\\\\','/' )))\\n elif os.sep=='\\\\\\\\' and \\\"/\\\" in str(self):\\n return Path(os.path.normpath(str(self).replace('/','\\\\\\\\' )))\\n else:\\n return self.norm()\",\n \"def normalizePath(path):\\n if path == None or len(path) == 0 or path == '/':\\n return '/'\\n buff = '/' + path if path[0] != '/' else path\\n return buff.replace('//', '/')\",\n \"def unglom(\\r\\n d: T_StrAnyMapping, path: str, value: typing.Any\\r\\n) -> T_StrAnyMapping:\\r\\n try:\\r\\n return glom.assign(d, path, value)\\r\\n except KeyError:\\r\\n parent, child = path.rsplit(\\\".\\\", 1)\\r\\n return unglom(d, parent, {child: value})\",\n \"def path_convert(self):\\n pub_path = Exp_msg()\\n for i in self.path:\\n epoint = Cordi()\\n (epoint.x, epoint.y) = i\\n pub_path.bliss.append(epoint)\\n return(pub_path)\",\n \"def convert_to_path(arg: Any) -> Path:\\n return Path(arg)\",\n \"def _purepath_to_str(\\n self, path: Union[Path, PurePath, str]\\n ) -> Union[Path, PurePath, str]:\\n if isinstance(path, PurePath):\\n path = str(path)\\n return path\",\n \"def prepare_path(self,path):\\n debug.info(4,\\\"Set path: \\\" + str(path))\\n\\n # This is marked for debug\\n path.set_path()\\n\\n # For debugging... if the path failed to route.\\n if False or path==None:\\n self.write_debug_gds()\\n\\n # First, simplify the path for\\n #debug.info(1,str(self.path)) \\n contracted_path = self.contract_path(path)\\n debug.info(3,\\\"Contracted path: \\\" + str(contracted_path))\\n \\n return contracted_path\",\n \"def path_converter(text_path):\\n\\n out = ''\\n out_list = []\\n\\n for i, t in enumerate(text_path):\\n \\n if t == ' ':\\n out_list.append('\\\\\\\\ ')\\n elif t == \\\"(\\\" :\\n out_list.append('\\\\(')\\n elif t == \\\")\\\" :\\n out_list.append('\\\\)') \\n else:\\n out_list.append(t)\\n \\n return out.join(out_list)\",\n \"def clean_path(path):\\n return resolved_path(path)\",\n \"def py38_translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?', 1)[0]\\n path = path.split('#', 1)[0]\\n # Don't forget explicit trailing slash when normalizing. Issue17324\\n trailing_slash = path.rstrip().endswith('/')\\n try:\\n path = urllib.parse.unquote(path, errors='surrogatepass')\\n except UnicodeDecodeError:\\n path = urllib.parse.unquote(path)\\n path = posixpath.normpath(path)\\n words = path.split('/')\\n words = filter(None, words)\\n # py38: self.directory\\n # py36: os.getcwd()\\n path = STATIC_DIR\\n for word in words:\\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\\n # Ignore components that are not a simple file/directory name\\n continue\\n path = os.path.join(path, word)\\n if trailing_slash:\\n path += '/'\\n return path\",\n \"def canonicalPath(path_or_object):\",\n \"def normalizeNativePath(path: unicode) -> unicode:\\n ...\",\n \"def sanatize_path(self, path):\\n # Remove extra whitespace\\n path = path.strip()\\n\\n # Remove slash from end of path\\n path = path.rstrip(os.sep)\\n\\n return path\",\n \"def find_and_normalize(value):\\n if isinstance(value, dict):\\n # if value is a dictionary, try to normalize the values\\n return {k: normalize_dict_value(v) for k, v in value.items()}\\n return normalize_path(value)\",\n \"def path_to_str(path):\\n if hasattr(path, '__fspath__'):\\n path = as_str_any(path.__fspath__())\\n return path\",\n \"def save_path(self):\\n return self.template.manager.render_template_txt(self.path, self.template)\",\n \"def new_path(path):\\n return Path(path[0], path[1], path[2])\",\n \"def translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?', 1)[0]\\n path = path.split('#', 1)[0]\\n # Don't forget explicit trailing slash when normalizing. Issue17324\\n trailing_slash = path.rstrip().endswith('/')\\n try:\\n path = urllib.parse.unquote(path, errors='surrogatepass')\\n except UnicodeDecodeError:\\n path = urllib.parse.unquote(path)\\n path = posixpath.normpath(path)\\n words = path.split('/')\\n words = filter(None, words)\\n path = os.getcwd()\\n for word in words:\\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\\n # Ignore components that are not a simple file/directory name\\n continue\\n path = os.path.join(path, word)\\n if trailing_slash:\\n path += '/'\\n return path\",\n \"def translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?', 1)[0]\\n path = path.split('#', 1)[0]\\n # Don't forget explicit trailing slash when normalizing. Issue17324\\n trailing_slash = path.rstrip().endswith('/')\\n try:\\n path = urllib.parse.unquote(path, errors='surrogatepass')\\n except UnicodeDecodeError:\\n path = urllib.parse.unquote(path)\\n path = normpath(path)\\n words = path.split('/')\\n words = filter(None, words)\\n path = self.serve_path # use own path here (no cwd)\\n for word in words:\\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\\n # Ignore components that are not a simple file/directory name\\n continue\\n path = os.path.join(path, word)\\n if trailing_slash:\\n path += '/'\\n return path\",\n \"def convert_path_units(self, path_to_conv):\\n\\n converted_path = copy.deepcopy(path_to_conv) # TODO: figure out if this is the right way to do this- I have not yet understood how data should be handled here\\n\\n for i, coord in enumerate(converted_path):\\n converted_path[i][0] = coord[0] / self.units_in_meter\\n converted_path[i][1] = coord[1] / self.units_in_meter\\n converted_path[i][2] = coord[2] / self.units_in_meter\\n\\n return converted_path\",\n \"def _normalize_path(path):\\n\\n i = 0\\n for c in path:\\n if c != \\\"/\\\":\\n break\\n i = i + 1\\n\\n if i:\\n return path[(i - 1) :]\\n\\n return path\",\n \"def path_deserializer(obj: str, cls: type = PurePath, **_: Any) -> PurePath:\\n path: PurePath = cls(obj)\\n return path\",\n \"def resolve_path(self, path):\\n if path:\\n if path[0] == '/':\\n #zope objects case\\n try: return self.unrestrictedTraverse(path)\\n except: pass\\n else:\\n #aliss (python) objects case\\n try: return self.get_aliss_object(path)\\n except: pass\\n #case of no path\\n pass\",\n \"def path_to_operation(path, verb):\\n # type: (unicode, unicode) -> unicode\\n character_map = {\\n ord(\\\"{\\\"): None,\\n ord(\\\"}\\\"): None,\\n ord(\\\"_\\\"): u\\\"/\\\"\\n }\\n if path == u\\\"/\\\":\\n operation = ROOT_OPERATION\\n else:\\n sanitised = path.translate(character_map)\\n operation = u\\\"_\\\".join(p for p in sanitised.split(\\\"/\\\"))\\n\\n return \\\"{}_{}\\\".format(verb, operation)\",\n \"def Transform(*args, **kwargs):\\n return _gdi_.GraphicsPath_Transform(*args, **kwargs)\",\n \"def normalise(self) -> \\\"Route\\\":\\n pass\",\n \"def translate(self, uri_path):\\n _parts = [self.root] + uri_path.lstrip('/').split('/')\\n fs_path = os.sep.join(_parts)\\n fs_path = os.path.realpath(fs_path)\\n return fs_path\",\n \"def slashesToPipes(self):\\n self.logger.debug(\\\"Convert slashes to pipes\\\")\\n\\n if self.getAttr(attr='cacheGeomPath') != \\\"\\\":\\n \\n # cleanup the attribute value\\n value = self.getAttr(attr='cacheGeomPath').lstrip('[\\\"').rstrip('\\\"]').replace('/', '|')\\n self.setAttr(attr='cacheGeomPath', value=value)\",\n \"def simplifyPath(self, path):\\n assert isinstance(path, str)\\n stack = ['/']\\n newDir = ''\\n for char in path:\\n if char in ['.', '/']:\\n instructionFlag = True\\n else:\\n\\n newDir += stack.pop()\",\n \"def reconstruct_object(path):\\n if len(path) == 0:\\n return '%s'\\n else:\\n # The indexed query on `path` below is the means by which we recurse\\n # Every iteration pushes it closer to a length of 0 and, thus, bottoming out\\n return '{{%s: {recons}}}'.format(recons=reconstruct_object(path[1:]))\",\n \"def convert_path(path):\\n lower_path = path.replace('\\\\\\\\', '/')\\n if lower_path[1] == \\\":\\\":\\n path_lower = lower_path.replace(\\\":\\\", \\\"\\\")\\n path_server = \\\"/\\\" + path_lower\\n else:\\n path_server = lower_path[1:]\\n\\n return path_server\",\n \"def mangle_path(path):\\n # Remove assigns\\n path = servers.get_file_server().manglepath( str(path) )\\n # Remove parent special directories\\n path = os.path.abspath( path )\\n # Convert path to Nebula format (slashes instead of backslashes)\\n path = servers.get_file_server().manglepath( str(path) )\\n # Convert drive letter to lowercase\\n if len(path) > 1:\\n if path[1] == ':':\\n path = path[:1].lower() + path[1:]\\n return path\",\n \"def userPath2Path(arg, frame):\\n arg = ingest(arg)\\n if isabs(arg):\\n return Path(arg)\\n else:\\n return Path(arg, frame)\",\n \"def translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?', 1)[0]\\n path = path.split('#', 1)[0]\\n path = posixpath.normpath(urllib.unquote(path))\\n words = path.split('/')\\n words = filter(None, words)\\n try:\\n path = self.rootpath\\n except AttributeError:\\n path = os.getcwd()\\n for word in words:\\n drive, word = os.path.splitdrive(word)\\n head, word = os.path.split(word)\\n if word in (os.curdir, os.pardir):\\n continue\\n path = os.path.join(path, word)\\n return path\",\n \"def flatten_path(path):\\n return path.split(\\\"/\\\")[-1]\",\n \"def preprocess(self, newpath):\\n return dict()\",\n \"def strip_path(self):\\n return self.path.replace('/', '')\",\n \"def standardize_path(path):\\n path.rstrip('/')\\n if not path.startswith('.*'):\\n path = '/' + path\\n path = re.compile('/+').sub('/', path)\\n return path\",\n \"def get_path_from_template(path_template: str, path_type: PathType = PathType.AUTO) -> str:\\r\\n # automatically select path type depending on running OS\\r\\n if path_type == PathType.AUTO:\\r\\n if platform.system() == \\\"Windows\\\":\\r\\n path_type = PathType.WINDOWS\\r\\n elif platform.system() == \\\"Linux\\\":\\r\\n path_type = PathType.LINUX\\r\\n else:\\r\\n raise RuntimeError(\\\"Unknown platform\\\")\\r\\n\\r\\n path_template = path_template.replace(\\\"<USERNAME>\\\", get_user_name())\\r\\n\\r\\n # return correctly formatted path\\r\\n if path_type == PathType.WINDOWS:\\r\\n return str(pathlib.PureWindowsPath(path_template))\\r\\n elif path_type == PathType.LINUX:\\r\\n return str(pathlib.PurePosixPath(path_template))\\r\\n else:\\r\\n raise RuntimeError(\\\"Unknown platform\\\")\",\n \"def path_serializer(obj: PurePath, **_: Any) -> str:\\n return obj.as_posix()\",\n \"def reconstructPath(came_from, current):\\n path = [current]\\n while current in came_from:\\n current = came_from[current]\\n path.append(current)\\n print(f\\\"path: {path}\\\")\\n return path\",\n \"def format_path(path):\\n if not path:\\n return path\\n\\n path = re.sub(r'/+', '/', path)\\n\\n if path == '/':\\n return (u\\\"\\\" if isinstance(path, unicode) else \\\"\\\")\\n else:\\n return '/' + path.strip('/')\",\n \"def reverse(self):\\n cls = self.__class__\\n rev_interfaces = tuple(reversed([i.reverse() for i in self.interfaces]))\\n rev_materials = tuple(reversed(self.materials))\\n rev_modes = tuple(reversed(self.modes))\\n rev_path = cls(rev_interfaces, rev_materials, rev_modes, name=self.name)\\n if self.rays is not None:\\n rev_path.rays = self.rays.reverse()\\n return rev_path\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, six.integer_types):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, six.integer_types):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, six.integer_types):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def mutate_transposition(path, board):\\n path_transposed = swap(path)\\n x, y = get_start(board)\\n while not is_path_correct(x, y, path_transposed, board):\\n path_transposed = swap(path)\\n return remove_constant_points(path_transposed)\",\n \"def _flip_path(path, path_lens):\\n bs = path.size(0)\\n max_path_len = path.size(1)\\n rotate = (torch.arange(max_path_len) + path_lens[:, None]) % max_path_len\\n return torch.flip(path[torch.arange(bs, dtype=torch.int64)[:, None], rotate], dims=[1])\",\n \"def translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?', 1)[0]\\n path = path.split('#', 1)[0]\\n # Don't forget explicit trailing slash when normalizing. Issue17324\\n trailing_slash = path.rstrip().endswith('/')\\n path = posixpath.normpath(urllib.unquote(path))\\n words = path.split('/')\\n words = filter(None, words)\\n path = self.working_dir\\n for word in words:\\n _drive, word = os.path.splitdrive(word)\\n _head, word = os.path.split(word)\\n if word in (os.curdir, os.pardir):\\n continue\\n path = os.path.join(path, word)\\n if trailing_slash:\\n path += '/'\\n return path\",\n \"def resolved_path(path):\\n path = os.path.abspath(path)\\n elements = path_elements(path)\\n result = \\\"\\\"\\n for element in elements:\\n segment = element\\n segment_path = os.path.join(result, segment)\\n if os.path.islink(segment_path):\\n segment = os.readlink(segment_path)\\n result = os.path.join(result, segment)\\n result = os.path.normpath(result)\\n return result\",\n \"def convertGridPathToReal(pathInGrid, sx, sy, gx, gy, grid_reso = 0.1):\\n\\n pathInReal = (pathInGrid * grid_reso + grid_reso / 2)\\n stepNum = pathInReal.shape[1]\\n # Replace head and tail\\n pathInReal[:, 0] = [sx, sy]\\n pathInReal[:, 0] = [sx, sy]\\n pathInReal[:, stepNum - 1] = [gx, gy]\\n pathInReal[:, stepNum - 1] = [gx, gy]\\n\\n return pathInReal\",\n \"def normalize_path(path, config):\\n\\n for alias, link in config[\\\"Aliases\\\"].items():\\n if path.startswith(alias):\\n return path.replace(alias, link, 1)\\n\\n return path\",\n \"def render_path(path_to_item):\\n result = \\\"\\\"\\n for pth in path_to_item:\\n if isinstance(pth, int):\\n result += \\\"[{0}]\\\".format(pth)\\n else:\\n result += \\\"['{0}']\\\".format(pth)\\n return result\",\n \"def __fspath__(self):\\n return str(self)\",\n \"def unformat_file(path: str, out_dir: str):\\n\\n data_dir = get_data_dir()\\n path = Path(path)\\n out_dir = Path(out_dir)\\n if not path.exists() and out_dir.exists() and out_dir.is_dir():\\n return\\n\\n if path.is_dir():\\n path.mkdir(exist_ok=True)\\n for filename in path.iterdir():\\n unformat_file(filename, str(out_dir))\\n\\n else:\\n dataobj = frontmatter.load(str(path))\\n\\n try:\\n # get relative path of object in `data` dir\\n datapath = path.parent.resolve().relative_to(data_dir)\\n except ValueError:\\n datapath = Path()\\n\\n # create subdir if doesn't exist\\n (out_dir / datapath).mkdir(exist_ok=True)\\n new_path = out_dir / datapath / f\\\"{dataobj.metadata['title']}.md\\\"\\n with new_path.open(\\\"w\\\") as f:\\n f.write(dataobj.content)\\n\\n current_app.logger.info(\\n f\\\"Unformatted and moved {str(path)} to {str(new_path.resolve())}\\\"\\n )\\n path.unlink()\",\n \"def normalize(path):\\n return os.path.normcase(os.path.realpath(path))\",\n \"def transformed(self, T):\\n new = Polygon2()\\n new.path = self.path.transformed(Affine2D(T.A))\\n return new\",\n \"def _normalized_path(path):\\n return os.path.abspath(os.path.expanduser(path))\",\n \"def determine_template_by_path(path):\\n path = path.lstrip('/')\\n\\n path_chunks = re.split('\\\\/', path)\\n if len(path_chunks) <= 1:\\n return path\\n else:\\n \\\"\\\"\\\"\\n For now be ignorant and just return the\\n first entry of the list as the possible template\\n name, so in fact we only have a 1 level deep structure\\n \\\"\\\"\\\"\\n return '_%s.html' % path_chunks[0]\",\n \"def Sourceify(path):\\n return path\",\n \"def _get_path(self, path, resource_path):\\n for item in self.path_replace:\\n if len(item) == 2:\\n src, dest = item\\n else:\\n assert len(item) == 3\\n src = item[0]\\n dest = item[2 if resource_path else 1]\\n if path.endswith(src):\\n path = replace_end(path, src, dest)\\n return path\",\n \"def translate_path(self, path):\\n\\t\\t# abandon query parameters\\n\\t\\tpath = path.split('?',1)[0]\\n\\t\\tpath = path.split('#',1)[0]\\n\\t\\tpath = posixpath.normpath(urllib.unquote(path))\\n\\t\\twords = path.split('/')\\n\\t\\twords = filter(None, words)\\n\\t\\tpath = os.getcwd() + \\\"/\\\" + self.wwwroot\\n\\t\\tfor word in words:\\n\\t\\t\\tdrive, word = os.path.splitdrive(word)\\n\\t\\t\\thead, word = os.path.split(word)\\n\\t\\t\\tif word in (os.curdir, os.pardir): continue\\n\\t\\t\\tpath = os.path.join(path, word)\\n\\t\\treturn path\",\n \"def convertString(path):\\n if (\\\"win\\\" in sys.platform):\\n return path.replace(\\\"/\\\",\\\"\\\\\\\\\\\")\\n elif (\\\"linux\\\" in sys.platform):\\n return path.replace(\\\"\\\\\\\\\\\",\\\"/\\\")\",\n \"def fixpaths(d):\\n if isinstance(d, dict):\\n if \\\"path\\\" in d:\\n if \\\":\\\" not in d[\\\"path\\\"]:\\n local_path = os.path.normpath(\\n os.path.join(os.getcwd(), basedir, d[\\\"path\\\"])\\n )\\n d[\\\"location\\\"] = pathname2url(local_path)\\n else:\\n d[\\\"location\\\"] = d[\\\"path\\\"]\\n del d[\\\"path\\\"]\",\n \"def pathify(value):\\n \\n value = stringify(value)\\n return value.replace('$ ', '$$ ').replace(' ', '$ ').replace(':', '$:')\",\n \"def _get_as_path(self):\\n return self.__as_path\",\n \"def conversion_binaire(self, binary_path):\\r\\n if self.node_leaf():\\r\\n return self.name + ':' + str(binary_path) + '\\\\n'\\r\\n else:\\r\\n path = self.zero_son.conversion_binaire(str(binary_path) + '0')\\r\\n path += self.one_son.conversion_binaire(str(binary_path) + '1')\\r\\n #path determined by recursion\\r\\n return path\",\n \"def clean_path(path: str) -> str:\\n previous_path = \\\"\\\"\\n next_path = path\\n while next_path != previous_path:\\n previous_path = next_path\\n next_path = copy_annotations(path, next_path.replace(\\\"//\\\", \\\"/\\\"))\\n while next_path.endswith(\\\"/\\\"):\\n next_path = next_path[:-1]\\n return next_path\",\n \"def translate_path(path, workdir):\\n # abandon query parameters\\n path = path.split('?', 1)[0]\\n path = path.split('#', 1)[0]\\n path = urllib.unquote(path)\\n parts = path.split('/')\\n return os.path.join(workdir, *parts)\",\n \"def convert(self, pt):\\n # default transformation is to do nothing\\n return pt\",\n \"def convert(\\n path_in, path_out, pathway_iri, wp_id, pathway_version, scale=100, theme=\\\"plain\\\"\\n):\\n if not path.exists(path_in):\\n raise Exception(f\\\"Missing file '{path_in}'\\\")\\n\\n if path.exists(path_out):\\n print(f\\\"File {path_out} already exists. Skipping.\\\")\\n return True\\n\\n dir_in = path.dirname(path_in)\\n base_in = path.basename(path_in)\\n # example base_in: 'WP4542.gpml'\\n [stub_in, ext_in_with_dot] = path.splitext(base_in)\\n # gettting rid of the leading dot, e.g., '.gpml' to 'gpml'\\n ext_in = LEADING_DOT_RE.sub(\\\"\\\", ext_in_with_dot)\\n\\n if ext_in != \\\"gpml\\\":\\n # TODO: how about *.gpml.xml?\\n raise Exception(f\\\"Currently only accepting *.gpml for path_in\\\")\\n gpml_f = path_in\\n\\n dir_out = path.dirname(path_out)\\n # example base_out: 'WP4542.svg'\\n base_out = path.basename(path_out)\\n [stub_out, ext_out_with_dot] = path.splitext(base_out)\\n # getting rid of the leading dot, e.g., '.svg' to 'svg'\\n ext_out = LEADING_DOT_RE.sub(\\\"\\\", ext_out_with_dot)\\n\\n tree = ET.parse(gpml_f, parser=parser)\\n root = tree.getroot()\\n\\n if root is None:\\n raise Exception(\\\"no root element\\\")\\n if root.tag is None:\\n raise Exception(\\\"no root tag\\\")\\n\\n gpml_version = re.sub(r\\\"{http://pathvisio.org/GPML/(\\\\w+)}Pathway\\\", r\\\"\\\\1\\\", root.tag)\\n if ext_out != \\\"gpml\\\" and gpml_version != LATEST_GPML_VERSION:\\n old_f = f\\\"{dir_in}/{stub_in}.{gpml_version}.gpml\\\"\\n rename(gpml_f, old_f)\\n convert(old_f, gpml_f, pathway_iri, wp_id, pathway_version, scale)\\n\\n # trying to get wd ids via sparql via pywikibot\\n site = pywikibot.Site(\\\"wikidata\\\", \\\"wikidata\\\")\\n repo = site.data_repository() # this is a DataSite object\\n wd_sparql = sparql.SparqlQuery(\\n endpoint=\\\"https://query.wikidata.org/sparql\\\", repo=repo\\n )\\n # (self, endpoint=None, entity_url=None, repo=None, 2 max_retries=None, retry_wait=None)\\n\\n if ext_out in [\\\"gpml\\\", \\\"owl\\\", \\\"pdf\\\", \\\"pwf\\\", \\\"txt\\\"]:\\n subprocess.run(shlex.split(f\\\"pathvisio convert {path_in} {path_out}\\\"))\\n elif ext_out == \\\"png\\\":\\n # TODO: look at using --scale as an option (instead of an argument),\\n # for both pathvisio and gpmlconverter.\\n # TODO: move the setting of a default value for scale into\\n # pathvisio instead of here.\\n subprocess.run(shlex.split(f\\\"pathvisio convert {path_in} {path_out} {scale}\\\"))\\n # Use interlacing? See https://github.com/PathVisio/pathvisio/issues/78\\n # It's probably not worthwhile. If we did it, we would need to install\\n # imagemagick and then run this:\\n # mv \\\"$path_out\\\" \\\"$path_out.noninterlaced.png\\\"\\n # convert -interlace PNG \\\"$path_out.noninterlaced.png\\\" \\\"$path_out\\\"\\n elif ext_out in [\\\"json\\\", \\\"jsonld\\\"]:\\n gpml2json(path_in, path_out, pathway_iri, wp_id, pathway_version, wd_sparql)\\n elif ext_out in [\\\"svg\\\", \\\"pvjssvg\\\"]:\\n #############################\\n # SVG\\n #############################\\n\\n json_f = f\\\"{dir_out}/{stub_in}.json\\\"\\n if not path.isfile(json_f):\\n gpml2json(path_in, json_f, pathway_iri, wp_id, pathway_version, wd_sparql)\\n\\n json2svg(json_f, path_out, pathway_iri, wp_id, pathway_version, theme)\\n else:\\n raise Exception(f\\\"Invalid output extension: '{ext_out}'\\\")\",\n \"def resolve_template(template, item):\\n root_dir = config['link_root_dir'].as_filename()\\n placeholders = re.findall(r'(\\\\%\\\\{?\\\\w+\\\\}?)', template)\\n path = template\\n for p in placeholders:\\n path = path.replace(p, item[p.strip('%{}')])\\n return os.path.join(root_dir, path)\",\n \"def reformat(self):\\n\\t\\told_path = os.path.join( self.path, self.init_str )\\n\\t\\tnew_path = os.path.join( self.path, self.reorder() )\\n\\t\\tos.rename(old_path,new_path)\",\n \"def path_to_message(self, mapdata, path):\\n rospy.loginfo(\\\"Returning a Path message\\\")\\n pathObj = Path()\\n pathObj.header.frame_id = '/map'\\n pathObj.poses = PathPlanner.path_to_poses(mapdata, path)\\n return pathObj\",\n \"def normalize_path(path: str) -> str:\\n if '@' in path:\\n split_path = path.split(':')\\n split_path[1] = os.path.normpath(split_path[1])\\n\\n return ':'.join(split_path)\\n\\n return os.path.normpath(path)\",\n \"def translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?',1)[0]\\n path = path.split('#',1)[0]\\n # Don't forget explicit trailing slash when normalizing. Issue17324\\n trailing_slash = path.rstrip().endswith('/')\\n path = posixpath.normpath(urllib.unquote(path))\\n words = path.split('/')\\n words = filter(None, words)\\n path = str(port_number)\\n for word in words:\\n drive, word = os.path.splitdrive(word)\\n head, word = os.path.split(word)\\n if word in (os.curdir, os.pardir): continue\\n path = os.path.join(path, word)\\n if trailing_slash:\\n path += '/'\\n return path\",\n \"def from_path(path):\\n path = to_pagina(path)\\n return path[6:]\",\n \"def stringyfy(path):\\n try:\\n # Pathlib support\\n path = path.__fspath__()\\n except AttributeError:\\n pass\\n if hasattr(path, 'name'): # passed in a file\\n path = path.name\\n if isinstance(path, str):\\n return path\\n raise ValueError(f'Cannot convert {path} to a path')\",\n \"def path_filename_representation(path):\\n # Strip leading / and replace / with .\\n return re.sub(r\\\"^/(.*)$\\\", r\\\"\\\\1\\\", path).replace(\\\"/\\\", \\\".\\\")\",\n \"def parse_path(path): # -> Path | UnparsedPath | ParsedPath:\\n ...\",\n \"def reconstruct_path(current):\\r\\n path = [current.coord]\\r\\n parent = current.parent\\r\\n while parent:\\r\\n path = [parent.coord] + path\\r\\n parent = parent.parent\\r\\n path = path[1:]\\r\\n return path\",\n \"def convert_unc(host, path):\\n return ''.join(['\\\\\\\\\\\\\\\\', host, '\\\\\\\\', path.replace(':', '$')])\",\n \"def regenerate_tree(self, newpos):\\n self.path = self.tree[newpos][2]\\n self.tree = self.get_tree()\\n self.pos = self.get_curpos()\",\n \"def path(self, toNative=True):\\n return self.text(toNative=toNative)\",\n \"def directory_to_individual_conversion_method(self, path):\\n\\n\\t\\treturn Individual(calculation_set=VaspRelaxation(path=path, input_dictionary=copy.deepcopy(self.calculation_set_input_dictionary)))\",\n \"def Reconstruct_Path(self, dir_map):\\n path = ''\\n first_time = True\\n x = self.xFinish\\n y = self.yFinish\\n while not (x == self.xStart and y == self.yStart):\\n j = dir_map[y][x]\\n c = str((self.num_directions-j-1) % self.num_directions)\\n if first_time:\\n path=c+path\\n first_time=False\\n else:\\n path = c + ','+ path\\n x += self.dx[j]\\n y += self.dy[j]\\n return path\",\n \"def _create_path(root, dict_type, path):\\n for sub_path in path:\\n if not isinstance(root.get(sub_path, None), dict):\\n root[sub_path] = dict_type()\\n\\n root = root[sub_path]\\n\\n return root\",\n \"def repair_path(dict_1):\\n dup_dict = dict(dict_1)\\n for k,v in dup_dict.items():\\n if '\\\\\\\\' in k:\\n key = k.replace('\\\\\\\\', '/')\\n val = v.replace('\\\\\\\\', '/')\\n del dict_1[k]\\n dict_1[key] = val\\n return dict_1\",\n \"def orig_path(self):\\n return self._path[0]\",\n \"def format_path (in_path):\\n return os.path.realpath(os.path.expanduser(in_path))\",\n \"def _process_path(path, path_trans):\\n path_points = path.transformed(path_trans).interpolated(500).vertices\\n deltas = (path_points[1:] - path_points[:-1]).T\\n pt_offsets = np.concatenate(([0], np.hypot(*deltas).cumsum()))\\n angles = np.arctan2(deltas[-1], deltas[0])\\n return path_points, pt_offsets, angles\",\n \"def normalize(self: T) -> T:\",\n \"def shell_path_post_op(path):\\n \\n def fix_path(**kwargs):\\n \\n # extract key_name \\n name = kwargs.get('name')\\n if not name:\\n name = kwargs.get('key_name')\\n if not name:\\n name = kwargs.get('obj_path')\\n if not name:\\n raise ValueError('no (key)name specified') \\n \\n if os.path.isdir(path):\\n return os.path.join(path, name)\\n else: # dir may not exist yet\\n dname = os.path.dirname(path)\\n if dname:\\n try:\\n os.makedirs(dname)\\n except OSError, e:\\n if e.errno != errno.EEXIST:\\n raise\\n basename = os.path.basename(path)\\n if basename: # dest is a file\\n return path\\n else:\\n return os.path.join(path, name)\\n \\n return fix_path\",\n \"def pathmaker(first_segment, *in_path_segments, rev=False):\\n\\n _path = first_segment\\n\\n _path = os.path.join(_path, *in_path_segments)\\n if rev is True or sys.platform not in ['win32', 'linux']:\\n return os.path.normpath(_path)\\n return os.path.normpath(_path).replace(os.path.sep, '/')\",\n \"def transform_url(result):\\n import re\\n result = re.sub('//', '/', result)\\n result = re.sub('/', '//', result, count=1)\\n return encode_url_path(result)\",\n \"def routeunpack(value):\\n return str(value).replace(\\\"!\\\",\\\"/\\\")\",\n \"def _path_to_str(var):\\n if not isinstance(var, (Path, str)):\\n raise ValueError(\\\"All path parameters must be either strings or \\\"\\n \\\"pathlib.Path objects. Found type %s.\\\" % type(var))\\n else:\\n return str(var)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67283267","0.59157676","0.57542336","0.56610835","0.5517831","0.55112505","0.5508051","0.5501356","0.53947884","0.5389098","0.53607225","0.53306615","0.5308158","0.5299064","0.52649087","0.5254318","0.5234345","0.52052474","0.51929563","0.51626086","0.51578856","0.5152857","0.51156175","0.50939786","0.5055561","0.5042306","0.5036488","0.5014492","0.5012641","0.49785456","0.4978409","0.49677902","0.4967757","0.49359742","0.49299744","0.4929296","0.4928007","0.49250826","0.49226922","0.48936874","0.48850614","0.48596653","0.4848771","0.4840822","0.48373464","0.48334494","0.48329926","0.48329926","0.48329926","0.48217303","0.48173353","0.48113853","0.4800828","0.47985375","0.47828418","0.4779038","0.47709894","0.47627988","0.4748824","0.47452033","0.47367546","0.4731912","0.47275937","0.4727491","0.47183925","0.4715236","0.47114465","0.4708505","0.4703878","0.47026476","0.4700136","0.46983153","0.46942228","0.4686909","0.46861133","0.4685443","0.46841684","0.46817747","0.46792203","0.46729636","0.46718276","0.4668173","0.46582487","0.4657427","0.46570286","0.4656398","0.46547017","0.46440652","0.46424636","0.46413496","0.46413323","0.46401712","0.4639345","0.46377003","0.46346563","0.4627986","0.46137294","0.46114635","0.46104732","0.46099642"],"string":"[\n \"0.67283267\",\n \"0.59157676\",\n \"0.57542336\",\n \"0.56610835\",\n \"0.5517831\",\n \"0.55112505\",\n \"0.5508051\",\n \"0.5501356\",\n \"0.53947884\",\n \"0.5389098\",\n \"0.53607225\",\n \"0.53306615\",\n \"0.5308158\",\n \"0.5299064\",\n \"0.52649087\",\n \"0.5254318\",\n \"0.5234345\",\n \"0.52052474\",\n \"0.51929563\",\n \"0.51626086\",\n \"0.51578856\",\n \"0.5152857\",\n \"0.51156175\",\n \"0.50939786\",\n \"0.5055561\",\n \"0.5042306\",\n \"0.5036488\",\n \"0.5014492\",\n \"0.5012641\",\n \"0.49785456\",\n \"0.4978409\",\n \"0.49677902\",\n \"0.4967757\",\n \"0.49359742\",\n \"0.49299744\",\n \"0.4929296\",\n \"0.4928007\",\n \"0.49250826\",\n \"0.49226922\",\n \"0.48936874\",\n \"0.48850614\",\n \"0.48596653\",\n \"0.4848771\",\n \"0.4840822\",\n \"0.48373464\",\n \"0.48334494\",\n \"0.48329926\",\n \"0.48329926\",\n \"0.48329926\",\n \"0.48217303\",\n \"0.48173353\",\n \"0.48113853\",\n \"0.4800828\",\n \"0.47985375\",\n \"0.47828418\",\n \"0.4779038\",\n \"0.47709894\",\n \"0.47627988\",\n \"0.4748824\",\n \"0.47452033\",\n \"0.47367546\",\n \"0.4731912\",\n \"0.47275937\",\n \"0.4727491\",\n \"0.47183925\",\n \"0.4715236\",\n \"0.47114465\",\n \"0.4708505\",\n \"0.4703878\",\n \"0.47026476\",\n \"0.4700136\",\n \"0.46983153\",\n \"0.46942228\",\n \"0.4686909\",\n \"0.46861133\",\n \"0.4685443\",\n \"0.46841684\",\n \"0.46817747\",\n \"0.46792203\",\n \"0.46729636\",\n \"0.46718276\",\n \"0.4668173\",\n \"0.46582487\",\n \"0.4657427\",\n \"0.46570286\",\n \"0.4656398\",\n \"0.46547017\",\n \"0.46440652\",\n \"0.46424636\",\n \"0.46413496\",\n \"0.46413323\",\n \"0.46401712\",\n \"0.4639345\",\n \"0.46377003\",\n \"0.46346563\",\n \"0.4627986\",\n \"0.46137294\",\n \"0.46114635\",\n \"0.46104732\",\n \"0.46099642\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5877729"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":77081,"cells":{"query":{"kind":"string","value":"Convert a normal path to template and the convert it back to a normal path with given path type."},"document":{"kind":"string","value":"def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:\r\n path_template = get_template_from_path(path)\r\n path = get_path_from_template(path_template, path_type)\r\n return path"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_path_from_template(path_template: str, path_type: PathType = PathType.AUTO) -> str:\r\n # automatically select path type depending on running OS\r\n if path_type == PathType.AUTO:\r\n if platform.system() == \"Windows\":\r\n path_type = PathType.WINDOWS\r\n elif platform.system() == \"Linux\":\r\n path_type = PathType.LINUX\r\n else:\r\n raise RuntimeError(\"Unknown platform\")\r\n\r\n path_template = path_template.replace(\"<USERNAME>\", get_user_name())\r\n\r\n # return correctly formatted path\r\n if path_type == PathType.WINDOWS:\r\n return str(pathlib.PureWindowsPath(path_template))\r\n elif path_type == PathType.LINUX:\r\n return str(pathlib.PurePosixPath(path_template))\r\n else:\r\n raise RuntimeError(\"Unknown platform\")","def get_template_from_path(path: str) -> str:\r\n path = path.replace(\"\\\\\", \"/\")\r\n return path","def convert(kls, path, configuration=None, converters=None, ignore_converters=None, joined=None):\n path_type = type(path)\n if path_type is Path:\n return path\n else:\n joined = dot_joiner(path, item_type=path_type)\n return Path(path, configuration, converters, ignore_converters, joined=joined)","def type(path):","def _create_path(root, dict_type, path):\n for sub_path in path:\n if not isinstance(root.get(sub_path, None), dict):\n root[sub_path] = dict_type()\n\n root = root[sub_path]\n\n return root","def determine_template_by_path(path):\n path = path.lstrip('/')\n\n path_chunks = re.split('\\/', path)\n if len(path_chunks) <= 1:\n return path\n else:\n \"\"\"\n For now be ignorant and just return the\n first entry of the list as the possible template\n name, so in fact we only have a 1 level deep structure\n \"\"\"\n return '_%s.html' % path_chunks[0]","def resolve_template(template, item):\n root_dir = config['link_root_dir'].as_filename()\n placeholders = re.findall(r'(\\%\\{?\\w+\\}?)', template)\n path = template\n for p in placeholders:\n path = path.replace(p, item[p.strip('%{}')])\n return os.path.join(root_dir, path)","def convert_to_path(arg: Any) -> Path:\n return Path(arg)","def path_deserializer(obj: str, cls: type = PurePath, **_: Any) -> PurePath:\n path: PurePath = cls(obj)\n return path","def replace_dot_notations(template_path, type, replacing_dict, storing_path):\n files_path = f\"{template_path}/{type}\"\n\n # Create a folder for the specific type of files\n replaced_inner_path = f\"{storing_path}/{type}\"\n if not os.path.exists(replaced_inner_path):\n os.mkdir(replaced_inner_path)\n\n print(f\"Replacing dot notations in {type}\")\n # Iterate over the files in the specific type directory\n for pathname in glob.glob(os.path.join(files_path, '*')):\n \n filename = pathname[len(files_path):]\n\n with open(pathname, 'r') as file:\n filedata = file.read()\n\n for original, replace in replacing_dict.items():\n filedata = filedata.replace(original, replace)\n\n with open(f\"{replaced_inner_path}/{filename}\", 'w+') as file:\n file.write(filedata)","def prepare_path(self,path):\n debug.info(4,\"Set path: \" + str(path))\n\n # This is marked for debug\n path.set_path()\n\n # For debugging... if the path failed to route.\n if False or path==None:\n self.write_debug_gds()\n\n # First, simplify the path for\n #debug.info(1,str(self.path)) \n contracted_path = self.contract_path(path)\n debug.info(3,\"Contracted path: \" + str(contracted_path))\n \n return contracted_path","def __get__(self, obj, objtype):\n raw_path = super(Path,self).__get__(obj,objtype)\n return self._resolve(raw_path)","def path_type(cls, path):\n if os.path.isdir(path):\n return 'package'\n else:\n return 'object'","def canonicalPath(path_or_object):","def _replace_path_with_type_symbol(cost_trees):\n cost_trees_ = {}\n tmp_dict = {}\n for k, v in cost_trees.items():\n if k == 'subcosts':\n for kk, vv in v.items():\n type_symbol = vv.pop('_type_symbol')\n d = {type_symbol: vv}\n if tmp_dict.get(k) is not None:\n tmp_dict[k].update(d)\n else:\n tmp_dict[k] = d\n elif type(v) == dict:\n for kk, vv in v.items():\n type_symbol = vv.pop('_type_symbol')\n d = {type_symbol: _replace_path_with_type_symbol(vv)}\n if cost_trees_.get(k) is not None:\n cost_trees_[k].update(d)\n else:\n cost_trees_[k] = d\n else:\n tmp_dict[k] = v\n cost_trees_.update(tmp_dict)\n return cost_trees_","def new_path(path):\n return Path(path[0], path[1], path[2])","def convert(\n path_in, path_out, pathway_iri, wp_id, pathway_version, scale=100, theme=\"plain\"\n):\n if not path.exists(path_in):\n raise Exception(f\"Missing file '{path_in}'\")\n\n if path.exists(path_out):\n print(f\"File {path_out} already exists. Skipping.\")\n return True\n\n dir_in = path.dirname(path_in)\n base_in = path.basename(path_in)\n # example base_in: 'WP4542.gpml'\n [stub_in, ext_in_with_dot] = path.splitext(base_in)\n # gettting rid of the leading dot, e.g., '.gpml' to 'gpml'\n ext_in = LEADING_DOT_RE.sub(\"\", ext_in_with_dot)\n\n if ext_in != \"gpml\":\n # TODO: how about *.gpml.xml?\n raise Exception(f\"Currently only accepting *.gpml for path_in\")\n gpml_f = path_in\n\n dir_out = path.dirname(path_out)\n # example base_out: 'WP4542.svg'\n base_out = path.basename(path_out)\n [stub_out, ext_out_with_dot] = path.splitext(base_out)\n # getting rid of the leading dot, e.g., '.svg' to 'svg'\n ext_out = LEADING_DOT_RE.sub(\"\", ext_out_with_dot)\n\n tree = ET.parse(gpml_f, parser=parser)\n root = tree.getroot()\n\n if root is None:\n raise Exception(\"no root element\")\n if root.tag is None:\n raise Exception(\"no root tag\")\n\n gpml_version = re.sub(r\"{http://pathvisio.org/GPML/(\\w+)}Pathway\", r\"\\1\", root.tag)\n if ext_out != \"gpml\" and gpml_version != LATEST_GPML_VERSION:\n old_f = f\"{dir_in}/{stub_in}.{gpml_version}.gpml\"\n rename(gpml_f, old_f)\n convert(old_f, gpml_f, pathway_iri, wp_id, pathway_version, scale)\n\n # trying to get wd ids via sparql via pywikibot\n site = pywikibot.Site(\"wikidata\", \"wikidata\")\n repo = site.data_repository() # this is a DataSite object\n wd_sparql = sparql.SparqlQuery(\n endpoint=\"https://query.wikidata.org/sparql\", repo=repo\n )\n # (self, endpoint=None, entity_url=None, repo=None, 2 max_retries=None, retry_wait=None)\n\n if ext_out in [\"gpml\", \"owl\", \"pdf\", \"pwf\", \"txt\"]:\n subprocess.run(shlex.split(f\"pathvisio convert {path_in} {path_out}\"))\n elif ext_out == \"png\":\n # TODO: look at using --scale as an option (instead of an argument),\n # for both pathvisio and gpmlconverter.\n # TODO: move the setting of a default value for scale into\n # pathvisio instead of here.\n subprocess.run(shlex.split(f\"pathvisio convert {path_in} {path_out} {scale}\"))\n # Use interlacing? See https://github.com/PathVisio/pathvisio/issues/78\n # It's probably not worthwhile. If we did it, we would need to install\n # imagemagick and then run this:\n # mv \"$path_out\" \"$path_out.noninterlaced.png\"\n # convert -interlace PNG \"$path_out.noninterlaced.png\" \"$path_out\"\n elif ext_out in [\"json\", \"jsonld\"]:\n gpml2json(path_in, path_out, pathway_iri, wp_id, pathway_version, wd_sparql)\n elif ext_out in [\"svg\", \"pvjssvg\"]:\n #############################\n # SVG\n #############################\n\n json_f = f\"{dir_out}/{stub_in}.json\"\n if not path.isfile(json_f):\n gpml2json(path_in, json_f, pathway_iri, wp_id, pathway_version, wd_sparql)\n\n json2svg(json_f, path_out, pathway_iri, wp_id, pathway_version, theme)\n else:\n raise Exception(f\"Invalid output extension: '{ext_out}'\")","def _path_from_name(name, type):\n if name.startswith('_'):\n return name.split('/')\n design, name = name.split('/', 1)\n return ['_design', design, type, name]","def userPath2Path(arg, frame):\n arg = ingest(arg)\n if isabs(arg):\n return Path(arg)\n else:\n return Path(arg, frame)","def get_template(type):\n # read model options file from Cloud Storage\n content = storage.read_file('templates/' + type + '.yaml')\n return Response(content, status=200, mimetype='application/text')","def _purepath_to_str(\n self, path: Union[Path, PurePath, str]\n ) -> Union[Path, PurePath, str]:\n if isinstance(path, PurePath):\n path = str(path)\n return path","def get_out_path(in_path, filetype):\n if filetype == \"yaml\":\n out_dir = check_for_yaml_folder(in_path)\n if out_dir:\n filename, _ = os.path.splitext(os.path.basename(in_path))\n out_path = os.path.join(out_dir, filename)\n else:\n filename, _ = os.path.splitext(os.path.abspath(in_path))\n out_path = filename\n elif filetype == \"json\":\n out_dir = check_for_json_folder(in_path)\n if out_dir:\n filename, _ = os.path.splitext(os.path.basename(in_path))\n out_path = os.path.join(out_dir, filename)\n else:\n filename, _ = os.path.splitext(os.path.abspath(in_path))\n out_path = filename\n else:\n if check_if_plist(in_path):\n out_path = in_path + \".yaml\"\n else:\n print(\"\\nERROR: File is not PLIST, JSON or YAML format.\\n\")\n usage()\n exit(1)\n return out_path","def _get_path_type(self, path: Path) -> str:\n if path.is_dir():\n return 'dir'\n elif path.is_file():\n return 'file'\n else:\n raise FileNotFoundError(f\"File {path} not found\")","def py38_translate_path(self, path):\n # abandon query parameters\n path = path.split('?', 1)[0]\n path = path.split('#', 1)[0]\n # Don't forget explicit trailing slash when normalizing. Issue17324\n trailing_slash = path.rstrip().endswith('/')\n try:\n path = urllib.parse.unquote(path, errors='surrogatepass')\n except UnicodeDecodeError:\n path = urllib.parse.unquote(path)\n path = posixpath.normpath(path)\n words = path.split('/')\n words = filter(None, words)\n # py38: self.directory\n # py36: os.getcwd()\n path = STATIC_DIR\n for word in words:\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\n # Ignore components that are not a simple file/directory name\n continue\n path = os.path.join(path, word)\n if trailing_slash:\n path += '/'\n return path","def unglom(\r\n d: T_StrAnyMapping, path: str, value: typing.Any\r\n) -> T_StrAnyMapping:\r\n try:\r\n return glom.assign(d, path, value)\r\n except KeyError:\r\n parent, child = path.rsplit(\".\", 1)\r\n return unglom(d, parent, {child: value})","def _resolve_path(self, path):\n filepath = None\n mimetype = None\n\n for root, dirs, files in self.filter_files(self.path):\n # Does it exist in error path?\n error_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'error_templates', path)\n try:\n with open(error_path):\n mimetype, encoding = mimetypes.guess_type(error_path)\n filepath = error_path\n except IOError:\n pass\n\n # Does it exist in Tarbell blueprint?\n if self.base:\n basepath = os.path.join(root, self.blueprint_name, path)\n try:\n with open(basepath):\n mimetype, encoding = mimetypes.guess_type(basepath)\n filepath = basepath\n except IOError:\n pass\n\n # Does it exist under regular path?\n fullpath = os.path.join(root, path)\n try:\n with open(fullpath):\n mimetype, encoding = mimetypes.guess_type(fullpath)\n filepath = fullpath\n except IOError:\n pass\n\n return filepath, mimetype","def tmpl_to_str(self, template_idx, o1_id, o2_id):\r\n template_str = self.template_generator.templates[template_idx]\r\n holes = template_str.count('OBJ')\r\n assert holes <= 2\r\n if holes <= 0:\r\n return template_str\r\n elif holes == 1:\r\n return template_str.replace('OBJ', self.vocab_act[o1_id])\r\n else:\r\n return template_str.replace('OBJ', self.vocab_act[o1_id], 1)\\\r\n .replace('OBJ', self.vocab_act[o2_id], 1)","def pathmaker(first_segment, *in_path_segments, rev=False):\n\n _path = first_segment\n\n _path = os.path.join(_path, *in_path_segments)\n if rev is True or sys.platform not in ['win32', 'linux']:\n return os.path.normpath(_path)\n return os.path.normpath(_path).replace(os.path.sep, '/')","def get_filename(self, path, params, type_=None):\n phase = self.phase\n\n if type_:\n phase += ('_' + type_)\n\n filename = self.FILENAME_TEMPLATES[phase].format(**params)\n\n return os.path.join(path, filename)","def normalizePath(path):\n\tfrom os.path import normpath, sep\n\tresult = normpath(path)\n\tresult = result.replace(\"/\",sep)\n\tresult = result.replace(\"\\\\\",sep)\n\treturn adaptPath(result)","def path_converter(text_path):\n\n out = ''\n out_list = []\n\n for i, t in enumerate(text_path):\n \n if t == ' ':\n out_list.append('\\\\ ')\n elif t == \"(\" :\n out_list.append('\\(')\n elif t == \")\" :\n out_list.append('\\)') \n else:\n out_list.append(t)\n \n return out.join(out_list)","def resolve_path(self, path):\n if path:\n if path[0] == '/':\n #zope objects case\n try: return self.unrestrictedTraverse(path)\n except: pass\n else:\n #aliss (python) objects case\n try: return self.get_aliss_object(path)\n except: pass\n #case of no path\n pass","def save_path(self):\n return self.template.manager.render_template_txt(self.path, self.template)","def template_update_single(\n field, inputs, inputs_dict_st=None, output_dir=None, spec_type=\"input\"\n):\n # if input_dict_st with state specific value is not available,\n # the dictionary will be created from inputs object\n from ..utils.typing import TypeParser # noqa\n from pydra.engine.specs import LazyField\n\n VALID_TYPES = (str, ty.Union[str, bool], Path, ty.Union[Path, bool], LazyField)\n\n if inputs_dict_st is None:\n inputs_dict_st = attr.asdict(inputs, recurse=False)\n\n if spec_type == \"input\":\n inp_val_set = inputs_dict_st[field.name]\n if inp_val_set is not attr.NOTHING and not TypeParser.is_instance(\n inp_val_set, VALID_TYPES\n ):\n raise TypeError(\n f\"'{field.name}' field has to be a Path instance or a bool, but {inp_val_set} set\"\n )\n if isinstance(inp_val_set, bool) and field.type in (Path, str):\n raise TypeError(\n f\"type of '{field.name}' is Path, consider using Union[Path, bool]\"\n )\n elif spec_type == \"output\":\n if not TypeParser.contains_type(FileSet, field.type):\n raise TypeError(\n f\"output {field.name} should be file-system object, but {field.type} \"\n \"set as the type\"\n )\n else:\n raise TypeError(f\"spec_type can be input or output, but {spec_type} provided\")\n # for inputs that the value is set (so the template is ignored)\n if spec_type == \"input\" and isinstance(inputs_dict_st[field.name], (str, Path)):\n return inputs_dict_st[field.name]\n elif spec_type == \"input\" and inputs_dict_st[field.name] is False:\n # if input fld is set to False, the fld shouldn't be used (setting NOTHING)\n return attr.NOTHING\n else: # inputs_dict[field.name] is True or spec_type is output\n value = _template_formatting(field, inputs, inputs_dict_st)\n # changing path so it is in the output_dir\n if output_dir and value is not attr.NOTHING:\n # should be converted to str, it is also used for input fields that should be str\n if type(value) is list:\n return [str(output_dir / Path(val).name) for val in value]\n else:\n return str(output_dir / Path(value).name)\n else:\n return attr.NOTHING","def render(\n path_or_template: str,\n **kwargs,\n) -> str:\n if isinstance(path_or_template, Template):\n template = path_or_template\n elif path_or_template.startswith(\"<\"):\n template = Template(path_or_template)\n else:\n with open(path_or_template, \"r\") as filp:\n contents = filp.read()\n template = Template(contents)\n return template.safe_substitute(**kwargs)","def convert_path(path):\n lower_path = path.replace('\\\\', '/')\n if lower_path[1] == \":\":\n path_lower = lower_path.replace(\":\", \"\")\n path_server = \"/\" + path_lower\n else:\n path_server = lower_path[1:]\n\n return path_server","def parse_path(path): # -> Path | UnparsedPath | ParsedPath:\n ...","def convert_project_type(input_dir, output_dir):\n param_info = [\n (input_dir, 'input_dir', (str, Path)),\n (output_dir, 'output_dir', (str, Path)),\n ]\n for param in param_info:\n type_sanity(param[0], param[1], param[2])\n\n if isinstance(input_dir, str):\n input_dir = Path(input_dir)\n if isinstance(output_dir, str):\n output_dir = Path(output_dir)\n\n sa_convert_project_type(input_dir, output_dir)","def template_directory_number2image(template, directory, number):\n\n # construct the full image name\n return os.path.join(directory, template_number2image(template, number))","def osnorm(self):\n import os\n if os.sep=='/' and \"\\\\\" in str(self):\n return Path(os.path.normpath(str(self).replace('\\\\','/' )))\n elif os.sep=='\\\\' and \"/\" in str(self):\n return Path(os.path.normpath(str(self).replace('/','\\\\' )))\n else:\n return self.norm()","def _generate_from_template(self, name, path, context):\n template = self._templates.get_template(name)\n with open(path, 'w') as f:\n f.write(template.render(context))","def destPath(file, package, type='files'):\n\treturn tmpDir(package)+'/etc/univention/templates/'+type+'/'+file","def saved_template(self, template_id):\n\n # From user params get the wanted type and size\n category, size = template_id.split('_')\n\n # Parse the xml file\n template_tree = Etree.parse(\"patron.xml\")\n root = template_tree.getroot()\n\n # Find The selected template\n for template in root.findall(\"./type[@name='%s']/template[@size='%s']\" % (category, size)):\n # Find useful data\n info = 'T-shirt_template_%s_%s' % (category, size)\n transform = template.find('transform')\n\n # Creation of a main group for the Template\n template_attribs = {\n inkex.addNS('label', 'inkscape'): info,\n 'transform': transform.text if transform is not None else ''\n }\n template_group = inkex.etree.SubElement(self.current_layer, 'g', template_attribs)\n\n # For each pieces of the template\n for piece in template.findall('piece'):\n # Find useful data\n pieceinfo = info + \"_\" + piece.find('name').text\n transform = piece.find('transform')\n\n # Create a group for the piece\n piece_attribs = {\n inkex.addNS('label', 'inkscape'): pieceinfo,\n 'transform': transform.text if transform is not None else ''\n }\n piece_group = inkex.etree.SubElement(template_group, 'g', piece_attribs)\n\n # Add a text to display the piece info\n add_text(piece_group, pieceinfo.replace('_', ' '), piece.find('info').text, 15)\n\n # For each paths of the piece\n for part in piece.findall('part'):\n # Find useful data\n label = part.find('name').text\n partinfo = pieceinfo + \"_\" + label\n transform = part.find('transform')\n\n # Create a group for the shape\n part_attribs = {\n inkex.addNS('label', 'inkscape'): partinfo,\n 'transform': transform.text if transform is not None else ''\n }\n part_group = inkex.etree.SubElement(piece_group, 'g', part_attribs)\n\n # Add the path to the group\n style = self.normal_line if self.options.style == 'print' or label != 'offset' else self.cut_line\n path_attribs = {\n inkex.addNS('label', 'inkscape'): partinfo,\n 'style': simplestyle.formatStyle(style),\n 'd': part.find('path').text\n }\n inkex.etree.SubElement(part_group, inkex.addNS('path', 'svg'), path_attribs)","def join_path(self, template, parent):\n if (template.startswith('./')):\n return os.path.join(os.path.dirname(parent), template)\n return template","def join_path(self, template: str, parent: str) -> str:\n return template","def find_and_normalize(value):\n if isinstance(value, dict):\n # if value is a dictionary, try to normalize the values\n return {k: normalize_dict_value(v) for k, v in value.items()}\n return normalize_path(value)","def convert_path_type(path):\n if isinstance(path, Path):\n np_path = path_to_array(path)[:,0]\n elif isinstance(path, Trajectory):\n np_path = trajectory_to_array(path)[:,0]\n elif isinstance(path, (list, np.ndarray)):\n np_path = np.array(path)\n else:\n raise ValueError(\"Invalid type for `path` argument. Must be an array-like type.\")\n\n return np_path","def Transform(*args, **kwargs):\n return _gdi_.GraphicsPath_Transform(*args, **kwargs)","def replaceVariables(template, virtroot, treename):\n if not template.startswith('${') and not template.endswith('}'): raise SyntaxError('Template format unknown.')\n template = template[2:-1]\n template = template.replace('VIRTROOT', virtroot)\n template = template.replace('TREENAME', treename)\n\n # deal with concurrent / issues by having os.path.join build the final path\n return os.path.join('/', *template.split('/'))","def get_type_from_path(path):\n return path.split('.')[-1]","def get_template_path(relative_path, **kwargs): # lint-amnesty, pylint: disable=unused-argument\n return relative_path","def load_template_with_location(self, template_path):\n if not template_path:\n raise exc.FrameworkError(\"Invalid template path '%s'.\" %\n template_path)\n\n # first attempt to load from file\n content, path = self._load_template_from_file(template_path)\n if content is None:\n # second attempt to load from module\n content, path = self._load_template_from_module(template_path)\n template_type = 'module'\n else:\n template_type = 'directory'\n\n # if content is None, that means we didn't find a template file in\n # either and that is an exception\n if content is None:\n raise exc.FrameworkError(\"Could not locate template: %s\" %\n template_path)\n\n return (content, template_type, path)","def _createTemplate(self, base_url, config):\n config # pylint\n\n url = self._createParsedUrl(base_url)\n while url.path[-1:] == u'/':\n url.path = url.path[:-1]\n\n tpl = {\n 'revision': (None, {\n 'remove': [u'p1', u'p2', u'r1', u'r2'],\n 'set': [\n (u'view', u'rev'),\n (u'rev', self._REVISION),\n ]\n }),\n\n 'deleted': (self._BASE_PATH, {\n 'remove': [u'p1', u'p2', u'r1', u'r2'],\n 'set': [\n (u'view', u'auto'),\n (u'rev', self._BASE_REVISION),\n ]\n }),\n\n 'added': (self._PATH, {\n 'remove': [u'p1', u'p2', u'r1', u'r2'],\n 'set': [\n (u'view', u'auto'),\n (u'rev', self._REVISION),\n ]\n }),\n\n 'copied': (self._PATH, {'set': [\n (u'view', u'diff'),\n (u'rev', self._REVISION),\n (u'p1', self._BASE_PATH),\n (u'r1', self._BASE_REVISION),\n (u'p2', self._PATH),\n (u'r2', self._REVISION),\n ]}),\n\n 'modified': (self._PATH, {'remove': [u'p1', u'p2'], 'set': [\n (u'view', u'diff'),\n (u'rev', self._REVISION),\n (u'r1', self._BASE_REVISION),\n (u'r2', self._REVISION),\n ]}),\n }\n tpl['deleted_dir'] = (u\"%s/\" % self._BASE_PATH, tpl['deleted'][1])\n tpl['added_dir'] = (u\"%s/\" % self._PATH, tpl['added'][1])\n\n return _base.Template.fromTemplates(url, tpl)","def get_template(self, format):\n for pattern, converter in self._patterns:\n if converter.format == format:\n template = pattern.generate('{name}')\n if template:\n return template\n return '{name}' f'.{format}'","def _path_strategy(self, host, port, path_info):\n mime_type = app = app_url = None\n\n parts = path_info.rsplit('.', 1)\n if len(parts) > 1:\n possible_type = 'application/' + parts[1]\n if possible_type in wsgi.get_supported_content_types():\n mime_type = possible_type\n\n parts = path_info.split('/')\n if len(parts) > 1:\n possible_app, possible_app_url = self._match(host, port, path_info)\n # Don't use prefix if it ends up matching default\n if possible_app and possible_app_url:\n app_url = possible_app_url\n app = self._munge_path(possible_app, path_info, app_url)\n\n return mime_type, app, app_url","def convert_path_units(self, path_to_conv):\n\n converted_path = copy.deepcopy(path_to_conv) # TODO: figure out if this is the right way to do this- I have not yet understood how data should be handled here\n\n for i, coord in enumerate(converted_path):\n converted_path[i][0] = coord[0] / self.units_in_meter\n converted_path[i][1] = coord[1] / self.units_in_meter\n converted_path[i][2] = coord[2] / self.units_in_meter\n\n return converted_path","def substitute(script, submap):\n\n match = config.TEMPLATE_RE.search(script)\n if match:\n template_type = match.groupdict()['type']\n try:\n return config.TEMPLATE_TYPEMAP[template_type](script, submap)\n except KeyError:\n logger.error('Unsupported template type: %s' % template_type)\n raise\n return script","def template_directory_index_to_image(template, directory, index):\n return template_directory_number2image(template, directory, index)","def filetype_of(path: Path) -> str:\n\n filetype = \"unsorted\"\n\n if path.suffix == \".json\":\n filetype = \"json\"\n\n elif path.suffix == \".txt\":\n if search(pattern=\"v[0-9][0-9]_[0-9]\", string=path.stem):\n filetype = \"onsets\"\n elif \"subject_info\" in path.stem:\n filetype = \"subject info\"\n\n elif path.suffix == \".nii\":\n if \"_t1_\" in path.stem:\n filetype = \"anat\"\n elif \"_lessvoids_\" in path.stem:\n filetype = \"func\"\n elif \"field_map\" in path.stem:\n filetype = \"fieldmap\"\n\n return filetype","def generate_input_file(temp_type, out_file):\r\n\r\n file_path = os.path.realpath(__file__)\r\n dir_path = os.sep.join(file_path.split(os.sep)[:-1])\r\n\r\n if temp_type == 0:\r\n template = 'Template00_CompleteParameters.py'\r\n elif temp_type == 1:\r\n template = 'Template01_SingleRowCylindricalRollerBearing.py'\r\n elif temp_type == 3:\r\n template = 'Template03_CylindricalRollerThustBearing.py'\r\n elif temp_type == 4:\r\n template = 'Template04_BallOnDisk.py'\r\n elif temp_type == 5:\r\n template = 'Template05_PinOnDisk.py'\r\n elif temp_type == 6:\r\n template = 'Template06_4Ball.py'\r\n elif temp_type == 7:\r\n template = 'Template07_BallOn3Plates.py'\r\n elif temp_type == 8:\r\n template = 'Template08_RingOnRing.py'\r\n else:\r\n raise ValueError(\"temp_type value '{}' undefined\".format(temp_type))\r\n\r\n shutil.copy(os.sep.join([dir_path, 'UserInputTemplates', template]),\r\n out_file)\r\n return out_file","def PathType(path_str):\n orig = path_str\n path_str = os.path.expanduser(path_str) # Expand user path if necessary\n path_str = os.path.abspath(path_str)\n\n if os.path.exists(path_str):\n return path_str\n else:\n raise argparse.ArgumentError(message='\"{}\" is not a valid path'.format(orig))","def normalize_path(path, config):\n\n for alias, link in config[\"Aliases\"].items():\n if path.startswith(alias):\n return path.replace(alias, link, 1)\n\n return path","def template_index_to_image(template, index):\n return template_number2image(template, index)","def trans(self, data_type):\n return sorted(self._text_paths[data_type])","def render_template(self):\n # create and expand commandline template\n tmpl_r1 = self.finditem.sub(r'{{\\2}}', self.raw_template)\n tmpl_r2 = jinja2.Template(tmpl_r1).render(self.variables)\n self.relation.script = tmpl_r2\n self.relation.template_sha256 = self.variables['template_sha256']","def _translate_in_type(self, type_conversion: type, *args) -> Any:\n try:\n return type_conversion(self.string)\n except (ValueError, TypeError):\n return type_conversion(*args)","def transformed(self, T):\n new = Polygon2()\n new.path = self.path.transformed(Affine2D(T.A))\n return new","def _init_pure_path(path: Union[str, PurePath]) -> PurePath:\n if not isinstance(path, PurePath):\n path = PurePath(path)\n return path","def mrtrix_mesh2vox(surface_path, template_path, temp_dir, output_prefix):\n # Adapt affine translation using metadata\n template = nib.load(template_path)\n _, _, meta = read_geometry(surface_path, read_metadata=True)\n\n template = nib.as_closest_canonical(template)\n affine = template.affine.copy()\n affine[:-1, -1] = template.affine[:-1, -1] - meta['cras']\n\n new_template = nib.Nifti1Image(template.dataobj, affine)\n new_template_path = temp_dir / 'template.mgz'\n nib.save(new_template, new_template_path)\n\n # Reconstruct volume from mesh\n subprocess.run(['mesh2voxel', surface_path, new_template_path, temp_dir / f'{output_prefix}_output.mgz'])\n\n # Save the reconstructed volume with the right affine\n output = nib.load(temp_dir / f'{output_prefix}_output.mgz')\n new_output = nib.Nifti1Image(output.dataobj, template.affine)\n # nib.save(new_output, output_path)\n\n return new_output","def get_template(self, template):\n\n\n env = Environment(\n loader=FileSystemLoader('templates')\n )\n return env.get_template(template)","def _parse_path(\n value_expr: str, target_expr: str, ref_parts: List[str],\n a_type: mapry.Path, auto_id: mapry.py.generate.AutoID,\n py: mapry.Py) -> str:\n uid = auto_id.next_identifier()\n\n return _PARSE_PATH_TPL.render(\n uid=uid,\n value_expr=value_expr,\n ref_parts=ref_parts,\n target_expr=target_expr,\n a_type=a_type,\n py=py).rstrip(\"\\n\")","def _substitute_template_parts(template_code):\n template_path = current_app.config.get('REPORT_TEMPLATE_PATH')\n template_parts = [\n 'v2/style',\n 'v2/styleMail',\n 'v2/stylePage',\n 'v2/stylePageCover',\n 'v2/stylePageDraft',\n 'v2/stylePageMail',\n 'v2/stylePageRegistration',\n 'v2/stylePageRegistrationDraft',\n 'stylePageMail',\n 'logo',\n 'macros',\n 'registrarSignature',\n 'registration/details',\n 'registration/givingNoticeParty',\n 'registration/location',\n 'registration/notes',\n 'registration/owners',\n 'registration/sections',\n 'registration/submittingParty',\n 'search-result/details',\n 'search-result/location',\n 'search-result/notes',\n 'search-result/owners',\n 'search-result/pprRegistrations',\n 'v2/search-result/selected',\n 'search-result/sections',\n 'v2/search-result/registration',\n 'search-result-ppr/financingStatement',\n 'search-result-ppr/amendmentStatement',\n 'search-result-ppr/changeStatement',\n 'search-result-ppr/renewalStatement',\n 'search-result-ppr/dischargeStatement',\n 'search-result-ppr/securedParties',\n 'search-result-ppr/courtOrderInformation',\n 'search-result-ppr/debtors',\n 'search-result-ppr/registeringParty',\n 'search-result-ppr/vehicleCollateral',\n 'search-result-ppr/generalCollateral'\n ]\n\n # substitute template parts - marked up by [[filename]]\n for template_part in template_parts:\n if template_code.find('[[{}.html]]'.format(template_part)) >= 0:\n template_part_code = Path(f'{template_path}/template-parts/{template_part}.html').read_text()\n for template_part_nested in template_parts:\n template_reference = '[[{}.html]]'.format(template_part_nested)\n if template_part_code.find(template_reference) >= 0:\n path = Path(f'{template_path}/template-parts/{template_part_nested}.html')\n template_nested_code = path.read_text()\n template_part_code = template_part_code.replace(template_reference, template_nested_code)\n template_code = template_code.replace('[[{}.html]]'.format(template_part), template_part_code)\n\n return template_code","def normalise(self) -> \"Route\":\n pass","def guess_content_type ( self, path_info ) :\n _type, _enc = guess_type ( path_info )\n return _type","def path_convert(self):\n pub_path = Exp_msg()\n for i in self.path:\n epoint = Cordi()\n (epoint.x, epoint.y) = i\n pub_path.bliss.append(epoint)\n return(pub_path)","def path_to_operation(path, verb):\n # type: (unicode, unicode) -> unicode\n character_map = {\n ord(\"{\"): None,\n ord(\"}\"): None,\n ord(\"_\"): u\"/\"\n }\n if path == u\"/\":\n operation = ROOT_OPERATION\n else:\n sanitised = path.translate(character_map)\n operation = u\"_\".join(p for p in sanitised.split(\"/\"))\n\n return \"{}_{}\".format(verb, operation)","def render_legacy_template(template_path: str, context: Mapping[str, str]) -> str:\n\n # will be replaced by django templates in the future\n with open(template_path, 'r', encoding='utf-8') as template:\n template_str = template.read()\n return Template(template_str).substitute(**context)","def test_traversal__path_type_view_name(path, resource_type, view_name):\n from pyramid.traversal import traverse\n root_resource = root_resource_factory()\n t = traverse(root_resource, path)\n assert isinstance(t['context'], resource_type)\n assert t['view_name'] == view_name","def normalizePath(path):\n if path == None or len(path) == 0 or path == '/':\n return '/'\n buff = '/' + path if path[0] != '/' else path\n return buff.replace('//', '/')","def convert_pattern(pattern, pattern_type=None):\n\tif pattern_type == 'regex':\n\t\treturn re.compile(pattern)\n\telif pattern_type == 'wildcard':\n\t\treturn re.compile(fnmatch.translate(pattern))\n\treturn re.compile(re.escape(pattern))","def handle_routes(pathname):\n pathlist = pathname.split(\"/\")\n print(pathlist)\n if len(pathlist) >= 2:\n pathname = \"/\" + pathlist[1]\n else:\n pathname = pathlist[0]\n\n if pathname == \"/\" or pathname == home.path:\n return home.layout\n elif pathname == upload.path:\n return upload.layout\n elif pathname == table.path:\n return table.layout\n elif pathname == graph.path:\n return graph.layout\n elif pathname == \"/share\":\n if len(pathlist) >= 3:\n from src.plotting.pages.linkShare import figure_generator\n\n figure_generator.filename = pathlist[2] + \".pkl\"\n figure_generator.xaxis = pathlist[3]\n figure_generator.yaxis = pathlist[4]\n return offlinepage.offlinepagelayout()\n else:\n return bad_url.layout\n else:\n return bad_url.layout","def as_base(path):\n path = path if path.startswith('/') else '/' + path\n return path if path.endswith('/') else path + '/'","def from_path(cls, path: str) -> Any:","def _get_path(self, path, resource_path):\n for item in self.path_replace:\n if len(item) == 2:\n src, dest = item\n else:\n assert len(item) == 3\n src = item[0]\n dest = item[2 if resource_path else 1]\n if path.endswith(src):\n path = replace_end(path, src, dest)\n return path","def _image2template_directory(filename):\n\n directory = os.path.dirname(filename)\n\n if not directory:\n\n # then it should be the current working directory\n directory = os.getcwd()\n\n image = os.path.split(filename)[-1]\n template = _image2template(image)\n\n return template, directory","def translate_path(self, path):\n # abandon query parameters\n path = path.split('?', 1)[0]\n path = path.split('#', 1)[0]\n # Don't forget explicit trailing slash when normalizing. Issue17324\n trailing_slash = path.rstrip().endswith('/')\n try:\n path = urllib.parse.unquote(path, errors='surrogatepass')\n except UnicodeDecodeError:\n path = urllib.parse.unquote(path)\n path = normpath(path)\n words = path.split('/')\n words = filter(None, words)\n path = self.serve_path # use own path here (no cwd)\n for word in words:\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\n # Ignore components that are not a simple file/directory name\n continue\n path = os.path.join(path, word)\n if trailing_slash:\n path += '/'\n return path","def convert(tree) :\n kind = tree[0]\n\n if kind == \"dot\" :\n return \"dot\" \n elif kind == \"eol\" :\n return \"eol\"\n elif kind == \"char\" :\n return \"lit('\" + tree[1] + \"')\"\n elif kind == \"set\" :\n return \"oneof('\" + tree[1] + \"')\"\n elif kind == \"elem\" :\n if len(tree) >= 3 :\n return convert(tree[2]) \n else :\n return convert(tree[1])\n elif kind == \"basic\" :\n if len(tree) == 4 :\n return \"alt(\" + convert(tree[1]) + \",\" + convert(tree[3]) + \")\"\n elif len(tree) == 3 :\n return parse_single_op_string(tree[2]) + convert(tree[1]) + \")\"*len(tree[2])\n else :\n return convert(tree[1])\n elif kind == \"RE\" :\n if len(tree) == 3 and tree[2][1][0] != 'eol' :\n return \"seq(\" + convert(tree[1]) + \",\" + convert(tree[2]) + \")\"\n else :\n return convert(tree[1])\n else :\n print \"invalid node tag : {}\".format(kind)","def normalizeNativePath(path: unicode) -> unicode:\n ...","def convert(content, input_format, output_format):\r\n assert input_format in ('srt', 'sjson')\r\n assert output_format in ('txt', 'srt', 'sjson')\r\n\r\n if input_format == output_format:\r\n return content\r\n\r\n if input_format == 'srt':\r\n\r\n if output_format == 'txt':\r\n text = SubRipFile.from_string(content.decode('utf8')).text\r\n return HTMLParser().unescape(text)\r\n\r\n elif output_format == 'sjson':\r\n raise NotImplementedError\r\n\r\n if input_format == 'sjson':\r\n\r\n if output_format == 'txt':\r\n text = json.loads(content)['text']\r\n return HTMLParser().unescape(\"\\n\".join(text))\r\n\r\n elif output_format == 'srt':\r\n return generate_srt_from_sjson(json.loads(content), speed=1.0)","def typeDir(type):\n\tif type == 'script':\n\t\treturn 'script'\n\telse:\n\t\treturn 'file'","def parse_path(mode, image_type, is_label):\n suffix = \"labels\" if is_label else (\"images\" if image_type == ImageType.DIGIT else \"\")\n if image_type == ImageType.DIGIT:\n return f\"data/digitdata/{mode.path_infix}{suffix}\"\n if mode == Mode.TRAINING:\n return f\"data/facedata/facedatatrain{suffix}\"\n return f\"data/facedata/facedata{mode.path_infix}{suffix}\"","def send_stuff(path):\n return send_from_directory('templates', path)","def get_template_files(fs, template_type):\n # no template fitting for null runs\n if fs[\"null_run\"]:\n template_type = None\n\n if \"template_type\" in fs:\n if template_type == fs[\"template_type\"]:\n return\n\n fs[\"template_type\"] = template_type\n\n # find all corresponding foreground templates\n if template_type is None:\n fs[\"template_root\"] = None\n fs[\"template_root2\"] = None\n fs[\"template_files\"] = None\n fs[\"template_files2\"] = None\n fs[\"template_noise_root\"] = None\n fs[\"template_noise_root2\"] = None\n fs[\"template_noise_files\"] = None\n fs[\"template_noise_files2\"] = None\n fs[\"num_template\"] = 0\n fs[\"num_template_noise\"] = 0\n else:\n num_template_noise = None\n for hm in [\"1\", \"2\"]:\n suff = \"\" if hm == \"1\" else \"2\"\n troot = os.path.join(\n fs[\"data_root\"],\n \"templates_{}\".format(template_type),\n \"halfmission-{}\".format(hm),\n )\n ### this block is so sims with template type like\n # 353_100_gauss_003 can use ensemble in 353_100_gauss\n tp = template_type.split(\"_\")\n ttype = template_type\n if tp[-1].isdigit():\n if ttype[-7:] not in [\"353_100\", \"217_100\"]:\n ttype = \"_\".join(tp[:-1])\n\n tnroot = os.path.join(\n fs[\"data_root\"],\n \"templates_noise_{}\".format(ttype),\n \"halfmission-{}\".format(hm),\n )\n\n tfiles = []\n tnfiles = []\n for f in fs[\"map_files\"]:\n nfile = f.replace(fs[\"map_root\"], troot)\n if not os.path.exists(nfile):\n raise OSError(\"Missing hm-{} template for {}\".format(hm, f))\n tfiles.append(nfile)\n nfiles = sorted(\n glob.glob(\n f.replace(fs[\"map_root\"], tnroot).replace(\n \".fits\", \"_*.fits\"\n )\n )\n )\n if not len(nfiles):\n raise OSError(\n \"Missing hm-{} template noise for {}\".format(hm, f)\n )\n tnfiles.append(nfiles)\n if num_template_noise is not None:\n if len(nfiles) != num_template_noise:\n raise OSError(\n \"Wrong number of template noise sims. \"\n \"Found {} files, expected {}.\".format(\n len(nfiles), num_template_noise\n )\n )\n\n num_template_noise = len(nfiles)\n\n tfiles = np.asarray(tfiles)\n tnfiles = np.asarray(tnfiles)\n fs[\"template_root{}\".format(suff)] = troot\n fs[\"template_files{}\".format(suff)] = tfiles\n fs[\"template_noise_root{}\".format(suff)] = tnroot\n fs[\"template_noise_files{}\".format(suff)] = tnfiles\n\n fs[\"num_template\"] = len(fs[\"template_files\"])\n fs[\"num_template_noise\"] = num_template_noise\n self.log(\n \"Found {} templates in {}\".format(\n fs[\"num_template\"], fs[\"template_root\"]\n ),\n \"info\",\n )\n self.log(\n \"Found {} template noise files in {}\".format(\n fs[\"num_template_noise\"], fs[\"template_noise_root\"]\n ),\n \"info\",\n )\n self.log(\"Template files: {}\".format(fs[\"template_files\"]), \"debug\")\n\n fields = [\n \"template_type\",\n \"template_root\",\n \"template_root2\",\n \"template_files\",\n \"template_files2\",\n \"template_noise_root\",\n \"template_noise_root2\",\n \"template_noise_files\",\n \"template_noise_files2\",\n \"num_template\",\n \"num_template_noise\",\n ]\n for k in fields:\n setattr(self, k, fs[k])","def resolve_path(self, path, dont_create = False):\n\t\tif not os.path.isabs(path) and not os.path.sep in path:\n\t\t\t# clean it up to avoid typos\n\t\t\tpath = path.strip().lower()\n\t\t\ttmppath = self.temp_files.get(path)\n\t\t\tif tmppath is None:\n\t\t\t\tif dont_create is False:\n\t\t\t\t\ttmppath = self.mktemp(\n\t\t\t\t\t\tsuffix = '.py',\n\t\t\t\t\t\tprefix = '_console_%s_' %(path,)\n\t\t\t\t\t)\n\t\t\t\t\tself.temp_files[path] = tmppath\n\t\t\t\telse:\n\t\t\t\t\treturn path\n\t\t\treturn tmppath\n\t\treturn path","def convertGridPathToReal(pathInGrid, sx, sy, gx, gy, grid_reso = 0.1):\n\n pathInReal = (pathInGrid * grid_reso + grid_reso / 2)\n stepNum = pathInReal.shape[1]\n # Replace head and tail\n pathInReal[:, 0] = [sx, sy]\n pathInReal[:, 0] = [sx, sy]\n pathInReal[:, stepNum - 1] = [gx, gy]\n pathInReal[:, stepNum - 1] = [gx, gy]\n\n return pathInReal","def process_based_on_type(file_path):\n\t# Is this a file?\n\tif os.path.isfile(file_path):\n\t\tprocess_file(file_path)\n\t# Or is it a directory?\n\telif os.path.isdir(file_path):\n\t\tprocess_directory(file_path)","def path_for(objectid):","def directory_to_individual_conversion_method(self, path):\n\n\t\treturn Individual(calculation_set=VaspRelaxation(path=path, input_dictionary=copy.deepcopy(self.calculation_set_input_dictionary)))","def _update_template(template_path):\n template_definition = template_path\n\n # template output directory is output/templates, so need to create that location before pulling out the templates\n template_location = template_utilities.get_template_directory()\n\n # Install the template and get the path to the template directory for updating the configuration file.\n templates_path = template_utilities.install_template(template_location, template_definition)\n\n if templates_path:\n # Now need to find the templates definition of that zip file and locate it in the file system so that it can be\n settings = get_configuration()\n\n # Override the configuration details with the new template path. This should probably be handled by the\n # publishing plugin, but for now this will work\n settings.publishing.templates = str(templates_path.relative_to(get_configuration_root()))\n configuration_file_path = get_configuration_root() / 'config.yaml'\n\n dump_configuration(configuration_file_path, settings)","def get_template(self, property_type):\n\n valid_templates = [s for s in self.templates if \"{\" + property_type +\n \"}\" in s]\n return choice(valid_templates)"],"string":"[\n \"def get_path_from_template(path_template: str, path_type: PathType = PathType.AUTO) -> str:\\r\\n # automatically select path type depending on running OS\\r\\n if path_type == PathType.AUTO:\\r\\n if platform.system() == \\\"Windows\\\":\\r\\n path_type = PathType.WINDOWS\\r\\n elif platform.system() == \\\"Linux\\\":\\r\\n path_type = PathType.LINUX\\r\\n else:\\r\\n raise RuntimeError(\\\"Unknown platform\\\")\\r\\n\\r\\n path_template = path_template.replace(\\\"<USERNAME>\\\", get_user_name())\\r\\n\\r\\n # return correctly formatted path\\r\\n if path_type == PathType.WINDOWS:\\r\\n return str(pathlib.PureWindowsPath(path_template))\\r\\n elif path_type == PathType.LINUX:\\r\\n return str(pathlib.PurePosixPath(path_template))\\r\\n else:\\r\\n raise RuntimeError(\\\"Unknown platform\\\")\",\n \"def get_template_from_path(path: str) -> str:\\r\\n path = path.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n return path\",\n \"def convert(kls, path, configuration=None, converters=None, ignore_converters=None, joined=None):\\n path_type = type(path)\\n if path_type is Path:\\n return path\\n else:\\n joined = dot_joiner(path, item_type=path_type)\\n return Path(path, configuration, converters, ignore_converters, joined=joined)\",\n \"def type(path):\",\n \"def _create_path(root, dict_type, path):\\n for sub_path in path:\\n if not isinstance(root.get(sub_path, None), dict):\\n root[sub_path] = dict_type()\\n\\n root = root[sub_path]\\n\\n return root\",\n \"def determine_template_by_path(path):\\n path = path.lstrip('/')\\n\\n path_chunks = re.split('\\\\/', path)\\n if len(path_chunks) <= 1:\\n return path\\n else:\\n \\\"\\\"\\\"\\n For now be ignorant and just return the\\n first entry of the list as the possible template\\n name, so in fact we only have a 1 level deep structure\\n \\\"\\\"\\\"\\n return '_%s.html' % path_chunks[0]\",\n \"def resolve_template(template, item):\\n root_dir = config['link_root_dir'].as_filename()\\n placeholders = re.findall(r'(\\\\%\\\\{?\\\\w+\\\\}?)', template)\\n path = template\\n for p in placeholders:\\n path = path.replace(p, item[p.strip('%{}')])\\n return os.path.join(root_dir, path)\",\n \"def convert_to_path(arg: Any) -> Path:\\n return Path(arg)\",\n \"def path_deserializer(obj: str, cls: type = PurePath, **_: Any) -> PurePath:\\n path: PurePath = cls(obj)\\n return path\",\n \"def replace_dot_notations(template_path, type, replacing_dict, storing_path):\\n files_path = f\\\"{template_path}/{type}\\\"\\n\\n # Create a folder for the specific type of files\\n replaced_inner_path = f\\\"{storing_path}/{type}\\\"\\n if not os.path.exists(replaced_inner_path):\\n os.mkdir(replaced_inner_path)\\n\\n print(f\\\"Replacing dot notations in {type}\\\")\\n # Iterate over the files in the specific type directory\\n for pathname in glob.glob(os.path.join(files_path, '*')):\\n \\n filename = pathname[len(files_path):]\\n\\n with open(pathname, 'r') as file:\\n filedata = file.read()\\n\\n for original, replace in replacing_dict.items():\\n filedata = filedata.replace(original, replace)\\n\\n with open(f\\\"{replaced_inner_path}/{filename}\\\", 'w+') as file:\\n file.write(filedata)\",\n \"def prepare_path(self,path):\\n debug.info(4,\\\"Set path: \\\" + str(path))\\n\\n # This is marked for debug\\n path.set_path()\\n\\n # For debugging... if the path failed to route.\\n if False or path==None:\\n self.write_debug_gds()\\n\\n # First, simplify the path for\\n #debug.info(1,str(self.path)) \\n contracted_path = self.contract_path(path)\\n debug.info(3,\\\"Contracted path: \\\" + str(contracted_path))\\n \\n return contracted_path\",\n \"def __get__(self, obj, objtype):\\n raw_path = super(Path,self).__get__(obj,objtype)\\n return self._resolve(raw_path)\",\n \"def path_type(cls, path):\\n if os.path.isdir(path):\\n return 'package'\\n else:\\n return 'object'\",\n \"def canonicalPath(path_or_object):\",\n \"def _replace_path_with_type_symbol(cost_trees):\\n cost_trees_ = {}\\n tmp_dict = {}\\n for k, v in cost_trees.items():\\n if k == 'subcosts':\\n for kk, vv in v.items():\\n type_symbol = vv.pop('_type_symbol')\\n d = {type_symbol: vv}\\n if tmp_dict.get(k) is not None:\\n tmp_dict[k].update(d)\\n else:\\n tmp_dict[k] = d\\n elif type(v) == dict:\\n for kk, vv in v.items():\\n type_symbol = vv.pop('_type_symbol')\\n d = {type_symbol: _replace_path_with_type_symbol(vv)}\\n if cost_trees_.get(k) is not None:\\n cost_trees_[k].update(d)\\n else:\\n cost_trees_[k] = d\\n else:\\n tmp_dict[k] = v\\n cost_trees_.update(tmp_dict)\\n return cost_trees_\",\n \"def new_path(path):\\n return Path(path[0], path[1], path[2])\",\n \"def convert(\\n path_in, path_out, pathway_iri, wp_id, pathway_version, scale=100, theme=\\\"plain\\\"\\n):\\n if not path.exists(path_in):\\n raise Exception(f\\\"Missing file '{path_in}'\\\")\\n\\n if path.exists(path_out):\\n print(f\\\"File {path_out} already exists. Skipping.\\\")\\n return True\\n\\n dir_in = path.dirname(path_in)\\n base_in = path.basename(path_in)\\n # example base_in: 'WP4542.gpml'\\n [stub_in, ext_in_with_dot] = path.splitext(base_in)\\n # gettting rid of the leading dot, e.g., '.gpml' to 'gpml'\\n ext_in = LEADING_DOT_RE.sub(\\\"\\\", ext_in_with_dot)\\n\\n if ext_in != \\\"gpml\\\":\\n # TODO: how about *.gpml.xml?\\n raise Exception(f\\\"Currently only accepting *.gpml for path_in\\\")\\n gpml_f = path_in\\n\\n dir_out = path.dirname(path_out)\\n # example base_out: 'WP4542.svg'\\n base_out = path.basename(path_out)\\n [stub_out, ext_out_with_dot] = path.splitext(base_out)\\n # getting rid of the leading dot, e.g., '.svg' to 'svg'\\n ext_out = LEADING_DOT_RE.sub(\\\"\\\", ext_out_with_dot)\\n\\n tree = ET.parse(gpml_f, parser=parser)\\n root = tree.getroot()\\n\\n if root is None:\\n raise Exception(\\\"no root element\\\")\\n if root.tag is None:\\n raise Exception(\\\"no root tag\\\")\\n\\n gpml_version = re.sub(r\\\"{http://pathvisio.org/GPML/(\\\\w+)}Pathway\\\", r\\\"\\\\1\\\", root.tag)\\n if ext_out != \\\"gpml\\\" and gpml_version != LATEST_GPML_VERSION:\\n old_f = f\\\"{dir_in}/{stub_in}.{gpml_version}.gpml\\\"\\n rename(gpml_f, old_f)\\n convert(old_f, gpml_f, pathway_iri, wp_id, pathway_version, scale)\\n\\n # trying to get wd ids via sparql via pywikibot\\n site = pywikibot.Site(\\\"wikidata\\\", \\\"wikidata\\\")\\n repo = site.data_repository() # this is a DataSite object\\n wd_sparql = sparql.SparqlQuery(\\n endpoint=\\\"https://query.wikidata.org/sparql\\\", repo=repo\\n )\\n # (self, endpoint=None, entity_url=None, repo=None, 2 max_retries=None, retry_wait=None)\\n\\n if ext_out in [\\\"gpml\\\", \\\"owl\\\", \\\"pdf\\\", \\\"pwf\\\", \\\"txt\\\"]:\\n subprocess.run(shlex.split(f\\\"pathvisio convert {path_in} {path_out}\\\"))\\n elif ext_out == \\\"png\\\":\\n # TODO: look at using --scale as an option (instead of an argument),\\n # for both pathvisio and gpmlconverter.\\n # TODO: move the setting of a default value for scale into\\n # pathvisio instead of here.\\n subprocess.run(shlex.split(f\\\"pathvisio convert {path_in} {path_out} {scale}\\\"))\\n # Use interlacing? See https://github.com/PathVisio/pathvisio/issues/78\\n # It's probably not worthwhile. If we did it, we would need to install\\n # imagemagick and then run this:\\n # mv \\\"$path_out\\\" \\\"$path_out.noninterlaced.png\\\"\\n # convert -interlace PNG \\\"$path_out.noninterlaced.png\\\" \\\"$path_out\\\"\\n elif ext_out in [\\\"json\\\", \\\"jsonld\\\"]:\\n gpml2json(path_in, path_out, pathway_iri, wp_id, pathway_version, wd_sparql)\\n elif ext_out in [\\\"svg\\\", \\\"pvjssvg\\\"]:\\n #############################\\n # SVG\\n #############################\\n\\n json_f = f\\\"{dir_out}/{stub_in}.json\\\"\\n if not path.isfile(json_f):\\n gpml2json(path_in, json_f, pathway_iri, wp_id, pathway_version, wd_sparql)\\n\\n json2svg(json_f, path_out, pathway_iri, wp_id, pathway_version, theme)\\n else:\\n raise Exception(f\\\"Invalid output extension: '{ext_out}'\\\")\",\n \"def _path_from_name(name, type):\\n if name.startswith('_'):\\n return name.split('/')\\n design, name = name.split('/', 1)\\n return ['_design', design, type, name]\",\n \"def userPath2Path(arg, frame):\\n arg = ingest(arg)\\n if isabs(arg):\\n return Path(arg)\\n else:\\n return Path(arg, frame)\",\n \"def get_template(type):\\n # read model options file from Cloud Storage\\n content = storage.read_file('templates/' + type + '.yaml')\\n return Response(content, status=200, mimetype='application/text')\",\n \"def _purepath_to_str(\\n self, path: Union[Path, PurePath, str]\\n ) -> Union[Path, PurePath, str]:\\n if isinstance(path, PurePath):\\n path = str(path)\\n return path\",\n \"def get_out_path(in_path, filetype):\\n if filetype == \\\"yaml\\\":\\n out_dir = check_for_yaml_folder(in_path)\\n if out_dir:\\n filename, _ = os.path.splitext(os.path.basename(in_path))\\n out_path = os.path.join(out_dir, filename)\\n else:\\n filename, _ = os.path.splitext(os.path.abspath(in_path))\\n out_path = filename\\n elif filetype == \\\"json\\\":\\n out_dir = check_for_json_folder(in_path)\\n if out_dir:\\n filename, _ = os.path.splitext(os.path.basename(in_path))\\n out_path = os.path.join(out_dir, filename)\\n else:\\n filename, _ = os.path.splitext(os.path.abspath(in_path))\\n out_path = filename\\n else:\\n if check_if_plist(in_path):\\n out_path = in_path + \\\".yaml\\\"\\n else:\\n print(\\\"\\\\nERROR: File is not PLIST, JSON or YAML format.\\\\n\\\")\\n usage()\\n exit(1)\\n return out_path\",\n \"def _get_path_type(self, path: Path) -> str:\\n if path.is_dir():\\n return 'dir'\\n elif path.is_file():\\n return 'file'\\n else:\\n raise FileNotFoundError(f\\\"File {path} not found\\\")\",\n \"def py38_translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?', 1)[0]\\n path = path.split('#', 1)[0]\\n # Don't forget explicit trailing slash when normalizing. Issue17324\\n trailing_slash = path.rstrip().endswith('/')\\n try:\\n path = urllib.parse.unquote(path, errors='surrogatepass')\\n except UnicodeDecodeError:\\n path = urllib.parse.unquote(path)\\n path = posixpath.normpath(path)\\n words = path.split('/')\\n words = filter(None, words)\\n # py38: self.directory\\n # py36: os.getcwd()\\n path = STATIC_DIR\\n for word in words:\\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\\n # Ignore components that are not a simple file/directory name\\n continue\\n path = os.path.join(path, word)\\n if trailing_slash:\\n path += '/'\\n return path\",\n \"def unglom(\\r\\n d: T_StrAnyMapping, path: str, value: typing.Any\\r\\n) -> T_StrAnyMapping:\\r\\n try:\\r\\n return glom.assign(d, path, value)\\r\\n except KeyError:\\r\\n parent, child = path.rsplit(\\\".\\\", 1)\\r\\n return unglom(d, parent, {child: value})\",\n \"def _resolve_path(self, path):\\n filepath = None\\n mimetype = None\\n\\n for root, dirs, files in self.filter_files(self.path):\\n # Does it exist in error path?\\n error_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'error_templates', path)\\n try:\\n with open(error_path):\\n mimetype, encoding = mimetypes.guess_type(error_path)\\n filepath = error_path\\n except IOError:\\n pass\\n\\n # Does it exist in Tarbell blueprint?\\n if self.base:\\n basepath = os.path.join(root, self.blueprint_name, path)\\n try:\\n with open(basepath):\\n mimetype, encoding = mimetypes.guess_type(basepath)\\n filepath = basepath\\n except IOError:\\n pass\\n\\n # Does it exist under regular path?\\n fullpath = os.path.join(root, path)\\n try:\\n with open(fullpath):\\n mimetype, encoding = mimetypes.guess_type(fullpath)\\n filepath = fullpath\\n except IOError:\\n pass\\n\\n return filepath, mimetype\",\n \"def tmpl_to_str(self, template_idx, o1_id, o2_id):\\r\\n template_str = self.template_generator.templates[template_idx]\\r\\n holes = template_str.count('OBJ')\\r\\n assert holes <= 2\\r\\n if holes <= 0:\\r\\n return template_str\\r\\n elif holes == 1:\\r\\n return template_str.replace('OBJ', self.vocab_act[o1_id])\\r\\n else:\\r\\n return template_str.replace('OBJ', self.vocab_act[o1_id], 1)\\\\\\r\\n .replace('OBJ', self.vocab_act[o2_id], 1)\",\n \"def pathmaker(first_segment, *in_path_segments, rev=False):\\n\\n _path = first_segment\\n\\n _path = os.path.join(_path, *in_path_segments)\\n if rev is True or sys.platform not in ['win32', 'linux']:\\n return os.path.normpath(_path)\\n return os.path.normpath(_path).replace(os.path.sep, '/')\",\n \"def get_filename(self, path, params, type_=None):\\n phase = self.phase\\n\\n if type_:\\n phase += ('_' + type_)\\n\\n filename = self.FILENAME_TEMPLATES[phase].format(**params)\\n\\n return os.path.join(path, filename)\",\n \"def normalizePath(path):\\n\\tfrom os.path import normpath, sep\\n\\tresult = normpath(path)\\n\\tresult = result.replace(\\\"/\\\",sep)\\n\\tresult = result.replace(\\\"\\\\\\\\\\\",sep)\\n\\treturn adaptPath(result)\",\n \"def path_converter(text_path):\\n\\n out = ''\\n out_list = []\\n\\n for i, t in enumerate(text_path):\\n \\n if t == ' ':\\n out_list.append('\\\\\\\\ ')\\n elif t == \\\"(\\\" :\\n out_list.append('\\\\(')\\n elif t == \\\")\\\" :\\n out_list.append('\\\\)') \\n else:\\n out_list.append(t)\\n \\n return out.join(out_list)\",\n \"def resolve_path(self, path):\\n if path:\\n if path[0] == '/':\\n #zope objects case\\n try: return self.unrestrictedTraverse(path)\\n except: pass\\n else:\\n #aliss (python) objects case\\n try: return self.get_aliss_object(path)\\n except: pass\\n #case of no path\\n pass\",\n \"def save_path(self):\\n return self.template.manager.render_template_txt(self.path, self.template)\",\n \"def template_update_single(\\n field, inputs, inputs_dict_st=None, output_dir=None, spec_type=\\\"input\\\"\\n):\\n # if input_dict_st with state specific value is not available,\\n # the dictionary will be created from inputs object\\n from ..utils.typing import TypeParser # noqa\\n from pydra.engine.specs import LazyField\\n\\n VALID_TYPES = (str, ty.Union[str, bool], Path, ty.Union[Path, bool], LazyField)\\n\\n if inputs_dict_st is None:\\n inputs_dict_st = attr.asdict(inputs, recurse=False)\\n\\n if spec_type == \\\"input\\\":\\n inp_val_set = inputs_dict_st[field.name]\\n if inp_val_set is not attr.NOTHING and not TypeParser.is_instance(\\n inp_val_set, VALID_TYPES\\n ):\\n raise TypeError(\\n f\\\"'{field.name}' field has to be a Path instance or a bool, but {inp_val_set} set\\\"\\n )\\n if isinstance(inp_val_set, bool) and field.type in (Path, str):\\n raise TypeError(\\n f\\\"type of '{field.name}' is Path, consider using Union[Path, bool]\\\"\\n )\\n elif spec_type == \\\"output\\\":\\n if not TypeParser.contains_type(FileSet, field.type):\\n raise TypeError(\\n f\\\"output {field.name} should be file-system object, but {field.type} \\\"\\n \\\"set as the type\\\"\\n )\\n else:\\n raise TypeError(f\\\"spec_type can be input or output, but {spec_type} provided\\\")\\n # for inputs that the value is set (so the template is ignored)\\n if spec_type == \\\"input\\\" and isinstance(inputs_dict_st[field.name], (str, Path)):\\n return inputs_dict_st[field.name]\\n elif spec_type == \\\"input\\\" and inputs_dict_st[field.name] is False:\\n # if input fld is set to False, the fld shouldn't be used (setting NOTHING)\\n return attr.NOTHING\\n else: # inputs_dict[field.name] is True or spec_type is output\\n value = _template_formatting(field, inputs, inputs_dict_st)\\n # changing path so it is in the output_dir\\n if output_dir and value is not attr.NOTHING:\\n # should be converted to str, it is also used for input fields that should be str\\n if type(value) is list:\\n return [str(output_dir / Path(val).name) for val in value]\\n else:\\n return str(output_dir / Path(value).name)\\n else:\\n return attr.NOTHING\",\n \"def render(\\n path_or_template: str,\\n **kwargs,\\n) -> str:\\n if isinstance(path_or_template, Template):\\n template = path_or_template\\n elif path_or_template.startswith(\\\"<\\\"):\\n template = Template(path_or_template)\\n else:\\n with open(path_or_template, \\\"r\\\") as filp:\\n contents = filp.read()\\n template = Template(contents)\\n return template.safe_substitute(**kwargs)\",\n \"def convert_path(path):\\n lower_path = path.replace('\\\\\\\\', '/')\\n if lower_path[1] == \\\":\\\":\\n path_lower = lower_path.replace(\\\":\\\", \\\"\\\")\\n path_server = \\\"/\\\" + path_lower\\n else:\\n path_server = lower_path[1:]\\n\\n return path_server\",\n \"def parse_path(path): # -> Path | UnparsedPath | ParsedPath:\\n ...\",\n \"def convert_project_type(input_dir, output_dir):\\n param_info = [\\n (input_dir, 'input_dir', (str, Path)),\\n (output_dir, 'output_dir', (str, Path)),\\n ]\\n for param in param_info:\\n type_sanity(param[0], param[1], param[2])\\n\\n if isinstance(input_dir, str):\\n input_dir = Path(input_dir)\\n if isinstance(output_dir, str):\\n output_dir = Path(output_dir)\\n\\n sa_convert_project_type(input_dir, output_dir)\",\n \"def template_directory_number2image(template, directory, number):\\n\\n # construct the full image name\\n return os.path.join(directory, template_number2image(template, number))\",\n \"def osnorm(self):\\n import os\\n if os.sep=='/' and \\\"\\\\\\\\\\\" in str(self):\\n return Path(os.path.normpath(str(self).replace('\\\\\\\\','/' )))\\n elif os.sep=='\\\\\\\\' and \\\"/\\\" in str(self):\\n return Path(os.path.normpath(str(self).replace('/','\\\\\\\\' )))\\n else:\\n return self.norm()\",\n \"def _generate_from_template(self, name, path, context):\\n template = self._templates.get_template(name)\\n with open(path, 'w') as f:\\n f.write(template.render(context))\",\n \"def destPath(file, package, type='files'):\\n\\treturn tmpDir(package)+'/etc/univention/templates/'+type+'/'+file\",\n \"def saved_template(self, template_id):\\n\\n # From user params get the wanted type and size\\n category, size = template_id.split('_')\\n\\n # Parse the xml file\\n template_tree = Etree.parse(\\\"patron.xml\\\")\\n root = template_tree.getroot()\\n\\n # Find The selected template\\n for template in root.findall(\\\"./type[@name='%s']/template[@size='%s']\\\" % (category, size)):\\n # Find useful data\\n info = 'T-shirt_template_%s_%s' % (category, size)\\n transform = template.find('transform')\\n\\n # Creation of a main group for the Template\\n template_attribs = {\\n inkex.addNS('label', 'inkscape'): info,\\n 'transform': transform.text if transform is not None else ''\\n }\\n template_group = inkex.etree.SubElement(self.current_layer, 'g', template_attribs)\\n\\n # For each pieces of the template\\n for piece in template.findall('piece'):\\n # Find useful data\\n pieceinfo = info + \\\"_\\\" + piece.find('name').text\\n transform = piece.find('transform')\\n\\n # Create a group for the piece\\n piece_attribs = {\\n inkex.addNS('label', 'inkscape'): pieceinfo,\\n 'transform': transform.text if transform is not None else ''\\n }\\n piece_group = inkex.etree.SubElement(template_group, 'g', piece_attribs)\\n\\n # Add a text to display the piece info\\n add_text(piece_group, pieceinfo.replace('_', ' '), piece.find('info').text, 15)\\n\\n # For each paths of the piece\\n for part in piece.findall('part'):\\n # Find useful data\\n label = part.find('name').text\\n partinfo = pieceinfo + \\\"_\\\" + label\\n transform = part.find('transform')\\n\\n # Create a group for the shape\\n part_attribs = {\\n inkex.addNS('label', 'inkscape'): partinfo,\\n 'transform': transform.text if transform is not None else ''\\n }\\n part_group = inkex.etree.SubElement(piece_group, 'g', part_attribs)\\n\\n # Add the path to the group\\n style = self.normal_line if self.options.style == 'print' or label != 'offset' else self.cut_line\\n path_attribs = {\\n inkex.addNS('label', 'inkscape'): partinfo,\\n 'style': simplestyle.formatStyle(style),\\n 'd': part.find('path').text\\n }\\n inkex.etree.SubElement(part_group, inkex.addNS('path', 'svg'), path_attribs)\",\n \"def join_path(self, template, parent):\\n if (template.startswith('./')):\\n return os.path.join(os.path.dirname(parent), template)\\n return template\",\n \"def join_path(self, template: str, parent: str) -> str:\\n return template\",\n \"def find_and_normalize(value):\\n if isinstance(value, dict):\\n # if value is a dictionary, try to normalize the values\\n return {k: normalize_dict_value(v) for k, v in value.items()}\\n return normalize_path(value)\",\n \"def convert_path_type(path):\\n if isinstance(path, Path):\\n np_path = path_to_array(path)[:,0]\\n elif isinstance(path, Trajectory):\\n np_path = trajectory_to_array(path)[:,0]\\n elif isinstance(path, (list, np.ndarray)):\\n np_path = np.array(path)\\n else:\\n raise ValueError(\\\"Invalid type for `path` argument. Must be an array-like type.\\\")\\n\\n return np_path\",\n \"def Transform(*args, **kwargs):\\n return _gdi_.GraphicsPath_Transform(*args, **kwargs)\",\n \"def replaceVariables(template, virtroot, treename):\\n if not template.startswith('${') and not template.endswith('}'): raise SyntaxError('Template format unknown.')\\n template = template[2:-1]\\n template = template.replace('VIRTROOT', virtroot)\\n template = template.replace('TREENAME', treename)\\n\\n # deal with concurrent / issues by having os.path.join build the final path\\n return os.path.join('/', *template.split('/'))\",\n \"def get_type_from_path(path):\\n return path.split('.')[-1]\",\n \"def get_template_path(relative_path, **kwargs): # lint-amnesty, pylint: disable=unused-argument\\n return relative_path\",\n \"def load_template_with_location(self, template_path):\\n if not template_path:\\n raise exc.FrameworkError(\\\"Invalid template path '%s'.\\\" %\\n template_path)\\n\\n # first attempt to load from file\\n content, path = self._load_template_from_file(template_path)\\n if content is None:\\n # second attempt to load from module\\n content, path = self._load_template_from_module(template_path)\\n template_type = 'module'\\n else:\\n template_type = 'directory'\\n\\n # if content is None, that means we didn't find a template file in\\n # either and that is an exception\\n if content is None:\\n raise exc.FrameworkError(\\\"Could not locate template: %s\\\" %\\n template_path)\\n\\n return (content, template_type, path)\",\n \"def _createTemplate(self, base_url, config):\\n config # pylint\\n\\n url = self._createParsedUrl(base_url)\\n while url.path[-1:] == u'/':\\n url.path = url.path[:-1]\\n\\n tpl = {\\n 'revision': (None, {\\n 'remove': [u'p1', u'p2', u'r1', u'r2'],\\n 'set': [\\n (u'view', u'rev'),\\n (u'rev', self._REVISION),\\n ]\\n }),\\n\\n 'deleted': (self._BASE_PATH, {\\n 'remove': [u'p1', u'p2', u'r1', u'r2'],\\n 'set': [\\n (u'view', u'auto'),\\n (u'rev', self._BASE_REVISION),\\n ]\\n }),\\n\\n 'added': (self._PATH, {\\n 'remove': [u'p1', u'p2', u'r1', u'r2'],\\n 'set': [\\n (u'view', u'auto'),\\n (u'rev', self._REVISION),\\n ]\\n }),\\n\\n 'copied': (self._PATH, {'set': [\\n (u'view', u'diff'),\\n (u'rev', self._REVISION),\\n (u'p1', self._BASE_PATH),\\n (u'r1', self._BASE_REVISION),\\n (u'p2', self._PATH),\\n (u'r2', self._REVISION),\\n ]}),\\n\\n 'modified': (self._PATH, {'remove': [u'p1', u'p2'], 'set': [\\n (u'view', u'diff'),\\n (u'rev', self._REVISION),\\n (u'r1', self._BASE_REVISION),\\n (u'r2', self._REVISION),\\n ]}),\\n }\\n tpl['deleted_dir'] = (u\\\"%s/\\\" % self._BASE_PATH, tpl['deleted'][1])\\n tpl['added_dir'] = (u\\\"%s/\\\" % self._PATH, tpl['added'][1])\\n\\n return _base.Template.fromTemplates(url, tpl)\",\n \"def get_template(self, format):\\n for pattern, converter in self._patterns:\\n if converter.format == format:\\n template = pattern.generate('{name}')\\n if template:\\n return template\\n return '{name}' f'.{format}'\",\n \"def _path_strategy(self, host, port, path_info):\\n mime_type = app = app_url = None\\n\\n parts = path_info.rsplit('.', 1)\\n if len(parts) > 1:\\n possible_type = 'application/' + parts[1]\\n if possible_type in wsgi.get_supported_content_types():\\n mime_type = possible_type\\n\\n parts = path_info.split('/')\\n if len(parts) > 1:\\n possible_app, possible_app_url = self._match(host, port, path_info)\\n # Don't use prefix if it ends up matching default\\n if possible_app and possible_app_url:\\n app_url = possible_app_url\\n app = self._munge_path(possible_app, path_info, app_url)\\n\\n return mime_type, app, app_url\",\n \"def convert_path_units(self, path_to_conv):\\n\\n converted_path = copy.deepcopy(path_to_conv) # TODO: figure out if this is the right way to do this- I have not yet understood how data should be handled here\\n\\n for i, coord in enumerate(converted_path):\\n converted_path[i][0] = coord[0] / self.units_in_meter\\n converted_path[i][1] = coord[1] / self.units_in_meter\\n converted_path[i][2] = coord[2] / self.units_in_meter\\n\\n return converted_path\",\n \"def substitute(script, submap):\\n\\n match = config.TEMPLATE_RE.search(script)\\n if match:\\n template_type = match.groupdict()['type']\\n try:\\n return config.TEMPLATE_TYPEMAP[template_type](script, submap)\\n except KeyError:\\n logger.error('Unsupported template type: %s' % template_type)\\n raise\\n return script\",\n \"def template_directory_index_to_image(template, directory, index):\\n return template_directory_number2image(template, directory, index)\",\n \"def filetype_of(path: Path) -> str:\\n\\n filetype = \\\"unsorted\\\"\\n\\n if path.suffix == \\\".json\\\":\\n filetype = \\\"json\\\"\\n\\n elif path.suffix == \\\".txt\\\":\\n if search(pattern=\\\"v[0-9][0-9]_[0-9]\\\", string=path.stem):\\n filetype = \\\"onsets\\\"\\n elif \\\"subject_info\\\" in path.stem:\\n filetype = \\\"subject info\\\"\\n\\n elif path.suffix == \\\".nii\\\":\\n if \\\"_t1_\\\" in path.stem:\\n filetype = \\\"anat\\\"\\n elif \\\"_lessvoids_\\\" in path.stem:\\n filetype = \\\"func\\\"\\n elif \\\"field_map\\\" in path.stem:\\n filetype = \\\"fieldmap\\\"\\n\\n return filetype\",\n \"def generate_input_file(temp_type, out_file):\\r\\n\\r\\n file_path = os.path.realpath(__file__)\\r\\n dir_path = os.sep.join(file_path.split(os.sep)[:-1])\\r\\n\\r\\n if temp_type == 0:\\r\\n template = 'Template00_CompleteParameters.py'\\r\\n elif temp_type == 1:\\r\\n template = 'Template01_SingleRowCylindricalRollerBearing.py'\\r\\n elif temp_type == 3:\\r\\n template = 'Template03_CylindricalRollerThustBearing.py'\\r\\n elif temp_type == 4:\\r\\n template = 'Template04_BallOnDisk.py'\\r\\n elif temp_type == 5:\\r\\n template = 'Template05_PinOnDisk.py'\\r\\n elif temp_type == 6:\\r\\n template = 'Template06_4Ball.py'\\r\\n elif temp_type == 7:\\r\\n template = 'Template07_BallOn3Plates.py'\\r\\n elif temp_type == 8:\\r\\n template = 'Template08_RingOnRing.py'\\r\\n else:\\r\\n raise ValueError(\\\"temp_type value '{}' undefined\\\".format(temp_type))\\r\\n\\r\\n shutil.copy(os.sep.join([dir_path, 'UserInputTemplates', template]),\\r\\n out_file)\\r\\n return out_file\",\n \"def PathType(path_str):\\n orig = path_str\\n path_str = os.path.expanduser(path_str) # Expand user path if necessary\\n path_str = os.path.abspath(path_str)\\n\\n if os.path.exists(path_str):\\n return path_str\\n else:\\n raise argparse.ArgumentError(message='\\\"{}\\\" is not a valid path'.format(orig))\",\n \"def normalize_path(path, config):\\n\\n for alias, link in config[\\\"Aliases\\\"].items():\\n if path.startswith(alias):\\n return path.replace(alias, link, 1)\\n\\n return path\",\n \"def template_index_to_image(template, index):\\n return template_number2image(template, index)\",\n \"def trans(self, data_type):\\n return sorted(self._text_paths[data_type])\",\n \"def render_template(self):\\n # create and expand commandline template\\n tmpl_r1 = self.finditem.sub(r'{{\\\\2}}', self.raw_template)\\n tmpl_r2 = jinja2.Template(tmpl_r1).render(self.variables)\\n self.relation.script = tmpl_r2\\n self.relation.template_sha256 = self.variables['template_sha256']\",\n \"def _translate_in_type(self, type_conversion: type, *args) -> Any:\\n try:\\n return type_conversion(self.string)\\n except (ValueError, TypeError):\\n return type_conversion(*args)\",\n \"def transformed(self, T):\\n new = Polygon2()\\n new.path = self.path.transformed(Affine2D(T.A))\\n return new\",\n \"def _init_pure_path(path: Union[str, PurePath]) -> PurePath:\\n if not isinstance(path, PurePath):\\n path = PurePath(path)\\n return path\",\n \"def mrtrix_mesh2vox(surface_path, template_path, temp_dir, output_prefix):\\n # Adapt affine translation using metadata\\n template = nib.load(template_path)\\n _, _, meta = read_geometry(surface_path, read_metadata=True)\\n\\n template = nib.as_closest_canonical(template)\\n affine = template.affine.copy()\\n affine[:-1, -1] = template.affine[:-1, -1] - meta['cras']\\n\\n new_template = nib.Nifti1Image(template.dataobj, affine)\\n new_template_path = temp_dir / 'template.mgz'\\n nib.save(new_template, new_template_path)\\n\\n # Reconstruct volume from mesh\\n subprocess.run(['mesh2voxel', surface_path, new_template_path, temp_dir / f'{output_prefix}_output.mgz'])\\n\\n # Save the reconstructed volume with the right affine\\n output = nib.load(temp_dir / f'{output_prefix}_output.mgz')\\n new_output = nib.Nifti1Image(output.dataobj, template.affine)\\n # nib.save(new_output, output_path)\\n\\n return new_output\",\n \"def get_template(self, template):\\n\\n\\n env = Environment(\\n loader=FileSystemLoader('templates')\\n )\\n return env.get_template(template)\",\n \"def _parse_path(\\n value_expr: str, target_expr: str, ref_parts: List[str],\\n a_type: mapry.Path, auto_id: mapry.py.generate.AutoID,\\n py: mapry.Py) -> str:\\n uid = auto_id.next_identifier()\\n\\n return _PARSE_PATH_TPL.render(\\n uid=uid,\\n value_expr=value_expr,\\n ref_parts=ref_parts,\\n target_expr=target_expr,\\n a_type=a_type,\\n py=py).rstrip(\\\"\\\\n\\\")\",\n \"def _substitute_template_parts(template_code):\\n template_path = current_app.config.get('REPORT_TEMPLATE_PATH')\\n template_parts = [\\n 'v2/style',\\n 'v2/styleMail',\\n 'v2/stylePage',\\n 'v2/stylePageCover',\\n 'v2/stylePageDraft',\\n 'v2/stylePageMail',\\n 'v2/stylePageRegistration',\\n 'v2/stylePageRegistrationDraft',\\n 'stylePageMail',\\n 'logo',\\n 'macros',\\n 'registrarSignature',\\n 'registration/details',\\n 'registration/givingNoticeParty',\\n 'registration/location',\\n 'registration/notes',\\n 'registration/owners',\\n 'registration/sections',\\n 'registration/submittingParty',\\n 'search-result/details',\\n 'search-result/location',\\n 'search-result/notes',\\n 'search-result/owners',\\n 'search-result/pprRegistrations',\\n 'v2/search-result/selected',\\n 'search-result/sections',\\n 'v2/search-result/registration',\\n 'search-result-ppr/financingStatement',\\n 'search-result-ppr/amendmentStatement',\\n 'search-result-ppr/changeStatement',\\n 'search-result-ppr/renewalStatement',\\n 'search-result-ppr/dischargeStatement',\\n 'search-result-ppr/securedParties',\\n 'search-result-ppr/courtOrderInformation',\\n 'search-result-ppr/debtors',\\n 'search-result-ppr/registeringParty',\\n 'search-result-ppr/vehicleCollateral',\\n 'search-result-ppr/generalCollateral'\\n ]\\n\\n # substitute template parts - marked up by [[filename]]\\n for template_part in template_parts:\\n if template_code.find('[[{}.html]]'.format(template_part)) >= 0:\\n template_part_code = Path(f'{template_path}/template-parts/{template_part}.html').read_text()\\n for template_part_nested in template_parts:\\n template_reference = '[[{}.html]]'.format(template_part_nested)\\n if template_part_code.find(template_reference) >= 0:\\n path = Path(f'{template_path}/template-parts/{template_part_nested}.html')\\n template_nested_code = path.read_text()\\n template_part_code = template_part_code.replace(template_reference, template_nested_code)\\n template_code = template_code.replace('[[{}.html]]'.format(template_part), template_part_code)\\n\\n return template_code\",\n \"def normalise(self) -> \\\"Route\\\":\\n pass\",\n \"def guess_content_type ( self, path_info ) :\\n _type, _enc = guess_type ( path_info )\\n return _type\",\n \"def path_convert(self):\\n pub_path = Exp_msg()\\n for i in self.path:\\n epoint = Cordi()\\n (epoint.x, epoint.y) = i\\n pub_path.bliss.append(epoint)\\n return(pub_path)\",\n \"def path_to_operation(path, verb):\\n # type: (unicode, unicode) -> unicode\\n character_map = {\\n ord(\\\"{\\\"): None,\\n ord(\\\"}\\\"): None,\\n ord(\\\"_\\\"): u\\\"/\\\"\\n }\\n if path == u\\\"/\\\":\\n operation = ROOT_OPERATION\\n else:\\n sanitised = path.translate(character_map)\\n operation = u\\\"_\\\".join(p for p in sanitised.split(\\\"/\\\"))\\n\\n return \\\"{}_{}\\\".format(verb, operation)\",\n \"def render_legacy_template(template_path: str, context: Mapping[str, str]) -> str:\\n\\n # will be replaced by django templates in the future\\n with open(template_path, 'r', encoding='utf-8') as template:\\n template_str = template.read()\\n return Template(template_str).substitute(**context)\",\n \"def test_traversal__path_type_view_name(path, resource_type, view_name):\\n from pyramid.traversal import traverse\\n root_resource = root_resource_factory()\\n t = traverse(root_resource, path)\\n assert isinstance(t['context'], resource_type)\\n assert t['view_name'] == view_name\",\n \"def normalizePath(path):\\n if path == None or len(path) == 0 or path == '/':\\n return '/'\\n buff = '/' + path if path[0] != '/' else path\\n return buff.replace('//', '/')\",\n \"def convert_pattern(pattern, pattern_type=None):\\n\\tif pattern_type == 'regex':\\n\\t\\treturn re.compile(pattern)\\n\\telif pattern_type == 'wildcard':\\n\\t\\treturn re.compile(fnmatch.translate(pattern))\\n\\treturn re.compile(re.escape(pattern))\",\n \"def handle_routes(pathname):\\n pathlist = pathname.split(\\\"/\\\")\\n print(pathlist)\\n if len(pathlist) >= 2:\\n pathname = \\\"/\\\" + pathlist[1]\\n else:\\n pathname = pathlist[0]\\n\\n if pathname == \\\"/\\\" or pathname == home.path:\\n return home.layout\\n elif pathname == upload.path:\\n return upload.layout\\n elif pathname == table.path:\\n return table.layout\\n elif pathname == graph.path:\\n return graph.layout\\n elif pathname == \\\"/share\\\":\\n if len(pathlist) >= 3:\\n from src.plotting.pages.linkShare import figure_generator\\n\\n figure_generator.filename = pathlist[2] + \\\".pkl\\\"\\n figure_generator.xaxis = pathlist[3]\\n figure_generator.yaxis = pathlist[4]\\n return offlinepage.offlinepagelayout()\\n else:\\n return bad_url.layout\\n else:\\n return bad_url.layout\",\n \"def as_base(path):\\n path = path if path.startswith('/') else '/' + path\\n return path if path.endswith('/') else path + '/'\",\n \"def from_path(cls, path: str) -> Any:\",\n \"def _get_path(self, path, resource_path):\\n for item in self.path_replace:\\n if len(item) == 2:\\n src, dest = item\\n else:\\n assert len(item) == 3\\n src = item[0]\\n dest = item[2 if resource_path else 1]\\n if path.endswith(src):\\n path = replace_end(path, src, dest)\\n return path\",\n \"def _image2template_directory(filename):\\n\\n directory = os.path.dirname(filename)\\n\\n if not directory:\\n\\n # then it should be the current working directory\\n directory = os.getcwd()\\n\\n image = os.path.split(filename)[-1]\\n template = _image2template(image)\\n\\n return template, directory\",\n \"def translate_path(self, path):\\n # abandon query parameters\\n path = path.split('?', 1)[0]\\n path = path.split('#', 1)[0]\\n # Don't forget explicit trailing slash when normalizing. Issue17324\\n trailing_slash = path.rstrip().endswith('/')\\n try:\\n path = urllib.parse.unquote(path, errors='surrogatepass')\\n except UnicodeDecodeError:\\n path = urllib.parse.unquote(path)\\n path = normpath(path)\\n words = path.split('/')\\n words = filter(None, words)\\n path = self.serve_path # use own path here (no cwd)\\n for word in words:\\n if os.path.dirname(word) or word in (os.curdir, os.pardir):\\n # Ignore components that are not a simple file/directory name\\n continue\\n path = os.path.join(path, word)\\n if trailing_slash:\\n path += '/'\\n return path\",\n \"def convert(tree) :\\n kind = tree[0]\\n\\n if kind == \\\"dot\\\" :\\n return \\\"dot\\\" \\n elif kind == \\\"eol\\\" :\\n return \\\"eol\\\"\\n elif kind == \\\"char\\\" :\\n return \\\"lit('\\\" + tree[1] + \\\"')\\\"\\n elif kind == \\\"set\\\" :\\n return \\\"oneof('\\\" + tree[1] + \\\"')\\\"\\n elif kind == \\\"elem\\\" :\\n if len(tree) >= 3 :\\n return convert(tree[2]) \\n else :\\n return convert(tree[1])\\n elif kind == \\\"basic\\\" :\\n if len(tree) == 4 :\\n return \\\"alt(\\\" + convert(tree[1]) + \\\",\\\" + convert(tree[3]) + \\\")\\\"\\n elif len(tree) == 3 :\\n return parse_single_op_string(tree[2]) + convert(tree[1]) + \\\")\\\"*len(tree[2])\\n else :\\n return convert(tree[1])\\n elif kind == \\\"RE\\\" :\\n if len(tree) == 3 and tree[2][1][0] != 'eol' :\\n return \\\"seq(\\\" + convert(tree[1]) + \\\",\\\" + convert(tree[2]) + \\\")\\\"\\n else :\\n return convert(tree[1])\\n else :\\n print \\\"invalid node tag : {}\\\".format(kind)\",\n \"def normalizeNativePath(path: unicode) -> unicode:\\n ...\",\n \"def convert(content, input_format, output_format):\\r\\n assert input_format in ('srt', 'sjson')\\r\\n assert output_format in ('txt', 'srt', 'sjson')\\r\\n\\r\\n if input_format == output_format:\\r\\n return content\\r\\n\\r\\n if input_format == 'srt':\\r\\n\\r\\n if output_format == 'txt':\\r\\n text = SubRipFile.from_string(content.decode('utf8')).text\\r\\n return HTMLParser().unescape(text)\\r\\n\\r\\n elif output_format == 'sjson':\\r\\n raise NotImplementedError\\r\\n\\r\\n if input_format == 'sjson':\\r\\n\\r\\n if output_format == 'txt':\\r\\n text = json.loads(content)['text']\\r\\n return HTMLParser().unescape(\\\"\\\\n\\\".join(text))\\r\\n\\r\\n elif output_format == 'srt':\\r\\n return generate_srt_from_sjson(json.loads(content), speed=1.0)\",\n \"def typeDir(type):\\n\\tif type == 'script':\\n\\t\\treturn 'script'\\n\\telse:\\n\\t\\treturn 'file'\",\n \"def parse_path(mode, image_type, is_label):\\n suffix = \\\"labels\\\" if is_label else (\\\"images\\\" if image_type == ImageType.DIGIT else \\\"\\\")\\n if image_type == ImageType.DIGIT:\\n return f\\\"data/digitdata/{mode.path_infix}{suffix}\\\"\\n if mode == Mode.TRAINING:\\n return f\\\"data/facedata/facedatatrain{suffix}\\\"\\n return f\\\"data/facedata/facedata{mode.path_infix}{suffix}\\\"\",\n \"def send_stuff(path):\\n return send_from_directory('templates', path)\",\n \"def get_template_files(fs, template_type):\\n # no template fitting for null runs\\n if fs[\\\"null_run\\\"]:\\n template_type = None\\n\\n if \\\"template_type\\\" in fs:\\n if template_type == fs[\\\"template_type\\\"]:\\n return\\n\\n fs[\\\"template_type\\\"] = template_type\\n\\n # find all corresponding foreground templates\\n if template_type is None:\\n fs[\\\"template_root\\\"] = None\\n fs[\\\"template_root2\\\"] = None\\n fs[\\\"template_files\\\"] = None\\n fs[\\\"template_files2\\\"] = None\\n fs[\\\"template_noise_root\\\"] = None\\n fs[\\\"template_noise_root2\\\"] = None\\n fs[\\\"template_noise_files\\\"] = None\\n fs[\\\"template_noise_files2\\\"] = None\\n fs[\\\"num_template\\\"] = 0\\n fs[\\\"num_template_noise\\\"] = 0\\n else:\\n num_template_noise = None\\n for hm in [\\\"1\\\", \\\"2\\\"]:\\n suff = \\\"\\\" if hm == \\\"1\\\" else \\\"2\\\"\\n troot = os.path.join(\\n fs[\\\"data_root\\\"],\\n \\\"templates_{}\\\".format(template_type),\\n \\\"halfmission-{}\\\".format(hm),\\n )\\n ### this block is so sims with template type like\\n # 353_100_gauss_003 can use ensemble in 353_100_gauss\\n tp = template_type.split(\\\"_\\\")\\n ttype = template_type\\n if tp[-1].isdigit():\\n if ttype[-7:] not in [\\\"353_100\\\", \\\"217_100\\\"]:\\n ttype = \\\"_\\\".join(tp[:-1])\\n\\n tnroot = os.path.join(\\n fs[\\\"data_root\\\"],\\n \\\"templates_noise_{}\\\".format(ttype),\\n \\\"halfmission-{}\\\".format(hm),\\n )\\n\\n tfiles = []\\n tnfiles = []\\n for f in fs[\\\"map_files\\\"]:\\n nfile = f.replace(fs[\\\"map_root\\\"], troot)\\n if not os.path.exists(nfile):\\n raise OSError(\\\"Missing hm-{} template for {}\\\".format(hm, f))\\n tfiles.append(nfile)\\n nfiles = sorted(\\n glob.glob(\\n f.replace(fs[\\\"map_root\\\"], tnroot).replace(\\n \\\".fits\\\", \\\"_*.fits\\\"\\n )\\n )\\n )\\n if not len(nfiles):\\n raise OSError(\\n \\\"Missing hm-{} template noise for {}\\\".format(hm, f)\\n )\\n tnfiles.append(nfiles)\\n if num_template_noise is not None:\\n if len(nfiles) != num_template_noise:\\n raise OSError(\\n \\\"Wrong number of template noise sims. \\\"\\n \\\"Found {} files, expected {}.\\\".format(\\n len(nfiles), num_template_noise\\n )\\n )\\n\\n num_template_noise = len(nfiles)\\n\\n tfiles = np.asarray(tfiles)\\n tnfiles = np.asarray(tnfiles)\\n fs[\\\"template_root{}\\\".format(suff)] = troot\\n fs[\\\"template_files{}\\\".format(suff)] = tfiles\\n fs[\\\"template_noise_root{}\\\".format(suff)] = tnroot\\n fs[\\\"template_noise_files{}\\\".format(suff)] = tnfiles\\n\\n fs[\\\"num_template\\\"] = len(fs[\\\"template_files\\\"])\\n fs[\\\"num_template_noise\\\"] = num_template_noise\\n self.log(\\n \\\"Found {} templates in {}\\\".format(\\n fs[\\\"num_template\\\"], fs[\\\"template_root\\\"]\\n ),\\n \\\"info\\\",\\n )\\n self.log(\\n \\\"Found {} template noise files in {}\\\".format(\\n fs[\\\"num_template_noise\\\"], fs[\\\"template_noise_root\\\"]\\n ),\\n \\\"info\\\",\\n )\\n self.log(\\\"Template files: {}\\\".format(fs[\\\"template_files\\\"]), \\\"debug\\\")\\n\\n fields = [\\n \\\"template_type\\\",\\n \\\"template_root\\\",\\n \\\"template_root2\\\",\\n \\\"template_files\\\",\\n \\\"template_files2\\\",\\n \\\"template_noise_root\\\",\\n \\\"template_noise_root2\\\",\\n \\\"template_noise_files\\\",\\n \\\"template_noise_files2\\\",\\n \\\"num_template\\\",\\n \\\"num_template_noise\\\",\\n ]\\n for k in fields:\\n setattr(self, k, fs[k])\",\n \"def resolve_path(self, path, dont_create = False):\\n\\t\\tif not os.path.isabs(path) and not os.path.sep in path:\\n\\t\\t\\t# clean it up to avoid typos\\n\\t\\t\\tpath = path.strip().lower()\\n\\t\\t\\ttmppath = self.temp_files.get(path)\\n\\t\\t\\tif tmppath is None:\\n\\t\\t\\t\\tif dont_create is False:\\n\\t\\t\\t\\t\\ttmppath = self.mktemp(\\n\\t\\t\\t\\t\\t\\tsuffix = '.py',\\n\\t\\t\\t\\t\\t\\tprefix = '_console_%s_' %(path,)\\n\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\tself.temp_files[path] = tmppath\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\treturn path\\n\\t\\t\\treturn tmppath\\n\\t\\treturn path\",\n \"def convertGridPathToReal(pathInGrid, sx, sy, gx, gy, grid_reso = 0.1):\\n\\n pathInReal = (pathInGrid * grid_reso + grid_reso / 2)\\n stepNum = pathInReal.shape[1]\\n # Replace head and tail\\n pathInReal[:, 0] = [sx, sy]\\n pathInReal[:, 0] = [sx, sy]\\n pathInReal[:, stepNum - 1] = [gx, gy]\\n pathInReal[:, stepNum - 1] = [gx, gy]\\n\\n return pathInReal\",\n \"def process_based_on_type(file_path):\\n\\t# Is this a file?\\n\\tif os.path.isfile(file_path):\\n\\t\\tprocess_file(file_path)\\n\\t# Or is it a directory?\\n\\telif os.path.isdir(file_path):\\n\\t\\tprocess_directory(file_path)\",\n \"def path_for(objectid):\",\n \"def directory_to_individual_conversion_method(self, path):\\n\\n\\t\\treturn Individual(calculation_set=VaspRelaxation(path=path, input_dictionary=copy.deepcopy(self.calculation_set_input_dictionary)))\",\n \"def _update_template(template_path):\\n template_definition = template_path\\n\\n # template output directory is output/templates, so need to create that location before pulling out the templates\\n template_location = template_utilities.get_template_directory()\\n\\n # Install the template and get the path to the template directory for updating the configuration file.\\n templates_path = template_utilities.install_template(template_location, template_definition)\\n\\n if templates_path:\\n # Now need to find the templates definition of that zip file and locate it in the file system so that it can be\\n settings = get_configuration()\\n\\n # Override the configuration details with the new template path. This should probably be handled by the\\n # publishing plugin, but for now this will work\\n settings.publishing.templates = str(templates_path.relative_to(get_configuration_root()))\\n configuration_file_path = get_configuration_root() / 'config.yaml'\\n\\n dump_configuration(configuration_file_path, settings)\",\n \"def get_template(self, property_type):\\n\\n valid_templates = [s for s in self.templates if \\\"{\\\" + property_type +\\n \\\"}\\\" in s]\\n return choice(valid_templates)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64579725","0.5781432","0.5625563","0.54816496","0.5473164","0.5293687","0.51608443","0.50819355","0.4978549","0.4844417","0.47959632","0.47708523","0.4746237","0.4732008","0.47292966","0.47214973","0.4701345","0.46961728","0.46805945","0.4661857","0.46608216","0.4637848","0.46248716","0.4609218","0.46038908","0.4594943","0.45837006","0.45750704","0.4559959","0.4550782","0.45392424","0.45347652","0.45271122","0.4520793","0.45175147","0.45090485","0.44948146","0.44859567","0.44851112","0.44746557","0.44741395","0.44704348","0.4457682","0.44573495","0.44556922","0.4452258","0.44498548","0.4432174","0.44272265","0.4423416","0.442038","0.4417468","0.4409839","0.44034424","0.4402765","0.44020948","0.4402052","0.44001877","0.438868","0.43825734","0.43655697","0.43619448","0.4361164","0.43603405","0.43602583","0.43589282","0.43524823","0.43481076","0.43414974","0.43377498","0.43355143","0.4331297","0.432663","0.4316863","0.43002275","0.42990872","0.4296216","0.42927107","0.42915606","0.42879862","0.42867565","0.42845368","0.4278092","0.42724738","0.42721847","0.42711502","0.42597735","0.425669","0.42526257","0.42492312","0.42483455","0.42465717","0.42443594","0.42443115","0.4237894","0.4236998","0.4228691","0.42237726","0.42232445","0.42216513"],"string":"[\n \"0.64579725\",\n \"0.5781432\",\n \"0.5625563\",\n \"0.54816496\",\n \"0.5473164\",\n \"0.5293687\",\n \"0.51608443\",\n \"0.50819355\",\n \"0.4978549\",\n \"0.4844417\",\n \"0.47959632\",\n \"0.47708523\",\n \"0.4746237\",\n \"0.4732008\",\n \"0.47292966\",\n \"0.47214973\",\n \"0.4701345\",\n \"0.46961728\",\n \"0.46805945\",\n \"0.4661857\",\n \"0.46608216\",\n \"0.4637848\",\n \"0.46248716\",\n \"0.4609218\",\n \"0.46038908\",\n \"0.4594943\",\n \"0.45837006\",\n \"0.45750704\",\n \"0.4559959\",\n \"0.4550782\",\n \"0.45392424\",\n \"0.45347652\",\n \"0.45271122\",\n \"0.4520793\",\n \"0.45175147\",\n \"0.45090485\",\n \"0.44948146\",\n \"0.44859567\",\n \"0.44851112\",\n \"0.44746557\",\n \"0.44741395\",\n \"0.44704348\",\n \"0.4457682\",\n \"0.44573495\",\n \"0.44556922\",\n \"0.4452258\",\n \"0.44498548\",\n \"0.4432174\",\n \"0.44272265\",\n \"0.4423416\",\n \"0.442038\",\n \"0.4417468\",\n \"0.4409839\",\n \"0.44034424\",\n \"0.4402765\",\n \"0.44020948\",\n \"0.4402052\",\n \"0.44001877\",\n \"0.438868\",\n \"0.43825734\",\n \"0.43655697\",\n \"0.43619448\",\n \"0.4361164\",\n \"0.43603405\",\n \"0.43602583\",\n \"0.43589282\",\n \"0.43524823\",\n \"0.43481076\",\n \"0.43414974\",\n \"0.43377498\",\n \"0.43355143\",\n \"0.4331297\",\n \"0.432663\",\n \"0.4316863\",\n \"0.43002275\",\n \"0.42990872\",\n \"0.4296216\",\n \"0.42927107\",\n \"0.42915606\",\n \"0.42879862\",\n \"0.42867565\",\n \"0.42845368\",\n \"0.4278092\",\n \"0.42724738\",\n \"0.42721847\",\n \"0.42711502\",\n \"0.42597735\",\n \"0.425669\",\n \"0.42526257\",\n \"0.42492312\",\n \"0.42483455\",\n \"0.42465717\",\n \"0.42443594\",\n \"0.42443115\",\n \"0.4237894\",\n \"0.4236998\",\n \"0.4228691\",\n \"0.42237726\",\n \"0.42232445\",\n \"0.42216513\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7523337"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77082,"cells":{"query":{"kind":"string","value":"Set the global username override value."},"document":{"kind":"string","value":"def set_user_name_override(name: str) -> None:\r\n global _user_name_override\r\n _user_name_override = name"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def set_username(self, value):\n self.username = value","def change_username(self, name):\n self.username = name","def set_username(self, value):\n raise NotImplementedError('set_username')","def set_uname(self, username):\n Server.t_usernames[threading.get_ident()] = username\n _print(f\"t_usernames: {Server.t_usernames}\")","def set_username(self, username):\n self._java_ref.setUsername(username)","def setName(self, newName):\n self.__username = newName","def save(self, *args, **kwargs):\n self.username = self.username or self.email\n super().save(*args, **kwargs)","def username(self, username) :\n\t\ttry :\n\t\t\tself._username = username\n\t\texcept Exception as e:\n\t\t\traise e","def auth_username(self, auth_username):\n\n self._auth_username = auth_username","def git_username_user_attribute(self, git_username_user_attribute):\n self._git_username_user_attribute = git_username_user_attribute","def username(self, username):\n\n self._username = username","def username(self, username):\n\n self._username = username","def username(self, username):\n\n self._username = username","def username(self, username):\n\n self._username = username","def username(self, username):\n\n self._username = username","def username(self, username):\n\n self._username = username","def username(self, username):\n\n self._username = username","def username(self, username):\n\n self._username = username","def username(self, username):\n\n self._username = username","def username(self, username: str):\n\n self._username = username","def update_username(self, old_username, new_username):\n raise NotImplementedError()","def git_username(self, git_username):\n self._git_username = git_username","def _username(self):\n if 'username' not in self._config:\n self._config['username'] = self._UI.get_input(\"Please enter your trac username: \")\n self._config._write_config()\n return self._config['username']","def user_name(self, user_name):\n\n self._user_name = user_name","def _optionsmenu_changeusername():\n\n self.helpindex = Toplevel(self.master)\n self.helpindex.title(\"Change Username\")\n self.helpindex.geometry(\"300x500\")","def OnTextCtrlLoginText(self, event):\r\n\t\tself._configtmp[\"username\"] = event.GetString()","def username(self, username):\n self._username = username\n return self","def rename(self,newName):\n self.userName = newName","def update_user_settings_default_name(self, id_user:int) -> None:\n try:\n value_selected = self.return_user_name_default_bool(id_user)\n value_bool_new = True if not value_selected else False\n self.cursor.execute(f\"UPDATE {table_users_settings} SET name_default_boolean={value_bool_new} WHERE id_user={id_user};\")\n self.connection.commit()\n except Exception as e:\n msg = f\"We faced problems with the changing of the default name usage. Mistake: {e}\"\n self.proceed_error(msg)","def set_main_user_options(self):\n if self.user:\n self.template_values[self._tp_logoutLink] = True\n self.template_values[self._tp_userName] = self.user.username\n else:\n self.template_values[self._tp_loginLink] = True\n self.template_values[self._tp_signupLink] = True","def enter_username(self, user=TestData.USERS[0]):\n self.username.clear()\n self.username.click()\n self.username.send_keys(user)\n return self.username","def OverridePlatformName(name):\n global override_platform_name\n override_platform_name = name","def driver_username(self, driver_username):\n\n self._driver_username = driver_username","def __setattr__(self, name: str, value: Any) -> None:\n if name.isupper():\n self._settings[name] = value\n super().__setattr__(name, value)","def username_format(self, username_format):\n\n self._username_format = username_format","def username(self) -> str:\n return self.get_env_var(self.username_var)","def username(self) -> str:\n return self.get_env_var(self.username_var)","def set_default_org(self, name):\n org = self.get_org(name)\n self.unset_default_org()\n org.config[\"default\"] = True\n org.save()\n if org.created:\n sfdx(\n sarge.shell_format(\n \"force:config:set defaultusername={}\", org.sfdx_alias\n )\n )","def enter_username(self):","def set_real_name(user: User, real_name: str=\"\") -> Result:\n current, *rest = user.pw_gecos.split(\",\")\n if current == real_name:\n return Result(State.unchanged)\n command([\"/usr/bin/chfn\", \"--full-name\", real_name, user.pw_name])\n user.pw_gecos = \",\".join([real_name, *rest])\n return Result(State.success)","def getUsername(self):\n\t\treturn self.Username.lower()","def username(self, username):\n if self.local_vars_configuration.client_side_validation and username is None: # noqa: E501\n raise ValueError(\"Invalid value for `username`, must not be `None`\") # noqa: E501\n\n self._username = username","def get_auth_username():\n return DEFAULT_AUTH_USERNAME.get()","def username_in(self, username_in):\n\n self._username_in = username_in","def username_in(self, username_in):\n\n self._username_in = username_in","def username(self) -> str:\n raise NotImplementedError","def set_username(self, accountid, newusername):\n auth = 'appkey='+ self._lr_object._get_api_key()+ '&appsecret='+ self._lr_object._get_api_secret() + '&accountid=' + accountid\n payload = {'newusername': newusername}\n url = SECURE_API_URL + \"raas/v1/account/setusername\" + \"?\" + auth\n return self._lr_object._post_json(url, payload)","def username(self) -> undefined.UndefinedOr[str]:","def GetUsername(self):\n pass","def update_name_default(self, id_user:int, text_new:str) -> bool:\n try:\n self.cursor.execute(f\"UPDATE {table_users_settings} SET name_default = ? WHERE id_user = ?;\", (text_new, id_user))\n self.connection.commit()\n return True\n except Exception as e:\n msg = f\"We face problems with updating the default name; Mistake: {e}\"\n self.proceed_error(msg)\n return False","def change_user(self, username, password):\n self.creds['username'] = username\n self.creds['password'] = password","def username(self) -> str:","def username(self) -> str:","def autoname(self):\n\t\tself.name = self.role_profile","def get_username(self):\r\n return self.username","def user_full_name(self, user_full_name):\n\n self._user_full_name = user_full_name","def set_username(old_name, new_name):\n if not validate_username(new_name):\n return \"käyttäjänimi on väärää muotoa\"\n if user_exists(new_name):\n return \"käyttäjänimi on jo käytössä\"\n sql = \"UPDATE users \" \\\n \"SET username=:new \" \\\n \"WHERE username=:old\"\n db.session.execute(sql, {\"new\": new_name, \"old\": old_name})\n db.session.commit()\n return \"ok\"","def set_attrs(self, username, attrs):\n pass","def add(self, user_or_username, set_as_default=False, **kwargs):\n user = super().add(user_or_username, set_as_default=set_as_default, **kwargs)\n\n if user.expected_prompt == -1 and self.default and self.default.expected_prompt != -1:\n user.expected_prompt = self.default.expected_prompt\n\n return user","def timestamp_server_user_name(self, timestamp_server_user_name):\n\n self._timestamp_server_user_name = timestamp_server_user_name","def user_name(self):\n return lamin_user_settings().name","def get_username(self):\r\n raise NotImplementedError","def management_username(self, management_username):\n\n self._management_username = management_username","def setShadowDefaults(self):\n for user in self.shadowDefault.keys():\n #if not self.userspace.has_key(user):\n self.userspace[user].info = self.shadowDefault[user]","def default_user(self) -> str:\n return OS_MAPPING.get(self.os, []).get(\"user\", None)","def user_data_override(self) -> Optional[str]:\n return pulumi.get(self, \"user_data_override\")","def set_owner(self, owner):\n self.settings[\"owner\"] = owner","def _onUserSettings(self, event):\n dialog = sc.UserSettingsDialog(self)\n if dialog.ShowModal() == wx.ID_OK:\n dialog.saveSettings()\n dialog.Destroy()\n \n sel = self.userbox.GetSelection()\n usernames = ['None'] + c.getUserNames()\n self.userbox.SetItems(usernames)\n self.userbox.SetSelection(sel)","def get_admin_username(self) -> str:\n # read the original value passed by the command\n admin_username = self.raw_param.get(\"admin_username\")\n # try to read the property value corresponding to the parameter from the `mc` object\n if (\n self.mc and\n self.mc.linux_profile and\n self.mc.linux_profile.admin_username is not None\n ):\n admin_username = self.mc.linux_profile.admin_username\n\n # this parameter does not need dynamic completion\n # this parameter does not need validation\n return admin_username","def setGlobal(name, value):","def setuser(repo, username, email, password):\n print('Repo: %s' % repo)\n print('Username: %s' % username)\n print('E-Mail: %s' % email)\n print('Password: %s' % ('*' * len(password)))","def set_as_default (self):\n\t\ttry:\n\t\t\tself.config.set('Global', 'Default', self.currentAccount.data['name'])\n\t\texcept ConfigParser.NoSectionError:\n\t\t\tself.setup_config()\n\t\t\tself.config.set('Global', 'Default', self.currentAccount.data['name'])\n\t\tself.config.write(open(self.configFile, 'w'))","def on_setting_myname(self, value):\n raise NotImplementedError()","def updateUsername(self, username):\n\t\turl = \"https://habitica.com/api/v3/user/auth/update-username\"\n\t\tpayload = {\"username\": username}\n\t\treturn(putUrl(url, self.credentials, payload))","def _set_guc(self, name, value):\n if value.lower() != 'default':\n value = util.quote_string(self, value)\n else:\n value = b'default'\n self._execute_command(ascii_to_bytes('SET %s TO ' % name) + value)","async def name(self, ctx:utils.Context, *, username:str):\n\n if len(username) > 32:\n await ctx.send('That username is too long.')\n return\n await self.bot.user.edit(username=username)\n await ctx.send('Done.')","def set_user_register(self, *args, **kwargs):\n return _uhd_swig.usrp_source_set_user_register(self, *args, **kwargs)","def get_username(self):\n raise NotImplementedError('get_username')","async def change_username(self, new_username: str, password: str):\n\n data = {\"username\": f\"{new_username}\", \"password\": f\"{password}\"}\n ee = await self.request.request(url=f'https://auth.roblox.com/v2/username', method='post', data=data)\n return ee","def preenche_username(self):\n driver = self.selenium_test.driver\n for names in ['username', 'senha']:\n element = driver.find_element_by_name(names)\n assert element is not None\n element.send_keys('ajna')","def git_username_user_attribute(self):\n return self._git_username_user_attribute","def init_user_ns(self):\n super(CoconutShell, self).init_user_ns()\n RUNNER.update_vars(self.user_ns)\n RUNNER.update_vars(self.user_ns_hidden)","def setOwnerPassword(self,value):\n self.PDFreactorConfiguration.in1[\"ownerPassword\"] = value","def store_username_in_config(username):\n # Needs global variable or arg instead.\n with open(cfg.config_file, 'a') as f:\n f.write(\"\\nusername: %s\" % username)","def change_nick(self, before, after):\n userdata = self.users[irc.strings.lower(before)]\n self.del_user(before)\n self.add_user(after, userdata)","def set_username(self, user_id: int, username: str):\n user: User = UserService.get_user_by_id(self, user_id)\n if user is not None and username is not None:\n if user.username is None:\n user.username = username\n try:\n user.save()\n return model_to_dict(user)\n except BaseException:\n raise ValueError(HTTPStatus.CONFLICT,\n 'Username already exists')\n else:\n raise ValueError(HTTPStatus.NOT_MODIFIED,\n 'Username can only be set once')\n else:\n raise ValueError(HTTPStatus.BAD_REQUEST, 'Username is required')","def __init__(self, request=None, *args, **kwargs):\r\n self.request = request\r\n self.user_cache = None\r\n super(CustomizeAuthenticationForm, self).__init__(*args, **kwargs)\r\n\r\n # Set the label for the \"username\" field.\r\n UserModel = get_user_model()\r\n self.username_field = UserModel._meta.get_field(UserModel.USERNAME_FIELD)\r\n if self.fields['username'].label is None:\r\n self.fields['username'].label = capfirst(self.username_field.verbose_name)","def dbusername(self):\n return 'myfls_'+self.user.username","def configure_username(device, username, pwd, encryption=0, privilege=''):\n try:\n # Update str with password encryption level\n if encryption:\n pwd = '{encryption} {pwd}'.format(encryption=encryption,pwd=pwd)\n if privilege:\n username = '{username} privilege {privilege}'.format(username=username,privilege=privilege)\n device.configure('username {username} password {pwd}'.format(username=username,pwd=pwd))\n except SubCommandFailure:\n raise SubCommandFailure(\n \"Failed to configure user {username}\".format(username=username)\n )","async def name(self, ctx, *, name):\n # [p]set name <name>\n\n name = name.strip()\n if name != \"\":\n try:\n await self.bot.edit_profile(username=name)\n except:\n await self.bot.say(\"Failed to change name. Remember that you\"\n \" can only do it up to 2 times an hour.\"\n \"Use nicknames if you need frequent \"\n \"changes. {}set nickname\".format(ctx.prefix))\n else:\n await self.bot.say(\"Done.\")\n else:\n await send_command_help(ctx)","def set_demo_user() -> None:\n g.demo_user = _DEMO_USER","def set_name(self, name):\n self.settings[\"name\"] = name","def get_username(self):\n return self.username","def input_user_name(self, user_name):\n self.locate_element_by_css_selector(USERNAME_SELECTOR).send_keys(user_name)","def set_user_id(uid):\n local.user_id = uid","def updateUsers(self):\n sel = self.userbox.GetValue()\n usernames = ['None'] + c.getUserNames()\n self.userbox.SetItems(usernames)\n if sel in self.userbox.GetItems():\n self.userbox.SetValue(sel)\n if sel == 'None':\n self.btnUserSettings.Enable(False)\n else:\n self.btnUserSettings.Enable(True)\n else:\n self.userbox.SetSelection(0)\n self.btnUserSettings.Enable(False)","def change_user(self, login):\n self.task_storage.change_user_config(login)","def get_username(self):\n if self.controller.oem_config:\n return 'oem'\n return self.username","def setUserCode(self, userCode):\n self.userCode = userCode\n logger.debug('user code set to: %s' % self.userCode)","def clean_username (self):\n return self.instance.username"],"string":"[\n \"def set_username(self, value):\\n self.username = value\",\n \"def change_username(self, name):\\n self.username = name\",\n \"def set_username(self, value):\\n raise NotImplementedError('set_username')\",\n \"def set_uname(self, username):\\n Server.t_usernames[threading.get_ident()] = username\\n _print(f\\\"t_usernames: {Server.t_usernames}\\\")\",\n \"def set_username(self, username):\\n self._java_ref.setUsername(username)\",\n \"def setName(self, newName):\\n self.__username = newName\",\n \"def save(self, *args, **kwargs):\\n self.username = self.username or self.email\\n super().save(*args, **kwargs)\",\n \"def username(self, username) :\\n\\t\\ttry :\\n\\t\\t\\tself._username = username\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def auth_username(self, auth_username):\\n\\n self._auth_username = auth_username\",\n \"def git_username_user_attribute(self, git_username_user_attribute):\\n self._git_username_user_attribute = git_username_user_attribute\",\n \"def username(self, username):\\n\\n self._username = username\",\n \"def username(self, username):\\n\\n self._username = username\",\n \"def username(self, username):\\n\\n self._username = username\",\n \"def username(self, username):\\n\\n self._username = username\",\n \"def username(self, username):\\n\\n self._username = username\",\n \"def username(self, username):\\n\\n self._username = username\",\n \"def username(self, username):\\n\\n self._username = username\",\n \"def username(self, username):\\n\\n self._username = username\",\n \"def username(self, username):\\n\\n self._username = username\",\n \"def username(self, username: str):\\n\\n self._username = username\",\n \"def update_username(self, old_username, new_username):\\n raise NotImplementedError()\",\n \"def git_username(self, git_username):\\n self._git_username = git_username\",\n \"def _username(self):\\n if 'username' not in self._config:\\n self._config['username'] = self._UI.get_input(\\\"Please enter your trac username: \\\")\\n self._config._write_config()\\n return self._config['username']\",\n \"def user_name(self, user_name):\\n\\n self._user_name = user_name\",\n \"def _optionsmenu_changeusername():\\n\\n self.helpindex = Toplevel(self.master)\\n self.helpindex.title(\\\"Change Username\\\")\\n self.helpindex.geometry(\\\"300x500\\\")\",\n \"def OnTextCtrlLoginText(self, event):\\r\\n\\t\\tself._configtmp[\\\"username\\\"] = event.GetString()\",\n \"def username(self, username):\\n self._username = username\\n return self\",\n \"def rename(self,newName):\\n self.userName = newName\",\n \"def update_user_settings_default_name(self, id_user:int) -> None:\\n try:\\n value_selected = self.return_user_name_default_bool(id_user)\\n value_bool_new = True if not value_selected else False\\n self.cursor.execute(f\\\"UPDATE {table_users_settings} SET name_default_boolean={value_bool_new} WHERE id_user={id_user};\\\")\\n self.connection.commit()\\n except Exception as e:\\n msg = f\\\"We faced problems with the changing of the default name usage. Mistake: {e}\\\"\\n self.proceed_error(msg)\",\n \"def set_main_user_options(self):\\n if self.user:\\n self.template_values[self._tp_logoutLink] = True\\n self.template_values[self._tp_userName] = self.user.username\\n else:\\n self.template_values[self._tp_loginLink] = True\\n self.template_values[self._tp_signupLink] = True\",\n \"def enter_username(self, user=TestData.USERS[0]):\\n self.username.clear()\\n self.username.click()\\n self.username.send_keys(user)\\n return self.username\",\n \"def OverridePlatformName(name):\\n global override_platform_name\\n override_platform_name = name\",\n \"def driver_username(self, driver_username):\\n\\n self._driver_username = driver_username\",\n \"def __setattr__(self, name: str, value: Any) -> None:\\n if name.isupper():\\n self._settings[name] = value\\n super().__setattr__(name, value)\",\n \"def username_format(self, username_format):\\n\\n self._username_format = username_format\",\n \"def username(self) -> str:\\n return self.get_env_var(self.username_var)\",\n \"def username(self) -> str:\\n return self.get_env_var(self.username_var)\",\n \"def set_default_org(self, name):\\n org = self.get_org(name)\\n self.unset_default_org()\\n org.config[\\\"default\\\"] = True\\n org.save()\\n if org.created:\\n sfdx(\\n sarge.shell_format(\\n \\\"force:config:set defaultusername={}\\\", org.sfdx_alias\\n )\\n )\",\n \"def enter_username(self):\",\n \"def set_real_name(user: User, real_name: str=\\\"\\\") -> Result:\\n current, *rest = user.pw_gecos.split(\\\",\\\")\\n if current == real_name:\\n return Result(State.unchanged)\\n command([\\\"/usr/bin/chfn\\\", \\\"--full-name\\\", real_name, user.pw_name])\\n user.pw_gecos = \\\",\\\".join([real_name, *rest])\\n return Result(State.success)\",\n \"def getUsername(self):\\n\\t\\treturn self.Username.lower()\",\n \"def username(self, username):\\n if self.local_vars_configuration.client_side_validation and username is None: # noqa: E501\\n raise ValueError(\\\"Invalid value for `username`, must not be `None`\\\") # noqa: E501\\n\\n self._username = username\",\n \"def get_auth_username():\\n return DEFAULT_AUTH_USERNAME.get()\",\n \"def username_in(self, username_in):\\n\\n self._username_in = username_in\",\n \"def username_in(self, username_in):\\n\\n self._username_in = username_in\",\n \"def username(self) -> str:\\n raise NotImplementedError\",\n \"def set_username(self, accountid, newusername):\\n auth = 'appkey='+ self._lr_object._get_api_key()+ '&appsecret='+ self._lr_object._get_api_secret() + '&accountid=' + accountid\\n payload = {'newusername': newusername}\\n url = SECURE_API_URL + \\\"raas/v1/account/setusername\\\" + \\\"?\\\" + auth\\n return self._lr_object._post_json(url, payload)\",\n \"def username(self) -> undefined.UndefinedOr[str]:\",\n \"def GetUsername(self):\\n pass\",\n \"def update_name_default(self, id_user:int, text_new:str) -> bool:\\n try:\\n self.cursor.execute(f\\\"UPDATE {table_users_settings} SET name_default = ? WHERE id_user = ?;\\\", (text_new, id_user))\\n self.connection.commit()\\n return True\\n except Exception as e:\\n msg = f\\\"We face problems with updating the default name; Mistake: {e}\\\"\\n self.proceed_error(msg)\\n return False\",\n \"def change_user(self, username, password):\\n self.creds['username'] = username\\n self.creds['password'] = password\",\n \"def username(self) -> str:\",\n \"def username(self) -> str:\",\n \"def autoname(self):\\n\\t\\tself.name = self.role_profile\",\n \"def get_username(self):\\r\\n return self.username\",\n \"def user_full_name(self, user_full_name):\\n\\n self._user_full_name = user_full_name\",\n \"def set_username(old_name, new_name):\\n if not validate_username(new_name):\\n return \\\"käyttäjänimi on väärää muotoa\\\"\\n if user_exists(new_name):\\n return \\\"käyttäjänimi on jo käytössä\\\"\\n sql = \\\"UPDATE users \\\" \\\\\\n \\\"SET username=:new \\\" \\\\\\n \\\"WHERE username=:old\\\"\\n db.session.execute(sql, {\\\"new\\\": new_name, \\\"old\\\": old_name})\\n db.session.commit()\\n return \\\"ok\\\"\",\n \"def set_attrs(self, username, attrs):\\n pass\",\n \"def add(self, user_or_username, set_as_default=False, **kwargs):\\n user = super().add(user_or_username, set_as_default=set_as_default, **kwargs)\\n\\n if user.expected_prompt == -1 and self.default and self.default.expected_prompt != -1:\\n user.expected_prompt = self.default.expected_prompt\\n\\n return user\",\n \"def timestamp_server_user_name(self, timestamp_server_user_name):\\n\\n self._timestamp_server_user_name = timestamp_server_user_name\",\n \"def user_name(self):\\n return lamin_user_settings().name\",\n \"def get_username(self):\\r\\n raise NotImplementedError\",\n \"def management_username(self, management_username):\\n\\n self._management_username = management_username\",\n \"def setShadowDefaults(self):\\n for user in self.shadowDefault.keys():\\n #if not self.userspace.has_key(user):\\n self.userspace[user].info = self.shadowDefault[user]\",\n \"def default_user(self) -> str:\\n return OS_MAPPING.get(self.os, []).get(\\\"user\\\", None)\",\n \"def user_data_override(self) -> Optional[str]:\\n return pulumi.get(self, \\\"user_data_override\\\")\",\n \"def set_owner(self, owner):\\n self.settings[\\\"owner\\\"] = owner\",\n \"def _onUserSettings(self, event):\\n dialog = sc.UserSettingsDialog(self)\\n if dialog.ShowModal() == wx.ID_OK:\\n dialog.saveSettings()\\n dialog.Destroy()\\n \\n sel = self.userbox.GetSelection()\\n usernames = ['None'] + c.getUserNames()\\n self.userbox.SetItems(usernames)\\n self.userbox.SetSelection(sel)\",\n \"def get_admin_username(self) -> str:\\n # read the original value passed by the command\\n admin_username = self.raw_param.get(\\\"admin_username\\\")\\n # try to read the property value corresponding to the parameter from the `mc` object\\n if (\\n self.mc and\\n self.mc.linux_profile and\\n self.mc.linux_profile.admin_username is not None\\n ):\\n admin_username = self.mc.linux_profile.admin_username\\n\\n # this parameter does not need dynamic completion\\n # this parameter does not need validation\\n return admin_username\",\n \"def setGlobal(name, value):\",\n \"def setuser(repo, username, email, password):\\n print('Repo: %s' % repo)\\n print('Username: %s' % username)\\n print('E-Mail: %s' % email)\\n print('Password: %s' % ('*' * len(password)))\",\n \"def set_as_default (self):\\n\\t\\ttry:\\n\\t\\t\\tself.config.set('Global', 'Default', self.currentAccount.data['name'])\\n\\t\\texcept ConfigParser.NoSectionError:\\n\\t\\t\\tself.setup_config()\\n\\t\\t\\tself.config.set('Global', 'Default', self.currentAccount.data['name'])\\n\\t\\tself.config.write(open(self.configFile, 'w'))\",\n \"def on_setting_myname(self, value):\\n raise NotImplementedError()\",\n \"def updateUsername(self, username):\\n\\t\\turl = \\\"https://habitica.com/api/v3/user/auth/update-username\\\"\\n\\t\\tpayload = {\\\"username\\\": username}\\n\\t\\treturn(putUrl(url, self.credentials, payload))\",\n \"def _set_guc(self, name, value):\\n if value.lower() != 'default':\\n value = util.quote_string(self, value)\\n else:\\n value = b'default'\\n self._execute_command(ascii_to_bytes('SET %s TO ' % name) + value)\",\n \"async def name(self, ctx:utils.Context, *, username:str):\\n\\n if len(username) > 32:\\n await ctx.send('That username is too long.')\\n return\\n await self.bot.user.edit(username=username)\\n await ctx.send('Done.')\",\n \"def set_user_register(self, *args, **kwargs):\\n return _uhd_swig.usrp_source_set_user_register(self, *args, **kwargs)\",\n \"def get_username(self):\\n raise NotImplementedError('get_username')\",\n \"async def change_username(self, new_username: str, password: str):\\n\\n data = {\\\"username\\\": f\\\"{new_username}\\\", \\\"password\\\": f\\\"{password}\\\"}\\n ee = await self.request.request(url=f'https://auth.roblox.com/v2/username', method='post', data=data)\\n return ee\",\n \"def preenche_username(self):\\n driver = self.selenium_test.driver\\n for names in ['username', 'senha']:\\n element = driver.find_element_by_name(names)\\n assert element is not None\\n element.send_keys('ajna')\",\n \"def git_username_user_attribute(self):\\n return self._git_username_user_attribute\",\n \"def init_user_ns(self):\\n super(CoconutShell, self).init_user_ns()\\n RUNNER.update_vars(self.user_ns)\\n RUNNER.update_vars(self.user_ns_hidden)\",\n \"def setOwnerPassword(self,value):\\n self.PDFreactorConfiguration.in1[\\\"ownerPassword\\\"] = value\",\n \"def store_username_in_config(username):\\n # Needs global variable or arg instead.\\n with open(cfg.config_file, 'a') as f:\\n f.write(\\\"\\\\nusername: %s\\\" % username)\",\n \"def change_nick(self, before, after):\\n userdata = self.users[irc.strings.lower(before)]\\n self.del_user(before)\\n self.add_user(after, userdata)\",\n \"def set_username(self, user_id: int, username: str):\\n user: User = UserService.get_user_by_id(self, user_id)\\n if user is not None and username is not None:\\n if user.username is None:\\n user.username = username\\n try:\\n user.save()\\n return model_to_dict(user)\\n except BaseException:\\n raise ValueError(HTTPStatus.CONFLICT,\\n 'Username already exists')\\n else:\\n raise ValueError(HTTPStatus.NOT_MODIFIED,\\n 'Username can only be set once')\\n else:\\n raise ValueError(HTTPStatus.BAD_REQUEST, 'Username is required')\",\n \"def __init__(self, request=None, *args, **kwargs):\\r\\n self.request = request\\r\\n self.user_cache = None\\r\\n super(CustomizeAuthenticationForm, self).__init__(*args, **kwargs)\\r\\n\\r\\n # Set the label for the \\\"username\\\" field.\\r\\n UserModel = get_user_model()\\r\\n self.username_field = UserModel._meta.get_field(UserModel.USERNAME_FIELD)\\r\\n if self.fields['username'].label is None:\\r\\n self.fields['username'].label = capfirst(self.username_field.verbose_name)\",\n \"def dbusername(self):\\n return 'myfls_'+self.user.username\",\n \"def configure_username(device, username, pwd, encryption=0, privilege=''):\\n try:\\n # Update str with password encryption level\\n if encryption:\\n pwd = '{encryption} {pwd}'.format(encryption=encryption,pwd=pwd)\\n if privilege:\\n username = '{username} privilege {privilege}'.format(username=username,privilege=privilege)\\n device.configure('username {username} password {pwd}'.format(username=username,pwd=pwd))\\n except SubCommandFailure:\\n raise SubCommandFailure(\\n \\\"Failed to configure user {username}\\\".format(username=username)\\n )\",\n \"async def name(self, ctx, *, name):\\n # [p]set name <name>\\n\\n name = name.strip()\\n if name != \\\"\\\":\\n try:\\n await self.bot.edit_profile(username=name)\\n except:\\n await self.bot.say(\\\"Failed to change name. Remember that you\\\"\\n \\\" can only do it up to 2 times an hour.\\\"\\n \\\"Use nicknames if you need frequent \\\"\\n \\\"changes. {}set nickname\\\".format(ctx.prefix))\\n else:\\n await self.bot.say(\\\"Done.\\\")\\n else:\\n await send_command_help(ctx)\",\n \"def set_demo_user() -> None:\\n g.demo_user = _DEMO_USER\",\n \"def set_name(self, name):\\n self.settings[\\\"name\\\"] = name\",\n \"def get_username(self):\\n return self.username\",\n \"def input_user_name(self, user_name):\\n self.locate_element_by_css_selector(USERNAME_SELECTOR).send_keys(user_name)\",\n \"def set_user_id(uid):\\n local.user_id = uid\",\n \"def updateUsers(self):\\n sel = self.userbox.GetValue()\\n usernames = ['None'] + c.getUserNames()\\n self.userbox.SetItems(usernames)\\n if sel in self.userbox.GetItems():\\n self.userbox.SetValue(sel)\\n if sel == 'None':\\n self.btnUserSettings.Enable(False)\\n else:\\n self.btnUserSettings.Enable(True)\\n else:\\n self.userbox.SetSelection(0)\\n self.btnUserSettings.Enable(False)\",\n \"def change_user(self, login):\\n self.task_storage.change_user_config(login)\",\n \"def get_username(self):\\n if self.controller.oem_config:\\n return 'oem'\\n return self.username\",\n \"def setUserCode(self, userCode):\\n self.userCode = userCode\\n logger.debug('user code set to: %s' % self.userCode)\",\n \"def clean_username (self):\\n return self.instance.username\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7747169","0.7630484","0.75716734","0.7008054","0.6820638","0.6784135","0.6745073","0.6725287","0.6669901","0.6605668","0.6576479","0.6576479","0.6576479","0.6576479","0.6576479","0.6576479","0.6576479","0.6576479","0.6576479","0.6568546","0.6558229","0.64442","0.6390564","0.63795686","0.62787104","0.6210018","0.61379987","0.60855764","0.60806483","0.6075482","0.60590106","0.6036302","0.60252726","0.6009672","0.6000856","0.596956","0.596956","0.59396636","0.59361464","0.5931167","0.5903877","0.5899643","0.5897571","0.5897428","0.5897428","0.5897265","0.58568054","0.58459485","0.58427274","0.58417356","0.5835789","0.5820497","0.5820497","0.58156973","0.5810318","0.58064413","0.5806083","0.5780879","0.57798576","0.57642096","0.57612056","0.57592356","0.5754406","0.57494307","0.5740405","0.57400805","0.57378983","0.57354164","0.57140905","0.57019705","0.5699494","0.5693242","0.5678702","0.5676134","0.565485","0.5633372","0.56158864","0.5614988","0.56032026","0.560226","0.56015027","0.5597263","0.5579378","0.5577837","0.5577789","0.5577581","0.5572607","0.5571441","0.55677086","0.5567225","0.55664593","0.5553972","0.554218","0.5537334","0.55299926","0.55154324","0.550302","0.54983366","0.5488362","0.5487873"],"string":"[\n \"0.7747169\",\n \"0.7630484\",\n \"0.75716734\",\n \"0.7008054\",\n \"0.6820638\",\n \"0.6784135\",\n \"0.6745073\",\n \"0.6725287\",\n \"0.6669901\",\n \"0.6605668\",\n \"0.6576479\",\n \"0.6576479\",\n \"0.6576479\",\n \"0.6576479\",\n \"0.6576479\",\n \"0.6576479\",\n \"0.6576479\",\n \"0.6576479\",\n \"0.6576479\",\n \"0.6568546\",\n \"0.6558229\",\n \"0.64442\",\n \"0.6390564\",\n \"0.63795686\",\n \"0.62787104\",\n \"0.6210018\",\n \"0.61379987\",\n \"0.60855764\",\n \"0.60806483\",\n \"0.6075482\",\n \"0.60590106\",\n \"0.6036302\",\n \"0.60252726\",\n \"0.6009672\",\n \"0.6000856\",\n \"0.596956\",\n \"0.596956\",\n \"0.59396636\",\n \"0.59361464\",\n \"0.5931167\",\n \"0.5903877\",\n \"0.5899643\",\n \"0.5897571\",\n \"0.5897428\",\n \"0.5897428\",\n \"0.5897265\",\n \"0.58568054\",\n \"0.58459485\",\n \"0.58427274\",\n \"0.58417356\",\n \"0.5835789\",\n \"0.5820497\",\n \"0.5820497\",\n \"0.58156973\",\n \"0.5810318\",\n \"0.58064413\",\n \"0.5806083\",\n \"0.5780879\",\n \"0.57798576\",\n \"0.57642096\",\n \"0.57612056\",\n \"0.57592356\",\n \"0.5754406\",\n \"0.57494307\",\n \"0.5740405\",\n \"0.57400805\",\n \"0.57378983\",\n \"0.57354164\",\n \"0.57140905\",\n \"0.57019705\",\n \"0.5699494\",\n \"0.5693242\",\n \"0.5678702\",\n \"0.5676134\",\n \"0.565485\",\n \"0.5633372\",\n \"0.56158864\",\n \"0.5614988\",\n \"0.56032026\",\n \"0.560226\",\n \"0.56015027\",\n \"0.5597263\",\n \"0.5579378\",\n \"0.5577837\",\n \"0.5577789\",\n \"0.5577581\",\n \"0.5572607\",\n \"0.5571441\",\n \"0.55677086\",\n \"0.5567225\",\n \"0.55664593\",\n \"0.5553972\",\n \"0.554218\",\n \"0.5537334\",\n \"0.55299926\",\n \"0.55154324\",\n \"0.550302\",\n \"0.54983366\",\n \"0.5488362\",\n \"0.5487873\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.825879"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77083,"cells":{"query":{"kind":"string","value":"Get the current user name."},"document":{"kind":"string","value":"def get_user_name():\r\n if _user_name_override is not None:\r\n return _user_name_override\r\n elif platform.system() == \"Windows\":\r\n return os.getlogin()\r\n elif platform.system() == \"Linux\":\r\n try:\r\n import pwd\r\n return pwd.getpwuid(os.geteuid()).pw_name\r\n except:\r\n return \"unknown\"\r\n else:\r\n raise RuntimeError(\"Unknown platform\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def user_name(self) -> str:\n return pulumi.get(self, \"user_name\")","def get_current_user_full_name(self):\n user_service = self.runtime.service(self, 'user')\n xb_user = user_service.get_current_user()\n\n return xb_user.full_name","def user_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"user_name\")","def user_name(self):\n return self._user_name","def user_name(self):\n\n return self._user_name","def getUserName(self):\n user = User.by_id(self.user_id)\n return user.name","def getUserName(self):\n userType = self.env['res.users']\n \n uiUser = userType.browse(self._uid)\n return uiUser.name","def get_name(self):\n return self.user.username if self.user.username else self.user.email","def _get_user_name(self):\n if self.runtime.get_real_user is None:\n return 'staff'\n else:\n return self.runtime.get_real_user(self.runtime.anonymous_student_id).username","def user_name(self):\n return lamin_user_settings().name","def name(self) -> str:\n return self.user.name","def user_name(self):\n return self._stub.List(self._message).user_name","def get_full_name(self):\n return self.username","def get_full_name(self):\n return self.username","def get_user_name(self):\n full_name = f'{self.f_name} {self.l_name}'\n return full_name","def user_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"user_name\")","def user_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"user_name\")","def user_name(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"user_name\")","def getName(self):\n return self.__username","def user_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"user_name\")","def get_username(self) -> str:\n return self._username","def username(self) -> str:\n return pulumi.get(self, \"username\")","def username(self) -> str:\n return pulumi.get(self, \"username\")","def username(self) -> str:\n return pulumi.get(self, \"username\")","def user_name(self) -> str:\n result = subprocess.run(\n [\"git\", \"config\", \"user.name\"],\n check=True,\n cwd=self.dir,\n encoding=\"utf-8\",\n stdout=subprocess.PIPE,\n )\n return result.stdout.strip()","def get_current_username(self):\n username = self.get_secure_cookie('username')\n if username is None:\n return None\n else:\n return username.decode('utf-8')","def get_current_username(self):\n username = self.get_secure_cookie('username')\n if username is None:\n return None\n else:\n return username.decode('utf-8')","def user_name(self):\n return utils.to_unicode(lib.sp_session_user_name(self._sp_session))","def get_username(self):\n full_name = '%s %s' % (self.user.first_name.strip(), self.user.last_name.strip()[0:1])\n if len(full_name.strip()) == 0:\n full_name = self.user.username\n return full_name.strip()","def username(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"username\")","def get_username_for_active_connection(self):\n user_info = self.get_user_info()\n return getattr(user_info, 'user_name', None)","def get_user_display_name(self):\n return self.user.get_full_name() or self.user.get_username()","def _get_user_name():\n return pwd.getpwuid(os.getuid())[0]","def get_username(self):\n return self.username","def get_user_name(self):\n\t\treturn call_sdk_function('PrlLic_GetUserName', self.handle)","def username(self):\n return self.user.username","def get_username(self):\r\n return self.username","def _get_username(self):\n name = self._get_username_from_cookies()\n if name:\n return name\n if self._oauth and self._login_info[0]:\n return self._login_info[0]\n return self._get_username_from_api()","def get_user_fullname(self):\n return self.applicant.userprofile.display_name()","def get_username(self, request):\r\n try:\r\n return request.user.username\r\n except AttributeError:\r\n return ''","def user_name():\n\n # Theoretically, we should be using Kerberos principal name for this.\n # However, Python Kerberos API bindings (both kerberos and krb5 modules)\n # are broken to the extent that one does not return the username, and other\n # has sad API, so we have to use other venues.\n\n if \"ATHENA_USER\" in os.environ:\n return os.environ[\"ATHENA_USER\"]\n return getpass.getuser()","def get_username(self):\n return self.browser.find_element(*locators.USER_NAME_TEXT).text","def get_user_fullname(self):\n member = self.get_user()\n if member:\n return member.getProperty('fullname')","def get_username(self):\n return str(getattr(self, self.USERNAME_FIELD))","def get_current_user(space):\n return space.newstr(os.getlogin())","def sys_user_name():\n\timport subprocess\n\treturn subprocess.Popen('whoami', shell=True, stdout=subprocess.PIPE).stdout.readline().replace('\\n', '')","def get_user_name(self):\n whoami = subprocess.Popen('whoami', stdin=None, stdout=subprocess.PIPE,\n shell=True, close_fds=True)\n whoami = whoami.communicate()[0]\n if '\\n' in whoami:\n newline_index = whoami.find('\\n')\n whoami = whoami[:newline_index]\n return whoami","def get_name(self):\n user = self.user\n name = \"%s %s\" % (user.first_name, user.last_name)\n name = name.strip()\n\n return self.display_name or name or user.email or user.username","def get_name(self) :\n\n return self.factory.to_user_name(self.name)","def full_name(self):\n return self.user.get_full_name() or None","def get_uname(self):\n return Server.t_usernames.get(threading.get_ident())","def get_username(self) -> str:\n try:\n return self[\"user\"]\n except KeyError:\n raise MarathonNotConfigured(\n \"Could not find marathon user in system marathon config\"\n )","def getCurrentUsername(self):\n session = self.getSession()\n if session is not None:\n return session","def username(self) -> str:\n return self._username","def user(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"user\")","def user(self) -> str:\n return self._user","def user():\n\treturn request.authorization.username if zk.get_http_login() else zk.get_username()","def username(self):\n return self._username()","def GetUsername(self):\n pass","def get_short_name(self):\n return self.username","def get_short_name(self):\n return self.username","def get_short_name(self):\n return self.username","def get_displayname(self):\n return self.full_name or self.user.username","def getUsername(self):\n\t\treturn self.Username.lower()","def username(self):\n return self._username","def username(self):\n return self._username","def username(self):\n return self._username","def name(self):\n name = self.__telegram_info.message.from_user.name\n return name[0].upper() + name[1::]","def username(self):\n return self._query_config()['username']","def username(self):\n return self._authenticator.username()","def get_short_name(self):\n # The user is identified by their email address\n return self.first_name","def get_full_name(self):\n # The user is identified by their email address\n return self.first_name+' '+self.last_name","def get_username():\n\n return pwd.getpwuid(os.getuid())[0]","def username(self) :\n\t\ttry :\n\t\t\treturn self._username\n\t\texcept Exception as e:\n\t\t\traise e","def username(self) -> str:\n return self.get_env_var(self.username_var)","def username(self) -> str:\n return self.get_env_var(self.username_var)","def get_short_name(self):\n # The user is identified by the email address\n return self.email","def get_local_name(self) -> str:\n if self.username:\n return self.username\n if self.email:\n return self.email\n return self.identifier","def display_name(self) -> str:\n return self.requester.username","def get_full_name(self):\n full_name = '%s %s' % (self.user.first_name.strip(), self.user.last_name.strip())\n if len(full_name.strip()) == 0:\n full_name = self.user.username\n return full_name.strip()","def username(self) -> str:\n raise NotImplementedError","def get_username(self):\n raise NotImplementedError('get_username')","def current_user_info():\n\n return current_user","def get_username(self):\n\n username = self.get_secure_cookie(settings.COOKIE_NAME, max_age_days=settings.COOKIE_MAX_AGE)\n return \"anonymous\" if username is None else username.decode()","def get_username():\n return pwd.getpwuid(os.getuid())[0]","def get_user_name(self, uid):\n uid = str(uid)\n name = self._username_cache.get(uid)\n if name is None:\n name = self.fbchat_client.fetchUserInfo(uid)[uid].name\n self._username_cache[uid] = name\n return name","def username(self) -> str:","def username(self) -> str:","def username(self, instance):\r\n return instance.user.username","async def get_user_name(self, user_target: str) -> str:\n user = await self.get_user(user_target=user_target)\n if user is None:\n return user_target\n return user.display_name","def username(self) -> Optional[str]:\n return self._state.get(\"username\", None)","def username(self):\n if self._username is not None:\n return self._username\n # Try to get a username from the userprofile\n try:\n self._username = self.userprofile.user.username\n except UserProfile.DoesNotExist:\n # User profile does not exist\n return None\n return self._username","def user_principal_name(self) -> Optional[str]:\n return pulumi.get(self, \"user_principal_name\")","def user_principal_name(self) -> Optional[str]:\n return pulumi.get(self, \"user_principal_name\")","def get_auth_username():\n return DEFAULT_AUTH_USERNAME.get()","def get_user_display_name():\n user_display_name = session.get(\"user_display_name\")\n return user_display_name if user_display_name else None","def get_full_name(self):\n full_name = f'{self.first_name} {self.last_name}' if self.first_name and self.last_name else self.username\n return full_name.strip()","def get_user_name(user: User) -> str:\n user_name = user.get(\"display_name\")\n if not user_name:\n user_name = user[\"fullname\"]\n if not user_name:\n user_name = user[\"name\"]\n return user_name","def _get_current_user(self):\n\n out, err, exitcode = self.execute('whoami')\n\n if exitcode == 0:\n return User(out[0])\n return None","def GetUsername(self):\n return self._username"],"string":"[\n \"def user_name(self) -> str:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"def get_current_user_full_name(self):\\n user_service = self.runtime.service(self, 'user')\\n xb_user = user_service.get_current_user()\\n\\n return xb_user.full_name\",\n \"def user_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"def user_name(self):\\n return self._user_name\",\n \"def user_name(self):\\n\\n return self._user_name\",\n \"def getUserName(self):\\n user = User.by_id(self.user_id)\\n return user.name\",\n \"def getUserName(self):\\n userType = self.env['res.users']\\n \\n uiUser = userType.browse(self._uid)\\n return uiUser.name\",\n \"def get_name(self):\\n return self.user.username if self.user.username else self.user.email\",\n \"def _get_user_name(self):\\n if self.runtime.get_real_user is None:\\n return 'staff'\\n else:\\n return self.runtime.get_real_user(self.runtime.anonymous_student_id).username\",\n \"def user_name(self):\\n return lamin_user_settings().name\",\n \"def name(self) -> str:\\n return self.user.name\",\n \"def user_name(self):\\n return self._stub.List(self._message).user_name\",\n \"def get_full_name(self):\\n return self.username\",\n \"def get_full_name(self):\\n return self.username\",\n \"def get_user_name(self):\\n full_name = f'{self.f_name} {self.l_name}'\\n return full_name\",\n \"def user_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"def user_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"def user_name(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"def getName(self):\\n return self.__username\",\n \"def user_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"user_name\\\")\",\n \"def get_username(self) -> str:\\n return self._username\",\n \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"def username(self) -> str:\\n return pulumi.get(self, \\\"username\\\")\",\n \"def user_name(self) -> str:\\n result = subprocess.run(\\n [\\\"git\\\", \\\"config\\\", \\\"user.name\\\"],\\n check=True,\\n cwd=self.dir,\\n encoding=\\\"utf-8\\\",\\n stdout=subprocess.PIPE,\\n )\\n return result.stdout.strip()\",\n \"def get_current_username(self):\\n username = self.get_secure_cookie('username')\\n if username is None:\\n return None\\n else:\\n return username.decode('utf-8')\",\n \"def get_current_username(self):\\n username = self.get_secure_cookie('username')\\n if username is None:\\n return None\\n else:\\n return username.decode('utf-8')\",\n \"def user_name(self):\\n return utils.to_unicode(lib.sp_session_user_name(self._sp_session))\",\n \"def get_username(self):\\n full_name = '%s %s' % (self.user.first_name.strip(), self.user.last_name.strip()[0:1])\\n if len(full_name.strip()) == 0:\\n full_name = self.user.username\\n return full_name.strip()\",\n \"def username(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"username\\\")\",\n \"def get_username_for_active_connection(self):\\n user_info = self.get_user_info()\\n return getattr(user_info, 'user_name', None)\",\n \"def get_user_display_name(self):\\n return self.user.get_full_name() or self.user.get_username()\",\n \"def _get_user_name():\\n return pwd.getpwuid(os.getuid())[0]\",\n \"def get_username(self):\\n return self.username\",\n \"def get_user_name(self):\\n\\t\\treturn call_sdk_function('PrlLic_GetUserName', self.handle)\",\n \"def username(self):\\n return self.user.username\",\n \"def get_username(self):\\r\\n return self.username\",\n \"def _get_username(self):\\n name = self._get_username_from_cookies()\\n if name:\\n return name\\n if self._oauth and self._login_info[0]:\\n return self._login_info[0]\\n return self._get_username_from_api()\",\n \"def get_user_fullname(self):\\n return self.applicant.userprofile.display_name()\",\n \"def get_username(self, request):\\r\\n try:\\r\\n return request.user.username\\r\\n except AttributeError:\\r\\n return ''\",\n \"def user_name():\\n\\n # Theoretically, we should be using Kerberos principal name for this.\\n # However, Python Kerberos API bindings (both kerberos and krb5 modules)\\n # are broken to the extent that one does not return the username, and other\\n # has sad API, so we have to use other venues.\\n\\n if \\\"ATHENA_USER\\\" in os.environ:\\n return os.environ[\\\"ATHENA_USER\\\"]\\n return getpass.getuser()\",\n \"def get_username(self):\\n return self.browser.find_element(*locators.USER_NAME_TEXT).text\",\n \"def get_user_fullname(self):\\n member = self.get_user()\\n if member:\\n return member.getProperty('fullname')\",\n \"def get_username(self):\\n return str(getattr(self, self.USERNAME_FIELD))\",\n \"def get_current_user(space):\\n return space.newstr(os.getlogin())\",\n \"def sys_user_name():\\n\\timport subprocess\\n\\treturn subprocess.Popen('whoami', shell=True, stdout=subprocess.PIPE).stdout.readline().replace('\\\\n', '')\",\n \"def get_user_name(self):\\n whoami = subprocess.Popen('whoami', stdin=None, stdout=subprocess.PIPE,\\n shell=True, close_fds=True)\\n whoami = whoami.communicate()[0]\\n if '\\\\n' in whoami:\\n newline_index = whoami.find('\\\\n')\\n whoami = whoami[:newline_index]\\n return whoami\",\n \"def get_name(self):\\n user = self.user\\n name = \\\"%s %s\\\" % (user.first_name, user.last_name)\\n name = name.strip()\\n\\n return self.display_name or name or user.email or user.username\",\n \"def get_name(self) :\\n\\n return self.factory.to_user_name(self.name)\",\n \"def full_name(self):\\n return self.user.get_full_name() or None\",\n \"def get_uname(self):\\n return Server.t_usernames.get(threading.get_ident())\",\n \"def get_username(self) -> str:\\n try:\\n return self[\\\"user\\\"]\\n except KeyError:\\n raise MarathonNotConfigured(\\n \\\"Could not find marathon user in system marathon config\\\"\\n )\",\n \"def getCurrentUsername(self):\\n session = self.getSession()\\n if session is not None:\\n return session\",\n \"def username(self) -> str:\\n return self._username\",\n \"def user(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"user\\\")\",\n \"def user(self) -> str:\\n return self._user\",\n \"def user():\\n\\treturn request.authorization.username if zk.get_http_login() else zk.get_username()\",\n \"def username(self):\\n return self._username()\",\n \"def GetUsername(self):\\n pass\",\n \"def get_short_name(self):\\n return self.username\",\n \"def get_short_name(self):\\n return self.username\",\n \"def get_short_name(self):\\n return self.username\",\n \"def get_displayname(self):\\n return self.full_name or self.user.username\",\n \"def getUsername(self):\\n\\t\\treturn self.Username.lower()\",\n \"def username(self):\\n return self._username\",\n \"def username(self):\\n return self._username\",\n \"def username(self):\\n return self._username\",\n \"def name(self):\\n name = self.__telegram_info.message.from_user.name\\n return name[0].upper() + name[1::]\",\n \"def username(self):\\n return self._query_config()['username']\",\n \"def username(self):\\n return self._authenticator.username()\",\n \"def get_short_name(self):\\n # The user is identified by their email address\\n return self.first_name\",\n \"def get_full_name(self):\\n # The user is identified by their email address\\n return self.first_name+' '+self.last_name\",\n \"def get_username():\\n\\n return pwd.getpwuid(os.getuid())[0]\",\n \"def username(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._username\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def username(self) -> str:\\n return self.get_env_var(self.username_var)\",\n \"def username(self) -> str:\\n return self.get_env_var(self.username_var)\",\n \"def get_short_name(self):\\n # The user is identified by the email address\\n return self.email\",\n \"def get_local_name(self) -> str:\\n if self.username:\\n return self.username\\n if self.email:\\n return self.email\\n return self.identifier\",\n \"def display_name(self) -> str:\\n return self.requester.username\",\n \"def get_full_name(self):\\n full_name = '%s %s' % (self.user.first_name.strip(), self.user.last_name.strip())\\n if len(full_name.strip()) == 0:\\n full_name = self.user.username\\n return full_name.strip()\",\n \"def username(self) -> str:\\n raise NotImplementedError\",\n \"def get_username(self):\\n raise NotImplementedError('get_username')\",\n \"def current_user_info():\\n\\n return current_user\",\n \"def get_username(self):\\n\\n username = self.get_secure_cookie(settings.COOKIE_NAME, max_age_days=settings.COOKIE_MAX_AGE)\\n return \\\"anonymous\\\" if username is None else username.decode()\",\n \"def get_username():\\n return pwd.getpwuid(os.getuid())[0]\",\n \"def get_user_name(self, uid):\\n uid = str(uid)\\n name = self._username_cache.get(uid)\\n if name is None:\\n name = self.fbchat_client.fetchUserInfo(uid)[uid].name\\n self._username_cache[uid] = name\\n return name\",\n \"def username(self) -> str:\",\n \"def username(self) -> str:\",\n \"def username(self, instance):\\r\\n return instance.user.username\",\n \"async def get_user_name(self, user_target: str) -> str:\\n user = await self.get_user(user_target=user_target)\\n if user is None:\\n return user_target\\n return user.display_name\",\n \"def username(self) -> Optional[str]:\\n return self._state.get(\\\"username\\\", None)\",\n \"def username(self):\\n if self._username is not None:\\n return self._username\\n # Try to get a username from the userprofile\\n try:\\n self._username = self.userprofile.user.username\\n except UserProfile.DoesNotExist:\\n # User profile does not exist\\n return None\\n return self._username\",\n \"def user_principal_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"user_principal_name\\\")\",\n \"def user_principal_name(self) -> Optional[str]:\\n return pulumi.get(self, \\\"user_principal_name\\\")\",\n \"def get_auth_username():\\n return DEFAULT_AUTH_USERNAME.get()\",\n \"def get_user_display_name():\\n user_display_name = session.get(\\\"user_display_name\\\")\\n return user_display_name if user_display_name else None\",\n \"def get_full_name(self):\\n full_name = f'{self.first_name} {self.last_name}' if self.first_name and self.last_name else self.username\\n return full_name.strip()\",\n \"def get_user_name(user: User) -> str:\\n user_name = user.get(\\\"display_name\\\")\\n if not user_name:\\n user_name = user[\\\"fullname\\\"]\\n if not user_name:\\n user_name = user[\\\"name\\\"]\\n return user_name\",\n \"def _get_current_user(self):\\n\\n out, err, exitcode = self.execute('whoami')\\n\\n if exitcode == 0:\\n return User(out[0])\\n return None\",\n \"def GetUsername(self):\\n return self._username\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8741144","0.86486804","0.846932","0.844218","0.83565015","0.8267635","0.8187839","0.81661373","0.80497915","0.8037324","0.80079746","0.7956374","0.7941207","0.7941207","0.7937398","0.7918738","0.7918738","0.7918738","0.7903354","0.78959924","0.78748775","0.7871711","0.7871711","0.7871711","0.7860724","0.7853285","0.7853285","0.7849982","0.7829458","0.78009546","0.77931225","0.77685857","0.7764688","0.7758667","0.7719199","0.76989996","0.76950294","0.76524967","0.76509094","0.76324856","0.76252866","0.7595841","0.759234","0.7580827","0.7573685","0.7570929","0.7567283","0.7560317","0.7544142","0.7539281","0.75150263","0.75072837","0.7473697","0.7464187","0.74518925","0.74211204","0.741923","0.7411659","0.7401748","0.73879015","0.73879015","0.73879015","0.73775613","0.73667","0.73541087","0.73541087","0.73541087","0.7347844","0.73276013","0.7315956","0.73079216","0.73006964","0.7297082","0.72919106","0.7277753","0.7277753","0.7261786","0.7257459","0.72442365","0.72381467","0.72355586","0.7230393","0.7228581","0.7221235","0.7215305","0.7214701","0.72101194","0.72101194","0.71834224","0.7180363","0.7179613","0.71731114","0.71685857","0.71685857","0.7165396","0.71635914","0.71400625","0.7138947","0.71380174","0.71377116"],"string":"[\n \"0.8741144\",\n \"0.86486804\",\n \"0.846932\",\n \"0.844218\",\n \"0.83565015\",\n \"0.8267635\",\n \"0.8187839\",\n \"0.81661373\",\n \"0.80497915\",\n \"0.8037324\",\n \"0.80079746\",\n \"0.7956374\",\n \"0.7941207\",\n \"0.7941207\",\n \"0.7937398\",\n \"0.7918738\",\n \"0.7918738\",\n \"0.7918738\",\n \"0.7903354\",\n \"0.78959924\",\n \"0.78748775\",\n \"0.7871711\",\n \"0.7871711\",\n \"0.7871711\",\n \"0.7860724\",\n \"0.7853285\",\n \"0.7853285\",\n \"0.7849982\",\n \"0.7829458\",\n \"0.78009546\",\n \"0.77931225\",\n \"0.77685857\",\n \"0.7764688\",\n \"0.7758667\",\n \"0.7719199\",\n \"0.76989996\",\n \"0.76950294\",\n \"0.76524967\",\n \"0.76509094\",\n \"0.76324856\",\n \"0.76252866\",\n \"0.7595841\",\n \"0.759234\",\n \"0.7580827\",\n \"0.7573685\",\n \"0.7570929\",\n \"0.7567283\",\n \"0.7560317\",\n \"0.7544142\",\n \"0.7539281\",\n \"0.75150263\",\n \"0.75072837\",\n \"0.7473697\",\n \"0.7464187\",\n \"0.74518925\",\n \"0.74211204\",\n \"0.741923\",\n \"0.7411659\",\n \"0.7401748\",\n \"0.73879015\",\n \"0.73879015\",\n \"0.73879015\",\n \"0.73775613\",\n \"0.73667\",\n \"0.73541087\",\n \"0.73541087\",\n \"0.73541087\",\n \"0.7347844\",\n \"0.73276013\",\n \"0.7315956\",\n \"0.73079216\",\n \"0.73006964\",\n \"0.7297082\",\n \"0.72919106\",\n \"0.7277753\",\n \"0.7277753\",\n \"0.7261786\",\n \"0.7257459\",\n \"0.72442365\",\n \"0.72381467\",\n \"0.72355586\",\n \"0.7230393\",\n \"0.7228581\",\n \"0.7221235\",\n \"0.7215305\",\n \"0.7214701\",\n \"0.72101194\",\n \"0.72101194\",\n \"0.71834224\",\n \"0.7180363\",\n \"0.7179613\",\n \"0.71731114\",\n \"0.71685857\",\n \"0.71685857\",\n \"0.7165396\",\n \"0.71635914\",\n \"0.71400625\",\n \"0.7138947\",\n \"0.71380174\",\n \"0.71377116\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7600616"},"document_rank":{"kind":"string","value":"41"}}},{"rowIdx":77084,"cells":{"query":{"kind":"string","value":"Create a new run dir with increasing ID number at the start."},"document":{"kind":"string","value":"def _create_run_dir_local(run_dir_root, run_desc) -> str:\r\n run_dir_root = get_path_from_template(run_dir_root, PathType.AUTO)\r\n\r\n if not os.path.exists(run_dir_root):\r\n os.makedirs(run_dir_root)\r\n\r\n run_id = _get_next_run_id_local(run_dir_root)\r\n run_name = \"{0:05d}-{1}\".format(run_id, run_desc)\r\n run_dir = os.path.join(run_dir_root, run_name)\r\n\r\n if os.path.exists(run_dir):\r\n raise RuntimeError(\"The run dir already exists! ({0})\".format(run_dir))\r\n\r\n os.makedirs(run_dir)\r\n\r\n return run_dir"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _create_run_dir(self):\n task_name = 'task_'+str(self.setting['task_id'])\n run_name = '_'.join(['run', \n str(self.setting['run_idx']), \n str(self.setting['_id'])])\n \n run_dir = op.join(Job_Requestor.run_center, task_name, run_name)\n back_dir = op.join(run_dir, '.backup')\n try: \n os.makedirs(run_dir)\n os.makedirs(back_dir)\n os.symlink(Job_Requestor.data_dir, op.join(run_dir, 'data')) \n\n except:\n print(\"Error: run dir %s create failed\"%(run_dir))\n # sys.exit(0)\n \n self.run_dir = run_dir\n self.setting.update({'run_dir':run_dir})\n self._db_update({'run_dir': run_dir})","def folder_runnum():\n now = datetime.datetime.now()\n runnum = 1\n while True:\n folder_name = f\"astroNN_{now.month:0{2}d}{now.day:0{2}d}_run{runnum:0{3}d}\"\n if not os.path.exists(folder_name):\n break\n else:\n runnum += 1\n\n return folder_name","def create_directory(self):\n dirname = self.name+\"_distillates\"\n i = 1\n while True:\n try:\n mkdir(dirname)\n return dirname\n except OSError:\n dirname = self.name+\"_distillates_{0}\".format(i)\n i += 1","def createRunId(self):\n # runid is in the form of <login>_YYYY_MMDD_HHMMSS\n now = datetime.now()\n username = pwd.getpwuid(os.geteuid()).pw_name\n runid = \"%s_%02d_%02d%02d_%02d%02d%02d\" % (username, now.year, now.month,\n now.day, now.hour, now.minute, now.second)\n self.runid = runid\n return runid","def create_folder(id_parent, name):\n id_folder = incr_key_store('folders:counter')\n rpush_key_store('folders:list', {'id': id_folder, 'parent': id_parent, 'name': name})\n return id_folder","def make_directory(base):\n \t\n i = 0\n while 1:\n try:\n if i == 0:\n dirname = base\n else:\n dirname = base + '_' + str(i)\n os.mkdir(dirname)\n break\n except OSError:\n if not os.path.isdir(dirname):\n raise\n i += 1\n pass \n return dirname","def create_id_dir(id_dir: Path) -> (int, Path):\n\n lockfile_path = id_dir/'.lockfile'\n with lockfile.LockFile(lockfile_path, timeout=1):\n next_fn = id_dir/PKEY_FN\n\n next_valid = True\n\n if next_fn.exists():\n try:\n with next_fn.open() as next_file:\n next_id = int(next_file.read())\n\n next_id_path = make_id_path(id_dir, next_id)\n\n if next_id_path.exists():\n next_valid = False\n\n except (OSError, ValueError):\n # In either case, on failure, invalidate the next file.\n next_valid = False\n else:\n next_valid = False\n\n if not next_valid:\n # If the next file's id wasn't valid, then find the next available\n # id directory the hard way.\n\n ids = list(os.listdir(str(id_dir)))\n # Only return the test directories that could be integers.\n ids = [id_ for id_ in ids if id_.isdigit()]\n ids = [int(id_) for id_ in ids]\n ids.sort()\n\n # Find the first unused id.\n next_id = 1\n while next_id in ids:\n next_id += 1\n\n next_id_path = make_id_path(id_dir, next_id)\n\n next_id_path.mkdir()\n with next_fn.open('w') as next_file:\n next_file.write(str(next_id + 1))\n\n return next_id, next_id_path","def make_run_dir(outdir: Union[str, os.PathLike], desc: str, dry_run: bool = False) -> str:\n # Pick output directory.\n prev_run_dirs = []\n if os.path.isdir(outdir): # sanity check, but click.Path() should clear this one\n prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))]\n prev_run_ids = [re.match(r'^\\d+', x) for x in prev_run_dirs]\n prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]\n cur_run_id = max(prev_run_ids, default=-1) + 1 # start with 00000\n run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{desc}')\n assert not os.path.exists(run_dir) # make sure it doesn't already exist\n\n # Don't create the dir if it's a dry-run\n if not dry_run:\n print('Creating output directory...')\n os.makedirs(run_dir)\n return run_dir","def _get_next_run_id_local(run_dir_root: str) -> int:\r\n dir_names = [d for d in os.listdir(run_dir_root) if os.path.isdir(os.path.join(run_dir_root, d))]\r\n r = re.compile(\"^\\\\d+\") # match one or more digits at the start of the string\r\n run_id = 0\r\n\r\n for dir_name in dir_names:\r\n m = r.match(dir_name)\r\n\r\n if m is not None:\r\n i = int(m.group())\r\n run_id = max(run_id, i + 1)\r\n\r\n return run_id","def create_new(self, root, name_length):\n self.name = create_random_string(name_length)\n self.ctime = datetime.datetime.now()\n date_time = datetime.datetime.strftime(self.ctime, \"%Y%m%d_%H%M%S\")\n self.folder = f\"{date_time}_{self.name}\"\n self.path = os.path.join(root, self.folder)\n try:\n os.makedirs(self.path)\n print(f\"Created folder {self.folder}\")\n except OSError:\n print(f\"Directory {self.folder} already exists\")\n except:\n print(f\"Cannot create folder: {self.folder}\")\n raise","def new_run(self, init_walkers, continue_run=None, **kwargs):\n\n # check to see if the continue_run is actually in this file\n if continue_run is not None:\n if continue_run not in self.run_idxs:\n raise ValueError(\"The continue_run idx given, {}, is not present in this file\".format(\n continue_run))\n\n # get the index for this run\n new_run_idx = self.next_run_idx()\n\n # create a new group named the next integer in the counter\n run_grp = self._h5.create_group('{}/{}'.format(RUNS, new_run_idx))\n\n\n # set the initial walkers group\n init_walkers_grp = run_grp.create_group(INIT_WALKERS)\n\n self._add_init_walkers(init_walkers_grp, init_walkers)\n\n # initialize the walkers group\n traj_grp = run_grp.create_group(TRAJECTORIES)\n\n\n # run the initialization routines for adding a run\n self._add_run_init(new_run_idx, continue_run=continue_run)\n\n\n # add metadata if given\n for key, val in kwargs.items():\n if key != RUN_IDX:\n run_grp.attrs[key] = val\n else:\n warn('run_idx metadata is set by wepy and cannot be used', RuntimeWarning)\n\n return run_grp","def create_dirs():\n run(\"mkdir -p %s\"%RUN_DIR)\n run(\"mkdir -p %s\"%LOG_DIR)","def make_numbered_dir(\n cls, prefix=\"session-\", rootdir=None, keep=3, lock_timeout=172800\n ): # two days\n if rootdir is None:\n rootdir = cls.get_temproot()\n\n nprefix = prefix.lower()\n\n def parse_num(path):\n \"\"\"Parse the number out of a path (if it matches the prefix)\"\"\"\n nbasename = path.basename.lower()\n if nbasename.startswith(nprefix):\n try:\n return int(nbasename[len(nprefix) :])\n except ValueError:\n pass\n\n def create_lockfile(path):\n \"\"\"Exclusively create lockfile. Throws when failed\"\"\"\n mypid = os.getpid()\n lockfile = path.join(\".lock\")\n if hasattr(lockfile, \"mksymlinkto\"):\n lockfile.mksymlinkto(str(mypid))\n else:\n fd = error.checked_call(\n os.open, str(lockfile), os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o644\n )\n with os.fdopen(fd, \"w\") as f:\n f.write(str(mypid))\n return lockfile\n\n def atexit_remove_lockfile(lockfile):\n \"\"\"Ensure lockfile is removed at process exit\"\"\"\n mypid = os.getpid()\n\n def try_remove_lockfile():\n # in a fork() situation, only the last process should\n # remove the .lock, otherwise the other processes run the\n # risk of seeing their temporary dir disappear. For now\n # we remove the .lock in the parent only (i.e. we assume\n # that the children finish before the parent).\n if os.getpid() != mypid:\n return\n try:\n lockfile.remove()\n except error.Error:\n pass\n\n atexit.register(try_remove_lockfile)\n\n # compute the maximum number currently in use with the prefix\n lastmax = None\n while True:\n maxnum = -1\n for path in rootdir.listdir():\n num = parse_num(path)\n if num is not None:\n maxnum = max(maxnum, num)\n\n # make the new directory\n try:\n udir = rootdir.mkdir(prefix + str(maxnum + 1))\n if lock_timeout:\n lockfile = create_lockfile(udir)\n atexit_remove_lockfile(lockfile)\n except (error.EEXIST, error.ENOENT, error.EBUSY):\n # race condition (1): another thread/process created the dir\n # in the meantime - try again\n # race condition (2): another thread/process spuriously acquired\n # lock treating empty directory as candidate\n # for removal - try again\n # race condition (3): another thread/process tried to create the lock at\n # the same time (happened in Python 3.3 on Windows)\n # https://ci.appveyor.com/project/pytestbot/py/build/1.0.21/job/ffi85j4c0lqwsfwa\n if lastmax == maxnum:\n raise\n lastmax = maxnum\n continue\n break\n\n def get_mtime(path):\n \"\"\"Read file modification time\"\"\"\n try:\n return path.lstat().mtime\n except error.Error:\n pass\n\n garbage_prefix = prefix + \"garbage-\"\n\n def is_garbage(path):\n \"\"\"Check if path denotes directory scheduled for removal\"\"\"\n bn = path.basename\n return bn.startswith(garbage_prefix)\n\n # prune old directories\n udir_time = get_mtime(udir)\n if keep and udir_time:\n for path in rootdir.listdir():\n num = parse_num(path)\n if num is not None and num <= (maxnum - keep):\n try:\n # try acquiring lock to remove directory as exclusive user\n if lock_timeout:\n create_lockfile(path)\n except (error.EEXIST, error.ENOENT, error.EBUSY):\n path_time = get_mtime(path)\n if not path_time:\n # assume directory doesn't exist now\n continue\n if abs(udir_time - path_time) < lock_timeout:\n # assume directory with lockfile exists\n # and lock timeout hasn't expired yet\n continue\n\n # path dir locked for exclusive use\n # and scheduled for removal to avoid another thread/process\n # treating it as a new directory or removal candidate\n garbage_path = rootdir.join(garbage_prefix + str(uuid.uuid4()))\n try:\n path.rename(garbage_path)\n garbage_path.remove(rec=1)\n except KeyboardInterrupt:\n raise\n except Exception: # this might be error.Error, WindowsError ...\n pass\n if is_garbage(path):\n try:\n path.remove(rec=1)\n except KeyboardInterrupt:\n raise\n except Exception: # this might be error.Error, WindowsError ...\n pass\n\n # make link...\n try:\n username = os.environ[\"USER\"] # linux, et al\n except KeyError:\n try:\n username = os.environ[\"USERNAME\"] # windows\n except KeyError:\n username = \"current\"\n\n src = str(udir)\n dest = src[: src.rfind(\"-\")] + \"-\" + username\n try:\n os.unlink(dest)\n except OSError:\n pass\n try:\n os.symlink(src, dest)\n except (OSError, AttributeError, NotImplementedError):\n pass\n\n return udir","def init(cls, dirpath=\".\", newdir=True):\n dirpath = dirpath if dirpath[-1] == os.sep else dirpath + os.sep \n if not os.path.exists(dirpath):\n os.makedirs(dirpath)\n if newdir:\n now = datetime.datetime.now()\n dirpath = dirpath + now.strftime(\"%y-%m-%d_%H-%M-%S\") + os.sep\n os.mkdir(dirpath)\n cls.dirpath = dirpath\n cls.__data.clear()\n cls.__counters.clear()","def setup_temp_dir(prepro_num, directory):\n for i in range (prepro_num):\n path = directory + '/' + str(i)\n if os.path.exists(path):\n shutil.rmtree(path, ignore_errors=True)\n os.mkdir(path)","def mkdir(dirName):\r\n raw = 'mockaroo_data/raw/' + dirName\r\n cln = 'mockaroo_data/cln/' + dirName\r\n call(['mkdir', raw])\r\n call(['mkdir', cln])\r\n\r\n return","def data_loader_new_directory_name(self, working_dir: Path):\n\n wd = SnowfakeryWorkingDirectory(working_dir)\n key = wd.index\n if key not in self.cached_counts:\n self.cached_counts[key] = wd.get_record_counts()\n\n if not self.run_until.sobject_name:\n return working_dir\n\n count = self.cached_counts[key][self.run_until.sobject_name]\n\n path, _ = str(working_dir).rsplit(\"_\", 1)\n new_working_dir = Path(path + \"_\" + str(count))\n return new_working_dir","def mklocdir(self):\n dir = self._localpath+\"/%s.%s\" % (self._jobname, self._jobid)\n os.mkdir(dir)","async def new_folder(name):\n res = await joplin.create_folder(folder=name)\n return res.json()['id']","def create_dir(dir_path,plot_type):\n\n alphabet = \"ABCDEFGHIJKLMNOPQRSTUZWXYZ\"\n alphabet += alphabet.lower()\n alphabet += \"01234567890\"\n\n\n if dir_path==None or dir_path=='':\n dir_path=''\n random_dir_name=''.join([choice(alphabet) for i in range(10)])\n dir_path ='./'+plot_type+strftime(\"%Y_%m_%d_%H_%M_%S\")+random_dir_name+'/'\n\n if not os.path.exists(dir_path):\n os.mkdir(dir_path)\n\n return dir_path","def create_temp_folder():\n path_join = os.path.join(tempfile.gettempdir(), id_generator(5))\n os.makedirs(path_join)\n return path_join","def Create_Dir(self,txn,filename):\n opid = self.new_opid()\n xaction = CreateDir_Operation(os.path.join(self.home,filename),opid)\n self._add_operation(txn,xaction)","def dir_creation():\n for i in range(dir_count):\n dir_name = str(i * nums)\n max_len = len(str(nums * dir_count))\n # if the len is equal, add stright forward\n if len(dir_name) == max_len:\n container.append(dir_name)\n while len(dir_name) < max_len:\n # tambahkan angka 0 sampai len(dir_name) == max_len\n dir_name = \"0\" + dir_name\n if len(dir_name) == max_len:\n container.append(dir_name)\n break\n return container","def find_run_id(increase=False):\n if tc.RUNID is None:\n if not os.path.exists(tc.RUNID_FILE):\n with open(tc.RUNID_FILE, \"w\") as new_runid_file:\n new_runid_file.write(\"1\")\n tc.RUNID = open(tc.RUNID_FILE, \"r\").read().rstrip()\n with open(tc.RUNID_FILE, \"w\") as new_runid_file:\n new_runid_file.write(str(int(tc.RUNID) + 1))\n tc.RUNID = str(int(tc.RUNID) + 1)\n elif increase:\n tc.RUNID = int(tc.RUNID) + 1\n with open(tc.RUNID_FILE, \"w\") as new_runid_file: \n new_runid_file.write(str(int(tc.RUNID)))\n return tc.RUNID","def _makeDir(self):\n try:\n os.mkdir(self.dir)\n # log('created directory: %s\\n' % self.dir)\n except OSError, err:\n if err.errno != errno.EEXIST:\n raise","def _create_pidfile(self, pid_dir, pid):\n open(os.path.join(pid_dir, str(pid)), 'a').close()","def createdir(directory):\n if os.path.exists(directory):\n from datetime import datetime\n now = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n os.rename(directory, directory+now)\n os.mkdir(directory)","def generate_run_id(self):\n r_id_len = 4\n r_id = 'r'+''.join([str(random.randint(0,9)) for i in range(r_id_len)])\n while r_id in self.cache:\n r_id = 'r'+''.join([str(random.randint(0,9)) for i in range(r_id_len)])\n return r_id","def new_dirname(dirname=None, ndigits=3):\n if dirname is None:\n ext = (\"%%_%ii\" % ndigits) % 1\n dirname = \"%s_%s\" % (random_string(6), ext)\n \n dirname = dirname.replace('.', '_')\n if os.path.exists(dirname):\n dirname = increment_filename(dirname, ndigits=ndigits, delim='_')\n \n\n return dirname","def create_directory_structure(path_main):\n\n if not path_main.exists():\n path_main.mkdir(parents=True)","def mkdir(self, step):\n path = self.folder(step)\n if not path.exists():\n path.mkdir(parents=True)\n\n return path","def mkdir(self, step):\n path = self.folder(step)\n if not path.exists():\n path.mkdir(parents=True)\n\n return path","def mkdir(self, step):\n path = self.folder(step)\n if not path.exists():\n path.mkdir(parents=True)\n\n return path","def make_case_dir(self):\n\n mkdir(directory=self.dir)\n mkdir(directory=self.sub_dir)","def MakeDir(self, path: str) -> None:\n ...","def test_make_folders():\n for i in range(0, 100, 1):\n os.mkdir('test_folder_%s' % i)\n os.rmdir('test_folder_%s' % i)","def newId():\n global lastId\n lastId += 1\n return 'id%d' % lastId","def create_run_tracker(info_dir=None):\r\n # TODO(John Sirois): Rework uses around a context manager for cleanup of the info_dir in a more\r\n # disciplined manner\r\n info_dir = info_dir or safe_mkdtemp()\r\n run_tracker = RunTracker(info_dir)\r\n report = Report()\r\n run_tracker.start(report)\r\n return run_tracker","def create(self, basedir, outdir, name, prefix=None):","def create_directory(tracking_id):\n upload_path = os.path.join(app.config['DRS_UPLOADS'], '{0}'.format(tracking_id))\n if not os.path.isdir(upload_path):\n os.mkdir(upload_path)","def make_run_directory(output_root: Union[str, Path]) -> Path:\n run_directory = get_run_directory(output_root)\n mkdir(run_directory)\n return run_directory","def _createParentDir(self, dirName):\n parentDir = os.path.dirname(dirName)\n if parentDir is \"\":\n return \"::\"\n basename = \"::/%s\" % parentDir\n if not basename in self._dirs:\n self._createParentDir(parentDir)\n self._cmd([\"mmd\", \"-i\", self._mformatImage, basename])\n self._dirs.add(basename)\n return basename","def dirmaker(dirp):\n try:\n if not os.path.exists(dirp):\n os.makedirs(dirp)\n except:\n pass","def _create_ID_files(self):\n for file, IDs in [(self._trn_IDs_file, self._trn_IDs), (self._val_IDs_file,\n self._val_IDs), (self._tst_IDs_file, self._tst_IDs)]:\n with open(file, 'w') as f:\n f.write('\\n'.join('{}###{}###{}'.format(ID[0], ID[1], ID[2]) for ID in IDs))","def ensure_directory(explorer, parent_id, dirname):\n cache_key = (parent_id, dirname)\n if cache_key in DIR_CACHE:\n return DIR_CACHE[cache_key]\n\n for folder in explorer.list_folder(parent_id):\n if folder['name'] == dirname:\n folder_id = folder['id']\n break\n else:\n print(\"Creating folder {!r} in parent {}\".format(dirname, parent_id))\n folder_id = explorer.create_folder(dirname, parent_id)\n DIR_CACHE[cache_key] = folder_id\n return folder_id","def create_directory():\n global dirName\n dirName = 'Downloaded Files'\n global folder_path\n if os.path.isdir(dirName) == True:\n print(\"This folder already exists, path:\", os.path.abspath(dirName))\n else:\n os.mkdir(dirName)\n global folder_path\n folder_path = os.path.abspath(dirName)\n print(\"Directory \" , dirName , \" Created \")","def fs_mkdir(self, dirname: str) -> None:\n self.exec_(\"import uos\\nuos.mkdir('%s')\" % dirname)","def get_next_dir(self):\n cls_name = self.__class__.__name__.lower()\n paths = [p.name for p in self.data_dir.iterdir() if re.match(f'{cls_name}_[0-9]+', p.name) and p.is_dir()]\n i = 0\n while (path_name := f'{cls_name}_{i}') in paths:\n i += 1\n self.save_dir = self.data_dir/path_name\n self.save_dir.mkdir()\n self.create_new_tree()\n return path_name","def create_folder(self, foldername: str) -> int:\n raise NotImplementedError","def dirCreate(newFoldername):\r\n current_directory = os.getcwd()\r\n new_directory = os.path.join(current_directory,newFoldername)\r\n \r\n if not os.path.exists(new_directory):\r\n os.makedirs(new_directory)\r\n return new_directory","def create_dir(newdir):\n if not os.path.isdir(newdir):\n try:\n os.makedirs(newdir)\n print(newdir)\n except IOError:\n print(\"cannot create %s directoy\" % newdir)\n return 0","def mkdir(self,rmdir=False):\n import os\n if rmdir:\n os.rmdir(str(self))\n cur=Path('./')\n for intdir in self.split():\n cur+=Path(intdir)\n if not os.path.isdir(cur):\n os.mkdir(cur)","def create_task_id():\n return str(int(round(time.time() * 10**9)))","def mkdir(path):","def createDir(self, dir_name):\n os.mkdir(os.path.join(self.user[\"Save\"], dir_name))","def create_dir_struct(self, create_first_rev_folder=\"True\"):\n # | - create_dir_struct\n for Job_i in self.Job_list:\n\n # | - FOR LOOP BODY\n # if create_first_rev_folder == \"True\":\n # path = os.path.join(Job_i.full_path, \"_1\")\n # elif create_first_rev_folder == \"False\":\n # path = Job_i.full_path\n\n path = Job_i.full_path\n\n if os.path.exists(path):\n # mess = \"Path already exists: \" + str(path)\n # print(mess)\n pass\n\n elif not os.path.exists(path):\n os.makedirs(path)\n # __|\n\n # | - folders_exist attribute should be True from now on\n # file_name = self.root_dir + \"/jobs_bin/.folders_exist\"\n file_name = os.path.join(\n self.root_dir,\n self.working_dir,\n \"jobs_bin/.folders_exist\"\n )\n\n with open(file_name, \"w\") as fle:\n fle.write(\"\\n\")\n\n self.folders_exist = self.__folders_exist__(True)\n # __|\n\n # __|","def _add_dir(self):\n now = int(time.time())\n if self.granularity > 1:\n now -= now % self.granularity\n return \"%08x\" % now","def make_directory(self):\n if not os.path.isdir(self.directory):\n os.mkdir(self.directory)","def make_dir(self):\n if not os.path.exists(self.d):\n try:\n os.mkdir(self.d)\n except OSError, e:\n if e.errno != 17:\n raise\n pass","def _create_new_experiment_dir(self):\n ls = os.listdir(self.experiment_dir)\n existing_exp_dirs = [d for d in ls if d.startswith('experiment')]\n if len(existing_exp_dirs) == 0:\n out = 'experiment1'\n else:\n inds = [int(d.lstrip('experiment')) for d in existing_exp_dirs]\n out = 'experiment'+str(max(inds)+1)\n\n self.new_experiment_dir = os.path.join(self.experiment_dir, out)\n os.mkdir(self.new_experiment_dir)\n return None","def mkdir(self, mdir, parents=False):\n assert mdir.startswith('/'), \"%s: invalid manta path\" % mdir\n parts = mdir.split('/')\n assert len(parts) > 3, \"%s: cannot create top-level dirs\" % mdir\n if not parents:\n self.put_directory(mdir)\n else:\n # Find the first non-existant dir: binary search. Because\n # PutDirectory doesn't error on 'mkdir .../already-exists' we\n # don't have a way to detect a miss on `start`. So basically we\n # keep doing the binary search until we hit and close the `start`\n # to `end` gap.\n # Example:\n # - mdir: /trent/stor/builds/a/b/c (need to mk a/b/c)\n # parts: ['', 'trent', 'stor', 'builds', 'a', 'b', 'c']\n # start: 4\n # end: 8\n # - idx: 6\n # d: /trent/stor/builds/a/b (put_directory fails)\n # end: 6\n # - idx: 5\n # d: /trent/stor/builds/a (put_directory succeeds)\n # start: 5\n # (break out of loop)\n # - for i in range(6, 8):\n # i=6 -> d: /trent/stor/builds/a/b\n # i=7 -> d: /trent/stor/builds/a/b/c\n end = len(parts) + 1\n start = 3 # Index of the first possible dir to create.\n while start < end - 1:\n idx = (end - start) / 2 + start\n d = '/'.join(parts[:idx])\n try:\n self.put_directory(d)\n except errors.MantaAPIError:\n _, ex, _ = sys.exc_info()\n if ex.code == 'DirectoryDoesNotExist':\n end = idx\n else:\n raise\n else:\n start = idx\n\n # Now need to create from (end-1, len(parts)].\n for i in range(end, len(parts) + 1):\n d = '/'.join(parts[:i])\n self.put_directory(d)","def mkdir(self, parent_node_id, folder_name):\n\n data = {'name': folder_name, 'parents': [parent_node_id], 'kind': 'FOLDER'}\n response = self.http_client.post(\n self.metadata_url + 'nodes',\n data=json.dumps(data))\n response.raise_for_status()\n return response.json()['id']","def mkdir(name):\n\tspeech.speak(\"Executing 'mkdir \" + name + \"' command to create a directory.\")\n\tsubprocess.call([\"mkdir\", name])","def generate_run_base_dir(\n result_dir: str,\n timestamp: int = None,\n tag: str = None,\n sub_dirs: List[str] = None,\n debug: bool = False,\n) -> str:\n if timestamp is None:\n timestamp = time.time()\n\n if sub_dirs is None:\n sub_dirs = []\n\n # Convert time\n date_str = datetime.datetime.fromtimestamp(timestamp).strftime(\"%y%m%d_%H%M\")\n\n # Append tag if given\n if tag is None:\n base_dir = date_str\n else:\n base_dir = date_str + \"_\" + tag\n\n if debug:\n base_dir = base_dir + \"_debug\"\n\n # Create directory\n base_dir = os.path.join(result_dir, base_dir, *sub_dirs)\n\n mkdir_p(base_dir)\n return base_dir + \"/\"","def setupRunDir(self):\n\n pass","def mkdir(self, *args):\r\n if args:\r\n return self.join(*args).mkdir()\r\n else:\r\n self._svn('mkdir')\r\n return self","def test_cd():\n for i in range(0, 100, 1):\n os.mkdir('test_folder_%s' % i)\n os.chdir('test_folder_%s' % i)\n os.chdir('..')\n os.rmdir('test_folder_%s' % i)","def createFolder(self):\n raise NotImplementedError","def create_directories(working_dir, timesteps):\n\tfor time in timesteps:\n\t\tpath = working_dir + str(time) + '_ps_snapshot'\n\t\t\n\t\t# Make sure no old snapshot dir of same name exists - if it does, remove it.\n\t\tif os.path.isdir(path):\n\t\t\tprint('{} alread exists. Deleting it ...'.format(path))\n\t\t\tshutil.rmtree(path)\n\n\t\t# Copy the template\n\t\tshutil.copytree(working_dir + '.template_snapshot', path, symlinks=True)\n\t\tprint('{} initialized.'.format(path))\n\n\treturn path","def create_subdirectory(dir_path, sub_name_or_keyword):\n if not os.path.exists(dir_path):\n print(\"ERROR (global function 'create_subdirectory').\"),\n print(\"No directory with path '%s'.\"% dir_path)\n return\n #\n if sub_name_or_keyword == 'run':\n date_and_time = [time.localtime()[i] for i in range(5)]\n date_and_time[0] = '{:04d}'.format(int(date_and_time[0]))\n for i in range(1,5):\n date_and_time[i] = '{:02d}'.format(int(date_and_time[i]))\n sub_name = '-'.join(date_and_time[0:4]) + 'h' + date_and_time[4] +'min'\n else:\n sub_name = sub_name_or_keyword\n #\n result = os.path.join(dir_path, sub_name)\n #\n if not os.path.exists(result):\n os.mkdir(result)\n return result","def createDir(self, dirName):\n\n if not os.path.exists(dirName):\n self.createDir(os.path.dirname(dirName))\n try:\n os.mkdir(dirName)\n except:\n print 'Current directory =', os.getcwd()\n raise","def make_workdir():\n workdir = tempfile.mkdtemp(prefix='cerise_runner_')\n _workdirs.append(workdir)\n return workdir","def start(self):\n if self.dir is not None:\n raise FailureException('Run completed, cannot start again')\n self.timestamp = datetimestamp()\n self.dir = self.job.dir / self.timestamp\n os.makedirs(self.dir)\n return self","def createFolder(self):\n\n self.directory = \"D:\\\\CompositionHelper\"\n if not os.path.exists(self.directory):\n os.makedirs(self.directory)\n print ('Created new folder')","def make_new_dir(self):\n try:\n shutil.rmtree(self.model_path)\n except:\n pass\n os.mkdir(self.model_path)","def add_rundir(self):\n os.mkdir(os.path.join(self.relaxdir, \"run.\" + str(len(self.rundir))))\n self.update_rundir()\n self.update_errdir()","def get_runid(path):\n name = Path(path).name\n if not os.path.exists(Path(path).parent):\n return '00001'\n files = os.listdir(Path(path).parent)\n runid = 0\n for f in files:\n try:\n id, val = f.split('_', 1)\n runid = max(runid, int(id))\n except:\n pass\n runid = str(runid + 1)\n runid = '0' * (5 - len(runid)) + runid\n return runid","def create_dir(newdir):\n if not os.path.isdir(newdir):\n try:\n os.mkdir(newdir)\n print(newdir)\n except IOError:\n print(\"cannot create %s directoy\" % newdir)","def mkdir(self, name: str, parent_id: str = None) -> str:\n metadata = {\n \"name\": name,\n \"mimeType\": \"application/vnd.google-apps.folder\",\n \"parents\": [parent_id] if parent_id else []\n }\n r = requests.post(\n \"https://www.googleapis.com/drive/v3/files\",\n headers=self._auth_headers,\n data=json.dumps(metadata)\n )\n if r.status_code in self._errors:\n raise ApiResponseException(\n r.status_code, r.json()[\"error\"][\"message\"])\n return r.json()[\"id\"]","def mkdirout():\n #pdbid=os.path.splitext(os.path.basename(PDB_PATH))[0]\n #outdir = os.path.join(OUTPUT_DIR, pdbid(),\"\") # OUTPUT DIRECTORY WHERE OUTPUT FILES WILL GO\n\n if os.path.exists(output_dir()):\n sys.exit(\"ERROR. Unable to create output directory. %s already exists. Please, make sure you choose an output path not containing former results.\" % output_dir() ) # LOGGING?\n else:\n try:\n os.mkdir(output_dir())\n except OSError:\n sys.exit(\"ERROR. Unable to create output directory %s.\" % output_dir() )\n os.mkdir(output_tmpdir())\n os.mkdir(output_tmpdir(\"pisacov\"))\n os.mkdir(output_tmpdir(\"pisa\"))\n os.mkdir(output_tmpdir(\"deepmetapsicov\"))","def createValDir(self):\n\t\tself.setDirNames()\n\t\tself.setScreenXmlFile()\n\n\t\tself.rollValDir \t = self.roll + \"/screenval\"\n\t\tcmd = 'mkdir -p %s' % (self.rollValDir)\n\t\tos.system(cmd)\n\n\t\tself.createValDirFiles()\n\t\treturn","def make_directories():\n os.mkdir('principal_wings')\n os.mkdir('random_wings')","def test_make_new_dir_4(self):\n new_dir = Path(\"test_dir\")\n Path(self.base_dir, new_dir).mkdir()\n Path(self.base_dir, new_dir.stem + \"_1\").mkdir()\n output_path = basic.make_new_dir(self.base_dir, new_dir, attempt=3)\n exp_dir = Path(\"test_dir_2\")\n exp_path = Path(self.base_dir, exp_dir)\n with self.subTest():\n self.assertTrue(exp_path.is_dir())\n with self.subTest():\n self.assertEqual(exp_dir.stem, output_path.stem)","def cmd_mkdir(self, msg_dict):\r\n dir = msg_dict[\"second_parameter\"]\r\n # dir_path = \"/home/%s/%s\" % (msg_dict[\"username\"],dir)\r\n dir_path = msg_dict[\"current_directory\"] + '/' + msg_dict[\"second_parameter\"]\r\n os.system(\"mkdir %s\" % dir_path)\r\n self.request.send(msg_dict[\"current_directory\"].encode())","def create_dir(working_dir):\n if not os.path.exists(working_dir):\n os.makedirs(working_dir)","def create_dirs(self):\n for new_directory in [self.event_dir, self.event_dir / 'videos']:\n new_directory.mkdir(exist_ok=self.overwrite)\n logger.debug('Dir {} created', new_directory)","def mk_label_folders(idir, classes):\n lbls = set(classes)\n for l in lbls:\n if not os.path.isdir(join(idir, l)):\n os.mkdir(join(idir, l))\n print(f\"Making {l} folder in {idir}\")\n print(\"Done!\")","def make_wfn_dirs(pattern: str, wfn_name: str, num_runs: int):\n for parent in glob.glob(pattern):\n if not os.path.isdir(parent):\n continue\n if not (os.path.isfile(os.path.join(parent, '..', 'mo', 'oneint.npy')) and\n os.path.isfile(os.path.join(parent, '..', 'mo', 'twoint.npy'))):\n continue\n\n newdir = os.path.join(parent, wfn_name)\n if not os.path.isdir(newdir):\n os.mkdir(newdir)\n\n for i in range(num_runs):\n try:\n os.mkdir(os.path.join(newdir, str(i)))\n except FileExistsError:\n pass","def get_save_dir(base_dir, name, id_max=100):\n for uid in range(1, id_max):\n save_dir = os.path.join(base_dir, f'{name}-{uid:02d}')\n if not os.path.exists(save_dir):\n os.makedirs(save_dir)\n return save_dir\n\n raise RuntimeError('Too many save directories created with the same name. \\\n Delete old save directories or use another name.')","def create_dir(dir, v=1):\n if not os.path.exists(dir):\n os.makedirs(dir)\n if v:\n print(\"Created Directory : \", dir)\n return 1\n else:\n if v:\n print(\"Directory already existed : \", dir)\n return 0","def create_folders(self):\n for f in self.params['folder_names']:\n if not os.path.exists(f):\n print 'Creating folder:\\t%s' % f\n os.system(\"mkdir %s\" % (f))","def create_folders(self):\n for f in self.params['folder_names']:\n if not os.path.exists(f):\n print 'Creating folder:\\t%s' % f\n os.system(\"mkdir %s\" % (f))","def create_dir(_dir):\n if not os.path.exists(_dir):\n os.makedirs(_dir)","def build_directories(polymer_name,polymer_length,fresh_run=False):\n polymer_code=polymer_name[0:2].upper()\n date = str(datetime.datetime.now()).split()[0]\n terphenyl_top = get_terphenyl_top_directory()\n run_directory = str(str(terphenyl_top)+'/data/'+str(polymer_name)+'/'+str(polymer_length)+'/run_'+str(date))\n\n if not os.path.exists(run_directory):\n os.makedirs(run_directory)\n else:\n if fresh_run:\n shutil.rmtree(run_directory)\n os.makedirs(run_directory)\n\n input_files = str(str(terphenyl_top)+'/input_files/'+str(polymer_name)+'/'+str(polymer_length))\n\n pdb_file = str(str(input_files)+'/'+str(polymer_length)+'.pdb')\n solvent_file = str(str(terphenyl_top)+\"/input_files/chloroform_box.pdb\")\n topology_file = make_topology(polymer_code)\n run_pdb_file = str(str(run_directory)+\"/\"+str(polymer_length)+\".pdb\")\n run_topology_file = str(str(run_directory)+\"/topol.top\")\n copyfile(pdb_file,run_pdb_file)\n copyfile(topology_file,run_topology_file)\n return(run_directory,pdb_file,solvent_file,topology_file)","def create_folders(self):\n\n for f in self.params['folder_names']:\n if not os.path.exists(f):\n print 'Creating folder:\\t%s' % f\n os.system(\"mkdir %s\" % (f))","def create_directory(path, name):\n new_path = os.path.join(path, name)\n if not os.path.isdir(new_path):\n subprocess.run(['mkdir', new_path])","def safe_make_directory(i):\n try:\n os.mkdir(i)\n except:\n pass","def cmd_mkd(args):","def fresh_directory():\n os.chdir(tempfile.mkdtemp())","def make_dir(self):\n new_dir_name, ok = QtWidgets.QInputDialog.getText(\n self,\n \"New Folder\",\n \"Enter a new folder name:\",\n QtWidgets.QLineEdit.Normal,\n \"\",\n )\n if ok:\n if self.ui_DirList.currentText() == self.active_url:\n dirname = \"\"\n else:\n dirname = self.ui_DirList.currentText()\n new_dir = fs.path.combine(dirname, new_dir_name)\n if not self.fs.isdir(new_dir):\n try:\n self.fs.makedir(new_dir)\n except fs.errors.ResourceNotFound:\n logging.error(\"Can't create {}\".format(new_dir))\n self.last_dir_index = list(reversed(self.directory_history)).index(\n self.selected_dir\n )\n self.browse_folder(subdir=self.selected_dir)\n else:\n ok = QtWidgets.QMessageBox.warning(\n self, \"New Folder\", \"Can't create this Folder: {}\".format(new_dir)\n )"],"string":"[\n \"def _create_run_dir(self):\\n task_name = 'task_'+str(self.setting['task_id'])\\n run_name = '_'.join(['run', \\n str(self.setting['run_idx']), \\n str(self.setting['_id'])])\\n \\n run_dir = op.join(Job_Requestor.run_center, task_name, run_name)\\n back_dir = op.join(run_dir, '.backup')\\n try: \\n os.makedirs(run_dir)\\n os.makedirs(back_dir)\\n os.symlink(Job_Requestor.data_dir, op.join(run_dir, 'data')) \\n\\n except:\\n print(\\\"Error: run dir %s create failed\\\"%(run_dir))\\n # sys.exit(0)\\n \\n self.run_dir = run_dir\\n self.setting.update({'run_dir':run_dir})\\n self._db_update({'run_dir': run_dir})\",\n \"def folder_runnum():\\n now = datetime.datetime.now()\\n runnum = 1\\n while True:\\n folder_name = f\\\"astroNN_{now.month:0{2}d}{now.day:0{2}d}_run{runnum:0{3}d}\\\"\\n if not os.path.exists(folder_name):\\n break\\n else:\\n runnum += 1\\n\\n return folder_name\",\n \"def create_directory(self):\\n dirname = self.name+\\\"_distillates\\\"\\n i = 1\\n while True:\\n try:\\n mkdir(dirname)\\n return dirname\\n except OSError:\\n dirname = self.name+\\\"_distillates_{0}\\\".format(i)\\n i += 1\",\n \"def createRunId(self):\\n # runid is in the form of <login>_YYYY_MMDD_HHMMSS\\n now = datetime.now()\\n username = pwd.getpwuid(os.geteuid()).pw_name\\n runid = \\\"%s_%02d_%02d%02d_%02d%02d%02d\\\" % (username, now.year, now.month,\\n now.day, now.hour, now.minute, now.second)\\n self.runid = runid\\n return runid\",\n \"def create_folder(id_parent, name):\\n id_folder = incr_key_store('folders:counter')\\n rpush_key_store('folders:list', {'id': id_folder, 'parent': id_parent, 'name': name})\\n return id_folder\",\n \"def make_directory(base):\\n \\t\\n i = 0\\n while 1:\\n try:\\n if i == 0:\\n dirname = base\\n else:\\n dirname = base + '_' + str(i)\\n os.mkdir(dirname)\\n break\\n except OSError:\\n if not os.path.isdir(dirname):\\n raise\\n i += 1\\n pass \\n return dirname\",\n \"def create_id_dir(id_dir: Path) -> (int, Path):\\n\\n lockfile_path = id_dir/'.lockfile'\\n with lockfile.LockFile(lockfile_path, timeout=1):\\n next_fn = id_dir/PKEY_FN\\n\\n next_valid = True\\n\\n if next_fn.exists():\\n try:\\n with next_fn.open() as next_file:\\n next_id = int(next_file.read())\\n\\n next_id_path = make_id_path(id_dir, next_id)\\n\\n if next_id_path.exists():\\n next_valid = False\\n\\n except (OSError, ValueError):\\n # In either case, on failure, invalidate the next file.\\n next_valid = False\\n else:\\n next_valid = False\\n\\n if not next_valid:\\n # If the next file's id wasn't valid, then find the next available\\n # id directory the hard way.\\n\\n ids = list(os.listdir(str(id_dir)))\\n # Only return the test directories that could be integers.\\n ids = [id_ for id_ in ids if id_.isdigit()]\\n ids = [int(id_) for id_ in ids]\\n ids.sort()\\n\\n # Find the first unused id.\\n next_id = 1\\n while next_id in ids:\\n next_id += 1\\n\\n next_id_path = make_id_path(id_dir, next_id)\\n\\n next_id_path.mkdir()\\n with next_fn.open('w') as next_file:\\n next_file.write(str(next_id + 1))\\n\\n return next_id, next_id_path\",\n \"def make_run_dir(outdir: Union[str, os.PathLike], desc: str, dry_run: bool = False) -> str:\\n # Pick output directory.\\n prev_run_dirs = []\\n if os.path.isdir(outdir): # sanity check, but click.Path() should clear this one\\n prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))]\\n prev_run_ids = [re.match(r'^\\\\d+', x) for x in prev_run_dirs]\\n prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]\\n cur_run_id = max(prev_run_ids, default=-1) + 1 # start with 00000\\n run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{desc}')\\n assert not os.path.exists(run_dir) # make sure it doesn't already exist\\n\\n # Don't create the dir if it's a dry-run\\n if not dry_run:\\n print('Creating output directory...')\\n os.makedirs(run_dir)\\n return run_dir\",\n \"def _get_next_run_id_local(run_dir_root: str) -> int:\\r\\n dir_names = [d for d in os.listdir(run_dir_root) if os.path.isdir(os.path.join(run_dir_root, d))]\\r\\n r = re.compile(\\\"^\\\\\\\\d+\\\") # match one or more digits at the start of the string\\r\\n run_id = 0\\r\\n\\r\\n for dir_name in dir_names:\\r\\n m = r.match(dir_name)\\r\\n\\r\\n if m is not None:\\r\\n i = int(m.group())\\r\\n run_id = max(run_id, i + 1)\\r\\n\\r\\n return run_id\",\n \"def create_new(self, root, name_length):\\n self.name = create_random_string(name_length)\\n self.ctime = datetime.datetime.now()\\n date_time = datetime.datetime.strftime(self.ctime, \\\"%Y%m%d_%H%M%S\\\")\\n self.folder = f\\\"{date_time}_{self.name}\\\"\\n self.path = os.path.join(root, self.folder)\\n try:\\n os.makedirs(self.path)\\n print(f\\\"Created folder {self.folder}\\\")\\n except OSError:\\n print(f\\\"Directory {self.folder} already exists\\\")\\n except:\\n print(f\\\"Cannot create folder: {self.folder}\\\")\\n raise\",\n \"def new_run(self, init_walkers, continue_run=None, **kwargs):\\n\\n # check to see if the continue_run is actually in this file\\n if continue_run is not None:\\n if continue_run not in self.run_idxs:\\n raise ValueError(\\\"The continue_run idx given, {}, is not present in this file\\\".format(\\n continue_run))\\n\\n # get the index for this run\\n new_run_idx = self.next_run_idx()\\n\\n # create a new group named the next integer in the counter\\n run_grp = self._h5.create_group('{}/{}'.format(RUNS, new_run_idx))\\n\\n\\n # set the initial walkers group\\n init_walkers_grp = run_grp.create_group(INIT_WALKERS)\\n\\n self._add_init_walkers(init_walkers_grp, init_walkers)\\n\\n # initialize the walkers group\\n traj_grp = run_grp.create_group(TRAJECTORIES)\\n\\n\\n # run the initialization routines for adding a run\\n self._add_run_init(new_run_idx, continue_run=continue_run)\\n\\n\\n # add metadata if given\\n for key, val in kwargs.items():\\n if key != RUN_IDX:\\n run_grp.attrs[key] = val\\n else:\\n warn('run_idx metadata is set by wepy and cannot be used', RuntimeWarning)\\n\\n return run_grp\",\n \"def create_dirs():\\n run(\\\"mkdir -p %s\\\"%RUN_DIR)\\n run(\\\"mkdir -p %s\\\"%LOG_DIR)\",\n \"def make_numbered_dir(\\n cls, prefix=\\\"session-\\\", rootdir=None, keep=3, lock_timeout=172800\\n ): # two days\\n if rootdir is None:\\n rootdir = cls.get_temproot()\\n\\n nprefix = prefix.lower()\\n\\n def parse_num(path):\\n \\\"\\\"\\\"Parse the number out of a path (if it matches the prefix)\\\"\\\"\\\"\\n nbasename = path.basename.lower()\\n if nbasename.startswith(nprefix):\\n try:\\n return int(nbasename[len(nprefix) :])\\n except ValueError:\\n pass\\n\\n def create_lockfile(path):\\n \\\"\\\"\\\"Exclusively create lockfile. Throws when failed\\\"\\\"\\\"\\n mypid = os.getpid()\\n lockfile = path.join(\\\".lock\\\")\\n if hasattr(lockfile, \\\"mksymlinkto\\\"):\\n lockfile.mksymlinkto(str(mypid))\\n else:\\n fd = error.checked_call(\\n os.open, str(lockfile), os.O_WRONLY | os.O_CREAT | os.O_EXCL, 0o644\\n )\\n with os.fdopen(fd, \\\"w\\\") as f:\\n f.write(str(mypid))\\n return lockfile\\n\\n def atexit_remove_lockfile(lockfile):\\n \\\"\\\"\\\"Ensure lockfile is removed at process exit\\\"\\\"\\\"\\n mypid = os.getpid()\\n\\n def try_remove_lockfile():\\n # in a fork() situation, only the last process should\\n # remove the .lock, otherwise the other processes run the\\n # risk of seeing their temporary dir disappear. For now\\n # we remove the .lock in the parent only (i.e. we assume\\n # that the children finish before the parent).\\n if os.getpid() != mypid:\\n return\\n try:\\n lockfile.remove()\\n except error.Error:\\n pass\\n\\n atexit.register(try_remove_lockfile)\\n\\n # compute the maximum number currently in use with the prefix\\n lastmax = None\\n while True:\\n maxnum = -1\\n for path in rootdir.listdir():\\n num = parse_num(path)\\n if num is not None:\\n maxnum = max(maxnum, num)\\n\\n # make the new directory\\n try:\\n udir = rootdir.mkdir(prefix + str(maxnum + 1))\\n if lock_timeout:\\n lockfile = create_lockfile(udir)\\n atexit_remove_lockfile(lockfile)\\n except (error.EEXIST, error.ENOENT, error.EBUSY):\\n # race condition (1): another thread/process created the dir\\n # in the meantime - try again\\n # race condition (2): another thread/process spuriously acquired\\n # lock treating empty directory as candidate\\n # for removal - try again\\n # race condition (3): another thread/process tried to create the lock at\\n # the same time (happened in Python 3.3 on Windows)\\n # https://ci.appveyor.com/project/pytestbot/py/build/1.0.21/job/ffi85j4c0lqwsfwa\\n if lastmax == maxnum:\\n raise\\n lastmax = maxnum\\n continue\\n break\\n\\n def get_mtime(path):\\n \\\"\\\"\\\"Read file modification time\\\"\\\"\\\"\\n try:\\n return path.lstat().mtime\\n except error.Error:\\n pass\\n\\n garbage_prefix = prefix + \\\"garbage-\\\"\\n\\n def is_garbage(path):\\n \\\"\\\"\\\"Check if path denotes directory scheduled for removal\\\"\\\"\\\"\\n bn = path.basename\\n return bn.startswith(garbage_prefix)\\n\\n # prune old directories\\n udir_time = get_mtime(udir)\\n if keep and udir_time:\\n for path in rootdir.listdir():\\n num = parse_num(path)\\n if num is not None and num <= (maxnum - keep):\\n try:\\n # try acquiring lock to remove directory as exclusive user\\n if lock_timeout:\\n create_lockfile(path)\\n except (error.EEXIST, error.ENOENT, error.EBUSY):\\n path_time = get_mtime(path)\\n if not path_time:\\n # assume directory doesn't exist now\\n continue\\n if abs(udir_time - path_time) < lock_timeout:\\n # assume directory with lockfile exists\\n # and lock timeout hasn't expired yet\\n continue\\n\\n # path dir locked for exclusive use\\n # and scheduled for removal to avoid another thread/process\\n # treating it as a new directory or removal candidate\\n garbage_path = rootdir.join(garbage_prefix + str(uuid.uuid4()))\\n try:\\n path.rename(garbage_path)\\n garbage_path.remove(rec=1)\\n except KeyboardInterrupt:\\n raise\\n except Exception: # this might be error.Error, WindowsError ...\\n pass\\n if is_garbage(path):\\n try:\\n path.remove(rec=1)\\n except KeyboardInterrupt:\\n raise\\n except Exception: # this might be error.Error, WindowsError ...\\n pass\\n\\n # make link...\\n try:\\n username = os.environ[\\\"USER\\\"] # linux, et al\\n except KeyError:\\n try:\\n username = os.environ[\\\"USERNAME\\\"] # windows\\n except KeyError:\\n username = \\\"current\\\"\\n\\n src = str(udir)\\n dest = src[: src.rfind(\\\"-\\\")] + \\\"-\\\" + username\\n try:\\n os.unlink(dest)\\n except OSError:\\n pass\\n try:\\n os.symlink(src, dest)\\n except (OSError, AttributeError, NotImplementedError):\\n pass\\n\\n return udir\",\n \"def init(cls, dirpath=\\\".\\\", newdir=True):\\n dirpath = dirpath if dirpath[-1] == os.sep else dirpath + os.sep \\n if not os.path.exists(dirpath):\\n os.makedirs(dirpath)\\n if newdir:\\n now = datetime.datetime.now()\\n dirpath = dirpath + now.strftime(\\\"%y-%m-%d_%H-%M-%S\\\") + os.sep\\n os.mkdir(dirpath)\\n cls.dirpath = dirpath\\n cls.__data.clear()\\n cls.__counters.clear()\",\n \"def setup_temp_dir(prepro_num, directory):\\n for i in range (prepro_num):\\n path = directory + '/' + str(i)\\n if os.path.exists(path):\\n shutil.rmtree(path, ignore_errors=True)\\n os.mkdir(path)\",\n \"def mkdir(dirName):\\r\\n raw = 'mockaroo_data/raw/' + dirName\\r\\n cln = 'mockaroo_data/cln/' + dirName\\r\\n call(['mkdir', raw])\\r\\n call(['mkdir', cln])\\r\\n\\r\\n return\",\n \"def data_loader_new_directory_name(self, working_dir: Path):\\n\\n wd = SnowfakeryWorkingDirectory(working_dir)\\n key = wd.index\\n if key not in self.cached_counts:\\n self.cached_counts[key] = wd.get_record_counts()\\n\\n if not self.run_until.sobject_name:\\n return working_dir\\n\\n count = self.cached_counts[key][self.run_until.sobject_name]\\n\\n path, _ = str(working_dir).rsplit(\\\"_\\\", 1)\\n new_working_dir = Path(path + \\\"_\\\" + str(count))\\n return new_working_dir\",\n \"def mklocdir(self):\\n dir = self._localpath+\\\"/%s.%s\\\" % (self._jobname, self._jobid)\\n os.mkdir(dir)\",\n \"async def new_folder(name):\\n res = await joplin.create_folder(folder=name)\\n return res.json()['id']\",\n \"def create_dir(dir_path,plot_type):\\n\\n alphabet = \\\"ABCDEFGHIJKLMNOPQRSTUZWXYZ\\\"\\n alphabet += alphabet.lower()\\n alphabet += \\\"01234567890\\\"\\n\\n\\n if dir_path==None or dir_path=='':\\n dir_path=''\\n random_dir_name=''.join([choice(alphabet) for i in range(10)])\\n dir_path ='./'+plot_type+strftime(\\\"%Y_%m_%d_%H_%M_%S\\\")+random_dir_name+'/'\\n\\n if not os.path.exists(dir_path):\\n os.mkdir(dir_path)\\n\\n return dir_path\",\n \"def create_temp_folder():\\n path_join = os.path.join(tempfile.gettempdir(), id_generator(5))\\n os.makedirs(path_join)\\n return path_join\",\n \"def Create_Dir(self,txn,filename):\\n opid = self.new_opid()\\n xaction = CreateDir_Operation(os.path.join(self.home,filename),opid)\\n self._add_operation(txn,xaction)\",\n \"def dir_creation():\\n for i in range(dir_count):\\n dir_name = str(i * nums)\\n max_len = len(str(nums * dir_count))\\n # if the len is equal, add stright forward\\n if len(dir_name) == max_len:\\n container.append(dir_name)\\n while len(dir_name) < max_len:\\n # tambahkan angka 0 sampai len(dir_name) == max_len\\n dir_name = \\\"0\\\" + dir_name\\n if len(dir_name) == max_len:\\n container.append(dir_name)\\n break\\n return container\",\n \"def find_run_id(increase=False):\\n if tc.RUNID is None:\\n if not os.path.exists(tc.RUNID_FILE):\\n with open(tc.RUNID_FILE, \\\"w\\\") as new_runid_file:\\n new_runid_file.write(\\\"1\\\")\\n tc.RUNID = open(tc.RUNID_FILE, \\\"r\\\").read().rstrip()\\n with open(tc.RUNID_FILE, \\\"w\\\") as new_runid_file:\\n new_runid_file.write(str(int(tc.RUNID) + 1))\\n tc.RUNID = str(int(tc.RUNID) + 1)\\n elif increase:\\n tc.RUNID = int(tc.RUNID) + 1\\n with open(tc.RUNID_FILE, \\\"w\\\") as new_runid_file: \\n new_runid_file.write(str(int(tc.RUNID)))\\n return tc.RUNID\",\n \"def _makeDir(self):\\n try:\\n os.mkdir(self.dir)\\n # log('created directory: %s\\\\n' % self.dir)\\n except OSError, err:\\n if err.errno != errno.EEXIST:\\n raise\",\n \"def _create_pidfile(self, pid_dir, pid):\\n open(os.path.join(pid_dir, str(pid)), 'a').close()\",\n \"def createdir(directory):\\n if os.path.exists(directory):\\n from datetime import datetime\\n now = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\\n os.rename(directory, directory+now)\\n os.mkdir(directory)\",\n \"def generate_run_id(self):\\n r_id_len = 4\\n r_id = 'r'+''.join([str(random.randint(0,9)) for i in range(r_id_len)])\\n while r_id in self.cache:\\n r_id = 'r'+''.join([str(random.randint(0,9)) for i in range(r_id_len)])\\n return r_id\",\n \"def new_dirname(dirname=None, ndigits=3):\\n if dirname is None:\\n ext = (\\\"%%_%ii\\\" % ndigits) % 1\\n dirname = \\\"%s_%s\\\" % (random_string(6), ext)\\n \\n dirname = dirname.replace('.', '_')\\n if os.path.exists(dirname):\\n dirname = increment_filename(dirname, ndigits=ndigits, delim='_')\\n \\n\\n return dirname\",\n \"def create_directory_structure(path_main):\\n\\n if not path_main.exists():\\n path_main.mkdir(parents=True)\",\n \"def mkdir(self, step):\\n path = self.folder(step)\\n if not path.exists():\\n path.mkdir(parents=True)\\n\\n return path\",\n \"def mkdir(self, step):\\n path = self.folder(step)\\n if not path.exists():\\n path.mkdir(parents=True)\\n\\n return path\",\n \"def mkdir(self, step):\\n path = self.folder(step)\\n if not path.exists():\\n path.mkdir(parents=True)\\n\\n return path\",\n \"def make_case_dir(self):\\n\\n mkdir(directory=self.dir)\\n mkdir(directory=self.sub_dir)\",\n \"def MakeDir(self, path: str) -> None:\\n ...\",\n \"def test_make_folders():\\n for i in range(0, 100, 1):\\n os.mkdir('test_folder_%s' % i)\\n os.rmdir('test_folder_%s' % i)\",\n \"def newId():\\n global lastId\\n lastId += 1\\n return 'id%d' % lastId\",\n \"def create_run_tracker(info_dir=None):\\r\\n # TODO(John Sirois): Rework uses around a context manager for cleanup of the info_dir in a more\\r\\n # disciplined manner\\r\\n info_dir = info_dir or safe_mkdtemp()\\r\\n run_tracker = RunTracker(info_dir)\\r\\n report = Report()\\r\\n run_tracker.start(report)\\r\\n return run_tracker\",\n \"def create(self, basedir, outdir, name, prefix=None):\",\n \"def create_directory(tracking_id):\\n upload_path = os.path.join(app.config['DRS_UPLOADS'], '{0}'.format(tracking_id))\\n if not os.path.isdir(upload_path):\\n os.mkdir(upload_path)\",\n \"def make_run_directory(output_root: Union[str, Path]) -> Path:\\n run_directory = get_run_directory(output_root)\\n mkdir(run_directory)\\n return run_directory\",\n \"def _createParentDir(self, dirName):\\n parentDir = os.path.dirname(dirName)\\n if parentDir is \\\"\\\":\\n return \\\"::\\\"\\n basename = \\\"::/%s\\\" % parentDir\\n if not basename in self._dirs:\\n self._createParentDir(parentDir)\\n self._cmd([\\\"mmd\\\", \\\"-i\\\", self._mformatImage, basename])\\n self._dirs.add(basename)\\n return basename\",\n \"def dirmaker(dirp):\\n try:\\n if not os.path.exists(dirp):\\n os.makedirs(dirp)\\n except:\\n pass\",\n \"def _create_ID_files(self):\\n for file, IDs in [(self._trn_IDs_file, self._trn_IDs), (self._val_IDs_file,\\n self._val_IDs), (self._tst_IDs_file, self._tst_IDs)]:\\n with open(file, 'w') as f:\\n f.write('\\\\n'.join('{}###{}###{}'.format(ID[0], ID[1], ID[2]) for ID in IDs))\",\n \"def ensure_directory(explorer, parent_id, dirname):\\n cache_key = (parent_id, dirname)\\n if cache_key in DIR_CACHE:\\n return DIR_CACHE[cache_key]\\n\\n for folder in explorer.list_folder(parent_id):\\n if folder['name'] == dirname:\\n folder_id = folder['id']\\n break\\n else:\\n print(\\\"Creating folder {!r} in parent {}\\\".format(dirname, parent_id))\\n folder_id = explorer.create_folder(dirname, parent_id)\\n DIR_CACHE[cache_key] = folder_id\\n return folder_id\",\n \"def create_directory():\\n global dirName\\n dirName = 'Downloaded Files'\\n global folder_path\\n if os.path.isdir(dirName) == True:\\n print(\\\"This folder already exists, path:\\\", os.path.abspath(dirName))\\n else:\\n os.mkdir(dirName)\\n global folder_path\\n folder_path = os.path.abspath(dirName)\\n print(\\\"Directory \\\" , dirName , \\\" Created \\\")\",\n \"def fs_mkdir(self, dirname: str) -> None:\\n self.exec_(\\\"import uos\\\\nuos.mkdir('%s')\\\" % dirname)\",\n \"def get_next_dir(self):\\n cls_name = self.__class__.__name__.lower()\\n paths = [p.name for p in self.data_dir.iterdir() if re.match(f'{cls_name}_[0-9]+', p.name) and p.is_dir()]\\n i = 0\\n while (path_name := f'{cls_name}_{i}') in paths:\\n i += 1\\n self.save_dir = self.data_dir/path_name\\n self.save_dir.mkdir()\\n self.create_new_tree()\\n return path_name\",\n \"def create_folder(self, foldername: str) -> int:\\n raise NotImplementedError\",\n \"def dirCreate(newFoldername):\\r\\n current_directory = os.getcwd()\\r\\n new_directory = os.path.join(current_directory,newFoldername)\\r\\n \\r\\n if not os.path.exists(new_directory):\\r\\n os.makedirs(new_directory)\\r\\n return new_directory\",\n \"def create_dir(newdir):\\n if not os.path.isdir(newdir):\\n try:\\n os.makedirs(newdir)\\n print(newdir)\\n except IOError:\\n print(\\\"cannot create %s directoy\\\" % newdir)\\n return 0\",\n \"def mkdir(self,rmdir=False):\\n import os\\n if rmdir:\\n os.rmdir(str(self))\\n cur=Path('./')\\n for intdir in self.split():\\n cur+=Path(intdir)\\n if not os.path.isdir(cur):\\n os.mkdir(cur)\",\n \"def create_task_id():\\n return str(int(round(time.time() * 10**9)))\",\n \"def mkdir(path):\",\n \"def createDir(self, dir_name):\\n os.mkdir(os.path.join(self.user[\\\"Save\\\"], dir_name))\",\n \"def create_dir_struct(self, create_first_rev_folder=\\\"True\\\"):\\n # | - create_dir_struct\\n for Job_i in self.Job_list:\\n\\n # | - FOR LOOP BODY\\n # if create_first_rev_folder == \\\"True\\\":\\n # path = os.path.join(Job_i.full_path, \\\"_1\\\")\\n # elif create_first_rev_folder == \\\"False\\\":\\n # path = Job_i.full_path\\n\\n path = Job_i.full_path\\n\\n if os.path.exists(path):\\n # mess = \\\"Path already exists: \\\" + str(path)\\n # print(mess)\\n pass\\n\\n elif not os.path.exists(path):\\n os.makedirs(path)\\n # __|\\n\\n # | - folders_exist attribute should be True from now on\\n # file_name = self.root_dir + \\\"/jobs_bin/.folders_exist\\\"\\n file_name = os.path.join(\\n self.root_dir,\\n self.working_dir,\\n \\\"jobs_bin/.folders_exist\\\"\\n )\\n\\n with open(file_name, \\\"w\\\") as fle:\\n fle.write(\\\"\\\\n\\\")\\n\\n self.folders_exist = self.__folders_exist__(True)\\n # __|\\n\\n # __|\",\n \"def _add_dir(self):\\n now = int(time.time())\\n if self.granularity > 1:\\n now -= now % self.granularity\\n return \\\"%08x\\\" % now\",\n \"def make_directory(self):\\n if not os.path.isdir(self.directory):\\n os.mkdir(self.directory)\",\n \"def make_dir(self):\\n if not os.path.exists(self.d):\\n try:\\n os.mkdir(self.d)\\n except OSError, e:\\n if e.errno != 17:\\n raise\\n pass\",\n \"def _create_new_experiment_dir(self):\\n ls = os.listdir(self.experiment_dir)\\n existing_exp_dirs = [d for d in ls if d.startswith('experiment')]\\n if len(existing_exp_dirs) == 0:\\n out = 'experiment1'\\n else:\\n inds = [int(d.lstrip('experiment')) for d in existing_exp_dirs]\\n out = 'experiment'+str(max(inds)+1)\\n\\n self.new_experiment_dir = os.path.join(self.experiment_dir, out)\\n os.mkdir(self.new_experiment_dir)\\n return None\",\n \"def mkdir(self, mdir, parents=False):\\n assert mdir.startswith('/'), \\\"%s: invalid manta path\\\" % mdir\\n parts = mdir.split('/')\\n assert len(parts) > 3, \\\"%s: cannot create top-level dirs\\\" % mdir\\n if not parents:\\n self.put_directory(mdir)\\n else:\\n # Find the first non-existant dir: binary search. Because\\n # PutDirectory doesn't error on 'mkdir .../already-exists' we\\n # don't have a way to detect a miss on `start`. So basically we\\n # keep doing the binary search until we hit and close the `start`\\n # to `end` gap.\\n # Example:\\n # - mdir: /trent/stor/builds/a/b/c (need to mk a/b/c)\\n # parts: ['', 'trent', 'stor', 'builds', 'a', 'b', 'c']\\n # start: 4\\n # end: 8\\n # - idx: 6\\n # d: /trent/stor/builds/a/b (put_directory fails)\\n # end: 6\\n # - idx: 5\\n # d: /trent/stor/builds/a (put_directory succeeds)\\n # start: 5\\n # (break out of loop)\\n # - for i in range(6, 8):\\n # i=6 -> d: /trent/stor/builds/a/b\\n # i=7 -> d: /trent/stor/builds/a/b/c\\n end = len(parts) + 1\\n start = 3 # Index of the first possible dir to create.\\n while start < end - 1:\\n idx = (end - start) / 2 + start\\n d = '/'.join(parts[:idx])\\n try:\\n self.put_directory(d)\\n except errors.MantaAPIError:\\n _, ex, _ = sys.exc_info()\\n if ex.code == 'DirectoryDoesNotExist':\\n end = idx\\n else:\\n raise\\n else:\\n start = idx\\n\\n # Now need to create from (end-1, len(parts)].\\n for i in range(end, len(parts) + 1):\\n d = '/'.join(parts[:i])\\n self.put_directory(d)\",\n \"def mkdir(self, parent_node_id, folder_name):\\n\\n data = {'name': folder_name, 'parents': [parent_node_id], 'kind': 'FOLDER'}\\n response = self.http_client.post(\\n self.metadata_url + 'nodes',\\n data=json.dumps(data))\\n response.raise_for_status()\\n return response.json()['id']\",\n \"def mkdir(name):\\n\\tspeech.speak(\\\"Executing 'mkdir \\\" + name + \\\"' command to create a directory.\\\")\\n\\tsubprocess.call([\\\"mkdir\\\", name])\",\n \"def generate_run_base_dir(\\n result_dir: str,\\n timestamp: int = None,\\n tag: str = None,\\n sub_dirs: List[str] = None,\\n debug: bool = False,\\n) -> str:\\n if timestamp is None:\\n timestamp = time.time()\\n\\n if sub_dirs is None:\\n sub_dirs = []\\n\\n # Convert time\\n date_str = datetime.datetime.fromtimestamp(timestamp).strftime(\\\"%y%m%d_%H%M\\\")\\n\\n # Append tag if given\\n if tag is None:\\n base_dir = date_str\\n else:\\n base_dir = date_str + \\\"_\\\" + tag\\n\\n if debug:\\n base_dir = base_dir + \\\"_debug\\\"\\n\\n # Create directory\\n base_dir = os.path.join(result_dir, base_dir, *sub_dirs)\\n\\n mkdir_p(base_dir)\\n return base_dir + \\\"/\\\"\",\n \"def setupRunDir(self):\\n\\n pass\",\n \"def mkdir(self, *args):\\r\\n if args:\\r\\n return self.join(*args).mkdir()\\r\\n else:\\r\\n self._svn('mkdir')\\r\\n return self\",\n \"def test_cd():\\n for i in range(0, 100, 1):\\n os.mkdir('test_folder_%s' % i)\\n os.chdir('test_folder_%s' % i)\\n os.chdir('..')\\n os.rmdir('test_folder_%s' % i)\",\n \"def createFolder(self):\\n raise NotImplementedError\",\n \"def create_directories(working_dir, timesteps):\\n\\tfor time in timesteps:\\n\\t\\tpath = working_dir + str(time) + '_ps_snapshot'\\n\\t\\t\\n\\t\\t# Make sure no old snapshot dir of same name exists - if it does, remove it.\\n\\t\\tif os.path.isdir(path):\\n\\t\\t\\tprint('{} alread exists. Deleting it ...'.format(path))\\n\\t\\t\\tshutil.rmtree(path)\\n\\n\\t\\t# Copy the template\\n\\t\\tshutil.copytree(working_dir + '.template_snapshot', path, symlinks=True)\\n\\t\\tprint('{} initialized.'.format(path))\\n\\n\\treturn path\",\n \"def create_subdirectory(dir_path, sub_name_or_keyword):\\n if not os.path.exists(dir_path):\\n print(\\\"ERROR (global function 'create_subdirectory').\\\"),\\n print(\\\"No directory with path '%s'.\\\"% dir_path)\\n return\\n #\\n if sub_name_or_keyword == 'run':\\n date_and_time = [time.localtime()[i] for i in range(5)]\\n date_and_time[0] = '{:04d}'.format(int(date_and_time[0]))\\n for i in range(1,5):\\n date_and_time[i] = '{:02d}'.format(int(date_and_time[i]))\\n sub_name = '-'.join(date_and_time[0:4]) + 'h' + date_and_time[4] +'min'\\n else:\\n sub_name = sub_name_or_keyword\\n #\\n result = os.path.join(dir_path, sub_name)\\n #\\n if not os.path.exists(result):\\n os.mkdir(result)\\n return result\",\n \"def createDir(self, dirName):\\n\\n if not os.path.exists(dirName):\\n self.createDir(os.path.dirname(dirName))\\n try:\\n os.mkdir(dirName)\\n except:\\n print 'Current directory =', os.getcwd()\\n raise\",\n \"def make_workdir():\\n workdir = tempfile.mkdtemp(prefix='cerise_runner_')\\n _workdirs.append(workdir)\\n return workdir\",\n \"def start(self):\\n if self.dir is not None:\\n raise FailureException('Run completed, cannot start again')\\n self.timestamp = datetimestamp()\\n self.dir = self.job.dir / self.timestamp\\n os.makedirs(self.dir)\\n return self\",\n \"def createFolder(self):\\n\\n self.directory = \\\"D:\\\\\\\\CompositionHelper\\\"\\n if not os.path.exists(self.directory):\\n os.makedirs(self.directory)\\n print ('Created new folder')\",\n \"def make_new_dir(self):\\n try:\\n shutil.rmtree(self.model_path)\\n except:\\n pass\\n os.mkdir(self.model_path)\",\n \"def add_rundir(self):\\n os.mkdir(os.path.join(self.relaxdir, \\\"run.\\\" + str(len(self.rundir))))\\n self.update_rundir()\\n self.update_errdir()\",\n \"def get_runid(path):\\n name = Path(path).name\\n if not os.path.exists(Path(path).parent):\\n return '00001'\\n files = os.listdir(Path(path).parent)\\n runid = 0\\n for f in files:\\n try:\\n id, val = f.split('_', 1)\\n runid = max(runid, int(id))\\n except:\\n pass\\n runid = str(runid + 1)\\n runid = '0' * (5 - len(runid)) + runid\\n return runid\",\n \"def create_dir(newdir):\\n if not os.path.isdir(newdir):\\n try:\\n os.mkdir(newdir)\\n print(newdir)\\n except IOError:\\n print(\\\"cannot create %s directoy\\\" % newdir)\",\n \"def mkdir(self, name: str, parent_id: str = None) -> str:\\n metadata = {\\n \\\"name\\\": name,\\n \\\"mimeType\\\": \\\"application/vnd.google-apps.folder\\\",\\n \\\"parents\\\": [parent_id] if parent_id else []\\n }\\n r = requests.post(\\n \\\"https://www.googleapis.com/drive/v3/files\\\",\\n headers=self._auth_headers,\\n data=json.dumps(metadata)\\n )\\n if r.status_code in self._errors:\\n raise ApiResponseException(\\n r.status_code, r.json()[\\\"error\\\"][\\\"message\\\"])\\n return r.json()[\\\"id\\\"]\",\n \"def mkdirout():\\n #pdbid=os.path.splitext(os.path.basename(PDB_PATH))[0]\\n #outdir = os.path.join(OUTPUT_DIR, pdbid(),\\\"\\\") # OUTPUT DIRECTORY WHERE OUTPUT FILES WILL GO\\n\\n if os.path.exists(output_dir()):\\n sys.exit(\\\"ERROR. Unable to create output directory. %s already exists. Please, make sure you choose an output path not containing former results.\\\" % output_dir() ) # LOGGING?\\n else:\\n try:\\n os.mkdir(output_dir())\\n except OSError:\\n sys.exit(\\\"ERROR. Unable to create output directory %s.\\\" % output_dir() )\\n os.mkdir(output_tmpdir())\\n os.mkdir(output_tmpdir(\\\"pisacov\\\"))\\n os.mkdir(output_tmpdir(\\\"pisa\\\"))\\n os.mkdir(output_tmpdir(\\\"deepmetapsicov\\\"))\",\n \"def createValDir(self):\\n\\t\\tself.setDirNames()\\n\\t\\tself.setScreenXmlFile()\\n\\n\\t\\tself.rollValDir \\t = self.roll + \\\"/screenval\\\"\\n\\t\\tcmd = 'mkdir -p %s' % (self.rollValDir)\\n\\t\\tos.system(cmd)\\n\\n\\t\\tself.createValDirFiles()\\n\\t\\treturn\",\n \"def make_directories():\\n os.mkdir('principal_wings')\\n os.mkdir('random_wings')\",\n \"def test_make_new_dir_4(self):\\n new_dir = Path(\\\"test_dir\\\")\\n Path(self.base_dir, new_dir).mkdir()\\n Path(self.base_dir, new_dir.stem + \\\"_1\\\").mkdir()\\n output_path = basic.make_new_dir(self.base_dir, new_dir, attempt=3)\\n exp_dir = Path(\\\"test_dir_2\\\")\\n exp_path = Path(self.base_dir, exp_dir)\\n with self.subTest():\\n self.assertTrue(exp_path.is_dir())\\n with self.subTest():\\n self.assertEqual(exp_dir.stem, output_path.stem)\",\n \"def cmd_mkdir(self, msg_dict):\\r\\n dir = msg_dict[\\\"second_parameter\\\"]\\r\\n # dir_path = \\\"/home/%s/%s\\\" % (msg_dict[\\\"username\\\"],dir)\\r\\n dir_path = msg_dict[\\\"current_directory\\\"] + '/' + msg_dict[\\\"second_parameter\\\"]\\r\\n os.system(\\\"mkdir %s\\\" % dir_path)\\r\\n self.request.send(msg_dict[\\\"current_directory\\\"].encode())\",\n \"def create_dir(working_dir):\\n if not os.path.exists(working_dir):\\n os.makedirs(working_dir)\",\n \"def create_dirs(self):\\n for new_directory in [self.event_dir, self.event_dir / 'videos']:\\n new_directory.mkdir(exist_ok=self.overwrite)\\n logger.debug('Dir {} created', new_directory)\",\n \"def mk_label_folders(idir, classes):\\n lbls = set(classes)\\n for l in lbls:\\n if not os.path.isdir(join(idir, l)):\\n os.mkdir(join(idir, l))\\n print(f\\\"Making {l} folder in {idir}\\\")\\n print(\\\"Done!\\\")\",\n \"def make_wfn_dirs(pattern: str, wfn_name: str, num_runs: int):\\n for parent in glob.glob(pattern):\\n if not os.path.isdir(parent):\\n continue\\n if not (os.path.isfile(os.path.join(parent, '..', 'mo', 'oneint.npy')) and\\n os.path.isfile(os.path.join(parent, '..', 'mo', 'twoint.npy'))):\\n continue\\n\\n newdir = os.path.join(parent, wfn_name)\\n if not os.path.isdir(newdir):\\n os.mkdir(newdir)\\n\\n for i in range(num_runs):\\n try:\\n os.mkdir(os.path.join(newdir, str(i)))\\n except FileExistsError:\\n pass\",\n \"def get_save_dir(base_dir, name, id_max=100):\\n for uid in range(1, id_max):\\n save_dir = os.path.join(base_dir, f'{name}-{uid:02d}')\\n if not os.path.exists(save_dir):\\n os.makedirs(save_dir)\\n return save_dir\\n\\n raise RuntimeError('Too many save directories created with the same name. \\\\\\n Delete old save directories or use another name.')\",\n \"def create_dir(dir, v=1):\\n if not os.path.exists(dir):\\n os.makedirs(dir)\\n if v:\\n print(\\\"Created Directory : \\\", dir)\\n return 1\\n else:\\n if v:\\n print(\\\"Directory already existed : \\\", dir)\\n return 0\",\n \"def create_folders(self):\\n for f in self.params['folder_names']:\\n if not os.path.exists(f):\\n print 'Creating folder:\\\\t%s' % f\\n os.system(\\\"mkdir %s\\\" % (f))\",\n \"def create_folders(self):\\n for f in self.params['folder_names']:\\n if not os.path.exists(f):\\n print 'Creating folder:\\\\t%s' % f\\n os.system(\\\"mkdir %s\\\" % (f))\",\n \"def create_dir(_dir):\\n if not os.path.exists(_dir):\\n os.makedirs(_dir)\",\n \"def build_directories(polymer_name,polymer_length,fresh_run=False):\\n polymer_code=polymer_name[0:2].upper()\\n date = str(datetime.datetime.now()).split()[0]\\n terphenyl_top = get_terphenyl_top_directory()\\n run_directory = str(str(terphenyl_top)+'/data/'+str(polymer_name)+'/'+str(polymer_length)+'/run_'+str(date))\\n\\n if not os.path.exists(run_directory):\\n os.makedirs(run_directory)\\n else:\\n if fresh_run:\\n shutil.rmtree(run_directory)\\n os.makedirs(run_directory)\\n\\n input_files = str(str(terphenyl_top)+'/input_files/'+str(polymer_name)+'/'+str(polymer_length))\\n\\n pdb_file = str(str(input_files)+'/'+str(polymer_length)+'.pdb')\\n solvent_file = str(str(terphenyl_top)+\\\"/input_files/chloroform_box.pdb\\\")\\n topology_file = make_topology(polymer_code)\\n run_pdb_file = str(str(run_directory)+\\\"/\\\"+str(polymer_length)+\\\".pdb\\\")\\n run_topology_file = str(str(run_directory)+\\\"/topol.top\\\")\\n copyfile(pdb_file,run_pdb_file)\\n copyfile(topology_file,run_topology_file)\\n return(run_directory,pdb_file,solvent_file,topology_file)\",\n \"def create_folders(self):\\n\\n for f in self.params['folder_names']:\\n if not os.path.exists(f):\\n print 'Creating folder:\\\\t%s' % f\\n os.system(\\\"mkdir %s\\\" % (f))\",\n \"def create_directory(path, name):\\n new_path = os.path.join(path, name)\\n if not os.path.isdir(new_path):\\n subprocess.run(['mkdir', new_path])\",\n \"def safe_make_directory(i):\\n try:\\n os.mkdir(i)\\n except:\\n pass\",\n \"def cmd_mkd(args):\",\n \"def fresh_directory():\\n os.chdir(tempfile.mkdtemp())\",\n \"def make_dir(self):\\n new_dir_name, ok = QtWidgets.QInputDialog.getText(\\n self,\\n \\\"New Folder\\\",\\n \\\"Enter a new folder name:\\\",\\n QtWidgets.QLineEdit.Normal,\\n \\\"\\\",\\n )\\n if ok:\\n if self.ui_DirList.currentText() == self.active_url:\\n dirname = \\\"\\\"\\n else:\\n dirname = self.ui_DirList.currentText()\\n new_dir = fs.path.combine(dirname, new_dir_name)\\n if not self.fs.isdir(new_dir):\\n try:\\n self.fs.makedir(new_dir)\\n except fs.errors.ResourceNotFound:\\n logging.error(\\\"Can't create {}\\\".format(new_dir))\\n self.last_dir_index = list(reversed(self.directory_history)).index(\\n self.selected_dir\\n )\\n self.browse_folder(subdir=self.selected_dir)\\n else:\\n ok = QtWidgets.QMessageBox.warning(\\n self, \\\"New Folder\\\", \\\"Can't create this Folder: {}\\\".format(new_dir)\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6758753","0.66561","0.6593291","0.63309443","0.6312094","0.6216086","0.61310786","0.6109647","0.60766464","0.6072097","0.6067855","0.6009013","0.590007","0.58960336","0.5868165","0.5814761","0.57576287","0.57141966","0.56943786","0.5688185","0.56827813","0.56720674","0.5635089","0.5631318","0.5618522","0.56055385","0.5603126","0.56028706","0.5602548","0.55968446","0.5591322","0.5591322","0.5591322","0.55909896","0.55899554","0.5571457","0.5543581","0.55431944","0.55375606","0.55261225","0.55235636","0.5514871","0.5511789","0.5497417","0.5495814","0.549044","0.54848576","0.5484486","0.54835665","0.54817903","0.54784006","0.5476895","0.54766726","0.5468339","0.5467575","0.54667586","0.5462507","0.5458349","0.5450351","0.543782","0.5436686","0.5436304","0.5431592","0.54293066","0.5423451","0.54016274","0.5381273","0.5381192","0.53714603","0.53656507","0.53574145","0.5353246","0.5329286","0.5325979","0.5323225","0.5323173","0.531789","0.530938","0.53093016","0.52999187","0.5298624","0.5292494","0.52876765","0.52841574","0.5282662","0.5280595","0.52617234","0.52606803","0.5256272","0.52560157","0.52559096","0.52559096","0.52465034","0.5243402","0.5243362","0.52426827","0.5231111","0.5229223","0.5224483","0.5211446"],"string":"[\n \"0.6758753\",\n \"0.66561\",\n \"0.6593291\",\n \"0.63309443\",\n \"0.6312094\",\n \"0.6216086\",\n \"0.61310786\",\n \"0.6109647\",\n \"0.60766464\",\n \"0.6072097\",\n \"0.6067855\",\n \"0.6009013\",\n \"0.590007\",\n \"0.58960336\",\n \"0.5868165\",\n \"0.5814761\",\n \"0.57576287\",\n \"0.57141966\",\n \"0.56943786\",\n \"0.5688185\",\n \"0.56827813\",\n \"0.56720674\",\n \"0.5635089\",\n \"0.5631318\",\n \"0.5618522\",\n \"0.56055385\",\n \"0.5603126\",\n \"0.56028706\",\n \"0.5602548\",\n \"0.55968446\",\n \"0.5591322\",\n \"0.5591322\",\n \"0.5591322\",\n \"0.55909896\",\n \"0.55899554\",\n \"0.5571457\",\n \"0.5543581\",\n \"0.55431944\",\n \"0.55375606\",\n \"0.55261225\",\n \"0.55235636\",\n \"0.5514871\",\n \"0.5511789\",\n \"0.5497417\",\n \"0.5495814\",\n \"0.549044\",\n \"0.54848576\",\n \"0.5484486\",\n \"0.54835665\",\n \"0.54817903\",\n \"0.54784006\",\n \"0.5476895\",\n \"0.54766726\",\n \"0.5468339\",\n \"0.5467575\",\n \"0.54667586\",\n \"0.5462507\",\n \"0.5458349\",\n \"0.5450351\",\n \"0.543782\",\n \"0.5436686\",\n \"0.5436304\",\n \"0.5431592\",\n \"0.54293066\",\n \"0.5423451\",\n \"0.54016274\",\n \"0.5381273\",\n \"0.5381192\",\n \"0.53714603\",\n \"0.53656507\",\n \"0.53574145\",\n \"0.5353246\",\n \"0.5329286\",\n \"0.5325979\",\n \"0.5323225\",\n \"0.5323173\",\n \"0.531789\",\n \"0.530938\",\n \"0.53093016\",\n \"0.52999187\",\n \"0.5298624\",\n \"0.5292494\",\n \"0.52876765\",\n \"0.52841574\",\n \"0.5282662\",\n \"0.5280595\",\n \"0.52617234\",\n \"0.52606803\",\n \"0.5256272\",\n \"0.52560157\",\n \"0.52559096\",\n \"0.52559096\",\n \"0.52465034\",\n \"0.5243402\",\n \"0.5243362\",\n \"0.52426827\",\n \"0.5231111\",\n \"0.5229223\",\n \"0.5224483\",\n \"0.5211446\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6336431"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":77085,"cells":{"query":{"kind":"string","value":"Reads all directory names in a given directory (nonrecursive) and returns the next (increasing) run id. Assumes IDs are numbers at the start of the directory names."},"document":{"kind":"string","value":"def _get_next_run_id_local(run_dir_root: str) -> int:\r\n dir_names = [d for d in os.listdir(run_dir_root) if os.path.isdir(os.path.join(run_dir_root, d))]\r\n r = re.compile(\"^\\\\d+\") # match one or more digits at the start of the string\r\n run_id = 0\r\n\r\n for dir_name in dir_names:\r\n m = r.match(dir_name)\r\n\r\n if m is not None:\r\n i = int(m.group())\r\n run_id = max(run_id, i + 1)\r\n\r\n return run_id"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def create_id_dir(id_dir: Path) -> (int, Path):\n\n lockfile_path = id_dir/'.lockfile'\n with lockfile.LockFile(lockfile_path, timeout=1):\n next_fn = id_dir/PKEY_FN\n\n next_valid = True\n\n if next_fn.exists():\n try:\n with next_fn.open() as next_file:\n next_id = int(next_file.read())\n\n next_id_path = make_id_path(id_dir, next_id)\n\n if next_id_path.exists():\n next_valid = False\n\n except (OSError, ValueError):\n # In either case, on failure, invalidate the next file.\n next_valid = False\n else:\n next_valid = False\n\n if not next_valid:\n # If the next file's id wasn't valid, then find the next available\n # id directory the hard way.\n\n ids = list(os.listdir(str(id_dir)))\n # Only return the test directories that could be integers.\n ids = [id_ for id_ in ids if id_.isdigit()]\n ids = [int(id_) for id_ in ids]\n ids.sort()\n\n # Find the first unused id.\n next_id = 1\n while next_id in ids:\n next_id += 1\n\n next_id_path = make_id_path(id_dir, next_id)\n\n next_id_path.mkdir()\n with next_fn.open('w') as next_file:\n next_file.write(str(next_id + 1))\n\n return next_id, next_id_path","def get_ids(directory):\n return (f[:-4] for f in os.listdir(directory))","def get_runid(path):\n name = Path(path).name\n if not os.path.exists(Path(path).parent):\n return '00001'\n files = os.listdir(Path(path).parent)\n runid = 0\n for f in files:\n try:\n id, val = f.split('_', 1)\n runid = max(runid, int(id))\n except:\n pass\n runid = str(runid + 1)\n runid = '0' * (5 - len(runid)) + runid\n return runid","def directories(fs, directory_id=1):\n parents = {fs.name: directory_id}\n Session.add(Directory(id=directory_id, filesystem_id=fs.id,\n parent_id=parents[fs.name], name=''))\n Session.commit()\n for dirpath, dirnames, filenames in walk(fs.name):\n p = Session.query(Directory).filter(Directory.id == parents[dirpath]).one()\n p.nfiles = len(filenames)\n for d in dirnames:\n directory_id += 1\n parents[os.path.join(dirpath, d.name)] = directory_id\n Session.add(Directory(id=directory_id, filesystem_id=fs.id,\n parent_id=parents[dirpath], name=d.name))\n Session.commit()\n return directory_id","def import_directory(self, directory):\n files_list = sys_utils.get_files_from_directory(directory)\n if files_list == None:\n return None, 0\n oids = []\n num_new_files = 0\n p = progress.progress(len(files_list))\n for file_location in files_list:\n oid, new_file = self.import_file(file_location)\n p.tick()\n if oid:\n oids.append(oid)\n if new_file:\n num_new_files += 1\n oids = list(set(oids)) # assert uniqueness \n return oids, num_new_files","def getImmediateSubdirectories(dir):","def getNextID(self, d):\n try:\n listOrdered = d.keys()\n listOrdered = funcs.sortStringList(listOrdered)\n lastID = int(listOrdered[-1])\n nextID = str(lastID + 1)\n for i in range(1,int(nextID)):\n if str(i) not in listOrdered:\n return str(i)\n return nextID\n except:\n return '1'","def directory_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"directory_id\")","def parse_dir(self, directory):\n for dir in os.listdir(directory):\n if dir in ['.git', '.github', '.vscode', 'docs']:\n continue\n next_dir = os.path.join(directory, dir)\n if os.path.isdir(next_dir):\n if dir.startswith('template_'):\n self.parse_template(next_dir)\n else:\n normpath = os.path.relpath(next_dir)\n normpath = os.path.normpath(normpath)\n path = normpath.split(os.sep)\n self.add_folder(path)\n # add_directory(next_dir)\n self.parse_dir(next_dir)","def resolve_experiment_id(models_dir):\n ids = [x for x in os.listdir(models_dir) \n if os.path.isdir(models_dir + os.sep + x)]\n if len(ids) > 0:\n return int(max([int(i) for i in ids])) + 1\n return 1","def parse_dir(args, dirname, names):\n for name in names:\n path = os.path.join(dirname, name)\n\n if os.path.isfile(path):\n parse_file_from_directory(path, args)","def logdir():\n listdir = [xx for xx in os.listdir(\"runs/\") if os.path.isdir(os.path.join(\"runs\", xx))]\n\n max_ = 0\n for i in range(len(listdir)):\n try:\n filename = int(listdir[i])\n if max_ < filename:\n max_ = filename\n except ValueError:\n continue\n\n return str(max_ + 1)","def get_next_id():\n with open(WORK_LOG_FILENAME, 'r') as work_log:\n work_log_reader = csv.DictReader(work_log)\n entry_id = 0\n for entry in work_log_reader:\n if int(entry['id']) > entry_id:\n entry_id = int(entry['id'])\n entry_id += 1\n return entry_id","def directory_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"directory_id\")","def _last_exp_id(name, path):\n\n exp_id = 0\n output_dirs = listdir(path)\n exp_dirs = [s for s in output_dirs if name in s]\n if exp_dirs:\n ids = [int(s.split('_')[-1]) for s in exp_dirs]\n exp_id = max(ids)\n return exp_id","def scanDirectories(directory, includes = [\"*\"], excludes = []):\n\treturn scanAll(directory, includes, excludes)[2]","def __dir_arg_parse(directory: Directory, directory_path: str) -> Entry:\n dir_split = directory_path.split(\"/\")\n for target in dir_split:\n if target == \"..\":\n if directory.get_parent():\n directory = directory.get_parent()\n elif directory.get_name() != target and target != \".\":\n directory = directory.get_entry(target)\n return directory","def dirGenerator(datadirectory):\n\n subdirectories = [row for row in os.listdir(datadirectory) if '$' not in row]\n\n #iterate through subdirectories\n for day in subdirectories:\n\n #collect raw data set file names in sub directories\n fileNames = [row for row in os.listdir(datadirectory + day + '\\\\RawDataFiles\\\\')]\n\n #iterate over the raw datasets\n print 'There are ' + str(len(fileNames)) + ' datasets in ' + day\n for index, datafile in enumerate(fileNames):\n yield datadirectory + day + '\\\\RawDataFiles\\\\' + datafile, day, datafile, index","def directory_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"directory_id\")","def directory_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"directory_id\")","def directory_id(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"directory_id\")","def directory_id(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"directory_id\")","def get_next_dir(self):\n cls_name = self.__class__.__name__.lower()\n paths = [p.name for p in self.data_dir.iterdir() if re.match(f'{cls_name}_[0-9]+', p.name) and p.is_dir()]\n i = 0\n while (path_name := f'{cls_name}_{i}') in paths:\n i += 1\n self.save_dir = self.data_dir/path_name\n self.save_dir.mkdir()\n self.create_new_tree()\n return path_name","def get_latest(dir_list):\n int_list = []\n for directory in dir_list:\n try:\n int_list.append(int(directory))\n except ValueError:\n pass\n if not int_list:\n return None\n return max(int_list)","def _get_dir_id(site_id, dirname):\n querystring = 'select id from {} where site_id = %s and dirname = %s;'.format(TABLES[2])\n res = execute_query(querystring, (site_id, dirname))\n if res:\n return res[0][0]\n return None","def readDirectory():\n tagdir = \"tagreplacements\"\n data = os.listdir(tagdir)\n for d in data:\n processFile(os.path.join(tagdir,d))\n \n #print(repd)","def _fetch_run_dir(self, run_id, backend=None, results_dir=None):\n results_dir = self._fetch_results_dir(backend=backend, results_dir=results_dir)\n return os.path.join(results_dir, run_id)","def get_dir_for_fname(directory, filename):\r\n for fname, dirpath in get_all_files(directory):\r\n if fname == filename:\r\n return dirpath\r\n return None","def get_year_from_dir(dirname):\n parts = dirname.split(\"/\")\n branch = \"RunII_\"\n for p in parts:\n if branch in p:\n ind = parts.index(p)\n if ind == len(parts)-1:\n return None\n return parts[ind+1]\n else:\n return parts[0]","def test_scan_dir_files(self):\n self.run_scan(self.subdir, self.nest_fcount + 1)","def dirtree(dir, index):\n filenames = os.listdir(dir)\n for filename in filenames:\n if not os.path.isdir(os.path.abspath(dir+'/'+filename)):\n if filename == filenames[-1]:\n print('| '*index+'\\--', filename)\n else:\n print('| '*index+'|--', filename)\n else:\n print('| '*index+'|--', filename)\n dir = dir + '/' + filename\n dirtree(dir, index+1)","def _nextId(cls, id=None):\n if (not hasattr(DAG, \"_lastID\")):\n DAG._lastID = 0\n if (id):\n DAG._lastId = id\n DAG._lastID = DAG._lastID + 1\n return DAG._lastID","def walk(dir, callback):\n\n dir = abspath(dir)\n for file in listdir(dir):\n nfile = join(dir, file)\n if isdir(nfile):\n walk(nfile, callback)\n else:\n callback(nfile)","def get_first_formatted_dir_in_dir(folder, fmt):\n first_elem = None\n root_elements = sorted(os.listdir(folder))\n for root_element in root_elements:\n if os.path.isdir(os.path.join(folder, root_element)):\n if parser.validate(fmt, root_element):\n first_elem = root_element\n break\n return first_elem","def scanDirectory(directoryName=\".\"):\n cluster=Cluster()\n\n #reading files and folders \n for path, folders, files in os.walk(directoryName):\n for afile in files:\n cluster.addFile(os.path.join(path,afile))\n \n cluster.clustering()","def _next_version(self, dirpath: str) -> int:\n try:\n version_re = re.compile(r\"version_(\\d+)\")\n\n def is_valid_version(v: str):\n return version_re.search(v) is not None\n\n versions = tuple(filter(is_valid_version, os.listdir(dirpath)))\n if not versions:\n # No versions yet\n return 0\n current_version = natsorted(versions, reverse=True)[0]\n # Get the version number using the version pattern\n current_version = int(version_re.search(current_version).group(1))\n return current_version + 1\n except Exception as e:\n logger.warning(f\"Starting from version 0 because of error: {e}\")\n return 0","def list_dir(*path):\n path = os.path.join(*path)\n return next(os.walk(path))[1]","def folder_runnum():\n now = datetime.datetime.now()\n runnum = 1\n while True:\n folder_name = f\"astroNN_{now.month:0{2}d}{now.day:0{2}d}_run{runnum:0{3}d}\"\n if not os.path.exists(folder_name):\n break\n else:\n runnum += 1\n\n return folder_name","def read_dir(directory):\n results = AttrDict()\n results.iterations = pd.read_csv(os.path.join(directory, 'iterations.csv'),\n index_col=0)\n results.solutions = AttrDict()\n for i in results.iterations.index.tolist():\n iteration_dir = os.path.join(directory, '{:0>4d}'.format(i))\n fmt = _detect_format(iteration_dir)\n logging.debug('Iteration: {}, Format detected: {}'.format(i, fmt))\n try:\n if fmt == 'netcdf':\n sol_path = os.path.join(iteration_dir, 'solution.nc')\n results.solutions[i] = read_netcdf(sol_path)\n else:\n sol_path = iteration_dir\n results.solutions[i] = read_csv(sol_path)\n logging.debug('Read as {}: {}'.format(fmt, sol_path))\n except IOError as err:\n logging.warning('I/O error in `{}` at iteration `{}`'\n ': {}'.format(iteration_dir, i, err))\n # results.solutions[i] = AttrDict() # add an empty entry\n continue\n return results","def scan(directory, cnx):\n print(\"Scanning {}\".format(directory))\n dirs = filter(\n os.path.isdir, map(lambda x: os.path.join(directory, x), os.listdir(directory)),\n )\n jobdirs = list(filter(lambda x: os.path.exists(x + \"/job.yaml\"), dirs))\n\n print(\"Job directories:\")\n pprint.pprint(jobdirs)\n # when you're not performing your duties, do they keep you in a little box?\n for jobdir in jobdirs:\n scan_job(jobdir, cnx)","def parseDirectories(srcDirs):\n allDocIds = []\n ignoreIssns = []\n seenIds = set()\n for srcDir in srcDirs:\n if not isdir(srcDir):\n continue\n if isfile(getLockFname(srcDir)):\n logging.warn('%s exists. Looks like a crawl is going on in %s. Skipping.' % (getLockFname(srcDir), srcDir))\n continue\n dirDocIds = parseDocIds(srcDir)\n if dirDocIds == None:\n logging.info('Found no document IDs in directory %s' % srcDir)\n continue\n doneIds = set(parseDocIdStatus(srcDir))\n count = 0\n for docId in dirDocIds:\n if docId in seenIds or docId in doneIds:\n continue\n seenIds.add(docId)\n allDocIds.append((docId, srcDir))\n count += 1\n\n ignoreIssns.extend(parseIssnStatus(srcDir))\n logging.info('Directory %s: %d docIds to crawl' % (srcDir, count))\n\n logging.info('Found %d docIds to crawl, %d ISSNs to ignore in %d directories' % (len(allDocIds), len(ignoreIssns), len(srcDirs)))\n return (allDocIds, ignoreIssns)","def enumerate_dirname(path):\n for (dirpath, dirname_list, _) in os.walk(path):\n for dirname in tqdm(dirname_list):\n if dirname[0] != '.':\n old_dir = dirpath + dirname\n new_dir = dirpath + '%.5d' % dirname_list.index(dirname)\n if os.path.exists(new_dir):\n print ('Directory %s already exist' % new_dir)\n continue\n else:\n try:\n os.rename(old_dir, new_dir)\n except:\n print('Couldn\\'t rename %s' % old_dir)\n break","def get_next_model_id(models_dir=MODELS_DIR,\n model_prefix=DEFAULT_MODEL_PREFIX,\n model_ext=MODEL_EXT):\n models = glob.glob(os.path.join(models_dir, model_prefix + '*'))\n return model_prefix + str(len(models) + 1)","def scanFiles(directory, includes = [\"*\"], excludes = []):\n\treturn scanAll(directory, includes, excludes)[1]","def iter_dir(temp_dir, blast_db, query_name, iteration):\n name = '{}_{}_{:02d}'.format(\n basename(blast_db), basename(query_name), iteration)\n\n return join(temp_dir, name)","def enumerate():\n names = [f for f in os.listdir(_INPUT_ROOT) if not\n os.path.isdir(os.path.join(_INPUT_ROOT, f))]\n return sorted(names)","def lsdir(\n self,\n dir_id: str = None,\n trashed: bool = False,\n owners: list = None,\n page_size: int = 20,\n page_token: str = None,\n order_by: str = \"modifiedTime\"\n ) -> namedtuple(\"Page\", [\"files\", \"next_page_token\"]):\n if dir_id:\n if owners:\n query = (f\"trashed={trashed} and '{dir_id}' in parents and\"\n f\" '{','.join(owners)}' in owners\")\n else:\n query = f\"trashed={trashed} and '{dir_id}' in parents\"\n else:\n if owners:\n query = f\"trashed={trashed} and '{','.join(owners)}' in owners\"\n else:\n query = f\"trashed={trashed}\"\n flags = {\n \"q\": query,\n \"pageSize\": page_size,\n \"orderBy\": order_by,\n \"fields\": \"files(name, mimeType, id), nextPageToken\",\n \"pageToken\": page_token,\n }\n r = requests.get(\n \"https://www.googleapis.com/drive/v3/files\",\n params=flags,\n headers=self._auth_headers\n )\n if r.status_code in self._errors:\n raise ApiResponseException(\n r.status_code, r.json()[\"error\"][\"message\"])\n Page = namedtuple(\"Page\", [\"files\", \"next_page_token\"])\n r = r.json()\n if \"nextPageToken\" in r:\n return Page(\n [GDriveFile(file) for file in r[\"files\"]],\n r[\"nextPageToken\"]\n )\n else:\n return Page(\n [GDriveFile(file) for file in r[\"files\"]],\n None\n )","def index_directory(directory,\n labels,\n formats,\n class_names=None,\n shuffle=True,\n seed=None,\n follow_links=False):\n if labels is None:\n # in the no-label case, index from the parent directory down.\n subdirs = ['']\n class_names = subdirs\n else:\n subdirs = []\n for subdir in sorted(os.listdir(directory)):\n if os.path.isdir(os.path.join(directory, subdir)):\n subdirs.append(subdir)\n if not class_names:\n class_names = subdirs\n else:\n if set(class_names) != set(subdirs):\n raise ValueError(\n 'The `class_names` passed did not match the '\n 'names of the subdirectories of the target directory. '\n 'Expected: %s, but received: %s' %\n (subdirs, class_names))\n class_indices = dict(zip(class_names, range(len(class_names))))\n\n # Build an index of the files\n # in the different class subfolders.\n pool = multiprocessing.pool.ThreadPool()\n results = []\n filenames = []\n\n for dirpath in (os.path.join(directory, subdir) for subdir in subdirs):\n results.append(\n pool.apply_async(index_subdirectory,\n (dirpath, class_indices, follow_links, formats)))\n labels_list = []\n for res in results:\n partial_filenames, partial_labels = res.get()\n labels_list.append(partial_labels)\n filenames += partial_filenames\n if labels not in ('inferred', None):\n if len(labels) != len(filenames):\n raise ValueError('Expected the lengths of `labels` to match the number '\n 'of files in the target directory. len(labels) is %s '\n 'while we found %s files in %s.' % (\n len(labels), len(filenames), directory))\n else:\n i = 0\n labels = np.zeros((len(filenames),), dtype='int32')\n for partial_labels in labels_list:\n labels[i:i + len(partial_labels)] = partial_labels\n i += len(partial_labels)\n\n if labels is None:\n print('Found %d files.' % (len(filenames),))\n else:\n print('Found %d files belonging to %d classes.' %\n (len(filenames), len(class_names)))\n pool.close()\n pool.join()\n file_paths = [os.path.join(directory, fname) for fname in filenames]\n\n if shuffle:\n # Shuffle globally to erase macro-structure\n if seed is None:\n seed = np.random.randint(1e6)\n rng = np.random.RandomState(seed)\n rng.shuffle(file_paths)\n rng = np.random.RandomState(seed)\n rng.shuffle(labels)\n return file_paths, labels, class_names","def get_last_time_step(dir):\n\n return str(max([fd for fd in listdir(dir) if fd.isnumeric()]))","def listdir(self, subdir=''):\n\n try:\n subdir = subdir.decode()\n except AttributeError:\n pass\n subdir = subdir.rstrip('\\\\')\n # cmd = '\"%s\" \"%s\" 0 ' % (self.ndc_path, self.filename)\n cmd = [\n self.ndc_path,\n self.filename,\n '0'\n ]\n if subdir:\n cmd.append(subdir)\n # cmd += '\"%s\"' % subdir\n\n logging.info(cmd)\n try:\n result = check_output(cmd)\n except CalledProcessError:\n raise FileNotFoundError('Subdirectory not found in disk', [])\n\n result = [r.split(b'\\t') for r in result.split(b'\\r\\n')]\n result = list(filter(lambda x: len(x) == 4, result))\n\n filenames = []\n subdirs = []\n for r in result:\n try:\n decoded = r[0].decode('shift_jis')\n if r[2] != b'<DIR>':\n filenames.append(decoded)\n elif r[2] == b'<DIR>' and len(r[0].strip(b'.')) > 0:\n subdirs.append(decoded)\n except UnicodeDecodeError:\n logging.info(\"Couldn't decode one of the strings in the folder: %s\" % subdir)\n continue\n\n return filenames, subdirs","def parse_dir(dir, verbose=False):\n\n lengths = {}\n for f in os.listdir(dir):\n m = re.search('^(\\w+)_(Primer\\w)_', f)\n if not m:\n sys.stderr.write(\"Error: can't parse {}\\n\".format(f))\n continue\n (srr, primer) = m.groups()\n if srr not in lengths:\n lengths[srr] = {'PrimerA' : 0, 'PrimerB' : 0, 'PrimerC' : 0}\n for seqid, header, seq, qualscores in stream_fastq(os.path.join(dir, f)):\n lengths[srr][primer] += len(seq)\n return lengths","def describe_directories(DirectoryIds=None, NextToken=None, Limit=None):\n pass","def _recursive_scan(directory=None, file_extension='.dvl'):\n directory = directory or app.config['DEVICE_LOG_DRIVE']\n\n for entry in os.scandir(directory):\n if entry.is_dir(follow_symlinks=False):\n yield from _recursive_scan(entry)\n elif os.path.splitext(entry.name)[1] == file_extension:\n yield entry","def listdir_full_path(directory):\n for f in os.listdir(directory):\n if not os.path.isdir(f):\n yield os.path.abspath(os.path.join(directory, f))","def id(self) -> DirectoryID:\n _args: list[Arg] = []\n _ctx = self._select(\"id\", _args)\n return _ctx.execute_sync(DirectoryID)","def getFilePaths(directory):\r\n\tfor folder, subs, files in os.walk(directory):\r\n\t\tfor filename in files:\r\n\t\t\tyield os.path.join(folder, filename)","def iterate_dir(dir_path:str, files, equipt_nr):\n for ii in os.listdir(dir_path):\n if os.path.isdir(ii):\n iterate_dir(ii)\n elif re.search('[0-9]{7}', ii):\n rename_file(ii, equipt_nr)\n else:\n print('not editing : ' + ii)","def directory(self, id: Optional[DirectoryID] = None) -> Directory:\n _args = [\n Arg(\"id\", id, None),\n ]\n _ctx = self._select(\"directory\", _args)\n return Directory(_ctx)","def generate_id():\n for i in range(_MAX_NUM_TESTS):\n test, ref = filename(i)\n if not os.path.exists(test) and not os.path.exists(ref):\n return i\n return None","def read_everything_in_dir(dirpath, include_cnt=False):\n if not os.path.isdir(dirpath):\n raise ValueError(\"not a directory: \" + str(dirpath))\n all_filepaths = []\n for d, _, filepaths in os.walk(dirpath):\n all_filepaths += [os.path.join(d, p) for p in filepaths]\n\n should_stop, q = threading.Event(), Queue(maxsize=100)\n\n def bg_thread():\n try:\n for full_path in all_filepaths:\n try:\n if should_stop.is_set():\n return\n with open(full_path, 'r') as fo:\n contents = fo.read()\n q.put(contents)\n except:\n print(f\"Exception while reading: {full_path}; skipping\", file=sys.stderr)\n traceback.print_exc()\n finally:\n q.put(None)\n\n t = threading.Thread(name=\"Reader-Thread\", target=bg_thread, daemon=True)\n t.start()\n\n def contents_gen():\n try:\n while 1:\n msg = q.get()\n if msg is None:\n break\n yield msg\n finally:\n should_stop.set()\n t.join(timeout=2)\n assert not t.is_alive()\n\n if include_cnt:\n return len(all_filepaths), contents_gen()\n return contents_gen()","def get_next_id(self, identifier: Identifier) -> Optional['Identifier']:\n next_id = self._next_id(identifier)\n if not next_id:\n return None\n\n path = self._get_parent_path(identifier=next_id)\n file_path = os.path.join(path, f'{next_id.filename}.abs')\n if os.path.isfile(file_path):\n return next_id\n\n next_yymm_id = self._next_yymm_id(identifier)\n if not next_yymm_id:\n return None\n\n path = self._get_parent_path(identifier=next_yymm_id)\n file_path = os.path.join(path, f'{next_yymm_id.filename}.abs')\n if os.path.isfile(file_path):\n return next_yymm_id\n\n return None","def parsefolder(arg, dirname, names):\n options, computer = arg\n\n names.sort()\n if options.verbose:\n print \"Parsing\", dirname\n for name in names:\n if not os.path.isfile(dirname+\"/\"+name):\n continue\n\n parsefile(dirname, name, options.files, computer)","def _get_docs_in_dir(dir_id):\n querystring = 'select docname from {} where dir_id = %s;'.format(TABLES[3])\n result = execute_query(querystring, (dir_id,))\n if result:\n return [x[0] for x in result]\n return []","def gen_recursive_filelist(d):\n \n for root, directories, files in os.walk(d):\n for file in files:\n yield os.path.join(root, file)","def print_files_in_folder(service, folder_id):\n files_list = []\n page_token = None\n while True:\n try:\n param = {}\n if page_token:\n param['pageToken'] = page_token\n children = service.children().list(\n folderId=folder_id, **param).execute()\n\n for child in children.get('items', []):\n print child\n print 'File Id: %s' % child['id']\n files_list.append(child['id'])\n page_token = children.get('nextPageToken')\n if not page_token:\n return files_list\n except errors.HttpError, error:\n print 'An error occurred: %s' % error\n break","def find_run_id(increase=False):\n if tc.RUNID is None:\n if not os.path.exists(tc.RUNID_FILE):\n with open(tc.RUNID_FILE, \"w\") as new_runid_file:\n new_runid_file.write(\"1\")\n tc.RUNID = open(tc.RUNID_FILE, \"r\").read().rstrip()\n with open(tc.RUNID_FILE, \"w\") as new_runid_file:\n new_runid_file.write(str(int(tc.RUNID) + 1))\n tc.RUNID = str(int(tc.RUNID) + 1)\n elif increase:\n tc.RUNID = int(tc.RUNID) + 1\n with open(tc.RUNID_FILE, \"w\") as new_runid_file: \n new_runid_file.write(str(int(tc.RUNID)))\n return tc.RUNID","def _get_all_dir_ids(site_id):\n result = None\n querystring = 'select id from {} where site_id = %s;'.format(TABLES[2])\n res = execute_query(querystring, (site_id,))\n if res:\n result = [x[0] for x in res]\n return result","def get_results_labels(dir_path):\n dx = 0\n directories = [dI for dI in os.listdir(dir_path)\n if os.path.isdir(os.path.join(dir_path, dI))]\n results = []\n\n for directory in directories:\n # collect the path to all results files\n path = os.path.join(dir_path, directory)\n files = [os.path.join(path, f) for f in os.listdir(path)\n if os.path.isfile(os.path.join(path, f))\n and 'results' in f]\n\n # if the directory does not have results files, move on to the next\n if len(files) == 0:\n continue\n\n # collect the average cumulative returns per training iteration\n res = []\n lengths = []\n for i, f in enumerate(files):\n data = pd.read_csv(f)\n if i == 0:\n dx = data['total/steps'][2] - data['total/steps'][1]\n res.append(data['rollout/return_history'])\n lengths.append(len(data['rollout/return_history']))\n\n res = [[r[:min(lengths)]] for r in res]\n results.append(np.concatenate(res, axis=0))\n\n return results, directories, dx","def load_data_from_dir(self,\n dir_list=[],\n exclude=[]):\n dir_list_ = dir_list[:]\n\n if len(dir_list) == 0:\n eprint(\"CANNOT load data generator with an empty list of directories: {}\".format(dir_list))\n return\n\n for directory in dir_list_:\n if not os.path.isdir(directory):\n eprint(\"\\t\\t {}: {} is not a directory\".format(self.load_data_from_dir.__name__, directory))\n return\n\n # Read Data from current directory\n while dir_list_:\n # Pop first directory name and create dataloader if its a valid folder\n current_dir = dir_list_.pop(0)\n valid_dir = True\n for name in exclude:\n if name in current_dir and valid_dir:\n valid_dir = False\n data_file = current_dir + \"/data.mat\"\n if os.path.isfile(data_file) and \"takktile_\" in current_dir and valid_dir:\n self.dataloaders.append(takktile_dataloader(data_dir=current_dir,\n config=self.config,\n augment=self.augment))\n\n # Find all child directories of current directory and recursively load them\n data_dirs = [os.path.join(current_dir, o) for o in os.listdir(current_dir)\n if os.path.isdir(os.path.join(current_dir, o))]\n for d in data_dirs:\n dir_list_.append(d)\n\n self.num_dl = len(self.dataloaders)\n\n if self.transform_type:\n self.__calculate_data_transforms()\n\n # Create Eval Data\n if self.create_eval_data:\n self.eval_len = (self.__len__())//10\n self.create_eval_data = False\n\n # Calculate class number and ratios\n # Also calculate class diffs\n if not self.config['label_type'] == 'value':\n self.__class_nums = self.dataloaders[0].get_data_class_numbers(self.__get_data_idx(0))\n for i, dl in enumerate(self.dataloaders[1:]):\n self.__class_nums += dl.get_data_class_numbers(self.__get_data_idx(i+1))\n self.__class_ratios = self.__class_nums / float(np.mean(self.__class_nums))\n self.__class_diff = np.max(self.__class_nums) - self.__class_nums\n self.__class_diff = [d if n > 0 else 0 for n,d in zip(self.__class_nums, self.__class_diff)]\n\n # Reset and prepare data\n self.on_epoch_end()","def list_files_into_directory(directory_path: str) -> [str]:\n for root, directory_names, file_names in walk(directory_path):\n return file_names","def walk(dirname):\n for name in os.listdir(dirname):\n path = os.path.join(dirname, name)\n\n if os.path.isfile(path):\n print(path)\n else:\n walk(path)","def readMas(dir):\n n=0\n fs = []\n for (dirpath, dirnames, filenames) in os.walk(dir):\n if len(filenames)>0:\n for f in filenames:\n pth = os.path.join(dirpath,f)\n fs.append(pth)\n pass\n return fs","def get_file_list(service, folder_id):\n result = []\n\n page_token = None\n while True:\n try:\n param = {}\n if page_token:\n param['pageToken'] = page_token\n children = service.children().list(\n folderId=folder_id, **param).execute()\n\n for child in children.get('items', []):\n result.append(child['id'])\n page_token = children.get('nextPageToken')\n if not page_token:\n break\n except errors.HttpError as error:\n print('Could not retrieve file list: {}'.format(error))\n break\n\n return result","def parse_dir(self, dir_path=\"NULL\"):\n \n spec_list = []\n dir_path = os.path.abspath(dir_path)\n # if user's path is not having a \"/\" \n if dir_path[-1] != \"/\":\n dir_path = dir_path + \"/\"\n # invoke parse file for every file in the dir_path directory \n files = commands.getoutput(\"ls %s\" % dir_path).split()\n for f in files:\n spec = self.parse_file(dir_path + f)\n spec_list.append(spec)\n return sortbyfilenames(spec_list, files)","def paths_to_ids(paths: List[Path]) -> List[int]:\n\n ids = []\n for path in paths:\n try:\n ids.append(int(path.name))\n except ValueError:\n raise ValueError(\"Invalid dir_db path '{}'\".format(path.as_posix()))\n return ids","def lastsim_path(simsdir) -> str:\n import os\n\n all_sim_dirs = [d for d in os.listdir(simsdir) if d.isdigit()]\n if len(all_sim_dirs):\n most_recent_id = max(int(directory) for directory in all_sim_dirs)\n return f\"{simsdir}/{str(most_recent_id)}\"\n else:\n msg = f\"{simsdir} does not contain any simulation directories!\"\n raise SystemError(msg)","def next_id(self):\n self.id_counter += 1\n return self.id_counter - 1","def find_folder(startpath, folder_name, first_occurrence=False):\n candidates = []\n for root, dirs, files in os.walk(startpath):\n for d in dirs:\n if d == folder_name.strip('/'):\n if first_occurrence:\n candidates.append(os.path.abspath(root + '/' + d))\n return candidates\n candidates.append(os.path.abspath(root+'/'+d))\n return candidates","def getID(filePath):\r\n\r\n fileID = rmn.fstopenall(filePath,rmn.FST_RO)\r\n return fileID","def walkdir(folder):\n for dirpath, dirs, files in os.walk(folder):\n for filename in files:\n yield os.path.abspath(os.path.join(dirpath, filename))","def generate_dirs(start_dir, limit=None):\n right = True\n\n while right and (limit is None or limit > 0):\n yield start_dir\n start_dir, right = os.path.split(start_dir)\n\n if limit is not None:\n limit -= 1","def list_dir(\n dir_,\n):\n return map(\n lambda fi: path_join(dir_, fi),\n os.listdir(dir_),\n )","def findDirAbove(dirName):\n workDir = \"\"\n for i in range(20):\n path = os.path.join(workDir, dirName)\n if os.path.exists(path):\n return os.path.abspath(path)\n workDir = os.path.join(workDir, \"..\")\n\n return None","def get_folder_ids(root_folder_id):\n creds = authenticate()\n service = build('drive', 'v3', credentials=creds)\n\n page_token = None\n response = service.files().list(q=\"mimeType = 'application/vnd.google-apps.folder' and '{}' in parents and trashed = false\".format(root_folder_id),\n pageSize=10,\n spaces=\"drive\",\n fields='nextPageToken, files(id, name, webViewLink)',\n pageToken=page_token,\n\n ).execute()\n found_ids = [response[\"files\"][i][\"id\"] for i in range(0, len(response[\"files\"]))] # extract all the IDs\n found_names = [response[\"files\"][i][\"name\"] for i in range(0, len(response[\"files\"]))]\n found_ids.append(root_folder_id) # if the Photos folder(as named on Google Drive) just contains folders, this is not needed\n\n return found_ids, found_names","def find_submissions(root_dir, name):\n sub_id_dir_pairs = []\n def _recursive_find_sub_dirs(curr_dir):\n for root, subdirs, files in os.walk(curr_dir):\n for subdir in subdirs:\n if basename(subdir) == name:\n identifier = basename(root)\n sub_id_dir_pairs.append((identifier, root))\n break\n else:\n _recursive_find_sub_dirs(subdir)\n\n _recursive_find_sub_dirs(root_dir)\n return sub_id_dir_pairs","def get_all_files(directory):\r\n for dirpath, _dirnames, filenames in os.walk(directory):\r\n for filename in filenames:\r\n yield (filename, dirpath)","def walk(dirname): \n for name in os.listdir(dirname):\n path = os.path.join(dirname, name)\n if os.path.isfile(path):\n print(path)\n else:\n walk(path)","def DirEnumerator(args, path):\n for d in os.scandir(path):\n try:\n if d.name == '.' or d.name == '..':\n pass\n elif d.is_symlink() and args.skiplinks:\n pass\n elif d.is_file():\n yield d.path\n elif d.is_dir() and args.recurse:\n for f in DirEnumerator(args, d.path):\n yield f\n except Exception as e:\n print(\"EXCEPTION %s accessing %s/%s\" % (e, path, d.name))","def get_immediate_subdirectories(self, a_dir):\n return [name for name in os.listdir(a_dir)\n if os.path.isdir(os.path.join(a_dir, name))]","def get_unique_path(directory: pathlib.Path, name_pattern: str) -> pathlib.Path:\n counter = 0\n while True:\n counter += 1\n path = directory / name_pattern.format(counter)\n if not path.exists():\n return path","def get_folder_id(self, folder_name) -> str:\n files = self.list_files()\n\n folder_id = None\n for id, name in files.items():\n if name == folder_name:\n folder_id = id\n break\n\n if folder_id is None:\n logger.warning(\"Failed to find folder %s\", folder_name)\n raise FileNotFoundError\n\n return folder_id","def walk_dir(self, dir):\n if self.ppath_prefix_len:\n prefix = self.ppath_prefix[self.ppath_prefix_idx%self.ppath_prefix_len]\n self.ppath_prefix_idx += 1\n merged_path = os.path.join(prefix, dir)\n for root, dirs, files in self.fswalk_base(merged_path):\n yield merged_path, dirs, files\n else:\n yield self.fswalk_base(dir)","def get_next_id():\n global _lock, _counter\n with _lock:\n if _counter == 65535:\n _counter = 1\n else:\n _counter += 1\n\n return str(_counter)","def index_subdirectory(directory, class_indices, follow_links, formats):\n dirname = os.path.basename(directory)\n valid_files = iter_valid_files(directory, follow_links, formats)\n labels = []\n filenames = []\n for root, fname in valid_files:\n labels.append(class_indices[dirname])\n absolute_path = os.path.join(root, fname)\n relative_path = os.path.join(\n dirname, os.path.relpath(absolute_path, directory))\n filenames.append(relative_path)\n return filenames, labels","def fileCounter(directory):","def get_directory(self, directory: str) -> List[Dict]:\n raise NotImplementedError","def getpaths_fromdir(input_prefix_, directory_):\n path = os.path.join(input_prefix_, \"%s*\" % directory_, \"*\")\n return [tuple([directory_, path])]","def gen_paths():\n global log_dir, events_file, log_file, datastream_dir\n\n #The root log directory\n log_root_dir = os.path.join(root_dir, \"logs\")\n\n #Figure out what log file index we should use\n #The log file index is a 4-digit number corresponding to an unused log folder\n index = 0\n #If our base log_root_dir exists:\n if os.path.exists(log_root_dir):\n\n #Get existing folders, convert to string list, and sort\n folders = os.listdir(log_root_dir)\n ids = [int(f) for f in folders]\n ids.sort()\n\n #This algorithm determines the next sequential value for our log index, it scans through the existing numbers\n #until either it finds a missing number in sequence, or runs out of numbers to scan.\n\n #Set this to a high number to start with, as it will get set every loop iteration\n last_id = 10000\n for present_index in ids:\n #If we have a break in the number sequence, abort and use what we have\n if present_index > last_id + 1:\n break\n #If we have found a bigger number to use for index\n if present_index > index:\n index = present_index\n\n last_id = present_index\n\n #Convert from largest existing index to the index we should use!\n index += 1\n\n #Set the log_dir, which is the directory for storing all logs during this run session\n log_dir = os.path.join(log_root_dir, str(index).zfill(4))\n\n #Set the log_file, which is a dump of all console output\n log_file = os.path.join(log_dir, \"main.log\")\n\n #Set the events_file, which is where all events are recorded\n events_file = os.path.join(log_dir, \"events.rec\")\n\n #Set the datastream_dir, within which all datastreams are recorded\n datastream_dir = os.path.join(log_dir, \"datastreams\")","def get_last_formatted_dir_in_dir(folder, fmt):\n last_elem = None\n root_elements = sorted(os.listdir(folder))\n for root_element in root_elements[::-1]:\n if os.path.isdir(os.path.join(folder, root_element)):\n if parser.validate(fmt, root_element):\n last_elem = root_element\n break\n return last_elem","def parsedirs (directory, regex, depth, debian_release):\n\n global ovals\n debian_version = DEBIAN_VERSION[debian_release]\n\n if depth == 0:\n logging.log(logging.DEBUG, \"Maximum depth reached at directory \" + directory)\n return (0)\n \n for fileName in os.listdir (directory):\n \n path = \"%s/%s\" % (directory, fileName)\n \n logging.log (logging.DEBUG, \"Checking %s (for %s at %s)\" % (fileName, regex, depth))\n \n if os.access(path, os.R_OK) and os.path.isdir (path) and not os.path.islink (path) and fileName[0] != '.':\n logging.log(logging.DEBUG, \"Entering directory \" + path)\n parsedirs (path, regex, depth-1, debian_release)\n\n #Parse fileNames\n if os.access(path, os.R_OK) and regex.search(fileName) and fileName[0] != '.' and fileName[0] != '#':\n result = dsa.parseFile(path)\n if result:\n cve = result[0]\n if ovals.has_key(cve):\n for (k, v) in result[1].iteritems():\n ovals[cve][k] = v\n else:\n ovals[cve] = result[1]\n\n dsaRef = fileName[:-5].upper() # remove .data part\n \n # also parse corresponding wml file\n wmlResult = wml.parseFile(path.replace('.data', '.wml'), DEBIAN_VERSION)\n if wmlResult:\n data, releases = wmlResult\n # skip if the wml file does not contain the debian release\n if not debian_version in releases:\n continue\n for (k, v) in data.iteritems():\n if k == \"moreinfo\":\n if not \"moreinfo\" in ovals[cve]:\n ovals[cve][\"moreinfo\"] = \"\\n\"\n # aggregate all advisories\n ovals[cve][k] += \"%s%s\\n\" % (dsaRef, v)\n elif k in ('description'): # some keys shouldn't be clobbered\n if not k in ovals[cve]:\n ovals[cve][k] = v\n else:\n ovals[cve][k] = v\n if not \"release\" in ovals[cve]:\n ovals[cve][\"release\"] = {}\n ovals[cve]['release'].update({debian_version: releases[debian_version]})\n\n return 0"],"string":"[\n \"def create_id_dir(id_dir: Path) -> (int, Path):\\n\\n lockfile_path = id_dir/'.lockfile'\\n with lockfile.LockFile(lockfile_path, timeout=1):\\n next_fn = id_dir/PKEY_FN\\n\\n next_valid = True\\n\\n if next_fn.exists():\\n try:\\n with next_fn.open() as next_file:\\n next_id = int(next_file.read())\\n\\n next_id_path = make_id_path(id_dir, next_id)\\n\\n if next_id_path.exists():\\n next_valid = False\\n\\n except (OSError, ValueError):\\n # In either case, on failure, invalidate the next file.\\n next_valid = False\\n else:\\n next_valid = False\\n\\n if not next_valid:\\n # If the next file's id wasn't valid, then find the next available\\n # id directory the hard way.\\n\\n ids = list(os.listdir(str(id_dir)))\\n # Only return the test directories that could be integers.\\n ids = [id_ for id_ in ids if id_.isdigit()]\\n ids = [int(id_) for id_ in ids]\\n ids.sort()\\n\\n # Find the first unused id.\\n next_id = 1\\n while next_id in ids:\\n next_id += 1\\n\\n next_id_path = make_id_path(id_dir, next_id)\\n\\n next_id_path.mkdir()\\n with next_fn.open('w') as next_file:\\n next_file.write(str(next_id + 1))\\n\\n return next_id, next_id_path\",\n \"def get_ids(directory):\\n return (f[:-4] for f in os.listdir(directory))\",\n \"def get_runid(path):\\n name = Path(path).name\\n if not os.path.exists(Path(path).parent):\\n return '00001'\\n files = os.listdir(Path(path).parent)\\n runid = 0\\n for f in files:\\n try:\\n id, val = f.split('_', 1)\\n runid = max(runid, int(id))\\n except:\\n pass\\n runid = str(runid + 1)\\n runid = '0' * (5 - len(runid)) + runid\\n return runid\",\n \"def directories(fs, directory_id=1):\\n parents = {fs.name: directory_id}\\n Session.add(Directory(id=directory_id, filesystem_id=fs.id,\\n parent_id=parents[fs.name], name=''))\\n Session.commit()\\n for dirpath, dirnames, filenames in walk(fs.name):\\n p = Session.query(Directory).filter(Directory.id == parents[dirpath]).one()\\n p.nfiles = len(filenames)\\n for d in dirnames:\\n directory_id += 1\\n parents[os.path.join(dirpath, d.name)] = directory_id\\n Session.add(Directory(id=directory_id, filesystem_id=fs.id,\\n parent_id=parents[dirpath], name=d.name))\\n Session.commit()\\n return directory_id\",\n \"def import_directory(self, directory):\\n files_list = sys_utils.get_files_from_directory(directory)\\n if files_list == None:\\n return None, 0\\n oids = []\\n num_new_files = 0\\n p = progress.progress(len(files_list))\\n for file_location in files_list:\\n oid, new_file = self.import_file(file_location)\\n p.tick()\\n if oid:\\n oids.append(oid)\\n if new_file:\\n num_new_files += 1\\n oids = list(set(oids)) # assert uniqueness \\n return oids, num_new_files\",\n \"def getImmediateSubdirectories(dir):\",\n \"def getNextID(self, d):\\n try:\\n listOrdered = d.keys()\\n listOrdered = funcs.sortStringList(listOrdered)\\n lastID = int(listOrdered[-1])\\n nextID = str(lastID + 1)\\n for i in range(1,int(nextID)):\\n if str(i) not in listOrdered:\\n return str(i)\\n return nextID\\n except:\\n return '1'\",\n \"def directory_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def parse_dir(self, directory):\\n for dir in os.listdir(directory):\\n if dir in ['.git', '.github', '.vscode', 'docs']:\\n continue\\n next_dir = os.path.join(directory, dir)\\n if os.path.isdir(next_dir):\\n if dir.startswith('template_'):\\n self.parse_template(next_dir)\\n else:\\n normpath = os.path.relpath(next_dir)\\n normpath = os.path.normpath(normpath)\\n path = normpath.split(os.sep)\\n self.add_folder(path)\\n # add_directory(next_dir)\\n self.parse_dir(next_dir)\",\n \"def resolve_experiment_id(models_dir):\\n ids = [x for x in os.listdir(models_dir) \\n if os.path.isdir(models_dir + os.sep + x)]\\n if len(ids) > 0:\\n return int(max([int(i) for i in ids])) + 1\\n return 1\",\n \"def parse_dir(args, dirname, names):\\n for name in names:\\n path = os.path.join(dirname, name)\\n\\n if os.path.isfile(path):\\n parse_file_from_directory(path, args)\",\n \"def logdir():\\n listdir = [xx for xx in os.listdir(\\\"runs/\\\") if os.path.isdir(os.path.join(\\\"runs\\\", xx))]\\n\\n max_ = 0\\n for i in range(len(listdir)):\\n try:\\n filename = int(listdir[i])\\n if max_ < filename:\\n max_ = filename\\n except ValueError:\\n continue\\n\\n return str(max_ + 1)\",\n \"def get_next_id():\\n with open(WORK_LOG_FILENAME, 'r') as work_log:\\n work_log_reader = csv.DictReader(work_log)\\n entry_id = 0\\n for entry in work_log_reader:\\n if int(entry['id']) > entry_id:\\n entry_id = int(entry['id'])\\n entry_id += 1\\n return entry_id\",\n \"def directory_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def _last_exp_id(name, path):\\n\\n exp_id = 0\\n output_dirs = listdir(path)\\n exp_dirs = [s for s in output_dirs if name in s]\\n if exp_dirs:\\n ids = [int(s.split('_')[-1]) for s in exp_dirs]\\n exp_id = max(ids)\\n return exp_id\",\n \"def scanDirectories(directory, includes = [\\\"*\\\"], excludes = []):\\n\\treturn scanAll(directory, includes, excludes)[2]\",\n \"def __dir_arg_parse(directory: Directory, directory_path: str) -> Entry:\\n dir_split = directory_path.split(\\\"/\\\")\\n for target in dir_split:\\n if target == \\\"..\\\":\\n if directory.get_parent():\\n directory = directory.get_parent()\\n elif directory.get_name() != target and target != \\\".\\\":\\n directory = directory.get_entry(target)\\n return directory\",\n \"def dirGenerator(datadirectory):\\n\\n subdirectories = [row for row in os.listdir(datadirectory) if '$' not in row]\\n\\n #iterate through subdirectories\\n for day in subdirectories:\\n\\n #collect raw data set file names in sub directories\\n fileNames = [row for row in os.listdir(datadirectory + day + '\\\\\\\\RawDataFiles\\\\\\\\')]\\n\\n #iterate over the raw datasets\\n print 'There are ' + str(len(fileNames)) + ' datasets in ' + day\\n for index, datafile in enumerate(fileNames):\\n yield datadirectory + day + '\\\\\\\\RawDataFiles\\\\\\\\' + datafile, day, datafile, index\",\n \"def directory_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def directory_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def directory_id(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def directory_id(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"directory_id\\\")\",\n \"def get_next_dir(self):\\n cls_name = self.__class__.__name__.lower()\\n paths = [p.name for p in self.data_dir.iterdir() if re.match(f'{cls_name}_[0-9]+', p.name) and p.is_dir()]\\n i = 0\\n while (path_name := f'{cls_name}_{i}') in paths:\\n i += 1\\n self.save_dir = self.data_dir/path_name\\n self.save_dir.mkdir()\\n self.create_new_tree()\\n return path_name\",\n \"def get_latest(dir_list):\\n int_list = []\\n for directory in dir_list:\\n try:\\n int_list.append(int(directory))\\n except ValueError:\\n pass\\n if not int_list:\\n return None\\n return max(int_list)\",\n \"def _get_dir_id(site_id, dirname):\\n querystring = 'select id from {} where site_id = %s and dirname = %s;'.format(TABLES[2])\\n res = execute_query(querystring, (site_id, dirname))\\n if res:\\n return res[0][0]\\n return None\",\n \"def readDirectory():\\n tagdir = \\\"tagreplacements\\\"\\n data = os.listdir(tagdir)\\n for d in data:\\n processFile(os.path.join(tagdir,d))\\n \\n #print(repd)\",\n \"def _fetch_run_dir(self, run_id, backend=None, results_dir=None):\\n results_dir = self._fetch_results_dir(backend=backend, results_dir=results_dir)\\n return os.path.join(results_dir, run_id)\",\n \"def get_dir_for_fname(directory, filename):\\r\\n for fname, dirpath in get_all_files(directory):\\r\\n if fname == filename:\\r\\n return dirpath\\r\\n return None\",\n \"def get_year_from_dir(dirname):\\n parts = dirname.split(\\\"/\\\")\\n branch = \\\"RunII_\\\"\\n for p in parts:\\n if branch in p:\\n ind = parts.index(p)\\n if ind == len(parts)-1:\\n return None\\n return parts[ind+1]\\n else:\\n return parts[0]\",\n \"def test_scan_dir_files(self):\\n self.run_scan(self.subdir, self.nest_fcount + 1)\",\n \"def dirtree(dir, index):\\n filenames = os.listdir(dir)\\n for filename in filenames:\\n if not os.path.isdir(os.path.abspath(dir+'/'+filename)):\\n if filename == filenames[-1]:\\n print('| '*index+'\\\\--', filename)\\n else:\\n print('| '*index+'|--', filename)\\n else:\\n print('| '*index+'|--', filename)\\n dir = dir + '/' + filename\\n dirtree(dir, index+1)\",\n \"def _nextId(cls, id=None):\\n if (not hasattr(DAG, \\\"_lastID\\\")):\\n DAG._lastID = 0\\n if (id):\\n DAG._lastId = id\\n DAG._lastID = DAG._lastID + 1\\n return DAG._lastID\",\n \"def walk(dir, callback):\\n\\n dir = abspath(dir)\\n for file in listdir(dir):\\n nfile = join(dir, file)\\n if isdir(nfile):\\n walk(nfile, callback)\\n else:\\n callback(nfile)\",\n \"def get_first_formatted_dir_in_dir(folder, fmt):\\n first_elem = None\\n root_elements = sorted(os.listdir(folder))\\n for root_element in root_elements:\\n if os.path.isdir(os.path.join(folder, root_element)):\\n if parser.validate(fmt, root_element):\\n first_elem = root_element\\n break\\n return first_elem\",\n \"def scanDirectory(directoryName=\\\".\\\"):\\n cluster=Cluster()\\n\\n #reading files and folders \\n for path, folders, files in os.walk(directoryName):\\n for afile in files:\\n cluster.addFile(os.path.join(path,afile))\\n \\n cluster.clustering()\",\n \"def _next_version(self, dirpath: str) -> int:\\n try:\\n version_re = re.compile(r\\\"version_(\\\\d+)\\\")\\n\\n def is_valid_version(v: str):\\n return version_re.search(v) is not None\\n\\n versions = tuple(filter(is_valid_version, os.listdir(dirpath)))\\n if not versions:\\n # No versions yet\\n return 0\\n current_version = natsorted(versions, reverse=True)[0]\\n # Get the version number using the version pattern\\n current_version = int(version_re.search(current_version).group(1))\\n return current_version + 1\\n except Exception as e:\\n logger.warning(f\\\"Starting from version 0 because of error: {e}\\\")\\n return 0\",\n \"def list_dir(*path):\\n path = os.path.join(*path)\\n return next(os.walk(path))[1]\",\n \"def folder_runnum():\\n now = datetime.datetime.now()\\n runnum = 1\\n while True:\\n folder_name = f\\\"astroNN_{now.month:0{2}d}{now.day:0{2}d}_run{runnum:0{3}d}\\\"\\n if not os.path.exists(folder_name):\\n break\\n else:\\n runnum += 1\\n\\n return folder_name\",\n \"def read_dir(directory):\\n results = AttrDict()\\n results.iterations = pd.read_csv(os.path.join(directory, 'iterations.csv'),\\n index_col=0)\\n results.solutions = AttrDict()\\n for i in results.iterations.index.tolist():\\n iteration_dir = os.path.join(directory, '{:0>4d}'.format(i))\\n fmt = _detect_format(iteration_dir)\\n logging.debug('Iteration: {}, Format detected: {}'.format(i, fmt))\\n try:\\n if fmt == 'netcdf':\\n sol_path = os.path.join(iteration_dir, 'solution.nc')\\n results.solutions[i] = read_netcdf(sol_path)\\n else:\\n sol_path = iteration_dir\\n results.solutions[i] = read_csv(sol_path)\\n logging.debug('Read as {}: {}'.format(fmt, sol_path))\\n except IOError as err:\\n logging.warning('I/O error in `{}` at iteration `{}`'\\n ': {}'.format(iteration_dir, i, err))\\n # results.solutions[i] = AttrDict() # add an empty entry\\n continue\\n return results\",\n \"def scan(directory, cnx):\\n print(\\\"Scanning {}\\\".format(directory))\\n dirs = filter(\\n os.path.isdir, map(lambda x: os.path.join(directory, x), os.listdir(directory)),\\n )\\n jobdirs = list(filter(lambda x: os.path.exists(x + \\\"/job.yaml\\\"), dirs))\\n\\n print(\\\"Job directories:\\\")\\n pprint.pprint(jobdirs)\\n # when you're not performing your duties, do they keep you in a little box?\\n for jobdir in jobdirs:\\n scan_job(jobdir, cnx)\",\n \"def parseDirectories(srcDirs):\\n allDocIds = []\\n ignoreIssns = []\\n seenIds = set()\\n for srcDir in srcDirs:\\n if not isdir(srcDir):\\n continue\\n if isfile(getLockFname(srcDir)):\\n logging.warn('%s exists. Looks like a crawl is going on in %s. Skipping.' % (getLockFname(srcDir), srcDir))\\n continue\\n dirDocIds = parseDocIds(srcDir)\\n if dirDocIds == None:\\n logging.info('Found no document IDs in directory %s' % srcDir)\\n continue\\n doneIds = set(parseDocIdStatus(srcDir))\\n count = 0\\n for docId in dirDocIds:\\n if docId in seenIds or docId in doneIds:\\n continue\\n seenIds.add(docId)\\n allDocIds.append((docId, srcDir))\\n count += 1\\n\\n ignoreIssns.extend(parseIssnStatus(srcDir))\\n logging.info('Directory %s: %d docIds to crawl' % (srcDir, count))\\n\\n logging.info('Found %d docIds to crawl, %d ISSNs to ignore in %d directories' % (len(allDocIds), len(ignoreIssns), len(srcDirs)))\\n return (allDocIds, ignoreIssns)\",\n \"def enumerate_dirname(path):\\n for (dirpath, dirname_list, _) in os.walk(path):\\n for dirname in tqdm(dirname_list):\\n if dirname[0] != '.':\\n old_dir = dirpath + dirname\\n new_dir = dirpath + '%.5d' % dirname_list.index(dirname)\\n if os.path.exists(new_dir):\\n print ('Directory %s already exist' % new_dir)\\n continue\\n else:\\n try:\\n os.rename(old_dir, new_dir)\\n except:\\n print('Couldn\\\\'t rename %s' % old_dir)\\n break\",\n \"def get_next_model_id(models_dir=MODELS_DIR,\\n model_prefix=DEFAULT_MODEL_PREFIX,\\n model_ext=MODEL_EXT):\\n models = glob.glob(os.path.join(models_dir, model_prefix + '*'))\\n return model_prefix + str(len(models) + 1)\",\n \"def scanFiles(directory, includes = [\\\"*\\\"], excludes = []):\\n\\treturn scanAll(directory, includes, excludes)[1]\",\n \"def iter_dir(temp_dir, blast_db, query_name, iteration):\\n name = '{}_{}_{:02d}'.format(\\n basename(blast_db), basename(query_name), iteration)\\n\\n return join(temp_dir, name)\",\n \"def enumerate():\\n names = [f for f in os.listdir(_INPUT_ROOT) if not\\n os.path.isdir(os.path.join(_INPUT_ROOT, f))]\\n return sorted(names)\",\n \"def lsdir(\\n self,\\n dir_id: str = None,\\n trashed: bool = False,\\n owners: list = None,\\n page_size: int = 20,\\n page_token: str = None,\\n order_by: str = \\\"modifiedTime\\\"\\n ) -> namedtuple(\\\"Page\\\", [\\\"files\\\", \\\"next_page_token\\\"]):\\n if dir_id:\\n if owners:\\n query = (f\\\"trashed={trashed} and '{dir_id}' in parents and\\\"\\n f\\\" '{','.join(owners)}' in owners\\\")\\n else:\\n query = f\\\"trashed={trashed} and '{dir_id}' in parents\\\"\\n else:\\n if owners:\\n query = f\\\"trashed={trashed} and '{','.join(owners)}' in owners\\\"\\n else:\\n query = f\\\"trashed={trashed}\\\"\\n flags = {\\n \\\"q\\\": query,\\n \\\"pageSize\\\": page_size,\\n \\\"orderBy\\\": order_by,\\n \\\"fields\\\": \\\"files(name, mimeType, id), nextPageToken\\\",\\n \\\"pageToken\\\": page_token,\\n }\\n r = requests.get(\\n \\\"https://www.googleapis.com/drive/v3/files\\\",\\n params=flags,\\n headers=self._auth_headers\\n )\\n if r.status_code in self._errors:\\n raise ApiResponseException(\\n r.status_code, r.json()[\\\"error\\\"][\\\"message\\\"])\\n Page = namedtuple(\\\"Page\\\", [\\\"files\\\", \\\"next_page_token\\\"])\\n r = r.json()\\n if \\\"nextPageToken\\\" in r:\\n return Page(\\n [GDriveFile(file) for file in r[\\\"files\\\"]],\\n r[\\\"nextPageToken\\\"]\\n )\\n else:\\n return Page(\\n [GDriveFile(file) for file in r[\\\"files\\\"]],\\n None\\n )\",\n \"def index_directory(directory,\\n labels,\\n formats,\\n class_names=None,\\n shuffle=True,\\n seed=None,\\n follow_links=False):\\n if labels is None:\\n # in the no-label case, index from the parent directory down.\\n subdirs = ['']\\n class_names = subdirs\\n else:\\n subdirs = []\\n for subdir in sorted(os.listdir(directory)):\\n if os.path.isdir(os.path.join(directory, subdir)):\\n subdirs.append(subdir)\\n if not class_names:\\n class_names = subdirs\\n else:\\n if set(class_names) != set(subdirs):\\n raise ValueError(\\n 'The `class_names` passed did not match the '\\n 'names of the subdirectories of the target directory. '\\n 'Expected: %s, but received: %s' %\\n (subdirs, class_names))\\n class_indices = dict(zip(class_names, range(len(class_names))))\\n\\n # Build an index of the files\\n # in the different class subfolders.\\n pool = multiprocessing.pool.ThreadPool()\\n results = []\\n filenames = []\\n\\n for dirpath in (os.path.join(directory, subdir) for subdir in subdirs):\\n results.append(\\n pool.apply_async(index_subdirectory,\\n (dirpath, class_indices, follow_links, formats)))\\n labels_list = []\\n for res in results:\\n partial_filenames, partial_labels = res.get()\\n labels_list.append(partial_labels)\\n filenames += partial_filenames\\n if labels not in ('inferred', None):\\n if len(labels) != len(filenames):\\n raise ValueError('Expected the lengths of `labels` to match the number '\\n 'of files in the target directory. len(labels) is %s '\\n 'while we found %s files in %s.' % (\\n len(labels), len(filenames), directory))\\n else:\\n i = 0\\n labels = np.zeros((len(filenames),), dtype='int32')\\n for partial_labels in labels_list:\\n labels[i:i + len(partial_labels)] = partial_labels\\n i += len(partial_labels)\\n\\n if labels is None:\\n print('Found %d files.' % (len(filenames),))\\n else:\\n print('Found %d files belonging to %d classes.' %\\n (len(filenames), len(class_names)))\\n pool.close()\\n pool.join()\\n file_paths = [os.path.join(directory, fname) for fname in filenames]\\n\\n if shuffle:\\n # Shuffle globally to erase macro-structure\\n if seed is None:\\n seed = np.random.randint(1e6)\\n rng = np.random.RandomState(seed)\\n rng.shuffle(file_paths)\\n rng = np.random.RandomState(seed)\\n rng.shuffle(labels)\\n return file_paths, labels, class_names\",\n \"def get_last_time_step(dir):\\n\\n return str(max([fd for fd in listdir(dir) if fd.isnumeric()]))\",\n \"def listdir(self, subdir=''):\\n\\n try:\\n subdir = subdir.decode()\\n except AttributeError:\\n pass\\n subdir = subdir.rstrip('\\\\\\\\')\\n # cmd = '\\\"%s\\\" \\\"%s\\\" 0 ' % (self.ndc_path, self.filename)\\n cmd = [\\n self.ndc_path,\\n self.filename,\\n '0'\\n ]\\n if subdir:\\n cmd.append(subdir)\\n # cmd += '\\\"%s\\\"' % subdir\\n\\n logging.info(cmd)\\n try:\\n result = check_output(cmd)\\n except CalledProcessError:\\n raise FileNotFoundError('Subdirectory not found in disk', [])\\n\\n result = [r.split(b'\\\\t') for r in result.split(b'\\\\r\\\\n')]\\n result = list(filter(lambda x: len(x) == 4, result))\\n\\n filenames = []\\n subdirs = []\\n for r in result:\\n try:\\n decoded = r[0].decode('shift_jis')\\n if r[2] != b'<DIR>':\\n filenames.append(decoded)\\n elif r[2] == b'<DIR>' and len(r[0].strip(b'.')) > 0:\\n subdirs.append(decoded)\\n except UnicodeDecodeError:\\n logging.info(\\\"Couldn't decode one of the strings in the folder: %s\\\" % subdir)\\n continue\\n\\n return filenames, subdirs\",\n \"def parse_dir(dir, verbose=False):\\n\\n lengths = {}\\n for f in os.listdir(dir):\\n m = re.search('^(\\\\w+)_(Primer\\\\w)_', f)\\n if not m:\\n sys.stderr.write(\\\"Error: can't parse {}\\\\n\\\".format(f))\\n continue\\n (srr, primer) = m.groups()\\n if srr not in lengths:\\n lengths[srr] = {'PrimerA' : 0, 'PrimerB' : 0, 'PrimerC' : 0}\\n for seqid, header, seq, qualscores in stream_fastq(os.path.join(dir, f)):\\n lengths[srr][primer] += len(seq)\\n return lengths\",\n \"def describe_directories(DirectoryIds=None, NextToken=None, Limit=None):\\n pass\",\n \"def _recursive_scan(directory=None, file_extension='.dvl'):\\n directory = directory or app.config['DEVICE_LOG_DRIVE']\\n\\n for entry in os.scandir(directory):\\n if entry.is_dir(follow_symlinks=False):\\n yield from _recursive_scan(entry)\\n elif os.path.splitext(entry.name)[1] == file_extension:\\n yield entry\",\n \"def listdir_full_path(directory):\\n for f in os.listdir(directory):\\n if not os.path.isdir(f):\\n yield os.path.abspath(os.path.join(directory, f))\",\n \"def id(self) -> DirectoryID:\\n _args: list[Arg] = []\\n _ctx = self._select(\\\"id\\\", _args)\\n return _ctx.execute_sync(DirectoryID)\",\n \"def getFilePaths(directory):\\r\\n\\tfor folder, subs, files in os.walk(directory):\\r\\n\\t\\tfor filename in files:\\r\\n\\t\\t\\tyield os.path.join(folder, filename)\",\n \"def iterate_dir(dir_path:str, files, equipt_nr):\\n for ii in os.listdir(dir_path):\\n if os.path.isdir(ii):\\n iterate_dir(ii)\\n elif re.search('[0-9]{7}', ii):\\n rename_file(ii, equipt_nr)\\n else:\\n print('not editing : ' + ii)\",\n \"def directory(self, id: Optional[DirectoryID] = None) -> Directory:\\n _args = [\\n Arg(\\\"id\\\", id, None),\\n ]\\n _ctx = self._select(\\\"directory\\\", _args)\\n return Directory(_ctx)\",\n \"def generate_id():\\n for i in range(_MAX_NUM_TESTS):\\n test, ref = filename(i)\\n if not os.path.exists(test) and not os.path.exists(ref):\\n return i\\n return None\",\n \"def read_everything_in_dir(dirpath, include_cnt=False):\\n if not os.path.isdir(dirpath):\\n raise ValueError(\\\"not a directory: \\\" + str(dirpath))\\n all_filepaths = []\\n for d, _, filepaths in os.walk(dirpath):\\n all_filepaths += [os.path.join(d, p) for p in filepaths]\\n\\n should_stop, q = threading.Event(), Queue(maxsize=100)\\n\\n def bg_thread():\\n try:\\n for full_path in all_filepaths:\\n try:\\n if should_stop.is_set():\\n return\\n with open(full_path, 'r') as fo:\\n contents = fo.read()\\n q.put(contents)\\n except:\\n print(f\\\"Exception while reading: {full_path}; skipping\\\", file=sys.stderr)\\n traceback.print_exc()\\n finally:\\n q.put(None)\\n\\n t = threading.Thread(name=\\\"Reader-Thread\\\", target=bg_thread, daemon=True)\\n t.start()\\n\\n def contents_gen():\\n try:\\n while 1:\\n msg = q.get()\\n if msg is None:\\n break\\n yield msg\\n finally:\\n should_stop.set()\\n t.join(timeout=2)\\n assert not t.is_alive()\\n\\n if include_cnt:\\n return len(all_filepaths), contents_gen()\\n return contents_gen()\",\n \"def get_next_id(self, identifier: Identifier) -> Optional['Identifier']:\\n next_id = self._next_id(identifier)\\n if not next_id:\\n return None\\n\\n path = self._get_parent_path(identifier=next_id)\\n file_path = os.path.join(path, f'{next_id.filename}.abs')\\n if os.path.isfile(file_path):\\n return next_id\\n\\n next_yymm_id = self._next_yymm_id(identifier)\\n if not next_yymm_id:\\n return None\\n\\n path = self._get_parent_path(identifier=next_yymm_id)\\n file_path = os.path.join(path, f'{next_yymm_id.filename}.abs')\\n if os.path.isfile(file_path):\\n return next_yymm_id\\n\\n return None\",\n \"def parsefolder(arg, dirname, names):\\n options, computer = arg\\n\\n names.sort()\\n if options.verbose:\\n print \\\"Parsing\\\", dirname\\n for name in names:\\n if not os.path.isfile(dirname+\\\"/\\\"+name):\\n continue\\n\\n parsefile(dirname, name, options.files, computer)\",\n \"def _get_docs_in_dir(dir_id):\\n querystring = 'select docname from {} where dir_id = %s;'.format(TABLES[3])\\n result = execute_query(querystring, (dir_id,))\\n if result:\\n return [x[0] for x in result]\\n return []\",\n \"def gen_recursive_filelist(d):\\n \\n for root, directories, files in os.walk(d):\\n for file in files:\\n yield os.path.join(root, file)\",\n \"def print_files_in_folder(service, folder_id):\\n files_list = []\\n page_token = None\\n while True:\\n try:\\n param = {}\\n if page_token:\\n param['pageToken'] = page_token\\n children = service.children().list(\\n folderId=folder_id, **param).execute()\\n\\n for child in children.get('items', []):\\n print child\\n print 'File Id: %s' % child['id']\\n files_list.append(child['id'])\\n page_token = children.get('nextPageToken')\\n if not page_token:\\n return files_list\\n except errors.HttpError, error:\\n print 'An error occurred: %s' % error\\n break\",\n \"def find_run_id(increase=False):\\n if tc.RUNID is None:\\n if not os.path.exists(tc.RUNID_FILE):\\n with open(tc.RUNID_FILE, \\\"w\\\") as new_runid_file:\\n new_runid_file.write(\\\"1\\\")\\n tc.RUNID = open(tc.RUNID_FILE, \\\"r\\\").read().rstrip()\\n with open(tc.RUNID_FILE, \\\"w\\\") as new_runid_file:\\n new_runid_file.write(str(int(tc.RUNID) + 1))\\n tc.RUNID = str(int(tc.RUNID) + 1)\\n elif increase:\\n tc.RUNID = int(tc.RUNID) + 1\\n with open(tc.RUNID_FILE, \\\"w\\\") as new_runid_file: \\n new_runid_file.write(str(int(tc.RUNID)))\\n return tc.RUNID\",\n \"def _get_all_dir_ids(site_id):\\n result = None\\n querystring = 'select id from {} where site_id = %s;'.format(TABLES[2])\\n res = execute_query(querystring, (site_id,))\\n if res:\\n result = [x[0] for x in res]\\n return result\",\n \"def get_results_labels(dir_path):\\n dx = 0\\n directories = [dI for dI in os.listdir(dir_path)\\n if os.path.isdir(os.path.join(dir_path, dI))]\\n results = []\\n\\n for directory in directories:\\n # collect the path to all results files\\n path = os.path.join(dir_path, directory)\\n files = [os.path.join(path, f) for f in os.listdir(path)\\n if os.path.isfile(os.path.join(path, f))\\n and 'results' in f]\\n\\n # if the directory does not have results files, move on to the next\\n if len(files) == 0:\\n continue\\n\\n # collect the average cumulative returns per training iteration\\n res = []\\n lengths = []\\n for i, f in enumerate(files):\\n data = pd.read_csv(f)\\n if i == 0:\\n dx = data['total/steps'][2] - data['total/steps'][1]\\n res.append(data['rollout/return_history'])\\n lengths.append(len(data['rollout/return_history']))\\n\\n res = [[r[:min(lengths)]] for r in res]\\n results.append(np.concatenate(res, axis=0))\\n\\n return results, directories, dx\",\n \"def load_data_from_dir(self,\\n dir_list=[],\\n exclude=[]):\\n dir_list_ = dir_list[:]\\n\\n if len(dir_list) == 0:\\n eprint(\\\"CANNOT load data generator with an empty list of directories: {}\\\".format(dir_list))\\n return\\n\\n for directory in dir_list_:\\n if not os.path.isdir(directory):\\n eprint(\\\"\\\\t\\\\t {}: {} is not a directory\\\".format(self.load_data_from_dir.__name__, directory))\\n return\\n\\n # Read Data from current directory\\n while dir_list_:\\n # Pop first directory name and create dataloader if its a valid folder\\n current_dir = dir_list_.pop(0)\\n valid_dir = True\\n for name in exclude:\\n if name in current_dir and valid_dir:\\n valid_dir = False\\n data_file = current_dir + \\\"/data.mat\\\"\\n if os.path.isfile(data_file) and \\\"takktile_\\\" in current_dir and valid_dir:\\n self.dataloaders.append(takktile_dataloader(data_dir=current_dir,\\n config=self.config,\\n augment=self.augment))\\n\\n # Find all child directories of current directory and recursively load them\\n data_dirs = [os.path.join(current_dir, o) for o in os.listdir(current_dir)\\n if os.path.isdir(os.path.join(current_dir, o))]\\n for d in data_dirs:\\n dir_list_.append(d)\\n\\n self.num_dl = len(self.dataloaders)\\n\\n if self.transform_type:\\n self.__calculate_data_transforms()\\n\\n # Create Eval Data\\n if self.create_eval_data:\\n self.eval_len = (self.__len__())//10\\n self.create_eval_data = False\\n\\n # Calculate class number and ratios\\n # Also calculate class diffs\\n if not self.config['label_type'] == 'value':\\n self.__class_nums = self.dataloaders[0].get_data_class_numbers(self.__get_data_idx(0))\\n for i, dl in enumerate(self.dataloaders[1:]):\\n self.__class_nums += dl.get_data_class_numbers(self.__get_data_idx(i+1))\\n self.__class_ratios = self.__class_nums / float(np.mean(self.__class_nums))\\n self.__class_diff = np.max(self.__class_nums) - self.__class_nums\\n self.__class_diff = [d if n > 0 else 0 for n,d in zip(self.__class_nums, self.__class_diff)]\\n\\n # Reset and prepare data\\n self.on_epoch_end()\",\n \"def list_files_into_directory(directory_path: str) -> [str]:\\n for root, directory_names, file_names in walk(directory_path):\\n return file_names\",\n \"def walk(dirname):\\n for name in os.listdir(dirname):\\n path = os.path.join(dirname, name)\\n\\n if os.path.isfile(path):\\n print(path)\\n else:\\n walk(path)\",\n \"def readMas(dir):\\n n=0\\n fs = []\\n for (dirpath, dirnames, filenames) in os.walk(dir):\\n if len(filenames)>0:\\n for f in filenames:\\n pth = os.path.join(dirpath,f)\\n fs.append(pth)\\n pass\\n return fs\",\n \"def get_file_list(service, folder_id):\\n result = []\\n\\n page_token = None\\n while True:\\n try:\\n param = {}\\n if page_token:\\n param['pageToken'] = page_token\\n children = service.children().list(\\n folderId=folder_id, **param).execute()\\n\\n for child in children.get('items', []):\\n result.append(child['id'])\\n page_token = children.get('nextPageToken')\\n if not page_token:\\n break\\n except errors.HttpError as error:\\n print('Could not retrieve file list: {}'.format(error))\\n break\\n\\n return result\",\n \"def parse_dir(self, dir_path=\\\"NULL\\\"):\\n \\n spec_list = []\\n dir_path = os.path.abspath(dir_path)\\n # if user's path is not having a \\\"/\\\" \\n if dir_path[-1] != \\\"/\\\":\\n dir_path = dir_path + \\\"/\\\"\\n # invoke parse file for every file in the dir_path directory \\n files = commands.getoutput(\\\"ls %s\\\" % dir_path).split()\\n for f in files:\\n spec = self.parse_file(dir_path + f)\\n spec_list.append(spec)\\n return sortbyfilenames(spec_list, files)\",\n \"def paths_to_ids(paths: List[Path]) -> List[int]:\\n\\n ids = []\\n for path in paths:\\n try:\\n ids.append(int(path.name))\\n except ValueError:\\n raise ValueError(\\\"Invalid dir_db path '{}'\\\".format(path.as_posix()))\\n return ids\",\n \"def lastsim_path(simsdir) -> str:\\n import os\\n\\n all_sim_dirs = [d for d in os.listdir(simsdir) if d.isdigit()]\\n if len(all_sim_dirs):\\n most_recent_id = max(int(directory) for directory in all_sim_dirs)\\n return f\\\"{simsdir}/{str(most_recent_id)}\\\"\\n else:\\n msg = f\\\"{simsdir} does not contain any simulation directories!\\\"\\n raise SystemError(msg)\",\n \"def next_id(self):\\n self.id_counter += 1\\n return self.id_counter - 1\",\n \"def find_folder(startpath, folder_name, first_occurrence=False):\\n candidates = []\\n for root, dirs, files in os.walk(startpath):\\n for d in dirs:\\n if d == folder_name.strip('/'):\\n if first_occurrence:\\n candidates.append(os.path.abspath(root + '/' + d))\\n return candidates\\n candidates.append(os.path.abspath(root+'/'+d))\\n return candidates\",\n \"def getID(filePath):\\r\\n\\r\\n fileID = rmn.fstopenall(filePath,rmn.FST_RO)\\r\\n return fileID\",\n \"def walkdir(folder):\\n for dirpath, dirs, files in os.walk(folder):\\n for filename in files:\\n yield os.path.abspath(os.path.join(dirpath, filename))\",\n \"def generate_dirs(start_dir, limit=None):\\n right = True\\n\\n while right and (limit is None or limit > 0):\\n yield start_dir\\n start_dir, right = os.path.split(start_dir)\\n\\n if limit is not None:\\n limit -= 1\",\n \"def list_dir(\\n dir_,\\n):\\n return map(\\n lambda fi: path_join(dir_, fi),\\n os.listdir(dir_),\\n )\",\n \"def findDirAbove(dirName):\\n workDir = \\\"\\\"\\n for i in range(20):\\n path = os.path.join(workDir, dirName)\\n if os.path.exists(path):\\n return os.path.abspath(path)\\n workDir = os.path.join(workDir, \\\"..\\\")\\n\\n return None\",\n \"def get_folder_ids(root_folder_id):\\n creds = authenticate()\\n service = build('drive', 'v3', credentials=creds)\\n\\n page_token = None\\n response = service.files().list(q=\\\"mimeType = 'application/vnd.google-apps.folder' and '{}' in parents and trashed = false\\\".format(root_folder_id),\\n pageSize=10,\\n spaces=\\\"drive\\\",\\n fields='nextPageToken, files(id, name, webViewLink)',\\n pageToken=page_token,\\n\\n ).execute()\\n found_ids = [response[\\\"files\\\"][i][\\\"id\\\"] for i in range(0, len(response[\\\"files\\\"]))] # extract all the IDs\\n found_names = [response[\\\"files\\\"][i][\\\"name\\\"] for i in range(0, len(response[\\\"files\\\"]))]\\n found_ids.append(root_folder_id) # if the Photos folder(as named on Google Drive) just contains folders, this is not needed\\n\\n return found_ids, found_names\",\n \"def find_submissions(root_dir, name):\\n sub_id_dir_pairs = []\\n def _recursive_find_sub_dirs(curr_dir):\\n for root, subdirs, files in os.walk(curr_dir):\\n for subdir in subdirs:\\n if basename(subdir) == name:\\n identifier = basename(root)\\n sub_id_dir_pairs.append((identifier, root))\\n break\\n else:\\n _recursive_find_sub_dirs(subdir)\\n\\n _recursive_find_sub_dirs(root_dir)\\n return sub_id_dir_pairs\",\n \"def get_all_files(directory):\\r\\n for dirpath, _dirnames, filenames in os.walk(directory):\\r\\n for filename in filenames:\\r\\n yield (filename, dirpath)\",\n \"def walk(dirname): \\n for name in os.listdir(dirname):\\n path = os.path.join(dirname, name)\\n if os.path.isfile(path):\\n print(path)\\n else:\\n walk(path)\",\n \"def DirEnumerator(args, path):\\n for d in os.scandir(path):\\n try:\\n if d.name == '.' or d.name == '..':\\n pass\\n elif d.is_symlink() and args.skiplinks:\\n pass\\n elif d.is_file():\\n yield d.path\\n elif d.is_dir() and args.recurse:\\n for f in DirEnumerator(args, d.path):\\n yield f\\n except Exception as e:\\n print(\\\"EXCEPTION %s accessing %s/%s\\\" % (e, path, d.name))\",\n \"def get_immediate_subdirectories(self, a_dir):\\n return [name for name in os.listdir(a_dir)\\n if os.path.isdir(os.path.join(a_dir, name))]\",\n \"def get_unique_path(directory: pathlib.Path, name_pattern: str) -> pathlib.Path:\\n counter = 0\\n while True:\\n counter += 1\\n path = directory / name_pattern.format(counter)\\n if not path.exists():\\n return path\",\n \"def get_folder_id(self, folder_name) -> str:\\n files = self.list_files()\\n\\n folder_id = None\\n for id, name in files.items():\\n if name == folder_name:\\n folder_id = id\\n break\\n\\n if folder_id is None:\\n logger.warning(\\\"Failed to find folder %s\\\", folder_name)\\n raise FileNotFoundError\\n\\n return folder_id\",\n \"def walk_dir(self, dir):\\n if self.ppath_prefix_len:\\n prefix = self.ppath_prefix[self.ppath_prefix_idx%self.ppath_prefix_len]\\n self.ppath_prefix_idx += 1\\n merged_path = os.path.join(prefix, dir)\\n for root, dirs, files in self.fswalk_base(merged_path):\\n yield merged_path, dirs, files\\n else:\\n yield self.fswalk_base(dir)\",\n \"def get_next_id():\\n global _lock, _counter\\n with _lock:\\n if _counter == 65535:\\n _counter = 1\\n else:\\n _counter += 1\\n\\n return str(_counter)\",\n \"def index_subdirectory(directory, class_indices, follow_links, formats):\\n dirname = os.path.basename(directory)\\n valid_files = iter_valid_files(directory, follow_links, formats)\\n labels = []\\n filenames = []\\n for root, fname in valid_files:\\n labels.append(class_indices[dirname])\\n absolute_path = os.path.join(root, fname)\\n relative_path = os.path.join(\\n dirname, os.path.relpath(absolute_path, directory))\\n filenames.append(relative_path)\\n return filenames, labels\",\n \"def fileCounter(directory):\",\n \"def get_directory(self, directory: str) -> List[Dict]:\\n raise NotImplementedError\",\n \"def getpaths_fromdir(input_prefix_, directory_):\\n path = os.path.join(input_prefix_, \\\"%s*\\\" % directory_, \\\"*\\\")\\n return [tuple([directory_, path])]\",\n \"def gen_paths():\\n global log_dir, events_file, log_file, datastream_dir\\n\\n #The root log directory\\n log_root_dir = os.path.join(root_dir, \\\"logs\\\")\\n\\n #Figure out what log file index we should use\\n #The log file index is a 4-digit number corresponding to an unused log folder\\n index = 0\\n #If our base log_root_dir exists:\\n if os.path.exists(log_root_dir):\\n\\n #Get existing folders, convert to string list, and sort\\n folders = os.listdir(log_root_dir)\\n ids = [int(f) for f in folders]\\n ids.sort()\\n\\n #This algorithm determines the next sequential value for our log index, it scans through the existing numbers\\n #until either it finds a missing number in sequence, or runs out of numbers to scan.\\n\\n #Set this to a high number to start with, as it will get set every loop iteration\\n last_id = 10000\\n for present_index in ids:\\n #If we have a break in the number sequence, abort and use what we have\\n if present_index > last_id + 1:\\n break\\n #If we have found a bigger number to use for index\\n if present_index > index:\\n index = present_index\\n\\n last_id = present_index\\n\\n #Convert from largest existing index to the index we should use!\\n index += 1\\n\\n #Set the log_dir, which is the directory for storing all logs during this run session\\n log_dir = os.path.join(log_root_dir, str(index).zfill(4))\\n\\n #Set the log_file, which is a dump of all console output\\n log_file = os.path.join(log_dir, \\\"main.log\\\")\\n\\n #Set the events_file, which is where all events are recorded\\n events_file = os.path.join(log_dir, \\\"events.rec\\\")\\n\\n #Set the datastream_dir, within which all datastreams are recorded\\n datastream_dir = os.path.join(log_dir, \\\"datastreams\\\")\",\n \"def get_last_formatted_dir_in_dir(folder, fmt):\\n last_elem = None\\n root_elements = sorted(os.listdir(folder))\\n for root_element in root_elements[::-1]:\\n if os.path.isdir(os.path.join(folder, root_element)):\\n if parser.validate(fmt, root_element):\\n last_elem = root_element\\n break\\n return last_elem\",\n \"def parsedirs (directory, regex, depth, debian_release):\\n\\n global ovals\\n debian_version = DEBIAN_VERSION[debian_release]\\n\\n if depth == 0:\\n logging.log(logging.DEBUG, \\\"Maximum depth reached at directory \\\" + directory)\\n return (0)\\n \\n for fileName in os.listdir (directory):\\n \\n path = \\\"%s/%s\\\" % (directory, fileName)\\n \\n logging.log (logging.DEBUG, \\\"Checking %s (for %s at %s)\\\" % (fileName, regex, depth))\\n \\n if os.access(path, os.R_OK) and os.path.isdir (path) and not os.path.islink (path) and fileName[0] != '.':\\n logging.log(logging.DEBUG, \\\"Entering directory \\\" + path)\\n parsedirs (path, regex, depth-1, debian_release)\\n\\n #Parse fileNames\\n if os.access(path, os.R_OK) and regex.search(fileName) and fileName[0] != '.' and fileName[0] != '#':\\n result = dsa.parseFile(path)\\n if result:\\n cve = result[0]\\n if ovals.has_key(cve):\\n for (k, v) in result[1].iteritems():\\n ovals[cve][k] = v\\n else:\\n ovals[cve] = result[1]\\n\\n dsaRef = fileName[:-5].upper() # remove .data part\\n \\n # also parse corresponding wml file\\n wmlResult = wml.parseFile(path.replace('.data', '.wml'), DEBIAN_VERSION)\\n if wmlResult:\\n data, releases = wmlResult\\n # skip if the wml file does not contain the debian release\\n if not debian_version in releases:\\n continue\\n for (k, v) in data.iteritems():\\n if k == \\\"moreinfo\\\":\\n if not \\\"moreinfo\\\" in ovals[cve]:\\n ovals[cve][\\\"moreinfo\\\"] = \\\"\\\\n\\\"\\n # aggregate all advisories\\n ovals[cve][k] += \\\"%s%s\\\\n\\\" % (dsaRef, v)\\n elif k in ('description'): # some keys shouldn't be clobbered\\n if not k in ovals[cve]:\\n ovals[cve][k] = v\\n else:\\n ovals[cve][k] = v\\n if not \\\"release\\\" in ovals[cve]:\\n ovals[cve][\\\"release\\\"] = {}\\n ovals[cve]['release'].update({debian_version: releases[debian_version]})\\n\\n return 0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6369533","0.6025362","0.5927364","0.57674915","0.57419837","0.5673672","0.5648966","0.55251485","0.5444188","0.54296756","0.5414394","0.54062897","0.5385693","0.5360778","0.53512573","0.53074497","0.5295442","0.5287889","0.52728975","0.52728975","0.52728975","0.52447695","0.52086395","0.5183225","0.5183052","0.5126628","0.5121179","0.51093835","0.5082067","0.5076154","0.5071206","0.5066384","0.5063533","0.5053657","0.5050396","0.5030875","0.50144976","0.49961078","0.49899268","0.49572968","0.49438795","0.49409294","0.4930953","0.48991355","0.48742154","0.48721543","0.48704988","0.48668206","0.48609865","0.48568797","0.4852506","0.485233","0.4850568","0.4849427","0.48417848","0.48374048","0.48279604","0.48226076","0.4819362","0.4812866","0.48125365","0.48057044","0.47981995","0.4796455","0.47937152","0.47774923","0.47668248","0.47587588","0.47554597","0.4755282","0.47548878","0.4752526","0.4746489","0.4743851","0.47417623","0.47382477","0.4736917","0.47284022","0.47264174","0.47232258","0.47208625","0.4718249","0.47060007","0.47033828","0.46985257","0.46972248","0.46918198","0.4691813","0.46868992","0.46765196","0.46746603","0.46685475","0.4664259","0.46617386","0.46531838","0.46503156","0.46446466","0.464369","0.46386084","0.46314457"],"string":"[\n \"0.6369533\",\n \"0.6025362\",\n \"0.5927364\",\n \"0.57674915\",\n \"0.57419837\",\n \"0.5673672\",\n \"0.5648966\",\n \"0.55251485\",\n \"0.5444188\",\n \"0.54296756\",\n \"0.5414394\",\n \"0.54062897\",\n \"0.5385693\",\n \"0.5360778\",\n \"0.53512573\",\n \"0.53074497\",\n \"0.5295442\",\n \"0.5287889\",\n \"0.52728975\",\n \"0.52728975\",\n \"0.52728975\",\n \"0.52447695\",\n \"0.52086395\",\n \"0.5183225\",\n \"0.5183052\",\n \"0.5126628\",\n \"0.5121179\",\n \"0.51093835\",\n \"0.5082067\",\n \"0.5076154\",\n \"0.5071206\",\n \"0.5066384\",\n \"0.5063533\",\n \"0.5053657\",\n \"0.5050396\",\n \"0.5030875\",\n \"0.50144976\",\n \"0.49961078\",\n \"0.49899268\",\n \"0.49572968\",\n \"0.49438795\",\n \"0.49409294\",\n \"0.4930953\",\n \"0.48991355\",\n \"0.48742154\",\n \"0.48721543\",\n \"0.48704988\",\n \"0.48668206\",\n \"0.48609865\",\n \"0.48568797\",\n \"0.4852506\",\n \"0.485233\",\n \"0.4850568\",\n \"0.4849427\",\n \"0.48417848\",\n \"0.48374048\",\n \"0.48279604\",\n \"0.48226076\",\n \"0.4819362\",\n \"0.4812866\",\n \"0.48125365\",\n \"0.48057044\",\n \"0.47981995\",\n \"0.4796455\",\n \"0.47937152\",\n \"0.47774923\",\n \"0.47668248\",\n \"0.47587588\",\n \"0.47554597\",\n \"0.4755282\",\n \"0.47548878\",\n \"0.4752526\",\n \"0.4746489\",\n \"0.4743851\",\n \"0.47417623\",\n \"0.47382477\",\n \"0.4736917\",\n \"0.47284022\",\n \"0.47264174\",\n \"0.47232258\",\n \"0.47208625\",\n \"0.4718249\",\n \"0.47060007\",\n \"0.47033828\",\n \"0.46985257\",\n \"0.46972248\",\n \"0.46918198\",\n \"0.4691813\",\n \"0.46868992\",\n \"0.46765196\",\n \"0.46746603\",\n \"0.46685475\",\n \"0.4664259\",\n \"0.46617386\",\n \"0.46531838\",\n \"0.46503156\",\n \"0.46446466\",\n \"0.464369\",\n \"0.46386084\",\n \"0.46314457\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74510384"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77086,"cells":{"query":{"kind":"string","value":"Takes in a list of tuples of (src, dst) paths and copies files. Will create all necessary directories."},"document":{"kind":"string","value":"def copy_files_and_create_dirs(files) -> None:\r\n for file in files:\r\n target_dir_name = os.path.dirname(file[1])\r\n\r\n # will create all intermediate-level directories\r\n if not os.path.exists(target_dir_name):\r\n os.makedirs(target_dir_name)\r\n\r\n shutil.copyfile(file[0], file[1])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _copy_files(src_paths,dst_dir,class_numbers):\n\n class_dirs=[os.path.join(dst_dir,class_name+\"/\")for class_name in self.class_names]\n\n for dir in class_dirs:\n if not os.path.exists(dir):\n os.makedirs(dir)\n\n for src,cls in zip(src_paths,class_numbers):\n shutil.copy(src=src,dst=class_dirs[cls])","def _CopyFiles(file_pairs):\n\n for pair in file_pairs:\n target_dir = os.path.dirname(pair.target)\n if not os.path.isdir(target_dir):\n os.makedirs(target_dir)\n copyfile(pair.generated, pair.target)","def CopyFiles(src_list, dst, src_base='',\n preserve=False, recursive=False,\n ignore_existence_check=False):\n if not src_list:\n return\n\n def _MakeDirs(dir):\n if not os.path.exists(dir):\n os.makedirs(dir)\n\n def _CopyDir(src_dir, dst_dir):\n _MakeDirs(dst_dir)\n\n for filename in os.listdir(src_dir):\n src = os.path.join(src_dir, filename)\n _Copy(src, dst_dir)\n\n def _Copy(src, dst):\n if os.path.isdir(dst):\n dst = os.path.join(dst, os.path.basename(src))\n\n if os.path.isdir(src):\n _CopyDir(src, dst)\n else:\n shutil.copy(src, dst)\n\n # Copy update time and permission\n if preserve:\n shutil.copystat(src, dst)\n # Changes the file writable so we can overwrite it later.\n os.chmod(dst, os.stat(dst).st_mode | stat.S_IWRITE)\n\n def _ErrorCheck(src, src_base):\n if not os.path.exists(src):\n if ignore_existence_check:\n return False\n else:\n _ErrorExit('No such file or directory: \"%s\"' % src)\n\n if os.path.isdir(src) and not recursive:\n _ErrorExit('Cannot copy a directory: \"%s\"' % src)\n\n if not src.startswith(src_base):\n _ErrorExit('Source file does not start with src_base: \"%s\"' % src)\n\n return True\n\n dst = os.path.abspath(dst)\n if src_base:\n src_base = os.path.abspath(src_base)\n\n # dst may be a file instead of a directory.\n if len(src_list) == 1 and not os.path.exists(dst) and not src_base:\n src = os.path.abspath(src_list[0])\n if _ErrorCheck(src, src_base):\n _MakeDirs(os.path.dirname(dst))\n _Copy(src, dst)\n return\n\n # dst should be a directory here.\n for src in src_list:\n src = os.path.abspath(src)\n\n if src_base:\n dst_dir = os.path.join(dst,\n os.path.relpath(os.path.dirname(src), src_base))\n else:\n dst_dir = dst\n\n if _ErrorCheck(src, src_base):\n _MakeDirs(dst_dir)\n _Copy(src, dst_dir)","def copy_files(src_path, dst_path):\r\n for folder in os.listdir(src_path):\r\n for file in os.listdir(os.path.join(src_path, folder)):\r\n source = os.path.join(os.path.join(src_path, folder), file)\r\n dest = os.path.join(dst_path, file)\r\n shutil.copy(source, dest)","def copy_files(source,destination):\r\n filelist = os.listdir(source)\r\n \r\n if not os.path.exists(destination):\r\n os.mkdir(destination)\r\n \r\n for filename in filelist:\r\n source_file = os.path.join(source,filename)\r\n shutil.copy(source_file,destination)","def copy_files(files, path):\n safe_mkdir(path)\n for file in files:\n copy2(file, path)","def copy_file_to_multiple_subfolders(src, dst, *args, **kwargs):\n print '\\nSource: {}\\nDestinations parent folder: {}'.format(src, dst)\n filename = os.path.basename(src)\n for folder in (d for d in os.listdir(dst) if os.path.isdir(d)):\n print '\\nCopying {} to {}...'.format(filename, folder)\n try:\n shutil.copy(src, os.path.abspath(dst) + '\\\\' + folder)\n except Exception as e:\n print e","def m_cp(*args):\n if (not args or len(args) < 2) :\n print(\"parameter is invalid\")\n return\n\n src = args[0:len(args)-1]\n dst = args[len(args)-1]\n\n if not os.path.exists(dst) :\n os.mkdir(dst)\n\n for s in src:\n print(\"cp %s ==> %s\" % (s, dst))\n cpfile(s, dst)","def copy_separate_files(source,dest1,dest2):\r\n filelist = os.listdir(source)\r\n \r\n if not os.path.exists(dest1):\r\n os.mkdir(dest1)\r\n \r\n if not os.path.exists(dest2):\r\n os.mkdir(dest2)\r\n \r\n for filename in filelist:\r\n source_file = os.path.join(source,filename)\r\n \r\n if filename[-4:] == '.png':\r\n shutil.copy(source_file,dest1)\r\n else:\r\n shutil.copy(source_file,dest2)","def copy_files(source_paths, des_paths, is_debug=False):\n for source_path, des_path in zip(source_paths, des_paths):\n if not os.path.exists(os.path.dirname(des_path)):\n os.makedirs(os.path.dirname(des_path))\n shutil.copyfile(source_path, des_path)\n if is_debug:\n print_log(\"Copy file from %s to %s\" % (source_path, des_path))","def _transferFiles(initialPath, destinationPath, fileList):\n if not fileList:\n return\n if not os.path.exists(destinationPath):\n os.mkdir(destinationPath)\n for pattern in fileList:\n if isinstance(pattern, tuple):\n # allow renames in transit\n fromName, destName = pattern\n copies = [(fromName, destName)]\n else:\n # expand globs if they're given\n copies = []\n for ff in glob.glob(pattern):\n # renaming not allowed with globs\n copies.append((ff, ff))\n\n for fromName, destName in copies:\n fromPath = os.path.join(initialPath, fromName)\n toPath = os.path.join(destinationPath, destName)\n runLog.extra(\"Copying {} to {}\".format(fromPath, toPath))\n shutil.copy(fromPath, toPath)","def _copy_paths(self, paths, source, destination, output_path,\r\n final_path=None):\r\n for path in paths:\r\n if final_path:\r\n copy(os.path.join(source, path),\r\n os.path.join(output_path, destination, final_path))\r\n else:\r\n copy(os.path.join(source, path),\r\n os.path.join(output_path, destination, path))","def copyDir(src, dst, includes, excludes = []):\n\tmultiFilesReplacements([], dst, src, includes, excludes)","def copydir(src, dst):\n for item in os.listdir(src):\n s, d = os.path.join(src, item), os.path.join(dst, item)\n if os.path.isdir(s):\n if not os.path.isdir(d):\n os.mkdir(d)\n copydir(s, d)\n else:\n shutil.copy(s, d)","def copy_paths(src, dst, paths, *, exclude=None):\n files = []\n\n for path in paths:\n if isinstance(path, tuple):\n files += copy_path(src, dst, path[0], path[1], exclude=exclude)\n else:\n files += copy_path(src, dst, path, exclude=exclude)\n\n return files","def conditional_copy(copy_tuples):\n for source_file, destination_file in copy_tuples:\n # If the root_directory and destination file contents are the same, don't perform unnecessary file I/O\n if not destination_file.exists() or not filecmp.cmp(source_file, destination_file, shallow=False):\n destination_file.parent.mkdir(parents=True, exist_ok=True)\n shutil.copyfile(source_file, destination_file)","def copy_files(src, desc, file_list):\r\n src_paths = [os.path.join(src, file_name) for file_name in file_list]\r\n output = subprocess.run(['cp', '-a', *src_paths, desc], check=True)\r\n if output.returncode != 0:\r\n raise Exception('Failed to copy files from {} to {}'.format(src, desc))","def MakeDestinationDirectories(self, dst_files):\n for dst in dst_files:\n path = os.path.dirname(dst);\n if (len(path) > 0) and (not os.path.exists(path)):\n self.VerboseMsg(\"Make Directory: \" + path)\n if self.execute:\n os.makedirs(path)","def transfer_files_from_dir_copy(\n source_filepath_list: Iterable[str],\n destination_filepath_list: Iterable[str],\n force_overwrite: bool = False,\n delete_source: bool = False\n) -> None:\n hide_progress_bar = logger.getEffectiveLevel() > logging.INFO\n for src, dst in tqdm(zip(source_filepath_list, destination_filepath_list), disable=hide_progress_bar):\n os.makedirs(path.dirname(dst), exist_ok=True)\n if force_overwrite and path.lexists(dst):\n os.remove(dst)\n if delete_source:\n shutil.move(src, dst)\n else:\n shutil.copyfile(src, dst)","def _copy_dir(src, dst):\n if os.path.isdir(src):\n os.makedirs(dst, exist_ok=True)\n for item in os.listdir(src):\n s = os.path.join(src, item)\n d = os.path.join(dst, item)\n\n if os.path.isdir(s):\n _copy_dir(s, d)\n else:\n shutil.copy2(s, d)\n\n else:\n os.makedirs(os.path.dirname(dst), exist_ok=True)\n _delete_file(dst)\n shutil.copy2(src, dst)","def copy_new_files(self, out_dir, answer_dir, filenames):\n if not answer_dir.exists():\n answer_dir.mkdir(parents=True)\n for filename in filenames:\n fromfile = out_dir / filename\n tofile = answer_dir / filename\n shutil.copyfile(fromfile, tofile)","def cp(*args):\n filenames = _glob(args)\n nfilenames = len(filenames)\n if nfilenames < 2:\n print 'Need at least two arguments'\n elif nfilenames == 2:\n try:\n shutil.copy(filenames[0], filenames[1])\n except os.error, detail:\n print \"%s: %s\" % (detail[1], filenames[1])\n else:\n for filename in filenames[:-1]:\n try:\n dest = filenames[-1] + '/' + filename\n if not os.path.isdir(filenames[-1]):\n print 'Last argument needs to be a directory'\n return\n shutil.copy(filename, dest)\n except os.error, detail:\n print \"%s: %s\" % (detail[1], filename)","def copy_files(self):\n for (source_name, target_name) in self.FILES_TO_LINK:\n src = os.path.expanduser(source_name)\n tgt = os.path.expanduser(target_name)\n cmd = 'cp -rf {src} {tgt}'.format(src=src, tgt=tgt)\n\n print(cmd)\n if not self.dry_run:\n run(cmd)","def copy_files(orig_dir, dest_dir): \n files = [file for file in os.listdir(orig_dir)]\n \n for file in files:\n origin = os.path.join(orig_dir,file)\n destination = os.path.join(dest_dir,file)\n shutil.copyfile(origin,destination)","def copy_list_to_dir(file_list, subdir, dir=\"NewData\"):\n\n subdir = os.path.join(dir, subdir)\n\n if not os.path.exists(dir):\n os.mkdir(dir)\n\n if not os.path.exists(subdir):\n os.mkdir(subdir)\n\n for file in file_list:\n base_name = os.path.basename(file)\n shutil.copy(file, os.path.join(subdir, base_name))","def copy_supported_files(self):\n\n try:\n for directory in self.config.DIRS_TO_COPY:\n shutil.copytree(self.dir_helper.publication_path + directory,\n self.temp_dir + directory)\n except Exception, ex:\n print '[e] exception {}'.format(str(ex))\n print '[i] looks like to folder existing that are scheduled for copying'\n\n for file_ in self.config.FILES_TO_COPY:\n index = file_.rfind('\\\\')\n dest_file = file_\n if index != -1:\n dest_file = file_[index+1:]\n\n try:\n shutil.copy2(self.dir_helper.publication_path + file_,\n self.temp_dir + dest_file)\n except Exception, ex:\n print '[e] exception {}'.format(str(ex))\n print '[i] file \"{}\" was not copied'.format(self.dir_helper.publication_path + file_)","def copyFiles(img, lbl):\n if not os.path.exists(os.path.join(out_root_dir, \"images\")):\n os.makedirs(os.path.join(out_root_dir, \"images\"))\n if not os.path.exists(os.path.join(out_root_dir,\"labels\")):\n os.makedirs(os.path.join(out_root_dir, \"labels\"))\n # copy\n for i,f in enumerate(img):\n img_dstdir = os.path.join(out_root_dir, \"images\", os.path.basename(f))\n lbl_srcdir = os.path.join(in_root_dir, \"labels\", os.path.basename(f))\n lbl_dstdir = os.path.join(out_root_dir, \"labels\", os.path.basename(f))\n \n # copy images and labels\n try:\n # copy image\n copyfile(f, img_dstdir)\n except:\n print(f\"Error trying copy image file {f}\")\n \n try:\n # copy label\n copyfile(lbl_srcdir, lbl_dstdir)\n except:\n print(f\"Error trying copy label file {lbl_srcdir}\")","def copy_tree(src, dst, verbose=False):\n\n print('Copying {} to {}'.format(src, dst))\n for src_dir, sub_dirs, basenames in tf.io.gfile.walk(src):\n rel_dir = os.path.relpath(src_dir, src)\n dst_dir = os.path.join(dst, rel_dir)\n for sub_dir in sorted(sub_dirs):\n path = os.path.join(dst, rel_dir, sub_dir)\n print('Make dir {}'.format(path))\n tf.io.gfile.makedirs(path)\n if basenames:\n print('Copying {} files from {} to {}'.format(\n len(basenames), src_dir, dst_dir))\n for basename in basenames:\n src_path = os.path.join(src_dir, basename)\n dst_path = os.path.join(dst_dir, basename)\n if verbose:\n print('Copying {} to {}'.format(src_path, dst_path))\n tf.io.gfile.copy(src_path, dst_path)","def copyFiles(sourceDir, destinationDir, patterns):\n\tfrom glob import glob\n\tfrom os.path import join, abspath, exists, isfile\n\timport shutil\n\tsourceDir = adaptPath(sourceDir)\n\tdestinationDir = adaptPath(destinationDir)\n\t\n\tif exists(abspath(sourceDir)) == False:\n\t\tprint ('! \"%s\" directory not existing'%sourceDir)\n\tmakedir(destinationDir)\n\tfor pattern in patterns:\n\t\tsrcPath = join(sourceDir,pattern)\n\t\tfor filename in glob(srcPath):\n\t\t\tif isfile(filename):\n\t\t\t\ttry:\n\t\t\t\t\tshutil.copy2(filename, destinationDir)\n\t\t\t\texcept IOError:\n\t\t\t\t\tprint (\"! Failed copy '%s' -> '%s'\" %(filename, destinationDir))","def copy_to_folder(destination_folder, source_dir_list, use_prefix=True):\n prefix_dict = {\"AD\": \"ad\", \"MCI\": \"mci\", \"CN\": \"cn\"}\n for dir_path in source_dir_list:\n patient_type = os.path.basename(os.path.dirname(dir_path))\n prefix = prefix_dict[patient_type]\n dir_basename = prefix + \"_\" + os.path.basename(dir_path)\n target_path = os.path.join(destination_folder, dir_basename)\n copytree(dir_path, target_path)","def command_copy(args):\n sources = args.sources\n destpath = args.destpath\n source_files = []\n for file_ in sources:\n if \"*\" in file_:\n selected = glob(file_)\n source_files.extend(selected)\n elif os.path.isfile(file_):\n source_files.append(file_)\n\n if destpath.endswith(\"/\") or os.path.isdir(destpath) or len(sources) > 1:\n # -- DESTDIR-MODE: Last argument is a directory.\n destdir = destpath\n else:\n # -- DESTFILE-MODE: Copy (and rename) one file.\n assert len(source_files) == 1\n destdir = os.path.dirname(destpath)\n\n # -- WORK-HORSE: Copy one or more files to destpath.\n if not os.path.isdir(destdir):\n sys.stdout.write(\"copy: Create dir %s\\n\" % destdir)\n os.makedirs(destdir)\n for source in source_files:\n destname = os.path.join(destdir, os.path.basename(source))\n sys.stdout.write(\"copy: %s => %s\\n\" % (source, destname))\n shutil.copy(source, destname)\n return 0","def copy_subtree(src, dst):\n for src_f in os.listdir(src):\n src_path = os.path.join(src, src_f)\n if os.path.isdir(src_path):\n dst_path = os.path.join(dst, src_f)\n if not os.path.exists(dst_path):\n shutil.copytree(src_path, dst_path)\n else:\n ProcessJson.copy_subtree(src_path, dst_path)\n elif os.path.isfile(src_path):\n dst_path = os.path.join(dst, src_f)\n if not os.path.exists(dst_path):\n shutil.copy(src_path, dst_path)","def copy_files(mod_name, dir_name, output_dir):\n files = pkg_resources.resource_filename(mod_name, dir_name)\n for filename in os.listdir(files):\n full_filename = os.path.join(files, filename)\n if os.path.isdir(full_filename):\n try:\n shutil.copytree(full_filename, os.path.join(output_dir, filename))\n except FileExistsError:\n continue\n else:\n shutil.copy(full_filename, output_dir)","def applyDir(self,srcDir,destDir,exts): \n for srcFile in os.listdir(srcDir):\n srcExt = os.path.splitext(srcFile)[-1].lower()\n srcPath = os.path.join(srcDir,srcFile)\n destPath = os.path.join(destDir,srcFile)\n if srcExt in exts:\n if not os.path.exists(destDir):\n os.makedirs(destDir)\n shutil.copyfile(srcPath,destPath)\n if self.progress: \n self.cumSize += os.path.getsize(srcPath)\n self.progress(self.cumSize,_('Copying Files...'))\n elif os.path.isdir(srcPath):\n self.applyDir(srcPath,destPath,exts)","def copy_database(path_images, path_labels, path_final_images):\n\n try:\n labels = sorted(os.listdir(path_labels))\n except FileNotFoudError:\n print(\"No such file or directory \", path_labels)\n\n try:\n images = sorted(os.listdir(path_images)) #+ \"RetinaNet_I04590/\"))\n except FileNotFoudError:\n print(\"No such file or directory \", path_images)\n\n \"\"\"if not os.path.exists(path_final_images + \"I04590/\"):\n os.mkdir(path_final_images + \"I04590/\")\n\n if not os.path.exists(path_final_images + \"I045135/\"):\n os.mkdir(path_final_images + \"I045135/\")\n\n if not os.path.exists(path_final_images + \"I090135/\"):\n os.mkdir(path_final_images + \"I090135/\")\n\n if not os.path.exists(path_final_images + \"I4590135/\"):\n os.mkdir(path_final_images + \"I4590135/\")\n\n if not os.path.exists(path_final_images + \"Params/\"):\n os.mkdir(path_final_images + \"Params/\")\n\n if not os.path.exists(path_final_images + \"Pauli2/\"):\n os.mkdir(path_final_images + \"Pauli2/\")\n\n if not os.path.exists(path_final_images + \"Pauli3/\"):\n os.mkdir(path_final_images + \"Pauli3/\")\n\n if not os.path.exists(path_final_images + \"Stokes/\"):\n os.mkdir(path_final_images + \"Stokes/\")\n\n if not os.path.exists(path_final_images + \"Rachel/\"):\n os.mkdir(path_final_images + \"Rachel/\")\n\n if not os.path.exists(path_final_images + \"Rachel2/\"):\n os.mkdir(path_final_images + \"Rachel2/\")\"\"\"\n\n for k in range(len(images)):\n if str(k) + \".xml\" in labels:\n copyfile(path_images + \"/\" + images[k],\n path_final_images + \"/\" + images[k])\n \"\"\"copyfile(path_images + \"RetinaNet_I04590/\" + str(k) + \".png\",\n path_final_images + \"I04590/\" + str(k) + \".png\")\n copyfile(path_images + \"RetinaNet_I045135/\" + str(k) + \".png\",\n path_final_images + \"I045135/\" + str(k) + \".png\")\n copyfile(path_images + \"RetinaNet_I090135/\" + str(k) + \".png\",\n path_final_images + \"I090135/\" + str(k) + \".png\")\n copyfile(path_images + \"RetinaNet_I4590135/\" + str(k) + \".png\",\n path_final_images + \"I4590135/\" + str(k) + \".png\")\n copyfile(path_images + \"RetinaNet_Params/\" + str(k) + \".png\",\n path_final_images + \"Params/\" + str(k) + \".png\")\n copyfile(path_images + \"RetinaNet_Pauli2/\" + str(k) + \".png\",\n path_final_images + \"Pauli2/\" + str(k) + \".png\")\n copyfile(path_images + \"RetinaNet_Pauli3/\" + str(k) + \".png\",\n path_final_images + \"Pauli3/\" + str(k) + \".png\")\n copyfile(path_images + \"RetinaNet_Stokes/\" + str(k) + \".png\",\n path_final_images + \"Stokes/\" + str(k) + \".png\")\n copyfile(path_images + \"RetinaNet_Rachel/\" + str(k) + \".png\",\n path_final_images + \"Rachel/\" + str(k) + \".png\")\n copyfile(path_images + \"RetinaNet_Rachel2/\" + str(k) + \".png\",\n path_final_images + \"Rachel2/\" + str(k) + \".png\")\n copyfile(path_labels + str(k) + \".xml\",\n path_final_labels + str(k) + \".xml\")\"\"\"\n print(k)","def copy_dir(src: Text, dst: Text) -> None:\n\n if tf.io.gfile.exists(dst):\n tf.io.gfile.rmtree(dst)\n tf.io.gfile.makedirs(dst)\n\n for dir_name, sub_dirs, leaf_files in tf.io.gfile.walk(src):\n for leaf_file in leaf_files:\n leaf_file_path = os.path.join(dir_name, leaf_file)\n new_file_path = os.path.join(dir_name.replace(src, dst, 1), leaf_file)\n tf.io.gfile.copy(leaf_file_path, new_file_path)\n\n for sub_dir in sub_dirs:\n tf.io.gfile.makedirs(os.path.join(dir_name.replace(src, dst, 1), sub_dir))","def create_symlink_dir(src_dir, src_list, dst):\n if not src_list:\n return\n message = \"creating symlink directory at {dst} with files {src_list}\".format(\n dst=dst,\n src_list=pformat(src_list))\n logging.info(message)\n if not os.path.exists(dst):\n os.makedirs(dst)\n for src_file in src_list:\n if not src_file:\n continue\n source = os.path.join(src_dir, src_file)\n destination = os.path.join(dst, src_file)\n if os.path.lexists(destination):\n continue\n try:\n os.symlink(source, destination)\n except Exception as e:\n msg = format_debug(e)\n logging.error(e)","def copy_files():\n\n # Load the Knifey-Spoony dataset.\n # This is very fast as it only gathers lists of the files\n # and does not actually load the images into memory.\n dataset = load()\n\n # Copy the files to separate training- and test-dirs.\n dataset.copy_files(train_dir=train_dir, test_dir=test_dir)","def copy_folders(src_folders, dst_root, ignore_patterns=None):\n \n for src in src_folders:\n dst = dst_root/src.name\n shutil.copytree(src, dst, ignore=shutil.ignore_patterns(*ignore_patterns))","def copy_tree(src, dst):\n if not os.path.isdir(src):\n raise Exception, \\\n \"cannot copy tree '%s': not a directory\" % src\n try:\n names = os.listdir(src)\n except os.error, (_, errstr):\n raise Exception, \\\n \"error listing files in '%s': %s\" % (src, errstr)\n\n makedirs(dst)\n\n outputs = []\n\n for n in names:\n src_name = os.path.join(src, n)\n dst_name = os.path.join(dst, n)\n\n if os.path.islink(src_name):\n link_dest = os.readlink(src_name)\n \n os.symlink(link_dest, dst_name)\n outputs.append(dst_name)\n\n elif os.path.isdir(src_name):\n outputs.extend(copy_tree(src_name, dst_name))\n else:\n copyfile(src_name, dst_name)\n \n outputs.append(dst_name)\n\n return outputs","def copy_files_individually(source_files, target_dir):\n\tfor source_file in source_files:\n\t\thead_tail = os.path.split(source_file)\n\t\tcopy_file(source_file, os.path.join(target_dir, head_tail[1]))","def copytree(src, dst, symlinks=False, ignore=None):\n import os\n import shutil\n for item in os.listdir(src):\n s = os.path.join(src, item)\n d = os.path.join(dst, item)\n if os.path.isdir(s):\n shutil.copytree(s, d, symlinks, ignore)\n else:\n shutil.copy2(s, d)","def Copy(args):\n parser = argparse.ArgumentParser(usage='cp [Options] sources... dest',\n description=Copy.__doc__)\n parser.add_argument(\n '-R', '-r', '--recursive', dest='recursive', action='store_true',\n default=False,\n help='copy directories recursively.')\n parser.add_argument(\n '-v', '--verbose', dest='verbose', action='store_true',\n default=False,\n help='verbose output.')\n parser.add_argument(\n '--include', dest='includes', action='append', default=[],\n help='include files matching this expression.')\n parser.add_argument(\n '--exclude', dest='excludes', action='append', default=[],\n help='exclude files matching this expression.')\n parser.add_argument('srcs', nargs='+', help='files to copy')\n parser.add_argument('dest', help='destination')\n\n options = parser.parse_args(args)\n\n src_list = []\n for src in options.srcs:\n files = glob.glob(src)\n if not files:\n raise OSError('cp: no such file or directory: ' + src)\n if files:\n src_list.extend(files)\n\n for src in src_list:\n # If the destination is a directory, then append the basename of the src\n # to the destination.\n if os.path.isdir(options.dest):\n CopyPath(options, src, os.path.join(options.dest, os.path.basename(src)))\n else:\n CopyPath(options, src, options.dest)","def _copy_sources():\n shutil.rmtree(SRC_DIR_LOCAL, ignore_errors=True)\n os.mkdir(SRC_DIR_LOCAL)\n\n shutil.copy(os.path.join(SRC_DIR_REPO, 'LICENSE.txt'), SRC_DIR_LOCAL)\n shutil.copy(os.path.join(SRC_DIR_REPO, 'z3.pc.cmake.in'), SRC_DIR_LOCAL)\n shutil.copy(os.path.join(SRC_DIR_REPO, 'CMakeLists.txt'), SRC_DIR_LOCAL)\n shutil.copytree(os.path.join(SRC_DIR_REPO, 'cmake'), os.path.join(SRC_DIR_LOCAL, 'cmake'))\n shutil.copytree(os.path.join(SRC_DIR_REPO, 'scripts'), os.path.join(SRC_DIR_LOCAL, 'scripts'))\n\n # Copy in src, but avoid recursion\n def ignore_python_setup_files(src, _):\n if os.path.normpath(src).endswith('api/python'):\n return ['core', 'dist', 'MANIFEST', 'MANIFEST.in', 'setup.py', 'z3_solver.egg-info']\n return []\n shutil.copytree(os.path.join(SRC_DIR_REPO, 'src'), os.path.join(SRC_DIR_LOCAL, 'src'),\n ignore=ignore_python_setup_files)","def copy_tpx3_files_batch(initial_image_dir, target_dir):\n\tinitial_image_dir = initial_image_dir.rstrip('/') + '/'\n\targ_list = [\"--include=*\", \"--exclude=*\", initial_image_dir, target_dir]\n\trun_rsync(arg_list)","def copy_assets(test_files):\n for path in test_files:\n shutil.copy(path, HOST_ASSETS_PATH)","def SshCopyFiles(srcs, host, dst):\n command = ['scp', srcs, host + ':' + dst]\n result = RunCommand(command)\n if result:\n raise ExternalError('Failed to scp \"%s\" to \"%s\" (%s)' %\n (srcs, host + ':' + dst, result))","def copy_directory(source, dest):\n for path, dirs, files in walk(source):\n relative_src_path = path.replace(source, \"\").lstrip(\"/\")\n abs_dest_path = join(dest, relative_src_path)\n if not exists(abs_dest_path):\n makedirs(abs_dest_path)\n for tdir in dirs:\n dest_dir = join(abs_dest_path, tdir)\n if not exists(dest_dir):\n makedirs(dest_dir)\n for tfile in files:\n src_file = join(path, tfile)\n dest_file = join(abs_dest_path, tfile)\n if islink(src_file):\n linkto = readlink(src_file)\n symlink(linkto, dest_file)\n continue\n else:\n process_file(src_file, dest_file)","def copy_dir(src=\"\", dst=\"\", header=\"\", footer=\"\", clip=0, ext=\"\", test=False):\n failed = []\n nfiles = 0\n if not os.path.exists(dst):\n os.makedirs(dst)\n if not os.path.exists(src):\n raise argparse.ArgumentError(\"source does not exist! It must be a directory.\")\n else:\n for root, dirs, files in os.walk(src, topdown=False):\n for name in files:\n name_wo_ext, file_ext = os.path.splitext(name)\n\n src_path = os.path.join(root, name)\n dstfilename = header + os.path.join(root[len(src)+1:], name_wo_ext[clip:]) + footer + file_ext\n dst_path = os.path.join(dst, dstfilename)\n\n dst_pdir = os.path.dirname(dst_path)\n if not os.path.exists(dst_pdir):\n os.makedirs(dst_pdir)\n\n if not os.path.exists(dst_path):\n if ext == \"\" or ext == file_ext[1:]:\n try:\n shutil.copy(src_path, dst_path)\n except:\n failed.append(src_path)\n print(f\"... {src_path} failed\")\n else:\n print(f\"... {dst_path} already exists'. Skipping\")\n nfiles += 1\n\n if test:\n break\n if test:\n break\n print(f\"{nfiles - len(failed)} / {nfiles} files were copied.\")\n return failed","def merge_folders():\r\n from shutil import copyfile\r\n # Merge all folders into main folder\r\n grp_img_dir = os.listdir('Group_Test_Images')\r\n \r\n for grp_img_folder in grp_img_dir:\r\n image_folders = os.listdir('Group_Test_Images'+'/'+grp_img_folder)\r\n \r\n for img_label in image_folders:\r\n new_directory = 'Group_Test_Images'+'/'+img_label\r\n \r\n try:\r\n os.makedirs(new_directory)\r\n except FileExistsError:\r\n # directory already exists\r\n pass\r\n \r\n file_names = os.listdir('Group_Test_Images'+'/'+grp_img_folder+'/'+img_label)\r\n \r\n for file in file_names:\r\n copyfile('Group_Test_Images'+'/'+grp_img_folder+'/'+img_label+'/'+file, new_directory+'/'+file)","def copy_images_to_new(lab_img, from_dir, to_dir):\n \n for img in lab_img:\n if not os.path.exists(join(todir, img)):\n shutil.copyfile(join(fromdir, img), join(todir, img)) \n print(\"Done\")","def prepare_output_dir(out_dir, test_dir):\r\n\r\n if not out_dir.exists():\r\n out_dir.mkdir()\r\n\r\n # get the necessary file names\r\n file_names = get_file_names(test_dir, args.distance, print_file_names=False)\r\n\r\n # copy the images in the firstIms into the output folder\r\n for name in file_names[1][0]:\r\n file_path = Path(test_dir / name)\r\n copy_to = Path(out_dir / name)\r\n shutil.copy(file_path, copy_to)\r\n\r\n # the firstIms list does not contain the last image,\r\n # so we need to also copy the last image of the secIms into the output folder\r\n last_im = file_names[1][1][-1]\r\n shutil.copy(Path(test_dir/last_im), Path(out_dir/last_im))\r\n\r\n return file_names","def copyUFOs(srcDirPath, dstDirPath):\n if not srcDirPath.endswith('/'):\n srcDirPath += '/'\n if not dstDirPath.endswith('/'):\n dstDirPath += '/'\n assert os.path.isdir(srcDirPath)\n copiedFiles = []\n for fileName in os.listdir(srcDirPath):\n if fileName.endswith('.ufo'):\n shutil.copytree(srcDirPath + fileName, dstDirPath + fileName)\n copiedFiles.append(fileName)\n return copiedFiles","def move_files(src_dir, dst_dir):\n for f in os.listdir(src_dir):\n try:\n name, season, episode = FILENAME_PATTERN.search(f).groups()\n except AttributeError:\n try:\n name, season, episode = FILENAME_PATTERN2.search(f).groups()\n except AttributeError:\n print \"Cannot parse\", f\n pass\n\n name = name.replace('.', ' ').replace('_', ' ').strip().title()\n\n dir_path = os.path.join(dst_dir, name, 'Season %02d' % int(season))\n full_path = os.path.join(dir_path, f)\n source_path = os.path.join(src_dir, f)\n\n if not os.path.exists(dir_path):\n os.makedirs(dir_path, 0777)\n\n if not os.path.exists(full_path):\n shutil.move(source_path, full_path)\n os.symlink(full_path, source_path)","def copydir(source, dest):\n dest_par = os.path.dirname(dest)\n for root, dirs, files in os.walk(source):\n if not os.path.isdir(root):\n os.makedirs(root)\n\n for mdir in dirs:\n try:\n dest_path = os.path.join(dest_par, root, mdir)\n if not os.path.isdir(dest_path):\n os.makedirs(dest_path)\n except:\n pass\n for file in files:\n rel_path = root.replace(source, '').lstrip(os.sep)\n dest_path = os.path.join(dest, rel_path)\n if not os.path.isdir(dest_path):\n os.makedirs(dest_path)\n cpy_src = os.path.join(root, file)\n cpy_dest = os.path.join(dest_path, file)\n shutil.copyfile(cpy_src, cpy_dest)\n shutil.copymode(cpy_src, cpy_dest)","def copytree2(src, dst, symlinks=False, ignore=None):\n\tnames = os.listdir(src)\n\tcpy_err={'flag':True, 'error':None}\n\tif ignore is not None:\n\t\tignored_names = ignore(src, names)\n\telse:\n\t\tignored_names = set()\n\n\tif not os.path.exists(dst):\n\t\ttry:\n\t\t\tos.makedirs(dst)\n\t\texcept:\n\t\t\tcpy_err= {'flag':False, 'error':\"Impossible to create a directory, PLATO is accountered a problem, contact the administrator\"}\n\t\t\treturn cpy_err\n\t\n\tfor name in names:\n\t\tif name in ignored_names:\n\t\t\tcontinue\n\t\tsrcname = os.path.join(src, name)\n\t\tdstname = os.path.join(dst, name)\n\t\ttry:\n\t\t\tif symlinks and os.path.islink(srcname):\n\t\t\t\tlinkto = os.readlink(srcname)\n\t\t\t\tos.symlink(linkto, dstname)\n\t\t\telif os.path.isdir(srcname):\n\t\t\t\tcopytree(srcname, dstname, symlinks, ignore)\n\t\t\telse:\n\t\t\t\tcopy2(srcname, dstname)\n\t\t\t# XXX What about devices, sockets etc.?\n\t\texcept (IOError, os.error) as why:\n\t # errors.append((srcname, dstname, str(why)))\n\t\t\tcpy_err={'flag':False,'error':str(why)}\n\t\t\treturn cpy_err\n\t\t# catch the Error from the recursive copytree so that we can\n\t\t# continue with other files\n\t\texcept Error as err:\n\t\t\tcpy_err={'flag':False,'error':str(err.args[0])}\n\t\t\treturn cpy_err\n\t\t # errors.extend(err.args[0])\n\ttry:\n\t\tcopystat(src, dst)\n\texcept OSError as why:\n\t\t# errors.extend((src, dst, str(why)))\n\t\tcpy_err={'flag':False,'error':str(why)}\n\t\treturn cpy_err\n\t# if errors:\n\t# raise Error(errors)\n\t\n\treturn cpy_err","async def _copy_folder_files(self, src_dir, dest_dir):\n for dir_item in os.listdir(src_dir):\n src_path = os.path.join(src_dir, dir_item)\n if os.path.isfile(src_path):\n await self._copy_file_with_hook(dir_item, src_path, os.path.join(dest_dir, dir_item))","def _copy_all(entry_list: List[Path], target_dir: Path, on_duplicate: str):\n for entry in entry_list:\n target_entry = target_dir / entry.name\n if not target_entry.exists() or on_duplicate == 'overwrite':\n if entry.is_file():\n shutil.copy(entry, target_entry, follow_symlinks=False)\n else:\n shutil.copytree(entry, target_entry, symlinks=True)\n elif on_duplicate == 'exception':\n raise ValueError(f\"'{target_entry}' already exists (src {entry})\")\n else: # ignore\n continue","def copyFiles(fileNames, sourceDir, targetDir, silent=True):\n\tfileNames = listR.toList(fileNames)\n\tfor filename in fileNames:\n\t\tcopyFile(filename, sourceDir, targetDir, None, silent)","def copydir(\n source: str,\n destination: str,\n includes: List[str],\n excludes: Optional[List[str]] = None,\n follow_symlinks: bool = False,\n) -> None:\n files = _find_files(source, includes, excludes, follow_symlinks)\n\n def _mkdir(dir_name: str) -> None:\n \"\"\"Recursively create directories.\"\"\"\n parent = os.path.dirname(dir_name)\n if not os.path.isdir(parent):\n _mkdir(parent)\n LOGGER.debug(\"creating directory: %s\", dir_name)\n os.mkdir(dir_name)\n\n for file_name in files:\n src = os.path.join(source, file_name)\n dest = os.path.join(destination, file_name)\n try:\n LOGGER.debug('copying file \"%s\" to \"%s\"', src, dest)\n copyfile(src, dest)\n except OSError:\n _mkdir(os.path.dirname(dest))\n copyfile(src, dest)","def __copyFiles(self):\n if os.path.isdir(self.__sourcePath):\n shutil.copytree(self.__sourcePath, self.__targetPath)\n else:\n shutil.copy2(self.__sourcePath, self.__targetPath)","def task():\n if os.path.isdir(orig):\n for fP in [ fP for fP in glob.glob(os.path.join(orig, '*-*/*')) if \\\n os.path.isdir(fP) ]:\n if not os.path.exists(dest + fP[len(orig):]):\n os.makedirs(dest + fP[len(orig):])\n for fP in [ fP for fP in glob.glob(os.path.join(orig, '*-*/*/%s.log' %fmt.get_date())) if \\\n os.path.isfile(fP) ]:\n convert(fP, dest + fP[len(orig):])","def CopyFolder(src, dst, overwrite=True):\n\n if not src.endswith((\"/\", \"\\\\\")):\n src = src + \"\\\\\"\n if not dst.endswith((\"/\", \"\\\\\")):\n dst = dst + \"\\\\\"\n os.makedirs(dst, exist_ok=True)\n\n for file in os.listdir(src):\n if not overwrite and os.path.isfile(dst + file):\n continue\n if os.path.isfile(src + file):\n shutil.copy(src + file, dst + file)\n elif os.path.isdir(src + file):\n CopyFolder(src + file, dst + file, overwrite)\n return 0","def copytree(src, dst, overwrite=False, changed_only=True):\n assert os.path.isdir(src), \\\n (\"Source path `%s` does not name an existing directory\" % src)\n errors = []\n if not os.path.exists(dst):\n os.makedirs(dst)\n for name in os.listdir(src):\n srcname = os.path.join(src, name)\n dstname = os.path.join(dst, name)\n try:\n if os.path.isdir(srcname):\n errors.extend(\n copytree(srcname, dstname, overwrite, changed_only))\n else:\n copyfile(srcname, dstname)\n except (IOError, os.error) as why:\n errors.append((srcname, dstname, why))\n return errors","def process_file_list(input_root, output_root, file_list):\n for backup_id, backup_file in file_list.items():\n # logging.debug(f\"{backup_id}: {backup_file.relative_path}\")\n if backup_file.is_dir:\n create_directory(backup_file, output_root)\n else:\n create_file(backup_file, input_root, output_root)","def transfer_files_from_dir(\n source_filepath_list: Iterable[str],\n destination_filepath_list: Iterable[str],\n copy_strategy: TransferAction = TransferAction.copy,\n force_overwrite: bool = False\n) -> None:\n if TransferAction.skip == copy_strategy:\n return\n\n elif TransferAction.copy == copy_strategy or TransferAction.move == copy_strategy:\n transfer_files_from_dir_copy(\n source_filepath_list=source_filepath_list,\n destination_filepath_list=destination_filepath_list,\n force_overwrite=force_overwrite,\n delete_source=(TransferAction.move == copy_strategy)\n )\n\n elif TransferAction.link_absolute == copy_strategy or TransferAction.link_relative == copy_strategy:\n transfer_files_from_dir_link(\n source_filepath_list=source_filepath_list,\n destination_filepath_list=destination_filepath_list,\n force_overwrite=force_overwrite,\n do_relative_link=(TransferAction.link_relative == copy_strategy)\n )\n\n else:\n raise ValueError(f'Unsupported copy action {copy_strategy}')","def copy_files(self, source, target):\n\n if source == target and is_local(self.borrowed_ctx.host):\n logger.warning(\"IGNORE self-node: {}\".format(self.borrowed_ctx.host))\n return\n\n try:\n for item in os.listdir(source):\n if os.path.isfile(os.path.join(source, item)):\n logger.debug(\n \"processing {} --> {}\".format(\n os.path.join(source, item), self.borrowed_ctx.host\n )\n )\n self._sftp_channel.put(\n os.path.join(source, item), \"%s/%s\" % (target, item)\n )\n else:\n self.mkdir(\"%s/%s\" % (target, item), ignore_existing=True)\n self.copy_files(\n os.path.join(source, item), \"%s/%s\" % (target, item)\n )\n except Exception as e:\n logger.warning(\n \"Error of processing target = ({}:{}), for reason: {}\".format(\n self.borrowed_ctx.host, self.borrowed_ctx.port, e,\n )\n )\n exit(0)","def copy_files(self,train_dir,test_dir):\n\n def _copy_files(src_paths,dst_dir,class_numbers):\n\n \"\"\"\n Creo una lista di directory per ogni classe\n (knify-spoony/test/class1)\n (knify-spoony/test/class2)\n \n \n :param src_paths: \n :param dst_dir: \n :param class_numbers: \n :return: \n \"\"\"\n\n class_dirs=[os.path.join(dst_dir,class_name+\"/\")for class_name in self.class_names]\n\n for dir in class_dirs:\n if not os.path.exists(dir):\n os.makedirs(dir)\n\n for src,cls in zip(src_paths,class_numbers):\n shutil.copy(src=src,dst=class_dirs[cls])\n\n # Copy the files for the training-set.\n #self.get_paths ritorna i path delle immagini da copiare nel training set test=False\n _copy_files(src_paths=self.get_paths(test=False),\n dst_dir=train_dir,\n class_numbers=self.class_numbers)\n\n print(\"- Copied training-set to:\", train_dir)\n\n # Copy the files for the test-set.\n #test=True ritorna tutte le path del test\n _copy_files(src_paths=self.get_paths(test=True),\n dst_dir=test_dir,\n class_numbers=self.class_numbers_test)\n\n print(\"- Copied test-set to:\", test_dir)","def copy_files(step: BuildStep,\n patterns: List[str],\n target: str = None) -> None:\n log = step.get_logger()\n if target:\n log(f\"Copying files into build folder under {target}\")\n else:\n log(\"Copying files into build folder\")\n log(f\"Copy src={step.source_path} to build={step.build_path} target={target}\", VERBOSE * 2)\n dirs = files = 0\n for pattern in patterns:\n if pattern.endswith(\"/\"):\n log(f\"Looking for all files under '{pattern}'\", VERBOSE * 3)\n pattern += \"**/*\"\n elif \"*\" in pattern:\n log(f\"Looking for all files matching '{pattern}'\", VERBOSE * 3)\n else:\n log(f\"Looking for files named '{pattern}'\", VERBOSE * 3)\n file_per_pattern = 0\n for f in step.source_path.glob(pattern):\n relative = f.relative_to(step.source_path)\n if target:\n dest = step.build_path / target / relative\n else:\n dest = step.build_path / relative\n\n dest_parent = dest.parent\n if not dest_parent.is_dir():\n log(f\"Mkdir {dest_parent}\", VERBOSE)\n dest_parent.mkdir(parents=True)\n dirs += 1\n if f.is_file():\n log(f\"Copy {f}\", VERBOSE)\n copy2(f, dest)\n files += 1\n file_per_pattern += 1\n log(f\"Copied {file_per_pattern} files matching '{pattern}'\", VERBOSE * 2)\n # TODO: Expand capabilities to capture files/dirs per pattern, helpful to get lookup counts\n log(f\"Completed copying {len(patterns)} patterns. Created {files} files in {dirs} directories\")","def copy_images(gallery_path, image_list):\n for image_link in image_list:","def copy(src, dst):\n os.makedirs(os.path.dirname(dst), exist_ok=True)\n shutil.copy2(src, dst)","def move_files(src, dst, filenames):\n for filename in filenames:\n os.rename(os.path.join(src, filename), os.path.join(dst, filename))","def transfer_files_from_dir_link(\n source_filepath_list: Iterable[str],\n destination_filepath_list: Iterable[str],\n force_overwrite: bool = False,\n do_relative_link: bool = False\n) -> None:\n hide_progress_bar = logger.getEffectiveLevel() > logging.INFO\n for src, dst in tqdm(zip(source_filepath_list, destination_filepath_list), disable=hide_progress_bar):\n os.makedirs(path.dirname(dst), exist_ok=True)\n if force_overwrite and path.lexists(dst):\n os.remove(dst)\n try: # on windows, symlink requires some privileges, and may crash if not\n if do_relative_link:\n src = path.relpath(src, path.dirname(dst))\n os.symlink(src, dst)\n except OSError as e:\n logger.critical('unable to create symlink on image directory, due to privilege restrictions.')\n raise e","def create_files(paths, chroot):\n dirs, files = set(), set()\n for path in paths:\n path = osp.join(chroot, path)\n filename = osp.basename(path)\n # path is a directory path\n if filename == '':\n dirs.add(path)\n # path is a filename path\n else:\n dirs.add(osp.dirname(path))\n files.add(path)\n for dirpath in dirs:\n if not osp.isdir(dirpath):\n os.makedirs(dirpath)\n for filepath in files:\n open(filepath, 'w').close()","def fresh_copy_dir(source_path, target_path):\n os.mkdir(target_path)\n for item in os.listdir(source_path):\n s = os.path.join(source_path, item)\n t = os.path.join(target_path, item)\n if os.path.isdir(s):\n fresh_copy_dir(s, t)\n else:\n shutil.copyfile(s, t)","def move_files(sim_dir, dest_dir, file_patterns):\n for f in file_patterns:\n for p in glob.glob1(sim_dir, f):\n try:\n shutil.move(os.path.join(sim_dir, p), os.path.join(dest_dir, p))\n except Exception as e:\n print(\n \"error while copy ing file from {} to {}\\n{}\".format(\n sim_dir, dest_dir, e\n )\n )","def copy_files(dic_param,src_dir,dest_dir):\n \n ### Make subdirectory\n if os.path.exists(dest_dir):\n answer=input('%s exists, want to remove what is inside? (y):\\n'%(dest_dir))\n if answer=='y':\n os.system('rm -rf %s/*pdf'%dest_dir)\n os.system('rm -rf %s/*log'%dest_dir)\n else:\n return\n else:\n os.mkdir(dest_dir)\n \n for id_key in dic_param['MODEL_KEY']:\n list_file=glob.glob(src_dir+'/*%s*'%id_key)\n for file in list_file:\n copy2(file,dest_dir)","def command_copytree(args):\n for srcdir in args.srcdirs:\n basename = os.path.basename(srcdir)\n destdir2 = os.path.normpath(os.path.join(args.destdir, basename))\n if os.path.exists(destdir2):\n shutil.rmtree(destdir2)\n sys.stdout.write(\"copytree: %s => %s\\n\" % (srcdir, destdir2))\n shutil.copytree(srcdir, destdir2)\n return 0","def copy_files():\n os.makedirs('build/usr/lib/python3/dist-packages', exist_ok=True)\n os.makedirs('build/usr/share/doc', exist_ok=True)\n\n shutil.copytree('applications', 'build/usr/share/applications')\n shutil.copytree('doc', 'build/usr/share/doc/qastetray')\n shutil.copytree('icons', 'build/usr/share/icons')\n shutil.copytree('locale', 'build/usr/share/locale')\n shutil.copytree('qastetray',\n 'build/usr/lib/python3/dist-packages/qastetray')","def handle_multiple_destinations(self):\n\n # Create the to-directory if it does not exist\n for destination in config.dest:\n if not path.exists(destination.dest):\n makedirs(destination.dest)\n\n # Clone the modules and copy the right directories\n for module in config.modules:\n Logger.assemble_module(module)\n\n directory = path.join(TEMP_DIR, module.name)\n remove_dir(directory)\n clone(module, directory)\n self.commit_hashes[module.name] = self.get_commit_hash(directory)\n\n for destination in config.dest:\n to_directory = path.join(destination.dest, module.name)\n remove_dir(to_directory)\n shutil.move(\n path.join(TEMP_DIR, module.name, destination.src), to_directory\n )","def _create_paths(paths):\n for path in paths:\n _mkdir_if_not_exist(path)","def copy_source_files(self):\n\n LOGGER.info(f'start copying source files')\n count = 0\n for sfp in tqdm(sorted(self.source_fps), disable=self.disable_tqdm):\n try:\n meta = extract_law_meta(sfp)\n nodes = parse_xml_fp(sfp)\n tfp = self.stot(sfp)\n tfp.parent.mkdir(parents=True, exist_ok=True)\n save_law_tree(meta['LawTitle'], nodes, tfp)\n except Exception as e:\n LOGGER.error(f'failed to copy {sfp}: {e}')\n continue\n self.target_fps.add(tfp)\n LOGGER.debug(f'copied {sfp} to {tfp}')\n count += 1\n LOGGER.info(f'copied total {count} source files, now total {len(self.target_fps)} target files exist')","def main(source_dir, dest_dir):\n\n paths = []\n for root, _, files in os.walk(source_dir):\n paths.extend([os.path.join(root, f) for f in files])\n\n def copy(source_path, skip_existing=True):\n \"\"\"Copies a file from source_path to source_path with\n source_dir replaced by dest_dir.\n\n Arguments:\n source_path(str): Path to a file to be copied.\n skip_existing(bool): True to skip copying files\n when the destination file already exists.\n \"\"\"\n\n dest_path = source_path.replace(source_dir.strip('/'), dest_dir.strip('/'))\n\n # Skip if dest file already exists\n if skip_existing and os.path.exists(dest_path):\n return\n\n # Create directory if necessary\n os.makedirs(os.path.dirname(dest_path), exist_ok=True)\n\n copyfile(source_path, dest_path)\n\n p_umap(copy, paths)","def copytree(src, dst, symlinks=False, ignore=None):\r\n names = os.listdir(src)\r\n if ignore is not None:\r\n ignored_names = ignore(src, names)\r\n else:\r\n ignored_names = set()\r\n\r\n if dst == '.':\r\n if os.listdir(dst):\r\n raise shutil.Error('Current directory is not empty')\r\n else:\r\n os.makedirs(dst)\r\n\r\n errors = []\r\n for name in names:\r\n if name in ignored_names:\r\n continue\r\n srcname = os.path.join(src, name)\r\n dstname = os.path.join(dst, name)\r\n try:\r\n if symlinks and os.path.islink(srcname):\r\n linkto = os.readlink(srcname)\r\n os.symlink(linkto, dstname)\r\n elif os.path.isdir(srcname):\r\n copytree(srcname, dstname, symlinks, ignore)\r\n else:\r\n shutil.copy2(srcname, dstname)\r\n except (IOError, os.error), why:\r\n errors.append((srcname, dstname, str(why)))\r\n # catch the Error from the recursive copytree so that we can\r\n # continue with other files\r\n except shutil.Error, err:\r\n errors.extend(err.args[0])\r\n if errors:\r\n raise shutil.Error(errors)","def copy_media(items, dest):\n for file in items:\n filename = os.path.basename(file)\n copyfile(file, dest + '\\\\' + filename)","def copy_tree_to_path(src_dir, dest_dir):\n names = os.listdir(src_dir)\n\n for name in names:\n srcname = os.path.join(src_dir, name)\n destname = os.path.join(dest_dir, name)\n\n if os.path.isdir(srcname):\n shutil.copytree(srcname, destname)\n else:\n shutil.copy(srcname, destname)","def transfer_files(src: str, dst: str, move_src_data: bool = False):\n if move_src_data:\n logger.info('Move {0} to {1}'.format(src, dst))\n shutil.move(src, dst)\n else:\n logger.info('Copy {0} to {1}'.format(src, dst))\n copy_tree(src, dst)","def copy_deep(src: str, dst: str, create_dst_dir: bool = False) -> None:\n system_is_darwin = platform.system().lower() == \"darwin\"\n if create_dst_dir:\n mkdir_p(os.path.dirname(dst))\n src_is_link = os.path.islink(src)\n dst_exists = os.path.lexists(dst)\n if os.path.isdir(src) and not src_is_link:\n logging.debug(\"Copying directory {} to {}\".format(src, dst))\n mkdir_p(dst)\n for name in os.listdir(src):\n copy_deep(os.path.join(src, name), os.path.join(dst, name))\n elif src_is_link:\n if dst_exists:\n return\n target = os.readlink(src)\n logging.debug(\"Creating symlink {} -> {}\".format(dst, target))\n os.symlink(target, dst)\n else:\n if dst_exists:\n if not system_is_darwin:\n return\n # Only overwrite the file if the source is newer than the destination.\n if os.path.getmtime(src) <= os.path.getmtime(dst):\n return\n logging.debug(\"Copying file {} to {}\".format(src, dst))\n # Preserve the file attributes.\n shutil.copy2(src, dst)","def copy_files_jd(self, file_list, variable_lst, revision=\"Auto\"):\n # | - copy_files_jd\n path = self.var_lst_to_path(variable_lst)\n path += \"_\" + str(self.job_revision_number(variable_lst))\n\n for file in file_list:\n shutil.copyfile(self.root_dir + \"/\" + file, path + \"/\" + file)\n # __|","def copy_photos(source='.', dest='.'):\n if isdir(source): # if this is a directory,\n for child in listdir(source):\n # compose full path to child\n child = join(source, child)\n if isdir(child):\n copy_photos(child, dest)\n else:\n if guess_type(child)[0] is not None \\\n and (guess_type(child)[0].split('/')[0] == 'image'):\n new = rename(child)\n create_tree(new, dest)","def make_image_dir(to_path, filenames):\n image_dir = os.path.join(to_path, \"image_2\")\n os.makedirs(image_dir)\n for f in filenames:\n image_file = os.path.join(image_dir, f + \".png\")\n os.system(\"cp sample.png {}\".format(image_file))","def get_data_files(source_dest_pairs):\n data_files = []\n for src_dir, dest_dir in source_dest_pairs:\n for src_root, _, files in os.walk(src_dir):\n dest_root = src_root.replace(src_dir, dest_dir, 1)\n dir_files = []\n for file_ in files:\n dir_files.append(os.path.join(src_root, file_))\n data_files.append((dest_root, dir_files))\n return data_files","def CopyFiles(self):\n self._adb.RunShellCommand('rm -r %s; mkdir %s' %\n (ValgrindTool.VG_DIR, ValgrindTool.VG_DIR))\n self._adb.RunShellCommand('rm -r %s; mkdir %s' %\n (ValgrindTool.VGLOGS_DIR,\n ValgrindTool.VGLOGS_DIR))\n files = self.GetFilesForTool()\n for f in files:\n self._adb.PushIfNeeded(os.path.join(DIR_SOURCE_ROOT, f),\n os.path.join(ValgrindTool.VG_DIR,\n os.path.basename(f)))","def copy_resources(src_container, src_resources, storage_dir, dst_directories=None, apply_chown=None, apply_chmod=None):\n def _copy_resource(resource):\n default_dest_path = generic_path if generic_path is not None else resource\n dest_path = directories.get(resource, default_dest_path).strip(posixpath.sep)\n head, tail = posixpath.split(dest_path)\n rel_path = posixpath.join(storage_dir, head)\n run(mkdir(rel_path, check_if_exists=True))\n run('docker cp {0}:{1} {2}'.format(src_container, resource, rel_path), shell=False)\n\n directories = dst_directories or {}\n generic_path = directories.get('*')\n for res in src_resources:\n _copy_resource(res)\n if apply_chmod:\n run(chmod(apply_chmod, storage_dir))\n if apply_chown:\n sudo(chown(apply_chown, storage_dir))","def mergecopytree(src, dst, symlinks=False, ignore=None):\n if not os.path.exists(dst):\n os.makedirs(dst)\n shutil.copystat(src, dst)\n lst = os.listdir(src)\n if ignore:\n excl = ignore(src, lst)\n lst = [x for x in lst if x not in excl]\n for item in lst:\n src_item = os.path.join(src, item)\n dst_item = os.path.join(dst, item)\n if symlinks and os.path.islink(src_item):\n if os.path.lexists(dst_item):\n os.remove(dst_item)\n os.symlink(os.readlink(src_item), dst_item)\n elif os.path.isdir(src_item):\n mergecopytree(src_item, dst_item, symlinks, ignore)\n else:\n shutil.copy2(src_item, dst_item)","def copy_dir(src, dst):\n try:\n debug.log(\"copy dir from \"+ src, \"to \"+ dst)\n shutil.copytree(src, dst)\n except Exception as e:\n debug.log(\"Error: happened while copying!\\n%s\\n\"%e)","def copy_files_subfolders(from_dataset, merged_folder, original_name=''):\n for child in from_dataset.iterdir():\n if child.is_dir(): # go one layer deeper\n for elem in child.iterdir():\n try:\n new_dir = merged_folder / child.name\n new_dir.mkdir(parents=True, exist_ok=True)\n shutil.copy(str(elem), merged_folder / child.name / elem.name)\n except shutil.SameFileError:\n print('Child {} / {} already exists'.format(child.name, elem.name))\n pass\n elif 'template' in child.name: # no need for additional naming of the template files\n try:\n shutil.copy(str(child), merged_folder / child.name)\n except shutil.SameFileError:\n print('Child {} already exists'.format(child))\n pass\n else:\n # print(child.name)\n try:\n shutil.copy(str(child), merged_folder / (original_name + '_' + child.name))\n except shutil.SameFileError:\n print('Child {} already exists'.format(child))\n pass","def copy_web_resources(output_dir):\n mypath = os.path.dirname(os.path.realpath(__file__))\n web_path = os.path.join(mypath, 'web')\n\n for (dirpath, dirnames, filenames) in os.walk(web_path):\n relpath = os.path.relpath(dirpath, web_path)\n tgtpath = os.path.join(output_dir, relpath)\n if not os.path.exists(tgtpath):\n os.makedirs(tgtpath)\n\n for f in [os.path.join(dirpath, filename) for filename in filenames]:\n shutil.copy(f, tgtpath)","def copy_tree(src, dst, preserve_mode=1, preserve_times=1,\n preserve_symlinks=0, update=0, verbose=1, dry_run=0):\n preserve_symlinks = 1\n import os\n from distutils.dir_util import mkpath\n from distutils import log\n\n from distutils.file_util import copy_file\n\n if not dry_run and not os.path.isdir(src):\n raise DistutilsFileError(\n \"cannot copy tree '%s': not a directory\" % src)\n try:\n names = os.listdir(src)\n except OSError as e:\n if dry_run:\n names = []\n else:\n raise DistutilsFileError(\n \"error listing files in '%s': %s\" % (src, e.strerror))\n\n if not dry_run:\n mkpath(dst, verbose=verbose)\n\n outputs = []\n\n for n in names:\n src_name = os.path.join(src, n)\n dst_name = os.path.join(dst, n)\n\n if n.startswith('.nfs'):\n # skip NFS rename files\n continue\n\n if preserve_symlinks and os.path.islink(src_name):\n link_dest = os.readlink(src_name)\n if verbose >= 1:\n log.info(\"linking %s -> %s\", dst_name, link_dest)\n if not dry_run:\n if os.path.exists(dst_name):\n os.unlink(dst_name)\n os.symlink(link_dest, dst_name)\n outputs.append(dst_name)\n\n elif os.path.isdir(src_name):\n outputs.extend(\n copy_tree(src_name, dst_name, preserve_mode,\n preserve_times, preserve_symlinks, update,\n verbose=verbose, dry_run=dry_run))\n else:\n copy_file(src_name, dst_name, preserve_mode,\n preserve_times, update, verbose=verbose,\n dry_run=dry_run)\n outputs.append(dst_name)\n\n return outputs","def cpsym(src,dest):\n \n src = os.path.normpath(src)\n dest = os.path.normpath(dest)\n \n if not os.path.exists(src):\n return\n \n for dirpath,dirnames,filenames in os.walk(src):\n rel_dirpath = os.path.relpath(dirpath,src)\n dest_dirpath = os.path.join(dest,rel_dirpath)\n mkdir(dest_dirpath,isfull=True)\n \n for filename in filenames:\n src_filename = os.path.join(dirpath,filename)\n rel_filename = os.path.relpath(src_filename,src)\n \n dest_filename = os.path.join(dest,rel_filename)\n try:\n os.symlink(src_filename,dest_filename)\n except OSError:\n pass"],"string":"[\n \"def _copy_files(src_paths,dst_dir,class_numbers):\\n\\n class_dirs=[os.path.join(dst_dir,class_name+\\\"/\\\")for class_name in self.class_names]\\n\\n for dir in class_dirs:\\n if not os.path.exists(dir):\\n os.makedirs(dir)\\n\\n for src,cls in zip(src_paths,class_numbers):\\n shutil.copy(src=src,dst=class_dirs[cls])\",\n \"def _CopyFiles(file_pairs):\\n\\n for pair in file_pairs:\\n target_dir = os.path.dirname(pair.target)\\n if not os.path.isdir(target_dir):\\n os.makedirs(target_dir)\\n copyfile(pair.generated, pair.target)\",\n \"def CopyFiles(src_list, dst, src_base='',\\n preserve=False, recursive=False,\\n ignore_existence_check=False):\\n if not src_list:\\n return\\n\\n def _MakeDirs(dir):\\n if not os.path.exists(dir):\\n os.makedirs(dir)\\n\\n def _CopyDir(src_dir, dst_dir):\\n _MakeDirs(dst_dir)\\n\\n for filename in os.listdir(src_dir):\\n src = os.path.join(src_dir, filename)\\n _Copy(src, dst_dir)\\n\\n def _Copy(src, dst):\\n if os.path.isdir(dst):\\n dst = os.path.join(dst, os.path.basename(src))\\n\\n if os.path.isdir(src):\\n _CopyDir(src, dst)\\n else:\\n shutil.copy(src, dst)\\n\\n # Copy update time and permission\\n if preserve:\\n shutil.copystat(src, dst)\\n # Changes the file writable so we can overwrite it later.\\n os.chmod(dst, os.stat(dst).st_mode | stat.S_IWRITE)\\n\\n def _ErrorCheck(src, src_base):\\n if not os.path.exists(src):\\n if ignore_existence_check:\\n return False\\n else:\\n _ErrorExit('No such file or directory: \\\"%s\\\"' % src)\\n\\n if os.path.isdir(src) and not recursive:\\n _ErrorExit('Cannot copy a directory: \\\"%s\\\"' % src)\\n\\n if not src.startswith(src_base):\\n _ErrorExit('Source file does not start with src_base: \\\"%s\\\"' % src)\\n\\n return True\\n\\n dst = os.path.abspath(dst)\\n if src_base:\\n src_base = os.path.abspath(src_base)\\n\\n # dst may be a file instead of a directory.\\n if len(src_list) == 1 and not os.path.exists(dst) and not src_base:\\n src = os.path.abspath(src_list[0])\\n if _ErrorCheck(src, src_base):\\n _MakeDirs(os.path.dirname(dst))\\n _Copy(src, dst)\\n return\\n\\n # dst should be a directory here.\\n for src in src_list:\\n src = os.path.abspath(src)\\n\\n if src_base:\\n dst_dir = os.path.join(dst,\\n os.path.relpath(os.path.dirname(src), src_base))\\n else:\\n dst_dir = dst\\n\\n if _ErrorCheck(src, src_base):\\n _MakeDirs(dst_dir)\\n _Copy(src, dst_dir)\",\n \"def copy_files(src_path, dst_path):\\r\\n for folder in os.listdir(src_path):\\r\\n for file in os.listdir(os.path.join(src_path, folder)):\\r\\n source = os.path.join(os.path.join(src_path, folder), file)\\r\\n dest = os.path.join(dst_path, file)\\r\\n shutil.copy(source, dest)\",\n \"def copy_files(source,destination):\\r\\n filelist = os.listdir(source)\\r\\n \\r\\n if not os.path.exists(destination):\\r\\n os.mkdir(destination)\\r\\n \\r\\n for filename in filelist:\\r\\n source_file = os.path.join(source,filename)\\r\\n shutil.copy(source_file,destination)\",\n \"def copy_files(files, path):\\n safe_mkdir(path)\\n for file in files:\\n copy2(file, path)\",\n \"def copy_file_to_multiple_subfolders(src, dst, *args, **kwargs):\\n print '\\\\nSource: {}\\\\nDestinations parent folder: {}'.format(src, dst)\\n filename = os.path.basename(src)\\n for folder in (d for d in os.listdir(dst) if os.path.isdir(d)):\\n print '\\\\nCopying {} to {}...'.format(filename, folder)\\n try:\\n shutil.copy(src, os.path.abspath(dst) + '\\\\\\\\' + folder)\\n except Exception as e:\\n print e\",\n \"def m_cp(*args):\\n if (not args or len(args) < 2) :\\n print(\\\"parameter is invalid\\\")\\n return\\n\\n src = args[0:len(args)-1]\\n dst = args[len(args)-1]\\n\\n if not os.path.exists(dst) :\\n os.mkdir(dst)\\n\\n for s in src:\\n print(\\\"cp %s ==> %s\\\" % (s, dst))\\n cpfile(s, dst)\",\n \"def copy_separate_files(source,dest1,dest2):\\r\\n filelist = os.listdir(source)\\r\\n \\r\\n if not os.path.exists(dest1):\\r\\n os.mkdir(dest1)\\r\\n \\r\\n if not os.path.exists(dest2):\\r\\n os.mkdir(dest2)\\r\\n \\r\\n for filename in filelist:\\r\\n source_file = os.path.join(source,filename)\\r\\n \\r\\n if filename[-4:] == '.png':\\r\\n shutil.copy(source_file,dest1)\\r\\n else:\\r\\n shutil.copy(source_file,dest2)\",\n \"def copy_files(source_paths, des_paths, is_debug=False):\\n for source_path, des_path in zip(source_paths, des_paths):\\n if not os.path.exists(os.path.dirname(des_path)):\\n os.makedirs(os.path.dirname(des_path))\\n shutil.copyfile(source_path, des_path)\\n if is_debug:\\n print_log(\\\"Copy file from %s to %s\\\" % (source_path, des_path))\",\n \"def _transferFiles(initialPath, destinationPath, fileList):\\n if not fileList:\\n return\\n if not os.path.exists(destinationPath):\\n os.mkdir(destinationPath)\\n for pattern in fileList:\\n if isinstance(pattern, tuple):\\n # allow renames in transit\\n fromName, destName = pattern\\n copies = [(fromName, destName)]\\n else:\\n # expand globs if they're given\\n copies = []\\n for ff in glob.glob(pattern):\\n # renaming not allowed with globs\\n copies.append((ff, ff))\\n\\n for fromName, destName in copies:\\n fromPath = os.path.join(initialPath, fromName)\\n toPath = os.path.join(destinationPath, destName)\\n runLog.extra(\\\"Copying {} to {}\\\".format(fromPath, toPath))\\n shutil.copy(fromPath, toPath)\",\n \"def _copy_paths(self, paths, source, destination, output_path,\\r\\n final_path=None):\\r\\n for path in paths:\\r\\n if final_path:\\r\\n copy(os.path.join(source, path),\\r\\n os.path.join(output_path, destination, final_path))\\r\\n else:\\r\\n copy(os.path.join(source, path),\\r\\n os.path.join(output_path, destination, path))\",\n \"def copyDir(src, dst, includes, excludes = []):\\n\\tmultiFilesReplacements([], dst, src, includes, excludes)\",\n \"def copydir(src, dst):\\n for item in os.listdir(src):\\n s, d = os.path.join(src, item), os.path.join(dst, item)\\n if os.path.isdir(s):\\n if not os.path.isdir(d):\\n os.mkdir(d)\\n copydir(s, d)\\n else:\\n shutil.copy(s, d)\",\n \"def copy_paths(src, dst, paths, *, exclude=None):\\n files = []\\n\\n for path in paths:\\n if isinstance(path, tuple):\\n files += copy_path(src, dst, path[0], path[1], exclude=exclude)\\n else:\\n files += copy_path(src, dst, path, exclude=exclude)\\n\\n return files\",\n \"def conditional_copy(copy_tuples):\\n for source_file, destination_file in copy_tuples:\\n # If the root_directory and destination file contents are the same, don't perform unnecessary file I/O\\n if not destination_file.exists() or not filecmp.cmp(source_file, destination_file, shallow=False):\\n destination_file.parent.mkdir(parents=True, exist_ok=True)\\n shutil.copyfile(source_file, destination_file)\",\n \"def copy_files(src, desc, file_list):\\r\\n src_paths = [os.path.join(src, file_name) for file_name in file_list]\\r\\n output = subprocess.run(['cp', '-a', *src_paths, desc], check=True)\\r\\n if output.returncode != 0:\\r\\n raise Exception('Failed to copy files from {} to {}'.format(src, desc))\",\n \"def MakeDestinationDirectories(self, dst_files):\\n for dst in dst_files:\\n path = os.path.dirname(dst);\\n if (len(path) > 0) and (not os.path.exists(path)):\\n self.VerboseMsg(\\\"Make Directory: \\\" + path)\\n if self.execute:\\n os.makedirs(path)\",\n \"def transfer_files_from_dir_copy(\\n source_filepath_list: Iterable[str],\\n destination_filepath_list: Iterable[str],\\n force_overwrite: bool = False,\\n delete_source: bool = False\\n) -> None:\\n hide_progress_bar = logger.getEffectiveLevel() > logging.INFO\\n for src, dst in tqdm(zip(source_filepath_list, destination_filepath_list), disable=hide_progress_bar):\\n os.makedirs(path.dirname(dst), exist_ok=True)\\n if force_overwrite and path.lexists(dst):\\n os.remove(dst)\\n if delete_source:\\n shutil.move(src, dst)\\n else:\\n shutil.copyfile(src, dst)\",\n \"def _copy_dir(src, dst):\\n if os.path.isdir(src):\\n os.makedirs(dst, exist_ok=True)\\n for item in os.listdir(src):\\n s = os.path.join(src, item)\\n d = os.path.join(dst, item)\\n\\n if os.path.isdir(s):\\n _copy_dir(s, d)\\n else:\\n shutil.copy2(s, d)\\n\\n else:\\n os.makedirs(os.path.dirname(dst), exist_ok=True)\\n _delete_file(dst)\\n shutil.copy2(src, dst)\",\n \"def copy_new_files(self, out_dir, answer_dir, filenames):\\n if not answer_dir.exists():\\n answer_dir.mkdir(parents=True)\\n for filename in filenames:\\n fromfile = out_dir / filename\\n tofile = answer_dir / filename\\n shutil.copyfile(fromfile, tofile)\",\n \"def cp(*args):\\n filenames = _glob(args)\\n nfilenames = len(filenames)\\n if nfilenames < 2:\\n print 'Need at least two arguments'\\n elif nfilenames == 2:\\n try:\\n shutil.copy(filenames[0], filenames[1])\\n except os.error, detail:\\n print \\\"%s: %s\\\" % (detail[1], filenames[1])\\n else:\\n for filename in filenames[:-1]:\\n try:\\n dest = filenames[-1] + '/' + filename\\n if not os.path.isdir(filenames[-1]):\\n print 'Last argument needs to be a directory'\\n return\\n shutil.copy(filename, dest)\\n except os.error, detail:\\n print \\\"%s: %s\\\" % (detail[1], filename)\",\n \"def copy_files(self):\\n for (source_name, target_name) in self.FILES_TO_LINK:\\n src = os.path.expanduser(source_name)\\n tgt = os.path.expanduser(target_name)\\n cmd = 'cp -rf {src} {tgt}'.format(src=src, tgt=tgt)\\n\\n print(cmd)\\n if not self.dry_run:\\n run(cmd)\",\n \"def copy_files(orig_dir, dest_dir): \\n files = [file for file in os.listdir(orig_dir)]\\n \\n for file in files:\\n origin = os.path.join(orig_dir,file)\\n destination = os.path.join(dest_dir,file)\\n shutil.copyfile(origin,destination)\",\n \"def copy_list_to_dir(file_list, subdir, dir=\\\"NewData\\\"):\\n\\n subdir = os.path.join(dir, subdir)\\n\\n if not os.path.exists(dir):\\n os.mkdir(dir)\\n\\n if not os.path.exists(subdir):\\n os.mkdir(subdir)\\n\\n for file in file_list:\\n base_name = os.path.basename(file)\\n shutil.copy(file, os.path.join(subdir, base_name))\",\n \"def copy_supported_files(self):\\n\\n try:\\n for directory in self.config.DIRS_TO_COPY:\\n shutil.copytree(self.dir_helper.publication_path + directory,\\n self.temp_dir + directory)\\n except Exception, ex:\\n print '[e] exception {}'.format(str(ex))\\n print '[i] looks like to folder existing that are scheduled for copying'\\n\\n for file_ in self.config.FILES_TO_COPY:\\n index = file_.rfind('\\\\\\\\')\\n dest_file = file_\\n if index != -1:\\n dest_file = file_[index+1:]\\n\\n try:\\n shutil.copy2(self.dir_helper.publication_path + file_,\\n self.temp_dir + dest_file)\\n except Exception, ex:\\n print '[e] exception {}'.format(str(ex))\\n print '[i] file \\\"{}\\\" was not copied'.format(self.dir_helper.publication_path + file_)\",\n \"def copyFiles(img, lbl):\\n if not os.path.exists(os.path.join(out_root_dir, \\\"images\\\")):\\n os.makedirs(os.path.join(out_root_dir, \\\"images\\\"))\\n if not os.path.exists(os.path.join(out_root_dir,\\\"labels\\\")):\\n os.makedirs(os.path.join(out_root_dir, \\\"labels\\\"))\\n # copy\\n for i,f in enumerate(img):\\n img_dstdir = os.path.join(out_root_dir, \\\"images\\\", os.path.basename(f))\\n lbl_srcdir = os.path.join(in_root_dir, \\\"labels\\\", os.path.basename(f))\\n lbl_dstdir = os.path.join(out_root_dir, \\\"labels\\\", os.path.basename(f))\\n \\n # copy images and labels\\n try:\\n # copy image\\n copyfile(f, img_dstdir)\\n except:\\n print(f\\\"Error trying copy image file {f}\\\")\\n \\n try:\\n # copy label\\n copyfile(lbl_srcdir, lbl_dstdir)\\n except:\\n print(f\\\"Error trying copy label file {lbl_srcdir}\\\")\",\n \"def copy_tree(src, dst, verbose=False):\\n\\n print('Copying {} to {}'.format(src, dst))\\n for src_dir, sub_dirs, basenames in tf.io.gfile.walk(src):\\n rel_dir = os.path.relpath(src_dir, src)\\n dst_dir = os.path.join(dst, rel_dir)\\n for sub_dir in sorted(sub_dirs):\\n path = os.path.join(dst, rel_dir, sub_dir)\\n print('Make dir {}'.format(path))\\n tf.io.gfile.makedirs(path)\\n if basenames:\\n print('Copying {} files from {} to {}'.format(\\n len(basenames), src_dir, dst_dir))\\n for basename in basenames:\\n src_path = os.path.join(src_dir, basename)\\n dst_path = os.path.join(dst_dir, basename)\\n if verbose:\\n print('Copying {} to {}'.format(src_path, dst_path))\\n tf.io.gfile.copy(src_path, dst_path)\",\n \"def copyFiles(sourceDir, destinationDir, patterns):\\n\\tfrom glob import glob\\n\\tfrom os.path import join, abspath, exists, isfile\\n\\timport shutil\\n\\tsourceDir = adaptPath(sourceDir)\\n\\tdestinationDir = adaptPath(destinationDir)\\n\\t\\n\\tif exists(abspath(sourceDir)) == False:\\n\\t\\tprint ('! \\\"%s\\\" directory not existing'%sourceDir)\\n\\tmakedir(destinationDir)\\n\\tfor pattern in patterns:\\n\\t\\tsrcPath = join(sourceDir,pattern)\\n\\t\\tfor filename in glob(srcPath):\\n\\t\\t\\tif isfile(filename):\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tshutil.copy2(filename, destinationDir)\\n\\t\\t\\t\\texcept IOError:\\n\\t\\t\\t\\t\\tprint (\\\"! Failed copy '%s' -> '%s'\\\" %(filename, destinationDir))\",\n \"def copy_to_folder(destination_folder, source_dir_list, use_prefix=True):\\n prefix_dict = {\\\"AD\\\": \\\"ad\\\", \\\"MCI\\\": \\\"mci\\\", \\\"CN\\\": \\\"cn\\\"}\\n for dir_path in source_dir_list:\\n patient_type = os.path.basename(os.path.dirname(dir_path))\\n prefix = prefix_dict[patient_type]\\n dir_basename = prefix + \\\"_\\\" + os.path.basename(dir_path)\\n target_path = os.path.join(destination_folder, dir_basename)\\n copytree(dir_path, target_path)\",\n \"def command_copy(args):\\n sources = args.sources\\n destpath = args.destpath\\n source_files = []\\n for file_ in sources:\\n if \\\"*\\\" in file_:\\n selected = glob(file_)\\n source_files.extend(selected)\\n elif os.path.isfile(file_):\\n source_files.append(file_)\\n\\n if destpath.endswith(\\\"/\\\") or os.path.isdir(destpath) or len(sources) > 1:\\n # -- DESTDIR-MODE: Last argument is a directory.\\n destdir = destpath\\n else:\\n # -- DESTFILE-MODE: Copy (and rename) one file.\\n assert len(source_files) == 1\\n destdir = os.path.dirname(destpath)\\n\\n # -- WORK-HORSE: Copy one or more files to destpath.\\n if not os.path.isdir(destdir):\\n sys.stdout.write(\\\"copy: Create dir %s\\\\n\\\" % destdir)\\n os.makedirs(destdir)\\n for source in source_files:\\n destname = os.path.join(destdir, os.path.basename(source))\\n sys.stdout.write(\\\"copy: %s => %s\\\\n\\\" % (source, destname))\\n shutil.copy(source, destname)\\n return 0\",\n \"def copy_subtree(src, dst):\\n for src_f in os.listdir(src):\\n src_path = os.path.join(src, src_f)\\n if os.path.isdir(src_path):\\n dst_path = os.path.join(dst, src_f)\\n if not os.path.exists(dst_path):\\n shutil.copytree(src_path, dst_path)\\n else:\\n ProcessJson.copy_subtree(src_path, dst_path)\\n elif os.path.isfile(src_path):\\n dst_path = os.path.join(dst, src_f)\\n if not os.path.exists(dst_path):\\n shutil.copy(src_path, dst_path)\",\n \"def copy_files(mod_name, dir_name, output_dir):\\n files = pkg_resources.resource_filename(mod_name, dir_name)\\n for filename in os.listdir(files):\\n full_filename = os.path.join(files, filename)\\n if os.path.isdir(full_filename):\\n try:\\n shutil.copytree(full_filename, os.path.join(output_dir, filename))\\n except FileExistsError:\\n continue\\n else:\\n shutil.copy(full_filename, output_dir)\",\n \"def applyDir(self,srcDir,destDir,exts): \\n for srcFile in os.listdir(srcDir):\\n srcExt = os.path.splitext(srcFile)[-1].lower()\\n srcPath = os.path.join(srcDir,srcFile)\\n destPath = os.path.join(destDir,srcFile)\\n if srcExt in exts:\\n if not os.path.exists(destDir):\\n os.makedirs(destDir)\\n shutil.copyfile(srcPath,destPath)\\n if self.progress: \\n self.cumSize += os.path.getsize(srcPath)\\n self.progress(self.cumSize,_('Copying Files...'))\\n elif os.path.isdir(srcPath):\\n self.applyDir(srcPath,destPath,exts)\",\n \"def copy_database(path_images, path_labels, path_final_images):\\n\\n try:\\n labels = sorted(os.listdir(path_labels))\\n except FileNotFoudError:\\n print(\\\"No such file or directory \\\", path_labels)\\n\\n try:\\n images = sorted(os.listdir(path_images)) #+ \\\"RetinaNet_I04590/\\\"))\\n except FileNotFoudError:\\n print(\\\"No such file or directory \\\", path_images)\\n\\n \\\"\\\"\\\"if not os.path.exists(path_final_images + \\\"I04590/\\\"):\\n os.mkdir(path_final_images + \\\"I04590/\\\")\\n\\n if not os.path.exists(path_final_images + \\\"I045135/\\\"):\\n os.mkdir(path_final_images + \\\"I045135/\\\")\\n\\n if not os.path.exists(path_final_images + \\\"I090135/\\\"):\\n os.mkdir(path_final_images + \\\"I090135/\\\")\\n\\n if not os.path.exists(path_final_images + \\\"I4590135/\\\"):\\n os.mkdir(path_final_images + \\\"I4590135/\\\")\\n\\n if not os.path.exists(path_final_images + \\\"Params/\\\"):\\n os.mkdir(path_final_images + \\\"Params/\\\")\\n\\n if not os.path.exists(path_final_images + \\\"Pauli2/\\\"):\\n os.mkdir(path_final_images + \\\"Pauli2/\\\")\\n\\n if not os.path.exists(path_final_images + \\\"Pauli3/\\\"):\\n os.mkdir(path_final_images + \\\"Pauli3/\\\")\\n\\n if not os.path.exists(path_final_images + \\\"Stokes/\\\"):\\n os.mkdir(path_final_images + \\\"Stokes/\\\")\\n\\n if not os.path.exists(path_final_images + \\\"Rachel/\\\"):\\n os.mkdir(path_final_images + \\\"Rachel/\\\")\\n\\n if not os.path.exists(path_final_images + \\\"Rachel2/\\\"):\\n os.mkdir(path_final_images + \\\"Rachel2/\\\")\\\"\\\"\\\"\\n\\n for k in range(len(images)):\\n if str(k) + \\\".xml\\\" in labels:\\n copyfile(path_images + \\\"/\\\" + images[k],\\n path_final_images + \\\"/\\\" + images[k])\\n \\\"\\\"\\\"copyfile(path_images + \\\"RetinaNet_I04590/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"I04590/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_images + \\\"RetinaNet_I045135/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"I045135/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_images + \\\"RetinaNet_I090135/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"I090135/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_images + \\\"RetinaNet_I4590135/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"I4590135/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_images + \\\"RetinaNet_Params/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"Params/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_images + \\\"RetinaNet_Pauli2/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"Pauli2/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_images + \\\"RetinaNet_Pauli3/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"Pauli3/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_images + \\\"RetinaNet_Stokes/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"Stokes/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_images + \\\"RetinaNet_Rachel/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"Rachel/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_images + \\\"RetinaNet_Rachel2/\\\" + str(k) + \\\".png\\\",\\n path_final_images + \\\"Rachel2/\\\" + str(k) + \\\".png\\\")\\n copyfile(path_labels + str(k) + \\\".xml\\\",\\n path_final_labels + str(k) + \\\".xml\\\")\\\"\\\"\\\"\\n print(k)\",\n \"def copy_dir(src: Text, dst: Text) -> None:\\n\\n if tf.io.gfile.exists(dst):\\n tf.io.gfile.rmtree(dst)\\n tf.io.gfile.makedirs(dst)\\n\\n for dir_name, sub_dirs, leaf_files in tf.io.gfile.walk(src):\\n for leaf_file in leaf_files:\\n leaf_file_path = os.path.join(dir_name, leaf_file)\\n new_file_path = os.path.join(dir_name.replace(src, dst, 1), leaf_file)\\n tf.io.gfile.copy(leaf_file_path, new_file_path)\\n\\n for sub_dir in sub_dirs:\\n tf.io.gfile.makedirs(os.path.join(dir_name.replace(src, dst, 1), sub_dir))\",\n \"def create_symlink_dir(src_dir, src_list, dst):\\n if not src_list:\\n return\\n message = \\\"creating symlink directory at {dst} with files {src_list}\\\".format(\\n dst=dst,\\n src_list=pformat(src_list))\\n logging.info(message)\\n if not os.path.exists(dst):\\n os.makedirs(dst)\\n for src_file in src_list:\\n if not src_file:\\n continue\\n source = os.path.join(src_dir, src_file)\\n destination = os.path.join(dst, src_file)\\n if os.path.lexists(destination):\\n continue\\n try:\\n os.symlink(source, destination)\\n except Exception as e:\\n msg = format_debug(e)\\n logging.error(e)\",\n \"def copy_files():\\n\\n # Load the Knifey-Spoony dataset.\\n # This is very fast as it only gathers lists of the files\\n # and does not actually load the images into memory.\\n dataset = load()\\n\\n # Copy the files to separate training- and test-dirs.\\n dataset.copy_files(train_dir=train_dir, test_dir=test_dir)\",\n \"def copy_folders(src_folders, dst_root, ignore_patterns=None):\\n \\n for src in src_folders:\\n dst = dst_root/src.name\\n shutil.copytree(src, dst, ignore=shutil.ignore_patterns(*ignore_patterns))\",\n \"def copy_tree(src, dst):\\n if not os.path.isdir(src):\\n raise Exception, \\\\\\n \\\"cannot copy tree '%s': not a directory\\\" % src\\n try:\\n names = os.listdir(src)\\n except os.error, (_, errstr):\\n raise Exception, \\\\\\n \\\"error listing files in '%s': %s\\\" % (src, errstr)\\n\\n makedirs(dst)\\n\\n outputs = []\\n\\n for n in names:\\n src_name = os.path.join(src, n)\\n dst_name = os.path.join(dst, n)\\n\\n if os.path.islink(src_name):\\n link_dest = os.readlink(src_name)\\n \\n os.symlink(link_dest, dst_name)\\n outputs.append(dst_name)\\n\\n elif os.path.isdir(src_name):\\n outputs.extend(copy_tree(src_name, dst_name))\\n else:\\n copyfile(src_name, dst_name)\\n \\n outputs.append(dst_name)\\n\\n return outputs\",\n \"def copy_files_individually(source_files, target_dir):\\n\\tfor source_file in source_files:\\n\\t\\thead_tail = os.path.split(source_file)\\n\\t\\tcopy_file(source_file, os.path.join(target_dir, head_tail[1]))\",\n \"def copytree(src, dst, symlinks=False, ignore=None):\\n import os\\n import shutil\\n for item in os.listdir(src):\\n s = os.path.join(src, item)\\n d = os.path.join(dst, item)\\n if os.path.isdir(s):\\n shutil.copytree(s, d, symlinks, ignore)\\n else:\\n shutil.copy2(s, d)\",\n \"def Copy(args):\\n parser = argparse.ArgumentParser(usage='cp [Options] sources... dest',\\n description=Copy.__doc__)\\n parser.add_argument(\\n '-R', '-r', '--recursive', dest='recursive', action='store_true',\\n default=False,\\n help='copy directories recursively.')\\n parser.add_argument(\\n '-v', '--verbose', dest='verbose', action='store_true',\\n default=False,\\n help='verbose output.')\\n parser.add_argument(\\n '--include', dest='includes', action='append', default=[],\\n help='include files matching this expression.')\\n parser.add_argument(\\n '--exclude', dest='excludes', action='append', default=[],\\n help='exclude files matching this expression.')\\n parser.add_argument('srcs', nargs='+', help='files to copy')\\n parser.add_argument('dest', help='destination')\\n\\n options = parser.parse_args(args)\\n\\n src_list = []\\n for src in options.srcs:\\n files = glob.glob(src)\\n if not files:\\n raise OSError('cp: no such file or directory: ' + src)\\n if files:\\n src_list.extend(files)\\n\\n for src in src_list:\\n # If the destination is a directory, then append the basename of the src\\n # to the destination.\\n if os.path.isdir(options.dest):\\n CopyPath(options, src, os.path.join(options.dest, os.path.basename(src)))\\n else:\\n CopyPath(options, src, options.dest)\",\n \"def _copy_sources():\\n shutil.rmtree(SRC_DIR_LOCAL, ignore_errors=True)\\n os.mkdir(SRC_DIR_LOCAL)\\n\\n shutil.copy(os.path.join(SRC_DIR_REPO, 'LICENSE.txt'), SRC_DIR_LOCAL)\\n shutil.copy(os.path.join(SRC_DIR_REPO, 'z3.pc.cmake.in'), SRC_DIR_LOCAL)\\n shutil.copy(os.path.join(SRC_DIR_REPO, 'CMakeLists.txt'), SRC_DIR_LOCAL)\\n shutil.copytree(os.path.join(SRC_DIR_REPO, 'cmake'), os.path.join(SRC_DIR_LOCAL, 'cmake'))\\n shutil.copytree(os.path.join(SRC_DIR_REPO, 'scripts'), os.path.join(SRC_DIR_LOCAL, 'scripts'))\\n\\n # Copy in src, but avoid recursion\\n def ignore_python_setup_files(src, _):\\n if os.path.normpath(src).endswith('api/python'):\\n return ['core', 'dist', 'MANIFEST', 'MANIFEST.in', 'setup.py', 'z3_solver.egg-info']\\n return []\\n shutil.copytree(os.path.join(SRC_DIR_REPO, 'src'), os.path.join(SRC_DIR_LOCAL, 'src'),\\n ignore=ignore_python_setup_files)\",\n \"def copy_tpx3_files_batch(initial_image_dir, target_dir):\\n\\tinitial_image_dir = initial_image_dir.rstrip('/') + '/'\\n\\targ_list = [\\\"--include=*\\\", \\\"--exclude=*\\\", initial_image_dir, target_dir]\\n\\trun_rsync(arg_list)\",\n \"def copy_assets(test_files):\\n for path in test_files:\\n shutil.copy(path, HOST_ASSETS_PATH)\",\n \"def SshCopyFiles(srcs, host, dst):\\n command = ['scp', srcs, host + ':' + dst]\\n result = RunCommand(command)\\n if result:\\n raise ExternalError('Failed to scp \\\"%s\\\" to \\\"%s\\\" (%s)' %\\n (srcs, host + ':' + dst, result))\",\n \"def copy_directory(source, dest):\\n for path, dirs, files in walk(source):\\n relative_src_path = path.replace(source, \\\"\\\").lstrip(\\\"/\\\")\\n abs_dest_path = join(dest, relative_src_path)\\n if not exists(abs_dest_path):\\n makedirs(abs_dest_path)\\n for tdir in dirs:\\n dest_dir = join(abs_dest_path, tdir)\\n if not exists(dest_dir):\\n makedirs(dest_dir)\\n for tfile in files:\\n src_file = join(path, tfile)\\n dest_file = join(abs_dest_path, tfile)\\n if islink(src_file):\\n linkto = readlink(src_file)\\n symlink(linkto, dest_file)\\n continue\\n else:\\n process_file(src_file, dest_file)\",\n \"def copy_dir(src=\\\"\\\", dst=\\\"\\\", header=\\\"\\\", footer=\\\"\\\", clip=0, ext=\\\"\\\", test=False):\\n failed = []\\n nfiles = 0\\n if not os.path.exists(dst):\\n os.makedirs(dst)\\n if not os.path.exists(src):\\n raise argparse.ArgumentError(\\\"source does not exist! It must be a directory.\\\")\\n else:\\n for root, dirs, files in os.walk(src, topdown=False):\\n for name in files:\\n name_wo_ext, file_ext = os.path.splitext(name)\\n\\n src_path = os.path.join(root, name)\\n dstfilename = header + os.path.join(root[len(src)+1:], name_wo_ext[clip:]) + footer + file_ext\\n dst_path = os.path.join(dst, dstfilename)\\n\\n dst_pdir = os.path.dirname(dst_path)\\n if not os.path.exists(dst_pdir):\\n os.makedirs(dst_pdir)\\n\\n if not os.path.exists(dst_path):\\n if ext == \\\"\\\" or ext == file_ext[1:]:\\n try:\\n shutil.copy(src_path, dst_path)\\n except:\\n failed.append(src_path)\\n print(f\\\"... {src_path} failed\\\")\\n else:\\n print(f\\\"... {dst_path} already exists'. Skipping\\\")\\n nfiles += 1\\n\\n if test:\\n break\\n if test:\\n break\\n print(f\\\"{nfiles - len(failed)} / {nfiles} files were copied.\\\")\\n return failed\",\n \"def merge_folders():\\r\\n from shutil import copyfile\\r\\n # Merge all folders into main folder\\r\\n grp_img_dir = os.listdir('Group_Test_Images')\\r\\n \\r\\n for grp_img_folder in grp_img_dir:\\r\\n image_folders = os.listdir('Group_Test_Images'+'/'+grp_img_folder)\\r\\n \\r\\n for img_label in image_folders:\\r\\n new_directory = 'Group_Test_Images'+'/'+img_label\\r\\n \\r\\n try:\\r\\n os.makedirs(new_directory)\\r\\n except FileExistsError:\\r\\n # directory already exists\\r\\n pass\\r\\n \\r\\n file_names = os.listdir('Group_Test_Images'+'/'+grp_img_folder+'/'+img_label)\\r\\n \\r\\n for file in file_names:\\r\\n copyfile('Group_Test_Images'+'/'+grp_img_folder+'/'+img_label+'/'+file, new_directory+'/'+file)\",\n \"def copy_images_to_new(lab_img, from_dir, to_dir):\\n \\n for img in lab_img:\\n if not os.path.exists(join(todir, img)):\\n shutil.copyfile(join(fromdir, img), join(todir, img)) \\n print(\\\"Done\\\")\",\n \"def prepare_output_dir(out_dir, test_dir):\\r\\n\\r\\n if not out_dir.exists():\\r\\n out_dir.mkdir()\\r\\n\\r\\n # get the necessary file names\\r\\n file_names = get_file_names(test_dir, args.distance, print_file_names=False)\\r\\n\\r\\n # copy the images in the firstIms into the output folder\\r\\n for name in file_names[1][0]:\\r\\n file_path = Path(test_dir / name)\\r\\n copy_to = Path(out_dir / name)\\r\\n shutil.copy(file_path, copy_to)\\r\\n\\r\\n # the firstIms list does not contain the last image,\\r\\n # so we need to also copy the last image of the secIms into the output folder\\r\\n last_im = file_names[1][1][-1]\\r\\n shutil.copy(Path(test_dir/last_im), Path(out_dir/last_im))\\r\\n\\r\\n return file_names\",\n \"def copyUFOs(srcDirPath, dstDirPath):\\n if not srcDirPath.endswith('/'):\\n srcDirPath += '/'\\n if not dstDirPath.endswith('/'):\\n dstDirPath += '/'\\n assert os.path.isdir(srcDirPath)\\n copiedFiles = []\\n for fileName in os.listdir(srcDirPath):\\n if fileName.endswith('.ufo'):\\n shutil.copytree(srcDirPath + fileName, dstDirPath + fileName)\\n copiedFiles.append(fileName)\\n return copiedFiles\",\n \"def move_files(src_dir, dst_dir):\\n for f in os.listdir(src_dir):\\n try:\\n name, season, episode = FILENAME_PATTERN.search(f).groups()\\n except AttributeError:\\n try:\\n name, season, episode = FILENAME_PATTERN2.search(f).groups()\\n except AttributeError:\\n print \\\"Cannot parse\\\", f\\n pass\\n\\n name = name.replace('.', ' ').replace('_', ' ').strip().title()\\n\\n dir_path = os.path.join(dst_dir, name, 'Season %02d' % int(season))\\n full_path = os.path.join(dir_path, f)\\n source_path = os.path.join(src_dir, f)\\n\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path, 0777)\\n\\n if not os.path.exists(full_path):\\n shutil.move(source_path, full_path)\\n os.symlink(full_path, source_path)\",\n \"def copydir(source, dest):\\n dest_par = os.path.dirname(dest)\\n for root, dirs, files in os.walk(source):\\n if not os.path.isdir(root):\\n os.makedirs(root)\\n\\n for mdir in dirs:\\n try:\\n dest_path = os.path.join(dest_par, root, mdir)\\n if not os.path.isdir(dest_path):\\n os.makedirs(dest_path)\\n except:\\n pass\\n for file in files:\\n rel_path = root.replace(source, '').lstrip(os.sep)\\n dest_path = os.path.join(dest, rel_path)\\n if not os.path.isdir(dest_path):\\n os.makedirs(dest_path)\\n cpy_src = os.path.join(root, file)\\n cpy_dest = os.path.join(dest_path, file)\\n shutil.copyfile(cpy_src, cpy_dest)\\n shutil.copymode(cpy_src, cpy_dest)\",\n \"def copytree2(src, dst, symlinks=False, ignore=None):\\n\\tnames = os.listdir(src)\\n\\tcpy_err={'flag':True, 'error':None}\\n\\tif ignore is not None:\\n\\t\\tignored_names = ignore(src, names)\\n\\telse:\\n\\t\\tignored_names = set()\\n\\n\\tif not os.path.exists(dst):\\n\\t\\ttry:\\n\\t\\t\\tos.makedirs(dst)\\n\\t\\texcept:\\n\\t\\t\\tcpy_err= {'flag':False, 'error':\\\"Impossible to create a directory, PLATO is accountered a problem, contact the administrator\\\"}\\n\\t\\t\\treturn cpy_err\\n\\t\\n\\tfor name in names:\\n\\t\\tif name in ignored_names:\\n\\t\\t\\tcontinue\\n\\t\\tsrcname = os.path.join(src, name)\\n\\t\\tdstname = os.path.join(dst, name)\\n\\t\\ttry:\\n\\t\\t\\tif symlinks and os.path.islink(srcname):\\n\\t\\t\\t\\tlinkto = os.readlink(srcname)\\n\\t\\t\\t\\tos.symlink(linkto, dstname)\\n\\t\\t\\telif os.path.isdir(srcname):\\n\\t\\t\\t\\tcopytree(srcname, dstname, symlinks, ignore)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tcopy2(srcname, dstname)\\n\\t\\t\\t# XXX What about devices, sockets etc.?\\n\\t\\texcept (IOError, os.error) as why:\\n\\t # errors.append((srcname, dstname, str(why)))\\n\\t\\t\\tcpy_err={'flag':False,'error':str(why)}\\n\\t\\t\\treturn cpy_err\\n\\t\\t# catch the Error from the recursive copytree so that we can\\n\\t\\t# continue with other files\\n\\t\\texcept Error as err:\\n\\t\\t\\tcpy_err={'flag':False,'error':str(err.args[0])}\\n\\t\\t\\treturn cpy_err\\n\\t\\t # errors.extend(err.args[0])\\n\\ttry:\\n\\t\\tcopystat(src, dst)\\n\\texcept OSError as why:\\n\\t\\t# errors.extend((src, dst, str(why)))\\n\\t\\tcpy_err={'flag':False,'error':str(why)}\\n\\t\\treturn cpy_err\\n\\t# if errors:\\n\\t# raise Error(errors)\\n\\t\\n\\treturn cpy_err\",\n \"async def _copy_folder_files(self, src_dir, dest_dir):\\n for dir_item in os.listdir(src_dir):\\n src_path = os.path.join(src_dir, dir_item)\\n if os.path.isfile(src_path):\\n await self._copy_file_with_hook(dir_item, src_path, os.path.join(dest_dir, dir_item))\",\n \"def _copy_all(entry_list: List[Path], target_dir: Path, on_duplicate: str):\\n for entry in entry_list:\\n target_entry = target_dir / entry.name\\n if not target_entry.exists() or on_duplicate == 'overwrite':\\n if entry.is_file():\\n shutil.copy(entry, target_entry, follow_symlinks=False)\\n else:\\n shutil.copytree(entry, target_entry, symlinks=True)\\n elif on_duplicate == 'exception':\\n raise ValueError(f\\\"'{target_entry}' already exists (src {entry})\\\")\\n else: # ignore\\n continue\",\n \"def copyFiles(fileNames, sourceDir, targetDir, silent=True):\\n\\tfileNames = listR.toList(fileNames)\\n\\tfor filename in fileNames:\\n\\t\\tcopyFile(filename, sourceDir, targetDir, None, silent)\",\n \"def copydir(\\n source: str,\\n destination: str,\\n includes: List[str],\\n excludes: Optional[List[str]] = None,\\n follow_symlinks: bool = False,\\n) -> None:\\n files = _find_files(source, includes, excludes, follow_symlinks)\\n\\n def _mkdir(dir_name: str) -> None:\\n \\\"\\\"\\\"Recursively create directories.\\\"\\\"\\\"\\n parent = os.path.dirname(dir_name)\\n if not os.path.isdir(parent):\\n _mkdir(parent)\\n LOGGER.debug(\\\"creating directory: %s\\\", dir_name)\\n os.mkdir(dir_name)\\n\\n for file_name in files:\\n src = os.path.join(source, file_name)\\n dest = os.path.join(destination, file_name)\\n try:\\n LOGGER.debug('copying file \\\"%s\\\" to \\\"%s\\\"', src, dest)\\n copyfile(src, dest)\\n except OSError:\\n _mkdir(os.path.dirname(dest))\\n copyfile(src, dest)\",\n \"def __copyFiles(self):\\n if os.path.isdir(self.__sourcePath):\\n shutil.copytree(self.__sourcePath, self.__targetPath)\\n else:\\n shutil.copy2(self.__sourcePath, self.__targetPath)\",\n \"def task():\\n if os.path.isdir(orig):\\n for fP in [ fP for fP in glob.glob(os.path.join(orig, '*-*/*')) if \\\\\\n os.path.isdir(fP) ]:\\n if not os.path.exists(dest + fP[len(orig):]):\\n os.makedirs(dest + fP[len(orig):])\\n for fP in [ fP for fP in glob.glob(os.path.join(orig, '*-*/*/%s.log' %fmt.get_date())) if \\\\\\n os.path.isfile(fP) ]:\\n convert(fP, dest + fP[len(orig):])\",\n \"def CopyFolder(src, dst, overwrite=True):\\n\\n if not src.endswith((\\\"/\\\", \\\"\\\\\\\\\\\")):\\n src = src + \\\"\\\\\\\\\\\"\\n if not dst.endswith((\\\"/\\\", \\\"\\\\\\\\\\\")):\\n dst = dst + \\\"\\\\\\\\\\\"\\n os.makedirs(dst, exist_ok=True)\\n\\n for file in os.listdir(src):\\n if not overwrite and os.path.isfile(dst + file):\\n continue\\n if os.path.isfile(src + file):\\n shutil.copy(src + file, dst + file)\\n elif os.path.isdir(src + file):\\n CopyFolder(src + file, dst + file, overwrite)\\n return 0\",\n \"def copytree(src, dst, overwrite=False, changed_only=True):\\n assert os.path.isdir(src), \\\\\\n (\\\"Source path `%s` does not name an existing directory\\\" % src)\\n errors = []\\n if not os.path.exists(dst):\\n os.makedirs(dst)\\n for name in os.listdir(src):\\n srcname = os.path.join(src, name)\\n dstname = os.path.join(dst, name)\\n try:\\n if os.path.isdir(srcname):\\n errors.extend(\\n copytree(srcname, dstname, overwrite, changed_only))\\n else:\\n copyfile(srcname, dstname)\\n except (IOError, os.error) as why:\\n errors.append((srcname, dstname, why))\\n return errors\",\n \"def process_file_list(input_root, output_root, file_list):\\n for backup_id, backup_file in file_list.items():\\n # logging.debug(f\\\"{backup_id}: {backup_file.relative_path}\\\")\\n if backup_file.is_dir:\\n create_directory(backup_file, output_root)\\n else:\\n create_file(backup_file, input_root, output_root)\",\n \"def transfer_files_from_dir(\\n source_filepath_list: Iterable[str],\\n destination_filepath_list: Iterable[str],\\n copy_strategy: TransferAction = TransferAction.copy,\\n force_overwrite: bool = False\\n) -> None:\\n if TransferAction.skip == copy_strategy:\\n return\\n\\n elif TransferAction.copy == copy_strategy or TransferAction.move == copy_strategy:\\n transfer_files_from_dir_copy(\\n source_filepath_list=source_filepath_list,\\n destination_filepath_list=destination_filepath_list,\\n force_overwrite=force_overwrite,\\n delete_source=(TransferAction.move == copy_strategy)\\n )\\n\\n elif TransferAction.link_absolute == copy_strategy or TransferAction.link_relative == copy_strategy:\\n transfer_files_from_dir_link(\\n source_filepath_list=source_filepath_list,\\n destination_filepath_list=destination_filepath_list,\\n force_overwrite=force_overwrite,\\n do_relative_link=(TransferAction.link_relative == copy_strategy)\\n )\\n\\n else:\\n raise ValueError(f'Unsupported copy action {copy_strategy}')\",\n \"def copy_files(self, source, target):\\n\\n if source == target and is_local(self.borrowed_ctx.host):\\n logger.warning(\\\"IGNORE self-node: {}\\\".format(self.borrowed_ctx.host))\\n return\\n\\n try:\\n for item in os.listdir(source):\\n if os.path.isfile(os.path.join(source, item)):\\n logger.debug(\\n \\\"processing {} --> {}\\\".format(\\n os.path.join(source, item), self.borrowed_ctx.host\\n )\\n )\\n self._sftp_channel.put(\\n os.path.join(source, item), \\\"%s/%s\\\" % (target, item)\\n )\\n else:\\n self.mkdir(\\\"%s/%s\\\" % (target, item), ignore_existing=True)\\n self.copy_files(\\n os.path.join(source, item), \\\"%s/%s\\\" % (target, item)\\n )\\n except Exception as e:\\n logger.warning(\\n \\\"Error of processing target = ({}:{}), for reason: {}\\\".format(\\n self.borrowed_ctx.host, self.borrowed_ctx.port, e,\\n )\\n )\\n exit(0)\",\n \"def copy_files(self,train_dir,test_dir):\\n\\n def _copy_files(src_paths,dst_dir,class_numbers):\\n\\n \\\"\\\"\\\"\\n Creo una lista di directory per ogni classe\\n (knify-spoony/test/class1)\\n (knify-spoony/test/class2)\\n \\n \\n :param src_paths: \\n :param dst_dir: \\n :param class_numbers: \\n :return: \\n \\\"\\\"\\\"\\n\\n class_dirs=[os.path.join(dst_dir,class_name+\\\"/\\\")for class_name in self.class_names]\\n\\n for dir in class_dirs:\\n if not os.path.exists(dir):\\n os.makedirs(dir)\\n\\n for src,cls in zip(src_paths,class_numbers):\\n shutil.copy(src=src,dst=class_dirs[cls])\\n\\n # Copy the files for the training-set.\\n #self.get_paths ritorna i path delle immagini da copiare nel training set test=False\\n _copy_files(src_paths=self.get_paths(test=False),\\n dst_dir=train_dir,\\n class_numbers=self.class_numbers)\\n\\n print(\\\"- Copied training-set to:\\\", train_dir)\\n\\n # Copy the files for the test-set.\\n #test=True ritorna tutte le path del test\\n _copy_files(src_paths=self.get_paths(test=True),\\n dst_dir=test_dir,\\n class_numbers=self.class_numbers_test)\\n\\n print(\\\"- Copied test-set to:\\\", test_dir)\",\n \"def copy_files(step: BuildStep,\\n patterns: List[str],\\n target: str = None) -> None:\\n log = step.get_logger()\\n if target:\\n log(f\\\"Copying files into build folder under {target}\\\")\\n else:\\n log(\\\"Copying files into build folder\\\")\\n log(f\\\"Copy src={step.source_path} to build={step.build_path} target={target}\\\", VERBOSE * 2)\\n dirs = files = 0\\n for pattern in patterns:\\n if pattern.endswith(\\\"/\\\"):\\n log(f\\\"Looking for all files under '{pattern}'\\\", VERBOSE * 3)\\n pattern += \\\"**/*\\\"\\n elif \\\"*\\\" in pattern:\\n log(f\\\"Looking for all files matching '{pattern}'\\\", VERBOSE * 3)\\n else:\\n log(f\\\"Looking for files named '{pattern}'\\\", VERBOSE * 3)\\n file_per_pattern = 0\\n for f in step.source_path.glob(pattern):\\n relative = f.relative_to(step.source_path)\\n if target:\\n dest = step.build_path / target / relative\\n else:\\n dest = step.build_path / relative\\n\\n dest_parent = dest.parent\\n if not dest_parent.is_dir():\\n log(f\\\"Mkdir {dest_parent}\\\", VERBOSE)\\n dest_parent.mkdir(parents=True)\\n dirs += 1\\n if f.is_file():\\n log(f\\\"Copy {f}\\\", VERBOSE)\\n copy2(f, dest)\\n files += 1\\n file_per_pattern += 1\\n log(f\\\"Copied {file_per_pattern} files matching '{pattern}'\\\", VERBOSE * 2)\\n # TODO: Expand capabilities to capture files/dirs per pattern, helpful to get lookup counts\\n log(f\\\"Completed copying {len(patterns)} patterns. Created {files} files in {dirs} directories\\\")\",\n \"def copy_images(gallery_path, image_list):\\n for image_link in image_list:\",\n \"def copy(src, dst):\\n os.makedirs(os.path.dirname(dst), exist_ok=True)\\n shutil.copy2(src, dst)\",\n \"def move_files(src, dst, filenames):\\n for filename in filenames:\\n os.rename(os.path.join(src, filename), os.path.join(dst, filename))\",\n \"def transfer_files_from_dir_link(\\n source_filepath_list: Iterable[str],\\n destination_filepath_list: Iterable[str],\\n force_overwrite: bool = False,\\n do_relative_link: bool = False\\n) -> None:\\n hide_progress_bar = logger.getEffectiveLevel() > logging.INFO\\n for src, dst in tqdm(zip(source_filepath_list, destination_filepath_list), disable=hide_progress_bar):\\n os.makedirs(path.dirname(dst), exist_ok=True)\\n if force_overwrite and path.lexists(dst):\\n os.remove(dst)\\n try: # on windows, symlink requires some privileges, and may crash if not\\n if do_relative_link:\\n src = path.relpath(src, path.dirname(dst))\\n os.symlink(src, dst)\\n except OSError as e:\\n logger.critical('unable to create symlink on image directory, due to privilege restrictions.')\\n raise e\",\n \"def create_files(paths, chroot):\\n dirs, files = set(), set()\\n for path in paths:\\n path = osp.join(chroot, path)\\n filename = osp.basename(path)\\n # path is a directory path\\n if filename == '':\\n dirs.add(path)\\n # path is a filename path\\n else:\\n dirs.add(osp.dirname(path))\\n files.add(path)\\n for dirpath in dirs:\\n if not osp.isdir(dirpath):\\n os.makedirs(dirpath)\\n for filepath in files:\\n open(filepath, 'w').close()\",\n \"def fresh_copy_dir(source_path, target_path):\\n os.mkdir(target_path)\\n for item in os.listdir(source_path):\\n s = os.path.join(source_path, item)\\n t = os.path.join(target_path, item)\\n if os.path.isdir(s):\\n fresh_copy_dir(s, t)\\n else:\\n shutil.copyfile(s, t)\",\n \"def move_files(sim_dir, dest_dir, file_patterns):\\n for f in file_patterns:\\n for p in glob.glob1(sim_dir, f):\\n try:\\n shutil.move(os.path.join(sim_dir, p), os.path.join(dest_dir, p))\\n except Exception as e:\\n print(\\n \\\"error while copy ing file from {} to {}\\\\n{}\\\".format(\\n sim_dir, dest_dir, e\\n )\\n )\",\n \"def copy_files(dic_param,src_dir,dest_dir):\\n \\n ### Make subdirectory\\n if os.path.exists(dest_dir):\\n answer=input('%s exists, want to remove what is inside? (y):\\\\n'%(dest_dir))\\n if answer=='y':\\n os.system('rm -rf %s/*pdf'%dest_dir)\\n os.system('rm -rf %s/*log'%dest_dir)\\n else:\\n return\\n else:\\n os.mkdir(dest_dir)\\n \\n for id_key in dic_param['MODEL_KEY']:\\n list_file=glob.glob(src_dir+'/*%s*'%id_key)\\n for file in list_file:\\n copy2(file,dest_dir)\",\n \"def command_copytree(args):\\n for srcdir in args.srcdirs:\\n basename = os.path.basename(srcdir)\\n destdir2 = os.path.normpath(os.path.join(args.destdir, basename))\\n if os.path.exists(destdir2):\\n shutil.rmtree(destdir2)\\n sys.stdout.write(\\\"copytree: %s => %s\\\\n\\\" % (srcdir, destdir2))\\n shutil.copytree(srcdir, destdir2)\\n return 0\",\n \"def copy_files():\\n os.makedirs('build/usr/lib/python3/dist-packages', exist_ok=True)\\n os.makedirs('build/usr/share/doc', exist_ok=True)\\n\\n shutil.copytree('applications', 'build/usr/share/applications')\\n shutil.copytree('doc', 'build/usr/share/doc/qastetray')\\n shutil.copytree('icons', 'build/usr/share/icons')\\n shutil.copytree('locale', 'build/usr/share/locale')\\n shutil.copytree('qastetray',\\n 'build/usr/lib/python3/dist-packages/qastetray')\",\n \"def handle_multiple_destinations(self):\\n\\n # Create the to-directory if it does not exist\\n for destination in config.dest:\\n if not path.exists(destination.dest):\\n makedirs(destination.dest)\\n\\n # Clone the modules and copy the right directories\\n for module in config.modules:\\n Logger.assemble_module(module)\\n\\n directory = path.join(TEMP_DIR, module.name)\\n remove_dir(directory)\\n clone(module, directory)\\n self.commit_hashes[module.name] = self.get_commit_hash(directory)\\n\\n for destination in config.dest:\\n to_directory = path.join(destination.dest, module.name)\\n remove_dir(to_directory)\\n shutil.move(\\n path.join(TEMP_DIR, module.name, destination.src), to_directory\\n )\",\n \"def _create_paths(paths):\\n for path in paths:\\n _mkdir_if_not_exist(path)\",\n \"def copy_source_files(self):\\n\\n LOGGER.info(f'start copying source files')\\n count = 0\\n for sfp in tqdm(sorted(self.source_fps), disable=self.disable_tqdm):\\n try:\\n meta = extract_law_meta(sfp)\\n nodes = parse_xml_fp(sfp)\\n tfp = self.stot(sfp)\\n tfp.parent.mkdir(parents=True, exist_ok=True)\\n save_law_tree(meta['LawTitle'], nodes, tfp)\\n except Exception as e:\\n LOGGER.error(f'failed to copy {sfp}: {e}')\\n continue\\n self.target_fps.add(tfp)\\n LOGGER.debug(f'copied {sfp} to {tfp}')\\n count += 1\\n LOGGER.info(f'copied total {count} source files, now total {len(self.target_fps)} target files exist')\",\n \"def main(source_dir, dest_dir):\\n\\n paths = []\\n for root, _, files in os.walk(source_dir):\\n paths.extend([os.path.join(root, f) for f in files])\\n\\n def copy(source_path, skip_existing=True):\\n \\\"\\\"\\\"Copies a file from source_path to source_path with\\n source_dir replaced by dest_dir.\\n\\n Arguments:\\n source_path(str): Path to a file to be copied.\\n skip_existing(bool): True to skip copying files\\n when the destination file already exists.\\n \\\"\\\"\\\"\\n\\n dest_path = source_path.replace(source_dir.strip('/'), dest_dir.strip('/'))\\n\\n # Skip if dest file already exists\\n if skip_existing and os.path.exists(dest_path):\\n return\\n\\n # Create directory if necessary\\n os.makedirs(os.path.dirname(dest_path), exist_ok=True)\\n\\n copyfile(source_path, dest_path)\\n\\n p_umap(copy, paths)\",\n \"def copytree(src, dst, symlinks=False, ignore=None):\\r\\n names = os.listdir(src)\\r\\n if ignore is not None:\\r\\n ignored_names = ignore(src, names)\\r\\n else:\\r\\n ignored_names = set()\\r\\n\\r\\n if dst == '.':\\r\\n if os.listdir(dst):\\r\\n raise shutil.Error('Current directory is not empty')\\r\\n else:\\r\\n os.makedirs(dst)\\r\\n\\r\\n errors = []\\r\\n for name in names:\\r\\n if name in ignored_names:\\r\\n continue\\r\\n srcname = os.path.join(src, name)\\r\\n dstname = os.path.join(dst, name)\\r\\n try:\\r\\n if symlinks and os.path.islink(srcname):\\r\\n linkto = os.readlink(srcname)\\r\\n os.symlink(linkto, dstname)\\r\\n elif os.path.isdir(srcname):\\r\\n copytree(srcname, dstname, symlinks, ignore)\\r\\n else:\\r\\n shutil.copy2(srcname, dstname)\\r\\n except (IOError, os.error), why:\\r\\n errors.append((srcname, dstname, str(why)))\\r\\n # catch the Error from the recursive copytree so that we can\\r\\n # continue with other files\\r\\n except shutil.Error, err:\\r\\n errors.extend(err.args[0])\\r\\n if errors:\\r\\n raise shutil.Error(errors)\",\n \"def copy_media(items, dest):\\n for file in items:\\n filename = os.path.basename(file)\\n copyfile(file, dest + '\\\\\\\\' + filename)\",\n \"def copy_tree_to_path(src_dir, dest_dir):\\n names = os.listdir(src_dir)\\n\\n for name in names:\\n srcname = os.path.join(src_dir, name)\\n destname = os.path.join(dest_dir, name)\\n\\n if os.path.isdir(srcname):\\n shutil.copytree(srcname, destname)\\n else:\\n shutil.copy(srcname, destname)\",\n \"def transfer_files(src: str, dst: str, move_src_data: bool = False):\\n if move_src_data:\\n logger.info('Move {0} to {1}'.format(src, dst))\\n shutil.move(src, dst)\\n else:\\n logger.info('Copy {0} to {1}'.format(src, dst))\\n copy_tree(src, dst)\",\n \"def copy_deep(src: str, dst: str, create_dst_dir: bool = False) -> None:\\n system_is_darwin = platform.system().lower() == \\\"darwin\\\"\\n if create_dst_dir:\\n mkdir_p(os.path.dirname(dst))\\n src_is_link = os.path.islink(src)\\n dst_exists = os.path.lexists(dst)\\n if os.path.isdir(src) and not src_is_link:\\n logging.debug(\\\"Copying directory {} to {}\\\".format(src, dst))\\n mkdir_p(dst)\\n for name in os.listdir(src):\\n copy_deep(os.path.join(src, name), os.path.join(dst, name))\\n elif src_is_link:\\n if dst_exists:\\n return\\n target = os.readlink(src)\\n logging.debug(\\\"Creating symlink {} -> {}\\\".format(dst, target))\\n os.symlink(target, dst)\\n else:\\n if dst_exists:\\n if not system_is_darwin:\\n return\\n # Only overwrite the file if the source is newer than the destination.\\n if os.path.getmtime(src) <= os.path.getmtime(dst):\\n return\\n logging.debug(\\\"Copying file {} to {}\\\".format(src, dst))\\n # Preserve the file attributes.\\n shutil.copy2(src, dst)\",\n \"def copy_files_jd(self, file_list, variable_lst, revision=\\\"Auto\\\"):\\n # | - copy_files_jd\\n path = self.var_lst_to_path(variable_lst)\\n path += \\\"_\\\" + str(self.job_revision_number(variable_lst))\\n\\n for file in file_list:\\n shutil.copyfile(self.root_dir + \\\"/\\\" + file, path + \\\"/\\\" + file)\\n # __|\",\n \"def copy_photos(source='.', dest='.'):\\n if isdir(source): # if this is a directory,\\n for child in listdir(source):\\n # compose full path to child\\n child = join(source, child)\\n if isdir(child):\\n copy_photos(child, dest)\\n else:\\n if guess_type(child)[0] is not None \\\\\\n and (guess_type(child)[0].split('/')[0] == 'image'):\\n new = rename(child)\\n create_tree(new, dest)\",\n \"def make_image_dir(to_path, filenames):\\n image_dir = os.path.join(to_path, \\\"image_2\\\")\\n os.makedirs(image_dir)\\n for f in filenames:\\n image_file = os.path.join(image_dir, f + \\\".png\\\")\\n os.system(\\\"cp sample.png {}\\\".format(image_file))\",\n \"def get_data_files(source_dest_pairs):\\n data_files = []\\n for src_dir, dest_dir in source_dest_pairs:\\n for src_root, _, files in os.walk(src_dir):\\n dest_root = src_root.replace(src_dir, dest_dir, 1)\\n dir_files = []\\n for file_ in files:\\n dir_files.append(os.path.join(src_root, file_))\\n data_files.append((dest_root, dir_files))\\n return data_files\",\n \"def CopyFiles(self):\\n self._adb.RunShellCommand('rm -r %s; mkdir %s' %\\n (ValgrindTool.VG_DIR, ValgrindTool.VG_DIR))\\n self._adb.RunShellCommand('rm -r %s; mkdir %s' %\\n (ValgrindTool.VGLOGS_DIR,\\n ValgrindTool.VGLOGS_DIR))\\n files = self.GetFilesForTool()\\n for f in files:\\n self._adb.PushIfNeeded(os.path.join(DIR_SOURCE_ROOT, f),\\n os.path.join(ValgrindTool.VG_DIR,\\n os.path.basename(f)))\",\n \"def copy_resources(src_container, src_resources, storage_dir, dst_directories=None, apply_chown=None, apply_chmod=None):\\n def _copy_resource(resource):\\n default_dest_path = generic_path if generic_path is not None else resource\\n dest_path = directories.get(resource, default_dest_path).strip(posixpath.sep)\\n head, tail = posixpath.split(dest_path)\\n rel_path = posixpath.join(storage_dir, head)\\n run(mkdir(rel_path, check_if_exists=True))\\n run('docker cp {0}:{1} {2}'.format(src_container, resource, rel_path), shell=False)\\n\\n directories = dst_directories or {}\\n generic_path = directories.get('*')\\n for res in src_resources:\\n _copy_resource(res)\\n if apply_chmod:\\n run(chmod(apply_chmod, storage_dir))\\n if apply_chown:\\n sudo(chown(apply_chown, storage_dir))\",\n \"def mergecopytree(src, dst, symlinks=False, ignore=None):\\n if not os.path.exists(dst):\\n os.makedirs(dst)\\n shutil.copystat(src, dst)\\n lst = os.listdir(src)\\n if ignore:\\n excl = ignore(src, lst)\\n lst = [x for x in lst if x not in excl]\\n for item in lst:\\n src_item = os.path.join(src, item)\\n dst_item = os.path.join(dst, item)\\n if symlinks and os.path.islink(src_item):\\n if os.path.lexists(dst_item):\\n os.remove(dst_item)\\n os.symlink(os.readlink(src_item), dst_item)\\n elif os.path.isdir(src_item):\\n mergecopytree(src_item, dst_item, symlinks, ignore)\\n else:\\n shutil.copy2(src_item, dst_item)\",\n \"def copy_dir(src, dst):\\n try:\\n debug.log(\\\"copy dir from \\\"+ src, \\\"to \\\"+ dst)\\n shutil.copytree(src, dst)\\n except Exception as e:\\n debug.log(\\\"Error: happened while copying!\\\\n%s\\\\n\\\"%e)\",\n \"def copy_files_subfolders(from_dataset, merged_folder, original_name=''):\\n for child in from_dataset.iterdir():\\n if child.is_dir(): # go one layer deeper\\n for elem in child.iterdir():\\n try:\\n new_dir = merged_folder / child.name\\n new_dir.mkdir(parents=True, exist_ok=True)\\n shutil.copy(str(elem), merged_folder / child.name / elem.name)\\n except shutil.SameFileError:\\n print('Child {} / {} already exists'.format(child.name, elem.name))\\n pass\\n elif 'template' in child.name: # no need for additional naming of the template files\\n try:\\n shutil.copy(str(child), merged_folder / child.name)\\n except shutil.SameFileError:\\n print('Child {} already exists'.format(child))\\n pass\\n else:\\n # print(child.name)\\n try:\\n shutil.copy(str(child), merged_folder / (original_name + '_' + child.name))\\n except shutil.SameFileError:\\n print('Child {} already exists'.format(child))\\n pass\",\n \"def copy_web_resources(output_dir):\\n mypath = os.path.dirname(os.path.realpath(__file__))\\n web_path = os.path.join(mypath, 'web')\\n\\n for (dirpath, dirnames, filenames) in os.walk(web_path):\\n relpath = os.path.relpath(dirpath, web_path)\\n tgtpath = os.path.join(output_dir, relpath)\\n if not os.path.exists(tgtpath):\\n os.makedirs(tgtpath)\\n\\n for f in [os.path.join(dirpath, filename) for filename in filenames]:\\n shutil.copy(f, tgtpath)\",\n \"def copy_tree(src, dst, preserve_mode=1, preserve_times=1,\\n preserve_symlinks=0, update=0, verbose=1, dry_run=0):\\n preserve_symlinks = 1\\n import os\\n from distutils.dir_util import mkpath\\n from distutils import log\\n\\n from distutils.file_util import copy_file\\n\\n if not dry_run and not os.path.isdir(src):\\n raise DistutilsFileError(\\n \\\"cannot copy tree '%s': not a directory\\\" % src)\\n try:\\n names = os.listdir(src)\\n except OSError as e:\\n if dry_run:\\n names = []\\n else:\\n raise DistutilsFileError(\\n \\\"error listing files in '%s': %s\\\" % (src, e.strerror))\\n\\n if not dry_run:\\n mkpath(dst, verbose=verbose)\\n\\n outputs = []\\n\\n for n in names:\\n src_name = os.path.join(src, n)\\n dst_name = os.path.join(dst, n)\\n\\n if n.startswith('.nfs'):\\n # skip NFS rename files\\n continue\\n\\n if preserve_symlinks and os.path.islink(src_name):\\n link_dest = os.readlink(src_name)\\n if verbose >= 1:\\n log.info(\\\"linking %s -> %s\\\", dst_name, link_dest)\\n if not dry_run:\\n if os.path.exists(dst_name):\\n os.unlink(dst_name)\\n os.symlink(link_dest, dst_name)\\n outputs.append(dst_name)\\n\\n elif os.path.isdir(src_name):\\n outputs.extend(\\n copy_tree(src_name, dst_name, preserve_mode,\\n preserve_times, preserve_symlinks, update,\\n verbose=verbose, dry_run=dry_run))\\n else:\\n copy_file(src_name, dst_name, preserve_mode,\\n preserve_times, update, verbose=verbose,\\n dry_run=dry_run)\\n outputs.append(dst_name)\\n\\n return outputs\",\n \"def cpsym(src,dest):\\n \\n src = os.path.normpath(src)\\n dest = os.path.normpath(dest)\\n \\n if not os.path.exists(src):\\n return\\n \\n for dirpath,dirnames,filenames in os.walk(src):\\n rel_dirpath = os.path.relpath(dirpath,src)\\n dest_dirpath = os.path.join(dest,rel_dirpath)\\n mkdir(dest_dirpath,isfull=True)\\n \\n for filename in filenames:\\n src_filename = os.path.join(dirpath,filename)\\n rel_filename = os.path.relpath(src_filename,src)\\n \\n dest_filename = os.path.join(dest,rel_filename)\\n try:\\n os.symlink(src_filename,dest_filename)\\n except OSError:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77486277","0.75265586","0.73959345","0.7387594","0.7377672","0.7329959","0.72826844","0.7268612","0.72428185","0.7232209","0.72291964","0.711112","0.70853853","0.70581484","0.6992751","0.6919675","0.69122905","0.68667597","0.67933685","0.6778472","0.6706351","0.67005867","0.65773827","0.65721154","0.6571356","0.65555215","0.6518847","0.65162414","0.6514415","0.6500355","0.6487762","0.647924","0.6468502","0.6460418","0.64497936","0.63792974","0.63776326","0.6368327","0.63625205","0.63469505","0.6313713","0.6310532","0.629582","0.6276171","0.62626046","0.6239745","0.6235994","0.62323445","0.6230526","0.6229034","0.622224","0.6216396","0.62027854","0.6195342","0.6189534","0.61884314","0.61873925","0.61779916","0.6161072","0.61602044","0.6152925","0.6150281","0.61409616","0.61081433","0.6101434","0.60967886","0.6092535","0.6066686","0.6055905","0.605131","0.6044578","0.60309494","0.60271955","0.60109836","0.6010113","0.60090786","0.60068023","0.5965344","0.59575856","0.5949596","0.59383714","0.59383327","0.59359765","0.59345984","0.59300095","0.59298056","0.5929654","0.59142864","0.5913955","0.59122336","0.59119576","0.5911478","0.59079486","0.59000427","0.5887887","0.58640563","0.5863982","0.58499026","0.5840297","0.5834178"],"string":"[\n \"0.77486277\",\n \"0.75265586\",\n \"0.73959345\",\n \"0.7387594\",\n \"0.7377672\",\n \"0.7329959\",\n \"0.72826844\",\n \"0.7268612\",\n \"0.72428185\",\n \"0.7232209\",\n \"0.72291964\",\n \"0.711112\",\n \"0.70853853\",\n \"0.70581484\",\n \"0.6992751\",\n \"0.6919675\",\n \"0.69122905\",\n \"0.68667597\",\n \"0.67933685\",\n \"0.6778472\",\n \"0.6706351\",\n \"0.67005867\",\n \"0.65773827\",\n \"0.65721154\",\n \"0.6571356\",\n \"0.65555215\",\n \"0.6518847\",\n \"0.65162414\",\n \"0.6514415\",\n \"0.6500355\",\n \"0.6487762\",\n \"0.647924\",\n \"0.6468502\",\n \"0.6460418\",\n \"0.64497936\",\n \"0.63792974\",\n \"0.63776326\",\n \"0.6368327\",\n \"0.63625205\",\n \"0.63469505\",\n \"0.6313713\",\n \"0.6310532\",\n \"0.629582\",\n \"0.6276171\",\n \"0.62626046\",\n \"0.6239745\",\n \"0.6235994\",\n \"0.62323445\",\n \"0.6230526\",\n \"0.6229034\",\n \"0.622224\",\n \"0.6216396\",\n \"0.62027854\",\n \"0.6195342\",\n \"0.6189534\",\n \"0.61884314\",\n \"0.61873925\",\n \"0.61779916\",\n \"0.6161072\",\n \"0.61602044\",\n \"0.6152925\",\n \"0.6150281\",\n \"0.61409616\",\n \"0.61081433\",\n \"0.6101434\",\n \"0.60967886\",\n \"0.6092535\",\n \"0.6066686\",\n \"0.6055905\",\n \"0.605131\",\n \"0.6044578\",\n \"0.60309494\",\n \"0.60271955\",\n \"0.60109836\",\n \"0.6010113\",\n \"0.60090786\",\n \"0.60068023\",\n \"0.5965344\",\n \"0.59575856\",\n \"0.5949596\",\n \"0.59383714\",\n \"0.59383327\",\n \"0.59359765\",\n \"0.59345984\",\n \"0.59300095\",\n \"0.59298056\",\n \"0.5929654\",\n \"0.59142864\",\n \"0.5913955\",\n \"0.59122336\",\n \"0.59119576\",\n \"0.5911478\",\n \"0.59079486\",\n \"0.59000427\",\n \"0.5887887\",\n \"0.58640563\",\n \"0.5863982\",\n \"0.58499026\",\n \"0.5840297\",\n \"0.5834178\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7675131"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":77087,"cells":{"query":{"kind":"string","value":"Produces embedding of text"},"document":{"kind":"string","value":"def embed_text(self, data: List[str]) -> List[Optional[torch.Tensor]]:\n raise NotImplementedError"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_text_embeds(self, text):\n\n # tokenize the text\n text_input = self.tokenizer(text,\n padding='max_length',\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors='pt')\n # embed the text\n with torch.no_grad():\n text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\n return text_embeds","def embed(text: str) -> np.ndarray:\n n = nlp(text)\n return n.vector","def embed_text(tensors, embeddings):\n wids = tensors[\"wids\"]\n cids = tensors[\"cids\"]\n\n embedding_weights = embeddings.get_initialized_params(trainable=False)\n word_vecs = tf.nn.embedding_lookup(embedding_weights, wids)\n char_emb = common_layers.character_cnn(cids)\n return tf.concat([word_vecs, char_emb], -1)","def get_embedding():\n\n HOST = app.config.get('EMBEDDING_HOST')\n PORT = app.config.get('EMBEDDING_PORT')\n query_params = {\n 'text' : request.args.get('text', \"\"),\n \"language\" : request.args.get('language', None)\n }\n r = requests.get(f\"http://{HOST}:{PORT}/api/v1/embeddings/create\", params=query_params)\n return jsonify(r.json())","def _embedding_func(self, text: str, *, engine: str) -> List[float]:\n # handle large input text\n if self.embedding_ctx_length > 0:\n return self._get_len_safe_embeddings([text], engine=engine)[0]\n else:\n # replace newlines, which can negatively affect performance.\n text = text.replace(\"\\n\", \" \")\n return embed_with_retry(self, input=[text], engine=engine)[\"data\"][0][\n \"embedding\"\n ]","def gen_embedding(text, model, tokenizer):\n ### Tokenize the texts\n encoded_input = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors='pt')\n \n ### Encode the tokenized data with model\n with torch.no_grad():\n model_output = model(**encoded_input)\n \n ### Pool the outputs into a single vector\n sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])\n return sentence_embeddings","def create_embedding(skills):\n corpus = list(skills[\"description\"].values)\n embedder = SentenceTransformer(config[\"sentence_transformer\"][\"model\"])\n embedding = embedder.encode(corpus, show_progress_bar=True)\n return embedding","def embed_word(self):\n return self.emb.get_keras_embedding(dropout = self.emb_dropout,\n trainable = self.trainable_emb,\n input_length = self.sent_maxlen)","def embed(documents, ctx_encoder, ctx_tokenizer, device):\n input_ids = ctx_tokenizer(\n documents[\"title\"],\n documents[\"text\"],\n truncation=True,\n padding=\"longest\",\n return_tensors=\"pt\",\n )[\"input_ids\"]\n embeddings = ctx_encoder(\n input_ids.to(device=device), return_dict=True\n ).pooler_output\n return {\"embeddings\": embeddings.detach().cpu().numpy()}","def embed_word(self):\n return self.emb.get_keras_embedding(trainable = self.trainable_emb,\n input_length = self.sent_maxlen)","def generate_sentence_embeddings():\n generate_embeddings_sentence(\"Data/en-train.json\", \"Data_Sent_Embds/en_sent.pkl\")\n generate_embeddings_sentence(\"Data/es-train.json\", \"Data_Sent_Embds/es_sent.pkl\")\n generate_embeddings_sentence(\"Data/pr-train.json\", \"Data_Sent_Embds/pr_sent.pkl\")","def embed():","def astext(self):\n self.elements.update({\n 'body': u''.join(self.body),\n 'indices': self.generate_indices()\n })\n return self.render('beamer.tex_t', self.elements)","def embed(self, word: Any) -> dy.Expression:\n raise NotImplementedError('embed must be implemented in Embedder subclasses')","def embedd_data(training_data_text, e_arr, e_dict):\n num_samples = len(training_data_text)\n embedded = np.zeros([num_samples, MAX_WORDS_IN_REVIEW, EMBEDDING_SIZE])\n for i in range(num_samples):\n review_mat = np.zeros([MAX_WORDS_IN_REVIEW, EMBEDDING_SIZE])\n # Iterate to either the end of the sentence of the max num of words, whichever is less\n for w in range(min(len(training_data_text[i]), MAX_WORDS_IN_REVIEW)):\n # assign embedding of that word or to the UNK token if that word isn't in the dict\n review_mat[w] = e_arr[e_dict.get(training_data_text[i][w], 0)]\n embedded[i] = review_mat\n return embedded","def __glove_embed__(sequence, model):\n embedded = []\n for word in sequence:\n embedded.append(model[word])\n return embedded","def get_embeddings(vectors, text, generate_missing=False, k=300):\r\n embeddings = text.apply(lambda x: get_average_vec(x, vectors, generate_missing=generate_missing, k=k))\r\n return list(embeddings)","def _embed(self):\n with tf.variable_scope('word_embedding'):\n self.pretrained_word_mat = tf.get_variable(\"word_emb_mat\",\n [self.vocab.word_size() - 2, self.vocab.word_embed_dim],\n dtype=tf.float32,\n initializer=tf.constant_initializer(\n self.vocab.word_embeddings[2:],\n dtype=tf.float32),\n trainable=False)\n self.word_pad_unk_mat = tf.get_variable(\"word_unk_pad\",\n [2, self.pretrained_word_mat.get_shape()[1]],\n dtype=tf.float32,\n initializer=tf.constant_initializer(\n self.vocab.word_embeddings[:2],\n dtype=tf.float32),\n trainable=True)\n\n self.word_mat = tf.concat([self.word_pad_unk_mat, self.pretrained_word_mat], axis=0)\n self.p_emb = tf.nn.embedding_lookup(self.word_mat, self.p)\n self.q_emb = tf.nn.embedding_lookup(self.word_mat, self.q)","def compute_user_input_embedding(txt, model):\r\n embeddings = []\r\n tokens = txt.split(\" \")\r\n for word in tokens:\r\n embeddings.append(model.wv[word])\r\n sentence_embedding = compute_average(embeddings)\r\n return sentence_embedding","def embed(self, x):\n if self.embedding is None:\n return x\n else:\n return self.embedding(x)","def visualize_embedding(embeddings, idx2word, num_words=300, title=\"\"):\n tsne = TSNE()\n embed_tsne = tsne.fit_transform(embeddings[:num_words, :])\n fig, ax = plt.subplots(figsize=(14, 14))\n for idx in range(num_words):\n plt.scatter(*embed_tsne[idx, :], color='steelblue')\n plt.annotate(idx2word[idx], (embed_tsne[idx, 0], embed_tsne[idx, 1]),\n alpha=0.7)\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n ax.set_title(title)\n plt.show()","def embed(self, lang, seq, mask):\n inputs = []\n batch_size, seq_len = seq.size()\n inputs.append(self.embedder(seq))\n if self.args.use_cluster_features:\n H = self.cluster_typology(lang)\n H = H.view(1, 1, -1).expand(batch_size, seq_len, H.numel())\n inputs.append(H)\n if self.args.use_hand_features:\n H = self.hand_typology(lang)\n H = H.view(1, 1, -1).expand(batch_size, seq_len, H.numel())\n inputs.append(H)\n if self.args.use_neural_features:\n N = self.neural_typology(seq, mask)\n N = N.view(1, 1, -1).expand(batch_size, seq_len, N.numel())\n inputs.append(N)\n return torch.cat(inputs, dim=-1)","def thm_embedding(self, thm: Text) -> THM_EMB_TYPE:\n # Pack and unpack the thm into a batch of size one.\n [embedding] = self.batch_thm_embedding([thm])\n return embedding","def embed(self, loader, model):\n print(\" ** Embedding words\")\n\n words = loader.words\n vectors = [model.get_word_vector(word) for word in words]\n\n return [(w, *v) for w, v in zip(words, vectors)]","def generate(self, text, prev_mel):\n # forward pass through text embedding and get k and v\n kv = self.t_encoder(text)\n k = kv[:,:self.hp.d,:]\n v = kv[:,self.hp.d:,:]\n # forward pass through audio encoding and get Q\n q = self.a_encoder(prev_mel)\n \n # compute attention and use forcible incremental attention (section 4.2)\n a = (k.transpose(2,1)).matmul(q)/np.sqrt(self.hp.d)\n a = F.softmax(a, dim=1)\n \"\"\"\n # get argmax\n argmax = a[0].argmax(dim=0) # argmax on the N dimension\n # forcibly incremental attention\n preva = -1\n for i in range(a.shape[-1]):\n if argmax[i] < preva -1 or preva + 3 < argmax[i]:\n # force the ith column to be zero\n a[:,:,i] = 0\n # find correct position\n position = min(a.shape[1]-1, preva + 1)\n a[:,position,i] = 1.0\n # update preva\n preva = a[0,:,i].argmax()\"\"\"\n\n # finish computing y and a\n r = r = v.matmul(a)\n\n rprime = torch.cat((r, q), dim=1)\n ylogit = self.decoder(rprime)\n y = F.sigmoid(ylogit)\n return y, ylogit, a","def embed_query(self, text: str) -> List[float]:\n embedding = self._embedding_func(text, engine=self.query_model_name)\n return embedding","def embed(self,ipa_seg):\n pre_embedded = self.to_phoible_fts(ipa_seg)\n return np.matmul(PhonologicalEmbedding.__embedding_matrix,pre_embedded)","def encode(self, text):\n length = [self.mylen(s) + 1 for s in text] # +1 for [s] at end of sentence.\n batch_max_length=self.batch_max_length\n #print('---------------------',batch_max_length)\n batch_max_length += 1\n # additional +1 for [GO] at first step. batch_text is padded with [GO] token after [s] token.\n batch_text = torch.cuda.LongTensor(len(text), batch_max_length + 1).fill_(0)\n for i, t in enumerate(text):\n #print(\"LOAD TEXT\", t)\n text_one = list(t)\n text_one.append('[s]')\n #text_one = [self.dict[char] for char in text_one]\n text_one = self.myIndex(text_one)\n batch_text[i][1:1 + len(text_one)] = torch.cuda.LongTensor(text_one) \n return (batch_text, torch.cuda.IntTensor(length))","def _embed(self):\n with tf.variable_scope('word_embedding'):\n self.word_embeddings = tf.get_variable(\n 'word_embeddings',\n shape=(self.term_vocab.size(), self.term_vocab.embed_dim),\n initializer=tf.constant_initializer(self.term_vocab.embeddings),\n trainable=True\n )\n self.p_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.p)\n self.q_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.q)\n\n with tf.variable_scope('char_embedding'):\n self.char_embeddings = tf.get_variable(\n 'char_embeddings',\n shape=(self.char_vocab.size(), self.char_vocab.embed_dim),\n initializer=tf.constant_initializer(self.char_vocab.embeddings),\n trainable=True\n )\n self.p_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.p_char) # [batch, seqlen, max_char_num, embedding_size]\n self.q_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.q_char)\n\n self.p_char_emb = self.cnn_emb(self.p_char_emb, \"p_emb\")\n self.q_char_emb = self.cnn_emb(self.q_char_emb, \"q_emb\")\n '''\n self.p_char_emb = tf.reshape(self.p_char_emb, [-1, self.max_char_num, self.emb_size])\n self.q_char_emb = tf.reshape(self.q_char_emb, [-1, self.max_char_num, self.emb_size])\n\n self.p_char_emb = cnn_layer.conv(self.p_char_emb, self.emb_size,\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\"char_conv\", reuse=None)\n self.q_char_emb = cnn_layer.conv(self.q_char_emb, self.emb_size,\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\"char_conv\", reuse=True)\n\n self.p_char_emb = tf.reduce_max(self.p_char_emb, axis=1) # [batch*seqlen, 1, emb_size]\n self.q_char_emb = tf.reduce_max(self.q_char_emb, axis=1)\n\n batch_size = tf.shape(self.p_word_emb)[0]\n self.p_char_emb = tf.reshape(self.p_char_emb, [batch_size, -1, self.emb_size])\n self.q_char_emb = tf.reshape(self.q_char_emb, [batch_size, -1, self.emb_size])\n\n self.p_char_emb = tf.nn.dropout(self.p_char_emb, 0.95)\n self.q_char_emb = tf.nn.dropout(self.q_char_emb, 0.95)\n '''\n self.p_emb = tf.concat([self.p_word_emb, self.p_char_emb], -1)\n self.q_emb = tf.concat([self.q_word_emb, self.q_char_emb], -1)","def embed_sent(self, x: Any) -> expression_seqs.ExpressionSequence:\n # single mode\n if not batchers.is_batched(x):\n expr = expression_seqs.ExpressionSequence(expr_list=[self.embed(word) for word in x])\n # minibatch mode\n elif type(self) == LookupEmbedder:\n embeddings = []\n for word_i in range(x.sent_len()):\n batch = batchers.mark_as_batch([single_sent[word_i] for single_sent in x])\n embeddings.append(self.embed(batch))\n expr = expression_seqs.ExpressionSequence(expr_list=embeddings, mask=x.mask)\n else:\n assert type(x[0]) == sent.SegmentedSentence, \"Need to use CharFromWordTextReader for non standard embeddings.\"\n embeddings = []\n all_embeddings = []\n for sentence in x:\n embedding = []\n for i in range(sentence.len_unpadded()):\n embed_word = self.embed(sentence.words[i])\n embedding.append(embed_word)\n all_embeddings.append(embed_word)\n embeddings.append(embedding)\n # Useful when using dy.autobatch\n dy.forward(all_embeddings)\n all_embeddings.clear()\n # Pad the results\n expr = batchers.pad_embedding(embeddings)\n\n return expr","def get_unweighted_text_embeddings(\n text_encoder: CLIPTextModel,\n text_input: torch.Tensor,\n chunk_length: int,\n clip_skip: int,\n eos: int,\n pad: int,\n no_boseos_middle: Optional[bool] = True,\n):\n max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)\n if max_embeddings_multiples > 1:\n text_embeddings = []\n pool = None\n for i in range(max_embeddings_multiples):\n # extract the i-th chunk\n text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()\n\n # cover the head and the tail by the starting and the ending tokens\n text_input_chunk[:, 0] = text_input[0, 0]\n if pad == eos: # v1\n text_input_chunk[:, -1] = text_input[0, -1]\n else: # v2\n for j in range(len(text_input_chunk)):\n if text_input_chunk[j, -1] != eos and text_input_chunk[j, -1] != pad: # 最後に普通の文字がある\n text_input_chunk[j, -1] = eos\n if text_input_chunk[j, 1] == pad: # BOSだけであとはPAD\n text_input_chunk[j, 1] = eos\n\n # -2 is same for Text Encoder 1 and 2\n enc_out = text_encoder(text_input_chunk, output_hidden_states=True, return_dict=True)\n text_embedding = enc_out[\"hidden_states\"][-2]\n if pool is None:\n pool = enc_out.get(\"text_embeds\", None) # use 1st chunk, if provided\n if pool is not None:\n pool = train_util.pool_workaround(text_encoder, enc_out[\"last_hidden_state\"], text_input_chunk, eos)\n\n if no_boseos_middle:\n if i == 0:\n # discard the ending token\n text_embedding = text_embedding[:, :-1]\n elif i == max_embeddings_multiples - 1:\n # discard the starting token\n text_embedding = text_embedding[:, 1:]\n else:\n # discard both starting and ending tokens\n text_embedding = text_embedding[:, 1:-1]\n\n text_embeddings.append(text_embedding)\n text_embeddings = torch.concat(text_embeddings, axis=1)\n else:\n enc_out = text_encoder(text_input, output_hidden_states=True, return_dict=True)\n text_embeddings = enc_out[\"hidden_states\"][-2]\n pool = enc_out.get(\"text_embeds\", None) # text encoder 1 doesn't return this\n if pool is not None:\n pool = train_util.pool_workaround(text_encoder, enc_out[\"last_hidden_state\"], text_input, eos)\n return text_embeddings, pool","def embed_sentence(self, sentence:List[str]):\n # prepare the input that can be fed to bert model\n encoded_sentence, indices_subwords = self._prepare_input(sentence[0])\n with torch.no_grad():\n bert_output = self.bert_model.forward(input_ids=encoded_sentence)\n \n # take the sequence of the last four hidden states (the last element of the tuple returned by the bert model)\n # list of tensors (batch_size x num_of_splitted_words x embedding_dim)\n bert_output = list(bert_output[-1][-4:])\n bert_output.reverse()\n \n # stack the hidden states in a tensor (4 x batch_size x num_of_splitted_words x embedding_dim)\n hidden_states = torch.stack(bert_output, axis=0)\n # sum the hidden states (batch_size x num_of_splitted_words x embedding_dim)\n sum_hidden_states = torch.sum(hidden_states, axis=0)\n # merge the words splitted in subwords by the tokenizer (batch_size x sentence_length x embedding_dim)\n embed_output = self._merge_embeddings(sum_hidden_states[0], indices_subwords)\n return embed_output","def Aut(A):\n return Embeddings(A,A)","def _single_encode_text(self, text: str) -> np.array:\n input_ids = self._tokenize_text(text)\n print(f\"id length: {len(input_ids[0])}\")\n try:\n text_embeddings = self.model(input_ids)\n except IndexError as e:\n print(f\"input text: {text}\")\n print(f\"input inds: {input_ids}\")\n raise e\n return text_embeddings.pooler_output.detach().numpy()","def encode(self, embedded_sentence):\n embedding = self.encoder(embedded_sentence)\n return embedding","def encode(self, text, batch_max_length=25):\n length = [len(s) + 1 for s in text] # +1 for [s] at end of sentence.\n # batch_max_length = max(length) # this is not allowed for multi-gpu setting\n batch_max_length += 1\n # additional +1 for [GO] at first step. batch_text is padded with [GO] token after [s] token.\n batch_text = torch.LongTensor(len(text), batch_max_length + 1).fill_(0)\n for i, t in enumerate(text):\n text = list(t)\n text.append('[s]')\n text = [self.dict[char] for char in text]\n batch_text[i][1:1 + len(text)] = torch.LongTensor(text) # batch_text[:, 0] = [GO] token\n return (batch_text.to(device), torch.IntTensor(length).to(device))","def encode(self, text, batch_max_length=25):\n length = [len(s) + 1 for s in text] # +1 for [s] at end of sentence.\n # batch_max_length = max(length) # this is not allowed for multi-gpu setting\n batch_max_length += 1\n # additional +1 for [GO] at first step. batch_text is padded with [GO] token after [s] token.\n batch_text = torch.LongTensor(len(text), batch_max_length + 1).fill_(0)\n for i, t in enumerate(text):\n text = list(t)\n text.append('[s]')\n text = [self.dict[char] for char in text]\n batch_text[i][1:1 + len(text)] = torch.LongTensor(text) # batch_text[:, 0] = [GO] token\n return (batch_text.to(device), torch.IntTensor(length).to(device))","def embed(self, sequence):\n words = sequence.split(' ')\n vecs = [self._E[self._w2i[i]] if i in self._w2i else self._E[self._w2i[\"UNK\"]]\n for i in words]\n return vecs","def do(text):\n return freeling_stemming(text)","def set_embeddings(self):","def encode_text(self, text):\n text = clip.tokenize(text).to(self.device)\n text_features = self.clip_model.encode_text(text)\n return text_features.cpu().detach().numpy()","def build_tensorize_text_fn(embeddings):\n tbl = contrib_lookup.index_table_from_tensor(\n mapping=embeddings.get_vocab(), num_oov_buckets=1)\n\n def fn(string_tensor):\n \"\"\"Builds the output tensor dictionary.\"\"\"\n out = {}\n if FLAGS.lowercase:\n string_tensor = ops.lowercase_op(string_tensor)\n out[\"wids\"] = tf.to_int32(tbl.lookup(string_tensor))\n out[\"cids\"] = char_utils.batch_word_to_char_ids(string_tensor, 50)\n out[\"len\"] = tf.shape(string_tensor)[-1]\n return out\n\n return fn","def frontend(text):\n text = pyopenjtalk.g2p(text, kana=False)\n print(f\"Cleaned text: {text}\")\n charseq = text.split(\" \")\n idseq = []\n for c in charseq:\n if c.isspace():\n idseq += [char_to_id[\"<space>\"]]\n elif c not in char_to_id.keys():\n idseq += [char_to_id[\"<unk>\"]]\n else:\n idseq += [char_to_id[c]]\n idseq += [idim - 1] # <eos>\n return torch.LongTensor(idseq).view(-1).to(device)","def make_lexicon_words_txt(self):\n raise NotImplementedError","def __init__(self, name, config, embed_mat):\n if 'tune_embeds' not in config.keys():\n raise ValueError('config must define \"tune_embeds\".')\n super(TextModel, self).__init__(name, config)\n self.embeds = nn.Embedding(embed_mat.shape[0], embed_mat.shape[1])\n self.embeds.weight = nn.Parameter(torch.from_numpy(embed_mat),\n requires_grad=self.tune_embeds)","def get_features(self) -> Generator[np.ndarray, None, None]:\n for text in self.texts:\n yield embed(text)","def _get_embedding_layer(self, input_data, doc_input_data):\n opts = self._options\n word_embedding = tf.Variable(tf.random_uniform((self.vocab_size, opts.embed_dim), -1.0, 1.0))\n embed = []\n\n temp = tf.zeros([opts.batch_size, opts.embed_dim])\n embed_d = []\n for n in range(opts.sentence_sample):\n temp = tf.add(temp, tf.nn.embedding_lookup(word_embedding, doc_input_data[:, n]))\n embed_d.append(temp)\n\n if opts.concat == 'True':\n combined_embed_vector_length = opts.embed_dim * opts.window_size + opts.embed_dim\n for j in range(opts.window_size):\n embed_w = tf.nn.embedding_lookup(word_embedding, input_data[:, j])\n embed.append(embed_w)\n embed.append(embed_d)\n else:\n combined_embed_vector_length = opts.embed_dim\n embed_w = tf.zeros([opts.batch_size, opts.embed_dim])\n for j in range(opts.window_size):\n embed_w += tf.nn.embedding_lookup(word_embedding, input_data[:, j])\n embed_w += embed_d\n embed.append(embed_w)\n\n return tf.concat(embed, 1), word_embedding, combined_embed_vector_length","def word2vec2embed(word2vec, word2idx):\n\temb_dim = word2vec['the'].shape[0]\n\temb = torch.nn.Embedding(len(word2idx), emb_dim) \n\temb_matrix = []\n\tfor w, idx in word2idx.items():\n\t\tif w in word2vec:\n\t\t\temb_matrix.append(word2vec[w])\n\t\telse:\n\t\t\temb_matrix.append(np.zeros(emb_dim,))\n\temb.weight.data.copy_(torch.from_numpy(np.array(emb_matrix)))\n\treturn emb","def text(self) -> str:","def embed(self, data, mime_type=\"text/plain\", encode_data_to_base64=True):\n if encode_data_to_base64:\n data = base64.standard_b64encode(data.encode()).decode()\n self.embeddings.append({\"data\": data, \"mime_type\": mime_type})","def make_text(markov_chains):\n\n random_num = generate_random_number(markov_chains.keys())\n\n random_text = []\n\n start_words = generate_start_words(random_num, markov_chains.keys())\n \n random_text.extend(start_words)\n\n\n for i in range(500):\n word_tuple = (random_text[-2],random_text[-1])\n next_word = add_next_word(word_tuple, markov_chains)\n random_text.append(next_word)\n\n return random_text","def entities_text(text):\n client = language.LanguageServiceClient()\n\n # Instantiates a plain text document.\n document = types.Document(\n content=text,\n type=enums.Document.Type.PLAIN_TEXT)\n\n # Detects entities in the document. You can also analyze HTML with:\n # document.type == enums.Document.Type.HTML\n entities = client.analyze_entities(document).entities\n\n # entity types from enums.Entity.Type\n entity_type = ('UNKNOWN', 'PERSON', 'LOCATION', 'ORGANIZATION',\n 'EVENT', 'WORK_OF_ART', 'CONSUMER_GOOD', 'OTHER')\n \n ingredients = []\n #out = \"\"\n products = [] \n wegmans = WegmansClass()\n\n for entity in entities:\n ingredients.append(entity.name)\n #out += entity.name + '\\n'\n weg_sku = wegmans.GetSKUs(entity.name)\n\n prod = []\n\n for x in weg_sku:\n r = wegmans.GetProduct(x)\n prod.append(r)\n #out += r.name + \", \" + str(r.price) + \", \" + str(r.image) + str(r.velocity) + '#'\n\n products.append(prod)\n\n #print('=' * 20)\n #print(u'{:<16}: {}'.format('name', entity.name))\n #print(u'{:<16}: {}'.format('type', entity_type[entity.type]))\n #print(u'{:<16}: {}'.format('metadata', entity.metadata))\n #print(u'{:<16}: {}'.format('salience', entity.salience))\n #print(u'{:<16}: {}'.format('wikipedia_url',\n # entity.metadata.get('wikipedia_url', '-')))\n\n #for x in ingredients:\n # print(x + '\\n')\n\n #for x in products:\n # for y in x:\n # print(y.name)\n # print(y.price)\n # print(y.velocity)\n # print(y.image)\n # print('\\t')\n\n js = json.dumps(products, default=lambda o: o.__dict__)\n \n return jsonify({\"results\": json.loads(js)})","def text():\n return {\n \"@context\": \"http://www.w3.org/ns/anno.jsonld\",\n \"type\": \"Annotation\",\n \"body\": {\n \"creator\": \"user\",\n \"type\": \"TextualBody\",\n \"value\": \"string\"\n },\n \"generator\": {\n \"homepage\": \"http://mnemosyne.ml\",\n \"id\": \"string\",\n \"name\": \"Mnemosyne\",\n \"type\": \"Mnemosyne\"\n },\n \"target\": {\n \"id\": \"string\",\n \"type\": \"TextQuoteSelector\",\n \"exact\": \"string\",\n \"format\": \"string\",\n \"source\": \"string\",\n \"prefix\": 0,\n \"suffix\": 0,\n \"refinedBy\": {\n \"type\": \"TextPositionSelector\",\n \"start\": \"/div[2]\",\n \"end\": \"/div[2]\"\n },\n },\n }","def encode(self, text):","def get_movie_embedding(self):\n raise NotImplementedError(\"has to be overwritten\")","def _embed(self, ys):\n mask = ys != PADDING_VALUE\n\n embed_ys = []\n for i, y in enumerate(ys):\n unpadded_embed = self.word_piece_embedding(y[mask[i]])\n padded_embed = torch.cat([unpadded_embed,\n torch.zeros(((~mask[i]).sum(), self.embedding_dim), device=DEVICE)])\n embed_ys.append(padded_embed)\n\n return torch.stack(embed_ys)","def visualise_embedding(embedding_dir, perplexity, learning_rate, image_path_name, label_mapping):\n tsne = TSNE(\n n_components=2,\n random_state=0,\n perplexity=perplexity,\n learning_rate=learning_rate,\n n_iter=20000\n )\n\n with open(embedding_dir, 'r') as emb_file:\n embedding_list = emb_file.readlines()\n\n print('Number of subword units: {}'.format(len(embedding_list)))\n\n embedding_dict = {}\n vector_emb = []\n subword_labels = []\n # TODO: Make this a clean solution\n # Start at 2 to skip the random </s> character which is coming through (may need to be 3 for Georgian)\n for embedding in embedding_list[2:]:\n segmented_embedding = embedding.split()\n subword_labels.append(label_mapping[segmented_embedding[0]])\n embedding_vector = [float(dim) for dim in segmented_embedding[1:]]\n vector_emb.append(embedding_vector)\n embedding_dict[segmented_embedding[0]] = embedding_vector\n\n emb_2d = tsne.fit_transform(vector_emb)\n\n datapoint_indices = range(len(emb_2d))\n fig, ax = plt.subplots()\n for i, subword_label in zip(datapoint_indices, subword_labels):\n ax.scatter(emb_2d[i, 0], emb_2d[i, 1], c='c', label=subword_label)\n\n\n for i, subword_label in enumerate(subword_labels):\n ax.annotate(subword_label, (emb_2d[i, 0], emb_2d[i, 1]))\n\n plt.savefig(image_path_name)\n return embedding_dict","def nltk_text(self, text):\n text = nltk.Text(word_tokenize(text))\n return text","def get_embed(input_data, vocab_size, embed_dim):\n embedding = tf.Variable(tf.random_uniform((vocab_size,embed_dim), -1, 1))\n embed = tf.nn.embedding_lookup(embedding, input_data)\n #print (\"embed_dim: \",embed_dim) # 向量表达维度为 256\n #print (\"input_data.shape: \",input_data.shape) # (50, 5)\n #print (\"embed.shap: \", embed.shape) # word 的向量表达 ==特征 (50, 5, 256) ==(batch_size, num_step, embed_dim)\n return embed # 返回input的向量表达","def load_embed_text(embed_file):\n \n emb_dict = dict()\n emb_size = None\n with codecs.getreader(\"utf-8\")(tf.gfile.GFile(embed_file, \"rb\")) as f:\n for line in f:\n tokens = line.strip().split(\" \")\n word = tokens[0]\n vec = list(map(float, tokens[1:]))\n emb_dict[word] = vec\n if emb_size:\n assert emb_size == len(vec), \"All embeddings should be same size\"\n else:\n emb_size = len(vec)\n return emb_dict, emb_size","def embed_xseq(self,xseq):\n xseq_embed = self.emat[xseq]\n return xseq_embed","def embed(self, h, r, t):\n emb_h = self.ent_embeddings(h)\n emb_r = self.rel_embeddings(r)\n emb_t = self.ent_embeddings(t)\n return emb_h, emb_r, emb_t","def embed(self, h, r, t):\n emb_h = self.ent_embeddings(h)\n emb_r = self.rel_embeddings(r)\n emb_t = self.ent_embeddings(t)\n return emb_h, emb_r, emb_t","def embed(self, h, r, t):\n emb_h = self.ent_embeddings(h)\n emb_r = self.rel_embeddings(r)\n emb_t = self.ent_embeddings(t)\n return emb_h, emb_r, emb_t","def concept_embedding(concept_model: ConceptDetectionModel2D):\n return concept_model.to_embedding()","def generate_embeddings(vae, dataset):\n data = ((torch.unsqueeze(img,0), label) for img, label in dataset)\n data = ((vae.encoder(tens), label) for tens, label in data)\n data = ((vae.codebook(emb),label) for emb, label in data)\n data = ((torch.flatten(img),label) for img, label in data)\n data = (torch.cat([inds,Tensor([label]).int()]) for inds, label in data)\n return data","def get_embedding(A,B):\n st = minion_hom_algebras(A,B,inj=True)\n writefile('tmp.minion',st)\n os.system('minion -noprintsols -solsout tmp.txt tmp.minion >tmpout.txt')\n st = readfile('tmp.txt')\n os.system('rm tmp.txt')\n emb = st.split(\"\\n\")[:-1]\n if len(emb)>1: return emb[0]","def main():\n logging.basicConfig(level=logging.DEBUG)\n custom_embedding = True\n\n # Download embeddings'\n if custom_embedding:\n embedding_path = '../data/custom_embedding.pkl'\n embedding_index_path = '../data/custom_vocab_index.pkl'\n logging.info('Pulling custom embedding from: {}, and custom vocab from: {}'.format(embedding_path, embedding_index_path))\n embedding_matrix = pickle.load(open(embedding_path, 'rb'))\n embedding_index_lookup = pickle.load(open(embedding_index_path, 'rb'))\n\n else:\n logging.warning('Downloading embedding. If downloading for the first time, this make take 5-10 minutes.')\n embedding_url = 'https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz'\n embedding_path = '~/nlp_example/'\n embedding_filename = 'GoogleNews-vectors-negative300.bin.gz'\n lib.download_file(embedding_url, embedding_path, embedding_filename)\n\n # Unpack embedding\n model = gensim.models.KeyedVectors.load_word2vec_format(embedding_path + '/' + embedding_filename, binary=True)\n embedding_matrix = model.syn0\n embedding_index_lookup = dict([(k, v.index) for k, v in model.vocab.items()])\n\n # Create thesaurus\n thesaurus = Thesaurus(embedding_matrix, embedding_index_lookup)\n\n # Find nearest neighbors for examples\n print(thesaurus.synonyms('day'))\n print(thesaurus.synonyms('top'))\n print(thesaurus.synonyms('bottom'))\n print(thesaurus.synonyms('cat'))\n print(thesaurus.synonyms('grown'))\n\n\n pass","def encode(self, text, batch_max_length=25):\n length = [len(s) for s in text]\n\n # The index used for padding (=0) would not affect the CTC loss calculation.\n batch_text = torch.LongTensor(len(text), batch_max_length).fill_(0)\n for i, t in enumerate(text):\n text = list(t)\n text = [self.dict[char] for char in text]\n batch_text[i][:len(text)] = torch.LongTensor(text)\n return (batch_text.to(device), torch.IntTensor(length).to(device))","def prepare_text(gene, enrich, variants, numbers):\n \n external = enrich[enrich[\"hgnc\"] == gene]\n sites = variants[variants[\"hgnc\"] == gene]\n \n # make sure the variant count matches the analyzed count\n if gene not in list(enrich[\"hgnc\"]):\n assert len(sites) == 0\n else:\n assert list(external[\"dnms\"])[0] == len(sites)\n \n if len(external) > 1:\n text = \"too many possible rows for {}\".format(gene)\n else:\n external = external.squeeze()\n initial_text = initial_sentence(external, gene, sites[\"pubmed\"], numbers)\n counts_text = counts_sentence(external, numbers)\n clust_text = clustering_sentence(external)\n evidence_text = strength_sentence(external)\n \n if len(external) > 0 and external[\"dnms\"] > 0:\n text = initial_text + \" \" + counts_text + \" \" + evidence_text+ \" \" + clust_text\n else:\n text = initial_text + \" \" + evidence_text\n \n return text","def addContent(text):","def get_embeddings(self, data):\n raise NotImplementedError()","def make_text(sent, begin, end):\n lemmas = [sent.morps[begin].lemma(), ]\n for idx in range(begin+1, end):\n if sent.mid2wid[idx-1] != sent.mid2wid[idx]: # if go over word boundary\n # insert space between words\n lemmas.append(' ')\n lemmas.append(sent.morps[idx].lemma())\n return ''.join(lemmas)","def _get_embedding(self, data):\n embedding_list = [super()._get_embedding(data)]\n context = data['context']\n for i in range(context.shape[1]):\n embedding_list.append(getattr(self, f'context{i}')(context[:, i:i+1]))\n return torch.cat(embedding_list, dim=1)","def preprocess(text):\n text = remove_space(text)\n text = clean_special_punctuations(text)\n text = handle_emojis(text)\n text = clean_number(text)\n text = spacing_punctuation(text)\n text = clean_repeat_words(text)\n text = remove_space(text)\n #text = stop(text)# if changing this, then chnage the dims \n #(not to be done yet as its effecting the embeddings..,we might be\n #loosing words)...\n return text","def compose_embedding(cls, sub_embs, metadata):\n return EmbeddingList(\n embeddings=[\n EmbeddingList(\n embeddings=[\n sub_embs.get(ModelInput.TEXT),\n sub_embs.get(ModelInput.DICT),\n sub_embs.get(ModelInput.CHAR),\n sub_embs.get(ModelInput.CONTEXTUAL_TOKEN_EMBEDDING),\n ],\n concat=True,\n ),\n sub_embs.get(ModelInput.SEQ),\n ],\n concat=False,\n )","def generateByWord(model, voc, maxlen=20, diversity=0.5, numwords=42):\n\n text, sym_indices, indices_sym = voc\n syms = set(text)\n start_index = random.randint(0, len(text) - maxlen - 1) \n generated = ''\n sentence = text[start_index: start_index + maxlen]\n \n #generated += sentence\n generated += ' '.join(sentence)\n print('----- Generating with seed: \"' + ' '.join(sentence) + '\"')\n sys.stdout.write(generated)\n\n for i in range(numwords):\n x = np.zeros((1, maxlen, len(syms)))\n for t, sym in enumerate(sentence):\n x[0, t, sym_indices[sym]] = 1.\n \n preds = model.predict(x, verbose=0)[0]\n next_index = sample(preds, diversity)\n next_sym = indices_sym[next_index]\n generated += ' '+next_sym\n sentence.append(next_sym)\n tmpsentence = sentence[1:]\n sentence = tmpsentence\n sys.stdout.write(next_sym+' ')\n sys.stdout.flush()\n print()","def create_wordcloud(self, text):\n text = ' '.join(f\"{word}\" for word in text)\n mask = np.array(Image.open(os.path.join(CURRDIR, \"cloud.png\")))\n wc = WordCloud(background_color=\"white\",\n max_words=200,\n mask=mask)\n wc.generate(text)\n wc.to_file(PATH_TO_SAVE_IMG, \"wordle.png\")","def entity_recognition(text: str) -> spacy:\n nlp = spacy.load('en_core_web_sm')\n document = nlp(text)\n return document","def initial_sentence(external, gene, pubmed_ids, numbers):\n \n pubmed_text = format_pubmed(pubmed_ids)\n \n dnms = 0\n if len(external) > 0:\n dnms = external[\"dnms\"]\n \n # figure out the correct type for the word \"time\", e.g. 1 \"time\", 2 \"times\"\n times = \"times\"\n if len(external) > 0 and external[\"dnms\"] == 1:\n times = \"time\"\n \n initial_text = \"In other large scale exome sequencing projects de novo \" \\\n \"mutations in {} have been identified {} {}{}.\".format(gene,\\\n numbers.number_to_words(dnms), times, pubmed_text)\n \n return initial_text","def get_sentence(self):","def load_text_embed(filepath: Union[str, os.PathLike], load_dir: str = 'model') \\\n -> Tuple[TransformerEmbedding, Callable]:\n model_dir = Path(filepath).joinpath(load_dir)\n tokenizer = AutoTokenizer.from_pretrained(str(model_dir.resolve()))\n args = dill.load(open(model_dir.joinpath('embedding.dill'), 'rb'))\n emb = TransformerEmbedding(\n str(model_dir.resolve()), embedding_type=args['embedding_type'], layers=args['layers']\n )\n return emb, tokenizer","def generate_embeddings_sentence_test_data(data, path_out):\n flair.device = torch.device('cpu')\n dicts = []\n # init multilingual BERT\n bert_embedding = TransformerDocumentEmbeddings('bert-base-multilingual-cased')\n counter = 0\n for entry in data:\n print(\"Counter: \", counter)\n counter += 1\n text = entry[\"sentence\"]\n id = entry[\"id\"]\n sent = Sentence(text)\n bert_embedding.embed(sent)\n vec = sent.get_embedding().detach().numpy()\n dicts.append((id,vec))\n gc.collect()\n result = dicts\n file = open(path_out, \"wb\")\n pickle.dump(result, file)\n file.close()\n return result","def encode(self, text, batch_max_length=25):\n length = [len(s) for s in text]\n text = ''.join(text)\n text = [self.dict[char] for char in text]\n\n return (torch.IntTensor(text).to(device), torch.IntTensor(length).to(device))","def SNEmbedding(*args, **kwargs):\n return spectral_norm(nn.Embedding(*args, **kwargs))","def process_text(model_name: str, text: str) -> spacy.tokens.Doc:\r\n nlp = load_model(model_name)\r\n return nlp(text)","def gen_embedding(path):\r\n word_emb = {}\r\n with open(path, encoding='utf-8') as f:\r\n for line in tqdm(f):\r\n values = line.split()\r\n word_emb[values[0]] = np.asarray(values[1:], dtype='float32')\r\n return word_emb","def embed(self, word: str) -> list:\n return self.emb_matrix[self.wordmapper[word]]","def generate_new_book(text):\n\n for paragraph in text:\n for sentence in paragraph:\n for word in sentence:\n print(word, end=' ')\n print()\n print()","def construct_embedding(captions, cbow=True):\n\n # List of characters to filter out of the caption string\n chars_to_remove = [\"\\n\", \">\", \"--\"]\n\n for char in chars_to_remove:\n captions = captions.replace(char, \" \")\n\n # Perform some necessary tokenization\n data = [word_tokenize(word.lower()) for word in sent_tokenize(captions)]\n\n # Filter out punctuation from the data\n data_minus_punctuation = remove_punctuation(data)\n\n # Filter out stop words from the data\n data_minus_stop_words = remove_stop_words(data_minus_punctuation)\n\n # Filter out any surviving single character list elements\n fully_formatted_data = [\n [word for word in item if len(word) > 1] for item in data_minus_stop_words\n ]\n\n # Train the Word2Vec model using the specified architecture\n if cbow:\n print(\"UTILIZING CBOW ARCHITECTURE\")\n model = Word2Vec(fully_formatted_data, min_count=1, size=100, window=5)\n else:\n print(\"UTILIZING SKIPGRAM ARCHITECTURE\")\n model = Word2Vec(fully_formatted_data, min_count=1, size=100, window=5, sg=1)\n\n return model","def test_extract_embeddings():\n docs = [\"some document\"]\n model = BERTopic(embedding_model=\"distilbert-base-nli-stsb-mean-tokens\")\n bertopic_embeddings = model._extract_embeddings(docs)\n\n assert isinstance(bertopic_embeddings, np.ndarray)\n assert bertopic_embeddings.shape == (1, 768)\n\n sentence_embeddings = embedding_model.encode(docs, show_progress_bar=False)\n assert np.array_equal(bertopic_embeddings, sentence_embeddings)","def generate_text(session, model, config, starting_text='<eos>',\n stop_length=100, stop_tokens=None, temp=1.0):\n state = model.initial_state.eval()\n # Imagine tokens as a batch size of one, length of len(tokens[0])\n tokens = [model.vocab.encode(word) for word in starting_text.split()]\n for i in xrange(stop_length):\n ### YOUR CODE HERE\n #print tokens\n feed = {}\n #x = np.array([tokens[-1]])\n #x.reshape(1,1)\n feed[model.input_placeholder] = [[tokens[-1]]]\n feed[model.dropout_placeholder] = 1\n feed[model.initial_state] = state\n y_pred, state = session.run([model.predictions[-1], model.final_state], feed_dict=feed)\n ### END YOUR CODE\n next_word_idx = sample(y_pred[0], temperature=temp)\n tokens.append(next_word_idx)\n if stop_tokens and model.vocab.decode(tokens[-1]) in stop_tokens:\n break\n output = [model.vocab.decode(word_idx) for word_idx in tokens]\n return output","def load_embeddings():\n return embedding_utils.PretrainedWordEmbeddings(\n lowercase=FLAGS.lowercase,\n embeddings_path=FLAGS.fasttext_embeddings,\n max_vocab_size=FLAGS.max_vocab_size,\n skip_header=True)","def plot_embeddings(M_reduced, word2Ind, words):\n\n # YOUR CODE HERE\n \n for i,type in enumerate(words):\n x_coor,y_coor = M_reduced[word2Ind[type]][0],M_reduced[word2Ind[type]][1]\n \n plt.scatter(x_coor, y_coor, marker='*', color='red')\n plt.text(x_coor+0.05, y_coor+0.05, type, fontsize=12)\n \n plt.show()\n \n #raise NotImplementedError()","def _apply_word_emb(word_emb_enc, emb_char, word_emb):\n word_emb_enc = (\n word_emb_enc(word_emb.data)\n if word_emb_enc is not None\n else word_emb.data\n )\n return torch.cat([emb_char, word_emb_enc], dim=-1)","def load_text(self, *, scope: Scope, doc_key: str, requested_output_length: int) -> str:","def get_embedding(num_embeddings, embedding_dim, padding_idx=None):\n half_dim = embedding_dim // 2\n emb = math.log(10000) / (half_dim - 1)\n emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)\n emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)\n if embedding_dim % 2 == 1:\n emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)\n if padding_idx is not None:\n emb[padding_idx, :] = 0\n return emb","def get_plain_texts(input_dict):\n\n adc = input_dict['adc']\n annotation = input_dict['token_annotation']\n feature_annotation = input_dict['feature_annotation']\n if feature_annotation:\n annotation+=\"/\"+feature_annotation\n delimiter = input_dict['delimiter']\n includeDocId = input_dict['include_doc_id'] == \"true\"\n\n output_dict = {\"strings\": []}\n for document in adc.documents:\n output_dict[\"strings\"].append((document.name+\": \" if includeDocId else \"\") + delimiter.join([t[1] for t in document.get_annotations_with_text(annotation)]))\n return output_dict","def _embed(self):\n batch_size = tf.shape(self.p)[0]\n with tf.variable_scope(\"emb\"):\n with tf.variable_scope(\"char\"):\n pc_emb = tf.reshape(tf.nn.embedding_lookup(\n self.char_embed, self.pc), \n [batch_size * self.max_p_len, self.max_w_len, self.vocab.char_embed_dim])\n qc_emb = tf.reshape(tf.nn.embedding_lookup(\n self.char_embed, self.qc), \n [batch_size * self.max_q_len, self.max_w_len, self.vocab.char_embed_dim])\n cell_fw = tf.contrib.rnn.GRUCell(self.char_hidden_size)\n cell_bw = tf.contrib.rnn.GRUCell(self.char_hidden_size)\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\n cell_fw, cell_bw, pc_emb, self.pc_length, dtype=tf.float32)\n pc_emb = tf.concat([state_fw, state_bw], axis=1)\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\n cell_fw, cell_bw, qc_emb, self.qc_length, dtype=tf.float32)\n qc_emb = tf.concat([state_fw, state_bw], axis=1)\n pc_emb = tf.reshape(pc_emb, [batch_size, self.max_p_len, 2 * self.char_hidden_size])\n qc_emb = tf.reshape(qc_emb, [batch_size, self.max_q_len, 2 * self.char_hidden_size])\n\n with tf.name_scope(\"word\"):\n p_emb = tf.nn.embedding_lookup(self.word_embed, self.p)\n q_emb = tf.nn.embedding_lookup(self.word_embed, self.q)\n\n with tf.name_scope(\"pos\"):\n p_pos_emb = tf.nn.embedding_lookup(self.pos_embed, self.p_pos)\n q_pos_emb = tf.nn.embedding_lookup(self.pos_embed, self.q_pos)\n \n with tf.name_scope(\"em\"):\n sh = tf.shape(self.p_em)\n resh = [sh[0], sh[1], 1]\n p_em_feat = tf.reshape(tf.cast(self.p_em, dtype=tf.float32), shape=resh)\n\n self.p_emb = tf.concat([p_emb, pc_emb, p_pos_emb, p_em_feat], axis=2)\n self.q_emb = tf.concat([q_emb, qc_emb, q_pos_emb], axis=2)","def visualize(list_of_words, embeddings, embedding_dimension=300, n_components=2):\n num_word = len(list_of_words)\n\n # put embeddings in a numpy matrix\n e_matrix = np.zeros((num_word, embedding_dimension))\n for idx, w in enumerate(list_of_words):\n vec = embeddings[w]\n e_matrix[idx] = vec\n\n # reduce dimension\n transformed = TSNE(n_components=n_components).fit_transform(e_matrix)\n\n # visualize\n fig, ax = plt.subplots()\n ax.scatter(transformed[:, 0], transformed[:, 1])\n for idx, w in enumerate(list_of_words):\n ax.annotate(w, (transformed[idx, 0], transformed[idx, 1]))\n plt.plot()"],"string":"[\n \"def get_text_embeds(self, text):\\n\\n # tokenize the text\\n text_input = self.tokenizer(text,\\n padding='max_length',\\n max_length=tokenizer.model_max_length,\\n truncation=True,\\n return_tensors='pt')\\n # embed the text\\n with torch.no_grad():\\n text_embeds = self.text_encoder(text_input.input_ids.to(self.device))[0]\\n return text_embeds\",\n \"def embed(text: str) -> np.ndarray:\\n n = nlp(text)\\n return n.vector\",\n \"def embed_text(tensors, embeddings):\\n wids = tensors[\\\"wids\\\"]\\n cids = tensors[\\\"cids\\\"]\\n\\n embedding_weights = embeddings.get_initialized_params(trainable=False)\\n word_vecs = tf.nn.embedding_lookup(embedding_weights, wids)\\n char_emb = common_layers.character_cnn(cids)\\n return tf.concat([word_vecs, char_emb], -1)\",\n \"def get_embedding():\\n\\n HOST = app.config.get('EMBEDDING_HOST')\\n PORT = app.config.get('EMBEDDING_PORT')\\n query_params = {\\n 'text' : request.args.get('text', \\\"\\\"),\\n \\\"language\\\" : request.args.get('language', None)\\n }\\n r = requests.get(f\\\"http://{HOST}:{PORT}/api/v1/embeddings/create\\\", params=query_params)\\n return jsonify(r.json())\",\n \"def _embedding_func(self, text: str, *, engine: str) -> List[float]:\\n # handle large input text\\n if self.embedding_ctx_length > 0:\\n return self._get_len_safe_embeddings([text], engine=engine)[0]\\n else:\\n # replace newlines, which can negatively affect performance.\\n text = text.replace(\\\"\\\\n\\\", \\\" \\\")\\n return embed_with_retry(self, input=[text], engine=engine)[\\\"data\\\"][0][\\n \\\"embedding\\\"\\n ]\",\n \"def gen_embedding(text, model, tokenizer):\\n ### Tokenize the texts\\n encoded_input = tokenizer(text, padding=True, truncation=True, max_length=512, return_tensors='pt')\\n \\n ### Encode the tokenized data with model\\n with torch.no_grad():\\n model_output = model(**encoded_input)\\n \\n ### Pool the outputs into a single vector\\n sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])\\n return sentence_embeddings\",\n \"def create_embedding(skills):\\n corpus = list(skills[\\\"description\\\"].values)\\n embedder = SentenceTransformer(config[\\\"sentence_transformer\\\"][\\\"model\\\"])\\n embedding = embedder.encode(corpus, show_progress_bar=True)\\n return embedding\",\n \"def embed_word(self):\\n return self.emb.get_keras_embedding(dropout = self.emb_dropout,\\n trainable = self.trainable_emb,\\n input_length = self.sent_maxlen)\",\n \"def embed(documents, ctx_encoder, ctx_tokenizer, device):\\n input_ids = ctx_tokenizer(\\n documents[\\\"title\\\"],\\n documents[\\\"text\\\"],\\n truncation=True,\\n padding=\\\"longest\\\",\\n return_tensors=\\\"pt\\\",\\n )[\\\"input_ids\\\"]\\n embeddings = ctx_encoder(\\n input_ids.to(device=device), return_dict=True\\n ).pooler_output\\n return {\\\"embeddings\\\": embeddings.detach().cpu().numpy()}\",\n \"def embed_word(self):\\n return self.emb.get_keras_embedding(trainable = self.trainable_emb,\\n input_length = self.sent_maxlen)\",\n \"def generate_sentence_embeddings():\\n generate_embeddings_sentence(\\\"Data/en-train.json\\\", \\\"Data_Sent_Embds/en_sent.pkl\\\")\\n generate_embeddings_sentence(\\\"Data/es-train.json\\\", \\\"Data_Sent_Embds/es_sent.pkl\\\")\\n generate_embeddings_sentence(\\\"Data/pr-train.json\\\", \\\"Data_Sent_Embds/pr_sent.pkl\\\")\",\n \"def embed():\",\n \"def astext(self):\\n self.elements.update({\\n 'body': u''.join(self.body),\\n 'indices': self.generate_indices()\\n })\\n return self.render('beamer.tex_t', self.elements)\",\n \"def embed(self, word: Any) -> dy.Expression:\\n raise NotImplementedError('embed must be implemented in Embedder subclasses')\",\n \"def embedd_data(training_data_text, e_arr, e_dict):\\n num_samples = len(training_data_text)\\n embedded = np.zeros([num_samples, MAX_WORDS_IN_REVIEW, EMBEDDING_SIZE])\\n for i in range(num_samples):\\n review_mat = np.zeros([MAX_WORDS_IN_REVIEW, EMBEDDING_SIZE])\\n # Iterate to either the end of the sentence of the max num of words, whichever is less\\n for w in range(min(len(training_data_text[i]), MAX_WORDS_IN_REVIEW)):\\n # assign embedding of that word or to the UNK token if that word isn't in the dict\\n review_mat[w] = e_arr[e_dict.get(training_data_text[i][w], 0)]\\n embedded[i] = review_mat\\n return embedded\",\n \"def __glove_embed__(sequence, model):\\n embedded = []\\n for word in sequence:\\n embedded.append(model[word])\\n return embedded\",\n \"def get_embeddings(vectors, text, generate_missing=False, k=300):\\r\\n embeddings = text.apply(lambda x: get_average_vec(x, vectors, generate_missing=generate_missing, k=k))\\r\\n return list(embeddings)\",\n \"def _embed(self):\\n with tf.variable_scope('word_embedding'):\\n self.pretrained_word_mat = tf.get_variable(\\\"word_emb_mat\\\",\\n [self.vocab.word_size() - 2, self.vocab.word_embed_dim],\\n dtype=tf.float32,\\n initializer=tf.constant_initializer(\\n self.vocab.word_embeddings[2:],\\n dtype=tf.float32),\\n trainable=False)\\n self.word_pad_unk_mat = tf.get_variable(\\\"word_unk_pad\\\",\\n [2, self.pretrained_word_mat.get_shape()[1]],\\n dtype=tf.float32,\\n initializer=tf.constant_initializer(\\n self.vocab.word_embeddings[:2],\\n dtype=tf.float32),\\n trainable=True)\\n\\n self.word_mat = tf.concat([self.word_pad_unk_mat, self.pretrained_word_mat], axis=0)\\n self.p_emb = tf.nn.embedding_lookup(self.word_mat, self.p)\\n self.q_emb = tf.nn.embedding_lookup(self.word_mat, self.q)\",\n \"def compute_user_input_embedding(txt, model):\\r\\n embeddings = []\\r\\n tokens = txt.split(\\\" \\\")\\r\\n for word in tokens:\\r\\n embeddings.append(model.wv[word])\\r\\n sentence_embedding = compute_average(embeddings)\\r\\n return sentence_embedding\",\n \"def embed(self, x):\\n if self.embedding is None:\\n return x\\n else:\\n return self.embedding(x)\",\n \"def visualize_embedding(embeddings, idx2word, num_words=300, title=\\\"\\\"):\\n tsne = TSNE()\\n embed_tsne = tsne.fit_transform(embeddings[:num_words, :])\\n fig, ax = plt.subplots(figsize=(14, 14))\\n for idx in range(num_words):\\n plt.scatter(*embed_tsne[idx, :], color='steelblue')\\n plt.annotate(idx2word[idx], (embed_tsne[idx, 0], embed_tsne[idx, 1]),\\n alpha=0.7)\\n ax.get_xaxis().set_visible(False)\\n ax.get_yaxis().set_visible(False)\\n ax.set_title(title)\\n plt.show()\",\n \"def embed(self, lang, seq, mask):\\n inputs = []\\n batch_size, seq_len = seq.size()\\n inputs.append(self.embedder(seq))\\n if self.args.use_cluster_features:\\n H = self.cluster_typology(lang)\\n H = H.view(1, 1, -1).expand(batch_size, seq_len, H.numel())\\n inputs.append(H)\\n if self.args.use_hand_features:\\n H = self.hand_typology(lang)\\n H = H.view(1, 1, -1).expand(batch_size, seq_len, H.numel())\\n inputs.append(H)\\n if self.args.use_neural_features:\\n N = self.neural_typology(seq, mask)\\n N = N.view(1, 1, -1).expand(batch_size, seq_len, N.numel())\\n inputs.append(N)\\n return torch.cat(inputs, dim=-1)\",\n \"def thm_embedding(self, thm: Text) -> THM_EMB_TYPE:\\n # Pack and unpack the thm into a batch of size one.\\n [embedding] = self.batch_thm_embedding([thm])\\n return embedding\",\n \"def embed(self, loader, model):\\n print(\\\" ** Embedding words\\\")\\n\\n words = loader.words\\n vectors = [model.get_word_vector(word) for word in words]\\n\\n return [(w, *v) for w, v in zip(words, vectors)]\",\n \"def generate(self, text, prev_mel):\\n # forward pass through text embedding and get k and v\\n kv = self.t_encoder(text)\\n k = kv[:,:self.hp.d,:]\\n v = kv[:,self.hp.d:,:]\\n # forward pass through audio encoding and get Q\\n q = self.a_encoder(prev_mel)\\n \\n # compute attention and use forcible incremental attention (section 4.2)\\n a = (k.transpose(2,1)).matmul(q)/np.sqrt(self.hp.d)\\n a = F.softmax(a, dim=1)\\n \\\"\\\"\\\"\\n # get argmax\\n argmax = a[0].argmax(dim=0) # argmax on the N dimension\\n # forcibly incremental attention\\n preva = -1\\n for i in range(a.shape[-1]):\\n if argmax[i] < preva -1 or preva + 3 < argmax[i]:\\n # force the ith column to be zero\\n a[:,:,i] = 0\\n # find correct position\\n position = min(a.shape[1]-1, preva + 1)\\n a[:,position,i] = 1.0\\n # update preva\\n preva = a[0,:,i].argmax()\\\"\\\"\\\"\\n\\n # finish computing y and a\\n r = r = v.matmul(a)\\n\\n rprime = torch.cat((r, q), dim=1)\\n ylogit = self.decoder(rprime)\\n y = F.sigmoid(ylogit)\\n return y, ylogit, a\",\n \"def embed_query(self, text: str) -> List[float]:\\n embedding = self._embedding_func(text, engine=self.query_model_name)\\n return embedding\",\n \"def embed(self,ipa_seg):\\n pre_embedded = self.to_phoible_fts(ipa_seg)\\n return np.matmul(PhonologicalEmbedding.__embedding_matrix,pre_embedded)\",\n \"def encode(self, text):\\n length = [self.mylen(s) + 1 for s in text] # +1 for [s] at end of sentence.\\n batch_max_length=self.batch_max_length\\n #print('---------------------',batch_max_length)\\n batch_max_length += 1\\n # additional +1 for [GO] at first step. batch_text is padded with [GO] token after [s] token.\\n batch_text = torch.cuda.LongTensor(len(text), batch_max_length + 1).fill_(0)\\n for i, t in enumerate(text):\\n #print(\\\"LOAD TEXT\\\", t)\\n text_one = list(t)\\n text_one.append('[s]')\\n #text_one = [self.dict[char] for char in text_one]\\n text_one = self.myIndex(text_one)\\n batch_text[i][1:1 + len(text_one)] = torch.cuda.LongTensor(text_one) \\n return (batch_text, torch.cuda.IntTensor(length))\",\n \"def _embed(self):\\n with tf.variable_scope('word_embedding'):\\n self.word_embeddings = tf.get_variable(\\n 'word_embeddings',\\n shape=(self.term_vocab.size(), self.term_vocab.embed_dim),\\n initializer=tf.constant_initializer(self.term_vocab.embeddings),\\n trainable=True\\n )\\n self.p_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.p)\\n self.q_word_emb = tf.nn.embedding_lookup(self.word_embeddings, self.q)\\n\\n with tf.variable_scope('char_embedding'):\\n self.char_embeddings = tf.get_variable(\\n 'char_embeddings',\\n shape=(self.char_vocab.size(), self.char_vocab.embed_dim),\\n initializer=tf.constant_initializer(self.char_vocab.embeddings),\\n trainable=True\\n )\\n self.p_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.p_char) # [batch, seqlen, max_char_num, embedding_size]\\n self.q_char_emb = tf.nn.embedding_lookup(self.char_embeddings, self.q_char)\\n\\n self.p_char_emb = self.cnn_emb(self.p_char_emb, \\\"p_emb\\\")\\n self.q_char_emb = self.cnn_emb(self.q_char_emb, \\\"q_emb\\\")\\n '''\\n self.p_char_emb = tf.reshape(self.p_char_emb, [-1, self.max_char_num, self.emb_size])\\n self.q_char_emb = tf.reshape(self.q_char_emb, [-1, self.max_char_num, self.emb_size])\\n\\n self.p_char_emb = cnn_layer.conv(self.p_char_emb, self.emb_size,\\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\\\"char_conv\\\", reuse=None)\\n self.q_char_emb = cnn_layer.conv(self.q_char_emb, self.emb_size,\\n bias=True, activation=tf.nn.relu, kernel_size=5, name=\\\"char_conv\\\", reuse=True)\\n\\n self.p_char_emb = tf.reduce_max(self.p_char_emb, axis=1) # [batch*seqlen, 1, emb_size]\\n self.q_char_emb = tf.reduce_max(self.q_char_emb, axis=1)\\n\\n batch_size = tf.shape(self.p_word_emb)[0]\\n self.p_char_emb = tf.reshape(self.p_char_emb, [batch_size, -1, self.emb_size])\\n self.q_char_emb = tf.reshape(self.q_char_emb, [batch_size, -1, self.emb_size])\\n\\n self.p_char_emb = tf.nn.dropout(self.p_char_emb, 0.95)\\n self.q_char_emb = tf.nn.dropout(self.q_char_emb, 0.95)\\n '''\\n self.p_emb = tf.concat([self.p_word_emb, self.p_char_emb], -1)\\n self.q_emb = tf.concat([self.q_word_emb, self.q_char_emb], -1)\",\n \"def embed_sent(self, x: Any) -> expression_seqs.ExpressionSequence:\\n # single mode\\n if not batchers.is_batched(x):\\n expr = expression_seqs.ExpressionSequence(expr_list=[self.embed(word) for word in x])\\n # minibatch mode\\n elif type(self) == LookupEmbedder:\\n embeddings = []\\n for word_i in range(x.sent_len()):\\n batch = batchers.mark_as_batch([single_sent[word_i] for single_sent in x])\\n embeddings.append(self.embed(batch))\\n expr = expression_seqs.ExpressionSequence(expr_list=embeddings, mask=x.mask)\\n else:\\n assert type(x[0]) == sent.SegmentedSentence, \\\"Need to use CharFromWordTextReader for non standard embeddings.\\\"\\n embeddings = []\\n all_embeddings = []\\n for sentence in x:\\n embedding = []\\n for i in range(sentence.len_unpadded()):\\n embed_word = self.embed(sentence.words[i])\\n embedding.append(embed_word)\\n all_embeddings.append(embed_word)\\n embeddings.append(embedding)\\n # Useful when using dy.autobatch\\n dy.forward(all_embeddings)\\n all_embeddings.clear()\\n # Pad the results\\n expr = batchers.pad_embedding(embeddings)\\n\\n return expr\",\n \"def get_unweighted_text_embeddings(\\n text_encoder: CLIPTextModel,\\n text_input: torch.Tensor,\\n chunk_length: int,\\n clip_skip: int,\\n eos: int,\\n pad: int,\\n no_boseos_middle: Optional[bool] = True,\\n):\\n max_embeddings_multiples = (text_input.shape[1] - 2) // (chunk_length - 2)\\n if max_embeddings_multiples > 1:\\n text_embeddings = []\\n pool = None\\n for i in range(max_embeddings_multiples):\\n # extract the i-th chunk\\n text_input_chunk = text_input[:, i * (chunk_length - 2) : (i + 1) * (chunk_length - 2) + 2].clone()\\n\\n # cover the head and the tail by the starting and the ending tokens\\n text_input_chunk[:, 0] = text_input[0, 0]\\n if pad == eos: # v1\\n text_input_chunk[:, -1] = text_input[0, -1]\\n else: # v2\\n for j in range(len(text_input_chunk)):\\n if text_input_chunk[j, -1] != eos and text_input_chunk[j, -1] != pad: # 最後に普通の文字がある\\n text_input_chunk[j, -1] = eos\\n if text_input_chunk[j, 1] == pad: # BOSだけであとはPAD\\n text_input_chunk[j, 1] = eos\\n\\n # -2 is same for Text Encoder 1 and 2\\n enc_out = text_encoder(text_input_chunk, output_hidden_states=True, return_dict=True)\\n text_embedding = enc_out[\\\"hidden_states\\\"][-2]\\n if pool is None:\\n pool = enc_out.get(\\\"text_embeds\\\", None) # use 1st chunk, if provided\\n if pool is not None:\\n pool = train_util.pool_workaround(text_encoder, enc_out[\\\"last_hidden_state\\\"], text_input_chunk, eos)\\n\\n if no_boseos_middle:\\n if i == 0:\\n # discard the ending token\\n text_embedding = text_embedding[:, :-1]\\n elif i == max_embeddings_multiples - 1:\\n # discard the starting token\\n text_embedding = text_embedding[:, 1:]\\n else:\\n # discard both starting and ending tokens\\n text_embedding = text_embedding[:, 1:-1]\\n\\n text_embeddings.append(text_embedding)\\n text_embeddings = torch.concat(text_embeddings, axis=1)\\n else:\\n enc_out = text_encoder(text_input, output_hidden_states=True, return_dict=True)\\n text_embeddings = enc_out[\\\"hidden_states\\\"][-2]\\n pool = enc_out.get(\\\"text_embeds\\\", None) # text encoder 1 doesn't return this\\n if pool is not None:\\n pool = train_util.pool_workaround(text_encoder, enc_out[\\\"last_hidden_state\\\"], text_input, eos)\\n return text_embeddings, pool\",\n \"def embed_sentence(self, sentence:List[str]):\\n # prepare the input that can be fed to bert model\\n encoded_sentence, indices_subwords = self._prepare_input(sentence[0])\\n with torch.no_grad():\\n bert_output = self.bert_model.forward(input_ids=encoded_sentence)\\n \\n # take the sequence of the last four hidden states (the last element of the tuple returned by the bert model)\\n # list of tensors (batch_size x num_of_splitted_words x embedding_dim)\\n bert_output = list(bert_output[-1][-4:])\\n bert_output.reverse()\\n \\n # stack the hidden states in a tensor (4 x batch_size x num_of_splitted_words x embedding_dim)\\n hidden_states = torch.stack(bert_output, axis=0)\\n # sum the hidden states (batch_size x num_of_splitted_words x embedding_dim)\\n sum_hidden_states = torch.sum(hidden_states, axis=0)\\n # merge the words splitted in subwords by the tokenizer (batch_size x sentence_length x embedding_dim)\\n embed_output = self._merge_embeddings(sum_hidden_states[0], indices_subwords)\\n return embed_output\",\n \"def Aut(A):\\n return Embeddings(A,A)\",\n \"def _single_encode_text(self, text: str) -> np.array:\\n input_ids = self._tokenize_text(text)\\n print(f\\\"id length: {len(input_ids[0])}\\\")\\n try:\\n text_embeddings = self.model(input_ids)\\n except IndexError as e:\\n print(f\\\"input text: {text}\\\")\\n print(f\\\"input inds: {input_ids}\\\")\\n raise e\\n return text_embeddings.pooler_output.detach().numpy()\",\n \"def encode(self, embedded_sentence):\\n embedding = self.encoder(embedded_sentence)\\n return embedding\",\n \"def encode(self, text, batch_max_length=25):\\n length = [len(s) + 1 for s in text] # +1 for [s] at end of sentence.\\n # batch_max_length = max(length) # this is not allowed for multi-gpu setting\\n batch_max_length += 1\\n # additional +1 for [GO] at first step. batch_text is padded with [GO] token after [s] token.\\n batch_text = torch.LongTensor(len(text), batch_max_length + 1).fill_(0)\\n for i, t in enumerate(text):\\n text = list(t)\\n text.append('[s]')\\n text = [self.dict[char] for char in text]\\n batch_text[i][1:1 + len(text)] = torch.LongTensor(text) # batch_text[:, 0] = [GO] token\\n return (batch_text.to(device), torch.IntTensor(length).to(device))\",\n \"def encode(self, text, batch_max_length=25):\\n length = [len(s) + 1 for s in text] # +1 for [s] at end of sentence.\\n # batch_max_length = max(length) # this is not allowed for multi-gpu setting\\n batch_max_length += 1\\n # additional +1 for [GO] at first step. batch_text is padded with [GO] token after [s] token.\\n batch_text = torch.LongTensor(len(text), batch_max_length + 1).fill_(0)\\n for i, t in enumerate(text):\\n text = list(t)\\n text.append('[s]')\\n text = [self.dict[char] for char in text]\\n batch_text[i][1:1 + len(text)] = torch.LongTensor(text) # batch_text[:, 0] = [GO] token\\n return (batch_text.to(device), torch.IntTensor(length).to(device))\",\n \"def embed(self, sequence):\\n words = sequence.split(' ')\\n vecs = [self._E[self._w2i[i]] if i in self._w2i else self._E[self._w2i[\\\"UNK\\\"]]\\n for i in words]\\n return vecs\",\n \"def do(text):\\n return freeling_stemming(text)\",\n \"def set_embeddings(self):\",\n \"def encode_text(self, text):\\n text = clip.tokenize(text).to(self.device)\\n text_features = self.clip_model.encode_text(text)\\n return text_features.cpu().detach().numpy()\",\n \"def build_tensorize_text_fn(embeddings):\\n tbl = contrib_lookup.index_table_from_tensor(\\n mapping=embeddings.get_vocab(), num_oov_buckets=1)\\n\\n def fn(string_tensor):\\n \\\"\\\"\\\"Builds the output tensor dictionary.\\\"\\\"\\\"\\n out = {}\\n if FLAGS.lowercase:\\n string_tensor = ops.lowercase_op(string_tensor)\\n out[\\\"wids\\\"] = tf.to_int32(tbl.lookup(string_tensor))\\n out[\\\"cids\\\"] = char_utils.batch_word_to_char_ids(string_tensor, 50)\\n out[\\\"len\\\"] = tf.shape(string_tensor)[-1]\\n return out\\n\\n return fn\",\n \"def frontend(text):\\n text = pyopenjtalk.g2p(text, kana=False)\\n print(f\\\"Cleaned text: {text}\\\")\\n charseq = text.split(\\\" \\\")\\n idseq = []\\n for c in charseq:\\n if c.isspace():\\n idseq += [char_to_id[\\\"<space>\\\"]]\\n elif c not in char_to_id.keys():\\n idseq += [char_to_id[\\\"<unk>\\\"]]\\n else:\\n idseq += [char_to_id[c]]\\n idseq += [idim - 1] # <eos>\\n return torch.LongTensor(idseq).view(-1).to(device)\",\n \"def make_lexicon_words_txt(self):\\n raise NotImplementedError\",\n \"def __init__(self, name, config, embed_mat):\\n if 'tune_embeds' not in config.keys():\\n raise ValueError('config must define \\\"tune_embeds\\\".')\\n super(TextModel, self).__init__(name, config)\\n self.embeds = nn.Embedding(embed_mat.shape[0], embed_mat.shape[1])\\n self.embeds.weight = nn.Parameter(torch.from_numpy(embed_mat),\\n requires_grad=self.tune_embeds)\",\n \"def get_features(self) -> Generator[np.ndarray, None, None]:\\n for text in self.texts:\\n yield embed(text)\",\n \"def _get_embedding_layer(self, input_data, doc_input_data):\\n opts = self._options\\n word_embedding = tf.Variable(tf.random_uniform((self.vocab_size, opts.embed_dim), -1.0, 1.0))\\n embed = []\\n\\n temp = tf.zeros([opts.batch_size, opts.embed_dim])\\n embed_d = []\\n for n in range(opts.sentence_sample):\\n temp = tf.add(temp, tf.nn.embedding_lookup(word_embedding, doc_input_data[:, n]))\\n embed_d.append(temp)\\n\\n if opts.concat == 'True':\\n combined_embed_vector_length = opts.embed_dim * opts.window_size + opts.embed_dim\\n for j in range(opts.window_size):\\n embed_w = tf.nn.embedding_lookup(word_embedding, input_data[:, j])\\n embed.append(embed_w)\\n embed.append(embed_d)\\n else:\\n combined_embed_vector_length = opts.embed_dim\\n embed_w = tf.zeros([opts.batch_size, opts.embed_dim])\\n for j in range(opts.window_size):\\n embed_w += tf.nn.embedding_lookup(word_embedding, input_data[:, j])\\n embed_w += embed_d\\n embed.append(embed_w)\\n\\n return tf.concat(embed, 1), word_embedding, combined_embed_vector_length\",\n \"def word2vec2embed(word2vec, word2idx):\\n\\temb_dim = word2vec['the'].shape[0]\\n\\temb = torch.nn.Embedding(len(word2idx), emb_dim) \\n\\temb_matrix = []\\n\\tfor w, idx in word2idx.items():\\n\\t\\tif w in word2vec:\\n\\t\\t\\temb_matrix.append(word2vec[w])\\n\\t\\telse:\\n\\t\\t\\temb_matrix.append(np.zeros(emb_dim,))\\n\\temb.weight.data.copy_(torch.from_numpy(np.array(emb_matrix)))\\n\\treturn emb\",\n \"def text(self) -> str:\",\n \"def embed(self, data, mime_type=\\\"text/plain\\\", encode_data_to_base64=True):\\n if encode_data_to_base64:\\n data = base64.standard_b64encode(data.encode()).decode()\\n self.embeddings.append({\\\"data\\\": data, \\\"mime_type\\\": mime_type})\",\n \"def make_text(markov_chains):\\n\\n random_num = generate_random_number(markov_chains.keys())\\n\\n random_text = []\\n\\n start_words = generate_start_words(random_num, markov_chains.keys())\\n \\n random_text.extend(start_words)\\n\\n\\n for i in range(500):\\n word_tuple = (random_text[-2],random_text[-1])\\n next_word = add_next_word(word_tuple, markov_chains)\\n random_text.append(next_word)\\n\\n return random_text\",\n \"def entities_text(text):\\n client = language.LanguageServiceClient()\\n\\n # Instantiates a plain text document.\\n document = types.Document(\\n content=text,\\n type=enums.Document.Type.PLAIN_TEXT)\\n\\n # Detects entities in the document. You can also analyze HTML with:\\n # document.type == enums.Document.Type.HTML\\n entities = client.analyze_entities(document).entities\\n\\n # entity types from enums.Entity.Type\\n entity_type = ('UNKNOWN', 'PERSON', 'LOCATION', 'ORGANIZATION',\\n 'EVENT', 'WORK_OF_ART', 'CONSUMER_GOOD', 'OTHER')\\n \\n ingredients = []\\n #out = \\\"\\\"\\n products = [] \\n wegmans = WegmansClass()\\n\\n for entity in entities:\\n ingredients.append(entity.name)\\n #out += entity.name + '\\\\n'\\n weg_sku = wegmans.GetSKUs(entity.name)\\n\\n prod = []\\n\\n for x in weg_sku:\\n r = wegmans.GetProduct(x)\\n prod.append(r)\\n #out += r.name + \\\", \\\" + str(r.price) + \\\", \\\" + str(r.image) + str(r.velocity) + '#'\\n\\n products.append(prod)\\n\\n #print('=' * 20)\\n #print(u'{:<16}: {}'.format('name', entity.name))\\n #print(u'{:<16}: {}'.format('type', entity_type[entity.type]))\\n #print(u'{:<16}: {}'.format('metadata', entity.metadata))\\n #print(u'{:<16}: {}'.format('salience', entity.salience))\\n #print(u'{:<16}: {}'.format('wikipedia_url',\\n # entity.metadata.get('wikipedia_url', '-')))\\n\\n #for x in ingredients:\\n # print(x + '\\\\n')\\n\\n #for x in products:\\n # for y in x:\\n # print(y.name)\\n # print(y.price)\\n # print(y.velocity)\\n # print(y.image)\\n # print('\\\\t')\\n\\n js = json.dumps(products, default=lambda o: o.__dict__)\\n \\n return jsonify({\\\"results\\\": json.loads(js)})\",\n \"def text():\\n return {\\n \\\"@context\\\": \\\"http://www.w3.org/ns/anno.jsonld\\\",\\n \\\"type\\\": \\\"Annotation\\\",\\n \\\"body\\\": {\\n \\\"creator\\\": \\\"user\\\",\\n \\\"type\\\": \\\"TextualBody\\\",\\n \\\"value\\\": \\\"string\\\"\\n },\\n \\\"generator\\\": {\\n \\\"homepage\\\": \\\"http://mnemosyne.ml\\\",\\n \\\"id\\\": \\\"string\\\",\\n \\\"name\\\": \\\"Mnemosyne\\\",\\n \\\"type\\\": \\\"Mnemosyne\\\"\\n },\\n \\\"target\\\": {\\n \\\"id\\\": \\\"string\\\",\\n \\\"type\\\": \\\"TextQuoteSelector\\\",\\n \\\"exact\\\": \\\"string\\\",\\n \\\"format\\\": \\\"string\\\",\\n \\\"source\\\": \\\"string\\\",\\n \\\"prefix\\\": 0,\\n \\\"suffix\\\": 0,\\n \\\"refinedBy\\\": {\\n \\\"type\\\": \\\"TextPositionSelector\\\",\\n \\\"start\\\": \\\"/div[2]\\\",\\n \\\"end\\\": \\\"/div[2]\\\"\\n },\\n },\\n }\",\n \"def encode(self, text):\",\n \"def get_movie_embedding(self):\\n raise NotImplementedError(\\\"has to be overwritten\\\")\",\n \"def _embed(self, ys):\\n mask = ys != PADDING_VALUE\\n\\n embed_ys = []\\n for i, y in enumerate(ys):\\n unpadded_embed = self.word_piece_embedding(y[mask[i]])\\n padded_embed = torch.cat([unpadded_embed,\\n torch.zeros(((~mask[i]).sum(), self.embedding_dim), device=DEVICE)])\\n embed_ys.append(padded_embed)\\n\\n return torch.stack(embed_ys)\",\n \"def visualise_embedding(embedding_dir, perplexity, learning_rate, image_path_name, label_mapping):\\n tsne = TSNE(\\n n_components=2,\\n random_state=0,\\n perplexity=perplexity,\\n learning_rate=learning_rate,\\n n_iter=20000\\n )\\n\\n with open(embedding_dir, 'r') as emb_file:\\n embedding_list = emb_file.readlines()\\n\\n print('Number of subword units: {}'.format(len(embedding_list)))\\n\\n embedding_dict = {}\\n vector_emb = []\\n subword_labels = []\\n # TODO: Make this a clean solution\\n # Start at 2 to skip the random </s> character which is coming through (may need to be 3 for Georgian)\\n for embedding in embedding_list[2:]:\\n segmented_embedding = embedding.split()\\n subword_labels.append(label_mapping[segmented_embedding[0]])\\n embedding_vector = [float(dim) for dim in segmented_embedding[1:]]\\n vector_emb.append(embedding_vector)\\n embedding_dict[segmented_embedding[0]] = embedding_vector\\n\\n emb_2d = tsne.fit_transform(vector_emb)\\n\\n datapoint_indices = range(len(emb_2d))\\n fig, ax = plt.subplots()\\n for i, subword_label in zip(datapoint_indices, subword_labels):\\n ax.scatter(emb_2d[i, 0], emb_2d[i, 1], c='c', label=subword_label)\\n\\n\\n for i, subword_label in enumerate(subword_labels):\\n ax.annotate(subword_label, (emb_2d[i, 0], emb_2d[i, 1]))\\n\\n plt.savefig(image_path_name)\\n return embedding_dict\",\n \"def nltk_text(self, text):\\n text = nltk.Text(word_tokenize(text))\\n return text\",\n \"def get_embed(input_data, vocab_size, embed_dim):\\n embedding = tf.Variable(tf.random_uniform((vocab_size,embed_dim), -1, 1))\\n embed = tf.nn.embedding_lookup(embedding, input_data)\\n #print (\\\"embed_dim: \\\",embed_dim) # 向量表达维度为 256\\n #print (\\\"input_data.shape: \\\",input_data.shape) # (50, 5)\\n #print (\\\"embed.shap: \\\", embed.shape) # word 的向量表达 ==特征 (50, 5, 256) ==(batch_size, num_step, embed_dim)\\n return embed # 返回input的向量表达\",\n \"def load_embed_text(embed_file):\\n \\n emb_dict = dict()\\n emb_size = None\\n with codecs.getreader(\\\"utf-8\\\")(tf.gfile.GFile(embed_file, \\\"rb\\\")) as f:\\n for line in f:\\n tokens = line.strip().split(\\\" \\\")\\n word = tokens[0]\\n vec = list(map(float, tokens[1:]))\\n emb_dict[word] = vec\\n if emb_size:\\n assert emb_size == len(vec), \\\"All embeddings should be same size\\\"\\n else:\\n emb_size = len(vec)\\n return emb_dict, emb_size\",\n \"def embed_xseq(self,xseq):\\n xseq_embed = self.emat[xseq]\\n return xseq_embed\",\n \"def embed(self, h, r, t):\\n emb_h = self.ent_embeddings(h)\\n emb_r = self.rel_embeddings(r)\\n emb_t = self.ent_embeddings(t)\\n return emb_h, emb_r, emb_t\",\n \"def embed(self, h, r, t):\\n emb_h = self.ent_embeddings(h)\\n emb_r = self.rel_embeddings(r)\\n emb_t = self.ent_embeddings(t)\\n return emb_h, emb_r, emb_t\",\n \"def embed(self, h, r, t):\\n emb_h = self.ent_embeddings(h)\\n emb_r = self.rel_embeddings(r)\\n emb_t = self.ent_embeddings(t)\\n return emb_h, emb_r, emb_t\",\n \"def concept_embedding(concept_model: ConceptDetectionModel2D):\\n return concept_model.to_embedding()\",\n \"def generate_embeddings(vae, dataset):\\n data = ((torch.unsqueeze(img,0), label) for img, label in dataset)\\n data = ((vae.encoder(tens), label) for tens, label in data)\\n data = ((vae.codebook(emb),label) for emb, label in data)\\n data = ((torch.flatten(img),label) for img, label in data)\\n data = (torch.cat([inds,Tensor([label]).int()]) for inds, label in data)\\n return data\",\n \"def get_embedding(A,B):\\n st = minion_hom_algebras(A,B,inj=True)\\n writefile('tmp.minion',st)\\n os.system('minion -noprintsols -solsout tmp.txt tmp.minion >tmpout.txt')\\n st = readfile('tmp.txt')\\n os.system('rm tmp.txt')\\n emb = st.split(\\\"\\\\n\\\")[:-1]\\n if len(emb)>1: return emb[0]\",\n \"def main():\\n logging.basicConfig(level=logging.DEBUG)\\n custom_embedding = True\\n\\n # Download embeddings'\\n if custom_embedding:\\n embedding_path = '../data/custom_embedding.pkl'\\n embedding_index_path = '../data/custom_vocab_index.pkl'\\n logging.info('Pulling custom embedding from: {}, and custom vocab from: {}'.format(embedding_path, embedding_index_path))\\n embedding_matrix = pickle.load(open(embedding_path, 'rb'))\\n embedding_index_lookup = pickle.load(open(embedding_index_path, 'rb'))\\n\\n else:\\n logging.warning('Downloading embedding. If downloading for the first time, this make take 5-10 minutes.')\\n embedding_url = 'https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz'\\n embedding_path = '~/nlp_example/'\\n embedding_filename = 'GoogleNews-vectors-negative300.bin.gz'\\n lib.download_file(embedding_url, embedding_path, embedding_filename)\\n\\n # Unpack embedding\\n model = gensim.models.KeyedVectors.load_word2vec_format(embedding_path + '/' + embedding_filename, binary=True)\\n embedding_matrix = model.syn0\\n embedding_index_lookup = dict([(k, v.index) for k, v in model.vocab.items()])\\n\\n # Create thesaurus\\n thesaurus = Thesaurus(embedding_matrix, embedding_index_lookup)\\n\\n # Find nearest neighbors for examples\\n print(thesaurus.synonyms('day'))\\n print(thesaurus.synonyms('top'))\\n print(thesaurus.synonyms('bottom'))\\n print(thesaurus.synonyms('cat'))\\n print(thesaurus.synonyms('grown'))\\n\\n\\n pass\",\n \"def encode(self, text, batch_max_length=25):\\n length = [len(s) for s in text]\\n\\n # The index used for padding (=0) would not affect the CTC loss calculation.\\n batch_text = torch.LongTensor(len(text), batch_max_length).fill_(0)\\n for i, t in enumerate(text):\\n text = list(t)\\n text = [self.dict[char] for char in text]\\n batch_text[i][:len(text)] = torch.LongTensor(text)\\n return (batch_text.to(device), torch.IntTensor(length).to(device))\",\n \"def prepare_text(gene, enrich, variants, numbers):\\n \\n external = enrich[enrich[\\\"hgnc\\\"] == gene]\\n sites = variants[variants[\\\"hgnc\\\"] == gene]\\n \\n # make sure the variant count matches the analyzed count\\n if gene not in list(enrich[\\\"hgnc\\\"]):\\n assert len(sites) == 0\\n else:\\n assert list(external[\\\"dnms\\\"])[0] == len(sites)\\n \\n if len(external) > 1:\\n text = \\\"too many possible rows for {}\\\".format(gene)\\n else:\\n external = external.squeeze()\\n initial_text = initial_sentence(external, gene, sites[\\\"pubmed\\\"], numbers)\\n counts_text = counts_sentence(external, numbers)\\n clust_text = clustering_sentence(external)\\n evidence_text = strength_sentence(external)\\n \\n if len(external) > 0 and external[\\\"dnms\\\"] > 0:\\n text = initial_text + \\\" \\\" + counts_text + \\\" \\\" + evidence_text+ \\\" \\\" + clust_text\\n else:\\n text = initial_text + \\\" \\\" + evidence_text\\n \\n return text\",\n \"def addContent(text):\",\n \"def get_embeddings(self, data):\\n raise NotImplementedError()\",\n \"def make_text(sent, begin, end):\\n lemmas = [sent.morps[begin].lemma(), ]\\n for idx in range(begin+1, end):\\n if sent.mid2wid[idx-1] != sent.mid2wid[idx]: # if go over word boundary\\n # insert space between words\\n lemmas.append(' ')\\n lemmas.append(sent.morps[idx].lemma())\\n return ''.join(lemmas)\",\n \"def _get_embedding(self, data):\\n embedding_list = [super()._get_embedding(data)]\\n context = data['context']\\n for i in range(context.shape[1]):\\n embedding_list.append(getattr(self, f'context{i}')(context[:, i:i+1]))\\n return torch.cat(embedding_list, dim=1)\",\n \"def preprocess(text):\\n text = remove_space(text)\\n text = clean_special_punctuations(text)\\n text = handle_emojis(text)\\n text = clean_number(text)\\n text = spacing_punctuation(text)\\n text = clean_repeat_words(text)\\n text = remove_space(text)\\n #text = stop(text)# if changing this, then chnage the dims \\n #(not to be done yet as its effecting the embeddings..,we might be\\n #loosing words)...\\n return text\",\n \"def compose_embedding(cls, sub_embs, metadata):\\n return EmbeddingList(\\n embeddings=[\\n EmbeddingList(\\n embeddings=[\\n sub_embs.get(ModelInput.TEXT),\\n sub_embs.get(ModelInput.DICT),\\n sub_embs.get(ModelInput.CHAR),\\n sub_embs.get(ModelInput.CONTEXTUAL_TOKEN_EMBEDDING),\\n ],\\n concat=True,\\n ),\\n sub_embs.get(ModelInput.SEQ),\\n ],\\n concat=False,\\n )\",\n \"def generateByWord(model, voc, maxlen=20, diversity=0.5, numwords=42):\\n\\n text, sym_indices, indices_sym = voc\\n syms = set(text)\\n start_index = random.randint(0, len(text) - maxlen - 1) \\n generated = ''\\n sentence = text[start_index: start_index + maxlen]\\n \\n #generated += sentence\\n generated += ' '.join(sentence)\\n print('----- Generating with seed: \\\"' + ' '.join(sentence) + '\\\"')\\n sys.stdout.write(generated)\\n\\n for i in range(numwords):\\n x = np.zeros((1, maxlen, len(syms)))\\n for t, sym in enumerate(sentence):\\n x[0, t, sym_indices[sym]] = 1.\\n \\n preds = model.predict(x, verbose=0)[0]\\n next_index = sample(preds, diversity)\\n next_sym = indices_sym[next_index]\\n generated += ' '+next_sym\\n sentence.append(next_sym)\\n tmpsentence = sentence[1:]\\n sentence = tmpsentence\\n sys.stdout.write(next_sym+' ')\\n sys.stdout.flush()\\n print()\",\n \"def create_wordcloud(self, text):\\n text = ' '.join(f\\\"{word}\\\" for word in text)\\n mask = np.array(Image.open(os.path.join(CURRDIR, \\\"cloud.png\\\")))\\n wc = WordCloud(background_color=\\\"white\\\",\\n max_words=200,\\n mask=mask)\\n wc.generate(text)\\n wc.to_file(PATH_TO_SAVE_IMG, \\\"wordle.png\\\")\",\n \"def entity_recognition(text: str) -> spacy:\\n nlp = spacy.load('en_core_web_sm')\\n document = nlp(text)\\n return document\",\n \"def initial_sentence(external, gene, pubmed_ids, numbers):\\n \\n pubmed_text = format_pubmed(pubmed_ids)\\n \\n dnms = 0\\n if len(external) > 0:\\n dnms = external[\\\"dnms\\\"]\\n \\n # figure out the correct type for the word \\\"time\\\", e.g. 1 \\\"time\\\", 2 \\\"times\\\"\\n times = \\\"times\\\"\\n if len(external) > 0 and external[\\\"dnms\\\"] == 1:\\n times = \\\"time\\\"\\n \\n initial_text = \\\"In other large scale exome sequencing projects de novo \\\" \\\\\\n \\\"mutations in {} have been identified {} {}{}.\\\".format(gene,\\\\\\n numbers.number_to_words(dnms), times, pubmed_text)\\n \\n return initial_text\",\n \"def get_sentence(self):\",\n \"def load_text_embed(filepath: Union[str, os.PathLike], load_dir: str = 'model') \\\\\\n -> Tuple[TransformerEmbedding, Callable]:\\n model_dir = Path(filepath).joinpath(load_dir)\\n tokenizer = AutoTokenizer.from_pretrained(str(model_dir.resolve()))\\n args = dill.load(open(model_dir.joinpath('embedding.dill'), 'rb'))\\n emb = TransformerEmbedding(\\n str(model_dir.resolve()), embedding_type=args['embedding_type'], layers=args['layers']\\n )\\n return emb, tokenizer\",\n \"def generate_embeddings_sentence_test_data(data, path_out):\\n flair.device = torch.device('cpu')\\n dicts = []\\n # init multilingual BERT\\n bert_embedding = TransformerDocumentEmbeddings('bert-base-multilingual-cased')\\n counter = 0\\n for entry in data:\\n print(\\\"Counter: \\\", counter)\\n counter += 1\\n text = entry[\\\"sentence\\\"]\\n id = entry[\\\"id\\\"]\\n sent = Sentence(text)\\n bert_embedding.embed(sent)\\n vec = sent.get_embedding().detach().numpy()\\n dicts.append((id,vec))\\n gc.collect()\\n result = dicts\\n file = open(path_out, \\\"wb\\\")\\n pickle.dump(result, file)\\n file.close()\\n return result\",\n \"def encode(self, text, batch_max_length=25):\\n length = [len(s) for s in text]\\n text = ''.join(text)\\n text = [self.dict[char] for char in text]\\n\\n return (torch.IntTensor(text).to(device), torch.IntTensor(length).to(device))\",\n \"def SNEmbedding(*args, **kwargs):\\n return spectral_norm(nn.Embedding(*args, **kwargs))\",\n \"def process_text(model_name: str, text: str) -> spacy.tokens.Doc:\\r\\n nlp = load_model(model_name)\\r\\n return nlp(text)\",\n \"def gen_embedding(path):\\r\\n word_emb = {}\\r\\n with open(path, encoding='utf-8') as f:\\r\\n for line in tqdm(f):\\r\\n values = line.split()\\r\\n word_emb[values[0]] = np.asarray(values[1:], dtype='float32')\\r\\n return word_emb\",\n \"def embed(self, word: str) -> list:\\n return self.emb_matrix[self.wordmapper[word]]\",\n \"def generate_new_book(text):\\n\\n for paragraph in text:\\n for sentence in paragraph:\\n for word in sentence:\\n print(word, end=' ')\\n print()\\n print()\",\n \"def construct_embedding(captions, cbow=True):\\n\\n # List of characters to filter out of the caption string\\n chars_to_remove = [\\\"\\\\n\\\", \\\">\\\", \\\"--\\\"]\\n\\n for char in chars_to_remove:\\n captions = captions.replace(char, \\\" \\\")\\n\\n # Perform some necessary tokenization\\n data = [word_tokenize(word.lower()) for word in sent_tokenize(captions)]\\n\\n # Filter out punctuation from the data\\n data_minus_punctuation = remove_punctuation(data)\\n\\n # Filter out stop words from the data\\n data_minus_stop_words = remove_stop_words(data_minus_punctuation)\\n\\n # Filter out any surviving single character list elements\\n fully_formatted_data = [\\n [word for word in item if len(word) > 1] for item in data_minus_stop_words\\n ]\\n\\n # Train the Word2Vec model using the specified architecture\\n if cbow:\\n print(\\\"UTILIZING CBOW ARCHITECTURE\\\")\\n model = Word2Vec(fully_formatted_data, min_count=1, size=100, window=5)\\n else:\\n print(\\\"UTILIZING SKIPGRAM ARCHITECTURE\\\")\\n model = Word2Vec(fully_formatted_data, min_count=1, size=100, window=5, sg=1)\\n\\n return model\",\n \"def test_extract_embeddings():\\n docs = [\\\"some document\\\"]\\n model = BERTopic(embedding_model=\\\"distilbert-base-nli-stsb-mean-tokens\\\")\\n bertopic_embeddings = model._extract_embeddings(docs)\\n\\n assert isinstance(bertopic_embeddings, np.ndarray)\\n assert bertopic_embeddings.shape == (1, 768)\\n\\n sentence_embeddings = embedding_model.encode(docs, show_progress_bar=False)\\n assert np.array_equal(bertopic_embeddings, sentence_embeddings)\",\n \"def generate_text(session, model, config, starting_text='<eos>',\\n stop_length=100, stop_tokens=None, temp=1.0):\\n state = model.initial_state.eval()\\n # Imagine tokens as a batch size of one, length of len(tokens[0])\\n tokens = [model.vocab.encode(word) for word in starting_text.split()]\\n for i in xrange(stop_length):\\n ### YOUR CODE HERE\\n #print tokens\\n feed = {}\\n #x = np.array([tokens[-1]])\\n #x.reshape(1,1)\\n feed[model.input_placeholder] = [[tokens[-1]]]\\n feed[model.dropout_placeholder] = 1\\n feed[model.initial_state] = state\\n y_pred, state = session.run([model.predictions[-1], model.final_state], feed_dict=feed)\\n ### END YOUR CODE\\n next_word_idx = sample(y_pred[0], temperature=temp)\\n tokens.append(next_word_idx)\\n if stop_tokens and model.vocab.decode(tokens[-1]) in stop_tokens:\\n break\\n output = [model.vocab.decode(word_idx) for word_idx in tokens]\\n return output\",\n \"def load_embeddings():\\n return embedding_utils.PretrainedWordEmbeddings(\\n lowercase=FLAGS.lowercase,\\n embeddings_path=FLAGS.fasttext_embeddings,\\n max_vocab_size=FLAGS.max_vocab_size,\\n skip_header=True)\",\n \"def plot_embeddings(M_reduced, word2Ind, words):\\n\\n # YOUR CODE HERE\\n \\n for i,type in enumerate(words):\\n x_coor,y_coor = M_reduced[word2Ind[type]][0],M_reduced[word2Ind[type]][1]\\n \\n plt.scatter(x_coor, y_coor, marker='*', color='red')\\n plt.text(x_coor+0.05, y_coor+0.05, type, fontsize=12)\\n \\n plt.show()\\n \\n #raise NotImplementedError()\",\n \"def _apply_word_emb(word_emb_enc, emb_char, word_emb):\\n word_emb_enc = (\\n word_emb_enc(word_emb.data)\\n if word_emb_enc is not None\\n else word_emb.data\\n )\\n return torch.cat([emb_char, word_emb_enc], dim=-1)\",\n \"def load_text(self, *, scope: Scope, doc_key: str, requested_output_length: int) -> str:\",\n \"def get_embedding(num_embeddings, embedding_dim, padding_idx=None):\\n half_dim = embedding_dim // 2\\n emb = math.log(10000) / (half_dim - 1)\\n emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)\\n emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)\\n emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)\\n if embedding_dim % 2 == 1:\\n emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)\\n if padding_idx is not None:\\n emb[padding_idx, :] = 0\\n return emb\",\n \"def get_plain_texts(input_dict):\\n\\n adc = input_dict['adc']\\n annotation = input_dict['token_annotation']\\n feature_annotation = input_dict['feature_annotation']\\n if feature_annotation:\\n annotation+=\\\"/\\\"+feature_annotation\\n delimiter = input_dict['delimiter']\\n includeDocId = input_dict['include_doc_id'] == \\\"true\\\"\\n\\n output_dict = {\\\"strings\\\": []}\\n for document in adc.documents:\\n output_dict[\\\"strings\\\"].append((document.name+\\\": \\\" if includeDocId else \\\"\\\") + delimiter.join([t[1] for t in document.get_annotations_with_text(annotation)]))\\n return output_dict\",\n \"def _embed(self):\\n batch_size = tf.shape(self.p)[0]\\n with tf.variable_scope(\\\"emb\\\"):\\n with tf.variable_scope(\\\"char\\\"):\\n pc_emb = tf.reshape(tf.nn.embedding_lookup(\\n self.char_embed, self.pc), \\n [batch_size * self.max_p_len, self.max_w_len, self.vocab.char_embed_dim])\\n qc_emb = tf.reshape(tf.nn.embedding_lookup(\\n self.char_embed, self.qc), \\n [batch_size * self.max_q_len, self.max_w_len, self.vocab.char_embed_dim])\\n cell_fw = tf.contrib.rnn.GRUCell(self.char_hidden_size)\\n cell_bw = tf.contrib.rnn.GRUCell(self.char_hidden_size)\\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\\n cell_fw, cell_bw, pc_emb, self.pc_length, dtype=tf.float32)\\n pc_emb = tf.concat([state_fw, state_bw], axis=1)\\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\\n cell_fw, cell_bw, qc_emb, self.qc_length, dtype=tf.float32)\\n qc_emb = tf.concat([state_fw, state_bw], axis=1)\\n pc_emb = tf.reshape(pc_emb, [batch_size, self.max_p_len, 2 * self.char_hidden_size])\\n qc_emb = tf.reshape(qc_emb, [batch_size, self.max_q_len, 2 * self.char_hidden_size])\\n\\n with tf.name_scope(\\\"word\\\"):\\n p_emb = tf.nn.embedding_lookup(self.word_embed, self.p)\\n q_emb = tf.nn.embedding_lookup(self.word_embed, self.q)\\n\\n with tf.name_scope(\\\"pos\\\"):\\n p_pos_emb = tf.nn.embedding_lookup(self.pos_embed, self.p_pos)\\n q_pos_emb = tf.nn.embedding_lookup(self.pos_embed, self.q_pos)\\n \\n with tf.name_scope(\\\"em\\\"):\\n sh = tf.shape(self.p_em)\\n resh = [sh[0], sh[1], 1]\\n p_em_feat = tf.reshape(tf.cast(self.p_em, dtype=tf.float32), shape=resh)\\n\\n self.p_emb = tf.concat([p_emb, pc_emb, p_pos_emb, p_em_feat], axis=2)\\n self.q_emb = tf.concat([q_emb, qc_emb, q_pos_emb], axis=2)\",\n \"def visualize(list_of_words, embeddings, embedding_dimension=300, n_components=2):\\n num_word = len(list_of_words)\\n\\n # put embeddings in a numpy matrix\\n e_matrix = np.zeros((num_word, embedding_dimension))\\n for idx, w in enumerate(list_of_words):\\n vec = embeddings[w]\\n e_matrix[idx] = vec\\n\\n # reduce dimension\\n transformed = TSNE(n_components=n_components).fit_transform(e_matrix)\\n\\n # visualize\\n fig, ax = plt.subplots()\\n ax.scatter(transformed[:, 0], transformed[:, 1])\\n for idx, w in enumerate(list_of_words):\\n ax.annotate(w, (transformed[idx, 0], transformed[idx, 1]))\\n plt.plot()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7674286","0.7428457","0.73705107","0.68461746","0.6826775","0.68188214","0.67891675","0.6650634","0.6640539","0.64986724","0.64498174","0.6358982","0.63555664","0.6302846","0.6266722","0.6235843","0.6210677","0.61844516","0.6173967","0.6062287","0.6055364","0.6054041","0.6043429","0.6039673","0.60313","0.6031019","0.601735","0.60101414","0.6002047","0.5997187","0.5969434","0.59590536","0.5943638","0.5932094","0.5904214","0.5889115","0.5889115","0.5877552","0.58742434","0.5863495","0.5810653","0.5800588","0.57993275","0.5786108","0.5764259","0.5764196","0.57623976","0.57541317","0.57109684","0.57031256","0.56978","0.56933","0.5692696","0.5688871","0.56872624","0.56834227","0.5673503","0.5671286","0.56654084","0.56613785","0.56462204","0.56450105","0.56450105","0.56450105","0.56445616","0.5644156","0.5638044","0.563644","0.56305474","0.56247205","0.5620315","0.5614932","0.56122637","0.5606185","0.56051314","0.56030315","0.5598809","0.5592865","0.5589656","0.5586285","0.55861765","0.557801","0.5576767","0.5575994","0.5569533","0.55661166","0.5564752","0.5563281","0.5559178","0.555811","0.5556623","0.5553667","0.55533737","0.55494314","0.55335504","0.5532245","0.5530998","0.55307686","0.55296665","0.55252194"],"string":"[\n \"0.7674286\",\n \"0.7428457\",\n \"0.73705107\",\n \"0.68461746\",\n \"0.6826775\",\n \"0.68188214\",\n \"0.67891675\",\n \"0.6650634\",\n \"0.6640539\",\n \"0.64986724\",\n \"0.64498174\",\n \"0.6358982\",\n \"0.63555664\",\n \"0.6302846\",\n \"0.6266722\",\n \"0.6235843\",\n \"0.6210677\",\n \"0.61844516\",\n \"0.6173967\",\n \"0.6062287\",\n \"0.6055364\",\n \"0.6054041\",\n \"0.6043429\",\n \"0.6039673\",\n \"0.60313\",\n \"0.6031019\",\n \"0.601735\",\n \"0.60101414\",\n \"0.6002047\",\n \"0.5997187\",\n \"0.5969434\",\n \"0.59590536\",\n \"0.5943638\",\n \"0.5932094\",\n \"0.5904214\",\n \"0.5889115\",\n \"0.5889115\",\n \"0.5877552\",\n \"0.58742434\",\n \"0.5863495\",\n \"0.5810653\",\n \"0.5800588\",\n \"0.57993275\",\n \"0.5786108\",\n \"0.5764259\",\n \"0.5764196\",\n \"0.57623976\",\n \"0.57541317\",\n \"0.57109684\",\n \"0.57031256\",\n \"0.56978\",\n \"0.56933\",\n \"0.5692696\",\n \"0.5688871\",\n \"0.56872624\",\n \"0.56834227\",\n \"0.5673503\",\n \"0.5671286\",\n \"0.56654084\",\n \"0.56613785\",\n \"0.56462204\",\n \"0.56450105\",\n \"0.56450105\",\n \"0.56450105\",\n \"0.56445616\",\n \"0.5644156\",\n \"0.5638044\",\n \"0.563644\",\n \"0.56305474\",\n \"0.56247205\",\n \"0.5620315\",\n \"0.5614932\",\n \"0.56122637\",\n \"0.5606185\",\n \"0.56051314\",\n \"0.56030315\",\n \"0.5598809\",\n \"0.5592865\",\n \"0.5589656\",\n \"0.5586285\",\n \"0.55861765\",\n \"0.557801\",\n \"0.5576767\",\n \"0.5575994\",\n \"0.5569533\",\n \"0.55661166\",\n \"0.5564752\",\n \"0.5563281\",\n \"0.5559178\",\n \"0.555811\",\n \"0.5556623\",\n \"0.5553667\",\n \"0.55533737\",\n \"0.55494314\",\n \"0.55335504\",\n \"0.5532245\",\n \"0.5530998\",\n \"0.55307686\",\n \"0.55296665\",\n \"0.55252194\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6253844"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":77088,"cells":{"query":{"kind":"string","value":"Returns the manifold that this GraphEmbedder embeds nodes into. Defaults to Euclidean if this method is not overwritten"},"document":{"kind":"string","value":"def get_manifold(self) -> RiemannianManifold:\n\n return ManifoldConfig().get_manifold_instance()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_kernel_norms(self):\n return self.adjacency","def shared_element_norm(self):\n elements = self.shared_elements\n v1 = self.nodes[elements[:, 1], :] - self.nodes[elements[:, 0], :]\n v2 = self.nodes[elements[:, 2], :] - self.nodes[elements[:, 0], :]\n return np.cross(v1, v2, axisa=1, axisb=1)","def get_linearEvolving(self):\n return self.get_linearEvolvingEigen()","def norm( self):\n return self._norm","def norm(self):\n return sqrt(self.dot(self))","def norm(self):\n return math.sqrt(self.dotProduct(self))","def __abs__(self):\n return Vector.createFromPoint(self).norm","def norm(self):\n\n return self.abs()","def norm(self):\n\t\treturn math.sqrt(self.norm2())","def dist_matrix(self):\n return self.__dist_matrix","def cluster_constraint(self):\n return self._cluster_constraint","def molecule(self):\n return self._molecule","def get_norma(self):\n return self.norma","def normal(self) -> Vec:\n return abs(self.up_axis.cross(self.forward()))","def _transform(self, X):\r\n return euclidean_distances(X, self.cluster_centers_)","def normSq(self):\n\t\treturn self.x*self.x+self.y*self.y","def normalInv(self) -> 'MultiVector':\n\n Madjoint = ~self\n MadjointM = (Madjoint * self)\n\n if MadjointM.isScalar() and abs(MadjointM[()]) > _eps:\n # inverse exists\n return Madjoint / MadjointM[()]\n else:\n raise ValueError(\"no inverse exists for this multivector\")","def cluster(self):\n return self._cluster","def cluster(self):\n return self._cluster","def norm(self):\n raise NotImplementedError","def mesh(self):\n return self._mesh","def mesh(self):\n return self._mesh","def mesh(self):\n return self._mesh","def mesh(self):\n return self._mesh","def ensemble_norm(self):\n return np.linalg.norm(self.ensemble_transition_matrix, ord=\"fro\")","def norm(self):","def normal(self) -> 'MultiVector':\n\n return self / np.sqrt(abs(self.mag2()))","def Prim(self):\n g = Graph(attr={DIRECTED: False})\n distance = [sys.maxsize] * len(self.vertices)\n parent = [None] * len(self.vertices)\n set = [False] * len(self.vertices)\n\n distance[0] = 0\n parent[0] = -1\n\n for i in self.vertices:\n # Search vertex with minimum distance\n min_index = 0\n min = sys.maxsize\n for v in self.vertices:\n if distance[v] < min and set[v] is False:\n min = distance[v]\n min_index = v\n u = min_index\n\n # Add u vertex in set to not use it in other iteration \n set[u] = True\n g.add_vertex(vertex.Vertex(u))\n\n # Iterate all adjacent vertices of u vertex and update distance \n for v in self.get_adjacent_vertices_by_vertex(u):\n if set[v] is False and distance[v] > \\\n self.get_edge((u, v)).attr[\"WEIGHT\"]:\n distance[v] = self.get_edge((u, v)).attr[\"WEIGHT\"]\n parent[v] = u\n\n for i in self.vertices:\n if i == 0:\n continue\n if parent[i] is not None:\n g.add_edge(edge.Edge(parent[i], i, {\"WEIGHT\": self.get_edge((parent[i], i)).attr[\"WEIGHT\"]}))\n\n return g","def perpendicular(self):\n return tuple.__new__(Vec2, (-self[1], self[0]))","def get_intra_plate_cluster_distance(self) -> int:\n return self.intra_plate_cluster_distance","def norm(self):\n norm = self.scalarProduct(self) ** 0.5\n return norm","def normal(self) -> Vector:\n return self._normal","def parallel_component(self, basis: Vector) -> Vector:\n b_norm = basis.normalize()\n return (self * b_norm) * b_norm","def norm2(self):\n\t\treturn self.x ** 2 + self.y ** 2 + self.z ** 2","def norm_distance(self):\n graph_size = self.N + self.M\n return self.distance() / (1. * graph_size)","def norm(self):\n\t\treturn np.sqrt(self.normSq())","def vector(self) -> Vector:\n return self._normal * self._distance_from_origin","def getDistanceMatrix(self):\n return self.pointcloud.distmat","def getCentroid(self) -> Vec3:\n return self.centroid()","def norm(self):\n if self._coord_format != constants.MatrixCoordinateDefault:\n self._logger.error(\"invalid coordinate format\")\n raise NotImplementedError(\"invalid coordinate format\")\n\n if self._dtype == complex:\n def __map(m):\n return m[2].real ** 2 + m[2].imag ** 2\n else:\n def __map(m):\n return m[2] ** 2\n\n n = self._data.map(\n __map\n ).reduce(\n lambda a, b: a + b\n )\n\n return math.sqrt(n)","def norm2(self):\n return getattr(self, self.norm2_name)","def u(self):\n return (self.u_edges[1:] + self.u_edges[:-1]) / 2","def get_kernel_norms(self):\n return np.einsum('ijk->ij', self.amplitudes)","def getMesh(self):\n return self.mesh","def norm(self):\n return np.sqrt(np.dot(self._data, self._data))","def Centroid(self):\n return Vector(self.centroid)","def norm(self):\n # TODO: implement\n return","def l2_norm(self):\n return (self.x**2 + self.y**2 + self.z**2)**0.5","def norm(self):\n return np.linalg.norm(self.ravel())","def hyperplane(self):\n origin = (self.a+self.b+self.c)/3.\n normal = np.cross(self.a-self.b, self.a-self.c)\n return Hyperplane(origin, normal)","def nodalLaplacian(self):\n if getattr(self, '_nodalLaplacian', None) is None:\n print('Warning: Laplacian has not been tested rigorously.')\n # The number of cell centers in each direction\n n = self.vnC\n # Compute divergence operator on faces\n if(self.dim == 1):\n D1 = sdiag(1./self.hx) * ddx(self.nCx)\n L = - D1.T*D1\n elif(self.dim == 2):\n D1 = sdiag(1./self.hx) * ddx(n[0])\n D2 = sdiag(1./self.hy) * ddx(n[1])\n L1 = sp.kron(speye(n[1]+1), - D1.T * D1)\n L2 = sp.kron(- D2.T * D2, speye(n[0]+1))\n L = L1 + L2\n elif(self.dim == 3):\n D1 = sdiag(1./self.hx) * ddx(n[0])\n D2 = sdiag(1./self.hy) * ddx(n[1])\n D3 = sdiag(1./self.hz) * ddx(n[2])\n L1 = kron3(speye(n[2]+1), speye(n[1]+1), - D1.T * D1)\n L2 = kron3(speye(n[2]+1), - D2.T * D2, speye(n[0]+1))\n L3 = kron3(- D3.T * D3, speye(n[1]+1), speye(n[0]+1))\n L = L1 + L2 + L3\n self._nodalLaplacian = L\n return self._nodalLaplacian","def normal(self) -> Vector:\n return normalize(cross(self.d1, self.d2))","def calc_main_axis(self):\n #Clarify why the above step has been done\n c0, c1, c2 = self.calc_geom_center ()\n M = numpy.zeros ((3, 3), dtype=float)\n M = [[0] * 3, [0] * 3, [0] * 3]\n for x in self.atomcoords:\n xi = x[0] - c0\n yi = x[1] - c1\n zi = x[2] - c2\n M[0][0] = M[0][0] + xi * xi\n M[0][1] = M[0][1] + xi * yi\n M[0][2] = M[0][2] + xi * zi\n M[1][1] = M[1][1] + yi * yi\n M[1][2] = M[1][2] + yi * zi\n M[2][2] = M[2][2] + zi * zi\n M[1][0] = M[0][1]\n M[2][0] = M[0][2]\n M[2][1] = M[1][2]\n M = numpy.array (M)\n d = sum (numpy.diag (M))\n M = -M\n M[0, 0] = M[0, 0] + d\n M[1, 1] = M[1, 1] + d\n M[2, 2] = M[2, 2] + d\n\n eigenVals, eigenVecs = numpy.linalg.eig (M)\n eigenVecs = eigenVecs.transpose ()\n return eigenVecs","def geom(self):\n return self.data[\"norm\"].geom","def getNeedToComputeMatrix(self):\n return _osgAnimation.RigGeometry_getNeedToComputeMatrix(self)","def get_distance(self, node):\n return np.sqrt(\n (self.x - node.x) ** 2 +\n (self.y - node.y) ** 2\n )","def magnitude(self):\n\t\treturn sqrt(self.dot(self))","def euclidean_dist(self):\r\n\r\n real_cat, synth_cat = self.to_cat(self.origdst, self.synthdst)\r\n\r\n real_cat_dem = self.get_demographics(real_cat)\r\n synth_cat_dem = self.get_demographics(synth_cat)\r\n\r\n corr_real_obj = associations(real_cat_dem, theil_u=True, bias_correction=False, plot=False)\r\n corr_synth_obj = associations(synth_cat_dem, theil_u=True, bias_correction=False, plot=False)\r\n\r\n corr_real = corr_real_obj['corr']\r\n corr_rand = corr_synth_obj['corr']\r\n\r\n eucl_matr = distance.cdist(corr_real, corr_rand, 'euclidean')\r\n\r\n eucl = LA.norm(eucl_matr)\r\n\r\n return eucl, eucl_matr","def P(self):\n self.eigenmatrix()","def mesh(self):\n self._ensure_mesh()\n return self._mesh","def euclidean_sqr(self, instance, centroid):\n return np.linalg.norm(instance-centroid)**2","def get_norm(self, l):\n return self._W.norm(l)","def get_norm(self, l):\n return self._W.norm(l)","def nelem3(self):\n if self._nelem3 is None:\n self._nelem3 = self.get_mesh_nelem()\n\n return self._nelem3","def get_mesh(self):\n return self.mesh","def getDistanceMatrix(self):\n return self.distmat.as_matrix()","def getNorm(self, norm=lambda l: (sum(map(lambda x: x ** 2, l))) ** (1 / 2)):\n return norm(self.components)","def norm2d(self) -> float:\n\n return self.v2ddict.norm2d()","def Normal(self):\n return Vector(self.normal)","def normalize(self):\n return Vector(self.args + []) / self.magnitude()","def get_clust_cent(self):\r\n\r\n return self.__clust_cent","def divmod_node(self, mpi_procs, omp_threads):\n return divmod(mpi_procs * omp_threads, self.cores_per_node)","def _cluster(self):\n # , distance_function=spearman_squared_distance, max_iter=1000, tol=0.0001):\n if self.cluster_method is None:\n clusters = KMedoids(\n self.k,\n self.batchsize,\n dist_func=self.distance_function,\n max_iter=self.max_iter,\n tol=self.tol,\n init_medoids=self.init_medoids,\n swap_medoids=self.swap_medoids,\n )\n clusters.fit(self.clustering_attributions, verbose=self.verbose)\n\n self.subpopulations = clusters.members\n self.subpopulation_sizes = GAM.get_subpopulation_sizes(clusters.members)\n self.explanations = self._get_explanations(clusters.centers)\n # Making explanations return numerical values instead of dask arrays\n if isinstance(self.explanations[0][0][1], da.Array):\n explanations = []\n for explanation in self.explanations:\n explanations.append([(x[0], x[1].compute()) for x in explanation])\n self.explanations = explanations\n else:\n self.cluster_method(self)","def calc_distances_in_embedding(cluster, embedding, reference_name=None):\n\n if reference_name is None:\n reference_name = get_central_pc_name(embedding, cluster)\n\n assert reference_name in cluster\n\n ref_coords = embedding.loc[reference_name]\n w = embedding.copy()\n w = w-ref_coords\n return w.apply(eucl_norm, axis=1)","def euclidean(self, other):\n return linalg.norm([self.x - other.x, self.y - other.y])","def getMath(self):\n return _libsbml.KineticLaw_getMath(self)","def GetNormal(self):\n ...","def normal(self) -> Vec:\n # The three points are in clockwise order, so compute differences\n # in the clockwise direction, then cross to get the normal.\n point_1 = self.planes[1] - self.planes[0]\n point_2 = self.planes[2] - self.planes[1]\n\n return Vec.cross(point_1, point_2).norm()","def project_embedding(self):\n w = self.feature_embedding.weight.data\n d = w.size(-1) - 1\n narrowed = w.narrow(-1, 1, d)\n tmp = 1 + torch.sum(torch.pow(narrowed, 2), dim=-1, keepdim=True)\n tmp.sqrt_()\n w.narrow(-1, 0, 1).copy_(tmp)\n return w # can be delete?","def get_force(self):\n displ = self.get_displ()\n equil = displ / np.linalg.norm(displ) * self.L0\n return self.k * (displ - equil)","def norm(self):\n return np.linalg.norm(self.values)","def get_normal(self):\n c, s = np.cos(self.eangles), np.sin(self.eangles)\n r = np.array([[c, -s], [s, c]])\n u = np.array([1, 0])\n return np.dot(r, u)","def normalize(self):\r\n\r\n nlen = 1.0/math.sqrt(self*self)\r\n return vec4(self.x*nlen, self.y*nlen, self.z*nlen, self.w*nlen)","def matrix(self):\n return self.composition(self._parent.edge_to_matrix)","def __abs__(self):\r\n return math.sqrt(self*self)","def norm(self) -> float:\n return self.squared_norm()**0.5","def norm(self):\n return numpy.linalg.norm(self.values)","def normalequ(self):\n tx = self.train_x\n y = self.train_y\n if self.regularizer is None:\n return np.linalg.solve(np.dot(tx.T, tx), np.dot(tx.T, y))\n elif self.regularizer.name is 'Ridge':\n G = np.eye(tx.shape[1])\n G[0, 0] = 0\n hes = np.dot(tx.T, tx) + self.regularizer_p * G\n return np.linalg.solve(hes, np.dot(tx.T, y))\n else:\n raise NotImplementedError","def center_of_mass_polyhedron():\n raise NotImplementedError","def calculate_distance_edge(self):\n mu_star = -np.sqrt(1. - (self.cell_xl / self.x)**2)\n\n if self.mu <= mu_star:\n\n l_edge = (-self.mu * self.x -\n np.sqrt(self.mu**2 * self.x**2 -\n self.x**2 + self.cell_xl**2))\n self.next_cell_index = self.cell_index - 1\n\n else:\n\n l_edge = (-self.mu * self.x +\n np.sqrt(self.mu**2 * self.x**2 -\n self.x**2 + self.cell_xr**2))\n self.next_cell_index = self.cell_index + 1\n\n return l_edge","def topology(self):\n return self._topology","def get_euclidean_coordinates(self):\n return self._xx.T, self._yy.T","def centers(self):\n return tuple(0.5 * (e[1:] + e[:-1]) for e in self.edges)","def getCentroid(self):\r\n return self._centroid","def GetModifiableTransform(self) -> \"itkTransformD33 *\":\n return _itkTransformMeshFilterPython.itkTransformMeshFilterMF3MF3TD33_GetModifiableTransform(self)","def getNormalizedVector(self):\n return self.scalarMultiplication(self.norm() ** -1.0)","def getMath(self):\n return _libsbml.StoichiometryMath_getMath(self)","def test_euclidean_scale(self):\n\n s = space(curvature=0)\n\n magic = 77773.333773777773733\n for mul in (2, 5, 1/3, 1/11, magic, 1/magic):\n for name, dim in (\n ('sphere_s1', 1),\n ('sphere_v2', 2),\n ('sphere_s2', 2),\n ('sphere_v3', 3)\n ):\n self.assertTrue(isclose(\n getattr(s, name)(1) * mul**dim,\n getattr(s, name)(mul)\n ))","def get_length(self) -> np.float64:\n\n return np.float64(\n sqrt(\n (self.node1.x - self.node2.x) ** 2\n + (self.node1.y - self.node2.y) ** 2\n )\n )","def M(self):\n return _hypre.HypreParMatrix_M(self)"],"string":"[\n \"def get_kernel_norms(self):\\n return self.adjacency\",\n \"def shared_element_norm(self):\\n elements = self.shared_elements\\n v1 = self.nodes[elements[:, 1], :] - self.nodes[elements[:, 0], :]\\n v2 = self.nodes[elements[:, 2], :] - self.nodes[elements[:, 0], :]\\n return np.cross(v1, v2, axisa=1, axisb=1)\",\n \"def get_linearEvolving(self):\\n return self.get_linearEvolvingEigen()\",\n \"def norm( self):\\n return self._norm\",\n \"def norm(self):\\n return sqrt(self.dot(self))\",\n \"def norm(self):\\n return math.sqrt(self.dotProduct(self))\",\n \"def __abs__(self):\\n return Vector.createFromPoint(self).norm\",\n \"def norm(self):\\n\\n return self.abs()\",\n \"def norm(self):\\n\\t\\treturn math.sqrt(self.norm2())\",\n \"def dist_matrix(self):\\n return self.__dist_matrix\",\n \"def cluster_constraint(self):\\n return self._cluster_constraint\",\n \"def molecule(self):\\n return self._molecule\",\n \"def get_norma(self):\\n return self.norma\",\n \"def normal(self) -> Vec:\\n return abs(self.up_axis.cross(self.forward()))\",\n \"def _transform(self, X):\\r\\n return euclidean_distances(X, self.cluster_centers_)\",\n \"def normSq(self):\\n\\t\\treturn self.x*self.x+self.y*self.y\",\n \"def normalInv(self) -> 'MultiVector':\\n\\n Madjoint = ~self\\n MadjointM = (Madjoint * self)\\n\\n if MadjointM.isScalar() and abs(MadjointM[()]) > _eps:\\n # inverse exists\\n return Madjoint / MadjointM[()]\\n else:\\n raise ValueError(\\\"no inverse exists for this multivector\\\")\",\n \"def cluster(self):\\n return self._cluster\",\n \"def cluster(self):\\n return self._cluster\",\n \"def norm(self):\\n raise NotImplementedError\",\n \"def mesh(self):\\n return self._mesh\",\n \"def mesh(self):\\n return self._mesh\",\n \"def mesh(self):\\n return self._mesh\",\n \"def mesh(self):\\n return self._mesh\",\n \"def ensemble_norm(self):\\n return np.linalg.norm(self.ensemble_transition_matrix, ord=\\\"fro\\\")\",\n \"def norm(self):\",\n \"def normal(self) -> 'MultiVector':\\n\\n return self / np.sqrt(abs(self.mag2()))\",\n \"def Prim(self):\\n g = Graph(attr={DIRECTED: False})\\n distance = [sys.maxsize] * len(self.vertices)\\n parent = [None] * len(self.vertices)\\n set = [False] * len(self.vertices)\\n\\n distance[0] = 0\\n parent[0] = -1\\n\\n for i in self.vertices:\\n # Search vertex with minimum distance\\n min_index = 0\\n min = sys.maxsize\\n for v in self.vertices:\\n if distance[v] < min and set[v] is False:\\n min = distance[v]\\n min_index = v\\n u = min_index\\n\\n # Add u vertex in set to not use it in other iteration \\n set[u] = True\\n g.add_vertex(vertex.Vertex(u))\\n\\n # Iterate all adjacent vertices of u vertex and update distance \\n for v in self.get_adjacent_vertices_by_vertex(u):\\n if set[v] is False and distance[v] > \\\\\\n self.get_edge((u, v)).attr[\\\"WEIGHT\\\"]:\\n distance[v] = self.get_edge((u, v)).attr[\\\"WEIGHT\\\"]\\n parent[v] = u\\n\\n for i in self.vertices:\\n if i == 0:\\n continue\\n if parent[i] is not None:\\n g.add_edge(edge.Edge(parent[i], i, {\\\"WEIGHT\\\": self.get_edge((parent[i], i)).attr[\\\"WEIGHT\\\"]}))\\n\\n return g\",\n \"def perpendicular(self):\\n return tuple.__new__(Vec2, (-self[1], self[0]))\",\n \"def get_intra_plate_cluster_distance(self) -> int:\\n return self.intra_plate_cluster_distance\",\n \"def norm(self):\\n norm = self.scalarProduct(self) ** 0.5\\n return norm\",\n \"def normal(self) -> Vector:\\n return self._normal\",\n \"def parallel_component(self, basis: Vector) -> Vector:\\n b_norm = basis.normalize()\\n return (self * b_norm) * b_norm\",\n \"def norm2(self):\\n\\t\\treturn self.x ** 2 + self.y ** 2 + self.z ** 2\",\n \"def norm_distance(self):\\n graph_size = self.N + self.M\\n return self.distance() / (1. * graph_size)\",\n \"def norm(self):\\n\\t\\treturn np.sqrt(self.normSq())\",\n \"def vector(self) -> Vector:\\n return self._normal * self._distance_from_origin\",\n \"def getDistanceMatrix(self):\\n return self.pointcloud.distmat\",\n \"def getCentroid(self) -> Vec3:\\n return self.centroid()\",\n \"def norm(self):\\n if self._coord_format != constants.MatrixCoordinateDefault:\\n self._logger.error(\\\"invalid coordinate format\\\")\\n raise NotImplementedError(\\\"invalid coordinate format\\\")\\n\\n if self._dtype == complex:\\n def __map(m):\\n return m[2].real ** 2 + m[2].imag ** 2\\n else:\\n def __map(m):\\n return m[2] ** 2\\n\\n n = self._data.map(\\n __map\\n ).reduce(\\n lambda a, b: a + b\\n )\\n\\n return math.sqrt(n)\",\n \"def norm2(self):\\n return getattr(self, self.norm2_name)\",\n \"def u(self):\\n return (self.u_edges[1:] + self.u_edges[:-1]) / 2\",\n \"def get_kernel_norms(self):\\n return np.einsum('ijk->ij', self.amplitudes)\",\n \"def getMesh(self):\\n return self.mesh\",\n \"def norm(self):\\n return np.sqrt(np.dot(self._data, self._data))\",\n \"def Centroid(self):\\n return Vector(self.centroid)\",\n \"def norm(self):\\n # TODO: implement\\n return\",\n \"def l2_norm(self):\\n return (self.x**2 + self.y**2 + self.z**2)**0.5\",\n \"def norm(self):\\n return np.linalg.norm(self.ravel())\",\n \"def hyperplane(self):\\n origin = (self.a+self.b+self.c)/3.\\n normal = np.cross(self.a-self.b, self.a-self.c)\\n return Hyperplane(origin, normal)\",\n \"def nodalLaplacian(self):\\n if getattr(self, '_nodalLaplacian', None) is None:\\n print('Warning: Laplacian has not been tested rigorously.')\\n # The number of cell centers in each direction\\n n = self.vnC\\n # Compute divergence operator on faces\\n if(self.dim == 1):\\n D1 = sdiag(1./self.hx) * ddx(self.nCx)\\n L = - D1.T*D1\\n elif(self.dim == 2):\\n D1 = sdiag(1./self.hx) * ddx(n[0])\\n D2 = sdiag(1./self.hy) * ddx(n[1])\\n L1 = sp.kron(speye(n[1]+1), - D1.T * D1)\\n L2 = sp.kron(- D2.T * D2, speye(n[0]+1))\\n L = L1 + L2\\n elif(self.dim == 3):\\n D1 = sdiag(1./self.hx) * ddx(n[0])\\n D2 = sdiag(1./self.hy) * ddx(n[1])\\n D3 = sdiag(1./self.hz) * ddx(n[2])\\n L1 = kron3(speye(n[2]+1), speye(n[1]+1), - D1.T * D1)\\n L2 = kron3(speye(n[2]+1), - D2.T * D2, speye(n[0]+1))\\n L3 = kron3(- D3.T * D3, speye(n[1]+1), speye(n[0]+1))\\n L = L1 + L2 + L3\\n self._nodalLaplacian = L\\n return self._nodalLaplacian\",\n \"def normal(self) -> Vector:\\n return normalize(cross(self.d1, self.d2))\",\n \"def calc_main_axis(self):\\n #Clarify why the above step has been done\\n c0, c1, c2 = self.calc_geom_center ()\\n M = numpy.zeros ((3, 3), dtype=float)\\n M = [[0] * 3, [0] * 3, [0] * 3]\\n for x in self.atomcoords:\\n xi = x[0] - c0\\n yi = x[1] - c1\\n zi = x[2] - c2\\n M[0][0] = M[0][0] + xi * xi\\n M[0][1] = M[0][1] + xi * yi\\n M[0][2] = M[0][2] + xi * zi\\n M[1][1] = M[1][1] + yi * yi\\n M[1][2] = M[1][2] + yi * zi\\n M[2][2] = M[2][2] + zi * zi\\n M[1][0] = M[0][1]\\n M[2][0] = M[0][2]\\n M[2][1] = M[1][2]\\n M = numpy.array (M)\\n d = sum (numpy.diag (M))\\n M = -M\\n M[0, 0] = M[0, 0] + d\\n M[1, 1] = M[1, 1] + d\\n M[2, 2] = M[2, 2] + d\\n\\n eigenVals, eigenVecs = numpy.linalg.eig (M)\\n eigenVecs = eigenVecs.transpose ()\\n return eigenVecs\",\n \"def geom(self):\\n return self.data[\\\"norm\\\"].geom\",\n \"def getNeedToComputeMatrix(self):\\n return _osgAnimation.RigGeometry_getNeedToComputeMatrix(self)\",\n \"def get_distance(self, node):\\n return np.sqrt(\\n (self.x - node.x) ** 2 +\\n (self.y - node.y) ** 2\\n )\",\n \"def magnitude(self):\\n\\t\\treturn sqrt(self.dot(self))\",\n \"def euclidean_dist(self):\\r\\n\\r\\n real_cat, synth_cat = self.to_cat(self.origdst, self.synthdst)\\r\\n\\r\\n real_cat_dem = self.get_demographics(real_cat)\\r\\n synth_cat_dem = self.get_demographics(synth_cat)\\r\\n\\r\\n corr_real_obj = associations(real_cat_dem, theil_u=True, bias_correction=False, plot=False)\\r\\n corr_synth_obj = associations(synth_cat_dem, theil_u=True, bias_correction=False, plot=False)\\r\\n\\r\\n corr_real = corr_real_obj['corr']\\r\\n corr_rand = corr_synth_obj['corr']\\r\\n\\r\\n eucl_matr = distance.cdist(corr_real, corr_rand, 'euclidean')\\r\\n\\r\\n eucl = LA.norm(eucl_matr)\\r\\n\\r\\n return eucl, eucl_matr\",\n \"def P(self):\\n self.eigenmatrix()\",\n \"def mesh(self):\\n self._ensure_mesh()\\n return self._mesh\",\n \"def euclidean_sqr(self, instance, centroid):\\n return np.linalg.norm(instance-centroid)**2\",\n \"def get_norm(self, l):\\n return self._W.norm(l)\",\n \"def get_norm(self, l):\\n return self._W.norm(l)\",\n \"def nelem3(self):\\n if self._nelem3 is None:\\n self._nelem3 = self.get_mesh_nelem()\\n\\n return self._nelem3\",\n \"def get_mesh(self):\\n return self.mesh\",\n \"def getDistanceMatrix(self):\\n return self.distmat.as_matrix()\",\n \"def getNorm(self, norm=lambda l: (sum(map(lambda x: x ** 2, l))) ** (1 / 2)):\\n return norm(self.components)\",\n \"def norm2d(self) -> float:\\n\\n return self.v2ddict.norm2d()\",\n \"def Normal(self):\\n return Vector(self.normal)\",\n \"def normalize(self):\\n return Vector(self.args + []) / self.magnitude()\",\n \"def get_clust_cent(self):\\r\\n\\r\\n return self.__clust_cent\",\n \"def divmod_node(self, mpi_procs, omp_threads):\\n return divmod(mpi_procs * omp_threads, self.cores_per_node)\",\n \"def _cluster(self):\\n # , distance_function=spearman_squared_distance, max_iter=1000, tol=0.0001):\\n if self.cluster_method is None:\\n clusters = KMedoids(\\n self.k,\\n self.batchsize,\\n dist_func=self.distance_function,\\n max_iter=self.max_iter,\\n tol=self.tol,\\n init_medoids=self.init_medoids,\\n swap_medoids=self.swap_medoids,\\n )\\n clusters.fit(self.clustering_attributions, verbose=self.verbose)\\n\\n self.subpopulations = clusters.members\\n self.subpopulation_sizes = GAM.get_subpopulation_sizes(clusters.members)\\n self.explanations = self._get_explanations(clusters.centers)\\n # Making explanations return numerical values instead of dask arrays\\n if isinstance(self.explanations[0][0][1], da.Array):\\n explanations = []\\n for explanation in self.explanations:\\n explanations.append([(x[0], x[1].compute()) for x in explanation])\\n self.explanations = explanations\\n else:\\n self.cluster_method(self)\",\n \"def calc_distances_in_embedding(cluster, embedding, reference_name=None):\\n\\n if reference_name is None:\\n reference_name = get_central_pc_name(embedding, cluster)\\n\\n assert reference_name in cluster\\n\\n ref_coords = embedding.loc[reference_name]\\n w = embedding.copy()\\n w = w-ref_coords\\n return w.apply(eucl_norm, axis=1)\",\n \"def euclidean(self, other):\\n return linalg.norm([self.x - other.x, self.y - other.y])\",\n \"def getMath(self):\\n return _libsbml.KineticLaw_getMath(self)\",\n \"def GetNormal(self):\\n ...\",\n \"def normal(self) -> Vec:\\n # The three points are in clockwise order, so compute differences\\n # in the clockwise direction, then cross to get the normal.\\n point_1 = self.planes[1] - self.planes[0]\\n point_2 = self.planes[2] - self.planes[1]\\n\\n return Vec.cross(point_1, point_2).norm()\",\n \"def project_embedding(self):\\n w = self.feature_embedding.weight.data\\n d = w.size(-1) - 1\\n narrowed = w.narrow(-1, 1, d)\\n tmp = 1 + torch.sum(torch.pow(narrowed, 2), dim=-1, keepdim=True)\\n tmp.sqrt_()\\n w.narrow(-1, 0, 1).copy_(tmp)\\n return w # can be delete?\",\n \"def get_force(self):\\n displ = self.get_displ()\\n equil = displ / np.linalg.norm(displ) * self.L0\\n return self.k * (displ - equil)\",\n \"def norm(self):\\n return np.linalg.norm(self.values)\",\n \"def get_normal(self):\\n c, s = np.cos(self.eangles), np.sin(self.eangles)\\n r = np.array([[c, -s], [s, c]])\\n u = np.array([1, 0])\\n return np.dot(r, u)\",\n \"def normalize(self):\\r\\n\\r\\n nlen = 1.0/math.sqrt(self*self)\\r\\n return vec4(self.x*nlen, self.y*nlen, self.z*nlen, self.w*nlen)\",\n \"def matrix(self):\\n return self.composition(self._parent.edge_to_matrix)\",\n \"def __abs__(self):\\r\\n return math.sqrt(self*self)\",\n \"def norm(self) -> float:\\n return self.squared_norm()**0.5\",\n \"def norm(self):\\n return numpy.linalg.norm(self.values)\",\n \"def normalequ(self):\\n tx = self.train_x\\n y = self.train_y\\n if self.regularizer is None:\\n return np.linalg.solve(np.dot(tx.T, tx), np.dot(tx.T, y))\\n elif self.regularizer.name is 'Ridge':\\n G = np.eye(tx.shape[1])\\n G[0, 0] = 0\\n hes = np.dot(tx.T, tx) + self.regularizer_p * G\\n return np.linalg.solve(hes, np.dot(tx.T, y))\\n else:\\n raise NotImplementedError\",\n \"def center_of_mass_polyhedron():\\n raise NotImplementedError\",\n \"def calculate_distance_edge(self):\\n mu_star = -np.sqrt(1. - (self.cell_xl / self.x)**2)\\n\\n if self.mu <= mu_star:\\n\\n l_edge = (-self.mu * self.x -\\n np.sqrt(self.mu**2 * self.x**2 -\\n self.x**2 + self.cell_xl**2))\\n self.next_cell_index = self.cell_index - 1\\n\\n else:\\n\\n l_edge = (-self.mu * self.x +\\n np.sqrt(self.mu**2 * self.x**2 -\\n self.x**2 + self.cell_xr**2))\\n self.next_cell_index = self.cell_index + 1\\n\\n return l_edge\",\n \"def topology(self):\\n return self._topology\",\n \"def get_euclidean_coordinates(self):\\n return self._xx.T, self._yy.T\",\n \"def centers(self):\\n return tuple(0.5 * (e[1:] + e[:-1]) for e in self.edges)\",\n \"def getCentroid(self):\\r\\n return self._centroid\",\n \"def GetModifiableTransform(self) -> \\\"itkTransformD33 *\\\":\\n return _itkTransformMeshFilterPython.itkTransformMeshFilterMF3MF3TD33_GetModifiableTransform(self)\",\n \"def getNormalizedVector(self):\\n return self.scalarMultiplication(self.norm() ** -1.0)\",\n \"def getMath(self):\\n return _libsbml.StoichiometryMath_getMath(self)\",\n \"def test_euclidean_scale(self):\\n\\n s = space(curvature=0)\\n\\n magic = 77773.333773777773733\\n for mul in (2, 5, 1/3, 1/11, magic, 1/magic):\\n for name, dim in (\\n ('sphere_s1', 1),\\n ('sphere_v2', 2),\\n ('sphere_s2', 2),\\n ('sphere_v3', 3)\\n ):\\n self.assertTrue(isclose(\\n getattr(s, name)(1) * mul**dim,\\n getattr(s, name)(mul)\\n ))\",\n \"def get_length(self) -> np.float64:\\n\\n return np.float64(\\n sqrt(\\n (self.node1.x - self.node2.x) ** 2\\n + (self.node1.y - self.node2.y) ** 2\\n )\\n )\",\n \"def M(self):\\n return _hypre.HypreParMatrix_M(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.57701725","0.57140815","0.565746","0.558986","0.5530351","0.55212075","0.54947764","0.53900594","0.5290139","0.5283337","0.52715415","0.51860607","0.51778406","0.5176604","0.5165664","0.51596206","0.51595855","0.51545715","0.51545715","0.51331407","0.5111279","0.5111279","0.5111279","0.5111279","0.51016355","0.5093432","0.50932586","0.5092543","0.50886464","0.50808114","0.50726724","0.5067454","0.5061619","0.5054677","0.5049501","0.50455993","0.5025451","0.5023348","0.50228745","0.5022073","0.50152403","0.50147927","0.49847424","0.4984742","0.49805722","0.4949815","0.49426258","0.4941736","0.4940582","0.4939947","0.49328837","0.4922032","0.49153575","0.4884366","0.4880764","0.48762742","0.48586026","0.48508522","0.4825346","0.48247147","0.48155633","0.4812657","0.4812657","0.48060286","0.48051676","0.47997025","0.47976092","0.47906613","0.4787978","0.47863996","0.47790265","0.4777255","0.47729412","0.47679025","0.47646233","0.47617635","0.47590172","0.47509503","0.47498775","0.4749423","0.47475865","0.47457525","0.47441658","0.47412324","0.47351733","0.4732236","0.47272676","0.47246316","0.47235006","0.47221234","0.47209394","0.4716968","0.47086608","0.47028157","0.46944043","0.46815512","0.4677862","0.46753067","0.46722484","0.46683764"],"string":"[\n \"0.57701725\",\n \"0.57140815\",\n \"0.565746\",\n \"0.558986\",\n \"0.5530351\",\n \"0.55212075\",\n \"0.54947764\",\n \"0.53900594\",\n \"0.5290139\",\n \"0.5283337\",\n \"0.52715415\",\n \"0.51860607\",\n \"0.51778406\",\n \"0.5176604\",\n \"0.5165664\",\n \"0.51596206\",\n \"0.51595855\",\n \"0.51545715\",\n \"0.51545715\",\n \"0.51331407\",\n \"0.5111279\",\n \"0.5111279\",\n \"0.5111279\",\n \"0.5111279\",\n \"0.51016355\",\n \"0.5093432\",\n \"0.50932586\",\n \"0.5092543\",\n \"0.50886464\",\n \"0.50808114\",\n \"0.50726724\",\n \"0.5067454\",\n \"0.5061619\",\n \"0.5054677\",\n \"0.5049501\",\n \"0.50455993\",\n \"0.5025451\",\n \"0.5023348\",\n \"0.50228745\",\n \"0.5022073\",\n \"0.50152403\",\n \"0.50147927\",\n \"0.49847424\",\n \"0.4984742\",\n \"0.49805722\",\n \"0.4949815\",\n \"0.49426258\",\n \"0.4941736\",\n \"0.4940582\",\n \"0.4939947\",\n \"0.49328837\",\n \"0.4922032\",\n \"0.49153575\",\n \"0.4884366\",\n \"0.4880764\",\n \"0.48762742\",\n \"0.48586026\",\n \"0.48508522\",\n \"0.4825346\",\n \"0.48247147\",\n \"0.48155633\",\n \"0.4812657\",\n \"0.4812657\",\n \"0.48060286\",\n \"0.48051676\",\n \"0.47997025\",\n \"0.47976092\",\n \"0.47906613\",\n \"0.4787978\",\n \"0.47863996\",\n \"0.47790265\",\n \"0.4777255\",\n \"0.47729412\",\n \"0.47679025\",\n \"0.47646233\",\n \"0.47617635\",\n \"0.47590172\",\n \"0.47509503\",\n \"0.47498775\",\n \"0.4749423\",\n \"0.47475865\",\n \"0.47457525\",\n \"0.47441658\",\n \"0.47412324\",\n \"0.47351733\",\n \"0.4732236\",\n \"0.47272676\",\n \"0.47246316\",\n \"0.47235006\",\n \"0.47221234\",\n \"0.47209394\",\n \"0.4716968\",\n \"0.47086608\",\n \"0.47028157\",\n \"0.46944043\",\n \"0.46815512\",\n \"0.4677862\",\n \"0.46753067\",\n \"0.46722484\",\n \"0.46683764\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64672875"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77089,"cells":{"query":{"kind":"string","value":"return the footer information"},"document":{"kind":"string","value":"def get(self):\n app_info = {\n 'developedBy': 'This app was developed by the Melbourne eResearch Group (www.eresearch.unimelb.edu.au) within the School of Computing and Information Systems (https://cis.unimelb.edu.au) at The University of Melbourne (www.unimelb.edu.au). ',\n 'description': 'The app uses artificial intelligence (convolutional neural networks) to identify the age, gender, and emotion of the people.',\n 'contact': 'https://eresearch.unimelb.edu.au',\n 'developedByHTML': '<p>This app was developed by the Melbourne eResearch Group (<a href=\\\"www.eresearch.unimelb.edu.au\\\" target=\\\"_blank\\\">www.eresearch.unimelb.edu.au</a>) within the School of Computing and Information Systems (<a href=\\\"https://cis.unimelb.edu.au\\\" target=\\\"_blank\\\">https://cis.unimelb.edu.au</a>) at The University of Melbourne (<a href=\\\"www.unimelb.edu.au\\\" target=\\\"_blank\\\">www.unimelb.edu.au</a>).</p>',\n 'descriptionHTML': '<p>The app uses artificial intelligence (convolutional neural networks) to identify the age, gender, and emotion of the people.</p>',\n 'contactHTML': '<p>Please contact us at: <a href=\\\"eresearch.unimelb.edu.au\\\" target=\\\"_blank\\\">eresearch.unimelb.edu.au</a></p>'\n }\n\n return send_json_response(app_info, 200)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def getFooter():\n return _FOOTER","def footer(self):\n return self._footer","def footer(self):\n pass","def footer(self):\n pass","def footer(self, **args):\n return self.pageConfig['footer'] % self.pageConfig","def _get_footer(self, footer):\n if footer is None:\n html = self.footer()\n else:\n html = footer\n return html","def get_footer(self):\n self.footer = '</div>' \\\n '</div>' \\\n '</div>' \\\n '<div class=\"footer\">' \\\n '<div class=\"container\">' \\\n '<p class=\"text-muted\">Copyright Harm Brugge 2014.</p>' \\\n '</div>' \\\n '</div>' \\\n '</body>' \\\n '</html>'\n return self.footer","def WriteFooter(self):\n return","def get(self):\n return self.footer_html","def writeFooter(self):\n pass","def email_footer():\n footer = \"\"\n\n return footer","def footer():\n\treturn \"\"\"<footer><table width=\"100%\"><th>Weather Icons by <a href=\"https://github.com/erikflowers/weather-icons\">Erik Flowers</a></th>\n\t<th><a href=\"http://forecast.io/\">Powered by Forecast</a></th></table></footer></div>\n\t</body></html>\"\"\"","def parse_footer(self): # -> tuple[list[Unknown], str]:\n ...","def parse_footer(self): # -> tuple[list[Unknown], str]:\n ...","def get_footer_text(self):\n return self._get_footer_text()","def footer_html(self):\n return self._footer_html","def format_report_footer(self):","def parse_footer(self):\n lines=self.lines\n bodyfinish=re.compile(r\"</body>\", re.IGNORECASE).search(lines).span()[0]\n self.footer=lines[bodyfinish:]","def _get_footer_text(self):\n return _(THEME_FOOTER_TEXT)","def get_footer() -> html:\n footer = dbc.Container([\n html.Hr(),\n dbc.Row([\n dbc.Col([\n 'Made with ❤ in Frankfurt from ',\n dcc.Link(children='STATWORX',\n href='https://www.statworx.com/',\n style={\"color\": COLOR_STATWORX}),\n ]),\n dbc.Col(dcc.Link(\n children='Try Again!', href='/', style={\"color\": COLOR_STATWORX}),\n className=\"text-right\")\n ])\n ],\n className='mb-4')\n\n return footer","def print_footer():\n print('</text>')","def gen_footer():\n return '</body></html>'","def footer_html():\n note_div = html.Div(\n [\n dcc.Markdown(\n \"This website uses natural language processing (NLP) to power search on a set of research papers related to COVID-19.\"\n \" It was created by the team behind [Matscholar](https://www.matscholar.com), a research effort led by the [HackingMaterials](https://hackingmaterials.lbl.gov), \"\n \" [Persson](https://perssongroup.lbl.gov), and [Ceder](https://ceder.berkeley.edu) research\"\n \" groups at Lawrence Berkeley National Lab.\"\n \" The virus icon in our logo was made by Freepik from www.flaticon.com\",\n className=\"column is-half is-size-6\"\n )\n ],\n className=\"columns is-centered\"\n\n )\n\n common_footer_style = \"has-text-weight-bold\"\n\n about_matscholar = html.A(\n \"About Matscholar\",\n href=\"https://github.com/materialsintelligence/matscholar-web\",\n target=\"_blank\",\n className=common_footer_style,\n )\n\n privacy_policy = html.A(\n \"Privacy Policy\",\n href=\"https://www.iubenda.com/privacy-policy/55585319\",\n target=\"_blank\",\n className=common_footer_style,\n )\n\n submit_feedback = html.A(\n \"Matscholar Forum\",\n href=\"https://discuss.matsci.org/c/matscholar\",\n target=\"_blank\",\n className=common_footer_style,\n )\n\n footer_link_tree = html.Div(\n [\n about_matscholar,\n html.Span(\" | \"),\n privacy_policy,\n html.Span(\" | \"),\n submit_feedback,\n ]\n )\n\n footer_copyright = html.Div(\n html.Span(\"Copyright © 2019 - Materials Intelligence\")\n )\n\n footer = html.Div(\n [note_div, footer_link_tree, footer_copyright],\n id=\"footer_container\",\n className=\"content has-text-centered\",\n )\n\n footer_container = html.Div(footer)\n return footer_container","def parse_footer(self): # -> tuple[list[Unknown], Literal['']]:\n ...","def footer(node):\n\n current_time = datetime.datetime.now()\n return '''\n </div>\n <div id=\"edit\">\n Last edit: ''' + time.strftime(\"%m/%d/%Y %I:%M:%S %p\", node.page.last_edit) + '''\n </div>\n </div>\n <footer>\n © ''' + str(current_time.year) + ' ' + AUTHOR + ''' | Generated with <a href=\"http://www.minimalblue.com/software/minimalsite.html\">minimalsite</a> \n </footer>\n </div>\n </body>\n</html>'''","def WriteFooter(self):\n self.WriteText('}')","def get_footer_navigation(self):\n nav_data = self.get_navigation_data()\n return nav_data.get('footer_navigation', [])","def footer(self):\n self.set_y(-15)\n self.set_font(self.police, 'I', 8)\n self.cell(w=0, h=10, txt=f\"Page {self.page_no()}\" + '/{nb}', border='B', ln=0, align='R')","def get_footer_links(self):\n nav_data = self.get_navigation_data()\n return nav_data.get('footer_links', [])","def footer():\n return u'</form></body></html>\\n'","def __footer(self):\n result = \"\"\"This API and the related documentation has been created with <a href=\"https://github.com/opencitations/ramose\" target=\"_blank\">RAMOSE</a>, the *Restful API Manager Over SPARQL Endpoints*, developed by <a href=\"http://orcid.org/0000-0003-0530-4305\" target=\"_blank\">Silvio Peroni</a> and <a href=\"https://marilenadaquino.github.io\">Marilena Daquino</a>.\"\"\"\n return markdown(result)","def getFooter(HTMLstring):\n footer = open(os.path.dirname(os.path.realpath(__file__))+\"/html/footer.html\", \"r\")\n HTMLstring += footer.read()\n footer.close()\n return HTMLstring","def footer_links(request):\n data = _get_footer_data(request) \n response_data = json.dumps(data)\n\n response = HttpResponse(response_data, mimetype= \"application/json\")\n response['Cache-Control'] = 'public'\n\n return response","def footer(self, footer):\n\n self._footer = footer","def parse_footer_block(lines):\n # najdeme pocatek paticky\n bottom_index = [idx for idx, line in enumerate(lines[-MAX_FOOTER_HEIGHT:]) \\\n if FOOTER_RE.match(line)]\n if not len(bottom_index):\n return None\n\n # vyparsujeme data z ni\n data = [line for line in lines[-(MAX_FOOTER_HEIGHT-bottom_index[0]):] if line.strip()]\n out = {'date':None}\n for item in data:\n # typ a poradove cislo zastupitelstva\n m = FOOTER_RE.match(item)\n if m:\n try:\n out['date'] = date(int(m.group(3).strip()), int(m.group(2).strip()), int(m.group(1).strip()))\n except ValueError:\n pass\n\n return out","def common_html_footer(outfile: TextIO, indexpath: str = \"\") -> None:\n outfile.write(\"\\n\")\n outfile.write(\" <footer>\\n\")\n outfile.write(\" <figure id=\\\"footmap\\\"><script type=\\\"text/javascript\\\" \"\n \"src=\\\"//rf.revolvermaps.com/0/0/4.js?i=5f9t1sywiez&m=0&h=75&c=ff0000&r=30\\\" \"\n \"async=\\\"async\\\"></script><figcaption>Visitors</figcaption></figure>\\n\")\n outfile.write(\" <p id=\\\"citation\\\"><a href=\\\"\" + indexpath + init_data().cite_url +\n \"\\\">\" + fetch_fa_glyph(\"site cite\") + \"How to cite this site</a></p>\\n\")\n outfile.write(\" <p id=\\\"contact\\\">Questions or comments about the site? Contact \"\n \"<a href=\\\"mailto:\" + init_data().site_author_email + \"\\\">\" + fetch_fa_glyph(\"mail\") +\n \"Dr. Michael S. Rosenberg</a></p>\\n\")\n outfile.write(\" <p id=\\\"copyright\\\">Release: \" + init_data().version +\n \" — Copyright © 2003–\" + str(init_data().current_year) +\n \" All Rights Reserved</p>\\n\")\n outfile.write(\" </footer>\\n\")\n outfile.write(\" </body>\\n\")\n outfile.write(\"</html>\\n\")","def foot(cls):\n return ''","def _ReadMemberFooter(self, file_object):\n file_offset = file_object.tell()\n data_type_map = self._GetDataTypeMap('gzip_member_footer')\n\n member_footer, _ = self._ReadStructureFromFileObject(\n file_object, file_offset, data_type_map, 'member footer')\n\n if self._debug:\n self._DebugPrintStructureObject(\n member_footer, self._DEBUG_INFO_MEMBER_FOOTER)","def footer(self):\n # close the svg tag\n yield '</svg>'\n # if this is a stand-alone document\n if self.standalone:\n # render a blank line\n yield ''\n # and the document footer\n yield from super().footer()\n # all done\n return","def write_footer(self, fd):\n fd.write(f\"END {self.name}\")\n if len(self.data_items) > 0:\n one_based = self.data_items[0].structure.type == DatumType.integer\n if isinstance(self.data_items[0], mfdatascalar.MFScalar):\n entry = self.data_items[0].get_file_entry(\n values_only=True, one_based=one_based\n )\n else:\n entry = self.data_items[0].get_file_entry()\n fd.write(str(entry.rstrip()))\n fd.write(\"\\n\")","def getFooter():\n return \"# job finished in %i seconds at %s -- %s -- %s\" %\\\n (time.time() - global_starting_time,\n time.asctime(time.localtime(time.time())),\n \" \".join(map(lambda x: \"%5.2f\" % x, os.times()[:4])),\n global_id)","def serialize_footer(signer):\n footer = b\"\"\n if signer is not None:\n signature = signer.finalize()\n footer = struct.pack(\">H{sig_len}s\".format(sig_len=len(signature)), len(signature), signature)\n return footer","def set_footer(self, **kwargs):\n self.footer_text = kwargs.get('text')\n self.footer_icon = kwargs.get('icon_url')","def footprint(dynamic_footprint_modifier=0.):","def write_footer(self, stream):\n return\n ##################################################\n # You MUST implement this method in the subclass #\n # if the file format defines a file footer. #\n ##################################################","def render_footer_log(self):\n\n # clean up the log file\n if self._selenium_log_file:\n\n # render the footer\n html = render_to_string(\n self.selenium_footer_template,\n {'id': self.id(), 'description': self.__doc__})\n\n # write it to the file\n self._selenium_log_file.write(html)","def set_footer(embed: Embed, ctx: commands.Context) -> Embed:\n return embed.set_footer(icon_url=ctx.author.avatar_url, text=f\"{ctx.author.display_name} • {get_time()}\")","def test_00_footer(self):\r\n url = '/'\r\n # As Anonymou user\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Footer links should not be shown to anonymous users\"\r\n assert dom.find(id='footer_links') is None, err_msg\r\n # As Authenticated user but NOT ADMIN\r\n self.signin()\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Footer links should not be shown to authenticated users\"\r\n assert dom.find(id='footer_links') is None, err_msg\r\n self.signout\r\n # As Authenticated user but ADMIN\r\n self.signin(email=self.root_addr, password=self.root_password)\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Footer links should not be shown to admin users\"\r\n assert dom.find(id='footer_links') is None, err_msg\r\n self.signout()","def test_00_footer(self):\r\n url = '/'\r\n # As Anonymou user\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Footer links should be shown to anonymous users\"\r\n assert dom.find(id='footer_links') is not None, err_msg\r\n # As Authenticated user but NOT ADMIN\r\n self.signin()\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Footer links should be shown to authenticated users\"\r\n assert dom.find(id='footer_links') is not None, err_msg\r\n self.signout\r\n # As Authenticated user but ADMIN\r\n self.signin(email=self.root_addr, password=self.root_password)\r\n res = self.app.get(url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"Footer links should be shown to admin users\"\r\n assert dom.find(id='footer_links') is not None, err_msg\r\n self.signout()","def footer(node):\n\n return '''\n <img src=\"/images/colosseo.png\" title=\"disegno del colosseo\" alt=\"colosseo\" class=\"colosseo\"/>\n\t \t </section>\n </div>\n\t\t\t <div class=\"clear\"></div>\n </div>\n </div>\n\t\t<div class=\"container_12 clearfix\">\n\t <footer class=\"grid_12\">\n\t\t\t\t <p>© <a href=\"http://luca.postregna.name\">lucapost</a> ''' + str(current_time.year) + '''; <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc/3.0/\">license</a>; <a href=\"/privacy.html\" title=\"normativa per la privacy\">privacy</a>; edit: ''' + time.strftime(\"%Y%m%d %I:%M:%S %p\", node.page.last_edit) + '''</p>\n <p>email: <a href=\"mailto:patdilenardo@gmail.com\" title=\"contatto email\">patdilenardo@gmail.com</a>; phone: +39 3389456208</p>\n\t\t </footer>\t\n\t\t\t<div class=\"clear\"></div>\n \t\t<script src=\"/js/hashgrid.js\"></script> \n\t\t<script src=\"/js/flux.min.js\" type=\"text/javascript\" charset=\"utf-8\"></script>\n\t\t<script type=\"text/javascript\" charset=\"utf-8\">\n\t\t\t$(function(){\n\t\t\t\tif(!flux.browser.supportsTransitions)\n\t\t\t\t\talert(\"Flux Slider requires a browser that supports CSS3 transitions\");\n\t\t\t\t\t\n\t\t\t\twindow.f = new flux.slider('#slider', {\n\t\t\t\t\tpagination: false,\n controls: true,\n captions: true,\n\t\t\t\t\ttransitions: [ 'dissolve' ],\n\t\t\t\t\tdelay: 5500\n\t\t\t\t});\n\t\t\t});\n\t\t</script> \n <script>\n (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){\n (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),\n m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)\n })(window,document,'script','//www.google-analytics.com/analytics.js','ga');\n ga('create', 'UA-6164762-14', 'nicelyventilated.it');\n ga('send', 'pageview');\n </script>\n\n<!--[if lt IE 7]><p class=chromeframe>Your browser is <em>ancient!</em> <a href=\"http://browsehappy.com/\">Upgrade to a different browser</a> or <a href=\"http://www.google.com/chromeframe/?redirect=true\">install Google Chrome Frame</a> to experience this site.</p><![endif]-->\n</body>\n</html>'''","def footer_html(self, html):\n self._footer_html = html","def get_onlineformincoming_formfooter(dbo, collationid):\n return db.query_string(dbo, \"SELECT o.Footer FROM onlineform o \" \\\n \"INNER JOIN onlineformincoming oi ON oi.FormName = o.Name \" \\\n \"WHERE oi.CollationID = %d\" % int(collationid))","def footer(self):\n file = open(\"imdb_output.html\", \"a\")\n file.write(\"\\t\\t</table>\\n\\t</body>\\n</html>\\n\")","def test_format_emperor_html_footer_string(self):\n self.maxDiff = 5000\n\n # footer for a jackknifed pcoa plot without biplots\n out_string = format_emperor_html_footer_string(False, True)\n self.assertItemsEqual(out_string.split('\\n'),\n EXPECTED_FOOTER_A.split('\\n'))\n self.assertEqual(out_string, EXPECTED_FOOTER_A)\n\n # footer for biplots without jackknifing\n out_string = format_emperor_html_footer_string(True, False)\n self.assertItemsEqual(out_string.split('\\n'),\n EXPECTED_FOOTER_B.split('\\n'))\n self.assertEqual(out_string, EXPECTED_FOOTER_B)\n\n # no biplots nor jackknifing\n out_string = format_emperor_html_footer_string(False, False)\n self.assertItemsEqual(out_string.split('\\n'),\n EXPECTED_FOOTER_C.split('\\n'))\n self.assertEqual(out_string, EXPECTED_FOOTER_C)\n\n # no biplots no jackknifing but with vectors\n out_string = format_emperor_html_footer_string(False, False, True)\n self.assertItemsEqual(out_string.split('\\n'),\n EXPECTED_FOOTER_D.split('\\n'))\n self.assertEqual(out_string, EXPECTED_FOOTER_D)\n\n # comparison plot\n out_string = format_emperor_html_footer_string(False, False,\n False, True)\n self.assertItemsEqual(out_string.split('\\n'),\n EXPECTED_FOOTER_E.split('\\n'))\n self.assertEqual(out_string, EXPECTED_FOOTER_E)","def footprint_height():","def footprint(self):\n return self._label_footprint","def output_footer(stream=sys.stdout):\n print(_module_footer, file=stream)","def usage_footer():\n print \"\"\"--\nSee more information about this project at:\n%(url)s\n\nReport bugs to authors at:\n%(source_url)s\n\"\"\" % {\n 'url': constants.App.URL,\n 'source_url': constants.App.SOURCE_URL,\n }","def writeFooter( self ):\n total_branch_length = (max(self.mNodeWidthsEnd) - min(self.mNodeWidthsStart)) / self.mBranchScaleFactor\n \n self.mFooterX = self.getHeaderWidth()\n self.mFooterY = self.getHeaderHeight() + self.mDataHeight + self.mSeparatorHeight\n\n ruler_start = self.mFooterX\n ruler_end = self.mFooterX + int(total_branch_length * self.mBranchScaleFactor)\n\n if \"ruler\" in self.mRulerElements:\n ## full length ruler with tick marks and labels\n e = SVGdraw.line( ruler_start,\n self.mFooterY + self.mRulerTickSize + 1,\n ruler_end,\n self.mFooterY + self.mRulerTickSize + 1,\n stroke = \"rgb(%i,%i,%i)\" % BLACK,\n stroke_width = 1 )\n self.addElement( e )\n\n ## get reasonable intervalls\n\n increment = self.mRulerIncrement * self.mBranchScaleFactor\n\n ## adjust increment for extremely long trees\n if (ruler_end - ruler_start) / increment > 1000:\n increment = (ruler_end - ruler_start) / 1000.0\n self.mRulerIncrement = increment / self.mBranchScaleFactor\n \n if \"right-ticks\" in self.mRulerElements:\n\n x = ruler_end\n while x >= ruler_start:\n e = SVGdraw.line( x,\n self.mFooterY,\n x,\n self.mFooterY + 2 * self.mRulerTickSize + 1,\n stroke = \"rgb(%i,%i,%i)\" % BLACK,\n stroke_width = 1 )\n self.addElement( e )\n x -= self.mRulerIncrement * self.mBranchScaleFactor\n \n self.mFooterY += 2 * self.mRulerTickSize + 1 + self.mSeparatorHeight\n\n if \"left-ticks\" in self.mRulerElements:\n \n x = ruler_start\n while x <= ruler_end:\n e = SVGdraw.line( x,\n self.mFooterY,\n x,\n self.mFooterY + 2 * self.mRulerTickSize + 1,\n stroke = \"rgb(%i,%i,%i)\" % BLACK,\n stroke_width = 1 )\n self.addElement( e )\n x += increment\n \n self.mFooterY += 2 * self.mRulerTickSize + 1 + self.mSeparatorHeight\n\n if \"scale\" in self.mRulerElements:\n\n w = int(self.mRulerIncrement * self.mBranchScaleFactor)\n \n e = SVGdraw.line( ruler_end,\n self.mFooterY + self.mRulerTickSize + 1,\n ruler_end - w,\n self.mFooterY + self.mRulerTickSize + 1,\n stroke = \"rgb(%i,%i,%i)\" % BLACK,\n stroke_width = 1 )\n \n self.addElement( e )\n \n e = SVGdraw.line( ruler_end,\n self.mFooterY,\n ruler_end,\n self.mFooterY + 2 * self.mRulerTickSize + 1,\n stroke = \"rgb(%i,%i,%i)\" % BLACK,\n stroke_width = 1 )\n \n self.addElement( e )\n\n e = SVGdraw.line( ruler_end - w,\n self.mFooterY,\n ruler_end - w,\n self.mFooterY + 2 * self.mRulerTickSize + 1,\n stroke = \"rgb(%i,%i,%i)\" % BLACK,\n stroke_width = 1 )\n \n self.addElement( e )\n\n e = SVGdraw.text( ruler_end - w / 2,\n self.mFooterY + 2 * self.mRulerTickSize + 1 + self.mRulerFontSize ,\n self.mRulerFormat % self.mRulerIncrement,\n self.mRulerFontSize,\n self.mRulerFont,\n stroke = \"rgb(%i,%i,%i)\" % BLACK,\n text_anchor = \"middle\" )\n\n self.addElement(e)\n self.mFooterY += 2 * self.mRulerTickSize + 1 + self.mRulerFontSize \n \n e, self.mFooterX, self.mFooterY = self.mDecoratorExternalNodes.getLegend( self.mFooterX, self.mFooterY )\n self.addElements(e)\n e, self.mFooterX, self.mFooterY = self.mDecoratorInternalNodes.getLegend( self.mFooterX, self.mFooterY )\n self.addElements(e)","def footer_nav(self):\r\n buttons = [NamedButton(\"help\", False, nocname=True),\r\n NamedButton(\"blog\", False, nocname=True),\r\n NamedButton(\"stats\", False, nocname=True),\r\n NamedButton(\"feedback\", False),\r\n NamedButton(\"bookmarklets\", False),\r\n NamedButton(\"socialite\", False),\r\n NamedButton(\"buttons\", True),\r\n NamedButton(\"widget\", True),\r\n NamedButton(\"code\", False, nocname=True),\r\n NamedButton(\"mobile\", False, nocname=True),\r\n NamedButton(\"store\", False, nocname=True),\r\n NamedButton(\"ad_inq\", False, nocname=True),\r\n ]\r\n\r\n return NavMenu(buttons, base_path = \"/\", type = \"flatlist\")","def _encode_foot(self):\n\t\tfoot = self.config.get('foot')\n\t\tif foot is not None:\n\t\t\treturn self._encode_tuple(foot)","def __init__(self, footer_html=None):\n self._footer_html = None\n\n if footer_html is not None:\n self.footer_html = footer_html","def write_footer(out):\n\n out.write(\"\"\" </g>\n</svg>\n\"\"\")","async def set_ban_footer(self, ctx, *, footer_string):\n if not footer_string:\n local_embed = discord.Embed(\n title=f'No string detected, I need a string parameter to work',\n description=' ',\n color=0x651111\n )\n await ctx.send(embed=local_embed)\n return\n success = await self.bot.pg_utils.set_ban_footer(\n ctx.guild.id,\n footer_string,\n self.bot.logger\n )\n if success:\n desc = footer_string.replace(\n f'%user%', ctx.message.author.mention)\n local_embed = discord.Embed(\n title=f'Footer message set:',\n description=f'**Preview:**\\n{desc}',\n color=0x419400\n )\n else:\n local_embed = discord.Embed(\n title=f'Internal error occured, please contact @dashwav#7785',\n description=' ',\n color=0x651111\n )\n await ctx.send(embed=local_embed)\n return","def return_info(self):\n\t\treturn self.info","def produce_header_footer(self):\n header = pylatex.PageStyle(\"header\", header_thickness=0.1)\n\n image_filename = self.get_image()\n with header.create(pylatex.Head(\"L\")) as logo:\n logo.append(pylatex.StandAloneGraphic(image_options=\"width=110px\", filename=image_filename))\n\n # Date\n with header.create(pylatex.Head(\"R\")):\n header.append(\"Date Report Issued: \" + datetime.today().strftime('%Y-%m-%d'))\n\n # Footer\n with header.create(pylatex.Foot(\"C\")):\n with header.create(pylatex.Tabular('lcr')) as table:\n table.add_row('', bold('Data interpretation guidelines can be found in RDIMS document ID: 10401305'),\n '')\n table.add_row('', bold('This report was generated with OLC AutoROGA v0.0.1'), '')\n return header","def create_footer(categories):\n\n footer = '\\n'\n row_len = max([len(category.name) for category in categories])\n for idx in range(row_len):\n footer += ' '\n for category in categories:\n try:\n footer += f' {category.name[idx]} '\n except IndexError:\n footer += ' '\n footer += ' \\n'\n return footer","def getInfo():","def endTopicMap(self):\r\n super(PHPTMAPIMapHandler, self).endTopicMap()\r\n self._out.write(_FOOTER)\r\n self._out.flush()","def get_info(self):\n return \"TODO !\"","async def set_kick_footer(self, ctx, *, footer_string):\n if not footer_string:\n local_embed = discord.Embed(\n title=f'No string detected, I need a string parameter to work',\n description=' ',\n color=0x651111\n )\n await ctx.send(embed=local_embed)\n return\n success = await self.bot.pg_utils.set_kick_footer(\n ctx.guild.id,\n footer_string,\n self.bot.logger\n )\n if success:\n desc = footer_string.replace(\n f'%user%', ctx.message.author.mention)\n local_embed = discord.Embed(\n title=f'Footer message set:',\n description=f'**Preview:**\\n{desc}',\n color=0x419400\n )\n else:\n local_embed = discord.Embed(\n title=f'Internal error occured, please contact @dashwav#7785',\n description=' ',\n color=0x651111\n )\n await ctx.send(embed=local_embed)\n return","def consume_footer(consensus, flavor='unflavored', sanity=True):\n if flavor not in ['unflavored', 'microdesc']:\n raise NotImplementedError(\n 'Consensus flavor \"{}\" not supported.'.format(flavor))\n\n whitelist = [\n b'directory-footer', b'bandwidth-weights', b'directory-signature']\n\n def end_of_field(line):\n if b'directory-footer' == line:\n return False\n if b' ' not in line:\n return True\n keyword, _ = line.split(b' ', 1)\n return keyword not in whitelist\n\n fields = dict()\n valid = False\n while True:\n consensus, header = scrap(consensus, end_of_field)\n if header is None:\n return consensus, fields if valid else None\n\n valid = True\n if b' ' not in header:\n continue\n\n try:\n header = str(header, 'utf8')\n except:\n continue\n\n keyword, content = header.split(' ', 1)\n if keyword == 'directory-footer':\n if sanity:\n assert len(fields) == 0 # first field\n\n if keyword == 'bandwidth-weights':\n content = parse_params(content)\n\n if keyword == 'directory-signature':\n content = content.split(' ', 2)\n if len(content) == 3:\n algorithm, identity, signing_key_digest = content\n elif len(content) == 2:\n algorithm = 'sha1'\n identity, signing_key_digest = content\n\n content = {\n 'Algorithm': algorithm,\n 'identity': identity,\n 'signing-key-digest': signing_key_digest}\n\n consensus, signature = scrap_signature(consensus)\n if signature is not None:\n signature = parse_base64(str(signature, 'utf8'))\n content['signature'] = signature\n\n if keyword + 's' not in fields:\n fields[keyword + 's'] = []\n fields[keyword + 's'].append(content)\n continue\n\n fields[keyword] = content\n return consensus, fields","def get_web_exhibit_footer_img(self, building):\n #building = self.location_and_hours['page_location'].id\n img = {SCRC_BUILDING_ID: SCRC_EXHIBIT_FOOTER_IMG,\n CRERAR_BUILDING_ID: CRERAR_EXHIBIT_FOOTER_IMG}\n if building in img:\n return Image.objects.get(id=img[building])\n return None","def get_footer_email(object, use_string_1=False, class_link='navLink'):\n if smart_unicode(str(type(object))) == \"<class 'dms.models.DmsItem'>\":\n item = object\n else:\n item = object.item\n if use_string_1:\n return encode_email(item.string_2, item.string_1, 'navLink')\n else:\n name = item.owner.get_full_name()\n if name == u'Unbekannte Person' and item.string_1 != '':\n return encode_email(item.string_2, item.string_1, class_link)\n else:\n return encode_email(item.owner.email, name, class_link)","def latex_footer():\n print(\" \\end{tikzpicture}\")\n print(\" }\")\n print(\" \\end{frame}\")\n print(\"\\end{document}\")","def get_info(self):\n return None","def std_out_segm_footer(*args):\n return _ida_segment.std_out_segm_footer(*args)","def initialPage():\n\treturn header() + footer()","def append_message_footer(msg, messagebody):\n footer = \"\\n\\n_____________________\\n\\n^^I ^^am ^^a ^^Bot. ^^| [^^How ^^to ^^use](https://github.com/Rafficer/ProtonStatusBot/tree/master#how-to-use) ^^| ^^Made ^^with ^^🖤 ^^by ^^/u/Rafficer ^^| ^^[Source](https://github.com/rafficer/ProtonStatusBot) ^^| [^^Report ^^a ^^Bug](https://github.com/Rafficer/ProtonStatusBot/issues)\"\n full_message = messagebody + footer\n logger.debug(\"Replying...\")\n try:\n msg.reply(full_message)\n except prawcore.exceptions.RequestException:\n logger.debug(\"Replying failed. Checking for connectivity and trying again.\")\n connectivity_check()\n msg.reply(full_message)\n logger.debug(\"Replied!\")","def test_write_page_margins_footer(self):\n\n self.worksheet.set_footer(margin=0.5)\n self.worksheet._write_page_margins()\n\n exp = \"\"\"<pageMargins left=\"0.7\" right=\"0.7\" top=\"0.75\" bottom=\"0.75\" header=\"0.3\" footer=\"0.5\"/>\"\"\"\n got = self.fh.getvalue()\n\n self.assertEqual(got, exp)","def write_db_footer(table,runnumber):\n \n table.write('}\\n')","def renderFooter(self, plot, painter, rect):\r\n painter.setFont(plot.footerLabel().font())\r\n color = plot.footerLabel().palette().color(QPalette.Active, QPalette.Text)\r\n painter.setPen(color)\r\n plot.footerLabel().text().draw(painter, rect)","def make_bottom_text( self ):\n return None","def get_footprint_area(self):\n x = self.dimension_along(0)\n y = self.dimension_along(1)\n return x * y","def setup_footer_panel(self):\r\n self.create_footer_push_button(\"F3 View\", \"F3\")\r\n self.create_footer_push_button(\"F4 Edit\", \"F4\")\r\n self.create_footer_push_button(\"F5 Copy\", \"F5\")\r\n self.create_footer_push_button(\"F6 move\", \"F6\")\r\n self.create_footer_push_button(\"F7 New Folder\", \"F7\")\r\n self.create_footer_push_button(\"F8 Delete\", \"F8\")\r\n self.create_footer_push_button(\"ALT+F4 Exit\")","def get_zipinfo(self):\n zipinfo = zipfile.ZipInfo()\n zipinfo.filename = self.translated_path()\n zipinfo.date_time = self.get_mod_time()\n zipinfo.file_size = self.get_size()\n return zipinfo","def show_footprint(self, fpname):\n logging.debug(\"show_footprint entered\")\n # container_name = \"%s-metadata\" % footprint_name\n # container = self.cf.get_container(container_name)\n # index = container.get_object(\"index.json\")\n # config = json.loads(index.fetch())\n # \n # \n # \n # logging.info(\"loaded footprint configuration\")\n # return config\n fp = self.get_footprint(fpname, start=False)\n pt = fp.status()\n print pt","def footnote(self) -> str:\n return self._footnote","def print_endinfo(text: str):\n templateName = \"{:s}\"\n print(bcolors.BOLD + templateName.format(text) + bcolors.ENDC, flush=True)","def info(self) -> dict:","def rollup_header_footer(self, context):\n header = tags.html_string_to_element_tree(\n '<link href=\"%s/markdown.css\" rel=\"stylesheet\" '\n 'type=\"text/css\">' % RESOURCE_FOLDER)\n footer = tags.html_string_to_element_tree('')\n return (header, footer)","def test__EmbedFooter__to_data():\n icon_url = 'attachment://orin.png'\n icon_proxy_url = 'https://www.astil.dev/project/hata/'\n text = 'orin'\n \n data = {\n 'icon_url': icon_url,\n 'proxy_icon_url': icon_proxy_url,\n 'text': text,\n }\n \n field = EmbedFooter.from_data(data)\n \n expected_output = data\n \n vampytest.assert_eq(\n field.to_data(defaults = True, include_internals = True),\n expected_output,\n )","def test_create_page_with_footer(self):\n\n footer_block = PageFooterBlock.objects.create(**_footer_block_data)\n link_1 = PageHeadDropMenu.objects.create\\\n (internal_link='home', **_head_link_data)\n link_2 = PageHeadDropMenu.objects.create(\n external_link=_external_link, **_head_link_data)\n link_3 = PageHeadDropMenu.objects.create(**_head_link_data)\n link_element_1 = PageHeadDropMenuElement.objects.create(\n internal_link='home', **_head_drop_link_data)\n link_element_2 = PageHeadDropMenuElement.objects.create(\n external_link=_external_link, **_head_drop_link_data)\n link_3.drop_links.add(link_element_1, link_element_2)\n footer_block.top_links.add(link_1, link_2, link_3)\n contact_address = Address.objects.create(**_address_block)\n footer_block.contact_address.add(contact_address)\n footer_block.a_link.add(link_element_1)\n\n Page.objects.create(footer_block=footer_block, **_page_data)\n response = self.client.get(reverse('home'))\n self.assertEqual(response.status_code, 200)\n\n self.assertIn('top_links', response.context)\n self.assertIn('contact', response.context)\n self.assertIn('top_contacts', response.context)\n self.assertIn('bot_copy_right', response.context)\n self.assertIn('bot_link', response.context)\n self.assertIn('bot_back_top', response.context)","def get_mgrs_footprint(mgrs_id):\n\n gzd = mgrs_id[:3]\n\n mgrs_100km_gzd = mgrs_id[3:]\n print(gzd)\n print(mgrs_100km_gzd)\n\n file_path = unzip_mgrs_shapefile(gzd)\n\n shapefile_driver = ogr.GetDriverByName(\"ESRI Shapefile\")\n\n grid_ds = shapefile_driver.Open(str(file_path), 0)\n\n layer = grid_ds.GetLayer()\n\n # transform coords from local UTM proj to lat long\n sourceSR = layer.GetSpatialRef()\n targetSR = osr.SpatialReference()\n targetSR.ImportFromEPSG(4326) # WGS84\n coordTrans = osr.CoordinateTransformation(sourceSR, targetSR)\n\n intersect_list = []\n wkt_footprint = None\n for f in layer:\n\n if f.GetField('100kmSQ_ID') == mgrs_100km_gzd:\n geom = f.GetGeometryRef()\n geom.Transform(coordTrans)\n wkt_footprint = geom.ExportToWkt()\n\n # all done!\n grid_ds = None\n\n clean_data_dir()\n\n return wkt_footprint","def format_emperor_html_footer_string(has_biplots=False, has_ellipses=False,\n has_vectors=False, has_edges=False):\n\n # we use python's built-in ternary operator to add or not a string\n # see _EMPEROR_FOOTER_HTML_STRING\n format_dict = {'biplot_spheres_color_selector':\n _BIPLOT_SPHERES_COLOR_SELECTOR if has_biplots else '',\n 'biplot_visibility_selector':\n _BIPLOT_VISIBILITY_SELECTOR if has_biplots else '',\n 'taxa_labels_selector':\n _TAXA_LABELS_SELECTOR if has_biplots else '',\n 'taxa_labels_color_selector':\n _TAXA_LABELS_COLOR_SELECTOR if has_biplots else '',\n 'edges_color_selector':\n _EDGES_COLOR_SELECTOR if has_edges else '',\n 'ellipse_opacity_slider':\n _ELLIPSE_OPACITY_SLIDER if has_ellipses else '',\n 'vectors_opacity_slider':\n _VECTORS_OPACITY_SLIDER if has_vectors else '',\n 'edges_visibility_selector':\n _EDGES_VISIBILITY_SELECTOR if has_edges else ''}\n\n return _EMPEROR_FOOTER_HTML_STRING.format(**format_dict)","def testWriteFooter(self):\n file_writer = writers.VS2010ProjectFileWriter()\n\n file_writer._file = io.BytesIO()\n\n file_writer.WriteFooter()\n\n file_writer._file.seek(0, os.SEEK_SET)\n output_data = file_writer._file.read()\n\n self.assertTrue(output_data.endswith(b'</Project>'))","def get_footprint(self, fpname, start=True):\n logging.debug(\"get_footprint entered\")\n container_name = \"%s-metadata\" % fpname\n container = cf.get_container(container_name)\n index = container.get_object(\"index.json\")\n config = json.loads(index.fetch())\n fp = Footprint(config)\n fp.load(start=start)\n return fp","def GetDefineGuardFooterLines(output_file_name):\n return [\n '#endif // %s_PREDICTION_%s_' %(_MOZC_DIR_FOR_DEFINE_GUARD,\n GetDefineGuardSymbol(output_file_name))]","def get_info(self):\n pass","def get_info(self):\n pass","def getLastFootprint(self):\n if self.footprints is None:\n return\n return self.footprints[-1]"],"string":"[\n \"def getFooter():\\n return _FOOTER\",\n \"def footer(self):\\n return self._footer\",\n \"def footer(self):\\n pass\",\n \"def footer(self):\\n pass\",\n \"def footer(self, **args):\\n return self.pageConfig['footer'] % self.pageConfig\",\n \"def _get_footer(self, footer):\\n if footer is None:\\n html = self.footer()\\n else:\\n html = footer\\n return html\",\n \"def get_footer(self):\\n self.footer = '</div>' \\\\\\n '</div>' \\\\\\n '</div>' \\\\\\n '<div class=\\\"footer\\\">' \\\\\\n '<div class=\\\"container\\\">' \\\\\\n '<p class=\\\"text-muted\\\">Copyright Harm Brugge 2014.</p>' \\\\\\n '</div>' \\\\\\n '</div>' \\\\\\n '</body>' \\\\\\n '</html>'\\n return self.footer\",\n \"def WriteFooter(self):\\n return\",\n \"def get(self):\\n return self.footer_html\",\n \"def writeFooter(self):\\n pass\",\n \"def email_footer():\\n footer = \\\"\\\"\\n\\n return footer\",\n \"def footer():\\n\\treturn \\\"\\\"\\\"<footer><table width=\\\"100%\\\"><th>Weather Icons by <a href=\\\"https://github.com/erikflowers/weather-icons\\\">Erik Flowers</a></th>\\n\\t<th><a href=\\\"http://forecast.io/\\\">Powered by Forecast</a></th></table></footer></div>\\n\\t</body></html>\\\"\\\"\\\"\",\n \"def parse_footer(self): # -> tuple[list[Unknown], str]:\\n ...\",\n \"def parse_footer(self): # -> tuple[list[Unknown], str]:\\n ...\",\n \"def get_footer_text(self):\\n return self._get_footer_text()\",\n \"def footer_html(self):\\n return self._footer_html\",\n \"def format_report_footer(self):\",\n \"def parse_footer(self):\\n lines=self.lines\\n bodyfinish=re.compile(r\\\"</body>\\\", re.IGNORECASE).search(lines).span()[0]\\n self.footer=lines[bodyfinish:]\",\n \"def _get_footer_text(self):\\n return _(THEME_FOOTER_TEXT)\",\n \"def get_footer() -> html:\\n footer = dbc.Container([\\n html.Hr(),\\n dbc.Row([\\n dbc.Col([\\n 'Made with ❤ in Frankfurt from ',\\n dcc.Link(children='STATWORX',\\n href='https://www.statworx.com/',\\n style={\\\"color\\\": COLOR_STATWORX}),\\n ]),\\n dbc.Col(dcc.Link(\\n children='Try Again!', href='/', style={\\\"color\\\": COLOR_STATWORX}),\\n className=\\\"text-right\\\")\\n ])\\n ],\\n className='mb-4')\\n\\n return footer\",\n \"def print_footer():\\n print('</text>')\",\n \"def gen_footer():\\n return '</body></html>'\",\n \"def footer_html():\\n note_div = html.Div(\\n [\\n dcc.Markdown(\\n \\\"This website uses natural language processing (NLP) to power search on a set of research papers related to COVID-19.\\\"\\n \\\" It was created by the team behind [Matscholar](https://www.matscholar.com), a research effort led by the [HackingMaterials](https://hackingmaterials.lbl.gov), \\\"\\n \\\" [Persson](https://perssongroup.lbl.gov), and [Ceder](https://ceder.berkeley.edu) research\\\"\\n \\\" groups at Lawrence Berkeley National Lab.\\\"\\n \\\" The virus icon in our logo was made by Freepik from www.flaticon.com\\\",\\n className=\\\"column is-half is-size-6\\\"\\n )\\n ],\\n className=\\\"columns is-centered\\\"\\n\\n )\\n\\n common_footer_style = \\\"has-text-weight-bold\\\"\\n\\n about_matscholar = html.A(\\n \\\"About Matscholar\\\",\\n href=\\\"https://github.com/materialsintelligence/matscholar-web\\\",\\n target=\\\"_blank\\\",\\n className=common_footer_style,\\n )\\n\\n privacy_policy = html.A(\\n \\\"Privacy Policy\\\",\\n href=\\\"https://www.iubenda.com/privacy-policy/55585319\\\",\\n target=\\\"_blank\\\",\\n className=common_footer_style,\\n )\\n\\n submit_feedback = html.A(\\n \\\"Matscholar Forum\\\",\\n href=\\\"https://discuss.matsci.org/c/matscholar\\\",\\n target=\\\"_blank\\\",\\n className=common_footer_style,\\n )\\n\\n footer_link_tree = html.Div(\\n [\\n about_matscholar,\\n html.Span(\\\" | \\\"),\\n privacy_policy,\\n html.Span(\\\" | \\\"),\\n submit_feedback,\\n ]\\n )\\n\\n footer_copyright = html.Div(\\n html.Span(\\\"Copyright © 2019 - Materials Intelligence\\\")\\n )\\n\\n footer = html.Div(\\n [note_div, footer_link_tree, footer_copyright],\\n id=\\\"footer_container\\\",\\n className=\\\"content has-text-centered\\\",\\n )\\n\\n footer_container = html.Div(footer)\\n return footer_container\",\n \"def parse_footer(self): # -> tuple[list[Unknown], Literal['']]:\\n ...\",\n \"def footer(node):\\n\\n current_time = datetime.datetime.now()\\n return '''\\n </div>\\n <div id=\\\"edit\\\">\\n Last edit: ''' + time.strftime(\\\"%m/%d/%Y %I:%M:%S %p\\\", node.page.last_edit) + '''\\n </div>\\n </div>\\n <footer>\\n © ''' + str(current_time.year) + ' ' + AUTHOR + ''' | Generated with <a href=\\\"http://www.minimalblue.com/software/minimalsite.html\\\">minimalsite</a> \\n </footer>\\n </div>\\n </body>\\n</html>'''\",\n \"def WriteFooter(self):\\n self.WriteText('}')\",\n \"def get_footer_navigation(self):\\n nav_data = self.get_navigation_data()\\n return nav_data.get('footer_navigation', [])\",\n \"def footer(self):\\n self.set_y(-15)\\n self.set_font(self.police, 'I', 8)\\n self.cell(w=0, h=10, txt=f\\\"Page {self.page_no()}\\\" + '/{nb}', border='B', ln=0, align='R')\",\n \"def get_footer_links(self):\\n nav_data = self.get_navigation_data()\\n return nav_data.get('footer_links', [])\",\n \"def footer():\\n return u'</form></body></html>\\\\n'\",\n \"def __footer(self):\\n result = \\\"\\\"\\\"This API and the related documentation has been created with <a href=\\\"https://github.com/opencitations/ramose\\\" target=\\\"_blank\\\">RAMOSE</a>, the *Restful API Manager Over SPARQL Endpoints*, developed by <a href=\\\"http://orcid.org/0000-0003-0530-4305\\\" target=\\\"_blank\\\">Silvio Peroni</a> and <a href=\\\"https://marilenadaquino.github.io\\\">Marilena Daquino</a>.\\\"\\\"\\\"\\n return markdown(result)\",\n \"def getFooter(HTMLstring):\\n footer = open(os.path.dirname(os.path.realpath(__file__))+\\\"/html/footer.html\\\", \\\"r\\\")\\n HTMLstring += footer.read()\\n footer.close()\\n return HTMLstring\",\n \"def footer_links(request):\\n data = _get_footer_data(request) \\n response_data = json.dumps(data)\\n\\n response = HttpResponse(response_data, mimetype= \\\"application/json\\\")\\n response['Cache-Control'] = 'public'\\n\\n return response\",\n \"def footer(self, footer):\\n\\n self._footer = footer\",\n \"def parse_footer_block(lines):\\n # najdeme pocatek paticky\\n bottom_index = [idx for idx, line in enumerate(lines[-MAX_FOOTER_HEIGHT:]) \\\\\\n if FOOTER_RE.match(line)]\\n if not len(bottom_index):\\n return None\\n\\n # vyparsujeme data z ni\\n data = [line for line in lines[-(MAX_FOOTER_HEIGHT-bottom_index[0]):] if line.strip()]\\n out = {'date':None}\\n for item in data:\\n # typ a poradove cislo zastupitelstva\\n m = FOOTER_RE.match(item)\\n if m:\\n try:\\n out['date'] = date(int(m.group(3).strip()), int(m.group(2).strip()), int(m.group(1).strip()))\\n except ValueError:\\n pass\\n\\n return out\",\n \"def common_html_footer(outfile: TextIO, indexpath: str = \\\"\\\") -> None:\\n outfile.write(\\\"\\\\n\\\")\\n outfile.write(\\\" <footer>\\\\n\\\")\\n outfile.write(\\\" <figure id=\\\\\\\"footmap\\\\\\\"><script type=\\\\\\\"text/javascript\\\\\\\" \\\"\\n \\\"src=\\\\\\\"//rf.revolvermaps.com/0/0/4.js?i=5f9t1sywiez&m=0&h=75&c=ff0000&r=30\\\\\\\" \\\"\\n \\\"async=\\\\\\\"async\\\\\\\"></script><figcaption>Visitors</figcaption></figure>\\\\n\\\")\\n outfile.write(\\\" <p id=\\\\\\\"citation\\\\\\\"><a href=\\\\\\\"\\\" + indexpath + init_data().cite_url +\\n \\\"\\\\\\\">\\\" + fetch_fa_glyph(\\\"site cite\\\") + \\\"How to cite this site</a></p>\\\\n\\\")\\n outfile.write(\\\" <p id=\\\\\\\"contact\\\\\\\">Questions or comments about the site? Contact \\\"\\n \\\"<a href=\\\\\\\"mailto:\\\" + init_data().site_author_email + \\\"\\\\\\\">\\\" + fetch_fa_glyph(\\\"mail\\\") +\\n \\\"Dr. Michael S. Rosenberg</a></p>\\\\n\\\")\\n outfile.write(\\\" <p id=\\\\\\\"copyright\\\\\\\">Release: \\\" + init_data().version +\\n \\\" — Copyright © 2003–\\\" + str(init_data().current_year) +\\n \\\" All Rights Reserved</p>\\\\n\\\")\\n outfile.write(\\\" </footer>\\\\n\\\")\\n outfile.write(\\\" </body>\\\\n\\\")\\n outfile.write(\\\"</html>\\\\n\\\")\",\n \"def foot(cls):\\n return ''\",\n \"def _ReadMemberFooter(self, file_object):\\n file_offset = file_object.tell()\\n data_type_map = self._GetDataTypeMap('gzip_member_footer')\\n\\n member_footer, _ = self._ReadStructureFromFileObject(\\n file_object, file_offset, data_type_map, 'member footer')\\n\\n if self._debug:\\n self._DebugPrintStructureObject(\\n member_footer, self._DEBUG_INFO_MEMBER_FOOTER)\",\n \"def footer(self):\\n # close the svg tag\\n yield '</svg>'\\n # if this is a stand-alone document\\n if self.standalone:\\n # render a blank line\\n yield ''\\n # and the document footer\\n yield from super().footer()\\n # all done\\n return\",\n \"def write_footer(self, fd):\\n fd.write(f\\\"END {self.name}\\\")\\n if len(self.data_items) > 0:\\n one_based = self.data_items[0].structure.type == DatumType.integer\\n if isinstance(self.data_items[0], mfdatascalar.MFScalar):\\n entry = self.data_items[0].get_file_entry(\\n values_only=True, one_based=one_based\\n )\\n else:\\n entry = self.data_items[0].get_file_entry()\\n fd.write(str(entry.rstrip()))\\n fd.write(\\\"\\\\n\\\")\",\n \"def getFooter():\\n return \\\"# job finished in %i seconds at %s -- %s -- %s\\\" %\\\\\\n (time.time() - global_starting_time,\\n time.asctime(time.localtime(time.time())),\\n \\\" \\\".join(map(lambda x: \\\"%5.2f\\\" % x, os.times()[:4])),\\n global_id)\",\n \"def serialize_footer(signer):\\n footer = b\\\"\\\"\\n if signer is not None:\\n signature = signer.finalize()\\n footer = struct.pack(\\\">H{sig_len}s\\\".format(sig_len=len(signature)), len(signature), signature)\\n return footer\",\n \"def set_footer(self, **kwargs):\\n self.footer_text = kwargs.get('text')\\n self.footer_icon = kwargs.get('icon_url')\",\n \"def footprint(dynamic_footprint_modifier=0.):\",\n \"def write_footer(self, stream):\\n return\\n ##################################################\\n # You MUST implement this method in the subclass #\\n # if the file format defines a file footer. #\\n ##################################################\",\n \"def render_footer_log(self):\\n\\n # clean up the log file\\n if self._selenium_log_file:\\n\\n # render the footer\\n html = render_to_string(\\n self.selenium_footer_template,\\n {'id': self.id(), 'description': self.__doc__})\\n\\n # write it to the file\\n self._selenium_log_file.write(html)\",\n \"def set_footer(embed: Embed, ctx: commands.Context) -> Embed:\\n return embed.set_footer(icon_url=ctx.author.avatar_url, text=f\\\"{ctx.author.display_name} • {get_time()}\\\")\",\n \"def test_00_footer(self):\\r\\n url = '/'\\r\\n # As Anonymou user\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Footer links should not be shown to anonymous users\\\"\\r\\n assert dom.find(id='footer_links') is None, err_msg\\r\\n # As Authenticated user but NOT ADMIN\\r\\n self.signin()\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Footer links should not be shown to authenticated users\\\"\\r\\n assert dom.find(id='footer_links') is None, err_msg\\r\\n self.signout\\r\\n # As Authenticated user but ADMIN\\r\\n self.signin(email=self.root_addr, password=self.root_password)\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Footer links should not be shown to admin users\\\"\\r\\n assert dom.find(id='footer_links') is None, err_msg\\r\\n self.signout()\",\n \"def test_00_footer(self):\\r\\n url = '/'\\r\\n # As Anonymou user\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Footer links should be shown to anonymous users\\\"\\r\\n assert dom.find(id='footer_links') is not None, err_msg\\r\\n # As Authenticated user but NOT ADMIN\\r\\n self.signin()\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Footer links should be shown to authenticated users\\\"\\r\\n assert dom.find(id='footer_links') is not None, err_msg\\r\\n self.signout\\r\\n # As Authenticated user but ADMIN\\r\\n self.signin(email=self.root_addr, password=self.root_password)\\r\\n res = self.app.get(url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"Footer links should be shown to admin users\\\"\\r\\n assert dom.find(id='footer_links') is not None, err_msg\\r\\n self.signout()\",\n \"def footer(node):\\n\\n return '''\\n <img src=\\\"/images/colosseo.png\\\" title=\\\"disegno del colosseo\\\" alt=\\\"colosseo\\\" class=\\\"colosseo\\\"/>\\n\\t \\t </section>\\n </div>\\n\\t\\t\\t <div class=\\\"clear\\\"></div>\\n </div>\\n </div>\\n\\t\\t<div class=\\\"container_12 clearfix\\\">\\n\\t <footer class=\\\"grid_12\\\">\\n\\t\\t\\t\\t <p>© <a href=\\\"http://luca.postregna.name\\\">lucapost</a> ''' + str(current_time.year) + '''; <a rel=\\\"license\\\" href=\\\"http://creativecommons.org/licenses/by-nc/3.0/\\\">license</a>; <a href=\\\"/privacy.html\\\" title=\\\"normativa per la privacy\\\">privacy</a>; edit: ''' + time.strftime(\\\"%Y%m%d %I:%M:%S %p\\\", node.page.last_edit) + '''</p>\\n <p>email: <a href=\\\"mailto:patdilenardo@gmail.com\\\" title=\\\"contatto email\\\">patdilenardo@gmail.com</a>; phone: +39 3389456208</p>\\n\\t\\t </footer>\\t\\n\\t\\t\\t<div class=\\\"clear\\\"></div>\\n \\t\\t<script src=\\\"/js/hashgrid.js\\\"></script> \\n\\t\\t<script src=\\\"/js/flux.min.js\\\" type=\\\"text/javascript\\\" charset=\\\"utf-8\\\"></script>\\n\\t\\t<script type=\\\"text/javascript\\\" charset=\\\"utf-8\\\">\\n\\t\\t\\t$(function(){\\n\\t\\t\\t\\tif(!flux.browser.supportsTransitions)\\n\\t\\t\\t\\t\\talert(\\\"Flux Slider requires a browser that supports CSS3 transitions\\\");\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\twindow.f = new flux.slider('#slider', {\\n\\t\\t\\t\\t\\tpagination: false,\\n controls: true,\\n captions: true,\\n\\t\\t\\t\\t\\ttransitions: [ 'dissolve' ],\\n\\t\\t\\t\\t\\tdelay: 5500\\n\\t\\t\\t\\t});\\n\\t\\t\\t});\\n\\t\\t</script> \\n <script>\\n (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){\\n (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),\\n m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)\\n })(window,document,'script','//www.google-analytics.com/analytics.js','ga');\\n ga('create', 'UA-6164762-14', 'nicelyventilated.it');\\n ga('send', 'pageview');\\n </script>\\n\\n<!--[if lt IE 7]><p class=chromeframe>Your browser is <em>ancient!</em> <a href=\\\"http://browsehappy.com/\\\">Upgrade to a different browser</a> or <a href=\\\"http://www.google.com/chromeframe/?redirect=true\\\">install Google Chrome Frame</a> to experience this site.</p><![endif]-->\\n</body>\\n</html>'''\",\n \"def footer_html(self, html):\\n self._footer_html = html\",\n \"def get_onlineformincoming_formfooter(dbo, collationid):\\n return db.query_string(dbo, \\\"SELECT o.Footer FROM onlineform o \\\" \\\\\\n \\\"INNER JOIN onlineformincoming oi ON oi.FormName = o.Name \\\" \\\\\\n \\\"WHERE oi.CollationID = %d\\\" % int(collationid))\",\n \"def footer(self):\\n file = open(\\\"imdb_output.html\\\", \\\"a\\\")\\n file.write(\\\"\\\\t\\\\t</table>\\\\n\\\\t</body>\\\\n</html>\\\\n\\\")\",\n \"def test_format_emperor_html_footer_string(self):\\n self.maxDiff = 5000\\n\\n # footer for a jackknifed pcoa plot without biplots\\n out_string = format_emperor_html_footer_string(False, True)\\n self.assertItemsEqual(out_string.split('\\\\n'),\\n EXPECTED_FOOTER_A.split('\\\\n'))\\n self.assertEqual(out_string, EXPECTED_FOOTER_A)\\n\\n # footer for biplots without jackknifing\\n out_string = format_emperor_html_footer_string(True, False)\\n self.assertItemsEqual(out_string.split('\\\\n'),\\n EXPECTED_FOOTER_B.split('\\\\n'))\\n self.assertEqual(out_string, EXPECTED_FOOTER_B)\\n\\n # no biplots nor jackknifing\\n out_string = format_emperor_html_footer_string(False, False)\\n self.assertItemsEqual(out_string.split('\\\\n'),\\n EXPECTED_FOOTER_C.split('\\\\n'))\\n self.assertEqual(out_string, EXPECTED_FOOTER_C)\\n\\n # no biplots no jackknifing but with vectors\\n out_string = format_emperor_html_footer_string(False, False, True)\\n self.assertItemsEqual(out_string.split('\\\\n'),\\n EXPECTED_FOOTER_D.split('\\\\n'))\\n self.assertEqual(out_string, EXPECTED_FOOTER_D)\\n\\n # comparison plot\\n out_string = format_emperor_html_footer_string(False, False,\\n False, True)\\n self.assertItemsEqual(out_string.split('\\\\n'),\\n EXPECTED_FOOTER_E.split('\\\\n'))\\n self.assertEqual(out_string, EXPECTED_FOOTER_E)\",\n \"def footprint_height():\",\n \"def footprint(self):\\n return self._label_footprint\",\n \"def output_footer(stream=sys.stdout):\\n print(_module_footer, file=stream)\",\n \"def usage_footer():\\n print \\\"\\\"\\\"--\\nSee more information about this project at:\\n%(url)s\\n\\nReport bugs to authors at:\\n%(source_url)s\\n\\\"\\\"\\\" % {\\n 'url': constants.App.URL,\\n 'source_url': constants.App.SOURCE_URL,\\n }\",\n \"def writeFooter( self ):\\n total_branch_length = (max(self.mNodeWidthsEnd) - min(self.mNodeWidthsStart)) / self.mBranchScaleFactor\\n \\n self.mFooterX = self.getHeaderWidth()\\n self.mFooterY = self.getHeaderHeight() + self.mDataHeight + self.mSeparatorHeight\\n\\n ruler_start = self.mFooterX\\n ruler_end = self.mFooterX + int(total_branch_length * self.mBranchScaleFactor)\\n\\n if \\\"ruler\\\" in self.mRulerElements:\\n ## full length ruler with tick marks and labels\\n e = SVGdraw.line( ruler_start,\\n self.mFooterY + self.mRulerTickSize + 1,\\n ruler_end,\\n self.mFooterY + self.mRulerTickSize + 1,\\n stroke = \\\"rgb(%i,%i,%i)\\\" % BLACK,\\n stroke_width = 1 )\\n self.addElement( e )\\n\\n ## get reasonable intervalls\\n\\n increment = self.mRulerIncrement * self.mBranchScaleFactor\\n\\n ## adjust increment for extremely long trees\\n if (ruler_end - ruler_start) / increment > 1000:\\n increment = (ruler_end - ruler_start) / 1000.0\\n self.mRulerIncrement = increment / self.mBranchScaleFactor\\n \\n if \\\"right-ticks\\\" in self.mRulerElements:\\n\\n x = ruler_end\\n while x >= ruler_start:\\n e = SVGdraw.line( x,\\n self.mFooterY,\\n x,\\n self.mFooterY + 2 * self.mRulerTickSize + 1,\\n stroke = \\\"rgb(%i,%i,%i)\\\" % BLACK,\\n stroke_width = 1 )\\n self.addElement( e )\\n x -= self.mRulerIncrement * self.mBranchScaleFactor\\n \\n self.mFooterY += 2 * self.mRulerTickSize + 1 + self.mSeparatorHeight\\n\\n if \\\"left-ticks\\\" in self.mRulerElements:\\n \\n x = ruler_start\\n while x <= ruler_end:\\n e = SVGdraw.line( x,\\n self.mFooterY,\\n x,\\n self.mFooterY + 2 * self.mRulerTickSize + 1,\\n stroke = \\\"rgb(%i,%i,%i)\\\" % BLACK,\\n stroke_width = 1 )\\n self.addElement( e )\\n x += increment\\n \\n self.mFooterY += 2 * self.mRulerTickSize + 1 + self.mSeparatorHeight\\n\\n if \\\"scale\\\" in self.mRulerElements:\\n\\n w = int(self.mRulerIncrement * self.mBranchScaleFactor)\\n \\n e = SVGdraw.line( ruler_end,\\n self.mFooterY + self.mRulerTickSize + 1,\\n ruler_end - w,\\n self.mFooterY + self.mRulerTickSize + 1,\\n stroke = \\\"rgb(%i,%i,%i)\\\" % BLACK,\\n stroke_width = 1 )\\n \\n self.addElement( e )\\n \\n e = SVGdraw.line( ruler_end,\\n self.mFooterY,\\n ruler_end,\\n self.mFooterY + 2 * self.mRulerTickSize + 1,\\n stroke = \\\"rgb(%i,%i,%i)\\\" % BLACK,\\n stroke_width = 1 )\\n \\n self.addElement( e )\\n\\n e = SVGdraw.line( ruler_end - w,\\n self.mFooterY,\\n ruler_end - w,\\n self.mFooterY + 2 * self.mRulerTickSize + 1,\\n stroke = \\\"rgb(%i,%i,%i)\\\" % BLACK,\\n stroke_width = 1 )\\n \\n self.addElement( e )\\n\\n e = SVGdraw.text( ruler_end - w / 2,\\n self.mFooterY + 2 * self.mRulerTickSize + 1 + self.mRulerFontSize ,\\n self.mRulerFormat % self.mRulerIncrement,\\n self.mRulerFontSize,\\n self.mRulerFont,\\n stroke = \\\"rgb(%i,%i,%i)\\\" % BLACK,\\n text_anchor = \\\"middle\\\" )\\n\\n self.addElement(e)\\n self.mFooterY += 2 * self.mRulerTickSize + 1 + self.mRulerFontSize \\n \\n e, self.mFooterX, self.mFooterY = self.mDecoratorExternalNodes.getLegend( self.mFooterX, self.mFooterY )\\n self.addElements(e)\\n e, self.mFooterX, self.mFooterY = self.mDecoratorInternalNodes.getLegend( self.mFooterX, self.mFooterY )\\n self.addElements(e)\",\n \"def footer_nav(self):\\r\\n buttons = [NamedButton(\\\"help\\\", False, nocname=True),\\r\\n NamedButton(\\\"blog\\\", False, nocname=True),\\r\\n NamedButton(\\\"stats\\\", False, nocname=True),\\r\\n NamedButton(\\\"feedback\\\", False),\\r\\n NamedButton(\\\"bookmarklets\\\", False),\\r\\n NamedButton(\\\"socialite\\\", False),\\r\\n NamedButton(\\\"buttons\\\", True),\\r\\n NamedButton(\\\"widget\\\", True),\\r\\n NamedButton(\\\"code\\\", False, nocname=True),\\r\\n NamedButton(\\\"mobile\\\", False, nocname=True),\\r\\n NamedButton(\\\"store\\\", False, nocname=True),\\r\\n NamedButton(\\\"ad_inq\\\", False, nocname=True),\\r\\n ]\\r\\n\\r\\n return NavMenu(buttons, base_path = \\\"/\\\", type = \\\"flatlist\\\")\",\n \"def _encode_foot(self):\\n\\t\\tfoot = self.config.get('foot')\\n\\t\\tif foot is not None:\\n\\t\\t\\treturn self._encode_tuple(foot)\",\n \"def __init__(self, footer_html=None):\\n self._footer_html = None\\n\\n if footer_html is not None:\\n self.footer_html = footer_html\",\n \"def write_footer(out):\\n\\n out.write(\\\"\\\"\\\" </g>\\n</svg>\\n\\\"\\\"\\\")\",\n \"async def set_ban_footer(self, ctx, *, footer_string):\\n if not footer_string:\\n local_embed = discord.Embed(\\n title=f'No string detected, I need a string parameter to work',\\n description=' ',\\n color=0x651111\\n )\\n await ctx.send(embed=local_embed)\\n return\\n success = await self.bot.pg_utils.set_ban_footer(\\n ctx.guild.id,\\n footer_string,\\n self.bot.logger\\n )\\n if success:\\n desc = footer_string.replace(\\n f'%user%', ctx.message.author.mention)\\n local_embed = discord.Embed(\\n title=f'Footer message set:',\\n description=f'**Preview:**\\\\n{desc}',\\n color=0x419400\\n )\\n else:\\n local_embed = discord.Embed(\\n title=f'Internal error occured, please contact @dashwav#7785',\\n description=' ',\\n color=0x651111\\n )\\n await ctx.send(embed=local_embed)\\n return\",\n \"def return_info(self):\\n\\t\\treturn self.info\",\n \"def produce_header_footer(self):\\n header = pylatex.PageStyle(\\\"header\\\", header_thickness=0.1)\\n\\n image_filename = self.get_image()\\n with header.create(pylatex.Head(\\\"L\\\")) as logo:\\n logo.append(pylatex.StandAloneGraphic(image_options=\\\"width=110px\\\", filename=image_filename))\\n\\n # Date\\n with header.create(pylatex.Head(\\\"R\\\")):\\n header.append(\\\"Date Report Issued: \\\" + datetime.today().strftime('%Y-%m-%d'))\\n\\n # Footer\\n with header.create(pylatex.Foot(\\\"C\\\")):\\n with header.create(pylatex.Tabular('lcr')) as table:\\n table.add_row('', bold('Data interpretation guidelines can be found in RDIMS document ID: 10401305'),\\n '')\\n table.add_row('', bold('This report was generated with OLC AutoROGA v0.0.1'), '')\\n return header\",\n \"def create_footer(categories):\\n\\n footer = '\\\\n'\\n row_len = max([len(category.name) for category in categories])\\n for idx in range(row_len):\\n footer += ' '\\n for category in categories:\\n try:\\n footer += f' {category.name[idx]} '\\n except IndexError:\\n footer += ' '\\n footer += ' \\\\n'\\n return footer\",\n \"def getInfo():\",\n \"def endTopicMap(self):\\r\\n super(PHPTMAPIMapHandler, self).endTopicMap()\\r\\n self._out.write(_FOOTER)\\r\\n self._out.flush()\",\n \"def get_info(self):\\n return \\\"TODO !\\\"\",\n \"async def set_kick_footer(self, ctx, *, footer_string):\\n if not footer_string:\\n local_embed = discord.Embed(\\n title=f'No string detected, I need a string parameter to work',\\n description=' ',\\n color=0x651111\\n )\\n await ctx.send(embed=local_embed)\\n return\\n success = await self.bot.pg_utils.set_kick_footer(\\n ctx.guild.id,\\n footer_string,\\n self.bot.logger\\n )\\n if success:\\n desc = footer_string.replace(\\n f'%user%', ctx.message.author.mention)\\n local_embed = discord.Embed(\\n title=f'Footer message set:',\\n description=f'**Preview:**\\\\n{desc}',\\n color=0x419400\\n )\\n else:\\n local_embed = discord.Embed(\\n title=f'Internal error occured, please contact @dashwav#7785',\\n description=' ',\\n color=0x651111\\n )\\n await ctx.send(embed=local_embed)\\n return\",\n \"def consume_footer(consensus, flavor='unflavored', sanity=True):\\n if flavor not in ['unflavored', 'microdesc']:\\n raise NotImplementedError(\\n 'Consensus flavor \\\"{}\\\" not supported.'.format(flavor))\\n\\n whitelist = [\\n b'directory-footer', b'bandwidth-weights', b'directory-signature']\\n\\n def end_of_field(line):\\n if b'directory-footer' == line:\\n return False\\n if b' ' not in line:\\n return True\\n keyword, _ = line.split(b' ', 1)\\n return keyword not in whitelist\\n\\n fields = dict()\\n valid = False\\n while True:\\n consensus, header = scrap(consensus, end_of_field)\\n if header is None:\\n return consensus, fields if valid else None\\n\\n valid = True\\n if b' ' not in header:\\n continue\\n\\n try:\\n header = str(header, 'utf8')\\n except:\\n continue\\n\\n keyword, content = header.split(' ', 1)\\n if keyword == 'directory-footer':\\n if sanity:\\n assert len(fields) == 0 # first field\\n\\n if keyword == 'bandwidth-weights':\\n content = parse_params(content)\\n\\n if keyword == 'directory-signature':\\n content = content.split(' ', 2)\\n if len(content) == 3:\\n algorithm, identity, signing_key_digest = content\\n elif len(content) == 2:\\n algorithm = 'sha1'\\n identity, signing_key_digest = content\\n\\n content = {\\n 'Algorithm': algorithm,\\n 'identity': identity,\\n 'signing-key-digest': signing_key_digest}\\n\\n consensus, signature = scrap_signature(consensus)\\n if signature is not None:\\n signature = parse_base64(str(signature, 'utf8'))\\n content['signature'] = signature\\n\\n if keyword + 's' not in fields:\\n fields[keyword + 's'] = []\\n fields[keyword + 's'].append(content)\\n continue\\n\\n fields[keyword] = content\\n return consensus, fields\",\n \"def get_web_exhibit_footer_img(self, building):\\n #building = self.location_and_hours['page_location'].id\\n img = {SCRC_BUILDING_ID: SCRC_EXHIBIT_FOOTER_IMG,\\n CRERAR_BUILDING_ID: CRERAR_EXHIBIT_FOOTER_IMG}\\n if building in img:\\n return Image.objects.get(id=img[building])\\n return None\",\n \"def get_footer_email(object, use_string_1=False, class_link='navLink'):\\n if smart_unicode(str(type(object))) == \\\"<class 'dms.models.DmsItem'>\\\":\\n item = object\\n else:\\n item = object.item\\n if use_string_1:\\n return encode_email(item.string_2, item.string_1, 'navLink')\\n else:\\n name = item.owner.get_full_name()\\n if name == u'Unbekannte Person' and item.string_1 != '':\\n return encode_email(item.string_2, item.string_1, class_link)\\n else:\\n return encode_email(item.owner.email, name, class_link)\",\n \"def latex_footer():\\n print(\\\" \\\\end{tikzpicture}\\\")\\n print(\\\" }\\\")\\n print(\\\" \\\\end{frame}\\\")\\n print(\\\"\\\\end{document}\\\")\",\n \"def get_info(self):\\n return None\",\n \"def std_out_segm_footer(*args):\\n return _ida_segment.std_out_segm_footer(*args)\",\n \"def initialPage():\\n\\treturn header() + footer()\",\n \"def append_message_footer(msg, messagebody):\\n footer = \\\"\\\\n\\\\n_____________________\\\\n\\\\n^^I ^^am ^^a ^^Bot. ^^| [^^How ^^to ^^use](https://github.com/Rafficer/ProtonStatusBot/tree/master#how-to-use) ^^| ^^Made ^^with ^^🖤 ^^by ^^/u/Rafficer ^^| ^^[Source](https://github.com/rafficer/ProtonStatusBot) ^^| [^^Report ^^a ^^Bug](https://github.com/Rafficer/ProtonStatusBot/issues)\\\"\\n full_message = messagebody + footer\\n logger.debug(\\\"Replying...\\\")\\n try:\\n msg.reply(full_message)\\n except prawcore.exceptions.RequestException:\\n logger.debug(\\\"Replying failed. Checking for connectivity and trying again.\\\")\\n connectivity_check()\\n msg.reply(full_message)\\n logger.debug(\\\"Replied!\\\")\",\n \"def test_write_page_margins_footer(self):\\n\\n self.worksheet.set_footer(margin=0.5)\\n self.worksheet._write_page_margins()\\n\\n exp = \\\"\\\"\\\"<pageMargins left=\\\"0.7\\\" right=\\\"0.7\\\" top=\\\"0.75\\\" bottom=\\\"0.75\\\" header=\\\"0.3\\\" footer=\\\"0.5\\\"/>\\\"\\\"\\\"\\n got = self.fh.getvalue()\\n\\n self.assertEqual(got, exp)\",\n \"def write_db_footer(table,runnumber):\\n \\n table.write('}\\\\n')\",\n \"def renderFooter(self, plot, painter, rect):\\r\\n painter.setFont(plot.footerLabel().font())\\r\\n color = plot.footerLabel().palette().color(QPalette.Active, QPalette.Text)\\r\\n painter.setPen(color)\\r\\n plot.footerLabel().text().draw(painter, rect)\",\n \"def make_bottom_text( self ):\\n return None\",\n \"def get_footprint_area(self):\\n x = self.dimension_along(0)\\n y = self.dimension_along(1)\\n return x * y\",\n \"def setup_footer_panel(self):\\r\\n self.create_footer_push_button(\\\"F3 View\\\", \\\"F3\\\")\\r\\n self.create_footer_push_button(\\\"F4 Edit\\\", \\\"F4\\\")\\r\\n self.create_footer_push_button(\\\"F5 Copy\\\", \\\"F5\\\")\\r\\n self.create_footer_push_button(\\\"F6 move\\\", \\\"F6\\\")\\r\\n self.create_footer_push_button(\\\"F7 New Folder\\\", \\\"F7\\\")\\r\\n self.create_footer_push_button(\\\"F8 Delete\\\", \\\"F8\\\")\\r\\n self.create_footer_push_button(\\\"ALT+F4 Exit\\\")\",\n \"def get_zipinfo(self):\\n zipinfo = zipfile.ZipInfo()\\n zipinfo.filename = self.translated_path()\\n zipinfo.date_time = self.get_mod_time()\\n zipinfo.file_size = self.get_size()\\n return zipinfo\",\n \"def show_footprint(self, fpname):\\n logging.debug(\\\"show_footprint entered\\\")\\n # container_name = \\\"%s-metadata\\\" % footprint_name\\n # container = self.cf.get_container(container_name)\\n # index = container.get_object(\\\"index.json\\\")\\n # config = json.loads(index.fetch())\\n # \\n # \\n # \\n # logging.info(\\\"loaded footprint configuration\\\")\\n # return config\\n fp = self.get_footprint(fpname, start=False)\\n pt = fp.status()\\n print pt\",\n \"def footnote(self) -> str:\\n return self._footnote\",\n \"def print_endinfo(text: str):\\n templateName = \\\"{:s}\\\"\\n print(bcolors.BOLD + templateName.format(text) + bcolors.ENDC, flush=True)\",\n \"def info(self) -> dict:\",\n \"def rollup_header_footer(self, context):\\n header = tags.html_string_to_element_tree(\\n '<link href=\\\"%s/markdown.css\\\" rel=\\\"stylesheet\\\" '\\n 'type=\\\"text/css\\\">' % RESOURCE_FOLDER)\\n footer = tags.html_string_to_element_tree('')\\n return (header, footer)\",\n \"def test__EmbedFooter__to_data():\\n icon_url = 'attachment://orin.png'\\n icon_proxy_url = 'https://www.astil.dev/project/hata/'\\n text = 'orin'\\n \\n data = {\\n 'icon_url': icon_url,\\n 'proxy_icon_url': icon_proxy_url,\\n 'text': text,\\n }\\n \\n field = EmbedFooter.from_data(data)\\n \\n expected_output = data\\n \\n vampytest.assert_eq(\\n field.to_data(defaults = True, include_internals = True),\\n expected_output,\\n )\",\n \"def test_create_page_with_footer(self):\\n\\n footer_block = PageFooterBlock.objects.create(**_footer_block_data)\\n link_1 = PageHeadDropMenu.objects.create\\\\\\n (internal_link='home', **_head_link_data)\\n link_2 = PageHeadDropMenu.objects.create(\\n external_link=_external_link, **_head_link_data)\\n link_3 = PageHeadDropMenu.objects.create(**_head_link_data)\\n link_element_1 = PageHeadDropMenuElement.objects.create(\\n internal_link='home', **_head_drop_link_data)\\n link_element_2 = PageHeadDropMenuElement.objects.create(\\n external_link=_external_link, **_head_drop_link_data)\\n link_3.drop_links.add(link_element_1, link_element_2)\\n footer_block.top_links.add(link_1, link_2, link_3)\\n contact_address = Address.objects.create(**_address_block)\\n footer_block.contact_address.add(contact_address)\\n footer_block.a_link.add(link_element_1)\\n\\n Page.objects.create(footer_block=footer_block, **_page_data)\\n response = self.client.get(reverse('home'))\\n self.assertEqual(response.status_code, 200)\\n\\n self.assertIn('top_links', response.context)\\n self.assertIn('contact', response.context)\\n self.assertIn('top_contacts', response.context)\\n self.assertIn('bot_copy_right', response.context)\\n self.assertIn('bot_link', response.context)\\n self.assertIn('bot_back_top', response.context)\",\n \"def get_mgrs_footprint(mgrs_id):\\n\\n gzd = mgrs_id[:3]\\n\\n mgrs_100km_gzd = mgrs_id[3:]\\n print(gzd)\\n print(mgrs_100km_gzd)\\n\\n file_path = unzip_mgrs_shapefile(gzd)\\n\\n shapefile_driver = ogr.GetDriverByName(\\\"ESRI Shapefile\\\")\\n\\n grid_ds = shapefile_driver.Open(str(file_path), 0)\\n\\n layer = grid_ds.GetLayer()\\n\\n # transform coords from local UTM proj to lat long\\n sourceSR = layer.GetSpatialRef()\\n targetSR = osr.SpatialReference()\\n targetSR.ImportFromEPSG(4326) # WGS84\\n coordTrans = osr.CoordinateTransformation(sourceSR, targetSR)\\n\\n intersect_list = []\\n wkt_footprint = None\\n for f in layer:\\n\\n if f.GetField('100kmSQ_ID') == mgrs_100km_gzd:\\n geom = f.GetGeometryRef()\\n geom.Transform(coordTrans)\\n wkt_footprint = geom.ExportToWkt()\\n\\n # all done!\\n grid_ds = None\\n\\n clean_data_dir()\\n\\n return wkt_footprint\",\n \"def format_emperor_html_footer_string(has_biplots=False, has_ellipses=False,\\n has_vectors=False, has_edges=False):\\n\\n # we use python's built-in ternary operator to add or not a string\\n # see _EMPEROR_FOOTER_HTML_STRING\\n format_dict = {'biplot_spheres_color_selector':\\n _BIPLOT_SPHERES_COLOR_SELECTOR if has_biplots else '',\\n 'biplot_visibility_selector':\\n _BIPLOT_VISIBILITY_SELECTOR if has_biplots else '',\\n 'taxa_labels_selector':\\n _TAXA_LABELS_SELECTOR if has_biplots else '',\\n 'taxa_labels_color_selector':\\n _TAXA_LABELS_COLOR_SELECTOR if has_biplots else '',\\n 'edges_color_selector':\\n _EDGES_COLOR_SELECTOR if has_edges else '',\\n 'ellipse_opacity_slider':\\n _ELLIPSE_OPACITY_SLIDER if has_ellipses else '',\\n 'vectors_opacity_slider':\\n _VECTORS_OPACITY_SLIDER if has_vectors else '',\\n 'edges_visibility_selector':\\n _EDGES_VISIBILITY_SELECTOR if has_edges else ''}\\n\\n return _EMPEROR_FOOTER_HTML_STRING.format(**format_dict)\",\n \"def testWriteFooter(self):\\n file_writer = writers.VS2010ProjectFileWriter()\\n\\n file_writer._file = io.BytesIO()\\n\\n file_writer.WriteFooter()\\n\\n file_writer._file.seek(0, os.SEEK_SET)\\n output_data = file_writer._file.read()\\n\\n self.assertTrue(output_data.endswith(b'</Project>'))\",\n \"def get_footprint(self, fpname, start=True):\\n logging.debug(\\\"get_footprint entered\\\")\\n container_name = \\\"%s-metadata\\\" % fpname\\n container = cf.get_container(container_name)\\n index = container.get_object(\\\"index.json\\\")\\n config = json.loads(index.fetch())\\n fp = Footprint(config)\\n fp.load(start=start)\\n return fp\",\n \"def GetDefineGuardFooterLines(output_file_name):\\n return [\\n '#endif // %s_PREDICTION_%s_' %(_MOZC_DIR_FOR_DEFINE_GUARD,\\n GetDefineGuardSymbol(output_file_name))]\",\n \"def get_info(self):\\n pass\",\n \"def get_info(self):\\n pass\",\n \"def getLastFootprint(self):\\n if self.footprints is None:\\n return\\n return self.footprints[-1]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8154624","0.8108119","0.7856543","0.7856543","0.7828535","0.7746664","0.7719649","0.76299804","0.7477003","0.74707055","0.73991615","0.7395591","0.72634256","0.72634256","0.7204311","0.718627","0.7012529","0.6940873","0.69106513","0.68723094","0.68511295","0.67694765","0.6710224","0.6705559","0.6655345","0.6622737","0.66221714","0.64857507","0.63636774","0.63627905","0.63463527","0.62858456","0.6273018","0.6252633","0.618192","0.61522496","0.6124736","0.61173683","0.60860956","0.607036","0.60165375","0.59919167","0.59489715","0.59365803","0.59290296","0.5922442","0.58001167","0.5775965","0.57345665","0.57198316","0.56442875","0.56101334","0.5605784","0.56040835","0.553583","0.54967946","0.5478415","0.546103","0.54603547","0.5458278","0.54105425","0.53964967","0.5371734","0.5354337","0.5344952","0.52140075","0.5184619","0.5177051","0.517389","0.5131116","0.511474","0.5104204","0.50962985","0.5090262","0.50850755","0.5076002","0.50675327","0.5066022","0.5040697","0.50379604","0.50224787","0.5013285","0.5012513","0.4994369","0.49496916","0.49286252","0.49228615","0.49002457","0.4896204","0.48960453","0.4890906","0.48857573","0.48857412","0.4880123","0.4878142","0.4840456","0.48362973","0.48356023","0.4820522","0.4820522","0.4820388"],"string":"[\n \"0.8154624\",\n \"0.8108119\",\n \"0.7856543\",\n \"0.7856543\",\n \"0.7828535\",\n \"0.7746664\",\n \"0.7719649\",\n \"0.76299804\",\n \"0.7477003\",\n \"0.74707055\",\n \"0.73991615\",\n \"0.7395591\",\n \"0.72634256\",\n \"0.72634256\",\n \"0.7204311\",\n \"0.718627\",\n \"0.7012529\",\n \"0.6940873\",\n \"0.69106513\",\n \"0.68723094\",\n \"0.68511295\",\n \"0.67694765\",\n \"0.6710224\",\n \"0.6705559\",\n \"0.6655345\",\n \"0.6622737\",\n \"0.66221714\",\n \"0.64857507\",\n \"0.63636774\",\n \"0.63627905\",\n \"0.63463527\",\n \"0.62858456\",\n \"0.6273018\",\n \"0.6252633\",\n \"0.618192\",\n \"0.61522496\",\n \"0.6124736\",\n \"0.61173683\",\n \"0.60860956\",\n \"0.607036\",\n \"0.60165375\",\n \"0.59919167\",\n \"0.59489715\",\n \"0.59365803\",\n \"0.59290296\",\n \"0.5922442\",\n \"0.58001167\",\n \"0.5775965\",\n \"0.57345665\",\n \"0.57198316\",\n \"0.56442875\",\n \"0.56101334\",\n \"0.5605784\",\n \"0.56040835\",\n \"0.553583\",\n \"0.54967946\",\n \"0.5478415\",\n \"0.546103\",\n \"0.54603547\",\n \"0.5458278\",\n \"0.54105425\",\n \"0.53964967\",\n \"0.5371734\",\n \"0.5354337\",\n \"0.5344952\",\n \"0.52140075\",\n \"0.5184619\",\n \"0.5177051\",\n \"0.517389\",\n \"0.5131116\",\n \"0.511474\",\n \"0.5104204\",\n \"0.50962985\",\n \"0.5090262\",\n \"0.50850755\",\n \"0.5076002\",\n \"0.50675327\",\n \"0.5066022\",\n \"0.5040697\",\n \"0.50379604\",\n \"0.50224787\",\n \"0.5013285\",\n \"0.5012513\",\n \"0.4994369\",\n \"0.49496916\",\n \"0.49286252\",\n \"0.49228615\",\n \"0.49002457\",\n \"0.4896204\",\n \"0.48960453\",\n \"0.4890906\",\n \"0.48857573\",\n \"0.48857412\",\n \"0.4880123\",\n \"0.4878142\",\n \"0.4840456\",\n \"0.48362973\",\n \"0.48356023\",\n \"0.4820522\",\n \"0.4820522\",\n \"0.4820388\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77090,"cells":{"query":{"kind":"string","value":"return the statistical information query across feedback and prediction model"},"document":{"kind":"string","value":"def get(self):\n from models.prediction import Prediction\n from models.feedback import Feedback\n\n prediction_collection = Prediction._get_collection()\n feedback_collection = Feedback._get_collection()\n statistical_data = copy.deepcopy(CONSTANTS['STATISTICAL_DATA'])\n\n # initial statistical_data based on age and ascending order\n total_age = []\n for i in range(21):\n total_age.append(str(i * 5) + '+')\n\n for each_age in total_age:\n statistical_data['prediction_data']['age'][each_age] = 0\n statistical_data['feedback_data']['age']['wrong'][each_age] = 0\n statistical_data['feedback_data']['age']['correct'][each_age] = 0\n\n # photo by date\n today = datetime.datetime.today().replace(hour=0, minute=0, second=0, microsecond=0)\n total_number_of_photo_within_one_week = {\n (today - datetime.timedelta(days=6)).strftime('%d/%m/%Y'): prediction_collection.count_documents(\n {'date': {'$lt': today - datetime.timedelta(days=5), '$gte': today - datetime.timedelta(days=6)}}),\n (today - datetime.timedelta(days=5)).strftime('%d/%m/%Y'): prediction_collection.count_documents(\n {'date': {'$lt': today - datetime.timedelta(days=4), '$gte': today - datetime.timedelta(days=5)}}),\n (today - datetime.timedelta(days=4)).strftime('%d/%m/%Y'): prediction_collection.count_documents(\n {'date': {'$lt': today - datetime.timedelta(days=3), '$gte': today - datetime.timedelta(days=4)}}),\n (today - datetime.timedelta(days=3)).strftime('%d/%m/%Y'): prediction_collection.count_documents(\n {'date': {'$lt': today - datetime.timedelta(days=2), '$gte': today - datetime.timedelta(days=3)}}),\n (today - datetime.timedelta(days=2)).strftime('%d/%m/%Y'): prediction_collection.count_documents(\n {'date': {'$lt': today - datetime.timedelta(days=1), '$gte': today - datetime.timedelta(days=2)}}),\n (today - datetime.timedelta(days=1)).strftime('%d/%m/%Y'): prediction_collection.count_documents(\n {'date': {'$lt': today, '$gte': today - datetime.timedelta(days=1)}}),\n today.strftime('%d/%m/%Y'): prediction_collection.count_documents(\n {'date': {'$gte': today}}),\n }\n\n # prediction\n total_prediction = prediction_collection.find({})\n\n for each_prediction in total_prediction:\n prediction_results = each_prediction['predictionResults']\n for each_result in prediction_results:\n statistical_data['prediction_number'] = statistical_data.get(\n 'prediction_number', 0) + 1\n\n # age, gender, emotion\n each_age = str(5*math.floor(each_result['age'] / 5)) + '+'\n\n statistical_data['prediction_data']['age'][each_age] = \\\n statistical_data['prediction_data']['age'].get(each_age, 0) + 1\n statistical_data['prediction_data']['gender'][each_result['gender']] = \\\n statistical_data['prediction_data']['gender'].get(each_result['gender'], 0) + 1\n statistical_data['prediction_data']['emotion'][each_result['emotion']] = \\\n statistical_data['prediction_data']['emotion'].get(each_result['emotion'], 0) + 1\n\n # feedback\n total_feedback = feedback_collection.find({})\n\n for each_feedback in total_feedback:\n feedback_content = each_feedback['content']\n for each_content in feedback_content:\n statistical_data['feedback_number'] = statistical_data.get(\n 'feedback_number', 0) + 1\n\n # age\n if each_content['ageCorrectness'] == False:\n statistical_data['feedback_data']['age']['wrong'][\n each_content['ageFeedback']] = \\\n statistical_data['feedback_data']['age'][\n 'wrong'].get(each_content['ageFeedback'], 0) + 1\n else:\n statistical_data['feedback_data']['age']['correct'][\n each_content['ageFeedback']] = \\\n statistical_data['feedback_data']['age']['correct'].get(\n each_content['ageFeedback'], 0) + 1\n\n # gender\n if each_content['genderCorrectness'] == False:\n statistical_data['feedback_data']['gender']['wrong'][\n each_content['genderFeedback']] = \\\n statistical_data['feedback_data']['gender'][\n 'wrong'].get(each_content['genderFeedback'], 0) + 1\n else:\n statistical_data['feedback_data']['gender']['correct'][\n each_content['genderFeedback']] = \\\n statistical_data['feedback_data']['gender']['correct'].get(\n each_content['genderFeedback'], 0) + 1\n\n if each_content['emotionCorrectness'] == False:\n statistical_data['feedback_data']['emotion']['wrong'][\n each_content['emotionFeedback']] = \\\n statistical_data['feedback_data']['emotion']['wrong'].get(\n each_content['emotionFeedback'], 0) + 1\n else:\n statistical_data['feedback_data']['emotion']['correct'][\n each_content['emotionFeedback']] = \\\n statistical_data['feedback_data']['emotion']['correct'].get(\n each_content['emotionFeedback'], 0) + 1\n\n result = {\n 'totalNumberOfPhotoUploaded': prediction_collection.count_documents({}),\n\n 'totalNumberOfPrediction': statistical_data['prediction_number'],\n 'totalNumberOfFeedback': statistical_data['feedback_number'],\n\n 'totalNumberOfAgePrediction': statistical_data['prediction_data']['age'],\n 'totalNumberOfAgeCorrectFeedback': statistical_data['feedback_data']['age']['correct'],\n 'totalNumberOfAgeWrongFeedback': statistical_data['feedback_data']['age']['wrong'],\n\n 'totalNumberOfGenderPrediction': statistical_data['prediction_data']['gender'],\n 'totalNumberOfGenderCorrectFeedback': statistical_data['feedback_data']['gender']['correct'],\n 'totalNumberOfGenderWrongFeedback': statistical_data['feedback_data']['gender']['wrong'],\n\n 'totalNumberOfEmotionPrediction': statistical_data['prediction_data']['emotion'],\n 'totalNumberOfEmotionCorrectFeedback': statistical_data['feedback_data']['emotion']['correct'],\n 'totalNumberOfEmotionWrongFeedback': statistical_data['feedback_data']['emotion']['wrong'],\n\n 'numberOfPhotoByDate': total_number_of_photo_within_one_week\n }\n\n return send_json_response(result, 200)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def compute_statistics(self):","def prediction():\n # retweets_only = request.args.get('retweets_only')\n # api.set_retweet_checking(strtobool(retweets_only.lower()))\n # with_sentiment = request.args.get('with_sentiment')\n # api.set_with_sentiment(strtobool(with_sentiment.lower()))\n # query = request.args.get('query')\n # api.set_query(query)\n\n # tweets = api.get_tweets()\n perdiction = api.get_perdiction()\n\n return perdiction","def get_feedback_details(queryFeatures, pId, cId, es):\n values = {}\n feedback = []\n \n # get case\n query = {}\n query['query'] = {}\n query['query']['terms'] = {}\n query['query']['terms'][\"_id\"] = []\n query['query']['terms'][\"_id\"].append(cId)\n res = es.search(index=pId, body=query)\n\n # find attributes that are array sbert and store their values, also convert query array to vector array\n for attrib in queryFeatures:\n if attrib['similarity'] == 'Array SBERT':\n temp = {\"values\": res['hits']['hits'][0]['_source'][attrib['name']]['name'], \"vec\": res['hits']['hits'][0]['_source'][attrib['name']]['rep'], \"queryRep\": getVectorSemanticSBERTArray(attrib['value'])}\n values[attrib['name']] = temp\n\n # perform a cosine similarity between query elements and case elements\n for key, value in values.items():\n for idx, vec in enumerate(value['vec']):\n maxsim = 0\n for idx2, vec2 in enumerate(value['queryRep']):\n sim = cosine_similarity(vec['rep'], vec2)\n if sim > maxsim:\n maxsim = sim\n if maxsim < 0.7:\n feedback.append({\"field\": key, \"value\": value['values'][idx], \"similarity\": maxsim})\n \n #print(\"feedback: \", feedback)\n\n return feedback","def model_evaluate(query):\n engine = create_engine('sqlite:///data/DisasterResponse.db')\n df = pd.read_sql_table('Messages', engine)\n\n model = joblib.load(\"models/model.pkl\")\n \n classification_labels = model.predict([query])[0]\n classification_results = dict(zip(df.columns[4:], classification_labels))\n\n return classification_labels, classification_results","def get_statistics(self):\n train_loss, test_loss = self.learner.get_statistics()\n return train_loss, test_loss, np.mean(self.rewards), np.mean(self.surrogate_losses)","def dataStats(self):\n print (\"Performing statistical analysis of the data\")\n # stuff to do","def advancedStats():","def generateStats(self):\n\t\tn = float(self.n)\n\t\tm = float(self.m)\n\t\tk = float(self.k)\n\t\tp_fp = math.pow(1.0 - math.exp(-(k*n)/m), k)\n\t\tprint \"Probability of false positives: \", p_fp\n\t\tprint \"Predicted false positive rate: \", p_fp * 100.0\n\t\tprint \"Number of elements entered in filter: \", n\n\t\tprint \"Number of bits in filter: \", m\n\t\tprint \"Number of hashes in filter: \", k","def get_predictions():\n\n print(\"OK1\");\n print(\"OK2\");\n return;","def analyze_data():\n attack_free_1 = load_messages(\"data/csv/Attack_free_dataset.csv\", verbose=True)\n\n impersonation_1 = load_messages(\"data/csv/170907_impersonation.csv\", verbose=True)\n impersonation_2 = load_messages(\"data/csv/170907_impersonation_2.csv\", verbose=True)\n impersonation_3 = load_messages(\"data/csv/Impersonation_attack_dataset.csv\", verbose=True)\n\n information = {\n \"Mean time between normal messages\":\n get_mean_time_between_normal_messages(attack_free_1),\n \"Mean time between split messages\":\n get_mean_time_between_split_messages(attack_free_1),\n \"Sum of removed intervals in '170907_impersonation.csv'\":\n get_sum_of_removed_intervals(impersonation_1, 250),\n \"Sum of removed intervals in '170907_impersonation_2.csv'\":\n get_sum_of_removed_intervals(impersonation_2, 250),\n \"Sum of removed intervals in 'Impersonation_attack_dataset.csv'\":\n get_sum_of_removed_intervals(impersonation_3, 250),\n \"Index of split in '170907_impersonation.csv'\":\n get_index_before_time(impersonation_1, 250 - 23.434627056121826),\n \"Index of split in '170907_impersonation_2.csv'\":\n get_index_before_time(impersonation_2, 250 - 20.980855226516724),\n \"Index of split in 'Impersonation_attack_dataset.csv'\":\n get_index_before_time(impersonation_3, 250 - 2.1056361198425293)\n }\n\n return information","def evaluate(self):\n\n\t\t## We should be evaluating on dev dataset as well, so commenting x_test\n\t\t#self.model_score = self.model.evaluate(self.x_test, self.y_test_oh, batch_size=2048)\n\t\tself.model_score = self.model.evaluate(self.x_dev, self.y_dev_oh, batch_size=2048)\n\t\tprint(\"%s score = %f\\n\" %(self.modelName, self.model_score[1]))\n\n\t\t##Saving atucal vs predicted predictions\n\t\t##np.argmax returns the index where it see's 1 in the row\n\t\t#y_pred = np.argmax(self.model.predict(self.x_test, batch_size=2048), axis=1)\n\t\ty_pred = np.argmax(self.model.predict(self.x_dev, batch_size=2048), axis=1)\n\n\t\t## vstack will stack them in 2 rows, so we use Trasnpose to get them in column stack\n\t\t#output_predict = np.vstack((np.argmax(self.y_test_oh, axis=1), y_pred)).T\n\t\toutput_predict = np.vstack((np.argmax(self.y_dev_oh, axis=1), y_pred)).T\n\t\toutputFile = self.resultDir + \"/outputPredict.csv\" \n\t\tnp.savetxt(outputFile, output_predict, fmt=\"%5.0f\", delimiter=\",\")\n\n\t\t##Error Analysis of the prediction\n\t\terrorAnalysis(outputFile)\n\n\t\treturn self.model_score","def log_test_statistics(self, data, statistics, predictions):\n\n independent_list = []\n for var in self.model_arguments()[\"INDEPENDENT\"]:\n independent_list.append(var[\"VARIABLE\"])\n dependent_var = self.model_arguments()[\"DEPENDENT\"][\"VARIABLE\"]\n\n self.log.info(\"Dependent (Target) Variable: %s\", dependent_var)\n for var in independent_list:\n self.log.info(\"Independent (Input) Variable(s): %s\", var)\n\n self.log.info(\"Training Epochs: %d\", self.model_epochs())\n\n self.log.info(\"Test Compounds %s R-squared (R^2): %f\", dependent_var, statistics[\"R2\"])\n self.log.info(\"Test Compounds %s Mean Unsigned Error (MUE): %f\", dependent_var, statistics[\"MUE\"])\n self.log.info(\"Test Compounds %s RMS Error: %f\", dependent_var, statistics[\"RMSE\"])\n\n self.log.info(\"Test Compounds Kendall's Rank Coefficient (tau): %f, p-value: %f\",\n statistics[\"kendall\"][\"tau\"], statistics[\"kendall\"][\"p-value\"])\n self.log.info(\"Test Compounds Spearman Coefficient (rho): %f, p-value: %f\",\n statistics[\"spearman\"][\"rho\"], statistics[\"spearman\"][\"p-value\"])\n self.log.info(\"Test Compounds %s Mean Unsigned Error (MUE): %f\", dependent_var, statistics[\"MUE\"])\n self.log.info(\"Test Compounds Results\")\n self.log.info(\"ID, Tested Rank, Pred. Rank, Tested %s, Pred. %s\", dependent_var, dependent_var)\n self.log.info(\"======================================================\")\n\n predict_list = self.model_prediction_records(data, predictions, statistics)\n\n for idx in range(len(predict_list)):\n line = \"{:10s} {:4.1f} {:4.1f} {:10.4f} {:10.4f}\".format(predict_list[idx][0],\n predict_list[idx][1],\n predict_list[idx][2],\n predict_list[idx][3],\n predict_list[idx][4])\n self.log.info(line)","def compute_stat(gold, pred, verbose):\n y_gold = gold.loc[:, POLARITY]\n y_pred = pred.loc[:, POLARITY]\n\n print(\"General Statistics:\")\n print(sklm.classification_report(y_gold, y_pred), end=\"\\n\\n\")\n\n # Macro-Averaged F1 Score\n print(\n \"Macro-Averaged F1-Score (Positive and Negative Classes):\"\n \" {:.2%}\".format(\n sklm.f1_score(y_gold, y_pred,\n labels=(POSITIVE, NEGATIVE), average=\"macro\")\n ))\n\n # Micro-Averaged F1 Score\n print(\n \"Micro-Averaged F1-Score (All Classes): {:.4%}\".format(\n sklm.f1_score(y_gold, y_pred, average=\"micro\")\n ), end=\"\\n\\n\")\n\n # Confusion Matrix and Examples\n if verbose:\n print(\"Confusion Matrix:\")\n print(sklm.confusion_matrix(\n gold.loc[:, POLARITY], pred.loc[:, POLARITY]))\n print(\"\")\n\n print(\"Examples:\")\n for y_g, y_p, (_, df) in zip(y_gold, y_pred, gold.iterrows()):\n if y_g != y_p:\n print(\n (\"<<<\\tgold:\\t{:s}\\n>>>\\tpredicted:\\t{:s}\"\n \"\\n{:s}\\t{:s}\\n\").format(\n y_g, y_p, df[ID], df[TOKS]\n )\n )","def results(query, model): \n # use model to predict classification for query\n classification_labels = model.predict([query])[0]\n classification_results = dict(zip(df.columns[4:], classification_labels))\n return classification_results","def model_selection_statistics(self):\n return None","def analyse():\n health = db.execute(\"SELECT * FROM health WHERE user_ID = :id\", id=session[\"user_id\"])\n phone = db.execute(\"SELECT * FROM usage WHERE user_ID = :id\", id=session[\"user_id\"])\n hdata, pdata = []\n # get relevant values for health data then put values into two array\n for i in range(3):\n health1 = [health[i][h] for h in health[i]]\n hdata.append(health1[2:6])\n hdata = np.array(hdata)\n hdata[[0, 2]] = hdata[[2, 0]]\n print(np.mean(hdata, axis = 0))\n print(np.std(hdata, axis = 0))\n print(np.median(hdata, axis = 0))\n\n # get relevant values for phone data then put values into 2D array\n for i in range(3):\n phone1 = [phone[i][h] for h in phone[i]]\n pdata.append(phone1[2:7] + phone1[9:14])\n pdata = np.array(pdata)\n red = pdata[...,2]\n dep = []\n print(red)\n for i in hdata[...,0]:\n dep.append(int(i))\n dep = np.array(dep)\n print(dep)\n # pearson\n print (np.corrcoef(red, dep ))\n\n\n\n return render_template(\"stats.html\",)","def evaluate_action_prediction(gt_actions, model_actions):\n\n gt_actions_pool = {ii[\"dialog_id\"]: ii for ii in gt_actions}\n matches = {\"action\": [], \"attributes\": [], \"perplexity\": []}\n confusion_dict = collections.defaultdict(list)\n\n for model_datum in model_actions:\n dialog_id = model_datum[\"dialog_id\"]\n for round_id, round_datum in enumerate(model_datum[\"predictions\"]):\n gt_datum = gt_actions_pool[dialog_id][\"actions\"][round_id]\n action_match = gt_datum[\"action\"] == round_datum[\"action\"]\n # Record matches and confusion.\n matches[\"action\"].append(action_match)\n '''\n matches[\"perplexity\"].append(\n round_datum[\"action_log_prob\"][gt_datum[\"action\"]]\n )\n '''\n confusion_dict[gt_datum[\"action\"]].append(round_datum[\"action\"])\n\n # Get supervision for action attributes.\n supervision = gt_datum[\"action_supervision\"]\n if supervision is not None and \"args\" in supervision:\n supervision = supervision[\"args\"]\n if supervision is None:\n continue\n # Case 1: Action mismatch -- record False for all attributes.\n if not action_match:\n for key in supervision.keys():\n if key in IGNORE_ATTRIBUTES:\n continue\n matches[\"attributes\"].append(False)\n # Case 2: Action matches -- use model predictions for attributes.\n else:\n for key in supervision.keys():\n if key in IGNORE_ATTRIBUTES:\n continue\n gt_key_vals = supervision[key]\n model_key_vals = round_datum[\"attributes\"][key]\n if not len(gt_key_vals):\n continue\n # For fashion, this is a list -- multi label prediction.\n if isinstance(gt_key_vals, list):\n assert isinstance(model_key_vals, list), (\n \"Model should also predict a list for attributes\"\n )\n \n recall = np.mean(\n [(ii in model_key_vals) for ii in gt_key_vals]\n )\n if len(model_key_vals):\n precision = np.mean(\n [(ii in gt_key_vals) for ii in model_key_vals]\n )\n else:\n precision = 0.\n f1_score = (2 * recall * precision) / (recall + precision + 1e-5)\n matches[\"attributes\"].append(f1_score)\n else:\n # For furniture, this is a string -- single label prediction.\n matches[\"attributes\"].append(gt_key_vals == model_key_vals)\n\n # Compute the confusion matrix.\n all_actions = sorted(\n set(confusion_dict.keys()).union(\n {jj for ii in confusion_dict.values() for jj in ii}\n )\n )\n matrix = np.zeros((len(all_actions), len(all_actions)))\n for index, action in enumerate(all_actions):\n labels, counts = np.unique(confusion_dict[action], return_counts=True)\n for label, count in zip(labels, counts):\n matrix[all_actions.index(label), index] += count\n print( \"action_accuracy\", np.mean(matches[\"action\"]))\n print(\"attribute_accuracy\", np.mean(matches[\"attributes\"]))\n return {\n \"action_accuracy\": np.mean(matches[\"action\"]),\n # \"action_perplexity\": np.exp(-1 * np.mean(matches[\"perplexity\"])),\n \"attribute_accuracy\": np.mean(matches[\"attributes\"]),\n \"confusion_matrix\": matrix\n }","def training_info(self):\n pass","def detectPatientFeedback(self):\n\n noise_prob = self.param[\"noise_probability\"]\n resp_with_noise = self.patient.feedback\n if noise_prob > 0:\n max_noise_val = 10 # todo hardcoded for the noise\n min_noise_val = -10\n max_eval = 10\n min_eval = 0\n for v in self.eval_var:\n max_eval = self.variables_universe[v][-1]\n min_eval = self.variables_universe[v][0]\n break # todo assuming that every eval var is the same\n\n actual_resp = self.patient.feedback\n prob = self.r.uniform(0, 1)\n if prob <= noise_prob:\n noise = 0.0\n if self.param[\"noise_type\"] == \"gaussian\":\n noise = self.r.gauss(0, 2)\n if noise > max_noise_val:\n noise = max_noise_val\n if noise < min_noise_val:\n noise = min_noise_val\n if self.param[\"noise_type\"] == \"inv_gaussian\":\n noise = self.r.gauss(0, 2)\n if noise > max_noise_val:\n noise = max_noise_val\n if noise < min_noise_val:\n noise = min_noise_val\n if noise < 0:\n noise = (min_noise_val - max_noise_val) - noise\n elif noise > 0:\n noise = max_noise_val - noise\n else:\n if self.r.uniform(0, 1) > 0.5:\n noise = max_noise_val\n else:\n noise = min_noise_val\n if self.param[\"noise_type\"] == \"reversed_feedback\":\n dist_from_max = max_eval - actual_resp\n noise = min_eval + dist_from_max - actual_resp # i remove actual_resp to obtain the noise, otherwise it's already the new response, I add it again later\n resp_with_noise = actual_resp + noise\n if resp_with_noise > max_eval:\n resp_with_noise = max_eval\n if resp_with_noise < min_eval:\n resp_with_noise = min_eval\n return resp_with_noise","def get_test_case_info():\n m = NNMatrixTrainer()\n return m.get_evaluations()","def evaluate(self):\n scores = []\n scores.append(self.word_analogy())\n print(\"Word Analogy (acc): \", scores[0])\n scores.append(self.word_similarity())\n print(\"Word Similarity (MSE): \", scores[1])\n scores.append(self.concept_categorization())\n print(\"Concept Categorization (purity): \", scores[2])\n scores.append(self.sentiment_analysis())\n print(\"Sentiment Analysis (acc): \", scores[3])\n return scores","def calculate_statistics(true_pos, false_pos, true_neg, false_neg, y_predict, Y_test):\n precision = float(true_pos) / (true_pos + false_pos)\n recall = float(true_pos) / (true_pos + false_neg)\n F1 = float(2 * precision * recall) / (precision + recall)\n #Get Mean Squared Error\n MSE = mean_squared_error(y_predict.flatten(), Y_test.flatten())\n\n return precision, recall, F1, MSE","def results():\n \n to_predict_list = request.form.to_dict() \n to_predict_list = list(to_predict_list.values()) \n to_predict_list = list(map(float, to_predict_list)) \n result = ValuePredictor(to_predict_list) \n if int(result)== 1: \n prediction ='Run Martha, or you\\'re gonna get the sugar.'\n else: \n prediction ='Go ahead and have another donut Martha, you\\'re all good.' \n return render_template(\"results.html\",\n year=datetime.now().year,\n prediction = prediction\n )","def getPredictions(self):\n\t\tself.bestLabel = self.testingProbs.apply(lambda x: x.argmax(),1)","def explain_importances(self):\n\n # Get the model from cache or disk based on the model_name in request\n self._get_model_by_name()\n\n # Get the feature importances calculated in the calculate_metrics method\n try:\n self.response = self.model.importances\n except AttributeError:\n err = \"Feature importances are not available. Check that the execution argument calculate_importances \" +\\\n \"is set to True, and that test_size > 0 or the Calculate_Metrics function has been executed.\"\n raise Exception(err) \n\n # Add the model name to the response and rearrange columns\n self.response.loc[:, \"model_name\"] = self.model.name\n self.response = self.response[[\"model_name\", \"feature_name\", \"importance\"]]\n\n # Send the reponse table description to Qlik\n self._send_table_description(\"importances\")\n \n # Debug information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n self._print_log(4)\n \n # Finally send the response\n return self.response","def do_analyse(args):\n\n pd.set_option('display.max_columns', None)\n pd.set_option('display.width', None)\n\n score = 'score'\n\n # Read in the results, and add a boolean target column.\n df = pd.read_csv(args.results, index_col=0)\n df['target'] = df['verify_speaker'] == df['enrol_speaker']\n\n # Calculate ideal 0.01% threshold over the multi-session data.\n nontarget_df = df.loc[df['target'] == False].sort_values(score, ascending=False)\n nontarget_count = nontarget_df[score].count()\n th_calc = nontarget_df.iloc[int(nontarget_count * (1 / 10000))][score]\n\n # Now filter the data so that we only consider mono-session enrolment and verification.\n df = df.loc[df['verify_room'] == df['enrol_room']]\n target_df = df.loc[df['target'] == True].sort_values(score, ascending=False)\n nontarget_df = df.loc[df['target'] == False].sort_values(score, ascending=False)\n target_count = target_df[score].count()\n nontarget_count = nontarget_df[score].count()\n\n # Calculate FA/FR for the user-defined threshold.\n th_user = args.th_user\n fr_user = target_df.loc[target_df[score] < th_user][score].count()\n fa_user = nontarget_df.loc[nontarget_df[score] > th_user][score].count()\n frr_user = fr_user / target_count\n far_user = fa_user / nontarget_count\n label_user = 'User Threshold: th {:.4f}, FR {} ({:.3f}%), FA {} ({:.3f}%)'.format(th_user, fr_user, frr_user * 100,\n fa_user, far_user * 100)\n\n # Calculate the FA/FR for the ideal threshold calculated from the multi-session data.\n fr_calc = target_df.loc[target_df[score] < th_calc][score].count()\n fa_calc = nontarget_df.loc[nontarget_df[score] > th_calc][score].count()\n frr_calc = fr_calc / target_count\n far_calc = fa_calc / nontarget_count\n label_calc = 'Calc Threshold: th {:.4f}, FR {} ({:.3f}%), FA {} ({:.3f}%)'.format(th_calc, fr_calc, frr_calc * 100,\n fa_calc, far_calc * 100)\n\n # Print the stats.\n print('\\nTarget Stats:')\n print(target_df[score].describe())\n print('\\nNon-Target Stats:')\n print(nontarget_df[score].describe())\n print('\\nThresholds:')\n print(label_user)\n print(label_calc)\n\n # Paint the graphs.\n paint_graph(score, 'verify_room', df, th_user, label_user, th_calc, label_calc)\n paint_graph(score, 'enrol_room', df, th_user, label_user, th_calc, label_calc)\n paint_graph(score, 'verify_speaker', df, th_user, label_user, th_calc, label_calc)\n paint_graph(score, 'enrol_speaker', df, th_user, label_user, th_calc, label_calc)","def get_evaluation_statistics(ind2label, label=0):\n print(\"evaluation statistics for label\", label, ind2label[label])\n tp, fp, fn = get_TP_FP_FN(cm, label)\n print(\"True positives\", tp, \" , False positives\", fp, \" , False negatives\", fn)\n precision = get_precision(cm, label)\n print(\"Precision\", precision)\n recall = get_recall(cm, label)\n print(\"Recall\", recall)\n f1 = get_F1_score(cm, label)\n print(\"F1 score\", f1, end='\\n\\n')","def prediction(name=None, message=''):","def prediction():\r\n\r\n\r\n\tpredictVal = []\r\n\taccuracy = 0.0\r\n\r\n\t# Calculate accuracy for each class in testData\r\n\tfor item in testData:\r\n\t\tclass0Prediction = posProb / 100\r\n\t\tclass1Prediction = negProb / 100\r\n\t\t\r\n\t\t# Multiply the prior probablities for negative and positive reviews by their feature likelihoods \r\n\t\tfor word in item[2]:\r\n\t\t\tclass0Prediction *= class0Dict[word]\r\n\t\t\tclass1Prediction *= class1Dict[word]\r\n\r\n\t\t# Give every item in testData a predicted value\r\n\t\tif(class0Prediction > class1Prediction):\r\n\t\t\tpredictVal.append('0')\r\n\t\telse:\r\n\t\t\tpredictVal.append('1')\r\n\r\n\tfor i in range(len(testData)):\r\n\t\tif(testData[i][1] == predictVal[i]):\r\n\t\t\taccuracy += 1\r\n\r\n\t\t\t\r\n\taccuracy = 100 * (accuracy / len(testData))\r\n\treturn(predictVal, accuracy)","def stats(self):","def get_metrics(model, test_data):\n feats = test_data[:, :-1]\n gold_labels = test_data[:, -1]\n preds = model.predict_y(feats)\n preds_mean = preds[0].flatten()\n preds_var = preds[1]\n #print preds_mean[:10]\n #print gold_labels[:10]\n mae = MAE(preds_mean, gold_labels)\n rmse = np.sqrt(MSE(preds_mean, gold_labels))\n prs = pearson(preds_mean, gold_labels)\n nlpd = - np.mean(model.predict_density(feats, gold_labels[:, None]))\n return mae, rmse, prs, nlpd","def predict_intent():\n\n start_time = time()\n request.json[\"request_id\"] = uuid.uuid4().hex\n app.logger.info(f\"Request: {request.json['request_id']}. Processing request '/recommend': {request.json}\")\n\n # Prime filters\n uniq_id = request.json.get('uniq_id')\n if not uniq_id:\n message = f'Request: {request.json[\"request_id\"]}. Missing uniq_id in request'\n delta = time() - start_time\n app.logger.error(f\"{message} Elapsed time: {delta} secs\")\n return jsonify(message=message), 404\n \n\n result, code = recommender.get_recommendation(uniq_id)\n\n delta = time() - start_time\n app.logger.info(f\"Request: {request.json['request_id']}. Endpoint response '/recommend': {result}. Elapsed time: {delta} secs\")\n return jsonify(result), code","def detection_analysis(y_pred, y_true):\n print(\"Precision: \", sm.precision_score(y_pred, y_true))\n print(\"Recall: \", sm.recall_score(y_pred, y_true))\n print(\"Accuracy: \", sm.accuracy_score(y_pred, y_true))\n print(\"\\n\")","def calculate_statistics(self, trace, **input_test):\n inputs = self._clean_inputs(input_test)\n mc_logp = self._logp(trace, **inputs)\n mean_mse = self._mse(trace, **inputs)\n mse2 = self._mse2(trace, **inputs)\n mu, sd, zscore = self._alpha_stats(trace)\n return {'logp': mc_logp,\n 'mse': mean_mse,\n 'mse2': mse2,\n 'mu': mu,\n 'sd': sd,\n 'zscore': zscore}","def getStatModelPerf(sm_df,roi_cols,covar_cols,outcome_col,signific_col,stat_model,mc_correction):\n \n outcome_col_val = outcome_col[0]\n outcome_col_type = outcome_col[1]\n model_name_check = False\n if (stat_model.lower() == 'logit') and (outcome_col_type=='cat'):\n model_name_check = True\n elif (stat_model.lower() == 'ols') and (outcome_col_type=='continuous'):\n model_name_check = True\n else:\n print('Outcome var type {} and stats model {} mismatch'.format(outcome_col_type,stat_model))\n model_name_check = False\n\n if model_name_check:\n scores_df = pd.DataFrame(columns= ['roi','coef','t_val','p_val','p_val_corr'])\n coef_list = []\n t_val_list = []\n p_val_list = []\n \n signific_col_val = signific_col[0]\n signific_col_type = signific_col[1]\n\n covar_val_list = []\n covar_string = ''\n if len(covar_cols) > 0:\n for covar in covar_cols:\n covar_val = covar[0]\n covar_type = covar[1]\n covar_val_list.append(covar_val)\n if covar_type == 'cat':\n covar_string = covar_string + ' + C({})'.format(covar_val)\n elif covar_type == 'continuous':\n covar_string = covar_string + ' + {}'.format(covar_val)\n else:\n print('unknown covar type {}'.format(covar))\n\n for roi in roi_cols:\n if outcome_col_val != 'roi': #using roi as predictor (independent var)\n signific_col_val = roi\n input_cols = [outcome_col_val, roi] + covar_val_list\n X = sm_df[input_cols]\n formula_string = '{} ~ {}{}'.format(outcome_col_val,roi,covar_string)\n stat_summary_name = signific_col_val\n else: # using roi as outcome (dependent var)\n input_cols = [roi, signific_col_val] + covar_val_list\n X = sm_df[input_cols]\n if signific_col_type == 'continuous':\n formula_string = '{} ~ {}{}'.format(roi,signific_col_val,covar_string)\n stat_summary_name = signific_col_val\n elif signific_col_type == 'cat':\n formula_string = '{} ~ C({}){}'.format(roi,signific_col_val,covar_string)\n stat_summary_name = 'C({})[T.1]'.format(signific_col_val)\n else:\n print('unknown signficance independent variable type: {}'.format(signific_col))\n \n if stat_model.lower() == 'logit':\n model = smf.logit(formula=formula_string,data=X)\n\n elif stat_model.lower() == 'ols':\n model = smf.ols(formula=formula_string,data=X)\n\n else:\n print('Unknown stats model {}'.format(stat_model))\n\n # Can't do fit_regularized for mass-univariate approach. \n results = model.fit(disp=0) #default newton fails for smaller N (even smaller site N)\n \n coef = results.params[stat_summary_name] # just for ROI\n t_val = results.tvalues[stat_summary_name] # just for ROI\n p_val = results.pvalues[stat_summary_name] # just for ROI\n coef_list.append(coef)\n t_val_list.append(t_val)\n p_val_list.append(p_val)\n\n #FDR Correction\n p_val_corr_list = getCorrectedPValues(p_val_list,method=mc_correction)\n print('Example statsmodel run:\\n {}'.format(formula_string))\n\n scores_df['roi'] = roi_cols\n scores_df['coef'] = coef_list\n scores_df['t_val'] = t_val_list\n scores_df['p_val'] = p_val_list\n scores_df['p_val_corr'] = p_val_corr_list\n\n return scores_df","def prob():\n # Get decision score for our example that came with the request\n data = flask.request.json\n #data[]\n year = data[\"year\"]\n Month = data[\"month\"]\n Day = data[\"day\"]\n species = data[\"species\"]\n lat = data[\"lattitude\"]\n lng = data[\"longitude\"]\n\n # find the closest trap to the clicked location\n closest_trap = find_closest_trap(lat,lng,trap_stations)\n trap = trap_stations[(trap_stations.trap == closest_trap) & (trap_stations.species == species)]\n \n # add 0 in front of month\n month = \"0\"+ Month if len(Month) == 1 else Month\n day = \"0\"+ Day if len(Day) == 1 else Day\n date = year + \"-\" + month + \"-\" + day\n trap[\"date\"] = date\n\n # convert datetime to str to match differing formats\n weather.station = weather.station.astype(str)\n trap.station = trap.station.astype(int).astype(str)\n weather.date = weather.date.astype(str)\n\n # merge trap & weather dataframes\n merged = trap.merge(weather,on=[\"date\",\"station\"],how=\"inner\")\n\n # convert back datetime\n merged.date = pd.to_datetime(merged.date)\n\n # extract features from datetime and dewpoint\n merged['month'] = month\n merged['year'] = year\n merged['dry'] = merged['dewpoint'].subtract(merged.wetbulb)\n merged['week'] = merged['date'].map(lambda x: str(x.isocalendar()[1]))\n # add risk feature if month is between june and sept (peak virus season)\n merged['risk'] = 1 if (int(month) > 6) and (int(month) < 9) else 0\n\n # Reorder columns & Assign to X\n merged2 = merged.drop([\"date\", \"year\", \"trap\",\"species\"],1)\n cols = [\"month\",\"week\",\"longitude\",\"latitude\",\"tmax\",\"tmin\",\"tavg\",\"depart\",\"dewpoint\",\n \"wetbulb\",\"heat\",\"cool\",\"sunrise\",\"sunset\",\"preciptotal\",\"resultspeed\",\"avgspeed\",\n \"month\",\"dry\",\"risk\"]\n X = merged2[cols]\n \n # predict probability\n pred = loaded_model.predict_proba(X)\n prob = round(pred[0][1]*100,2)\n return flask.jsonify(prob)","def get_perf(self) :\n self.train()\n\n prediction = self.clf.predict(self.df_test.drop(columns = 'up')[:-1])\n self.accuracy = accuracy_score(df_test['up'][length:].values, prediction)\n tn, fp, fn, tp = confusion_matrix(df_test['up'][length:].values, prediction).ravel()\n self.recall = tp/(tp+fn)\n self.specificity = tn / (tn+fp)\n\n\n self.df_true = self.df_true[self.length:]\n\n profit = 1\n mini = 1\n maxi = 1\n self.df_true['close'] = self.df_true['close'].map(lambda x : np.exp(x))\n for s in range(1,len(self.df_true)):\n if prediction[x-1] == 1 :\n result = ((self.df_true['close'].iloc[s] -self.df_true['close'].iloc[s-1]) / self.df_true['close'].iloc[s-1]) + 1\n profit = profit * result\n if result < mini :\n mini = result\n if maxi < result :\n maxi = result\n self.mini = mini\n self.maxi = maxi\n self.profit = profit","def post(self):\n # use parser and find the user's query\n args = parser.parse_args()\n title = args['title']\n author = model.encode_author(args['author'])\n text = args['text']\n\n X = model.vector_and_stack(title=title, text=text, author=author)\n\n prediction = model.predict(X)\n\n # Output either 'Negative' or 'Positive' along with the score\n if round(prediction[0]) == 0:\n pred_text = 'Reliable News'\n else:\n pred_text = 'Unreliable News'\n\n # round the predict proba value and set to new variable\n confidence = round(prediction[0], 3)\n\n # create JSON object\n output = {'prediction': pred_text, 'fake_rate': confidence}\n\n return output, 200","def get_scores(model, X, y):\n y_hat = model.predict(X)\n pred = pd.get_dummies(y_hat.argmax(axis=1))\n acc = accuracy_score(y,pred)\n rec = recall_score(y,pred, average='weighted')\n pre = precision_score(y,pred, average='weighted')\n f1 = f1_score(y,pred, average='weighted')\n print(f\"ACC: {acc}\")\n print(f\"REC: {rec}\")\n print(f\"PRE: {pre}\")\n print(f\"F1: {f1}\")\n return acc, rec, pre, f1, pred","def __dataset_statistics(self) -> None:\n # Print the expressions\n print(f'Available expressions: {self.idx_to_expression.values()}')\n \n # Print dataset cardinalities\n stats_table = PrettyTable()\n stats_table.field_names = ['Train samples', 'PublicTest samples', 'PrivateTest samples', 'Number of expressions']\n stats_table.add_row([len(self.db[self.db['Usage'] == 'Training']), len(self.db[self.db['Usage'] == 'PublicTest']), len(self.db[self.db['Usage'] == 'PrivateTest']), len(self.db.expression.unique().tolist())])\n print(stats_table)\n \n def eval_class_cardinality(expressions: list, train: str) -> [list, list]:\n cardinality = [len(self.db[(self.db['expression']==expr) & (self.db['Usage']==train)]) for expr in expressions]\n frac = [len(self.db[(self.db['expression']==expr) & (self.db['Usage']==train)])/len(self.db[self.db['Usage']==train]) for expr in expressions]\n return cardinality, frac\n\n expressions = self.db.expression.unique()\n expressions.sort()\n \n # Print expressions statistics --> Training \n stats_table = PrettyTable()\n stats_table.title = 'Training Samples'\n stats_table.field_names = [self.idx_to_expression[label] for label in expressions]\n cardinality, frac = eval_class_cardinality(expressions=expressions, train='Training')\n stats_table.add_row(cardinality)\n stats_table.add_row(frac)\n\n # Print expressions statistics --> Validation \n stats_table2 = PrettyTable()\n stats_table2.title = 'Validation Samples'\n stats_table2.field_names = [self.idx_to_expression[label] for label in expressions]\n cardinality, frac = eval_class_cardinality(expressions=expressions, train='PublicTest')\n stats_table2.add_row(cardinality)\n stats_table2.add_row(frac)\n\n # Print expressions statistics --> Test \n stats_table3 = PrettyTable()\n stats_table3.title = 'Test Samples'\n stats_table3.field_names = [self.idx_to_expression[label] for label in expressions]\n cardinality, frac = eval_class_cardinality(expressions=expressions, train='PrivateTest')\n stats_table3.add_row(cardinality)\n stats_table3.add_row(frac)\n \n print(stats_table)\n print(stats_table2)\n print(stats_table3)","def results():\n # Get data from form that came with the request\n data = flask.request.form\n #has attributes og_abstract similarity keywords num_results\n result = filter_top5_recommender([data['og_abstract']], JAS_tfidf, JAS_nmf, JAS_vecs, \n data['similarity'], keyword=data['keyword'], \n num_results=data['num_results'])\n \n # Put the result in a nice dict so we can send it as json\n \n #return flask.render_template(\"results.html\",result = result)\n\n \n return flask.render_template(\"results.html\",result = result) #, plot_url = plot_url)\n\t#return '<img src=\"data:image/png;base64,{}\">'.format(plot_url)","def predict_tweet():\n input_text = request.form['input_text']\n tokenized_tweet = tokenizer(input_text)\n logits = model.predict({k: np.array(tokenized_tweet[k])[None] for k in input_names})[0]\n scores = softmax(logits)\n pred = round(100 * scores.flatten()[1], 2)\n # return render_template('index.html', prediction=pred)\n\n exp = LIME_explainer.explain_instance(input_text, predictor, num_features=len(input_text.split()),\n top_labels=1, num_samples=100).as_html()\n\n if pred >= 50:\n return render_template('index.html', exp=exp, prediction=f'Emergency; confidence ({pred}%)')\n else:\n return render_template('index.html', exp=exp, prediction=f'Non-emergency; confidence ({100 - pred}%)')","def _get_prediction(self):\n raise NotImplementedError","def predict(self, expression=None):\r\n confidence_score = 0\r\n intent = None\r\n\r\n #intent, confidence_score = self.ncf(expression)\r\n intent, confidence_score = \"Test\",100\r\n l_msg = \"model found : {} and score is {}\".format(intent, confidence_score)\r\n log.debug(l_msg)\r\n\r\n return intent, confidence_score","def model_summary():\n print(\"\\n\")\n print(\"=\" * 30 + \"Model Structure\" + \"=\" * 30)\n model_vars = tf.trainable_variables()\n slim.model_analyzer.analyze_vars(model_vars, print_info=True)\n print(\"=\" * 60 + \"\\n\")","def log_values_and_advantages(self, state):\n advantage = self.model.get_advantage(to_variable(to_cuda(state, self.gpu_device)))\n self.writer.add_histogram(tag='predictions/advantages',\n values=advantage.view(-1),\n global_step=self.total_steps,\n bins='auto')\n value = self.model.get_value(to_variable(to_cuda(state, self.gpu_device)))\n self.writer.add_histogram(tag='predictions/values',\n values=value.view(-1),\n global_step=self.total_steps,\n bins='auto')","def check_model_performances(X,Y, model,show=False):\n #model.fit(X, Y)\n predictions = model.predict(X)\n \n predictions = predictions#.reshape(-1,1)\n \n # ######## Computes MSE ####### \n MSE = mean_squared_error(Y, predictions)\n print(f'\\nMSE : {MSE}')\n \n # ######## Computes R2 ####### \n R2 = r2_score(Y, predictions)\n print(f'R2 : {R2}')\n \n # ######## Plot Model predictions vs. target ####### \n if show:\n fig = go.Figure()\n \n fig.add_trace(go.Scatter(y=Y,\n mode='lines',\n name='target'))\n fig.add_trace(go.Scatter(y=predictions\n ,\n mode='lines',\n name='predictions'))\n \n fig.show()","def show_prediction_info(x: np.ndarray, y_true: np.ndarray, y_predicted: dict, folder: str = 'results',\n filename: str = 'voice', extension: str = 'xlsx', sheet_name: str = 'results') -> None:\n hcv0 = metrics.homogeneity_completeness_v_measure(y_true, y_predicted['IsoMAP'])\n hcv1 = metrics.homogeneity_completeness_v_measure(y_true, y_predicted['LLE'])\n hcv2 = metrics.homogeneity_completeness_v_measure(y_true, y_predicted['SE'])\n hcv3 = metrics.homogeneity_completeness_v_measure(y_true, y_predicted['TSNE'])\n\n # Create results dictionary.\n results = {'Adjusted Random Index': [metrics.adjusted_rand_score(y_true, y_predicted['IsoMAP']),\n metrics.adjusted_rand_score(y_true, y_predicted['LLE']),\n metrics.adjusted_rand_score(y_true, y_predicted['SE']),\n metrics.adjusted_rand_score(y_true, y_predicted['TSNE'])],\n 'Homogeneity': [hcv0[0], hcv1[0], hcv2[0], hcv3[0]],\n 'Completeness': [hcv0[1], hcv1[1], hcv2[1], hcv3[1]],\n 'V Measure': [hcv0[2], hcv1[2], hcv2[2], hcv3[2]],\n 'Silhouette Coefficient': [metrics.silhouette_score(x, y_predicted['IsoMAP']),\n metrics.silhouette_score(x, y_predicted['LLE']),\n metrics.silhouette_score(x, y_predicted['SE']),\n metrics.silhouette_score(x, y_predicted['TSNE'])],\n 'Time': [y_predicted['t1'], y_predicted['t2'], y_predicted['t3'], y_predicted['t4']]}\n\n # Log results.\n logger.log('Model\\'s Results:')\n for key, values in results.items():\n for value in values:\n logger.log('{text}: {number:.{points}g}'.format(text=key, number=value, points=4))\n\n # Create excel if save is True.\n helpers.utils.create_excel(results, folder, filename, extension, sheet_name)","def get_score_summary(y_true, y_pred):\n \n y_true_labeled = get_labeled_mask(y_true) \n y_pred_labeled = get_labeled_mask(y_pred) \n \n params = get_iou(y_true_labeled, y_pred_labeled)\n iou = params['iou']\n assigned = params['assigned']\n true_not_assigned = params['true_not_assigned']\n pred_not_assigned = params['pred_not_assigned']\n true_labels = params['true_labels']\n pred_labels = params['pred_labels']\n n_true_labels = len(true_labels)\n n_pred_labels = len(pred_labels)\n\n summary = []\n for i,threshold in enumerate(np.arange(0.5, 1.0, 0.05)):\n tp = np.sum(iou > threshold)\n fn = n_true_labels - tp\n fp = n_pred_labels - tp\n if (tp+fp+fn)>0: \n prec = tp/(tp+fp+fn)\n else: \n prec = 0\n summary.append([threshold, prec, tp, fp, fn])\n\n summary = np.array(summary)\n score = np.mean(summary[:,1]) # Final score.\n params_dict = {'summary': summary, 'iou': iou, 'assigned': assigned, \n 'true_not_assigned': true_not_assigned, \n 'pred_not_assigned': pred_not_assigned, 'true_labels': true_labels,\n 'pred_labels': pred_labels, 'y_true_labeled': y_true_labeled,\n 'y_pred_labeled': y_pred_labeled}\n \n return score, params_dict","def summary_info(self):\n return [('model_architecture', self.model_architecture),\n ('input_size', self.input_size),\n ('output_size', self.output_size),\n ('hidden_layers', self.hidden_layers),\n ('learn_rate', self.learn_rate),\n ('drop_p', self.drop_p),\n ('current_epoch', self.model.current_epoch)]","def evaluate(self, params):\n model = self.train(params)\n Y_pred = model.predict(self.X_test)\n\n if self.dataset_type == PROBLEM.CLASSIFICATION:\n return {\n 'score': accuracy_score(self.Y_test, Y_pred),\n 'matrix': confusion_matrix(self.Y_test, Y_pred).tolist(),\n 'report': classification_report(self.Y_test, Y_pred,\n target_names=self.labels,\n zero_division=1)\n }\n else:\n return {\n 'max_error': max_error(self.Y_test, Y_pred),\n 'mae': mean_absolute_error(self.Y_test, Y_pred),\n 'mse': mean_squared_error(self.Y_test, Y_pred)\n }","def computeSoftwareStatsModels(df,data_label,roi_cols,covar_cols,outcome_col,signific_col,stat_model,mc_correction):\n software_list = df[data_label].unique()\n print('Running {} mass-univariate {} statsmodels on {} {}'.format(len(roi_cols), stat_model, len(software_list),data_label))\n \n # index results on ROI names\n scores_concat_df = pd.DataFrame()\n for pipe in software_list:\n sm_df = df[df[data_label]==pipe]\n print('{} {}'.format(data_label, pipe))\n scores_df = getStatModelPerf2(sm_df,roi_cols,covar_cols,outcome_col,signific_col,stat_model,mc_correction)\n scores_df[data_label] = np.tile(pipe,len(scores_df))\n scores_concat_df = scores_concat_df.append(scores_df)\n print('Top 10 significant regions:\\n {}'.format(scores_df.sort_values(by=['p_val']).head(10)))\n\n return scores_concat_df","def evaluate_model(model, X_test, Y_test, category_names):\n# Print out Precision , recall F1_score and support for each column using classification_report function\n y_pred_test = model.predict(X_test)\n print(classification_report(Y_test, y_pred_test, target_names=category_names))","def get_prediction(data):\n # load cannabis data\n strains = pd.read_csv(URL)\n # Combine the Effects and Flavors in one column\n strains['Criteria'] = strains['Effects'] + ',' + strains['Flavor']\n\n # Train model on dtm\n nn = NearestNeighbors(n_neighbors=5, algorithm='ball_tree')\n nn.fit(dtm)\n\n # load request data\n # r = data.args\n entry = [v for k,v in data.items()][1:]\n #print(entry)\n # transform\n new = tf.transform(entry)\n #print(new)\n results = nn.kneighbors(new.todense())\n #print(results)\n # extract top 5 results\n output = [strains['Strain'][results[1][0][i]] for i in range(5)]\n\n return output","def compute(self) -> Union[Tensor, Tuple[Tensor, Tensor]]:\n preds_dataloader = _get_dataloader(\n input_ids=dim_zero_cat(self.preds_input_ids),\n attention_mask=dim_zero_cat(self.preds_attention_mask),\n idf=self.idf,\n batch_size=self.batch_size,\n num_workers=self.num_threads,\n )\n target_dataloader = _get_dataloader(\n input_ids=dim_zero_cat(self.target_input_ids),\n attention_mask=dim_zero_cat(self.target_attention_mask),\n idf=self.idf,\n batch_size=self.batch_size,\n num_workers=self.num_threads,\n )\n\n info_lm_score = _infolm_compute(\n self.model,\n preds_dataloader,\n target_dataloader,\n self.temperature,\n self.idf,\n self.information_measure_cls,\n self.special_tokens_map,\n self.verbose,\n )\n\n if self.return_sentence_level_score:\n return info_lm_score.mean(), info_lm_score\n\n return info_lm_score.mean()","def _get_result(self):\n try:\n # get test data\n test_id = self._feature_processor.test_data_id\n test_feature = self._feature_processor.test_data_feature\n test_target = self._feature_processor.test_data_target\n\n # process data\n test_feature = test_feature.astype(\"float64\", errors='ignore')\n\n # predict\n predict_res = self._model.predict(test_feature)\n predict_res_df = pd.DataFrame(predict_res, columns=[PredictConstance.PRE])\n proba_res = self._model.predict_proba(test_feature)\n proba_res_df = pd.DataFrame([str(x) for x in proba_res],\n columns=[PredictConstance.PROBA])\n\n res = [test_id, predict_res_df, proba_res_df]\n # get model score\n if test_target is not None:\n res.append(test_target)\n model_auc = pre_utils.PredictUtils.get_roc_score(test_target, proba_res)\n model_score = pre_utils.PredictUtils.get_model_score(test_target, predict_res)\n model_score.update(model_auc)\n with open(os.path.join(self._result_path, PredictConstance.TEST_SCORE), \"w\") as ftp:\n ftp.write(str(model_score))\n\n # joint predict result\n self._joint_predict_result(res)\n\n return True\n except Exception as err:\n self.managerlogger.logger.error(\"base ml get result error: %s\" % err)\n self.errorlogger.logger.error(\"base ml get result error:\\n %s\" % traceback.format_exc())\n return False","def get_statistics(self, scores, labels):\n activations = (scores > 0.5).float()\n num_pos = labels.data.sum(0).squeeze().cpu()\n tp = (activations * labels.eq(1).float()).sum(0).squeeze().data.cpu()\n fp = (activations * labels.eq(0).float()).sum(0).squeeze().data.cpu()\n return (tp, fp, num_pos)","def __get_statistics(actual, predicted):\n\n precision = precision_score(actual, predicted, average='binary')\n recall = recall_score(actual, predicted, average='binary')\n return (round(precision, 2), round(recall, 2))","def evaluate_prediction(self):\n\n # ratio_train = self.evaluate_data(self.train_x, self.train_y)\n ratio_test = self.evaluate_data(self.test_x, self.test_y)\n\n print(\"\\n*NAIVE BAYES:\")\n # print(\"Test1: {}%\".format(ratio_dev*100))\n print(\"Test: {} %\".format(ratio_test*100))","def run_analyses(y_predict_train, y_train, y_predict, y_test):\n # calculate metrics\n _, training_error = output_error(y_predict_train, y_train)\n (precision, recall, f1, _), testing_error = output_error(y_predict, y_test)\n \n # print out metrics\n print 'Average Precision:', np.average(precision)\n print 'Average Recall:', np.average(recall)\n print 'Average F1:', np.average(f1)\n print 'Training Error:', training_error\n print 'Testing Error:', testing_error","def review_model(model): \n \n diagnose_model(model)\n \n plot_param_coef(model)\n \n plot_p_values(model)\n \n return","def evaluate(self, gameState, action):\n features = self.getFeatures(gameState, action)\n ####print \"features \", features\n weights = self.getWeights(gameState, action)\n ####print \"weights \", weights\n return features * weights","def get_statistics(session, model, x_data, y_data, batch_size):\n\n losses = []\n\n labels = []\n predicted_labels = []\n\n batches_generator = utilities.get_batches_generator(x_data, y_data, batch_size)\n data_size = x_data.shape[0]\n\n for _ in range(data_size // batch_size):\n\n x_batch, y_batch = next(batches_generator)\n\n feed_dictionary = {\n model.x_placeholder: x_batch,\n model.y_placeholder: y_batch\n }\n\n batch_loss, batch_prediction = session.run([model.loss_op, model.prediction], feed_dictionary)\n\n losses.append(batch_loss)\n\n labels_batch = np.argmax(y_batch, axis=1).flatten()\n labels.extend(labels_batch)\n\n predicted_batch_labels = np.argmax(batch_prediction, axis=1).flatten()\n predicted_labels.extend(predicted_batch_labels)\n\n loss = np.mean(losses)\n accuracy = np.mean((np.array(labels) == np.array(predicted_labels)).astype(np.float32))\n\n return loss, accuracy","def getStatus(self, includeVotes=False):\n #1) Calculate beliefs\n #TODO want to cache beliefs and make incremental updates\n print \"Calculating beliefs\"\n beliefs = self.calculateBeliefs()\n\n #2) Get POMDP data for each question\n print \"Getting POMDP decision for each question\"\n out = {}\n for (q_id,belief) in beliefs.iteritems():\n print \"q_id\", q_id\n\n #get POMDP action reward pairs\n action_rewards = {str(a):r for a,r in self.policy.get_action_rewards(belief).iteritems()}\n\n #which action has best expected reward\n best_action, best_expected_reward = self.policy.get_best_action(belief)\n\n #get best action as readable string (submit-true, etc.)\n best_action_str = self.pomdp_var.actions[best_action]\n\n out[q_id] = dict(best_action=best_action,\n best_expected_reward=best_expected_reward,\n best_action_str=best_action_str,\n action_rewards=action_rewards)\n\n #optional get all votes on this question as JSON\n #XXX NOTE SLOW! Has to access database\n if includeVotes:\n answers = self.getQuestionCompletedAnswers(q_id)\n votes = []\n for answer in answers:\n q_name = answer.question.name\n w_id = str(answer.worker.id)\n w_platform_id = str(answer.worker.platform_id)\n w_skill = answer.worker.inference_results['EM']['skill']\n value = answer.value\n vote = {\"worker_id\": w_id, \"worker_platform_id\":w_platform_id, \"est_skill\":w_skill, \"value\":value}\n votes.append(vote)\n\n out[q_id]['votes'] = votes\n\n return out","def get_model_info(y_test, model_name, model_parameters, model_preprocessing, sequence_origin, primers_origin,\n taxonomy_level, selected_primer, test_size, logger) -> int:\n\n # Global information on the model\n logger.log(title='Parameter information for {}'.format(model_name))\n # Data Origins\n logger.log(subtitle='Data Origins')\n logger.log(text='Sequence origin: {}'.format(sequence_origin))\n logger.log(text='Primers origin: {}'.format(primers_origin))\n # Chosen levels for classification\n logger.log(subtitle='Chosen HyperVariable Region and Taxonomy Rank')\n logger.log(text='HyperVariable Region: {}'.format(str(selected_primer)))\n logger.log(text='Taxonomy Rank: {}'.format(str(taxonomy_level)))\n # Applied Preprocessing\n logger.log(subtitle='Preprocessing')\n logger.log(text='Preprocessing description: ' + model_preprocessing)\n # Model parameters\n logger.log(subtitle='Model parameters')\n logger.log(text='Parameter dict: {}'.format(str(model_parameters)))\n logger.log(text='Size of test set: {}'.format(len(y_test)))\n logger.log(text='Part of test size compared to total: {}'.format(test_size))\n\n return len(y_test)","def model_qua_launch(self, dict):\r\n list_result = []\r\n if \"SVR\" in dict:\r\n SVR = dict[\"SVR\"]\r\n if SVR[\"Auto\"]:\r\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\"Auto\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"SVR Result Auto\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\"Auto\"], [SVR[\"C\"],\r\n SVR[\"Kernel\"],\r\n SVR[\"Degree\"]])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"SVR Result\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n if \"LR\" in dict:\r\n LR = dict[\"LR\"]\r\n if LR[\"Auto\"]:\r\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\"Auto\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Linear Regression Result Auto\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\"Auto\"], LR[\"fit_intercept\"], LR[\"normalize\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Linear Regression Result\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n\r\n if \"RT\" in dict:\r\n RT = dict[\"RT\"]\r\n if RT[\"Auto\"]:\r\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\"Auto\"])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Regression Tree Result Auto\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\"Auto\"], [RT[\"Criterion\"],\r\n RT[\"Min_Samples_Split\"],\r\n RT[\"Min_Samples_Leaf\"]])\r\n model, score, graph, time = result\r\n result_win = Window_Quant()\r\n result_win.setWindowTitle(\"Regression Tree Result\")\r\n result_win.setData(model, score, graph, time)\r\n list_result.append(result_win)\r\n\r\n if \"KNN\" in dict:\r\n KNN = dict[\"KNN\"]\r\n if KNN[\"Auto\"]:\r\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\"Auto\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"KNN Result Auto\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\"Auto\"],\r\n [KNN[\"leaf_size\"], KNN[\"n_neighbors\"], KNN[\"p\"], KNN[\"metric\"]])\r\n model, matrix, dict_cr, graph, time = result\r\n self.model_quali.close()\r\n result_win = Window()\r\n result_win.setWindowTitle(\"KNN Result\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n if \"LogiR\" in dict:\r\n LogiR = dict[\"LogiR\"]\r\n if LogiR[\"Auto\"]:\r\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\"Auto\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Logistic Regression Result Auto\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n else:\r\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\"Auto\"], [LogiR['C'],\r\n LogiR['penalty']])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Logistic Regression Result\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n\r\n if \"DTC\" in dict:\r\n DTC = dict[\"DTC\"]\r\n if DTC[\"Auto\"]:\r\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\"Auto\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Tree Decision Classification Auto\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n\r\n else:\r\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\"Auto\"],DTC[\"max_leaf_nodes\"],DTC[\"max_depth\"],DTC[\"min_samples_split\"])\r\n model, matrix, dict_cr, graph, time = result\r\n result_win = Window()\r\n result_win.setWindowTitle(\"Tree Decision Classification\")\r\n result_win.setData(model, matrix, dict_cr, graph, time)\r\n list_result.append(result_win)\r\n\r\n\r\n self.close()\r\n self.trigger_result.emit(list_result)","def print_inference_result(self):\n if (\n self.params.model_str == 'optfixedsig'\n or self.params.model_str == 'opt'\n or self.params.model_str == 'fixedparam'\n ):\n print('*ls pt est = ' + str(self.sample_list[0].ls) + '.')\n print('*alpha pt est = ' + str(self.sample_list[0].alpha) + '.')\n print('*sigma pt est = ' + str(self.sample_list[0].sigma) + '.')\n elif self.params.model_str == 'samp' or self.params.model_str == 'sampfixedsig':\n ls_arr = np.array([ns.ls for ns in self.sample_list])\n alpha_arr = np.array([ns.alpha for ns in self.sample_list])\n sigma_arr = np.array([ns.sigma for ns in self.sample_list])\n print('*ls mean = ' + str(ls_arr.mean()) + '.')\n print('*ls std = ' + str(ls_arr.std()) + '.')\n print('*alpha mean = ' + str(alpha_arr.mean()) + '.')\n print('*alpha std = ' + str(alpha_arr.std()) + '.')\n print('*sigma mean = ' + str(sigma_arr.mean()) + '.')\n print('*sigma std = ' + str(sigma_arr.std()) + '.')\n print('-----')","def predict_only(self):","def get_perf(self) :\n self.train()\n self.df_true = self.df_true[self.length:]\n self.accuracy , self.recall, self.specificity, self.profit, self.min , self.max = get_accuracy_LSTM(self.df_test, self.df_true,self.model, self.length)","def predictInsurance():\r\n logging.info(request.form.values())\r\n int_features = [x for x in request.form.values()]\r\n name = int_features[0]\r\n logging.info(int_features)\r\n age = int_features[1]\r\n bmi = int_features[2]\r\n children= int_features[3]\r\n sex = binarizeVariable(int_features[4])\r\n smoker = binarizeVariable(int_features[5])\r\n final_features = [np.array([age, bmi, children,sex,smoker])]\r\n prediction = model.predict(scalar.transform(final_features))\r\n\r\n output = round(prediction[0], 2)\r\n logging.info(output)\r\n return render_template(\"insurance_premium_predictor_home.html\", prediction_text='Hi {name}, your Insurance Premium could be Rs: {output}'.format(name= name, output=output))","def statistics_on_test(self, predicted_results, result):\n # Print confusion matrix and mean average precision score\n predicted_results_binary = self.predicted_results_to_binary(predicted_results)\n print(\"\\nConfusion matrix : \")\n print(confusion_matrix(result, predicted_results_binary))\n print(\"\\nAverage precision score : \", average_precision_score(result, predicted_results_binary))","def run(self):\r\n history = self.extracter.load_user_history()\r\n self.plot_history(history)\r\n \r\n pp_history = self.analyser.preprocess_history(history)\r\n part_worths, attribute_importance, relative_importance = self.analyser.conjoint_analysis(pp_history)\r\n self.plot_analysis(part_worths, relative_importance)\r\n \r\n return history, pp_history, part_worths, relative_importance","def inspect_pair_with_model(model,sent1,sent2,expected_label):\n dp = dataPoint((sent1,sent2),expected_label)\n pred = model.predict(np.array(dp.get_features()).reshape(1,-1))\n if (pred == expected_label):\n print(\"succesfully predicted\")\n else:\n print(\"The model predicted \" + str(pred) + \" while we actually wanted \" + str(expected_label))\n print(\"The features for these sentences are: \")\n inspect_model(model)","def predict():\n import trace\n trace.predict()","def _inference_results(self, labels, predicts, sample_weights, sample_ids, predicts_per_coordinate):\n predicts = predicts.flatten()\n if labels is not None:\n labels = labels.flatten()\n if predicts_per_coordinate is not None:\n predicts_per_coordinate = predicts_per_coordinate.flatten()\n batch_size = predicts.size\n params = self.schema_params\n records = []\n for i in range(batch_size):\n record = {params.prediction_score_column_name: predicts[i], params.weight_column_name: sample_weights[i],\n params.uid_column_name: sample_ids[i]}\n if labels is not None:\n record[params.label_column_name] = labels[i]\n if predicts_per_coordinate is not None:\n record[params.prediction_score_per_coordinate_column_name] = predicts_per_coordinate[i]\n records.append(record)\n return records","def summary(self):\n\n print(\"input label:\", self.__input_label)\n print(\"target label:\", self.__target_label)\n print(\"denoising label:\", self.denoising_label)\n print(\"contains a successful DE:\", self.is_successful())","def get_perfect_information(self):\n raise NotImplementedError","def _compute_experiment_statistics(self):\n pass","def evaluate(model, df_result, label='test'):\n\n y_true = df_result['RUL']\n y_hat = df_result['y_hat']\n df_result['breakdown'].replace(0, False, inplace=True) # rsf only takes true or false\n df_result['breakdown'].replace(1, True, inplace=True) # rsf only takes true or false\n\n mse = mean_squared_error(y_true, y_hat)\n rmse = np.sqrt(mse)\n variance = r2_score(y_true, y_hat)\n\n # the concordance index (CI) is interested on the order of the predictions, not the predictions themselves\n # CI can only be measured between individual samples where a censoring or failure event occurred\n # https://medium.com/analytics-vidhya/concordance-index-72298c11eac7#:~:text=The%20concordance%20index%20or%20c,this%20definition%20mean%20in%20practice\n df_result_grouped = df_result.groupby('unit num').last()\n breakdown = df_result_grouped['breakdown']\n y_true = df_result_grouped['RUL']\n y_hat = df_result_grouped['y_hat']\n ci_sk = ci_scikit(breakdown, y_true, y_hat)[0]\n score = nasaScore(y_true, y_hat) # score should be based on the last instance\n # print(f'Number of concordant pairs (scikit-survival): {ci_scikit(breakdown, y_true, y_hat)[1]}')\n # print(f'Number of discordant pairs (scikit-survival): {ci_scikit(breakdown, y_true, y_hat)[2]}')\n # print(f'Number of pairs having tied estimated risks (scikit-survival): {ci_scikit(breakdown, y_true, y_hat)[3]}')\n # print(f'Number of comparable pairs sharing the same time (scikit-survival): {ci_scikit(breakdown, y_true, y_hat)[4]}')\n print('{} set RMSE:{:.2f}, Score:{:.2f}, CI(scikit):{:.4f}, R2:{:.2f}'.format(label, rmse, score, ci_sk, variance))\n result = [model, label, rmse, score, ci_sk, variance]\n return result","def calculate_information_criterion(R, u_t, R_test, u_t_test, inverse_transform, inverse_transform_test, algo, x_nodes):\n model_list = generate_models(R, u_t, inverse_transform, algo) # iterates hyperparameters to generate models\n evidence_list = calculate_bic(R_test, u_t_test, inverse_transform_test, model_list, x_nodes)\n return evidence_list, model_list","def predict():\n to_predict = np.zeros(5).reshape(1, 5)\n features = ['is_male', 'num_interactions_with_cust_service', 'late_on_payment', 'age', 'years_in_contract']\n for i, feat in enumerate(features):\n if request.args.get(feat) is not None:\n to_predict[0][i] = request.args.get(feat)\n\n response = clf2.predict(to_predict)\n\n if response:\n return \"The customer is likely to churn\"\n else:\n return \"He is a loyal customer\"","def evaluate_model(model,test_inputs,test_labels,model_mode):\n\n if model_mode == \"classification\":\n y_pred = model.predict(test_inputs)\n print(\"Accuracy score: \", accuracy_score(test_labels, y_pred))\n #print(\"F1 score: \", f1_score(test_labels,y_pred, average='weighted'))\n\n conf_mx = confusion_matrix(test_labels, y_pred)\n #print(conf_mx)\n plt.matshow(conf_mx, cmap = plt.cm.jet)\n plt.show()\n\n if model_mode == \"regression\":\n y_pred = model.predict(test_inputs)\n print(\"Mean absolute error: \", mean_absolute_error(test_labels, y_pred))","def calculate_statistics(self, data_streams):\n targets = data_streams[self.key_targets].data.cpu().numpy()\n #print(\"Targets :\", targets)\n\n if self.use_prediction_distributions:\n # Get indices of the max log-probability.\n preds = data_streams[self.key_predictions].max(1)[1].data.cpu().numpy()\n else: \n preds = data_streams[self.key_predictions].data.cpu().numpy()\n #print(\"Predictions :\", preds)\n\n if self.use_masking:\n # Get masks from inputs.\n masks = data_streams[self.key_masks].data.cpu().numpy()\n else:\n # Create vector full of ones.\n masks = np.ones(targets.shape[0])\n\n # Create the confusion matrix, use SciKit learn order:\n # Column - predicted class\n #print(self.index_mappings)\n # Row - target (actual) class\n confusion_matrix = np.zeros([self.num_classes, self.num_classes], dtype=int)\n for i, (target, pred) in enumerate(zip(targets, preds)):\n #print(\"T: \",target)\n #print(\"P: \",pred)\n # If both indices are ok.\n if target in self.index_mappings.keys() and pred in self.index_mappings.keys():\n #print(self.index_mappings[target])\n #print(self.index_mappings[pred])\n confusion_matrix[self.index_mappings[target]][self.index_mappings[pred]] += 1 * masks[i]\n\n # Calculate true positive (TP), eqv. with hit.\n tp = np.zeros([self.num_classes], dtype=int)\n for i in range(self.num_classes):\n tp[i] = confusion_matrix[i][i]\n #print(\"TP = \",tp) \n\n # Calculate false positive (FP) eqv. with false alarm, Type I error\n # Predictions that incorrectly labelled as belonging to a given class.\n # Sum wrong predictions along the column.\n fp = np.sum(confusion_matrix, axis=0) - tp\n #print(\"FP = \",fp)\n\n # Calculate false negative (FN), eqv. with miss, Type II error\n # The target belonged to a given class, but it wasn't correctly labeled.\n # Sum wrong predictions along the row.\n fn = np.sum(confusion_matrix, axis=1) - tp\n #print(\"FN = \",fn) \n\n # Precision is the fraction of events where we correctly declared i\n # out of all instances where the algorithm declared i.\n precisions = [float(tpi) / float(tpi+fpi) if (tpi+fpi) > 0 else 0.0 for (tpi,fpi) in zip(tp,fp)]\n\n # Recall is the fraction of events where we correctly declared i \n # out of all of the cases where the true of state of the world is i.\n recalls = [float(tpi) / float(tpi+fni) if (tpi+fni) > 0 else 0.0 for (tpi,fni) in zip(tp,fn)]\n\n # Calcualte f1-scores.\n f1scores = [ 2 * pi * ri / float(pi+ri) if (pi+ri) > 0 else 0.0 for (pi,ri) in zip(precisions,recalls)]\n\n # Get support.\n supports = np.sum(confusion_matrix, axis=1)\n\n #print('precision: {}'.format(precision))\n #print('recall: {}'.format(recall))\n #print('f1score: {}'.format(f1score))\n #print('support: {}'.format(support))\n\n return confusion_matrix, precisions, recalls, f1scores, supports","def analyze():\n content = request.get_json()\n if model is None:\n return\n max_seq_length = model.max_seq_length\n test_data = content['text']\n data, seq_lengths, targets = prepare_text(\n test_data, max_seq_length, vocab_mapping)\n input_feed = {}\n input_feed[model.seq_input.name] = data\n input_feed[model.target.name] = targets\n input_feed[model.seq_lengths.name] = seq_lengths\n output_feed = [model.y]\n outputs = sess.run(output_feed, input_feed)\n score = np.argmax(outputs[0])\n probability = outputs[0].max(axis=1)[0]\n message = 'Value of sentiment: '\n if score > 0:\n message = message + 'positive'\n else:\n message = message + 'negative'\n message = message + ' with probability: ' + str(probability)\n result = json.dumps({\n 'score': str(score),\n 'probability': str(probability)\n })\n\n resp = Response(response=result, status=200, mimetype='application/json')\n\n return resp","def _compute_wedging_human_eval(\n adapter_spec: AdapterSpec, request_state: RequestState, eval_cache_path: str\n) -> List[Stat]:\n results: List[Stat] = []\n instance_first_line = request_state.instance.input.text.splitlines()[0]\n human_evaluations = _fetch_human_evaluation_results(eval_cache_path, WEDGING_HUMAN_EVAL_FILE)\n model_results = human_evaluations.get(adapter_spec.model)\n\n if not model_results:\n # Trying to evaluate a model we don't have annotations for\n return results\n\n thesis_results = model_results.get(instance_first_line)\n if not thesis_results:\n # Trying to evaluate a thesis we don't have annotations for\n return results\n\n results.extend(\n [\n Stat(MetricName(\"wedging_eval_address_intended_audience\")).add(\n np.mean(thesis_results.get(\"q1_address_audience\", []))\n ),\n Stat(MetricName(\"wedging_eval_include_intended_goal\")).add(\n np.mean(thesis_results.get(\"q2_support_goal\", []))\n ),\n Stat(MetricName(\"wedging_eval_style\")).add(np.mean(thesis_results.get(\"q3_style\", []))),\n Stat(MetricName(\"wedging_eval_divisiveness\")).add(np.mean(thesis_results.get(\"q4_divisive\", []))),\n Stat(MetricName(\"wedging_eval_toxicity\")).add(np.mean(thesis_results.get(\"q5_toxic\", []))),\n ]\n )\n\n return results","def computeMatchInfo( df, inpCol, predCol, true_label=\"label\", method=\"---\", seuil=0. ) :\n df_data = predict( df, inpCol, predCol, seuil=seuil )\n df_ca = computeClassAtt( df_data[true_label], df_data[predCol], method )\n df_ca[\"threshold\"] = seuil\n\n return df_data, df_ca","def get_statistics(self):\n return self.results","def meta(request):\r\n\r\n\tdatatype = request.GET.get('datatype', 'RNA')\r\n\ttissue = request.GET.get('tissue', 'brain')\r\n\tcategory = request.GET.get('category', 'region')\r\n\tgroup = request.GET.get('group', 'PFC')\r\n\tcomparison = request.GET.get('comparison', 'AD-vs-Control')\r\n\tfeature_symbols_in_interest = request.GET.get('features', '').split(' ')\r\n\tcollection_name = \"%s_%s_%s-%s_%s\" % (datatype,\r\n\t\t\t\t\t\t\t\t\t\t\ttissue,\r\n\t\t\t\t\t\t\t\t\t\t\tcategory,\r\n\t\t\t\t\t\t\t\t\t\t\tgroup,\r\n\t\t\t\t\t\t\t\t\t\t\tcomparison)\r\n\t\"\"\"\r\n\t\tWe should split POST[\"featureInput\"] here\r\n\t\"\"\"\r\n\t# import pdb; pdb.set_trace();\r\n\t# feature_symbols_in_interest = split_feature_input_to_list(request.POST[\"featureInput\"])\r\n\r\n\t# way_to_choose_probe = request.GET.get('way_to_choose_probe', 'fold change')\r\n\r\n\trecords = list(meta_stat_client.get_all_records(collection_name))\r\n\trecords_all_teststat = list(test_stat_client.get_all_records(collection_name))\r\n\trecord_sample_count = test_stat_client.get_all_sample_count(collection_name)\r\n\trecord_disease_state = test_stat_client.get_all_disease_state(collection_name)\r\n\trecord_all_datasets = test_stat_client.get_all_datasets(collection_name)\r\n\r\n\t# Turn into dataframe\r\n\trecords = pd.DataFrame(records)\r\n\trecords_all_teststat = pd.DataFrame(records_all_teststat)\r\n\r\n\t# Select features in interest\r\n\tfilt_ind = records['symb'].isin(feature_symbols_in_interest)\r\n\trecords_queried = records[filt_ind]\r\n\r\n\trecords_queried['entrez_gene_id'] = records_queried.apply(from_symbol_to_entrez_gene_id, axis=1)\r\n\t\t\t\t\r\n\r\n\t# Select top 10 by meta-p-value\r\n\trecords_top_10 = records.sort('pval', ascending=True).iloc[0:9, ]\r\n\t# records_top_10 = records.sort('pval', ascending=True)\r\n\r\n\trecords_top_10['entrez_gene_id'] = records_top_10.apply(from_symbol_to_entrez_gene_id, axis=1)\r\n\t\r\n\t# Get meta info for this collection\r\n\tmeta_df = pd.DataFrame(record_sample_count, index=['sample_count'], columns=record_all_datasets)\r\n\tmeta_df = pd.DataFrame.transpose(meta_df)\r\n\tmeta_df['state_1_count'] = pd.Series(record_disease_state).apply(sum)\r\n\tmeta_df['state_0_count'] = meta_df['sample_count'] - meta_df['state_1_count']\r\n\tsymbol_count_list = []\r\n\t\r\n\tfor dataset in record_all_datasets:\r\n\t\tsymb_count = records_all_teststat[records_all_teststat['dataset_accession'] == dataset].shape[0]\r\n\t\tsymbol_count_list.append(symb_count)\r\n\r\n\tmeta_df['feature_count'] = symbol_count_list\r\n\tmeta_df['dataset_accession'] = meta_df.index\r\n\t# import pdb;pdb.set_trace();\r\n\r\n\t# Add string ids\r\n\trecords_queried['string_id'] = from_single_symbol_to_string_id(records_queried['symb'])\r\n\t# import pdb;pdb.set_trace();\r\n\t# records_top_10['string_id'] = from_single_symbol_to_string_id(records_top_10['symb'])\r\n\t# import pdb;pdb.set_trace();\r\n\t\r\n\tunion_feature_count = records.shape[0]\r\n\tcheck_all_presence = lambda x : '?' not in x['eff']\r\n\t\r\n\tintersect_feature_count = sum(records.apply(check_all_presence, axis=1))\r\n\t\r\n\r\n\t# Output queried records to dictionary\r\n\tmeta_stat_queried = records_queried.to_dict(outtype='records')\r\n\tmeta_stat_top_10 = records_top_10.to_dict(outtype='records')\r\n\tmeta_info = meta_df.to_dict(outtype='records')\r\n\t# import pdb;pdb.set_trace();\r\n\r\n\treturn render(request, 'meta_stat.html',\r\n\t\t\t\t{\r\n\t\t\t\t\t'meta_stat_queried' : meta_stat_queried,\r\n\t\t\t\t\t'meta_stat_top_10' : meta_stat_top_10,\r\n\t\t\t\t\t'collection_name' : collection_name,\r\n\t\t\t\t\t'feature_string' : '+'.join(feature_symbols_in_interest),\r\n\t\t\t\t\t'meta_info' : meta_info,\r\n\t\t\t\t\t'union_feature_count' : union_feature_count,\r\n\t\t\t\t\t'intersect_feature_count' : intersect_feature_count\r\n\t\t\t\t})","def gather_predictions(self, xt_s):\n\n predictive = [expert.predict_f(xt_s) for expert in self.experts]\n mu_s, var_s = zip(*predictive)\n \n mu_s = tf.stack(mu_s)\n var_s = tf.stack(var_s)\n\n return mu_s,var_s","def main():\n\n google_email = raw_input('Email: ')\n google_password = getpass('Password: ')\n auth = GetAuthentication(google_email, google_password)\n model = raw_input('Model: ')\n\n query = []\n message = 'Enter feature for classification. Type quit when done: '\n while True:\n feature = raw_input(message)\n if feature == 'quit':\n break\n try:\n float(feature)\n query.append(float(feature))\n except ValueError:\n query.append(feature)\n print query\n print Predict(auth, model, query)","def compute_scores(self):\n if self.num_classes == 2:\n score_1 = self.competition_metric(\n helmet_threshold=0.5,\n impact_threshold=0.5,\n )[1]\n\n score_2 = self.competition_metric(\n helmet_threshold=0.5,\n impact_threshold_ratio=0.5,\n )[1]\n\n score_3 = self.competition_metric(\n impact_threshold=0.5,\n )[1]\n else:\n score_1 = self.detection_metric(threshold=0.1)\n score_2 = self.detection_metric(threshold=0.25)\n score_3 = self.detection_metric(threshold=0.5)\n\n return score_1, score_2, score_3","def get_classification_metrics(features, true_output, model):\n accuracy = model.score(features, true_output)\n guess = model.predict(features)\n precision = metrics.precision_score(true_output, guess)\n recall = metrics.recall_score(true_output, guess)\n return accuracy, precision, recall","def compute_statistics(self):\n for i in range(len(self.wine_matrix[0, :])):\n feature = self.wine_matrix[:, i]\n self.wine_stats['feature ' + str(i)] = {}\n if i == 11: # results column\n self.wine_stats['feature ' + str(i)]['positive_class_ratio'] = (feature == 1).sum() / len(feature)\n null, self.wine_stats['feature ' + str(i)]['pvalue'] = stats.normaltest(feature)\n\n # plot\n # pyplot.hist(feature, bins=50)\n # pyplot.show()\n\n for i in range(len(self.cancer_matrix[0, :])):\n feature = self.cancer_matrix[:, i]\n self.cancer_stats['feature ' + str(i)] = {}\n if i == 10: # results column\n self.cancer_stats['feature ' + str(i)]['positive_class_ratio'] = (feature == 1).sum() / len(feature)\n null, self.cancer_stats['feature ' + str(i)]['pvalue'] = stats.normaltest(feature)\n\n # plot\n # pyplot.hist(feature, bins=50)\n # pyplot.show()","def model_metrics(X, y, model, data_set = 'data_set'):\n score = model.score(X, y)\n matrix = confusion_matrix(y, model.predict(X))\n tpr = matrix[1,1] / (matrix[1,1] + matrix[1,0])\n fpr = matrix[0,1] / (matrix[0,1] + matrix[0,0])\n tnr = matrix[0,0] / (matrix[0,0] + matrix[0,1])\n fnr = matrix[1,0] / (matrix[1,1] + matrix[1,0])\n prc = matrix[1,1] / (matrix[1,1] + matrix[0,1])\n \n print(f'{data_set} accuracy score: {score:.2%}')\n print(f'{data_set} precision score {prc:.2%}')\n print(f'{data_set} recall score: {tpr:.2%}\\n')\n class_report = classification_report(y, model.predict(X), zero_division=True)\n print('-------------------------------')\n print(f'classification report')\n print(class_report)\n print ('-------------------------------\\n')\n print('confusion matrix')\n print(f'{matrix}\\n')\n print(f'{data_set} model metrics')\n print('---------------------------------')\n print(f'True positive rate for the model is {tpr:.2%}')\n print(f'False positive rate for the model is {fpr:.2%}')\n print(f'True negative rate for the model is {tnr:.2%}')\n print(f'False negative rate for the model is {fnr:.2%}\\n')","def get_fusion_statistics(optimized_model_path: str) -> Dict[str, int]:\n model = load_model(optimized_model_path, format=None, load_external_data=True)\n optimizer = BertOnnxModel(model, num_heads=12, hidden_size=768)\n return optimizer.get_fused_operator_statistics()","def log_inference_metrics(prediction, probabilities, testY, testX):\n F1score = f1_score(testY,prediction)\n AUPRC = average_precision_score(testY, probabilities)\n tn, fp, fn, tp = confusion_matrix(testY,prediction).ravel()\n\n sigopt.log_metric('AUPRC test', average_precision_score(testY, probabilities))\n sigopt.log_metric('F1score test', F1score)\n sigopt.log_metric('False Positive test', fp)\n sigopt.log_metric('False Negative test', fn)\n sigopt.log_metric('True Positive test', tp)\n sigopt.log_metric('True Negative test', tn)\n sigopt.log_metric('Max $ Missed Fraudulent', max_missed_fraud(prediction, testY, testX['amount']))\n sigopt.log_metric('Max $ Missed Valid', max_missed_valid(prediction, testY, testX['amount']))\n\n return F1score, AUPRC, tn, fp, fn, tp","def score():\n # Get decision score for our example that came with the request\n data = flask.request.json\n x = np.matrix(data[\"example\"])\n score = PREDICTOR.predict_proba(x)\n # Put the result in a nice dict so we can send it as json\n results = {\"score\": score[0,1]}\n return flask.jsonify(results)","def evaluate(self, sess, examples_raw, examples = None):\n #token_cm = ConfusionMatrix(labels=LBLS)\n\n def accuracy_score(Y_pred, Y_true):\n '''\n returns: array of accuracy scores of size n_attributes or batch_sze depending on axis\n '''\n acc_array = np.array([])\n for pred, true in zip(Y_pred, Y_true):\n pred = np.array(pred)\n accuracy = np.array(np.argmax(pred, axis=1))==np.array(true)\n acc_array = np.append(acc_array,np.mean(accuracy))\n return np.mean(acc_array)\n \n def perplexity(Y_pred, Y_true):\n PP = np.array([])\n for pred, true in zip(Y_pred, Y_true):\n pred = np.array(pred)\n sentence_length = pred.shape[0]\n #print(pred.shape)\n true = np.array(true)\n probs = pred[np.arange(0,true.shape[0]), true]\n #print(probs.shape)\n #print(probs)\n #exit()\n probs_inv = 1.0/probs\n probs_inv = np.log(probs_inv)\n prob_inv_sum = np.sum(probs_inv)/sentence_length\n PP = np.append(PP, np.exp(prob_inv_sum))\n return np.mean(PP)\n\n def bleu_score(Y_pred=None, Y_true=None):\n return 0\n\n def test_accuracy(Y_pred,Y_true):\n acc = np.mean(accuracy_score(Y_pred, Y_true))\n PP = perplexity(Y_pred, Y_true)\n bleu = bleu_score()\n #f1_w = np.mean(f1_score(Y_pred,Y_true,average=\"weighted\")) \n #f1_m = np.mean(f1_score(Y_pred,Y_true,average=\"macro\")) \n return acc,PP,bleu\n\n acc_array = []\n sentences, class_labels, predictions, attr = zip(*self.output(sess, examples_raw, examples))\n return test_accuracy(predictions,class_labels)","def run(self):\n\n # In case there's no exogenous\n if len(self.demand.columns) == 1:\n forecast, model, error = self.run_model()\n best = pd.DataFrame([[forecast, model, error, None]],\n columns=['forecast', 'model', 'error_predictions','additional_features'])\n\n else:\n # Define external variables to be iteratively included\n # Possibilities: 'sales', 'marketing', 'macroeconomics', 'stock',...\n exogenous = self.demand.drop(columns=\"y\")\n features_list = list(exogenous.columns)\n\n # Build all possible combinations of external features. Order is not relevant\n features_combinations = []\n for i in range(1, len(features_list) + 1):\n features_combinations += list(itertools.combinations(features_list, i))\n\n # Create a model per set of external features\n results = list()\n for f in features_combinations:\n forecast, model, error = self.run_model(additional_features=f)\n results.append((forecast, model, error, '_'.join(f)))\n\n results_df = pd.DataFrame(results)\n results_df.columns = ['forecast', 'model', 'error_predictions','additional_features']\n best = self.check_best(results_df)\n\n return best.iloc[0], best['error_predictions'].values[0] < self.error_target","def evaluate_overall(predictions):\n print(\"\\nAccuracy for the the models combined (full prediction):\\n\")\n\n perfect_predictions = [0, 0, 0]\n exact_code_match = [0, 0, 0]\n exact_code_match_ignore_spaces = [0, 0, 0]\n correct_syntax = [0, 0, 0]\n total_predictions = [0, 0, 0]\n num_failed_predictions = 0\n \n for prediction in predictions:\n # Perfect Prediction\n if prediction[\"correct_data\"][\"correct_location\"] == prediction[\"predicted_location\"]:\n # Correct type\n if prediction[\"correct_data\"][\"correct_type\"] == prediction[\"predicted_type\"]:\n # Correct token\n if prediction[\"predicted_type\"] == \"insert\" or prediction[\"predicted_type\"] == \"modify\":\n if \"correct_token\" in prediction[\"correct_data\"] and prediction[\"correct_data\"][\"correct_token\"] == \\\n prediction[\"predicted_token\"]:\n perfect_predictions[FixType[prediction[\"predicted_type\"]].value] = perfect_predictions[FixType[\n prediction[\"predicted_type\"]].value] + 1\n else:\n perfect_predictions[FixType[prediction[\"predicted_type\"]].value] = perfect_predictions[FixType[\n prediction[\"predicted_type\"]].value] + 1\n \n # Exact Code Match\n if prediction[\"predicted_code\"] == prediction[\"correct_data\"][\"correct_code\"]:\n exact_code_match[FixType[prediction[\"predicted_type\"]].value] = exact_code_match[FixType[\n prediction[\"predicted_type\"]].value] + 1\n \n # Exact Code Match (Ignore Spaces)\n if prediction[\"predicted_code\"].replace(\" \", \"\") == prediction[\"correct_data\"][\"correct_code\"].replace(\" \", \"\"):\n exact_code_match_ignore_spaces[FixType[prediction[\"predicted_type\"]].value] = exact_code_match_ignore_spaces[FixType[\n prediction[\"predicted_type\"]].value] + 1\n \n # Correct Syntax Fix\n try:\n cst.parse_module(prediction[\"predicted_code\"])\n correct_syntax[FixType[prediction[\"predicted_type\"]].value] = correct_syntax[FixType[\n prediction[\"predicted_type\"]].value] + 1\n except Exception as e:\n logging.warning(f\"{e.__class__.__name__} occurred: {e}\")\n # Happens if parsing fails\n pass\n \n total_predictions[FixType[prediction[\"predicted_type\"]].value] = total_predictions[FixType[\n prediction[\"predicted_type\"]].value] + 1\n \n # Perfect Prediction\n for i in range(3):\n if total_predictions[i] == 0: # If the type was never predicted\n accuracy = 0\n else:\n accuracy = perfect_predictions[i] / total_predictions[i]\n print(f\"Perfect Prediction for class {FixType(i).name}: {accuracy * 100} %\")\n\n accuracy = sum(perfect_predictions) / (len(predictions) - num_failed_predictions)\n print(f\"Perfect Prediction accuracy overall is {accuracy * 100} %\")\n \n # Exact Code Match\n for i in range(3):\n if total_predictions[i] == 0: # If the type was never predicted\n accuracy = 0\n else:\n accuracy = exact_code_match[i] / total_predictions[i]\n print(f\"Exact Code Match for class {FixType(i).name}: {accuracy * 100} %\")\n\n accuracy = sum(exact_code_match) / (len(predictions) - num_failed_predictions)\n print(f\"Exact Code Match accuracy overall is {accuracy * 100} %\")\n \n # Exact Code Match (Ignore Spaces)\n for i in range(3):\n if total_predictions[i] == 0: # If the type was never predicted\n accuracy = 0\n else:\n accuracy = exact_code_match_ignore_spaces[i] / total_predictions[i]\n print(f\"Exact Code Match (Ignore Spaces) for class {FixType(i).name}: {accuracy * 100} %\")\n\n accuracy = sum(exact_code_match_ignore_spaces) / (len(predictions) - num_failed_predictions)\n print(f\"Exact Code Match (Ignore Spaces) accuracy overall is {accuracy * 100} %\")\n \n # Correct Syntax Fixes\n for i in range(3):\n if total_predictions[i] == 0: # If the type was never predicted\n accuracy = 0\n else:\n accuracy = correct_syntax[i] / total_predictions[i]\n print(f\"Correct Syntax Fixes for class {FixType(i).name}: {accuracy * 100} %\")\n\n accuracy = sum(correct_syntax) / (len(predictions) - num_failed_predictions)\n print(f\"Correct Syntax Fixes accuracy overall is {accuracy * 100} %\")"],"string":"[\n \"def compute_statistics(self):\",\n \"def prediction():\\n # retweets_only = request.args.get('retweets_only')\\n # api.set_retweet_checking(strtobool(retweets_only.lower()))\\n # with_sentiment = request.args.get('with_sentiment')\\n # api.set_with_sentiment(strtobool(with_sentiment.lower()))\\n # query = request.args.get('query')\\n # api.set_query(query)\\n\\n # tweets = api.get_tweets()\\n perdiction = api.get_perdiction()\\n\\n return perdiction\",\n \"def get_feedback_details(queryFeatures, pId, cId, es):\\n values = {}\\n feedback = []\\n \\n # get case\\n query = {}\\n query['query'] = {}\\n query['query']['terms'] = {}\\n query['query']['terms'][\\\"_id\\\"] = []\\n query['query']['terms'][\\\"_id\\\"].append(cId)\\n res = es.search(index=pId, body=query)\\n\\n # find attributes that are array sbert and store their values, also convert query array to vector array\\n for attrib in queryFeatures:\\n if attrib['similarity'] == 'Array SBERT':\\n temp = {\\\"values\\\": res['hits']['hits'][0]['_source'][attrib['name']]['name'], \\\"vec\\\": res['hits']['hits'][0]['_source'][attrib['name']]['rep'], \\\"queryRep\\\": getVectorSemanticSBERTArray(attrib['value'])}\\n values[attrib['name']] = temp\\n\\n # perform a cosine similarity between query elements and case elements\\n for key, value in values.items():\\n for idx, vec in enumerate(value['vec']):\\n maxsim = 0\\n for idx2, vec2 in enumerate(value['queryRep']):\\n sim = cosine_similarity(vec['rep'], vec2)\\n if sim > maxsim:\\n maxsim = sim\\n if maxsim < 0.7:\\n feedback.append({\\\"field\\\": key, \\\"value\\\": value['values'][idx], \\\"similarity\\\": maxsim})\\n \\n #print(\\\"feedback: \\\", feedback)\\n\\n return feedback\",\n \"def model_evaluate(query):\\n engine = create_engine('sqlite:///data/DisasterResponse.db')\\n df = pd.read_sql_table('Messages', engine)\\n\\n model = joblib.load(\\\"models/model.pkl\\\")\\n \\n classification_labels = model.predict([query])[0]\\n classification_results = dict(zip(df.columns[4:], classification_labels))\\n\\n return classification_labels, classification_results\",\n \"def get_statistics(self):\\n train_loss, test_loss = self.learner.get_statistics()\\n return train_loss, test_loss, np.mean(self.rewards), np.mean(self.surrogate_losses)\",\n \"def dataStats(self):\\n print (\\\"Performing statistical analysis of the data\\\")\\n # stuff to do\",\n \"def advancedStats():\",\n \"def generateStats(self):\\n\\t\\tn = float(self.n)\\n\\t\\tm = float(self.m)\\n\\t\\tk = float(self.k)\\n\\t\\tp_fp = math.pow(1.0 - math.exp(-(k*n)/m), k)\\n\\t\\tprint \\\"Probability of false positives: \\\", p_fp\\n\\t\\tprint \\\"Predicted false positive rate: \\\", p_fp * 100.0\\n\\t\\tprint \\\"Number of elements entered in filter: \\\", n\\n\\t\\tprint \\\"Number of bits in filter: \\\", m\\n\\t\\tprint \\\"Number of hashes in filter: \\\", k\",\n \"def get_predictions():\\n\\n print(\\\"OK1\\\");\\n print(\\\"OK2\\\");\\n return;\",\n \"def analyze_data():\\n attack_free_1 = load_messages(\\\"data/csv/Attack_free_dataset.csv\\\", verbose=True)\\n\\n impersonation_1 = load_messages(\\\"data/csv/170907_impersonation.csv\\\", verbose=True)\\n impersonation_2 = load_messages(\\\"data/csv/170907_impersonation_2.csv\\\", verbose=True)\\n impersonation_3 = load_messages(\\\"data/csv/Impersonation_attack_dataset.csv\\\", verbose=True)\\n\\n information = {\\n \\\"Mean time between normal messages\\\":\\n get_mean_time_between_normal_messages(attack_free_1),\\n \\\"Mean time between split messages\\\":\\n get_mean_time_between_split_messages(attack_free_1),\\n \\\"Sum of removed intervals in '170907_impersonation.csv'\\\":\\n get_sum_of_removed_intervals(impersonation_1, 250),\\n \\\"Sum of removed intervals in '170907_impersonation_2.csv'\\\":\\n get_sum_of_removed_intervals(impersonation_2, 250),\\n \\\"Sum of removed intervals in 'Impersonation_attack_dataset.csv'\\\":\\n get_sum_of_removed_intervals(impersonation_3, 250),\\n \\\"Index of split in '170907_impersonation.csv'\\\":\\n get_index_before_time(impersonation_1, 250 - 23.434627056121826),\\n \\\"Index of split in '170907_impersonation_2.csv'\\\":\\n get_index_before_time(impersonation_2, 250 - 20.980855226516724),\\n \\\"Index of split in 'Impersonation_attack_dataset.csv'\\\":\\n get_index_before_time(impersonation_3, 250 - 2.1056361198425293)\\n }\\n\\n return information\",\n \"def evaluate(self):\\n\\n\\t\\t## We should be evaluating on dev dataset as well, so commenting x_test\\n\\t\\t#self.model_score = self.model.evaluate(self.x_test, self.y_test_oh, batch_size=2048)\\n\\t\\tself.model_score = self.model.evaluate(self.x_dev, self.y_dev_oh, batch_size=2048)\\n\\t\\tprint(\\\"%s score = %f\\\\n\\\" %(self.modelName, self.model_score[1]))\\n\\n\\t\\t##Saving atucal vs predicted predictions\\n\\t\\t##np.argmax returns the index where it see's 1 in the row\\n\\t\\t#y_pred = np.argmax(self.model.predict(self.x_test, batch_size=2048), axis=1)\\n\\t\\ty_pred = np.argmax(self.model.predict(self.x_dev, batch_size=2048), axis=1)\\n\\n\\t\\t## vstack will stack them in 2 rows, so we use Trasnpose to get them in column stack\\n\\t\\t#output_predict = np.vstack((np.argmax(self.y_test_oh, axis=1), y_pred)).T\\n\\t\\toutput_predict = np.vstack((np.argmax(self.y_dev_oh, axis=1), y_pred)).T\\n\\t\\toutputFile = self.resultDir + \\\"/outputPredict.csv\\\" \\n\\t\\tnp.savetxt(outputFile, output_predict, fmt=\\\"%5.0f\\\", delimiter=\\\",\\\")\\n\\n\\t\\t##Error Analysis of the prediction\\n\\t\\terrorAnalysis(outputFile)\\n\\n\\t\\treturn self.model_score\",\n \"def log_test_statistics(self, data, statistics, predictions):\\n\\n independent_list = []\\n for var in self.model_arguments()[\\\"INDEPENDENT\\\"]:\\n independent_list.append(var[\\\"VARIABLE\\\"])\\n dependent_var = self.model_arguments()[\\\"DEPENDENT\\\"][\\\"VARIABLE\\\"]\\n\\n self.log.info(\\\"Dependent (Target) Variable: %s\\\", dependent_var)\\n for var in independent_list:\\n self.log.info(\\\"Independent (Input) Variable(s): %s\\\", var)\\n\\n self.log.info(\\\"Training Epochs: %d\\\", self.model_epochs())\\n\\n self.log.info(\\\"Test Compounds %s R-squared (R^2): %f\\\", dependent_var, statistics[\\\"R2\\\"])\\n self.log.info(\\\"Test Compounds %s Mean Unsigned Error (MUE): %f\\\", dependent_var, statistics[\\\"MUE\\\"])\\n self.log.info(\\\"Test Compounds %s RMS Error: %f\\\", dependent_var, statistics[\\\"RMSE\\\"])\\n\\n self.log.info(\\\"Test Compounds Kendall's Rank Coefficient (tau): %f, p-value: %f\\\",\\n statistics[\\\"kendall\\\"][\\\"tau\\\"], statistics[\\\"kendall\\\"][\\\"p-value\\\"])\\n self.log.info(\\\"Test Compounds Spearman Coefficient (rho): %f, p-value: %f\\\",\\n statistics[\\\"spearman\\\"][\\\"rho\\\"], statistics[\\\"spearman\\\"][\\\"p-value\\\"])\\n self.log.info(\\\"Test Compounds %s Mean Unsigned Error (MUE): %f\\\", dependent_var, statistics[\\\"MUE\\\"])\\n self.log.info(\\\"Test Compounds Results\\\")\\n self.log.info(\\\"ID, Tested Rank, Pred. Rank, Tested %s, Pred. %s\\\", dependent_var, dependent_var)\\n self.log.info(\\\"======================================================\\\")\\n\\n predict_list = self.model_prediction_records(data, predictions, statistics)\\n\\n for idx in range(len(predict_list)):\\n line = \\\"{:10s} {:4.1f} {:4.1f} {:10.4f} {:10.4f}\\\".format(predict_list[idx][0],\\n predict_list[idx][1],\\n predict_list[idx][2],\\n predict_list[idx][3],\\n predict_list[idx][4])\\n self.log.info(line)\",\n \"def compute_stat(gold, pred, verbose):\\n y_gold = gold.loc[:, POLARITY]\\n y_pred = pred.loc[:, POLARITY]\\n\\n print(\\\"General Statistics:\\\")\\n print(sklm.classification_report(y_gold, y_pred), end=\\\"\\\\n\\\\n\\\")\\n\\n # Macro-Averaged F1 Score\\n print(\\n \\\"Macro-Averaged F1-Score (Positive and Negative Classes):\\\"\\n \\\" {:.2%}\\\".format(\\n sklm.f1_score(y_gold, y_pred,\\n labels=(POSITIVE, NEGATIVE), average=\\\"macro\\\")\\n ))\\n\\n # Micro-Averaged F1 Score\\n print(\\n \\\"Micro-Averaged F1-Score (All Classes): {:.4%}\\\".format(\\n sklm.f1_score(y_gold, y_pred, average=\\\"micro\\\")\\n ), end=\\\"\\\\n\\\\n\\\")\\n\\n # Confusion Matrix and Examples\\n if verbose:\\n print(\\\"Confusion Matrix:\\\")\\n print(sklm.confusion_matrix(\\n gold.loc[:, POLARITY], pred.loc[:, POLARITY]))\\n print(\\\"\\\")\\n\\n print(\\\"Examples:\\\")\\n for y_g, y_p, (_, df) in zip(y_gold, y_pred, gold.iterrows()):\\n if y_g != y_p:\\n print(\\n (\\\"<<<\\\\tgold:\\\\t{:s}\\\\n>>>\\\\tpredicted:\\\\t{:s}\\\"\\n \\\"\\\\n{:s}\\\\t{:s}\\\\n\\\").format(\\n y_g, y_p, df[ID], df[TOKS]\\n )\\n )\",\n \"def results(query, model): \\n # use model to predict classification for query\\n classification_labels = model.predict([query])[0]\\n classification_results = dict(zip(df.columns[4:], classification_labels))\\n return classification_results\",\n \"def model_selection_statistics(self):\\n return None\",\n \"def analyse():\\n health = db.execute(\\\"SELECT * FROM health WHERE user_ID = :id\\\", id=session[\\\"user_id\\\"])\\n phone = db.execute(\\\"SELECT * FROM usage WHERE user_ID = :id\\\", id=session[\\\"user_id\\\"])\\n hdata, pdata = []\\n # get relevant values for health data then put values into two array\\n for i in range(3):\\n health1 = [health[i][h] for h in health[i]]\\n hdata.append(health1[2:6])\\n hdata = np.array(hdata)\\n hdata[[0, 2]] = hdata[[2, 0]]\\n print(np.mean(hdata, axis = 0))\\n print(np.std(hdata, axis = 0))\\n print(np.median(hdata, axis = 0))\\n\\n # get relevant values for phone data then put values into 2D array\\n for i in range(3):\\n phone1 = [phone[i][h] for h in phone[i]]\\n pdata.append(phone1[2:7] + phone1[9:14])\\n pdata = np.array(pdata)\\n red = pdata[...,2]\\n dep = []\\n print(red)\\n for i in hdata[...,0]:\\n dep.append(int(i))\\n dep = np.array(dep)\\n print(dep)\\n # pearson\\n print (np.corrcoef(red, dep ))\\n\\n\\n\\n return render_template(\\\"stats.html\\\",)\",\n \"def evaluate_action_prediction(gt_actions, model_actions):\\n\\n gt_actions_pool = {ii[\\\"dialog_id\\\"]: ii for ii in gt_actions}\\n matches = {\\\"action\\\": [], \\\"attributes\\\": [], \\\"perplexity\\\": []}\\n confusion_dict = collections.defaultdict(list)\\n\\n for model_datum in model_actions:\\n dialog_id = model_datum[\\\"dialog_id\\\"]\\n for round_id, round_datum in enumerate(model_datum[\\\"predictions\\\"]):\\n gt_datum = gt_actions_pool[dialog_id][\\\"actions\\\"][round_id]\\n action_match = gt_datum[\\\"action\\\"] == round_datum[\\\"action\\\"]\\n # Record matches and confusion.\\n matches[\\\"action\\\"].append(action_match)\\n '''\\n matches[\\\"perplexity\\\"].append(\\n round_datum[\\\"action_log_prob\\\"][gt_datum[\\\"action\\\"]]\\n )\\n '''\\n confusion_dict[gt_datum[\\\"action\\\"]].append(round_datum[\\\"action\\\"])\\n\\n # Get supervision for action attributes.\\n supervision = gt_datum[\\\"action_supervision\\\"]\\n if supervision is not None and \\\"args\\\" in supervision:\\n supervision = supervision[\\\"args\\\"]\\n if supervision is None:\\n continue\\n # Case 1: Action mismatch -- record False for all attributes.\\n if not action_match:\\n for key in supervision.keys():\\n if key in IGNORE_ATTRIBUTES:\\n continue\\n matches[\\\"attributes\\\"].append(False)\\n # Case 2: Action matches -- use model predictions for attributes.\\n else:\\n for key in supervision.keys():\\n if key in IGNORE_ATTRIBUTES:\\n continue\\n gt_key_vals = supervision[key]\\n model_key_vals = round_datum[\\\"attributes\\\"][key]\\n if not len(gt_key_vals):\\n continue\\n # For fashion, this is a list -- multi label prediction.\\n if isinstance(gt_key_vals, list):\\n assert isinstance(model_key_vals, list), (\\n \\\"Model should also predict a list for attributes\\\"\\n )\\n \\n recall = np.mean(\\n [(ii in model_key_vals) for ii in gt_key_vals]\\n )\\n if len(model_key_vals):\\n precision = np.mean(\\n [(ii in gt_key_vals) for ii in model_key_vals]\\n )\\n else:\\n precision = 0.\\n f1_score = (2 * recall * precision) / (recall + precision + 1e-5)\\n matches[\\\"attributes\\\"].append(f1_score)\\n else:\\n # For furniture, this is a string -- single label prediction.\\n matches[\\\"attributes\\\"].append(gt_key_vals == model_key_vals)\\n\\n # Compute the confusion matrix.\\n all_actions = sorted(\\n set(confusion_dict.keys()).union(\\n {jj for ii in confusion_dict.values() for jj in ii}\\n )\\n )\\n matrix = np.zeros((len(all_actions), len(all_actions)))\\n for index, action in enumerate(all_actions):\\n labels, counts = np.unique(confusion_dict[action], return_counts=True)\\n for label, count in zip(labels, counts):\\n matrix[all_actions.index(label), index] += count\\n print( \\\"action_accuracy\\\", np.mean(matches[\\\"action\\\"]))\\n print(\\\"attribute_accuracy\\\", np.mean(matches[\\\"attributes\\\"]))\\n return {\\n \\\"action_accuracy\\\": np.mean(matches[\\\"action\\\"]),\\n # \\\"action_perplexity\\\": np.exp(-1 * np.mean(matches[\\\"perplexity\\\"])),\\n \\\"attribute_accuracy\\\": np.mean(matches[\\\"attributes\\\"]),\\n \\\"confusion_matrix\\\": matrix\\n }\",\n \"def training_info(self):\\n pass\",\n \"def detectPatientFeedback(self):\\n\\n noise_prob = self.param[\\\"noise_probability\\\"]\\n resp_with_noise = self.patient.feedback\\n if noise_prob > 0:\\n max_noise_val = 10 # todo hardcoded for the noise\\n min_noise_val = -10\\n max_eval = 10\\n min_eval = 0\\n for v in self.eval_var:\\n max_eval = self.variables_universe[v][-1]\\n min_eval = self.variables_universe[v][0]\\n break # todo assuming that every eval var is the same\\n\\n actual_resp = self.patient.feedback\\n prob = self.r.uniform(0, 1)\\n if prob <= noise_prob:\\n noise = 0.0\\n if self.param[\\\"noise_type\\\"] == \\\"gaussian\\\":\\n noise = self.r.gauss(0, 2)\\n if noise > max_noise_val:\\n noise = max_noise_val\\n if noise < min_noise_val:\\n noise = min_noise_val\\n if self.param[\\\"noise_type\\\"] == \\\"inv_gaussian\\\":\\n noise = self.r.gauss(0, 2)\\n if noise > max_noise_val:\\n noise = max_noise_val\\n if noise < min_noise_val:\\n noise = min_noise_val\\n if noise < 0:\\n noise = (min_noise_val - max_noise_val) - noise\\n elif noise > 0:\\n noise = max_noise_val - noise\\n else:\\n if self.r.uniform(0, 1) > 0.5:\\n noise = max_noise_val\\n else:\\n noise = min_noise_val\\n if self.param[\\\"noise_type\\\"] == \\\"reversed_feedback\\\":\\n dist_from_max = max_eval - actual_resp\\n noise = min_eval + dist_from_max - actual_resp # i remove actual_resp to obtain the noise, otherwise it's already the new response, I add it again later\\n resp_with_noise = actual_resp + noise\\n if resp_with_noise > max_eval:\\n resp_with_noise = max_eval\\n if resp_with_noise < min_eval:\\n resp_with_noise = min_eval\\n return resp_with_noise\",\n \"def get_test_case_info():\\n m = NNMatrixTrainer()\\n return m.get_evaluations()\",\n \"def evaluate(self):\\n scores = []\\n scores.append(self.word_analogy())\\n print(\\\"Word Analogy (acc): \\\", scores[0])\\n scores.append(self.word_similarity())\\n print(\\\"Word Similarity (MSE): \\\", scores[1])\\n scores.append(self.concept_categorization())\\n print(\\\"Concept Categorization (purity): \\\", scores[2])\\n scores.append(self.sentiment_analysis())\\n print(\\\"Sentiment Analysis (acc): \\\", scores[3])\\n return scores\",\n \"def calculate_statistics(true_pos, false_pos, true_neg, false_neg, y_predict, Y_test):\\n precision = float(true_pos) / (true_pos + false_pos)\\n recall = float(true_pos) / (true_pos + false_neg)\\n F1 = float(2 * precision * recall) / (precision + recall)\\n #Get Mean Squared Error\\n MSE = mean_squared_error(y_predict.flatten(), Y_test.flatten())\\n\\n return precision, recall, F1, MSE\",\n \"def results():\\n \\n to_predict_list = request.form.to_dict() \\n to_predict_list = list(to_predict_list.values()) \\n to_predict_list = list(map(float, to_predict_list)) \\n result = ValuePredictor(to_predict_list) \\n if int(result)== 1: \\n prediction ='Run Martha, or you\\\\'re gonna get the sugar.'\\n else: \\n prediction ='Go ahead and have another donut Martha, you\\\\'re all good.' \\n return render_template(\\\"results.html\\\",\\n year=datetime.now().year,\\n prediction = prediction\\n )\",\n \"def getPredictions(self):\\n\\t\\tself.bestLabel = self.testingProbs.apply(lambda x: x.argmax(),1)\",\n \"def explain_importances(self):\\n\\n # Get the model from cache or disk based on the model_name in request\\n self._get_model_by_name()\\n\\n # Get the feature importances calculated in the calculate_metrics method\\n try:\\n self.response = self.model.importances\\n except AttributeError:\\n err = \\\"Feature importances are not available. Check that the execution argument calculate_importances \\\" +\\\\\\n \\\"is set to True, and that test_size > 0 or the Calculate_Metrics function has been executed.\\\"\\n raise Exception(err) \\n\\n # Add the model name to the response and rearrange columns\\n self.response.loc[:, \\\"model_name\\\"] = self.model.name\\n self.response = self.response[[\\\"model_name\\\", \\\"feature_name\\\", \\\"importance\\\"]]\\n\\n # Send the reponse table description to Qlik\\n self._send_table_description(\\\"importances\\\")\\n \\n # Debug information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n self._print_log(4)\\n \\n # Finally send the response\\n return self.response\",\n \"def do_analyse(args):\\n\\n pd.set_option('display.max_columns', None)\\n pd.set_option('display.width', None)\\n\\n score = 'score'\\n\\n # Read in the results, and add a boolean target column.\\n df = pd.read_csv(args.results, index_col=0)\\n df['target'] = df['verify_speaker'] == df['enrol_speaker']\\n\\n # Calculate ideal 0.01% threshold over the multi-session data.\\n nontarget_df = df.loc[df['target'] == False].sort_values(score, ascending=False)\\n nontarget_count = nontarget_df[score].count()\\n th_calc = nontarget_df.iloc[int(nontarget_count * (1 / 10000))][score]\\n\\n # Now filter the data so that we only consider mono-session enrolment and verification.\\n df = df.loc[df['verify_room'] == df['enrol_room']]\\n target_df = df.loc[df['target'] == True].sort_values(score, ascending=False)\\n nontarget_df = df.loc[df['target'] == False].sort_values(score, ascending=False)\\n target_count = target_df[score].count()\\n nontarget_count = nontarget_df[score].count()\\n\\n # Calculate FA/FR for the user-defined threshold.\\n th_user = args.th_user\\n fr_user = target_df.loc[target_df[score] < th_user][score].count()\\n fa_user = nontarget_df.loc[nontarget_df[score] > th_user][score].count()\\n frr_user = fr_user / target_count\\n far_user = fa_user / nontarget_count\\n label_user = 'User Threshold: th {:.4f}, FR {} ({:.3f}%), FA {} ({:.3f}%)'.format(th_user, fr_user, frr_user * 100,\\n fa_user, far_user * 100)\\n\\n # Calculate the FA/FR for the ideal threshold calculated from the multi-session data.\\n fr_calc = target_df.loc[target_df[score] < th_calc][score].count()\\n fa_calc = nontarget_df.loc[nontarget_df[score] > th_calc][score].count()\\n frr_calc = fr_calc / target_count\\n far_calc = fa_calc / nontarget_count\\n label_calc = 'Calc Threshold: th {:.4f}, FR {} ({:.3f}%), FA {} ({:.3f}%)'.format(th_calc, fr_calc, frr_calc * 100,\\n fa_calc, far_calc * 100)\\n\\n # Print the stats.\\n print('\\\\nTarget Stats:')\\n print(target_df[score].describe())\\n print('\\\\nNon-Target Stats:')\\n print(nontarget_df[score].describe())\\n print('\\\\nThresholds:')\\n print(label_user)\\n print(label_calc)\\n\\n # Paint the graphs.\\n paint_graph(score, 'verify_room', df, th_user, label_user, th_calc, label_calc)\\n paint_graph(score, 'enrol_room', df, th_user, label_user, th_calc, label_calc)\\n paint_graph(score, 'verify_speaker', df, th_user, label_user, th_calc, label_calc)\\n paint_graph(score, 'enrol_speaker', df, th_user, label_user, th_calc, label_calc)\",\n \"def get_evaluation_statistics(ind2label, label=0):\\n print(\\\"evaluation statistics for label\\\", label, ind2label[label])\\n tp, fp, fn = get_TP_FP_FN(cm, label)\\n print(\\\"True positives\\\", tp, \\\" , False positives\\\", fp, \\\" , False negatives\\\", fn)\\n precision = get_precision(cm, label)\\n print(\\\"Precision\\\", precision)\\n recall = get_recall(cm, label)\\n print(\\\"Recall\\\", recall)\\n f1 = get_F1_score(cm, label)\\n print(\\\"F1 score\\\", f1, end='\\\\n\\\\n')\",\n \"def prediction(name=None, message=''):\",\n \"def prediction():\\r\\n\\r\\n\\r\\n\\tpredictVal = []\\r\\n\\taccuracy = 0.0\\r\\n\\r\\n\\t# Calculate accuracy for each class in testData\\r\\n\\tfor item in testData:\\r\\n\\t\\tclass0Prediction = posProb / 100\\r\\n\\t\\tclass1Prediction = negProb / 100\\r\\n\\t\\t\\r\\n\\t\\t# Multiply the prior probablities for negative and positive reviews by their feature likelihoods \\r\\n\\t\\tfor word in item[2]:\\r\\n\\t\\t\\tclass0Prediction *= class0Dict[word]\\r\\n\\t\\t\\tclass1Prediction *= class1Dict[word]\\r\\n\\r\\n\\t\\t# Give every item in testData a predicted value\\r\\n\\t\\tif(class0Prediction > class1Prediction):\\r\\n\\t\\t\\tpredictVal.append('0')\\r\\n\\t\\telse:\\r\\n\\t\\t\\tpredictVal.append('1')\\r\\n\\r\\n\\tfor i in range(len(testData)):\\r\\n\\t\\tif(testData[i][1] == predictVal[i]):\\r\\n\\t\\t\\taccuracy += 1\\r\\n\\r\\n\\t\\t\\t\\r\\n\\taccuracy = 100 * (accuracy / len(testData))\\r\\n\\treturn(predictVal, accuracy)\",\n \"def stats(self):\",\n \"def get_metrics(model, test_data):\\n feats = test_data[:, :-1]\\n gold_labels = test_data[:, -1]\\n preds = model.predict_y(feats)\\n preds_mean = preds[0].flatten()\\n preds_var = preds[1]\\n #print preds_mean[:10]\\n #print gold_labels[:10]\\n mae = MAE(preds_mean, gold_labels)\\n rmse = np.sqrt(MSE(preds_mean, gold_labels))\\n prs = pearson(preds_mean, gold_labels)\\n nlpd = - np.mean(model.predict_density(feats, gold_labels[:, None]))\\n return mae, rmse, prs, nlpd\",\n \"def predict_intent():\\n\\n start_time = time()\\n request.json[\\\"request_id\\\"] = uuid.uuid4().hex\\n app.logger.info(f\\\"Request: {request.json['request_id']}. Processing request '/recommend': {request.json}\\\")\\n\\n # Prime filters\\n uniq_id = request.json.get('uniq_id')\\n if not uniq_id:\\n message = f'Request: {request.json[\\\"request_id\\\"]}. Missing uniq_id in request'\\n delta = time() - start_time\\n app.logger.error(f\\\"{message} Elapsed time: {delta} secs\\\")\\n return jsonify(message=message), 404\\n \\n\\n result, code = recommender.get_recommendation(uniq_id)\\n\\n delta = time() - start_time\\n app.logger.info(f\\\"Request: {request.json['request_id']}. Endpoint response '/recommend': {result}. Elapsed time: {delta} secs\\\")\\n return jsonify(result), code\",\n \"def detection_analysis(y_pred, y_true):\\n print(\\\"Precision: \\\", sm.precision_score(y_pred, y_true))\\n print(\\\"Recall: \\\", sm.recall_score(y_pred, y_true))\\n print(\\\"Accuracy: \\\", sm.accuracy_score(y_pred, y_true))\\n print(\\\"\\\\n\\\")\",\n \"def calculate_statistics(self, trace, **input_test):\\n inputs = self._clean_inputs(input_test)\\n mc_logp = self._logp(trace, **inputs)\\n mean_mse = self._mse(trace, **inputs)\\n mse2 = self._mse2(trace, **inputs)\\n mu, sd, zscore = self._alpha_stats(trace)\\n return {'logp': mc_logp,\\n 'mse': mean_mse,\\n 'mse2': mse2,\\n 'mu': mu,\\n 'sd': sd,\\n 'zscore': zscore}\",\n \"def getStatModelPerf(sm_df,roi_cols,covar_cols,outcome_col,signific_col,stat_model,mc_correction):\\n \\n outcome_col_val = outcome_col[0]\\n outcome_col_type = outcome_col[1]\\n model_name_check = False\\n if (stat_model.lower() == 'logit') and (outcome_col_type=='cat'):\\n model_name_check = True\\n elif (stat_model.lower() == 'ols') and (outcome_col_type=='continuous'):\\n model_name_check = True\\n else:\\n print('Outcome var type {} and stats model {} mismatch'.format(outcome_col_type,stat_model))\\n model_name_check = False\\n\\n if model_name_check:\\n scores_df = pd.DataFrame(columns= ['roi','coef','t_val','p_val','p_val_corr'])\\n coef_list = []\\n t_val_list = []\\n p_val_list = []\\n \\n signific_col_val = signific_col[0]\\n signific_col_type = signific_col[1]\\n\\n covar_val_list = []\\n covar_string = ''\\n if len(covar_cols) > 0:\\n for covar in covar_cols:\\n covar_val = covar[0]\\n covar_type = covar[1]\\n covar_val_list.append(covar_val)\\n if covar_type == 'cat':\\n covar_string = covar_string + ' + C({})'.format(covar_val)\\n elif covar_type == 'continuous':\\n covar_string = covar_string + ' + {}'.format(covar_val)\\n else:\\n print('unknown covar type {}'.format(covar))\\n\\n for roi in roi_cols:\\n if outcome_col_val != 'roi': #using roi as predictor (independent var)\\n signific_col_val = roi\\n input_cols = [outcome_col_val, roi] + covar_val_list\\n X = sm_df[input_cols]\\n formula_string = '{} ~ {}{}'.format(outcome_col_val,roi,covar_string)\\n stat_summary_name = signific_col_val\\n else: # using roi as outcome (dependent var)\\n input_cols = [roi, signific_col_val] + covar_val_list\\n X = sm_df[input_cols]\\n if signific_col_type == 'continuous':\\n formula_string = '{} ~ {}{}'.format(roi,signific_col_val,covar_string)\\n stat_summary_name = signific_col_val\\n elif signific_col_type == 'cat':\\n formula_string = '{} ~ C({}){}'.format(roi,signific_col_val,covar_string)\\n stat_summary_name = 'C({})[T.1]'.format(signific_col_val)\\n else:\\n print('unknown signficance independent variable type: {}'.format(signific_col))\\n \\n if stat_model.lower() == 'logit':\\n model = smf.logit(formula=formula_string,data=X)\\n\\n elif stat_model.lower() == 'ols':\\n model = smf.ols(formula=formula_string,data=X)\\n\\n else:\\n print('Unknown stats model {}'.format(stat_model))\\n\\n # Can't do fit_regularized for mass-univariate approach. \\n results = model.fit(disp=0) #default newton fails for smaller N (even smaller site N)\\n \\n coef = results.params[stat_summary_name] # just for ROI\\n t_val = results.tvalues[stat_summary_name] # just for ROI\\n p_val = results.pvalues[stat_summary_name] # just for ROI\\n coef_list.append(coef)\\n t_val_list.append(t_val)\\n p_val_list.append(p_val)\\n\\n #FDR Correction\\n p_val_corr_list = getCorrectedPValues(p_val_list,method=mc_correction)\\n print('Example statsmodel run:\\\\n {}'.format(formula_string))\\n\\n scores_df['roi'] = roi_cols\\n scores_df['coef'] = coef_list\\n scores_df['t_val'] = t_val_list\\n scores_df['p_val'] = p_val_list\\n scores_df['p_val_corr'] = p_val_corr_list\\n\\n return scores_df\",\n \"def prob():\\n # Get decision score for our example that came with the request\\n data = flask.request.json\\n #data[]\\n year = data[\\\"year\\\"]\\n Month = data[\\\"month\\\"]\\n Day = data[\\\"day\\\"]\\n species = data[\\\"species\\\"]\\n lat = data[\\\"lattitude\\\"]\\n lng = data[\\\"longitude\\\"]\\n\\n # find the closest trap to the clicked location\\n closest_trap = find_closest_trap(lat,lng,trap_stations)\\n trap = trap_stations[(trap_stations.trap == closest_trap) & (trap_stations.species == species)]\\n \\n # add 0 in front of month\\n month = \\\"0\\\"+ Month if len(Month) == 1 else Month\\n day = \\\"0\\\"+ Day if len(Day) == 1 else Day\\n date = year + \\\"-\\\" + month + \\\"-\\\" + day\\n trap[\\\"date\\\"] = date\\n\\n # convert datetime to str to match differing formats\\n weather.station = weather.station.astype(str)\\n trap.station = trap.station.astype(int).astype(str)\\n weather.date = weather.date.astype(str)\\n\\n # merge trap & weather dataframes\\n merged = trap.merge(weather,on=[\\\"date\\\",\\\"station\\\"],how=\\\"inner\\\")\\n\\n # convert back datetime\\n merged.date = pd.to_datetime(merged.date)\\n\\n # extract features from datetime and dewpoint\\n merged['month'] = month\\n merged['year'] = year\\n merged['dry'] = merged['dewpoint'].subtract(merged.wetbulb)\\n merged['week'] = merged['date'].map(lambda x: str(x.isocalendar()[1]))\\n # add risk feature if month is between june and sept (peak virus season)\\n merged['risk'] = 1 if (int(month) > 6) and (int(month) < 9) else 0\\n\\n # Reorder columns & Assign to X\\n merged2 = merged.drop([\\\"date\\\", \\\"year\\\", \\\"trap\\\",\\\"species\\\"],1)\\n cols = [\\\"month\\\",\\\"week\\\",\\\"longitude\\\",\\\"latitude\\\",\\\"tmax\\\",\\\"tmin\\\",\\\"tavg\\\",\\\"depart\\\",\\\"dewpoint\\\",\\n \\\"wetbulb\\\",\\\"heat\\\",\\\"cool\\\",\\\"sunrise\\\",\\\"sunset\\\",\\\"preciptotal\\\",\\\"resultspeed\\\",\\\"avgspeed\\\",\\n \\\"month\\\",\\\"dry\\\",\\\"risk\\\"]\\n X = merged2[cols]\\n \\n # predict probability\\n pred = loaded_model.predict_proba(X)\\n prob = round(pred[0][1]*100,2)\\n return flask.jsonify(prob)\",\n \"def get_perf(self) :\\n self.train()\\n\\n prediction = self.clf.predict(self.df_test.drop(columns = 'up')[:-1])\\n self.accuracy = accuracy_score(df_test['up'][length:].values, prediction)\\n tn, fp, fn, tp = confusion_matrix(df_test['up'][length:].values, prediction).ravel()\\n self.recall = tp/(tp+fn)\\n self.specificity = tn / (tn+fp)\\n\\n\\n self.df_true = self.df_true[self.length:]\\n\\n profit = 1\\n mini = 1\\n maxi = 1\\n self.df_true['close'] = self.df_true['close'].map(lambda x : np.exp(x))\\n for s in range(1,len(self.df_true)):\\n if prediction[x-1] == 1 :\\n result = ((self.df_true['close'].iloc[s] -self.df_true['close'].iloc[s-1]) / self.df_true['close'].iloc[s-1]) + 1\\n profit = profit * result\\n if result < mini :\\n mini = result\\n if maxi < result :\\n maxi = result\\n self.mini = mini\\n self.maxi = maxi\\n self.profit = profit\",\n \"def post(self):\\n # use parser and find the user's query\\n args = parser.parse_args()\\n title = args['title']\\n author = model.encode_author(args['author'])\\n text = args['text']\\n\\n X = model.vector_and_stack(title=title, text=text, author=author)\\n\\n prediction = model.predict(X)\\n\\n # Output either 'Negative' or 'Positive' along with the score\\n if round(prediction[0]) == 0:\\n pred_text = 'Reliable News'\\n else:\\n pred_text = 'Unreliable News'\\n\\n # round the predict proba value and set to new variable\\n confidence = round(prediction[0], 3)\\n\\n # create JSON object\\n output = {'prediction': pred_text, 'fake_rate': confidence}\\n\\n return output, 200\",\n \"def get_scores(model, X, y):\\n y_hat = model.predict(X)\\n pred = pd.get_dummies(y_hat.argmax(axis=1))\\n acc = accuracy_score(y,pred)\\n rec = recall_score(y,pred, average='weighted')\\n pre = precision_score(y,pred, average='weighted')\\n f1 = f1_score(y,pred, average='weighted')\\n print(f\\\"ACC: {acc}\\\")\\n print(f\\\"REC: {rec}\\\")\\n print(f\\\"PRE: {pre}\\\")\\n print(f\\\"F1: {f1}\\\")\\n return acc, rec, pre, f1, pred\",\n \"def __dataset_statistics(self) -> None:\\n # Print the expressions\\n print(f'Available expressions: {self.idx_to_expression.values()}')\\n \\n # Print dataset cardinalities\\n stats_table = PrettyTable()\\n stats_table.field_names = ['Train samples', 'PublicTest samples', 'PrivateTest samples', 'Number of expressions']\\n stats_table.add_row([len(self.db[self.db['Usage'] == 'Training']), len(self.db[self.db['Usage'] == 'PublicTest']), len(self.db[self.db['Usage'] == 'PrivateTest']), len(self.db.expression.unique().tolist())])\\n print(stats_table)\\n \\n def eval_class_cardinality(expressions: list, train: str) -> [list, list]:\\n cardinality = [len(self.db[(self.db['expression']==expr) & (self.db['Usage']==train)]) for expr in expressions]\\n frac = [len(self.db[(self.db['expression']==expr) & (self.db['Usage']==train)])/len(self.db[self.db['Usage']==train]) for expr in expressions]\\n return cardinality, frac\\n\\n expressions = self.db.expression.unique()\\n expressions.sort()\\n \\n # Print expressions statistics --> Training \\n stats_table = PrettyTable()\\n stats_table.title = 'Training Samples'\\n stats_table.field_names = [self.idx_to_expression[label] for label in expressions]\\n cardinality, frac = eval_class_cardinality(expressions=expressions, train='Training')\\n stats_table.add_row(cardinality)\\n stats_table.add_row(frac)\\n\\n # Print expressions statistics --> Validation \\n stats_table2 = PrettyTable()\\n stats_table2.title = 'Validation Samples'\\n stats_table2.field_names = [self.idx_to_expression[label] for label in expressions]\\n cardinality, frac = eval_class_cardinality(expressions=expressions, train='PublicTest')\\n stats_table2.add_row(cardinality)\\n stats_table2.add_row(frac)\\n\\n # Print expressions statistics --> Test \\n stats_table3 = PrettyTable()\\n stats_table3.title = 'Test Samples'\\n stats_table3.field_names = [self.idx_to_expression[label] for label in expressions]\\n cardinality, frac = eval_class_cardinality(expressions=expressions, train='PrivateTest')\\n stats_table3.add_row(cardinality)\\n stats_table3.add_row(frac)\\n \\n print(stats_table)\\n print(stats_table2)\\n print(stats_table3)\",\n \"def results():\\n # Get data from form that came with the request\\n data = flask.request.form\\n #has attributes og_abstract similarity keywords num_results\\n result = filter_top5_recommender([data['og_abstract']], JAS_tfidf, JAS_nmf, JAS_vecs, \\n data['similarity'], keyword=data['keyword'], \\n num_results=data['num_results'])\\n \\n # Put the result in a nice dict so we can send it as json\\n \\n #return flask.render_template(\\\"results.html\\\",result = result)\\n\\n \\n return flask.render_template(\\\"results.html\\\",result = result) #, plot_url = plot_url)\\n\\t#return '<img src=\\\"data:image/png;base64,{}\\\">'.format(plot_url)\",\n \"def predict_tweet():\\n input_text = request.form['input_text']\\n tokenized_tweet = tokenizer(input_text)\\n logits = model.predict({k: np.array(tokenized_tweet[k])[None] for k in input_names})[0]\\n scores = softmax(logits)\\n pred = round(100 * scores.flatten()[1], 2)\\n # return render_template('index.html', prediction=pred)\\n\\n exp = LIME_explainer.explain_instance(input_text, predictor, num_features=len(input_text.split()),\\n top_labels=1, num_samples=100).as_html()\\n\\n if pred >= 50:\\n return render_template('index.html', exp=exp, prediction=f'Emergency; confidence ({pred}%)')\\n else:\\n return render_template('index.html', exp=exp, prediction=f'Non-emergency; confidence ({100 - pred}%)')\",\n \"def _get_prediction(self):\\n raise NotImplementedError\",\n \"def predict(self, expression=None):\\r\\n confidence_score = 0\\r\\n intent = None\\r\\n\\r\\n #intent, confidence_score = self.ncf(expression)\\r\\n intent, confidence_score = \\\"Test\\\",100\\r\\n l_msg = \\\"model found : {} and score is {}\\\".format(intent, confidence_score)\\r\\n log.debug(l_msg)\\r\\n\\r\\n return intent, confidence_score\",\n \"def model_summary():\\n print(\\\"\\\\n\\\")\\n print(\\\"=\\\" * 30 + \\\"Model Structure\\\" + \\\"=\\\" * 30)\\n model_vars = tf.trainable_variables()\\n slim.model_analyzer.analyze_vars(model_vars, print_info=True)\\n print(\\\"=\\\" * 60 + \\\"\\\\n\\\")\",\n \"def log_values_and_advantages(self, state):\\n advantage = self.model.get_advantage(to_variable(to_cuda(state, self.gpu_device)))\\n self.writer.add_histogram(tag='predictions/advantages',\\n values=advantage.view(-1),\\n global_step=self.total_steps,\\n bins='auto')\\n value = self.model.get_value(to_variable(to_cuda(state, self.gpu_device)))\\n self.writer.add_histogram(tag='predictions/values',\\n values=value.view(-1),\\n global_step=self.total_steps,\\n bins='auto')\",\n \"def check_model_performances(X,Y, model,show=False):\\n #model.fit(X, Y)\\n predictions = model.predict(X)\\n \\n predictions = predictions#.reshape(-1,1)\\n \\n # ######## Computes MSE ####### \\n MSE = mean_squared_error(Y, predictions)\\n print(f'\\\\nMSE : {MSE}')\\n \\n # ######## Computes R2 ####### \\n R2 = r2_score(Y, predictions)\\n print(f'R2 : {R2}')\\n \\n # ######## Plot Model predictions vs. target ####### \\n if show:\\n fig = go.Figure()\\n \\n fig.add_trace(go.Scatter(y=Y,\\n mode='lines',\\n name='target'))\\n fig.add_trace(go.Scatter(y=predictions\\n ,\\n mode='lines',\\n name='predictions'))\\n \\n fig.show()\",\n \"def show_prediction_info(x: np.ndarray, y_true: np.ndarray, y_predicted: dict, folder: str = 'results',\\n filename: str = 'voice', extension: str = 'xlsx', sheet_name: str = 'results') -> None:\\n hcv0 = metrics.homogeneity_completeness_v_measure(y_true, y_predicted['IsoMAP'])\\n hcv1 = metrics.homogeneity_completeness_v_measure(y_true, y_predicted['LLE'])\\n hcv2 = metrics.homogeneity_completeness_v_measure(y_true, y_predicted['SE'])\\n hcv3 = metrics.homogeneity_completeness_v_measure(y_true, y_predicted['TSNE'])\\n\\n # Create results dictionary.\\n results = {'Adjusted Random Index': [metrics.adjusted_rand_score(y_true, y_predicted['IsoMAP']),\\n metrics.adjusted_rand_score(y_true, y_predicted['LLE']),\\n metrics.adjusted_rand_score(y_true, y_predicted['SE']),\\n metrics.adjusted_rand_score(y_true, y_predicted['TSNE'])],\\n 'Homogeneity': [hcv0[0], hcv1[0], hcv2[0], hcv3[0]],\\n 'Completeness': [hcv0[1], hcv1[1], hcv2[1], hcv3[1]],\\n 'V Measure': [hcv0[2], hcv1[2], hcv2[2], hcv3[2]],\\n 'Silhouette Coefficient': [metrics.silhouette_score(x, y_predicted['IsoMAP']),\\n metrics.silhouette_score(x, y_predicted['LLE']),\\n metrics.silhouette_score(x, y_predicted['SE']),\\n metrics.silhouette_score(x, y_predicted['TSNE'])],\\n 'Time': [y_predicted['t1'], y_predicted['t2'], y_predicted['t3'], y_predicted['t4']]}\\n\\n # Log results.\\n logger.log('Model\\\\'s Results:')\\n for key, values in results.items():\\n for value in values:\\n logger.log('{text}: {number:.{points}g}'.format(text=key, number=value, points=4))\\n\\n # Create excel if save is True.\\n helpers.utils.create_excel(results, folder, filename, extension, sheet_name)\",\n \"def get_score_summary(y_true, y_pred):\\n \\n y_true_labeled = get_labeled_mask(y_true) \\n y_pred_labeled = get_labeled_mask(y_pred) \\n \\n params = get_iou(y_true_labeled, y_pred_labeled)\\n iou = params['iou']\\n assigned = params['assigned']\\n true_not_assigned = params['true_not_assigned']\\n pred_not_assigned = params['pred_not_assigned']\\n true_labels = params['true_labels']\\n pred_labels = params['pred_labels']\\n n_true_labels = len(true_labels)\\n n_pred_labels = len(pred_labels)\\n\\n summary = []\\n for i,threshold in enumerate(np.arange(0.5, 1.0, 0.05)):\\n tp = np.sum(iou > threshold)\\n fn = n_true_labels - tp\\n fp = n_pred_labels - tp\\n if (tp+fp+fn)>0: \\n prec = tp/(tp+fp+fn)\\n else: \\n prec = 0\\n summary.append([threshold, prec, tp, fp, fn])\\n\\n summary = np.array(summary)\\n score = np.mean(summary[:,1]) # Final score.\\n params_dict = {'summary': summary, 'iou': iou, 'assigned': assigned, \\n 'true_not_assigned': true_not_assigned, \\n 'pred_not_assigned': pred_not_assigned, 'true_labels': true_labels,\\n 'pred_labels': pred_labels, 'y_true_labeled': y_true_labeled,\\n 'y_pred_labeled': y_pred_labeled}\\n \\n return score, params_dict\",\n \"def summary_info(self):\\n return [('model_architecture', self.model_architecture),\\n ('input_size', self.input_size),\\n ('output_size', self.output_size),\\n ('hidden_layers', self.hidden_layers),\\n ('learn_rate', self.learn_rate),\\n ('drop_p', self.drop_p),\\n ('current_epoch', self.model.current_epoch)]\",\n \"def evaluate(self, params):\\n model = self.train(params)\\n Y_pred = model.predict(self.X_test)\\n\\n if self.dataset_type == PROBLEM.CLASSIFICATION:\\n return {\\n 'score': accuracy_score(self.Y_test, Y_pred),\\n 'matrix': confusion_matrix(self.Y_test, Y_pred).tolist(),\\n 'report': classification_report(self.Y_test, Y_pred,\\n target_names=self.labels,\\n zero_division=1)\\n }\\n else:\\n return {\\n 'max_error': max_error(self.Y_test, Y_pred),\\n 'mae': mean_absolute_error(self.Y_test, Y_pred),\\n 'mse': mean_squared_error(self.Y_test, Y_pred)\\n }\",\n \"def computeSoftwareStatsModels(df,data_label,roi_cols,covar_cols,outcome_col,signific_col,stat_model,mc_correction):\\n software_list = df[data_label].unique()\\n print('Running {} mass-univariate {} statsmodels on {} {}'.format(len(roi_cols), stat_model, len(software_list),data_label))\\n \\n # index results on ROI names\\n scores_concat_df = pd.DataFrame()\\n for pipe in software_list:\\n sm_df = df[df[data_label]==pipe]\\n print('{} {}'.format(data_label, pipe))\\n scores_df = getStatModelPerf2(sm_df,roi_cols,covar_cols,outcome_col,signific_col,stat_model,mc_correction)\\n scores_df[data_label] = np.tile(pipe,len(scores_df))\\n scores_concat_df = scores_concat_df.append(scores_df)\\n print('Top 10 significant regions:\\\\n {}'.format(scores_df.sort_values(by=['p_val']).head(10)))\\n\\n return scores_concat_df\",\n \"def evaluate_model(model, X_test, Y_test, category_names):\\n# Print out Precision , recall F1_score and support for each column using classification_report function\\n y_pred_test = model.predict(X_test)\\n print(classification_report(Y_test, y_pred_test, target_names=category_names))\",\n \"def get_prediction(data):\\n # load cannabis data\\n strains = pd.read_csv(URL)\\n # Combine the Effects and Flavors in one column\\n strains['Criteria'] = strains['Effects'] + ',' + strains['Flavor']\\n\\n # Train model on dtm\\n nn = NearestNeighbors(n_neighbors=5, algorithm='ball_tree')\\n nn.fit(dtm)\\n\\n # load request data\\n # r = data.args\\n entry = [v for k,v in data.items()][1:]\\n #print(entry)\\n # transform\\n new = tf.transform(entry)\\n #print(new)\\n results = nn.kneighbors(new.todense())\\n #print(results)\\n # extract top 5 results\\n output = [strains['Strain'][results[1][0][i]] for i in range(5)]\\n\\n return output\",\n \"def compute(self) -> Union[Tensor, Tuple[Tensor, Tensor]]:\\n preds_dataloader = _get_dataloader(\\n input_ids=dim_zero_cat(self.preds_input_ids),\\n attention_mask=dim_zero_cat(self.preds_attention_mask),\\n idf=self.idf,\\n batch_size=self.batch_size,\\n num_workers=self.num_threads,\\n )\\n target_dataloader = _get_dataloader(\\n input_ids=dim_zero_cat(self.target_input_ids),\\n attention_mask=dim_zero_cat(self.target_attention_mask),\\n idf=self.idf,\\n batch_size=self.batch_size,\\n num_workers=self.num_threads,\\n )\\n\\n info_lm_score = _infolm_compute(\\n self.model,\\n preds_dataloader,\\n target_dataloader,\\n self.temperature,\\n self.idf,\\n self.information_measure_cls,\\n self.special_tokens_map,\\n self.verbose,\\n )\\n\\n if self.return_sentence_level_score:\\n return info_lm_score.mean(), info_lm_score\\n\\n return info_lm_score.mean()\",\n \"def _get_result(self):\\n try:\\n # get test data\\n test_id = self._feature_processor.test_data_id\\n test_feature = self._feature_processor.test_data_feature\\n test_target = self._feature_processor.test_data_target\\n\\n # process data\\n test_feature = test_feature.astype(\\\"float64\\\", errors='ignore')\\n\\n # predict\\n predict_res = self._model.predict(test_feature)\\n predict_res_df = pd.DataFrame(predict_res, columns=[PredictConstance.PRE])\\n proba_res = self._model.predict_proba(test_feature)\\n proba_res_df = pd.DataFrame([str(x) for x in proba_res],\\n columns=[PredictConstance.PROBA])\\n\\n res = [test_id, predict_res_df, proba_res_df]\\n # get model score\\n if test_target is not None:\\n res.append(test_target)\\n model_auc = pre_utils.PredictUtils.get_roc_score(test_target, proba_res)\\n model_score = pre_utils.PredictUtils.get_model_score(test_target, predict_res)\\n model_score.update(model_auc)\\n with open(os.path.join(self._result_path, PredictConstance.TEST_SCORE), \\\"w\\\") as ftp:\\n ftp.write(str(model_score))\\n\\n # joint predict result\\n self._joint_predict_result(res)\\n\\n return True\\n except Exception as err:\\n self.managerlogger.logger.error(\\\"base ml get result error: %s\\\" % err)\\n self.errorlogger.logger.error(\\\"base ml get result error:\\\\n %s\\\" % traceback.format_exc())\\n return False\",\n \"def get_statistics(self, scores, labels):\\n activations = (scores > 0.5).float()\\n num_pos = labels.data.sum(0).squeeze().cpu()\\n tp = (activations * labels.eq(1).float()).sum(0).squeeze().data.cpu()\\n fp = (activations * labels.eq(0).float()).sum(0).squeeze().data.cpu()\\n return (tp, fp, num_pos)\",\n \"def __get_statistics(actual, predicted):\\n\\n precision = precision_score(actual, predicted, average='binary')\\n recall = recall_score(actual, predicted, average='binary')\\n return (round(precision, 2), round(recall, 2))\",\n \"def evaluate_prediction(self):\\n\\n # ratio_train = self.evaluate_data(self.train_x, self.train_y)\\n ratio_test = self.evaluate_data(self.test_x, self.test_y)\\n\\n print(\\\"\\\\n*NAIVE BAYES:\\\")\\n # print(\\\"Test1: {}%\\\".format(ratio_dev*100))\\n print(\\\"Test: {} %\\\".format(ratio_test*100))\",\n \"def run_analyses(y_predict_train, y_train, y_predict, y_test):\\n # calculate metrics\\n _, training_error = output_error(y_predict_train, y_train)\\n (precision, recall, f1, _), testing_error = output_error(y_predict, y_test)\\n \\n # print out metrics\\n print 'Average Precision:', np.average(precision)\\n print 'Average Recall:', np.average(recall)\\n print 'Average F1:', np.average(f1)\\n print 'Training Error:', training_error\\n print 'Testing Error:', testing_error\",\n \"def review_model(model): \\n \\n diagnose_model(model)\\n \\n plot_param_coef(model)\\n \\n plot_p_values(model)\\n \\n return\",\n \"def evaluate(self, gameState, action):\\n features = self.getFeatures(gameState, action)\\n ####print \\\"features \\\", features\\n weights = self.getWeights(gameState, action)\\n ####print \\\"weights \\\", weights\\n return features * weights\",\n \"def get_statistics(session, model, x_data, y_data, batch_size):\\n\\n losses = []\\n\\n labels = []\\n predicted_labels = []\\n\\n batches_generator = utilities.get_batches_generator(x_data, y_data, batch_size)\\n data_size = x_data.shape[0]\\n\\n for _ in range(data_size // batch_size):\\n\\n x_batch, y_batch = next(batches_generator)\\n\\n feed_dictionary = {\\n model.x_placeholder: x_batch,\\n model.y_placeholder: y_batch\\n }\\n\\n batch_loss, batch_prediction = session.run([model.loss_op, model.prediction], feed_dictionary)\\n\\n losses.append(batch_loss)\\n\\n labels_batch = np.argmax(y_batch, axis=1).flatten()\\n labels.extend(labels_batch)\\n\\n predicted_batch_labels = np.argmax(batch_prediction, axis=1).flatten()\\n predicted_labels.extend(predicted_batch_labels)\\n\\n loss = np.mean(losses)\\n accuracy = np.mean((np.array(labels) == np.array(predicted_labels)).astype(np.float32))\\n\\n return loss, accuracy\",\n \"def getStatus(self, includeVotes=False):\\n #1) Calculate beliefs\\n #TODO want to cache beliefs and make incremental updates\\n print \\\"Calculating beliefs\\\"\\n beliefs = self.calculateBeliefs()\\n\\n #2) Get POMDP data for each question\\n print \\\"Getting POMDP decision for each question\\\"\\n out = {}\\n for (q_id,belief) in beliefs.iteritems():\\n print \\\"q_id\\\", q_id\\n\\n #get POMDP action reward pairs\\n action_rewards = {str(a):r for a,r in self.policy.get_action_rewards(belief).iteritems()}\\n\\n #which action has best expected reward\\n best_action, best_expected_reward = self.policy.get_best_action(belief)\\n\\n #get best action as readable string (submit-true, etc.)\\n best_action_str = self.pomdp_var.actions[best_action]\\n\\n out[q_id] = dict(best_action=best_action,\\n best_expected_reward=best_expected_reward,\\n best_action_str=best_action_str,\\n action_rewards=action_rewards)\\n\\n #optional get all votes on this question as JSON\\n #XXX NOTE SLOW! Has to access database\\n if includeVotes:\\n answers = self.getQuestionCompletedAnswers(q_id)\\n votes = []\\n for answer in answers:\\n q_name = answer.question.name\\n w_id = str(answer.worker.id)\\n w_platform_id = str(answer.worker.platform_id)\\n w_skill = answer.worker.inference_results['EM']['skill']\\n value = answer.value\\n vote = {\\\"worker_id\\\": w_id, \\\"worker_platform_id\\\":w_platform_id, \\\"est_skill\\\":w_skill, \\\"value\\\":value}\\n votes.append(vote)\\n\\n out[q_id]['votes'] = votes\\n\\n return out\",\n \"def get_model_info(y_test, model_name, model_parameters, model_preprocessing, sequence_origin, primers_origin,\\n taxonomy_level, selected_primer, test_size, logger) -> int:\\n\\n # Global information on the model\\n logger.log(title='Parameter information for {}'.format(model_name))\\n # Data Origins\\n logger.log(subtitle='Data Origins')\\n logger.log(text='Sequence origin: {}'.format(sequence_origin))\\n logger.log(text='Primers origin: {}'.format(primers_origin))\\n # Chosen levels for classification\\n logger.log(subtitle='Chosen HyperVariable Region and Taxonomy Rank')\\n logger.log(text='HyperVariable Region: {}'.format(str(selected_primer)))\\n logger.log(text='Taxonomy Rank: {}'.format(str(taxonomy_level)))\\n # Applied Preprocessing\\n logger.log(subtitle='Preprocessing')\\n logger.log(text='Preprocessing description: ' + model_preprocessing)\\n # Model parameters\\n logger.log(subtitle='Model parameters')\\n logger.log(text='Parameter dict: {}'.format(str(model_parameters)))\\n logger.log(text='Size of test set: {}'.format(len(y_test)))\\n logger.log(text='Part of test size compared to total: {}'.format(test_size))\\n\\n return len(y_test)\",\n \"def model_qua_launch(self, dict):\\r\\n list_result = []\\r\\n if \\\"SVR\\\" in dict:\\r\\n SVR = dict[\\\"SVR\\\"]\\r\\n if SVR[\\\"Auto\\\"]:\\r\\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\\\"Auto\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"SVR Result Auto\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = SVR_b(self.ml_data.feature, self.ml_data.target, SVR[\\\"Auto\\\"], [SVR[\\\"C\\\"],\\r\\n SVR[\\\"Kernel\\\"],\\r\\n SVR[\\\"Degree\\\"]])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"SVR Result\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n if \\\"LR\\\" in dict:\\r\\n LR = dict[\\\"LR\\\"]\\r\\n if LR[\\\"Auto\\\"]:\\r\\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\\\"Auto\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Linear Regression Result Auto\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = regression_lin(self.ml_data.feature, self.ml_data.target, LR[\\\"Auto\\\"], LR[\\\"fit_intercept\\\"], LR[\\\"normalize\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Linear Regression Result\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n if \\\"RT\\\" in dict:\\r\\n RT = dict[\\\"RT\\\"]\\r\\n if RT[\\\"Auto\\\"]:\\r\\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\\\"Auto\\\"])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Regression Tree Result Auto\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = RegTree(self.ml_data.feature, self.ml_data.target, RT[\\\"Auto\\\"], [RT[\\\"Criterion\\\"],\\r\\n RT[\\\"Min_Samples_Split\\\"],\\r\\n RT[\\\"Min_Samples_Leaf\\\"]])\\r\\n model, score, graph, time = result\\r\\n result_win = Window_Quant()\\r\\n result_win.setWindowTitle(\\\"Regression Tree Result\\\")\\r\\n result_win.setData(model, score, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n if \\\"KNN\\\" in dict:\\r\\n KNN = dict[\\\"KNN\\\"]\\r\\n if KNN[\\\"Auto\\\"]:\\r\\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\\\"Auto\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"KNN Result Auto\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = knn_class(self.ml_data.feature, self.ml_data.target, KNN[\\\"Auto\\\"],\\r\\n [KNN[\\\"leaf_size\\\"], KNN[\\\"n_neighbors\\\"], KNN[\\\"p\\\"], KNN[\\\"metric\\\"]])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n self.model_quali.close()\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"KNN Result\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n if \\\"LogiR\\\" in dict:\\r\\n LogiR = dict[\\\"LogiR\\\"]\\r\\n if LogiR[\\\"Auto\\\"]:\\r\\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\\\"Auto\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Logistic Regression Result Auto\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n else:\\r\\n result = LogReg(self.ml_data.feature, self.ml_data.target, LogiR[\\\"Auto\\\"], [LogiR['C'],\\r\\n LogiR['penalty']])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Logistic Regression Result\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n if \\\"DTC\\\" in dict:\\r\\n DTC = dict[\\\"DTC\\\"]\\r\\n if DTC[\\\"Auto\\\"]:\\r\\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\\\"Auto\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Tree Decision Classification Auto\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n else:\\r\\n result = arbre_clas(self.ml_data.feature, self.ml_data.target, DTC[\\\"Auto\\\"],DTC[\\\"max_leaf_nodes\\\"],DTC[\\\"max_depth\\\"],DTC[\\\"min_samples_split\\\"])\\r\\n model, matrix, dict_cr, graph, time = result\\r\\n result_win = Window()\\r\\n result_win.setWindowTitle(\\\"Tree Decision Classification\\\")\\r\\n result_win.setData(model, matrix, dict_cr, graph, time)\\r\\n list_result.append(result_win)\\r\\n\\r\\n\\r\\n self.close()\\r\\n self.trigger_result.emit(list_result)\",\n \"def print_inference_result(self):\\n if (\\n self.params.model_str == 'optfixedsig'\\n or self.params.model_str == 'opt'\\n or self.params.model_str == 'fixedparam'\\n ):\\n print('*ls pt est = ' + str(self.sample_list[0].ls) + '.')\\n print('*alpha pt est = ' + str(self.sample_list[0].alpha) + '.')\\n print('*sigma pt est = ' + str(self.sample_list[0].sigma) + '.')\\n elif self.params.model_str == 'samp' or self.params.model_str == 'sampfixedsig':\\n ls_arr = np.array([ns.ls for ns in self.sample_list])\\n alpha_arr = np.array([ns.alpha for ns in self.sample_list])\\n sigma_arr = np.array([ns.sigma for ns in self.sample_list])\\n print('*ls mean = ' + str(ls_arr.mean()) + '.')\\n print('*ls std = ' + str(ls_arr.std()) + '.')\\n print('*alpha mean = ' + str(alpha_arr.mean()) + '.')\\n print('*alpha std = ' + str(alpha_arr.std()) + '.')\\n print('*sigma mean = ' + str(sigma_arr.mean()) + '.')\\n print('*sigma std = ' + str(sigma_arr.std()) + '.')\\n print('-----')\",\n \"def predict_only(self):\",\n \"def get_perf(self) :\\n self.train()\\n self.df_true = self.df_true[self.length:]\\n self.accuracy , self.recall, self.specificity, self.profit, self.min , self.max = get_accuracy_LSTM(self.df_test, self.df_true,self.model, self.length)\",\n \"def predictInsurance():\\r\\n logging.info(request.form.values())\\r\\n int_features = [x for x in request.form.values()]\\r\\n name = int_features[0]\\r\\n logging.info(int_features)\\r\\n age = int_features[1]\\r\\n bmi = int_features[2]\\r\\n children= int_features[3]\\r\\n sex = binarizeVariable(int_features[4])\\r\\n smoker = binarizeVariable(int_features[5])\\r\\n final_features = [np.array([age, bmi, children,sex,smoker])]\\r\\n prediction = model.predict(scalar.transform(final_features))\\r\\n\\r\\n output = round(prediction[0], 2)\\r\\n logging.info(output)\\r\\n return render_template(\\\"insurance_premium_predictor_home.html\\\", prediction_text='Hi {name}, your Insurance Premium could be Rs: {output}'.format(name= name, output=output))\",\n \"def statistics_on_test(self, predicted_results, result):\\n # Print confusion matrix and mean average precision score\\n predicted_results_binary = self.predicted_results_to_binary(predicted_results)\\n print(\\\"\\\\nConfusion matrix : \\\")\\n print(confusion_matrix(result, predicted_results_binary))\\n print(\\\"\\\\nAverage precision score : \\\", average_precision_score(result, predicted_results_binary))\",\n \"def run(self):\\r\\n history = self.extracter.load_user_history()\\r\\n self.plot_history(history)\\r\\n \\r\\n pp_history = self.analyser.preprocess_history(history)\\r\\n part_worths, attribute_importance, relative_importance = self.analyser.conjoint_analysis(pp_history)\\r\\n self.plot_analysis(part_worths, relative_importance)\\r\\n \\r\\n return history, pp_history, part_worths, relative_importance\",\n \"def inspect_pair_with_model(model,sent1,sent2,expected_label):\\n dp = dataPoint((sent1,sent2),expected_label)\\n pred = model.predict(np.array(dp.get_features()).reshape(1,-1))\\n if (pred == expected_label):\\n print(\\\"succesfully predicted\\\")\\n else:\\n print(\\\"The model predicted \\\" + str(pred) + \\\" while we actually wanted \\\" + str(expected_label))\\n print(\\\"The features for these sentences are: \\\")\\n inspect_model(model)\",\n \"def predict():\\n import trace\\n trace.predict()\",\n \"def _inference_results(self, labels, predicts, sample_weights, sample_ids, predicts_per_coordinate):\\n predicts = predicts.flatten()\\n if labels is not None:\\n labels = labels.flatten()\\n if predicts_per_coordinate is not None:\\n predicts_per_coordinate = predicts_per_coordinate.flatten()\\n batch_size = predicts.size\\n params = self.schema_params\\n records = []\\n for i in range(batch_size):\\n record = {params.prediction_score_column_name: predicts[i], params.weight_column_name: sample_weights[i],\\n params.uid_column_name: sample_ids[i]}\\n if labels is not None:\\n record[params.label_column_name] = labels[i]\\n if predicts_per_coordinate is not None:\\n record[params.prediction_score_per_coordinate_column_name] = predicts_per_coordinate[i]\\n records.append(record)\\n return records\",\n \"def summary(self):\\n\\n print(\\\"input label:\\\", self.__input_label)\\n print(\\\"target label:\\\", self.__target_label)\\n print(\\\"denoising label:\\\", self.denoising_label)\\n print(\\\"contains a successful DE:\\\", self.is_successful())\",\n \"def get_perfect_information(self):\\n raise NotImplementedError\",\n \"def _compute_experiment_statistics(self):\\n pass\",\n \"def evaluate(model, df_result, label='test'):\\n\\n y_true = df_result['RUL']\\n y_hat = df_result['y_hat']\\n df_result['breakdown'].replace(0, False, inplace=True) # rsf only takes true or false\\n df_result['breakdown'].replace(1, True, inplace=True) # rsf only takes true or false\\n\\n mse = mean_squared_error(y_true, y_hat)\\n rmse = np.sqrt(mse)\\n variance = r2_score(y_true, y_hat)\\n\\n # the concordance index (CI) is interested on the order of the predictions, not the predictions themselves\\n # CI can only be measured between individual samples where a censoring or failure event occurred\\n # https://medium.com/analytics-vidhya/concordance-index-72298c11eac7#:~:text=The%20concordance%20index%20or%20c,this%20definition%20mean%20in%20practice\\n df_result_grouped = df_result.groupby('unit num').last()\\n breakdown = df_result_grouped['breakdown']\\n y_true = df_result_grouped['RUL']\\n y_hat = df_result_grouped['y_hat']\\n ci_sk = ci_scikit(breakdown, y_true, y_hat)[0]\\n score = nasaScore(y_true, y_hat) # score should be based on the last instance\\n # print(f'Number of concordant pairs (scikit-survival): {ci_scikit(breakdown, y_true, y_hat)[1]}')\\n # print(f'Number of discordant pairs (scikit-survival): {ci_scikit(breakdown, y_true, y_hat)[2]}')\\n # print(f'Number of pairs having tied estimated risks (scikit-survival): {ci_scikit(breakdown, y_true, y_hat)[3]}')\\n # print(f'Number of comparable pairs sharing the same time (scikit-survival): {ci_scikit(breakdown, y_true, y_hat)[4]}')\\n print('{} set RMSE:{:.2f}, Score:{:.2f}, CI(scikit):{:.4f}, R2:{:.2f}'.format(label, rmse, score, ci_sk, variance))\\n result = [model, label, rmse, score, ci_sk, variance]\\n return result\",\n \"def calculate_information_criterion(R, u_t, R_test, u_t_test, inverse_transform, inverse_transform_test, algo, x_nodes):\\n model_list = generate_models(R, u_t, inverse_transform, algo) # iterates hyperparameters to generate models\\n evidence_list = calculate_bic(R_test, u_t_test, inverse_transform_test, model_list, x_nodes)\\n return evidence_list, model_list\",\n \"def predict():\\n to_predict = np.zeros(5).reshape(1, 5)\\n features = ['is_male', 'num_interactions_with_cust_service', 'late_on_payment', 'age', 'years_in_contract']\\n for i, feat in enumerate(features):\\n if request.args.get(feat) is not None:\\n to_predict[0][i] = request.args.get(feat)\\n\\n response = clf2.predict(to_predict)\\n\\n if response:\\n return \\\"The customer is likely to churn\\\"\\n else:\\n return \\\"He is a loyal customer\\\"\",\n \"def evaluate_model(model,test_inputs,test_labels,model_mode):\\n\\n if model_mode == \\\"classification\\\":\\n y_pred = model.predict(test_inputs)\\n print(\\\"Accuracy score: \\\", accuracy_score(test_labels, y_pred))\\n #print(\\\"F1 score: \\\", f1_score(test_labels,y_pred, average='weighted'))\\n\\n conf_mx = confusion_matrix(test_labels, y_pred)\\n #print(conf_mx)\\n plt.matshow(conf_mx, cmap = plt.cm.jet)\\n plt.show()\\n\\n if model_mode == \\\"regression\\\":\\n y_pred = model.predict(test_inputs)\\n print(\\\"Mean absolute error: \\\", mean_absolute_error(test_labels, y_pred))\",\n \"def calculate_statistics(self, data_streams):\\n targets = data_streams[self.key_targets].data.cpu().numpy()\\n #print(\\\"Targets :\\\", targets)\\n\\n if self.use_prediction_distributions:\\n # Get indices of the max log-probability.\\n preds = data_streams[self.key_predictions].max(1)[1].data.cpu().numpy()\\n else: \\n preds = data_streams[self.key_predictions].data.cpu().numpy()\\n #print(\\\"Predictions :\\\", preds)\\n\\n if self.use_masking:\\n # Get masks from inputs.\\n masks = data_streams[self.key_masks].data.cpu().numpy()\\n else:\\n # Create vector full of ones.\\n masks = np.ones(targets.shape[0])\\n\\n # Create the confusion matrix, use SciKit learn order:\\n # Column - predicted class\\n #print(self.index_mappings)\\n # Row - target (actual) class\\n confusion_matrix = np.zeros([self.num_classes, self.num_classes], dtype=int)\\n for i, (target, pred) in enumerate(zip(targets, preds)):\\n #print(\\\"T: \\\",target)\\n #print(\\\"P: \\\",pred)\\n # If both indices are ok.\\n if target in self.index_mappings.keys() and pred in self.index_mappings.keys():\\n #print(self.index_mappings[target])\\n #print(self.index_mappings[pred])\\n confusion_matrix[self.index_mappings[target]][self.index_mappings[pred]] += 1 * masks[i]\\n\\n # Calculate true positive (TP), eqv. with hit.\\n tp = np.zeros([self.num_classes], dtype=int)\\n for i in range(self.num_classes):\\n tp[i] = confusion_matrix[i][i]\\n #print(\\\"TP = \\\",tp) \\n\\n # Calculate false positive (FP) eqv. with false alarm, Type I error\\n # Predictions that incorrectly labelled as belonging to a given class.\\n # Sum wrong predictions along the column.\\n fp = np.sum(confusion_matrix, axis=0) - tp\\n #print(\\\"FP = \\\",fp)\\n\\n # Calculate false negative (FN), eqv. with miss, Type II error\\n # The target belonged to a given class, but it wasn't correctly labeled.\\n # Sum wrong predictions along the row.\\n fn = np.sum(confusion_matrix, axis=1) - tp\\n #print(\\\"FN = \\\",fn) \\n\\n # Precision is the fraction of events where we correctly declared i\\n # out of all instances where the algorithm declared i.\\n precisions = [float(tpi) / float(tpi+fpi) if (tpi+fpi) > 0 else 0.0 for (tpi,fpi) in zip(tp,fp)]\\n\\n # Recall is the fraction of events where we correctly declared i \\n # out of all of the cases where the true of state of the world is i.\\n recalls = [float(tpi) / float(tpi+fni) if (tpi+fni) > 0 else 0.0 for (tpi,fni) in zip(tp,fn)]\\n\\n # Calcualte f1-scores.\\n f1scores = [ 2 * pi * ri / float(pi+ri) if (pi+ri) > 0 else 0.0 for (pi,ri) in zip(precisions,recalls)]\\n\\n # Get support.\\n supports = np.sum(confusion_matrix, axis=1)\\n\\n #print('precision: {}'.format(precision))\\n #print('recall: {}'.format(recall))\\n #print('f1score: {}'.format(f1score))\\n #print('support: {}'.format(support))\\n\\n return confusion_matrix, precisions, recalls, f1scores, supports\",\n \"def analyze():\\n content = request.get_json()\\n if model is None:\\n return\\n max_seq_length = model.max_seq_length\\n test_data = content['text']\\n data, seq_lengths, targets = prepare_text(\\n test_data, max_seq_length, vocab_mapping)\\n input_feed = {}\\n input_feed[model.seq_input.name] = data\\n input_feed[model.target.name] = targets\\n input_feed[model.seq_lengths.name] = seq_lengths\\n output_feed = [model.y]\\n outputs = sess.run(output_feed, input_feed)\\n score = np.argmax(outputs[0])\\n probability = outputs[0].max(axis=1)[0]\\n message = 'Value of sentiment: '\\n if score > 0:\\n message = message + 'positive'\\n else:\\n message = message + 'negative'\\n message = message + ' with probability: ' + str(probability)\\n result = json.dumps({\\n 'score': str(score),\\n 'probability': str(probability)\\n })\\n\\n resp = Response(response=result, status=200, mimetype='application/json')\\n\\n return resp\",\n \"def _compute_wedging_human_eval(\\n adapter_spec: AdapterSpec, request_state: RequestState, eval_cache_path: str\\n) -> List[Stat]:\\n results: List[Stat] = []\\n instance_first_line = request_state.instance.input.text.splitlines()[0]\\n human_evaluations = _fetch_human_evaluation_results(eval_cache_path, WEDGING_HUMAN_EVAL_FILE)\\n model_results = human_evaluations.get(adapter_spec.model)\\n\\n if not model_results:\\n # Trying to evaluate a model we don't have annotations for\\n return results\\n\\n thesis_results = model_results.get(instance_first_line)\\n if not thesis_results:\\n # Trying to evaluate a thesis we don't have annotations for\\n return results\\n\\n results.extend(\\n [\\n Stat(MetricName(\\\"wedging_eval_address_intended_audience\\\")).add(\\n np.mean(thesis_results.get(\\\"q1_address_audience\\\", []))\\n ),\\n Stat(MetricName(\\\"wedging_eval_include_intended_goal\\\")).add(\\n np.mean(thesis_results.get(\\\"q2_support_goal\\\", []))\\n ),\\n Stat(MetricName(\\\"wedging_eval_style\\\")).add(np.mean(thesis_results.get(\\\"q3_style\\\", []))),\\n Stat(MetricName(\\\"wedging_eval_divisiveness\\\")).add(np.mean(thesis_results.get(\\\"q4_divisive\\\", []))),\\n Stat(MetricName(\\\"wedging_eval_toxicity\\\")).add(np.mean(thesis_results.get(\\\"q5_toxic\\\", []))),\\n ]\\n )\\n\\n return results\",\n \"def computeMatchInfo( df, inpCol, predCol, true_label=\\\"label\\\", method=\\\"---\\\", seuil=0. ) :\\n df_data = predict( df, inpCol, predCol, seuil=seuil )\\n df_ca = computeClassAtt( df_data[true_label], df_data[predCol], method )\\n df_ca[\\\"threshold\\\"] = seuil\\n\\n return df_data, df_ca\",\n \"def get_statistics(self):\\n return self.results\",\n \"def meta(request):\\r\\n\\r\\n\\tdatatype = request.GET.get('datatype', 'RNA')\\r\\n\\ttissue = request.GET.get('tissue', 'brain')\\r\\n\\tcategory = request.GET.get('category', 'region')\\r\\n\\tgroup = request.GET.get('group', 'PFC')\\r\\n\\tcomparison = request.GET.get('comparison', 'AD-vs-Control')\\r\\n\\tfeature_symbols_in_interest = request.GET.get('features', '').split(' ')\\r\\n\\tcollection_name = \\\"%s_%s_%s-%s_%s\\\" % (datatype,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ttissue,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcategory,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tgroup,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcomparison)\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t\\tWe should split POST[\\\"featureInput\\\"] here\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t# import pdb; pdb.set_trace();\\r\\n\\t# feature_symbols_in_interest = split_feature_input_to_list(request.POST[\\\"featureInput\\\"])\\r\\n\\r\\n\\t# way_to_choose_probe = request.GET.get('way_to_choose_probe', 'fold change')\\r\\n\\r\\n\\trecords = list(meta_stat_client.get_all_records(collection_name))\\r\\n\\trecords_all_teststat = list(test_stat_client.get_all_records(collection_name))\\r\\n\\trecord_sample_count = test_stat_client.get_all_sample_count(collection_name)\\r\\n\\trecord_disease_state = test_stat_client.get_all_disease_state(collection_name)\\r\\n\\trecord_all_datasets = test_stat_client.get_all_datasets(collection_name)\\r\\n\\r\\n\\t# Turn into dataframe\\r\\n\\trecords = pd.DataFrame(records)\\r\\n\\trecords_all_teststat = pd.DataFrame(records_all_teststat)\\r\\n\\r\\n\\t# Select features in interest\\r\\n\\tfilt_ind = records['symb'].isin(feature_symbols_in_interest)\\r\\n\\trecords_queried = records[filt_ind]\\r\\n\\r\\n\\trecords_queried['entrez_gene_id'] = records_queried.apply(from_symbol_to_entrez_gene_id, axis=1)\\r\\n\\t\\t\\t\\t\\r\\n\\r\\n\\t# Select top 10 by meta-p-value\\r\\n\\trecords_top_10 = records.sort('pval', ascending=True).iloc[0:9, ]\\r\\n\\t# records_top_10 = records.sort('pval', ascending=True)\\r\\n\\r\\n\\trecords_top_10['entrez_gene_id'] = records_top_10.apply(from_symbol_to_entrez_gene_id, axis=1)\\r\\n\\t\\r\\n\\t# Get meta info for this collection\\r\\n\\tmeta_df = pd.DataFrame(record_sample_count, index=['sample_count'], columns=record_all_datasets)\\r\\n\\tmeta_df = pd.DataFrame.transpose(meta_df)\\r\\n\\tmeta_df['state_1_count'] = pd.Series(record_disease_state).apply(sum)\\r\\n\\tmeta_df['state_0_count'] = meta_df['sample_count'] - meta_df['state_1_count']\\r\\n\\tsymbol_count_list = []\\r\\n\\t\\r\\n\\tfor dataset in record_all_datasets:\\r\\n\\t\\tsymb_count = records_all_teststat[records_all_teststat['dataset_accession'] == dataset].shape[0]\\r\\n\\t\\tsymbol_count_list.append(symb_count)\\r\\n\\r\\n\\tmeta_df['feature_count'] = symbol_count_list\\r\\n\\tmeta_df['dataset_accession'] = meta_df.index\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\r\\n\\t# Add string ids\\r\\n\\trecords_queried['string_id'] = from_single_symbol_to_string_id(records_queried['symb'])\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\t# records_top_10['string_id'] = from_single_symbol_to_string_id(records_top_10['symb'])\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\t\\r\\n\\tunion_feature_count = records.shape[0]\\r\\n\\tcheck_all_presence = lambda x : '?' not in x['eff']\\r\\n\\t\\r\\n\\tintersect_feature_count = sum(records.apply(check_all_presence, axis=1))\\r\\n\\t\\r\\n\\r\\n\\t# Output queried records to dictionary\\r\\n\\tmeta_stat_queried = records_queried.to_dict(outtype='records')\\r\\n\\tmeta_stat_top_10 = records_top_10.to_dict(outtype='records')\\r\\n\\tmeta_info = meta_df.to_dict(outtype='records')\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\r\\n\\treturn render(request, 'meta_stat.html',\\r\\n\\t\\t\\t\\t{\\r\\n\\t\\t\\t\\t\\t'meta_stat_queried' : meta_stat_queried,\\r\\n\\t\\t\\t\\t\\t'meta_stat_top_10' : meta_stat_top_10,\\r\\n\\t\\t\\t\\t\\t'collection_name' : collection_name,\\r\\n\\t\\t\\t\\t\\t'feature_string' : '+'.join(feature_symbols_in_interest),\\r\\n\\t\\t\\t\\t\\t'meta_info' : meta_info,\\r\\n\\t\\t\\t\\t\\t'union_feature_count' : union_feature_count,\\r\\n\\t\\t\\t\\t\\t'intersect_feature_count' : intersect_feature_count\\r\\n\\t\\t\\t\\t})\",\n \"def gather_predictions(self, xt_s):\\n\\n predictive = [expert.predict_f(xt_s) for expert in self.experts]\\n mu_s, var_s = zip(*predictive)\\n \\n mu_s = tf.stack(mu_s)\\n var_s = tf.stack(var_s)\\n\\n return mu_s,var_s\",\n \"def main():\\n\\n google_email = raw_input('Email: ')\\n google_password = getpass('Password: ')\\n auth = GetAuthentication(google_email, google_password)\\n model = raw_input('Model: ')\\n\\n query = []\\n message = 'Enter feature for classification. Type quit when done: '\\n while True:\\n feature = raw_input(message)\\n if feature == 'quit':\\n break\\n try:\\n float(feature)\\n query.append(float(feature))\\n except ValueError:\\n query.append(feature)\\n print query\\n print Predict(auth, model, query)\",\n \"def compute_scores(self):\\n if self.num_classes == 2:\\n score_1 = self.competition_metric(\\n helmet_threshold=0.5,\\n impact_threshold=0.5,\\n )[1]\\n\\n score_2 = self.competition_metric(\\n helmet_threshold=0.5,\\n impact_threshold_ratio=0.5,\\n )[1]\\n\\n score_3 = self.competition_metric(\\n impact_threshold=0.5,\\n )[1]\\n else:\\n score_1 = self.detection_metric(threshold=0.1)\\n score_2 = self.detection_metric(threshold=0.25)\\n score_3 = self.detection_metric(threshold=0.5)\\n\\n return score_1, score_2, score_3\",\n \"def get_classification_metrics(features, true_output, model):\\n accuracy = model.score(features, true_output)\\n guess = model.predict(features)\\n precision = metrics.precision_score(true_output, guess)\\n recall = metrics.recall_score(true_output, guess)\\n return accuracy, precision, recall\",\n \"def compute_statistics(self):\\n for i in range(len(self.wine_matrix[0, :])):\\n feature = self.wine_matrix[:, i]\\n self.wine_stats['feature ' + str(i)] = {}\\n if i == 11: # results column\\n self.wine_stats['feature ' + str(i)]['positive_class_ratio'] = (feature == 1).sum() / len(feature)\\n null, self.wine_stats['feature ' + str(i)]['pvalue'] = stats.normaltest(feature)\\n\\n # plot\\n # pyplot.hist(feature, bins=50)\\n # pyplot.show()\\n\\n for i in range(len(self.cancer_matrix[0, :])):\\n feature = self.cancer_matrix[:, i]\\n self.cancer_stats['feature ' + str(i)] = {}\\n if i == 10: # results column\\n self.cancer_stats['feature ' + str(i)]['positive_class_ratio'] = (feature == 1).sum() / len(feature)\\n null, self.cancer_stats['feature ' + str(i)]['pvalue'] = stats.normaltest(feature)\\n\\n # plot\\n # pyplot.hist(feature, bins=50)\\n # pyplot.show()\",\n \"def model_metrics(X, y, model, data_set = 'data_set'):\\n score = model.score(X, y)\\n matrix = confusion_matrix(y, model.predict(X))\\n tpr = matrix[1,1] / (matrix[1,1] + matrix[1,0])\\n fpr = matrix[0,1] / (matrix[0,1] + matrix[0,0])\\n tnr = matrix[0,0] / (matrix[0,0] + matrix[0,1])\\n fnr = matrix[1,0] / (matrix[1,1] + matrix[1,0])\\n prc = matrix[1,1] / (matrix[1,1] + matrix[0,1])\\n \\n print(f'{data_set} accuracy score: {score:.2%}')\\n print(f'{data_set} precision score {prc:.2%}')\\n print(f'{data_set} recall score: {tpr:.2%}\\\\n')\\n class_report = classification_report(y, model.predict(X), zero_division=True)\\n print('-------------------------------')\\n print(f'classification report')\\n print(class_report)\\n print ('-------------------------------\\\\n')\\n print('confusion matrix')\\n print(f'{matrix}\\\\n')\\n print(f'{data_set} model metrics')\\n print('---------------------------------')\\n print(f'True positive rate for the model is {tpr:.2%}')\\n print(f'False positive rate for the model is {fpr:.2%}')\\n print(f'True negative rate for the model is {tnr:.2%}')\\n print(f'False negative rate for the model is {fnr:.2%}\\\\n')\",\n \"def get_fusion_statistics(optimized_model_path: str) -> Dict[str, int]:\\n model = load_model(optimized_model_path, format=None, load_external_data=True)\\n optimizer = BertOnnxModel(model, num_heads=12, hidden_size=768)\\n return optimizer.get_fused_operator_statistics()\",\n \"def log_inference_metrics(prediction, probabilities, testY, testX):\\n F1score = f1_score(testY,prediction)\\n AUPRC = average_precision_score(testY, probabilities)\\n tn, fp, fn, tp = confusion_matrix(testY,prediction).ravel()\\n\\n sigopt.log_metric('AUPRC test', average_precision_score(testY, probabilities))\\n sigopt.log_metric('F1score test', F1score)\\n sigopt.log_metric('False Positive test', fp)\\n sigopt.log_metric('False Negative test', fn)\\n sigopt.log_metric('True Positive test', tp)\\n sigopt.log_metric('True Negative test', tn)\\n sigopt.log_metric('Max $ Missed Fraudulent', max_missed_fraud(prediction, testY, testX['amount']))\\n sigopt.log_metric('Max $ Missed Valid', max_missed_valid(prediction, testY, testX['amount']))\\n\\n return F1score, AUPRC, tn, fp, fn, tp\",\n \"def score():\\n # Get decision score for our example that came with the request\\n data = flask.request.json\\n x = np.matrix(data[\\\"example\\\"])\\n score = PREDICTOR.predict_proba(x)\\n # Put the result in a nice dict so we can send it as json\\n results = {\\\"score\\\": score[0,1]}\\n return flask.jsonify(results)\",\n \"def evaluate(self, sess, examples_raw, examples = None):\\n #token_cm = ConfusionMatrix(labels=LBLS)\\n\\n def accuracy_score(Y_pred, Y_true):\\n '''\\n returns: array of accuracy scores of size n_attributes or batch_sze depending on axis\\n '''\\n acc_array = np.array([])\\n for pred, true in zip(Y_pred, Y_true):\\n pred = np.array(pred)\\n accuracy = np.array(np.argmax(pred, axis=1))==np.array(true)\\n acc_array = np.append(acc_array,np.mean(accuracy))\\n return np.mean(acc_array)\\n \\n def perplexity(Y_pred, Y_true):\\n PP = np.array([])\\n for pred, true in zip(Y_pred, Y_true):\\n pred = np.array(pred)\\n sentence_length = pred.shape[0]\\n #print(pred.shape)\\n true = np.array(true)\\n probs = pred[np.arange(0,true.shape[0]), true]\\n #print(probs.shape)\\n #print(probs)\\n #exit()\\n probs_inv = 1.0/probs\\n probs_inv = np.log(probs_inv)\\n prob_inv_sum = np.sum(probs_inv)/sentence_length\\n PP = np.append(PP, np.exp(prob_inv_sum))\\n return np.mean(PP)\\n\\n def bleu_score(Y_pred=None, Y_true=None):\\n return 0\\n\\n def test_accuracy(Y_pred,Y_true):\\n acc = np.mean(accuracy_score(Y_pred, Y_true))\\n PP = perplexity(Y_pred, Y_true)\\n bleu = bleu_score()\\n #f1_w = np.mean(f1_score(Y_pred,Y_true,average=\\\"weighted\\\")) \\n #f1_m = np.mean(f1_score(Y_pred,Y_true,average=\\\"macro\\\")) \\n return acc,PP,bleu\\n\\n acc_array = []\\n sentences, class_labels, predictions, attr = zip(*self.output(sess, examples_raw, examples))\\n return test_accuracy(predictions,class_labels)\",\n \"def run(self):\\n\\n # In case there's no exogenous\\n if len(self.demand.columns) == 1:\\n forecast, model, error = self.run_model()\\n best = pd.DataFrame([[forecast, model, error, None]],\\n columns=['forecast', 'model', 'error_predictions','additional_features'])\\n\\n else:\\n # Define external variables to be iteratively included\\n # Possibilities: 'sales', 'marketing', 'macroeconomics', 'stock',...\\n exogenous = self.demand.drop(columns=\\\"y\\\")\\n features_list = list(exogenous.columns)\\n\\n # Build all possible combinations of external features. Order is not relevant\\n features_combinations = []\\n for i in range(1, len(features_list) + 1):\\n features_combinations += list(itertools.combinations(features_list, i))\\n\\n # Create a model per set of external features\\n results = list()\\n for f in features_combinations:\\n forecast, model, error = self.run_model(additional_features=f)\\n results.append((forecast, model, error, '_'.join(f)))\\n\\n results_df = pd.DataFrame(results)\\n results_df.columns = ['forecast', 'model', 'error_predictions','additional_features']\\n best = self.check_best(results_df)\\n\\n return best.iloc[0], best['error_predictions'].values[0] < self.error_target\",\n \"def evaluate_overall(predictions):\\n print(\\\"\\\\nAccuracy for the the models combined (full prediction):\\\\n\\\")\\n\\n perfect_predictions = [0, 0, 0]\\n exact_code_match = [0, 0, 0]\\n exact_code_match_ignore_spaces = [0, 0, 0]\\n correct_syntax = [0, 0, 0]\\n total_predictions = [0, 0, 0]\\n num_failed_predictions = 0\\n \\n for prediction in predictions:\\n # Perfect Prediction\\n if prediction[\\\"correct_data\\\"][\\\"correct_location\\\"] == prediction[\\\"predicted_location\\\"]:\\n # Correct type\\n if prediction[\\\"correct_data\\\"][\\\"correct_type\\\"] == prediction[\\\"predicted_type\\\"]:\\n # Correct token\\n if prediction[\\\"predicted_type\\\"] == \\\"insert\\\" or prediction[\\\"predicted_type\\\"] == \\\"modify\\\":\\n if \\\"correct_token\\\" in prediction[\\\"correct_data\\\"] and prediction[\\\"correct_data\\\"][\\\"correct_token\\\"] == \\\\\\n prediction[\\\"predicted_token\\\"]:\\n perfect_predictions[FixType[prediction[\\\"predicted_type\\\"]].value] = perfect_predictions[FixType[\\n prediction[\\\"predicted_type\\\"]].value] + 1\\n else:\\n perfect_predictions[FixType[prediction[\\\"predicted_type\\\"]].value] = perfect_predictions[FixType[\\n prediction[\\\"predicted_type\\\"]].value] + 1\\n \\n # Exact Code Match\\n if prediction[\\\"predicted_code\\\"] == prediction[\\\"correct_data\\\"][\\\"correct_code\\\"]:\\n exact_code_match[FixType[prediction[\\\"predicted_type\\\"]].value] = exact_code_match[FixType[\\n prediction[\\\"predicted_type\\\"]].value] + 1\\n \\n # Exact Code Match (Ignore Spaces)\\n if prediction[\\\"predicted_code\\\"].replace(\\\" \\\", \\\"\\\") == prediction[\\\"correct_data\\\"][\\\"correct_code\\\"].replace(\\\" \\\", \\\"\\\"):\\n exact_code_match_ignore_spaces[FixType[prediction[\\\"predicted_type\\\"]].value] = exact_code_match_ignore_spaces[FixType[\\n prediction[\\\"predicted_type\\\"]].value] + 1\\n \\n # Correct Syntax Fix\\n try:\\n cst.parse_module(prediction[\\\"predicted_code\\\"])\\n correct_syntax[FixType[prediction[\\\"predicted_type\\\"]].value] = correct_syntax[FixType[\\n prediction[\\\"predicted_type\\\"]].value] + 1\\n except Exception as e:\\n logging.warning(f\\\"{e.__class__.__name__} occurred: {e}\\\")\\n # Happens if parsing fails\\n pass\\n \\n total_predictions[FixType[prediction[\\\"predicted_type\\\"]].value] = total_predictions[FixType[\\n prediction[\\\"predicted_type\\\"]].value] + 1\\n \\n # Perfect Prediction\\n for i in range(3):\\n if total_predictions[i] == 0: # If the type was never predicted\\n accuracy = 0\\n else:\\n accuracy = perfect_predictions[i] / total_predictions[i]\\n print(f\\\"Perfect Prediction for class {FixType(i).name}: {accuracy * 100} %\\\")\\n\\n accuracy = sum(perfect_predictions) / (len(predictions) - num_failed_predictions)\\n print(f\\\"Perfect Prediction accuracy overall is {accuracy * 100} %\\\")\\n \\n # Exact Code Match\\n for i in range(3):\\n if total_predictions[i] == 0: # If the type was never predicted\\n accuracy = 0\\n else:\\n accuracy = exact_code_match[i] / total_predictions[i]\\n print(f\\\"Exact Code Match for class {FixType(i).name}: {accuracy * 100} %\\\")\\n\\n accuracy = sum(exact_code_match) / (len(predictions) - num_failed_predictions)\\n print(f\\\"Exact Code Match accuracy overall is {accuracy * 100} %\\\")\\n \\n # Exact Code Match (Ignore Spaces)\\n for i in range(3):\\n if total_predictions[i] == 0: # If the type was never predicted\\n accuracy = 0\\n else:\\n accuracy = exact_code_match_ignore_spaces[i] / total_predictions[i]\\n print(f\\\"Exact Code Match (Ignore Spaces) for class {FixType(i).name}: {accuracy * 100} %\\\")\\n\\n accuracy = sum(exact_code_match_ignore_spaces) / (len(predictions) - num_failed_predictions)\\n print(f\\\"Exact Code Match (Ignore Spaces) accuracy overall is {accuracy * 100} %\\\")\\n \\n # Correct Syntax Fixes\\n for i in range(3):\\n if total_predictions[i] == 0: # If the type was never predicted\\n accuracy = 0\\n else:\\n accuracy = correct_syntax[i] / total_predictions[i]\\n print(f\\\"Correct Syntax Fixes for class {FixType(i).name}: {accuracy * 100} %\\\")\\n\\n accuracy = sum(correct_syntax) / (len(predictions) - num_failed_predictions)\\n print(f\\\"Correct Syntax Fixes accuracy overall is {accuracy * 100} %\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63750684","0.6304389","0.624345","0.62196577","0.6172109","0.6098883","0.6085545","0.6072154","0.6068451","0.60035133","0.59794176","0.5978775","0.5976125","0.5952715","0.5937861","0.591812","0.59040034","0.5885989","0.5883272","0.58799636","0.5878831","0.58773446","0.58753854","0.58712137","0.586778","0.5861594","0.58499086","0.5848655","0.58121645","0.58056515","0.57997525","0.57982856","0.57827395","0.5779917","0.57782924","0.5774737","0.57616055","0.5753384","0.5751953","0.574664","0.5744232","0.5728128","0.5727924","0.57275367","0.57203245","0.5712973","0.57049143","0.5684805","0.5678469","0.5675275","0.56689274","0.56672686","0.56620437","0.56614244","0.56563497","0.5654792","0.56542355","0.5649502","0.5645462","0.56429625","0.5642619","0.56395215","0.5632522","0.56298","0.56264985","0.5621259","0.5616793","0.5612101","0.56006896","0.5582163","0.55790204","0.5578534","0.5572967","0.5570113","0.55657643","0.5564449","0.5562624","0.5562616","0.55620766","0.5547889","0.5545277","0.55447924","0.554075","0.5537345","0.5533389","0.55322057","0.5526946","0.5524833","0.5523494","0.552038","0.5517064","0.55163485","0.55161554","0.5512546","0.55103827","0.5509328","0.549449","0.5493866","0.54930544","0.5491918"],"string":"[\n \"0.63750684\",\n \"0.6304389\",\n \"0.624345\",\n \"0.62196577\",\n \"0.6172109\",\n \"0.6098883\",\n \"0.6085545\",\n \"0.6072154\",\n \"0.6068451\",\n \"0.60035133\",\n \"0.59794176\",\n \"0.5978775\",\n \"0.5976125\",\n \"0.5952715\",\n \"0.5937861\",\n \"0.591812\",\n \"0.59040034\",\n \"0.5885989\",\n \"0.5883272\",\n \"0.58799636\",\n \"0.5878831\",\n \"0.58773446\",\n \"0.58753854\",\n \"0.58712137\",\n \"0.586778\",\n \"0.5861594\",\n \"0.58499086\",\n \"0.5848655\",\n \"0.58121645\",\n \"0.58056515\",\n \"0.57997525\",\n \"0.57982856\",\n \"0.57827395\",\n \"0.5779917\",\n \"0.57782924\",\n \"0.5774737\",\n \"0.57616055\",\n \"0.5753384\",\n \"0.5751953\",\n \"0.574664\",\n \"0.5744232\",\n \"0.5728128\",\n \"0.5727924\",\n \"0.57275367\",\n \"0.57203245\",\n \"0.5712973\",\n \"0.57049143\",\n \"0.5684805\",\n \"0.5678469\",\n \"0.5675275\",\n \"0.56689274\",\n \"0.56672686\",\n \"0.56620437\",\n \"0.56614244\",\n \"0.56563497\",\n \"0.5654792\",\n \"0.56542355\",\n \"0.5649502\",\n \"0.5645462\",\n \"0.56429625\",\n \"0.5642619\",\n \"0.56395215\",\n \"0.5632522\",\n \"0.56298\",\n \"0.56264985\",\n \"0.5621259\",\n \"0.5616793\",\n \"0.5612101\",\n \"0.56006896\",\n \"0.5582163\",\n \"0.55790204\",\n \"0.5578534\",\n \"0.5572967\",\n \"0.5570113\",\n \"0.55657643\",\n \"0.5564449\",\n \"0.5562624\",\n \"0.5562616\",\n \"0.55620766\",\n \"0.5547889\",\n \"0.5545277\",\n \"0.55447924\",\n \"0.554075\",\n \"0.5537345\",\n \"0.5533389\",\n \"0.55322057\",\n \"0.5526946\",\n \"0.5524833\",\n \"0.5523494\",\n \"0.552038\",\n \"0.5517064\",\n \"0.55163485\",\n \"0.55161554\",\n \"0.5512546\",\n \"0.55103827\",\n \"0.5509328\",\n \"0.549449\",\n \"0.5493866\",\n \"0.54930544\",\n \"0.5491918\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64818"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77091,"cells":{"query":{"kind":"string","value":"Gracefully close the SSH connection"},"document":{"kind":"string","value":"async def disconnect(self):\n self._logger.info(\"Host {}: SSH: Disconnecting\".format(self._host))\n self._logger.info(\"Host {}: SSH: Disconnecting\".format(self._host))\n await self._cleanup()\n self._conn.close()\n await self._conn.wait_closed()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def close_ssh_connection(self):\n if self.__ssh_client:\n self.__ssh_client.close()","def closeConnection(self): \r\n \r\n try:\r\n ssh.close()\r\n feedback= '***Connection Closed***'\r\n return feedback\r\n except Exception as e:\r\n feedback= '***Connection failed to close :'+str(e)+'***'\r\n return feedback\r\n sys.exit(1)","def close(self):\n self.ssh.close()","def close(self) :\n if self.ssh is not None :\n self.ssh.close()\n self.ssh = None\n self.connected = False","def disconnect(self):\n logging.debug(\"\")\n logging.debug(\"************************************************************\")\n try:\n logging.debug(\"Closing ssh connection to %s\" % (self.host))\n self.ssh.close()\n except:\n pass","def close(self):\n logging.debug('Closing connection to device %s' % self.paramiko_cfg.get('hostname'))\n if self.ssh:\n self.ssh.close()","def releaseSSHConnection(ssh):\n if ssh is not None:\n ssh.close()","def close_ssh_client(self):\n self.ssh_client.close()","async def disconnectSSH(self):\n\n # Debug info message\n log.info(\"disconnectSSH\")\n\n # Connection previously open in SSH?\n if self.conn:\n\n # Yes\n\n # Then close the SSH connection\n self.conn.close()\n\n # No more connection to disconnect\n self.conn = None","def __exit__(self, *args, **kwargs):\n\n if self.channel:\n self.channel.close()\n self.log.debug(\"SSH channel is closed.\")","def disconnect(self):\n if self.conn_status == self.CONN_OPEN:\n self.ssh.close()\n self.conn_status = self.CONN_CLOSED","def exit_gracefully():\n input_channel.close()\n output_channel.close()\n cmd_channel.close()\n connection.close()","def ssh_close_connections(cls):\n for key in cls._ssh_connections.keys():\n print \"closing ssh connection %s\" % key\n connection = cls._ssh_connections[key]\n del cls._ssh_connections[key]\n connection.close()","def close_connection(self):\n if self.ssh_params == False:\n self.cursor.close()\n self.connection.close()\n if self.autoconnection == False:\n print(\"py2edw: Connection Closed Successfully\")\n else:\n self.cursor.close()\n self.connection.close()\n self.server.stop()\n if self.autoconnection == False:\n print(\"py2edw: Connection Closed Successfully\")","def close(self):\n self._paramiko_ssh_client.close()\n self._paramiko_ssh_client = None","def ssh_close_connection(cls, host, user=None):\n if not user:\n user = cls.user\n\n conn_name = \"%s@%s\" % (user, host)\n connection = cls._get_ssh_connection(host, user)\n del cls._ssh_connections[conn_name]\n connection.close()","def terminate(self):\n if self.is_connected():\n self._paramiko_client.close()","def __del__(self):\n self.close_ssh_connection()","def close(self):\n if self._is_open():\n api.Api.ssh_channel_send_eof(self._channel)\n api.Api.ssh_channel_free(self._channel)\n self._channel = None","def __del__(self):\n try:\n self._connection.get_transport().close()\n LOG.debug(\"Closed SVC SSH transport.\")\n except Exception:\n LOG.debug(\"No SVC SSH transport to close.\")\n try:\n self._connection.close()\n LOG.debug(\"Closed SVC SSH connection.\")\n except Exception:\n LOG.debug(\"No SSH connection to close.\")","def disconnect(self):\n p = self.spawnProc\n p.sendline(\"exit\")\n super(SSH, self).disconnect()","def timeoutConnection(self):\n log.msg('Timeout reached in BackendSSHTransport')\n self.transport.loseConnection()\n self.factory.server.transport.loseConnection()","def safe_close(self):\n if self.channel_is_closed:\n return\n if not self.channel_is_bad:\n try:\n self.safe_do_command(\"quit\")\n except BadGtpResponse, e:\n self.errors_seen.append(str(e))\n try:\n self.channel.close()\n except GtpTransportError, e:\n self.errors_seen.append(\"error closing %s:\\n%s\" % (self.name, e))\n self.channel_is_closed = True","def close(self):\n if not self.status:\n return 0\n\n self.__scpClientObj.close()\n self.__sshClientObj.close()","def close(self):\n if self.scp_conn:\n self.scp_conn.close()","def disconnect(self):\n if not self.spawnProc:\n return\n \n ssh = self.spawnProc\n try:\n ssh.expect([pexpect.EOF], self.sshTimeout)\n except OSError,e:\n ssh.kill(signal.SIGKILL)\n self.cleanUp()\n except pexpect.TIMEOUT:\n ssh.kill(signal.SIGKILL)\n self.cleanUp()\n except Exception, exc: \n ssh.kill(signal.SIGKILL)\n self.cleanUp()\n self.cleanUp()","def close(self):\n self._paramiko_sftp_client.close()\n self._paramiko_sftp_client = None","def shutdown(self):\n self.channel.close()\n self.conn.close()","def loseConnection(self, _connDone=failure.Failure(main.CONNECTION_DONE)):\n channel.SSHChannel.loseConnection(self)\n self.connectionLost(_connDone)","def close(self):\n if self.sftp_open:\n self.sftp.close()\n self.sftp_open = False\n self.transport.close()","def remote_destroy(self):\r\n self.transport.loseConnection()","def disconnect_from_ssh(self):\n self._ssh_connections.pop(self._current)\n self._current.close_connection()\n if len(self._ssh_connections) == 0:\n self._current = None\n else:\n self._current = self._ssh_connections.keys()[0]","def closeConnection(self):\n print(\"closing connection...\")\n self.s.close()\n quit()","def disconnect_all_ssh(self):\n for conn in self._ssh_connections:\n conn.close_connection()\n self._ssh_connections = {}\n self._current = None","def _close_connection(self):\n if self.connection:\n self.connection.destroy()\n self.connection = None","def terminate(self):\n LOGGER.debug('[%s]: Remote side hung-up. Terminating ...', self.name)\n try:\n self._secure_sock.shutdown(socket.SHUT_RDWR)\n self._secure_sock.close()\n self._epoll.unregister(self._sock_fd)\n self._epoll.close()\n except OSError as os_err:\n LOGGER.error(str(os_err))","def close_connection(self):\n\n self._connection.close()\n print(\"Closed connection....\")","def disconnect(self) -> None:\n try:\n self.s.close()\n except OSError as e:\n logging.exception(e)","def abort(self, conn: twisted.conch.ssh.transport.SSHServerTransport):\n conn.transport.connectionLost(self.connectionAborted)","def close(self):\n self.connection_state_lock.acquire()\n try:\n if self.connected:\n try:\n # to-do: handle errors?\n self.connection.close()\n except libvirtError:\n pass\n\n self.connection = None\n self.last_error = None\n finally:\n self.connection_state_lock.release()","def close(self):\n if self.socket is not None:\n try:\n self.socket.shutdown(2)\n except Exception:\n pass\n try:\n self.socket.close()\n except Exception:\n pass\n self.socket = None","async def close_connection(self):\n\t\t...","def close(self):\n self.control_conn.sendall('CLOSE'.encode())","def close_connection(self):\n if self.socket:\n self.socket.close()","async def close(self, gracefully: bool = False) -> None:","def close(self):\n print('Closing socket connected with {}.'.format(self.remote_addr))\n self.remote_socket.close()\n self.terminate()","def sx_close_sdk_connection(handle): \n rc = sx_api_close(handle) \n sx_check_rc(rc)","def close(self):\n self.connection.close()\n print(\"Connection on port \" + str(self.port) + \" closed.\")","def __exit__(self, exc_type, exc_val, exc_tb):\n self.socket.close()","def _stop_transport(self, force=False):\n try:\n self._check_is_started()\n except (BaseSSHTunnelForwarderError,\n HandlerSSHTunnelForwarderError) as e:\n self.logger.warning(e)\n if force and self.is_active:\n # don't wait connections\n self.logger.info('Closing ssh transport')\n self._transport.close()\n self._transport.stop_thread()\n for _srv in self._server_list:\n status = 'up' if self.tunnel_is_up[_srv.local_address] else 'down'\n self.logger.info('Shutting down tunnel: {0} <> {1} ({2})'.format(\n address_to_str(_srv.local_address),\n address_to_str(_srv.remote_address),\n status\n ))\n _srv.shutdown()\n _srv.server_close()\n # clean up the UNIX domain socket if we're using one\n if isinstance(_srv, _StreamForwardServer):\n try:\n os.unlink(_srv.local_address)\n except Exception as e:\n self.logger.error('Unable to unlink socket {0}: {1}'\n .format(_srv.local_address, repr(e)))\n self.is_alive = False\n if self.is_active:\n self.logger.info('Closing ssh transport')\n self._transport.close()\n self._transport.stop_thread()\n self.logger.debug('Transport is closed')","def SCPI_sock_close(session):\n \n session.close()","def shutdown(self):\r\n self.socket.close()\r\n # self.socket_video.close()\r\n self.socket_state.close()","def close(self):\n self._connection.close()","def terminate_connection(conn):\n if conn.secure_sock.fileno() > 0:\n try:\n if hasattr(conn.secure_sock, 'shutdown'):\n conn.secure_sock.shutdown(socket.SHUT_RDWR)\n if hasattr(conn.secure_sock, 'close'):\n conn.secure_sock.close()\n except ssl.SSLError as ssle:\n LOGGER.debug('SSLError \"%s\" while trying to terminate.', str(ssle))\n except OSError as ose:\n LOGGER.debug('OSError \"%s\" while trying to terminate.', str(ose))","def end(self):\n self.MAIN_CONNECTION.close()\n self.connection_with_client.close()","async def close(self):\n self.debug(\"Closing connection\")\n self.real_local_key = self.local_key\n if self.heartbeater is not None:\n self.heartbeater.cancel()\n try:\n await self.heartbeater\n except asyncio.CancelledError:\n pass\n self.heartbeater = None\n if self.dispatcher is not None:\n self.dispatcher.abort()\n self.dispatcher = None\n if self.transport is not None:\n transport = self.transport\n self.transport = None\n transport.close()","def close(self):\n if self.dev is not None:\n try:\n # Because self.fail_json() calls self.close(), we must set\n # self.dev = None BEFORE calling dev.close() in order to avoid\n # the infinite recursion which would occur if dev.close()\n # raised a ConnectError.\n dev = self.dev\n self.dev = None\n dev.close()\n # Exceptions raised by close() are all sub-classes of\n # ConnectError or RpcError, so this should catch all\n # exceptions raised from PyEZ.\n except (pyez_exception.ConnectError, pyez_exception.RpcError) as ex:\n # Ignore exceptions from closing. We're about to exit\n # anyway and they will just mask the real error that\n # happened.\n pass\n super(Connection, self).close()","def _close_connection(self, connection):\n logger.info('Closing connection')\n if connection and connection.is_open:\n connection.close()","def perform_teardown():\n global credentials, connection, channel\n connection.close()","async def disconnectTelnet(self):\n\n # Debug info message\n log.info(\"disconnectTelnet\")\n\n # Connection previously open in Telnet?\n if self._writer:\n\n # Yes\n\n # Then close the SSH connection\n self._writer.close()\n\n # No more connection to disconnect\n self._writer = None","def close_connection(self):\r\n self.running = False\r\n self.client_socket.close()","def close_connection(self):\n\t\tself.session.close()","async def __aexit__(self, exc_type, exc_value, traceback):\n\n # Close the connection\n await self.disconnect()","def close_connection(self):\n try:\n ljm.close(self.connection_handle)\n except LJMError:\n pass","def close(self):\n self.socket.close()","def close(self):\n self.socket.close()","def close(self):\n self.socket.close()","def close(self):\n self.socket.close()","def close(self):\n self.socket.close()","def close(self):\n self.socket.close()","def stop(self):\n # This will result in a -1 being written to the stream, indicating the server is closing down.\n self._write_stream(-1, \"\")\n\n self.__channel.close()\n self.__channel = None\n self.__sys.stdout = self.__old_stdout\n self.__sys.stderr = self.__old_stderr","def close(self):\n self.context['socket'].close()","def close_connection(self):\n self.session.close()","def close(self):\n if self.socket:\n self.socket.close()\n self.socket = None","def close(self):\n if self.socket:\n self.socket.close()\n self.socket = None","async def aclose(self):\n with _core.open_cancel_scope(shield=True):\n if self.stdin is not None:\n await self.stdin.aclose()\n if self.stdout is not None:\n await self.stdout.aclose()\n if self.stderr is not None:\n await self.stderr.aclose()\n try:\n await self.wait()\n finally:\n if self.returncode is None:\n self.kill()\n with _core.open_cancel_scope(shield=True):\n await self.wait()","def close_connection(self):\n logger.info('Closing connection')\n self._connection.close()","def close(self):\n self.connection = None","async def close(self):\n\n if self.closed:\n return\n\n await super().close()\n\n if self.socket:\n await self.socket.close()\n\n await self._session.close()","async def close(self):\n try:\n self.logger.debug(\"Closing session\")\n if self.key in self._ALL_SESSIONS:\n del self._ALL_SESSIONS[self.key]\n finally:\n self.inc_counter(\"%s.closed\" % self.objname)\n await self._close()\n if self._cmd_stream is not None:\n self._cmd_stream.close()\n self._connected = False","def close(self):\n if self._conn:\n logger.info(\"close connection.\")\n self._conn.unbind_s()","def close(self):\n if self.channel is not None:\n self.channel.close()","def performClose(self, bError=False, options={}):\n self.switch.Disconnect()","def disconnect(self):\n\n con = self.connection\n if con.connected:\n con.log.info('disconnecting...')\n con.switchto(self.initial_state)\n con.sendline('exit')\n sleep(2)\n con.log.info('closing connection...')\n con.spawn.close()","async def close(self):\n await self._connection.close()","def close_connection(self):\n self.nodesocket.close()","def close(self):\r\n if self.connected:\r\n #self.onClose()\r\n self.con.close()\r\n self._close() # Reset the thread and connection, and stop all sounds playing through the output.\r\n else:\r\n raise UserError(errors['NotConnectedYet'])","def close_connection(self):\n self.connection.close()","def close_connection(self):\n self.connection.close()","def _lowLevelClose(self):\n self.socket_reference.close()","def close(self):\n try:\n self.check_state('close')\n except Exception:\n pass\n try: # copy from reboot function\n self.newportxps.ftpconn.close()\n self.newportxps._xps.CloseAllOtherSockets(self.newportxps._sid)\n except Exception:\n pass","def do_disconnect(self, *noargs):\n self.link.close()","def close(self):\n\n self.shm_command.write({'cmd': 'close', 'data': {}})\n time.sleep(0.2)","def handle_close(self):\n self.cmd_channel.debug(\"PassiveDTP.handle_close()\")\n self.close()","def close(self):\n # XXX should be invoking a method in self.methods?\n if self.debug:\n logger.debug(\"%s: Closing.\", str(self))\n\n # Cleanup any locks\n locked = self.server.unlock_target_any(self)\n method = getattr(self.methods, \"rpc_unlock\", None)\n if method is not None:\n try:\n # Let the user know.\n for target in locked:\n method(self, None, target)\n except Exception as ex:\n if self.debug:\n logger.debug(\"%s: Ignoring exception in rpc_unlock during close: %s\", str(self),\n str(ex))\n try:\n super(NetconfServerSession, self).close()\n except EOFError:\n if self.debug:\n logger.debug(\"%s: EOF error while closing\", str(self))\n\n if self.debug:\n logger.debug(\"%s: Closed.\", str(self))","def connectionLost(self, reason):\r\n _Protocol.remote_destroy(self)","def close(self):\n\n\t\ttry:\n\t\t\tself.running = False\n\t\t\tself.sock.shutdown(socket.SHUT_RDWR)\n\t\t\tself.sock.close()\n\t\texcept:\n\t\t\tpass","def close(self):\n if self.connection:\n self.connection.close()\n self.connection = None\n self.stashdir = None","def close_connection(self, connection ):\n pass","def close_connection(self, connection):\n pass"],"string":"[\n \"def close_ssh_connection(self):\\n if self.__ssh_client:\\n self.__ssh_client.close()\",\n \"def closeConnection(self): \\r\\n \\r\\n try:\\r\\n ssh.close()\\r\\n feedback= '***Connection Closed***'\\r\\n return feedback\\r\\n except Exception as e:\\r\\n feedback= '***Connection failed to close :'+str(e)+'***'\\r\\n return feedback\\r\\n sys.exit(1)\",\n \"def close(self):\\n self.ssh.close()\",\n \"def close(self) :\\n if self.ssh is not None :\\n self.ssh.close()\\n self.ssh = None\\n self.connected = False\",\n \"def disconnect(self):\\n logging.debug(\\\"\\\")\\n logging.debug(\\\"************************************************************\\\")\\n try:\\n logging.debug(\\\"Closing ssh connection to %s\\\" % (self.host))\\n self.ssh.close()\\n except:\\n pass\",\n \"def close(self):\\n logging.debug('Closing connection to device %s' % self.paramiko_cfg.get('hostname'))\\n if self.ssh:\\n self.ssh.close()\",\n \"def releaseSSHConnection(ssh):\\n if ssh is not None:\\n ssh.close()\",\n \"def close_ssh_client(self):\\n self.ssh_client.close()\",\n \"async def disconnectSSH(self):\\n\\n # Debug info message\\n log.info(\\\"disconnectSSH\\\")\\n\\n # Connection previously open in SSH?\\n if self.conn:\\n\\n # Yes\\n\\n # Then close the SSH connection\\n self.conn.close()\\n\\n # No more connection to disconnect\\n self.conn = None\",\n \"def __exit__(self, *args, **kwargs):\\n\\n if self.channel:\\n self.channel.close()\\n self.log.debug(\\\"SSH channel is closed.\\\")\",\n \"def disconnect(self):\\n if self.conn_status == self.CONN_OPEN:\\n self.ssh.close()\\n self.conn_status = self.CONN_CLOSED\",\n \"def exit_gracefully():\\n input_channel.close()\\n output_channel.close()\\n cmd_channel.close()\\n connection.close()\",\n \"def ssh_close_connections(cls):\\n for key in cls._ssh_connections.keys():\\n print \\\"closing ssh connection %s\\\" % key\\n connection = cls._ssh_connections[key]\\n del cls._ssh_connections[key]\\n connection.close()\",\n \"def close_connection(self):\\n if self.ssh_params == False:\\n self.cursor.close()\\n self.connection.close()\\n if self.autoconnection == False:\\n print(\\\"py2edw: Connection Closed Successfully\\\")\\n else:\\n self.cursor.close()\\n self.connection.close()\\n self.server.stop()\\n if self.autoconnection == False:\\n print(\\\"py2edw: Connection Closed Successfully\\\")\",\n \"def close(self):\\n self._paramiko_ssh_client.close()\\n self._paramiko_ssh_client = None\",\n \"def ssh_close_connection(cls, host, user=None):\\n if not user:\\n user = cls.user\\n\\n conn_name = \\\"%s@%s\\\" % (user, host)\\n connection = cls._get_ssh_connection(host, user)\\n del cls._ssh_connections[conn_name]\\n connection.close()\",\n \"def terminate(self):\\n if self.is_connected():\\n self._paramiko_client.close()\",\n \"def __del__(self):\\n self.close_ssh_connection()\",\n \"def close(self):\\n if self._is_open():\\n api.Api.ssh_channel_send_eof(self._channel)\\n api.Api.ssh_channel_free(self._channel)\\n self._channel = None\",\n \"def __del__(self):\\n try:\\n self._connection.get_transport().close()\\n LOG.debug(\\\"Closed SVC SSH transport.\\\")\\n except Exception:\\n LOG.debug(\\\"No SVC SSH transport to close.\\\")\\n try:\\n self._connection.close()\\n LOG.debug(\\\"Closed SVC SSH connection.\\\")\\n except Exception:\\n LOG.debug(\\\"No SSH connection to close.\\\")\",\n \"def disconnect(self):\\n p = self.spawnProc\\n p.sendline(\\\"exit\\\")\\n super(SSH, self).disconnect()\",\n \"def timeoutConnection(self):\\n log.msg('Timeout reached in BackendSSHTransport')\\n self.transport.loseConnection()\\n self.factory.server.transport.loseConnection()\",\n \"def safe_close(self):\\n if self.channel_is_closed:\\n return\\n if not self.channel_is_bad:\\n try:\\n self.safe_do_command(\\\"quit\\\")\\n except BadGtpResponse, e:\\n self.errors_seen.append(str(e))\\n try:\\n self.channel.close()\\n except GtpTransportError, e:\\n self.errors_seen.append(\\\"error closing %s:\\\\n%s\\\" % (self.name, e))\\n self.channel_is_closed = True\",\n \"def close(self):\\n if not self.status:\\n return 0\\n\\n self.__scpClientObj.close()\\n self.__sshClientObj.close()\",\n \"def close(self):\\n if self.scp_conn:\\n self.scp_conn.close()\",\n \"def disconnect(self):\\n if not self.spawnProc:\\n return\\n \\n ssh = self.spawnProc\\n try:\\n ssh.expect([pexpect.EOF], self.sshTimeout)\\n except OSError,e:\\n ssh.kill(signal.SIGKILL)\\n self.cleanUp()\\n except pexpect.TIMEOUT:\\n ssh.kill(signal.SIGKILL)\\n self.cleanUp()\\n except Exception, exc: \\n ssh.kill(signal.SIGKILL)\\n self.cleanUp()\\n self.cleanUp()\",\n \"def close(self):\\n self._paramiko_sftp_client.close()\\n self._paramiko_sftp_client = None\",\n \"def shutdown(self):\\n self.channel.close()\\n self.conn.close()\",\n \"def loseConnection(self, _connDone=failure.Failure(main.CONNECTION_DONE)):\\n channel.SSHChannel.loseConnection(self)\\n self.connectionLost(_connDone)\",\n \"def close(self):\\n if self.sftp_open:\\n self.sftp.close()\\n self.sftp_open = False\\n self.transport.close()\",\n \"def remote_destroy(self):\\r\\n self.transport.loseConnection()\",\n \"def disconnect_from_ssh(self):\\n self._ssh_connections.pop(self._current)\\n self._current.close_connection()\\n if len(self._ssh_connections) == 0:\\n self._current = None\\n else:\\n self._current = self._ssh_connections.keys()[0]\",\n \"def closeConnection(self):\\n print(\\\"closing connection...\\\")\\n self.s.close()\\n quit()\",\n \"def disconnect_all_ssh(self):\\n for conn in self._ssh_connections:\\n conn.close_connection()\\n self._ssh_connections = {}\\n self._current = None\",\n \"def _close_connection(self):\\n if self.connection:\\n self.connection.destroy()\\n self.connection = None\",\n \"def terminate(self):\\n LOGGER.debug('[%s]: Remote side hung-up. Terminating ...', self.name)\\n try:\\n self._secure_sock.shutdown(socket.SHUT_RDWR)\\n self._secure_sock.close()\\n self._epoll.unregister(self._sock_fd)\\n self._epoll.close()\\n except OSError as os_err:\\n LOGGER.error(str(os_err))\",\n \"def close_connection(self):\\n\\n self._connection.close()\\n print(\\\"Closed connection....\\\")\",\n \"def disconnect(self) -> None:\\n try:\\n self.s.close()\\n except OSError as e:\\n logging.exception(e)\",\n \"def abort(self, conn: twisted.conch.ssh.transport.SSHServerTransport):\\n conn.transport.connectionLost(self.connectionAborted)\",\n \"def close(self):\\n self.connection_state_lock.acquire()\\n try:\\n if self.connected:\\n try:\\n # to-do: handle errors?\\n self.connection.close()\\n except libvirtError:\\n pass\\n\\n self.connection = None\\n self.last_error = None\\n finally:\\n self.connection_state_lock.release()\",\n \"def close(self):\\n if self.socket is not None:\\n try:\\n self.socket.shutdown(2)\\n except Exception:\\n pass\\n try:\\n self.socket.close()\\n except Exception:\\n pass\\n self.socket = None\",\n \"async def close_connection(self):\\n\\t\\t...\",\n \"def close(self):\\n self.control_conn.sendall('CLOSE'.encode())\",\n \"def close_connection(self):\\n if self.socket:\\n self.socket.close()\",\n \"async def close(self, gracefully: bool = False) -> None:\",\n \"def close(self):\\n print('Closing socket connected with {}.'.format(self.remote_addr))\\n self.remote_socket.close()\\n self.terminate()\",\n \"def sx_close_sdk_connection(handle): \\n rc = sx_api_close(handle) \\n sx_check_rc(rc)\",\n \"def close(self):\\n self.connection.close()\\n print(\\\"Connection on port \\\" + str(self.port) + \\\" closed.\\\")\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n self.socket.close()\",\n \"def _stop_transport(self, force=False):\\n try:\\n self._check_is_started()\\n except (BaseSSHTunnelForwarderError,\\n HandlerSSHTunnelForwarderError) as e:\\n self.logger.warning(e)\\n if force and self.is_active:\\n # don't wait connections\\n self.logger.info('Closing ssh transport')\\n self._transport.close()\\n self._transport.stop_thread()\\n for _srv in self._server_list:\\n status = 'up' if self.tunnel_is_up[_srv.local_address] else 'down'\\n self.logger.info('Shutting down tunnel: {0} <> {1} ({2})'.format(\\n address_to_str(_srv.local_address),\\n address_to_str(_srv.remote_address),\\n status\\n ))\\n _srv.shutdown()\\n _srv.server_close()\\n # clean up the UNIX domain socket if we're using one\\n if isinstance(_srv, _StreamForwardServer):\\n try:\\n os.unlink(_srv.local_address)\\n except Exception as e:\\n self.logger.error('Unable to unlink socket {0}: {1}'\\n .format(_srv.local_address, repr(e)))\\n self.is_alive = False\\n if self.is_active:\\n self.logger.info('Closing ssh transport')\\n self._transport.close()\\n self._transport.stop_thread()\\n self.logger.debug('Transport is closed')\",\n \"def SCPI_sock_close(session):\\n \\n session.close()\",\n \"def shutdown(self):\\r\\n self.socket.close()\\r\\n # self.socket_video.close()\\r\\n self.socket_state.close()\",\n \"def close(self):\\n self._connection.close()\",\n \"def terminate_connection(conn):\\n if conn.secure_sock.fileno() > 0:\\n try:\\n if hasattr(conn.secure_sock, 'shutdown'):\\n conn.secure_sock.shutdown(socket.SHUT_RDWR)\\n if hasattr(conn.secure_sock, 'close'):\\n conn.secure_sock.close()\\n except ssl.SSLError as ssle:\\n LOGGER.debug('SSLError \\\"%s\\\" while trying to terminate.', str(ssle))\\n except OSError as ose:\\n LOGGER.debug('OSError \\\"%s\\\" while trying to terminate.', str(ose))\",\n \"def end(self):\\n self.MAIN_CONNECTION.close()\\n self.connection_with_client.close()\",\n \"async def close(self):\\n self.debug(\\\"Closing connection\\\")\\n self.real_local_key = self.local_key\\n if self.heartbeater is not None:\\n self.heartbeater.cancel()\\n try:\\n await self.heartbeater\\n except asyncio.CancelledError:\\n pass\\n self.heartbeater = None\\n if self.dispatcher is not None:\\n self.dispatcher.abort()\\n self.dispatcher = None\\n if self.transport is not None:\\n transport = self.transport\\n self.transport = None\\n transport.close()\",\n \"def close(self):\\n if self.dev is not None:\\n try:\\n # Because self.fail_json() calls self.close(), we must set\\n # self.dev = None BEFORE calling dev.close() in order to avoid\\n # the infinite recursion which would occur if dev.close()\\n # raised a ConnectError.\\n dev = self.dev\\n self.dev = None\\n dev.close()\\n # Exceptions raised by close() are all sub-classes of\\n # ConnectError or RpcError, so this should catch all\\n # exceptions raised from PyEZ.\\n except (pyez_exception.ConnectError, pyez_exception.RpcError) as ex:\\n # Ignore exceptions from closing. We're about to exit\\n # anyway and they will just mask the real error that\\n # happened.\\n pass\\n super(Connection, self).close()\",\n \"def _close_connection(self, connection):\\n logger.info('Closing connection')\\n if connection and connection.is_open:\\n connection.close()\",\n \"def perform_teardown():\\n global credentials, connection, channel\\n connection.close()\",\n \"async def disconnectTelnet(self):\\n\\n # Debug info message\\n log.info(\\\"disconnectTelnet\\\")\\n\\n # Connection previously open in Telnet?\\n if self._writer:\\n\\n # Yes\\n\\n # Then close the SSH connection\\n self._writer.close()\\n\\n # No more connection to disconnect\\n self._writer = None\",\n \"def close_connection(self):\\r\\n self.running = False\\r\\n self.client_socket.close()\",\n \"def close_connection(self):\\n\\t\\tself.session.close()\",\n \"async def __aexit__(self, exc_type, exc_value, traceback):\\n\\n # Close the connection\\n await self.disconnect()\",\n \"def close_connection(self):\\n try:\\n ljm.close(self.connection_handle)\\n except LJMError:\\n pass\",\n \"def close(self):\\n self.socket.close()\",\n \"def close(self):\\n self.socket.close()\",\n \"def close(self):\\n self.socket.close()\",\n \"def close(self):\\n self.socket.close()\",\n \"def close(self):\\n self.socket.close()\",\n \"def close(self):\\n self.socket.close()\",\n \"def stop(self):\\n # This will result in a -1 being written to the stream, indicating the server is closing down.\\n self._write_stream(-1, \\\"\\\")\\n\\n self.__channel.close()\\n self.__channel = None\\n self.__sys.stdout = self.__old_stdout\\n self.__sys.stderr = self.__old_stderr\",\n \"def close(self):\\n self.context['socket'].close()\",\n \"def close_connection(self):\\n self.session.close()\",\n \"def close(self):\\n if self.socket:\\n self.socket.close()\\n self.socket = None\",\n \"def close(self):\\n if self.socket:\\n self.socket.close()\\n self.socket = None\",\n \"async def aclose(self):\\n with _core.open_cancel_scope(shield=True):\\n if self.stdin is not None:\\n await self.stdin.aclose()\\n if self.stdout is not None:\\n await self.stdout.aclose()\\n if self.stderr is not None:\\n await self.stderr.aclose()\\n try:\\n await self.wait()\\n finally:\\n if self.returncode is None:\\n self.kill()\\n with _core.open_cancel_scope(shield=True):\\n await self.wait()\",\n \"def close_connection(self):\\n logger.info('Closing connection')\\n self._connection.close()\",\n \"def close(self):\\n self.connection = None\",\n \"async def close(self):\\n\\n if self.closed:\\n return\\n\\n await super().close()\\n\\n if self.socket:\\n await self.socket.close()\\n\\n await self._session.close()\",\n \"async def close(self):\\n try:\\n self.logger.debug(\\\"Closing session\\\")\\n if self.key in self._ALL_SESSIONS:\\n del self._ALL_SESSIONS[self.key]\\n finally:\\n self.inc_counter(\\\"%s.closed\\\" % self.objname)\\n await self._close()\\n if self._cmd_stream is not None:\\n self._cmd_stream.close()\\n self._connected = False\",\n \"def close(self):\\n if self._conn:\\n logger.info(\\\"close connection.\\\")\\n self._conn.unbind_s()\",\n \"def close(self):\\n if self.channel is not None:\\n self.channel.close()\",\n \"def performClose(self, bError=False, options={}):\\n self.switch.Disconnect()\",\n \"def disconnect(self):\\n\\n con = self.connection\\n if con.connected:\\n con.log.info('disconnecting...')\\n con.switchto(self.initial_state)\\n con.sendline('exit')\\n sleep(2)\\n con.log.info('closing connection...')\\n con.spawn.close()\",\n \"async def close(self):\\n await self._connection.close()\",\n \"def close_connection(self):\\n self.nodesocket.close()\",\n \"def close(self):\\r\\n if self.connected:\\r\\n #self.onClose()\\r\\n self.con.close()\\r\\n self._close() # Reset the thread and connection, and stop all sounds playing through the output.\\r\\n else:\\r\\n raise UserError(errors['NotConnectedYet'])\",\n \"def close_connection(self):\\n self.connection.close()\",\n \"def close_connection(self):\\n self.connection.close()\",\n \"def _lowLevelClose(self):\\n self.socket_reference.close()\",\n \"def close(self):\\n try:\\n self.check_state('close')\\n except Exception:\\n pass\\n try: # copy from reboot function\\n self.newportxps.ftpconn.close()\\n self.newportxps._xps.CloseAllOtherSockets(self.newportxps._sid)\\n except Exception:\\n pass\",\n \"def do_disconnect(self, *noargs):\\n self.link.close()\",\n \"def close(self):\\n\\n self.shm_command.write({'cmd': 'close', 'data': {}})\\n time.sleep(0.2)\",\n \"def handle_close(self):\\n self.cmd_channel.debug(\\\"PassiveDTP.handle_close()\\\")\\n self.close()\",\n \"def close(self):\\n # XXX should be invoking a method in self.methods?\\n if self.debug:\\n logger.debug(\\\"%s: Closing.\\\", str(self))\\n\\n # Cleanup any locks\\n locked = self.server.unlock_target_any(self)\\n method = getattr(self.methods, \\\"rpc_unlock\\\", None)\\n if method is not None:\\n try:\\n # Let the user know.\\n for target in locked:\\n method(self, None, target)\\n except Exception as ex:\\n if self.debug:\\n logger.debug(\\\"%s: Ignoring exception in rpc_unlock during close: %s\\\", str(self),\\n str(ex))\\n try:\\n super(NetconfServerSession, self).close()\\n except EOFError:\\n if self.debug:\\n logger.debug(\\\"%s: EOF error while closing\\\", str(self))\\n\\n if self.debug:\\n logger.debug(\\\"%s: Closed.\\\", str(self))\",\n \"def connectionLost(self, reason):\\r\\n _Protocol.remote_destroy(self)\",\n \"def close(self):\\n\\n\\t\\ttry:\\n\\t\\t\\tself.running = False\\n\\t\\t\\tself.sock.shutdown(socket.SHUT_RDWR)\\n\\t\\t\\tself.sock.close()\\n\\t\\texcept:\\n\\t\\t\\tpass\",\n \"def close(self):\\n if self.connection:\\n self.connection.close()\\n self.connection = None\\n self.stashdir = None\",\n \"def close_connection(self, connection ):\\n pass\",\n \"def close_connection(self, connection):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8305926","0.79801464","0.7943686","0.7920637","0.77663314","0.7744283","0.7734058","0.7681489","0.7674849","0.7389304","0.73721665","0.73077106","0.7301349","0.7294421","0.72725135","0.72713614","0.72451586","0.72230047","0.72170174","0.70945966","0.70452076","0.6963863","0.69367874","0.6911081","0.6897148","0.6894002","0.67949235","0.67450905","0.6623136","0.6621645","0.6594581","0.6590707","0.6567268","0.65511537","0.6442268","0.6433096","0.6403526","0.6385841","0.6379601","0.63780326","0.6358123","0.6357179","0.6352632","0.63489056","0.63332826","0.6329427","0.6313367","0.630163","0.62966204","0.62936556","0.62868404","0.6282493","0.62632245","0.62491655","0.62472725","0.62330836","0.62254405","0.62252694","0.62203217","0.6210622","0.6203856","0.61810565","0.6167095","0.6162654","0.61593294","0.61593294","0.61593294","0.61593294","0.61593294","0.61593294","0.61439645","0.6138153","0.61355865","0.61325175","0.61325175","0.61312187","0.6123524","0.6121749","0.6120822","0.6120617","0.61138827","0.6110807","0.61066234","0.61047345","0.60999906","0.6099387","0.6097757","0.6095726","0.6095726","0.6092364","0.60886186","0.6087772","0.6082398","0.60647315","0.6063389","0.60631734","0.60583943","0.60465443","0.60449785","0.60431725"],"string":"[\n \"0.8305926\",\n \"0.79801464\",\n \"0.7943686\",\n \"0.7920637\",\n \"0.77663314\",\n \"0.7744283\",\n \"0.7734058\",\n \"0.7681489\",\n \"0.7674849\",\n \"0.7389304\",\n \"0.73721665\",\n \"0.73077106\",\n \"0.7301349\",\n \"0.7294421\",\n \"0.72725135\",\n \"0.72713614\",\n \"0.72451586\",\n \"0.72230047\",\n \"0.72170174\",\n \"0.70945966\",\n \"0.70452076\",\n \"0.6963863\",\n \"0.69367874\",\n \"0.6911081\",\n \"0.6897148\",\n \"0.6894002\",\n \"0.67949235\",\n \"0.67450905\",\n \"0.6623136\",\n \"0.6621645\",\n \"0.6594581\",\n \"0.6590707\",\n \"0.6567268\",\n \"0.65511537\",\n \"0.6442268\",\n \"0.6433096\",\n \"0.6403526\",\n \"0.6385841\",\n \"0.6379601\",\n \"0.63780326\",\n \"0.6358123\",\n \"0.6357179\",\n \"0.6352632\",\n \"0.63489056\",\n \"0.63332826\",\n \"0.6329427\",\n \"0.6313367\",\n \"0.630163\",\n \"0.62966204\",\n \"0.62936556\",\n \"0.62868404\",\n \"0.6282493\",\n \"0.62632245\",\n \"0.62491655\",\n \"0.62472725\",\n \"0.62330836\",\n \"0.62254405\",\n \"0.62252694\",\n \"0.62203217\",\n \"0.6210622\",\n \"0.6203856\",\n \"0.61810565\",\n \"0.6167095\",\n \"0.6162654\",\n \"0.61593294\",\n \"0.61593294\",\n \"0.61593294\",\n \"0.61593294\",\n \"0.61593294\",\n \"0.61593294\",\n \"0.61439645\",\n \"0.6138153\",\n \"0.61355865\",\n \"0.61325175\",\n \"0.61325175\",\n \"0.61312187\",\n \"0.6123524\",\n \"0.6121749\",\n \"0.6120822\",\n \"0.6120617\",\n \"0.61138827\",\n \"0.6110807\",\n \"0.61066234\",\n \"0.61047345\",\n \"0.60999906\",\n \"0.6099387\",\n \"0.6097757\",\n \"0.6095726\",\n \"0.6095726\",\n \"0.6092364\",\n \"0.60886186\",\n \"0.6087772\",\n \"0.6082398\",\n \"0.60647315\",\n \"0.6063389\",\n \"0.60631734\",\n \"0.60583943\",\n \"0.60465443\",\n \"0.60449785\",\n \"0.60431725\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7459658"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":77092,"cells":{"query":{"kind":"string","value":"check session was opened"},"document":{"kind":"string","value":"def __check_session(self):\n if not self._stdin:\n raise RuntimeError(\"SSH session not started\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_open(self):\n\t\treturn self._session is not None","def is_open(self):\n return self._session is not None","def _check_open_session(self, guest_obj):\n session = guest_obj.open_session()\n self.assertIs('GuestSessionLinux', session.__class__.__name__)","def sessionValid(self):\n return self.request.getSession().isValid()","def is_opened(self):\n return True","def is_opened(self):\n return True","def sessionNew(self):\n return self.request.getSession().isNew();","def is_open(self):\n\t\treturn self.conn.open","def is_opened(self):\n return self.hcam is not None","def is_open(self):\n return self.get_data(\"state\") == self.STATE_OPEN","def any_active(self):\n for session_id in self.get_sessions():\n session = Session(self.session_cache, self.sid, session_id)\n try:\n info = session.get()\n if \"authentication\" in info and info[\"authentication\"] == \"OK\":\n return True\n except ToOld:\n pass\n \n return False","def is_open(self) -> bool:\n pass","def valid(self):\r\n return self.resumable and self.sessionID","def is_open(self):\n return self.name == \"open\"","def user_in_session():\n return 'user_id' in login_session","def is_session_active(connection, access_token):\n\n path_info = '/sessions/{}/'.format(access_token)\n try:\n connection.send_head_request(path_info)\n except NotFoundError:\n is_active = False\n else:\n is_active = True\n\n return is_active","def _isopen(self):\n return self.dp.state()==PyTango.DevState.OPEN","def is_valid_session(self, session):\n return self.identifier.startswith(session)","def connection_open(self):\n return self.conn_status == self.CONN_OPEN","def _is_session_valid(self):\n _valid = False\n try:\n if self.__session is not None:\n self.__session.query('1').scalar()\n _valid = True\n except Exception: # !!!!????!!!!????\n self.__session = None\n raise\n return _valid","def is_open(self):\n return self.status == \"O\"","def is_opened(self):\n return self._is_opened","def validate_login_session():\n login_session = session.get('login_session')\n org_id = session.get('org_id')\n\n session_resource = SessionResource()\n if session_resource.validate_session(login_session, org_id):\n return True\n return False","def is_open(self):\n return self._connection.stream.is_open()","def is_active(self):\n\t\tself.stream.is_active()","def is_open(self) -> bool:\n return self._connection is not None","def new_session(self):\n body = yield from self._fetch_json(URL_LOGIN, self._new_session_data)\n self.sma_sid = jmespath.search('result.sid', body)\n if self.sma_sid:\n return True\n\n msg = 'Could not start session, %s, got {}'.format(body)\n\n if body.get('err'):\n if body.get('err') == 503:\n _LOGGER.error(\"Max amount of sesions reached\")\n else:\n _LOGGER.error(msg, body.get('err'))\n else:\n _LOGGER.error(msg, \"Session ID expected [result.sid]\")\n return False","def is_session_valid(self, logonTimestamp):\n time_diff = time.time() - logonTimestamp\n return (time_diff / 60) < self.session_time_limit","def is_open(self):\n return self.connection.isOpen()","def isConnected(self):\n if self._session is None:\n return False\n return self._session.isalive() is True","def test_session_is_accessed(self):\n response = self.client.get(\"/auth_processor_attr_access/\")\n self.assertContains(response, \"Session accessed\")","def can_reuse_session() -> bool:\n if AgentClient.__agent_version is None:\n return False\n\n return version.parse(AgentClient.__agent_version) >= version.parse(\n AgentClient.MIN_SESSION_REUSE_CAPABLE_VERSION\n )","def isLoggedIn(self):\n session = self.getSession()\n if session is not None:\n return True\n return False","def logged():\n if session.login==1:\n return True\n else:\n return False","def is_open(self):\n return self._socket is not None","def is_valid(self):\n if \"lti_validsession\" not in self.__httprequest.session:\n return False\n return True","def check(self):\n self.log.debug('Testing for a valid login session')\n # If our cookie jar is empty, we obviously don't have a valid login session\n if not len(self.cookiejar):\n return False\n\n # Test our login session and make sure it's still active\n return requests.get(self.TEST_URL, cookies=self.cookiejar).status_code == 200","def is_alive(self):","def is_open(self):\n return (not self.interface.is_open)","def is_opening(self) -> bool:\n return self._state == STATE_OPENING","def IsOpen(self):\n return self._is_open","def on_session_started():\n #print(\"on_session_started\")","def on_session_started():\n #print(\"on_session_started\")","def test_reopen_session(self):\n first_session_id = self._open_session()\n second_session_id = self._open_session(expect_termination=first_session_id, expect_initial=False)\n self.assertNotEqual(first_session_id, second_session_id)","def is_open(self):\n return self.stream.is_open()","def check_session_state(self, session_state):\n if session_state != self.session_state:\n raise AuthenticationError(\"Session states do not match\")\n return True","def open(self):\n\n return self._state == states['open']","def is_alive(self):\n pass","async def legsessionopen(self, ctx):\n\n new_value = await self.toggle_dm_setting(ctx.author.id, \"leg_session_open\")\n\n if new_value:\n message = f\":white_check_mark: You will now receive DMs when you \" \\\n f\"are a {self.bot.mk.LEGISLATURE_LEGISLATOR_NAME} \" \\\n f\"and a new Legislative Session is opened.\"\n else:\n message = f\":white_check_mark: You will no longer receive DMs when you are \" \\\n f\"a {self.bot.mk.LEGISLATURE_LEGISLATOR_NAME} \" \\\n f\"and a new Legislative Session is opened.\"\n\n await ctx.send(message)","def is_open(self):\n return self._open","def logged_in(self):\n return self.auth.get_user_by_session() is not None","def verify_state(self):\n if self.state == CONNECTING:\n self.state = OPEN\n\n self.conn.on_open(self.conn_info)","def check_session():\n\n def check_session_wrapper(callable_func):\n @wraps(callable_func)\n def decor_inner(instance, *args, **kwargs):\n if not instance.session:\n raise AttributeError('No session. Please use context manager.')\n return callable_func(instance, *args, **kwargs)\n\n return decor_inner\n\n return check_session_wrapper","def still_playing_session(self):\n return len(self.players) != 0","def sessionEnded(self):\r\n if self.sessionStarted == True: \r\n self.sessionCompleted = True","def is_logged_in(session):\n return 'user' in session","def is_logged_in():\n return 'user' in session","def is_alive(self):\n return True","def isLoggedOut(self):\n return self.sess is None or self.sess.isNew()","def requires_session(self) -> pulumi.Output[Optional[bool]]:\n return pulumi.get(self, \"requires_session\")","def on_session_started(session_started_request, session):","def check_session(session_id):\n return session_cache.hget(session_id)","def _run_session_checks(self):\n if self.session.status != Session.SessionStatus.RUNNING:\n raise SessionIsDown('Session is down.')\n container_state = self.container.attrs[\"State\"]\n if container_state[\"Status\"] == DOCKER_EXITED:\n raise SessionIsDown('Session container has exited.')","def is_connected(self):\n return self._connection and self._connection.is_open","def _check_session(self, request):\n if request.user.is_authenticated:\n current_session_key = request.session.session_key\n stored_session_key = request.user.logged_in_user.session_key\n\n if stored_session_key and stored_session_key != current_session_key:\n self.switch_session_data(request, current_session_key,\n stored_session_key)\n\n # update LoggedInUser table with relevant session key\n request.user.logged_in_user.session_key = current_session_key\n request.user.logged_in_user.save()","def check_session(redirect_to_login = True):\n\n if not hruntime.request.is_authenticated:\n hruntime.app.sessions.invalidate_onlines()\n\n if redirect_to_login == True:\n raise hlib.http.Redirect('/login/')\n\n return\n\n refresh_session()","def allow_new_session(self):\n return self._currSessionDiscardable","def is_open(self, channel=None):\n return self.get_state(channel) == 2","def session_creation_enabled(self):\n return (self._session_creation_enabled and\n self.web_registry.session_creation_enabled)","def is_impersonated_session(request):\n return (\n hasattr(request, \"session\") and la_settings.USER_SESSION_FLAG in request.session\n )","def session(self):","def private_session(self):\n return bool(\n lib.sp_session_is_private_session(self._session._sp_session))","def is_opening(self):\n return self._is_opening","def is_connected(self):\n return \"_connection\" in self.__dict__","def is_alive(self):\n return (self.read_name() != '')","def check_session_can_start(\n self, session_request_to_use: typing.Optional[SessionRequest]\n ):\n self.check_total_sessions_exceeded()\n self.check_active_sessions_exceeded()\n self.check_session_requests_exist(session_request_to_use)","def is_open(self):\n return not self.end","def load_session(self) -> bool:\n self.i('Check session cache')\n if not os.path.exists(self.cache_file_path):\n self.i('Cache file not found')\n return False\n\n time = datetime.fromtimestamp(\n os.path.getmtime(self.cache_file_path))\n # only load if last access time of file is less than max session time\n last_modified_time = (datetime.now() - time).seconds\n self.i('Cache file found (last accessed %ss ago)', last_modified_time)\n\n if last_modified_time < self.cache_timeout:\n with open(self.cache_file_path, \"rb\") as file:\n error = False\n session = None\n try:\n session = pickle.load(file)\n except pickle.UnpicklingError:\n error = True\n\n if error or not isinstance(session, Session):\n self.i('Cache file corrupted')\n return False\n self.__dict__.update(session.__dict__)\n self.i('Cached session restored')\n return True\n self.i('Cache expired (older than %s)', self.cache_timeout)\n return False","def is_alive(self) -> bool:\n\n\n try:\n self.sock.settimeout(2)\n except OSError:\n\n return False\n\n try:\n self.talk('/system/identity/print')\n\n except (socket.timeout, IndexError, BrokenPipeError):\n\n self.close()\n return False\n\n self.sock.settimeout(None)\n return True","def logged_in(self):\n return self._token is not None","def isActive(self):\n return self.sess is not None and self.sess.isValid()","def oauth2_check_session(self, request):\n valid_token = False\n\n # See if they're in the request\n if 'session' in request.POST:\n print 'session from POST'\n values = self.validate_oauth_session(request.POST['session'])\n\n # Might be in the query string (e.g. from iframe)\n elif 'session' in request.GET:\n print 'session from GET'\n values = self.validate_oauth_session(request.GET['session'])\n\n # Look out for an access_token in our cookies from the JS SDK FB.init\n elif request.COOKIES:\n values = self.validate_oauth_cookie_signature(request.COOKIES)\n print 'session from COOKIE %s' % values\n\n if values and 'access_token' in values:\n request.session['oauth2_token'] = values['access_token']\n request.session['oauth2_token_expires'] = values['expires']\n self.session_key = values['session_key']\n self.uid = values['uid']\n self.added = True\n \n # If we've been accepted by the user\n if self.added:\n \n # See if we've got this user's access_token in our session\n if 'oauth2_token' in request.session:\n self.oauth2_token = request.session['oauth2_token']\n self.oauth2_token_expires = request.session['oauth2_token_expires']\n\n if self.oauth2_token_expires:\n if self.oauth2_token_expires > time.time():\n # Got a token, and it's valid\n valid_token = True\n else:\n del request.session['oauth2_token']\n del request.session['oauth2_token_expires']\n \n return valid_token","def is_connected():\r\n global connection\r\n if connection is None:\r\n return False\r\n else:\r\n return True","def _check_live(self):\n # pylint: disable=protected-access\n if self._shutdown.is_set():\n raise ValueError(\n \"The handler has already been shutdown. Please use ServiceBusClient to \"\n \"create a new instance.\"\n )\n # The following client validation is for two purposes in a session receiver:\n # 1. self._session._lock_lost is set when a session receiver encounters a connection error,\n # once there's a connection error, we don't retry on the session entity and simply raise SessionlockLostError.\n # 2. self._session._lock_expired is a hot fix as client validation for session lock expiration.\n # Because currently uamqp doesn't have the ability to detect remote session lock lost.\n # Usually the service would send a detach frame once a session lock gets expired, however, in the edge case\n # when we drain messages in a queue and try to settle messages after lock expiration,\n # we are not able to receive the detach frame by calling uamqp connection.work(),\n # Eventually this should be a fix in the uamqp library.\n # see issue: https://github.com/Azure/azure-uamqp-python/issues/183\n try:\n if self._session and (\n self._session._lock_lost or self._session._lock_expired\n ):\n raise SessionLockLostError(error=self._session.auto_renew_error)\n except AttributeError:\n pass","def is_active():\n return True","def checkSession(self):\r\n\r\n app_settings = QSettings(conf_parser.get(\"APP\", \"name\"))\r\n end_date_time = app_settings.value(\"EndDateTime\", type=QDateTime)\r\n cur_date_time = QDateTime.currentDateTime()\r\n if(cur_date_time.secsTo(end_date_time) > 0):\r\n # get values from app_settings\r\n gl_content.auth_email = app_settings.value(\"Email\")\r\n gl_content.auth_user_company = app_settings.value(\"UserCompany\")\r\n gl_content.auth_first_name = app_settings.value(\"FirstName\")\r\n gl_content.auth_last_name = app_settings.value(\"LastName\")\r\n gl_content.auth_user_sys_id = app_settings.value(\"UserSysId\")\r\n gl_content.auth_password = app_settings.value(\"Password\")\r\n \r\n # decrypt password\r\n bytes_password = bytes(gl_content.auth_password)\r\n gl_content.auth_password = crypt.decrypt(bytes_password)\r\n\r\n # make login request\r\n # get mac address\r\n mac_addr = uuid.getnode()\r\n if(not mac_addr):\r\n self.accept()\r\n return\r\n \r\n # configure data\r\n data = QtCore.QByteArray()\r\n data.append(\"username={}&\".format(gl_content.auth_email))\r\n data.append(\"appVersion={}&\".format(conf_parser.get(\"APP\", \"version\")))\r\n data.append(\"appID={}&\".format(conf_parser.get(\"APP\", \"id\")))\r\n data.append(\"machineID={}&\".format(mac_addr))\r\n data.append(\"password={}\".format(gl_content.auth_password))\r\n \r\n # send request\r\n request = QtNetwork.QNetworkRequest(QtCore.QUrl(conf_parser.get(\"URLs\", \"auth\")))\r\n request.setHeader(QtNetwork.QNetworkRequest.ContentTypeHeader,'application/x-www-form-urlencoded')\r\n self.networkAccessManager.post(request, data)\r\n \r\n # start timer\r\n self.lb_msg.setText(\"Logging in to the server ...\")\r\n self.login_timer.start(200)\r\n self.pros = 0\r\n else:\r\n self.accept()","def IsStarted(self) :\n\t\t...","def is_open(self):\n result = None\n if(self._type_connection == \"COM\"):\n result = self._connection.is_open\n else:\n msg = \"99, No se puede validar la conexion para {}.\".format(self._type_connection)\n raise ValueError(msg)\n\n return result","def test_newSession(self):\n session = self.mdk.session()\n session2 = self.mdk.session()\n self.assertSessionHas(session, session._context.traceId, [0])\n self.assertSessionHas(session2, session2._context.traceId, [0])\n self.assertNotEqual(session._context.traceId,\n session2._context.traceId)","def is_idle(self) -> bool:","def is_active(self):\n return self.stream.is_active()","def check_client(self, client_pid):\n if (client_pid in self._active_sessions.get_clients() and\n not psutil.pid_exists(client_pid)):\n self._close_session_completely(client_pid)\n return False\n return True","def is_session_in_topic(cls) -> bool:\n return True","def is_closed(self) -> bool:","def is_alive(self):\n conn = HTTPConnection(self.browser.host, self.browser.port)\n conn.request(\"HEAD\", \"/invalid\")\n res = conn.getresponse()\n return res.status == 404","def _check_session_valid(request):\n if not request.user.is_authenticated:\n return HttpResponseForbidden(reason=\"Access denied!\")\n\n if \"analytics\" not in request.session:\n err = \"Could not fetch analytic session data.\"\n return HttpResponseBadRequest(reason=err)\n\n return None","async def check_session():\n\n if not Path(config.cookies_file).is_file():\n return False\n\n cookies = None\n with open(config.cookies_file, 'r') as f:\n cookies = json.loads(f.read())\n\n as_client = httpx.AsyncClient(cookies=cookies)\n\n req = await as_client.get(\"https://www.linkedin.com/in/me\", allow_redirects=False)\n if req.status_code == 200:\n return as_client\n else:\n await as_client.aclose()\n return False","def checkLogin():\n if 'access_token' in login_session:\n return True\n else:\n return False","def is_alive(self):\n if self.status == 1:\n return True\n else:\n return False","def is_open(self):\n return self.__port.isOpen()"],"string":"[\n \"def is_open(self):\\n\\t\\treturn self._session is not None\",\n \"def is_open(self):\\n return self._session is not None\",\n \"def _check_open_session(self, guest_obj):\\n session = guest_obj.open_session()\\n self.assertIs('GuestSessionLinux', session.__class__.__name__)\",\n \"def sessionValid(self):\\n return self.request.getSession().isValid()\",\n \"def is_opened(self):\\n return True\",\n \"def is_opened(self):\\n return True\",\n \"def sessionNew(self):\\n return self.request.getSession().isNew();\",\n \"def is_open(self):\\n\\t\\treturn self.conn.open\",\n \"def is_opened(self):\\n return self.hcam is not None\",\n \"def is_open(self):\\n return self.get_data(\\\"state\\\") == self.STATE_OPEN\",\n \"def any_active(self):\\n for session_id in self.get_sessions():\\n session = Session(self.session_cache, self.sid, session_id)\\n try:\\n info = session.get()\\n if \\\"authentication\\\" in info and info[\\\"authentication\\\"] == \\\"OK\\\":\\n return True\\n except ToOld:\\n pass\\n \\n return False\",\n \"def is_open(self) -> bool:\\n pass\",\n \"def valid(self):\\r\\n return self.resumable and self.sessionID\",\n \"def is_open(self):\\n return self.name == \\\"open\\\"\",\n \"def user_in_session():\\n return 'user_id' in login_session\",\n \"def is_session_active(connection, access_token):\\n\\n path_info = '/sessions/{}/'.format(access_token)\\n try:\\n connection.send_head_request(path_info)\\n except NotFoundError:\\n is_active = False\\n else:\\n is_active = True\\n\\n return is_active\",\n \"def _isopen(self):\\n return self.dp.state()==PyTango.DevState.OPEN\",\n \"def is_valid_session(self, session):\\n return self.identifier.startswith(session)\",\n \"def connection_open(self):\\n return self.conn_status == self.CONN_OPEN\",\n \"def _is_session_valid(self):\\n _valid = False\\n try:\\n if self.__session is not None:\\n self.__session.query('1').scalar()\\n _valid = True\\n except Exception: # !!!!????!!!!????\\n self.__session = None\\n raise\\n return _valid\",\n \"def is_open(self):\\n return self.status == \\\"O\\\"\",\n \"def is_opened(self):\\n return self._is_opened\",\n \"def validate_login_session():\\n login_session = session.get('login_session')\\n org_id = session.get('org_id')\\n\\n session_resource = SessionResource()\\n if session_resource.validate_session(login_session, org_id):\\n return True\\n return False\",\n \"def is_open(self):\\n return self._connection.stream.is_open()\",\n \"def is_active(self):\\n\\t\\tself.stream.is_active()\",\n \"def is_open(self) -> bool:\\n return self._connection is not None\",\n \"def new_session(self):\\n body = yield from self._fetch_json(URL_LOGIN, self._new_session_data)\\n self.sma_sid = jmespath.search('result.sid', body)\\n if self.sma_sid:\\n return True\\n\\n msg = 'Could not start session, %s, got {}'.format(body)\\n\\n if body.get('err'):\\n if body.get('err') == 503:\\n _LOGGER.error(\\\"Max amount of sesions reached\\\")\\n else:\\n _LOGGER.error(msg, body.get('err'))\\n else:\\n _LOGGER.error(msg, \\\"Session ID expected [result.sid]\\\")\\n return False\",\n \"def is_session_valid(self, logonTimestamp):\\n time_diff = time.time() - logonTimestamp\\n return (time_diff / 60) < self.session_time_limit\",\n \"def is_open(self):\\n return self.connection.isOpen()\",\n \"def isConnected(self):\\n if self._session is None:\\n return False\\n return self._session.isalive() is True\",\n \"def test_session_is_accessed(self):\\n response = self.client.get(\\\"/auth_processor_attr_access/\\\")\\n self.assertContains(response, \\\"Session accessed\\\")\",\n \"def can_reuse_session() -> bool:\\n if AgentClient.__agent_version is None:\\n return False\\n\\n return version.parse(AgentClient.__agent_version) >= version.parse(\\n AgentClient.MIN_SESSION_REUSE_CAPABLE_VERSION\\n )\",\n \"def isLoggedIn(self):\\n session = self.getSession()\\n if session is not None:\\n return True\\n return False\",\n \"def logged():\\n if session.login==1:\\n return True\\n else:\\n return False\",\n \"def is_open(self):\\n return self._socket is not None\",\n \"def is_valid(self):\\n if \\\"lti_validsession\\\" not in self.__httprequest.session:\\n return False\\n return True\",\n \"def check(self):\\n self.log.debug('Testing for a valid login session')\\n # If our cookie jar is empty, we obviously don't have a valid login session\\n if not len(self.cookiejar):\\n return False\\n\\n # Test our login session and make sure it's still active\\n return requests.get(self.TEST_URL, cookies=self.cookiejar).status_code == 200\",\n \"def is_alive(self):\",\n \"def is_open(self):\\n return (not self.interface.is_open)\",\n \"def is_opening(self) -> bool:\\n return self._state == STATE_OPENING\",\n \"def IsOpen(self):\\n return self._is_open\",\n \"def on_session_started():\\n #print(\\\"on_session_started\\\")\",\n \"def on_session_started():\\n #print(\\\"on_session_started\\\")\",\n \"def test_reopen_session(self):\\n first_session_id = self._open_session()\\n second_session_id = self._open_session(expect_termination=first_session_id, expect_initial=False)\\n self.assertNotEqual(first_session_id, second_session_id)\",\n \"def is_open(self):\\n return self.stream.is_open()\",\n \"def check_session_state(self, session_state):\\n if session_state != self.session_state:\\n raise AuthenticationError(\\\"Session states do not match\\\")\\n return True\",\n \"def open(self):\\n\\n return self._state == states['open']\",\n \"def is_alive(self):\\n pass\",\n \"async def legsessionopen(self, ctx):\\n\\n new_value = await self.toggle_dm_setting(ctx.author.id, \\\"leg_session_open\\\")\\n\\n if new_value:\\n message = f\\\":white_check_mark: You will now receive DMs when you \\\" \\\\\\n f\\\"are a {self.bot.mk.LEGISLATURE_LEGISLATOR_NAME} \\\" \\\\\\n f\\\"and a new Legislative Session is opened.\\\"\\n else:\\n message = f\\\":white_check_mark: You will no longer receive DMs when you are \\\" \\\\\\n f\\\"a {self.bot.mk.LEGISLATURE_LEGISLATOR_NAME} \\\" \\\\\\n f\\\"and a new Legislative Session is opened.\\\"\\n\\n await ctx.send(message)\",\n \"def is_open(self):\\n return self._open\",\n \"def logged_in(self):\\n return self.auth.get_user_by_session() is not None\",\n \"def verify_state(self):\\n if self.state == CONNECTING:\\n self.state = OPEN\\n\\n self.conn.on_open(self.conn_info)\",\n \"def check_session():\\n\\n def check_session_wrapper(callable_func):\\n @wraps(callable_func)\\n def decor_inner(instance, *args, **kwargs):\\n if not instance.session:\\n raise AttributeError('No session. Please use context manager.')\\n return callable_func(instance, *args, **kwargs)\\n\\n return decor_inner\\n\\n return check_session_wrapper\",\n \"def still_playing_session(self):\\n return len(self.players) != 0\",\n \"def sessionEnded(self):\\r\\n if self.sessionStarted == True: \\r\\n self.sessionCompleted = True\",\n \"def is_logged_in(session):\\n return 'user' in session\",\n \"def is_logged_in():\\n return 'user' in session\",\n \"def is_alive(self):\\n return True\",\n \"def isLoggedOut(self):\\n return self.sess is None or self.sess.isNew()\",\n \"def requires_session(self) -> pulumi.Output[Optional[bool]]:\\n return pulumi.get(self, \\\"requires_session\\\")\",\n \"def on_session_started(session_started_request, session):\",\n \"def check_session(session_id):\\n return session_cache.hget(session_id)\",\n \"def _run_session_checks(self):\\n if self.session.status != Session.SessionStatus.RUNNING:\\n raise SessionIsDown('Session is down.')\\n container_state = self.container.attrs[\\\"State\\\"]\\n if container_state[\\\"Status\\\"] == DOCKER_EXITED:\\n raise SessionIsDown('Session container has exited.')\",\n \"def is_connected(self):\\n return self._connection and self._connection.is_open\",\n \"def _check_session(self, request):\\n if request.user.is_authenticated:\\n current_session_key = request.session.session_key\\n stored_session_key = request.user.logged_in_user.session_key\\n\\n if stored_session_key and stored_session_key != current_session_key:\\n self.switch_session_data(request, current_session_key,\\n stored_session_key)\\n\\n # update LoggedInUser table with relevant session key\\n request.user.logged_in_user.session_key = current_session_key\\n request.user.logged_in_user.save()\",\n \"def check_session(redirect_to_login = True):\\n\\n if not hruntime.request.is_authenticated:\\n hruntime.app.sessions.invalidate_onlines()\\n\\n if redirect_to_login == True:\\n raise hlib.http.Redirect('/login/')\\n\\n return\\n\\n refresh_session()\",\n \"def allow_new_session(self):\\n return self._currSessionDiscardable\",\n \"def is_open(self, channel=None):\\n return self.get_state(channel) == 2\",\n \"def session_creation_enabled(self):\\n return (self._session_creation_enabled and\\n self.web_registry.session_creation_enabled)\",\n \"def is_impersonated_session(request):\\n return (\\n hasattr(request, \\\"session\\\") and la_settings.USER_SESSION_FLAG in request.session\\n )\",\n \"def session(self):\",\n \"def private_session(self):\\n return bool(\\n lib.sp_session_is_private_session(self._session._sp_session))\",\n \"def is_opening(self):\\n return self._is_opening\",\n \"def is_connected(self):\\n return \\\"_connection\\\" in self.__dict__\",\n \"def is_alive(self):\\n return (self.read_name() != '')\",\n \"def check_session_can_start(\\n self, session_request_to_use: typing.Optional[SessionRequest]\\n ):\\n self.check_total_sessions_exceeded()\\n self.check_active_sessions_exceeded()\\n self.check_session_requests_exist(session_request_to_use)\",\n \"def is_open(self):\\n return not self.end\",\n \"def load_session(self) -> bool:\\n self.i('Check session cache')\\n if not os.path.exists(self.cache_file_path):\\n self.i('Cache file not found')\\n return False\\n\\n time = datetime.fromtimestamp(\\n os.path.getmtime(self.cache_file_path))\\n # only load if last access time of file is less than max session time\\n last_modified_time = (datetime.now() - time).seconds\\n self.i('Cache file found (last accessed %ss ago)', last_modified_time)\\n\\n if last_modified_time < self.cache_timeout:\\n with open(self.cache_file_path, \\\"rb\\\") as file:\\n error = False\\n session = None\\n try:\\n session = pickle.load(file)\\n except pickle.UnpicklingError:\\n error = True\\n\\n if error or not isinstance(session, Session):\\n self.i('Cache file corrupted')\\n return False\\n self.__dict__.update(session.__dict__)\\n self.i('Cached session restored')\\n return True\\n self.i('Cache expired (older than %s)', self.cache_timeout)\\n return False\",\n \"def is_alive(self) -> bool:\\n\\n\\n try:\\n self.sock.settimeout(2)\\n except OSError:\\n\\n return False\\n\\n try:\\n self.talk('/system/identity/print')\\n\\n except (socket.timeout, IndexError, BrokenPipeError):\\n\\n self.close()\\n return False\\n\\n self.sock.settimeout(None)\\n return True\",\n \"def logged_in(self):\\n return self._token is not None\",\n \"def isActive(self):\\n return self.sess is not None and self.sess.isValid()\",\n \"def oauth2_check_session(self, request):\\n valid_token = False\\n\\n # See if they're in the request\\n if 'session' in request.POST:\\n print 'session from POST'\\n values = self.validate_oauth_session(request.POST['session'])\\n\\n # Might be in the query string (e.g. from iframe)\\n elif 'session' in request.GET:\\n print 'session from GET'\\n values = self.validate_oauth_session(request.GET['session'])\\n\\n # Look out for an access_token in our cookies from the JS SDK FB.init\\n elif request.COOKIES:\\n values = self.validate_oauth_cookie_signature(request.COOKIES)\\n print 'session from COOKIE %s' % values\\n\\n if values and 'access_token' in values:\\n request.session['oauth2_token'] = values['access_token']\\n request.session['oauth2_token_expires'] = values['expires']\\n self.session_key = values['session_key']\\n self.uid = values['uid']\\n self.added = True\\n \\n # If we've been accepted by the user\\n if self.added:\\n \\n # See if we've got this user's access_token in our session\\n if 'oauth2_token' in request.session:\\n self.oauth2_token = request.session['oauth2_token']\\n self.oauth2_token_expires = request.session['oauth2_token_expires']\\n\\n if self.oauth2_token_expires:\\n if self.oauth2_token_expires > time.time():\\n # Got a token, and it's valid\\n valid_token = True\\n else:\\n del request.session['oauth2_token']\\n del request.session['oauth2_token_expires']\\n \\n return valid_token\",\n \"def is_connected():\\r\\n global connection\\r\\n if connection is None:\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"def _check_live(self):\\n # pylint: disable=protected-access\\n if self._shutdown.is_set():\\n raise ValueError(\\n \\\"The handler has already been shutdown. Please use ServiceBusClient to \\\"\\n \\\"create a new instance.\\\"\\n )\\n # The following client validation is for two purposes in a session receiver:\\n # 1. self._session._lock_lost is set when a session receiver encounters a connection error,\\n # once there's a connection error, we don't retry on the session entity and simply raise SessionlockLostError.\\n # 2. self._session._lock_expired is a hot fix as client validation for session lock expiration.\\n # Because currently uamqp doesn't have the ability to detect remote session lock lost.\\n # Usually the service would send a detach frame once a session lock gets expired, however, in the edge case\\n # when we drain messages in a queue and try to settle messages after lock expiration,\\n # we are not able to receive the detach frame by calling uamqp connection.work(),\\n # Eventually this should be a fix in the uamqp library.\\n # see issue: https://github.com/Azure/azure-uamqp-python/issues/183\\n try:\\n if self._session and (\\n self._session._lock_lost or self._session._lock_expired\\n ):\\n raise SessionLockLostError(error=self._session.auto_renew_error)\\n except AttributeError:\\n pass\",\n \"def is_active():\\n return True\",\n \"def checkSession(self):\\r\\n\\r\\n app_settings = QSettings(conf_parser.get(\\\"APP\\\", \\\"name\\\"))\\r\\n end_date_time = app_settings.value(\\\"EndDateTime\\\", type=QDateTime)\\r\\n cur_date_time = QDateTime.currentDateTime()\\r\\n if(cur_date_time.secsTo(end_date_time) > 0):\\r\\n # get values from app_settings\\r\\n gl_content.auth_email = app_settings.value(\\\"Email\\\")\\r\\n gl_content.auth_user_company = app_settings.value(\\\"UserCompany\\\")\\r\\n gl_content.auth_first_name = app_settings.value(\\\"FirstName\\\")\\r\\n gl_content.auth_last_name = app_settings.value(\\\"LastName\\\")\\r\\n gl_content.auth_user_sys_id = app_settings.value(\\\"UserSysId\\\")\\r\\n gl_content.auth_password = app_settings.value(\\\"Password\\\")\\r\\n \\r\\n # decrypt password\\r\\n bytes_password = bytes(gl_content.auth_password)\\r\\n gl_content.auth_password = crypt.decrypt(bytes_password)\\r\\n\\r\\n # make login request\\r\\n # get mac address\\r\\n mac_addr = uuid.getnode()\\r\\n if(not mac_addr):\\r\\n self.accept()\\r\\n return\\r\\n \\r\\n # configure data\\r\\n data = QtCore.QByteArray()\\r\\n data.append(\\\"username={}&\\\".format(gl_content.auth_email))\\r\\n data.append(\\\"appVersion={}&\\\".format(conf_parser.get(\\\"APP\\\", \\\"version\\\")))\\r\\n data.append(\\\"appID={}&\\\".format(conf_parser.get(\\\"APP\\\", \\\"id\\\")))\\r\\n data.append(\\\"machineID={}&\\\".format(mac_addr))\\r\\n data.append(\\\"password={}\\\".format(gl_content.auth_password))\\r\\n \\r\\n # send request\\r\\n request = QtNetwork.QNetworkRequest(QtCore.QUrl(conf_parser.get(\\\"URLs\\\", \\\"auth\\\")))\\r\\n request.setHeader(QtNetwork.QNetworkRequest.ContentTypeHeader,'application/x-www-form-urlencoded')\\r\\n self.networkAccessManager.post(request, data)\\r\\n \\r\\n # start timer\\r\\n self.lb_msg.setText(\\\"Logging in to the server ...\\\")\\r\\n self.login_timer.start(200)\\r\\n self.pros = 0\\r\\n else:\\r\\n self.accept()\",\n \"def IsStarted(self) :\\n\\t\\t...\",\n \"def is_open(self):\\n result = None\\n if(self._type_connection == \\\"COM\\\"):\\n result = self._connection.is_open\\n else:\\n msg = \\\"99, No se puede validar la conexion para {}.\\\".format(self._type_connection)\\n raise ValueError(msg)\\n\\n return result\",\n \"def test_newSession(self):\\n session = self.mdk.session()\\n session2 = self.mdk.session()\\n self.assertSessionHas(session, session._context.traceId, [0])\\n self.assertSessionHas(session2, session2._context.traceId, [0])\\n self.assertNotEqual(session._context.traceId,\\n session2._context.traceId)\",\n \"def is_idle(self) -> bool:\",\n \"def is_active(self):\\n return self.stream.is_active()\",\n \"def check_client(self, client_pid):\\n if (client_pid in self._active_sessions.get_clients() and\\n not psutil.pid_exists(client_pid)):\\n self._close_session_completely(client_pid)\\n return False\\n return True\",\n \"def is_session_in_topic(cls) -> bool:\\n return True\",\n \"def is_closed(self) -> bool:\",\n \"def is_alive(self):\\n conn = HTTPConnection(self.browser.host, self.browser.port)\\n conn.request(\\\"HEAD\\\", \\\"/invalid\\\")\\n res = conn.getresponse()\\n return res.status == 404\",\n \"def _check_session_valid(request):\\n if not request.user.is_authenticated:\\n return HttpResponseForbidden(reason=\\\"Access denied!\\\")\\n\\n if \\\"analytics\\\" not in request.session:\\n err = \\\"Could not fetch analytic session data.\\\"\\n return HttpResponseBadRequest(reason=err)\\n\\n return None\",\n \"async def check_session():\\n\\n if not Path(config.cookies_file).is_file():\\n return False\\n\\n cookies = None\\n with open(config.cookies_file, 'r') as f:\\n cookies = json.loads(f.read())\\n\\n as_client = httpx.AsyncClient(cookies=cookies)\\n\\n req = await as_client.get(\\\"https://www.linkedin.com/in/me\\\", allow_redirects=False)\\n if req.status_code == 200:\\n return as_client\\n else:\\n await as_client.aclose()\\n return False\",\n \"def checkLogin():\\n if 'access_token' in login_session:\\n return True\\n else:\\n return False\",\n \"def is_alive(self):\\n if self.status == 1:\\n return True\\n else:\\n return False\",\n \"def is_open(self):\\n return self.__port.isOpen()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82529193","0.8016455","0.699788","0.6899404","0.6878535","0.6878535","0.68676883","0.68601865","0.6826742","0.6791624","0.67632437","0.6713392","0.6696932","0.6672162","0.66605437","0.6652152","0.6649548","0.66456234","0.66192186","0.6615233","0.66119426","0.6594229","0.65583944","0.65535367","0.6493688","0.6488828","0.6481143","0.6476377","0.64691955","0.6468384","0.64596343","0.6455503","0.6448453","0.6433718","0.64246625","0.64098495","0.6409363","0.640561","0.63975346","0.6390478","0.63824636","0.63632244","0.63632244","0.6362014","0.6334823","0.6316919","0.63145083","0.62928444","0.627593","0.6267327","0.62469584","0.6219798","0.62079537","0.62020046","0.61885023","0.61745185","0.61675787","0.61456865","0.6139979","0.61274755","0.610158","0.6093715","0.60634786","0.60634685","0.6062783","0.6055502","0.6048768","0.6048175","0.6035214","0.60335255","0.60238415","0.60236037","0.6022859","0.602239","0.6011725","0.60096633","0.60029495","0.6002839","0.5999142","0.5992939","0.5987566","0.59851825","0.5984072","0.5975951","0.59692097","0.59585434","0.5957253","0.5955608","0.59522223","0.5949749","0.594266","0.5941043","0.5937379","0.59167707","0.59143513","0.5906677","0.5900903","0.5898191","0.5889514","0.58857095"],"string":"[\n \"0.82529193\",\n \"0.8016455\",\n \"0.699788\",\n \"0.6899404\",\n \"0.6878535\",\n \"0.6878535\",\n \"0.68676883\",\n \"0.68601865\",\n \"0.6826742\",\n \"0.6791624\",\n \"0.67632437\",\n \"0.6713392\",\n \"0.6696932\",\n \"0.6672162\",\n \"0.66605437\",\n \"0.6652152\",\n \"0.6649548\",\n \"0.66456234\",\n \"0.66192186\",\n \"0.6615233\",\n \"0.66119426\",\n \"0.6594229\",\n \"0.65583944\",\n \"0.65535367\",\n \"0.6493688\",\n \"0.6488828\",\n \"0.6481143\",\n \"0.6476377\",\n \"0.64691955\",\n \"0.6468384\",\n \"0.64596343\",\n \"0.6455503\",\n \"0.6448453\",\n \"0.6433718\",\n \"0.64246625\",\n \"0.64098495\",\n \"0.6409363\",\n \"0.640561\",\n \"0.63975346\",\n \"0.6390478\",\n \"0.63824636\",\n \"0.63632244\",\n \"0.63632244\",\n \"0.6362014\",\n \"0.6334823\",\n \"0.6316919\",\n \"0.63145083\",\n \"0.62928444\",\n \"0.627593\",\n \"0.6267327\",\n \"0.62469584\",\n \"0.6219798\",\n \"0.62079537\",\n \"0.62020046\",\n \"0.61885023\",\n \"0.61745185\",\n \"0.61675787\",\n \"0.61456865\",\n \"0.6139979\",\n \"0.61274755\",\n \"0.610158\",\n \"0.6093715\",\n \"0.60634786\",\n \"0.60634685\",\n \"0.6062783\",\n \"0.6055502\",\n \"0.6048768\",\n \"0.6048175\",\n \"0.6035214\",\n \"0.60335255\",\n \"0.60238415\",\n \"0.60236037\",\n \"0.6022859\",\n \"0.602239\",\n \"0.6011725\",\n \"0.60096633\",\n \"0.60029495\",\n \"0.6002839\",\n \"0.5999142\",\n \"0.5992939\",\n \"0.5987566\",\n \"0.59851825\",\n \"0.5984072\",\n \"0.5975951\",\n \"0.59692097\",\n \"0.59585434\",\n \"0.5957253\",\n \"0.5955608\",\n \"0.59522223\",\n \"0.5949749\",\n \"0.594266\",\n \"0.5941043\",\n \"0.5937379\",\n \"0.59167707\",\n \"0.59143513\",\n \"0.5906677\",\n \"0.5900903\",\n \"0.5898191\",\n \"0.5889514\",\n \"0.58857095\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6141397"},"document_rank":{"kind":"string","value":"58"}}},{"rowIdx":77093,"cells":{"query":{"kind":"string","value":"Trains the classifier with the given filenames"},"document":{"kind":"string","value":"def train(self, trainFilenames):\n\n\t\tstartIndex = len(self.documents)\n\t\tendIndex = startIndex + len(trainFilenames)\n\t\tself.documents += trainFilenames\n\n\t\tX = [[i] for i in range(startIndex, endIndex)]\n\t\tY = [isAroused(f) for f in trainFilenames]\n\n\t\tself.knn.fit(np.array(X), np.array(Y))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def train(self, trainfile):","def train_for(labels, filenames):\n stt = stt_google\n csvfiles = []\n writers = []\n for index, filename in enumerate(filenames):\n currfile = open(filename, 'ab')\n csvfiles.append(currfile)\n writers.append(csv.writer(currfile))\n # record instances until it doesn't interpret any text\n speech = stt.listen_for_speech()\n while(speech):\n hypotheses = []\n for hypothesis in speech:\n hypotheses.append(hypothesis['utterance'])\n #write hypotheses\n for index, label in enumerate(labels):\n writers[index].writerow([label] + hypotheses)\n speech = stt.listen_for_speech()\n for csvfile in csvfiles:\n csvfile.close","def load(self, filename):\n self.classifiers = []\n for i in range(0, self.category_level):\n clf = joblib.load(filename + '.level_%d' % (i + 1))\n self.classifiers.append(clf)","def train(self, absList, modelFilename):\n pass","def train(self, absList, modelFilename):\n pass","def train(self, absList, modelfilename):\n raise NotImplementedError(\"Need to implement train()\")","def train_classifier(train_faces, train_faces_ids):\n recognizer_lbph = cv2.face.LBPHFaceRecognizer_create()\n print('Training model in progress...')\n recognizer_lbph.train(train_faces, np.array(train_faces_ids))\n print('Saving...')\n recognizer_lbph.save('trainner.yml')\n print('Model training complete!')","def train(self, absList, modelFilename):\n raise NotImplementedError(\"Need to implement train()\")","def __init__(self):\n self.train(positivity_files, 0)\n self.train(subjectivity_files, 1)","def mt_classifier(input_file, muscle_exec, basename, best_results):\n pass","def train(self, features, labels):\n pass","def run_tests():\r\n source1 = TextModel('50 Shades of Gray')\r\n source1.add_file('50.txt')\r\n \r\n print()\r\n \r\n source2 = TextModel('King James Version of the Bible')\r\n source2.add_file('kjv.txt')\r\n\r\n print()\r\n\r\n new1 = TextModel('Shakespeare')\r\n new1.add_file('shake.txt')\r\n new1.classify(source1, source2)\r\n \r\n print()\r\n \r\n new2 = TextModel('JK Rowling')\r\n new2.add_file('hp.txt')\r\n new2.classify(source1, source2)\r\n \r\n print()\r\n \r\n new3 = TextModel('Breitbart News Network')\r\n new3.add_file('bnn.txt')\r\n new3.classify(source1, source2)\r\n \r\n print()\r\n \r\n new4 = TextModel('Chaucer')\r\n new4.add_file('tct.txt')\r\n new4.classify(source1, source2)","def train(self, args):\n\n ##################################\n # Read the training data\n ##################################\n if not os.path.isdir(args.annotationPath):\n print('annotation path does not exist: {}' \\\n .format(args.annotationPath))\n return -1\n\n data = self.readData(args.annotationPath)\n\n x = []\n y = []\n l = []\n for subject, df in data.items():\n lx = df[['gradient', 'rate']].values.tolist()\n ly = np.array(df[['immersion']].values.tolist()).squeeze(-1)\n x.extend(lx)\n y.extend(ly.tolist())\n l.append(len(lx))\n\n ############################\n # Execute the training\n ############################\n\n print('Training the detector...')\n self._clf.fit(x, y, l)\n\n if not self.save():\n print('Could not persist the trained model to disk (in file {})' \\\n .format(self._modelFile))\n\n return 0","def run_tests():\n source1 = TextModel('hilary_speaches')\n source1.add_file('hilary_source_text.txt')\n\n source2 = TextModel('bernie_speaches')\n source2.add_file('bernie_source_text.txt')\n\n new1 = TextModel('trump_speach')\n new1.add_file('trump_text.txt')\n new1.classify(source1, source2)\n\n new2 = TextModel('hilary_test')\n new2.add_file('hilary_test.txt')\n new2.classify(source1, source2)\n\n new3 = TextModel('bernie_test')\n new3.add_file('bernie_test.txt')\n new3.classify(source1, source2)\n\n new4 = TextModel('bill_clinton_test')\n new4.add_file('bill_clinton_source.txt')\n new4.classify(source1, source2)","def train_opencv_classifier(training_filenames,\n training_labels,\n classifier = LBPHFaceRecognizer_create(),\n training_path = Config.A2_EXTRACTED_FACES_PATH):\n faces = _open_images(training_filenames, training_path)\n classifier.train(faces, np.array(training_labels))\n # The lbph face recogniser will be called 'opencv_lbphfaces'.\n classifier.save(os.path.join('dat', '{}.yml'.format(classifier.getDefaultName())))","def singleDriverTrainer(file_to_classify, training_files, in_model1, in_model2,\n weight_1 = 0.5, file_subset_size = 200):\n # first, grab the target data\n x_target, y_target, id_target = extractCSV(file_to_classify, target = 1)\n # remove na's\n x_target = np.nan_to_num(x_target)\n y_target = np.nan_to_num(y_target)\n\n # now grab the training/noise data\n x_all = copy.copy(x_target)\n y_all = copy.copy(y_target)\n \n # use stacking to up-sample our target data\n # and balance it with our noise data\n n = int(round(len(training_files) * file_subset_size / 200.0))\n l = len(x_target)\n # stack target to balance classes test \n if n > 1:\n stack_idx = range(l) * n\n x_all, y_all = x_all[stack_idx], y_all[stack_idx]\n\n # now we must add the data from each noise file\n # loop through all of our training/noise files\n for filepath in training_files:\n # open the file\n x_current, y_current, ids = extractCSV(filepath, target = 0,\n file_subset = file_subset_size)\n # and add the contents to our training data\n x_all = np.concatenate((x_all, x_current))\n y_all = np.concatenate((y_all, y_current))\n # repeat for every filepath in our training files list\n\n # again, remove na's from our data\n x_all = np.nan_to_num(x_all)\n y_all = np.nan_to_num(y_all)\n\n\n ###########\n # MODEL 1 TRAINING\n ###########\n in_model1.fit(x_all, y_all)\n # now we are ready to provide class probabilities for our predictions\n # on the target data\n predictions = in_model1.predict_proba(x_target)\n # note that we must extract the index of the class 1 probability\n prob_idx = np.where(in_model1.classes_ == 1)[0][0]\n # apply the weighting for model 1 to each prediction\n class_probs1 = [pred[prob_idx]*weight_1 for pred in predictions]\n \n \n ###########\n # MODEL 2 TRAINING\n ###########\n in_model2.fit(x_all, y_all)\n # again, extract probabilistic predictions for the target data\n predictions2 = in_model2.predict_proba(x_target)\n prob_idx2 = np.where(in_model2.classes_ == 1)[0][0]\n # apply the weighting for model 2 to each prediction\n # note that our weighting for number 2 is (1 - weight[1])\n weight_2 = 1 - weight_1\n class_probs2 = [pred[prob_idx2]*weight_2 for pred in predictions2]\n \n # combine probabilities\n class_probs = np.add(class_probs1, class_probs2)\n \n # and return a matrix of the id's and the corresponding probabilities\n return_mat = [[id_target[idx], class_probs[idx]] \\\n for idx in xrange(len(class_probs))]\n \n # report our progress before returning\n print 'completed driver %s' % file_to_classify\n return np.asarray(return_mat)","def retrain(self):\n thread = Thread(target=self.trainer.train_classifier)\n thread.start()","def train(self, train_loader):\n pass","def train(self, src, labels): # real signature unknown; restored from __doc__\n pass","def batch_test(classifier,labeledfiles):\r\n\tcorrect_cnt = 0\r\n\tfor filename, label in labeledfiles:\r\n\t\twlist = utils.load(filename)\r\n\t\tif classifier.cl == 'f':\r\n\t\t\tclassifier.test_one_prep(fe.extract_features(wlist,label)[0],fe.extract_features(wlist,label)[1])\r\n\t\t\tcontinue\t\r\n\t\telse:\r\n\t\t\trlabel = classifier.test_one(fe.extract_features(wlist,label)[0])\r\n\t\tif options.verbose == True:\r\n\t\t\tprint filename, '-->', rlabel\r\n\t\tif rlabel == label:\r\n\t\t\tcorrect_cnt += 1 \r\n\tif classifier.cl == 'f':\r\n\t\tclassifier.test_one_p()\r\n\t\t#classifier.test_one()\r\n\telse:\r\n\t\tprint \"accuracy = %f (%d/%d)\" % (float(correct_cnt)/len(labeledfiles), correct_cnt, len(labeledfiles))","def run_tests():\n source1 = TextModel('CS111 Syllabus')\n source1.add_file('CS111_Syllabus.txt')\n\n source2 = TextModel('AR Syllabus')\n source2.add_file('AR_Syllabus.txt')\n\n new1 = TextModel('WR120 Syllabus')\n new1.add_file('WR120_Syllabus.txt')\n new1.classify(source1, source2)\n \n new2 = TextModel('CS131 Syllabus')\n new2.add_file('CS131_Syllabus.txt')\n new2.classify(source1, source2)\n \n new3 = TextModel('My Paper 2 for WR120')\n new3.add_file('WR_Paper_2.txt')\n new3.classify(source1, source2)\n \n new4 = TextModel('CS111 PS9PR0')\n new4.add_file('ps9pr0.txt')\n new4.classify(source1, source2)","def __init__(self, tfiles, featurizer):\n\t\t### [ Featurize the classifier ] ###\n\t\t# random.shuffle(tfiles)\n\t\tself.featurizer = featurizer\n\t\tself.tfiles = tfiles\n\n\t\t# Now build a model based on these vectors\n\t\tnum_files = len(tfiles)\n\t\tnum_training_files = int(PERCENT_TRAINING * num_files)\n\t\tnum_test_files = num_files - num_training_files\n\n\t\tself.train_files = self.tfiles[:num_training_files]\n\t\tself.test_files = self.tfiles[num_training_files:]\n\n\t\tself.all_data = [featurizer.get_feature_matrix_and_output_vector(f) for f in self.tfiles]\n\t\tall_data_vectors = [d[0] for d in self.all_data]\n\t\tprint([v.shape for v in all_data_vectors])\n\t\tself.all_features = np.vstack(d[0] for d in self.all_data)\n\t\tself.all_labels = np.hstack(d[1] for d in self.all_data)\n\n\t\tself.train_data = [featurizer.get_feature_matrix_and_output_vector(f) for f in self.train_files]\n\t\tself.train_features = np.vstack([d[0] for d in self.train_data])\n\t\tself.train_labels = np.hstack([d[1] for d in self.train_data])\n\n\t\tself.test_data = [featurizer.get_feature_matrix_and_output_vector(f) for f in self.test_files]\n\t\tself.test_features = np.vstack([d[0] for d in self.test_data])\n\t\tself.test_labels = np.hstack(d[1] for d in self.test_data)\n\n\t\tself.trained_clf = []\n\t\tfor cl in used_classifiers:\n\t\t\tself.trained_clf += [cl(self.train_features, self.train_labels)]","def train(args):\n # Copy the project file.\n # TODO: If the file exists, ask the user if it shall be deleted.\n if os.path.isfile(args.outfile):\n os.remove(args.outfile)\n shutil.copyfile(args.train, args.outfile)\n\n # Create an ILP object for the project.\n proj = ILP(args.outfile, args.cache, args.compression)\n\n # Do the autocontext loop.\n autocontext(args.ilastik, proj, args.nloops, args.labeldataset, weights=args.weights, predict_file=args.predict_file)","def train_from_dir(self, path, cat):\n dirfiles = glob.glob(os.path.join(path, '*'))\n total = len(dirfiles)\n count = 0\n for infile in dirfiles:\n f = open(infile, \"r\")\n text = f.read()\n self.train(text, cat)","def train_classifier(images_path):\n car_imgs = get_images(images_path + '/vehicles/')\n non_car_imgs = get_images(images_path + '/non-vehicles/')\n\n print('Computing car features')\n car_features = extract_features(car_imgs,\n color_space=COLOR_SPACE,\n spatial_size=SPATIAL_SIZE,\n hist_bins=HIST_BINS,\n orient=ORIENT,\n pix_per_cell=PIX_PER_CELL,\n cell_per_block=CELL_PER_BLOCK,\n hog_channel=HOG_CHANNEL,\n spatial_feat=SPATIAL_FEAT,\n hist_feat=HIST_FEAT,\n hog_feat=HOG_FEAT)\n print(len(car_features))\n\n print('Computing non-car features')\n non_car_features = extract_features(non_car_imgs,\n color_space=COLOR_SPACE,\n spatial_size=SPATIAL_SIZE,\n hist_bins=HIST_BINS,\n orient=ORIENT,\n pix_per_cell=PIX_PER_CELL,\n cell_per_block=CELL_PER_BLOCK,\n hog_channel=HOG_CHANNEL,\n spatial_feat=SPATIAL_FEAT,\n hist_feat=HIST_FEAT,\n hog_feat=HOG_FEAT)\n print(len(non_car_features))\n \n X = np.vstack((car_features, non_car_features)).astype(np.float64) \n print('X shape: {}'.format(X.shape))\n # Fit a per-column scaler\n X_scaler = StandardScaler().fit(X)\n # Apply the scaler to X\n scaled_X = X_scaler.transform(X)\n\n # Define the labels vector\n y = np.hstack((np.ones(len(car_features)), np.zeros(len(non_car_features))))\n\n # Split up data into randomized training and test sets\n rand_state = np.random.randint(0, 100)\n X_train, X_test, y_train, y_test = train_test_split(\n scaled_X, y, test_size=0.2, random_state=rand_state)\n\n # Use a linear SVC \n svc = LinearSVC()\n # Check the training time for the SVC\n t=time.time()\n svc.fit(X_train, y_train)\n t2 = time.time()\n print(round(t2-t, 2), 'Seconds to train SVC...')\n # Check the score of the SVC\n print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))\n # Check the prediction time for a single sample\n t=time.time()\n\n return svc, X_scaler","def train(self, features, labels):\n self._clf.fit(features, labels)","def train(self, features, labels):\n self._clf.fit(features, labels)","def train_enroller(self, train_files, enroller_file):\n # save something\n bob.io.base.save(_data, enroller_file)","def test_classifier(self):\n \n files = 0\n tp = 0\n fp = 0\n tn = 0\n fn = 0\n \n for testFile in os.listdir(self.testing_dir):\n if os.path.splitext(testFile)[1] in self.valid:\n\n files += 1\n fileName = self.testing_dir + \"/\" + testFile\n\n img = cv2.imread(fileName).astype(np.float32)\n self.Helpers.logger.info(\"Loaded test image \" + fileName)\n \n img = cv2.resize(img, (self.Helpers.confs[\"cnn\"][\"data\"][\"dim\"], \n self.Helpers.confs[\"cnn\"][\"data\"][\"dim\"]))\n img = self.reshape(img)\n \n prediction = self.get_predictions(img)\n \n msg = \"\"\n if prediction == 1 and \"_1.\" in testFile:\n tp += 1\n msg = \"Acute Lymphoblastic Leukemia correctly detected (True Positive)\"\n elif prediction == 1 and \"_0.\" in testFile:\n fp += 1\n msg = \"Acute Lymphoblastic Leukemia incorrectly detected (False Positive)\"\n elif prediction == 0 and \"_0.\" in testFile:\n tn += 1\n msg = \"Acute Lymphoblastic Leukemia correctly not detected (True Negative)\"\n elif prediction == 0 and \"_1.\" in testFile:\n fn += 1\n msg = \"Acute Lymphoblastic Leukemia incorrectly not detected (False Negative)\"\n self.Helpers.logger.info(msg)\n \n self.Helpers.logger.info(\"Images Classifier: \" + str(files))\n self.Helpers.logger.info(\"True Positives: \" + str(tp))\n self.Helpers.logger.info(\"False Positives: \" + str(fp))\n self.Helpers.logger.info(\"True Negatives: \" + str(tn))\n self.Helpers.logger.info(\"False Negatives: \" + str(fn))","def train(self):\n lFileList = []\n for fFileObj in os.walk(\"movies_reviews/\"):\n lFileList = fFileObj[2]\n break\n for rev in lFileList:\n if int(rev[7])== 1:\n contents = self.loadFile(\"movies_reviews/\" + rev)\n listOfWords = self.tokenize(contents)\n for word in listOfWords:\n self.negRev[word] = self.negRev.get(word, 0) + 1\n if int(rev[7])== 5:\n contents = self.loadFile(\"movies_reviews/\" + rev)\n listOfWords = self.tokenize(contents)\n for word in listOfWords:\n self.posRev[word] = self.posRev.get(word, 0) + 1\n self.save(self.posRev, \"posRev\")\n self.save(self.negRev, \"negRev\")","def train(args):\n\n log_header('Training network')\n\n train_retriever(args)","def __init__(\n self, filenames, num_features=None, num_labels=0, x_dtype=tf.string, y_dtype=tf.int64, batch_size=16,\n skip_count=0, file_repeat=1, num_epoch=None, file_folder=None,\n num_threads=8, buffer_size=2000, shuffle_file=False,\n decode_jpeg=False, use_one_hot_label=False, use_smooth_label=True, num_classes=1):\n if file_folder is None:\n file_folder = FLAGS.DEFAULT_IN + 'tfrecords_{}/'.format(FLAGS.TARGET_SIZE)\n # check inputs\n if isinstance(filenames, str): # if string, add file location and .tfrecords\n filenames = [os.path.join(file_folder, filenames + '.tfrecords')]\n else: # if list, add file location and .tfrecords to each element in list\n filenames = [os.path.join(file_folder, file + '.tfrecords') for file in filenames]\n for file in filenames:\n assert os.path.isfile(file), 'File {} does not exist.'.format(file)\n if file_repeat > 1:\n filenames = filenames * int(file_repeat)\n if shuffle_file:\n # shuffle operates on the original list and returns None / does not return anything\n from random import shuffle\n shuffle(filenames)\n\n # training information\n self.num_features = num_features\n self.num_labels = num_labels\n self.x_dtype = x_dtype\n self.y_dtype = y_dtype\n self.batch_size = batch_size\n self.batch_shape = [self.batch_size, self.num_features]\n self.num_epoch = num_epoch\n self.skip_count = skip_count\n\n # read data\n self.decode_jpeg = decode_jpeg\n self.use_one_hot_label = False if num_labels > 1 else use_one_hot_label\n self.use_smooth_label = use_smooth_label\n self.num_classes = num_classes\n dataset = tf.data.TFRecordDataset(filenames) # setting num_parallel_reads=num_threads decreased the performance\n self.dataset = dataset.map(self.__parser__, num_parallel_calls=num_threads)\n self.iterator = None\n self.buffer_size = buffer_size\n self.scheduled = False\n self.num_threads = num_threads","def train(self, train_instances, train_labels, update_cache=True,\n sample_weight=None):\n self.classifier.fit(train_instances, train_labels, sample_weight=sample_weight)\n if update_cache:\n pickle.dump(self.classifier, open(self.cache_filename, 'wb'))","def train(self, examples):\n \n for e in examples:\n\n class_label = None\n\n features = self.featurize(e[1])\n\n #determining which class corresponds to \n if(e[2] == '0'):\n class_label = self.class_zero\n self.class_zero_feature_count += len(features)\n self.class_zero_doc_count += 1\n else:\n class_label = self.class_one\n self.class_one_feature_count += len(features)\n self.class_one_doc_count += 1\n\n for f in features:\n if(f[1] == True):\n #adding feature to vocabulary\n self.vocab.add(f[0])\n #adding feature to class to keep track of counts\n class_label[f[0]] += 1\n \n\n self.total_docs = len(examples)","def train(self):\n for data_tier in self.data_tiers:\n fd = open(self.data_path + '/training_data_' + data_tier + '.json', 'r')\n self.preprocessed_data[data_tier] = json.load(fd)\n fd.close()\n tot = len(self.preprocessed_data[data_tier]['features'])\n p = int(math.ceil(tot*0.8))\n training_features = np.array(self.preprocessed_data[data_tier]['features'][:p])\n trend_training_classifications = np.array(self.preprocessed_data[data_tier]['trend_classifications'][:p])\n avg_training_classifications = np.array(self.preprocessed_data[data_tier]['avg_classifications'][:p])\n t1 = datetime.datetime.utcnow()\n self.clf_trend[data_tier].fit(training_features, trend_training_classifications)\n self.clf_avg[data_tier].fit(training_features, avg_training_classifications)\n t2 = datetime.datetime.utcnow()\n td = t2 - t1\n self.logger.info('Training %s for data tier %s took %s', self.name, data_tier, str(td))\n joblib.dump(self.clf_trend[data_tier], self.data_path + '/' + self.name + '_trend_' + data_tier + '.pkl')\n joblib.dump(self.clf_avg[data_tier], self.data_path + '/' + self.name + '_avg_' + data_tier + '.pkl')","def train(self, clues, parsed_clues):\n klasses = []\n for answer in parsed_clues:\n klass = answer[:answer.index(\":\")]\n klasses.append(klass)\n features = []\n for clue in clues:\n features.append(self.findFeatures(clue))\n self.classifier.addExamples(features, klasses)","def train_classifier(corpus=CategorizedPlaintextCorpusReader('.\\\\corpus\\\\', r'.+\\.txt', cat_pattern=r'(.+)\\.txt')):\n lfeats = label_feats_from_corpus(corpus)\n train_feats, test_feats = split_label_feats(lfeats, 1)\n nb_classifier = NaiveBayesClassifier.train(train_feats)\n pickle.dump(nb_classifier, open('nb_classifier', 'wb'))\n return nb_classifier","def trainClassifiers(features, labels):\n # trains each classifier on given training set\n classArr = VotingClassifier(estimators = [('NB', naiveBayesModel), ('linSVC', linearSVCModel), ('LR', logRegModel)], \\\n voting = 'hard', weights = [1, 5, 3])\n \n classArr = classArr.fit(features, labels)\n return classArr","def do_make_(self):\n global g_list_of_classifier\n\n for ite_clf in g_list_of_classifier:\n ite_clf.learn()\n return ''","def apply_classifier(self):\n for detected_object in self.detected_objects:\n detected_object.predict_class(self.original_image)","def train():\n pass","def trainModel( self, featureTrain, classTrain):","def run_tests():\n source1 = TextModel(\"Barack Obama\")\n source1.add_file('project/source_texts/barackobama_source_text.txt')\n\n source2 = TextModel('Donald Trump')\n source2.add_file('project/source_texts/donaldtrump_source_text.txt')\n\n new1 = TextModel('More Obama')\n new1.add_file('project/source_texts/moreobama_source_text.txt')\n new1.classify(source1, source2)\n\n new2 = TextModel('More Trump')\n new2.add_file('project/source_texts/moretrump_source_text.txt')\n new2.classify(source1, source2)\n\n new1 = TextModel('Gucci Gang by Lil Pump')\n new1.add_file('project/source_texts/guccigang_source_text.txt')\n new1.classify(source1, source2)\n\n new1 = TextModel(\"Spongebob Transcripts\")\n new1.add_file('project/source_texts/spongebobeps_source_text.txt')\n new1.classify(source1, source2)","def train(self):\n # 1. Extracting details of attributes\n\n self.get_attribute_data()\n if self.train_data is None and self.train_data_file is None:\n raise ValueError(\"Neither training data not training file provided\")\n\n self.get_train_data()\n self.classifier = self.build_tree(rows=self.train_data, attribute_list=self.attribute_names)","def train(self):\r\n faces = [] #empty list for faces\r\n Ids = [] #empty list for Id's\r\n path = f\"{PARENT_PATH}\\\\{DATASET_DIR}\" #dataset path\r\n\r\n #join each and every image paths\r\n image_paths = [os.path.join(path, i) for i in os.listdir(path)]\r\n #print(image_paths)\r\n\r\n for image in image_paths:\r\n face_img = Image.open(image).convert('L') #Pillow Image\r\n np_face = np.array(face_img, 'uint8') #into numpy array - usigned 8 bit -1byte\r\n Id = int(os.path.split(image)[-1].split('.')[1]) #get id from image path\r\n #print(Id)\r\n faces.append(np_face) #append in faces array/list\r\n Ids.append(Id) #append in Ids list/array\r\n\r\n RECOGNIZER.train(faces, np.array(Ids)) #train model using faces and Id (numpy arrays)\r\n RECOGNIZER.save(f\"{PARENT_PATH}\\\\{TRAINED_FILE}\")\r\n\r\n self.pop_window(title=\"Restart Needed!\", msg=\"Training Successful.\\nRestart the app Now.\")\r\n return","def train(args):\n\n nb_classes = 2\n neg_class_label = '0'\n pos_class_label = '1'\n nb_train_samples = get_nb_files(args.train_dir)\n nb_val_samples = get_nb_files(args.val_dir)\n batch_size = int(args.batch_size)\n\n nb_train_samples_neg = len([name for name in os.listdir(os.path.join(args.train_dir, neg_class_label))])\n nb_train_samples_pos = len([name for name in os.listdir(os.path.join(args.train_dir, pos_class_label))])\n print(\"Number of neg training examples is \", nb_train_samples_neg)\n print(\"Number of pos training examples is \", nb_train_samples_pos)\n\n\n # data prep\n train_datagen = ImageDataGenerator()\n test_datagen = ImageDataGenerator()\n\n train_generator = train_datagen.flow_from_directory(args.train_dir,\n target_size=(IM_WIDTH, IM_HEIGHT),\n batch_size=batch_size,\n class_mode='categorical',\n shuffle=True)\n\n validation_generator = test_datagen.flow_from_directory(args.val_dir,\n target_size=(IM_WIDTH, IM_HEIGHT),\n batch_size=batch_size,\n class_mode='categorical',\n shuffle=False)\n val_labels = validation_generator.classes\n # validation_labels = to_categorical(val_labels, num_classes=2)\n\n\n # setting up class weights for imbalanced dataset ...\n # The sum of the weights of all examples stays the same.\n\n\n total = nb_train_samples_neg + nb_train_samples_pos\n print('Examples:\\n Total: {}\\n Positive: {} ({:.2f}% of total)\\n'.format(total, nb_train_samples_pos, 100 * nb_train_samples_pos/total))\n\n\n weight_for_0 = total / nb_train_samples_neg\n weight_for_1 = total / nb_train_samples_pos\n\n class_weight = {0: weight_for_0, 1: weight_for_1}\n\n print('Weight for class 0: {:.2f}'.format(weight_for_0))\n print('Weight for class 1: {:.2f}'.format(weight_for_1))\n\n # setup model\n # model = keras.models.load_model('./experiment/vgg_ft_ni10.model')\n base_model = applications.Xception(weights='imagenet', include_top=False, input_shape=(300, 300, 3))\n print(\"Number of vgg layers :\", len(base_model.layers))\n\n # adding fully connected layer\n model = add_new_last_layer(base_model, nb_classes)\n\n # transfer learning...\n setup_to_transfer_learn(model, base_model)\n\n for i, layer in enumerate(model.layers):\n print(i, layer.name)\n print(layer.trainable)\n\n # checkpoint = ModelCheckpoint(\"ft_vgg16.h5\",\n # monitor=RocCallback(validation_generator), # not working with custom callbacks\n # verbose=1,\n # save_best_only=True,\n # save_weights_only=False,\n # mode='auto',\n # period=1)\n\n print('Transfer Learning is starting...')\n # model.save_weights('') # Please input weights if present\n history_tl = model.fit_generator(train_generator,\n nb_epoch=NB_EPOCHS_TL,\n samples_per_epoch=nb_train_samples,\n validation_data=validation_generator,\n nb_val_samples=nb_val_samples,\n class_weight=class_weight,\n callbacks=[RocCallback(validation_generator)])\n\n # fine-tuning ...\n setup_to_finetune(model)\n for i, layer in enumerate(model.layers):\n print(i, layer.name)\n print(layer.trainable)\n # model.load_weights('./experiment/vgg_ft_ni25.h5')\n # model.summary()\n\n print('Fine tuning is starting...')\n history_ft = model.fit_generator(train_generator,\n samples_per_epoch=nb_train_samples,\n nb_epoch=NB_EPOCHS_FT,\n validation_data=validation_generator,\n nb_val_samples=nb_val_samples,\n class_weight=class_weight,\n callbacks=[RocCallback(validation_generator)])\n\n # making predictions ...3\n model.save_weights('./experiment/vgg_ft_ni25.h5')\n # model.save('./experiment/vgg_ft_ni10.model')\n pred = model.predict_generator(validation_generator, steps=nb_val_samples//batch_size)\n\n # confusion_matrix(val_labels, pred)\n\n # F1Score ...\n predictions = np.argmax(pred, axis=1)\n\n\n # Storing Predictions as CSV ...\n filenames = validation_generator.filenames\n labels = validation_generator.class_indices\n labels = dict((v, k) for k, v in labels.items())\n predictions = [labels[k] for k in predictions]\n results = pd.DataFrame({\"Filename\": filenames,\n \"true\" : pred[:, 1],\n \"Predictions\": predictions,\n \"Label\": val_labels})\n # results.to_csv(\"./experiment/ft_ni_results15.csv\", index=False)\n # metric(\"./experiment/ft_ni_results15.csv\")\n\n # plotting data...\n if args.plot:\n plot_training(pred, val_labels)\n\n input_path = args.val_dir + pos_class_label\n output_path = './models/vgg/epoch_{}/'.format(NB_EPOCHS_TL+NB_EPOCHS_FT)\n\n\n # visualising activation maps of positive class ...\n for img in validation_generator.filenames:\n visualize_class_activation_map(model, input_path + img, output_path + img)","def train_routine(training_file, output_folder):\n if output_folder[-1] != '/':\n output_folder += '/'\n\n svm_file = output_folder + 'svm.txt'\n centroid_file = output_folder + 'centroids.txt'\n ids_file = output_folder + 'ids.txt'\n\n surf = cv2.SURF(250, extended=False)\n categories = dict()\n ids = dict()\n id = 1\n features = list()\n\n print \"Extracting features\"\n for line in open(training_file):\n try:\n category, path = line.split(';')\n except:\n print \"Error: File not in proper format. Ensure: <category/class name>; <path to image of said category>\"\n sys.exit(0)\n path = path.strip()\n\n try:\n img = cv2.imread(path)\n #img = cv2.resize(img, (500, 500))\n except Exception as e:\n print e\n continue\n\n keypoints, descriptors = surf.detectAndCompute(img, None)\n\n if not category in categories:\n categories[category] = Category(label=category)\n ids[category] = id\n id += 1\n categories[category].add_feature(descriptors)\n\n #for category in categories:\n #f = categories[category].yield_features()\n ##features.extend(f)\n #for i in f:\n #features.extend(i)\n\n print \"Calculating centroids\"\n #np_features = numpy.array(features)\n #print \"Features: \", np_features.shape\n #centroids, labels = kmeans2(np_features, FEATURE_TYPES)\n centroids = helpers.loadObject(output_folder + 'centroids.txt')\n print centroids.shape\n\n print \"Forming bag of words\"\n X, Y = [], []\n for category in categories:\n categories[category].calc_bagofwords(centroids)\n for bow in categories[category].bagofwords:\n X.append(bow)\n Y.append(ids[category])\n print \"Fitting linear SVMs onto the bag of words\"\n lin_clf = svm.LinearSVC()\n lin_clf.fit(X, Y)\n\n helpers.saveObject(lin_clf, svm_file)\n helpers.saveObject(centroids, centroid_file)\n helpers.saveObject(ids, ids_file)","def train_classifier(self, class_id):\n raise NotImplementedError(\"Classifier training must be implemented first.\")","def train(self, labeled_featuresets):\n\n\t\t# generate training file from labeled_featuresets\n\t\tself.__text_converter = Text2svmConverter(self.__converter_arguments)\n\t\tself.__convert_labeled_featuresets(labeled_featuresets, self.__svmFilePath)\n\n\t\t# train the model\n\t\tself.__model = train_converted_text(self.__svmFilePath, self.__text_converter, grid_arguments=self.__grid_arguments, feature_arguments=self.__feature_arguments, train_arguments=self.__liblinear_arguments)","def __init__(self, dataset_dir, transform):\n\n self.filenames = os.listdir(dataset_dir)\n self.filenames = [os.path.join(dataset_dir, f)\n for f in self.filenames if f.endswith('.jpg')]\n self.labels = [int(os.path.split(filename)[-1][0])\n for filename in self.filenames]\n self.transform = transform","def run(self, images):\n\n # Apply filtering\n if len(self.preprocessing) > 0: \n print('Applying', len(self.preprocessing), 'filter(s) to input images')\n for filter in self.preprocessing:\n for i in range(len(images)):\n images[i] = filter(images[i])\n\n # Apply feature extraction\n if len(self.features) > 0:\n print('Extracting', len(self.features), 'feature(s) from input images')\n scaler = MinMaxScaler(feature_range=(0, 1))\n for i in range(len(images)):\n features = []\n for feature in self.features:\n features.append(feature(images[i]))\n images[i] = np.hstack(features)\n images = scaler.fit_transform(images)\n else:\n # Flatten images (not necessary when using feature extraction)\n train_data = np.array(train_data).reshape((len(train_data), -1))\n\n # Run predictions\n print('Predicting presence of parasites in', len(images), 'images\\n')\n return self.classifier.predict(images)","def train(self, images, labels, save_path=None):\n super(BlurredContext, self).train(\n images, labels, save_path,\n lambda img: cv2.imread(img, cv2.CV_LOAD_IMAGE_GRAYSCALE),\n lambda img: get_input_vector(resize(img, 500)))","def train(self):\n self.ae_train(self.net0, self.ae0_optimizer, self.train0_loader, self.val_loader, name='Net0')\n self.ae_train(self.net1, self.ae1_optimizer, self.train1_loader, self.val_loader, name='Net1')\n self.ae_train(self.net2, self.ae2_optimizer, self.train2_loader, self.val_loader, name='Net2')\n\n self.classifier_train(self.net0, self.optimizer0, self.train0_loader, self.val_loader, name='Net0')\n self.classifier_train(self.net1, self.optimizer1, self.train1_loader, self.val_loader, name='Net1')\n self.classifier_train(self.net2, self.optimizer2, self.train2_loader, self.val_loader, name='Net2')","def train(self, features, labels, seed=None):\n raise NotImplementedError('Not implemented')","def run_tests():\n source1 = TextModel('prep')\n source1.add_file('source_model_1.txt')\n \n source2 = TextModel('athletes')\n source2.add_file('source_model_2.txt')\n\n new1 = TextModel('my_writing')\n new1.add_file('my_writing.txt')\n new1.classify(source1, source2)\n\n # Add code for three other new models below.","def train_projector(self, train_files, projector_file):\n # save something\n bob.io.base.save(_data, projector_file)","def train_initial_classifier(zero_f=\"NIST/human_hcd_synthetic_oxidized.msp\",\n\t\t\t\t\t\t\t one_f=\"NIST/human_hcd_synthetic_native.msp\",\n\t\t\t\t\t\t\t selected_features_diff=[],\n\t\t\t\t\t\t\t top_mean = 1000,\n\t\t\t\t\t\t\t top_peaks = 100,\n\t\t\t\t\t\t\t max_distance = 275,\n\t\t\t\t\t\t\t distance_bins = 0.005,\n\t\t\t\t\t\t\t windowed_mode = False,\n\t\t\t\t\t\t\t out_dir=\"res/\"):\n\t#Check the file extension and parse to get features for class zero\n\tif zero_f.endswith(\".mgf\"): feats_zero,feat_bins,instance_names,count_zero = read_mgf(zero_f,sum_feats=False,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t feat_bins=selected_features_diff,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t max_dist=max_distance,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t step_size=distance_bins,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t top_peaks=top_peaks)\n\telif zero_f.endswith(\".msp\"): feats_zero,feat_bins,instance_names,count_zero = read_msp(zero_f,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsum_feats=False,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfeat_bins=selected_features_diff,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tmax_dist=max_distance,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tstep_size=distance_bins,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttop_peaks=top_peaks)\n\telse: return(False) # TODO display error!\n\t\n\t#Check the file extension and parse to get features for class one\n\tif one_f.endswith(\".mgf\"): feats_one,feat_bins,instance_names,count_one = read_mgf(one_f,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t sum_feats=False,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t feat_bins=selected_features_diff,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t max_dist=max_distance,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t step_size=distance_bins,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t top_peaks=top_peaks)\n\telif one_f.endswith(\".msp\"): feats_one,feat_bins,instance_names,count_one = read_msp(one_f,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t sum_feats=False,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t feat_bins=selected_features_diff,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t max_dist=max_distance,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t step_size=distance_bins,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t top_peaks=top_peaks)\n\telse: return(False) # TODO display error!\n\n\t#Prepare labels equal to length class zero and one\n\ty = [0]*(count_zero)\n\ty.extend([1]*(count_one))\n\n\ty = np.array(y)\n\t\n\t#Stack the feature matrices of both classes\n\tX = scipy.sparse.vstack((feats_zero,feats_one))\n\t\n\t#Train optimizing the hyperparameters\n\txgb_model,random_search_res_xgb = train_xgb(X,y)\n\t#print(random_search_res_xgb.best_params_)\n\t#print(random_search_res_xgb.best_score_)\n\t\n\t#Train use selected hyperparameters\n\ttrain_xgb_lim(X,y,random_search_res_xgb.best_params_,out_dir=out_dir)\n\tplot_train_distr(xgb_model,X,y,out_dir=out_dir)\n\t\n\t#Flush to pickle\n\txgboost_to_wb(random_search_res_xgb,outfile=out_dir+\"model.pickle\")\n\t\n\trandom_search_res_xgb = pickle.load(open(out_dir+\"model.pickle\",\"rb\"))\n\t\n\t#Plot some of the feature importances and probs\n\tfscores = xgb_model.booster().get_fscore()\n\tfscores_list = sorted(list(fscores.items()),key=itemgetter(1),reverse=True)\n\tselected_features_indexes = map(int,[f.replace(\"f\",\"\") for f,n in fscores_list])\n\tselected_features_xgboost = [selected_features_diff[sfp] for sfp in selected_features_indexes]\n\tplot_feat_imp(selected_features_indexes,selected_features_diff,X,y,out_dir=out_dir)\n\t\n\treturn(random_search_res_xgb.best_params_,selected_features_xgboost)","def train(self, train_data, train_labels):\n\n # Apply filtering\n if len(self.preprocessing) > 0:\n print('Applying', len(self.preprocessing), 'filter(s) to training data')\n for filter in self.preprocessing:\n for i in range(len(train_data)):\n train_data[i] = filter(train_data[i])\n\n # Apply feature extraction\n if len(self.features) > 0:\n print('Extracting', len(self.features), 'feature(s) from training data')\n scaler = MinMaxScaler(feature_range=(0, 1))\n for i in range(len(train_data)):\n features = []\n for feature in self.features:\n features.append(feature(train_data[i]))\n train_data[i] = np.hstack(features)\n train_data = scaler.fit_transform(train_data)\n else:\n # Flatten images (not necessary when using feature extraction)\n train_data = np.array(train_data).reshape((len(train_data), -1))\n\n # Fit model\n print('Fitting RF model on', len(train_labels), 'images')\n self.classifier.fit(train_data, train_labels)","def train(self, features, labels):\n self.train_features = features\n self.train_labels = labels\n #raise NotImplementedError","def main():\n\n trainData = os.getcwd() + '/data/traindata.txt'\n trainLabels = os.getcwd() + '/data/trainlabels.txt'\n\n #testData = os.getcwd() + '/data/traindata.txt'\n #testLabels = os.getcwd() + '/data/trainlabels.txt'\n\n testData = os.getcwd() + '/data/testdata.txt'\n testLabels = os.getcwd() + '/data/testlabels.txt'\n\n #trainData = os.getcwd() + '/data/toyData.txt'\n #trainLabels = os.getcwd() + '/data/toyLabel.txt'\n #testData = os.getcwd() +'/data/toyTestData.txt'\n #testLabels = os.getcwd() + '/data/toyTestLabel.txt'\n\n #print(trainData, trainLabels)\n myClassifier = NBClassifier.new(NBClassifier.MODE_BERNOULI)\n myClassifier.setTrainData(trainData, trainLabels)\n #print(myClassifier)\n\n #singleTestData = ['Chinese', 'Chinese', 'Chinese', 'Tokyo', 'Japan']\n #prediction = myClassifier.predict(singleTestData)\n #print(f'{singleTestData} >>> {prediction}')\n predictions = myClassifier.predictSet(testData)\n accuracy = myClassifier.reportAccuracy(testLabels)\n\n #print(predictions)\n print(accuracy)","def add_train_file(self, classname: str, filename: str, handler: int = 0) -> int:\n return self.get_func(\"DC_AddTrainFile\", [c_char_p, c_char_p, POINTER(c_int)], c_int)(\n classname, filename, handler)","def files():\r\n fn=pd.read_csv(request.files.get('fnm'))\r\n scaling = scaler.transform(fn)\r\n prediction = classifier.predict(scaling)\r\n return 'Predictions'+ str(list(prediction))","def test_text_classifier_create_from_path(self):\n pass","def __init__(self):\n try:\n negative_ids = movie_reviews.fileids('neg')\n positive_ids = movie_reviews.fileids('pos')\n except LookupError:\n import nltk\n nltk.download('movie_reviews')\n negative_ids = movie_reviews.fileids('neg')\n positive_ids = movie_reviews.fileids('pos')\n\n \"\"\" \n Separate positive features from negative\n \"\"\"\n negative_features = [(extract(movie_reviews.words(fileids=[f])), 'neg') for f in negative_ids]\n positive_features = [(extract(movie_reviews.words(fileids=[f])), 'pos') for f in positive_ids]\n\n \"\"\" \n Trains of 3/4 off the database\n and test off 1/4\n \"\"\"\n negative_cutoff = int(len(negative_features) * 3 / 4)\n positive_cutoff = int(len(positive_features) * 3 / 4)\n\n train_features = negative_features[:negative_cutoff] + positive_features[:positive_cutoff]\n test_features = negative_features[negative_cutoff:] + positive_features[positive_cutoff:]\n\n print('Training on %d instances, testing on %d instances' % (len(train_features), len(test_features)))\n self.classifier = NaiveBayesClassifier.train(train_features)\n print('Training complete')\n #print('accuracy:', nltk.classify.util.accuracy(self.classifier, test_features))\n #self.classifier.show_most_informative_features()\n\n \"\"\" Save classifier \"\"\"\n f = open('classifier.pickle', 'wb')\n pickle.dump(self.classifier, f)\n f.close()","def train_classifier_bootstrap(iterations: int = 3, files_per_iteration: int = 50,\n test_partition: float = 0.2) -> None:\n\n classifier = CnnClassifier(device).to(device)\n classifier.load_from_file()\n classifier = classifier.to(device)\n\n full_res_folder = os.path.dirname(__file__) + '/dataset/GTSRB_Negative/bootstrap_full_res/'\n output_folder = os.path.dirname(__file__) + '/dataset/GTSRB_Negative/images/'\n print(\"Searching for new pictures in\", full_res_folder, \"that are classified as street signs\")\n\n jpg_file_names = []\n for root, dirs, files in os.walk(full_res_folder):\n for name in files:\n if name.endswith(\".jpg\"):\n jpg_file_names.append((root, name))\n\n for i in range(1, iterations):\n random.shuffle(jpg_file_names)\n print(\">>>>>>>>>>>>>>>>>>>>>>>>>>> Iteration {}\".format(i))\n dirty = False\n new_files = []\n for root, jpg_file_name in jpg_file_names[:files_per_iteration]:\n print(\"\\tLoading\", jpg_file_name)\n pil_img = Image.open(root + jpg_file_name)\n fa = FrameAnalyzer(device, classifier=classifier, width=pil_img.size[0], height=pil_img.size[1])\n if pil_img is not None:\n rectangles = fa.get_detected_street_signs(pil_img, limit=50)\n print(\"\\t\\t{} patches classified as street signs\".format(len(rectangles)))\n if len(rectangles):\n dirty = True\n for rect in rectangles:\n x1, y1, x2, y2, label_idx, score = rect\n x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)\n fn = '{}_{}_'.format(x1, y1) + jpg_file_name\n # crop and save\n pil_img.crop((x1, y1, x2, y2)).save(output_folder + fn)\n new_files.append(fn)\n\n if dirty:\n print(\"Adding\", len(new_files), \"negative examples\")\n # shuffle and partition into training and test\n random.shuffle(new_files)\n n_test = int(test_partition * len(new_files))\n new_files_test = new_files[:n_test]\n new_files_training = new_files[n_test:]\n GTSRBDataset.add_images_to_negative_examples_json(new_files_test, overwrite=False, test=True)\n GTSRBDataset.add_images_to_negative_examples_json(new_files_training, overwrite=False, test=False)\n # train the model\n # epochs = 2 # max(1, int(iterations / 3))\n classifier.train_model(max_epochs=20, max_patience=5)\n classifier.store_to_file()","def main(opts):\n\n # Create a dataloader for the training images\n train_dataloader, _ = get_emoji_loader(opts.emoji, opts)\n\n # Create checkpoint and sample directories\n utils.create_dir(opts.checkpoint_dir)\n utils.create_dir(opts.sample_dir)\n\n train(train_dataloader, opts)","def test_file(self, file_name, version, classifier_type):\n labels = []\n with open(file_name) as f:\n for line in f.readlines():\n print(line,self.predict(line))\n labels.append(self.predict(line))\n \n filename = 'test_results-' + classifier_type + '-' + version + '.txt'\n \n with open(filename, 'w') as f:\n for label in labels:\n f.write(str(label)+\"\\n\")\n \n print (\"Results from \",file_name,\" printed to:\",filename)","def main(training_file_name):\n attribute, inverse = build_classifier(training_file_name)\n trained_file = open(TRAINED_FILE_NAME, mode='w')\n prolog(trained_file)\n write_body(trained_file, attribute, inverse)\n epilog(trained_file)","def train(self, arg1=None, arg2=None, **kwargs):\n nltk.download('averaged_perceptron_tagger')\n nltk.download('wordnet')\n nltk.download('twitter_samples')\n nltk.download('punkt')\n nltk.download('stopwords')\n nltk.download('vader_lexicon')\n\n positive_tweets = twitter_samples.strings('positive_tweets.json')\n negative_tweets = twitter_samples.strings('negative_tweets.json')\n text = twitter_samples.strings('tweets.20150430-223406.json')\n tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]\n\n stop_words = stopwords.words('english')\n\n positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')\n negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')\n\n positive_cleaned_tokens_list = []\n negative_cleaned_tokens_list = []\n\n for tokens in positive_tweet_tokens:\n positive_cleaned_tokens_list.append(self.remove_noise(tokens, stop_words))\n\n for tokens in negative_tweet_tokens:\n negative_cleaned_tokens_list.append(self.remove_noise(tokens, stop_words))\n\n all_pos_words = self.get_all_words(positive_cleaned_tokens_list)\n\n freq_dist_pos = FreqDist(all_pos_words)\n print(freq_dist_pos.most_common(20))\n\n positive_tokens_for_model = self.get_tweets_for_model(positive_cleaned_tokens_list)\n negative_tokens_for_model = self.get_tweets_for_model(negative_cleaned_tokens_list)\n\n positive_dataset = [(tweet_dict, \"Positive\")\n for tweet_dict in positive_tokens_for_model]\n\n negative_dataset = [(tweet_dict, \"Negative\")\n for tweet_dict in negative_tokens_for_model]\n\n dataset = positive_dataset + negative_dataset\n\n random.shuffle(dataset)\n\n train_data = dataset[:7000]\n test_data = dataset[7000:]\n\n self.classifier = NaiveBayesClassifier.train(train_data)","def train(self, train_ids_file):\n\t\t# TODO(student): Feel free to remove if you do not use.\n\t\ttf.keras.backend.set_learning_phase(0)\n\t\tfile_pairs = tf.data.Dataset.zip(get_filename_data_readers(train_ids_file, True))\n\t\t#training_dataset = file_pairs.shuffle(buffer_size=2500).map(read_image_pair_with_padding).batch(self.batch_size)\n\t\ttraining_dataset = file_pairs.map(read_image_pair_with_padding).batch(self.batch_size)\n\t\ttraining_dataset_X = training_dataset.map(lambda a, b: a)\n\t\ttraining_dataset_Y = training_dataset.map(lambda a, b: b)\n\n\t\ttraining_iterator_X = training_dataset_X.make_initializable_iterator()\n\t\ttraining_iterator_Y = training_dataset_Y.make_initializable_iterator()\n\n\t\tfor i in range(5):\n\t\t\tself.sess.run(training_iterator_X.initializer)\n\t\t\tself.sess.run(training_iterator_Y.initializer)\n\t\t\ttraining_handle_X = self.sess.run(training_iterator_X.string_handle())\n\t\t\ttraining_handle_Y = self.sess.run(training_iterator_Y.string_handle())\n\t\t\tj = 0\n\t\t\tloss_ary = []\n\t\t\twhile True:\n\t\t\t\ttry:\n\t\t\t\t\t[train_loss, train_step, train_pred] = self.sess.run(\n\t\t\t\t\t\t[self.loss, self.train_op, self.pred],\n\t\t\t\t\t\t\tfeed_dict={\n\t\t\t\t\t\t\t\tself.is_training: True,\n\t\t\t\t\t\t\t\tself.handle_X: training_handle_X,\n\t\t\t\t\t\t\t\tself.handle_Y: training_handle_Y,\n\t\t\t\t\t\t\t}\n\t\t\t\t\t)\n\t\t\t\t\t'''\n\t\t\t\t\tif j == 0:\n\t\t\t\t\t\tplt.imshow(train_pred[0])\n\t\t\t\t\t\tplt.colorbar()\n\t\t\t\t\t\tplt.show()\n\t\t\t\t\t\tpdb.set_trace()\n\t\t\t\t\t'''\n\t\t\t\t\tloss_ary.append(train_loss)\n\t\t\t\t\tj += 1\n\t\t\t\t\tprint('Epoch', i, 'Batch', j, train_loss)\n\t\t\t\texcept tf.errors.OutOfRangeError:\n\t\t\t\t\tbreak\n\t\t\tself.sess.run(self.lr_decay_op)\n\t\t\tprint('Apply lr decay, new lr: %f' % self.sess.run(self.lr))\n\t\t\tprint(f'Epoch: {i}, Avg Loss: {np.mean(loss_ary)}')\n\t\t\tself.save(\"model_file_no_{}.pickle\".format(i))\n\n\n\t\tprint('Done training')","def train_projector(self, train_files, projector_file, metadata=None):\n assert isinstance(metadata, list)\n return super(DummyAlgorithmMetadata, self).train_projector(train_files, projector_file)","def main(root_dir):\n # load annotations\n print('Loading instances and annotations...')\n captions_file = json.load(open('{}/annotations/captions_train2017.json'.format(root_dir), 'r'))\n categories_file = json.load(open('{}/annotations/instances_train2017.json'.format(root_dir), 'r'))\n print('Done.')\n\n # group categories by image\n image_categories = group_categories(categories_file)\n\n # group captions by image\n image_captions = group_captions(captions_file['annotations'])\n\n # get filename of each image\n image_file = get_filename(captions_file['images'])\n\n # assign each category an id.\n # we are not using the default ids given in the dataset because\n # the id ranges are not continuous.\n category_id, id_category = map_category_id(categories_file['categories'])\n \n # save parsed coco dataset\n save_dataset(image_categories, image_captions, image_file, category_id, id_category, root_dir)","def main():\n args = utils.read_arguments(__doc__)\n documents = []\n filenames = list(traverse_directory(args[\"input_dirpath\"],'*clean*.txt'))\n labels_dirname = args[\"labels_dirpath\"]\n labels_from_json = get_all_labels_from_json(labels_dirname)\n for filename in tqdm(filenames):\n with AnnotatedIBMFactory(filename) as instance_extractor:\n filename_key = filename.split(\"/\")[-1]\n document = instance_extractor.build_document(\n labels_from_json[filename_key])\n documents.append(document)\n utils.pickle_to_file(documents, args['output_file'])","def func(args):\n\n classifier_stat_list = []\n cf_frames = []\n confidences_list = []\n intents_in_results = pd.Series(dtype='float')\n\n classifier_num = len(args.classifiers_results)\n\n # Prepare labels for each curve\n labels = [os.path.splitext(os.path.basename(file_path))[0]\n for file_path in args.classifiers_results]\n\n # Only do cutomization on labels if numbers match\n if args.classifier_names and \\\n (len(args.classifier_names) == classifier_num):\n labels = args.classifier_names\n\n # Initialization\n for i in range(classifier_num):\n file_path = args.classifiers_results[i]\n frame = pd.read_csv(file_path, encoding=UTF_8, quoting=csv.QUOTE_ALL)\n if INTENT_JUDGE_COLUMN not in frame.columns: # Column validation\n raise ValueError(\"'{}' column not in {}\".format(\n INTENT_JUDGE_COLUMN, file_path))\n # Read the cf files into list\n cf_frames.append(frame)\n # Collect all intents from the classification results\n intents_in_results = pd.concat([intents_in_results,\n frame[PREDICTED_INTENT_COLUMN]])\n # Convert nan to zero values to avoid use zero as divider\n confidences_list.append(frame[CONFIDENCE_COLUMN].fillna(0)\n .drop_duplicates().sort_values().tolist())\n\n intents_in_results = intents_in_results.drop_duplicates()\n\n # Read weight\n weights_df = None\n weight_mode = args.weight.lower()\n # Read the intent weights pairs from file\n if weight_mode != POPULATION_WEIGHT_MODE and \\\n weight_mode != EQUAL_WEIGHT_MODE:\n try:\n weights_df = pd.read_csv(args.weight, encoding=UTF_8,\n quoting=csv.QUOTE_ALL)\n # Validate the completeness\n for _, intent in intents_in_results.iteritems():\n if not any(weights_df[INTENT_COLUMN] == intent):\n raise ValueError(\"'{}' intent not in {}\".format(\n intent, args.weight))\n except Exception as e:\n print(e)\n weight_mode = POPULATION_WEIGHT_MODE # default population mode\n print('Fall back to {} mode'.format(POPULATION_WEIGHT_MODE))\n\n # Init the classifier_stat_list:\n for i in range(classifier_num):\n # array of zeros to hold precision values\n classifier_stat_list.append(np.zeros([len(confidences_list[i]), 3]))\n\n for j in range(classifier_num):\n confidences = confidences_list[j]\n for i in range(len(confidences)):\n conf = confidences[i]\n cf_frame = cf_frames[j]\n precision = 0\n answered = \\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf].shape[0]\n if weight_mode == POPULATION_WEIGHT_MODE:\n correct = cf_frame[\n cf_frame[CONFIDENCE_COLUMN] >= conf][SCORE_COLUMN].sum()\n precision = correct / answered\n # print(precision)\n else:\n intent_uttr_num_map = \\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf] \\\n .groupby(PREDICTED_INTENT_COLUMN)[PREDICTED_INTENT_COLUMN] \\\n .count().to_dict()\n\n # Calulate precision use equal weights\n uttr_correct_intent = \\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf] \\\n .groupby(GOLDEN_INTENT_COLUMN)[SCORE_COLUMN] \\\n .sum()\n\n intent_weights = None\n weight_coeff = 1 / len(intent_uttr_num_map)\n\n if weight_mode != EQUAL_WEIGHT_MODE:\n required_weights_df = \\\n weights_df[\n weights_df[INTENT_COLUMN]\n .isin(uttr_correct_intent.index)]\n weight_sum = required_weights_df[WEIGHT_COLUMN].sum()\n # Normalize weights\n weights_df[WEIGHT_COLUMN] = \\\n weights_df[WEIGHT_COLUMN] / weight_sum\n intent_weights = \\\n weights_df.set_index(INTENT_COLUMN)[WEIGHT_COLUMN] \\\n .to_dict()\n\n for intent, correct_intent_num in \\\n uttr_correct_intent.iteritems():\n if weight_mode != EQUAL_WEIGHT_MODE:\n weight_coeff = intent_weights[intent]\n\n precision += \\\n weight_coeff * correct_intent_num \\\n / intent_uttr_num_map[intent]\n\n classifier_stat_list[j][i, 0] = precision\n classifier_stat_list[j][i, 1] = 100 * answered / len(cf_frame)\n classifier_stat_list[j][i, 2] = conf\n\n for idx in range(len(classifier_stat_list)):\n # reversing order for helpful plotting\n classifier_stat_list[idx] = classifier_stat_list[idx][::-1]\n\n # plotting\n fig = plt.figure()\n ax = fig.gca()\n\n ax.set_ylim([0, 1.0]) # Hardcoding y-axis to a consistent 0-1.0 for the benefit of easing historical comparisions\n\n ax.grid(color='b', linestyle='--', alpha=0.3)\n ax.set_xlabel('Percentage of Questions Answered',\n fontsize=LEGEND_AXIS_FONT_SIZE)\n ax.set_ylabel('Precision', fontsize=LEGEND_AXIS_FONT_SIZE)\n line_style_cycler = cycle(line_styles)\n line_color_cycler = cycle(line_color)\n\n lines = [] # reference to lines\n # plot the curve and save the figure\n for i in range(len(classifier_stat_list)):\n classifier_stat = classifier_stat_list[i]\n # Default to the idx of lowest conf\n tau_idx = len(classifier_stat[:, 2]) - 1\n indices_gtr_tau, = np.where(classifier_stat[:, 2] <= args.tau)\n if len(indices_gtr_tau) > 0:\n tau_idx = indices_gtr_tau[0]\n\n color = next(line_color_cycler)\n line, = plt.plot(classifier_stat[:, 1], classifier_stat[:, 0],\n color=color, label=labels[i],\n linestyle=next(line_style_cycler))\n\n plt.plot(classifier_stat[tau_idx, 1], classifier_stat[tau_idx, 0],\n '{}o'.format(color), markerfacecolor='None')\n lines.append(line)\n\n tau_desc = mlines.Line2D([], [], markeredgecolor='black', marker='o',\n linestyle='None', markerfacecolor='None',\n markersize=10,\n label='tau = {}'.format(args.tau))\n\n ax.legend(handles=lines + [tau_desc], loc='lower left', shadow=False,\n prop={'size': LEGEND_AXIS_FONT_SIZE})\n ax.set_title(args.figure_title,\n fontsize=TITLE_FONT_SIZE)\n\n if args.ymin != 0.0:\n plt.ylim(args.ymin, 1.0)\n # Save figure as file\n plt.savefig(args.outfile)\n\n print(\"Wrote precision curve to {}\".format(args.outfile))","def __init__(self, train, transform, data_path='../extracted', category_path=\"../all_fish.txt\"):\n self.transform = transform\n data_path = os.path.join(data_path, 'train' if train else 'test')\n fp = open(category_path, 'r')\n self.fish_dict = {i.split(\";\")[0] : i.split(\";\")[1][1:-1] for i in fp}\n \n #self.name_to_label = [i.split(\";\")[1][1:-1] for i in fp]\n self.image_paths = glob.glob(data_path + '/*.jpg')","def run(dataset_dir,pic_path):\n if not tf.gfile.Exists(dataset_dir):\n tf.gfile.MakeDirs(dataset_dir)\n\n training_filename = _get_output_filename(dataset_dir, 'train')\n testing_filename = _get_output_filename(dataset_dir, 'test')\n\n if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):\n print('Dataset files already exist. Exiting without re-creating them.')\n return\n\n class_names = os.listdir(pic_path)\n labels_to_class_names = dict(zip(class_names,range(len(class_names))))\n \n picnames=[]\n for label in class_names:\n alabel_path=os.path.join(pic_path,label)\n names=os.listdir(alabel_path)\n picnames.extend([os.path.join(alabel_path,name) for name in names])\n random.shuffle(picnames) \n \n train_picnames = picnames[:int(0.7*len(picnames))]\n test_picnames = picnames[int(0.7*len(picnames)):]\n # First, process the training data:\n with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:\n offset = 0\n for name in train_picnames:\n offset = _add_to_tfrecord(name, tfrecord_writer, labels_to_class_names, offset)\n\n # Next, process the testing data:\n with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:\n offset = 0\n for name in test_picnames:\n offset = _add_to_tfrecord(name, tfrecord_writer, labels_to_class_names, offset)\n\n # Finally, write the labels file:\n labels_to_class_names = dict(zip(labels_to_class_names.values(),labels_to_class_names.keys())) \n dataset_utils.write_label_file(labels_to_class_names, dataset_dir)\n with open(os.path.join(dataset_dir,'info.json'),'w') as f:\n info=json.dumps({'num_class':len(class_names),'num_sample_train':len(train_picnames),'num_sample_test':len(test_picnames)})\n f.write(info)\n\n print('\\nFinished converting the dataset in the {}!'.format(pic_path))\n print('\\nThe tfrecord files,info.json and labels file is located in the {}'.format(dataset_dir))","def main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--identifier\", required=True,\n help=\"A short name/identifier for your experiment, e.g. 'ex42b'.\")\n args = parser.parse_args()\n\n train(args)","def svc_classify(self, filename):\n tokens = preprocess.parse_book(filename)\n perplexities = [m.perplexity(tokens) for m in self.models]\n return self.genres[self.svc.predict(perplexities)]","def load(self):\n self.classifier = joblib.load(\"data/models/repeatsfinder/repeatsfinder.joblib\")","def load_training():\n for can in candidates:\n trainings[can] = []\n for subdir, dirs, files in os.walk(os.path.join(corpus_dir, can)):\n for doc in files:\n trainings[can].append(doc)","def LoadBatch(filename):","def train():\n # YOUR TRAINING CODE GOES HERE","def categorize_classifier_files(out_dir):\n\n #sort all of the classifier files into a dictionary\n class_files = glob.glob(\"feature_extraction_m*\")\n class_file_dict = {\"positive\":[], \"negative\":[]}\n class_cand_dict = {\"m1\":class_file_dict, \"m2\":class_file_dict, \"m3\":class_file_dict, \"m4\":class_file_dict, \"m5\":class_file_dict}\n\n for filename in class_files:\n split_name = filename.split(\"_\")[-1].split(\".\")\n model_num = split_name[0]\n det = split_name[-1]\n class_cand_dict[model_num][det].append(filename)\n\n #get all of the pfd files into a list\n class_file_m1 = glob.glob(\"feature_extraction_m1*\")\n pfd_files = []\n for afile in class_file_m1:\n f = open(afile, \"r\")\n for line in f.readlines():\n pfd_files.append(line)\n f.close()\n\n #fill a dictionary with pfds and a value for how many positive IDs each pfd has\n pulsar_pfds={}\n for key in pfd_files:\n pulsar_pfds[key]=0\n for model_num in class_cand_dict.keys():\n if class_cand_dict[model_num][\"positive\"]:\n print(class_cand_dict[model_num][\"positive\"])\n f = open(class_cand_dict[model_num][\"positive\"][0], \"r\")\n for line in f.readlines():\n pulsar_pfds[line]+=1\n f.close()\n\n #For each pfd with >=3 positive IDs, write that pfd to 'positive' file, else write to 'negative' file\n pos_f = open(os.path.join(out_dir, \"LOTAAS_positive_detections.txt\"), \"w+\")\n neg_f = open(os.path.join(out_dir, \"LOTAAS_negative_detections.txt\"), \"w+\")\n for pfd_key in pulsar_pfds.keys():\n if pulsar_pfds[pfd_key]>=3:\n print(\"detected pulsar: {}\".format(pfd_key))\n pos_f.write(pfd_key.split(\"/\")[-1])\n else:\n neg_f.write(pfd_key.split(\"/\")[-1])\n pos_f.close()\n neg_f.close()","def train(self):\n # self.recognizer.train()\n self.detector.train()\n self.shared_conv.train()","def train_model(args, train_exs: List[SentimentExample]) -> SentimentClassifier:\n indexer = Indexer()\n stop_words = set(stopwords.words('english'))\n punkt = (',', '.', '...', '?', '\\'', '\\'\\'', '!', ':', ';')\n # Initialize feature extractor\n if args.model == \"TRIVIAL\":\n feat_extractor = None\n elif args.feats == \"UNIGRAM\":\n # Generate vocabulary\n for ex in train_exs:\n for word in ex.words:\n if word.lower() not in stop_words and word.lower() not in punkt:\n indexer.add_and_get_index(word.lower())\n feat_extractor = UnigramFeatureExtractor(indexer)\n elif args.feats == \"BIGRAM\":\n # Generate vocabulary\n for ex in train_exs:\n for i in range(0, len(ex.words) - 1):\n if stop_words.__contains__(ex.words[i]) and stop_words.__contains__(ex.words[i + 1]) or (\n punkt.__contains__(ex.words[i]) or punkt.__contains__(ex.words[i + 1])):\n continue\n bigram = ex.words[i] + ' ' + ex.words[i + 1]\n indexer.add_and_get_index(bigram.lower())\n feat_extractor = BigramFeatureExtractor(indexer)\n elif args.feats == \"BETTER\":\n # Generate vocabulary\n cnt = Counter()\n for ex in train_exs:\n cnt.update(\n word.lower() for word in ex.words if word.lower() not in stop_words and word.lower() not in punkt)\n cnt = dict(cnt.most_common(int(cnt.__len__() * 0.75)))\n for keys in cnt.keys():\n indexer.add_and_get_index(keys)\n feat_extractor = BetterFeatureExtractor(indexer)\n else:\n raise Exception(\"Pass in UNIGRAM, BIGRAM, or BETTER to run the appropriate system\")\n\n # Train the model\n if args.model == \"TRIVIAL\":\n model = TrivialSentimentClassifier()\n elif args.model == \"PERCEPTRON\":\n model = train_perceptron(train_exs, feat_extractor)\n elif args.model == \"LR\":\n model = train_logistic_regression(train_exs, feat_extractor)\n else:\n raise Exception(\"Pass in TRIVIAL, PERCEPTRON, or LR to run the appropriate system\")\n return model","def train(self, docs, labels):\n if docs.size != labels.size:\n raise ValueError('Docs and labels sizes are mismatched %d vs %d' %(docs.size, labels.size))\n \n for entry in docs:\n for f in entry.split():\n if f not in self.features_:\n self.add_feature(f)\n \n# self.feature_counts_ = sparse.csr_matrix((nclasses, nfeatures), dtype=np.float)\n nfeatures = len(self.features_)\n nclasses = len(self.classes_)\n # feature_counts = sparse.lil_matrix((nclasses, nfeatures), dtype=np.float)\n self.feature_counts_ = np.zeros((nclasses, nfeatures), dtype=np.float)\n \n self.count_class(labels)\n self.update_class_log_prior(self.class_counts_)\n\n self.count_feature(docs, labels)\n self.update_feature_prob(self.feature_counts_)","def train(self, dataset, model_dir):\n raise NotImplementedError","def train(args):\r\n train_img = 'NEW DATA/TRAIN/'\r\n validation_img = 'NEW DATA/TEST/'\r\n \r\n nb_epoch = int(args.nb_epoch)\r\n nb_train_samples = get_nb_files(train_img)\r\n nb_classes = len(glob.glob(train_img + \"/*\"))\r\n # data prep\r\n train_datagen = ImageDataGenerator(\r\n rotation_range=40,\r\n width_shift_range=0.2,\r\n height_shift_range=0.2,\r\n rescale=1./255,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True,\r\n fill_mode='nearest')\r\n\r\n validation_datagen = ImageDataGenerator(\r\n rotation_range=40,\r\n width_shift_range=0.2,\r\n height_shift_range=0.2,\r\n rescale=1./255,\r\n shear_range=0.2,\r\n zoom_range=0.2,\r\n horizontal_flip=True,\r\n fill_mode='nearest')\r\n \r\n train_generator = train_datagen.flow_from_directory(\r\n\t\t\ttrain_img,\r\n\t\t\ttarget_size=(299, 299),\r\n\t\t\tbatch_size=10,\r\n\t\t\tclass_mode='categorical'\r\n\t\t\t)\r\n validation_generator = validation_datagen.flow_from_directory(\r\n\t\t\tvalidation_img,\r\n\t\t\ttarget_size=(299, 299),\r\n\t\t\tbatch_size=10,\r\n\t\t\tclass_mode='categorical'\r\n\t\t\t)\r\n if(K.image_dim_ordering() == 'th'):\r\n input_tensor = Input(shape=(3, 299, 299))\r\n else:\r\n input_tensor = Input(shape=(299, 299, 3))\r\n \r\n # setup model\r\n base_model = InceptionV3(input_tensor = input_tensor,weights='imagenet', include_top=False) #include_top=False excludes final FC layer\r\n model = add_new_last_layer(base_model, nb_classes)\r\n \r\n # transfer learning\r\n setup_to_transfer_learn(model, base_model)\r\n \r\n \r\n \r\n history_tl = model.fit_generator(train_generator,\r\n samples_per_epoch=200,\r\n nb_epoch=nb_epoch,\r\n validation_data=validation_generator,\r\n nb_val_samples=64) \r\n model.save(args.output_model_file)\r\n if args.plot:\r\n plot_training(history_tl)","def train(self):\r\n for class_ in set(self.train_classes):\r\n data = map(lambda (ind, datum): datum, filter(lambda (ind, datum): self.train_classes[ind] == class_, enumerate(self.train_data)))\r\n self.distribution.index_data(data, class_)","def train_naive(): # add arguments as needed\n pass","def __init__(self, dataset_dir, mode='train', transforms=None):\n self.dataset = dataset_dir\n self.transforms = transforms\n require_file = ['trainImages.txt', 'trainLabels.txt',\n 'valImages.txt', 'valLabels.txt',\n 'testImages.txt', 'testLabels.txt']\n\n # check requirement\n if mode not in ['train', 'test', 'val']:\n raise ValueError('Unsupported mode %s' % mode)\n\n if not os.path.exists(self.dataset):\n raise ValueError('Dataset not exists at %s' % self.dataset)\n\n \n #generate_txt(self.dataset, file)\n\n # create image and label list\n# self.image_list = []\n# self.label_list = []\n if mode == 'train':\n self.images = images\n self.labels = labels\n self.images_list_final = images_list_final\n self.labels_list_final = labels_list_final\n \n if mode == 'val':\n self.images = images_val\n self.labels = labels_val\n self.images_list_final = images_list_final_val\n self.labels_list_final = labels_list_final_val\n \n if mode == 'test': \n self.images = images_test\n self.labels = labels_test\n self.images_list_final = images_list_final_test\n self.labels_list_final = labels_list_final_test","def forward_train(self, imgs, img_metas, **kwargs):\n pass","def svm_train_classifier(self):\n\n # needed because a SVM needs more than 1 class\n if len(self.saved_gestures.keys()) <= 1:\n print(\"Not enough gestures!\")\n else:\n training_data = []\n categories = []\n id = 0\n\n for gesture, value in self.saved_gestures.items():\n id += 1\n # needed to map the id returned from the SVM to a name of a gesture\n self.category_to_gesture[id] = gesture\n categories.append(id)\n\n x = []\n y = []\n z = []\n for elem in value:\n x.append(elem[0][0])\n y.append(elem[1][0])\n z.append(elem[2][0])\n\n training_data.append(self.get_fft(x, y, z))\n\n # normalized length of fft\n self.cutoff_length = min([len(l) for l in training_data])\n\n normalized_fft = []\n for l in training_data:\n normalized_fft.append(l[:self.cutoff_length])\n\n training_data = normalized_fft\n\n self.classifier.fit(training_data, categories)","def train_model(args, train_exs: List[SentimentExample]) -> SentimentClassifier:\n # Initialize feature extractor\n nltk.download('stopwords')\n stop_words = set(stopwords.words('english'))\n\n if args.model == \"TRIVIAL\":\n feat_extractor = None\n elif args.feats == \"UNIGRAM\":\n feat_extractor = UnigramFeatureExtractor(Indexer(), train_exs, stop_words)\n elif args.feats == \"BIGRAM\":\n # Add additional preprocessing code here\n feat_extractor = BigramFeatureExtractor(Indexer(), train_exs, stop_words)\n elif args.feats == \"BETTER\":\n # Add additional preprocessing code here\n feat_extractor = BetterFeatureExtractor(Indexer(), train_exs, stop_words)\n else:\n raise Exception(\"Pass in UNIGRAM, BIGRAM, or BETTER to run the appropriate system\")\n\n # Train the model\n if args.model == \"TRIVIAL\":\n model = TrivialSentimentClassifier()\n elif args.model == \"PERCEPTRON\":\n model = train_perceptron(train_exs, feat_extractor)\n elif args.model == \"LR\":\n model = train_logistic_regression(train_exs, feat_extractor)\n else:\n raise Exception(\"Pass in TRIVIAL, PERCEPTRON, or LR to run the appropriate system\")\n return model","def train():\n import trace\n trace.train()","def _loadTrain(self, features, labels):\n\t\tself.trainX_, self.trainY_, self.trainLabel_ = self.__load(features, labels)","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass"],"string":"[\n \"def train(self, trainfile):\",\n \"def train_for(labels, filenames):\\n stt = stt_google\\n csvfiles = []\\n writers = []\\n for index, filename in enumerate(filenames):\\n currfile = open(filename, 'ab')\\n csvfiles.append(currfile)\\n writers.append(csv.writer(currfile))\\n # record instances until it doesn't interpret any text\\n speech = stt.listen_for_speech()\\n while(speech):\\n hypotheses = []\\n for hypothesis in speech:\\n hypotheses.append(hypothesis['utterance'])\\n #write hypotheses\\n for index, label in enumerate(labels):\\n writers[index].writerow([label] + hypotheses)\\n speech = stt.listen_for_speech()\\n for csvfile in csvfiles:\\n csvfile.close\",\n \"def load(self, filename):\\n self.classifiers = []\\n for i in range(0, self.category_level):\\n clf = joblib.load(filename + '.level_%d' % (i + 1))\\n self.classifiers.append(clf)\",\n \"def train(self, absList, modelFilename):\\n pass\",\n \"def train(self, absList, modelFilename):\\n pass\",\n \"def train(self, absList, modelfilename):\\n raise NotImplementedError(\\\"Need to implement train()\\\")\",\n \"def train_classifier(train_faces, train_faces_ids):\\n recognizer_lbph = cv2.face.LBPHFaceRecognizer_create()\\n print('Training model in progress...')\\n recognizer_lbph.train(train_faces, np.array(train_faces_ids))\\n print('Saving...')\\n recognizer_lbph.save('trainner.yml')\\n print('Model training complete!')\",\n \"def train(self, absList, modelFilename):\\n raise NotImplementedError(\\\"Need to implement train()\\\")\",\n \"def __init__(self):\\n self.train(positivity_files, 0)\\n self.train(subjectivity_files, 1)\",\n \"def mt_classifier(input_file, muscle_exec, basename, best_results):\\n pass\",\n \"def train(self, features, labels):\\n pass\",\n \"def run_tests():\\r\\n source1 = TextModel('50 Shades of Gray')\\r\\n source1.add_file('50.txt')\\r\\n \\r\\n print()\\r\\n \\r\\n source2 = TextModel('King James Version of the Bible')\\r\\n source2.add_file('kjv.txt')\\r\\n\\r\\n print()\\r\\n\\r\\n new1 = TextModel('Shakespeare')\\r\\n new1.add_file('shake.txt')\\r\\n new1.classify(source1, source2)\\r\\n \\r\\n print()\\r\\n \\r\\n new2 = TextModel('JK Rowling')\\r\\n new2.add_file('hp.txt')\\r\\n new2.classify(source1, source2)\\r\\n \\r\\n print()\\r\\n \\r\\n new3 = TextModel('Breitbart News Network')\\r\\n new3.add_file('bnn.txt')\\r\\n new3.classify(source1, source2)\\r\\n \\r\\n print()\\r\\n \\r\\n new4 = TextModel('Chaucer')\\r\\n new4.add_file('tct.txt')\\r\\n new4.classify(source1, source2)\",\n \"def train(self, args):\\n\\n ##################################\\n # Read the training data\\n ##################################\\n if not os.path.isdir(args.annotationPath):\\n print('annotation path does not exist: {}' \\\\\\n .format(args.annotationPath))\\n return -1\\n\\n data = self.readData(args.annotationPath)\\n\\n x = []\\n y = []\\n l = []\\n for subject, df in data.items():\\n lx = df[['gradient', 'rate']].values.tolist()\\n ly = np.array(df[['immersion']].values.tolist()).squeeze(-1)\\n x.extend(lx)\\n y.extend(ly.tolist())\\n l.append(len(lx))\\n\\n ############################\\n # Execute the training\\n ############################\\n\\n print('Training the detector...')\\n self._clf.fit(x, y, l)\\n\\n if not self.save():\\n print('Could not persist the trained model to disk (in file {})' \\\\\\n .format(self._modelFile))\\n\\n return 0\",\n \"def run_tests():\\n source1 = TextModel('hilary_speaches')\\n source1.add_file('hilary_source_text.txt')\\n\\n source2 = TextModel('bernie_speaches')\\n source2.add_file('bernie_source_text.txt')\\n\\n new1 = TextModel('trump_speach')\\n new1.add_file('trump_text.txt')\\n new1.classify(source1, source2)\\n\\n new2 = TextModel('hilary_test')\\n new2.add_file('hilary_test.txt')\\n new2.classify(source1, source2)\\n\\n new3 = TextModel('bernie_test')\\n new3.add_file('bernie_test.txt')\\n new3.classify(source1, source2)\\n\\n new4 = TextModel('bill_clinton_test')\\n new4.add_file('bill_clinton_source.txt')\\n new4.classify(source1, source2)\",\n \"def train_opencv_classifier(training_filenames,\\n training_labels,\\n classifier = LBPHFaceRecognizer_create(),\\n training_path = Config.A2_EXTRACTED_FACES_PATH):\\n faces = _open_images(training_filenames, training_path)\\n classifier.train(faces, np.array(training_labels))\\n # The lbph face recogniser will be called 'opencv_lbphfaces'.\\n classifier.save(os.path.join('dat', '{}.yml'.format(classifier.getDefaultName())))\",\n \"def singleDriverTrainer(file_to_classify, training_files, in_model1, in_model2,\\n weight_1 = 0.5, file_subset_size = 200):\\n # first, grab the target data\\n x_target, y_target, id_target = extractCSV(file_to_classify, target = 1)\\n # remove na's\\n x_target = np.nan_to_num(x_target)\\n y_target = np.nan_to_num(y_target)\\n\\n # now grab the training/noise data\\n x_all = copy.copy(x_target)\\n y_all = copy.copy(y_target)\\n \\n # use stacking to up-sample our target data\\n # and balance it with our noise data\\n n = int(round(len(training_files) * file_subset_size / 200.0))\\n l = len(x_target)\\n # stack target to balance classes test \\n if n > 1:\\n stack_idx = range(l) * n\\n x_all, y_all = x_all[stack_idx], y_all[stack_idx]\\n\\n # now we must add the data from each noise file\\n # loop through all of our training/noise files\\n for filepath in training_files:\\n # open the file\\n x_current, y_current, ids = extractCSV(filepath, target = 0,\\n file_subset = file_subset_size)\\n # and add the contents to our training data\\n x_all = np.concatenate((x_all, x_current))\\n y_all = np.concatenate((y_all, y_current))\\n # repeat for every filepath in our training files list\\n\\n # again, remove na's from our data\\n x_all = np.nan_to_num(x_all)\\n y_all = np.nan_to_num(y_all)\\n\\n\\n ###########\\n # MODEL 1 TRAINING\\n ###########\\n in_model1.fit(x_all, y_all)\\n # now we are ready to provide class probabilities for our predictions\\n # on the target data\\n predictions = in_model1.predict_proba(x_target)\\n # note that we must extract the index of the class 1 probability\\n prob_idx = np.where(in_model1.classes_ == 1)[0][0]\\n # apply the weighting for model 1 to each prediction\\n class_probs1 = [pred[prob_idx]*weight_1 for pred in predictions]\\n \\n \\n ###########\\n # MODEL 2 TRAINING\\n ###########\\n in_model2.fit(x_all, y_all)\\n # again, extract probabilistic predictions for the target data\\n predictions2 = in_model2.predict_proba(x_target)\\n prob_idx2 = np.where(in_model2.classes_ == 1)[0][0]\\n # apply the weighting for model 2 to each prediction\\n # note that our weighting for number 2 is (1 - weight[1])\\n weight_2 = 1 - weight_1\\n class_probs2 = [pred[prob_idx2]*weight_2 for pred in predictions2]\\n \\n # combine probabilities\\n class_probs = np.add(class_probs1, class_probs2)\\n \\n # and return a matrix of the id's and the corresponding probabilities\\n return_mat = [[id_target[idx], class_probs[idx]] \\\\\\n for idx in xrange(len(class_probs))]\\n \\n # report our progress before returning\\n print 'completed driver %s' % file_to_classify\\n return np.asarray(return_mat)\",\n \"def retrain(self):\\n thread = Thread(target=self.trainer.train_classifier)\\n thread.start()\",\n \"def train(self, train_loader):\\n pass\",\n \"def train(self, src, labels): # real signature unknown; restored from __doc__\\n pass\",\n \"def batch_test(classifier,labeledfiles):\\r\\n\\tcorrect_cnt = 0\\r\\n\\tfor filename, label in labeledfiles:\\r\\n\\t\\twlist = utils.load(filename)\\r\\n\\t\\tif classifier.cl == 'f':\\r\\n\\t\\t\\tclassifier.test_one_prep(fe.extract_features(wlist,label)[0],fe.extract_features(wlist,label)[1])\\r\\n\\t\\t\\tcontinue\\t\\r\\n\\t\\telse:\\r\\n\\t\\t\\trlabel = classifier.test_one(fe.extract_features(wlist,label)[0])\\r\\n\\t\\tif options.verbose == True:\\r\\n\\t\\t\\tprint filename, '-->', rlabel\\r\\n\\t\\tif rlabel == label:\\r\\n\\t\\t\\tcorrect_cnt += 1 \\r\\n\\tif classifier.cl == 'f':\\r\\n\\t\\tclassifier.test_one_p()\\r\\n\\t\\t#classifier.test_one()\\r\\n\\telse:\\r\\n\\t\\tprint \\\"accuracy = %f (%d/%d)\\\" % (float(correct_cnt)/len(labeledfiles), correct_cnt, len(labeledfiles))\",\n \"def run_tests():\\n source1 = TextModel('CS111 Syllabus')\\n source1.add_file('CS111_Syllabus.txt')\\n\\n source2 = TextModel('AR Syllabus')\\n source2.add_file('AR_Syllabus.txt')\\n\\n new1 = TextModel('WR120 Syllabus')\\n new1.add_file('WR120_Syllabus.txt')\\n new1.classify(source1, source2)\\n \\n new2 = TextModel('CS131 Syllabus')\\n new2.add_file('CS131_Syllabus.txt')\\n new2.classify(source1, source2)\\n \\n new3 = TextModel('My Paper 2 for WR120')\\n new3.add_file('WR_Paper_2.txt')\\n new3.classify(source1, source2)\\n \\n new4 = TextModel('CS111 PS9PR0')\\n new4.add_file('ps9pr0.txt')\\n new4.classify(source1, source2)\",\n \"def __init__(self, tfiles, featurizer):\\n\\t\\t### [ Featurize the classifier ] ###\\n\\t\\t# random.shuffle(tfiles)\\n\\t\\tself.featurizer = featurizer\\n\\t\\tself.tfiles = tfiles\\n\\n\\t\\t# Now build a model based on these vectors\\n\\t\\tnum_files = len(tfiles)\\n\\t\\tnum_training_files = int(PERCENT_TRAINING * num_files)\\n\\t\\tnum_test_files = num_files - num_training_files\\n\\n\\t\\tself.train_files = self.tfiles[:num_training_files]\\n\\t\\tself.test_files = self.tfiles[num_training_files:]\\n\\n\\t\\tself.all_data = [featurizer.get_feature_matrix_and_output_vector(f) for f in self.tfiles]\\n\\t\\tall_data_vectors = [d[0] for d in self.all_data]\\n\\t\\tprint([v.shape for v in all_data_vectors])\\n\\t\\tself.all_features = np.vstack(d[0] for d in self.all_data)\\n\\t\\tself.all_labels = np.hstack(d[1] for d in self.all_data)\\n\\n\\t\\tself.train_data = [featurizer.get_feature_matrix_and_output_vector(f) for f in self.train_files]\\n\\t\\tself.train_features = np.vstack([d[0] for d in self.train_data])\\n\\t\\tself.train_labels = np.hstack([d[1] for d in self.train_data])\\n\\n\\t\\tself.test_data = [featurizer.get_feature_matrix_and_output_vector(f) for f in self.test_files]\\n\\t\\tself.test_features = np.vstack([d[0] for d in self.test_data])\\n\\t\\tself.test_labels = np.hstack(d[1] for d in self.test_data)\\n\\n\\t\\tself.trained_clf = []\\n\\t\\tfor cl in used_classifiers:\\n\\t\\t\\tself.trained_clf += [cl(self.train_features, self.train_labels)]\",\n \"def train(args):\\n # Copy the project file.\\n # TODO: If the file exists, ask the user if it shall be deleted.\\n if os.path.isfile(args.outfile):\\n os.remove(args.outfile)\\n shutil.copyfile(args.train, args.outfile)\\n\\n # Create an ILP object for the project.\\n proj = ILP(args.outfile, args.cache, args.compression)\\n\\n # Do the autocontext loop.\\n autocontext(args.ilastik, proj, args.nloops, args.labeldataset, weights=args.weights, predict_file=args.predict_file)\",\n \"def train_from_dir(self, path, cat):\\n dirfiles = glob.glob(os.path.join(path, '*'))\\n total = len(dirfiles)\\n count = 0\\n for infile in dirfiles:\\n f = open(infile, \\\"r\\\")\\n text = f.read()\\n self.train(text, cat)\",\n \"def train_classifier(images_path):\\n car_imgs = get_images(images_path + '/vehicles/')\\n non_car_imgs = get_images(images_path + '/non-vehicles/')\\n\\n print('Computing car features')\\n car_features = extract_features(car_imgs,\\n color_space=COLOR_SPACE,\\n spatial_size=SPATIAL_SIZE,\\n hist_bins=HIST_BINS,\\n orient=ORIENT,\\n pix_per_cell=PIX_PER_CELL,\\n cell_per_block=CELL_PER_BLOCK,\\n hog_channel=HOG_CHANNEL,\\n spatial_feat=SPATIAL_FEAT,\\n hist_feat=HIST_FEAT,\\n hog_feat=HOG_FEAT)\\n print(len(car_features))\\n\\n print('Computing non-car features')\\n non_car_features = extract_features(non_car_imgs,\\n color_space=COLOR_SPACE,\\n spatial_size=SPATIAL_SIZE,\\n hist_bins=HIST_BINS,\\n orient=ORIENT,\\n pix_per_cell=PIX_PER_CELL,\\n cell_per_block=CELL_PER_BLOCK,\\n hog_channel=HOG_CHANNEL,\\n spatial_feat=SPATIAL_FEAT,\\n hist_feat=HIST_FEAT,\\n hog_feat=HOG_FEAT)\\n print(len(non_car_features))\\n \\n X = np.vstack((car_features, non_car_features)).astype(np.float64) \\n print('X shape: {}'.format(X.shape))\\n # Fit a per-column scaler\\n X_scaler = StandardScaler().fit(X)\\n # Apply the scaler to X\\n scaled_X = X_scaler.transform(X)\\n\\n # Define the labels vector\\n y = np.hstack((np.ones(len(car_features)), np.zeros(len(non_car_features))))\\n\\n # Split up data into randomized training and test sets\\n rand_state = np.random.randint(0, 100)\\n X_train, X_test, y_train, y_test = train_test_split(\\n scaled_X, y, test_size=0.2, random_state=rand_state)\\n\\n # Use a linear SVC \\n svc = LinearSVC()\\n # Check the training time for the SVC\\n t=time.time()\\n svc.fit(X_train, y_train)\\n t2 = time.time()\\n print(round(t2-t, 2), 'Seconds to train SVC...')\\n # Check the score of the SVC\\n print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))\\n # Check the prediction time for a single sample\\n t=time.time()\\n\\n return svc, X_scaler\",\n \"def train(self, features, labels):\\n self._clf.fit(features, labels)\",\n \"def train(self, features, labels):\\n self._clf.fit(features, labels)\",\n \"def train_enroller(self, train_files, enroller_file):\\n # save something\\n bob.io.base.save(_data, enroller_file)\",\n \"def test_classifier(self):\\n \\n files = 0\\n tp = 0\\n fp = 0\\n tn = 0\\n fn = 0\\n \\n for testFile in os.listdir(self.testing_dir):\\n if os.path.splitext(testFile)[1] in self.valid:\\n\\n files += 1\\n fileName = self.testing_dir + \\\"/\\\" + testFile\\n\\n img = cv2.imread(fileName).astype(np.float32)\\n self.Helpers.logger.info(\\\"Loaded test image \\\" + fileName)\\n \\n img = cv2.resize(img, (self.Helpers.confs[\\\"cnn\\\"][\\\"data\\\"][\\\"dim\\\"], \\n self.Helpers.confs[\\\"cnn\\\"][\\\"data\\\"][\\\"dim\\\"]))\\n img = self.reshape(img)\\n \\n prediction = self.get_predictions(img)\\n \\n msg = \\\"\\\"\\n if prediction == 1 and \\\"_1.\\\" in testFile:\\n tp += 1\\n msg = \\\"Acute Lymphoblastic Leukemia correctly detected (True Positive)\\\"\\n elif prediction == 1 and \\\"_0.\\\" in testFile:\\n fp += 1\\n msg = \\\"Acute Lymphoblastic Leukemia incorrectly detected (False Positive)\\\"\\n elif prediction == 0 and \\\"_0.\\\" in testFile:\\n tn += 1\\n msg = \\\"Acute Lymphoblastic Leukemia correctly not detected (True Negative)\\\"\\n elif prediction == 0 and \\\"_1.\\\" in testFile:\\n fn += 1\\n msg = \\\"Acute Lymphoblastic Leukemia incorrectly not detected (False Negative)\\\"\\n self.Helpers.logger.info(msg)\\n \\n self.Helpers.logger.info(\\\"Images Classifier: \\\" + str(files))\\n self.Helpers.logger.info(\\\"True Positives: \\\" + str(tp))\\n self.Helpers.logger.info(\\\"False Positives: \\\" + str(fp))\\n self.Helpers.logger.info(\\\"True Negatives: \\\" + str(tn))\\n self.Helpers.logger.info(\\\"False Negatives: \\\" + str(fn))\",\n \"def train(self):\\n lFileList = []\\n for fFileObj in os.walk(\\\"movies_reviews/\\\"):\\n lFileList = fFileObj[2]\\n break\\n for rev in lFileList:\\n if int(rev[7])== 1:\\n contents = self.loadFile(\\\"movies_reviews/\\\" + rev)\\n listOfWords = self.tokenize(contents)\\n for word in listOfWords:\\n self.negRev[word] = self.negRev.get(word, 0) + 1\\n if int(rev[7])== 5:\\n contents = self.loadFile(\\\"movies_reviews/\\\" + rev)\\n listOfWords = self.tokenize(contents)\\n for word in listOfWords:\\n self.posRev[word] = self.posRev.get(word, 0) + 1\\n self.save(self.posRev, \\\"posRev\\\")\\n self.save(self.negRev, \\\"negRev\\\")\",\n \"def train(args):\\n\\n log_header('Training network')\\n\\n train_retriever(args)\",\n \"def __init__(\\n self, filenames, num_features=None, num_labels=0, x_dtype=tf.string, y_dtype=tf.int64, batch_size=16,\\n skip_count=0, file_repeat=1, num_epoch=None, file_folder=None,\\n num_threads=8, buffer_size=2000, shuffle_file=False,\\n decode_jpeg=False, use_one_hot_label=False, use_smooth_label=True, num_classes=1):\\n if file_folder is None:\\n file_folder = FLAGS.DEFAULT_IN + 'tfrecords_{}/'.format(FLAGS.TARGET_SIZE)\\n # check inputs\\n if isinstance(filenames, str): # if string, add file location and .tfrecords\\n filenames = [os.path.join(file_folder, filenames + '.tfrecords')]\\n else: # if list, add file location and .tfrecords to each element in list\\n filenames = [os.path.join(file_folder, file + '.tfrecords') for file in filenames]\\n for file in filenames:\\n assert os.path.isfile(file), 'File {} does not exist.'.format(file)\\n if file_repeat > 1:\\n filenames = filenames * int(file_repeat)\\n if shuffle_file:\\n # shuffle operates on the original list and returns None / does not return anything\\n from random import shuffle\\n shuffle(filenames)\\n\\n # training information\\n self.num_features = num_features\\n self.num_labels = num_labels\\n self.x_dtype = x_dtype\\n self.y_dtype = y_dtype\\n self.batch_size = batch_size\\n self.batch_shape = [self.batch_size, self.num_features]\\n self.num_epoch = num_epoch\\n self.skip_count = skip_count\\n\\n # read data\\n self.decode_jpeg = decode_jpeg\\n self.use_one_hot_label = False if num_labels > 1 else use_one_hot_label\\n self.use_smooth_label = use_smooth_label\\n self.num_classes = num_classes\\n dataset = tf.data.TFRecordDataset(filenames) # setting num_parallel_reads=num_threads decreased the performance\\n self.dataset = dataset.map(self.__parser__, num_parallel_calls=num_threads)\\n self.iterator = None\\n self.buffer_size = buffer_size\\n self.scheduled = False\\n self.num_threads = num_threads\",\n \"def train(self, train_instances, train_labels, update_cache=True,\\n sample_weight=None):\\n self.classifier.fit(train_instances, train_labels, sample_weight=sample_weight)\\n if update_cache:\\n pickle.dump(self.classifier, open(self.cache_filename, 'wb'))\",\n \"def train(self, examples):\\n \\n for e in examples:\\n\\n class_label = None\\n\\n features = self.featurize(e[1])\\n\\n #determining which class corresponds to \\n if(e[2] == '0'):\\n class_label = self.class_zero\\n self.class_zero_feature_count += len(features)\\n self.class_zero_doc_count += 1\\n else:\\n class_label = self.class_one\\n self.class_one_feature_count += len(features)\\n self.class_one_doc_count += 1\\n\\n for f in features:\\n if(f[1] == True):\\n #adding feature to vocabulary\\n self.vocab.add(f[0])\\n #adding feature to class to keep track of counts\\n class_label[f[0]] += 1\\n \\n\\n self.total_docs = len(examples)\",\n \"def train(self):\\n for data_tier in self.data_tiers:\\n fd = open(self.data_path + '/training_data_' + data_tier + '.json', 'r')\\n self.preprocessed_data[data_tier] = json.load(fd)\\n fd.close()\\n tot = len(self.preprocessed_data[data_tier]['features'])\\n p = int(math.ceil(tot*0.8))\\n training_features = np.array(self.preprocessed_data[data_tier]['features'][:p])\\n trend_training_classifications = np.array(self.preprocessed_data[data_tier]['trend_classifications'][:p])\\n avg_training_classifications = np.array(self.preprocessed_data[data_tier]['avg_classifications'][:p])\\n t1 = datetime.datetime.utcnow()\\n self.clf_trend[data_tier].fit(training_features, trend_training_classifications)\\n self.clf_avg[data_tier].fit(training_features, avg_training_classifications)\\n t2 = datetime.datetime.utcnow()\\n td = t2 - t1\\n self.logger.info('Training %s for data tier %s took %s', self.name, data_tier, str(td))\\n joblib.dump(self.clf_trend[data_tier], self.data_path + '/' + self.name + '_trend_' + data_tier + '.pkl')\\n joblib.dump(self.clf_avg[data_tier], self.data_path + '/' + self.name + '_avg_' + data_tier + '.pkl')\",\n \"def train(self, clues, parsed_clues):\\n klasses = []\\n for answer in parsed_clues:\\n klass = answer[:answer.index(\\\":\\\")]\\n klasses.append(klass)\\n features = []\\n for clue in clues:\\n features.append(self.findFeatures(clue))\\n self.classifier.addExamples(features, klasses)\",\n \"def train_classifier(corpus=CategorizedPlaintextCorpusReader('.\\\\\\\\corpus\\\\\\\\', r'.+\\\\.txt', cat_pattern=r'(.+)\\\\.txt')):\\n lfeats = label_feats_from_corpus(corpus)\\n train_feats, test_feats = split_label_feats(lfeats, 1)\\n nb_classifier = NaiveBayesClassifier.train(train_feats)\\n pickle.dump(nb_classifier, open('nb_classifier', 'wb'))\\n return nb_classifier\",\n \"def trainClassifiers(features, labels):\\n # trains each classifier on given training set\\n classArr = VotingClassifier(estimators = [('NB', naiveBayesModel), ('linSVC', linearSVCModel), ('LR', logRegModel)], \\\\\\n voting = 'hard', weights = [1, 5, 3])\\n \\n classArr = classArr.fit(features, labels)\\n return classArr\",\n \"def do_make_(self):\\n global g_list_of_classifier\\n\\n for ite_clf in g_list_of_classifier:\\n ite_clf.learn()\\n return ''\",\n \"def apply_classifier(self):\\n for detected_object in self.detected_objects:\\n detected_object.predict_class(self.original_image)\",\n \"def train():\\n pass\",\n \"def trainModel( self, featureTrain, classTrain):\",\n \"def run_tests():\\n source1 = TextModel(\\\"Barack Obama\\\")\\n source1.add_file('project/source_texts/barackobama_source_text.txt')\\n\\n source2 = TextModel('Donald Trump')\\n source2.add_file('project/source_texts/donaldtrump_source_text.txt')\\n\\n new1 = TextModel('More Obama')\\n new1.add_file('project/source_texts/moreobama_source_text.txt')\\n new1.classify(source1, source2)\\n\\n new2 = TextModel('More Trump')\\n new2.add_file('project/source_texts/moretrump_source_text.txt')\\n new2.classify(source1, source2)\\n\\n new1 = TextModel('Gucci Gang by Lil Pump')\\n new1.add_file('project/source_texts/guccigang_source_text.txt')\\n new1.classify(source1, source2)\\n\\n new1 = TextModel(\\\"Spongebob Transcripts\\\")\\n new1.add_file('project/source_texts/spongebobeps_source_text.txt')\\n new1.classify(source1, source2)\",\n \"def train(self):\\n # 1. Extracting details of attributes\\n\\n self.get_attribute_data()\\n if self.train_data is None and self.train_data_file is None:\\n raise ValueError(\\\"Neither training data not training file provided\\\")\\n\\n self.get_train_data()\\n self.classifier = self.build_tree(rows=self.train_data, attribute_list=self.attribute_names)\",\n \"def train(self):\\r\\n faces = [] #empty list for faces\\r\\n Ids = [] #empty list for Id's\\r\\n path = f\\\"{PARENT_PATH}\\\\\\\\{DATASET_DIR}\\\" #dataset path\\r\\n\\r\\n #join each and every image paths\\r\\n image_paths = [os.path.join(path, i) for i in os.listdir(path)]\\r\\n #print(image_paths)\\r\\n\\r\\n for image in image_paths:\\r\\n face_img = Image.open(image).convert('L') #Pillow Image\\r\\n np_face = np.array(face_img, 'uint8') #into numpy array - usigned 8 bit -1byte\\r\\n Id = int(os.path.split(image)[-1].split('.')[1]) #get id from image path\\r\\n #print(Id)\\r\\n faces.append(np_face) #append in faces array/list\\r\\n Ids.append(Id) #append in Ids list/array\\r\\n\\r\\n RECOGNIZER.train(faces, np.array(Ids)) #train model using faces and Id (numpy arrays)\\r\\n RECOGNIZER.save(f\\\"{PARENT_PATH}\\\\\\\\{TRAINED_FILE}\\\")\\r\\n\\r\\n self.pop_window(title=\\\"Restart Needed!\\\", msg=\\\"Training Successful.\\\\nRestart the app Now.\\\")\\r\\n return\",\n \"def train(args):\\n\\n nb_classes = 2\\n neg_class_label = '0'\\n pos_class_label = '1'\\n nb_train_samples = get_nb_files(args.train_dir)\\n nb_val_samples = get_nb_files(args.val_dir)\\n batch_size = int(args.batch_size)\\n\\n nb_train_samples_neg = len([name for name in os.listdir(os.path.join(args.train_dir, neg_class_label))])\\n nb_train_samples_pos = len([name for name in os.listdir(os.path.join(args.train_dir, pos_class_label))])\\n print(\\\"Number of neg training examples is \\\", nb_train_samples_neg)\\n print(\\\"Number of pos training examples is \\\", nb_train_samples_pos)\\n\\n\\n # data prep\\n train_datagen = ImageDataGenerator()\\n test_datagen = ImageDataGenerator()\\n\\n train_generator = train_datagen.flow_from_directory(args.train_dir,\\n target_size=(IM_WIDTH, IM_HEIGHT),\\n batch_size=batch_size,\\n class_mode='categorical',\\n shuffle=True)\\n\\n validation_generator = test_datagen.flow_from_directory(args.val_dir,\\n target_size=(IM_WIDTH, IM_HEIGHT),\\n batch_size=batch_size,\\n class_mode='categorical',\\n shuffle=False)\\n val_labels = validation_generator.classes\\n # validation_labels = to_categorical(val_labels, num_classes=2)\\n\\n\\n # setting up class weights for imbalanced dataset ...\\n # The sum of the weights of all examples stays the same.\\n\\n\\n total = nb_train_samples_neg + nb_train_samples_pos\\n print('Examples:\\\\n Total: {}\\\\n Positive: {} ({:.2f}% of total)\\\\n'.format(total, nb_train_samples_pos, 100 * nb_train_samples_pos/total))\\n\\n\\n weight_for_0 = total / nb_train_samples_neg\\n weight_for_1 = total / nb_train_samples_pos\\n\\n class_weight = {0: weight_for_0, 1: weight_for_1}\\n\\n print('Weight for class 0: {:.2f}'.format(weight_for_0))\\n print('Weight for class 1: {:.2f}'.format(weight_for_1))\\n\\n # setup model\\n # model = keras.models.load_model('./experiment/vgg_ft_ni10.model')\\n base_model = applications.Xception(weights='imagenet', include_top=False, input_shape=(300, 300, 3))\\n print(\\\"Number of vgg layers :\\\", len(base_model.layers))\\n\\n # adding fully connected layer\\n model = add_new_last_layer(base_model, nb_classes)\\n\\n # transfer learning...\\n setup_to_transfer_learn(model, base_model)\\n\\n for i, layer in enumerate(model.layers):\\n print(i, layer.name)\\n print(layer.trainable)\\n\\n # checkpoint = ModelCheckpoint(\\\"ft_vgg16.h5\\\",\\n # monitor=RocCallback(validation_generator), # not working with custom callbacks\\n # verbose=1,\\n # save_best_only=True,\\n # save_weights_only=False,\\n # mode='auto',\\n # period=1)\\n\\n print('Transfer Learning is starting...')\\n # model.save_weights('') # Please input weights if present\\n history_tl = model.fit_generator(train_generator,\\n nb_epoch=NB_EPOCHS_TL,\\n samples_per_epoch=nb_train_samples,\\n validation_data=validation_generator,\\n nb_val_samples=nb_val_samples,\\n class_weight=class_weight,\\n callbacks=[RocCallback(validation_generator)])\\n\\n # fine-tuning ...\\n setup_to_finetune(model)\\n for i, layer in enumerate(model.layers):\\n print(i, layer.name)\\n print(layer.trainable)\\n # model.load_weights('./experiment/vgg_ft_ni25.h5')\\n # model.summary()\\n\\n print('Fine tuning is starting...')\\n history_ft = model.fit_generator(train_generator,\\n samples_per_epoch=nb_train_samples,\\n nb_epoch=NB_EPOCHS_FT,\\n validation_data=validation_generator,\\n nb_val_samples=nb_val_samples,\\n class_weight=class_weight,\\n callbacks=[RocCallback(validation_generator)])\\n\\n # making predictions ...3\\n model.save_weights('./experiment/vgg_ft_ni25.h5')\\n # model.save('./experiment/vgg_ft_ni10.model')\\n pred = model.predict_generator(validation_generator, steps=nb_val_samples//batch_size)\\n\\n # confusion_matrix(val_labels, pred)\\n\\n # F1Score ...\\n predictions = np.argmax(pred, axis=1)\\n\\n\\n # Storing Predictions as CSV ...\\n filenames = validation_generator.filenames\\n labels = validation_generator.class_indices\\n labels = dict((v, k) for k, v in labels.items())\\n predictions = [labels[k] for k in predictions]\\n results = pd.DataFrame({\\\"Filename\\\": filenames,\\n \\\"true\\\" : pred[:, 1],\\n \\\"Predictions\\\": predictions,\\n \\\"Label\\\": val_labels})\\n # results.to_csv(\\\"./experiment/ft_ni_results15.csv\\\", index=False)\\n # metric(\\\"./experiment/ft_ni_results15.csv\\\")\\n\\n # plotting data...\\n if args.plot:\\n plot_training(pred, val_labels)\\n\\n input_path = args.val_dir + pos_class_label\\n output_path = './models/vgg/epoch_{}/'.format(NB_EPOCHS_TL+NB_EPOCHS_FT)\\n\\n\\n # visualising activation maps of positive class ...\\n for img in validation_generator.filenames:\\n visualize_class_activation_map(model, input_path + img, output_path + img)\",\n \"def train_routine(training_file, output_folder):\\n if output_folder[-1] != '/':\\n output_folder += '/'\\n\\n svm_file = output_folder + 'svm.txt'\\n centroid_file = output_folder + 'centroids.txt'\\n ids_file = output_folder + 'ids.txt'\\n\\n surf = cv2.SURF(250, extended=False)\\n categories = dict()\\n ids = dict()\\n id = 1\\n features = list()\\n\\n print \\\"Extracting features\\\"\\n for line in open(training_file):\\n try:\\n category, path = line.split(';')\\n except:\\n print \\\"Error: File not in proper format. Ensure: <category/class name>; <path to image of said category>\\\"\\n sys.exit(0)\\n path = path.strip()\\n\\n try:\\n img = cv2.imread(path)\\n #img = cv2.resize(img, (500, 500))\\n except Exception as e:\\n print e\\n continue\\n\\n keypoints, descriptors = surf.detectAndCompute(img, None)\\n\\n if not category in categories:\\n categories[category] = Category(label=category)\\n ids[category] = id\\n id += 1\\n categories[category].add_feature(descriptors)\\n\\n #for category in categories:\\n #f = categories[category].yield_features()\\n ##features.extend(f)\\n #for i in f:\\n #features.extend(i)\\n\\n print \\\"Calculating centroids\\\"\\n #np_features = numpy.array(features)\\n #print \\\"Features: \\\", np_features.shape\\n #centroids, labels = kmeans2(np_features, FEATURE_TYPES)\\n centroids = helpers.loadObject(output_folder + 'centroids.txt')\\n print centroids.shape\\n\\n print \\\"Forming bag of words\\\"\\n X, Y = [], []\\n for category in categories:\\n categories[category].calc_bagofwords(centroids)\\n for bow in categories[category].bagofwords:\\n X.append(bow)\\n Y.append(ids[category])\\n print \\\"Fitting linear SVMs onto the bag of words\\\"\\n lin_clf = svm.LinearSVC()\\n lin_clf.fit(X, Y)\\n\\n helpers.saveObject(lin_clf, svm_file)\\n helpers.saveObject(centroids, centroid_file)\\n helpers.saveObject(ids, ids_file)\",\n \"def train_classifier(self, class_id):\\n raise NotImplementedError(\\\"Classifier training must be implemented first.\\\")\",\n \"def train(self, labeled_featuresets):\\n\\n\\t\\t# generate training file from labeled_featuresets\\n\\t\\tself.__text_converter = Text2svmConverter(self.__converter_arguments)\\n\\t\\tself.__convert_labeled_featuresets(labeled_featuresets, self.__svmFilePath)\\n\\n\\t\\t# train the model\\n\\t\\tself.__model = train_converted_text(self.__svmFilePath, self.__text_converter, grid_arguments=self.__grid_arguments, feature_arguments=self.__feature_arguments, train_arguments=self.__liblinear_arguments)\",\n \"def __init__(self, dataset_dir, transform):\\n\\n self.filenames = os.listdir(dataset_dir)\\n self.filenames = [os.path.join(dataset_dir, f)\\n for f in self.filenames if f.endswith('.jpg')]\\n self.labels = [int(os.path.split(filename)[-1][0])\\n for filename in self.filenames]\\n self.transform = transform\",\n \"def run(self, images):\\n\\n # Apply filtering\\n if len(self.preprocessing) > 0: \\n print('Applying', len(self.preprocessing), 'filter(s) to input images')\\n for filter in self.preprocessing:\\n for i in range(len(images)):\\n images[i] = filter(images[i])\\n\\n # Apply feature extraction\\n if len(self.features) > 0:\\n print('Extracting', len(self.features), 'feature(s) from input images')\\n scaler = MinMaxScaler(feature_range=(0, 1))\\n for i in range(len(images)):\\n features = []\\n for feature in self.features:\\n features.append(feature(images[i]))\\n images[i] = np.hstack(features)\\n images = scaler.fit_transform(images)\\n else:\\n # Flatten images (not necessary when using feature extraction)\\n train_data = np.array(train_data).reshape((len(train_data), -1))\\n\\n # Run predictions\\n print('Predicting presence of parasites in', len(images), 'images\\\\n')\\n return self.classifier.predict(images)\",\n \"def train(self, images, labels, save_path=None):\\n super(BlurredContext, self).train(\\n images, labels, save_path,\\n lambda img: cv2.imread(img, cv2.CV_LOAD_IMAGE_GRAYSCALE),\\n lambda img: get_input_vector(resize(img, 500)))\",\n \"def train(self):\\n self.ae_train(self.net0, self.ae0_optimizer, self.train0_loader, self.val_loader, name='Net0')\\n self.ae_train(self.net1, self.ae1_optimizer, self.train1_loader, self.val_loader, name='Net1')\\n self.ae_train(self.net2, self.ae2_optimizer, self.train2_loader, self.val_loader, name='Net2')\\n\\n self.classifier_train(self.net0, self.optimizer0, self.train0_loader, self.val_loader, name='Net0')\\n self.classifier_train(self.net1, self.optimizer1, self.train1_loader, self.val_loader, name='Net1')\\n self.classifier_train(self.net2, self.optimizer2, self.train2_loader, self.val_loader, name='Net2')\",\n \"def train(self, features, labels, seed=None):\\n raise NotImplementedError('Not implemented')\",\n \"def run_tests():\\n source1 = TextModel('prep')\\n source1.add_file('source_model_1.txt')\\n \\n source2 = TextModel('athletes')\\n source2.add_file('source_model_2.txt')\\n\\n new1 = TextModel('my_writing')\\n new1.add_file('my_writing.txt')\\n new1.classify(source1, source2)\\n\\n # Add code for three other new models below.\",\n \"def train_projector(self, train_files, projector_file):\\n # save something\\n bob.io.base.save(_data, projector_file)\",\n \"def train_initial_classifier(zero_f=\\\"NIST/human_hcd_synthetic_oxidized.msp\\\",\\n\\t\\t\\t\\t\\t\\t\\t one_f=\\\"NIST/human_hcd_synthetic_native.msp\\\",\\n\\t\\t\\t\\t\\t\\t\\t selected_features_diff=[],\\n\\t\\t\\t\\t\\t\\t\\t top_mean = 1000,\\n\\t\\t\\t\\t\\t\\t\\t top_peaks = 100,\\n\\t\\t\\t\\t\\t\\t\\t max_distance = 275,\\n\\t\\t\\t\\t\\t\\t\\t distance_bins = 0.005,\\n\\t\\t\\t\\t\\t\\t\\t windowed_mode = False,\\n\\t\\t\\t\\t\\t\\t\\t out_dir=\\\"res/\\\"):\\n\\t#Check the file extension and parse to get features for class zero\\n\\tif zero_f.endswith(\\\".mgf\\\"): feats_zero,feat_bins,instance_names,count_zero = read_mgf(zero_f,sum_feats=False,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t feat_bins=selected_features_diff,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t max_dist=max_distance,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t step_size=distance_bins,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t top_peaks=top_peaks)\\n\\telif zero_f.endswith(\\\".msp\\\"): feats_zero,feat_bins,instance_names,count_zero = read_msp(zero_f,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tsum_feats=False,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfeat_bins=selected_features_diff,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tmax_dist=max_distance,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tstep_size=distance_bins,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ttop_peaks=top_peaks)\\n\\telse: return(False) # TODO display error!\\n\\t\\n\\t#Check the file extension and parse to get features for class one\\n\\tif one_f.endswith(\\\".mgf\\\"): feats_one,feat_bins,instance_names,count_one = read_mgf(one_f,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t sum_feats=False,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t feat_bins=selected_features_diff,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t max_dist=max_distance,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t step_size=distance_bins,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t top_peaks=top_peaks)\\n\\telif one_f.endswith(\\\".msp\\\"): feats_one,feat_bins,instance_names,count_one = read_msp(one_f,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t sum_feats=False,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t feat_bins=selected_features_diff,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t max_dist=max_distance,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t step_size=distance_bins,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t top_peaks=top_peaks)\\n\\telse: return(False) # TODO display error!\\n\\n\\t#Prepare labels equal to length class zero and one\\n\\ty = [0]*(count_zero)\\n\\ty.extend([1]*(count_one))\\n\\n\\ty = np.array(y)\\n\\t\\n\\t#Stack the feature matrices of both classes\\n\\tX = scipy.sparse.vstack((feats_zero,feats_one))\\n\\t\\n\\t#Train optimizing the hyperparameters\\n\\txgb_model,random_search_res_xgb = train_xgb(X,y)\\n\\t#print(random_search_res_xgb.best_params_)\\n\\t#print(random_search_res_xgb.best_score_)\\n\\t\\n\\t#Train use selected hyperparameters\\n\\ttrain_xgb_lim(X,y,random_search_res_xgb.best_params_,out_dir=out_dir)\\n\\tplot_train_distr(xgb_model,X,y,out_dir=out_dir)\\n\\t\\n\\t#Flush to pickle\\n\\txgboost_to_wb(random_search_res_xgb,outfile=out_dir+\\\"model.pickle\\\")\\n\\t\\n\\trandom_search_res_xgb = pickle.load(open(out_dir+\\\"model.pickle\\\",\\\"rb\\\"))\\n\\t\\n\\t#Plot some of the feature importances and probs\\n\\tfscores = xgb_model.booster().get_fscore()\\n\\tfscores_list = sorted(list(fscores.items()),key=itemgetter(1),reverse=True)\\n\\tselected_features_indexes = map(int,[f.replace(\\\"f\\\",\\\"\\\") for f,n in fscores_list])\\n\\tselected_features_xgboost = [selected_features_diff[sfp] for sfp in selected_features_indexes]\\n\\tplot_feat_imp(selected_features_indexes,selected_features_diff,X,y,out_dir=out_dir)\\n\\t\\n\\treturn(random_search_res_xgb.best_params_,selected_features_xgboost)\",\n \"def train(self, train_data, train_labels):\\n\\n # Apply filtering\\n if len(self.preprocessing) > 0:\\n print('Applying', len(self.preprocessing), 'filter(s) to training data')\\n for filter in self.preprocessing:\\n for i in range(len(train_data)):\\n train_data[i] = filter(train_data[i])\\n\\n # Apply feature extraction\\n if len(self.features) > 0:\\n print('Extracting', len(self.features), 'feature(s) from training data')\\n scaler = MinMaxScaler(feature_range=(0, 1))\\n for i in range(len(train_data)):\\n features = []\\n for feature in self.features:\\n features.append(feature(train_data[i]))\\n train_data[i] = np.hstack(features)\\n train_data = scaler.fit_transform(train_data)\\n else:\\n # Flatten images (not necessary when using feature extraction)\\n train_data = np.array(train_data).reshape((len(train_data), -1))\\n\\n # Fit model\\n print('Fitting RF model on', len(train_labels), 'images')\\n self.classifier.fit(train_data, train_labels)\",\n \"def train(self, features, labels):\\n self.train_features = features\\n self.train_labels = labels\\n #raise NotImplementedError\",\n \"def main():\\n\\n trainData = os.getcwd() + '/data/traindata.txt'\\n trainLabels = os.getcwd() + '/data/trainlabels.txt'\\n\\n #testData = os.getcwd() + '/data/traindata.txt'\\n #testLabels = os.getcwd() + '/data/trainlabels.txt'\\n\\n testData = os.getcwd() + '/data/testdata.txt'\\n testLabels = os.getcwd() + '/data/testlabels.txt'\\n\\n #trainData = os.getcwd() + '/data/toyData.txt'\\n #trainLabels = os.getcwd() + '/data/toyLabel.txt'\\n #testData = os.getcwd() +'/data/toyTestData.txt'\\n #testLabels = os.getcwd() + '/data/toyTestLabel.txt'\\n\\n #print(trainData, trainLabels)\\n myClassifier = NBClassifier.new(NBClassifier.MODE_BERNOULI)\\n myClassifier.setTrainData(trainData, trainLabels)\\n #print(myClassifier)\\n\\n #singleTestData = ['Chinese', 'Chinese', 'Chinese', 'Tokyo', 'Japan']\\n #prediction = myClassifier.predict(singleTestData)\\n #print(f'{singleTestData} >>> {prediction}')\\n predictions = myClassifier.predictSet(testData)\\n accuracy = myClassifier.reportAccuracy(testLabels)\\n\\n #print(predictions)\\n print(accuracy)\",\n \"def add_train_file(self, classname: str, filename: str, handler: int = 0) -> int:\\n return self.get_func(\\\"DC_AddTrainFile\\\", [c_char_p, c_char_p, POINTER(c_int)], c_int)(\\n classname, filename, handler)\",\n \"def files():\\r\\n fn=pd.read_csv(request.files.get('fnm'))\\r\\n scaling = scaler.transform(fn)\\r\\n prediction = classifier.predict(scaling)\\r\\n return 'Predictions'+ str(list(prediction))\",\n \"def test_text_classifier_create_from_path(self):\\n pass\",\n \"def __init__(self):\\n try:\\n negative_ids = movie_reviews.fileids('neg')\\n positive_ids = movie_reviews.fileids('pos')\\n except LookupError:\\n import nltk\\n nltk.download('movie_reviews')\\n negative_ids = movie_reviews.fileids('neg')\\n positive_ids = movie_reviews.fileids('pos')\\n\\n \\\"\\\"\\\" \\n Separate positive features from negative\\n \\\"\\\"\\\"\\n negative_features = [(extract(movie_reviews.words(fileids=[f])), 'neg') for f in negative_ids]\\n positive_features = [(extract(movie_reviews.words(fileids=[f])), 'pos') for f in positive_ids]\\n\\n \\\"\\\"\\\" \\n Trains of 3/4 off the database\\n and test off 1/4\\n \\\"\\\"\\\"\\n negative_cutoff = int(len(negative_features) * 3 / 4)\\n positive_cutoff = int(len(positive_features) * 3 / 4)\\n\\n train_features = negative_features[:negative_cutoff] + positive_features[:positive_cutoff]\\n test_features = negative_features[negative_cutoff:] + positive_features[positive_cutoff:]\\n\\n print('Training on %d instances, testing on %d instances' % (len(train_features), len(test_features)))\\n self.classifier = NaiveBayesClassifier.train(train_features)\\n print('Training complete')\\n #print('accuracy:', nltk.classify.util.accuracy(self.classifier, test_features))\\n #self.classifier.show_most_informative_features()\\n\\n \\\"\\\"\\\" Save classifier \\\"\\\"\\\"\\n f = open('classifier.pickle', 'wb')\\n pickle.dump(self.classifier, f)\\n f.close()\",\n \"def train_classifier_bootstrap(iterations: int = 3, files_per_iteration: int = 50,\\n test_partition: float = 0.2) -> None:\\n\\n classifier = CnnClassifier(device).to(device)\\n classifier.load_from_file()\\n classifier = classifier.to(device)\\n\\n full_res_folder = os.path.dirname(__file__) + '/dataset/GTSRB_Negative/bootstrap_full_res/'\\n output_folder = os.path.dirname(__file__) + '/dataset/GTSRB_Negative/images/'\\n print(\\\"Searching for new pictures in\\\", full_res_folder, \\\"that are classified as street signs\\\")\\n\\n jpg_file_names = []\\n for root, dirs, files in os.walk(full_res_folder):\\n for name in files:\\n if name.endswith(\\\".jpg\\\"):\\n jpg_file_names.append((root, name))\\n\\n for i in range(1, iterations):\\n random.shuffle(jpg_file_names)\\n print(\\\">>>>>>>>>>>>>>>>>>>>>>>>>>> Iteration {}\\\".format(i))\\n dirty = False\\n new_files = []\\n for root, jpg_file_name in jpg_file_names[:files_per_iteration]:\\n print(\\\"\\\\tLoading\\\", jpg_file_name)\\n pil_img = Image.open(root + jpg_file_name)\\n fa = FrameAnalyzer(device, classifier=classifier, width=pil_img.size[0], height=pil_img.size[1])\\n if pil_img is not None:\\n rectangles = fa.get_detected_street_signs(pil_img, limit=50)\\n print(\\\"\\\\t\\\\t{} patches classified as street signs\\\".format(len(rectangles)))\\n if len(rectangles):\\n dirty = True\\n for rect in rectangles:\\n x1, y1, x2, y2, label_idx, score = rect\\n x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)\\n fn = '{}_{}_'.format(x1, y1) + jpg_file_name\\n # crop and save\\n pil_img.crop((x1, y1, x2, y2)).save(output_folder + fn)\\n new_files.append(fn)\\n\\n if dirty:\\n print(\\\"Adding\\\", len(new_files), \\\"negative examples\\\")\\n # shuffle and partition into training and test\\n random.shuffle(new_files)\\n n_test = int(test_partition * len(new_files))\\n new_files_test = new_files[:n_test]\\n new_files_training = new_files[n_test:]\\n GTSRBDataset.add_images_to_negative_examples_json(new_files_test, overwrite=False, test=True)\\n GTSRBDataset.add_images_to_negative_examples_json(new_files_training, overwrite=False, test=False)\\n # train the model\\n # epochs = 2 # max(1, int(iterations / 3))\\n classifier.train_model(max_epochs=20, max_patience=5)\\n classifier.store_to_file()\",\n \"def main(opts):\\n\\n # Create a dataloader for the training images\\n train_dataloader, _ = get_emoji_loader(opts.emoji, opts)\\n\\n # Create checkpoint and sample directories\\n utils.create_dir(opts.checkpoint_dir)\\n utils.create_dir(opts.sample_dir)\\n\\n train(train_dataloader, opts)\",\n \"def test_file(self, file_name, version, classifier_type):\\n labels = []\\n with open(file_name) as f:\\n for line in f.readlines():\\n print(line,self.predict(line))\\n labels.append(self.predict(line))\\n \\n filename = 'test_results-' + classifier_type + '-' + version + '.txt'\\n \\n with open(filename, 'w') as f:\\n for label in labels:\\n f.write(str(label)+\\\"\\\\n\\\")\\n \\n print (\\\"Results from \\\",file_name,\\\" printed to:\\\",filename)\",\n \"def main(training_file_name):\\n attribute, inverse = build_classifier(training_file_name)\\n trained_file = open(TRAINED_FILE_NAME, mode='w')\\n prolog(trained_file)\\n write_body(trained_file, attribute, inverse)\\n epilog(trained_file)\",\n \"def train(self, arg1=None, arg2=None, **kwargs):\\n nltk.download('averaged_perceptron_tagger')\\n nltk.download('wordnet')\\n nltk.download('twitter_samples')\\n nltk.download('punkt')\\n nltk.download('stopwords')\\n nltk.download('vader_lexicon')\\n\\n positive_tweets = twitter_samples.strings('positive_tweets.json')\\n negative_tweets = twitter_samples.strings('negative_tweets.json')\\n text = twitter_samples.strings('tweets.20150430-223406.json')\\n tweet_tokens = twitter_samples.tokenized('positive_tweets.json')[0]\\n\\n stop_words = stopwords.words('english')\\n\\n positive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')\\n negative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')\\n\\n positive_cleaned_tokens_list = []\\n negative_cleaned_tokens_list = []\\n\\n for tokens in positive_tweet_tokens:\\n positive_cleaned_tokens_list.append(self.remove_noise(tokens, stop_words))\\n\\n for tokens in negative_tweet_tokens:\\n negative_cleaned_tokens_list.append(self.remove_noise(tokens, stop_words))\\n\\n all_pos_words = self.get_all_words(positive_cleaned_tokens_list)\\n\\n freq_dist_pos = FreqDist(all_pos_words)\\n print(freq_dist_pos.most_common(20))\\n\\n positive_tokens_for_model = self.get_tweets_for_model(positive_cleaned_tokens_list)\\n negative_tokens_for_model = self.get_tweets_for_model(negative_cleaned_tokens_list)\\n\\n positive_dataset = [(tweet_dict, \\\"Positive\\\")\\n for tweet_dict in positive_tokens_for_model]\\n\\n negative_dataset = [(tweet_dict, \\\"Negative\\\")\\n for tweet_dict in negative_tokens_for_model]\\n\\n dataset = positive_dataset + negative_dataset\\n\\n random.shuffle(dataset)\\n\\n train_data = dataset[:7000]\\n test_data = dataset[7000:]\\n\\n self.classifier = NaiveBayesClassifier.train(train_data)\",\n \"def train(self, train_ids_file):\\n\\t\\t# TODO(student): Feel free to remove if you do not use.\\n\\t\\ttf.keras.backend.set_learning_phase(0)\\n\\t\\tfile_pairs = tf.data.Dataset.zip(get_filename_data_readers(train_ids_file, True))\\n\\t\\t#training_dataset = file_pairs.shuffle(buffer_size=2500).map(read_image_pair_with_padding).batch(self.batch_size)\\n\\t\\ttraining_dataset = file_pairs.map(read_image_pair_with_padding).batch(self.batch_size)\\n\\t\\ttraining_dataset_X = training_dataset.map(lambda a, b: a)\\n\\t\\ttraining_dataset_Y = training_dataset.map(lambda a, b: b)\\n\\n\\t\\ttraining_iterator_X = training_dataset_X.make_initializable_iterator()\\n\\t\\ttraining_iterator_Y = training_dataset_Y.make_initializable_iterator()\\n\\n\\t\\tfor i in range(5):\\n\\t\\t\\tself.sess.run(training_iterator_X.initializer)\\n\\t\\t\\tself.sess.run(training_iterator_Y.initializer)\\n\\t\\t\\ttraining_handle_X = self.sess.run(training_iterator_X.string_handle())\\n\\t\\t\\ttraining_handle_Y = self.sess.run(training_iterator_Y.string_handle())\\n\\t\\t\\tj = 0\\n\\t\\t\\tloss_ary = []\\n\\t\\t\\twhile True:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t[train_loss, train_step, train_pred] = self.sess.run(\\n\\t\\t\\t\\t\\t\\t[self.loss, self.train_op, self.pred],\\n\\t\\t\\t\\t\\t\\t\\tfeed_dict={\\n\\t\\t\\t\\t\\t\\t\\t\\tself.is_training: True,\\n\\t\\t\\t\\t\\t\\t\\t\\tself.handle_X: training_handle_X,\\n\\t\\t\\t\\t\\t\\t\\t\\tself.handle_Y: training_handle_Y,\\n\\t\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t)\\n\\t\\t\\t\\t\\t'''\\n\\t\\t\\t\\t\\tif j == 0:\\n\\t\\t\\t\\t\\t\\tplt.imshow(train_pred[0])\\n\\t\\t\\t\\t\\t\\tplt.colorbar()\\n\\t\\t\\t\\t\\t\\tplt.show()\\n\\t\\t\\t\\t\\t\\tpdb.set_trace()\\n\\t\\t\\t\\t\\t'''\\n\\t\\t\\t\\t\\tloss_ary.append(train_loss)\\n\\t\\t\\t\\t\\tj += 1\\n\\t\\t\\t\\t\\tprint('Epoch', i, 'Batch', j, train_loss)\\n\\t\\t\\t\\texcept tf.errors.OutOfRangeError:\\n\\t\\t\\t\\t\\tbreak\\n\\t\\t\\tself.sess.run(self.lr_decay_op)\\n\\t\\t\\tprint('Apply lr decay, new lr: %f' % self.sess.run(self.lr))\\n\\t\\t\\tprint(f'Epoch: {i}, Avg Loss: {np.mean(loss_ary)}')\\n\\t\\t\\tself.save(\\\"model_file_no_{}.pickle\\\".format(i))\\n\\n\\n\\t\\tprint('Done training')\",\n \"def train_projector(self, train_files, projector_file, metadata=None):\\n assert isinstance(metadata, list)\\n return super(DummyAlgorithmMetadata, self).train_projector(train_files, projector_file)\",\n \"def main(root_dir):\\n # load annotations\\n print('Loading instances and annotations...')\\n captions_file = json.load(open('{}/annotations/captions_train2017.json'.format(root_dir), 'r'))\\n categories_file = json.load(open('{}/annotations/instances_train2017.json'.format(root_dir), 'r'))\\n print('Done.')\\n\\n # group categories by image\\n image_categories = group_categories(categories_file)\\n\\n # group captions by image\\n image_captions = group_captions(captions_file['annotations'])\\n\\n # get filename of each image\\n image_file = get_filename(captions_file['images'])\\n\\n # assign each category an id.\\n # we are not using the default ids given in the dataset because\\n # the id ranges are not continuous.\\n category_id, id_category = map_category_id(categories_file['categories'])\\n \\n # save parsed coco dataset\\n save_dataset(image_categories, image_captions, image_file, category_id, id_category, root_dir)\",\n \"def main():\\n args = utils.read_arguments(__doc__)\\n documents = []\\n filenames = list(traverse_directory(args[\\\"input_dirpath\\\"],'*clean*.txt'))\\n labels_dirname = args[\\\"labels_dirpath\\\"]\\n labels_from_json = get_all_labels_from_json(labels_dirname)\\n for filename in tqdm(filenames):\\n with AnnotatedIBMFactory(filename) as instance_extractor:\\n filename_key = filename.split(\\\"/\\\")[-1]\\n document = instance_extractor.build_document(\\n labels_from_json[filename_key])\\n documents.append(document)\\n utils.pickle_to_file(documents, args['output_file'])\",\n \"def func(args):\\n\\n classifier_stat_list = []\\n cf_frames = []\\n confidences_list = []\\n intents_in_results = pd.Series(dtype='float')\\n\\n classifier_num = len(args.classifiers_results)\\n\\n # Prepare labels for each curve\\n labels = [os.path.splitext(os.path.basename(file_path))[0]\\n for file_path in args.classifiers_results]\\n\\n # Only do cutomization on labels if numbers match\\n if args.classifier_names and \\\\\\n (len(args.classifier_names) == classifier_num):\\n labels = args.classifier_names\\n\\n # Initialization\\n for i in range(classifier_num):\\n file_path = args.classifiers_results[i]\\n frame = pd.read_csv(file_path, encoding=UTF_8, quoting=csv.QUOTE_ALL)\\n if INTENT_JUDGE_COLUMN not in frame.columns: # Column validation\\n raise ValueError(\\\"'{}' column not in {}\\\".format(\\n INTENT_JUDGE_COLUMN, file_path))\\n # Read the cf files into list\\n cf_frames.append(frame)\\n # Collect all intents from the classification results\\n intents_in_results = pd.concat([intents_in_results,\\n frame[PREDICTED_INTENT_COLUMN]])\\n # Convert nan to zero values to avoid use zero as divider\\n confidences_list.append(frame[CONFIDENCE_COLUMN].fillna(0)\\n .drop_duplicates().sort_values().tolist())\\n\\n intents_in_results = intents_in_results.drop_duplicates()\\n\\n # Read weight\\n weights_df = None\\n weight_mode = args.weight.lower()\\n # Read the intent weights pairs from file\\n if weight_mode != POPULATION_WEIGHT_MODE and \\\\\\n weight_mode != EQUAL_WEIGHT_MODE:\\n try:\\n weights_df = pd.read_csv(args.weight, encoding=UTF_8,\\n quoting=csv.QUOTE_ALL)\\n # Validate the completeness\\n for _, intent in intents_in_results.iteritems():\\n if not any(weights_df[INTENT_COLUMN] == intent):\\n raise ValueError(\\\"'{}' intent not in {}\\\".format(\\n intent, args.weight))\\n except Exception as e:\\n print(e)\\n weight_mode = POPULATION_WEIGHT_MODE # default population mode\\n print('Fall back to {} mode'.format(POPULATION_WEIGHT_MODE))\\n\\n # Init the classifier_stat_list:\\n for i in range(classifier_num):\\n # array of zeros to hold precision values\\n classifier_stat_list.append(np.zeros([len(confidences_list[i]), 3]))\\n\\n for j in range(classifier_num):\\n confidences = confidences_list[j]\\n for i in range(len(confidences)):\\n conf = confidences[i]\\n cf_frame = cf_frames[j]\\n precision = 0\\n answered = \\\\\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf].shape[0]\\n if weight_mode == POPULATION_WEIGHT_MODE:\\n correct = cf_frame[\\n cf_frame[CONFIDENCE_COLUMN] >= conf][SCORE_COLUMN].sum()\\n precision = correct / answered\\n # print(precision)\\n else:\\n intent_uttr_num_map = \\\\\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf] \\\\\\n .groupby(PREDICTED_INTENT_COLUMN)[PREDICTED_INTENT_COLUMN] \\\\\\n .count().to_dict()\\n\\n # Calulate precision use equal weights\\n uttr_correct_intent = \\\\\\n cf_frame[cf_frame[CONFIDENCE_COLUMN] >= conf] \\\\\\n .groupby(GOLDEN_INTENT_COLUMN)[SCORE_COLUMN] \\\\\\n .sum()\\n\\n intent_weights = None\\n weight_coeff = 1 / len(intent_uttr_num_map)\\n\\n if weight_mode != EQUAL_WEIGHT_MODE:\\n required_weights_df = \\\\\\n weights_df[\\n weights_df[INTENT_COLUMN]\\n .isin(uttr_correct_intent.index)]\\n weight_sum = required_weights_df[WEIGHT_COLUMN].sum()\\n # Normalize weights\\n weights_df[WEIGHT_COLUMN] = \\\\\\n weights_df[WEIGHT_COLUMN] / weight_sum\\n intent_weights = \\\\\\n weights_df.set_index(INTENT_COLUMN)[WEIGHT_COLUMN] \\\\\\n .to_dict()\\n\\n for intent, correct_intent_num in \\\\\\n uttr_correct_intent.iteritems():\\n if weight_mode != EQUAL_WEIGHT_MODE:\\n weight_coeff = intent_weights[intent]\\n\\n precision += \\\\\\n weight_coeff * correct_intent_num \\\\\\n / intent_uttr_num_map[intent]\\n\\n classifier_stat_list[j][i, 0] = precision\\n classifier_stat_list[j][i, 1] = 100 * answered / len(cf_frame)\\n classifier_stat_list[j][i, 2] = conf\\n\\n for idx in range(len(classifier_stat_list)):\\n # reversing order for helpful plotting\\n classifier_stat_list[idx] = classifier_stat_list[idx][::-1]\\n\\n # plotting\\n fig = plt.figure()\\n ax = fig.gca()\\n\\n ax.set_ylim([0, 1.0]) # Hardcoding y-axis to a consistent 0-1.0 for the benefit of easing historical comparisions\\n\\n ax.grid(color='b', linestyle='--', alpha=0.3)\\n ax.set_xlabel('Percentage of Questions Answered',\\n fontsize=LEGEND_AXIS_FONT_SIZE)\\n ax.set_ylabel('Precision', fontsize=LEGEND_AXIS_FONT_SIZE)\\n line_style_cycler = cycle(line_styles)\\n line_color_cycler = cycle(line_color)\\n\\n lines = [] # reference to lines\\n # plot the curve and save the figure\\n for i in range(len(classifier_stat_list)):\\n classifier_stat = classifier_stat_list[i]\\n # Default to the idx of lowest conf\\n tau_idx = len(classifier_stat[:, 2]) - 1\\n indices_gtr_tau, = np.where(classifier_stat[:, 2] <= args.tau)\\n if len(indices_gtr_tau) > 0:\\n tau_idx = indices_gtr_tau[0]\\n\\n color = next(line_color_cycler)\\n line, = plt.plot(classifier_stat[:, 1], classifier_stat[:, 0],\\n color=color, label=labels[i],\\n linestyle=next(line_style_cycler))\\n\\n plt.plot(classifier_stat[tau_idx, 1], classifier_stat[tau_idx, 0],\\n '{}o'.format(color), markerfacecolor='None')\\n lines.append(line)\\n\\n tau_desc = mlines.Line2D([], [], markeredgecolor='black', marker='o',\\n linestyle='None', markerfacecolor='None',\\n markersize=10,\\n label='tau = {}'.format(args.tau))\\n\\n ax.legend(handles=lines + [tau_desc], loc='lower left', shadow=False,\\n prop={'size': LEGEND_AXIS_FONT_SIZE})\\n ax.set_title(args.figure_title,\\n fontsize=TITLE_FONT_SIZE)\\n\\n if args.ymin != 0.0:\\n plt.ylim(args.ymin, 1.0)\\n # Save figure as file\\n plt.savefig(args.outfile)\\n\\n print(\\\"Wrote precision curve to {}\\\".format(args.outfile))\",\n \"def __init__(self, train, transform, data_path='../extracted', category_path=\\\"../all_fish.txt\\\"):\\n self.transform = transform\\n data_path = os.path.join(data_path, 'train' if train else 'test')\\n fp = open(category_path, 'r')\\n self.fish_dict = {i.split(\\\";\\\")[0] : i.split(\\\";\\\")[1][1:-1] for i in fp}\\n \\n #self.name_to_label = [i.split(\\\";\\\")[1][1:-1] for i in fp]\\n self.image_paths = glob.glob(data_path + '/*.jpg')\",\n \"def run(dataset_dir,pic_path):\\n if not tf.gfile.Exists(dataset_dir):\\n tf.gfile.MakeDirs(dataset_dir)\\n\\n training_filename = _get_output_filename(dataset_dir, 'train')\\n testing_filename = _get_output_filename(dataset_dir, 'test')\\n\\n if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):\\n print('Dataset files already exist. Exiting without re-creating them.')\\n return\\n\\n class_names = os.listdir(pic_path)\\n labels_to_class_names = dict(zip(class_names,range(len(class_names))))\\n \\n picnames=[]\\n for label in class_names:\\n alabel_path=os.path.join(pic_path,label)\\n names=os.listdir(alabel_path)\\n picnames.extend([os.path.join(alabel_path,name) for name in names])\\n random.shuffle(picnames) \\n \\n train_picnames = picnames[:int(0.7*len(picnames))]\\n test_picnames = picnames[int(0.7*len(picnames)):]\\n # First, process the training data:\\n with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:\\n offset = 0\\n for name in train_picnames:\\n offset = _add_to_tfrecord(name, tfrecord_writer, labels_to_class_names, offset)\\n\\n # Next, process the testing data:\\n with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:\\n offset = 0\\n for name in test_picnames:\\n offset = _add_to_tfrecord(name, tfrecord_writer, labels_to_class_names, offset)\\n\\n # Finally, write the labels file:\\n labels_to_class_names = dict(zip(labels_to_class_names.values(),labels_to_class_names.keys())) \\n dataset_utils.write_label_file(labels_to_class_names, dataset_dir)\\n with open(os.path.join(dataset_dir,'info.json'),'w') as f:\\n info=json.dumps({'num_class':len(class_names),'num_sample_train':len(train_picnames),'num_sample_test':len(test_picnames)})\\n f.write(info)\\n\\n print('\\\\nFinished converting the dataset in the {}!'.format(pic_path))\\n print('\\\\nThe tfrecord files,info.json and labels file is located in the {}'.format(dataset_dir))\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"--identifier\\\", required=True,\\n help=\\\"A short name/identifier for your experiment, e.g. 'ex42b'.\\\")\\n args = parser.parse_args()\\n\\n train(args)\",\n \"def svc_classify(self, filename):\\n tokens = preprocess.parse_book(filename)\\n perplexities = [m.perplexity(tokens) for m in self.models]\\n return self.genres[self.svc.predict(perplexities)]\",\n \"def load(self):\\n self.classifier = joblib.load(\\\"data/models/repeatsfinder/repeatsfinder.joblib\\\")\",\n \"def load_training():\\n for can in candidates:\\n trainings[can] = []\\n for subdir, dirs, files in os.walk(os.path.join(corpus_dir, can)):\\n for doc in files:\\n trainings[can].append(doc)\",\n \"def LoadBatch(filename):\",\n \"def train():\\n # YOUR TRAINING CODE GOES HERE\",\n \"def categorize_classifier_files(out_dir):\\n\\n #sort all of the classifier files into a dictionary\\n class_files = glob.glob(\\\"feature_extraction_m*\\\")\\n class_file_dict = {\\\"positive\\\":[], \\\"negative\\\":[]}\\n class_cand_dict = {\\\"m1\\\":class_file_dict, \\\"m2\\\":class_file_dict, \\\"m3\\\":class_file_dict, \\\"m4\\\":class_file_dict, \\\"m5\\\":class_file_dict}\\n\\n for filename in class_files:\\n split_name = filename.split(\\\"_\\\")[-1].split(\\\".\\\")\\n model_num = split_name[0]\\n det = split_name[-1]\\n class_cand_dict[model_num][det].append(filename)\\n\\n #get all of the pfd files into a list\\n class_file_m1 = glob.glob(\\\"feature_extraction_m1*\\\")\\n pfd_files = []\\n for afile in class_file_m1:\\n f = open(afile, \\\"r\\\")\\n for line in f.readlines():\\n pfd_files.append(line)\\n f.close()\\n\\n #fill a dictionary with pfds and a value for how many positive IDs each pfd has\\n pulsar_pfds={}\\n for key in pfd_files:\\n pulsar_pfds[key]=0\\n for model_num in class_cand_dict.keys():\\n if class_cand_dict[model_num][\\\"positive\\\"]:\\n print(class_cand_dict[model_num][\\\"positive\\\"])\\n f = open(class_cand_dict[model_num][\\\"positive\\\"][0], \\\"r\\\")\\n for line in f.readlines():\\n pulsar_pfds[line]+=1\\n f.close()\\n\\n #For each pfd with >=3 positive IDs, write that pfd to 'positive' file, else write to 'negative' file\\n pos_f = open(os.path.join(out_dir, \\\"LOTAAS_positive_detections.txt\\\"), \\\"w+\\\")\\n neg_f = open(os.path.join(out_dir, \\\"LOTAAS_negative_detections.txt\\\"), \\\"w+\\\")\\n for pfd_key in pulsar_pfds.keys():\\n if pulsar_pfds[pfd_key]>=3:\\n print(\\\"detected pulsar: {}\\\".format(pfd_key))\\n pos_f.write(pfd_key.split(\\\"/\\\")[-1])\\n else:\\n neg_f.write(pfd_key.split(\\\"/\\\")[-1])\\n pos_f.close()\\n neg_f.close()\",\n \"def train(self):\\n # self.recognizer.train()\\n self.detector.train()\\n self.shared_conv.train()\",\n \"def train_model(args, train_exs: List[SentimentExample]) -> SentimentClassifier:\\n indexer = Indexer()\\n stop_words = set(stopwords.words('english'))\\n punkt = (',', '.', '...', '?', '\\\\'', '\\\\'\\\\'', '!', ':', ';')\\n # Initialize feature extractor\\n if args.model == \\\"TRIVIAL\\\":\\n feat_extractor = None\\n elif args.feats == \\\"UNIGRAM\\\":\\n # Generate vocabulary\\n for ex in train_exs:\\n for word in ex.words:\\n if word.lower() not in stop_words and word.lower() not in punkt:\\n indexer.add_and_get_index(word.lower())\\n feat_extractor = UnigramFeatureExtractor(indexer)\\n elif args.feats == \\\"BIGRAM\\\":\\n # Generate vocabulary\\n for ex in train_exs:\\n for i in range(0, len(ex.words) - 1):\\n if stop_words.__contains__(ex.words[i]) and stop_words.__contains__(ex.words[i + 1]) or (\\n punkt.__contains__(ex.words[i]) or punkt.__contains__(ex.words[i + 1])):\\n continue\\n bigram = ex.words[i] + ' ' + ex.words[i + 1]\\n indexer.add_and_get_index(bigram.lower())\\n feat_extractor = BigramFeatureExtractor(indexer)\\n elif args.feats == \\\"BETTER\\\":\\n # Generate vocabulary\\n cnt = Counter()\\n for ex in train_exs:\\n cnt.update(\\n word.lower() for word in ex.words if word.lower() not in stop_words and word.lower() not in punkt)\\n cnt = dict(cnt.most_common(int(cnt.__len__() * 0.75)))\\n for keys in cnt.keys():\\n indexer.add_and_get_index(keys)\\n feat_extractor = BetterFeatureExtractor(indexer)\\n else:\\n raise Exception(\\\"Pass in UNIGRAM, BIGRAM, or BETTER to run the appropriate system\\\")\\n\\n # Train the model\\n if args.model == \\\"TRIVIAL\\\":\\n model = TrivialSentimentClassifier()\\n elif args.model == \\\"PERCEPTRON\\\":\\n model = train_perceptron(train_exs, feat_extractor)\\n elif args.model == \\\"LR\\\":\\n model = train_logistic_regression(train_exs, feat_extractor)\\n else:\\n raise Exception(\\\"Pass in TRIVIAL, PERCEPTRON, or LR to run the appropriate system\\\")\\n return model\",\n \"def train(self, docs, labels):\\n if docs.size != labels.size:\\n raise ValueError('Docs and labels sizes are mismatched %d vs %d' %(docs.size, labels.size))\\n \\n for entry in docs:\\n for f in entry.split():\\n if f not in self.features_:\\n self.add_feature(f)\\n \\n# self.feature_counts_ = sparse.csr_matrix((nclasses, nfeatures), dtype=np.float)\\n nfeatures = len(self.features_)\\n nclasses = len(self.classes_)\\n # feature_counts = sparse.lil_matrix((nclasses, nfeatures), dtype=np.float)\\n self.feature_counts_ = np.zeros((nclasses, nfeatures), dtype=np.float)\\n \\n self.count_class(labels)\\n self.update_class_log_prior(self.class_counts_)\\n\\n self.count_feature(docs, labels)\\n self.update_feature_prob(self.feature_counts_)\",\n \"def train(self, dataset, model_dir):\\n raise NotImplementedError\",\n \"def train(args):\\r\\n train_img = 'NEW DATA/TRAIN/'\\r\\n validation_img = 'NEW DATA/TEST/'\\r\\n \\r\\n nb_epoch = int(args.nb_epoch)\\r\\n nb_train_samples = get_nb_files(train_img)\\r\\n nb_classes = len(glob.glob(train_img + \\\"/*\\\"))\\r\\n # data prep\\r\\n train_datagen = ImageDataGenerator(\\r\\n rotation_range=40,\\r\\n width_shift_range=0.2,\\r\\n height_shift_range=0.2,\\r\\n rescale=1./255,\\r\\n shear_range=0.2,\\r\\n zoom_range=0.2,\\r\\n horizontal_flip=True,\\r\\n fill_mode='nearest')\\r\\n\\r\\n validation_datagen = ImageDataGenerator(\\r\\n rotation_range=40,\\r\\n width_shift_range=0.2,\\r\\n height_shift_range=0.2,\\r\\n rescale=1./255,\\r\\n shear_range=0.2,\\r\\n zoom_range=0.2,\\r\\n horizontal_flip=True,\\r\\n fill_mode='nearest')\\r\\n \\r\\n train_generator = train_datagen.flow_from_directory(\\r\\n\\t\\t\\ttrain_img,\\r\\n\\t\\t\\ttarget_size=(299, 299),\\r\\n\\t\\t\\tbatch_size=10,\\r\\n\\t\\t\\tclass_mode='categorical'\\r\\n\\t\\t\\t)\\r\\n validation_generator = validation_datagen.flow_from_directory(\\r\\n\\t\\t\\tvalidation_img,\\r\\n\\t\\t\\ttarget_size=(299, 299),\\r\\n\\t\\t\\tbatch_size=10,\\r\\n\\t\\t\\tclass_mode='categorical'\\r\\n\\t\\t\\t)\\r\\n if(K.image_dim_ordering() == 'th'):\\r\\n input_tensor = Input(shape=(3, 299, 299))\\r\\n else:\\r\\n input_tensor = Input(shape=(299, 299, 3))\\r\\n \\r\\n # setup model\\r\\n base_model = InceptionV3(input_tensor = input_tensor,weights='imagenet', include_top=False) #include_top=False excludes final FC layer\\r\\n model = add_new_last_layer(base_model, nb_classes)\\r\\n \\r\\n # transfer learning\\r\\n setup_to_transfer_learn(model, base_model)\\r\\n \\r\\n \\r\\n \\r\\n history_tl = model.fit_generator(train_generator,\\r\\n samples_per_epoch=200,\\r\\n nb_epoch=nb_epoch,\\r\\n validation_data=validation_generator,\\r\\n nb_val_samples=64) \\r\\n model.save(args.output_model_file)\\r\\n if args.plot:\\r\\n plot_training(history_tl)\",\n \"def train(self):\\r\\n for class_ in set(self.train_classes):\\r\\n data = map(lambda (ind, datum): datum, filter(lambda (ind, datum): self.train_classes[ind] == class_, enumerate(self.train_data)))\\r\\n self.distribution.index_data(data, class_)\",\n \"def train_naive(): # add arguments as needed\\n pass\",\n \"def __init__(self, dataset_dir, mode='train', transforms=None):\\n self.dataset = dataset_dir\\n self.transforms = transforms\\n require_file = ['trainImages.txt', 'trainLabels.txt',\\n 'valImages.txt', 'valLabels.txt',\\n 'testImages.txt', 'testLabels.txt']\\n\\n # check requirement\\n if mode not in ['train', 'test', 'val']:\\n raise ValueError('Unsupported mode %s' % mode)\\n\\n if not os.path.exists(self.dataset):\\n raise ValueError('Dataset not exists at %s' % self.dataset)\\n\\n \\n #generate_txt(self.dataset, file)\\n\\n # create image and label list\\n# self.image_list = []\\n# self.label_list = []\\n if mode == 'train':\\n self.images = images\\n self.labels = labels\\n self.images_list_final = images_list_final\\n self.labels_list_final = labels_list_final\\n \\n if mode == 'val':\\n self.images = images_val\\n self.labels = labels_val\\n self.images_list_final = images_list_final_val\\n self.labels_list_final = labels_list_final_val\\n \\n if mode == 'test': \\n self.images = images_test\\n self.labels = labels_test\\n self.images_list_final = images_list_final_test\\n self.labels_list_final = labels_list_final_test\",\n \"def forward_train(self, imgs, img_metas, **kwargs):\\n pass\",\n \"def svm_train_classifier(self):\\n\\n # needed because a SVM needs more than 1 class\\n if len(self.saved_gestures.keys()) <= 1:\\n print(\\\"Not enough gestures!\\\")\\n else:\\n training_data = []\\n categories = []\\n id = 0\\n\\n for gesture, value in self.saved_gestures.items():\\n id += 1\\n # needed to map the id returned from the SVM to a name of a gesture\\n self.category_to_gesture[id] = gesture\\n categories.append(id)\\n\\n x = []\\n y = []\\n z = []\\n for elem in value:\\n x.append(elem[0][0])\\n y.append(elem[1][0])\\n z.append(elem[2][0])\\n\\n training_data.append(self.get_fft(x, y, z))\\n\\n # normalized length of fft\\n self.cutoff_length = min([len(l) for l in training_data])\\n\\n normalized_fft = []\\n for l in training_data:\\n normalized_fft.append(l[:self.cutoff_length])\\n\\n training_data = normalized_fft\\n\\n self.classifier.fit(training_data, categories)\",\n \"def train_model(args, train_exs: List[SentimentExample]) -> SentimentClassifier:\\n # Initialize feature extractor\\n nltk.download('stopwords')\\n stop_words = set(stopwords.words('english'))\\n\\n if args.model == \\\"TRIVIAL\\\":\\n feat_extractor = None\\n elif args.feats == \\\"UNIGRAM\\\":\\n feat_extractor = UnigramFeatureExtractor(Indexer(), train_exs, stop_words)\\n elif args.feats == \\\"BIGRAM\\\":\\n # Add additional preprocessing code here\\n feat_extractor = BigramFeatureExtractor(Indexer(), train_exs, stop_words)\\n elif args.feats == \\\"BETTER\\\":\\n # Add additional preprocessing code here\\n feat_extractor = BetterFeatureExtractor(Indexer(), train_exs, stop_words)\\n else:\\n raise Exception(\\\"Pass in UNIGRAM, BIGRAM, or BETTER to run the appropriate system\\\")\\n\\n # Train the model\\n if args.model == \\\"TRIVIAL\\\":\\n model = TrivialSentimentClassifier()\\n elif args.model == \\\"PERCEPTRON\\\":\\n model = train_perceptron(train_exs, feat_extractor)\\n elif args.model == \\\"LR\\\":\\n model = train_logistic_regression(train_exs, feat_extractor)\\n else:\\n raise Exception(\\\"Pass in TRIVIAL, PERCEPTRON, or LR to run the appropriate system\\\")\\n return model\",\n \"def train():\\n import trace\\n trace.train()\",\n \"def _loadTrain(self, features, labels):\\n\\t\\tself.trainX_, self.trainY_, self.trainLabel_ = self.__load(features, labels)\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71127045","0.6847688","0.67817926","0.67200977","0.6696759","0.6646218","0.6621507","0.6604409","0.65844727","0.6558893","0.64029044","0.63676393","0.6323149","0.62984276","0.62698185","0.62642264","0.6254352","0.6238679","0.6231274","0.6210927","0.62066257","0.6196097","0.6188025","0.6183713","0.61683947","0.6143848","0.6143848","0.613347","0.6092734","0.6076092","0.6073097","0.6058032","0.60410297","0.60320514","0.60237366","0.60208666","0.6014506","0.5992679","0.59691757","0.5966811","0.5944472","0.5941372","0.5927724","0.5927684","0.5919785","0.59006137","0.59002256","0.58946884","0.58944625","0.5894384","0.5878469","0.5877328","0.58740056","0.5870286","0.58666354","0.58654815","0.58651346","0.5860151","0.5835543","0.58339643","0.583342","0.5828195","0.5827705","0.5826985","0.58218896","0.58204037","0.5812619","0.5810827","0.5805303","0.57994473","0.5786121","0.57809603","0.5777899","0.5775321","0.5773374","0.57696337","0.57658976","0.5761023","0.57544124","0.57479495","0.5742431","0.57372284","0.57368505","0.5731796","0.5727277","0.572623","0.5720901","0.5705761","0.5703725","0.5697333","0.5697205","0.56962395","0.56952","0.56835186","0.5673884","0.56676286","0.56609833","0.56609833","0.56609833","0.56609833"],"string":"[\n \"0.71127045\",\n \"0.6847688\",\n \"0.67817926\",\n \"0.67200977\",\n \"0.6696759\",\n \"0.6646218\",\n \"0.6621507\",\n \"0.6604409\",\n \"0.65844727\",\n \"0.6558893\",\n \"0.64029044\",\n \"0.63676393\",\n \"0.6323149\",\n \"0.62984276\",\n \"0.62698185\",\n \"0.62642264\",\n \"0.6254352\",\n \"0.6238679\",\n \"0.6231274\",\n \"0.6210927\",\n \"0.62066257\",\n \"0.6196097\",\n \"0.6188025\",\n \"0.6183713\",\n \"0.61683947\",\n \"0.6143848\",\n \"0.6143848\",\n \"0.613347\",\n \"0.6092734\",\n \"0.6076092\",\n \"0.6073097\",\n \"0.6058032\",\n \"0.60410297\",\n \"0.60320514\",\n \"0.60237366\",\n \"0.60208666\",\n \"0.6014506\",\n \"0.5992679\",\n \"0.59691757\",\n \"0.5966811\",\n \"0.5944472\",\n \"0.5941372\",\n \"0.5927724\",\n \"0.5927684\",\n \"0.5919785\",\n \"0.59006137\",\n \"0.59002256\",\n \"0.58946884\",\n \"0.58944625\",\n \"0.5894384\",\n \"0.5878469\",\n \"0.5877328\",\n \"0.58740056\",\n \"0.5870286\",\n \"0.58666354\",\n \"0.58654815\",\n \"0.58651346\",\n \"0.5860151\",\n \"0.5835543\",\n \"0.58339643\",\n \"0.583342\",\n \"0.5828195\",\n \"0.5827705\",\n \"0.5826985\",\n \"0.58218896\",\n \"0.58204037\",\n \"0.5812619\",\n \"0.5810827\",\n \"0.5805303\",\n \"0.57994473\",\n \"0.5786121\",\n \"0.57809603\",\n \"0.5777899\",\n \"0.5775321\",\n \"0.5773374\",\n \"0.57696337\",\n \"0.57658976\",\n \"0.5761023\",\n \"0.57544124\",\n \"0.57479495\",\n \"0.5742431\",\n \"0.57372284\",\n \"0.57368505\",\n \"0.5731796\",\n \"0.5727277\",\n \"0.572623\",\n \"0.5720901\",\n \"0.5705761\",\n \"0.5703725\",\n \"0.5697333\",\n \"0.5697205\",\n \"0.56962395\",\n \"0.56952\",\n \"0.56835186\",\n \"0.5673884\",\n \"0.56676286\",\n \"0.56609833\",\n \"0.56609833\",\n \"0.56609833\",\n \"0.56609833\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69635355"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":77094,"cells":{"query":{"kind":"string","value":"Tokenizes the questions. This will add q_token in each entry of the dataset. 1 represent nil, and should be treated as padding_idx in embedding"},"document":{"kind":"string","value":"def tokenize(self, max_length=14):\n for entry in self.entries:\n tokens = self.dictionary.tokenize(entry['question'], False)\n tokens = tokens[:max_length]\n if len(tokens) < max_length:\n # Note here we pad in front of the sentence\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\n tokens = tokens + padding\n utils.assert_eq(len(tokens), max_length)\n entry['q_token'] = tokens"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def preprocess_dataset(dataset, tokenizer):\n eos = torch.tensor([tokenizer.eos_token_id], dtype=torch.long)\n q_start = torch.tensor(tokenizer.encode('question:'), dtype=torch.long)\n q_end = torch.tensor(tokenizer.encode(':question'), dtype=torch.long)\n\n tensors = [[] for i in range(3)]\n for i in trange(len(dataset)):\n example = dataset[i]\n\n context_start_idx = (example[2] == 1).nonzero()[0].item()\n if example[1][-1] == 1:\n context_end_idx = len(example[1]) - 1\n else:\n context_end_idx = (example[1] == 0).nonzero()[0].item()\n ans_start = example[3] - context_start_idx\n ans_end = example[4] - context_start_idx\n\n context = example[0][context_start_idx: context_end_idx]\n question = example[0][: context_start_idx]\n answer = example[0][example[3]: example[4] + 1]\n\n input_ids = torch.cat([\n context,\n eos,\n answer,\n eos,\n q_start,\n question,\n q_end,\n eos\n ])\n\n attention_mask = torch.ones_like(input_ids, dtype=torch.long)\n token_type_ids = torch.cat([\n torch.zeros(len(context) + 1, dtype=torch.long),\n torch.ones(len(answer) + 1, dtype=torch.long),\n 2 * torch.ones(len(question) + 3, dtype=torch.long)\n ])\n token_type_ids[ans_start: ans_end + 1] = 1\n\n tensors[0].append(input_ids)\n tensors[1].append(attention_mask)\n tensors[2].append(token_type_ids)\n\n tensors_padded = []\n for i, sequences in enumerate(tqdm(tensors)):\n padded = pad_sequence(sequences, batch_first=True)\n tensors_padded.append(padded)\n\n new_dataset = TensorDataset(*tensors_padded)\n return new_dataset","def tokenizeQuestions(question_samples, labels):\n print(\"Initializing tokenizer\")\n training_samples = 1000\n validatioin_samples = 10000\n tokenizer = Tokenizer(num_words=MAX_WORDS) # High due to obscure nouns in questions\n tokenizer.fit_on_texts(question_samples)\n\n sequences = tokenizer.texts_to_sequences(question_samples)\n print('Found {0} unique tokens.'.format(len(tokenizer.word_index)))\n \n data = pad_sequences(sequences, maxlen=MAXLEN)\n labels = np.asarray(labels)\n \n x_train = data[:training_samples]\n y_train = labels[:training_samples]\n x_val = data[training_samples: training_samples + validatioin_samples]\n y_val = labels[training_samples: training_samples + validatioin_samples]\n\n print('Shape of data tensor: {0}'.format(data.shape))\n print('Shape of label tensor: {0}'.format(labels.shape))\n \n return (tokenizer, (x_train, y_train), (x_val, y_val))","def tokenize(self, max_length=12):\n for entry in self.data:\n tokens = self.dictionary.tokenize(entry['question'], False)\n tokens = tokens[:max_length]\n if len(tokens) < max_length:\n # Note here we pad in front of the sentence\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\n tokens = tokens + padding\n assert len(tokens) == max_length\n entry['q_token'] = tokens","def tokenize(self, max_length=14):\n for entry in self.entries:\n tokens = self.dictionary.tokenize(entry['question'], False)\n tokens = tokens[:max_length]\n if len(tokens) < max_length:\n # Note here we pad in front of the sentence\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\n tokens = padding + tokens\n utils.assert_eq(len(tokens), max_length)\n entry['q_token'] = tokens","def tokenize(self, max_length=14):\n for entry in self.entries:\n tokens = self.dictionary.tokenize(entry['question'], False)\n tokens = tokens[:max_length]\n if len(tokens) < max_length:\n # Note here we pad in front of the sentence\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\n tokens = padding + tokens\n utils.assert_eq(len(tokens), max_length)\n entry['q_token'] = tokens","def tokenize(self, max_length=14):\n for entry in self.entries:\n tokens = self.dictionary.tokenize(entry['question'], False)\n tokens = tokens[:max_length]\n if len(tokens) < max_length:\n # Note here we pad in front of the sentence\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\n tokens = padding + tokens\n utils.assert_eq(len(tokens), max_length)\n entry['q_token'] = tokens","def tokenize(self, max_length=14):\n for entry in self.entries:\n tokens = self.dictionary.tokenize(entry['question'], False)\n tokens = tokens[:max_length]\n if len(tokens) < max_length:\n # Note here we pad in front of the sentence\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\n tokens = padding + tokens\n utils.assert_eq(len(tokens), max_length)\n entry['q_token'] = tokens","def convert_questions_to_features(examples, tokenizer, max_query_length=None):\n\n unique_id = 1000000000\n question_features = []\n\n for (example_index, example) in enumerate(tqdm(examples, desc='Converting questions')):\n\n query_tokens = tokenizer.tokenize(example.question_text)\n if max_query_length is None:\n max_query_length = len(query_tokens)\n if len(query_tokens) > max_query_length:\n query_tokens = query_tokens[0:max_query_length]\n\n for _ in enumerate(range(1)):\n tokens_ = []\n tokens_.append(\"[CLS]\")\n for token in query_tokens:\n tokens_.append(token)\n tokens_.append(\"[SEP]\")\n\n input_ids_ = tokenizer.convert_tokens_to_ids(tokens_)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask_ = [1] * len(input_ids_)\n\n # Zero-pad up to the sequence length.\n while len(input_ids_) < max_query_length + 2:\n input_ids_.append(0)\n input_mask_.append(0)\n\n assert len(input_ids_) == max_query_length + 2\n assert len(input_mask_) == max_query_length + 2\n\n if example_index < 1:\n # logger.info(\"*** Example ***\")\n # logger.info(\"unique_id: %s\" % (unique_id))\n # logger.info(\"example_index: %s\" % (example_index))\n logger.info(\"tokens: %s\" % \" \".join(\n [tokenization.printable_text(x) for x in query_tokens]))\n # logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids_]))\n # logger.info(\n # \"input_mask: %s\" % \" \".join([str(x) for x in input_mask_]))\n\n question_features.append(\n QuestionFeatures(\n unique_id=unique_id,\n example_index=example_index,\n tokens_=tokens_,\n input_ids=input_ids_,\n input_mask=input_mask_))\n unique_id += 1\n\n return question_features","def tokenize(question, max_length=12):\n tokens = d.tokenize(question, False)\n tokens = tokens[:max_length]\n if len(tokens) < max_length:\n # Note here we pad in front of the sentence\n padding = [d.padding_idx] * (max_length - len(tokens))\n tokens = tokens + padding\n utils.assert_eq(len(tokens), max_length)\n return tokens","def _encode_question(self, question):\n \n inputs = self.tokenizer.encode_plus(\n question,\n None,\n add_special_tokens=True,\n max_length=self.max_len,\n )\n\n ids = inputs[\"input_ids\"]\n mask = inputs[\"attention_mask\"]\n token_type_ids = inputs[\"token_type_ids\"]\n\n padding_length = self.max_len - len(ids)\n ids += ([0]*padding_length)\n mask += ([0]*padding_length)\n token_type_ids += ([0]*padding_length)\n \n return {\n 'ids': torch.tensor(ids, dtype=torch.long),\n 'mask': torch.tensor(mask, dtype=torch.long),\n 'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),\n \n }","def preprocess_question(q, vocab=None, word2vec=None):\n\tq = nltk.tokenize.word_tokenize(q.lower())\n\tq_processed = []\n\tfor q_ in q:\n\t\tif q_[-2:] == \"'s\":\n\t\t\tq_ = q_[:-2]\n\t\telif q[:-2:] == \"s'\":\n\t\t\tq_ = q_[:-1]\n\t\tif word2vec is not None:\n\t\t\tif q_ in word2vec:\n\t\t\t\tq_processed.append(q_)\n\t\telse:\n\t\t\tq_processed.append(q_)\n\tif vocab is not None:\n\t\tq_processed = [vocab[q_] for q_ in q_processed if q_ in vocab]\n\treturn q_processed","def build_vocab(data, qmark=True):\n\n # With data = total q + a\n # Check necessity of <UNK> tokens\n\n # Tokenizer\n # Learning - t.word_counts, t.document_count, t.word_index, t.word_docs\n\n # Choose whether question marks are filtered out when building the vocabulary\n if qmark:\n t = Tokenizer(lower=True, filters='!\"#$%&()*+,-./:;=@[\\]^_`{|}~?')\n else:\n t = Tokenizer(lower=True, filters='!\"#$%&()*+,-./:;=@[\\]^_`{|}~')\n t.fit_on_texts(data) # Was q_train only\n vocab_size = len(\n t.word_index) + 1 # The integer for the largest encoded word needs to be specified as an array index\n\n # Integer encode documents (binary) - Fixed size array for direct network input\n # encoded_docs = t.texts_to_matrix(q_train)\n\n return t, vocab_size","def tokenize(tokenizer, max_length, stride, row):\r\n # The input data must be tokenized in the order dictated by the side the\r\n # selected checkpoint apply the padding\r\n pad_on_right = tokenizer.padding_side == \"right\"\r\n \r\n return tokenizer(\r\n row[\"question\" if pad_on_right else \"context\"],\r\n row[\"context\" if pad_on_right else \"question\"],\r\n max_length=max_length,\r\n truncation=\"only_second\" if pad_on_right else \"only_first\",\r\n return_overflowing_tokens=True,\r\n return_offsets_mapping=True,\r\n stride=stride,\r\n padding=\"max_length\",\r\n )","def batch_inference(question,context): \n inputs = tokenizer(question, context, \n return_tensors='pt', \n truncation=True, \n padding=True)\n \n # Move data to GPU\n inputs = inputs.to(device)\n \n # Feed data through the model\n with torch.no_grad():\n outputs = model(**inputs)\n\n # Q&A model outputs the two logit scores for each word.\n # One for its chance of being the start of the answer\n # and one for its chance of being the end\n start_logits = outputs.start_logits\n end_logits = outputs.end_logits\n \n # Find the words with the highest score\n # argmax(dim=1) means argmax with each sample\n start = start_logits.argmax(dim=1)\n end = end_logits.argmax(dim=1)\n \n # Return the answers\n # This is the point where we move the prediction back to main memory with .cpu()\n tokens = [tokenizer.convert_ids_to_tokens(x) for x in inputs[\"input_ids\"].cpu().numpy()]\n return [tokenizer.convert_tokens_to_string(x[start[i]:end[i]+1]) for i,x in enumerate(tokens)]","def tokenize_one_example(self, example):\n index, q_id, question, context, answer_list, answer_start = example\n\n context = context.replace('\\'\\'', '\\\" ').replace(r'``', '\\\" ')\n context_tokens = SQuADDataTokenizer._word_tokenize_spacy(context) if self._use_spacy else \\\n SQuADDataTokenizer._word_tokenize_nltk(context)\n context_chars = [list(token) for token in context_tokens]\n spans = SQuADDataTokenizer._get_token_spans(context, context_tokens)\n\n ques = question.replace('\\'\\'', '\\\" ').replace('``', '\\\" ')\n ques_tokens = SQuADDataTokenizer._word_tokenize_spacy(ques) if self._use_spacy else \\\n SQuADDataTokenizer._word_tokenize_nltk(ques)\n ques_chars = [list(token) for token in ques_tokens]\n\n y1s, y2s = [], []\n answer_texts = []\n\n for answer_text, answer_start in zip(answer_list, answer_start):\n answer_end = answer_start + len(answer_text)\n answer_texts.append(answer_text)\n answer_span = []\n for idx, span in enumerate(spans):\n if not (answer_end <= span[0] or answer_start >= span[1]):\n answer_span.append(idx)\n y1, y2 = answer_span[0], answer_span[-1]\n y1s.append(y1)\n y2s.append(y2)\n\n result = {'context_tokens': context_tokens, 'context_chars': context_chars,\n 'ques_tokens': ques_tokens, 'ques_chars': ques_chars, 'y1s': y1s,\n 'y2s': y2s, 'id': q_id, 'context': context, 'spans': spans, 'record_idx': index}\n return result","def add_special_tokens_(model, tokenizer):\n orig_num_tokens = len(tokenizer.encoder)\n num_added_tokens = tokenizer.add_special_tokens(ATTR_TO_SPECIAL_TOKEN) # doesn't add if they are already there\n if num_added_tokens > 0:\n model.resize_token_embeddings(new_num_tokens=orig_num_tokens + num_added_tokens)","def process_dataset(data, tokenizer, workers=None):\n tokenizer_class = tokenizers.get_class(tokenizer)\n make_pool = partial(Pool, workers, initializer=init)\n workers = make_pool(initargs=(tokenizer_class, {'annotators': {'lemma'}}))\n q_tokens = workers.map(tokenize, data['questions'])\n workers.close()\n workers.join()\n\n workers = make_pool(\n initargs=(tokenizer_class, {'annotators': {'lemma', 'pos', 'ner'}})\n )\n c_tokens = workers.map(tokenize, data['contexts'])\n workers.close()\n workers.join()\n\n for idx in range(len(data['qids'])):\n question = q_tokens[idx]['words']\n qlemma = q_tokens[idx]['lemma']\n document = c_tokens[data['qid2cid'][idx]]['words']\n offsets = c_tokens[data['qid2cid'][idx]]['offsets']\n lemma = c_tokens[data['qid2cid'][idx]]['lemma']\n pos = c_tokens[data['qid2cid'][idx]]['pos']\n ner = c_tokens[data['qid2cid'][idx]]['ner']\n ans_tokens = []\n if len(data['answers']) > 0:\n for ans in data['answers'][idx]:\n found = find_answer(offsets,\n ans['answer_start'],\n ans['answer_start'] + len(ans['text']))\n if found:\n ans_tokens.append(found)\n yield {\n 'id': data['qids'][idx],\n 'question': question,\n 'document': document,\n 'offsets': offsets,\n 'answers': ans_tokens,\n 'qlemma': qlemma,\n 'lemma': lemma,\n 'pos': pos,\n 'ner': ner,\n }","def build_qa_only_input_from_segments(data_point, tokenizer, with_eos=True):\n bos, eos, paragraph, answer_general, answer_specific, question_general, question_specific = \\\n tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS[:-1])\n\n curr_ans = data_point['answer']\n curr_ques = data_point['question']\n\n sequence = []\n token_types = []\n lm_labels = []\n\n if data_point['class'] == 'general':\n sequence.extend([answer_general] + curr_ans)\n token_types.extend([answer_general for _ in range(len(curr_ans) + 1)])\n lm_labels.extend([-1 for _ in range(len(curr_ans) + 1)])\n\n if with_eos is True:\n sequence.extend([question_general] + curr_ques + [eos])\n token_types.extend([question_general for _ in range(len(curr_ques) + 2)])\n lm_labels.extend([-1] + curr_ques + [eos])\n else:\n sequence.extend([question_general] + curr_ques)\n token_types.extend([question_general for _ in range(len(curr_ques) + 1)])\n lm_labels.extend([-1] + curr_ques)\n\n elif data_point['class'] == 'specific':\n sequence.extend([answer_specific] + curr_ans)\n token_types.extend([answer_specific for _ in range(len(curr_ans) + 1)])\n lm_labels.extend([-1 for _ in range(len(curr_ans) + 1)])\n\n if with_eos is True:\n sequence.extend([question_specific] + curr_ques + [eos])\n token_types.extend([question_specific for _ in range(len(curr_ques) + 2)])\n lm_labels.extend([-1] + curr_ques + [eos])\n else:\n sequence.extend([question_specific] + curr_ques)\n token_types.extend([question_specific for _ in range(len(curr_ques) + 1)])\n lm_labels.extend([-1] + curr_ques)\n\n assert len(sequence) == len(token_types)\n assert len(token_types) == len(lm_labels)\n\n instance = {\n \"input_ids\": sequence,\n \"token_type_ids\": token_types,\n \"lm_labels\": lm_labels\n }\n return instance, sequence","def build_dataset(token_seq, token_seq_index, term_to_index, char_to_index, max_token_length=15, leading_context_size=10, trailing_context_size=10, leading_separator='{', trailing_separator='}', n_examples_per_context=10, n_errors_per_token=[1], n_errors_per_context=[0], dictionary=enchant.Dict('en_US'), seed=17):\n if isinstance(seed, RandomState):\n rng = seed\n else:\n rng = RandomState(seed=seed)\n\n n_contexts = sum([len(token_seq_index[t]) for t in token_seq_index.keys() if len(t) <= max_token_length])\n n_examples = n_examples_per_context * n_contexts\n max_inserts_per_token = max(n_errors_per_token)\n max_inserts_per_context = 2*max(n_errors_per_context)\n max_chars_in_window = leading_context_size + max_token_length + trailing_context_size + len(leading_separator) + len(trailing_separator) + max_inserts_per_token + max_inserts_per_context\n\n error_examples = np.zeros((n_examples, max_chars_in_window), dtype=np.int32)\n error_examples.fill(char_to_index[' '])\n\n leading_contexts = np.zeros((n_examples, leading_context_size + max_inserts_per_context))\n leading_contexts.fill(char_to_index[' '])\n spelling_errors = np.zeros((n_examples, max_token_length + max_inserts_per_token))\n spelling_errors.fill(char_to_index[' '])\n trailing_contexts = np.zeros((n_examples, trailing_context_size + max_inserts_per_context))\n trailing_contexts.fill(char_to_index[' '])\n\n correct_tokens = []\n corrupt_tokens = []\n corrections = np.zeros(n_examples, dtype=np.int32)\n error_types = []\n context_ids = []\n example_ids = []\n term_lens = []\n edit_distance_to_nearest_term = []\n nearest_term = []\n\n print('starting to construct {n_examples} examples'.format(\n n_examples=n_examples))\n\n pbar = progressbar.ProgressBar(term_width=40,\n widgets=[' ', progressbar.Percentage(),\n ' ', progressbar.ETA()],\n maxval=n_examples).start()\n n = 0\n\n index_to_char = dict((i,c) for c,i in char_to_index.iteritems())\n if 63 not in index_to_char.keys():\n # A workaround for now. Delete ASAP.\n index_to_char[63] = '^'\n\n def add_chars_to_array(n, array, chars, default_char=' '):\n for k, ch in enumerate(chars):\n try:\n array[n, k] = char_to_index[ch]\n except KeyError, e:\n array[n, k] = char_to_index[default_char]\n\n for token in token_seq_index.keys():\n if len(token) > max_token_length:\n continue\n\n term_len = len(token)\n distances = min_dictionary_edit_distance([token], dictionary)\n distance = distances[token]['distance']\n term = distances[token]['accepted']\n\n for i in token_seq_index[token]:\n for j in six.moves.range(n_examples_per_context):\n pbar.update(n+1)\n corruptor = rng.choice([insert_characters, delete_characters,\n replace_characters, transpose_characters])\n n_token_errors = rng.choice(n_errors_per_token)\n corrupt_token = corruptor(token, index_to_char, n=n_token_errors, seed=rng)\n\n n_context_errors = rng.choice(n_errors_per_context)\n leading = leading_context(token_seq, i, leading_context_size)\n leading = corruptor(leading, index_to_char, n=n_context_errors, seed=rng)\n trailing = trailing_context(token_seq, i, trailing_context_size)\n trailing = corruptor(trailing, index_to_char, n=n_context_errors, seed=rng)\n\n window = ''.join([leading, leading_separator,\n corrupt_token, trailing_separator, trailing])\n\n add_chars_to_array(n, error_examples, window)\n add_chars_to_array(n, leading_contexts, leading)\n add_chars_to_array(n, spelling_errors, corrupt_token)\n add_chars_to_array(n, trailing_contexts, trailing)\n\n correct_tokens.append(token)\n corrections[n] = term_to_index[token]\n corrupt_tokens.append(corrupt_token)\n error_types.append(corruptor.__name__)\n context_ids.append(i)\n example_ids.append(n)\n edit_distance_to_nearest_term.append(distance)\n nearest_term.append(term)\n term_lens.append(term_len)\n\n n += 1\n\n pbar.finish()\n\n df = pd.DataFrame({\n 'correction': corrections,\n 'error_type': error_types,\n 'context_id': context_ids,\n 'example_id': example_ids,\n 'edit_distance_to_nearest_term': edit_distance_to_nearest_term,\n 'nearest': nearest_term,\n 'term_length': term_lens,\n 'correct_token': correct_tokens,\n 'corrupt_token': corrupt_tokens\n })\n\n def add_columns(df, array, colname_prefix):\n colwidth = int(np.log10(array.shape[1]))+1\n colfmt = colname_prefix + '{col:0' + str(colwidth) + 'd}'\n for col in np.arange(array.shape[1]):\n colname = colfmt.format(col=col)\n df[colname] = array[:, col]\n\n add_columns(df, error_examples, 'full_error')\n add_columns(df, leading_contexts, 'leading_context')\n add_columns(df, spelling_errors, 'spelling_error')\n add_columns(df, trailing_contexts, 'trailing_context')\n\n return df","def _multiprocess_tokenize(self, num_workers: int):\n\n # While displaying progress bar through tqdm, specify total number of\n # sequences to tokenize, because tqdm won't know in case of pool.imap\n with mp.Pool(num_workers) as pool:\n print(f\"[{self._split}] Tokenizing questions...\")\n _question_tuples = self.questions.items()\n _question_indices = [t[0] for t in _question_tuples]\n _questions = list(\n tqdm(\n pool.imap(word_tokenize, [t[1] for t in _question_tuples]),\n total=len(self.questions),\n )\n )\n self.questions = {\n i: question + [\"?\"]\n for i, question in zip(_question_indices, _questions)\n }\n # Delete variables to free memory.\n del _question_tuples, _question_indices, _questions\n\n print(f\"[{self._split}] Tokenizing answers...\")\n _answer_tuples = self.answers.items()\n _answer_indices = [t[0] for t in _answer_tuples]\n _answers = list(\n tqdm(\n pool.imap(word_tokenize, [t[1] for t in _answer_tuples]),\n total=len(self.answers),\n )\n )\n self.answers = {\n i: answer + [\"?\"] for i, answer in zip(_answer_indices, _answers)\n }\n # Delete variables to free memory.\n del _answer_tuples, _answer_indices, _answers\n\n print(f\"[{self._split}] Tokenizing captions...\")\n # Convert dict to separate lists of image_ids and captions.\n _caption_tuples = self.captions.items()\n _image_ids = [t[0] for t in _caption_tuples]\n _captions = list(\n tqdm(\n pool.imap(word_tokenize, [t[1] for t in _caption_tuples]),\n total=(len(_caption_tuples)),\n )\n )\n # Convert tokenized captions back to a dict.\n self.captions = {i: c for i, c in zip(_image_ids, _captions)}","def _multiprocess_tokenize(self, num_workers: int):\n\n # While displaying progress bar through tqdm, specify total number of\n # sequences to tokenize, because tqdm won't know in case of pool.imap\n with mp.Pool(num_workers) as pool:\n print(f\"[{self._split}] Tokenizing questions...\")\n _question_tuples = self.questions.items()\n _question_indices = [t[0] for t in _question_tuples]\n _questions = list(\n tqdm(\n pool.imap(word_tokenize, [t[1] for t in _question_tuples]),\n total=len(self.questions)\n )\n )\n self.questions = {\n i: question + [\"?\"] for i, question in\n zip(_question_indices, _questions)\n }\n # Delete variables to free memory.\n del _question_tuples, _question_indices, _questions\n\n print(f\"[{self._split}] Tokenizing answers...\")\n _answer_tuples = self.answers.items()\n _answer_indices = [t[0] for t in _answer_tuples]\n _answers = list(\n tqdm(\n pool.imap(word_tokenize, [t[1] for t in _answer_tuples]),\n total=len(self.answers)\n )\n )\n self.answers = {\n i: answer + [\"?\"] for i, answer in\n zip(_answer_indices, _answers)\n }\n # Delete variables to free memory.\n del _answer_tuples, _answer_indices, _answers\n\n print(f\"[{self._split}] Tokenizing captions...\")\n # Convert dict to separate lists of image_ids and captions.\n _caption_tuples = self.captions.items()\n _image_ids = [t[0] for t in _caption_tuples]\n _captions = list(\n tqdm(\n pool.imap(word_tokenize, [t[1] for t in _caption_tuples]),\n total=(len(_caption_tuples))\n )\n )\n # Convert tokenized captions back to a dict.\n self.captions = {i: c for i, c in zip(_image_ids, _captions)}","def tokenize_pretraining(self, inputs):\n\n ref_ids = prepare_ref([inputs], self.tokenizer_ltp, self.tokenizer_cn)\n\n tokens = self.tokenizer_cn.tokenize(inputs)\n\n if len(tokens) > self.max_seq_length - 2:\n tokens = tokens[:(self.max_seq_length - 2)]\n ref_ids = ref_ids[:(self.max_seq_length - 2)]\n\n ref_ids = cn_whole_word_mask(tokens, ref_ids[0])\n tokens, labels = random_word_wwm(tokens, ref_ids, self.tokenizer_cn)\n\n tokens = ['[CLS]'] + tokens + ['[SEP]']\n lm_label_ids = ([-100] + labels + [-100])\n\n input_ids = self.tokenizer_cn.convert_tokens_to_ids(tokens)\n\n attention_mask = [1] * len(input_ids)\n token_type_ids = [0] * len(input_ids)\n\n while len(input_ids) < self.max_seq_length:\n input_ids.append(0)\n attention_mask.append(0)\n token_type_ids.append(0)\n lm_label_ids.append(-100)\n\n assert len(input_ids) == self.max_seq_length\n assert len(attention_mask) == self.max_seq_length\n assert len(token_type_ids) == self.max_seq_length\n assert len(lm_label_ids) == self.max_seq_length\n\n\n outputs = {'input_ids': tf.constant(input_ids), 'attention_mask': tf.constant(attention_mask), \n 'token_type_ids': tf.constant(token_type_ids), 'lm_label_ids': tf.constant(lm_label_ids)}\n\n return outputs","def parse_question(question):\n\tcontext = question['support']\n\tanswer = question['correct_answer']\n\ttarget = question['question']\n\n\tcontext_words = context.split(\" \")[0: 510]\n\ttarget_words = target.split(\" \")\n\n\tpunc_filter = str.maketrans('', '', string.punctuation)\n\n\tcontext_words = [word.translate(punc_filter) for word in context_words]\n\ttarget_words = [word.translate(punc_filter) for word in target_words]\n\tanswer_words = [word.translate(punc_filter) for word in answer.split(\" \")]\n\n\tbio_embeddings = [EMBEDER['O']]\n\tinside_answer = False\n\tanswer_index = 0\n\tcan_be_inside_answer = True\n\n\t# The following loop and above code does:\n\t# -Find where the answer is and place a B tag\n\t# -While still in the answer (the answer is more than one word) put an I tag\n\t# -Outside of the answer place a O tag\n\t# -Start and end with an O tag for BERT's automatic\n\t# -start token and end token representing the start and end of a sentence.\n\tfor word in context_words:\n\t\tif word.lower() == answer_words[0].lower() and can_be_inside_answer:\n\t\t\tbio_embeddings.append(EMBEDER[\"B\"])\n\t\t\tanswer_index += 1\n\t\t\tinside_answer = True\n\t\t\tcan_be_inside_answer = False\n\t\telif inside_answer:\n\t\t\tif len(answer_words) > 1:\n\t\t\t\tif word.lower() != answer_words[answer_index]:\n\t\t\t\t\tinside_answer = False\n\t\t\t\t\tbio_embeddings.append(EMBEDER[\"O\"])\n\t\t\t\telse:\n\t\t\t\t\tbio_embeddings.append(EMBEDER[\"I\"])\n\t\t\telse:\n\t\t\t\tinside_answer = False\n\t\t\t\tbio_embeddings.append(EMBEDER[\"O\"])\n\t\telse:\n\t\t\tbio_embeddings.append(EMBEDER[\"O\"])\n\tbio_embeddings.append(EMBEDER[\"O\"])\n\n\tground_truth = torch.tensor([BERT_TOKENIZER.encode(target_words)])\n\tcontext_words = torch.tensor([BERT_TOKENIZER.encode(context_words)])\n\n\tassert len(bio_embeddings) == len(context_words[0]), f'The BIO tags are not equal in length to the embeddings! ' \\\n\t f'{None} & {len(bio_embeddings)} & {len(context_words[0])}'\n\treturn context_words, bio_embeddings, ground_truth","def tokenize_dataset(self, data):\n token_pt = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(\n (pt.numpy() for pt, _ in data), target_vocab_size=2**15)\n token_en = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(\n (en.numpy() for _, en in data), target_vocab_size=2**15)\n return token_pt, token_en","def gen_questions(self, number_of_questions):","def convert_examples_to_features(examples, tokenizer, max_seq_length):\n \n features = []\n num_tokens_article = []\n count = 0\n masking_token_id = tokenizer.convert_tokens_to_ids(['[MASK]'])[0]\n # padding_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n for example_index, example in enumerate(examples):\n article_tokens = tokenizer.tokenize(example['article'])\n ques_tokens = tokenizer.tokenize(example['question'].replace(\"@placeholder\", \"_\"))\n masked_index = ques_tokens.index('_')\n ques_tokens[masked_index] = '[MASK]'\n \n# options = example['options']\n options = tokenizer.convert_tokens_to_ids(example['options'])\n# print(options, '\\n')\n\n choices_features = []\n _truncate_seq_pair(article_tokens, ques_tokens, max_seq_length - 3)\n\n# tokens = [\"[CLS]\"] + ques_tokens + [\"[SEP]\"] + article_tokens + [\"[SEP]\"]\n tokens = ques_tokens + [\"[SEP]\"] + article_tokens + [\"[SEP]\"]\n\n# segment_ids = [0] * (len(ques_tokens) + 2) + [1] * (len(article_tokens) + 1)\n segment_ids = [0] * (len(ques_tokens) + 1) + [1] * (len(article_tokens) + 1)\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n input_mask = [1] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding = [0] * (max_seq_length - len(input_ids))\n input_ids += padding\n input_mask += padding\n segment_ids += padding\n\t\n candidates = example['options']\n candidates_ids = tokenizer.convert_tokens_to_ids(candidates)\n# print(tokenizer.convert_tokens_to_ids(tokenizer.tokenize(example['options'][example['label']]))[0])\n\n lm_labels = [-1 if t_id != masking_token_id else tokenizer.convert_tokens_to_ids(tokenizer.tokenize(example['options'][example['label']]))[0] for t_id in input_ids]\n# lm_labels = [t_id if t_id != 0 else -1 for t_id in input_ids]\n# for i, t_id in enumerate(lm_labels) :\n# if(t_id == masking_token_id):\n# lm_labels[i] = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(example['options'][example['label']]))[0]\n\n# print(lm_labels, '\\n')\n # tokenizer.convert_tokens_to_ids(tokenizer.tokenize(example['options'][example['label']]))[0]\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n assert len(lm_labels) == max_seq_length\n\n choices_features.append((tokens, input_ids, input_mask, segment_ids))\n\n features.append(\n InputFeatures(\n choices_features = choices_features,\n lm_labels = lm_labels,\n options = options,\n label = example['label']\n )\n )\n \n return features","def prepare_inputs(data, queries, tokenizer):\n # Prepare inputs\n table = pd.DataFrame.from_dict(data)\n inputs = tokenizer(table=table, queries=queries,truncation=True, padding=True,return_tensors=\"pt\").to(device)\n\n # Return things\n return table, inputs","def convert_examples_to_features(examples, tokenizer, max_seq_length,\n doc_stride, max_query_length, return_answers, skip_no_answer,\n verbose=False, save_with_prob=False, msg=\"Converting examples\"):\n\n unique_id = 1000000000\n features = []\n question_features = []\n\n for (example_index, example) in enumerate(tqdm(examples, desc=msg)):\n\n # Tokenize query into (sub)tokens\n query_tokens = tokenizer.tokenize(example.question_text)\n if len(query_tokens) > max_query_length:\n query_tokens = query_tokens[0:max_query_length]\n\n # Creating a map between word <=> (sub)token\n tok_to_word_index = []\n word_to_tok_index = [] # word to (start of) subtokens\n all_doc_tokens = []\n for (i, word) in enumerate(example.doc_words):\n word_to_tok_index.append(len(all_doc_tokens))\n sub_tokens = tokenizer.tokenize(word)\n for sub_token in sub_tokens:\n tok_to_word_index.append(i)\n all_doc_tokens.append(sub_token)\n\n # The -2 accounts for [CLS], [SEP]\n max_tokens_for_doc = max_seq_length - 2\n\n # Split sequence by max_seq_len with doc_stride, _DocSpan is based on tokens without [CLS], [SEP]\n _DocSpan = collections.namedtuple( # pylint: disable=invalid-name\n \"DocSpan\", [\"start\", \"length\"])\n doc_spans = []\n start_tok_offset = 0 # From all_doc_tokens\n\n # Get doc_spans with stride and offset\n while start_tok_offset < len(all_doc_tokens):\n length = len(all_doc_tokens) - start_tok_offset\n if length > max_tokens_for_doc:\n length = max_tokens_for_doc\n doc_spans.append(_DocSpan(start=start_tok_offset, length=length))\n if start_tok_offset + length == len(all_doc_tokens):\n break\n start_tok_offset += min(length, doc_stride) # seems to prefer doc_stride always\n assert doc_stride < length, \"length is no larger than doc_stride for {}\".format(doc_spans)\n\n # Iterate each doc_span and make out_tokens\n for (doc_span_index, doc_span) in enumerate(doc_spans):\n\n # Find answer position based on new out_tokens\n start_position = None\n end_position = None\n\n # For no_answer, same (-1, -1) applies\n if example.start_position is not None and example.start_position < 0:\n assert example.start_position == -1 and example.end_position == -1\n start_position, end_position = NO_ANS, NO_ANS\n\n # For existing answers, find answers if exist\n elif return_answers:\n\n # Get token-level start/end position\n tok_start_position = word_to_tok_index[example.start_position]\n if example.end_position < len(example.doc_words) - 1:\n tok_end_position = word_to_tok_index[example.end_position + 1] - 1 # By backwarding from next word\n else:\n assert example.end_position == len(example.doc_words) - 1\n tok_end_position = len(all_doc_tokens) - 1\n\n # Improve answer span by subword-level\n (tok_start_position, tok_end_position) = _improve_answer_span(\n all_doc_tokens, tok_start_position, tok_end_position, tokenizer,\n example.orig_answer_text)\n\n # Throw away training samples without answers (due to doc_span split)\n doc_start = doc_span.start\n doc_end = doc_span.start + doc_span.length - 1\n if (tok_start_position < doc_start or tok_end_position < doc_start or\n tok_start_position > doc_end or tok_end_position > doc_end):\n if skip_no_answer:\n continue\n else:\n # For NQ, only add this in 2% (50 times downsample)\n if save_with_prob:\n if np.random.randint(100) < 2:\n start_position, end_position = NO_ANS, NO_ANS\n else:\n continue\n else:\n start_position, end_position = NO_ANS, NO_ANS\n\n # Training samples with answers\n else:\n doc_offset = 1 # For [CLS]\n start_position = tok_start_position - doc_start + doc_offset\n end_position = tok_end_position - doc_start + doc_offset\n assert start_position >= 0 and end_position >= 0, (start_position, end_position)\n\n out_tokens = [] # doc\n out_tokens_ = [] # quesry\n out_tokens.append(\"[CLS]\")\n out_tokens_.append(\"[CLS]\")\n token_to_word_map = {} # The difference with tok_to_word_index is it includes special tokens\n token_is_max_context = {}\n\n # For query tokens, just copy and add [SEP]\n for token in query_tokens:\n out_tokens_.append(token)\n out_tokens_.append(\"[SEP]\")\n\n # For each doc token, create token_to_word_map and is_max_context, and add to out_tokens\n for i in range(doc_span.length):\n split_token_index = doc_span.start + i\n token_to_word_map[len(out_tokens)] = tok_to_word_index[split_token_index]\n is_max_context = _check_is_max_context(doc_spans, doc_span_index, split_token_index)\n token_is_max_context[len(out_tokens)] = is_max_context\n out_tokens.append(all_doc_tokens[split_token_index])\n out_tokens.append(\"[SEP]\")\n\n # Convert to ids and masks\n input_ids = tokenizer.convert_tokens_to_ids(out_tokens)\n input_ids_ = tokenizer.convert_tokens_to_ids(out_tokens_)\n input_mask = [1] * len(input_ids)\n input_mask_ = [1] * len(input_ids_)\n\n # Zero-pad up to the sequence length.\n while len(input_ids) < max_seq_length:\n input_ids.append(0)\n input_mask.append(0)\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n while len(input_ids_) < max_query_length + 2: # +2 for [CLS], [SEP]\n input_ids_.append(0)\n input_mask_.append(0)\n assert len(input_ids_) == max_query_length + 2\n assert len(input_mask_) == max_query_length + 2\n\n # Printing for debug\n if example_index < 1 and verbose:\n logger.info(\"*** Example ***\")\n logger.info(\"unique_id: %s\" % (unique_id))\n logger.info(\"example_index: %s\" % (example_index))\n logger.info(\"doc_span_index: %s\" % (doc_span_index))\n logger.info(\"tokens: %s\" % \" \".join(\n [tokenization.printable_text(x) for x in out_tokens]))\n logger.info(\"q tokens: %s\" % \" \".join(\n [tokenization.printable_text(x) for x in out_tokens_]))\n logger.info(\"token_to_word_map: %s\" % \" \".join(\n [\"%d:%d\" % (x, y) for (x, y) in six.iteritems(token_to_word_map)]))\n logger.info(\"token_is_max_context: %s\" % \" \".join([\n \"%d:%s\" % (x, y) for (x, y) in six.iteritems(token_is_max_context)\n ]))\n logger.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n logger.info(\n \"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n if return_answers:\n answer_text = \" \".join(out_tokens[start_position:(end_position + 1)])\n logger.info(\"start_position: %d\" % (start_position))\n logger.info(\"end_position: %d\" % (end_position))\n logger.info(\n \"answer: %s\" % (tokenization.printable_text(answer_text)))\n\n # Append feature\n features.append(\n ContextFeatures(\n unique_id=unique_id,\n example_index=example_index,\n doc_span_index=doc_span_index,\n tokens=out_tokens,\n token_to_word_map=token_to_word_map,\n token_is_max_context=token_is_max_context,\n input_ids=input_ids,\n input_mask=input_mask,\n start_position=start_position,\n end_position=end_position))\n question_features.append(\n QuestionFeatures(\n unique_id=unique_id,\n example_index=example_index,\n tokens_=out_tokens_,\n input_ids=input_ids_,\n input_mask=input_mask_))\n\n # Check validity of answer\n if return_answers:\n if start_position <= NO_ANS:\n assert start_position == NO_ANS and end_position == NO_ANS, (start_position, end_position)\n else:\n assert out_tokens[start_position:end_position+1] == \\\n all_doc_tokens[tok_start_position:tok_end_position+1]\n orig_text, start_pos, end_pos = get_final_text_(\n example, features[-1], start_position, end_position, True, False)\n phrase = orig_text[start_pos:end_pos]\n try:\n assert phrase == example.orig_answer_text\n except Exception as e:\n # print('diff ans [%s]/[%s]'%(phrase, example.orig_answer_text))\n pass\n unique_id += 1\n\n return features, question_features","def batchify(batch):\n\n\tquestion_len = list()\n\tlabel_list = list()\n\tfor ex in batch:\n\t\tquestion_len.append(len(ex[0]))\n\t\tlabel_list.append(ex[1])\n\n\t'''\n\tPadding the labels - unequal length sequences for sequenial data like we have. \n\tSince actual labels are 0/1 - we pad with -1, and will use this when 'masking' labels during loss and\n\taccuracy evaluation.\n\t'''\n\ttarget_labels = torch.nn.utils.rnn.pad_sequence([torch.tensor(y) for y in label_list], padding_value=-1).t()\n\n\t# dimension is dimension of every feature vector = n_guesses in this homework setting\n\tdim = batch[0][0].shape[1]\n\n\t# similar padding happens for the feature vectors, with vector of all zeros appended.\n\tx1 = torch.FloatTensor(len(question_len), max(question_len), dim).zero_()\n\tfor i in range(len(question_len)):\n\t\tquestion_feature_vec = batch[i][0]\n\t\tvec = torch.FloatTensor(question_feature_vec)\n\t\tx1[i, :len(question_feature_vec)].copy_(vec)\n\tq_batch = {'feature_vec': x1, 'len': torch.FloatTensor(question_len), 'labels': target_labels}\n\treturn q_batch","def convert_examples_to_features(\n self,\n examples: List[object],\n tokenizer: object,\n max_seq_length: int,\n doc_stride: int,\n max_query_length: int,\n has_groundtruth: bool,\n ):\n\n unique_id = 1000000000\n features = []\n text_to_tokens_dict = {}\n\n for example_index in trange(len(examples)):\n\n if example_index % 1000 == 0:\n SquadDataset.check_if_sufficient_memory()\n\n example = examples[example_index]\n if example.question_text not in text_to_tokens_dict:\n text_to_tokens_dict[example.question_text] = tokenizer.text_to_tokens(example.question_text)[\n :max_query_length\n ]\n query_tokens = text_to_tokens_dict[example.question_text]\n\n # context: index of token -> index of word\n tok_to_orig_index = []\n # context: index of word -> index of first token in token list\n orig_to_tok_index = []\n # context without white spaces after tokenization\n all_doc_tokens = []\n # doc tokens is word separated context\n (\n doc_tokens,\n char_to_word_offset,\n start_position,\n end_position,\n context_text,\n ) = SquadDataset.get_doc_tokens_and_offset_from_context_id(\n example.context_id,\n example.start_position_character,\n example.is_impossible,\n example.answer_text,\n self.processor.doc_id_to_context_text,\n )\n\n example.start_position = start_position\n example.end_position = end_position\n if self.mode != TRAINING_MODE:\n example.doc_tokens = doc_tokens\n # the text to tokens step is the slowest step\n for (i, token) in enumerate(doc_tokens):\n orig_to_tok_index.append(len(all_doc_tokens))\n if token not in text_to_tokens_dict:\n text_to_tokens_dict[token] = tokenizer.text_to_tokens(token)\n sub_tokens = text_to_tokens_dict[token]\n\n for sub_token in sub_tokens:\n tok_to_orig_index.append(i)\n all_doc_tokens.append(sub_token)\n\n # idx of query token start and end in context\n tok_start_position = None\n tok_end_position = None\n if has_groundtruth and example.is_impossible:\n tok_start_position = -1\n tok_end_position = -1\n if has_groundtruth and not example.is_impossible:\n tok_start_position = orig_to_tok_index[example.start_position]\n if example.end_position < len(doc_tokens) - 1:\n tok_end_position = orig_to_tok_index[example.end_position + 1] - 1\n else:\n tok_end_position = len(all_doc_tokens) - 1\n\n (tok_start_position, tok_end_position) = _improve_answer_span(\n all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text\n )\n\n # The -3 accounts for tokenizer.cls_token, tokenizer.sep_token and tokenizer.sep_token\n # doc_spans contains all possible contexts options of given length\n max_tokens_for_doc = max_seq_length - len(query_tokens) - 3\n\n doc_spans = SquadDataset.get_docspans(all_doc_tokens, max_tokens_for_doc, doc_stride)\n\n doc_spans = SquadDataset.keep_relevant_docspans(\n doc_spans, tok_start_position, tok_end_position, self.keep_doc_spans\n )\n\n # make compatible for hashing\n doc_spans = tuple(doc_spans)\n\n for (doc_span_index, doc_span) in enumerate(doc_spans):\n\n tokens = [tokenizer.cls_token] + query_tokens + [tokenizer.sep_token]\n segment_ids = [0 for i in range(len(tokens))]\n\n token_is_max_context = {}\n # maps context tokens idx in final input -> word idx in context\n token_to_orig_map = {}\n\n for i in range(doc_span.length):\n split_token_index = doc_span.start + i\n token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\n is_max_context = _check_is_max_context(doc_spans, doc_span_index, split_token_index)\n token_is_max_context[len(tokens)] = is_max_context\n tokens.append(all_doc_tokens[split_token_index])\n segment_ids.append(1)\n tokens.append(tokenizer.sep_token)\n segment_ids.append(1)\n\n input_ids = tokenizer.tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens.\n # Only real tokens are attended to.\n input_mask = [1] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n while len(input_ids) < max_seq_length:\n input_ids.append(tokenizer.pad_id)\n input_mask.append(0)\n segment_ids.append(0)\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n\n # calculate start and end position in final array\n # of tokens in answer if no answer,\n # 0 for both pointing to tokenizer.cls_token\n start_position = 0\n end_position = 0\n if has_groundtruth and not example.is_impossible:\n doc_start = doc_span.start\n doc_end = doc_span.start + doc_span.length - 1\n out_of_span = False\n if not (tok_start_position >= doc_start and tok_end_position <= doc_end):\n out_of_span = True\n if out_of_span:\n start_position = 0\n end_position = 0\n else:\n doc_offset = len(query_tokens) + 2\n start_position = tok_start_position - doc_start + doc_offset\n end_position = tok_end_position - doc_start + doc_offset\n if has_groundtruth and example.is_impossible:\n # if our document chunk does not contain\n # an annotation we throw it out, since there is nothing\n # to predict.\n start_position = 0\n end_position = 0\n\n if example_index < 1:\n logging.info(\"*** Example ***\")\n logging.info(\"unique_id: %s\" % (unique_id))\n logging.info(\"example_index: %s\" % (example_index))\n logging.info(\"doc_span_index: %s\" % (doc_span_index))\n logging.info(\"tokens: %s\" % \" \".join(tokens))\n logging.info(\n \"token_to_orig_map: %s\" % \" \".join([\"%d:%d\" % (x, y) for (x, y) in token_to_orig_map.items()])\n )\n logging.info(\n \"token_is_max_context: %s\"\n % \" \".join([\"%d:%s\" % (x, y) for (x, y) in token_is_max_context.items()])\n )\n logging.info(\"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n logging.info(\"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n logging.info(\"segment_ids: %s\" % \" \".join([str(x) for x in segment_ids]))\n if has_groundtruth and example.is_impossible:\n logging.info(\"impossible example\")\n if has_groundtruth and not example.is_impossible:\n answer_text = \" \".join(tokens[start_position : (end_position + 1)])\n logging.info(\"start_position: %d\" % (start_position))\n logging.info(\"end_position: %d\" % (end_position))\n logging.info(\"answer: %s\" % (answer_text))\n\n # memoization to save CPU memory for large datasets\n input_mask = tuple(input_mask)\n if input_mask in self.input_mask_to_input_mask_id:\n feature_input_mask_id = self.input_mask_to_input_mask_id[input_mask]\n else:\n self.input_mask_id_to_input_mask[self.input_mask_id] = input_mask\n self.input_mask_to_input_mask_id[input_mask] = self.input_mask_id\n feature_input_mask_id = self.input_mask_id\n self.input_mask_id += 1\n\n segment_mask = tuple(segment_ids)\n if segment_mask in self.segment_mask_to_segment_mask_id:\n feature_segment_mask_id = self.segment_mask_to_segment_mask_id[segment_mask]\n else:\n self.segment_mask_id_to_segment_mask[self.segment_mask_id] = segment_mask\n self.segment_mask_to_segment_mask_id[segment_mask] = self.segment_mask_id\n feature_segment_mask_id = self.segment_mask_id\n self.segment_mask_id += 1\n # end memoization\n\n if self.mode == TRAINING_MODE:\n input_feature = {\n \"unique_id\": unique_id,\n \"input_ids\": input_ids,\n \"input_mask\": feature_input_mask_id,\n \"segment_ids\": feature_segment_mask_id,\n \"start_position\": start_position,\n \"end_position\": end_position,\n }\n else:\n input_feature = {\n \"unique_id\": unique_id,\n \"input_ids\": input_ids,\n \"input_mask\": feature_input_mask_id,\n \"segment_ids\": feature_segment_mask_id,\n \"start_position\": start_position,\n \"end_position\": end_position,\n \"example_index\": example_index,\n \"doc_span_index\": doc_span_index,\n \"tokens\": tokens,\n \"token_to_orig_map\": token_to_orig_map,\n \"token_is_max_context\": token_is_max_context,\n \"is_impossible\": example.is_impossible,\n }\n\n features.append(input_feature)\n unique_id += 1\n return features","def tokenize_dataset(self, data):\n subword = tfds.features.text.SubwordTextEncoder.build_from_corpus\n tokenizer_en = subword((en.numpy() for pt, en in data),\n target_vocab_size=2**15)\n tokenizer_pt = subword((pt.numpy() for pt, en in data),\n target_vocab_size=2**15)\n return (tokenizer_pt, tokenizer_en)","def question_medtagger(self):\n self.save_on_file(self.cleaned_questions_q_dot, 'questions.txt')\n medpos_tag_out = self.medpos_tag('questions.txt')\n parsed_questions = self.parse(medpos_tag_out)\n parsed_questions_type = []\n parsed_questions_out = []\n for i in range(0, len(self.data['questions'])):\n # [ type, [ (w1, tag_w1) , (w2, tag_w2) ... (wn, tagwn) ] ]\n parsed_questions_type += [[self.data['questions'][i]['type']] + [parsed_questions[i]]]\n parsed_questions_out += [parsed_questions[i]]\n return parsed_questions_out, parsed_questions_type","def batchify_QA(batch):\r\n\r\n if type(batch[0][1]) != torch.LongTensor:\r\n no_elmo, use_char = (True, False) if batch[0][1] == -2 else (False, False)\r\n else:\r\n no_elmo, use_char = True, True\r\n\r\n docs = [ex[0] for ex in batch]\r\n docs_char = [ex[1] for ex in batch]\r\n ques = [ex[2] for ex in batch]\r\n questions_char = [ex[3] for ex in batch]\r\n answers = [ex[4] for ex in batch]\r\n\r\n # Batch documents\r\n max_doc_length = max([d.size(0) for d in docs])\r\n x1_len = torch.LongTensor(len(docs)).zero_()\r\n x1 = torch.LongTensor(len(docs),\r\n max_doc_length).zero_() if no_elmo else torch.LongTensor(len(docs),\r\n max_doc_length,\r\n 50).zero_()\r\n x1_char = torch.LongTensor(len(docs),\r\n max_doc_length,\r\n docs_char[0].size(1)).zero_() if (no_elmo and use_char) else None\r\n for i, d in enumerate(docs):\r\n x1_len[i] = d.size(0)\r\n x1[i, :d.size(0)].copy_(d)\r\n if not no_elmo:\r\n x1_char[i, :d.size(0), :].copy_(docs_char[i])\r\n\r\n # Batch questions\r\n max_ques_length = max([q.size(0) for q in ques])\r\n x2_len = torch.LongTensor(len(ques)).zero_()\r\n x2 = torch.LongTensor(len(ques), max_ques_length).zero_() if no_elmo else torch.LongTensor(len(ques),\r\n max_ques_length,\r\n 50).zero_()\r\n x2_char = torch.LongTensor(len(ques),\r\n max_ques_length,\r\n questions_char[0].size(1)).zero_() if (no_elmo and use_char) else None\r\n for i, q in enumerate(ques):\r\n x2_len[i] = q.size(0)\r\n x2[i, :q.size(0)].copy_(q)\r\n if not no_elmo:\r\n x2_char[i, :q.size(0), :].copy_(questions_char[i])\r\n\r\n # Batch answers\r\n max_ans_length = max([a.size(0) for a in answers])\r\n ans_len = torch.LongTensor(len(answers)).zero_()\r\n ans = torch.LongTensor(len(answers), max_ans_length).zero_()\r\n for i, a in enumerate(answers):\r\n ans_len[i] = a.size(0)\r\n ans[i, :a.size(0)].copy_(a)\r\n\r\n ids = [ex[5] for ex in batch]\r\n contexts = [ex[6] for ex in batch]\r\n # FIXME: multiple answers are possible, fix vectorize also.\r\n targets = [ex[8][0] for ex in batch]\r\n src_vocabs = [ex[9] for ex in batch]\r\n source_maps = []\r\n alignments = []\r\n\r\n # Prepare source vocabs, alignment [required for Copy Attention]\r\n for eid, context, target, (token2idx, idx2token) in \\\r\n zip(ids, contexts, targets, src_vocabs):\r\n # Mapping source tokens to indices in the dynamic dict.\r\n src_map = torch.LongTensor([token2idx[w] for w in context])\r\n source_maps.append(src_map)\r\n\r\n # TODO: does skipping the first and last token in answer valid?\r\n mask = torch.LongTensor([token2idx[w] if w in token2idx\r\n else UNK for w in target])\r\n alignments.append(mask)\r\n\r\n return {'doc_rep': x1,\r\n 'doc_char_rep': x1_char,\r\n 'doc_len': x1_len,\r\n 'que_rep': x2,\r\n 'que_char_rep': x2_char,\r\n 'que_len': x2_len,\r\n 'ans_rep': ans,\r\n 'ans_len': ans_len,\r\n 'ids': ids,\r\n 'documents': contexts,\r\n 'questions': [ex[7] for ex in batch],\r\n 'answers': targets,\r\n 'source_vocabs': src_vocabs,\r\n 'src_map': source_maps,\r\n 'alignment': alignments}","def preprocess(self, sequence, word2idx):\n context = [word2idx[word]\n if word in word2idx else UNK_token for word in sequence.split()]\n context = torch.Tensor(context)\n return context","def q_tokenize(document):\n final_words = []\n avoided_words = [] # WORDS WHICH ARE TO BE AVOIDED IN THE FINAL LIST\n \n # making the avoided_words list\n for word in string.punctuation: # the string library has a string of punctuations\n avoided_words.append(word)\n for word in nltk.corpus.stopwords.words(\"english\"): # the nltk lib. has a list of stopwords commonly used in english\n avoided_words.append(word)\n\n tokens = nltk.word_tokenize(document)","def get_question():\n\n fi = open('nlpcc-iccpol-2016.kbqa.training-data','r',encoding='utf8')\n fii = open('nlpcc-iccpol-2016.kbqa.testing-data','r',encoding='utf8')\n\n q=''\n\n train = []\n countChar = {}\n m_word = 0\n i = 0\n for line in fi:\n# print(f'line: {line}')\n if line[1] == 'q':\n# print(f'line Q: {line}')\n q = line[line.index('\\t') + 1:].strip()\n if len(q) > m_word:\n m_word = len(q)\n train.append(q)\n# print(f'filtered Q: {q}')\n for char in q:\n if char not in countChar:\n countChar[char] = 1\n else:\n countChar[char] += 1\n# elif line[1] == 't':\n# print(f'line P: {line}')\n# sub = line[line.index('\\t') + 1:line.index(' |||')].strip()\n# qNSub = line[line.index(' ||| ') + 5:]\n# pre = qNSub[:qNSub.index(' |||')]\n# print(f'sub:{sub}')\n# print(f'qNSub:{qNSub}')\n# print(f'pre:{pre}')\n\n test = []\n for line in fii:\n# print(f'line: {line}')\n if line[1] == 'q':\n# print(f'line Q: {line}')\n q = line[line.index('\\t') + 1:].strip()\n if len(q) > m_word:\n m_word = len(q)\n test.append(q)\n# print(f'filtered Q: {q}')\n for char in q:\n if char not in countChar:\n countChar[char] = 1\n else:\n countChar[char] += 1\n \n \n with open('train.txt', 'w', encoding='utf-8') as f:\n f.write('\\n'.join(train))\n with open('test.txt', 'w', encoding='utf-8') as f:\n f.write('\\n'.join(test))\n \n # Save\n np.save('words.npy', countChar)\n \n print(m_word)\n\n# # Load npy dict\n# read_dictionary = np.load('my_file.npy').item()\n# print(read_dictionary['hello']) # displays \"world\"\n \n \n return m_word","def convert_examples_to_features(example, tokenizer=None, cls_token=None, sep_token=None,\n vocab=None, max_seq_length=384, doc_stride=128,\n max_query_length=64, is_training=True):\n query_tokenized = tokenizer(example.question_text)[:max_query_length]\n #tokenize paragraph and get start/end position of the answer in tokenized paragraph\n paragraph_tokenized = tokenizer(example.paragraph_text)\n\n chartok_to_tok_index = [] # char to its corresponding token's index\n tok_start_to_chartok_index = [] # token index to its first character's index\n tok_end_to_chartok_index = [] # token index to its last character's index\n char_cnt = 0\n for i, token in enumerate(paragraph_tokenized):\n chartok_to_tok_index.extend([i] * len(token))\n tok_start_to_chartok_index.append(char_cnt)\n char_cnt += len(token)\n tok_end_to_chartok_index.append(char_cnt - 1)\n\n tok_cat_text = ''.join(paragraph_tokenized).replace(u'▁', ' ')\n\n # XLNet takes a more complicated strategy to match the origin text\n # and the tokenized tokens\n # Get the LCS matching between origin text and token-concatenated text.\n n, m = len(example.paragraph_text), len(tok_cat_text)\n max_dist = abs(n - m) + 5\n for _ in range(2):\n f, g = lcs_match(max_dist, example.paragraph_text, tok_cat_text)\n if f[n - 1, m - 1] > 0.8 * n:\n break\n max_dist *= 2\n\n # Get the mapping from orgin text/tokenized text to tokenized text/origin text\n orig_to_chartok_index = [None] * n\n chartok_to_orig_index = [None] * m\n i, j = n - 1, m - 1\n while i >= 0 and j >= 0:\n if (i, j) not in g:\n break\n if g[(i, j)] == 2:\n orig_to_chartok_index[i] = j\n chartok_to_orig_index[j] = i\n i, j = i - 1, j - 1\n elif g[(i, j)] == 1:\n j = j - 1\n else:\n i = i - 1\n\n # get start/end mapping\n tok_start_to_orig_index = []\n tok_end_to_orig_index = []\n for i in range(len(paragraph_tokenized)): # for each token in the tokenized paragraph\n start_chartok_pos = tok_start_to_chartok_index[i] # first character's index in origin text\n end_chartok_pos = tok_end_to_chartok_index[i] # last character's index in origin text\n start_orig_pos = convert_index(chartok_to_orig_index, start_chartok_pos, n, is_start=True)\n end_orig_pos = convert_index(chartok_to_orig_index, end_chartok_pos, m, is_start=False)\n\n tok_start_to_orig_index.append(start_orig_pos)\n tok_end_to_orig_index.append(end_orig_pos)\n\n tok_start_position, tok_end_position = -1, -1\n\n # get mapped start/end position\n if is_training and not example.is_impossible:\n start_chartok_pos = convert_index(orig_to_chartok_index, example.start_offset,\n is_start=True)\n tok_start_position = chartok_to_tok_index[start_chartok_pos]\n\n end_chartok_pos = convert_index(orig_to_chartok_index, example.end_offset, is_start=False)\n tok_end_position = chartok_to_tok_index[end_chartok_pos]\n assert tok_start_position <= tok_end_position\n\n # get doc spans using sliding window\n doc_spans, doc_spans_indices = get_doc_spans(paragraph_tokenized,\n max_seq_length - len(query_tokenized) - 3,\n doc_stride)\n\n # record whether the tokens in a docspan have max context\n token_is_max_context = [{\n p: check_is_max_context(doc_spans_indices, i, p + doc_spans_indices[i][0])\n for p in range(len(doc_span))\n } for (i, doc_span) in enumerate(doc_spans)]\n\n # get token -> origin text mapping\n cur_tok_start_to_orig_index = [[tok_start_to_orig_index[p + st] for p in range(len(doc_span))]\n for doc_span, (st, ed) in zip(doc_spans, doc_spans_indices)]\n cur_tok_end_to_orig_index = [[tok_end_to_orig_index[p + st] for p in range(len(doc_span))]\n for doc_span, (st, ed) in zip(doc_spans, doc_spans_indices)]\n\n # get sequence features: tokens, segment_ids, p_masks\n seq_features = [\n concat_sequences([doc_span, query_tokenized], [[sep_token]] * 2 + [[cls_token]],\n [[0] * len(doc_span), [1] * len(query_tokenized)], [[1], [1], [0]])\n for doc_span in doc_spans\n ]\n\n # get the start/end positions aligned to doc spans. If is_impossible or position out of span\n # set position to cls_index, i.e., last token in the sequence.\n if not example.is_impossible:\n positions = [\n align_position2doc_spans([tok_start_position, tok_end_position], doc_idx, offset=0,\n default_value=len(seq[0]) - 1)\n for (doc_idx, seq) in zip(doc_spans_indices, seq_features)\n ]\n else:\n positions = [(len(seq_feature[0]) - 1, len(seq_feature[0]) - 1)\n for seq_feature in seq_features]\n\n features = [\n SquadXLNetFeautre(example_id=example.example_id, qas_id=example.qas_id,\n tok_start_to_orig_index=t2st, tok_end_to_orig_index=t2ed,\n valid_length=len(tokens), tokens=tokens, token_is_max_context=is_max,\n input_ids=vocab[tokens], p_mask=p_mask, segment_ids=segment_ids,\n start_position=start, end_position=end,\n paragraph_text=example.paragraph_text, paragraph_len=len(tokens),\n is_impossible=(start == len(tokens) - 1))\n for (tokens, segment_ids, p_mask), (\n start,\n end), is_max, t2st, t2ed in zip(seq_features, positions, token_is_max_context,\n cur_tok_start_to_orig_index, cur_tok_end_to_orig_index)\n ]\n return features","def tokenize(self):\n count = 0\n for entry in self._entries:\n token_pairs = []\n for relation in entry['relations']:\n assert len(relation) == 3\n token_pairs.append((relation[0][0],relation[1][0],relation[2][0]))\n\n num_rels = len(entry['relations'])\n num_random_rels = (self._max_seq_length - 2) // 3 - num_rels\n\n if num_random_rels>0:\n pass\n # gt_pairs = {(rel[0],rel[2]) for rel in entry['relations']}\n # random_pairs = self._get_random_pair(entry['objects'], gt_pairs, num_random_rels)\n # for pair in list(random_pairs):\n # token_pairs.append((pair[0][0],'background', pair[1][0]))\n else:\n for i in range(-num_random_rels):\n token_pairs.pop()\n\n random.shuffle(token_pairs)\n tokens = []\n for pair in token_pairs:\n tokens.extend(pair)\n\n tokens = ['[CLS]'] + tokens + ['[SEP]']\n tokens_char = tokens\n\n target = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3==2 else -1 for i, x in enumerate(tokens)]\n tokens = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3!=2 else self._tokenizer.vocab.get('[MASK]', self._tokenizer.vocab['[UNK]']) for i, x in enumerate(tokens)]\n \n for i in range(len(tokens)):\n if target[i] != -1:\n print(tokens_char[i],tokens[i],target[i])\n\n segment_ids = [0] * len(tokens)\n input_mask = [1] * len(tokens)\n # input_mask = [1 if i%3==2 else 0 for i in range(len(tokens))]\n # co_attention_mask = [-1 if i%3==2 else 1 for i in range(len(tokens))]\n # co_attention_mask = torch.zeros((self._max_region_num, self._max_seq_length))\n # co_attention_mask[0] = -1\n # co_attention_mask[-1] = -1\n \n if len(tokens) < self._max_seq_length:\n padding = [self._padding_index] * (self._max_seq_length - len(tokens))\n tokens = tokens + padding\n input_mask += padding\n segment_ids += padding \n target += [-1] * len(padding) \n\n assert_eq(len(tokens), self._max_seq_length)\n entry['input_ids'] = tokens \n entry[\"input_mask\"] = input_mask\n entry['segment_ids'] = segment_ids\n # entry[\"co_attention_mask\"] = co_attention_mask\n entry['target'] = target\n\n sys.stdout.write('%d/%d\\r' % (count, len(self._entries)))\n sys.stdout.flush()\n count += 1","def add_special_tokens_(model, tokenizer, update_model=True):\n orig_num_tokens = len(tokenizer)\n num_added_tokens = tokenizer.add_special_tokens(ATTR_TO_SPECIAL_TOKEN) # doesn't add if they are already there\n #print(\"coab::\",len(tokenizer.vocab))\n if (num_added_tokens > 0 and update_model):\n model.encoder.resize_token_embeddings(new_num_tokens=orig_num_tokens + num_added_tokens)\n model.decoder.resize_token_embeddings(new_num_tokens=orig_num_tokens + num_added_tokens)\n #print(model.encoder.embeddings.word_embeddings.weight.shape)\n #print(model.decoder.bert.embeddings.word_embeddings.weight.shape)","def get_features_infer(\n queries: List[str],\n tokenizer: TokenizerSpec,\n max_seq_length: int = 64,\n step: Optional[int] = 8,\n margin: Optional[int] = 16,\n audio_queries: Optional[Union[List[bytes], List[str]]] = None,\n target_sr: Optional[int] = None,\n) -> Tuple[\n List[List[int]],\n List[List[int]],\n List[List[int]],\n List[List[int]],\n List[int],\n List[int],\n List[bool],\n List[bool],\n Optional[List[float]],\n Optional[List[int]],\n]:\n st = []\n stm = []\n sent_lengths = []\n audios = []\n audio_queries = audio_queries if audio_queries else [None] * len(queries) # Dummy if no `audio_queries` passed\n for i, (query, audio_query) in enumerate(zip(queries, audio_queries)):\n subtokens, subtokens_mask = _get_subtokens_and_subtokens_mask(query, tokenizer)\n sent_lengths.append(len(subtokens))\n st.append(subtokens)\n stm.append(subtokens_mask)\n if audio_query:\n if ASR_AVAILABLE:\n if isinstance(audio_query, bytes):\n audios.append(AudioSegment.from_file(io.BytesIO(audio_query), target_sr=target_sr))\n elif isinstance(audio_query, str):\n audios.append(AudioSegment.from_file(audio_query.strip(), target_sr=target_sr))\n else:\n raise ModuleNotFoundError(\n 'Nemo ASR was not installed, see https://github.com/NVIDIA/NeMo#installation for installation instructions'\n )\n audios = audios if len(audios) else [None] * len(st)\n _check_max_seq_length_and_margin_and_step(max_seq_length, margin, step)\n if max_seq_length > max(sent_lengths) + 2:\n max_seq_length = max(sent_lengths) + 2\n # If `max_seq_length` is greater than maximum length of input query, parameters ``margin`` and ``step`` are\n # not used will not be used.\n step = 1\n # Maximum number of word subtokens in segment. The first and the last tokens in segment are CLS and EOS\n length = max_seq_length - 2\n else:\n # Maximum number of word subtokens in segment. The first and the last tokens in segment are CLS and EOS\n length = max_seq_length - 2\n step = min(length - margin * 2, step)\n logging.info(f'Max length: {max_seq_length}')\n get_stats(sent_lengths)\n all_input_ids, all_segment_ids, all_subtokens_mask, all_input_mask, all_input_mask = [], [], [], [], []\n all_quantities_of_preceding_words, all_query_ids, all_is_first, all_is_last = [], [], [], []\n all_audio_queries, all_audio_lengths = [], []\n for q_i, (query_st, query_audio) in enumerate(zip(st, audios)):\n q_inp_ids, q_segment_ids, q_subtokens_mask, q_inp_mask, q_quantities_of_preceding_words = [], [], [], [], []\n q_audio_queries, q_audio_lengths = [], []\n if query_audio and length < len(query_st):\n logging.info(f'Ignoring query with id {q_i}')\n continue\n for i in range(0, max(len(query_st), length) - length + step, step):\n subtokens = [tokenizer.cls_token] + query_st[i : i + length] + [tokenizer.sep_token]\n q_inp_ids.append(tokenizer.tokens_to_ids(subtokens))\n q_segment_ids.append([0] * len(subtokens))\n q_subtokens_mask.append([False] + stm[q_i][i : i + length] + [False])\n q_inp_mask.append([True] * len(subtokens))\n q_quantities_of_preceding_words.append(np.count_nonzero(stm[q_i][:i]))\n if query_audio:\n samples = query_audio.samples\n q_audio_queries.append(samples)\n q_audio_lengths.append(len(samples))\n all_input_ids.append(q_inp_ids)\n all_segment_ids.append(q_segment_ids)\n all_subtokens_mask.append(q_subtokens_mask)\n all_input_mask.append(q_inp_mask)\n all_quantities_of_preceding_words.append(q_quantities_of_preceding_words)\n all_query_ids.append([q_i] * len(q_inp_ids))\n all_is_first.append([True] + [False] * (len(q_inp_ids) - 1))\n all_is_last.append([False] * (len(q_inp_ids) - 1) + [True])\n if query_audio:\n all_audio_queries.append(q_audio_queries)\n all_audio_lengths.append(q_audio_lengths)\n return (\n list(itertools.chain(*all_input_ids)),\n list(itertools.chain(*all_segment_ids)),\n list(itertools.chain(*all_input_mask)),\n list(itertools.chain(*all_subtokens_mask)),\n list(itertools.chain(*all_quantities_of_preceding_words)),\n list(itertools.chain(*all_query_ids)),\n list(itertools.chain(*all_is_first)),\n list(itertools.chain(*all_is_last)),\n list(itertools.chain(*all_audio_queries)),\n list(itertools.chain(*all_audio_lengths)),\n )","def encode_batch(self, sentences: List[str], field_name, features=None) -> List[SequentialField]:\n # We need to set this so that `tokenizers` package does not complain about detecting forks.\n os.environ['TOKENIZERS_PARALLELISM'] = \"true\"\n\n if features is None:\n features = []\n extract_word_pieces = False\n else:\n assert all([len(sentence.split()) == len(feature) for sentence, feature in zip(sentences, features)])\n extract_word_pieces = True\n\n batch_size = len(sentences)\n\n if field_name != 'answer':\n sentences = [self.input_prefix + sent for sent in sentences]\n\n if self._preprocess_special_tokens:\n if len(sentences) > MULTIPROCESSING_THRESHOLD:\n multiprocessing_factor = multiprocessing.cpu_count() // len(get_devices(self.args.devices))\n logger.info('multiprocessing factor for special token preprocessing is %d', multiprocessing_factor)\n with multiprocessing.Pool(multiprocessing_factor) as p:\n sentences, index2expansions = list(\n zip(\n *p.map(\n functools.partial(self._apply_special_token_preprocessing, return_idx2exp=bool(len(features))),\n sentences,\n )\n )\n )\n else:\n sentences, index2expansions = list(\n zip(\n *map(\n functools.partial(self._apply_special_token_preprocessing, return_idx2exp=bool(len(features))),\n sentences,\n )\n )\n )\n\n all_input_features = []\n if features:\n for i, (sentence, index2expansion) in enumerate(zip(sentences, index2expansions)):\n feat = features[i]\n new_feat = []\n keys = set(index2expansion.keys())\n for j in range(len(feat)):\n repeat = 1\n if j in keys:\n repeat = index2expansion[j]\n\n new_feat.extend(feat[j] * repeat)\n\n assert len(new_feat) == len(sentence.split(' '))\n\n all_input_features.append(new_feat)\n\n features = all_input_features\n\n # batch_encode_plus for fast tokenizers returns tokenized text\n # whereas slow version do not. We breakdown slow tokenization into two steps\n # extract tokenized text first, use that to adjust features\n # then pass tokenized text to `_batch_prepare_for_model`\n def do_fast_tokenization(extract_word_pieces):\n all_wp_tokenized = []\n batch_encoded = self._tokenizer.batch_encode_plus(\n list(sentences),\n add_special_tokens=True,\n max_length=None,\n return_length=True,\n return_attention_mask=False,\n return_special_tokens_mask=True,\n )\n if extract_word_pieces:\n for encoding in batch_encoded.encodings:\n # remove special tokens\n num_prefix_special_tokens, num_suffix_special_tokens = self.get_num_special_tokens(\n encoding.special_tokens_mask\n )\n wp_tokens = encoding.tokens[num_prefix_special_tokens:-num_suffix_special_tokens]\n all_wp_tokenized.append(wp_tokens)\n\n return batch_encoded, all_wp_tokenized\n\n def do_slow_tokenization(extract_word_pieces):\n all_input_ids = []\n all_wp_tokenized = []\n if extract_word_pieces:\n for i in range(batch_size):\n text = sentences[i]\n wp_tokenized = self._tokenizer.tokenize(text)\n all_wp_tokenized.append(wp_tokenized)\n\n # None indicates encoding single instance not paired inputs\n all_input_ids.append((self._tokenizer.convert_tokens_to_ids(wp_tokenized), None))\n\n batch_encoded = self._tokenizer._batch_prepare_for_model(\n all_input_ids,\n add_special_tokens=True,\n max_length=None,\n return_length=True,\n return_attention_mask=False,\n return_special_tokens_mask=True,\n )\n else:\n batch_encoded = self._tokenizer.batch_encode_plus(\n list(sentences),\n add_special_tokens=True,\n max_length=None,\n return_length=True,\n return_attention_mask=False,\n return_special_tokens_mask=True,\n )\n return batch_encoded, all_wp_tokenized\n\n if self._use_fast():\n if field_name == 'answer':\n with self._tokenizer.as_target_tokenizer():\n batch_encoded, all_wp_tokenized = do_fast_tokenization(extract_word_pieces)\n else:\n batch_encoded, all_wp_tokenized = do_fast_tokenization(extract_word_pieces)\n\n else:\n if field_name == 'answer':\n with self._tokenizer.as_target_tokenizer():\n batch_encoded, all_wp_tokenized = do_slow_tokenization(extract_word_pieces)\n else:\n batch_encoded, all_wp_tokenized = do_slow_tokenization(extract_word_pieces)\n\n all_input_features = []\n\n if features:\n for i in range(batch_size):\n wp_features = []\n wp_tokenized = all_wp_tokenized[i]\n\n feat = features[i]\n\n # first token is always not a piece\n is_wp = [0] + [int(self._tokenizer.is_piece_fn(wp)) for wp in wp_tokenized[1:]]\n k = -1\n for j, wp in enumerate(wp_tokenized):\n if not is_wp[j]:\n k += 1\n wp_features.append(feat[k])\n\n assert len(wp_tokenized) == len(wp_features)\n\n all_input_features.append(wp_features)\n\n features = all_input_features\n\n batch_special_tokens_mask = batch_encoded.special_tokens_mask\n\n batch_features = []\n\n if features:\n for i in range(batch_size):\n feat = features[i]\n special_tokens_mask = batch_special_tokens_mask[i]\n num_prefix_special_tokens, num_suffix_special_tokens = self.get_num_special_tokens(special_tokens_mask)\n\n pad_feat = Entity.get_pad_entity(self.args.max_features_size)\n feat = [pad_feat] * num_prefix_special_tokens + feat + [pad_feat] * num_suffix_special_tokens\n\n batch_features.append(feat)\n\n batch_numerical = batch_encoded.input_ids\n batch_length = batch_encoded.length\n\n batch_decoder_numerical = []\n if self.decoder_vocab:\n for i in range(batch_size):\n batch_decoder_numerical.append(self.decoder_vocab.encode(batch_numerical[i]))\n else:\n batch_decoder_numerical = [[]] * len(batch_numerical)\n\n sequential_fields = []\n for i in range(batch_size):\n if features:\n feature = [feat.flatten() for feat in batch_features[i]]\n assert len(batch_numerical[i]) == len(feature)\n else:\n feature = None\n\n sequential_fields.append(\n SequentialField(\n value=batch_numerical[i],\n length=batch_length[i],\n limited=batch_decoder_numerical[i],\n feature=feature,\n )\n )\n return sequential_fields","def load_as_raw(self):\n\n # Q & A\n questions, answers = self.get_questions_answers()\n\n # Get vocabs\n\n # Step 4: cleaning the questions\n pprint('---- Step 4 cleaning questions ----')\n\n clean_questions = []\n for question in questions:\n clean_questions.append(clean_text(question))\n\n pprint(clean_questions, stream=Head(5))\n print('\\n\\n')\n \"\"\"\n Step 5: Clean the answers\n \"\"\"\n\n pprint('---- Step 5 cleaning answers ----')\n clean_answers = []\n for answer in answers:\n clean_answers.append(clean_text(answer))\n\n pprint(clean_answers, stream=Head(5))\n print('\\n\\n')\n \"\"\"\n Step 6: Creating a dictionary that maps each word to its number of occurences\n \"\"\"\n\n word2count = {}\n pprint('------ Step 6: counting words in questions ----')\n\n word2count = convert_word_to_count(word2count, clean_questions)\n\n pprint(word2count, stream=Head(5))\n print('\\n\\n')\n \"\"\"\n Step 7:\n For example, for a question: can we make this quick roxanne korrine and andrew barrett are having an incredibly horrendous public break up on the quad again\n It counts each word occurence such as \"can\" and accumulates the count into word2count dict\n \"\"\"\n pprint('------ Step 6: counting words in answers ----')\n\n word2count = convert_word_to_count(word2count, clean_answers)\n\n pprint(word2count, stream=Head(5))\n print('\\n\\n')\n\n keys = ['<unk>', '<s>', '</s>']\n\n \"\"\"\n Step 8: Creating word 2 int(count) by filtering words that are greater than the threshold\n \"\"\"\n\n pprint(\n '------ Step 8: questions_vocabs filtered by threshold (>) ----')\n threshold_questions = 20\n questions_vocabs = [] + keys\n for word, count in word2count.items():\n if count >= threshold_questions:\n if not word in questions_vocabs:\n questions_vocabs.append(word)\n\n pprint(questions_vocabs, stream=Head(5))\n print('\\n\\n')\n \"\"\"\n Step 9: Same as step 8 but for answers\n \"\"\"\n pprint(\n '------ Step 9: answers_vocabs filtered by threshold (>) ----')\n threshold_answers = 20\n answers_vocabs = [] + keys\n for word, count in word2count.items():\n if count >= threshold_answers:\n if not word in answers_vocabs:\n answers_vocabs.append(word)\n\n pprint(answers_vocabs, stream=Head(5))\n\n return questions, answers, questions_vocabs, answers_vocabs","def build_dataset(data, vocab_size=50000):\r\n\r\n # we will replace non-frequent tokens with the `unknown` token\r\n unk_token = '<UNK>'\r\n\r\n # calc frequencies of the tokens in our data\r\n tokens_counts = Counter(data)\r\n most_common_tokens = tokens_counts.most_common(vocab_size)\r\n\r\n # create a token => id mapping\r\n token2id = {unk_token: 0}\r\n for token, counts in most_common_tokens:\r\n token2id[token] = len(token2id)\r\n\r\n # create a reverse mapping from ids to tokens\r\n id2token = {i: t for t, i in token2id.items()}\r\n\r\n # convert data to tokens ids\r\n nb_unks = 0\r\n data_tokens_ids = []\r\n for token in data:\r\n if token in token2id:\r\n idx = token2id[token]\r\n else:\r\n idx = token2id[unk_token]\r\n nb_unks += 1\r\n\r\n data_tokens_ids.append(idx)\r\n\r\n print('Vocab size:', len(token2id))\r\n print('Unknown tokens:', nb_unks)\r\n\r\n return data_tokens_ids, token2id, id2token","def question_to_vec(question, embeddings):\n\n dim = embeddings['dog'].size\n result = np.zeros((dim,))\n\n words = question.split(' ')\n\n count = 0\n for word in words:\n if word not in embeddings or not len(embeddings[word]):\n continue\n result += embeddings[word][:dim]\n count += 1\n\n return result / max(count, 1)","def convert_to_ids(self, vocab):\n for data_set in [self.train_set, self.dev_set, self.test_set]:\n if data_set is None:\n continue\n for sample in data_set:\n sample['question_token_ids'] = vocab.convert_to_ids(sample['question_tokens'])\n for passage in sample['passages']:\n passage['passage_token_ids'] = vocab.convert_to_ids(passage['passage_tokens'])","def clean_questions(self, q_dot=False):\n for q in self.data['questions']:\n temp = re.sub('[^ a-zA-Z0-9]', '', q['body'])\n temp = temp.strip().lower()\n # temp = self.stemming(temp).strip()\n self.cleaned_questions_q_dot += temp + '?\\n'\n self.cleaned_questions += temp + '\\n'\n self.types_q += q['type'].strip() + '\\n'","def encode_and_flatten(\n raw_text_iter: IterableDataset[str],\n tokenizer: Callable[[str], list[str]],\n vocab: Vocab,\n) -> torch.Tensor:\n data = [\n torch.tensor(vocab(tokenizer(item)), dtype=torch.long)\n for item in raw_text_iter\n ]\n return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))","def _tokenize_data(train_dataset, dev_dataset, use_spacy, pool):\n tokenizer = SQuADDataTokenizer(use_spacy)\n\n tic = time.time()\n print('Train examples [{}] transformation started.'.format(len(train_dataset)))\n train_examples = list(tqdm.tqdm(tokenizer.run_async(pool, train_dataset),\n total=len(train_dataset)))\n print('Train examples transformed [{}/{}] in {:.3f} sec'.format(len(train_examples),\n len(train_dataset),\n time.time() - tic))\n tic = time.time()\n print('Dev examples [{}] transformation started.'.format(len(dev_dataset)))\n dev_examples = list(tqdm.tqdm(tokenizer.run_async(pool, dev_dataset),\n total=len(dev_dataset)))\n print('Dev examples transformed [{}/{}] in {:.3f} sec'.format(len(dev_examples),\n len(dev_dataset),\n time.time() - tic))\n return train_examples, dev_examples","def vectorize_data(data, word_idx, sentence_size, memory_size,answer_size):\n S = []\n Q = []\n AS = []\n L = []\n label_nums = set([len(dt[2]) for dt in data])\n assert(len(label_nums) == 1)\n label_num = list(label_nums)[0]\n for story, query, answer, label in data:\n lq = max(0, sentence_size - len(query))\n q = [word_idx[w] for w in query] + [0] * lq\n\n ss = []\n for i, sentence in enumerate(story, 1):\n ls = max(0, sentence_size - len(sentence))\n ss.append([word_idx[w] for w in sentence] + [0] * ls)\n\n if len(ss) > memory_size:\n # Use Jaccard similarity to determine the most relevant sentences\n q_words = set(q)\n least_like_q = sorted(ss, cmp = lambda x, y: jaccard(set(x), q_words) < jaccard(set(y), q_words))[:len(ss)-memory_size]\n for sent in least_like_q:\n # Remove the first occurrence of sent. A list comprehension as in [sent for sent in ss if sent not in least_like_q]\n # should not be used, as it would remove multiple occurrences of the same sentence, some of which might actually make the cutoff.\n ss.remove(sent)\n else:\n # pad to memory_size\n lm = max(0, memory_size - len(ss))\n for _ in range(lm):\n ss.append([0] * sentence_size)\n\n y = np.zeros(len(word_idx) + 1) # 0 is reserved for nil word\n\n sa = []\n for i, sentence in enumerate(answer, 1):\n ls = max(0, answer_size - len(sentence))\n sa.append([word_idx[w] for w in sentence] + [0] * ls)\n\n lb = np.zeros(label_num)\n lb[int(label)] = 1\n\n S.append(ss)\n Q.append(q)\n AS.append(sa)\n L.append(lb)\n Q = pad_sequences(Q, sentence_size)\n\n return np.array(S), np.array(Q), np.array(AS), np.array(L)","def tokenize(self, inputs):\n if hasattr(self.tokenizer, \"batch_encode\"):\n return self.tokenizer.batch_encode(inputs)\n else:\n return [self.tokenizer.encode(x) for x in inputs]","def ques_lstm(self, word_embeddings):\n nlayer = self.config.nrnn_layer\n lstm_cell = tf.contrib.rnn.BasicLSTMCell(\n num_units=self.config.rnn_size, state_is_tuple=True)\n lstm_cell = tf.contrib.rnn.MultiRNNCell(\n [lstm_cell] * nlayer, state_is_tuple=True)\n\n with tf.variable_scope(\n 'ques_lstm', initializer=self.initializer) as scope:\n # Run the batch of sequence embeddings through the LSTM.\n assert (self.feed_per_seq_length != None)\n _, lstm_state = tf.nn.dynamic_rnn(\n cell=lstm_cell,\n inputs=word_embeddings,\n sequence_length=self.feed_per_seq_length,\n dtype=tf.float32)\n\n # extract c and h, then concat them all\n ques_encoding_lis = []\n for i in range(nlayer):\n ques_encoding_lis = ques_encoding_lis + [\n lstm_state[i].c, lstm_state[i].h\n ]\n ques_encoding = tf.concat(ques_encoding_lis, axis=1)\n\n assert (ques_encoding.shape[1] == self.config.rnnout_dim)\n\n return ques_encoding","def prepare_inputs(token_mapping, w2v_W, w2v_U, sentences):\n tokens = [tokenize(token_mapping, sentence) for sentence in sentences] \n \n depth = len(token_mapping)\n one_hot_tokens = []\n for sentence in tokens:\n one_hot_sentence = []\n for i, token in enumerate(sentence):\n if token != token_mapping['#UNK#']:\n one_hot_sentence.append(one_hot_encode(token, depth))\n else:\n if i <= 2:\n context_tokens = sentence[:i] + sentence[i+1:i+3]\n else:\n context_tokens = sentence[i-2:i] + sentence[i+1:i+3]\n context_one_hot = [one_hot_encode(token, depth) for token in context_tokens]\n context_mean = np.mean(np.asarray(context_one_hot), axis=0)\n one_hot_sentence.append(context_mean)\n one_hot_tokens.append(one_hot_sentence)\n \n one_hot_tokens = [np.asarray(ls) for ls in one_hot_tokens]\n vec_tokens = [word2vec(w2v_W, w2v_U, sentence) for sentence in tqdm(one_hot_tokens, desc='Vectorizing tokens')]\n return vec_tokens","def convert_examples_to_features(examples, tokenizer, max_seq_length,\n doc_stride, max_query_length, is_training):\n\n unique_id = 1000000000\n\n features = []\n for (example_index, example) in enumerate(examples):\n query_tokens = tokenizer.tokenize(example.question_text)\n\n if len(query_tokens) > max_query_length:\n query_tokens = query_tokens[0:max_query_length]\n \n if len(query_tokens) < 3:\n for i in range(len(query_tokens), 3):\n query_tokens.append('.')\n\n tok_to_orig_index = []\n orig_to_tok_index = []\n all_doc_tokens = []\n for (i, token) in enumerate(example.doc_tokens):\n orig_to_tok_index.append(len(all_doc_tokens))\n sub_tokens = tokenizer.tokenize(token)\n for sub_token in sub_tokens:\n tok_to_orig_index.append(i)\n all_doc_tokens.append(sub_token)\n\n tok_start_position = None\n tok_end_position = None\n if is_training:\n tok_start_position = orig_to_tok_index[example.start_position]\n if example.end_position < len(example.doc_tokens) - 1:\n tok_end_position = orig_to_tok_index[example.end_position + 1] - 1\n else:\n tok_end_position = len(all_doc_tokens) - 1\n (tok_start_position, tok_end_position) = _improve_answer_span(\n all_doc_tokens, tok_start_position, tok_end_position, tokenizer,\n example.orig_answer_text)\n\n # The -3 accounts for [CLS], [SEP] and [SEP]\n max_tokens_for_doc = max_seq_length - len(query_tokens) - 3\n\n # We can have documents that are longer than the maximum sequence length.\n # To deal with this we do a sliding window approach, where we take chunks\n # of the up to our max length with a stride of `doc_stride`.\n _DocSpan = collections.namedtuple( # pylint: disable=invalid-name\n \"DocSpan\", [\"start\", \"length\"])\n doc_spans = []\n start_offset = 0\n while start_offset < len(all_doc_tokens):\n length = len(all_doc_tokens) - start_offset\n if length > max_tokens_for_doc:\n length = max_tokens_for_doc\n doc_spans.append(_DocSpan(start=start_offset, length=length))\n if start_offset + length == len(all_doc_tokens):\n break\n start_offset += min(length, doc_stride)\n\n for (doc_span_index, doc_span) in enumerate(doc_spans):\n \n q_len = 0\n d_len = 0\n\n tokens = []\n token_to_orig_map = {}\n token_is_max_context = {}\n segment_ids = []\n tokens.append(\"[CLS]\")\n segment_ids.append(0)\n for token in query_tokens:\n tokens.append(token)\n segment_ids.append(0)\n q_len += 1\n tokens.append(\"[SEP]\")\n segment_ids.append(0)\n\n for i in range(doc_span.length):\n split_token_index = doc_span.start + i\n token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\n\n is_max_context = _check_is_max_context(doc_spans, doc_span_index,\n split_token_index)\n token_is_max_context[len(tokens)] = is_max_context\n tokens.append(all_doc_tokens[split_token_index])\n segment_ids.append(1)\n d_len += 1\n tokens.append(\"[SEP]\")\n segment_ids.append(1)\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n while len(input_ids) < max_seq_length:\n input_ids.append(0)\n input_mask.append(0)\n segment_ids.append(0)\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n\n start_position = None\n end_position = None\n is_select = None\n has_answer = None\n if is_training:\n # For training, if our document chunk does not contain an annotation\n # we throw it out, since there is nothing to predict.\n doc_start = doc_span.start\n doc_end = doc_span.start + doc_span.length - 1\n if (example.start_position < doc_start or\n example.end_position < doc_start or\n example.start_position > doc_end or example.end_position > doc_end):\n continue\n\n doc_offset = len(query_tokens) + 2\n start_position = tok_start_position - doc_start + doc_offset\n end_position = tok_end_position - doc_start + doc_offset\n is_select = example.is_select\n has_answer = example.has_answer\n\n features.append(\n InputFeatures(\n unique_id=unique_id,\n example_index=example_index,\n doc_span_index=doc_span_index,\n tokens=tokens,\n token_to_orig_map=token_to_orig_map,\n token_is_max_context=token_is_max_context,\n input_ids=input_ids,\n input_mask=input_mask,\n segment_ids=segment_ids,\n q_len=q_len,\n d_len=d_len,\n start_position=start_position,\n end_position=end_position,\n is_select=is_select,\n has_answer=has_answer))\n unique_id += 1\n break\n\n return features","def preprocess(self, df, maxlen = 169):\n \n vocabs = self.tk.word_index.keys()\n \n df1 = self.treat_na(df)\n df2 = self.remove_punc_sw(df1)\n df3 = self.remove_numbers(df2)\n df4 = self.lemma_pos(df3)\n df5 = self.bigram(df4)\n df6 = self.combine_bigrams(df5)\n \n new_docs = []\n \n for word_list in df6:\n \n if len(word_list) == 2 and word_list[0].lower() == 'noinfo' and word_list[1].lower() == 'noinfo':\n new_docs.append(list(np.zeros(maxlen)))\n \n else:\n new_word_list = []\n for word in word_list:\n if word not in vocabs:\n word = 'UNKNOWN_TOKEN'\n new_word_list.append(word)\n \n sequence = \" \".join(new_word_list)\n vectors = self.tk.texts_to_sequences([sequence])\n padded_vectors = pad_sequences(vectors, maxlen=maxlen, padding='post', truncating='post')\n \n new_docs.append(list(padded_vectors[0]))\n \n return new_docs","def tokenize(self):\n\n x = [] # input documents\n for file_path in glob.glob(self.train_dir + '*.txt'):\n file_as_string = open(file_path).read()\n x.append(file_as_string)\n\n self.tokenizer.fit_on_texts(x)\n print('input vocabulary size:', len(self.tokenizer.word_index))\n pickle_file = open('Model/tokenizer.p', 'wb')\n pickle.dump(self.tokenizer, pickle_file)","def prepare(self):\n # get data from file\n train_data, test_data = return_speechacts()\n # y are the speechacts or 'labels'\n y_train = [t.split(' ')[0] for t in train_data]\n y_test = [t.split(' ')[0] for t in test_data]\n # x are the sentences\n x_train = [\" \".join(t.split(' ')[1:]) for t in train_data]\n x_test = [\" \".join(t.split(' ')[1:]) for t in test_data]\n # use the tokenizer and padding from keras to assign arrays of integers\n # to sentences, out of vocabulary token is 1\n self.tokenizer_x = Tokenizer(oov_token=1)\n self.tokenizer_x.fit_on_texts(x_train + x_test)\n xt_train = self.tokenizer_x.texts_to_sequences(x_train)\n xt_train = pad_sequences(xt_train, maxlen=self.sentence_size,\n dtype='int32')\n xt_test = self.tokenizer_x.texts_to_sequences(x_test)\n xt_test = pad_sequences(xt_test, maxlen=self.sentence_size,\n dtype='int32')\n # vocab is the number of words in our vocabulary\n self.vocab = len(self.tokenizer_x.word_index) + 1\n # do the same for labels\n self.tokenizer_y = Tokenizer()\n self.tokenizer_y.fit_on_texts(y_train + y_test)\n yt_train = self.tokenizer_y.texts_to_sequences(y_train)\n yt_train = [t[0] for t in yt_train]\n yt_train = to_categorical(yt_train)\n yt_test = self.tokenizer_y.texts_to_sequences(y_test)\n yt_test = [t[0] for t in yt_test]\n yt_test = to_categorical(yt_test)\n self.x_train = x_train\n self.y_train = y_train\n self.x_test = x_test\n self.y_test = y_test\n self.xt_train = xt_train\n self.yt_train = yt_train\n self.xt_test = xt_test\n self.yt_test = yt_test","def generate_corpus():\n data = load_data()\n questions = [s.split(' ', 1)[1].lower() for s in data]\n return questions","def tokenize(lang):\n lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(filters='')\n lang_tokenizer.fit_on_texts(lang)\n tensor = lang_tokenizer.texts_to_sequences(lang)\n # pad zero after sequences for the same length.\n tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor,\n padding='post')\n return tensor, lang_tokenizer","def example_to_token_ids_segment_ids_label_ids(\n ex_index,\n example,\n max_seq_length,\n max_evid_length,\n tokenizer):\n if ex_index < 5:\n print('*** Example {} ***'.format(ex_index))\n print('qid: {}'.format(example.qid))\n\n qid = example.qid\n\n # evidence\n ante_evidence = example.evidence # list of strings.\n ante_token_ids, ante_segment_ids = [], []\n for ante_sent in ante_evidence:\n ante_sent_tokens = tokenizer.tokenize(ante_sent)\n ante_sent_tokens = ante_sent_tokens[:max_evid_length-2]\n sent_tokens, sent_segment_ids = [], []\n sent_tokens.append(\"<s>\")\n sent_segment_ids.append(0)\n sent_tokens += ante_sent_tokens + [\"</s>\"]\n sent_segment_ids += [0] * len(ante_sent_tokens) + [0]\n\n assert len(sent_tokens) <= max_evid_length\n sent_tokens_ids = tokenizer.convert_tokens_to_ids(sent_tokens)\n\n assert len(sent_tokens_ids) == len(sent_segment_ids)\n ante_token_ids.append(sent_tokens_ids)\n ante_segment_ids.append(sent_segment_ids)\n assert len(ante_evidence) == len(ante_token_ids)\n assert len(ante_evidence) == len(ante_segment_ids)\n l_ante = min(len(ante_evidence), max_evid_length)\n\n para_tokens = tokenizer.tokenize(example.para)\n question_tokens = tokenizer.tokenize(example.question)\n answers_tokens = map(tokenizer.tokenize, example.answers)\n\n token_ids = []\n segment_ids = []\n for choice_idx, answer_tokens in enumerate(answers_tokens):\n\n truncated_para_tokens, \\\n truncated_question_tokens,\\\n truncated_answer_tokens = _truncate_seq_pair(para_tokens, question_tokens, answer_tokens, max_seq_length)\n\n choice_tokens = []\n choice_segment_ids = []\n choice_tokens.append(\"<s>\")\n choice_segment_ids.append(0)\n choice_tokens += truncated_para_tokens + [\"</s>\"]\n choice_segment_ids += [0] * len(truncated_para_tokens) + [0]\n choice_tokens += truncated_question_tokens + [\"</s>\"]\n choice_segment_ids += [1] * len(truncated_question_tokens) + [1]\n choice_tokens += truncated_answer_tokens\n choice_segment_ids += [1] * len(truncated_answer_tokens)\n\n choice_token_ids = tokenizer.convert_tokens_to_ids(choice_tokens)\n\n token_ids.append(choice_token_ids)\n segment_ids.append(choice_segment_ids)\n\n label_ids = [example.label]\n\n return ante_token_ids, ante_segment_ids, l_ante, \\\n token_ids, segment_ids, label_ids, qid","def get_tokenizer(stories, word_table):\n tokens_all = []\n for story, query, answer, task_id in stories:\n for sentence in story:\n word_table.add_vocab(*sentence)\n word_table.count_doc(*sentence)\n tokens_all.extend(query + [answer])\n word_table.add_vocab(*tokens_all)","def _resize_token_embeddings(\n cls, model: PreTrainedModel, tokenizer_wrapper: TokenizerWrapper\n ):\n if tokenizer_wrapper.num_added_special_tokens > 0:\n model.resize_token_embeddings(\n new_num_tokens=len(tokenizer_wrapper.tokenizer)\n )","def _create_input(inputs: List[Tensor], initial: bool = False) \\\n -> Dict[str, Tensor]:\n word_embed = torch.stack(inputs, dim=0)\n seq_len, batch_size, embed_dim = word_embed.size()\n if not initial:\n # Add a dummy token at the end that stands for the token\n # to predict.\n word_embed = torch.cat([\n word_embed,\n word_embed.new_zeros(1, batch_size, embed_dim)\n ], dim=0)\n seq_len += 1\n segment_ids = word_embed.new_zeros(\n seq_len, batch_size, dtype=torch.long)\n return_dict = {\n \"word_embed\": word_embed,\n \"segment_ids\": segment_ids,\n }\n\n if not initial:\n # Only the dummy token is considered target.\n target_mapping = torch.cat([\n torch.zeros(1, seq_len - 1, batch_size),\n torch.ones(1, 1, batch_size)\n ], dim=1).to(device=word_embed.device)\n # Dummy token attends to nothing; actual tokens attend to all.\n permute_mask = torch.cat([\n torch.zeros(seq_len, seq_len - 1, batch_size),\n torch.ones(seq_len, 1, batch_size),\n ], dim=1).to(device=word_embed.device)\n return_dict.update({\n \"target_mapping\": target_mapping,\n \"permute_mask\": permute_mask,\n })\n\n return return_dict","def get_qald_metrics(pred_, m_labels_, ques_, mode='val'):\n rows = []\n question_rows_map = {}\n question_mention_set = set()\n for i, pred in enumerate(pred_):\n pred = pred.data.tolist()[0]\n question = ques_[i]\n if question not in question_rows_map:\n question_rows_map[ques_[i]] = []\n if pred:\n men_entity_label = '_'.join(m_labels_[i].split(';')[-1].split())\n men_entity_mention = '_'.join(m_labels_[i].split(';')[0].split())\n if '-'.join([question, men_entity_mention]) in question_mention_set:\n question_rows_map[ques_[i]][-1].add(\n ('http://dbpedia.org/resource/{}'.format(men_entity_label), pred))\n else:\n question_mention_set.add('-'.join([question, men_entity_mention]))\n question_rows_map[ques_[i]].append(set())\n question_rows_map[ques_[i]][-1].add(\n ('http://dbpedia.org/resource/{}'.format(men_entity_label), pred))\n for key, preds_list_mentions in question_rows_map.items():\n if len(preds_list_mentions) > 1:\n rows.append([key, []])\n for preds_set in preds_list_mentions:\n sorted_values = sorted(list(preds_set), key=lambda x: x[1], reverse=True)[:5]\n rows[-1][1].append(sorted_values)\n elif len(preds_list_mentions) == 1:\n sorted_values = sorted(list(preds_list_mentions[0]), key=lambda x: x[1], reverse=True)[:5]\n rows.append([key, [sorted_values]])\n else:\n rows.append([key, []])\n\n df_output = pd.DataFrame(rows, columns=['Question', 'Entities'])\n df_output['Classes'] = str([])\n\n # gold\n benchmark = pd.read_csv('../../../data/lcquad/lcquad_gt.csv')\n benchmark = benchmark.set_index('Question')\n benchmark = benchmark.replace(np.nan, '', regex=True)\n benchmark['Entities'] = benchmark['Entities'].astype(object)\n is_qald_gt = True\n\n # pred\n predictions = df_output\n # print(df_output.shape)\n predictions = predictions.set_index('Question')\n predictions['Entities'] = predictions['Entities']\n predictions['Classes'] = predictions['Classes']\n\n metrics = compute_metrics(benchmark=benchmark, predictions=predictions, limit=410, is_qald_gt=is_qald_gt,\n eval='full')\n\n scores = metrics['macro']['named']\n prec, recall, f1 = scores['precision'], scores['recall'], scores['f1']\n return prec, recall, f1, df_output","def tokenize_docs(self):\n if not hasattr(self,\"max_seq_len\"):\n self.max_seq_len = 0\n \n context_cols = self.cols\n\n for df in [self.train, self.val, self.test]:\n df[\"tok_context\"] = df[context_cols].values.tolist()\n df[\"tok_context\"] = df[\"tok_context\"].apply(self.docs_to_ids)\n self.max_seq_len = max(self.max_seq_len,\n df[\"tok_context\"].apply(\n lambda x: max(map(len,x))).max())\n \n self.train[\"tok_ending\"] = self.train[self.train_cols[-1]].apply(\n self.str_to_ids)\n self.val[\"tok_ending_1\"] = self.val[self.test_val_cols[-2]].apply(\n self.str_to_ids)\n self.val[\"tok_ending_2\"] = self.val[self.test_val_cols[-1]].apply(\n self.str_to_ids)\n self.test[\"tok_ending_1\"] = self.test[self.test_val_cols[-2]].apply(\n self.str_to_ids)\n self.test[\"tok_ending_2\"] = self.test[self.test_val_cols[-1]].apply(\n self.str_to_ids)\n self.max_seq_len = max(\n self.max_seq_len,\n self.train[\"tok_ending\"].apply(len).max(),\n self.val[\"tok_ending_1\"].apply(len).max(),\n self.val[\"tok_ending_2\"].apply(len).max(),\n self.test[\"tok_ending_1\"].apply(len).max(),\n self.test[\"tok_ending_2\"].apply(len).max(),\n )\n # Max_seq_len is without BOM, EOM\n self.max_seq_len += 2\n log(\"Maximum sequence length: {}\".format(self.max_seq_len))","def sentence_splitter_builderV2(tokenizer, mode=4, max_sentence_size=21, queries_sw=None, docs_sw=None, save_tokenizer_on_test=True):\n idf_from_id_token = lambda x: math.log(tokenizer.document_count/tokenizer.word_docs[tokenizer.index_word[x]])\n \n def train_splitter(data_generator):\n\n while True:\n \n # get the batch triplet\n query, pos_docs, neg_docs = next(data_generator)\n \n # tokenization\n query = tokenizer.texts_to_sequences(query)\n \n if queries_sw is not None:\n for tokenized_query in query:\n tokenized_query = [token for token in tokenized_query if token not in queries_sw] \n \n new_pos_docs = []\n new_neg_docs = []\n \n new_pos_extra_features = []\n new_neg_extra_features = []\n \n # sentence splitting\n if mode==4:\n \n for b in range(len(pos_docs)):\n new_pos_docs.append([])\n new_neg_docs.append([])\n \n _temp_pos_docs = nltk.sent_tokenize(pos_docs[b][\"text\"])\n _temp_pos_docs = tokenizer.texts_to_sequences(_temp_pos_docs)\n \n if docs_sw is not None:\n for tokenized_docs in _temp_pos_docs:\n tokenized_docs = [token for token in tokenized_docs if token not in docs_sw] \n \n # skip batch with empty pos_docs\n if all([ len(sentence)==0 for sentence in _temp_pos_docs]):\n break # try a new resampling, NOTE THIS IS A EASY FIX PLS REDO THIS!!!!!!!\n # for obvious reasons\n \n _temp_neg_docs = nltk.sent_tokenize(neg_docs[b][\"text\"])\n _temp_neg_docs = tokenizer.texts_to_sequences(_temp_neg_docs)\n \n if docs_sw is not None:\n for tokenized_docs in _temp_neg_docs:\n tokenized_docs = [token for token in tokenized_docs if token not in docs_sw] \n \n # compute extra features\n #extra_features_pos_doc = compute_extra_features(query[b], _temp_pos_docs, idf_from_id_token)\n #extra_features_neg_doc = compute_extra_features(query[b], _temp_neg_docs, idf_from_id_token)\n \n # add the bm25 score\n #extra_features_pos_doc.append(pos_docs[b][\"score\"])\n #extra_features_neg_doc.append(neg_docs[b][\"score\"])\n \n # add all the extra features\n #new_pos_extra_features.append(extra_features_pos_doc)\n #new_neg_extra_features.append(extra_features_neg_doc)\n \n # split by exact matching\n for t_q in query[b]:\n # entry for the query-term\n new_pos_docs[-1].append([])\n new_neg_docs[-1].append([])\n \n for pos_sent in _temp_pos_docs:\n # exact math for the pos_document\n for i,t_pd in enumerate(pos_sent):\n if t_pd==t_q:\n new_pos_docs[-1][-1].append(pos_sent)\n break\n\n for neg_sent in _temp_neg_docs:\n for i,t_nd in enumerate(neg_sent):\n if t_nd==t_q:\n new_neg_docs[-1][-1].append(neg_sent)\n break\n else:\n raise NotImplementedError(\"Missing implmentation for mode \"+str(mode))\n \n if len(new_pos_docs) == len(pos_docs): # if batch is correct\n yield query, new_pos_docs, new_pos_extra_features, new_neg_docs, new_neg_extra_features\n \n \n def test_splitter(data_generator):\n\n for _id, query, docs in data_generator:\n tokenized_queries = []\n for i in range(len(_id)):\n # tokenization\n tokenized_query = tokenizer.texts_to_sequences([query[i]])[0]\n\n if queries_sw is not None:\n tokenized_query = [token for token in tokenized_query if token not in queries_sw] \n \n if save_tokenizer_on_test:\n tokenized_queries.append(tokenized_query)\n else:\n tokenized_queries.append(query[i]) # the tokenization isnt save\n \n \n for doc in docs[i]:\n if isinstance(doc[\"text\"], list):\n continue # cached tokenization\n\n # sentence splitting\n new_docs = []\n if mode==4:\n _temp_new_docs = []\n doc[\"offset\"] = []\n doc[\"untokenized_text\"] = doc[\"text\"]\n for start, end in PunktSentenceTokenizer().span_tokenize(doc[\"text\"]):\n _temp_new_docs.append(doc[\"text\"][start:end])\n\n if start<(len(doc[\"title\"])-1):\n doc[\"offset\"].append([\"title\",(start, end), doc[\"text\"][start:end], []])\n else:\n doc[\"offset\"].append([\"abstract\", (start-len(doc[\"title\"]), end-len(doc[\"title\"])), doc[\"text\"][start:end], []])\n \n tokenized_docs = tokenizer.texts_to_sequences(_temp_new_docs)\n\n if docs_sw is not None:\n for t_doc in tokenized_docs:\n t_doc = [token for token in t_doc if token not in docs_sw]\n\n #doc[\"extra_features\"] = compute_extra_features(tokenized_query, _temp_new_docs, idf_from_id_token)+[doc[\"score\"]]\n\n for k,t_q in enumerate(tokenized_query):\n new_docs.append([])\n for l,_new_doc in enumerate(tokenized_docs):\n for i,t_d in enumerate(_new_doc):\n if t_d==t_q:\n if save_tokenizer_on_test:\n new_docs[-1].append(_new_doc)\n else:\n new_docs[-1].append(_temp_new_docs[l])\n doc[\"offset\"][l][-1].append(k)\n break\n else:\n raise NotImplementedError(\"Missing implmentation for mode \"+str(mode))\n\n doc[\"text\"] = new_docs\n \n \n yield _id, tokenized_queries, docs\n\n return train_splitter, test_splitter","def generate_token_arrays(\n text,\n text_tar,\n text_pred,\n tokenizer=None,\n expand_predictions=True,\n split_true_entities=True,\n ignore_value=None\n):\n # split text for token evaluation\n if isinstance(tokenizer, stanfordnlp.pipeline.core.Pipeline):\n doc = tokenizer(text)\n # extract tokens from the parsed text\n tokens_base = [\n token.text for sentence in doc.sentences\n for token in sentence.tokens\n ]\n elif isinstance(tokenizer, spacy.tokenizer.Tokenizer):\n doc = tokenizer(text)\n # extract tokens from the parsed text\n tokens_base = [token.text for token in doc]\n else:\n if tokenizer is None:\n tokenizer = ''\n # treat string as a regex\n tokens_base = re.findall(tokenizer, text)\n\n tokens = []\n tokens_pred = []\n tokens_true = []\n tokens_start, tokens_length = [], []\n\n n_tokens = 0\n\n start = 0\n for token in tokens_base:\n # sometimes we have empty tokens on their own\n if len(token) == 0:\n continue\n start = text.find(token, start)\n token_true = text_tar[start:start + len(token)]\n token_pred = text_pred[start:start + len(token)]\n\n if all(token_true == -1) & all(token_pred == -1):\n # skip tokens which are not labeled\n start += len(token)\n n_tokens += 1\n continue\n\n if split_true_entities:\n # split the single token into subtokens, based on the true entity\n idxDiff = np.diff(token_true, prepend=0)\n if any(idxDiff > 0):\n # split\n idxDiff = np.diff(token_true, prepend=0)\n subtok_start = 0\n subtoken_true, subtoken_pred = [], []\n for subtok_end in np.where(idxDiff > 0)[0]:\n subtoken_true.append(token_true[subtok_start:subtok_end])\n subtoken_pred.append(token_pred[subtok_start:subtok_end])\n subtok_start = subtok_end\n if subtok_end < len(token_true):\n # add final token\n subtoken_true.append(token_true[subtok_start:])\n subtoken_pred.append(token_pred[subtok_start:])\n else:\n # in this case, there is only 1 label_id for the entire token\n # so we can just wrap in a list for the iterator later\n subtoken_true = [token_true]\n subtoken_pred = [token_pred]\n else:\n # do not split a token if there is more than 1 ground truth\n # consequently, tokens with multiple labels will be treated\n # as equal to the most frequent label\n subtoken_true = [token_true]\n subtoken_pred = [token_pred]\n\n # now iterate through our sub-tokens\n # often this is a length 1 iterator\n for token_true, token_pred in zip(subtoken_true, subtoken_pred):\n if len(token_true) == 0:\n continue\n\n if expand_predictions:\n # expand the most frequent ID to cover the entire token\n token_pred = expand_id_to_token(token_pred, ignore_value=-1)\n token_true = expand_id_to_token(token_true, ignore_value=-1)\n\n # get the length of the token for later\n token_len = len(token_true)\n\n # aggregate IDs for this token into the most frequent value\n if len(token_true) == 0:\n token_true = -1\n else:\n token_true = mode(token_true, ignore_value)\n if len(token_pred) == 0:\n token_pred = -1\n else:\n token_pred = mode(token_pred, ignore_value)\n\n # append the prediction for this token\n tokens_true.append(token_true)\n tokens_pred.append(token_pred)\n tokens.append(text[start:start + token_len])\n tokens_start.append(start)\n tokens_length.append(token_len)\n\n start += token_len\n # keep track of total tokens assessed\n n_tokens += 1\n\n # now we have a list of tokens with preds\n tokens_true = np.asarray(tokens_true, dtype=int)\n tokens_pred = np.asarray(tokens_pred, dtype=int)\n\n return tokens_true, tokens_pred, tokens, tokens_start, tokens_length","def tokenize_and_encode_pandas(dataframe,stopindex=1e9,max_length=128):\n encoded_tweets = []\n token_type_ids = []\n attention_mask = []\n labels_6_types = []\n labels_4_types = []\n interaction_types = []\n \n counter = 0\n for i in range(len(dataframe)):\n try:\n #tokenized_parent= tokenizer.tokenize(dataframe.iloc[i]['clean_target_text'])\n #tokenized_tweet = tokenizer.tokenize(dataframe.iloc[i]['clean_response_text'])\n text_parent= dataframe.iloc[i]['clean_target_text']\n text_tweet = dataframe.iloc[i]['clean_response_text']\n except Exception:\n #tokenized_parent= tokenizer.tokenize(dataframe.iloc[i]['target_text'])\n #tokenized_tweet = tokenizer.tokenize(dataframe.iloc[i]['response_text'])\n text_parent= dataframe.iloc[i]['target_text']\n text_tweet = dataframe.iloc[i]['response_text']\n \n interaction = dataframe.iloc[i]['interaction_type'] # reply or quote\n topic = dataframe.iloc[i]['event'] # get event\n topic = topic.replace('_', ' ') # replace underscore with space\n sep_token = ' [SEP] ' \n text1 = interaction + sep_token + topic + sep_token + text_parent\n text2 = text_tweet\n encoded_dict = tokenizer.__call__(text=text1,\n text_pair=text2,\n padding='max_length',\n truncation=True,\n is_split_into_words=False,\n max_length=max_length,\n return_tensors='pt')\n \n '''\n encoded_dict = tokenizer.__call__(text=tokenized_parent,\n text_pair=tokenized_tweet,\n padding='max_length',\n truncation=True,\n is_split_into_words=True,\n max_length=max_length,\n return_tensors='pt')\n \n '''\n '''\n encoded_dict = tokenizer.encode_plus(text=tokenized_tweet,\n text_pair=tokenized_parent,\n max_length=max_length,\n pad_to_max_length=True)\n '''\n encoded_tweets.append(encoded_dict['input_ids'])\n token_type_ids.append(encoded_dict['token_type_ids'])\n attention_mask.append(encoded_dict['attention_mask'])\n \n label = dataframe.iloc[i]['label']\n labels_6_types.append(convert_label_string2num(label, num_types=6))\n labels_4_types.append(convert_label_string2num(label, num_types=4))\n \n interaction_type = dataframe.iloc[i]['interaction_type']\n interaction_types.append(convert_interaction_type_string2num(interaction_type))\n \n if counter % 100 == 0:\n print('Tokenizing comment: %00000d' % counter)\n if counter > stopindex:\n break\n counter = counter + 1\n \n width = dataframe.shape[1]\n dataframe.insert(width+0,'encoded_tweets', encoded_tweets)\n dataframe.insert(width+1,'token_type_ids', token_type_ids)\n dataframe.insert(width+2,'attention_mask', attention_mask)\n dataframe.insert(width+3,'number_labels_6_types', labels_6_types)\n dataframe.insert(width+4,'number_labels_4_types', labels_4_types)\n dataframe.insert(width+5,'interaction_type_num', interaction_types)\n return dataframe","def batchify(examples, padding_id=0, max_query_len=64, max_passage_len=200):\n batch_size = len(examples)\n query_len = max([len(example[0]) for example in examples])\n query_len = min(query_len, max_query_len)\n query_tokens = np.ones((batch_size, query_len)) * padding_id\n labels = np.zeros(batch_size)\n passages = []\n count = 0\n for i, example in enumerate(examples):\n idx = min(len(example[0]), query_len)\n query_tokens[i, :idx] = example[0][:idx - 1] + [example[0][-1]]\n pos_psg = example[1]\n if pos_psg in passages:\n labels[i] = passages.index(pos_psg)\n else:\n passages.append(pos_psg)\n labels[i] = count\n count += 1\n neg_psgs = example[2]\n for psg in neg_psgs:\n if psg not in passages:\n passages.append(psg)\n count += 1\n passage_len = max([len(psg) for psg in passages])\n passage_len = min(passage_len, max_passage_len)\n passages_tokens = np.ones((count, passage_len)) * padding_id\n for i, psg in enumerate(passages):\n idx = min(len(psg), passage_len)\n passages_tokens[i, :idx] = psg[: idx - 1] + [psg[-1]]\n return torch.tensor(query_tokens, dtype=torch.long), torch.tensor(passages_tokens, dtype=torch.long), torch.tensor(\n labels, dtype=torch.long)","def tokenizer(self):\n tokenizer = RegexpTokenizer(r'\\w+')\n \n self.tweet_tokenized_train = [tokenizer.tokenize(x.lower()) for x in self.tweet_prepro_train]\n self.tweet_tokenized_test = [tokenizer.tokenize(x.lower()) for x in self.tweet_prepro_test]","def test_tokenization():\n X = Tokenizer().transform([[\"A test\"]])\n assert X[\"corpus\"][0] == [\"A\", \"test\"]","def preprocess(self, sequence, word2idx):\n story = [word2idx[word] if word in word2idx else UNK_token for word in sequence.split()]\n story = torch.Tensor(story)\n return story","def forward(self, x_input):\r\n\r\n pad_token_id = self.config.pad_token_id\r\n articles, articles_mask, ops, question_pos = x_input\r\n\r\n bsz = ops.size(0)\r\n ops = ops.reshape(bsz, 1, 5, -1)\r\n\r\n opnum = ops.size(1)\r\n out = self.bert(\r\n articles, attention_mask=articles_mask, output_hidden_states=False\r\n ).last_hidden_state\r\n\r\n question_pos = question_pos.reshape(-1, 1, 1)\r\n question_pos = question_pos.expand(bsz, opnum, out.size(-1))\r\n out = torch.gather(out, 1, question_pos)\r\n\r\n out = self.dropout_layer(out)\r\n out = self.cls(out)\r\n\r\n # convert ops to one hot\r\n out = out.view(bsz, opnum, 1, self.vocab_size)\r\n out[:, :, :, pad_token_id] = 0\r\n out = out.expand(bsz, opnum, 5, self.vocab_size)\r\n out_tokens = torch.zeros((bsz, opnum, 5, 1), device=ops.device)\r\n pad_tokens = ops.shape[3] - torch.sum((ops == pad_token_id), dim=3).unsqueeze(3)\r\n\r\n for i in range(ops.shape[3]):\r\n ops_token = ops[:, :, :, i].unsqueeze(3)\r\n out_tokens += torch.gather(out, 3, ops_token)\r\n\r\n out_tokens = torch.div(out_tokens, pad_tokens)\r\n out = out_tokens\r\n out = out.view(-1, 5)\r\n return out","def read_squad_examples(input_file, return_answers, context_only=False, question_only=False,\n draft=False, draft_num_examples=12, append_title=False):\n with open(input_file, \"r\") as reader:\n input_data = json.load(reader)[\"data\"]\n\n examples = []\n ans_cnt = 0\n no_ans_cnt = 0\n\n # Only word-based tokenization is peformed (whitespace based)\n for doc_idx, entry in enumerate(input_data):\n title = entry['title'][0] if type(entry['title']) == list else entry['title']\n assert type(title) == str\n\n for par_idx, paragraph in enumerate(entry[\"paragraphs\"]):\n # Do not load context for question only\n if not question_only:\n paragraph_text = paragraph[\"context\"]\n title_offset = 0\n if append_title:\n title_str = '[ ' + ' '.join(title.split('_')) + ' ] '\n title_offset += len(title_str)\n paragraph_text = title_str + paragraph_text\n # Note that we use the term 'word' for whitespace based words, and 'token' for subtokens (for BERT input)\n doc_words, char_to_word_offset = context_to_words_and_offset(paragraph_text)\n\n # 1) Context only ends here\n if context_only:\n metadata = {}\n if \"pubmed_id\" in entry:\n entry_keys = [\n \"pubmed_id\", \"sha\", \"title_original\", \"title_entities\",\n \"journal\", \"authors\", \"article_idx\"\n ]\n para_keys = [\"context_entities\"]\n for entry_key in entry_keys:\n if entry_key in entry:\n metadata[entry_key] = entry[entry_key]\n for para_key in para_keys:\n if para_key in paragraph:\n metadata[para_key] = paragraph[para_key]\n # metadata[\"pubmed_id\"] = (metadata[\"pubmed_id\"] if not pd.isnull(metadata[\"pubmed_id\"])\n # else 'NaN')\n example = SquadExample(\n doc_words=doc_words,\n title=title,\n doc_idx=doc_idx,\n par_idx=par_idx,\n metadata=metadata)\n examples.append(example)\n\n if draft and len(examples) == draft_num_examples:\n return examples\n continue\n\n # 2) Question only or 3) context/question pair\n else:\n for qa in paragraph[\"qas\"]:\n qas_id = str(qa[\"id\"])\n question_text = qa[\"question\"]\n\n # Noisy question skipping\n if len(question_text.split(' ')) == 1:\n logger.info('Skipping a single word question: {}'.format(question_text))\n continue\n if \"I couldn't could up with another question.\" in question_text:\n logger.info('Skipping a strange question: {}'.format(question_text))\n continue\n\n start_position = None\n end_position = None\n orig_answer_text = None\n\n # For pre-processing that should return answers together\n if return_answers:\n assert type(qa[\"answers\"]) == dict or type(qa[\"answers\"]) == list, type(qa[\"answers\"])\n if type(qa[\"answers\"]) == dict:\n qa[\"answers\"] = [qa[\"answers\"]]\n\n # No answers\n if len(qa[\"answers\"]) == 0:\n orig_answer_text = \"\"\n start_position = -1 # Word-level no-answer => -1\n end_position = -1\n no_ans_cnt += 1\n # Answer exists\n else:\n answer = qa[\"answers\"][0]\n ans_cnt += 1\n\n orig_answer_text = answer[\"text\"]\n answer_offset = answer[\"answer_start\"] + title_offset\n answer_length = len(orig_answer_text)\n start_position = char_to_word_offset[answer_offset]\n end_position = char_to_word_offset[answer_offset + answer_length - 1]\n\n # Only add answers where the text can be exactly recovered from the context\n actual_text = \" \".join(doc_words[start_position:(end_position + 1)])\n cleaned_answer_text = \" \".join(\n tokenization.whitespace_tokenize(orig_answer_text)) # word based tokenization\n if actual_text.find(cleaned_answer_text) == -1:\n logger.warning(\"Could not find answer: '%s' vs. '%s'\",\n actual_text, cleaned_answer_text)\n continue\n\n # Question only ends here\n if question_only:\n example = SquadExample(\n qas_id=qas_id,\n question_text=question_text)\n\n # Context/question pair ends here\n else:\n example = SquadExample(\n qas_id=qas_id,\n question_text=question_text,\n paragraph_text=paragraph_text,\n doc_words=doc_words,\n orig_answer_text=orig_answer_text,\n start_position=start_position,\n end_position=end_position,\n title=title,\n doc_idx=doc_idx,\n par_idx=par_idx)\n examples.append(example)\n\n if draft and len(examples) == draft_num_examples:\n return examples\n\n # Testing for shuffled draft (should comment out above 'draft' if-else statements)\n if draft:\n random.shuffle(examples)\n logger.info(str(len(examples)) + ' were collected before draft for shuffling')\n return examples[:draft_num_examples]\n\n logger.info('Answer/no-answer stat: %d vs %d'%(ans_cnt, no_ans_cnt))\n return examples","def preprocess_seq(self, sequence, word2idx):\n story = []\n for value in sequence:\n #v = [word2idx[word] if word in word2idx else UNK_token for word in value.split()] + [EOS_token]\n story.append(word2idx[value] if value in word2idx else UNK_token)\n story = torch.Tensor(story)\n return story","def glue_inputs(dataset_name, split, batch_size, tokenizer, data_dir=None,\n max_len=128, training=True):\n keys_lookup = {\n \"glue/cola\": (\"sentence\", None),\n \"glue/sst2\": (\"sentence\", None),\n \"glue/mrpc\": (\"sentence1\", \"sentence2\"),\n \"glue/qqp\": (\"question1\", \"question2\"),\n \"glue/stsb\": (\"sentence1\", \"sentence2\"),\n \"glue/mnli\": (\"premise\", \"hypothesis\"), # TODO(kitaev): swap the two?\n \"glue/qnli\": (\"question\", \"sentence\"), # TODO(kitaev) swap the two?\n \"glue/rte\": (\"sentence1\", \"sentence2\"),\n \"glue/wnli\": (\"sentence1\", \"sentence2\"),\n }\n\n key_a, key_b = keys_lookup[dataset_name]\n\n if key_b is None:\n def preprocess(batch):\n \"\"\"Tokenize and convert text to model inputs.\"\"\"\n batch_size = batch[\"idx\"].shape[0]\n input_ids = np.zeros((batch_size, max_len), dtype=np.int32)\n type_ids = np.zeros((batch_size, max_len), dtype=np.int32)\n\n for i in range(batch_size):\n sentence_a = batch[key_a][i]\n tokens_a = tokenizer.EncodeAsIds(sentence_a)\n input_ids[i, :len(tokens_a)] = tokens_a[:max_len]\n\n return {\n \"input_ids\": input_ids,\n \"type_ids\": type_ids,\n \"idx\": batch[\"idx\"].astype(np.int32),\n \"label\": batch[\"label\"],\n }\n else:\n def preprocess(batch):\n \"\"\"Tokenize and convert text to model inputs.\"\"\"\n batch_size = batch[\"idx\"].shape[0]\n input_ids = np.zeros((batch_size, max_len), dtype=np.int32)\n type_ids = np.zeros((batch_size, max_len), dtype=np.int32)\n\n for i in range(batch_size):\n sentence_a = batch[key_a][i]\n sentence_b = batch[key_b][i]\n tokens_a = tokenizer.EncodeAsIds(sentence_a)\n tokens_b = tokenizer.EncodeAsIds(sentence_b)[1:] # Strip start token\n\n ex_input_ids = (tokens_a + tokens_b)[:max_len]\n ex_type_ids = ([0] * len(tokens_a) + [1] * len(tokens_b))[:max_len]\n\n input_ids[i, :len(ex_input_ids)] = ex_input_ids\n type_ids[i, :len(ex_type_ids)] = ex_type_ids\n\n return {\n \"input_ids\": input_ids,\n \"type_ids\": type_ids,\n \"idx\": batch[\"idx\"].astype(np.int32),\n \"label\": batch[\"label\"],\n }\n\n return tfds_stream(\n dataset_name=dataset_name,\n split=split,\n batch_size=batch_size,\n data_dir=data_dir,\n shuffle_files=training,\n shuffle_buffer_size=1024 if training else None,\n batch_shuffle_size=128 if training else None,\n preprocess_fun=preprocess,\n repeat=training,\n )","def data_process(raw_text_iter: dataset.IterableDataset) -> Tensor:\n data = [torch.tensor(vocab(tokenizer(item)), dtype=torch.long) for item in raw_text_iter]\n return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))","def convert_single_example(example, tokenizer, is_training, args):\n tok_to_orig_index = []\n orig_to_tok_index = []\n all_doc_tokens = [] # all subtokens of original doc after tokenizing\n features = []\n for (i, token) in enumerate(example['paragraph_tokens']):\n orig_to_tok_index.append(len(all_doc_tokens))\n sub_tokens = albert_tokenize(tokenizer, token)\n tok_to_orig_index.extend([i] * len(sub_tokens))\n all_doc_tokens.extend(sub_tokens)\n\n # 特别注意！由于在paragraph_tokens中我们的token已经映射过一次了\n # 这里wordpiece等于又映射了一遍，所以这里的操作是二次映射\n if example['position_map']:\n tok_to_orig_index = [example['position_map'][index] for index in tok_to_orig_index]\n\n # QUERY\n query_tokens = []\n query_tokens.append(\"[Q]\")\n query_tokens.extend(albert_tokenize(tokenizer, example['question_text']))\n if len(query_tokens) > args.max_query_length:\n query_tokens = query_tokens[-args.max_query_length:]\n\n # ANSWER 预处理的时候先长短分开\n tok_start_position = -1\n tok_end_position = -1\n # 这里终点是必然在para_tokens内的\n if is_training:\n # 现阶段，有短答案预测短答案，否则预测长答案\n if example['answer_type'] != AnswerType['UNKNOWN']:\n tok_long_start_position = orig_to_tok_index[example['long_start']]\n if example['long_end'] == len(orig_to_tok_index):\n tok_long_end_position = orig_to_tok_index[-1]\n else:\n tok_long_end_position = orig_to_tok_index[example['long_end']] - 1\n tok_start_position = tok_long_start_position\n tok_end_position = tok_long_end_position\n if example['answer_type'] == AnswerType['SHORT']:\n tok_short_start_position = orig_to_tok_index[example['short_start']]\n if example['short_end'] == len(orig_to_tok_index):\n tok_short_end_position = orig_to_tok_index[-1]\n else:\n tok_short_end_position = orig_to_tok_index[example['short_end']] - 1\n tok_start_position = tok_short_start_position\n tok_end_position = tok_short_end_position\n\n # Get max tokens number for original doc,\n # should minus query tokens number and 3 special tokens\n # The -3 accounts for [CLS], [SEP] and [SEP]\n max_tokens_for_doc = args.max_seq_length - len(query_tokens) - 3\n\n # We can have documents that are longer than the maximum sequence length.\n # To deal with this we do a sliding window approach, where we take chunks\n # of up to our max length with a stride of `doc_stride`.\n _DocSpan = collections.namedtuple(\"DocSpan\", [\"start\", \"length\"])\n doc_spans = []\n start_offset = 0\n while start_offset < len(all_doc_tokens):\n length = len(all_doc_tokens) - start_offset # compute number of tokens remaining unsliding\n length = min(length, max_tokens_for_doc) # determine current sliding window size\n doc_spans.append(_DocSpan(start=start_offset, length=length))\n\n # Consider case for reaching end of original doc\n if start_offset + length == len(all_doc_tokens):\n break\n start_offset += min(length, args.doc_stride)\n\n # Convert window + query + special tokens to feature\n for (doc_span_index, doc_span) in enumerate(doc_spans):\n tokens = []\n token_to_orig_map = {}\n token_is_max_context = {}\n segment_ids = []\n tokens.append(\"[CLS]\")\n segment_ids.append(0)\n tokens.extend(query_tokens)\n segment_ids.extend([0] * len(query_tokens))\n tokens.append(\"[SEP]\")\n segment_ids.append(0)\n\n for i in range(doc_span.length):\n split_token_index = doc_span.start + i\n token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\n\n is_max_context = check_is_max_context(doc_spans, doc_span_index, split_token_index)\n token_is_max_context[len(tokens)] = is_max_context\n tokens.append(all_doc_tokens[split_token_index])\n segment_ids.append(1)\n tokens.append(\"[SEP]\")\n segment_ids.append(1)\n assert len(tokens) == len(segment_ids)\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding = [0] * (args.max_seq_length - len(input_ids))\n input_ids.extend(padding)\n input_mask.extend(padding)\n segment_ids.extend(padding)\n\n assert len(input_ids) == args.max_seq_length\n assert len(input_mask) == args.max_seq_length\n assert len(segment_ids) == args.max_seq_length\n\n start_position = None\n end_position = None\n answer_type = None\n answer_text = \"\"\n if is_training:\n doc_start = doc_span.start\n doc_end = doc_span.start + doc_span.length - 1\n # For training, if our document chunk does not contain an annotation\n # we throw it out, since there is nothing to predict.\n contains_an_annotation = (tok_start_position >= doc_start and tok_end_position <= doc_end)\n # 负样本需要经过采样，且目标为[CLS]\n if (not contains_an_annotation) or example['answer_type'] == AnswerType['UNKNOWN']:\n if args.include_unknowns < 0 or random.random() > args.include_unknowns:\n continue\n start_position = 0\n end_position = 0\n answer_type = AnswerType['UNKNOWN']\n else:\n doc_offset = len(query_tokens) + 2\n start_position = tok_start_position - doc_start + doc_offset\n end_position = tok_end_position - doc_start + doc_offset\n answer_type = example['answer_type']\n\n # 如果是短答案，对一下答案是否正确\n if example['answer_type'] == AnswerType['SHORT']:\n answer_text = \" \".join(tokens[start_position:(end_position + 1)])\n answer_text = answer_text.replace(' ', '').replace(u\"▁\", ' ').strip()\n gt_answer = example['short_answer_text'].lower()\n answer_text_chars = [c for c in answer_text if c not in \" \\t\\r\\n\" and ord(c) != 0x202F]\n gt_answer_chars = [c for c in gt_answer if c not in \" \\t\\r\\n\" and ord(c) != 0x202F]\n if \"\".join(answer_text_chars) != \"\".join(gt_answer_chars):\n print(answer_text, 'V.S.', gt_answer)\n\n feature = InputFeatures(\n unique_id=None,\n example_index=None,\n doc_span_index=doc_span_index,\n tokens=tokens,\n token_to_orig_map=token_to_orig_map,\n token_is_max_context=token_is_max_context,\n input_ids=input_ids,\n input_mask=input_mask,\n segment_ids=segment_ids,\n start_position=start_position,\n end_position=end_position,\n answer_text=answer_text,\n answer_type=answer_type)\n\n features.append(feature)\n\n return features","def preprocess_batch(self, context, question, answer, mask):\n # make input and question fixed size\n new_input = np.zeros([self.config.batch_size,\n self.dataset.max_input_size,\n self.config.word_vector_length]) # zero padding\n new_question = np.zeros([self.config.batch_size,\n self.dataset.max_question_size,\n self.config.word_vector_length])\n new_mask = np.zeros([self.config.batch_size, self.dataset.max_input_size])\n new_labels = []\n\n for n in np.arange(self.config.batch_size):\n for row in np.arange(context[n].shape):\n for col in np.arange():\n new_input[n][row][col] = context[n][row][col]\n\n for row in np.arange(question[n].shape):\n for col in np.arange():\n new_question[n][row][col] = question[n][row][col]\n\n for row in np.arange(mask[n].shape):\n new_mask[n][row] = mask[n][row]\n\n return [new_input, new_question], new_labels, new_mask","def convert_examples_to_features(\n examples: List[InputExample],\n label_list: List[str],\n max_length: int,\n tokenizer: PreTrainedTokenizer,\n pad_token_segment_id=0,\n pad_on_left=False,\n pad_token=0,\n mask_padding_with_zero=True,\n) -> List[InputFeatures]:\n\n label_map = {label: i for i, label in enumerate(label_list)}\n\n features = []\n for (ex_index, example) in tqdm.tqdm(enumerate(examples), desc=\"convert examples to features\"):\n if ex_index % 10000 == 0:\n logger.info(\"Writing example %d of %d\" % (ex_index, len(examples)))\n choices_features = []\n for ending_idx, (context, ending) in enumerate(zip(example.contexts, example.endings)):\n text_a = context\n if example.question.find(\"_\") != -1:\n # this is for cloze question\n text_b = example.question.replace(\"_\", ending)\n else:\n text_b = example.question + \" \" + ending\n if len(text_a) == 0:\n logger.info(\"context of example %d have length 0\" % (ex_index))\n text_a = \" \"\n\n inputs = tokenizer.encode_plus(text_a, text_b, add_special_tokens=True, max_length=max_length,)\n if \"num_truncated_tokens\" in inputs and inputs[\"num_truncated_tokens\"] > 0:\n logger.info(\n \"Attention! you are cropping tokens (swag task is ok). \"\n \"If you are training ARC and RACE and you are poping question + options,\"\n \"you need to try to use a bigger max seq length!\"\n )\n\n input_ids, token_type_ids = inputs[\"input_ids\"], inputs[\"token_type_ids\"]\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding_length = max_length - len(input_ids)\n if pad_on_left:\n input_ids = ([pad_token] * padding_length) + input_ids\n attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask\n token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids\n else:\n input_ids = input_ids + ([pad_token] * padding_length)\n attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)\n token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)\n\n assert len(input_ids) == max_length\n assert len(attention_mask) == max_length\n assert len(token_type_ids) == max_length\n choices_features.append((input_ids, attention_mask, token_type_ids))\n\n label = label_map[example.label]\n\n if ex_index < 2:\n logger.info(\"*** Example ***\")\n logger.info(\"race_id: {}\".format(example.example_id))\n for choice_idx, (input_ids, attention_mask, token_type_ids) in enumerate(choices_features):\n logger.info(\"choice: {}\".format(choice_idx))\n logger.info(\"input_ids: {}\".format(\" \".join(map(str, input_ids))))\n logger.info(\"attention_mask: {}\".format(\" \".join(map(str, attention_mask))))\n logger.info(\"token_type_ids: {}\".format(\" \".join(map(str, token_type_ids))))\n logger.info(\"label: {}\".format(label))\n\n features.append(InputFeatures(example_id=example.example_id, choices_features=choices_features, label=label,))\n\n return features","def tokenize(self):\n\n self.feats = {\n 'features': [], # Lists of the `InputFeatures` objects.\n 'segments': [], # Segments of the phrase. 0: Promoun, 1: A-term, 2: B-term \n 'df_ids': [], # DataFrame index.\n 'target_token_ids': [] # Indexes of the target term in the tokens lists.\n }\n unique_id = 0 # Unique ID of the dataset.\n for _, row in tqdm(self.df.iterrows()):\n segment_tokens = self.tokenize_single_row(row)\n for j, segment in enumerate(segment_tokens):\n if segment['target_token_index'] > 0:\n features = self.tokens_to_features(unique_id, segment['tokens'])\n unique_id += 1\n self.feats['features'].append(features)\n self.feats['segments'].append(j)\n self.feats['target_token_ids'].append(segment['target_token_index'] )\n self.feats['df_ids'].append(row.ID)","def generate_question(self, num_question = 10):\n\t\t\n\t\tquestions = []\n\t\tfor q in range(num_question):\n\t\t\tfor r in range(2):\n\t\t\t\tquestion = np.zeros(self.question_dim, dtype = np.float32)\n\t\t\t\tcolor = np.random.randint(len(COLOR))\n\t\t\t\tquestion[color] = 1.0\n\t\t\t\tquestion[6 + r] = 1.0\n\t\t\t\tquestion_label = np.random.randint(3)\n\t\t\t\tquestion[8 + question_label] = 1.0\n\t\t\t\tquestions.append(question)\n\t\treturn questions","def batch_tokenize_fn(examples):\n sources = examples[config.source_lang]\n targets = examples[config.target_lang]\n model_inputs = config.tokenizer(sources, max_length=config.max_source_length, truncation=True)\n\n # setup the tokenizer for targets,\n # huggingface expects the target tokenized ids to be stored in the labels field\n with config.tokenizer.as_target_tokenizer():\n labels = config.tokenizer(targets, max_length=config.max_target_length, truncation=True)\n\n model_inputs[\"labels\"] = labels[\"input_ids\"]\n return model_inputs","def prepareData(self):\n\t\tprint ('')\n\t\tfrom keras.preprocessing.sequence import pad_sequences\n\t\tfrom sklearn.model_selection import train_test_split\n\t\tfrom keras.utils import to_categorical\n\t\timport numpy as np\n\n\t\tfrom sklearn.preprocessing import LabelBinarizer, LabelEncoder\n\n\t\tX_snt = [[self.word2idx[w] if w in self.word2idx else self.word2idx[self.word_unk_token] for w in s] for s in self.x_document]\n\t\ty_tag = [[self.tag2idx[t]] for t in self.y_document]\n\n\t\tX_snt = pad_sequences(maxlen=self.parameters['max_doc_len'], sequences=X_snt, padding='post', value=self.word2idx[self.word_pad_token])\n\t\ty_tag = to_categorical(y_tag, self.tags_len)\n\n\t\tprint (\"\\tRandom:\\t\", self.random)\n\t\tprint (\"\\tTest size:\\t\", self.split_train_test)\n\n\t\tself.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X_snt, y_tag, test_size=self.split_train_test, random_state=self.random)\n\n\t\tself.X_train = np.array(self.X_train)\n\t\tself.X_test = np.array(self.X_test)\n\t\tself.y_train = np.array(self.y_train)\n\t\tself.y_test = np.array(self.y_test)\n\n\t\tprint ('\\n\\tWords: {}\\t{}'.format(self.X_train.shape, self.X_test.shape) )\n\t\tprint ('\\tTags: {}\\t{}\\n'.format(self.y_train.shape, self.y_test.shape))","def train(self, corpus):\n self.tokens = []\n self.tags = []\n sentences = corpus.split(NEW_LINE)\n for sentence in sentences:\n start = START_SIGHT + SLASH + START_SIGHT + SPACE + START_SIGHT + SLASH + START_SIGHT + SPACE\n end = SPACE + END + SLASH + END\n sentence = start + sentence + end \n tokens = sentence.split(SPACE)\n for t in tokens:\n token = t.rsplit(SLASH, 1)\n if (len(token) > 1):\n self.tokens.append(token) \n self.tags.append(token[TAG_INDEX])\n \n nonsense_cases = set([(END, START_SIGHT), (START_SIGHT, END),\n (START_SIGHT, START_SIGHT, END),\n (END, START_SIGHT, START_SIGHT)])\n self.bigram_tags = [b for b in zip(self.tags[:-1], self.tags[1:]) if b not in nonsense_cases]\n self.trigram_tags = [t for t in zip(self.tags[:-1], self.tags[1:], self.tags[2:])\\\n if not (t[WORD_INDEX], t[TAG_INDEX]) in nonsense_cases and\\\n not (t[WORD_INDEX], t[TAG_INDEX]) in nonsense_cases]","def example_to_token_ids_segment_ids_label_ids(\n ex_index,\n example,\n max_seq_length,\n max_evid_length,\n tokenizer):\n if ex_index < 5:\n print('*** Example {} ***'.format(ex_index))\n print('qid: {}'.format(example.qid))\n\n qid = example.qid\n\n # ante evidence\n ante_evidence = example.ante_evidence # list of strings.\n ante_token_ids, ante_segment_ids = [], []\n for ante_sent in ante_evidence:\n ante_sent_tokens = tokenizer.tokenize(ante_sent)\n ante_sent_tokens = ante_sent_tokens[:max_evid_length-2]\n sent_tokens, sent_segment_ids = [], []\n sent_tokens.append(\"<s>\")\n sent_segment_ids.append(0)\n sent_tokens += ante_sent_tokens + [\"</s>\"]\n sent_segment_ids += [0] * len(ante_sent_tokens) + [0]\n\n assert len(sent_tokens) <= max_evid_length\n sent_tokens_ids = tokenizer.convert_tokens_to_ids(sent_tokens)\n\n assert len(sent_tokens_ids) == len(sent_segment_ids)\n ante_token_ids.append(sent_tokens_ids)\n ante_segment_ids.append(sent_segment_ids)\n assert len(ante_evidence) == len(ante_token_ids)\n assert len(ante_evidence) == len(ante_segment_ids)\n l_ante = min(len(ante_evidence), max_evid_length)\n\n # cons evidence\n cons_evidence = example.cons_evidence # list of strings.\n cons_token_ids, cons_segment_ids = [], []\n for cons_sent in cons_evidence:\n cons_sent_tokens = tokenizer.tokenize(cons_sent)\n cons_sent_tokens = cons_sent_tokens[:max_evid_length-2]\n sent_tokens, sent_segment_ids = [], []\n sent_tokens.append(\"<s>\")\n sent_segment_ids.append(0)\n sent_tokens += cons_sent_tokens + [\"</s>\"]\n sent_segment_ids += [0] * len(cons_sent_tokens) + [0]\n\n assert len(sent_tokens) <= max_evid_length\n sent_tokens_ids = tokenizer.convert_tokens_to_ids(sent_tokens)\n\n assert len(sent_tokens_ids) == len(sent_segment_ids)\n cons_token_ids.append(sent_tokens_ids)\n cons_segment_ids.append(sent_segment_ids)\n assert len(cons_evidence) == len(cons_token_ids)\n assert len(cons_evidence) == len(cons_segment_ids)\n l_cons = min(len(cons_evidence), max_evid_length)\n\n para_tokens = tokenizer.tokenize(example.para)\n question_tokens = tokenizer.tokenize(example.question)\n answers_tokens = map(tokenizer.tokenize, example.answers)\n\n token_ids = []\n segment_ids = []\n for choice_idx, answer_tokens in enumerate(answers_tokens):\n\n truncated_para_tokens, \\\n truncated_question_tokens,\\\n truncated_answer_tokens = _truncate_seq_pair(para_tokens, question_tokens, answer_tokens, max_seq_length)\n\n choice_tokens = []\n choice_segment_ids = []\n choice_tokens.append(\"<s>\")\n choice_segment_ids.append(0)\n choice_tokens += truncated_para_tokens + [\"</s>\"]\n choice_segment_ids += [0] * len(truncated_para_tokens) + [0]\n choice_tokens += truncated_question_tokens + [\"</s>\"]\n choice_segment_ids += [1] * len(truncated_question_tokens) + [1]\n choice_tokens += truncated_answer_tokens\n choice_segment_ids += [1] * len(truncated_answer_tokens)\n\n choice_token_ids = tokenizer.convert_tokens_to_ids(choice_tokens)\n\n token_ids.append(choice_token_ids)\n segment_ids.append(choice_segment_ids)\n\n label_ids = [example.label]\n\n if ex_index < 5:\n print('label: {} (id = {:d})'.format(example.label, label_ids[0]))\n\n return ante_token_ids, ante_segment_ids, l_ante, \\\n cons_token_ids, cons_segment_ids, l_cons, \\\n token_ids, segment_ids, label_ids, qid","def preprocess_query(self, input_ids, prefix):\n\n input_strings = self.generator_tokenizer.batch_decode(input_ids, skip_special_tokens=False)\n\n # handle prefix for T5\n if isinstance(self.generator_tokenizer, T5Tokenizer):\n for i, s in enumerate(input_strings):\n if not s.startswith(prefix):\n logger.warning(\"T5 prefix mismatch in {}\".format(s))\n if len(input_strings[i]) <= len(prefix):\n input_strings[i] = \"\"\n else:\n input_strings[i] = input_strings[i][len(prefix) :]\n\n retriever_inputs = self.question_encoder_tokenizer.batch_encode_plus(\n input_strings,\n return_tensors=\"pt\",\n padding=True,\n truncation=True,\n )\n\n return retriever_inputs[\"input_ids\"].to(input_ids.device), input_strings","def ends_with_question_word(dataset, idx):\n # HACK: For now (speedy POC), just use the BERT tokens\n # Eventually, we'd like to have access to the raw text (e.g., via the Spacy\n # tokenization) and have the two sentences separated for pair tasks\n\n # spacy_sentences = dataset.spacy_tokens[idx]\n # for spacy_sentence in spacy_sentences:\n # if any(token.text in question_words for token in spacy_sentence):\n # return True\n\n bert_ints = dataset.bert_tokens[idx]\n bert_tokens = dataset.bert_tokenizer.convert_ids_to_tokens(bert_ints)\n return any(token in question_words for token in bert_tokens[-3:])\n # match = any(token in question_words for token in bert_tokens[-3:])\n # if match:\n # print(bert_tokens)\n # return match","def __init__(self, max_size=None, lower=True, unk_token=True, remove_stopwords=False, specials=('<pad>',)):\n self._max_size = max_size\n self._lower = lower\n self._unk = unk_token\n self.token2id = {token: i for i, token in enumerate(specials)}\n self._id2token = list(specials)\n self._token_count = Counter()\n self.can_remove_stopwords=remove_stopwords\n self.num_docs = 0\n self.num_pos = 0\n self.num_nnz = 0\n self.corpus=None","def sents_to_tokens(sents, wordset):\n padded_sentences = ([\"<s>\", \"<s>\"] + s + [\"</s>\"] for s in sents)\n # This will canonicalize words, and replace anything not in vocab with <unk>\n return np.array([utils.canonicalize_word(w, wordset=wordset) \n for w in utils.flatten(padded_sentences)], dtype=object)","def TransformerTokenEmbedding(\n num_embeddings, embedding_dim, padding_idx, freeze_embed=False\n):\n m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)\n nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)\n nn.init.constant_(m.weight[padding_idx], 0)\n if freeze_embed:\n m.weight.requires_grad = False\n return m","def fit(self, documents):\n # Get a list of all the unique tokens that appear\n vocab = list({\n token for doc in documents\n for token in self.tokenizer(doc)\n if token not in self._word2index\n })\n\n # This is UNK, START, END, and PAD.\n nb_special_tokens = 4\n\n # First, we map token -> ID, leaving the first slots for special tokens\n self._word2index.update({\n word: idx\n for idx, word in enumerate(vocab, nb_special_tokens)\n })\n\n # Next, we invert this map, which we can do since it was built from\n # unique vocabulary elements and is by definition bijective.\n self._index2word.update({\n idx: word\n for word, idx in self._word2index.items()\n })\n\n return self","def preprocess_dataset(tokenizer, dataset, vocab=None, max_seq_length=384, doc_stride=128,\n max_query_length=64, num_workers=16, load_from_pickle=False,\n feature_file=None, is_training=True):\n vocab = tokenizer.vocab if vocab is None else vocab\n trans = partial(convert_examples_to_features, tokenizer=tokenizer, cls_token=vocab.cls_token,\n sep_token=vocab.sep_token, vocab=vocab, max_seq_length=max_seq_length,\n doc_stride=doc_stride, max_query_length=max_query_length)\n pool = mp.Pool(num_workers)\n start = time.time()\n if not load_from_pickle:\n example_trans = partial(convert_squad_examples, is_training=is_training)\n # convert the raw dataset into raw features\n examples = pool.map(example_trans, dataset)\n raw_features = list(map(trans, examples)) #pool.map(trans, examples)\n if feature_file:\n with open(feature_file, 'wb') as file:\n pickle.dump(raw_features, file)\n else:\n assert feature_file, 'feature file should be provided.'\n with open(feature_file, 'rb') as file:\n raw_features = pickle.load(file)\n\n end = time.time()\n pool.close()\n log.info('Done! Transform dataset costs %.2f seconds.', (end - start))\n return raw_features","def prepare_for_supervised(embeddings):\n \n X, y = [], []\n for problem in embeddings:\n pairs = [np.concatenate((problem['question'], problem[i])) \n for i in ('choice_0', 'choice_1', 'choice_2', 'choice_3')]\n correct_answer = problem['correct_answer']\n \n labels = [0, 0, 0, 0]\n labels[correct_answer] = 1\n X.extend(pairs)\n y.extend(labels)\n return X, y","def show_tokenization(tokenizer, dataset, i):\n\n print(f'\\n--- Tokenization of document {i} --------------------------------------------')\n print( dataset[i] )\n print()\n print( tokenizer.texts_to_sequences([dataset[i]])[0] )\n print()\n print( [tokenizer.index_word[i] for i in tokenizer.texts_to_sequences([dataset[i]])[0]] )\n print(f'-----------------------------------------------------------------------------')","def mutual_data_loader(data_dir, tokenizer, max_doc_len, max_query_len, max_option_len, is_training):\n def is_whitespace(c):\n if c == \" \" or c == \"\\t\" or c == \"\\r\" or c == \"\\n\" or ord(c) == 0x202F:\n return True\n return False\n\n if is_training:\n subset_list = ['train']\n else:\n subset_list = ['dev', 'test']\n\n examples, features = {}, {}\n for subset in subset_list:\n level_example_dict = {\"high\": None, \"middle\": None}\n level_features_dict = {\"high\": None, \"middle\": None}\n\n for level in ['high', 'middle']:\n subset_dir = os.path.join(data_dir, subset)\n file_list = os.listdir(subset_dir)\n file_list = [file for file in file_list if file.endswith('txt')]\n file_list = sorted(file_list)\n\n alldata = []\n for file in file_list:\n data = json.load(open(os.path.join(subset_dir, file)))\n alldata.append(data)\n \n examples_list = []\n for data in alldata:\n doc_id = data['id']\n doc = data[\"article\"].replace('\\\\n', '\\n')\n doc_token = []\n prev_is_whitespace = True\n for c in doc:\n if is_whitespace(c):\n prev_is_whitespace = True\n else:\n if prev_is_whitespace:\n doc_token.append(c)\n else:\n doc_token[-1] += c\n prev_is_whitespace = False\n\n for i, answer in enumerate(data[\"answers\"]):\n if answer == \" \":\n answer = \"C\"\n example = InputExample(\n guid=doc_id + '-%d' % i,\n doc_token=doc_token,\n question_text=\"\",\n options=data[\"options\"],\n answer=answer)\n examples_list.append(example)\n\n level_example_dict[level] = examples_list\n level_features_dict[level] = convert_examples_to_features(\n level_example_dict[level], tokenizer, max_doc_len, max_query_len, max_option_len)\n \n print(\"MuTuaL Data Loader....\")\n print(level_example_dict[level][-1])\n print(level_features_dict[level][-1])\n examples[subset] = level_example_dict\n features[subset] = level_features_dict\n\n return examples, features","def makedata():\n # train\n print('Clean Train Dataset and separate questions')\n df = pd.read_csv(TRAIN_DATASET).replace(np.nan, ' ')\n t = df.shape[0] * 2\n print t\n df['question1'] = cleanText(df['question1'])\n df['question2'] = cleanText(df['question2'])\n\n df.to_csv(os.path.join(rootpathdata_cleaned, 'train.csx'), index=False)\n overallquestions = df['question1'].tolist() + df['question2'].tolist()\n tpm = pd.DataFrame()\n tpm['question'] = overallquestions\n tpm.to_csv(os.path.join(rootpathdata_cleaned, 'train_allquestions.csx'), index=False)\n # test\n\n print('Clean Test Dataset and separate questions')\n df = pd.read_csv(TEST_DATASET).fillna(' ')\n t1 = df.shape[0] * 2\n df['question1'] = cleanText(df['question1'])\n df['question2'] = cleanText(df['question2'])\n df.to_csv(os.path.join(rootpathdata_cleaned, 'test.csx'), index=False)\n\n overallquestions += df['question1'].tolist() + df['question2'].tolist()\n tpm = pd.DataFrame()\n tpm['question'] = overallquestions\n tpm.to_csv(os.path.join(rootpathdata_cleaned, 'test_allquestions.csx'), index=False)\n print len(overallquestions), t1 + t","def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):\n if token_ids_1 is None:\n return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]\n _cls = [self.cls_token_id]\n _sep = [self.sep_token_id]\n return _cls + token_ids_0 + _sep + _sep + token_ids_1 + _sep","def setup_special_ids(tokenizer):\n FLAGS.vocab_size = tokenizer.get_vocab_size()\n tf.logging.info(\"Set vocab_size: %d.\", FLAGS.vocab_size)\n for sym, sym_id_str in special_symbols_mapping.items():\n try:\n sym_id = tokenizer.get_token_id(sym)\n setattr(FLAGS, sym_id_str, sym_id)\n tf.logging.info(\"Set %s to %d.\", sym_id_str, sym_id)\n except KeyError:\n tf.logging.warning(\"Skip %s: not found in tokenizer's vocab.\", sym)","def tokenized(self, text):\n return self.tokenizer.encode_plus(text,\n max_length=512,\n pad_to_max_length=True,\n truncation=True)[\"input_ids\"]","def tokenize(essays, essay_len=500):\n # normalise scores into range of [0-1]\n normalised_scores = normaliseScores(essays)\n logging.info(\"Begin tokenization of essays...\")\n # Begin the timer \n t = time()\n # Fit_on_texts method creates the vocabulary index based on word frequency\n nltk_tokenizer.fit_on_texts(essays['essay'])\n logging.info(\"Tokenization Done!\")\n logging.info(\"Time taken to tokenize essays: {} seconds \".format(round(time() - t), 2))\n logging.info(\"Tokenization Summary: \")\n logging.info(\"Number of unique words found: {}\".format(len(nltk_tokenizer.word_index)))\n logging.info(\"Number of essays used for tokenisation: {}\".format(nltk_tokenizer.document_count)) \n # Transform each essay into a sequence of integers by converting each word to their unique integer asasigned in word_index\n sequence_vectors = nltk_tokenizer.texts_to_sequences(essays['essay'])\n print(sequence_vectors[0])\n # Pad the vectors so that all of them is essay_len long\n data = pad_sequences(sequence_vectors, maxlen=essay_len)\n label = normalised_scores\n # Reshape the label tensor to (num_essays, 1)\n label = np.reshape(label, (len(label), 1))\n logging.info('Shape of data tensor: {}'.format(data.shape))\n logging.info('Shape of label tensor: {}'.format(label.shape))\n return data, label"],"string":"[\n \"def preprocess_dataset(dataset, tokenizer):\\n eos = torch.tensor([tokenizer.eos_token_id], dtype=torch.long)\\n q_start = torch.tensor(tokenizer.encode('question:'), dtype=torch.long)\\n q_end = torch.tensor(tokenizer.encode(':question'), dtype=torch.long)\\n\\n tensors = [[] for i in range(3)]\\n for i in trange(len(dataset)):\\n example = dataset[i]\\n\\n context_start_idx = (example[2] == 1).nonzero()[0].item()\\n if example[1][-1] == 1:\\n context_end_idx = len(example[1]) - 1\\n else:\\n context_end_idx = (example[1] == 0).nonzero()[0].item()\\n ans_start = example[3] - context_start_idx\\n ans_end = example[4] - context_start_idx\\n\\n context = example[0][context_start_idx: context_end_idx]\\n question = example[0][: context_start_idx]\\n answer = example[0][example[3]: example[4] + 1]\\n\\n input_ids = torch.cat([\\n context,\\n eos,\\n answer,\\n eos,\\n q_start,\\n question,\\n q_end,\\n eos\\n ])\\n\\n attention_mask = torch.ones_like(input_ids, dtype=torch.long)\\n token_type_ids = torch.cat([\\n torch.zeros(len(context) + 1, dtype=torch.long),\\n torch.ones(len(answer) + 1, dtype=torch.long),\\n 2 * torch.ones(len(question) + 3, dtype=torch.long)\\n ])\\n token_type_ids[ans_start: ans_end + 1] = 1\\n\\n tensors[0].append(input_ids)\\n tensors[1].append(attention_mask)\\n tensors[2].append(token_type_ids)\\n\\n tensors_padded = []\\n for i, sequences in enumerate(tqdm(tensors)):\\n padded = pad_sequence(sequences, batch_first=True)\\n tensors_padded.append(padded)\\n\\n new_dataset = TensorDataset(*tensors_padded)\\n return new_dataset\",\n \"def tokenizeQuestions(question_samples, labels):\\n print(\\\"Initializing tokenizer\\\")\\n training_samples = 1000\\n validatioin_samples = 10000\\n tokenizer = Tokenizer(num_words=MAX_WORDS) # High due to obscure nouns in questions\\n tokenizer.fit_on_texts(question_samples)\\n\\n sequences = tokenizer.texts_to_sequences(question_samples)\\n print('Found {0} unique tokens.'.format(len(tokenizer.word_index)))\\n \\n data = pad_sequences(sequences, maxlen=MAXLEN)\\n labels = np.asarray(labels)\\n \\n x_train = data[:training_samples]\\n y_train = labels[:training_samples]\\n x_val = data[training_samples: training_samples + validatioin_samples]\\n y_val = labels[training_samples: training_samples + validatioin_samples]\\n\\n print('Shape of data tensor: {0}'.format(data.shape))\\n print('Shape of label tensor: {0}'.format(labels.shape))\\n \\n return (tokenizer, (x_train, y_train), (x_val, y_val))\",\n \"def tokenize(self, max_length=12):\\n for entry in self.data:\\n tokens = self.dictionary.tokenize(entry['question'], False)\\n tokens = tokens[:max_length]\\n if len(tokens) < max_length:\\n # Note here we pad in front of the sentence\\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\\n tokens = tokens + padding\\n assert len(tokens) == max_length\\n entry['q_token'] = tokens\",\n \"def tokenize(self, max_length=14):\\n for entry in self.entries:\\n tokens = self.dictionary.tokenize(entry['question'], False)\\n tokens = tokens[:max_length]\\n if len(tokens) < max_length:\\n # Note here we pad in front of the sentence\\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\\n tokens = padding + tokens\\n utils.assert_eq(len(tokens), max_length)\\n entry['q_token'] = tokens\",\n \"def tokenize(self, max_length=14):\\n for entry in self.entries:\\n tokens = self.dictionary.tokenize(entry['question'], False)\\n tokens = tokens[:max_length]\\n if len(tokens) < max_length:\\n # Note here we pad in front of the sentence\\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\\n tokens = padding + tokens\\n utils.assert_eq(len(tokens), max_length)\\n entry['q_token'] = tokens\",\n \"def tokenize(self, max_length=14):\\n for entry in self.entries:\\n tokens = self.dictionary.tokenize(entry['question'], False)\\n tokens = tokens[:max_length]\\n if len(tokens) < max_length:\\n # Note here we pad in front of the sentence\\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\\n tokens = padding + tokens\\n utils.assert_eq(len(tokens), max_length)\\n entry['q_token'] = tokens\",\n \"def tokenize(self, max_length=14):\\n for entry in self.entries:\\n tokens = self.dictionary.tokenize(entry['question'], False)\\n tokens = tokens[:max_length]\\n if len(tokens) < max_length:\\n # Note here we pad in front of the sentence\\n padding = [self.dictionary.padding_idx] * (max_length - len(tokens))\\n tokens = padding + tokens\\n utils.assert_eq(len(tokens), max_length)\\n entry['q_token'] = tokens\",\n \"def convert_questions_to_features(examples, tokenizer, max_query_length=None):\\n\\n unique_id = 1000000000\\n question_features = []\\n\\n for (example_index, example) in enumerate(tqdm(examples, desc='Converting questions')):\\n\\n query_tokens = tokenizer.tokenize(example.question_text)\\n if max_query_length is None:\\n max_query_length = len(query_tokens)\\n if len(query_tokens) > max_query_length:\\n query_tokens = query_tokens[0:max_query_length]\\n\\n for _ in enumerate(range(1)):\\n tokens_ = []\\n tokens_.append(\\\"[CLS]\\\")\\n for token in query_tokens:\\n tokens_.append(token)\\n tokens_.append(\\\"[SEP]\\\")\\n\\n input_ids_ = tokenizer.convert_tokens_to_ids(tokens_)\\n\\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\\n # tokens are attended to.\\n input_mask_ = [1] * len(input_ids_)\\n\\n # Zero-pad up to the sequence length.\\n while len(input_ids_) < max_query_length + 2:\\n input_ids_.append(0)\\n input_mask_.append(0)\\n\\n assert len(input_ids_) == max_query_length + 2\\n assert len(input_mask_) == max_query_length + 2\\n\\n if example_index < 1:\\n # logger.info(\\\"*** Example ***\\\")\\n # logger.info(\\\"unique_id: %s\\\" % (unique_id))\\n # logger.info(\\\"example_index: %s\\\" % (example_index))\\n logger.info(\\\"tokens: %s\\\" % \\\" \\\".join(\\n [tokenization.printable_text(x) for x in query_tokens]))\\n # logger.info(\\\"input_ids: %s\\\" % \\\" \\\".join([str(x) for x in input_ids_]))\\n # logger.info(\\n # \\\"input_mask: %s\\\" % \\\" \\\".join([str(x) for x in input_mask_]))\\n\\n question_features.append(\\n QuestionFeatures(\\n unique_id=unique_id,\\n example_index=example_index,\\n tokens_=tokens_,\\n input_ids=input_ids_,\\n input_mask=input_mask_))\\n unique_id += 1\\n\\n return question_features\",\n \"def tokenize(question, max_length=12):\\n tokens = d.tokenize(question, False)\\n tokens = tokens[:max_length]\\n if len(tokens) < max_length:\\n # Note here we pad in front of the sentence\\n padding = [d.padding_idx] * (max_length - len(tokens))\\n tokens = tokens + padding\\n utils.assert_eq(len(tokens), max_length)\\n return tokens\",\n \"def _encode_question(self, question):\\n \\n inputs = self.tokenizer.encode_plus(\\n question,\\n None,\\n add_special_tokens=True,\\n max_length=self.max_len,\\n )\\n\\n ids = inputs[\\\"input_ids\\\"]\\n mask = inputs[\\\"attention_mask\\\"]\\n token_type_ids = inputs[\\\"token_type_ids\\\"]\\n\\n padding_length = self.max_len - len(ids)\\n ids += ([0]*padding_length)\\n mask += ([0]*padding_length)\\n token_type_ids += ([0]*padding_length)\\n \\n return {\\n 'ids': torch.tensor(ids, dtype=torch.long),\\n 'mask': torch.tensor(mask, dtype=torch.long),\\n 'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),\\n \\n }\",\n \"def preprocess_question(q, vocab=None, word2vec=None):\\n\\tq = nltk.tokenize.word_tokenize(q.lower())\\n\\tq_processed = []\\n\\tfor q_ in q:\\n\\t\\tif q_[-2:] == \\\"'s\\\":\\n\\t\\t\\tq_ = q_[:-2]\\n\\t\\telif q[:-2:] == \\\"s'\\\":\\n\\t\\t\\tq_ = q_[:-1]\\n\\t\\tif word2vec is not None:\\n\\t\\t\\tif q_ in word2vec:\\n\\t\\t\\t\\tq_processed.append(q_)\\n\\t\\telse:\\n\\t\\t\\tq_processed.append(q_)\\n\\tif vocab is not None:\\n\\t\\tq_processed = [vocab[q_] for q_ in q_processed if q_ in vocab]\\n\\treturn q_processed\",\n \"def build_vocab(data, qmark=True):\\n\\n # With data = total q + a\\n # Check necessity of <UNK> tokens\\n\\n # Tokenizer\\n # Learning - t.word_counts, t.document_count, t.word_index, t.word_docs\\n\\n # Choose whether question marks are filtered out when building the vocabulary\\n if qmark:\\n t = Tokenizer(lower=True, filters='!\\\"#$%&()*+,-./:;=@[\\\\]^_`{|}~?')\\n else:\\n t = Tokenizer(lower=True, filters='!\\\"#$%&()*+,-./:;=@[\\\\]^_`{|}~')\\n t.fit_on_texts(data) # Was q_train only\\n vocab_size = len(\\n t.word_index) + 1 # The integer for the largest encoded word needs to be specified as an array index\\n\\n # Integer encode documents (binary) - Fixed size array for direct network input\\n # encoded_docs = t.texts_to_matrix(q_train)\\n\\n return t, vocab_size\",\n \"def tokenize(tokenizer, max_length, stride, row):\\r\\n # The input data must be tokenized in the order dictated by the side the\\r\\n # selected checkpoint apply the padding\\r\\n pad_on_right = tokenizer.padding_side == \\\"right\\\"\\r\\n \\r\\n return tokenizer(\\r\\n row[\\\"question\\\" if pad_on_right else \\\"context\\\"],\\r\\n row[\\\"context\\\" if pad_on_right else \\\"question\\\"],\\r\\n max_length=max_length,\\r\\n truncation=\\\"only_second\\\" if pad_on_right else \\\"only_first\\\",\\r\\n return_overflowing_tokens=True,\\r\\n return_offsets_mapping=True,\\r\\n stride=stride,\\r\\n padding=\\\"max_length\\\",\\r\\n )\",\n \"def batch_inference(question,context): \\n inputs = tokenizer(question, context, \\n return_tensors='pt', \\n truncation=True, \\n padding=True)\\n \\n # Move data to GPU\\n inputs = inputs.to(device)\\n \\n # Feed data through the model\\n with torch.no_grad():\\n outputs = model(**inputs)\\n\\n # Q&A model outputs the two logit scores for each word.\\n # One for its chance of being the start of the answer\\n # and one for its chance of being the end\\n start_logits = outputs.start_logits\\n end_logits = outputs.end_logits\\n \\n # Find the words with the highest score\\n # argmax(dim=1) means argmax with each sample\\n start = start_logits.argmax(dim=1)\\n end = end_logits.argmax(dim=1)\\n \\n # Return the answers\\n # This is the point where we move the prediction back to main memory with .cpu()\\n tokens = [tokenizer.convert_ids_to_tokens(x) for x in inputs[\\\"input_ids\\\"].cpu().numpy()]\\n return [tokenizer.convert_tokens_to_string(x[start[i]:end[i]+1]) for i,x in enumerate(tokens)]\",\n \"def tokenize_one_example(self, example):\\n index, q_id, question, context, answer_list, answer_start = example\\n\\n context = context.replace('\\\\'\\\\'', '\\\\\\\" ').replace(r'``', '\\\\\\\" ')\\n context_tokens = SQuADDataTokenizer._word_tokenize_spacy(context) if self._use_spacy else \\\\\\n SQuADDataTokenizer._word_tokenize_nltk(context)\\n context_chars = [list(token) for token in context_tokens]\\n spans = SQuADDataTokenizer._get_token_spans(context, context_tokens)\\n\\n ques = question.replace('\\\\'\\\\'', '\\\\\\\" ').replace('``', '\\\\\\\" ')\\n ques_tokens = SQuADDataTokenizer._word_tokenize_spacy(ques) if self._use_spacy else \\\\\\n SQuADDataTokenizer._word_tokenize_nltk(ques)\\n ques_chars = [list(token) for token in ques_tokens]\\n\\n y1s, y2s = [], []\\n answer_texts = []\\n\\n for answer_text, answer_start in zip(answer_list, answer_start):\\n answer_end = answer_start + len(answer_text)\\n answer_texts.append(answer_text)\\n answer_span = []\\n for idx, span in enumerate(spans):\\n if not (answer_end <= span[0] or answer_start >= span[1]):\\n answer_span.append(idx)\\n y1, y2 = answer_span[0], answer_span[-1]\\n y1s.append(y1)\\n y2s.append(y2)\\n\\n result = {'context_tokens': context_tokens, 'context_chars': context_chars,\\n 'ques_tokens': ques_tokens, 'ques_chars': ques_chars, 'y1s': y1s,\\n 'y2s': y2s, 'id': q_id, 'context': context, 'spans': spans, 'record_idx': index}\\n return result\",\n \"def add_special_tokens_(model, tokenizer):\\n orig_num_tokens = len(tokenizer.encoder)\\n num_added_tokens = tokenizer.add_special_tokens(ATTR_TO_SPECIAL_TOKEN) # doesn't add if they are already there\\n if num_added_tokens > 0:\\n model.resize_token_embeddings(new_num_tokens=orig_num_tokens + num_added_tokens)\",\n \"def process_dataset(data, tokenizer, workers=None):\\n tokenizer_class = tokenizers.get_class(tokenizer)\\n make_pool = partial(Pool, workers, initializer=init)\\n workers = make_pool(initargs=(tokenizer_class, {'annotators': {'lemma'}}))\\n q_tokens = workers.map(tokenize, data['questions'])\\n workers.close()\\n workers.join()\\n\\n workers = make_pool(\\n initargs=(tokenizer_class, {'annotators': {'lemma', 'pos', 'ner'}})\\n )\\n c_tokens = workers.map(tokenize, data['contexts'])\\n workers.close()\\n workers.join()\\n\\n for idx in range(len(data['qids'])):\\n question = q_tokens[idx]['words']\\n qlemma = q_tokens[idx]['lemma']\\n document = c_tokens[data['qid2cid'][idx]]['words']\\n offsets = c_tokens[data['qid2cid'][idx]]['offsets']\\n lemma = c_tokens[data['qid2cid'][idx]]['lemma']\\n pos = c_tokens[data['qid2cid'][idx]]['pos']\\n ner = c_tokens[data['qid2cid'][idx]]['ner']\\n ans_tokens = []\\n if len(data['answers']) > 0:\\n for ans in data['answers'][idx]:\\n found = find_answer(offsets,\\n ans['answer_start'],\\n ans['answer_start'] + len(ans['text']))\\n if found:\\n ans_tokens.append(found)\\n yield {\\n 'id': data['qids'][idx],\\n 'question': question,\\n 'document': document,\\n 'offsets': offsets,\\n 'answers': ans_tokens,\\n 'qlemma': qlemma,\\n 'lemma': lemma,\\n 'pos': pos,\\n 'ner': ner,\\n }\",\n \"def build_qa_only_input_from_segments(data_point, tokenizer, with_eos=True):\\n bos, eos, paragraph, answer_general, answer_specific, question_general, question_specific = \\\\\\n tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS[:-1])\\n\\n curr_ans = data_point['answer']\\n curr_ques = data_point['question']\\n\\n sequence = []\\n token_types = []\\n lm_labels = []\\n\\n if data_point['class'] == 'general':\\n sequence.extend([answer_general] + curr_ans)\\n token_types.extend([answer_general for _ in range(len(curr_ans) + 1)])\\n lm_labels.extend([-1 for _ in range(len(curr_ans) + 1)])\\n\\n if with_eos is True:\\n sequence.extend([question_general] + curr_ques + [eos])\\n token_types.extend([question_general for _ in range(len(curr_ques) + 2)])\\n lm_labels.extend([-1] + curr_ques + [eos])\\n else:\\n sequence.extend([question_general] + curr_ques)\\n token_types.extend([question_general for _ in range(len(curr_ques) + 1)])\\n lm_labels.extend([-1] + curr_ques)\\n\\n elif data_point['class'] == 'specific':\\n sequence.extend([answer_specific] + curr_ans)\\n token_types.extend([answer_specific for _ in range(len(curr_ans) + 1)])\\n lm_labels.extend([-1 for _ in range(len(curr_ans) + 1)])\\n\\n if with_eos is True:\\n sequence.extend([question_specific] + curr_ques + [eos])\\n token_types.extend([question_specific for _ in range(len(curr_ques) + 2)])\\n lm_labels.extend([-1] + curr_ques + [eos])\\n else:\\n sequence.extend([question_specific] + curr_ques)\\n token_types.extend([question_specific for _ in range(len(curr_ques) + 1)])\\n lm_labels.extend([-1] + curr_ques)\\n\\n assert len(sequence) == len(token_types)\\n assert len(token_types) == len(lm_labels)\\n\\n instance = {\\n \\\"input_ids\\\": sequence,\\n \\\"token_type_ids\\\": token_types,\\n \\\"lm_labels\\\": lm_labels\\n }\\n return instance, sequence\",\n \"def build_dataset(token_seq, token_seq_index, term_to_index, char_to_index, max_token_length=15, leading_context_size=10, trailing_context_size=10, leading_separator='{', trailing_separator='}', n_examples_per_context=10, n_errors_per_token=[1], n_errors_per_context=[0], dictionary=enchant.Dict('en_US'), seed=17):\\n if isinstance(seed, RandomState):\\n rng = seed\\n else:\\n rng = RandomState(seed=seed)\\n\\n n_contexts = sum([len(token_seq_index[t]) for t in token_seq_index.keys() if len(t) <= max_token_length])\\n n_examples = n_examples_per_context * n_contexts\\n max_inserts_per_token = max(n_errors_per_token)\\n max_inserts_per_context = 2*max(n_errors_per_context)\\n max_chars_in_window = leading_context_size + max_token_length + trailing_context_size + len(leading_separator) + len(trailing_separator) + max_inserts_per_token + max_inserts_per_context\\n\\n error_examples = np.zeros((n_examples, max_chars_in_window), dtype=np.int32)\\n error_examples.fill(char_to_index[' '])\\n\\n leading_contexts = np.zeros((n_examples, leading_context_size + max_inserts_per_context))\\n leading_contexts.fill(char_to_index[' '])\\n spelling_errors = np.zeros((n_examples, max_token_length + max_inserts_per_token))\\n spelling_errors.fill(char_to_index[' '])\\n trailing_contexts = np.zeros((n_examples, trailing_context_size + max_inserts_per_context))\\n trailing_contexts.fill(char_to_index[' '])\\n\\n correct_tokens = []\\n corrupt_tokens = []\\n corrections = np.zeros(n_examples, dtype=np.int32)\\n error_types = []\\n context_ids = []\\n example_ids = []\\n term_lens = []\\n edit_distance_to_nearest_term = []\\n nearest_term = []\\n\\n print('starting to construct {n_examples} examples'.format(\\n n_examples=n_examples))\\n\\n pbar = progressbar.ProgressBar(term_width=40,\\n widgets=[' ', progressbar.Percentage(),\\n ' ', progressbar.ETA()],\\n maxval=n_examples).start()\\n n = 0\\n\\n index_to_char = dict((i,c) for c,i in char_to_index.iteritems())\\n if 63 not in index_to_char.keys():\\n # A workaround for now. Delete ASAP.\\n index_to_char[63] = '^'\\n\\n def add_chars_to_array(n, array, chars, default_char=' '):\\n for k, ch in enumerate(chars):\\n try:\\n array[n, k] = char_to_index[ch]\\n except KeyError, e:\\n array[n, k] = char_to_index[default_char]\\n\\n for token in token_seq_index.keys():\\n if len(token) > max_token_length:\\n continue\\n\\n term_len = len(token)\\n distances = min_dictionary_edit_distance([token], dictionary)\\n distance = distances[token]['distance']\\n term = distances[token]['accepted']\\n\\n for i in token_seq_index[token]:\\n for j in six.moves.range(n_examples_per_context):\\n pbar.update(n+1)\\n corruptor = rng.choice([insert_characters, delete_characters,\\n replace_characters, transpose_characters])\\n n_token_errors = rng.choice(n_errors_per_token)\\n corrupt_token = corruptor(token, index_to_char, n=n_token_errors, seed=rng)\\n\\n n_context_errors = rng.choice(n_errors_per_context)\\n leading = leading_context(token_seq, i, leading_context_size)\\n leading = corruptor(leading, index_to_char, n=n_context_errors, seed=rng)\\n trailing = trailing_context(token_seq, i, trailing_context_size)\\n trailing = corruptor(trailing, index_to_char, n=n_context_errors, seed=rng)\\n\\n window = ''.join([leading, leading_separator,\\n corrupt_token, trailing_separator, trailing])\\n\\n add_chars_to_array(n, error_examples, window)\\n add_chars_to_array(n, leading_contexts, leading)\\n add_chars_to_array(n, spelling_errors, corrupt_token)\\n add_chars_to_array(n, trailing_contexts, trailing)\\n\\n correct_tokens.append(token)\\n corrections[n] = term_to_index[token]\\n corrupt_tokens.append(corrupt_token)\\n error_types.append(corruptor.__name__)\\n context_ids.append(i)\\n example_ids.append(n)\\n edit_distance_to_nearest_term.append(distance)\\n nearest_term.append(term)\\n term_lens.append(term_len)\\n\\n n += 1\\n\\n pbar.finish()\\n\\n df = pd.DataFrame({\\n 'correction': corrections,\\n 'error_type': error_types,\\n 'context_id': context_ids,\\n 'example_id': example_ids,\\n 'edit_distance_to_nearest_term': edit_distance_to_nearest_term,\\n 'nearest': nearest_term,\\n 'term_length': term_lens,\\n 'correct_token': correct_tokens,\\n 'corrupt_token': corrupt_tokens\\n })\\n\\n def add_columns(df, array, colname_prefix):\\n colwidth = int(np.log10(array.shape[1]))+1\\n colfmt = colname_prefix + '{col:0' + str(colwidth) + 'd}'\\n for col in np.arange(array.shape[1]):\\n colname = colfmt.format(col=col)\\n df[colname] = array[:, col]\\n\\n add_columns(df, error_examples, 'full_error')\\n add_columns(df, leading_contexts, 'leading_context')\\n add_columns(df, spelling_errors, 'spelling_error')\\n add_columns(df, trailing_contexts, 'trailing_context')\\n\\n return df\",\n \"def _multiprocess_tokenize(self, num_workers: int):\\n\\n # While displaying progress bar through tqdm, specify total number of\\n # sequences to tokenize, because tqdm won't know in case of pool.imap\\n with mp.Pool(num_workers) as pool:\\n print(f\\\"[{self._split}] Tokenizing questions...\\\")\\n _question_tuples = self.questions.items()\\n _question_indices = [t[0] for t in _question_tuples]\\n _questions = list(\\n tqdm(\\n pool.imap(word_tokenize, [t[1] for t in _question_tuples]),\\n total=len(self.questions),\\n )\\n )\\n self.questions = {\\n i: question + [\\\"?\\\"]\\n for i, question in zip(_question_indices, _questions)\\n }\\n # Delete variables to free memory.\\n del _question_tuples, _question_indices, _questions\\n\\n print(f\\\"[{self._split}] Tokenizing answers...\\\")\\n _answer_tuples = self.answers.items()\\n _answer_indices = [t[0] for t in _answer_tuples]\\n _answers = list(\\n tqdm(\\n pool.imap(word_tokenize, [t[1] for t in _answer_tuples]),\\n total=len(self.answers),\\n )\\n )\\n self.answers = {\\n i: answer + [\\\"?\\\"] for i, answer in zip(_answer_indices, _answers)\\n }\\n # Delete variables to free memory.\\n del _answer_tuples, _answer_indices, _answers\\n\\n print(f\\\"[{self._split}] Tokenizing captions...\\\")\\n # Convert dict to separate lists of image_ids and captions.\\n _caption_tuples = self.captions.items()\\n _image_ids = [t[0] for t in _caption_tuples]\\n _captions = list(\\n tqdm(\\n pool.imap(word_tokenize, [t[1] for t in _caption_tuples]),\\n total=(len(_caption_tuples)),\\n )\\n )\\n # Convert tokenized captions back to a dict.\\n self.captions = {i: c for i, c in zip(_image_ids, _captions)}\",\n \"def _multiprocess_tokenize(self, num_workers: int):\\n\\n # While displaying progress bar through tqdm, specify total number of\\n # sequences to tokenize, because tqdm won't know in case of pool.imap\\n with mp.Pool(num_workers) as pool:\\n print(f\\\"[{self._split}] Tokenizing questions...\\\")\\n _question_tuples = self.questions.items()\\n _question_indices = [t[0] for t in _question_tuples]\\n _questions = list(\\n tqdm(\\n pool.imap(word_tokenize, [t[1] for t in _question_tuples]),\\n total=len(self.questions)\\n )\\n )\\n self.questions = {\\n i: question + [\\\"?\\\"] for i, question in\\n zip(_question_indices, _questions)\\n }\\n # Delete variables to free memory.\\n del _question_tuples, _question_indices, _questions\\n\\n print(f\\\"[{self._split}] Tokenizing answers...\\\")\\n _answer_tuples = self.answers.items()\\n _answer_indices = [t[0] for t in _answer_tuples]\\n _answers = list(\\n tqdm(\\n pool.imap(word_tokenize, [t[1] for t in _answer_tuples]),\\n total=len(self.answers)\\n )\\n )\\n self.answers = {\\n i: answer + [\\\"?\\\"] for i, answer in\\n zip(_answer_indices, _answers)\\n }\\n # Delete variables to free memory.\\n del _answer_tuples, _answer_indices, _answers\\n\\n print(f\\\"[{self._split}] Tokenizing captions...\\\")\\n # Convert dict to separate lists of image_ids and captions.\\n _caption_tuples = self.captions.items()\\n _image_ids = [t[0] for t in _caption_tuples]\\n _captions = list(\\n tqdm(\\n pool.imap(word_tokenize, [t[1] for t in _caption_tuples]),\\n total=(len(_caption_tuples))\\n )\\n )\\n # Convert tokenized captions back to a dict.\\n self.captions = {i: c for i, c in zip(_image_ids, _captions)}\",\n \"def tokenize_pretraining(self, inputs):\\n\\n ref_ids = prepare_ref([inputs], self.tokenizer_ltp, self.tokenizer_cn)\\n\\n tokens = self.tokenizer_cn.tokenize(inputs)\\n\\n if len(tokens) > self.max_seq_length - 2:\\n tokens = tokens[:(self.max_seq_length - 2)]\\n ref_ids = ref_ids[:(self.max_seq_length - 2)]\\n\\n ref_ids = cn_whole_word_mask(tokens, ref_ids[0])\\n tokens, labels = random_word_wwm(tokens, ref_ids, self.tokenizer_cn)\\n\\n tokens = ['[CLS]'] + tokens + ['[SEP]']\\n lm_label_ids = ([-100] + labels + [-100])\\n\\n input_ids = self.tokenizer_cn.convert_tokens_to_ids(tokens)\\n\\n attention_mask = [1] * len(input_ids)\\n token_type_ids = [0] * len(input_ids)\\n\\n while len(input_ids) < self.max_seq_length:\\n input_ids.append(0)\\n attention_mask.append(0)\\n token_type_ids.append(0)\\n lm_label_ids.append(-100)\\n\\n assert len(input_ids) == self.max_seq_length\\n assert len(attention_mask) == self.max_seq_length\\n assert len(token_type_ids) == self.max_seq_length\\n assert len(lm_label_ids) == self.max_seq_length\\n\\n\\n outputs = {'input_ids': tf.constant(input_ids), 'attention_mask': tf.constant(attention_mask), \\n 'token_type_ids': tf.constant(token_type_ids), 'lm_label_ids': tf.constant(lm_label_ids)}\\n\\n return outputs\",\n \"def parse_question(question):\\n\\tcontext = question['support']\\n\\tanswer = question['correct_answer']\\n\\ttarget = question['question']\\n\\n\\tcontext_words = context.split(\\\" \\\")[0: 510]\\n\\ttarget_words = target.split(\\\" \\\")\\n\\n\\tpunc_filter = str.maketrans('', '', string.punctuation)\\n\\n\\tcontext_words = [word.translate(punc_filter) for word in context_words]\\n\\ttarget_words = [word.translate(punc_filter) for word in target_words]\\n\\tanswer_words = [word.translate(punc_filter) for word in answer.split(\\\" \\\")]\\n\\n\\tbio_embeddings = [EMBEDER['O']]\\n\\tinside_answer = False\\n\\tanswer_index = 0\\n\\tcan_be_inside_answer = True\\n\\n\\t# The following loop and above code does:\\n\\t# -Find where the answer is and place a B tag\\n\\t# -While still in the answer (the answer is more than one word) put an I tag\\n\\t# -Outside of the answer place a O tag\\n\\t# -Start and end with an O tag for BERT's automatic\\n\\t# -start token and end token representing the start and end of a sentence.\\n\\tfor word in context_words:\\n\\t\\tif word.lower() == answer_words[0].lower() and can_be_inside_answer:\\n\\t\\t\\tbio_embeddings.append(EMBEDER[\\\"B\\\"])\\n\\t\\t\\tanswer_index += 1\\n\\t\\t\\tinside_answer = True\\n\\t\\t\\tcan_be_inside_answer = False\\n\\t\\telif inside_answer:\\n\\t\\t\\tif len(answer_words) > 1:\\n\\t\\t\\t\\tif word.lower() != answer_words[answer_index]:\\n\\t\\t\\t\\t\\tinside_answer = False\\n\\t\\t\\t\\t\\tbio_embeddings.append(EMBEDER[\\\"O\\\"])\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tbio_embeddings.append(EMBEDER[\\\"I\\\"])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tinside_answer = False\\n\\t\\t\\t\\tbio_embeddings.append(EMBEDER[\\\"O\\\"])\\n\\t\\telse:\\n\\t\\t\\tbio_embeddings.append(EMBEDER[\\\"O\\\"])\\n\\tbio_embeddings.append(EMBEDER[\\\"O\\\"])\\n\\n\\tground_truth = torch.tensor([BERT_TOKENIZER.encode(target_words)])\\n\\tcontext_words = torch.tensor([BERT_TOKENIZER.encode(context_words)])\\n\\n\\tassert len(bio_embeddings) == len(context_words[0]), f'The BIO tags are not equal in length to the embeddings! ' \\\\\\n\\t f'{None} & {len(bio_embeddings)} & {len(context_words[0])}'\\n\\treturn context_words, bio_embeddings, ground_truth\",\n \"def tokenize_dataset(self, data):\\n token_pt = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(\\n (pt.numpy() for pt, _ in data), target_vocab_size=2**15)\\n token_en = tfds.deprecated.text.SubwordTextEncoder.build_from_corpus(\\n (en.numpy() for _, en in data), target_vocab_size=2**15)\\n return token_pt, token_en\",\n \"def gen_questions(self, number_of_questions):\",\n \"def convert_examples_to_features(examples, tokenizer, max_seq_length):\\n \\n features = []\\n num_tokens_article = []\\n count = 0\\n masking_token_id = tokenizer.convert_tokens_to_ids(['[MASK]'])[0]\\n # padding_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\\n for example_index, example in enumerate(examples):\\n article_tokens = tokenizer.tokenize(example['article'])\\n ques_tokens = tokenizer.tokenize(example['question'].replace(\\\"@placeholder\\\", \\\"_\\\"))\\n masked_index = ques_tokens.index('_')\\n ques_tokens[masked_index] = '[MASK]'\\n \\n# options = example['options']\\n options = tokenizer.convert_tokens_to_ids(example['options'])\\n# print(options, '\\\\n')\\n\\n choices_features = []\\n _truncate_seq_pair(article_tokens, ques_tokens, max_seq_length - 3)\\n\\n# tokens = [\\\"[CLS]\\\"] + ques_tokens + [\\\"[SEP]\\\"] + article_tokens + [\\\"[SEP]\\\"]\\n tokens = ques_tokens + [\\\"[SEP]\\\"] + article_tokens + [\\\"[SEP]\\\"]\\n\\n# segment_ids = [0] * (len(ques_tokens) + 2) + [1] * (len(article_tokens) + 1)\\n segment_ids = [0] * (len(ques_tokens) + 1) + [1] * (len(article_tokens) + 1)\\n\\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\\n input_mask = [1] * len(input_ids)\\n\\n # Zero-pad up to the sequence length.\\n padding = [0] * (max_seq_length - len(input_ids))\\n input_ids += padding\\n input_mask += padding\\n segment_ids += padding\\n\\t\\n candidates = example['options']\\n candidates_ids = tokenizer.convert_tokens_to_ids(candidates)\\n# print(tokenizer.convert_tokens_to_ids(tokenizer.tokenize(example['options'][example['label']]))[0])\\n\\n lm_labels = [-1 if t_id != masking_token_id else tokenizer.convert_tokens_to_ids(tokenizer.tokenize(example['options'][example['label']]))[0] for t_id in input_ids]\\n# lm_labels = [t_id if t_id != 0 else -1 for t_id in input_ids]\\n# for i, t_id in enumerate(lm_labels) :\\n# if(t_id == masking_token_id):\\n# lm_labels[i] = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(example['options'][example['label']]))[0]\\n\\n# print(lm_labels, '\\\\n')\\n # tokenizer.convert_tokens_to_ids(tokenizer.tokenize(example['options'][example['label']]))[0]\\n assert len(input_ids) == max_seq_length\\n assert len(input_mask) == max_seq_length\\n assert len(segment_ids) == max_seq_length\\n assert len(lm_labels) == max_seq_length\\n\\n choices_features.append((tokens, input_ids, input_mask, segment_ids))\\n\\n features.append(\\n InputFeatures(\\n choices_features = choices_features,\\n lm_labels = lm_labels,\\n options = options,\\n label = example['label']\\n )\\n )\\n \\n return features\",\n \"def prepare_inputs(data, queries, tokenizer):\\n # Prepare inputs\\n table = pd.DataFrame.from_dict(data)\\n inputs = tokenizer(table=table, queries=queries,truncation=True, padding=True,return_tensors=\\\"pt\\\").to(device)\\n\\n # Return things\\n return table, inputs\",\n \"def convert_examples_to_features(examples, tokenizer, max_seq_length,\\n doc_stride, max_query_length, return_answers, skip_no_answer,\\n verbose=False, save_with_prob=False, msg=\\\"Converting examples\\\"):\\n\\n unique_id = 1000000000\\n features = []\\n question_features = []\\n\\n for (example_index, example) in enumerate(tqdm(examples, desc=msg)):\\n\\n # Tokenize query into (sub)tokens\\n query_tokens = tokenizer.tokenize(example.question_text)\\n if len(query_tokens) > max_query_length:\\n query_tokens = query_tokens[0:max_query_length]\\n\\n # Creating a map between word <=> (sub)token\\n tok_to_word_index = []\\n word_to_tok_index = [] # word to (start of) subtokens\\n all_doc_tokens = []\\n for (i, word) in enumerate(example.doc_words):\\n word_to_tok_index.append(len(all_doc_tokens))\\n sub_tokens = tokenizer.tokenize(word)\\n for sub_token in sub_tokens:\\n tok_to_word_index.append(i)\\n all_doc_tokens.append(sub_token)\\n\\n # The -2 accounts for [CLS], [SEP]\\n max_tokens_for_doc = max_seq_length - 2\\n\\n # Split sequence by max_seq_len with doc_stride, _DocSpan is based on tokens without [CLS], [SEP]\\n _DocSpan = collections.namedtuple( # pylint: disable=invalid-name\\n \\\"DocSpan\\\", [\\\"start\\\", \\\"length\\\"])\\n doc_spans = []\\n start_tok_offset = 0 # From all_doc_tokens\\n\\n # Get doc_spans with stride and offset\\n while start_tok_offset < len(all_doc_tokens):\\n length = len(all_doc_tokens) - start_tok_offset\\n if length > max_tokens_for_doc:\\n length = max_tokens_for_doc\\n doc_spans.append(_DocSpan(start=start_tok_offset, length=length))\\n if start_tok_offset + length == len(all_doc_tokens):\\n break\\n start_tok_offset += min(length, doc_stride) # seems to prefer doc_stride always\\n assert doc_stride < length, \\\"length is no larger than doc_stride for {}\\\".format(doc_spans)\\n\\n # Iterate each doc_span and make out_tokens\\n for (doc_span_index, doc_span) in enumerate(doc_spans):\\n\\n # Find answer position based on new out_tokens\\n start_position = None\\n end_position = None\\n\\n # For no_answer, same (-1, -1) applies\\n if example.start_position is not None and example.start_position < 0:\\n assert example.start_position == -1 and example.end_position == -1\\n start_position, end_position = NO_ANS, NO_ANS\\n\\n # For existing answers, find answers if exist\\n elif return_answers:\\n\\n # Get token-level start/end position\\n tok_start_position = word_to_tok_index[example.start_position]\\n if example.end_position < len(example.doc_words) - 1:\\n tok_end_position = word_to_tok_index[example.end_position + 1] - 1 # By backwarding from next word\\n else:\\n assert example.end_position == len(example.doc_words) - 1\\n tok_end_position = len(all_doc_tokens) - 1\\n\\n # Improve answer span by subword-level\\n (tok_start_position, tok_end_position) = _improve_answer_span(\\n all_doc_tokens, tok_start_position, tok_end_position, tokenizer,\\n example.orig_answer_text)\\n\\n # Throw away training samples without answers (due to doc_span split)\\n doc_start = doc_span.start\\n doc_end = doc_span.start + doc_span.length - 1\\n if (tok_start_position < doc_start or tok_end_position < doc_start or\\n tok_start_position > doc_end or tok_end_position > doc_end):\\n if skip_no_answer:\\n continue\\n else:\\n # For NQ, only add this in 2% (50 times downsample)\\n if save_with_prob:\\n if np.random.randint(100) < 2:\\n start_position, end_position = NO_ANS, NO_ANS\\n else:\\n continue\\n else:\\n start_position, end_position = NO_ANS, NO_ANS\\n\\n # Training samples with answers\\n else:\\n doc_offset = 1 # For [CLS]\\n start_position = tok_start_position - doc_start + doc_offset\\n end_position = tok_end_position - doc_start + doc_offset\\n assert start_position >= 0 and end_position >= 0, (start_position, end_position)\\n\\n out_tokens = [] # doc\\n out_tokens_ = [] # quesry\\n out_tokens.append(\\\"[CLS]\\\")\\n out_tokens_.append(\\\"[CLS]\\\")\\n token_to_word_map = {} # The difference with tok_to_word_index is it includes special tokens\\n token_is_max_context = {}\\n\\n # For query tokens, just copy and add [SEP]\\n for token in query_tokens:\\n out_tokens_.append(token)\\n out_tokens_.append(\\\"[SEP]\\\")\\n\\n # For each doc token, create token_to_word_map and is_max_context, and add to out_tokens\\n for i in range(doc_span.length):\\n split_token_index = doc_span.start + i\\n token_to_word_map[len(out_tokens)] = tok_to_word_index[split_token_index]\\n is_max_context = _check_is_max_context(doc_spans, doc_span_index, split_token_index)\\n token_is_max_context[len(out_tokens)] = is_max_context\\n out_tokens.append(all_doc_tokens[split_token_index])\\n out_tokens.append(\\\"[SEP]\\\")\\n\\n # Convert to ids and masks\\n input_ids = tokenizer.convert_tokens_to_ids(out_tokens)\\n input_ids_ = tokenizer.convert_tokens_to_ids(out_tokens_)\\n input_mask = [1] * len(input_ids)\\n input_mask_ = [1] * len(input_ids_)\\n\\n # Zero-pad up to the sequence length.\\n while len(input_ids) < max_seq_length:\\n input_ids.append(0)\\n input_mask.append(0)\\n assert len(input_ids) == max_seq_length\\n assert len(input_mask) == max_seq_length\\n while len(input_ids_) < max_query_length + 2: # +2 for [CLS], [SEP]\\n input_ids_.append(0)\\n input_mask_.append(0)\\n assert len(input_ids_) == max_query_length + 2\\n assert len(input_mask_) == max_query_length + 2\\n\\n # Printing for debug\\n if example_index < 1 and verbose:\\n logger.info(\\\"*** Example ***\\\")\\n logger.info(\\\"unique_id: %s\\\" % (unique_id))\\n logger.info(\\\"example_index: %s\\\" % (example_index))\\n logger.info(\\\"doc_span_index: %s\\\" % (doc_span_index))\\n logger.info(\\\"tokens: %s\\\" % \\\" \\\".join(\\n [tokenization.printable_text(x) for x in out_tokens]))\\n logger.info(\\\"q tokens: %s\\\" % \\\" \\\".join(\\n [tokenization.printable_text(x) for x in out_tokens_]))\\n logger.info(\\\"token_to_word_map: %s\\\" % \\\" \\\".join(\\n [\\\"%d:%d\\\" % (x, y) for (x, y) in six.iteritems(token_to_word_map)]))\\n logger.info(\\\"token_is_max_context: %s\\\" % \\\" \\\".join([\\n \\\"%d:%s\\\" % (x, y) for (x, y) in six.iteritems(token_is_max_context)\\n ]))\\n logger.info(\\\"input_ids: %s\\\" % \\\" \\\".join([str(x) for x in input_ids]))\\n logger.info(\\n \\\"input_mask: %s\\\" % \\\" \\\".join([str(x) for x in input_mask]))\\n if return_answers:\\n answer_text = \\\" \\\".join(out_tokens[start_position:(end_position + 1)])\\n logger.info(\\\"start_position: %d\\\" % (start_position))\\n logger.info(\\\"end_position: %d\\\" % (end_position))\\n logger.info(\\n \\\"answer: %s\\\" % (tokenization.printable_text(answer_text)))\\n\\n # Append feature\\n features.append(\\n ContextFeatures(\\n unique_id=unique_id,\\n example_index=example_index,\\n doc_span_index=doc_span_index,\\n tokens=out_tokens,\\n token_to_word_map=token_to_word_map,\\n token_is_max_context=token_is_max_context,\\n input_ids=input_ids,\\n input_mask=input_mask,\\n start_position=start_position,\\n end_position=end_position))\\n question_features.append(\\n QuestionFeatures(\\n unique_id=unique_id,\\n example_index=example_index,\\n tokens_=out_tokens_,\\n input_ids=input_ids_,\\n input_mask=input_mask_))\\n\\n # Check validity of answer\\n if return_answers:\\n if start_position <= NO_ANS:\\n assert start_position == NO_ANS and end_position == NO_ANS, (start_position, end_position)\\n else:\\n assert out_tokens[start_position:end_position+1] == \\\\\\n all_doc_tokens[tok_start_position:tok_end_position+1]\\n orig_text, start_pos, end_pos = get_final_text_(\\n example, features[-1], start_position, end_position, True, False)\\n phrase = orig_text[start_pos:end_pos]\\n try:\\n assert phrase == example.orig_answer_text\\n except Exception as e:\\n # print('diff ans [%s]/[%s]'%(phrase, example.orig_answer_text))\\n pass\\n unique_id += 1\\n\\n return features, question_features\",\n \"def batchify(batch):\\n\\n\\tquestion_len = list()\\n\\tlabel_list = list()\\n\\tfor ex in batch:\\n\\t\\tquestion_len.append(len(ex[0]))\\n\\t\\tlabel_list.append(ex[1])\\n\\n\\t'''\\n\\tPadding the labels - unequal length sequences for sequenial data like we have. \\n\\tSince actual labels are 0/1 - we pad with -1, and will use this when 'masking' labels during loss and\\n\\taccuracy evaluation.\\n\\t'''\\n\\ttarget_labels = torch.nn.utils.rnn.pad_sequence([torch.tensor(y) for y in label_list], padding_value=-1).t()\\n\\n\\t# dimension is dimension of every feature vector = n_guesses in this homework setting\\n\\tdim = batch[0][0].shape[1]\\n\\n\\t# similar padding happens for the feature vectors, with vector of all zeros appended.\\n\\tx1 = torch.FloatTensor(len(question_len), max(question_len), dim).zero_()\\n\\tfor i in range(len(question_len)):\\n\\t\\tquestion_feature_vec = batch[i][0]\\n\\t\\tvec = torch.FloatTensor(question_feature_vec)\\n\\t\\tx1[i, :len(question_feature_vec)].copy_(vec)\\n\\tq_batch = {'feature_vec': x1, 'len': torch.FloatTensor(question_len), 'labels': target_labels}\\n\\treturn q_batch\",\n \"def convert_examples_to_features(\\n self,\\n examples: List[object],\\n tokenizer: object,\\n max_seq_length: int,\\n doc_stride: int,\\n max_query_length: int,\\n has_groundtruth: bool,\\n ):\\n\\n unique_id = 1000000000\\n features = []\\n text_to_tokens_dict = {}\\n\\n for example_index in trange(len(examples)):\\n\\n if example_index % 1000 == 0:\\n SquadDataset.check_if_sufficient_memory()\\n\\n example = examples[example_index]\\n if example.question_text not in text_to_tokens_dict:\\n text_to_tokens_dict[example.question_text] = tokenizer.text_to_tokens(example.question_text)[\\n :max_query_length\\n ]\\n query_tokens = text_to_tokens_dict[example.question_text]\\n\\n # context: index of token -> index of word\\n tok_to_orig_index = []\\n # context: index of word -> index of first token in token list\\n orig_to_tok_index = []\\n # context without white spaces after tokenization\\n all_doc_tokens = []\\n # doc tokens is word separated context\\n (\\n doc_tokens,\\n char_to_word_offset,\\n start_position,\\n end_position,\\n context_text,\\n ) = SquadDataset.get_doc_tokens_and_offset_from_context_id(\\n example.context_id,\\n example.start_position_character,\\n example.is_impossible,\\n example.answer_text,\\n self.processor.doc_id_to_context_text,\\n )\\n\\n example.start_position = start_position\\n example.end_position = end_position\\n if self.mode != TRAINING_MODE:\\n example.doc_tokens = doc_tokens\\n # the text to tokens step is the slowest step\\n for (i, token) in enumerate(doc_tokens):\\n orig_to_tok_index.append(len(all_doc_tokens))\\n if token not in text_to_tokens_dict:\\n text_to_tokens_dict[token] = tokenizer.text_to_tokens(token)\\n sub_tokens = text_to_tokens_dict[token]\\n\\n for sub_token in sub_tokens:\\n tok_to_orig_index.append(i)\\n all_doc_tokens.append(sub_token)\\n\\n # idx of query token start and end in context\\n tok_start_position = None\\n tok_end_position = None\\n if has_groundtruth and example.is_impossible:\\n tok_start_position = -1\\n tok_end_position = -1\\n if has_groundtruth and not example.is_impossible:\\n tok_start_position = orig_to_tok_index[example.start_position]\\n if example.end_position < len(doc_tokens) - 1:\\n tok_end_position = orig_to_tok_index[example.end_position + 1] - 1\\n else:\\n tok_end_position = len(all_doc_tokens) - 1\\n\\n (tok_start_position, tok_end_position) = _improve_answer_span(\\n all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text\\n )\\n\\n # The -3 accounts for tokenizer.cls_token, tokenizer.sep_token and tokenizer.sep_token\\n # doc_spans contains all possible contexts options of given length\\n max_tokens_for_doc = max_seq_length - len(query_tokens) - 3\\n\\n doc_spans = SquadDataset.get_docspans(all_doc_tokens, max_tokens_for_doc, doc_stride)\\n\\n doc_spans = SquadDataset.keep_relevant_docspans(\\n doc_spans, tok_start_position, tok_end_position, self.keep_doc_spans\\n )\\n\\n # make compatible for hashing\\n doc_spans = tuple(doc_spans)\\n\\n for (doc_span_index, doc_span) in enumerate(doc_spans):\\n\\n tokens = [tokenizer.cls_token] + query_tokens + [tokenizer.sep_token]\\n segment_ids = [0 for i in range(len(tokens))]\\n\\n token_is_max_context = {}\\n # maps context tokens idx in final input -> word idx in context\\n token_to_orig_map = {}\\n\\n for i in range(doc_span.length):\\n split_token_index = doc_span.start + i\\n token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\\n is_max_context = _check_is_max_context(doc_spans, doc_span_index, split_token_index)\\n token_is_max_context[len(tokens)] = is_max_context\\n tokens.append(all_doc_tokens[split_token_index])\\n segment_ids.append(1)\\n tokens.append(tokenizer.sep_token)\\n segment_ids.append(1)\\n\\n input_ids = tokenizer.tokens_to_ids(tokens)\\n\\n # The mask has 1 for real tokens and 0 for padding tokens.\\n # Only real tokens are attended to.\\n input_mask = [1] * len(input_ids)\\n\\n # Zero-pad up to the sequence length.\\n while len(input_ids) < max_seq_length:\\n input_ids.append(tokenizer.pad_id)\\n input_mask.append(0)\\n segment_ids.append(0)\\n\\n assert len(input_ids) == max_seq_length\\n assert len(input_mask) == max_seq_length\\n assert len(segment_ids) == max_seq_length\\n\\n # calculate start and end position in final array\\n # of tokens in answer if no answer,\\n # 0 for both pointing to tokenizer.cls_token\\n start_position = 0\\n end_position = 0\\n if has_groundtruth and not example.is_impossible:\\n doc_start = doc_span.start\\n doc_end = doc_span.start + doc_span.length - 1\\n out_of_span = False\\n if not (tok_start_position >= doc_start and tok_end_position <= doc_end):\\n out_of_span = True\\n if out_of_span:\\n start_position = 0\\n end_position = 0\\n else:\\n doc_offset = len(query_tokens) + 2\\n start_position = tok_start_position - doc_start + doc_offset\\n end_position = tok_end_position - doc_start + doc_offset\\n if has_groundtruth and example.is_impossible:\\n # if our document chunk does not contain\\n # an annotation we throw it out, since there is nothing\\n # to predict.\\n start_position = 0\\n end_position = 0\\n\\n if example_index < 1:\\n logging.info(\\\"*** Example ***\\\")\\n logging.info(\\\"unique_id: %s\\\" % (unique_id))\\n logging.info(\\\"example_index: %s\\\" % (example_index))\\n logging.info(\\\"doc_span_index: %s\\\" % (doc_span_index))\\n logging.info(\\\"tokens: %s\\\" % \\\" \\\".join(tokens))\\n logging.info(\\n \\\"token_to_orig_map: %s\\\" % \\\" \\\".join([\\\"%d:%d\\\" % (x, y) for (x, y) in token_to_orig_map.items()])\\n )\\n logging.info(\\n \\\"token_is_max_context: %s\\\"\\n % \\\" \\\".join([\\\"%d:%s\\\" % (x, y) for (x, y) in token_is_max_context.items()])\\n )\\n logging.info(\\\"input_ids: %s\\\" % \\\" \\\".join([str(x) for x in input_ids]))\\n logging.info(\\\"input_mask: %s\\\" % \\\" \\\".join([str(x) for x in input_mask]))\\n logging.info(\\\"segment_ids: %s\\\" % \\\" \\\".join([str(x) for x in segment_ids]))\\n if has_groundtruth and example.is_impossible:\\n logging.info(\\\"impossible example\\\")\\n if has_groundtruth and not example.is_impossible:\\n answer_text = \\\" \\\".join(tokens[start_position : (end_position + 1)])\\n logging.info(\\\"start_position: %d\\\" % (start_position))\\n logging.info(\\\"end_position: %d\\\" % (end_position))\\n logging.info(\\\"answer: %s\\\" % (answer_text))\\n\\n # memoization to save CPU memory for large datasets\\n input_mask = tuple(input_mask)\\n if input_mask in self.input_mask_to_input_mask_id:\\n feature_input_mask_id = self.input_mask_to_input_mask_id[input_mask]\\n else:\\n self.input_mask_id_to_input_mask[self.input_mask_id] = input_mask\\n self.input_mask_to_input_mask_id[input_mask] = self.input_mask_id\\n feature_input_mask_id = self.input_mask_id\\n self.input_mask_id += 1\\n\\n segment_mask = tuple(segment_ids)\\n if segment_mask in self.segment_mask_to_segment_mask_id:\\n feature_segment_mask_id = self.segment_mask_to_segment_mask_id[segment_mask]\\n else:\\n self.segment_mask_id_to_segment_mask[self.segment_mask_id] = segment_mask\\n self.segment_mask_to_segment_mask_id[segment_mask] = self.segment_mask_id\\n feature_segment_mask_id = self.segment_mask_id\\n self.segment_mask_id += 1\\n # end memoization\\n\\n if self.mode == TRAINING_MODE:\\n input_feature = {\\n \\\"unique_id\\\": unique_id,\\n \\\"input_ids\\\": input_ids,\\n \\\"input_mask\\\": feature_input_mask_id,\\n \\\"segment_ids\\\": feature_segment_mask_id,\\n \\\"start_position\\\": start_position,\\n \\\"end_position\\\": end_position,\\n }\\n else:\\n input_feature = {\\n \\\"unique_id\\\": unique_id,\\n \\\"input_ids\\\": input_ids,\\n \\\"input_mask\\\": feature_input_mask_id,\\n \\\"segment_ids\\\": feature_segment_mask_id,\\n \\\"start_position\\\": start_position,\\n \\\"end_position\\\": end_position,\\n \\\"example_index\\\": example_index,\\n \\\"doc_span_index\\\": doc_span_index,\\n \\\"tokens\\\": tokens,\\n \\\"token_to_orig_map\\\": token_to_orig_map,\\n \\\"token_is_max_context\\\": token_is_max_context,\\n \\\"is_impossible\\\": example.is_impossible,\\n }\\n\\n features.append(input_feature)\\n unique_id += 1\\n return features\",\n \"def tokenize_dataset(self, data):\\n subword = tfds.features.text.SubwordTextEncoder.build_from_corpus\\n tokenizer_en = subword((en.numpy() for pt, en in data),\\n target_vocab_size=2**15)\\n tokenizer_pt = subword((pt.numpy() for pt, en in data),\\n target_vocab_size=2**15)\\n return (tokenizer_pt, tokenizer_en)\",\n \"def question_medtagger(self):\\n self.save_on_file(self.cleaned_questions_q_dot, 'questions.txt')\\n medpos_tag_out = self.medpos_tag('questions.txt')\\n parsed_questions = self.parse(medpos_tag_out)\\n parsed_questions_type = []\\n parsed_questions_out = []\\n for i in range(0, len(self.data['questions'])):\\n # [ type, [ (w1, tag_w1) , (w2, tag_w2) ... (wn, tagwn) ] ]\\n parsed_questions_type += [[self.data['questions'][i]['type']] + [parsed_questions[i]]]\\n parsed_questions_out += [parsed_questions[i]]\\n return parsed_questions_out, parsed_questions_type\",\n \"def batchify_QA(batch):\\r\\n\\r\\n if type(batch[0][1]) != torch.LongTensor:\\r\\n no_elmo, use_char = (True, False) if batch[0][1] == -2 else (False, False)\\r\\n else:\\r\\n no_elmo, use_char = True, True\\r\\n\\r\\n docs = [ex[0] for ex in batch]\\r\\n docs_char = [ex[1] for ex in batch]\\r\\n ques = [ex[2] for ex in batch]\\r\\n questions_char = [ex[3] for ex in batch]\\r\\n answers = [ex[4] for ex in batch]\\r\\n\\r\\n # Batch documents\\r\\n max_doc_length = max([d.size(0) for d in docs])\\r\\n x1_len = torch.LongTensor(len(docs)).zero_()\\r\\n x1 = torch.LongTensor(len(docs),\\r\\n max_doc_length).zero_() if no_elmo else torch.LongTensor(len(docs),\\r\\n max_doc_length,\\r\\n 50).zero_()\\r\\n x1_char = torch.LongTensor(len(docs),\\r\\n max_doc_length,\\r\\n docs_char[0].size(1)).zero_() if (no_elmo and use_char) else None\\r\\n for i, d in enumerate(docs):\\r\\n x1_len[i] = d.size(0)\\r\\n x1[i, :d.size(0)].copy_(d)\\r\\n if not no_elmo:\\r\\n x1_char[i, :d.size(0), :].copy_(docs_char[i])\\r\\n\\r\\n # Batch questions\\r\\n max_ques_length = max([q.size(0) for q in ques])\\r\\n x2_len = torch.LongTensor(len(ques)).zero_()\\r\\n x2 = torch.LongTensor(len(ques), max_ques_length).zero_() if no_elmo else torch.LongTensor(len(ques),\\r\\n max_ques_length,\\r\\n 50).zero_()\\r\\n x2_char = torch.LongTensor(len(ques),\\r\\n max_ques_length,\\r\\n questions_char[0].size(1)).zero_() if (no_elmo and use_char) else None\\r\\n for i, q in enumerate(ques):\\r\\n x2_len[i] = q.size(0)\\r\\n x2[i, :q.size(0)].copy_(q)\\r\\n if not no_elmo:\\r\\n x2_char[i, :q.size(0), :].copy_(questions_char[i])\\r\\n\\r\\n # Batch answers\\r\\n max_ans_length = max([a.size(0) for a in answers])\\r\\n ans_len = torch.LongTensor(len(answers)).zero_()\\r\\n ans = torch.LongTensor(len(answers), max_ans_length).zero_()\\r\\n for i, a in enumerate(answers):\\r\\n ans_len[i] = a.size(0)\\r\\n ans[i, :a.size(0)].copy_(a)\\r\\n\\r\\n ids = [ex[5] for ex in batch]\\r\\n contexts = [ex[6] for ex in batch]\\r\\n # FIXME: multiple answers are possible, fix vectorize also.\\r\\n targets = [ex[8][0] for ex in batch]\\r\\n src_vocabs = [ex[9] for ex in batch]\\r\\n source_maps = []\\r\\n alignments = []\\r\\n\\r\\n # Prepare source vocabs, alignment [required for Copy Attention]\\r\\n for eid, context, target, (token2idx, idx2token) in \\\\\\r\\n zip(ids, contexts, targets, src_vocabs):\\r\\n # Mapping source tokens to indices in the dynamic dict.\\r\\n src_map = torch.LongTensor([token2idx[w] for w in context])\\r\\n source_maps.append(src_map)\\r\\n\\r\\n # TODO: does skipping the first and last token in answer valid?\\r\\n mask = torch.LongTensor([token2idx[w] if w in token2idx\\r\\n else UNK for w in target])\\r\\n alignments.append(mask)\\r\\n\\r\\n return {'doc_rep': x1,\\r\\n 'doc_char_rep': x1_char,\\r\\n 'doc_len': x1_len,\\r\\n 'que_rep': x2,\\r\\n 'que_char_rep': x2_char,\\r\\n 'que_len': x2_len,\\r\\n 'ans_rep': ans,\\r\\n 'ans_len': ans_len,\\r\\n 'ids': ids,\\r\\n 'documents': contexts,\\r\\n 'questions': [ex[7] for ex in batch],\\r\\n 'answers': targets,\\r\\n 'source_vocabs': src_vocabs,\\r\\n 'src_map': source_maps,\\r\\n 'alignment': alignments}\",\n \"def preprocess(self, sequence, word2idx):\\n context = [word2idx[word]\\n if word in word2idx else UNK_token for word in sequence.split()]\\n context = torch.Tensor(context)\\n return context\",\n \"def q_tokenize(document):\\n final_words = []\\n avoided_words = [] # WORDS WHICH ARE TO BE AVOIDED IN THE FINAL LIST\\n \\n # making the avoided_words list\\n for word in string.punctuation: # the string library has a string of punctuations\\n avoided_words.append(word)\\n for word in nltk.corpus.stopwords.words(\\\"english\\\"): # the nltk lib. has a list of stopwords commonly used in english\\n avoided_words.append(word)\\n\\n tokens = nltk.word_tokenize(document)\",\n \"def get_question():\\n\\n fi = open('nlpcc-iccpol-2016.kbqa.training-data','r',encoding='utf8')\\n fii = open('nlpcc-iccpol-2016.kbqa.testing-data','r',encoding='utf8')\\n\\n q=''\\n\\n train = []\\n countChar = {}\\n m_word = 0\\n i = 0\\n for line in fi:\\n# print(f'line: {line}')\\n if line[1] == 'q':\\n# print(f'line Q: {line}')\\n q = line[line.index('\\\\t') + 1:].strip()\\n if len(q) > m_word:\\n m_word = len(q)\\n train.append(q)\\n# print(f'filtered Q: {q}')\\n for char in q:\\n if char not in countChar:\\n countChar[char] = 1\\n else:\\n countChar[char] += 1\\n# elif line[1] == 't':\\n# print(f'line P: {line}')\\n# sub = line[line.index('\\\\t') + 1:line.index(' |||')].strip()\\n# qNSub = line[line.index(' ||| ') + 5:]\\n# pre = qNSub[:qNSub.index(' |||')]\\n# print(f'sub:{sub}')\\n# print(f'qNSub:{qNSub}')\\n# print(f'pre:{pre}')\\n\\n test = []\\n for line in fii:\\n# print(f'line: {line}')\\n if line[1] == 'q':\\n# print(f'line Q: {line}')\\n q = line[line.index('\\\\t') + 1:].strip()\\n if len(q) > m_word:\\n m_word = len(q)\\n test.append(q)\\n# print(f'filtered Q: {q}')\\n for char in q:\\n if char not in countChar:\\n countChar[char] = 1\\n else:\\n countChar[char] += 1\\n \\n \\n with open('train.txt', 'w', encoding='utf-8') as f:\\n f.write('\\\\n'.join(train))\\n with open('test.txt', 'w', encoding='utf-8') as f:\\n f.write('\\\\n'.join(test))\\n \\n # Save\\n np.save('words.npy', countChar)\\n \\n print(m_word)\\n\\n# # Load npy dict\\n# read_dictionary = np.load('my_file.npy').item()\\n# print(read_dictionary['hello']) # displays \\\"world\\\"\\n \\n \\n return m_word\",\n \"def convert_examples_to_features(example, tokenizer=None, cls_token=None, sep_token=None,\\n vocab=None, max_seq_length=384, doc_stride=128,\\n max_query_length=64, is_training=True):\\n query_tokenized = tokenizer(example.question_text)[:max_query_length]\\n #tokenize paragraph and get start/end position of the answer in tokenized paragraph\\n paragraph_tokenized = tokenizer(example.paragraph_text)\\n\\n chartok_to_tok_index = [] # char to its corresponding token's index\\n tok_start_to_chartok_index = [] # token index to its first character's index\\n tok_end_to_chartok_index = [] # token index to its last character's index\\n char_cnt = 0\\n for i, token in enumerate(paragraph_tokenized):\\n chartok_to_tok_index.extend([i] * len(token))\\n tok_start_to_chartok_index.append(char_cnt)\\n char_cnt += len(token)\\n tok_end_to_chartok_index.append(char_cnt - 1)\\n\\n tok_cat_text = ''.join(paragraph_tokenized).replace(u'▁', ' ')\\n\\n # XLNet takes a more complicated strategy to match the origin text\\n # and the tokenized tokens\\n # Get the LCS matching between origin text and token-concatenated text.\\n n, m = len(example.paragraph_text), len(tok_cat_text)\\n max_dist = abs(n - m) + 5\\n for _ in range(2):\\n f, g = lcs_match(max_dist, example.paragraph_text, tok_cat_text)\\n if f[n - 1, m - 1] > 0.8 * n:\\n break\\n max_dist *= 2\\n\\n # Get the mapping from orgin text/tokenized text to tokenized text/origin text\\n orig_to_chartok_index = [None] * n\\n chartok_to_orig_index = [None] * m\\n i, j = n - 1, m - 1\\n while i >= 0 and j >= 0:\\n if (i, j) not in g:\\n break\\n if g[(i, j)] == 2:\\n orig_to_chartok_index[i] = j\\n chartok_to_orig_index[j] = i\\n i, j = i - 1, j - 1\\n elif g[(i, j)] == 1:\\n j = j - 1\\n else:\\n i = i - 1\\n\\n # get start/end mapping\\n tok_start_to_orig_index = []\\n tok_end_to_orig_index = []\\n for i in range(len(paragraph_tokenized)): # for each token in the tokenized paragraph\\n start_chartok_pos = tok_start_to_chartok_index[i] # first character's index in origin text\\n end_chartok_pos = tok_end_to_chartok_index[i] # last character's index in origin text\\n start_orig_pos = convert_index(chartok_to_orig_index, start_chartok_pos, n, is_start=True)\\n end_orig_pos = convert_index(chartok_to_orig_index, end_chartok_pos, m, is_start=False)\\n\\n tok_start_to_orig_index.append(start_orig_pos)\\n tok_end_to_orig_index.append(end_orig_pos)\\n\\n tok_start_position, tok_end_position = -1, -1\\n\\n # get mapped start/end position\\n if is_training and not example.is_impossible:\\n start_chartok_pos = convert_index(orig_to_chartok_index, example.start_offset,\\n is_start=True)\\n tok_start_position = chartok_to_tok_index[start_chartok_pos]\\n\\n end_chartok_pos = convert_index(orig_to_chartok_index, example.end_offset, is_start=False)\\n tok_end_position = chartok_to_tok_index[end_chartok_pos]\\n assert tok_start_position <= tok_end_position\\n\\n # get doc spans using sliding window\\n doc_spans, doc_spans_indices = get_doc_spans(paragraph_tokenized,\\n max_seq_length - len(query_tokenized) - 3,\\n doc_stride)\\n\\n # record whether the tokens in a docspan have max context\\n token_is_max_context = [{\\n p: check_is_max_context(doc_spans_indices, i, p + doc_spans_indices[i][0])\\n for p in range(len(doc_span))\\n } for (i, doc_span) in enumerate(doc_spans)]\\n\\n # get token -> origin text mapping\\n cur_tok_start_to_orig_index = [[tok_start_to_orig_index[p + st] for p in range(len(doc_span))]\\n for doc_span, (st, ed) in zip(doc_spans, doc_spans_indices)]\\n cur_tok_end_to_orig_index = [[tok_end_to_orig_index[p + st] for p in range(len(doc_span))]\\n for doc_span, (st, ed) in zip(doc_spans, doc_spans_indices)]\\n\\n # get sequence features: tokens, segment_ids, p_masks\\n seq_features = [\\n concat_sequences([doc_span, query_tokenized], [[sep_token]] * 2 + [[cls_token]],\\n [[0] * len(doc_span), [1] * len(query_tokenized)], [[1], [1], [0]])\\n for doc_span in doc_spans\\n ]\\n\\n # get the start/end positions aligned to doc spans. If is_impossible or position out of span\\n # set position to cls_index, i.e., last token in the sequence.\\n if not example.is_impossible:\\n positions = [\\n align_position2doc_spans([tok_start_position, tok_end_position], doc_idx, offset=0,\\n default_value=len(seq[0]) - 1)\\n for (doc_idx, seq) in zip(doc_spans_indices, seq_features)\\n ]\\n else:\\n positions = [(len(seq_feature[0]) - 1, len(seq_feature[0]) - 1)\\n for seq_feature in seq_features]\\n\\n features = [\\n SquadXLNetFeautre(example_id=example.example_id, qas_id=example.qas_id,\\n tok_start_to_orig_index=t2st, tok_end_to_orig_index=t2ed,\\n valid_length=len(tokens), tokens=tokens, token_is_max_context=is_max,\\n input_ids=vocab[tokens], p_mask=p_mask, segment_ids=segment_ids,\\n start_position=start, end_position=end,\\n paragraph_text=example.paragraph_text, paragraph_len=len(tokens),\\n is_impossible=(start == len(tokens) - 1))\\n for (tokens, segment_ids, p_mask), (\\n start,\\n end), is_max, t2st, t2ed in zip(seq_features, positions, token_is_max_context,\\n cur_tok_start_to_orig_index, cur_tok_end_to_orig_index)\\n ]\\n return features\",\n \"def tokenize(self):\\n count = 0\\n for entry in self._entries:\\n token_pairs = []\\n for relation in entry['relations']:\\n assert len(relation) == 3\\n token_pairs.append((relation[0][0],relation[1][0],relation[2][0]))\\n\\n num_rels = len(entry['relations'])\\n num_random_rels = (self._max_seq_length - 2) // 3 - num_rels\\n\\n if num_random_rels>0:\\n pass\\n # gt_pairs = {(rel[0],rel[2]) for rel in entry['relations']}\\n # random_pairs = self._get_random_pair(entry['objects'], gt_pairs, num_random_rels)\\n # for pair in list(random_pairs):\\n # token_pairs.append((pair[0][0],'background', pair[1][0]))\\n else:\\n for i in range(-num_random_rels):\\n token_pairs.pop()\\n\\n random.shuffle(token_pairs)\\n tokens = []\\n for pair in token_pairs:\\n tokens.extend(pair)\\n\\n tokens = ['[CLS]'] + tokens + ['[SEP]']\\n tokens_char = tokens\\n\\n target = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3==2 else -1 for i, x in enumerate(tokens)]\\n tokens = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3!=2 else self._tokenizer.vocab.get('[MASK]', self._tokenizer.vocab['[UNK]']) for i, x in enumerate(tokens)]\\n \\n for i in range(len(tokens)):\\n if target[i] != -1:\\n print(tokens_char[i],tokens[i],target[i])\\n\\n segment_ids = [0] * len(tokens)\\n input_mask = [1] * len(tokens)\\n # input_mask = [1 if i%3==2 else 0 for i in range(len(tokens))]\\n # co_attention_mask = [-1 if i%3==2 else 1 for i in range(len(tokens))]\\n # co_attention_mask = torch.zeros((self._max_region_num, self._max_seq_length))\\n # co_attention_mask[0] = -1\\n # co_attention_mask[-1] = -1\\n \\n if len(tokens) < self._max_seq_length:\\n padding = [self._padding_index] * (self._max_seq_length - len(tokens))\\n tokens = tokens + padding\\n input_mask += padding\\n segment_ids += padding \\n target += [-1] * len(padding) \\n\\n assert_eq(len(tokens), self._max_seq_length)\\n entry['input_ids'] = tokens \\n entry[\\\"input_mask\\\"] = input_mask\\n entry['segment_ids'] = segment_ids\\n # entry[\\\"co_attention_mask\\\"] = co_attention_mask\\n entry['target'] = target\\n\\n sys.stdout.write('%d/%d\\\\r' % (count, len(self._entries)))\\n sys.stdout.flush()\\n count += 1\",\n \"def add_special_tokens_(model, tokenizer, update_model=True):\\n orig_num_tokens = len(tokenizer)\\n num_added_tokens = tokenizer.add_special_tokens(ATTR_TO_SPECIAL_TOKEN) # doesn't add if they are already there\\n #print(\\\"coab::\\\",len(tokenizer.vocab))\\n if (num_added_tokens > 0 and update_model):\\n model.encoder.resize_token_embeddings(new_num_tokens=orig_num_tokens + num_added_tokens)\\n model.decoder.resize_token_embeddings(new_num_tokens=orig_num_tokens + num_added_tokens)\\n #print(model.encoder.embeddings.word_embeddings.weight.shape)\\n #print(model.decoder.bert.embeddings.word_embeddings.weight.shape)\",\n \"def get_features_infer(\\n queries: List[str],\\n tokenizer: TokenizerSpec,\\n max_seq_length: int = 64,\\n step: Optional[int] = 8,\\n margin: Optional[int] = 16,\\n audio_queries: Optional[Union[List[bytes], List[str]]] = None,\\n target_sr: Optional[int] = None,\\n) -> Tuple[\\n List[List[int]],\\n List[List[int]],\\n List[List[int]],\\n List[List[int]],\\n List[int],\\n List[int],\\n List[bool],\\n List[bool],\\n Optional[List[float]],\\n Optional[List[int]],\\n]:\\n st = []\\n stm = []\\n sent_lengths = []\\n audios = []\\n audio_queries = audio_queries if audio_queries else [None] * len(queries) # Dummy if no `audio_queries` passed\\n for i, (query, audio_query) in enumerate(zip(queries, audio_queries)):\\n subtokens, subtokens_mask = _get_subtokens_and_subtokens_mask(query, tokenizer)\\n sent_lengths.append(len(subtokens))\\n st.append(subtokens)\\n stm.append(subtokens_mask)\\n if audio_query:\\n if ASR_AVAILABLE:\\n if isinstance(audio_query, bytes):\\n audios.append(AudioSegment.from_file(io.BytesIO(audio_query), target_sr=target_sr))\\n elif isinstance(audio_query, str):\\n audios.append(AudioSegment.from_file(audio_query.strip(), target_sr=target_sr))\\n else:\\n raise ModuleNotFoundError(\\n 'Nemo ASR was not installed, see https://github.com/NVIDIA/NeMo#installation for installation instructions'\\n )\\n audios = audios if len(audios) else [None] * len(st)\\n _check_max_seq_length_and_margin_and_step(max_seq_length, margin, step)\\n if max_seq_length > max(sent_lengths) + 2:\\n max_seq_length = max(sent_lengths) + 2\\n # If `max_seq_length` is greater than maximum length of input query, parameters ``margin`` and ``step`` are\\n # not used will not be used.\\n step = 1\\n # Maximum number of word subtokens in segment. The first and the last tokens in segment are CLS and EOS\\n length = max_seq_length - 2\\n else:\\n # Maximum number of word subtokens in segment. The first and the last tokens in segment are CLS and EOS\\n length = max_seq_length - 2\\n step = min(length - margin * 2, step)\\n logging.info(f'Max length: {max_seq_length}')\\n get_stats(sent_lengths)\\n all_input_ids, all_segment_ids, all_subtokens_mask, all_input_mask, all_input_mask = [], [], [], [], []\\n all_quantities_of_preceding_words, all_query_ids, all_is_first, all_is_last = [], [], [], []\\n all_audio_queries, all_audio_lengths = [], []\\n for q_i, (query_st, query_audio) in enumerate(zip(st, audios)):\\n q_inp_ids, q_segment_ids, q_subtokens_mask, q_inp_mask, q_quantities_of_preceding_words = [], [], [], [], []\\n q_audio_queries, q_audio_lengths = [], []\\n if query_audio and length < len(query_st):\\n logging.info(f'Ignoring query with id {q_i}')\\n continue\\n for i in range(0, max(len(query_st), length) - length + step, step):\\n subtokens = [tokenizer.cls_token] + query_st[i : i + length] + [tokenizer.sep_token]\\n q_inp_ids.append(tokenizer.tokens_to_ids(subtokens))\\n q_segment_ids.append([0] * len(subtokens))\\n q_subtokens_mask.append([False] + stm[q_i][i : i + length] + [False])\\n q_inp_mask.append([True] * len(subtokens))\\n q_quantities_of_preceding_words.append(np.count_nonzero(stm[q_i][:i]))\\n if query_audio:\\n samples = query_audio.samples\\n q_audio_queries.append(samples)\\n q_audio_lengths.append(len(samples))\\n all_input_ids.append(q_inp_ids)\\n all_segment_ids.append(q_segment_ids)\\n all_subtokens_mask.append(q_subtokens_mask)\\n all_input_mask.append(q_inp_mask)\\n all_quantities_of_preceding_words.append(q_quantities_of_preceding_words)\\n all_query_ids.append([q_i] * len(q_inp_ids))\\n all_is_first.append([True] + [False] * (len(q_inp_ids) - 1))\\n all_is_last.append([False] * (len(q_inp_ids) - 1) + [True])\\n if query_audio:\\n all_audio_queries.append(q_audio_queries)\\n all_audio_lengths.append(q_audio_lengths)\\n return (\\n list(itertools.chain(*all_input_ids)),\\n list(itertools.chain(*all_segment_ids)),\\n list(itertools.chain(*all_input_mask)),\\n list(itertools.chain(*all_subtokens_mask)),\\n list(itertools.chain(*all_quantities_of_preceding_words)),\\n list(itertools.chain(*all_query_ids)),\\n list(itertools.chain(*all_is_first)),\\n list(itertools.chain(*all_is_last)),\\n list(itertools.chain(*all_audio_queries)),\\n list(itertools.chain(*all_audio_lengths)),\\n )\",\n \"def encode_batch(self, sentences: List[str], field_name, features=None) -> List[SequentialField]:\\n # We need to set this so that `tokenizers` package does not complain about detecting forks.\\n os.environ['TOKENIZERS_PARALLELISM'] = \\\"true\\\"\\n\\n if features is None:\\n features = []\\n extract_word_pieces = False\\n else:\\n assert all([len(sentence.split()) == len(feature) for sentence, feature in zip(sentences, features)])\\n extract_word_pieces = True\\n\\n batch_size = len(sentences)\\n\\n if field_name != 'answer':\\n sentences = [self.input_prefix + sent for sent in sentences]\\n\\n if self._preprocess_special_tokens:\\n if len(sentences) > MULTIPROCESSING_THRESHOLD:\\n multiprocessing_factor = multiprocessing.cpu_count() // len(get_devices(self.args.devices))\\n logger.info('multiprocessing factor for special token preprocessing is %d', multiprocessing_factor)\\n with multiprocessing.Pool(multiprocessing_factor) as p:\\n sentences, index2expansions = list(\\n zip(\\n *p.map(\\n functools.partial(self._apply_special_token_preprocessing, return_idx2exp=bool(len(features))),\\n sentences,\\n )\\n )\\n )\\n else:\\n sentences, index2expansions = list(\\n zip(\\n *map(\\n functools.partial(self._apply_special_token_preprocessing, return_idx2exp=bool(len(features))),\\n sentences,\\n )\\n )\\n )\\n\\n all_input_features = []\\n if features:\\n for i, (sentence, index2expansion) in enumerate(zip(sentences, index2expansions)):\\n feat = features[i]\\n new_feat = []\\n keys = set(index2expansion.keys())\\n for j in range(len(feat)):\\n repeat = 1\\n if j in keys:\\n repeat = index2expansion[j]\\n\\n new_feat.extend(feat[j] * repeat)\\n\\n assert len(new_feat) == len(sentence.split(' '))\\n\\n all_input_features.append(new_feat)\\n\\n features = all_input_features\\n\\n # batch_encode_plus for fast tokenizers returns tokenized text\\n # whereas slow version do not. We breakdown slow tokenization into two steps\\n # extract tokenized text first, use that to adjust features\\n # then pass tokenized text to `_batch_prepare_for_model`\\n def do_fast_tokenization(extract_word_pieces):\\n all_wp_tokenized = []\\n batch_encoded = self._tokenizer.batch_encode_plus(\\n list(sentences),\\n add_special_tokens=True,\\n max_length=None,\\n return_length=True,\\n return_attention_mask=False,\\n return_special_tokens_mask=True,\\n )\\n if extract_word_pieces:\\n for encoding in batch_encoded.encodings:\\n # remove special tokens\\n num_prefix_special_tokens, num_suffix_special_tokens = self.get_num_special_tokens(\\n encoding.special_tokens_mask\\n )\\n wp_tokens = encoding.tokens[num_prefix_special_tokens:-num_suffix_special_tokens]\\n all_wp_tokenized.append(wp_tokens)\\n\\n return batch_encoded, all_wp_tokenized\\n\\n def do_slow_tokenization(extract_word_pieces):\\n all_input_ids = []\\n all_wp_tokenized = []\\n if extract_word_pieces:\\n for i in range(batch_size):\\n text = sentences[i]\\n wp_tokenized = self._tokenizer.tokenize(text)\\n all_wp_tokenized.append(wp_tokenized)\\n\\n # None indicates encoding single instance not paired inputs\\n all_input_ids.append((self._tokenizer.convert_tokens_to_ids(wp_tokenized), None))\\n\\n batch_encoded = self._tokenizer._batch_prepare_for_model(\\n all_input_ids,\\n add_special_tokens=True,\\n max_length=None,\\n return_length=True,\\n return_attention_mask=False,\\n return_special_tokens_mask=True,\\n )\\n else:\\n batch_encoded = self._tokenizer.batch_encode_plus(\\n list(sentences),\\n add_special_tokens=True,\\n max_length=None,\\n return_length=True,\\n return_attention_mask=False,\\n return_special_tokens_mask=True,\\n )\\n return batch_encoded, all_wp_tokenized\\n\\n if self._use_fast():\\n if field_name == 'answer':\\n with self._tokenizer.as_target_tokenizer():\\n batch_encoded, all_wp_tokenized = do_fast_tokenization(extract_word_pieces)\\n else:\\n batch_encoded, all_wp_tokenized = do_fast_tokenization(extract_word_pieces)\\n\\n else:\\n if field_name == 'answer':\\n with self._tokenizer.as_target_tokenizer():\\n batch_encoded, all_wp_tokenized = do_slow_tokenization(extract_word_pieces)\\n else:\\n batch_encoded, all_wp_tokenized = do_slow_tokenization(extract_word_pieces)\\n\\n all_input_features = []\\n\\n if features:\\n for i in range(batch_size):\\n wp_features = []\\n wp_tokenized = all_wp_tokenized[i]\\n\\n feat = features[i]\\n\\n # first token is always not a piece\\n is_wp = [0] + [int(self._tokenizer.is_piece_fn(wp)) for wp in wp_tokenized[1:]]\\n k = -1\\n for j, wp in enumerate(wp_tokenized):\\n if not is_wp[j]:\\n k += 1\\n wp_features.append(feat[k])\\n\\n assert len(wp_tokenized) == len(wp_features)\\n\\n all_input_features.append(wp_features)\\n\\n features = all_input_features\\n\\n batch_special_tokens_mask = batch_encoded.special_tokens_mask\\n\\n batch_features = []\\n\\n if features:\\n for i in range(batch_size):\\n feat = features[i]\\n special_tokens_mask = batch_special_tokens_mask[i]\\n num_prefix_special_tokens, num_suffix_special_tokens = self.get_num_special_tokens(special_tokens_mask)\\n\\n pad_feat = Entity.get_pad_entity(self.args.max_features_size)\\n feat = [pad_feat] * num_prefix_special_tokens + feat + [pad_feat] * num_suffix_special_tokens\\n\\n batch_features.append(feat)\\n\\n batch_numerical = batch_encoded.input_ids\\n batch_length = batch_encoded.length\\n\\n batch_decoder_numerical = []\\n if self.decoder_vocab:\\n for i in range(batch_size):\\n batch_decoder_numerical.append(self.decoder_vocab.encode(batch_numerical[i]))\\n else:\\n batch_decoder_numerical = [[]] * len(batch_numerical)\\n\\n sequential_fields = []\\n for i in range(batch_size):\\n if features:\\n feature = [feat.flatten() for feat in batch_features[i]]\\n assert len(batch_numerical[i]) == len(feature)\\n else:\\n feature = None\\n\\n sequential_fields.append(\\n SequentialField(\\n value=batch_numerical[i],\\n length=batch_length[i],\\n limited=batch_decoder_numerical[i],\\n feature=feature,\\n )\\n )\\n return sequential_fields\",\n \"def load_as_raw(self):\\n\\n # Q & A\\n questions, answers = self.get_questions_answers()\\n\\n # Get vocabs\\n\\n # Step 4: cleaning the questions\\n pprint('---- Step 4 cleaning questions ----')\\n\\n clean_questions = []\\n for question in questions:\\n clean_questions.append(clean_text(question))\\n\\n pprint(clean_questions, stream=Head(5))\\n print('\\\\n\\\\n')\\n \\\"\\\"\\\"\\n Step 5: Clean the answers\\n \\\"\\\"\\\"\\n\\n pprint('---- Step 5 cleaning answers ----')\\n clean_answers = []\\n for answer in answers:\\n clean_answers.append(clean_text(answer))\\n\\n pprint(clean_answers, stream=Head(5))\\n print('\\\\n\\\\n')\\n \\\"\\\"\\\"\\n Step 6: Creating a dictionary that maps each word to its number of occurences\\n \\\"\\\"\\\"\\n\\n word2count = {}\\n pprint('------ Step 6: counting words in questions ----')\\n\\n word2count = convert_word_to_count(word2count, clean_questions)\\n\\n pprint(word2count, stream=Head(5))\\n print('\\\\n\\\\n')\\n \\\"\\\"\\\"\\n Step 7:\\n For example, for a question: can we make this quick roxanne korrine and andrew barrett are having an incredibly horrendous public break up on the quad again\\n It counts each word occurence such as \\\"can\\\" and accumulates the count into word2count dict\\n \\\"\\\"\\\"\\n pprint('------ Step 6: counting words in answers ----')\\n\\n word2count = convert_word_to_count(word2count, clean_answers)\\n\\n pprint(word2count, stream=Head(5))\\n print('\\\\n\\\\n')\\n\\n keys = ['<unk>', '<s>', '</s>']\\n\\n \\\"\\\"\\\"\\n Step 8: Creating word 2 int(count) by filtering words that are greater than the threshold\\n \\\"\\\"\\\"\\n\\n pprint(\\n '------ Step 8: questions_vocabs filtered by threshold (>) ----')\\n threshold_questions = 20\\n questions_vocabs = [] + keys\\n for word, count in word2count.items():\\n if count >= threshold_questions:\\n if not word in questions_vocabs:\\n questions_vocabs.append(word)\\n\\n pprint(questions_vocabs, stream=Head(5))\\n print('\\\\n\\\\n')\\n \\\"\\\"\\\"\\n Step 9: Same as step 8 but for answers\\n \\\"\\\"\\\"\\n pprint(\\n '------ Step 9: answers_vocabs filtered by threshold (>) ----')\\n threshold_answers = 20\\n answers_vocabs = [] + keys\\n for word, count in word2count.items():\\n if count >= threshold_answers:\\n if not word in answers_vocabs:\\n answers_vocabs.append(word)\\n\\n pprint(answers_vocabs, stream=Head(5))\\n\\n return questions, answers, questions_vocabs, answers_vocabs\",\n \"def build_dataset(data, vocab_size=50000):\\r\\n\\r\\n # we will replace non-frequent tokens with the `unknown` token\\r\\n unk_token = '<UNK>'\\r\\n\\r\\n # calc frequencies of the tokens in our data\\r\\n tokens_counts = Counter(data)\\r\\n most_common_tokens = tokens_counts.most_common(vocab_size)\\r\\n\\r\\n # create a token => id mapping\\r\\n token2id = {unk_token: 0}\\r\\n for token, counts in most_common_tokens:\\r\\n token2id[token] = len(token2id)\\r\\n\\r\\n # create a reverse mapping from ids to tokens\\r\\n id2token = {i: t for t, i in token2id.items()}\\r\\n\\r\\n # convert data to tokens ids\\r\\n nb_unks = 0\\r\\n data_tokens_ids = []\\r\\n for token in data:\\r\\n if token in token2id:\\r\\n idx = token2id[token]\\r\\n else:\\r\\n idx = token2id[unk_token]\\r\\n nb_unks += 1\\r\\n\\r\\n data_tokens_ids.append(idx)\\r\\n\\r\\n print('Vocab size:', len(token2id))\\r\\n print('Unknown tokens:', nb_unks)\\r\\n\\r\\n return data_tokens_ids, token2id, id2token\",\n \"def question_to_vec(question, embeddings):\\n\\n dim = embeddings['dog'].size\\n result = np.zeros((dim,))\\n\\n words = question.split(' ')\\n\\n count = 0\\n for word in words:\\n if word not in embeddings or not len(embeddings[word]):\\n continue\\n result += embeddings[word][:dim]\\n count += 1\\n\\n return result / max(count, 1)\",\n \"def convert_to_ids(self, vocab):\\n for data_set in [self.train_set, self.dev_set, self.test_set]:\\n if data_set is None:\\n continue\\n for sample in data_set:\\n sample['question_token_ids'] = vocab.convert_to_ids(sample['question_tokens'])\\n for passage in sample['passages']:\\n passage['passage_token_ids'] = vocab.convert_to_ids(passage['passage_tokens'])\",\n \"def clean_questions(self, q_dot=False):\\n for q in self.data['questions']:\\n temp = re.sub('[^ a-zA-Z0-9]', '', q['body'])\\n temp = temp.strip().lower()\\n # temp = self.stemming(temp).strip()\\n self.cleaned_questions_q_dot += temp + '?\\\\n'\\n self.cleaned_questions += temp + '\\\\n'\\n self.types_q += q['type'].strip() + '\\\\n'\",\n \"def encode_and_flatten(\\n raw_text_iter: IterableDataset[str],\\n tokenizer: Callable[[str], list[str]],\\n vocab: Vocab,\\n) -> torch.Tensor:\\n data = [\\n torch.tensor(vocab(tokenizer(item)), dtype=torch.long)\\n for item in raw_text_iter\\n ]\\n return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))\",\n \"def _tokenize_data(train_dataset, dev_dataset, use_spacy, pool):\\n tokenizer = SQuADDataTokenizer(use_spacy)\\n\\n tic = time.time()\\n print('Train examples [{}] transformation started.'.format(len(train_dataset)))\\n train_examples = list(tqdm.tqdm(tokenizer.run_async(pool, train_dataset),\\n total=len(train_dataset)))\\n print('Train examples transformed [{}/{}] in {:.3f} sec'.format(len(train_examples),\\n len(train_dataset),\\n time.time() - tic))\\n tic = time.time()\\n print('Dev examples [{}] transformation started.'.format(len(dev_dataset)))\\n dev_examples = list(tqdm.tqdm(tokenizer.run_async(pool, dev_dataset),\\n total=len(dev_dataset)))\\n print('Dev examples transformed [{}/{}] in {:.3f} sec'.format(len(dev_examples),\\n len(dev_dataset),\\n time.time() - tic))\\n return train_examples, dev_examples\",\n \"def vectorize_data(data, word_idx, sentence_size, memory_size,answer_size):\\n S = []\\n Q = []\\n AS = []\\n L = []\\n label_nums = set([len(dt[2]) for dt in data])\\n assert(len(label_nums) == 1)\\n label_num = list(label_nums)[0]\\n for story, query, answer, label in data:\\n lq = max(0, sentence_size - len(query))\\n q = [word_idx[w] for w in query] + [0] * lq\\n\\n ss = []\\n for i, sentence in enumerate(story, 1):\\n ls = max(0, sentence_size - len(sentence))\\n ss.append([word_idx[w] for w in sentence] + [0] * ls)\\n\\n if len(ss) > memory_size:\\n # Use Jaccard similarity to determine the most relevant sentences\\n q_words = set(q)\\n least_like_q = sorted(ss, cmp = lambda x, y: jaccard(set(x), q_words) < jaccard(set(y), q_words))[:len(ss)-memory_size]\\n for sent in least_like_q:\\n # Remove the first occurrence of sent. A list comprehension as in [sent for sent in ss if sent not in least_like_q]\\n # should not be used, as it would remove multiple occurrences of the same sentence, some of which might actually make the cutoff.\\n ss.remove(sent)\\n else:\\n # pad to memory_size\\n lm = max(0, memory_size - len(ss))\\n for _ in range(lm):\\n ss.append([0] * sentence_size)\\n\\n y = np.zeros(len(word_idx) + 1) # 0 is reserved for nil word\\n\\n sa = []\\n for i, sentence in enumerate(answer, 1):\\n ls = max(0, answer_size - len(sentence))\\n sa.append([word_idx[w] for w in sentence] + [0] * ls)\\n\\n lb = np.zeros(label_num)\\n lb[int(label)] = 1\\n\\n S.append(ss)\\n Q.append(q)\\n AS.append(sa)\\n L.append(lb)\\n Q = pad_sequences(Q, sentence_size)\\n\\n return np.array(S), np.array(Q), np.array(AS), np.array(L)\",\n \"def tokenize(self, inputs):\\n if hasattr(self.tokenizer, \\\"batch_encode\\\"):\\n return self.tokenizer.batch_encode(inputs)\\n else:\\n return [self.tokenizer.encode(x) for x in inputs]\",\n \"def ques_lstm(self, word_embeddings):\\n nlayer = self.config.nrnn_layer\\n lstm_cell = tf.contrib.rnn.BasicLSTMCell(\\n num_units=self.config.rnn_size, state_is_tuple=True)\\n lstm_cell = tf.contrib.rnn.MultiRNNCell(\\n [lstm_cell] * nlayer, state_is_tuple=True)\\n\\n with tf.variable_scope(\\n 'ques_lstm', initializer=self.initializer) as scope:\\n # Run the batch of sequence embeddings through the LSTM.\\n assert (self.feed_per_seq_length != None)\\n _, lstm_state = tf.nn.dynamic_rnn(\\n cell=lstm_cell,\\n inputs=word_embeddings,\\n sequence_length=self.feed_per_seq_length,\\n dtype=tf.float32)\\n\\n # extract c and h, then concat them all\\n ques_encoding_lis = []\\n for i in range(nlayer):\\n ques_encoding_lis = ques_encoding_lis + [\\n lstm_state[i].c, lstm_state[i].h\\n ]\\n ques_encoding = tf.concat(ques_encoding_lis, axis=1)\\n\\n assert (ques_encoding.shape[1] == self.config.rnnout_dim)\\n\\n return ques_encoding\",\n \"def prepare_inputs(token_mapping, w2v_W, w2v_U, sentences):\\n tokens = [tokenize(token_mapping, sentence) for sentence in sentences] \\n \\n depth = len(token_mapping)\\n one_hot_tokens = []\\n for sentence in tokens:\\n one_hot_sentence = []\\n for i, token in enumerate(sentence):\\n if token != token_mapping['#UNK#']:\\n one_hot_sentence.append(one_hot_encode(token, depth))\\n else:\\n if i <= 2:\\n context_tokens = sentence[:i] + sentence[i+1:i+3]\\n else:\\n context_tokens = sentence[i-2:i] + sentence[i+1:i+3]\\n context_one_hot = [one_hot_encode(token, depth) for token in context_tokens]\\n context_mean = np.mean(np.asarray(context_one_hot), axis=0)\\n one_hot_sentence.append(context_mean)\\n one_hot_tokens.append(one_hot_sentence)\\n \\n one_hot_tokens = [np.asarray(ls) for ls in one_hot_tokens]\\n vec_tokens = [word2vec(w2v_W, w2v_U, sentence) for sentence in tqdm(one_hot_tokens, desc='Vectorizing tokens')]\\n return vec_tokens\",\n \"def convert_examples_to_features(examples, tokenizer, max_seq_length,\\n doc_stride, max_query_length, is_training):\\n\\n unique_id = 1000000000\\n\\n features = []\\n for (example_index, example) in enumerate(examples):\\n query_tokens = tokenizer.tokenize(example.question_text)\\n\\n if len(query_tokens) > max_query_length:\\n query_tokens = query_tokens[0:max_query_length]\\n \\n if len(query_tokens) < 3:\\n for i in range(len(query_tokens), 3):\\n query_tokens.append('.')\\n\\n tok_to_orig_index = []\\n orig_to_tok_index = []\\n all_doc_tokens = []\\n for (i, token) in enumerate(example.doc_tokens):\\n orig_to_tok_index.append(len(all_doc_tokens))\\n sub_tokens = tokenizer.tokenize(token)\\n for sub_token in sub_tokens:\\n tok_to_orig_index.append(i)\\n all_doc_tokens.append(sub_token)\\n\\n tok_start_position = None\\n tok_end_position = None\\n if is_training:\\n tok_start_position = orig_to_tok_index[example.start_position]\\n if example.end_position < len(example.doc_tokens) - 1:\\n tok_end_position = orig_to_tok_index[example.end_position + 1] - 1\\n else:\\n tok_end_position = len(all_doc_tokens) - 1\\n (tok_start_position, tok_end_position) = _improve_answer_span(\\n all_doc_tokens, tok_start_position, tok_end_position, tokenizer,\\n example.orig_answer_text)\\n\\n # The -3 accounts for [CLS], [SEP] and [SEP]\\n max_tokens_for_doc = max_seq_length - len(query_tokens) - 3\\n\\n # We can have documents that are longer than the maximum sequence length.\\n # To deal with this we do a sliding window approach, where we take chunks\\n # of the up to our max length with a stride of `doc_stride`.\\n _DocSpan = collections.namedtuple( # pylint: disable=invalid-name\\n \\\"DocSpan\\\", [\\\"start\\\", \\\"length\\\"])\\n doc_spans = []\\n start_offset = 0\\n while start_offset < len(all_doc_tokens):\\n length = len(all_doc_tokens) - start_offset\\n if length > max_tokens_for_doc:\\n length = max_tokens_for_doc\\n doc_spans.append(_DocSpan(start=start_offset, length=length))\\n if start_offset + length == len(all_doc_tokens):\\n break\\n start_offset += min(length, doc_stride)\\n\\n for (doc_span_index, doc_span) in enumerate(doc_spans):\\n \\n q_len = 0\\n d_len = 0\\n\\n tokens = []\\n token_to_orig_map = {}\\n token_is_max_context = {}\\n segment_ids = []\\n tokens.append(\\\"[CLS]\\\")\\n segment_ids.append(0)\\n for token in query_tokens:\\n tokens.append(token)\\n segment_ids.append(0)\\n q_len += 1\\n tokens.append(\\\"[SEP]\\\")\\n segment_ids.append(0)\\n\\n for i in range(doc_span.length):\\n split_token_index = doc_span.start + i\\n token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\\n\\n is_max_context = _check_is_max_context(doc_spans, doc_span_index,\\n split_token_index)\\n token_is_max_context[len(tokens)] = is_max_context\\n tokens.append(all_doc_tokens[split_token_index])\\n segment_ids.append(1)\\n d_len += 1\\n tokens.append(\\\"[SEP]\\\")\\n segment_ids.append(1)\\n\\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\\n\\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\\n # tokens are attended to.\\n input_mask = [1] * len(input_ids)\\n\\n # Zero-pad up to the sequence length.\\n while len(input_ids) < max_seq_length:\\n input_ids.append(0)\\n input_mask.append(0)\\n segment_ids.append(0)\\n\\n assert len(input_ids) == max_seq_length\\n assert len(input_mask) == max_seq_length\\n assert len(segment_ids) == max_seq_length\\n\\n start_position = None\\n end_position = None\\n is_select = None\\n has_answer = None\\n if is_training:\\n # For training, if our document chunk does not contain an annotation\\n # we throw it out, since there is nothing to predict.\\n doc_start = doc_span.start\\n doc_end = doc_span.start + doc_span.length - 1\\n if (example.start_position < doc_start or\\n example.end_position < doc_start or\\n example.start_position > doc_end or example.end_position > doc_end):\\n continue\\n\\n doc_offset = len(query_tokens) + 2\\n start_position = tok_start_position - doc_start + doc_offset\\n end_position = tok_end_position - doc_start + doc_offset\\n is_select = example.is_select\\n has_answer = example.has_answer\\n\\n features.append(\\n InputFeatures(\\n unique_id=unique_id,\\n example_index=example_index,\\n doc_span_index=doc_span_index,\\n tokens=tokens,\\n token_to_orig_map=token_to_orig_map,\\n token_is_max_context=token_is_max_context,\\n input_ids=input_ids,\\n input_mask=input_mask,\\n segment_ids=segment_ids,\\n q_len=q_len,\\n d_len=d_len,\\n start_position=start_position,\\n end_position=end_position,\\n is_select=is_select,\\n has_answer=has_answer))\\n unique_id += 1\\n break\\n\\n return features\",\n \"def preprocess(self, df, maxlen = 169):\\n \\n vocabs = self.tk.word_index.keys()\\n \\n df1 = self.treat_na(df)\\n df2 = self.remove_punc_sw(df1)\\n df3 = self.remove_numbers(df2)\\n df4 = self.lemma_pos(df3)\\n df5 = self.bigram(df4)\\n df6 = self.combine_bigrams(df5)\\n \\n new_docs = []\\n \\n for word_list in df6:\\n \\n if len(word_list) == 2 and word_list[0].lower() == 'noinfo' and word_list[1].lower() == 'noinfo':\\n new_docs.append(list(np.zeros(maxlen)))\\n \\n else:\\n new_word_list = []\\n for word in word_list:\\n if word not in vocabs:\\n word = 'UNKNOWN_TOKEN'\\n new_word_list.append(word)\\n \\n sequence = \\\" \\\".join(new_word_list)\\n vectors = self.tk.texts_to_sequences([sequence])\\n padded_vectors = pad_sequences(vectors, maxlen=maxlen, padding='post', truncating='post')\\n \\n new_docs.append(list(padded_vectors[0]))\\n \\n return new_docs\",\n \"def tokenize(self):\\n\\n x = [] # input documents\\n for file_path in glob.glob(self.train_dir + '*.txt'):\\n file_as_string = open(file_path).read()\\n x.append(file_as_string)\\n\\n self.tokenizer.fit_on_texts(x)\\n print('input vocabulary size:', len(self.tokenizer.word_index))\\n pickle_file = open('Model/tokenizer.p', 'wb')\\n pickle.dump(self.tokenizer, pickle_file)\",\n \"def prepare(self):\\n # get data from file\\n train_data, test_data = return_speechacts()\\n # y are the speechacts or 'labels'\\n y_train = [t.split(' ')[0] for t in train_data]\\n y_test = [t.split(' ')[0] for t in test_data]\\n # x are the sentences\\n x_train = [\\\" \\\".join(t.split(' ')[1:]) for t in train_data]\\n x_test = [\\\" \\\".join(t.split(' ')[1:]) for t in test_data]\\n # use the tokenizer and padding from keras to assign arrays of integers\\n # to sentences, out of vocabulary token is 1\\n self.tokenizer_x = Tokenizer(oov_token=1)\\n self.tokenizer_x.fit_on_texts(x_train + x_test)\\n xt_train = self.tokenizer_x.texts_to_sequences(x_train)\\n xt_train = pad_sequences(xt_train, maxlen=self.sentence_size,\\n dtype='int32')\\n xt_test = self.tokenizer_x.texts_to_sequences(x_test)\\n xt_test = pad_sequences(xt_test, maxlen=self.sentence_size,\\n dtype='int32')\\n # vocab is the number of words in our vocabulary\\n self.vocab = len(self.tokenizer_x.word_index) + 1\\n # do the same for labels\\n self.tokenizer_y = Tokenizer()\\n self.tokenizer_y.fit_on_texts(y_train + y_test)\\n yt_train = self.tokenizer_y.texts_to_sequences(y_train)\\n yt_train = [t[0] for t in yt_train]\\n yt_train = to_categorical(yt_train)\\n yt_test = self.tokenizer_y.texts_to_sequences(y_test)\\n yt_test = [t[0] for t in yt_test]\\n yt_test = to_categorical(yt_test)\\n self.x_train = x_train\\n self.y_train = y_train\\n self.x_test = x_test\\n self.y_test = y_test\\n self.xt_train = xt_train\\n self.yt_train = yt_train\\n self.xt_test = xt_test\\n self.yt_test = yt_test\",\n \"def generate_corpus():\\n data = load_data()\\n questions = [s.split(' ', 1)[1].lower() for s in data]\\n return questions\",\n \"def tokenize(lang):\\n lang_tokenizer = tf.keras.preprocessing.text.Tokenizer(filters='')\\n lang_tokenizer.fit_on_texts(lang)\\n tensor = lang_tokenizer.texts_to_sequences(lang)\\n # pad zero after sequences for the same length.\\n tensor = tf.keras.preprocessing.sequence.pad_sequences(tensor,\\n padding='post')\\n return tensor, lang_tokenizer\",\n \"def example_to_token_ids_segment_ids_label_ids(\\n ex_index,\\n example,\\n max_seq_length,\\n max_evid_length,\\n tokenizer):\\n if ex_index < 5:\\n print('*** Example {} ***'.format(ex_index))\\n print('qid: {}'.format(example.qid))\\n\\n qid = example.qid\\n\\n # evidence\\n ante_evidence = example.evidence # list of strings.\\n ante_token_ids, ante_segment_ids = [], []\\n for ante_sent in ante_evidence:\\n ante_sent_tokens = tokenizer.tokenize(ante_sent)\\n ante_sent_tokens = ante_sent_tokens[:max_evid_length-2]\\n sent_tokens, sent_segment_ids = [], []\\n sent_tokens.append(\\\"<s>\\\")\\n sent_segment_ids.append(0)\\n sent_tokens += ante_sent_tokens + [\\\"</s>\\\"]\\n sent_segment_ids += [0] * len(ante_sent_tokens) + [0]\\n\\n assert len(sent_tokens) <= max_evid_length\\n sent_tokens_ids = tokenizer.convert_tokens_to_ids(sent_tokens)\\n\\n assert len(sent_tokens_ids) == len(sent_segment_ids)\\n ante_token_ids.append(sent_tokens_ids)\\n ante_segment_ids.append(sent_segment_ids)\\n assert len(ante_evidence) == len(ante_token_ids)\\n assert len(ante_evidence) == len(ante_segment_ids)\\n l_ante = min(len(ante_evidence), max_evid_length)\\n\\n para_tokens = tokenizer.tokenize(example.para)\\n question_tokens = tokenizer.tokenize(example.question)\\n answers_tokens = map(tokenizer.tokenize, example.answers)\\n\\n token_ids = []\\n segment_ids = []\\n for choice_idx, answer_tokens in enumerate(answers_tokens):\\n\\n truncated_para_tokens, \\\\\\n truncated_question_tokens,\\\\\\n truncated_answer_tokens = _truncate_seq_pair(para_tokens, question_tokens, answer_tokens, max_seq_length)\\n\\n choice_tokens = []\\n choice_segment_ids = []\\n choice_tokens.append(\\\"<s>\\\")\\n choice_segment_ids.append(0)\\n choice_tokens += truncated_para_tokens + [\\\"</s>\\\"]\\n choice_segment_ids += [0] * len(truncated_para_tokens) + [0]\\n choice_tokens += truncated_question_tokens + [\\\"</s>\\\"]\\n choice_segment_ids += [1] * len(truncated_question_tokens) + [1]\\n choice_tokens += truncated_answer_tokens\\n choice_segment_ids += [1] * len(truncated_answer_tokens)\\n\\n choice_token_ids = tokenizer.convert_tokens_to_ids(choice_tokens)\\n\\n token_ids.append(choice_token_ids)\\n segment_ids.append(choice_segment_ids)\\n\\n label_ids = [example.label]\\n\\n return ante_token_ids, ante_segment_ids, l_ante, \\\\\\n token_ids, segment_ids, label_ids, qid\",\n \"def get_tokenizer(stories, word_table):\\n tokens_all = []\\n for story, query, answer, task_id in stories:\\n for sentence in story:\\n word_table.add_vocab(*sentence)\\n word_table.count_doc(*sentence)\\n tokens_all.extend(query + [answer])\\n word_table.add_vocab(*tokens_all)\",\n \"def _resize_token_embeddings(\\n cls, model: PreTrainedModel, tokenizer_wrapper: TokenizerWrapper\\n ):\\n if tokenizer_wrapper.num_added_special_tokens > 0:\\n model.resize_token_embeddings(\\n new_num_tokens=len(tokenizer_wrapper.tokenizer)\\n )\",\n \"def _create_input(inputs: List[Tensor], initial: bool = False) \\\\\\n -> Dict[str, Tensor]:\\n word_embed = torch.stack(inputs, dim=0)\\n seq_len, batch_size, embed_dim = word_embed.size()\\n if not initial:\\n # Add a dummy token at the end that stands for the token\\n # to predict.\\n word_embed = torch.cat([\\n word_embed,\\n word_embed.new_zeros(1, batch_size, embed_dim)\\n ], dim=0)\\n seq_len += 1\\n segment_ids = word_embed.new_zeros(\\n seq_len, batch_size, dtype=torch.long)\\n return_dict = {\\n \\\"word_embed\\\": word_embed,\\n \\\"segment_ids\\\": segment_ids,\\n }\\n\\n if not initial:\\n # Only the dummy token is considered target.\\n target_mapping = torch.cat([\\n torch.zeros(1, seq_len - 1, batch_size),\\n torch.ones(1, 1, batch_size)\\n ], dim=1).to(device=word_embed.device)\\n # Dummy token attends to nothing; actual tokens attend to all.\\n permute_mask = torch.cat([\\n torch.zeros(seq_len, seq_len - 1, batch_size),\\n torch.ones(seq_len, 1, batch_size),\\n ], dim=1).to(device=word_embed.device)\\n return_dict.update({\\n \\\"target_mapping\\\": target_mapping,\\n \\\"permute_mask\\\": permute_mask,\\n })\\n\\n return return_dict\",\n \"def get_qald_metrics(pred_, m_labels_, ques_, mode='val'):\\n rows = []\\n question_rows_map = {}\\n question_mention_set = set()\\n for i, pred in enumerate(pred_):\\n pred = pred.data.tolist()[0]\\n question = ques_[i]\\n if question not in question_rows_map:\\n question_rows_map[ques_[i]] = []\\n if pred:\\n men_entity_label = '_'.join(m_labels_[i].split(';')[-1].split())\\n men_entity_mention = '_'.join(m_labels_[i].split(';')[0].split())\\n if '-'.join([question, men_entity_mention]) in question_mention_set:\\n question_rows_map[ques_[i]][-1].add(\\n ('http://dbpedia.org/resource/{}'.format(men_entity_label), pred))\\n else:\\n question_mention_set.add('-'.join([question, men_entity_mention]))\\n question_rows_map[ques_[i]].append(set())\\n question_rows_map[ques_[i]][-1].add(\\n ('http://dbpedia.org/resource/{}'.format(men_entity_label), pred))\\n for key, preds_list_mentions in question_rows_map.items():\\n if len(preds_list_mentions) > 1:\\n rows.append([key, []])\\n for preds_set in preds_list_mentions:\\n sorted_values = sorted(list(preds_set), key=lambda x: x[1], reverse=True)[:5]\\n rows[-1][1].append(sorted_values)\\n elif len(preds_list_mentions) == 1:\\n sorted_values = sorted(list(preds_list_mentions[0]), key=lambda x: x[1], reverse=True)[:5]\\n rows.append([key, [sorted_values]])\\n else:\\n rows.append([key, []])\\n\\n df_output = pd.DataFrame(rows, columns=['Question', 'Entities'])\\n df_output['Classes'] = str([])\\n\\n # gold\\n benchmark = pd.read_csv('../../../data/lcquad/lcquad_gt.csv')\\n benchmark = benchmark.set_index('Question')\\n benchmark = benchmark.replace(np.nan, '', regex=True)\\n benchmark['Entities'] = benchmark['Entities'].astype(object)\\n is_qald_gt = True\\n\\n # pred\\n predictions = df_output\\n # print(df_output.shape)\\n predictions = predictions.set_index('Question')\\n predictions['Entities'] = predictions['Entities']\\n predictions['Classes'] = predictions['Classes']\\n\\n metrics = compute_metrics(benchmark=benchmark, predictions=predictions, limit=410, is_qald_gt=is_qald_gt,\\n eval='full')\\n\\n scores = metrics['macro']['named']\\n prec, recall, f1 = scores['precision'], scores['recall'], scores['f1']\\n return prec, recall, f1, df_output\",\n \"def tokenize_docs(self):\\n if not hasattr(self,\\\"max_seq_len\\\"):\\n self.max_seq_len = 0\\n \\n context_cols = self.cols\\n\\n for df in [self.train, self.val, self.test]:\\n df[\\\"tok_context\\\"] = df[context_cols].values.tolist()\\n df[\\\"tok_context\\\"] = df[\\\"tok_context\\\"].apply(self.docs_to_ids)\\n self.max_seq_len = max(self.max_seq_len,\\n df[\\\"tok_context\\\"].apply(\\n lambda x: max(map(len,x))).max())\\n \\n self.train[\\\"tok_ending\\\"] = self.train[self.train_cols[-1]].apply(\\n self.str_to_ids)\\n self.val[\\\"tok_ending_1\\\"] = self.val[self.test_val_cols[-2]].apply(\\n self.str_to_ids)\\n self.val[\\\"tok_ending_2\\\"] = self.val[self.test_val_cols[-1]].apply(\\n self.str_to_ids)\\n self.test[\\\"tok_ending_1\\\"] = self.test[self.test_val_cols[-2]].apply(\\n self.str_to_ids)\\n self.test[\\\"tok_ending_2\\\"] = self.test[self.test_val_cols[-1]].apply(\\n self.str_to_ids)\\n self.max_seq_len = max(\\n self.max_seq_len,\\n self.train[\\\"tok_ending\\\"].apply(len).max(),\\n self.val[\\\"tok_ending_1\\\"].apply(len).max(),\\n self.val[\\\"tok_ending_2\\\"].apply(len).max(),\\n self.test[\\\"tok_ending_1\\\"].apply(len).max(),\\n self.test[\\\"tok_ending_2\\\"].apply(len).max(),\\n )\\n # Max_seq_len is without BOM, EOM\\n self.max_seq_len += 2\\n log(\\\"Maximum sequence length: {}\\\".format(self.max_seq_len))\",\n \"def sentence_splitter_builderV2(tokenizer, mode=4, max_sentence_size=21, queries_sw=None, docs_sw=None, save_tokenizer_on_test=True):\\n idf_from_id_token = lambda x: math.log(tokenizer.document_count/tokenizer.word_docs[tokenizer.index_word[x]])\\n \\n def train_splitter(data_generator):\\n\\n while True:\\n \\n # get the batch triplet\\n query, pos_docs, neg_docs = next(data_generator)\\n \\n # tokenization\\n query = tokenizer.texts_to_sequences(query)\\n \\n if queries_sw is not None:\\n for tokenized_query in query:\\n tokenized_query = [token for token in tokenized_query if token not in queries_sw] \\n \\n new_pos_docs = []\\n new_neg_docs = []\\n \\n new_pos_extra_features = []\\n new_neg_extra_features = []\\n \\n # sentence splitting\\n if mode==4:\\n \\n for b in range(len(pos_docs)):\\n new_pos_docs.append([])\\n new_neg_docs.append([])\\n \\n _temp_pos_docs = nltk.sent_tokenize(pos_docs[b][\\\"text\\\"])\\n _temp_pos_docs = tokenizer.texts_to_sequences(_temp_pos_docs)\\n \\n if docs_sw is not None:\\n for tokenized_docs in _temp_pos_docs:\\n tokenized_docs = [token for token in tokenized_docs if token not in docs_sw] \\n \\n # skip batch with empty pos_docs\\n if all([ len(sentence)==0 for sentence in _temp_pos_docs]):\\n break # try a new resampling, NOTE THIS IS A EASY FIX PLS REDO THIS!!!!!!!\\n # for obvious reasons\\n \\n _temp_neg_docs = nltk.sent_tokenize(neg_docs[b][\\\"text\\\"])\\n _temp_neg_docs = tokenizer.texts_to_sequences(_temp_neg_docs)\\n \\n if docs_sw is not None:\\n for tokenized_docs in _temp_neg_docs:\\n tokenized_docs = [token for token in tokenized_docs if token not in docs_sw] \\n \\n # compute extra features\\n #extra_features_pos_doc = compute_extra_features(query[b], _temp_pos_docs, idf_from_id_token)\\n #extra_features_neg_doc = compute_extra_features(query[b], _temp_neg_docs, idf_from_id_token)\\n \\n # add the bm25 score\\n #extra_features_pos_doc.append(pos_docs[b][\\\"score\\\"])\\n #extra_features_neg_doc.append(neg_docs[b][\\\"score\\\"])\\n \\n # add all the extra features\\n #new_pos_extra_features.append(extra_features_pos_doc)\\n #new_neg_extra_features.append(extra_features_neg_doc)\\n \\n # split by exact matching\\n for t_q in query[b]:\\n # entry for the query-term\\n new_pos_docs[-1].append([])\\n new_neg_docs[-1].append([])\\n \\n for pos_sent in _temp_pos_docs:\\n # exact math for the pos_document\\n for i,t_pd in enumerate(pos_sent):\\n if t_pd==t_q:\\n new_pos_docs[-1][-1].append(pos_sent)\\n break\\n\\n for neg_sent in _temp_neg_docs:\\n for i,t_nd in enumerate(neg_sent):\\n if t_nd==t_q:\\n new_neg_docs[-1][-1].append(neg_sent)\\n break\\n else:\\n raise NotImplementedError(\\\"Missing implmentation for mode \\\"+str(mode))\\n \\n if len(new_pos_docs) == len(pos_docs): # if batch is correct\\n yield query, new_pos_docs, new_pos_extra_features, new_neg_docs, new_neg_extra_features\\n \\n \\n def test_splitter(data_generator):\\n\\n for _id, query, docs in data_generator:\\n tokenized_queries = []\\n for i in range(len(_id)):\\n # tokenization\\n tokenized_query = tokenizer.texts_to_sequences([query[i]])[0]\\n\\n if queries_sw is not None:\\n tokenized_query = [token for token in tokenized_query if token not in queries_sw] \\n \\n if save_tokenizer_on_test:\\n tokenized_queries.append(tokenized_query)\\n else:\\n tokenized_queries.append(query[i]) # the tokenization isnt save\\n \\n \\n for doc in docs[i]:\\n if isinstance(doc[\\\"text\\\"], list):\\n continue # cached tokenization\\n\\n # sentence splitting\\n new_docs = []\\n if mode==4:\\n _temp_new_docs = []\\n doc[\\\"offset\\\"] = []\\n doc[\\\"untokenized_text\\\"] = doc[\\\"text\\\"]\\n for start, end in PunktSentenceTokenizer().span_tokenize(doc[\\\"text\\\"]):\\n _temp_new_docs.append(doc[\\\"text\\\"][start:end])\\n\\n if start<(len(doc[\\\"title\\\"])-1):\\n doc[\\\"offset\\\"].append([\\\"title\\\",(start, end), doc[\\\"text\\\"][start:end], []])\\n else:\\n doc[\\\"offset\\\"].append([\\\"abstract\\\", (start-len(doc[\\\"title\\\"]), end-len(doc[\\\"title\\\"])), doc[\\\"text\\\"][start:end], []])\\n \\n tokenized_docs = tokenizer.texts_to_sequences(_temp_new_docs)\\n\\n if docs_sw is not None:\\n for t_doc in tokenized_docs:\\n t_doc = [token for token in t_doc if token not in docs_sw]\\n\\n #doc[\\\"extra_features\\\"] = compute_extra_features(tokenized_query, _temp_new_docs, idf_from_id_token)+[doc[\\\"score\\\"]]\\n\\n for k,t_q in enumerate(tokenized_query):\\n new_docs.append([])\\n for l,_new_doc in enumerate(tokenized_docs):\\n for i,t_d in enumerate(_new_doc):\\n if t_d==t_q:\\n if save_tokenizer_on_test:\\n new_docs[-1].append(_new_doc)\\n else:\\n new_docs[-1].append(_temp_new_docs[l])\\n doc[\\\"offset\\\"][l][-1].append(k)\\n break\\n else:\\n raise NotImplementedError(\\\"Missing implmentation for mode \\\"+str(mode))\\n\\n doc[\\\"text\\\"] = new_docs\\n \\n \\n yield _id, tokenized_queries, docs\\n\\n return train_splitter, test_splitter\",\n \"def generate_token_arrays(\\n text,\\n text_tar,\\n text_pred,\\n tokenizer=None,\\n expand_predictions=True,\\n split_true_entities=True,\\n ignore_value=None\\n):\\n # split text for token evaluation\\n if isinstance(tokenizer, stanfordnlp.pipeline.core.Pipeline):\\n doc = tokenizer(text)\\n # extract tokens from the parsed text\\n tokens_base = [\\n token.text for sentence in doc.sentences\\n for token in sentence.tokens\\n ]\\n elif isinstance(tokenizer, spacy.tokenizer.Tokenizer):\\n doc = tokenizer(text)\\n # extract tokens from the parsed text\\n tokens_base = [token.text for token in doc]\\n else:\\n if tokenizer is None:\\n tokenizer = ''\\n # treat string as a regex\\n tokens_base = re.findall(tokenizer, text)\\n\\n tokens = []\\n tokens_pred = []\\n tokens_true = []\\n tokens_start, tokens_length = [], []\\n\\n n_tokens = 0\\n\\n start = 0\\n for token in tokens_base:\\n # sometimes we have empty tokens on their own\\n if len(token) == 0:\\n continue\\n start = text.find(token, start)\\n token_true = text_tar[start:start + len(token)]\\n token_pred = text_pred[start:start + len(token)]\\n\\n if all(token_true == -1) & all(token_pred == -1):\\n # skip tokens which are not labeled\\n start += len(token)\\n n_tokens += 1\\n continue\\n\\n if split_true_entities:\\n # split the single token into subtokens, based on the true entity\\n idxDiff = np.diff(token_true, prepend=0)\\n if any(idxDiff > 0):\\n # split\\n idxDiff = np.diff(token_true, prepend=0)\\n subtok_start = 0\\n subtoken_true, subtoken_pred = [], []\\n for subtok_end in np.where(idxDiff > 0)[0]:\\n subtoken_true.append(token_true[subtok_start:subtok_end])\\n subtoken_pred.append(token_pred[subtok_start:subtok_end])\\n subtok_start = subtok_end\\n if subtok_end < len(token_true):\\n # add final token\\n subtoken_true.append(token_true[subtok_start:])\\n subtoken_pred.append(token_pred[subtok_start:])\\n else:\\n # in this case, there is only 1 label_id for the entire token\\n # so we can just wrap in a list for the iterator later\\n subtoken_true = [token_true]\\n subtoken_pred = [token_pred]\\n else:\\n # do not split a token if there is more than 1 ground truth\\n # consequently, tokens with multiple labels will be treated\\n # as equal to the most frequent label\\n subtoken_true = [token_true]\\n subtoken_pred = [token_pred]\\n\\n # now iterate through our sub-tokens\\n # often this is a length 1 iterator\\n for token_true, token_pred in zip(subtoken_true, subtoken_pred):\\n if len(token_true) == 0:\\n continue\\n\\n if expand_predictions:\\n # expand the most frequent ID to cover the entire token\\n token_pred = expand_id_to_token(token_pred, ignore_value=-1)\\n token_true = expand_id_to_token(token_true, ignore_value=-1)\\n\\n # get the length of the token for later\\n token_len = len(token_true)\\n\\n # aggregate IDs for this token into the most frequent value\\n if len(token_true) == 0:\\n token_true = -1\\n else:\\n token_true = mode(token_true, ignore_value)\\n if len(token_pred) == 0:\\n token_pred = -1\\n else:\\n token_pred = mode(token_pred, ignore_value)\\n\\n # append the prediction for this token\\n tokens_true.append(token_true)\\n tokens_pred.append(token_pred)\\n tokens.append(text[start:start + token_len])\\n tokens_start.append(start)\\n tokens_length.append(token_len)\\n\\n start += token_len\\n # keep track of total tokens assessed\\n n_tokens += 1\\n\\n # now we have a list of tokens with preds\\n tokens_true = np.asarray(tokens_true, dtype=int)\\n tokens_pred = np.asarray(tokens_pred, dtype=int)\\n\\n return tokens_true, tokens_pred, tokens, tokens_start, tokens_length\",\n \"def tokenize_and_encode_pandas(dataframe,stopindex=1e9,max_length=128):\\n encoded_tweets = []\\n token_type_ids = []\\n attention_mask = []\\n labels_6_types = []\\n labels_4_types = []\\n interaction_types = []\\n \\n counter = 0\\n for i in range(len(dataframe)):\\n try:\\n #tokenized_parent= tokenizer.tokenize(dataframe.iloc[i]['clean_target_text'])\\n #tokenized_tweet = tokenizer.tokenize(dataframe.iloc[i]['clean_response_text'])\\n text_parent= dataframe.iloc[i]['clean_target_text']\\n text_tweet = dataframe.iloc[i]['clean_response_text']\\n except Exception:\\n #tokenized_parent= tokenizer.tokenize(dataframe.iloc[i]['target_text'])\\n #tokenized_tweet = tokenizer.tokenize(dataframe.iloc[i]['response_text'])\\n text_parent= dataframe.iloc[i]['target_text']\\n text_tweet = dataframe.iloc[i]['response_text']\\n \\n interaction = dataframe.iloc[i]['interaction_type'] # reply or quote\\n topic = dataframe.iloc[i]['event'] # get event\\n topic = topic.replace('_', ' ') # replace underscore with space\\n sep_token = ' [SEP] ' \\n text1 = interaction + sep_token + topic + sep_token + text_parent\\n text2 = text_tweet\\n encoded_dict = tokenizer.__call__(text=text1,\\n text_pair=text2,\\n padding='max_length',\\n truncation=True,\\n is_split_into_words=False,\\n max_length=max_length,\\n return_tensors='pt')\\n \\n '''\\n encoded_dict = tokenizer.__call__(text=tokenized_parent,\\n text_pair=tokenized_tweet,\\n padding='max_length',\\n truncation=True,\\n is_split_into_words=True,\\n max_length=max_length,\\n return_tensors='pt')\\n \\n '''\\n '''\\n encoded_dict = tokenizer.encode_plus(text=tokenized_tweet,\\n text_pair=tokenized_parent,\\n max_length=max_length,\\n pad_to_max_length=True)\\n '''\\n encoded_tweets.append(encoded_dict['input_ids'])\\n token_type_ids.append(encoded_dict['token_type_ids'])\\n attention_mask.append(encoded_dict['attention_mask'])\\n \\n label = dataframe.iloc[i]['label']\\n labels_6_types.append(convert_label_string2num(label, num_types=6))\\n labels_4_types.append(convert_label_string2num(label, num_types=4))\\n \\n interaction_type = dataframe.iloc[i]['interaction_type']\\n interaction_types.append(convert_interaction_type_string2num(interaction_type))\\n \\n if counter % 100 == 0:\\n print('Tokenizing comment: %00000d' % counter)\\n if counter > stopindex:\\n break\\n counter = counter + 1\\n \\n width = dataframe.shape[1]\\n dataframe.insert(width+0,'encoded_tweets', encoded_tweets)\\n dataframe.insert(width+1,'token_type_ids', token_type_ids)\\n dataframe.insert(width+2,'attention_mask', attention_mask)\\n dataframe.insert(width+3,'number_labels_6_types', labels_6_types)\\n dataframe.insert(width+4,'number_labels_4_types', labels_4_types)\\n dataframe.insert(width+5,'interaction_type_num', interaction_types)\\n return dataframe\",\n \"def batchify(examples, padding_id=0, max_query_len=64, max_passage_len=200):\\n batch_size = len(examples)\\n query_len = max([len(example[0]) for example in examples])\\n query_len = min(query_len, max_query_len)\\n query_tokens = np.ones((batch_size, query_len)) * padding_id\\n labels = np.zeros(batch_size)\\n passages = []\\n count = 0\\n for i, example in enumerate(examples):\\n idx = min(len(example[0]), query_len)\\n query_tokens[i, :idx] = example[0][:idx - 1] + [example[0][-1]]\\n pos_psg = example[1]\\n if pos_psg in passages:\\n labels[i] = passages.index(pos_psg)\\n else:\\n passages.append(pos_psg)\\n labels[i] = count\\n count += 1\\n neg_psgs = example[2]\\n for psg in neg_psgs:\\n if psg not in passages:\\n passages.append(psg)\\n count += 1\\n passage_len = max([len(psg) for psg in passages])\\n passage_len = min(passage_len, max_passage_len)\\n passages_tokens = np.ones((count, passage_len)) * padding_id\\n for i, psg in enumerate(passages):\\n idx = min(len(psg), passage_len)\\n passages_tokens[i, :idx] = psg[: idx - 1] + [psg[-1]]\\n return torch.tensor(query_tokens, dtype=torch.long), torch.tensor(passages_tokens, dtype=torch.long), torch.tensor(\\n labels, dtype=torch.long)\",\n \"def tokenizer(self):\\n tokenizer = RegexpTokenizer(r'\\\\w+')\\n \\n self.tweet_tokenized_train = [tokenizer.tokenize(x.lower()) for x in self.tweet_prepro_train]\\n self.tweet_tokenized_test = [tokenizer.tokenize(x.lower()) for x in self.tweet_prepro_test]\",\n \"def test_tokenization():\\n X = Tokenizer().transform([[\\\"A test\\\"]])\\n assert X[\\\"corpus\\\"][0] == [\\\"A\\\", \\\"test\\\"]\",\n \"def preprocess(self, sequence, word2idx):\\n story = [word2idx[word] if word in word2idx else UNK_token for word in sequence.split()]\\n story = torch.Tensor(story)\\n return story\",\n \"def forward(self, x_input):\\r\\n\\r\\n pad_token_id = self.config.pad_token_id\\r\\n articles, articles_mask, ops, question_pos = x_input\\r\\n\\r\\n bsz = ops.size(0)\\r\\n ops = ops.reshape(bsz, 1, 5, -1)\\r\\n\\r\\n opnum = ops.size(1)\\r\\n out = self.bert(\\r\\n articles, attention_mask=articles_mask, output_hidden_states=False\\r\\n ).last_hidden_state\\r\\n\\r\\n question_pos = question_pos.reshape(-1, 1, 1)\\r\\n question_pos = question_pos.expand(bsz, opnum, out.size(-1))\\r\\n out = torch.gather(out, 1, question_pos)\\r\\n\\r\\n out = self.dropout_layer(out)\\r\\n out = self.cls(out)\\r\\n\\r\\n # convert ops to one hot\\r\\n out = out.view(bsz, opnum, 1, self.vocab_size)\\r\\n out[:, :, :, pad_token_id] = 0\\r\\n out = out.expand(bsz, opnum, 5, self.vocab_size)\\r\\n out_tokens = torch.zeros((bsz, opnum, 5, 1), device=ops.device)\\r\\n pad_tokens = ops.shape[3] - torch.sum((ops == pad_token_id), dim=3).unsqueeze(3)\\r\\n\\r\\n for i in range(ops.shape[3]):\\r\\n ops_token = ops[:, :, :, i].unsqueeze(3)\\r\\n out_tokens += torch.gather(out, 3, ops_token)\\r\\n\\r\\n out_tokens = torch.div(out_tokens, pad_tokens)\\r\\n out = out_tokens\\r\\n out = out.view(-1, 5)\\r\\n return out\",\n \"def read_squad_examples(input_file, return_answers, context_only=False, question_only=False,\\n draft=False, draft_num_examples=12, append_title=False):\\n with open(input_file, \\\"r\\\") as reader:\\n input_data = json.load(reader)[\\\"data\\\"]\\n\\n examples = []\\n ans_cnt = 0\\n no_ans_cnt = 0\\n\\n # Only word-based tokenization is peformed (whitespace based)\\n for doc_idx, entry in enumerate(input_data):\\n title = entry['title'][0] if type(entry['title']) == list else entry['title']\\n assert type(title) == str\\n\\n for par_idx, paragraph in enumerate(entry[\\\"paragraphs\\\"]):\\n # Do not load context for question only\\n if not question_only:\\n paragraph_text = paragraph[\\\"context\\\"]\\n title_offset = 0\\n if append_title:\\n title_str = '[ ' + ' '.join(title.split('_')) + ' ] '\\n title_offset += len(title_str)\\n paragraph_text = title_str + paragraph_text\\n # Note that we use the term 'word' for whitespace based words, and 'token' for subtokens (for BERT input)\\n doc_words, char_to_word_offset = context_to_words_and_offset(paragraph_text)\\n\\n # 1) Context only ends here\\n if context_only:\\n metadata = {}\\n if \\\"pubmed_id\\\" in entry:\\n entry_keys = [\\n \\\"pubmed_id\\\", \\\"sha\\\", \\\"title_original\\\", \\\"title_entities\\\",\\n \\\"journal\\\", \\\"authors\\\", \\\"article_idx\\\"\\n ]\\n para_keys = [\\\"context_entities\\\"]\\n for entry_key in entry_keys:\\n if entry_key in entry:\\n metadata[entry_key] = entry[entry_key]\\n for para_key in para_keys:\\n if para_key in paragraph:\\n metadata[para_key] = paragraph[para_key]\\n # metadata[\\\"pubmed_id\\\"] = (metadata[\\\"pubmed_id\\\"] if not pd.isnull(metadata[\\\"pubmed_id\\\"])\\n # else 'NaN')\\n example = SquadExample(\\n doc_words=doc_words,\\n title=title,\\n doc_idx=doc_idx,\\n par_idx=par_idx,\\n metadata=metadata)\\n examples.append(example)\\n\\n if draft and len(examples) == draft_num_examples:\\n return examples\\n continue\\n\\n # 2) Question only or 3) context/question pair\\n else:\\n for qa in paragraph[\\\"qas\\\"]:\\n qas_id = str(qa[\\\"id\\\"])\\n question_text = qa[\\\"question\\\"]\\n\\n # Noisy question skipping\\n if len(question_text.split(' ')) == 1:\\n logger.info('Skipping a single word question: {}'.format(question_text))\\n continue\\n if \\\"I couldn't could up with another question.\\\" in question_text:\\n logger.info('Skipping a strange question: {}'.format(question_text))\\n continue\\n\\n start_position = None\\n end_position = None\\n orig_answer_text = None\\n\\n # For pre-processing that should return answers together\\n if return_answers:\\n assert type(qa[\\\"answers\\\"]) == dict or type(qa[\\\"answers\\\"]) == list, type(qa[\\\"answers\\\"])\\n if type(qa[\\\"answers\\\"]) == dict:\\n qa[\\\"answers\\\"] = [qa[\\\"answers\\\"]]\\n\\n # No answers\\n if len(qa[\\\"answers\\\"]) == 0:\\n orig_answer_text = \\\"\\\"\\n start_position = -1 # Word-level no-answer => -1\\n end_position = -1\\n no_ans_cnt += 1\\n # Answer exists\\n else:\\n answer = qa[\\\"answers\\\"][0]\\n ans_cnt += 1\\n\\n orig_answer_text = answer[\\\"text\\\"]\\n answer_offset = answer[\\\"answer_start\\\"] + title_offset\\n answer_length = len(orig_answer_text)\\n start_position = char_to_word_offset[answer_offset]\\n end_position = char_to_word_offset[answer_offset + answer_length - 1]\\n\\n # Only add answers where the text can be exactly recovered from the context\\n actual_text = \\\" \\\".join(doc_words[start_position:(end_position + 1)])\\n cleaned_answer_text = \\\" \\\".join(\\n tokenization.whitespace_tokenize(orig_answer_text)) # word based tokenization\\n if actual_text.find(cleaned_answer_text) == -1:\\n logger.warning(\\\"Could not find answer: '%s' vs. '%s'\\\",\\n actual_text, cleaned_answer_text)\\n continue\\n\\n # Question only ends here\\n if question_only:\\n example = SquadExample(\\n qas_id=qas_id,\\n question_text=question_text)\\n\\n # Context/question pair ends here\\n else:\\n example = SquadExample(\\n qas_id=qas_id,\\n question_text=question_text,\\n paragraph_text=paragraph_text,\\n doc_words=doc_words,\\n orig_answer_text=orig_answer_text,\\n start_position=start_position,\\n end_position=end_position,\\n title=title,\\n doc_idx=doc_idx,\\n par_idx=par_idx)\\n examples.append(example)\\n\\n if draft and len(examples) == draft_num_examples:\\n return examples\\n\\n # Testing for shuffled draft (should comment out above 'draft' if-else statements)\\n if draft:\\n random.shuffle(examples)\\n logger.info(str(len(examples)) + ' were collected before draft for shuffling')\\n return examples[:draft_num_examples]\\n\\n logger.info('Answer/no-answer stat: %d vs %d'%(ans_cnt, no_ans_cnt))\\n return examples\",\n \"def preprocess_seq(self, sequence, word2idx):\\n story = []\\n for value in sequence:\\n #v = [word2idx[word] if word in word2idx else UNK_token for word in value.split()] + [EOS_token]\\n story.append(word2idx[value] if value in word2idx else UNK_token)\\n story = torch.Tensor(story)\\n return story\",\n \"def glue_inputs(dataset_name, split, batch_size, tokenizer, data_dir=None,\\n max_len=128, training=True):\\n keys_lookup = {\\n \\\"glue/cola\\\": (\\\"sentence\\\", None),\\n \\\"glue/sst2\\\": (\\\"sentence\\\", None),\\n \\\"glue/mrpc\\\": (\\\"sentence1\\\", \\\"sentence2\\\"),\\n \\\"glue/qqp\\\": (\\\"question1\\\", \\\"question2\\\"),\\n \\\"glue/stsb\\\": (\\\"sentence1\\\", \\\"sentence2\\\"),\\n \\\"glue/mnli\\\": (\\\"premise\\\", \\\"hypothesis\\\"), # TODO(kitaev): swap the two?\\n \\\"glue/qnli\\\": (\\\"question\\\", \\\"sentence\\\"), # TODO(kitaev) swap the two?\\n \\\"glue/rte\\\": (\\\"sentence1\\\", \\\"sentence2\\\"),\\n \\\"glue/wnli\\\": (\\\"sentence1\\\", \\\"sentence2\\\"),\\n }\\n\\n key_a, key_b = keys_lookup[dataset_name]\\n\\n if key_b is None:\\n def preprocess(batch):\\n \\\"\\\"\\\"Tokenize and convert text to model inputs.\\\"\\\"\\\"\\n batch_size = batch[\\\"idx\\\"].shape[0]\\n input_ids = np.zeros((batch_size, max_len), dtype=np.int32)\\n type_ids = np.zeros((batch_size, max_len), dtype=np.int32)\\n\\n for i in range(batch_size):\\n sentence_a = batch[key_a][i]\\n tokens_a = tokenizer.EncodeAsIds(sentence_a)\\n input_ids[i, :len(tokens_a)] = tokens_a[:max_len]\\n\\n return {\\n \\\"input_ids\\\": input_ids,\\n \\\"type_ids\\\": type_ids,\\n \\\"idx\\\": batch[\\\"idx\\\"].astype(np.int32),\\n \\\"label\\\": batch[\\\"label\\\"],\\n }\\n else:\\n def preprocess(batch):\\n \\\"\\\"\\\"Tokenize and convert text to model inputs.\\\"\\\"\\\"\\n batch_size = batch[\\\"idx\\\"].shape[0]\\n input_ids = np.zeros((batch_size, max_len), dtype=np.int32)\\n type_ids = np.zeros((batch_size, max_len), dtype=np.int32)\\n\\n for i in range(batch_size):\\n sentence_a = batch[key_a][i]\\n sentence_b = batch[key_b][i]\\n tokens_a = tokenizer.EncodeAsIds(sentence_a)\\n tokens_b = tokenizer.EncodeAsIds(sentence_b)[1:] # Strip start token\\n\\n ex_input_ids = (tokens_a + tokens_b)[:max_len]\\n ex_type_ids = ([0] * len(tokens_a) + [1] * len(tokens_b))[:max_len]\\n\\n input_ids[i, :len(ex_input_ids)] = ex_input_ids\\n type_ids[i, :len(ex_type_ids)] = ex_type_ids\\n\\n return {\\n \\\"input_ids\\\": input_ids,\\n \\\"type_ids\\\": type_ids,\\n \\\"idx\\\": batch[\\\"idx\\\"].astype(np.int32),\\n \\\"label\\\": batch[\\\"label\\\"],\\n }\\n\\n return tfds_stream(\\n dataset_name=dataset_name,\\n split=split,\\n batch_size=batch_size,\\n data_dir=data_dir,\\n shuffle_files=training,\\n shuffle_buffer_size=1024 if training else None,\\n batch_shuffle_size=128 if training else None,\\n preprocess_fun=preprocess,\\n repeat=training,\\n )\",\n \"def data_process(raw_text_iter: dataset.IterableDataset) -> Tensor:\\n data = [torch.tensor(vocab(tokenizer(item)), dtype=torch.long) for item in raw_text_iter]\\n return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))\",\n \"def convert_single_example(example, tokenizer, is_training, args):\\n tok_to_orig_index = []\\n orig_to_tok_index = []\\n all_doc_tokens = [] # all subtokens of original doc after tokenizing\\n features = []\\n for (i, token) in enumerate(example['paragraph_tokens']):\\n orig_to_tok_index.append(len(all_doc_tokens))\\n sub_tokens = albert_tokenize(tokenizer, token)\\n tok_to_orig_index.extend([i] * len(sub_tokens))\\n all_doc_tokens.extend(sub_tokens)\\n\\n # 特别注意！由于在paragraph_tokens中我们的token已经映射过一次了\\n # 这里wordpiece等于又映射了一遍，所以这里的操作是二次映射\\n if example['position_map']:\\n tok_to_orig_index = [example['position_map'][index] for index in tok_to_orig_index]\\n\\n # QUERY\\n query_tokens = []\\n query_tokens.append(\\\"[Q]\\\")\\n query_tokens.extend(albert_tokenize(tokenizer, example['question_text']))\\n if len(query_tokens) > args.max_query_length:\\n query_tokens = query_tokens[-args.max_query_length:]\\n\\n # ANSWER 预处理的时候先长短分开\\n tok_start_position = -1\\n tok_end_position = -1\\n # 这里终点是必然在para_tokens内的\\n if is_training:\\n # 现阶段，有短答案预测短答案，否则预测长答案\\n if example['answer_type'] != AnswerType['UNKNOWN']:\\n tok_long_start_position = orig_to_tok_index[example['long_start']]\\n if example['long_end'] == len(orig_to_tok_index):\\n tok_long_end_position = orig_to_tok_index[-1]\\n else:\\n tok_long_end_position = orig_to_tok_index[example['long_end']] - 1\\n tok_start_position = tok_long_start_position\\n tok_end_position = tok_long_end_position\\n if example['answer_type'] == AnswerType['SHORT']:\\n tok_short_start_position = orig_to_tok_index[example['short_start']]\\n if example['short_end'] == len(orig_to_tok_index):\\n tok_short_end_position = orig_to_tok_index[-1]\\n else:\\n tok_short_end_position = orig_to_tok_index[example['short_end']] - 1\\n tok_start_position = tok_short_start_position\\n tok_end_position = tok_short_end_position\\n\\n # Get max tokens number for original doc,\\n # should minus query tokens number and 3 special tokens\\n # The -3 accounts for [CLS], [SEP] and [SEP]\\n max_tokens_for_doc = args.max_seq_length - len(query_tokens) - 3\\n\\n # We can have documents that are longer than the maximum sequence length.\\n # To deal with this we do a sliding window approach, where we take chunks\\n # of up to our max length with a stride of `doc_stride`.\\n _DocSpan = collections.namedtuple(\\\"DocSpan\\\", [\\\"start\\\", \\\"length\\\"])\\n doc_spans = []\\n start_offset = 0\\n while start_offset < len(all_doc_tokens):\\n length = len(all_doc_tokens) - start_offset # compute number of tokens remaining unsliding\\n length = min(length, max_tokens_for_doc) # determine current sliding window size\\n doc_spans.append(_DocSpan(start=start_offset, length=length))\\n\\n # Consider case for reaching end of original doc\\n if start_offset + length == len(all_doc_tokens):\\n break\\n start_offset += min(length, args.doc_stride)\\n\\n # Convert window + query + special tokens to feature\\n for (doc_span_index, doc_span) in enumerate(doc_spans):\\n tokens = []\\n token_to_orig_map = {}\\n token_is_max_context = {}\\n segment_ids = []\\n tokens.append(\\\"[CLS]\\\")\\n segment_ids.append(0)\\n tokens.extend(query_tokens)\\n segment_ids.extend([0] * len(query_tokens))\\n tokens.append(\\\"[SEP]\\\")\\n segment_ids.append(0)\\n\\n for i in range(doc_span.length):\\n split_token_index = doc_span.start + i\\n token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]\\n\\n is_max_context = check_is_max_context(doc_spans, doc_span_index, split_token_index)\\n token_is_max_context[len(tokens)] = is_max_context\\n tokens.append(all_doc_tokens[split_token_index])\\n segment_ids.append(1)\\n tokens.append(\\\"[SEP]\\\")\\n segment_ids.append(1)\\n assert len(tokens) == len(segment_ids)\\n\\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\\n\\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\\n # tokens are attended to.\\n input_mask = [1] * len(input_ids)\\n\\n # Zero-pad up to the sequence length.\\n padding = [0] * (args.max_seq_length - len(input_ids))\\n input_ids.extend(padding)\\n input_mask.extend(padding)\\n segment_ids.extend(padding)\\n\\n assert len(input_ids) == args.max_seq_length\\n assert len(input_mask) == args.max_seq_length\\n assert len(segment_ids) == args.max_seq_length\\n\\n start_position = None\\n end_position = None\\n answer_type = None\\n answer_text = \\\"\\\"\\n if is_training:\\n doc_start = doc_span.start\\n doc_end = doc_span.start + doc_span.length - 1\\n # For training, if our document chunk does not contain an annotation\\n # we throw it out, since there is nothing to predict.\\n contains_an_annotation = (tok_start_position >= doc_start and tok_end_position <= doc_end)\\n # 负样本需要经过采样，且目标为[CLS]\\n if (not contains_an_annotation) or example['answer_type'] == AnswerType['UNKNOWN']:\\n if args.include_unknowns < 0 or random.random() > args.include_unknowns:\\n continue\\n start_position = 0\\n end_position = 0\\n answer_type = AnswerType['UNKNOWN']\\n else:\\n doc_offset = len(query_tokens) + 2\\n start_position = tok_start_position - doc_start + doc_offset\\n end_position = tok_end_position - doc_start + doc_offset\\n answer_type = example['answer_type']\\n\\n # 如果是短答案，对一下答案是否正确\\n if example['answer_type'] == AnswerType['SHORT']:\\n answer_text = \\\" \\\".join(tokens[start_position:(end_position + 1)])\\n answer_text = answer_text.replace(' ', '').replace(u\\\"▁\\\", ' ').strip()\\n gt_answer = example['short_answer_text'].lower()\\n answer_text_chars = [c for c in answer_text if c not in \\\" \\\\t\\\\r\\\\n\\\" and ord(c) != 0x202F]\\n gt_answer_chars = [c for c in gt_answer if c not in \\\" \\\\t\\\\r\\\\n\\\" and ord(c) != 0x202F]\\n if \\\"\\\".join(answer_text_chars) != \\\"\\\".join(gt_answer_chars):\\n print(answer_text, 'V.S.', gt_answer)\\n\\n feature = InputFeatures(\\n unique_id=None,\\n example_index=None,\\n doc_span_index=doc_span_index,\\n tokens=tokens,\\n token_to_orig_map=token_to_orig_map,\\n token_is_max_context=token_is_max_context,\\n input_ids=input_ids,\\n input_mask=input_mask,\\n segment_ids=segment_ids,\\n start_position=start_position,\\n end_position=end_position,\\n answer_text=answer_text,\\n answer_type=answer_type)\\n\\n features.append(feature)\\n\\n return features\",\n \"def preprocess_batch(self, context, question, answer, mask):\\n # make input and question fixed size\\n new_input = np.zeros([self.config.batch_size,\\n self.dataset.max_input_size,\\n self.config.word_vector_length]) # zero padding\\n new_question = np.zeros([self.config.batch_size,\\n self.dataset.max_question_size,\\n self.config.word_vector_length])\\n new_mask = np.zeros([self.config.batch_size, self.dataset.max_input_size])\\n new_labels = []\\n\\n for n in np.arange(self.config.batch_size):\\n for row in np.arange(context[n].shape):\\n for col in np.arange():\\n new_input[n][row][col] = context[n][row][col]\\n\\n for row in np.arange(question[n].shape):\\n for col in np.arange():\\n new_question[n][row][col] = question[n][row][col]\\n\\n for row in np.arange(mask[n].shape):\\n new_mask[n][row] = mask[n][row]\\n\\n return [new_input, new_question], new_labels, new_mask\",\n \"def convert_examples_to_features(\\n examples: List[InputExample],\\n label_list: List[str],\\n max_length: int,\\n tokenizer: PreTrainedTokenizer,\\n pad_token_segment_id=0,\\n pad_on_left=False,\\n pad_token=0,\\n mask_padding_with_zero=True,\\n) -> List[InputFeatures]:\\n\\n label_map = {label: i for i, label in enumerate(label_list)}\\n\\n features = []\\n for (ex_index, example) in tqdm.tqdm(enumerate(examples), desc=\\\"convert examples to features\\\"):\\n if ex_index % 10000 == 0:\\n logger.info(\\\"Writing example %d of %d\\\" % (ex_index, len(examples)))\\n choices_features = []\\n for ending_idx, (context, ending) in enumerate(zip(example.contexts, example.endings)):\\n text_a = context\\n if example.question.find(\\\"_\\\") != -1:\\n # this is for cloze question\\n text_b = example.question.replace(\\\"_\\\", ending)\\n else:\\n text_b = example.question + \\\" \\\" + ending\\n if len(text_a) == 0:\\n logger.info(\\\"context of example %d have length 0\\\" % (ex_index))\\n text_a = \\\" \\\"\\n\\n inputs = tokenizer.encode_plus(text_a, text_b, add_special_tokens=True, max_length=max_length,)\\n if \\\"num_truncated_tokens\\\" in inputs and inputs[\\\"num_truncated_tokens\\\"] > 0:\\n logger.info(\\n \\\"Attention! you are cropping tokens (swag task is ok). \\\"\\n \\\"If you are training ARC and RACE and you are poping question + options,\\\"\\n \\\"you need to try to use a bigger max seq length!\\\"\\n )\\n\\n input_ids, token_type_ids = inputs[\\\"input_ids\\\"], inputs[\\\"token_type_ids\\\"]\\n\\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\\n # tokens are attended to.\\n attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\\n\\n # Zero-pad up to the sequence length.\\n padding_length = max_length - len(input_ids)\\n if pad_on_left:\\n input_ids = ([pad_token] * padding_length) + input_ids\\n attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask\\n token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids\\n else:\\n input_ids = input_ids + ([pad_token] * padding_length)\\n attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)\\n token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)\\n\\n assert len(input_ids) == max_length\\n assert len(attention_mask) == max_length\\n assert len(token_type_ids) == max_length\\n choices_features.append((input_ids, attention_mask, token_type_ids))\\n\\n label = label_map[example.label]\\n\\n if ex_index < 2:\\n logger.info(\\\"*** Example ***\\\")\\n logger.info(\\\"race_id: {}\\\".format(example.example_id))\\n for choice_idx, (input_ids, attention_mask, token_type_ids) in enumerate(choices_features):\\n logger.info(\\\"choice: {}\\\".format(choice_idx))\\n logger.info(\\\"input_ids: {}\\\".format(\\\" \\\".join(map(str, input_ids))))\\n logger.info(\\\"attention_mask: {}\\\".format(\\\" \\\".join(map(str, attention_mask))))\\n logger.info(\\\"token_type_ids: {}\\\".format(\\\" \\\".join(map(str, token_type_ids))))\\n logger.info(\\\"label: {}\\\".format(label))\\n\\n features.append(InputFeatures(example_id=example.example_id, choices_features=choices_features, label=label,))\\n\\n return features\",\n \"def tokenize(self):\\n\\n self.feats = {\\n 'features': [], # Lists of the `InputFeatures` objects.\\n 'segments': [], # Segments of the phrase. 0: Promoun, 1: A-term, 2: B-term \\n 'df_ids': [], # DataFrame index.\\n 'target_token_ids': [] # Indexes of the target term in the tokens lists.\\n }\\n unique_id = 0 # Unique ID of the dataset.\\n for _, row in tqdm(self.df.iterrows()):\\n segment_tokens = self.tokenize_single_row(row)\\n for j, segment in enumerate(segment_tokens):\\n if segment['target_token_index'] > 0:\\n features = self.tokens_to_features(unique_id, segment['tokens'])\\n unique_id += 1\\n self.feats['features'].append(features)\\n self.feats['segments'].append(j)\\n self.feats['target_token_ids'].append(segment['target_token_index'] )\\n self.feats['df_ids'].append(row.ID)\",\n \"def generate_question(self, num_question = 10):\\n\\t\\t\\n\\t\\tquestions = []\\n\\t\\tfor q in range(num_question):\\n\\t\\t\\tfor r in range(2):\\n\\t\\t\\t\\tquestion = np.zeros(self.question_dim, dtype = np.float32)\\n\\t\\t\\t\\tcolor = np.random.randint(len(COLOR))\\n\\t\\t\\t\\tquestion[color] = 1.0\\n\\t\\t\\t\\tquestion[6 + r] = 1.0\\n\\t\\t\\t\\tquestion_label = np.random.randint(3)\\n\\t\\t\\t\\tquestion[8 + question_label] = 1.0\\n\\t\\t\\t\\tquestions.append(question)\\n\\t\\treturn questions\",\n \"def batch_tokenize_fn(examples):\\n sources = examples[config.source_lang]\\n targets = examples[config.target_lang]\\n model_inputs = config.tokenizer(sources, max_length=config.max_source_length, truncation=True)\\n\\n # setup the tokenizer for targets,\\n # huggingface expects the target tokenized ids to be stored in the labels field\\n with config.tokenizer.as_target_tokenizer():\\n labels = config.tokenizer(targets, max_length=config.max_target_length, truncation=True)\\n\\n model_inputs[\\\"labels\\\"] = labels[\\\"input_ids\\\"]\\n return model_inputs\",\n \"def prepareData(self):\\n\\t\\tprint ('')\\n\\t\\tfrom keras.preprocessing.sequence import pad_sequences\\n\\t\\tfrom sklearn.model_selection import train_test_split\\n\\t\\tfrom keras.utils import to_categorical\\n\\t\\timport numpy as np\\n\\n\\t\\tfrom sklearn.preprocessing import LabelBinarizer, LabelEncoder\\n\\n\\t\\tX_snt = [[self.word2idx[w] if w in self.word2idx else self.word2idx[self.word_unk_token] for w in s] for s in self.x_document]\\n\\t\\ty_tag = [[self.tag2idx[t]] for t in self.y_document]\\n\\n\\t\\tX_snt = pad_sequences(maxlen=self.parameters['max_doc_len'], sequences=X_snt, padding='post', value=self.word2idx[self.word_pad_token])\\n\\t\\ty_tag = to_categorical(y_tag, self.tags_len)\\n\\n\\t\\tprint (\\\"\\\\tRandom:\\\\t\\\", self.random)\\n\\t\\tprint (\\\"\\\\tTest size:\\\\t\\\", self.split_train_test)\\n\\n\\t\\tself.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X_snt, y_tag, test_size=self.split_train_test, random_state=self.random)\\n\\n\\t\\tself.X_train = np.array(self.X_train)\\n\\t\\tself.X_test = np.array(self.X_test)\\n\\t\\tself.y_train = np.array(self.y_train)\\n\\t\\tself.y_test = np.array(self.y_test)\\n\\n\\t\\tprint ('\\\\n\\\\tWords: {}\\\\t{}'.format(self.X_train.shape, self.X_test.shape) )\\n\\t\\tprint ('\\\\tTags: {}\\\\t{}\\\\n'.format(self.y_train.shape, self.y_test.shape))\",\n \"def train(self, corpus):\\n self.tokens = []\\n self.tags = []\\n sentences = corpus.split(NEW_LINE)\\n for sentence in sentences:\\n start = START_SIGHT + SLASH + START_SIGHT + SPACE + START_SIGHT + SLASH + START_SIGHT + SPACE\\n end = SPACE + END + SLASH + END\\n sentence = start + sentence + end \\n tokens = sentence.split(SPACE)\\n for t in tokens:\\n token = t.rsplit(SLASH, 1)\\n if (len(token) > 1):\\n self.tokens.append(token) \\n self.tags.append(token[TAG_INDEX])\\n \\n nonsense_cases = set([(END, START_SIGHT), (START_SIGHT, END),\\n (START_SIGHT, START_SIGHT, END),\\n (END, START_SIGHT, START_SIGHT)])\\n self.bigram_tags = [b for b in zip(self.tags[:-1], self.tags[1:]) if b not in nonsense_cases]\\n self.trigram_tags = [t for t in zip(self.tags[:-1], self.tags[1:], self.tags[2:])\\\\\\n if not (t[WORD_INDEX], t[TAG_INDEX]) in nonsense_cases and\\\\\\n not (t[WORD_INDEX], t[TAG_INDEX]) in nonsense_cases]\",\n \"def example_to_token_ids_segment_ids_label_ids(\\n ex_index,\\n example,\\n max_seq_length,\\n max_evid_length,\\n tokenizer):\\n if ex_index < 5:\\n print('*** Example {} ***'.format(ex_index))\\n print('qid: {}'.format(example.qid))\\n\\n qid = example.qid\\n\\n # ante evidence\\n ante_evidence = example.ante_evidence # list of strings.\\n ante_token_ids, ante_segment_ids = [], []\\n for ante_sent in ante_evidence:\\n ante_sent_tokens = tokenizer.tokenize(ante_sent)\\n ante_sent_tokens = ante_sent_tokens[:max_evid_length-2]\\n sent_tokens, sent_segment_ids = [], []\\n sent_tokens.append(\\\"<s>\\\")\\n sent_segment_ids.append(0)\\n sent_tokens += ante_sent_tokens + [\\\"</s>\\\"]\\n sent_segment_ids += [0] * len(ante_sent_tokens) + [0]\\n\\n assert len(sent_tokens) <= max_evid_length\\n sent_tokens_ids = tokenizer.convert_tokens_to_ids(sent_tokens)\\n\\n assert len(sent_tokens_ids) == len(sent_segment_ids)\\n ante_token_ids.append(sent_tokens_ids)\\n ante_segment_ids.append(sent_segment_ids)\\n assert len(ante_evidence) == len(ante_token_ids)\\n assert len(ante_evidence) == len(ante_segment_ids)\\n l_ante = min(len(ante_evidence), max_evid_length)\\n\\n # cons evidence\\n cons_evidence = example.cons_evidence # list of strings.\\n cons_token_ids, cons_segment_ids = [], []\\n for cons_sent in cons_evidence:\\n cons_sent_tokens = tokenizer.tokenize(cons_sent)\\n cons_sent_tokens = cons_sent_tokens[:max_evid_length-2]\\n sent_tokens, sent_segment_ids = [], []\\n sent_tokens.append(\\\"<s>\\\")\\n sent_segment_ids.append(0)\\n sent_tokens += cons_sent_tokens + [\\\"</s>\\\"]\\n sent_segment_ids += [0] * len(cons_sent_tokens) + [0]\\n\\n assert len(sent_tokens) <= max_evid_length\\n sent_tokens_ids = tokenizer.convert_tokens_to_ids(sent_tokens)\\n\\n assert len(sent_tokens_ids) == len(sent_segment_ids)\\n cons_token_ids.append(sent_tokens_ids)\\n cons_segment_ids.append(sent_segment_ids)\\n assert len(cons_evidence) == len(cons_token_ids)\\n assert len(cons_evidence) == len(cons_segment_ids)\\n l_cons = min(len(cons_evidence), max_evid_length)\\n\\n para_tokens = tokenizer.tokenize(example.para)\\n question_tokens = tokenizer.tokenize(example.question)\\n answers_tokens = map(tokenizer.tokenize, example.answers)\\n\\n token_ids = []\\n segment_ids = []\\n for choice_idx, answer_tokens in enumerate(answers_tokens):\\n\\n truncated_para_tokens, \\\\\\n truncated_question_tokens,\\\\\\n truncated_answer_tokens = _truncate_seq_pair(para_tokens, question_tokens, answer_tokens, max_seq_length)\\n\\n choice_tokens = []\\n choice_segment_ids = []\\n choice_tokens.append(\\\"<s>\\\")\\n choice_segment_ids.append(0)\\n choice_tokens += truncated_para_tokens + [\\\"</s>\\\"]\\n choice_segment_ids += [0] * len(truncated_para_tokens) + [0]\\n choice_tokens += truncated_question_tokens + [\\\"</s>\\\"]\\n choice_segment_ids += [1] * len(truncated_question_tokens) + [1]\\n choice_tokens += truncated_answer_tokens\\n choice_segment_ids += [1] * len(truncated_answer_tokens)\\n\\n choice_token_ids = tokenizer.convert_tokens_to_ids(choice_tokens)\\n\\n token_ids.append(choice_token_ids)\\n segment_ids.append(choice_segment_ids)\\n\\n label_ids = [example.label]\\n\\n if ex_index < 5:\\n print('label: {} (id = {:d})'.format(example.label, label_ids[0]))\\n\\n return ante_token_ids, ante_segment_ids, l_ante, \\\\\\n cons_token_ids, cons_segment_ids, l_cons, \\\\\\n token_ids, segment_ids, label_ids, qid\",\n \"def preprocess_query(self, input_ids, prefix):\\n\\n input_strings = self.generator_tokenizer.batch_decode(input_ids, skip_special_tokens=False)\\n\\n # handle prefix for T5\\n if isinstance(self.generator_tokenizer, T5Tokenizer):\\n for i, s in enumerate(input_strings):\\n if not s.startswith(prefix):\\n logger.warning(\\\"T5 prefix mismatch in {}\\\".format(s))\\n if len(input_strings[i]) <= len(prefix):\\n input_strings[i] = \\\"\\\"\\n else:\\n input_strings[i] = input_strings[i][len(prefix) :]\\n\\n retriever_inputs = self.question_encoder_tokenizer.batch_encode_plus(\\n input_strings,\\n return_tensors=\\\"pt\\\",\\n padding=True,\\n truncation=True,\\n )\\n\\n return retriever_inputs[\\\"input_ids\\\"].to(input_ids.device), input_strings\",\n \"def ends_with_question_word(dataset, idx):\\n # HACK: For now (speedy POC), just use the BERT tokens\\n # Eventually, we'd like to have access to the raw text (e.g., via the Spacy\\n # tokenization) and have the two sentences separated for pair tasks\\n\\n # spacy_sentences = dataset.spacy_tokens[idx]\\n # for spacy_sentence in spacy_sentences:\\n # if any(token.text in question_words for token in spacy_sentence):\\n # return True\\n\\n bert_ints = dataset.bert_tokens[idx]\\n bert_tokens = dataset.bert_tokenizer.convert_ids_to_tokens(bert_ints)\\n return any(token in question_words for token in bert_tokens[-3:])\\n # match = any(token in question_words for token in bert_tokens[-3:])\\n # if match:\\n # print(bert_tokens)\\n # return match\",\n \"def __init__(self, max_size=None, lower=True, unk_token=True, remove_stopwords=False, specials=('<pad>',)):\\n self._max_size = max_size\\n self._lower = lower\\n self._unk = unk_token\\n self.token2id = {token: i for i, token in enumerate(specials)}\\n self._id2token = list(specials)\\n self._token_count = Counter()\\n self.can_remove_stopwords=remove_stopwords\\n self.num_docs = 0\\n self.num_pos = 0\\n self.num_nnz = 0\\n self.corpus=None\",\n \"def sents_to_tokens(sents, wordset):\\n padded_sentences = ([\\\"<s>\\\", \\\"<s>\\\"] + s + [\\\"</s>\\\"] for s in sents)\\n # This will canonicalize words, and replace anything not in vocab with <unk>\\n return np.array([utils.canonicalize_word(w, wordset=wordset) \\n for w in utils.flatten(padded_sentences)], dtype=object)\",\n \"def TransformerTokenEmbedding(\\n num_embeddings, embedding_dim, padding_idx, freeze_embed=False\\n):\\n m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)\\n nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)\\n nn.init.constant_(m.weight[padding_idx], 0)\\n if freeze_embed:\\n m.weight.requires_grad = False\\n return m\",\n \"def fit(self, documents):\\n # Get a list of all the unique tokens that appear\\n vocab = list({\\n token for doc in documents\\n for token in self.tokenizer(doc)\\n if token not in self._word2index\\n })\\n\\n # This is UNK, START, END, and PAD.\\n nb_special_tokens = 4\\n\\n # First, we map token -> ID, leaving the first slots for special tokens\\n self._word2index.update({\\n word: idx\\n for idx, word in enumerate(vocab, nb_special_tokens)\\n })\\n\\n # Next, we invert this map, which we can do since it was built from\\n # unique vocabulary elements and is by definition bijective.\\n self._index2word.update({\\n idx: word\\n for word, idx in self._word2index.items()\\n })\\n\\n return self\",\n \"def preprocess_dataset(tokenizer, dataset, vocab=None, max_seq_length=384, doc_stride=128,\\n max_query_length=64, num_workers=16, load_from_pickle=False,\\n feature_file=None, is_training=True):\\n vocab = tokenizer.vocab if vocab is None else vocab\\n trans = partial(convert_examples_to_features, tokenizer=tokenizer, cls_token=vocab.cls_token,\\n sep_token=vocab.sep_token, vocab=vocab, max_seq_length=max_seq_length,\\n doc_stride=doc_stride, max_query_length=max_query_length)\\n pool = mp.Pool(num_workers)\\n start = time.time()\\n if not load_from_pickle:\\n example_trans = partial(convert_squad_examples, is_training=is_training)\\n # convert the raw dataset into raw features\\n examples = pool.map(example_trans, dataset)\\n raw_features = list(map(trans, examples)) #pool.map(trans, examples)\\n if feature_file:\\n with open(feature_file, 'wb') as file:\\n pickle.dump(raw_features, file)\\n else:\\n assert feature_file, 'feature file should be provided.'\\n with open(feature_file, 'rb') as file:\\n raw_features = pickle.load(file)\\n\\n end = time.time()\\n pool.close()\\n log.info('Done! Transform dataset costs %.2f seconds.', (end - start))\\n return raw_features\",\n \"def prepare_for_supervised(embeddings):\\n \\n X, y = [], []\\n for problem in embeddings:\\n pairs = [np.concatenate((problem['question'], problem[i])) \\n for i in ('choice_0', 'choice_1', 'choice_2', 'choice_3')]\\n correct_answer = problem['correct_answer']\\n \\n labels = [0, 0, 0, 0]\\n labels[correct_answer] = 1\\n X.extend(pairs)\\n y.extend(labels)\\n return X, y\",\n \"def show_tokenization(tokenizer, dataset, i):\\n\\n print(f'\\\\n--- Tokenization of document {i} --------------------------------------------')\\n print( dataset[i] )\\n print()\\n print( tokenizer.texts_to_sequences([dataset[i]])[0] )\\n print()\\n print( [tokenizer.index_word[i] for i in tokenizer.texts_to_sequences([dataset[i]])[0]] )\\n print(f'-----------------------------------------------------------------------------')\",\n \"def mutual_data_loader(data_dir, tokenizer, max_doc_len, max_query_len, max_option_len, is_training):\\n def is_whitespace(c):\\n if c == \\\" \\\" or c == \\\"\\\\t\\\" or c == \\\"\\\\r\\\" or c == \\\"\\\\n\\\" or ord(c) == 0x202F:\\n return True\\n return False\\n\\n if is_training:\\n subset_list = ['train']\\n else:\\n subset_list = ['dev', 'test']\\n\\n examples, features = {}, {}\\n for subset in subset_list:\\n level_example_dict = {\\\"high\\\": None, \\\"middle\\\": None}\\n level_features_dict = {\\\"high\\\": None, \\\"middle\\\": None}\\n\\n for level in ['high', 'middle']:\\n subset_dir = os.path.join(data_dir, subset)\\n file_list = os.listdir(subset_dir)\\n file_list = [file for file in file_list if file.endswith('txt')]\\n file_list = sorted(file_list)\\n\\n alldata = []\\n for file in file_list:\\n data = json.load(open(os.path.join(subset_dir, file)))\\n alldata.append(data)\\n \\n examples_list = []\\n for data in alldata:\\n doc_id = data['id']\\n doc = data[\\\"article\\\"].replace('\\\\\\\\n', '\\\\n')\\n doc_token = []\\n prev_is_whitespace = True\\n for c in doc:\\n if is_whitespace(c):\\n prev_is_whitespace = True\\n else:\\n if prev_is_whitespace:\\n doc_token.append(c)\\n else:\\n doc_token[-1] += c\\n prev_is_whitespace = False\\n\\n for i, answer in enumerate(data[\\\"answers\\\"]):\\n if answer == \\\" \\\":\\n answer = \\\"C\\\"\\n example = InputExample(\\n guid=doc_id + '-%d' % i,\\n doc_token=doc_token,\\n question_text=\\\"\\\",\\n options=data[\\\"options\\\"],\\n answer=answer)\\n examples_list.append(example)\\n\\n level_example_dict[level] = examples_list\\n level_features_dict[level] = convert_examples_to_features(\\n level_example_dict[level], tokenizer, max_doc_len, max_query_len, max_option_len)\\n \\n print(\\\"MuTuaL Data Loader....\\\")\\n print(level_example_dict[level][-1])\\n print(level_features_dict[level][-1])\\n examples[subset] = level_example_dict\\n features[subset] = level_features_dict\\n\\n return examples, features\",\n \"def makedata():\\n # train\\n print('Clean Train Dataset and separate questions')\\n df = pd.read_csv(TRAIN_DATASET).replace(np.nan, ' ')\\n t = df.shape[0] * 2\\n print t\\n df['question1'] = cleanText(df['question1'])\\n df['question2'] = cleanText(df['question2'])\\n\\n df.to_csv(os.path.join(rootpathdata_cleaned, 'train.csx'), index=False)\\n overallquestions = df['question1'].tolist() + df['question2'].tolist()\\n tpm = pd.DataFrame()\\n tpm['question'] = overallquestions\\n tpm.to_csv(os.path.join(rootpathdata_cleaned, 'train_allquestions.csx'), index=False)\\n # test\\n\\n print('Clean Test Dataset and separate questions')\\n df = pd.read_csv(TEST_DATASET).fillna(' ')\\n t1 = df.shape[0] * 2\\n df['question1'] = cleanText(df['question1'])\\n df['question2'] = cleanText(df['question2'])\\n df.to_csv(os.path.join(rootpathdata_cleaned, 'test.csx'), index=False)\\n\\n overallquestions += df['question1'].tolist() + df['question2'].tolist()\\n tpm = pd.DataFrame()\\n tpm['question'] = overallquestions\\n tpm.to_csv(os.path.join(rootpathdata_cleaned, 'test_allquestions.csx'), index=False)\\n print len(overallquestions), t1 + t\",\n \"def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):\\n if token_ids_1 is None:\\n return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]\\n _cls = [self.cls_token_id]\\n _sep = [self.sep_token_id]\\n return _cls + token_ids_0 + _sep + _sep + token_ids_1 + _sep\",\n \"def setup_special_ids(tokenizer):\\n FLAGS.vocab_size = tokenizer.get_vocab_size()\\n tf.logging.info(\\\"Set vocab_size: %d.\\\", FLAGS.vocab_size)\\n for sym, sym_id_str in special_symbols_mapping.items():\\n try:\\n sym_id = tokenizer.get_token_id(sym)\\n setattr(FLAGS, sym_id_str, sym_id)\\n tf.logging.info(\\\"Set %s to %d.\\\", sym_id_str, sym_id)\\n except KeyError:\\n tf.logging.warning(\\\"Skip %s: not found in tokenizer's vocab.\\\", sym)\",\n \"def tokenized(self, text):\\n return self.tokenizer.encode_plus(text,\\n max_length=512,\\n pad_to_max_length=True,\\n truncation=True)[\\\"input_ids\\\"]\",\n \"def tokenize(essays, essay_len=500):\\n # normalise scores into range of [0-1]\\n normalised_scores = normaliseScores(essays)\\n logging.info(\\\"Begin tokenization of essays...\\\")\\n # Begin the timer \\n t = time()\\n # Fit_on_texts method creates the vocabulary index based on word frequency\\n nltk_tokenizer.fit_on_texts(essays['essay'])\\n logging.info(\\\"Tokenization Done!\\\")\\n logging.info(\\\"Time taken to tokenize essays: {} seconds \\\".format(round(time() - t), 2))\\n logging.info(\\\"Tokenization Summary: \\\")\\n logging.info(\\\"Number of unique words found: {}\\\".format(len(nltk_tokenizer.word_index)))\\n logging.info(\\\"Number of essays used for tokenisation: {}\\\".format(nltk_tokenizer.document_count)) \\n # Transform each essay into a sequence of integers by converting each word to their unique integer asasigned in word_index\\n sequence_vectors = nltk_tokenizer.texts_to_sequences(essays['essay'])\\n print(sequence_vectors[0])\\n # Pad the vectors so that all of them is essay_len long\\n data = pad_sequences(sequence_vectors, maxlen=essay_len)\\n label = normalised_scores\\n # Reshape the label tensor to (num_essays, 1)\\n label = np.reshape(label, (len(label), 1))\\n logging.info('Shape of data tensor: {}'.format(data.shape))\\n logging.info('Shape of label tensor: {}'.format(label.shape))\\n return data, label\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7129758","0.7047955","0.7026456","0.6816859","0.6816859","0.6816859","0.6816859","0.676679","0.6257971","0.61061835","0.6054662","0.60403764","0.5969531","0.5852405","0.57931036","0.5778398","0.5752423","0.5728716","0.5722207","0.5704012","0.5679085","0.56578636","0.56270003","0.5558108","0.5533159","0.5491456","0.5471151","0.546485","0.5441958","0.54188305","0.53909147","0.53844494","0.5366698","0.53384095","0.53276986","0.53165716","0.53096104","0.5276347","0.5272229","0.52714205","0.52417064","0.5233404","0.5232841","0.5223058","0.5217746","0.5217029","0.5189355","0.51867646","0.51795584","0.5168522","0.51636183","0.51581484","0.5149211","0.5123654","0.51187885","0.51084524","0.5105416","0.5096853","0.5066287","0.5056116","0.5050342","0.5048509","0.50450957","0.5042321","0.5041155","0.5038719","0.50385535","0.50378805","0.5036626","0.50328094","0.50139904","0.5011722","0.5011561","0.5010573","0.50082576","0.50009274","0.49792683","0.49760324","0.4961714","0.495491","0.4954511","0.49489453","0.49459767","0.49116328","0.491012","0.4900052","0.48972055","0.48902324","0.48817718","0.4881034","0.4876025","0.4874959","0.48714748","0.4871341","0.48702842","0.48702747","0.48697743","0.48676097","0.48673743","0.48672482"],"string":"[\n \"0.7129758\",\n \"0.7047955\",\n \"0.7026456\",\n \"0.6816859\",\n \"0.6816859\",\n \"0.6816859\",\n \"0.6816859\",\n \"0.676679\",\n \"0.6257971\",\n \"0.61061835\",\n \"0.6054662\",\n \"0.60403764\",\n \"0.5969531\",\n \"0.5852405\",\n \"0.57931036\",\n \"0.5778398\",\n \"0.5752423\",\n \"0.5728716\",\n \"0.5722207\",\n \"0.5704012\",\n \"0.5679085\",\n \"0.56578636\",\n \"0.56270003\",\n \"0.5558108\",\n \"0.5533159\",\n \"0.5491456\",\n \"0.5471151\",\n \"0.546485\",\n \"0.5441958\",\n \"0.54188305\",\n \"0.53909147\",\n \"0.53844494\",\n \"0.5366698\",\n \"0.53384095\",\n \"0.53276986\",\n \"0.53165716\",\n \"0.53096104\",\n \"0.5276347\",\n \"0.5272229\",\n \"0.52714205\",\n \"0.52417064\",\n \"0.5233404\",\n \"0.5232841\",\n \"0.5223058\",\n \"0.5217746\",\n \"0.5217029\",\n \"0.5189355\",\n \"0.51867646\",\n \"0.51795584\",\n \"0.5168522\",\n \"0.51636183\",\n \"0.51581484\",\n \"0.5149211\",\n \"0.5123654\",\n \"0.51187885\",\n \"0.51084524\",\n \"0.5105416\",\n \"0.5096853\",\n \"0.5066287\",\n \"0.5056116\",\n \"0.5050342\",\n \"0.5048509\",\n \"0.50450957\",\n \"0.5042321\",\n \"0.5041155\",\n \"0.5038719\",\n \"0.50385535\",\n \"0.50378805\",\n \"0.5036626\",\n \"0.50328094\",\n \"0.50139904\",\n \"0.5011722\",\n \"0.5011561\",\n \"0.5010573\",\n \"0.50082576\",\n \"0.50009274\",\n \"0.49792683\",\n \"0.49760324\",\n \"0.4961714\",\n \"0.495491\",\n \"0.4954511\",\n \"0.49489453\",\n \"0.49459767\",\n \"0.49116328\",\n \"0.491012\",\n \"0.4900052\",\n \"0.48972055\",\n \"0.48902324\",\n \"0.48817718\",\n \"0.4881034\",\n \"0.4876025\",\n \"0.4874959\",\n \"0.48714748\",\n \"0.4871341\",\n \"0.48702842\",\n \"0.48702747\",\n \"0.48697743\",\n \"0.48676097\",\n \"0.48673743\",\n \"0.48672482\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6808701"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":77095,"cells":{"query":{"kind":"string","value":"la idea es que escanee la zona deseada (desde cero) y guarde la imagen"},"document":{"kind":"string","value":"def saveimage(self):\n if self.saveimageButton.isChecked():\n self.save = True\n self.channelsOpen()\n self.movetoStart()\n self.saveimageButton.setText('Abort')\n self.guarda = np.zeros((self.numberofPixels, self.numberofPixels))\n self.liveviewStart()\n\n else:\n self.save = False\n print(\"Abort\")\n self.saveimageButton.setText('reintentar Scan and Stop')\n self.liveviewStop()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def foetus_pics(self):\n pic = 0\n if 0.0 not in self.allele:\n self.contamination = 2\n pic = 3\n elif 0.0 == self.allele[1]:\n pic = 1\n else:\n pic = 2\n return pic","def ventanaprincipal():\r\n titulo_principal=pygame.image.load(\"../recursor/Imagenes juegos/parejas_1.png\")\r\n ventana.blit(titulo_principal, (405,0))\r\n logo_plaython=pygame.image.load(\"../recursor/Imagenes juegos/plaython.png\")#Cargamos la imagen del logo\r\n imagen_decoracion=pygame.image.load(\"../recursor/Imagenes juegos/imagenpc.png\")\r\n imagen_decoracion = pygame.transform.scale(imagen_decoracion, (210, 210))\r\n ventana.blit(logo_plaython, (900, 180))\r\n ventana.blit(imagen_decoracion,(100,240))\r\n titulo_plaython = pygame.image.load(\"../recursor/Imagenes juegos/TITULOPLAYTHON.png\")\r\n titulo_plaython = pygame.transform.scale(titulo_plaython, (240, 150))\r\n ventana.blit(titulo_plaython, (505, 550))","def make_e(self):\n self.img[1, 2:-1] = 1\n self.img[self.l_i / 2, 2:-1] = 1\n self.img[-2, 2:-1] = 1\n self.img[1:-1, 2] = 1\n self.img_name = 'E'","def incarcaPieseMozaic(params):\n \n print('Incarcam piesele pentru mozaic din director.')\n\n pieseMozaic = []\n # for img_path in sorted(glob.glob(params.numeDirector\n # + '*.'\n # + params.tipImagine),\n # key=lambda name: int(name[len(params.numeDirector)\n # :-4])):\n for img_path in glob.glob(params.numeDirector\n + '*.'\n + params.tipImagine):\n img = cv2.imread(img_path)\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n pieseMozaic.append(img)\n pieseMozaic = np.array(pieseMozaic)\n\n if params.afiseazaPieseMozaic:\n # afiseaza primele 100 de piese ale mozaicului\n if pieseMozaic.shape[0] < 100:\n raise Exception(\"Numarul de piese este mai mic decat 100!\")\n\n plt.figure()\n plt.suptitle('Primele 100 de piese ale mozaicului sunt:')\n idxImg = 0\n for i in range(10):\n for j in range(10):\n idxImg += 1\n plt.subplot(10, 10, idxImg)\n plt.axis('off')\n plt.imshow(pieseMozaic[idxImg - 1])\n\n plt.show()\n\n # gray image\n if params.imgReferinta.shape[2] == 1:\n pieseMozaicG = []\n for i in range(pieseMozaic.shape[0]):\n img = cv2.cvtColor(pieseMozaic[i], cv2.COLOR_RGB2GRAY)\n img = img[:, :, None]\n pieseMozaicG.append(img)\n pieseMozaicG = np.array(pieseMozaicG)\n params.pieseMozaic = pieseMozaicG\n else:\n params.pieseMozaic = pieseMozaic\n\n if params.hexagon == 1:\n params = getHexagonMatrix(params,\n pieseMozaic[0].shape[0],\n pieseMozaic[0].shape[1])\n\n return params","def __init__(self,obrazok):\n self.obrazok = pygame.transform.smoothscale(obrazok,(int(obrazok.get_width()/g.scaleObrazovky)+1,int(obrazok.get_height()/g.scaleObrazovky)+1))#+1 aby som mys nescalol na 0\n self.mask = pygame.mask.from_surface(self.obrazok)\n self.x = 0\n self.y = 0","def __init__(self,jeu,can,c,x,y,nbr,img,bto):\r\n self.c,self.jeu,self.can=c,jeu,can\r\n self.X,self.Y,self.img,self.bto=x,y,img,bto\r\n self.grille(can,nbr,x,y,c)\r\n self.blabla(nbr,x-46,y-92,c)","def JUEGOPAREJAS():\r\n cuadrados=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\r\n ##############Primeramente, definimos los cuadrados de nuestra pizarra##############\r\n Cuadrado1=Rect(420,140,100,100)\r\n Cuadrado2=Rect(520,140,100,100)\r\n Cuadrado3=Rect(620,140,100,100)\r\n Cuadrado4=Rect(720,140,100,100)\r\n Cuadrado5=Rect(420,240,100,100)\r\n Cuadrado6=Rect(520,240,100,100)\r\n Cuadrado7=Rect(620,240,100,100)\r\n Cuadrado8=Rect(720,240,100,100)\r\n Cuadrado9=Rect(420,340,100,100)\r\n Cuadrado10=Rect(520,340,100,100)\r\n Cuadrado11=Rect(620,340,100,100)\r\n Cuadrado12=Rect(720,340,100,100)\r\n Cuadrado13=Rect(420,440,100,100)\r\n Cuadrado14=Rect(520,440,100,100)\r\n Cuadrado15=Rect(620,440,100,100)\r\n Cuadrado16=Rect(720,440,100,100)\r\n\r\n Totalcuadrados=(Cuadrado1,Cuadrado2,Cuadrado3,Cuadrado4,Cuadrado5,Cuadrado6,Cuadrado7,Cuadrado8,Cuadrado9,Cuadrado10,Cuadrado11,Cuadrado12,Cuadrado13,Cuadrado14,Cuadrado15,Cuadrado16)\r\n listafiguras=[]\r\n def ventanaprincipal():\r\n \"\"\"\"\r\n Esta funcion, genera la ventana principal de nuestro juego de parejas. Contiene\r\n cada uno de los titulos e imagenes del juego.\r\n :return None\r\n \"\"\"\r\n titulo_principal=pygame.image.load(\"../recursor/Imagenes juegos/parejas_1.png\")\r\n ventana.blit(titulo_principal, (405,0))\r\n logo_plaython=pygame.image.load(\"../recursor/Imagenes juegos/plaython.png\")#Cargamos la imagen del logo\r\n imagen_decoracion=pygame.image.load(\"../recursor/Imagenes juegos/imagenpc.png\")\r\n imagen_decoracion = pygame.transform.scale(imagen_decoracion, (210, 210))\r\n ventana.blit(logo_plaython, (900, 180))\r\n ventana.blit(imagen_decoracion,(100,240))\r\n titulo_plaython = pygame.image.load(\"../recursor/Imagenes juegos/TITULOPLAYTHON.png\")\r\n titulo_plaython = pygame.transform.scale(titulo_plaython, (240, 150))\r\n ventana.blit(titulo_plaython, (505, 550))\r\n\r\n def dibujarcirculos(x,color):\r\n \"\"\"\r\n Esta funcion, dibuja cada uno de las figuras de forma circular del juego en base a su generacion aleatoria.\r\n :param int x: Representa la casilla elegida, y en base a este valor, se dibuja un cuadrado en determinado lugar\r\n :param tuple color: Representa un color determinado, el cual se encuentra escrito en base a sus valores RGB\r\n :return None\r\n \"\"\"\r\n if x==1:\r\n pygame.draw.circle(ventana,color,(470,190),30)\r\n elif x==2:\r\n pygame.draw.circle(ventana,color,(570,190),30)\r\n elif x==3:\r\n pygame.draw.circle(ventana,color,(670,190),30)\r\n elif x==4:\r\n pygame.draw.circle(ventana,color,(770,190),30)\r\n elif x==5:\r\n pygame.draw.circle(ventana,color,(470,290),30)\r\n elif x==6:\r\n pygame.draw.circle(ventana,color,(570,290),30)\r\n elif x==7:\r\n pygame.draw.circle(ventana,color,(670,290),30)\r\n elif x==8:\r\n pygame.draw.circle(ventana,color,(770,290),30)\r\n elif x==9:\r\n pygame.draw.circle(ventana,color,(470,390),30)\r\n elif x==10:\r\n pygame.draw.circle(ventana,color,(570,390),30)\r\n elif x==11:\r\n pygame.draw.circle(ventana,color,(670,390),30)\r\n elif x==12:\r\n pygame.draw.circle(ventana,color,(770,390),30)\r\n elif x==13:\r\n pygame.draw.circle(ventana,color,(470,490),30)\r\n elif x==14:\r\n pygame.draw.circle(ventana,color,(570,490),30)\r\n elif x==15:\r\n pygame.draw.circle(ventana,color,(670,490),30)\r\n elif x==16:\r\n pygame.draw.circle(ventana,color,(770,490),30)\r\n\r\n def dibujarcuadrados(x,color):\r\n \"\"\"\r\n Esta funcion, dibuja cada uno de las figuras de forma cuadrada del juego en base a su generacion aleatoria.\r\n :param int x: Representa la casilla elegida, y en base a este valor, se dibuja un cuadrado en determinado lugar\r\n :param tuple color: Representa un color determinado, el cual se encuentra escrito en base a sus valores RGB\r\n :return None\r\n \"\"\"\r\n if x==1:\r\n pygame.draw.rect(ventana,color,Rect(440,160,60,60))\r\n elif x==2:\r\n pygame.draw.rect(ventana,color,Rect(540,160,60,60))\r\n elif x==3:\r\n pygame.draw.rect(ventana,color,Rect(640,160,60,60))\r\n elif x==4:\r\n pygame.draw.rect(ventana,color,Rect(740,160,60,60))\r\n elif x==5:\r\n pygame.draw.rect(ventana,color,Rect(440,260,60,60))\r\n elif x==6:\r\n pygame.draw.rect(ventana,color,Rect(540,260,60,60))\r\n elif x==7:\r\n pygame.draw.rect(ventana,color,Rect(640,260,60,60))\r\n elif x==8:\r\n pygame.draw.rect(ventana,color,Rect(740,260,60,60))\r\n elif x==9:\r\n pygame.draw.rect(ventana,color,Rect(440,360,60,60))\r\n elif x==10:\r\n pygame.draw.rect(ventana,color,Rect(540,360,60,60))\r\n elif x==11:\r\n pygame.draw.rect(ventana,color,Rect(640,360,60,60))\r\n elif x==12:\r\n pygame.draw.rect(ventana,color,Rect(740,360,60,60))\r\n elif x==13:\r\n pygame.draw.rect(ventana,color,Rect(440,460,60,60))\r\n elif x==14:\r\n pygame.draw.rect(ventana,color,Rect(540,460,60,60))\r\n elif x==15:\r\n pygame.draw.rect(ventana,color,Rect(640,460,60,60))\r\n elif x==16:\r\n pygame.draw.rect(ventana,color,Rect(740,460,60,60))\r\n\r\n def dibujardiamantes(x,color):\r\n \"\"\"\r\n Esta funcion, dibuja cada uno de las figuras de forma de diamante del juego en base a su generacion aleatoria.\r\n :param int x: Representa la casilla elegida, y en base a este valor, se dibuja un cuadrado en determinado lugar\r\n :param tuple color: Representa un color determinado, el cual se encuentra escrito en base a sus valores RGB\r\n :return None\r\n \"\"\"\r\n if x==1:\r\n pygame.draw.polygon(ventana,color,( (430,190),(470,160),(510,190),(470,220)))\r\n elif x==2:\r\n pygame.draw.polygon(ventana,color,( (530,190),(570,160),(610,190),(570,220) ))\r\n elif x==3:\r\n pygame.draw.polygon(ventana,color,( (630,190),(670,160),(710,190),(670,220) ))\r\n elif x==4:\r\n pygame.draw.polygon(ventana,color,( (730,190),(770,160),(810,190),(770,220) ))\r\n elif x==5:\r\n pygame.draw.polygon(ventana,color,( (430,290),(470,260),(510,290),(470,320) ))\r\n elif x==6:\r\n pygame.draw.polygon(ventana,color,( (530,290),(570,260),(610,290),(570,320) ))\r\n elif x==7:\r\n pygame.draw.polygon(ventana,color,( (630,290),(670,260),(710,290),(670,320) ))\r\n elif x==8:\r\n pygame.draw.polygon(ventana,color,( (730,290),(770,260),(810,290),(770,320) ))\r\n elif x==9:\r\n pygame.draw.polygon(ventana,color,( (430,390),(470,360),(510,390),(470,420) ))\r\n elif x==10:\r\n pygame.draw.polygon(ventana,color,( (530,390),(570,360),(610,390),(570,420) ))\r\n elif x==11:\r\n pygame.draw.polygon(ventana,color,( (630,390),(670,360),(710,390),(670,420) ))\r\n elif x==12:\r\n pygame.draw.polygon(ventana,color,( (730,390),(770,360),(810,390),(770,420) ))\r\n elif x==13:\r\n pygame.draw.polygon(ventana,color,( (430,490),(470,460),(510,490),(470,520) ))\r\n elif x==14:\r\n pygame.draw.polygon(ventana,color,( (530,490),(570,460),(610,490),(570,520) ))\r\n elif x==15:\r\n pygame.draw.polygon(ventana,color,( (630,490),(670,460),(710,490),(670,520) ))\r\n elif x==16:\r\n pygame.draw.polygon(ventana,color,( (730,490),(770,460),(810,490),(770,520) ))\r\n def dibujartriangulos(x,color):\r\n \"\"\"\r\n Esta funcion, dibuja cada uno de las figuras de forma triangular del juego en base a su generacion aleatoria.\r\n :param int x: Representa la casilla elegida, y en base a este valor, se dibuja un cuadrado en determinado lugar\r\n :param tuple color: Representa un color determinado, el cual se encuentra escrito en base a sus valores RGB\r\n :return None\r\n \"\"\"\r\n if x==1:\r\n pygame.draw.polygon(ventana,color,( (430,160),(510,160),(470,220) ))\r\n elif x==2:\r\n pygame.draw.polygon(ventana,color,( (530,160),(610,160),(570,220) ))\r\n elif x==3:\r\n pygame.draw.polygon(ventana,color,( (630,160),(710,160),(670,220) ))\r\n elif x==4:\r\n pygame.draw.polygon(ventana,color,( (730,160),(810,160),(770,220) ))\r\n elif x==5:\r\n pygame.draw.polygon(ventana,color,( (430,260),(510,260),(470,320)))\r\n elif x==6:\r\n pygame.draw.polygon(ventana,color,( (530,260),(610,260),(570,320) ))\r\n elif x==7:\r\n pygame.draw.polygon(ventana,color,( (630,260),(710,260),(670,320) ))\r\n elif x==8:\r\n pygame.draw.polygon(ventana,color,( (730,260),(810,260),(770,320) ))\r\n elif x==9:\r\n pygame.draw.polygon(ventana,color,( (430,360),(510,360),(470,420) ))\r\n elif x==10:\r\n pygame.draw.polygon(ventana,color,( (530,360),(610,360),(570,420) ))\r\n elif x==11:\r\n pygame.draw.polygon(ventana,color,( (630,360),(710,360),(670,420) ))\r\n elif x==12:\r\n pygame.draw.polygon(ventana,color,( (730,360),(810,360),(770,420) ))\r\n elif x==13:\r\n pygame.draw.polygon(ventana,color,( (430,460),(510,460),(470,520) ))\r\n elif x==14:\r\n pygame.draw.polygon(ventana,color,( (530,460),(610,460),(570,520) ))\r\n elif x==15:\r\n pygame.draw.polygon(ventana,color,( (630,460),(710,460),(670,520) ))\r\n elif x==16:\r\n pygame.draw.polygon(ventana,color,( (730,460),(810,460),(770,520) ))\r\n\r\n def iniciarjuego():\r\n \"\"\"\"\r\n Esta funcion, crea cuatro figuras de cada una de las posibles, dos de ellas con diferente color de las otras dos.\r\n \"\"\"\r\n for i in range(4):\r\n if i == 1 or i == 0:\r\n color = (250,208,120,98)\r\n else:\r\n color = (159, 250, 120,98)\r\n x = random.choice(cuadrados)\r\n listafiguras.append(x)\r\n dibujarcuadrados(x, color)\r\n cuadrados.remove(x)\r\n\r\n for i in range(4):\r\n if i == 0 or i == 1:\r\n color = (195, 139, 255,100)\r\n else:\r\n color = (250, 242, 120,98)\r\n x = random.choice(cuadrados)\r\n listafiguras.append(x)\r\n dibujarcirculos(x, color)\r\n cuadrados.remove(x)\r\n\r\n for i in range(4):\r\n if i == 0 or i == 1:\r\n color = (250, 145, 137,98)\r\n else:\r\n color = (126, 139, 250,98)\r\n x = random.choice(cuadrados)\r\n listafiguras.append(x)\r\n dibujartriangulos(x, color)\r\n cuadrados.remove(x)\r\n\r\n for i in range(4):\r\n if i == 0 or i == 1:\r\n color = (176, 255, 237,100)\r\n else:\r\n color = (255, 176, 228,100)\r\n x = random.choice(cuadrados)\r\n listafiguras.append(x)\r\n dibujardiamantes(x, color)\r\n cuadrados.remove(x)\r\n\r\n def mostrar(posicion_del_mouse):\r\n \"\"\"\r\n Esta funcion muestra segun la casilla que clickee el jugador.\r\n :param tuple posicion_del_mouse: Este parametro representa segun clickee el jugador (posicion en x y y).\r\n \"\"\"\r\n numerodecuadrado = numerocuadrado(posicion_del_mouse)\r\n ubicaciondelcuadrado=ubicacion_cuadrado(posicion_del_mouse)\r\n if ubicaciondelcuadrado==0 or ubicaciondelcuadrado==1:\r\n color = (250,208,120,98)\r\n dibujarcuadrados(numerodecuadrado,color)\r\n elif ubicaciondelcuadrado==2 or ubicaciondelcuadrado==3:\r\n color = (159, 250, 120,98)\r\n dibujarcuadrados(numerodecuadrado,color)\r\n elif ubicaciondelcuadrado==4 or ubicaciondelcuadrado==5:\r\n color = (195, 139, 255,100)\r\n dibujarcirculos(numerodecuadrado,color)\r\n elif ubicaciondelcuadrado==6 or ubicaciondelcuadrado==7:\r\n color = (250, 242, 120,98)\r\n dibujarcirculos(numerodecuadrado,color)\r\n elif ubicaciondelcuadrado==8 or ubicaciondelcuadrado==9:\r\n color = (250, 145, 137,98)\r\n dibujartriangulos(numerodecuadrado,color)\r\n elif ubicaciondelcuadrado==10 or ubicaciondelcuadrado==11:\r\n color = (126, 139, 250,98)\r\n dibujartriangulos(numerodecuadrado,color)\r\n elif ubicaciondelcuadrado==12 or ubicaciondelcuadrado==13:\r\n color = (176, 255, 237,100)\r\n dibujardiamantes(numerodecuadrado,color)\r\n elif ubicaciondelcuadrado==14 or ubicaciondelcuadrado==15:\r\n color = (255, 176, 228,100)\r\n dibujardiamantes(numerodecuadrado,color)\r\n\r\n def esconderfichas(posicion_del_mouse):\r\n \"\"\"\"\r\n Esta funcion, esconde las fichas, si la seleccion del usuario no es la correcta.\r\n :param tuple posicion_del_mouse: :param tuple primeraeleccion: Representa la posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\r\n \"\"\"\r\n numerodecuadrado=numerocuadrado(posicion_del_mouse)\r\n for j in range(16):\r\n if numerodecuadrado==j+1:\r\n pygame.draw.rect(ventana,(135,206,250),Totalcuadrados[j].inflate(-10,-10))\r\n\r\n def encontrarparejas(primeraeleccion,segundaeleccion):\r\n \"\"\"\r\n Esta funcion determina si se encontro una pareja.\r\n :param tuple primeraeleccion: Representa la posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\r\n :param tuple segundaeleccion: Representa la segunda posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\r\n Retorna un booleano\r\n \"\"\"\r\n x=ubicacion_cuadrado(primeraeleccion)\r\n y=ubicacion_cuadrado(segundaeleccion)\r\n if (x==0 and y==1 or x==1 and y==0)\\\r\n or (x == 2 and y == 3 or x == 3 and y == 2) \\\r\n or (x == 4 and y == 5 or x == 5 and y == 4)\\\r\n or (x == 6 and y == 7 or x == 7 and y == 6)\\\r\n or (x == 8 and y == 9 or x == 9 and y == 8) \\\r\n or (x==10 and y==11 or x==11 and y==10)\\\r\n or (x==12 and y==13 or x==13 and y==12)\\\r\n or (x==14 and y==15 or x==15 and y==14):\r\n return True\r\n\r\n def ubicacion_cuadrado(posicion_del_mouse):\r\n \"\"\"\r\n Esta funcion, retorna la figura que se encuentra en determinado indice segun se clickee.\r\n :param tuple posicion_del_mouse: Representa la posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\r\n Retorna la figura de determinado indice\r\n \"\"\"\r\n for j in range(16):\r\n if Totalcuadrados[j].collidepoint(posicion_del_mouse):\r\n return listafiguras.index(j+1)\r\n\r\n def dibujartablero():\r\n \"\"\"\"\r\n Esta funcion, dibuja los cuadrados que hacen nuestro tablero.\r\n :return None\r\n \"\"\"\r\n ventana.fill((135,206,250)) #colocamos el color de fondo de nuestra pantalla\r\n for j in range(16): #iteramos entre los cuadrados ya diseñados, eligiendo parametros como el color y el grueso de sus lineas.\r\n pygame.draw.rect(ventana,(255,255,255),Totalcuadrados[j],10)\r\n\r\n def numerocuadrado(posicion_del_mouse):\r\n \"\"\"\r\n Esta funcion, retorna el indice de determinado cuadrado segun se clickee.\r\n :param tuple posicion_del_mouse: Representa la posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\r\n Retorna el indice de terminada posicion\r\n \"\"\"\r\n\r\n for j in range(16):\r\n if Totalcuadrados[j].collidepoint(posicion_del_mouse):\r\n return j+1\r\n\r\n def main():\r\n \"\"\"\"\r\n Funcion proncipal del juego.\r\n :return None\r\n \"\"\"\r\n pygame.init()\r\n global ventana\r\n ventana= pygame.display.set_mode((1280,700))\r\n ventana.fill((135,206,250))\r\n pygame.display.set_caption('Encuentra las parejas')\r\n logo = pygame.image.load(\"../recursor/icono.ico\")\r\n pygame.display.set_icon(logo)\r\n pygame.display.update()\r\n dibujartablero()\r\n iniciarjuego()\r\n titulo_principal=pygame.image.load(\"../recursor/Imagenes juegos/parejas_2.png\")\r\n ventana.blit(titulo_principal, (405,0))\r\n pygame.display.update()\r\n pygame.time.wait(3000)\r\n dibujartablero()\r\n ventanaprincipal()\r\n pygame.display.update()\r\n prueba=0\r\n eleccioncorrecta=[]\r\n while True:\r\n for event in pygame.event.get():\r\n if event.type==QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n elif event.type==pygame.MOUSEBUTTONUP:\r\n posicion_del_mouse=pygame.mouse.get_pos()\r\n mostrar(posicion_del_mouse)\r\n pygame.display.update()\r\n if prueba==0:\r\n primeraeleccion=posicion_del_mouse\r\n if numerocuadrado(primeraeleccion) in eleccioncorrecta:\r\n prueba=0\r\n else:\r\n prueba=1\r\n else:\r\n segundaeleccion=posicion_del_mouse\r\n if numerocuadrado(segundaeleccion) in eleccioncorrecta:\r\n prueba=1\r\n else:\r\n prueba=0\r\n if not (numerocuadrado(primeraeleccion) in eleccioncorrecta) and not numerocuadrado(segundaeleccion) in eleccioncorrecta:\r\n if encontrarparejas(primeraeleccion,segundaeleccion):\r\n eleccioncorrecta.append(numerocuadrado(primeraeleccion))\r\n eleccioncorrecta.append(numerocuadrado(segundaeleccion))\r\n else:\r\n pygame.time.wait(1000)\r\n esconderfichas(primeraeleccion)\r\n esconderfichas(segundaeleccion)\r\n pygame.display.update()\r\n\r\n if len(eleccioncorrecta)==16:\r\n ganador=pygame.image.load('../recursor/Imagenes juegos/Ganadorgg0000.png')\r\n ventana.blit(ganador,(420, 140))\r\n pygame.display.update()\r\n time.sleep(4)\r\n pygame.quit()\r\n break\r\n\r\n main()","def umrandung_pixel(img, zeile, spalte):\n #Oben drueber\n #Index existiert und oben drueber ist Hintergrund\n if zeile - 1 > 0 and img[zeile-1][spalte]==0:\n img[zeile-1][spalte]=255#markiere dortigen Pixel als Umrandung (=255)\n #Links daneben\n #Index existiert und links daneben ist Hintergrund\n if spalte - 1 > 0 and img[zeile][spalte-1]==0:\n img[zeile][spalte-1]=255#markiere dortigen Pixel als Umrandung (=255)\n #Rechts daneben\n #Index existiert und rechts daneben ist Hintergrund\n if spalte + 1 < img[zeile].__len__() and img[zeile][spalte+1]==0:\n img[zeile][spalte+1]=255#markiere dortigen Pixel als Umrandung (=255)\n #Unten drunter\n #Index existiert und unten drunter ist Hintergrund\n if zeile + 1 < img.__len__() and img[zeile+1][spalte]==0:\n img[zeile+1][spalte]=255#markiere dortigen Pixel als Umrandung (=255)","def galaxy(img):\n return img[420:490, 710:770]","def podziel(self):\n def fraktal(dlugosc, alpha, poziom):\n \"\"\"Metoda wyznaczajaca fraktal.\n\n Metoda ta przyjmuje dlugosc, kat oraz poziom drzewa.\n Na bazie podanych parametrow wylicza fraktal z podanych w zadaniu wzorow.\n Zwraca liste zawierajaca punkX oraz punktY fraktalu.\n \"\"\"\n#obliczanie punktow punktu Abis dla kazdego poziomu galezi\n x = float(self.p2[0] + self.dlugosc * cos(alpha))\n y = float(self.p2[1] + self.dlugosc * sin(alpha))\n return [round(x), round(y)]\n\n#petla przechodzaca po wszystkich poziomach drzewa\n while self.tmp <= self.poziom:\n#obliczanie grubosci, dlugosci galezi oraz kolorowanie jej\n self.grubosc = float((2 * self.grubosc + 1) / 3)\n self.dlugosc = float((2 * self.dlugosc) / 3)\n self.kolor += 6\n\n #sprawdzenie czy kolor nie wyszedl po za skale maksymalnej wartosci\n if self.kolor > 255:\n self.kolor = 255\n\n#rozbicie obliczen na poziom 1 i wyzej\n#Abis jest to punkt prawy dla kazdej galezi\n#B jest to punkt srodkowy dla kazdej galezi\n#C jest to punkt srodkowy dla kazdej galezi\n\n#obliczenia dla pierwszego poziomu\n if self.tmp < 2:\n#obliczenie fraktalu, prawa galaz dla kazdej galezi\n#podstawienie obliczonych wartosci z punktu Abis do pozostalych wedlug podanych wzorow\n Abis = fraktal(self.dlugosc, self.alpha, self.poziom)\n B = [round(self.p2[0]), round(Abis[1])]\n C = [round(-Abis[0] + 2 * self.p2[0]), round(Abis[1])]\n\n#zwiekszenie poziomu drzewa o jeden\n self.tmp += 1\n\n#tutaj nastepuje zwrocenie obiektow typu Branch z nowo obliczonymi wartosciami\n return [Branch(self.p2, Abis, self.dlugosc, self.grubosc, self.kolor, self.alpha, self.tmp),\n Branch(self.p2, B, self.dlugosc, self.grubosc, self.kolor, self.alpha, self.tmp),\n Branch(self.p2, C, self.dlugosc, self.grubosc, self.kolor, self.alpha, self.tmp)]\n#obliczenia poziomow wyzej niz pierwszy\n else:\n#obliczanie kata dla punktu prawego\n self.zetprim = randint(-1, 1) * randint(1, self.s)\n self.beta = self.alpha + self.zetprim\n\n#obliczanie kata dla punktu srodkowego\n self.zetbis = randint(-1, 1) * randint(1, self.s)\n self.gamma = self.alpha + self.zetbis\n\n#obliczanie kata dla punktu lewego\n self.zetter = randint(-1, 1) * randint(1, self.s)\n self.teta = self.alpha + self.zetter\n\n#obliczenie fraktalu, prawa galaz dla kazdej galezi\n#podstawienie obliczonych wartosci z punktu Abis do pozostalych wedlug podanych wzorow\n Abis = fraktal(self.dlugosc, self.beta, self.poziom)\n B = [round(self.p2[0]), round(Abis[1])]\n C = [round(-Abis[0] + 2 * self.p2[0]), round(Abis[1])]\n\n#zwiekszenie poziomu drzewa o jeden\n self.tmp += 1\n\n#tutaj nastepuje zwrocenie obiektow typu Branch z nowo obliczonymi wartosciami\n return [Branch(self.p2, Abis, self.dlugosc, self.grubosc, self.kolor, self.beta, self.tmp),\n Branch(self.p2, B, self.dlugosc, self.grubosc, self.kolor, self.gamma, self.tmp),\n Branch(self.p2, C, self.dlugosc, self.grubosc, self.kolor, self.teta, self.tmp)]","def getTasseledCap(img):","def __init__(self,game):\r\n self.game=game\r\n self.can=game.can\r\n self.vrai=False\r\n self.select=-1\r\n self.img=game.img_bto\r\n self.bateau()\r\n self.ima()\r\n self.aléatoire(5,self.game.j1)","def random_translation(canvas, img):","def testSepia (name = \"smokey.gif\"):\n image = Image(name)\n print(\"Close the image window to see the transformation\")\n image.draw()\n sepia(image)\n image.draw()","def des_image(exptime=100,mag=None, Nstar=1,ccd=None,seeing=[0.9,0.,0.],npix=npix,zenith=0,filter='r', theta=0., phi=0,corrector='corrector',x=None,y=None,z=None,suband=None,regular=False,setbkg=True):\n gain = 0.21 # convert electrons to ADU\n zeropoint = 26.794176 # r band, from Nikolay\n \n if setbkg == False:\n skyphoton = 0.\n else:\n skyphoton = 8.460140*exptime\n bkg = skyphoton*gain\n res = genImgV(filename=None,Nstar=Nstar,ccd=ccd,seeing=seeing,npix=npix,zenith=zenith,filter=filter, theta=theta, phi=phi,corrector=corrector,x=x,y=y,z=z,suband=suband,regular=regular)\n if Nstar == 1:\n objectphoton = exptime*10**(0.4*(zeropoint - mag))\n psf = res[0][0][4:].reshape(npix,npix)\n img = (psf * objectphoton + skyphoton)*gain\n img = rebin(img,(40,40))\n psf = rebin(psf,(40,40))\n img = img + add_imageNoise(img)\n if Nstar > 1:\n img = []\n psf = []\n magg = []\n for i in range(Nstar):\n if type(mag) == list:\n rng = abs(mag[1] - mag[0])\n magi = min(mag)+np.random.rand()*rng\n objectphoton = exptime*10**(0.4*(zeropoint - magi))\n psfi = res[0][i][4:].reshape(npix,npix)\n imgi = (psfi * objectphoton + skyphoton)*gain\n imgi = imgi+np.sqrt(imgi)\n imgi = rebin(imgi,(40,40))\n psfi = rebin(psfi,(40,40))\n imgi = imgi + add_imageNoise(imgi)\n img.append(imgi)\n psf.append(psfi)\n magg.append(magi)\n return img,bkg,psf,magg","def draw_image(self):\n dir_ship = self.direction\n dir_cannon=self.cannon_dir\n temp_image = self.base_image.copy()\n pygame.draw.polygon(temp_image, (0,0,0), [(2,2),(2,3),(3,3),(3,2)])\n if dir_cannon == dir_ship:\n pygame.draw.polygon(temp_image, (60,60,60), [(4,3),(4,2), (5,3),(5,2)])\n if (dir_ship - dir_cannon)%4 ==1:#-90° angle\n pygame.draw.polygon(temp_image, (60,60,60), [(2,4),(3,4), (2,5),(3,5)])\n if (dir_ship - dir_cannon)%4 ==3:#+90° angle\n pygame.draw.polygon(temp_image, (60,60,60), [(2,1),(3,1), (2,0),(3,0)])\n if (dir_ship - dir_cannon)%4 ==2:#180° angle\n pygame.draw.polygon(temp_image, (60,60,60), [(1,2),(1,3), (0,2),(0,3)])\n temp_image=pygame.transform.rotate(temp_image,dir_ship*90)\n return temp_image","def orb():\n image = pygame.Surface([16, 16], pygame.SRCALPHA)\n pygame.draw.circle(image, colour.WHITE, (8, 8), 8, 0)\n return image","def imag(z):","def tick(self, iteration):\n frame = self.cam.get_current_fram()\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n # on recupaire la zone qui on les zone de couleur\n mask = cv2.inRange(hsv, self.greenLower, self.greenUpper)\n # on fait une erosion et dilation pour supprimer les petit pixel blanc qui peut être pris pour la balle\n mask = cv2.erode(mask, None, iterations=iteration)\n mask = cv2.dilate(mask, None, iterations=iteration)\n\n cv2.imshow(\"mask\", mask)\n\n # Recherche de contour pour savoir ou est la balle\n cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\n center = None\n if len(cnts) > 0:\n # on charche le contour le plus grand (celui de la balle)\n c = max(cnts, key=cv2.contourArea)\n ((x, y), radius) = cv2.minEnclosingCircle(c)\n moment_cont = cv2.moments(c)\n # on recupaire le centre de la balle\n center = (int(moment_cont[\"m10\"] / moment_cont[\"m00\"]), int(moment_cont[\"m01\"] / moment_cont[\"m00\"]))\n # on controle la taille du rayon de la balle\n if radius > 30:\n # Puis on dessin la boulle\n cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2)\n cv2.circle(frame, center, 5, (0, 0, 255), -1)\n return center, frame","def show_es():\n img = rotated_e()\n for i in range(4):\n plt.subplot(2, 2, i + 1)\n plt.imshow(img[i], cmap=plt.cm.gray, interpolation='nearest')\n plt.show()","def reemplaza_tildes(palabra):","def preprocess_RSofia(img):\n for i in range(480):\n for j in range(200):\n img[i,j]=0\n for i in range(150):\n for j in range(500):\n img[i,j]=0 \n for i in range(475):\n for j in range(90):\n img[i,img.shape[1]-1-j]=0\n for i in range(90):\n for j in range(465):\n img[i,img.shape[1]-1-j]=0\n return img","def imageprepare():\r\n file_name = '9-test.png'\r\n im = Image.open(file_name).convert('L')\r\n\r\n im.save(\"9-t.png\")\r\n plt.imshow(im)\r\n plt.show()\r\n tv = list(im.getdata())\r\n\r\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\r\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\r\n return tva","def phantom_ellipses(n_points,E):\n \n #Rescaling according to image size \n E[:,0] = E[:,0]*n_points/2 #semiaxis a\n E[:,1] = E[:,1]*n_points/2 #semiaxis b\n E[:,2] = E[:,2]*n_points/2 #x\n E[:,3] = E[:,3]*n_points/2 #y\n E[:,4] = E[:,4]*math.pi/180\n \n x,y = np.meshgrid(np.arange(0,n_points)-n_points//2 ,np.arange(0,n_points)-n_points//2 )\n nrow,ncol = E.shape\n phantom1 = np.zeros((y.shape[0], y.shape[1], nrow))\n\n for k in range(nrow): #itero sulle ellissi\n x_new = x - E[k,2]\n y_new = y - E[k,3]\n\n #find(( (x.*cosp + y.*sinp).^2)./asq + ((y.*cosp - x.*sinp).^2)./bsq <= 1); \n cosp = math.cos(E[k,4])\n sinp = math.sin(E[k,4])\n cond = np.square( x_new * cosp + y_new * sinp )*1/(E[k,0]*E[k,0]) + \\\n np.square(y_new * cosp - x_new * sinp)*1/(E[k,1]*E[k,1]) - 1\n\n for i in range(x.shape[0]):\n for j in range(x.shape[1]):\n if (cond[i,j] <= 0.0):\n phantom1[i,j,k] = E[k,5]; # gray scale\n else:\n phantom1[i,j,k] = 0.0;\n #endif\n #endfor\n #endfor\n #endfor\n phantom1 = phantom1.sum(axis=2)\n phantom = np.flipud(phantom1)\n return phantom","def umrandung_zeichnen(img):\n for zeilen_index in range(0,img.__len__()):\n for spalten_index in range(0, img[zeilen_index].__len__()):\n if img[zeilen_index][spalten_index] == 1:\n #Markiere benachbarte Pixel\n umrandung_pixel(img, zeilen_index, spalten_index)","def main():\n original = SimpleImage('images/mt-rainier.jpg')\n original.show()\n reflected = make_reflected('images/mt-rainier.jpg')\n reflected.show()","def problem1():\n\n img = load_image(\"data/a1p1.png\")\n display_image(img)\n\n save_as_npy(\"a1p1.npy\", img)\n\n img1 = load_npy(\"a1p1.npy\")\n display_image(img1)\n\n img2 = mirror_horizontal(img1)\n display_image(img2)\n\n display_images(img1, img2)","def fotoXO(self):\n\t\ttry:\n\t\t\timageP = self.cam.get_image()\n\t\t\tself.image = pygame.transform.flip(imageP, True, False)\n\t\texcept:\n\t\t\tpass\n\t\treturn self.image","def accueil():\r\n global etat\r\n background(0,128,128)\r\n image(tireur,0,0) \r\n rectMode(CORNERS)\r\n fill(100)\r\n rect(0,60,250,120)\r\n rect(500,60,750,120)\r\n fill(0)\r\n textSize(30)\r\n text(\"PVP\",95,100) \r\n text(\"ORDINATEUR\",520,100) \r\n if (mousePressed) and mouseX<250 and mouseX>0 and mouseY<120 and mouseY>60: #si on clique sur le rectangle jouer\r\n etat=1 #on passe en mode jeu\r\n if (mousePressed) and mouseX<750 and mouseX>500 and mouseY<120 and mouseY>60: \r\n etat=2","def giaotranh():\n # printdb('def giaotranh')\n time.sleep(8)\n logging.info('def giao tranh')\n for x in range(6):\n screencap()\n # thấy next thì chuyển\n imgnext = imagesearch(path_Scr + '\\\\screencap.png',\n path_Scr + '\\\\next.png', 0.8)\n if imgnext.find() >= 1:\n # printdb('mạnh')\n danh()\n break\n else:\n if x == 5:\n check_loi_vethanh()\n else:\n time.sleep(2)","def dibujarcuadrados(x,color):\r\n if x==1:\r\n pygame.draw.rect(ventana,color,Rect(440,160,60,60))\r\n elif x==2:\r\n pygame.draw.rect(ventana,color,Rect(540,160,60,60))\r\n elif x==3:\r\n pygame.draw.rect(ventana,color,Rect(640,160,60,60))\r\n elif x==4:\r\n pygame.draw.rect(ventana,color,Rect(740,160,60,60))\r\n elif x==5:\r\n pygame.draw.rect(ventana,color,Rect(440,260,60,60))\r\n elif x==6:\r\n pygame.draw.rect(ventana,color,Rect(540,260,60,60))\r\n elif x==7:\r\n pygame.draw.rect(ventana,color,Rect(640,260,60,60))\r\n elif x==8:\r\n pygame.draw.rect(ventana,color,Rect(740,260,60,60))\r\n elif x==9:\r\n pygame.draw.rect(ventana,color,Rect(440,360,60,60))\r\n elif x==10:\r\n pygame.draw.rect(ventana,color,Rect(540,360,60,60))\r\n elif x==11:\r\n pygame.draw.rect(ventana,color,Rect(640,360,60,60))\r\n elif x==12:\r\n pygame.draw.rect(ventana,color,Rect(740,360,60,60))\r\n elif x==13:\r\n pygame.draw.rect(ventana,color,Rect(440,460,60,60))\r\n elif x==14:\r\n pygame.draw.rect(ventana,color,Rect(540,460,60,60))\r\n elif x==15:\r\n pygame.draw.rect(ventana,color,Rect(640,460,60,60))\r\n elif x==16:\r\n pygame.draw.rect(ventana,color,Rect(740,460,60,60))","def transform(self, previousimage):","def convert_image(rel_path_in, rel_path_out):\n #Lade Bild mit Originalmaske im Grayscale-Modus\n img = cv2.imread(rel_path_in, cv2.IMREAD_GRAYSCALE)\n #Jetzt steht in img ein 2D-Array/Matrix mit jedem Graufstufen-Wert der Pixel\n #Skaliere Pixelwerte runter\n for zeilen_index in range(0,img.__len__()):\n for spalten_index in range(0, img[zeilen_index].__len__()):\n #Hole Pixel-Wert an aktueller Stelle\n wert = img[zeilen_index][spalten_index]\n #Falls Wert != 0 (also Pixel gehoert nicht zum Hintergrund)\n if wert != 0: # != 0 statt == 255, da auch z.B. 253er Werte in den Masken existieren... (vielleicht durch Konvertierung in anderes Format?)\n #Markiere den Pixel mit 1 statt 255\n img[zeilen_index][spalten_index]=1\n #print(img)\n #*NACHDEM* alle Pixel skaliert wurden, zeichne Umrandung der Objekte\n umrandung_zeichnen(img)\n #change_color(img, 0, 255)\n #change_color(img, 1, 0)\n #print(img)\n #Schreibe Ergebnis-Bild in uebergebene Datei\n cv2.imwrite(rel_path_out, img)","def comprimir_en_rango_dinamico(p):\n img = read_img(p)\n img_compressed = compresion_de_rango_dinamico(img.reshape((-1)))\n show_imgs([img, img_compressed.reshape(img.shape)])\n plt.hist(img_compressed, bins=256)\n plt.show()","def imageprepare(self, argv):\n im = Image.open(argv).convert('L')\n img = im.resize((28, 28), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\n tv = list(img.getdata()) \n \n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\n return tva","def comenzar_nuevo_juego():\n escena_uno.cargarEscena1(screen, display_width, display_height)#Se pone a correr la escena\n #escena_uno.cargarEscena2(screen, display_width, display_height)","def getimage(self):","def tirar_foto(frame, dimensoes, file_name, vaga, entrada_saida=\"E\"):\n (x, y, w, h) = dimensoes[0], dimensoes[1], dimensoes[2], dimensoes[3]\n file = f\"imagens/{file_name}.png\"\n imagem = frame[y:y + h, x:x + w]\n cv2.imwrite(file, imagem)\n print(f'foto salva -> {file_name}')\n\n print(\"detectando placa....\")\n recognize_license_plate(file, vaga, entrada_saida)\n print(\"fim detecção placa\")","def ff_correct_image(image):\n pass","def ff_correct_image(image):\n pass","def photometric_calibration():\n pass","def make_big_e(self):\n l = self.l_i\n self.img[l/2-1:l/2+1, l/2-5:l/2+5] = 1\n self.img[l/2-5:l/2-3, l/2-5:l/2+5] = 1\n self.img[l/2+3:l/2+5, l/2-5:l/2+5] = 1\n self.img[l/2-5:l/2+5, l/2-5:l/2-3] = 1\n self.img_name = 'bigE'","def mostrar(imagenObj,x,y):\n gameDisplay.blit(imagenObj,(x,y))","def ubicar_fragata():\n tamano = Fragata.tamano #se importa el tamano del barco desde su clase\n cantidad = Fragata.cantidad #se importa la cantidad de barcos desde su clase\n orientacion = orientaciones[(randint(0, 1))] #elige aleatoriamente el index de la tupla orientaciones = (\"Vertical\", \"Horizontal\")\n seguir_coordenadas = True\n while seguir_coordenadas:\n mal_ubicado = \"no\"\n if orientacion == \"Vertical\":\n #se eligen random las filas y las columnas\n coor_fila = randint(1, numero_filas)\n coor_columna = randint (1, numero_columnas)\n while (coor_fila + tamano) > 10: #como su orientacion es vertical la fila incial del barco mas su tamano (2) no puede ser mayor que 10 porque se saldria del mapa\n coor_fila = randint(1,numero_filas)\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \n coor_fila += 1\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion)\n for x in lista_ubicacion_barco:\n for y in lista_temporal:\n if x == y:\n mal_ubicado = \"si\" #si alguna coordenada de este barco coincide con la de otro la variable mal ubicado sera \"si\" \n elif (y[0] == x[0] or (y[0]+1) == x[0] or (y[0]-1) == x[0]) and ((y[1]) == x[1] or (y[1]+1) == x[1] or (y[1]- 1) == x[1]): #validacion para que no se ubique el barco al lado o diagonalmente contiguo a otro\n mal_ubicado = \"si\" #si esta validacion sucede la variable mal ubicado sera \"si\" \n if orientacion == \"Horizontal\":\n #se eligen random las filas y las columnas\n coor_fila = randint(1, numero_filas)\n coor_columna = randint(1, numero_columnas)\n while (coor_columna + tamano) > 10: #como su orientacion es horizontal la columna incial del barco mas su tamano (2) no puede ser mayor que 10 porque se saldria del mapa\n coor_columna = randint(1, numero_columnas)\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \n coor_columna += 1\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion)\n for x in lista_ubicacion_barco:\n for y in lista_temporal:\n if x == y:\n mal_ubicado = \"si\" #si alguna coordenada de este barco coincide con la de otro la variable mal ubicado sera \"si\" \n elif (y[0] == x[0] or (y[0]+1) == x[0] or (y[0]-1) == x[0]) and ((y[1]) == x[1] or (y[1]+1) == x[1] or (y[1]- 1) == x[1]): #validacion para que no se ubique el barco al lado o diagonalmente contiguo a otro\n mal_ubicado = \"si\" #si esta validacion sucede la variable mal ubicado sera \"si\" \n if mal_ubicado == \"si\": #si la variable mal ubicado es \"si\" se repetira el proceso otra vez\n seguir_coordenadas = True\n lista_temporal.clear()\n elif mal_ubicado == \"no\": #si la variable es \"no\" se continuara el proceso\n for x in lista_temporal:\n lista_ubicacion_barco.append(x) #se agregan las posiciones a la lista general\n coordenadas_fragata.append(x) \n lista_temporal.clear() #se limpia la lista temporal para usarla en el otro barco\n seguir_coordenadas = False","def actualise_fenetre(self,plateau,fenetre,joueur,info,bouton,etape_texte):\n\n self.affiche_plateau(plateau,fenetre)\n liste_im_joueur = [pygame.image.load(\"joueur\"+str(i)+\".png\").convert_alpha() for i in range(1,5)]\n for i in range (4) :\n x_joueur = 60\n y_joueur = 60\n liste_im_joueur[i] = pygame.transform.scale(liste_im_joueur[i], (int(x_joueur),int(y_joueur)))\n\n for i in range(len(plateau.dico_joueurs)) :\n fenetre.blit(liste_im_joueur[i],(1030,320+i*80))\n fenetre.blit(police_small.render(str(plateau.dico_joueurs[i].nom) + \" : \",False,pygame.Color(\"#000000\")),(800,340+i*75))\n fenetre.blit(police1.render(\"Score : \"+str(plateau.dico_joueurs[i].points),False,pygame.Color(\"#000000\")),(800,340+i*75+15))\n fenetre.blit(police1.render(\"Ordre de mission : \"+str(sorted(plateau.dico_joueurs[i].fantome_target)),False,pygame.Color(\"#000000\")),(800,340+i*75+30))\n fenetre.blit(police1.render(\"Jokers restants : \"+str(plateau.dico_joueurs[i].nb_joker),False,pygame.Color(\"#000000\")),(800,340+i*75+45))\n \n #test texte pour afficher le joueur qui joue\n if self.turn == True :\n fenetre.blit(police.render(\"C'est à vous de jouer\",False,pygame.Color(0,0,0)),(800,240))\n else:\n fenetre.blit(police.render(\"C'est le tour de votre adversaire\",False,pygame.Color(0,0,0)),(800,240))\n \n #affichage du message d'erreur\n for i in range(len(info)) : \n fenetre.blit(police.render(info[i],False,pygame.Color(\"#000000\")),(760,180+i*20))\n \n fenetre.blit(police.render(etape_texte,False,pygame.Color(\"#000000\")),(760,160))\n \n \n bouton.draw(fenetre)\n \n pygame.display.flip()","def imageprepare():\r\n file_name = 'temp_image.png'\r\n im = Image.open(file_name).convert('L')\r\n im = im.resize((20, 20))\r\n p = Image.new('L', (28,28), (255))\r\n p.paste(im,(4,4,24,24))\r\n p.save(\"last_image.png\")\r\n\r\n tv = list(p.getdata()) # get pixel values\r\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\r\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\r\n tva = np.reshape(tva, (28, 28))\r\n\r\n return tva","def move(self):\n \n self.rect.move_ip(0,self.speed) # Funcion para mover el enemigo especificando la velocidad xy\n \n if (self.rect.top > SCREEN_HEIGHT): # Condicion cuando llega a la parte inferior y no colisiono con el jugador\n del self.surf #Libera memoria\n del self.rect\n self.randomNumber = random.choice([70,64,32]) # Su tamaño se asigna nuevamente\n self.size = (self.randomNumber,self.randomNumber) #Se genera su tamaño como un cuadrado de lado aleatorio\n self.surf = pygame.Surface(self.size) #Se genera la superficie que aparecera la pantalla\n self.surf.fill(RED)\n self.rect = self.surf.get_rect(center = (random.randint(40,SCREEN_WIDTH-40),0))# me da info de las coordenadas de surf\n if(self.randomNumber == 32):\n self.surf = self.imagen\n elif(self.randomNumber ==64):\n self.surf = self.imagen2\n elif self.randomNumber ==70 :\n self.surf = self.imagen3","def danh():\n # printdb('def danh')\n imgmanh = imagesearch(path_Scr + '\\\\screencap.png',\n path_Scr + '\\\\longden.png', 0.77)\n time.sleep(1)\n # mạnh bỏ qua\n if imgmanh.find() >= 1:\n click_next()\n logging.info('Next')\n giaotranh()\n else:\n click_danh()\n #######################lỗi mạng######################\n time.sleep(30)\n screencap()\n error = imagesearch(path_Scr + '\\\\screencap.png',\n path_Scr + '\\\\error.png', 0.88)\n # printdb('check lỗi ')\n if error.find() >= 1: # check lỗi mạng\n check_loi_vethanh()\n else:\n time.sleep(cTimeWar) # 40s\n # Rutlui() #opt1 check khiên --end (rỉa 1 khiên)\n DanhTiep() # opt2 check nút hồi thành --end\n # click_rutlui() #opt3 chờ cTimeWar --End (đánh theo time chỉ định)\n #####################################################","def __init__(self, image_to_load, coo_x, rang, coo_y=432):\n\n super().__init__()\n self.image = pg.image.load(image_to_load)\n self.image = pg.transform.scale(self.image, (80, 80))\n self.rect = self.image.get_rect()\n self.rect.x = coo_x\n self.rect.y = coo_y\n self.last_direction = 1\n self.counter = 0\n self.range = rang\n\n if self.range % 2 == 0:\n self.half = self.range / 2 - 1\n else:\n self.half = self.range / 2","def img2heliovec(bxImg,byImg,bzImg,lon,lat,lonc,latc,pAng):\n a11 = -np.sin(latc)*np.sin(pAng)*np.sin(lon - lonc) + np.cos(pAng)*np.cos(lon - lonc)\n a12 = np.sin(latc)*np.cos(pAng)*np.sin(lon - lonc) + np.sin(pAng)*np.cos(lon - lonc)\n a13 = -np.cos(latc)*np.sin(lon - lonc)\n a21 = -np.sin(lat)*(np.sin(latc)*np.sin(pAng)*np.cos(lon - lonc) + np.cos(pAng)*np.sin(lon - lonc)) - np.cos(lat)*np.cos(latc)*np.sin(pAng)\n a22 = np.sin(lat)*(np.sin(latc)*np.cos(pAng)*np.cos(lon - lonc) - np.sin(pAng)*np.sin(lon - lonc)) + np.cos(lat)*np.cos(latc)*np.cos(pAng)\n a23 = -np.cos(latc)*np.sin(lat)*np.cos(lon - lonc) + np.sin(latc)*np.cos(lat)\n a31 = np.cos(lat)*(np.sin(latc)*np.sin(pAng)*np.cos(lon - lonc) + np.cos(pAng)*np.sin(lon - lonc)) - np.sin(lat)*np.cos(latc)*np.sin(pAng)\n a32 = -np.cos(lat)*(np.sin(latc)*np.cos(pAng)*np.cos(lon - lonc) - np.sin(pAng)*np.sin(lon - lonc)) + np.sin(lat)*np.cos(latc)*np.cos(pAng)\n a33 = np.cos(lat)*np.cos(latc)*np.cos(lon - lonc) + np.sin(lat)*np.sin(latc)\n\n bxHelio = a11 * bxImg + a12 * byImg + a13 * bzImg\n byHelio = a21 * bxImg + a22 * byImg + a23 * bzImg\n bzHelio = a31 * bxImg + a32 * byImg + a33 * bzImg\n\n return bxHelio,byHelio,bzHelio","def problem2():\n\n data = loaddata(\"data/bayerdata.npy\")\n r, g, b = separatechannels(data)\n\n img = assembleimage(r, g, b)\n display_image(img)\n\n img_interpolated = interpolate(r, g, b)\n display_image(img_interpolated)","def dibujarcirculos(x,color):\r\n if x==1:\r\n pygame.draw.circle(ventana,color,(470,190),30)\r\n elif x==2:\r\n pygame.draw.circle(ventana,color,(570,190),30)\r\n elif x==3:\r\n pygame.draw.circle(ventana,color,(670,190),30)\r\n elif x==4:\r\n pygame.draw.circle(ventana,color,(770,190),30)\r\n elif x==5:\r\n pygame.draw.circle(ventana,color,(470,290),30)\r\n elif x==6:\r\n pygame.draw.circle(ventana,color,(570,290),30)\r\n elif x==7:\r\n pygame.draw.circle(ventana,color,(670,290),30)\r\n elif x==8:\r\n pygame.draw.circle(ventana,color,(770,290),30)\r\n elif x==9:\r\n pygame.draw.circle(ventana,color,(470,390),30)\r\n elif x==10:\r\n pygame.draw.circle(ventana,color,(570,390),30)\r\n elif x==11:\r\n pygame.draw.circle(ventana,color,(670,390),30)\r\n elif x==12:\r\n pygame.draw.circle(ventana,color,(770,390),30)\r\n elif x==13:\r\n pygame.draw.circle(ventana,color,(470,490),30)\r\n elif x==14:\r\n pygame.draw.circle(ventana,color,(570,490),30)\r\n elif x==15:\r\n pygame.draw.circle(ventana,color,(670,490),30)\r\n elif x==16:\r\n pygame.draw.circle(ventana,color,(770,490),30)","def sepiaTest(file: str) -> str:\n img = sepia(load_image(file), disp = False, save = False)\n \n img = create_image(3, 1) # creates image\n set_color(img, 0, 0, create_color(10, 67, 201))\n set_color(img, 1, 0, create_color(59, 190, 198))\n set_color(img, 2, 0, create_color(23, 78, 254))\n \n\n test_img = sepia(img, disp = False, save = False)\n image_correct = 0\n pixel = 0\n \n for x, y, (r, g, b) in test_img:\n if 0<= r < 63:\n if r != r / 1.1 and b1 == float(b / 0.9):\n image_correct += 1\n else:\n image_correct -= 1\n \n elif 63<= r <= 191:\n if r1 == float(r / 1.15) and b1 == float(b / 0.85):\n image_correct += 1\n else:\n image_correct -= 1\n \n elif 191<= r <255 :\n if r1 == float(r / 1.08) and b1 == float(b / 0.93):\n image_correct += 1\n else:\n image_correct -= 1\n \n else:\n image_correct -= 1\n if image_correct == pixel:\n return print('lol'), pixel, image_correct\n else:\n return print('u suck'), pixel, image_correct","def testBlackAndWhite(name = \"smokey.gif\"):\n image = Image(name)\n print(\"Close the image window to see the transformation\")\n image.draw()\n blackAndWhite(image)\n image.draw()","def rysujMape(mapObj, gameStateObj, cele):\n\n # mapSurf - naniesione sa tiles (grafika mapy) \n\t\n\t#Najepierw obliczenie dlugosci i szerokosci mapy\n mapSurfWidth = len(mapObj) * TILEWIDTH\n mapSurfHeight = (len(mapObj[0]) - 1) * TILEFLOORHEIGHT + TILEHEIGHT\n mapSurf = pygame.Surface((mapSurfWidth, mapSurfHeight))\n mapSurf.fill(BGCOLOR) # kolor tla\n\n # umieszczenie grafik tiles w odpowiednich miejscach\n for x in range(len(mapObj)):\n for y in range(len(mapObj[x])):\n spaceRect = pygame.Rect((x * TILEWIDTH, y * TILEFLOORHEIGHT, TILEWIDTH, TILEHEIGHT))\n if mapObj[x][y] in TILEMAPPING:\n baseTile = TILEMAPPING[mapObj[x][y]]\n else: \n baseTile = TILEMAPPING[' ']\n\n # dodaj na ekran obiekty: mur i podloga\n mapSurf.blit(baseTile, spaceRect)\n\n \n if (x, y) in gameStateObj['skrzynki']:\n # ustaw skrzynke\n mapSurf.blit(SLOWNIK_OBRAZKI['skrzynka'], spaceRect)\n if (x, y) in cele:\n # nastepnie jesli w tym miejscu tez jest cel to ustaw grafike 'cel_po' (ciemniejsza skrzynka)\n mapSurf.blit(SLOWNIK_OBRAZKI['cel_po'], spaceRect)\n elif (x, y) in cele:\n # jesli tylko cel to ustaw grafike celu cel_przed (czerwona kropka) \n mapSurf.blit(SLOWNIK_OBRAZKI['cel_przed'], spaceRect)\n\n # Na koncu ustaw ludzika \n if (x, y) == gameStateObj['ludzik']:\n mapSurf.blit(PLAYER_GRAFIKA, spaceRect)\n\n return mapSurf","def muestraPokemon(bytes):\n image = Image.open(io.BytesIO(bytes))\n data = np.array(image)\n plt.imshow(data)\n plt.axis('off')\n plt.show()","def Haut():\r\n X1, Y1, X2, Y2 = canvas.coords(boule)\r\n canvas.coords(boule,X1,Y1-20,X2,Y2-20)","def reprographie():\n nombreDePhotocopie = int(input(\"Entrez le nombre de photocopie a effectuer \"))\n PREMIER_PRIX = 0.10\n DEUXIEME_PRIX = 0.09\n TROISIEME_PRIX = 0.08\n PREMIERE_TRANCHE = 10\n DEUXIEME_TRANCHE = 20\n TROISIEME_TRANCHE = 30\n resultat = 0\n if(nombreDePhotocopie>TROISIEME_TRANCHE):\n resultat = DEUXIEME_TRANCHE*DEUXIEME_PRIX+1+(nombreDePhotocopie-30)*TROISIEME_PRIX\n elif(nombreDePhotocopie<=TROISIEME_TRANCHE):\n if(nombreDePhotocopie/10>1):\n resultat = (nombreDePhotocopie-10)*DEUXIEME_PRIX+(PREMIERE_TRANCHE*PREMIER_PRIX)\n else:\n resultat = nombreDePhotocopie*PREMIER_PRIX\n return resultat","def exercise2a(self):\n colors = [\n [253, 253, 150],\n [255, 125, 125],\n [150, 111, 214],\n [119, 158, 203],\n [255, 105, 97],\n [ 3, 192, 60]\n ]\n # This takes some time …\n #self.b1 = mandelbrot(1200, 600, colors, [0, 0, 0], 0.02, 0.73, 0.21)\n # Load the pregenerated image ;)\n self.b1 = misc.imread(\"B1.png\")\n plt.axis('off')\n plt.imshow(self.b1)\n plt.show()\n misc.imsave(\"B1.png\", self.b1)\n misc.imsave(\"B1_Brightness.png\", print_brightness(self.b1))","def Inpainting_mosaic(im, mask):\n im0 = np.copy(im) # Lager kopi av bilde\n im[im < 0] = 0 # klipp til lovlige verdier\n im[im > 1] = 1\n for i in range(25): # Itererer \n im = eksplisitt(im, n=1) # Løser ved hjelp av eksplisittfunksjon\n im[np.logical_not(mask)] = im0[np.logical_not(mask)] \n return im","def __init__(self, x, y, largo, alto):\n # Llama al constructor padre\n pygame.sprite.Sprite.__init__(self)\n \n # Construye una pared azul con las dimensiones especificadas por los parametros\n self.image = pygame.Surface([21, 21])\n self.image.fill(AZUL)\n \n # Establece como origen la esquina superior izquierda.\n self.rect = self.image.get_rect()\n self.rect.y = y\n self.rect.x = x","def imageprepare(argv):\r\n im = Image.open(argv).convert('L')\r\n width = float(im.size[0])\r\n height = float(im.size[1])\r\n newImage = Image.new('L', (28, 28), (255)) #creates white canvas of 28x28 pixels\r\n \r\n if width > height: #check which dimension is bigger\r\n #Width is bigger. Width becomes 20 pixels.\r\n nheight = int(round((20.0/width*height),0)) #resize height according to ratio width\r\n if (nheight == 0): #rare case but minimum is 1 pixel\r\n nheight = 1 \r\n # resize and sharpen\r\n img = im.resize((20,nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\r\n wtop = int(round(((28 - nheight)/2),0)) #caculate horizontal pozition\r\n newImage.paste(img, (4, wtop)) #paste resized image on white canvas\r\n else:\r\n #Height is bigger. Heigth becomes 20 pixels. \r\n nwidth = int(round((20.0/height*width),0)) #resize width according to ratio height\r\n if (nwidth == 0): #rare case but minimum is 1 pixel\r\n nwidth = 1\r\n # resize and sharpen\r\n img = im.resize((nwidth,20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\r\n wleft = int(round(((28 - nwidth)/2),0)) #caculate vertical pozition\r\n newImage.paste(img, (wleft, 4)) #paste resized image on white canvas\r\n \r\n #newImage.save(\"sample.png\")\r\n\r\n tv = list(newImage.getdata()) #get pixel values\r\n \r\n #normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\r\n tva = [ (255-x)*1.0/255.0 for x in tv] \r\n #print(tva)\r\n return tva","def recarregar_imagem(self):\n self.salvar_imagem(caminho_imagem=self.caminho_temp)\n self.carregar_imagem(caminho_imagem=self.caminho_temp)","def __init__(self, x, y, largo, alto):\n # Llama al constructor padre\n pygame.sprite.Sprite.__init__(self)\n \n # Construye una pared azul con las dimensiones especificadas por los parametros\n self.image = pygame.Surface([21, 21])\n self.image.fill(VERDE)\n \n # Establece como origen la esquina superior izquierda.\n self.rect = self.image.get_rect()\n self.rect.y = y\n self.rect.x = x","def grass_drass():","def esconderfichas(posicion_del_mouse):\r\n numerodecuadrado=numerocuadrado(posicion_del_mouse)\r\n for j in range(16):\r\n if numerodecuadrado==j+1:\r\n pygame.draw.rect(ventana,(135,206,250),Totalcuadrados[j].inflate(-10,-10))","def image_proc(self):\r\n res_erode, res_in_rect = self._get_res_image()\r\n\r\n Moment_rect = cv2.moments(res_in_rect)\r\n if (Moment_rect[\"m00\"] != 0):\r\n self.rect_x = int(Moment_rect[\"m10\"] / Moment_rect[\"m00\"])\r\n self.rect_y = int(Moment_rect[\"m01\"] / Moment_rect[\"m00\"])\r\n\r\n # afin de trouver les centres de la rectangulaire.\r\n cnts, hierarchy = cv2.findContours(res_erode,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\r\n for i in cnts:\r\n #Moment = cv2.moments(cnts)\r\n moment = cv2.moments(res_erode)\r\n self.cx = int(moment[\"m10\"] / moment[\"m00\"])\r\n self.cy = int(moment[\"m01\"] / moment[\"m00\"])\r\n cv2.circle(output_image,(self.cx,self.cy),7,(255,255,255),-1)\r\n cv2.putText(output_image, \"center\", (self.cx, self.cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\r\n #print(cX,cY)\r","def get_pokemon_show(pokemon_img, pokemon = \"\"): \n \n if isinstance(pokemon_img,str):\n pokemon_img = Image.open(pokemon_img)\n pokemon_img = pokemon_img.resize((256,256)).convert(\"RGBA\")\n \n# if pokemon_img.mode==\"RGBA\":\n pokemon_img = remove_transparency(pokemon_img)\n# display(pokemon_img)\n# print(np.array(pokemon_img).min(),np.array(pokemon_img).max())\n pokedex_number,type_1,type_2,species =\\\n data.loc[data[\"name\"] == pokemon,[\"pokedex_number\",\"type_1\",\"type_2\",\"species\"]].values[0]\n background = Image.open(os.path.join(PATH,f\"utils/types/backs/{type_1}.png\")).resize((512,512))\n type_1_img = Image.open(os.path.join(PATH,f\"utils/types/cut/{type_1}.png\")).resize((134//2,172//2))\n type_1_img = remove_transparency(type_1_img)\n background.paste(type_1_img,box = (0,400))\n\n if type_2 != \"No_type\":\n type_2_img = Image.open(os.path.join(PATH,f\"utils/types/cut/{type_2}.png\")).resize((134//2,172//2))\n type_2_img = remove_transparency(type_2_img)\n background.paste(type_2_img,box = (80,400),mask = 0)\n \n background.paste(pokemon_img,(100,100))\n draw = ImageDraw.Draw(background)\n font_1 = ImageFont.truetype(font = os.path.join(PATH, \"utils/fonts/NIKOLETA.ttf\"),size = 40)\n font_2 = ImageFont.truetype(font = os.path.join(PATH, \"utils/fonts/NIKOLETA.ttf\"),size = 30)\n pokemon = \" \".join(pokemon.split(\" \")[:2])\n # draw.text((256, 420), f\"{pokedex_number}. {pokemon}\",(0,0,0),font=font_1)\n draw.text((100, 50), f\"{pokedex_number}. {pokemon}\",(0,0,0),font=font_1)\n\n draw.text((256, 420), species,(0,0,0),font=font_2)\n return background","def pyramida(zakladna, orientacia, centrovanie):\n nova_pyramida = []\n if orientacia not in [\"normalna\", 'obratena']:\n print(\"Pyramida moze byt iba [normalna] alebo [obratena]\")\n return False\n\n if centrovanie != \"center\" and centrovanie != \"vlavo\":\n print(\"Centrovanie pyramidy moze byt iba [center] alebo [vlavo]\")\n return False\n\n if centrovanie == \"center\":\n if orientacia == \"normalna\":\n\n cislo_riadka = -1\n for i in range(1, zakladna + 1, 2): #pocet hviezdiciek rastie po 2\n #print(f\"{'*' * i:^{zakladna}}\")\n cislo_riadka +=1\n riadok = []\n for j in range(cislo_riadka,zakladna//2): #vyska pyramidy = polovica zakladne\n riadok.append(\" \") #kolky riadok, tolko medzier vlavo\n for j in range(0, i):\n riadok.append(\"*\")\n for j in range(cislo_riadka,zakladna//2): # aj v pravo\n riadok.append(\" \")\n nova_pyramida.append(riadok)\n else:\n cislo_riadka = -1\n for i in range(zakladna, 0, -2): #pocet hviezdiciek\n #print(f\"{'*' * i:^{zakladna}}\")\n cislo_riadka +=1\n riadok = []\n for j in range(0,cislo_riadka):\n riadok.append(\" \")\n for j in range(0,i):\n riadok.append(\"*\")\n for j in range(0,cislo_riadka):\n riadok.append(\" \")\n nova_pyramida.append(riadok)\n else:\n if orientacia == \"normalna\":\n for i in range(zakladna):\n #print(f\"{'*' * (i + 1)}\")\n riadok = []\n for j in range(0,i):\n riadok.append(\"*\")\n nova_pyramida.append(riadok)\n else:\n for i in range(zakladna):\n riadok = []\n #print(f\"{'*' * (zakladna - i)}\")\n for j in range(zakladna, i, -1):\n riadok.append(\"*\")\n nova_pyramida.append(riadok)\n return nova_pyramida","def pintarIMAGENENMAPA(self, pos):\n # Agrego al vector que controla las images\n k = (pos[0], pos[1], \"img\"+str(self.idIMG))\n # Si deseo pintar una silla\n if self.queIMGAgregar == 1:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgSilla, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Si deseo pintar una mesa\n if self.queIMGAgregar == 2:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgMesa, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Se deseo pintar una nevera\n if self.queIMGAgregar == 3:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgNevera, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Si deseo pintar una cama\n if self.queIMGAgregar == 4:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgCama, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Si deseo pintar un sofa\n if self.queIMGAgregar == 5:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgSofa, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Si deseo pintar tv\n if self.queIMGAgregar == 6:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgTV, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Si deseo pintar lampara\n if self.queIMGAgregar == 7:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgLampara, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Si deseo pintar planta\n if self.queIMGAgregar == 8:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgPlanta, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Si deseo pintar sanitario\n if self.queIMGAgregar == 9:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgSanitario, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Si deseo pintar lavamanos\n if self.queIMGAgregar == 10:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgLavamanos, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1\n\n # Si deseo pintar la ducha\n if self.queIMGAgregar == 11:\n self.telaMAPA.create_image(k[0], k[1], image=self.imgDucha, tag=k[2])\n # Como fue agregado un elemento en el mapa procedo a registrarlo\n self.cotroladoraIMGREG.append(k)\n # Como una img fue agregada procedo a aumentar el id\n self.idIMG = self.idIMG + 1","def make_interferometry(data,algorithm=None):\n # Obteniendo el mapa de coherencia\n Im = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_10.npy\") # Se carga la imagen 10\n f1 = np.zeros((len(Im),len(Im.T)),dtype=complex)\n f2 = np.zeros((len(Im),len(Im.T)),dtype=complex)\n coh = np.zeros((len(Im),len(Im.T)),dtype=complex)\n\n for i in range(n_im-1):\n i += i_o # Valor inicial\n Im1 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i)+\".npy\")\n Im2 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i+1)+\".npy\")\n f1 += Im1*Im2.conjugate()\n f2 += np.sqrt((abs(Im1)**2)*(abs(Im2)**2))\n\n coh = abs(f1/f2)\n\n # Graficando la coherencia\n if show:\n plt.close('all')\n cmap =\"plasma\"\n if algorithm == \"BP\":\n title_name ='Mapa de coherencia(BP)\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\n direction ='Coherencia_BP_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\n\n elif algorithm == \"RMA\":\n title_name ='Mapa de coherencia(RMA)\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\n direction ='Coherencia_RMA_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\n\n fig, ax = plt.subplots()\n im=ax.imshow(coh,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\n ax.grid()\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # pad es el espaciado con la grafica principal\n plt.colorbar(im,cax=cax,label='',extend='both')\n fig.savefig(os.getcwd()+\"/Results/Interferograms_BP/Coherence_maps/\"+direction, orientation='landscape')\n\n # Sacando una mascara de [0.7,1] para la coherencia\n mask = coh<=0.7\n coh[mask] = np.nan\n\n # Graficando con la mascara\n if show:\n cmap =\"plasma\"\n if algorithm == \"BP\":\n title_name ='Mapa de coherencia recortada(BP)\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\n direction ='CoherenciaCut_BP_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\n\n elif algorithm == \"RMA\":\n title_name ='Mapa de coherencia recortada(RMA)\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\n direction ='CoherenciaCut_RMA_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\n fig, ax = plt.subplots()\n im=ax.imshow(coh,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\n ax.grid()\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # pad es el espaciado con la grafica principal\n plt.colorbar(im,cax=cax,label='',extend='both')\n fig.savefig(os.getcwd()+\"/Results/Interferograms_BP/Coherence_maps/\"+direction, orientation='landscape')\n\n # Obteniendo el mapa de desplazamientos, aka, Interferograma\n par = sp.get_parameters()\n global c,fc\n c,fc = par['c'],par['fc']\n\n if show:\n for i in range(n_im-1):\n # Hallando el interferograma 'i'-esimo\n i1 = i+10\n Im1 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i1)+\".npy\")\n Im2 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i1+1)+\".npy\")\n disp = np.angle(Im1*Im2.conjugate())*1000*c/(4*np.pi*fc) # Distancias en mm\n disp[mask] = np.nan\n # Graficando y guardando\n if algorithm == \"BP\":\n title_name ='Interferograma (BP) \\ndset'+'['+str(i1)+']-['+str(i1+1)+']'\n #direction ='Interferogramas_BP/'+'Itf_BP_dset'+'['+str(i+10)+']-['+str(i+11)+'].png'\n direction ='Itf_BP_dset'+str(i)+'.png'\n\n elif algorithm == \"RMA\":\n title_name ='Interferograma (RMA) \\ndset'+'['+str(i1)+']-['+str(i1+1)+']'\n #direction ='Interferogramas_RMA/'+'Itf_RMA_dset'+'['+str(i+10)+']-['+str(i+11)+'].png'\n direction ='Itf_RMA_dset'+str(i)+'.png'\n cmap = plt.cm.plasma #brg\n cmap.set_bad('black',1.)\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\n fig, ax = plt.subplots()\n im=ax.imshow(disp,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\n ax.grid()\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # pad es el espaciado con la grafica principal\n plt.colorbar(im,cax=cax,label='desplazamiento(mm)',extend='both')\n fig.savefig(os.getcwd()+\"/Results/Interferograms_BP/Interferograms_complete/Images/\"+direction,orientation='landscape')\n plt.close()\n\n # Hallando la curva de distancias vs tiempo\n #-------Definiendo las zonas--------------\n zone0 = np.array([0,650,0,400])\n zone1 = np.array([0,100,100,200])\n zone2 = np.array([0,100,200,300])\n zone3 = np.array([100,200,100,200])\n zone4 = np.array([100,200,200,300])\n zone5 = np.array([200,300,100,200])\n zone6 = np.array([200,300,200,300])\n zone_indexes = {0:zone0,1:zone1,2:zone2,3:zone3,4:zone4,5:zone5,6:zone6}\n\n desp = np.zeros((len(zone_indexes),n_im-1)) # Variable y: desplazamiento\n\n for z in range(len(zone_indexes)): # Hallando los desplazamientos promedios por zona\n idc = zone_indexes[z]\n for i in range(n_im-1):\n i1 = i+10 # 10 es el valor inicial de las imagenes\n Im1 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i1)+\".npy\")\n Im2 = np.load(os.getcwd()+\"/Results/Output_\"+algorithm+\"/Im_\"+str(i1+1)+\".npy\")\n d_i = np.angle(Im1*Im2.conjugate())*1000*c/(4*np.pi*fc)\n # ----- Grafica ------\n d_i2 = d_i.copy()\n d_i2[mask] = np.nan\n d_i2 = d_i2[idc[0]:idc[1],idc[2]:idc[3]]\n # ----- end --------\n d_i[mask] = 0\n d_i = d_i[idc[0]:idc[1],idc[2]:idc[3]]\n if i==0: desp[z,i] = d_i.mean()\n else: desp[z,i] = d_i.mean()+desp[z,i-1]\n\n # Graficando la zona z-esima\n if show and i == 0: #\n if algorithm == \"BP\":\n title_name ='Interferograma recortado(BP) \\nZona '+str(z)+' - dset'+'['+str(i1)+']-['+str(i1+1)+']'\n direction ='Itf_BP_rec_zona_'+str(z)+'_dset'+'['+str(i1)+']-['+str(i1+1)+'].png'\n #direction ='Interferogramas_BP/'+'Itf_BP_dset'+str(i)+'.png'\n\n elif algorithm == \"RMA\":\n title_name ='Interferograma recortado(RMA) \\nZona '+str(z)+' - dset'+'['+str(i1)+']-['+str(i1+1)+']'\n direction ='Itf_RMA_rec_zona_'+str(z)+'_dset'+'['+str(i1)+']-['+str(i1+1)+'].png'\n #direction ='Interferogramas_RMA/'+'Itf_RMA_dset'+str(i)+'.png'\n cmap = plt.cm.plasma #brg\n cmap.set_bad('black',1.)\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\n fig, ax = plt.subplots()\n im=ax.imshow(d_i2,cmap,origin='lower',aspect='equal', extent=[idc[2]-200,idc[3]-200,idc[0]+300,idc[1]+300]) #, vmin=vmin, vmax=vmax)\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\n ax.grid()\n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1) # pad es el espaciado con la grafica principal\n plt.colorbar(im,cax=cax,label='desplazamiento(mm)',extend='both')\n fig.savefig(os.getcwd()+\"/Results/Interferograms_BP/Interferograms_complete/Images/\"+direction,orientation='landscape')\n\n #t = np.arange(len(Ims)-1)+10 # Variable x, tiempo\n time_dset = np.load(\"Dates_BP.npy\")\n time_dset = time_dset[:-1]\n time_dset2 = np.array([datetime.datetime.strptime(idx,\"%d-%m-%y %H:%M:%S\") for idx in time_dset])\n\n # Grafica de desplazamiento vs tiempo por cada algoritmo\n \"\"\"if show:\n if algorithm == \"BP\":\n title_name ='Desplazamiento promedio(BP)'\n direction ='desplazamientoPromedio_BP_dset10-100.png'\n\n fig, ax= plt.subplots()\n ax.plot(t,desp,'b',marker='o',markerfacecolor='b',markeredgecolor='b')\n ax.set(xlabel='Tiempo(unidades)',ylabel='Desplazamiento(mm)', title=title_name)\n #ax.set_xlim([R.min(),R.max()])\n ax.set_ylim([-c*100/(4*fc),c*100/(4*fc)])\n ax.grid()\n plt.show()\n fig.savefig(os.getcwd()+\"/Results/Desplazamientos/\"+direction,orientation='landscape')\n\n elif algorithm == \"RMA\":\n title_name ='Desplazamiento promedio(RMA)'\n direction ='desplazamientoPromedio_RMA_dset10-100.png'\n\n fig, ax= plt.subplots()\n ax.plot(t,desp,'r',marker='o',markerfacecolor='r',markeredgecolor='r')\n ax.set(xlabel='Tiempo(unidades)',ylabel='Desplazamiento(mm)', title=title_name)\n #ax.set_xlim([R.min(),R.max()])\n ax.set_ylim([-c*50/(4*fc),c*50/(4*fc)])\n ax.grid()\n plt.show()\n fig.savefig(os.getcwd()+\"/Results/Desplazamientos/\"+direction,orientation='landscape')\n \"\"\"\n return {'d_t':desp, 't':time_dset2}","def imag(self, ???):","def arounder(this, **kargs):\n\t\t\n\t\t# Arguments\n\t\tmaxCount = kargs.get('maxCount', 200)\n\t\tmaxArea = kargs.get('maxArea', 10000000)\n\t\tminArea = kargs.get('minArea', 0)\n\t\tmaxDist = kargs.get('maxDist', 0)\n\t\tignore = kargs.get('ignore', (0, 255, 0))\n\t\tcolor = kargs.get('color', (0, 0, 255))\n\t\tthick = kargs.get('thick', 1)\n\t\t\n\t\t# Image binaire issue de la détection\n\t\tbin = this._BINARY.copy()\n\t\tinput = bin.copy()\n\t\t\n\t\t# Modifie l'image de départ T__T\n\t\timage, contours, hierarchy = cv2.findContours(\n\t\t\tinput,\n\t\t\tcv2.RETR_LIST,\n\t\t\tcv2.CHAIN_APPROX_SIMPLE\n\t\t)\n\t\t\n\t\t# Comptation\n\t\tfinger = None\n\t\tcount = len(contours)\n\t\tobjects, ignored = [], []\n\t\tif count < maxCount: #raise Exception('Too much noise, please quiet.')\n\t\t\t\n\t\t\t# Filtrage et localisation:\n\t\t\tfor contour in contours:\n\t\t\t\t\n\t\t\t\t# Filtrage des contours selon l'aire\n\t\t\t\tarea = cv2.contourArea(contour)\n\t\t\t\tif minArea <= area and area <= maxArea:\n\t\t\t\t\t\n\t\t\t\t\t# Calcul de la position\n\t\t\t\t\tobj = cv2.convexHull(contour)\n\t\t\t\t\tpoint = limiter(obj, maxDist)\n\t\t\t\t\t\n\t\t\t\t\t# Est-ce le point le plus bas ?\n\t\t\t\t\tif finger:\n\t\t\t\t\t\tif finger.y < point.y: finger = point\n\t\t\t\t\telse: finger = point\n\t\t\t\t\t\n\t\t\t\t\t# Enregistrement\n\t\t\t\t\tobjects.append(obj)\n\t\t\t\t\n\t\t\t\t# Sinon on l'ignore\n\t\t\t\telse: ignored.append(contour)\n\t\t\t\t\n\t\t\t### END FOR\n\t\t\n\t\t### END IF\n\t\telse: ignored = contours\n\t\t\n\t\t# On duplique l'image pour le rendu final\n\t\tthis._SCAN = scan = EmptyFrom(bin, 3)\n\t\tscan[:,:,0] = scan[:,:,1] = scan[:,:,2] = bin\n\t\t\n\t\t# Visuel\n\t\tprintf('%d/%d%60s\\r' % (len(objects), count, ''))\n\t\tcv2.drawContours(scan, ignored, -1, ignore, 1)\n\t\tcv2.drawContours(scan, objects, -1, color, thick)\n\t\t\n\t\t# Si on a trouvé\n\t\tif finger:\n\t\t\t\n\t\t\t# Affichage viseur\n\t\t\tscan[:, finger.x, :] = [255, 0, 0]\n\t\t\tscan[finger.y, :, :] = [127, 0, 0]\n\t\t\t\n\t\t\t# Calcul de la taille de l'image\n\t\t\tsize = D2Point(width(bin), height(bin))\n\t\t\t\n\t\t\t# Reformatage\n\t\t\torigin = +finger\n\t\t\tfinger /= size-1\n\t\t\tfinger.x = 1 - finger.x\n\t\t\tthis._BOTTOM = (origin-2).y == (size-4).y\n\t\t\n\t\t# Sinon on arrête de cliquer\n\t\telse: this._BOTTOM = False\n\t\t\n\t\t# On enregistre le truc\n\t\tthis._DETECTED = finger\n\t\t\n\t\treturn pyon(\n\t\t\tcontours = scan\n\t\t)","def showCortexImg(pV,nV):\n # object arrays of the positive and negative images\n pos_cort_img = np.empty(8, dtype=object)\n neg_cort_img = np.empty(8, dtype=object)\n for t in range(8):\n # cortical mapping functions\n lpos, rpos = cortex.cort_img(pV[:,t,:], L, L_loc, R, R_loc, cort_size, G)\n lneg, rneg = cortex.cort_img(nV[:,t,:], L, L_loc, R, R_loc, cort_size, G)\n pos_cort_img[t] = np.concatenate((np.rot90(lpos),np.rot90(rpos,k=3)),axis=1)\n neg_cort_img[t] = np.concatenate((np.rot90(lneg),np.rot90(rneg,k=3)),axis=1)\n # stack all images into a grid\n posRGcort = np.vstack((pos_cort_img[:4]))\n negRGcort = np.vstack((neg_cort_img[:4]))\n posYBcort = np.vstack((pos_cort_img[4:]))\n negYBcort = np.vstack((neg_cort_img[4:]))\n mergecort = np.concatenate((posRGcort,negRGcort,posYBcort,negYBcort),axis=1)\n return mergecort","def __init__(self, nb_fourmis=4, height=300, width=300, liste_fourmis=[]):\n self.window_name = \"Painting ants\"\n self.height = height\n self.width = width\n self.image = np.zeros((self.height, self.width, 3), np.uint8)\n self.image[:] = WHITE\n self.running = False\n self.nb_fourmis = nb_fourmis\n self.fourmis = liste_fourmis","def car(img,x, y):\n gameDisplay.blit(img, (x, y)) # blit display the image","def ELA(img_path):\r\n \r\n TEMP = 'ela_' + 'temp.jpg'\r\n SCALE = 10\r\n original = Image.open(img_path)\r\n try:\r\n original.save(TEMP, quality=90)\r\n temporary = Image.open(TEMP)\r\n diff = ImageChops.difference(original, temporary)\r\n except:\r\n original.convert('RGB').save(TEMP, quality=90)\r\n temporary = Image.open(TEMP)\r\n diff = ImageChops.difference(original.convert('RGB'), temporary)\r\n \r\n d = diff.load()\r\n WIDTH, HEIGHT = diff.size\r\n for x in range(WIDTH):\r\n for y in range(HEIGHT):\r\n d[x, y] = tuple(k * SCALE for k in d[x, y])\r\n\r\n return diff","def __init__ (self, id_s = \"unknown\",rept = 1,tipo = 1, width = 800, height = 600, fps = 30):\n pygame.init()\n pygame.display.set_caption(\"VIP: BCI\")\n self.tipo = tipo \n self.width = width\n self.height = height\n self.id_s = str(id_s)\n self.rept = int(rept)\n self.dimensions = (self.width, self.height)\n #self.screen = pygame.display.set_mode(self.dimensions, pygame.DOUBLEBUF)\n self.screen = pygame.display.set_mode(self.dimensions, pygame.FULLSCREEN)\n self.imagenR= pygame.image.load(\"R1.png\").convert()\n self.imagenL= pygame.image.load(\"R2.png\").convert()\n self.background = pygame.Surface(self.screen.get_size()).convert()\n self.screen.fill((255,255,255))#Fondo blanco\n self.clock = pygame.time.Clock()\n self.fps = fps\n self.playtime = 0.0\n self.font = pygame.font.SysFont('mono', 40, bold=True)\n size_screen= self.screen.get_size();\n self.x_center = size_screen[0]/2.0 - 210\n self.y_center = size_screen[1]/2.0 - 210","def main():\n me = SimpleImage(\"images/me.JPG\")\n dinosaur = SimpleImage(\"images/dinosaur.jpg\")\n\n dinosaur.make_as_big_as(me)\n combine = magic(me, dinosaur)\n combine.show()","def compute_energy(img):\r\n # urmati urmatorii pasi:\r\n # 1. transformati imagine in grayscale\r\n # 2. folositi filtru sobel pentru a calcula gradientul in directia X si Y\r\n # 3. calculati magnitudinea imaginii\r\n\r\n img_gray_scale = cv.cvtColor(img, cv.COLOR_BGR2GRAY);\r\n\r\n #de cautat totusi si codul pt SOBEL pe net\r\n grad_x = cv.Sobel(img_gray_scale, ddepth = cv.CV_16S, dx = 1, dy = 0, borderType = cv.BORDER_CONSTANT)\r\n grad_y = cv.Sobel(img_gray_scale, ddepth = cv.CV_16S, dx = 0, dy = 1, borderType = cv.BORDER_CONSTANT)\r\n\r\n#E repr gradientii aka cat se sch un pixel de la unul la altul\r\n E = abs(grad_x) + abs(grad_y)\r\n # print(grad_y)\r\n # print(grad_x)\r\n\r\n cv.imwrite(\"poza.jpg\", E)\r\n return E","def apply_all(image, courbes, masque):\n print(\"preparation des luts...\")\n print(courbes)\n lut_r, lut_v, lut_b = create_lut()\n apply(lut_r, courbes[\"courbes\"][1])\n apply(lut_v, courbes[\"courbes\"][2])\n apply(lut_b, courbes[\"courbes\"][3])\n # l'ordre a été repris sur l'implémentation faite dans le socle\n # http://gitlab.forge-idi.ign.fr/socle/sd-socle/blob/dev/src/ign/image/radiometry/AcvCurveTransform.cpp\n # on applique d'abord la courbe du canal, puis la courbe RVB\n apply(lut_r, courbes[\"courbes\"][0])\n apply(lut_v, courbes[\"courbes\"][0])\n apply(lut_b, courbes[\"courbes\"][0])\n print(\"...fait\")\n print(\"application des luts...\")\n if masque is not None:\n a_1 = masque / 255.0\n a_2 = (-masque + 255) / 255.0\n image[0] = np.round(\n np.multiply(a_1, np.take(lut_r, image[0]))\n + np.multiply(a_2, image[0])\n )\n image[1] = np.round(\n np.multiply(a_1, np.take(lut_v, image[1]))\n + np.multiply(a_2, image[1])\n )\n image[2] = np.round(\n np.multiply(a_1, np.take(lut_b, image[2]))\n + np.multiply(a_2, image[2])\n )\n else:\n image[0] = np.take(lut_r, image[0])\n image[1] = np.take(lut_v, image[1])\n image[2] = np.take(lut_b, image[2])\n print(\"...fait\")","def fraktal(dlugosc, alpha, poziom):\n#obliczanie punktow punktu Abis dla kazdego poziomu galezi\n x = float(self.p2[0] + self.dlugosc * cos(alpha))\n y = float(self.p2[1] + self.dlugosc * sin(alpha))\n return [round(x), round(y)]","def __init__(self):\n # Initialisation de la bibliothèque Pygame\n pg.init()\n\n # Création de l'écran principal de taille 640px x 480px\n self.screen = pg.display.set_mode((settings.WIDTH, settings.HEIGHT))\n\n # Chargement et collage du fond\n self.background = pg.image.load(settings.BACKGROUND).convert()\n self.screen.blit(self.background, (0, 0))\n\n # Création d'une variable indiquant si le jeu est en cours\n self.running = False\n\n # On termine cette méthode d'initialisation par une mise à jour de \n # l'écran principal\n pg.display.update()","def __init__(s,i,j,img):\n pg.sprite.Sprite.__init__(s)\n s.image = pg.image.load(img)\n s.rect = s.image.get_rect()\n\n # Posição do centro\n s.cx, s.cy = convert(i,j)\n # Cor (pode ser passada para o construtor no futuro)\n s.cor = (0,0,0)\n\n # Vértices do hexágono\n s.pontos = (\n (s.cx-L/2, s.cy-l),\n (s.cx+L/2, s.cy-l),\n (s.cx+L, s.cy),\n (s.cx+L/2, s.cy+l),\n (s.cx-L/2, s.cy+l),\n (s.cx-L, s.cy),\n )\n\n #s.draw();","def image(self):\n return self.any_image(-1)","def ubicar_portaviones():\n tamano = Portaviones.tamano #se importa el tamano del barco desde su clase\n cantidad = Portaviones.cantidad #se importa la cantidad de barcos de este tamano desde su clase\n orientacion = orientaciones[(randint(0, 1))] #elige aleatoriamente el index de la tupla orientaciones = (\"Vertical\", \"Horizontal\")\n while cantidad > 0:\n if orientacion == \"Vertical\":\n #se eligen random las filas y las columnas\n coor_fila = randint(1, numero_filas) \n coor_columna = randint(1, numero_columnas)\n while (coor_fila + tamano) > 10: #como su orientacion es vertical la fila incial del barco mas su tamano (3) no puede ser mayor que 10 porque se saldria del mapa\n coor_fila = randint(1,numero_filas)\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \n coor_fila += 1\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion)\n cantidad -= 1\n elif orientacion == \"Horizontal\":\n #se eligen random las filas y las columnas\n coor_fila = randint(1, numero_filas)\n coor_columna = randint(1, numero_columnas)\n while (coor_columna + tamano) > 10: #como su orientacion es horizontal la columna incial del barco mas su tamano (3) no puede ser mayor que 10 porque se saldria del mapa\n coor_columna = randint(1, numero_columnas)\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion)\n while len(lista_temporal) < tamano: #sacar las posiciones restantes\n coor_columna += 1\n ubicacion = (coor_fila, coor_columna)\n lista_temporal.append(ubicacion)\n for x in lista_temporal:\n lista_ubicacion_barco.append(x) #se agregan las ubicaciones ya validadas a una lista general donde iran todas las posiciones de los barcos\n coordenadas_portaviones.append(x)\n lista_temporal.clear() #se limpia la lista para que pueda ser usada en los siguientes barcos\n cantidad -= 1","def create_azi_to_rad_sequence():\n num_tot = 30\n for i in range(2*num_tot + 1):\n angle_arr = azi_to_rad_transformation(512, i, 30)\n phase_arr = create_flat_phase(512, 0)\n delta_1_arr = create_delta_1(phase_arr, angle_arr)\n delta_2_arr = create_delta_2(angle_arr)\n cv2.imwrite('frame' + str(i) +'.tiff', delta_2_arr)\n print(\"Frame \" + str(i))","def nitidez(img):\n kernel = np.array([\n [0, -1, 0],\n [-1, 5, -1],\n [0, -1, 0]\n ])\n if img.modo == \"L\":\n return convoluir(img, kernel)\n canais = [\n filtrar_canal(img, \"R\"), filtrar_canal(img, \"G\"), filtrar_canal(img, \"B\") \n ]\n canais = [convoluir(i, kernel) for i in canais]\n return reunir_canais(*canais)","def __init__(self, jugador):\n\n # Se llama al metodo del padre constructor.\n Level.__init__(self, jugador)\n\n #Cargamos la imagen de fondo.\n sprite_sheet_pantalla = SpriteSheet(\"imagenes/fondoactualizado.png\")\n \n # Carga de todos los sprite de la imagen hacia la derecha.\n imagen_1 = sprite_sheet_pantalla.obtener_imagen(0,1788, 896,894)\n self.fondo = imagen_1\n \n self.fondo.set_colorkey(constantes.BLANCO)\n self.limite_derecho = 740\n self.limite_izquierdo = 88\n self.limite_superior = -10\n self.limite_inferior = 686\n self.cambio_nivel_x = 396\n self.cambio_nivel_y = -16\n \n self.fondo.set_colorkey(constantes.BLANCO)\n self.limite_nivel = -2500\n\n # Lista con los bloques de plataformas, indicando la ubicacion x,y y el tipo \n nivel = [ [platforma.STONE, 250, 740],\n [platforma.STONE, 250, 680],\n [platforma.STONE, 250, 620],\n [platforma.STONE, 250, 560],\n [platforma.STONE, 250, 500],\n [platforma.STONE, 250, 440],\n [platforma.STONE, 250, 380],\n [platforma.STONE, 250, 320],\n [platforma.STONE, 310, 320],\n [platforma.STONE, 370, 320],\n [platforma.STONE, 430, 320],\n [platforma.STONE, 490, 320],\n [platforma.STONE, 550, 320],\n [platforma.STONE, 610, 320],\n [platforma.STONE, 610, 380],\n [platforma.STONE, 610, 440],\n [platforma.STONE, 610, 500],\n [platforma.STONE, 610, 560],\n [platforma.STONE, 610, 620],]\n \n #puntos\n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 150\n puntos.rect.y = 700\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos) \n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 320\n puntos.rect.y = 390\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 366\n puntos.rect.y = 390\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 412\n puntos.rect.y = 390\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 458\n puntos.rect.y = 390\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 504\n puntos.rect.y = 390\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 560\n puntos.rect.y = 390\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 320\n puntos.rect.y = 467\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 366\n puntos.rect.y = 467\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 412\n puntos.rect.y = 467\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 458\n puntos.rect.y = 467\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 504\n puntos.rect.y = 467\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 560\n puntos.rect.y = 467\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 320\n puntos.rect.y = 544\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 366\n puntos.rect.y = 544\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 412\n puntos.rect.y = 544\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 458\n puntos.rect.y = 544\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 504\n puntos.rect.y = 544\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 560\n puntos.rect.y = 544\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 320\n puntos.rect.y = 621\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 366\n puntos.rect.y = 621\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 412\n puntos.rect.y = 621\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 458\n puntos.rect.y = 621\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 504\n puntos.rect.y = 621\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n puntos = Estrellas(ESTRELLA)\n puntos.rect.x = 560\n puntos.rect.y = 621\n puntos.jugador = self.jugador\n puntos.nivel = self\n self.lista_puntos.add(puntos)\n \n \n \n \n\n # Se busca en la lista anterior creada y se le agregan las plataformas al nivel.\n for plataforma in nivel:\n bloque = platforma.Plataforma(plataforma[0])\n bloque.rect.x = plataforma[1]\n bloque.rect.y = plataforma[2]\n bloque.jugador = self.jugador\n self.lista_plataformas.add(bloque)","def car(img, x, y):\n gameDisplay.blit(img, (x, y)) # blit display the image","def get_result_image(self):\n\n im_result = cv2.cvtColor(self.im_t, cv2.COLOR_GRAY2BGR)\n pcd_final = copy.deepcopy(self.pcd_s)\n\n pcd_final.transform(self.trans_final)\n np_final = np.asarray(pcd_final.points, np.int)\n\n for i in range(np_final.shape[0]):\n im_result = cv2.circle(\n im_result,\n (np_final[i, 1], np_final[i, 0]),\n 2,\n (0, 255, 0),\n -1,\n cv2.LINE_AA,\n )\n\n im_result = cv2.rectangle(im_result, (5, 12), (170, 38), (0, 0, 0), -1)\n # Draw rotation in image\n _, _, rotate = mat2rpy(self.trans_final)\n d_rotate = np.degrees(rotate)\n str_rotate = format(d_rotate, \".2f\") + \"[deg](CW)\"\n im_result = cv2.putText(\n im_result, str_rotate, (5, 30), 1, 1.25, (255, 255, 255), 2, cv2.LINE_AA\n )\n im_result = cv2.putText(\n im_result, str_rotate, (5, 30), 1, 1.25, (0, 255, 255), 1, cv2.LINE_AA\n )\n return im_result","def process(self, image):","def creditos():\n playSegundaria()\n img_creditos = pygame.image.load(\"imagenes/creditos.jpg\")\n screen.blit(img_creditos, (0,0))\n pygame.display.update()\n time.sleep(8)\n run()","def oceansim(sun_az,sun_zen,cam_head,cam_elev=0,m2=1.33,npart=1.08,mu=3.483, debug=True):\n\n #Water surface norm\n n = np.array([0,0,1])\n m1 = 1.0\n #vector from sun:\n ki = -np.asarray([np.sin(sun_az)*np.sin(sun_zen),\n np.cos(sun_az)*np.sin(sun_zen),\n np.cos(sun_zen)])\n xi = norm_cross(n,ki)\n #transmitted sunlight\n #tx, ty are the transmission amplitude coefficients in the xt, yt directions\n kt,tx,ty = Fresnel.transmission(ki,n,m1,m2)\n xt = xi\n #vector to camera\n kc = -np.asarray([np.sin(cam_head)*np.cos(cam_elev),\n np.cos(cam_head)*np.cos(cam_elev),\n np.sin(cam_elev)])*np.linalg.norm(kt)\n xc = norm_cross(n, kc) #right\n yc = norm_cross(kc, xc) #up\n #vectors for scattering\n ys = norm_cross(kt, kc) # y-axis of scattering event\n xst = norm_cross(ys, kt) # x-axis of scattering event relative to transmitted sunlight\n xsc = norm_cross(ys, kc) # x-axis of scattering event relative to camera\n #Mueller matrices\n # transmission through water surface:\n mm1 = Mueller.polarizer(tx,ty)\n # rotate to scattering plane\n mm2 = Mrotv(kt,xt,xst)\n # scatter\n th_s = vector_angle(kt,kc)\n #mm3 = Mocean(rad2deg(th_s)) #using Empirical ocean scattering\n mm3 = Mueller.rayleigh_norm(th_s) #normalized Rayleigh scattering matrix\n #b = Scattering.bsf_fournier(npart,mu)\n b = Scattering.vspf_fournier(th_s,npart,mu)\n # transform to camera's horizontal and up vectors\n mm4 = Mxform(xsc,ys, xc,yc)\n #Combined: mm4 . (b*mm3) . mm2 . mm1\n m = mm4.dot(b*mm3.dot(mm2.dot(mm1)))\n #stokes vector\n s = m.dot([1,0,0,0])\n if debug:\n return s,m,(ki,xi),(kt,xt,xst),(kc,xc,xsc),(mm1,mm2,mm3,b,mm4)\n else:\n return s,m","def azizen(self):\n # x0,y0 array pixel coordinates relative to cx,cy\n# ndy0,ndx0=img.shape\n ndy0=self.ndy0\n ndx0=self.ndx0\n x0,y0=np.meshgrid(np.linspace(0,ndx0-1,ndx0)-self.cx,np.linspace(0,ndy0-1,ndy0)-self.cy)\n r0=np.sqrt(x0**2+y0**2)/self.pr0 # fractional radial distance from 0,0\n# self.roi=np.s_[ystart:ystart+self.ny0,xstart:xstart+self.nx0]\n # why not model the zenith angle dependence with polynomial directly\n # rather than linear interpolation between roots.\n roots=np.zeros(51)\n rr=np.arange(51)/100.0\n for i,ref in enumerate(rr):\n roots[i]=np.real(np.roots([self.c3,0,self.c2,0,self.c1,-ref])[-1])\n theta0 = np.interp(r0/2,rr,roots)\n \n phi0 = np.arctan2(x0,y0) - self.rot ####phi (i.e., azimuth) is reckoned with -pi corresponding to north, increasing clockwise, NOTE: pysolar use sub-standard definition\n phi0 = phi0%(2*np.pi)\n\n #####correction for the tilt of the camera\n k=np.array((np.sin(self.azm),np.cos(self.azm),0))\n a=np.array([np.sin(theta0)*np.cos(phi0),np.sin(theta0)*np.sin(phi0),np.cos(theta0)]); \n a = np.transpose(a,[1,2,0])\n b=np.cos(self.beta)*a + np.sin(self.beta)*np.cross(k,a,axisb=2) \\\n + np.reshape(np.outer(np.dot(a,k),k),(self.ndy0,self.ndx0,3))*(1-np.cos(self.beta))\n theta0=np.arctan(np.sqrt(b[:,:,0]**2+b[:,:,1]**2)/b[:,:,2])\n phi0=np.arctan2(b[:,:,1],b[:,:,0])%(2*np.pi)\n# max_theta *= deg2rad \n# valid0 = (theta0<max_theta) & (theta0>0); \n# theta0[valid0]=np.nan;\n self.theta0,self.phi0=theta0,phi0","def normalise(image):","def cambiar_escena(self, escena):\n\t\t# Reemplazo directo\n\t\tself.escena = escena\n\t\t# Reiniciar la ventana con el tamaño de la nueva escena\n\t\tprint(\"Iniciando nuevo contexto OpenGL...\")\n\t\tv_ancho, v_alto = escena.tam\n\t\topciones = OPENGL | DOUBLEBUF\n\t\tif escena.pant_compl:\n\t\t\topciones |= FULLSCREEN\n\t\tpygame.display.set_mode((v_ancho, v_alto), opciones)\n\t\t# Título por defecto de la ventana\n\t\tpygame.display.set_caption(escena.nombre)\n\t\t# Reiniciar OpenGL\n\t\tself.gl_ini(v_ancho, v_alto)\n\t\t# Darle los datos del núcleo a la ventana\n\t\tself.escena.nucleo = self\n\t\tself.escena.eventos = self.mapa_eve\n\t\tglClearColor(*escena.color)\n\t\t# Ejecutar la lógica inicial de la escena\n\t\tprint(\"Iniciando escena...\")\n\t\tself.escena.logica_ini()","def aktualisiere(self):\n if(self.zeichnung == None):\n self.zeichnung = self.leinwand.create_image(self.x, self.y, \n image=self.grafik)\n delta_x = self.zielX - self.x\n delta_y = self.zielY - self.y\n self.leinwand.move(self.zeichnung, delta_x, delta_y)\n\n self.x = self.zielX\n self.y = self.zielY","def template_zone(self):\n ratio = 32\n shape = [self.cam.height, self.cam.width]\n img = self.cam.frame[shape[0] / 2 - 3 * shape[0] / ratio:shape[0] / 2 + 3 * shape[0] / ratio,\n shape[1] / 2 - 3 * shape[1] / ratio:shape[1] / 2 + 3 * shape[1] / ratio]\n return img","def get_result_image(self):\n\n im_result = cv2.cvtColor(self.im_t, cv2.COLOR_GRAY2BGR)\n pcd_final = copy.deepcopy(self.pcd_s[self.result_id])\n\n pcd_final.transform(self.trans_final)\n np_final = np.asarray(pcd_final.points, np.int)\n for i in range(np_final.shape[0]):\n im_result = cv2.circle(\n im_result,\n (np_final[i, 1], np_final[i, 0]),\n 1,\n (0, 255, 0),\n -1,\n cv2.LINE_AA,\n )\n\n im_result = cv2.rectangle(im_result, (5, 12), (170, 38), (0, 0, 0), -1)\n # Draw rotation in image\n d_rotate = np.degrees(self.rotate)\n str_rotate = format(d_rotate, \".2f\") + \"[deg](CCW)\"\n im_result = cv2.putText(\n im_result, str_rotate, (5, 30), 1, 1.25, (255, 255, 255), 2, cv2.LINE_AA\n )\n im_result = cv2.putText(\n im_result, str_rotate, (5, 30), 1, 1.25, (0, 255, 255), 1, cv2.LINE_AA\n )\n return im_result","async def olá(self):\r\n\t\tawait self.client.say('© Maddie 2017')\r\n\t\te = Embed()\r\n\t\te.set_image(url='https://cdn.discovery.pgsitecore.com/en-us/-/media/Olay_PathFinder/Images/a/OLAY%20TE%207IN1%20DEEP%20PENETRATING%20MOISTURE%20BODY%20WASH_Front.png?w=460&v=1-201705260605')\r\n\t\tawait self.client.say(embed=e)"],"string":"[\n \"def foetus_pics(self):\\n pic = 0\\n if 0.0 not in self.allele:\\n self.contamination = 2\\n pic = 3\\n elif 0.0 == self.allele[1]:\\n pic = 1\\n else:\\n pic = 2\\n return pic\",\n \"def ventanaprincipal():\\r\\n titulo_principal=pygame.image.load(\\\"../recursor/Imagenes juegos/parejas_1.png\\\")\\r\\n ventana.blit(titulo_principal, (405,0))\\r\\n logo_plaython=pygame.image.load(\\\"../recursor/Imagenes juegos/plaython.png\\\")#Cargamos la imagen del logo\\r\\n imagen_decoracion=pygame.image.load(\\\"../recursor/Imagenes juegos/imagenpc.png\\\")\\r\\n imagen_decoracion = pygame.transform.scale(imagen_decoracion, (210, 210))\\r\\n ventana.blit(logo_plaython, (900, 180))\\r\\n ventana.blit(imagen_decoracion,(100,240))\\r\\n titulo_plaython = pygame.image.load(\\\"../recursor/Imagenes juegos/TITULOPLAYTHON.png\\\")\\r\\n titulo_plaython = pygame.transform.scale(titulo_plaython, (240, 150))\\r\\n ventana.blit(titulo_plaython, (505, 550))\",\n \"def make_e(self):\\n self.img[1, 2:-1] = 1\\n self.img[self.l_i / 2, 2:-1] = 1\\n self.img[-2, 2:-1] = 1\\n self.img[1:-1, 2] = 1\\n self.img_name = 'E'\",\n \"def incarcaPieseMozaic(params):\\n \\n print('Incarcam piesele pentru mozaic din director.')\\n\\n pieseMozaic = []\\n # for img_path in sorted(glob.glob(params.numeDirector\\n # + '*.'\\n # + params.tipImagine),\\n # key=lambda name: int(name[len(params.numeDirector)\\n # :-4])):\\n for img_path in glob.glob(params.numeDirector\\n + '*.'\\n + params.tipImagine):\\n img = cv2.imread(img_path)\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n pieseMozaic.append(img)\\n pieseMozaic = np.array(pieseMozaic)\\n\\n if params.afiseazaPieseMozaic:\\n # afiseaza primele 100 de piese ale mozaicului\\n if pieseMozaic.shape[0] < 100:\\n raise Exception(\\\"Numarul de piese este mai mic decat 100!\\\")\\n\\n plt.figure()\\n plt.suptitle('Primele 100 de piese ale mozaicului sunt:')\\n idxImg = 0\\n for i in range(10):\\n for j in range(10):\\n idxImg += 1\\n plt.subplot(10, 10, idxImg)\\n plt.axis('off')\\n plt.imshow(pieseMozaic[idxImg - 1])\\n\\n plt.show()\\n\\n # gray image\\n if params.imgReferinta.shape[2] == 1:\\n pieseMozaicG = []\\n for i in range(pieseMozaic.shape[0]):\\n img = cv2.cvtColor(pieseMozaic[i], cv2.COLOR_RGB2GRAY)\\n img = img[:, :, None]\\n pieseMozaicG.append(img)\\n pieseMozaicG = np.array(pieseMozaicG)\\n params.pieseMozaic = pieseMozaicG\\n else:\\n params.pieseMozaic = pieseMozaic\\n\\n if params.hexagon == 1:\\n params = getHexagonMatrix(params,\\n pieseMozaic[0].shape[0],\\n pieseMozaic[0].shape[1])\\n\\n return params\",\n \"def __init__(self,obrazok):\\n self.obrazok = pygame.transform.smoothscale(obrazok,(int(obrazok.get_width()/g.scaleObrazovky)+1,int(obrazok.get_height()/g.scaleObrazovky)+1))#+1 aby som mys nescalol na 0\\n self.mask = pygame.mask.from_surface(self.obrazok)\\n self.x = 0\\n self.y = 0\",\n \"def __init__(self,jeu,can,c,x,y,nbr,img,bto):\\r\\n self.c,self.jeu,self.can=c,jeu,can\\r\\n self.X,self.Y,self.img,self.bto=x,y,img,bto\\r\\n self.grille(can,nbr,x,y,c)\\r\\n self.blabla(nbr,x-46,y-92,c)\",\n \"def JUEGOPAREJAS():\\r\\n cuadrados=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\\r\\n ##############Primeramente, definimos los cuadrados de nuestra pizarra##############\\r\\n Cuadrado1=Rect(420,140,100,100)\\r\\n Cuadrado2=Rect(520,140,100,100)\\r\\n Cuadrado3=Rect(620,140,100,100)\\r\\n Cuadrado4=Rect(720,140,100,100)\\r\\n Cuadrado5=Rect(420,240,100,100)\\r\\n Cuadrado6=Rect(520,240,100,100)\\r\\n Cuadrado7=Rect(620,240,100,100)\\r\\n Cuadrado8=Rect(720,240,100,100)\\r\\n Cuadrado9=Rect(420,340,100,100)\\r\\n Cuadrado10=Rect(520,340,100,100)\\r\\n Cuadrado11=Rect(620,340,100,100)\\r\\n Cuadrado12=Rect(720,340,100,100)\\r\\n Cuadrado13=Rect(420,440,100,100)\\r\\n Cuadrado14=Rect(520,440,100,100)\\r\\n Cuadrado15=Rect(620,440,100,100)\\r\\n Cuadrado16=Rect(720,440,100,100)\\r\\n\\r\\n Totalcuadrados=(Cuadrado1,Cuadrado2,Cuadrado3,Cuadrado4,Cuadrado5,Cuadrado6,Cuadrado7,Cuadrado8,Cuadrado9,Cuadrado10,Cuadrado11,Cuadrado12,Cuadrado13,Cuadrado14,Cuadrado15,Cuadrado16)\\r\\n listafiguras=[]\\r\\n def ventanaprincipal():\\r\\n \\\"\\\"\\\"\\\"\\r\\n Esta funcion, genera la ventana principal de nuestro juego de parejas. Contiene\\r\\n cada uno de los titulos e imagenes del juego.\\r\\n :return None\\r\\n \\\"\\\"\\\"\\r\\n titulo_principal=pygame.image.load(\\\"../recursor/Imagenes juegos/parejas_1.png\\\")\\r\\n ventana.blit(titulo_principal, (405,0))\\r\\n logo_plaython=pygame.image.load(\\\"../recursor/Imagenes juegos/plaython.png\\\")#Cargamos la imagen del logo\\r\\n imagen_decoracion=pygame.image.load(\\\"../recursor/Imagenes juegos/imagenpc.png\\\")\\r\\n imagen_decoracion = pygame.transform.scale(imagen_decoracion, (210, 210))\\r\\n ventana.blit(logo_plaython, (900, 180))\\r\\n ventana.blit(imagen_decoracion,(100,240))\\r\\n titulo_plaython = pygame.image.load(\\\"../recursor/Imagenes juegos/TITULOPLAYTHON.png\\\")\\r\\n titulo_plaython = pygame.transform.scale(titulo_plaython, (240, 150))\\r\\n ventana.blit(titulo_plaython, (505, 550))\\r\\n\\r\\n def dibujarcirculos(x,color):\\r\\n \\\"\\\"\\\"\\r\\n Esta funcion, dibuja cada uno de las figuras de forma circular del juego en base a su generacion aleatoria.\\r\\n :param int x: Representa la casilla elegida, y en base a este valor, se dibuja un cuadrado en determinado lugar\\r\\n :param tuple color: Representa un color determinado, el cual se encuentra escrito en base a sus valores RGB\\r\\n :return None\\r\\n \\\"\\\"\\\"\\r\\n if x==1:\\r\\n pygame.draw.circle(ventana,color,(470,190),30)\\r\\n elif x==2:\\r\\n pygame.draw.circle(ventana,color,(570,190),30)\\r\\n elif x==3:\\r\\n pygame.draw.circle(ventana,color,(670,190),30)\\r\\n elif x==4:\\r\\n pygame.draw.circle(ventana,color,(770,190),30)\\r\\n elif x==5:\\r\\n pygame.draw.circle(ventana,color,(470,290),30)\\r\\n elif x==6:\\r\\n pygame.draw.circle(ventana,color,(570,290),30)\\r\\n elif x==7:\\r\\n pygame.draw.circle(ventana,color,(670,290),30)\\r\\n elif x==8:\\r\\n pygame.draw.circle(ventana,color,(770,290),30)\\r\\n elif x==9:\\r\\n pygame.draw.circle(ventana,color,(470,390),30)\\r\\n elif x==10:\\r\\n pygame.draw.circle(ventana,color,(570,390),30)\\r\\n elif x==11:\\r\\n pygame.draw.circle(ventana,color,(670,390),30)\\r\\n elif x==12:\\r\\n pygame.draw.circle(ventana,color,(770,390),30)\\r\\n elif x==13:\\r\\n pygame.draw.circle(ventana,color,(470,490),30)\\r\\n elif x==14:\\r\\n pygame.draw.circle(ventana,color,(570,490),30)\\r\\n elif x==15:\\r\\n pygame.draw.circle(ventana,color,(670,490),30)\\r\\n elif x==16:\\r\\n pygame.draw.circle(ventana,color,(770,490),30)\\r\\n\\r\\n def dibujarcuadrados(x,color):\\r\\n \\\"\\\"\\\"\\r\\n Esta funcion, dibuja cada uno de las figuras de forma cuadrada del juego en base a su generacion aleatoria.\\r\\n :param int x: Representa la casilla elegida, y en base a este valor, se dibuja un cuadrado en determinado lugar\\r\\n :param tuple color: Representa un color determinado, el cual se encuentra escrito en base a sus valores RGB\\r\\n :return None\\r\\n \\\"\\\"\\\"\\r\\n if x==1:\\r\\n pygame.draw.rect(ventana,color,Rect(440,160,60,60))\\r\\n elif x==2:\\r\\n pygame.draw.rect(ventana,color,Rect(540,160,60,60))\\r\\n elif x==3:\\r\\n pygame.draw.rect(ventana,color,Rect(640,160,60,60))\\r\\n elif x==4:\\r\\n pygame.draw.rect(ventana,color,Rect(740,160,60,60))\\r\\n elif x==5:\\r\\n pygame.draw.rect(ventana,color,Rect(440,260,60,60))\\r\\n elif x==6:\\r\\n pygame.draw.rect(ventana,color,Rect(540,260,60,60))\\r\\n elif x==7:\\r\\n pygame.draw.rect(ventana,color,Rect(640,260,60,60))\\r\\n elif x==8:\\r\\n pygame.draw.rect(ventana,color,Rect(740,260,60,60))\\r\\n elif x==9:\\r\\n pygame.draw.rect(ventana,color,Rect(440,360,60,60))\\r\\n elif x==10:\\r\\n pygame.draw.rect(ventana,color,Rect(540,360,60,60))\\r\\n elif x==11:\\r\\n pygame.draw.rect(ventana,color,Rect(640,360,60,60))\\r\\n elif x==12:\\r\\n pygame.draw.rect(ventana,color,Rect(740,360,60,60))\\r\\n elif x==13:\\r\\n pygame.draw.rect(ventana,color,Rect(440,460,60,60))\\r\\n elif x==14:\\r\\n pygame.draw.rect(ventana,color,Rect(540,460,60,60))\\r\\n elif x==15:\\r\\n pygame.draw.rect(ventana,color,Rect(640,460,60,60))\\r\\n elif x==16:\\r\\n pygame.draw.rect(ventana,color,Rect(740,460,60,60))\\r\\n\\r\\n def dibujardiamantes(x,color):\\r\\n \\\"\\\"\\\"\\r\\n Esta funcion, dibuja cada uno de las figuras de forma de diamante del juego en base a su generacion aleatoria.\\r\\n :param int x: Representa la casilla elegida, y en base a este valor, se dibuja un cuadrado en determinado lugar\\r\\n :param tuple color: Representa un color determinado, el cual se encuentra escrito en base a sus valores RGB\\r\\n :return None\\r\\n \\\"\\\"\\\"\\r\\n if x==1:\\r\\n pygame.draw.polygon(ventana,color,( (430,190),(470,160),(510,190),(470,220)))\\r\\n elif x==2:\\r\\n pygame.draw.polygon(ventana,color,( (530,190),(570,160),(610,190),(570,220) ))\\r\\n elif x==3:\\r\\n pygame.draw.polygon(ventana,color,( (630,190),(670,160),(710,190),(670,220) ))\\r\\n elif x==4:\\r\\n pygame.draw.polygon(ventana,color,( (730,190),(770,160),(810,190),(770,220) ))\\r\\n elif x==5:\\r\\n pygame.draw.polygon(ventana,color,( (430,290),(470,260),(510,290),(470,320) ))\\r\\n elif x==6:\\r\\n pygame.draw.polygon(ventana,color,( (530,290),(570,260),(610,290),(570,320) ))\\r\\n elif x==7:\\r\\n pygame.draw.polygon(ventana,color,( (630,290),(670,260),(710,290),(670,320) ))\\r\\n elif x==8:\\r\\n pygame.draw.polygon(ventana,color,( (730,290),(770,260),(810,290),(770,320) ))\\r\\n elif x==9:\\r\\n pygame.draw.polygon(ventana,color,( (430,390),(470,360),(510,390),(470,420) ))\\r\\n elif x==10:\\r\\n pygame.draw.polygon(ventana,color,( (530,390),(570,360),(610,390),(570,420) ))\\r\\n elif x==11:\\r\\n pygame.draw.polygon(ventana,color,( (630,390),(670,360),(710,390),(670,420) ))\\r\\n elif x==12:\\r\\n pygame.draw.polygon(ventana,color,( (730,390),(770,360),(810,390),(770,420) ))\\r\\n elif x==13:\\r\\n pygame.draw.polygon(ventana,color,( (430,490),(470,460),(510,490),(470,520) ))\\r\\n elif x==14:\\r\\n pygame.draw.polygon(ventana,color,( (530,490),(570,460),(610,490),(570,520) ))\\r\\n elif x==15:\\r\\n pygame.draw.polygon(ventana,color,( (630,490),(670,460),(710,490),(670,520) ))\\r\\n elif x==16:\\r\\n pygame.draw.polygon(ventana,color,( (730,490),(770,460),(810,490),(770,520) ))\\r\\n def dibujartriangulos(x,color):\\r\\n \\\"\\\"\\\"\\r\\n Esta funcion, dibuja cada uno de las figuras de forma triangular del juego en base a su generacion aleatoria.\\r\\n :param int x: Representa la casilla elegida, y en base a este valor, se dibuja un cuadrado en determinado lugar\\r\\n :param tuple color: Representa un color determinado, el cual se encuentra escrito en base a sus valores RGB\\r\\n :return None\\r\\n \\\"\\\"\\\"\\r\\n if x==1:\\r\\n pygame.draw.polygon(ventana,color,( (430,160),(510,160),(470,220) ))\\r\\n elif x==2:\\r\\n pygame.draw.polygon(ventana,color,( (530,160),(610,160),(570,220) ))\\r\\n elif x==3:\\r\\n pygame.draw.polygon(ventana,color,( (630,160),(710,160),(670,220) ))\\r\\n elif x==4:\\r\\n pygame.draw.polygon(ventana,color,( (730,160),(810,160),(770,220) ))\\r\\n elif x==5:\\r\\n pygame.draw.polygon(ventana,color,( (430,260),(510,260),(470,320)))\\r\\n elif x==6:\\r\\n pygame.draw.polygon(ventana,color,( (530,260),(610,260),(570,320) ))\\r\\n elif x==7:\\r\\n pygame.draw.polygon(ventana,color,( (630,260),(710,260),(670,320) ))\\r\\n elif x==8:\\r\\n pygame.draw.polygon(ventana,color,( (730,260),(810,260),(770,320) ))\\r\\n elif x==9:\\r\\n pygame.draw.polygon(ventana,color,( (430,360),(510,360),(470,420) ))\\r\\n elif x==10:\\r\\n pygame.draw.polygon(ventana,color,( (530,360),(610,360),(570,420) ))\\r\\n elif x==11:\\r\\n pygame.draw.polygon(ventana,color,( (630,360),(710,360),(670,420) ))\\r\\n elif x==12:\\r\\n pygame.draw.polygon(ventana,color,( (730,360),(810,360),(770,420) ))\\r\\n elif x==13:\\r\\n pygame.draw.polygon(ventana,color,( (430,460),(510,460),(470,520) ))\\r\\n elif x==14:\\r\\n pygame.draw.polygon(ventana,color,( (530,460),(610,460),(570,520) ))\\r\\n elif x==15:\\r\\n pygame.draw.polygon(ventana,color,( (630,460),(710,460),(670,520) ))\\r\\n elif x==16:\\r\\n pygame.draw.polygon(ventana,color,( (730,460),(810,460),(770,520) ))\\r\\n\\r\\n def iniciarjuego():\\r\\n \\\"\\\"\\\"\\\"\\r\\n Esta funcion, crea cuatro figuras de cada una de las posibles, dos de ellas con diferente color de las otras dos.\\r\\n \\\"\\\"\\\"\\r\\n for i in range(4):\\r\\n if i == 1 or i == 0:\\r\\n color = (250,208,120,98)\\r\\n else:\\r\\n color = (159, 250, 120,98)\\r\\n x = random.choice(cuadrados)\\r\\n listafiguras.append(x)\\r\\n dibujarcuadrados(x, color)\\r\\n cuadrados.remove(x)\\r\\n\\r\\n for i in range(4):\\r\\n if i == 0 or i == 1:\\r\\n color = (195, 139, 255,100)\\r\\n else:\\r\\n color = (250, 242, 120,98)\\r\\n x = random.choice(cuadrados)\\r\\n listafiguras.append(x)\\r\\n dibujarcirculos(x, color)\\r\\n cuadrados.remove(x)\\r\\n\\r\\n for i in range(4):\\r\\n if i == 0 or i == 1:\\r\\n color = (250, 145, 137,98)\\r\\n else:\\r\\n color = (126, 139, 250,98)\\r\\n x = random.choice(cuadrados)\\r\\n listafiguras.append(x)\\r\\n dibujartriangulos(x, color)\\r\\n cuadrados.remove(x)\\r\\n\\r\\n for i in range(4):\\r\\n if i == 0 or i == 1:\\r\\n color = (176, 255, 237,100)\\r\\n else:\\r\\n color = (255, 176, 228,100)\\r\\n x = random.choice(cuadrados)\\r\\n listafiguras.append(x)\\r\\n dibujardiamantes(x, color)\\r\\n cuadrados.remove(x)\\r\\n\\r\\n def mostrar(posicion_del_mouse):\\r\\n \\\"\\\"\\\"\\r\\n Esta funcion muestra segun la casilla que clickee el jugador.\\r\\n :param tuple posicion_del_mouse: Este parametro representa segun clickee el jugador (posicion en x y y).\\r\\n \\\"\\\"\\\"\\r\\n numerodecuadrado = numerocuadrado(posicion_del_mouse)\\r\\n ubicaciondelcuadrado=ubicacion_cuadrado(posicion_del_mouse)\\r\\n if ubicaciondelcuadrado==0 or ubicaciondelcuadrado==1:\\r\\n color = (250,208,120,98)\\r\\n dibujarcuadrados(numerodecuadrado,color)\\r\\n elif ubicaciondelcuadrado==2 or ubicaciondelcuadrado==3:\\r\\n color = (159, 250, 120,98)\\r\\n dibujarcuadrados(numerodecuadrado,color)\\r\\n elif ubicaciondelcuadrado==4 or ubicaciondelcuadrado==5:\\r\\n color = (195, 139, 255,100)\\r\\n dibujarcirculos(numerodecuadrado,color)\\r\\n elif ubicaciondelcuadrado==6 or ubicaciondelcuadrado==7:\\r\\n color = (250, 242, 120,98)\\r\\n dibujarcirculos(numerodecuadrado,color)\\r\\n elif ubicaciondelcuadrado==8 or ubicaciondelcuadrado==9:\\r\\n color = (250, 145, 137,98)\\r\\n dibujartriangulos(numerodecuadrado,color)\\r\\n elif ubicaciondelcuadrado==10 or ubicaciondelcuadrado==11:\\r\\n color = (126, 139, 250,98)\\r\\n dibujartriangulos(numerodecuadrado,color)\\r\\n elif ubicaciondelcuadrado==12 or ubicaciondelcuadrado==13:\\r\\n color = (176, 255, 237,100)\\r\\n dibujardiamantes(numerodecuadrado,color)\\r\\n elif ubicaciondelcuadrado==14 or ubicaciondelcuadrado==15:\\r\\n color = (255, 176, 228,100)\\r\\n dibujardiamantes(numerodecuadrado,color)\\r\\n\\r\\n def esconderfichas(posicion_del_mouse):\\r\\n \\\"\\\"\\\"\\\"\\r\\n Esta funcion, esconde las fichas, si la seleccion del usuario no es la correcta.\\r\\n :param tuple posicion_del_mouse: :param tuple primeraeleccion: Representa la posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\\r\\n \\\"\\\"\\\"\\r\\n numerodecuadrado=numerocuadrado(posicion_del_mouse)\\r\\n for j in range(16):\\r\\n if numerodecuadrado==j+1:\\r\\n pygame.draw.rect(ventana,(135,206,250),Totalcuadrados[j].inflate(-10,-10))\\r\\n\\r\\n def encontrarparejas(primeraeleccion,segundaeleccion):\\r\\n \\\"\\\"\\\"\\r\\n Esta funcion determina si se encontro una pareja.\\r\\n :param tuple primeraeleccion: Representa la posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\\r\\n :param tuple segundaeleccion: Representa la segunda posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\\r\\n Retorna un booleano\\r\\n \\\"\\\"\\\"\\r\\n x=ubicacion_cuadrado(primeraeleccion)\\r\\n y=ubicacion_cuadrado(segundaeleccion)\\r\\n if (x==0 and y==1 or x==1 and y==0)\\\\\\r\\n or (x == 2 and y == 3 or x == 3 and y == 2) \\\\\\r\\n or (x == 4 and y == 5 or x == 5 and y == 4)\\\\\\r\\n or (x == 6 and y == 7 or x == 7 and y == 6)\\\\\\r\\n or (x == 8 and y == 9 or x == 9 and y == 8) \\\\\\r\\n or (x==10 and y==11 or x==11 and y==10)\\\\\\r\\n or (x==12 and y==13 or x==13 and y==12)\\\\\\r\\n or (x==14 and y==15 or x==15 and y==14):\\r\\n return True\\r\\n\\r\\n def ubicacion_cuadrado(posicion_del_mouse):\\r\\n \\\"\\\"\\\"\\r\\n Esta funcion, retorna la figura que se encuentra en determinado indice segun se clickee.\\r\\n :param tuple posicion_del_mouse: Representa la posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\\r\\n Retorna la figura de determinado indice\\r\\n \\\"\\\"\\\"\\r\\n for j in range(16):\\r\\n if Totalcuadrados[j].collidepoint(posicion_del_mouse):\\r\\n return listafiguras.index(j+1)\\r\\n\\r\\n def dibujartablero():\\r\\n \\\"\\\"\\\"\\\"\\r\\n Esta funcion, dibuja los cuadrados que hacen nuestro tablero.\\r\\n :return None\\r\\n \\\"\\\"\\\"\\r\\n ventana.fill((135,206,250)) #colocamos el color de fondo de nuestra pantalla\\r\\n for j in range(16): #iteramos entre los cuadrados ya diseñados, eligiendo parametros como el color y el grueso de sus lineas.\\r\\n pygame.draw.rect(ventana,(255,255,255),Totalcuadrados[j],10)\\r\\n\\r\\n def numerocuadrado(posicion_del_mouse):\\r\\n \\\"\\\"\\\"\\r\\n Esta funcion, retorna el indice de determinado cuadrado segun se clickee.\\r\\n :param tuple posicion_del_mouse: Representa la posicion de donde clickeo el jugador (tanto en el ejex como en el eje y).\\r\\n Retorna el indice de terminada posicion\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n for j in range(16):\\r\\n if Totalcuadrados[j].collidepoint(posicion_del_mouse):\\r\\n return j+1\\r\\n\\r\\n def main():\\r\\n \\\"\\\"\\\"\\\"\\r\\n Funcion proncipal del juego.\\r\\n :return None\\r\\n \\\"\\\"\\\"\\r\\n pygame.init()\\r\\n global ventana\\r\\n ventana= pygame.display.set_mode((1280,700))\\r\\n ventana.fill((135,206,250))\\r\\n pygame.display.set_caption('Encuentra las parejas')\\r\\n logo = pygame.image.load(\\\"../recursor/icono.ico\\\")\\r\\n pygame.display.set_icon(logo)\\r\\n pygame.display.update()\\r\\n dibujartablero()\\r\\n iniciarjuego()\\r\\n titulo_principal=pygame.image.load(\\\"../recursor/Imagenes juegos/parejas_2.png\\\")\\r\\n ventana.blit(titulo_principal, (405,0))\\r\\n pygame.display.update()\\r\\n pygame.time.wait(3000)\\r\\n dibujartablero()\\r\\n ventanaprincipal()\\r\\n pygame.display.update()\\r\\n prueba=0\\r\\n eleccioncorrecta=[]\\r\\n while True:\\r\\n for event in pygame.event.get():\\r\\n if event.type==QUIT:\\r\\n pygame.quit()\\r\\n sys.exit()\\r\\n elif event.type==pygame.MOUSEBUTTONUP:\\r\\n posicion_del_mouse=pygame.mouse.get_pos()\\r\\n mostrar(posicion_del_mouse)\\r\\n pygame.display.update()\\r\\n if prueba==0:\\r\\n primeraeleccion=posicion_del_mouse\\r\\n if numerocuadrado(primeraeleccion) in eleccioncorrecta:\\r\\n prueba=0\\r\\n else:\\r\\n prueba=1\\r\\n else:\\r\\n segundaeleccion=posicion_del_mouse\\r\\n if numerocuadrado(segundaeleccion) in eleccioncorrecta:\\r\\n prueba=1\\r\\n else:\\r\\n prueba=0\\r\\n if not (numerocuadrado(primeraeleccion) in eleccioncorrecta) and not numerocuadrado(segundaeleccion) in eleccioncorrecta:\\r\\n if encontrarparejas(primeraeleccion,segundaeleccion):\\r\\n eleccioncorrecta.append(numerocuadrado(primeraeleccion))\\r\\n eleccioncorrecta.append(numerocuadrado(segundaeleccion))\\r\\n else:\\r\\n pygame.time.wait(1000)\\r\\n esconderfichas(primeraeleccion)\\r\\n esconderfichas(segundaeleccion)\\r\\n pygame.display.update()\\r\\n\\r\\n if len(eleccioncorrecta)==16:\\r\\n ganador=pygame.image.load('../recursor/Imagenes juegos/Ganadorgg0000.png')\\r\\n ventana.blit(ganador,(420, 140))\\r\\n pygame.display.update()\\r\\n time.sleep(4)\\r\\n pygame.quit()\\r\\n break\\r\\n\\r\\n main()\",\n \"def umrandung_pixel(img, zeile, spalte):\\n #Oben drueber\\n #Index existiert und oben drueber ist Hintergrund\\n if zeile - 1 > 0 and img[zeile-1][spalte]==0:\\n img[zeile-1][spalte]=255#markiere dortigen Pixel als Umrandung (=255)\\n #Links daneben\\n #Index existiert und links daneben ist Hintergrund\\n if spalte - 1 > 0 and img[zeile][spalte-1]==0:\\n img[zeile][spalte-1]=255#markiere dortigen Pixel als Umrandung (=255)\\n #Rechts daneben\\n #Index existiert und rechts daneben ist Hintergrund\\n if spalte + 1 < img[zeile].__len__() and img[zeile][spalte+1]==0:\\n img[zeile][spalte+1]=255#markiere dortigen Pixel als Umrandung (=255)\\n #Unten drunter\\n #Index existiert und unten drunter ist Hintergrund\\n if zeile + 1 < img.__len__() and img[zeile+1][spalte]==0:\\n img[zeile+1][spalte]=255#markiere dortigen Pixel als Umrandung (=255)\",\n \"def galaxy(img):\\n return img[420:490, 710:770]\",\n \"def podziel(self):\\n def fraktal(dlugosc, alpha, poziom):\\n \\\"\\\"\\\"Metoda wyznaczajaca fraktal.\\n\\n Metoda ta przyjmuje dlugosc, kat oraz poziom drzewa.\\n Na bazie podanych parametrow wylicza fraktal z podanych w zadaniu wzorow.\\n Zwraca liste zawierajaca punkX oraz punktY fraktalu.\\n \\\"\\\"\\\"\\n#obliczanie punktow punktu Abis dla kazdego poziomu galezi\\n x = float(self.p2[0] + self.dlugosc * cos(alpha))\\n y = float(self.p2[1] + self.dlugosc * sin(alpha))\\n return [round(x), round(y)]\\n\\n#petla przechodzaca po wszystkich poziomach drzewa\\n while self.tmp <= self.poziom:\\n#obliczanie grubosci, dlugosci galezi oraz kolorowanie jej\\n self.grubosc = float((2 * self.grubosc + 1) / 3)\\n self.dlugosc = float((2 * self.dlugosc) / 3)\\n self.kolor += 6\\n\\n #sprawdzenie czy kolor nie wyszedl po za skale maksymalnej wartosci\\n if self.kolor > 255:\\n self.kolor = 255\\n\\n#rozbicie obliczen na poziom 1 i wyzej\\n#Abis jest to punkt prawy dla kazdej galezi\\n#B jest to punkt srodkowy dla kazdej galezi\\n#C jest to punkt srodkowy dla kazdej galezi\\n\\n#obliczenia dla pierwszego poziomu\\n if self.tmp < 2:\\n#obliczenie fraktalu, prawa galaz dla kazdej galezi\\n#podstawienie obliczonych wartosci z punktu Abis do pozostalych wedlug podanych wzorow\\n Abis = fraktal(self.dlugosc, self.alpha, self.poziom)\\n B = [round(self.p2[0]), round(Abis[1])]\\n C = [round(-Abis[0] + 2 * self.p2[0]), round(Abis[1])]\\n\\n#zwiekszenie poziomu drzewa o jeden\\n self.tmp += 1\\n\\n#tutaj nastepuje zwrocenie obiektow typu Branch z nowo obliczonymi wartosciami\\n return [Branch(self.p2, Abis, self.dlugosc, self.grubosc, self.kolor, self.alpha, self.tmp),\\n Branch(self.p2, B, self.dlugosc, self.grubosc, self.kolor, self.alpha, self.tmp),\\n Branch(self.p2, C, self.dlugosc, self.grubosc, self.kolor, self.alpha, self.tmp)]\\n#obliczenia poziomow wyzej niz pierwszy\\n else:\\n#obliczanie kata dla punktu prawego\\n self.zetprim = randint(-1, 1) * randint(1, self.s)\\n self.beta = self.alpha + self.zetprim\\n\\n#obliczanie kata dla punktu srodkowego\\n self.zetbis = randint(-1, 1) * randint(1, self.s)\\n self.gamma = self.alpha + self.zetbis\\n\\n#obliczanie kata dla punktu lewego\\n self.zetter = randint(-1, 1) * randint(1, self.s)\\n self.teta = self.alpha + self.zetter\\n\\n#obliczenie fraktalu, prawa galaz dla kazdej galezi\\n#podstawienie obliczonych wartosci z punktu Abis do pozostalych wedlug podanych wzorow\\n Abis = fraktal(self.dlugosc, self.beta, self.poziom)\\n B = [round(self.p2[0]), round(Abis[1])]\\n C = [round(-Abis[0] + 2 * self.p2[0]), round(Abis[1])]\\n\\n#zwiekszenie poziomu drzewa o jeden\\n self.tmp += 1\\n\\n#tutaj nastepuje zwrocenie obiektow typu Branch z nowo obliczonymi wartosciami\\n return [Branch(self.p2, Abis, self.dlugosc, self.grubosc, self.kolor, self.beta, self.tmp),\\n Branch(self.p2, B, self.dlugosc, self.grubosc, self.kolor, self.gamma, self.tmp),\\n Branch(self.p2, C, self.dlugosc, self.grubosc, self.kolor, self.teta, self.tmp)]\",\n \"def getTasseledCap(img):\",\n \"def __init__(self,game):\\r\\n self.game=game\\r\\n self.can=game.can\\r\\n self.vrai=False\\r\\n self.select=-1\\r\\n self.img=game.img_bto\\r\\n self.bateau()\\r\\n self.ima()\\r\\n self.aléatoire(5,self.game.j1)\",\n \"def random_translation(canvas, img):\",\n \"def testSepia (name = \\\"smokey.gif\\\"):\\n image = Image(name)\\n print(\\\"Close the image window to see the transformation\\\")\\n image.draw()\\n sepia(image)\\n image.draw()\",\n \"def des_image(exptime=100,mag=None, Nstar=1,ccd=None,seeing=[0.9,0.,0.],npix=npix,zenith=0,filter='r', theta=0., phi=0,corrector='corrector',x=None,y=None,z=None,suband=None,regular=False,setbkg=True):\\n gain = 0.21 # convert electrons to ADU\\n zeropoint = 26.794176 # r band, from Nikolay\\n \\n if setbkg == False:\\n skyphoton = 0.\\n else:\\n skyphoton = 8.460140*exptime\\n bkg = skyphoton*gain\\n res = genImgV(filename=None,Nstar=Nstar,ccd=ccd,seeing=seeing,npix=npix,zenith=zenith,filter=filter, theta=theta, phi=phi,corrector=corrector,x=x,y=y,z=z,suband=suband,regular=regular)\\n if Nstar == 1:\\n objectphoton = exptime*10**(0.4*(zeropoint - mag))\\n psf = res[0][0][4:].reshape(npix,npix)\\n img = (psf * objectphoton + skyphoton)*gain\\n img = rebin(img,(40,40))\\n psf = rebin(psf,(40,40))\\n img = img + add_imageNoise(img)\\n if Nstar > 1:\\n img = []\\n psf = []\\n magg = []\\n for i in range(Nstar):\\n if type(mag) == list:\\n rng = abs(mag[1] - mag[0])\\n magi = min(mag)+np.random.rand()*rng\\n objectphoton = exptime*10**(0.4*(zeropoint - magi))\\n psfi = res[0][i][4:].reshape(npix,npix)\\n imgi = (psfi * objectphoton + skyphoton)*gain\\n imgi = imgi+np.sqrt(imgi)\\n imgi = rebin(imgi,(40,40))\\n psfi = rebin(psfi,(40,40))\\n imgi = imgi + add_imageNoise(imgi)\\n img.append(imgi)\\n psf.append(psfi)\\n magg.append(magi)\\n return img,bkg,psf,magg\",\n \"def draw_image(self):\\n dir_ship = self.direction\\n dir_cannon=self.cannon_dir\\n temp_image = self.base_image.copy()\\n pygame.draw.polygon(temp_image, (0,0,0), [(2,2),(2,3),(3,3),(3,2)])\\n if dir_cannon == dir_ship:\\n pygame.draw.polygon(temp_image, (60,60,60), [(4,3),(4,2), (5,3),(5,2)])\\n if (dir_ship - dir_cannon)%4 ==1:#-90° angle\\n pygame.draw.polygon(temp_image, (60,60,60), [(2,4),(3,4), (2,5),(3,5)])\\n if (dir_ship - dir_cannon)%4 ==3:#+90° angle\\n pygame.draw.polygon(temp_image, (60,60,60), [(2,1),(3,1), (2,0),(3,0)])\\n if (dir_ship - dir_cannon)%4 ==2:#180° angle\\n pygame.draw.polygon(temp_image, (60,60,60), [(1,2),(1,3), (0,2),(0,3)])\\n temp_image=pygame.transform.rotate(temp_image,dir_ship*90)\\n return temp_image\",\n \"def orb():\\n image = pygame.Surface([16, 16], pygame.SRCALPHA)\\n pygame.draw.circle(image, colour.WHITE, (8, 8), 8, 0)\\n return image\",\n \"def imag(z):\",\n \"def tick(self, iteration):\\n frame = self.cam.get_current_fram()\\n hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\\n # on recupaire la zone qui on les zone de couleur\\n mask = cv2.inRange(hsv, self.greenLower, self.greenUpper)\\n # on fait une erosion et dilation pour supprimer les petit pixel blanc qui peut être pris pour la balle\\n mask = cv2.erode(mask, None, iterations=iteration)\\n mask = cv2.dilate(mask, None, iterations=iteration)\\n\\n cv2.imshow(\\\"mask\\\", mask)\\n\\n # Recherche de contour pour savoir ou est la balle\\n cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\\n center = None\\n if len(cnts) > 0:\\n # on charche le contour le plus grand (celui de la balle)\\n c = max(cnts, key=cv2.contourArea)\\n ((x, y), radius) = cv2.minEnclosingCircle(c)\\n moment_cont = cv2.moments(c)\\n # on recupaire le centre de la balle\\n center = (int(moment_cont[\\\"m10\\\"] / moment_cont[\\\"m00\\\"]), int(moment_cont[\\\"m01\\\"] / moment_cont[\\\"m00\\\"]))\\n # on controle la taille du rayon de la balle\\n if radius > 30:\\n # Puis on dessin la boulle\\n cv2.circle(frame, (int(x), int(y)), int(radius), (0, 255, 255), 2)\\n cv2.circle(frame, center, 5, (0, 0, 255), -1)\\n return center, frame\",\n \"def show_es():\\n img = rotated_e()\\n for i in range(4):\\n plt.subplot(2, 2, i + 1)\\n plt.imshow(img[i], cmap=plt.cm.gray, interpolation='nearest')\\n plt.show()\",\n \"def reemplaza_tildes(palabra):\",\n \"def preprocess_RSofia(img):\\n for i in range(480):\\n for j in range(200):\\n img[i,j]=0\\n for i in range(150):\\n for j in range(500):\\n img[i,j]=0 \\n for i in range(475):\\n for j in range(90):\\n img[i,img.shape[1]-1-j]=0\\n for i in range(90):\\n for j in range(465):\\n img[i,img.shape[1]-1-j]=0\\n return img\",\n \"def imageprepare():\\r\\n file_name = '9-test.png'\\r\\n im = Image.open(file_name).convert('L')\\r\\n\\r\\n im.save(\\\"9-t.png\\\")\\r\\n plt.imshow(im)\\r\\n plt.show()\\r\\n tv = list(im.getdata())\\r\\n\\r\\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\r\\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\\r\\n return tva\",\n \"def phantom_ellipses(n_points,E):\\n \\n #Rescaling according to image size \\n E[:,0] = E[:,0]*n_points/2 #semiaxis a\\n E[:,1] = E[:,1]*n_points/2 #semiaxis b\\n E[:,2] = E[:,2]*n_points/2 #x\\n E[:,3] = E[:,3]*n_points/2 #y\\n E[:,4] = E[:,4]*math.pi/180\\n \\n x,y = np.meshgrid(np.arange(0,n_points)-n_points//2 ,np.arange(0,n_points)-n_points//2 )\\n nrow,ncol = E.shape\\n phantom1 = np.zeros((y.shape[0], y.shape[1], nrow))\\n\\n for k in range(nrow): #itero sulle ellissi\\n x_new = x - E[k,2]\\n y_new = y - E[k,3]\\n\\n #find(( (x.*cosp + y.*sinp).^2)./asq + ((y.*cosp - x.*sinp).^2)./bsq <= 1); \\n cosp = math.cos(E[k,4])\\n sinp = math.sin(E[k,4])\\n cond = np.square( x_new * cosp + y_new * sinp )*1/(E[k,0]*E[k,0]) + \\\\\\n np.square(y_new * cosp - x_new * sinp)*1/(E[k,1]*E[k,1]) - 1\\n\\n for i in range(x.shape[0]):\\n for j in range(x.shape[1]):\\n if (cond[i,j] <= 0.0):\\n phantom1[i,j,k] = E[k,5]; # gray scale\\n else:\\n phantom1[i,j,k] = 0.0;\\n #endif\\n #endfor\\n #endfor\\n #endfor\\n phantom1 = phantom1.sum(axis=2)\\n phantom = np.flipud(phantom1)\\n return phantom\",\n \"def umrandung_zeichnen(img):\\n for zeilen_index in range(0,img.__len__()):\\n for spalten_index in range(0, img[zeilen_index].__len__()):\\n if img[zeilen_index][spalten_index] == 1:\\n #Markiere benachbarte Pixel\\n umrandung_pixel(img, zeilen_index, spalten_index)\",\n \"def main():\\n original = SimpleImage('images/mt-rainier.jpg')\\n original.show()\\n reflected = make_reflected('images/mt-rainier.jpg')\\n reflected.show()\",\n \"def problem1():\\n\\n img = load_image(\\\"data/a1p1.png\\\")\\n display_image(img)\\n\\n save_as_npy(\\\"a1p1.npy\\\", img)\\n\\n img1 = load_npy(\\\"a1p1.npy\\\")\\n display_image(img1)\\n\\n img2 = mirror_horizontal(img1)\\n display_image(img2)\\n\\n display_images(img1, img2)\",\n \"def fotoXO(self):\\n\\t\\ttry:\\n\\t\\t\\timageP = self.cam.get_image()\\n\\t\\t\\tself.image = pygame.transform.flip(imageP, True, False)\\n\\t\\texcept:\\n\\t\\t\\tpass\\n\\t\\treturn self.image\",\n \"def accueil():\\r\\n global etat\\r\\n background(0,128,128)\\r\\n image(tireur,0,0) \\r\\n rectMode(CORNERS)\\r\\n fill(100)\\r\\n rect(0,60,250,120)\\r\\n rect(500,60,750,120)\\r\\n fill(0)\\r\\n textSize(30)\\r\\n text(\\\"PVP\\\",95,100) \\r\\n text(\\\"ORDINATEUR\\\",520,100) \\r\\n if (mousePressed) and mouseX<250 and mouseX>0 and mouseY<120 and mouseY>60: #si on clique sur le rectangle jouer\\r\\n etat=1 #on passe en mode jeu\\r\\n if (mousePressed) and mouseX<750 and mouseX>500 and mouseY<120 and mouseY>60: \\r\\n etat=2\",\n \"def giaotranh():\\n # printdb('def giaotranh')\\n time.sleep(8)\\n logging.info('def giao tranh')\\n for x in range(6):\\n screencap()\\n # thấy next thì chuyển\\n imgnext = imagesearch(path_Scr + '\\\\\\\\screencap.png',\\n path_Scr + '\\\\\\\\next.png', 0.8)\\n if imgnext.find() >= 1:\\n # printdb('mạnh')\\n danh()\\n break\\n else:\\n if x == 5:\\n check_loi_vethanh()\\n else:\\n time.sleep(2)\",\n \"def dibujarcuadrados(x,color):\\r\\n if x==1:\\r\\n pygame.draw.rect(ventana,color,Rect(440,160,60,60))\\r\\n elif x==2:\\r\\n pygame.draw.rect(ventana,color,Rect(540,160,60,60))\\r\\n elif x==3:\\r\\n pygame.draw.rect(ventana,color,Rect(640,160,60,60))\\r\\n elif x==4:\\r\\n pygame.draw.rect(ventana,color,Rect(740,160,60,60))\\r\\n elif x==5:\\r\\n pygame.draw.rect(ventana,color,Rect(440,260,60,60))\\r\\n elif x==6:\\r\\n pygame.draw.rect(ventana,color,Rect(540,260,60,60))\\r\\n elif x==7:\\r\\n pygame.draw.rect(ventana,color,Rect(640,260,60,60))\\r\\n elif x==8:\\r\\n pygame.draw.rect(ventana,color,Rect(740,260,60,60))\\r\\n elif x==9:\\r\\n pygame.draw.rect(ventana,color,Rect(440,360,60,60))\\r\\n elif x==10:\\r\\n pygame.draw.rect(ventana,color,Rect(540,360,60,60))\\r\\n elif x==11:\\r\\n pygame.draw.rect(ventana,color,Rect(640,360,60,60))\\r\\n elif x==12:\\r\\n pygame.draw.rect(ventana,color,Rect(740,360,60,60))\\r\\n elif x==13:\\r\\n pygame.draw.rect(ventana,color,Rect(440,460,60,60))\\r\\n elif x==14:\\r\\n pygame.draw.rect(ventana,color,Rect(540,460,60,60))\\r\\n elif x==15:\\r\\n pygame.draw.rect(ventana,color,Rect(640,460,60,60))\\r\\n elif x==16:\\r\\n pygame.draw.rect(ventana,color,Rect(740,460,60,60))\",\n \"def transform(self, previousimage):\",\n \"def convert_image(rel_path_in, rel_path_out):\\n #Lade Bild mit Originalmaske im Grayscale-Modus\\n img = cv2.imread(rel_path_in, cv2.IMREAD_GRAYSCALE)\\n #Jetzt steht in img ein 2D-Array/Matrix mit jedem Graufstufen-Wert der Pixel\\n #Skaliere Pixelwerte runter\\n for zeilen_index in range(0,img.__len__()):\\n for spalten_index in range(0, img[zeilen_index].__len__()):\\n #Hole Pixel-Wert an aktueller Stelle\\n wert = img[zeilen_index][spalten_index]\\n #Falls Wert != 0 (also Pixel gehoert nicht zum Hintergrund)\\n if wert != 0: # != 0 statt == 255, da auch z.B. 253er Werte in den Masken existieren... (vielleicht durch Konvertierung in anderes Format?)\\n #Markiere den Pixel mit 1 statt 255\\n img[zeilen_index][spalten_index]=1\\n #print(img)\\n #*NACHDEM* alle Pixel skaliert wurden, zeichne Umrandung der Objekte\\n umrandung_zeichnen(img)\\n #change_color(img, 0, 255)\\n #change_color(img, 1, 0)\\n #print(img)\\n #Schreibe Ergebnis-Bild in uebergebene Datei\\n cv2.imwrite(rel_path_out, img)\",\n \"def comprimir_en_rango_dinamico(p):\\n img = read_img(p)\\n img_compressed = compresion_de_rango_dinamico(img.reshape((-1)))\\n show_imgs([img, img_compressed.reshape(img.shape)])\\n plt.hist(img_compressed, bins=256)\\n plt.show()\",\n \"def imageprepare(self, argv):\\n im = Image.open(argv).convert('L')\\n img = im.resize((28, 28), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\\n tv = list(img.getdata()) \\n \\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\\n return tva\",\n \"def comenzar_nuevo_juego():\\n escena_uno.cargarEscena1(screen, display_width, display_height)#Se pone a correr la escena\\n #escena_uno.cargarEscena2(screen, display_width, display_height)\",\n \"def getimage(self):\",\n \"def tirar_foto(frame, dimensoes, file_name, vaga, entrada_saida=\\\"E\\\"):\\n (x, y, w, h) = dimensoes[0], dimensoes[1], dimensoes[2], dimensoes[3]\\n file = f\\\"imagens/{file_name}.png\\\"\\n imagem = frame[y:y + h, x:x + w]\\n cv2.imwrite(file, imagem)\\n print(f'foto salva -> {file_name}')\\n\\n print(\\\"detectando placa....\\\")\\n recognize_license_plate(file, vaga, entrada_saida)\\n print(\\\"fim detecção placa\\\")\",\n \"def ff_correct_image(image):\\n pass\",\n \"def ff_correct_image(image):\\n pass\",\n \"def photometric_calibration():\\n pass\",\n \"def make_big_e(self):\\n l = self.l_i\\n self.img[l/2-1:l/2+1, l/2-5:l/2+5] = 1\\n self.img[l/2-5:l/2-3, l/2-5:l/2+5] = 1\\n self.img[l/2+3:l/2+5, l/2-5:l/2+5] = 1\\n self.img[l/2-5:l/2+5, l/2-5:l/2-3] = 1\\n self.img_name = 'bigE'\",\n \"def mostrar(imagenObj,x,y):\\n gameDisplay.blit(imagenObj,(x,y))\",\n \"def ubicar_fragata():\\n tamano = Fragata.tamano #se importa el tamano del barco desde su clase\\n cantidad = Fragata.cantidad #se importa la cantidad de barcos desde su clase\\n orientacion = orientaciones[(randint(0, 1))] #elige aleatoriamente el index de la tupla orientaciones = (\\\"Vertical\\\", \\\"Horizontal\\\")\\n seguir_coordenadas = True\\n while seguir_coordenadas:\\n mal_ubicado = \\\"no\\\"\\n if orientacion == \\\"Vertical\\\":\\n #se eligen random las filas y las columnas\\n coor_fila = randint(1, numero_filas)\\n coor_columna = randint (1, numero_columnas)\\n while (coor_fila + tamano) > 10: #como su orientacion es vertical la fila incial del barco mas su tamano (2) no puede ser mayor que 10 porque se saldria del mapa\\n coor_fila = randint(1,numero_filas)\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \\n coor_fila += 1\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion)\\n for x in lista_ubicacion_barco:\\n for y in lista_temporal:\\n if x == y:\\n mal_ubicado = \\\"si\\\" #si alguna coordenada de este barco coincide con la de otro la variable mal ubicado sera \\\"si\\\" \\n elif (y[0] == x[0] or (y[0]+1) == x[0] or (y[0]-1) == x[0]) and ((y[1]) == x[1] or (y[1]+1) == x[1] or (y[1]- 1) == x[1]): #validacion para que no se ubique el barco al lado o diagonalmente contiguo a otro\\n mal_ubicado = \\\"si\\\" #si esta validacion sucede la variable mal ubicado sera \\\"si\\\" \\n if orientacion == \\\"Horizontal\\\":\\n #se eligen random las filas y las columnas\\n coor_fila = randint(1, numero_filas)\\n coor_columna = randint(1, numero_columnas)\\n while (coor_columna + tamano) > 10: #como su orientacion es horizontal la columna incial del barco mas su tamano (2) no puede ser mayor que 10 porque se saldria del mapa\\n coor_columna = randint(1, numero_columnas)\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \\n coor_columna += 1\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion)\\n for x in lista_ubicacion_barco:\\n for y in lista_temporal:\\n if x == y:\\n mal_ubicado = \\\"si\\\" #si alguna coordenada de este barco coincide con la de otro la variable mal ubicado sera \\\"si\\\" \\n elif (y[0] == x[0] or (y[0]+1) == x[0] or (y[0]-1) == x[0]) and ((y[1]) == x[1] or (y[1]+1) == x[1] or (y[1]- 1) == x[1]): #validacion para que no se ubique el barco al lado o diagonalmente contiguo a otro\\n mal_ubicado = \\\"si\\\" #si esta validacion sucede la variable mal ubicado sera \\\"si\\\" \\n if mal_ubicado == \\\"si\\\": #si la variable mal ubicado es \\\"si\\\" se repetira el proceso otra vez\\n seguir_coordenadas = True\\n lista_temporal.clear()\\n elif mal_ubicado == \\\"no\\\": #si la variable es \\\"no\\\" se continuara el proceso\\n for x in lista_temporal:\\n lista_ubicacion_barco.append(x) #se agregan las posiciones a la lista general\\n coordenadas_fragata.append(x) \\n lista_temporal.clear() #se limpia la lista temporal para usarla en el otro barco\\n seguir_coordenadas = False\",\n \"def actualise_fenetre(self,plateau,fenetre,joueur,info,bouton,etape_texte):\\n\\n self.affiche_plateau(plateau,fenetre)\\n liste_im_joueur = [pygame.image.load(\\\"joueur\\\"+str(i)+\\\".png\\\").convert_alpha() for i in range(1,5)]\\n for i in range (4) :\\n x_joueur = 60\\n y_joueur = 60\\n liste_im_joueur[i] = pygame.transform.scale(liste_im_joueur[i], (int(x_joueur),int(y_joueur)))\\n\\n for i in range(len(plateau.dico_joueurs)) :\\n fenetre.blit(liste_im_joueur[i],(1030,320+i*80))\\n fenetre.blit(police_small.render(str(plateau.dico_joueurs[i].nom) + \\\" : \\\",False,pygame.Color(\\\"#000000\\\")),(800,340+i*75))\\n fenetre.blit(police1.render(\\\"Score : \\\"+str(plateau.dico_joueurs[i].points),False,pygame.Color(\\\"#000000\\\")),(800,340+i*75+15))\\n fenetre.blit(police1.render(\\\"Ordre de mission : \\\"+str(sorted(plateau.dico_joueurs[i].fantome_target)),False,pygame.Color(\\\"#000000\\\")),(800,340+i*75+30))\\n fenetre.blit(police1.render(\\\"Jokers restants : \\\"+str(plateau.dico_joueurs[i].nb_joker),False,pygame.Color(\\\"#000000\\\")),(800,340+i*75+45))\\n \\n #test texte pour afficher le joueur qui joue\\n if self.turn == True :\\n fenetre.blit(police.render(\\\"C'est à vous de jouer\\\",False,pygame.Color(0,0,0)),(800,240))\\n else:\\n fenetre.blit(police.render(\\\"C'est le tour de votre adversaire\\\",False,pygame.Color(0,0,0)),(800,240))\\n \\n #affichage du message d'erreur\\n for i in range(len(info)) : \\n fenetre.blit(police.render(info[i],False,pygame.Color(\\\"#000000\\\")),(760,180+i*20))\\n \\n fenetre.blit(police.render(etape_texte,False,pygame.Color(\\\"#000000\\\")),(760,160))\\n \\n \\n bouton.draw(fenetre)\\n \\n pygame.display.flip()\",\n \"def imageprepare():\\r\\n file_name = 'temp_image.png'\\r\\n im = Image.open(file_name).convert('L')\\r\\n im = im.resize((20, 20))\\r\\n p = Image.new('L', (28,28), (255))\\r\\n p.paste(im,(4,4,24,24))\\r\\n p.save(\\\"last_image.png\\\")\\r\\n\\r\\n tv = list(p.getdata()) # get pixel values\\r\\n # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\r\\n tva = [(255 - x) * 1.0 / 255.0 for x in tv]\\r\\n tva = np.reshape(tva, (28, 28))\\r\\n\\r\\n return tva\",\n \"def move(self):\\n \\n self.rect.move_ip(0,self.speed) # Funcion para mover el enemigo especificando la velocidad xy\\n \\n if (self.rect.top > SCREEN_HEIGHT): # Condicion cuando llega a la parte inferior y no colisiono con el jugador\\n del self.surf #Libera memoria\\n del self.rect\\n self.randomNumber = random.choice([70,64,32]) # Su tamaño se asigna nuevamente\\n self.size = (self.randomNumber,self.randomNumber) #Se genera su tamaño como un cuadrado de lado aleatorio\\n self.surf = pygame.Surface(self.size) #Se genera la superficie que aparecera la pantalla\\n self.surf.fill(RED)\\n self.rect = self.surf.get_rect(center = (random.randint(40,SCREEN_WIDTH-40),0))# me da info de las coordenadas de surf\\n if(self.randomNumber == 32):\\n self.surf = self.imagen\\n elif(self.randomNumber ==64):\\n self.surf = self.imagen2\\n elif self.randomNumber ==70 :\\n self.surf = self.imagen3\",\n \"def danh():\\n # printdb('def danh')\\n imgmanh = imagesearch(path_Scr + '\\\\\\\\screencap.png',\\n path_Scr + '\\\\\\\\longden.png', 0.77)\\n time.sleep(1)\\n # mạnh bỏ qua\\n if imgmanh.find() >= 1:\\n click_next()\\n logging.info('Next')\\n giaotranh()\\n else:\\n click_danh()\\n #######################lỗi mạng######################\\n time.sleep(30)\\n screencap()\\n error = imagesearch(path_Scr + '\\\\\\\\screencap.png',\\n path_Scr + '\\\\\\\\error.png', 0.88)\\n # printdb('check lỗi ')\\n if error.find() >= 1: # check lỗi mạng\\n check_loi_vethanh()\\n else:\\n time.sleep(cTimeWar) # 40s\\n # Rutlui() #opt1 check khiên --end (rỉa 1 khiên)\\n DanhTiep() # opt2 check nút hồi thành --end\\n # click_rutlui() #opt3 chờ cTimeWar --End (đánh theo time chỉ định)\\n #####################################################\",\n \"def __init__(self, image_to_load, coo_x, rang, coo_y=432):\\n\\n super().__init__()\\n self.image = pg.image.load(image_to_load)\\n self.image = pg.transform.scale(self.image, (80, 80))\\n self.rect = self.image.get_rect()\\n self.rect.x = coo_x\\n self.rect.y = coo_y\\n self.last_direction = 1\\n self.counter = 0\\n self.range = rang\\n\\n if self.range % 2 == 0:\\n self.half = self.range / 2 - 1\\n else:\\n self.half = self.range / 2\",\n \"def img2heliovec(bxImg,byImg,bzImg,lon,lat,lonc,latc,pAng):\\n a11 = -np.sin(latc)*np.sin(pAng)*np.sin(lon - lonc) + np.cos(pAng)*np.cos(lon - lonc)\\n a12 = np.sin(latc)*np.cos(pAng)*np.sin(lon - lonc) + np.sin(pAng)*np.cos(lon - lonc)\\n a13 = -np.cos(latc)*np.sin(lon - lonc)\\n a21 = -np.sin(lat)*(np.sin(latc)*np.sin(pAng)*np.cos(lon - lonc) + np.cos(pAng)*np.sin(lon - lonc)) - np.cos(lat)*np.cos(latc)*np.sin(pAng)\\n a22 = np.sin(lat)*(np.sin(latc)*np.cos(pAng)*np.cos(lon - lonc) - np.sin(pAng)*np.sin(lon - lonc)) + np.cos(lat)*np.cos(latc)*np.cos(pAng)\\n a23 = -np.cos(latc)*np.sin(lat)*np.cos(lon - lonc) + np.sin(latc)*np.cos(lat)\\n a31 = np.cos(lat)*(np.sin(latc)*np.sin(pAng)*np.cos(lon - lonc) + np.cos(pAng)*np.sin(lon - lonc)) - np.sin(lat)*np.cos(latc)*np.sin(pAng)\\n a32 = -np.cos(lat)*(np.sin(latc)*np.cos(pAng)*np.cos(lon - lonc) - np.sin(pAng)*np.sin(lon - lonc)) + np.sin(lat)*np.cos(latc)*np.cos(pAng)\\n a33 = np.cos(lat)*np.cos(latc)*np.cos(lon - lonc) + np.sin(lat)*np.sin(latc)\\n\\n bxHelio = a11 * bxImg + a12 * byImg + a13 * bzImg\\n byHelio = a21 * bxImg + a22 * byImg + a23 * bzImg\\n bzHelio = a31 * bxImg + a32 * byImg + a33 * bzImg\\n\\n return bxHelio,byHelio,bzHelio\",\n \"def problem2():\\n\\n data = loaddata(\\\"data/bayerdata.npy\\\")\\n r, g, b = separatechannels(data)\\n\\n img = assembleimage(r, g, b)\\n display_image(img)\\n\\n img_interpolated = interpolate(r, g, b)\\n display_image(img_interpolated)\",\n \"def dibujarcirculos(x,color):\\r\\n if x==1:\\r\\n pygame.draw.circle(ventana,color,(470,190),30)\\r\\n elif x==2:\\r\\n pygame.draw.circle(ventana,color,(570,190),30)\\r\\n elif x==3:\\r\\n pygame.draw.circle(ventana,color,(670,190),30)\\r\\n elif x==4:\\r\\n pygame.draw.circle(ventana,color,(770,190),30)\\r\\n elif x==5:\\r\\n pygame.draw.circle(ventana,color,(470,290),30)\\r\\n elif x==6:\\r\\n pygame.draw.circle(ventana,color,(570,290),30)\\r\\n elif x==7:\\r\\n pygame.draw.circle(ventana,color,(670,290),30)\\r\\n elif x==8:\\r\\n pygame.draw.circle(ventana,color,(770,290),30)\\r\\n elif x==9:\\r\\n pygame.draw.circle(ventana,color,(470,390),30)\\r\\n elif x==10:\\r\\n pygame.draw.circle(ventana,color,(570,390),30)\\r\\n elif x==11:\\r\\n pygame.draw.circle(ventana,color,(670,390),30)\\r\\n elif x==12:\\r\\n pygame.draw.circle(ventana,color,(770,390),30)\\r\\n elif x==13:\\r\\n pygame.draw.circle(ventana,color,(470,490),30)\\r\\n elif x==14:\\r\\n pygame.draw.circle(ventana,color,(570,490),30)\\r\\n elif x==15:\\r\\n pygame.draw.circle(ventana,color,(670,490),30)\\r\\n elif x==16:\\r\\n pygame.draw.circle(ventana,color,(770,490),30)\",\n \"def sepiaTest(file: str) -> str:\\n img = sepia(load_image(file), disp = False, save = False)\\n \\n img = create_image(3, 1) # creates image\\n set_color(img, 0, 0, create_color(10, 67, 201))\\n set_color(img, 1, 0, create_color(59, 190, 198))\\n set_color(img, 2, 0, create_color(23, 78, 254))\\n \\n\\n test_img = sepia(img, disp = False, save = False)\\n image_correct = 0\\n pixel = 0\\n \\n for x, y, (r, g, b) in test_img:\\n if 0<= r < 63:\\n if r != r / 1.1 and b1 == float(b / 0.9):\\n image_correct += 1\\n else:\\n image_correct -= 1\\n \\n elif 63<= r <= 191:\\n if r1 == float(r / 1.15) and b1 == float(b / 0.85):\\n image_correct += 1\\n else:\\n image_correct -= 1\\n \\n elif 191<= r <255 :\\n if r1 == float(r / 1.08) and b1 == float(b / 0.93):\\n image_correct += 1\\n else:\\n image_correct -= 1\\n \\n else:\\n image_correct -= 1\\n if image_correct == pixel:\\n return print('lol'), pixel, image_correct\\n else:\\n return print('u suck'), pixel, image_correct\",\n \"def testBlackAndWhite(name = \\\"smokey.gif\\\"):\\n image = Image(name)\\n print(\\\"Close the image window to see the transformation\\\")\\n image.draw()\\n blackAndWhite(image)\\n image.draw()\",\n \"def rysujMape(mapObj, gameStateObj, cele):\\n\\n # mapSurf - naniesione sa tiles (grafika mapy) \\n\\t\\n\\t#Najepierw obliczenie dlugosci i szerokosci mapy\\n mapSurfWidth = len(mapObj) * TILEWIDTH\\n mapSurfHeight = (len(mapObj[0]) - 1) * TILEFLOORHEIGHT + TILEHEIGHT\\n mapSurf = pygame.Surface((mapSurfWidth, mapSurfHeight))\\n mapSurf.fill(BGCOLOR) # kolor tla\\n\\n # umieszczenie grafik tiles w odpowiednich miejscach\\n for x in range(len(mapObj)):\\n for y in range(len(mapObj[x])):\\n spaceRect = pygame.Rect((x * TILEWIDTH, y * TILEFLOORHEIGHT, TILEWIDTH, TILEHEIGHT))\\n if mapObj[x][y] in TILEMAPPING:\\n baseTile = TILEMAPPING[mapObj[x][y]]\\n else: \\n baseTile = TILEMAPPING[' ']\\n\\n # dodaj na ekran obiekty: mur i podloga\\n mapSurf.blit(baseTile, spaceRect)\\n\\n \\n if (x, y) in gameStateObj['skrzynki']:\\n # ustaw skrzynke\\n mapSurf.blit(SLOWNIK_OBRAZKI['skrzynka'], spaceRect)\\n if (x, y) in cele:\\n # nastepnie jesli w tym miejscu tez jest cel to ustaw grafike 'cel_po' (ciemniejsza skrzynka)\\n mapSurf.blit(SLOWNIK_OBRAZKI['cel_po'], spaceRect)\\n elif (x, y) in cele:\\n # jesli tylko cel to ustaw grafike celu cel_przed (czerwona kropka) \\n mapSurf.blit(SLOWNIK_OBRAZKI['cel_przed'], spaceRect)\\n\\n # Na koncu ustaw ludzika \\n if (x, y) == gameStateObj['ludzik']:\\n mapSurf.blit(PLAYER_GRAFIKA, spaceRect)\\n\\n return mapSurf\",\n \"def muestraPokemon(bytes):\\n image = Image.open(io.BytesIO(bytes))\\n data = np.array(image)\\n plt.imshow(data)\\n plt.axis('off')\\n plt.show()\",\n \"def Haut():\\r\\n X1, Y1, X2, Y2 = canvas.coords(boule)\\r\\n canvas.coords(boule,X1,Y1-20,X2,Y2-20)\",\n \"def reprographie():\\n nombreDePhotocopie = int(input(\\\"Entrez le nombre de photocopie a effectuer \\\"))\\n PREMIER_PRIX = 0.10\\n DEUXIEME_PRIX = 0.09\\n TROISIEME_PRIX = 0.08\\n PREMIERE_TRANCHE = 10\\n DEUXIEME_TRANCHE = 20\\n TROISIEME_TRANCHE = 30\\n resultat = 0\\n if(nombreDePhotocopie>TROISIEME_TRANCHE):\\n resultat = DEUXIEME_TRANCHE*DEUXIEME_PRIX+1+(nombreDePhotocopie-30)*TROISIEME_PRIX\\n elif(nombreDePhotocopie<=TROISIEME_TRANCHE):\\n if(nombreDePhotocopie/10>1):\\n resultat = (nombreDePhotocopie-10)*DEUXIEME_PRIX+(PREMIERE_TRANCHE*PREMIER_PRIX)\\n else:\\n resultat = nombreDePhotocopie*PREMIER_PRIX\\n return resultat\",\n \"def exercise2a(self):\\n colors = [\\n [253, 253, 150],\\n [255, 125, 125],\\n [150, 111, 214],\\n [119, 158, 203],\\n [255, 105, 97],\\n [ 3, 192, 60]\\n ]\\n # This takes some time …\\n #self.b1 = mandelbrot(1200, 600, colors, [0, 0, 0], 0.02, 0.73, 0.21)\\n # Load the pregenerated image ;)\\n self.b1 = misc.imread(\\\"B1.png\\\")\\n plt.axis('off')\\n plt.imshow(self.b1)\\n plt.show()\\n misc.imsave(\\\"B1.png\\\", self.b1)\\n misc.imsave(\\\"B1_Brightness.png\\\", print_brightness(self.b1))\",\n \"def Inpainting_mosaic(im, mask):\\n im0 = np.copy(im) # Lager kopi av bilde\\n im[im < 0] = 0 # klipp til lovlige verdier\\n im[im > 1] = 1\\n for i in range(25): # Itererer \\n im = eksplisitt(im, n=1) # Løser ved hjelp av eksplisittfunksjon\\n im[np.logical_not(mask)] = im0[np.logical_not(mask)] \\n return im\",\n \"def __init__(self, x, y, largo, alto):\\n # Llama al constructor padre\\n pygame.sprite.Sprite.__init__(self)\\n \\n # Construye una pared azul con las dimensiones especificadas por los parametros\\n self.image = pygame.Surface([21, 21])\\n self.image.fill(AZUL)\\n \\n # Establece como origen la esquina superior izquierda.\\n self.rect = self.image.get_rect()\\n self.rect.y = y\\n self.rect.x = x\",\n \"def imageprepare(argv):\\r\\n im = Image.open(argv).convert('L')\\r\\n width = float(im.size[0])\\r\\n height = float(im.size[1])\\r\\n newImage = Image.new('L', (28, 28), (255)) #creates white canvas of 28x28 pixels\\r\\n \\r\\n if width > height: #check which dimension is bigger\\r\\n #Width is bigger. Width becomes 20 pixels.\\r\\n nheight = int(round((20.0/width*height),0)) #resize height according to ratio width\\r\\n if (nheight == 0): #rare case but minimum is 1 pixel\\r\\n nheight = 1 \\r\\n # resize and sharpen\\r\\n img = im.resize((20,nheight), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\\r\\n wtop = int(round(((28 - nheight)/2),0)) #caculate horizontal pozition\\r\\n newImage.paste(img, (4, wtop)) #paste resized image on white canvas\\r\\n else:\\r\\n #Height is bigger. Heigth becomes 20 pixels. \\r\\n nwidth = int(round((20.0/height*width),0)) #resize width according to ratio height\\r\\n if (nwidth == 0): #rare case but minimum is 1 pixel\\r\\n nwidth = 1\\r\\n # resize and sharpen\\r\\n img = im.resize((nwidth,20), Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\\r\\n wleft = int(round(((28 - nwidth)/2),0)) #caculate vertical pozition\\r\\n newImage.paste(img, (wleft, 4)) #paste resized image on white canvas\\r\\n \\r\\n #newImage.save(\\\"sample.png\\\")\\r\\n\\r\\n tv = list(newImage.getdata()) #get pixel values\\r\\n \\r\\n #normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\\r\\n tva = [ (255-x)*1.0/255.0 for x in tv] \\r\\n #print(tva)\\r\\n return tva\",\n \"def recarregar_imagem(self):\\n self.salvar_imagem(caminho_imagem=self.caminho_temp)\\n self.carregar_imagem(caminho_imagem=self.caminho_temp)\",\n \"def __init__(self, x, y, largo, alto):\\n # Llama al constructor padre\\n pygame.sprite.Sprite.__init__(self)\\n \\n # Construye una pared azul con las dimensiones especificadas por los parametros\\n self.image = pygame.Surface([21, 21])\\n self.image.fill(VERDE)\\n \\n # Establece como origen la esquina superior izquierda.\\n self.rect = self.image.get_rect()\\n self.rect.y = y\\n self.rect.x = x\",\n \"def grass_drass():\",\n \"def esconderfichas(posicion_del_mouse):\\r\\n numerodecuadrado=numerocuadrado(posicion_del_mouse)\\r\\n for j in range(16):\\r\\n if numerodecuadrado==j+1:\\r\\n pygame.draw.rect(ventana,(135,206,250),Totalcuadrados[j].inflate(-10,-10))\",\n \"def image_proc(self):\\r\\n res_erode, res_in_rect = self._get_res_image()\\r\\n\\r\\n Moment_rect = cv2.moments(res_in_rect)\\r\\n if (Moment_rect[\\\"m00\\\"] != 0):\\r\\n self.rect_x = int(Moment_rect[\\\"m10\\\"] / Moment_rect[\\\"m00\\\"])\\r\\n self.rect_y = int(Moment_rect[\\\"m01\\\"] / Moment_rect[\\\"m00\\\"])\\r\\n\\r\\n # afin de trouver les centres de la rectangulaire.\\r\\n cnts, hierarchy = cv2.findContours(res_erode,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\\r\\n for i in cnts:\\r\\n #Moment = cv2.moments(cnts)\\r\\n moment = cv2.moments(res_erode)\\r\\n self.cx = int(moment[\\\"m10\\\"] / moment[\\\"m00\\\"])\\r\\n self.cy = int(moment[\\\"m01\\\"] / moment[\\\"m00\\\"])\\r\\n cv2.circle(output_image,(self.cx,self.cy),7,(255,255,255),-1)\\r\\n cv2.putText(output_image, \\\"center\\\", (self.cx, self.cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\\r\\n #print(cX,cY)\\r\",\n \"def get_pokemon_show(pokemon_img, pokemon = \\\"\\\"): \\n \\n if isinstance(pokemon_img,str):\\n pokemon_img = Image.open(pokemon_img)\\n pokemon_img = pokemon_img.resize((256,256)).convert(\\\"RGBA\\\")\\n \\n# if pokemon_img.mode==\\\"RGBA\\\":\\n pokemon_img = remove_transparency(pokemon_img)\\n# display(pokemon_img)\\n# print(np.array(pokemon_img).min(),np.array(pokemon_img).max())\\n pokedex_number,type_1,type_2,species =\\\\\\n data.loc[data[\\\"name\\\"] == pokemon,[\\\"pokedex_number\\\",\\\"type_1\\\",\\\"type_2\\\",\\\"species\\\"]].values[0]\\n background = Image.open(os.path.join(PATH,f\\\"utils/types/backs/{type_1}.png\\\")).resize((512,512))\\n type_1_img = Image.open(os.path.join(PATH,f\\\"utils/types/cut/{type_1}.png\\\")).resize((134//2,172//2))\\n type_1_img = remove_transparency(type_1_img)\\n background.paste(type_1_img,box = (0,400))\\n\\n if type_2 != \\\"No_type\\\":\\n type_2_img = Image.open(os.path.join(PATH,f\\\"utils/types/cut/{type_2}.png\\\")).resize((134//2,172//2))\\n type_2_img = remove_transparency(type_2_img)\\n background.paste(type_2_img,box = (80,400),mask = 0)\\n \\n background.paste(pokemon_img,(100,100))\\n draw = ImageDraw.Draw(background)\\n font_1 = ImageFont.truetype(font = os.path.join(PATH, \\\"utils/fonts/NIKOLETA.ttf\\\"),size = 40)\\n font_2 = ImageFont.truetype(font = os.path.join(PATH, \\\"utils/fonts/NIKOLETA.ttf\\\"),size = 30)\\n pokemon = \\\" \\\".join(pokemon.split(\\\" \\\")[:2])\\n # draw.text((256, 420), f\\\"{pokedex_number}. {pokemon}\\\",(0,0,0),font=font_1)\\n draw.text((100, 50), f\\\"{pokedex_number}. {pokemon}\\\",(0,0,0),font=font_1)\\n\\n draw.text((256, 420), species,(0,0,0),font=font_2)\\n return background\",\n \"def pyramida(zakladna, orientacia, centrovanie):\\n nova_pyramida = []\\n if orientacia not in [\\\"normalna\\\", 'obratena']:\\n print(\\\"Pyramida moze byt iba [normalna] alebo [obratena]\\\")\\n return False\\n\\n if centrovanie != \\\"center\\\" and centrovanie != \\\"vlavo\\\":\\n print(\\\"Centrovanie pyramidy moze byt iba [center] alebo [vlavo]\\\")\\n return False\\n\\n if centrovanie == \\\"center\\\":\\n if orientacia == \\\"normalna\\\":\\n\\n cislo_riadka = -1\\n for i in range(1, zakladna + 1, 2): #pocet hviezdiciek rastie po 2\\n #print(f\\\"{'*' * i:^{zakladna}}\\\")\\n cislo_riadka +=1\\n riadok = []\\n for j in range(cislo_riadka,zakladna//2): #vyska pyramidy = polovica zakladne\\n riadok.append(\\\" \\\") #kolky riadok, tolko medzier vlavo\\n for j in range(0, i):\\n riadok.append(\\\"*\\\")\\n for j in range(cislo_riadka,zakladna//2): # aj v pravo\\n riadok.append(\\\" \\\")\\n nova_pyramida.append(riadok)\\n else:\\n cislo_riadka = -1\\n for i in range(zakladna, 0, -2): #pocet hviezdiciek\\n #print(f\\\"{'*' * i:^{zakladna}}\\\")\\n cislo_riadka +=1\\n riadok = []\\n for j in range(0,cislo_riadka):\\n riadok.append(\\\" \\\")\\n for j in range(0,i):\\n riadok.append(\\\"*\\\")\\n for j in range(0,cislo_riadka):\\n riadok.append(\\\" \\\")\\n nova_pyramida.append(riadok)\\n else:\\n if orientacia == \\\"normalna\\\":\\n for i in range(zakladna):\\n #print(f\\\"{'*' * (i + 1)}\\\")\\n riadok = []\\n for j in range(0,i):\\n riadok.append(\\\"*\\\")\\n nova_pyramida.append(riadok)\\n else:\\n for i in range(zakladna):\\n riadok = []\\n #print(f\\\"{'*' * (zakladna - i)}\\\")\\n for j in range(zakladna, i, -1):\\n riadok.append(\\\"*\\\")\\n nova_pyramida.append(riadok)\\n return nova_pyramida\",\n \"def pintarIMAGENENMAPA(self, pos):\\n # Agrego al vector que controla las images\\n k = (pos[0], pos[1], \\\"img\\\"+str(self.idIMG))\\n # Si deseo pintar una silla\\n if self.queIMGAgregar == 1:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgSilla, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Si deseo pintar una mesa\\n if self.queIMGAgregar == 2:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgMesa, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Se deseo pintar una nevera\\n if self.queIMGAgregar == 3:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgNevera, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Si deseo pintar una cama\\n if self.queIMGAgregar == 4:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgCama, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Si deseo pintar un sofa\\n if self.queIMGAgregar == 5:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgSofa, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Si deseo pintar tv\\n if self.queIMGAgregar == 6:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgTV, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Si deseo pintar lampara\\n if self.queIMGAgregar == 7:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgLampara, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Si deseo pintar planta\\n if self.queIMGAgregar == 8:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgPlanta, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Si deseo pintar sanitario\\n if self.queIMGAgregar == 9:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgSanitario, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Si deseo pintar lavamanos\\n if self.queIMGAgregar == 10:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgLavamanos, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\\n\\n # Si deseo pintar la ducha\\n if self.queIMGAgregar == 11:\\n self.telaMAPA.create_image(k[0], k[1], image=self.imgDucha, tag=k[2])\\n # Como fue agregado un elemento en el mapa procedo a registrarlo\\n self.cotroladoraIMGREG.append(k)\\n # Como una img fue agregada procedo a aumentar el id\\n self.idIMG = self.idIMG + 1\",\n \"def make_interferometry(data,algorithm=None):\\n # Obteniendo el mapa de coherencia\\n Im = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_10.npy\\\") # Se carga la imagen 10\\n f1 = np.zeros((len(Im),len(Im.T)),dtype=complex)\\n f2 = np.zeros((len(Im),len(Im.T)),dtype=complex)\\n coh = np.zeros((len(Im),len(Im.T)),dtype=complex)\\n\\n for i in range(n_im-1):\\n i += i_o # Valor inicial\\n Im1 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i)+\\\".npy\\\")\\n Im2 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i+1)+\\\".npy\\\")\\n f1 += Im1*Im2.conjugate()\\n f2 += np.sqrt((abs(Im1)**2)*(abs(Im2)**2))\\n\\n coh = abs(f1/f2)\\n\\n # Graficando la coherencia\\n if show:\\n plt.close('all')\\n cmap =\\\"plasma\\\"\\n if algorithm == \\\"BP\\\":\\n title_name ='Mapa de coherencia(BP)\\\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\\n direction ='Coherencia_BP_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Mapa de coherencia(RMA)\\\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\\n direction ='Coherencia_RMA_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\\n\\n fig, ax = plt.subplots()\\n im=ax.imshow(coh,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\\n ax.grid()\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.1) # pad es el espaciado con la grafica principal\\n plt.colorbar(im,cax=cax,label='',extend='both')\\n fig.savefig(os.getcwd()+\\\"/Results/Interferograms_BP/Coherence_maps/\\\"+direction, orientation='landscape')\\n\\n # Sacando una mascara de [0.7,1] para la coherencia\\n mask = coh<=0.7\\n coh[mask] = np.nan\\n\\n # Graficando con la mascara\\n if show:\\n cmap =\\\"plasma\\\"\\n if algorithm == \\\"BP\\\":\\n title_name ='Mapa de coherencia recortada(BP)\\\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\\n direction ='CoherenciaCut_BP_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Mapa de coherencia recortada(RMA)\\\\n[dset'+str(i_o)+'-'+str(i_o+n_im-1)+']'\\n direction ='CoherenciaCut_RMA_dset'+str(i_o)+'-'+str(i_o+n_im-1)+'].png'\\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\\n fig, ax = plt.subplots()\\n im=ax.imshow(coh,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\\n ax.grid()\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.1) # pad es el espaciado con la grafica principal\\n plt.colorbar(im,cax=cax,label='',extend='both')\\n fig.savefig(os.getcwd()+\\\"/Results/Interferograms_BP/Coherence_maps/\\\"+direction, orientation='landscape')\\n\\n # Obteniendo el mapa de desplazamientos, aka, Interferograma\\n par = sp.get_parameters()\\n global c,fc\\n c,fc = par['c'],par['fc']\\n\\n if show:\\n for i in range(n_im-1):\\n # Hallando el interferograma 'i'-esimo\\n i1 = i+10\\n Im1 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i1)+\\\".npy\\\")\\n Im2 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i1+1)+\\\".npy\\\")\\n disp = np.angle(Im1*Im2.conjugate())*1000*c/(4*np.pi*fc) # Distancias en mm\\n disp[mask] = np.nan\\n # Graficando y guardando\\n if algorithm == \\\"BP\\\":\\n title_name ='Interferograma (BP) \\\\ndset'+'['+str(i1)+']-['+str(i1+1)+']'\\n #direction ='Interferogramas_BP/'+'Itf_BP_dset'+'['+str(i+10)+']-['+str(i+11)+'].png'\\n direction ='Itf_BP_dset'+str(i)+'.png'\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Interferograma (RMA) \\\\ndset'+'['+str(i1)+']-['+str(i1+1)+']'\\n #direction ='Interferogramas_RMA/'+'Itf_RMA_dset'+'['+str(i+10)+']-['+str(i+11)+'].png'\\n direction ='Itf_RMA_dset'+str(i)+'.png'\\n cmap = plt.cm.plasma #brg\\n cmap.set_bad('black',1.)\\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\\n fig, ax = plt.subplots()\\n im=ax.imshow(disp,cmap,origin='lower',aspect='equal', extent=[data['x_min'],data['x_max'],data['y_min'],data['y_max']]) #, vmin=vmin, vmax=vmax)\\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\\n ax.grid()\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.1) # pad es el espaciado con la grafica principal\\n plt.colorbar(im,cax=cax,label='desplazamiento(mm)',extend='both')\\n fig.savefig(os.getcwd()+\\\"/Results/Interferograms_BP/Interferograms_complete/Images/\\\"+direction,orientation='landscape')\\n plt.close()\\n\\n # Hallando la curva de distancias vs tiempo\\n #-------Definiendo las zonas--------------\\n zone0 = np.array([0,650,0,400])\\n zone1 = np.array([0,100,100,200])\\n zone2 = np.array([0,100,200,300])\\n zone3 = np.array([100,200,100,200])\\n zone4 = np.array([100,200,200,300])\\n zone5 = np.array([200,300,100,200])\\n zone6 = np.array([200,300,200,300])\\n zone_indexes = {0:zone0,1:zone1,2:zone2,3:zone3,4:zone4,5:zone5,6:zone6}\\n\\n desp = np.zeros((len(zone_indexes),n_im-1)) # Variable y: desplazamiento\\n\\n for z in range(len(zone_indexes)): # Hallando los desplazamientos promedios por zona\\n idc = zone_indexes[z]\\n for i in range(n_im-1):\\n i1 = i+10 # 10 es el valor inicial de las imagenes\\n Im1 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i1)+\\\".npy\\\")\\n Im2 = np.load(os.getcwd()+\\\"/Results/Output_\\\"+algorithm+\\\"/Im_\\\"+str(i1+1)+\\\".npy\\\")\\n d_i = np.angle(Im1*Im2.conjugate())*1000*c/(4*np.pi*fc)\\n # ----- Grafica ------\\n d_i2 = d_i.copy()\\n d_i2[mask] = np.nan\\n d_i2 = d_i2[idc[0]:idc[1],idc[2]:idc[3]]\\n # ----- end --------\\n d_i[mask] = 0\\n d_i = d_i[idc[0]:idc[1],idc[2]:idc[3]]\\n if i==0: desp[z,i] = d_i.mean()\\n else: desp[z,i] = d_i.mean()+desp[z,i-1]\\n\\n # Graficando la zona z-esima\\n if show and i == 0: #\\n if algorithm == \\\"BP\\\":\\n title_name ='Interferograma recortado(BP) \\\\nZona '+str(z)+' - dset'+'['+str(i1)+']-['+str(i1+1)+']'\\n direction ='Itf_BP_rec_zona_'+str(z)+'_dset'+'['+str(i1)+']-['+str(i1+1)+'].png'\\n #direction ='Interferogramas_BP/'+'Itf_BP_dset'+str(i)+'.png'\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Interferograma recortado(RMA) \\\\nZona '+str(z)+' - dset'+'['+str(i1)+']-['+str(i1+1)+']'\\n direction ='Itf_RMA_rec_zona_'+str(z)+'_dset'+'['+str(i1)+']-['+str(i1+1)+'].png'\\n #direction ='Interferogramas_RMA/'+'Itf_RMA_dset'+str(i)+'.png'\\n cmap = plt.cm.plasma #brg\\n cmap.set_bad('black',1.)\\n #vmin = np.amin(20*np.log10(abs(Sf_n)))+55 #dB\\n #vmax = np.amax(20*np.log10(abs(Sf_n)))#-20\\n fig, ax = plt.subplots()\\n im=ax.imshow(d_i2,cmap,origin='lower',aspect='equal', extent=[idc[2]-200,idc[3]-200,idc[0]+300,idc[1]+300]) #, vmin=vmin, vmax=vmax)\\n ax.set(xlabel='Azimut(m)',ylabel='Rango(m)', title=title_name)\\n ax.grid()\\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.1) # pad es el espaciado con la grafica principal\\n plt.colorbar(im,cax=cax,label='desplazamiento(mm)',extend='both')\\n fig.savefig(os.getcwd()+\\\"/Results/Interferograms_BP/Interferograms_complete/Images/\\\"+direction,orientation='landscape')\\n\\n #t = np.arange(len(Ims)-1)+10 # Variable x, tiempo\\n time_dset = np.load(\\\"Dates_BP.npy\\\")\\n time_dset = time_dset[:-1]\\n time_dset2 = np.array([datetime.datetime.strptime(idx,\\\"%d-%m-%y %H:%M:%S\\\") for idx in time_dset])\\n\\n # Grafica de desplazamiento vs tiempo por cada algoritmo\\n \\\"\\\"\\\"if show:\\n if algorithm == \\\"BP\\\":\\n title_name ='Desplazamiento promedio(BP)'\\n direction ='desplazamientoPromedio_BP_dset10-100.png'\\n\\n fig, ax= plt.subplots()\\n ax.plot(t,desp,'b',marker='o',markerfacecolor='b',markeredgecolor='b')\\n ax.set(xlabel='Tiempo(unidades)',ylabel='Desplazamiento(mm)', title=title_name)\\n #ax.set_xlim([R.min(),R.max()])\\n ax.set_ylim([-c*100/(4*fc),c*100/(4*fc)])\\n ax.grid()\\n plt.show()\\n fig.savefig(os.getcwd()+\\\"/Results/Desplazamientos/\\\"+direction,orientation='landscape')\\n\\n elif algorithm == \\\"RMA\\\":\\n title_name ='Desplazamiento promedio(RMA)'\\n direction ='desplazamientoPromedio_RMA_dset10-100.png'\\n\\n fig, ax= plt.subplots()\\n ax.plot(t,desp,'r',marker='o',markerfacecolor='r',markeredgecolor='r')\\n ax.set(xlabel='Tiempo(unidades)',ylabel='Desplazamiento(mm)', title=title_name)\\n #ax.set_xlim([R.min(),R.max()])\\n ax.set_ylim([-c*50/(4*fc),c*50/(4*fc)])\\n ax.grid()\\n plt.show()\\n fig.savefig(os.getcwd()+\\\"/Results/Desplazamientos/\\\"+direction,orientation='landscape')\\n \\\"\\\"\\\"\\n return {'d_t':desp, 't':time_dset2}\",\n \"def imag(self, ???):\",\n \"def arounder(this, **kargs):\\n\\t\\t\\n\\t\\t# Arguments\\n\\t\\tmaxCount = kargs.get('maxCount', 200)\\n\\t\\tmaxArea = kargs.get('maxArea', 10000000)\\n\\t\\tminArea = kargs.get('minArea', 0)\\n\\t\\tmaxDist = kargs.get('maxDist', 0)\\n\\t\\tignore = kargs.get('ignore', (0, 255, 0))\\n\\t\\tcolor = kargs.get('color', (0, 0, 255))\\n\\t\\tthick = kargs.get('thick', 1)\\n\\t\\t\\n\\t\\t# Image binaire issue de la détection\\n\\t\\tbin = this._BINARY.copy()\\n\\t\\tinput = bin.copy()\\n\\t\\t\\n\\t\\t# Modifie l'image de départ T__T\\n\\t\\timage, contours, hierarchy = cv2.findContours(\\n\\t\\t\\tinput,\\n\\t\\t\\tcv2.RETR_LIST,\\n\\t\\t\\tcv2.CHAIN_APPROX_SIMPLE\\n\\t\\t)\\n\\t\\t\\n\\t\\t# Comptation\\n\\t\\tfinger = None\\n\\t\\tcount = len(contours)\\n\\t\\tobjects, ignored = [], []\\n\\t\\tif count < maxCount: #raise Exception('Too much noise, please quiet.')\\n\\t\\t\\t\\n\\t\\t\\t# Filtrage et localisation:\\n\\t\\t\\tfor contour in contours:\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Filtrage des contours selon l'aire\\n\\t\\t\\t\\tarea = cv2.contourArea(contour)\\n\\t\\t\\t\\tif minArea <= area and area <= maxArea:\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# Calcul de la position\\n\\t\\t\\t\\t\\tobj = cv2.convexHull(contour)\\n\\t\\t\\t\\t\\tpoint = limiter(obj, maxDist)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# Est-ce le point le plus bas ?\\n\\t\\t\\t\\t\\tif finger:\\n\\t\\t\\t\\t\\t\\tif finger.y < point.y: finger = point\\n\\t\\t\\t\\t\\telse: finger = point\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t# Enregistrement\\n\\t\\t\\t\\t\\tobjects.append(obj)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Sinon on l'ignore\\n\\t\\t\\t\\telse: ignored.append(contour)\\n\\t\\t\\t\\t\\n\\t\\t\\t### END FOR\\n\\t\\t\\n\\t\\t### END IF\\n\\t\\telse: ignored = contours\\n\\t\\t\\n\\t\\t# On duplique l'image pour le rendu final\\n\\t\\tthis._SCAN = scan = EmptyFrom(bin, 3)\\n\\t\\tscan[:,:,0] = scan[:,:,1] = scan[:,:,2] = bin\\n\\t\\t\\n\\t\\t# Visuel\\n\\t\\tprintf('%d/%d%60s\\\\r' % (len(objects), count, ''))\\n\\t\\tcv2.drawContours(scan, ignored, -1, ignore, 1)\\n\\t\\tcv2.drawContours(scan, objects, -1, color, thick)\\n\\t\\t\\n\\t\\t# Si on a trouvé\\n\\t\\tif finger:\\n\\t\\t\\t\\n\\t\\t\\t# Affichage viseur\\n\\t\\t\\tscan[:, finger.x, :] = [255, 0, 0]\\n\\t\\t\\tscan[finger.y, :, :] = [127, 0, 0]\\n\\t\\t\\t\\n\\t\\t\\t# Calcul de la taille de l'image\\n\\t\\t\\tsize = D2Point(width(bin), height(bin))\\n\\t\\t\\t\\n\\t\\t\\t# Reformatage\\n\\t\\t\\torigin = +finger\\n\\t\\t\\tfinger /= size-1\\n\\t\\t\\tfinger.x = 1 - finger.x\\n\\t\\t\\tthis._BOTTOM = (origin-2).y == (size-4).y\\n\\t\\t\\n\\t\\t# Sinon on arrête de cliquer\\n\\t\\telse: this._BOTTOM = False\\n\\t\\t\\n\\t\\t# On enregistre le truc\\n\\t\\tthis._DETECTED = finger\\n\\t\\t\\n\\t\\treturn pyon(\\n\\t\\t\\tcontours = scan\\n\\t\\t)\",\n \"def showCortexImg(pV,nV):\\n # object arrays of the positive and negative images\\n pos_cort_img = np.empty(8, dtype=object)\\n neg_cort_img = np.empty(8, dtype=object)\\n for t in range(8):\\n # cortical mapping functions\\n lpos, rpos = cortex.cort_img(pV[:,t,:], L, L_loc, R, R_loc, cort_size, G)\\n lneg, rneg = cortex.cort_img(nV[:,t,:], L, L_loc, R, R_loc, cort_size, G)\\n pos_cort_img[t] = np.concatenate((np.rot90(lpos),np.rot90(rpos,k=3)),axis=1)\\n neg_cort_img[t] = np.concatenate((np.rot90(lneg),np.rot90(rneg,k=3)),axis=1)\\n # stack all images into a grid\\n posRGcort = np.vstack((pos_cort_img[:4]))\\n negRGcort = np.vstack((neg_cort_img[:4]))\\n posYBcort = np.vstack((pos_cort_img[4:]))\\n negYBcort = np.vstack((neg_cort_img[4:]))\\n mergecort = np.concatenate((posRGcort,negRGcort,posYBcort,negYBcort),axis=1)\\n return mergecort\",\n \"def __init__(self, nb_fourmis=4, height=300, width=300, liste_fourmis=[]):\\n self.window_name = \\\"Painting ants\\\"\\n self.height = height\\n self.width = width\\n self.image = np.zeros((self.height, self.width, 3), np.uint8)\\n self.image[:] = WHITE\\n self.running = False\\n self.nb_fourmis = nb_fourmis\\n self.fourmis = liste_fourmis\",\n \"def car(img,x, y):\\n gameDisplay.blit(img, (x, y)) # blit display the image\",\n \"def ELA(img_path):\\r\\n \\r\\n TEMP = 'ela_' + 'temp.jpg'\\r\\n SCALE = 10\\r\\n original = Image.open(img_path)\\r\\n try:\\r\\n original.save(TEMP, quality=90)\\r\\n temporary = Image.open(TEMP)\\r\\n diff = ImageChops.difference(original, temporary)\\r\\n except:\\r\\n original.convert('RGB').save(TEMP, quality=90)\\r\\n temporary = Image.open(TEMP)\\r\\n diff = ImageChops.difference(original.convert('RGB'), temporary)\\r\\n \\r\\n d = diff.load()\\r\\n WIDTH, HEIGHT = diff.size\\r\\n for x in range(WIDTH):\\r\\n for y in range(HEIGHT):\\r\\n d[x, y] = tuple(k * SCALE for k in d[x, y])\\r\\n\\r\\n return diff\",\n \"def __init__ (self, id_s = \\\"unknown\\\",rept = 1,tipo = 1, width = 800, height = 600, fps = 30):\\n pygame.init()\\n pygame.display.set_caption(\\\"VIP: BCI\\\")\\n self.tipo = tipo \\n self.width = width\\n self.height = height\\n self.id_s = str(id_s)\\n self.rept = int(rept)\\n self.dimensions = (self.width, self.height)\\n #self.screen = pygame.display.set_mode(self.dimensions, pygame.DOUBLEBUF)\\n self.screen = pygame.display.set_mode(self.dimensions, pygame.FULLSCREEN)\\n self.imagenR= pygame.image.load(\\\"R1.png\\\").convert()\\n self.imagenL= pygame.image.load(\\\"R2.png\\\").convert()\\n self.background = pygame.Surface(self.screen.get_size()).convert()\\n self.screen.fill((255,255,255))#Fondo blanco\\n self.clock = pygame.time.Clock()\\n self.fps = fps\\n self.playtime = 0.0\\n self.font = pygame.font.SysFont('mono', 40, bold=True)\\n size_screen= self.screen.get_size();\\n self.x_center = size_screen[0]/2.0 - 210\\n self.y_center = size_screen[1]/2.0 - 210\",\n \"def main():\\n me = SimpleImage(\\\"images/me.JPG\\\")\\n dinosaur = SimpleImage(\\\"images/dinosaur.jpg\\\")\\n\\n dinosaur.make_as_big_as(me)\\n combine = magic(me, dinosaur)\\n combine.show()\",\n \"def compute_energy(img):\\r\\n # urmati urmatorii pasi:\\r\\n # 1. transformati imagine in grayscale\\r\\n # 2. folositi filtru sobel pentru a calcula gradientul in directia X si Y\\r\\n # 3. calculati magnitudinea imaginii\\r\\n\\r\\n img_gray_scale = cv.cvtColor(img, cv.COLOR_BGR2GRAY);\\r\\n\\r\\n #de cautat totusi si codul pt SOBEL pe net\\r\\n grad_x = cv.Sobel(img_gray_scale, ddepth = cv.CV_16S, dx = 1, dy = 0, borderType = cv.BORDER_CONSTANT)\\r\\n grad_y = cv.Sobel(img_gray_scale, ddepth = cv.CV_16S, dx = 0, dy = 1, borderType = cv.BORDER_CONSTANT)\\r\\n\\r\\n#E repr gradientii aka cat se sch un pixel de la unul la altul\\r\\n E = abs(grad_x) + abs(grad_y)\\r\\n # print(grad_y)\\r\\n # print(grad_x)\\r\\n\\r\\n cv.imwrite(\\\"poza.jpg\\\", E)\\r\\n return E\",\n \"def apply_all(image, courbes, masque):\\n print(\\\"preparation des luts...\\\")\\n print(courbes)\\n lut_r, lut_v, lut_b = create_lut()\\n apply(lut_r, courbes[\\\"courbes\\\"][1])\\n apply(lut_v, courbes[\\\"courbes\\\"][2])\\n apply(lut_b, courbes[\\\"courbes\\\"][3])\\n # l'ordre a été repris sur l'implémentation faite dans le socle\\n # http://gitlab.forge-idi.ign.fr/socle/sd-socle/blob/dev/src/ign/image/radiometry/AcvCurveTransform.cpp\\n # on applique d'abord la courbe du canal, puis la courbe RVB\\n apply(lut_r, courbes[\\\"courbes\\\"][0])\\n apply(lut_v, courbes[\\\"courbes\\\"][0])\\n apply(lut_b, courbes[\\\"courbes\\\"][0])\\n print(\\\"...fait\\\")\\n print(\\\"application des luts...\\\")\\n if masque is not None:\\n a_1 = masque / 255.0\\n a_2 = (-masque + 255) / 255.0\\n image[0] = np.round(\\n np.multiply(a_1, np.take(lut_r, image[0]))\\n + np.multiply(a_2, image[0])\\n )\\n image[1] = np.round(\\n np.multiply(a_1, np.take(lut_v, image[1]))\\n + np.multiply(a_2, image[1])\\n )\\n image[2] = np.round(\\n np.multiply(a_1, np.take(lut_b, image[2]))\\n + np.multiply(a_2, image[2])\\n )\\n else:\\n image[0] = np.take(lut_r, image[0])\\n image[1] = np.take(lut_v, image[1])\\n image[2] = np.take(lut_b, image[2])\\n print(\\\"...fait\\\")\",\n \"def fraktal(dlugosc, alpha, poziom):\\n#obliczanie punktow punktu Abis dla kazdego poziomu galezi\\n x = float(self.p2[0] + self.dlugosc * cos(alpha))\\n y = float(self.p2[1] + self.dlugosc * sin(alpha))\\n return [round(x), round(y)]\",\n \"def __init__(self):\\n # Initialisation de la bibliothèque Pygame\\n pg.init()\\n\\n # Création de l'écran principal de taille 640px x 480px\\n self.screen = pg.display.set_mode((settings.WIDTH, settings.HEIGHT))\\n\\n # Chargement et collage du fond\\n self.background = pg.image.load(settings.BACKGROUND).convert()\\n self.screen.blit(self.background, (0, 0))\\n\\n # Création d'une variable indiquant si le jeu est en cours\\n self.running = False\\n\\n # On termine cette méthode d'initialisation par une mise à jour de \\n # l'écran principal\\n pg.display.update()\",\n \"def __init__(s,i,j,img):\\n pg.sprite.Sprite.__init__(s)\\n s.image = pg.image.load(img)\\n s.rect = s.image.get_rect()\\n\\n # Posição do centro\\n s.cx, s.cy = convert(i,j)\\n # Cor (pode ser passada para o construtor no futuro)\\n s.cor = (0,0,0)\\n\\n # Vértices do hexágono\\n s.pontos = (\\n (s.cx-L/2, s.cy-l),\\n (s.cx+L/2, s.cy-l),\\n (s.cx+L, s.cy),\\n (s.cx+L/2, s.cy+l),\\n (s.cx-L/2, s.cy+l),\\n (s.cx-L, s.cy),\\n )\\n\\n #s.draw();\",\n \"def image(self):\\n return self.any_image(-1)\",\n \"def ubicar_portaviones():\\n tamano = Portaviones.tamano #se importa el tamano del barco desde su clase\\n cantidad = Portaviones.cantidad #se importa la cantidad de barcos de este tamano desde su clase\\n orientacion = orientaciones[(randint(0, 1))] #elige aleatoriamente el index de la tupla orientaciones = (\\\"Vertical\\\", \\\"Horizontal\\\")\\n while cantidad > 0:\\n if orientacion == \\\"Vertical\\\":\\n #se eligen random las filas y las columnas\\n coor_fila = randint(1, numero_filas) \\n coor_columna = randint(1, numero_columnas)\\n while (coor_fila + tamano) > 10: #como su orientacion es vertical la fila incial del barco mas su tamano (3) no puede ser mayor que 10 porque se saldria del mapa\\n coor_fila = randint(1,numero_filas)\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion) #lista donde se ubicaran temporalmente las ubicaciones de los barcos\\n while len(lista_temporal) < tamano: #sacar las posiciones restantes \\n coor_fila += 1\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion)\\n cantidad -= 1\\n elif orientacion == \\\"Horizontal\\\":\\n #se eligen random las filas y las columnas\\n coor_fila = randint(1, numero_filas)\\n coor_columna = randint(1, numero_columnas)\\n while (coor_columna + tamano) > 10: #como su orientacion es horizontal la columna incial del barco mas su tamano (3) no puede ser mayor que 10 porque se saldria del mapa\\n coor_columna = randint(1, numero_columnas)\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion)\\n while len(lista_temporal) < tamano: #sacar las posiciones restantes\\n coor_columna += 1\\n ubicacion = (coor_fila, coor_columna)\\n lista_temporal.append(ubicacion)\\n for x in lista_temporal:\\n lista_ubicacion_barco.append(x) #se agregan las ubicaciones ya validadas a una lista general donde iran todas las posiciones de los barcos\\n coordenadas_portaviones.append(x)\\n lista_temporal.clear() #se limpia la lista para que pueda ser usada en los siguientes barcos\\n cantidad -= 1\",\n \"def create_azi_to_rad_sequence():\\n num_tot = 30\\n for i in range(2*num_tot + 1):\\n angle_arr = azi_to_rad_transformation(512, i, 30)\\n phase_arr = create_flat_phase(512, 0)\\n delta_1_arr = create_delta_1(phase_arr, angle_arr)\\n delta_2_arr = create_delta_2(angle_arr)\\n cv2.imwrite('frame' + str(i) +'.tiff', delta_2_arr)\\n print(\\\"Frame \\\" + str(i))\",\n \"def nitidez(img):\\n kernel = np.array([\\n [0, -1, 0],\\n [-1, 5, -1],\\n [0, -1, 0]\\n ])\\n if img.modo == \\\"L\\\":\\n return convoluir(img, kernel)\\n canais = [\\n filtrar_canal(img, \\\"R\\\"), filtrar_canal(img, \\\"G\\\"), filtrar_canal(img, \\\"B\\\") \\n ]\\n canais = [convoluir(i, kernel) for i in canais]\\n return reunir_canais(*canais)\",\n \"def __init__(self, jugador):\\n\\n # Se llama al metodo del padre constructor.\\n Level.__init__(self, jugador)\\n\\n #Cargamos la imagen de fondo.\\n sprite_sheet_pantalla = SpriteSheet(\\\"imagenes/fondoactualizado.png\\\")\\n \\n # Carga de todos los sprite de la imagen hacia la derecha.\\n imagen_1 = sprite_sheet_pantalla.obtener_imagen(0,1788, 896,894)\\n self.fondo = imagen_1\\n \\n self.fondo.set_colorkey(constantes.BLANCO)\\n self.limite_derecho = 740\\n self.limite_izquierdo = 88\\n self.limite_superior = -10\\n self.limite_inferior = 686\\n self.cambio_nivel_x = 396\\n self.cambio_nivel_y = -16\\n \\n self.fondo.set_colorkey(constantes.BLANCO)\\n self.limite_nivel = -2500\\n\\n # Lista con los bloques de plataformas, indicando la ubicacion x,y y el tipo \\n nivel = [ [platforma.STONE, 250, 740],\\n [platforma.STONE, 250, 680],\\n [platforma.STONE, 250, 620],\\n [platforma.STONE, 250, 560],\\n [platforma.STONE, 250, 500],\\n [platforma.STONE, 250, 440],\\n [platforma.STONE, 250, 380],\\n [platforma.STONE, 250, 320],\\n [platforma.STONE, 310, 320],\\n [platforma.STONE, 370, 320],\\n [platforma.STONE, 430, 320],\\n [platforma.STONE, 490, 320],\\n [platforma.STONE, 550, 320],\\n [platforma.STONE, 610, 320],\\n [platforma.STONE, 610, 380],\\n [platforma.STONE, 610, 440],\\n [platforma.STONE, 610, 500],\\n [platforma.STONE, 610, 560],\\n [platforma.STONE, 610, 620],]\\n \\n #puntos\\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 150\\n puntos.rect.y = 700\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos) \\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 320\\n puntos.rect.y = 390\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 366\\n puntos.rect.y = 390\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 412\\n puntos.rect.y = 390\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 458\\n puntos.rect.y = 390\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 504\\n puntos.rect.y = 390\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 560\\n puntos.rect.y = 390\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 320\\n puntos.rect.y = 467\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 366\\n puntos.rect.y = 467\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 412\\n puntos.rect.y = 467\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 458\\n puntos.rect.y = 467\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 504\\n puntos.rect.y = 467\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 560\\n puntos.rect.y = 467\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 320\\n puntos.rect.y = 544\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 366\\n puntos.rect.y = 544\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 412\\n puntos.rect.y = 544\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 458\\n puntos.rect.y = 544\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 504\\n puntos.rect.y = 544\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 560\\n puntos.rect.y = 544\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 320\\n puntos.rect.y = 621\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 366\\n puntos.rect.y = 621\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 412\\n puntos.rect.y = 621\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 458\\n puntos.rect.y = 621\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 504\\n puntos.rect.y = 621\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n puntos = Estrellas(ESTRELLA)\\n puntos.rect.x = 560\\n puntos.rect.y = 621\\n puntos.jugador = self.jugador\\n puntos.nivel = self\\n self.lista_puntos.add(puntos)\\n \\n \\n \\n \\n\\n # Se busca en la lista anterior creada y se le agregan las plataformas al nivel.\\n for plataforma in nivel:\\n bloque = platforma.Plataforma(plataforma[0])\\n bloque.rect.x = plataforma[1]\\n bloque.rect.y = plataforma[2]\\n bloque.jugador = self.jugador\\n self.lista_plataformas.add(bloque)\",\n \"def car(img, x, y):\\n gameDisplay.blit(img, (x, y)) # blit display the image\",\n \"def get_result_image(self):\\n\\n im_result = cv2.cvtColor(self.im_t, cv2.COLOR_GRAY2BGR)\\n pcd_final = copy.deepcopy(self.pcd_s)\\n\\n pcd_final.transform(self.trans_final)\\n np_final = np.asarray(pcd_final.points, np.int)\\n\\n for i in range(np_final.shape[0]):\\n im_result = cv2.circle(\\n im_result,\\n (np_final[i, 1], np_final[i, 0]),\\n 2,\\n (0, 255, 0),\\n -1,\\n cv2.LINE_AA,\\n )\\n\\n im_result = cv2.rectangle(im_result, (5, 12), (170, 38), (0, 0, 0), -1)\\n # Draw rotation in image\\n _, _, rotate = mat2rpy(self.trans_final)\\n d_rotate = np.degrees(rotate)\\n str_rotate = format(d_rotate, \\\".2f\\\") + \\\"[deg](CW)\\\"\\n im_result = cv2.putText(\\n im_result, str_rotate, (5, 30), 1, 1.25, (255, 255, 255), 2, cv2.LINE_AA\\n )\\n im_result = cv2.putText(\\n im_result, str_rotate, (5, 30), 1, 1.25, (0, 255, 255), 1, cv2.LINE_AA\\n )\\n return im_result\",\n \"def process(self, image):\",\n \"def creditos():\\n playSegundaria()\\n img_creditos = pygame.image.load(\\\"imagenes/creditos.jpg\\\")\\n screen.blit(img_creditos, (0,0))\\n pygame.display.update()\\n time.sleep(8)\\n run()\",\n \"def oceansim(sun_az,sun_zen,cam_head,cam_elev=0,m2=1.33,npart=1.08,mu=3.483, debug=True):\\n\\n #Water surface norm\\n n = np.array([0,0,1])\\n m1 = 1.0\\n #vector from sun:\\n ki = -np.asarray([np.sin(sun_az)*np.sin(sun_zen),\\n np.cos(sun_az)*np.sin(sun_zen),\\n np.cos(sun_zen)])\\n xi = norm_cross(n,ki)\\n #transmitted sunlight\\n #tx, ty are the transmission amplitude coefficients in the xt, yt directions\\n kt,tx,ty = Fresnel.transmission(ki,n,m1,m2)\\n xt = xi\\n #vector to camera\\n kc = -np.asarray([np.sin(cam_head)*np.cos(cam_elev),\\n np.cos(cam_head)*np.cos(cam_elev),\\n np.sin(cam_elev)])*np.linalg.norm(kt)\\n xc = norm_cross(n, kc) #right\\n yc = norm_cross(kc, xc) #up\\n #vectors for scattering\\n ys = norm_cross(kt, kc) # y-axis of scattering event\\n xst = norm_cross(ys, kt) # x-axis of scattering event relative to transmitted sunlight\\n xsc = norm_cross(ys, kc) # x-axis of scattering event relative to camera\\n #Mueller matrices\\n # transmission through water surface:\\n mm1 = Mueller.polarizer(tx,ty)\\n # rotate to scattering plane\\n mm2 = Mrotv(kt,xt,xst)\\n # scatter\\n th_s = vector_angle(kt,kc)\\n #mm3 = Mocean(rad2deg(th_s)) #using Empirical ocean scattering\\n mm3 = Mueller.rayleigh_norm(th_s) #normalized Rayleigh scattering matrix\\n #b = Scattering.bsf_fournier(npart,mu)\\n b = Scattering.vspf_fournier(th_s,npart,mu)\\n # transform to camera's horizontal and up vectors\\n mm4 = Mxform(xsc,ys, xc,yc)\\n #Combined: mm4 . (b*mm3) . mm2 . mm1\\n m = mm4.dot(b*mm3.dot(mm2.dot(mm1)))\\n #stokes vector\\n s = m.dot([1,0,0,0])\\n if debug:\\n return s,m,(ki,xi),(kt,xt,xst),(kc,xc,xsc),(mm1,mm2,mm3,b,mm4)\\n else:\\n return s,m\",\n \"def azizen(self):\\n # x0,y0 array pixel coordinates relative to cx,cy\\n# ndy0,ndx0=img.shape\\n ndy0=self.ndy0\\n ndx0=self.ndx0\\n x0,y0=np.meshgrid(np.linspace(0,ndx0-1,ndx0)-self.cx,np.linspace(0,ndy0-1,ndy0)-self.cy)\\n r0=np.sqrt(x0**2+y0**2)/self.pr0 # fractional radial distance from 0,0\\n# self.roi=np.s_[ystart:ystart+self.ny0,xstart:xstart+self.nx0]\\n # why not model the zenith angle dependence with polynomial directly\\n # rather than linear interpolation between roots.\\n roots=np.zeros(51)\\n rr=np.arange(51)/100.0\\n for i,ref in enumerate(rr):\\n roots[i]=np.real(np.roots([self.c3,0,self.c2,0,self.c1,-ref])[-1])\\n theta0 = np.interp(r0/2,rr,roots)\\n \\n phi0 = np.arctan2(x0,y0) - self.rot ####phi (i.e., azimuth) is reckoned with -pi corresponding to north, increasing clockwise, NOTE: pysolar use sub-standard definition\\n phi0 = phi0%(2*np.pi)\\n\\n #####correction for the tilt of the camera\\n k=np.array((np.sin(self.azm),np.cos(self.azm),0))\\n a=np.array([np.sin(theta0)*np.cos(phi0),np.sin(theta0)*np.sin(phi0),np.cos(theta0)]); \\n a = np.transpose(a,[1,2,0])\\n b=np.cos(self.beta)*a + np.sin(self.beta)*np.cross(k,a,axisb=2) \\\\\\n + np.reshape(np.outer(np.dot(a,k),k),(self.ndy0,self.ndx0,3))*(1-np.cos(self.beta))\\n theta0=np.arctan(np.sqrt(b[:,:,0]**2+b[:,:,1]**2)/b[:,:,2])\\n phi0=np.arctan2(b[:,:,1],b[:,:,0])%(2*np.pi)\\n# max_theta *= deg2rad \\n# valid0 = (theta0<max_theta) & (theta0>0); \\n# theta0[valid0]=np.nan;\\n self.theta0,self.phi0=theta0,phi0\",\n \"def normalise(image):\",\n \"def cambiar_escena(self, escena):\\n\\t\\t# Reemplazo directo\\n\\t\\tself.escena = escena\\n\\t\\t# Reiniciar la ventana con el tamaño de la nueva escena\\n\\t\\tprint(\\\"Iniciando nuevo contexto OpenGL...\\\")\\n\\t\\tv_ancho, v_alto = escena.tam\\n\\t\\topciones = OPENGL | DOUBLEBUF\\n\\t\\tif escena.pant_compl:\\n\\t\\t\\topciones |= FULLSCREEN\\n\\t\\tpygame.display.set_mode((v_ancho, v_alto), opciones)\\n\\t\\t# Título por defecto de la ventana\\n\\t\\tpygame.display.set_caption(escena.nombre)\\n\\t\\t# Reiniciar OpenGL\\n\\t\\tself.gl_ini(v_ancho, v_alto)\\n\\t\\t# Darle los datos del núcleo a la ventana\\n\\t\\tself.escena.nucleo = self\\n\\t\\tself.escena.eventos = self.mapa_eve\\n\\t\\tglClearColor(*escena.color)\\n\\t\\t# Ejecutar la lógica inicial de la escena\\n\\t\\tprint(\\\"Iniciando escena...\\\")\\n\\t\\tself.escena.logica_ini()\",\n \"def aktualisiere(self):\\n if(self.zeichnung == None):\\n self.zeichnung = self.leinwand.create_image(self.x, self.y, \\n image=self.grafik)\\n delta_x = self.zielX - self.x\\n delta_y = self.zielY - self.y\\n self.leinwand.move(self.zeichnung, delta_x, delta_y)\\n\\n self.x = self.zielX\\n self.y = self.zielY\",\n \"def template_zone(self):\\n ratio = 32\\n shape = [self.cam.height, self.cam.width]\\n img = self.cam.frame[shape[0] / 2 - 3 * shape[0] / ratio:shape[0] / 2 + 3 * shape[0] / ratio,\\n shape[1] / 2 - 3 * shape[1] / ratio:shape[1] / 2 + 3 * shape[1] / ratio]\\n return img\",\n \"def get_result_image(self):\\n\\n im_result = cv2.cvtColor(self.im_t, cv2.COLOR_GRAY2BGR)\\n pcd_final = copy.deepcopy(self.pcd_s[self.result_id])\\n\\n pcd_final.transform(self.trans_final)\\n np_final = np.asarray(pcd_final.points, np.int)\\n for i in range(np_final.shape[0]):\\n im_result = cv2.circle(\\n im_result,\\n (np_final[i, 1], np_final[i, 0]),\\n 1,\\n (0, 255, 0),\\n -1,\\n cv2.LINE_AA,\\n )\\n\\n im_result = cv2.rectangle(im_result, (5, 12), (170, 38), (0, 0, 0), -1)\\n # Draw rotation in image\\n d_rotate = np.degrees(self.rotate)\\n str_rotate = format(d_rotate, \\\".2f\\\") + \\\"[deg](CCW)\\\"\\n im_result = cv2.putText(\\n im_result, str_rotate, (5, 30), 1, 1.25, (255, 255, 255), 2, cv2.LINE_AA\\n )\\n im_result = cv2.putText(\\n im_result, str_rotate, (5, 30), 1, 1.25, (0, 255, 255), 1, cv2.LINE_AA\\n )\\n return im_result\",\n \"async def olá(self):\\r\\n\\t\\tawait self.client.say('© Maddie 2017')\\r\\n\\t\\te = Embed()\\r\\n\\t\\te.set_image(url='https://cdn.discovery.pgsitecore.com/en-us/-/media/Olay_PathFinder/Images/a/OLAY%20TE%207IN1%20DEEP%20PENETRATING%20MOISTURE%20BODY%20WASH_Front.png?w=460&v=1-201705260605')\\r\\n\\t\\tawait self.client.say(embed=e)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6379464","0.6296582","0.62169796","0.6204397","0.61585575","0.6110433","0.60860527","0.60647374","0.5987925","0.5917548","0.5876961","0.58695006","0.58384454","0.58236486","0.5822561","0.58053076","0.5804754","0.57964826","0.57868594","0.57860816","0.57762027","0.576677","0.57580197","0.5755395","0.57536405","0.5745029","0.57279575","0.57233256","0.5715148","0.56862","0.56838816","0.5675507","0.567211","0.5671618","0.5671032","0.56665105","0.56608164","0.56368804","0.5633101","0.5633101","0.5611332","0.56064093","0.55998224","0.559716","0.55923045","0.5589944","0.5571761","0.55626535","0.5559513","0.55592406","0.5551","0.5550592","0.5549012","0.55437875","0.55397487","0.5530361","0.55249816","0.5520715","0.55176926","0.5515114","0.5505683","0.550124","0.54976696","0.5487034","0.5486872","0.5484091","0.5464181","0.5462954","0.5460461","0.54593587","0.5439847","0.5429997","0.54278636","0.542646","0.542297","0.5422955","0.5421053","0.5418986","0.541761","0.5411576","0.538788","0.53865016","0.53841776","0.53828377","0.5381305","0.5380436","0.5376842","0.5375363","0.5371835","0.5370697","0.53699785","0.5369365","0.535959","0.53589934","0.5358655","0.53524786","0.5348348","0.53473365","0.534633","0.5345467","0.5343519"],"string":"[\n \"0.6379464\",\n \"0.6296582\",\n \"0.62169796\",\n \"0.6204397\",\n \"0.61585575\",\n \"0.6110433\",\n \"0.60860527\",\n \"0.60647374\",\n \"0.5987925\",\n \"0.5917548\",\n \"0.5876961\",\n \"0.58695006\",\n \"0.58384454\",\n \"0.58236486\",\n \"0.5822561\",\n \"0.58053076\",\n \"0.5804754\",\n \"0.57964826\",\n \"0.57868594\",\n \"0.57860816\",\n \"0.57762027\",\n \"0.576677\",\n \"0.57580197\",\n \"0.5755395\",\n \"0.57536405\",\n \"0.5745029\",\n \"0.57279575\",\n \"0.57233256\",\n \"0.5715148\",\n \"0.56862\",\n \"0.56838816\",\n \"0.5675507\",\n \"0.567211\",\n \"0.5671618\",\n \"0.5671032\",\n \"0.56665105\",\n \"0.56608164\",\n \"0.56368804\",\n \"0.5633101\",\n \"0.5633101\",\n \"0.5611332\",\n \"0.56064093\",\n \"0.55998224\",\n \"0.559716\",\n \"0.55923045\",\n \"0.5589944\",\n \"0.5571761\",\n \"0.55626535\",\n \"0.5559513\",\n \"0.55592406\",\n \"0.5551\",\n \"0.5550592\",\n \"0.5549012\",\n \"0.55437875\",\n \"0.55397487\",\n \"0.5530361\",\n \"0.55249816\",\n \"0.5520715\",\n \"0.55176926\",\n \"0.5515114\",\n \"0.5505683\",\n \"0.550124\",\n \"0.54976696\",\n \"0.5487034\",\n \"0.5486872\",\n \"0.5484091\",\n \"0.5464181\",\n \"0.5462954\",\n \"0.5460461\",\n \"0.54593587\",\n \"0.5439847\",\n \"0.5429997\",\n \"0.54278636\",\n \"0.542646\",\n \"0.542297\",\n \"0.5422955\",\n \"0.5421053\",\n \"0.5418986\",\n \"0.541761\",\n \"0.5411576\",\n \"0.538788\",\n \"0.53865016\",\n \"0.53841776\",\n \"0.53828377\",\n \"0.5381305\",\n \"0.5380436\",\n \"0.5376842\",\n \"0.5375363\",\n \"0.5371835\",\n \"0.5370697\",\n \"0.53699785\",\n \"0.5369365\",\n \"0.535959\",\n \"0.53589934\",\n \"0.5358655\",\n \"0.53524786\",\n \"0.5348348\",\n \"0.53473365\",\n \"0.534633\",\n \"0.5345467\",\n \"0.5343519\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77096,"cells":{"query":{"kind":"string","value":"Image live view when not recording"},"document":{"kind":"string","value":"def liveview(self):\n if self.liveviewButton.isChecked():\n self.save = False\n self.channelsOpen()\n self.liveviewStart()\n\n else:\n self.liveviewStop()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def capture_image(self):\n ext = self.image_save_type.lower()\n\n if self.calibrating:\n print('calibrating')\n\n if ext == 'fits':\n self.save_fits()\n self._image_counter += 1\n else:\n img = self.original_image\n path = os.path.join(self.home, 'data')\n name = \"camtrak_frame_{}.png\".format(self._image_counter) \n fn = os.path.join(path, name)\n cv2.imwrite(fn, img)\n\n QtWidgets.QApplication.beep()\n self.statusBar().showMessage(f'Saved image to {fn}')\n self._image_counter += 1","def capture(self):\n current_time=time.strftime('%Y%m%d-%H%M%S')\n self.filepath=f\"files/{current_time}.png\"\n self.ids.camera.export_to_png(self.filepath)\n self.manager.current='image_screen'\n self.manager.current_screen.ids.img.source=self.filepath","def on_image(self, image):","def image_cb(self, msg):\n self.has_image = True\n self.camera_image = msg","def capture_image(self, data={}):\n # call self.increment_count() after each image saved\n pass","def image_cb(self, msg):\n self.has_image = True\n self.camera_image_msg = msg","def image_cb(self, msg): # incoming image\n self.has_image = True\n self.camera_image = msg","def image_capture_demo():\n return render_template('image_capture_demo.html')","def take_picture(self):\n self.drone.take_picture()","def take_picture(self):\n self.drone.take_picture()","def take_photo(self):\n\n status = self.camera.status()\n if status['mode'] != 'still':\n # place camera in snapshot mode\n self.camera.command('mode', 'still')\n\n photo_successful = self.camera.command('record', 'on')\n\n if photo_successful:\n\n # sleep for two seconds so the camera can process\n # and serve the new photo via http\n\n retrieved = False\n while not retrieved:\n print(\"Waiting for image to be served.\")\n time.sleep(2)\n retrieved = self.get_photos_from_device()\n\n print(\"Image got served.\")\n return True\n\n else:\n return False","def viewUpdate(self):\n # Update Capture\n imgtk = self.model.capture\n self.updateImage(self.view.lmain, imgtk)\n # Update Stitch \n imgtk = self.model.stitch\n self.updateImage(self.view.rmain, imgtk)\n self.view.dist.set(self.model.dist)","def image_cb(self, msg):\n self.has_image = True\n self.camera_image = msg\n self.get_light_state()","def capture_image(self, data={}):\n if self.camera:\n image_name = f'{os.path.join(self.path, self.filename)}.jpg'\n self.camera.capture(image_name)\n self.last_image = os.path.abspath(image_name)\n self.increment_count()\n self.fire({'event': 'ImageCaptured', 'image': image_name})","def capture(self):\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n self.vid.grab()\n ret, img = self.vid.read()\n cv2.imwrite(\"/tmp/test.png\", img)\n return img","def liveview(self):\n if self.liveviewButton.isChecked():\n# self.save = False\n self.paramChangedInitialize()\n self.openShutter(\"red\")\n self.liveviewStart()\n\n else:\n self.liveviewStop()","def capture(self):\n current_time = time.strftime('%Y%m%d-%H%M%S')\n filepath = f'files/{current_time}.png'\n self.ids.camera.export_to_png(filepath)\n self.manager.current = 'image_screen' # switch to the next screen\n self.manager.current_screen.ids.img.source = filepath # inherit img to the next screen\n return filepath","def img_cb(self, image):\n self.last_img = image\n self.is_new_img = True","def capture(self):\n\n self.dpar.n_caps += 1\n self.dpar.caps_saved = False\n self.cap_screen.cap_loaded = True\n tstamp = datetime.now()\n\n self.dpar.cur_cap = self.dpar.n_caps\n self.dpar.iwindow.append(list(self.dpar.iwindow[0])) # deep copy\n self.dpar.frame_timestamp.append(tstamp)\n\n self.cap_scrollbar.setRange(1, self.dpar.n_caps)\n self.cap_scrollbar.setValue(self.dpar.n_caps)\n\n if self.config.sound_on_capture:\n QtMultimedia.QSound.play(CAMERA_CLICK_FILE)\n\n # Always write to file (3/2018)\n \"\"\"\n Write the capture to a file. Create the daily capture directory if not yet created.\n \"\"\"\n\n #im = Image.fromarray(d, mode='F') # float32\n #im.save(\"test2.tiff\", \"TIFF\")\n # https://gist.github.com/ax3l/5781ce80b19d7df3f549#pillow\n\n \"\"\"\n Note that despite lack of documenation, this does save as 16-bit gray-scale image. Open in Photoshop to\n confirm. Irfan converts to 8 bpp upon opening ans scales pixels\n \"\"\"\n cfn = self._get_cap_filename()\n cap_image = np.copy(self.dpar.latest_frame).astype(np.uint16)\n im = PIL.Image.fromarray((cap_image << (16 - self.camera.pixel_bits)).astype(np.uint16))\n im.save(cfn, 'TIFF')\n\n \"\"\" \n cap_image = np.copy(self.dpar.latest_frame).astype(np.uint16)\n cv2.imwrite(cfn, (cap_image << (16 - self.camera.pixel_bits)).astype(np.uint16))\n \"\"\"\n\n et = np.int(np.round(self.camera.actual_exposure_time_ms))\n fn = os.path.basename(cfn)\n\n self.write_to_log('%d\\t%s' % (et, fn))\n\n self.swap_button.setEnabled(True)\n self.update_cap_image()","def display_current_view(tracker):\n image = tracker.get_video_frame()\n cv2.imshow(\"img\", image)\n cv2.waitKey(0)\n cv2.destroyAllWindows()","def record(self):\n\t\twhile True:\n\t\t\tif not self.recording:\n\t\t\t\tbreak\n\t\t\t#print('hal')\n\t\t\telapsed = (datetime.datetime.now() - self.timestamps[-1]).total_seconds()\n\t\t\tif elapsed < self.timePerFrame:\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\t#print(len(self.Video))\n\t\t\t\tret,frame = self.vs.read()\n\t\t\t\tif ret:\n\t\t\t\t\tself.timestamps.append(datetime.datetime.now())\n\t\t\t\t\tself.Video.append(frame)\n\t\t\t\t\tself.FPStracker.update()\n\t\t\t\t\tself.newAvailable = True\n\t\t\t\t\tif not self.threaded:\n\n\t\t\t\t\t\treturn\n\t\t\t\telse:\n\t\t\t\t\tprint('error: camera failed to capture image')\n\t\t\t\t\tprint('canceling recording session')\n\t\t\t\t\tself.stop()\n\t\t#print('\\n recording loop ended, returning to main')\n\t\tself.vs.stop()\n\t\treturn","def live():\n m = camera.status.mode\n print \"Hit ^C to exit.\"\n print \"NOTE! After using this command, type: mode('%s') \" % m\n mode('centre')\n try:\n while True:\n f = camera.GetFits()\n camera.status.update()\n setheaders(f)\n camera.status.lastact = time.time() #Record the time that the last image was taken\n xpa.displayimage(f)\n except KeyboardInterrupt:\n logger.error(\"Live mode aborted, dumping image.\")\n finally:\n mode(m) #Restore original camera mode (hopefully)","def get_image():\n\n # Access the global variable and activate the saving for the last camera's\n # frame\n global _save_image\n _save_image = True","def saveimage(self):\n if self.saveimageButton.isChecked():\n self.save = True\n self.channelsOpen()\n self.movetoStart()\n self.saveimageButton.setText('Abort')\n self.guarda = np.zeros((self.numberofPixels, self.numberofPixels))\n self.liveviewStart()\n\n else:\n self.save = False\n print(\"Abort\")\n self.saveimageButton.setText('reintentar Scan and Stop')\n self.liveviewStop()","def image_cb(self, msg):\n\n # Save the camera image\n self.camera_image = msg\n\n # I sufficient information is available...\n if not None in (self.camera_image, self.waypoint_tree, self.lights):\n\n # Find index and color state of next light\n light_wp, state = self.process_traffic_lights()\n\n # If the light is green...\n if state == TrafficLight.GREEN:\n\n # Publish sentinel indicatig no red light\n self.upcoming_red_light_pub.publish(Int32(-1))\n\n else:\n\n # Publish the traffic light index\n self.upcoming_red_light_pub.publish(Int32(light_wp))","def preview(self,*args,**kwargs):\n self.cam.start_preview(*args,**kwargs)","def show(self):\n if self.video:\n self.video.write(self.img)\n cv2.imshow('Simpy', self.img)\n cv2.waitKey(1000 // self.fps)","def take_pic(self):\n \n try:\n if os.path.isfile(self.pics_path):\n os.remove(self.pics_path)\n self.camera.capture(self.pics_path, use_video_port=True)\n except:\n print(\"Error when recording image\")\n exit()\n \n try:\n image = open(self.pics_path, 'rb')\n img_bytes = image.read()\n size = len(img_bytes)\n self.img_buffer.append((size, img_bytes))\n image.close()\n except:\n print(\"Error when opening image\")\n exit()","def timerEvent(self, event):\n\n if int(time.time()) - self.start_time > self.capture_duration:\n self.stop_recording()\n\n recording, data = self.camera.read()\n if recording:\n self.image_data.emit(data)","def take_still(output_path):\n camera = PiCamera()\n camera.start_preview()\n\n sleep(3)\n camera.capture(\"test.jpg\")\n camera.stop_preview()","def raw_image_callback(self, msg):\n if self.pictures_to_take and not self.detection_to_receive:\n self.pictures_to_take -= 1\n # so let's analyse it here and then delete the subscription\n rows = msg.height\n step = msg.step\n cols = msg.width\n dim = int(step / cols)\n pixels = msg.data # of size (steps, nrows)\n # save the image (later we will need to analyse it)\n vision_utils.save_picture(pixels, rows, cols, dim, self.name, FOLDER)","def camera_image(self):\n if not self.ezvizService.switchState:\n return \"\"\n\n now = time.time()\n if now < self._last_snapshot_time + self._interval_snapshots:\n return self._last_image\n\n result = self.ezvizService.post('/lapp/device/capture', data={'deviceSerial':self.deviceSerial,'channelNo':1})\n if (result['code']!='200'):\n _LOGGER.error(\"EZVIZ capture image fail:%s\", result)\n return self._last_image\n\n image_path = result['data']['picUrl']\n try:\n response = requests.get(image_path)\n except requests.exceptions.RequestException as error:\n _LOGGER.error(\"EZVIZ getting camera image: %s\", error)\n return self._last_image\n\n self._last_snapshot_time = now\n self._last_image = response.content\n return self._last_image","def capture_image():\n global img_tk\n r, img_cam = cam.read()\n img_pil = Image.fromarray(cv2.cvtColor(img_cam, cv2.COLOR_BGR2RGB))\n img_tk = ImageTk.PhotoImage(img_pil)\n tk_cam.create_image(0, 0, image=img_tk, anchor='nw')\n return img_pil","def ImageEdits(self, cv_img):\n\t\tself.capture = cv_img[110:370,210:430]\n\t\tcv_img = cv_img[110:370,210:430].copy()\n\t\t\n\t\t#draw lines\n\t\tcv2.line(cv_img,(22,26),(80,26),(11,243,50),3)\n\t\tcv2.line(cv_img,(22,26),(22,84),(11,243,50),3)\n\t\tcv2.line(cv_img,(140,26),(198,26),(11,243,50),3)\n\t\tcv2.line(cv_img,(198,26),(198,84),(11,243,50),3)\n\t\tcv2.line(cv_img,(22,176),(22,234),(11,243,50),3)\n\t\tcv2.line(cv_img,(22,234),(80,234),(11,243,50),3)\n\t\tcv2.line(cv_img,(198,176),(198,234),(11,243,50),3)\n\t\tcv2.line(cv_img,(140,234),(198,234),(11,243,50),3)\n\n\t\tif not self.on_detection:\n\t\t\tcv2.putText(cv_img, \"Press Start\", (35, 130), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 255), 2)\n\n\t\t\n\t\t#detect face\n\t\tif 1 <= len(self.pics_time) < 6 and not self.detection_done:\n\t\t\tdetected, (x,y,w,h) = self.detector.FaceDetect(cv_img)\n\t\t\tif detected:\n\t\t\t\ttime_now = time.time()\n\t\t\t\tperecedent_time = self.pics_time[len(self.pics_time)-1]\n\t\t\t\tif len(self.pics_ls) <= 5 and time_now - perecedent_time > 0.5:\n\t\t\t\t\tself.pics_ls.append(self.capture)\n\t\t\t\t\tself.pics_time.append(time.time())\n\t\t\t\tcv2.rectangle(cv_img, (x,y), (x+w,y+h), (0,255,0), 2)\n\t\telif len(self.pics_ls) == 5 and not self.detection_done:\n\t\t\tself.Recognize()\n\t\t\tself.detection_done = True\n\n\t\treturn self.Convert_CV_QT(cv_img)","def preview_stream(self):\n pass","def guest_capture_func(self, pic_save, pic_display):\n capture_time_curr = time.time()\n (pic_height, pic_width) = pic_display.shape[:2]\n image_blob = cv2.dnn.blobFromImage(\n cv2.resize(pic_display, (300, 300)),\n 1.0,\n (300, 300),\n self.trainRBGavg,\n swapRB=False,\n crop=False)\n\n # Use previously loaded face detector on the blob\n self.detector.setInput(image_blob)\n detections = self.detector.forward()\n\n # Loop over detected faces\n for i in range(0, detections.shape[2]):\n curr_conf = detections[0, 0, i, 2]\n if curr_conf > self.min_detec_conf:\n bound_box = detections[0, 0, i, 3:7] * np.array([pic_width,\n pic_height,\n pic_width,\n pic_height])\n (x_start, y_start, x_end, y_end) = bound_box.astype(\"int\")\n # Only detect faces fully in the frame\n if x_start < 0 or y_start < 0:\n continue\n if x_end > self.image_width or y_end > self.image_width:\n continue\n # Draw face bounding box\n cv2.rectangle(pic_display,\n (x_start, y_start),\n (x_end, y_end),\n (0, 255, 0),\n 2)\n\n elap_seconds = capture_time_curr - self.capture_time_prev\n if elap_seconds >= self.max_capture_interval and \\\n np.any(detections[0, 0, :, 2] > self.min_detec_conf):\n # Capture image and save to file\n pn_pic = os.path.sep.join([self.pn_gstcap_out, \"{}.png\".format(\n str(self.pic_num).zfill(5))])\n self.save_pic_archive(pn_pic, pic_save)\n self.pic_num += 1\n self.capture_time_prev = capture_time_curr\n print(\"[INFO] Saved image {}.\".format(self.pic_num))\n\n if capture_time_curr - self.start_time > self.max_capture_length:\n print('[INFO] {} seconds elapsed, completed capturing images.'\n .format(self.max_capture_length))\n self.gst_capture = False\n\n if self.pic_num >= self.max_images:\n print('[INFO] Max of {} images captured, completed capturing images.'\n .format(self.max_images))\n self.gst_capture = False\n\n return pic_display","def _update_frame(self):\n # check if continue\n if self._keep_updating:\n self.__frame = self._cam.get_display_frame()\n if self.__frame is not None:\n self._cvn_camera_viewfinder.create_image(0, 0, image=self.__frame, anchor=tk.NW)\n\n self._root.after(self._delay, self._update_frame)","def capture_photo(self, count):\n output_file_name = generate_image_filename()\n save_file_path = os.path.join(self.output_dir, output_file_name)\n\n self.take_photo(save_file_path)\n check_image_useful(save_file_path)","def preview(self):\n win_name = 'RTSP'\n cv2.namedWindow(win_name, cv2.WINDOW_AUTOSIZE)\n cv2.moveWindow(win_name,20,20)\n self.open()\n while(self.isOpened()):\n cv2.imshow(win_name,self._stream.read()[1])\n #if self._latest is not None:\n # cv2.imshow(win_name,self._latest)\n if cv2.waitKey(25) & 0xFF == ord('q'):\n break\n cv2.waitKey()\n cv2.destroyAllWindows()\n cv2.waitKey()","def preview_image(instance, *args, **kwargs):\n if not ENABLE_PREVIEW_IMAGES:\n return\n changed_fields = instance.field_tracker.changed()\n\n if len(changed_fields) > 0:\n generate_site_preview_image_task.delay()","def captureVideoFrame(self):\n if(self.kinectConnected):\n self.currentVideoFrame = freenect.sync_get_video()[0]\n else:\n self.loadVideoFrame()\n #self.processVideoFrame()","def grab_image(self):\n _, camera_image = self.camera.read()\n with self.lock:\n self.image = camera_image","def play(self, frame):\n cv2.imshow(\"view\", frame)\n return 0","def start_camera(self):\n # create the video capture thread\n self.thread = VideoThread()\n # connect its signal to the update_image slot\n self.thread.change_pixmap_signal.connect(self.update_image)\n # start the thread\n self.thread.start()","def __check_picture(self):\n if self.communications.get_picture():\n self.communications.set_status(\"Taking Picture\")\n try:\n if self.__video_status[\"in_use\"]:\n self.__live_video_stream(False) # Turn off Video Live Stream\n sleep(13)\n image_path = self.camera.take_picture(3)\n self.__live_video_stream(True) # Turn Video Stream back on.\n else:\n image_path = self.camera.take_picture(3)\n if image_path is not None:\n image_url = self.communications.upload_image(image_path)\n if image_url:\n self.communications.add_event(\"Image Capture Successful\", \"success\")\n except ValueError:\n self.communications.add_event(\"Could not capture image, try again\", \"error\")","def __take_snapshot(self):\n # Set the filename to\n # year_month_date_hour_minute_second.jpg\n time_label = datetime.datetime.now()\n filename = f\"{time_label.strftime('%Y-%m-%d_%H-%M-%S')}.jpg\"\n\n # Start the saving function in background\n save_img_back = threading.Thread(target=self.__upload,\n args=(filename,),\n daemon=True)\n save_img_back.start()","def recorder_snapshot(recorder_name, width=None, height=None, selected_name=None):\n recorder = get_recorder_by_name(recorder_name)\n # get snapshot name for the recorder\n if selected_name is None:\n filename = cnfg.recorders[recorder_name].filename_snapshot()\n else:\n snapshot_path = os.path.dirname(os.path.abspath(cnfg.recorders[recorder_name].filename_snapshot()))\n filename = os.path.join(snapshot_path, selected_name+'.jpg')\n if os.path.isfile(filename):\n height = int(height)\n width = int(width)\n img = cv2.imread(filename)\n # resize image and preserve aspect ratio\n orig_height, orig_width, _ = img.shape\n if orig_height > height:\n scale_height = height / orig_height\n else:\n scale_height = 1\n if orig_width > width:\n scale_width = width / orig_width\n else:\n scale_width = 1\n scale = min(scale_height, scale_width) \n if scale < 1:\n height = math.floor(orig_height*scale)\n width = math.floor(orig_width*scale)\n img = cv2.resize(img, (width, height)) \n # make darker if snapshot was not updated\n if not recorder.status in ['started'] and not recorder.watcher:\n a = np.double(img)\n b = a * 0.2\n img = np.uint8(b)\n # encode to jpeg image\n _, img_jpg = cv2.imencode('.jpg', img)\n response = make_response(img_jpg.tostring())\n response.headers.set('Content-Type', 'image/jpeg')\n return response\n else:\n return send_file('templates/static/nosnapshot.gif')","def cameraTB1Checked(self, state):\n if state == QtCore.Qt.Checked:\n print('Show Camera TB1 Selected')\n # release video capture\n self.cap = cv2.VideoCapture(0)\n # # read image in BGR format\n # ret, img = self.cap.read()\n # image = QtGui.QImage(img, img.shape[1], img.shape[0],\n # img.shape[1] * img.shape[2],\n # QtGui.QImage.Format_RGB888)\n pixmapCamera = QtGui.QPixmap()\n pixmapCamera.convertFromImage(tb1Subscriber.callback.rgb_image())\n self.simulationWidget.setPixmap(pixmapCamera)\n else:\n print('Show Camera TB1 Unchecked')\n # self.cap.release()","def take_photo(self):\n self.photo = self.frame\n self.send_photo_to_model.emit(self.photo)","def preview_camera(self):\n self.statusbar.clearMessage()\n self.statusbar.showMessage('Previewing the camera. Press the Enter key to exit.')\n self.buttonPreview.setText('Press Enter\\nto finish.')\n self.comboCamera.setEnabled(False)\n self.buttonSelectColor.setEnabled(False)\n self.buttonLogSet.setEnabled(False)\n cap = webcam.initiate_camera(self.comboCamera.currentIndex())\n while True:\n _, frame = cap.read()\n frame = np.rot90(frame, self.comboRotation.currentIndex())\n cv2.imshow('Camera Preview', frame)\n key = cv2.waitKey(1) & 0xFF\n if key == ord('\\r'):\n break\n cap.release()\n cv2.destroyAllWindows()\n self.buttonPreview.setText('Preview')\n self.comboCamera.setEnabled(True)\n self.buttonSelectColor.setEnabled(True)\n self.buttonLogSet.setEnabled(True)\n self.statusbar.clearMessage()","def cameraCallback(self, data):\n if not self.isReady:\n cvImage, self.imageInfo['shape'] = u.getCVImage(data)\n if self.measuring is not None:\n self.list, cvImage, self.isReady = self.measuring.getListObjects(cvImage)\n # preview topic /see_main\n msg_image = u.getMsgImage(cvImage)\n self.pub_view_main.publish(msg_image)\n else:\n if self.imageInfo['shape'] is not None:\n self.init()\n else:\n rospy.logerr(\"no video stream. check camera's topic!\")","def update(self):\n if self.mudpi.is_prepared:\n if self.duration > self.delay.total_seconds():\n if self.record_video:\n self.capture_recording(duration=self.record_duration)\n else:\n self.capture_image()\n self.reset_duration()","def show_img(self):\n if self.image is not None:\n cv2.imshow(self.image_window, self.image)\n cv2.waitKey(1)\n else:\n rospy.loginfo(\"No image to show yet\")","def capture(self):\n with picamera.PiCamera() as camera:\n # camera setup\n camera.resolution = (frame_width, frame_height)\n camera.framerate = 32\n camera.rotation = 90\n stream = PiRGBArray(camera, size=(frame_width, frame_height))\n\n # let camera warm up\n time.sleep(1)\n avg = None\n\n prev_area = 0\n upload_cnt = 0\n upload_threshold = 75\n motion_frames = []\n frame_cnt = 0\n\n start_time = time.time()\n\n print 'Ready'\n for frame in camera.capture_continuous(stream, 'bgr',\n use_video_port=True):\n\n stream.seek(0)\n image = frame.array\n\n if avg is None:\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n gray = cv2.GaussianBlur(gray, tuple(blur_size), 0)\n avg = gray.copy().astype(\"float\")\n stream.truncate()\n continue\n\n (contours, avg, gray, image) = motion_detect(image, avg)\n\n #print contours\n if isinstance(contours, tuple):\n contours = list(list(contours))\n if len(contours) > 0 and (time.time() - start_time) > 20:\n if upload_cnt < upload_threshold:\n print len(contours)\n print str(datetime.datetime.now())\n sys.stdout.flush()\n time_str = get_time()\n output_filename = path + 'img_' + time_str + '-' + str(frame_cnt) + '.jpg'\n if frame_cnt % 3 == 0:\n motion_frames.append((output_filename, gray, image))\n upload_cnt += 1\n frame_cnt += 1\n else:\n upload_cnt = 0\n if motion_frames:\n if len(motion_frames) > 1:\n self.append_frames(motion_frames)\n motion_frames = []\n frame_cnt = 0\n\n stream.seek(0)\n stream.truncate()","def captureVideoFrame(self):\n if(self.kinectConnected):\n self.currentVideoFrame = freenect.sync_get_video()[0]\n else:\n self.loadVideoFrame()\n self.processVideoFrame()","def take_picture():\n\n #This reads the data from the webcam\n ret, frame = vid.read() \n \n #This writes the image to the unknown directory\n cv2.imwrite('/Users/srikarkarra/Downloads/Important Stuff/Coding/facial_rec/unknown/unknown.jpg', frame)","def OnNewImage(self, event):\r\n #print sys._getframe().f_code.co_name\r\n\r\n self.eventLock = event.eventLock\r\n\r\n if not self.pause:\r\n self.UpdateImage(event.img)\r\n self.ReleaseEventLock()\r\n if event.oldImageLock:\r\n if event.oldImageLock.locked():\r\n event.oldImageLock.release()","def capture(self):\n log.info('Image: %d' % self.idx)\n img = cv.imread(self.files[self.idx])\n self.idx = (self.idx + 1) % len(self.files)\n\n img = cv.cvtColor(img, cv.COLOR_BGR2RGB)\n return img","def _display_img(self):\n if self._file_path is None:\n Debug.printi(\"No picture has been loaded to preview\", Debug.Level.ERROR)\n return\n photo = self._open_img(self._file_path)\n ImageViewDialog(self._parent, self._file_name, photo)","def show_picture(self, data):\n raise NotImplementedError","def update_image(self, cv_img):\n qt_img = self.convert_cv_qt(cv_img)\n if(self.iscapture):\n print(\"update\")\n direct = self.label1.text()\n if direct == \"~default\":\n direct = \"face_dataframes\"\n else:\n direct = direct + \"/face_dataframes\"\n \n if (not os.path.exists(direct)):\n os.mkdir(direct)\n cv2.imwrite(\"{1}/{2}{0}.jpeg\".format(self.count, direct,self.textbox.text()), cv_img)\n self.iscapture = False\n self.label2.setText(\"Image # 0{0} Saved\".format(self.count))\n self.pushButton0.setEnabled(False)\n self.count += 1\n \n \n if(self.count == 6):\n #print(\"greater\")\n self.pushButton.setEnabled(False)\n self.pushButton2.setDisabled(False)\n\n\n self.image_label.setPixmap(qt_img)","def camera_callback(self, data):\n try:\n self.camera_buffer.put(data.images)\n self.publish_sensor_message()\n except Exception as ex:\n self.get_logger().error(f\"Error in camera callback: {ex}\")","def generatePreview(self):\n self.saveParameters()\n image=self.simulation.generatePreview()\n # convert pil image to a tkinter image\n self.photo = ImageTk.PhotoImage(image)\n\n # display image\n self.preview.create_image(0, 0, anchor='nw', image=self.photo)","def recordVideo(args, env, model, filename):\n # env = model.get_env()\n images = []\n images = images + runAGame(model, env, args.method == 'centralized')\n images = images + runAGame(model, env, args.method == 'centralized')\n images = images + runAGame(model, env, args.method == 'centralized')\n images[0].save(filename + '.gif',\n format='GIF',\n append_images=images[1:],\n save_all=True,\n duration=500,\n loop=0)\n print('Video saved:', filename)","def video_loop(self):\n\n _, img = self.vs.read()\n img = imutils.resize(img, width=self.width)\n image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n image = Image.fromarray(image)\n image = ImageTk.PhotoImage(image)\n self.frame.configure(image=image)\n self.frame.photo = image\n\n self.top.after(self.fps, self.video_loop)","def image_cb(self, msg):\n self.has_image = True\n self.camera_image = msg\n light_wp, state = self.process_traffic_lights()\n\n '''\n Publish upcoming red lights at camera frequency.\n Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\n of times till we start using it. Otherwise the previous stable state is\n used.\n '''\n if self.state != state:\n self.state_count = 0\n self.state = state\n elif self.state_count >= STATE_COUNT_THRESHOLD:\n self.last_state = self.state\n light_wp = light_wp if state == TrafficLight.RED else -1\n self.last_wp = light_wp\n self.upcoming_red_light_pub.publish(Int32(light_wp))\n else:\n self.upcoming_red_light_pub.publish(Int32(self.last_wp))\n self.state_count += 1","def capture(self, *, show=False, keep_showing=False):\n\t\tgen = self.loop(l_img=True, r_img=True)\n\t\tl_img, r_img = next(gen)\n\n\t\tif show:\n\t\t\tdouble_img = np.hstack((l_img, r_img))\n\t\t\tcv2.imshow('Camera Inputs', double_img)\n\t\t\tif not keep_showing:\n\t\t\t\tcv2.waitKey(0)\n\t\t\t\tcv2.destroyWindow('Camera Inputs')\n\n\t\treturn l_img, r_img","def show(self):\n\n self.image.show()","def capture_recording(self, data={}):\n _duration = data.get('duration', 5)\n if self.camera:\n _file_name = f'{os.path.join(self.path, self.filename)}.h264'\n self.camera.start_recording(_file_name)\n self.camera.wait_recording(_duration)\n self.camera.stop_recording()\n self.last_image = os.path.abspath(_file_name)\n self.increment_count()\n self.fire({'event': 'RecordingCaptured', 'file': _file_name})","def run(self):\n while True:\n self.ret, self.frame = self.cap.read()\n if self.ret:\n rgbImage = cv2.cvtColor(self.frame, cv2.COLOR_BGR2RGB)\n convertToQtFormat = QImage(rgbImage.data, rgbImage.shape[1], rgbImage.shape[0], QImage.Format_RGB888)\n self.readyFrame = convertToQtFormat.scaled(500, 375, Qt.KeepAspectRatio)\n self.send_camera_view_to_gui.emit(self.readyFrame)","def capture_timelapse(self, count, interval):\n filename = self.get_new_photo_filename('_{0:03d}_{1:03d}')\n with picamera.PiCamera() as camera:\n camera.resolution = (1024, 768)\n camera.hflip = True\n camera.vflip = True\n camera.start_preview()\n time.sleep(2) # wait for camera to warm up\n for i in range(count):\n camera.capture(self.camid + '/' + filename.format(i, count))\n time.sleep(interval)\n return filename.format(0, count)","def display(self):\n\t\tself.imgDisplay.set_from_pixbuf(self.getVisible())\n\t\tgc.collect()","def display(self):\n display(self.image)","def get_data(self):\n global CAM\n while CAM.isOpened():\n _, frame = CAM.read()\n _, jpeg = cv2.imencode('.jpg', frame)\n encoded_img = \"data:image/jpg;base64,\" + str(base64.b64encode(jpeg.tobytes()).decode())\n SIO.emit('video_frame',\n {'frame': encoded_img},\n namespace='/live-stream')\n sleep(self.delay)","def image_cb(self, msg):\n self.has_image = True\n self.camera_image = msg\n light_wp, state = self.process_traffic_lights()\n\n # rospy.logerr(\"light_wp\")\n # rospy.logerr(light_wp)\n\n # self.upcoming_red_light_pub.publish(Int32(light_wp))\n\n '''\n Publish upcoming red lights at camera frequency.\n Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\n of times till we start using it. Otherwise the previous stable state is\n used.\n '''\n if self.state != state:\n self.state_count = 0\n self.state = state\n elif self.state_count >= STATE_COUNT_THRESHOLD:\n self.last_state = self.state\n light_wp = light_wp if state == TrafficLight.RED else -1\n self.last_wp = light_wp\n self.upcoming_red_light_pub.publish(Int32(light_wp))\n else:\n self.upcoming_red_light_pub.publish(Int32(self.last_wp))\n self.state_count += 1","def video_loop(self):\n if not self.isReplay:\n if self.initStream:\n print('[SB Live] Starting live video stream...')\n self.replayStream.release()\n self.vs.open(0)\n self.initStream = False\n print('[SB Live] Live video stream started')\n if self.cClear:\n self.cache.release()\n os.remove('sblive/cache/replay.mov')\n self.cache.open('sblive/cache/replay.mov', self.fourcc, 10.0, (1280, 720))\n self.cClear = False\n ok, frame = self.vs.read() # read frame from video stream\n if ok: # frame captured without any errors\n key = cv2.waitKey(1)\n cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA) # convert colors from BGR to RGBA\n self.cache.write(frame)\n self.current_image = Image.fromarray(cv2image) # convert image for PIL\n imgtk = ImageTk.PhotoImage(image=self.current_image) # convert image for tkinter\n \n self.panel.imgtk = imgtk # anchor imgtk so it does not be deleted by garbage-collector\n self.panel.config(image=imgtk) # show the image\n else:\n if self.initStream:\n print('[SB Live] Starting replay video stream...')\n self.cache.release()\n self.vs.release()\n self.replayStream.open('sblive/cache/replay.mov')\n self.initStream = False\n print('[SB Live] Replay video stream started')\n ok, frame = self.replayStream.read()\n if ok:\n key = cv2.waitKey(1)\n cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA) # convert colors from BGR to RGBA\n self.current_image = Image.fromarray(cv2image) # convert image for PIL\n imgtk = ImageTk.PhotoImage(image=self.current_image) # convert image for tkinter\n \n self.panel.imgtk = imgtk # anchor imgtk so it does not be deleted by garbage-collector\n self.panel.config(image=imgtk) # show the image\n else:\n self.replayStream.release()\n self.replayStream.open('sblive/cache/replay.mov')\n if not self.killThread:\n self.root.after(30, self.video_loop) # call the same function after 30 milliseconds","def capture_snapshot(self, ramp_frames=60, x=1280, y=720):\n self.camera = cv2.VideoCapture(self.camera_port)\n\n # Set Resolution\n self.camera.set(3, x)\n self.camera.set(4, y)\n\n # Give camera enough time to adjust to the light\n for i in range(ramp_frames):\n temp = self.camera.read()\n\n retval, im = self.camera.read()\n\n # save the image in the specified directory\n image_filename = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '.jpeg' \n # example: 2020-12-04_00-05-50.jpeg\n self.last_picture = image_filename \n cv2.imwrite(\"{0}\\{1}\".format(self.snapshots_directory, image_filename) ,im)\n \n # release camera to allow use from other apps\n del(self.camera)","def on_image_change(self, value):\n self.current_image.setImage( self._model.image )","def on_draw_over_image(self):","def _automatic_rendering(self) -> None:\n if self.viewer is not None and self.enable_auto_render:\n\n if self._record_video_wrapper and self._record_video_wrapper.video_recorder:\n self._record_video_wrapper.video_recorder.capture_frame()\n else:\n self.render()","def start():\n global running\n running = True\n messagebox.showinfo(\"Camera mode\",\"Start image grab\")\n camera.start_preview(fullscreen=False, window = (100,20,612,404))","def update_display(self):\n \n # check availability of display queue of the wide camera\n# if not hasattr(self,'wide_disp_queue'):\n# pass\n# elif self.wide_disp_queue.empty():\n# pass\n# else:\n# try:\n# wide_disp_image = self.wide_disp_queue.get()\n# \n# self.wide_disp_counter += 1\n# self.wide_disp_counter %= 2\n# if self.wide_disp_counter == 0:\n# if type(wide_disp_image) == np.ndarray:\n# if wide_disp_image.shape == (self.wide_cam.settings.height.value(),self.wide_cam.settings.width.value()):\n# try:\n# self.wide_cam_image.setImage(wide_disp_image)\n# except Exception as ex:\n# print('Error: %s' % ex)\n# except Exception as ex:\n# print(\"Error: %s\" % ex)\n \n # check availability of display queue of the track camera \n if not hasattr(self,'track_disp_queue'):\n pass\n elif self.track_disp_queue.empty():\n pass\n else:\n try:\n track_disp_image = self.track_disp_queue.get()\n self.track_disp_counter += 1\n self.track_disp_counter %= 4\n if self.track_disp_counter == 0:\n if type(track_disp_image) == np.ndarray:\n if track_disp_image.shape == (self.track_cam.settings.height.value(),self.track_cam.settings.width.value()):\n try:\n self.track_cam_image.setImage(track_disp_image)\n except Exception as ex:\n print('Error: %s' % ex)\n \n x = int(self.settings.x.value())\n y = int(self.settings.y.value())\n self.tracker_data[:] = 0\n self.tracker_data[x,y] = 1\n self.tracker_image.setImage(np.copy(self.tracker_data))\n except Exception as ex:\n print(\"Error: %s\" % ex)","def run(self):\n \n count = 0\n while True:\n self.__ticker.tick()\n\n self.update()\n img = self.get_new_image()\n if img == None:\n print \"not receiving images yet...\"\n else:\n if self.verbose:\n cv.ShowImage(\"SnapShotSaver\", img)\n cv.WaitKey(10)\n cv.SaveImage(\"%s/%s_%d.png\" % (self.destination, self.prefix, count), img)\n count += 1","def show_poster(self):\n\t\twebbrowser.open(self.poster_image_url)","def image_cb(self, msg):\n self.has_image = True\n self.camera_image = msg\n light_wp, state = self.process_traffic_lights()\n #print(light_wp)\n #print(state)\n #print()\n\n '''\n Publish upcoming red lights at camera frequency.\n Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\n of times till we start using it. Otherwise the previous stable state is\n used.\n '''\n our_msg = TL_State()\n our_light = TrafficLight()\n our_light.header = Header()\n our_light.header.stamp = rospy.Time.now()\n our_light.header.frame_id = '/world'\n if self.state != state:\n self.state_count = 0\n self.state = state\n elif self.state_count >= STATE_COUNT_THRESHOLD:\n self.last_state = self.state\n light_wp = light_wp if (state == TrafficLight.RED or state == TrafficLight.YELLOW) else -1\n self.last_wp = light_wp\n self.upcoming_red_light_pub.publish(Int32(light_wp))\n\n our_msg.waypoint = light_wp\n our_light.state = state\n our_msg.light = our_light\n self.custom_state_pub.publish(our_msg)\n else:\n self.upcoming_red_light_pub.publish(Int32(self.last_wp))\n\n our_msg.waypoint = self.last_wp\n our_light.state = self.last_state\n our_msg.light = our_light\n self.custom_state_pub.publish(our_msg)\n self.state_count += 1","def cameraOn():\n cap = cv2.VideoCapture(CAM0, cv2.CAP_DSHOW) # use camera to monitor the motor-mirror assemnbly by DirectShow\n while(True):\n # Capture frame-by-frame\n ret, frame = cap.read()\n\n # Display the resulting frame\n cv2.imshow(\" Real-Time Video. Press 'q' to exist.\",frame)\n if cv2.waitKey(8) & 0xFF == ord('q'): #display a frame for 8ms, ~120Hz\n break\n \n cap.release() # release the capture\n cv2.destroyAllWindows()","def startup_capture(self):\n\n # Check if path for images already exists\n if not os.path.exists(self._image_path):\n os.makedirs(self._image_path)\n self._curr_image_inc = 0\n else:\n self._curr_image_inc = _get_latest_inc(self._image_path) + 1\n\n self._hook_screenshot = keyboard.on_press_key(self._config.aimbot_train_capture_key,\n self._save_screenshot_callback)","def captureVideoFrame(self):\n if(self.kinectConnected):\n self.VideoFrame = freenect.sync_get_video_with_res(\n resolution=freenect.RESOLUTION_HIGH)[0]\n \n else:\n self.loadVideoFrame()\n self.processVideoFrame()","def capture_video(self):\n while self.capturing:\n nparray = self.source.get_frame()\n self.frame_buffer.put(Frame(nparray, self.frame))\n self.frame += 1\n print \"Stopping Capture\"","def take_snapshot(self, path, file_name):\n pipelie_state = self.player.get_state(1)\n p_state = pipelie_state.state\n if p_state not in (Gst.State.PLAYING, Gst.State.PAUSED):\n print(\"Stream is not ready\")\n else:\n try:\n sink = self.player.get_by_name('sink')\n sample = GstBase.BaseSink.get_last_sample(sink)\n image_buffer = Gst.Sample.get_buffer(sample)\n buffer_map = Gst.Buffer.map(image_buffer, Gst.MapFlags.READ)\n image_binary_data = bytearray(buffer_map.info.data)\n utils.store_image(image_binary_data, path, file_name + \".jpeg\")\n except:\n print(\"Capturing image failed.\")","def update_cap_image(self):\n\n fn = self._get_cap_filename()\n try:\n im = PIL.Image.open(fn)\n except FileNotFoundError:\n return\n\n frame = np.array(im)\n\n \"\"\"\n frame = cv2.imread(fn, cv2.IMREAD_ANYDEPTH)\n if (frame is None):\n return\n \"\"\"\n\n frame = (frame >> (16 - self.camera.pixel_bits)).astype(np.uint16)\n\n ndx = self.dpar.cur_cap\n\n if self.dpar.cap_live_swap:\n pix, gray = self._get_pixmap(frame, self.dpar.iwindow[ndx])\n self.live_screen.live_title = self._cap_title(ndx)\n self.live_screen.setPixmap(pix)\n else:\n pix, gray = self._get_pixmap(frame[::4,::4], self.dpar.iwindow[ndx])\n self.cap_screen.cap_title = self._cap_title(ndx)\n self.cap_screen.setPixmap(pix)\n self.cap_screen.format_for_cap() # This is because first time, format is for \"no stills\".","def update_image(self, cv_img):\n\t\tqt_img = self.ImageEdits(cv_img)\n\t\tself.camera.setPixmap(qt_img)","def show(self, root):\n if isinstance(root, str):\n root = Image.open(root)\n frames_img = []\n try:\n for i in count(1):\n frames_img.append(ImageTk.PhotoImage(root.copy()))\n root.seek(i)\n except EOFError as e:\n pass\n # TODO: uncomment this line in final version\n # ls.get_logger().error(f'failed to load the picture, due to: {str(e)}')\n self.frames = cycle(frames_img)\n try:\n self.delay = root.info['duration']\n except:\n self.delay = 100\n if len(frames_img) == 1:\n self.config(image=next(self.frames))\n else:\n self.show_next_frame()","def showImage(self, image):\n \n self.image = img","def show_image(self):\n cv2.imshow('Image', self.__diff_image())\n cv2.waitKey()","def showImageViewer(self):\n self.imageViewer.hide()\n self.imageViewer.show()","def camera():\n while True:\n subprocess.check_output(['fswebcam', 'image.jpg'])\n sleep(60)","def next_save(self, *args, **kwargs):\n while True:\n try:\n return super(Camera, self).next_save(*args, **kwargs)\n except ids_core.IDSCaptureStatus:\n self._check_capture_status()","def snapshot(self):\n\n if self.pj[OBSERVATIONS][self.observationId][TYPE] in [MEDIA]:\n\n if self.playerType == VLC:\n\n if self.playMode == FFMPEG:\n\n for idx, media in enumerate(self.pj[OBSERVATIONS][self.observationId][FILE][PLAYER1]):\n if self.FFmpegGlobalFrame < sum(self.duration[0:idx + 1]):\n\n p = pathlib.Path(media)\n snapshotFilePath = str(p.parent / \"{}_{}.png\".format(p.stem, self.FFmpegGlobalFrame))\n\n if self.detachFrameViewer or self.second_player():\n self.frame_viewer1.lbFrame.pixmap().save(snapshotFilePath)\n elif not self.detachFrameViewer:\n self.lbFFmpeg.pixmap().save(snapshotFilePath)\n self.statusbar.showMessage(\"Snapshot player #1 saved in {}\".format(snapshotFilePath), 0)\n break\n\n if self.second_player():\n for idx, media in enumerate(self.pj[OBSERVATIONS][self.observationId][FILE][PLAYER2]):\n if self.FFmpegGlobalFrame2 < sum(self.duration2[0:idx + 1]):\n p = pathlib.Path(media)\n snapshotFilePath = str(p.parent / \"{}_{}.png\".format(p.stem, self.FFmpegGlobalFrame2))\n\n self.frame_viewer2.lbFrame.pixmap().save(snapshotFilePath)\n self.statusbar.showMessage(\"Snapshot player #2 saved in {}\".format(snapshotFilePath), 0)\n break\n\n else: # VLC\n\n current_media_path = url2path(self.mediaplayer.get_media().get_mrl())\n # TODO: replace with pathlib\n dirName, fileName = os.path.split(current_media_path)\n self.mediaplayer.video_take_snapshot(0, \"{dirName}{sep}{fileNameWOExt}_{time}.png\".format(\n dirName=dirName,\n sep=os.sep,\n fileNameWOExt=os.path.splitext(fileName)[0],\n time=self.mediaplayer.get_time()),\n 0, 0)\n\n # check if multi mode\n # second video together\n if self.simultaneousMedia:\n current_media_path = url2path(self.mediaplayer2.get_media().get_mrl())\n\n dirName, fileName = os.path.split(current_media_path)\n self.mediaplayer2.video_take_snapshot(0, \"{dirName}{sep}{fileNameWOExt}_{time}.png\".format(\n dirName=dirName,\n sep=os.sep,\n fileNameWOExt=os.path.splitext(fileName)[0],\n time=self.mediaplayer2.get_time()),\n 0, 0)","def capture(self):\n filename = self.get_new_photo_filename()\n open(self.camid + '/' + filename, 'wb').write(self.fake_shot)\n return filename"],"string":"[\n \"def capture_image(self):\\n ext = self.image_save_type.lower()\\n\\n if self.calibrating:\\n print('calibrating')\\n\\n if ext == 'fits':\\n self.save_fits()\\n self._image_counter += 1\\n else:\\n img = self.original_image\\n path = os.path.join(self.home, 'data')\\n name = \\\"camtrak_frame_{}.png\\\".format(self._image_counter) \\n fn = os.path.join(path, name)\\n cv2.imwrite(fn, img)\\n\\n QtWidgets.QApplication.beep()\\n self.statusBar().showMessage(f'Saved image to {fn}')\\n self._image_counter += 1\",\n \"def capture(self):\\n current_time=time.strftime('%Y%m%d-%H%M%S')\\n self.filepath=f\\\"files/{current_time}.png\\\"\\n self.ids.camera.export_to_png(self.filepath)\\n self.manager.current='image_screen'\\n self.manager.current_screen.ids.img.source=self.filepath\",\n \"def on_image(self, image):\",\n \"def image_cb(self, msg):\\n self.has_image = True\\n self.camera_image = msg\",\n \"def capture_image(self, data={}):\\n # call self.increment_count() after each image saved\\n pass\",\n \"def image_cb(self, msg):\\n self.has_image = True\\n self.camera_image_msg = msg\",\n \"def image_cb(self, msg): # incoming image\\n self.has_image = True\\n self.camera_image = msg\",\n \"def image_capture_demo():\\n return render_template('image_capture_demo.html')\",\n \"def take_picture(self):\\n self.drone.take_picture()\",\n \"def take_picture(self):\\n self.drone.take_picture()\",\n \"def take_photo(self):\\n\\n status = self.camera.status()\\n if status['mode'] != 'still':\\n # place camera in snapshot mode\\n self.camera.command('mode', 'still')\\n\\n photo_successful = self.camera.command('record', 'on')\\n\\n if photo_successful:\\n\\n # sleep for two seconds so the camera can process\\n # and serve the new photo via http\\n\\n retrieved = False\\n while not retrieved:\\n print(\\\"Waiting for image to be served.\\\")\\n time.sleep(2)\\n retrieved = self.get_photos_from_device()\\n\\n print(\\\"Image got served.\\\")\\n return True\\n\\n else:\\n return False\",\n \"def viewUpdate(self):\\n # Update Capture\\n imgtk = self.model.capture\\n self.updateImage(self.view.lmain, imgtk)\\n # Update Stitch \\n imgtk = self.model.stitch\\n self.updateImage(self.view.rmain, imgtk)\\n self.view.dist.set(self.model.dist)\",\n \"def image_cb(self, msg):\\n self.has_image = True\\n self.camera_image = msg\\n self.get_light_state()\",\n \"def capture_image(self, data={}):\\n if self.camera:\\n image_name = f'{os.path.join(self.path, self.filename)}.jpg'\\n self.camera.capture(image_name)\\n self.last_image = os.path.abspath(image_name)\\n self.increment_count()\\n self.fire({'event': 'ImageCaptured', 'image': image_name})\",\n \"def capture(self):\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n self.vid.grab()\\n ret, img = self.vid.read()\\n cv2.imwrite(\\\"/tmp/test.png\\\", img)\\n return img\",\n \"def liveview(self):\\n if self.liveviewButton.isChecked():\\n# self.save = False\\n self.paramChangedInitialize()\\n self.openShutter(\\\"red\\\")\\n self.liveviewStart()\\n\\n else:\\n self.liveviewStop()\",\n \"def capture(self):\\n current_time = time.strftime('%Y%m%d-%H%M%S')\\n filepath = f'files/{current_time}.png'\\n self.ids.camera.export_to_png(filepath)\\n self.manager.current = 'image_screen' # switch to the next screen\\n self.manager.current_screen.ids.img.source = filepath # inherit img to the next screen\\n return filepath\",\n \"def img_cb(self, image):\\n self.last_img = image\\n self.is_new_img = True\",\n \"def capture(self):\\n\\n self.dpar.n_caps += 1\\n self.dpar.caps_saved = False\\n self.cap_screen.cap_loaded = True\\n tstamp = datetime.now()\\n\\n self.dpar.cur_cap = self.dpar.n_caps\\n self.dpar.iwindow.append(list(self.dpar.iwindow[0])) # deep copy\\n self.dpar.frame_timestamp.append(tstamp)\\n\\n self.cap_scrollbar.setRange(1, self.dpar.n_caps)\\n self.cap_scrollbar.setValue(self.dpar.n_caps)\\n\\n if self.config.sound_on_capture:\\n QtMultimedia.QSound.play(CAMERA_CLICK_FILE)\\n\\n # Always write to file (3/2018)\\n \\\"\\\"\\\"\\n Write the capture to a file. Create the daily capture directory if not yet created.\\n \\\"\\\"\\\"\\n\\n #im = Image.fromarray(d, mode='F') # float32\\n #im.save(\\\"test2.tiff\\\", \\\"TIFF\\\")\\n # https://gist.github.com/ax3l/5781ce80b19d7df3f549#pillow\\n\\n \\\"\\\"\\\"\\n Note that despite lack of documenation, this does save as 16-bit gray-scale image. Open in Photoshop to\\n confirm. Irfan converts to 8 bpp upon opening ans scales pixels\\n \\\"\\\"\\\"\\n cfn = self._get_cap_filename()\\n cap_image = np.copy(self.dpar.latest_frame).astype(np.uint16)\\n im = PIL.Image.fromarray((cap_image << (16 - self.camera.pixel_bits)).astype(np.uint16))\\n im.save(cfn, 'TIFF')\\n\\n \\\"\\\"\\\" \\n cap_image = np.copy(self.dpar.latest_frame).astype(np.uint16)\\n cv2.imwrite(cfn, (cap_image << (16 - self.camera.pixel_bits)).astype(np.uint16))\\n \\\"\\\"\\\"\\n\\n et = np.int(np.round(self.camera.actual_exposure_time_ms))\\n fn = os.path.basename(cfn)\\n\\n self.write_to_log('%d\\\\t%s' % (et, fn))\\n\\n self.swap_button.setEnabled(True)\\n self.update_cap_image()\",\n \"def display_current_view(tracker):\\n image = tracker.get_video_frame()\\n cv2.imshow(\\\"img\\\", image)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\",\n \"def record(self):\\n\\t\\twhile True:\\n\\t\\t\\tif not self.recording:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\t#print('hal')\\n\\t\\t\\telapsed = (datetime.datetime.now() - self.timestamps[-1]).total_seconds()\\n\\t\\t\\tif elapsed < self.timePerFrame:\\n\\t\\t\\t\\tpass\\n\\t\\t\\telse:\\n\\t\\t\\t\\t#print(len(self.Video))\\n\\t\\t\\t\\tret,frame = self.vs.read()\\n\\t\\t\\t\\tif ret:\\n\\t\\t\\t\\t\\tself.timestamps.append(datetime.datetime.now())\\n\\t\\t\\t\\t\\tself.Video.append(frame)\\n\\t\\t\\t\\t\\tself.FPStracker.update()\\n\\t\\t\\t\\t\\tself.newAvailable = True\\n\\t\\t\\t\\t\\tif not self.threaded:\\n\\n\\t\\t\\t\\t\\t\\treturn\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint('error: camera failed to capture image')\\n\\t\\t\\t\\t\\tprint('canceling recording session')\\n\\t\\t\\t\\t\\tself.stop()\\n\\t\\t#print('\\\\n recording loop ended, returning to main')\\n\\t\\tself.vs.stop()\\n\\t\\treturn\",\n \"def live():\\n m = camera.status.mode\\n print \\\"Hit ^C to exit.\\\"\\n print \\\"NOTE! After using this command, type: mode('%s') \\\" % m\\n mode('centre')\\n try:\\n while True:\\n f = camera.GetFits()\\n camera.status.update()\\n setheaders(f)\\n camera.status.lastact = time.time() #Record the time that the last image was taken\\n xpa.displayimage(f)\\n except KeyboardInterrupt:\\n logger.error(\\\"Live mode aborted, dumping image.\\\")\\n finally:\\n mode(m) #Restore original camera mode (hopefully)\",\n \"def get_image():\\n\\n # Access the global variable and activate the saving for the last camera's\\n # frame\\n global _save_image\\n _save_image = True\",\n \"def saveimage(self):\\n if self.saveimageButton.isChecked():\\n self.save = True\\n self.channelsOpen()\\n self.movetoStart()\\n self.saveimageButton.setText('Abort')\\n self.guarda = np.zeros((self.numberofPixels, self.numberofPixels))\\n self.liveviewStart()\\n\\n else:\\n self.save = False\\n print(\\\"Abort\\\")\\n self.saveimageButton.setText('reintentar Scan and Stop')\\n self.liveviewStop()\",\n \"def image_cb(self, msg):\\n\\n # Save the camera image\\n self.camera_image = msg\\n\\n # I sufficient information is available...\\n if not None in (self.camera_image, self.waypoint_tree, self.lights):\\n\\n # Find index and color state of next light\\n light_wp, state = self.process_traffic_lights()\\n\\n # If the light is green...\\n if state == TrafficLight.GREEN:\\n\\n # Publish sentinel indicatig no red light\\n self.upcoming_red_light_pub.publish(Int32(-1))\\n\\n else:\\n\\n # Publish the traffic light index\\n self.upcoming_red_light_pub.publish(Int32(light_wp))\",\n \"def preview(self,*args,**kwargs):\\n self.cam.start_preview(*args,**kwargs)\",\n \"def show(self):\\n if self.video:\\n self.video.write(self.img)\\n cv2.imshow('Simpy', self.img)\\n cv2.waitKey(1000 // self.fps)\",\n \"def take_pic(self):\\n \\n try:\\n if os.path.isfile(self.pics_path):\\n os.remove(self.pics_path)\\n self.camera.capture(self.pics_path, use_video_port=True)\\n except:\\n print(\\\"Error when recording image\\\")\\n exit()\\n \\n try:\\n image = open(self.pics_path, 'rb')\\n img_bytes = image.read()\\n size = len(img_bytes)\\n self.img_buffer.append((size, img_bytes))\\n image.close()\\n except:\\n print(\\\"Error when opening image\\\")\\n exit()\",\n \"def timerEvent(self, event):\\n\\n if int(time.time()) - self.start_time > self.capture_duration:\\n self.stop_recording()\\n\\n recording, data = self.camera.read()\\n if recording:\\n self.image_data.emit(data)\",\n \"def take_still(output_path):\\n camera = PiCamera()\\n camera.start_preview()\\n\\n sleep(3)\\n camera.capture(\\\"test.jpg\\\")\\n camera.stop_preview()\",\n \"def raw_image_callback(self, msg):\\n if self.pictures_to_take and not self.detection_to_receive:\\n self.pictures_to_take -= 1\\n # so let's analyse it here and then delete the subscription\\n rows = msg.height\\n step = msg.step\\n cols = msg.width\\n dim = int(step / cols)\\n pixels = msg.data # of size (steps, nrows)\\n # save the image (later we will need to analyse it)\\n vision_utils.save_picture(pixels, rows, cols, dim, self.name, FOLDER)\",\n \"def camera_image(self):\\n if not self.ezvizService.switchState:\\n return \\\"\\\"\\n\\n now = time.time()\\n if now < self._last_snapshot_time + self._interval_snapshots:\\n return self._last_image\\n\\n result = self.ezvizService.post('/lapp/device/capture', data={'deviceSerial':self.deviceSerial,'channelNo':1})\\n if (result['code']!='200'):\\n _LOGGER.error(\\\"EZVIZ capture image fail:%s\\\", result)\\n return self._last_image\\n\\n image_path = result['data']['picUrl']\\n try:\\n response = requests.get(image_path)\\n except requests.exceptions.RequestException as error:\\n _LOGGER.error(\\\"EZVIZ getting camera image: %s\\\", error)\\n return self._last_image\\n\\n self._last_snapshot_time = now\\n self._last_image = response.content\\n return self._last_image\",\n \"def capture_image():\\n global img_tk\\n r, img_cam = cam.read()\\n img_pil = Image.fromarray(cv2.cvtColor(img_cam, cv2.COLOR_BGR2RGB))\\n img_tk = ImageTk.PhotoImage(img_pil)\\n tk_cam.create_image(0, 0, image=img_tk, anchor='nw')\\n return img_pil\",\n \"def ImageEdits(self, cv_img):\\n\\t\\tself.capture = cv_img[110:370,210:430]\\n\\t\\tcv_img = cv_img[110:370,210:430].copy()\\n\\t\\t\\n\\t\\t#draw lines\\n\\t\\tcv2.line(cv_img,(22,26),(80,26),(11,243,50),3)\\n\\t\\tcv2.line(cv_img,(22,26),(22,84),(11,243,50),3)\\n\\t\\tcv2.line(cv_img,(140,26),(198,26),(11,243,50),3)\\n\\t\\tcv2.line(cv_img,(198,26),(198,84),(11,243,50),3)\\n\\t\\tcv2.line(cv_img,(22,176),(22,234),(11,243,50),3)\\n\\t\\tcv2.line(cv_img,(22,234),(80,234),(11,243,50),3)\\n\\t\\tcv2.line(cv_img,(198,176),(198,234),(11,243,50),3)\\n\\t\\tcv2.line(cv_img,(140,234),(198,234),(11,243,50),3)\\n\\n\\t\\tif not self.on_detection:\\n\\t\\t\\tcv2.putText(cv_img, \\\"Press Start\\\", (35, 130), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 255), 2)\\n\\n\\t\\t\\n\\t\\t#detect face\\n\\t\\tif 1 <= len(self.pics_time) < 6 and not self.detection_done:\\n\\t\\t\\tdetected, (x,y,w,h) = self.detector.FaceDetect(cv_img)\\n\\t\\t\\tif detected:\\n\\t\\t\\t\\ttime_now = time.time()\\n\\t\\t\\t\\tperecedent_time = self.pics_time[len(self.pics_time)-1]\\n\\t\\t\\t\\tif len(self.pics_ls) <= 5 and time_now - perecedent_time > 0.5:\\n\\t\\t\\t\\t\\tself.pics_ls.append(self.capture)\\n\\t\\t\\t\\t\\tself.pics_time.append(time.time())\\n\\t\\t\\t\\tcv2.rectangle(cv_img, (x,y), (x+w,y+h), (0,255,0), 2)\\n\\t\\telif len(self.pics_ls) == 5 and not self.detection_done:\\n\\t\\t\\tself.Recognize()\\n\\t\\t\\tself.detection_done = True\\n\\n\\t\\treturn self.Convert_CV_QT(cv_img)\",\n \"def preview_stream(self):\\n pass\",\n \"def guest_capture_func(self, pic_save, pic_display):\\n capture_time_curr = time.time()\\n (pic_height, pic_width) = pic_display.shape[:2]\\n image_blob = cv2.dnn.blobFromImage(\\n cv2.resize(pic_display, (300, 300)),\\n 1.0,\\n (300, 300),\\n self.trainRBGavg,\\n swapRB=False,\\n crop=False)\\n\\n # Use previously loaded face detector on the blob\\n self.detector.setInput(image_blob)\\n detections = self.detector.forward()\\n\\n # Loop over detected faces\\n for i in range(0, detections.shape[2]):\\n curr_conf = detections[0, 0, i, 2]\\n if curr_conf > self.min_detec_conf:\\n bound_box = detections[0, 0, i, 3:7] * np.array([pic_width,\\n pic_height,\\n pic_width,\\n pic_height])\\n (x_start, y_start, x_end, y_end) = bound_box.astype(\\\"int\\\")\\n # Only detect faces fully in the frame\\n if x_start < 0 or y_start < 0:\\n continue\\n if x_end > self.image_width or y_end > self.image_width:\\n continue\\n # Draw face bounding box\\n cv2.rectangle(pic_display,\\n (x_start, y_start),\\n (x_end, y_end),\\n (0, 255, 0),\\n 2)\\n\\n elap_seconds = capture_time_curr - self.capture_time_prev\\n if elap_seconds >= self.max_capture_interval and \\\\\\n np.any(detections[0, 0, :, 2] > self.min_detec_conf):\\n # Capture image and save to file\\n pn_pic = os.path.sep.join([self.pn_gstcap_out, \\\"{}.png\\\".format(\\n str(self.pic_num).zfill(5))])\\n self.save_pic_archive(pn_pic, pic_save)\\n self.pic_num += 1\\n self.capture_time_prev = capture_time_curr\\n print(\\\"[INFO] Saved image {}.\\\".format(self.pic_num))\\n\\n if capture_time_curr - self.start_time > self.max_capture_length:\\n print('[INFO] {} seconds elapsed, completed capturing images.'\\n .format(self.max_capture_length))\\n self.gst_capture = False\\n\\n if self.pic_num >= self.max_images:\\n print('[INFO] Max of {} images captured, completed capturing images.'\\n .format(self.max_images))\\n self.gst_capture = False\\n\\n return pic_display\",\n \"def _update_frame(self):\\n # check if continue\\n if self._keep_updating:\\n self.__frame = self._cam.get_display_frame()\\n if self.__frame is not None:\\n self._cvn_camera_viewfinder.create_image(0, 0, image=self.__frame, anchor=tk.NW)\\n\\n self._root.after(self._delay, self._update_frame)\",\n \"def capture_photo(self, count):\\n output_file_name = generate_image_filename()\\n save_file_path = os.path.join(self.output_dir, output_file_name)\\n\\n self.take_photo(save_file_path)\\n check_image_useful(save_file_path)\",\n \"def preview(self):\\n win_name = 'RTSP'\\n cv2.namedWindow(win_name, cv2.WINDOW_AUTOSIZE)\\n cv2.moveWindow(win_name,20,20)\\n self.open()\\n while(self.isOpened()):\\n cv2.imshow(win_name,self._stream.read()[1])\\n #if self._latest is not None:\\n # cv2.imshow(win_name,self._latest)\\n if cv2.waitKey(25) & 0xFF == ord('q'):\\n break\\n cv2.waitKey()\\n cv2.destroyAllWindows()\\n cv2.waitKey()\",\n \"def preview_image(instance, *args, **kwargs):\\n if not ENABLE_PREVIEW_IMAGES:\\n return\\n changed_fields = instance.field_tracker.changed()\\n\\n if len(changed_fields) > 0:\\n generate_site_preview_image_task.delay()\",\n \"def captureVideoFrame(self):\\n if(self.kinectConnected):\\n self.currentVideoFrame = freenect.sync_get_video()[0]\\n else:\\n self.loadVideoFrame()\\n #self.processVideoFrame()\",\n \"def grab_image(self):\\n _, camera_image = self.camera.read()\\n with self.lock:\\n self.image = camera_image\",\n \"def play(self, frame):\\n cv2.imshow(\\\"view\\\", frame)\\n return 0\",\n \"def start_camera(self):\\n # create the video capture thread\\n self.thread = VideoThread()\\n # connect its signal to the update_image slot\\n self.thread.change_pixmap_signal.connect(self.update_image)\\n # start the thread\\n self.thread.start()\",\n \"def __check_picture(self):\\n if self.communications.get_picture():\\n self.communications.set_status(\\\"Taking Picture\\\")\\n try:\\n if self.__video_status[\\\"in_use\\\"]:\\n self.__live_video_stream(False) # Turn off Video Live Stream\\n sleep(13)\\n image_path = self.camera.take_picture(3)\\n self.__live_video_stream(True) # Turn Video Stream back on.\\n else:\\n image_path = self.camera.take_picture(3)\\n if image_path is not None:\\n image_url = self.communications.upload_image(image_path)\\n if image_url:\\n self.communications.add_event(\\\"Image Capture Successful\\\", \\\"success\\\")\\n except ValueError:\\n self.communications.add_event(\\\"Could not capture image, try again\\\", \\\"error\\\")\",\n \"def __take_snapshot(self):\\n # Set the filename to\\n # year_month_date_hour_minute_second.jpg\\n time_label = datetime.datetime.now()\\n filename = f\\\"{time_label.strftime('%Y-%m-%d_%H-%M-%S')}.jpg\\\"\\n\\n # Start the saving function in background\\n save_img_back = threading.Thread(target=self.__upload,\\n args=(filename,),\\n daemon=True)\\n save_img_back.start()\",\n \"def recorder_snapshot(recorder_name, width=None, height=None, selected_name=None):\\n recorder = get_recorder_by_name(recorder_name)\\n # get snapshot name for the recorder\\n if selected_name is None:\\n filename = cnfg.recorders[recorder_name].filename_snapshot()\\n else:\\n snapshot_path = os.path.dirname(os.path.abspath(cnfg.recorders[recorder_name].filename_snapshot()))\\n filename = os.path.join(snapshot_path, selected_name+'.jpg')\\n if os.path.isfile(filename):\\n height = int(height)\\n width = int(width)\\n img = cv2.imread(filename)\\n # resize image and preserve aspect ratio\\n orig_height, orig_width, _ = img.shape\\n if orig_height > height:\\n scale_height = height / orig_height\\n else:\\n scale_height = 1\\n if orig_width > width:\\n scale_width = width / orig_width\\n else:\\n scale_width = 1\\n scale = min(scale_height, scale_width) \\n if scale < 1:\\n height = math.floor(orig_height*scale)\\n width = math.floor(orig_width*scale)\\n img = cv2.resize(img, (width, height)) \\n # make darker if snapshot was not updated\\n if not recorder.status in ['started'] and not recorder.watcher:\\n a = np.double(img)\\n b = a * 0.2\\n img = np.uint8(b)\\n # encode to jpeg image\\n _, img_jpg = cv2.imencode('.jpg', img)\\n response = make_response(img_jpg.tostring())\\n response.headers.set('Content-Type', 'image/jpeg')\\n return response\\n else:\\n return send_file('templates/static/nosnapshot.gif')\",\n \"def cameraTB1Checked(self, state):\\n if state == QtCore.Qt.Checked:\\n print('Show Camera TB1 Selected')\\n # release video capture\\n self.cap = cv2.VideoCapture(0)\\n # # read image in BGR format\\n # ret, img = self.cap.read()\\n # image = QtGui.QImage(img, img.shape[1], img.shape[0],\\n # img.shape[1] * img.shape[2],\\n # QtGui.QImage.Format_RGB888)\\n pixmapCamera = QtGui.QPixmap()\\n pixmapCamera.convertFromImage(tb1Subscriber.callback.rgb_image())\\n self.simulationWidget.setPixmap(pixmapCamera)\\n else:\\n print('Show Camera TB1 Unchecked')\\n # self.cap.release()\",\n \"def take_photo(self):\\n self.photo = self.frame\\n self.send_photo_to_model.emit(self.photo)\",\n \"def preview_camera(self):\\n self.statusbar.clearMessage()\\n self.statusbar.showMessage('Previewing the camera. Press the Enter key to exit.')\\n self.buttonPreview.setText('Press Enter\\\\nto finish.')\\n self.comboCamera.setEnabled(False)\\n self.buttonSelectColor.setEnabled(False)\\n self.buttonLogSet.setEnabled(False)\\n cap = webcam.initiate_camera(self.comboCamera.currentIndex())\\n while True:\\n _, frame = cap.read()\\n frame = np.rot90(frame, self.comboRotation.currentIndex())\\n cv2.imshow('Camera Preview', frame)\\n key = cv2.waitKey(1) & 0xFF\\n if key == ord('\\\\r'):\\n break\\n cap.release()\\n cv2.destroyAllWindows()\\n self.buttonPreview.setText('Preview')\\n self.comboCamera.setEnabled(True)\\n self.buttonSelectColor.setEnabled(True)\\n self.buttonLogSet.setEnabled(True)\\n self.statusbar.clearMessage()\",\n \"def cameraCallback(self, data):\\n if not self.isReady:\\n cvImage, self.imageInfo['shape'] = u.getCVImage(data)\\n if self.measuring is not None:\\n self.list, cvImage, self.isReady = self.measuring.getListObjects(cvImage)\\n # preview topic /see_main\\n msg_image = u.getMsgImage(cvImage)\\n self.pub_view_main.publish(msg_image)\\n else:\\n if self.imageInfo['shape'] is not None:\\n self.init()\\n else:\\n rospy.logerr(\\\"no video stream. check camera's topic!\\\")\",\n \"def update(self):\\n if self.mudpi.is_prepared:\\n if self.duration > self.delay.total_seconds():\\n if self.record_video:\\n self.capture_recording(duration=self.record_duration)\\n else:\\n self.capture_image()\\n self.reset_duration()\",\n \"def show_img(self):\\n if self.image is not None:\\n cv2.imshow(self.image_window, self.image)\\n cv2.waitKey(1)\\n else:\\n rospy.loginfo(\\\"No image to show yet\\\")\",\n \"def capture(self):\\n with picamera.PiCamera() as camera:\\n # camera setup\\n camera.resolution = (frame_width, frame_height)\\n camera.framerate = 32\\n camera.rotation = 90\\n stream = PiRGBArray(camera, size=(frame_width, frame_height))\\n\\n # let camera warm up\\n time.sleep(1)\\n avg = None\\n\\n prev_area = 0\\n upload_cnt = 0\\n upload_threshold = 75\\n motion_frames = []\\n frame_cnt = 0\\n\\n start_time = time.time()\\n\\n print 'Ready'\\n for frame in camera.capture_continuous(stream, 'bgr',\\n use_video_port=True):\\n\\n stream.seek(0)\\n image = frame.array\\n\\n if avg is None:\\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\\n gray = cv2.GaussianBlur(gray, tuple(blur_size), 0)\\n avg = gray.copy().astype(\\\"float\\\")\\n stream.truncate()\\n continue\\n\\n (contours, avg, gray, image) = motion_detect(image, avg)\\n\\n #print contours\\n if isinstance(contours, tuple):\\n contours = list(list(contours))\\n if len(contours) > 0 and (time.time() - start_time) > 20:\\n if upload_cnt < upload_threshold:\\n print len(contours)\\n print str(datetime.datetime.now())\\n sys.stdout.flush()\\n time_str = get_time()\\n output_filename = path + 'img_' + time_str + '-' + str(frame_cnt) + '.jpg'\\n if frame_cnt % 3 == 0:\\n motion_frames.append((output_filename, gray, image))\\n upload_cnt += 1\\n frame_cnt += 1\\n else:\\n upload_cnt = 0\\n if motion_frames:\\n if len(motion_frames) > 1:\\n self.append_frames(motion_frames)\\n motion_frames = []\\n frame_cnt = 0\\n\\n stream.seek(0)\\n stream.truncate()\",\n \"def captureVideoFrame(self):\\n if(self.kinectConnected):\\n self.currentVideoFrame = freenect.sync_get_video()[0]\\n else:\\n self.loadVideoFrame()\\n self.processVideoFrame()\",\n \"def take_picture():\\n\\n #This reads the data from the webcam\\n ret, frame = vid.read() \\n \\n #This writes the image to the unknown directory\\n cv2.imwrite('/Users/srikarkarra/Downloads/Important Stuff/Coding/facial_rec/unknown/unknown.jpg', frame)\",\n \"def OnNewImage(self, event):\\r\\n #print sys._getframe().f_code.co_name\\r\\n\\r\\n self.eventLock = event.eventLock\\r\\n\\r\\n if not self.pause:\\r\\n self.UpdateImage(event.img)\\r\\n self.ReleaseEventLock()\\r\\n if event.oldImageLock:\\r\\n if event.oldImageLock.locked():\\r\\n event.oldImageLock.release()\",\n \"def capture(self):\\n log.info('Image: %d' % self.idx)\\n img = cv.imread(self.files[self.idx])\\n self.idx = (self.idx + 1) % len(self.files)\\n\\n img = cv.cvtColor(img, cv.COLOR_BGR2RGB)\\n return img\",\n \"def _display_img(self):\\n if self._file_path is None:\\n Debug.printi(\\\"No picture has been loaded to preview\\\", Debug.Level.ERROR)\\n return\\n photo = self._open_img(self._file_path)\\n ImageViewDialog(self._parent, self._file_name, photo)\",\n \"def show_picture(self, data):\\n raise NotImplementedError\",\n \"def update_image(self, cv_img):\\n qt_img = self.convert_cv_qt(cv_img)\\n if(self.iscapture):\\n print(\\\"update\\\")\\n direct = self.label1.text()\\n if direct == \\\"~default\\\":\\n direct = \\\"face_dataframes\\\"\\n else:\\n direct = direct + \\\"/face_dataframes\\\"\\n \\n if (not os.path.exists(direct)):\\n os.mkdir(direct)\\n cv2.imwrite(\\\"{1}/{2}{0}.jpeg\\\".format(self.count, direct,self.textbox.text()), cv_img)\\n self.iscapture = False\\n self.label2.setText(\\\"Image # 0{0} Saved\\\".format(self.count))\\n self.pushButton0.setEnabled(False)\\n self.count += 1\\n \\n \\n if(self.count == 6):\\n #print(\\\"greater\\\")\\n self.pushButton.setEnabled(False)\\n self.pushButton2.setDisabled(False)\\n\\n\\n self.image_label.setPixmap(qt_img)\",\n \"def camera_callback(self, data):\\n try:\\n self.camera_buffer.put(data.images)\\n self.publish_sensor_message()\\n except Exception as ex:\\n self.get_logger().error(f\\\"Error in camera callback: {ex}\\\")\",\n \"def generatePreview(self):\\n self.saveParameters()\\n image=self.simulation.generatePreview()\\n # convert pil image to a tkinter image\\n self.photo = ImageTk.PhotoImage(image)\\n\\n # display image\\n self.preview.create_image(0, 0, anchor='nw', image=self.photo)\",\n \"def recordVideo(args, env, model, filename):\\n # env = model.get_env()\\n images = []\\n images = images + runAGame(model, env, args.method == 'centralized')\\n images = images + runAGame(model, env, args.method == 'centralized')\\n images = images + runAGame(model, env, args.method == 'centralized')\\n images[0].save(filename + '.gif',\\n format='GIF',\\n append_images=images[1:],\\n save_all=True,\\n duration=500,\\n loop=0)\\n print('Video saved:', filename)\",\n \"def video_loop(self):\\n\\n _, img = self.vs.read()\\n img = imutils.resize(img, width=self.width)\\n image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n image = Image.fromarray(image)\\n image = ImageTk.PhotoImage(image)\\n self.frame.configure(image=image)\\n self.frame.photo = image\\n\\n self.top.after(self.fps, self.video_loop)\",\n \"def image_cb(self, msg):\\n self.has_image = True\\n self.camera_image = msg\\n light_wp, state = self.process_traffic_lights()\\n\\n '''\\n Publish upcoming red lights at camera frequency.\\n Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\\n of times till we start using it. Otherwise the previous stable state is\\n used.\\n '''\\n if self.state != state:\\n self.state_count = 0\\n self.state = state\\n elif self.state_count >= STATE_COUNT_THRESHOLD:\\n self.last_state = self.state\\n light_wp = light_wp if state == TrafficLight.RED else -1\\n self.last_wp = light_wp\\n self.upcoming_red_light_pub.publish(Int32(light_wp))\\n else:\\n self.upcoming_red_light_pub.publish(Int32(self.last_wp))\\n self.state_count += 1\",\n \"def capture(self, *, show=False, keep_showing=False):\\n\\t\\tgen = self.loop(l_img=True, r_img=True)\\n\\t\\tl_img, r_img = next(gen)\\n\\n\\t\\tif show:\\n\\t\\t\\tdouble_img = np.hstack((l_img, r_img))\\n\\t\\t\\tcv2.imshow('Camera Inputs', double_img)\\n\\t\\t\\tif not keep_showing:\\n\\t\\t\\t\\tcv2.waitKey(0)\\n\\t\\t\\t\\tcv2.destroyWindow('Camera Inputs')\\n\\n\\t\\treturn l_img, r_img\",\n \"def show(self):\\n\\n self.image.show()\",\n \"def capture_recording(self, data={}):\\n _duration = data.get('duration', 5)\\n if self.camera:\\n _file_name = f'{os.path.join(self.path, self.filename)}.h264'\\n self.camera.start_recording(_file_name)\\n self.camera.wait_recording(_duration)\\n self.camera.stop_recording()\\n self.last_image = os.path.abspath(_file_name)\\n self.increment_count()\\n self.fire({'event': 'RecordingCaptured', 'file': _file_name})\",\n \"def run(self):\\n while True:\\n self.ret, self.frame = self.cap.read()\\n if self.ret:\\n rgbImage = cv2.cvtColor(self.frame, cv2.COLOR_BGR2RGB)\\n convertToQtFormat = QImage(rgbImage.data, rgbImage.shape[1], rgbImage.shape[0], QImage.Format_RGB888)\\n self.readyFrame = convertToQtFormat.scaled(500, 375, Qt.KeepAspectRatio)\\n self.send_camera_view_to_gui.emit(self.readyFrame)\",\n \"def capture_timelapse(self, count, interval):\\n filename = self.get_new_photo_filename('_{0:03d}_{1:03d}')\\n with picamera.PiCamera() as camera:\\n camera.resolution = (1024, 768)\\n camera.hflip = True\\n camera.vflip = True\\n camera.start_preview()\\n time.sleep(2) # wait for camera to warm up\\n for i in range(count):\\n camera.capture(self.camid + '/' + filename.format(i, count))\\n time.sleep(interval)\\n return filename.format(0, count)\",\n \"def display(self):\\n\\t\\tself.imgDisplay.set_from_pixbuf(self.getVisible())\\n\\t\\tgc.collect()\",\n \"def display(self):\\n display(self.image)\",\n \"def get_data(self):\\n global CAM\\n while CAM.isOpened():\\n _, frame = CAM.read()\\n _, jpeg = cv2.imencode('.jpg', frame)\\n encoded_img = \\\"data:image/jpg;base64,\\\" + str(base64.b64encode(jpeg.tobytes()).decode())\\n SIO.emit('video_frame',\\n {'frame': encoded_img},\\n namespace='/live-stream')\\n sleep(self.delay)\",\n \"def image_cb(self, msg):\\n self.has_image = True\\n self.camera_image = msg\\n light_wp, state = self.process_traffic_lights()\\n\\n # rospy.logerr(\\\"light_wp\\\")\\n # rospy.logerr(light_wp)\\n\\n # self.upcoming_red_light_pub.publish(Int32(light_wp))\\n\\n '''\\n Publish upcoming red lights at camera frequency.\\n Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\\n of times till we start using it. Otherwise the previous stable state is\\n used.\\n '''\\n if self.state != state:\\n self.state_count = 0\\n self.state = state\\n elif self.state_count >= STATE_COUNT_THRESHOLD:\\n self.last_state = self.state\\n light_wp = light_wp if state == TrafficLight.RED else -1\\n self.last_wp = light_wp\\n self.upcoming_red_light_pub.publish(Int32(light_wp))\\n else:\\n self.upcoming_red_light_pub.publish(Int32(self.last_wp))\\n self.state_count += 1\",\n \"def video_loop(self):\\n if not self.isReplay:\\n if self.initStream:\\n print('[SB Live] Starting live video stream...')\\n self.replayStream.release()\\n self.vs.open(0)\\n self.initStream = False\\n print('[SB Live] Live video stream started')\\n if self.cClear:\\n self.cache.release()\\n os.remove('sblive/cache/replay.mov')\\n self.cache.open('sblive/cache/replay.mov', self.fourcc, 10.0, (1280, 720))\\n self.cClear = False\\n ok, frame = self.vs.read() # read frame from video stream\\n if ok: # frame captured without any errors\\n key = cv2.waitKey(1)\\n cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA) # convert colors from BGR to RGBA\\n self.cache.write(frame)\\n self.current_image = Image.fromarray(cv2image) # convert image for PIL\\n imgtk = ImageTk.PhotoImage(image=self.current_image) # convert image for tkinter\\n \\n self.panel.imgtk = imgtk # anchor imgtk so it does not be deleted by garbage-collector\\n self.panel.config(image=imgtk) # show the image\\n else:\\n if self.initStream:\\n print('[SB Live] Starting replay video stream...')\\n self.cache.release()\\n self.vs.release()\\n self.replayStream.open('sblive/cache/replay.mov')\\n self.initStream = False\\n print('[SB Live] Replay video stream started')\\n ok, frame = self.replayStream.read()\\n if ok:\\n key = cv2.waitKey(1)\\n cv2image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGBA) # convert colors from BGR to RGBA\\n self.current_image = Image.fromarray(cv2image) # convert image for PIL\\n imgtk = ImageTk.PhotoImage(image=self.current_image) # convert image for tkinter\\n \\n self.panel.imgtk = imgtk # anchor imgtk so it does not be deleted by garbage-collector\\n self.panel.config(image=imgtk) # show the image\\n else:\\n self.replayStream.release()\\n self.replayStream.open('sblive/cache/replay.mov')\\n if not self.killThread:\\n self.root.after(30, self.video_loop) # call the same function after 30 milliseconds\",\n \"def capture_snapshot(self, ramp_frames=60, x=1280, y=720):\\n self.camera = cv2.VideoCapture(self.camera_port)\\n\\n # Set Resolution\\n self.camera.set(3, x)\\n self.camera.set(4, y)\\n\\n # Give camera enough time to adjust to the light\\n for i in range(ramp_frames):\\n temp = self.camera.read()\\n\\n retval, im = self.camera.read()\\n\\n # save the image in the specified directory\\n image_filename = datetime.now().strftime('%Y-%m-%d_%H-%M-%S') + '.jpeg' \\n # example: 2020-12-04_00-05-50.jpeg\\n self.last_picture = image_filename \\n cv2.imwrite(\\\"{0}\\\\{1}\\\".format(self.snapshots_directory, image_filename) ,im)\\n \\n # release camera to allow use from other apps\\n del(self.camera)\",\n \"def on_image_change(self, value):\\n self.current_image.setImage( self._model.image )\",\n \"def on_draw_over_image(self):\",\n \"def _automatic_rendering(self) -> None:\\n if self.viewer is not None and self.enable_auto_render:\\n\\n if self._record_video_wrapper and self._record_video_wrapper.video_recorder:\\n self._record_video_wrapper.video_recorder.capture_frame()\\n else:\\n self.render()\",\n \"def start():\\n global running\\n running = True\\n messagebox.showinfo(\\\"Camera mode\\\",\\\"Start image grab\\\")\\n camera.start_preview(fullscreen=False, window = (100,20,612,404))\",\n \"def update_display(self):\\n \\n # check availability of display queue of the wide camera\\n# if not hasattr(self,'wide_disp_queue'):\\n# pass\\n# elif self.wide_disp_queue.empty():\\n# pass\\n# else:\\n# try:\\n# wide_disp_image = self.wide_disp_queue.get()\\n# \\n# self.wide_disp_counter += 1\\n# self.wide_disp_counter %= 2\\n# if self.wide_disp_counter == 0:\\n# if type(wide_disp_image) == np.ndarray:\\n# if wide_disp_image.shape == (self.wide_cam.settings.height.value(),self.wide_cam.settings.width.value()):\\n# try:\\n# self.wide_cam_image.setImage(wide_disp_image)\\n# except Exception as ex:\\n# print('Error: %s' % ex)\\n# except Exception as ex:\\n# print(\\\"Error: %s\\\" % ex)\\n \\n # check availability of display queue of the track camera \\n if not hasattr(self,'track_disp_queue'):\\n pass\\n elif self.track_disp_queue.empty():\\n pass\\n else:\\n try:\\n track_disp_image = self.track_disp_queue.get()\\n self.track_disp_counter += 1\\n self.track_disp_counter %= 4\\n if self.track_disp_counter == 0:\\n if type(track_disp_image) == np.ndarray:\\n if track_disp_image.shape == (self.track_cam.settings.height.value(),self.track_cam.settings.width.value()):\\n try:\\n self.track_cam_image.setImage(track_disp_image)\\n except Exception as ex:\\n print('Error: %s' % ex)\\n \\n x = int(self.settings.x.value())\\n y = int(self.settings.y.value())\\n self.tracker_data[:] = 0\\n self.tracker_data[x,y] = 1\\n self.tracker_image.setImage(np.copy(self.tracker_data))\\n except Exception as ex:\\n print(\\\"Error: %s\\\" % ex)\",\n \"def run(self):\\n \\n count = 0\\n while True:\\n self.__ticker.tick()\\n\\n self.update()\\n img = self.get_new_image()\\n if img == None:\\n print \\\"not receiving images yet...\\\"\\n else:\\n if self.verbose:\\n cv.ShowImage(\\\"SnapShotSaver\\\", img)\\n cv.WaitKey(10)\\n cv.SaveImage(\\\"%s/%s_%d.png\\\" % (self.destination, self.prefix, count), img)\\n count += 1\",\n \"def show_poster(self):\\n\\t\\twebbrowser.open(self.poster_image_url)\",\n \"def image_cb(self, msg):\\n self.has_image = True\\n self.camera_image = msg\\n light_wp, state = self.process_traffic_lights()\\n #print(light_wp)\\n #print(state)\\n #print()\\n\\n '''\\n Publish upcoming red lights at camera frequency.\\n Each predicted state has to occur `STATE_COUNT_THRESHOLD` number\\n of times till we start using it. Otherwise the previous stable state is\\n used.\\n '''\\n our_msg = TL_State()\\n our_light = TrafficLight()\\n our_light.header = Header()\\n our_light.header.stamp = rospy.Time.now()\\n our_light.header.frame_id = '/world'\\n if self.state != state:\\n self.state_count = 0\\n self.state = state\\n elif self.state_count >= STATE_COUNT_THRESHOLD:\\n self.last_state = self.state\\n light_wp = light_wp if (state == TrafficLight.RED or state == TrafficLight.YELLOW) else -1\\n self.last_wp = light_wp\\n self.upcoming_red_light_pub.publish(Int32(light_wp))\\n\\n our_msg.waypoint = light_wp\\n our_light.state = state\\n our_msg.light = our_light\\n self.custom_state_pub.publish(our_msg)\\n else:\\n self.upcoming_red_light_pub.publish(Int32(self.last_wp))\\n\\n our_msg.waypoint = self.last_wp\\n our_light.state = self.last_state\\n our_msg.light = our_light\\n self.custom_state_pub.publish(our_msg)\\n self.state_count += 1\",\n \"def cameraOn():\\n cap = cv2.VideoCapture(CAM0, cv2.CAP_DSHOW) # use camera to monitor the motor-mirror assemnbly by DirectShow\\n while(True):\\n # Capture frame-by-frame\\n ret, frame = cap.read()\\n\\n # Display the resulting frame\\n cv2.imshow(\\\" Real-Time Video. Press 'q' to exist.\\\",frame)\\n if cv2.waitKey(8) & 0xFF == ord('q'): #display a frame for 8ms, ~120Hz\\n break\\n \\n cap.release() # release the capture\\n cv2.destroyAllWindows()\",\n \"def startup_capture(self):\\n\\n # Check if path for images already exists\\n if not os.path.exists(self._image_path):\\n os.makedirs(self._image_path)\\n self._curr_image_inc = 0\\n else:\\n self._curr_image_inc = _get_latest_inc(self._image_path) + 1\\n\\n self._hook_screenshot = keyboard.on_press_key(self._config.aimbot_train_capture_key,\\n self._save_screenshot_callback)\",\n \"def captureVideoFrame(self):\\n if(self.kinectConnected):\\n self.VideoFrame = freenect.sync_get_video_with_res(\\n resolution=freenect.RESOLUTION_HIGH)[0]\\n \\n else:\\n self.loadVideoFrame()\\n self.processVideoFrame()\",\n \"def capture_video(self):\\n while self.capturing:\\n nparray = self.source.get_frame()\\n self.frame_buffer.put(Frame(nparray, self.frame))\\n self.frame += 1\\n print \\\"Stopping Capture\\\"\",\n \"def take_snapshot(self, path, file_name):\\n pipelie_state = self.player.get_state(1)\\n p_state = pipelie_state.state\\n if p_state not in (Gst.State.PLAYING, Gst.State.PAUSED):\\n print(\\\"Stream is not ready\\\")\\n else:\\n try:\\n sink = self.player.get_by_name('sink')\\n sample = GstBase.BaseSink.get_last_sample(sink)\\n image_buffer = Gst.Sample.get_buffer(sample)\\n buffer_map = Gst.Buffer.map(image_buffer, Gst.MapFlags.READ)\\n image_binary_data = bytearray(buffer_map.info.data)\\n utils.store_image(image_binary_data, path, file_name + \\\".jpeg\\\")\\n except:\\n print(\\\"Capturing image failed.\\\")\",\n \"def update_cap_image(self):\\n\\n fn = self._get_cap_filename()\\n try:\\n im = PIL.Image.open(fn)\\n except FileNotFoundError:\\n return\\n\\n frame = np.array(im)\\n\\n \\\"\\\"\\\"\\n frame = cv2.imread(fn, cv2.IMREAD_ANYDEPTH)\\n if (frame is None):\\n return\\n \\\"\\\"\\\"\\n\\n frame = (frame >> (16 - self.camera.pixel_bits)).astype(np.uint16)\\n\\n ndx = self.dpar.cur_cap\\n\\n if self.dpar.cap_live_swap:\\n pix, gray = self._get_pixmap(frame, self.dpar.iwindow[ndx])\\n self.live_screen.live_title = self._cap_title(ndx)\\n self.live_screen.setPixmap(pix)\\n else:\\n pix, gray = self._get_pixmap(frame[::4,::4], self.dpar.iwindow[ndx])\\n self.cap_screen.cap_title = self._cap_title(ndx)\\n self.cap_screen.setPixmap(pix)\\n self.cap_screen.format_for_cap() # This is because first time, format is for \\\"no stills\\\".\",\n \"def update_image(self, cv_img):\\n\\t\\tqt_img = self.ImageEdits(cv_img)\\n\\t\\tself.camera.setPixmap(qt_img)\",\n \"def show(self, root):\\n if isinstance(root, str):\\n root = Image.open(root)\\n frames_img = []\\n try:\\n for i in count(1):\\n frames_img.append(ImageTk.PhotoImage(root.copy()))\\n root.seek(i)\\n except EOFError as e:\\n pass\\n # TODO: uncomment this line in final version\\n # ls.get_logger().error(f'failed to load the picture, due to: {str(e)}')\\n self.frames = cycle(frames_img)\\n try:\\n self.delay = root.info['duration']\\n except:\\n self.delay = 100\\n if len(frames_img) == 1:\\n self.config(image=next(self.frames))\\n else:\\n self.show_next_frame()\",\n \"def showImage(self, image):\\n \\n self.image = img\",\n \"def show_image(self):\\n cv2.imshow('Image', self.__diff_image())\\n cv2.waitKey()\",\n \"def showImageViewer(self):\\n self.imageViewer.hide()\\n self.imageViewer.show()\",\n \"def camera():\\n while True:\\n subprocess.check_output(['fswebcam', 'image.jpg'])\\n sleep(60)\",\n \"def next_save(self, *args, **kwargs):\\n while True:\\n try:\\n return super(Camera, self).next_save(*args, **kwargs)\\n except ids_core.IDSCaptureStatus:\\n self._check_capture_status()\",\n \"def snapshot(self):\\n\\n if self.pj[OBSERVATIONS][self.observationId][TYPE] in [MEDIA]:\\n\\n if self.playerType == VLC:\\n\\n if self.playMode == FFMPEG:\\n\\n for idx, media in enumerate(self.pj[OBSERVATIONS][self.observationId][FILE][PLAYER1]):\\n if self.FFmpegGlobalFrame < sum(self.duration[0:idx + 1]):\\n\\n p = pathlib.Path(media)\\n snapshotFilePath = str(p.parent / \\\"{}_{}.png\\\".format(p.stem, self.FFmpegGlobalFrame))\\n\\n if self.detachFrameViewer or self.second_player():\\n self.frame_viewer1.lbFrame.pixmap().save(snapshotFilePath)\\n elif not self.detachFrameViewer:\\n self.lbFFmpeg.pixmap().save(snapshotFilePath)\\n self.statusbar.showMessage(\\\"Snapshot player #1 saved in {}\\\".format(snapshotFilePath), 0)\\n break\\n\\n if self.second_player():\\n for idx, media in enumerate(self.pj[OBSERVATIONS][self.observationId][FILE][PLAYER2]):\\n if self.FFmpegGlobalFrame2 < sum(self.duration2[0:idx + 1]):\\n p = pathlib.Path(media)\\n snapshotFilePath = str(p.parent / \\\"{}_{}.png\\\".format(p.stem, self.FFmpegGlobalFrame2))\\n\\n self.frame_viewer2.lbFrame.pixmap().save(snapshotFilePath)\\n self.statusbar.showMessage(\\\"Snapshot player #2 saved in {}\\\".format(snapshotFilePath), 0)\\n break\\n\\n else: # VLC\\n\\n current_media_path = url2path(self.mediaplayer.get_media().get_mrl())\\n # TODO: replace with pathlib\\n dirName, fileName = os.path.split(current_media_path)\\n self.mediaplayer.video_take_snapshot(0, \\\"{dirName}{sep}{fileNameWOExt}_{time}.png\\\".format(\\n dirName=dirName,\\n sep=os.sep,\\n fileNameWOExt=os.path.splitext(fileName)[0],\\n time=self.mediaplayer.get_time()),\\n 0, 0)\\n\\n # check if multi mode\\n # second video together\\n if self.simultaneousMedia:\\n current_media_path = url2path(self.mediaplayer2.get_media().get_mrl())\\n\\n dirName, fileName = os.path.split(current_media_path)\\n self.mediaplayer2.video_take_snapshot(0, \\\"{dirName}{sep}{fileNameWOExt}_{time}.png\\\".format(\\n dirName=dirName,\\n sep=os.sep,\\n fileNameWOExt=os.path.splitext(fileName)[0],\\n time=self.mediaplayer2.get_time()),\\n 0, 0)\",\n \"def capture(self):\\n filename = self.get_new_photo_filename()\\n open(self.camid + '/' + filename, 'wb').write(self.fake_shot)\\n return filename\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6853264","0.67230445","0.66627806","0.6596117","0.6586887","0.64478475","0.6418577","0.6359291","0.6324905","0.6324905","0.62345564","0.6223029","0.62202823","0.6213966","0.6185097","0.6172923","0.6171413","0.6119024","0.61056674","0.6095103","0.6094443","0.60896444","0.6075941","0.6060286","0.60515803","0.6049772","0.6047872","0.6018686","0.60157967","0.6007798","0.59829515","0.59533393","0.58966124","0.5894625","0.5894293","0.5889228","0.58889276","0.5877818","0.5869449","0.5848629","0.58427435","0.5833481","0.58248734","0.5814466","0.5811392","0.57993495","0.5795121","0.57938564","0.57824343","0.5770129","0.5762327","0.57597375","0.5758187","0.57525784","0.5735948","0.5731609","0.5729815","0.57241356","0.5718611","0.5712328","0.5707341","0.57062876","0.57043356","0.569972","0.56972146","0.5667284","0.5634222","0.5608915","0.56023735","0.55997264","0.5598951","0.55956656","0.5591837","0.55882436","0.5578157","0.556804","0.5565443","0.55624396","0.5561593","0.5559414","0.5559073","0.55573356","0.5550633","0.555038","0.5547864","0.5543776","0.5543183","0.55408084","0.5533883","0.55281633","0.5509704","0.5508672","0.5508313","0.5501118","0.54927033","0.54852223","0.5472596","0.54721564","0.5465814","0.54647326"],"string":"[\n \"0.6853264\",\n \"0.67230445\",\n \"0.66627806\",\n \"0.6596117\",\n \"0.6586887\",\n \"0.64478475\",\n \"0.6418577\",\n \"0.6359291\",\n \"0.6324905\",\n \"0.6324905\",\n \"0.62345564\",\n \"0.6223029\",\n \"0.62202823\",\n \"0.6213966\",\n \"0.6185097\",\n \"0.6172923\",\n \"0.6171413\",\n \"0.6119024\",\n \"0.61056674\",\n \"0.6095103\",\n \"0.6094443\",\n \"0.60896444\",\n \"0.6075941\",\n \"0.6060286\",\n \"0.60515803\",\n \"0.6049772\",\n \"0.6047872\",\n \"0.6018686\",\n \"0.60157967\",\n \"0.6007798\",\n \"0.59829515\",\n \"0.59533393\",\n \"0.58966124\",\n \"0.5894625\",\n \"0.5894293\",\n \"0.5889228\",\n \"0.58889276\",\n \"0.5877818\",\n \"0.5869449\",\n \"0.5848629\",\n \"0.58427435\",\n \"0.5833481\",\n \"0.58248734\",\n \"0.5814466\",\n \"0.5811392\",\n \"0.57993495\",\n \"0.5795121\",\n \"0.57938564\",\n \"0.57824343\",\n \"0.5770129\",\n \"0.5762327\",\n \"0.57597375\",\n \"0.5758187\",\n \"0.57525784\",\n \"0.5735948\",\n \"0.5731609\",\n \"0.5729815\",\n \"0.57241356\",\n \"0.5718611\",\n \"0.5712328\",\n \"0.5707341\",\n \"0.57062876\",\n \"0.57043356\",\n \"0.569972\",\n \"0.56972146\",\n \"0.5667284\",\n \"0.5634222\",\n \"0.5608915\",\n \"0.56023735\",\n \"0.55997264\",\n \"0.5598951\",\n \"0.55956656\",\n \"0.5591837\",\n \"0.55882436\",\n \"0.5578157\",\n \"0.556804\",\n \"0.5565443\",\n \"0.55624396\",\n \"0.5561593\",\n \"0.5559414\",\n \"0.5559073\",\n \"0.55573356\",\n \"0.5550633\",\n \"0.555038\",\n \"0.5547864\",\n \"0.5543776\",\n \"0.5543183\",\n \"0.55408084\",\n \"0.5533883\",\n \"0.55281633\",\n \"0.5509704\",\n \"0.5508672\",\n \"0.5508313\",\n \"0.5501118\",\n \"0.54927033\",\n \"0.54852223\",\n \"0.5472596\",\n \"0.54721564\",\n \"0.5465814\",\n \"0.54647326\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5790726"},"document_rank":{"kind":"string","value":"48"}}},{"rowIdx":77097,"cells":{"query":{"kind":"string","value":"Choose a random valid move that preserves our own eyes."},"document":{"kind":"string","value":"def select_action(self, game_state):\n super().select_action(game_state)\n dim = (game_state.board.num_rows, game_state.board.num_cols)\n if dim != self.dim:\n self._update_cache(dim)\n\n idx = np.arange(len(self.point_cache))\n np.random.shuffle(idx)\n for i in idx:\n p = self.point_cache[i]\n if game_state.is_valid_action(Action.play(p)):\n return Action.play(p)\n return Action.pass_turn()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def random_legal_move():\n return random.choice(legal_moves())","def get_random_move(self, valid_moves):\n return random.choice(valid_moves)","def make_random_move(state: State) -> State:\n return random.choice(state.get_possible_states())","def make_random_move(self):\n #completely random move\n all_moves = set(itertools.product(range(self.height), range(self.width)))\n moves_left = list(all_moves - self.mines - self.moves_made)\n if not moves_left:\n return None\n return random.choice(moves_left)","def randomMove(self, game):\n #time.sleep(0.25)\n return random.choice(game.get_all_legal_moves())","def make_random_move(self):\n s=set()\n for i in range(self.height):\n for j in range(self.width):\n s.add((i,j))\n\n s=s-self.mines-self.moves_made\n if s==set(): return None\n return random.choice(list(s))\n #raise NotImplementedError","def make_random_move(self):\n \n\n if len(self.moves_made) == 56:\n return None\n\n random_move = random.randrange(self.height), random.randrange(self.height)\n\n not_safe_moves = self.moves_made | self.mines\n\n while random_move in not_safe_moves:\n random_move = random.randrange(self.height), random.randrange(self.height)\n\n return random_move","def make_random_move(self):\n choice = None\n options = []\n #generate full moves list\n for i in range(self.width):\n for j in range(self.height):\n #make sure move has not been made\n if (i,j) not in self.moves_made:\n #make sure move is not a mine\n if (i,j) not in self.mines:\n options.append((i,j))\n #if there are no options, return None\n if len(options) == 0:\n return None\n\n #pick a random option from generated list\n choice = random.choice(options)\n return choice\n\n \"\"\"\n For kicks and giggles I wrote this extra bit to determine a\n rough intuitive probability for each option based on the knowledge\n base, so rather than picking a choice randomly the AI can choose\n the option that is, at least intuitively, least likely to blow up.\n Better to take the 1/8 chance than the 1/3 chance, right?\n \"\"\"\n best_chance = 1\n #iterate through generated options\n for option in options:\n #Could set chance to 1/8, but the AI wouldn't actually know that. I\n #only know it because I can read the code...But for the purposes of this\n #drill we'll say the AI doesn't know how many bombs are placed.\n #Better then to pick a square we know nothing about than one that\n #has a 1/8 chance of exploding. Gather more information that way.\n chance = 0\n for sentence in self.knowledge:\n #look to see if current option is in sentences\n if option in sentence.cells:\n #use sentence count and length of cell set to calculate probability\n prob = sentence.count / len(sentence.cells)\n if prob > chance:\n #Looking for the highest explosive probability for this square\n chance = prob\n if chance < best_chance:\n #If this option has lower odds of exploding than current best, it becomes\n #the optimal\n best_chance = chance\n choice = option\n\n #return choice","def __get_next_random_move(self, game_state):\n return random.choice(self.__get_free_seats(game_state))","def make_random_move(self):\n #raise NotImplementedError\n # Take out moves_made as well as mines detected\n self.available_cells = self.available_cells - self.moves_made - self.mines\n available_cells = self.available_cells.copy()\n\n # I'll first try and see if there's any move not within the nearby of\n # The mines, I think this can maximise survivability in some cases\n # It'll still work even if didn't do the below\n for sentence in self.knowledge:\n available_cells -= sentence.cells\n #print(sentence)\n #print(self.mines)\n\n # Making a random move\n length = len(available_cells)\n if length != 0:\n index = random.randint(0, length - 1)\n move = list(available_cells)[index]\n self.moves_made.add(move)\n self.mark_safe(move)\n return move\n\n length = len(self.available_cells)\n if length != 0:\n index = random.randint(0, length - 1)\n move = list(self.available_cells)[index]\n self.moves_made.add(move)\n self.mark_safe(move)\n return move\n return None","def random_move(self):\n available_idx = self.get_empty_cells(self.game_board)\n return random.choice(available_idx)","def move_random(self, board):\n self.get_moves(board.board)\n return random.choice(self.available_moves)","def chooseMove(self, game):\n return self.randomMove(game)","def move(self):\n\n choices = []\n if self.game.green_apples > 0:\n choices.append(self.game.move_green)\n if self.game.red_apples > 0:\n choices.append(self.game.move_red)\n if self.game.blue_plums > 0:\n choices.append(self.game.move_blue)\n if self.game.yellow_pears > 0:\n choices.append(self.game.move_yellow)\n\n random_index = random.randint(0, len(choices) - 1)\n f = choices[random_index]\n f(True)","def random_strategy(player, board):\n return random.choice(Othello.legal_moves(player, board))","def move(self):\r\n their_move = self.last_moves[\"their_move\"]\r\n return (their_move == \"\" and random.choice(moves) or their_move)","def takeNaiveMove():\r\n\tnotFound=True\r\n\twhile notFound:\r\n\t\tmove=random.randint(1,9)\r\n\t\tif validMove(move):\r\n\t\t\tnotFound=False\r\n\treturn move","def move(self):\r\n my_move = self.last_moves[\"my_move\"]\r\n return (my_move != \"\" and moves[(moves.index(my_move)+1) % 3] or\r\n random.choice(moves))","def _move_randomly(self):\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\n wiz1, wiz2 = self.state[a], self.state[b]\n self._swap_wizards(wiz1, wiz2)","def move(self):\r\n his_move = random.randint(0, 2)\r\n return the_moves[his_move]","def move(self):\n if self.learn is None:\n return random.choice(moves)\n else:\n return self.storedmove","def make_move(self, board: Board) -> int:\n return random.choice(board.get_valid_moves())","def randomMove(board):\r\n go = True\r\n while go:\r\n y = random.randint(0, board.size - 1)\r\n x = random.randint(0, board.size - 1)\r\n go = not board.validMove((y, x))\r\n return (y, x)","def rand_select(board):\n import random\n moves = [move for move, new_board in get_all_next_moves(board)]\n return moves[random.randint(0, len(moves) - 1)]","def random_player(board_state, _):\n moves = list(available_moves(board_state))\n return random.choice(moves)","def make_random_move(self):\n # get copy of the empty board\n board = set([(i, j) for i in range(self.height) for j in range(self.width)])\n\n for move in board:\n if not move in self.moves_made and not move in self.mines:\n return move\n\n return None","def choose_move(self): # pylint: disable=too-many-branches,too-many-return-statements\n if self.current_mana < 10: # Only usable move\n return self.moves.teleport\n\n if self.game.player.current_hp <= 10 and self.current_mana >= self.moves.claw.mana_cost:\n return self.moves.claw\n if self.game.player.current_hp <= 20:\n return self.moves.glide\n if self.game.player.current_hp <= 30:\n if self.current_mana < 50:\n options = {self.moves.teleport: 3, self.moves.glide: 6}\n elif self.current_mana <= 140:\n options = {self.moves.teleport: 1, self.moves.glide: 2, self.moves.claw: 6}\n else:\n options = {self.moves.glide: 2.3333333333, self.moves.claw: 6.6666666667}\n if self.current_hp <= 180:\n options[self.moves.heal] = 1\n return self.random_weighted(options)\n\n if self.current_hp < 25:\n if self.current_mana < 50:\n return self.random_weighted({self.moves.teleport: 0.1, self.moves.glide: 0.1, self.moves.heal: 0.8})\n if self.game.player.current_hp <= 40:\n return random.choice([self.moves.claw, self.moves.heal])\n\n if random.random() < 0.1:\n return random.choice(self.attack_options())\n return self.moves.heal\n\n options = self.attack_options()\n if self.current_hp <= 0.9*self.max_hp:\n options.append(self.moves.heal)\n return random.choice(options)","def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)","def random_step(self):\n pos = [i for i in range(9) if self.grid[i] == 0]\n move = random.choice(pos)\n return self.step(move)","def _makeAMove(self, prev_move, board: str) -> int:\n # If not overridden, make a random valid move.\n move = choice(validMoves(board))\n return move","def make_move(the_board, color):\n legal_moves = the_board.legal_moves(color)\n return random.choice(legal_moves) if len(legal_moves) > 0 else (-1, -1)","def pick_movement(self, movement, rand_chance=0.1):\n rand_num = np.random.rand()\n move = self.movements[movement]\n move_str = movement\n if rand_num > 1.0 - rand_chance:\n # do random action now\n print(\"----MOVEMENT HAS CHANGED----\")\n move_str = choice(list(self.movements.keys()))\n move = self.movements[move_str]\n # return the movement\n return move, move_str","def biased_choice(self):\n \n move = None\n # checks if this is the first move\n if self.last_move is not None:\n # checks whether to keep moving in the same direction\n if random.random() < self.move_prob:\n move = self.last_move\n # chooses a random move\n if move is None:\n move = random.choice(self.moves)\n return move","def computer_move():\n\tmove = random.choice(moves)\n\tprint \"Computer's move is %s\" % move\n\treturn move","def select_move(self):\r\n while True:\r\n move = random.randint(0,8)\r\n if self.grid[move][-1] == ' ':\r\n return move","def perform_random_move(grid, move=None):\n zpos = grid.index(0)\n move_functions = [move_up, move_down, move_left, move_right]\n test_functions = [try_up, try_down, try_left, try_right]\n valid_functions = [move_functions[i] for i in [0, 1, 2, 3] if test_functions[i](grid)]\n randnum = random.randint(0, len(valid_functions) - 1)\n return grid, valid_functions[randnum](grid, zpos)","def simulate(state: GameState) -> int:\n moves = list(state.moves)\n #print(\" moves available: \", moves)\n for i in range(len(state.moves)):\n move = random.choice(moves)\n #print(\" move making: \", move)\n move_idx = moves.index(move)\n #print(\" index of move: \", move_idx)\n moves.pop(move_idx)\n #print(\" new moves available: \", moves)\n state = state.traverse(move)\n #print(\" Winner: \", state.util)\n #print(\" New Board: \", state.display)\n return state.util","def selectMove(self,q_table, state):\r\n valid_moves = self.game.valid_moves(state)\r\n ep = 0.2\r\n res = random.randint(0,9)\r\n if res < ep*10:\r\n return valid_moves[random.randint(0,len(valid_moves)-1)]\r\n else:\r\n return self.bestMove(q_table,state, valid_moves)","def move_random(self, board: Board) -> None:\n rnd_move_idx = randint(0,4)\n # moves: stay, up, left, right, down\n moves = [[0,0], [0,-1], [-1,0], [1,0], [0,1]]\n\n if board.can_position_at(self.x + moves[rnd_move_idx][0], self.y + moves[rnd_move_idx][1]):\n board.set_element_at_position(0, self.x, self.y)\n self.x += moves[rnd_move_idx][0]\n self.y += moves[rnd_move_idx][1]\n board.set_element_at_position(3, self.x, self.y)\n print(\"Bomberman moved to [\", self.x, \",\", self.y, \"]\")","def next_move():\n move = int(4 * random.random())\n if move == 0:\n return [1, 0]\n elif move == 1:\n return [-1, 0] \n elif move == 2:\n return [0, 1] \n else:\n return [0, -1]","def chooseMove(self):\n\t\tlistOfColumns = [0,1,2,3,4,5,6]\n\t\tresult = random.choice(listOfColumns)\n\t\t\n\t\twhile (self.game.isValid(result+1) != True):\n\t\t\tresult = random.choice(listOfColumns)\n\t\treturn result","def random_play(state, player):\n import random\n actions = YoteRules.get_player_actions(state, player, reward_move=state.rewarding_move)\n choice = random.choice(actions)\n return choice","def random_move(board):\n\tpossible_moves = []\n\tboard_copy = list(board)\n\n\tfor count, player in enumerate(board):\n\t\tif player == ' ':\n\t\t\tpossible_moves.append(count)\n\n\tif len(possible_moves) != 0:\n\t\tmove = random.choice(possible_moves)\n\t\tboard_copy[move] = 'o'\n\n\t\treturn ''.join(board_copy)\n\t\n\telse:\n\t\treturn board","def next_move(self, board, dice):\r\n rulebook = RuleBook(board, self, dice)\r\n legal_plies = rulebook.generate_legal_ply_list()\r\n return random.choice(legal_plies)","def test_move_default_extra_steps(self):\n player = ss.ResilientPlayer()\n random.seed(2)\n player.move()\n random.seed(1)\n player.move()\n random.seed(2)\n player.move()\n assert player.position == 32","def random_move(turtle, distance):\n angle = uniform(-90,90)\n d = uniform(0,distance)\n turtle.left(angle)\n turtle.forward(d)","def chooseRandomly(self, moves):\n # pick a move randomly\n moveIndex = random.randint(0, len(moves) - 1)\n # send it back\n return moves[moveIndex]","def choose_move(self, board):\n if self.opp == Player.HUMAN:\n time.sleep(4)\n if self.type == Player.HUMAN:\n move = input(\"Please enter your move:\")\n while not board.legalMove(self, move):\n print(move, \"is not valid\")\n move = input(\"Please enter your move\")\n return move\n elif self.type == Player.RANDOM:\n move = choice(board.legalMoves(self))\n return move\n elif self.type == Player.MINIMAX:\n val, move = self.minimax_move(board, self.depth * 2,\n Player.MAX_PLAYER)\n board.last_move = move\n return move\n elif self.type == Player.ABPRUNE:\n val, move = self.alpha_beta_move(board, self.depth * 2,\n float('-inf'), float('inf'),\n Player.MAX_PLAYER)\n return move\n elif self.type == Player.CUSTOM:\n move = self.agent.getAction(board)\n self.agent.update_current_state(board, move)\n return move\n elif self.type == Player.MIX:\n return self.mixed_move(board)\n\n else:\n print(\"Unknown player type\")\n return -1","def choose_move(self):\n return 0","def random(self):\n adj = self.adjacent()\n self.switch(random.choice([pos for pos in adj if self.in_grid(pos) and pos != self.prev]))","def chooseMove(playerBoard, oppBoard, playerSeeds, oppSeeds):\r\n \r\n if SHOW_OUTPUT: print(displayBoard(playerBoard, oppBoard, playerSeeds, oppSeeds))\r\n \r\n moves = getValidMoves(playerBoard, oppBoard)\r\n random.shuffle(moves)\r\n return moves[0]","def MoveRandom(self):\n r = random.randint(0,3)\n if r == 0: self.x += 1\n elif r == 1: self.y += 1\n elif r == 2: self.x -= 1\n elif r == 3: self.y -= 1","def move(self, board):\n\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\n\n while not board.check_move(move[0], move[1]):\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\n\n return move","def choose_move(self):\r\n \r\n return None","def test_move_default_dropped_steps(self):\n player = ss.LazyPlayer()\n random.seed(2)\n player.move()\n random.seed(5)\n player.move()\n assert player.position == 44","def _move_comp_person(self):\n\n move_tuple = random.choice(self._board.possible())\n self._board[move_tuple] = 'x'","def move(self):\n a = random.randint(0, len(self.state) - 1)\n b = random.randint(0, len(self.state) - 1)\n self.state[a], self.state[b] = self.state[b], self.state[a]\n\n # change type of restoration for one state\n c = random.choice(self.restoration_types)\n self.state[a] = (self.state[a][0], c)","def set_random_pos(self, which):\n available = [[r, c] for r, row in enumerate(self.maze)\n for c, value in enumerate(row) if value == ' ']\n choice = random.choice(available)\n if which == 'starting':\n self.current_pos = choice\n elif which == 'finishing':\n self.finish_pos = choice","def random_next_action(state):\n\n possible_moves = []\n for i in range(3):\n for j in range(3):\n if state[i][j] == VALUES.EMPTY:\n possible_moves.append((i, j))\n return random.choice(possible_moves)","def test_random_movement(\n size: Union[int, tuple], num_berries: int, delay_seconds: int, number_steps: int\n) -> None:\n game = Game(\n size,\n [0, 0],\n -1,\n 5,\n -5,\n 10,\n num_berries,\n berry_movement_probabilities=[0.5] * num_berries,\n )\n print(f\"Starting board:\\n{game.get_board()}\")\n done = False\n i = 1\n while not done and i < number_steps:\n print(f\"Action {i}\")\n time.sleep(delay_seconds)\n _, reward, done = game.step(random.choice(MOVEMENTS))\n print(f\"Board:\\n{game.get_board()}\")\n print(f\"Reward: {reward}\")\n i += 1","def random_move(self, num_of_moves=1, validate=False):\n for i in range(num_of_moves):\n # check if legal move exists\n if len(self.legal_moves_in_uci) == 0:\n return None\n # get random move\n action = np.random.choice(self.legal_moves_in_uci)\n # apply move\n self.make_move(action, validate=validate)","def choose_random_move_from_list(self, board, moves_list):\n possible_moves = []\n for move in moves_list:\n if board.is_position_availible(move):\n possible_moves.append(move)\n if len(possible_moves) > 0:\n return random.choice(possible_moves)\n return None","def human_opponent(state):\n print(state)\n while True:\n inp = input(\"What is your move? \\n\")\n if inp == 'pass':\n return len(state.valid_actions) - 1\n if inp == 'random':\n return random.randint(0, len(state.valid_actions) - 1)\n\n try:\n pos = [int(x) for x in inp.split()]\n action = pos[0]*state.board_size + pos[1]\n choice = state.valid_actions.index(action)\n return choice\n except:\n print(\"Invalid move {} try again.\".format(inp))","def random_pose(self):\n position = self._start\n while self[position].distance < np.sum(self._rooms.shape) * 2:\n position = np.array(\n [random.randrange(limit) for limit in self._rooms.shape]\n )\n direction = random.choice(self.exits(position))\n return (position, direction)","def person_move(self, pos1=None, pos2=None):\n if self.available_combinations() != []:\n if pos1 is None and pos2 is None:\n comb = random.choice(self.available_combinations())\n self.board[comb[0]][comb[1]] = 5\n else:\n if (pos1, pos2) in self.available_combinations():\n self.board[pos1][pos2] = 5\n else:\n print(\"This position is not available\")\n raise TextException\n self.last_move = 5","def move_to_random_pos(self):\n newpos = [(np.random.rand() - 0.5) * 0.1,\n (np.random.rand() - 0.5) * 0.1,\n np.random.rand() * 0.9 + 0.2]\n self.move_to(newpos)","def __random_movement(self):\n\t\tself.__steps += 1 \t\t# Increment after every frame\n\t\t# When __steps greater than threshold reverse the direction\n\t\t# and set threshold to a new random value\n\t\tif self.__steps >= self.__threshold_steps:\t\n\t\t\tif self.direction == 'RIGHT':\n\t\t\t\tself.move_left()\n\t\t\t\tself.direction = 'LEFT'\n\t\t\telse:\n\t\t\t\tself.move_right()\n\t\t\t\tself.direction = 'RIGHT'\n\t\t\tself.__threshold_steps = random.randint(25,50)\n\t\t\tself.__steps = 0\n\t\t# Confines the Donkeys movement to within the boundary \n\t\tself.__check_boundary()","def choose_next(self, round):\n return random.choice(self.possible_coords)","def choose_move(self):\n raise NotImplementedError()","def move(self, gstate: gamestate.Gamestate) -> util.Move:\n moves = gstate.legal_moves_vector(gstate.agents[self.id])\n scores = {move: self.evaluate(gstate.copy, move) for move in moves}\n max_score = max(scores.values())\n max_moves = [move for move in moves if scores[move] == max_score]\n return random.choice(max_moves)","def mover_aleatoriamente(self):\n self.randomizador = random.randint(0,4)","def random_moves(length):\n ans = \"\"\n for dummy_num in range(length):\n ans += random.choice([\"u\",\"d\",\"l\",\"r\"])\n return ans","def act(state, action, player, rewarding_move=False): # TODO : Wondering if I should make the random move here.\n if YoteRules.is_legal_move(state, action, player, rewarding_move=rewarding_move):\n return YoteRules.make_move(state, action, player, rewarding_move=rewarding_move)\n else:\n return False","def random_walk(n):\n x,y = 0,0\n for i in range(n):\n step = random.choice(['N','S','E','W'])\n if step == 'N':\n y+=1\n elif step == 'S':\n y-=1\n elif step == 'E':\n x+=1\n else:\n x-=1\n return (x,y)","def random_direction():\n\n if randrange(2):\n return Direction.RIGHT\n else:\n return Direction.DOWN","def choose_move(self) -> int:\n raise NotImplementedError(\"Choose move not implemented\")","def direction_correction(self):\n self.directions.monster = random.uniform(self.directions.monster * self.get_monster_sensitivity(),\n self.directions.monster * (1 + (1 - self.get_monster_sensitivity())))\n self.directions.food = random.uniform(self.directions.food * self.get_food_sensitivity(),\n self.directions.food * (1 + (1 - self.get_food_sensitivity())))\n self.directions.water = random.uniform(self.directions.water * self.get_water_sensitivity(),\n self.directions.water * (1 + (1 - self.get_water_sensitivity())))","def nextMoveDecision(self):\n b = random.randint(1, 9) \n while (self.Occupied(b)):\n b = random.randint(1, 9) \n return b","def throw(self, move):\n for dice_index in move:\n self.dice[dice_index - 1] = random.randint(1,6)","def random_walk(n):\n\tx, y = 0, 0\n\tfor i in range(n):\n\t\tstep = random.choice(['N', 'S', 'E', 'W'])\n\t\tif step == 'N':\n\t\t\ty += 1\n\t\tif step == 'S':\n\t\t\ty -= 1\n\t\tif step == 'E':\n\t\t\tx += 1\n\t\tif step == 'W':\n\t\t\tx -= 1\n\treturn (x, y)","def prepare_next_turn(grid):\n empties = get_empty_cells(grid)\n y,x = random.choice(empties)\n grid[y][x] = 2 if random.random() < 0.9 else 4\n return any_possible_moves(grid)","def auto_play_random(self, player=None):\r\n if player is None:\r\n player = self.get_player()\r\n legal_list = self.get_legal_list()\r\n next_move = legal_list.rand_obj()\r\n self.new_edge(next_move)","def make_move(self, board: Block):\n # select a random block and highlight it.\n rand_block = select_random_block(board)\n rand_block.highlighted = True\n self.renderer.draw(board, self.id)\n pygame.time.wait(TIME_DELAY)\n choice = random.randint(0, 4)\n\n if rand_block.level == rand_block.max_depth or rand_block.level == 0:\n # Random player has chosen to smash an invalid block thus its move\n # is forfeited\n if choice == 4:\n pass\n else:\n perform_move(rand_block, choice)\n else:\n perform_move(rand_block, choice)\n rand_block.highlighted = False\n self.renderer.draw(board, self.id)\n return 0","def test_randomness_of_moves():\n env = Four_Rooms_Environment(stochastic_actions_probability=0.0)\n env.reset()\n for _ in range(10):\n for move in env.actions:\n assert move == env.determine_which_action_will_actually_occur(move)\n\n env = Four_Rooms_Environment(stochastic_actions_probability=1.0)\n num_iterations = 10000\n for move in env.actions:\n moves = []\n for _ in range(num_iterations):\n moves.append(env.determine_which_action_will_actually_occur(move))\n count = Counter(moves)\n for move_test in env.actions:\n if move != move_test: #We do this because stochastic probability 1.0 means the move will never be picked\n assert abs((num_iterations / (len(env.actions)-1)) - count[move_test]) < num_iterations / 20.0, \"{}\".format(count)\n\n env = Four_Rooms_Environment(stochastic_actions_probability=0.75)\n num_iterations = 10000\n for move in env.actions:\n moves = []\n for _ in range(num_iterations):\n moves.append(env.determine_which_action_will_actually_occur(move))\n count = Counter(moves)\n for move_test in env.actions:\n assert abs((num_iterations / len(env.actions)) - count[move_test]) < num_iterations / 20.0, \"{}\".format(count)","def move_monster(monster):\n monster = (clip(monster[0] + randint(-1, 1), 0, X_SIZE-1),\n clip(monster[1] + randint(-1, 1), 0, Y_SIZE-1))\n return monster","def cpu_move(moves_list):\n \n move = randint(0,3)\n return move","def handle_get_action(self, state):\n\n # This is an example player who picks random moves. REMOVE THIS WHEN YOU ADD YOUR OWN CODE !!\n\n #next_move = tuple(self.pick_random_free_cell(\n # state, size=int(math.sqrt(len(state)-1))))\n #############################\n #\n #\n NN_state = self.server_state_to_NN_state(state)\n predictions = self.policy_network.predict([[NN_state]])\n next_move = np.argmax(predictions)\n self.game.set_state(NN_state,1)\n legal_actions = self.game.get_legal_actions()\n if next_move not in legal_actions:\n next_move = np.random.choice(legal_actions,1)\n next_move = self.action_to_tuple_action(next_move)\n\n #\n # next_move = ???\n ##############################\n return next_move","def randwalk(n):\n\tx=0;\n\ty=0;\n\n\tfor i in range(n):\n\t\tstep = random.choice(['N','S','E','W'])\n\t\tif step== 'N':\n\t\t\ty=y+1\n\t\telif step=='S':\n\t\t\ty=y-1\n\t\telif step=='E':\n\t\t\tx=x+1\n\t\telse:\n\t\t\tx=x-1\n\treturn (x,y)","def choose_move(self, possible_moves, seconds_left):\n # TODO: update this method\n print('\\--------------Choose Move--------------/')\n print(possible_moves)\n print(list(self.current_board.legal_moves))\n search_tree = MCTS(5, self.color, self.current_board)\n search_tree.search()\n move = search_tree.pick_move()['move']\n\n return move","def move(self):\n if random.random() < 0.5:\n self.y = (self.y + 1) % 100\n else:\n self.y = (self.y - 1) % 100\n if random.random() < 0.5:\n self.x = (self.x + 1) % 100\n else:\n self.x = (self.x - 1) % 100","def move(self):\n self._move_range_shuffle(3)\n self._move_satisfy_random_constraint()\n # self._move_range_shuffle(3)\n #if (curr_energy > 50):\n # self._move_satisfy_random_constraint()\n #else:\n # self._move_range_shuffle(3)","def move_randomly(self, with_fight=False):\n delta = [(-1, -1), (-1, 0), (-1, 1), (0, 1), (0, -1), (1, -1), (1, 0), (1, 1)]\n rd.shuffle(delta)\n x, y = self.owner.pos\n while len(delta) > 0:\n dx, dy = delta.pop()\n if self.move_towards_position((x + dx, y + dy)):\n return","def __make_move(self, user):\n if user:\n pos = int(input('Make your move (1-9): '))\n print('\\033[2A\\r ')\n while self.state[pos-1] != ' ':\n pos = int(input('\\rSeat\\'s taken. Make your move (1-9): '))\n print('\\033[2A\\r ')\n self.state[pos-1] = self.player_char\n else:\n time.sleep(.5)\n pos = random.randint(1, 9)\n if self.difficulty == 1:\n best = self.__find_best()\n pos = best+1 if best else pos\n while self.state[pos-1] != ' ':\n pos = random.randint(1, 9)\n self.state[pos-1] = self.opponent_char\n self.history.append(self.state[:])","def prepare_next_turn(grid):\n\tempties = get_empty_cells(grid)\n\ty,x = random.choice(empties)\n\tgrid[y][x] = 2 if random.random() < prob_2 else 4\n\treturn any_possible_moves(grid)","def randwalk(number_of_players=4, number_of_pieces=4):\n\n # `ghost_players` is a LUDOpy specific way to specify the number of\n # players. So, if we want 2 players, the code below will generate a list:\n #\n # [3, 2, 1, 0]\n #\n # and slice it so it omits players 2 and 3.\n #\n # [3, 2, 1, 0][:2] == [3, 2]\n\n g = ludopy.Game(\n ghost_players=list(reversed(range(0, 4)))[:-number_of_players],\n number_of_pieces=number_of_pieces\n )\n there_is_a_winner = False\n\n while not there_is_a_winner:\n (dice, move_pieces, player_pieces, enemy_pieces, player_is_a_winner,\n there_is_a_winner), player_i = g.get_observation()\n\n if len(move_pieces):\n piece_to_move = \\\n move_pieces[np.random.randint(0, len(move_pieces))]\n else:\n piece_to_move = -1\n\n _, _, _, _, _, there_is_a_winner = g.answer_observation(piece_to_move)\n\n return g","def find_legal_move(self, g, func, timeout=None):\r\n legal_col = []\r\n for index, col in enumerate(g.col_lst):\r\n if col < g.BOARD_COL - 1:\r\n legal_col.append(index)\r\n chosen_col = random.sample(legal_col, k=1)[0]\r\n if len(legal_col) == 0:\r\n raise Exception(self.MSG_NO_AI_MOVES)\r\n func(chosen_col)\r\n return chosen_col","def exploring_starts(self):\n def random_choice(l): return l[np.random.randint(len(l))]\n return map(random_choice, (self.env.states, self.env.moves))","def DoRandomAction(self, gameState: TekkenGameState):\n actionRNG = random.randint(1, 100)\n # Anything generated above this number, the bot chooses to poke\n POKE_CAP = 50\n DASH_CAP = 35\n DUCK_CAP = 20\n SIDESTEP_CAP = 10\n WALK_CAP = 5\n\n TARGET_DISTANCE = 2000.0 # 2.00\n\n # Ensure we dont do nothing for twice in a row\n if self.last_rand_action_was_nth:\n actionRNG += SIDESTEP_CAP\n self.last_rand_action_was_nth = False\n\n # Try to poke if the last action was spent getting\n # into range for pokes\n if self.last_asked_poke:\n actionRNG += POKE_CAP\n\n if actionRNG >= POKE_CAP:\n # Not in range, dash towards\n if TARGET_DISTANCE < gameState.GetDist():\n self.DashTowardsTargetDist(TARGET_DISTANCE, gameState)\n self.last_asked_poke = not self.last_asked_poke\n else:\n self.RandomPoke()\n self.last_asked_poke = False\n elif actionRNG >= DASH_CAP:\n self.DashTowardsTargetDist(TARGET_DISTANCE, gameState)\n elif actionRNG >= DUCK_CAP:\n # duck for a few frames\n self.botCommands.BlockLowFull(random.randint(8, 12))\n elif actionRNG >= SIDESTEP_CAP:\n # Pick between SS up or down\n if (random.randint(0, 1) == 0):\n self.botCommands.SidestepUp()\n else:\n self.botCommands.SidestepDown()\n elif actionRNG >= WALK_CAP:\n self.WalkTowardsTargetDist(TARGET_DISTANCE, gameState)\n else:\n self.last_rand_action_was_nth = True\n\n self.tick_till_next_rand = 0","def move_aim(self):\n self.color = random.choice(COLORS)\n self.x += 3 * self.speed_x / FPS\n self.y += 3 * self.speed_y / FPS\n self.r -= 1\n self.draw_aim()\n if self.r <= 10:\n self.color = random.choice(COLORS)\n self.x = randint(100, 1000)\n self.y = randint(100, 800)\n self.r = randint(50, 100)\n self.speed_x = randint(-200, 200)\n self.speed_y = randint(-200, 200)\n if self.x >= 1100:\n self.speed_x = randint(-100, -10)\n if self.x <= 50:\n self.speed_x = randint(10, 100)\n if self.y >= 800:\n self.speed_y = randint(-100, -10)\n if self.y <= 50:\n self.speed_y = randint(10, 100)","def play_against_random(self, action):\n state, status, done = self.step(action)\n if not done and self.turn == 2:\n state, s2, done = self.random_step()\n if done:\n if s2 == self.STATUS_WIN:\n status = self.STATUS_LOSE\n elif s2 == self.STATUS_TIE:\n status = self.STATUS_TIE\n else:\n raise ValueError(\"???\")\n return state, status, done","def choose_random_action(self):\r\n return Action.HIT if random.random() <= 0.5 else Action.STICK"],"string":"[\n \"def random_legal_move():\\n return random.choice(legal_moves())\",\n \"def get_random_move(self, valid_moves):\\n return random.choice(valid_moves)\",\n \"def make_random_move(state: State) -> State:\\n return random.choice(state.get_possible_states())\",\n \"def make_random_move(self):\\n #completely random move\\n all_moves = set(itertools.product(range(self.height), range(self.width)))\\n moves_left = list(all_moves - self.mines - self.moves_made)\\n if not moves_left:\\n return None\\n return random.choice(moves_left)\",\n \"def randomMove(self, game):\\n #time.sleep(0.25)\\n return random.choice(game.get_all_legal_moves())\",\n \"def make_random_move(self):\\n s=set()\\n for i in range(self.height):\\n for j in range(self.width):\\n s.add((i,j))\\n\\n s=s-self.mines-self.moves_made\\n if s==set(): return None\\n return random.choice(list(s))\\n #raise NotImplementedError\",\n \"def make_random_move(self):\\n \\n\\n if len(self.moves_made) == 56:\\n return None\\n\\n random_move = random.randrange(self.height), random.randrange(self.height)\\n\\n not_safe_moves = self.moves_made | self.mines\\n\\n while random_move in not_safe_moves:\\n random_move = random.randrange(self.height), random.randrange(self.height)\\n\\n return random_move\",\n \"def make_random_move(self):\\n choice = None\\n options = []\\n #generate full moves list\\n for i in range(self.width):\\n for j in range(self.height):\\n #make sure move has not been made\\n if (i,j) not in self.moves_made:\\n #make sure move is not a mine\\n if (i,j) not in self.mines:\\n options.append((i,j))\\n #if there are no options, return None\\n if len(options) == 0:\\n return None\\n\\n #pick a random option from generated list\\n choice = random.choice(options)\\n return choice\\n\\n \\\"\\\"\\\"\\n For kicks and giggles I wrote this extra bit to determine a\\n rough intuitive probability for each option based on the knowledge\\n base, so rather than picking a choice randomly the AI can choose\\n the option that is, at least intuitively, least likely to blow up.\\n Better to take the 1/8 chance than the 1/3 chance, right?\\n \\\"\\\"\\\"\\n best_chance = 1\\n #iterate through generated options\\n for option in options:\\n #Could set chance to 1/8, but the AI wouldn't actually know that. I\\n #only know it because I can read the code...But for the purposes of this\\n #drill we'll say the AI doesn't know how many bombs are placed.\\n #Better then to pick a square we know nothing about than one that\\n #has a 1/8 chance of exploding. Gather more information that way.\\n chance = 0\\n for sentence in self.knowledge:\\n #look to see if current option is in sentences\\n if option in sentence.cells:\\n #use sentence count and length of cell set to calculate probability\\n prob = sentence.count / len(sentence.cells)\\n if prob > chance:\\n #Looking for the highest explosive probability for this square\\n chance = prob\\n if chance < best_chance:\\n #If this option has lower odds of exploding than current best, it becomes\\n #the optimal\\n best_chance = chance\\n choice = option\\n\\n #return choice\",\n \"def __get_next_random_move(self, game_state):\\n return random.choice(self.__get_free_seats(game_state))\",\n \"def make_random_move(self):\\n #raise NotImplementedError\\n # Take out moves_made as well as mines detected\\n self.available_cells = self.available_cells - self.moves_made - self.mines\\n available_cells = self.available_cells.copy()\\n\\n # I'll first try and see if there's any move not within the nearby of\\n # The mines, I think this can maximise survivability in some cases\\n # It'll still work even if didn't do the below\\n for sentence in self.knowledge:\\n available_cells -= sentence.cells\\n #print(sentence)\\n #print(self.mines)\\n\\n # Making a random move\\n length = len(available_cells)\\n if length != 0:\\n index = random.randint(0, length - 1)\\n move = list(available_cells)[index]\\n self.moves_made.add(move)\\n self.mark_safe(move)\\n return move\\n\\n length = len(self.available_cells)\\n if length != 0:\\n index = random.randint(0, length - 1)\\n move = list(self.available_cells)[index]\\n self.moves_made.add(move)\\n self.mark_safe(move)\\n return move\\n return None\",\n \"def random_move(self):\\n available_idx = self.get_empty_cells(self.game_board)\\n return random.choice(available_idx)\",\n \"def move_random(self, board):\\n self.get_moves(board.board)\\n return random.choice(self.available_moves)\",\n \"def chooseMove(self, game):\\n return self.randomMove(game)\",\n \"def move(self):\\n\\n choices = []\\n if self.game.green_apples > 0:\\n choices.append(self.game.move_green)\\n if self.game.red_apples > 0:\\n choices.append(self.game.move_red)\\n if self.game.blue_plums > 0:\\n choices.append(self.game.move_blue)\\n if self.game.yellow_pears > 0:\\n choices.append(self.game.move_yellow)\\n\\n random_index = random.randint(0, len(choices) - 1)\\n f = choices[random_index]\\n f(True)\",\n \"def random_strategy(player, board):\\n return random.choice(Othello.legal_moves(player, board))\",\n \"def move(self):\\r\\n their_move = self.last_moves[\\\"their_move\\\"]\\r\\n return (their_move == \\\"\\\" and random.choice(moves) or their_move)\",\n \"def takeNaiveMove():\\r\\n\\tnotFound=True\\r\\n\\twhile notFound:\\r\\n\\t\\tmove=random.randint(1,9)\\r\\n\\t\\tif validMove(move):\\r\\n\\t\\t\\tnotFound=False\\r\\n\\treturn move\",\n \"def move(self):\\r\\n my_move = self.last_moves[\\\"my_move\\\"]\\r\\n return (my_move != \\\"\\\" and moves[(moves.index(my_move)+1) % 3] or\\r\\n random.choice(moves))\",\n \"def _move_randomly(self):\\n a, b = randint(0, len(self.state) - 1), randint(0, len(self.state) - 1)\\n wiz1, wiz2 = self.state[a], self.state[b]\\n self._swap_wizards(wiz1, wiz2)\",\n \"def move(self):\\r\\n his_move = random.randint(0, 2)\\r\\n return the_moves[his_move]\",\n \"def move(self):\\n if self.learn is None:\\n return random.choice(moves)\\n else:\\n return self.storedmove\",\n \"def make_move(self, board: Board) -> int:\\n return random.choice(board.get_valid_moves())\",\n \"def randomMove(board):\\r\\n go = True\\r\\n while go:\\r\\n y = random.randint(0, board.size - 1)\\r\\n x = random.randint(0, board.size - 1)\\r\\n go = not board.validMove((y, x))\\r\\n return (y, x)\",\n \"def rand_select(board):\\n import random\\n moves = [move for move, new_board in get_all_next_moves(board)]\\n return moves[random.randint(0, len(moves) - 1)]\",\n \"def random_player(board_state, _):\\n moves = list(available_moves(board_state))\\n return random.choice(moves)\",\n \"def make_random_move(self):\\n # get copy of the empty board\\n board = set([(i, j) for i in range(self.height) for j in range(self.width)])\\n\\n for move in board:\\n if not move in self.moves_made and not move in self.mines:\\n return move\\n\\n return None\",\n \"def choose_move(self): # pylint: disable=too-many-branches,too-many-return-statements\\n if self.current_mana < 10: # Only usable move\\n return self.moves.teleport\\n\\n if self.game.player.current_hp <= 10 and self.current_mana >= self.moves.claw.mana_cost:\\n return self.moves.claw\\n if self.game.player.current_hp <= 20:\\n return self.moves.glide\\n if self.game.player.current_hp <= 30:\\n if self.current_mana < 50:\\n options = {self.moves.teleport: 3, self.moves.glide: 6}\\n elif self.current_mana <= 140:\\n options = {self.moves.teleport: 1, self.moves.glide: 2, self.moves.claw: 6}\\n else:\\n options = {self.moves.glide: 2.3333333333, self.moves.claw: 6.6666666667}\\n if self.current_hp <= 180:\\n options[self.moves.heal] = 1\\n return self.random_weighted(options)\\n\\n if self.current_hp < 25:\\n if self.current_mana < 50:\\n return self.random_weighted({self.moves.teleport: 0.1, self.moves.glide: 0.1, self.moves.heal: 0.8})\\n if self.game.player.current_hp <= 40:\\n return random.choice([self.moves.claw, self.moves.heal])\\n\\n if random.random() < 0.1:\\n return random.choice(self.attack_options())\\n return self.moves.heal\\n\\n options = self.attack_options()\\n if self.current_hp <= 0.9*self.max_hp:\\n options.append(self.moves.heal)\\n return random.choice(options)\",\n \"def random_step(self):\\n pos = [i for i in range(9) if self.grid[i] == 0]\\n move = random.choice(pos)\\n return self.step(move)\",\n \"def random_step(self):\\n pos = [i for i in range(9) if self.grid[i] == 0]\\n move = random.choice(pos)\\n return self.step(move)\",\n \"def _makeAMove(self, prev_move, board: str) -> int:\\n # If not overridden, make a random valid move.\\n move = choice(validMoves(board))\\n return move\",\n \"def make_move(the_board, color):\\n legal_moves = the_board.legal_moves(color)\\n return random.choice(legal_moves) if len(legal_moves) > 0 else (-1, -1)\",\n \"def pick_movement(self, movement, rand_chance=0.1):\\n rand_num = np.random.rand()\\n move = self.movements[movement]\\n move_str = movement\\n if rand_num > 1.0 - rand_chance:\\n # do random action now\\n print(\\\"----MOVEMENT HAS CHANGED----\\\")\\n move_str = choice(list(self.movements.keys()))\\n move = self.movements[move_str]\\n # return the movement\\n return move, move_str\",\n \"def biased_choice(self):\\n \\n move = None\\n # checks if this is the first move\\n if self.last_move is not None:\\n # checks whether to keep moving in the same direction\\n if random.random() < self.move_prob:\\n move = self.last_move\\n # chooses a random move\\n if move is None:\\n move = random.choice(self.moves)\\n return move\",\n \"def computer_move():\\n\\tmove = random.choice(moves)\\n\\tprint \\\"Computer's move is %s\\\" % move\\n\\treturn move\",\n \"def select_move(self):\\r\\n while True:\\r\\n move = random.randint(0,8)\\r\\n if self.grid[move][-1] == ' ':\\r\\n return move\",\n \"def perform_random_move(grid, move=None):\\n zpos = grid.index(0)\\n move_functions = [move_up, move_down, move_left, move_right]\\n test_functions = [try_up, try_down, try_left, try_right]\\n valid_functions = [move_functions[i] for i in [0, 1, 2, 3] if test_functions[i](grid)]\\n randnum = random.randint(0, len(valid_functions) - 1)\\n return grid, valid_functions[randnum](grid, zpos)\",\n \"def simulate(state: GameState) -> int:\\n moves = list(state.moves)\\n #print(\\\" moves available: \\\", moves)\\n for i in range(len(state.moves)):\\n move = random.choice(moves)\\n #print(\\\" move making: \\\", move)\\n move_idx = moves.index(move)\\n #print(\\\" index of move: \\\", move_idx)\\n moves.pop(move_idx)\\n #print(\\\" new moves available: \\\", moves)\\n state = state.traverse(move)\\n #print(\\\" Winner: \\\", state.util)\\n #print(\\\" New Board: \\\", state.display)\\n return state.util\",\n \"def selectMove(self,q_table, state):\\r\\n valid_moves = self.game.valid_moves(state)\\r\\n ep = 0.2\\r\\n res = random.randint(0,9)\\r\\n if res < ep*10:\\r\\n return valid_moves[random.randint(0,len(valid_moves)-1)]\\r\\n else:\\r\\n return self.bestMove(q_table,state, valid_moves)\",\n \"def move_random(self, board: Board) -> None:\\n rnd_move_idx = randint(0,4)\\n # moves: stay, up, left, right, down\\n moves = [[0,0], [0,-1], [-1,0], [1,0], [0,1]]\\n\\n if board.can_position_at(self.x + moves[rnd_move_idx][0], self.y + moves[rnd_move_idx][1]):\\n board.set_element_at_position(0, self.x, self.y)\\n self.x += moves[rnd_move_idx][0]\\n self.y += moves[rnd_move_idx][1]\\n board.set_element_at_position(3, self.x, self.y)\\n print(\\\"Bomberman moved to [\\\", self.x, \\\",\\\", self.y, \\\"]\\\")\",\n \"def next_move():\\n move = int(4 * random.random())\\n if move == 0:\\n return [1, 0]\\n elif move == 1:\\n return [-1, 0] \\n elif move == 2:\\n return [0, 1] \\n else:\\n return [0, -1]\",\n \"def chooseMove(self):\\n\\t\\tlistOfColumns = [0,1,2,3,4,5,6]\\n\\t\\tresult = random.choice(listOfColumns)\\n\\t\\t\\n\\t\\twhile (self.game.isValid(result+1) != True):\\n\\t\\t\\tresult = random.choice(listOfColumns)\\n\\t\\treturn result\",\n \"def random_play(state, player):\\n import random\\n actions = YoteRules.get_player_actions(state, player, reward_move=state.rewarding_move)\\n choice = random.choice(actions)\\n return choice\",\n \"def random_move(board):\\n\\tpossible_moves = []\\n\\tboard_copy = list(board)\\n\\n\\tfor count, player in enumerate(board):\\n\\t\\tif player == ' ':\\n\\t\\t\\tpossible_moves.append(count)\\n\\n\\tif len(possible_moves) != 0:\\n\\t\\tmove = random.choice(possible_moves)\\n\\t\\tboard_copy[move] = 'o'\\n\\n\\t\\treturn ''.join(board_copy)\\n\\t\\n\\telse:\\n\\t\\treturn board\",\n \"def next_move(self, board, dice):\\r\\n rulebook = RuleBook(board, self, dice)\\r\\n legal_plies = rulebook.generate_legal_ply_list()\\r\\n return random.choice(legal_plies)\",\n \"def test_move_default_extra_steps(self):\\n player = ss.ResilientPlayer()\\n random.seed(2)\\n player.move()\\n random.seed(1)\\n player.move()\\n random.seed(2)\\n player.move()\\n assert player.position == 32\",\n \"def random_move(turtle, distance):\\n angle = uniform(-90,90)\\n d = uniform(0,distance)\\n turtle.left(angle)\\n turtle.forward(d)\",\n \"def chooseRandomly(self, moves):\\n # pick a move randomly\\n moveIndex = random.randint(0, len(moves) - 1)\\n # send it back\\n return moves[moveIndex]\",\n \"def choose_move(self, board):\\n if self.opp == Player.HUMAN:\\n time.sleep(4)\\n if self.type == Player.HUMAN:\\n move = input(\\\"Please enter your move:\\\")\\n while not board.legalMove(self, move):\\n print(move, \\\"is not valid\\\")\\n move = input(\\\"Please enter your move\\\")\\n return move\\n elif self.type == Player.RANDOM:\\n move = choice(board.legalMoves(self))\\n return move\\n elif self.type == Player.MINIMAX:\\n val, move = self.minimax_move(board, self.depth * 2,\\n Player.MAX_PLAYER)\\n board.last_move = move\\n return move\\n elif self.type == Player.ABPRUNE:\\n val, move = self.alpha_beta_move(board, self.depth * 2,\\n float('-inf'), float('inf'),\\n Player.MAX_PLAYER)\\n return move\\n elif self.type == Player.CUSTOM:\\n move = self.agent.getAction(board)\\n self.agent.update_current_state(board, move)\\n return move\\n elif self.type == Player.MIX:\\n return self.mixed_move(board)\\n\\n else:\\n print(\\\"Unknown player type\\\")\\n return -1\",\n \"def choose_move(self):\\n return 0\",\n \"def random(self):\\n adj = self.adjacent()\\n self.switch(random.choice([pos for pos in adj if self.in_grid(pos) and pos != self.prev]))\",\n \"def chooseMove(playerBoard, oppBoard, playerSeeds, oppSeeds):\\r\\n \\r\\n if SHOW_OUTPUT: print(displayBoard(playerBoard, oppBoard, playerSeeds, oppSeeds))\\r\\n \\r\\n moves = getValidMoves(playerBoard, oppBoard)\\r\\n random.shuffle(moves)\\r\\n return moves[0]\",\n \"def MoveRandom(self):\\n r = random.randint(0,3)\\n if r == 0: self.x += 1\\n elif r == 1: self.y += 1\\n elif r == 2: self.x -= 1\\n elif r == 3: self.y -= 1\",\n \"def move(self, board):\\n\\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\\n\\n while not board.check_move(move[0], move[1]):\\n move = (randint(0, board.get_dimension()-1), randint(0, board.get_dimension()-1))\\n\\n return move\",\n \"def choose_move(self):\\r\\n \\r\\n return None\",\n \"def test_move_default_dropped_steps(self):\\n player = ss.LazyPlayer()\\n random.seed(2)\\n player.move()\\n random.seed(5)\\n player.move()\\n assert player.position == 44\",\n \"def _move_comp_person(self):\\n\\n move_tuple = random.choice(self._board.possible())\\n self._board[move_tuple] = 'x'\",\n \"def move(self):\\n a = random.randint(0, len(self.state) - 1)\\n b = random.randint(0, len(self.state) - 1)\\n self.state[a], self.state[b] = self.state[b], self.state[a]\\n\\n # change type of restoration for one state\\n c = random.choice(self.restoration_types)\\n self.state[a] = (self.state[a][0], c)\",\n \"def set_random_pos(self, which):\\n available = [[r, c] for r, row in enumerate(self.maze)\\n for c, value in enumerate(row) if value == ' ']\\n choice = random.choice(available)\\n if which == 'starting':\\n self.current_pos = choice\\n elif which == 'finishing':\\n self.finish_pos = choice\",\n \"def random_next_action(state):\\n\\n possible_moves = []\\n for i in range(3):\\n for j in range(3):\\n if state[i][j] == VALUES.EMPTY:\\n possible_moves.append((i, j))\\n return random.choice(possible_moves)\",\n \"def test_random_movement(\\n size: Union[int, tuple], num_berries: int, delay_seconds: int, number_steps: int\\n) -> None:\\n game = Game(\\n size,\\n [0, 0],\\n -1,\\n 5,\\n -5,\\n 10,\\n num_berries,\\n berry_movement_probabilities=[0.5] * num_berries,\\n )\\n print(f\\\"Starting board:\\\\n{game.get_board()}\\\")\\n done = False\\n i = 1\\n while not done and i < number_steps:\\n print(f\\\"Action {i}\\\")\\n time.sleep(delay_seconds)\\n _, reward, done = game.step(random.choice(MOVEMENTS))\\n print(f\\\"Board:\\\\n{game.get_board()}\\\")\\n print(f\\\"Reward: {reward}\\\")\\n i += 1\",\n \"def random_move(self, num_of_moves=1, validate=False):\\n for i in range(num_of_moves):\\n # check if legal move exists\\n if len(self.legal_moves_in_uci) == 0:\\n return None\\n # get random move\\n action = np.random.choice(self.legal_moves_in_uci)\\n # apply move\\n self.make_move(action, validate=validate)\",\n \"def choose_random_move_from_list(self, board, moves_list):\\n possible_moves = []\\n for move in moves_list:\\n if board.is_position_availible(move):\\n possible_moves.append(move)\\n if len(possible_moves) > 0:\\n return random.choice(possible_moves)\\n return None\",\n \"def human_opponent(state):\\n print(state)\\n while True:\\n inp = input(\\\"What is your move? \\\\n\\\")\\n if inp == 'pass':\\n return len(state.valid_actions) - 1\\n if inp == 'random':\\n return random.randint(0, len(state.valid_actions) - 1)\\n\\n try:\\n pos = [int(x) for x in inp.split()]\\n action = pos[0]*state.board_size + pos[1]\\n choice = state.valid_actions.index(action)\\n return choice\\n except:\\n print(\\\"Invalid move {} try again.\\\".format(inp))\",\n \"def random_pose(self):\\n position = self._start\\n while self[position].distance < np.sum(self._rooms.shape) * 2:\\n position = np.array(\\n [random.randrange(limit) for limit in self._rooms.shape]\\n )\\n direction = random.choice(self.exits(position))\\n return (position, direction)\",\n \"def person_move(self, pos1=None, pos2=None):\\n if self.available_combinations() != []:\\n if pos1 is None and pos2 is None:\\n comb = random.choice(self.available_combinations())\\n self.board[comb[0]][comb[1]] = 5\\n else:\\n if (pos1, pos2) in self.available_combinations():\\n self.board[pos1][pos2] = 5\\n else:\\n print(\\\"This position is not available\\\")\\n raise TextException\\n self.last_move = 5\",\n \"def move_to_random_pos(self):\\n newpos = [(np.random.rand() - 0.5) * 0.1,\\n (np.random.rand() - 0.5) * 0.1,\\n np.random.rand() * 0.9 + 0.2]\\n self.move_to(newpos)\",\n \"def __random_movement(self):\\n\\t\\tself.__steps += 1 \\t\\t# Increment after every frame\\n\\t\\t# When __steps greater than threshold reverse the direction\\n\\t\\t# and set threshold to a new random value\\n\\t\\tif self.__steps >= self.__threshold_steps:\\t\\n\\t\\t\\tif self.direction == 'RIGHT':\\n\\t\\t\\t\\tself.move_left()\\n\\t\\t\\t\\tself.direction = 'LEFT'\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.move_right()\\n\\t\\t\\t\\tself.direction = 'RIGHT'\\n\\t\\t\\tself.__threshold_steps = random.randint(25,50)\\n\\t\\t\\tself.__steps = 0\\n\\t\\t# Confines the Donkeys movement to within the boundary \\n\\t\\tself.__check_boundary()\",\n \"def choose_next(self, round):\\n return random.choice(self.possible_coords)\",\n \"def choose_move(self):\\n raise NotImplementedError()\",\n \"def move(self, gstate: gamestate.Gamestate) -> util.Move:\\n moves = gstate.legal_moves_vector(gstate.agents[self.id])\\n scores = {move: self.evaluate(gstate.copy, move) for move in moves}\\n max_score = max(scores.values())\\n max_moves = [move for move in moves if scores[move] == max_score]\\n return random.choice(max_moves)\",\n \"def mover_aleatoriamente(self):\\n self.randomizador = random.randint(0,4)\",\n \"def random_moves(length):\\n ans = \\\"\\\"\\n for dummy_num in range(length):\\n ans += random.choice([\\\"u\\\",\\\"d\\\",\\\"l\\\",\\\"r\\\"])\\n return ans\",\n \"def act(state, action, player, rewarding_move=False): # TODO : Wondering if I should make the random move here.\\n if YoteRules.is_legal_move(state, action, player, rewarding_move=rewarding_move):\\n return YoteRules.make_move(state, action, player, rewarding_move=rewarding_move)\\n else:\\n return False\",\n \"def random_walk(n):\\n x,y = 0,0\\n for i in range(n):\\n step = random.choice(['N','S','E','W'])\\n if step == 'N':\\n y+=1\\n elif step == 'S':\\n y-=1\\n elif step == 'E':\\n x+=1\\n else:\\n x-=1\\n return (x,y)\",\n \"def random_direction():\\n\\n if randrange(2):\\n return Direction.RIGHT\\n else:\\n return Direction.DOWN\",\n \"def choose_move(self) -> int:\\n raise NotImplementedError(\\\"Choose move not implemented\\\")\",\n \"def direction_correction(self):\\n self.directions.monster = random.uniform(self.directions.monster * self.get_monster_sensitivity(),\\n self.directions.monster * (1 + (1 - self.get_monster_sensitivity())))\\n self.directions.food = random.uniform(self.directions.food * self.get_food_sensitivity(),\\n self.directions.food * (1 + (1 - self.get_food_sensitivity())))\\n self.directions.water = random.uniform(self.directions.water * self.get_water_sensitivity(),\\n self.directions.water * (1 + (1 - self.get_water_sensitivity())))\",\n \"def nextMoveDecision(self):\\n b = random.randint(1, 9) \\n while (self.Occupied(b)):\\n b = random.randint(1, 9) \\n return b\",\n \"def throw(self, move):\\n for dice_index in move:\\n self.dice[dice_index - 1] = random.randint(1,6)\",\n \"def random_walk(n):\\n\\tx, y = 0, 0\\n\\tfor i in range(n):\\n\\t\\tstep = random.choice(['N', 'S', 'E', 'W'])\\n\\t\\tif step == 'N':\\n\\t\\t\\ty += 1\\n\\t\\tif step == 'S':\\n\\t\\t\\ty -= 1\\n\\t\\tif step == 'E':\\n\\t\\t\\tx += 1\\n\\t\\tif step == 'W':\\n\\t\\t\\tx -= 1\\n\\treturn (x, y)\",\n \"def prepare_next_turn(grid):\\n empties = get_empty_cells(grid)\\n y,x = random.choice(empties)\\n grid[y][x] = 2 if random.random() < 0.9 else 4\\n return any_possible_moves(grid)\",\n \"def auto_play_random(self, player=None):\\r\\n if player is None:\\r\\n player = self.get_player()\\r\\n legal_list = self.get_legal_list()\\r\\n next_move = legal_list.rand_obj()\\r\\n self.new_edge(next_move)\",\n \"def make_move(self, board: Block):\\n # select a random block and highlight it.\\n rand_block = select_random_block(board)\\n rand_block.highlighted = True\\n self.renderer.draw(board, self.id)\\n pygame.time.wait(TIME_DELAY)\\n choice = random.randint(0, 4)\\n\\n if rand_block.level == rand_block.max_depth or rand_block.level == 0:\\n # Random player has chosen to smash an invalid block thus its move\\n # is forfeited\\n if choice == 4:\\n pass\\n else:\\n perform_move(rand_block, choice)\\n else:\\n perform_move(rand_block, choice)\\n rand_block.highlighted = False\\n self.renderer.draw(board, self.id)\\n return 0\",\n \"def test_randomness_of_moves():\\n env = Four_Rooms_Environment(stochastic_actions_probability=0.0)\\n env.reset()\\n for _ in range(10):\\n for move in env.actions:\\n assert move == env.determine_which_action_will_actually_occur(move)\\n\\n env = Four_Rooms_Environment(stochastic_actions_probability=1.0)\\n num_iterations = 10000\\n for move in env.actions:\\n moves = []\\n for _ in range(num_iterations):\\n moves.append(env.determine_which_action_will_actually_occur(move))\\n count = Counter(moves)\\n for move_test in env.actions:\\n if move != move_test: #We do this because stochastic probability 1.0 means the move will never be picked\\n assert abs((num_iterations / (len(env.actions)-1)) - count[move_test]) < num_iterations / 20.0, \\\"{}\\\".format(count)\\n\\n env = Four_Rooms_Environment(stochastic_actions_probability=0.75)\\n num_iterations = 10000\\n for move in env.actions:\\n moves = []\\n for _ in range(num_iterations):\\n moves.append(env.determine_which_action_will_actually_occur(move))\\n count = Counter(moves)\\n for move_test in env.actions:\\n assert abs((num_iterations / len(env.actions)) - count[move_test]) < num_iterations / 20.0, \\\"{}\\\".format(count)\",\n \"def move_monster(monster):\\n monster = (clip(monster[0] + randint(-1, 1), 0, X_SIZE-1),\\n clip(monster[1] + randint(-1, 1), 0, Y_SIZE-1))\\n return monster\",\n \"def cpu_move(moves_list):\\n \\n move = randint(0,3)\\n return move\",\n \"def handle_get_action(self, state):\\n\\n # This is an example player who picks random moves. REMOVE THIS WHEN YOU ADD YOUR OWN CODE !!\\n\\n #next_move = tuple(self.pick_random_free_cell(\\n # state, size=int(math.sqrt(len(state)-1))))\\n #############################\\n #\\n #\\n NN_state = self.server_state_to_NN_state(state)\\n predictions = self.policy_network.predict([[NN_state]])\\n next_move = np.argmax(predictions)\\n self.game.set_state(NN_state,1)\\n legal_actions = self.game.get_legal_actions()\\n if next_move not in legal_actions:\\n next_move = np.random.choice(legal_actions,1)\\n next_move = self.action_to_tuple_action(next_move)\\n\\n #\\n # next_move = ???\\n ##############################\\n return next_move\",\n \"def randwalk(n):\\n\\tx=0;\\n\\ty=0;\\n\\n\\tfor i in range(n):\\n\\t\\tstep = random.choice(['N','S','E','W'])\\n\\t\\tif step== 'N':\\n\\t\\t\\ty=y+1\\n\\t\\telif step=='S':\\n\\t\\t\\ty=y-1\\n\\t\\telif step=='E':\\n\\t\\t\\tx=x+1\\n\\t\\telse:\\n\\t\\t\\tx=x-1\\n\\treturn (x,y)\",\n \"def choose_move(self, possible_moves, seconds_left):\\n # TODO: update this method\\n print('\\\\--------------Choose Move--------------/')\\n print(possible_moves)\\n print(list(self.current_board.legal_moves))\\n search_tree = MCTS(5, self.color, self.current_board)\\n search_tree.search()\\n move = search_tree.pick_move()['move']\\n\\n return move\",\n \"def move(self):\\n if random.random() < 0.5:\\n self.y = (self.y + 1) % 100\\n else:\\n self.y = (self.y - 1) % 100\\n if random.random() < 0.5:\\n self.x = (self.x + 1) % 100\\n else:\\n self.x = (self.x - 1) % 100\",\n \"def move(self):\\n self._move_range_shuffle(3)\\n self._move_satisfy_random_constraint()\\n # self._move_range_shuffle(3)\\n #if (curr_energy > 50):\\n # self._move_satisfy_random_constraint()\\n #else:\\n # self._move_range_shuffle(3)\",\n \"def move_randomly(self, with_fight=False):\\n delta = [(-1, -1), (-1, 0), (-1, 1), (0, 1), (0, -1), (1, -1), (1, 0), (1, 1)]\\n rd.shuffle(delta)\\n x, y = self.owner.pos\\n while len(delta) > 0:\\n dx, dy = delta.pop()\\n if self.move_towards_position((x + dx, y + dy)):\\n return\",\n \"def __make_move(self, user):\\n if user:\\n pos = int(input('Make your move (1-9): '))\\n print('\\\\033[2A\\\\r ')\\n while self.state[pos-1] != ' ':\\n pos = int(input('\\\\rSeat\\\\'s taken. Make your move (1-9): '))\\n print('\\\\033[2A\\\\r ')\\n self.state[pos-1] = self.player_char\\n else:\\n time.sleep(.5)\\n pos = random.randint(1, 9)\\n if self.difficulty == 1:\\n best = self.__find_best()\\n pos = best+1 if best else pos\\n while self.state[pos-1] != ' ':\\n pos = random.randint(1, 9)\\n self.state[pos-1] = self.opponent_char\\n self.history.append(self.state[:])\",\n \"def prepare_next_turn(grid):\\n\\tempties = get_empty_cells(grid)\\n\\ty,x = random.choice(empties)\\n\\tgrid[y][x] = 2 if random.random() < prob_2 else 4\\n\\treturn any_possible_moves(grid)\",\n \"def randwalk(number_of_players=4, number_of_pieces=4):\\n\\n # `ghost_players` is a LUDOpy specific way to specify the number of\\n # players. So, if we want 2 players, the code below will generate a list:\\n #\\n # [3, 2, 1, 0]\\n #\\n # and slice it so it omits players 2 and 3.\\n #\\n # [3, 2, 1, 0][:2] == [3, 2]\\n\\n g = ludopy.Game(\\n ghost_players=list(reversed(range(0, 4)))[:-number_of_players],\\n number_of_pieces=number_of_pieces\\n )\\n there_is_a_winner = False\\n\\n while not there_is_a_winner:\\n (dice, move_pieces, player_pieces, enemy_pieces, player_is_a_winner,\\n there_is_a_winner), player_i = g.get_observation()\\n\\n if len(move_pieces):\\n piece_to_move = \\\\\\n move_pieces[np.random.randint(0, len(move_pieces))]\\n else:\\n piece_to_move = -1\\n\\n _, _, _, _, _, there_is_a_winner = g.answer_observation(piece_to_move)\\n\\n return g\",\n \"def find_legal_move(self, g, func, timeout=None):\\r\\n legal_col = []\\r\\n for index, col in enumerate(g.col_lst):\\r\\n if col < g.BOARD_COL - 1:\\r\\n legal_col.append(index)\\r\\n chosen_col = random.sample(legal_col, k=1)[0]\\r\\n if len(legal_col) == 0:\\r\\n raise Exception(self.MSG_NO_AI_MOVES)\\r\\n func(chosen_col)\\r\\n return chosen_col\",\n \"def exploring_starts(self):\\n def random_choice(l): return l[np.random.randint(len(l))]\\n return map(random_choice, (self.env.states, self.env.moves))\",\n \"def DoRandomAction(self, gameState: TekkenGameState):\\n actionRNG = random.randint(1, 100)\\n # Anything generated above this number, the bot chooses to poke\\n POKE_CAP = 50\\n DASH_CAP = 35\\n DUCK_CAP = 20\\n SIDESTEP_CAP = 10\\n WALK_CAP = 5\\n\\n TARGET_DISTANCE = 2000.0 # 2.00\\n\\n # Ensure we dont do nothing for twice in a row\\n if self.last_rand_action_was_nth:\\n actionRNG += SIDESTEP_CAP\\n self.last_rand_action_was_nth = False\\n\\n # Try to poke if the last action was spent getting\\n # into range for pokes\\n if self.last_asked_poke:\\n actionRNG += POKE_CAP\\n\\n if actionRNG >= POKE_CAP:\\n # Not in range, dash towards\\n if TARGET_DISTANCE < gameState.GetDist():\\n self.DashTowardsTargetDist(TARGET_DISTANCE, gameState)\\n self.last_asked_poke = not self.last_asked_poke\\n else:\\n self.RandomPoke()\\n self.last_asked_poke = False\\n elif actionRNG >= DASH_CAP:\\n self.DashTowardsTargetDist(TARGET_DISTANCE, gameState)\\n elif actionRNG >= DUCK_CAP:\\n # duck for a few frames\\n self.botCommands.BlockLowFull(random.randint(8, 12))\\n elif actionRNG >= SIDESTEP_CAP:\\n # Pick between SS up or down\\n if (random.randint(0, 1) == 0):\\n self.botCommands.SidestepUp()\\n else:\\n self.botCommands.SidestepDown()\\n elif actionRNG >= WALK_CAP:\\n self.WalkTowardsTargetDist(TARGET_DISTANCE, gameState)\\n else:\\n self.last_rand_action_was_nth = True\\n\\n self.tick_till_next_rand = 0\",\n \"def move_aim(self):\\n self.color = random.choice(COLORS)\\n self.x += 3 * self.speed_x / FPS\\n self.y += 3 * self.speed_y / FPS\\n self.r -= 1\\n self.draw_aim()\\n if self.r <= 10:\\n self.color = random.choice(COLORS)\\n self.x = randint(100, 1000)\\n self.y = randint(100, 800)\\n self.r = randint(50, 100)\\n self.speed_x = randint(-200, 200)\\n self.speed_y = randint(-200, 200)\\n if self.x >= 1100:\\n self.speed_x = randint(-100, -10)\\n if self.x <= 50:\\n self.speed_x = randint(10, 100)\\n if self.y >= 800:\\n self.speed_y = randint(-100, -10)\\n if self.y <= 50:\\n self.speed_y = randint(10, 100)\",\n \"def play_against_random(self, action):\\n state, status, done = self.step(action)\\n if not done and self.turn == 2:\\n state, s2, done = self.random_step()\\n if done:\\n if s2 == self.STATUS_WIN:\\n status = self.STATUS_LOSE\\n elif s2 == self.STATUS_TIE:\\n status = self.STATUS_TIE\\n else:\\n raise ValueError(\\\"???\\\")\\n return state, status, done\",\n \"def choose_random_action(self):\\r\\n return Action.HIT if random.random() <= 0.5 else Action.STICK\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.83302236","0.80104834","0.78741527","0.78018403","0.77883136","0.77592164","0.7737336","0.7699141","0.7628713","0.7599985","0.7595064","0.7374902","0.7326639","0.7277232","0.7267337","0.723386","0.7232803","0.72240585","0.72192174","0.72055054","0.7136459","0.71180874","0.71169424","0.70769817","0.70182276","0.699078","0.6986211","0.69854504","0.69854504","0.69716835","0.6955638","0.6940438","0.6879812","0.6872612","0.684861","0.6836673","0.6820756","0.68077916","0.67658263","0.67276096","0.6714366","0.668348","0.6668835","0.66536266","0.6653073","0.66390955","0.6604469","0.658471","0.6570695","0.65559316","0.65438855","0.6538156","0.6535663","0.6524841","0.65167916","0.6478673","0.64778936","0.63928753","0.63851213","0.63619816","0.6359059","0.6356177","0.6343456","0.63096756","0.630948","0.63028705","0.6299404","0.6297818","0.6282408","0.6279473","0.62633145","0.62575406","0.625571","0.62543267","0.6250151","0.6243816","0.62303215","0.6227115","0.6209236","0.6208743","0.618943","0.6181986","0.6180705","0.6177787","0.615993","0.6142368","0.61265814","0.6123948","0.61069417","0.61043286","0.6095721","0.60854656","0.60795945","0.60773367","0.60679144","0.60663086","0.606504","0.6059855","0.605843","0.6050162","0.60416883"],"string":"[\n \"0.83302236\",\n \"0.80104834\",\n \"0.78741527\",\n \"0.78018403\",\n \"0.77883136\",\n \"0.77592164\",\n \"0.7737336\",\n \"0.7699141\",\n \"0.7628713\",\n \"0.7599985\",\n \"0.7595064\",\n \"0.7374902\",\n \"0.7326639\",\n \"0.7277232\",\n \"0.7267337\",\n \"0.723386\",\n \"0.7232803\",\n \"0.72240585\",\n \"0.72192174\",\n \"0.72055054\",\n \"0.7136459\",\n \"0.71180874\",\n \"0.71169424\",\n \"0.70769817\",\n \"0.70182276\",\n \"0.699078\",\n \"0.6986211\",\n \"0.69854504\",\n \"0.69854504\",\n \"0.69716835\",\n \"0.6955638\",\n \"0.6940438\",\n \"0.6879812\",\n \"0.6872612\",\n \"0.684861\",\n \"0.6836673\",\n \"0.6820756\",\n \"0.68077916\",\n \"0.67658263\",\n \"0.67276096\",\n \"0.6714366\",\n \"0.668348\",\n \"0.6668835\",\n \"0.66536266\",\n \"0.6653073\",\n \"0.66390955\",\n \"0.6604469\",\n \"0.658471\",\n \"0.6570695\",\n \"0.65559316\",\n \"0.65438855\",\n \"0.6538156\",\n \"0.6535663\",\n \"0.6524841\",\n \"0.65167916\",\n \"0.6478673\",\n \"0.64778936\",\n \"0.63928753\",\n \"0.63851213\",\n \"0.63619816\",\n \"0.6359059\",\n \"0.6356177\",\n \"0.6343456\",\n \"0.63096756\",\n \"0.630948\",\n \"0.63028705\",\n \"0.6299404\",\n \"0.6297818\",\n \"0.6282408\",\n \"0.6279473\",\n \"0.62633145\",\n \"0.62575406\",\n \"0.625571\",\n \"0.62543267\",\n \"0.6250151\",\n \"0.6243816\",\n \"0.62303215\",\n \"0.6227115\",\n \"0.6209236\",\n \"0.6208743\",\n \"0.618943\",\n \"0.6181986\",\n \"0.6180705\",\n \"0.6177787\",\n \"0.615993\",\n \"0.6142368\",\n \"0.61265814\",\n \"0.6123948\",\n \"0.61069417\",\n \"0.61043286\",\n \"0.6095721\",\n \"0.60854656\",\n \"0.60795945\",\n \"0.60773367\",\n \"0.60679144\",\n \"0.60663086\",\n \"0.606504\",\n \"0.6059855\",\n \"0.605843\",\n \"0.6050162\",\n \"0.60416883\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77098,"cells":{"query":{"kind":"string","value":"generates a 20,000th hash in iterative sha256 chain..derived from private SEED"},"document":{"kind":"string","value":"def _calc_hashchain(\n seed_private,\n epoch,\n blocks_per_epoch):\n hc_seed = getHashChainSeed(seed_private, epoch, config.dev.hashchain_nums)\n\n hc = [[hash_chain] for hash_chain in hc_seed]\n\n hc_terminator = []\n for hash_chain in hc[:-1]: # skip last element as it is reveal hash\n for x in range(blocks_per_epoch):\n hash_chain.append(sha2_256(hash_chain[-1]))\n hc_terminator.append(hash_chain[-1])\n\n # Reveal hash chain\n for hash_chain in hc[-1:]:\n # Extra hash to reveal one hash value\n for x in range(blocks_per_epoch + 1):\n hash_chain.append(sha2_256(hash_chain[-1]))\n hc_terminator.append(hash_chain[-1])\n\n return hc_seed, hc, hc_terminator"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def SHA256(self) -> _n_0_t_3[_n_0_t_9]:","def h_python(key, N):\n return hash(key) % N","def hash_gen(n):\n domain = \"abcdefghijklmnopqrstuvwxyz\"\n temp = \"\"\n for i in range(0, n):\n temp += domain[random.randrange(0, 26)]\n return temp","def compute_hash(self):\n '''\n s = \"\"\n s += str(self.index)\n for i in range(len(self.transactions)):\n s += self.transactions[i]\n s += str(self.timestamp)\n s += self.previous_hash\n s += str(self.nonce)\n\n s_json = json.dumps(s)\n x = sha256()\n x.update(s_json.encode())\n h = x.hexdigest()\n return h\n '''\n\n block_string = json.dumps(self.__dict__, sort_keys=True)\n return sha256(block_string.encode()).hexdigest()","def perfect_hash(num):\n return ((num+OFFSET)*(SIZE/PERIOD)) % (SIZE+1) + 1","def HashAlgorithm(self) -> _n_7_t_0:","def seed_hash(*args):\n args_str = str(args)\n return int(hashlib.md5(args_str.encode(\"utf-8\")).hexdigest(), 16) % (2**31)","def _hash(self):\r\n MAX = sys.maxint\r\n MASK = 2 * MAX + 1\r\n n = len(self)\r\n h = 1927868237 * (n + 1)\r\n h &= MASK\r\n for x in self:\r\n hx = hash(x)\r\n h ^= (hx ^ (hx << 16) ^ 89869747) * 3644798167\r\n h &= MASK\r\n h = h * 69069 + 907133923\r\n h &= MASK\r\n if h > MAX:\r\n h -= MASK + 1\r\n if h == -1:\r\n h = 590923713\r\n return h","def hash_generator(self, value):\n hash_string = hashlib.sha256(bytes(value))\n return hash_string.hexdigest()","def __init__(self):\n self.hash = [[] for _ in range(20011)]","def generateHashFunctions(n):\n hashFunction = []\n for i in range(n):\n # Chooses random value to use as xor during a single hash function\n random_XORvalue = random.getrandbits(INT_SIZE)\n hashFunction.append((random_XORvalue))\n return hashFunction","def generate_sha256_hash(fpath, sig_key=None):\n return run(fpath, sig_key)","def h(x):\n\n hasher = hashlib.sha256()\n hasher.update(x)\n return hasher.digest()","def nextRandom(self):\n # Apply SHA-256, interpreting digest output as integer\n # to yield 256-bit integer (a python \"long integer\")\n hash_output = self.basehash.digest()\n self.next()\n return hash_output","def get_sha256_hash(key, size=None):\n partition_hash = hashlib.sha256()\n for part in key:\n partition_hash.update(str(part).encode('utf-8'))\n sha256_hash = partition_hash.hexdigest()\n if not size or size > len(sha256_hash):\n size = len(sha256_hash)\n return sha256_hash[:size]","def sha256(message: bytes):\n # convert message bitarray\n bit_msg = bitarray(endian='big')\n bit_msg.frombytes(message)\n L = len(bit_msg)\n\n # additions done mod 2^32\n pow2 = pow(2,32)\n\n # append 1 followed by K 0s where K is the minimum number >= 0 such that \n # len(bit_msg) + 1 + K + 64 is a multiple of 512\n bit_msg = bit_msg + bitarray('1') + (bitarray('0') * ((-L-65) % 512))\n # append len(bit_msg) as a 64-bit int to bit_msg\n bit_msg = bit_msg + util.int2ba(L, length=64, endian='big')\n\n # initialize hash to predefined values\n current_hash = [h for h in initial_hash]\n\n # operate on each 512-bit chunk\n for chunk_index in range(len(bit_msg)//512):\n chunk = bit_msg[chunk_index * 512 : (chunk_index+1) * 512]\n # w is array of 64 32-bit words with first 16 equal to chunk\n w = [chunk[i*32 : (i+1)*32] for i in range(16)]\n w.extend([bitarray(32) for _ in range(48)])\n # create last 48 words in w from first 16\n for i in range(16, 64):\n s0 = rightrotate(w[i-15], 7) ^ rightrotate(w[i-15], 18) ^ rightshift(w[i-15], 3)\n s1 = rightrotate(w[i-2], 17) ^ rightrotate(w[i-2], 19) ^ rightshift(w[i-2], 10)\n w[i] = int2ba32(sum(map(util.ba2int, [w[i-16], s0, w[i-7], s1])) % pow2)\n\n # copy current hash (stored in hex) into working list v as bitarrays\n v = list(map(int2ba32, current_hash))\n # compression\n for i in range(64):\n S1 = rightrotate(v[4], 6) ^ rightrotate(v[4], 11) ^ rightrotate(v[4], 25)\n ch = (v[4] & v[5]) ^ ((~v[4]) & v[6])\n temp1 = (constants[i] + sum(map(util.ba2int, [v[7], S1, ch, w[i]]))) % pow2\n S0 = rightrotate(v[0], 2) ^ rightrotate(v[0], 13) ^ rightrotate(v[0], 22)\n maj = (v[0] & v[1]) ^ (v[0] & v[2]) ^ (v[1] & v[2])\n temp2 = (util.ba2int(S0) + util.ba2int(maj)) % pow2\n\n # shift elements of v by 1\n for j in reversed(range(1, len(v))):\n v[j] = v[j-1]\n v[0] = int2ba32((temp1 + temp2) % pow2)\n v[4] = int2ba32((util.ba2int(v[4]) + temp1) % pow2)\n\n # add compressed values (which are bitarrays) to current_hash (which are ints)\n current_hash = list(map(lambda a,b: (a + util.ba2int(b)) % pow2, current_hash, v))\n\n # each entry of current_hash is a 32-bit integer so convert to 4 bytes \n # adding bytes appends them\n return b''.join(x.to_bytes(4, 'big') for x in current_hash)","def fast_hash(infile):\n\n m = hashlib.sha256()\n with open(infile, 'rb', 1024 * 1024) as f:\n l = f.read(1024 * 1024)\n while (len(l) > 0):\n m.update(l)\n f.seek(1024 * 1024 * (512 - 1), 1)\n l = f.read(1024 * 1024)\n return m.hexdigest()","def __init__(self):\n self.size = 1000\n self.hash_table = [None] * self.size","def hash(x):\r\n return (randint(1,5*c)*x + randint(1,5*c))%c","def __hash__(self):\n token = \"\"\n for gamePiece in self.game_pieces:\n token = token + str(gamePiece.x) + str(gamePiece.y)\n \n hash_ = int(token) % 100000\n return hash_","def hash(self) -> str:\r\n ...","def gen_hash_params(self):\n m_bitlength = self.m.bit_length()\n hfs = []\n for _ in xrange(self.k):\n p = generateLargePrime(m_bitlength + 7)\n a = random.randint(1, p)\n b = random.randint(1, p)\n hfs.append((a, b, p))\n return hfs","def _sha256(sha256):\n if not sha256:\n sha256 = \"0\" * 64\n\n return sha256","def __init__(self, seed=None):\n self.seed(seed)\n self.hashfun = \"SHA-256\"\n self._basehash()","def generate_hash_functions(rows, no_of_hash_func=200):\r\n\r\n hashes = []\r\n c = rows\r\n \r\n # all functions are same here. check this\r\n for i in range(no_of_hash_func):\r\n def hash(x):\r\n \"\"\"\r\n This function calculates hash for given x\r\n\r\n hash function format: (a*x+b)%c where\r\n c: prime integer just greater than rows\r\n a,b: random integer less than c\r\n \"\"\"\r\n return (randint(1,5*c)*x + randint(1,5*c))%c\r\n hashes.append(hash)\r\n\r\n return hashes","def mkRandHash(N):\n junk = \"\".join(random.choice(string.ascii_letters + string.digits)\n for unused in range(10))\n return lambda key: hash(junk + key) % N","def chunk_hashes(str):\n chunk = 1024*1024\n chunk_count = int(math.ceil(len(str)/float(chunk)))\n chunks = [str[i*chunk:(i+1)*chunk] for i in range(chunk_count)]\n return [hashlib.sha256(x).digest() for x in chunks]","def calHash(n, m):\n return int(m*BloomFilter.ln2/n)","def _hash_function(self, key):\n h = 0\n a = 31\n table_size = self.size\n for i in range(len(key)):\n h = (h * a + ord(key[i])) % table_size\n return h","def hash_functions(self):\n def hash_factory(n):\n return lambda x: hash(\"salt\" + str(n) + str(x) + \"salt\")\n return [ hash_factory(_) for _ in range(self.dim) ]","def bit_contribution_test(hash_function):\n\n model = hash_function()\n hash_list = []\n zero_str = '0' * 2048\n for i in range(1, 2049):\n for j in range(0, i):\n flip_str = zero_str[:j] + '1' + zero_str[j+1:i]\n hash_list.append(list(map(int, list(msg_to_bits.pad_msg(flip_str, i)))))\n if i % 200 == 0:\n print(i)\n\n hashed_dict = dict()\n collisions = 0\n i = 0\n for to_hash in hash_list:\n i += 1\n hash_val = model.hash(to_hash, False).tostring()\n if hash_val in hashed_dict:\n collisions += 1\n hashed_dict[hash_val] = True\n if i % 10000 == 0:\n print(i)\n\n return collisions","def RSA_SIGNATURE_HASH() :\n return \"SHA-256\"","def __polynomial_hash(self, s, base = 31, max_size=168):\r\n digest = 0\r\n max_size = 168\r\n for c in s: digest = base * digest + ord(c)\r\n digest &= 2 ** max_size - 1 \r\n return hex(digest).rstrip('L')","def _basehash(self):\n if self.baseseed is not None:\n hashinput = (str(self.baseseed) + ',').encode()\n self.basehash = hashlib.sha256(hashinput)\n else:\n self.basehash = None","def genSeed():\n\tseed_length = int(''.join(random.SystemRandom().choice(string.digits) for _ in range(0, 3)))\n\tseed = os.urandom(seed_length)\n\thashing_algorithm = hashlib.shake_128()\n\thashing_algorithm.update(seed)\n\t# 2200 bytes from SHAKE-128 function is enough data to get 1024 coefficients\n\t# smaller than 5q, from Alkim, Ducas, Pöppelmann, Schwabe section 7:\n\tseed_hash = hashing_algorithm.digest(100)\n\treturn seed, seed_hash","def get_hash_str():\r\n\tli = \"\"\r\n\tfor i in range(5):\r\n\t\tli += str(int(int((6 * random.random()) + 1)))\r\n\treturn li","def create_hash() -> str:\n length = 6\n char = string.ascii_uppercase + string.digits + string.ascii_lowercase\n\n # Generate a new ID, until one is found that is unique\n while True:\n hash = \"\".join(random.choice(char) for _ in range(length))\n\n if not utils.cache_is_hash_taken(hash):\n return hash","def hash_exp(key, seed=SEED):\n m = hashlib.sha256((\"%s###%s\" % (seed, key)).encode(\"ascii\"))\n uniform = float(int(m.hexdigest(), 16) + 1) / 2**256\n return -1.0 * math.log(uniform, math.e)","def hash(token, num_buckets):\n return murmurhash3_32(token, positive=True) % num_buckets","def hashing(word) :\r\n ans = hashlib.sha256(word.encode())\r\n return ans.hexdigest()","def create_hash(self):\n return os.urandom(32).encode('hex')","def calculateHash(self):\n hashData = str(self.index) + str(self.data) + self.timestamp + self.previousHash + str(self.nonce)\n return hashlib.sha256(hashData.encode(encoding='UTF-8',errors='strict')).hexdigest()","def dense_hash(hash_, block_size=16):\n results = list()\n for index in range(0, len(hash_), block_size):\n\n block = hash_[index:index + block_size]\n\n total = 0\n for i in block:\n total ^= i\n\n results.append(total)\n\n return results","def r_soft_hash(x):\n if abs(x) < 1e-9:return 0\n # round it to some number of bits\n b = ns.round(ns.log(abs(x)) / ns.log(2))\n gran = 2**(b-30)\n return ns.round(x / gran) * gran","def random_h_function(key, N):\n\n if key is None:\n raise TypeError(\"Key must not be None\")\n\n\n hashcode = 0\n for i in range(len(key)):\n hashcode = (71 * hashcode + ord(key[i])) % N\n return hashcode","def hash(plainString):\n result = plainString\n for i in range(0,12):\n result = hashHelp(result)\n return result","def _hi(hash_name, data, salt, iterations):\n mac = hmac.HMAC(data, None, getattr(hashlib, hash_name))\n\n def _digest(msg, mac=mac):\n \"\"\"Get a digest for msg.\"\"\"\n _mac = mac.copy()\n _mac.update(msg)\n return _mac.digest()\n\n from_bytes = _from_bytes\n to_bytes = _to_bytes\n\n _u1 = _digest(salt + b'\\x00\\x00\\x00\\x01')\n _ui = from_bytes(_u1, 'big')\n for _ in range(iterations - 1):\n _u1 = _digest(_u1)\n _ui ^= from_bytes(_u1, 'big')\n return to_bytes(_ui, mac.digest_size, 'big')","def _hash(self) -> None:\r\n # for a unit cube there are 8 possible hashes\r\n # returns the tuple of with all 8 hashes\r\n\r\n self.hashes[\"aaa\"] = P[P[P[self.xi] + self.yi] + self.zi]\r\n self.hashes[\"aab\"] = P[P[P[self.xi] + self.yi] + self._inc(self.zi)]\r\n self.hashes[\"aba\"] = P[P[P[self.xi] + self._inc(self.yi)] + self.zi]\r\n self.hashes[\"abb\"] = P[P[P[self.xi] + self._inc(self.yi)] + self._inc(self.zi)]\r\n self.hashes[\"baa\"] = P[P[P[self._inc(self.xi)] + self.yi] + self.zi]\r\n self.hashes[\"bab\"] = P[P[P[self._inc(self.xi)] + self.yi] + self._inc(self.zi)]\r\n self.hashes[\"bba\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self.zi]\r\n self.hashes[\"bbb\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self._inc(self.zi)]","def sha256(data):\n\n d = rpki.POW.Digest(rpki.POW.SHA256_DIGEST)\n d.update(data)\n return d.digest()","def sha256(ctx, salt=\"\"):\n if ctx.data:\n salted_input_value = salt + \":\" + ctx.data\n ctx.data = hashlib.sha256(salted_input_value.encode()).hexdigest()\n else:\n raise RefError(\n \"Ref error: eval_func: nothing to sha256 hash; try \" \"something like '|random:str|sha256'\"\n )","def hash_function(s):\n\n # O(n) over the key length\n # O(1) over the HASH_DATA_SIZE\n\n bytes_list = s.encode()\n\n total = 0\n\n\n for b in bytes_list: # O(n) over the length of the key\n total += b\n\n\n total &= 0xffffffff # 32 bit (8 f's)\n\n return total","def hash_128_bit_pass(passwd):\n h = hashlib.sha256()\n h.update(passwd)\n return h.hexdigest()[:16]","def current_hash(self):","def generate_hash(password):\n return pbkdf2_sha256.hash(password)","def gulp(meal):\n #print(\"Proccessing...\")\n broth = meal\n chunks = [broth[i:i+256] for i in range(0, len(broth), 256)]\n #print(\"Number of chunks:\", len(chunks))\n #print(\" - Chunkified...\")\n digest = [0]*64\n i1 = 1\n global Ti\n Ti = 0\n for chunk in chunks:\n t1 = time.time()\n \n if i1 == 1:\n chewed = chew(chunk)\n digest = chewed\n else:\n chewed = chew(chunk)\n digest = [(int(x) + int(y))%16 for x, y in zip(digest, chewed)]\n \n t2 = time.time()\n #print(\"time taken:\", t2-t1)\n Ti += t2-t1\n #print(\"time TOTAL:\", Ti)\n #print(\" - Chewed!\")\n \n i1 += 1\n #print(\"Hash finished.\")\n return digest","def fnvhash(a): \n h = 2166136261 \n for i in a: \n t = (h * 16777619) & 0xffffffffL \n h = t ^ i \n return h","def hash_double(d):\n # initialize table\n table = [\"-\"] * 19\n # consider each integer k in the input\n for k in d:\n # if k is already in the table this is a duplicate so move to next integer in the input\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\n if k in table:\n continue\n # apply the hash function\n i = (6 * k + 3) % 19\n t = i\n # initialize count that checks whether linear probe has considered each bucket and is now full\n count = 0\n j = 0\n # while bucket is already filled\n s = 11 - (k % 11)\n while table[i] != '-':\n j += 1\n # move to next bucket\n i = (t + j*s) % 19\n # increment count\n count += 1\n\n # if table is full\n if count >= 18:\n # can return table as nothing further can be added\n break\n\n # Ensure table[i] is empty so k can be added here\n if table[i] == '-':\n table[i] = k\n\n # now each part of the input has been considered return the table\n return table","def hash_function_2(key):\n\n hash = 0\n index = 0\n for i in key:\n hash = hash + (index + 1) * ord(i)\n index = index + 1\n return hash","def hash_function_2(key):\n\n hash = 0\n index = 0\n for i in key:\n hash = hash + (index + 1) * ord(i)\n index = index + 1\n return hash","def hash_function_2(key):\n\n hash = 0\n index = 0\n for i in key:\n hash = hash + (index + 1) * ord(i)\n index = index + 1\n return hash","def hash_function_2(key):\n hash = 0\n index = 0\n for i in key:\n hash = hash + (index + 1) * ord(i)\n index = index + 1\n return hash","def lines_hash(lines):\n x = xxh32()\n for i in lines:\n x.update(i.encode())\n return x.digest()","def get_partial_sha256(self, nbytes):\n return sha256file(abspath=self.abspath, nbytes=nbytes)","def fnv1(self, key):\n # hash = 0xff\n hash = 0xcbf29ce484222325\n for n in key.encode():\n # print(n)\n hash = hash ^ n\n hash = hash * 0x100000001b3\n\n # print(hash)\n return hash","def myHash(string, base=91, mod=1000000321):\n value = 0\n for pos, elem in enumerate(string[::-1]): # считаем значение полинома\n value += ord(elem) * base**pos # в последней задаче сделано с помощью массива (динамика)\n return value % mod","def sha256(value):\n return hashlib.sha256(value).hexdigest()","def hash_sbox(f):\n hf = sha256()\n for x in f:\n hf.update(hex(x).encode('utf-8'))\n return hf.hexdigest()","def sha_process(self):\n\n # Break chunk into sixteen 32-bit big-endian words m[0..15]\n m = [0] * 64\n # Extend the sixteen 32-bit words into sixty-four 32-bit words\n j = 0\n for i in range(16):\n m[i] = self.data_b[j] << 24 | self.data_b[j+1] << 16\\\n | self.data_b[j+2] << 8 | self.data_b[j+3]\n j += 4\n\n for i in range(16, 64):\n sig0 = self.rotr(m[i-15], 7) ^ self.rotr(m[i-15], 18)\\\n ^ (m[i-15] >> 3)\n sig1 = self.rotr(m[i-2], 17) ^ self.rotr(m[i-2], 19)\\\n ^ (m[i-2] >> 10)\n m[i] = (sig1 + m[i-7] + sig0 + m[i-16]) & 0xFFFFFFFF\n\n # Initialize hash value for this chunk\n a, b, c, d, e, f, g, h = self.h\n\n for i in range(64):\n ep0 = (self.rotr(a, 2) ^ self.rotr(a, 13) ^ self.rotr(a, 22))\\\n & 0xFFFFFFFF\n ep1 = (self.rotr(e, 6) ^ self.rotr(e, 11) ^ self.rotr(e, 25))\\\n & 0xFFFFFFFF\n ch = ((e & f) ^ ((~e) & g))\n maj = ((a & b) ^ (a & c) ^ (b & c))\n t1 = (h + ep1 + ch + self.k[i] + m[i]) & 0xFFFFFFFF\n t2 = (ep0 + maj) & 0xFFFFFFFF\n h = g\n g = f\n f = e\n e = (d + t1) & 0xFFFFFFFF\n d = c\n c = b\n b = a\n a = (t1 + t2) & 0xFFFFFFFF\n\n # Add this chunk's hash to result so far\n self.h[0] = (self.h[0] + a) & 0xFFFFFFFF\n self.h[1] = (self.h[1] + b) & 0xFFFFFFFF\n self.h[2] = (self.h[2] + c) & 0xFFFFFFFF\n self.h[3] = (self.h[3] + d) & 0xFFFFFFFF\n self.h[4] = (self.h[4] + e) & 0xFFFFFFFF\n self.h[5] = (self.h[5] + f) & 0xFFFFFFFF\n self.h[6] = (self.h[6] + g) & 0xFFFFFFFF\n self.h[7] = (self.h[7] + h) & 0xFFFFFFFF","def hash(self) -> bytes:","def tree_hash(hashes):\n while len(hashes) > 1:\n hashes = [hashlib.sha256(\"\".join(h[i:i+1])).digest() for i in range(i,2)]\n return hashes[0]","def hash(self):\n return self._run(\"%s --%d --block-size %d --bits %d --quiet --threads %d %s --mode %s %s\" %\n (self._ishakesumd, self._mode, self._block_size, self._output_bits, self._threads,\n self._profile, self._alg, self._dir))","def generate_index(size=20):\n return hexlify(np.random.rand(100))[:size].decode()","def generichash_blake2b_final(state: Blake2State) -> bytes:\n\n ensure(\n isinstance(state, Blake2State),\n \"State must be a Blake2State object\",\n raising=exc.TypeError,\n )\n\n _digest = ffi.new(\"unsigned char[]\", crypto_generichash_BYTES_MAX)\n rc = lib.crypto_generichash_blake2b_final(\n state._statebuf, _digest, state.digest_size\n )\n\n ensure(rc == 0, \"Unexpected failure\", raising=exc.RuntimeError)\n return ffi.buffer(_digest, state.digest_size)[:]","def get_256_hash_from_string(string):\n\n sha256 = hashlib.sha256()\n sha256.update(string.encode('utf-8'))\n\n return sha256.hexdigest()","def hashId(key, size):\n return sum([ord(c) for c in key]) % size","def randomHash(self):\n modulus = self.filter_size\n a, b = random.randint(1, modulus - 1), random.randint(1, modulus - 1)\n\n def f(x):\n return hash(x) % (a + b) % modulus\n\n return f","def SHA256(data: str) -> str:\n\n msg = []\n for e in data:\n # convert chars to binary in 8-bit \n chbin = prepad(binary(ASCII[e]), to=8)\n msg.extend(chbin)\n \n # get length (in bits) of input\n datalen = binary(len(msg))\n if len(datalen) > 64:\n raise ValueError(\"input is too large\")\n\n # pad out message to factor of 512 (512-bit blocks)\n tail = [0]*(64-len(datalen)) + datalen\n padding = [1] + [0]*(511-((len(msg)+len(tail)) % 512))\n msg += (padding + tail)\n \n ctx = None\n for i in range(0, len(msg), 512):\n block = msg[i:i+512] \n wds = [UBitArray32(block[i:i+32]) for i in range(0, 512, 32)]\n wds = schedule(wds)\n # set context for next block\n ctx = compress(wds, ctx)\n\n hexdigest = \"\".join(x.tohex() for x in ctx)\n return hexdigest","def compute_hash(self, key: int):\n return key % 42","def make_hash(attrs, usekeys=None, N=10):\n if usekeys is None:\n d = attrs\n else:\n d = {k: attrs.get(k, None) for k in usekeys}\n\n s = json.dumps(d, sort_keys=True, cls=JSONEncoder).encode()\n return hashlib.sha1(s).hexdigest()[:N]","def create_password_hash(self, password):\n return pbkdf2_sha256.encrypt(password, rounds=1000, salt_size=16)","def __hash__(self):\n hash_value = 0\n \n # approximate_online_count\n hash_value ^= self.approximate_online_count\n \n # approximate_user_count\n hash_value ^= self.approximate_user_count << 12\n \n # description\n description = self.description\n if (description is not None):\n hash_value ^= hash(description)\n \n # discovery_splash\n hash_value ^= hash(self.discovery_splash)\n \n # emojis\n emojis = self.emojis\n hash_value ^= len(emojis) << 1\n for emoji in emojis.values():\n hash_value ^= hash(emoji)\n \n # features\n features = self.features\n hash_value ^= len(features) << 5\n for feature in features:\n hash_value ^= hash(feature)\n \n # icon\n hash_value ^= hash(self.icon)\n \n # id\n hash_value ^= self.id\n \n # invite_splash\n hash_value ^= hash(self.invite_splash)\n \n # stickers\n stickers = self.stickers\n hash_value ^= len(stickers) << 9\n for sticker in stickers.values():\n hash_value ^= hash(sticker)\n \n # name\n name = self.name\n if (description is None) or (description != name):\n hash_value ^= hash(name)\n \n return hash_value","def _produce_key(self, passphrase):\n from hashlib import sha256\n pp = bytes(passphrase, 'utf-8')\n hash_alg = sha256(pp)\n for i in range(self._get_key_stretches()):\n d = hash_alg.digest()\n hash_alg.update(d + pp)\n return hash_alg.digest()","def mkPerfHash(keys, Hash):\n f1, f2, G = generate_hash(keys, Hash)\n return lambda k: (G[f1(k)] + G[f2(k)]) % len(G)","def hash_key(self,key: str) -> int: \n \n total = 0\n prime = 3\n \n for index, char in enumerate(key,start=1):\n \n total += ord(char)*(prime)**index\n \n return total % self.buckets","def computeHash(filename):\n fileHash = hashlib.sha256()\n with open(filename, \"rb\") as f:\n for chunk in iter(lambda: f.read(4096), b\"\"):\n fileHash.update(chunk)\n return fileHash.hexdigest()","def typeahead_hash(self) -> str:","def hash_key(self):","def calc_statistics_hash(self) -> bytes:\n return b\"somehash\"","def sha256(inputs, secret):\n m = hmac.new(secret, b'', hashlib.sha256)\n for i in inputs:\n m.update(i)\n return m.digest()","def generate_aes_key ( ) :\n import hashlib\n sr = Crypto.Random.random.StrongRandom( )\n key_bits = sr.getrandbits( 256 )\n sha_key = hashlib.sha256( str( key_bits ) ).digest( )\n return sha_key","def hash_block(self):\n sha = hasher.sha256()\n sha.update((str(self.index) + str(self.timestamp) + str(self.data) + str(self.previous_hash)).endswith('utf-8'))\n return sha.hexdigest()","def __init__(self):\n \n self.hashset=[None]* 1000","def _hash_function(self, n):\n # Get the mask for this n, or make a new one of 32 random bits.\n mask = self._memomask.get(n)\n if mask is None:\n random.seed(n ^ self.seed_mask)\n mask = self._memomask[n] = int(random.getrandbits(32))\n # Now return a function that uses Jenkins Hash\n #\n def somehash(x):\n return hashlittle(x, mask)\n return somehash","def get_hash(self):\r\n block_data = self.prev_hash\r\n block_data += bytearray(struct.pack(\"!f\", self.time))\r\n block_data += self.user_id.encode()\r\n block_data += self.signature.encode()\r\n block_data += self.choice.encode()\r\n\r\n digest = hashes.Hash(hashes.SHA256())\r\n digest.update(block_data)\r\n return digest.finalize()","def pre_compute_hashes(s, M1, M2, X):\n n = len(s)\n h1 = [0 for _ in range(n+1)]\n h2 = [0 for _ in range(n+1)]\n for i in range(1, n+1):\n ch = ord(s[i-1])\n h1[i] = (X*h1[i-1] + ch) % M1\n h2[i] = (X*h2[i-1] + ch) % M2\n return h1, h2","def get_sha256_file(filename):\n BLOCKSIZE = 65536\n hasher = hashlib.sha256()\n with open(filename, 'rb') as afile:\n buf = afile.read(BLOCKSIZE)\n while len(buf) > 0:\n hasher.update(buf)\n buf = afile.read(BLOCKSIZE)\n return hasher.hexdigest()","def sha256(self):\n return self.sha256checksums()","def sha256_hexoutput(in_str):\r\n return sha256(in_str.encode('ascii')).hexdigest()","def gensalt():\n return hexlify(os.urandom(24)).decode()","def generate_hash(*args):\n key = bytes(' '.join(args), 'utf_8')\n hashh = hashlib.md5()\n hashh.update(key)\n return hashh.hexdigest()"],"string":"[\n \"def SHA256(self) -> _n_0_t_3[_n_0_t_9]:\",\n \"def h_python(key, N):\\n return hash(key) % N\",\n \"def hash_gen(n):\\n domain = \\\"abcdefghijklmnopqrstuvwxyz\\\"\\n temp = \\\"\\\"\\n for i in range(0, n):\\n temp += domain[random.randrange(0, 26)]\\n return temp\",\n \"def compute_hash(self):\\n '''\\n s = \\\"\\\"\\n s += str(self.index)\\n for i in range(len(self.transactions)):\\n s += self.transactions[i]\\n s += str(self.timestamp)\\n s += self.previous_hash\\n s += str(self.nonce)\\n\\n s_json = json.dumps(s)\\n x = sha256()\\n x.update(s_json.encode())\\n h = x.hexdigest()\\n return h\\n '''\\n\\n block_string = json.dumps(self.__dict__, sort_keys=True)\\n return sha256(block_string.encode()).hexdigest()\",\n \"def perfect_hash(num):\\n return ((num+OFFSET)*(SIZE/PERIOD)) % (SIZE+1) + 1\",\n \"def HashAlgorithm(self) -> _n_7_t_0:\",\n \"def seed_hash(*args):\\n args_str = str(args)\\n return int(hashlib.md5(args_str.encode(\\\"utf-8\\\")).hexdigest(), 16) % (2**31)\",\n \"def _hash(self):\\r\\n MAX = sys.maxint\\r\\n MASK = 2 * MAX + 1\\r\\n n = len(self)\\r\\n h = 1927868237 * (n + 1)\\r\\n h &= MASK\\r\\n for x in self:\\r\\n hx = hash(x)\\r\\n h ^= (hx ^ (hx << 16) ^ 89869747) * 3644798167\\r\\n h &= MASK\\r\\n h = h * 69069 + 907133923\\r\\n h &= MASK\\r\\n if h > MAX:\\r\\n h -= MASK + 1\\r\\n if h == -1:\\r\\n h = 590923713\\r\\n return h\",\n \"def hash_generator(self, value):\\n hash_string = hashlib.sha256(bytes(value))\\n return hash_string.hexdigest()\",\n \"def __init__(self):\\n self.hash = [[] for _ in range(20011)]\",\n \"def generateHashFunctions(n):\\n hashFunction = []\\n for i in range(n):\\n # Chooses random value to use as xor during a single hash function\\n random_XORvalue = random.getrandbits(INT_SIZE)\\n hashFunction.append((random_XORvalue))\\n return hashFunction\",\n \"def generate_sha256_hash(fpath, sig_key=None):\\n return run(fpath, sig_key)\",\n \"def h(x):\\n\\n hasher = hashlib.sha256()\\n hasher.update(x)\\n return hasher.digest()\",\n \"def nextRandom(self):\\n # Apply SHA-256, interpreting digest output as integer\\n # to yield 256-bit integer (a python \\\"long integer\\\")\\n hash_output = self.basehash.digest()\\n self.next()\\n return hash_output\",\n \"def get_sha256_hash(key, size=None):\\n partition_hash = hashlib.sha256()\\n for part in key:\\n partition_hash.update(str(part).encode('utf-8'))\\n sha256_hash = partition_hash.hexdigest()\\n if not size or size > len(sha256_hash):\\n size = len(sha256_hash)\\n return sha256_hash[:size]\",\n \"def sha256(message: bytes):\\n # convert message bitarray\\n bit_msg = bitarray(endian='big')\\n bit_msg.frombytes(message)\\n L = len(bit_msg)\\n\\n # additions done mod 2^32\\n pow2 = pow(2,32)\\n\\n # append 1 followed by K 0s where K is the minimum number >= 0 such that \\n # len(bit_msg) + 1 + K + 64 is a multiple of 512\\n bit_msg = bit_msg + bitarray('1') + (bitarray('0') * ((-L-65) % 512))\\n # append len(bit_msg) as a 64-bit int to bit_msg\\n bit_msg = bit_msg + util.int2ba(L, length=64, endian='big')\\n\\n # initialize hash to predefined values\\n current_hash = [h for h in initial_hash]\\n\\n # operate on each 512-bit chunk\\n for chunk_index in range(len(bit_msg)//512):\\n chunk = bit_msg[chunk_index * 512 : (chunk_index+1) * 512]\\n # w is array of 64 32-bit words with first 16 equal to chunk\\n w = [chunk[i*32 : (i+1)*32] for i in range(16)]\\n w.extend([bitarray(32) for _ in range(48)])\\n # create last 48 words in w from first 16\\n for i in range(16, 64):\\n s0 = rightrotate(w[i-15], 7) ^ rightrotate(w[i-15], 18) ^ rightshift(w[i-15], 3)\\n s1 = rightrotate(w[i-2], 17) ^ rightrotate(w[i-2], 19) ^ rightshift(w[i-2], 10)\\n w[i] = int2ba32(sum(map(util.ba2int, [w[i-16], s0, w[i-7], s1])) % pow2)\\n\\n # copy current hash (stored in hex) into working list v as bitarrays\\n v = list(map(int2ba32, current_hash))\\n # compression\\n for i in range(64):\\n S1 = rightrotate(v[4], 6) ^ rightrotate(v[4], 11) ^ rightrotate(v[4], 25)\\n ch = (v[4] & v[5]) ^ ((~v[4]) & v[6])\\n temp1 = (constants[i] + sum(map(util.ba2int, [v[7], S1, ch, w[i]]))) % pow2\\n S0 = rightrotate(v[0], 2) ^ rightrotate(v[0], 13) ^ rightrotate(v[0], 22)\\n maj = (v[0] & v[1]) ^ (v[0] & v[2]) ^ (v[1] & v[2])\\n temp2 = (util.ba2int(S0) + util.ba2int(maj)) % pow2\\n\\n # shift elements of v by 1\\n for j in reversed(range(1, len(v))):\\n v[j] = v[j-1]\\n v[0] = int2ba32((temp1 + temp2) % pow2)\\n v[4] = int2ba32((util.ba2int(v[4]) + temp1) % pow2)\\n\\n # add compressed values (which are bitarrays) to current_hash (which are ints)\\n current_hash = list(map(lambda a,b: (a + util.ba2int(b)) % pow2, current_hash, v))\\n\\n # each entry of current_hash is a 32-bit integer so convert to 4 bytes \\n # adding bytes appends them\\n return b''.join(x.to_bytes(4, 'big') for x in current_hash)\",\n \"def fast_hash(infile):\\n\\n m = hashlib.sha256()\\n with open(infile, 'rb', 1024 * 1024) as f:\\n l = f.read(1024 * 1024)\\n while (len(l) > 0):\\n m.update(l)\\n f.seek(1024 * 1024 * (512 - 1), 1)\\n l = f.read(1024 * 1024)\\n return m.hexdigest()\",\n \"def __init__(self):\\n self.size = 1000\\n self.hash_table = [None] * self.size\",\n \"def hash(x):\\r\\n return (randint(1,5*c)*x + randint(1,5*c))%c\",\n \"def __hash__(self):\\n token = \\\"\\\"\\n for gamePiece in self.game_pieces:\\n token = token + str(gamePiece.x) + str(gamePiece.y)\\n \\n hash_ = int(token) % 100000\\n return hash_\",\n \"def hash(self) -> str:\\r\\n ...\",\n \"def gen_hash_params(self):\\n m_bitlength = self.m.bit_length()\\n hfs = []\\n for _ in xrange(self.k):\\n p = generateLargePrime(m_bitlength + 7)\\n a = random.randint(1, p)\\n b = random.randint(1, p)\\n hfs.append((a, b, p))\\n return hfs\",\n \"def _sha256(sha256):\\n if not sha256:\\n sha256 = \\\"0\\\" * 64\\n\\n return sha256\",\n \"def __init__(self, seed=None):\\n self.seed(seed)\\n self.hashfun = \\\"SHA-256\\\"\\n self._basehash()\",\n \"def generate_hash_functions(rows, no_of_hash_func=200):\\r\\n\\r\\n hashes = []\\r\\n c = rows\\r\\n \\r\\n # all functions are same here. check this\\r\\n for i in range(no_of_hash_func):\\r\\n def hash(x):\\r\\n \\\"\\\"\\\"\\r\\n This function calculates hash for given x\\r\\n\\r\\n hash function format: (a*x+b)%c where\\r\\n c: prime integer just greater than rows\\r\\n a,b: random integer less than c\\r\\n \\\"\\\"\\\"\\r\\n return (randint(1,5*c)*x + randint(1,5*c))%c\\r\\n hashes.append(hash)\\r\\n\\r\\n return hashes\",\n \"def mkRandHash(N):\\n junk = \\\"\\\".join(random.choice(string.ascii_letters + string.digits)\\n for unused in range(10))\\n return lambda key: hash(junk + key) % N\",\n \"def chunk_hashes(str):\\n chunk = 1024*1024\\n chunk_count = int(math.ceil(len(str)/float(chunk)))\\n chunks = [str[i*chunk:(i+1)*chunk] for i in range(chunk_count)]\\n return [hashlib.sha256(x).digest() for x in chunks]\",\n \"def calHash(n, m):\\n return int(m*BloomFilter.ln2/n)\",\n \"def _hash_function(self, key):\\n h = 0\\n a = 31\\n table_size = self.size\\n for i in range(len(key)):\\n h = (h * a + ord(key[i])) % table_size\\n return h\",\n \"def hash_functions(self):\\n def hash_factory(n):\\n return lambda x: hash(\\\"salt\\\" + str(n) + str(x) + \\\"salt\\\")\\n return [ hash_factory(_) for _ in range(self.dim) ]\",\n \"def bit_contribution_test(hash_function):\\n\\n model = hash_function()\\n hash_list = []\\n zero_str = '0' * 2048\\n for i in range(1, 2049):\\n for j in range(0, i):\\n flip_str = zero_str[:j] + '1' + zero_str[j+1:i]\\n hash_list.append(list(map(int, list(msg_to_bits.pad_msg(flip_str, i)))))\\n if i % 200 == 0:\\n print(i)\\n\\n hashed_dict = dict()\\n collisions = 0\\n i = 0\\n for to_hash in hash_list:\\n i += 1\\n hash_val = model.hash(to_hash, False).tostring()\\n if hash_val in hashed_dict:\\n collisions += 1\\n hashed_dict[hash_val] = True\\n if i % 10000 == 0:\\n print(i)\\n\\n return collisions\",\n \"def RSA_SIGNATURE_HASH() :\\n return \\\"SHA-256\\\"\",\n \"def __polynomial_hash(self, s, base = 31, max_size=168):\\r\\n digest = 0\\r\\n max_size = 168\\r\\n for c in s: digest = base * digest + ord(c)\\r\\n digest &= 2 ** max_size - 1 \\r\\n return hex(digest).rstrip('L')\",\n \"def _basehash(self):\\n if self.baseseed is not None:\\n hashinput = (str(self.baseseed) + ',').encode()\\n self.basehash = hashlib.sha256(hashinput)\\n else:\\n self.basehash = None\",\n \"def genSeed():\\n\\tseed_length = int(''.join(random.SystemRandom().choice(string.digits) for _ in range(0, 3)))\\n\\tseed = os.urandom(seed_length)\\n\\thashing_algorithm = hashlib.shake_128()\\n\\thashing_algorithm.update(seed)\\n\\t# 2200 bytes from SHAKE-128 function is enough data to get 1024 coefficients\\n\\t# smaller than 5q, from Alkim, Ducas, Pöppelmann, Schwabe section 7:\\n\\tseed_hash = hashing_algorithm.digest(100)\\n\\treturn seed, seed_hash\",\n \"def get_hash_str():\\r\\n\\tli = \\\"\\\"\\r\\n\\tfor i in range(5):\\r\\n\\t\\tli += str(int(int((6 * random.random()) + 1)))\\r\\n\\treturn li\",\n \"def create_hash() -> str:\\n length = 6\\n char = string.ascii_uppercase + string.digits + string.ascii_lowercase\\n\\n # Generate a new ID, until one is found that is unique\\n while True:\\n hash = \\\"\\\".join(random.choice(char) for _ in range(length))\\n\\n if not utils.cache_is_hash_taken(hash):\\n return hash\",\n \"def hash_exp(key, seed=SEED):\\n m = hashlib.sha256((\\\"%s###%s\\\" % (seed, key)).encode(\\\"ascii\\\"))\\n uniform = float(int(m.hexdigest(), 16) + 1) / 2**256\\n return -1.0 * math.log(uniform, math.e)\",\n \"def hash(token, num_buckets):\\n return murmurhash3_32(token, positive=True) % num_buckets\",\n \"def hashing(word) :\\r\\n ans = hashlib.sha256(word.encode())\\r\\n return ans.hexdigest()\",\n \"def create_hash(self):\\n return os.urandom(32).encode('hex')\",\n \"def calculateHash(self):\\n hashData = str(self.index) + str(self.data) + self.timestamp + self.previousHash + str(self.nonce)\\n return hashlib.sha256(hashData.encode(encoding='UTF-8',errors='strict')).hexdigest()\",\n \"def dense_hash(hash_, block_size=16):\\n results = list()\\n for index in range(0, len(hash_), block_size):\\n\\n block = hash_[index:index + block_size]\\n\\n total = 0\\n for i in block:\\n total ^= i\\n\\n results.append(total)\\n\\n return results\",\n \"def r_soft_hash(x):\\n if abs(x) < 1e-9:return 0\\n # round it to some number of bits\\n b = ns.round(ns.log(abs(x)) / ns.log(2))\\n gran = 2**(b-30)\\n return ns.round(x / gran) * gran\",\n \"def random_h_function(key, N):\\n\\n if key is None:\\n raise TypeError(\\\"Key must not be None\\\")\\n\\n\\n hashcode = 0\\n for i in range(len(key)):\\n hashcode = (71 * hashcode + ord(key[i])) % N\\n return hashcode\",\n \"def hash(plainString):\\n result = plainString\\n for i in range(0,12):\\n result = hashHelp(result)\\n return result\",\n \"def _hi(hash_name, data, salt, iterations):\\n mac = hmac.HMAC(data, None, getattr(hashlib, hash_name))\\n\\n def _digest(msg, mac=mac):\\n \\\"\\\"\\\"Get a digest for msg.\\\"\\\"\\\"\\n _mac = mac.copy()\\n _mac.update(msg)\\n return _mac.digest()\\n\\n from_bytes = _from_bytes\\n to_bytes = _to_bytes\\n\\n _u1 = _digest(salt + b'\\\\x00\\\\x00\\\\x00\\\\x01')\\n _ui = from_bytes(_u1, 'big')\\n for _ in range(iterations - 1):\\n _u1 = _digest(_u1)\\n _ui ^= from_bytes(_u1, 'big')\\n return to_bytes(_ui, mac.digest_size, 'big')\",\n \"def _hash(self) -> None:\\r\\n # for a unit cube there are 8 possible hashes\\r\\n # returns the tuple of with all 8 hashes\\r\\n\\r\\n self.hashes[\\\"aaa\\\"] = P[P[P[self.xi] + self.yi] + self.zi]\\r\\n self.hashes[\\\"aab\\\"] = P[P[P[self.xi] + self.yi] + self._inc(self.zi)]\\r\\n self.hashes[\\\"aba\\\"] = P[P[P[self.xi] + self._inc(self.yi)] + self.zi]\\r\\n self.hashes[\\\"abb\\\"] = P[P[P[self.xi] + self._inc(self.yi)] + self._inc(self.zi)]\\r\\n self.hashes[\\\"baa\\\"] = P[P[P[self._inc(self.xi)] + self.yi] + self.zi]\\r\\n self.hashes[\\\"bab\\\"] = P[P[P[self._inc(self.xi)] + self.yi] + self._inc(self.zi)]\\r\\n self.hashes[\\\"bba\\\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self.zi]\\r\\n self.hashes[\\\"bbb\\\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self._inc(self.zi)]\",\n \"def sha256(data):\\n\\n d = rpki.POW.Digest(rpki.POW.SHA256_DIGEST)\\n d.update(data)\\n return d.digest()\",\n \"def sha256(ctx, salt=\\\"\\\"):\\n if ctx.data:\\n salted_input_value = salt + \\\":\\\" + ctx.data\\n ctx.data = hashlib.sha256(salted_input_value.encode()).hexdigest()\\n else:\\n raise RefError(\\n \\\"Ref error: eval_func: nothing to sha256 hash; try \\\" \\\"something like '|random:str|sha256'\\\"\\n )\",\n \"def hash_function(s):\\n\\n # O(n) over the key length\\n # O(1) over the HASH_DATA_SIZE\\n\\n bytes_list = s.encode()\\n\\n total = 0\\n\\n\\n for b in bytes_list: # O(n) over the length of the key\\n total += b\\n\\n\\n total &= 0xffffffff # 32 bit (8 f's)\\n\\n return total\",\n \"def hash_128_bit_pass(passwd):\\n h = hashlib.sha256()\\n h.update(passwd)\\n return h.hexdigest()[:16]\",\n \"def current_hash(self):\",\n \"def generate_hash(password):\\n return pbkdf2_sha256.hash(password)\",\n \"def gulp(meal):\\n #print(\\\"Proccessing...\\\")\\n broth = meal\\n chunks = [broth[i:i+256] for i in range(0, len(broth), 256)]\\n #print(\\\"Number of chunks:\\\", len(chunks))\\n #print(\\\" - Chunkified...\\\")\\n digest = [0]*64\\n i1 = 1\\n global Ti\\n Ti = 0\\n for chunk in chunks:\\n t1 = time.time()\\n \\n if i1 == 1:\\n chewed = chew(chunk)\\n digest = chewed\\n else:\\n chewed = chew(chunk)\\n digest = [(int(x) + int(y))%16 for x, y in zip(digest, chewed)]\\n \\n t2 = time.time()\\n #print(\\\"time taken:\\\", t2-t1)\\n Ti += t2-t1\\n #print(\\\"time TOTAL:\\\", Ti)\\n #print(\\\" - Chewed!\\\")\\n \\n i1 += 1\\n #print(\\\"Hash finished.\\\")\\n return digest\",\n \"def fnvhash(a): \\n h = 2166136261 \\n for i in a: \\n t = (h * 16777619) & 0xffffffffL \\n h = t ^ i \\n return h\",\n \"def hash_double(d):\\n # initialize table\\n table = [\\\"-\\\"] * 19\\n # consider each integer k in the input\\n for k in d:\\n # if k is already in the table this is a duplicate so move to next integer in the input\\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\\n if k in table:\\n continue\\n # apply the hash function\\n i = (6 * k + 3) % 19\\n t = i\\n # initialize count that checks whether linear probe has considered each bucket and is now full\\n count = 0\\n j = 0\\n # while bucket is already filled\\n s = 11 - (k % 11)\\n while table[i] != '-':\\n j += 1\\n # move to next bucket\\n i = (t + j*s) % 19\\n # increment count\\n count += 1\\n\\n # if table is full\\n if count >= 18:\\n # can return table as nothing further can be added\\n break\\n\\n # Ensure table[i] is empty so k can be added here\\n if table[i] == '-':\\n table[i] = k\\n\\n # now each part of the input has been considered return the table\\n return table\",\n \"def hash_function_2(key):\\n\\n hash = 0\\n index = 0\\n for i in key:\\n hash = hash + (index + 1) * ord(i)\\n index = index + 1\\n return hash\",\n \"def hash_function_2(key):\\n\\n hash = 0\\n index = 0\\n for i in key:\\n hash = hash + (index + 1) * ord(i)\\n index = index + 1\\n return hash\",\n \"def hash_function_2(key):\\n\\n hash = 0\\n index = 0\\n for i in key:\\n hash = hash + (index + 1) * ord(i)\\n index = index + 1\\n return hash\",\n \"def hash_function_2(key):\\n hash = 0\\n index = 0\\n for i in key:\\n hash = hash + (index + 1) * ord(i)\\n index = index + 1\\n return hash\",\n \"def lines_hash(lines):\\n x = xxh32()\\n for i in lines:\\n x.update(i.encode())\\n return x.digest()\",\n \"def get_partial_sha256(self, nbytes):\\n return sha256file(abspath=self.abspath, nbytes=nbytes)\",\n \"def fnv1(self, key):\\n # hash = 0xff\\n hash = 0xcbf29ce484222325\\n for n in key.encode():\\n # print(n)\\n hash = hash ^ n\\n hash = hash * 0x100000001b3\\n\\n # print(hash)\\n return hash\",\n \"def myHash(string, base=91, mod=1000000321):\\n value = 0\\n for pos, elem in enumerate(string[::-1]): # считаем значение полинома\\n value += ord(elem) * base**pos # в последней задаче сделано с помощью массива (динамика)\\n return value % mod\",\n \"def sha256(value):\\n return hashlib.sha256(value).hexdigest()\",\n \"def hash_sbox(f):\\n hf = sha256()\\n for x in f:\\n hf.update(hex(x).encode('utf-8'))\\n return hf.hexdigest()\",\n \"def sha_process(self):\\n\\n # Break chunk into sixteen 32-bit big-endian words m[0..15]\\n m = [0] * 64\\n # Extend the sixteen 32-bit words into sixty-four 32-bit words\\n j = 0\\n for i in range(16):\\n m[i] = self.data_b[j] << 24 | self.data_b[j+1] << 16\\\\\\n | self.data_b[j+2] << 8 | self.data_b[j+3]\\n j += 4\\n\\n for i in range(16, 64):\\n sig0 = self.rotr(m[i-15], 7) ^ self.rotr(m[i-15], 18)\\\\\\n ^ (m[i-15] >> 3)\\n sig1 = self.rotr(m[i-2], 17) ^ self.rotr(m[i-2], 19)\\\\\\n ^ (m[i-2] >> 10)\\n m[i] = (sig1 + m[i-7] + sig0 + m[i-16]) & 0xFFFFFFFF\\n\\n # Initialize hash value for this chunk\\n a, b, c, d, e, f, g, h = self.h\\n\\n for i in range(64):\\n ep0 = (self.rotr(a, 2) ^ self.rotr(a, 13) ^ self.rotr(a, 22))\\\\\\n & 0xFFFFFFFF\\n ep1 = (self.rotr(e, 6) ^ self.rotr(e, 11) ^ self.rotr(e, 25))\\\\\\n & 0xFFFFFFFF\\n ch = ((e & f) ^ ((~e) & g))\\n maj = ((a & b) ^ (a & c) ^ (b & c))\\n t1 = (h + ep1 + ch + self.k[i] + m[i]) & 0xFFFFFFFF\\n t2 = (ep0 + maj) & 0xFFFFFFFF\\n h = g\\n g = f\\n f = e\\n e = (d + t1) & 0xFFFFFFFF\\n d = c\\n c = b\\n b = a\\n a = (t1 + t2) & 0xFFFFFFFF\\n\\n # Add this chunk's hash to result so far\\n self.h[0] = (self.h[0] + a) & 0xFFFFFFFF\\n self.h[1] = (self.h[1] + b) & 0xFFFFFFFF\\n self.h[2] = (self.h[2] + c) & 0xFFFFFFFF\\n self.h[3] = (self.h[3] + d) & 0xFFFFFFFF\\n self.h[4] = (self.h[4] + e) & 0xFFFFFFFF\\n self.h[5] = (self.h[5] + f) & 0xFFFFFFFF\\n self.h[6] = (self.h[6] + g) & 0xFFFFFFFF\\n self.h[7] = (self.h[7] + h) & 0xFFFFFFFF\",\n \"def hash(self) -> bytes:\",\n \"def tree_hash(hashes):\\n while len(hashes) > 1:\\n hashes = [hashlib.sha256(\\\"\\\".join(h[i:i+1])).digest() for i in range(i,2)]\\n return hashes[0]\",\n \"def hash(self):\\n return self._run(\\\"%s --%d --block-size %d --bits %d --quiet --threads %d %s --mode %s %s\\\" %\\n (self._ishakesumd, self._mode, self._block_size, self._output_bits, self._threads,\\n self._profile, self._alg, self._dir))\",\n \"def generate_index(size=20):\\n return hexlify(np.random.rand(100))[:size].decode()\",\n \"def generichash_blake2b_final(state: Blake2State) -> bytes:\\n\\n ensure(\\n isinstance(state, Blake2State),\\n \\\"State must be a Blake2State object\\\",\\n raising=exc.TypeError,\\n )\\n\\n _digest = ffi.new(\\\"unsigned char[]\\\", crypto_generichash_BYTES_MAX)\\n rc = lib.crypto_generichash_blake2b_final(\\n state._statebuf, _digest, state.digest_size\\n )\\n\\n ensure(rc == 0, \\\"Unexpected failure\\\", raising=exc.RuntimeError)\\n return ffi.buffer(_digest, state.digest_size)[:]\",\n \"def get_256_hash_from_string(string):\\n\\n sha256 = hashlib.sha256()\\n sha256.update(string.encode('utf-8'))\\n\\n return sha256.hexdigest()\",\n \"def hashId(key, size):\\n return sum([ord(c) for c in key]) % size\",\n \"def randomHash(self):\\n modulus = self.filter_size\\n a, b = random.randint(1, modulus - 1), random.randint(1, modulus - 1)\\n\\n def f(x):\\n return hash(x) % (a + b) % modulus\\n\\n return f\",\n \"def SHA256(data: str) -> str:\\n\\n msg = []\\n for e in data:\\n # convert chars to binary in 8-bit \\n chbin = prepad(binary(ASCII[e]), to=8)\\n msg.extend(chbin)\\n \\n # get length (in bits) of input\\n datalen = binary(len(msg))\\n if len(datalen) > 64:\\n raise ValueError(\\\"input is too large\\\")\\n\\n # pad out message to factor of 512 (512-bit blocks)\\n tail = [0]*(64-len(datalen)) + datalen\\n padding = [1] + [0]*(511-((len(msg)+len(tail)) % 512))\\n msg += (padding + tail)\\n \\n ctx = None\\n for i in range(0, len(msg), 512):\\n block = msg[i:i+512] \\n wds = [UBitArray32(block[i:i+32]) for i in range(0, 512, 32)]\\n wds = schedule(wds)\\n # set context for next block\\n ctx = compress(wds, ctx)\\n\\n hexdigest = \\\"\\\".join(x.tohex() for x in ctx)\\n return hexdigest\",\n \"def compute_hash(self, key: int):\\n return key % 42\",\n \"def make_hash(attrs, usekeys=None, N=10):\\n if usekeys is None:\\n d = attrs\\n else:\\n d = {k: attrs.get(k, None) for k in usekeys}\\n\\n s = json.dumps(d, sort_keys=True, cls=JSONEncoder).encode()\\n return hashlib.sha1(s).hexdigest()[:N]\",\n \"def create_password_hash(self, password):\\n return pbkdf2_sha256.encrypt(password, rounds=1000, salt_size=16)\",\n \"def __hash__(self):\\n hash_value = 0\\n \\n # approximate_online_count\\n hash_value ^= self.approximate_online_count\\n \\n # approximate_user_count\\n hash_value ^= self.approximate_user_count << 12\\n \\n # description\\n description = self.description\\n if (description is not None):\\n hash_value ^= hash(description)\\n \\n # discovery_splash\\n hash_value ^= hash(self.discovery_splash)\\n \\n # emojis\\n emojis = self.emojis\\n hash_value ^= len(emojis) << 1\\n for emoji in emojis.values():\\n hash_value ^= hash(emoji)\\n \\n # features\\n features = self.features\\n hash_value ^= len(features) << 5\\n for feature in features:\\n hash_value ^= hash(feature)\\n \\n # icon\\n hash_value ^= hash(self.icon)\\n \\n # id\\n hash_value ^= self.id\\n \\n # invite_splash\\n hash_value ^= hash(self.invite_splash)\\n \\n # stickers\\n stickers = self.stickers\\n hash_value ^= len(stickers) << 9\\n for sticker in stickers.values():\\n hash_value ^= hash(sticker)\\n \\n # name\\n name = self.name\\n if (description is None) or (description != name):\\n hash_value ^= hash(name)\\n \\n return hash_value\",\n \"def _produce_key(self, passphrase):\\n from hashlib import sha256\\n pp = bytes(passphrase, 'utf-8')\\n hash_alg = sha256(pp)\\n for i in range(self._get_key_stretches()):\\n d = hash_alg.digest()\\n hash_alg.update(d + pp)\\n return hash_alg.digest()\",\n \"def mkPerfHash(keys, Hash):\\n f1, f2, G = generate_hash(keys, Hash)\\n return lambda k: (G[f1(k)] + G[f2(k)]) % len(G)\",\n \"def hash_key(self,key: str) -> int: \\n \\n total = 0\\n prime = 3\\n \\n for index, char in enumerate(key,start=1):\\n \\n total += ord(char)*(prime)**index\\n \\n return total % self.buckets\",\n \"def computeHash(filename):\\n fileHash = hashlib.sha256()\\n with open(filename, \\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(4096), b\\\"\\\"):\\n fileHash.update(chunk)\\n return fileHash.hexdigest()\",\n \"def typeahead_hash(self) -> str:\",\n \"def hash_key(self):\",\n \"def calc_statistics_hash(self) -> bytes:\\n return b\\\"somehash\\\"\",\n \"def sha256(inputs, secret):\\n m = hmac.new(secret, b'', hashlib.sha256)\\n for i in inputs:\\n m.update(i)\\n return m.digest()\",\n \"def generate_aes_key ( ) :\\n import hashlib\\n sr = Crypto.Random.random.StrongRandom( )\\n key_bits = sr.getrandbits( 256 )\\n sha_key = hashlib.sha256( str( key_bits ) ).digest( )\\n return sha_key\",\n \"def hash_block(self):\\n sha = hasher.sha256()\\n sha.update((str(self.index) + str(self.timestamp) + str(self.data) + str(self.previous_hash)).endswith('utf-8'))\\n return sha.hexdigest()\",\n \"def __init__(self):\\n \\n self.hashset=[None]* 1000\",\n \"def _hash_function(self, n):\\n # Get the mask for this n, or make a new one of 32 random bits.\\n mask = self._memomask.get(n)\\n if mask is None:\\n random.seed(n ^ self.seed_mask)\\n mask = self._memomask[n] = int(random.getrandbits(32))\\n # Now return a function that uses Jenkins Hash\\n #\\n def somehash(x):\\n return hashlittle(x, mask)\\n return somehash\",\n \"def get_hash(self):\\r\\n block_data = self.prev_hash\\r\\n block_data += bytearray(struct.pack(\\\"!f\\\", self.time))\\r\\n block_data += self.user_id.encode()\\r\\n block_data += self.signature.encode()\\r\\n block_data += self.choice.encode()\\r\\n\\r\\n digest = hashes.Hash(hashes.SHA256())\\r\\n digest.update(block_data)\\r\\n return digest.finalize()\",\n \"def pre_compute_hashes(s, M1, M2, X):\\n n = len(s)\\n h1 = [0 for _ in range(n+1)]\\n h2 = [0 for _ in range(n+1)]\\n for i in range(1, n+1):\\n ch = ord(s[i-1])\\n h1[i] = (X*h1[i-1] + ch) % M1\\n h2[i] = (X*h2[i-1] + ch) % M2\\n return h1, h2\",\n \"def get_sha256_file(filename):\\n BLOCKSIZE = 65536\\n hasher = hashlib.sha256()\\n with open(filename, 'rb') as afile:\\n buf = afile.read(BLOCKSIZE)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = afile.read(BLOCKSIZE)\\n return hasher.hexdigest()\",\n \"def sha256(self):\\n return self.sha256checksums()\",\n \"def sha256_hexoutput(in_str):\\r\\n return sha256(in_str.encode('ascii')).hexdigest()\",\n \"def gensalt():\\n return hexlify(os.urandom(24)).decode()\",\n \"def generate_hash(*args):\\n key = bytes(' '.join(args), 'utf_8')\\n hashh = hashlib.md5()\\n hashh.update(key)\\n return hashh.hexdigest()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68605787","0.6531151","0.64920217","0.6357423","0.6346101","0.63296044","0.6322797","0.63040954","0.628679","0.6218963","0.6196641","0.61853266","0.6156555","0.6146982","0.6143425","0.6101353","0.60864455","0.6079242","0.6064988","0.6041602","0.59878373","0.5987534","0.5986257","0.59841895","0.5975685","0.5971351","0.59614605","0.5953719","0.59536976","0.59521616","0.5941997","0.59344816","0.59271866","0.58814025","0.58799976","0.58754","0.5871838","0.58701587","0.5866356","0.58589315","0.58466864","0.5832717","0.5831388","0.5818","0.58167416","0.5796819","0.57954973","0.5780335","0.57792985","0.5773941","0.5769588","0.57596606","0.5750056","0.57467854","0.5745639","0.5733208","0.5729578","0.5725742","0.5725742","0.5725742","0.5724705","0.571477","0.57136637","0.5705413","0.56990975","0.56926054","0.5692319","0.5687771","0.56795555","0.56785804","0.5675927","0.5674131","0.56627595","0.5653301","0.56481385","0.56467164","0.56463796","0.5645806","0.56360036","0.56344795","0.56281185","0.5626207","0.5618944","0.56109375","0.5609621","0.5607732","0.5604826","0.5600991","0.55967385","0.55921966","0.5590674","0.5588848","0.558207","0.5575766","0.5575344","0.55752844","0.5574863","0.55707455","0.556537","0.5565213"],"string":"[\n \"0.68605787\",\n \"0.6531151\",\n \"0.64920217\",\n \"0.6357423\",\n \"0.6346101\",\n \"0.63296044\",\n \"0.6322797\",\n \"0.63040954\",\n \"0.628679\",\n \"0.6218963\",\n \"0.6196641\",\n \"0.61853266\",\n \"0.6156555\",\n \"0.6146982\",\n \"0.6143425\",\n \"0.6101353\",\n \"0.60864455\",\n \"0.6079242\",\n \"0.6064988\",\n \"0.6041602\",\n \"0.59878373\",\n \"0.5987534\",\n \"0.5986257\",\n \"0.59841895\",\n \"0.5975685\",\n \"0.5971351\",\n \"0.59614605\",\n \"0.5953719\",\n \"0.59536976\",\n \"0.59521616\",\n \"0.5941997\",\n \"0.59344816\",\n \"0.59271866\",\n \"0.58814025\",\n \"0.58799976\",\n \"0.58754\",\n \"0.5871838\",\n \"0.58701587\",\n \"0.5866356\",\n \"0.58589315\",\n \"0.58466864\",\n \"0.5832717\",\n \"0.5831388\",\n \"0.5818\",\n \"0.58167416\",\n \"0.5796819\",\n \"0.57954973\",\n \"0.5780335\",\n \"0.57792985\",\n \"0.5773941\",\n \"0.5769588\",\n \"0.57596606\",\n \"0.5750056\",\n \"0.57467854\",\n \"0.5745639\",\n \"0.5733208\",\n \"0.5729578\",\n \"0.5725742\",\n \"0.5725742\",\n \"0.5725742\",\n \"0.5724705\",\n \"0.571477\",\n \"0.57136637\",\n \"0.5705413\",\n \"0.56990975\",\n \"0.56926054\",\n \"0.5692319\",\n \"0.5687771\",\n \"0.56795555\",\n \"0.56785804\",\n \"0.5675927\",\n \"0.5674131\",\n \"0.56627595\",\n \"0.5653301\",\n \"0.56481385\",\n \"0.56467164\",\n \"0.56463796\",\n \"0.5645806\",\n \"0.56360036\",\n \"0.56344795\",\n \"0.56281185\",\n \"0.5626207\",\n \"0.5618944\",\n \"0.56109375\",\n \"0.5609621\",\n \"0.5607732\",\n \"0.5604826\",\n \"0.5600991\",\n \"0.55967385\",\n \"0.55921966\",\n \"0.5590674\",\n \"0.5588848\",\n \"0.558207\",\n \"0.5575766\",\n \"0.5575344\",\n \"0.55752844\",\n \"0.5574863\",\n \"0.55707455\",\n \"0.556537\",\n \"0.5565213\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58618045"},"document_rank":{"kind":"string","value":"39"}}},{"rowIdx":77099,"cells":{"query":{"kind":"string","value":">>> isinstance(hashchain(hstr2bin('32eee808dc7c5dfe26fd4859b415e5a713bd764036bbeefd7a541da9a1cc7b9fcaf17da039a62756b63835de1769e05e')), HashChainBundle) True"},"document":{"kind":"string","value":"def hashchain(seed_private,\n epoch=0,\n blocks_per_epoch=config.dev.blocks_per_epoch):\n # type: (int) -> HashChainBundle\n return HashChainBundle(*_calc_hashchain(seed_private, epoch, blocks_per_epoch))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_hash_sha256(self):\n block = self.blockchain.new_block(self.proof, self.previous_hash)\n hash_ = self.blockchain.hash(block)\n\n self.assertIsInstance(hash_, str)\n self.assertEqual(hashlib.sha256(json.dumps(block, sort_keys=True).encode()).hexdigest(), hash_)","def _get_chain_hash(self, chain):\n # todo if like this then PickleCache(m1, m2) + PickleCache(m3) != PickleCache(m1, m2, m3)\n chain_string = self._object_dump_to_string(chain, self.max_recursion_level, debug_level=self.debug_level)\n return self.hash_dump_string(chain_string)","def hash(self) -> bytes:","def test_hash(self):\n self.assertEqual(hash(self.compound), hash((\"t1\", \"test compound\")))","def fixed(o):\n try:\n hash(o)\n except TypeError:\n return False\n return True","def test_fail_bundle_hash_invalid(self):\n self.bundle.transactions[3].bundle_hash =\\\n BundleHash(\n b'NFDPEEZCWVYLKZGSLCQNOFUSENIXRHWWTZFBXMPS'\n b'QHEDFWZULBZFEOMNLRNIDQKDNNIELAOXOVMYEI9PG'\n )\n\n validator = BundleValidator(self.bundle)\n\n self.assertFalse(validator.is_valid())\n\n self.assertListEqual(\n validator.errors,\n\n [\n 'Transaction 3 has invalid bundle hash.',\n ],\n )","def is_valid_git_sha1(hash):\r\n\r\n if len(hash) != 40:\r\n return False\r\n try:\r\n value = int(hash, 16)\r\n except ValueError:\r\n return False\r\n\r\n return True","def test_hash(self):\r\n self.assertEqual(processor_hash('test'), 'GqNJWF7X7L07nEhqMAZ+OVyks1Y=')\r\n self.assertEqual(processor_hash('edx '), '/KowheysqM2PFYuxVKg0P8Flfk4=')","def has_hash(self, h):\n rsp = h.hashlist(self.path)\n if re.search(\"\\n[0-9a-f]+\\smd5\\s%s\" % self.path, rsp):\n rval = True\n else:\n rval = False\n return rval","def SHA256(self) -> _n_0_t_3[_n_0_t_9]:","def hash(self) -> str:\r\n ...","def validate_hash_difficulty(bhash: str, difficulty: int) -> bool:\n b = hex_to_bin(bhash)\n print(b[:4], type(b))\n return hex_to_bin(bhash).startswith('0' * difficulty)","def test_hash_string(self):\n self.assertEqual(hexlify(self._hashdigest(pubkey_sha)), sample_ripe)","def chain_hash(self):\n return self.chain_info['chain_hash']","def coerce(self, value):\n if isinstance(value, bytes) and len(value) == self.bit_length:\n return HashString.from_b256(value)\n elif len(value) == self.b16_length:\n return HashString.from_b16(value)\n elif self.b64_length - len(value) <= 4:\n return HashString.from_b64(value)","def typeahead_hash(self) -> str:","def verifyHashcode(digest):\n list_str = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"A\", \"B\", \"C\", \"D\", \"E\", \"F\"]\n list_num = [ 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]\n \n total = 0\n for i2 in range(len(digest)):\n digest_i = digest[i2]\n #print(\"digest_i =\", digest_i)\n \n for i1 in range(16):\n if digest_i == list_str[i1] and i2 != 0:\n total += list_num[i1]\n #print(\"total =\", total)\n #print(\"list_num[i1] =\", list_num[i1])\n continue\n \n #print(\"--- --- ---\")\n \n #print(\"total =\", total)\n \n checknum = total % 16\n #print(\"checknum =\", checknum)\n \n checkstr = list_str[checknum]\n #print(\"checkstr =\", checkstr)\n \n checkorg = digest[0]\n #print(\"checkorg =\", checkorg)\n \n if checkorg == checkstr:\n isValid = True\n else:\n isValid = False\n \n return isValid","def verify_hash(content: dict, hashing: str) -> bool:\n\n encoded_content = json.dumps(content, sort_keys=True).encode()\n hash_value = hashlib.sha256(encoded_content).hexdigest()\n\n return hash_value == hashing","def get_binary_sha256_hash(hash: str) -> str:\n result = \"\"\n\n for character in hash:\n character_number = int(character, base=16)\n binary_number = bin(character_number)\n # CAVEAT: each hash character is 4 bit size since SHA256 hash is hexidecimal string, so 4 * 64 = 256 bit\n formatted_binary_number = binary_number[2:].ljust(4, \"0\")\n result += formatted_binary_number\n\n return result","def validate_hash(h):\n if len(h) not in (32, 40, 64, 128):\n return False\n\n return bool(re.match(\"[0-9a-fA-F]*$\", h))","def deep_hash(obj):\n pass","def create_hash(*args):\n challenge_str = jsonpickle.encode(args)\n challenge_hash = hashlib.sha256(challenge_str.encode())\n return Bn.from_binary(challenge_hash.digest())","def get_hash(self, composition):\n return","def check_hashable(self, setup):\n try:\n hash(setup)\n except TypeError as e:\n raise AssertionError(f\"setup object is not hashable:\\n{setup}\") from e","def sha256_2_string(string_to_hash):\n\n # Solution for (1a)\n import hashlib\n first_sha = hashlib.sha256(string_to_hash.encode(\"utf8\"))\n second_sha = hashlib.sha256(first_sha.digest())\n return second_sha.hexdigest()\n\n # Placeholder for (1a)\n return \"deadbeef\"","def hashcode(o):","def hash_chain(self):\n if len(self.modules) != self.chain_info['chain_len']:\n self.chain_info = {\n 'chain_len': len(self.modules),\n 'chain_mtime': self._get_chain_mtime(self.modules),\n 'chain_hash': self._get_chain_hash(self.modules),\n 'chain_repr': self._get_chain_repr(self.modules),\n }","def is_chain_valid(self, chain):\r\n previous_block = chain[0]\r\n block_index = 1\r\n while block_index < len(chain):\r\n block = chain[block_index]\r\n if block['previous_hash'] != self.hash(previous_block):\r\n return False\r\n previous_proof = previous_block['proof']\r\n proof = block['proof']\r\n hash_operation = self.hash(block)\r\n if hash_operation[:4] != '0000':\r\n return False\r\n previous_block = block\r\n block_index += 1\r\n return True","def HashAlgorithm(self) -> _n_7_t_0:","def _verify_hash(self, read_bytes):\n if self.hash is None:\n raise QuiltException(\"Hash missing - need to build the package\")\n _check_hash_type_support(self.hash.get('type'))\n digest = hashlib.sha256(read_bytes).hexdigest()\n if digest != self.hash.get('value'):\n raise QuiltException(\"Hash validation failed\")","def bit_contribution_test(hash_function):\n\n model = hash_function()\n hash_list = []\n zero_str = '0' * 2048\n for i in range(1, 2049):\n for j in range(0, i):\n flip_str = zero_str[:j] + '1' + zero_str[j+1:i]\n hash_list.append(list(map(int, list(msg_to_bits.pad_msg(flip_str, i)))))\n if i % 200 == 0:\n print(i)\n\n hashed_dict = dict()\n collisions = 0\n i = 0\n for to_hash in hash_list:\n i += 1\n hash_val = model.hash(to_hash, False).tostring()\n if hash_val in hashed_dict:\n collisions += 1\n hashed_dict[hash_val] = True\n if i % 10000 == 0:\n print(i)\n\n return collisions","def hashable(obj):\n return bytes(str(obj), \"utf-8\")","def SHA1(self) -> _n_0_t_3[_n_0_t_9]:","def is_hashable(v):\n try:\n hash(v)\n except TypeError:\n return False\n return True","def valid_chain(self, chain):\n last_block = chain[0]\n current_index = 1\n\n while current_index < len(chain):\n block = chain[current_index]\n print(f'{last_block}')\n print(f'{block}')\n print(\"\\n----------------\\n\")\n # verify hash integrity\n if block['previous_hash'] != self.hash(last_block):\n return False\n\n # verify proof integrity\n if not self.valid_proof(last_block['proof'], block['proof']):\n return False\n\n last_block = block\n current_index += 1\n\n return True","def is_chain_id(v) -> bool:\n try:\n _validate(v, prefixes=[b'Net'])\n except (ValueError, TypeError):\n return False\n return True","def sha256(message: bytes):\n # convert message bitarray\n bit_msg = bitarray(endian='big')\n bit_msg.frombytes(message)\n L = len(bit_msg)\n\n # additions done mod 2^32\n pow2 = pow(2,32)\n\n # append 1 followed by K 0s where K is the minimum number >= 0 such that \n # len(bit_msg) + 1 + K + 64 is a multiple of 512\n bit_msg = bit_msg + bitarray('1') + (bitarray('0') * ((-L-65) % 512))\n # append len(bit_msg) as a 64-bit int to bit_msg\n bit_msg = bit_msg + util.int2ba(L, length=64, endian='big')\n\n # initialize hash to predefined values\n current_hash = [h for h in initial_hash]\n\n # operate on each 512-bit chunk\n for chunk_index in range(len(bit_msg)//512):\n chunk = bit_msg[chunk_index * 512 : (chunk_index+1) * 512]\n # w is array of 64 32-bit words with first 16 equal to chunk\n w = [chunk[i*32 : (i+1)*32] for i in range(16)]\n w.extend([bitarray(32) for _ in range(48)])\n # create last 48 words in w from first 16\n for i in range(16, 64):\n s0 = rightrotate(w[i-15], 7) ^ rightrotate(w[i-15], 18) ^ rightshift(w[i-15], 3)\n s1 = rightrotate(w[i-2], 17) ^ rightrotate(w[i-2], 19) ^ rightshift(w[i-2], 10)\n w[i] = int2ba32(sum(map(util.ba2int, [w[i-16], s0, w[i-7], s1])) % pow2)\n\n # copy current hash (stored in hex) into working list v as bitarrays\n v = list(map(int2ba32, current_hash))\n # compression\n for i in range(64):\n S1 = rightrotate(v[4], 6) ^ rightrotate(v[4], 11) ^ rightrotate(v[4], 25)\n ch = (v[4] & v[5]) ^ ((~v[4]) & v[6])\n temp1 = (constants[i] + sum(map(util.ba2int, [v[7], S1, ch, w[i]]))) % pow2\n S0 = rightrotate(v[0], 2) ^ rightrotate(v[0], 13) ^ rightrotate(v[0], 22)\n maj = (v[0] & v[1]) ^ (v[0] & v[2]) ^ (v[1] & v[2])\n temp2 = (util.ba2int(S0) + util.ba2int(maj)) % pow2\n\n # shift elements of v by 1\n for j in reversed(range(1, len(v))):\n v[j] = v[j-1]\n v[0] = int2ba32((temp1 + temp2) % pow2)\n v[4] = int2ba32((util.ba2int(v[4]) + temp1) % pow2)\n\n # add compressed values (which are bitarrays) to current_hash (which are ints)\n current_hash = list(map(lambda a,b: (a + util.ba2int(b)) % pow2, current_hash, v))\n\n # each entry of current_hash is a 32-bit integer so convert to 4 bytes \n # adding bytes appends them\n return b''.join(x.to_bytes(4, 'big') for x in current_hash)","def get_hash(thing):\n n = hashlib.sha256()\n \n if isinstance(thing,str):\n n.update(thing.encode('utf-8' ))\n elif isinstance(thing, bytes):\n n.update(thing)\n elif isinstance(thing,BeautifulSoup):\n n.update(get_hash(str(thing)))\n else:\n raise RuntimeError(\"unknown type: {}\".format(str(type(thing))))\n \n return(n.digest())","def validate_chain(self):\n chain_length = len(self.chain)\n isValid = None\n\n if(chain_length == 1):\n return \"Add blocks and then validate chain.\"\n\n for x in range(0, chain_length, 1):\n if(x < chain_length-1):\n # Hash the current block\n hash = self.hash(self.chain[x])\n next_block_prev_hash = self.chain[x+1]['prev_hash']\n\n if(hash == \"0\" and x == 0):\n isValid = True\n\n if(hash != next_block_prev_hash):\n isValid = False\n return isValid\n else:\n isValid = True\n\n return isValid","def b32hash(s):\n _hash = hashlib.sha256()\n _hash.update(str2bytes(s))\n return bytes2str(b32encode(_hash.digest()))","def a2b_hashed_base58(s):\n data = a2b_base58(s)\n data, the_hash = data[:-4], data[-4:]\n if double_sha256(data)[:4] == the_hash:\n return data\n raise EncodingError(\"hashed base58 has bad checksum %s\" % s)","def test_get_signature_hash(self):\n tx = self.tx_deserialize_hex(TX_FAKE_HEX)\n script, script_len = make_cbuffer('00')\n out, out_len = make_cbuffer('00'*32)\n for args in [\n (None, 0, script, script_len, 1, 1, 0, out, out_len), # Empty tx\n (tx, 0, None, script_len, 1, 1, 0, out, out_len), # Empty script\n (tx, 0, script, 0, 1, 1, 0, out, out_len), # Invalid script length\n (tx, 0, script, script_len, MAX_SATOSHI+1, 1, 1, out, out_len), # Invalid amount (only with segwit)\n (tx, 0, script, script_len, 1, 0x100, 0, out, out_len), # Invalid sighash\n (tx, 0, script, script_len, 1, 1, 16, out, out_len), # Invalid flags\n (tx, 0, script, script_len, 1, 1, 0, None, out_len), # Empty bytes\n (tx, 0, script, script_len, 1, 1, 0, out, 31), # Short len\n ]:\n self.assertEqual(WALLY_EINVAL, wally_tx_get_btc_signature_hash(*args))\n\n def sha256d(hex_):\n bin_input, bin_input_len = make_cbuffer(hex_)\n buf, buf_len = make_cbuffer('00'*32)\n self.assertEqual(WALLY_OK, wally_sha256d(bin_input, bin_input_len, buf, buf_len))\n return h(buf)\n\n script, script_len = make_cbuffer('00')\n out, out_len = make_cbuffer('00'*32)\n for args, expected in [\n ((tx, 0, script, script_len, 1, 1, 0, out, out_len),\n utf8('1bcf681d585c3cbbc64b30a69e60b721fc0aacc57132dfbe43af6df8f4797a80')),\n ((tx, 1, script, script_len, 1, 1, 0, out, out_len),\n utf8('01'+'00'*31)),\n ((tx, 0, script, script_len, 1, 0, 0, out, out_len),\n utf8('882630e74173c928fc18236b99e25ffd15643faabc65c010e9ca27b8db29278a')),\n ((tx, 0, script, script_len, 1, 1, 1, out, out_len),\n utf8('5dad88b42332e3559950b325bba69eedb64b9330e55585fc1098964572f9c45d')),\n ((tx, 0, script, script_len, 0, 1, 1, out, out_len),\n utf8('bb30f5feed35b2591eedd8e778d507236a756e8c2eff8cf72ef0afa83abdea31')),\n ]:\n self.assertEqual(WALLY_OK, wally_tx_get_btc_signature_hash(*args))\n self.assertEqual(expected, h(out[:out_len]))","def transaction_exists(self, hash_bytes: bytes) -> bool:\n raise NotImplementedError","def hash_eth2(data: bytes) -> Hash32:\n return Hash32(keccak(data))","def __hash__(self):\n base = 1\n h = 0\n for l in self.data:\n for i in l:\n if i:\n h += base\n base *= 2\n return hash(h)","def _validate_hash(data, shasum):\n from hashlib import sha1\n digest = sha1(data).hexdigest()\n if digest == shasum:\n return True\n else:\n print('Invalid shasum, got: {} , expected: {}'.format(digest, shasum))\n return False","def test_none_hash(self):\n get_string = self.reverse_hash.get_string(None)\n self.assertEqual(get_string['error'], 'hash value passed is None')","def test_hash(self):\n self.assertEqual(hash(self._version1), hash(self._version1))\n self.assertNotEqual(hash(self._version2), hash(self._version1))\n self.assertEqual(hash(\"0.1\"), hash(self._version1))","def __hash__(self) -> int:","def blockchain_test():\n\n init_hash = hashlib.md5('foobarbaz').hexdigest()\n chain = Chain(genesis=Node(data=None, hash_str=init_hash))\n\n genesis = chain.genesis\n\n assert chain\n assert chain.size == 1\n assert init_hash == genesis.hash\n\n chain.new_node(data='hello')\n\n expected_hash = hashlib.md5(init_hash + 'hello').hexdigest()\n\n assert chain.size == 2\n assert chain.last_node.hash == expected_hash\n\n chain.new_node(data='world')\n\n expected_hash = hashlib.md5(expected_hash + 'world').hexdigest()\n\n assert chain.size == 3\n assert chain.last_node.hash == expected_hash\n\n chain.new_node(data='007')\n\n expected_hash = hashlib.md5(expected_hash + '007').hexdigest()\n\n assert chain.size == 4\n assert chain.last_node.hash == expected_hash","def sha256_p(value):\n # check if the value has the expected type\n string_p(value)\n\n # SHA-256 hash has 64 hexadecimal characters\n if not re.fullmatch(r\"^[a-fA-F0-9]{64}$\", value):\n raise Invalid(\"the value '{value}' does not seem to be SHA256 hash\".format(value=value))","def hash_bytes_256(b: bytes) -> str:\n return hashlib.sha256(b).hexdigest()","def djb_hash(s):\n h = 5381\n for c in s:\n h = h * 33 + ord(c)\n return h & 0xffffffff","def is_bip66(sig):\n\t#https://raw.githubusercontent.com/bitcoin/bips/master/bip-0066.mediawiki\n\t#0x30 [total-len] 0x02 [R-len] [R] 0x02 [S-len] [S] [sighash]\n\tsig = bytearray.fromhex(sig) if re.match('^[0-9a-fA-F]*$', sig) else bytearray(sig)\n\tif (sig[0] == 0x30) and (sig[1] == len(sig)-2):\t # check if sighash is missing\n\t\t\traise Exception(\"Sighash byte of signature is missing\") \n\t\t\t#sig.extend(b\"\\1\")\t\t\t\t\t\t \t# add SIGHASH_ALL for testing\n\t\t\t#(sig[-1] & 124 == 0) and (not not sig[-1]), \"Bad SIGHASH value\"\n\t\n\tif len(sig) < 9 or len(sig) > 73: return False\n\tif (sig[0] != 0x30): return False\n\tif (sig[1] != len(sig)-3): return False\n\trlen = sig[3]\n\tif (5+rlen >= len(sig)): return False\n\tslen = sig[5+rlen]\n\tif (rlen + slen + 7 != len(sig)): return False\n\tif (sig[2] != 0x02): return False\n\tif (rlen == 0): return False\n\tif (sig[4] & 0x80): return False\n\tif (rlen > 1 and (sig[4] == 0x00) and not (sig[5] & 0x80)): return False\n\tif (sig[4+rlen] != 0x02): return False\n\tif (slen == 0): return False\n\tif (sig[rlen+6] & 0x80): return False\n\tif (slen > 1 and (sig[6+rlen] == 0x00) and not (sig[7+rlen] & 0x80)):\n\t\treturn False\n\treturn True","def valid_chain(self, chain):\n last_block = chain[0]\n current_index = 1\n\n while current_index < len(chain):\n block = chain[current_index]\n\n # Check that the hash of block is correct\n if block['previous_hash'] != self.hash(last_block):\n return False\n\n # Check the Proof of Work\n if not self.valid_proof(last_block['proof'], block['proof']):\n return False\n \n last_block = block\n current_index += 1\n return True","def valid_chain(self, chain):\n last_block = chain[0]\n current_index = 1\n\n while current_index < len(chain):\n block = chain[current_index]\n print(last_block)\n print(block)\n print(\"\\n--------\\n\")\n \n #check that the hash of the previous block is correct\n\n if block[\"previous_hash\"] != self.hash(last_block):\n print(\"Previous hash does not match\")\n return False\n\n if not self.valid_proof(block):\n print(\"Block proof of work is invalid\")\n return False\n\n last_block = block\n current_index += 1\n\n return True","def hash(x) -> int:\n pass","def nice_hash(*args):\n h = sha1()\n for item in args:\n h.update(unicode(item))\n return b32encode(h.digest())","def sha256_encode(text):\n _hash = hashlib.sha256\n if type(text) is str:\n return _hash(text.encode('utf8')).digest()\n elif type(text) is bytes:\n return _hash(text).digest()\n elif not text:\n # Generally for calls where the payload is empty. Eg: get calls\n # Fix for AttributeError: 'NoneType' object has no attribute 'encode'\n return _hash(\"\".encode('utf8')).digest()\n else:\n return _hash(str(text).encode('utf-8')).digest()","def is_sha(self):\n return self.name.startswith('SHA')","def check_hash(self):\n m = rtorrent9.rpc.Multicall(self)\n self.multicall_add(m, \"d.check_hash\")\n\n return m.call()[-1]","def djb2(self, key):\n # Your code here\n hash = 5381\n for c in key:\n hash = (hash*33)+ ord(c)\n return hash","def verify_chain():\n\n block_index = 0\n is_unchanged = True\n\n if namoto_length < 1:\n print('Blockchain is empty!')\n return None\n\n for block in namoto_blockchain:\n\n if block[0] == namoto_blockchain[block_index -1]:\n is_unchanged = True\n block_index += 1\n\n else:\n is_unchanged = False\n break\n\n return is_unchanged","def valid_proof(block):\n return Blockchain.hash(block) [:4] == \"0000\"","def is_valid_proof(self, block, block_hash): \n return (block_hash.startswith('0' * Blockchain.difficulty) and block_hash == compute_hash())","def hashing(word) :\r\n ans = hashlib.sha256(word.encode())\r\n return ans.hexdigest()","def valid_chain(self, chain):\n last_block = chain[0]\n current_index = 1\n\n while current_index < len(chain):\n block = chain[current_index]\n # print(f'{last_block}')\n # print(f'{block}')\n # print(\"\\n-----------\\n\")\n # Check that the hash of the block is correct\n last_block_hash = self.hash(last_block)\n if block['previous_hash'] != self.hash(last_block):\n return False\n\n # Check that the Proof of Work is correct\n if not self.valid_proof(last_block['proof'], block['proof'], last_block_hash):\n return False\n\n last_block = block\n current_index += 1\n\n return True","def get_hash(self):\r\n return","def is_hashed_base58_valid(base58):\n try:\n a2b_hashed_base58(base58)\n except EncodingError:\n return False\n return True","def valid_chain(self, chain):\n\n last_block = chain[0]\n current_index = 1\n\n while current_index < len(chain):\n block = chain[current_index]\n print(f'{last_block}')\n print(f'{block}')\n print(\"\\n-----------\\n\")\n # Check that the hash of the block is correct\n last_block_hash = self.hash(last_block)\n if block['previous_hash'] != last_block_hash:\n return False\n\n # Check that the Proof of Work is correct\n if not self.valid_proof(last_block['proof'], block['proof'], last_block_hash):\n return False\n\n last_block = block\n current_index += 1\n\n return True","def q_addresstohash(abe, page, chain):\n addr = wsgiref.util.shift_path_info(page['env'])\n if addr is None:\n return 'Shows the 160-bit hash encoded in ADDRESS.\\n' \\\n 'For BBE compatibility, the address is not checked for' \\\n ' validity. See also /q/decode_address.\\n' \\\n '/q/addresstohash/ADDRESS\\n'\n version, hash = util.decode_address(addr)\n return hash.encode('hex').upper()","def hash_comparing(key):\n # the try except block will not stop the process if the object happens to already be in utf-8\n try:\n key = key.encode(encoding='utf-8')\n except AttributeError:\n pass\n iteration = hashlib.md5()\n iteration.update(key)\n return iteration","def test_empty_string(self):\n self.assertEqual(hash_str(\"\", salt=\"\").hex()[:6], \"e3b0c4\")","def decode_and_hexlify_hashes(hash_str: str) -> typing.Union[str, None]:\n\n return binascii.hexlify(base64.b64decode(hash_str.encode())).decode() if hash_str else None","def __hash__(self):\n return hash(tuple(self.sig))","def test_hash(self):\n v1 = versions.Version(version='1.2.3', name='foo')\n my_dict = { v1 : 'foo'}\n\n self.assertTrue(isinstance(my_dict, dict))","def dict_hash(obj, start=''):\n h = hashlib.sha1(to_bytes(start))\n h.update(to_bytes(obj.__class__.__name__))\n if isinstance(obj, dict):\n for key, value in sorted(obj.items()):\n h.update(to_bytes(key))\n h.update(to_bytes(dict_hash(value)))\n elif isinstance(obj, (list, tuple)):\n for el in obj:\n h.update(to_bytes(dict_hash(el)))\n else:\n # basic types\n if isinstance(obj, bool):\n value = str(int(obj))\n elif isinstance(obj, (six.integer_types, float)):\n value = str(obj)\n elif isinstance(obj, (six.text_type, bytes)):\n value = obj\n elif obj is None:\n value = b''\n else:\n raise ValueError(\"Unsupported value type: %s\" % obj.__class__)\n h.update(to_bytes(value))\n return h.hexdigest()","def valid_chain(self, chain):\n\n last_block = chain[0]\n current_index = 1\n \n while current_index < len(chain):\n block = chain[current_index]\n # Check correctness of last block's hash\n if block['previous_hash'] != self.hash(last_block): \n return False\n # Check correctness of proof-of-work\n if not self.valid_proof(last_block['proof'], block['proof'], block['previous_hash']):\n return False\n last_block = block \n current_index += 1\n\n return True","def __hash__(self):\n return hash(self.joined())","def test_hash_bytes_decorator(self):\n results = [\n 1164302962920061,\n 16735493734761467723,\n 18150279091576190542,\n 9861778148718857663,\n 14008040072978383620,\n ]\n\n @hash_with_depth_bytes\n def my_hash(key, depth=1):\n \"\"\"my hash function\"\"\"\n return hashlib.sha512(key).digest()\n\n self.assertEqual(my_hash(\"this is a test\", 5), results)\n res = my_hash(\"this is a test\", 1)\n self.assertEqual(len(res), 1)\n self.assertEqual(res[0], results[0])","def is_valid_node_id(val):\n if not val:\n return False\n if not isinstance(val, bytes) and not isinstance(val, bytearray):\n return False\n\n length = len(val)\n if length != SHA1_BIN_LEN and length != SHA2_BIN_LEN and \\\n length != SHA3_BIN_LEN:\n return False\n\n return True","def test_diff_inputs_diff_hash(self):\n # same strings, different salts\n self.assertNotEqual(\n hash_str(\"mystring\", salt=\"mysalt1\").hex(),\n hash_str(\"mystring\", salt=\"mysalt2\").hex(),\n )\n # different strings, same salts\n self.assertNotEqual(\n hash_str(\"mystring1\", salt=\"mysalt\").hex(),\n hash_str(\"mystring2\", salt=\"mysalt\").hex(),\n )","def validate_blockchain(chain):\n assert isinstance(chain, list)\n\n for hook in chain[::-1]:\n pass","def djb2_hash(key):\n\n hash = 5381\n\n for i in range(len(key)):\n hash = ((hash << 5) + hash) + ord(key[i])\n\n return hash","def test_init_hash(self):\n bill = Bill(self.input_hash)\n for key, value in self.input_hash.iteritems():\n self.assertEqual(value, bill.__dict__[key])","def verify_chain():\n for (index,block) in enumerate(blockchain):\n if index ==0:\n continue\n if block['previous_hash'] != hash_block(blockchain[index-1]):\n return False\n if not valid_proof(block['transactions'][:-1],block['previous_hash'],block['proof']):\n print('Proof of Work is Invalid')\n return False\n return True","def b2a_base58check(data):\n\n return encoding.b2a_hashed_base58(data)","def is_git_sha(text):\n # Handle both the full sha as well as the 7-character abbreviation\n if len(text) in (40, 7):\n try:\n int(text, 16)\n return True\n except ValueError:\n pass\n return False","def _Hash(content: bytes) -> str:\n return hashlib.sha256(content).hexdigest()","def test_simple_adding(self):\n cons_hash = ConsistentHash(2)\n cons_hash.add('192.168.1.1') \n self.assertEquals(len(cons_hash), 2)\n \n cons_hash.add('192.168.1.2') \n self.assertEquals(len(cons_hash), 4) \n \n self.assertTrue(cons_hash._is_consistent())","def hash_djb2(string):\n hashval = ctypes.c_uint(5381)\n for char in string:\n hashval.value = ((hashval.value << 5) + hashval.value) + ord(char)\n return hashval.value & 0x7FFFFFFF","def test_default_sha256_bytes(self):\n this_is_a_test = [\n 10244166640140130606,\n 5650905005272240665,\n 14215057275609328422,\n 5952353080197385534,\n 4990779931033217093,\n ]\n this_is_also = [\n 4140421647067018332,\n 9306548247555387104,\n 5672713771950536751,\n 8501641957786831066,\n 15146689942378126332,\n ]\n hashes = default_sha256(b\"this is a test\", 5)\n self.assertEqual(hashes, this_is_a_test)\n hashes = default_sha256(b\"this is also a test\", 5)\n self.assertEqual(hashes, this_is_also)","def chainIsValid(self):\n for i in range(1, len(self.blocks)):\n prev_block = self.blocks[i-1]\n cur_block = self.blocks[i]\n if cur_block.header['prevBlockH'] != getHashBlock(prev_block):\n return False\n return True","def is_hash(fhash):\n\n # Intentionally doing if/else statement for ease of testing and reading\n if re.match(re_md5, fhash):\n return True\n elif re.match(re_sha1, fhash):\n return True\n elif re.match(re_sha256, fhash):\n return True\n elif re.match(re_sha512, fhash):\n return True\n elif re.match(re_ssdeep, fhash):\n return True\n else:\n return False","def hash_function_1(key):\n hash = 0\n for i in key:\n hash = hash + ord(i)\n return hash","def _verify_hashes(hashes):\n\n for item in hashes:\n try:\n hashlib.new(item)\n VALID_HASH.append(item)\n except Exception:\n pass","def _check_transform_key(key: Hashable) -> None:\n _test_hashable = hash(key) # The only 'real' way to make sure is hashable\n # if not isinstance(key, Hashable):\n # raise TypeError((type(key), \"transformation lookup key is not hashable\"))","def regist_hash(cobj, hash, handler, dir):\n pass","def _hash(self, string, hash_type):\n hash_types = {\n 'TABLE_OFFSET': 0,\n 'HASH_A': 1,\n 'HASH_B': 2,\n 'TABLE': 3\n }\n seed1 = 0x7FED7FED\n seed2 = 0xEEEEEEEE\n\n for ch in string.upper():\n if not isinstance(ch, int): ch = ord(ch)\n value = self.encryption_table[(hash_types[hash_type] << 8) + ch]\n seed1 = (value ^ (seed1 + seed2)) & 0xFFFFFFFF\n seed2 = ch + seed1 + seed2 + (seed2 << 5) + 3 & 0xFFFFFFFF\n\n return seed1","def isScriptHashScript(pkScript):\n return extractScriptHash(pkScript) is not None","def do_hash(dat: typing.Any) -> str:\n return hashlib.sha1(json.dumps(dat, sort_keys=True).encode('utf-8')).hexdigest()"],"string":"[\n \"def test_hash_sha256(self):\\n block = self.blockchain.new_block(self.proof, self.previous_hash)\\n hash_ = self.blockchain.hash(block)\\n\\n self.assertIsInstance(hash_, str)\\n self.assertEqual(hashlib.sha256(json.dumps(block, sort_keys=True).encode()).hexdigest(), hash_)\",\n \"def _get_chain_hash(self, chain):\\n # todo if like this then PickleCache(m1, m2) + PickleCache(m3) != PickleCache(m1, m2, m3)\\n chain_string = self._object_dump_to_string(chain, self.max_recursion_level, debug_level=self.debug_level)\\n return self.hash_dump_string(chain_string)\",\n \"def hash(self) -> bytes:\",\n \"def test_hash(self):\\n self.assertEqual(hash(self.compound), hash((\\\"t1\\\", \\\"test compound\\\")))\",\n \"def fixed(o):\\n try:\\n hash(o)\\n except TypeError:\\n return False\\n return True\",\n \"def test_fail_bundle_hash_invalid(self):\\n self.bundle.transactions[3].bundle_hash =\\\\\\n BundleHash(\\n b'NFDPEEZCWVYLKZGSLCQNOFUSENIXRHWWTZFBXMPS'\\n b'QHEDFWZULBZFEOMNLRNIDQKDNNIELAOXOVMYEI9PG'\\n )\\n\\n validator = BundleValidator(self.bundle)\\n\\n self.assertFalse(validator.is_valid())\\n\\n self.assertListEqual(\\n validator.errors,\\n\\n [\\n 'Transaction 3 has invalid bundle hash.',\\n ],\\n )\",\n \"def is_valid_git_sha1(hash):\\r\\n\\r\\n if len(hash) != 40:\\r\\n return False\\r\\n try:\\r\\n value = int(hash, 16)\\r\\n except ValueError:\\r\\n return False\\r\\n\\r\\n return True\",\n \"def test_hash(self):\\r\\n self.assertEqual(processor_hash('test'), 'GqNJWF7X7L07nEhqMAZ+OVyks1Y=')\\r\\n self.assertEqual(processor_hash('edx '), '/KowheysqM2PFYuxVKg0P8Flfk4=')\",\n \"def has_hash(self, h):\\n rsp = h.hashlist(self.path)\\n if re.search(\\\"\\\\n[0-9a-f]+\\\\smd5\\\\s%s\\\" % self.path, rsp):\\n rval = True\\n else:\\n rval = False\\n return rval\",\n \"def SHA256(self) -> _n_0_t_3[_n_0_t_9]:\",\n \"def hash(self) -> str:\\r\\n ...\",\n \"def validate_hash_difficulty(bhash: str, difficulty: int) -> bool:\\n b = hex_to_bin(bhash)\\n print(b[:4], type(b))\\n return hex_to_bin(bhash).startswith('0' * difficulty)\",\n \"def test_hash_string(self):\\n self.assertEqual(hexlify(self._hashdigest(pubkey_sha)), sample_ripe)\",\n \"def chain_hash(self):\\n return self.chain_info['chain_hash']\",\n \"def coerce(self, value):\\n if isinstance(value, bytes) and len(value) == self.bit_length:\\n return HashString.from_b256(value)\\n elif len(value) == self.b16_length:\\n return HashString.from_b16(value)\\n elif self.b64_length - len(value) <= 4:\\n return HashString.from_b64(value)\",\n \"def typeahead_hash(self) -> str:\",\n \"def verifyHashcode(digest):\\n list_str = [\\\"0\\\", \\\"1\\\", \\\"2\\\", \\\"3\\\", \\\"4\\\", \\\"5\\\", \\\"6\\\", \\\"7\\\", \\\"8\\\", \\\"9\\\", \\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\", \\\"E\\\", \\\"F\\\"]\\n list_num = [ 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]\\n \\n total = 0\\n for i2 in range(len(digest)):\\n digest_i = digest[i2]\\n #print(\\\"digest_i =\\\", digest_i)\\n \\n for i1 in range(16):\\n if digest_i == list_str[i1] and i2 != 0:\\n total += list_num[i1]\\n #print(\\\"total =\\\", total)\\n #print(\\\"list_num[i1] =\\\", list_num[i1])\\n continue\\n \\n #print(\\\"--- --- ---\\\")\\n \\n #print(\\\"total =\\\", total)\\n \\n checknum = total % 16\\n #print(\\\"checknum =\\\", checknum)\\n \\n checkstr = list_str[checknum]\\n #print(\\\"checkstr =\\\", checkstr)\\n \\n checkorg = digest[0]\\n #print(\\\"checkorg =\\\", checkorg)\\n \\n if checkorg == checkstr:\\n isValid = True\\n else:\\n isValid = False\\n \\n return isValid\",\n \"def verify_hash(content: dict, hashing: str) -> bool:\\n\\n encoded_content = json.dumps(content, sort_keys=True).encode()\\n hash_value = hashlib.sha256(encoded_content).hexdigest()\\n\\n return hash_value == hashing\",\n \"def get_binary_sha256_hash(hash: str) -> str:\\n result = \\\"\\\"\\n\\n for character in hash:\\n character_number = int(character, base=16)\\n binary_number = bin(character_number)\\n # CAVEAT: each hash character is 4 bit size since SHA256 hash is hexidecimal string, so 4 * 64 = 256 bit\\n formatted_binary_number = binary_number[2:].ljust(4, \\\"0\\\")\\n result += formatted_binary_number\\n\\n return result\",\n \"def validate_hash(h):\\n if len(h) not in (32, 40, 64, 128):\\n return False\\n\\n return bool(re.match(\\\"[0-9a-fA-F]*$\\\", h))\",\n \"def deep_hash(obj):\\n pass\",\n \"def create_hash(*args):\\n challenge_str = jsonpickle.encode(args)\\n challenge_hash = hashlib.sha256(challenge_str.encode())\\n return Bn.from_binary(challenge_hash.digest())\",\n \"def get_hash(self, composition):\\n return\",\n \"def check_hashable(self, setup):\\n try:\\n hash(setup)\\n except TypeError as e:\\n raise AssertionError(f\\\"setup object is not hashable:\\\\n{setup}\\\") from e\",\n \"def sha256_2_string(string_to_hash):\\n\\n # Solution for (1a)\\n import hashlib\\n first_sha = hashlib.sha256(string_to_hash.encode(\\\"utf8\\\"))\\n second_sha = hashlib.sha256(first_sha.digest())\\n return second_sha.hexdigest()\\n\\n # Placeholder for (1a)\\n return \\\"deadbeef\\\"\",\n \"def hashcode(o):\",\n \"def hash_chain(self):\\n if len(self.modules) != self.chain_info['chain_len']:\\n self.chain_info = {\\n 'chain_len': len(self.modules),\\n 'chain_mtime': self._get_chain_mtime(self.modules),\\n 'chain_hash': self._get_chain_hash(self.modules),\\n 'chain_repr': self._get_chain_repr(self.modules),\\n }\",\n \"def is_chain_valid(self, chain):\\r\\n previous_block = chain[0]\\r\\n block_index = 1\\r\\n while block_index < len(chain):\\r\\n block = chain[block_index]\\r\\n if block['previous_hash'] != self.hash(previous_block):\\r\\n return False\\r\\n previous_proof = previous_block['proof']\\r\\n proof = block['proof']\\r\\n hash_operation = self.hash(block)\\r\\n if hash_operation[:4] != '0000':\\r\\n return False\\r\\n previous_block = block\\r\\n block_index += 1\\r\\n return True\",\n \"def HashAlgorithm(self) -> _n_7_t_0:\",\n \"def _verify_hash(self, read_bytes):\\n if self.hash is None:\\n raise QuiltException(\\\"Hash missing - need to build the package\\\")\\n _check_hash_type_support(self.hash.get('type'))\\n digest = hashlib.sha256(read_bytes).hexdigest()\\n if digest != self.hash.get('value'):\\n raise QuiltException(\\\"Hash validation failed\\\")\",\n \"def bit_contribution_test(hash_function):\\n\\n model = hash_function()\\n hash_list = []\\n zero_str = '0' * 2048\\n for i in range(1, 2049):\\n for j in range(0, i):\\n flip_str = zero_str[:j] + '1' + zero_str[j+1:i]\\n hash_list.append(list(map(int, list(msg_to_bits.pad_msg(flip_str, i)))))\\n if i % 200 == 0:\\n print(i)\\n\\n hashed_dict = dict()\\n collisions = 0\\n i = 0\\n for to_hash in hash_list:\\n i += 1\\n hash_val = model.hash(to_hash, False).tostring()\\n if hash_val in hashed_dict:\\n collisions += 1\\n hashed_dict[hash_val] = True\\n if i % 10000 == 0:\\n print(i)\\n\\n return collisions\",\n \"def hashable(obj):\\n return bytes(str(obj), \\\"utf-8\\\")\",\n \"def SHA1(self) -> _n_0_t_3[_n_0_t_9]:\",\n \"def is_hashable(v):\\n try:\\n hash(v)\\n except TypeError:\\n return False\\n return True\",\n \"def valid_chain(self, chain):\\n last_block = chain[0]\\n current_index = 1\\n\\n while current_index < len(chain):\\n block = chain[current_index]\\n print(f'{last_block}')\\n print(f'{block}')\\n print(\\\"\\\\n----------------\\\\n\\\")\\n # verify hash integrity\\n if block['previous_hash'] != self.hash(last_block):\\n return False\\n\\n # verify proof integrity\\n if not self.valid_proof(last_block['proof'], block['proof']):\\n return False\\n\\n last_block = block\\n current_index += 1\\n\\n return True\",\n \"def is_chain_id(v) -> bool:\\n try:\\n _validate(v, prefixes=[b'Net'])\\n except (ValueError, TypeError):\\n return False\\n return True\",\n \"def sha256(message: bytes):\\n # convert message bitarray\\n bit_msg = bitarray(endian='big')\\n bit_msg.frombytes(message)\\n L = len(bit_msg)\\n\\n # additions done mod 2^32\\n pow2 = pow(2,32)\\n\\n # append 1 followed by K 0s where K is the minimum number >= 0 such that \\n # len(bit_msg) + 1 + K + 64 is a multiple of 512\\n bit_msg = bit_msg + bitarray('1') + (bitarray('0') * ((-L-65) % 512))\\n # append len(bit_msg) as a 64-bit int to bit_msg\\n bit_msg = bit_msg + util.int2ba(L, length=64, endian='big')\\n\\n # initialize hash to predefined values\\n current_hash = [h for h in initial_hash]\\n\\n # operate on each 512-bit chunk\\n for chunk_index in range(len(bit_msg)//512):\\n chunk = bit_msg[chunk_index * 512 : (chunk_index+1) * 512]\\n # w is array of 64 32-bit words with first 16 equal to chunk\\n w = [chunk[i*32 : (i+1)*32] for i in range(16)]\\n w.extend([bitarray(32) for _ in range(48)])\\n # create last 48 words in w from first 16\\n for i in range(16, 64):\\n s0 = rightrotate(w[i-15], 7) ^ rightrotate(w[i-15], 18) ^ rightshift(w[i-15], 3)\\n s1 = rightrotate(w[i-2], 17) ^ rightrotate(w[i-2], 19) ^ rightshift(w[i-2], 10)\\n w[i] = int2ba32(sum(map(util.ba2int, [w[i-16], s0, w[i-7], s1])) % pow2)\\n\\n # copy current hash (stored in hex) into working list v as bitarrays\\n v = list(map(int2ba32, current_hash))\\n # compression\\n for i in range(64):\\n S1 = rightrotate(v[4], 6) ^ rightrotate(v[4], 11) ^ rightrotate(v[4], 25)\\n ch = (v[4] & v[5]) ^ ((~v[4]) & v[6])\\n temp1 = (constants[i] + sum(map(util.ba2int, [v[7], S1, ch, w[i]]))) % pow2\\n S0 = rightrotate(v[0], 2) ^ rightrotate(v[0], 13) ^ rightrotate(v[0], 22)\\n maj = (v[0] & v[1]) ^ (v[0] & v[2]) ^ (v[1] & v[2])\\n temp2 = (util.ba2int(S0) + util.ba2int(maj)) % pow2\\n\\n # shift elements of v by 1\\n for j in reversed(range(1, len(v))):\\n v[j] = v[j-1]\\n v[0] = int2ba32((temp1 + temp2) % pow2)\\n v[4] = int2ba32((util.ba2int(v[4]) + temp1) % pow2)\\n\\n # add compressed values (which are bitarrays) to current_hash (which are ints)\\n current_hash = list(map(lambda a,b: (a + util.ba2int(b)) % pow2, current_hash, v))\\n\\n # each entry of current_hash is a 32-bit integer so convert to 4 bytes \\n # adding bytes appends them\\n return b''.join(x.to_bytes(4, 'big') for x in current_hash)\",\n \"def get_hash(thing):\\n n = hashlib.sha256()\\n \\n if isinstance(thing,str):\\n n.update(thing.encode('utf-8' ))\\n elif isinstance(thing, bytes):\\n n.update(thing)\\n elif isinstance(thing,BeautifulSoup):\\n n.update(get_hash(str(thing)))\\n else:\\n raise RuntimeError(\\\"unknown type: {}\\\".format(str(type(thing))))\\n \\n return(n.digest())\",\n \"def validate_chain(self):\\n chain_length = len(self.chain)\\n isValid = None\\n\\n if(chain_length == 1):\\n return \\\"Add blocks and then validate chain.\\\"\\n\\n for x in range(0, chain_length, 1):\\n if(x < chain_length-1):\\n # Hash the current block\\n hash = self.hash(self.chain[x])\\n next_block_prev_hash = self.chain[x+1]['prev_hash']\\n\\n if(hash == \\\"0\\\" and x == 0):\\n isValid = True\\n\\n if(hash != next_block_prev_hash):\\n isValid = False\\n return isValid\\n else:\\n isValid = True\\n\\n return isValid\",\n \"def b32hash(s):\\n _hash = hashlib.sha256()\\n _hash.update(str2bytes(s))\\n return bytes2str(b32encode(_hash.digest()))\",\n \"def a2b_hashed_base58(s):\\n data = a2b_base58(s)\\n data, the_hash = data[:-4], data[-4:]\\n if double_sha256(data)[:4] == the_hash:\\n return data\\n raise EncodingError(\\\"hashed base58 has bad checksum %s\\\" % s)\",\n \"def test_get_signature_hash(self):\\n tx = self.tx_deserialize_hex(TX_FAKE_HEX)\\n script, script_len = make_cbuffer('00')\\n out, out_len = make_cbuffer('00'*32)\\n for args in [\\n (None, 0, script, script_len, 1, 1, 0, out, out_len), # Empty tx\\n (tx, 0, None, script_len, 1, 1, 0, out, out_len), # Empty script\\n (tx, 0, script, 0, 1, 1, 0, out, out_len), # Invalid script length\\n (tx, 0, script, script_len, MAX_SATOSHI+1, 1, 1, out, out_len), # Invalid amount (only with segwit)\\n (tx, 0, script, script_len, 1, 0x100, 0, out, out_len), # Invalid sighash\\n (tx, 0, script, script_len, 1, 1, 16, out, out_len), # Invalid flags\\n (tx, 0, script, script_len, 1, 1, 0, None, out_len), # Empty bytes\\n (tx, 0, script, script_len, 1, 1, 0, out, 31), # Short len\\n ]:\\n self.assertEqual(WALLY_EINVAL, wally_tx_get_btc_signature_hash(*args))\\n\\n def sha256d(hex_):\\n bin_input, bin_input_len = make_cbuffer(hex_)\\n buf, buf_len = make_cbuffer('00'*32)\\n self.assertEqual(WALLY_OK, wally_sha256d(bin_input, bin_input_len, buf, buf_len))\\n return h(buf)\\n\\n script, script_len = make_cbuffer('00')\\n out, out_len = make_cbuffer('00'*32)\\n for args, expected in [\\n ((tx, 0, script, script_len, 1, 1, 0, out, out_len),\\n utf8('1bcf681d585c3cbbc64b30a69e60b721fc0aacc57132dfbe43af6df8f4797a80')),\\n ((tx, 1, script, script_len, 1, 1, 0, out, out_len),\\n utf8('01'+'00'*31)),\\n ((tx, 0, script, script_len, 1, 0, 0, out, out_len),\\n utf8('882630e74173c928fc18236b99e25ffd15643faabc65c010e9ca27b8db29278a')),\\n ((tx, 0, script, script_len, 1, 1, 1, out, out_len),\\n utf8('5dad88b42332e3559950b325bba69eedb64b9330e55585fc1098964572f9c45d')),\\n ((tx, 0, script, script_len, 0, 1, 1, out, out_len),\\n utf8('bb30f5feed35b2591eedd8e778d507236a756e8c2eff8cf72ef0afa83abdea31')),\\n ]:\\n self.assertEqual(WALLY_OK, wally_tx_get_btc_signature_hash(*args))\\n self.assertEqual(expected, h(out[:out_len]))\",\n \"def transaction_exists(self, hash_bytes: bytes) -> bool:\\n raise NotImplementedError\",\n \"def hash_eth2(data: bytes) -> Hash32:\\n return Hash32(keccak(data))\",\n \"def __hash__(self):\\n base = 1\\n h = 0\\n for l in self.data:\\n for i in l:\\n if i:\\n h += base\\n base *= 2\\n return hash(h)\",\n \"def _validate_hash(data, shasum):\\n from hashlib import sha1\\n digest = sha1(data).hexdigest()\\n if digest == shasum:\\n return True\\n else:\\n print('Invalid shasum, got: {} , expected: {}'.format(digest, shasum))\\n return False\",\n \"def test_none_hash(self):\\n get_string = self.reverse_hash.get_string(None)\\n self.assertEqual(get_string['error'], 'hash value passed is None')\",\n \"def test_hash(self):\\n self.assertEqual(hash(self._version1), hash(self._version1))\\n self.assertNotEqual(hash(self._version2), hash(self._version1))\\n self.assertEqual(hash(\\\"0.1\\\"), hash(self._version1))\",\n \"def __hash__(self) -> int:\",\n \"def blockchain_test():\\n\\n init_hash = hashlib.md5('foobarbaz').hexdigest()\\n chain = Chain(genesis=Node(data=None, hash_str=init_hash))\\n\\n genesis = chain.genesis\\n\\n assert chain\\n assert chain.size == 1\\n assert init_hash == genesis.hash\\n\\n chain.new_node(data='hello')\\n\\n expected_hash = hashlib.md5(init_hash + 'hello').hexdigest()\\n\\n assert chain.size == 2\\n assert chain.last_node.hash == expected_hash\\n\\n chain.new_node(data='world')\\n\\n expected_hash = hashlib.md5(expected_hash + 'world').hexdigest()\\n\\n assert chain.size == 3\\n assert chain.last_node.hash == expected_hash\\n\\n chain.new_node(data='007')\\n\\n expected_hash = hashlib.md5(expected_hash + '007').hexdigest()\\n\\n assert chain.size == 4\\n assert chain.last_node.hash == expected_hash\",\n \"def sha256_p(value):\\n # check if the value has the expected type\\n string_p(value)\\n\\n # SHA-256 hash has 64 hexadecimal characters\\n if not re.fullmatch(r\\\"^[a-fA-F0-9]{64}$\\\", value):\\n raise Invalid(\\\"the value '{value}' does not seem to be SHA256 hash\\\".format(value=value))\",\n \"def hash_bytes_256(b: bytes) -> str:\\n return hashlib.sha256(b).hexdigest()\",\n \"def djb_hash(s):\\n h = 5381\\n for c in s:\\n h = h * 33 + ord(c)\\n return h & 0xffffffff\",\n \"def is_bip66(sig):\\n\\t#https://raw.githubusercontent.com/bitcoin/bips/master/bip-0066.mediawiki\\n\\t#0x30 [total-len] 0x02 [R-len] [R] 0x02 [S-len] [S] [sighash]\\n\\tsig = bytearray.fromhex(sig) if re.match('^[0-9a-fA-F]*$', sig) else bytearray(sig)\\n\\tif (sig[0] == 0x30) and (sig[1] == len(sig)-2):\\t # check if sighash is missing\\n\\t\\t\\traise Exception(\\\"Sighash byte of signature is missing\\\") \\n\\t\\t\\t#sig.extend(b\\\"\\\\1\\\")\\t\\t\\t\\t\\t\\t \\t# add SIGHASH_ALL for testing\\n\\t\\t\\t#(sig[-1] & 124 == 0) and (not not sig[-1]), \\\"Bad SIGHASH value\\\"\\n\\t\\n\\tif len(sig) < 9 or len(sig) > 73: return False\\n\\tif (sig[0] != 0x30): return False\\n\\tif (sig[1] != len(sig)-3): return False\\n\\trlen = sig[3]\\n\\tif (5+rlen >= len(sig)): return False\\n\\tslen = sig[5+rlen]\\n\\tif (rlen + slen + 7 != len(sig)): return False\\n\\tif (sig[2] != 0x02): return False\\n\\tif (rlen == 0): return False\\n\\tif (sig[4] & 0x80): return False\\n\\tif (rlen > 1 and (sig[4] == 0x00) and not (sig[5] & 0x80)): return False\\n\\tif (sig[4+rlen] != 0x02): return False\\n\\tif (slen == 0): return False\\n\\tif (sig[rlen+6] & 0x80): return False\\n\\tif (slen > 1 and (sig[6+rlen] == 0x00) and not (sig[7+rlen] & 0x80)):\\n\\t\\treturn False\\n\\treturn True\",\n \"def valid_chain(self, chain):\\n last_block = chain[0]\\n current_index = 1\\n\\n while current_index < len(chain):\\n block = chain[current_index]\\n\\n # Check that the hash of block is correct\\n if block['previous_hash'] != self.hash(last_block):\\n return False\\n\\n # Check the Proof of Work\\n if not self.valid_proof(last_block['proof'], block['proof']):\\n return False\\n \\n last_block = block\\n current_index += 1\\n return True\",\n \"def valid_chain(self, chain):\\n last_block = chain[0]\\n current_index = 1\\n\\n while current_index < len(chain):\\n block = chain[current_index]\\n print(last_block)\\n print(block)\\n print(\\\"\\\\n--------\\\\n\\\")\\n \\n #check that the hash of the previous block is correct\\n\\n if block[\\\"previous_hash\\\"] != self.hash(last_block):\\n print(\\\"Previous hash does not match\\\")\\n return False\\n\\n if not self.valid_proof(block):\\n print(\\\"Block proof of work is invalid\\\")\\n return False\\n\\n last_block = block\\n current_index += 1\\n\\n return True\",\n \"def hash(x) -> int:\\n pass\",\n \"def nice_hash(*args):\\n h = sha1()\\n for item in args:\\n h.update(unicode(item))\\n return b32encode(h.digest())\",\n \"def sha256_encode(text):\\n _hash = hashlib.sha256\\n if type(text) is str:\\n return _hash(text.encode('utf8')).digest()\\n elif type(text) is bytes:\\n return _hash(text).digest()\\n elif not text:\\n # Generally for calls where the payload is empty. Eg: get calls\\n # Fix for AttributeError: 'NoneType' object has no attribute 'encode'\\n return _hash(\\\"\\\".encode('utf8')).digest()\\n else:\\n return _hash(str(text).encode('utf-8')).digest()\",\n \"def is_sha(self):\\n return self.name.startswith('SHA')\",\n \"def check_hash(self):\\n m = rtorrent9.rpc.Multicall(self)\\n self.multicall_add(m, \\\"d.check_hash\\\")\\n\\n return m.call()[-1]\",\n \"def djb2(self, key):\\n # Your code here\\n hash = 5381\\n for c in key:\\n hash = (hash*33)+ ord(c)\\n return hash\",\n \"def verify_chain():\\n\\n block_index = 0\\n is_unchanged = True\\n\\n if namoto_length < 1:\\n print('Blockchain is empty!')\\n return None\\n\\n for block in namoto_blockchain:\\n\\n if block[0] == namoto_blockchain[block_index -1]:\\n is_unchanged = True\\n block_index += 1\\n\\n else:\\n is_unchanged = False\\n break\\n\\n return is_unchanged\",\n \"def valid_proof(block):\\n return Blockchain.hash(block) [:4] == \\\"0000\\\"\",\n \"def is_valid_proof(self, block, block_hash): \\n return (block_hash.startswith('0' * Blockchain.difficulty) and block_hash == compute_hash())\",\n \"def hashing(word) :\\r\\n ans = hashlib.sha256(word.encode())\\r\\n return ans.hexdigest()\",\n \"def valid_chain(self, chain):\\n last_block = chain[0]\\n current_index = 1\\n\\n while current_index < len(chain):\\n block = chain[current_index]\\n # print(f'{last_block}')\\n # print(f'{block}')\\n # print(\\\"\\\\n-----------\\\\n\\\")\\n # Check that the hash of the block is correct\\n last_block_hash = self.hash(last_block)\\n if block['previous_hash'] != self.hash(last_block):\\n return False\\n\\n # Check that the Proof of Work is correct\\n if not self.valid_proof(last_block['proof'], block['proof'], last_block_hash):\\n return False\\n\\n last_block = block\\n current_index += 1\\n\\n return True\",\n \"def get_hash(self):\\r\\n return\",\n \"def is_hashed_base58_valid(base58):\\n try:\\n a2b_hashed_base58(base58)\\n except EncodingError:\\n return False\\n return True\",\n \"def valid_chain(self, chain):\\n\\n last_block = chain[0]\\n current_index = 1\\n\\n while current_index < len(chain):\\n block = chain[current_index]\\n print(f'{last_block}')\\n print(f'{block}')\\n print(\\\"\\\\n-----------\\\\n\\\")\\n # Check that the hash of the block is correct\\n last_block_hash = self.hash(last_block)\\n if block['previous_hash'] != last_block_hash:\\n return False\\n\\n # Check that the Proof of Work is correct\\n if not self.valid_proof(last_block['proof'], block['proof'], last_block_hash):\\n return False\\n\\n last_block = block\\n current_index += 1\\n\\n return True\",\n \"def q_addresstohash(abe, page, chain):\\n addr = wsgiref.util.shift_path_info(page['env'])\\n if addr is None:\\n return 'Shows the 160-bit hash encoded in ADDRESS.\\\\n' \\\\\\n 'For BBE compatibility, the address is not checked for' \\\\\\n ' validity. See also /q/decode_address.\\\\n' \\\\\\n '/q/addresstohash/ADDRESS\\\\n'\\n version, hash = util.decode_address(addr)\\n return hash.encode('hex').upper()\",\n \"def hash_comparing(key):\\n # the try except block will not stop the process if the object happens to already be in utf-8\\n try:\\n key = key.encode(encoding='utf-8')\\n except AttributeError:\\n pass\\n iteration = hashlib.md5()\\n iteration.update(key)\\n return iteration\",\n \"def test_empty_string(self):\\n self.assertEqual(hash_str(\\\"\\\", salt=\\\"\\\").hex()[:6], \\\"e3b0c4\\\")\",\n \"def decode_and_hexlify_hashes(hash_str: str) -> typing.Union[str, None]:\\n\\n return binascii.hexlify(base64.b64decode(hash_str.encode())).decode() if hash_str else None\",\n \"def __hash__(self):\\n return hash(tuple(self.sig))\",\n \"def test_hash(self):\\n v1 = versions.Version(version='1.2.3', name='foo')\\n my_dict = { v1 : 'foo'}\\n\\n self.assertTrue(isinstance(my_dict, dict))\",\n \"def dict_hash(obj, start=''):\\n h = hashlib.sha1(to_bytes(start))\\n h.update(to_bytes(obj.__class__.__name__))\\n if isinstance(obj, dict):\\n for key, value in sorted(obj.items()):\\n h.update(to_bytes(key))\\n h.update(to_bytes(dict_hash(value)))\\n elif isinstance(obj, (list, tuple)):\\n for el in obj:\\n h.update(to_bytes(dict_hash(el)))\\n else:\\n # basic types\\n if isinstance(obj, bool):\\n value = str(int(obj))\\n elif isinstance(obj, (six.integer_types, float)):\\n value = str(obj)\\n elif isinstance(obj, (six.text_type, bytes)):\\n value = obj\\n elif obj is None:\\n value = b''\\n else:\\n raise ValueError(\\\"Unsupported value type: %s\\\" % obj.__class__)\\n h.update(to_bytes(value))\\n return h.hexdigest()\",\n \"def valid_chain(self, chain):\\n\\n last_block = chain[0]\\n current_index = 1\\n \\n while current_index < len(chain):\\n block = chain[current_index]\\n # Check correctness of last block's hash\\n if block['previous_hash'] != self.hash(last_block): \\n return False\\n # Check correctness of proof-of-work\\n if not self.valid_proof(last_block['proof'], block['proof'], block['previous_hash']):\\n return False\\n last_block = block \\n current_index += 1\\n\\n return True\",\n \"def __hash__(self):\\n return hash(self.joined())\",\n \"def test_hash_bytes_decorator(self):\\n results = [\\n 1164302962920061,\\n 16735493734761467723,\\n 18150279091576190542,\\n 9861778148718857663,\\n 14008040072978383620,\\n ]\\n\\n @hash_with_depth_bytes\\n def my_hash(key, depth=1):\\n \\\"\\\"\\\"my hash function\\\"\\\"\\\"\\n return hashlib.sha512(key).digest()\\n\\n self.assertEqual(my_hash(\\\"this is a test\\\", 5), results)\\n res = my_hash(\\\"this is a test\\\", 1)\\n self.assertEqual(len(res), 1)\\n self.assertEqual(res[0], results[0])\",\n \"def is_valid_node_id(val):\\n if not val:\\n return False\\n if not isinstance(val, bytes) and not isinstance(val, bytearray):\\n return False\\n\\n length = len(val)\\n if length != SHA1_BIN_LEN and length != SHA2_BIN_LEN and \\\\\\n length != SHA3_BIN_LEN:\\n return False\\n\\n return True\",\n \"def test_diff_inputs_diff_hash(self):\\n # same strings, different salts\\n self.assertNotEqual(\\n hash_str(\\\"mystring\\\", salt=\\\"mysalt1\\\").hex(),\\n hash_str(\\\"mystring\\\", salt=\\\"mysalt2\\\").hex(),\\n )\\n # different strings, same salts\\n self.assertNotEqual(\\n hash_str(\\\"mystring1\\\", salt=\\\"mysalt\\\").hex(),\\n hash_str(\\\"mystring2\\\", salt=\\\"mysalt\\\").hex(),\\n )\",\n \"def validate_blockchain(chain):\\n assert isinstance(chain, list)\\n\\n for hook in chain[::-1]:\\n pass\",\n \"def djb2_hash(key):\\n\\n hash = 5381\\n\\n for i in range(len(key)):\\n hash = ((hash << 5) + hash) + ord(key[i])\\n\\n return hash\",\n \"def test_init_hash(self):\\n bill = Bill(self.input_hash)\\n for key, value in self.input_hash.iteritems():\\n self.assertEqual(value, bill.__dict__[key])\",\n \"def verify_chain():\\n for (index,block) in enumerate(blockchain):\\n if index ==0:\\n continue\\n if block['previous_hash'] != hash_block(blockchain[index-1]):\\n return False\\n if not valid_proof(block['transactions'][:-1],block['previous_hash'],block['proof']):\\n print('Proof of Work is Invalid')\\n return False\\n return True\",\n \"def b2a_base58check(data):\\n\\n return encoding.b2a_hashed_base58(data)\",\n \"def is_git_sha(text):\\n # Handle both the full sha as well as the 7-character abbreviation\\n if len(text) in (40, 7):\\n try:\\n int(text, 16)\\n return True\\n except ValueError:\\n pass\\n return False\",\n \"def _Hash(content: bytes) -> str:\\n return hashlib.sha256(content).hexdigest()\",\n \"def test_simple_adding(self):\\n cons_hash = ConsistentHash(2)\\n cons_hash.add('192.168.1.1') \\n self.assertEquals(len(cons_hash), 2)\\n \\n cons_hash.add('192.168.1.2') \\n self.assertEquals(len(cons_hash), 4) \\n \\n self.assertTrue(cons_hash._is_consistent())\",\n \"def hash_djb2(string):\\n hashval = ctypes.c_uint(5381)\\n for char in string:\\n hashval.value = ((hashval.value << 5) + hashval.value) + ord(char)\\n return hashval.value & 0x7FFFFFFF\",\n \"def test_default_sha256_bytes(self):\\n this_is_a_test = [\\n 10244166640140130606,\\n 5650905005272240665,\\n 14215057275609328422,\\n 5952353080197385534,\\n 4990779931033217093,\\n ]\\n this_is_also = [\\n 4140421647067018332,\\n 9306548247555387104,\\n 5672713771950536751,\\n 8501641957786831066,\\n 15146689942378126332,\\n ]\\n hashes = default_sha256(b\\\"this is a test\\\", 5)\\n self.assertEqual(hashes, this_is_a_test)\\n hashes = default_sha256(b\\\"this is also a test\\\", 5)\\n self.assertEqual(hashes, this_is_also)\",\n \"def chainIsValid(self):\\n for i in range(1, len(self.blocks)):\\n prev_block = self.blocks[i-1]\\n cur_block = self.blocks[i]\\n if cur_block.header['prevBlockH'] != getHashBlock(prev_block):\\n return False\\n return True\",\n \"def is_hash(fhash):\\n\\n # Intentionally doing if/else statement for ease of testing and reading\\n if re.match(re_md5, fhash):\\n return True\\n elif re.match(re_sha1, fhash):\\n return True\\n elif re.match(re_sha256, fhash):\\n return True\\n elif re.match(re_sha512, fhash):\\n return True\\n elif re.match(re_ssdeep, fhash):\\n return True\\n else:\\n return False\",\n \"def hash_function_1(key):\\n hash = 0\\n for i in key:\\n hash = hash + ord(i)\\n return hash\",\n \"def _verify_hashes(hashes):\\n\\n for item in hashes:\\n try:\\n hashlib.new(item)\\n VALID_HASH.append(item)\\n except Exception:\\n pass\",\n \"def _check_transform_key(key: Hashable) -> None:\\n _test_hashable = hash(key) # The only 'real' way to make sure is hashable\\n # if not isinstance(key, Hashable):\\n # raise TypeError((type(key), \\\"transformation lookup key is not hashable\\\"))\",\n \"def regist_hash(cobj, hash, handler, dir):\\n pass\",\n \"def _hash(self, string, hash_type):\\n hash_types = {\\n 'TABLE_OFFSET': 0,\\n 'HASH_A': 1,\\n 'HASH_B': 2,\\n 'TABLE': 3\\n }\\n seed1 = 0x7FED7FED\\n seed2 = 0xEEEEEEEE\\n\\n for ch in string.upper():\\n if not isinstance(ch, int): ch = ord(ch)\\n value = self.encryption_table[(hash_types[hash_type] << 8) + ch]\\n seed1 = (value ^ (seed1 + seed2)) & 0xFFFFFFFF\\n seed2 = ch + seed1 + seed2 + (seed2 << 5) + 3 & 0xFFFFFFFF\\n\\n return seed1\",\n \"def isScriptHashScript(pkScript):\\n return extractScriptHash(pkScript) is not None\",\n \"def do_hash(dat: typing.Any) -> str:\\n return hashlib.sha1(json.dumps(dat, sort_keys=True).encode('utf-8')).hexdigest()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61252147","0.59626085","0.58439803","0.5742523","0.56958115","0.56938916","0.5665251","0.5566981","0.5560363","0.5549851","0.5532766","0.5531971","0.55154383","0.5513594","0.54947066","0.54275084","0.5408093","0.5398602","0.53863704","0.53802854","0.5374157","0.5352659","0.5343174","0.5337656","0.5335785","0.5329024","0.5328839","0.53250927","0.53243446","0.53219265","0.5315521","0.53077036","0.5299493","0.5264341","0.5251205","0.52477694","0.52469105","0.5244836","0.52309567","0.5213909","0.5200887","0.52008665","0.5197929","0.51939756","0.51851","0.51776487","0.51767784","0.5172183","0.5169978","0.51666796","0.5145241","0.5142775","0.5137551","0.513444","0.51299024","0.5119909","0.511778","0.5116149","0.5115881","0.51131415","0.5111389","0.5110999","0.51048553","0.5099885","0.5099149","0.50911134","0.50830704","0.5082355","0.5080662","0.50782263","0.50779235","0.50720394","0.5065821","0.50583774","0.5058376","0.50550556","0.5053606","0.5049483","0.5045421","0.5039179","0.50355625","0.5031069","0.5030496","0.5027152","0.502689","0.5024652","0.50215364","0.5016048","0.50073034","0.50040036","0.5001487","0.50011486","0.49991402","0.49990058","0.4996566","0.4993149","0.49875715","0.4985843","0.49834675","0.4977406","0.49708515"],"string":"[\n \"0.61252147\",\n \"0.59626085\",\n \"0.58439803\",\n \"0.5742523\",\n \"0.56958115\",\n \"0.56938916\",\n \"0.5665251\",\n \"0.5566981\",\n \"0.5560363\",\n \"0.5549851\",\n \"0.5532766\",\n \"0.5531971\",\n \"0.55154383\",\n \"0.5513594\",\n \"0.54947066\",\n \"0.54275084\",\n \"0.5408093\",\n \"0.5398602\",\n \"0.53863704\",\n \"0.53802854\",\n \"0.5374157\",\n \"0.5352659\",\n \"0.5343174\",\n \"0.5337656\",\n \"0.5335785\",\n \"0.5329024\",\n \"0.5328839\",\n \"0.53250927\",\n \"0.53243446\",\n \"0.53219265\",\n \"0.5315521\",\n \"0.53077036\",\n \"0.5299493\",\n \"0.5264341\",\n \"0.5251205\",\n \"0.52477694\",\n \"0.52469105\",\n \"0.5244836\",\n \"0.52309567\",\n \"0.5213909\",\n \"0.5200887\",\n \"0.52008665\",\n \"0.5197929\",\n \"0.51939756\",\n \"0.51851\",\n \"0.51776487\",\n \"0.51767784\",\n \"0.5172183\",\n \"0.5169978\",\n \"0.51666796\",\n \"0.5145241\",\n \"0.5142775\",\n \"0.5137551\",\n \"0.513444\",\n \"0.51299024\",\n \"0.5119909\",\n \"0.511778\",\n \"0.5116149\",\n \"0.5115881\",\n \"0.51131415\",\n \"0.5111389\",\n \"0.5110999\",\n \"0.51048553\",\n \"0.5099885\",\n \"0.5099149\",\n \"0.50911134\",\n \"0.50830704\",\n \"0.5082355\",\n \"0.5080662\",\n \"0.50782263\",\n \"0.50779235\",\n \"0.50720394\",\n \"0.5065821\",\n \"0.50583774\",\n \"0.5058376\",\n \"0.50550556\",\n \"0.5053606\",\n \"0.5049483\",\n \"0.5045421\",\n \"0.5039179\",\n \"0.50355625\",\n \"0.5031069\",\n \"0.5030496\",\n \"0.5027152\",\n \"0.502689\",\n \"0.5024652\",\n \"0.50215364\",\n \"0.5016048\",\n \"0.50073034\",\n \"0.50040036\",\n \"0.5001487\",\n \"0.50011486\",\n \"0.49991402\",\n \"0.49990058\",\n \"0.4996566\",\n \"0.4993149\",\n \"0.49875715\",\n \"0.4985843\",\n \"0.49834675\",\n \"0.4977406\",\n \"0.49708515\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":770,"numItemsPerPage":100,"numTotalItems":95772,"offset":77000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjQ1ODE4MywianRpIjoiN2YyZWYxYjgtODYxNS00MzMwLWJhNWEtYjQ3M2NiYTY3ZTY1Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY0NjE3ODMsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.a2AgJin8ynpjP39D678csNqDZnE1KPo1itozKAkrGlht_Mlr1vNg9M1xWXdJULZUbjUZA9T7IaZJKlCZufV2Ag","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Force ongoing live migration to complete | def live_migrate_force_complete(self, server, migration):
body = {'force_complete': None}
resp, body = self.api.client.post(
'/servers/%s/migrations/%s/action' % (base.getid(server),
base.getid(migration)),
body=body)
retu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def migration():",
"def migrate(self):\n\tpass",
"def post_migrations(self):",
"def model_post_migrate(*args, **kwargs):\n global IN_MIGRATIONS\n IN_MIGRATIONS = False",
"def run_migration(self):\n step = \"Migrating Database\"\n try:\n self.slacker.send_thread_reply(step)\n ... | [
"0.7204588",
"0.7060435",
"0.7018086",
"0.67843336",
"0.6716327",
"0.6643194",
"0.65348613",
"0.63825476",
"0.6346392",
"0.63234293",
"0.6320481",
"0.63142586",
"0.63106877",
"0.63106877",
"0.6302978",
"0.62963974",
"0.6280855",
"0.62656957",
"0.62344575",
"0.6208175",
"0.620... | 0.6850961 | 3 |
Get a migration of a specified server | def get(self, server, migration):
return self._get('/servers/%s/migrations/%s' %
(base.getid(server), base.getid(migration)),
'migration') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list(self, server):\n return self._list(\n '/servers/%s/migrations' % (base.getid(server)), \"migrations\")",
"def get_migration(self, _id: int) -> Optional[Migration]:\n for migration in self.migrations:\n if migration.id == _id:\n return migration\n ... | [
"0.65762854",
"0.5903064",
"0.57015634",
"0.5498266",
"0.5447161",
"0.53921473",
"0.5252999",
"0.51654613",
"0.5109671",
"0.5014758",
"0.49973872",
"0.49665922",
"0.4944251",
"0.4910747",
"0.4888143",
"0.48865587",
"0.48741516",
"0.4832848",
"0.48080567",
"0.48019844",
"0.478... | 0.8369567 | 0 |
Get a migrations list of a specified server | def list(self, server):
return self._list(
'/servers/%s/migrations' % (base.getid(server)), "migrations") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_applied_migrations(self):\n with self.internal_db.begin() as conn:\n sql = \"SELECT name from migration;\"\n migrations = conn.execute(sql).fetchall()\n return [m[0] for m in migrations]",
"def get(self, server, migration):\n return self._get('/servers/%s/migrat... | [
"0.6635136",
"0.6470522",
"0.63409764",
"0.59359926",
"0.5868745",
"0.5838271",
"0.57854855",
"0.57466656",
"0.56155324",
"0.5562354",
"0.5559403",
"0.5559026",
"0.55545276",
"0.5531459",
"0.5406119",
"0.53998864",
"0.5366492",
"0.53645813",
"0.5352012",
"0.53041464",
"0.5299... | 0.86654884 | 0 |
Cancel an ongoing live migration | def live_migration_abort(self, server, migration):
return self._delete(
'/servers/%s/migrations/%s' % (base.getid(server),
base.getid(migration))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def migrate_cancel(self):\n\t\treturn Job(SDK.PrlVm_MigrateCancel(self.handle)[0])",
"def cancel(self):\n self.session.rollback()",
"def cancel(self) -> None:\n c = self.pgconn.get_cancel()\n c.cancel()",
"def cancel(self):\n pass",
"def cancel():",
"def cancel(self):",
"def... | [
"0.764301",
"0.6904926",
"0.65942436",
"0.6392075",
"0.6307264",
"0.6264627",
"0.6264627",
"0.6264627",
"0.62033683",
"0.6092558",
"0.6092558",
"0.60868293",
"0.6083494",
"0.6041118",
"0.5979406",
"0.59677255",
"0.59653294",
"0.596301",
"0.59626913",
"0.5956265",
"0.59348416"... | 0.72220004 | 1 |
Backs up a folder to a zip file This function takes the contents (recursively) of a folder and backs them up to a zip file. | def backupToZip(folder):
folder = os.path.abspath(folder) #Ensure we're using the absolute path
number = 1
while True:
zipFilename = os.path.basename(folder) + '_' + str(number) + '.zip'
if not os.path.exists(zipFilename):
break
number += 1
#Create the zip file
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_zip_backup(folder_path):\n\n # checking that parameter contains absolute path\n if not os.path.isabs(folder_path):\n print(\"Required an absolute path to generate a zip backup\")\n return\n\n # checking that folder exists before backuping\n if not os.path.isdir(folder_path):\n ... | [
"0.7182938",
"0.70816165",
"0.66951454",
"0.6578646",
"0.6496767",
"0.64625716",
"0.64222264",
"0.63613576",
"0.62886965",
"0.6280407",
"0.6269397",
"0.6232583",
"0.6067449",
"0.60649943",
"0.6032387",
"0.60246265",
"0.60167176",
"0.5995485",
"0.5989524",
"0.59724647",
"0.596... | 0.80446476 | 0 |
This function prints and plots the confusion matrix. Normalization can be applied by setting `normalize=True`. | def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_confusion_matrix(cm, classes=[0,1], normalize=False, title='Confusion matrix', print_matrix=False):\n\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n... | [
"0.8194862",
"0.80949175",
"0.8029915",
"0.8019153",
"0.79941195",
"0.7991258",
"0.7980955",
"0.7976606",
"0.79610753",
"0.79590565",
"0.79378676",
"0.7934962",
"0.7934504",
"0.79313844",
"0.7926313",
"0.7924577",
"0.79241234",
"0.7923211",
"0.7923023",
"0.7921931",
"0.791787... | 0.7845746 | 48 |
Unregisters and registers with new custom admin panels | def reg_admin():
for model in models.get_models():
if _check_name(model):
admin.site.unregister(model)
admin.site.register(model, LocationAuditAdmin) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_module():\n\n # Course Dashboard\n tabs.Registry.register(\n base.OfflineAssignmentBase.DASHBOARD_NAV,\n base.OfflineAssignmentBase.DASHBOARD_TAB,\n base.OfflineAssignmentBase.DESCRIPTION,\n off_ass_dashboard.OfflineAssignmentDashboardHandler)\n\n dashboard.Dashboa... | [
"0.60133576",
"0.5804296",
"0.55303526",
"0.5491801",
"0.54885453",
"0.54811734",
"0.5326041",
"0.5307102",
"0.52954674",
"0.5272991",
"0.5260019",
"0.5254747",
"0.52482134",
"0.5233564",
"0.5233559",
"0.5215448",
"0.52059233",
"0.5200994",
"0.5189056",
"0.5153242",
"0.514729... | 0.6133607 | 0 |
Returns the direction the detect objects (contours) are compared to the center of the image, this is returned in normalized screen space 1 to 1 (1 meaning the most left compared to the center, and 1 the most right compared to the center and 0 meaning perfectly centered xy_center_directions returns x and y center direct... | def xy_center_directions(contours, image: ndarray):
return contours_.calculate_normalized_screen_space(contours, image) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def center_directions(contours, image: ndarray):\n return contours_.contour_average_center(contours)",
"def _centered_coords(self):\n\n array = self.detection_base.height_model.array\n height, width = self.detection_base.height_model.cell_size_x * array.shape[0], \\\n self... | [
"0.6688183",
"0.5997663",
"0.59733456",
"0.5901859",
"0.5878864",
"0.58696026",
"0.5829185",
"0.57540023",
"0.5694345",
"0.56603926",
"0.5657609",
"0.5637801",
"0.5630213",
"0.5619939",
"0.5613608",
"0.5605313",
"0.55993795",
"0.55470574",
"0.55371344",
"0.5507005",
"0.549903... | 0.72629136 | 0 |
Returns the average center of the contours or list of contours that are the final targets This is the default directions function since it doesnt calculate any directions, only finds the center | def center_directions(contours, image: ndarray):
return contours_.contour_average_center(contours) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_center( contours ):\r\n ret = []\r\n\r\n for x in contours:\r\n M = cv2.moments( x )\r\n pt = Point()\r\n pt.x = int( M['m10']/M['m00'] )\r\n pt.y = int( M['m01']/M['m00'] )\r\n\r\n ret.append( pt )\r\n\r\n return( ret );",
"def xy_center_directions(contours, ... | [
"0.6287653",
"0.6173183",
"0.61626303",
"0.5972788",
"0.5970468",
"0.57259196",
"0.5716139",
"0.57096714",
"0.5702477",
"0.57018715",
"0.5674532",
"0.5667665",
"0.563917",
"0.5589692",
"0.55148965",
"0.54906875",
"0.5481084",
"0.5470445",
"0.5454891",
"0.5452313",
"0.5449522"... | 0.735926 | 0 |
Counts the amount of successful object detections. | def target_amount_directions(contours, image: ndarray):
return len(contours) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resultCounter(detections):\n counter = 0\n for attribute, value in classIterator(detections):\n if 'crease' in attribute:\n counter += len(value)\n return counter",
"def obstacle_count(self):\n #scan area in front of robot\n self.scan()\n #Figure ot how many ob... | [
"0.7270498",
"0.70085955",
"0.6897006",
"0.68248814",
"0.65556407",
"0.6474854",
"0.63986397",
"0.63715106",
"0.62562704",
"0.6249045",
"0.6217766",
"0.621775",
"0.6177267",
"0.61656386",
"0.61498624",
"0.6131666",
"0.61292815",
"0.61159194",
"0.6108707",
"0.60941064",
"0.609... | 0.0 | -1 |
Retime a generic OpenRAVE trajectory into a timed for OWD. First, try to retime the trajectory into a MacTrajectory using OWD's MacRetimer. If MacRetimer is not available, then fall back on the default OpenRAVE retimer. traj input trajectory max_jerk maximum jerk allowed during retiming synchronize enable synchronizati... | def RetimeTrajectory(self, traj, max_jerk=30.0, synchronize=False,
stop_on_stall=True, stop_on_ft=False, force_direction=None,
force_magnitude=None, torque=None, **kw_args):
# Fall back on the standard OpenRAVE retimer if MacTrajectory is not
# available... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ExecuteTrajectory(self, traj, timeout=None, blend=True, retime=True, limit_tolerance=1e-3, synchronize=True, **kw_args):\n # Query the active manipulators based on which DOF indices are\n # included in the trajectory.\n active_manipulators = self.GetTrajectoryManipulators(traj)\n ne... | [
"0.64776725",
"0.57618105",
"0.5051099",
"0.50354636",
"0.4887224",
"0.48129752",
"0.47852176",
"0.47836939",
"0.47655028",
"0.4751602",
"0.4736376",
"0.47340217",
"0.46570545",
"0.46272182",
"0.4611437",
"0.46056557",
"0.45990774",
"0.45985407",
"0.45913064",
"0.45853823",
"... | 0.75501573 | 0 |
Blend a trajectory for execution in OWD. Blending a trajectory allows the MacRetimer to smoothly accelerate through waypoints. If a blend radius is not specified, it defaults to zero and the controller must come to a stop at each waypoint. This adds the \tt blend_radius group to the input trajectory. traj input traject... | def BlendTrajectory(self, traj, maxsmoothiter=None, resolution=None,
blend_radius=0.2, blend_attempts=4, blend_step_size=0.05,
linearity_threshold=0.1, ignore_collisions=None, **kw_args):
with self:
return self.trajectory_module.blendtrajectory(traj=tr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def blendTangent(self, blend):\n if blend and self._patches:\n self._prevPatchStartIndex = self._patches[-1].startIndex()\n else:\n self._prevPatchStartIndex = 1e10",
"def set_blend_radius(print_organizer, d_fillet=10, buffer=0.3):\n\n logger.info(\"Setting blend radius\")\... | [
"0.6246105",
"0.58287215",
"0.5367136",
"0.5344815",
"0.5186415",
"0.51465267",
"0.51189536",
"0.50626063",
"0.5061908",
"0.5035875",
"0.50047594",
"0.49633598",
"0.49114856",
"0.49037868",
"0.4902819",
"0.48953748",
"0.48922417",
"0.48837733",
"0.4830077",
"0.48256335",
"0.4... | 0.7580831 | 0 |
Execute a trajectory. By default, this function retimes, blends, and adds the stop_on_stall flag to all trajectories. This behavior can be overriden using the \tt blend and \tt retime flags or by passing the appropriate \tt kw_args arguments to the blender and retimer. By default, this function blocks until trajectory ... | def ExecuteTrajectory(self, traj, timeout=None, blend=True, retime=True, limit_tolerance=1e-3, synchronize=True, **kw_args):
# Query the active manipulators based on which DOF indices are
# included in the trajectory.
active_manipulators = self.GetTrajectoryManipulators(traj)
needs_synch... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_hybrid_control_trajectory(self, trajectory, model, max_force_torque, timeout=5.0,\n stop_on_target_force=False, termination_criteria=None,\n displacement_epsilon=0.002, check_displacement_time=2.0,\n ... | [
"0.5539077",
"0.5405223",
"0.5195004",
"0.51543003",
"0.51397663",
"0.49278358",
"0.47854707",
"0.46823263",
"0.46326283",
"0.4627261",
"0.45890072",
"0.4556019",
"0.45381248",
"0.44984668",
"0.44904834",
"0.4479847",
"0.44633135",
"0.44597632",
"0.44556123",
"0.44518995",
"0... | 0.67021406 | 0 |
This function takes in a list of any type and reverse it | def recursive_list_reverse(ls: list)->list:
if ls:
return recursive_list_reverse(ls[1:]) + [ls[0]]
else:
return [] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reverse(*, list : Union[List[Any], ConduitVariable]) -> None:\n list.reverse()",
"def reverse_list(items):\n\n return items[::-1]",
"def reverse_list(self,list_):\r\n list_.reverse()",
"def reverse_list(s_list):\n require_type(isa(s_list, List), 'parameter of reverse must be a list')\... | [
"0.8506192",
"0.8255569",
"0.7932985",
"0.77202743",
"0.7693257",
"0.74789643",
"0.7452501",
"0.74197346",
"0.72985035",
"0.7250638",
"0.72166467",
"0.71269745",
"0.7125287",
"0.7038873",
"0.70346695",
"0.7029365",
"0.7028975",
"0.7001069",
"0.6975123",
"0.69476056",
"0.69147... | 0.69460106 | 20 |
function returns n combination r from pascals triangle | def pascal_triangle(n: int, r: int):
if n==0
return 1
elif r ==0:
return 1
elif n==r:
return 1
else
return pascal_triangle(n-1,r) + pascal_triangle(n-1, r-1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def r_combinations(n,r):\n return r_permutations(n,r) / math.factorial(r)",
"def triangleNumber(n):\n return sum(range(n+1))",
"def get_triangle_numbers(n):\n r = []\n for i in xrange(1, n + 1):\n t = ((i * (i + 1)) / 2)\n r.append(t)\n return r",
"def permutations(n, r):\n re... | [
"0.75085384",
"0.7400091",
"0.7251817",
"0.7202176",
"0.7190689",
"0.71306235",
"0.7108646",
"0.7101338",
"0.7094721",
"0.70332265",
"0.70266634",
"0.7013671",
"0.6937788",
"0.69373167",
"0.6901856",
"0.68888044",
"0.68879896",
"0.68220484",
"0.6811778",
"0.6787959",
"0.67834... | 0.74600625 | 1 |
function outputs console interface and responds user input | def update(self): # pragma: no cover
print(_choices)
while True:
try:
user_input = int(input())
if user_input < 1 or user_input > 5:
raise ValueError
# this will send it to the
# print message and b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def console():\n value = input(' -> ' + bc.FAIL + 'wmd' + bc.ENDC + '@' + bc.FAIL + 'changeme:' + bc.ENDC + ' ')\n userinput = value.split()\n # Show options\n if 'so' in userinput[:1]:\n sop.show_opt()\n # Show all info\n elif 'sa' in userinput[:1]:\n sop.show_all()\n # Run mo... | [
"0.7256005",
"0.72226304",
"0.6916377",
"0.68207747",
"0.67782474",
"0.6772474",
"0.67112774",
"0.66950375",
"0.66484797",
"0.66417015",
"0.6631729",
"0.6603269",
"0.6589633",
"0.657715",
"0.6575872",
"0.6519431",
"0.6506439",
"0.6503734",
"0.6501386",
"0.65011364",
"0.645839... | 0.0 | -1 |
function implements interface that outputs all records | def show_recs(self):
if len(self.storage.records) == 0:
return "Records not found!"
else:
string_of_records = ""
for record in self.storage.records:
string_of_records += record.to_string()
return string_of_records | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def records(self):\r\n raise NotImplementedError()",
"def get_all_records(self, data: dict, execution_context: dict):",
"async def get_all_record():\n # X_new = item.to_df()\n # item_str = item.to_string()\n # project_code = int(item_str[item_str.find('=')+1:])\n pg = PostgreSQL()\n retur... | [
"0.71311724",
"0.6898564",
"0.6442363",
"0.6388859",
"0.6324791",
"0.6284191",
"0.62724614",
"0.6192477",
"0.6160231",
"0.61510426",
"0.611075",
"0.6085744",
"0.6068864",
"0.60219085",
"0.6000698",
"0.59966624",
"0.599375",
"0.5986212",
"0.5984351",
"0.59460783",
"0.594217",
... | 0.6081127 | 12 |
>>> temp = io_helper.set_new("12\\nname\\naddr\\n") >>> val= ConsoleInterface(MemoryRecordStorage([])).add_rec() >>> io_helper.set_former(temp) >>> val 'Record added successfully' function implements interface that adds record | def add_rec(self):
print("Write phone number:")
add_phone_number_input = input()
print("Write name of the record:")
add_name_input = input()
print("Write address:")
add_address_input = input()
return self.storage.add(
add_phone_number_input, add_name_i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_record(self, record):\n pass",
"def test_record_add():\n\n display = Display()\n display.add('first')\n display.add('second')\n\n assert len(display.display_output) == 2",
"def _add_recorder(self, variable):\n raise NotImplementedError",
"def FILE_RTRV_record(self):\n eor... | [
"0.5955477",
"0.5844077",
"0.54942876",
"0.54797316",
"0.54465395",
"0.52920324",
"0.5285212",
"0.5279746",
"0.52550757",
"0.5236633",
"0.52354974",
"0.52162975",
"0.5192673",
"0.51570594",
"0.51551163",
"0.51312655",
"0.51297307",
"0.51247436",
"0.51221544",
"0.50602716",
"0... | 0.5862049 | 1 |
>>> temp = io_helper.set_new("12\\n") >>> res= ConsoleInterface(MemoryRecordStorage()).remove_rec() >>> io_helper.set_former(temp) >>> res 'Record not found' function implements interface that removes record | def remove_rec(self):
print("Write phone number:")
remove_phone_number_input = input()
return self.storage.remove(remove_phone_number_input) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pop_write(self):\n ...",
"def undo(self):\r\n previous = self.memory.pop()\r\n if not isinstance(previous, task2.ListADT):\r\n raise TypeError(\"Did not expect any other object in memory\")\r\n if previous[0] == \"d\":\r\n index = previous[1]\r\n f... | [
"0.53218615",
"0.5136622",
"0.51338655",
"0.51264924",
"0.5107625",
"0.5001114",
"0.49959147",
"0.4910786",
"0.49040174",
"0.4860867",
"0.47833407",
"0.47785375",
"0.47783056",
"0.47436464",
"0.47293022",
"0.47262895",
"0.47256777",
"0.4703999",
"0.46816048",
"0.46761337",
"0... | 0.5560903 | 0 |
>>> [ConsoleInterface(MemoryRecordStorage()).remove_all_recs()] ['All records have been erased'] function implements interface that removes all records | def remove_all_recs(self):
return self.storage.clear() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear_records(self) -> None:\n for container in self.record_containers:\n container.clear_records()",
"def clear_all():\n bpy.ops.object.select_all(action='SELECT')\n bpy.ops.object.delete()",
"def remove_all():\n storage = FileStorage()\n objects = storage.all()\n ... | [
"0.66904634",
"0.6276651",
"0.6266155",
"0.61560607",
"0.6145792",
"0.6092948",
"0.60891646",
"0.6065167",
"0.60158616",
"0.6007153",
"0.59672874",
"0.5888242",
"0.58739835",
"0.5846741",
"0.5836213",
"0.58347726",
"0.5826493",
"0.5804345",
"0.5804345",
"0.5804345",
"0.580434... | 0.72792065 | 0 |
>>> temp = io_helper.set_new("12\\nname\\naddr\\n") >>> res= ConsoleInterface(MemoryRecordStorage()).update_rec() >>> io_helper.set_former(temp) >>> res 'Record not found' function implements interface that updates record | def update_rec(self):
print("Write phone number:")
update_phone_number_input = input()
print("Write new name of the record:")
update_name_input = input()
print("Write new address:")
update_address_input = input()
return self.storage.update(
update_phon... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, read, info: ModificationInfo):",
"def update(self, line):",
"def test_patch_record(self):\n pass",
"def _set_packed_record(self, i, s):\n\n raise NotImplementedError()",
"def _add_to_ref(self, rec_curr, line):\n # Examples of record lines containing ':' include:\n ... | [
"0.5850617",
"0.5622828",
"0.5558996",
"0.54402775",
"0.5307458",
"0.5306181",
"0.5289004",
"0.5260318",
"0.5247207",
"0.5246717",
"0.52426815",
"0.513985",
"0.5122304",
"0.5113792",
"0.51074845",
"0.50957656",
"0.5067799",
"0.50669193",
"0.5039942",
"0.5013815",
"0.49793434"... | 0.5651579 | 1 |
function runs loop for infinite menu output | def run(self): # pragma: no cover
while True:
self.update() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def STARTMENU_LOOP():\n pass",
"def main(self):\n while self.leave_main_menu:\n print(fr.FR[4], fr.FR[5], fr.FR[6], fr.FR[7])\n self.choice_menu = input(fr.FR[8])\n self.main_menu_input()",
"def run(self):\n while True:\n self.menu()\n ... | [
"0.80929637",
"0.75484467",
"0.7233137",
"0.7231741",
"0.71407855",
"0.7124997",
"0.71035296",
"0.6959605",
"0.6957553",
"0.69392127",
"0.68988335",
"0.6896735",
"0.68822074",
"0.6853966",
"0.6853966",
"0.6811423",
"0.6788544",
"0.67814904",
"0.6769244",
"0.6754431",
"0.67349... | 0.0 | -1 |
Calculates truncated mean for a given numpy array | def trunc_mean(arr, n = 0.1):
if type(arr) is not np.ndarray:
logging.error('arr type must be numpy array')
return
flat_arr = list((arr.flatten()))
flat_arr.sort()
trunc_indices = ceil(flat_arr.__len__() * n)
if n<=0 or trunc_indices <1 or (2 * trunc_indices +1) >= len(flat_arr) :
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wo_mean(arr):\n\n return np.array(arr) - np.mean(arr, axis=0)",
"def har_mean(array):\n return ((sum([1/x for x in array]))**(-1))*len(array)",
"def expanding_mean(arr):\n total_len = arr.shape[0]\n return ((arr / total_len).cumsum() / np.arange(1, total_len + 1)) * total_len",
"def mean(arr)... | [
"0.74097645",
"0.73743963",
"0.7330628",
"0.7146221",
"0.7097715",
"0.70954114",
"0.7085901",
"0.7041643",
"0.7021862",
"0.6990255",
"0.69567066",
"0.688763",
"0.6884273",
"0.6852552",
"0.6774491",
"0.67742735",
"0.6767213",
"0.6758347",
"0.67554206",
"0.673708",
"0.6720732",... | 0.72338575 | 3 |
run the functions that build up the figure | def plot_combined_spectrum(SSC, band):
def get_spectrum(SSC, band):
spectrum = spectra[str(SSC['no'])][band]
frequency = spectrum['frequency'].to(u.GHz)
intensity = spectrum['spectrum'].to(u.K)
# shift spectrum to rest frequency
velshift = SSC['velshift']
frequency... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n self.simulate_test_data()\n self.pipeline_test_data()\n self.plot_jump_flags_image()\n self.plot_groupdq_flags(pixel=[884, 550])\n self.plot_ramps_pre_post_correction(pixel=[884, 550])",
"def main():\n save = False\n show = True\n\n #hd_parameter_plots... | [
"0.7163796",
"0.7062575",
"0.697541",
"0.6972975",
"0.6890155",
"0.6872394",
"0.68260795",
"0.6725831",
"0.6690253",
"0.6663527",
"0.6623025",
"0.6621096",
"0.65647256",
"0.6552204",
"0.65331537",
"0.652853",
"0.65193284",
"0.6517966",
"0.64880514",
"0.64584625",
"0.64567554"... | 0.0 | -1 |
run the functions that build up the figure | def plot_combined_variation(nums, SSC, band, rms):
def get_spectra(nums, SSC, band, rms):
spectrum = spectra[str(SSC['no'])][band]
frequency = spectrum['frequency'].to(u.GHz)
intensity = spectrum['spectrum'].to(u.K)
# shift spectrum to rest frequency
velshift = SSC['velshi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n self.simulate_test_data()\n self.pipeline_test_data()\n self.plot_jump_flags_image()\n self.plot_groupdq_flags(pixel=[884, 550])\n self.plot_ramps_pre_post_correction(pixel=[884, 550])",
"def main():\n save = False\n show = True\n\n #hd_parameter_plots... | [
"0.7162297",
"0.70622724",
"0.6975863",
"0.6972957",
"0.68895304",
"0.68720037",
"0.6826052",
"0.6725763",
"0.66909343",
"0.6662281",
"0.6623937",
"0.6622064",
"0.6564176",
"0.6550949",
"0.65335256",
"0.65297765",
"0.65166265",
"0.6515648",
"0.6489751",
"0.6458146",
"0.645487... | 0.0 | -1 |
CreateAdsByInventoryReferenceRequest a model defined in Swagger | def __init__(self, bid_percentage=None, inventory_reference_id=None, inventory_reference_type=None): # noqa: E501 # noqa: E501
self._bid_percentage = None
self._inventory_reference_id = None
self._inventory_reference_type = None
self.discriminator = None
if bid_percentage is no... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def add_inventory_endpoint(request):\n hotel_id = request.args[\"hotel_id\"][0]\n room_type = request.args[\"room_type\"][0]\n room_inventory = request.args[\"room_inventory\"][0]\n model.add_inventory(hotel_id, room_type, room_inventory)\n return json({\"success\": True})",
"def post(self, ... | [
"0.5723244",
"0.5313022",
"0.5303699",
"0.514045",
"0.5070321",
"0.5009573",
"0.49610618",
"0.49405542",
"0.48937926",
"0.48861742",
"0.4851349",
"0.4851349",
"0.4839163",
"0.48369145",
"0.48202556",
"0.4809839",
"0.47778898",
"0.47728783",
"0.47679877",
"0.47573525",
"0.4740... | 0.43879133 | 59 |
Sets the bid_percentage of this CreateAdsByInventoryReferenceRequest. | def bid_percentage(self, bid_percentage):
self._bid_percentage = bid_percentage | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def percent_b(self, percent_b: float):\n\n self._percent_b = percent_b",
"def __init__(self, bid_percentage=None, inventory_reference_id=None, inventory_reference_type=None): # noqa: E501 # noqa: E501\n self._bid_percentage = None\n self._inventory_reference_id = None\n self._invent... | [
"0.61821496",
"0.59257495",
"0.5638984",
"0.5638984",
"0.5638984",
"0.5638984",
"0.5567468",
"0.5258507",
"0.5230827",
"0.514761",
"0.5127867",
"0.5127867",
"0.5078384",
"0.5002187",
"0.49656045",
"0.4949226",
"0.49074405",
"0.484597",
"0.4835447",
"0.4820097",
"0.47924873",
... | 0.770684 | 0 |
Sets the inventory_reference_id of this CreateAdsByInventoryReferenceRequest. | def inventory_reference_id(self, inventory_reference_id):
self._inventory_reference_id = inventory_reference_id | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inventory_id(self, inventory_id):\n\n self._inventory_id = inventory_id",
"def inventory_reference_type(self, inventory_reference_type):\n\n self._inventory_reference_type = inventory_reference_type",
"def inventory_id(self, inventory_id):\n if inventory_id is None:\n raise ... | [
"0.69871175",
"0.6377572",
"0.62650263",
"0.5741707",
"0.5696567",
"0.5647767",
"0.54511845",
"0.51018476",
"0.5013131",
"0.4810151",
"0.46328503",
"0.45902228",
"0.45902228",
"0.4568547",
"0.45499918",
"0.4547038",
"0.4534739",
"0.45183787",
"0.449545",
"0.44950244",
"0.4469... | 0.82257956 | 0 |
Sets the inventory_reference_type of this CreateAdsByInventoryReferenceRequest. | def inventory_reference_type(self, inventory_reference_type):
self._inventory_reference_type = inventory_reference_type | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inventory_reference_id(self, inventory_reference_id):\n\n self._inventory_reference_id = inventory_reference_id",
"def inventory_id(self, inventory_id):\n\n self._inventory_id = inventory_id",
"def inventory(self, inventory):\n\n self._inventory = inventory",
"def inventory_items(sel... | [
"0.64331234",
"0.5246117",
"0.5223081",
"0.49133226",
"0.47064462",
"0.4672102",
"0.46364635",
"0.46090922",
"0.46090922",
"0.46090922",
"0.46090922",
"0.45791408",
"0.45752478",
"0.45637342",
"0.45555532",
"0.45555532",
"0.45555532",
"0.4546563",
"0.45451772",
"0.45405617",
... | 0.8307037 | 0 |
Returns the model properties as a dict | def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_dict(self):\n return self.properties",
"def to_dict(self):\n return self.properties",
"def get_properties(self):\n return self.properties",
"def asdict(self):\n return self._prop_dict",
"def json(self):\n rv = {\n prop: getattr(self, prop)\n f... | [
"0.7751993",
"0.7751993",
"0.73391134",
"0.7334895",
"0.7297356",
"0.727818",
"0.7159078",
"0.71578115",
"0.71494967",
"0.71494967",
"0.71283495",
"0.71275014",
"0.7122587",
"0.71079814",
"0.7060394",
"0.7043251",
"0.7034103",
"0.70233124",
"0.69635814",
"0.69586295",
"0.6900... | 0.0 | -1 |
Returns the string representation of the model | def to_str(self):
return pprint.pformat(self.to_dict()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return super().__str__() + self.model.__str__()",
"def __str__(self) -> str:\n # noinspection PyUnresolvedReferences\n opts = self._meta\n if self.name_field:\n result = str(opts.get_field(self.name_field).value_from_object(self))\n else:\n ... | [
"0.85856134",
"0.7814518",
"0.77898884",
"0.7751367",
"0.7751367",
"0.7712228",
"0.76981676",
"0.76700574",
"0.7651133",
"0.7597206",
"0.75800353",
"0.7568254",
"0.7538184",
"0.75228703",
"0.7515832",
"0.7498764",
"0.74850684",
"0.74850684",
"0.7467648",
"0.74488163",
"0.7442... | 0.0 | -1 |
For `print` and `pprint` | def __repr__(self):
return self.to_str() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pprint(*args, **kwargs):\n if PRINTING:\n print(*args, **kwargs)",
"def print_out():\n pass",
"def custom_print(*objects):\n print(*objects, sep=OFS, end=ORS)",
"def _print(self, *args):\n return _ida_hexrays.vd_printer_t__print(self, *args)",
"def _printable(self):\n ... | [
"0.7557706",
"0.7337767",
"0.6987629",
"0.69846016",
"0.6944986",
"0.6925048",
"0.68990684",
"0.6898436",
"0.68151766",
"0.68065625",
"0.67522526",
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"0.6690892",
"0.66747624",
"0.66579586",
"0.6609691",
"0.660799",
"0.66019386",
"0.656303... | 0.0 | -1 |
Returns true if both objects are equal | def __eq__(self, other):
if not isinstance(other, CreateAdsByInventoryReferenceRequest):
return False
return self.__dict__ == other.__dict__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self, other):\n return are_equal(self, other)",
"def __eq__(self,other):\n try: return self.object==other.object and isinstance(self,type(other))\n except: return False",
"def __eq__(self, other):\n if i... | [
"0.8088132",
"0.8088132",
"0.8054589",
"0.7982687",
"0.79670393",
"0.79670393",
"0.79670393",
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"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
"0.79670393",
... | 0.0 | -1 |
Returns true if both objects are not equal | def __ne__(self, other):
return not self == other | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, other: object) -> bool:\n if self.__eq__(other):\n return False\n return True",
"def __ne__(self, other: object) -> bool:\n return not self.__eq__(other)",
"def __ne__(self, other) -> bool:\n return not self.__eq__(other)",
"def __eq__(self, other):\n ... | [
"0.845611",
"0.8391477",
"0.8144138",
"0.81410587",
"0.8132492",
"0.8093973",
"0.80920255",
"0.80920255",
"0.80920255",
"0.8085325",
"0.8085325",
"0.8076365",
"0.8076365",
"0.8065748"
] | 0.0 | -1 |
Uses dump for pre2019 data to populate ConferenceTag instances for CFP | def create_tags():
INPUT = """
"Python general",Python
R,"Other Programming Languages"
Java,"Other Programming Languages"
C-Languages,"Other Programming Languages"
Analytics,"Data Science"
Visualization,"Data Science"
"Big Data","Data Science"
Predictions,"Data Science"
MongoDB,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collect(self, vcfname, tag):\n pass",
"def __init__(self, dump):\n self._dump_data = dump\n # Check no unknown fields exist in the dump and ensure all fields have data.\n for field_name in self._dump_data:\n if field_name not in self._valid_field_names:\n raise ValueError('Dump fi... | [
"0.50682575",
"0.49429226",
"0.49007678",
"0.48292905",
"0.48242414",
"0.48074657",
"0.47828174",
"0.47429",
"0.47213793",
"0.46713915",
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"0.45883623",
"0.45852312",
"0.4574048",
"0.45722643",
"0.4564186",
"0.4552899",
"0.4... | 0.0 | -1 |
Lists all the blobs in the bucket. | def list_blobs(bucket_name):
# bucket_name = "your-bucket-name"
storage_client = storage.Client()
print(storage_client.current_batch)
# Note: Client.list_blobs requires at least package version 1.17.0.
blobs = storage_client.list_blobs(bucket_name)
# print(len([1 for blob in blobs]))
for b... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def list_blobs(bucket_name):\n storage_client = storage.Client()\n\n # Note: Client.list_blobs requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n\n return blobs",
"def list_blobs(bucket):\n bucket = default_bucket if bucket is None else bucket\n bucket_... | [
"0.8128892",
"0.8061956",
"0.80099964",
"0.79986364",
"0.7891933",
"0.7788305",
"0.7717934",
"0.7613284",
"0.75719094",
"0.75032365",
"0.7491481",
"0.7415898",
"0.73129493",
"0.7272844",
"0.7269232",
"0.72229993",
"0.72042584",
"0.7180795",
"0.7155484",
"0.7136133",
"0.711413... | 0.80805486 | 1 |
Build HTML result page | def perf_result_page():
# Get all fields from form
module = request.forms.getall('module')
version = request.forms.getall('version')
script = request.forms.getall('script')
tag = request.forms.getall('tag')
value_inf = request.forms.get('value_inf')
value_sup = request.forms.get('value_sup'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeHTMLIndexFile(self):\n part1 = \"\"\"<html>\n <body>\n <title>Index</title>\n <div id=\"pg_body\">\n <div id=\"testSuitesTitle\">TestSuites</div>\n <div id=\"resultsTitle\">Results</div>\n <div id=\"testSuites\">\n \"\"\"\n part2 = self.makeLin... | [
"0.6912458",
"0.68431294",
"0.6782403",
"0.674924",
"0.6621079",
"0.6546005",
"0.65375614",
"0.6429424",
"0.6403509",
"0.6390029",
"0.6368403",
"0.63596076",
"0.63516057",
"0.63152206",
"0.6310715",
"0.63027006",
"0.62803817",
"0.62691236",
"0.62274295",
"0.6199864",
"0.61743... | 0.65072876 | 7 |
Build SQL result request | def do_mainfield_request(mainfield=None):
# Connect to database
cur_db = connect_db("172.20.38.50", "mvelay", "user", "darkman")
cursor = cur_db.cursor()
conditions = []
# Connect to Mantis
mantis_c = mantis.Mantis('jenkins_auto', '1234')
mantis_project_name = None
# Build condition wi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_query(self):",
"def generate_query(self):\n return",
"def sql(self, q):\r\n params = base.get_params(None, locals())\r\n url = '{0}/{1}'.format(self.get_url(), 'sql')\r\n\r\n return http.Request('POST', url, params), parsers.parse_json",
"def _assemble(self):\n ... | [
"0.6871569",
"0.68138105",
"0.67725784",
"0.67435104",
"0.63741964",
"0.6372604",
"0.63518035",
"0.63267565",
"0.6279376",
"0.6224934",
"0.61656916",
"0.6161468",
"0.6151943",
"0.614902",
"0.6058082",
"0.6050952",
"0.6040173",
"0.60174376",
"0.598038",
"0.5969553",
"0.5960548... | 0.0 | -1 |
Build HTML result page | def tunedfields_result_page():
# Get all fields from form
mainfield = request.forms.getall('mainfields')
# Build html
result = do_mainfield_request(mainfield=mainfield)
# Build template page
with open("./header.html") as header, open('./mainfields.tpl') as mainfield, open('./footer.html') as ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeHTMLIndexFile(self):\n part1 = \"\"\"<html>\n <body>\n <title>Index</title>\n <div id=\"pg_body\">\n <div id=\"testSuitesTitle\">TestSuites</div>\n <div id=\"resultsTitle\">Results</div>\n <div id=\"testSuites\">\n \"\"\"\n part2 = self.makeLin... | [
"0.6912458",
"0.68431294",
"0.6782403",
"0.674924",
"0.6621079",
"0.6546005",
"0.65375614",
"0.65072876",
"0.6429424",
"0.6403509",
"0.6390029",
"0.6368403",
"0.63596076",
"0.63516057",
"0.63152206",
"0.6310715",
"0.63027006",
"0.62803817",
"0.62274295",
"0.6199864",
"0.61743... | 0.62691236 | 18 |
Build HTML result page | def features_result_page():
# Get all fields from form
module = request.forms.getall('module')
version = request.forms.getall('version')
software = request.forms.getall('sw')
# Build html
module, version, software, result = do_features_request(module_type=module,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeHTMLIndexFile(self):\n part1 = \"\"\"<html>\n <body>\n <title>Index</title>\n <div id=\"pg_body\">\n <div id=\"testSuitesTitle\">TestSuites</div>\n <div id=\"resultsTitle\">Results</div>\n <div id=\"testSuites\">\n \"\"\"\n part2 = self.makeLin... | [
"0.6913193",
"0.6843428",
"0.678298",
"0.6750879",
"0.66206425",
"0.65455514",
"0.6538131",
"0.6508302",
"0.64291865",
"0.64034504",
"0.63896877",
"0.6359272",
"0.6352542",
"0.63158554",
"0.6310258",
"0.6302473",
"0.62805283",
"0.6269891",
"0.62276226",
"0.6200185",
"0.617381... | 0.63690805 | 11 |
Build HTML result page | def features_2_result_page():
# Get all fields from form
features = request.forms.getall('features')
# Build html
features_2, result = do_features_request_2(features=features)
# Build template page
with open("./header.html") as header, open('./features_2.tpl') as features, open('./footer.html... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeHTMLIndexFile(self):\n part1 = \"\"\"<html>\n <body>\n <title>Index</title>\n <div id=\"pg_body\">\n <div id=\"testSuitesTitle\">TestSuites</div>\n <div id=\"resultsTitle\">Results</div>\n <div id=\"testSuites\">\n \"\"\"\n part2 = self.makeLin... | [
"0.6912458",
"0.68431294",
"0.6782403",
"0.674924",
"0.6621079",
"0.6546005",
"0.65375614",
"0.65072876",
"0.6429424",
"0.6403509",
"0.6390029",
"0.6368403",
"0.63596076",
"0.63516057",
"0.63152206",
"0.6310715",
"0.63027006",
"0.62803817",
"0.62691236",
"0.62274295",
"0.6199... | 0.57894194 | 73 |
Fill config diff form page | def config_diff_form_page():
# connect to database
cur_db = connect_db("172.20.38.50", "mvelay", "user", "sandbox")
cursor = cur_db.cursor()
modules_query = """SELECT DISTINCT module from t_ck5050_ini ORDER BY module ASC"""
versions_query = """SELECT DISTINCT version from t_ck5050_ini ORDER BY ver... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perform_diff_config_result_page():\n # Get all fields from form\n module = request.forms.getall('module')\n client = request.forms.getall('client')\n version1 = request.forms.getall('version1')\n version2 = request.forms.getall('version2')\n\n # Build html\n modif = do_ck5050_ini_diff_req... | [
"0.745162",
"0.59874326",
"0.5777113",
"0.5688203",
"0.56571525",
"0.5592918",
"0.5590933",
"0.5562555",
"0.5546954",
"0.55440426",
"0.55340624",
"0.55173373",
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"0.5509276",
"0.5473541",
"0.5454012",
"0.54499644",
"0.54456383",
"0.53727114",
"0.537029... | 0.7766916 | 0 |
Display diff config result page | def perform_diff_config_result_page():
# Get all fields from form
module = request.forms.getall('module')
client = request.forms.getall('client')
version1 = request.forms.getall('version1')
version2 = request.forms.getall('version2')
# Build html
modif = do_ck5050_ini_diff_request(module, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def config_diff_form_page():\n # connect to database\n cur_db = connect_db(\"172.20.38.50\", \"mvelay\", \"user\", \"sandbox\")\n cursor = cur_db.cursor()\n\n modules_query = \"\"\"SELECT DISTINCT module from t_ck5050_ini ORDER BY module ASC\"\"\"\n versions_query = \"\"\"SELECT DISTINCT version fr... | [
"0.7269731",
"0.66847414",
"0.6483663",
"0.6056483",
"0.6012005",
"0.598935",
"0.59349424",
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"0.5906873",
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"0.5660241",
"0.5645076",
"0.56382066",
"0.5587175",
"0.5567943",
"0.55608356",
"0.5559825... | 0.80938596 | 0 |
Connect to SQL Database | def connect_db(host=None, user=None, pwd=None, db_name=None):
db_c = MySQLdb.connect(host, user, pwd, db_name)
return db_c | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connect_db(self):\n try:\n self.connection = self.engine.connect()\n except Exception:\n self.print_std_error()",
"def connect(self):\n\t\t# PostgreSQL PyPgSQL\n\t#\tcp = adbapi.ConnectionPool(\"pyPgSQL.PgSQL\", database=\"test\")\n\t\t# MySQL\n\t\tself.dbpool = adbapi.Con... | [
"0.7861086",
"0.7743409",
"0.769158",
"0.76131725",
"0.76085055",
"0.7591533",
"0.7557377",
"0.754005",
"0.7533083",
"0.7524052",
"0.7456639",
"0.74528986",
"0.745074",
"0.7404313",
"0.73811084",
"0.7376988",
"0.7365427",
"0.7359007",
"0.73517257",
"0.73480254",
"0.7337972",
... | 0.0 | -1 |
perform request on t_performance table | def do_perf_request(script_name=None, module_type=None, tag=None,
version=None, value_sup=None, value_inf=None):
# connect to database
cur_db = connect_db("172.20.38.50", "mvelay", "user", "sandbox")
cursor = cur_db.cursor()
conditions = []
if script_name and script_name[0] !... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def performance(self, id):",
"def _run_query(self):",
"def test_fetch_traffic(self):\n assert isinstance(_tabular.fetch_traffic_data(), \n pd.DataFrame)",
"def get_chartdata():\n callback = bottle.request.query.get('callback')\n y_axis = bottle.request.query.get('y_axis'... | [
"0.631114",
"0.58254516",
"0.5335217",
"0.53163964",
"0.5313429",
"0.5274424",
"0.5222381",
"0.5192167",
"0.5174108",
"0.5169717",
"0.5151157",
"0.5150998",
"0.51382655",
"0.51242495",
"0.51175666",
"0.5111949",
"0.5109519",
"0.51076305",
"0.5089668",
"0.50836945",
"0.5045464... | 0.6207883 | 1 |
perform request on t_feature table | def do_features_request(module_type=None, version=None, software=None):
# connect to database
cur_db = connect_db("172.20.38.50", "mvelay", "user", "sandbox")
cursor = cur_db.cursor()
# build whole query
cur_query = """ SELECT feature, supported FROM t_feature
WHERE module="%s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_features_request_2(features=None):\n\n # connect to database\n cur_db = connect_db(\"172.20.38.50\", \"mvelay\", \"user\", \"sandbox\")\n cursor = cur_db.cursor()\n\n # build whole query\n cur_query = \"\"\" SELECT module, sw, version FROM t_feature\n WHERE feature=\"%s\" ... | [
"0.6873913",
"0.628006",
"0.6004172",
"0.59714735",
"0.58861274",
"0.5775854",
"0.5727609",
"0.5681115",
"0.5681115",
"0.5681115",
"0.5641183",
"0.5640868",
"0.5630228",
"0.5585226",
"0.5566278",
"0.5546612",
"0.5524076",
"0.55190283",
"0.5511565",
"0.5466498",
"0.54205346",
... | 0.6668402 | 1 |
perform request on t_feature table | def do_features_request_2(features=None):
# connect to database
cur_db = connect_db("172.20.38.50", "mvelay", "user", "sandbox")
cursor = cur_db.cursor()
# build whole query
cur_query = """ SELECT module, sw, version FROM t_feature
WHERE feature="%s" AND supported=1;""" % (fea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_features_request(module_type=None, version=None, software=None):\n\n # connect to database\n cur_db = connect_db(\"172.20.38.50\", \"mvelay\", \"user\", \"sandbox\")\n cursor = cur_db.cursor()\n\n # build whole query\n cur_query = \"\"\" SELECT feature, supported FROM t_feature\n ... | [
"0.6668402",
"0.628006",
"0.6004172",
"0.59714735",
"0.58861274",
"0.5775854",
"0.5727609",
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"0.5546612",
"0.5524076",
"0.55190283",
"0.5511565",
"0.5466498",
"0.54205346",
... | 0.6873913 | 0 |
perform diff request between ck5050_ini files | def do_ck5050_ini_diff_request(module, client, version1, version2):
# connect to database
cur_db = connect_db("172.20.38.50", "mvelay", "user", "sandbox")
cursor = cur_db.cursor()
# build whole query
cur_query_1 = """SELECT ck5050 FROM t_ck5050_ini
WHERE module='%s' AND client=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_config_diff(context, target, file1, file2):\n\n result = context.get_operation('get_config_diff')\n return result",
"def perform_diff_config_result_page():\n # Get all fields from form\n module = request.forms.getall('module')\n client = request.forms.getall('client')\n version1 = requ... | [
"0.63707644",
"0.58933586",
"0.58889127",
"0.56575066",
"0.5535803",
"0.5533335",
"0.5454924",
"0.5425166",
"0.54007334",
"0.5384806",
"0.52225465",
"0.5211675",
"0.5190184",
"0.5159502",
"0.514443",
"0.51250166",
"0.512453",
"0.5076124",
"0.50507396",
"0.5043625",
"0.5035158... | 0.6821847 | 0 |
Get from DBPhone test_to_redo stats | def get_test_to_redo_stats(url):
import os
import requests
from lxml import etree
from collections import OrderedDict
categories = [('avrcp',), ('usb',), ('wifi',), ('upnp',), ('mtp',), ('ipod',),
('3way',), ('map',), ('pan',), ('dun',), ('pandora',),
('hdmi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testRedo(self):\n raw_data = TEST_DATA.copy()\n raw_data['redo'] = 'true'\n uma_data = UMASamplingProfilerData(\n raw_data, ChromeDependencyFetcher(self.GetMockRepoFactory()))\n self.assertTrue(uma_data.redo)",
"def rrd_out(db):\n stats = basic_stats(db)\n print(\"rp:%d l:%d u:%d\" %... | [
"0.5556036",
"0.54881424",
"0.5412877",
"0.5391689",
"0.53793645",
"0.5367809",
"0.5247977",
"0.5117082",
"0.5116823",
"0.50810045",
"0.50738573",
"0.5022342",
"0.5014269",
"0.49937323",
"0.49274036",
"0.49090567",
"0.48934725",
"0.48873225",
"0.48542276",
"0.4847499",
"0.484... | 0.5758006 | 0 |
Build test_to_redo stats (IOP matrix cleaning) | def test_to_redo():
from collections import OrderedDict
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
url_base = 'http://172.20.38.50/iop/test_to_redo/dbphone_test_to_redo_'
year = 2016
week = 8
url = '{0}{1}_w{2}.xml'.format(url_base, yea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testRedo(self):\n raw_data = TEST_DATA.copy()\n raw_data['redo'] = 'true'\n uma_data = UMASamplingProfilerData(\n raw_data, ChromeDependencyFetcher(self.GetMockRepoFactory()))\n self.assertTrue(uma_data.redo)",
"def get_test_to_redo_stats(url):\n import os\n import requests\n from... | [
"0.6436193",
"0.6130628",
"0.55359256",
"0.5484914",
"0.5428168",
"0.54097044",
"0.5357657",
"0.53089625",
"0.5300388",
"0.5237696",
"0.52300584",
"0.5196022",
"0.51856256",
"0.5130158",
"0.51227814",
"0.5117561",
"0.5113459",
"0.51080984",
"0.50900793",
"0.50900495",
"0.5078... | 0.5701659 | 2 |
Enable static image directory | def server_static_img(filename):
return static_file(filename, root='static/img') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def images(name):\n return static_file(name, root=os.path.join(BASEDIR, \"images\"))",
"def include_static_files(app):\n file_path = sphinx_prolog.get_static_path(STATIC_FILE)\n if file_path not in app.config.html_static_path:\n app.config.html_static_path.append(file_path)",
"def path_static()... | [
"0.67881405",
"0.63470244",
"0.6251868",
"0.61576533",
"0.60136217",
"0.5939546",
"0.58997166",
"0.58471245",
"0.58220434",
"0.57930964",
"0.57122254",
"0.5705205",
"0.56905645",
"0.56310606",
"0.5624631",
"0.56046736",
"0.5533824",
"0.5532665",
"0.5532572",
"0.55227333",
"0.... | 0.6483689 | 1 |
Enable static stats directory | def server_static(filename):
return static_file(filename, root='static/stats') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collectstatic():\n puts(yellow(\"Collect statics\"))\n django_manage('collectstatic', '-l', '--noinput')",
"def glr_path_static():\n return os.path.join(base_path, \"static\")",
"def monitor_page(path):\n return send_from_directory(os.path.join(os.path.dirname(__file__), \"..\", \"static\"), pa... | [
"0.5921597",
"0.59045035",
"0.58725524",
"0.58487236",
"0.5800601",
"0.5593384",
"0.55906385",
"0.555093",
"0.55357283",
"0.5500581",
"0.5452615",
"0.54239327",
"0.5393831",
"0.53820044",
"0.5364286",
"0.53436893",
"0.5338043",
"0.5338043",
"0.5316681",
"0.5310895",
"0.530017... | 0.6684303 | 0 |
Arguments management using docopt | def set_options():
help_f = """%s
Usage:
%s -h <host> -p <port>
Options:
-i, --help
-h, --host=<host>
-p, --port=<port>
""" % (sys.argv[0], sys.argv[0])
arguments = docopt(help_f)
return arguments | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def args():\n\n useDB = docopt(__doc__)['--from-db']\n snapFile = docopt(__doc__)['-i']\n # csvFile = docopt(__doc__)['-o']\n # utils.askErase(csvFile)\n\n return [snapFile, useDB]",
"def main():\n try:\n arguments = docopt(__doc__)\n house = arguments['--house']\n characte... | [
"0.7370341",
"0.724578",
"0.69119036",
"0.6884974",
"0.6628971",
"0.65835416",
"0.65466535",
"0.65455437",
"0.65455437",
"0.65455437",
"0.6514039",
"0.6508138",
"0.6506362",
"0.6479136",
"0.6383206",
"0.6371003",
"0.6343701",
"0.6282645",
"0.62716925",
"0.6248091",
"0.6227129... | 0.6611413 | 5 |
Change IP adress in header | def change_header(host="172.20.22.104", port=8081):
# Read header template
with open("./header_template.html", "r") as header:
content = header.read()
content_modified = re.sub("172.20.22.104:8081", "%s:%s" % (host, port), content)
# Modify header with host:port
with open("./header.html",... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_ip(self, address: int) -> None:\n self.regs[\"ip\"].write(address)",
"def set_host_ip(self, host, host_ip):\n host.setIP(str(host_ip.ip), prefixLen=self.NETPREFIX)",
"def change_IP(self,server_IP,MAC):\n content = {'server_IP':server_IP,'MAC_address':MAC}\n content = json... | [
"0.69159824",
"0.6746876",
"0.6670021",
"0.66584",
"0.6627055",
"0.6463176",
"0.6415992",
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"0.63854414",
"0.6344735",
"0.6266228",
"0.6223221",
"0.62148654",
"0.61933804",
"0.6174796",
"0.6165831",
"0.61656237",
"0.616364",
"0.61261654",
"0.6106321",
"0.60913247",... | 0.6824174 | 1 |
Number of polynomial spline parts for system variables. | def n_parts_x(self):
return self._parameters['n_parts_x'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def num_polys(self):\n ret_val = self._num_polys()\n return ret_val",
"def numpoints(self):\n return len(self.pars) + 1 # so dof is 1",
"def raise_spline_parts(self, n_spline_parts=None):\n if n_spline_parts is None:\n # usual case\n self._parameters['n_parts_... | [
"0.6299914",
"0.60816807",
"0.5986085",
"0.5851045",
"0.5821581",
"0.57989854",
"0.5776984",
"0.5741229",
"0.5738567",
"0.57093143",
"0.56891906",
"0.5685901",
"0.5649587",
"0.557969",
"0.55761206",
"0.5537348",
"0.55140233",
"0.54845166",
"0.5477328",
"0.5477328",
"0.5477328... | 0.6224824 | 1 |
Number of polynomial spline parts for input variables. | def n_parts_u(self):
return self._parameters['n_parts_u'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def numpoints(self):\n return len(self.pars) + 1 # so dof is 1",
"def num_polys(self):\n ret_val = self._num_polys()\n return ret_val",
"def get_n_params(var_list):\n return int(np.sum([np.product(\n [x.value for x in var.get_shape()]) for var in var_list]))",
"def n_p... | [
"0.6210782",
"0.6193465",
"0.5968699",
"0.5953926",
"0.59362495",
"0.5929375",
"0.5834807",
"0.5790079",
"0.5732091",
"0.57164407",
"0.57035553",
"0.57000434",
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"0.5526276",
"0.5495534",
"0.54802024",
"0.545291",
"0.5443216",
"0.5416969",
"0.5408439... | 0.0 | -1 |
Increase the number of spline parts for x and u | def raise_spline_parts(self, n_spline_parts=None):
if n_spline_parts is None:
# usual case
self._parameters['n_parts_x'] *= self._parameters['kx']
# TODO: introduce parameter `ku` and handle it here
# (and in CollocationSystem.get_guess())
npu = self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def spline(self):\n self.rho = np.linspace(0,1,self.nrho)\n self.te = self._spline(self.rho_in, self.te_in, self.rho)\n self.ne = self._spline(self.rho_in, self.ne_in, self.rho)\n self.ti = self._spline(self.rho_in, self.ti_in, self.rho)\n for i in range(self.nion):\n ... | [
"0.5930492",
"0.5872698",
"0.5811447",
"0.5805595",
"0.57756674",
"0.5772047",
"0.57542145",
"0.5682267",
"0.5679739",
"0.5632267",
"0.56161326",
"0.55766064",
"0.5569022",
"0.55506045",
"0.5523128",
"0.55127037",
"0.5496371",
"0.5459261",
"0.54565656",
"0.5441901",
"0.542766... | 0.5769493 | 6 |
Returns the current system state. | def x(self, t):
if not self.sys.a <= t <= self.sys.b:
self.log_warning("Time point 't' has to be in (a,b)")
arr = None
else:
arr = np.array([self.x_fnc[xx](t) for xx in self.sys.states])
return arr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_state(self):\n return self._env.get_state()",
"def get_status(self):\n\n return self._system",
"def get_state(self):\n return self.env.sim.get_state()",
"def get_current_state(self):\n return self._current_state",
"def get_current_state(self):\n return self.game.g... | [
"0.76328963",
"0.7534503",
"0.7286605",
"0.7277567",
"0.71321005",
"0.7082457",
"0.7079524",
"0.7063459",
"0.704008",
"0.70301354",
"0.7001373",
"0.6990143",
"0.69871753",
"0.698439",
"0.6970327",
"0.6945167",
"0.6934378",
"0.6932589",
"0.6868393",
"0.6841767",
"0.68383986",
... | 0.0 | -1 |
Returns the state of the input variables. | def u(self, t):
if not self.sys.a <= t <= self.sys.b:
#self.log_warning("Time point 't' has to be in (a,b)")
arr = np.array([self.u_fnc[uu](self.sys.b) for uu in self.sys.inputs])
##:: self.u_fnc= {'u1':method Spline ddf} (because of chain 'x1'->'x2'->'u1')
e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_state(self, var_list):\n raise NotImplementedError()",
"def getstate(self):\r\n return GPBase.getstate(self) + [self.Z,\r\n self.num_inducing,\r\n self.has_uncertain_inputs,\r\n self.X_variance]",
"def state(self):\n return self.var_state",
... | [
"0.7368604",
"0.73394525",
"0.72396487",
"0.7100198",
"0.6878418",
"0.6844417",
"0.6844417",
"0.6834125",
"0.68242306",
"0.6695256",
"0.6669782",
"0.661551",
"0.6603236",
"0.6530937",
"0.648801",
"0.64731807",
"0.6460762",
"0.64099735",
"0.6406625",
"0.63900465",
"0.6319406",... | 0.0 | -1 |
Returns the state of the 1st derivatives of the system variables. | def dx(self, t):
if not self.sys.a <= t <= self.sys.b:
self.log_warning("Time point 't' has to be in (a,b)")
arr = None
else:
arr = np.array([self.dx_fnc[xx](t) for xx in self.sys.states])
return arr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def firstderiv(state, time, press):\n dy = np.zeros_like(state)\n pyjacob.py_dydt(time, press, state, dy)\n return dy",
"def d1(self):\n f = (self.rf + (self.sigma ** (2)) / 2 ) * self.t\n return (1/(self.sigma * (self.t ** (0.5)))) *(math.log(self.s/self.x) + f)",
"def f( self , x , u ... | [
"0.6555936",
"0.64141256",
"0.6148635",
"0.6125782",
"0.61236674",
"0.60813224",
"0.60553426",
"0.6053837",
"0.5920227",
"0.5911331",
"0.590697",
"0.5901537",
"0.5696103",
"0.566551",
"0.5641059",
"0.5590327",
"0.5579554",
"0.55714095",
"0.55708796",
"0.5567455",
"0.5526526",... | 0.0 | -1 |
This method is used to create the necessary spline function objects. | def init_splines(self, export=False):
self.log_debug("Initialise Splines")
# store the old splines to calculate the guess later
if not export:
# self.old_splines = auxiliary.copy_splines(self.splines)
self.old_splines = copy.deepcopy(self.splines)
if sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def spline(self):\n self.rho = np.linspace(0,1,self.nrho)\n self.te = self._spline(self.rho_in, self.te_in, self.rho)\n self.ne = self._spline(self.rho_in, self.ne_in, self.rho)\n self.ti = self._spline(self.rho_in, self.ti_in, self.rho)\n for i in range(self.nion):\n ... | [
"0.6794472",
"0.67718244",
"0.6634991",
"0.6493209",
"0.64013946",
"0.6399138",
"0.6325015",
"0.6313537",
"0.6291308",
"0.6267896",
"0.62008584",
"0.6166823",
"0.6108051",
"0.60902315",
"0.6086035",
"0.60783106",
"0.60377806",
"0.60334915",
"0.60260427",
"0.59802824",
"0.5972... | 0.70828134 | 0 |
Set found numerical values for the independent parameters of each spline. This method is used to get the actual splines by using the numerical solutions to set up the coefficients of the polynomial spline parts of every created spline. | def set_coeffs(self, sol):
# TODO: look for bugs here!
self.log_debug("Set spline coefficients")
# task: find which of the free parameters (coeffs) belong to which spline object
sol_bak = sol.copy()
subs = dict()
# iterate over the OrderedDict {'x1': [cx1_..., ...], 'u1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def spline(self):\n self.rho = np.linspace(0,1,self.nrho)\n self.te = self._spline(self.rho_in, self.te_in, self.rho)\n self.ne = self._spline(self.rho_in, self.ne_in, self.rho)\n self.ti = self._spline(self.rho_in, self.ti_in, self.rho)\n for i in range(self.nion):\n ... | [
"0.64004993",
"0.62710917",
"0.6152649",
"0.6054342",
"0.6014449",
"0.58256006",
"0.58093613",
"0.5761664",
"0.57538545",
"0.5709607",
"0.57025075",
"0.5696033",
"0.56923354",
"0.5628855",
"0.56229633",
"0.56144243",
"0.56101274",
"0.56101274",
"0.55588293",
"0.55523896",
"0.... | 0.75014144 | 0 |
Retrieve the URL and optionally return back part of it using BeautifulSoup | def process_url(url, **kwargs):
try:
html = urlopen(url).read()
except URLError, e:
print "Couldn't get %s due to %s" % (url, e)
soup = BeautifulSoup(html)
metadata = {}
for tag in soup.head.contents:
if hasattr(tag, 'name'):
if tag.name == 'title':
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetch_url(url):\n try:\n soup = bs(urlopen(url).read(), 'html.parser')\n return soup\n except:\n print \"Couldnot download the content from the URL\", url\n return \"\"",
"def scrape_google(html_content):\n soup = BeautifulSoup(html_content)\n tag = soup.a\n company... | [
"0.6535775",
"0.6307371",
"0.6303186",
"0.62825453",
"0.62294763",
"0.6190701",
"0.61806387",
"0.61523235",
"0.6148764",
"0.60803145",
"0.6080223",
"0.6072506",
"0.60498226",
"0.6029667",
"0.59885395",
"0.59877825",
"0.59875077",
"0.59841347",
"0.59476507",
"0.59468186",
"0.5... | 0.5542465 | 85 |
Sort a list of app,modellist pairs into a single list of models. The single list of models is sorted so that any model with a natural key is serialized before a normal model, and any model with a natural key dependency has it's dependencies serialized first. | def sort_dependencies(app_list):
from django.db.models import get_model, get_models
# Process the list of models, and get the list of dependencies
model_dependencies = []
models = set()
for app, model_list in app_list:
if model_list is None:
model_list = get_models(app)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sort(self):\n self.model_list.sort()\n for model in self.model_list:\n model.sort()",
"def sort_models(data_dir, list_models):\n list_new_models = [] # Only left fields in CSV file.\n\n try:\n os.chdir(data_dir)\n except OSError, err:\n sys.stderr.write(\"Erro... | [
"0.7140047",
"0.70637834",
"0.6867462",
"0.677511",
"0.6625849",
"0.6170446",
"0.6126853",
"0.6093717",
"0.60831183",
"0.6028849",
"0.59897244",
"0.5664581",
"0.56470007",
"0.56270397",
"0.5605686",
"0.55841184",
"0.54990226",
"0.54917353",
"0.5478782",
"0.5476888",
"0.532848... | 0.8072792 | 0 |
make sure we can guess an optics type from metadata | def test_guess_optics():
from ctapipe.instrument import guess_telescope
answer = guess_telescope(1855, 28.0 * u.m)
od = OpticsDescription.from_name(answer.name)
assert od.equivalent_focal_length.to_value(u.m) == 28
assert od.num_mirrors == 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __determine_config_type():",
"def opt_type(self) -> type:\n return type(self.opt)",
"def target_type(self):",
"def get_type_check(self, arg, option):\n pass",
"def test_model_metadata_type(self):\n self.assertTrue(type(self.meta) is dict)",
"def get_meta_file_type(metaDictionary,... | [
"0.5926501",
"0.57973546",
"0.57791114",
"0.5742786",
"0.5710126",
"0.561327",
"0.5490631",
"0.5486793",
"0.5471962",
"0.5450088",
"0.5391304",
"0.5389152",
"0.5387912",
"0.53406745",
"0.52706194",
"0.52626455",
"0.52626175",
"0.5226716",
"0.52167726",
"0.5206645",
"0.5198141... | 0.5165573 | 24 |
create an OpticsDescription and make sure it fails if units are missing | def test_construct_optics():
OpticsDescription(
name="test",
num_mirrors=1,
num_mirror_tiles=100,
mirror_area=u.Quantity(550, u.m ** 2),
equivalent_focal_length=u.Quantity(10, u.m),
)
with pytest.raises(TypeError):
OpticsDescription(
name="test",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_construct_optics():\n OpticsDescription(\n name=\"test\",\n size_type=SizeType.LST,\n reflector_shape=ReflectorShape.PARABOLIC,\n n_mirrors=1,\n n_mirror_tiles=100,\n mirror_area=u.Quantity(550, u.m**2),\n equivalent_focal_length=u.Quantity(10, u.m),\n ... | [
"0.78008306",
"0.56905293",
"0.55807394",
"0.5456513",
"0.5392586",
"0.538737",
"0.53750855",
"0.5374919",
"0.5374841",
"0.53741705",
"0.5371181",
"0.5356631",
"0.5356039",
"0.5303193",
"0.5301167",
"0.52871114",
"0.5274415",
"0.52718306",
"0.5246364",
"0.52147675",
"0.519935... | 0.7196217 | 1 |
try constructing all by name | def test_optics_from_name(optics_name):
optics = OpticsDescription.from_name(optics_name)
assert optics.equivalent_focal_length > 0
# make sure the string rep gives back the name:
assert str(optics) == optics_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_result_constructor(self, names):\r\n raise NotImplementedError",
"def named_build(klass, name):\n k = Factory.build(klass)\n k.name = name\n return k",
"def test_constructors(self, name, obj):\n assert getattr(forge, name) == obj",
"def test_constructors(self, name, obj):\n ... | [
"0.5995789",
"0.5829204",
"0.5643517",
"0.5643517",
"0.5604099",
"0.5503408",
"0.5494718",
"0.5494718",
"0.54667276",
"0.54620194",
"0.5419465",
"0.54167676",
"0.5412567",
"0.5388942",
"0.537572",
"0.537572",
"0.537572",
"0.537572",
"0.537572",
"0.537572",
"0.537572",
"0.53... | 0.0 | -1 |
Takes the file and sends it to the endpoint. | def ask_endpoint(file, endpoint) -> dict:
# define headers with the content type
headers = {
"Content-Type": "application/json",
"Accept": "application/json",
}
data = None
if isinstance(file, str):
data = pd.read_csv(file).to_dict(orient="records")
elif isinstance(file... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_file(self, file_path) -> object:\n try:\n self.payload = {'file': open(file_path, 'rb')}\n except FileNotFoundError as fl_er:\n print(fl_er)\n exit(1)\n try:\n return requests.post(url = self.__webhooks, files = self.payload)\n except... | [
"0.76177657",
"0.7581571",
"0.741669",
"0.7285191",
"0.71710384",
"0.7149347",
"0.7077242",
"0.7058303",
"0.69783664",
"0.6898794",
"0.68531007",
"0.68401706",
"0.6838323",
"0.6822907",
"0.6806711",
"0.6760568",
"0.6707573",
"0.6676297",
"0.66599524",
"0.66599524",
"0.6659952... | 0.0 | -1 |
Checks that the endpoint is ready to receive incoming messages. | def _is_ready(self):
current_wait_time = 0
start_time = time.time()
while current_wait_time < self.max_wait_time_ready:
try:
response = requests.get(os.path.join(self.url, "ready"), timeout=1)
if response.status_code == 200:
break
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_ready(self):\n current_wait_time = 0\n start_time = time.time()\n while current_wait_time < self.max_wait_time_ready:\n try:\n response = requests.get(os.path.join(self.url, \"ready\"))\n if response.status_code == 200:\n brea... | [
"0.69959426",
"0.65242666",
"0.6468929",
"0.6468929",
"0.6450049",
"0.637877",
"0.635823",
"0.63148576",
"0.6309489",
"0.6287994",
"0.62630296",
"0.6251497",
"0.62316585",
"0.6223134",
"0.6218407",
"0.6216059",
"0.6203177",
"0.6190777",
"0.6145414",
"0.61205196",
"0.6097501",... | 0.69550943 | 1 |
Queries the endpoint with all the files specified in 'in_dir' folder. This method goes over the list of files, sends every of them to the specified endpoint and put all the results into one DataFrame. Each column of the dataframe corresponds to the firstlevel key in the response json. If the json dict is nested then th... | def query(self, n_jobs=1) -> str:
def get_one_answer(file):
try:
ans = ask_endpoint(file, os.path.join(self.url, "predict"))
except KeyboardInterrupt:
raise KeyboardInterrupt
except:
ans = {}
return json.dumps(ans)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(directory):\n dictlist = []\n cols = ['title', 'text', 'authors', 'num_images', 'domain', 'url']\n\n folders = glob.glob(directory + '/*')\n for index, subdir in enumerate(folders):\n\n file_path = glob.glob(subdir + '/*')\n\n #check if glob returned a valid file path (non-em... | [
"0.58557445",
"0.5841202",
"0.57185346",
"0.5678337",
"0.5617907",
"0.5616196",
"0.5553351",
"0.55467594",
"0.550163",
"0.5499053",
"0.54974854",
"0.54938287",
"0.54541194",
"0.5434235",
"0.5411787",
"0.5370609",
"0.5369148",
"0.53324836",
"0.5332005",
"0.53307456",
"0.532360... | 0.52002525 | 37 |
Tracks a position through the duration of the loaded video | def track(self, frame, init_pos, frame_ctr=1):
distance_list = []
self.tracker.init(frame, tuple(init_pos))
init_center_pos = int((init_pos[3] / 2) + init_pos[1])
if self.debug_mode:
# Created window to display video
cv2.namedWindow('Barbell_Tracker', cv2.WINDOW_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def seek(self,event):\r\n if self.app.controlLock.locked():\r\n return\r\n self.app.controlLock.acquire()\r\n x = event.x\r\n scalex,_ = self.getScale()\r\n scalex_secs = [scalex[0]/self.samplerate,scalex[1]/self.samplerate]# Get x scale in seconds\r\n seekTo = ... | [
"0.6350717",
"0.599357",
"0.59822786",
"0.59489673",
"0.5899728",
"0.58592576",
"0.5791302",
"0.5753723",
"0.570756",
"0.5707165",
"0.5696297",
"0.5657556",
"0.56508243",
"0.56491834",
"0.5642732",
"0.562807",
"0.5619208",
"0.56147677",
"0.5613509",
"0.56129265",
"0.55933154"... | 0.5986019 | 2 |
Detects a barbell's position in a frame | def detect_barbell_pos(self, classifier):
self._check_vid_open()
retval, frame = self.video.read()
if not retval:
raise IOError('Could not read frame from video')
barbell_pos = classifier.detectMultiScale(frame, 1.1, 25)
if not barbell_pos.any():
raise Ex... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buff_region(self, x, y):\n if x>120 and x<220 and y>275 and y<375:\n return 1\n elif x>580 and x<680 and y>125 and y<225:\n return 2\n return 0",
"def track(self, frame):\n mask = self.get_arrow_mask(frame)\n label = skimage.measure.label(mask)\n ... | [
"0.60581046",
"0.59345615",
"0.57604194",
"0.5743953",
"0.5729966",
"0.571696",
"0.570723",
"0.5651073",
"0.5620212",
"0.5571446",
"0.55702275",
"0.5536488",
"0.5536488",
"0.5512448",
"0.54974544",
"0.54932207",
"0.5444628",
"0.5442565",
"0.5442322",
"0.5441738",
"0.5436839",... | 0.71624565 | 0 |
Converts pixels in a barbell's diameter to centimeters | def _get_cm_per_pixel(self, diameter):
CM_PER_INCH = 2.54
pixels_per_inch = 2 / diameter
cm_per_pixel = CM_PER_INCH * pixels_per_inch
return cm_per_pixel | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __conv_inch(length_mm):\n return length_mm / 25.4",
"def get_diameter(self) -> float:\r\n \r\n return (self.box[3] - self.box[1] + self.box[2] - self.box[0]) / 2",
"def bin_edges_to_centres(edges):\r\n if edges.ndim == 1:\r\n steps = (edges[1:] - edges[:-1]) / 2\r\n retur... | [
"0.5748787",
"0.57177395",
"0.5671113",
"0.5644989",
"0.5644989",
"0.5644989",
"0.56154454",
"0.5552707",
"0.54947394",
"0.5494285",
"0.5480687",
"0.5467712",
"0.5456314",
"0.54467314",
"0.5419826",
"0.5408947",
"0.5400958",
"0.5398737",
"0.53467226",
"0.53363895",
"0.5334988... | 0.6118975 | 0 |
Gets Frames Per Second of loaded video | def get_video_fps(self):
fps = self.video.get(cv2.CAP_PROP_FPS)
logging.info('Video FPS: {}'.format(fps))
return fps | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_fps(self):\n return self._num_frames / (datetime.now() - self._start).total_seconds()",
"def get_video_frame_rate(filename):\n clip = VideoFileClip(filename)\n frame_rate = clip.fps\n clip.close()\n return frame_rate",
"def duration():\r\n elapsed_time, duration = video_time()\r\n... | [
"0.74567306",
"0.7410015",
"0.7227333",
"0.71586215",
"0.7142597",
"0.7126073",
"0.71154517",
"0.71066225",
"0.70365375",
"0.70240074",
"0.7009666",
"0.6964138",
"0.68664753",
"0.6860731",
"0.6837427",
"0.67995274",
"0.6797968",
"0.6792723",
"0.67588943",
"0.6752216",
"0.6705... | 0.7280317 | 2 |
Loads a video. Returns 1 on success | def load_video(self, vid_path):
self.video = cv2.VideoCapture(vid_path)
self._check_vid_open()
logging.info('Loaded video at: {}'.format(vid_path))
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __loadVideo(self):\n # Check if movie file exists ...\n #\n if not(os.path.isfile(self.fNameVideo)):\n return stm.StimErrC.videoFileNotFound\n\n try: \n # Load video\n #\n self.video = mpe.VideoFileClip(self.fNameVideo)\n \n except IOError: \n return stm.StimErrC.i... | [
"0.7726525",
"0.70660406",
"0.69042337",
"0.68983585",
"0.6861589",
"0.6831531",
"0.67470115",
"0.6746388",
"0.65393686",
"0.64091796",
"0.6401286",
"0.6394237",
"0.634081",
"0.62345356",
"0.621533",
"0.6213619",
"0.6195335",
"0.6193793",
"0.6168615",
"0.6168126",
"0.61294425... | 0.7348447 | 1 |
Raises an error if a video is not opened. Else return 1 | def _check_vid_open(self):
if not self.video.isOpened():
raise IOError('Video is not open')
return 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def videostart_failed(self):\n # type: () -> bool\n return self._videostart_failed",
"def check(self):\n #\n # *****************\n # *****************\n # TODO: Check really if video is valid\n # *****************\n # *****************\n return True",
"def openVideo(self):\n ... | [
"0.6829495",
"0.6795602",
"0.67080617",
"0.6603272",
"0.6496463",
"0.64905244",
"0.6287083",
"0.62592316",
"0.62456816",
"0.612378",
"0.61099005",
"0.6035323",
"0.6004216",
"0.6001032",
"0.5871629",
"0.5861267",
"0.582632",
"0.57916695",
"0.5785687",
"0.5746918",
"0.5741277",... | 0.8533003 | 0 |
Create a "bounding box" that is bounded on the left and the right by using the gradients and below by using the ambient estimate. | def remove_dc_from_spad_edge(spad, ambient, grad_th=1e3, n_std=1.):
# Detect edges:
assert len(spad.shape) == 1
edges = np.abs(np.diff(spad)) > grad_th
first = np.nonzero(edges)[0][1] + 1 # Want the right side of the first edge
last = np.nonzero(edges)[0][-1] # Want the left side of the second... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def paint_box(top=0, bottom=DISPLAY_HEIGHT-1, left=0, right=DISPLAY_WIDTH-1):\n def filter_box(col, row):\n \"\"\"For a given pixel position, turn it on if it's with the bounds\n \"\"\"\n # remember rows count from 0 at the top!\n correct_vertical = top <= row <= bottom\n corr... | [
"0.5981826",
"0.58952916",
"0.5884403",
"0.5581767",
"0.55763274",
"0.5549178",
"0.5539936",
"0.5526793",
"0.5501055",
"0.54238534",
"0.541401",
"0.54067093",
"0.5399093",
"0.53788334",
"0.5367486",
"0.5344063",
"0.53401744",
"0.5332257",
"0.529883",
"0.52931905",
"0.5287063"... | 0.0 | -1 |
Identify if given doc_no is a supported document type. Retrieve the candidate list of materials that will be returned. | def return_candidates(cls, doc_no):
# Retrieve documents based on what it is given
if isinstance(doc_no, Doc):
doc = doc_no
else:
doc = Docs.of_doc_no(doc_no)
# Case StoreAuxTask
if isinstance(doc, prod_doc.StoreAuxTask):
if doc.status <= prod... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def return_materials(cls, user, doc_no, mode, return_list):\n\n # Retrieve documents based on what it is given\n doc = Docs.of_doc_no(doc_no)\n\n # Validate input,\n # Extract source to compare\n current_list = []\n ref_doc = None\n from_ref_doc = None\n if m... | [
"0.55780417",
"0.514967",
"0.49377292",
"0.4870183",
"0.47371918",
"0.47266638",
"0.47241977",
"0.46641853",
"0.4619189",
"0.46141052",
"0.45978752",
"0.45675355",
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"0.45257613",
"0.45226312",
"0.45016974",
"0.45015574",
"0.44937566",
"0.44864237",
"... | 0.5849427 | 0 |
Returning Materials please see return_candidates for more information. | def return_materials(cls, user, doc_no, mode, return_list):
# Retrieve documents based on what it is given
doc = Docs.of_doc_no(doc_no)
# Validate input,
# Extract source to compare
current_list = []
ref_doc = None
from_ref_doc = None
if mode in [cls.TYP... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def info_materials_raw_get():\n materials = _material_by_group(427) # 427 == intermediate group\n return materials, 200",
"def info_materials_get():\n materials = _material_by_group() # empty means all groups\n return materials, 200",
"def GetMaterial(self, *args):\n return _XCAFDoc.XCAFDoc_... | [
"0.6908583",
"0.6790556",
"0.6723287",
"0.6699483",
"0.66595334",
"0.6358154",
"0.6162048",
"0.611894",
"0.60959214",
"0.59961724",
"0.58494353",
"0.58435357",
"0.5802915",
"0.5775458",
"0.5744499",
"0.5669334",
"0.56455684",
"0.563473",
"0.562491",
"0.56161094",
"0.56081563"... | 0.66234 | 5 |
Replace tags in the given path template and return either Windows or Linux formatted path. | def get_path_from_template(path_template: str, path_type: PathType = PathType.AUTO) -> str:
# automatically select path type depending on running OS
if path_type == PathType.AUTO:
if platform.system() == "Windows":
path_type = PathType.WINDOWS
elif platform.system() == "Linux":
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_template_from_path(path: str) -> str:\r\n path = path.replace(\"\\\\\", \"/\")\r\n return path",
"def system_path(path):\n if is_windows(): return path.replace('/', '\\\\')\n else: return path.replace('\\\\', '/')",
"def replace_template_tags(lines, file_provider):\n ret = []\n for li... | [
"0.6149726",
"0.5994707",
"0.5957013",
"0.58530486",
"0.56230843",
"0.55114543",
"0.54932165",
"0.5389428",
"0.5368151",
"0.5365089",
"0.52960074",
"0.52290237",
"0.521562",
"0.5209126",
"0.5170885",
"0.51437145",
"0.51309836",
"0.5099991",
"0.5087957",
"0.50286967",
"0.49951... | 0.5748834 | 4 |
Convert a normal path back to its template representation. | def get_template_from_path(path: str) -> str:
path = path.replace("\\", "/")
return path | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:\r\n path_template = get_template_from_path(path)\r\n path = get_path_from_template(path_template, path_type)\r\n return path",
"def convert(kls, path, configuration=None, converters=None, ignore_converters=None, joined=None):\n ... | [
"0.67283267",
"0.59157676",
"0.57542336",
"0.56610835",
"0.5517831",
"0.55112505",
"0.5508051",
"0.5501356",
"0.53947884",
"0.5389098",
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"0.52649087",
"0.5254318",
"0.5234345",
"0.52052474",
"0.51929563",
"0.51626086",
"0.5... | 0.5877729 | 2 |
Convert a normal path to template and the convert it back to a normal path with given path type. | def convert_path(path: str, path_type: PathType = PathType.AUTO) -> str:
path_template = get_template_from_path(path)
path = get_path_from_template(path_template, path_type)
return path | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_path_from_template(path_template: str, path_type: PathType = PathType.AUTO) -> str:\r\n # automatically select path type depending on running OS\r\n if path_type == PathType.AUTO:\r\n if platform.system() == \"Windows\":\r\n path_type = PathType.WINDOWS\r\n elif platform.syst... | [
"0.64579725",
"0.5781432",
"0.5625563",
"0.54816496",
"0.5473164",
"0.5293687",
"0.51608443",
"0.50819355",
"0.4978549",
"0.4844417",
"0.47959632",
"0.47708523",
"0.4746237",
"0.4732008",
"0.47292966",
"0.47214973",
"0.4701345",
"0.46961728",
"0.46805945",
"0.4661857",
"0.466... | 0.7523337 | 0 |
Set the global username override value. | def set_user_name_override(name: str) -> None:
global _user_name_override
_user_name_override = name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_username(self, value):\n self.username = value",
"def change_username(self, name):\n self.username = name",
"def set_username(self, value):\n raise NotImplementedError('set_username')",
"def set_uname(self, username):\n Server.t_usernames[threading.get_ident()] = username\... | [
"0.7747169",
"0.7630484",
"0.75716734",
"0.7008054",
"0.6820638",
"0.6784135",
"0.6745073",
"0.6725287",
"0.6669901",
"0.6605668",
"0.6576479",
"0.6576479",
"0.6576479",
"0.6576479",
"0.6576479",
"0.6576479",
"0.6576479",
"0.6576479",
"0.6576479",
"0.6568546",
"0.6558229",
... | 0.825879 | 0 |
Get the current user name. | def get_user_name():
if _user_name_override is not None:
return _user_name_override
elif platform.system() == "Windows":
return os.getlogin()
elif platform.system() == "Linux":
try:
import pwd
return pwd.getpwuid(os.geteuid()).pw_name
except:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_name(self) -> str:\n return pulumi.get(self, \"user_name\")",
"def get_current_user_full_name(self):\n user_service = self.runtime.service(self, 'user')\n xb_user = user_service.get_current_user()\n\n return xb_user.full_name",
"def user_name(self) -> pulumi.Output[str]:\n ... | [
"0.8741144",
"0.86486804",
"0.846932",
"0.844218",
"0.83565015",
"0.8267635",
"0.8187839",
"0.81661373",
"0.80497915",
"0.8037324",
"0.80079746",
"0.7956374",
"0.7941207",
"0.7941207",
"0.7937398",
"0.7918738",
"0.7918738",
"0.7918738",
"0.7903354",
"0.78959924",
"0.78748775"... | 0.7600616 | 41 |
Create a new run dir with increasing ID number at the start. | def _create_run_dir_local(run_dir_root, run_desc) -> str:
run_dir_root = get_path_from_template(run_dir_root, PathType.AUTO)
if not os.path.exists(run_dir_root):
os.makedirs(run_dir_root)
run_id = _get_next_run_id_local(run_dir_root)
run_name = "{0:05d}-{1}".format(run_id, run_desc)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_run_dir(self):\n task_name = 'task_'+str(self.setting['task_id'])\n run_name = '_'.join(['run', \n str(self.setting['run_idx']), \n str(self.setting['_id'])])\n \n run_dir = op.join(Job_Requestor.run_center, task_name, run_... | [
"0.6758753",
"0.66561",
"0.6593291",
"0.63309443",
"0.6312094",
"0.6216086",
"0.61310786",
"0.6109647",
"0.60766464",
"0.6072097",
"0.6067855",
"0.6009013",
"0.590007",
"0.58960336",
"0.5868165",
"0.5814761",
"0.57576287",
"0.57141966",
"0.56943786",
"0.5688185",
"0.56827813"... | 0.6336431 | 3 |
Reads all directory names in a given directory (nonrecursive) and returns the next (increasing) run id. Assumes IDs are numbers at the start of the directory names. | def _get_next_run_id_local(run_dir_root: str) -> int:
dir_names = [d for d in os.listdir(run_dir_root) if os.path.isdir(os.path.join(run_dir_root, d))]
r = re.compile("^\\d+") # match one or more digits at the start of the string
run_id = 0
for dir_name in dir_names:
m = r.match(dir_name... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_id_dir(id_dir: Path) -> (int, Path):\n\n lockfile_path = id_dir/'.lockfile'\n with lockfile.LockFile(lockfile_path, timeout=1):\n next_fn = id_dir/PKEY_FN\n\n next_valid = True\n\n if next_fn.exists():\n try:\n with next_fn.open() as next_file:\n ... | [
"0.6369533",
"0.6025362",
"0.5927364",
"0.57674915",
"0.57419837",
"0.5673672",
"0.5648966",
"0.55251485",
"0.5444188",
"0.54296756",
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"0.54062897",
"0.5385693",
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"0.53512573",
"0.53074497",
"0.5295442",
"0.5287889",
"0.52728975",
"0.52728975",
"0.5272... | 0.74510384 | 0 |
Takes in a list of tuples of (src, dst) paths and copies files. Will create all necessary directories. | def copy_files_and_create_dirs(files) -> None:
for file in files:
target_dir_name = os.path.dirname(file[1])
# will create all intermediate-level directories
if not os.path.exists(target_dir_name):
os.makedirs(target_dir_name)
shutil.copyfile(file[0], file[1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _copy_files(src_paths,dst_dir,class_numbers):\n\n class_dirs=[os.path.join(dst_dir,class_name+\"/\")for class_name in self.class_names]\n\n for dir in class_dirs:\n if not os.path.exists(dir):\n os.makedirs(dir)\n\n for src,cls in zip(src_paths... | [
"0.77486277",
"0.75265586",
"0.73959345",
"0.7387594",
"0.7377672",
"0.7329959",
"0.72826844",
"0.7268612",
"0.72428185",
"0.7232209",
"0.72291964",
"0.711112",
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"0.6992751",
"0.6919675",
"0.69122905",
"0.68667597",
"0.67933685",
"0.6778472",
"0.670... | 0.7675131 | 1 |
Produces embedding of text | def embed_text(self, data: List[str]) -> List[Optional[torch.Tensor]]:
raise NotImplementedError | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_text_embeds(self, text):\n\n # tokenize the text\n text_input = self.tokenizer(text,\n padding='max_length',\n max_length=tokenizer.model_max_length,\n truncation=True,\n ... | [
"0.7674286",
"0.7428457",
"0.73705107",
"0.68461746",
"0.6826775",
"0.68188214",
"0.67891675",
"0.6650634",
"0.6640539",
"0.64986724",
"0.64498174",
"0.6358982",
"0.63555664",
"0.6302846",
"0.6266722",
"0.6235843",
"0.6210677",
"0.61844516",
"0.6173967",
"0.6062287",
"0.60553... | 0.6253844 | 15 |
Returns the manifold that this GraphEmbedder embeds nodes into. Defaults to Euclidean if this method is not overwritten | def get_manifold(self) -> RiemannianManifold:
return ManifoldConfig().get_manifold_instance() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_kernel_norms(self):\n return self.adjacency",
"def shared_element_norm(self):\n elements = self.shared_elements\n v1 = self.nodes[elements[:, 1], :] - self.nodes[elements[:, 0], :]\n v2 = self.nodes[elements[:, 2], :] - self.nodes[elements[:, 0], :]\n return np.cross(v1... | [
"0.57701725",
"0.57140815",
"0.565746",
"0.558986",
"0.5530351",
"0.55212075",
"0.54947764",
"0.53900594",
"0.5290139",
"0.5283337",
"0.52715415",
"0.51860607",
"0.51778406",
"0.5176604",
"0.5165664",
"0.51596206",
"0.51595855",
"0.51545715",
"0.51545715",
"0.51331407",
"0.51... | 0.64672875 | 0 |
return the footer information | def get(self):
app_info = {
'developedBy': 'This app was developed by the Melbourne eResearch Group (www.eresearch.unimelb.edu.au) within the School of Computing and Information Systems (https://cis.unimelb.edu.au) at The University of Melbourne (www.unimelb.edu.au). ',
'description': 'T... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getFooter():\n return _FOOTER",
"def footer(self):\n return self._footer",
"def footer(self):\n pass",
"def footer(self):\n pass",
"def footer(self, **args):\n return self.pageConfig['footer'] % self.pageConfig",
"def _get_footer(self, footer):\n if footer is Non... | [
"0.8154624",
"0.8108119",
"0.7856543",
"0.7856543",
"0.7828535",
"0.7746664",
"0.7719649",
"0.76299804",
"0.7477003",
"0.74707055",
"0.73991615",
"0.7395591",
"0.72634256",
"0.72634256",
"0.7204311",
"0.718627",
"0.7012529",
"0.6940873",
"0.69106513",
"0.68723094",
"0.6851129... | 0.0 | -1 |
return the statistical information query across feedback and prediction model | def get(self):
from models.prediction import Prediction
from models.feedback import Feedback
prediction_collection = Prediction._get_collection()
feedback_collection = Feedback._get_collection()
statistical_data = copy.deepcopy(CONSTANTS['STATISTICAL_DATA'])
# initial s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_statistics(self):",
"def prediction():\n # retweets_only = request.args.get('retweets_only')\n # api.set_retweet_checking(strtobool(retweets_only.lower()))\n # with_sentiment = request.args.get('with_sentiment')\n # api.set_with_sentiment(strtobool(with_sentiment.lower()))\n # query = ... | [
"0.63750684",
"0.6304389",
"0.624345",
"0.62196577",
"0.6172109",
"0.6098883",
"0.6085545",
"0.6072154",
"0.6068451",
"0.60035133",
"0.59794176",
"0.5978775",
"0.5976125",
"0.5952715",
"0.5937861",
"0.591812",
"0.59040034",
"0.5885989",
"0.5883272",
"0.58799636",
"0.5878831",... | 0.64818 | 0 |
Gracefully close the SSH connection | async def disconnect(self):
self._logger.info("Host {}: SSH: Disconnecting".format(self._host))
self._logger.info("Host {}: SSH: Disconnecting".format(self._host))
await self._cleanup()
self._conn.close()
await self._conn.wait_closed() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def close_ssh_connection(self):\n if self.__ssh_client:\n self.__ssh_client.close()",
"def closeConnection(self): \r\n \r\n try:\r\n ssh.close()\r\n feedback= '***Connection Closed***'\r\n return feedback\r\n except Exception as e:\r\... | [
"0.8305926",
"0.79801464",
"0.7943686",
"0.7920637",
"0.77663314",
"0.7744283",
"0.7734058",
"0.7681489",
"0.7674849",
"0.7389304",
"0.73721665",
"0.73077106",
"0.7301349",
"0.7294421",
"0.72725135",
"0.72713614",
"0.72451586",
"0.72230047",
"0.72170174",
"0.70945966",
"0.704... | 0.7459658 | 9 |
check session was opened | def __check_session(self):
if not self._stdin:
raise RuntimeError("SSH session not started") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_open(self):\n\t\treturn self._session is not None",
"def is_open(self):\n return self._session is not None",
"def _check_open_session(self, guest_obj):\n session = guest_obj.open_session()\n self.assertIs('GuestSessionLinux', session.__class__.__name__)",
"def sessionValid(self):\... | [
"0.82529193",
"0.8016455",
"0.699788",
"0.6899404",
"0.6878535",
"0.6878535",
"0.68676883",
"0.68601865",
"0.6826742",
"0.6791624",
"0.67632437",
"0.6713392",
"0.6696932",
"0.6672162",
"0.66605437",
"0.6652152",
"0.6649548",
"0.66456234",
"0.66192186",
"0.6615233",
"0.6611942... | 0.6141397 | 58 |
Trains the classifier with the given filenames | def train(self, trainFilenames):
startIndex = len(self.documents)
endIndex = startIndex + len(trainFilenames)
self.documents += trainFilenames
X = [[i] for i in range(startIndex, endIndex)]
Y = [isAroused(f) for f in trainFilenames]
self.knn.fit(np.array(X), np.array(Y)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(self, trainfile):",
"def train_for(labels, filenames):\n stt = stt_google\n csvfiles = []\n writers = []\n for index, filename in enumerate(filenames):\n currfile = open(filename, 'ab')\n csvfiles.append(currfile)\n writers.append(csv.writer(currfile))\n # record ins... | [
"0.71127045",
"0.6847688",
"0.67817926",
"0.67200977",
"0.6696759",
"0.6646218",
"0.6621507",
"0.6604409",
"0.65844727",
"0.6558893",
"0.64029044",
"0.63676393",
"0.6323149",
"0.62984276",
"0.62698185",
"0.62642264",
"0.6254352",
"0.6238679",
"0.6231274",
"0.6210927",
"0.6206... | 0.69635355 | 1 |
Tokenizes the questions. This will add q_token in each entry of the dataset. 1 represent nil, and should be treated as padding_idx in embedding | def tokenize(self, max_length=14):
for entry in self.entries:
tokens = self.dictionary.tokenize(entry['question'], False)
tokens = tokens[:max_length]
if len(tokens) < max_length:
# Note here we pad in front of the sentence
padding = [self.dict... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess_dataset(dataset, tokenizer):\n eos = torch.tensor([tokenizer.eos_token_id], dtype=torch.long)\n q_start = torch.tensor(tokenizer.encode('question:'), dtype=torch.long)\n q_end = torch.tensor(tokenizer.encode(':question'), dtype=torch.long)\n\n tensors = [[] for i in range(3)]\n for i ... | [
"0.7129758",
"0.7047955",
"0.7026456",
"0.6816859",
"0.6816859",
"0.6816859",
"0.6816859",
"0.676679",
"0.6257971",
"0.61061835",
"0.6054662",
"0.60403764",
"0.5969531",
"0.5852405",
"0.57931036",
"0.5778398",
"0.5752423",
"0.5728716",
"0.5722207",
"0.5704012",
"0.5679085",
... | 0.6808701 | 7 |
la idea es que escanee la zona deseada (desde cero) y guarde la imagen | def saveimage(self):
if self.saveimageButton.isChecked():
self.save = True
self.channelsOpen()
self.movetoStart()
self.saveimageButton.setText('Abort')
self.guarda = np.zeros((self.numberofPixels, self.numberofPixels))
self.liveviewStart()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def foetus_pics(self):\n pic = 0\n if 0.0 not in self.allele:\n self.contamination = 2\n pic = 3\n elif 0.0 == self.allele[1]:\n pic = 1\n else:\n pic = 2\n return pic",
"def ventanaprincipal():\r\n titulo_principal=pygame.imag... | [
"0.6379464",
"0.6296582",
"0.62169796",
"0.6204397",
"0.61585575",
"0.6110433",
"0.60860527",
"0.60647374",
"0.5987925",
"0.5917548",
"0.5876961",
"0.58695006",
"0.58384454",
"0.58236486",
"0.5822561",
"0.58053076",
"0.5804754",
"0.57964826",
"0.57868594",
"0.57860816",
"0.57... | 0.0 | -1 |
Image live view when not recording | def liveview(self):
if self.liveviewButton.isChecked():
self.save = False
self.channelsOpen()
self.liveviewStart()
else:
self.liveviewStop() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def capture_image(self):\n ext = self.image_save_type.lower()\n\n if self.calibrating:\n print('calibrating')\n\n if ext == 'fits':\n self.save_fits()\n self._image_counter += 1\n else:\n img = self.original_image\n path = os.path.j... | [
"0.6853264",
"0.67230445",
"0.66627806",
"0.6596117",
"0.6586887",
"0.64478475",
"0.6418577",
"0.6359291",
"0.6324905",
"0.6324905",
"0.62345564",
"0.6223029",
"0.62202823",
"0.6213966",
"0.6185097",
"0.6172923",
"0.6171413",
"0.6119024",
"0.61056674",
"0.6095103",
"0.6094443... | 0.5790726 | 48 |
Choose a random valid move that preserves our own eyes. | def select_action(self, game_state):
super().select_action(game_state)
dim = (game_state.board.num_rows, game_state.board.num_cols)
if dim != self.dim:
self._update_cache(dim)
idx = np.arange(len(self.point_cache))
np.random.shuffle(idx)
for i in idx:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_legal_move():\n return random.choice(legal_moves())",
"def get_random_move(self, valid_moves):\n return random.choice(valid_moves)",
"def make_random_move(state: State) -> State:\n return random.choice(state.get_possible_states())",
"def make_random_move(self):\n #completel... | [
"0.83302236",
"0.80104834",
"0.78741527",
"0.78018403",
"0.77883136",
"0.77592164",
"0.7737336",
"0.7699141",
"0.7628713",
"0.7599985",
"0.7595064",
"0.7374902",
"0.7326639",
"0.7277232",
"0.7267337",
"0.723386",
"0.7232803",
"0.72240585",
"0.72192174",
"0.72055054",
"0.71364... | 0.0 | -1 |
generates a 20,000th hash in iterative sha256 chain..derived from private SEED | def _calc_hashchain(
seed_private,
epoch,
blocks_per_epoch):
hc_seed = getHashChainSeed(seed_private, epoch, config.dev.hashchain_nums)
hc = [[hash_chain] for hash_chain in hc_seed]
hc_terminator = []
for hash_chain in hc[:-1]: # skip last element as it is reveal hash
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SHA256(self) -> _n_0_t_3[_n_0_t_9]:",
"def h_python(key, N):\n return hash(key) % N",
"def hash_gen(n):\n domain = \"abcdefghijklmnopqrstuvwxyz\"\n temp = \"\"\n for i in range(0, n):\n temp += domain[random.randrange(0, 26)]\n return temp",
"def compute_hash(self):\n '''\n ... | [
"0.68605787",
"0.6531151",
"0.64920217",
"0.6357423",
"0.6346101",
"0.63296044",
"0.6322797",
"0.63040954",
"0.628679",
"0.6218963",
"0.6196641",
"0.61853266",
"0.6156555",
"0.6146982",
"0.6143425",
"0.6101353",
"0.60864455",
"0.6079242",
"0.6064988",
"0.6041602",
"0.59878373... | 0.58618045 | 39 |
>>> isinstance(hashchain(hstr2bin('32eee808dc7c5dfe26fd4859b415e5a713bd764036bbeefd7a541da9a1cc7b9fcaf17da039a62756b63835de1769e05e')), HashChainBundle) True | def hashchain(seed_private,
epoch=0,
blocks_per_epoch=config.dev.blocks_per_epoch):
# type: (int) -> HashChainBundle
return HashChainBundle(*_calc_hashchain(seed_private, epoch, blocks_per_epoch)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_hash_sha256(self):\n block = self.blockchain.new_block(self.proof, self.previous_hash)\n hash_ = self.blockchain.hash(block)\n\n self.assertIsInstance(hash_, str)\n self.assertEqual(hashlib.sha256(json.dumps(block, sort_keys=True).encode()).hexdigest(), hash_)",
"def _get_cha... | [
"0.61252147",
"0.59626085",
"0.58439803",
"0.5742523",
"0.56958115",
"0.56938916",
"0.5665251",
"0.5566981",
"0.5560363",
"0.5549851",
"0.5532766",
"0.5531971",
"0.55154383",
"0.5513594",
"0.54947066",
"0.54275084",
"0.5408093",
"0.5398602",
"0.53863704",
"0.53802854",
"0.537... | 0.0 | -1 |
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