_ResultBase:\\n return simulate_request(app, 'PUT', path, **kwargs)\",\n \"def put(self, url, body):\\n return self._query(url, 'PUT', json=body)\",\n \"def do_put_request(self, uri, headers, payload, timeout_ms):\\n return self._do_request('PUT', uri, headers, payload, timeout_ms, None)\",\n \"def put(self, *args, **kwargs):\\n return self.handle_put_request()\",\n \"def put(self, request, pk=None):\\n return Response({'method': 'PUT'})\",\n \"def put(self, request, pk=None):\\n return Response({'method': 'PUT'})\",\n \"def put(self, request, pk=None):\\n return Response({'method': 'PUT'})\",\n \"async def put(url, session=None, **kwargs):\\n\\n method = 'PUT'\\n resp = await _request(method, url, session=session, **kwargs)\\n return resp\",\n \"def put(self, request, pk=None):\\n\\n return Response({'method': 'put'})\",\n \"def simulate_put(self, path='/', **kwargs):\\n return self.simulate_request('PUT', path, **kwargs)\",\n \"def put_http(self, path, data, content_type):\\n req_url = self.normalize_cdmi_url(path)\\n headers = {\\\"user-agent\\\": self.u_agent, \\\"Content-type\\\": content_type}\\n res = requests.put(\\n req_url, headers=headers, auth=self.auth, data=data, verify=False\\n )\\n if res.status_code in [400, 401, 403, 404, 406]:\\n return Response(res.status_code, res)\\n return Response(0, res)\",\n \"def _put(self, path, data=None):\\n headers = {'content-type': 'application/json'}\\n if data:\\n data = json.dumps(data)\\n r = requests.put(self._url(path), data=data, headers=headers)\\n assert r.status_code == 200\\n return r\",\n \"def _put(self, url, data=None):\\n url = urljoin(self.base_url, url)\\n try:\\n r = self._make_request(**dict(\\n method='PUT',\\n url=url,\\n json=data,\\n auth=self.auth,\\n timeout=self.timeout,\\n hooks=self.request_hooks,\\n headers=self.request_headers\\n ))\\n except requests.exceptions.RequestException as e:\\n raise e\\n else:\\n if r.status_code >= 400:\\n _raise_response_error(r)\\n return r.json()\",\n \"def simulate_put(self, path='/', **kwargs) -> _ResultBase:\\n return self.simulate_request('PUT', path, **kwargs)\",\n \"def put(self, url, data=None):\\r\\n response = self.requestHelper.put(url, data=data)\\r\\n return self.process(response)\",\n \"async def simulate_put(self, path='/', **kwargs) -> _ResultBase:\\n return await self.simulate_request('PUT', path, **kwargs)\",\n \"def put(self, path, data):\\n response = self._create_connection(\\\"PUT\\\", path, json.dumps(data))\\n return self._handle_response(response)\",\n \"def putrequest(self, full_path, data=None, headers=None, files=None):\\n return self.request('PUT', full_path, data, headers, files)\",\n \"def put(self, path: str, params):\\n return self.request(\\\"PUT\\\", path, params)\",\n \"def put(self,request, pk =None):\\n return Response({'method': 'PUT'})\",\n \"def do_PUT(self):\\n content_len = int(self.headers.get('content-length', 0))\\n post_body = self.rfile.read(content_len)\\n post_body = json.loads(post_body)\\n (resource, id) = self.parse_url(self.path)\\n\\n success = False\\n\\n if resource == \\\"categories\\\":\\n success = update_category(id, post_body)\\n if resource == \\\"comments\\\":\\n success = edit_comment(id, post_body)\\n if resource == \\\"posts\\\":\\n success = update_post(id, post_body)\\n\\n if success:\\n self._set_headers(204)\\n else:\\n self._set_headers(404)\\n\\n self.wfile.write(\\\"\\\".encode())\",\n \"def put(self, url, payload={}):\\n response = self._make_request(\\\"PUT\\\", url, payload)\\n\\n return response\",\n \"def _put(self, url, data, extra_headers=None):\\n headers = {'X-Requested-By': 'Unit Tests'}\\n headers.update(extra_headers)\\n return self.client.put(\\n url,\\n content_type='application/json',\\n data=utils.as_json(data),\\n headers=headers,\\n )\",\n \"def put(url, data={}, verify=True):\\n data = requests.put(url=url, data=data, verify=verify)\\n if data.status_code == 200:\\n return data\",\n \"def put(path: str, data={}):\\n token = get_token()\\n headers = {\\n \\\"Authorization\\\": f\\\"Bearer {token}\\\"\\n }\\n return requests.put(get_base_url() + path, headers=headers, json=data)\",\n \"def put(api, url, payload, headers=None, auth=_KERBEROS_AUTH, proxies=None,\\n retries=_NUM_OF_RETRIES, timeout=None):\\n return call(api, url, 'put', payload=payload,\\n headers=headers, auth=auth, proxies=proxies, retries=retries,\\n timeout=timeout)\",\n \"def put(call,\\n headers=None,\\n data=None,\\n base=cloudBase,\\n no_headers=False,\\n raw=False,\\n **kwargs): # pragma: no cover\\n return _call(method=requests.put,\\n call='{0}{1}'.format(base, call),\\n headers=headers,\\n no_headers=no_headers,\\n data=data,\\n raw=raw,\\n **kwargs)\",\n \"def put(self, path, request):\\n\\n try:\\n data = json_decode(request.body)\\n self.interface_data.set(path, data)\\n response = self.interface_data.get(path, False)\\n status_code = 200\\n except MetadataParameterError as e:\\n response = {'error': str(e)}\\n status_code = 400\\n except (TypeError, ValueError) as e:\\n response = {'error': 'Failed to decode PUT request body: {}'.format(str(e))}\\n status_code = 400\\n return ApiAdapterResponse(response, status_code=status_code)\",\n \"def base_put(url_path, content):\\n response = requests.put(url=settings.URL_API + url_path, json=content)\\n return response\",\n \"def put(self, api_path, *args, **kwargs):\\n\\n\\t\\treturn self._do_operation(u'put', api_path, *args, **kwargs)\",\n \"def test_put(self):\\n url, port = self.server.address\\n\\n #couple of basic POSTs\\n r = self.client.get(\\\"http://{0}:{1}/\\\".format(url, port))\\n self.assertEqual(200, r.status_code)\\n r = self.client.get(\\\"http://{0}:{1}\\\".format(url, port))\\n self.assertEqual(200, r.status_code)\\n r = self.client.get(\\\"http://{0}:{1}/200\\\".format(url, port))\\n self.assertEqual(200, r.status_code)\\n r = self.client.get(\\\"http://{0}:{1}/400\\\".format(url, port))\\n self.assertEqual(400, r.status_code)\\n\\n r = self.client.put(\\\"http://{0}:{1}/400?foo=bar\\\".format(url, port))\\n self.assertEqual(400, r.status_code)\",\n \"def put(self ,request, pk = None):\\r\\n\\r\\n return Response({'method ': 'put'})\",\n \"def test_put_method(self):\\n self.getPage('/blah', method='PUT')\\n self.assertStatus('200 OK')\\n self.assertHeader('Content-Type', 'application/json')\\n self.assertBody('{\\\"mystring\\\": \\\"blah\\\"}')\",\n \"def update(self, request, pk=None):\\n\\n return Response({'http_method': 'PUT'})\",\n \"def put(self, path, body):\\n url = urljoin(self.api_endpoint, path)\\n response = requests.put(url, json=body, headers=self.headers)\\n return self._check_response(response)\",\n \"def put(self, request, pk=None):\\n return Response({'method': 'patch'})\",\n \"def put(self, api_path, *args, **kwargs):\\n\\n return self._do_operation(u'put', api_path, *args, **kwargs)\",\n \"def PUT(url, mime_type='text/html'):\\n def_app = DefaultHttpRequestHandler()\\n return def_app.RequestMap(url, methods=['PUT'], produces=mime_type)\",\n \"def put(self, request, pk=None): #pk of id of objects to be updated (DB)\\n return Response({'method':'PUT'})\",\n \"def PUT(self):\\n return json.dumps(self.update_new(cherrypy.request.body.read().strip()))\",\n \"def put(self, path, payload):\\n \\n # prep\\n put_url = self.url(path)\\n json_payload = json.dumps(payload)\\n\\n # request\\n response = requests.put(put_url, data=json_payload, auth=self.auth, headers=API.HEADERS)\\n\\n # test and return\\n self.raise_for_status(response)\\n return response.json()\",\n \"def put(self, path, value):\\n req = self.session.put(self.url + path,\\n json={'data': value},\\n verify=False)\\n jdata = req.json()\\n if jdata['status'] != 'ok':\\n raise Exception(\\\"Failed to qeury put: \\\\n\\\" + req.text)\\n\\n return True\",\n \"def put(self,request,pk=None):\\n return Response({'method':'Put'})\",\n \"def test_PUT4(self):\\n payload = {\\n \\\"make\\\": \\\"Nissan\\\",\\n \\\"model\\\": \\\"Skyline\\\",\\n \\\"year\\\": 1999,\\n \\\"price\\\": 2200\\n }\\n r = requests.put(self.address + \\\"/loremipsum/42\\\", json=payload)\\n self.assertEqual(r.status_code, 400)\",\n \"async def put(self, resource: str, data: Optional[Sequence]):\\r\\n return await self.request(\\\"put\\\", self._api_base + resource, data=data)\",\n \"def put_request(\\n self,\\n alias,\\n uri,\\n data=None,\\n json=None,\\n params=None,\\n files=None,\\n headers=None,\\n allow_redirects=None,\\n timeout=None):\\n session = self._cache.switch(alias)\\n data = utils.format_data_according_to_header(session, data, headers)\\n # XXX workaround to restore library default behaviour. Not needed in new keywords\\n redir = True if allow_redirects is None else allow_redirects\\n\\n response = self._common_request(\\n \\\"put\\\",\\n session,\\n uri,\\n data=data,\\n json=json,\\n params=params,\\n files=files,\\n headers=headers,\\n allow_redirects=redir,\\n timeout=timeout)\\n\\n return response\",\n \"def putRequest(self, resource, data):\\n\\n try:\\n req = self._put(self.fullUrl(resource), json=data)\\n return req\\n except requests.exceptions.RequestException as err:\\n print('request failed:', err)\\n return None\",\n \"def put(self, data, **kwargs):\\n return put.put(self._host, self._session, data, **kwargs)\",\n \"def test_client_can_do_put_request(self):\\n response = self.httpbin_4.test_requests_put_method()\\n self.assertEqual(response.request.method, 'PUT')\\n self.assertEqual(response.status_code, 200)\",\n \"def _put(self, url, json=None, **kwargs):\\n kwargs = Connection._prepare_json_payload(json, **(kwargs or {}))\\n return self._http.put(self.cluster + url, timeout=self.timeout, **kwargs)\",\n \"def put(self, path, request):\\n\\n content_type = 'application/json'\\n\\n try:\\n data = json_decode(request.body)\\n self.fileInterface.set(path, data)\\n response = self.fileInterface.get(path)\\n status_code = 200\\n except FileInterfaceError as e:\\n response = {'error': str(e)}\\n status_code = 400\\n except (TypeError, ValueError) as e:\\n response = {'error': 'Failed to decode PUT request body: {}'.format(str(e))}\\n status_code = 400\\n\\n logging.debug(response)\\n\\n return ApiAdapterResponse(response, content_type=content_type,\\n status_code=status_code)\",\n \"def put(self,id):\\r\\n data = request.json\\r\\n return update(id=id,data=data)\",\n \"def put(self, url_pattern):\\n return self.route(url_pattern, methods=['PUT'])\",\n \"def api_put(self, *args, **kwargs):\\n return self.api_put_with_response(*args, **kwargs)[0]\",\n \"def putRequestSimple(self, resource):\\n\\n req = self.s.put(self.fullUrl(resource))\\n return req.json()\",\n \"def put(self, endpoint, params=None, data=None):\\n params = params or dict()\\n data = data or dict()\\n return self.request(verb=requests.put, address=self.project_address + endpoint,\\n params=params, data=data)\",\n \"def put(self, app_prefix, path, payload):\\n return self.handle_request('put', app_prefix, path, payload=payload)\",\n \"def put(call, data, hub_token_header=True, base=apiPath, **kwargs):\\n return _call(method=requests.put,\\n call='{0}{1}'.format(base, call),\\n hub_token_header=hub_token_header,\\n data=data,\\n **kwargs)\",\n \"def put(self, url):\\n return self.session.put(url=self.base_url + url)\",\n \"def PUT(self, req):\\n if req.is_object_request:\\n headers = {}\\n src_path = '/%s/%s' % (req.container_name, req.object_name)\\n\\n # object-sysmeta' can be updated by 'Copy' method,\\n # but can not be by 'POST' method.\\n # So headers['X-Copy-From'] for copy request is added here.\\n headers['X-Copy-From'] = quote(src_path)\\n headers['Content-Length'] = 0\\n req.get_response(self.app, 'PUT', headers=headers)\\n else:\\n req.get_response(self.app, 'POST')\\n\\n return HTTPOk()\",\n \"def put_call(url, params=None, headers=None):\\n if params is None:\\n params = {}\\n\\n response = requests.put(url, data=params, headers=headers)\\n\\n if response.status_code == 401:\\n raise RedirectException(reverse('b2b_control:logout'))\\n\\n return response\",\n \"def PUT(self):\\n return json.dumps(self.create_new(cherrypy.request.body.read().strip()))\",\n \"def put(self, key, headers, value, metadata=None):\",\n \"def put(self, call, params={}): \\n # Build an endpoint using the parameters...\\n endpoint = self._calls[call](params)\\n url = '{}/{}'.format(str(self), str(endpoint))\\n return self.putter.respond(url)\",\n \"def put(self, uri, data=None, **kwargs):\\n return self.session.put(uri, data=data, **kwargs)\",\n \"def Put(Path: str, Params: Any = None, Data: Any = None, *, Callback: Callable[[int, Dict[str, Any]], None]) -> None:\\n Request(\\\"PUT\\\", Path, Params, Data, Callback=Callback)\",\n \"def put(resource, data, **kwargs):\\n\\tresp = requests.put(\\n\\t\\t_endpoint(resource, 'PUT'),\\n\\t\\tparams=_jsonify_dict_values(kwargs),\\n\\t\\tdata=json.dumps(data),\\n\\t\\theaders=PAYLOAD_HEADERS,\\n\\t\\tverify=SERVER_CERT\\n\\t)\\n\\tresp.raise_for_status()\\n\\treturn resp.json()\",\n \"def put(path, *params, **kwparams):\\n def method(f):\\n return config(f, 'PUT', path, **kwparams)\\n return method\",\n \"def test_PUT(self):\\n if not self.url:\\n return\\n response = self.client.put(self.url, {}, format='json')\\n self.assertIn(response.status_code, [status.HTTP_405_METHOD_NOT_ALLOWED,\\n status.HTTP_401_UNAUTHORIZED])\",\n \"def put(self, endpoint, params=None, data=None):\\n\\n return self._call(requests.put, endpoint, params=params, data=data)\",\n \"def update(self, request, pk=None):\\n return Response({'http_method': 'PUT'})\",\n \"def do_PUT(self):\\n print(\\\"Received PUT request\\\")\\n \\n try:\\n url = urllib.parse.urlparse(self.path)\\n \\n # validate key\\n key = url.path[1:] \\n if not self.valid_key(key):\\n self.send_response(403)\\n self.end_headers()\\n return\\n \\n # TODO handle missing content-length\\n content_length = int(self.headers.get(\\\"Content-length\\\"))\\n \\n # set up the paths for writing\\n data = self.rfile.read(content_length)\\n\\n tmp_file = self.server.tmp_dir + \\\"/\\\" + key\\n data_file = self.server.keys_dir + \\\"/\\\" + key\\n \\n # write to tmp directory so not to corrupt existing data on failure\\n with open(tmp_file, mode='wb') as the_file:\\n the_file.write(data)\\n \\n # write succeded, move file\\n os.rename(tmp_file, data_file)\\n \\n # TODO clean up tmp if rename fails\\n \\n # push to nodes, best effort\\n w = parse_int_from_qs(url.query, \\\"w\\\", DEFAULT_W)\\n w = min(w, len(self.server.nodes) + 1)\\n \\n successfully_updated = 1\\n if w > 0:\\n for node in self.server.nodes:\\n try:\\n node.update(key, data, successfully_updated >= w)\\n successfully_updated = successfully_updated + 1\\n except Exception:\\n pass\\n \\n # respond to client\\n if successfully_updated >= w:\\n self.send_response(201)\\n self.send_header(\\\"Content-type\\\", \\\"text/plain\\\")\\n self.end_headers()\\n else:\\n self.send_response(500, \\\"Only {} nodes updated\\\".format(successfully_updated))\\n self.end_headers()\\n \\n except Exception as err:\\n print(err)\\n self.send_response(500)\\n self.end_headers()\",\n \"def sendUpdateRequest(self, url:str, originator:str, data:Any, parameters:Parameters=None, ct:ContentSerializationType=None, targetResource:Resource=None) -> Result:\\n\\t\\tif Utils.isHttpUrl(url):\\n\\t\\t\\tCSE.event.httpSendUpdate() # type: ignore\\n\\t\\t\\treturn CSE.httpServer.sendHttpRequest(requests.put, url, originator, data=data, parameters=parameters, ct=ct, targetResource=targetResource)\\n\\t\\tLogging.logWarn(dbg := f'unsupported url scheme: {url}')\\n\\t\\treturn Result(status=True, rsc=RC.badRequest, dbg=dbg)\",\n \"def _put(self, uri, data=None, headers=None):\\n if self.openam_url[-1:] == '/':\\n openam_path = self.openam_url + uri\\n else:\\n openam_path = self.openam_url + \\\"/\\\" + uri\\n\\n try:\\n data = requests.put(openam_path, headers=headers, data=data, timeout=self.timeout, verify=self.verify)\\n except requests.exceptions.RequestException as e:\\n data = {'error': e}\\n return data\",\n \"def put(self, request, *args, **kwargs):\\n verify_secure(request)\\n return super().put(request, args, kwargs)\",\n \"def put(self, request, *args, **kwargs):\\n verify_secure(request)\\n return super().put(request, args, kwargs)\",\n \"def put(self, url, data, token=None):\\n return self.app.put(url,\\n data=json.dumps(data),\\n headers=_token_header(token),\\n content_type='application/json')\",\n \"def put(self, url, user, data):\\n token = self.login(user)\\n response = requests.put(\\n url_root + url, headers={\\\"access-token\\\": token}, json=data\\n )\\n return response.json(), response.status_code\",\n \"def put(url, headers=None, data=None, json=None, parameters=None):\\n log = log_utilities.create_logger()\\n response = None\\n http_code = None\\n\\n try:\\n r = requests.put(url, data=data, headers=headers, json=json, params=parameters)\\n log.info(\\\"[http_utilities.put] URL: {url}\\\".format(url=url))\\n if parameters:\\n log.info(\\\"[http_utilities.put] parameters: {parameters}\\\".format(parameters=parameters))\\n if headers:\\n log.info(\\\"[http_utilities.put] headers: {headers}\\\".format(headers=headers))\\n if json:\\n log.info(\\\"[http_utilities.put] json: {json}\\\".format(json=json))\\n if data:\\n log.info(\\\"[http_utilities.put] data: {data}\\\".format(data=data))\\n log.info(\\\"[http_utilities.put] Request sent for URL: {url}\\\".format(url=r.url))\\n response = r\\n http_code = r.status_code\\n log.info(\\\"[http_utilities.put] HTTP status code: {http_code}\\\".format(http_code=http_code))\\n except requests.exceptions.RequestException as e:\\n log.exception(\\\"[http_utilities.put] Exception {type} = {exception}\\\".format(type=type(e), exception=e.message))\\n\\n return response, http_code\",\n \"def put(self, location, data=None, headers={}):\\n return self._communicate(vxg.core.request.PutRequest,\\n location, data, headers)\",\n \"def update(self,request,pk = None):\\n return Response({'http_method':'PUT'})\",\n \"def update(self, request, pk=None):\\n\\n return Response({'http_method': 'PUT'})\",\n \"def update(self, request, pk=None):\\n\\n return Response({'http_method':'PUT'})\",\n \"def put(self, url: str, json: Dict[str, Any] = None, headers: Dict[str, Any] = None) -> Response:\\n return self._api_client._put(url, json=json, headers=headers)\",\n \"def test_put(self):\\n client = RestClient(host=self.host, username='')\\n rest_url = 'some/url/'\\n \\n # Mock good response\\n with responses.RequestsMock() as rsps:\\n rsps.add(responses.PUT, f'{self.host}/{rest_url}', status=200,\\n json={'value':\\\"good!\\\"})\\n r = client.put(rest_url)\",\n \"def update(self, request, pk=None): #update a specific object\\n return Response({'http_method': 'PUT'})\",\n \"def _put(self, path=None, version=None, params=None,\\n data=None, json=None, header=None):\\n return self.client.put(module='mam', path=path, version=version,\\n params=params, data=data,\\n json=json, header=header)\",\n \"def put(self, endpoint, content=None, params=None):\\n\\t\\treturn self._call(\\\"PUT\\\", endpoint, content, params)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8385138","0.8301353","0.82257414","0.8194025","0.81546164","0.8014394","0.7950364","0.7868214","0.7865386","0.7856735","0.77816135","0.7774543","0.7757993","0.7712685","0.7612626","0.7602484","0.75962627","0.75962627","0.75962627","0.7547503","0.7535183","0.7485096","0.74673814","0.74638367","0.7460749","0.7453108","0.7444599","0.74367714","0.74117464","0.73871905","0.73595566","0.7353991","0.7330357","0.7306541","0.72882223","0.7281075","0.72190285","0.72123104","0.7173495","0.71586823","0.71344334","0.712274","0.7066028","0.7060247","0.7054181","0.70443606","0.70369315","0.70330304","0.7032377","0.7020784","0.7008231","0.6998258","0.6990589","0.6990295","0.69881916","0.69734746","0.696734","0.69623786","0.695801","0.6950718","0.695023","0.69500077","0.69269335","0.69095314","0.69055116","0.69030607","0.6853882","0.68355393","0.6821766","0.68069875","0.68041414","0.67996943","0.6790171","0.6765412","0.67644036","0.67474145","0.6746365","0.6728647","0.67279977","0.6726768","0.67238915","0.67182636","0.6709666","0.6707055","0.6705771","0.66946733","0.6690609","0.6690609","0.66791636","0.66726214","0.6651685","0.6649403","0.66233164","0.6620394","0.6618066","0.66149616","0.66070557","0.6602666","0.65982705","0.6575807"],"string":"[\n \"0.8385138\",\n \"0.8301353\",\n \"0.82257414\",\n \"0.8194025\",\n \"0.81546164\",\n \"0.8014394\",\n \"0.7950364\",\n \"0.7868214\",\n \"0.7865386\",\n \"0.7856735\",\n \"0.77816135\",\n \"0.7774543\",\n \"0.7757993\",\n \"0.7712685\",\n \"0.7612626\",\n \"0.7602484\",\n \"0.75962627\",\n \"0.75962627\",\n \"0.75962627\",\n \"0.7547503\",\n \"0.7535183\",\n \"0.7485096\",\n \"0.74673814\",\n \"0.74638367\",\n \"0.7460749\",\n \"0.7453108\",\n \"0.7444599\",\n \"0.74367714\",\n \"0.74117464\",\n \"0.73871905\",\n \"0.73595566\",\n \"0.7353991\",\n \"0.7330357\",\n \"0.7306541\",\n \"0.72882223\",\n \"0.7281075\",\n \"0.72190285\",\n \"0.72123104\",\n \"0.7173495\",\n \"0.71586823\",\n \"0.71344334\",\n \"0.712274\",\n \"0.7066028\",\n \"0.7060247\",\n \"0.7054181\",\n \"0.70443606\",\n \"0.70369315\",\n \"0.70330304\",\n \"0.7032377\",\n \"0.7020784\",\n \"0.7008231\",\n \"0.6998258\",\n \"0.6990589\",\n \"0.6990295\",\n \"0.69881916\",\n \"0.69734746\",\n \"0.696734\",\n \"0.69623786\",\n \"0.695801\",\n \"0.6950718\",\n \"0.695023\",\n \"0.69500077\",\n \"0.69269335\",\n \"0.69095314\",\n \"0.69055116\",\n \"0.69030607\",\n \"0.6853882\",\n \"0.68355393\",\n \"0.6821766\",\n \"0.68069875\",\n \"0.68041414\",\n \"0.67996943\",\n \"0.6790171\",\n \"0.6765412\",\n \"0.67644036\",\n \"0.67474145\",\n \"0.6746365\",\n \"0.6728647\",\n \"0.67279977\",\n \"0.6726768\",\n \"0.67238915\",\n \"0.67182636\",\n \"0.6709666\",\n \"0.6707055\",\n \"0.6705771\",\n \"0.66946733\",\n \"0.6690609\",\n \"0.6690609\",\n \"0.66791636\",\n \"0.66726214\",\n \"0.6651685\",\n \"0.6649403\",\n \"0.66233164\",\n \"0.6620394\",\n \"0.6618066\",\n \"0.66149616\",\n \"0.66070557\",\n \"0.6602666\",\n \"0.65982705\",\n \"0.6575807\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7208611"},"document_rank":{"kind":"string","value":"38"}}},{"rowIdx":83088,"cells":{"query":{"kind":"string","value":"Make an HTTP DELETE request."},"document":{"kind":"string","value":"def handle_delete(self, api, command):\n return self._make_request_from_command('DELETE', command)"},"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 httpDelete(self, url='', data='', params={}, headers={}):\n\n return self.httpRequest('DELETE', url, data, params, headers)","def do_DELETE(self,):\n self.http_method = 'DELETE'\n self.response()","def delete(self):\r\n return http.Request('DELETE', '{0}'.format(\r\n self.get_url())), parsers.parse_json","def http_delete(self, **kwargs):\n return self.rabjcallable.delete(**kwargs)","def delete(url, **kwargs):\n\n return request('delete', url, **kwargs)","def _delete(self, url):\n return self._request(url, method=\"DELETE\")","def delete(self):\r\n request = http.Request('DELETE', self.get_url())\r\n\r\n return request, parsers.parse_empty","def delete(self, url):\n return self.request(url, \"DELETE\")","def _delete(self, *args, **kwargs):\n return self._request('delete', *args, **kwargs)","def delete(self, url):\n return self._request('DELETE', url)","def delete(url, to_error=_default_to_error, **kwargs):\n\n return request('delete', url, to_error=to_error, **kwargs)","def _delete(self, url, **kwargs):\n return self._call('DELETE', url, kwargs)","def __delete(self, url, headers=None):\n return self.__req(url, \"DELETE\", headers=headers)","def send_delete(url, data={}, headers={}, return_output=False):\n req = requests.delete(url=url, data=json.dumps(data), headers=headers)\n if return_output:\n return req\n if str(req.status_code).startswith('2'):\n print 'SUCCESS! {0} {1} {2}'.format(req.status_code, req.reason, req.content)\n else:\n print 'FAIL! {0} {1} {2}'.format(req.status_code, req.reason, req.content)\n exit(77)","def delete(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"delete\"), kwargs)","def delete(self, **kwargs):\n\n return self.api_request(self._get_method_fullname(\"delete\"), kwargs)","def delete(self, *args, **kwargs):\n self.request(\"delete\", *args, **kwargs)","def do_delete_request(self, uri, headers, timeout_ms):\n return self._do_request('DELETE', uri, headers, None, timeout_ms, None)","def _delete(self, path=\"\", **kwargs):\n uri = force_json(self.uri + path)\n return self.client.request(uri, method=\"DELETE\", **kwargs)","def delete(self, *args, **kw):\n kw['method'] = 'DELETE'\n return self.open(*args, **kw)","def _delete(self, url):\n url = urljoin(self.base_url, url)\n try:\n r = self._make_request(**dict(\n method='DELETE',\n url=url,\n auth=self.auth,\n timeout=self.timeout,\n hooks=self.request_hooks,\n headers=self.request_headers\n ))\n except requests.exceptions.RequestException as e:\n raise e\n else:\n if r.status_code >= 400:\n _raise_response_error(r)\n if r.status_code == 204:\n return\n return r.json()","def _delete(self, url, **kwargs):\n return self._http.delete(self.cluster + url, timeout=self.timeout, **kwargs)","def api_delete(self, path):\n return self._api_request(path, 'DELETE')","def adel(url, **kwargs):\n return requests.delete(url, **kwargs)","def delete(self, method, uri, query_param, request_param, headers, **kwargs):\n raise NotImplementedError","def delete(self):\n\n headers = self._default_headers()\n\n return self._request(self.name,\n ok_status=None,\n data=None,\n headers=headers,\n method=\"DELETE\")","def delete(self, url: str, params: Dict[str, Any] = None, headers: Dict[str, Any] = None) -> Response:\n return self._api_client._delete(url, params=params, headers=headers)","async def delete(url, session=None, **kwargs):\n\n method = 'DELETE'\n resp = await _request(method, url, session=session, **kwargs)\n return resp","def delete(self, path: str, params: dict):\n return self.request(\"DELETE\", path, params)","def sendDeleteRequest(self, url:str, originator:str, parameters:Parameters=None, ct:ContentSerializationType=None, targetResource:Resource=None) -> Result:\n\t\tif Utils.isHttpUrl(url):\n\t\t\tCSE.event.httpSendDelete() # type: ignore\n\t\t\treturn CSE.httpServer.sendHttpRequest(requests.delete, url, originator, parameters=parameters, ct=ct, targetResource=targetResource)\n\t\tLogging.logWarn(dbg := f'unsupported url scheme: {url}')\n\t\treturn Result(status=True, rsc=RC.badRequest, dbg=dbg)","def delete(self):\n self.method = \"DELETE\"\n self.send()","def delete(self, url, payload={}):\n response = self._make_request(\"DELETE\", url, payload)\n\n return response","def delete(api, url, payload=None, headers=None, auth=_KERBEROS_AUTH,\n proxies=None, retries=_NUM_OF_RETRIES, timeout=None):\n return call(api, url, 'delete', payload=payload,\n headers=headers, auth=auth, proxies=proxies, retries=retries,\n timeout=timeout)","def delete(self, url):\n return self._query(url, 'DELETE')","def delete(self):\n self.call('DELETE', expect=error.NO_CONTENT)","def delete(cls, uri):\n return cls._perform_request(uri, 'DELETE')","def delete(self):\r\n self.require_collection()\r\n request = http.Request('DELETE', self.get_url())\r\n\r\n return request, parsers.parse_empty","def delete(resource, params=None, expected_status_code=204, user=user_data):\n return call(requests.delete, resource, expected_status_code, user, params=params)","def simulate_delete(app, path, **kwargs) -> _ResultBase:\n return simulate_request(app, 'DELETE', path, **kwargs)","def delete(self) -> requests.request:\n # Check if id is set\n if self.args.id is None:\n raise Exception('Provide id of asset you want to delete')\n\n # Send DELETE request\n return requests.delete(self.REQUEST_URL + str(self.args.id))","def delete(self, *args, **kwargs):\n return self.handle_delete_request()","def delete(self):\n return self.request('', pylastica.request.Request.DELETE)","def base_delete(url_path):\n response = requests.delete(url=settings.URL_API + url_path)\n return response","def delete(self, _uri):\n print(\"Deleting '%s'\"%(_uri))\n response = self.__httpsRequest('DELETE', _uri, '')","def __Delete(self, url, id = None):\n\n conn = self.__GetConnection()\n if (id != None):\n url += \"/\" + str(id)\n conn.request(\"DELETE\", url, \"\", self.__MakeHeaders(True))\n response = conn.getresponse()\n self.__CheckResponse(response)","def delete(self, params=None):\n params = self.parameters(additional_parameters=params)\n res = delete(self.endpoint_url, params=params)\n return Response(res)","def DELETE(url, mime_type='text/html'):\n def_app = DefaultHttpRequestHandler()\n return def_app.RequestMap(url, methods=['DELETE'], produces=mime_type)","def simulate_delete(self, path='/', **kwargs):\n return self.simulate_request('DELETE', path, **kwargs)","def test_client_can_do_delete_request(self):\n response = self.httpbin_4.test_requests_delete_method()\n self.assertEqual(response.request.method, 'DELETE')\n self.assertEqual(response.status_code, 200)","def simulate_delete(self, path='/', **kwargs) -> _ResultBase:\n return self.simulate_request('DELETE', path, **kwargs)","async def delete(self, delete: TPayload) -> None:","def delete(self):\n self.request().delete()","def delete(self, uri, body=None, headers=None, auth=False):\n return self.send_request('DELETE', uri, body, headers, auth)","async def simulate_delete(self, path='/', **kwargs) -> _ResultBase:\n return await self.simulate_request('DELETE', path, **kwargs)","def do_DELETE(self): # pylint: disable=missing-docstring\n self._set_headers(204)\n (resource, id) = self.parse_url(self.path)\n\n if resource == \"comments\":\n delete_comment(id)\n elif resource == \"posts\":\n delete_post(id)\n elif resource == \"categories\":\n delete_category(id)\n elif resource == \"posttags\":\n remove_tag(id)\n \n self.wfile.write(\"\".encode())","def delete(self, path):\n \n # prep\n delete_url = self.url(path)\n\n # request\n response = requests.delete(delete_url, auth=self.auth, headers=API.HEADERS)\n\n # test and return\n self.raise_for_status(response)","def delete_request(\n self,\n alias,\n uri,\n data=None,\n json=None,\n params=None,\n headers=None,\n allow_redirects=None,\n timeout=None):\n session = self._cache.switch(alias)\n data = utils.format_data_according_to_header(session, data, headers)\n # XXX workaround to restore library default behaviour. Not needed in new keywords\n redir = True if allow_redirects is None else allow_redirects\n\n response = self._common_request(\n \"delete\",\n session,\n uri,\n data=data,\n json=json,\n params=params,\n headers=headers,\n allow_redirects=redir,\n timeout=timeout)\n\n return response","def delete(path: str):\n token = get_token()\n headers = {\n \"Authorization\": f\"Bearer {token}\"\n }\n return requests.delete(get_base_url() + path, headers=headers)","def destroy(self, request, pk=None):\n\n return Response({'http_method': 'DELETE'})","def delete(self, url_pattern):\n return self.route(url_pattern, methods=['DELETE'])","def delete(self):\r\n request = http.Request('DELETE', self.get_push_url(), None)\r\n return request, parsers.parse_empty","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def api_delete(self, *args, **kwargs):\n return self.api_delete_with_response(*args, **kwargs)[0]","def revoke(self):\r\n return http.Request('DELETE', self.get_url()), parsers.parse_empty","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def delete(self, ids):\r\n params = base.get_params(None, locals())\r\n request = http.Request('DELETE', self.get_url(), params)\r\n return request, parsers.parse_json","def do_DELETE(self):\n note_details = NoteDetails\n if self.path == '/note/api/delete':\n response_data=note_details.delete_data(self)\n Response(self).jsonResponse(status=200, data=response_data)","def delete_call(url, headers=None):\n response = requests.delete(url, headers=headers)\n\n if response.status_code == 401:\n raise RedirectException(reverse('b2b_control:logout'))\n\n return response","def delete(self, path):\n req_url = self.normalize_cdmi_url(path)\n res = requests.delete(req_url, auth=self.auth, verify=False)\n if res.status_code == 204:\n return Response(0, \"ok\")\n else:\n return Response(res.status_code, res)","def delete(self, call, params={}): \n # Build an endpoint using the parameters...\n endpoint = self._calls[call](params)\n url = '{}/{}'.format(str(self), str(endpoint))\n return self.deleter.respond(url)","def test_delete():\n sample_uuid = get_sample_id()\n response = requests.delete(f'http://localhost:5000/api/persons/{sample_uuid}')\n\n assert response.status_code == 200","def delete(self, url):\n return self.session.delete(url=self.base_url + url)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)","def Delete(self, request, global_params=None):\n config = self.GetMethodConfig('Delete')\n return self._RunMethod(\n config, request, global_params=global_params)"],"string":"[\n \"def httpDelete(self, url='', data='', params={}, headers={}):\\n\\n return self.httpRequest('DELETE', url, data, params, headers)\",\n \"def do_DELETE(self,):\\n self.http_method = 'DELETE'\\n self.response()\",\n \"def delete(self):\\r\\n return http.Request('DELETE', '{0}'.format(\\r\\n self.get_url())), parsers.parse_json\",\n \"def http_delete(self, **kwargs):\\n return self.rabjcallable.delete(**kwargs)\",\n \"def delete(url, **kwargs):\\n\\n return request('delete', url, **kwargs)\",\n \"def _delete(self, url):\\n return self._request(url, method=\\\"DELETE\\\")\",\n \"def delete(self):\\r\\n request = http.Request('DELETE', self.get_url())\\r\\n\\r\\n return request, parsers.parse_empty\",\n \"def delete(self, url):\\n return self.request(url, \\\"DELETE\\\")\",\n \"def _delete(self, *args, **kwargs):\\n return self._request('delete', *args, **kwargs)\",\n \"def delete(self, url):\\n return self._request('DELETE', url)\",\n \"def delete(url, to_error=_default_to_error, **kwargs):\\n\\n return request('delete', url, to_error=to_error, **kwargs)\",\n \"def _delete(self, url, **kwargs):\\n return self._call('DELETE', url, kwargs)\",\n \"def __delete(self, url, headers=None):\\n return self.__req(url, \\\"DELETE\\\", headers=headers)\",\n \"def send_delete(url, data={}, headers={}, return_output=False):\\n req = requests.delete(url=url, data=json.dumps(data), headers=headers)\\n if return_output:\\n return req\\n if str(req.status_code).startswith('2'):\\n print 'SUCCESS! {0} {1} {2}'.format(req.status_code, req.reason, req.content)\\n else:\\n print 'FAIL! {0} {1} {2}'.format(req.status_code, req.reason, req.content)\\n exit(77)\",\n \"def delete(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"delete\\\"), kwargs)\",\n \"def delete(self, **kwargs):\\n\\n return self.api_request(self._get_method_fullname(\\\"delete\\\"), kwargs)\",\n \"def delete(self, *args, **kwargs):\\n self.request(\\\"delete\\\", *args, **kwargs)\",\n \"def do_delete_request(self, uri, headers, timeout_ms):\\n return self._do_request('DELETE', uri, headers, None, timeout_ms, None)\",\n \"def _delete(self, path=\\\"\\\", **kwargs):\\n uri = force_json(self.uri + path)\\n return self.client.request(uri, method=\\\"DELETE\\\", **kwargs)\",\n \"def delete(self, *args, **kw):\\n kw['method'] = 'DELETE'\\n return self.open(*args, **kw)\",\n \"def _delete(self, url):\\n url = urljoin(self.base_url, url)\\n try:\\n r = self._make_request(**dict(\\n method='DELETE',\\n url=url,\\n auth=self.auth,\\n timeout=self.timeout,\\n hooks=self.request_hooks,\\n headers=self.request_headers\\n ))\\n except requests.exceptions.RequestException as e:\\n raise e\\n else:\\n if r.status_code >= 400:\\n _raise_response_error(r)\\n if r.status_code == 204:\\n return\\n return r.json()\",\n \"def _delete(self, url, **kwargs):\\n return self._http.delete(self.cluster + url, timeout=self.timeout, **kwargs)\",\n \"def api_delete(self, path):\\n return self._api_request(path, 'DELETE')\",\n \"def adel(url, **kwargs):\\n return requests.delete(url, **kwargs)\",\n \"def delete(self, method, uri, query_param, request_param, headers, **kwargs):\\n raise NotImplementedError\",\n \"def delete(self):\\n\\n headers = self._default_headers()\\n\\n return self._request(self.name,\\n ok_status=None,\\n data=None,\\n headers=headers,\\n method=\\\"DELETE\\\")\",\n \"def delete(self, url: str, params: Dict[str, Any] = None, headers: Dict[str, Any] = None) -> Response:\\n return self._api_client._delete(url, params=params, headers=headers)\",\n \"async def delete(url, session=None, **kwargs):\\n\\n method = 'DELETE'\\n resp = await _request(method, url, session=session, **kwargs)\\n return resp\",\n \"def delete(self, path: str, params: dict):\\n return self.request(\\\"DELETE\\\", path, params)\",\n \"def sendDeleteRequest(self, url:str, originator:str, parameters:Parameters=None, ct:ContentSerializationType=None, targetResource:Resource=None) -> Result:\\n\\t\\tif Utils.isHttpUrl(url):\\n\\t\\t\\tCSE.event.httpSendDelete() # type: ignore\\n\\t\\t\\treturn CSE.httpServer.sendHttpRequest(requests.delete, url, originator, parameters=parameters, ct=ct, targetResource=targetResource)\\n\\t\\tLogging.logWarn(dbg := f'unsupported url scheme: {url}')\\n\\t\\treturn Result(status=True, rsc=RC.badRequest, dbg=dbg)\",\n \"def delete(self):\\n self.method = \\\"DELETE\\\"\\n self.send()\",\n \"def delete(self, url, payload={}):\\n response = self._make_request(\\\"DELETE\\\", url, payload)\\n\\n return response\",\n \"def delete(api, url, payload=None, headers=None, auth=_KERBEROS_AUTH,\\n proxies=None, retries=_NUM_OF_RETRIES, timeout=None):\\n return call(api, url, 'delete', payload=payload,\\n headers=headers, auth=auth, proxies=proxies, retries=retries,\\n timeout=timeout)\",\n \"def delete(self, url):\\n return self._query(url, 'DELETE')\",\n \"def delete(self):\\n self.call('DELETE', expect=error.NO_CONTENT)\",\n \"def delete(cls, uri):\\n return cls._perform_request(uri, 'DELETE')\",\n \"def delete(self):\\r\\n self.require_collection()\\r\\n request = http.Request('DELETE', self.get_url())\\r\\n\\r\\n return request, parsers.parse_empty\",\n \"def delete(resource, params=None, expected_status_code=204, user=user_data):\\n return call(requests.delete, resource, expected_status_code, user, params=params)\",\n \"def simulate_delete(app, path, **kwargs) -> _ResultBase:\\n return simulate_request(app, 'DELETE', path, **kwargs)\",\n \"def delete(self) -> requests.request:\\n # Check if id is set\\n if self.args.id is None:\\n raise Exception('Provide id of asset you want to delete')\\n\\n # Send DELETE request\\n return requests.delete(self.REQUEST_URL + str(self.args.id))\",\n \"def delete(self, *args, **kwargs):\\n return self.handle_delete_request()\",\n \"def delete(self):\\n return self.request('', pylastica.request.Request.DELETE)\",\n \"def base_delete(url_path):\\n response = requests.delete(url=settings.URL_API + url_path)\\n return response\",\n \"def delete(self, _uri):\\n print(\\\"Deleting '%s'\\\"%(_uri))\\n response = self.__httpsRequest('DELETE', _uri, '')\",\n \"def __Delete(self, url, id = None):\\n\\n conn = self.__GetConnection()\\n if (id != None):\\n url += \\\"/\\\" + str(id)\\n conn.request(\\\"DELETE\\\", url, \\\"\\\", self.__MakeHeaders(True))\\n response = conn.getresponse()\\n self.__CheckResponse(response)\",\n \"def delete(self, params=None):\\n params = self.parameters(additional_parameters=params)\\n res = delete(self.endpoint_url, params=params)\\n return Response(res)\",\n \"def DELETE(url, mime_type='text/html'):\\n def_app = DefaultHttpRequestHandler()\\n return def_app.RequestMap(url, methods=['DELETE'], produces=mime_type)\",\n \"def simulate_delete(self, path='/', **kwargs):\\n return self.simulate_request('DELETE', path, **kwargs)\",\n \"def test_client_can_do_delete_request(self):\\n response = self.httpbin_4.test_requests_delete_method()\\n self.assertEqual(response.request.method, 'DELETE')\\n self.assertEqual(response.status_code, 200)\",\n \"def simulate_delete(self, path='/', **kwargs) -> _ResultBase:\\n return self.simulate_request('DELETE', path, **kwargs)\",\n \"async def delete(self, delete: TPayload) -> None:\",\n \"def delete(self):\\n self.request().delete()\",\n \"def delete(self, uri, body=None, headers=None, auth=False):\\n return self.send_request('DELETE', uri, body, headers, auth)\",\n \"async def simulate_delete(self, path='/', **kwargs) -> _ResultBase:\\n return await self.simulate_request('DELETE', path, **kwargs)\",\n \"def do_DELETE(self): # pylint: disable=missing-docstring\\n self._set_headers(204)\\n (resource, id) = self.parse_url(self.path)\\n\\n if resource == \\\"comments\\\":\\n delete_comment(id)\\n elif resource == \\\"posts\\\":\\n delete_post(id)\\n elif resource == \\\"categories\\\":\\n delete_category(id)\\n elif resource == \\\"posttags\\\":\\n remove_tag(id)\\n \\n self.wfile.write(\\\"\\\".encode())\",\n \"def delete(self, path):\\n \\n # prep\\n delete_url = self.url(path)\\n\\n # request\\n response = requests.delete(delete_url, auth=self.auth, headers=API.HEADERS)\\n\\n # test and return\\n self.raise_for_status(response)\",\n \"def delete_request(\\n self,\\n alias,\\n uri,\\n data=None,\\n json=None,\\n params=None,\\n headers=None,\\n allow_redirects=None,\\n timeout=None):\\n session = self._cache.switch(alias)\\n data = utils.format_data_according_to_header(session, data, headers)\\n # XXX workaround to restore library default behaviour. Not needed in new keywords\\n redir = True if allow_redirects is None else allow_redirects\\n\\n response = self._common_request(\\n \\\"delete\\\",\\n session,\\n uri,\\n data=data,\\n json=json,\\n params=params,\\n headers=headers,\\n allow_redirects=redir,\\n timeout=timeout)\\n\\n return response\",\n \"def delete(path: str):\\n token = get_token()\\n headers = {\\n \\\"Authorization\\\": f\\\"Bearer {token}\\\"\\n }\\n return requests.delete(get_base_url() + path, headers=headers)\",\n \"def destroy(self, request, pk=None):\\n\\n return Response({'http_method': 'DELETE'})\",\n \"def delete(self, url_pattern):\\n return self.route(url_pattern, methods=['DELETE'])\",\n \"def delete(self):\\r\\n request = http.Request('DELETE', self.get_push_url(), None)\\r\\n return request, parsers.parse_empty\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def api_delete(self, *args, **kwargs):\\n return self.api_delete_with_response(*args, **kwargs)[0]\",\n \"def revoke(self):\\r\\n return http.Request('DELETE', self.get_url()), parsers.parse_empty\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def delete(self, ids):\\r\\n params = base.get_params(None, locals())\\r\\n request = http.Request('DELETE', self.get_url(), params)\\r\\n return request, parsers.parse_json\",\n \"def do_DELETE(self):\\n note_details = NoteDetails\\n if self.path == '/note/api/delete':\\n response_data=note_details.delete_data(self)\\n Response(self).jsonResponse(status=200, data=response_data)\",\n \"def delete_call(url, headers=None):\\n response = requests.delete(url, headers=headers)\\n\\n if response.status_code == 401:\\n raise RedirectException(reverse('b2b_control:logout'))\\n\\n return response\",\n \"def delete(self, path):\\n req_url = self.normalize_cdmi_url(path)\\n res = requests.delete(req_url, auth=self.auth, verify=False)\\n if res.status_code == 204:\\n return Response(0, \\\"ok\\\")\\n else:\\n return Response(res.status_code, res)\",\n \"def delete(self, call, params={}): \\n # Build an endpoint using the parameters...\\n endpoint = self._calls[call](params)\\n url = '{}/{}'.format(str(self), str(endpoint))\\n return self.deleter.respond(url)\",\n \"def test_delete():\\n sample_uuid = get_sample_id()\\n response = requests.delete(f'http://localhost:5000/api/persons/{sample_uuid}')\\n\\n assert response.status_code == 200\",\n \"def delete(self, url):\\n return self.session.delete(url=self.base_url + url)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Delete(self, request, global_params=None):\\n config = self.GetMethodConfig('Delete')\\n return self._RunMethod(\\n config, request, global_params=global_params)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.86554825","0.8388014","0.830307","0.81576777","0.8146794","0.81088203","0.8056914","0.8012024","0.8011185","0.79084957","0.7869481","0.7860623","0.78572756","0.77129704","0.7690312","0.7690312","0.76605344","0.7657503","0.7538217","0.7509946","0.7497349","0.7495778","0.747285","0.7464505","0.7430828","0.74264854","0.7403058","0.7383994","0.73783445","0.7368197","0.73266226","0.7324984","0.7319125","0.7316048","0.7299779","0.72835505","0.72805196","0.72640514","0.7249236","0.72470903","0.72419846","0.72231615","0.72019464","0.7200508","0.7149553","0.7144593","0.71434","0.71285206","0.71135336","0.71104926","0.7102879","0.7093949","0.70777345","0.7077713","0.7014437","0.70057577","0.6966438","0.6961326","0.6944376","0.6940707","0.6937969","0.6933806","0.6933806","0.6933806","0.6933806","0.6933806","0.6933806","0.6933806","0.6927929","0.69237405","0.68968487","0.68968487","0.68968487","0.68968487","0.6894617","0.6891618","0.6879725","0.6873823","0.6861536","0.68574977","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346","0.6847346"],"string":"[\n \"0.86554825\",\n \"0.8388014\",\n \"0.830307\",\n \"0.81576777\",\n \"0.8146794\",\n \"0.81088203\",\n \"0.8056914\",\n \"0.8012024\",\n \"0.8011185\",\n \"0.79084957\",\n \"0.7869481\",\n \"0.7860623\",\n \"0.78572756\",\n \"0.77129704\",\n \"0.7690312\",\n \"0.7690312\",\n \"0.76605344\",\n \"0.7657503\",\n \"0.7538217\",\n \"0.7509946\",\n \"0.7497349\",\n \"0.7495778\",\n \"0.747285\",\n \"0.7464505\",\n \"0.7430828\",\n \"0.74264854\",\n \"0.7403058\",\n \"0.7383994\",\n \"0.73783445\",\n \"0.7368197\",\n \"0.73266226\",\n \"0.7324984\",\n \"0.7319125\",\n \"0.7316048\",\n \"0.7299779\",\n \"0.72835505\",\n \"0.72805196\",\n \"0.72640514\",\n \"0.7249236\",\n \"0.72470903\",\n \"0.72419846\",\n \"0.72231615\",\n \"0.72019464\",\n \"0.7200508\",\n \"0.7149553\",\n \"0.7144593\",\n \"0.71434\",\n \"0.71285206\",\n \"0.71135336\",\n \"0.71104926\",\n \"0.7102879\",\n \"0.7093949\",\n \"0.70777345\",\n \"0.7077713\",\n \"0.7014437\",\n \"0.70057577\",\n \"0.6966438\",\n \"0.6961326\",\n \"0.6944376\",\n \"0.6940707\",\n \"0.6937969\",\n \"0.6933806\",\n \"0.6933806\",\n \"0.6933806\",\n \"0.6933806\",\n \"0.6933806\",\n \"0.6933806\",\n \"0.6933806\",\n \"0.6927929\",\n \"0.69237405\",\n \"0.68968487\",\n \"0.68968487\",\n \"0.68968487\",\n \"0.68968487\",\n \"0.6894617\",\n \"0.6891618\",\n \"0.6879725\",\n \"0.6873823\",\n \"0.6861536\",\n \"0.68574977\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\",\n \"0.6847346\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.707418"},"document_rank":{"kind":"string","value":"54"}}},{"rowIdx":83089,"cells":{"query":{"kind":"string","value":"Make an HTTP HEAD request."},"document":{"kind":"string","value":"def handle_head(self, api, command):\n return self._make_request_from_command('HEAD', command)"},"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 do_HEAD(self):\n self.do_GET(True)","def do_HEAD(self):\n self.log.debug('do_HEAD called')\n self.HeadGet('HEAD')","async def head(url, session=None, **kwargs):\n\n method = 'HEAD'\n resp = await _request(method, url, session=session, **kwargs)\n return resp","def head(self, url, *args, **kwargs):\n with LOG_JWS_HEAD().context():\n return DeferredContext(\n self._send_request(u'HEAD', url, *args, **kwargs)\n ).addActionFinish()","def head(self, *args, **kw):\n kw['method'] = 'HEAD'\n return self.open(*args, **kw)","def HEAD(self, req):\r\n resp = req.get_response(self.app)\r\n\r\n return HTTPOk(headers=resp.headers)","def do_HEAD(self):\n self._try_to_process_request(self._handle_head_request)","def HEAD(self, req):\n resp = req.get_response(self.app)\n\n return HTTPOk(headers=resp.headers)","def do_HEAD(self):\n self.send_response(200)","def simulate_head(app, path, **kwargs) -> _ResultBase:\n return simulate_request(app, 'HEAD', path, **kwargs)","def do_HEAD(self):\r\n f = self.send_head()\r\n if f:\r\n f.close()","def do_HEAD(self):\n f = self.send_head()\n if f:\n f.close()","def do_HEAD(self):\n f = self.send_head()\n if f:\n f.close()","def do_HEAD(self):\n f = self.send_head()\n if f:\n f.close()","def do_HEAD(self):\n f = self.send_head()\n if f:\n f.close()","def HEAD(self, req):\n return self.GETorHEAD(req)","def simulate_head(self, path='/', **kwargs):\n return self.simulate_request('HEAD', path, **kwargs)","def simulate_head(self, path='/', **kwargs) -> _ResultBase:\n return self.simulate_request('HEAD', path, **kwargs)","async def simulate_head(self, path='/', **kwargs) -> _ResultBase:\n return await self.simulate_request('HEAD', path, **kwargs)","def head(url, **kwargs):\n\n kwargs.setdefault('allow_redirects', False)\n return request('head', url, **kwargs)","def head(url, to_error=_default_to_error, **kwargs):\n\n return request('head', url, **kwargs)","def HEAD(url, mime_type='text/html'):\n def_app = DefaultHttpRequestHandler()\n return def_app.RequestMap(url, methods=['HEAD'], produces=mime_type)","def do_head(self) -> bool:\n with FileHTTPResponse(self.request) as response:\n response.send_head(self.conn)\n return response.close_connection()","def test_HEAD(self):\n if not self.url:\n return\n response = self.client.head(self.url, {}, format='json')\n self.assertIn(response.status_code, [status.HTTP_405_METHOD_NOT_ALLOWED,\n status.HTTP_200_OK,\n status.HTTP_401_UNAUTHORIZED])","def do_HEAD(self):\n content = self._regenerate(self.path)\n if content:\n self._send_regenerated_head(content)\n else:\n SimpleHTTPRequestHandler.do_HEAD(self)","def make_head_request(self, headers):\n\n lmt, etag = headers.get('last-modified'), headers.get('etag')\n if lmt != None or etag != None:\n req_header = {}\n if lmt != None and self.config.flag_use_last_modified:\n req_header['if-modified-since'] = lmt\n if etag != None and self.config.flag_use_etags:\n req_header['if-none-match'] = etag\n\n try:\n # print 'Making a head request =>',self.url\n fhead = urlhelper.head_url(self.url, headers=req_header,\n verify = self.config.flag_ssl_validate)\n\n # Status code is 304 ?\n if fhead.status_code == 304:\n return True\n except urlhelper.FetchUrlException, e:\n pass\n else:\n log.debug(\"Required meta-data headers (lmt, etag) not present =>\", self.url)\n\n # No lmt or etag or URL is not uptodate\n return False","def do_HEAD(self):\n self.range_from, self.range_to = self._get_range_header()\n self.response_type = HTTPStatus.OK if self.range_from == None else HTTPStatus.PARTIAL_CONTENT\n self.cors_req = self._has_cors_header()\n f = self.send_headers()\n # don't send the file\n if f:\n f.close()","def head(self, url, params=None, headers=None, timeout=10):\r\n headers = headers or self.headers\r\n try:\r\n r = self.request(url=url, method='HEAD', params=params, extra_headers=headers, timeout=timeout)\r\n return r.status_code < 403\r\n except requests.exceptions.ProxyError:\r\n return None\r\n except requests.RequestException as error:\r\n if self._debug:\r\n logging.exception(\r\n ''.join(traceback.format_exception(etype=type(error), value=error, tb=error.__traceback__)))\r\n return None","def head_request(\n self,\n alias,\n uri,\n headers=None,\n allow_redirects=None,\n timeout=None):\n session = self._cache.switch(alias)\n # XXX workaround to restore library default behaviour. Not needed in new keywords\n redir = False if allow_redirects is None else allow_redirects\n response = self._common_request(\n \"head\",\n session,\n uri,\n headers=headers,\n allow_redirects=redir,\n timeout=timeout)\n\n return response","def test_head(self):\n client = RestClient(host=self.host, username='')\n rest_url = 'some/url/'\n \n # Mock good response\n with responses.RequestsMock() as rsps:\n rsps.add(responses.HEAD, f'{self.host}/{rest_url}', status=200,\n json={'value':\"good!\"})\n r = client.head(rest_url)","def head_obj(cls, client, spec):\n return cls.set_header(client.get(cls.HEAD.format(**spec))[0])","def head(self, pattern, handler):\n return self.route(HEAD, pattern, handler)","def send_head(self):\n\t\tif self.is_cgi():\n\t\t\treturn self.run_cgi()\n\t\telse:\n\t\t\treturn SimpleHTTPServer.SimpleHTTPRequestHandler.send_head(self)","def head(self, api_path, *args, **kwargs):\n\n\t\treturn self._do_operation(u'head', api_path, *args, **kwargs)","async def get_file_head(self, path: str) -> Optional[CIMultiDictProxy]:\n session = aiohttp.ClientSession(loop=self._loop)\n try:\n async with session.request(hdrs.METH_HEAD, path) as res: # type: aiohttp.ClientResponse\n return res.headers\n except (aiohttp.ServerDisconnectedError, aiohttp.ClientResponseError):\n self.log_debug(\"Could not get a HEAD for the {!r}.\".format(path))\n finally:\n session.close()\n return None","def perform_http_head(self, system_name, variable_config=None,\n request_id=None, data=None,\n url=None, expected_response=None,\n headers=None, user=None, password=None,\n allow_redirects=None, timeout=None,\n json=None, cookies=None, files=None, proxies=None,\n verify=None, stream=None, cert=None, var_sub=None):\n arguments = {'system_name': system_name, 'variable_config': variable_config,\n 'request_id': request_id, 'url': url, 'data': data,\n 'expected_response': expected_response, 'headers': headers,\n 'user': user, 'password': password, 'allow_redirects': allow_redirects,\n 'timeout': timeout, 'json': json, 'cookies': cookies,\n 'files': files, 'proxies': proxies, 'verify': verify,\n 'stream': stream, 'cert': cert, 'var_sub': var_sub}\n wdesc = \"Perform a http head to the url\"\n pSubStep(wdesc)\n pNote(system_name)\n output_dict = {}\n result = True\n\n for element in arguments:\n if element in [\"json\", \"data\", \"variable_config\"] and \\\n arguments[element] and arguments[element] is not None:\n arguments[element] = Utils.rest_Utils.\\\n check_ext_get_abspath(arguments[element], self.tc_path)\n\n credentials = Utils.data_Utils.\\\n get_user_specified_tag_values_in_tc(self.datafile, **arguments)\n\n if credentials[\"variable_config\"] and \\\n credentials[\"variable_config\"] is not None:\n credentials[\"variable_config\"] = Utils.rest_Utils.\\\n check_ext_get_abspath(credentials[\"variable_config\"],\n os.path.dirname(self.datafile))\n\n for element in credentials:\n credentials = Utils.rest_Utils.\\\n resolve_credentials_for_rest(credentials, element,\n self.datafile, system_name)\n credentials[\"auth\"] = (credentials['user'], credentials['password'])\n\n credentials, popped_args = Utils.rest_Utils.\\\n remove_invalid_req_args(credentials, [\"user\", \"password\",\n \"request_id\",\n \"variable_config\",\n \"var_sub\", \"json\"])\n\n pNote(\"url is: {0}\".format(credentials['url']))\n for i in range(0, len(popped_args[\"json\"])):\n if popped_args[\"json\"][i] != \"Error\":\n credentials[\"json\"] = popped_args[\"json\"][i]\n for key in credentials:\n pNote(\"Sending argument '{0}': {1}\"\n .format(key, credentials[key]))\n status, api_response = self.rest_object.head(**credentials)\n result = result and status\n output_dict.update(self.rest_object.\n update_output_dict(system_name,\n api_response, request_id, status, i+1))\n else:\n pNote(\"Request not sent.\", \"error\")\n status = False\n result = result and status\n if result:\n msg = \"http head successful\"\n else:\n msg = \"http head failed\"\n pNote(msg)\n report_substep_status(result)\n return result, output_dict","def head(self, params=None):\n params = self.parameters(additional_parameters=params)\n res = head(self.endpoint_url, params=params)\n return Response(res)","def send_head(self):\n path = self.path\n # separate the query\n i = path.find( '?' )\n if i >= 0:\n query = path[ i: ]\n path = path[ : i ]\n else:\n query = ''\n if os.path.isdir( '.' + path ): # os.getcwd() ?\n # check if index.py exists\n pathindexpy = os.path.join( path, 'index.py' )\n if os.path.exists( '.' + pathindexpy ):\n path = pathindexpy + query\n self.path = path\n if self.is_cgi():\n return self.run_cgi()\n else:\n return SimpleHTTPServer.SimpleHTTPRequestHandler.send_head(self)","def HeadGet(self, method):\n self.log.debug('HeadGet called: %s' % self.path)\n orig_request = ssl_proxy_log.Request(self.path, method, self.headers,\n self.log, self.http_redirect_table, self.ssl_redirect_table)\n self.log.debug('orig_request built, calling fetch2')\n\n request_log = self.fetch2(orig_request, self.wfile)\n\n if not request_log:\n return\n\n if self.display_filter:\n if not self.display_filter.search(self.path):\n return\n\n if self.interactive:\n self.Log(request_log)\n\n #log.SetValue('url', self.path)\n #self.Log(log)","def head(self, uri, format='json', data=None, authentication=None, **kwargs):\r\n content_type = self.get_content_type(format)\r\n kwargs['HTTP_ACCEPT'] = content_type\r\n\r\n # GET & DELETE are the only times we don't serialize the data.\r\n if data is not None:\r\n kwargs['data'] = data\r\n\r\n if authentication is not None:\r\n kwargs['HTTP_AUTHORIZATION'] = authentication\r\n\r\n return self.client.head(uri, **kwargs)","def _handle_head_request(self):\n self._header_only = True\n self._handle_get_request()","def make_request(url):\n response = {}\n try:\n resp = requests.head(url, allow_redirects=False, timeout=1)\n response['url'] = resp.url\n response['status'] = resp.status_code\n except BaseException:\n response['url'] = url\n response['status'] = -1\n\n return response","def head_jenkins_with_http_info(self, **kwargs):\n\n all_params = []\n all_params.append('async')\n all_params.append('_return_http_data_only')\n all_params.append('_preload_content')\n all_params.append('_request_timeout')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method head_jenkins\" % key\n )\n params[key] = val\n del params['kwargs']\n\n collection_formats = {}\n\n path_params = {}\n\n query_params = []\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n # Authentication setting\n auth_settings = ['jenkins_auth']\n\n return self.api_client.call_api('/api/json', 'HEAD',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type=None,\n auth_settings=auth_settings,\n async=params.get('async'),\n _return_http_data_only=params.get('_return_http_data_only'),\n _preload_content=params.get('_preload_content', True),\n _request_timeout=params.get('_request_timeout'),\n collection_formats=collection_formats)","def head(cls, client, spec, headers=None):\n return cls(client, spec, headers)","def head(*args, **kwargs):\r\n\r\n return HttpResponse()","def head(self, filesystem, acc_dir, cont_dir, account, container, req):\n try:\n # create path\n path, headers = '', ''\n if 'x-updater-request' in req.headers:\n path = self.create_updater_path(filesystem, acc_dir, cont_dir, account, container)\n self.logger.debug(('HEAD container called for path: %(path)s'),\n {'path' : path})\n # get headers for updater request\n headers = self.__updater_headers(path , True) #pass True in updater request\n else:\n path = self.create_path(filesystem, acc_dir, cont_dir, account, container)\n self.logger.debug(('HEAD container called for path: %(path)s'),\n {'path' : path})\n # get headers for request\n headers = self.__head_or_get(path)\n if headers == HTTP_INTERNAL_SERVER_ERROR:\n self.logger.debug('Internal error raised from library')\n raise HTTPInternalServerError(request=req)\n if headers == HTTP_NOT_FOUND and 'x-updater-request' in req.headers:\n self.logger.debug('File not found error raised from library: updater case')\n raise HTTPNotFound(request=req)\n elif headers == HTTP_NOT_FOUND:\n self.logger.debug('File not found error raised from library')\n raise HTTPNotFound(request=req)\n else:\n out_content_type = get_listing_content_type(req)\n headers['Content-Type'] = out_content_type\n return headers\n except HTTPException as error:\n self.logger.exception(error)\n raise error\n except Exception as err:\n self.logger.error \\\n (('HEAD request failed for container: %(container)s '),\n {'container' : container})\n self.logger.exception(err)\n raise err","def test_no_head(self):\n\n def get_200_response(req):\n return HttpResponse(status=200)\n\n request = self.request_factory.head(\"/\")\n conditional_get_response = ConditionalGetMiddleware(get_200_response)(request)\n self.assertNotIn(\"ETag\", conditional_get_response)","def assertHttpOk(self, url):\n host = urlsplit(url)\n conn = HTTPConnection(host.netloc, timeout=4)\n conn.request('HEAD', host.path)\n res = conn.getresponse()\n self.assertEqual(res.status, 200)","def send_head(self):\n print(\"[*] send_head\")\n path = self.translate_path(self.path)\n f = None\n if os.path.isdir(path):\n parts = urllib.parse.urlsplit(self.path)\n if not parts.path.endswith('/'):\n # redirect browser - doing basically what apache does\n self.send_response(HTTPStatus.MOVED_PERMANENTLY)\n new_parts = (parts[0], parts[1], parts[2] + '/',\n parts[3], parts[4])\n new_url = urllib.parse.urlunsplit(new_parts)\n self.send_header(\"Location\", new_url)\n self.end_headers()\n return None\n for index in \"index.html\", \"index.htm\":\n index = os.path.join(path, index)\n if os.path.exists(index):\n path = index\n break\n else:\n return self.list_directory(path)\n ctype = self.guess_type(path)\n # check for trailing \"/\" which should return 404. See Issue17324\n # The test for this was added in test_httpserver.py\n # However, some OS platforms accept a trailingSlash as a filename\n # See discussion on python-dev and Issue34711 regarding\n # parseing and rejection of filenames with a trailing slash\n if path.endswith(\"/\"):\n self.send_error(HTTPStatus.NOT_FOUND, \"File not found\")\n return None\n try:\n f = open(path, 'rb')\n except OSError:\n self.send_error(HTTPStatus.NOT_FOUND, \"File not found\")\n return None\n\n try:\n fs = os.fstat(f.fileno())\n # Use browser cache if possible\n if (\"If-Modified-Since\" in self.headers\n and \"If-None-Match\" not in self.headers):\n # compare If-Modified-Since and time of last file modification\n try:\n ims = email.utils.parsedate_to_datetime(\n self.headers[\"If-Modified-Since\"])\n except (TypeError, IndexError, OverflowError, ValueError):\n # ignore ill-formed values\n pass\n else:\n if ims.tzinfo is None:\n # obsolete format with no timezone, cf.\n # https://tools.ietf.org/html/rfc7231#section-7.1.1.1\n ims = ims.replace(tzinfo=datetime.timezone.utc)\n if ims.tzinfo is datetime.timezone.utc:\n # compare to UTC datetime of last modification\n last_modif = datetime.datetime.fromtimestamp(\n fs.st_mtime, datetime.timezone.utc)\n # remove microseconds, like in If-Modified-Since\n last_modif = last_modif.replace(microsecond=0)\n\n if last_modif <= ims:\n self.send_response(HTTPStatus.NOT_MODIFIED)\n self.end_headers()\n f.close()\n return None\n\n self.send_response(HTTPStatus.OK)\n self.send_header(\"Content-type\", ctype)\n self.send_header(\"Content-Length\", str(fs[6]))\n self.send_header(\"Last-Modified\",\n self.date_time_string(fs.st_mtime))\n self.end_headers()\n return f\n except:\n f.close()\n raise","def do_GET(self):\r\n f = self.send_head()\r\n if f:\r\n self.copyfile(f, self.wfile)\r\n f.close()","def connect(self):\n # sends User-Agent and Refferer (main page on the server) in the header, \n # it's necessary when the server blocks downloading via links from other resources\n headers = {'User-Agent': self.user_agent, 'Refferer': '{}://{}/'.format(self.url.protocol, self.url.host)}\n self.conn = self.protocol(self.url.host, timeout=self.timeout)\n self.conn.request('HEAD', self.url.request, headers=headers)\n response = self.conn.getresponse()\n\n # status 3xx\n if response.status // 100 == 3:\n location = response.getheader('Location')\n return self.redirect(location, response.status)\n\n if response.status != 200: # HTTP(S) error\n return TaskHeadError(self.url.host, response.status)\n\n file_size = int(response.getheader('Content-Length'))\n info = TaskHeadData(self.url.host, response.status, file_size)\n response.close()\n return info","def do_GET(self):\n f = self.send_head()\n if f:\n try:\n self.copyfile(f, self.wfile)\n finally:\n f.close()","def _send_regenerated_head(self, content):\n self.send_response(200)\n self.send_header(\"Content-type\", 'text/html')\n self.send_header(\"Content-Length\", len(content))\n self.send_header(\"Last-Modified\", self.date_time_string())\n self.end_headers()","def send_head(self):\n path = self.translate_path(self.path)\n f = None\n if os.path.isdir(path):\n parts = urllib.parse.urlsplit(self.path)\n if not parts.path.endswith('/'):\n # redirect browser - doing basically what apache does\n self.send_response(HTTPStatus.MOVED_PERMANENTLY)\n new_parts = (parts[0], parts[1], parts[2] + '/',\n parts[3], parts[4])\n new_url = urllib.parse.urlunsplit(new_parts)\n self.send_header(\"Location\", new_url)\n self.end_headers()\n return None\n # If there is only one file, return it.\n try:\n file_list = os.listdir(path)\n except OSError:\n self.send_error(\n HTTPStatus.NOT_FOUND,\n \"No permission to list directory\")\n\n # If there is more than one file, return\n # the one called openapi.yaml\n if len(file_list) == 1:\n path = os.path.join(path, file_list[0])\n elif len(file_list) > 1:\n for index in \"openapi.yml\", \"openapi.yaml\":\n index = os.path.join(path, index)\n if os.path.exists(index):\n path = index\n break\n else:\n self.send_error(HTTPStatus.NOT_FOUND, \"File not found\")\n ctype = self.guess_type(path)\n try:\n f = open(path, 'rb')\n except OSError:\n self.send_error(HTTPStatus.NOT_FOUND, \"File not found\")\n return None\n try:\n self.send_response(HTTPStatus.OK)\n self.send_header(\"Content-type\", ctype)\n fs = os.fstat(f.fileno())\n self.send_header(\"Content-Length\", str(fs[6]))\n self.send_header(\"Last-Modified\", self.date_time_string(fs.st_mtime))\n self.end_headers()\n return f\n except Exception:\n f.close()\n raise","def do_GET(self):\n self.log.debug('do_GET called')\n self.HeadGet('GET')","def send_head(self):\n path = translate_path(self.headers[\"URI\"], DOCUMENT_ROOT)\n if not validate_root_escape(DOCUMENT_ROOT, path):\n self.send_error(404)\n return None\n if os.path.isdir(path):\n path = os.path.join(path, \"index.html\")\n\n if not os.path.isfile(path):\n fdir, fname = url_basename(path)\n if fname == \"index.html\":\n if RECREATE_INDEX_HTML:\n index_html = self.list_directory(fdir)\n return index_html\n else:\n self.send_error(403)\n self.send_error(404)\n return None\n try:\n fd = open(path, \"rb\")\n except Exception:\n self.send_error(500, \"Error opening file\")\n return None\n\n self.send_response(200)\n self.send_header(\"Content-type\", guess_content_type(path))\n self.send_header(\"Content-Length\", str(os.fstat(fd.fileno())[6]))\n self.end_headers()\n return fd","def do_GET(self):\n f = self.send_head()\n if f:\n try:\n \n self.copyfile(f, self.wfile)\n finally:\n f.close()","def _get_url_http_headers(self) -> Dict[str, str]:\n h = requests.head(self.url, allow_redirects=True)\n return h.headers","def head300(\n self, custom_headers=None, raw=False, **operation_config):\n # Construct URL\n url = '/http/redirect/300'\n\n # Construct parameters\n query_parameters = {}\n\n # Construct headers\n header_parameters = {}\n header_parameters['Content-Type'] = 'application/json; charset=utf-8'\n if custom_headers:\n header_parameters.update(custom_headers)\n\n # Construct and send request\n request = self._client.head(url, query_parameters)\n response = self._client.send(request, header_parameters, **operation_config)\n\n if response.status_code not in [200, 300]:\n raise models.ErrorException(self._deserialize, response)\n\n if raw:\n client_raw_response = ClientRawResponse(None, response)\n client_raw_response.add_headers({\n 'Location': 'str',\n })\n return client_raw_response","def test_post_head_unauthorized(self):\n url = reverse('post-list')\n response = self.client.head(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def send_head(self):\n print 'url_path', self.path\n file_path = self.url_path_to_file_path(self.path)\n print 'file_path', file_path\n f = None\n if os.path.isdir(file_path):\n if not self.path.endswith('/'):\n # redirect browser - doing basically what apache does\n self.send_response(301)\n self.send_header(\"Location\", self.path + \"/\")\n self.end_headers()\n return None\n for index in \"index.html\", \"index.htm\":\n index = os.path.join(file_path, index)\n if os.path.exists(index):\n file_path = index\n break\n\n self.counter.incr_counter(file_path)\n\n if os.path.isdir(file_path):\n return self.list_directory(file_path)\n ctype = self.guess_type(file_path)\n\n try:\n # Always read in binary mode. Opening files in text mode may cause\n # newline translations, making the actual size of the content\n # transmitted *less* than the content-length!\n f = open(file_path, 'rb')\n except IOError:\n self.send_error(404, \"File not found \" + file_path)\n return None\n self.send_response(200)\n self.send_header(\"Content-type\", ctype)\n fs = os.fstat(f.fileno())\n self.send_header(\"Content-Length\", str(fs[6]))\n self.send_header(\"Last-Modified\", self.date_time_string(fs.st_mtime))\n self.end_headers()\n return f","def access_gemini_url_head_method(context, endpoint):\n url = urljoin(context.gemini_api_url, endpoint)\n context.response = requests.head(url)","def get_server_status_code(url):\n host, path = urlparse.urlparse(url)[1:3] # elems [1] and [2]\n print \"host : \", host, \" | path : \", path\n try:\n conn = httplib.HTTPConnection(host)\n conn.request('HEAD', path)\n print \" | status : \" , conn.getresponse().status\n return conn.getresponse().status\n except StandardError as e:\n print \"StandardError : \", e\n return None","def do_GET(self):\n #if self.path.startswith('/api/'):\n # f = self.send_response_headers('api call')\n #else:\n f=self.route()\n if f==False:\n f = self.send_head()\n if f:\n try:\n self.copyfile(f, self.wfile)\n finally:\n f.close()","def send_head(self, conn: socket.socket, encoding: str = HTTP_DEFAULT_ENCODING) -> bool:\n conn_addr = conn.getpeername()\n if 'Connection' not in self.__headers:\n self.set_header('Connection', self.request.headers.get('Connection', 'close').lower() if self.request else 'close')\n if self.get_header('Connection') == 'keep-alive' and (timeout := conn.gettimeout()):\n logging.debug('%s timeout = %d', conn_addr, timeout)\n self.set_header('Keep-Alive', f\"timeout={int(timeout + self.request.ctime - time.time() if self.request else timeout)}\")\n head = END_LINE.join([self.get_status_line(), *self.get_headers(), END_LINE]).encode(encoding)\n logging.debug('%s <- sending head [%s]', conn_addr, head)\n if self.protocol.version != (0, 9):\n with suppress(BrokenPipeError):\n conn.sendall(head)\n logging.info('%s <- head sent - ok', conn_addr)","def head(self, url, **kwargs):\n kwargs.setdefault(\"timeout\", 300)\n return super(PacerSession, self).head(url, **kwargs)","def send_head(self):\n path = self.translate_path(self.path)\n f = None\n if os.path.isdir(path):\n if not self.path.endswith('/'):\n # redirect browser - doing basically what apache does\n self.send_response(301)\n self.send_header(\"Location\", self.path + \"/\")\n self.end_headers()\n return None\n for index in \"index.html\", \"index.htm\":\n index = os.path.join(path, index)\n if os.path.exists(index):\n path = index\n break\n else:\n return self.list_directory(path)\n ctype = self.guess_type(path)\n try:\n # Always read in binary mode. Opening files in text mode may cause\n # newline translations, making the actual size of the content\n # transmitted *less* than the content-length!\n f = open(path, 'rb')\n except IOError:\n self.send_error(404, \"File not found\")\n return None\n try:\n self.send_response(200)\n self.send_header(\"Content-type\", ctype)\n fs = os.fstat(f.fileno())\n self.send_header(\"Content-Length\", str(fs[6]))\n self.send_header(\"Last-Modified\", self.date_time_string(fs.st_mtime))\n self.end_headers()\n return f\n except:\n f.close()\n raise","def _head_object(\n s3_conn: S3Client, bucket: str, key: str\n) -> Optional[HeadObjectOutputTypeDef]:\n try:\n return s3_conn.head_object(Bucket=bucket, Key=key)\n except botocore.exceptions.ClientError as err:\n if err.response[\"Error\"][\"Code\"] == \"404\":\n return None\n raise","def assert_has_valid_head(self, response, expected):\r\n assert 'head' in response\r\n head = response['head']\r\n assert isinstance(head, str)\r\n assert head == expected","def __head_or_get(self, path):\n try:\n info = self.get_cont_stat(path)\n if not isinstance(info, types.DictType):\n raise info()\n headers = HeaderKeyDict({\n 'X-Container-Object-Count': info['object_count'],\n 'X-Container-Bytes-Used': info['bytes_used'],\n 'X-Timestamp': info['created_at'],\n 'X-PUT-Timestamp': info['put_timestamp'],\n })\n metadata = info['metadata']\n for key, value in metadata.iteritems():\n if key == 'r-':\n headers.update({'x-container-read' : value})\n elif key == 'w-':\n headers.update({'x-container-write' : value})\n else:\n ser_key = key.split('-')[0]\n if ser_key == 'm':\n #Supported a single word key till first '-' \n #in the entire metadata header as X-Container-Meta-A\n #key = 'x-container-meta-' + key.split('-')[1]\n \n #SANCHIT: This supports multi-part key for metadata \n #such as X-Container-Meta-A-B-C\n key = 'x-container-meta-' + key.split('-', 1)[1]\n else:\n #key = 'x-container-sysmeta-' + key.split('-')[1]\n key = 'x-container-sysmeta-' + key.split('-', 1)[1]\n headers.update({key : value})\n return headers\n except HTTPException as error:\n self.logger.exception(error)\n return error.status_int\n except Exception as err:\n self.logger.exception(err)\n return HTTP_INTERNAL_SERVER_ERROR","def create_head(\n self,\n path: PathLike,\n commit: Union[\"SymbolicReference\", \"str\"] = \"HEAD\",\n force: bool = False,\n logmsg: Optional[str] = None,\n ) -> \"Head\":\n return Head.create(self, path, commit, logmsg, force)","def _send_head(self, text, code):\n size = len(text)\n self.send_response(code, None, size)\n # Trying to figure out what we are going to send out.\n # Maybe this could be improved a bit further but should do\n # it for now.\n extension_start = self.path.rfind('.')\n extension = self.path[extension_start:]\n try:\n self.send_header('Content-Type', CONTENT_TYPES[extension])\n except KeyError:\n self.send_header('Content-Type', 'text/html')\n self.send_header(\"Content-Length\", size)\n self.end_headers()","def GET(self, req):\n resp = req.get_response(self.app, method='HEAD')\n\n acl = resp.object_acl if req.is_object_request else resp.bucket_acl\n\n resp = HTTPOk()\n resp.body = tostring(acl.elem())\n\n return resp","def url_exist(url:str) -> bool:\r\n with closing(requests.head(url, allow_redirects=True)) as r:\r\n return r.ok","def head(self, tenant: str, filename: str) -> None:\n try:\n if not self.allow_info:\n raise ClientMethodNotAllowed\n if not filename:\n raise ClientError(\"No resource specified\")\n self.path = self.export_dir\n self.filepath = f\"{self.path}/{url_unescape(self.resource)}\"\n # ensure there are no symlinks in filepath\n any_path_islink(self.filepath, opts=options)\n if not os.path.lexists(self.filepath):\n raise ClientResourceNotFoundError(f\"{self.filepath} not found\")\n size, mime_type, mtime = self.get_file_metadata(self.filepath)\n logging.info(\n f\"user: {self.requestor}, checked file: {self.filepath} , MIME type: {mime_type}\"\n )\n self.set_header(\"Content-Length\", size)\n self.set_header(\"Accept-Ranges\", \"bytes\")\n self.set_header(\"Content-Type\", mime_type)\n self.set_header(\"Modified-Time\", str(mtime))\n self.set_status(HTTPStatus.OK.value)\n except Exception as e:\n error = error_for_exception(e, details=self.additional_log_details())\n logging.error(error.message)\n for name, value in error.headers.items():\n self.set_header(name, value)\n self.set_status(error.status, reason=error.reason)\n self.finish()","def send_head(self):\n path = self.translate_path(self.path)\n f = None\n if os.path.isdir(path):\n parts = urlparse.urlsplit(self.path)\n if not parts.path.endswith('/'):\n # redirect browser - doing basically what apache does\n self.send_response(301)\n new_parts = (parts[0], parts[1], parts[2] + '/',\n parts[3], parts[4])\n new_url = urlparse.urlunsplit(new_parts)\n self.send_header(\"Location\", new_url)\n self.end_headers()\n return None\n for index in \"index.html\", \"index.htm\":\n index = os.path.join(path, index)\n if os.path.exists(index):\n path = index\n break\n else:\n return self.list_directory(path)\n ctype = self.guess_type(path)\n parts = path.split('/')\n if len(parts)>=3 and parts[1]=='raw':\n path = '/'.join(parts[:1]+parts[2:])\n ctype = self.extensions_map['']\n print(os.path.basename(path))\n if ctype=='text/plain':\n return self.display_source(path)\n try:\n # Always read in binary mode. Opening files in text mode may cause\n # newline translations, making the actual size of the content\n # transmitted *less* than the content-length!\n f = open(path, 'rb')\n except IOError:\n self.send_error(404, \"File not found\")\n return None\n try:\n self.send_response(200)\n self.send_header(\"Content-type\", ctype)\n fs = os.fstat(f.fileno())\n self.send_header(\"Content-Disposition\", 'filename=\"%s\"' % (os.path.basename(path)))\n self.send_header(\"Content-Length\", str(fs[6]))\n self.send_header(\"Last-Modified\", self.date_time_string(fs.st_mtime))\n self.end_headers()\n return f\n except:\n f.close()\n raise","def _get_head(wit_path):\n\n head = _get_references_data(wit_path)['HEAD']\n if len(head) == 40:\n return head\n return _get_references_data(wit_path)[head]","def get_status_code(host, path=\"/\"):\n try:\n conn = httplib.HTTPConnection(host)\n conn.request(\"HEAD\", path)\n return conn.getresponse().status\n except StandardError:\n return None","def test_h2_header_ok(self):\n self.set_frang_config(frang_config=\"http_strict_host_checking true;\")\n client = self.get_client(\"deproxy-1\")\n client.start()\n client.parsing = False\n\n first_headers = [(\":authority\", \"localhost\"), (\":path\", \"/\")]\n second_headers = [(\":path\", \"/\"), (\"host\", \"localhost\")]\n third_headers = [(\":authority\", \"localhost\"), (\":path\", \"/\"), (\"host\", \"localhost\")]\n fourth_headers = [\n (\":authority\", \"tempesta-tech.com\"),\n (\":path\", \"/\"),\n (\"forwarded\", \"host=tempesta-tech.com\"),\n (\"forwarded\", \"for=tempesta.com\"),\n ]\n\n header_list = [\n first_headers,\n first_headers, # as byte\n second_headers,\n second_headers, # as byte\n third_headers,\n third_headers, # as byte\n fourth_headers,\n fourth_headers, # as byte\n ]\n for header in header_list:\n head = [\n (\":scheme\", \"https\"),\n (\":method\", \"HEAD\"),\n ]\n head.extend(header)\n client.make_request(head)\n self.assertTrue(client.wait_for_response(1))\n\n self.check_response(client, status_code=\"200\", warning_msg=\"frang: \")","def head_object(self, bucket_name, key):\n url = self.__key_url(bucket_name, key)\n res = self.infinispan_client.head(url, headers=self.headers, auth=self.basicAuth)\n return res.status_code","def do_GET(self):\n content = self._regenerate(self.path)\n if content:\n self._send_regenerated_head(content)\n self.wfile.write(content)\n else:\n SimpleHTTPRequestHandler.do_GET(self)","def handle_head(self, tag, attrs):\n self.head = 'open'","def viewHeadOn(*args, **kwargs)->None:\n pass","def url_check(url):\n try:\n request = urllib.request.Request(url)\n request.get_method = lambda: 'HEAD'\n urllib.request.urlopen(request)\n return True\n \n except ValueError:\n return False\n\n except urllib.request.HTTPError:\n return False\n \n except URLError:\n return False","def test_allow(self) -> None:\n response = self.request(\"/\", method=\"HEAD\")\n self.assert_allowed(response, (\"GET\", \"POST\"))","def ping():\n\treturn HTTPResponse(status=200)","def _url_exists(url):\n h = httplib2.Http()\n try:\n resp = h.request(url, 'HEAD')\n if resp[0].status == 200:\n return True\n except (httplib2.RelativeURIError, httplib2.ServerNotFoundError):\n return False","def get_status_code_for_head(file: str) -> int:\n if file == \"/\":\n file = \"/index.html\"\n\n if not os.path.isfile(file[1:]):\n return 404\n\n return 200","def set_header(cls, response):\n head = {key: response[key] for key in (response.keys() & cls.HEAD_KEYS)}\n\n for key_name, key_path in cls.HEAD_EXTRA:\n value = response\n try:\n for key in key_path:\n value = value[key]\n except KeyError:\n continue\n head[key_name] = value\n\n return head","def test_server_should_be_http_1_1(httpbin):\n resp = get_raw_http_response(httpbin.host, httpbin.port, \"/get\")\n assert resp.startswith(b\"HTTP/1.1\")","def head(filename, n=10):\n\tprint(\"[HEAD {}] {}\".format(n,filename))\n\tif filename[-3:].casefold()=='.gz':\n\t\twith gzip.open(filename, 'rt') as previewfile:\n\t\t\tprint(*(next(previewfile) for x in range(n)))\n\telse:\n\t\twith open(filename, 'r') as f:\n\t\t\tfor linenumber in range(n):\n\t\t\t\tline = f.readline()\n\t\t\t\tprint(line)\n\tprint(\"[END HEAD]\")","def exists(path):\n r = requests.head(path)\n # print(r.status_code)\n return r.status_code == requests.codes.ok","def is_page_available(host, path=\"/\"):\n try:\n conn = httplib.HTTPConnection(host)\n conn.request(\"HEAD\", path)\n if re.match(\"^[23]\\d\\d$\", str(conn.getresponse().status)):\n return True\n except StandardError:\n return None","def dispatch_request(self, **kwargs):\n meth = getattr(self, request.method.lower(), None)\n if meth is None and request.method == 'HEAD':\n meth = getattr(self, 'get', None)\n return meth(**kwargs)","def head301(\n self, custom_headers=None, raw=False, **operation_config):\n # Construct URL\n url = '/http/redirect/301'\n\n # Construct parameters\n query_parameters = {}\n\n # Construct headers\n header_parameters = {}\n header_parameters['Content-Type'] = 'application/json; charset=utf-8'\n if custom_headers:\n header_parameters.update(custom_headers)\n\n # Construct and send request\n request = self._client.head(url, query_parameters)\n response = self._client.send(request, header_parameters, **operation_config)\n\n if response.status_code not in [200, 301]:\n raise models.ErrorException(self._deserialize, response)\n\n if raw:\n client_raw_response = ClientRawResponse(None, response)\n client_raw_response.add_headers({\n 'Location': 'str',\n })\n return client_raw_response","def GetStatusCode(host, path=\"/\"):\n try:\n conn = HTTPConnection(host)\n conn.request(\"HEAD\", path)\n return conn.getresponse().status\n except Exception:\n return None","def get_server_status_code(url):\n # http://stackoverflow.com/questions/1140661\n host, path = urlparse.urlparse(url)[1:3] # elems [1] and [2]\n try:\n conn = httplib.HTTPConnection(host)\n conn.request('HEAD', path)\n return conn.getresponse().status\n except StandardError:\n return None","def head307(\n self, custom_headers=None, raw=False, **operation_config):\n # Construct URL\n url = '/http/redirect/307'\n\n # Construct parameters\n query_parameters = {}\n\n # Construct headers\n header_parameters = {}\n header_parameters['Content-Type'] = 'application/json; charset=utf-8'\n if custom_headers:\n header_parameters.update(custom_headers)\n\n # Construct and send request\n request = self._client.head(url, query_parameters)\n response = self._client.send(request, header_parameters, **operation_config)\n\n if response.status_code not in [200, 307]:\n raise models.ErrorException(self._deserialize, response)\n\n if raw:\n client_raw_response = ClientRawResponse(None, response)\n client_raw_response.add_headers({\n 'Location': 'str',\n })\n return client_raw_response","def connect_head_namespaced_node_proxy_with_http_info(self, name, **kwargs):\n\n all_params = ['name', 'path']\n all_params.append('callback')\n all_params.append('_return_http_data_only')\n\n params = locals()\n for key, val in iteritems(params['kwargs']):\n if key not in all_params:\n raise TypeError(\n \"Got an unexpected keyword argument '%s'\"\n \" to method connect_head_namespaced_node_proxy\" % key\n )\n params[key] = val\n del params['kwargs']\n # verify the required parameter 'name' is set\n if ('name' not in params) or (params['name'] is None):\n raise ValueError(\"Missing the required parameter `name` when calling `connect_head_namespaced_node_proxy`\")\n\n resource_path = '/api/v1/nodes/{name}/proxy'.replace('{format}', 'json')\n path_params = {}\n if 'name' in params:\n path_params['name'] = params['name']\n\n query_params = {}\n if 'path' in params:\n query_params['path'] = params['path']\n\n header_params = {}\n\n form_params = []\n local_var_files = {}\n\n body_params = None\n\n # HTTP header `Accept`\n header_params['Accept'] = self.api_client.\\\n select_header_accept(['*/*'])\n if not header_params['Accept']:\n del header_params['Accept']\n\n # HTTP header `Content-Type`\n header_params['Content-Type'] = self.api_client.\\\n select_header_content_type(['*/*'])\n\n # Authentication setting\n auth_settings = []\n\n return self.api_client.call_api(resource_path, 'HEAD',\n path_params,\n query_params,\n header_params,\n body=body_params,\n post_params=form_params,\n files=local_var_files,\n response_type='str',\n auth_settings=auth_settings,\n callback=params.get('callback'),\n _return_http_data_only=params.get('_return_http_data_only'))","def _get_head_commit(self, branch_name) -> dict:\n try:\n commit_response = self.repo.get_branch(branch_name)\n commit = commit_response.commit\n commit_head = commit.raw_data.get('sha')\n return make_success_response(200, commit_head)\n except GithubException as github_exc:\n return make_error_response(github_exc.status, github_exc.data)"],"string":"[\n \"def do_HEAD(self):\\n self.do_GET(True)\",\n \"def do_HEAD(self):\\n self.log.debug('do_HEAD called')\\n self.HeadGet('HEAD')\",\n \"async def head(url, session=None, **kwargs):\\n\\n method = 'HEAD'\\n resp = await _request(method, url, session=session, **kwargs)\\n return resp\",\n \"def head(self, url, *args, **kwargs):\\n with LOG_JWS_HEAD().context():\\n return DeferredContext(\\n self._send_request(u'HEAD', url, *args, **kwargs)\\n ).addActionFinish()\",\n \"def head(self, *args, **kw):\\n kw['method'] = 'HEAD'\\n return self.open(*args, **kw)\",\n \"def HEAD(self, req):\\r\\n resp = req.get_response(self.app)\\r\\n\\r\\n return HTTPOk(headers=resp.headers)\",\n \"def do_HEAD(self):\\n self._try_to_process_request(self._handle_head_request)\",\n \"def HEAD(self, req):\\n resp = req.get_response(self.app)\\n\\n return HTTPOk(headers=resp.headers)\",\n \"def do_HEAD(self):\\n self.send_response(200)\",\n \"def simulate_head(app, path, **kwargs) -> _ResultBase:\\n return simulate_request(app, 'HEAD', path, **kwargs)\",\n \"def do_HEAD(self):\\r\\n f = self.send_head()\\r\\n if f:\\r\\n f.close()\",\n \"def do_HEAD(self):\\n f = self.send_head()\\n if f:\\n f.close()\",\n \"def do_HEAD(self):\\n f = self.send_head()\\n if f:\\n f.close()\",\n \"def do_HEAD(self):\\n f = self.send_head()\\n if f:\\n f.close()\",\n \"def do_HEAD(self):\\n f = self.send_head()\\n if f:\\n f.close()\",\n \"def HEAD(self, req):\\n return self.GETorHEAD(req)\",\n \"def simulate_head(self, path='/', **kwargs):\\n return self.simulate_request('HEAD', path, **kwargs)\",\n \"def simulate_head(self, path='/', **kwargs) -> _ResultBase:\\n return self.simulate_request('HEAD', path, **kwargs)\",\n \"async def simulate_head(self, path='/', **kwargs) -> _ResultBase:\\n return await self.simulate_request('HEAD', path, **kwargs)\",\n \"def head(url, **kwargs):\\n\\n kwargs.setdefault('allow_redirects', False)\\n return request('head', url, **kwargs)\",\n \"def head(url, to_error=_default_to_error, **kwargs):\\n\\n return request('head', url, **kwargs)\",\n \"def HEAD(url, mime_type='text/html'):\\n def_app = DefaultHttpRequestHandler()\\n return def_app.RequestMap(url, methods=['HEAD'], produces=mime_type)\",\n \"def do_head(self) -> bool:\\n with FileHTTPResponse(self.request) as response:\\n response.send_head(self.conn)\\n return response.close_connection()\",\n \"def test_HEAD(self):\\n if not self.url:\\n return\\n response = self.client.head(self.url, {}, format='json')\\n self.assertIn(response.status_code, [status.HTTP_405_METHOD_NOT_ALLOWED,\\n status.HTTP_200_OK,\\n status.HTTP_401_UNAUTHORIZED])\",\n \"def do_HEAD(self):\\n content = self._regenerate(self.path)\\n if content:\\n self._send_regenerated_head(content)\\n else:\\n SimpleHTTPRequestHandler.do_HEAD(self)\",\n \"def make_head_request(self, headers):\\n\\n lmt, etag = headers.get('last-modified'), headers.get('etag')\\n if lmt != None or etag != None:\\n req_header = {}\\n if lmt != None and self.config.flag_use_last_modified:\\n req_header['if-modified-since'] = lmt\\n if etag != None and self.config.flag_use_etags:\\n req_header['if-none-match'] = etag\\n\\n try:\\n # print 'Making a head request =>',self.url\\n fhead = urlhelper.head_url(self.url, headers=req_header,\\n verify = self.config.flag_ssl_validate)\\n\\n # Status code is 304 ?\\n if fhead.status_code == 304:\\n return True\\n except urlhelper.FetchUrlException, e:\\n pass\\n else:\\n log.debug(\\\"Required meta-data headers (lmt, etag) not present =>\\\", self.url)\\n\\n # No lmt or etag or URL is not uptodate\\n return False\",\n \"def do_HEAD(self):\\n self.range_from, self.range_to = self._get_range_header()\\n self.response_type = HTTPStatus.OK if self.range_from == None else HTTPStatus.PARTIAL_CONTENT\\n self.cors_req = self._has_cors_header()\\n f = self.send_headers()\\n # don't send the file\\n if f:\\n f.close()\",\n \"def head(self, url, params=None, headers=None, timeout=10):\\r\\n headers = headers or self.headers\\r\\n try:\\r\\n r = self.request(url=url, method='HEAD', params=params, extra_headers=headers, timeout=timeout)\\r\\n return r.status_code < 403\\r\\n except requests.exceptions.ProxyError:\\r\\n return None\\r\\n except requests.RequestException as error:\\r\\n if self._debug:\\r\\n logging.exception(\\r\\n ''.join(traceback.format_exception(etype=type(error), value=error, tb=error.__traceback__)))\\r\\n return None\",\n \"def head_request(\\n self,\\n alias,\\n uri,\\n headers=None,\\n allow_redirects=None,\\n timeout=None):\\n session = self._cache.switch(alias)\\n # XXX workaround to restore library default behaviour. Not needed in new keywords\\n redir = False if allow_redirects is None else allow_redirects\\n response = self._common_request(\\n \\\"head\\\",\\n session,\\n uri,\\n headers=headers,\\n allow_redirects=redir,\\n timeout=timeout)\\n\\n return response\",\n \"def test_head(self):\\n client = RestClient(host=self.host, username='')\\n rest_url = 'some/url/'\\n \\n # Mock good response\\n with responses.RequestsMock() as rsps:\\n rsps.add(responses.HEAD, f'{self.host}/{rest_url}', status=200,\\n json={'value':\\\"good!\\\"})\\n r = client.head(rest_url)\",\n \"def head_obj(cls, client, spec):\\n return cls.set_header(client.get(cls.HEAD.format(**spec))[0])\",\n \"def head(self, pattern, handler):\\n return self.route(HEAD, pattern, handler)\",\n \"def send_head(self):\\n\\t\\tif self.is_cgi():\\n\\t\\t\\treturn self.run_cgi()\\n\\t\\telse:\\n\\t\\t\\treturn SimpleHTTPServer.SimpleHTTPRequestHandler.send_head(self)\",\n \"def head(self, api_path, *args, **kwargs):\\n\\n\\t\\treturn self._do_operation(u'head', api_path, *args, **kwargs)\",\n \"async def get_file_head(self, path: str) -> Optional[CIMultiDictProxy]:\\n session = aiohttp.ClientSession(loop=self._loop)\\n try:\\n async with session.request(hdrs.METH_HEAD, path) as res: # type: aiohttp.ClientResponse\\n return res.headers\\n except (aiohttp.ServerDisconnectedError, aiohttp.ClientResponseError):\\n self.log_debug(\\\"Could not get a HEAD for the {!r}.\\\".format(path))\\n finally:\\n session.close()\\n return None\",\n \"def perform_http_head(self, system_name, variable_config=None,\\n request_id=None, data=None,\\n url=None, expected_response=None,\\n headers=None, user=None, password=None,\\n allow_redirects=None, timeout=None,\\n json=None, cookies=None, files=None, proxies=None,\\n verify=None, stream=None, cert=None, var_sub=None):\\n arguments = {'system_name': system_name, 'variable_config': variable_config,\\n 'request_id': request_id, 'url': url, 'data': data,\\n 'expected_response': expected_response, 'headers': headers,\\n 'user': user, 'password': password, 'allow_redirects': allow_redirects,\\n 'timeout': timeout, 'json': json, 'cookies': cookies,\\n 'files': files, 'proxies': proxies, 'verify': verify,\\n 'stream': stream, 'cert': cert, 'var_sub': var_sub}\\n wdesc = \\\"Perform a http head to the url\\\"\\n pSubStep(wdesc)\\n pNote(system_name)\\n output_dict = {}\\n result = True\\n\\n for element in arguments:\\n if element in [\\\"json\\\", \\\"data\\\", \\\"variable_config\\\"] and \\\\\\n arguments[element] and arguments[element] is not None:\\n arguments[element] = Utils.rest_Utils.\\\\\\n check_ext_get_abspath(arguments[element], self.tc_path)\\n\\n credentials = Utils.data_Utils.\\\\\\n get_user_specified_tag_values_in_tc(self.datafile, **arguments)\\n\\n if credentials[\\\"variable_config\\\"] and \\\\\\n credentials[\\\"variable_config\\\"] is not None:\\n credentials[\\\"variable_config\\\"] = Utils.rest_Utils.\\\\\\n check_ext_get_abspath(credentials[\\\"variable_config\\\"],\\n os.path.dirname(self.datafile))\\n\\n for element in credentials:\\n credentials = Utils.rest_Utils.\\\\\\n resolve_credentials_for_rest(credentials, element,\\n self.datafile, system_name)\\n credentials[\\\"auth\\\"] = (credentials['user'], credentials['password'])\\n\\n credentials, popped_args = Utils.rest_Utils.\\\\\\n remove_invalid_req_args(credentials, [\\\"user\\\", \\\"password\\\",\\n \\\"request_id\\\",\\n \\\"variable_config\\\",\\n \\\"var_sub\\\", \\\"json\\\"])\\n\\n pNote(\\\"url is: {0}\\\".format(credentials['url']))\\n for i in range(0, len(popped_args[\\\"json\\\"])):\\n if popped_args[\\\"json\\\"][i] != \\\"Error\\\":\\n credentials[\\\"json\\\"] = popped_args[\\\"json\\\"][i]\\n for key in credentials:\\n pNote(\\\"Sending argument '{0}': {1}\\\"\\n .format(key, credentials[key]))\\n status, api_response = self.rest_object.head(**credentials)\\n result = result and status\\n output_dict.update(self.rest_object.\\n update_output_dict(system_name,\\n api_response, request_id, status, i+1))\\n else:\\n pNote(\\\"Request not sent.\\\", \\\"error\\\")\\n status = False\\n result = result and status\\n if result:\\n msg = \\\"http head successful\\\"\\n else:\\n msg = \\\"http head failed\\\"\\n pNote(msg)\\n report_substep_status(result)\\n return result, output_dict\",\n \"def head(self, params=None):\\n params = self.parameters(additional_parameters=params)\\n res = head(self.endpoint_url, params=params)\\n return Response(res)\",\n \"def send_head(self):\\n path = self.path\\n # separate the query\\n i = path.find( '?' )\\n if i >= 0:\\n query = path[ i: ]\\n path = path[ : i ]\\n else:\\n query = ''\\n if os.path.isdir( '.' + path ): # os.getcwd() ?\\n # check if index.py exists\\n pathindexpy = os.path.join( path, 'index.py' )\\n if os.path.exists( '.' + pathindexpy ):\\n path = pathindexpy + query\\n self.path = path\\n if self.is_cgi():\\n return self.run_cgi()\\n else:\\n return SimpleHTTPServer.SimpleHTTPRequestHandler.send_head(self)\",\n \"def HeadGet(self, method):\\n self.log.debug('HeadGet called: %s' % self.path)\\n orig_request = ssl_proxy_log.Request(self.path, method, self.headers,\\n self.log, self.http_redirect_table, self.ssl_redirect_table)\\n self.log.debug('orig_request built, calling fetch2')\\n\\n request_log = self.fetch2(orig_request, self.wfile)\\n\\n if not request_log:\\n return\\n\\n if self.display_filter:\\n if not self.display_filter.search(self.path):\\n return\\n\\n if self.interactive:\\n self.Log(request_log)\\n\\n #log.SetValue('url', self.path)\\n #self.Log(log)\",\n \"def head(self, uri, format='json', data=None, authentication=None, **kwargs):\\r\\n content_type = self.get_content_type(format)\\r\\n kwargs['HTTP_ACCEPT'] = content_type\\r\\n\\r\\n # GET & DELETE are the only times we don't serialize the data.\\r\\n if data is not None:\\r\\n kwargs['data'] = data\\r\\n\\r\\n if authentication is not None:\\r\\n kwargs['HTTP_AUTHORIZATION'] = authentication\\r\\n\\r\\n return self.client.head(uri, **kwargs)\",\n \"def _handle_head_request(self):\\n self._header_only = True\\n self._handle_get_request()\",\n \"def make_request(url):\\n response = {}\\n try:\\n resp = requests.head(url, allow_redirects=False, timeout=1)\\n response['url'] = resp.url\\n response['status'] = resp.status_code\\n except BaseException:\\n response['url'] = url\\n response['status'] = -1\\n\\n return response\",\n \"def head_jenkins_with_http_info(self, **kwargs):\\n\\n all_params = []\\n all_params.append('async')\\n all_params.append('_return_http_data_only')\\n all_params.append('_preload_content')\\n all_params.append('_request_timeout')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method head_jenkins\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n\\n collection_formats = {}\\n\\n path_params = {}\\n\\n query_params = []\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n # Authentication setting\\n auth_settings = ['jenkins_auth']\\n\\n return self.api_client.call_api('/api/json', 'HEAD',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type=None,\\n auth_settings=auth_settings,\\n async=params.get('async'),\\n _return_http_data_only=params.get('_return_http_data_only'),\\n _preload_content=params.get('_preload_content', True),\\n _request_timeout=params.get('_request_timeout'),\\n collection_formats=collection_formats)\",\n \"def head(cls, client, spec, headers=None):\\n return cls(client, spec, headers)\",\n \"def head(*args, **kwargs):\\r\\n\\r\\n return HttpResponse()\",\n \"def head(self, filesystem, acc_dir, cont_dir, account, container, req):\\n try:\\n # create path\\n path, headers = '', ''\\n if 'x-updater-request' in req.headers:\\n path = self.create_updater_path(filesystem, acc_dir, cont_dir, account, container)\\n self.logger.debug(('HEAD container called for path: %(path)s'),\\n {'path' : path})\\n # get headers for updater request\\n headers = self.__updater_headers(path , True) #pass True in updater request\\n else:\\n path = self.create_path(filesystem, acc_dir, cont_dir, account, container)\\n self.logger.debug(('HEAD container called for path: %(path)s'),\\n {'path' : path})\\n # get headers for request\\n headers = self.__head_or_get(path)\\n if headers == HTTP_INTERNAL_SERVER_ERROR:\\n self.logger.debug('Internal error raised from library')\\n raise HTTPInternalServerError(request=req)\\n if headers == HTTP_NOT_FOUND and 'x-updater-request' in req.headers:\\n self.logger.debug('File not found error raised from library: updater case')\\n raise HTTPNotFound(request=req)\\n elif headers == HTTP_NOT_FOUND:\\n self.logger.debug('File not found error raised from library')\\n raise HTTPNotFound(request=req)\\n else:\\n out_content_type = get_listing_content_type(req)\\n headers['Content-Type'] = out_content_type\\n return headers\\n except HTTPException as error:\\n self.logger.exception(error)\\n raise error\\n except Exception as err:\\n self.logger.error \\\\\\n (('HEAD request failed for container: %(container)s '),\\n {'container' : container})\\n self.logger.exception(err)\\n raise err\",\n \"def test_no_head(self):\\n\\n def get_200_response(req):\\n return HttpResponse(status=200)\\n\\n request = self.request_factory.head(\\\"/\\\")\\n conditional_get_response = ConditionalGetMiddleware(get_200_response)(request)\\n self.assertNotIn(\\\"ETag\\\", conditional_get_response)\",\n \"def assertHttpOk(self, url):\\n host = urlsplit(url)\\n conn = HTTPConnection(host.netloc, timeout=4)\\n conn.request('HEAD', host.path)\\n res = conn.getresponse()\\n self.assertEqual(res.status, 200)\",\n \"def send_head(self):\\n print(\\\"[*] send_head\\\")\\n path = self.translate_path(self.path)\\n f = None\\n if os.path.isdir(path):\\n parts = urllib.parse.urlsplit(self.path)\\n if not parts.path.endswith('/'):\\n # redirect browser - doing basically what apache does\\n self.send_response(HTTPStatus.MOVED_PERMANENTLY)\\n new_parts = (parts[0], parts[1], parts[2] + '/',\\n parts[3], parts[4])\\n new_url = urllib.parse.urlunsplit(new_parts)\\n self.send_header(\\\"Location\\\", new_url)\\n self.end_headers()\\n return None\\n for index in \\\"index.html\\\", \\\"index.htm\\\":\\n index = os.path.join(path, index)\\n if os.path.exists(index):\\n path = index\\n break\\n else:\\n return self.list_directory(path)\\n ctype = self.guess_type(path)\\n # check for trailing \\\"/\\\" which should return 404. See Issue17324\\n # The test for this was added in test_httpserver.py\\n # However, some OS platforms accept a trailingSlash as a filename\\n # See discussion on python-dev and Issue34711 regarding\\n # parseing and rejection of filenames with a trailing slash\\n if path.endswith(\\\"/\\\"):\\n self.send_error(HTTPStatus.NOT_FOUND, \\\"File not found\\\")\\n return None\\n try:\\n f = open(path, 'rb')\\n except OSError:\\n self.send_error(HTTPStatus.NOT_FOUND, \\\"File not found\\\")\\n return None\\n\\n try:\\n fs = os.fstat(f.fileno())\\n # Use browser cache if possible\\n if (\\\"If-Modified-Since\\\" in self.headers\\n and \\\"If-None-Match\\\" not in self.headers):\\n # compare If-Modified-Since and time of last file modification\\n try:\\n ims = email.utils.parsedate_to_datetime(\\n self.headers[\\\"If-Modified-Since\\\"])\\n except (TypeError, IndexError, OverflowError, ValueError):\\n # ignore ill-formed values\\n pass\\n else:\\n if ims.tzinfo is None:\\n # obsolete format with no timezone, cf.\\n # https://tools.ietf.org/html/rfc7231#section-7.1.1.1\\n ims = ims.replace(tzinfo=datetime.timezone.utc)\\n if ims.tzinfo is datetime.timezone.utc:\\n # compare to UTC datetime of last modification\\n last_modif = datetime.datetime.fromtimestamp(\\n fs.st_mtime, datetime.timezone.utc)\\n # remove microseconds, like in If-Modified-Since\\n last_modif = last_modif.replace(microsecond=0)\\n\\n if last_modif <= ims:\\n self.send_response(HTTPStatus.NOT_MODIFIED)\\n self.end_headers()\\n f.close()\\n return None\\n\\n self.send_response(HTTPStatus.OK)\\n self.send_header(\\\"Content-type\\\", ctype)\\n self.send_header(\\\"Content-Length\\\", str(fs[6]))\\n self.send_header(\\\"Last-Modified\\\",\\n self.date_time_string(fs.st_mtime))\\n self.end_headers()\\n return f\\n except:\\n f.close()\\n raise\",\n \"def do_GET(self):\\r\\n f = self.send_head()\\r\\n if f:\\r\\n self.copyfile(f, self.wfile)\\r\\n f.close()\",\n \"def connect(self):\\n # sends User-Agent and Refferer (main page on the server) in the header, \\n # it's necessary when the server blocks downloading via links from other resources\\n headers = {'User-Agent': self.user_agent, 'Refferer': '{}://{}/'.format(self.url.protocol, self.url.host)}\\n self.conn = self.protocol(self.url.host, timeout=self.timeout)\\n self.conn.request('HEAD', self.url.request, headers=headers)\\n response = self.conn.getresponse()\\n\\n # status 3xx\\n if response.status // 100 == 3:\\n location = response.getheader('Location')\\n return self.redirect(location, response.status)\\n\\n if response.status != 200: # HTTP(S) error\\n return TaskHeadError(self.url.host, response.status)\\n\\n file_size = int(response.getheader('Content-Length'))\\n info = TaskHeadData(self.url.host, response.status, file_size)\\n response.close()\\n return info\",\n \"def do_GET(self):\\n f = self.send_head()\\n if f:\\n try:\\n self.copyfile(f, self.wfile)\\n finally:\\n f.close()\",\n \"def _send_regenerated_head(self, content):\\n self.send_response(200)\\n self.send_header(\\\"Content-type\\\", 'text/html')\\n self.send_header(\\\"Content-Length\\\", len(content))\\n self.send_header(\\\"Last-Modified\\\", self.date_time_string())\\n self.end_headers()\",\n \"def send_head(self):\\n path = self.translate_path(self.path)\\n f = None\\n if os.path.isdir(path):\\n parts = urllib.parse.urlsplit(self.path)\\n if not parts.path.endswith('/'):\\n # redirect browser - doing basically what apache does\\n self.send_response(HTTPStatus.MOVED_PERMANENTLY)\\n new_parts = (parts[0], parts[1], parts[2] + '/',\\n parts[3], parts[4])\\n new_url = urllib.parse.urlunsplit(new_parts)\\n self.send_header(\\\"Location\\\", new_url)\\n self.end_headers()\\n return None\\n # If there is only one file, return it.\\n try:\\n file_list = os.listdir(path)\\n except OSError:\\n self.send_error(\\n HTTPStatus.NOT_FOUND,\\n \\\"No permission to list directory\\\")\\n\\n # If there is more than one file, return\\n # the one called openapi.yaml\\n if len(file_list) == 1:\\n path = os.path.join(path, file_list[0])\\n elif len(file_list) > 1:\\n for index in \\\"openapi.yml\\\", \\\"openapi.yaml\\\":\\n index = os.path.join(path, index)\\n if os.path.exists(index):\\n path = index\\n break\\n else:\\n self.send_error(HTTPStatus.NOT_FOUND, \\\"File not found\\\")\\n ctype = self.guess_type(path)\\n try:\\n f = open(path, 'rb')\\n except OSError:\\n self.send_error(HTTPStatus.NOT_FOUND, \\\"File not found\\\")\\n return None\\n try:\\n self.send_response(HTTPStatus.OK)\\n self.send_header(\\\"Content-type\\\", ctype)\\n fs = os.fstat(f.fileno())\\n self.send_header(\\\"Content-Length\\\", str(fs[6]))\\n self.send_header(\\\"Last-Modified\\\", self.date_time_string(fs.st_mtime))\\n self.end_headers()\\n return f\\n except Exception:\\n f.close()\\n raise\",\n \"def do_GET(self):\\n self.log.debug('do_GET called')\\n self.HeadGet('GET')\",\n \"def send_head(self):\\n path = translate_path(self.headers[\\\"URI\\\"], DOCUMENT_ROOT)\\n if not validate_root_escape(DOCUMENT_ROOT, path):\\n self.send_error(404)\\n return None\\n if os.path.isdir(path):\\n path = os.path.join(path, \\\"index.html\\\")\\n\\n if not os.path.isfile(path):\\n fdir, fname = url_basename(path)\\n if fname == \\\"index.html\\\":\\n if RECREATE_INDEX_HTML:\\n index_html = self.list_directory(fdir)\\n return index_html\\n else:\\n self.send_error(403)\\n self.send_error(404)\\n return None\\n try:\\n fd = open(path, \\\"rb\\\")\\n except Exception:\\n self.send_error(500, \\\"Error opening file\\\")\\n return None\\n\\n self.send_response(200)\\n self.send_header(\\\"Content-type\\\", guess_content_type(path))\\n self.send_header(\\\"Content-Length\\\", str(os.fstat(fd.fileno())[6]))\\n self.end_headers()\\n return fd\",\n \"def do_GET(self):\\n f = self.send_head()\\n if f:\\n try:\\n \\n self.copyfile(f, self.wfile)\\n finally:\\n f.close()\",\n \"def _get_url_http_headers(self) -> Dict[str, str]:\\n h = requests.head(self.url, allow_redirects=True)\\n return h.headers\",\n \"def head300(\\n self, custom_headers=None, raw=False, **operation_config):\\n # Construct URL\\n url = '/http/redirect/300'\\n\\n # Construct parameters\\n query_parameters = {}\\n\\n # Construct headers\\n header_parameters = {}\\n header_parameters['Content-Type'] = 'application/json; charset=utf-8'\\n if custom_headers:\\n header_parameters.update(custom_headers)\\n\\n # Construct and send request\\n request = self._client.head(url, query_parameters)\\n response = self._client.send(request, header_parameters, **operation_config)\\n\\n if response.status_code not in [200, 300]:\\n raise models.ErrorException(self._deserialize, response)\\n\\n if raw:\\n client_raw_response = ClientRawResponse(None, response)\\n client_raw_response.add_headers({\\n 'Location': 'str',\\n })\\n return client_raw_response\",\n \"def test_post_head_unauthorized(self):\\n url = reverse('post-list')\\n response = self.client.head(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def send_head(self):\\n print 'url_path', self.path\\n file_path = self.url_path_to_file_path(self.path)\\n print 'file_path', file_path\\n f = None\\n if os.path.isdir(file_path):\\n if not self.path.endswith('/'):\\n # redirect browser - doing basically what apache does\\n self.send_response(301)\\n self.send_header(\\\"Location\\\", self.path + \\\"/\\\")\\n self.end_headers()\\n return None\\n for index in \\\"index.html\\\", \\\"index.htm\\\":\\n index = os.path.join(file_path, index)\\n if os.path.exists(index):\\n file_path = index\\n break\\n\\n self.counter.incr_counter(file_path)\\n\\n if os.path.isdir(file_path):\\n return self.list_directory(file_path)\\n ctype = self.guess_type(file_path)\\n\\n try:\\n # Always read in binary mode. Opening files in text mode may cause\\n # newline translations, making the actual size of the content\\n # transmitted *less* than the content-length!\\n f = open(file_path, 'rb')\\n except IOError:\\n self.send_error(404, \\\"File not found \\\" + file_path)\\n return None\\n self.send_response(200)\\n self.send_header(\\\"Content-type\\\", ctype)\\n fs = os.fstat(f.fileno())\\n self.send_header(\\\"Content-Length\\\", str(fs[6]))\\n self.send_header(\\\"Last-Modified\\\", self.date_time_string(fs.st_mtime))\\n self.end_headers()\\n return f\",\n \"def access_gemini_url_head_method(context, endpoint):\\n url = urljoin(context.gemini_api_url, endpoint)\\n context.response = requests.head(url)\",\n \"def get_server_status_code(url):\\n host, path = urlparse.urlparse(url)[1:3] # elems [1] and [2]\\n print \\\"host : \\\", host, \\\" | path : \\\", path\\n try:\\n conn = httplib.HTTPConnection(host)\\n conn.request('HEAD', path)\\n print \\\" | status : \\\" , conn.getresponse().status\\n return conn.getresponse().status\\n except StandardError as e:\\n print \\\"StandardError : \\\", e\\n return None\",\n \"def do_GET(self):\\n #if self.path.startswith('/api/'):\\n # f = self.send_response_headers('api call')\\n #else:\\n f=self.route()\\n if f==False:\\n f = self.send_head()\\n if f:\\n try:\\n self.copyfile(f, self.wfile)\\n finally:\\n f.close()\",\n \"def send_head(self, conn: socket.socket, encoding: str = HTTP_DEFAULT_ENCODING) -> bool:\\n conn_addr = conn.getpeername()\\n if 'Connection' not in self.__headers:\\n self.set_header('Connection', self.request.headers.get('Connection', 'close').lower() if self.request else 'close')\\n if self.get_header('Connection') == 'keep-alive' and (timeout := conn.gettimeout()):\\n logging.debug('%s timeout = %d', conn_addr, timeout)\\n self.set_header('Keep-Alive', f\\\"timeout={int(timeout + self.request.ctime - time.time() if self.request else timeout)}\\\")\\n head = END_LINE.join([self.get_status_line(), *self.get_headers(), END_LINE]).encode(encoding)\\n logging.debug('%s <- sending head [%s]', conn_addr, head)\\n if self.protocol.version != (0, 9):\\n with suppress(BrokenPipeError):\\n conn.sendall(head)\\n logging.info('%s <- head sent - ok', conn_addr)\",\n \"def head(self, url, **kwargs):\\n kwargs.setdefault(\\\"timeout\\\", 300)\\n return super(PacerSession, self).head(url, **kwargs)\",\n \"def send_head(self):\\n path = self.translate_path(self.path)\\n f = None\\n if os.path.isdir(path):\\n if not self.path.endswith('/'):\\n # redirect browser - doing basically what apache does\\n self.send_response(301)\\n self.send_header(\\\"Location\\\", self.path + \\\"/\\\")\\n self.end_headers()\\n return None\\n for index in \\\"index.html\\\", \\\"index.htm\\\":\\n index = os.path.join(path, index)\\n if os.path.exists(index):\\n path = index\\n break\\n else:\\n return self.list_directory(path)\\n ctype = self.guess_type(path)\\n try:\\n # Always read in binary mode. Opening files in text mode may cause\\n # newline translations, making the actual size of the content\\n # transmitted *less* than the content-length!\\n f = open(path, 'rb')\\n except IOError:\\n self.send_error(404, \\\"File not found\\\")\\n return None\\n try:\\n self.send_response(200)\\n self.send_header(\\\"Content-type\\\", ctype)\\n fs = os.fstat(f.fileno())\\n self.send_header(\\\"Content-Length\\\", str(fs[6]))\\n self.send_header(\\\"Last-Modified\\\", self.date_time_string(fs.st_mtime))\\n self.end_headers()\\n return f\\n except:\\n f.close()\\n raise\",\n \"def _head_object(\\n s3_conn: S3Client, bucket: str, key: str\\n) -> Optional[HeadObjectOutputTypeDef]:\\n try:\\n return s3_conn.head_object(Bucket=bucket, Key=key)\\n except botocore.exceptions.ClientError as err:\\n if err.response[\\\"Error\\\"][\\\"Code\\\"] == \\\"404\\\":\\n return None\\n raise\",\n \"def assert_has_valid_head(self, response, expected):\\r\\n assert 'head' in response\\r\\n head = response['head']\\r\\n assert isinstance(head, str)\\r\\n assert head == expected\",\n \"def __head_or_get(self, path):\\n try:\\n info = self.get_cont_stat(path)\\n if not isinstance(info, types.DictType):\\n raise info()\\n headers = HeaderKeyDict({\\n 'X-Container-Object-Count': info['object_count'],\\n 'X-Container-Bytes-Used': info['bytes_used'],\\n 'X-Timestamp': info['created_at'],\\n 'X-PUT-Timestamp': info['put_timestamp'],\\n })\\n metadata = info['metadata']\\n for key, value in metadata.iteritems():\\n if key == 'r-':\\n headers.update({'x-container-read' : value})\\n elif key == 'w-':\\n headers.update({'x-container-write' : value})\\n else:\\n ser_key = key.split('-')[0]\\n if ser_key == 'm':\\n #Supported a single word key till first '-' \\n #in the entire metadata header as X-Container-Meta-A\\n #key = 'x-container-meta-' + key.split('-')[1]\\n \\n #SANCHIT: This supports multi-part key for metadata \\n #such as X-Container-Meta-A-B-C\\n key = 'x-container-meta-' + key.split('-', 1)[1]\\n else:\\n #key = 'x-container-sysmeta-' + key.split('-')[1]\\n key = 'x-container-sysmeta-' + key.split('-', 1)[1]\\n headers.update({key : value})\\n return headers\\n except HTTPException as error:\\n self.logger.exception(error)\\n return error.status_int\\n except Exception as err:\\n self.logger.exception(err)\\n return HTTP_INTERNAL_SERVER_ERROR\",\n \"def create_head(\\n self,\\n path: PathLike,\\n commit: Union[\\\"SymbolicReference\\\", \\\"str\\\"] = \\\"HEAD\\\",\\n force: bool = False,\\n logmsg: Optional[str] = None,\\n ) -> \\\"Head\\\":\\n return Head.create(self, path, commit, logmsg, force)\",\n \"def _send_head(self, text, code):\\n size = len(text)\\n self.send_response(code, None, size)\\n # Trying to figure out what we are going to send out.\\n # Maybe this could be improved a bit further but should do\\n # it for now.\\n extension_start = self.path.rfind('.')\\n extension = self.path[extension_start:]\\n try:\\n self.send_header('Content-Type', CONTENT_TYPES[extension])\\n except KeyError:\\n self.send_header('Content-Type', 'text/html')\\n self.send_header(\\\"Content-Length\\\", size)\\n self.end_headers()\",\n \"def GET(self, req):\\n resp = req.get_response(self.app, method='HEAD')\\n\\n acl = resp.object_acl if req.is_object_request else resp.bucket_acl\\n\\n resp = HTTPOk()\\n resp.body = tostring(acl.elem())\\n\\n return resp\",\n \"def url_exist(url:str) -> bool:\\r\\n with closing(requests.head(url, allow_redirects=True)) as r:\\r\\n return r.ok\",\n \"def head(self, tenant: str, filename: str) -> None:\\n try:\\n if not self.allow_info:\\n raise ClientMethodNotAllowed\\n if not filename:\\n raise ClientError(\\\"No resource specified\\\")\\n self.path = self.export_dir\\n self.filepath = f\\\"{self.path}/{url_unescape(self.resource)}\\\"\\n # ensure there are no symlinks in filepath\\n any_path_islink(self.filepath, opts=options)\\n if not os.path.lexists(self.filepath):\\n raise ClientResourceNotFoundError(f\\\"{self.filepath} not found\\\")\\n size, mime_type, mtime = self.get_file_metadata(self.filepath)\\n logging.info(\\n f\\\"user: {self.requestor}, checked file: {self.filepath} , MIME type: {mime_type}\\\"\\n )\\n self.set_header(\\\"Content-Length\\\", size)\\n self.set_header(\\\"Accept-Ranges\\\", \\\"bytes\\\")\\n self.set_header(\\\"Content-Type\\\", mime_type)\\n self.set_header(\\\"Modified-Time\\\", str(mtime))\\n self.set_status(HTTPStatus.OK.value)\\n except Exception as e:\\n error = error_for_exception(e, details=self.additional_log_details())\\n logging.error(error.message)\\n for name, value in error.headers.items():\\n self.set_header(name, value)\\n self.set_status(error.status, reason=error.reason)\\n self.finish()\",\n \"def send_head(self):\\n path = self.translate_path(self.path)\\n f = None\\n if os.path.isdir(path):\\n parts = urlparse.urlsplit(self.path)\\n if not parts.path.endswith('/'):\\n # redirect browser - doing basically what apache does\\n self.send_response(301)\\n new_parts = (parts[0], parts[1], parts[2] + '/',\\n parts[3], parts[4])\\n new_url = urlparse.urlunsplit(new_parts)\\n self.send_header(\\\"Location\\\", new_url)\\n self.end_headers()\\n return None\\n for index in \\\"index.html\\\", \\\"index.htm\\\":\\n index = os.path.join(path, index)\\n if os.path.exists(index):\\n path = index\\n break\\n else:\\n return self.list_directory(path)\\n ctype = self.guess_type(path)\\n parts = path.split('/')\\n if len(parts)>=3 and parts[1]=='raw':\\n path = '/'.join(parts[:1]+parts[2:])\\n ctype = self.extensions_map['']\\n print(os.path.basename(path))\\n if ctype=='text/plain':\\n return self.display_source(path)\\n try:\\n # Always read in binary mode. Opening files in text mode may cause\\n # newline translations, making the actual size of the content\\n # transmitted *less* than the content-length!\\n f = open(path, 'rb')\\n except IOError:\\n self.send_error(404, \\\"File not found\\\")\\n return None\\n try:\\n self.send_response(200)\\n self.send_header(\\\"Content-type\\\", ctype)\\n fs = os.fstat(f.fileno())\\n self.send_header(\\\"Content-Disposition\\\", 'filename=\\\"%s\\\"' % (os.path.basename(path)))\\n self.send_header(\\\"Content-Length\\\", str(fs[6]))\\n self.send_header(\\\"Last-Modified\\\", self.date_time_string(fs.st_mtime))\\n self.end_headers()\\n return f\\n except:\\n f.close()\\n raise\",\n \"def _get_head(wit_path):\\n\\n head = _get_references_data(wit_path)['HEAD']\\n if len(head) == 40:\\n return head\\n return _get_references_data(wit_path)[head]\",\n \"def get_status_code(host, path=\\\"/\\\"):\\n try:\\n conn = httplib.HTTPConnection(host)\\n conn.request(\\\"HEAD\\\", path)\\n return conn.getresponse().status\\n except StandardError:\\n return None\",\n \"def test_h2_header_ok(self):\\n self.set_frang_config(frang_config=\\\"http_strict_host_checking true;\\\")\\n client = self.get_client(\\\"deproxy-1\\\")\\n client.start()\\n client.parsing = False\\n\\n first_headers = [(\\\":authority\\\", \\\"localhost\\\"), (\\\":path\\\", \\\"/\\\")]\\n second_headers = [(\\\":path\\\", \\\"/\\\"), (\\\"host\\\", \\\"localhost\\\")]\\n third_headers = [(\\\":authority\\\", \\\"localhost\\\"), (\\\":path\\\", \\\"/\\\"), (\\\"host\\\", \\\"localhost\\\")]\\n fourth_headers = [\\n (\\\":authority\\\", \\\"tempesta-tech.com\\\"),\\n (\\\":path\\\", \\\"/\\\"),\\n (\\\"forwarded\\\", \\\"host=tempesta-tech.com\\\"),\\n (\\\"forwarded\\\", \\\"for=tempesta.com\\\"),\\n ]\\n\\n header_list = [\\n first_headers,\\n first_headers, # as byte\\n second_headers,\\n second_headers, # as byte\\n third_headers,\\n third_headers, # as byte\\n fourth_headers,\\n fourth_headers, # as byte\\n ]\\n for header in header_list:\\n head = [\\n (\\\":scheme\\\", \\\"https\\\"),\\n (\\\":method\\\", \\\"HEAD\\\"),\\n ]\\n head.extend(header)\\n client.make_request(head)\\n self.assertTrue(client.wait_for_response(1))\\n\\n self.check_response(client, status_code=\\\"200\\\", warning_msg=\\\"frang: \\\")\",\n \"def head_object(self, bucket_name, key):\\n url = self.__key_url(bucket_name, key)\\n res = self.infinispan_client.head(url, headers=self.headers, auth=self.basicAuth)\\n return res.status_code\",\n \"def do_GET(self):\\n content = self._regenerate(self.path)\\n if content:\\n self._send_regenerated_head(content)\\n self.wfile.write(content)\\n else:\\n SimpleHTTPRequestHandler.do_GET(self)\",\n \"def handle_head(self, tag, attrs):\\n self.head = 'open'\",\n \"def viewHeadOn(*args, **kwargs)->None:\\n pass\",\n \"def url_check(url):\\n try:\\n request = urllib.request.Request(url)\\n request.get_method = lambda: 'HEAD'\\n urllib.request.urlopen(request)\\n return True\\n \\n except ValueError:\\n return False\\n\\n except urllib.request.HTTPError:\\n return False\\n \\n except URLError:\\n return False\",\n \"def test_allow(self) -> None:\\n response = self.request(\\\"/\\\", method=\\\"HEAD\\\")\\n self.assert_allowed(response, (\\\"GET\\\", \\\"POST\\\"))\",\n \"def ping():\\n\\treturn HTTPResponse(status=200)\",\n \"def _url_exists(url):\\n h = httplib2.Http()\\n try:\\n resp = h.request(url, 'HEAD')\\n if resp[0].status == 200:\\n return True\\n except (httplib2.RelativeURIError, httplib2.ServerNotFoundError):\\n return False\",\n \"def get_status_code_for_head(file: str) -> int:\\n if file == \\\"/\\\":\\n file = \\\"/index.html\\\"\\n\\n if not os.path.isfile(file[1:]):\\n return 404\\n\\n return 200\",\n \"def set_header(cls, response):\\n head = {key: response[key] for key in (response.keys() & cls.HEAD_KEYS)}\\n\\n for key_name, key_path in cls.HEAD_EXTRA:\\n value = response\\n try:\\n for key in key_path:\\n value = value[key]\\n except KeyError:\\n continue\\n head[key_name] = value\\n\\n return head\",\n \"def test_server_should_be_http_1_1(httpbin):\\n resp = get_raw_http_response(httpbin.host, httpbin.port, \\\"/get\\\")\\n assert resp.startswith(b\\\"HTTP/1.1\\\")\",\n \"def head(filename, n=10):\\n\\tprint(\\\"[HEAD {}] {}\\\".format(n,filename))\\n\\tif filename[-3:].casefold()=='.gz':\\n\\t\\twith gzip.open(filename, 'rt') as previewfile:\\n\\t\\t\\tprint(*(next(previewfile) for x in range(n)))\\n\\telse:\\n\\t\\twith open(filename, 'r') as f:\\n\\t\\t\\tfor linenumber in range(n):\\n\\t\\t\\t\\tline = f.readline()\\n\\t\\t\\t\\tprint(line)\\n\\tprint(\\\"[END HEAD]\\\")\",\n \"def exists(path):\\n r = requests.head(path)\\n # print(r.status_code)\\n return r.status_code == requests.codes.ok\",\n \"def is_page_available(host, path=\\\"/\\\"):\\n try:\\n conn = httplib.HTTPConnection(host)\\n conn.request(\\\"HEAD\\\", path)\\n if re.match(\\\"^[23]\\\\d\\\\d$\\\", str(conn.getresponse().status)):\\n return True\\n except StandardError:\\n return None\",\n \"def dispatch_request(self, **kwargs):\\n meth = getattr(self, request.method.lower(), None)\\n if meth is None and request.method == 'HEAD':\\n meth = getattr(self, 'get', None)\\n return meth(**kwargs)\",\n \"def head301(\\n self, custom_headers=None, raw=False, **operation_config):\\n # Construct URL\\n url = '/http/redirect/301'\\n\\n # Construct parameters\\n query_parameters = {}\\n\\n # Construct headers\\n header_parameters = {}\\n header_parameters['Content-Type'] = 'application/json; charset=utf-8'\\n if custom_headers:\\n header_parameters.update(custom_headers)\\n\\n # Construct and send request\\n request = self._client.head(url, query_parameters)\\n response = self._client.send(request, header_parameters, **operation_config)\\n\\n if response.status_code not in [200, 301]:\\n raise models.ErrorException(self._deserialize, response)\\n\\n if raw:\\n client_raw_response = ClientRawResponse(None, response)\\n client_raw_response.add_headers({\\n 'Location': 'str',\\n })\\n return client_raw_response\",\n \"def GetStatusCode(host, path=\\\"/\\\"):\\n try:\\n conn = HTTPConnection(host)\\n conn.request(\\\"HEAD\\\", path)\\n return conn.getresponse().status\\n except Exception:\\n return None\",\n \"def get_server_status_code(url):\\n # http://stackoverflow.com/questions/1140661\\n host, path = urlparse.urlparse(url)[1:3] # elems [1] and [2]\\n try:\\n conn = httplib.HTTPConnection(host)\\n conn.request('HEAD', path)\\n return conn.getresponse().status\\n except StandardError:\\n return None\",\n \"def head307(\\n self, custom_headers=None, raw=False, **operation_config):\\n # Construct URL\\n url = '/http/redirect/307'\\n\\n # Construct parameters\\n query_parameters = {}\\n\\n # Construct headers\\n header_parameters = {}\\n header_parameters['Content-Type'] = 'application/json; charset=utf-8'\\n if custom_headers:\\n header_parameters.update(custom_headers)\\n\\n # Construct and send request\\n request = self._client.head(url, query_parameters)\\n response = self._client.send(request, header_parameters, **operation_config)\\n\\n if response.status_code not in [200, 307]:\\n raise models.ErrorException(self._deserialize, response)\\n\\n if raw:\\n client_raw_response = ClientRawResponse(None, response)\\n client_raw_response.add_headers({\\n 'Location': 'str',\\n })\\n return client_raw_response\",\n \"def connect_head_namespaced_node_proxy_with_http_info(self, name, **kwargs):\\n\\n all_params = ['name', 'path']\\n all_params.append('callback')\\n all_params.append('_return_http_data_only')\\n\\n params = locals()\\n for key, val in iteritems(params['kwargs']):\\n if key not in all_params:\\n raise TypeError(\\n \\\"Got an unexpected keyword argument '%s'\\\"\\n \\\" to method connect_head_namespaced_node_proxy\\\" % key\\n )\\n params[key] = val\\n del params['kwargs']\\n # verify the required parameter 'name' is set\\n if ('name' not in params) or (params['name'] is None):\\n raise ValueError(\\\"Missing the required parameter `name` when calling `connect_head_namespaced_node_proxy`\\\")\\n\\n resource_path = '/api/v1/nodes/{name}/proxy'.replace('{format}', 'json')\\n path_params = {}\\n if 'name' in params:\\n path_params['name'] = params['name']\\n\\n query_params = {}\\n if 'path' in params:\\n query_params['path'] = params['path']\\n\\n header_params = {}\\n\\n form_params = []\\n local_var_files = {}\\n\\n body_params = None\\n\\n # HTTP header `Accept`\\n header_params['Accept'] = self.api_client.\\\\\\n select_header_accept(['*/*'])\\n if not header_params['Accept']:\\n del header_params['Accept']\\n\\n # HTTP header `Content-Type`\\n header_params['Content-Type'] = self.api_client.\\\\\\n select_header_content_type(['*/*'])\\n\\n # Authentication setting\\n auth_settings = []\\n\\n return self.api_client.call_api(resource_path, 'HEAD',\\n path_params,\\n query_params,\\n header_params,\\n body=body_params,\\n post_params=form_params,\\n files=local_var_files,\\n response_type='str',\\n auth_settings=auth_settings,\\n callback=params.get('callback'),\\n _return_http_data_only=params.get('_return_http_data_only'))\",\n \"def _get_head_commit(self, branch_name) -> dict:\\n try:\\n commit_response = self.repo.get_branch(branch_name)\\n commit = commit_response.commit\\n commit_head = commit.raw_data.get('sha')\\n return make_success_response(200, commit_head)\\n except GithubException as github_exc:\\n return make_error_response(github_exc.status, github_exc.data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8344407","0.818973","0.813251","0.8088553","0.80747944","0.7905858","0.78950536","0.7855229","0.7854368","0.7849326","0.7813137","0.77831864","0.77831864","0.77831864","0.77831864","0.7763726","0.77396655","0.77286154","0.76971126","0.7648268","0.75887346","0.75780445","0.73940194","0.737736","0.73397243","0.7335625","0.731812","0.7058429","0.70118755","0.6875258","0.67489314","0.6723788","0.66205007","0.65088826","0.64914596","0.64849335","0.64522356","0.6345388","0.6315368","0.6295614","0.62487537","0.62212175","0.61596835","0.60993177","0.6087559","0.6039716","0.60264945","0.6011665","0.60063607","0.5970877","0.5962886","0.5930681","0.591879","0.5917101","0.5876208","0.587498","0.5847588","0.5741512","0.5722587","0.57066596","0.56911755","0.56532043","0.5643784","0.56429845","0.56383836","0.5614577","0.55940855","0.557862","0.5567933","0.55592006","0.5557213","0.55545795","0.5522956","0.5506864","0.54779226","0.54544693","0.5403571","0.53809017","0.53794754","0.53787684","0.53543967","0.53497833","0.5338658","0.5309923","0.529239","0.5281718","0.5278953","0.5265548","0.52436465","0.52143824","0.52126944","0.52041864","0.51979333","0.51940066","0.5180363","0.51580536","0.513846","0.5127353","0.5120488","0.5103531"],"string":"[\n \"0.8344407\",\n \"0.818973\",\n \"0.813251\",\n \"0.8088553\",\n \"0.80747944\",\n \"0.7905858\",\n \"0.78950536\",\n \"0.7855229\",\n \"0.7854368\",\n \"0.7849326\",\n \"0.7813137\",\n \"0.77831864\",\n \"0.77831864\",\n \"0.77831864\",\n \"0.77831864\",\n \"0.7763726\",\n \"0.77396655\",\n \"0.77286154\",\n \"0.76971126\",\n \"0.7648268\",\n \"0.75887346\",\n \"0.75780445\",\n \"0.73940194\",\n \"0.737736\",\n \"0.73397243\",\n \"0.7335625\",\n \"0.731812\",\n \"0.7058429\",\n \"0.70118755\",\n \"0.6875258\",\n \"0.67489314\",\n \"0.6723788\",\n \"0.66205007\",\n \"0.65088826\",\n \"0.64914596\",\n \"0.64849335\",\n \"0.64522356\",\n \"0.6345388\",\n \"0.6315368\",\n \"0.6295614\",\n \"0.62487537\",\n \"0.62212175\",\n \"0.61596835\",\n \"0.60993177\",\n \"0.6087559\",\n \"0.6039716\",\n \"0.60264945\",\n \"0.6011665\",\n \"0.60063607\",\n \"0.5970877\",\n \"0.5962886\",\n \"0.5930681\",\n \"0.591879\",\n \"0.5917101\",\n \"0.5876208\",\n \"0.587498\",\n \"0.5847588\",\n \"0.5741512\",\n \"0.5722587\",\n \"0.57066596\",\n \"0.56911755\",\n \"0.56532043\",\n \"0.5643784\",\n \"0.56429845\",\n \"0.56383836\",\n \"0.5614577\",\n \"0.55940855\",\n \"0.557862\",\n \"0.5567933\",\n \"0.55592006\",\n \"0.5557213\",\n \"0.55545795\",\n \"0.5522956\",\n \"0.5506864\",\n \"0.54779226\",\n \"0.54544693\",\n \"0.5403571\",\n \"0.53809017\",\n \"0.53794754\",\n \"0.53787684\",\n \"0.53543967\",\n \"0.53497833\",\n \"0.5338658\",\n \"0.5309923\",\n \"0.529239\",\n \"0.5281718\",\n \"0.5278953\",\n \"0.5265548\",\n \"0.52436465\",\n \"0.52143824\",\n \"0.52126944\",\n \"0.52041864\",\n \"0.51979333\",\n \"0.51940066\",\n \"0.5180363\",\n \"0.51580536\",\n \"0.513846\",\n \"0.5127353\",\n \"0.5120488\",\n \"0.5103531\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7776205"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":83090,"cells":{"query":{"kind":"string","value":"Make an HTTP POST request."},"document":{"kind":"string","value":"def handle_post(self, api, command):\n return self._make_request_from_command('POST', command)"},"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 httpPost(self, url='', data='', params={}, headers={}):\n\n return self.httpRequest('POST', url, data, params, headers)","def post(self, *args, **kwargs):\n return self._requests_call(util.requests_post, *args, **kwargs)","def do_POST(self,):\n self.http_method = 'POST'\n self.response()","def httpPost(self, url, post_parameters=None):\r\n return self.auth.post(url, post_parameters)","def _post(self, *args, **kwargs):\n return self._request('post', *args, **kwargs)","def send_post(url):\n HEADERS['accept'] = 'application/vnd.yang.data+json'\n if not url.startswith('/'):\n url = \"/{}\".format(url)\n url = BASE_URL + url\n resp = requests.post(url, headers=HEADERS)\n return resp","def http_post(self, **kwargs):\n return self.rabjcallable.post(**kwargs)","def _post(self, url, data=None):\n if data is not None:\n data = urllib.urlencode(data)\n return self._request(url, method='POST', payload=data)","def post(self, *args, **kw):\n kw['method'] = 'POST'\n return self.open(*args, **kw)","def make_post_request(self, url, data):\n auth = (self.AUTH_ID, self.AUTH_TOKEN)\n headers = {'content-type': 'application/json'}\n return requests.post(url, data=data, auth=auth, headers=headers)","def http_method_post():\n return 'POST'","def post(self, url, body=None, headers=None, serialize=True):\n return self._request('POST', url, body, headers, serialize)","def post(self, *args, **kwargs):\n headers = self.post_headers\n headers.update(kwargs.get('headers', {}))\n kwargs['headers'] = headers\n return self._request('post', *args, **kwargs)","def PostRequest(self):\n if self.__Payload: \n self.__Answer = requests.post(self.__URL, data = self.__Payload, headers = self.__Headers)\n Logs.WriteToLog(\"Data transited to web server\")\n else:\n Logs.WriteToLog(\"No payload in HTTP request\")\n raise Exception(\"Payload must be setted\")","def test_post(self):\n return self.doRequest(self.url, method=\"POST\", body=self.input)","def _post(self, url, **kwargs):\n return self._call('POST', url, kwargs)","def post(self, *path, **data):\n\t\treturn self.request('POST', *path, **data)","def make_post_request(url:str, post_params:dict, **kwargs):\n\n print(\"Making call to '{}'...\".format(url))\n resp = requests.post(url, data=post_params, **kwargs)\n print(\"Received response.\")\n\n if not resp.ok:\n return False, resp.status_code, json.loads(resp.content)\n\n return True, resp.status_code, json.loads(resp.content)","def post(self, *args, **kwargs):\n self.request(\"post\", *args, **kwargs)","def post(self, *args, **kwargs):\n return self._hit(\"POST\", *args, **kwargs)","def make_post_request(client, endpoint, data):\n return client.post(endpoint, data=data)","def post(self, path, **post_args):\n return self.request(path, data=post_args, method='POST')","def post(self, data):\n req = self._new_http_request()\n req.add_data(self._prepare_data(data))\n\n return self._urllib2.urlopen(req)","def do_POST(self):\r\n self._send_handler_response('POST')","def post(self, url, data):\r\n print(f\"POST {url}\")\r\n print(\"data:\")\r\n self.pp.pprint(data)\r\n response = self.session.post(url, data=data)\r\n print(f\"STATUS {response.status_code}\")\r\n self.print_cookies()\r\n return response","def post(url, data=None, json=None, **kwargs):\n\n return request('post', url, data=data, json=json, **kwargs)","def post(self, data):\n return requests.post(self.url, headers=self.headers, data=data)","def post(url, to_error=_default_to_error, data=None, json=None, **kwargs):\n\n return request('post',\n url, to_error=to_error, data=data, json=json, **kwargs)","def __post(self, url, payload=None, headers=None):\n if headers is None:\n headers = {\"Content-Type\": \"application/json\"}\n return self.__req(url, \"POST\", body=payload, headers=headers)","def apost(url, **kwargs):\n return requests.post(url, **kwargs)","def post(self, url, data=None):\r\n response = self.requestHelper.post(url, data=data)\r\n return self.process(response)","def post_form(url, headers, payload):\n\n headers['Content-Type'] = 'application/x-www-form-urlencoded'\n\n return RestClient.make_post_request(url, headers=headers, data=payload)","def _http_post(self, path, data):\n # Prepare the request path\n if path[0] == '/':\n path = path[1:]\n path = urljoin(self.servlet_path, path)\n\n # Request the end points\n conn = httplib.HTTPConnection(\"localhost\", self.port)\n conn.request(\"POST\", path, data, {\"Content-Type\": \"application/json\"})\n result = conn.getresponse()\n data = result.read()\n conn.close()\n\n # Convert the response to a string\n return result.status, to_str(data)","def _post_request(url, params):\n data = dumps(params).encode(\"utf-8\")\n request = requests.post(url, data=data)\n return request","def post(self, url, payload={}):\n response = self._make_request(\"POST\", url, payload)\n\n return response","def post(self, url, data):\n return self.app.post(get_url(url), data=data, follow_redirects=True)","def send_post(url, data, headers, return_output=False):\n req = requests.post(url=url, data=json.dumps(data), headers=headers)\n if return_output:\n return req\n if str(req.status_code).startswith('2'):\n print 'SUCCESS! {0} {1} {2}'.format(req.status_code, req.reason, req.content)\n else:\n print 'FAIL! {0} {1} {2}'.format(req.status_code, req.reason, req.content)\n exit(77)","def do_post_request(self, uri, headers, payload, timeout_ms, stats_config):\n return self._do_request('POST', uri, headers, payload, timeout_ms,\n stats_config)","def post(self, url, data):\n\t\treturn self.session.post(url, data, headers=self.headers)","async def post(url, session=None, **kwargs):\n\n method = 'POST'\n resp = await _request(method, url, session=session, **kwargs)\n return resp","def post(self, url, data=None, params=None):\n return self.session.post(url=self.base_url + url, data=data,\n params=params)","def post(self):\n code, status = run_handlers.handle_data_post(self.request.headers, self.request.body)\n self.set_status(code)\n self.write(status)\n self.finish()","def _post(self, url, data=None):\n url = urljoin(self.base_url, url)\n try:\n r = self._make_request(**dict(\n method='POST',\n url=url,\n json=data,\n auth=self.auth,\n timeout=self.timeout,\n hooks=self.request_hooks,\n headers=self.request_headers\n ))\n except requests.exceptions.RequestException as e:\n raise e\n else:\n if r.status_code >= 400:\n _raise_response_error(r)\n\n if r.status_code == 204:\n return None\n return r.json()","def post(self, url, body):\n return self._query(url, 'POST', json=body)","def api_post(self, path, data):\n return self._api_request(path, 'POST', data)","def post(self, url_or_path, *args, **kwargs):\n return self.request.post(url_or_path, *args, **kwargs).json()","def do_POST(self):\n\n # do this before sending any response as we may raise an exception\n content = self.__get_content(self.__get_post_body())\n\n # send the response back to the client\n self.send_response(200)\n self.send_header(\"Content-type\", \"text/json\")\n self.end_headers()\n self.wfile.write(content)","def post(self, path: str, params: dict) -> dict:\n return self.request(\"POST\", path, params)","def post(self, url: str, **kwargs: Dict[str, Any]) -> Any:\n pass","def simulate_post(app, path, **kwargs) -> _ResultBase:\n return simulate_request(app, 'POST', path, **kwargs)","def _http_post(self, url, params={}):\n url = self.server + '/api2' + url\n try:\n r = requests.post(url=url, data=params)\n except requests.exceptions.RequestException as e:\n return check_failed(e)\n # raise ClientHttpError(None, e)\n if r.status_code != 200:\n return check_failed(r.status_code)\n # return ClientHttpError(r.status_code, r.text)\n data = json.loads(r.text)\n # TODO: check data\n return data","def test_client_can_do_post_request(self):\n response = self.httpbin_4.test_requests_post_method()\n self.assertEqual(response.request.method, 'POST')\n self.assertEqual(response.status_code, 200)","async def post(self, path, params=None, json_data=None):\n response = await self.request('POST', path, params, json_data)\n return response","def post(self, url, *args):\n\n req_method = type(self.client).__name__\n\n if not url.startswith(\"http\"):\n if not url.startswith(\"/\"):\n url = \"/%s\" % url\n url = \"%s%s\" % (self.base, url)\n\n if req_method == \"FlaskClient\":\n self.client.post(url, headers=self.headers, *args)\n\n else:\n self.client.post(url, headers=self.headers, *args)","def do_POST(self):\r\n self.do_GET()","async def post(self, url, params=None, json_data=None):\n if self._authenticated:\n return await self.__open(url, method=POST, headers=self._head, params=params, json_data=json_data)","def post(self, **kwargs):\n post_args = deepcopy(kwargs)\n post_args['apikey'] = self.apikey\n post_args['version'] = self.version\n post_args['timestamp'] = timestamp()\n\n # calculate a checksum based on the values and secret key\n post_checksum = self.checksum(**post_args)\n\n # use urllib to post the values\n post_args['checksum'] = post_checksum\n\n params = urlencode(post_args)\n try:\n if Monitis.debug is True:\n print \"Request URL: \" + self.url\n print \"Request params: \" + str(post_args)\n result = urlopen(self.url, params)\n except HTTPError, error:\n raise MonitisError('API Error: ' + error.read())\n ret = result.read()\n if Monitis.debug is True:\n print \"Response: \" + ret\n result.close()\n return ret","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 _req_post(self, url: str, data, raw_res: bool = False):\n self._get_cookies()\n if not self._cookies:\n return\n r = reqtry.post(url, cookies=self._cookies, data=data, allow_redirects=False, timeout=(3, 3), tries=3, delay=1,\n backoff=1.5, jitter=(1, 1.5))\n if raw_res:\n return r\n assert r.status_code == 200, f\"Post request: Invalid http status code: {r.status_code}\"\n assert '\"errCode\":0' in r.text, f'Post response with error from server. Response: {r.text}'\n return r.text","def post(self, uri, body=None, headers=None, auth=False):\n return self.send_request('POST', uri, body, headers, auth)","def _post(self, path=\"\", body=None, **kwargs):\n uri = force_json(self.uri + path)\n return self.client.request(uri, method=\"POST\", d=body, **kwargs)","def send_post(self, uri, data=None, ofile=None, ojson=None):\n return self.__send_request('POST', uri=uri, data=data, ofile=ofile, ojson=ojson)","def post(self, *args, **kwargs):\n return self.handle_post_request()","def post(self, url, postParameters=None, urlParameters=None):\r\n if self.authorized_client:\r\n if urlParameters:\r\n getString = self.getParameters(urlParameters)\r\n req = urllib2.Request(url + \"?\" + getString)\r\n else:\r\n req = urllib2.Request(url)\r\n postString = self.postParameters(postParameters)\r\n resp,content = self.authorized_client.request(req, method=\"POST\", body=postString)\r\n return toUnicode(content)\r\n else:\r\n raise IOError(\"No authorized client available.\")","def do_POST(self):\n self._try_to_process_request(self._handle_post_request)","def _post(self, request_obj):\n return self._execute_action(request_obj, 'POST')","def post():\n pass","def _post(self, path, data=None):\n headers = {'content-type': 'application/json'}\n if data:\n data = json.dumps(data)\n r = requests.post(self._url(path), data=data, headers=headers)\n assert r.status_code == 200\n return r","def post(self, url, postParameters=None, urlParameters=None):\r\n if self._action_token == None:\r\n self._action_token = self.get(ReaderUrl.ACTION_TOKEN_URL)\r\n\r\n if self._http == None:\r\n self._setupHttp()\r\n uri = url + \"?\" + self.getParameters(urlParameters)\r\n postParameters.update({'T':self._action_token})\r\n body = self.postParameters(postParameters)\r\n response, content = self._http.request(uri, \"POST\", body=body)\r\n return content","def post(api, url, payload, headers=None, auth=_KERBEROS_AUTH, proxies=None,\n retries=_NUM_OF_RETRIES, timeout=None):\n return call(api, url, 'post', payload=payload,\n headers=headers, auth=auth, proxies=proxies, retries=retries,\n timeout=timeout)","def post(self, url, data=None, headers=None, timeout=10):\r\n try:\r\n return self.request(url=url, method='POST', data=data, extra_headers=headers, timeout=timeout)\r\n except requests.exceptions.ProxyError:\r\n return None\r\n except requests.RequestException as error:\r\n if self._debug:\r\n logging.exception(\r\n ''.join(traceback.format_exception(etype=type(error), value=error, tb=error.__traceback__)))\r\n return None","def post_action(self, path, data=None):\n response = self._request(\"POST\", path, urllib.urlencode(data) if data else None)\n return self._handle_response(response)","def post(url, data, auth=None, retries=10):\n if not url.startswith('http://'):\n url = 'http://' + url\n\n request = urllib2.Request(url)\n if auth:\n request.add_header('Authorization', 'Basic %s' % b64encode('%s:%s' % auth))\n\n params = urllib.urlencode(data)\n response = urllib2.urlopen(request, params)\n return response.read()","def post(cls, uri, payload):\n return cls._perform_request(uri, 'POST', payload)","def do_POST(self):\n self.send_response(200)\n self.send_header('Content-type','text/html')\n self.send_header('Access-Control-Allow-Origin','*')\n self.end_headers()\n # Send the html message\n self.wfile.write(\"Aivis Panovs, 161REB125\")\n return","def post(url_ext, query_params={}, payload={}, custom_err=None, timeout=DEFAULT_TIMEOUT):\r\n url = get_url() + url_ext\r\n headers = get_headers()\r\n r = requests.post(url, headers=headers, params=query_params, data=payload, timeout=timeout)\r\n return handle_response(r, \"POST\", custom_err)","def post(self):\n created = post_tool(request.json)\n return created, 201","def post(self):\n data = request.json\n return save_new_post(data=data)","def make_post_request(url, headers=None, json_data=None, data=None):\n logger.info(\"Inside: make_post_request\")\n logger.debug(\"make_post_request: parameters - {}, {}, {}, {}\".format(url, headers, json_data, data))\n\n timeout = get_config(REQUEST_TIMEOUT,\"timeout\")\n\n if not headers:\n headers = {}\n\n if json_data:\n resp = requests.post(url, verify=False, headers=headers, json=json_data, timeout=timeout)\n elif data:\n resp = requests.post(url, verify=False, headers=headers, data=data, timeout=timeout)\n\n logger.debug('received status : {}'.format(resp.status_code))\n logger.debug('received text : {}'.format(resp.text))\n logger.info(\"Exit: make_post_request\")\n if RestClient.result_success(resp.status_code):\n return resp\n else:\n err_msg = 'ERROR, received {} code during API call {}'.format(resp.status_code, url)\n logger.error(err_msg)\n raise APIException(err_msg, resp.text)","def raw_post(\n self, uri: str, data: Optional[Dict] = None, json: Optional[Dict] = None, **kwargs\n ) -> requests.Response:\n return self.session.post(url=self._url(uri), data=data, json=json, **kwargs)","def test_doPost(self) -> None:\n status_code = apicall.doPost(URL, self._browserheader)\n assert status_code != API_SUCCESS","def post(self, data=None, params=None):\n params = self.parameters(additional_parameters=params)\n res = post(self.endpoint_url, data=data, params=params)\n return Response(res)","def post(self, url, postParameters=None, urlParameters=None):\r\n if not self.access_token:\r\n raise IOError(\"No authorized client available.\")\r\n if not self.action_token:\r\n raise IOError(\"Need to generate action token.\")\r\n if urlParameters is None:\r\n urlParameters = {}\r\n headers = {'Authorization': 'Bearer ' + self.access_token,\r\n 'Content-Type': 'application/x-www-form-urlencoded'}\r\n postParameters.update({'T':self.action_token})\r\n request = requests.post(url + '?' + self.getParameters(urlParameters),\r\n data=postParameters, headers=headers)\r\n if request.status_code != 200:\r\n return None\r\n else:\r\n return toUnicode(request.text)","def do_POST(self): # pylint: disable=invalid-name\n self.handle_request()","def simulate_post(self, path='/', **kwargs):\n return self.simulate_request('POST', path, **kwargs)","def submitPost(self):\n headers, params, proxy = self.getHeaderParamProxyInfo()\n try:\n resp = requests.post(self.FullURL, data=self.PostData, headers=headers, params=params, proxies=proxy, verify=False)\n return str(resp.content)\n except ConnectionError as ce:\n try:\n self.postErrorMessage('[-] Cannot connect to {url}. Server response is {resp} Server error code is {code}'.\n format(url=self.FullURL, resp=ce.message[0], code=ce.message[1][0]))\n except:\n self.postErrorMessage('[-] Cannot connect to ' + self.FullURL)\n except:\n self.postErrorMessage('[-] Cannot connect to ' + self.FullURL)","def test_post(self):\n url, port = self.server.address\n\n #couple of basic POSTs\n #request parameters\n r = self.client.get(\"http://{0}:{1}/\".format(url, port))\n self.assertEqual(200, r.status_code)\n r = self.client.get(\"http://{0}:{1}\".format(url, port))\n self.assertEqual(200, r.status_code)\n r = self.client.get(\"http://{0}:{1}/200\".format(url, port))\n self.assertEqual(200, r.status_code)\n r = self.client.get(\"http://{0}:{1}/400\".format(url, port))\n self.assertEqual(400, r.status_code)","def post(url, data={}, verify=True):\n data = requests.post(url=url, data=json.dumps(data), verify=verify)\n if data.status_code == 201:\n return data","def post(self, **kwargs):\n return self.client.post(\n self.url(), data=json.dumps(kwargs),\n content_type='application/json')","def _createPostRequest(self, postBody: dict) -> object:\n request = HttpRequest()\n request.method = \"POST\"\n for name,value in postBody.items():\n request.POST[name]= value\n return request","def post(self, url, postParameters=None, urlParameters=None):\r\n if urlParameters:\r\n url = url + \"?\" + self.getParameters(urlParameters)\r\n headers = {'Authorization':'GoogleLogin auth=%s' % self.auth_token,\r\n 'Content-Type': 'application/x-www-form-urlencoded'\r\n }\r\n postString = self.postParameters(postParameters)\r\n req = requests.post(url, data=postString, headers=headers)\r\n return req.text","async def _post_request(self, url, data):\n # Request the specific URL\n async with self.session.post(url, headers=self.headers, data=data) as resp:\n # Finally return the response\n return await resp.json()","def _api_post(self, query, data):\n r = requests.post(self._url + query,\n headers={'Content-Type': 'application/json', 'Accept': 'application/json'},\n auth=self._auth, data=json.dumps(data), timeout=self._request_timeout_secs)\n r.raise_for_status()\n return r","def _PostRequest(self, data=None):\n # requests will use about 3 times of data size's memory.\n req = requests.Request(\n 'POST',\n url=self._target_url,\n headers={'Multi-Event': 'True',\n 'Node-ID': str(self.GetNodeID())},\n files=data).prepare()\n clen = int(req.headers.get('Content-Length'))\n # Checks the size of request, and doesn't send if bigger than maximum size.\n if clen > self._max_bytes:\n return (413, 'Request Entity Too Large: The request is bigger '\n 'than %d bytes' % self._max_bytes, clen)\n resp = requests.Session().send(req, timeout=http_common.HTTP_TIMEOUT)\n if resp.headers['Maximum-Bytes']:\n self._max_bytes = int(resp.headers['Maximum-Bytes'])\n return resp.status_code, resp.reason, clen","def postTo(self,conn,data):\n #log(\"postTo: \"+str(conn))\n conn.request(self.command,self.path,data,self.headers)\n resp = conn.getresponse()\n log(\"postTo: \"+str(resp.status)+\", \"+str(resp.reason)+\", \"+str(resp.version))\n return resp","def http_post(call):\n\n verify_ssl = (\n True if \"verify_ssl\" not in call.data.keys() else call.data[\"verify_ssl\"]\n )\n\n headers = basic_headers\n if \"headers\" in call.data.keys():\n headers.update(call.data[\"headers\"])\n\n auth = None\n if \"auth_username\" in call.data.keys() and \"auth_password\" in call.data.keys():\n auth = (\n call.data[\"auth_username\"]\n if \"auth_username\" in call.data.keys()\n else None,\n call.data[\"auth_password\"]\n if \"auth_password\" in call.data.keys()\n else None,\n )\n\n data = None\n\n if \"data\" in call.data.keys():\n data = call.data[\"data\"]\n\n resp = requests.post(\n url=call.data[\"url\"],\n data=data,\n headers=headers,\n verify=verify_ssl,\n timeout=10,\n auth=auth,\n )\n\n return resp.status_code == 200","async def simulate_post(self, path='/', **kwargs) -> _ResultBase:\n return await self.simulate_request('POST', path, **kwargs)","def simulate_post(self, path='/', **kwargs) -> _ResultBase:\n return self.simulate_request('POST', path, **kwargs)","def post_resource(**kwargs) -> dict:\n\n response = requests.post(**kwargs)\n print(f\"HTTP {response.request.method}: {response.url}\")\n print(f'HTTP Status code: {response.status_code}')\n\n # Raise an exception if the response is not OK\n if not response.ok:\n print(response.text)\n response.raise_for_status()\n\n # Convert the reply to JSON\n response_json = response.json()\n\n # Return json parsed data as a python dictionary\n return response_json","def sr_post(self, route_or_uri, data, params=None, raw_response=False, **kwargs):\n op = lambda r: self.post(r, json=data)\n return self.__req(\n route_or_uri,\n params=params,\n query={},\n op=op,\n raw_response=raw_response,\n **kwargs,\n )","def POST(url, mime_type='text/html'):\n def_app = DefaultHttpRequestHandler()\n return def_app.RequestMap(url, methods=['POST'], produces=mime_type)"],"string":"[\n \"def httpPost(self, url='', data='', params={}, headers={}):\\n\\n return self.httpRequest('POST', url, data, params, headers)\",\n \"def post(self, *args, **kwargs):\\n return self._requests_call(util.requests_post, *args, **kwargs)\",\n \"def do_POST(self,):\\n self.http_method = 'POST'\\n self.response()\",\n \"def httpPost(self, url, post_parameters=None):\\r\\n return self.auth.post(url, post_parameters)\",\n \"def _post(self, *args, **kwargs):\\n return self._request('post', *args, **kwargs)\",\n \"def send_post(url):\\n HEADERS['accept'] = 'application/vnd.yang.data+json'\\n if not url.startswith('/'):\\n url = \\\"/{}\\\".format(url)\\n url = BASE_URL + url\\n resp = requests.post(url, headers=HEADERS)\\n return resp\",\n \"def http_post(self, **kwargs):\\n return self.rabjcallable.post(**kwargs)\",\n \"def _post(self, url, data=None):\\n if data is not None:\\n data = urllib.urlencode(data)\\n return self._request(url, method='POST', payload=data)\",\n \"def post(self, *args, **kw):\\n kw['method'] = 'POST'\\n return self.open(*args, **kw)\",\n \"def make_post_request(self, url, data):\\n auth = (self.AUTH_ID, self.AUTH_TOKEN)\\n headers = {'content-type': 'application/json'}\\n return requests.post(url, data=data, auth=auth, headers=headers)\",\n \"def http_method_post():\\n return 'POST'\",\n \"def post(self, url, body=None, headers=None, serialize=True):\\n return self._request('POST', url, body, headers, serialize)\",\n \"def post(self, *args, **kwargs):\\n headers = self.post_headers\\n headers.update(kwargs.get('headers', {}))\\n kwargs['headers'] = headers\\n return self._request('post', *args, **kwargs)\",\n \"def PostRequest(self):\\n if self.__Payload: \\n self.__Answer = requests.post(self.__URL, data = self.__Payload, headers = self.__Headers)\\n Logs.WriteToLog(\\\"Data transited to web server\\\")\\n else:\\n Logs.WriteToLog(\\\"No payload in HTTP request\\\")\\n raise Exception(\\\"Payload must be setted\\\")\",\n \"def test_post(self):\\n return self.doRequest(self.url, method=\\\"POST\\\", body=self.input)\",\n \"def _post(self, url, **kwargs):\\n return self._call('POST', url, kwargs)\",\n \"def post(self, *path, **data):\\n\\t\\treturn self.request('POST', *path, **data)\",\n \"def make_post_request(url:str, post_params:dict, **kwargs):\\n\\n print(\\\"Making call to '{}'...\\\".format(url))\\n resp = requests.post(url, data=post_params, **kwargs)\\n print(\\\"Received response.\\\")\\n\\n if not resp.ok:\\n return False, resp.status_code, json.loads(resp.content)\\n\\n return True, resp.status_code, json.loads(resp.content)\",\n \"def post(self, *args, **kwargs):\\n self.request(\\\"post\\\", *args, **kwargs)\",\n \"def post(self, *args, **kwargs):\\n return self._hit(\\\"POST\\\", *args, **kwargs)\",\n \"def make_post_request(client, endpoint, data):\\n return client.post(endpoint, data=data)\",\n \"def post(self, path, **post_args):\\n return self.request(path, data=post_args, method='POST')\",\n \"def post(self, data):\\n req = self._new_http_request()\\n req.add_data(self._prepare_data(data))\\n\\n return self._urllib2.urlopen(req)\",\n \"def do_POST(self):\\r\\n self._send_handler_response('POST')\",\n \"def post(self, url, data):\\r\\n print(f\\\"POST {url}\\\")\\r\\n print(\\\"data:\\\")\\r\\n self.pp.pprint(data)\\r\\n response = self.session.post(url, data=data)\\r\\n print(f\\\"STATUS {response.status_code}\\\")\\r\\n self.print_cookies()\\r\\n return response\",\n \"def post(url, data=None, json=None, **kwargs):\\n\\n return request('post', url, data=data, json=json, **kwargs)\",\n \"def post(self, data):\\n return requests.post(self.url, headers=self.headers, data=data)\",\n \"def post(url, to_error=_default_to_error, data=None, json=None, **kwargs):\\n\\n return request('post',\\n url, to_error=to_error, data=data, json=json, **kwargs)\",\n \"def __post(self, url, payload=None, headers=None):\\n if headers is None:\\n headers = {\\\"Content-Type\\\": \\\"application/json\\\"}\\n return self.__req(url, \\\"POST\\\", body=payload, headers=headers)\",\n \"def apost(url, **kwargs):\\n return requests.post(url, **kwargs)\",\n \"def post(self, url, data=None):\\r\\n response = self.requestHelper.post(url, data=data)\\r\\n return self.process(response)\",\n \"def post_form(url, headers, payload):\\n\\n headers['Content-Type'] = 'application/x-www-form-urlencoded'\\n\\n return RestClient.make_post_request(url, headers=headers, data=payload)\",\n \"def _http_post(self, path, data):\\n # Prepare the request path\\n if path[0] == '/':\\n path = path[1:]\\n path = urljoin(self.servlet_path, path)\\n\\n # Request the end points\\n conn = httplib.HTTPConnection(\\\"localhost\\\", self.port)\\n conn.request(\\\"POST\\\", path, data, {\\\"Content-Type\\\": \\\"application/json\\\"})\\n result = conn.getresponse()\\n data = result.read()\\n conn.close()\\n\\n # Convert the response to a string\\n return result.status, to_str(data)\",\n \"def _post_request(url, params):\\n data = dumps(params).encode(\\\"utf-8\\\")\\n request = requests.post(url, data=data)\\n return request\",\n \"def post(self, url, payload={}):\\n response = self._make_request(\\\"POST\\\", url, payload)\\n\\n return response\",\n \"def post(self, url, data):\\n return self.app.post(get_url(url), data=data, follow_redirects=True)\",\n \"def send_post(url, data, headers, return_output=False):\\n req = requests.post(url=url, data=json.dumps(data), headers=headers)\\n if return_output:\\n return req\\n if str(req.status_code).startswith('2'):\\n print 'SUCCESS! {0} {1} {2}'.format(req.status_code, req.reason, req.content)\\n else:\\n print 'FAIL! {0} {1} {2}'.format(req.status_code, req.reason, req.content)\\n exit(77)\",\n \"def do_post_request(self, uri, headers, payload, timeout_ms, stats_config):\\n return self._do_request('POST', uri, headers, payload, timeout_ms,\\n stats_config)\",\n \"def post(self, url, data):\\n\\t\\treturn self.session.post(url, data, headers=self.headers)\",\n \"async def post(url, session=None, **kwargs):\\n\\n method = 'POST'\\n resp = await _request(method, url, session=session, **kwargs)\\n return resp\",\n \"def post(self, url, data=None, params=None):\\n return self.session.post(url=self.base_url + url, data=data,\\n params=params)\",\n \"def post(self):\\n code, status = run_handlers.handle_data_post(self.request.headers, self.request.body)\\n self.set_status(code)\\n self.write(status)\\n self.finish()\",\n \"def _post(self, url, data=None):\\n url = urljoin(self.base_url, url)\\n try:\\n r = self._make_request(**dict(\\n method='POST',\\n url=url,\\n json=data,\\n auth=self.auth,\\n timeout=self.timeout,\\n hooks=self.request_hooks,\\n headers=self.request_headers\\n ))\\n except requests.exceptions.RequestException as e:\\n raise e\\n else:\\n if r.status_code >= 400:\\n _raise_response_error(r)\\n\\n if r.status_code == 204:\\n return None\\n return r.json()\",\n \"def post(self, url, body):\\n return self._query(url, 'POST', json=body)\",\n \"def api_post(self, path, data):\\n return self._api_request(path, 'POST', data)\",\n \"def post(self, url_or_path, *args, **kwargs):\\n return self.request.post(url_or_path, *args, **kwargs).json()\",\n \"def do_POST(self):\\n\\n # do this before sending any response as we may raise an exception\\n content = self.__get_content(self.__get_post_body())\\n\\n # send the response back to the client\\n self.send_response(200)\\n self.send_header(\\\"Content-type\\\", \\\"text/json\\\")\\n self.end_headers()\\n self.wfile.write(content)\",\n \"def post(self, path: str, params: dict) -> dict:\\n return self.request(\\\"POST\\\", path, params)\",\n \"def post(self, url: str, **kwargs: Dict[str, Any]) -> Any:\\n pass\",\n \"def simulate_post(app, path, **kwargs) -> _ResultBase:\\n return simulate_request(app, 'POST', path, **kwargs)\",\n \"def _http_post(self, url, params={}):\\n url = self.server + '/api2' + url\\n try:\\n r = requests.post(url=url, data=params)\\n except requests.exceptions.RequestException as e:\\n return check_failed(e)\\n # raise ClientHttpError(None, e)\\n if r.status_code != 200:\\n return check_failed(r.status_code)\\n # return ClientHttpError(r.status_code, r.text)\\n data = json.loads(r.text)\\n # TODO: check data\\n return data\",\n \"def test_client_can_do_post_request(self):\\n response = self.httpbin_4.test_requests_post_method()\\n self.assertEqual(response.request.method, 'POST')\\n self.assertEqual(response.status_code, 200)\",\n \"async def post(self, path, params=None, json_data=None):\\n response = await self.request('POST', path, params, json_data)\\n return response\",\n \"def post(self, url, *args):\\n\\n req_method = type(self.client).__name__\\n\\n if not url.startswith(\\\"http\\\"):\\n if not url.startswith(\\\"/\\\"):\\n url = \\\"/%s\\\" % url\\n url = \\\"%s%s\\\" % (self.base, url)\\n\\n if req_method == \\\"FlaskClient\\\":\\n self.client.post(url, headers=self.headers, *args)\\n\\n else:\\n self.client.post(url, headers=self.headers, *args)\",\n \"def do_POST(self):\\r\\n self.do_GET()\",\n \"async def post(self, url, params=None, json_data=None):\\n if self._authenticated:\\n return await self.__open(url, method=POST, headers=self._head, params=params, json_data=json_data)\",\n \"def post(self, **kwargs):\\n post_args = deepcopy(kwargs)\\n post_args['apikey'] = self.apikey\\n post_args['version'] = self.version\\n post_args['timestamp'] = timestamp()\\n\\n # calculate a checksum based on the values and secret key\\n post_checksum = self.checksum(**post_args)\\n\\n # use urllib to post the values\\n post_args['checksum'] = post_checksum\\n\\n params = urlencode(post_args)\\n try:\\n if Monitis.debug is True:\\n print \\\"Request URL: \\\" + self.url\\n print \\\"Request params: \\\" + str(post_args)\\n result = urlopen(self.url, params)\\n except HTTPError, error:\\n raise MonitisError('API Error: ' + error.read())\\n ret = result.read()\\n if Monitis.debug is True:\\n print \\\"Response: \\\" + ret\\n result.close()\\n return ret\",\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 _req_post(self, url: str, data, raw_res: bool = False):\\n self._get_cookies()\\n if not self._cookies:\\n return\\n r = reqtry.post(url, cookies=self._cookies, data=data, allow_redirects=False, timeout=(3, 3), tries=3, delay=1,\\n backoff=1.5, jitter=(1, 1.5))\\n if raw_res:\\n return r\\n assert r.status_code == 200, f\\\"Post request: Invalid http status code: {r.status_code}\\\"\\n assert '\\\"errCode\\\":0' in r.text, f'Post response with error from server. Response: {r.text}'\\n return r.text\",\n \"def post(self, uri, body=None, headers=None, auth=False):\\n return self.send_request('POST', uri, body, headers, auth)\",\n \"def _post(self, path=\\\"\\\", body=None, **kwargs):\\n uri = force_json(self.uri + path)\\n return self.client.request(uri, method=\\\"POST\\\", d=body, **kwargs)\",\n \"def send_post(self, uri, data=None, ofile=None, ojson=None):\\n return self.__send_request('POST', uri=uri, data=data, ofile=ofile, ojson=ojson)\",\n \"def post(self, *args, **kwargs):\\n return self.handle_post_request()\",\n \"def post(self, url, postParameters=None, urlParameters=None):\\r\\n if self.authorized_client:\\r\\n if urlParameters:\\r\\n getString = self.getParameters(urlParameters)\\r\\n req = urllib2.Request(url + \\\"?\\\" + getString)\\r\\n else:\\r\\n req = urllib2.Request(url)\\r\\n postString = self.postParameters(postParameters)\\r\\n resp,content = self.authorized_client.request(req, method=\\\"POST\\\", body=postString)\\r\\n return toUnicode(content)\\r\\n else:\\r\\n raise IOError(\\\"No authorized client available.\\\")\",\n \"def do_POST(self):\\n self._try_to_process_request(self._handle_post_request)\",\n \"def _post(self, request_obj):\\n return self._execute_action(request_obj, 'POST')\",\n \"def post():\\n pass\",\n \"def _post(self, path, data=None):\\n headers = {'content-type': 'application/json'}\\n if data:\\n data = json.dumps(data)\\n r = requests.post(self._url(path), data=data, headers=headers)\\n assert r.status_code == 200\\n return r\",\n \"def post(self, url, postParameters=None, urlParameters=None):\\r\\n if self._action_token == None:\\r\\n self._action_token = self.get(ReaderUrl.ACTION_TOKEN_URL)\\r\\n\\r\\n if self._http == None:\\r\\n self._setupHttp()\\r\\n uri = url + \\\"?\\\" + self.getParameters(urlParameters)\\r\\n postParameters.update({'T':self._action_token})\\r\\n body = self.postParameters(postParameters)\\r\\n response, content = self._http.request(uri, \\\"POST\\\", body=body)\\r\\n return content\",\n \"def post(api, url, payload, headers=None, auth=_KERBEROS_AUTH, proxies=None,\\n retries=_NUM_OF_RETRIES, timeout=None):\\n return call(api, url, 'post', payload=payload,\\n headers=headers, auth=auth, proxies=proxies, retries=retries,\\n timeout=timeout)\",\n \"def post(self, url, data=None, headers=None, timeout=10):\\r\\n try:\\r\\n return self.request(url=url, method='POST', data=data, extra_headers=headers, timeout=timeout)\\r\\n except requests.exceptions.ProxyError:\\r\\n return None\\r\\n except requests.RequestException as error:\\r\\n if self._debug:\\r\\n logging.exception(\\r\\n ''.join(traceback.format_exception(etype=type(error), value=error, tb=error.__traceback__)))\\r\\n return None\",\n \"def post_action(self, path, data=None):\\n response = self._request(\\\"POST\\\", path, urllib.urlencode(data) if data else None)\\n return self._handle_response(response)\",\n \"def post(url, data, auth=None, retries=10):\\n if not url.startswith('http://'):\\n url = 'http://' + url\\n\\n request = urllib2.Request(url)\\n if auth:\\n request.add_header('Authorization', 'Basic %s' % b64encode('%s:%s' % auth))\\n\\n params = urllib.urlencode(data)\\n response = urllib2.urlopen(request, params)\\n return response.read()\",\n \"def post(cls, uri, payload):\\n return cls._perform_request(uri, 'POST', payload)\",\n \"def do_POST(self):\\n self.send_response(200)\\n self.send_header('Content-type','text/html')\\n self.send_header('Access-Control-Allow-Origin','*')\\n self.end_headers()\\n # Send the html message\\n self.wfile.write(\\\"Aivis Panovs, 161REB125\\\")\\n return\",\n \"def post(url_ext, query_params={}, payload={}, custom_err=None, timeout=DEFAULT_TIMEOUT):\\r\\n url = get_url() + url_ext\\r\\n headers = get_headers()\\r\\n r = requests.post(url, headers=headers, params=query_params, data=payload, timeout=timeout)\\r\\n return handle_response(r, \\\"POST\\\", custom_err)\",\n \"def post(self):\\n created = post_tool(request.json)\\n return created, 201\",\n \"def post(self):\\n data = request.json\\n return save_new_post(data=data)\",\n \"def make_post_request(url, headers=None, json_data=None, data=None):\\n logger.info(\\\"Inside: make_post_request\\\")\\n logger.debug(\\\"make_post_request: parameters - {}, {}, {}, {}\\\".format(url, headers, json_data, data))\\n\\n timeout = get_config(REQUEST_TIMEOUT,\\\"timeout\\\")\\n\\n if not headers:\\n headers = {}\\n\\n if json_data:\\n resp = requests.post(url, verify=False, headers=headers, json=json_data, timeout=timeout)\\n elif data:\\n resp = requests.post(url, verify=False, headers=headers, data=data, timeout=timeout)\\n\\n logger.debug('received status : {}'.format(resp.status_code))\\n logger.debug('received text : {}'.format(resp.text))\\n logger.info(\\\"Exit: make_post_request\\\")\\n if RestClient.result_success(resp.status_code):\\n return resp\\n else:\\n err_msg = 'ERROR, received {} code during API call {}'.format(resp.status_code, url)\\n logger.error(err_msg)\\n raise APIException(err_msg, resp.text)\",\n \"def raw_post(\\n self, uri: str, data: Optional[Dict] = None, json: Optional[Dict] = None, **kwargs\\n ) -> requests.Response:\\n return self.session.post(url=self._url(uri), data=data, json=json, **kwargs)\",\n \"def test_doPost(self) -> None:\\n status_code = apicall.doPost(URL, self._browserheader)\\n assert status_code != API_SUCCESS\",\n \"def post(self, data=None, params=None):\\n params = self.parameters(additional_parameters=params)\\n res = post(self.endpoint_url, data=data, params=params)\\n return Response(res)\",\n \"def post(self, url, postParameters=None, urlParameters=None):\\r\\n if not self.access_token:\\r\\n raise IOError(\\\"No authorized client available.\\\")\\r\\n if not self.action_token:\\r\\n raise IOError(\\\"Need to generate action token.\\\")\\r\\n if urlParameters is None:\\r\\n urlParameters = {}\\r\\n headers = {'Authorization': 'Bearer ' + self.access_token,\\r\\n 'Content-Type': 'application/x-www-form-urlencoded'}\\r\\n postParameters.update({'T':self.action_token})\\r\\n request = requests.post(url + '?' + self.getParameters(urlParameters),\\r\\n data=postParameters, headers=headers)\\r\\n if request.status_code != 200:\\r\\n return None\\r\\n else:\\r\\n return toUnicode(request.text)\",\n \"def do_POST(self): # pylint: disable=invalid-name\\n self.handle_request()\",\n \"def simulate_post(self, path='/', **kwargs):\\n return self.simulate_request('POST', path, **kwargs)\",\n \"def submitPost(self):\\n headers, params, proxy = self.getHeaderParamProxyInfo()\\n try:\\n resp = requests.post(self.FullURL, data=self.PostData, headers=headers, params=params, proxies=proxy, verify=False)\\n return str(resp.content)\\n except ConnectionError as ce:\\n try:\\n self.postErrorMessage('[-] Cannot connect to {url}. Server response is {resp} Server error code is {code}'.\\n format(url=self.FullURL, resp=ce.message[0], code=ce.message[1][0]))\\n except:\\n self.postErrorMessage('[-] Cannot connect to ' + self.FullURL)\\n except:\\n self.postErrorMessage('[-] Cannot connect to ' + self.FullURL)\",\n \"def test_post(self):\\n url, port = self.server.address\\n\\n #couple of basic POSTs\\n #request parameters\\n r = self.client.get(\\\"http://{0}:{1}/\\\".format(url, port))\\n self.assertEqual(200, r.status_code)\\n r = self.client.get(\\\"http://{0}:{1}\\\".format(url, port))\\n self.assertEqual(200, r.status_code)\\n r = self.client.get(\\\"http://{0}:{1}/200\\\".format(url, port))\\n self.assertEqual(200, r.status_code)\\n r = self.client.get(\\\"http://{0}:{1}/400\\\".format(url, port))\\n self.assertEqual(400, r.status_code)\",\n \"def post(url, data={}, verify=True):\\n data = requests.post(url=url, data=json.dumps(data), verify=verify)\\n if data.status_code == 201:\\n return data\",\n \"def post(self, **kwargs):\\n return self.client.post(\\n self.url(), data=json.dumps(kwargs),\\n content_type='application/json')\",\n \"def _createPostRequest(self, postBody: dict) -> object:\\n request = HttpRequest()\\n request.method = \\\"POST\\\"\\n for name,value in postBody.items():\\n request.POST[name]= value\\n return request\",\n \"def post(self, url, postParameters=None, urlParameters=None):\\r\\n if urlParameters:\\r\\n url = url + \\\"?\\\" + self.getParameters(urlParameters)\\r\\n headers = {'Authorization':'GoogleLogin auth=%s' % self.auth_token,\\r\\n 'Content-Type': 'application/x-www-form-urlencoded'\\r\\n }\\r\\n postString = self.postParameters(postParameters)\\r\\n req = requests.post(url, data=postString, headers=headers)\\r\\n return req.text\",\n \"async def _post_request(self, url, data):\\n # Request the specific URL\\n async with self.session.post(url, headers=self.headers, data=data) as resp:\\n # Finally return the response\\n return await resp.json()\",\n \"def _api_post(self, query, data):\\n r = requests.post(self._url + query,\\n headers={'Content-Type': 'application/json', 'Accept': 'application/json'},\\n auth=self._auth, data=json.dumps(data), timeout=self._request_timeout_secs)\\n r.raise_for_status()\\n return r\",\n \"def _PostRequest(self, data=None):\\n # requests will use about 3 times of data size's memory.\\n req = requests.Request(\\n 'POST',\\n url=self._target_url,\\n headers={'Multi-Event': 'True',\\n 'Node-ID': str(self.GetNodeID())},\\n files=data).prepare()\\n clen = int(req.headers.get('Content-Length'))\\n # Checks the size of request, and doesn't send if bigger than maximum size.\\n if clen > self._max_bytes:\\n return (413, 'Request Entity Too Large: The request is bigger '\\n 'than %d bytes' % self._max_bytes, clen)\\n resp = requests.Session().send(req, timeout=http_common.HTTP_TIMEOUT)\\n if resp.headers['Maximum-Bytes']:\\n self._max_bytes = int(resp.headers['Maximum-Bytes'])\\n return resp.status_code, resp.reason, clen\",\n \"def postTo(self,conn,data):\\n #log(\\\"postTo: \\\"+str(conn))\\n conn.request(self.command,self.path,data,self.headers)\\n resp = conn.getresponse()\\n log(\\\"postTo: \\\"+str(resp.status)+\\\", \\\"+str(resp.reason)+\\\", \\\"+str(resp.version))\\n return resp\",\n \"def http_post(call):\\n\\n verify_ssl = (\\n True if \\\"verify_ssl\\\" not in call.data.keys() else call.data[\\\"verify_ssl\\\"]\\n )\\n\\n headers = basic_headers\\n if \\\"headers\\\" in call.data.keys():\\n headers.update(call.data[\\\"headers\\\"])\\n\\n auth = None\\n if \\\"auth_username\\\" in call.data.keys() and \\\"auth_password\\\" in call.data.keys():\\n auth = (\\n call.data[\\\"auth_username\\\"]\\n if \\\"auth_username\\\" in call.data.keys()\\n else None,\\n call.data[\\\"auth_password\\\"]\\n if \\\"auth_password\\\" in call.data.keys()\\n else None,\\n )\\n\\n data = None\\n\\n if \\\"data\\\" in call.data.keys():\\n data = call.data[\\\"data\\\"]\\n\\n resp = requests.post(\\n url=call.data[\\\"url\\\"],\\n data=data,\\n headers=headers,\\n verify=verify_ssl,\\n timeout=10,\\n auth=auth,\\n )\\n\\n return resp.status_code == 200\",\n \"async def simulate_post(self, path='/', **kwargs) -> _ResultBase:\\n return await self.simulate_request('POST', path, **kwargs)\",\n \"def simulate_post(self, path='/', **kwargs) -> _ResultBase:\\n return self.simulate_request('POST', path, **kwargs)\",\n \"def post_resource(**kwargs) -> dict:\\n\\n response = requests.post(**kwargs)\\n print(f\\\"HTTP {response.request.method}: {response.url}\\\")\\n print(f'HTTP Status code: {response.status_code}')\\n\\n # Raise an exception if the response is not OK\\n if not response.ok:\\n print(response.text)\\n response.raise_for_status()\\n\\n # Convert the reply to JSON\\n response_json = response.json()\\n\\n # Return json parsed data as a python dictionary\\n return response_json\",\n \"def sr_post(self, route_or_uri, data, params=None, raw_response=False, **kwargs):\\n op = lambda r: self.post(r, json=data)\\n return self.__req(\\n route_or_uri,\\n params=params,\\n query={},\\n op=op,\\n raw_response=raw_response,\\n **kwargs,\\n )\",\n \"def POST(url, mime_type='text/html'):\\n def_app = DefaultHttpRequestHandler()\\n return def_app.RequestMap(url, methods=['POST'], produces=mime_type)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7948866","0.78074974","0.775014","0.77245474","0.7586205","0.7512639","0.7453403","0.73794913","0.7378032","0.73769873","0.73720765","0.7345682","0.72977006","0.726105","0.7250581","0.72415143","0.72306734","0.7205217","0.7157622","0.7133639","0.71161723","0.71075433","0.7106713","0.707298","0.70619005","0.70554304","0.7036653","0.7012903","0.70087165","0.7000057","0.6980748","0.69726914","0.6944447","0.687227","0.6849894","0.68422306","0.68367344","0.6809654","0.67620337","0.6758897","0.6749642","0.6748007","0.67403716","0.673126","0.67240256","0.6704173","0.6697699","0.66809005","0.66783154","0.6677158","0.66466165","0.66433346","0.6628051","0.66148907","0.6610932","0.66106015","0.65963274","0.6592714","0.6584113","0.65773606","0.65444475","0.654","0.65106565","0.650899","0.6502104","0.650048","0.649795","0.6491686","0.6485103","0.64832896","0.64631325","0.6461783","0.644181","0.643729","0.6434932","0.6432336","0.6431839","0.6402868","0.64022875","0.6399916","0.63894546","0.63806415","0.63685596","0.6358507","0.6357756","0.63564795","0.6338249","0.6337162","0.6325394","0.632427","0.6323746","0.63172257","0.63151014","0.6306534","0.630511","0.6304475","0.6296479","0.6282167","0.62793666","0.62671375","0.62612486"],"string":"[\n \"0.7948866\",\n \"0.78074974\",\n \"0.775014\",\n \"0.77245474\",\n \"0.7586205\",\n \"0.7512639\",\n \"0.7453403\",\n \"0.73794913\",\n \"0.7378032\",\n \"0.73769873\",\n \"0.73720765\",\n \"0.7345682\",\n \"0.72977006\",\n \"0.726105\",\n \"0.7250581\",\n \"0.72415143\",\n \"0.72306734\",\n \"0.7205217\",\n \"0.7157622\",\n \"0.7133639\",\n \"0.71161723\",\n \"0.71075433\",\n \"0.7106713\",\n \"0.707298\",\n \"0.70619005\",\n \"0.70554304\",\n \"0.7036653\",\n \"0.7012903\",\n \"0.70087165\",\n \"0.7000057\",\n \"0.6980748\",\n \"0.69726914\",\n \"0.6944447\",\n \"0.687227\",\n \"0.6849894\",\n \"0.68422306\",\n \"0.68367344\",\n \"0.6809654\",\n \"0.67620337\",\n \"0.6758897\",\n \"0.6749642\",\n \"0.6748007\",\n \"0.67403716\",\n \"0.673126\",\n \"0.67240256\",\n \"0.6704173\",\n \"0.6697699\",\n \"0.66809005\",\n \"0.66783154\",\n \"0.6677158\",\n \"0.66466165\",\n \"0.66433346\",\n \"0.6628051\",\n \"0.66148907\",\n \"0.6610932\",\n \"0.66106015\",\n \"0.65963274\",\n \"0.6592714\",\n \"0.6584113\",\n \"0.65773606\",\n \"0.65444475\",\n \"0.654\",\n \"0.65106565\",\n \"0.650899\",\n \"0.6502104\",\n \"0.650048\",\n \"0.649795\",\n \"0.6491686\",\n \"0.6485103\",\n \"0.64832896\",\n \"0.64631325\",\n \"0.6461783\",\n \"0.644181\",\n \"0.643729\",\n \"0.6434932\",\n \"0.6432336\",\n \"0.6431839\",\n \"0.6402868\",\n \"0.64022875\",\n \"0.6399916\",\n \"0.63894546\",\n \"0.63806415\",\n \"0.63685596\",\n \"0.6358507\",\n \"0.6357756\",\n \"0.63564795\",\n \"0.6338249\",\n \"0.6337162\",\n \"0.6325394\",\n \"0.632427\",\n \"0.6323746\",\n \"0.63172257\",\n \"0.63151014\",\n \"0.6306534\",\n \"0.630511\",\n \"0.6304475\",\n \"0.6296479\",\n \"0.6282167\",\n \"0.62793666\",\n \"0.62671375\",\n \"0.62612486\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":83091,"cells":{"query":{"kind":"string","value":"Make an HTTP PATCH request."},"document":{"kind":"string","value":"def handle_patch(self, api, command):\n return self._make_request_from_command('PATCH', command)"},"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 httpPatch(self, url='', data='', params={}, headers={}):\n\n return self.httpRequest('PATCH', url, data, params, headers)","def patch(url, data=None, **kwargs):\n\n return request('patch', url, data=data, **kwargs)","def patch(self, *args, **kwargs):\n self.request(\"patch\", *args, **kwargs)","def patch(self, url, body=None, headers=None):\n return self._request('PATCH', url, body, headers)","def test_client_can_do_patch_request(self):\n response = self.httpbin_4.test_requests_patch_method()\n self.assertEqual(response.request.method, 'PATCH')\n self.assertEqual(response.status_code, 200)","def simulate_patch(app, path, **kwargs) -> _ResultBase:\n return simulate_request(app, 'PATCH', path, **kwargs)","def _patch(self, url, data=None):\n url = urljoin(self.base_url, url)\n try:\n r = self._make_request(**dict(\n method='PATCH',\n url=url,\n json=data,\n auth=self.auth,\n timeout=self.timeout,\n hooks=self.request_hooks,\n headers=self.request_headers\n ))\n except requests.exceptions.RequestException as e:\n raise e\n else:\n if r.status_code >= 400:\n _raise_response_error(r)\n return r.json()","def patch(self, url_or_path, *args, **kwargs):\n return self.request.patch(url_or_path, *args, **kwargs).json()","def patch(url, to_error=_default_to_error, data=None, **kwargs):\n\n return request('patch', url, to_error=to_error, data=data, **kwargs)","def patch(self, url, params='', headers=None, extra_environ=None,\n status=None, upload_files=None, expect_errors=False,\n content_type=None):\n return self._gen_request(RequestMethods.PATCH,\n url, params=params, headers=headers,\n extra_environ=extra_environ, status=status,\n upload_files=upload_files,\n expect_errors=expect_errors,\n content_type=content_type)","def simulate_patch(self, path='/', **kwargs):\n return self.simulate_request('PATCH', path, **kwargs)","def patch(self , request , pk = None ):\r\n return Response({'method':'patch'})","def patch(self, request, pk=None):\n return Response({'method': 'PATCH'})","def patch(self, request, pk=None):\n return Response({'method': 'PATCH'})","def patch(self, request, pk=None):\n return Response({'method': 'PATCH'})","def patch(self, request , pk=None):\n return Response({'message':'PATCH'})","def patch(self, request, pk=None):\n\n return Response({'method': 'patch'})","def simulate_patch(self, path='/', **kwargs) -> _ResultBase:\n return self.simulate_request('PATCH', path, **kwargs)","async def simulate_patch(self, path='/', **kwargs) -> _ResultBase:\n return await self.simulate_request('PATCH', path, **kwargs)","def requestfactory_patch(self, path, data=None, content_type=client.MULTIPART_CONTENT, **extra):\r\n\r\n data = data or {}\r\n patch_data = self._encode_data(data, content_type)\r\n\r\n parsed = urlparse.urlparse(path)\r\n request = {\r\n 'CONTENT_LENGTH': len(patch_data),\r\n 'CONTENT_TYPE': content_type,\r\n 'PATH_INFO': self._get_path(parsed),\r\n 'QUERY_STRING': parsed[4],\r\n 'REQUEST_METHOD': 'PATCH',\r\n 'wsgi.input': client.FakePayload(patch_data),\r\n }\r\n request.update(extra)\r\n return self.request(**request)","def patch(self,request,pk = None):\n return Response({'method': 'PATCH'})","def patch(self, obj):\r\n self.require_item()\r\n request = http.Request('PATCH', self.get_url(), self.wrap_object(obj))\r\n\r\n return request, parsers.parse_json","async def patch(url, session=None, **kwargs):\n\n method = 'PATCH'\n resp = await _request(method, url, session=session, **kwargs)\n return resp","def patch(self, request, pk=None):\n return Response({'message': 'patch'})","def partial_update(self, request, pk=None):\n\n return Response({'http_method': 'PATCH'})","def patch(self, endpoint, params=None, data=None):\n params = params or dict()\n data = data or dict()\n return self.request(verb=requests.patch, address=self.project_address + endpoint,\n params=params, data=data)","def patch(self, api_path, *args, **kwargs):\n\n\t\treturn self._do_operation(u'patch', api_path, *args, **kwargs)","def patch(self,request,pk=None):\n return Response({'method':'Patch'})","def __patch(self, url):\n\n res = requests.patch(url, headers=self.auth_header)\n res.raise_for_status()\n return res","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def Patch(self, request, global_params=None):\n config = self.GetMethodConfig('Patch')\n return self._RunMethod(\n config, request, global_params=global_params)","def patch(self, path, body):\n url = urljoin(self.api_endpoint, path)\n response = requests.patch(url, json=body, headers=self.headers)\n return self._check_response(response)","def patch(resource, data, **kwargs):\n\tresp = requests.patch(\n\t\t_endpoint(resource, 'PATCH'),\n\t\tparams=_jsonify_dict_values(kwargs),\n\t\tdata=json.dumps(data),\n\t\theaders=PAYLOAD_HEADERS,\n\t\tverify=SERVER_CERT\n\t)\n\tresp.raise_for_status()\n\treturn resp.json()","def patch(self, endpoint, content=None, params=None):\n\t\treturn self._call(\"PATCH\", endpoint, content, params)","def partial_update(self, request, pk=None):\n return Response({'http_method':'PATCH'})","def _patch(self, path=None, version=None, params=None,\n data=None, json=None, header=None):\n return self.client.patch(module='mam', path=path, version=version,\n params=params, data=data,\n json=json, header=header)","def partial_update(self, request, pk=None):\n\n return Response({'http_method':'PATCH'})","def partial_update(self, request, pk=None):\n\n return Response({'http_method': 'PATCH'})","def patch(self, *args, **kwargs):\n return self.handle_patch_request()","def patch(self, request, pk=None): #pk of id of objects to be updated (DB)\n return Response({'method':'PATCH'})","def patch(self, uri, format='json', data=None, authentication=None, **kwargs):\r\n content_type = self.get_content_type(format)\r\n kwargs['content_type'] = content_type\r\n\r\n if data is not None:\r\n kwargs['data'] = self.serializer.serialize(data, format=content_type)\r\n\r\n if authentication is not None:\r\n kwargs['HTTP_AUTHORIZATION'] = authentication\r\n\r\n # This hurts because Django doesn't support PATCH natively.\r\n parsed = urlparse(uri)\r\n r = {\r\n 'CONTENT_LENGTH': len(kwargs['data']),\r\n 'CONTENT_TYPE': content_type,\r\n 'PATH_INFO': self.client._get_path(parsed),\r\n 'QUERY_STRING': parsed[4],\r\n 'REQUEST_METHOD': 'PATCH',\r\n 'wsgi.input': FakePayload(kwargs['data']),\r\n }\r\n r.update(kwargs)\r\n return self.client.request(**r)","def client_patch(self, path, data=None, content_type=client.MULTIPART_CONTENT, follow=False, **extra):\r\n\r\n data = data or {}\r\n response = super(client.Client, self).patch(path, data=data, content_type=content_type, **extra)\r\n if follow:\r\n response = self._handle_redirects(response, **extra)\r\n return response","def Patch(Path: str, Params: Any = None, Data: Any = None, *, Callback: Callable[[int, Dict[str, Any]], None]) -> None:\n Request(\"PATCH\", Path, Params, Data, Callback=Callback)","def patch(*args, **kwargs):\n return update(*args, patch=True, **kwargs)","def partial_update(self,request,pk= None):\n return Response({'http_method':'PATCH'})","def test_PATCH(self):\n if not self.url:\n return\n response = self.client.patch(self.url, {}, format='json')\n self.assertIn(response.status_code, [status.HTTP_405_METHOD_NOT_ALLOWED,\n status.HTTP_401_UNAUTHORIZED])","def partial_update(self, request, pk=None): #partial update a specific object\n return Response({'http_method': 'PATCH'})","def partial_update(self,request,pk = None):\r\n\r\n return Response({'HTTP method':'PATCH'})","def patch(self, uri, data=None, **kwargs):\n return self.session.patch(uri, data=data, **kwargs)","def patch(self, url_pattern):\n return self.route(url_pattern, methods=['PATCH'])","def test_patch(self):\n client = RestClient(host=self.host, username='')\n rest_url = 'some/url/'\n \n # Mock good response\n with responses.RequestsMock() as rsps:\n rsps.add(responses.PATCH, f'{self.host}/{rest_url}', status=200,\n json={'value':\"good!\"})\n r = client.patch(rest_url)","def _patch(self, url, json=None, **kwargs):\n kwargs = Connection._prepare_json_payload(json, **(kwargs or {}))\n return self._http.patch(self.cluster + url, timeout=self.timeout, **(kwargs or {}))","def patch_request(\n self,\n alias,\n uri,\n data=None,\n json=None,\n params=None,\n headers=None,\n files=None,\n allow_redirects=None,\n timeout=None):\n session = self._cache.switch(alias)\n data = utils.format_data_according_to_header(session, data, headers)\n # XXX workaround to restore library default behaviour. Not needed in new keywords\n redir = True if allow_redirects is None else allow_redirects\n\n response = self._common_request(\n \"patch\",\n session,\n uri,\n data=data,\n json=json,\n params=params,\n files=files,\n headers=headers,\n allow_redirects=redir,\n timeout=timeout)\n\n return response","def view_patch():\n\n return jsonify(\n get_dict(\"url\", \"args\", \"form\", \"data\", \"origin\", \"headers\", \"files\", \"json\")\n )","def put(self, request, pk=None):\n return Response({'method': 'patch'})","def patch(self, id_or_uri, operation, path, value, timeout=-1):\n headers = {'Content-Type': 'application/json-patch+json'}\n return self._client.patch(id_or_uri, operation, path, value, timeout=timeout, custom_headers=headers)","def update(self, request, pk=None):\n\n return Response({'http_method': 'PUT'})","def patch(self, resource, data, target=None, verb='patch', **kwargs):\n return self._modify_resource(resource, data, target, verb, **kwargs)","def patch(self, method, uri, query_param, request_param, headers, **kwargs):\n raise NotImplementedError","def patch(self, request, pk):\n return self.partial_update(request, pk)","def fusion_api_generic_patch(self, body, uri, api=None, headers=None):\n if api:\n headers = self.fusion_client._set_req_api_version(api=api)\n elif not headers:\n headers = self.fusion_client._headers\n uri = 'https://%s%s' % (self.fusion_client._host, uri)\n return self.fusion_client.patch(uri=uri, headers=headers, body=json.dumps(body))","def test_partial_update_should_not_be_allowed(self):\n response = self.client.patch(self.get_url(), {})\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)","def perform_http_patch(self, system_name, variable_config=None,\n request_id=None, data=None,\n url=None, expected_response=None,\n headers=None, user=None, password=None,\n allow_redirects=None, timeout=None,\n json=None, cookies=None, files=None, proxies=None,\n verify=None, stream=None, cert=None, var_sub=None):\n arguments = {'system_name': system_name, 'variable_config': variable_config,\n 'request_id': request_id, 'url': url, 'data': data,\n 'expected_response': expected_response, 'headers': headers,\n 'user': user, 'password': password, 'allow_redirects': allow_redirects,\n 'timeout': timeout, 'json': json, 'cookies': cookies,\n 'files': files, 'proxies': proxies, 'verify': verify,\n 'stream': stream, 'cert': cert, 'var_sub': var_sub}\n wdesc = \"Perform a http patch to the url\"\n pSubStep(wdesc)\n pNote(system_name)\n output_dict = {}\n result = True\n\n for element in arguments:\n if element in [\"json\", \"data\", \"variable_config\"] and \\\n arguments[element] and arguments[element] is not None:\n arguments[element] = Utils.rest_Utils.\\\n check_ext_get_abspath(arguments[element], self.tc_path)\n\n credentials = Utils.data_Utils.\\\n get_user_specified_tag_values_in_tc(self.datafile, **arguments)\n\n if credentials[\"variable_config\"] and \\\n credentials[\"variable_config\"] is not None:\n credentials[\"variable_config\"] = Utils.rest_Utils.\\\n check_ext_get_abspath(credentials[\"variable_config\"],\n os.path.dirname(self.datafile))\n\n for element in credentials:\n credentials = Utils.rest_Utils.\\\n resolve_credentials_for_rest(credentials, element,\n self.datafile, system_name)\n credentials[\"auth\"] = (credentials['user'], credentials['password'])\n\n credentials, popped_args = Utils.rest_Utils.\\\n remove_invalid_req_args(credentials, [\"user\", \"password\",\n \"request_id\",\n \"variable_config\",\n \"var_sub\", \"json\"])\n\n pNote(\"url is: {0}\".format(credentials['url']))\n for i in range(0, len(popped_args[\"json\"])):\n if popped_args[\"json\"][i] != \"Error\":\n credentials[\"json\"] = popped_args[\"json\"][i]\n for key in credentials:\n pNote(\"Sending argument '{0}': {1}\"\n .format(key, credentials[key]))\n status, api_response = self.rest_object.patch(**credentials)\n result = result and status\n output_dict.update(self.rest_object.\n update_output_dict(system_name,\n api_response, request_id, status, i+1))\n else:\n pNote(\"Request not sent.\", \"error\")\n status = False\n result = result and status\n if result:\n msg = \"http patch successful\"\n else:\n msg = \"http patch failed\"\n pNote(msg)\n report_substep_status(result)\n return result, output_dict"],"string":"[\n \"def httpPatch(self, url='', data='', params={}, headers={}):\\n\\n return self.httpRequest('PATCH', url, data, params, headers)\",\n \"def patch(url, data=None, **kwargs):\\n\\n return request('patch', url, data=data, **kwargs)\",\n \"def patch(self, *args, **kwargs):\\n self.request(\\\"patch\\\", *args, **kwargs)\",\n \"def patch(self, url, body=None, headers=None):\\n return self._request('PATCH', url, body, headers)\",\n \"def test_client_can_do_patch_request(self):\\n response = self.httpbin_4.test_requests_patch_method()\\n self.assertEqual(response.request.method, 'PATCH')\\n self.assertEqual(response.status_code, 200)\",\n \"def simulate_patch(app, path, **kwargs) -> _ResultBase:\\n return simulate_request(app, 'PATCH', path, **kwargs)\",\n \"def _patch(self, url, data=None):\\n url = urljoin(self.base_url, url)\\n try:\\n r = self._make_request(**dict(\\n method='PATCH',\\n url=url,\\n json=data,\\n auth=self.auth,\\n timeout=self.timeout,\\n hooks=self.request_hooks,\\n headers=self.request_headers\\n ))\\n except requests.exceptions.RequestException as e:\\n raise e\\n else:\\n if r.status_code >= 400:\\n _raise_response_error(r)\\n return r.json()\",\n \"def patch(self, url_or_path, *args, **kwargs):\\n return self.request.patch(url_or_path, *args, **kwargs).json()\",\n \"def patch(url, to_error=_default_to_error, data=None, **kwargs):\\n\\n return request('patch', url, to_error=to_error, data=data, **kwargs)\",\n \"def patch(self, url, params='', headers=None, extra_environ=None,\\n status=None, upload_files=None, expect_errors=False,\\n content_type=None):\\n return self._gen_request(RequestMethods.PATCH,\\n url, params=params, headers=headers,\\n extra_environ=extra_environ, status=status,\\n upload_files=upload_files,\\n expect_errors=expect_errors,\\n content_type=content_type)\",\n \"def simulate_patch(self, path='/', **kwargs):\\n return self.simulate_request('PATCH', path, **kwargs)\",\n \"def patch(self , request , pk = None ):\\r\\n return Response({'method':'patch'})\",\n \"def patch(self, request, pk=None):\\n return Response({'method': 'PATCH'})\",\n \"def patch(self, request, pk=None):\\n return Response({'method': 'PATCH'})\",\n \"def patch(self, request, pk=None):\\n return Response({'method': 'PATCH'})\",\n \"def patch(self, request , pk=None):\\n return Response({'message':'PATCH'})\",\n \"def patch(self, request, pk=None):\\n\\n return Response({'method': 'patch'})\",\n \"def simulate_patch(self, path='/', **kwargs) -> _ResultBase:\\n return self.simulate_request('PATCH', path, **kwargs)\",\n \"async def simulate_patch(self, path='/', **kwargs) -> _ResultBase:\\n return await self.simulate_request('PATCH', path, **kwargs)\",\n \"def requestfactory_patch(self, path, data=None, content_type=client.MULTIPART_CONTENT, **extra):\\r\\n\\r\\n data = data or {}\\r\\n patch_data = self._encode_data(data, content_type)\\r\\n\\r\\n parsed = urlparse.urlparse(path)\\r\\n request = {\\r\\n 'CONTENT_LENGTH': len(patch_data),\\r\\n 'CONTENT_TYPE': content_type,\\r\\n 'PATH_INFO': self._get_path(parsed),\\r\\n 'QUERY_STRING': parsed[4],\\r\\n 'REQUEST_METHOD': 'PATCH',\\r\\n 'wsgi.input': client.FakePayload(patch_data),\\r\\n }\\r\\n request.update(extra)\\r\\n return self.request(**request)\",\n \"def patch(self,request,pk = None):\\n return Response({'method': 'PATCH'})\",\n \"def patch(self, obj):\\r\\n self.require_item()\\r\\n request = http.Request('PATCH', self.get_url(), self.wrap_object(obj))\\r\\n\\r\\n return request, parsers.parse_json\",\n \"async def patch(url, session=None, **kwargs):\\n\\n method = 'PATCH'\\n resp = await _request(method, url, session=session, **kwargs)\\n return resp\",\n \"def patch(self, request, pk=None):\\n return Response({'message': 'patch'})\",\n \"def partial_update(self, request, pk=None):\\n\\n return Response({'http_method': 'PATCH'})\",\n \"def patch(self, endpoint, params=None, data=None):\\n params = params or dict()\\n data = data or dict()\\n return self.request(verb=requests.patch, address=self.project_address + endpoint,\\n params=params, data=data)\",\n \"def patch(self, api_path, *args, **kwargs):\\n\\n\\t\\treturn self._do_operation(u'patch', api_path, *args, **kwargs)\",\n \"def patch(self,request,pk=None):\\n return Response({'method':'Patch'})\",\n \"def __patch(self, url):\\n\\n res = requests.patch(url, headers=self.auth_header)\\n res.raise_for_status()\\n return res\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def Patch(self, request, global_params=None):\\n config = self.GetMethodConfig('Patch')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def patch(self, path, body):\\n url = urljoin(self.api_endpoint, path)\\n response = requests.patch(url, json=body, headers=self.headers)\\n return self._check_response(response)\",\n \"def patch(resource, data, **kwargs):\\n\\tresp = requests.patch(\\n\\t\\t_endpoint(resource, 'PATCH'),\\n\\t\\tparams=_jsonify_dict_values(kwargs),\\n\\t\\tdata=json.dumps(data),\\n\\t\\theaders=PAYLOAD_HEADERS,\\n\\t\\tverify=SERVER_CERT\\n\\t)\\n\\tresp.raise_for_status()\\n\\treturn resp.json()\",\n \"def patch(self, endpoint, content=None, params=None):\\n\\t\\treturn self._call(\\\"PATCH\\\", endpoint, content, params)\",\n \"def partial_update(self, request, pk=None):\\n return Response({'http_method':'PATCH'})\",\n \"def _patch(self, path=None, version=None, params=None,\\n data=None, json=None, header=None):\\n return self.client.patch(module='mam', path=path, version=version,\\n params=params, data=data,\\n json=json, header=header)\",\n \"def partial_update(self, request, pk=None):\\n\\n return Response({'http_method':'PATCH'})\",\n \"def partial_update(self, request, pk=None):\\n\\n return Response({'http_method': 'PATCH'})\",\n \"def patch(self, *args, **kwargs):\\n return self.handle_patch_request()\",\n \"def patch(self, request, pk=None): #pk of id of objects to be updated (DB)\\n return Response({'method':'PATCH'})\",\n \"def patch(self, uri, format='json', data=None, authentication=None, **kwargs):\\r\\n content_type = self.get_content_type(format)\\r\\n kwargs['content_type'] = content_type\\r\\n\\r\\n if data is not None:\\r\\n kwargs['data'] = self.serializer.serialize(data, format=content_type)\\r\\n\\r\\n if authentication is not None:\\r\\n kwargs['HTTP_AUTHORIZATION'] = authentication\\r\\n\\r\\n # This hurts because Django doesn't support PATCH natively.\\r\\n parsed = urlparse(uri)\\r\\n r = {\\r\\n 'CONTENT_LENGTH': len(kwargs['data']),\\r\\n 'CONTENT_TYPE': content_type,\\r\\n 'PATH_INFO': self.client._get_path(parsed),\\r\\n 'QUERY_STRING': parsed[4],\\r\\n 'REQUEST_METHOD': 'PATCH',\\r\\n 'wsgi.input': FakePayload(kwargs['data']),\\r\\n }\\r\\n r.update(kwargs)\\r\\n return self.client.request(**r)\",\n \"def client_patch(self, path, data=None, content_type=client.MULTIPART_CONTENT, follow=False, **extra):\\r\\n\\r\\n data = data or {}\\r\\n response = super(client.Client, self).patch(path, data=data, content_type=content_type, **extra)\\r\\n if follow:\\r\\n response = self._handle_redirects(response, **extra)\\r\\n return response\",\n \"def Patch(Path: str, Params: Any = None, Data: Any = None, *, Callback: Callable[[int, Dict[str, Any]], None]) -> None:\\n Request(\\\"PATCH\\\", Path, Params, Data, Callback=Callback)\",\n \"def patch(*args, **kwargs):\\n return update(*args, patch=True, **kwargs)\",\n \"def partial_update(self,request,pk= None):\\n return Response({'http_method':'PATCH'})\",\n \"def test_PATCH(self):\\n if not self.url:\\n return\\n response = self.client.patch(self.url, {}, format='json')\\n self.assertIn(response.status_code, [status.HTTP_405_METHOD_NOT_ALLOWED,\\n status.HTTP_401_UNAUTHORIZED])\",\n \"def partial_update(self, request, pk=None): #partial update a specific object\\n return Response({'http_method': 'PATCH'})\",\n \"def partial_update(self,request,pk = None):\\r\\n\\r\\n return Response({'HTTP method':'PATCH'})\",\n \"def patch(self, uri, data=None, **kwargs):\\n return self.session.patch(uri, data=data, **kwargs)\",\n \"def patch(self, url_pattern):\\n return self.route(url_pattern, methods=['PATCH'])\",\n \"def test_patch(self):\\n client = RestClient(host=self.host, username='')\\n rest_url = 'some/url/'\\n \\n # Mock good response\\n with responses.RequestsMock() as rsps:\\n rsps.add(responses.PATCH, f'{self.host}/{rest_url}', status=200,\\n json={'value':\\\"good!\\\"})\\n r = client.patch(rest_url)\",\n \"def _patch(self, url, json=None, **kwargs):\\n kwargs = Connection._prepare_json_payload(json, **(kwargs or {}))\\n return self._http.patch(self.cluster + url, timeout=self.timeout, **(kwargs or {}))\",\n \"def patch_request(\\n self,\\n alias,\\n uri,\\n data=None,\\n json=None,\\n params=None,\\n headers=None,\\n files=None,\\n allow_redirects=None,\\n timeout=None):\\n session = self._cache.switch(alias)\\n data = utils.format_data_according_to_header(session, data, headers)\\n # XXX workaround to restore library default behaviour. Not needed in new keywords\\n redir = True if allow_redirects is None else allow_redirects\\n\\n response = self._common_request(\\n \\\"patch\\\",\\n session,\\n uri,\\n data=data,\\n json=json,\\n params=params,\\n files=files,\\n headers=headers,\\n allow_redirects=redir,\\n timeout=timeout)\\n\\n return response\",\n \"def view_patch():\\n\\n return jsonify(\\n get_dict(\\\"url\\\", \\\"args\\\", \\\"form\\\", \\\"data\\\", \\\"origin\\\", \\\"headers\\\", \\\"files\\\", \\\"json\\\")\\n )\",\n \"def put(self, request, pk=None):\\n return Response({'method': 'patch'})\",\n \"def patch(self, id_or_uri, operation, path, value, timeout=-1):\\n headers = {'Content-Type': 'application/json-patch+json'}\\n return self._client.patch(id_or_uri, operation, path, value, timeout=timeout, custom_headers=headers)\",\n \"def update(self, request, pk=None):\\n\\n return Response({'http_method': 'PUT'})\",\n \"def patch(self, resource, data, target=None, verb='patch', **kwargs):\\n return self._modify_resource(resource, data, target, verb, **kwargs)\",\n \"def patch(self, method, uri, query_param, request_param, headers, **kwargs):\\n raise NotImplementedError\",\n \"def patch(self, request, pk):\\n return self.partial_update(request, pk)\",\n \"def fusion_api_generic_patch(self, body, uri, api=None, headers=None):\\n if api:\\n headers = self.fusion_client._set_req_api_version(api=api)\\n elif not headers:\\n headers = self.fusion_client._headers\\n uri = 'https://%s%s' % (self.fusion_client._host, uri)\\n return self.fusion_client.patch(uri=uri, headers=headers, body=json.dumps(body))\",\n \"def test_partial_update_should_not_be_allowed(self):\\n response = self.client.patch(self.get_url(), {})\\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\",\n \"def perform_http_patch(self, system_name, variable_config=None,\\n request_id=None, data=None,\\n url=None, expected_response=None,\\n headers=None, user=None, password=None,\\n allow_redirects=None, timeout=None,\\n json=None, cookies=None, files=None, proxies=None,\\n verify=None, stream=None, cert=None, var_sub=None):\\n arguments = {'system_name': system_name, 'variable_config': variable_config,\\n 'request_id': request_id, 'url': url, 'data': data,\\n 'expected_response': expected_response, 'headers': headers,\\n 'user': user, 'password': password, 'allow_redirects': allow_redirects,\\n 'timeout': timeout, 'json': json, 'cookies': cookies,\\n 'files': files, 'proxies': proxies, 'verify': verify,\\n 'stream': stream, 'cert': cert, 'var_sub': var_sub}\\n wdesc = \\\"Perform a http patch to the url\\\"\\n pSubStep(wdesc)\\n pNote(system_name)\\n output_dict = {}\\n result = True\\n\\n for element in arguments:\\n if element in [\\\"json\\\", \\\"data\\\", \\\"variable_config\\\"] and \\\\\\n arguments[element] and arguments[element] is not None:\\n arguments[element] = Utils.rest_Utils.\\\\\\n check_ext_get_abspath(arguments[element], self.tc_path)\\n\\n credentials = Utils.data_Utils.\\\\\\n get_user_specified_tag_values_in_tc(self.datafile, **arguments)\\n\\n if credentials[\\\"variable_config\\\"] and \\\\\\n credentials[\\\"variable_config\\\"] is not None:\\n credentials[\\\"variable_config\\\"] = Utils.rest_Utils.\\\\\\n check_ext_get_abspath(credentials[\\\"variable_config\\\"],\\n os.path.dirname(self.datafile))\\n\\n for element in credentials:\\n credentials = Utils.rest_Utils.\\\\\\n resolve_credentials_for_rest(credentials, element,\\n self.datafile, system_name)\\n credentials[\\\"auth\\\"] = (credentials['user'], credentials['password'])\\n\\n credentials, popped_args = Utils.rest_Utils.\\\\\\n remove_invalid_req_args(credentials, [\\\"user\\\", \\\"password\\\",\\n \\\"request_id\\\",\\n \\\"variable_config\\\",\\n \\\"var_sub\\\", \\\"json\\\"])\\n\\n pNote(\\\"url is: {0}\\\".format(credentials['url']))\\n for i in range(0, len(popped_args[\\\"json\\\"])):\\n if popped_args[\\\"json\\\"][i] != \\\"Error\\\":\\n credentials[\\\"json\\\"] = popped_args[\\\"json\\\"][i]\\n for key in credentials:\\n pNote(\\\"Sending argument '{0}': {1}\\\"\\n .format(key, credentials[key]))\\n status, api_response = self.rest_object.patch(**credentials)\\n result = result and status\\n output_dict.update(self.rest_object.\\n update_output_dict(system_name,\\n api_response, request_id, status, i+1))\\n else:\\n pNote(\\\"Request not sent.\\\", \\\"error\\\")\\n status = False\\n result = result and status\\n if result:\\n msg = \\\"http patch successful\\\"\\n else:\\n msg = \\\"http patch failed\\\"\\n pNote(msg)\\n report_substep_status(result)\\n return result, output_dict\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.8377986\",\n \"0.77408034\",\n \"0.7737159\",\n \"0.7708333\",\n \"0.7624386\",\n \"0.7548285\",\n \"0.75311804\",\n \"0.75118834\",\n \"0.74857336\",\n \"0.73733705\",\n \"0.7367035\",\n \"0.7365305\",\n \"0.73553634\",\n \"0.73553634\",\n \"0.73553634\",\n \"0.7353132\",\n \"0.7337053\",\n \"0.7293803\",\n \"0.72613055\",\n \"0.7235854\",\n \"0.7191032\",\n \"0.71903783\",\n \"0.71838886\",\n \"0.7173512\",\n \"0.7173215\",\n \"0.713293\",\n \"0.7130985\",\n \"0.7106572\",\n \"0.704597\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.70449394\",\n \"0.7034015\",\n \"0.7019616\",\n \"0.7017634\",\n \"0.7012777\",\n \"0.7002533\",\n \"0.6969554\",\n \"0.6955208\",\n \"0.6940651\",\n \"0.69229\",\n \"0.6899751\",\n \"0.688609\",\n \"0.68528795\",\n \"0.68052363\",\n \"0.6789266\",\n \"0.6739031\",\n \"0.67374\",\n \"0.67303246\",\n \"0.66781694\",\n \"0.667281\",\n \"0.6648208\",\n \"0.66096604\",\n \"0.6604829\",\n \"0.650952\",\n \"0.64184856\",\n \"0.6406848\",\n \"0.63902754\",\n \"0.6378\",\n \"0.63202566\",\n \"0.63111943\",\n \"0.6307346\",\n \"0.62674326\",\n \"0.62511265\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72490084"},"document_rank":{"kind":"string","value":"19"}}},{"rowIdx":83092,"cells":{"query":{"kind":"string","value":"Return a sandbox id for a message or event. Subclasses may override this to retrieve an appropriate id."},"document":{"kind":"string","value":"def sandbox_id_for_message(self, msg_or_event):\n return msg_or_event['sandbox_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 id(self) -> str:\n return self._event.get('id')","def getId(self):\n return _libsbml.Event_getId(self)","def getMessageID(self):\n return self._payload[1]","def message_id(self) -> int:\n enforce(self.is_set(\"message_id\"), \"message_id is not set.\")\n return cast(int, self.get(\"message_id\"))","def message_id(self) -> str:\n return self[\"Sns\"][\"MessageId\"]","def event_id(self):\n return self._event_id","def _get_id(self):\n return self.id","def getID(self):\r\n return self._interface.UID","def getId(self):\n return _libsbml.SBase_getId(self)","def get_session_id(self):\n return 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return self.internalid\n else:\n return self.Id","def getid(self):\n if self.Id is None:\n return self.internalid\n else:\n return self.Id","def get_id(self):\n pass","def get_id(self):\n pass","def get_id(self):\n pass","def get_id(self):\n pass","def GetEventIdentifier(self):\n return self._event_identifier","def get_assessment_id(self):\n if not self.has_assessment():\n raise IllegalState()\n else:\n return Id(self._my_map['assessmentId'])","def get_id(self):\n return self.__id","def get_id(self):\n return self.__id","def get_id(self):\n return self._id","def get_id(self):\n return self._id","def get_id(self):\n return self._id","def get_id(self):\n return self._id","def get_id(self):\n return self._id","def get_id(self):\n return self._id","def getID(self):\n return self.__id","def get_id(self):\n return self.iid","def get_stream_id(self) -> str:\n return self.id","def getID():","def alert_id(self):\n return self._alert_id","def get_id(self):\n\n\t\treturn self.__id","def getID(self):\n return self._id","def get_id(self): # real signature unknown; restored from __doc__\n return \"\"","def get_sandbox_dataset_id(dataset_id):\n return '{dataset_id}_{sandbox_suffix}'.format(dataset_id=dataset_id,\n sandbox_suffix=SANDBOX_SUFFIX)","def getID(self) -> int:\n ...","def getIdent (self) :\n return self.id","def message_id(self):\n return self._message_id","def get_id(asset_details):\n\n return Script.get_uid(asset_details)","def id(self):\n return self.get_data(\"id\")","def id(self): # type: () -> str\n return self.inspection['Id']","def get_id(self): # pragma: no cover\n pass","def getUniqueId(self, unitCode=0):\n res = self.XAPCommand(\"UID\", unitCode=unitCode)\n return res","def test_event_id(self):\n result = self.test_client.event_id\n\n assert result == \"2130389\"","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id","def get_id(self):\n return self.id"],"string":"[\n \"def id(self) -> str:\\n return self._event.get('id')\",\n \"def getId(self):\\n return _libsbml.Event_getId(self)\",\n \"def getMessageID(self):\\n return self._payload[1]\",\n \"def message_id(self) -> int:\\n enforce(self.is_set(\\\"message_id\\\"), \\\"message_id is not set.\\\")\\n return cast(int, self.get(\\\"message_id\\\"))\",\n \"def message_id(self) -> str:\\n return self[\\\"Sns\\\"][\\\"MessageId\\\"]\",\n \"def event_id(self):\\n return self._event_id\",\n \"def _get_id(self):\\n return self.id\",\n \"def getID(self):\\r\\n return self._interface.UID\",\n \"def getId(self):\\n return _libsbml.SBase_getId(self)\",\n \"def get_session_id(self):\\n return self.request_data['id']\",\n \"def getid(self):\\n return self.__id\",\n \"def get_id(self):\\n return self.uid\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def 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return self.Id\",\n \"def getid(self):\\n if self.Id is None:\\n return self.internalid\\n else:\\n return self.Id\",\n \"def getid(self):\\n if self.Id is None:\\n return self.internalid\\n else:\\n return self.Id\",\n \"def getid(self):\\n if self.Id is None:\\n return self.internalid\\n else:\\n return self.Id\",\n \"def getid(self):\\n if self.Id is None:\\n return self.internalid\\n else:\\n return self.Id\",\n \"def get_id(self):\\n pass\",\n \"def get_id(self):\\n pass\",\n \"def get_id(self):\\n pass\",\n \"def get_id(self):\\n pass\",\n \"def GetEventIdentifier(self):\\n return self._event_identifier\",\n \"def get_assessment_id(self):\\n if not self.has_assessment():\\n raise IllegalState()\\n else:\\n return Id(self._my_map['assessmentId'])\",\n \"def get_id(self):\\n return self.__id\",\n \"def get_id(self):\\n return self.__id\",\n \"def get_id(self):\\n return self._id\",\n \"def get_id(self):\\n return self._id\",\n \"def get_id(self):\\n return self._id\",\n \"def get_id(self):\\n return self._id\",\n \"def get_id(self):\\n return self._id\",\n \"def get_id(self):\\n return self._id\",\n \"def getID(self):\\n return self.__id\",\n \"def get_id(self):\\n return self.iid\",\n \"def get_stream_id(self) -> str:\\n return self.id\",\n \"def getID():\",\n \"def alert_id(self):\\n return self._alert_id\",\n \"def get_id(self):\\n\\n\\t\\treturn self.__id\",\n \"def getID(self):\\n return self._id\",\n \"def get_id(self): # real signature unknown; restored from __doc__\\n return \\\"\\\"\",\n \"def get_sandbox_dataset_id(dataset_id):\\n return '{dataset_id}_{sandbox_suffix}'.format(dataset_id=dataset_id,\\n sandbox_suffix=SANDBOX_SUFFIX)\",\n \"def getID(self) -> int:\\n ...\",\n \"def getIdent (self) :\\n return self.id\",\n \"def message_id(self):\\n return self._message_id\",\n \"def get_id(asset_details):\\n\\n return Script.get_uid(asset_details)\",\n \"def id(self):\\n return self.get_data(\\\"id\\\")\",\n \"def id(self): # type: () -> str\\n return self.inspection['Id']\",\n \"def get_id(self): # pragma: no cover\\n pass\",\n \"def getUniqueId(self, unitCode=0):\\n res = self.XAPCommand(\\\"UID\\\", unitCode=unitCode)\\n return res\",\n \"def test_event_id(self):\\n result = self.test_client.event_id\\n\\n assert result == \\\"2130389\\\"\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\",\n \"def get_id(self):\\n return self.id\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.5858612\",\n \"0.5858366\",\n \"0.5858212\",\n \"0.5839398\",\n \"0.5839398\",\n \"0.5812437\",\n \"0.5812437\",\n \"0.5812437\",\n \"0.5812437\",\n \"0.5812437\",\n \"0.5812437\",\n \"0.5804843\",\n \"0.5804553\",\n \"0.57902646\",\n \"0.5783508\",\n \"0.5781176\",\n \"0.5771331\",\n \"0.57708746\",\n \"0.5770545\",\n \"0.5760327\",\n \"0.5748555\",\n \"0.57480216\",\n \"0.5729912\",\n \"0.5729162\",\n \"0.5707881\",\n \"0.56947905\",\n \"0.56947446\",\n \"0.5688431\",\n \"0.5669388\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\",\n \"0.5664851\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.88027954"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":83093,"cells":{"query":{"kind":"string","value":"Return a sandbox protocol for a message or event. Subclasses may override this to retrieve an appropriate protocol."},"document":{"kind":"string","value":"def sandbox_protocol_for_message(self, msg_or_event, config):\n api = self.create_sandbox_api(self.resources, config)\n protocol = self.create_sandbox_protocol(api)\n return protocol"},"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 protocol(self):\n self._recv_protocol()\n return self._protocol","def protocol(self) -> MessageProtocol:\n return self._sender.protocol","def getProtocol(self, _):\r\n return self._protocol","def protocol(self) -> NetworkProtocol:\n if hasattr(self, \"_protocol\"):\n return self._protocol\n _args: list[Arg] = []\n _ctx = self._select(\"protocol\", _args)\n return _ctx.execute_sync(NetworkProtocol)","def protocol(self):\n return helpers.get_protocol()","def protocol(self):\n return self._protocol","def _get_protocol_type(self):\n return self.__protocol_type","def protocol(self) -> Optional[pulumi.Input[Union[str, 'Protocol']]]:\n return pulumi.get(self, \"protocol\")","def protocol(self) -> Optional['ListenerProtocol']:\n return pulumi.get(self, \"protocol\")","def protocol(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"protocol\")","def protocol(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"protocol\")","def protocol(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"protocol\")","def protocol(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"protocol\")","def protocol(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"protocol\")","def v_protocol(self):\n return self._protocol","def v_protocol(self):\n return self._protocol","def protocol(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"protocol\")","def streaming_protocol(self) -> pulumi.Input[Union[str, 'LiveEventInputProtocol']]:\n return pulumi.get(self, \"streaming_protocol\")","def protocol(self):\n return self._info.next # pylint: disable=E1101","def protocol(self):\n return self._host[CONF_PROTOCOL]","def provides_protocol(type_, protocol):\n return AdaptationManager.provides_protocol(type_, protocol)","def getProtocol(self) -> str:\n ...","def protocol(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"protocol\")","def protocol(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"protocol\")","def protocol(self):\n raise UnsupportedCall(f\"'{self.__class__.__name__}' object has no attribute 'protocol'\")","def protocol(self) -> Optional[pulumi.Input['TargetServerProtocol']]:\n return pulumi.get(self, \"protocol\")","def proto(self):\n return self.sock.proto","def protocol(self) -> str:\n return pulumi.get(self, \"protocol\")","def getProtocol(self, endpoint):\r\n if not (self._serverProtocol and self._serverEndpoint and\r\n self._clientProtocol and self._clientEndpoint):\r\n raise ConnectionError('Endpoint connection is dead.')\r\n\r\n if self._serverEndpoint == endpoint:\r\n return self._serverProtocol\r\n elif self._clientEndpoint == endpoint:\r\n return self._clientProtocol\r\n else:\r\n raise InternalError('The endpoint is not part of this connection.')","def sandbox_id_for_message(self, msg_or_event):\n return msg_or_event['sandbox_id']","def protocol(self):\n return PROTO_INT_VAR if self._type == VAR_INTEGER else PROTO_STATE_VAR","def protocol_type(self):\n return self._read(MX_PROTOCOL_TYPE)","def protocol(self):\n return self._config[\"security.protocol\"]","def get_protocol(binding_id):\n binding_to_protocol = {VID_TAXII_HTTP_10: \"http\", VID_TAXII_HTTPS_10: \"https\"}\n try:\n return binding_to_protocol[binding_id]\n except:\n raise ValueError(\"Unknown Protocol Binding ID %s\" % binding_id)","def ProtocolType(self) -> ProtocolType:","def protocol(self) -> str:\n return self.__parameters.protocol","def in_protocol(self) -> str:\n return pulumi.get(self, \"in_protocol\")","def protocol(self) -> Optional[pulumi.Input[Union[str, 'GatewayRouteConfigProtocol']]]:\n return pulumi.get(self, \"protocol\")","def protocol(self):\n\n raise NotImplementedError()","def protocol(self) -> str:\n return __name__","def get_client_protocol(self, machine_id: str):\n peer = self.peer_registry.peers.get(machine_id)\n return peer.peer if peer is not None else None","def get_protocol(self):\n if self.ssl:\n return \"https\"\n else:\n return \"http\"","def backend_protocol(self) -> Optional[pulumi.Input[Union[str, 'BackendProtocol']]]:\n return pulumi.get(self, \"backend_protocol\")","def transportprotocol(self) :\n\t\ttry :\n\t\t\treturn self._transportprotocol\n\t\texcept Exception as e:\n\t\t\traise e","def get_interface(cls, message):\r\n if message is not None:\r\n if isinstance(message, PlatformMessage):\r\n return message.interface\r\n assert isinstance(message, (list, tuple)), \"Message is expected to be a list or a tuple\"\r\n assert len(message) >= 4, \"Message's length expected to be at least 4\"\r\n assert message[0] == PlatformMessage._signature, \"Message's signature is incorrect\"\r\n return message[2]\r\n return None","def get_protocols(self):\r\n\r\n return None","def getPeerToPeerProtocol(self, peerType):\r\n raise NotImplementedError()","def FlowStatIpProtocol(self):\n\t\treturn self._get_attribute('flowStatIpProtocol')","def buildProtocol(self, addr):\n return _SSLServerProtocol(self)","def get_protocol():\n if https():\n protocol = 'https'\n else:\n protocol = 'http'\n return protocol","def protocol(self):\n v = uint16_packer.unpack(self[2:4])[0]\n mask = 0b111111111111\n return v & mask","def protocol(self):\n\n if '://' in self.host:\n scheme, host = self.host.split('://', 1)\n return scheme\n elif self.port == 21:\n return 'ftp'\n elif self.port == 22:\n return 'sftp'\n elif self.port == 990:\n return 'ftps'\n else:\n # Uncertain, assume FTP.\n return 'ftp'","def protocol_factory_method(self):\n pass","def proto_check(proto):\n # Check for TCP\n if proto == 6:\n return 'tcp'\n # Check for UDP\n elif proto == 17:\n return 'udp'\n else:\n return None","def buildProtocol(self, addr):\n return _UIDServerProtocol(self)","def getProtocol(self, transport: Any) -> TCompactProtocol:\n return TCompactProtocol.TCompactProtocol(transport)","def protocol(self):\n ...","def trafficProtocol(self):\n #\n # TODO: Reimplement this if possible.\n #\n return client.trafficProtocol(self)","def protocol(self) -> typing.Optional[\"RedirectProtocol\"]:\n return self._values.get('protocol')","def protocol(self) -> typing.Optional[\"RedirectProtocol\"]:\n return self._values.get('protocol')","def protocols(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"protocols\")","def SocketType(self) -> SocketType:","def SocketClientAccessPolicyProtocol(self) -> SocketClientAccessPolicyProtocol:","def protocols(self) -> Optional[pulumi.Input['ServiceProtocolsArgs']]:\n return pulumi.get(self, \"protocols\")","def protocols(self) -> Optional[pulumi.Input['ServiceProtocolsArgs']]:\n return pulumi.get(self, \"protocols\")","def value_for_protocol(self, proto):\n proto = protocols.protocol_with_any(proto)\n for proto2, value in self.items():\n if proto2 is proto or proto2 == proto:\n return value\n raise exceptions.ProtocolLookupError(proto, str(self))","def base_protocol(self, timeout=None):\n base_protocol = self._acquire_base_protocol(timeout)\n yield base_protocol\n self._return_base_protocol(base_protocol)","def protocol_details(self) -> Optional[pulumi.Input['ServerProtocolDetailsArgs']]:\n return pulumi.get(self, \"protocol_details\")","def protocol_details(self) -> Optional[pulumi.Input['ServerProtocolDetailsArgs']]:\n return pulumi.get(self, \"protocol_details\")","def protocol_details(self) -> pulumi.Output['outputs.ServerProtocolDetails']:\n return pulumi.get(self, \"protocol_details\")","def type(self):\n return self.sock.type","def server_protocol(self) -> Optional['LocationObjectStorageServerProtocol']:\n return pulumi.get(self, \"server_protocol\")","def buildProtocol(self, addr):\n return _UIDClientProtocol(self._deferred)","def buildProtocol(self, addr):\n return _UIDClientProtocol(self._deferred)","def buildProtocol(self, addr):\n p = self.protocol()\n p.factory = self\n\n return p","def protocol(self, code: str) -> str:\n return 'https'","def proxy_protocol(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"proxy_protocol\")","def proxy_protocol(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"proxy_protocol\")","def buildProtocol(self, addr):\r\n p = RobotWebSocketProtocol(self._realm)\r\n p.factory = self\r\n return p","def get_message_decoder(self, topic):\n decoder = None\n if topic == \"apprf\":\n decoder = apprf_session()\n elif topic == \"audit\":\n decoder = audit_message()\n elif topic == \"location\":\n decoder = stream_location()\n elif topic == \"monitoring\":\n decoder = MonitoringInformation()\n elif topic == \"presence\":\n decoder = presence_event()\n elif topic == \"security\":\n decoder = RapidsStreamingEvent()\n return decoder","def identify_client(self,protocol):\n if protocol.resident:\n return protocol.peer\n #pdb.set_trace()","def sandbox_type(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"sandbox_type\")","def provides_protocol(type_, protocol):\n return issubclass(type_, protocol)","def protocol(self, code):\n return self.url.scheme","def protocol(ver):\r\n if ver == 1:\r\n return 1\r\n\r\n if ver == 2:\r\n return 2\r\n\r\n\r\n raise ValueError","def raw_socket(self) -> SocketType:","def proxy_protocol(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"proxy_protocol\")","def available_protocols(self):\n return [\"dropbox://\"]","def FlowAggregatedStatIpProtocol(self):\n\t\treturn self._get_attribute('flowAggregatedStatIpProtocol')","def _new_server_protocol_handler(self):\n socket = mock.MagicMock()\n socket.fileno.return_value = 42\n local = mock.MagicMock()\n return epdb_server.TelnetServerProtocolHandler(socket, local)","def get_supported_protocol_enum(self, supported_protocol):\n\n supported_protocol = \"MULTI_PROTOCOL\" if \\\n supported_protocol == \"MULTIPROTOCOL\" else supported_protocol\n if supported_protocol in utils.FSSupportedProtocolEnum.__members__:\n return utils.FSSupportedProtocolEnum[supported_protocol]\n else:\n errormsg = \"Invalid choice {0} for supported_protocol\".format(\n supported_protocol)\n LOG.error(errormsg)\n self.module.fail_json(msg=errormsg)","def default_protocol(self):\n return \"dropbox://\"","def protocols(self) -> pulumi.Output['outputs.ServiceProtocols']:\n return pulumi.get(self, \"protocols\")","def _build_protocol(self):\n self._protocol = SBE37Protocol(SBE37Prompt, SBE37_NEWLINE, self._driver_event)","def gotProtocol(self,p): \n p.send_hello()","def buildProtocol(self, addr):\n if hasattr(settings, \"DISCORD_SESSION_CLASS\"):\n protocol_class = class_from_module(\n settings.DISCORD_SESSION_CLASS, fallback=DiscordClient\n )\n protocol = protocol_class()\n else:\n protocol = DiscordClient()\n\n protocol.factory = self\n protocol.sessionhandler = self.sessionhandler\n return protocol","def protocol(self):\n return 'https' if self.allow_https and self.is_secure else 'http'","def get_http_protocol(self):\n if self.cfg.ssl:\n return \"https\"\n else:\n return \"http\"","def getProtocolPfn( self, pfnDict, withPort ):\n # pfnDict['Protocol'] = ''\n # pfnDict['Host'] = ''\n # pfnDict['Port'] = ''\n # pfnDict['WSUrl'] = ''\n res = pfnunparse( pfnDict )\n return res","def __find_protocol(self, url):\n match = self.__REGEX_SCHEMA.search(url)\n if match:\n protocol = match.group(0).split(':')[0]\n return protocol\n return None"],"string":"[\n \"def protocol(self):\\n self._recv_protocol()\\n return self._protocol\",\n \"def protocol(self) -> MessageProtocol:\\n return self._sender.protocol\",\n \"def getProtocol(self, _):\\r\\n return self._protocol\",\n \"def protocol(self) -> NetworkProtocol:\\n if hasattr(self, \\\"_protocol\\\"):\\n return self._protocol\\n _args: list[Arg] = []\\n _ctx = self._select(\\\"protocol\\\", _args)\\n return _ctx.execute_sync(NetworkProtocol)\",\n \"def protocol(self):\\n return helpers.get_protocol()\",\n \"def protocol(self):\\n return self._protocol\",\n \"def _get_protocol_type(self):\\n return self.__protocol_type\",\n \"def protocol(self) -> Optional[pulumi.Input[Union[str, 'Protocol']]]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def protocol(self) -> Optional['ListenerProtocol']:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def protocol(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def protocol(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def protocol(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def protocol(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def protocol(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def v_protocol(self):\\n return self._protocol\",\n \"def v_protocol(self):\\n return self._protocol\",\n \"def protocol(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def streaming_protocol(self) -> pulumi.Input[Union[str, 'LiveEventInputProtocol']]:\\n return pulumi.get(self, \\\"streaming_protocol\\\")\",\n \"def protocol(self):\\n return self._info.next # pylint: disable=E1101\",\n \"def protocol(self):\\n return self._host[CONF_PROTOCOL]\",\n \"def provides_protocol(type_, protocol):\\n return AdaptationManager.provides_protocol(type_, protocol)\",\n \"def getProtocol(self) -> str:\\n ...\",\n \"def protocol(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def protocol(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def protocol(self):\\n raise UnsupportedCall(f\\\"'{self.__class__.__name__}' object has no attribute 'protocol'\\\")\",\n \"def protocol(self) -> Optional[pulumi.Input['TargetServerProtocol']]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def proto(self):\\n return self.sock.proto\",\n \"def protocol(self) -> str:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def getProtocol(self, endpoint):\\r\\n if not (self._serverProtocol and self._serverEndpoint and\\r\\n self._clientProtocol and self._clientEndpoint):\\r\\n raise ConnectionError('Endpoint connection is dead.')\\r\\n\\r\\n if self._serverEndpoint == endpoint:\\r\\n return self._serverProtocol\\r\\n elif self._clientEndpoint == endpoint:\\r\\n return self._clientProtocol\\r\\n else:\\r\\n raise InternalError('The endpoint is not part of this connection.')\",\n \"def sandbox_id_for_message(self, msg_or_event):\\n return msg_or_event['sandbox_id']\",\n \"def protocol(self):\\n return PROTO_INT_VAR if self._type == VAR_INTEGER else PROTO_STATE_VAR\",\n \"def protocol_type(self):\\n return self._read(MX_PROTOCOL_TYPE)\",\n \"def protocol(self):\\n return self._config[\\\"security.protocol\\\"]\",\n \"def get_protocol(binding_id):\\n binding_to_protocol = {VID_TAXII_HTTP_10: \\\"http\\\", VID_TAXII_HTTPS_10: \\\"https\\\"}\\n try:\\n return binding_to_protocol[binding_id]\\n except:\\n raise ValueError(\\\"Unknown Protocol Binding ID %s\\\" % binding_id)\",\n \"def ProtocolType(self) -> ProtocolType:\",\n \"def protocol(self) -> str:\\n return self.__parameters.protocol\",\n \"def in_protocol(self) -> str:\\n return pulumi.get(self, \\\"in_protocol\\\")\",\n \"def protocol(self) -> Optional[pulumi.Input[Union[str, 'GatewayRouteConfigProtocol']]]:\\n return pulumi.get(self, \\\"protocol\\\")\",\n \"def protocol(self):\\n\\n raise NotImplementedError()\",\n \"def protocol(self) -> str:\\n return __name__\",\n \"def get_client_protocol(self, machine_id: str):\\n peer = self.peer_registry.peers.get(machine_id)\\n return peer.peer if peer is not None else None\",\n \"def get_protocol(self):\\n if self.ssl:\\n return \\\"https\\\"\\n else:\\n return \\\"http\\\"\",\n \"def backend_protocol(self) -> Optional[pulumi.Input[Union[str, 'BackendProtocol']]]:\\n return pulumi.get(self, \\\"backend_protocol\\\")\",\n \"def transportprotocol(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._transportprotocol\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def get_interface(cls, message):\\r\\n if message is not None:\\r\\n if isinstance(message, PlatformMessage):\\r\\n return message.interface\\r\\n assert isinstance(message, (list, tuple)), \\\"Message is expected to be a list or a tuple\\\"\\r\\n assert len(message) >= 4, \\\"Message's length expected to be at least 4\\\"\\r\\n assert message[0] == PlatformMessage._signature, \\\"Message's signature is incorrect\\\"\\r\\n return message[2]\\r\\n return None\",\n \"def get_protocols(self):\\r\\n\\r\\n return None\",\n \"def getPeerToPeerProtocol(self, peerType):\\r\\n raise NotImplementedError()\",\n \"def FlowStatIpProtocol(self):\\n\\t\\treturn self._get_attribute('flowStatIpProtocol')\",\n \"def buildProtocol(self, addr):\\n return _SSLServerProtocol(self)\",\n \"def get_protocol():\\n if https():\\n protocol = 'https'\\n else:\\n protocol = 'http'\\n return protocol\",\n \"def protocol(self):\\n v = uint16_packer.unpack(self[2:4])[0]\\n mask = 0b111111111111\\n return v & mask\",\n \"def protocol(self):\\n\\n if '://' in self.host:\\n scheme, host = self.host.split('://', 1)\\n return scheme\\n elif self.port == 21:\\n return 'ftp'\\n elif self.port == 22:\\n return 'sftp'\\n elif self.port == 990:\\n return 'ftps'\\n else:\\n # Uncertain, assume FTP.\\n return 'ftp'\",\n \"def protocol_factory_method(self):\\n pass\",\n \"def proto_check(proto):\\n # Check for TCP\\n if proto == 6:\\n return 'tcp'\\n # Check for UDP\\n elif proto == 17:\\n return 'udp'\\n else:\\n return None\",\n \"def buildProtocol(self, addr):\\n return _UIDServerProtocol(self)\",\n \"def getProtocol(self, transport: Any) -> TCompactProtocol:\\n return TCompactProtocol.TCompactProtocol(transport)\",\n \"def protocol(self):\\n ...\",\n \"def trafficProtocol(self):\\n #\\n # TODO: Reimplement this if possible.\\n #\\n return client.trafficProtocol(self)\",\n \"def protocol(self) -> typing.Optional[\\\"RedirectProtocol\\\"]:\\n return self._values.get('protocol')\",\n \"def protocol(self) -> typing.Optional[\\\"RedirectProtocol\\\"]:\\n return self._values.get('protocol')\",\n \"def protocols(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"protocols\\\")\",\n \"def SocketType(self) -> SocketType:\",\n \"def SocketClientAccessPolicyProtocol(self) -> SocketClientAccessPolicyProtocol:\",\n \"def protocols(self) -> Optional[pulumi.Input['ServiceProtocolsArgs']]:\\n return pulumi.get(self, \\\"protocols\\\")\",\n \"def protocols(self) -> Optional[pulumi.Input['ServiceProtocolsArgs']]:\\n return pulumi.get(self, \\\"protocols\\\")\",\n \"def value_for_protocol(self, proto):\\n proto = protocols.protocol_with_any(proto)\\n for proto2, value in self.items():\\n if proto2 is proto or proto2 == proto:\\n return value\\n raise exceptions.ProtocolLookupError(proto, str(self))\",\n \"def base_protocol(self, timeout=None):\\n base_protocol = self._acquire_base_protocol(timeout)\\n yield base_protocol\\n self._return_base_protocol(base_protocol)\",\n \"def protocol_details(self) -> Optional[pulumi.Input['ServerProtocolDetailsArgs']]:\\n return pulumi.get(self, \\\"protocol_details\\\")\",\n \"def protocol_details(self) -> Optional[pulumi.Input['ServerProtocolDetailsArgs']]:\\n return pulumi.get(self, \\\"protocol_details\\\")\",\n \"def protocol_details(self) -> pulumi.Output['outputs.ServerProtocolDetails']:\\n return pulumi.get(self, \\\"protocol_details\\\")\",\n \"def type(self):\\n return self.sock.type\",\n \"def server_protocol(self) -> Optional['LocationObjectStorageServerProtocol']:\\n return pulumi.get(self, \\\"server_protocol\\\")\",\n \"def buildProtocol(self, addr):\\n return _UIDClientProtocol(self._deferred)\",\n \"def buildProtocol(self, addr):\\n return _UIDClientProtocol(self._deferred)\",\n \"def buildProtocol(self, addr):\\n p = self.protocol()\\n p.factory = self\\n\\n return p\",\n \"def protocol(self, code: str) -> str:\\n return 'https'\",\n \"def proxy_protocol(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"proxy_protocol\\\")\",\n \"def proxy_protocol(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"proxy_protocol\\\")\",\n \"def buildProtocol(self, addr):\\r\\n p = RobotWebSocketProtocol(self._realm)\\r\\n p.factory = self\\r\\n return p\",\n \"def get_message_decoder(self, topic):\\n decoder = None\\n if topic == \\\"apprf\\\":\\n decoder = apprf_session()\\n elif topic == \\\"audit\\\":\\n decoder = audit_message()\\n elif topic == \\\"location\\\":\\n decoder = stream_location()\\n elif topic == \\\"monitoring\\\":\\n decoder = MonitoringInformation()\\n elif topic == \\\"presence\\\":\\n decoder = presence_event()\\n elif topic == \\\"security\\\":\\n decoder = RapidsStreamingEvent()\\n return decoder\",\n \"def identify_client(self,protocol):\\n if protocol.resident:\\n return protocol.peer\\n #pdb.set_trace()\",\n \"def sandbox_type(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"sandbox_type\\\")\",\n \"def provides_protocol(type_, protocol):\\n return issubclass(type_, protocol)\",\n \"def protocol(self, code):\\n return self.url.scheme\",\n \"def protocol(ver):\\r\\n if ver == 1:\\r\\n return 1\\r\\n\\r\\n if ver == 2:\\r\\n return 2\\r\\n\\r\\n\\r\\n raise ValueError\",\n \"def raw_socket(self) -> SocketType:\",\n \"def proxy_protocol(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"proxy_protocol\\\")\",\n \"def available_protocols(self):\\n return [\\\"dropbox://\\\"]\",\n \"def FlowAggregatedStatIpProtocol(self):\\n\\t\\treturn self._get_attribute('flowAggregatedStatIpProtocol')\",\n \"def _new_server_protocol_handler(self):\\n socket = mock.MagicMock()\\n socket.fileno.return_value = 42\\n local = mock.MagicMock()\\n return epdb_server.TelnetServerProtocolHandler(socket, local)\",\n \"def get_supported_protocol_enum(self, supported_protocol):\\n\\n supported_protocol = \\\"MULTI_PROTOCOL\\\" if \\\\\\n supported_protocol == \\\"MULTIPROTOCOL\\\" else supported_protocol\\n if supported_protocol in utils.FSSupportedProtocolEnum.__members__:\\n return utils.FSSupportedProtocolEnum[supported_protocol]\\n else:\\n errormsg = \\\"Invalid choice {0} for supported_protocol\\\".format(\\n supported_protocol)\\n LOG.error(errormsg)\\n self.module.fail_json(msg=errormsg)\",\n \"def default_protocol(self):\\n return \\\"dropbox://\\\"\",\n \"def protocols(self) -> pulumi.Output['outputs.ServiceProtocols']:\\n return pulumi.get(self, \\\"protocols\\\")\",\n \"def _build_protocol(self):\\n self._protocol = SBE37Protocol(SBE37Prompt, SBE37_NEWLINE, self._driver_event)\",\n \"def gotProtocol(self,p): \\n p.send_hello()\",\n \"def buildProtocol(self, addr):\\n if hasattr(settings, \\\"DISCORD_SESSION_CLASS\\\"):\\n protocol_class = class_from_module(\\n settings.DISCORD_SESSION_CLASS, fallback=DiscordClient\\n )\\n protocol = protocol_class()\\n else:\\n protocol = DiscordClient()\\n\\n protocol.factory = self\\n protocol.sessionhandler = self.sessionhandler\\n return protocol\",\n \"def protocol(self):\\n return 'https' if self.allow_https and self.is_secure else 'http'\",\n \"def get_http_protocol(self):\\n if self.cfg.ssl:\\n return \\\"https\\\"\\n else:\\n return \\\"http\\\"\",\n \"def getProtocolPfn( self, pfnDict, withPort ):\\n # pfnDict['Protocol'] = ''\\n # pfnDict['Host'] = ''\\n # pfnDict['Port'] = ''\\n # pfnDict['WSUrl'] = ''\\n res = pfnunparse( pfnDict )\\n return res\",\n \"def __find_protocol(self, url):\\n match = self.__REGEX_SCHEMA.search(url)\\n if match:\\n protocol = match.group(0).split(':')[0]\\n return protocol\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6581834","0.65799445","0.65358585","0.645299","0.62877756","0.6196647","0.6158876","0.6152343","0.6143666","0.60666347","0.60666347","0.60666347","0.60666347","0.60666347","0.606265","0.606265","0.5934752","0.59274423","0.58809066","0.5875745","0.5863043","0.5833028","0.58186245","0.58186245","0.5809929","0.5777468","0.5777353","0.5772579","0.57697225","0.563891","0.5605231","0.55834687","0.55315554","0.552696","0.54704595","0.544869","0.53965014","0.53892034","0.53884137","0.53872246","0.5374239","0.5365142","0.534955","0.53444","0.53429896","0.533692","0.5307532","0.52975273","0.5295226","0.5291372","0.524151","0.52007616","0.5172212","0.5171716","0.51541716","0.5137776","0.5117567","0.51021475","0.5090907","0.5090907","0.50680363","0.5044608","0.5022636","0.50000584","0.50000584","0.49817735","0.49761298","0.49739373","0.49739373","0.4967663","0.4960528","0.4959228","0.4955929","0.4955929","0.4948097","0.4946131","0.49437043","0.49437043","0.49298313","0.4926765","0.48995122","0.48794138","0.48736906","0.48726836","0.48596454","0.48564813","0.48184982","0.48099154","0.48054248","0.47891307","0.4783755","0.4779457","0.4770134","0.47615033","0.47517028","0.47488952","0.4740172","0.47363907","0.47312483","0.4726048"],"string":"[\n \"0.6581834\",\n \"0.65799445\",\n \"0.65358585\",\n \"0.645299\",\n \"0.62877756\",\n \"0.6196647\",\n \"0.6158876\",\n \"0.6152343\",\n \"0.6143666\",\n \"0.60666347\",\n \"0.60666347\",\n \"0.60666347\",\n \"0.60666347\",\n \"0.60666347\",\n \"0.606265\",\n \"0.606265\",\n \"0.5934752\",\n \"0.59274423\",\n \"0.58809066\",\n \"0.5875745\",\n \"0.5863043\",\n \"0.5833028\",\n \"0.58186245\",\n \"0.58186245\",\n \"0.5809929\",\n \"0.5777468\",\n \"0.5777353\",\n \"0.5772579\",\n \"0.57697225\",\n \"0.563891\",\n \"0.5605231\",\n \"0.55834687\",\n \"0.55315554\",\n \"0.552696\",\n \"0.54704595\",\n \"0.544869\",\n \"0.53965014\",\n \"0.53892034\",\n \"0.53884137\",\n \"0.53872246\",\n \"0.5374239\",\n \"0.5365142\",\n \"0.534955\",\n \"0.53444\",\n \"0.53429896\",\n \"0.533692\",\n \"0.5307532\",\n \"0.52975273\",\n \"0.5295226\",\n \"0.5291372\",\n \"0.524151\",\n \"0.52007616\",\n \"0.5172212\",\n \"0.5171716\",\n \"0.51541716\",\n \"0.5137776\",\n \"0.5117567\",\n \"0.51021475\",\n \"0.5090907\",\n \"0.5090907\",\n \"0.50680363\",\n \"0.5044608\",\n \"0.5022636\",\n \"0.50000584\",\n \"0.50000584\",\n \"0.49817735\",\n \"0.49761298\",\n \"0.49739373\",\n \"0.49739373\",\n \"0.4967663\",\n \"0.4960528\",\n \"0.4959228\",\n \"0.4955929\",\n \"0.4955929\",\n \"0.4948097\",\n \"0.4946131\",\n \"0.49437043\",\n \"0.49437043\",\n \"0.49298313\",\n \"0.4926765\",\n \"0.48995122\",\n \"0.48794138\",\n \"0.48736906\",\n \"0.48726836\",\n \"0.48596454\",\n \"0.48564813\",\n \"0.48184982\",\n \"0.48099154\",\n \"0.48054248\",\n \"0.47891307\",\n \"0.4783755\",\n \"0.4779457\",\n \"0.4770134\",\n \"0.47615033\",\n \"0.47517028\",\n \"0.47488952\",\n \"0.4740172\",\n \"0.47363907\",\n \"0.47312483\",\n \"0.4726048\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80012"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":83094,"cells":{"query":{"kind":"string","value":"Sort table by given column number."},"document":{"kind":"string","value":"def sort(self, p_int, order=None):\n try:\n self.layoutAboutToBeChanged.emit()\n\n source_array = self.get_source_array()\n\n if len(source_array):\n source_array = sorted(source_array,\n key=lambda x: (self.get_item(x, p_int) is None,\n str(type(self.get_item(x, p_int))),\n self.get_item(x, p_int)))\n if order == Qt.DescendingOrder:\n source_array = source_array[::-1]\n\n self.set_source_array(source_array)\n\n self.init_cache()\n self.layoutChanged.emit()\n except Exception as e:\n logging.error(str(e))"},"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 sortTable(self, table, cols):\n #productive\n profprint()\n for col in reversed(cols):\n table = sorted(table, key=operator.itemgetter(col))\n return table","def sortTable(self, table, cols):\r\n # productive\r\n profprint()\r\n for col in reversed(cols):\r\n table = sorted(table, key=operator.itemgetter(col))\r\n return table","def sort_table_by_column(\n self, table: Table, column: Column, ascending: bool = True\n ):\n self._requires_table(table)\n table.sort_by_column(column, ascending=ascending)","def sort_by_column(self, columns, ascending=False):\n columns = to_list(columns)\n\n # Create sort criteria list, with each row as tuple of column values\n values = (self.get_column(column, as_list=True) for column in columns)\n values = list(zip(*values))\n assert len(values) == self.size\n\n def sorter(row):\n \"\"\"Sort table by given values, while allowing for disparate types.\n Order priority:\n - Values by typename\n - Numeric types\n - None values\n \"\"\"\n criteria = []\n for value in row[1]: # Ignore enumeration\n criteria.append(\n (\n value is not None,\n \"\" if isinstance(value, Number) else type(value).__name__,\n value,\n )\n )\n return criteria\n\n # Store original index order using enumerate() before sort,\n # and use it to sort data later\n values = sorted(enumerate(values), key=sorter, reverse=not ascending)\n idxs = [value[0] for value in values]\n\n # Re-order data\n self._data = [self._data[idx] for idx in idxs]","def sortByColumn(data_file, column_to_sort=1):\n cmp = lambda qvar: float(qvar.split()[column_to_sort-1]) # used in \"sorted\" method\n in_file = open(data_file, \"r\")\n out_file = open(data_file+\".TEMP\", \"w\")\n for a_line in sorted(in_file.readlines(),key=cmp):\n out_file.write(a_line)\n in_file.close()\n out_file.close()\n shutil.copy(data_file+\".TEMP\", data_file)\n os.remove(data_file+\".TEMP\")","def sortby(tree, col, descending):\r\n # grab values to sort\r\n data = [(tree.set(child, col), child) for child in tree.get_children('')]\r\n \r\n #Figure out if this is a float column. If it is,\r\n #transform to float so the ordering will be meaningful\r\n try:\r\n \r\n data = [( float(x[0]), x[1]) for x in data]\r\n \r\n except:\r\n \r\n #Nope!\r\n pass\r\n \r\n # reorder data\r\n data.sort(reverse=descending)\r\n for indx, item in enumerate(data):\r\n tree.move(item[1], '', indx)\r\n\r\n # switch the heading so that it will sort in the opposite direction\r\n tree.heading(col,\r\n command=lambda col=col: sortby(tree, col, int(not descending)))","def sort(self, column: int, order: Qt.SortOrder = ...) -> None:\n col = self._dataframe.columns[column]\n # Temporary column with display value of column\n self._dataframe['_FOR_SORT'] = self._dataframe.apply(\n lambda row: self._get_cell_value(self._dataframe.index.get_loc(row.name), column), axis=1)\n # Sort by temp column\n self._dataframe.sort_values(by=['_FOR_SORT'], ascending=True if order == 0 else False,\n inplace=True, axis=0)\n # drop temp column\n self._dataframe.drop('_FOR_SORT', axis=1, inplace=True)\n self.layoutChanged.emit()\n super().sort(column, order)","def sortby_num(tree, col, descending): # 重新排序 <-- 數字版\n # grab values to sort\n data = [(tree.set(child, col), child) \\\n for child in tree.get_children('')]\n\n # if the data to be sorted is numeric change to float\n #data = change_numeric(data)\n # now sort the data in place\n #data.sort(reverse=descending)\n # 數字的排法(但文字部分就無法排序)\n data.sort(key=lambda data: int(data[0]), reverse=descending)\n\n for ix, item in enumerate(data):\n tree.move(item[1], '', ix)\n\n # switch the heading so it will sort in the opposite direction\n tree.heading(col, command=lambda col=col: sortby_num(tree, col, \\\n int(not descending)))","def _sort_column(self, column, reverse):\n if tk.DISABLED in self.state():\n return\n # get list of (value, item) tuple where value is the value in column for the item\n l = [(self.set(child, column), child) for child in self.get_children('')]\n # sort list using the column type\n l.sort(reverse=reverse, key=lambda x: self._column_types[column](x[0]))\n # reorder items\n for index, (val, child) in enumerate(l):\n self.move(child, \"\", index)\n # reverse sorting direction for the next time\n self.heading(column, command=lambda: self._sort_column(column, not reverse))","def treeview_sort_column(self, treeview, column, reverse):\r\n data = [(treeview.set(ticker, column), ticker) for ticker in treeview.get_children('')]\r\n data.sort(reverse=reverse)\r\n\r\n # sort the stock(s)\r\n for index, (val, k) in enumerate(data):\r\n treeview.move(k, '', index)\r\n\r\n # reverse sort next time\r\n treeview.heading(column, command=lambda: self.treeview_sort_column(treeview, column, not reverse))","def sort(self, Ncol, order):\n self.emit(SIGNAL(\"layoutAboutToBeChanged()\"))\n self.arraydata = sorted(self.arraydata, key=operator.itemgetter(Ncol)) \n if order != Qt.DescendingOrder:\n self.arraydata.reverse()\n self.emit(SIGNAL(\"layoutChanged()\"))","def sort(self, Ncol, order):\n self.emit(SIGNAL(\"layoutAboutToBeChanged()\"))\n self.arraydata = sorted(self.arraydata, key=operator.itemgetter(Ncol)) \n if order == Qt.DescendingOrder:\n self.arraydata.reverse()\n self.emit(SIGNAL(\"layoutChanged()\"))","def sort_re_analysis_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.re_analysis_grid_div_id, column_name, descending_order)","def sortby(tree, col, descending):\n # grab values to sort\n data = [(tree.set(child, col), child) for child in tree.get_children('')]\n # if the data to be sorted is numeric change to float\n #data = change_numeric(data)\n # now sort the data in place\n data.sort(reverse=descending)\n for ix, item in enumerate(data):\n tree.move(item[1], '', ix)\n # switch the heading so it will sort in the opposite direction\n tree.heading(col, command=lambda col=col: sortby(tree, col, int(not descending)))","def sort_column(self, column, reverse):\n data = [(self.treeview.set(child, column), child) for child in self.treeview.get_children(\"\")]\n data.sort(reverse=reverse)\n\n # set sort symbol on column heading\n if reverse:\n self.treeview.heading(column, text=f\"{column} ▼\")\n else:\n self.treeview.heading(column, text=f\"{column} ▲\")\n\n # rearrange items in sorted positions\n for index, (value, child) in enumerate(data):\n self.treeview.move(child, \"\", index)\n\n # reverse sort direction\n self.treeview.heading(column, command=lambda: self.sort_column(column, not reverse))","def sort_dialed_digits_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.dialed_digits_grid_div_id, column_name, descending_order)","def sort_vendors_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.vendors_div_id, column_name, descending_order)","def SortBy(self,tree, col, descending):\n \n # grab values to sort\n data = [(tree.set(child, col), child) for child in tree.get_children('')]\n \n # now sort the data in place\n data.sort(reverse=descending)\n \n for ix, item in enumerate(data):\n tree.move(item[1], '', ix)\n \n # switch the heading so it will sort in the opposite direction\n tree.heading(col,command=lambda col=col: self.SortBy(tree, col,int(not descending)))","def _sort_dataframe(self, dataframe):\r\n columns = list(dataframe.columns)\r\n columns.sort()\r\n dataframe = dataframe[columns]\r\n return dataframe","def sort_column(self, column):\n if column == 1: # type\n self.sorted_keys = sorted(self.data_dict.keys(),\n key=lambda x: (self.data_dict[x]['type']),\n reverse=self.sorted_type_top)\n # Invert sorting method\n self.sorted_type_top = not self.sorted_type_top\n\n elif column == 2: # Score\n self.sorted_keys = sorted(self.data_dict.keys(),\n key=lambda x: (float(self.data_dict[x]['score'])),\n reverse=self.sorted_score_top)\n # Invert sorting method\n self.sorted_score_top = not self.sorted_score_top\n\n elif column == 4: # Duration\n d = dict()\n for k in self.sorted_keys:\n duration_string = self.data_dict[k]['duration']\n\n # Get amount of episodes\n if 'episode' in duration_string:\n if 'Some' in duration_string:\n episodes = 0\n else:\n episodes = int(duration_string.split(' episodes')[0])\n else:\n episodes = 1\n\n # Get the duration in minutes\n minutes = 0\n if 'min' in duration_string:\n minutes = int(re.findall('([0-9]+)min', duration_string)[0])\n if 'h' in duration_string:\n minutes += int(re.findall('([0-9]+)h', duration_string)[0]) * 60\n\n # Get total duration of the whole show\n minutes *= episodes\n\n # Store it for sorting\n d[k] = minutes\n\n # Sort titles based on duration\n self.sorted_keys = sorted(d.keys(),\n key=lambda x: d[x],\n reverse=self.sorted_duration_top)\n # Invert sorting method\n self.sorted_duration_top = not self.sorted_duration_top\n\n elif column == 5: # release year\n self.sorted_keys = sorted(self.data_dict.keys(),\n key=lambda x: (float(self.data_dict[x]['released'])),\n reverse=self.sorted_year_top)\n # Invert sorting method\n self.sorted_year_top = not self.sorted_year_top\n\n if column != 2:\n # Make sure next time we click to sort by score,\n # the highest score is on top\n self.sorted_score_top = True\n\n # Redraw the table\n self.setup_tableview()","def sort_table(table, sats_table):","def sort_rates_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.rates_grid_div_id, column_name, descending_order)","def sort_vendor_price_list_detail_dial_digits_grid_column(self, column_name, descending_order):\n self.wait_for_ajax_spinner_load(300)\n self.sort_grid_column(self.vendor_price_list_detail_dial_digits_grid_div_id, column_name, descending_order)","def sort_re_analysis_detail_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.re_analysis_detail_grid_div_id, column_name, descending_order)","def sort_by_col(array, idx=1):\n order = np.argsort(array[:, idx])[::-1]\n return array[order]","def sort(self, trans, query, ascending, column_name=None):\n return grids.GridColumn.sort(self, trans, query, ascending, column_name=\"history_id\")","def sortTableReverse(self, table, cols):\n #productive\n profprint()\n for col in reversed(cols):\n table = sorted(table, key=operator.itemgetter(col), reverse=True)\n return table","def click_on_sort(self):\r\n self.driver.find_element_by_xpath('//*[@id=\"dataTable\"]/thead/tr[3]/th[4]').click()","def sort_vendor_price_list_detail_grid_column(self, column_name, descending_order):\n self.wait_for_ajax_spinner_load(300)\n self.sort_grid_column(self.vendor_price_list_detail_rates_grid_div_id, column_name, descending_order)","def sortby(self, col, descending):\n\t\t# grab values to sort\n\t\ttree = self.albumList\n\t\tdata = [(tree.set(child, col), child) for child in tree.get_children('')]\n\n\t\t# reorder data\n\t\tdata.sort(reverse=descending)\n\t\tfor indx, item in enumerate(data):\n\t\t tree.move(item[1], '', indx)\n\n\t\t# switch the heading so that it will sort in the opposite direction\n\t\ttree.heading(col,\n\t\t command=lambda col=col: self.sortby(col, int(not descending)))","def sortTableReverse(self, table, cols):\r\n # productive\r\n profprint()\r\n for col in reversed(cols):\r\n table = sorted(table, key=operator.itemgetter(col), reverse=True)\r\n return table","def sort_col(col):\n return (col[0], sorted(col[1], key=lambda pair: pair[0]))","def sort(self, column, order=Qt.AscendingOrder):\n if(column == Columns.Date):\n self.sorting = Sorting.Date\n elif(column == Columns.Code):\n self.sorting = Sorting.Code\n elif(column == Columns.User):\n self.sorting = Sorting.User\n elif(column == Columns.Tags):\n self.sorting = Sorting.Priviledges\n elif(column == Columns.TimesRequested):\n self.sorting = Sorting.TimesRequested\n\n if(order == Qt.DescendingOrder):\n self.sorting |= Sorting.Reversed\n\n self._reset_view()","def sort(self, col, order):\r\n self.emit(SIGNAL(\"layoutAboutToBeChanged()\"))\r\n self.mylist = sorted(self.mylist,\r\n key=operator.itemgetter(col))\r\n if order == Qt.DescendingOrder:\r\n self.mylist.reverse()\r\n self.emit(SIGNAL(\"layoutChanged()\"))","def OnColumnClick(self, event):\r\n columns = self.data.getParam('columns')\r\n self.SortItems(columns[event.GetColumn()],'INVERT')","def sortby(tree, col, descending): # 重新排序 <-- 文字版\n # grab values to sort\n data = [(tree.set(child, col), child) \\\n for child in tree.get_children('')]\n\n # if the data to be sorted is numeric change to float\n #data = change_numeric(data)\n # now sort the data in place\n data.sort(reverse=descending)\n # 數字的排法(但文字部分就無法排序)\n #data.sort(key=lambda data: int(data[0]), reverse=descending)\n\n for ix, item in enumerate(data):\n tree.move(item[1], '', ix)\n\n # switch the heading so it will sort in the opposite direction\n tree.heading(col, command=lambda col=col: sortby(tree, col, \\\n int(not descending)))","def sort(self, col, order):\r\n self.emit(SIGNAL(\"layoutAboutToBeChanged()\"))\r\n self.mylist = sorted(self.mylist,\r\n key=operator.itemgetter(col))\r\n if order == QtCore.Qt.DescendingOrder:\r\n self.mylist.reverse()\r\n self.emit(SIGNAL(\"layoutChanged()\"))","def sortDataByAscendingOrderOFValuesInColumn(self, data, colIndex):\n def sortFunc(x):\n return float(x[colIndex])\n data.sort(key=sortFunc)","def sort_data(df, col_order=[\"id\", 'event_name', 'specialty', 'plan_type']):\n df.sort_values(col_order, inplace = True)\n df.reset_index(drop=1, inplace=True)\n return df","def sort_vendor_price_lists_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.view_price_list_div_id, column_name, descending_order)","def sort_vendor_price_list_detail_reference_rates_grid_column(self, column_name, descending_order):\n self.wait_for_ajax_spinner_load(300)\n self.sort_grid_column(self.vendor_price_list_detail_reference_rates_grid_div_id, column_name, descending_order)","def _change_sort_order(self, column):\n if self.tableView.horizontalHeader().sortIndicatorOrder() == Qt.AscendingOrder and \\\n column == self._last_sorted_column:\n self.tableView.horizontalHeader().setSortIndicator(column, Qt.DescendingOrder)\n self.tableView.horizontalHeader().setSortIndicatorShown(False)\n self.tableView.model().unsort(column)\n self._last_sorted_column = -1\n self._last_sort_order = None\n else:\n self.tableView.horizontalHeader().setSortIndicatorShown(True)\n self._last_sorted_column = column\n self._last_sort_order = self.tableView.horizontalHeader().sortIndicatorOrder()","def sort(self, col, order):\n self.layoutAboutToBeChanged.emit()\n self.mylist = sorted(self.mylist,\n key=operator.itemgetter(col))\n if order == Qt.DescendingOrder:\n self.mylist.reverse()\n self.layoutChanged.emit()","def cols_sorted(self, cmp=None, key=None, reverse=False):\n return self.select(*sorted(self.names, cmp, key, reverse))","def sort(headers, data): # extension\n\tcolumn_matrix=data.get_data(headers) # get raw matrix data for numeric values\n\tprint \"\\n before sorting \\n \"\n\tprint column_matrix\n\t\n\tcolumn_matrix=column_matrix.tolist()\n\tcolumn_array=np.asarray(column_matrix)\n\t\n\tcolumn_array.sort(axis=0)\n\t\n\tprint \"\\n \\n done sorting here is your matrix \\n\"\n\t\n\treturn column_array","def sort(self, columnId, order=Qt.AscendingOrder):\n self.layoutAboutToBeChanged.emit()\n self.sortingAboutToStart.emit()\n column = self._dataFrame.columns[columnId]\n self._dataFrame.sort_values(column,\n ascending=not bool(order),\n inplace=True)\n self._dataFrame.reset_index(drop=True, inplace=True)\n self.layoutChanged.emit()\n self.sortingFinished.emit()","def _sortHandler(self) -> None:\n response, columnIndex, ascending = self._sortDialog()\n order = Qt.AscendingOrder if ascending else Qt.DescendingOrder\n if response:\n self._mainFileView.sortByColumn(columnIndex, order)","def change_column_order(p_df, col_name, index):\n cols = p_df.columns.tolist()\n cols.remove(col_name)\n cols.insert(index, col_name)\n return p_df[cols]","def sort(self, column_or_label, descending=False, distinct=False):\n column = self._get_column(column_or_label)\n if distinct:\n _, row_numbers = np.unique(column, return_index=True)\n else:\n row_numbers = np.argsort(column, axis=0)\n assert (row_numbers < self.num_rows).all(), row_numbers\n if descending:\n row_numbers = np.array(row_numbers[::-1])\n return self.take(row_numbers)","def move_card_column(issue_number: int, column: str):\n # move this issue to the active column\n try:\n issue_column([\"issue_column\", SCRUM_BOARD_NAME, issue_number, column])\n except:\n print(\"Unable to move card to the {} column, is it in triage?\".format(column))","def _sorting_order_columns(\n self,\n m,\n byRow=0,\n usingCellValue=0,\n descending=True,\n file_name=None,\n client_name=None):\n\n def _get_list_without_excluded_items(client_name, _keep_at_end):\n\n if _keep_at_end != []:\n return [str(c[usingCellValue].Value) + \" \" + \\\n c.TopMember.Label for c in m[byRow] if not \\\n c.TopMember.Label in _keep_at_end]\n\n elif client_name is not None:\n return [str(c[usingCellValue].Value) + \" \" + \\\n c.TopMember.Label for c in m[byRow] if \\\n c.TopMember.Label != client_name]\n\n else:\n return [str(c[usingCellValue].Value) + \" \" +\n c.TopMember.Label for c in m[byRow]]\n\n _keep_at_end = self._get_keep_at_end(file_name)\n\n _lst = _get_list_without_excluded_items(client_name, _keep_at_end)\n\n # reverse the list to make ascending order\n if descending:\n _lst_incl_cells = list(reversed(self._sorted_nicely(_lst)))\n else:\n _lst_incl_cells = list(self._sorted_nicely(_lst))\n\n return self._rank_position(_lst_incl_cells)","def sort(self, label, descending=True):\n new_label = []\n new_rows1 = []\n for x in self.column_labels:\n new_label.append(x)\n \n # self.column_labels.index(label)\n for x in self.rows:\n new_row = []\n new_row += x\n new_rows1.append(new_row)\n \n \n \n \n new_rows = sorted(new_rows1, key=lambda s: s[self.column_labels.index(label)], reverse = descending)\n \n new_Table = T88ble(new_rows, new_label)\n\n return new_Table","def sort_country_groups_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.country_groups_grid_div_id, column_name, descending_order)","def sort_buy_and_sell_management_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.buy_and_sell_management_grid_div_id, column_name, descending_order)","def sort_nicely(col):\n convert = lambda text: int(text) if text.isdigit() else text\n alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key[col])]\n return alphanum_key","def sort(self, cmp=None, key=None, reverse=False):\n o = order(list(self), cmp, key, reverse)\n # Modify the table in place, more than one variable may be referencing it:\n r=list(self._table); [self._table.__setitem__(i2, r[i1]) for i2, i1 in enumerate(o)]","def order_by(self, columns, reverse=False):\n\n return self._get(\"order\", (columns, reverse), Table)","def _sort(self):\n self.rows.sort(key=lambda x: (x['PERC1'], x['EQ'], x['PASS'], x['W2']),\n reverse=True)\n\n rank = 0\n prev_perc = 0\n prev_rank = 0\n for row in self.rows:\n if row[\"NR\"] == 0:\n # Something has already populated NR as 0 - so we set rank as\n # 0 too\n row['_RANK'] = 0\n row['_NR'] = 0\n continue\n\n # Increment our count\n rank += 1\n if row['PERC1'] == prev_perc:\n row['NR'] = \"\"\n row['_NR'] = prev_rank # I.e. joint 6th will be 6 here\n row['_RANK'] = rank # I.e. joint 6th could be 7, or 8 etc. here\n else:\n row['NR'] = rank\n row['_NR'] = rank\n row['_RANK'] = rank\n prev_perc = row['PERC1']\n prev_rank = rank","def sort_otu_table_by_mapping_field(otu_table_data,\r\n mapping_file_data,\r\n sort_field,\r\n sort_f=natsort):\r\n mapping_data, header_data, comments = mapping_file_data\r\n\r\n mapping_field_index = header_data.index(sort_field)\r\n sorted_sample_ids = [(e[mapping_field_index], e[0]) for e in mapping_data]\r\n sorted_sample_ids = sort_f(sorted_sample_ids)\r\n sorted_sample_ids = [e[1] for e in sorted_sample_ids]\r\n\r\n return sort_otu_table(otu_table_data, sorted_sample_ids)","def _sort(self, row):\n if not self._head:\n self._head = self._create_head(row)\n if self._args.head:\n return row\n\n if 'key' not in self._state:\n self._state['key'] = self._replace_fields(self._args.key)\n\n r = list(map(self._convert, row))\n self._sorting_insert(self._result, r, key=lambda r: eval(self._state['key']))","def sort_destinations_grid_column(self, column_name, descending_order):\n self.sort_grid_column(self.destinations_grid_div_id, column_name, descending_order)","def SortItems(self,column=None,reverse='CURRENT'):\r\n #--Parse column and reverse arguments.\r\n data = self.data\r\n if self.sortDirty:\r\n self.sortDirty = False\r\n (column, reverse) = (None,'CURRENT')\r\n curColumn = data.defaultParam('colSort',data.tankColumns[0])\r\n column = column or curColumn\r\n curReverse = data.defaultParam('colReverse',{}).get(column,False)\r\n if reverse == 'INVERT' and column == curColumn:\r\n reverse = not curReverse\r\n elif reverse in ('INVERT','CURRENT'):\r\n reverse = curReverse\r\n data.updateParam('colReverse')[column] = reverse\r\n data.setParam('colSort',column)\r\n #--Sort\r\n items = self.data.getSorted(column,reverse)\r\n sortDict = dict((self.item_itemId[y],x) for x,y in enumerate(items))\r\n self.gList.SortItems(lambda x,y: cmp(sortDict[x],sortDict[y]))\r\n #--Done\r\n self.mouseTexts.clear()","def OnReorder( self, event ):\n column = self.columns[event.GetColumn()]\n if column.sortOn:\n # multiple sorts for the click...\n columns = [ self.columnByAttribute( attr ) for attr in column.sortOn ]\n diff = [ (a,b) for a,b in zip( self.sortOrder, columns ) if b is not a[1]]\n if not diff:\n self.sortOrder[0] = (not self.sortOrder[0][0], column)\n else:\n self.sortOrder = [\n (c.defaultOrder,c) for c in columns \n ] + [ (a,b) for (a,b) in self.sortOrder if b not in columns]\n else:\n if column is self.sortOrder[0][1]:\n # reverse current major order\n self.sortOrder[0] = (not self.sortOrder[0][0], column)\n else:\n self.sortOrder = [(column.defaultOrder,column)] + [\n (a,b) \n for (a,b) in self.sortOrder if b is not column \n ]\n # TODO: store current selection and re-select after sorting...\n self.reorder()\n self.Refresh()","def nat_sort(x, df):\n unordered = df.swing_drop.tolist()\n reordered = natsorted(unordered, alg=ns.IGNORECASE)\n return reordered.index(x)","def _sort_columns(self, order):\n unknown = set(self._columns) - set(order)\n if unknown:\n names = \", \".join(str(name) for name in unknown)\n raise ValueError(f\"Unknown columns: {names}\")\n\n cols = [self.column_location(column) for column in order]\n\n self._columns = [self._columns[col] for col in cols]\n self._data = [[row[col] for col in cols] for row in self._data]","def sort(df, cols):\n if not df:\n return []\n\n return sorted(df, key=lambda row: [row[col] for col in cols])","def on_combo_sort_col_names_currentIndexChanged(self, index):\n if self.ui.sort_radio_asc.isChecked():\n self.model.setSort(index, Qt.AscendingOrder)\n else:\n self.model.setSort(index, Qt.DescendingOrder)\n self.model.select()","def on_sort_column_change(self, model):\n id, order = self.tv.get_model().get_sort_column_id()\n if id is None and order is None:\n return False\n if self.prev_sort_column_id and self.prev_sort_column_id[0][0] == id:\n self.prev_sort_column_id.pop(0)\n self.prev_sort_column_id.insert(0, (id, order))\n if len(self.prev_sort_column_id) > 2:\n self.prev_sort_column_id.pop()","def order_by(columns):\r\n def col(cd):\r\n return f\"{cd[0]} ASC\" if cd[1] else f\"{cd[0]} DESC\"\r\n return '' if len(columns) == 0 else f\"ORDER BY {', '.join(map(col, columns.items()))}\"","def sort_columns(self):\n extra_cols = []\n for col in self.data.columns:\n if col not in self._required_columns:\n extra_cols.append(col)\n sorted_colnames = list(self._required_columns) + sorted(extra_cols)\n assert len(sorted_colnames) == len(self.data.columns)\n self.data = self.data.reindex(columns=sorted_colnames)","def sort(self, column: Union[str, List[str], List[Tuple], Any], order: str = 'ASC') -> B[B, E]:\n pass","def sortby(self):\n ...","def reorder_cols(df):\n # print(df.columns)\n req_order = [df.columns.tolist()[-1]] + df.columns.tolist()[:-1]\n # print(req_order)\n df = df.loc[:, req_order]\n df = df.sort_values(by=['participant_id'], ignore_index=True)\n return df","def sort_rows_by_column_values(\n self, columns, ascending=True, **kwargs\n ): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.sort_values)(\n self, by=columns, axis=0, ascending=ascending, **kwargs\n )","def sort_col_by_sim(col):\n sims = sorted(col[1], key=lambda pair: (pair[0], pair[1]), reverse=True)\n return (col[0], sims[0:k])","def column_number(self, column_number):\n\n self._column_number = column_number","def test_sort_columns(self):\n before_b = \"\"\"\\\n first line\n line 1\n line a\n line b\n line c\n last line\n \"\"\"\n after_b = \"\"\"\\\n line b\n line a\n first line\n last line\n line 1\n line c\n \"\"\"\n self.run_test(\n before_b=before_b,\n after_b=after_b,\n before_sel=(\"1.0\", \"6.2\"),\n after_sel=(\"1.0\", \"7.0\"),\n command_name=\"sort-columns\",\n )","def order_by(self, order):\n new_table = self.select()\n new_table.rows.sort(key=order)\n return new_table","def sort(tv, col, parent='', reverse=False):\n\n # build a sorting list\n sort_index = list()\n for iid in tv.get_children(parent):\n sort_value = tv.set(iid, col) if col != '#0' else iid\n sort_index.append((sort_value, iid))\n\n # sort the list\n sort_index.sort(reverse=reverse)\n\n # move each node according to its index in the sort list\n for index, (_, iid) in enumerate(sort_index):\n tv.move(iid, parent, index)\n\n # sort each child node\n sort(tv, col, parent=iid, reverse=reverse)\n\n # If this is the top level, reset the headings for reverse sort\n if parent == '':\n tv.heading(\n col,\n command=lambda col=col: sort(tv, col, reverse=not reverse)\n )","def sort():\n return -1","def reorder_columns(dataframe, col_name, position):\r\n temp_col = dataframe[col_name]\r\n dataframe = dataframe.drop(columns=[col_name])\r\n dataframe.insert(loc=position, column=col_name, value=temp_col)\r\n return dataframe","def sort_animals(all_animals):\n def get_key(a):\n return a.row + 0.001 * a.col\n\n all_animals.sort(key=get_key)","def sort(self, desc):\n self.__sortByIndex(0, desc)","def setColumn(self, column_number, column):\n self.data[column_number] = column\n return","def canonicalize_column_index(self, line, col):\n if col < 0:\n col += self.col_lens[line] + 1\n assert col >= 0\n return col","def sort_reg_time_column_in_descending_order(self):\n self.set_jobs_grid_settings(self.reset_all_grid_settings)\n try:\n ok_button_element = self.wait(10).until(EC.presence_of_element_located(self.ok_button_locator), 'ok button locator not found before specified time out')\n ok_button_element.click()\n except:\n pass\n column_name_element = self.wait().until(EC.presence_of_element_located(self.reg_time_column_name_locator), 'reg time column name locator not found before specified time out')\n self.hover(column_name_element)\n self.script_executor_click(column_name_element)\n self.click_element(self.sort_descending_option_locator)","def sort_by(rows: [dict], col_name: str, rev: bool) -> [dict]:\n def get_col(row):\n return row[col_name]\n return sorted(rows, key=get_col, reverse=rev)","def SetColumnOrder(self, columns):\n\n # Validate the columns list.\n # \n # Since this information comes from a serialization\n # and the badge levels may have changed\n # We need to compensate for deleted/new columns.\n hasWhinedOnce = False;\n columnsToRemove = []\n for column in columns:\n if not column in self.__columns.keys():\n if not hasWhinedOnce:\n print \"WARNING: Grid columns have changed. Attempting to compensate.\"\n print \" File->Preferences may contain hidden columns.\"\n hasWhinedOnce = True\n columnsToRemove.append(column)\n for column in columnsToRemove:\n columns.remove(column)\n \n # Use this (serialized) column order.\n self.__colsKey = columns\n for cKey in self.__columns.keys():\n column = self.__columns[cKey]\n if (column[FTable.__POSITION] != None):\n column[FTable.__ATTR].IncRef()\n column[FTable.__POSITION] = None\n for i in range(len(self.__colsKey)):\n cKey = self.__colsKey[i]\n column = self.__columns[cKey]\n column[FTable.__POSITION] = i\n self.SetColSize(cKey, column[FTable.__SIZE])\n self.__grid.SetColAttr(i, column[FTable.__ATTR])","def sortByRecordNum(self, records, verbose=False, in_place=False):\n tmp = records # copy of records for sorting\n if (verbose):\n print \"[EVT] Sorting by record number\"\n\n swapped = True\n while swapped:\n swapped = False\n for i in xrange(len(tmp)-1):\n ni = tmp[i].getField(\"recordNumber\")\n nj = tmp[i+1].getField(\"recordNumber\")\n if ni > nj:\n t = tmp[i+1]\n tmp[i+1] = tmp[i]\n tmp[i] = t\n swapped = True\n return tmp","def sortByIndex(self,_index,desc):\n\treturn self.sortByPostText(\"[%s]\"%_index,desc)","def row_major_sort(dataframe):\n t1_column = dataframe.columns.get_loc(\"t1\")\n winding_column = dataframe.columns.get_loc(\"winding\")\n t_array = np.flip(dataframe.iloc[:,t1_column:winding_column].values, axis=1)\n n_ts = t_array.shape[1]\n for column in range(n_ts):\n sorted_args = np.argsort(t_array[:,column], kind = \"mergesort\")\n dataframe = dataframe.iloc[sorted_args,:]\n t_array = t_array[sorted_args]\n return dataframe","def order_columns(dataset, col_names, feature_to_predict):\n col_names.remove(feature_to_predict)\n col_names.sort()\n\n sorted_column_list = [feature_to_predict]\n for col_name in col_names:\n if col_name != feature_to_predict:\n sorted_column_list.append(col_name)\n\n dataset = dataset.reindex(sorted_column_list, axis=1)\n return dataset","def _column_sorting_key(self, c):\n first_index = 0\n if c.startswith('hybrid'):\n first_index = 1\n elif c.startswith('solar'):\n first_index = 2\n elif c.startswith('wind'):\n first_index = 3\n elif c == MERGE_COLUMN:\n first_index = -1\n return first_index, self._hybrid_meta.columns.get_loc(c)","def sort(self, key: Callable):\n self.data.sort(key=key)","def sort(self, key: Callable):\n self.data.sort(key=key)","def column_order(self):\n return ((1, 2), (1, 0), (1, 1))","def _sort_dataframe(self, dataframe):\r\n print('Not allowed')","def _sort_rows(matrix, num_rows):\n tmatrix = array_ops.transpose(matrix, [1, 0])\n sorted_tmatrix = nn_ops.top_k(tmatrix, num_rows)[0]\n return array_ops.transpose(sorted_tmatrix, [1, 0])","def _sorting_order_rows(\n self,\n m,\n byColumn=0,\n usingCellValue=0,\n descending=True,\n file_name=None,\n client_name=None):\n\n def _get_list_without_excluded_items(client_name, _keep_at_end):\n\n if _keep_at_end != []:\n return [str(r[byColumn][usingCellValue].Value) + \" \" +\n r.Member.Label for r in m if not r.Member.Label in \\\n _keep_at_end]\n\n elif client_name is not None:\n return [str(r[byColumn][usingCellValue].Value) + \" \" +\n r.Member.Label for r in m if \\\n r.Member.Label != client_name]\n\n else:\n return [str(r[byColumn][usingCellValue].Value) +\n \" \" + r.Member.Label for r in m]\n\n _keep_at_end = self._get_keep_at_end(file_name)\n\n _lst = _get_list_without_excluded_items(client_name, _keep_at_end)\n\n # reverse the list to make ascending order\n if descending:\n _lst_incl_cells = list(reversed(self._sorted_nicely(_lst)))\n else:\n _lst_incl_cells = list(self._sorted_nicely(_lst))\n\n return self._rank_position(_lst_incl_cells)","def order_by(self, column, direction=\"ASC\"):\n self._order_by += ((column, direction),)\n return self","def sort_dataset(self, identifier, columns, reversed, datastore):\n # Get dataset. Raise exception if dataset is unknown\n dataset = datastore.get_dataset(identifier)\n if dataset is None:\n raise ValueError(\"unknown dataset '{}'\".format(identifier))\n # Sort the data frame on the specified columns.\n df = dataset.to_dataframe()\n df = vizual.sort_dataset(df=df, colids=columns, reversed=reversed)\n # Store updated dataset to get new identifier.\n ds = datastore.update_dataset(\n origin=dataset,\n df=df,\n annotations=dataset.annotations\n )\n return VizualApiResult(ds)"],"string":"[\n \"def sortTable(self, table, cols):\\n #productive\\n profprint()\\n for col in reversed(cols):\\n table = sorted(table, key=operator.itemgetter(col))\\n return table\",\n \"def sortTable(self, table, cols):\\r\\n # productive\\r\\n profprint()\\r\\n for col in reversed(cols):\\r\\n table = sorted(table, key=operator.itemgetter(col))\\r\\n return table\",\n \"def sort_table_by_column(\\n self, table: Table, column: Column, ascending: bool = True\\n ):\\n self._requires_table(table)\\n table.sort_by_column(column, ascending=ascending)\",\n \"def sort_by_column(self, columns, ascending=False):\\n columns = to_list(columns)\\n\\n # Create sort criteria list, with each row as tuple of column values\\n values = (self.get_column(column, as_list=True) for column in columns)\\n values = list(zip(*values))\\n assert len(values) == self.size\\n\\n def sorter(row):\\n \\\"\\\"\\\"Sort table by given values, while allowing for disparate types.\\n Order priority:\\n - Values by typename\\n - Numeric types\\n - None values\\n \\\"\\\"\\\"\\n criteria = []\\n for value in row[1]: # Ignore enumeration\\n criteria.append(\\n (\\n value is not None,\\n \\\"\\\" if isinstance(value, Number) else type(value).__name__,\\n value,\\n )\\n )\\n return criteria\\n\\n # Store original index order using enumerate() before sort,\\n # and use it to sort data later\\n values = sorted(enumerate(values), key=sorter, reverse=not ascending)\\n idxs = [value[0] for value in values]\\n\\n # Re-order data\\n self._data = [self._data[idx] for idx in idxs]\",\n \"def sortByColumn(data_file, column_to_sort=1):\\n cmp = lambda qvar: float(qvar.split()[column_to_sort-1]) # used in \\\"sorted\\\" method\\n in_file = open(data_file, \\\"r\\\")\\n out_file = open(data_file+\\\".TEMP\\\", \\\"w\\\")\\n for a_line in sorted(in_file.readlines(),key=cmp):\\n out_file.write(a_line)\\n in_file.close()\\n out_file.close()\\n shutil.copy(data_file+\\\".TEMP\\\", data_file)\\n os.remove(data_file+\\\".TEMP\\\")\",\n \"def sortby(tree, col, descending):\\r\\n # grab values to sort\\r\\n data = [(tree.set(child, col), child) for child in tree.get_children('')]\\r\\n \\r\\n #Figure out if this is a float column. If it is,\\r\\n #transform to float so the ordering will be meaningful\\r\\n try:\\r\\n \\r\\n data = [( float(x[0]), x[1]) for x in data]\\r\\n \\r\\n except:\\r\\n \\r\\n #Nope!\\r\\n pass\\r\\n \\r\\n # reorder data\\r\\n data.sort(reverse=descending)\\r\\n for indx, item in enumerate(data):\\r\\n tree.move(item[1], '', indx)\\r\\n\\r\\n # switch the heading so that it will sort in the opposite direction\\r\\n tree.heading(col,\\r\\n command=lambda col=col: sortby(tree, col, int(not descending)))\",\n \"def sort(self, column: int, order: Qt.SortOrder = ...) -> None:\\n col = self._dataframe.columns[column]\\n # Temporary column with display value of column\\n self._dataframe['_FOR_SORT'] = self._dataframe.apply(\\n lambda row: self._get_cell_value(self._dataframe.index.get_loc(row.name), column), axis=1)\\n # Sort by temp column\\n self._dataframe.sort_values(by=['_FOR_SORT'], ascending=True if order == 0 else False,\\n inplace=True, axis=0)\\n # drop temp column\\n self._dataframe.drop('_FOR_SORT', axis=1, inplace=True)\\n self.layoutChanged.emit()\\n super().sort(column, order)\",\n \"def sortby_num(tree, col, descending): # 重新排序 <-- 數字版\\n # grab values to sort\\n data = [(tree.set(child, col), child) \\\\\\n for child in tree.get_children('')]\\n\\n # if the data to be sorted is numeric change to float\\n #data = change_numeric(data)\\n # now sort the data in place\\n #data.sort(reverse=descending)\\n # 數字的排法(但文字部分就無法排序)\\n data.sort(key=lambda data: int(data[0]), reverse=descending)\\n\\n for ix, item in enumerate(data):\\n tree.move(item[1], '', ix)\\n\\n # switch the heading so it will sort in the opposite direction\\n tree.heading(col, command=lambda col=col: sortby_num(tree, col, \\\\\\n int(not descending)))\",\n \"def _sort_column(self, column, reverse):\\n if tk.DISABLED in self.state():\\n return\\n # get list of (value, item) tuple where value is the value in column for the item\\n l = [(self.set(child, column), child) for child in self.get_children('')]\\n # sort list using the column type\\n l.sort(reverse=reverse, key=lambda x: self._column_types[column](x[0]))\\n # reorder items\\n for index, (val, child) in enumerate(l):\\n self.move(child, \\\"\\\", index)\\n # reverse sorting direction for the next time\\n self.heading(column, command=lambda: self._sort_column(column, not reverse))\",\n \"def treeview_sort_column(self, treeview, column, reverse):\\r\\n data = [(treeview.set(ticker, column), ticker) for ticker in treeview.get_children('')]\\r\\n data.sort(reverse=reverse)\\r\\n\\r\\n # sort the stock(s)\\r\\n for index, (val, k) in enumerate(data):\\r\\n treeview.move(k, '', index)\\r\\n\\r\\n # reverse sort next time\\r\\n treeview.heading(column, command=lambda: self.treeview_sort_column(treeview, column, not reverse))\",\n \"def sort(self, Ncol, order):\\n self.emit(SIGNAL(\\\"layoutAboutToBeChanged()\\\"))\\n self.arraydata = sorted(self.arraydata, key=operator.itemgetter(Ncol)) \\n if order != Qt.DescendingOrder:\\n self.arraydata.reverse()\\n self.emit(SIGNAL(\\\"layoutChanged()\\\"))\",\n \"def sort(self, Ncol, order):\\n self.emit(SIGNAL(\\\"layoutAboutToBeChanged()\\\"))\\n self.arraydata = sorted(self.arraydata, key=operator.itemgetter(Ncol)) \\n if order == Qt.DescendingOrder:\\n self.arraydata.reverse()\\n self.emit(SIGNAL(\\\"layoutChanged()\\\"))\",\n \"def sort_re_analysis_grid_column(self, column_name, descending_order):\\n self.sort_grid_column(self.re_analysis_grid_div_id, column_name, descending_order)\",\n \"def sortby(tree, col, descending):\\n # grab values to sort\\n data = [(tree.set(child, col), child) for child in tree.get_children('')]\\n # if the data to be sorted is numeric change to float\\n #data = change_numeric(data)\\n # now sort the data in place\\n data.sort(reverse=descending)\\n for ix, item in enumerate(data):\\n tree.move(item[1], '', ix)\\n # switch the heading so it will sort in the opposite direction\\n tree.heading(col, command=lambda col=col: sortby(tree, col, int(not descending)))\",\n \"def sort_column(self, column, reverse):\\n data = [(self.treeview.set(child, column), child) for child in self.treeview.get_children(\\\"\\\")]\\n data.sort(reverse=reverse)\\n\\n # set sort symbol on column heading\\n if reverse:\\n self.treeview.heading(column, text=f\\\"{column} ▼\\\")\\n else:\\n self.treeview.heading(column, text=f\\\"{column} ▲\\\")\\n\\n # rearrange items in sorted positions\\n for index, (value, child) in enumerate(data):\\n self.treeview.move(child, \\\"\\\", index)\\n\\n # reverse sort direction\\n self.treeview.heading(column, command=lambda: self.sort_column(column, not reverse))\",\n \"def sort_dialed_digits_grid_column(self, column_name, descending_order):\\n self.sort_grid_column(self.dialed_digits_grid_div_id, column_name, descending_order)\",\n \"def sort_vendors_grid_column(self, column_name, descending_order):\\n self.sort_grid_column(self.vendors_div_id, column_name, descending_order)\",\n \"def SortBy(self,tree, col, descending):\\n \\n # grab values to sort\\n data = [(tree.set(child, col), child) for child in tree.get_children('')]\\n \\n # now sort the data in place\\n data.sort(reverse=descending)\\n \\n for ix, item in enumerate(data):\\n tree.move(item[1], '', ix)\\n \\n # switch the heading so it will sort in the opposite direction\\n tree.heading(col,command=lambda col=col: self.SortBy(tree, col,int(not descending)))\",\n \"def _sort_dataframe(self, dataframe):\\r\\n columns = list(dataframe.columns)\\r\\n columns.sort()\\r\\n dataframe = dataframe[columns]\\r\\n return dataframe\",\n \"def sort_column(self, column):\\n if column == 1: # type\\n self.sorted_keys = sorted(self.data_dict.keys(),\\n key=lambda x: (self.data_dict[x]['type']),\\n reverse=self.sorted_type_top)\\n # Invert sorting method\\n self.sorted_type_top = not self.sorted_type_top\\n\\n elif column == 2: # Score\\n self.sorted_keys = sorted(self.data_dict.keys(),\\n key=lambda x: (float(self.data_dict[x]['score'])),\\n reverse=self.sorted_score_top)\\n # Invert sorting method\\n self.sorted_score_top = not self.sorted_score_top\\n\\n elif column == 4: # Duration\\n d = dict()\\n for k in self.sorted_keys:\\n duration_string = self.data_dict[k]['duration']\\n\\n # Get amount of episodes\\n if 'episode' in duration_string:\\n if 'Some' in duration_string:\\n episodes = 0\\n else:\\n episodes = int(duration_string.split(' episodes')[0])\\n else:\\n episodes = 1\\n\\n # Get the duration in minutes\\n minutes = 0\\n if 'min' in duration_string:\\n minutes = int(re.findall('([0-9]+)min', duration_string)[0])\\n if 'h' in duration_string:\\n minutes += int(re.findall('([0-9]+)h', duration_string)[0]) * 60\\n\\n # Get total duration of the whole show\\n minutes *= episodes\\n\\n # Store it for sorting\\n d[k] = minutes\\n\\n # Sort titles based on duration\\n self.sorted_keys = sorted(d.keys(),\\n key=lambda x: d[x],\\n reverse=self.sorted_duration_top)\\n # Invert sorting method\\n self.sorted_duration_top = not self.sorted_duration_top\\n\\n elif column == 5: # release year\\n self.sorted_keys = sorted(self.data_dict.keys(),\\n key=lambda x: (float(self.data_dict[x]['released'])),\\n reverse=self.sorted_year_top)\\n # Invert sorting method\\n self.sorted_year_top = not self.sorted_year_top\\n\\n if column != 2:\\n # Make sure next time we click to sort by score,\\n # the highest score is on top\\n self.sorted_score_top = True\\n\\n # Redraw the table\\n self.setup_tableview()\",\n \"def sort_table(table, sats_table):\",\n \"def sort_rates_grid_column(self, column_name, descending_order):\\n self.sort_grid_column(self.rates_grid_div_id, column_name, descending_order)\",\n \"def sort_vendor_price_list_detail_dial_digits_grid_column(self, column_name, descending_order):\\n self.wait_for_ajax_spinner_load(300)\\n self.sort_grid_column(self.vendor_price_list_detail_dial_digits_grid_div_id, column_name, descending_order)\",\n \"def sort_re_analysis_detail_grid_column(self, column_name, descending_order):\\n self.sort_grid_column(self.re_analysis_detail_grid_div_id, column_name, descending_order)\",\n \"def sort_by_col(array, idx=1):\\n order = np.argsort(array[:, idx])[::-1]\\n return array[order]\",\n \"def sort(self, trans, query, ascending, column_name=None):\\n return grids.GridColumn.sort(self, trans, query, ascending, column_name=\\\"history_id\\\")\",\n \"def sortTableReverse(self, table, cols):\\n #productive\\n profprint()\\n for col in reversed(cols):\\n table = sorted(table, key=operator.itemgetter(col), reverse=True)\\n return table\",\n \"def click_on_sort(self):\\r\\n self.driver.find_element_by_xpath('//*[@id=\\\"dataTable\\\"]/thead/tr[3]/th[4]').click()\",\n \"def sort_vendor_price_list_detail_grid_column(self, column_name, descending_order):\\n self.wait_for_ajax_spinner_load(300)\\n self.sort_grid_column(self.vendor_price_list_detail_rates_grid_div_id, column_name, descending_order)\",\n \"def sortby(self, col, descending):\\n\\t\\t# grab values to sort\\n\\t\\ttree = self.albumList\\n\\t\\tdata = [(tree.set(child, col), child) for child in tree.get_children('')]\\n\\n\\t\\t# reorder data\\n\\t\\tdata.sort(reverse=descending)\\n\\t\\tfor indx, item in enumerate(data):\\n\\t\\t tree.move(item[1], '', indx)\\n\\n\\t\\t# switch the heading so that it will sort in the opposite direction\\n\\t\\ttree.heading(col,\\n\\t\\t command=lambda col=col: self.sortby(col, int(not descending)))\",\n \"def sortTableReverse(self, table, cols):\\r\\n # productive\\r\\n profprint()\\r\\n for col in reversed(cols):\\r\\n table = sorted(table, key=operator.itemgetter(col), reverse=True)\\r\\n return table\",\n \"def sort_col(col):\\n return (col[0], sorted(col[1], key=lambda pair: pair[0]))\",\n \"def sort(self, column, order=Qt.AscendingOrder):\\n if(column == Columns.Date):\\n self.sorting = Sorting.Date\\n elif(column == Columns.Code):\\n self.sorting = Sorting.Code\\n elif(column == Columns.User):\\n self.sorting = Sorting.User\\n elif(column == Columns.Tags):\\n self.sorting = Sorting.Priviledges\\n elif(column == Columns.TimesRequested):\\n self.sorting = Sorting.TimesRequested\\n\\n if(order == Qt.DescendingOrder):\\n self.sorting |= Sorting.Reversed\\n\\n self._reset_view()\",\n \"def sort(self, col, order):\\r\\n self.emit(SIGNAL(\\\"layoutAboutToBeChanged()\\\"))\\r\\n self.mylist = sorted(self.mylist,\\r\\n key=operator.itemgetter(col))\\r\\n if order == Qt.DescendingOrder:\\r\\n self.mylist.reverse()\\r\\n self.emit(SIGNAL(\\\"layoutChanged()\\\"))\",\n \"def OnColumnClick(self, event):\\r\\n columns = self.data.getParam('columns')\\r\\n self.SortItems(columns[event.GetColumn()],'INVERT')\",\n \"def sortby(tree, col, descending): # 重新排序 <-- 文字版\\n # grab values to sort\\n data = [(tree.set(child, col), child) \\\\\\n for child in tree.get_children('')]\\n\\n # if the data to be sorted is numeric change to float\\n #data = change_numeric(data)\\n # now sort the data in place\\n data.sort(reverse=descending)\\n # 數字的排法(但文字部分就無法排序)\\n #data.sort(key=lambda data: int(data[0]), reverse=descending)\\n\\n for ix, item in enumerate(data):\\n tree.move(item[1], '', ix)\\n\\n # switch the heading so it will sort in the opposite direction\\n tree.heading(col, command=lambda col=col: sortby(tree, col, \\\\\\n int(not descending)))\",\n \"def sort(self, col, order):\\r\\n self.emit(SIGNAL(\\\"layoutAboutToBeChanged()\\\"))\\r\\n self.mylist = sorted(self.mylist,\\r\\n key=operator.itemgetter(col))\\r\\n if order == QtCore.Qt.DescendingOrder:\\r\\n self.mylist.reverse()\\r\\n self.emit(SIGNAL(\\\"layoutChanged()\\\"))\",\n \"def sortDataByAscendingOrderOFValuesInColumn(self, data, colIndex):\\n def sortFunc(x):\\n return float(x[colIndex])\\n data.sort(key=sortFunc)\",\n \"def sort_data(df, col_order=[\\\"id\\\", 'event_name', 'specialty', 'plan_type']):\\n df.sort_values(col_order, inplace = True)\\n df.reset_index(drop=1, inplace=True)\\n return df\",\n \"def sort_vendor_price_lists_grid_column(self, column_name, descending_order):\\n self.sort_grid_column(self.view_price_list_div_id, column_name, descending_order)\",\n \"def sort_vendor_price_list_detail_reference_rates_grid_column(self, column_name, descending_order):\\n self.wait_for_ajax_spinner_load(300)\\n self.sort_grid_column(self.vendor_price_list_detail_reference_rates_grid_div_id, column_name, descending_order)\",\n \"def _change_sort_order(self, column):\\n if self.tableView.horizontalHeader().sortIndicatorOrder() == Qt.AscendingOrder and \\\\\\n column == self._last_sorted_column:\\n self.tableView.horizontalHeader().setSortIndicator(column, Qt.DescendingOrder)\\n self.tableView.horizontalHeader().setSortIndicatorShown(False)\\n self.tableView.model().unsort(column)\\n self._last_sorted_column = -1\\n self._last_sort_order = None\\n else:\\n self.tableView.horizontalHeader().setSortIndicatorShown(True)\\n self._last_sorted_column = column\\n self._last_sort_order = self.tableView.horizontalHeader().sortIndicatorOrder()\",\n \"def sort(self, col, order):\\n self.layoutAboutToBeChanged.emit()\\n self.mylist = sorted(self.mylist,\\n key=operator.itemgetter(col))\\n if order == Qt.DescendingOrder:\\n self.mylist.reverse()\\n self.layoutChanged.emit()\",\n \"def cols_sorted(self, cmp=None, key=None, reverse=False):\\n return self.select(*sorted(self.names, cmp, key, reverse))\",\n \"def sort(headers, data): # extension\\n\\tcolumn_matrix=data.get_data(headers) # get raw matrix data for numeric values\\n\\tprint \\\"\\\\n before sorting \\\\n \\\"\\n\\tprint column_matrix\\n\\t\\n\\tcolumn_matrix=column_matrix.tolist()\\n\\tcolumn_array=np.asarray(column_matrix)\\n\\t\\n\\tcolumn_array.sort(axis=0)\\n\\t\\n\\tprint \\\"\\\\n \\\\n done sorting here is your matrix \\\\n\\\"\\n\\t\\n\\treturn column_array\",\n \"def sort(self, columnId, order=Qt.AscendingOrder):\\n self.layoutAboutToBeChanged.emit()\\n self.sortingAboutToStart.emit()\\n column = self._dataFrame.columns[columnId]\\n self._dataFrame.sort_values(column,\\n ascending=not bool(order),\\n inplace=True)\\n self._dataFrame.reset_index(drop=True, inplace=True)\\n self.layoutChanged.emit()\\n self.sortingFinished.emit()\",\n \"def _sortHandler(self) -> None:\\n response, columnIndex, ascending = self._sortDialog()\\n order = Qt.AscendingOrder if ascending else Qt.DescendingOrder\\n if response:\\n self._mainFileView.sortByColumn(columnIndex, order)\",\n \"def change_column_order(p_df, col_name, index):\\n cols = p_df.columns.tolist()\\n cols.remove(col_name)\\n cols.insert(index, col_name)\\n return p_df[cols]\",\n \"def sort(self, column_or_label, descending=False, distinct=False):\\n column = self._get_column(column_or_label)\\n if distinct:\\n _, row_numbers = np.unique(column, return_index=True)\\n else:\\n row_numbers = np.argsort(column, axis=0)\\n assert (row_numbers < self.num_rows).all(), row_numbers\\n if descending:\\n row_numbers = np.array(row_numbers[::-1])\\n return self.take(row_numbers)\",\n \"def move_card_column(issue_number: int, column: str):\\n # move this issue to the active column\\n try:\\n issue_column([\\\"issue_column\\\", SCRUM_BOARD_NAME, issue_number, column])\\n except:\\n print(\\\"Unable to move card to the {} column, is it in triage?\\\".format(column))\",\n \"def _sorting_order_columns(\\n self,\\n m,\\n byRow=0,\\n usingCellValue=0,\\n descending=True,\\n file_name=None,\\n client_name=None):\\n\\n def _get_list_without_excluded_items(client_name, _keep_at_end):\\n\\n if _keep_at_end != []:\\n return [str(c[usingCellValue].Value) + \\\" \\\" + \\\\\\n c.TopMember.Label for c in m[byRow] if not \\\\\\n c.TopMember.Label in _keep_at_end]\\n\\n elif client_name is not None:\\n return [str(c[usingCellValue].Value) + \\\" \\\" + \\\\\\n c.TopMember.Label for c in m[byRow] if \\\\\\n c.TopMember.Label != client_name]\\n\\n else:\\n return [str(c[usingCellValue].Value) + \\\" \\\" +\\n c.TopMember.Label for c in m[byRow]]\\n\\n _keep_at_end = self._get_keep_at_end(file_name)\\n\\n _lst = _get_list_without_excluded_items(client_name, _keep_at_end)\\n\\n # reverse the list to make ascending order\\n if descending:\\n _lst_incl_cells = list(reversed(self._sorted_nicely(_lst)))\\n else:\\n _lst_incl_cells = list(self._sorted_nicely(_lst))\\n\\n return self._rank_position(_lst_incl_cells)\",\n \"def sort(self, label, descending=True):\\n new_label = []\\n new_rows1 = []\\n for x in self.column_labels:\\n new_label.append(x)\\n \\n # self.column_labels.index(label)\\n for x in self.rows:\\n new_row = []\\n new_row += x\\n new_rows1.append(new_row)\\n \\n \\n \\n \\n new_rows = sorted(new_rows1, key=lambda s: s[self.column_labels.index(label)], reverse = descending)\\n \\n new_Table = T88ble(new_rows, new_label)\\n\\n return new_Table\",\n \"def sort_country_groups_grid_column(self, column_name, descending_order):\\n self.sort_grid_column(self.country_groups_grid_div_id, column_name, descending_order)\",\n \"def sort_buy_and_sell_management_grid_column(self, column_name, descending_order):\\n self.sort_grid_column(self.buy_and_sell_management_grid_div_id, column_name, descending_order)\",\n \"def sort_nicely(col):\\n convert = lambda text: int(text) if text.isdigit() else text\\n alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key[col])]\\n return alphanum_key\",\n \"def sort(self, cmp=None, key=None, reverse=False):\\n o = order(list(self), cmp, key, reverse)\\n # Modify the table in place, more than one variable may be referencing it:\\n r=list(self._table); [self._table.__setitem__(i2, r[i1]) for i2, i1 in enumerate(o)]\",\n \"def order_by(self, columns, reverse=False):\\n\\n return self._get(\\\"order\\\", (columns, reverse), Table)\",\n \"def _sort(self):\\n self.rows.sort(key=lambda x: (x['PERC1'], x['EQ'], x['PASS'], x['W2']),\\n reverse=True)\\n\\n rank = 0\\n prev_perc = 0\\n prev_rank = 0\\n for row in self.rows:\\n if row[\\\"NR\\\"] == 0:\\n # Something has already populated NR as 0 - so we set rank as\\n # 0 too\\n row['_RANK'] = 0\\n row['_NR'] = 0\\n continue\\n\\n # Increment our count\\n rank += 1\\n if row['PERC1'] == prev_perc:\\n row['NR'] = \\\"\\\"\\n row['_NR'] = prev_rank # I.e. joint 6th will be 6 here\\n row['_RANK'] = rank # I.e. joint 6th could be 7, or 8 etc. here\\n else:\\n row['NR'] = rank\\n row['_NR'] = rank\\n row['_RANK'] = rank\\n prev_perc = row['PERC1']\\n prev_rank = rank\",\n \"def sort_otu_table_by_mapping_field(otu_table_data,\\r\\n mapping_file_data,\\r\\n sort_field,\\r\\n sort_f=natsort):\\r\\n mapping_data, header_data, comments = mapping_file_data\\r\\n\\r\\n mapping_field_index = header_data.index(sort_field)\\r\\n sorted_sample_ids = [(e[mapping_field_index], e[0]) for e in mapping_data]\\r\\n sorted_sample_ids = sort_f(sorted_sample_ids)\\r\\n sorted_sample_ids = [e[1] for e in sorted_sample_ids]\\r\\n\\r\\n return sort_otu_table(otu_table_data, sorted_sample_ids)\",\n \"def _sort(self, row):\\n if not self._head:\\n self._head = self._create_head(row)\\n if self._args.head:\\n return row\\n\\n if 'key' not in self._state:\\n self._state['key'] = self._replace_fields(self._args.key)\\n\\n r = list(map(self._convert, row))\\n self._sorting_insert(self._result, r, key=lambda r: eval(self._state['key']))\",\n \"def sort_destinations_grid_column(self, column_name, descending_order):\\n self.sort_grid_column(self.destinations_grid_div_id, column_name, descending_order)\",\n \"def SortItems(self,column=None,reverse='CURRENT'):\\r\\n #--Parse column and reverse arguments.\\r\\n data = self.data\\r\\n if self.sortDirty:\\r\\n self.sortDirty = False\\r\\n (column, reverse) = (None,'CURRENT')\\r\\n curColumn = data.defaultParam('colSort',data.tankColumns[0])\\r\\n column = column or curColumn\\r\\n curReverse = data.defaultParam('colReverse',{}).get(column,False)\\r\\n if reverse == 'INVERT' and column == curColumn:\\r\\n reverse = not curReverse\\r\\n elif reverse in ('INVERT','CURRENT'):\\r\\n reverse = curReverse\\r\\n data.updateParam('colReverse')[column] = reverse\\r\\n data.setParam('colSort',column)\\r\\n #--Sort\\r\\n items = self.data.getSorted(column,reverse)\\r\\n sortDict = dict((self.item_itemId[y],x) for x,y in enumerate(items))\\r\\n self.gList.SortItems(lambda x,y: cmp(sortDict[x],sortDict[y]))\\r\\n #--Done\\r\\n self.mouseTexts.clear()\",\n \"def OnReorder( self, event ):\\n column = self.columns[event.GetColumn()]\\n if column.sortOn:\\n # multiple sorts for the click...\\n columns = [ self.columnByAttribute( attr ) for attr in column.sortOn ]\\n diff = [ (a,b) for a,b in zip( self.sortOrder, columns ) if b is not a[1]]\\n if not diff:\\n self.sortOrder[0] = (not self.sortOrder[0][0], column)\\n else:\\n self.sortOrder = [\\n (c.defaultOrder,c) for c in columns \\n ] + [ (a,b) for (a,b) in self.sortOrder if b not in columns]\\n else:\\n if column is self.sortOrder[0][1]:\\n # reverse current major order\\n self.sortOrder[0] = (not self.sortOrder[0][0], column)\\n else:\\n self.sortOrder = [(column.defaultOrder,column)] + [\\n (a,b) \\n for (a,b) in self.sortOrder if b is not column \\n ]\\n # TODO: store current selection and re-select after sorting...\\n self.reorder()\\n self.Refresh()\",\n \"def nat_sort(x, df):\\n unordered = df.swing_drop.tolist()\\n reordered = natsorted(unordered, alg=ns.IGNORECASE)\\n return reordered.index(x)\",\n \"def _sort_columns(self, order):\\n unknown = set(self._columns) - set(order)\\n if unknown:\\n names = \\\", \\\".join(str(name) for name in unknown)\\n raise ValueError(f\\\"Unknown columns: {names}\\\")\\n\\n cols = [self.column_location(column) for column in order]\\n\\n self._columns = [self._columns[col] for col in cols]\\n self._data = [[row[col] for col in cols] for row in self._data]\",\n \"def sort(df, cols):\\n if not df:\\n return []\\n\\n return sorted(df, key=lambda row: [row[col] for col in cols])\",\n \"def on_combo_sort_col_names_currentIndexChanged(self, index):\\n if self.ui.sort_radio_asc.isChecked():\\n self.model.setSort(index, Qt.AscendingOrder)\\n else:\\n self.model.setSort(index, Qt.DescendingOrder)\\n self.model.select()\",\n \"def on_sort_column_change(self, model):\\n id, order = self.tv.get_model().get_sort_column_id()\\n if id is None and order is None:\\n return False\\n if self.prev_sort_column_id and self.prev_sort_column_id[0][0] == id:\\n self.prev_sort_column_id.pop(0)\\n self.prev_sort_column_id.insert(0, (id, order))\\n if len(self.prev_sort_column_id) > 2:\\n self.prev_sort_column_id.pop()\",\n \"def order_by(columns):\\r\\n def col(cd):\\r\\n return f\\\"{cd[0]} ASC\\\" if cd[1] else f\\\"{cd[0]} DESC\\\"\\r\\n return '' if len(columns) == 0 else f\\\"ORDER BY {', '.join(map(col, columns.items()))}\\\"\",\n \"def sort_columns(self):\\n extra_cols = []\\n for col in self.data.columns:\\n if col not in self._required_columns:\\n extra_cols.append(col)\\n sorted_colnames = list(self._required_columns) + sorted(extra_cols)\\n assert len(sorted_colnames) == len(self.data.columns)\\n self.data = self.data.reindex(columns=sorted_colnames)\",\n \"def sort(self, column: Union[str, List[str], List[Tuple], Any], order: str = 'ASC') -> B[B, E]:\\n pass\",\n \"def sortby(self):\\n ...\",\n \"def reorder_cols(df):\\n # print(df.columns)\\n req_order = [df.columns.tolist()[-1]] + df.columns.tolist()[:-1]\\n # print(req_order)\\n df = df.loc[:, req_order]\\n df = df.sort_values(by=['participant_id'], ignore_index=True)\\n return df\",\n \"def sort_rows_by_column_values(\\n self, columns, ascending=True, **kwargs\\n ): # noqa: PR02\\n return DataFrameDefault.register(pandas.DataFrame.sort_values)(\\n self, by=columns, axis=0, ascending=ascending, **kwargs\\n )\",\n \"def sort_col_by_sim(col):\\n sims = sorted(col[1], key=lambda pair: (pair[0], pair[1]), reverse=True)\\n return (col[0], sims[0:k])\",\n \"def column_number(self, column_number):\\n\\n self._column_number = column_number\",\n \"def test_sort_columns(self):\\n before_b = \\\"\\\"\\\"\\\\\\n first line\\n line 1\\n line a\\n line b\\n line c\\n last line\\n \\\"\\\"\\\"\\n after_b = \\\"\\\"\\\"\\\\\\n line b\\n line a\\n first line\\n last line\\n line 1\\n line c\\n \\\"\\\"\\\"\\n self.run_test(\\n before_b=before_b,\\n after_b=after_b,\\n before_sel=(\\\"1.0\\\", \\\"6.2\\\"),\\n after_sel=(\\\"1.0\\\", \\\"7.0\\\"),\\n command_name=\\\"sort-columns\\\",\\n )\",\n \"def order_by(self, order):\\n new_table = self.select()\\n new_table.rows.sort(key=order)\\n return new_table\",\n \"def sort(tv, col, parent='', reverse=False):\\n\\n # build a sorting list\\n sort_index = list()\\n for iid in tv.get_children(parent):\\n sort_value = tv.set(iid, col) if col != '#0' else iid\\n sort_index.append((sort_value, iid))\\n\\n # sort the list\\n sort_index.sort(reverse=reverse)\\n\\n # move each node according to its index in the sort list\\n for index, (_, iid) in enumerate(sort_index):\\n tv.move(iid, parent, index)\\n\\n # sort each child node\\n sort(tv, col, parent=iid, reverse=reverse)\\n\\n # If this is the top level, reset the headings for reverse sort\\n if parent == '':\\n tv.heading(\\n col,\\n command=lambda col=col: sort(tv, col, reverse=not reverse)\\n )\",\n \"def sort():\\n return -1\",\n \"def reorder_columns(dataframe, col_name, position):\\r\\n temp_col = dataframe[col_name]\\r\\n dataframe = dataframe.drop(columns=[col_name])\\r\\n dataframe.insert(loc=position, column=col_name, value=temp_col)\\r\\n return dataframe\",\n \"def sort_animals(all_animals):\\n def get_key(a):\\n return a.row + 0.001 * a.col\\n\\n all_animals.sort(key=get_key)\",\n \"def sort(self, desc):\\n self.__sortByIndex(0, desc)\",\n \"def setColumn(self, column_number, column):\\n self.data[column_number] = column\\n return\",\n \"def canonicalize_column_index(self, line, col):\\n if col < 0:\\n col += self.col_lens[line] + 1\\n assert col >= 0\\n return col\",\n \"def sort_reg_time_column_in_descending_order(self):\\n self.set_jobs_grid_settings(self.reset_all_grid_settings)\\n try:\\n ok_button_element = self.wait(10).until(EC.presence_of_element_located(self.ok_button_locator), 'ok button locator not found before specified time out')\\n ok_button_element.click()\\n except:\\n pass\\n column_name_element = self.wait().until(EC.presence_of_element_located(self.reg_time_column_name_locator), 'reg time column name locator not found before specified time out')\\n self.hover(column_name_element)\\n self.script_executor_click(column_name_element)\\n self.click_element(self.sort_descending_option_locator)\",\n \"def sort_by(rows: [dict], col_name: str, rev: bool) -> [dict]:\\n def get_col(row):\\n return row[col_name]\\n return sorted(rows, key=get_col, reverse=rev)\",\n \"def SetColumnOrder(self, columns):\\n\\n # Validate the columns list.\\n # \\n # Since this information comes from a serialization\\n # and the badge levels may have changed\\n # We need to compensate for deleted/new columns.\\n hasWhinedOnce = False;\\n columnsToRemove = []\\n for column in columns:\\n if not column in self.__columns.keys():\\n if not hasWhinedOnce:\\n print \\\"WARNING: Grid columns have changed. Attempting to compensate.\\\"\\n print \\\" File->Preferences may contain hidden columns.\\\"\\n hasWhinedOnce = True\\n columnsToRemove.append(column)\\n for column in columnsToRemove:\\n columns.remove(column)\\n \\n # Use this (serialized) column order.\\n self.__colsKey = columns\\n for cKey in self.__columns.keys():\\n column = self.__columns[cKey]\\n if (column[FTable.__POSITION] != None):\\n column[FTable.__ATTR].IncRef()\\n column[FTable.__POSITION] = None\\n for i in range(len(self.__colsKey)):\\n cKey = self.__colsKey[i]\\n column = self.__columns[cKey]\\n column[FTable.__POSITION] = i\\n self.SetColSize(cKey, column[FTable.__SIZE])\\n self.__grid.SetColAttr(i, column[FTable.__ATTR])\",\n \"def sortByRecordNum(self, records, verbose=False, in_place=False):\\n tmp = records # copy of records for sorting\\n if (verbose):\\n print \\\"[EVT] Sorting by record number\\\"\\n\\n swapped = True\\n while swapped:\\n swapped = False\\n for i in xrange(len(tmp)-1):\\n ni = tmp[i].getField(\\\"recordNumber\\\")\\n nj = tmp[i+1].getField(\\\"recordNumber\\\")\\n if ni > nj:\\n t = tmp[i+1]\\n tmp[i+1] = tmp[i]\\n tmp[i] = t\\n swapped = True\\n return tmp\",\n \"def sortByIndex(self,_index,desc):\\n\\treturn self.sortByPostText(\\\"[%s]\\\"%_index,desc)\",\n \"def row_major_sort(dataframe):\\n t1_column = dataframe.columns.get_loc(\\\"t1\\\")\\n winding_column = dataframe.columns.get_loc(\\\"winding\\\")\\n t_array = np.flip(dataframe.iloc[:,t1_column:winding_column].values, axis=1)\\n n_ts = t_array.shape[1]\\n for column in range(n_ts):\\n sorted_args = np.argsort(t_array[:,column], kind = \\\"mergesort\\\")\\n dataframe = dataframe.iloc[sorted_args,:]\\n t_array = t_array[sorted_args]\\n return dataframe\",\n \"def order_columns(dataset, col_names, feature_to_predict):\\n col_names.remove(feature_to_predict)\\n col_names.sort()\\n\\n sorted_column_list = [feature_to_predict]\\n for col_name in col_names:\\n if col_name != feature_to_predict:\\n sorted_column_list.append(col_name)\\n\\n dataset = dataset.reindex(sorted_column_list, axis=1)\\n return dataset\",\n \"def _column_sorting_key(self, c):\\n first_index = 0\\n if c.startswith('hybrid'):\\n first_index = 1\\n elif c.startswith('solar'):\\n first_index = 2\\n elif c.startswith('wind'):\\n first_index = 3\\n elif c == MERGE_COLUMN:\\n first_index = -1\\n return first_index, self._hybrid_meta.columns.get_loc(c)\",\n \"def sort(self, key: Callable):\\n self.data.sort(key=key)\",\n \"def sort(self, key: Callable):\\n self.data.sort(key=key)\",\n \"def column_order(self):\\n return ((1, 2), (1, 0), (1, 1))\",\n \"def _sort_dataframe(self, dataframe):\\r\\n print('Not allowed')\",\n \"def _sort_rows(matrix, num_rows):\\n tmatrix = array_ops.transpose(matrix, [1, 0])\\n sorted_tmatrix = nn_ops.top_k(tmatrix, num_rows)[0]\\n return array_ops.transpose(sorted_tmatrix, [1, 0])\",\n \"def _sorting_order_rows(\\n self,\\n m,\\n byColumn=0,\\n usingCellValue=0,\\n descending=True,\\n file_name=None,\\n client_name=None):\\n\\n def _get_list_without_excluded_items(client_name, _keep_at_end):\\n\\n if _keep_at_end != []:\\n return [str(r[byColumn][usingCellValue].Value) + \\\" \\\" +\\n r.Member.Label for r in m if not r.Member.Label in \\\\\\n _keep_at_end]\\n\\n elif client_name is not None:\\n return [str(r[byColumn][usingCellValue].Value) + \\\" \\\" +\\n r.Member.Label for r in m if \\\\\\n r.Member.Label != client_name]\\n\\n else:\\n return [str(r[byColumn][usingCellValue].Value) +\\n \\\" \\\" + r.Member.Label for r in m]\\n\\n _keep_at_end = self._get_keep_at_end(file_name)\\n\\n _lst = _get_list_without_excluded_items(client_name, _keep_at_end)\\n\\n # reverse the list to make ascending order\\n if descending:\\n _lst_incl_cells = list(reversed(self._sorted_nicely(_lst)))\\n else:\\n _lst_incl_cells = list(self._sorted_nicely(_lst))\\n\\n return self._rank_position(_lst_incl_cells)\",\n \"def order_by(self, column, direction=\\\"ASC\\\"):\\n self._order_by += ((column, direction),)\\n return self\",\n \"def sort_dataset(self, identifier, columns, reversed, datastore):\\n # Get dataset. Raise exception if dataset is unknown\\n dataset = datastore.get_dataset(identifier)\\n if dataset is None:\\n raise ValueError(\\\"unknown dataset '{}'\\\".format(identifier))\\n # Sort the data frame on the specified columns.\\n df = dataset.to_dataframe()\\n df = vizual.sort_dataset(df=df, colids=columns, reversed=reversed)\\n # Store updated dataset to get new identifier.\\n ds = datastore.update_dataset(\\n origin=dataset,\\n df=df,\\n annotations=dataset.annotations\\n )\\n return VizualApiResult(ds)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6988205","0.6941003","0.688725","0.6766009","0.6726265","0.6554286","0.64665633","0.63434094","0.62454164","0.62412584","0.62091345","0.6206283","0.61986214","0.6197047","0.61965746","0.619618","0.61853695","0.6185017","0.61779505","0.61375666","0.61233777","0.6080344","0.603452","0.60091764","0.59979427","0.598492","0.59704196","0.596269","0.5917345","0.59060097","0.59037524","0.5901894","0.59016114","0.5881996","0.58689237","0.58520603","0.5834396","0.5819347","0.58084774","0.58032924","0.5780326","0.57759696","0.57666796","0.5757012","0.5742443","0.5682243","0.5648145","0.56294954","0.56218266","0.5615849","0.5559375","0.5559289","0.5550812","0.55232406","0.55101323","0.55065405","0.548511","0.54713565","0.5445692","0.54220694","0.54209673","0.5410091","0.5405058","0.53947794","0.53746855","0.5373299","0.53730494","0.53626716","0.5359953","0.5339048","0.5322181","0.52976334","0.52971363","0.5292541","0.52903426","0.52554816","0.52473676","0.52419305","0.52178276","0.5216362","0.520309","0.51934195","0.5182151","0.5177957","0.51696134","0.5166014","0.5119549","0.5119357","0.511474","0.51142585","0.51101","0.5109075","0.50972795","0.50947404","0.50947404","0.5087681","0.5070184","0.50523627","0.50341666","0.5028366","0.5005076"],"string":"[\n \"0.6988205\",\n \"0.6941003\",\n \"0.688725\",\n \"0.6766009\",\n \"0.6726265\",\n \"0.6554286\",\n \"0.64665633\",\n \"0.63434094\",\n \"0.62454164\",\n \"0.62412584\",\n \"0.62091345\",\n \"0.6206283\",\n \"0.61986214\",\n \"0.6197047\",\n \"0.61965746\",\n \"0.619618\",\n \"0.61853695\",\n \"0.6185017\",\n \"0.61779505\",\n \"0.61375666\",\n \"0.61233777\",\n \"0.6080344\",\n \"0.603452\",\n \"0.60091764\",\n \"0.59979427\",\n \"0.598492\",\n \"0.59704196\",\n \"0.596269\",\n \"0.5917345\",\n \"0.59060097\",\n \"0.59037524\",\n \"0.5901894\",\n \"0.59016114\",\n \"0.5881996\",\n \"0.58689237\",\n \"0.58520603\",\n \"0.5834396\",\n \"0.5819347\",\n \"0.58084774\",\n \"0.58032924\",\n \"0.5780326\",\n \"0.57759696\",\n \"0.57666796\",\n \"0.5757012\",\n \"0.5742443\",\n \"0.5682243\",\n \"0.5648145\",\n \"0.56294954\",\n \"0.56218266\",\n \"0.5615849\",\n \"0.5559375\",\n \"0.5559289\",\n \"0.5550812\",\n \"0.55232406\",\n \"0.55101323\",\n \"0.55065405\",\n \"0.548511\",\n \"0.54713565\",\n \"0.5445692\",\n \"0.54220694\",\n \"0.54209673\",\n \"0.5410091\",\n \"0.5405058\",\n \"0.53947794\",\n \"0.53746855\",\n \"0.5373299\",\n \"0.53730494\",\n \"0.53626716\",\n \"0.5359953\",\n \"0.5339048\",\n \"0.5322181\",\n \"0.52976334\",\n \"0.52971363\",\n \"0.5292541\",\n \"0.52903426\",\n \"0.52554816\",\n \"0.52473676\",\n \"0.52419305\",\n \"0.52178276\",\n \"0.5216362\",\n \"0.520309\",\n \"0.51934195\",\n \"0.5182151\",\n \"0.5177957\",\n \"0.51696134\",\n \"0.5166014\",\n \"0.5119549\",\n \"0.5119357\",\n \"0.511474\",\n \"0.51142585\",\n \"0.51101\",\n \"0.5109075\",\n \"0.50972795\",\n \"0.50947404\",\n \"0.50947404\",\n \"0.5087681\",\n \"0.5070184\",\n \"0.50523627\",\n \"0.50341666\",\n \"0.5028366\",\n \"0.5005076\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":83095,"cells":{"query":{"kind":"string","value":"Objective function, necessary for optimizing with optuna."},"document":{"kind":"string","value":"def objective(trial, \n bounds: Optional[Iterable]=None, \n func: Optional[Callable]=None, \n param_names: Optional[List[str]]=None):\n if param_names is None:\n param_names = PARAM_NAMES\n if (bounds is None):\n bounds = ((-10, 10) for _ in param_names)\n if not isinstance(bounds, dict):\n bounds = dict((p, (min(b), max(b))) \n for p, b in zip(param_names, bounds))\n if func is None:\n func = DEFAULT_METRIC_FUNC\n\n params = dict(\n (p, trial.suggest_float(p, bounds.get(p)[0], bounds.get(p)[1])) \n for p in param_names \n )\n # x = trial.suggest_float('x', -10, 10)\n return func((params[p] for p in param_names))"},"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 objective(self):\n pass","def objective(self, x):\n pass","def objective(self, x):\n pass","def objective_function(x):\n return x * 1 # change this to our actual function","def get_Objective(self):\r\n \r\n ans = 0\r\n for i in range(len(self.X.shape)):\r\n ans += self.parameter['phi'][i] * self.parameter['lambda1'] *norm(tl.unfold(self.Wlist[i], i), 'nuc') + (1 / self.parameter['m']) * norm(tl.unfold(self.X - self.Wlist[i] - self.V, i))\r\n\r\n # Augmented part is calculated seperately. \r\n augment_part1 = 0.5 * self.parameter['rho1'] * norm(self.V - self.T + self.F1)\r\n augment_part2 = 0.5 * self.parameter['rho2'] * norm(tl.fold(np.dot(self.Dmatrix, tl.unfold(self.T, 0)), 0, self.T.shape) - self.S + self.F2)\r\n\r\n # Combine the result for final objective function\r\n ans += self.parameter['beta1'] * norm(self.V.reshape(self.totaldim), 1) + self.parameter['beta2'] * norm(self.S.reshape(self.totaldim), 1) + augment_part1 + augment_part2 \r\n return ans","def objective(self, args: Dict[str, Any]) -> float:\n pass","def get_objective(self, X_v, U_v, X_last_p, U_last_p):\n objective = None\n return objective","def objective(args: Namespace, trial: optuna.trial._trial.Trial) -> float:\n # Paramters (to tune)\n args.embedding_dim = trial.suggest_int(\"embedding_dim\", 128, 512)\n args.num_filters = trial.suggest_int(\"num_filters\", 128, 512)\n args.hidden_dim = trial.suggest_int(\"hidden_dim\", 128, 512)\n args.dropout_p = trial.suggest_uniform(\"dropout_p\", 0.3, 0.8)\n args.lr = trial.suggest_loguniform(\"lr\", 5e-5, 5e-4)\n\n # Train (can move some of these outside for efficiency)\n logger.info(f\"\\nTrial {trial.number}:\")\n logger.info(json.dumps(trial.params, indent=2))\n artifacts = run(args=args, trial=trial)\n\n # Set additional attributes\n args = artifacts[\"args\"]\n performance = artifacts[\"performance\"]\n logger.info(json.dumps(performance[\"overall\"], indent=2))\n trial.set_user_attr(\"threshold\", args.threshold)\n trial.set_user_attr(\"precision\", performance[\"overall\"][\"precision\"])\n trial.set_user_attr(\"recall\", performance[\"overall\"][\"recall\"])\n trial.set_user_attr(\"f1\", performance[\"overall\"][\"f1\"])\n\n return performance[\"overall\"][\"f1\"]","def objective_function(self, y_true, y_predicted, X=None):","def add_objective(self): \n \n if \"CSS\" in self.algorithm:\n \n if self.num_hidden == 0:\n \n data_term = self.compute_energy(self.x, self.batch_size)\n \n else:\n \n data_term = self.compute_free_energy(self.x)\n \n normalizer_term = self.add_css_approximation(data_term)\n \n if \"CD\" in self.algorithm and self.num_hidden ==0:\n \n data_term = self.compute_energy(self.x, self.batch_size)\n \n normalizer_term = self.compute_energy(self.x_gibbs, \n self.batch_size)\n \n normalizer_term = -T.mean(normalizer_term)\n \n if \"CD\" in self.algorithm and self.num_hidden > 0:\n \n data_term = self.compute_free_energy(self.x)\n \n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\n \n normalizer_term = -T.mean(normalizer_term)\n \n # cost is negative log likelihood \n self.cost = T.mean(data_term) + normalizer_term","def objective(trial):\n # The parameters that we will calibrate the model for are shown here.\n # Optuna trial i\n BOD = trial.suggest_uniform(\"BOD\", 0, 1) #Review ranges here\n k_r = trial.suggest_uniform(\"k_r\", 0, 1) #Review Ranges here \n \n def ChLa(t):\n return 1 # Need to link to data\n\n def I(x):\n return 1 # Need to link to data\n\n K_z = 2 * 10**(-5) # p.51\n a = K_z\n k_b = 0.1 # Table 5\n th_b = 1.047 # Table 5\n k_r = 0.1 # Table 5\n YCHO2 = 0.0083 # Table 5\n th_p = 1.036 # Table 5\n th_s = 1.065 # Table 5\n th_r = 1.047 # Table 5\n\n def Temp(t):\n \"\"\"\n Function that maps time to temperature\n \"\"\"\n return 20 # Need to link to data\n\n def P_max(t):\n return 9.6 * 1.036 **(Temp(t) - 20) # Eq. 4\n\n def L_min(t):\n I = 1 # Need to link to PAR data\n K_1 = 0.687 * 1.086**(Temp(t) - 20)\n K_2 = 15\n return I * (1 + 2 * np.sqrt(K_1 / K_2)) / (I + K_1 + I**2 / K_2) # Eq. 5\n \n # f deals with sink and source terms \n def f(x, t):\n return -1 / YCHO2 * k_r * th_r**(Temp(t) - 20) * ChLa(t) + P_max(t) * L_min(t) * ChLa(t) - k_b * th_b**(Temp(t)-20) * BOD \n\n L = 200 # Length of domain\n dt = 1 / 48 # Mesh spacing in t\n F = a * dt # a * dt / dx**2\n T = 100 # Simulation time stop\n\n # Solving the PDE\n DO, x, t, _ = solver_FE_simple(I, a, f, L, dt, F, T)\n \n # Creating some bogus targets while database errors are happening\n DO_data = DO + np.random.random(len(DO))\n\n # Using mean squared error as the measure of fit, where we want\n # to minimize this number\n return ((DO - DO_data)**2).mean()","def objective(self, adjacency=None, R=None):\n raise NotImplementedError()","def objective(hyperparams): \n global iteration #necessary with a global variable because of implementation from hyperopt. \n iteration += 1\n\n result = run_model(hyperparams, iteration)\n loss = -result #transform to loss in order to minimize\n\n return {'loss': loss, 'hyperparams': hyperparams, 'iteration': iteration, 'status': STATUS_OK}","def objective(\n self,\n parameters: object\n ) -> float:\n pass","def objective(self, param):\n self.__init__(param, self.data)\n # return self.rmse() + self.penalty()\n return self.rmse() + self.penalty()","def _objective(self,output, expected,device):\r\n #\r\n #output = output.to(device = expected.device) #\r\n \r\n output = torch.where(output == 0,self.saf,output)\r\n #output = torch.where(output == 1,torch.tensor([0.999999],device = device),output)\r\n out1 = torch.mul(-expected,torch.log(output))\r\n #out2 = torch.mul(expected-1,torch.log(1 - output))\r\n #out = torch.add(out1,out2)\r\n return torch.sum(out1, dim = 1)","def objective(rp,n=5000,C=-2*10**11,a=300,b=1):\n l = log(rp)/n\n r = exp(l)\n rm1 = r-1\n return (rp-1)*((a-b*n)*rm1 + 1) - C*(rm1)*(rm1)\n #return rm1","def buildObjective(self):\r\n\r\n # self.z_prior might be the modified version\r\n self.L_elbo = T.mean(self.reconst + self.conditional_prior + self.w_prior + self.z_prior)\r\n\r\n self.L_elbo_modif = T.mean(self.reconst + self.conditional_prior + self.w_prior_modif + self.z_prior_modif)\r\n\r\n #---Getting model parameter---#\r\n cg = ComputationGraph(self.L_elbo)\r\n #self.phi_theta is the list of all the parameters in q and p.\r\n self.params = VariableFilter(roles=[PARAMETER])(cg.variables)","def objective(self):\n return self._objective","def _objective(self, trial, X, y, weights=None, split=None):\n\n # Generate even weights if none\n if weights is None:\n weights = pd.Series(np.ones(len(y)), index=y.index)\n else:\n weights = pd.Series(weights, index=y.index)\n\n # Execute trial function\n try:\n res = eval(self.function)\n except:\n raise RuntimeError(f\"Optuna execution error: {self.function}\")\n\n # If indicator result is tuple, select the one of interest\n if isinstance(res, tuple):\n res = res[self.idx]\n\n # Ensure result is a dataframe with same index as X\n res = pd.DataFrame(res, index=X.index)\n\n # If indicator result is dataframe, select the one of interest\n if len(res.columns) > 1:\n res = pd.DataFrame(res.iloc[:, self.idx])\n\n # y may be a subset of X, so reduce result to y and convert to series\n res_y = res.reindex(y.index).iloc[:, 0].replace([np.inf, -np.inf], np.nan)\n\n # Save all trial results for pruning and reporting\n # Only the best trial will eventually be saved to limit storage requirements\n self.res_y.append(res_y) # Save results\n\n # Indicator result may be all NANs based on parameter set\n # Return FALSE and alert\n if np.isnan(res_y).sum() / len(res_y) > .95: # Most or all NANs\n self.res_y_corr.append(np.zeros(len(y)))\n if split is not None:\n return tuple([False] * (len(split) - 1))\n else:\n return False\n\n # Obtain correlation for entire dataset\n if self.spearman:\n corr = _weighted_spearman(np.array(y), np.array(res_y), np.array(weights))\n else:\n corr = _weighted_pearson(np.array(y), np.array(res_y), np.array(weights))\n\n # Save correlation for res_y\n self.res_y_corr.append(corr)\n\n # Multi-objective optimization\n # Obtain correlation to target for each split for Optuna to maximize\n if split is not None:\n mo = []\n for i, e in enumerate(split):\n if i == 0:\n s = e\n continue\n\n # y could be a subset of X, use index of X to filter y\n idx = X[s:e].index\n\n # Filter y based on X split\n y_se = np.array(y[y.index.isin(idx)]).astype('float64')\n\n # Filter y predictions based on X split\n res_y_se = np.array(res_y[res_y.index.isin(idx)]).astype('float64')\n\n # Filter weights based on X split\n weights_se = np.array(weights[weights.index.isin(idx)]).astype('float64')\n\n if np.isnan(res_y_se).sum() / len(res_y_se) > .95:\n return tuple([False]*(len(split)-1))\n\n if self.spearman:\n mo.append(_weighted_spearman(y_se, res_y_se, weights_se))\n else:\n mo.append(_weighted_pearson(y_se, res_y_se, weights_se))\n s = e\n return tuple(mo)\n\n # Single objective optimization return corr for entire dataset\n else:\n return corr","def _l1m_objective(a,X,*args):\n \n return(np.sum(np.apply_along_axis(_euclidnorm,1,_diffmat_objective(a,X))))","def objective_function(params):\n\n\tenergy = 0 # Initialize the energy in 0\n\n\tqc = get_var_form(params) # Obtain a quantum circuit instance from the parameters\n\n\tfor key in pauli_weights.keys(): # Iterate over the pauli string in the Pauli weight\n\n\t\tmc, n_measures = measure_circuit_factory(key) # Obtain the measurement circuit from the Pauli string\n\t\tqc_final = qc.compose(mc) # Combine both circuits\n\n\t\t# Execute the quantum circuit to obtain the probability distribution associated with the current parameters\n\t\tt_qc = transpile(qc_final, backend)\n\t\tq_obj = assemble(t_qc, shots=NUM_SHOTS)\n\t\tcounts = backend.run(q_obj).result().get_counts(qc_final)\n\n\t\tdistribution = get_distribution(counts, n_measures) # Convert the measured counts into a probability vector\n\n\t\t# Weight each probability by the diagonal factor, them sum all of them, and later multiply by the Pauli Weight\n\t\tenergy += np.sum(distribution * generate_diagonal_factors(n_measures)) * pauli_weights[key]\n\n\tenergy_list.append(energy) # Append the new computed energy\n\n\t# Print the iteration of the VQE and the energy\n\tprint('Iteration {}, Energy: {:.4f}'.format(len(energy_list), energy))\n\n\treturn energy","def objective_function(num, x, fe_count, best):\n if num == 1:\n return sphere(x, fe_count, best)\n elif num == 2:\n return rastrigin(x, fe_count, best)\n elif num == 3:\n return rosenbrock(x, fe_count, best)\n elif num == 4:\n return schwefel(x, fe_count, best)\n elif num == 5:\n return quartic(x, fe_count, best)\n elif num == 6:\n return ackley(x, fe_count, best)\n elif num == 7:\n return schaffer(x, fe_count, best)\n elif num == 8:\n return griewank(x, fe_count, best)\n elif num == 9:\n return matyas(x, fe_count, best)\n elif num == 10:\n return trid(x, fe_count, best)\n else:\n pass","def _optfun(theta):\n return optfun(theta)[:2]","def objective(self) -> Optional[Union[int, float]]:\n if self.solution is not None:\n if isinstance(self.solution, list):\n return getattr(self.solution[-1], \"objective\", None)\n else:\n return getattr(self.solution, \"objective\", None)\n else:\n return None","def _optimize(self, objective):\n # Initial value\n initial = self.get_initial()[0]\n\n if self.vector_to_matrix_transform is not None:\n initial = self.vector_to_matrix_transform(initial)\n\n if self.solver_type is 'NelderMead' or self.solver_type is 'ParticleSwarm':\n initial = None\n\n # Create tensorflow variable\n if self.matrix_manifold_dimension is None:\n x_tf = tf.Variable(tf.zeros(self.dimension, dtype=tf.float64))\n else:\n x_tf = tf.Variable(tf.zeros([self.matrix_manifold_dimension, self.matrix_manifold_dimension], dtype=tf.float64))\n\n # Cost function for pymanopt\n def objective_fct(x):\n if self.matrix_to_vector_transform_tf is not None:\n # Reshape x from matrix to vector form to compute the objective function (tensorflow format)\n x = self.matrix_to_vector_transform_tf(x, self.matrix_manifold_dimension)\n return objective(x)[0]\n\n # Transform the cost function to tensorflow function\n cost = tf.py_function(objective_fct, [x_tf], tf.float64)\n\n # Gradient function for pymanopt\n def objective_grad(x):\n if self.matrix_to_vector_transform is not None:\n # Reshape x from matrix to vector form to compute the gradient\n x = self.matrix_to_vector_transform(x)\n\n # Compute the gradient\n grad = np.array(objective(x)[1])[0]\n\n if self.vector_to_matrix_transform is not None:\n # Reshape the gradient in matrix form for the optimization on the manifold\n grad = self.vector_to_matrix_transform(grad)\n return grad\n\n # Define pymanopt problem\n problem = pyman.Problem(manifold=self.manifold, cost=cost, egrad=objective_grad, arg=x_tf, verbosity=2)\n\n # Optimize the parameters of the problem\n opt_x, opt_log = self.solver.solve(problem, x=initial)\n\n if self.matrix_to_vector_transform is not None:\n # Reshape the optimum from matrix to vector form\n opt_x = self.matrix_to_vector_transform(opt_x)\n\n # Format the result to fit with GPflowOpt\n result = sc_opt.OptimizeResult(x=opt_x, fun=opt_log['final_values']['f(x)'], nit=opt_log['final_values']['iterations'], message=opt_log['stoppingreason'], success=True)\n\n return result","def relaxed_obj_u_opt_N_fixed(u, w_0, w, w_lin, eta_0, eta, eta_lin, T, H, L_lhs, alpha, B):\n tumor_dose = T.dot(u)\n OAR_doses = [Hi.dot(u) for Hi in H]\n linear = L_lhs.dot(u)\n relaxed_obj = alpha.T.dot(w_0) - w_0.T.dot(B*w_0) + (1/(2*eta_0))*(np.linalg.norm(\n w_0-tumor_dose))**2 + np.sum([(1/(2*eta[i]))*(np.linalg.norm(w[i]-OAR_doses[i]))**2 for i in range(len(H))]) + (np.linalg.norm(\n (np.sqrt(1/(2*eta_lin)))*(w_lin-linear)))**2\n return relaxed_obj","def rpt_objective(opt_objective):\n if opt_objective == \"lgst\": return \"logl\"\n else: return opt_objective","def objective_function(theta, X, y):\n # m number of training instances\n m = X.shape[0]\n jtheta = sum((np.dot(X, theta) - y)**2) / (2.0*m)\n return jtheta","def _update_objective(self):\n # rewrap the cost if the solver has been run\n self.Finalize()\n return","def optimizer(grad, method, init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N):\r\n\r\n\t\r\n\tif grad == 'NO':\r\n\t\tif method == 'Powell' :\r\n\t\t\tres = opt.minimize(Ulike,init_par, method = method,\r\n\t\t\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N))\r\n\t\t\treturn res.x, res.nit\r\n\t\telif method == 'Nelder-Mead':\r\n\t\t\tres = opt.minimize(Ulike,init_par, method = method,\r\n\t\t\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N),\r\n\t\t\t\t options = {'ftol': 0.0001})\r\n\t\t\treturn res.x, res.nit\r\n\t\telif method == 'default':\r\n\t\t\tres = opt.minimize(Ulike,init_par, \r\n\t\t\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N))\r\n\t\t\treturn res.x, res.nit\r\n\r\n\telif grad == 'YES':\r\n\t\tres = opt.minimize(Ulike, init_par, method = method, jac = stella_grad_full, \r\n \t\t\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N),\r\n\t\t\t options={'disp': True, 'maxiter': 4000, 'xtol': 1e-4})\r\n\t\treturn res.x, res.nit \r\n\t\t\t\r\n\t\t\r\n\telif grad == 'HESS':\r\n\t\tres = opt.minimize(Ulike, init_par, method = method, jac = stella_grad_full, hess = stella_hessian,\r\n\t\t\t\t\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N),\r\n\t\t\t\t\t options = {'disp': True, 'maxiter': 4000, 'xtol': 1.e-06}) \r\n\t\treturn res.x, res.nit","def optim_func(params, model):\n if model.model == 'ARD':\n model.alpha, model.beta = params\n lik = model.pruning_algorithm()\n\n else:\n model.alpha = params[0]\n lik = model.pruning_algorithm()\n \n return -lik","def addObjective(self, *args):\n return _libsbml.FbcModelPlugin_addObjective(self, *args)","def get_objective(X_t, xattr, Y_t, s):\n return eloglik(X_t, xattr, Y_t, s) - (s['KL']).sum()","def objective_function(params):\n cirq_circuit = variational_state_evolve(params)\n mean_value = self.expectation(\n all_qubits_in_circuit, cirq_circuit, hamiltonian)\n self._current_expectation = mean_value\n return mean_value","def objective_fn(samples):\n # TODO(chelseaf): This is the inefficient way to do it. Would be faster\n # to tile after the conv layers.\n cem_state = np.tile(np.expand_dims(state, 0), [samples.shape[0], 1, 1, 1])\n np_inputs = self._t2r_model.pack_features(cem_state,\n self._prev_episode_data,\n timestep, samples)\n q_values = self._predictor.predict(np_inputs)[prediction_key]\n if not self._prev_episode_data:\n q_values *= 0\n return q_values[0]","def objective(trial):\n %time\n env = gym.make('Delivery-v0')\n alpha = trial.suggest_discrete_uniform('alpha', 0.3,0.9,0.3)\n gamma = trial.suggest_discrete_uniform('gamma', 0.6, 1,0.1)\n epsilon = trial.suggest_discrete_uniform('epsilon', 0.01, 0.11, 0.04)\n episodes = 1000000\n \n # For plotting metrics\n all_epochs = []\n all_penalties = []\n rewards = []\n \n #Initialize Q table of 22500 x 8 size (22500 states and 8 actions) with all zeroes\n q_table = np.zeros([env.observation_space.n, env.action_space.n]) \n \n for i in range(1, episodes+1):\n state = env.reset()\n episode_rewards = []\n\n epochs, penalties, reward, = 0, 0, 0\n done = False\n\n while not done:\n if random.uniform(0, 1) < epsilon:\n action = env.action_space.sample() # Explore action space randomly\n else:\n action = np.argmax(q_table[state]) # Exploit learned values by choosing optimal values\n\n next_state, reward, done, info = env.step(action) \n\n old_value = q_table[state, action]\n next_max = np.max(q_table[next_state])\n\n new_value = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)\n q_table[state, action] = new_value\n\n if reward == -10:\n penalties += 1\n \n\n state = next_state\n episode_rewards.append(reward)\n epochs += 1\n \n if done == True:\n break \n if epochs == 1000:\n break \n rewards.append(np.sum(episode_rewards))\n \n last_reward = np.mean(rewards)\n # trial.report(-1 * last_reward)\n\n return -1 * last_reward","def objective(self, objective):\n\n self._objective = objective","def objective(beta, lambdat, X, y):\n return 1/len(y) * (np.sum(\n (np.maximum(0, 1-((y[:, np.newaxis]*X).dot(beta)))**2)))\\\n + lambdat * np.linalg.norm(beta)**2","def propose_optimize():\n pass","def objective_function(self, variables, mask, annuity_scalar=1):\n ice_gen = variables['ice_gen']\n self.costs = {'ice_fuel': cvx.sum(cvx.multiply(self.efficiency * self.fuel_cost * self.dt * annuity_scalar, variables['ice_gen'])),\n 'ice_fixed': self.fixed_om * annuity_scalar,\n 'ice_variable': cvx.sum(cvx.multiply(self.vari_om * self.dt * annuity_scalar, ice_gen)),\n 'ice_ccost': self.capital_cost * self.n + self.ccost_kw * self.rated_power * self.n\n }\n\n return self.costs","def _objective_cost(self):\n\n def obj_expression_simple(model):\n total = model.A_total + model.A2_total + model.A3_total + \\\n model.A4_total\n return -total\n\n def obj_expression(model):\n total = model.A_total + model.A2_total + model.A3_total + \\\n model.A4_total\n total += model.Completion_total\n total += model.Affinity_cognitive_total\n total += model.CTu_total + model.CTl_total + model.S_total\n return -total\n\n # self.model.exp_cost = Expression(rule=obj_expression)\n # self.model.obj_cost = Objective(rule=self.model.exp_cost)\n # self.model.obj_cost = Objective(rule=obj_expression_simple)\n self.model.obj_cost = Objective(rule=obj_expression)","def optimize(opt, target, n_agents, n_variables, n_iterations, lb, ub, hyperparams):\n\n # Creating the SearchSpace\n space = SearchSpace(n_agents=n_agents, n_variables=n_variables,\n n_iterations=n_iterations, lower_bound=lb, upper_bound=ub)\n\n # Creating the Function\n function = Function(pointer=target)\n\n # Creating Optimizer\n if opt.__name__ is not 'BH':\n optimizer = opt(hyperparams=hyperparams)\n else:\n optimizer = opt()\n\n # Creating the optimization task\n task = Opytimizer(space=space, optimizer=optimizer, function=function)\n\n return task.start(store_best_only=True)","def objective(self,w):\n diffs = self.get_y_times_diffs(self.get_split_weights(w))\n #print diffs, sigmoid(diffs)\n obj = -np.sum(np.log(sigmoid(diffs))) #negative, since minimising\n # regularisation\n obj += 0.5 * self.alpha * np.dot(w[:self.interp_index[0]], w[:self.interp_index[0]])\n return obj","def slo(self, objective=99.99):\n self.objective = objective\n return objective","def objective(params):\n\t# hyperopt casts as float\n\tparams['num_boost_round'] = int(params['num_boost_round'])\n\tparams['num_leaves'] = int(params['num_leaves'])\n\n\t# need to be passed as parameter\n\tparams['is_unbalance'] = True\n\tparams['verbose'] = -1\n\tparams['seed'] = 1\n\n\tcv_result = lgb.cv(\n\t\tparams,\n\t\tdtrain,\n\t\tnum_boost_round=params['num_boost_round'],\n\t\tmetrics='binary_logloss',\n\t\tnfold=3,\n\t\tearly_stopping_rounds=20,\n\t\tstratified=False)\n\tearly_stop_dict[objective.i] = len(cv_result['binary_logloss-mean'])\n\terror = round(cv_result['binary_logloss-mean'][-1], 4)\n\tobjective.i+=1\n\treturn error","def obj_u_opt_N_fixed(u, T, alpha, B):\n x = T.dot(u)\n return alpha.T.dot(x) - x.T.dot(B*x)","def _objective_function(self, thetas, X, Y):\n \n # Convert thetas vector to form total_cost can understand\n thetas = self.reshape_thetas(thetas, 'list')\n self.thetas = thetas\n \n # Get cost function value\n fval = self.total_cost(X, Y, thetas)\n \n # Get derivatives using back propagation\n Deltas = self.get_gradients(X, Y)\n dfval = self.reshape_thetas(Deltas, 'vector')\n \n return fval, dfval","def objective_func(self, topology, grad_func, tmax, eta):\n f = objective_function_numpy\n x_func = partial(self._optimize_form, topology=topology, tmax=tmax, eta=eta)\n return partial(f, x_func=x_func, grad_func=grad_func)","def define_objective(m):\r\n\r\n # Dual objective function\r\n m.OBJECTIVE = Objective(expr=m.DUAL_OBJECTIVE_EXPRESSION, sense=maximize)\r\n\r\n return m","def cost_function(self, u):\n # enforce that the input is a numpy array\n u = np.asarray(u)\n\n # Reshape the control input to (mxT). Vector input is required for some optimization libraries\n T = int(len(u)/2)\n u = u.reshape((2,T))\n\n J = - self.rho(u)\n\n return J","def maximize(self, budget, optimizer):\n\n\t\tpass","def _objective(self, beta, y, mask=None):\n # if \"mask\" vector isn't already computed, compute it\n mask = 1 - y*self.X_train.dot(beta) if mask is None else mask\n\n if self.large:\n # more efficient for larger feature matrices\n empirical = 0\n for i in range(self.n):\n if mask[i] >= -self.h:\n if mask[i] > +self.h:\n empirical += mask[i]\n else:\n empirical += (mask[i] + self.h)**2/(4*self.h)\n empirical /= self.n\n\n else:\n # more efficient for smaller feature matrices\n empirical = np.zeros(self.n)\n piecewise_2 = np.where(abs(mask) <= self.h)\n piecewise_3 = np.where(mask > self.h)\n empirical[piecewise_2] = (mask[piecewise_2] + self.h)**2/(4*self.h)\n empirical[piecewise_3] = mask[piecewise_3]\n empirical = np.mean(empirical)\n\n regularization = self.lambduh*np.linalg.norm(beta)**2\n return empirical + regularization, mask","def run_qae_optimization(training_states, n_repetitions, exact=no_noise, noisy=gate_error):\n result_list = []\n def proxy(params, training_states, n_repetitions, exact=no_noise, noisy=gate_error):\n \"\"\"Embedded function version\n \"\"\"\n input_list = fix_list(params, all_param_array=all_param, var_param_array=var_param, fixed_vals_array=fixed_vals)\n fidelities = []\n for training_state in training_states:\n fid = cusp_stage2.compute_stage2_cost_function(*input_list, alpha=training_state, n_repetitions=n_repetitions,\n exact=exact, noisy=noisy)\n fidelities.append(fid)\n avg_fid = np.mean(fidelities)\n result_list.append(1-avg_fid)\n print(1-avg_fid)\n return 1. - avg_fid\n\n \n # Initialize parameters\n half_turn_min = 0\n half_turn_max = 2\n init_params = np.random.uniform(low=half_turn_min, high=half_turn_max,\n size=num_param)\n\n # Optimization using Nelder-Mead.\n h2_qae_wrap = lambda params: proxy(params, training_states=training_states,\n n_repetitions=n_repetitions, exact=exact, noisy=noisy)\n \n if noisy:\n maxiter = 60\n else:\n maxiter = None\n \n res = minimize(h2_qae_wrap, init_params, args=(),\n method='Nelder-Mead', tol=None, \n options={'disp': False, 'maxiter': maxiter, 'xatol': 0.001,\n 'return_all': False, 'fatol': 0.001})\n np.savetxt('stage2_data.csv',result_list, delimiter=',')\n return res.x","def custom_method(*args, **kwargs):\n return objective_return","def custom_method(*args, **kwargs):\n return objective_return","def get_objective(self):\n self.objective = 0\n for r in self.routes:\n r.update_route(self.vrpdata)\n self.objective += r.distance\n # all() returns True if all elements of the iterable are true\n self.solutionValid = (all([r.tourValid for r in self.routes]) and len(self.routes) <= self.vrpdata.MaxNumVeh)\n if self.solutionValid:\n return self.objective\n return -1","def optofitness(op_array, n_obj=1):\n d = 5\n f_speed = f1(op_array[1], df.v_flock, df.v_tol)\n f_coll = f3(op_array[3], df.a_tol)\n f_disc = f3(op_array[4], df.num_agents / 5)\n f_wall = f2(op_array[0], df.r_tol)\n f_cluster = f1(op_array[5], df.num_agents / 5, df.num_agents / 5)\n if op_array[2] > 0:\n f_corr = op_array[2]\n else:\n f_corr = 0\n time_fit = 1 # (1-sigmoid_decay(op_array[6], df.max_sim_time-df.wait_time, 200))\n if n_obj == 2:\n # F1 = -time_fit * f_speed * f_corr * f_disc * f_cluster\n # F2 = -time_fit * f_wall * f_coll\n F2 = -time_fit *f_coll * f_corr * f_disc * f_cluster\n F1 = -time_fit * f_wall * f_speed\n return round(F1, d), round(F2, d)\n\n elif n_obj == 3:\n F1 = -time_fit * f_speed * f_corr * f_disc * f_cluster\n F2 = -time_fit * f_wall\n F3 = -time_fit * f_coll\n return round(F1, d), round(F2, d), round(F3, d)\n elif n_obj == 'all':\n return round(f_wall, d), round(f_speed, d), round(f_corr, d), round(f_coll, d), round(f_disc, d), round(f_cluster, d)\n F1 = -time_fit * f_speed * f_coll * f_disc * f_wall * f_corr * f_cluster\n return round(F1, d)","def update_op(self, loss, learning_rate,var):\n #train_op = None\n ####### Implementation Here ######\n #pass\n train_op = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(loss = loss,var_list = var )\n return train_op","def optimization_step(self):\n \n if \"CSS\" in self.algorithm:\n \n input_dict = {self.x: self.train_inputs[self.minibatch_set,:]}\n \n var_list = [self.x_tilda, self.minibatch_set]\n \n if (self.num_samples > 0) and (not self.mixture):\n \n if ((self.mf_steps > 0) and self.alpha >0) or\\\n self.gibbs_steps > 0: \n \n var_list.append(self.sampler_theta)\n \n elif \"CD\" in self.algorithm:\n \n input_dict = {self.x : self.train_inputs[self.minibatch_set,:]} \n \n var_list = [self.minibatch_set]\n \n var_list.append(self.learning_rate)\n \n if self.use_momentum:\n \n var_list.append(self.momentum)\n \n output_vars = [self.pseudo_cost]\n \n if self.report_p_tilda:\n \n output_vars.append(self.p_tilda)\n \n else:\n \n output_vars.append(theano.shared(0))\n \n opt_step = theano.function(inputs = var_list,\n outputs = output_vars,\n updates = self.updates,\n givens = input_dict,\n on_unused_input='warn')\n \n return opt_step","def qp(self, eta_u, eta_v):\n z = 1\n v = np.array(eta_u)\n n_features = v.shape[0]\n u = np.sort(v)[::-1]\n cssv = np.cumsum(u) - z\n ind = np.arange(n_features) + 1\n cond = u - cssv / ind > 0\n rho = ind[cond][-1]\n theta = cssv[cond][-1] / float(rho)\n uu = np.maximum(v - theta, 0)\n vv = np.array(())\n return uu, vv","def objective_function(self, flow_values):\n\n objective_value = -1.0 * np.dot(self.turbine_params, flow_values)\n\n return objective_value","def _objective_switching(self):\n # FIXME(cathywu) consider removing, not currently used\n\n def obj_expression(model):\n total = 0\n for j in range(1, self.num_tasks + 1):\n for i in range(1, self.num_timeslots):\n total += abs(model.A[i, j] - model.A[i + 1, j])\n return total\n\n # TODO objective with multiple parts\n self.model.exp_switching = Expression(rule=obj_expression)","def set_objective_fn(self, objective_fn):\n self.objective_fn = objective_fn","def objective(self, x):\n rvs = frozenset(map(frozenset, self._rvs))\n joint = self.construct_joint(x)\n joint = joint.sum(axis=self._others, keepdims=True)\n crv = joint.sum(axis=tuple(flatten(rvs)))\n\n H_crv = h(crv.ravel())\n H = h(joint.ravel()) - H_crv\n\n def I_P(part):\n margs = [ joint.sum(axis=tuple(flatten(rvs - p))) for p in part ]\n a = sum(h(marg.ravel()) - H_crv for marg in margs)\n return (a - H)/(len(part) - 1)\n\n parts = [p for p in partitions(map(frozenset, rvs)) if len(p) > 1]\n\n caekl = min(I_P(p) for p in parts)\n\n return caekl","def objective(self, var: ndarray) -> float:\n beta, gamma = self.get_vars(var)\n r = self.get_residual(beta)\n d = self.get_varmat(gamma)\n\n val = 0.5*(d.logdet() + r.dot(d.invdot(r)))\n val += self.fevar.prior_objective(beta)\n val += self.revar.prior_objective(gamma)\n\n return val","def scipy_minus_objective(w,all_vector_graphs,all_correct_rows,\\\n all_batches,sigma=None,perceptron=None):\n if perceptron:\n perceptron._obj_iter += 1\n obj = 0.0\n index = 0\n for vector_graphs,correct_rows,batches in zip(all_vector_graphs,all_correct_rows,all_batches):\n all_scores = vector_graphs * w\n sum_log_Z = 0.0\n for batch in batches:\n batch_scores = all_scores[batch]\n sum_log_Z += logsumexp(batch_scores) #np.log(np.exp(batch_scores).sum())\n obj += all_scores[correct_rows].sum() - sum_log_Z\n index += 1\n if index % 100 == 0:\n print('Objective '+str(index)+' processed')\n obj = obj / len(all_vector_graphs)\n if sigma != None:\n obj += - 0.5 * sigma * (w * w).sum()\n print('Objective:'+str(obj))\n return -1.0 * obj","def best_policy(self, U):\n pi = np.zeros((self.nstates),np.int)\n for s in range(self.nstates):\n pi[s] = self.best_action(s,U)\n return pi","def objective(params):\n param0, param1, param2 = params\n model = Model(param0, param1, param3)\n\n train_loss = train(model)\n val_loss = validate(model)\n\n return val_loss","def _objective_decorator(func):\n def inner(preds, dmatrix):\n \"\"\"internal function\"\"\"\n labels = dmatrix.get_label()\n return func(labels, preds)\n return inner","def objective(self, trial):\n # Suggest start row IDs\n selected = [self.all_df.index.max()]\n for i in range(self.n_points):\n id_min = 3 * (self.n_points - len(selected) + 1)\n id_max = selected[-1] - 3\n if id_min + 3 > id_max:\n return np.inf\n new = trial.suggest_int(str(i), id_min, id_max)\n selected.append(new)\n start_ids = sorted(selected)[:-1]\n # Create subsets\n subsets = self.create_subsets(start_ids)\n # Curve fitting for each subset\n df_list = [self.curve_fit(df, num)\n for (num, df) in enumerate(subsets, start=1)]\n # Calculate the error\n return self.error_f(df_list)","def objective(self, params):\n \n model_params = dict()\n training_params = dict()\n for param_name in self.model_param_names:\n model_params[param_name] = params[param_name]\n for param_name in self.training_param_names:\n training_params[param_name] = params[param_name]\n \n copy = self.m['model']\n self.m['model'] = self.m['model'](**model_params)\n self.m.update(training_params)\n \n model = Model(**self.m)\n data = self.datasets[model.data_type]\n\n start = timer()\n res, full_res = CV_fit(model, data, self.datasets)\n run_time = timer()-start\n\n loss = res[0]\n self.m['model'] = copy\n \n return {'loss': loss, 'params': params, 'run_time': run_time, 'status': STATUS_OK}","def _optimize(self, objective):\n points = self._get_eval_points()\n\n if self.matrix_to_vector_transform is not None:\n # Transform the sampled matrix points in vectors\n points = np.array([self.matrix_to_vector_transform(points[i]) for i in range(self._nb_samples)])\n\n evaluations = objective(points)\n idx_best = np.argmin(evaluations, axis=0)\n\n return sc_opt.OptimizeResult(x=points[idx_best, :], success=True, fun=evaluations[idx_best, :],\n nfev=points.shape[0], message=\"OK\")","def penalty_objective(Z_0, Z_1, Z_2, psi, theta):\n return theta * sum(map(l1_od_norm, Z_0)) + (1 - theta) * sum(map(psi, Z_2 - Z_1))","def min_scalar(objective, **kwargs):\n result = minimize_scalar(objective, **kwargs)\n return result.fun","def obj_u_opt_N_opt(u, T, alpha, B, N, num_tumor_voxels, Td = 2):\n x = T.dot(u)\n alpha_tilde = alpha #np.repeat(N, num_tumor_voxels)*alpha\n B_tilde = B #np.repeat(N, num_tumor_voxels)*B\n #Note that all modalities must have the same number of tumor voxels:\n return alpha_tilde.T.dot(x) - x.T.dot(B_tilde*x) + num_tumor_voxels*(np.sum(N)-1)*(np.log(2)/Td)","def uniformCostSearch(problem):\n \"*** YOUR CODE HERE IF YOU WANT TO PRACTICE ***\"\n aStarSearch(problem)","def compute_likelihood_given(O_a, skills, beta, g, theta, vals, Zs, povs):\n O_os, P_os = gen_option_trajectories(O_a, skills, beta, g, theta, vals, Zs, povs) #Generate all option-trajectories\n if povs != []:\n rewards, dests, discounts, _, _ = povs\n probs = []\n for index, O_o in enumerate(O_os): #For option-trajectory in option-trajectories\n state = O_a[0] #Initial state\n prob = 0\n for o_index, o in enumerate(O_o): #For option in option-trajectory (either state to go to or [goal, beta_k] pair\n #Now compute the prob of choosing the option o that was actually chosen\n if type(o) != list and o == theta:\n to_add = np.exp(beta*20) / Zs[state]\n state = o\n elif type(o) != list:\n to_add = np.exp(beta*(-1 + g*vals[o])) / Zs[state]\n state = o\n else: #Compound option\n Q = (rewards[tuple([state, o_dict[o[0]]])] +\n vals[dests[tuple([state, o_dict[o[0]]])]]*discounts[tuple([state, o_dict[o[0]]])])\n # out = option(state, o[0], o[1], g, povs[3][:, o_dict[o[0]]], povs[4][:, o_dict[o[0]]], 10) #Gives samples, discounted rewards, lengths\n # to_add = np.exp(beta*(out[1] + (pow(g,out[2]))*vals[o[0]])) / Zs[state]\n to_add = np.exp(beta*Q) / Zs[state]\n state = o[0]\n if prob == 0:\n prob = to_add\n else:\n prob *= to_add\n if prob == 0:\n print O_a\n print '??'\n probs.append(prob)\n return np.sum(probs)","def cost_fun(x, problem):\n j = 0\n if problem['use_log_bar']:\n c = ineqconstr(x, problem)\n j += np.sum(logbarrierfunc(0.1, c, problem['use_sigma']))\n\n x, t_final = matrify(x, problem)\n if problem['T']!=0:\n j += np.sum([problem['cost_fun_single'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])\n else:\n j = t_final\n return j","def evaluate_objective(x):\n\n x_points_cartesian = x_to_cartesian(x)\n hull = ConvexHull(x_points_cartesian)\n\n # Return the negative value because the optimization is a minimization\n return -hull.volume","def _SD_optimal(t):","def _optfn(self, x):\n\n logger.debug(\" optfn(theta=%s)\", str(x))\n\n wmx = max(self.weights) * self.weighttrunc\n\n ip = []\n for i,w in enumerate(self.weights):\n if w < wmx:\n continue\n ip.append((i,w,x))\n\n if self.pool is None:\n itr = map(self.worker.loglik_grad, ip)\n else:\n itr = self.pool.imap_unordered(_pool_loglik_grad, ip, 10)\n\n if self._prior_shape is None:\n ll = 0.\n grad = np.zeros(len(x))\n else:\n ll = sum(sp.special.xlogy(self._prior_shape-1,x)-(x/self._prior_scale))\n grad = (self._prior_shape - 1)/x - 1/self._prior_scale\n\n for l,g in itr:\n ll += l\n grad += g\n\n logger.debug(\" optfn=%g\", ll)\n\n return -ll, -grad","def objective(xs, fps, fns):\n Wp = - np.log(fpr / (1 - fpr)) # Weight for false positives\n Wn = - np.log(fnr / (1 - fnr)) # Weight for false negatives\n Wx = - np.log(f / (1 - f)) # Weight for all positives\n return np.sum(xs) * Wx + np.sum(fps) * Wp + np.sum(fns) * Wn","def optimize_qaoa(self, x: tuple, *args: tuple) -> float:\n if args:\n rho = self.simulate_qaoa(\n params=x,\n with_noise=args[0]\n )\n else:\n rho = self.simulate_qaoa(\n params=x\n )\n return numpy.trace(self.cost * rho).real","def costFun(self, S, x):","def objective(self, x):\n rvs = self._rvs\n joint = self.construct_joint(x)\n joint = joint.sum(axis=self._others, keepdims=True)\n margs = [ joint.sum(axis=tuple(rv)) for rv in rvs ]\n crv = joint.sum(axis=tuple(flatten(rvs)))\n\n a = sum(h(marg.ravel()) for marg in margs)\n b = h(joint.ravel())\n c = h(crv.ravel())\n\n dtc = a - (len(rvs) - 1)*b - c\n\n return dtc","def objective(data: VLEPoints, params: typing.List[float]) -> float:\n error = 0\n mixture = Mixture(\n name=\"\",\n first_component=data.components[0],\n second_component=data.components[1],\n uniquac_params=UNIQUACParameters.from_array(params),\n )\n for point in data:\n error += (get_partial_pressures(temperature=point.temperature,\n composition=point.composition,\n mixture=mixture,\n calculation_type=\"UNIQUAC\",\n )[0] - point.pressures[0]) ** 2 \\\n + (get_partial_pressures(temperature=point.temperature,\n composition=point.composition,\n mixture=mixture,\n calculation_type=\"UNIQUAC\",\n )[1] - point.pressures[1]) ** 2\n return numpy.sqrt(error / len(data))","def optimize(self):\n optimizer = tf.train.RMSPropOptimizer(self.config.learning_rate)\n return optimizer.minimize(self.cost)","def cost_function(H, n_qubits, p, params):\n ini_state=plus_state(n_qubits)\n for i in range(p):\n ini_state=qaoa_step(ini_state,H,n_qubits,params=[params[2*i],params[2*i+1]])\n return ((sparse.spmatrix.getH(ini_state)).dot(H.dot(ini_state))).real, ini_state","def solver(u_init, eta_0, eta, eta_lin, T, H, L_lhs, L_rhs, alpha, gamma, B, D, C, ftol = 1e-3, max_iter = 5000, verbose = 0, nnls_max_iter=30):\n\n # Raise('NotImplementedError: only adjusted the arguments.')\n #Need to incorporate L_lhs into stacked and appropriate w_lin updates, u_update and eta_lin increments\n #precompute the expensive operation:\n lin_penalties = 1/np.sqrt(2*eta_lin)\n eta_T_H_L_stacked = scipy.sparse.vstack([T.multiply(1/np.sqrt(2*eta_0))] + [H[i].multiply(1/np.sqrt(2*eta[i])) for i in range(len(H))] + [L_lhs.multiply(lin_penalties[:,None])])\n #!!!!\n# premultiplied_lhs = eta_T_H_stacked.T.dot(eta_T_H_stacked).toarray()\n #!!!!\n u_prev = u_init + 1\n u = u_init\n count = 0\n obj_history = []\n relaxed_obj_history = [-1, 0.1] #just two initial values to enter the loop\n while np.abs((relaxed_obj_history[-2] - relaxed_obj_history[-1])/relaxed_obj_history[-2]) > ftol and count < max_iter:#np.linalg.norm(u - u_prev, np.inf) > 1e-3 and count < max_iter: #Maybe all of them stop changing\n start = time.time()\n \n u_prev = np.copy(u)\n w_0 = w_0_update(eta_0, u, T, alpha, B) \n w = w_update(u, H, gamma, D, C) \n w_lin = w_lin_update(u, L_lhs, L_rhs)\n# u = u_update(eta_0, eta, w_0, w, eta_T_H_stacked, nnls_max_iter=50)\n #!!!!\n # u = u_update(eta_0, eta, w_0, w, eta_T_H_L_stacked, nnls_max_iter=30)\n u = u_update(eta_0, eta, eta_lin, w_0, w, w_lin, eta_T_H_L_stacked, premultiplied_lhs = None, nnls_max_iter=nnls_max_iter)\n #!!!!\n count += 1 \n if count == 10:\n u_inf = np.copy(u)\n w_0_inf = w_0[:]\n w_inf = w[:]\n w_lin_inf = w_lin[:]\n if count > 10 and np.abs(cur_obj) > 1e+15: #HANDLE THIS BETTER!!!\n print('INFINITY! RETURNING u at the 10-th iteration to enter the feasibility loop')\n return u_inf, w_0_inf, w_inf, w_lin_inf, obj_history, relaxed_obj_history\n \n cur_obj = obj_u_opt_N_fixed(u, T, alpha, B)\n obj_history.append(cur_obj)\n cur_relaxed_obj = relaxed_obj_u_opt_N_fixed(u, w_0, w, w_lin, eta_0, eta, eta_lin, T, H, L_lhs, alpha, B)\n # relaxed_obj_u_opt_N_fixed(u, w_0, w, eta_0, eta, T, H, alpha, B)\n relaxed_obj_history.append(cur_relaxed_obj) \n \n stop = time.time()\n duration = stop-start\n \n if count%1 == 0 and verbose: \n stopping_criterion = np.abs((relaxed_obj_history[-2] - relaxed_obj_history[-1])/relaxed_obj_history[-2])\n print(' iter = {}, stopping criterion:{}, OBJ {}'.format(count, stopping_criterion, cur_obj))\n print(' This iteration took: {}'.format(duration))\n return u, w_0, w, w_lin, obj_history, relaxed_obj_history","def arg_min_scalar(objective, **kwargs):\n return minimize_scalar(objective, **kwargs).x","def buildTheta():\n mylambda = 0.\n initial_theta = np.zeros((X.shape[1],1)).reshape(-1)\n Theta = np.zeros((10,X.shape[1]))\n for i in range(10):\n iclass = i if i else 10 #class \"10\" corresponds to handwritten zero\n logic_Y = np.array([1 if x == iclass else 0 for x in y])#.reshape((X.shape[0],1))\n itheta, imincost = optimizeTheta(initial_theta,X,logic_Y,mylambda)\n Theta[i,:] = itheta\n return Theta","def _optimise(self):\n pass","def compute_objective(X, C):\n # similar as mse\n\n acc = 0\n for x in range(len(X)):\n acc += (nearest_distance(X.iloc[x], C)) ** 2\n\n return acc","def return_lu_func(RunningCost='Minimize Input Energy'):\n if type(RunningCost)==str:\n assert RunningCost in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\"\n else:\n assert type(RunningCost)==list, \"RunningCost must be a list of cost types.\"\n for el in RunningCost:\n assert type(el)==str, \"Each element of RunningCost must be a string. Not \" + str(type(el)) + \".\"\n assert el in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\" + el + \"' not accepted.\"\n\n if \"Minimize Input Energy\" in RunningCost:\n result1 = lambda X,U,dt: np.matrix([[k3*U*dt]])\n else:\n result1 = lambda X,U,dt: np.matrix([[0]])\n\n if \"Minimize time away from target angle\" in RunningCost:\n result2 = lambda X,U,dt: np.matrix([[0]])\n else:\n result2 = lambda X,U,dt: np.matrix([[0]])\n\n if \"Minimize time away from target angular velocity\" in RunningCost:\n result3 = lambda X,U,dt: np.matrix([[0]])\n else:\n result3 = lambda X,U,dt: np.matrix([[0]])\n\n result = lambda X,U,dt: result1(X,U,dt) \\\n + result2(X,U,dt) \\\n + result3(X,U,dt)\n return(result)","def compute_surr_objective(A_hat, old_policy_model, policy_model, j, state_part_mean, state_part_std, config=PPO_config):\n \n def clip(c, epsilon):\n if c < 1-epsilon:\n return 1- epsilon\n elif c > 1+epsilon:\n return 1+ epsilon\n else:\n return c\n \n clipping_param = np.amax([0.01, (1-j/config['J'])*config['clipping']])\n print(\"Clipping parameters: {}\".format(clipping_param))\n #K = PPO_config['K']\n #assert(K==len(A_hat))\n policy_model.to(device)\n old_policy_model.to(device)\n surr_objective = 0\n kl_est_list = []\n for k in range(len(A_hat)):\n for i in range(len(A_hat[k])):\n # CHECK: if the action is an integer corresponding to the state\n curr_state = torch.tensor(A_hat[k][i][0]).float()\n try:\n curr_state_norm_input = torch.cat((curr_state[:,0].unsqueeze(1), (curr_state[:,1:]-state_part_mean)/state_part_std))\n except:\n curr_state_norm_input = torch.cat((curr_state[0].unsqueeze(0), (curr_state[1:]-state_part_mean)/state_part_std))\n\n curr_state_norm_input[np.isnan(curr_state_norm_input)] = 0\n curr_state_norm_input.to(device)\n curr_action = A_hat[k][i][1]\n\n r_theta_ksi = policy_model(curr_state_norm_input.to(device))[curr_action] / old_policy_model(curr_state_norm_input.to(device))[curr_action] \n r_theta_ksi_num = r_theta_ksi.cpu().detach().numpy()\n single_est_from_r = r_theta_ksi_num-1-np.log(r_theta_ksi_num)\n kl_est_list.append(single_est_from_r)\n A_hat_ksi = A_hat[k][i][2] # Estimate the A_hat\n surr_objective += min(r_theta_ksi * A_hat_ksi, clip(r_theta_ksi, clipping_param) * A_hat_ksi)\n surr_objective /= len(A_hat)\n kl_est = np.mean(np.asarray(kl_est_list))\n\n return surr_objective, kl_est","def u_update(eta_0, eta, eta_lin, w_0, w, w_lin, eta_T_H_L_stacked, premultiplied_lhs = None, nnls_max_iter=50): \n # PREMULTIPLIED LHS IS AN EXTRA ARGUMENT! Set it to None and add solver! \n \"\"\"In the following +[[]] and [:-1] are added to keep thing 1dim array of objects and still multiply it elemtwisely\"\"\" \n# #B.append([]) #THIS IS WRONG, CHANGES THE LIST \n# B_concat = np.concatenate((1/np.sqrt(2*eta))*np.array(B+[[]])[:-1], axis = 0) \n# A_ls = np.concatenate([(1/np.sqrt(2*eta0))*A, B_concat], axis = 0) \n# #print(np.array(B).shape) \n# #print(w[0].shape) \n# #print(w, eta) \n# #w.append([]) THIS IS WRONG, CHANGES THE LIST \n# w_concat = np.concatenate((1/np.sqrt(2*eta))*np.array(w+[[]])[:-1], axis = 0) #[:-1] Added as a hack to keep it one-dim array of objects \n# eta_w = np.expand_dims(1/np.sqrt(2*eta),1)*np.array(w) \n# print(eta_w.shape) \n# b_ls = np.concatenate([(1/np.sqrt(2*eta_0))*w_0, eta_w.flatten()], axis = 0) \n #Use correct broadcasting?\n w_concat = np.concatenate((1/np.sqrt(2*eta))*np.array(w+[[]])[:-1], axis = 0) #[:-1] Added as a hack to keep it one-dim array of objects \n b_ls = np.concatenate([(1/np.sqrt(2*eta_0))*w_0, w_concat, (1/np.sqrt(2*eta_lin))*w_lin], axis = 0) \n# print(np.sum(eta_w.flatten() != w_concat)) \n# premultiplied_time_start = time.time() \n# premultiplied_lhs = eta_T_H_stacked.T.dot(eta_T_H_stacked).toarray() \n# premultiplied_time_end = time.time() \n# print('premultiplying took {}'.format(premultiplied_time_end - premultiplied_time_start)) \n# premultiplied_rhs = eta_T_H_stacked.T.dot(b_ls) \n# u_next = nnls_predotted(premultiplied_lhs, premultiplied_rhs, tol=1e-5) \n# print(eta_T_H_stacked.shape, b_ls.shape) \n# A_ls_t_b = eta_T_H_stacked.T.dot(b_ls) \n# w =scipy.sparse.linalg.spsolve_triangular(RT, A_ls_t_b, lower = True) \n# x = scipy.sparse.linalg.spsolve_triangular(R, w, lower = False) \n# u_next = x \n u_next = scipy.optimize.lsq_linear(eta_T_H_L_stacked, b_ls, bounds = (0, np.inf), tol=1e-3, lsmr_tol=1e-3, max_iter=nnls_max_iter, verbose=1).x \n# u = scipy.optimize.lsq_linear(premultiplied_lhs, premultiplied_rhs, bounds = (0, np.inf), tol=1e-5).x \n return u_next","def return_luu_func(RunningCost='Minimize Input Energy'):\n if type(RunningCost)==str:\n assert RunningCost in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\"\n else:\n assert type(RunningCost)==list, \"RunningCost must be a list of cost types.\"\n for el in RunningCost:\n assert type(el)==str, \"Each element of RunningCost must be a string. Not \" + str(type(el)) + \".\"\n assert el in ['Minimize Input Energy',\n 'Minimize time away from target angle',\n 'Minimize time away from target angular velocity'],\\\n \"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\" + el + \"' not accepted.\"\n\n if \"Minimize Input Energy\" in RunningCost:\n result1 = lambda X,U,dt: np.matrix([[k3*dt]])\n else:\n result1 = lambda X,U,dt: np.matrix([[0]])\n\n if \"Minimize time away from target angle\" in RunningCost:\n result2 = lambda X,U,dt: np.matrix([[0]])\n else:\n result2 = lambda X,U,dt: np.matrix([[0]])\n if \"Minimize time away from target angular velocity\" in RunningCost:\n result3 = lambda X,U,dt: np.matrix([[0]])\n else:\n result3 = lambda X,U,dt: np.matrix([[0]])\n\n result = lambda X,U,dt: result1(X,U,dt) \\\n + result2(X,U,dt) \\\n + result3(X,U,dt)\n return(result)","def annealing_objective(x: npt.NDArray[np.float64], *args: Any) -> float:\n blocks = [int(ind) for ind in x]\n\n approx_threshold = args[0]\n upper_limit = args[1]\n psol_configs = args[2]\n dists = args[3]\n utrys = args[4]\n n_cxs = args[5]\n p = args[6]\n w = args[7]\n\n if blocks in psol_configs:\n return 1.2\n\n approx_dist = sum(dists[i][b] for i, b in enumerate(blocks))\n\n if approx_dist > approx_threshold:\n return 1.1\n\n n_cx = w * (sum(n_cxs[i][b] for i, b in enumerate(blocks)) / upper_limit)\n\n if len(psol_configs) == 0:\n return n_cx\n\n distances = [0] * len(psol_configs)\n\n for index, config in enumerate(psol_configs):\n distances[index] = sum(\n utrys[i][config[i]].get_distance_from(utrys[i][b])\n < max(dists[i][config[i]], dists[i][b])\n for i, b in enumerate(blocks)\n ) / len(blocks)\n\n b_dv = (1 - w) * np.percentile(distances, p)\n\n return n_cx + b_dv","def _objective(self, params, model_ID, model_dict, X, y, **kwargs):\n model = model_dict['model']\n param_grid = model_dict['param_grid'].copy()\n params = params.copy()\n \n obj_verbose = max(0,self.verbose-2)\n \n type_X = str(type(X))\n \n if 'dask' in type_X:\n X = X.compute()\n y = y.compute()\n \n if obj_verbose>=2:\n print('params',params)\n \n params_transform, model = self._update_model_params(params, \n model_ID,\n model, \n param_grid)\n type_model = str(type(model))\n \n if obj_verbose>=2:\n print('params_transform',params_transform)\n if 'sklearn' in type_model or 'xgboost' in type_model:\n \n cv_scores = _sklearn_model_selection.cross_val_score(model, X, y,\n scoring= self.scoring['metric'],\n cv = self.cv,\n n_jobs= self.n_jobs,\n verbose = obj_verbose\n )\n\n else: #using neural net function\n import tensorflow as _tf\n #check for kwargs\n epochs = 100\n batch_size = 32\n callbacks = [_tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience =10)]\n for item in kwargs.items():\n if 'epochs' in item[0]: \n epochs = item[1]\n elif 'batch_size' in item[0]: \n batch_size = item[1]\n elif 'callbacks' in item[0]: \n callbacks = item[1] \n cv_scores = _NeuralNet.cross_val_score(model,\n batch_size,\n epochs,\n X, y,\n callbacks,\n scoring = self.scoring['metric'],\n cv = self.cv,\n verbose= obj_verbose)\n \n cv_score = _np.mean(cv_scores)\n \n if 'sklearn' in type_model or 'xgboost' in type_model:\n if self.scoring['maximize']==True or self.scoring['metric']==None:\n cv_score = 1/cv_score \n else:\n if self.scoring['maximize']==True and self.scoring['metric']!=None :\n cv_score = 1/cv_score \n \n objective = {'loss': cv_score,\n 'params': params,\n 'status': _hyperopt.STATUS_OK,\n 'eval_time': _time.time()}\n return objective","def objective_function(self, variables, mask, load, generation, annuity_scalar=1):\n size = sum(mask)\n # pay for reg down energy, get paid for reg up energy\n # paid revenue for capacity to do both\n\n p_regu = cvx.Parameter(size, value=self.p_regu.loc[mask].values, name='p_regu')\n p_regd = cvx.Parameter(size, value=self.p_regd.loc[mask].values, name='p_regd')\n p_ene = cvx.Parameter(size, value=self.price.loc[mask].values, name='price')\n\n regup_charge_payment = cvx.sum(variables['regu_c'] * -p_regu) * annuity_scalar\n regup_charge_settlement = cvx.sum(variables['regu_c'] * -p_ene) * self.dt * self.kru_avg * annuity_scalar\n\n regup_disch_payment = cvx.sum(variables['regu_d'] * -p_regu) * annuity_scalar\n regup_disch_settlement = cvx.sum(variables['regu_d'] * -p_ene) * self.dt * self.kru_avg * annuity_scalar\n\n regdown_charge_payment = cvx.sum(variables['regd_c'] * -p_regd) * annuity_scalar\n regdown_charge_settlement = cvx.sum(variables['regd_c'] * p_ene) * self.dt * self.krd_avg * annuity_scalar\n\n regdown_disch_payment = cvx.sum(variables['regd_d'] * -p_regd) * annuity_scalar\n regdown_disch_settlement = cvx.sum(variables['regd_d'] * p_ene) * self.dt * self.krd_avg * annuity_scalar\n\n return {'regup_payment': regup_charge_payment + regup_disch_payment,\n 'regdown_payment': regdown_charge_payment + regdown_disch_payment,\n 'fr_energy_settlement': regup_disch_settlement + regdown_disch_settlement + regup_charge_settlement + regdown_charge_settlement}"],"string":"[\n \"def objective(self):\\n pass\",\n \"def objective(self, x):\\n pass\",\n \"def objective(self, x):\\n pass\",\n \"def objective_function(x):\\n return x * 1 # change this to our actual function\",\n \"def get_Objective(self):\\r\\n \\r\\n ans = 0\\r\\n for i in range(len(self.X.shape)):\\r\\n ans += self.parameter['phi'][i] * self.parameter['lambda1'] *norm(tl.unfold(self.Wlist[i], i), 'nuc') + (1 / self.parameter['m']) * norm(tl.unfold(self.X - self.Wlist[i] - self.V, i))\\r\\n\\r\\n # Augmented part is calculated seperately. \\r\\n augment_part1 = 0.5 * self.parameter['rho1'] * norm(self.V - self.T + self.F1)\\r\\n augment_part2 = 0.5 * self.parameter['rho2'] * norm(tl.fold(np.dot(self.Dmatrix, tl.unfold(self.T, 0)), 0, self.T.shape) - self.S + self.F2)\\r\\n\\r\\n # Combine the result for final objective function\\r\\n ans += self.parameter['beta1'] * norm(self.V.reshape(self.totaldim), 1) + self.parameter['beta2'] * norm(self.S.reshape(self.totaldim), 1) + augment_part1 + augment_part2 \\r\\n return ans\",\n \"def objective(self, args: Dict[str, Any]) -> float:\\n pass\",\n \"def get_objective(self, X_v, U_v, X_last_p, U_last_p):\\n objective = None\\n return objective\",\n \"def objective(args: Namespace, trial: optuna.trial._trial.Trial) -> float:\\n # Paramters (to tune)\\n args.embedding_dim = trial.suggest_int(\\\"embedding_dim\\\", 128, 512)\\n args.num_filters = trial.suggest_int(\\\"num_filters\\\", 128, 512)\\n args.hidden_dim = trial.suggest_int(\\\"hidden_dim\\\", 128, 512)\\n args.dropout_p = trial.suggest_uniform(\\\"dropout_p\\\", 0.3, 0.8)\\n args.lr = trial.suggest_loguniform(\\\"lr\\\", 5e-5, 5e-4)\\n\\n # Train (can move some of these outside for efficiency)\\n logger.info(f\\\"\\\\nTrial {trial.number}:\\\")\\n logger.info(json.dumps(trial.params, indent=2))\\n artifacts = run(args=args, trial=trial)\\n\\n # Set additional attributes\\n args = artifacts[\\\"args\\\"]\\n performance = artifacts[\\\"performance\\\"]\\n logger.info(json.dumps(performance[\\\"overall\\\"], indent=2))\\n trial.set_user_attr(\\\"threshold\\\", args.threshold)\\n trial.set_user_attr(\\\"precision\\\", performance[\\\"overall\\\"][\\\"precision\\\"])\\n trial.set_user_attr(\\\"recall\\\", performance[\\\"overall\\\"][\\\"recall\\\"])\\n trial.set_user_attr(\\\"f1\\\", performance[\\\"overall\\\"][\\\"f1\\\"])\\n\\n return performance[\\\"overall\\\"][\\\"f1\\\"]\",\n \"def objective_function(self, y_true, y_predicted, X=None):\",\n \"def add_objective(self): \\n \\n if \\\"CSS\\\" in self.algorithm:\\n \\n if self.num_hidden == 0:\\n \\n data_term = self.compute_energy(self.x, self.batch_size)\\n \\n else:\\n \\n data_term = self.compute_free_energy(self.x)\\n \\n normalizer_term = self.add_css_approximation(data_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden ==0:\\n \\n data_term = self.compute_energy(self.x, self.batch_size)\\n \\n normalizer_term = self.compute_energy(self.x_gibbs, \\n self.batch_size)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden > 0:\\n \\n data_term = self.compute_free_energy(self.x)\\n \\n normalizer_term = self.compute_free_energy(self.rbm_cd_samples)\\n \\n normalizer_term = -T.mean(normalizer_term)\\n \\n # cost is negative log likelihood \\n self.cost = T.mean(data_term) + normalizer_term\",\n \"def objective(trial):\\n # The parameters that we will calibrate the model for are shown here.\\n # Optuna trial i\\n BOD = trial.suggest_uniform(\\\"BOD\\\", 0, 1) #Review ranges here\\n k_r = trial.suggest_uniform(\\\"k_r\\\", 0, 1) #Review Ranges here \\n \\n def ChLa(t):\\n return 1 # Need to link to data\\n\\n def I(x):\\n return 1 # Need to link to data\\n\\n K_z = 2 * 10**(-5) # p.51\\n a = K_z\\n k_b = 0.1 # Table 5\\n th_b = 1.047 # Table 5\\n k_r = 0.1 # Table 5\\n YCHO2 = 0.0083 # Table 5\\n th_p = 1.036 # Table 5\\n th_s = 1.065 # Table 5\\n th_r = 1.047 # Table 5\\n\\n def Temp(t):\\n \\\"\\\"\\\"\\n Function that maps time to temperature\\n \\\"\\\"\\\"\\n return 20 # Need to link to data\\n\\n def P_max(t):\\n return 9.6 * 1.036 **(Temp(t) - 20) # Eq. 4\\n\\n def L_min(t):\\n I = 1 # Need to link to PAR data\\n K_1 = 0.687 * 1.086**(Temp(t) - 20)\\n K_2 = 15\\n return I * (1 + 2 * np.sqrt(K_1 / K_2)) / (I + K_1 + I**2 / K_2) # Eq. 5\\n \\n # f deals with sink and source terms \\n def f(x, t):\\n return -1 / YCHO2 * k_r * th_r**(Temp(t) - 20) * ChLa(t) + P_max(t) * L_min(t) * ChLa(t) - k_b * th_b**(Temp(t)-20) * BOD \\n\\n L = 200 # Length of domain\\n dt = 1 / 48 # Mesh spacing in t\\n F = a * dt # a * dt / dx**2\\n T = 100 # Simulation time stop\\n\\n # Solving the PDE\\n DO, x, t, _ = solver_FE_simple(I, a, f, L, dt, F, T)\\n \\n # Creating some bogus targets while database errors are happening\\n DO_data = DO + np.random.random(len(DO))\\n\\n # Using mean squared error as the measure of fit, where we want\\n # to minimize this number\\n return ((DO - DO_data)**2).mean()\",\n \"def objective(self, adjacency=None, R=None):\\n raise NotImplementedError()\",\n \"def objective(hyperparams): \\n global iteration #necessary with a global variable because of implementation from hyperopt. \\n iteration += 1\\n\\n result = run_model(hyperparams, iteration)\\n loss = -result #transform to loss in order to minimize\\n\\n return {'loss': loss, 'hyperparams': hyperparams, 'iteration': iteration, 'status': STATUS_OK}\",\n \"def objective(\\n self,\\n parameters: object\\n ) -> float:\\n pass\",\n \"def objective(self, param):\\n self.__init__(param, self.data)\\n # return self.rmse() + self.penalty()\\n return self.rmse() + self.penalty()\",\n \"def _objective(self,output, expected,device):\\r\\n #\\r\\n #output = output.to(device = expected.device) #\\r\\n \\r\\n output = torch.where(output == 0,self.saf,output)\\r\\n #output = torch.where(output == 1,torch.tensor([0.999999],device = device),output)\\r\\n out1 = torch.mul(-expected,torch.log(output))\\r\\n #out2 = torch.mul(expected-1,torch.log(1 - output))\\r\\n #out = torch.add(out1,out2)\\r\\n return torch.sum(out1, dim = 1)\",\n \"def objective(rp,n=5000,C=-2*10**11,a=300,b=1):\\n l = log(rp)/n\\n r = exp(l)\\n rm1 = r-1\\n return (rp-1)*((a-b*n)*rm1 + 1) - C*(rm1)*(rm1)\\n #return rm1\",\n \"def buildObjective(self):\\r\\n\\r\\n # self.z_prior might be the modified version\\r\\n self.L_elbo = T.mean(self.reconst + self.conditional_prior + self.w_prior + self.z_prior)\\r\\n\\r\\n self.L_elbo_modif = T.mean(self.reconst + self.conditional_prior + self.w_prior_modif + self.z_prior_modif)\\r\\n\\r\\n #---Getting model parameter---#\\r\\n cg = ComputationGraph(self.L_elbo)\\r\\n #self.phi_theta is the list of all the parameters in q and p.\\r\\n self.params = VariableFilter(roles=[PARAMETER])(cg.variables)\",\n \"def objective(self):\\n return self._objective\",\n \"def _objective(self, trial, X, y, weights=None, split=None):\\n\\n # Generate even weights if none\\n if weights is None:\\n weights = pd.Series(np.ones(len(y)), index=y.index)\\n else:\\n weights = pd.Series(weights, index=y.index)\\n\\n # Execute trial function\\n try:\\n res = eval(self.function)\\n except:\\n raise RuntimeError(f\\\"Optuna execution error: {self.function}\\\")\\n\\n # If indicator result is tuple, select the one of interest\\n if isinstance(res, tuple):\\n res = res[self.idx]\\n\\n # Ensure result is a dataframe with same index as X\\n res = pd.DataFrame(res, index=X.index)\\n\\n # If indicator result is dataframe, select the one of interest\\n if len(res.columns) > 1:\\n res = pd.DataFrame(res.iloc[:, self.idx])\\n\\n # y may be a subset of X, so reduce result to y and convert to series\\n res_y = res.reindex(y.index).iloc[:, 0].replace([np.inf, -np.inf], np.nan)\\n\\n # Save all trial results for pruning and reporting\\n # Only the best trial will eventually be saved to limit storage requirements\\n self.res_y.append(res_y) # Save results\\n\\n # Indicator result may be all NANs based on parameter set\\n # Return FALSE and alert\\n if np.isnan(res_y).sum() / len(res_y) > .95: # Most or all NANs\\n self.res_y_corr.append(np.zeros(len(y)))\\n if split is not None:\\n return tuple([False] * (len(split) - 1))\\n else:\\n return False\\n\\n # Obtain correlation for entire dataset\\n if self.spearman:\\n corr = _weighted_spearman(np.array(y), np.array(res_y), np.array(weights))\\n else:\\n corr = _weighted_pearson(np.array(y), np.array(res_y), np.array(weights))\\n\\n # Save correlation for res_y\\n self.res_y_corr.append(corr)\\n\\n # Multi-objective optimization\\n # Obtain correlation to target for each split for Optuna to maximize\\n if split is not None:\\n mo = []\\n for i, e in enumerate(split):\\n if i == 0:\\n s = e\\n continue\\n\\n # y could be a subset of X, use index of X to filter y\\n idx = X[s:e].index\\n\\n # Filter y based on X split\\n y_se = np.array(y[y.index.isin(idx)]).astype('float64')\\n\\n # Filter y predictions based on X split\\n res_y_se = np.array(res_y[res_y.index.isin(idx)]).astype('float64')\\n\\n # Filter weights based on X split\\n weights_se = np.array(weights[weights.index.isin(idx)]).astype('float64')\\n\\n if np.isnan(res_y_se).sum() / len(res_y_se) > .95:\\n return tuple([False]*(len(split)-1))\\n\\n if self.spearman:\\n mo.append(_weighted_spearman(y_se, res_y_se, weights_se))\\n else:\\n mo.append(_weighted_pearson(y_se, res_y_se, weights_se))\\n s = e\\n return tuple(mo)\\n\\n # Single objective optimization return corr for entire dataset\\n else:\\n return corr\",\n \"def _l1m_objective(a,X,*args):\\n \\n return(np.sum(np.apply_along_axis(_euclidnorm,1,_diffmat_objective(a,X))))\",\n \"def objective_function(params):\\n\\n\\tenergy = 0 # Initialize the energy in 0\\n\\n\\tqc = get_var_form(params) # Obtain a quantum circuit instance from the parameters\\n\\n\\tfor key in pauli_weights.keys(): # Iterate over the pauli string in the Pauli weight\\n\\n\\t\\tmc, n_measures = measure_circuit_factory(key) # Obtain the measurement circuit from the Pauli string\\n\\t\\tqc_final = qc.compose(mc) # Combine both circuits\\n\\n\\t\\t# Execute the quantum circuit to obtain the probability distribution associated with the current parameters\\n\\t\\tt_qc = transpile(qc_final, backend)\\n\\t\\tq_obj = assemble(t_qc, shots=NUM_SHOTS)\\n\\t\\tcounts = backend.run(q_obj).result().get_counts(qc_final)\\n\\n\\t\\tdistribution = get_distribution(counts, n_measures) # Convert the measured counts into a probability vector\\n\\n\\t\\t# Weight each probability by the diagonal factor, them sum all of them, and later multiply by the Pauli Weight\\n\\t\\tenergy += np.sum(distribution * generate_diagonal_factors(n_measures)) * pauli_weights[key]\\n\\n\\tenergy_list.append(energy) # Append the new computed energy\\n\\n\\t# Print the iteration of the VQE and the energy\\n\\tprint('Iteration {}, Energy: {:.4f}'.format(len(energy_list), energy))\\n\\n\\treturn energy\",\n \"def objective_function(num, x, fe_count, best):\\n if num == 1:\\n return sphere(x, fe_count, best)\\n elif num == 2:\\n return rastrigin(x, fe_count, best)\\n elif num == 3:\\n return rosenbrock(x, fe_count, best)\\n elif num == 4:\\n return schwefel(x, fe_count, best)\\n elif num == 5:\\n return quartic(x, fe_count, best)\\n elif num == 6:\\n return ackley(x, fe_count, best)\\n elif num == 7:\\n return schaffer(x, fe_count, best)\\n elif num == 8:\\n return griewank(x, fe_count, best)\\n elif num == 9:\\n return matyas(x, fe_count, best)\\n elif num == 10:\\n return trid(x, fe_count, best)\\n else:\\n pass\",\n \"def _optfun(theta):\\n return optfun(theta)[:2]\",\n \"def objective(self) -> Optional[Union[int, float]]:\\n if self.solution is not None:\\n if isinstance(self.solution, list):\\n return getattr(self.solution[-1], \\\"objective\\\", None)\\n else:\\n return getattr(self.solution, \\\"objective\\\", None)\\n else:\\n return None\",\n \"def _optimize(self, objective):\\n # Initial value\\n initial = self.get_initial()[0]\\n\\n if self.vector_to_matrix_transform is not None:\\n initial = self.vector_to_matrix_transform(initial)\\n\\n if self.solver_type is 'NelderMead' or self.solver_type is 'ParticleSwarm':\\n initial = None\\n\\n # Create tensorflow variable\\n if self.matrix_manifold_dimension is None:\\n x_tf = tf.Variable(tf.zeros(self.dimension, dtype=tf.float64))\\n else:\\n x_tf = tf.Variable(tf.zeros([self.matrix_manifold_dimension, self.matrix_manifold_dimension], dtype=tf.float64))\\n\\n # Cost function for pymanopt\\n def objective_fct(x):\\n if self.matrix_to_vector_transform_tf is not None:\\n # Reshape x from matrix to vector form to compute the objective function (tensorflow format)\\n x = self.matrix_to_vector_transform_tf(x, self.matrix_manifold_dimension)\\n return objective(x)[0]\\n\\n # Transform the cost function to tensorflow function\\n cost = tf.py_function(objective_fct, [x_tf], tf.float64)\\n\\n # Gradient function for pymanopt\\n def objective_grad(x):\\n if self.matrix_to_vector_transform is not None:\\n # Reshape x from matrix to vector form to compute the gradient\\n x = self.matrix_to_vector_transform(x)\\n\\n # Compute the gradient\\n grad = np.array(objective(x)[1])[0]\\n\\n if self.vector_to_matrix_transform is not None:\\n # Reshape the gradient in matrix form for the optimization on the manifold\\n grad = self.vector_to_matrix_transform(grad)\\n return grad\\n\\n # Define pymanopt problem\\n problem = pyman.Problem(manifold=self.manifold, cost=cost, egrad=objective_grad, arg=x_tf, verbosity=2)\\n\\n # Optimize the parameters of the problem\\n opt_x, opt_log = self.solver.solve(problem, x=initial)\\n\\n if self.matrix_to_vector_transform is not None:\\n # Reshape the optimum from matrix to vector form\\n opt_x = self.matrix_to_vector_transform(opt_x)\\n\\n # Format the result to fit with GPflowOpt\\n result = sc_opt.OptimizeResult(x=opt_x, fun=opt_log['final_values']['f(x)'], nit=opt_log['final_values']['iterations'], message=opt_log['stoppingreason'], success=True)\\n\\n return result\",\n \"def relaxed_obj_u_opt_N_fixed(u, w_0, w, w_lin, eta_0, eta, eta_lin, T, H, L_lhs, alpha, B):\\n tumor_dose = T.dot(u)\\n OAR_doses = [Hi.dot(u) for Hi in H]\\n linear = L_lhs.dot(u)\\n relaxed_obj = alpha.T.dot(w_0) - w_0.T.dot(B*w_0) + (1/(2*eta_0))*(np.linalg.norm(\\n w_0-tumor_dose))**2 + np.sum([(1/(2*eta[i]))*(np.linalg.norm(w[i]-OAR_doses[i]))**2 for i in range(len(H))]) + (np.linalg.norm(\\n (np.sqrt(1/(2*eta_lin)))*(w_lin-linear)))**2\\n return relaxed_obj\",\n \"def rpt_objective(opt_objective):\\n if opt_objective == \\\"lgst\\\": return \\\"logl\\\"\\n else: return opt_objective\",\n \"def objective_function(theta, X, y):\\n # m number of training instances\\n m = X.shape[0]\\n jtheta = sum((np.dot(X, theta) - y)**2) / (2.0*m)\\n return jtheta\",\n \"def _update_objective(self):\\n # rewrap the cost if the solver has been run\\n self.Finalize()\\n return\",\n \"def optimizer(grad, method, init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N):\\r\\n\\r\\n\\t\\r\\n\\tif grad == 'NO':\\r\\n\\t\\tif method == 'Powell' :\\r\\n\\t\\t\\tres = opt.minimize(Ulike,init_par, method = method,\\r\\n\\t\\t\\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N))\\r\\n\\t\\t\\treturn res.x, res.nit\\r\\n\\t\\telif method == 'Nelder-Mead':\\r\\n\\t\\t\\tres = opt.minimize(Ulike,init_par, method = method,\\r\\n\\t\\t\\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N),\\r\\n\\t\\t\\t\\t options = {'ftol': 0.0001})\\r\\n\\t\\t\\treturn res.x, res.nit\\r\\n\\t\\telif method == 'default':\\r\\n\\t\\t\\tres = opt.minimize(Ulike,init_par, \\r\\n\\t\\t\\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N))\\r\\n\\t\\t\\treturn res.x, res.nit\\r\\n\\r\\n\\telif grad == 'YES':\\r\\n\\t\\tres = opt.minimize(Ulike, init_par, method = method, jac = stella_grad_full, \\r\\n \\t\\t\\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N),\\r\\n\\t\\t\\t options={'disp': True, 'maxiter': 4000, 'xtol': 1e-4})\\r\\n\\t\\treturn res.x, res.nit \\r\\n\\t\\t\\t\\r\\n\\t\\t\\r\\n\\telif grad == 'HESS':\\r\\n\\t\\tres = opt.minimize(Ulike, init_par, method = method, jac = stella_grad_full, hess = stella_hessian,\\r\\n\\t\\t\\t\\t\\t args = (alpha, delta, plx_obs, mualpha_obs,mudelta_obs, vrad_obs, sigma_obs, sigma_vrad, ccoeff, N),\\r\\n\\t\\t\\t\\t\\t options = {'disp': True, 'maxiter': 4000, 'xtol': 1.e-06}) \\r\\n\\t\\treturn res.x, res.nit\",\n \"def optim_func(params, model):\\n if model.model == 'ARD':\\n model.alpha, model.beta = params\\n lik = model.pruning_algorithm()\\n\\n else:\\n model.alpha = params[0]\\n lik = model.pruning_algorithm()\\n \\n return -lik\",\n \"def addObjective(self, *args):\\n return _libsbml.FbcModelPlugin_addObjective(self, *args)\",\n \"def get_objective(X_t, xattr, Y_t, s):\\n return eloglik(X_t, xattr, Y_t, s) - (s['KL']).sum()\",\n \"def objective_function(params):\\n cirq_circuit = variational_state_evolve(params)\\n mean_value = self.expectation(\\n all_qubits_in_circuit, cirq_circuit, hamiltonian)\\n self._current_expectation = mean_value\\n return mean_value\",\n \"def objective_fn(samples):\\n # TODO(chelseaf): This is the inefficient way to do it. Would be faster\\n # to tile after the conv layers.\\n cem_state = np.tile(np.expand_dims(state, 0), [samples.shape[0], 1, 1, 1])\\n np_inputs = self._t2r_model.pack_features(cem_state,\\n self._prev_episode_data,\\n timestep, samples)\\n q_values = self._predictor.predict(np_inputs)[prediction_key]\\n if not self._prev_episode_data:\\n q_values *= 0\\n return q_values[0]\",\n \"def objective(trial):\\n %time\\n env = gym.make('Delivery-v0')\\n alpha = trial.suggest_discrete_uniform('alpha', 0.3,0.9,0.3)\\n gamma = trial.suggest_discrete_uniform('gamma', 0.6, 1,0.1)\\n epsilon = trial.suggest_discrete_uniform('epsilon', 0.01, 0.11, 0.04)\\n episodes = 1000000\\n \\n # For plotting metrics\\n all_epochs = []\\n all_penalties = []\\n rewards = []\\n \\n #Initialize Q table of 22500 x 8 size (22500 states and 8 actions) with all zeroes\\n q_table = np.zeros([env.observation_space.n, env.action_space.n]) \\n \\n for i in range(1, episodes+1):\\n state = env.reset()\\n episode_rewards = []\\n\\n epochs, penalties, reward, = 0, 0, 0\\n done = False\\n\\n while not done:\\n if random.uniform(0, 1) < epsilon:\\n action = env.action_space.sample() # Explore action space randomly\\n else:\\n action = np.argmax(q_table[state]) # Exploit learned values by choosing optimal values\\n\\n next_state, reward, done, info = env.step(action) \\n\\n old_value = q_table[state, action]\\n next_max = np.max(q_table[next_state])\\n\\n new_value = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)\\n q_table[state, action] = new_value\\n\\n if reward == -10:\\n penalties += 1\\n \\n\\n state = next_state\\n episode_rewards.append(reward)\\n epochs += 1\\n \\n if done == True:\\n break \\n if epochs == 1000:\\n break \\n rewards.append(np.sum(episode_rewards))\\n \\n last_reward = np.mean(rewards)\\n # trial.report(-1 * last_reward)\\n\\n return -1 * last_reward\",\n \"def objective(self, objective):\\n\\n self._objective = objective\",\n \"def objective(beta, lambdat, X, y):\\n return 1/len(y) * (np.sum(\\n (np.maximum(0, 1-((y[:, np.newaxis]*X).dot(beta)))**2)))\\\\\\n + lambdat * np.linalg.norm(beta)**2\",\n \"def propose_optimize():\\n pass\",\n \"def objective_function(self, variables, mask, annuity_scalar=1):\\n ice_gen = variables['ice_gen']\\n self.costs = {'ice_fuel': cvx.sum(cvx.multiply(self.efficiency * self.fuel_cost * self.dt * annuity_scalar, variables['ice_gen'])),\\n 'ice_fixed': self.fixed_om * annuity_scalar,\\n 'ice_variable': cvx.sum(cvx.multiply(self.vari_om * self.dt * annuity_scalar, ice_gen)),\\n 'ice_ccost': self.capital_cost * self.n + self.ccost_kw * self.rated_power * self.n\\n }\\n\\n return self.costs\",\n \"def _objective_cost(self):\\n\\n def obj_expression_simple(model):\\n total = model.A_total + model.A2_total + model.A3_total + \\\\\\n model.A4_total\\n return -total\\n\\n def obj_expression(model):\\n total = model.A_total + model.A2_total + model.A3_total + \\\\\\n model.A4_total\\n total += model.Completion_total\\n total += model.Affinity_cognitive_total\\n total += model.CTu_total + model.CTl_total + model.S_total\\n return -total\\n\\n # self.model.exp_cost = Expression(rule=obj_expression)\\n # self.model.obj_cost = Objective(rule=self.model.exp_cost)\\n # self.model.obj_cost = Objective(rule=obj_expression_simple)\\n self.model.obj_cost = Objective(rule=obj_expression)\",\n \"def optimize(opt, target, n_agents, n_variables, n_iterations, lb, ub, hyperparams):\\n\\n # Creating the SearchSpace\\n space = SearchSpace(n_agents=n_agents, n_variables=n_variables,\\n n_iterations=n_iterations, lower_bound=lb, upper_bound=ub)\\n\\n # Creating the Function\\n function = Function(pointer=target)\\n\\n # Creating Optimizer\\n if opt.__name__ is not 'BH':\\n optimizer = opt(hyperparams=hyperparams)\\n else:\\n optimizer = opt()\\n\\n # Creating the optimization task\\n task = Opytimizer(space=space, optimizer=optimizer, function=function)\\n\\n return task.start(store_best_only=True)\",\n \"def objective(self,w):\\n diffs = self.get_y_times_diffs(self.get_split_weights(w))\\n #print diffs, sigmoid(diffs)\\n obj = -np.sum(np.log(sigmoid(diffs))) #negative, since minimising\\n # regularisation\\n obj += 0.5 * self.alpha * np.dot(w[:self.interp_index[0]], w[:self.interp_index[0]])\\n return obj\",\n \"def slo(self, objective=99.99):\\n self.objective = objective\\n return objective\",\n \"def objective(params):\\n\\t# hyperopt casts as float\\n\\tparams['num_boost_round'] = int(params['num_boost_round'])\\n\\tparams['num_leaves'] = int(params['num_leaves'])\\n\\n\\t# need to be passed as parameter\\n\\tparams['is_unbalance'] = True\\n\\tparams['verbose'] = -1\\n\\tparams['seed'] = 1\\n\\n\\tcv_result = lgb.cv(\\n\\t\\tparams,\\n\\t\\tdtrain,\\n\\t\\tnum_boost_round=params['num_boost_round'],\\n\\t\\tmetrics='binary_logloss',\\n\\t\\tnfold=3,\\n\\t\\tearly_stopping_rounds=20,\\n\\t\\tstratified=False)\\n\\tearly_stop_dict[objective.i] = len(cv_result['binary_logloss-mean'])\\n\\terror = round(cv_result['binary_logloss-mean'][-1], 4)\\n\\tobjective.i+=1\\n\\treturn error\",\n \"def obj_u_opt_N_fixed(u, T, alpha, B):\\n x = T.dot(u)\\n return alpha.T.dot(x) - x.T.dot(B*x)\",\n \"def _objective_function(self, thetas, X, Y):\\n \\n # Convert thetas vector to form total_cost can understand\\n thetas = self.reshape_thetas(thetas, 'list')\\n self.thetas = thetas\\n \\n # Get cost function value\\n fval = self.total_cost(X, Y, thetas)\\n \\n # Get derivatives using back propagation\\n Deltas = self.get_gradients(X, Y)\\n dfval = self.reshape_thetas(Deltas, 'vector')\\n \\n return fval, dfval\",\n \"def objective_func(self, topology, grad_func, tmax, eta):\\n f = objective_function_numpy\\n x_func = partial(self._optimize_form, topology=topology, tmax=tmax, eta=eta)\\n return partial(f, x_func=x_func, grad_func=grad_func)\",\n \"def define_objective(m):\\r\\n\\r\\n # Dual objective function\\r\\n m.OBJECTIVE = Objective(expr=m.DUAL_OBJECTIVE_EXPRESSION, sense=maximize)\\r\\n\\r\\n return m\",\n \"def cost_function(self, u):\\n # enforce that the input is a numpy array\\n u = np.asarray(u)\\n\\n # Reshape the control input to (mxT). Vector input is required for some optimization libraries\\n T = int(len(u)/2)\\n u = u.reshape((2,T))\\n\\n J = - self.rho(u)\\n\\n return J\",\n \"def maximize(self, budget, optimizer):\\n\\n\\t\\tpass\",\n \"def _objective(self, beta, y, mask=None):\\n # if \\\"mask\\\" vector isn't already computed, compute it\\n mask = 1 - y*self.X_train.dot(beta) if mask is None else mask\\n\\n if self.large:\\n # more efficient for larger feature matrices\\n empirical = 0\\n for i in range(self.n):\\n if mask[i] >= -self.h:\\n if mask[i] > +self.h:\\n empirical += mask[i]\\n else:\\n empirical += (mask[i] + self.h)**2/(4*self.h)\\n empirical /= self.n\\n\\n else:\\n # more efficient for smaller feature matrices\\n empirical = np.zeros(self.n)\\n piecewise_2 = np.where(abs(mask) <= self.h)\\n piecewise_3 = np.where(mask > self.h)\\n empirical[piecewise_2] = (mask[piecewise_2] + self.h)**2/(4*self.h)\\n empirical[piecewise_3] = mask[piecewise_3]\\n empirical = np.mean(empirical)\\n\\n regularization = self.lambduh*np.linalg.norm(beta)**2\\n return empirical + regularization, mask\",\n \"def run_qae_optimization(training_states, n_repetitions, exact=no_noise, noisy=gate_error):\\n result_list = []\\n def proxy(params, training_states, n_repetitions, exact=no_noise, noisy=gate_error):\\n \\\"\\\"\\\"Embedded function version\\n \\\"\\\"\\\"\\n input_list = fix_list(params, all_param_array=all_param, var_param_array=var_param, fixed_vals_array=fixed_vals)\\n fidelities = []\\n for training_state in training_states:\\n fid = cusp_stage2.compute_stage2_cost_function(*input_list, alpha=training_state, n_repetitions=n_repetitions,\\n exact=exact, noisy=noisy)\\n fidelities.append(fid)\\n avg_fid = np.mean(fidelities)\\n result_list.append(1-avg_fid)\\n print(1-avg_fid)\\n return 1. - avg_fid\\n\\n \\n # Initialize parameters\\n half_turn_min = 0\\n half_turn_max = 2\\n init_params = np.random.uniform(low=half_turn_min, high=half_turn_max,\\n size=num_param)\\n\\n # Optimization using Nelder-Mead.\\n h2_qae_wrap = lambda params: proxy(params, training_states=training_states,\\n n_repetitions=n_repetitions, exact=exact, noisy=noisy)\\n \\n if noisy:\\n maxiter = 60\\n else:\\n maxiter = None\\n \\n res = minimize(h2_qae_wrap, init_params, args=(),\\n method='Nelder-Mead', tol=None, \\n options={'disp': False, 'maxiter': maxiter, 'xatol': 0.001,\\n 'return_all': False, 'fatol': 0.001})\\n np.savetxt('stage2_data.csv',result_list, delimiter=',')\\n return res.x\",\n \"def custom_method(*args, **kwargs):\\n return objective_return\",\n \"def custom_method(*args, **kwargs):\\n return objective_return\",\n \"def get_objective(self):\\n self.objective = 0\\n for r in self.routes:\\n r.update_route(self.vrpdata)\\n self.objective += r.distance\\n # all() returns True if all elements of the iterable are true\\n self.solutionValid = (all([r.tourValid for r in self.routes]) and len(self.routes) <= self.vrpdata.MaxNumVeh)\\n if self.solutionValid:\\n return self.objective\\n return -1\",\n \"def optofitness(op_array, n_obj=1):\\n d = 5\\n f_speed = f1(op_array[1], df.v_flock, df.v_tol)\\n f_coll = f3(op_array[3], df.a_tol)\\n f_disc = f3(op_array[4], df.num_agents / 5)\\n f_wall = f2(op_array[0], df.r_tol)\\n f_cluster = f1(op_array[5], df.num_agents / 5, df.num_agents / 5)\\n if op_array[2] > 0:\\n f_corr = op_array[2]\\n else:\\n f_corr = 0\\n time_fit = 1 # (1-sigmoid_decay(op_array[6], df.max_sim_time-df.wait_time, 200))\\n if n_obj == 2:\\n # F1 = -time_fit * f_speed * f_corr * f_disc * f_cluster\\n # F2 = -time_fit * f_wall * f_coll\\n F2 = -time_fit *f_coll * f_corr * f_disc * f_cluster\\n F1 = -time_fit * f_wall * f_speed\\n return round(F1, d), round(F2, d)\\n\\n elif n_obj == 3:\\n F1 = -time_fit * f_speed * f_corr * f_disc * f_cluster\\n F2 = -time_fit * f_wall\\n F3 = -time_fit * f_coll\\n return round(F1, d), round(F2, d), round(F3, d)\\n elif n_obj == 'all':\\n return round(f_wall, d), round(f_speed, d), round(f_corr, d), round(f_coll, d), round(f_disc, d), round(f_cluster, d)\\n F1 = -time_fit * f_speed * f_coll * f_disc * f_wall * f_corr * f_cluster\\n return round(F1, d)\",\n \"def update_op(self, loss, learning_rate,var):\\n #train_op = None\\n ####### Implementation Here ######\\n #pass\\n train_op = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(loss = loss,var_list = var )\\n return train_op\",\n \"def optimization_step(self):\\n \\n if \\\"CSS\\\" in self.algorithm:\\n \\n input_dict = {self.x: self.train_inputs[self.minibatch_set,:]}\\n \\n var_list = [self.x_tilda, self.minibatch_set]\\n \\n if (self.num_samples > 0) and (not self.mixture):\\n \\n if ((self.mf_steps > 0) and self.alpha >0) or\\\\\\n self.gibbs_steps > 0: \\n \\n var_list.append(self.sampler_theta)\\n \\n elif \\\"CD\\\" in self.algorithm:\\n \\n input_dict = {self.x : self.train_inputs[self.minibatch_set,:]} \\n \\n var_list = [self.minibatch_set]\\n \\n var_list.append(self.learning_rate)\\n \\n if self.use_momentum:\\n \\n var_list.append(self.momentum)\\n \\n output_vars = [self.pseudo_cost]\\n \\n if self.report_p_tilda:\\n \\n output_vars.append(self.p_tilda)\\n \\n else:\\n \\n output_vars.append(theano.shared(0))\\n \\n opt_step = theano.function(inputs = var_list,\\n outputs = output_vars,\\n updates = self.updates,\\n givens = input_dict,\\n on_unused_input='warn')\\n \\n return opt_step\",\n \"def qp(self, eta_u, eta_v):\\n z = 1\\n v = np.array(eta_u)\\n n_features = v.shape[0]\\n u = np.sort(v)[::-1]\\n cssv = np.cumsum(u) - z\\n ind = np.arange(n_features) + 1\\n cond = u - cssv / ind > 0\\n rho = ind[cond][-1]\\n theta = cssv[cond][-1] / float(rho)\\n uu = np.maximum(v - theta, 0)\\n vv = np.array(())\\n return uu, vv\",\n \"def objective_function(self, flow_values):\\n\\n objective_value = -1.0 * np.dot(self.turbine_params, flow_values)\\n\\n return objective_value\",\n \"def _objective_switching(self):\\n # FIXME(cathywu) consider removing, not currently used\\n\\n def obj_expression(model):\\n total = 0\\n for j in range(1, self.num_tasks + 1):\\n for i in range(1, self.num_timeslots):\\n total += abs(model.A[i, j] - model.A[i + 1, j])\\n return total\\n\\n # TODO objective with multiple parts\\n self.model.exp_switching = Expression(rule=obj_expression)\",\n \"def set_objective_fn(self, objective_fn):\\n self.objective_fn = objective_fn\",\n \"def objective(self, x):\\n rvs = frozenset(map(frozenset, self._rvs))\\n joint = self.construct_joint(x)\\n joint = joint.sum(axis=self._others, keepdims=True)\\n crv = joint.sum(axis=tuple(flatten(rvs)))\\n\\n H_crv = h(crv.ravel())\\n H = h(joint.ravel()) - H_crv\\n\\n def I_P(part):\\n margs = [ joint.sum(axis=tuple(flatten(rvs - p))) for p in part ]\\n a = sum(h(marg.ravel()) - H_crv for marg in margs)\\n return (a - H)/(len(part) - 1)\\n\\n parts = [p for p in partitions(map(frozenset, rvs)) if len(p) > 1]\\n\\n caekl = min(I_P(p) for p in parts)\\n\\n return caekl\",\n \"def objective(self, var: ndarray) -> float:\\n beta, gamma = self.get_vars(var)\\n r = self.get_residual(beta)\\n d = self.get_varmat(gamma)\\n\\n val = 0.5*(d.logdet() + r.dot(d.invdot(r)))\\n val += self.fevar.prior_objective(beta)\\n val += self.revar.prior_objective(gamma)\\n\\n return val\",\n \"def scipy_minus_objective(w,all_vector_graphs,all_correct_rows,\\\\\\n all_batches,sigma=None,perceptron=None):\\n if perceptron:\\n perceptron._obj_iter += 1\\n obj = 0.0\\n index = 0\\n for vector_graphs,correct_rows,batches in zip(all_vector_graphs,all_correct_rows,all_batches):\\n all_scores = vector_graphs * w\\n sum_log_Z = 0.0\\n for batch in batches:\\n batch_scores = all_scores[batch]\\n sum_log_Z += logsumexp(batch_scores) #np.log(np.exp(batch_scores).sum())\\n obj += all_scores[correct_rows].sum() - sum_log_Z\\n index += 1\\n if index % 100 == 0:\\n print('Objective '+str(index)+' processed')\\n obj = obj / len(all_vector_graphs)\\n if sigma != None:\\n obj += - 0.5 * sigma * (w * w).sum()\\n print('Objective:'+str(obj))\\n return -1.0 * obj\",\n \"def best_policy(self, U):\\n pi = np.zeros((self.nstates),np.int)\\n for s in range(self.nstates):\\n pi[s] = self.best_action(s,U)\\n return pi\",\n \"def objective(params):\\n param0, param1, param2 = params\\n model = Model(param0, param1, param3)\\n\\n train_loss = train(model)\\n val_loss = validate(model)\\n\\n return val_loss\",\n \"def _objective_decorator(func):\\n def inner(preds, dmatrix):\\n \\\"\\\"\\\"internal function\\\"\\\"\\\"\\n labels = dmatrix.get_label()\\n return func(labels, preds)\\n return inner\",\n \"def objective(self, trial):\\n # Suggest start row IDs\\n selected = [self.all_df.index.max()]\\n for i in range(self.n_points):\\n id_min = 3 * (self.n_points - len(selected) + 1)\\n id_max = selected[-1] - 3\\n if id_min + 3 > id_max:\\n return np.inf\\n new = trial.suggest_int(str(i), id_min, id_max)\\n selected.append(new)\\n start_ids = sorted(selected)[:-1]\\n # Create subsets\\n subsets = self.create_subsets(start_ids)\\n # Curve fitting for each subset\\n df_list = [self.curve_fit(df, num)\\n for (num, df) in enumerate(subsets, start=1)]\\n # Calculate the error\\n return self.error_f(df_list)\",\n \"def objective(self, params):\\n \\n model_params = dict()\\n training_params = dict()\\n for param_name in self.model_param_names:\\n model_params[param_name] = params[param_name]\\n for param_name in self.training_param_names:\\n training_params[param_name] = params[param_name]\\n \\n copy = self.m['model']\\n self.m['model'] = self.m['model'](**model_params)\\n self.m.update(training_params)\\n \\n model = Model(**self.m)\\n data = self.datasets[model.data_type]\\n\\n start = timer()\\n res, full_res = CV_fit(model, data, self.datasets)\\n run_time = timer()-start\\n\\n loss = res[0]\\n self.m['model'] = copy\\n \\n return {'loss': loss, 'params': params, 'run_time': run_time, 'status': STATUS_OK}\",\n \"def _optimize(self, objective):\\n points = self._get_eval_points()\\n\\n if self.matrix_to_vector_transform is not None:\\n # Transform the sampled matrix points in vectors\\n points = np.array([self.matrix_to_vector_transform(points[i]) for i in range(self._nb_samples)])\\n\\n evaluations = objective(points)\\n idx_best = np.argmin(evaluations, axis=0)\\n\\n return sc_opt.OptimizeResult(x=points[idx_best, :], success=True, fun=evaluations[idx_best, :],\\n nfev=points.shape[0], message=\\\"OK\\\")\",\n \"def penalty_objective(Z_0, Z_1, Z_2, psi, theta):\\n return theta * sum(map(l1_od_norm, Z_0)) + (1 - theta) * sum(map(psi, Z_2 - Z_1))\",\n \"def min_scalar(objective, **kwargs):\\n result = minimize_scalar(objective, **kwargs)\\n return result.fun\",\n \"def obj_u_opt_N_opt(u, T, alpha, B, N, num_tumor_voxels, Td = 2):\\n x = T.dot(u)\\n alpha_tilde = alpha #np.repeat(N, num_tumor_voxels)*alpha\\n B_tilde = B #np.repeat(N, num_tumor_voxels)*B\\n #Note that all modalities must have the same number of tumor voxels:\\n return alpha_tilde.T.dot(x) - x.T.dot(B_tilde*x) + num_tumor_voxels*(np.sum(N)-1)*(np.log(2)/Td)\",\n \"def uniformCostSearch(problem):\\n \\\"*** YOUR CODE HERE IF YOU WANT TO PRACTICE ***\\\"\\n aStarSearch(problem)\",\n \"def compute_likelihood_given(O_a, skills, beta, g, theta, vals, Zs, povs):\\n O_os, P_os = gen_option_trajectories(O_a, skills, beta, g, theta, vals, Zs, povs) #Generate all option-trajectories\\n if povs != []:\\n rewards, dests, discounts, _, _ = povs\\n probs = []\\n for index, O_o in enumerate(O_os): #For option-trajectory in option-trajectories\\n state = O_a[0] #Initial state\\n prob = 0\\n for o_index, o in enumerate(O_o): #For option in option-trajectory (either state to go to or [goal, beta_k] pair\\n #Now compute the prob of choosing the option o that was actually chosen\\n if type(o) != list and o == theta:\\n to_add = np.exp(beta*20) / Zs[state]\\n state = o\\n elif type(o) != list:\\n to_add = np.exp(beta*(-1 + g*vals[o])) / Zs[state]\\n state = o\\n else: #Compound option\\n Q = (rewards[tuple([state, o_dict[o[0]]])] +\\n vals[dests[tuple([state, o_dict[o[0]]])]]*discounts[tuple([state, o_dict[o[0]]])])\\n # out = option(state, o[0], o[1], g, povs[3][:, o_dict[o[0]]], povs[4][:, o_dict[o[0]]], 10) #Gives samples, discounted rewards, lengths\\n # to_add = np.exp(beta*(out[1] + (pow(g,out[2]))*vals[o[0]])) / Zs[state]\\n to_add = np.exp(beta*Q) / Zs[state]\\n state = o[0]\\n if prob == 0:\\n prob = to_add\\n else:\\n prob *= to_add\\n if prob == 0:\\n print O_a\\n print '??'\\n probs.append(prob)\\n return np.sum(probs)\",\n \"def cost_fun(x, problem):\\n j = 0\\n if problem['use_log_bar']:\\n c = ineqconstr(x, problem)\\n j += np.sum(logbarrierfunc(0.1, c, problem['use_sigma']))\\n\\n x, t_final = matrify(x, problem)\\n if problem['T']!=0:\\n j += np.sum([problem['cost_fun_single'](x[:, :, i], t_final, problem) for i in range(problem['Nv'])])\\n else:\\n j = t_final\\n return j\",\n \"def evaluate_objective(x):\\n\\n x_points_cartesian = x_to_cartesian(x)\\n hull = ConvexHull(x_points_cartesian)\\n\\n # Return the negative value because the optimization is a minimization\\n return -hull.volume\",\n \"def _SD_optimal(t):\",\n \"def _optfn(self, x):\\n\\n logger.debug(\\\" optfn(theta=%s)\\\", str(x))\\n\\n wmx = max(self.weights) * self.weighttrunc\\n\\n ip = []\\n for i,w in enumerate(self.weights):\\n if w < wmx:\\n continue\\n ip.append((i,w,x))\\n\\n if self.pool is None:\\n itr = map(self.worker.loglik_grad, ip)\\n else:\\n itr = self.pool.imap_unordered(_pool_loglik_grad, ip, 10)\\n\\n if self._prior_shape is None:\\n ll = 0.\\n grad = np.zeros(len(x))\\n else:\\n ll = sum(sp.special.xlogy(self._prior_shape-1,x)-(x/self._prior_scale))\\n grad = (self._prior_shape - 1)/x - 1/self._prior_scale\\n\\n for l,g in itr:\\n ll += l\\n grad += g\\n\\n logger.debug(\\\" optfn=%g\\\", ll)\\n\\n return -ll, -grad\",\n \"def objective(xs, fps, fns):\\n Wp = - np.log(fpr / (1 - fpr)) # Weight for false positives\\n Wn = - np.log(fnr / (1 - fnr)) # Weight for false negatives\\n Wx = - np.log(f / (1 - f)) # Weight for all positives\\n return np.sum(xs) * Wx + np.sum(fps) * Wp + np.sum(fns) * Wn\",\n \"def optimize_qaoa(self, x: tuple, *args: tuple) -> float:\\n if args:\\n rho = self.simulate_qaoa(\\n params=x,\\n with_noise=args[0]\\n )\\n else:\\n rho = self.simulate_qaoa(\\n params=x\\n )\\n return numpy.trace(self.cost * rho).real\",\n \"def costFun(self, S, x):\",\n \"def objective(self, x):\\n rvs = self._rvs\\n joint = self.construct_joint(x)\\n joint = joint.sum(axis=self._others, keepdims=True)\\n margs = [ joint.sum(axis=tuple(rv)) for rv in rvs ]\\n crv = joint.sum(axis=tuple(flatten(rvs)))\\n\\n a = sum(h(marg.ravel()) for marg in margs)\\n b = h(joint.ravel())\\n c = h(crv.ravel())\\n\\n dtc = a - (len(rvs) - 1)*b - c\\n\\n return dtc\",\n \"def objective(data: VLEPoints, params: typing.List[float]) -> float:\\n error = 0\\n mixture = Mixture(\\n name=\\\"\\\",\\n first_component=data.components[0],\\n second_component=data.components[1],\\n uniquac_params=UNIQUACParameters.from_array(params),\\n )\\n for point in data:\\n error += (get_partial_pressures(temperature=point.temperature,\\n composition=point.composition,\\n mixture=mixture,\\n calculation_type=\\\"UNIQUAC\\\",\\n )[0] - point.pressures[0]) ** 2 \\\\\\n + (get_partial_pressures(temperature=point.temperature,\\n composition=point.composition,\\n mixture=mixture,\\n calculation_type=\\\"UNIQUAC\\\",\\n )[1] - point.pressures[1]) ** 2\\n return numpy.sqrt(error / len(data))\",\n \"def optimize(self):\\n optimizer = tf.train.RMSPropOptimizer(self.config.learning_rate)\\n return optimizer.minimize(self.cost)\",\n \"def cost_function(H, n_qubits, p, params):\\n ini_state=plus_state(n_qubits)\\n for i in range(p):\\n ini_state=qaoa_step(ini_state,H,n_qubits,params=[params[2*i],params[2*i+1]])\\n return ((sparse.spmatrix.getH(ini_state)).dot(H.dot(ini_state))).real, ini_state\",\n \"def solver(u_init, eta_0, eta, eta_lin, T, H, L_lhs, L_rhs, alpha, gamma, B, D, C, ftol = 1e-3, max_iter = 5000, verbose = 0, nnls_max_iter=30):\\n\\n # Raise('NotImplementedError: only adjusted the arguments.')\\n #Need to incorporate L_lhs into stacked and appropriate w_lin updates, u_update and eta_lin increments\\n #precompute the expensive operation:\\n lin_penalties = 1/np.sqrt(2*eta_lin)\\n eta_T_H_L_stacked = scipy.sparse.vstack([T.multiply(1/np.sqrt(2*eta_0))] + [H[i].multiply(1/np.sqrt(2*eta[i])) for i in range(len(H))] + [L_lhs.multiply(lin_penalties[:,None])])\\n #!!!!\\n# premultiplied_lhs = eta_T_H_stacked.T.dot(eta_T_H_stacked).toarray()\\n #!!!!\\n u_prev = u_init + 1\\n u = u_init\\n count = 0\\n obj_history = []\\n relaxed_obj_history = [-1, 0.1] #just two initial values to enter the loop\\n while np.abs((relaxed_obj_history[-2] - relaxed_obj_history[-1])/relaxed_obj_history[-2]) > ftol and count < max_iter:#np.linalg.norm(u - u_prev, np.inf) > 1e-3 and count < max_iter: #Maybe all of them stop changing\\n start = time.time()\\n \\n u_prev = np.copy(u)\\n w_0 = w_0_update(eta_0, u, T, alpha, B) \\n w = w_update(u, H, gamma, D, C) \\n w_lin = w_lin_update(u, L_lhs, L_rhs)\\n# u = u_update(eta_0, eta, w_0, w, eta_T_H_stacked, nnls_max_iter=50)\\n #!!!!\\n # u = u_update(eta_0, eta, w_0, w, eta_T_H_L_stacked, nnls_max_iter=30)\\n u = u_update(eta_0, eta, eta_lin, w_0, w, w_lin, eta_T_H_L_stacked, premultiplied_lhs = None, nnls_max_iter=nnls_max_iter)\\n #!!!!\\n count += 1 \\n if count == 10:\\n u_inf = np.copy(u)\\n w_0_inf = w_0[:]\\n w_inf = w[:]\\n w_lin_inf = w_lin[:]\\n if count > 10 and np.abs(cur_obj) > 1e+15: #HANDLE THIS BETTER!!!\\n print('INFINITY! RETURNING u at the 10-th iteration to enter the feasibility loop')\\n return u_inf, w_0_inf, w_inf, w_lin_inf, obj_history, relaxed_obj_history\\n \\n cur_obj = obj_u_opt_N_fixed(u, T, alpha, B)\\n obj_history.append(cur_obj)\\n cur_relaxed_obj = relaxed_obj_u_opt_N_fixed(u, w_0, w, w_lin, eta_0, eta, eta_lin, T, H, L_lhs, alpha, B)\\n # relaxed_obj_u_opt_N_fixed(u, w_0, w, eta_0, eta, T, H, alpha, B)\\n relaxed_obj_history.append(cur_relaxed_obj) \\n \\n stop = time.time()\\n duration = stop-start\\n \\n if count%1 == 0 and verbose: \\n stopping_criterion = np.abs((relaxed_obj_history[-2] - relaxed_obj_history[-1])/relaxed_obj_history[-2])\\n print(' iter = {}, stopping criterion:{}, OBJ {}'.format(count, stopping_criterion, cur_obj))\\n print(' This iteration took: {}'.format(duration))\\n return u, w_0, w, w_lin, obj_history, relaxed_obj_history\",\n \"def arg_min_scalar(objective, **kwargs):\\n return minimize_scalar(objective, **kwargs).x\",\n \"def buildTheta():\\n mylambda = 0.\\n initial_theta = np.zeros((X.shape[1],1)).reshape(-1)\\n Theta = np.zeros((10,X.shape[1]))\\n for i in range(10):\\n iclass = i if i else 10 #class \\\"10\\\" corresponds to handwritten zero\\n logic_Y = np.array([1 if x == iclass else 0 for x in y])#.reshape((X.shape[0],1))\\n itheta, imincost = optimizeTheta(initial_theta,X,logic_Y,mylambda)\\n Theta[i,:] = itheta\\n return Theta\",\n \"def _optimise(self):\\n pass\",\n \"def compute_objective(X, C):\\n # similar as mse\\n\\n acc = 0\\n for x in range(len(X)):\\n acc += (nearest_distance(X.iloc[x], C)) ** 2\\n\\n return acc\",\n \"def return_lu_func(RunningCost='Minimize Input Energy'):\\n if type(RunningCost)==str:\\n assert RunningCost in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\\\"\\n else:\\n assert type(RunningCost)==list, \\\"RunningCost must be a list of cost types.\\\"\\n for el in RunningCost:\\n assert type(el)==str, \\\"Each element of RunningCost must be a string. Not \\\" + str(type(el)) + \\\".\\\"\\n assert el in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\\\" + el + \\\"' not accepted.\\\"\\n\\n if \\\"Minimize Input Energy\\\" in RunningCost:\\n result1 = lambda X,U,dt: np.matrix([[k3*U*dt]])\\n else:\\n result1 = lambda X,U,dt: np.matrix([[0]])\\n\\n if \\\"Minimize time away from target angle\\\" in RunningCost:\\n result2 = lambda X,U,dt: np.matrix([[0]])\\n else:\\n result2 = lambda X,U,dt: np.matrix([[0]])\\n\\n if \\\"Minimize time away from target angular velocity\\\" in RunningCost:\\n result3 = lambda X,U,dt: np.matrix([[0]])\\n else:\\n result3 = lambda X,U,dt: np.matrix([[0]])\\n\\n result = lambda X,U,dt: result1(X,U,dt) \\\\\\n + result2(X,U,dt) \\\\\\n + result3(X,U,dt)\\n return(result)\",\n \"def compute_surr_objective(A_hat, old_policy_model, policy_model, j, state_part_mean, state_part_std, config=PPO_config):\\n \\n def clip(c, epsilon):\\n if c < 1-epsilon:\\n return 1- epsilon\\n elif c > 1+epsilon:\\n return 1+ epsilon\\n else:\\n return c\\n \\n clipping_param = np.amax([0.01, (1-j/config['J'])*config['clipping']])\\n print(\\\"Clipping parameters: {}\\\".format(clipping_param))\\n #K = PPO_config['K']\\n #assert(K==len(A_hat))\\n policy_model.to(device)\\n old_policy_model.to(device)\\n surr_objective = 0\\n kl_est_list = []\\n for k in range(len(A_hat)):\\n for i in range(len(A_hat[k])):\\n # CHECK: if the action is an integer corresponding to the state\\n curr_state = torch.tensor(A_hat[k][i][0]).float()\\n try:\\n curr_state_norm_input = torch.cat((curr_state[:,0].unsqueeze(1), (curr_state[:,1:]-state_part_mean)/state_part_std))\\n except:\\n curr_state_norm_input = torch.cat((curr_state[0].unsqueeze(0), (curr_state[1:]-state_part_mean)/state_part_std))\\n\\n curr_state_norm_input[np.isnan(curr_state_norm_input)] = 0\\n curr_state_norm_input.to(device)\\n curr_action = A_hat[k][i][1]\\n\\n r_theta_ksi = policy_model(curr_state_norm_input.to(device))[curr_action] / old_policy_model(curr_state_norm_input.to(device))[curr_action] \\n r_theta_ksi_num = r_theta_ksi.cpu().detach().numpy()\\n single_est_from_r = r_theta_ksi_num-1-np.log(r_theta_ksi_num)\\n kl_est_list.append(single_est_from_r)\\n A_hat_ksi = A_hat[k][i][2] # Estimate the A_hat\\n surr_objective += min(r_theta_ksi * A_hat_ksi, clip(r_theta_ksi, clipping_param) * A_hat_ksi)\\n surr_objective /= len(A_hat)\\n kl_est = np.mean(np.asarray(kl_est_list))\\n\\n return surr_objective, kl_est\",\n \"def u_update(eta_0, eta, eta_lin, w_0, w, w_lin, eta_T_H_L_stacked, premultiplied_lhs = None, nnls_max_iter=50): \\n # PREMULTIPLIED LHS IS AN EXTRA ARGUMENT! Set it to None and add solver! \\n \\\"\\\"\\\"In the following +[[]] and [:-1] are added to keep thing 1dim array of objects and still multiply it elemtwisely\\\"\\\"\\\" \\n# #B.append([]) #THIS IS WRONG, CHANGES THE LIST \\n# B_concat = np.concatenate((1/np.sqrt(2*eta))*np.array(B+[[]])[:-1], axis = 0) \\n# A_ls = np.concatenate([(1/np.sqrt(2*eta0))*A, B_concat], axis = 0) \\n# #print(np.array(B).shape) \\n# #print(w[0].shape) \\n# #print(w, eta) \\n# #w.append([]) THIS IS WRONG, CHANGES THE LIST \\n# w_concat = np.concatenate((1/np.sqrt(2*eta))*np.array(w+[[]])[:-1], axis = 0) #[:-1] Added as a hack to keep it one-dim array of objects \\n# eta_w = np.expand_dims(1/np.sqrt(2*eta),1)*np.array(w) \\n# print(eta_w.shape) \\n# b_ls = np.concatenate([(1/np.sqrt(2*eta_0))*w_0, eta_w.flatten()], axis = 0) \\n #Use correct broadcasting?\\n w_concat = np.concatenate((1/np.sqrt(2*eta))*np.array(w+[[]])[:-1], axis = 0) #[:-1] Added as a hack to keep it one-dim array of objects \\n b_ls = np.concatenate([(1/np.sqrt(2*eta_0))*w_0, w_concat, (1/np.sqrt(2*eta_lin))*w_lin], axis = 0) \\n# print(np.sum(eta_w.flatten() != w_concat)) \\n# premultiplied_time_start = time.time() \\n# premultiplied_lhs = eta_T_H_stacked.T.dot(eta_T_H_stacked).toarray() \\n# premultiplied_time_end = time.time() \\n# print('premultiplying took {}'.format(premultiplied_time_end - premultiplied_time_start)) \\n# premultiplied_rhs = eta_T_H_stacked.T.dot(b_ls) \\n# u_next = nnls_predotted(premultiplied_lhs, premultiplied_rhs, tol=1e-5) \\n# print(eta_T_H_stacked.shape, b_ls.shape) \\n# A_ls_t_b = eta_T_H_stacked.T.dot(b_ls) \\n# w =scipy.sparse.linalg.spsolve_triangular(RT, A_ls_t_b, lower = True) \\n# x = scipy.sparse.linalg.spsolve_triangular(R, w, lower = False) \\n# u_next = x \\n u_next = scipy.optimize.lsq_linear(eta_T_H_L_stacked, b_ls, bounds = (0, np.inf), tol=1e-3, lsmr_tol=1e-3, max_iter=nnls_max_iter, verbose=1).x \\n# u = scipy.optimize.lsq_linear(premultiplied_lhs, premultiplied_rhs, bounds = (0, np.inf), tol=1e-5).x \\n return u_next\",\n \"def return_luu_func(RunningCost='Minimize Input Energy'):\\n if type(RunningCost)==str:\\n assert RunningCost in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'.\\\"\\n else:\\n assert type(RunningCost)==list, \\\"RunningCost must be a list of cost types.\\\"\\n for el in RunningCost:\\n assert type(el)==str, \\\"Each element of RunningCost must be a string. Not \\\" + str(type(el)) + \\\".\\\"\\n assert el in ['Minimize Input Energy',\\n 'Minimize time away from target angle',\\n 'Minimize time away from target angular velocity'],\\\\\\n \\\"Each element of RunningCost must be either 'Minimize Input Energy','Minimize time away from target angle', or 'Minimize time away from target angular velocity'. '\\\" + el + \\\"' not accepted.\\\"\\n\\n if \\\"Minimize Input Energy\\\" in RunningCost:\\n result1 = lambda X,U,dt: np.matrix([[k3*dt]])\\n else:\\n result1 = lambda X,U,dt: np.matrix([[0]])\\n\\n if \\\"Minimize time away from target angle\\\" in RunningCost:\\n result2 = lambda X,U,dt: np.matrix([[0]])\\n else:\\n result2 = lambda X,U,dt: np.matrix([[0]])\\n if \\\"Minimize time away from target angular velocity\\\" in RunningCost:\\n result3 = lambda X,U,dt: np.matrix([[0]])\\n else:\\n result3 = lambda X,U,dt: np.matrix([[0]])\\n\\n result = lambda X,U,dt: result1(X,U,dt) \\\\\\n + result2(X,U,dt) \\\\\\n + result3(X,U,dt)\\n return(result)\",\n \"def annealing_objective(x: npt.NDArray[np.float64], *args: Any) -> float:\\n blocks = [int(ind) for ind in x]\\n\\n approx_threshold = args[0]\\n upper_limit = args[1]\\n psol_configs = args[2]\\n dists = args[3]\\n utrys = args[4]\\n n_cxs = args[5]\\n p = args[6]\\n w = args[7]\\n\\n if blocks in psol_configs:\\n return 1.2\\n\\n approx_dist = sum(dists[i][b] for i, b in enumerate(blocks))\\n\\n if approx_dist > approx_threshold:\\n return 1.1\\n\\n n_cx = w * (sum(n_cxs[i][b] for i, b in enumerate(blocks)) / upper_limit)\\n\\n if len(psol_configs) == 0:\\n return n_cx\\n\\n distances = [0] * len(psol_configs)\\n\\n for index, config in enumerate(psol_configs):\\n distances[index] = sum(\\n utrys[i][config[i]].get_distance_from(utrys[i][b])\\n < max(dists[i][config[i]], dists[i][b])\\n for i, b in enumerate(blocks)\\n ) / len(blocks)\\n\\n b_dv = (1 - w) * np.percentile(distances, p)\\n\\n return n_cx + b_dv\",\n \"def _objective(self, params, model_ID, model_dict, X, y, **kwargs):\\n model = model_dict['model']\\n param_grid = model_dict['param_grid'].copy()\\n params = params.copy()\\n \\n obj_verbose = max(0,self.verbose-2)\\n \\n type_X = str(type(X))\\n \\n if 'dask' in type_X:\\n X = X.compute()\\n y = y.compute()\\n \\n if obj_verbose>=2:\\n print('params',params)\\n \\n params_transform, model = self._update_model_params(params, \\n model_ID,\\n model, \\n param_grid)\\n type_model = str(type(model))\\n \\n if obj_verbose>=2:\\n print('params_transform',params_transform)\\n if 'sklearn' in type_model or 'xgboost' in type_model:\\n \\n cv_scores = _sklearn_model_selection.cross_val_score(model, X, y,\\n scoring= self.scoring['metric'],\\n cv = self.cv,\\n n_jobs= self.n_jobs,\\n verbose = obj_verbose\\n )\\n\\n else: #using neural net function\\n import tensorflow as _tf\\n #check for kwargs\\n epochs = 100\\n batch_size = 32\\n callbacks = [_tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience =10)]\\n for item in kwargs.items():\\n if 'epochs' in item[0]: \\n epochs = item[1]\\n elif 'batch_size' in item[0]: \\n batch_size = item[1]\\n elif 'callbacks' in item[0]: \\n callbacks = item[1] \\n cv_scores = _NeuralNet.cross_val_score(model,\\n batch_size,\\n epochs,\\n X, y,\\n callbacks,\\n scoring = self.scoring['metric'],\\n cv = self.cv,\\n verbose= obj_verbose)\\n \\n cv_score = _np.mean(cv_scores)\\n \\n if 'sklearn' in type_model or 'xgboost' in type_model:\\n if self.scoring['maximize']==True or self.scoring['metric']==None:\\n cv_score = 1/cv_score \\n else:\\n if self.scoring['maximize']==True and self.scoring['metric']!=None :\\n cv_score = 1/cv_score \\n \\n objective = {'loss': cv_score,\\n 'params': params,\\n 'status': _hyperopt.STATUS_OK,\\n 'eval_time': _time.time()}\\n return objective\",\n \"def objective_function(self, variables, mask, load, generation, annuity_scalar=1):\\n size = sum(mask)\\n # pay for reg down energy, get paid for reg up energy\\n # paid revenue for capacity to do both\\n\\n p_regu = cvx.Parameter(size, value=self.p_regu.loc[mask].values, name='p_regu')\\n p_regd = cvx.Parameter(size, value=self.p_regd.loc[mask].values, name='p_regd')\\n p_ene = cvx.Parameter(size, value=self.price.loc[mask].values, name='price')\\n\\n regup_charge_payment = cvx.sum(variables['regu_c'] * -p_regu) * annuity_scalar\\n regup_charge_settlement = cvx.sum(variables['regu_c'] * -p_ene) * self.dt * self.kru_avg * annuity_scalar\\n\\n regup_disch_payment = cvx.sum(variables['regu_d'] * -p_regu) * annuity_scalar\\n regup_disch_settlement = cvx.sum(variables['regu_d'] * -p_ene) * self.dt * self.kru_avg * annuity_scalar\\n\\n regdown_charge_payment = cvx.sum(variables['regd_c'] * -p_regd) * annuity_scalar\\n regdown_charge_settlement = cvx.sum(variables['regd_c'] * p_ene) * self.dt * self.krd_avg * annuity_scalar\\n\\n regdown_disch_payment = cvx.sum(variables['regd_d'] * -p_regd) * annuity_scalar\\n regdown_disch_settlement = cvx.sum(variables['regd_d'] * p_ene) * self.dt * self.krd_avg * annuity_scalar\\n\\n return {'regup_payment': regup_charge_payment + regup_disch_payment,\\n 'regdown_payment': regdown_charge_payment + regdown_disch_payment,\\n 'fr_energy_settlement': regup_disch_settlement + regdown_disch_settlement + regup_charge_settlement + regdown_charge_settlement}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7534007","0.75032496","0.75032496","0.733393","0.70547366","0.6989981","0.67604387","0.6705372","0.65997154","0.65972316","0.6582349","0.6576692","0.6509474","0.64613736","0.63616884","0.6350423","0.6277985","0.62494683","0.6182294","0.6176217","0.61698246","0.6161798","0.61309665","0.6122308","0.61018056","0.6022587","0.5975683","0.5967913","0.5963623","0.5946506","0.59325355","0.5903355","0.58849037","0.5878727","0.5868404","0.5867751","0.58646876","0.5860697","0.5849144","0.58351356","0.5830843","0.58264226","0.5825919","0.5820146","0.5788786","0.5781775","0.5775983","0.5773021","0.5772958","0.57677555","0.5761686","0.5754224","0.5746678","0.5737704","0.5733742","0.5733742","0.57336","0.5733348","0.57146037","0.56881166","0.5674221","0.5672358","0.5668293","0.56681085","0.5660374","0.5654576","0.5644123","0.56414896","0.5640519","0.56291264","0.5605276","0.55882126","0.55787367","0.5573993","0.5573663","0.557242","0.5570128","0.55701023","0.5566639","0.5552163","0.5535199","0.5523764","0.5521847","0.5519568","0.5515221","0.5507067","0.5505733","0.5497814","0.5487531","0.54852295","0.5468235","0.54568964","0.5453294","0.54511213","0.54415953","0.5434186","0.5428943","0.5418477","0.5416099","0.5414987","0.54137003"],"string":"[\n \"0.7534007\",\n \"0.75032496\",\n \"0.75032496\",\n \"0.733393\",\n \"0.70547366\",\n \"0.6989981\",\n \"0.67604387\",\n \"0.6705372\",\n \"0.65997154\",\n \"0.65972316\",\n \"0.6582349\",\n \"0.6576692\",\n \"0.6509474\",\n \"0.64613736\",\n \"0.63616884\",\n \"0.6350423\",\n \"0.6277985\",\n \"0.62494683\",\n \"0.6182294\",\n \"0.6176217\",\n \"0.61698246\",\n \"0.6161798\",\n \"0.61309665\",\n \"0.6122308\",\n \"0.61018056\",\n \"0.6022587\",\n \"0.5975683\",\n \"0.5967913\",\n \"0.5963623\",\n \"0.5946506\",\n \"0.59325355\",\n \"0.5903355\",\n \"0.58849037\",\n \"0.5878727\",\n \"0.5868404\",\n \"0.5867751\",\n \"0.58646876\",\n \"0.5860697\",\n \"0.5849144\",\n \"0.58351356\",\n \"0.5830843\",\n \"0.58264226\",\n \"0.5825919\",\n \"0.5820146\",\n \"0.5788786\",\n \"0.5781775\",\n \"0.5775983\",\n \"0.5773021\",\n \"0.5772958\",\n \"0.57677555\",\n \"0.5761686\",\n \"0.5754224\",\n \"0.5746678\",\n \"0.5737704\",\n \"0.5733742\",\n \"0.5733742\",\n \"0.57336\",\n \"0.5733348\",\n \"0.57146037\",\n \"0.56881166\",\n \"0.5674221\",\n \"0.5672358\",\n \"0.5668293\",\n \"0.56681085\",\n \"0.5660374\",\n \"0.5654576\",\n \"0.5644123\",\n \"0.56414896\",\n \"0.5640519\",\n \"0.56291264\",\n \"0.5605276\",\n \"0.55882126\",\n \"0.55787367\",\n \"0.5573993\",\n \"0.5573663\",\n \"0.557242\",\n \"0.5570128\",\n \"0.55701023\",\n \"0.5566639\",\n \"0.5552163\",\n \"0.5535199\",\n \"0.5523764\",\n \"0.5521847\",\n \"0.5519568\",\n \"0.5515221\",\n \"0.5507067\",\n \"0.5505733\",\n \"0.5497814\",\n \"0.5487531\",\n \"0.54852295\",\n \"0.5468235\",\n \"0.54568964\",\n \"0.5453294\",\n \"0.54511213\",\n \"0.54415953\",\n \"0.5434186\",\n \"0.5428943\",\n \"0.5418477\",\n \"0.5416099\",\n \"0.5414987\",\n \"0.54137003\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":83096,"cells":{"query":{"kind":"string","value":"Returns formatted output for a dictionary with string keys and float values."},"document":{"kind":"string","value":"def format_dict(d: Dict[str, float], format: str=\".4f\") -> Dict[str, float]:\n return dict((k, float(f'{v:{format}}')) for k,v in d.items())"},"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 print_dict(dictionary):\n for key in sorted(dictionary.keys()):\n value = dictionary[key]\n if type(value) == float:\n value = round(value, 8)\n print key, value","def print_dict(dictionary):\r\n for key in sorted(dictionary.keys()):\r\n value = dictionary[key]\r\n if type(value) == float:\r\n value = round(value, 8)\r\n print (key, value)","def _DictToString(self, value_dict, str_length=5):\n\n def FormatValue(v, value_format, str_length):\n if isinstance(v, (int, float)):\n return value_format % v\n else:\n return str(v).rjust(str_length)\n\n text = []\n blank = '--'.rjust(str_length)\n\n if self._show_label:\n text.append(' '.join(k.rjust(str_length) for k in self._node_labels))\n\n if not self._precision:\n value_format = '%% %dd' % str_length\n else:\n value_format = '%% %d.%df' % (str_length, self._precision)\n\n text.append(' '.join(\n [FormatValue(value_dict[k], value_format, str_length)\n if k in value_dict else blank for k in self._node_labels]))\n\n return '\\n'.join(text)","def format_metrics(d: dict) -> dict:\n new = {}\n\n for key in d:\n if isinstance(d[key], dict):\n new[key] = format_metrics(d[key])\n elif isinstance(d[key], float):\n new[key] = float(\"{:.8f}\".format(d[key]))\n else:\n new[key] = d[key]\n\n return new","def print_metric_dict(self, metric_dict):\n print(\"\".join([\" {}: {:4f},\".format(k, v) for k, v in metric_dict.items()]))","def nice_string_output(d, extra_spacing=5, decimals=3):\n\n names = d.keys()\n max_names = len_of_longest_string(names)\n\n values = values_to_string(d.values(), decimals=decimals)\n max_values = len_of_longest_string(values)\n\n string = \"\"\n for name, value in zip(names, values):\n spacing = extra_spacing + max_values + max_names - len(name) - 1\n string += \"{name:s} {value:>{spacing}} \\n\".format(name=name, value=value, spacing=spacing)\n return string[:-2]","def float_format(self):\n ...","def format_dict(kv_list):\n return '\\n'.join(['{} - {}'.format(key, value) for\n key, value in kv_list])","def format(self, valDict):\n return self._formatStr % valDict","def _print_table(stats):\n max_key_len = max([len(key) for key in stats])\n width_right = 15\n width_left = max(width_right, max_key_len)\n divider = '+-' + '-' * width_left + '-+-' + '-' * width_right + '-+'\n\n def get_format_char(value):\n if isinstance(value, int):\n return 'd'\n elif isinstance(value, float):\n return '.4f'\n else:\n return 's'\n\n print(divider)\n for name, value in stats.items():\n left_format = f':>{width_left}s'\n right_format = f':<{width_right}{get_format_char(value)}'\n line_format = f'| {{{left_format}}} | {{{right_format}}} |'\n line = line_format.format(name, value)\n print(line)\n print(divider)","def print_formatted_values(**kwargs):\n string = ', '.join([f'{k}: {format_number(kwargs[k])}' for k in kwargs])\n print(string)","def format(self):\r\n output = {}\r\n\r\n for k, v in self:\r\n if type(v) == collections.InstrumentedList: # pragma: no cover\r\n output[k] = [item.to_dict() for item in v]\r\n elif isinstance(v, (date, datetime, time)): # pragma: no cover\r\n output[k] = v.isoformat()\r\n elif isinstance(v, (float, Decimal)): # pragma: no cover\r\n output[k] = str(v)\r\n else:\r\n output[k] = v\r\n\r\n return output","def pretty_print(name, input, val_width=40, key_width=0):\n\n # root\n pretty_str = name + ': {\\n'\n\n # determine key width\n for key in input.keys(): key_width = max(key_width, len(str(key)) + 4)\n\n # cycle keys\n for key in input.keys():\n\n val = input[key]\n\n # round values to 3 decimals..\n if type(val) == np.ndarray: val = np.round(val, 3).tolist()\n\n # difficult formatting\n val_str = str(val)\n if len(val_str) > val_width:\n val_str = pprint.pformat(val, width=val_width, compact=True)\n val_str = val_str.replace('\\n', '\\n{tab}')\n tab = ('{0:' + str(4 + key_width) + '}').format('')\n val_str = val_str.replace('{tab}', tab)\n\n # more difficult formatting\n format_str = '{0:' + str(4) + '}{1:' + str(key_width) + '} {2:' + str(val_width) + '}\\n'\n pretty_str += format_str.format('', key + ':', val_str)\n\n # close root object\n pretty_str += '}'\n\n return pretty_str","def format_value(self, value: float) -> str:\r\n ...","def format_value(self, value: float) -> str:\r\n ...","def _format_dict(self, dict):\n\n result = \"\"\n for k, v in dict.items():\n result += \"\\n{0}: {1}\".format(k.capitalize(), v)\n\n return result","def format_pair(self, k, v):\n if isinstance(v, int):\n data_width = len(str(v)) + 1\n header_width = len(str(k))\n w = max(data_width, header_width)\n h = ('%% %us'%w)%k\n return ' '*len(h), h, '%%%ud'%w\n elif k=='dt':\n fmt = '%6.3f'\n return 6*' ', '%6s'%k, fmt\n elif isinstance(v, float):\n fmt = '% .3e'\n data_width = len(fmt%1)\n header_width = len(str(k))\n w = max(data_width, header_width)\n spaces = ' '*(w-data_width)\n h = ('%%%us'%w)%k\n return ' '*len(h), h, spaces+fmt\n elif isinstance(v, dict):\n results = [self.format_pair(k_, v_) for k_, v_ in v.items()]\n keys = ' '.join([str(r[-2]) for r in results])\n fmts = ' '.join([str(r[-1]) for r in results])\n h1 = ('%%.%us'%(len(keys))) % k\n pl = (len(keys)-len(h1)) // 2\n pr = (len(keys)-len(h1)) - pl\n h1 = '.' * pl + h1 + '.' * pr\n return h1, keys, fmts\n elif isinstance(v, h5py.ExternalLink):\n data_width = len('hdf5-link')\n header_width = len(str(k))\n w = max(data_width, header_width)\n h = ('%%%us'%w)%k\n return ' '*len(h), h, '%%%us'%w\n elif isinstance(v, h5py.VirtualLayout):\n data_width = len('hdf5-vds')\n header_width = len(str(k))\n w = max(data_width, header_width)\n h = ('%%%us'%w)%k\n return ' '*len(h), h, '%%%us'%w\n else:\n fmt = '%%%u.%us' % (self.min_str_len, self.max_str_len)\n w = len(fmt%v)\n h = ('%%%us'%w)%k\n return ' '*len(h), h, fmt","def printMap(values, klab, vlab, precision, offset=16):\n\tprint(klab.ljust(offset, \" \") + vlab)\n\tfor k in values.keys():\n\t\tv = values[k]\n\t\tks = toStr(k, precision).ljust(offset, \" \")\n\t\tvs = toStr(v, precision)\n\t\tprint(ks + vs)","def dict(self) -> Dict[str, List[NumericType]]:\n return {r.name: r.floats() for r in self._records}","def format_str() -> str:\r\n decimal_places = conf.instance[\"general\"][\"output\"][\"model_results_decimal_places\"]\r\n return f\"{{:.{decimal_places}f}}\"","def _createTextProfile(self, indict):\r\n\r\n keys = map(lambda x: x[0], indict)\r\n vals = map(lambda x: x[1], indict)\r\n\r\n outstrs = [\"\\n\"]\r\n propDict = {}\r\n total = sum(vals)\r\n maxLenKey = max([len(a) for a in keys])\r\n maxLenVal = max([len(repr(a)) for a in vals]) \r\n\r\n for k, v in indict:\r\n outstr = \" \"\r\n outstr += k.ljust(maxLenKey + 1)\r\n outstr += (\"%.2f\" % v).ljust(maxLenVal + 1)\r\n outstr += \"-\" * int(self.numBars * (v / total))\r\n outstrs.append(outstr)\r\n\r\n return \"\\n\".join(outstrs)","def dict_pretty_print(D: dict, indent_lvl=0):\n print(\"Using 3 decimal places.\")\n base_indent = indent_lvl * \" \"\n indent = (indent_lvl+2)*\" \"\n print(f\"{base_indent}\" + \"{\")\n for key, value in D.items():\n print(f\"{indent}{key}: \", end=\"\")\n if type(value) is dict:\n print(\"\")\n dict_pretty_print(value, indent_lvl + 2)\n else:\n print(f\"{value:.3f}\")\n print(f\"{base_indent}\" + \"}\")","def _format_output(predictions, descriptors):\n # type: (list,dict) -> list\n\n output = []\n for item in predictions:\n output.append(' - '.join([descriptors.get(item[1]), str(item[0]).format('0.00f')]))\n\n return output","def nice_dict_format(d):\n return ''.join([key+\": \"+str(d[key])+\"\\n\" for key in list(d.keys())])","def print_object(dict_to_print, *, name='', uppercase=False):\n string = '' if name == '' else name.ljust(10)\n for key, value in dict_to_print.items():\n string += f'{key.upper() if uppercase else key}: {\"\" if value < 0 else \" \"}{float(value):.4}'.ljust(\n len(key) + 10)\n\n print(string)","def prettyVal( value, key=None):\n\n frmt = '%.4f %c'\n if value <= 1E-9: # Pico\n s = frmt % (value * 1E12, 'p')\n elif value < 1E-6: # Nano\n s = frmt % (value * 1E9, 'n')\n elif value < 1E-3: # Micro\n s = frmt % (value * 1E6, 'u')\n elif value < 1: # Milli\n s = frmt % (value * 1E3, 'm')\n else: # (whole units)\n s = frmt % (value, ' ')\n\n if key is not None: # Add unit suffix if given unit\n if key[0] == 'C':\n s += 'F' # Capacitors have units Farads\n if value < 1e-12 or value > 1e-3:\n s += ' BAD!' # outside of reasonable capacitors\n elif key[0] == 'L':\n s += 'H' # Inductors have units Henries\n if value < 1e-12 or value > 1e-3:\n s += ' BAD!' # outside of reasonable henries\n\n return s","def _format_values_in_map(self, dict_values: dict) -> dict:\n\n for key in dict_values.keys():\n if str(key).lower() == \"exception\":\n dict_values[key] = self._format_exception(str(dict_values[key]))\n break\n\n new_map = {}\n for key, value in dict_values.items():\n value = str(value)\n if ' ' in value:\n value = f'\"{value}\"'\n\n new_map[key] = f\"{key.replace(' ', '')}={value}\"\n return new_map","def print_eval(eval_dict):\n\n status = ''.join(\n ['{}: {:.6f}, '.format(key, eval_dict[key]) for key in sorted(eval_dict)])\n print(status[:-2])","def show_values():\n dic_drg = {}\n dic_age = {}\n dic_sex = {}\n dic_sline = {}\n for tup in all_data:\n drg = tup[7]\n age = tup[9]\n sex = tup[10]\n sline = tup[14]\n\n dic_drg[drg] = 1\n dic_age[age] = 1\n dic_sex[sex] = 1\n dic_sline[sline] = 1\n\n print \"Age values\"\n for key in sorted(dic_age.keys()):\n print key\n\n print \"Sex values\"\n for key in sorted(dic_sex.keys()):\n print key\n\n print \"Service line values\"\n for key in sorted(dic_sline.keys()):\n if key is None or len(key) == 0:\n continue\n print \"'\" + key + \"',\",\n print\n\n print \"Drg values\"\n for key in sorted(dic_drg.keys()):\n if key is None or len(key) == 0:\n continue\n print\"'\" + key + \"',\",\n print","def format_data(self)->float: \n try:\n formatted = chr(self.data[0])\n for i in range(1, len(self.data)): \n formatted = formatted + (chr(self.data[i])) \n return str(formatted)\n except ValueError as e:\n print(e)\n except TypeError as e:\n print(e)","def pretty(self):\n table = [\"\".join([\"%4s \" % s for s in self.alpha.getSymbols()])]\n table.append(\"\".join([\"%3.2f \" % y for y in Distrib.getFreq(self)]))\n return table","def format_to_print(key, value):\n if value < 0:\n sign = \"-\"\n else:\n sign = \"\"\n\n key = key.upper()\n if key.replace(\"_ERR\", \"\") not in (FLOAT_PARAMS + STR_PARAMS):\n raise ValueError(\"invalid key %r\" % key)\n\n if key in FLOAT_PARAMS or isinstance(value, basestring):\n return str(value)\n elif key in ['RA', 'RAJ', 'RA_ERR', 'RAJ_ERR']:\n sec = value / SIDFREQ\n if '_ERR' in key:\n return str(sec)\n m, s = divmod(sec, 60)\n h, m = divmod(m, 60)\n sign = \"\"\n if s >= 9.9995:\n return \"%s%.2d:%.2d:%.5f\" % (sign, h, m, s)\n else:\n return \"%s%.2d:%.2d:0%.5f\" % (sign, h, m, s)\n elif key in ['DEC', 'DECJ', 'DEC_ERR', 'DECJ_ERR']:\n # taken from: lscsoft/src/lalsuite/lalapps/src/pulsar\n # /HeterodyneSearch/pulsarpputils.py\n arc = np.degrees(np.fmod(np.fabs(value), np.pi))\n d = int(arc)\n arc = (arc - d) * 60.0\n m = int(arc)\n s = (arc - m) * 60.0\n if '_ERR' in key:\n return str(s)\n if s >= 9.9995:\n return \"%s%.2d:%.2d:%.5f\" % (sign, d, m, s)\n else:\n return \"%s%.2d:%.2d:0%.5f\" % (sign, d, m, s)\n else:\n raise TypeError(\"cannot format argument %s with value %r\"\n % (key, value))","def test_format_decimal_width() -> None:\n table: Dict[str, int] = {\"Sjoerd\": 4127, \"Jack\": 254098}\n\n assert f\"{'Sjoerd':10} ==> {table['Sjoerd']:10d}\" == \"Sjoerd ==> 4127\"\n\n names_phones: List[str] = [\n f\"{name + ' ':.<10}{' ' + str(phone):.>10}\" for name, phone in table.items()\n ]\n assert names_phones[0] == \"Sjoerd ........ 4127\"\n assert names_phones[1] == \"Jack ........ 254098\"","def format_val(val):\n if isinstance(val, str):\n out = '\"%s\"' % val\n else:\n if 'float' in val.dtype.name:\n if val.dtype.itemsize >= 8:\n out = '%.16g' % val\n else:\n out = '%.7g' % val\n else:\n out = '%s' % val\n\n return out","def get_dict_of_float2(self):\n pass","def format_dict(\n last_value, last_date, evol, evol_percentage, level,\n code_level, dfvalues, column, trendType\n):\n resdict = {}\n resdict['last_value'] = str(last_value)\n resdict['last_date'] = str(last_date)\n resdict['evol'] = str(evol)\n if (np.isinf(evol_percentage)) | (pd.isnull(evol_percentage)):\n evol_percentage = 0 \n resdict['evol_percentage'] = str(round(evol_percentage, 2))\n resdict['evol_color'] = get_color(round(evol_percentage, 2), trendType)\n resdict['level'] = level\n resdict['code_level'] = str(code_level)\n resdict['values'] = []\n for index, row in dfvalues.iterrows():\n interdict = {}\n interdict['value'] = str(round(row[column], 2))\n interdict['date'] = row['date']\n if 'date_entree_en_vigueur' in dfvalues.columns:\n interdict['protocole'] = row['date_entree_en_vigueur']\n if 'nombre_total_classes' in dfvalues.columns:\n interdict['total'] = str(row['nombre_total_classes'])\n if 'nombre_total_structures' in dfvalues.columns:\n interdict['total'] = str(row['nombre_total_structures'])\n resdict['values'].append(interdict)\n return resdict","def FixFloats(Data):\n\n if isinstance(Data, dict):\n return { k: FixFloats(v) for k, v in Data.items() }\n\n if isinstance(Data, list):\n return [ FixFloats(v) for v in Data ]\n\n if isinstance(Data, float): # Only floats being changed here\n return str(Data)\n\n return Data","def formated_print(dic):\n print dic['Year'] + '/' + get_month_number(dic['Month']) + '/' + \\\n dic['Day'] + ' | ' + dic['Hour'] + ':' + dic['Min'] + ':' + \\\n dic['Seg'] + ' | ' + dic['Energy'] + ' Watts'","def _format_entries(self):\n\n def format_item(key, value):\n if value is None:\n return str(key)\n else:\n return '%s -> %x' % (key, value,)\n\n items = self._entries.items()\n items.sort()\n return '{%s}' % (', '.join([format_item(*item) for item in items]),)","def _format_dict(self, dict_, indent=0):\n prefix = indent*\" \"*4\n output = \"{\\n\"\n for key, val in sorted(dict_.items()):\n if isinstance(val, dict):\n rval = self._format_dict(val, indent+1)\n else:\n rval = repr(val)\n output += prefix + \" \"*4 + repr(key) + \" : \" + rval + \",\\n\"\n output += prefix + \"}\"\n return output","def pretty_float_formatter(field, ndigits=None):\n def _formatter(obj):\n value = obj[field] if isinstance(obj, dict) else getattr(obj, field)\n if type(value) in (int, float):\n if ndigits:\n return round(value, ndigits)\n return value\n return \"n/a\"\n return _formatter","def myformat(table):\n m = 0\n table = sorted(table, key=itemgetter(0))\n for t in table:\n t = str(t)\n if len(t[0]) > m:\n m = len(t[0])\n m += 10\n fstr = \"{0:}\" + m*\" \" + \"{1:}\"\n s = \"\"\n for x in table:\n try:\n a = float(x[0])\n b = float(x[1])\n s += \"{0:.5f}{1:{width}}\".format(a, b, width=m) + \"\\n\"\n except IndexError:\n pass\n return s\n \"\"\"\n out = \"\"\n for pair in table:\n out += str(pair[0]) + 5*\" \" + str(pair[1]) + \"\\n\"\n return out\"\"\"","def format_string_1(file_num, float_num1, int_num, float_num2):\n\n text = \"file_{:0>3d} :{:9.2f}, {:.2e}, {:.3g}\"\n result = text.format(file_num, float_num1, int_num, float_num2)\n print(result)\n return result","def __str__(self):\n r = []\n for item in sorted(self._data.keys()):\n correct, incorrect = self._data[item][True], self._data[item][False]\n acc = correct / (correct + incorrect)\n s = f\"{item:4} | Accuracy: {acc:.2f}% (diff {'+' if acc-item >=0 else ''}{acc-item:.2f}%) | correct: {correct:2}, incorrect: {incorrect:2}\" \n r.append(s)\n\n return \"\\n\".join(r)","def __str__(self):\n if len(self.__keys) == 0:\n return '{}'\n output = '{'\n fmt = '{}: {}, '\n for key, val in zip(self.__keys, self.__vals):\n output += fmt.format(repr(key), repr(val))\n return output[:-2] + '}'","def formatAllToFloat(self):\n SectionList = [self.Accel[\"X\"], self.Accel[\"Y\"], self.Accel[\"Z\"], self.Gyro[\"X\"], self.Gyro[\"Y\"], self.Gyro[\"Z\"], self.Millis,self.GForce,self.Rot[\"X\"],self.Rot[\"Y\"],self.Rot[\"Z\"],self.DDMillis.Gyro,self.DDMillis.Accel,self.DDMillis.Rot,self.DDMillis.Mag,\n self.GPSDat[\"lng\"], self.GPSDat[\"lat\"], self.GPSDat[\"Speed\"], self.GPSDat[\"Altitude\"], self.GPSDat[\"Accuracy\"], self.GPSDat[\"Bearing\"],self.Mag[\"X\"],self.Mag[\"Y\"],self.Mag[\"Z\"]]\n\n for item in SectionList:\n if not len(item) == 0:\n GoToThisDangit = item[:]\n for SubItem in range(len(GoToThisDangit)):\n try:\n if SubItem > len(item):\n break\n else:\n item[SubItem] = float(item[SubItem])\n except ValueError:\n try:\n del item[SubItem]\n except IndexError:\n \"Slightly dangerous catch....\"\n except IndexError:\n \"yo\"","def engFormat(f):\n if f == 0.0:\n value = 0.0\n exponent = 0\n else:\n exponent = math.log10(-f if f < 0 else f)\n if exponent < 0:\n exponent = -int(math.ceil(-exponent))\n else:\n exponent = int(math.floor(exponent))\n for i in range(3):\n if (exponent % 3) == 0:\n break\n exponent = exponent - 1\n value = f * 10 ** -exponent\n # Choose a format to maintain the number of useful digits we print.\n if abs(value) < 10:\n fmt = \"%6.3f%s\"\n elif abs(value) < 100:\n fmt = \"%6.2f%s\"\n else:\n fmt = \"%6.1f%s\"\n\n return fmt % (value, (\"\" if exponent == 0 else \"e%d\" % exponent))","def format_string_2(file_num, float_num1, int_num, float_num2):\n\n result = f\"file_{file_num:0>3d} :{float_num1:9.2f}, {int_num:.2e}, {float_num2:.3g}\"\n print(result)\n return result","def float_vct_str ( vct , format = '%.5g' ) :\n try :\n return '[ ' + ', '.join ( [ format % v for v in vct ] ) + ' ]' \n except TypeError :\n pass\n return float_vct_str ( vct , format = '%.5g' )","def log_metric(name, values, tags={}):\n value_list = []\n for key in sorted(values.keys()):\n value = values[key]\n value_list.append(f\"{key}:{value:7.3f}\")\n values = \", \".join(value_list)\n tag_list = []\n for key, tag in tags.items():\n tag_list.append(f\"{key}:{tag}\")\n tags = \", \".join(tag_list)\n print(\"{name:30s} - {values} ({tags})\".format(name=name, values=values, tags=tags))","def print_dict(dictionary, format_=None):\n\n format_ = format_ or DEFAULT\n\n if format_ == TEXT:\n for key, value in iter(sorted(dictionary.items())):\n print(\"%s = %s\" % (key, value))\n elif format_ == DOCKERENV:\n for key, value in iter(sorted(dictionary.items())):\n print(\"%s=%s\" % (key, value))\n elif format_ == BASH:\n for key, value in iter(sorted(dictionary.items())):\n print(\"export %s=%s\" % (key, value))\n elif format_ == JSON:\n print(json.dumps(dictionary))\n elif format_ == NAME_VALUE_DICT:\n print(\"[\")\n for key, value in iter(sorted(dictionary.items())):\n print('{\"name\": \"%s\", \"value\": \"%s\"},' % (key, value))\n print(\"]\")","def _reprOfStringToValueMap (stringMap : Map) -> String:\n\n entrySeparator = u\"§\"\n entryTemplate = \"%s: %s\"\n keyList = sorted(list(stringMap.keys()))\n result = \"\"\n \n for key in keyList:\n value = stringMap[key] \n result += (iif(result == \"\", \"\", entrySeparator)\n + entryTemplate % (key, value))\n \n result = \"{\" + result + \"}\";\n return result","def clean_dict(self,dict_to_clean):\n for i in dict_to_clean: \n try:\n float( dict_to_clean[i] ) \n except:\n dict_to_clean[ i ] = \"'%s'\"%( dict_to_clean[i ].replace(\"'\",\"\").replace('\"',\"\") )","def __str__(self):\n #Get an ordered list of the elements strings so it outputs always the same\n #string given a mass function.\n elements = []\n for element in self.focals:\n elements.append((element, str(element)))\n sortedList = sorted(elements, key=lambda x:x[1])\n \n result = \"\"\n first = True\n for t in sortedList:\n if first:\n result += t[1] + \":\" + \"{:.4f}\".format(self.focals[t[0]])\n first = False\n else:\n result += \", \" + t[1] + \":\" + \"{:.4f}\".format(self.focals[t[0]])\n return \"{\" + result + \"}\"","def _fmt_map(self, string_list):\n return self._fmt_csv(string_list, list_braces=\"{}\")","def _write_dict(pdf, input_dict, indent=0, border=BORDER):\n for key, value in input_dict.items():\n pdf.set_font(\"arial\", \"B\", 14)\n pdf.cell(75, 5, key, border, 1, \"L\")\n pdf.cell(indent, 0)\n pdf.set_font(\"arial\", \"\", 12)\n pdf.multi_cell(150, 5, value, border, \"L\")\n pdf.ln(h=5)","def displaySummary(self, dictionnary, lineCount):\n for key in self.summaryDict.keys():\n dictionnary[key] = (dictionnary[key] / lineCount) * 100\n #print(str(key)+\" => \"+str(dictionnary[key])+\" %\")","def func_plot_fmt_map():\n # line_styles = ['o-', 'x-', '*-', '-_', 'D-', 'h-', '+-', 's-', 'v-', \n # ',-', '1-']\n func_tuples = get_func_tuples()\n M = {k:v for (k, _, v) in func_tuples}\n return M","def print_data(d, indent=0):\n prefix = indent * ' '\n for k in sorted(d):\n v = d[k]\n k = prefix + str(k)\n if isinstance(v, dict):\n print(k)\n print_data(v, indent + 1)\n else:\n if k.endswith('cent'):\n v = ' '.join(\n str(tuple(int(j) if j.is_integer() else j for j in i))\n for i in v\n )\n elif isinstance(v, np.ndarray):\n v = str(v).replace('\\n', '')\n print(k, '=', v)","def printDict(myDict):\n for key in myDict:\n print(f\"Version: --> {myDict[key]['version']} \")\n print(f\"Accuracy: --> {myDict[key]['accuracy']}\")\n print(f\"Time --> {myDict[key]['time_per_target']}\")\n print(f\"Penalty --> {myDict[key]['target_w_penalty']}\")\n print(f\"ID --> {myDict[key]['assessed_by']}\")\n print(f\"# --> {myDict[key]['attempt']}\")\n\n print()","def __str__(self):\n if len(self.keys()):\n return '{' + repr(self.keys()[0]) + ':' + repr(self[self.keys()[0]]) + ', ...'\n else:\n return super(FSDict, self).__str__()","def to_string(inputs, outputs):\n r_val = '# Column 01: frequency\\n'\n r_val += '# 02: hp - real\\n'\n r_val += '# 03: hp - imaginary\\n'\n r_val += '# 04: hc - real\\n'\n r_val += '# 05: hc - imaginary\\n'\n for f_i, hp_i, hc_i in zip(inputs.freqs, outputs.hp, outputs.hc):\n r_val += \"%8.2f %12.5e %12.5e %12.5e %12.5e\\n\" % (f_i, hp_i.real, hp_i.imag, hc_i.real, hc_i.imag)\n return r_val","def dist_dict_to_json(dist_dict: Dict[Tuple[str, str], float]) -> Dict[str, float]:\n \n return {str(custs_key): dist for custs_key, dist in dist_dict.items()}","def sensible_format_data(self, value):\n if abs(value) > 1e4 or abs(value)<1e-3:\n s = '%1.4e' % value\n return self._formatSciNotation(s)\n else:\n return '%4.3f' % value","def pretty_print_table(data, list_of_dicts):\n # ensure that each dict has the same set of keys\n keys = None\n for d in list_of_dicts:\n if keys is None:\n keys = d.keys()\n else:\n if d.keys() != keys:\n print(\"Error! not all dicts have the same keys!\")\n return\n header = \"\\t\" + \"\\t\".join(['{:11.10s}'] * len(data))\n header = header.format(*data)\n rows = []\n for k in keys:\n r = k + \"\\t\"\n for d in list_of_dicts:\n if type(d[k]) is float:\n r += '{:.9f}'.format(d[k]) + \"\\t\"\n else:\n r += '{:10.9s}'.format(str(d[k])) + \"\\t\"\n rows.append(r)\n print(header)\n for row in rows:\n print(row)","def value_str(value):\n # cast to float to remove 0-padding\n return \":\".join(map(str, map(float, value)))","def __str__(self):\n try:\n delim = ', ' if len(self) < 8 else ',\\n '\n s = delim.join('%s: %s' % (repr(k), repr(self[k])) for k in self.peys())\n return '{' + s + '}'\n except Exception:\n return dict.__repr__(self)","def format_dict(dictionary, depth=0):\n tab = \" \" * 4\n string = \"{\\n\"\n for key, val in dictionary.items():\n string += depth * tab \n string += \"{}: \".format(key)\n if type(val) is dict:\n string += format_dict(val, depth + 1)\n \n else:\n if type(val) is str:\n fmt = \"'{}'\\n\"\n else:\n fmt = \"{}\\n\"\n string += fmt.format(val)\n string += (depth) * tab + '}\\n'\n return string","def display(self):\n new_list = []\n for key, value in self.model.items():\n interne = []\n # FIXME print price value in {:.2f} format\n if key in ['price', 'efficiency']:\n interne.extend([f'{value:.2f}'])\n else:\n interne.extend([f'{value}'])\n new_list.append(list(interne))\n return new_list","def to_text(self, floatformat=\".4f\"):\n return (\"f_{{{ind}}} < {thr:\" + floatformat + \"}\").format(\n ind=self.val[\"best_feature_index\"], thr=self.val[\"thr\"]\n )","def pretty_dict(d):\n return '{%s}' % ', '.join('%r: %r' % (k, v)\n for k, v in sorted(d.items(), key=repr))","def loss_dict_to_str(running_loss_dict: Dict[str, float], logging_period: int) -> str:\n loss_str = \", \".join(\n [\n f\"{key}: {value / logging_period:.2f}\"\n for key, value in running_loss_dict.items()\n ]\n )\n return loss_str","def get_formatter(self, **kwargs):\n config = dict([\n (attr, getattr(self, attr))\n for attr in [\n \"include_sign\",\n \"group_with_commas\",\n \"num_decimal_places\",\n ]\n ])\n config.update(kwargs)\n return \"\".join([\n \"{\",\n config.get(\"field_name\", \"\"),\n \":\",\n \"+\" if config[\"include_sign\"] else \"\",\n \",\" if config[\"group_with_commas\"] else \"\",\n \".\", str(config[\"num_decimal_places\"]), \"f\",\n \"}\",\n ])","def pretty(self):\n #table = [\"\".join([\"%8s \" % s for s in self.alpha.getSymbols()])]\n table = []\n for row in PWM.getFreq(self):\n table.append(\"\".join([\"%8.6f \" % y for y in row]))\n return table","def __str__(self):\n return \"{:.3f} {:+.3f}i {:+.3f}j {:+.3f}k\".format(self.q[0], self.q[1], self.q[2], self.q[3])","def _to_string(self) -> str:\n\n string_list = []\n for key, value in self.__dict__.items():\n if isinstance(value, dict):\n string_list.append(key)\n string_list.extend('\\n'.join([\"Key: {:24}\\tValue: {}\".format(_key, _value) for _key, _value in value.items()]))\n else:\n string_list.append(\"Key: {:24}\\tValue: {}\\n\".format(key, value))\n return ''.join(string_list)","def __format__(self, formatstr):\n if formatstr.strip() == '': # Defualt behaviour mirrors self.__str__()\n formatstr = '+.3f'\n\n string = \\\n \"{:\" + formatstr +\"} \" + \\\n \"{:\" + formatstr +\"}i \" + \\\n \"{:\" + formatstr +\"}j \" + \\\n \"{:\" + formatstr +\"}k\"\n return string.format(self.q[0], self.q[1], self.q[2], self.q[3])","def ppdict(d):\n print '{'\n keys=d.keys()\n keys.sort()\n for k in keys:\n spacing=\" \" * (16-(len(repr(k))+1))\n print \"%s:%s%s,\" % (repr(k),spacing,repr(d[k]))\n print '}'","def _trans_format(self):\n config_dict = vars(self._config)\n for item, value in config_dict.items():\n if value == 'None':\n config_dict[item] = None\n elif isinstance(value, str) and is_number(value):\n if value.isdigit():\n value = int(value)\n else:\n value = float(value)\n config_dict[item] = value","def func_dict(func):\n fdict = {\n 'doc': '',\n 'unit': '',\n 'str': 'NA',\n 'func': func}\n\n value = func\n try:\n value = value()\n except:\n pass\n\n try:\n fdict['attr'] = func.__func__.__name__\n except:\n fdict['attr'] = None\n \n if isinstance(value,str):\n fdict['str'] = value\n else:\n if hasattr(value,'mean'):\n fdict['str'] = '<{:.4}>'.format(value.mean())\n else:\n try:\n fdict['attr'] = func.__func__.__name__\n doc = func.__doc__.split('\\n')[-1].lstrip(' ')\n fdict['doc'] = doc\n fdict['str'] = '{:10.5g}'.format(value)\n fdict['unit'] = get_unit_from_doc(doc)\n fdict['type'] = get_type_from_doc(doc)\n except:\n try:\n fdict['attr'] = func.__func__.__name__\n fdict['str'] = value.__str__()\n except:\n pass\n\n fdict['value'] = value\n\n return fdict","def _format_parameter_output(self, parameters: dict) -> str:\n \n output = ''\n for key, value in parameters.items():\n output = output + '\\t\\t' + str(key) + ': ' + str(value) + '\\n'\n \n return output","def convert_font_dict_to_ttoptions_dict(d):\n return {\n (\"f\" + k if k in (underline_s, overstrike_s) else k): v\n for k, v in d.items()\n }","def __str__(self):\n return \"{\" + (\", \".join(\"%s: %s\"%(ngram, value) for (ngram, value) in self.items())) + \"}\"","def __str__(self):\n columns = list(self.metrics.keys())\n columns.sort()\n out = '%s\\n' % ','.join(columns)\n values = [str(self.metrics[c]) for c in columns]\n out += '%s\\n' % ','.join(values)\n return out","def to_dict(self):\n return {k[1:]: round(v, 5) for k, v in self.__dict__.items()}","def output_metrics(self):\n print('')\n for key in sorted(self.metrics):\n print('{}:'.format(key), end='')\n for k, v in self.metrics[key].items():\n if type(v[-1]) is list:\n print('\\t' + k + ': ' + ''.join('{:5.3f} '.format(vs) for vs in v[-1]), end='')\n else:\n print('\\t{}: {:5.3f}'.format(k, v[-1]), end='')\n print('\\n', end='')","def __str__(self):\n prob = str(round(self.probability, 5))\n dprob = str(round(self.postdProbability, 5))\n output = \"dprob: \" + dprob + \" \\tprob: \" + prob + \"\\t: \"\n for key in self.attackDict.keys():\n output += key + \" \"\n return output","def _drop_float_precision(dict_obj, point):\n for keyword in dict_obj.keys():\n for lang_id, prob in enumerate(dict_obj[keyword]):\n dict_obj[keyword][lang_id] = round(prob, point)","def to_dump(self):\n s = []\n for k in self.keys():\n if isinstance(self[k], int) or isinstance(self[k], long):\n s.append(\"%s=%d\" % (k, self[k]))\n elif isinstance(self[k], float):\n s.append(\"%s=%f\" % (k, self[k]))\n else:\n for v2 in self.list(k):\n if isinstance(v2, str):\n s.append(\"%s=%s\" % (k, v2))\n else:\n s.append(\"%s=%s\" % (k, util.encode(v2)))\n s.append(\"~format=%s\" % self.format)\n s.append(\"\")\n return \"\\n\".join(s)","def __str__(self):\n try:\n delim = ', ' if len(self) < 8 else ',\\n '\n s = delim.join('%s: %s' % (repr(k), repr(self[k])) for k in self.peys())\n return '{' + s + '}'\n except Exception:\n return defaultdict.__repr__(self)","def pretty_float(v: Union[float, int], n_sigfigs: Optional[int] = 5) -> str:\n\t\tv0 = v.__class__(v)\n\t\t# first, handle NaN and infinities\n\t\tif np.isneginf(v):\n\t\t\treturn Chars.minus + Chars.inf\n\t\telif np.isposinf(v):\n\t\t\treturn '+' + Chars.inf\n\t\telif np.isnan(v):\n\t\t\treturn Chars.null\n\t\t# sweet. it's a regular float or int.\n\t\tisint = isinstance(v, int)\n\t\tif isint:\n\t\t\tv = int(round(v))\n\t\tif n_sigfigs is None:\n\t\t\ts = StringTools.strip_empty_decimal(str(v))\n\t\telse:\n\t\t\ts = str(float(str(('%.{}g'.format(n_sigfigs)) % v)))\n\t\t# remove the .0 if the precision doesn't support it\n\t\t# if v >= 1 and n_sigfigs<2, it couldn't have a decimal\n\t\t# and if n_sigfigs<1, it definitely can't\n\t\tif isint or n_sigfigs is not None and v0>=1 and n_sigfigs<2 or n_sigfigs is not None and n_sigfigs < 1:\n\t\t\ts = StringTools.strip_empty_decimal(s)\n\t\t# and ... %g does this.\n\t\tif s.startswith('.'):\n\t\t\ts = '0' + s\n\t\t# prepend + or -\n\t\tif s.startswith('-'):\n\t\t\treturn Chars.minus + s[1:]\n\t\telse:\n\t\t\treturn '+' + s","def formatall(obj):\n result = \"\"\n if isinstance(obj, list):\n# i = 0\n for obj in obj:\n #printf(\">>> [%d] >>> \", i)\n result += format(obj)\n result += \"\\n\"\n# i += 1\n return result\n if isinstance(obj, dict):\n for key, value in obj.items():\n result += \"%-15s : \" % key\n result += format(value)\n result += \"\\n\"\n return result\n return format(obj)","def _float_to_string(v):\n\n if not isinstance(v,float):\n raise InstrumentParameterException('Value %s is not a float.' % v)\n else:\n return '%e' % v","def format_float(value):\n if isinstance(value, float):\n value = '{:.3f}'.format(value * 1e-3)\n return value","def get_column_formatters(df):\n formatters = {}\n for p in df.columns:\n (a, b) = p\n if b.endswith(\"Q\"):\n formatters[p] = lambda x: \"%.2f\" % x\n elif b.startswith(\"T_\"):\n formatters[p] = lambda x: \"%.1f\" % x\n else:\n formatters[p] = lambda x: \"%.0f\" % x\n return formatters","def _print_report(self, result: Dict[str, Dict[str, float]]):\n\n print('\\n\\tprecision recall f1_score num')\n for type_ in self.types:\n print(type_, end='\\t')\n print('{0: .3f}'.format(result[type_]['precision']), end=' ')\n print('{0: .3f}'.format(result[type_]['recall']), end=' ')\n print('{0: .3f}'.format(result[type_]['f1_score']), end=' ')\n print('{0: d}'.format(result[type_]['num']), end='\\n')","def format_to_store(key, value):\n key = key.upper()\n if not isparam(key):\n raise ValueError(\"invalid key %r\" % key)\n\n if key in ['RA_ERR', 'RAJ_ERR']:\n if isinstance(value, basestring):\n floatvalue = hms_rad(0., 0., value)\n elif isinstance(value, float):\n floatvalue = value\n else:\n raise TypeError('invalid %s value type %r' % (key, value))\n elif key in ['DEC_ERR', 'DECJ_ERR']:\n if isinstance(value, basestring):\n floatvalue = dms_rad(0., 0., value)\n elif isinstance(value, float):\n floatvalue = value\n else:\n raise TypeError('invalid %s value type %r' % (key, value))\n else:\n try:\n floatvalue = float(value)\n except ValueError:\n if key in ['RA', 'RAJ']:\n floatvalue = hms_rad(value)\n elif key in ['DEC', 'DECJ']:\n floatvalue = dms_rad(value)\n elif isinstance(value, basestring):\n floatvalue = value\n else:\n raise ValueError('invalid PAR %s value %r.' % (key, value))\n return floatvalue","def __repr__(self, *args, **kwargs):\n result ='{'\n for (k, v) in self.items(*args, **kwargs):\n result += repr(k) + \": \" + repr(v) + \",\"\n\n result = result[:-1] + '}'\n return result","def __init_key_value_fmts__(self):\n self.test_desc_fmt = \"{0}.description\"\n self.test_svc_name_fmt = \"{0}.svc\"\n self.test_svc_ipc_id_fmt = \"{0}.svc.{1}.ipc_id\"\n self.test_svc_rsp_code_fmt = \"{0}.svc.{1}.svc_rsp_code\"\n self.test_svc_rsp_stage_fmt = \"{0}.svc.{1}.svc_rsp_stage\"\n self.test_pre_hook_stage_fmt = \"{0}.pre_hook.stage\"\n self.test_pre_hook_script_fmt = \"{0}.pre_hook.script\"\n self.test_post_hook_stage_fmt = \"{0}.post_hook.stage\"\n self.test_post_hook_script_fmt = \"{0}.post_hook.script\"\n self.test_svc_rsp_time_delay_fmt = \"{0}.svc.{1}.rsp_time_delay\"\n\n self.test_expected_result_fmt = \"{0}.expected_result\"\n self.test_exp_api_return_fmt = \"{0}.expected_result.api_return_code\"\n self.test_exp_log_file_fmt = \"{0}.expected_result.log\"\n self.test_exp_log_fmt = \"{0}.expected_result.log.{1}\"","def _kv_to_float(kv, keys):\n for k, v in kv.items():\n if k in keys:\n kv[k] = _to_float(v)\n return kv"],"string":"[\n \"def print_dict(dictionary):\\n for key in sorted(dictionary.keys()):\\n value = dictionary[key]\\n if type(value) == float:\\n value = round(value, 8)\\n print key, value\",\n \"def print_dict(dictionary):\\r\\n for key in sorted(dictionary.keys()):\\r\\n value = dictionary[key]\\r\\n if type(value) == float:\\r\\n value = round(value, 8)\\r\\n print (key, value)\",\n \"def _DictToString(self, value_dict, str_length=5):\\n\\n def FormatValue(v, value_format, str_length):\\n if isinstance(v, (int, float)):\\n return value_format % v\\n else:\\n return str(v).rjust(str_length)\\n\\n text = []\\n blank = '--'.rjust(str_length)\\n\\n if self._show_label:\\n text.append(' '.join(k.rjust(str_length) for k in self._node_labels))\\n\\n if not self._precision:\\n value_format = '%% %dd' % str_length\\n else:\\n value_format = '%% %d.%df' % (str_length, self._precision)\\n\\n text.append(' '.join(\\n [FormatValue(value_dict[k], value_format, str_length)\\n if k in value_dict else blank for k in self._node_labels]))\\n\\n return '\\\\n'.join(text)\",\n \"def format_metrics(d: dict) -> dict:\\n new = {}\\n\\n for key in d:\\n if isinstance(d[key], dict):\\n new[key] = format_metrics(d[key])\\n elif isinstance(d[key], float):\\n new[key] = float(\\\"{:.8f}\\\".format(d[key]))\\n else:\\n new[key] = d[key]\\n\\n return new\",\n \"def print_metric_dict(self, metric_dict):\\n print(\\\"\\\".join([\\\" {}: {:4f},\\\".format(k, v) for k, v in metric_dict.items()]))\",\n \"def nice_string_output(d, extra_spacing=5, decimals=3):\\n\\n names = d.keys()\\n max_names = len_of_longest_string(names)\\n\\n values = values_to_string(d.values(), decimals=decimals)\\n max_values = len_of_longest_string(values)\\n\\n string = \\\"\\\"\\n for name, value in zip(names, values):\\n spacing = extra_spacing + max_values + max_names - len(name) - 1\\n string += \\\"{name:s} {value:>{spacing}} \\\\n\\\".format(name=name, value=value, spacing=spacing)\\n return string[:-2]\",\n \"def float_format(self):\\n ...\",\n \"def format_dict(kv_list):\\n return '\\\\n'.join(['{} - {}'.format(key, value) for\\n key, value in kv_list])\",\n \"def format(self, valDict):\\n return self._formatStr % valDict\",\n \"def _print_table(stats):\\n max_key_len = max([len(key) for key in stats])\\n width_right = 15\\n width_left = max(width_right, max_key_len)\\n divider = '+-' + '-' * width_left + '-+-' + '-' * width_right + '-+'\\n\\n def get_format_char(value):\\n if isinstance(value, int):\\n return 'd'\\n elif isinstance(value, float):\\n return '.4f'\\n else:\\n return 's'\\n\\n print(divider)\\n for name, value in stats.items():\\n left_format = f':>{width_left}s'\\n right_format = f':<{width_right}{get_format_char(value)}'\\n line_format = f'| {{{left_format}}} | {{{right_format}}} |'\\n line = line_format.format(name, value)\\n print(line)\\n print(divider)\",\n \"def print_formatted_values(**kwargs):\\n string = ', '.join([f'{k}: {format_number(kwargs[k])}' for k in kwargs])\\n print(string)\",\n \"def format(self):\\r\\n output = {}\\r\\n\\r\\n for k, v in self:\\r\\n if type(v) == collections.InstrumentedList: # pragma: no cover\\r\\n output[k] = [item.to_dict() for item in v]\\r\\n elif isinstance(v, (date, datetime, time)): # pragma: no cover\\r\\n output[k] = v.isoformat()\\r\\n elif isinstance(v, (float, Decimal)): # pragma: no cover\\r\\n output[k] = str(v)\\r\\n else:\\r\\n output[k] = v\\r\\n\\r\\n return output\",\n \"def pretty_print(name, input, val_width=40, key_width=0):\\n\\n # root\\n pretty_str = name + ': {\\\\n'\\n\\n # determine key width\\n for key in input.keys(): key_width = max(key_width, len(str(key)) + 4)\\n\\n # cycle keys\\n for key in input.keys():\\n\\n val = input[key]\\n\\n # round values to 3 decimals..\\n if type(val) == np.ndarray: val = np.round(val, 3).tolist()\\n\\n # difficult formatting\\n val_str = str(val)\\n if len(val_str) > val_width:\\n val_str = pprint.pformat(val, width=val_width, compact=True)\\n val_str = val_str.replace('\\\\n', '\\\\n{tab}')\\n tab = ('{0:' + str(4 + key_width) + '}').format('')\\n val_str = val_str.replace('{tab}', tab)\\n\\n # more difficult formatting\\n format_str = '{0:' + str(4) + '}{1:' + str(key_width) + '} {2:' + str(val_width) + '}\\\\n'\\n pretty_str += format_str.format('', key + ':', val_str)\\n\\n # close root object\\n pretty_str += '}'\\n\\n return pretty_str\",\n \"def format_value(self, value: float) -> str:\\r\\n ...\",\n \"def format_value(self, value: float) -> str:\\r\\n ...\",\n \"def _format_dict(self, dict):\\n\\n result = \\\"\\\"\\n for k, v in dict.items():\\n result += \\\"\\\\n{0}: {1}\\\".format(k.capitalize(), v)\\n\\n return result\",\n \"def format_pair(self, k, v):\\n if isinstance(v, int):\\n data_width = len(str(v)) + 1\\n header_width = len(str(k))\\n w = max(data_width, header_width)\\n h = ('%% %us'%w)%k\\n return ' '*len(h), h, '%%%ud'%w\\n elif k=='dt':\\n fmt = '%6.3f'\\n return 6*' ', '%6s'%k, fmt\\n elif isinstance(v, float):\\n fmt = '% .3e'\\n data_width = len(fmt%1)\\n header_width = len(str(k))\\n w = max(data_width, header_width)\\n spaces = ' '*(w-data_width)\\n h = ('%%%us'%w)%k\\n return ' '*len(h), h, spaces+fmt\\n elif isinstance(v, dict):\\n results = [self.format_pair(k_, v_) for k_, v_ in v.items()]\\n keys = ' '.join([str(r[-2]) for r in results])\\n fmts = ' '.join([str(r[-1]) for r in results])\\n h1 = ('%%.%us'%(len(keys))) % k\\n pl = (len(keys)-len(h1)) // 2\\n pr = (len(keys)-len(h1)) - pl\\n h1 = '.' * pl + h1 + '.' * pr\\n return h1, keys, fmts\\n elif isinstance(v, h5py.ExternalLink):\\n data_width = len('hdf5-link')\\n header_width = len(str(k))\\n w = max(data_width, header_width)\\n h = ('%%%us'%w)%k\\n return ' '*len(h), h, '%%%us'%w\\n elif isinstance(v, h5py.VirtualLayout):\\n data_width = len('hdf5-vds')\\n header_width = len(str(k))\\n w = max(data_width, header_width)\\n h = ('%%%us'%w)%k\\n return ' '*len(h), h, '%%%us'%w\\n else:\\n fmt = '%%%u.%us' % (self.min_str_len, self.max_str_len)\\n w = len(fmt%v)\\n h = ('%%%us'%w)%k\\n return ' '*len(h), h, fmt\",\n \"def printMap(values, klab, vlab, precision, offset=16):\\n\\tprint(klab.ljust(offset, \\\" \\\") + vlab)\\n\\tfor k in values.keys():\\n\\t\\tv = values[k]\\n\\t\\tks = toStr(k, precision).ljust(offset, \\\" \\\")\\n\\t\\tvs = toStr(v, precision)\\n\\t\\tprint(ks + vs)\",\n \"def dict(self) -> Dict[str, List[NumericType]]:\\n return {r.name: r.floats() for r in self._records}\",\n \"def format_str() -> str:\\r\\n decimal_places = conf.instance[\\\"general\\\"][\\\"output\\\"][\\\"model_results_decimal_places\\\"]\\r\\n return f\\\"{{:.{decimal_places}f}}\\\"\",\n \"def _createTextProfile(self, indict):\\r\\n\\r\\n keys = map(lambda x: x[0], indict)\\r\\n vals = map(lambda x: x[1], indict)\\r\\n\\r\\n outstrs = [\\\"\\\\n\\\"]\\r\\n propDict = {}\\r\\n total = sum(vals)\\r\\n maxLenKey = max([len(a) for a in keys])\\r\\n maxLenVal = max([len(repr(a)) for a in vals]) \\r\\n\\r\\n for k, v in indict:\\r\\n outstr = \\\" \\\"\\r\\n outstr += k.ljust(maxLenKey + 1)\\r\\n outstr += (\\\"%.2f\\\" % v).ljust(maxLenVal + 1)\\r\\n outstr += \\\"-\\\" * int(self.numBars * (v / total))\\r\\n outstrs.append(outstr)\\r\\n\\r\\n return \\\"\\\\n\\\".join(outstrs)\",\n \"def dict_pretty_print(D: dict, indent_lvl=0):\\n print(\\\"Using 3 decimal places.\\\")\\n base_indent = indent_lvl * \\\" \\\"\\n indent = (indent_lvl+2)*\\\" \\\"\\n print(f\\\"{base_indent}\\\" + \\\"{\\\")\\n for key, value in D.items():\\n print(f\\\"{indent}{key}: \\\", end=\\\"\\\")\\n if type(value) is dict:\\n print(\\\"\\\")\\n dict_pretty_print(value, indent_lvl + 2)\\n else:\\n print(f\\\"{value:.3f}\\\")\\n print(f\\\"{base_indent}\\\" + \\\"}\\\")\",\n \"def _format_output(predictions, descriptors):\\n # type: (list,dict) -> list\\n\\n output = []\\n for item in predictions:\\n output.append(' - '.join([descriptors.get(item[1]), str(item[0]).format('0.00f')]))\\n\\n return output\",\n \"def nice_dict_format(d):\\n return ''.join([key+\\\": \\\"+str(d[key])+\\\"\\\\n\\\" for key in list(d.keys())])\",\n \"def print_object(dict_to_print, *, name='', uppercase=False):\\n string = '' if name == '' else name.ljust(10)\\n for key, value in dict_to_print.items():\\n string += f'{key.upper() if uppercase else key}: {\\\"\\\" if value < 0 else \\\" \\\"}{float(value):.4}'.ljust(\\n len(key) + 10)\\n\\n print(string)\",\n \"def prettyVal( value, key=None):\\n\\n frmt = '%.4f %c'\\n if value <= 1E-9: # Pico\\n s = frmt % (value * 1E12, 'p')\\n elif value < 1E-6: # Nano\\n s = frmt % (value * 1E9, 'n')\\n elif value < 1E-3: # Micro\\n s = frmt % (value * 1E6, 'u')\\n elif value < 1: # Milli\\n s = frmt % (value * 1E3, 'm')\\n else: # (whole units)\\n s = frmt % (value, ' ')\\n\\n if key is not None: # Add unit suffix if given unit\\n if key[0] == 'C':\\n s += 'F' # Capacitors have units Farads\\n if value < 1e-12 or value > 1e-3:\\n s += ' BAD!' # outside of reasonable capacitors\\n elif key[0] == 'L':\\n s += 'H' # Inductors have units Henries\\n if value < 1e-12 or value > 1e-3:\\n s += ' BAD!' # outside of reasonable henries\\n\\n return s\",\n \"def _format_values_in_map(self, dict_values: dict) -> dict:\\n\\n for key in dict_values.keys():\\n if str(key).lower() == \\\"exception\\\":\\n dict_values[key] = self._format_exception(str(dict_values[key]))\\n break\\n\\n new_map = {}\\n for key, value in dict_values.items():\\n value = str(value)\\n if ' ' in value:\\n value = f'\\\"{value}\\\"'\\n\\n new_map[key] = f\\\"{key.replace(' ', '')}={value}\\\"\\n return new_map\",\n \"def print_eval(eval_dict):\\n\\n status = ''.join(\\n ['{}: {:.6f}, '.format(key, eval_dict[key]) for key in sorted(eval_dict)])\\n print(status[:-2])\",\n \"def show_values():\\n dic_drg = {}\\n dic_age = {}\\n dic_sex = {}\\n dic_sline = {}\\n for tup in all_data:\\n drg = tup[7]\\n age = tup[9]\\n sex = tup[10]\\n sline = tup[14]\\n\\n dic_drg[drg] = 1\\n dic_age[age] = 1\\n dic_sex[sex] = 1\\n dic_sline[sline] = 1\\n\\n print \\\"Age values\\\"\\n for key in sorted(dic_age.keys()):\\n print key\\n\\n print \\\"Sex values\\\"\\n for key in sorted(dic_sex.keys()):\\n print key\\n\\n print \\\"Service line values\\\"\\n for key in sorted(dic_sline.keys()):\\n if key is None or len(key) == 0:\\n continue\\n print \\\"'\\\" + key + \\\"',\\\",\\n print\\n\\n print \\\"Drg values\\\"\\n for key in sorted(dic_drg.keys()):\\n if key is None or len(key) == 0:\\n continue\\n print\\\"'\\\" + key + \\\"',\\\",\\n print\",\n \"def format_data(self)->float: \\n try:\\n formatted = chr(self.data[0])\\n for i in range(1, len(self.data)): \\n formatted = formatted + (chr(self.data[i])) \\n return str(formatted)\\n except ValueError as e:\\n print(e)\\n except TypeError as e:\\n print(e)\",\n \"def pretty(self):\\n table = [\\\"\\\".join([\\\"%4s \\\" % s for s in self.alpha.getSymbols()])]\\n table.append(\\\"\\\".join([\\\"%3.2f \\\" % y for y in Distrib.getFreq(self)]))\\n return table\",\n \"def format_to_print(key, value):\\n if value < 0:\\n sign = \\\"-\\\"\\n else:\\n sign = \\\"\\\"\\n\\n key = key.upper()\\n if key.replace(\\\"_ERR\\\", \\\"\\\") not in (FLOAT_PARAMS + STR_PARAMS):\\n raise ValueError(\\\"invalid key %r\\\" % key)\\n\\n if key in FLOAT_PARAMS or isinstance(value, basestring):\\n return str(value)\\n elif key in ['RA', 'RAJ', 'RA_ERR', 'RAJ_ERR']:\\n sec = value / SIDFREQ\\n if '_ERR' in key:\\n return str(sec)\\n m, s = divmod(sec, 60)\\n h, m = divmod(m, 60)\\n sign = \\\"\\\"\\n if s >= 9.9995:\\n return \\\"%s%.2d:%.2d:%.5f\\\" % (sign, h, m, s)\\n else:\\n return \\\"%s%.2d:%.2d:0%.5f\\\" % (sign, h, m, s)\\n elif key in ['DEC', 'DECJ', 'DEC_ERR', 'DECJ_ERR']:\\n # taken from: lscsoft/src/lalsuite/lalapps/src/pulsar\\n # /HeterodyneSearch/pulsarpputils.py\\n arc = np.degrees(np.fmod(np.fabs(value), np.pi))\\n d = int(arc)\\n arc = (arc - d) * 60.0\\n m = int(arc)\\n s = (arc - m) * 60.0\\n if '_ERR' in key:\\n return str(s)\\n if s >= 9.9995:\\n return \\\"%s%.2d:%.2d:%.5f\\\" % (sign, d, m, s)\\n else:\\n return \\\"%s%.2d:%.2d:0%.5f\\\" % (sign, d, m, s)\\n else:\\n raise TypeError(\\\"cannot format argument %s with value %r\\\"\\n % (key, value))\",\n \"def test_format_decimal_width() -> None:\\n table: Dict[str, int] = {\\\"Sjoerd\\\": 4127, \\\"Jack\\\": 254098}\\n\\n assert f\\\"{'Sjoerd':10} ==> {table['Sjoerd']:10d}\\\" == \\\"Sjoerd ==> 4127\\\"\\n\\n names_phones: List[str] = [\\n f\\\"{name + ' ':.<10}{' ' + str(phone):.>10}\\\" for name, phone in table.items()\\n ]\\n assert names_phones[0] == \\\"Sjoerd ........ 4127\\\"\\n assert names_phones[1] == \\\"Jack ........ 254098\\\"\",\n \"def format_val(val):\\n if isinstance(val, str):\\n out = '\\\"%s\\\"' % val\\n else:\\n if 'float' in val.dtype.name:\\n if val.dtype.itemsize >= 8:\\n out = '%.16g' % val\\n else:\\n out = '%.7g' % val\\n else:\\n out = '%s' % val\\n\\n return out\",\n \"def get_dict_of_float2(self):\\n pass\",\n \"def format_dict(\\n last_value, last_date, evol, evol_percentage, level,\\n code_level, dfvalues, column, trendType\\n):\\n resdict = {}\\n resdict['last_value'] = str(last_value)\\n resdict['last_date'] = str(last_date)\\n resdict['evol'] = str(evol)\\n if (np.isinf(evol_percentage)) | (pd.isnull(evol_percentage)):\\n evol_percentage = 0 \\n resdict['evol_percentage'] = str(round(evol_percentage, 2))\\n resdict['evol_color'] = get_color(round(evol_percentage, 2), trendType)\\n resdict['level'] = level\\n resdict['code_level'] = str(code_level)\\n resdict['values'] = []\\n for index, row in dfvalues.iterrows():\\n interdict = {}\\n interdict['value'] = str(round(row[column], 2))\\n interdict['date'] = row['date']\\n if 'date_entree_en_vigueur' in dfvalues.columns:\\n interdict['protocole'] = row['date_entree_en_vigueur']\\n if 'nombre_total_classes' in dfvalues.columns:\\n interdict['total'] = str(row['nombre_total_classes'])\\n if 'nombre_total_structures' in dfvalues.columns:\\n interdict['total'] = str(row['nombre_total_structures'])\\n resdict['values'].append(interdict)\\n return resdict\",\n \"def FixFloats(Data):\\n\\n if isinstance(Data, dict):\\n return { k: FixFloats(v) for k, v in Data.items() }\\n\\n if isinstance(Data, list):\\n return [ FixFloats(v) for v in Data ]\\n\\n if isinstance(Data, float): # Only floats being changed here\\n return str(Data)\\n\\n return Data\",\n \"def formated_print(dic):\\n print dic['Year'] + '/' + get_month_number(dic['Month']) + '/' + \\\\\\n dic['Day'] + ' | ' + dic['Hour'] + ':' + dic['Min'] + ':' + \\\\\\n dic['Seg'] + ' | ' + dic['Energy'] + ' Watts'\",\n \"def _format_entries(self):\\n\\n def format_item(key, value):\\n if value is None:\\n return str(key)\\n else:\\n return '%s -> %x' % (key, value,)\\n\\n items = self._entries.items()\\n items.sort()\\n return '{%s}' % (', '.join([format_item(*item) for item in items]),)\",\n \"def _format_dict(self, dict_, indent=0):\\n prefix = indent*\\\" \\\"*4\\n output = \\\"{\\\\n\\\"\\n for key, val in sorted(dict_.items()):\\n if isinstance(val, dict):\\n rval = self._format_dict(val, indent+1)\\n else:\\n rval = repr(val)\\n output += prefix + \\\" \\\"*4 + repr(key) + \\\" : \\\" + rval + \\\",\\\\n\\\"\\n output += prefix + \\\"}\\\"\\n return output\",\n \"def pretty_float_formatter(field, ndigits=None):\\n def _formatter(obj):\\n value = obj[field] if isinstance(obj, dict) else getattr(obj, field)\\n if type(value) in (int, float):\\n if ndigits:\\n return round(value, ndigits)\\n return value\\n return \\\"n/a\\\"\\n return _formatter\",\n \"def myformat(table):\\n m = 0\\n table = sorted(table, key=itemgetter(0))\\n for t in table:\\n t = str(t)\\n if len(t[0]) > m:\\n m = len(t[0])\\n m += 10\\n fstr = \\\"{0:}\\\" + m*\\\" \\\" + \\\"{1:}\\\"\\n s = \\\"\\\"\\n for x in table:\\n try:\\n a = float(x[0])\\n b = float(x[1])\\n s += \\\"{0:.5f}{1:{width}}\\\".format(a, b, width=m) + \\\"\\\\n\\\"\\n except IndexError:\\n pass\\n return s\\n \\\"\\\"\\\"\\n out = \\\"\\\"\\n for pair in table:\\n out += str(pair[0]) + 5*\\\" \\\" + str(pair[1]) + \\\"\\\\n\\\"\\n return out\\\"\\\"\\\"\",\n \"def format_string_1(file_num, float_num1, int_num, float_num2):\\n\\n text = \\\"file_{:0>3d} :{:9.2f}, {:.2e}, {:.3g}\\\"\\n result = text.format(file_num, float_num1, int_num, float_num2)\\n print(result)\\n return result\",\n \"def __str__(self):\\n r = []\\n for item in sorted(self._data.keys()):\\n correct, incorrect = self._data[item][True], self._data[item][False]\\n acc = correct / (correct + incorrect)\\n s = f\\\"{item:4} | Accuracy: {acc:.2f}% (diff {'+' if acc-item >=0 else ''}{acc-item:.2f}%) | correct: {correct:2}, incorrect: {incorrect:2}\\\" \\n r.append(s)\\n\\n return \\\"\\\\n\\\".join(r)\",\n \"def __str__(self):\\n if len(self.__keys) == 0:\\n return '{}'\\n output = '{'\\n fmt = '{}: {}, '\\n for key, val in zip(self.__keys, self.__vals):\\n output += fmt.format(repr(key), repr(val))\\n return output[:-2] + '}'\",\n \"def formatAllToFloat(self):\\n SectionList = [self.Accel[\\\"X\\\"], self.Accel[\\\"Y\\\"], self.Accel[\\\"Z\\\"], self.Gyro[\\\"X\\\"], self.Gyro[\\\"Y\\\"], self.Gyro[\\\"Z\\\"], self.Millis,self.GForce,self.Rot[\\\"X\\\"],self.Rot[\\\"Y\\\"],self.Rot[\\\"Z\\\"],self.DDMillis.Gyro,self.DDMillis.Accel,self.DDMillis.Rot,self.DDMillis.Mag,\\n self.GPSDat[\\\"lng\\\"], self.GPSDat[\\\"lat\\\"], self.GPSDat[\\\"Speed\\\"], self.GPSDat[\\\"Altitude\\\"], self.GPSDat[\\\"Accuracy\\\"], self.GPSDat[\\\"Bearing\\\"],self.Mag[\\\"X\\\"],self.Mag[\\\"Y\\\"],self.Mag[\\\"Z\\\"]]\\n\\n for item in SectionList:\\n if not len(item) == 0:\\n GoToThisDangit = item[:]\\n for SubItem in range(len(GoToThisDangit)):\\n try:\\n if SubItem > len(item):\\n break\\n else:\\n item[SubItem] = float(item[SubItem])\\n except ValueError:\\n try:\\n del item[SubItem]\\n except IndexError:\\n \\\"Slightly dangerous catch....\\\"\\n except IndexError:\\n \\\"yo\\\"\",\n \"def engFormat(f):\\n if f == 0.0:\\n value = 0.0\\n exponent = 0\\n else:\\n exponent = math.log10(-f if f < 0 else f)\\n if exponent < 0:\\n exponent = -int(math.ceil(-exponent))\\n else:\\n exponent = int(math.floor(exponent))\\n for i in range(3):\\n if (exponent % 3) == 0:\\n break\\n exponent = exponent - 1\\n value = f * 10 ** -exponent\\n # Choose a format to maintain the number of useful digits we print.\\n if abs(value) < 10:\\n fmt = \\\"%6.3f%s\\\"\\n elif abs(value) < 100:\\n fmt = \\\"%6.2f%s\\\"\\n else:\\n fmt = \\\"%6.1f%s\\\"\\n\\n return fmt % (value, (\\\"\\\" if exponent == 0 else \\\"e%d\\\" % exponent))\",\n \"def format_string_2(file_num, float_num1, int_num, float_num2):\\n\\n result = f\\\"file_{file_num:0>3d} :{float_num1:9.2f}, {int_num:.2e}, {float_num2:.3g}\\\"\\n print(result)\\n return result\",\n \"def float_vct_str ( vct , format = '%.5g' ) :\\n try :\\n return '[ ' + ', '.join ( [ format % v for v in vct ] ) + ' ]' \\n except TypeError :\\n pass\\n return float_vct_str ( vct , format = '%.5g' )\",\n \"def log_metric(name, values, tags={}):\\n value_list = []\\n for key in sorted(values.keys()):\\n value = values[key]\\n value_list.append(f\\\"{key}:{value:7.3f}\\\")\\n values = \\\", \\\".join(value_list)\\n tag_list = []\\n for key, tag in tags.items():\\n tag_list.append(f\\\"{key}:{tag}\\\")\\n tags = \\\", \\\".join(tag_list)\\n print(\\\"{name:30s} - {values} ({tags})\\\".format(name=name, values=values, tags=tags))\",\n \"def print_dict(dictionary, format_=None):\\n\\n format_ = format_ or DEFAULT\\n\\n if format_ == TEXT:\\n for key, value in iter(sorted(dictionary.items())):\\n print(\\\"%s = %s\\\" % (key, value))\\n elif format_ == DOCKERENV:\\n for key, value in iter(sorted(dictionary.items())):\\n print(\\\"%s=%s\\\" % (key, value))\\n elif format_ == BASH:\\n for key, value in iter(sorted(dictionary.items())):\\n print(\\\"export %s=%s\\\" % (key, value))\\n elif format_ == JSON:\\n print(json.dumps(dictionary))\\n elif format_ == NAME_VALUE_DICT:\\n print(\\\"[\\\")\\n for key, value in iter(sorted(dictionary.items())):\\n print('{\\\"name\\\": \\\"%s\\\", \\\"value\\\": \\\"%s\\\"},' % (key, value))\\n print(\\\"]\\\")\",\n \"def _reprOfStringToValueMap (stringMap : Map) -> String:\\n\\n entrySeparator = u\\\"§\\\"\\n entryTemplate = \\\"%s: %s\\\"\\n keyList = sorted(list(stringMap.keys()))\\n result = \\\"\\\"\\n \\n for key in keyList:\\n value = stringMap[key] \\n result += (iif(result == \\\"\\\", \\\"\\\", entrySeparator)\\n + entryTemplate % (key, value))\\n \\n result = \\\"{\\\" + result + \\\"}\\\";\\n return result\",\n \"def clean_dict(self,dict_to_clean):\\n for i in dict_to_clean: \\n try:\\n float( dict_to_clean[i] ) \\n except:\\n dict_to_clean[ i ] = \\\"'%s'\\\"%( dict_to_clean[i ].replace(\\\"'\\\",\\\"\\\").replace('\\\"',\\\"\\\") )\",\n \"def __str__(self):\\n #Get an ordered list of the elements strings so it outputs always the same\\n #string given a mass function.\\n elements = []\\n for element in self.focals:\\n elements.append((element, str(element)))\\n sortedList = sorted(elements, key=lambda x:x[1])\\n \\n result = \\\"\\\"\\n first = True\\n for t in sortedList:\\n if first:\\n result += t[1] + \\\":\\\" + \\\"{:.4f}\\\".format(self.focals[t[0]])\\n first = False\\n else:\\n result += \\\", \\\" + t[1] + \\\":\\\" + \\\"{:.4f}\\\".format(self.focals[t[0]])\\n return \\\"{\\\" + result + \\\"}\\\"\",\n \"def _fmt_map(self, string_list):\\n return self._fmt_csv(string_list, list_braces=\\\"{}\\\")\",\n \"def _write_dict(pdf, input_dict, indent=0, border=BORDER):\\n for key, value in input_dict.items():\\n pdf.set_font(\\\"arial\\\", \\\"B\\\", 14)\\n pdf.cell(75, 5, key, border, 1, \\\"L\\\")\\n pdf.cell(indent, 0)\\n pdf.set_font(\\\"arial\\\", \\\"\\\", 12)\\n pdf.multi_cell(150, 5, value, border, \\\"L\\\")\\n pdf.ln(h=5)\",\n \"def displaySummary(self, dictionnary, lineCount):\\n for key in self.summaryDict.keys():\\n dictionnary[key] = (dictionnary[key] / lineCount) * 100\\n #print(str(key)+\\\" => \\\"+str(dictionnary[key])+\\\" %\\\")\",\n \"def func_plot_fmt_map():\\n # line_styles = ['o-', 'x-', '*-', '-_', 'D-', 'h-', '+-', 's-', 'v-', \\n # ',-', '1-']\\n func_tuples = get_func_tuples()\\n M = {k:v for (k, _, v) in func_tuples}\\n return M\",\n \"def print_data(d, indent=0):\\n prefix = indent * ' '\\n for k in sorted(d):\\n v = d[k]\\n k = prefix + str(k)\\n if isinstance(v, dict):\\n print(k)\\n print_data(v, indent + 1)\\n else:\\n if k.endswith('cent'):\\n v = ' '.join(\\n str(tuple(int(j) if j.is_integer() else j for j in i))\\n for i in v\\n )\\n elif isinstance(v, np.ndarray):\\n v = str(v).replace('\\\\n', '')\\n print(k, '=', v)\",\n \"def printDict(myDict):\\n for key in myDict:\\n print(f\\\"Version: --> {myDict[key]['version']} \\\")\\n print(f\\\"Accuracy: --> {myDict[key]['accuracy']}\\\")\\n print(f\\\"Time --> {myDict[key]['time_per_target']}\\\")\\n print(f\\\"Penalty --> {myDict[key]['target_w_penalty']}\\\")\\n print(f\\\"ID --> {myDict[key]['assessed_by']}\\\")\\n print(f\\\"# --> {myDict[key]['attempt']}\\\")\\n\\n print()\",\n \"def __str__(self):\\n if len(self.keys()):\\n return '{' + repr(self.keys()[0]) + ':' + repr(self[self.keys()[0]]) + ', ...'\\n else:\\n return super(FSDict, self).__str__()\",\n \"def to_string(inputs, outputs):\\n r_val = '# Column 01: frequency\\\\n'\\n r_val += '# 02: hp - real\\\\n'\\n r_val += '# 03: hp - imaginary\\\\n'\\n r_val += '# 04: hc - real\\\\n'\\n r_val += '# 05: hc - imaginary\\\\n'\\n for f_i, hp_i, hc_i in zip(inputs.freqs, outputs.hp, outputs.hc):\\n r_val += \\\"%8.2f %12.5e %12.5e %12.5e %12.5e\\\\n\\\" % (f_i, hp_i.real, hp_i.imag, hc_i.real, hc_i.imag)\\n return r_val\",\n \"def dist_dict_to_json(dist_dict: Dict[Tuple[str, str], float]) -> Dict[str, float]:\\n \\n return {str(custs_key): dist for custs_key, dist in dist_dict.items()}\",\n \"def sensible_format_data(self, value):\\n if abs(value) > 1e4 or abs(value)<1e-3:\\n s = '%1.4e' % value\\n return self._formatSciNotation(s)\\n else:\\n return '%4.3f' % value\",\n \"def pretty_print_table(data, list_of_dicts):\\n # ensure that each dict has the same set of keys\\n keys = None\\n for d in list_of_dicts:\\n if keys is None:\\n keys = d.keys()\\n else:\\n if d.keys() != keys:\\n print(\\\"Error! not all dicts have the same keys!\\\")\\n return\\n header = \\\"\\\\t\\\" + \\\"\\\\t\\\".join(['{:11.10s}'] * len(data))\\n header = header.format(*data)\\n rows = []\\n for k in keys:\\n r = k + \\\"\\\\t\\\"\\n for d in list_of_dicts:\\n if type(d[k]) is float:\\n r += '{:.9f}'.format(d[k]) + \\\"\\\\t\\\"\\n else:\\n r += '{:10.9s}'.format(str(d[k])) + \\\"\\\\t\\\"\\n rows.append(r)\\n print(header)\\n for row in rows:\\n print(row)\",\n \"def value_str(value):\\n # cast to float to remove 0-padding\\n return \\\":\\\".join(map(str, map(float, value)))\",\n \"def __str__(self):\\n try:\\n delim = ', ' if len(self) < 8 else ',\\\\n '\\n s = delim.join('%s: %s' % (repr(k), repr(self[k])) for k in self.peys())\\n return '{' + s + '}'\\n except Exception:\\n return dict.__repr__(self)\",\n \"def format_dict(dictionary, depth=0):\\n tab = \\\" \\\" * 4\\n string = \\\"{\\\\n\\\"\\n for key, val in dictionary.items():\\n string += depth * tab \\n string += \\\"{}: \\\".format(key)\\n if type(val) is dict:\\n string += format_dict(val, depth + 1)\\n \\n else:\\n if type(val) is str:\\n fmt = \\\"'{}'\\\\n\\\"\\n else:\\n fmt = \\\"{}\\\\n\\\"\\n string += fmt.format(val)\\n string += (depth) * tab + '}\\\\n'\\n return string\",\n \"def display(self):\\n new_list = []\\n for key, value in self.model.items():\\n interne = []\\n # FIXME print price value in {:.2f} format\\n if key in ['price', 'efficiency']:\\n interne.extend([f'{value:.2f}'])\\n else:\\n interne.extend([f'{value}'])\\n new_list.append(list(interne))\\n return new_list\",\n \"def to_text(self, floatformat=\\\".4f\\\"):\\n return (\\\"f_{{{ind}}} < {thr:\\\" + floatformat + \\\"}\\\").format(\\n ind=self.val[\\\"best_feature_index\\\"], thr=self.val[\\\"thr\\\"]\\n )\",\n \"def pretty_dict(d):\\n return '{%s}' % ', '.join('%r: %r' % (k, v)\\n for k, v in sorted(d.items(), key=repr))\",\n \"def loss_dict_to_str(running_loss_dict: Dict[str, float], logging_period: int) -> str:\\n loss_str = \\\", \\\".join(\\n [\\n f\\\"{key}: {value / logging_period:.2f}\\\"\\n for key, value in running_loss_dict.items()\\n ]\\n )\\n return loss_str\",\n \"def get_formatter(self, **kwargs):\\n config = dict([\\n (attr, getattr(self, attr))\\n for attr in [\\n \\\"include_sign\\\",\\n \\\"group_with_commas\\\",\\n \\\"num_decimal_places\\\",\\n ]\\n ])\\n config.update(kwargs)\\n return \\\"\\\".join([\\n \\\"{\\\",\\n config.get(\\\"field_name\\\", \\\"\\\"),\\n \\\":\\\",\\n \\\"+\\\" if config[\\\"include_sign\\\"] else \\\"\\\",\\n \\\",\\\" if config[\\\"group_with_commas\\\"] else \\\"\\\",\\n \\\".\\\", str(config[\\\"num_decimal_places\\\"]), \\\"f\\\",\\n \\\"}\\\",\\n ])\",\n \"def pretty(self):\\n #table = [\\\"\\\".join([\\\"%8s \\\" % s for s in self.alpha.getSymbols()])]\\n table = []\\n for row in PWM.getFreq(self):\\n table.append(\\\"\\\".join([\\\"%8.6f \\\" % y for y in row]))\\n return table\",\n \"def __str__(self):\\n return \\\"{:.3f} {:+.3f}i {:+.3f}j {:+.3f}k\\\".format(self.q[0], self.q[1], self.q[2], self.q[3])\",\n \"def _to_string(self) -> str:\\n\\n string_list = []\\n for key, value in self.__dict__.items():\\n if isinstance(value, dict):\\n string_list.append(key)\\n string_list.extend('\\\\n'.join([\\\"Key: {:24}\\\\tValue: {}\\\".format(_key, _value) for _key, _value in value.items()]))\\n else:\\n string_list.append(\\\"Key: {:24}\\\\tValue: {}\\\\n\\\".format(key, value))\\n return ''.join(string_list)\",\n \"def __format__(self, formatstr):\\n if formatstr.strip() == '': # Defualt behaviour mirrors self.__str__()\\n formatstr = '+.3f'\\n\\n string = \\\\\\n \\\"{:\\\" + formatstr +\\\"} \\\" + \\\\\\n \\\"{:\\\" + formatstr +\\\"}i \\\" + \\\\\\n \\\"{:\\\" + formatstr +\\\"}j \\\" + \\\\\\n \\\"{:\\\" + formatstr +\\\"}k\\\"\\n return string.format(self.q[0], self.q[1], self.q[2], self.q[3])\",\n \"def ppdict(d):\\n print '{'\\n keys=d.keys()\\n keys.sort()\\n for k in keys:\\n spacing=\\\" \\\" * (16-(len(repr(k))+1))\\n print \\\"%s:%s%s,\\\" % (repr(k),spacing,repr(d[k]))\\n print '}'\",\n \"def _trans_format(self):\\n config_dict = vars(self._config)\\n for item, value in config_dict.items():\\n if value == 'None':\\n config_dict[item] = None\\n elif isinstance(value, str) and is_number(value):\\n if value.isdigit():\\n value = int(value)\\n else:\\n value = float(value)\\n config_dict[item] = value\",\n \"def func_dict(func):\\n fdict = {\\n 'doc': '',\\n 'unit': '',\\n 'str': 'NA',\\n 'func': func}\\n\\n value = func\\n try:\\n value = value()\\n except:\\n pass\\n\\n try:\\n fdict['attr'] = func.__func__.__name__\\n except:\\n fdict['attr'] = None\\n \\n if isinstance(value,str):\\n fdict['str'] = value\\n else:\\n if hasattr(value,'mean'):\\n fdict['str'] = '<{:.4}>'.format(value.mean())\\n else:\\n try:\\n fdict['attr'] = func.__func__.__name__\\n doc = func.__doc__.split('\\\\n')[-1].lstrip(' ')\\n fdict['doc'] = doc\\n fdict['str'] = '{:10.5g}'.format(value)\\n fdict['unit'] = get_unit_from_doc(doc)\\n fdict['type'] = get_type_from_doc(doc)\\n except:\\n try:\\n fdict['attr'] = func.__func__.__name__\\n fdict['str'] = value.__str__()\\n except:\\n pass\\n\\n fdict['value'] = value\\n\\n return fdict\",\n \"def _format_parameter_output(self, parameters: dict) -> str:\\n \\n output = ''\\n for key, value in parameters.items():\\n output = output + '\\\\t\\\\t' + str(key) + ': ' + str(value) + '\\\\n'\\n \\n return output\",\n \"def convert_font_dict_to_ttoptions_dict(d):\\n return {\\n (\\\"f\\\" + k if k in (underline_s, overstrike_s) else k): v\\n for k, v in d.items()\\n }\",\n \"def __str__(self):\\n return \\\"{\\\" + (\\\", \\\".join(\\\"%s: %s\\\"%(ngram, value) for (ngram, value) in self.items())) + \\\"}\\\"\",\n \"def __str__(self):\\n columns = list(self.metrics.keys())\\n columns.sort()\\n out = '%s\\\\n' % ','.join(columns)\\n values = [str(self.metrics[c]) for c in columns]\\n out += '%s\\\\n' % ','.join(values)\\n return out\",\n \"def to_dict(self):\\n return {k[1:]: round(v, 5) for k, v in self.__dict__.items()}\",\n \"def output_metrics(self):\\n print('')\\n for key in sorted(self.metrics):\\n print('{}:'.format(key), end='')\\n for k, v in self.metrics[key].items():\\n if type(v[-1]) is list:\\n print('\\\\t' + k + ': ' + ''.join('{:5.3f} '.format(vs) for vs in v[-1]), end='')\\n else:\\n print('\\\\t{}: {:5.3f}'.format(k, v[-1]), end='')\\n print('\\\\n', end='')\",\n \"def __str__(self):\\n prob = str(round(self.probability, 5))\\n dprob = str(round(self.postdProbability, 5))\\n output = \\\"dprob: \\\" + dprob + \\\" \\\\tprob: \\\" + prob + \\\"\\\\t: \\\"\\n for key in self.attackDict.keys():\\n output += key + \\\" \\\"\\n return output\",\n \"def _drop_float_precision(dict_obj, point):\\n for keyword in dict_obj.keys():\\n for lang_id, prob in enumerate(dict_obj[keyword]):\\n dict_obj[keyword][lang_id] = round(prob, point)\",\n \"def to_dump(self):\\n s = []\\n for k in self.keys():\\n if isinstance(self[k], int) or isinstance(self[k], long):\\n s.append(\\\"%s=%d\\\" % (k, self[k]))\\n elif isinstance(self[k], float):\\n s.append(\\\"%s=%f\\\" % (k, self[k]))\\n else:\\n for v2 in self.list(k):\\n if isinstance(v2, str):\\n s.append(\\\"%s=%s\\\" % (k, v2))\\n else:\\n s.append(\\\"%s=%s\\\" % (k, util.encode(v2)))\\n s.append(\\\"~format=%s\\\" % self.format)\\n s.append(\\\"\\\")\\n return \\\"\\\\n\\\".join(s)\",\n \"def __str__(self):\\n try:\\n delim = ', ' if len(self) < 8 else ',\\\\n '\\n s = delim.join('%s: %s' % (repr(k), repr(self[k])) for k in self.peys())\\n return '{' + s + '}'\\n except Exception:\\n return defaultdict.__repr__(self)\",\n \"def pretty_float(v: Union[float, int], n_sigfigs: Optional[int] = 5) -> str:\\n\\t\\tv0 = v.__class__(v)\\n\\t\\t# first, handle NaN and infinities\\n\\t\\tif np.isneginf(v):\\n\\t\\t\\treturn Chars.minus + Chars.inf\\n\\t\\telif np.isposinf(v):\\n\\t\\t\\treturn '+' + Chars.inf\\n\\t\\telif np.isnan(v):\\n\\t\\t\\treturn Chars.null\\n\\t\\t# sweet. it's a regular float or int.\\n\\t\\tisint = isinstance(v, int)\\n\\t\\tif isint:\\n\\t\\t\\tv = int(round(v))\\n\\t\\tif n_sigfigs is None:\\n\\t\\t\\ts = StringTools.strip_empty_decimal(str(v))\\n\\t\\telse:\\n\\t\\t\\ts = str(float(str(('%.{}g'.format(n_sigfigs)) % v)))\\n\\t\\t# remove the .0 if the precision doesn't support it\\n\\t\\t# if v >= 1 and n_sigfigs<2, it couldn't have a decimal\\n\\t\\t# and if n_sigfigs<1, it definitely can't\\n\\t\\tif isint or n_sigfigs is not None and v0>=1 and n_sigfigs<2 or n_sigfigs is not None and n_sigfigs < 1:\\n\\t\\t\\ts = StringTools.strip_empty_decimal(s)\\n\\t\\t# and ... %g does this.\\n\\t\\tif s.startswith('.'):\\n\\t\\t\\ts = '0' + s\\n\\t\\t# prepend + or -\\n\\t\\tif s.startswith('-'):\\n\\t\\t\\treturn Chars.minus + s[1:]\\n\\t\\telse:\\n\\t\\t\\treturn '+' + s\",\n \"def formatall(obj):\\n result = \\\"\\\"\\n if isinstance(obj, list):\\n# i = 0\\n for obj in obj:\\n #printf(\\\">>> [%d] >>> \\\", i)\\n result += format(obj)\\n result += \\\"\\\\n\\\"\\n# i += 1\\n return result\\n if isinstance(obj, dict):\\n for key, value in obj.items():\\n result += \\\"%-15s : \\\" % key\\n result += format(value)\\n result += \\\"\\\\n\\\"\\n return result\\n return format(obj)\",\n \"def _float_to_string(v):\\n\\n if not isinstance(v,float):\\n raise InstrumentParameterException('Value %s is not a float.' % v)\\n else:\\n return '%e' % v\",\n \"def format_float(value):\\n if isinstance(value, float):\\n value = '{:.3f}'.format(value * 1e-3)\\n return value\",\n \"def get_column_formatters(df):\\n formatters = {}\\n for p in df.columns:\\n (a, b) = p\\n if b.endswith(\\\"Q\\\"):\\n formatters[p] = lambda x: \\\"%.2f\\\" % x\\n elif b.startswith(\\\"T_\\\"):\\n formatters[p] = lambda x: \\\"%.1f\\\" % x\\n else:\\n formatters[p] = lambda x: \\\"%.0f\\\" % x\\n return formatters\",\n \"def _print_report(self, result: Dict[str, Dict[str, float]]):\\n\\n print('\\\\n\\\\tprecision recall f1_score num')\\n for type_ in self.types:\\n print(type_, end='\\\\t')\\n print('{0: .3f}'.format(result[type_]['precision']), end=' ')\\n print('{0: .3f}'.format(result[type_]['recall']), end=' ')\\n print('{0: .3f}'.format(result[type_]['f1_score']), end=' ')\\n print('{0: d}'.format(result[type_]['num']), end='\\\\n')\",\n \"def format_to_store(key, value):\\n key = key.upper()\\n if not isparam(key):\\n raise ValueError(\\\"invalid key %r\\\" % key)\\n\\n if key in ['RA_ERR', 'RAJ_ERR']:\\n if isinstance(value, basestring):\\n floatvalue = hms_rad(0., 0., value)\\n elif isinstance(value, float):\\n floatvalue = value\\n else:\\n raise TypeError('invalid %s value type %r' % (key, value))\\n elif key in ['DEC_ERR', 'DECJ_ERR']:\\n if isinstance(value, basestring):\\n floatvalue = dms_rad(0., 0., value)\\n elif isinstance(value, float):\\n floatvalue = value\\n else:\\n raise TypeError('invalid %s value type %r' % (key, value))\\n else:\\n try:\\n floatvalue = float(value)\\n except ValueError:\\n if key in ['RA', 'RAJ']:\\n floatvalue = hms_rad(value)\\n elif key in ['DEC', 'DECJ']:\\n floatvalue = dms_rad(value)\\n elif isinstance(value, basestring):\\n floatvalue = value\\n else:\\n raise ValueError('invalid PAR %s value %r.' % (key, value))\\n return floatvalue\",\n \"def __repr__(self, *args, **kwargs):\\n result ='{'\\n for (k, v) in self.items(*args, **kwargs):\\n result += repr(k) + \\\": \\\" + repr(v) + \\\",\\\"\\n\\n result = result[:-1] + '}'\\n return result\",\n \"def __init_key_value_fmts__(self):\\n self.test_desc_fmt = \\\"{0}.description\\\"\\n self.test_svc_name_fmt = \\\"{0}.svc\\\"\\n self.test_svc_ipc_id_fmt = \\\"{0}.svc.{1}.ipc_id\\\"\\n self.test_svc_rsp_code_fmt = \\\"{0}.svc.{1}.svc_rsp_code\\\"\\n self.test_svc_rsp_stage_fmt = \\\"{0}.svc.{1}.svc_rsp_stage\\\"\\n self.test_pre_hook_stage_fmt = \\\"{0}.pre_hook.stage\\\"\\n self.test_pre_hook_script_fmt = \\\"{0}.pre_hook.script\\\"\\n self.test_post_hook_stage_fmt = \\\"{0}.post_hook.stage\\\"\\n self.test_post_hook_script_fmt = \\\"{0}.post_hook.script\\\"\\n self.test_svc_rsp_time_delay_fmt = \\\"{0}.svc.{1}.rsp_time_delay\\\"\\n\\n self.test_expected_result_fmt = \\\"{0}.expected_result\\\"\\n self.test_exp_api_return_fmt = \\\"{0}.expected_result.api_return_code\\\"\\n self.test_exp_log_file_fmt = \\\"{0}.expected_result.log\\\"\\n self.test_exp_log_fmt = \\\"{0}.expected_result.log.{1}\\\"\",\n \"def _kv_to_float(kv, keys):\\n for k, v in kv.items():\\n if k in keys:\\n kv[k] = _to_float(v)\\n return kv\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70268685","0.70015323","0.6506103","0.65015024","0.63505834","0.63133466","0.61922085","0.6134083","0.6098064","0.6060226","0.6022871","0.6014576","0.5958936","0.5937073","0.5937073","0.5933473","0.5883104","0.5847088","0.5822004","0.5818801","0.581338","0.5791235","0.5785255","0.57714146","0.5768837","0.57627803","0.576219","0.5761243","0.5741955","0.5736133","0.57349676","0.57111526","0.5710091","0.5689559","0.5687905","0.56756353","0.5668683","0.5658287","0.5655828","0.5634439","0.5620644","0.5596378","0.55684793","0.5561877","0.55559224","0.5553523","0.55473614","0.55443555","0.554161","0.5526257","0.55229765","0.5511857","0.5510066","0.55099434","0.5509016","0.54926586","0.5485991","0.54667217","0.5464419","0.54627776","0.54565233","0.5447183","0.5446046","0.5441025","0.5438854","0.5434458","0.54232365","0.5400997","0.53918785","0.5388907","0.53810143","0.5364278","0.5360961","0.5358202","0.53533417","0.5346492","0.5343345","0.53418404","0.5334174","0.533046","0.5328994","0.5315904","0.5312906","0.53104347","0.5296254","0.52870876","0.52856153","0.52849644","0.5279316","0.5272995","0.52664757","0.52598804","0.5254569","0.5250928","0.52506745","0.52480346","0.52362984","0.5232264","0.522636","0.52223724"],"string":"[\n \"0.70268685\",\n \"0.70015323\",\n \"0.6506103\",\n \"0.65015024\",\n \"0.63505834\",\n \"0.63133466\",\n \"0.61922085\",\n \"0.6134083\",\n \"0.6098064\",\n \"0.6060226\",\n \"0.6022871\",\n \"0.6014576\",\n \"0.5958936\",\n \"0.5937073\",\n \"0.5937073\",\n \"0.5933473\",\n \"0.5883104\",\n \"0.5847088\",\n \"0.5822004\",\n \"0.5818801\",\n \"0.581338\",\n \"0.5791235\",\n \"0.5785255\",\n \"0.57714146\",\n \"0.5768837\",\n \"0.57627803\",\n \"0.576219\",\n \"0.5761243\",\n \"0.5741955\",\n \"0.5736133\",\n \"0.57349676\",\n \"0.57111526\",\n \"0.5710091\",\n \"0.5689559\",\n \"0.5687905\",\n \"0.56756353\",\n \"0.5668683\",\n \"0.5658287\",\n \"0.5655828\",\n \"0.5634439\",\n \"0.5620644\",\n \"0.5596378\",\n \"0.55684793\",\n \"0.5561877\",\n \"0.55559224\",\n \"0.5553523\",\n \"0.55473614\",\n \"0.55443555\",\n \"0.554161\",\n \"0.5526257\",\n \"0.55229765\",\n \"0.5511857\",\n \"0.5510066\",\n \"0.55099434\",\n \"0.5509016\",\n \"0.54926586\",\n \"0.5485991\",\n \"0.54667217\",\n \"0.5464419\",\n \"0.54627776\",\n \"0.54565233\",\n \"0.5447183\",\n \"0.5446046\",\n \"0.5441025\",\n \"0.5438854\",\n \"0.5434458\",\n \"0.54232365\",\n \"0.5400997\",\n \"0.53918785\",\n \"0.5388907\",\n \"0.53810143\",\n \"0.5364278\",\n \"0.5360961\",\n \"0.5358202\",\n \"0.53533417\",\n \"0.5346492\",\n \"0.5343345\",\n \"0.53418404\",\n \"0.5334174\",\n \"0.533046\",\n \"0.5328994\",\n \"0.5315904\",\n \"0.5312906\",\n \"0.53104347\",\n \"0.5296254\",\n \"0.52870876\",\n \"0.52856153\",\n \"0.52849644\",\n \"0.5279316\",\n \"0.5272995\",\n \"0.52664757\",\n \"0.52598804\",\n \"0.5254569\",\n \"0.5250928\",\n \"0.52506745\",\n \"0.52480346\",\n \"0.52362984\",\n \"0.5232264\",\n \"0.522636\",\n \"0.52223724\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7233693"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":83097,"cells":{"query":{"kind":"string","value":"Create contour plots for a study, given a list or tuple of two parameter names."},"document":{"kind":"string","value":"def study_contour_plot(study: optuna.Study, \n params: Optional[List[str]]=None, \n width: int=560, \n height: int=500):\n if params is None:\n params = [\"dv\", \"vc\"]\n fig = optuna.visualization.plot_contour(study, params=params)\n fig.update_layout(\n title=f'Contour Plot: {study.study_name} ({params[0]}, {params[1]})', \n autosize=False,\n width=width, \n height=height,\n margin=dict(l=65, r=50, b=65, t=90))\n fig.show()"},"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 make_plots(fig_title, \n t_csd_x, t_csd_y, t_csd_z, true_csd, \n ele_x, ele_y, ele_z, pots,\n k_csd_x, k_csd_y, k_csd_z, est_csd):\n fig = plt.figure(figsize=(10,16))\n #True CSD\n z_steps = 5\n height_ratios = [1 for i in range(z_steps)]\n height_ratios.append(0.1)\n gs = gridspec.GridSpec(z_steps+1, 3, height_ratios=height_ratios)\n t_max = np.max(np.abs(true_csd))\n levels = np.linspace(-1*t_max, t_max, 16)\n ind_interest = np.mgrid[0:t_csd_z.shape[2]:np.complex(0,z_steps+2)]\n ind_interest = np.array(ind_interest, dtype=np.int)[1:-1]\n for ii, idx in enumerate(ind_interest):\n ax = plt.subplot(gs[ii, 0])\n im = plt.contourf(t_csd_x[:,:,idx], t_csd_y[:,:,idx], true_csd[:,:,idx], \n levels=levels, cmap=cm.bwr_r)\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n title = str(t_csd_z[:,:,idx][0][0])[:4]\n ax.set_title(label=title, fontdict={'x':0.8, 'y':0.8})\n ax.set_aspect('equal')\n cax = plt.subplot(gs[z_steps,0])\n cbar = plt.colorbar(im, cax=cax, orientation='horizontal')\n cbar.set_ticks(levels[::2])\n cbar.set_ticklabels(np.around(levels[::2], decimals=2))\n #Potentials\n v_max = np.max(np.abs(pots))\n levels_pot = np.linspace(-1*v_max, v_max, 16)\n ele_res = int(np.ceil(len(pots)**(3**-1))) \n ele_x = ele_x.reshape(ele_res, ele_res, ele_res)\n ele_y = ele_y.reshape(ele_res, ele_res, ele_res)\n ele_z = ele_z.reshape(ele_res, ele_res, ele_res)\n pots = pots.reshape(ele_res, ele_res, ele_res)\n for idx in range(min(5,ele_res)):\n X,Y,Z = grid(ele_x[:,:,idx], ele_y[:,:,idx], pots[:,:,idx])\n ax = plt.subplot(gs[idx, 1])\n im = plt.contourf(X, Y, Z, levels=levels_pot, cmap=cm.PRGn)\n ax.hold(True)\n plt.scatter(ele_x[:,:,idx], ele_y[:,:,idx], 5)\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n title = str(ele_z[:,:,idx][0][0])[:4]\n ax.set_title(label=title, fontdict={'x':0.8, 'y':0.8})\n ax.set_aspect('equal')\n ax.set_xlim([0.,1.])\n ax.set_ylim([0.,1.])\n cax = plt.subplot(gs[z_steps,1])\n cbar2 = plt.colorbar(im, cax=cax, orientation='horizontal')\n cbar2.set_ticks(levels_pot[::2])\n cbar2.set_ticklabels(np.around(levels_pot[::2], decimals=2))\n # #KCSD\n t_max = np.max(np.abs(est_csd[:,:,:,0]))\n levels_kcsd = np.linspace(-1*t_max, t_max, 16)\n ind_interest = np.mgrid[0:k_csd_z.shape[2]:np.complex(0,z_steps+2)]\n ind_interest = np.array(ind_interest, dtype=np.int)[1:-1]\n for ii, idx in enumerate(ind_interest):\n ax = plt.subplot(gs[ii, 2])\n im = plt.contourf(k_csd_x[:,:,idx], k_csd_y[:,:,idx], est_csd[:,:,idx,0], \n levels=levels_kcsd, cmap=cm.bwr_r)\n #im = plt.contourf(k_csd_x[:,:,idx], k_csd_y[:,:,idx], est_csd[:,:,idx,0], \n # levels=levels, cmap=cm.bwr_r)\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n title = str(k_csd_z[:,:,idx][0][0])[:4]\n ax.set_title(label=title, fontdict={'x':0.8, 'y':0.8})\n ax.set_aspect('equal')\n cax = plt.subplot(gs[z_steps,2])\n cbar3 = plt.colorbar(im, cax=cax, orientation='horizontal')\n cbar3.set_ticks(levels_kcsd[::2])\n #cbar3.set_ticks(levels[::2])\n cbar3.set_ticklabels(np.around(levels_kcsd[::2], decimals=2))\n #cbar3.set_ticklabels(np.around(levels[::2], decimals=2))\n fig.suptitle(\"Lambda,R,CV_Error,RMS_Error,Time = \"+fig_title)\n gs.tight_layout(fig, rect=[0, 0.03, 1, 0.95]) \n # #Showing\n #plt.tight_layout()\n plt.show()\n return","def make_plot(\n X: np.ndarray, Y: np.ndarray, Z: np.ndarray, \n int_coords: List[np.ndarray], levels: np.ndarray, \n color_range: List[str], inter_color: List[Union[str, float]], \n SAVE: bool, SAVE_NAME: str):\n color1 = Color(color_range[0])\n color2 = Color(color_range[1])\n \n # List of Color objects, need to convert for matplotlib\n color_list = list(color1.range_to(color2, len(levels)))\n \n contour_colors = []\n for color in color_list:\n contour_colors.append(color.hex)\n\n fig = plt.figure(figsize = [8, 6])\n ax1 = fig.add_subplot(111)\n\n # Set title\n fig.suptitle('Quartz Deformation Mechanism Map')\n \n CS = ax1.contour(X, Y, Z, levels, colors = contour_colors) \n \n # Add intersection lines to plot\n for coord_arr in int_coords:\n # Get x indices and y indices\n x, y = coord_arr.T\n \n # Get actual points to plot\n points_x = X[x, y]\n points_y = Y[x, y]\n\n ax1.plot(points_x, points_y, \n color = inter_color[0], alpha = inter_color[1])\n\n # Labels... hardcoded. May find a way to make this generic\n sr_labels = [\n r'$10^{-15}$', r'$10^{-14}$', r'$10^{-13}$', \n r'$10^{-12}$', r'$10^{-11}$', r'$10^{-10}$', \n r'$10^{-9}$', r'$10^{-8}$', r'$10^{-7}$', \n r'$10^{-6}$']\n\n # Make legend equal to contour values from sr_labels\n for i in range(len(sr_labels)):\n CS.collections[i].set_label(sr_labels[i])\n\n # Reverse legend order, set location and title\n handles, labels = ax1.get_legend_handles_labels()\n ax1.legend(reversed(handles), reversed(labels), \n loc = 'upper right', title = r'$\\.\\epsilon$')\n\n # Set log scale and ylim\n ax1.set_yscale('log')\n ax1.set_ylim([10 ** -6, 1])\n \n # Set labels... could be done better in Illustrator...\n ax1.set_ylabel(\n r'Shear Stress $log\\left( \\frac{\\sigma}{\\mu}\\right)$'\n )\n ax1.set_xlabel(\n r'Homologous Temperature $\\left( \\frac{T}{T_m} \\right)$'\n )\n\n # Plot tick marks\n ax1.grid()\n\n if SAVE:\n fig.savefig(SAVE_NAME)\n else:\n plt.show()","def plot_contour(xynodes, stress, mytitle='contour plot'):\n\n stress = np.array(stress)\n fig2 = plt.figure()\n\n fig2 = plt.figure()\n sigma1 = stress[:, 0]\n sigma2 = stress[:, 1]\n sigma12 = stress[:, 2]\n vonmises = np.sqrt(sigma1 ** 2 - sigma1 * sigma2 + sigma2 ** 2 + 3 * sigma12 ** 2)\n prins1 = (sigma1 + sigma2) / 2 + np.sqrt(((sigma1 - sigma2) / 2) ** 2 + sigma12 ** 2)\n prins2 = (sigma1 + sigma2) / 2 - np.sqrt(((sigma1 - sigma2) / 2) ** 2 + sigma12 ** 2)\n\n sigma1norm = sigma1 / np.max(np.abs(sigma1))\n sigma2norm = sigma2 / np.max(np.abs(sigma2))\n sigma12norm = sigma12 / np.max(np.abs(sigma12))\n vonmisesnorm = vonmises / np.max(np.abs(vonmises))\n prins1norm = prins1 / np.max(np.abs(prins1))\n prins2norm = prins2 / np.max(np.abs(prins2))\n\n pltsigma = sigma1norm\n for i, xy in enumerate(xynodes):\n x = [k[0] for k in xy]\n y = [k[1] for k in xy]\n # red for tension, blue for compression\n pltcolor = 'r' if pltsigma[i] >= 0 else 'b'\n sigmavalnorm = pltsigma[i] if pltsigma[i] > 0 else pltsigma[i] * -1\n plt.fill(x, y, pltcolor, alpha=sigmavalnorm)\n plt.plot(x, y, 'k')\n\n title(mytitle)\n tmpx = [x[0] for k in xynodes for x in k]\n tmpy = [x[1] for k in xynodes for x in k]\n plt.xlim([np.min(tmpx) - 3, np.max(tmpx) + 3])\n plt.ylim([np.min(tmpy) - 3, np.max(tmpy) + 3])\n plt.show()","def plotter(parameter1_range, parameter2_range, value_grid, title_string, x_label, y_label, colormap = plt.cm.viridis):\n\n plt.contourf(parameter1_range, parameter2_range, value_grid, cmap=colormap)\n plt.colorbar()\n\n plt.title(title_string)\n\n plt.xlabel(x_label)\n plt.ylabel(y_label)\n\n plt.show()","def prepare_subplot(fig, point_list, cols, rows, number, lims):\n ax = fig.add_subplot(cols, rows, number)\n ax.scatter(list(map(lambda e: e[0], point_list)), list(map(lambda e: e[1], point_list)), s=9)\n ax.set_xlim(lims[0], lims[1])\n ax.set_ylim(lims[0], lims[1])\n return ax","def plot_contours(clf, xx, yy, num_features, locs, **params):\n predict = fix_examples(np.c_[xx.ravel(), yy.ravel()], num_features, locs)\n Z = clf.predict(predict)\n Z = Z.reshape(xx.shape)\n out = plt.contourf(xx, yy, Z, **params)\n return out","def create_four_subplots():\n pass","def plot_sherpa_contours():\n log.info(\"plotting parameters contours obtained from sherpa\")\n # where to take the results, configurations for the individual butterflies\n instruments = [\"fermi\", \"magic\", \"hess\", \"fact\", \"veritas\", \"joint\"]\n labels = [\"Fermi-LAT\", \"MAGIC\", \"H.E.S.S.\", \"FACT\", \"VERITAS\", \"joint fit\"]\n colors = [\"#21ABCD\", \"#FF9933\", \"#5A4FCF\", \"#5CC184\", \"#702963\", \"crimson\"]\n lss = [\"--\", \"--\", \"--\", \"--\", \"--\", \"-\"]\n\n fig, axarr = plt.subplots(1, 3, figsize=(18, 6))\n\n # with one loop we realize all the contour plots\n for instrument, label, color, ls in zip(instruments, labels, colors, lss):\n\n path = config.repo_path / f\"results/fit/sherpa/{instrument}\"\n\n contours_path = path / \"fit_contours_logparabola.npy\"\n results_path = path / \"fit_results_logparabola.yaml\"\n\n if not path.exists():\n log.warning(f\"Missing: {path} . Skipping.\")\n continue\n\n # load the contours and the results of the fit\n contours = np.load(contours_path).tolist()\n results = load_yaml(results_path)\n\n # define a 2 x 2 matrix to visualise the plot\n # we will delete one of the subplots and make something like a corner plot\n # useful variables for the plot\n ampl_range = contours[\"contour_ampl_c1\"][\"x0_range\"]\n c1_range = contours[\"contour_ampl_c1\"][\"x1_range\"]\n c2_range = contours[\"contour_ampl_c2\"][\"x1_range\"]\n # actual values output of the fit\n # remember in sherpa notation: (amplitude->ampl, alpha->c1, beta->c2)\n ampl = results[\"parameters\"][0][\"value\"]\n c1 = results[\"parameters\"][2][\"value\"]\n c2 = results[\"parameters\"][3][\"value\"]\n\n # axarr[0,0]\n extent = [ampl_range[0] * 1e9, ampl_range[1] * 1e9, c1_range[0], c1_range[1]]\n\n axarr[0].contour(\n contours[\"contour_ampl_c1\"][\"like_values\"],\n contours[\"contour_ampl_c1\"][\"levels\"],\n origin=\"lower\",\n extent=extent,\n colors=color,\n linewidths=(2., 1.5, 1.3),\n linestyles=(\"-\", \"--\", \":\"),\n )\n\n # print actual value\n axarr[0].plot(ampl, c1, marker=\"X\", markersize=7, color=color)\n axarr[0].set_xlabel(\n r\"$f_0 / (\\mathrm{TeV} \\, \\mathrm{cm}^{-2} \\mathrm{s}^{-1})$\"\n )\n axarr[0].set_ylabel(r\"$\\Gamma$\")\n\n extent = [ampl_range[0] * 1e9, ampl_range[1] * 1e9, c2_range[0], c2_range[1]]\n\n axarr[1].contour(\n contours[\"contour_ampl_c2\"][\"like_values\"],\n contours[\"contour_ampl_c2\"][\"levels\"],\n origin=\"lower\",\n extent=extent,\n colors=color,\n linewidths=(2., 1.5, 1.3),\n linestyles=(\"-\", \"--\", \":\"),\n )\n\n # print actual value\n axarr[1].plot(ampl, c2, marker=\"X\", markersize=7, color=color)\n axarr[1].set_ylabel(r\"$\\beta$\")\n axarr[1].set_xlabel(\n r\"$f_0 / (\\mathrm{TeV} \\, \\mathrm{cm}^{-2} \\, \\mathrm{s}^{-1})$\"\n )\n\n extent = [c1_range[0], c1_range[1], c2_range[0], c2_range[1]]\n\n axarr[2].contour(\n contours[\"contour_c1_c2\"][\"like_values\"],\n contours[\"contour_c1_c2\"][\"levels\"],\n origin=\"lower\",\n extent=extent,\n colors=color,\n linewidths=(2., 1.5, 1.3),\n linestyles=(\"-\", \"--\", \":\"),\n )\n\n # print actual value\n axarr[2].plot(c1, c2, marker=\"X\", markersize=7, color=color)\n axarr[2].set_ylabel(r\"$\\beta$\")\n axarr[2].set_xlabel(r\"$\\Gamma$\")\n\n # axarr[0,1] is for the legend\n import matplotlib.lines as mlines\n\n sigma_1 = mlines.Line2D(\n [], [], color=\"k\", marker=\"\", ls=\"-\", lw=2., label=r\"1 $\\sigma$ contour\"\n )\n sigma_2 = mlines.Line2D(\n [], [], color=\"k\", marker=\"\", ls=\"--\", lw=1.5, label=r\"2 $\\sigma$ contour\"\n )\n sigma_3 = mlines.Line2D(\n [], [], color=\"k\", marker=\"\", ls=\":\", lw=1.3, label=r\"3 $\\sigma$ contour\"\n )\n fermi = mlines.Line2D(\n [], [], color=\"#21ABCD\", marker=\"\", ls=\"-\", lw=2., label=\"Fermi-LAT\"\n )\n magic = mlines.Line2D(\n [], [], color=\"#FF9933\", marker=\"\", ls=\"-\", lw=2., label=\"MAGIC\"\n )\n hess = mlines.Line2D(\n [], [], color=\"#5A4FCF\", marker=\"\", ls=\"-\", lw=2., label=\"H.E.S.S.\"\n )\n fact = mlines.Line2D(\n [], [], color=\"#5CC184\", marker=\"\", ls=\"-\", lw=2., label=\"FACT\"\n )\n veritas = mlines.Line2D(\n [], [], color=\"#702963\", marker=\"\", ls=\"-\", lw=2., label=\"VERITAS\"\n )\n joint = mlines.Line2D(\n [], [], color=\"crimson\", marker=\"\", ls=\"-\", lw=2., label=\"joint fit\"\n )\n axarr[2].legend(\n handles=[sigma_1, sigma_2, sigma_3, fermi, magic, hess, fact, veritas, joint],\n loc=3,\n fontsize=12,\n )\n # axarr[2].set_axis_off()\n\n plt.tight_layout()\n filename = \"results/figures/sherpa_logparabola_contour.png\"\n fig.savefig(filename)\n log.info(f\"Writing {filename}\")\n fig.savefig(filename)","def fig_template(name):\n fig, axs = plt.subplots(ncols=2, figsize=(9, 4))\n if name == \"circle\":\n for ax in axs:\n circle = plt.Circle(\n (0, 0), np.sqrt(2 / np.pi), color=\"black\", fill=False, zorder=10\n )\n ax.add_artist(circle)\n\n elif name == \"3_circles\":\n centers = np.array([[-1, 1], [1, 0], [-0.5, -0.5]])\n radii = np.array([1, np.sqrt(6 / np.pi - 1), 1 / 2])\n for c, r in zip(centers, radii):\n for ax in axs:\n circle = plt.Circle(c, r, color=\"black\", fill=False, zorder=10)\n ax.add_artist(circle)\n\n elif name == \"square\":\n p = 0.5 * np.sqrt(2)\n for ax in axs:\n ax.plot([-p, p, p, -p, -p], [-p, -p, p, p, -p], color=\"black\", zorder=10)\n\n elif name == \"4_squares\":\n for ax in axs:\n ax.plot([0, 0], [-1, 1], color=\"black\", zorder=10)\n ax.plot([-1, 1], [0, 0], color=\"black\", zorder=10)\n\n elif name == \"crown\" or name == \"tricrown\":\n centers = [[0, 0], [0, 0]]\n radii = [np.sqrt(0.8), np.sqrt(0.8 - 2 / np.pi)]\n for c, r in zip(centers, radii):\n for ax in axs:\n circle = plt.Circle(c, r, color=\"black\", fill=False, zorder=10)\n ax.add_artist(circle)\n\n elif name == \"wavy_lines\":\n freq = 1\n\n def fun1(s):\n return s + np.sin(freq * np.pi * s)\n\n def fun2(s):\n return -s + np.sin(freq * np.pi * s)\n\n x = np.linspace(-1, 1)\n for ax in axs:\n ax.plot(x, np.clip(fun1(x), -1, 1), color=\"black\", zorder=10)\n ax.plot(x, np.clip(fun2(x), -1, 1), color=\"black\", zorder=10)\n\n axs[0].set(xlabel=r\"$x_0$\", ylabel=r\"$x_1$\", xlim=[-1, 1], ylim=[-1, 1])\n axs[0].axis(\"equal\")\n axs[1].set(xlabel=r\"$x_0$\", xlim=[-1, 1], ylim=[-1, 1])\n axs[1].axis(\"equal\")\n\n return fig, axs","def create_plots(self, keys):\n\n self.plots = VPlotContainer(resizable = \"hv\", bgcolor=\"lightgray\",\n fill_padding=True, padding = 10)\n # this looks cryptic, but it is equivalent to\n # ArrayPlotData(a=[], b=[], c=[])\n # if the keys are a,b,c. This just does it for all of the keys.\n self.plotdata = ArrayPlotData(**dict(zip(keys, [[]]*len(keys))))\n\n # figure out which key will be the x axis\n if 'Step' in keys:\n x = 'Step'\n elif 'Time (ps)' in keys:\n x = 'Time (ps)'\n else:\n raise ValueError('The reporter published neither the step nor time'\n 'count, so I don\\'t know what to plot on the x-axis!')\n\n\n colors = itertools.cycle(['blue', 'green', 'silver', 'pink', 'lightblue',\n 'red', 'darkgray', 'lightgreen',])\n for y in filter(lambda y: y != x, keys):\n self.plots.add(chaco_scatter(self.plotdata, x_name=x, y_name=y,\n color=colors.next()))","def _2d_plot_samples(self, **kwargs):\n\n from pesummary.core.plots.bounded_2d_kde import Bounded_2d_kde\n\n # get bounds\n lows = []\n highs = []\n methods = []\n for param in self.parameters[0:2]:\n if param in DEFAULT_BOUNDS:\n lows.append(\n DEFAULT_BOUNDS[param][\"low\"]\n if \"low\" in DEFAULT_BOUNDS[param]\n else None\n )\n highs.append(\n DEFAULT_BOUNDS[param][\"high\"]\n if \"high\" in DEFAULT_BOUNDS[param]\n else None\n )\n methods.append(\n DEFAULT_BOUNDS[param][\"method\"]\n if \"method\" in DEFAULT_BOUNDS[param]\n else \"Reflection\"\n )\n\n if self.plottype == \"triangle\":\n from pesummary.core.plots.publication import triangle_plot as plotfunc\n elif self.plottype == \"reverse_triangle\":\n from pesummary.core.plots.publication import (\n reverse_triangle_plot as plotfunc,\n )\n else:\n # contour plot\n from pesummary.core.plots.publication import (\n comparison_twod_contour_plot as plotfunc,\n )\n\n # set KDE information\n kwargs.update(\n {\n \"kde\": Bounded_2d_kde,\n \"kde_kwargs\": {\n \"xlow\": lows[0],\n \"xhigh\": highs[0],\n \"ylow\": lows[1],\n \"yhigh\": highs[1],\n },\n }\n )\n\n # default to not showing data points\n if \"plot_datapoints\" not in kwargs:\n kwargs[\"plot_datapoints\"] = False\n\n if \"triangle\" in self.plottype:\n from pesummary.core.plots.bounded_1d_kde import bounded_1d_kde\n\n # set KDE informaiton\n kwargs.update(\n {\n \"kde_2d\": Bounded_2d_kde,\n \"kde_2d_kwargs\": {\n \"xlow\": lows[0],\n \"xhigh\": highs[0],\n \"ylow\": lows[1],\n \"yhigh\": highs[1],\n },\n \"kde\": bounded_1d_kde,\n }\n )\n\n kwargs[\"kde_kwargs\"] = {\n \"x_axis\": {\"xlow\": lows[0], \"xhigh\": highs[0], \"method\": methods[0]},\n \"y_axis\": {\"xlow\": lows[1], \"xhigh\": highs[1], \"method\": methods[1]},\n }\n\n args = [\n [samps[self.parameters[0]].values for samps in self._samples.values()],\n [samps[self.parameters[1]].values for samps in self._samples.values()],\n ]\n\n if \"xlabel\" not in kwargs:\n kwargs[\"xlabel\"] = self.latex_labels[self.parameters[0]]\n if \"ylabel\" not in kwargs:\n kwargs[\"ylabel\"] = self.latex_labels[self.parameters[1]]\n\n if \"labels\" not in kwargs and len(self.results) > 1:\n kwargs[\"labels\"] = list(self._samples.keys())\n\n # set injection parameter values\n if self.injection_parameters is not None:\n if (\n self.injection_parameters[self.parameters[0]] is not None\n and self.injection_parameters[self.parameters[1]] is not None\n ):\n kwargname = \"truths\" if self.plottype == \"corner\" else \"truth\"\n kwargs[kwargname] = [\n self.injection_parameters[self.parameters[0]]\n - self.parameter_offsets[self.parameters[0]],\n self.injection_parameters[self.parameters[1]]\n - self.parameter_offsets[self.parameters[1]],\n ]\n\n # create plot\n with DisableLogger():\n fig = plotfunc(*args, **kwargs)\n\n return fig","def plot_samples(s):\r\n assert len(s[0, :]) >= 2, ('The Phase space dimensions are less than two.', ' Need at least two to plot.')\r\n fig = plt.figure(1)\r\n if len(s[0, :]) >= 3:\r\n ax = fig.add_subplot(111, projection='3d')\r\n ax.scatter(s[:, 0], s[:, 1], s[:, 2])\r\n fig = plt.figure(2)\r\n plt.scatter(s[:, 0], s[:, 1])\r\n plt.show()","def plot_all(\n template,\n v: list,\n n_e: list,\n l: list,\n tm: str,\n ) -> None:\n\n scatterplot(template, tm, v[0], v[1], \"Constraint Energy X vs Y\", \"Constraint Energy X (J)\", \"Constraint Energy Y (J)\")\n scatterplot(template, tm, v[3], v[4], \"Internal Energy X vs Y\", \"Internal Energy X (J)\", \"Internal Energy Y (J)\")\n\n scatterplot(template, tm, n_e, v[6], \"Elements Removed vs Hausdorff Distance\", \"Number of Elements Removed\", \"Hausdorff Distance\")\n\n # # Loop through the types of data\n # for i in range(0, len(v)):\n\n # # Plot the histogram\n # histogram(template, tm, v[i], l[i], \"Frequency\", \"Energy (J)\")\n\n # # Plot the scatterplot\n # scatterplot(template, tm, n_e, v[i], l[i], \"Energy (J)\", \"Number of Elements Removed\")\n\n # # Plot a scatterplot\n # scatterplot(template, tm, v[0], v[1], \"Constraint Energy (J)\", \"Y-direction\", \"X-direction\")\n\n return","def ice_plot(curves, **kwargs):\n \n n,m = curves.shape\n \n #grid of plots\n gridspec_kw = {'height_ratios':[3, 1], \n 'wspace':0.0, \n 'hspace':0.0}\n fig, (ax_curves, ax_feature) = plt.subplots(2,1, \n gridspec_kw=gridspec_kw, \n figsize=kwargs.get('figsize', (6,6)))\n \n # top graph - curves\n for curve in curves:\n ax_curves.plot(np.arange(m), curve)\n ax_curves.set_xticklabels([])\n ax_curves.set_xlim((0, m))\n ax_curves.set_ylabel(kwargs.get('ylabel', 'decision function $\\Delta$'))\n \n # bottom graph - feature values\n if 'feature_values' in kwargs:\n ax_feature.plot(np.arange(m), kwargs.get('feature_values'))\n ax_feature.set_xlim((0, m)) \n ax_feature.set_xlabel(kwargs.get('xlabel', '$X_S$ values'))\n \n return fig, (ax_curves, ax_feature)","def plot(self, *, ax=..., n_cols=..., line_kw=..., contour_kw=...):\n ...","def subplot_gen(\n var, # variable\n name, # file name\n loc, # location of where to save\n color = \"jet\", # color scheme (default set to jet)\n cb = False, # color bar flag (default set to off)\n xlab = False, # x-label flag (default set to off)\n ylab = False, # y-label flag (default set to off)\n lab_ticks = False): # x and y ticks (default set to off)\n #---------------------------------------------------------------------#\n # Checking the location path to make sure it has a OS separator #\n #---------------------------------------------------------------------#\n sep = os.sep # OS separator\n if loc[-1] != sep: # adding the separator\n loc = loc + sep\n #---------------------------------------------------------------------#\n # Verifying the location #\n #---------------------------------------------------------------------#\n isFile = os.path.isdir(loc) # checking existence of path\n if isFile is False:\n os.mkdir(loc) # creating directory\n #---------------------------------------------------------------------#\n # Defining domain variables #\n #---------------------------------------------------------------------#\n N = 64 # number of data pts.\n pi = np.pi # pi\n X1 = np.linspace(0.0, 2.0*pi, N) # x_{1} domain\n X2 = np.linspace(0.0, 2.0*pi, N) # x_{2} domain\n (X1, X2) = np.meshgrid(X1, X2) # mesh grid (plotting)\n #---------------------------------------------------------------------#\n # Generating contours #\n #---------------------------------------------------------------------#\n print_count = 20 # print counter\n dim = var.shape # dimensions of the quantity\n for i in range(0, dim[2]):\n plt.subplot(8,8,i+1)\n cnt = plt.contourf(X1, X2, var[:,:,i], 500, cmap=color)\n for c in cnt.collections: # setting the edge colors\n c.set_edgecolors(\"face\")\n if cb is not False: # generating the color bar\n plt.colorbar()\n if xlab is not False: # generating the x_{1}-label\n plt.xlabel(\"$0 \\leq x_{1} \\leq 2\\pi$\")\n if ylab is not False: # generating the x_{2}-label\n plt.ylabel(\"$0 \\leq x_{1} \\leq 2\\pi$\")\n if lab_ticks is False:\n plt.axis(\"off\")\n #-----------------------------------------------------------------#\n # Print counter #\n #-----------------------------------------------------------------#\n if print_count >= 7:\n print(\"\\tz step --> %i\" %(i+1))\n print_count = 0\n print_count += 1\n #---------------------------------------------------------------------#\n # Saving and clearing figure #\n #---------------------------------------------------------------------#\n plt.savefig(loc + name + \".png\") # storing figure\n plt.clf() # clearing figure\n print(\"\\t**** Plots saved ****\")\n\n return","def plot_summary(self, **kwargs):\n ncols = 2\n nparams = len(self.distribution_parameter_names)\n d, r = divmod(nparams, 2)\n if r > 0:\n nrows = d + 1\n else:\n nrows = d\n\n gs = gridspec.GridSpec(nrows, ncols)\n fig = plt.figure(num=1, figsize=(16, 3*nrows))\n ax = []\n for n, parameter in enumerate(self):\n r, c = divmod(n, 2)\n ax.append(fig.add_subplot(gs[r, c]))\n\n if c == 0 and (n <= nparams-1):\n self._plot_posterior_pdf(parameter, ax[-1],\n y_label='Posterior pdf',\n x_label=parameter)\n elif n <= nparams-1:\n self._plot_posterior_pdf(parameter, ax[-1],\n y_label=None,\n x_label=parameter)\n\n fig.tight_layout()\n\n return fig, ax","def plot_individual(xdict, ydict, xprop, yprop, documents, spline):\n figure_array = {}\n for item in documents:\n xlabel = \"\\\\textbf{\" + label_dict[xprop] + \"}\"\n ylabel = \"\\\\textbf{\" + label_dict[yprop] + \"}\"\n x = xdict[item[\"path_id\"]]\n y = ydict[item[\"path_id\"]]\n # fig_title = item[\"path_id\"] + \"(\" + item[\"pretty_formula\"] + \")\" # Individual traces\n # fig_title = yprop + item[\"cation_type\"] # Plot by cation\n fig_title = yprop # All together\n figure_array[item[\"path_id\"]] = plt.figure(fig_title, figsize=(6,6), dpi=plotting_dpi)\n ax = figure_array[item[\"path_id\"]].add_subplot(111) \n ax.scatter(x,y, s=70, zorder=2, color=color_dict[item[\"cation_type\"]], linewidths=2.5, edgecolors='black')\n if spline:\n tck = interpolate.splrep(x, y, s=0)\n xnew = np.arange(0, 100, 0.1)\n splfit = interpolate.splev(xnew, tck, der=0)\n x = xnew\n y = splfit\n if item[\"path_id\"][-3:] == \"002\":\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\"cation_type\"]], linestyle='dashed')\n elif item[\"path_id\"][-3:] == \"003\":\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\"cation_type\"]], linestyle='dotted')\n else:\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\"cation_type\"]])\n ax.set_xlabel(xlabel, fontsize=24)\n # ax.set_ylim([0,1200])\n # ax.set_xlim([0,100])\n ax.set_ylabel(ylabel, fontsize=24)\n ax.tick_params(axis='x', labelsize=22)\n ax.tick_params(axis='y', labelsize=22)\n border_width = 2\n [i.set_linewidth(border_width) for i in ax.spines.itervalues()]\n plt.tight_layout()\n plt.legend(loc='best', prop={'size': 14})\n plt.rc('text', usetex=True)\n plt.rc('font', family='sans-serif')\n plt.tight_layout()\n plt.show()","def create_scattering(\n self, plot_name=\"S Parameter Plot Nominal\", sweep_name=None, port_names=None, port_excited=None, variations=None\n ):\n Families = [\"Freq:=\", [\"All\"]]\n if variations:\n Families += variations\n else:\n Families += self.get_nominal_variation()\n if not sweep_name:\n sweep_name = self.existing_analysis_sweeps[1]\n if not port_names:\n port_names = self.get_excitations_name\n if not port_excited:\n port_excited = port_names\n Trace = [\n \"X Component:=\",\n \"Freq\",\n \"Y Component:=\",\n [\"dB(S(\" + p + \",\" + q + \"))\" for p, q in zip(list(port_names), list(port_excited))],\n ]\n solution_data = \"\"\n if self.solution_type == \"DrivenModal\":\n solution_data = \"Modal Solution Data\"\n elif self.solution_type == \"DrivenTerminal\":\n solution_data = \"Terminal Solution Data\"\n elif self.solution_type == \"HFSS3DLayout\":\n solution_data = \"Standard\"\n if solution_data != \"\":\n # run CreateReport function\n self.post.oreportsetup.CreateReport(\n plot_name, solution_data, \"Rectangular Plot\", sweep_name, [\"Domain:=\", \"Sweep\"], Families, Trace, []\n )\n return True\n else:\n return False","def make_plot(d0,t0,d1,t1,d2,t2,d3,t3,suptitle,path_out):\n fig0 = plt.figure(1,(10.,7.))\n grid = ImageGrid(fig0, 111, # similar to subplot(111)\n nrows_ncols = (2, 2), # creates 2x2 grid of axes\n axes_pad=0.4, # pad between axes in inch.\n share_all=True, # share axes\n cbar_mode='each')\n \n im = grid[0].pcolor(d0)\n grid[0].set_title(t0)\n grid.cbar_axes[0].colorbar(im)\n grid[0].axis([0,d0.shape[1],0,d0.shape[0]])\n \n im = grid[1].pcolor(d1)\n grid[1].set_title(t1)\n grid.cbar_axes[1].colorbar(im)\n grid[0].axis([0,d1.shape[1],0,d1.shape[0]])\n \n im = grid[2].pcolor(d2)\n grid[2].set_title(t2)\n grid.cbar_axes[2].colorbar(im)\n grid[0].axis([0,d2.shape[1],0,d2.shape[0]])\n \n im = grid[3].pcolor(d3)\n grid[3].set_title(t3) \n grid.cbar_axes[3].colorbar(im)\n grid[0].axis([0,d3.shape[1],0,d3.shape[0]])\n\n \n fig0.suptitle(suptitle,fontsize=18)\n \n fig0.savefig(path_out, dpi=300)\n fig0.clf()\n return","def plot_graphs(graphs, sel_str, plotname, contour, ranges):\n logging.debug('Creating plot for sel_str = \\'{}\\''.format(sel_str))\n sel_graphs = select_graphs(graphs, sel_str)\n logging.debug('Got {} graphs'.format(len(sel_graphs)))\n\n binnings, graphs = sort_graphs(sel_graphs)\n graphs = list(graphs) # need a list in any case\n\n if not contour:\n set_marker_styles(graphs)\n draw_opt = 'PE'\n else:\n set_line_styles(graphs)\n draw_opt='L'\n\n make_leg = lambda x: 'thresh = {}, bins = {}'.format(x[0], x[1])\n\n mkplot(graphs,\n legEntries=[make_leg(b) for b in binnings],\n xRange=ranges[0], yRange=ranges[1],\n saveAs=plotname, grid=True, drawOpt=draw_opt,\n legPos='botleft', yLabel='#Delta_{#lambda}', xLabel='#lambda_{ref}')","def plot_profiles(self, parameters, fig=1):\n # Make sure the parameters are a list\n if isinstance(parameters, str) or isinstance(parameters, unicode):\n parameters = [parameters]\n \n # Decide how many subplots to create\n nrows = int(len(parameters) / 3)\n if len(parameters) % 3 > 0:\n nrows += 1\n \n # Initialize a figure for plotting\n width = 8\n height = 4 * nrows\n plt.figure(fig, figsize=(width, height))\n plt.clf()\n \n # Plot each parameter\n for parm in parameters:\n ax = plt.subplot(nrows, 3, parameters.index(parm)+1)\n self.plot_parameter(parm)\n \n plt.tight_layout()\n \n plt.show()","def make_plots(self,indices=None,hardcopy=False,hardcopydir='.',hardcopyprefix='',hardcopytype='png'):\n\t\tfor (i,E) in enumerate(self.experiments):\n\t\t\tif(indices==None) or (i in indices):\n\t\t\t\tE.show_plot(hardcopy,hardcopydir,hardcopyprefix,hardcopytype)","def plot_modelparametercollections(plotname, parametercollection_SF, parametercollection_AGN,\n stat_SF, stat_AGN, AGNcol='blue',SFcol='red', constraintsstr=None,\n fluxratiodictionarylist=None, verbose=True):\n\n Nobj = len(parametercollection_SF)\n if verbose: print(' - Will generate plots of NEOGAL \"PDFs\" for all '+str(Nobj)+' objects in parameter collections')\n for oo in np.arange(Nobj):\n objid = parametercollection_SF[oo]['id']\n if verbose:\n infostr = ' plotting info for '+str(objid)+' ('+str(\"%.5d\" % (oo+1))+' / '+str(\"%.5d\" % Nobj)+') '\n sys.stdout.write(\"%s\\r\" % infostr)\n sys.stdout.flush()\n plotname_obj = plotname.replace('.pdf','_id'+str(objid)+'.pdf')\n # if verbose: print(' - Generating the figure '+plotname_obj)\n figuresize_x = 6\n figuresize_y = 5\n fig = plt.figure(figsize=(figuresize_x,figuresize_y))\n Fsize = 9\n LW = 2\n plt.rc('text', usetex=True) # enabling LaTex rendering of text\n plt.rc('font', family='serif',size=Fsize) # setting text font\n plt.rc('xtick', labelsize=Fsize)\n plt.rc('ytick', labelsize=Fsize)\n plt.clf()\n plt.ioff()\n\n left = 0.10 # the left side of the subplots of the figure\n right = 0.95 # the right side of the subplots of the figure\n bottom = 0.10 # the bottom of the subplots of the figure\n top = 0.90 # the top of the subplots of the figure\n wspace = 1.50 # the amount of width reserved for blank space between subplots\n hspace = 0.50 # the amount of height reserved for white space between subplots\n plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)\n\n Nrows, Ncols = 3, 6\n ylabel = 'Number of NEOGAL SF ('+str(SFcol)+') and AGN ('+str(AGNcol)+') models'\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n Nmodels_SF = float(len(parametercollection_SF[oo]['Zgas']))\n Nmodels_AGN = float(len(parametercollection_AGN[oo]['Zgas']))\n\n titlestr = 'Models satisfying ID='+str(objid)+' cuts: SF='+str(Nmodels_SF)+'; AGN='+str(Nmodels_AGN)\n if (Nmodels_AGN > 0) & (Nmodels_SF > 0):\n Nmodels_ratio = Nmodels_SF/Nmodels_AGN\n titlestr_addition = '; SF/AGN='+str(\"%.4f\" % Nmodels_ratio)\n titlestr = titlestr+titlestr_addition\n\n if fluxratiodictionarylist is not None:\n constraints = fluxratiodictionarylist[oo]\n constraintslist = [key+':['+str(\"%.2f\" % constraints[key][0])+','+str(\"%.2f\" % constraints[key][1])+']'\n for key in constraints.keys() if key not in ['id']]\n\n if len(constraintslist) < 4:\n constraintsstr = '; '.join(constraintslist)\n elif (len(constraintslist) > 3) & (len(constraintslist) < 7):\n constraintsstr = '; '.join(constraintslist[:3])+'\\n'+'; '.join(constraintslist[3:6])\n elif (len(constraintslist) > 6) & (len(constraintslist) < 10):\n constraintsstr = '; '.join(constraintslist[:3])+'\\n'+'; '.join(constraintslist[3:6])+\\\n '\\n'+'; '.join(constraintslist[6:])\n else:\n constraintsstr = '; '.join(constraintslist[:3])+'\\n'+'; '.join(constraintslist[3:6])+\\\n '\\n'+'; '.join(constraintslist[6:9])+'\\n'+'; '.join(constraintslist[9:])\n\n constraintsstr = constraintsstr.replace('10000000000.00','1e10')\n\n titlestr = titlestr+'\\n'+constraintsstr\n # titlestr = r'{\\fontsize{'+str(Fsize)+'pt}{3em}\\selectfont{}{'+titlestr+'\\r}{\\fontsize{'+str((Fsize-2.))+'pt}{3em}\\selectfont{}('+constraintsstr+'}'\n\n # plt.text(x=0.5, y=0.94, s=titlestr, fontsize=Fsize, ha=\"center\", transform=fig.transFigure)\n # plt.text(x=0.5, y=0.88, s=constraintsstr, fontsize=Fsize-2, ha=\"center\", transform=fig.transFigure)\n # fig.title(titlestr,fontsize=Fsize)\n fig.suptitle(titlestr,fontsize=Fsize-2)\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # Zgas\n plt.subplot(Nrows, Ncols, (1,3))\n\n bindefs = np.array([0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.004, 0.006, 0.008, 0.014,\n 0.017, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07])-0.00001\n\n plotkey = 'Zgas'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xscale('log')\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([0.00001,0.1])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # logUs\n plt.subplot(Nrows, Ncols, (4,6))\n\n bindefs = np.arange(-4.75, -0.25, 0.5)\n\n plotkey = 'logUs'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([-5,-0.5])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # xid\n plt.subplot(Nrows, Ncols, (7,9))\n\n bindefs = np.array([0.0, 0.2, 0.4, 0.6])\n\n plotkey = 'xid'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([-0.05,0.65])\n plt.ylabel(ylabel)\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # nh\n plt.subplot(Nrows, Ncols, (10,12))\n\n bindefs = 10**np.array([1.5, 2.5, 3.5, 4.5])\n\n plotkey = 'nh'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n plt.xscale('log')\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([10,1e5])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # COCOsol\n plt.subplot(Nrows, Ncols, (13,14))\n\n #bindefs = np.array([0.10, 0.14, 0.20, 0.27, 0.38, 0.52, 0.72, 1.00, 1.40])\n bindefs = np.arange(0.05,1.5,0.06)\n\n plotkey = 'COCOsol'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],None,\n stat_SF[oo][plotkey],None,\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([0.00,1.55])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # mup\n plt.subplot(Nrows, Ncols, (15,16))\n\n bindefs = np.array([0,200,400])\n\n plotkey = 'mup'\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],None,\n stat_SF[oo][plotkey],None,\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([-10,410])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n # alpha\n plt.subplot(Nrows, Ncols, (17,18))\n\n bindefs = np.array([-2.15,-1.85,-1.55,-1.25,-0.95])\n\n plotkey = 'alpha'\n Nbins = 10\n nm.plot_modelparametercollections_addhist(None,parametercollection_AGN[oo][plotkey],\n None,stat_AGN[oo][plotkey],\n SFcol,AGNcol,LW,bindefs=None,Nbins=Nbins)\n\n plt.xlabel(nm.keylabels(plotkey))\n plt.xlim([-2.2,-0.9])\n\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n plt.savefig(plotname_obj)\n plt.clf()\n plt.close('all')\n # if verbose: print(' - Successfully saved figure to file')\n if verbose: print('\\n done...')","def plot_cdf(self, param, plot_type, Nsplit=50, **kwargs):\n title = self.family.capitalize() + \" Copula CDF\" \n\n bounds = [0+1e-2, 1-1e-2]\n U_grid, V_grid = np.meshgrid(\n np.linspace(bounds[0], bounds[1], Nsplit),\n np.linspace(bounds[0], bounds[1], Nsplit))\n \n Z = np.array(\n [self.get_cdf(uu, vv, param) for uu, vv in zip(np.ravel(U_grid), np.ravel(V_grid)) ] )\n \n Z = Z.reshape(U_grid.shape)\n\n if plot_type == \"3d\":\n plot_bivariate_3d(U_grid,V_grid,Z, [0,1], title, **kwargs)\n elif plot_type == \"contour\":\n plot_bivariate_contour(U_grid,V_grid,Z, [0,1], title, **kwargs)\n else:\n print(\"only \\\"contour\\\" or \\\"3d\\\" arguments supported for type\")\n raise ValueError","def plot(self, n_levels = 200, axes = None, figsize = (10, 4), cont_kws = {}):\n if not isinstance(n_levels, int) or n_levels < 1:\n raise ValueError(\"n_levels must be a positive integer\")\n if axes is not None and not isinstance(axes, Axes):\n raise ValueError(\"axes must be Axes\")\n if not isinstance(figsize, (list, tuple)) or len(figsize) != 2:\n raise ValueError(\"figsize must be a tuple with 2 elements\")\n if not isinstance(cont_kws, dict):\n raise ValueError(\"cont_kws must be a dictionary\")\n\n if self._n_dim == 2:\n if axes is None:\n fig = plt.figure(figsize = figsize, facecolor = \"white\")\n ax1 = fig.add_subplot(1, 1, 1)\n else:\n ax1 = axes\n cax = ax1.contourf(self._xaxis, self._zaxis, self._velocity_model, n_levels, **cont_kws)\n ax1.set_xlabel(\"X (m)\")\n ax1.set_ylabel(\"Depth (m)\")\n ax1.invert_yaxis()\n return cax\n else:\n raise ValueError(\"plot unavailable for 3-D grid\")","def ShowOneContour(index,all_images,all_pointing,thex0,they0,all_titles,object_name,all_expo,dir_top_img,all_filt,figname):\n plt.figure(figsize=(15,6))\n spec_index_min=100 # cut the left border\n spec_index_max=1900 # cut the right border\n star_halfwidth=70\n \n YMIN=-15\n YMAX=15\n \n figfilename=os.path.join(dir_top_img,figname) \n \n #center is approximately the one on the original raw image (may be changed)\n #x0=int(all_pointing[index][0])\n x0=int(thex0[index])\n \n \n # Extract the image \n full_image=np.copy(all_images[index])\n \n # refine center in X,Y\n star_region_X=full_image[:,x0-star_halfwidth:x0+star_halfwidth]\n \n profile_X=np.sum(star_region_X,axis=0)\n profile_Y=np.sum(star_region_X,axis=1)\n\n NX=profile_X.shape[0]\n NY=profile_Y.shape[0]\n \n X_=np.arange(NX)\n Y_=np.arange(NY)\n \n avX,sigX=weighted_avg_and_std(X_,profile_X**4) # take squared on purpose (weigh must be >0)\n avY,sigY=weighted_avg_and_std(Y_,profile_Y**4)\n \n x0=int(avX+x0-star_halfwidth)\n \n \n # find the center in Y on the spectrum\n yprofile=np.sum(full_image[:,spec_index_min:spec_index_max],axis=1)\n y0=np.where(yprofile==yprofile.max())[0][0]\n\n # cut the image in vertical and normalise by exposition time\n reduc_image=full_image[y0-20:y0+20,x0:spec_index_max]/all_expo[index] \n reduc_image[:,0:100]=0 # erase central star\n \n X_Size_Pixels=np.arange(0,reduc_image.shape[1])\n Y_Size_Pixels=np.arange(0,reduc_image.shape[0])\n Transverse_Pixel_Size=Y_Size_Pixels-int(float(Y_Size_Pixels.shape[0])/2.)\n \n # calibration in wavelength\n #grating_name=all_filt[index].replace('dia ','')\n grating_name=get_disperser_filtname(all_filt[index])\n \n lambdas=Pixel_To_Lambdas(grating_name,X_Size_Pixels,all_pointing[index],True)\n \n #if grating_name=='Ron200':\n # holo = Hologram('Ron400',verbose=True)\n #else: \n # holo = Hologram(grating_name,verbose=True)\n #lambdas=holo.grating_pixel_to_lambda(X_Size_Pixels,all_pointing[index])\n #if grating_name=='Ron200':\n # lambdas=lambdas*2.\n \n\n X,Y=np.meshgrid(lambdas,Transverse_Pixel_Size) \n T=np.transpose(reduc_image)\n \n \n plt.contourf(X, Y, reduc_image, 100, alpha=1., cmap='jet',origin='lower')\n C = plt.contour(X, Y, reduc_image , 20, colors='black', linewidth=.5,origin='lower')\n \n \n for line in LINES:\n if line == O2 or line == HALPHA or line == HBETA or line == HGAMMA:\n plt.plot([line['lambda'],line['lambda']],[YMIN,YMAX],'-',color='lime',lw=0.5)\n plt.text(line['lambda'],YMAX-3,line['label'],verticalalignment='bottom', horizontalalignment='center',color='lime', fontweight='bold',fontsize=16)\n \n \n \n plt.axis([X.min(), X.max(), Y.min(), Y.max()]); plt.grid(True)\n plt.title(all_titles[index])\n plt.grid(color='white', ls='solid')\n plt.text(200,-5.,all_filt[index],verticalalignment='bottom', horizontalalignment='center',color='yellow', fontweight='bold',fontsize=16)\n plt.xlabel('$\\lambda$ (nm)')\n plt.ylabel('pixels')\n plt.ylim(YMIN,YMAX)\n plt.xlim(0.,1200.)\n plt.savefig(figfilename)","def create_scatterplots(PrimaryParticleName, LongVectorSignals, LongVectorSignalsCher,\n\tShortVectorSignals, ShortVectorSignalsCher, ScinTreshold,\n\tCherTreshold):\n\n\t#Set ROOT plots\n\tTH2LongScinSignals = TH2F(\"LongScatterplotSignals\", PrimaryParticleName, 46, 0., 93., 93, 0., 93.)\n\tTH2ShortScinSignals = TH2F(\"ShortScatterplotSignals\", PrimaryParticleName, 46, 0., 93., 93, 0., 93.)\n\tTH2LongCherSignals = TH2F(\"LongCherScatterplotSignals\", PrimaryParticleName, 46, 0., 93., 92, 0., 93.)\n\tTH2ShortCherSignals = TH2F(\"ShortCherScatterplotSignals\", PrimaryParticleName, 46, 0., 93., 92, 0., 93.)\n\tTH2Uniformity = TH2F(\"Uniformity\", \"Uniformity\", 46, 0., 93., 92, 0., 93.)\n\n\t#Fill plots in for loop\n\tfor index in range(len(LongVectorSignals)):\n\t\tif LongVectorSignals[index] > ScinTreshold:\n\t\t\tX,Y = Staggeredmap.mapXYLongScin(index)\n\t\t\tTH2LongScinSignals.Fill(X,Y,LongVectorSignals[index])\n\t\tif LongVectorSignalsCher[index] > CherTreshold:\n\t\t\tX,Y = Staggeredmap.mapXYLongCherenkov(index)\n\t\t\tTH2LongCherSignals.Fill(X,Y,LongVectorSignalsCher[index])\n\t\tif ShortVectorSignals[index] > ScinTreshold:\n\t\t\tX,Y = Staggeredmap.mapXYShortScin(index)\n\t\t\tTH2ShortScinSignals.Fill(X,Y, ShortVectorSignals[index])\n\t\tif ShortVectorSignalsCher[index] > CherTreshold:\n\t\t\tX,Y = Staggeredmap.mapXYShortCherenkov(index)\n\t\t\tTH2ShortCherSignals.Fill(X,Y, ShortVectorSignalsCher[index])\n\n\t#Fill uniformity plot\n\tfor index in range(len(LongVectorSignals)):\n\t\tX,Y = Staggeredmap.mapXYLongScin(index)\n\t\tTH2Uniformity.Fill(X,Y,1.)\t\n\t\tX,Y = Staggeredmap.mapXYShortScin(index)\n\t\tTH2Uniformity.Fill(X,Y,1.)\n\t\tX,Y = Staggeredmap.mapXYLongCherenkov(index)\n\t\tTH2Uniformity.Fill(X,Y,1.)\n\t\tX,Y = Staggeredmap.mapXYShortCherenkov(index)\n\t\tTH2Uniformity.Fill(X,Y,1.)\n\n\t#Draw + DrawOptions plots\n\tStyle = gStyle\n\tStyle.SetPalette(1) #Root palette style\n\tStyle.SetOptStat(0) #Do not show statistics\n\tTH2LongScinSignals.SetLineWidth(0) #TH2LongScinSignals #No line width\n\tTH2LongScinSignals.SetLineColor(2)\n\t#TH2Signals.SetFillColorAlpha(2, 0.)\n\tXAxis = TH2LongScinSignals.GetXaxis()\n\tXAxis.SetTitle(\"x (mm)\")\n\tXAxis.CenterTitle()\n\tXAxis.SetTitleOffset(1.8)\n\tYAxis = TH2LongScinSignals.GetYaxis()\n\tYAxis.SetTitle(\"y (mm)\")\n\tYAxis.CenterTitle()\n\tYAxis.SetTitleOffset(1.8)\n\tZAxis = TH2LongScinSignals.GetZaxis()\n\tZAxis.SetTitle(\"Energy (MeV)\")\n\tZAxis.SetTitleOffset(1.4)\n\tTH2LongScinSignals.Draw(\"LEGO2Z 0 FB\")\n\tgPad.SaveAs(\"ImageLongScintillation.eps\")\n\tTH2ShortScinSignals.SetLineWidth(0) #TH2ShortScinSignals #No line width\n\tTH2ShortScinSignals.SetLineColor(2)\n\t#TH2Signals.SetFillColorAlpha(2, 0.)\n\tXAxis = TH2ShortScinSignals.GetXaxis()\n\tXAxis.SetTitle(\"x (mm)\")\n\tXAxis.CenterTitle()\n\tXAxis.SetTitleOffset(1.8)\n\tYAxis = TH2ShortScinSignals.GetYaxis()\n\tYAxis.SetTitle(\"y (mm)\")\n\tYAxis.CenterTitle()\n\tYAxis.SetTitleOffset(1.8)\n\tZAxis = TH2ShortScinSignals.GetZaxis()\n\tZAxis.SetTitle(\"Energy (MeV)\")\n\tZAxis.SetTitleOffset(1.4)\n\tTH2ShortScinSignals.Draw(\"LEGO2Z 0 FB\")\n\tgPad.SaveAs(\"ImageShortScintillation.eps\")\n\tTH2ShortCherSignals.SetLineWidth(0) #TH2ShortCherSignals #No line width\n\tTH2ShortCherSignals.SetLineColor(4)\n\t#TH2Signals.SetFillColorAlpha(2, 0.)\n\tXAxis = TH2ShortCherSignals.GetXaxis()\n\tXAxis.SetTitle(\"x (mm)\")\n\tXAxis.CenterTitle()\n\tXAxis.SetTitleOffset(1.8)\n\tYAxis = TH2ShortCherSignals.GetYaxis()\n\tYAxis.SetTitle(\"y (mm)\")\n\tYAxis.CenterTitle()\n\tYAxis.SetTitleOffset(1.8)\n\tZAxis = TH2ShortCherSignals.GetZaxis()\n\tZAxis.SetTitle(\"Cher p.e.\")\n\tZAxis.SetTitleOffset(1.4)\n\tTH2ShortCherSignals.Draw(\"LEGO2Z 0 FB\")\n\tgPad.SaveAs(\"ImageShortCherenkov.eps\")\n\tTH2LongCherSignals.SetLineWidth(0) #TH2LongCherSignals #No line width\n\tTH2LongCherSignals.SetLineColor(4)\n\tXAxis = TH2LongCherSignals.GetXaxis()\n\tXAxis.SetTitle(\"x (mm)\")\n\tXAxis.CenterTitle()\n\tXAxis.SetTitleOffset(1.8)\n\tYAxis = TH2LongCherSignals.GetYaxis()\n\tYAxis.SetTitle(\"y (mm)\")\n\tYAxis.CenterTitle()\n\tYAxis.SetTitleOffset(1.8)\n\tZAxis = TH2LongCherSignals.GetZaxis()\n\tZAxis.SetTitle(\"Cher p.e.\")\n\tZAxis.SetTitleOffset(1.4)\n\tTH2LongCherSignals.Draw(\"LEGO2Z FB 0\")\n\tgPad.SaveAs(\"ImageLongCherenkov.eps\")\n\tTH2Uniformity.SetLineWidth(0) #TH2Uniformity #No line width\n\tTH2Uniformity.SetLineColor(2)\n\t#TH2Signals.SetFillColorAlpha(2, 0.)\n\tXAxis = TH2Uniformity.GetXaxis()\n\tXAxis.SetTitle(\"x (mm)\")\n\tXAxis.CenterTitle()\n\tXAxis.SetTitleOffset(1.8)\n\tYAxis = TH2Uniformity.GetYaxis()\n\tYAxis.SetTitle(\"y (mm)\")\n\tYAxis.CenterTitle()\n\tYAxis.SetTitleOffset(1.8)\n\tZAxis = TH2Uniformity.GetZaxis()\n\tZAxis.SetTitle(\"Set to 1\")\n\tZAxis.SetTitleOffset(1.4)\n\tTH2Uniformity.Draw(\"LEGO2Z 0 FB\")\n\t#gPad.SaveAs(\"ImageUniformity.eps\")","def plot_pca(basis, weights):\n file_name = '../Data/lhc_512_5.txt'\n params = np.loadtxt(file_name)\n\n ncomp, imsize = basis.shape\n npix = int(np.sqrt(imsize))\n ncol = int(ncomp//2)\n nsamp, _ = weights.shape\n\n # Shows the basis images\n for i in range(ncomp):\n plt.subplot(2, ncol, i+1)\n plt.imshow(basis[i].reshape((npix, npix)))\n plt.show()\n\n # Scatter plots\n for i in range(ncomp):\n # parameter number (0: flux, 1: radius, 2: g1 shear, 3: g2 shear, 4: psf fwhm)\n par = 3\n # weight number (x-axis) 0 -> ncomp-1\n w = 11\n plt.subplot(2, ncol, i+1)\n plt.scatter(weights[:, w], weights[:, i], s=1, c=params[:, par])\n plt.ylabel('Weight '+str(i+1), size=15)\n plt.xlabel('Weight '+str(w+1), size=15)\n plt.colorbar()\n plt.show()","def contour_manual(ctx, a):\n \n plotid = a.plotid()\n z = a.dbl_2d('z')\n x = a.dbl_1d('x')\n y = a.dbl_1d('y')\n levels = a.dbl_1d('levels')\n\n label = a.bool('label')\n line = a.line()\n display = a.display()\n\n ctx.set(plotid)\n ctx.fail_if_polar()\n ctx.fail_if_log_symlog()\n\n # Apply defaults for optional args\n if len(x) == 0:\n x = np.arange(z.shape[1])\n if len(y) == 0:\n y = np.arange(z.shape[0])\n \n # Make all the same size, and filter out rows/cols corresponding to NaN\n x, y, z = filters.filter_2d(x, y, z)\n \n # Can't contour datasets which end up smaller than 2x2\n if z.shape[0] < 2 or z.shape[1] < 2:\n return\n \n # MPL pitches a fit if any elements are NaN, so use a masked array\n if np.any(~np.isfinite(z)):\n z = np.ma.masked_invalid(z)\n\n # MPL doesn't like this\n if len(levels) == 0:\n return\n\n # Limit to 500 to avoid \"MAXTICKS\" issue\n levels = levels[0:500]\n\n # Note we handle line props manually, as contour uses non-standard\n # naming for the keyword arguments.\n k = { 'colors': 'k' if line.color is None else line.color,\n 'linestyles': line.style,\n 'linewidths': line.width, }\n k.update(display._k())\n remove_none(k)\n\n contourset = plt.contour(x, y, z, levels, **k)\n \n if label:\n plt.clabel(contourset)","def plot(self, *names):\r\n for name in names:\r\n if name in self.__obs.keys():\r\n list_obs = self.__obs[name]\r\n if not isinstance(list_obs[0], matrix):\r\n fig = plt.figure()\r\n plt.plot(self.__obs[name])\r\n else:\r\n fig = plt.figure()\r\n for i in range(list_obs[0].size):\r\n plt.plot([float(obs[i]) for obs in list_obs], label=\"Dimension {0}\".format(i))\r\n plt.legend()\r\n plt.ylabel(name)\r\n plt.show()\r\n else:\r\n for sous_objet in self.__sous_objets:\r\n if re.match((sous_objet+\"?\").encode('string-escape'), name.lower()):\r\n self.__dict__[sous_objet].plot(name)","def plot_bivariate_contour(X, Y, Z, bounds, title, **kwargs):\n plt.figure()\n CS = plt.contour(X, Y, Z, colors='k', linewidths=1., linestyles=None, **kwargs)\n plt.clabel(CS, fontsize=8, inline=1)\n plt.xlim(bounds)\n plt.ylim(bounds)\n plt.title(title)\n plt.show()","def visualize(s):\n n = 3\n fig, ax = plt.subplots(1, n, sharex=True, sharey=True)\n for x, y in zip(range(n), [s.x, s.y, s.z]):\n plot(ax, x, y)\n\n plt.tight_layout()\n plt.show()","def plotGrid(parameters, plot_name):\n\n allNames = sorted(parameters)\n combinations = it.product(*(parameters[Name] for Name in allNames))\n\n combs = [list(c) for c in list(combinations) ]\n combs_df = pd.DataFrame(combs, columns=[\"criterion\", \"max_features\", \"n_estimators\"])\n\n trace = go.Scatter3d(\n x=combs_df['criterion'],\n y=combs_df['max_features'],\n z=combs_df['n_estimators'],\n mode='markers',\n marker=dict(\n size= 5,\n color='green',\n opacity=0.99,\n colorscale='Viridis',\n showscale=False\n )\n )\n data = [trace]\n layout = go.Layout(\n margin=dict(\n l=30,\n r=30,\n b=30,\n t=30\n ),\n height=700,\n width=960,\n showlegend=True,\n scene=dict(\n xaxis=dict(\n title='criterion',\n nticks=10,\n ),\n yaxis=dict(\n title='max_features',\n nticks=10,\n ),\n zaxis=dict(\n title='n_estimators',\n nticks=10,\n ),\n camera=dict(\n eye=dict(\n y=2.089757339892154,\n x=-0.5464711077183096,\n z=0.14759264478960377,\n )\n ),\n ),\n )\n\n fig = go.Figure(data=data, layout=layout)\n fig.show()\n fig.write_image(plot_name)\n fig.write_html(script_path + \"/results/GridSearchPlot.html\")","def vplots(vname:str, *args):\n for arg in args:\n plt.figure()\n N = len(arg.vm)\n for i in range(N):\n plt.plot(arg.t/b2.ms, arg.getattr(vname)[i]/b2.mV)","def plot_multiLyapunov(systems, mode=2, savefig=True, figname=None):\n if mode == 2:\n print(systems)\n# divnorm = colors.DivergingNorm(vmin=max([np.nanmin(np.nanmax(np.nanmax(system.lyapunov_2, axis=0), axis=0)) for system in systems]), vcenter=0, vmax=max[np.nanmax(system.lyapunov_2) for system in systems])\n if figname == None:\n figname = 'sum_of_first_2_lyapunov'\n \n fig, ax = plt.subplots()\n for system in systems:\n\n lyapunov_2 = system.lyapunov_2\n x = system.x\n y = system.y\n l = system.l\n a = system.a\n\n\n\n plt.contourf(a[0,0,:,:],l[0,0,:,:],np.nanmax(np.nanmax(lyapunov_2, axis=0), axis=0), levels = 100, cmap = 'RdBu_r')\n# , norm=divnorm)\n for i in range(lyapunov_2.shape[0]):\n for j in range(lyapunov_2.shape[1]):\n plt.contour(a[0,0,:,:],l[0,0,:,:],lyapunov_2[i,j], levels = [0,], colors=('k',),alpha=0.1)\n lyap_sum = plt.contour(a[0,0,:,:],l[0,0,:,:],lyapunov_2.max(axis=0).max(axis=0), levels = [0,], colors=('blue',),alpha=1)\n\n# cbar = plt.colorbar()\n plt.plot(wild_chaos[:,0],wild_chaos[:,1],'--r',lw=3)\n plt.title('Sum of the first 2 Lyapunov exponents ')\n plt.ylabel('$\\lambda$')\n plt.xlabel('a')\n# cbar.ax.set_ylabel('Sum of the first 2 Lyapunov exponents')\n\n ax.set_ylim([l.min(),l.max()])\n ax.set_xlim([a.min(),a.max()])\n if savefig:\n plt.savefig(f'images/{figname}.pdf')\n plt.show()","def syst_contour():\n\n fig, axarr = plt.subplots(1, 3, figsize=(16, 5))\n\n # first plot in light gray the stat contours\n instruments = [\"fermi\", \"magic\", \"veritas\", \"fact\", \"hess\", \"joint\"]\n\n # with one loop we realize all the contour plots\n color = \"lightgray\"\n for instrument in instruments:\n path = f\"{config.repo_path}/results/fit/gammapy/{instrument}\"\n\n contours_path = f\"{path}/fit_1.0_sigma_contours_logparabola.npy\"\n results_path = f\"{path}/fit_results_logparabola.yaml\"\n\n # load the contours and the results of the fit\n contours = np.load(contours_path).tolist()\n results = load_yaml(results_path)\n # true values to be plotted\n amplitude = float(results[\"parameters\"][0][\"value\"])\n alpha = float(results[\"parameters\"][2][\"value\"])\n beta = float(results[\"parameters\"][3][\"value\"])\n\n # amplitude vs alpha\n amplitude_alpha = contours[\"contour_amplitude_alpha\"]\n axarr[0].plot(\n amplitude_alpha[\"amplitude\"] * 10,\n amplitude_alpha[\"alpha\"],\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n color=color,\n )\n # plot actual value\n axarr[0].plot(\n amplitude * 1e11, alpha, marker=\"X\", markersize=7, color=color, lw=2.5\n )\n\n # amplitude vs beta\n amplitude_beta = contours[\"contour_amplitude_beta\"]\n axarr[1].plot(\n amplitude_beta[\"amplitude\"] * 10,\n # contour have a scale factor of 1e-10, parameters are in units of 1e-11\n amplitude_beta[\"beta\"],\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n color=color,\n )\n # plot actual value\n axarr[1].plot(amplitude * 1e11, beta, marker=\"X\", markersize=7, color=color)\n\n # alpha vs beta\n alpha_beta = contours[\"contour_alpha_beta\"]\n axarr[2].plot(\n alpha_beta[\"alpha\"],\n alpha_beta[\"beta\"],\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n color=color,\n )\n # plot actual value\n axarr[2].plot(alpha, beta, marker=\"X\", markersize=7, color=color)\n\n # plotting stat vs syst result on top of instrument-wise contours in gray\n path = config.repo_path / f\"results/fit/gammapy/joint\"\n\n stat_contours_path = f\"{path}/fit_1.0_sigma_contours_logparabola.npy\"\n stat_results_path = f\"{path}/fit_results_logparabola.yaml\"\n\n syst_contours_path = f\"{path}/fit_1.0_sigma_contours_logparabola_energy_scale.npy\"\n syst_results_path = f\"{path}/fit_results_logparabola_energy_scale.yaml\"\n\n for (contours_path, results_path, color, label) in zip(\n [stat_contours_path, syst_contours_path],\n [stat_results_path, syst_results_path],\n [\"crimson\", \"#002E63\"],\n [\"stat. only\", \"stat. + syst.\"],\n ):\n # load the contours and the results of the fit\n contours = np.load(contours_path).tolist()\n results = load_yaml(results_path)\n # true values to be plotted\n amplitude = float(results[\"parameters\"][0][\"value\"])\n alpha = float(results[\"parameters\"][2][\"value\"])\n beta = float(results[\"parameters\"][3][\"value\"])\n\n # amplitude vs alpha\n amplitude_alpha = contours[\"contour_amplitude_alpha\"]\n if label == \"stat. only\":\n axarr[0].plot(\n amplitude_alpha[\"amplitude\"] * 10,\n # gammapy contour have a scale factor of 1e-10, parameters are in units of 1e-11\n amplitude_alpha[\"alpha\"],\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n color=color,\n )\n else:\n axarr[0].plot(\n amplitude_alpha[\"amplitude\"] * 1e11,\n amplitude_alpha[\"alpha\"],\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n color=color,\n )\n\n # plot actual value\n axarr[0].plot(\n amplitude * 1e11, alpha, marker=\"X\", markersize=7, color=color, lw=2.5\n )\n\n # amplitude vs beta\n amplitude_beta = contours[\"contour_amplitude_beta\"]\n if label == \"stat. only\":\n axarr[1].plot(\n amplitude_beta[\"amplitude\"] * 10,\n # gammapy contour have a scale factor of 1e-10, parameters are in units of 1e-11\n amplitude_beta[\"beta\"],\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n color=color,\n )\n else:\n axarr[1].plot(\n amplitude_beta[\"amplitude\"] * 1e11,\n amplitude_beta[\"beta\"],\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n color=color,\n )\n\n # plot actual value\n axarr[1].plot(amplitude * 1e11, beta, marker=\"X\", markersize=7, color=color)\n\n # alpha vs beta\n alpha_beta = contours[\"contour_alpha_beta\"]\n axarr[2].plot(\n alpha_beta[\"alpha\"],\n alpha_beta[\"beta\"],\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n color=color,\n )\n # plot actual value\n axarr[2].plot(alpha, beta, marker=\"X\", markersize=7, color=color)\n\n # legend\n import matplotlib.lines as mlines\n\n stat_label = mlines.Line2D(\n [],\n [],\n color=\"crimson\",\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n label=\"joint fit \\n stat. only\",\n )\n syst_label = mlines.Line2D(\n [],\n [],\n color=\"#002E63\",\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n label=\"joint fit \\n stat. + syst.\",\n )\n single_label = mlines.Line2D(\n [],\n [],\n color=\"lightgray\",\n marker=\"\",\n ls=\"-\",\n lw=2.5,\n label=\"instr. fit \\n stat. only\",\n )\n\n axarr[0].set_xlabel(\n r\"$\\phi_0 \\,/\\,(10^{-11}\\,{\\rm TeV} \\, {\\rm cm}^{-2} {\\rm s}^{-1})$\",\n size=FONTSIZE_CONTOURS,\n )\n axarr[0].set_ylabel(r\"$\\Gamma$\", size=FONTSIZE_CONTOURS)\n # make axis thicker\n axarr[0].set_yticks([2.2, 2.4, 2.6, 2.8])\n axarr[0].set_ylim([2.1, 2.9])\n axarr[0].set_xticks([3, 4, 5])\n axarr[0].set_xlim([2.8, 5.2])\n for axis in [\"top\", \"bottom\", \"left\", \"right\"]:\n axarr[0].spines[axis].set_linewidth(2.5)\n axarr[0].tick_params(\n \"both\", length=7, width=1.6, which=\"major\", labelsize=FONTSIZE_CONTOURS\n )\n axarr[0].tick_params(\n \"both\", length=4, width=1.6, which=\"minor\", labelsize=FONTSIZE_CONTOURS\n )\n\n axarr[1].set_xlabel(\n r\"$\\phi_0 \\,/\\,(10^{-11}\\,{\\rm TeV} \\, {\\rm cm}^{-2} {\\rm s}^{-1})$\",\n size=FONTSIZE_CONTOURS,\n )\n axarr[1].set_ylabel(r\"$\\beta$\", size=FONTSIZE_CONTOURS)\n # make axis thicker\n axarr[1].set_xticks([3, 4, 5])\n axarr[1].set_xlim([2.8, 5.2])\n axarr[1].set_yticks([0.2, 0.4, 0.6])\n axarr[1].set_ylim([0.0, 0.8])\n for axis in [\"top\", \"bottom\", \"left\", \"right\"]:\n axarr[1].spines[axis].set_linewidth(2.5)\n axarr[1].tick_params(\n \"both\", length=7, width=1.6, which=\"major\", labelsize=FONTSIZE_CONTOURS\n )\n axarr[1].tick_params(\n \"both\", length=4, width=1.6, which=\"minor\", labelsize=FONTSIZE_CONTOURS\n )\n\n axarr[2].set_xlabel(r\"$\\Gamma$\", size=FONTSIZE_CONTOURS)\n axarr[2].set_ylabel(r\"$\\beta$\", size=FONTSIZE_CONTOURS)\n axarr[2].set_xticks([2.2, 2.4, 2.6, 2.8])\n axarr[2].set_xlim([2.1, 2.9])\n axarr[2].set_yticks([0.2, 0.4, 0.6])\n axarr[2].set_ylim([0.0, 0.8])\n # make axis thicker\n for axis in [\"top\", \"bottom\", \"left\", \"right\"]:\n axarr[2].spines[axis].set_linewidth(2.5)\n axarr[2].tick_params(\n \"both\", length=7, width=1.6, which=\"major\", labelsize=FONTSIZE_CONTOURS\n )\n axarr[2].tick_params(\n \"both\", length=4, width=1.6, which=\"minor\", labelsize=FONTSIZE_CONTOURS\n )\n\n box = axarr[2].get_position()\n axarr[2].set_position([box.x0, box.y0, box.width * 0.97, box.height])\n # plot the legend on top of the central plot\n axarr[2].legend(\n handles=[stat_label, syst_label, single_label],\n loc=\"center left\",\n fontsize=FONTSIZE_CONTOURS,\n bbox_to_anchor=(1., 0.5),\n )\n\n plt.tight_layout()\n filename = \"results/figures/iminuit_logparabola_energy_scale_contour.png\"\n filename_pdf = \"results/figures/iminuit_logparabola_energy_scale_contour.pdf\"\n fig.savefig(filename)\n log.info(f\"Writing {filename}\")\n fig.savefig(filename)\n fig.savefig(filename_pdf)","def add_contours(self, levels=5, **kwargs):\n\n rs, ts = NP.meshgrid(NP.linspace(self.smin, self.smax),\n NP.linspace(0, NP.pi/2))\n # Compute centered RMS difference\n rms = NP.sqrt(self.refstd**2 + rs**2 - 2*self.refstd*rs*NP.cos(ts))\n\n contours = self.ax.contour(ts, rs, rms, levels, **kwargs)\n\n return contours","def add_contours(self, levels=5, **kwargs):\n\n rs, ts = NP.meshgrid(NP.linspace(self.smin, self.smax),\n NP.linspace(0, NP.pi/2))\n # Compute centered RMS difference\n rms = NP.sqrt(self.refstd**2 + rs**2 - 2*self.refstd*rs*NP.cos(ts))\n\n contours = self.ax.contour(ts, rs, rms, levels, **kwargs)\n\n return contours","def check_plots(tracklist_subset, tracklist):\n\n for trial in tracklist_subset: # Iterates over all available trials\n\n # Scale time for colormap\n scaled_time = (tracklist[trial]['data'].index.values -\n tracklist[trial]['data'].index.values.min()) / \\\n tracklist[trial]['data'].index.values.ptp()\n timemax = max(tracklist[trial]['data'].index.values)\n data = tracklist[trial]['data']\n colors = plt.cm.cubehelix(scaled_time)\n m = cm.ScalarMappable(cmap=cm.cubehelix)\n m.set_array(tracklist[trial]['data'].index.values)\n\n # filename = str(trial) + '.pdf'\n\n fig = plt.figure(figsize=(20, 20))\n fig.suptitle(tracklist[trial]['sequence'] + ' ' +\n tracklist[trial]['behavior'])\n\n ax1 = fig.add_subplot(4, 2, 1, projection='3d')\n ax1.set_title('Pt 1 Position')\n ax1.scatter3D(xs=tracklist[trial]['data']['pt1x_smth'],\n ys=tracklist[trial]['data']['pt1y_smth'],\n zs=tracklist[trial]['data']['pt1z_smth'],\n zdir='z', s=3, c=colors, marker='o',\n edgecolor='none') # 3D Scatter plot\n ax1.autoscale(enable=True, tight=True)\n ax1.set_xlabel('X position')\n ax1.set_ylabel('Y position')\n ax1.set_zlabel('Z position')\n plt.colorbar(m, shrink=0.5, aspect=10)\n\n ax2 = fig.add_subplot(4, 2, 3)\n ax2.plot(data.index.values, data['pt1y_v_smth'], 'bo')\n ax2.set_ylabel('Y velocity (cm/s)', color='b')\n ax2.tick_params('y', colors='b')\n\n ax3 = fig.add_subplot(4, 2, 5)\n ax3.plot(data.index.values, data['pt1_net_v'], 'bo')\n ax3.set_ylabel('Net Velocity (cm/s)', color='b')\n ax3.tick_params('y', colors='b')\n\n ax4 = fig.add_subplot(4, 2, 7)\n ax4.plot(data.index.values, data['pt1_net_a'], 'bo')\n ax4.set_ylabel('Net accel (cm/s2)', color='b')\n ax4.tick_params('y', colors='b')\n\n ax5 = fig.add_subplot(4, 2, 2, projection='3d')\n ax5.set_title('Pt 2 Position')\n ax5.scatter3D(xs=tracklist[trial]['data']['pt2x_smth'],\n ys=tracklist[trial]['data']['pt2y_smth'],\n zs=tracklist[trial]['data']['pt2z_smth'],\n zdir='z', s=3, c=colors, marker='o',\n edgecolor='none') # 3D Scatter plot\n ax5.autoscale(enable=True, tight=True)\n ax5.set_xlabel('X position')\n ax5.set_ylabel('Y position')\n ax5.set_zlabel('Z position')\n plt.colorbar(m, shrink=0.5, aspect=10)\n\n ax6 = fig.add_subplot(4, 2, 4)\n ax6.plot(data.index.values, data['pt2y_v_smth'], 'bo')\n ax6.set_ylabel('Y velocity (cm/s)', color='b')\n ax6.tick_params('y', colors='b')\n\n ax7 = fig.add_subplot(4, 2, 6)\n ax7.plot(data.index.values, data['pt2_net_v'], 'bo')\n ax7.set_ylabel('Net Velocity (cm/s)', color='b')\n ax7.tick_params('y', colors='b')\n\n ax8 = fig.add_subplot(4, 2, 8)\n ax8.plot(data.index.values, data['pt2_net_a'], 'bo')\n ax8.set_ylabel('Net accel (cm/s2)', color='b')\n ax8.tick_params('y', colors='b')\n\n plt.show()","def convexHull(stimulus_class, subject_list, data_dir, save_dir, save_fig=False):\n\t\n\tfig = plt.figure(figsize=(15,9))\n\tax = fig.subplots()\n\tif stimulus_class == 'TIMIT':\n\t\tprint('TIMIT')\n\t\tplt.plot([-0.2, 1.0], [-0.2, 1.0], 'black', label='unity')\n\n\t\t\n\telif stimulus_class == 'MT':\n\t\tprint('trailers')\n\t\tplt.plot([-0.1, 0.5], [-0.1, 0.5], 'black', label='unity')\n\n\telse:\n\t\tprint('Undefined stimulus class')\n\t# plt.axis('tight')\n\t# plt.axis('square')\n\n\tcorrs = []\n\tcorrs_sig = []\n\t#corrs_nonsig = []\n\tfor idx, s in enumerate(subject_list):\n\t\twith h5py.File('%s/%s/%s_STRF_by_binned_pitches_%s.hf5'%(data_dir, s, s, stimulus_class), 'r') as fh: #full model\n\t\t\tfullModel = fh['corrs_%s_norm' %(stimulus_class.lower())][:] \n\t\t\tfullModel[np.isinf(fullModel)]=0\n\t\t\tp_vals_full = fh['pvals_%s' %(stimulus_class.lower())][:]\n\t\t\t#full_nonsig = (fullModel[np.where(p_vals_full[0] > 0.05)])\n\t\t\tfull_sig = (fullModel[np.where(p_vals_full[0] < 0.05)])\n\n\t\t\t#binned pitch full model \n\t\twith h5py.File('%s/%s/%s_STRF_by_binned_pitch_only_%s.hf5'%(data_dir, s, s, stimulus_class), 'r') as fh: #full model\n\t\t\tpitch = fh['corrs_%s_norm' %(stimulus_class.lower())][:] \n\t\t\tpitch[np.isinf(pitch)]=0\n\t\t\tp_val_pitch = fh['pvals_%s' %(stimulus_class.lower())][:]\n\t\t\t#binned_full_nonsig = (pitch[np.where(p_vals_full[0] > 0.05)])\n\t\t\tbinned_full_sig = (pitch[np.where(p_vals_full[0] < 0.05)])\n\t\t\tprint(pitch)\n\n\t\twith h5py.File('%s/%s/%s_STRF_by_envs_%s.hf5'%(data_dir, s, s, stimulus_class), 'r') as h: #envs model only \n\t\t\tenvs = h['corrs_%s_norm' %(stimulus_class.lower())][:]\n\t\t\tenvs[np.isinf(envs)]=0\n\t\t\tp_vals_envs = h['pvals_%s' %(stimulus_class.lower())][:]\n\t\t\t#envs_nonsig = (envs[np.where(p_vals_full[0] > 0.05)])\n\t\t\tenvs_sig = (envs[np.where(p_vals_full[0] < 0.05)])\n\n\t\twith h5py.File('%s/%s/%s_STRF_by_phnfeat_%s.hf5'%(data_dir, s, s, stimulus_class), 'r') as h: #phnfeat model only \n\t\t\tphnfeat = h['corrs_%s_norm' %(stimulus_class.lower())][:]\n\t\t\tphnfeat[np.isinf(phnfeat)]=0\n\t\t\tp_vals_phnfeat = h['pvals_%s' %(stimulus_class.lower())][:]\n\t\t\t#phnfeat_nonsig = (phnfeat[np.where(p_vals_full[0] > 0.05)])\n\t\t\tphnfeat_sig = (phnfeat[np.where(p_vals_full[0] < 0.05)])\n\n\t\tcorrs.append([fullModel, envs, phnfeat, pitch])\n\t\t#corrs_nonsig.append([full_nonsig, envs_nonsig, phnfeat_nonsig, binned_full_nonsig])\n\t\tcorrs_sig.append([full_sig, envs_sig, phnfeat_sig, binned_full_sig])\n\n\tpoints = np.hstack(corrs).T\n\tpoints_sig = np.hstack(corrs_sig).T\n\t#points_nonsig = np.hstack(corrs_nonsig).T\n\n\thull1=ConvexHull(np.vstack((points_sig[:,0] , points_sig[:,1])).T) #full model, envs\n\thullv1 = hull1.vertices.copy()\n\thullv1 = np.append(hullv1, hullv1[0])\n\tprint(hullv1)\n\n\thull2=ConvexHull(np.vstack((points_sig[:,0] , points_sig[:,2])).T) #full model, phnfeat\n\thullv2 = hull2.vertices.copy()\n\thullv2 = np.append(hullv2, hullv2[0])\n\tprint(hullv2)\n\n\thull3=ConvexHull(np.vstack((points_sig[:,0] , points_sig[:,3])).T) #full model, pitch\n\thullv3 = hull3.vertices.copy()\n\thullv3 = np.append(hullv3, hullv3[0])\n\tprint(hullv3)\n\n\n\t#fill between: \n\tplt.fill(points_sig[hullv2,0], points_sig[hullv2,2], facecolor='#cd1a1e', alpha=0.4, zorder=3, label='full vs. phnfeat') #phnfeat\n\tplt.fill(points_sig[hullv3,0], points_sig[hullv3,3], facecolor='#64a7bc', alpha=0.4, zorder=1, label='full vs. pitch') #pitch \n\tplt.fill(points_sig[hullv1,0], points_sig[hullv1,1], facecolor='#808080', alpha=0.4, zorder=2, label='full vs. envs') #envs\n\n\t# plt.fill(points[hullv4,0], points[hullv4,4], facecolor='#4B8B3B', alpha=0.4, zorder=4, label='full vs. binned_pitch')\n\n\n\tplt.plot(points_sig[:,0], points_sig[:,1], '.', color='#808080', alpha=0.8) #envs\n\tplt.plot(points_sig[:,0], points_sig[:,2], '.', color='#cd1a1e', alpha=0.8) #phnfeat\n\tplt.plot(points_sig[:,0], points_sig[:,3], '.', color='#64a7bc',alpha=0.8) #pitch\n\t# plt.plot(points_sig[:,0], points_sig[:,4], '.', color='#4B8B3B', alpha=0.8) #binned pitch \n\n\t# plt.plot(points_nonsig[:,0], points_nonsig[:,1], '.', color='#CDC1C5', alpha=0.7) #envs\n\t# plt.plot(points_nonsig[:,0], points_nonsig[:,2], '.', color='#CDC1C5', alpha=0.7) #phnfeat\n\t# plt.plot(points_nonsig[:,0], points_nonsig[:,3], '.', color='#CDC1C5',alpha=0.7) #pitch\n\t# # plt.plot(points_nonsig[:,0], points_nonsig[:,4], '.', color='#CDC1C5', alpha=0.7) #binned pitch non sig\n\t\n\tplt.legend(bbox_to_anchor=(1, 1), loc='upper left')\n\tplt.xlabel('Full model correlation values (r)')\n\tplt.ylabel('Individual model correlation values (r)')\n\tplt.title('Convex-Hull for %s (Feature distribution across subjects)' %(stimulus_class))\n\tplt.axis('tight')\n\tplt.axis('square')\n\t\n\t#save figure\n\tif save_fig:\n\t\tplt.savefig('%s/%s_ConvexHull.pdf' %(save_dir, stimulus_class))","def add_contours(self, levels=5, **kwargs):\n\n rs, ts = NP.meshgrid(NP.linspace(self.smin, self.smax),\n NP.linspace(0, NP.pi / 2))\n # Compute centered RMS difference\n rms = NP.sqrt(self.refstd ** 2 + rs ** 2 - 2 * self.refstd * rs * NP.cos(ts))\n\n contours = self.ax.contour(ts, rs, rms, levels, **kwargs)\n\n return contours","def PlotCollectionAddOneTwoThree(title, frit, ulist, addvalues):\n\tfig = pp.figure( figsize=printsize )\n\tnxpoints = 907\n\tfig.suptitle = \"TEST SUP TITLE\" #title\n\tplotspots = [231,232,233,234,235,236]\n\tfor i,(uvalue,ulabel,xrange,yrange) in enumerate(ulist):\n\t print('making sub-plot for u=%g' % (uvalue,)\t\n\t\tPlotOneSubplot( fig.add_subplot(plotspots[i]), frit, xrange, nxpoints, yrange, uvalue,ulabel, addvalues)\n\t#pp.show() \n\tfig.savefig(title+\".eps\", format='eps', pad_inches=0.02)\n\n\naddvalues = [\n # value to \"add\", color\n\t[ -2, \"#6688ee\" ],\n\t[ -1, \"#66aacc\" ],\n\t[ 0, \"#000000\" ],\n\t[ 1, \"#ee6688\" ],\n\t[ 2, \"#dd9966\" ],\n\t[ 3, \"#bbaa66\" ],","def plot_contours(ax, clf, xx, yy, **params):\r\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\r\n Z = Z.reshape(xx.shape)\r\n out = ax.contourf(xx, yy, Z, **params)\r\n return out","def plot_contours(ax, clf, xx, yy, **params):\r\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\r\n Z = Z.reshape(xx.shape)\r\n out = ax.contourf(xx, yy, Z, **params)\r\n return out","def plot_contours(ax, clf, xx, yy, **params):\r\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\r\n Z = Z.reshape(xx.shape)\r\n out = ax.contourf(xx, yy, Z, **params)\r\n return out","def coffee_plot(times, Temps, axes, legends=[None, None], split=2):\n plot_model(times[split:], Temps[split:], axes[0], legends[0])\n plot_samples(times[:split], Temps[:split], axes[1], legends[1])","def generate_plot(self, xlimg = None , ylimg =None , exname = '' , prefix = True , save = True):\n print ('start with the generation of plots')\n #plot of condensation energy\n self.plotwrap(0,2, 'energy (a.u.)' , name = 'ge'+ exname, titel = 'the energy (a.u.)', xlim = xlimg , ylim = ylimg , prefix = prefix ,save = save )\n self.plotwrap(0,1, 'condensation energy (a.u.)' , name = 'ce' + exname ,titel = 'the condensation energy (a.u.)',xlim = xlimg , ylim = ylimg , prefix = prefix,save = save )","def plot_contours(ax, clf, xx, yy, **params):\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n out = ax.contourf(xx, yy, Z, **params)\n return out","def plot_contours(ax, clf, xx, yy, **params):\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n out = ax.contourf(xx, yy, Z, **params)\n return out","def plot_contours(ax, clf, xx, yy, **params):\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n out = ax.contourf(xx, yy, Z, **params)\n return out","def plot_contours(ax, clf, xx, yy, **params):\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n out = ax.contourf(xx, yy, Z, **params)\n return out","def plt_sclr(**kwargs):\n import argparse\n import os.path\n import numpy as np\n import matplotlib\n from read_xdr import readXDRsclr, readXDRstruct\n import gc\n \n dirname=os.getcwd() + '/'\n if not os.path.exists(dirname+\"plots\"):\n os.makedirs(dirname+\"plots\")\n \n class C(object):\n pass\n arg=C()\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument('-i', type=int, help='time index')\n parser.add_argument('-p', type=int, help='probe')\n parser.add_argument('-minx', type=float, help='minimum x')\n parser.add_argument('-maxx', type=float, help='maximum x')\n parser.add_argument('-minz', type=float, help='minimum z')\n parser.add_argument('-maxz', type=float, help='maximum z')\n parser.add_argument('-theta', type=float, action='append', help='azimuthal angle (range)')\n parser.add_argument('-polarz', type=float, action='append', help='polar projection at z (range)')\n \n parser.add_argument('-cmap', type=str, help='colormap')\n parser.add_argument('-cmin', type=float, help='min value')\n parser.add_argument('-cmax', type=float, help='max value')\n parser.add_argument('-slicex', type=float, action='append', help='z-slice along given x')\n parser.add_argument('-slicez', type=float, action='append', help='x-slice along given z')\n \n parser.add_argument('-f', action='store_true', help=\"add horizontal mirror image\")\n parser.add_argument('-a', action='store_true', help=\"keep aspect ratio\")\n parser.add_argument('-log', action='store_true', help=\"log scale\")\n parser.add_argument('-smooth', action='store_true', help=\"smooth contour\")\n parser.add_argument('-save', action='store_true', help=\"save only\")\n parser.add_argument('-noaxis', action='store_true', help=\"show axis\")\n parser.add_argument('-crit', type=float, help=\"use density criterion\")\n \n \n if \"arglist\" in kwargs:\n print(kwargs[\"arglist\"])\n parser.parse_args(kwargs[\"arglist\"],namespace=arg)\n else:\n parser.parse_args(namespace=arg) \n \n num = int(arg.i) if arg.i != None else 1\n ivar = int(arg.p) if arg.p != None else 0\n \n x0 = float(arg.minx) if arg.minx != None else None \n x1 = float(arg.maxx) if arg.maxx != None else None \n z0 = float(arg.minz) if arg.minz != None else None \n z1 = float(arg.maxz) if arg.maxz != None else None\n \n if arg.theta != None:\n theta = arg.theta \n if type(theta)!=list: \n theta=[theta]\n theta = np.sort(theta)\n else:\n theta = None\n \n if arg.polarz != None:\n polarZ = arg.polarz \n if type(polarZ)!=list: \n polarZ=[polarZ]\n polarZ = np.sort(polarZ)\n else:\n polarZ = None\n \n cmap = arg.cmap if arg.cmap != None else None \n cmin = float(arg.cmin) if arg.cmin != None else None \n cmax = float(arg.cmax) if arg.cmax != None else None \n \n sliceX = arg.slicex if arg.slicex != None else None\n sliceZ = arg.slicez if arg.slicez != None else None \n \n flip = arg.f\n aspect= arg.a\n logScale = arg.log\n smooth = arg.smooth\n noShow = arg.save\n noAxis = arg.noaxis\n \n crit = float(arg.crit) if arg.crit != None else None \n \n if noShow: matplotlib.use('Agg') # to run in pdebug (not using $DISPLAY)\n import matplotlib.pyplot as plt\n if 'classic' in plt.style.available: plt.style.use('classic')\n import matplotlib.collections as mc\n from matplotlib.colors import LogNorm\n from matplotlib import ticker\n from mpl_toolkits.axes_grid1 import make_axes_locatable\n import pylab as pl\n \n def line2d(x0,x1,z0,z1,X,Z,dat,val):\n from scipy import interpolate\n \n if (z1==z0): return()\n \n indx=np.argwhere((X>=np.min([x0,x1]))&(X<=np.max([x0,x1])))\n indx=indx.reshape(indx.shape[0],1)\n indz=np.argwhere((Z>=z0)&(Z<=z1))\n indz=indz.reshape(1,indz.shape[0])\n \n tanA=(x1-x0)/(z1-z0)\n # cosA = np.cos(np.arctan(tanA))\n npt=100\n z = np.linspace(z0,z1,npt)\n x = x0 + (z-z0)*tanA\n r = np.sqrt((x-x0)**2 + (z-z0)**2).reshape((npt,1))\n f=interpolate.interp2d(X[indx].flatten(), Z[indz].flatten(), dat[indx,indz].transpose(), kind='cubic')\n lnout = np.zeros((npt,1))\n for i in range(npt):\n lnout[i]=f(x[i], z[i])\n \n f, ax = plt.subplots()\n # ax.plot(z[1:],np.diff(lnout[:,0])/np.diff(r[:,0]),linewidth=2)\n ax.plot(z,lnout,linewidth=2)\n i = np.nonzero(lnout[::-1]>val)[0][0] \n print(x[::-1][i])\n print(z[::-1][i])\n plt.show()\n return(lnout)\n \n \n def plotSliceZ():\n data=np.zeros((len(X),1));\n if len(sliceZ)>1:\n zrange = np.linspace(sliceZ[0],sliceZ[1],100)\n else:\n zrange = sliceZ\n \n for i in range(len(X)):\n data[i,0]=np.mean(np.interp(zrange,Z[:],dat[i,:]));\n f, ax = plt.subplots()\n ax.plot(X[:],data[:,0],linewidth=2)\n ax.plot(X[:],np.ones(len(data[:,0]))*np.mean(data[:,0]),linewidth=2)\n print(\"SliceZ mean %.2e\" %(np.mean(data[:,0])))\n ax.set(title=VarNames[ivar]+\" (\" + \\\n VarUnits[ivar] + \")\" +\" X-slice at Z = %.2f\" %(sliceZ[0]) + \" at \"+tstamp+\"ns\")\n ax.set_xlim([xmin,xmax]);\n ax.set_ylim([cmin,cmax]);\n ax.set(xlabel='X (cm)', ylabel=VarUnits[ivar])\n if logScale: ax.set(yscale='log')\n f.savefig(dirname +\"plots/\" + VarNames[ivar] + \"_%06.1f\" %(time) + '_sliceZ.png',dpi=200, bbox_inches=\"tight\")\n # fout = open(dirname +\"plots/slicez.out\", \"a+\")\n # fout.write(\"%.8e %.8e\\n\" %(time, np.mean(data[0,:])));\n # fout.close() \n fout = open(dirname +\"plots/\" + VarNames[ivar] + \"_%06.1f\" %(time) + '_sliceZ.out', \"w\")\n for i in range(len(X)):\n fout.write(\"%.8e %.8e\\n\" %(X[i],data[i,0]))\n fout.close()\n if not noShow: \n plt.show()\n # # sheath front locator\n # zdum=Z[0,:]\n # ydum=np.diff(data[0,:]);\n # zfront=zdum[np.argwhere(ydum==np.min(ydum))][0]\n # print(\"sheath front z-location %.2f\" %(zfront))\n # fout = open(dirname+'sheath_front.out', \"a\")\n # fout.write(\"%.8e %.8e\" %(time, zfront));\n # fout.write(\"\\n\");\n # fout.close() \n return()\n \n def plotSliceX():\n data=np.zeros((1,len(Z)));\n if len(sliceX)>1:\n xrange = np.linspace(sliceX[0],sliceX[1],100)\n else:\n xrange = sliceX\n \n for j in range(len(Z)):\n data[0,j]=np.mean(np.interp(xrange,X[:],dat[:,j]));\n f, ax = plt.subplots()\n ax.plot(Z[:],data[0,:],linewidth=2)\n ax.plot(Z[:],np.ones(len(data[0,:]))*np.mean(data[0,:]),linewidth=2)\n print(\"SliceX integral %.2e\" %(np.trapz(data[0,:],Z)))\n ax.set(title=VarNames[ivar]+\" (\" + \\\n VarUnits[ivar] + \")\" +\" Z-slice at X = %.4f\" %(sliceX[0]) + \" at \"+tstamp+\"ns\")\n ax.set_xlim([zmin,zmax]);\n ax.set_ylim([cmin,cmax]);\n ax.set(xlabel='Z (cm)', ylabel=VarUnits[ivar])\n if logScale: ax.set(yscale='log')\n f.savefig(dirname +\"plots/\" + VarNames[ivar] + \"_%06.1f\" %(time) + '_sliceX.png',dpi=200, bbox_inches=\"tight\")\n fout = open(dirname +\"plots/slicex.out\", \"a+\")\n fout.write(\"%.8e %.8e\\n\" %(time, np.mean(data[0,:])));\n fout.close()\n fout = open(dirname +\"plots/\" + VarNames[ivar] + \"_%06.1f\" %(time) + '_sliceX.out', \"w\")\n for i in range(len(Z)):\n fout.write(\"%.8e %.8e\\n\" %(Z[i],data[0,i]))\n fout.close()\n\n ind = np.nonzero(data[0,::-1]>2e18)[0][0]\n fout = open(dirname +\"plots/front.out\", \"a+\")\n fout.write(\"%.8e %.8e\\n\" %(time, Z[::-1][ind]));\n fout.close() \n \n if not noShow: \n plt.show()\n return()\n \n def addStructure(ax):\n (xa,ya,za,xb,yb,zb)=readXDRstruct(dirname+'struct.p4')\n lines=[[(za[i],xa[i]),(zb[i],xb[i])] for i in range(len(xa))]\n lc = mc.LineCollection(lines, color=(0.9,0.9,0.9),linewidths=1)\n ax.add_collection(lc)\n if flip:\n lines=[[(za[i],-xa[i]),(zb[i],-xb[i])] for i in range(len(xa))]\n lc = mc.LineCollection(lines, color=(0.9,0.9,0.9),linewidths=1)\n ax.add_collection(lc)\n return()\n \n def plotPolar():\n r, th = np.meshgrid(X, Y)\n f, ax = plt.subplots(subplot_kw=dict(projection='polar'),figsize=(8,8))\n cm = matplotlib.cm.get_cmap(cmap)\n val = dat.transpose(1,0)\n # contour_levels = arange(cmin, 3, 0.05)\n if logScale:\n # im = ax.pcolor(th, r, dat.transpose(1,0),norm=LogNorm(vmin=cmin,vmax=cmax),cmap=cm)\n im = ax.contourf(th, r, val,norm=LogNorm(vmin=cmin,vmax=cmax),cmap=cm)\n else:\n # im = ax.pcolor(th, r, dat.transpose(1,0),vmin=cmin,vmax=cmax,cmap=cm)\n im = ax.contourf(th, r, val,100,vmin=cmin,vmax=cmax,cmap=cm)\n ax.xaxis.grid(True, zorder=0)\n ax.yaxis.grid(True, zorder=0)\n \n \n if noAxis:\n ax.set(title=VarNames[ivar]) \n ax.get_xaxis().set_visible(True)\n ax.get_yaxis().set_visible(False)\n else:\n title = VarNames[ivar] + \" (\" + VarUnits[ivar] + \")\" +\"/Time=%.1fns/Z=%.1fcm\" %(time,polarZ[0])\n ax.set(title=title, xlabel=\"R (cm)\")\n plt.colorbar(im)\n \n \n f.savefig(dirname + \"plots/\" + VarNames[ivar] + \"_T%06.1f_Z%04.1f.png\" %(time,polarZ[0]),dpi=200, bbox_inches=\"tight\")\n if not noShow: \n plt.show()\n return()\n \n \n \n #######################################################\n \n fname=dirname+'sclr'+str(num)+'.p4'\n print(fname)\n \n if \"sdata\" in kwargs:\n (X,Y,Z,Var,VarNames,VarUnits,time)=kwargs[\"sdata\"]\n else:\n (X,Y,Z,Var,VarNames,VarUnits,time)=readXDRsclr(fname,silent=noShow)\n \n # if not noShow:\n # for i in range(len(Var)):\n # print(str(i) + \" \" + VarNames[i] + \" \" + VarUnits[i])\n tstamp = \"%.2f\" % time\n \n dat=Var[ivar]\n if crit!=None:\n cvar = VarNames.index(\"RhoT\" + VarNames[ivar][4:])\n dat[np.nonzero(Var[cvar]<crit)]=0.0\n \n if flip: x0=min(X)\n xmin=x0 if (x0!=None) and (x0>min(X)) and (x0<=max(X)) else min(X)\n xmax=x1 if (x1!=None) and (x1>xmin) and (x1<=max(X)) else max(X)\n zmin=z0 if (z0!=None) and (z0>min(Z)) and (z0<max(Z)) else min(Z)\n zmax=z1 if (z1!=None) and (z1>zmin) and (z1<=max(Z)) else max(Z)\n \n indx=np.argwhere((X>=xmin)&(X<=xmax))\n indx=indx.reshape(indx.shape[0],1,1)\n indz=np.argwhere((Z>=zmin)&(Z<=zmax))\n indz=indz.reshape(1,1,indz.shape[0])\n indy=np.arange(len(Y)).reshape(1,len(Y),1)\n \n #indz=np.transpose(indz)\n X=X[indx].flatten()\n Y=Y[indy].flatten()\n Z=Z[indz].flatten()\n \n dat=dat[indx,indy,indz]\n \n if type(polarZ)!=type(None):\n # calculate weights for nonuniform grid spacing\n dz = np.diff(Z)\n wZ = np.ones(len(Z))\n wZ[0] = 0.5*dz[0]\n wZ[1:-1]=0.5*(dz + np.roll(dz,-1))[0:-1]\n wZ[-1]=0.5*dz[-1]\n \n zdum = polarZ[0]\n k10 = np.max([1,int(np.argwhere(Z>=zdum)[0][0])])\n k00 = int(k10-1)\n w00 = (Z[k10] - zdum)/(Z[k10] - Z[k00])\n wZ[k00] *= w00\n \n zdum = polarZ[-1]\n k11 = np.max([1,int(np.argwhere(Z>=zdum)[0][0])])\n k01 = int(k11-1)\n w01 = (Z[k11] - zdum)/(Z[k11] - Z[k01])\n wZ[k11] *= (1-w01)\n dat = np.average(dat[:,:,k00:k11+1], axis=2, weights=wZ[k00:k11+1])\n \n plotPolar()\n exit() \n \n if type(theta)!=type(None):\n wY = np.ones(len(Y))\n # calculate weights for nonuniform grid spacing\n if len(Y)>1:\n dy = np.diff(Y)\n wY[0] = 0.5*dy[0]\n wY[1:-1]=0.5*(dy + np.roll(dy,-1))[0:-1]\n wY[-1]=0.5*dy[-1] \n \n thdum = np.radians(theta[0])\n thdum = divmod(thdum,np.pi*2)[1] if thdum>np.pi*2 else thdum\n j10 = int(np.argwhere(Y>=thdum)[0][0])\n if j10==0: \n j10+=1\n j00 = int(j10-1)\n w00 = (Y[j10] - thdum)/(Y[j10] - Y[j00])\n wY[j00] = w00*wY[j00] # weight of outermost left grid point\n \n thdum = np.radians(theta[-1])\n thdum = divmod(thdum,np.pi*2)[1] if thdum>np.pi*2 else thdum\n j11 = int(np.argwhere(Y>=thdum)[0][0])\n if j11==0: \n j11+=1 \n j01 = int(j11-1)\n w01 = (Y[j11] - thdum)/(Y[j11] - Y[j01])\n wY[j11] = (1 - w01)*wY[j11] # # weight of outermost right grid point\n \n dat = np.average(dat[:,j00:j11+1,:], axis=1, weights=wY[j00:j11+1])\n else:\n dat=np.squeeze(dat[:,0,:])\n \n if cmin is None:\n cmin = dat.min()\n if cmax is None:\n cmax = dat.max()\n \n fig, ax = plt.subplots(figsize=(8,8))\n if flip:\n dat=np.concatenate((np.flipud(dat),dat),axis=0);\n X = np.concatenate([-X[::-1],X])\n \n cm = matplotlib.cm.get_cmap(cmap)\n \n if logScale:\n locator = ticker.LogLocator(base=10)\n if smooth:\n dat[np.nonzero(dat>cmax)] = cmax\n dat[np.nonzero(dat<cmin)] = cmin\n levels = np.power(10,np.linspace(np.log10(cmin),np.log10(cmax),num=100))\n im = ax.contourf(Z,X,dat,norm=LogNorm(vmin=cmin,vmax=cmax),cmap=cm,locator=locator,levels=levels)\n else:\n im = ax.pcolor(Z,X,dat,norm=LogNorm(vmin=cmin,vmax=cmax),cmap=cm)\n else:\n if smooth:\n dat[np.nonzero(dat>cmax)] = cmax\n dat[np.nonzero(dat<cmin)] = cmin \n levels = ticker.MaxNLocator(nbins=100).tick_values(cmin, cmax)\n im = ax.contourf(Z,X,dat,vmin=cmin,vmax=cmax,cmap=cm,levels=levels)\n else:\n im = ax.pcolor(Z,X,dat,vmin=cmin,vmax=cmax,cmap=cm)\n \n ax.set_xlim([min(Z[:]),max(Z[:])])\n ax.set_ylim([min(X[:]),max(X[:])])\n \n title = VarNames[ivar] + \" (\" + VarUnits[ivar] + \")\" + \"/Time %.1fns\" %(time)\n if type(theta)!=type(None):\n title = title + \"/Angle %.1f-%.1fdeg\" %(theta[0],theta[-1])\n ax.set(title=title,xlabel=\"Z (cm)\",ylabel=\"R (cm)\")\n \n # add structure\n if os.path.isfile(dirname+'struct.p4'):\n addStructure(ax)\n \n if (aspect or flip):\n ax.set_aspect('equal');\n else:\n ax.set_aspect('auto') \n \n divider = make_axes_locatable(ax)\n cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n \n if not logScale: #set exponent base for colorbar formatter\n CB = plt.colorbar(im, cax=cax)\n CB.formatter.set_powerlimits((0, 2))\n CB.update_ticks()\n else:\n CB = plt.colorbar(im, cax=cax, ticks=locator)\n \n if noAxis:\n ax.set(title=VarNames[ivar],\n xlabel=\"Z (cm)\", ylabel=\"R (cm)\") \n plt.axis('off')\n ax.get_xaxis().set_visible(False)\n ax.get_yaxis().set_visible(False)\n \n ftitle = VarNames[ivar] + \"_T%07.1f\" %(time)\n if type(theta)!=type(None):\n ftitle = ftitle + \"_A%5.1f-%5.1f\" %(theta[0],theta[-1])\n fig.savefig(dirname + \"plots/\" + ftitle + '.png',dpi=200, bbox_inches=\"tight\")\n #cax.yaxis.major.locator.set_params(nbins=8) \n \n if sliceX!= None and sliceX[-1]<=xmax and sliceX[0]>=xmin: # plot slice along Z\n plotSliceX()\n \n if sliceZ!= None and sliceZ[-1]<=zmax and sliceZ[0]>=zmin: # plot slice along X\n plotSliceZ()\n \n # line2d(0,2,0,8,X,Z,dat,1.5e18)\n \n if not noShow: \n plt.show()\n \n return()","def _nd_plot_samples(self, **kwargs):\n\n from pesummary.core.plots.plot import _make_comparison_corner_plot as plotfunc\n\n plotkwargs = kwargs.copy()\n\n args = [self._samples]\n plotkwargs[\"corner_parameters\"] = self.parameters\n if \"latex_labels\" not in kwargs:\n plotkwargs[\"latex_labels\"] = self.latex_labels\n\n if \"plot_percentile\" not in kwargs:\n plotkwargs[\"plot_percentile\"] = False\n\n # get ranges for each parameter to set figure axes extents\n if \"range\" not in kwargs:\n range = []\n for param in self.parameters:\n range.append(\n [\n np.min(\n [samps[param].min() for samps in self._samples.values()]\n ),\n np.max(\n [samps[param].max() for samps in self._samples.values()]\n ),\n ]\n )\n plotkwargs[\"range\"] = range\n\n # default to not show quantile lines\n plotkwargs.setdefault(\"quantiles\", None)\n\n # set default injection line color\n plotkwargs.setdefault(\"truth_color\", \"k\")\n\n # set injection parameter values\n if self.injection_parameters is not None:\n injpars = [\n self.injection_parameters[p] - self.parameter_offsets[p]\n for p in self.parameters\n if self.injection_parameters[p] is not None\n ]\n if len(injpars) == self._num_parameters:\n plotkwargs[\"truths\"] = injpars\n\n # create plot\n with DisableLogger():\n fig = plotfunc(*args, **plotkwargs)\n\n # turn frame off on legend\n fig.legends[0].set_frame_on(False)\n\n return fig","def plot(self,nwins,show=True,fname=None):\n #setup figure and axes ...\n maxcolumns = 5\n ncolumns = min(maxcolumns,nwins)\n nrows = np.ceil(nwins/ncolumns).astype(int)\n figsize = ncolumns * 1.2, nrows * 1.2 + 0.5\n fig, axes = plt.subplots(nrows, ncolumns, figsize=figsize)\n for ax in axes[:-1,:].flatten():\n for xlabel_i in ax.get_xticklabels():\n xlabel_i.set_visible(False)\n for ax in axes[:,1:].flatten():\n for ylabel_i in ax.get_yticklabels():\n ylabel_i.set_visible(False)\n\n #loop through tapers and plot them\n for itaper in range( min(self.nwins, nwins) ):\n evalue = self.eigenvalues[itaper]\n coeffs = self._coeffs(itaper)\n ax = axes.flatten()[itaper]\n grid = MakeGridDH(coeffs)\n ax.imshow(grid)\n ax.set_title('concentration: {:2.2f}'.format(evalue))\n fig.tight_layout(pad=0.5)\n\n if show: plt.show()\n if fname is not None:\n fig.savefig(fname)","def plotit(X,Y=None,clf=None, markers = ('s','o'), hold = False, transform = None):\n eps=1e-6\n minx, maxx = np.min(X[:,0]), np.max(X[:,0])\n miny, maxy = np.min(X[:,1]), np.max(X[:,1])\n \n if clf is not None:\n npts = 150\n x = np.linspace(minx,maxx,npts)\n y = np.linspace(miny,maxy,npts)\n t = np.array(list(itertools.product(x,y)))\n if transform is not None:\n t = transform(t)\n z = clf(t)\n z = np.reshape(z,(npts,npts)).T \n extent = [minx,maxx,miny,maxy]\n plt.contour(x,y,z,[-1+eps,0,1-eps],linewidths = [2],colors=('b','k','r'),extent=extent, label='f(x)=0')\n #plt.imshow(np.flipud(z), extent = extent, cmap=plt.cm.Purples, vmin = -2, vmax = +2); plt.colorbar()\n plt.pcolormesh(x, y, z,cmap=plt.cm.Purples,vmin=-2,vmax=+2);plt.colorbar()\n plt.axis([minx,maxx,miny,maxy])\n \n if Y is not None:\n \n plt.scatter(X[Y==1,0],X[Y==1,1],marker = markers[0], c = 'y', s = 30)\n plt.scatter(X[Y==-1,0],X[Y==-1,1],marker = markers[1],c = 'c', s = 30)\n plt.xlabel('$x_1$')\n plt.ylabel('$x_2$') \n \n else:\n plt.scatter(X[:,0],X[:,1],marker = '.', c = 'k', s = 5)\n if not hold:\n plt.grid()\n plt.show()","def individual_plot(z, cf_lvls, cl_lvls, cline_label_format=cline_label_format_default, log_scale=False ,\n extend=\"neither\"):\n map_plot.drawcoastlines(linewidth=.4)\n\n if log_scale:\n norm = colors.LogNorm(vmin=cf_lvls[0], vmax=cf_lvls[-1])\n else:\n norm = None\n\n if extend == 'neither':\n contour_fills = map_plot.contourf(x_grid, y_grid, z, cf_lvls, cmap=color_map, norm=norm)\n else:\n contour_fills = map_plot.contourf(x_grid, y_grid, z, cf_lvls, cmap=color_map, norm=norm, extend=extend)\n contour_lines = plt.contour(x_grid, y_grid, z, cl_lvls, colors='0.1', linewidths=1)\n\n # Label levels with specially formatted floats\n plt.rcParams['font.weight'] = 'bold'\n plt.clabel(contour_lines, fmt=cline_label_format, inline=1, fontsize=9, colors='k')\n plt.rcParams['font.weight'] = 'normal'\n\n return contour_fills","def make_plots(river_name, values):\n\n fig, ax = plt.subplots()\n\n ax.set_title(river_name)\n ax.set_ylabel('Cubic Feet per Second (cfs)')\n ax.axhline(y=sum(values)/len(values), color='red', linestyle=\"--\", label='Average Water Level')\n ax.legend()\n\n ax.set_ylim(min(values) * .95, max(values) * 1.05)\n\n return ax","def plot_multidimensional_function_slices(\n func: Callable[[np.ndarray], NDAorTuple],\n slice_loc: np.ndarray,\n bounds: Union[np.ndarray, List[Tuple[float, float]]],\n input_names: Optional[List[str]] = None,\n obs_points: Optional[Union[np.ndarray, List[np.ndarray]]] = None,\n input_scales: Optional[List[PLOT_SCALE]] = None,\n output_scale: PLOT_SCALE = \"linear\",\n output_label: str = \"Objective Value\",\n size: float = 3,\n slice_2d_resolution: int = 50,\n # slide_1d_resolution: int = 100,\n func_returns_confidence_intervals: bool = False,\n) -> Tuple[plt.Figure, np.ndarray]:\n # Input validation checks\n assert output_scale in [\"linear\", \"log\", \"symlog\"]\n\n def func_return_just_mean(x):\n \"\"\"\n If the supplied function is a predictor returning lower and upper confidence bounds as well as mean,\n return just the mean prediction. If not, return the function value evaluated at x.\n \"\"\"\n return func(x)[0] if func_returns_confidence_intervals else func(x)\n\n n_dims: int = len(bounds)\n # If multiple batches of points supplied as a list in obs_points, make a colour palette\n n_batches = len(obs_points) if isinstance(obs_points, (list, tuple)) else 1\n scatter_colours = sns.color_palette(\"viridis\", n_colors=n_batches)\n # If input_scales not specified, default all to 'linear'\n input_scales = input_scales if input_scales else [\"linear\"] * n_dims # type: ignore # auto\n # Keep track of contour sets returned for each axis\n contour_sets = []\n\n # Construct axes\n fig = plt.figure(figsize=(size * n_dims, size * n_dims))\n axes, cbar_axes = make_lower_triangular_axis_grid_with_colorbar_axes(\n fig=fig, num_cols=n_dims, num_colorbars=2, share_y_on_diagonal=True\n )\n\n # Keep a running minimum and maximum of function values in 2D slices\n func_values_min: float = np.inf\n func_values_max: float = -np.inf\n\n with sns.axes_style(\"darkgrid\"):\n for i in range(n_dims): # i iterates over the rows of the plots\n for j in range(n_dims): # j iterates over the columns of the plots\n ax = axes[i, j]\n # 1D-slice plots along the diagonal\n if i == j:\n if func_returns_confidence_intervals:\n plot_1d_slice_through_function_with_confidence_intervals(\n func, # type: ignore\n dim=i,\n slice_loc=slice_loc,\n slice_bounds=bounds[i],\n ax=ax,\n x_scale=input_scales[i],\n )\n else:\n plot_1d_slice_through_function(\n func, # type: ignore\n dim=i,\n slice_loc=slice_loc,\n slice_bounds=bounds[i],\n ax=ax,\n x_scale=input_scales[i],\n )\n ax.set_yscale(output_scale)\n\n # lower triangle\n elif i > j:\n dim_x, dim_y = j, i\n # Compute the data for the 2D slice plots\n xx, yy, func_values_slice = calc_2d_slice(\n func=func_return_just_mean, # type: ignore # auto\n dim_x=dim_x,\n dim_y=dim_y,\n slice_loc=slice_loc,\n slice_bounds_x=bounds[dim_x],\n slice_bounds_y=bounds[dim_y],\n x_scale=input_scales[dim_x],\n y_scale=input_scales[dim_y],\n resolution=slice_2d_resolution,\n )\n # Plot the 2D slice\n _, im = plot_2d_slice_from_arrays(\n xx,\n yy,\n func_values_slice,\n ax=ax,\n x_scale=input_scales[dim_x],\n y_scale=input_scales[dim_y],\n output_scale=output_scale,\n )\n contour_sets.append(im)\n # Keep a running minimum and maximum of function values in slices\n func_values_min = min(func_values_min, func_values_slice.min()) # type: ignore\n func_values_max = max(func_values_max, func_values_slice.max()) # type: ignore\n # Scatter points on the slices if given\n if obs_points is not None: # pragma: no cover\n if isinstance(obs_points, np.ndarray):\n # If just one array given, scatter with the colour reflecting objective value\n ax.scatter(\n obs_points[:, dim_x], obs_points[:, dim_y], color=scatter_colours[0], s=20, zorder=15\n )\n else:\n assert isinstance(obs_points, (list, tuple))\n # If multiple arrays given, colour the points according to the batch number\n for batch_num, batch_arr in enumerate(obs_points):\n ax.scatter(\n batch_arr[:, dim_x],\n batch_arr[:, dim_y],\n color=scatter_colours[batch_num],\n s=25,\n lw=0.0,\n alpha=0.8,\n zorder=15,\n )\n # Add axis labels\n if input_names is not None: # pragma: no cover\n # If plot in the first column (but not first row), add a y_label\n if i != 0 and j == 0:\n axes[i, j].set_ylabel(input_names[i])\n # If plot is at the bottom, add an x_label\n if i == n_dims - 1:\n axes[i, j].set_xlabel(input_names[j])\n if i >= j:\n # Remove redundant ticks on inner plots\n if i != n_dims - 1:\n axes[i, j].xaxis.set_visible(False)\n if j != 0:\n axes[i, j].yaxis.set_visible(False)\n # # Prune the upper-most tick from plot, so that the ticks don't overlap each other between plots\n # ax.yaxis.set_major_locator(ticker.MaxNLocator(prune='upper'))\n ax.tick_params(axis=\"both\", which=\"major\", labelsize=9)\n ax.tick_params(axis=\"both\", which=\"minor\", labelsize=6)\n # Update the colour limits of the slice plots\n for contour_set in contour_sets:\n contour_set.set_clim(vmin=func_values_min, vmax=func_values_max)\n # Add the colourbars\n if n_dims > 1:\n # make a colourbar for the contour plots\n cb1 = fig.colorbar(contour_sets[-1], cax=cbar_axes[0], aspect=50)\n cb1.set_label(output_label)\n cbar_axes[0].yaxis.set_ticks_position(\"left\")\n # make a colourbar for different batches\n if n_batches > 1: # pragma: no cover\n cb2 = matplotlib.colorbar.ColorbarBase( # type: ignore # auto\n cbar_axes[1],\n cmap=matplotlib.colors.ListedColormap(scatter_colours),\n boundaries=[x - 0.5 for x in range(n_batches + 1)],\n ticks=list(range(n_batches)),\n spacing=\"proportional\",\n )\n cb2.set_label(\"Batch Number\")\n else:\n cbar_axes[1].set_visible(False)\n return fig, axes","def visualizeObs():\n fcontourf(fObs, [-2, 2], [-1, 1], [0, 10])","def build_scatter_plots(dep_vars, cont_corr_data, continous, combined_data, drop_outliers=True, tps=False,\n replacement=0.01):\n\n for i, column in enumerate(dep_vars.columns):\n # Calculating number of graphs\n cont_cols = continous.columns.to_list()\n if i != len(dep_vars.columns) - 1 and not tps:\n cont_cols = cont_cols + dep_vars.columns.to_list()[i + 1:]\n if not tps:\n num_rows = len(cont_cols) + 2 * len(continous.columns) + 2 * len(dep_vars.columns.to_list()[i + 1:])\n else:\n num_rows = 3 * len(cont_cols)\n fig = make_subplots(rows=num_rows, subplot_titles=range(num_rows))\n row_num = 1\n for value in cont_cols:\n # Getting data\n scatter_data = pd.DataFrame({\"x\": combined_data[value],\n \"y\": combined_data[column]})\n if drop_outliers:\n # Apply 3 sigma rule\n scatter_data = scatter_data.loc[rem_outliers(scatter_data[\"x\"])]\n scatter_data = scatter_data.loc[rem_outliers(scatter_data[\"y\"])]\n # Replace zeros with little values and create logarithmic variation\n scatter_data[\"y\"] = scatter_data[\"y\"].apply(lambda x: replacement if x == 0 else x)\n log_y = scatter_data[\"y\"].apply(lambda x: log(x))\n log_x = scatter_data[\"x\"].apply(lambda x: replacement if x == 0 else x).apply(lambda x: log(x))\n\n # Creating graph on initial data, stating first pearson p-value, kendall p-value and sample size\n p_value_pearson = cont_corr_data.loc[f\"pearson_{column}\", value]\n p_value_kendall = cont_corr_data.loc[f\"kendall_{column}\", value]\n sample_size = int(cont_corr_data.loc[f\"sample_size_{column}\", value])\n text = f\"pearson: {p_value_pearson:.4f}, kendall: {p_value_kendall:.4f}, sample: {sample_size}\"\n build_scatter_subplot(fig, scatter_data[\"x\"], scatter_data[\"y\"], row_num, text, value, column)\n row_num = row_num + 1\n\n # Creating graph on log data, stating second pearson p-value, kendall p-value and sample size\n p_value_pearson = cont_corr_data.loc[f\"log_pearson_{column}\", value]\n p_value_kendall = cont_corr_data.loc[f\"kendall_{column}\", value]\n sample_size = int(cont_corr_data.loc[f\"sample_size_{column}\", value])\n text = f\"log({column}) pearson: {p_value_pearson:.4f}, kendall: {p_value_kendall:.4f}, \" \\\n f\"sample: {sample_size}\"\n build_scatter_subplot(fig, scatter_data[\"x\"], log_y, row_num, text, value, column)\n row_num = row_num + 1\n\n # Creating graph on log indep variable, stating first pearson p-value, kendall p-value and sample size\n p_value_pearson = cont_corr_data.loc[f\"log({value})_{column}_pearson\", value]\n sample_size = int(cont_corr_data.loc[f\"sample_size_{column}\", value])\n text = f\"log({value}) pearson: {p_value_pearson:.4f}, sample: {sample_size}\"\n build_scatter_subplot(fig, log_x, scatter_data[\"y\"], row_num, text, value, column)\n row_num = row_num + 1\n if not tps:\n fig.update_layout(height=4800, width=600)\n else:\n fig.update_layout(height=600, width=600)\n fig.update_layout(showlegend=False, plot_bgcolor='rgba(0,0,0,0)')\n fig.write_html(f'figures/scatter_{column}_{time.time()}.html', auto_open=True)","def plot_violins(times, func_names, output_file):\n fig, axes = plt.subplots(1, len(func_names))\n for name, func_time, ax in zip(func_names, times, axes):\n sns.violinplot(y=func_time, ax=ax)\n ax.set_ylabel('Time (ms)')\n ax.set_title(name)\n fig.tight_layout()\n fig.savefig(output_file)","def contours_and_data(epoch, model, features, filters, figname, fgal=0.5,\n idx=-1, data='s82', N=60000):\n if data == 's82':\n # fetch Stripe 82 data\n X, Xcov = fetch_prepped_s82data(epoch, fgal, features, filters)\n Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, features,\n filters, use_single=False)\n sind = np.abs(Xcoadd[:, idx]) < 0.03\n gind = np.abs(Xcoadd[:, idx]) > 0.03\n\n else:\n # fetch DR10 data\n X, Xcov = fetch_prepped_dr10data(N, fgal, features, filters)\n sind = np.abs(X[:, idx]) < 0.145\n gind = np.abs(X[:, idx]) > 0.145\n\n # unpickle the XD model\n if type(model) == str: \n f = open(model, 'rb')\n model = cPickle.load(f)\n f.close()\n\n fs = 5\n ms = 1\n lsize = 20\n idx = [[0, -1], [2, 3], [3, 4]]\n xlim = [(18., 22), (-0.5, 2.5), (-0.5, 2)]\n ylim = [(-0.1, 0.5), (-0.5, 2.5), (-0.5, 1.5)]\n xlab = ['psfmag $r$', 'modelmag $g-r$', 'modelmag $r-i$']\n ylab = ['psfmag - modelmag $r$', 'modelmag $r-i$', 'modelmag $i-z$']\n\n f = pl.figure(figsize=(3 * fs, 3 * fs))\n Nstar = len(np.where(model.fixed_means[:, idx] != np.inf)[0])\n pl.subplots_adjust(wspace=0.3)\n for i in range(1, 10):\n k = (i - 1) % 3\n if i < 4:\n ind = np.arange(X.shape[0], dtype=np.int)\n rng = range(model.n_components)\n elif 3 < i < 7:\n ind = sind\n rng = range(Nstar)\n else:\n ind = gind\n rng = range(Nstar, model.n_components)\n ax = pl.subplot(3, 3, i)\n for j in rng:\n if model.alpha[j] > 1.e-3:\n draw_ellipse(model.mu[j, idx[k]],\n model.V[j, idx[k]][:, idx[k]],\n scales=[2], ec='k', fc='gray', alpha=0.2)\n pl.plot(X[ind][::10, idx[k][0]],\n X[ind][::10, idx[k][1]], '.k',ms=ms)\n pl.xlim(xlim[k])\n pl.ylim(ylim[k])\n pl.xlabel(xlab[k], fontsize=lsize)\n pl.ylabel(ylab[k], fontsize=lsize)\n if ('psf' in ylab[k]) & ('model' in ylab[k]):\n ytick = ['%0.1f' % v for v in np.linspace(-.1, 0.4, 6)]\n ytick[0] = ''\n ax.set_yticklabels(ytick)\n if i == 1:\n s = 'All'\n elif i == 3:\n s = '\"Stars\"'\n else:\n s = '\"Galaxies\"'\n ax.text(-.3, 0.5, s, ha='center', va='center', fontsize=25,\n rotation='vertical', transform=ax.transAxes)\n f.savefig(figname, bbox_inches='tight')","def scatterPlot(data_dict, features_list, name=\"image.png\", f1_name=\"feature 1\", f2_name=\"feature 2\"):\n data = featureFormat(data_dict, features_list)\n poi, finance_features = targetFeatureSplit(data)\n for i, p in enumerate(poi):\n non_poi = plt.scatter(finance_features[i][0], finance_features[i][1])\n if p:\n poi = plt.scatter(finance_features[i][0], finance_features[i][1], color=\"r\", marker=\"*\") \n plt.xlabel(f1_name)\n plt.ylabel(f2_name)\n plt.legend((non_poi, poi), ('Non POI', 'POI'),loc='upper left')\n plt.title('scatter plot of 2 features')\n plt.savefig(name)\n plt.show()","def plot_spatial_correlations_with_significance(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/timeseries/INDICIES/', indicies = ['SAM', 'IPO', 'DMI'], seaice_source = 'nsidc'):\n for temp_res, temp_decomp, dt in itertools.product(temporal_resolution, temporal_decomposition, detrend):\n plot_spatial_correlation_with_significance(anomlous = temp_decomp, temporal_resolution = temp_res, detrend = dt, temp_decomp = temp_decomp, imagefolder = 'images/correlations/spatial/', n = 1, seaice_source = seaice_source)","def set_subplots(self, names: [str]) -> None:\n self._plots = list()\n self._logger.debug(\"running\")\n if len(names) < 1:\n return\n r = len(names)\n c = 1\n for i in range(0, r):\n self._plots.append((names[i], (r, c, i + 1), True))\n self._logger.debug(\"done\")","def CII_vs_CO(**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\n fig,ax1 = plt.subplots()\n L_CII = getattr(GR,'L_[CII]158_sun')\n L_CO = getattr(GR,'L_CO(1-0)_sun')\n Zsfr = getattr(GR,'Zsfr')\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\n sc = ax1.scatter(np.log10(L_CO)-10, np.log10(L_CII)-10, marker='o', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\n vmin=np.log10(0.05), vmax=np.log10(3.1), \\\n s=10, alpha=0.8)#, label='SIGAME 100Mpc_arepoPDF')\n # print('Min Zsfr in Simba sample: ',np.min(Zsfr))\n # print('indices with L_CO < 1e0:')\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\n 4:lL_CII.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=8,zorder=10)\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\n\n GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0],grid_ext=p.grid_exts[1])\n L_CII = getattr(GR,'L_[CII]158_sun')\n L_CO = getattr(GR,'L_CO(1-0)_sun')\n Zsfr = getattr(GR,'Zsfr')\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\n lL_CO = np.append(lL_CO,np.array([6.1,5]))\n lL_CII = np.append(lL_CII,np.array([8.9,9.7]))\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\n # s=10, alpha=0.8, label='SIGAME 25Mpc_arepoPDF')\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\n 4:lL_CII.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='deepskyblue',linestyles='dotted',levels=6)\n CS.collections[0].set_label('SIGAME 25Mpc_arepoPDF')\n\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\n L_CII = getattr(GR,'L_[CII]158_sun')\n L_CO = getattr(GR,'L_CO(1-0)_sun')\n Zsfr = getattr(GR,'Zsfr')\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\n lL_CO = np.append(lL_CO,np.array([-2.2,4.7]))\n lL_CII = np.append(lL_CII,np.array([8,9.3]))\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\n # s=10, alpha=0.8, label='SIGAME v3 Simba-%s' % (p.sim_runs[0].replace('_','').replace('Mpc','')))\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\n 4:lL_CII.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='brown',levels=8,zorder=5,linestyles='dashed')\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\n\n # Observations\n K16 = pd.read_pickle('data/observations/AHIMSA_sample_lit')\n K16_LCII = K16['[CII]158_Lsun']\n K16_LCO = K16['CO(1-0)_Lsun']\n ax1.plot(np.log10(K16_LCO), np.log10(K16_LCII), '>', color='grey', ms=6, fillstyle='none',alpha=0.8, mew=1,zorder=0,\\\n label='Mixed type galaxies [Kamenetzky+16]')\n\n C15 = pd.read_pickle('data/observations/DGS_Cormier_2015')\n C15_LCII = C15['L_[CII]158']\n C15_LCO = C15['L_CO(1-0)']\n C15_Z = C15['Z']\n # L_ul = C15['L_[CII]158'][(C15['L_[CII]158'] < 0) & (C15['L_CO(1-0)'] > 0)]\n # if len(L_ul) > 0:\n # ax1.plot(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul),'s',ms=5,mew=0,color='grey',alpha=0.8)\n # ax1.errorbar(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul), elinewidth=1,\\\n # uplims=np.ones(len(L_ul)),yerr=np.ones(len(L_ul))*1,color='grey',alpha=0.8,lw=0)\n ax1.scatter(np.log10(C15_LCO), np.log10(C15_LCII), marker='+', c=np.log10(C15_Z), cmap='viridis', zorder=0,\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\n s=100, lw=3, alpha=0.8, label='Dwarf galaxies [Cormier+15]')\n\n A17 = pd.read_pickle('data/observations/xCOLD_GASS_Accurso_2017')\n A17 = A17.loc[np.argwhere(A17['L_CO(1-0)'].values > 0).flatten()]\n ax1.scatter(A17['L_CO(1-0)'],A17['L_[CII]158'], marker='d', c=np.log10(A17['Z']), cmap='viridis', zorder=0,\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\n s=50, lw=0, alpha=0.8, label='COLD GASS [Accurso+17]') #c=np.log10(A17['Z']), \n\n CII_obs = np.log10(np.append(K16_LCII.values,C15_LCII.values))\n CO_obs = np.log10(np.append(K16_LCO.values,C15_LCO.values))\n CII_obs = np.append(CII_obs,A17['L_[CII]158'].values)\n CO_obs = np.append(CO_obs,A17['L_CO(1-0)'].values)\n index = np.argwhere((CII_obs > 0) & (CO_obs > 0)).flatten()\n CII_obs = CII_obs[index]\n CO_obs = CO_obs[index]\n\n x = np.linspace(0, 7, 100)\n fit = LinearRegression().fit(CO_obs.reshape(-1, 1),\\\n CII_obs.reshape(-1, 1))\n L_fit = fit.predict(x.reshape(-1, 1))\n ax1.plot(x, L_fit, color='black', linestyle='--', label='Log-linear fit to observations')\n\n ax1.set_ylabel('log ' + getlabel('[CII]158'))\n ax1.set_xlabel('log ' + getlabel('CO(1-0)'))\n plt.colorbar(sc,label=r'log $\\langle$Z$\\rangle_{\\rm SFR}$ [Z$_{\\rm \\odot}$]')\n\n handles, labels = ax1.get_legend_handles_labels()\n print(labels) # labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))\n # handles = [handles[_] for _ in [2,4,3,5,0,6,7,1]]\n # labels = [labels[_] for _ in [2,4,3,5,0,6,7,1]]\n handles = [handles[_] for _ in [2,4,3,5,6,0,1]]\n labels = [labels[_] for _ in [2,4,3,5,6,0,1]]\n plt.legend(handles,labels,loc='lower left',fontsize=10.,frameon=True)\n\n ax1.set_xlim([-3,6.2])\n ax1.set_ylim([4,10])\n\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \n plt.savefig(p.d_plot + 'luminosity/CO_vs_CII%s%s.png' % (p.grid_ext,p.table_ext), dpi=300)","def CombinedPlotHelper(self,minc=70,maxc=120,num=25):\n levels = np.linspace(minc,maxc,num+1)\n title = textwrap.dedent(\"\"\"\\\n Orography difference between LGM and Modern ICE-5G data\n using {0} meter contour interval\"\"\").format((maxc-minc)/num)\n plt.figure()\n ax = plt.subplot(111)\n contourset = plt.contourf(self.difference_in_ice_5g_orography,\n levels=levels,hold=True)\n cbar = plt.colorbar()\n cbar.ax.set_ylabel('Orography difference in meters')\n plt.contour(self.difference_in_ice_5g_orography,levels=contourset.levels,\n colors='black',hold=True)\n ufcntr = plt.contourf(self.difference_in_ice_5g_orography,\n levels=[np.min(self.difference_in_ice_5g_orography),minc],\n colors='white',\n hatches=['/'],hold=True)\n ofcntr = plt.contourf(self.difference_in_ice_5g_orography,\n levels=[maxc,np.max(self.difference_in_ice_5g_orography)],\n colors='white',\n hatches=['\\\\'],hold=True)\n ufartists,uflabels = ufcntr.legend_elements() #@UnusedVariable\n ofartists,oflabels = ofcntr.legend_elements() #@UnusedVariable\n uflabels=['Difference $\\\\leq {0}$'.format(minc)]\n oflabels=['${0} <$ Difference'.format(maxc)]\n artists = ufartists + ofartists\n labels = uflabels + oflabels\n plt.title(title)\n pts.set_ticks_to_zero()\n axbounds = ax.get_position()\n #Shrink box by 5%\n ax.set_position([axbounds.x0,axbounds.y0 + axbounds.height*0.05,\n axbounds.width,axbounds.height*0.95])\n ax.legend(artists,labels,loc='upper center',\n bbox_to_anchor=(0.5,-0.025),fancybox=True,ncol=2)\n #if self.save:\n #plt.savefig('something')\n print(\"Combined plot created\")","def display_contour(cnts, img):\n for c in cnts:\n cv2.drawContours(img, [c], -1, (0, 255, 0), 2)\n\n img = imutils.resize(img, width=1200)\n cv2.imshow(\"Image\", img)\n cv2.moveWindow('Image', 30, 30)\n cv2.waitKey(0)","def cov_plot(self, matrix, station=\"\", hour = \"\", date=\"\" , averaged = \"\" ):\n var = self.var_dics[self.var]['name'] \n fig,ax = plt.subplots()\n date = self.date_prettyfier(date)\n hour = str(hour).replace('0','00:00').replace('1','12:00')\n if not averaged:\n title = \"Stat: \" + station + ', H: ' + hour + ', Date: ' + date + ', ' + var\n filename = 'Cov_' + station + '_hour_' + hour.replace(':','') + '_date_' + str(date).replace('/','') + '_' +var\n \n elif averaged :\n title = var.replace('temp','Temp.') + \" , Stat: \" + station + ', H: ' + str(hour) + ', Date: ' + str(date)\n filename ='Cov_' + station + '_hour_' + str(hour).replace(':','') + '_averaged_' + str(date).replace('/','') + '_' + var \n\n plt.title(title.replace('_', ' ' ), y=1.03, fontsize = self.font-2)\n\n num = len(matrix[0,:])\n Num = range(num)\n\n vmin, vmax = -3, 3\n if self.var == 'direction': \n vmin, vmax = -10, 10\n color_map= plt.imshow(matrix, interpolation= 'nearest', cmap = 'RdYlBu', vmin = vmin, vmax = vmax ) # nearest serves for discreete grid # cmaps blue, seismic \n plt.ylim(-0.5, 15.5)\n plt.xlim(-0.5, 15.5)\n plt.xticks(Num, Num)\n plt.xlabel('Pressure level an_dep [hPa]', fontsize = self.font-2)\n plt.yticks(Num, Num)\n plt.ylabel('Pressure level fg_dep [hPa]', fontsize = self.font-2)\n ax.set_xticklabels(labels = self.pretty_pressure, fontsize = self.font-4, rotation=45)\n ax.set_yticklabels(labels = self.pretty_pressure, fontsize = self.font-4)\n\n bar = plt.colorbar()\n bar.ax.set_ylabel(\"Covariance\", fontsize = self.font)\n \n for i in Num: # creating text labels\n for j in Num:\n value = '{0:.2f}'.format(matrix[i,j])\n text = ax.text( j,i, value , ha = 'center' , va = 'center', color = 'black', fontsize = 5)\n\n if not os.path.isdir('plots/covariances/'+station): os.mkdir('plots/covariances/'+station)\n plt.savefig('plots/covariances/' + station + '/' + filename + '.png', bbox_inches='tight', dpi = 200)\n plt.close()","def visualize(self, features, targets, my_title=\"untitled\"):\n try:\n import matplotlib.pyplot as plt\n except:\n import matplotlib\n matplotlib.use('Agg')\n import matplotlib.pyplot as plt\n\n plt.figure(figsize=(6,4))\n #plt.contourf(self.out, cmap=plt.cm.Paired)\n plt.axis('off')\n plt.scatter(features[:, 0], features[:, 1], c=self.out)\n plt.title(my_title)\n plt.savefig(f'{my_title}.png')","def plot_xy(nc,params,tms,lev=None):\n \n import matplotlib.pyplot as plt\n import ggWRFutils as gW\n from datetime import datetime\n import numpy as np\n wvar={}\n for p in params:\n if p != 'Times':\n if p=='WS10':\n wvar[p]=np.sqrt(nc.variables['U10'][:]**2+nc.variables['U10'][:]**2)\n elif p=='UV10': \n wvar['U10']=nc.variables['U10'][:,:,:] \n wvar['V10']=nc.variables['V10'][:,:,:] \n elif p=='UV':\n wvar['U']=nc.variables['U'][:,lev,:,:] \n wvar['V']=nc.variables['V'][:,lev,:,:] \n elif len(nc.variables[p].shape) > 3:\n wvar[p]=nc.variables[p][:,lev,:,:] \n else: \n wvar[p]=nc.variables[p][:] \n Nx,Ny,Nz,lon,lat,dx,dy=gW.getDimensions(nc)\n for p in params:\n if params[p]=='pcolor':\n plt.pcolor(lon,lat,wvar[p][tms,:,:],shading='flat')\n plt.colorbar()\n if params[p]=='contourf':\n plt.contourf(lon,lat,wvar[p][tms,:,:],50)\n plt.colorbar()\n if params[p]=='contour':\n plt.contourf(lon,lat,wvar[p][tms,:,:])\n plt.colorbar()\n if params[p]=='quiver':\n if p=='UV10':\n plt.quiver(lon[::10,::10],lat[::10,::10],wvar['U10'][tms,::10,::10],wvar['V10'][tms,::10,::10],units='width')\n elif p=='UV':\n plt.quiver(lon,lat,wvar['U'][tms,:,:],wvar['V'][tms,:,:])\n plt.hold(True)\n plt.xlim(lon.min(),lon.max())\n plt.ylim(lat.min(),lat.max())\n fig=plt.gcf()\n return fig","def visualize(**images):\r\n n_images = len(images)\r\n plt.figure(figsize=(20, 8))\r\n for idx, (name, image) in enumerate(images.items()):\r\n plt.subplot(1, n_images, idx + 1)\r\n plt.xticks([])\r\n plt.yticks([])\r\n # get title from the parameter names\r\n plt.title(name.replace('_', ' ').title(), fontsize=20)\r\n plt.imshow(image)\r\n plt.show()","def plot_HDres_histos_vs_z(\n df,\n nameout,\n threshold_var=\"class0\",\n threshold_list=[0.5, 0.7, 0.9],\n threshold_sign=\">\",\n):\n\n P = df[df[\"class0\"] > 0.5]\n Ias = df[df[\"target\"] == 0]\n\n TP = P[P[\"target\"] == 0]\n FP = P[P[\"target\"] != 0]\n\n sel_TP_dic = {}\n sel_FP_dic = {}\n for t in threshold_list:\n if threshold_sign == \">\":\n sel_TP_dic[t] = TP[TP[threshold_var] > t]\n sel_FP_dic[t] = FP[FP[threshold_var] > t]\n else:\n sel_TP_dic[t] = TP[TP[threshold_var] < t]\n sel_FP_dic[t] = FP[FP[threshold_var] < t]\n\n plt.clf()\n cm = CMAP\n fig = plt.figure(figsize=(14, 14))\n # gs = gridspec.GridSpec(4, 2, width_ratios=[3, 1], height_ratios=[2, 2, 1, 1])\n # gs.update(wspace=0.1, hspace=0.3)\n\n # # gridspec init\n # ax00 = plt.subplot(gs[0, 0]) # Hres Ia\n # ax10 = plt.subplot(gs[1, 0], sharex=ax00) # Hres CC\n # ax20 = plt.subplot(gs[2:, 0], sharex=ax00) # efficiency\n # ax01 = plt.subplot(gs[0, 1], sharey=ax00) # histo Ia\n # ax11 = plt.subplot(gs[1, 1], sharey=ax10) # histo CC\n # ax21 = plt.subplot(gs[2, 1]) # histo x1\n # ax31 = plt.subplot(gs[3, 1]) # histo c\n gs = gridspec.GridSpec(3, 3, height_ratios=[2, 2, 1])\n # gs.update(wspace=0.2, hspace=0.1)\n\n # gridspec init\n ax00 = plt.subplot(gs[0, 0:2]) # Hres Ia\n ax10 = plt.subplot(gs[1, 0:2], sharex=ax00) # Hres CC\n ax20 = plt.subplot(gs[2, 0]) # redshift dist\n ax01 = plt.subplot(gs[0, 2], sharey=ax00) # histo Ia\n ax11 = plt.subplot(gs[1, 2], sharey=ax10) # histo CC\n ax21 = plt.subplot(gs[2, 1]) # histo x1\n ax31 = plt.subplot(gs[2, 2]) # histo c\n\n # lines\n ax00.plot([0, 1.2], np.zeros(len([0, 1.2])), \"k:\")\n ax10.plot([0, 1.2], np.zeros(len([0, 1.2])), \"k:\")\n\n mubins = np.arange(-2, 2 + 0.1, 0.1)\n\n # Hres w. histogram\n def HRwhisto(\n df, sel_dic, ax_left, ax_right, threshold_sign, ylabel=\"TP\", visible=False\n ):\n if ylabel == \"TP\":\n sntyp = \"Ia\"\n else:\n sntyp = \"CC\"\n ax_left.scatter(\n df[\"SIM_REDSHIFT_CMB\"],\n df[\"delmu\"],\n c=df[\"class0\"],\n cmap=CMAP,\n vmin=0.5,\n vmax=1,\n s=8,\n )\n ax_left.errorbar(\n df[\"SIM_REDSHIFT_CMB\"],\n df[\"delmu\"],\n yerr=df[\"delmu_err\"],\n color=\"gray\",\n zorder=0,\n fmt=\"none\",\n marker=\"none\",\n )\n\n ax_left.set_ylim(-2, 2)\n ax_left.set_xlim(0, 1.2)\n ax_left.set_ylabel(f\"{ylabel} residual\", fontsize=18)\n ax_left.tick_params(labelsize=14)\n plt.setp(ax_left.get_xticklabels(), visible=visible)\n if visible is True:\n ax_left.set_xlabel(\"simulated redshift\", fontsize=18)\n for t in threshold_list:\n sel = sel_dic[t]\n n_SNe = len(sel)\n ax_right.hist(\n sel[\"delmu\"],\n orientation=\"horizontal\",\n histtype=\"step\",\n color=cm(t),\n bins=mubins,\n density=True,\n label=f\"{n_SNe} {sntyp} {threshold_sign} {t}\",\n lw=2,\n )\n ax_right.legend(loc=\"lower center\", prop={\"size\": 13})\n plt.setp(ax_right.get_yticklabels(), visible=False)\n plt.setp(ax_right.get_xticklabels(), visible=False)\n ax_right.plot(\n [ax_right.get_xlim()[0], ax_right.get_xlim()[1]],\n np.zeros(len([ax_right.get_xlim()[0], ax_right.get_xlim()[1]])),\n \"k:\",\n )\n\n HRwhisto(TP, sel_TP_dic, ax00, ax01, threshold_sign, ylabel=\"TP\", visible=False)\n HRwhisto(FP, sel_FP_dic, ax10, ax11, threshold_sign, ylabel=\"FP\", visible=True)\n\n # z histos\n n, bins_to_use, tmp = ax20.hist(\n Ias[\"SIM_REDSHIFT_CMB\"], histtype=\"step\", color=\"black\", bins=15, lw=3\n )\n\n for t in threshold_list:\n sel_TP = sel_TP_dic[t]\n sel_FP = sel_FP_dic[t]\n ax20.hist(\n sel_TP[\"SIM_REDSHIFT_CMB\"], histtype=\"step\", color=cm(t), bins=bins_to_use\n )\n ax20.hist(\n sel_FP[\"SIM_REDSHIFT_CMB\"],\n histtype=\"step\",\n color=cm(t),\n linestyle=\"--\",\n bins=bins_to_use,\n )\n ax20.set_xlim(0, 1.2)\n ax20.tick_params(labelsize=14)\n ax20.set_xlabel(\"simulated redshift\", fontsize=18)\n\n # hist stretch\n n, bins_to_use, tmp = ax21.hist(Ias[\"x1\"], color=\"black\", histtype=\"step\", lw=3)\n for t in threshold_list:\n sel_TP = sel_TP_dic[t]\n ax21.hist(\n sel_TP[\"x1\"],\n orientation=\"vertical\",\n histtype=\"step\",\n color=cm(t),\n bins=bins_to_use,\n lw=2,\n )\n ax21.set_xlabel(\"x1\", fontsize=18)\n ax21.yaxis.set_label_position(\"right\")\n ax21.set_xlim(-3, 3)\n ax21.tick_params(labelsize=14)\n # color histo\n n, bins_to_use, tmp = ax31.hist(Ias[\"c\"], color=\"black\", histtype=\"step\", lw=3)\n for t in threshold_list:\n sel_TP = sel_TP_dic[t]\n ax31.hist(\n sel_TP[\"c\"],\n orientation=\"vertical\",\n histtype=\"step\",\n color=cm(t),\n bins=bins_to_use,\n lw=2,\n )\n ax31.set_xlabel(\"c\", fontsize=18)\n ax31.set_xlim(-1, 1)\n ax31.tick_params(labelsize=14)\n ax31.yaxis.set_label_position(\"right\")\n\n gs.tight_layout(fig)\n plt.savefig(nameout)\n plt.close()\n del fig","def plotContour(verts, data, comp=2):\n z = data[:,:,comp].flatten()\n x = verts[:,0]\n y = verts[:,1]\n\n xi = np.linspace(x.min(), x.max(), x.size)\n yi = np.linspace(y.min(), y.max(), y.size)\n zi = griddata(x,y,z, xi,yi, interp='nn') #'nn' #NOTE: for irregularly spaced data\n\n plt.figure()\n #plt.gca().set_aspect('equal')\n compDict = {0:'X',1:'Y',2:'Z'}\n C = compDict[comp]\n\n CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')\n CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.jet)\n plt.title( C + ' Displacement')\n plt.xlabel('Distance [m]')\n plt.ylabel('Distance [m]')\n cb = plt.colorbar()\n cb.set_label('[m]')","def subject(s):\n ir(s)\n contours(s, colour='green')\n matplotlib.pyplot.xlim(0, config['surveys']['atlas']['fits_width'])\n matplotlib.pyplot.ylim(0, config['surveys']['atlas']['fits_height'])","def plot_case(casepath, casename, framelist, max_level, subtitle):\n\n # paths\n repo_path = os.path.dirname(os.path.abspath(__file__))\n casepath = os.path.abspath(casepath)\n figspath = os.path.join(repo_path, \"figs\")\n casefigpath = os.path.join(figspath, casename, \"level{:02}\".format(max_level))\n\n # check figs folder\n if not os.path.isdir(casefigpath):\n os.makedirs(casefigpath)\n\n for fno in framelist:\n outputfile = os.path.join(casefigpath, \"depth{:04}.png\".format(fno))\n plot_single_frame(casepath, fno, max_level, subtitle, outputfile)","def test_arguments_7(self):\n fig = plt.figure()\n ax = fig.add_subplot(projection='ternary')\n tn0, tn1, tn2 = get_spiral()\n ax.plot(tn0, tn1, tn2, 'C3:', tn1, tn2, tn0)","def create_plots(self):\n shutil.rmtree(self.param.path, ignore_errors=True)\n os.makedirs(self.param.path)\n\n ids = list(range(len(self.signs)))\n\n \"\"\"True positives\"\"\"\n values, kinds = self.get_evaluations(ids)\n plots.create_plot(\n kinds,\n [e[0] for e in values], # True positives\n save_dir=self.param.path,\n y_label=\"number_tp\",\n file_name=\"number_tp\",\n title=\"Amount of true positives\",\n )\n\n # Only signs with at least one detection!\n ids = [i for i, _ in enumerate(self.signs) if self.signs[i].evaluate()[2] > 0]\n values, kinds = self.get_evaluations(ids)\n\n \"\"\"Distance\"\"\"\n plots.create_plot(\n kinds,\n values=[e[2] for e in values], # Distances\n save_dir=self.param.path,\n y_label=\"distance\",\n file_name=\"distance\",\n title=\"Distance\",\n )\n \"\"\"Precision\"\"\"\n plots.create_plot(\n kinds,\n # Precision signs with at least one detection are used, e[0]+e[1] > 0)\n values=[e[0] / (e[0] + e[1]) for e in values],\n save_dir=self.param.path,\n y_label=\"precision\",\n file_name=\"precision\",\n title=\"Precision\",\n )","def surface_contacts(struc,SASA):\n\tplot_single = False\n\n\tcmap_cutoff = 5.0 # nm\n\tsasa_cutoff = 0.5\n\t# identify surface residues\n\tS = []\n\tN = np.array(range(struc.n_residues))\n\tfor r in N:\n\t\tind = struc.topology.select('resid '+str(r))\n\t\tSASA_per_res = np.sum(SASA[0][ind[0]:ind[-1]],axis=0)\n\t\tif SASA_per_res > sasa_cutoff:\n\t\t\tS.append(SASA_per_res)\n\t\telse:\n\t\t\tS.append(0.0)\n\t#import ipdb; ipdb.set_trace()\n\tsurf_res = N[np.where(np.array(S) > 0)]\n\tcmap = contact_maps(struc.atom_slice(struc.topology.select('name CA'))[0].xyz[0])\n\tfor n in N:\n\t\tfor m in N:\n\t\t\tif (n not in surf_res or m not in surf_res) or n < m:\n\t\t\t\tcmap[n][m] = 0\n\t\t\telif cmap[n][m] < cmap_cutoff:\n\t\t\t\tcmap[n][m] = 1\n\tif plot_single == True:\n\t\tfor it,rn in enumerate(resnames):\n\t\t\tif rn in hydrophobic:\n\t\t\t\tplt.axhline(y=it,c='grey',linewidth=2)\n\t\t\t\tplt.axvline(x=it,c='grey',linewidth=2)\n\t\t\telif rn in hydrophilic:\n\t\t\t\tplt.axhline(y=it,c='g',linewidth= 2)\n\t\t\t\tplt.axvline(x=it,c='g',linewidth= 2)\n\t\t\telif rn in poscharge:\n\t\t\t\tplt.axhline(y=it,c='b',linewidth= 2)\n\t\t\t\tplt.axvline(x=it,c='b',linewidth= 2)\n\t\t\telif rn in negcharge:\n\t\t\t\tplt.axhline(y=it,c='r',linewidth= 2)\n\t\t\t\tplt.axvline(x=it,c='r',linewidth= 2)\n\t\t\telse:\n\t\t\t\tprint \"unknown restype:\", rn\n\t\tscat = np.array(zip(np.where(np.array(cmap) > 0)[0], np.where(np.array(cmap) > 0)[1])).T\n\t\tplt.scatter(scat[0], scat[1],marker='s',c='k',s=90)\n\t\t#plt.imshow(cmap,cmap='Greys')\n\t\tplt.show()\n\treturn cmap","def plot_data(samples, snps, indels, filename):\n categories = ['SNP Precision', 'SNP Sensitivity', 'Indel Precision',\n 'Indel Sensitivity']\n\n plot_data = defaultdict(list)\n for sample in samples:\n # Here we add the data, in the SAME ORDER as categories\n for data in snps[sample]:\n # We want to use the data without a threshold\n if data['Threshold'] == 'None':\n plot_data[sample].append(data['Precision'])\n plot_data[sample].append(data['Sensitivity'])\n for data in indels[sample]:\n # We want to use the data without a threshold\n if data['Threshold'] == 'None':\n plot_data[sample].append(data['Precision'])\n plot_data[sample].append(data['Sensitivity'])\n\n fig = go.Figure(data=[\n go.Bar(\n name=sample,\n x=categories,\n y=plot_data[sample]) for sample in samples])\n\n fig.update_layout(barmode='group')\n fig.write_html(filename)","def display4(*args):\n #-------------------- unpack\n twiss_func = args[0]\n cos_like = args[1]\n sin_like = args[2]\n lat_plot = args[3]\n #-------------------- beta x,y & dispersion x\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)] # Abszisse\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)] # beta x\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)] # beta y\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)] # dispersion x\n#-------------------- longitudinal trajectories\n z1= [cos_like(i,'s') for i in range(cos_like.nbpoints)]\n cz= [cos_like(i,'cz') for i in range(cos_like.nbpoints)]\n cdp= [cos_like(i,'cdp') for i in range(cos_like.nbpoints)]\n\n z2= [sin_like(i,'s') for i in range(sin_like.nbpoints)]\n sz= [sin_like(i,'sz') for i in range(sin_like.nbpoints)]\n sdp= [sin_like(i,'sdp') for i in range(sin_like.nbpoints)]\n #-------------------- lattice viseo\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\n #-------------------- figure frame\n width=14; height=7.6\n # fighdr = 'lattice version = {}, input file = {}'.format(PARAMS['lattice_version'],PARAMS['input_file'])\n fig = plt.figure(num=1,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\n\n #-------------------- beta functions\n splot211=plt.subplot(211)\n splot211.set_title('beta x,y')\n # mapping box\n splot211.text(0.01, 1.1, UTIL.FLAGS.get('mapping'),transform=splot211.transAxes,fontsize=8,bbox=dict(boxstyle='round',facecolor='wheat',alpha=0.5),verticalalignment='top')\n # function plots\n plt.plot(s,bx, label=r\"$\\beta$x [m]\", color='black', linestyle='-')\n plt.plot(s,by, label=r\"$\\beta$y [m]\", color='red', linestyle='-')\n plt.plot(s,dx, label=r'$\\eta_x$ [m]' , color='green', linestyle='-') # dispersion x\n vscale=splot211.axis()[3]*0.25\n viseoz = [x*vscale for x in vis_ordinate]\n plt.plot(vis_abszisse,viseoz,label='',color='black')\n plt.plot(vis_abszisse,vzero,color='green',linestyle='--')\n # zero line\n splot211.plot(vis_abszisse,vzero,color='green',linestyle='--')\n plt.legend(loc='lower right',fontsize='x-small')\n\n #-------------------- longitudinal tracks z, dP/P\n # ax_l = left abszisse\n ax_l=plt.subplot(212)\n # ax_l=plt.subplot(10,1,(7,9))\n ax_l.set_title('synchrotron oscillation')\n ax_l.set_ylabel(r\"z [mm]\")\n ax_l.tick_params(axis='y', colors='green')\n ax_l.yaxis.label.set_color('green')\n ax_l.plot(z1,cz,label='C',color='green')\n ax_l.plot(z2,sz,label='S',color='green',linestyle=':')\n plt.legend(loc='lower left',fontsize='x-small')\n # ax_r = right abszisse\n ax_r = ax_l.twinx()\n ax_r.set_ylabel(r'$\\Delta$p/p [%]')\n ax_r.tick_params(axis='y', colors='red')\n ax_r.yaxis.label.set_color('red')\n ax_r.plot(z2,cdp,label='C',color='red')\n ax_r.plot(z2,sdp,label='S',color='red',linestyle=':')\n ax_r.plot(vis_abszisse,vzero,color='red', linestyle='--')\n plt.legend(loc='lower right',fontsize='x-small')\n # lattice elements\n vscale=ax_l.axis()[3]*0.25\n viseoz = [x*vscale for x in vis_ordinate]\n ax_l.plot(vis_abszisse,viseoz,label='',color='black')\n ax_l.plot(vis_abszisse,vzero,color='green',linestyle='--')","def plot_all_regression_multiple_scatter(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/regressions/single_multiple/', indicies = ['SAM', 'IPO', 'DMI', 'ENSO']):\n for temp_res, temp_decomp, dt in itertools.product(temporal_resolution, temporal_decomposition, detrend):\n plot_regression_multiple_scatter(anomlous = temp_decomp, temporal_resolution = temp_res, detrend = dt, temp_decomp = temp_decomp, imagefolder = 'images/regressions/single_multiple/', n = 1)","def plot_sbc(theta_samples, theta_test, param_names, bins=25, figsize=(8, 4), interval=0.99, show=True, filename=None, font_size=12):\n\n # Plot settings\n plt.rcParams['font.size'] = font_size\n N = int(theta_test.shape[0])\n\n # Determine n_subplots dynamically\n n_row = int(np.ceil(len(param_names) / 6))\n n_col = int(np.ceil(len(param_names) / n_row))\n\n # Initialize figure\n f, axarr = plt.subplots(n_row, n_col, figsize=figsize)\n if n_row > 1:\n axarr = axarr.flat\n\n # Compute ranks (using broadcasting) \n ranks = np.sum(theta_samples < theta_test[:, np.newaxis, :], axis=1)\n \n # Compute interval\n endpoints = binom.interval(interval, N, 1 / (bins))\n\n # Plot histograms\n for j in range(len(param_names)):\n \n # Add interval\n axarr[j].axhspan(endpoints[0], endpoints[1], facecolor='gray', alpha=0.3)\n axarr[j].axhline(np.mean(endpoints), color='gray', zorder=0, alpha=0.5)\n \n sns.histplot(ranks[:, j], kde=False, ax=axarr[j], color='#a34f4f', bins=bins, alpha=0.95)\n \n axarr[j].set_title(param_names[j])\n axarr[j].spines['right'].set_visible(False)\n axarr[j].spines['top'].set_visible(False)\n if j == 0:\n axarr[j].set_xlabel('Rank statistic')\n axarr[j].get_yaxis().set_ticks([])\n axarr[j].set_ylabel('')\n \n f.tight_layout()\n # Show, if specified\n if show:\n plt.show()\n # Save if specified\n if filename is not None:\n f.savefig(\"figures/{}_SBC.png\".format(filename), dpi=600, bbox_inches='tight')\n return f","def crossplot(self, analytes=None, lognorm=True,\n bins=25, filt=False, samples=None, subset=None, **kwargs):\n if analytes is None:\n analytes = [a for a in self.analytes if 'Ca' not in a]\n\n if subset is None:\n subset = 'All_Samples'\n\n self.get_focus(filt, samples, subset)\n\n numvars = len(analytes)\n fig, axes = plt.subplots(nrows=numvars, ncols=numvars,\n figsize=(12, 12))\n fig.subplots_adjust(hspace=0.05, wspace=0.05)\n\n for ax in axes.flat:\n ax.xaxis.set_visible(False)\n ax.yaxis.set_visible(False)\n\n if ax.is_first_col():\n ax.yaxis.set_ticks_position('left')\n if ax.is_last_col():\n ax.yaxis.set_ticks_position('right')\n if ax.is_first_row():\n ax.xaxis.set_ticks_position('top')\n if ax.is_last_row():\n ax.xaxis.set_ticks_position('bottom')\n\n cmlist = ['Blues', 'BuGn', 'BuPu', 'GnBu',\n 'Greens', 'Greys', 'Oranges', 'OrRd',\n 'PuBu', 'PuBuGn', 'PuRd', 'Purples',\n 'RdPu', 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']\n\n # isolate nominal_values for all analytes\n focus = {k: nominal_values(v) for k, v in self.focus.items()}\n # determine units for all analytes\n udict = {a: unitpicker(np.nanmean(focus[a])) for a in analytes}\n # determine ranges for all analytes\n rdict = {a: (np.nanmin(focus[a] * udict[a][0]),\n np.nanmax(focus[a] * udict[a][0])) for a in analytes}\n\n for i, j in zip(*np.triu_indices_from(axes, k=1)):\n # get analytes\n ai = analytes[i]\n aj = analytes[j]\n\n # remove nan, apply multipliers\n pi = focus[ai][~np.isnan(focus[ai])] * udict[ai][0]\n pj = focus[aj][~np.isnan(focus[aj])] * udict[aj][0]\n\n # make plot\n if lognorm:\n axes[i, j].hist2d(pj, pi, bins,\n norm=mpl.colors.LogNorm(),\n cmap=plt.get_cmap(cmlist[i]))\n axes[j, i].hist2d(pi, pj, bins,\n norm=mpl.colors.LogNorm(),\n cmap=plt.get_cmap(cmlist[j]))\n else:\n axes[i, j].hist2d(pj, pi, bins,\n cmap=plt.get_cmap(cmlist[i]))\n axes[j, i].hist2d(pi, pj, bins,\n cmap=plt.get_cmap(cmlist[j]))\n\n axes[i, j].set_ylim(*rdict[ai])\n axes[i, j].set_xlim(*rdict[aj])\n\n axes[j, i].set_ylim(*rdict[aj])\n axes[j, i].set_xlim(*rdict[ai])\n\n # diagonal labels\n for a, n in zip(analytes, np.arange(len(analytes))):\n axes[n, n].annotate(a + '\\n' + udict[a][1], (0.5, 0.5),\n xycoords='axes fraction',\n ha='center', va='center')\n axes[n, n].set_xlim(*rdict[a])\n axes[n, n].set_ylim(*rdict[a])\n\n # switch on alternating axes\n for i, j in zip(range(numvars), itertools.cycle((-1, 0))):\n axes[j, i].xaxis.set_visible(True)\n for label in axes[j, i].get_xticklabels():\n label.set_rotation(90)\n axes[i, j].yaxis.set_visible(True)\n\n return fig, axes","def scatter_plot(dictionary, variable1, variable2): \r\n plt.clf() # Deletes the previous plot \r\n plt.scatter(dictionary[variable1], dictionary[variable2], color='purple')\r\n plt.title('Scatter plot between ' + variable1 + \" and \" + variable2)\r\n plt.xlabel(variable1)\r\n plt.ylabel(variable2)\r\n plt.savefig(variable1 + \" and \" + variable2)","def plot_implicit(ax, fn, bbox=(-2.5, 2.5)):\n xmin, xmax, ymin, ymax, zmin, zmax = bbox*3\n A = np.linspace(xmin, xmax, 100) # resolution of the contour\n B = np.linspace(xmin, xmax, 15) # number of slices\n A1, A2 = np.meshgrid(A, A) # grid on which the contour is plotted\n for z in B: # plot contours in the XY plane\n X, Y = A1, A2\n Z = fn(X, Y, z)\n ax.contour(X, Y, Z+z, [z], zdir='z')\n # [z] defines the only level to plot for this contour for\n # this value of z\n\n for y in B: # plot contours in the XZ plane\n X, Z = A1, A2\n Y = fn(X, y, Z)\n ax.contour(X, Y+y, Z, [y], zdir='y')\n\n for x in B: # plot contours in the YZ plane\n Y, Z = A1, A2\n X = fn(x, Y, Z)\n ax.contour(X+x, Y, Z, [x], zdir='x')\n\n ax.tick_params(labelsize=6) # smaller label size\n\n # must set plot limits because the contour will likely extend\n # way beyond the displayed level. Otherwise matplotlib extends\n # the plot limits to encompass all values in the contour.\n ax.set_zlim3d(zmin, zmax)\n ax.set_xlim3d(xmin, xmax)\n ax.set_ylim3d(ymin, ymax)","def _plot_imgs_and_ctrs_np(self, config) -> None:\n imgs = config['imgs']\n ctrs = config['ctrs']\n orig_slices = config['slices']\n plane = config['plane']\n title = config['title']\n draw_only_ctrs = config['draw_only_ctrs']\n file_name_prefix = config['file_name_prefix']\n labels = config['labels']\n\n create_folder(self._output_folder)\n if plane != PlaneTypes.ALL: # Draw single plane\n # This should plot a single image with all the contours overlayed\n first_img_shape = imgs[0].shape\n # Validate the size of the images\n for c_img in imgs:\n if c_img.shape != first_img_shape:\n raise Exception('The shape of the images must be the same')\n\n slices = get_slices(orig_slices, imgs[0], plane)\n for c_slice in slices:\n draw_slice = should_display_slice(ctrs, c_slice, plane, draw_only_ctrs)\n if draw_slice:\n fig, ax = plt.subplots(1, len(imgs), squeeze=True, figsize=(8, 8))\n for img_idx, c_img in enumerate(imgs):\n img_slice = get_proper_plane(c_img, plane, c_slice)\n ctrs_slice = [get_proper_plane(np_ctr, plane, c_slice) for np_ctr in ctrs]\n if len(imgs) > 1:\n plot_slice(img_slice, ctrs_slice, ax[img_idx], labels)\n else:\n plot_slice(img_slice, ctrs_slice, ax, labels)\n c_title = F'{title} {plane.value} {c_slice:04d}'\n file_name = F'{file_name_prefix}_{plane.value}_{c_slice:04d}'\n plt.title(c_title, fontsize=20)\n pylab.savefig(join(self._output_folder, F'{file_name}.jpg'), bbox_inches='tight')\n else:\n if len(imgs) != 1:\n raise Exception('The number of image allowed for Plane type ALL must be 1')\n # In this case it should plot 3 images, one for each plane. Here we force it to plot\n # only the middle slice\n c_img = imgs[0]\n plt.subplots(1, 3, squeeze=True, figsize=(8 * 3, 8))\n for id_plane, plane in enumerate([PlaneTypes.AXIAL, PlaneTypes.SAGITTAL, PlaneTypes.CORONAL]):\n ax = plt.subplot(1, 3, id_plane + 1)\n c_slice = get_slices(SliceMode.MIDDLE, c_img, plane)[0]\n img_slice = get_proper_plane(c_img, plane, c_slice)\n ctrs_slice = [get_proper_plane(np_ctr, plane, c_slice) for np_ctr in ctrs]\n plot_slice(img_slice, ctrs_slice, ax, labels)\n c_title = F'{title} ALL {c_slice:04d}'\n plt.title(c_title, fontsize=20)\n\n file_name = F'{file_name_prefix}_{plane.value}_{c_slice:04d}'\n pylab.savefig(join(self._output_folder, F'{file_name}.jpg'), bbox_inches='tight')\n\n self._close_figure()","def plot_all_regression_scatter(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/regressions/single/', indicies = ['SAM', 'IPO', 'DMI', 'ENSO'],seaice_source='nsidc'):\n for temp_res, temp_decomp, dt in itertools.product(temporal_resolution, temporal_decomposition, detrend):\n plot_regression_scatter(anomlous = temp_decomp, temporal_resolution = temp_res, detrend = dt, temp_decomp = temp_decomp, imagefolder = 'images/regressions/single/', n = 1,seaice_source=seaice_source)","def plot_all_regression_multiple_spatial(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/regressions/spatial_multiple/', indicies = ['SAM', 'IPO', 'DMI', 'ENSO']):\n for temp_res, temp_decomp, dt in itertools.product(temporal_resolution, temporal_decomposition, detrend):\n plot_regression_multiple_spatial(anomlous = temp_decomp, temporal_resolution = temp_res, detrend = dt, temp_decomp = temp_decomp, imagefolder = 'images/regressions/spatial_multiple/', n = 1)","def plot_figure4():\n column_titles = [\"5th percentile\", \"32nd percentile\", \"50th percentile\"]\n\n fixed_height_ref = 100.\n fixed_height_id = list(fixed_heights).index(fixed_height_ref)\n\n plot_item0 = {\n 'data': nc.variables[\"p_fixed_perc5\"][fixed_height_id, :, :]*1e-3,\n 'contour_fill_levels': np.linspace(0, .03, 21),\n 'contour_line_levels': sorted([.003]+list(np.linspace(0, .03, 21)[::5])),\n 'contour_line_label_fmt': '%.3f',\n 'colorbar_ticks': np.linspace(0, .03, 21)[::5],\n 'colorbar_tick_fmt': '{:.3f}',\n 'colorbar_label': 'Power density [$kW/m^2$]',\n }\n plot_item1 = {\n 'data': nc.variables[\"p_fixed_perc32\"][fixed_height_id, :, :]*1e-3,\n 'contour_fill_levels': np.linspace(0, .45, 21),\n 'contour_line_levels': sorted([.04]+list(np.linspace(0, .45, 21)[::4])),\n 'contour_line_label_fmt': '%.2f',\n 'colorbar_ticks': np.linspace(0, .45, 21)[::4],\n 'colorbar_tick_fmt': '{:.2f}',\n 'colorbar_label': 'Power density [$kW/m^2$]',\n }\n plot_item2 = {\n 'data': nc.variables[\"p_fixed_perc50\"][fixed_height_id, :, :]*1e-3,\n 'contour_fill_levels': np.linspace(0, 1, 21),\n 'contour_line_levels': sorted([.1]+list(np.linspace(0, 1, 21)[::4])),\n 'contour_line_label_fmt': '%.2f',\n 'colorbar_ticks': np.linspace(0, 1, 21)[::4],\n 'colorbar_tick_fmt': '{:.2f}',\n 'colorbar_label': 'Power density [$kW/m^2$]',\n }\n\n plot_items = [plot_item0, plot_item1, plot_item2]\n\n eval_contour_fill_levels(plot_items)\n plot_panel_1x3_seperate_colorbar(plot_items, column_titles)","def _add_contours(\n *coords,\n zi=None,\n ax=None,\n contours=[],\n cmap=DEFAULT_CONT_COLORMAP,\n vmin=0.0,\n extent=None,\n **kwargs\n):\n # get the contour levels\n percentiles = kwargs.pop(\"percentiles\", True)\n levels = contours or kwargs.get(\"levels\", None)\n pcolor, contour, contourf = get_axis_density_methods(ax)\n if percentiles and not isinstance(levels, int):\n # plot individual percentile contours\n _cs = plot_Z_percentiles(\n *coords,\n zi=zi,\n ax=ax,\n percentiles=levels,\n extent=extent,\n cmap=cmap,\n **kwargs\n )\n mappable = _cs\n else:\n # plot interval contours\n if levels is None:\n levels = MaxNLocator(nbins=10).tick_values(zi.min(), zi.max())\n elif isinstance(levels, int):\n levels = MaxNLocator(nbins=levels).tick_values(zi.min(), zi.max())\n else:\n raise NotImplementedError\n # filled contours\n mappable = contourf(\n *coords, zi, extent=extent, levels=levels, cmap=cmap, vmin=vmin, **kwargs\n )\n # contours\n contour(\n *coords, zi, extent=extent, levels=levels, cmap=cmap, vmin=vmin, **kwargs\n )\n return mappable","def plot_data(array_list, params):\n\tkey = array_list[0]\n\tvals = array_list[1]\n\tprint key\n\tprint len(vals)\n\tfigure_id = 1\n\tfor item in params:\n\t\tx_axis = get_by_keys(item['x_keys'], key, vals)\n\t\ty_axis = get_by_keys(item['y_keys'], key, vals)\n\t\tplt.figure(figure_id)\n\t\tplt.plot(x_axis, y_axis)\n\t\tfigure_id = figure_id + 1\n\tplt.show()","def plot_solutions(self, solutions_list, plot_w=False, savefig_filename=None, display=True):\n plt.figure(figsize=(13, 8))\n for s in solutions_list:\n U = s[0]\n method, T = s[1]\n plt.plot(self.x, U[:self.N], label=rf\"$U$ : {method}, $T = {T}$\")\n if plot_w:\n plt.plot(self.x, U[self.N:], label=rf\"$U_t$ : {method}, $T = {T}$\")\n plt.xlabel(r\"$x$\")\n plt.ylabel(r\"$U, U_t$\")\n plt.title(\"Plot of Various Models at Different Times\")\n plt.legend()\n plt.grid()\n if savefig_filename:\n plt.savefig(savefig_filename) if savefig_filename.endswith(\".png\") else plt.savefig(savefig_filename+\".jpg\")\n if display:\n plt.show()","def render(static, tour_indices, save_path):\n\n plt.close('all')\n print('static_shape', static.shape)\n print('tour_indices_shape', tour_indices.shape)\n\n num_plots = 3 if int(np.sqrt(len(tour_indices))) >= 3 else 1\n\n _, axes = plt.subplots(nrows=num_plots, ncols=num_plots,\n sharex='col', sharey='row')\n\n if num_plots == 1:\n axes = [[axes]]\n axes = [a for ax in axes for a in ax]\n\n for i, ax in enumerate(axes):\n\n # Convert the indices back into a tour\n idx = tour_indices[i]\n print('idx0', idx)\n if len(idx.size()) == 1:\n idx = idx.unsqueeze(0)\n print('idx1', idx)\n\n idx = idx.expand(static.size(1), -1)\n print('idx2', idx)\n data = torch.gather(static[i].data, 1, idx).cpu().numpy()\n print('data', data)\n\n start = static[i, :, 0].cpu().data.numpy()\n x = np.hstack((start[0], data[0], start[0]))\n y = np.hstack((start[1], data[1], start[1]))\n print('x', x)\n print('y', y)\n\n # Assign each subtour a different colour & label in order traveled\n idx = np.hstack((0, tour_indices[i].cpu().numpy().flatten(), 0))\n print('idx3', idx)\n where = np.where(idx == 0)[0]\n print('where', where)\n\n for j in range(len(where) - 1):\n\n low = where[j]\n high = where[j + 1]\n\n if low + 1 == high:\n continue\n\n ax.plot(x[low: high + 1], y[low: high + 1], zorder=1, label=j)\n\n ax.legend(loc=\"upper right\", fontsize=3, framealpha=0.5)\n ax.scatter(x, y, s=4, c='r', zorder=2)\n ax.scatter(x[0], y[0], s=20, c='k', marker='*', zorder=3)\n\n ax.set_xlim(0, 1)\n ax.set_ylim(0, 1)\n\n plt.tight_layout()\n plt.savefig(save_path, bbox_inches='tight', dpi=400)","def step_plots(X, labs, steps_vec, perplexity = 30, ncols = 3, \\\n verbose = False, cdict = {1: 'red', 2: 'mediumspringgreen', 3: 'royalblue'}):\n \n # Set dimensions of subplots\n nrows = math.ceil(len(steps_vec) / ncols)\n \n # Configure axes\n axes = []\n fig = plt.figure(figsize = (16, 3 * nrows))\n \n # Run t-SNE\n low_d = tsne(X = X, perplexity = perplexity, niter = np.max(steps_vec), verbose = verbose, optim = \"fastest\")\n \n # Iteratively generate plots\n for step in range(len(steps_vec)):\n axes.append(fig.add_subplot(nrows, ncols, step + 1))\n axes[-1].set_title(\"Perplexity = \" + str(perplexity) + \", Step = \" + str(steps_vec[step]))\n plt.scatter(x = low_d[steps_vec[step], :, 0], y = low_d[steps_vec[step], :, 1], \\\n edgecolor = None, alpha = 0.8, c = np.array(list(map(lambda x: cdict[x], labs))))\n axes[-1].set_xticklabels([])\n axes[-1].set_yticklabels([])\n axes[-1].xaxis.set_ticks_position('none')\n axes[-1].yaxis.set_ticks_position('none')","def plot_all_seaice(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/timeseries/SIC/',seaice_source='nsidc'):\n for n, temp_res, temp_decomp, dt in itertools.product(resolutions, temporal_resolution, temporal_decomposition, detrend):\n plot_seaice_timeseries(anomlous = 'anomalous' == temp_decomp, temporal_resolution = temp_res, spatial_resolution = n, detrend = dt == 'detrended',seaice_source=seaice_source)","def gen_plots(uf_dict, f_dict, min_x, max_x, min_y, max_y, axes, name, histogram, total):\n with PdfPages(name) as pdf:\n total_xuf = []\n total_yuf = []\n total_xf = []\n total_yf = []\n for entry in uf_dict:\n print 'Making plot for ' + entry\n xuf, yuf = zip(*uf_dict[entry])\n fig = plt.figure()\n ax1 = fig.add_subplot(111)\n ax1.scatter(xuf, yuf, c='#ad4851', marker='o', label='initial structures')\n try:\n xf, yf = zip(*f_dict[entry])\n ax1.scatter(xf, yf, c='orange', marker='x', label='selected structures')\n except ValueError:\n xf = []\n yf = []\n plt.legend(loc='upper right')\n plt.title(entry, fontsize=30)\n plt.xlim(min_x, max_x)\n plt.ylim(min_y, max_y)\n plt.xlabel(axes[0], fontsize=20)\n plt.ylabel(axes[1], fontsize=20)\n pdf.savefig(fig)\n plt.close()\n\n if total:\n total_xuf.extend(xuf)\n total_yuf.extend(yuf)\n total_xf.extend(xf)\n total_yf.extend(yf)\n\n if histogram:\n bins = np.linspace(min_y, max_y, num=10)\n plt.hist(yuf, bins, alpha=0.5, color='b', label='initial structures')\n try:\n plt.hist(yf, bins, alpha=0.5, color='orange', label='selected structures')\n except ValueError:\n pass\n plt.legend(loc='upper right')\n plt.title(entry, fontsize=30)\n plt.xlabel(axes[1], fontsize=20)\n plt.ylabel('Frequency', fontsize=20)\n pdf.savefig()\n plt.close()\n\n if total:\n print 'Making composite plot'\n fig = plt.figure()\n ax1 = fig.add_subplot(111)\n ax1.scatter(total_xuf, total_yuf, c='#ad4851', marker='o', label='initial structures')\n ax1.scatter(total_xf, total_yf, c='orange', marker='x', label='selected structures')\n plt.legend(loc='upper right')\n plt.title('Composite Plot', fontsize=30)\n plt.xlim(min_x, max_x)\n plt.ylim(min_y, max_y)\n plt.xlabel(axes[0], fontsize=20)\n plt.ylabel(axes[1], fontsize=20)\n pdf.savefig(fig)\n plt.close()","def _create_initial_plots(\n criterion_history,\n params_history,\n group_to_param_ids,\n group_to_param_names,\n):\n param_plots = []\n for group, param_ids in group_to_param_ids.items():\n param_names = group_to_param_names[group]\n param_group_plot = plot_time_series(\n data=params_history,\n y_keys=param_ids,\n y_names=param_names,\n x_name=\"iteration\",\n title=str(group),\n )\n param_plots.append(param_group_plot)\n\n arranged_param_plots = [Row(plot) for plot in param_plots]\n\n criterion_plot = plot_time_series(\n data=criterion_history,\n x_name=\"iteration\",\n y_keys=[\"criterion\"],\n y_names=[\"criterion\"],\n title=\"Criterion\",\n name=\"criterion_plot\",\n )\n\n plots = [Row(criterion_plot), *arranged_param_plots]\n return plots","def plot3DOS(infileA, nameA, infileB, nameB, infileC, nameC):\n\t\n\tspin1occsA, spin2occsA, energiesA = readfile(infileA)\n\tspin1occsB, spin2occsB, energiesB = readfile(infileB)\n\tspin1occsC, spin2occsC, energiesC = readfile(infileC)\n\t\n\t\n\tspin1names = [spin1occsA, spin1occsB, spin1occsC]\n\tspin2names = [spin2occsA, spin2occsB, spin2occsC]\n\tenergynames = [energiesA, energiesB, energiesC]\n\tnames = [nameA, nameB, nameC]\n\t\n\t# Create figure\n\tfig, axs = plt.subplots(1,3,figsize=(18,12), facecolor='w', edgecolor='k')\n\n\taxs = axs.ravel()\n\tfor i in range(0,3):\n\t\taxs[i].plot(spin1names[i], energynames[i], \"b\", label='Spin 1')\n\t\taxs[i].plot(spin2names[i], energynames[i], \"g\", label='Spin 2')\n\t\t\n\t\taxs[i].set_title(str(names[i]))\n\t\taxs[i].get_xaxis().set_visible(False)\n\taxs[0].set_ylabel('Energy')\n\thandles, labels = axs[0].get_legend_handles_labels()\n\tfig.legend(handles, labels)\n\thandles, labels = axs[0].get_legend_handles_labels()\n\tplt.savefig(\"DOSPLOTS\")\n\tplt.show()"],"string":"[\n \"def make_plots(fig_title, \\n t_csd_x, t_csd_y, t_csd_z, true_csd, \\n ele_x, ele_y, ele_z, pots,\\n k_csd_x, k_csd_y, k_csd_z, est_csd):\\n fig = plt.figure(figsize=(10,16))\\n #True CSD\\n z_steps = 5\\n height_ratios = [1 for i in range(z_steps)]\\n height_ratios.append(0.1)\\n gs = gridspec.GridSpec(z_steps+1, 3, height_ratios=height_ratios)\\n t_max = np.max(np.abs(true_csd))\\n levels = np.linspace(-1*t_max, t_max, 16)\\n ind_interest = np.mgrid[0:t_csd_z.shape[2]:np.complex(0,z_steps+2)]\\n ind_interest = np.array(ind_interest, dtype=np.int)[1:-1]\\n for ii, idx in enumerate(ind_interest):\\n ax = plt.subplot(gs[ii, 0])\\n im = plt.contourf(t_csd_x[:,:,idx], t_csd_y[:,:,idx], true_csd[:,:,idx], \\n levels=levels, cmap=cm.bwr_r)\\n ax.get_xaxis().set_visible(False)\\n ax.get_yaxis().set_visible(False)\\n title = str(t_csd_z[:,:,idx][0][0])[:4]\\n ax.set_title(label=title, fontdict={'x':0.8, 'y':0.8})\\n ax.set_aspect('equal')\\n cax = plt.subplot(gs[z_steps,0])\\n cbar = plt.colorbar(im, cax=cax, orientation='horizontal')\\n cbar.set_ticks(levels[::2])\\n cbar.set_ticklabels(np.around(levels[::2], decimals=2))\\n #Potentials\\n v_max = np.max(np.abs(pots))\\n levels_pot = np.linspace(-1*v_max, v_max, 16)\\n ele_res = int(np.ceil(len(pots)**(3**-1))) \\n ele_x = ele_x.reshape(ele_res, ele_res, ele_res)\\n ele_y = ele_y.reshape(ele_res, ele_res, ele_res)\\n ele_z = ele_z.reshape(ele_res, ele_res, ele_res)\\n pots = pots.reshape(ele_res, ele_res, ele_res)\\n for idx in range(min(5,ele_res)):\\n X,Y,Z = grid(ele_x[:,:,idx], ele_y[:,:,idx], pots[:,:,idx])\\n ax = plt.subplot(gs[idx, 1])\\n im = plt.contourf(X, Y, Z, levels=levels_pot, cmap=cm.PRGn)\\n ax.hold(True)\\n plt.scatter(ele_x[:,:,idx], ele_y[:,:,idx], 5)\\n ax.get_xaxis().set_visible(False)\\n ax.get_yaxis().set_visible(False)\\n title = str(ele_z[:,:,idx][0][0])[:4]\\n ax.set_title(label=title, fontdict={'x':0.8, 'y':0.8})\\n ax.set_aspect('equal')\\n ax.set_xlim([0.,1.])\\n ax.set_ylim([0.,1.])\\n cax = plt.subplot(gs[z_steps,1])\\n cbar2 = plt.colorbar(im, cax=cax, orientation='horizontal')\\n cbar2.set_ticks(levels_pot[::2])\\n cbar2.set_ticklabels(np.around(levels_pot[::2], decimals=2))\\n # #KCSD\\n t_max = np.max(np.abs(est_csd[:,:,:,0]))\\n levels_kcsd = np.linspace(-1*t_max, t_max, 16)\\n ind_interest = np.mgrid[0:k_csd_z.shape[2]:np.complex(0,z_steps+2)]\\n ind_interest = np.array(ind_interest, dtype=np.int)[1:-1]\\n for ii, idx in enumerate(ind_interest):\\n ax = plt.subplot(gs[ii, 2])\\n im = plt.contourf(k_csd_x[:,:,idx], k_csd_y[:,:,idx], est_csd[:,:,idx,0], \\n levels=levels_kcsd, cmap=cm.bwr_r)\\n #im = plt.contourf(k_csd_x[:,:,idx], k_csd_y[:,:,idx], est_csd[:,:,idx,0], \\n # levels=levels, cmap=cm.bwr_r)\\n ax.get_xaxis().set_visible(False)\\n ax.get_yaxis().set_visible(False)\\n title = str(k_csd_z[:,:,idx][0][0])[:4]\\n ax.set_title(label=title, fontdict={'x':0.8, 'y':0.8})\\n ax.set_aspect('equal')\\n cax = plt.subplot(gs[z_steps,2])\\n cbar3 = plt.colorbar(im, cax=cax, orientation='horizontal')\\n cbar3.set_ticks(levels_kcsd[::2])\\n #cbar3.set_ticks(levels[::2])\\n cbar3.set_ticklabels(np.around(levels_kcsd[::2], decimals=2))\\n #cbar3.set_ticklabels(np.around(levels[::2], decimals=2))\\n fig.suptitle(\\\"Lambda,R,CV_Error,RMS_Error,Time = \\\"+fig_title)\\n gs.tight_layout(fig, rect=[0, 0.03, 1, 0.95]) \\n # #Showing\\n #plt.tight_layout()\\n plt.show()\\n return\",\n \"def make_plot(\\n X: np.ndarray, Y: np.ndarray, Z: np.ndarray, \\n int_coords: List[np.ndarray], levels: np.ndarray, \\n color_range: List[str], inter_color: List[Union[str, float]], \\n SAVE: bool, SAVE_NAME: str):\\n color1 = Color(color_range[0])\\n color2 = Color(color_range[1])\\n \\n # List of Color objects, need to convert for matplotlib\\n color_list = list(color1.range_to(color2, len(levels)))\\n \\n contour_colors = []\\n for color in color_list:\\n contour_colors.append(color.hex)\\n\\n fig = plt.figure(figsize = [8, 6])\\n ax1 = fig.add_subplot(111)\\n\\n # Set title\\n fig.suptitle('Quartz Deformation Mechanism Map')\\n \\n CS = ax1.contour(X, Y, Z, levels, colors = contour_colors) \\n \\n # Add intersection lines to plot\\n for coord_arr in int_coords:\\n # Get x indices and y indices\\n x, y = coord_arr.T\\n \\n # Get actual points to plot\\n points_x = X[x, y]\\n points_y = Y[x, y]\\n\\n ax1.plot(points_x, points_y, \\n color = inter_color[0], alpha = inter_color[1])\\n\\n # Labels... hardcoded. May find a way to make this generic\\n sr_labels = [\\n r'$10^{-15}$', r'$10^{-14}$', r'$10^{-13}$', \\n r'$10^{-12}$', r'$10^{-11}$', r'$10^{-10}$', \\n r'$10^{-9}$', r'$10^{-8}$', r'$10^{-7}$', \\n r'$10^{-6}$']\\n\\n # Make legend equal to contour values from sr_labels\\n for i in range(len(sr_labels)):\\n CS.collections[i].set_label(sr_labels[i])\\n\\n # Reverse legend order, set location and title\\n handles, labels = ax1.get_legend_handles_labels()\\n ax1.legend(reversed(handles), reversed(labels), \\n loc = 'upper right', title = r'$\\\\.\\\\epsilon$')\\n\\n # Set log scale and ylim\\n ax1.set_yscale('log')\\n ax1.set_ylim([10 ** -6, 1])\\n \\n # Set labels... could be done better in Illustrator...\\n ax1.set_ylabel(\\n r'Shear Stress $log\\\\left( \\\\frac{\\\\sigma}{\\\\mu}\\\\right)$'\\n )\\n ax1.set_xlabel(\\n r'Homologous Temperature $\\\\left( \\\\frac{T}{T_m} \\\\right)$'\\n )\\n\\n # Plot tick marks\\n ax1.grid()\\n\\n if SAVE:\\n fig.savefig(SAVE_NAME)\\n else:\\n plt.show()\",\n \"def plot_contour(xynodes, stress, mytitle='contour plot'):\\n\\n stress = np.array(stress)\\n fig2 = plt.figure()\\n\\n fig2 = plt.figure()\\n sigma1 = stress[:, 0]\\n sigma2 = stress[:, 1]\\n sigma12 = stress[:, 2]\\n vonmises = np.sqrt(sigma1 ** 2 - sigma1 * sigma2 + sigma2 ** 2 + 3 * sigma12 ** 2)\\n prins1 = (sigma1 + sigma2) / 2 + np.sqrt(((sigma1 - sigma2) / 2) ** 2 + sigma12 ** 2)\\n prins2 = (sigma1 + sigma2) / 2 - np.sqrt(((sigma1 - sigma2) / 2) ** 2 + sigma12 ** 2)\\n\\n sigma1norm = sigma1 / np.max(np.abs(sigma1))\\n sigma2norm = sigma2 / np.max(np.abs(sigma2))\\n sigma12norm = sigma12 / np.max(np.abs(sigma12))\\n vonmisesnorm = vonmises / np.max(np.abs(vonmises))\\n prins1norm = prins1 / np.max(np.abs(prins1))\\n prins2norm = prins2 / np.max(np.abs(prins2))\\n\\n pltsigma = sigma1norm\\n for i, xy in enumerate(xynodes):\\n x = [k[0] for k in xy]\\n y = [k[1] for k in xy]\\n # red for tension, blue for compression\\n pltcolor = 'r' if pltsigma[i] >= 0 else 'b'\\n sigmavalnorm = pltsigma[i] if pltsigma[i] > 0 else pltsigma[i] * -1\\n plt.fill(x, y, pltcolor, alpha=sigmavalnorm)\\n plt.plot(x, y, 'k')\\n\\n title(mytitle)\\n tmpx = [x[0] for k in xynodes for x in k]\\n tmpy = [x[1] for k in xynodes for x in k]\\n plt.xlim([np.min(tmpx) - 3, np.max(tmpx) + 3])\\n plt.ylim([np.min(tmpy) - 3, np.max(tmpy) + 3])\\n plt.show()\",\n \"def plotter(parameter1_range, parameter2_range, value_grid, title_string, x_label, y_label, colormap = plt.cm.viridis):\\n\\n plt.contourf(parameter1_range, parameter2_range, value_grid, cmap=colormap)\\n plt.colorbar()\\n\\n plt.title(title_string)\\n\\n plt.xlabel(x_label)\\n plt.ylabel(y_label)\\n\\n plt.show()\",\n \"def prepare_subplot(fig, point_list, cols, rows, number, lims):\\n ax = fig.add_subplot(cols, rows, number)\\n ax.scatter(list(map(lambda e: e[0], point_list)), list(map(lambda e: e[1], point_list)), s=9)\\n ax.set_xlim(lims[0], lims[1])\\n ax.set_ylim(lims[0], lims[1])\\n return ax\",\n \"def plot_contours(clf, xx, yy, num_features, locs, **params):\\n predict = fix_examples(np.c_[xx.ravel(), yy.ravel()], num_features, locs)\\n Z = clf.predict(predict)\\n Z = Z.reshape(xx.shape)\\n out = plt.contourf(xx, yy, Z, **params)\\n return out\",\n \"def create_four_subplots():\\n pass\",\n \"def plot_sherpa_contours():\\n log.info(\\\"plotting parameters contours obtained from sherpa\\\")\\n # where to take the results, configurations for the individual butterflies\\n instruments = [\\\"fermi\\\", \\\"magic\\\", \\\"hess\\\", \\\"fact\\\", \\\"veritas\\\", \\\"joint\\\"]\\n labels = [\\\"Fermi-LAT\\\", \\\"MAGIC\\\", \\\"H.E.S.S.\\\", \\\"FACT\\\", \\\"VERITAS\\\", \\\"joint fit\\\"]\\n colors = [\\\"#21ABCD\\\", \\\"#FF9933\\\", \\\"#5A4FCF\\\", \\\"#5CC184\\\", \\\"#702963\\\", \\\"crimson\\\"]\\n lss = [\\\"--\\\", \\\"--\\\", \\\"--\\\", \\\"--\\\", \\\"--\\\", \\\"-\\\"]\\n\\n fig, axarr = plt.subplots(1, 3, figsize=(18, 6))\\n\\n # with one loop we realize all the contour plots\\n for instrument, label, color, ls in zip(instruments, labels, colors, lss):\\n\\n path = config.repo_path / f\\\"results/fit/sherpa/{instrument}\\\"\\n\\n contours_path = path / \\\"fit_contours_logparabola.npy\\\"\\n results_path = path / \\\"fit_results_logparabola.yaml\\\"\\n\\n if not path.exists():\\n log.warning(f\\\"Missing: {path} . Skipping.\\\")\\n continue\\n\\n # load the contours and the results of the fit\\n contours = np.load(contours_path).tolist()\\n results = load_yaml(results_path)\\n\\n # define a 2 x 2 matrix to visualise the plot\\n # we will delete one of the subplots and make something like a corner plot\\n # useful variables for the plot\\n ampl_range = contours[\\\"contour_ampl_c1\\\"][\\\"x0_range\\\"]\\n c1_range = contours[\\\"contour_ampl_c1\\\"][\\\"x1_range\\\"]\\n c2_range = contours[\\\"contour_ampl_c2\\\"][\\\"x1_range\\\"]\\n # actual values output of the fit\\n # remember in sherpa notation: (amplitude->ampl, alpha->c1, beta->c2)\\n ampl = results[\\\"parameters\\\"][0][\\\"value\\\"]\\n c1 = results[\\\"parameters\\\"][2][\\\"value\\\"]\\n c2 = results[\\\"parameters\\\"][3][\\\"value\\\"]\\n\\n # axarr[0,0]\\n extent = [ampl_range[0] * 1e9, ampl_range[1] * 1e9, c1_range[0], c1_range[1]]\\n\\n axarr[0].contour(\\n contours[\\\"contour_ampl_c1\\\"][\\\"like_values\\\"],\\n contours[\\\"contour_ampl_c1\\\"][\\\"levels\\\"],\\n origin=\\\"lower\\\",\\n extent=extent,\\n colors=color,\\n linewidths=(2., 1.5, 1.3),\\n linestyles=(\\\"-\\\", \\\"--\\\", \\\":\\\"),\\n )\\n\\n # print actual value\\n axarr[0].plot(ampl, c1, marker=\\\"X\\\", markersize=7, color=color)\\n axarr[0].set_xlabel(\\n r\\\"$f_0 / (\\\\mathrm{TeV} \\\\, \\\\mathrm{cm}^{-2} \\\\mathrm{s}^{-1})$\\\"\\n )\\n axarr[0].set_ylabel(r\\\"$\\\\Gamma$\\\")\\n\\n extent = [ampl_range[0] * 1e9, ampl_range[1] * 1e9, c2_range[0], c2_range[1]]\\n\\n axarr[1].contour(\\n contours[\\\"contour_ampl_c2\\\"][\\\"like_values\\\"],\\n contours[\\\"contour_ampl_c2\\\"][\\\"levels\\\"],\\n origin=\\\"lower\\\",\\n extent=extent,\\n colors=color,\\n linewidths=(2., 1.5, 1.3),\\n linestyles=(\\\"-\\\", \\\"--\\\", \\\":\\\"),\\n )\\n\\n # print actual value\\n axarr[1].plot(ampl, c2, marker=\\\"X\\\", markersize=7, color=color)\\n axarr[1].set_ylabel(r\\\"$\\\\beta$\\\")\\n axarr[1].set_xlabel(\\n r\\\"$f_0 / (\\\\mathrm{TeV} \\\\, \\\\mathrm{cm}^{-2} \\\\, \\\\mathrm{s}^{-1})$\\\"\\n )\\n\\n extent = [c1_range[0], c1_range[1], c2_range[0], c2_range[1]]\\n\\n axarr[2].contour(\\n contours[\\\"contour_c1_c2\\\"][\\\"like_values\\\"],\\n contours[\\\"contour_c1_c2\\\"][\\\"levels\\\"],\\n origin=\\\"lower\\\",\\n extent=extent,\\n colors=color,\\n linewidths=(2., 1.5, 1.3),\\n linestyles=(\\\"-\\\", \\\"--\\\", \\\":\\\"),\\n )\\n\\n # print actual value\\n axarr[2].plot(c1, c2, marker=\\\"X\\\", markersize=7, color=color)\\n axarr[2].set_ylabel(r\\\"$\\\\beta$\\\")\\n axarr[2].set_xlabel(r\\\"$\\\\Gamma$\\\")\\n\\n # axarr[0,1] is for the legend\\n import matplotlib.lines as mlines\\n\\n sigma_1 = mlines.Line2D(\\n [], [], color=\\\"k\\\", marker=\\\"\\\", ls=\\\"-\\\", lw=2., label=r\\\"1 $\\\\sigma$ contour\\\"\\n )\\n sigma_2 = mlines.Line2D(\\n [], [], color=\\\"k\\\", marker=\\\"\\\", ls=\\\"--\\\", lw=1.5, label=r\\\"2 $\\\\sigma$ contour\\\"\\n )\\n sigma_3 = mlines.Line2D(\\n [], [], color=\\\"k\\\", marker=\\\"\\\", ls=\\\":\\\", lw=1.3, label=r\\\"3 $\\\\sigma$ contour\\\"\\n )\\n fermi = mlines.Line2D(\\n [], [], color=\\\"#21ABCD\\\", marker=\\\"\\\", ls=\\\"-\\\", lw=2., label=\\\"Fermi-LAT\\\"\\n )\\n magic = mlines.Line2D(\\n [], [], color=\\\"#FF9933\\\", marker=\\\"\\\", ls=\\\"-\\\", lw=2., label=\\\"MAGIC\\\"\\n )\\n hess = mlines.Line2D(\\n [], [], color=\\\"#5A4FCF\\\", marker=\\\"\\\", ls=\\\"-\\\", lw=2., label=\\\"H.E.S.S.\\\"\\n )\\n fact = mlines.Line2D(\\n [], [], color=\\\"#5CC184\\\", marker=\\\"\\\", ls=\\\"-\\\", lw=2., label=\\\"FACT\\\"\\n )\\n veritas = mlines.Line2D(\\n [], [], color=\\\"#702963\\\", marker=\\\"\\\", ls=\\\"-\\\", lw=2., label=\\\"VERITAS\\\"\\n )\\n joint = mlines.Line2D(\\n [], [], color=\\\"crimson\\\", marker=\\\"\\\", ls=\\\"-\\\", lw=2., label=\\\"joint fit\\\"\\n )\\n axarr[2].legend(\\n handles=[sigma_1, sigma_2, sigma_3, fermi, magic, hess, fact, veritas, joint],\\n loc=3,\\n fontsize=12,\\n )\\n # axarr[2].set_axis_off()\\n\\n plt.tight_layout()\\n filename = \\\"results/figures/sherpa_logparabola_contour.png\\\"\\n fig.savefig(filename)\\n log.info(f\\\"Writing {filename}\\\")\\n fig.savefig(filename)\",\n \"def fig_template(name):\\n fig, axs = plt.subplots(ncols=2, figsize=(9, 4))\\n if name == \\\"circle\\\":\\n for ax in axs:\\n circle = plt.Circle(\\n (0, 0), np.sqrt(2 / np.pi), color=\\\"black\\\", fill=False, zorder=10\\n )\\n ax.add_artist(circle)\\n\\n elif name == \\\"3_circles\\\":\\n centers = np.array([[-1, 1], [1, 0], [-0.5, -0.5]])\\n radii = np.array([1, np.sqrt(6 / np.pi - 1), 1 / 2])\\n for c, r in zip(centers, radii):\\n for ax in axs:\\n circle = plt.Circle(c, r, color=\\\"black\\\", fill=False, zorder=10)\\n ax.add_artist(circle)\\n\\n elif name == \\\"square\\\":\\n p = 0.5 * np.sqrt(2)\\n for ax in axs:\\n ax.plot([-p, p, p, -p, -p], [-p, -p, p, p, -p], color=\\\"black\\\", zorder=10)\\n\\n elif name == \\\"4_squares\\\":\\n for ax in axs:\\n ax.plot([0, 0], [-1, 1], color=\\\"black\\\", zorder=10)\\n ax.plot([-1, 1], [0, 0], color=\\\"black\\\", zorder=10)\\n\\n elif name == \\\"crown\\\" or name == \\\"tricrown\\\":\\n centers = [[0, 0], [0, 0]]\\n radii = [np.sqrt(0.8), np.sqrt(0.8 - 2 / np.pi)]\\n for c, r in zip(centers, radii):\\n for ax in axs:\\n circle = plt.Circle(c, r, color=\\\"black\\\", fill=False, zorder=10)\\n ax.add_artist(circle)\\n\\n elif name == \\\"wavy_lines\\\":\\n freq = 1\\n\\n def fun1(s):\\n return s + np.sin(freq * np.pi * s)\\n\\n def fun2(s):\\n return -s + np.sin(freq * np.pi * s)\\n\\n x = np.linspace(-1, 1)\\n for ax in axs:\\n ax.plot(x, np.clip(fun1(x), -1, 1), color=\\\"black\\\", zorder=10)\\n ax.plot(x, np.clip(fun2(x), -1, 1), color=\\\"black\\\", zorder=10)\\n\\n axs[0].set(xlabel=r\\\"$x_0$\\\", ylabel=r\\\"$x_1$\\\", xlim=[-1, 1], ylim=[-1, 1])\\n axs[0].axis(\\\"equal\\\")\\n axs[1].set(xlabel=r\\\"$x_0$\\\", xlim=[-1, 1], ylim=[-1, 1])\\n axs[1].axis(\\\"equal\\\")\\n\\n return fig, axs\",\n \"def create_plots(self, keys):\\n\\n self.plots = VPlotContainer(resizable = \\\"hv\\\", bgcolor=\\\"lightgray\\\",\\n fill_padding=True, padding = 10)\\n # this looks cryptic, but it is equivalent to\\n # ArrayPlotData(a=[], b=[], c=[])\\n # if the keys are a,b,c. This just does it for all of the keys.\\n self.plotdata = ArrayPlotData(**dict(zip(keys, [[]]*len(keys))))\\n\\n # figure out which key will be the x axis\\n if 'Step' in keys:\\n x = 'Step'\\n elif 'Time (ps)' in keys:\\n x = 'Time (ps)'\\n else:\\n raise ValueError('The reporter published neither the step nor time'\\n 'count, so I don\\\\'t know what to plot on the x-axis!')\\n\\n\\n colors = itertools.cycle(['blue', 'green', 'silver', 'pink', 'lightblue',\\n 'red', 'darkgray', 'lightgreen',])\\n for y in filter(lambda y: y != x, keys):\\n self.plots.add(chaco_scatter(self.plotdata, x_name=x, y_name=y,\\n color=colors.next()))\",\n \"def _2d_plot_samples(self, **kwargs):\\n\\n from pesummary.core.plots.bounded_2d_kde import Bounded_2d_kde\\n\\n # get bounds\\n lows = []\\n highs = []\\n methods = []\\n for param in self.parameters[0:2]:\\n if param in DEFAULT_BOUNDS:\\n lows.append(\\n DEFAULT_BOUNDS[param][\\\"low\\\"]\\n if \\\"low\\\" in DEFAULT_BOUNDS[param]\\n else None\\n )\\n highs.append(\\n DEFAULT_BOUNDS[param][\\\"high\\\"]\\n if \\\"high\\\" in DEFAULT_BOUNDS[param]\\n else None\\n )\\n methods.append(\\n DEFAULT_BOUNDS[param][\\\"method\\\"]\\n if \\\"method\\\" in DEFAULT_BOUNDS[param]\\n else \\\"Reflection\\\"\\n )\\n\\n if self.plottype == \\\"triangle\\\":\\n from pesummary.core.plots.publication import triangle_plot as plotfunc\\n elif self.plottype == \\\"reverse_triangle\\\":\\n from pesummary.core.plots.publication import (\\n reverse_triangle_plot as plotfunc,\\n )\\n else:\\n # contour plot\\n from pesummary.core.plots.publication import (\\n comparison_twod_contour_plot as plotfunc,\\n )\\n\\n # set KDE information\\n kwargs.update(\\n {\\n \\\"kde\\\": Bounded_2d_kde,\\n \\\"kde_kwargs\\\": {\\n \\\"xlow\\\": lows[0],\\n \\\"xhigh\\\": highs[0],\\n \\\"ylow\\\": lows[1],\\n \\\"yhigh\\\": highs[1],\\n },\\n }\\n )\\n\\n # default to not showing data points\\n if \\\"plot_datapoints\\\" not in kwargs:\\n kwargs[\\\"plot_datapoints\\\"] = False\\n\\n if \\\"triangle\\\" in self.plottype:\\n from pesummary.core.plots.bounded_1d_kde import bounded_1d_kde\\n\\n # set KDE informaiton\\n kwargs.update(\\n {\\n \\\"kde_2d\\\": Bounded_2d_kde,\\n \\\"kde_2d_kwargs\\\": {\\n \\\"xlow\\\": lows[0],\\n \\\"xhigh\\\": highs[0],\\n \\\"ylow\\\": lows[1],\\n \\\"yhigh\\\": highs[1],\\n },\\n \\\"kde\\\": bounded_1d_kde,\\n }\\n )\\n\\n kwargs[\\\"kde_kwargs\\\"] = {\\n \\\"x_axis\\\": {\\\"xlow\\\": lows[0], \\\"xhigh\\\": highs[0], \\\"method\\\": methods[0]},\\n \\\"y_axis\\\": {\\\"xlow\\\": lows[1], \\\"xhigh\\\": highs[1], \\\"method\\\": methods[1]},\\n }\\n\\n args = [\\n [samps[self.parameters[0]].values for samps in self._samples.values()],\\n [samps[self.parameters[1]].values for samps in self._samples.values()],\\n ]\\n\\n if \\\"xlabel\\\" not in kwargs:\\n kwargs[\\\"xlabel\\\"] = self.latex_labels[self.parameters[0]]\\n if \\\"ylabel\\\" not in kwargs:\\n kwargs[\\\"ylabel\\\"] = self.latex_labels[self.parameters[1]]\\n\\n if \\\"labels\\\" not in kwargs and len(self.results) > 1:\\n kwargs[\\\"labels\\\"] = list(self._samples.keys())\\n\\n # set injection parameter values\\n if self.injection_parameters is not None:\\n if (\\n self.injection_parameters[self.parameters[0]] is not None\\n and self.injection_parameters[self.parameters[1]] is not None\\n ):\\n kwargname = \\\"truths\\\" if self.plottype == \\\"corner\\\" else \\\"truth\\\"\\n kwargs[kwargname] = [\\n self.injection_parameters[self.parameters[0]]\\n - self.parameter_offsets[self.parameters[0]],\\n self.injection_parameters[self.parameters[1]]\\n - self.parameter_offsets[self.parameters[1]],\\n ]\\n\\n # create plot\\n with DisableLogger():\\n fig = plotfunc(*args, **kwargs)\\n\\n return fig\",\n \"def plot_samples(s):\\r\\n assert len(s[0, :]) >= 2, ('The Phase space dimensions are less than two.', ' Need at least two to plot.')\\r\\n fig = plt.figure(1)\\r\\n if len(s[0, :]) >= 3:\\r\\n ax = fig.add_subplot(111, projection='3d')\\r\\n ax.scatter(s[:, 0], s[:, 1], s[:, 2])\\r\\n fig = plt.figure(2)\\r\\n plt.scatter(s[:, 0], s[:, 1])\\r\\n plt.show()\",\n \"def plot_all(\\n template,\\n v: list,\\n n_e: list,\\n l: list,\\n tm: str,\\n ) -> None:\\n\\n scatterplot(template, tm, v[0], v[1], \\\"Constraint Energy X vs Y\\\", \\\"Constraint Energy X (J)\\\", \\\"Constraint Energy Y (J)\\\")\\n scatterplot(template, tm, v[3], v[4], \\\"Internal Energy X vs Y\\\", \\\"Internal Energy X (J)\\\", \\\"Internal Energy Y (J)\\\")\\n\\n scatterplot(template, tm, n_e, v[6], \\\"Elements Removed vs Hausdorff Distance\\\", \\\"Number of Elements Removed\\\", \\\"Hausdorff Distance\\\")\\n\\n # # Loop through the types of data\\n # for i in range(0, len(v)):\\n\\n # # Plot the histogram\\n # histogram(template, tm, v[i], l[i], \\\"Frequency\\\", \\\"Energy (J)\\\")\\n\\n # # Plot the scatterplot\\n # scatterplot(template, tm, n_e, v[i], l[i], \\\"Energy (J)\\\", \\\"Number of Elements Removed\\\")\\n\\n # # Plot a scatterplot\\n # scatterplot(template, tm, v[0], v[1], \\\"Constraint Energy (J)\\\", \\\"Y-direction\\\", \\\"X-direction\\\")\\n\\n return\",\n \"def ice_plot(curves, **kwargs):\\n \\n n,m = curves.shape\\n \\n #grid of plots\\n gridspec_kw = {'height_ratios':[3, 1], \\n 'wspace':0.0, \\n 'hspace':0.0}\\n fig, (ax_curves, ax_feature) = plt.subplots(2,1, \\n gridspec_kw=gridspec_kw, \\n figsize=kwargs.get('figsize', (6,6)))\\n \\n # top graph - curves\\n for curve in curves:\\n ax_curves.plot(np.arange(m), curve)\\n ax_curves.set_xticklabels([])\\n ax_curves.set_xlim((0, m))\\n ax_curves.set_ylabel(kwargs.get('ylabel', 'decision function $\\\\Delta$'))\\n \\n # bottom graph - feature values\\n if 'feature_values' in kwargs:\\n ax_feature.plot(np.arange(m), kwargs.get('feature_values'))\\n ax_feature.set_xlim((0, m)) \\n ax_feature.set_xlabel(kwargs.get('xlabel', '$X_S$ values'))\\n \\n return fig, (ax_curves, ax_feature)\",\n \"def plot(self, *, ax=..., n_cols=..., line_kw=..., contour_kw=...):\\n ...\",\n \"def subplot_gen(\\n var, # variable\\n name, # file name\\n loc, # location of where to save\\n color = \\\"jet\\\", # color scheme (default set to jet)\\n cb = False, # color bar flag (default set to off)\\n xlab = False, # x-label flag (default set to off)\\n ylab = False, # y-label flag (default set to off)\\n lab_ticks = False): # x and y ticks (default set to off)\\n #---------------------------------------------------------------------#\\n # Checking the location path to make sure it has a OS separator #\\n #---------------------------------------------------------------------#\\n sep = os.sep # OS separator\\n if loc[-1] != sep: # adding the separator\\n loc = loc + sep\\n #---------------------------------------------------------------------#\\n # Verifying the location #\\n #---------------------------------------------------------------------#\\n isFile = os.path.isdir(loc) # checking existence of path\\n if isFile is False:\\n os.mkdir(loc) # creating directory\\n #---------------------------------------------------------------------#\\n # Defining domain variables #\\n #---------------------------------------------------------------------#\\n N = 64 # number of data pts.\\n pi = np.pi # pi\\n X1 = np.linspace(0.0, 2.0*pi, N) # x_{1} domain\\n X2 = np.linspace(0.0, 2.0*pi, N) # x_{2} domain\\n (X1, X2) = np.meshgrid(X1, X2) # mesh grid (plotting)\\n #---------------------------------------------------------------------#\\n # Generating contours #\\n #---------------------------------------------------------------------#\\n print_count = 20 # print counter\\n dim = var.shape # dimensions of the quantity\\n for i in range(0, dim[2]):\\n plt.subplot(8,8,i+1)\\n cnt = plt.contourf(X1, X2, var[:,:,i], 500, cmap=color)\\n for c in cnt.collections: # setting the edge colors\\n c.set_edgecolors(\\\"face\\\")\\n if cb is not False: # generating the color bar\\n plt.colorbar()\\n if xlab is not False: # generating the x_{1}-label\\n plt.xlabel(\\\"$0 \\\\leq x_{1} \\\\leq 2\\\\pi$\\\")\\n if ylab is not False: # generating the x_{2}-label\\n plt.ylabel(\\\"$0 \\\\leq x_{1} \\\\leq 2\\\\pi$\\\")\\n if lab_ticks is False:\\n plt.axis(\\\"off\\\")\\n #-----------------------------------------------------------------#\\n # Print counter #\\n #-----------------------------------------------------------------#\\n if print_count >= 7:\\n print(\\\"\\\\tz step --> %i\\\" %(i+1))\\n print_count = 0\\n print_count += 1\\n #---------------------------------------------------------------------#\\n # Saving and clearing figure #\\n #---------------------------------------------------------------------#\\n plt.savefig(loc + name + \\\".png\\\") # storing figure\\n plt.clf() # clearing figure\\n print(\\\"\\\\t**** Plots saved ****\\\")\\n\\n return\",\n \"def plot_summary(self, **kwargs):\\n ncols = 2\\n nparams = len(self.distribution_parameter_names)\\n d, r = divmod(nparams, 2)\\n if r > 0:\\n nrows = d + 1\\n else:\\n nrows = d\\n\\n gs = gridspec.GridSpec(nrows, ncols)\\n fig = plt.figure(num=1, figsize=(16, 3*nrows))\\n ax = []\\n for n, parameter in enumerate(self):\\n r, c = divmod(n, 2)\\n ax.append(fig.add_subplot(gs[r, c]))\\n\\n if c == 0 and (n <= nparams-1):\\n self._plot_posterior_pdf(parameter, ax[-1],\\n y_label='Posterior pdf',\\n x_label=parameter)\\n elif n <= nparams-1:\\n self._plot_posterior_pdf(parameter, ax[-1],\\n y_label=None,\\n x_label=parameter)\\n\\n fig.tight_layout()\\n\\n return fig, ax\",\n \"def plot_individual(xdict, ydict, xprop, yprop, documents, spline):\\n figure_array = {}\\n for item in documents:\\n xlabel = \\\"\\\\\\\\textbf{\\\" + label_dict[xprop] + \\\"}\\\"\\n ylabel = \\\"\\\\\\\\textbf{\\\" + label_dict[yprop] + \\\"}\\\"\\n x = xdict[item[\\\"path_id\\\"]]\\n y = ydict[item[\\\"path_id\\\"]]\\n # fig_title = item[\\\"path_id\\\"] + \\\"(\\\" + item[\\\"pretty_formula\\\"] + \\\")\\\" # Individual traces\\n # fig_title = yprop + item[\\\"cation_type\\\"] # Plot by cation\\n fig_title = yprop # All together\\n figure_array[item[\\\"path_id\\\"]] = plt.figure(fig_title, figsize=(6,6), dpi=plotting_dpi)\\n ax = figure_array[item[\\\"path_id\\\"]].add_subplot(111) \\n ax.scatter(x,y, s=70, zorder=2, color=color_dict[item[\\\"cation_type\\\"]], linewidths=2.5, edgecolors='black')\\n if spline:\\n tck = interpolate.splrep(x, y, s=0)\\n xnew = np.arange(0, 100, 0.1)\\n splfit = interpolate.splev(xnew, tck, der=0)\\n x = xnew\\n y = splfit\\n if item[\\\"path_id\\\"][-3:] == \\\"002\\\":\\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\\\"cation_type\\\"]], linestyle='dashed')\\n elif item[\\\"path_id\\\"][-3:] == \\\"003\\\":\\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\\\"cation_type\\\"]], linestyle='dotted')\\n else:\\n ax.plot(x,y, linewidth=2.5, zorder=1, color=color_dict[item[\\\"cation_type\\\"]])\\n ax.set_xlabel(xlabel, fontsize=24)\\n # ax.set_ylim([0,1200])\\n # ax.set_xlim([0,100])\\n ax.set_ylabel(ylabel, fontsize=24)\\n ax.tick_params(axis='x', labelsize=22)\\n ax.tick_params(axis='y', labelsize=22)\\n border_width = 2\\n [i.set_linewidth(border_width) for i in ax.spines.itervalues()]\\n plt.tight_layout()\\n plt.legend(loc='best', prop={'size': 14})\\n plt.rc('text', usetex=True)\\n plt.rc('font', family='sans-serif')\\n plt.tight_layout()\\n plt.show()\",\n \"def create_scattering(\\n self, plot_name=\\\"S Parameter Plot Nominal\\\", sweep_name=None, port_names=None, port_excited=None, variations=None\\n ):\\n Families = [\\\"Freq:=\\\", [\\\"All\\\"]]\\n if variations:\\n Families += variations\\n else:\\n Families += self.get_nominal_variation()\\n if not sweep_name:\\n sweep_name = self.existing_analysis_sweeps[1]\\n if not port_names:\\n port_names = self.get_excitations_name\\n if not port_excited:\\n port_excited = port_names\\n Trace = [\\n \\\"X Component:=\\\",\\n \\\"Freq\\\",\\n \\\"Y Component:=\\\",\\n [\\\"dB(S(\\\" + p + \\\",\\\" + q + \\\"))\\\" for p, q in zip(list(port_names), list(port_excited))],\\n ]\\n solution_data = \\\"\\\"\\n if self.solution_type == \\\"DrivenModal\\\":\\n solution_data = \\\"Modal Solution Data\\\"\\n elif self.solution_type == \\\"DrivenTerminal\\\":\\n solution_data = \\\"Terminal Solution Data\\\"\\n elif self.solution_type == \\\"HFSS3DLayout\\\":\\n solution_data = \\\"Standard\\\"\\n if solution_data != \\\"\\\":\\n # run CreateReport function\\n self.post.oreportsetup.CreateReport(\\n plot_name, solution_data, \\\"Rectangular Plot\\\", sweep_name, [\\\"Domain:=\\\", \\\"Sweep\\\"], Families, Trace, []\\n )\\n return True\\n else:\\n return False\",\n \"def make_plot(d0,t0,d1,t1,d2,t2,d3,t3,suptitle,path_out):\\n fig0 = plt.figure(1,(10.,7.))\\n grid = ImageGrid(fig0, 111, # similar to subplot(111)\\n nrows_ncols = (2, 2), # creates 2x2 grid of axes\\n axes_pad=0.4, # pad between axes in inch.\\n share_all=True, # share axes\\n cbar_mode='each')\\n \\n im = grid[0].pcolor(d0)\\n grid[0].set_title(t0)\\n grid.cbar_axes[0].colorbar(im)\\n grid[0].axis([0,d0.shape[1],0,d0.shape[0]])\\n \\n im = grid[1].pcolor(d1)\\n grid[1].set_title(t1)\\n grid.cbar_axes[1].colorbar(im)\\n grid[0].axis([0,d1.shape[1],0,d1.shape[0]])\\n \\n im = grid[2].pcolor(d2)\\n grid[2].set_title(t2)\\n grid.cbar_axes[2].colorbar(im)\\n grid[0].axis([0,d2.shape[1],0,d2.shape[0]])\\n \\n im = grid[3].pcolor(d3)\\n grid[3].set_title(t3) \\n grid.cbar_axes[3].colorbar(im)\\n grid[0].axis([0,d3.shape[1],0,d3.shape[0]])\\n\\n \\n fig0.suptitle(suptitle,fontsize=18)\\n \\n fig0.savefig(path_out, dpi=300)\\n fig0.clf()\\n return\",\n \"def plot_graphs(graphs, sel_str, plotname, contour, ranges):\\n logging.debug('Creating plot for sel_str = \\\\'{}\\\\''.format(sel_str))\\n sel_graphs = select_graphs(graphs, sel_str)\\n logging.debug('Got {} graphs'.format(len(sel_graphs)))\\n\\n binnings, graphs = sort_graphs(sel_graphs)\\n graphs = list(graphs) # need a list in any case\\n\\n if not contour:\\n set_marker_styles(graphs)\\n draw_opt = 'PE'\\n else:\\n set_line_styles(graphs)\\n draw_opt='L'\\n\\n make_leg = lambda x: 'thresh = {}, bins = {}'.format(x[0], x[1])\\n\\n mkplot(graphs,\\n legEntries=[make_leg(b) for b in binnings],\\n xRange=ranges[0], yRange=ranges[1],\\n saveAs=plotname, grid=True, drawOpt=draw_opt,\\n legPos='botleft', yLabel='#Delta_{#lambda}', xLabel='#lambda_{ref}')\",\n \"def plot_profiles(self, parameters, fig=1):\\n # Make sure the parameters are a list\\n if isinstance(parameters, str) or isinstance(parameters, unicode):\\n parameters = [parameters]\\n \\n # Decide how many subplots to create\\n nrows = int(len(parameters) / 3)\\n if len(parameters) % 3 > 0:\\n nrows += 1\\n \\n # Initialize a figure for plotting\\n width = 8\\n height = 4 * nrows\\n plt.figure(fig, figsize=(width, height))\\n plt.clf()\\n \\n # Plot each parameter\\n for parm in parameters:\\n ax = plt.subplot(nrows, 3, parameters.index(parm)+1)\\n self.plot_parameter(parm)\\n \\n plt.tight_layout()\\n \\n plt.show()\",\n \"def make_plots(self,indices=None,hardcopy=False,hardcopydir='.',hardcopyprefix='',hardcopytype='png'):\\n\\t\\tfor (i,E) in enumerate(self.experiments):\\n\\t\\t\\tif(indices==None) or (i in indices):\\n\\t\\t\\t\\tE.show_plot(hardcopy,hardcopydir,hardcopyprefix,hardcopytype)\",\n \"def plot_modelparametercollections(plotname, parametercollection_SF, parametercollection_AGN,\\n stat_SF, stat_AGN, AGNcol='blue',SFcol='red', constraintsstr=None,\\n fluxratiodictionarylist=None, verbose=True):\\n\\n Nobj = len(parametercollection_SF)\\n if verbose: print(' - Will generate plots of NEOGAL \\\"PDFs\\\" for all '+str(Nobj)+' objects in parameter collections')\\n for oo in np.arange(Nobj):\\n objid = parametercollection_SF[oo]['id']\\n if verbose:\\n infostr = ' plotting info for '+str(objid)+' ('+str(\\\"%.5d\\\" % (oo+1))+' / '+str(\\\"%.5d\\\" % Nobj)+') '\\n sys.stdout.write(\\\"%s\\\\r\\\" % infostr)\\n sys.stdout.flush()\\n plotname_obj = plotname.replace('.pdf','_id'+str(objid)+'.pdf')\\n # if verbose: print(' - Generating the figure '+plotname_obj)\\n figuresize_x = 6\\n figuresize_y = 5\\n fig = plt.figure(figsize=(figuresize_x,figuresize_y))\\n Fsize = 9\\n LW = 2\\n plt.rc('text', usetex=True) # enabling LaTex rendering of text\\n plt.rc('font', family='serif',size=Fsize) # setting text font\\n plt.rc('xtick', labelsize=Fsize)\\n plt.rc('ytick', labelsize=Fsize)\\n plt.clf()\\n plt.ioff()\\n\\n left = 0.10 # the left side of the subplots of the figure\\n right = 0.95 # the right side of the subplots of the figure\\n bottom = 0.10 # the bottom of the subplots of the figure\\n top = 0.90 # the top of the subplots of the figure\\n wspace = 1.50 # the amount of width reserved for blank space between subplots\\n hspace = 0.50 # the amount of height reserved for white space between subplots\\n plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)\\n\\n Nrows, Ncols = 3, 6\\n ylabel = 'Number of NEOGAL SF ('+str(SFcol)+') and AGN ('+str(AGNcol)+') models'\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n Nmodels_SF = float(len(parametercollection_SF[oo]['Zgas']))\\n Nmodels_AGN = float(len(parametercollection_AGN[oo]['Zgas']))\\n\\n titlestr = 'Models satisfying ID='+str(objid)+' cuts: SF='+str(Nmodels_SF)+'; AGN='+str(Nmodels_AGN)\\n if (Nmodels_AGN > 0) & (Nmodels_SF > 0):\\n Nmodels_ratio = Nmodels_SF/Nmodels_AGN\\n titlestr_addition = '; SF/AGN='+str(\\\"%.4f\\\" % Nmodels_ratio)\\n titlestr = titlestr+titlestr_addition\\n\\n if fluxratiodictionarylist is not None:\\n constraints = fluxratiodictionarylist[oo]\\n constraintslist = [key+':['+str(\\\"%.2f\\\" % constraints[key][0])+','+str(\\\"%.2f\\\" % constraints[key][1])+']'\\n for key in constraints.keys() if key not in ['id']]\\n\\n if len(constraintslist) < 4:\\n constraintsstr = '; '.join(constraintslist)\\n elif (len(constraintslist) > 3) & (len(constraintslist) < 7):\\n constraintsstr = '; '.join(constraintslist[:3])+'\\\\n'+'; '.join(constraintslist[3:6])\\n elif (len(constraintslist) > 6) & (len(constraintslist) < 10):\\n constraintsstr = '; '.join(constraintslist[:3])+'\\\\n'+'; '.join(constraintslist[3:6])+\\\\\\n '\\\\n'+'; '.join(constraintslist[6:])\\n else:\\n constraintsstr = '; '.join(constraintslist[:3])+'\\\\n'+'; '.join(constraintslist[3:6])+\\\\\\n '\\\\n'+'; '.join(constraintslist[6:9])+'\\\\n'+'; '.join(constraintslist[9:])\\n\\n constraintsstr = constraintsstr.replace('10000000000.00','1e10')\\n\\n titlestr = titlestr+'\\\\n'+constraintsstr\\n # titlestr = r'{\\\\fontsize{'+str(Fsize)+'pt}{3em}\\\\selectfont{}{'+titlestr+'\\\\r}{\\\\fontsize{'+str((Fsize-2.))+'pt}{3em}\\\\selectfont{}('+constraintsstr+'}'\\n\\n # plt.text(x=0.5, y=0.94, s=titlestr, fontsize=Fsize, ha=\\\"center\\\", transform=fig.transFigure)\\n # plt.text(x=0.5, y=0.88, s=constraintsstr, fontsize=Fsize-2, ha=\\\"center\\\", transform=fig.transFigure)\\n # fig.title(titlestr,fontsize=Fsize)\\n fig.suptitle(titlestr,fontsize=Fsize-2)\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # Zgas\\n plt.subplot(Nrows, Ncols, (1,3))\\n\\n bindefs = np.array([0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.004, 0.006, 0.008, 0.014,\\n 0.017, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07])-0.00001\\n\\n plotkey = 'Zgas'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xscale('log')\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([0.00001,0.1])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # logUs\\n plt.subplot(Nrows, Ncols, (4,6))\\n\\n bindefs = np.arange(-4.75, -0.25, 0.5)\\n\\n plotkey = 'logUs'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([-5,-0.5])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # xid\\n plt.subplot(Nrows, Ncols, (7,9))\\n\\n bindefs = np.array([0.0, 0.2, 0.4, 0.6])\\n\\n plotkey = 'xid'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([-0.05,0.65])\\n plt.ylabel(ylabel)\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # nh\\n plt.subplot(Nrows, Ncols, (10,12))\\n\\n bindefs = 10**np.array([1.5, 2.5, 3.5, 4.5])\\n\\n plotkey = 'nh'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],parametercollection_AGN[oo][plotkey],\\n stat_SF[oo][plotkey],stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n plt.xscale('log')\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([10,1e5])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # COCOsol\\n plt.subplot(Nrows, Ncols, (13,14))\\n\\n #bindefs = np.array([0.10, 0.14, 0.20, 0.27, 0.38, 0.52, 0.72, 1.00, 1.40])\\n bindefs = np.arange(0.05,1.5,0.06)\\n\\n plotkey = 'COCOsol'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],None,\\n stat_SF[oo][plotkey],None,\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([0.00,1.55])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # mup\\n plt.subplot(Nrows, Ncols, (15,16))\\n\\n bindefs = np.array([0,200,400])\\n\\n plotkey = 'mup'\\n nm.plot_modelparametercollections_addhist(parametercollection_SF[oo][plotkey],None,\\n stat_SF[oo][plotkey],None,\\n SFcol,AGNcol,LW,bindefs=bindefs,Nbins=None)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([-10,410])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n # alpha\\n plt.subplot(Nrows, Ncols, (17,18))\\n\\n bindefs = np.array([-2.15,-1.85,-1.55,-1.25,-0.95])\\n\\n plotkey = 'alpha'\\n Nbins = 10\\n nm.plot_modelparametercollections_addhist(None,parametercollection_AGN[oo][plotkey],\\n None,stat_AGN[oo][plotkey],\\n SFcol,AGNcol,LW,bindefs=None,Nbins=Nbins)\\n\\n plt.xlabel(nm.keylabels(plotkey))\\n plt.xlim([-2.2,-0.9])\\n\\n # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\\n plt.savefig(plotname_obj)\\n plt.clf()\\n plt.close('all')\\n # if verbose: print(' - Successfully saved figure to file')\\n if verbose: print('\\\\n done...')\",\n \"def plot_cdf(self, param, plot_type, Nsplit=50, **kwargs):\\n title = self.family.capitalize() + \\\" Copula CDF\\\" \\n\\n bounds = [0+1e-2, 1-1e-2]\\n U_grid, V_grid = np.meshgrid(\\n np.linspace(bounds[0], bounds[1], Nsplit),\\n np.linspace(bounds[0], bounds[1], Nsplit))\\n \\n Z = np.array(\\n [self.get_cdf(uu, vv, param) for uu, vv in zip(np.ravel(U_grid), np.ravel(V_grid)) ] )\\n \\n Z = Z.reshape(U_grid.shape)\\n\\n if plot_type == \\\"3d\\\":\\n plot_bivariate_3d(U_grid,V_grid,Z, [0,1], title, **kwargs)\\n elif plot_type == \\\"contour\\\":\\n plot_bivariate_contour(U_grid,V_grid,Z, [0,1], title, **kwargs)\\n else:\\n print(\\\"only \\\\\\\"contour\\\\\\\" or \\\\\\\"3d\\\\\\\" arguments supported for type\\\")\\n raise ValueError\",\n \"def plot(self, n_levels = 200, axes = None, figsize = (10, 4), cont_kws = {}):\\n if not isinstance(n_levels, int) or n_levels < 1:\\n raise ValueError(\\\"n_levels must be a positive integer\\\")\\n if axes is not None and not isinstance(axes, Axes):\\n raise ValueError(\\\"axes must be Axes\\\")\\n if not isinstance(figsize, (list, tuple)) or len(figsize) != 2:\\n raise ValueError(\\\"figsize must be a tuple with 2 elements\\\")\\n if not isinstance(cont_kws, dict):\\n raise ValueError(\\\"cont_kws must be a dictionary\\\")\\n\\n if self._n_dim == 2:\\n if axes is None:\\n fig = plt.figure(figsize = figsize, facecolor = \\\"white\\\")\\n ax1 = fig.add_subplot(1, 1, 1)\\n else:\\n ax1 = axes\\n cax = ax1.contourf(self._xaxis, self._zaxis, self._velocity_model, n_levels, **cont_kws)\\n ax1.set_xlabel(\\\"X (m)\\\")\\n ax1.set_ylabel(\\\"Depth (m)\\\")\\n ax1.invert_yaxis()\\n return cax\\n else:\\n raise ValueError(\\\"plot unavailable for 3-D grid\\\")\",\n \"def ShowOneContour(index,all_images,all_pointing,thex0,they0,all_titles,object_name,all_expo,dir_top_img,all_filt,figname):\\n plt.figure(figsize=(15,6))\\n spec_index_min=100 # cut the left border\\n spec_index_max=1900 # cut the right border\\n star_halfwidth=70\\n \\n YMIN=-15\\n YMAX=15\\n \\n figfilename=os.path.join(dir_top_img,figname) \\n \\n #center is approximately the one on the original raw image (may be changed)\\n #x0=int(all_pointing[index][0])\\n x0=int(thex0[index])\\n \\n \\n # Extract the image \\n full_image=np.copy(all_images[index])\\n \\n # refine center in X,Y\\n star_region_X=full_image[:,x0-star_halfwidth:x0+star_halfwidth]\\n \\n profile_X=np.sum(star_region_X,axis=0)\\n profile_Y=np.sum(star_region_X,axis=1)\\n\\n NX=profile_X.shape[0]\\n NY=profile_Y.shape[0]\\n \\n X_=np.arange(NX)\\n Y_=np.arange(NY)\\n \\n avX,sigX=weighted_avg_and_std(X_,profile_X**4) # take squared on purpose (weigh must be >0)\\n avY,sigY=weighted_avg_and_std(Y_,profile_Y**4)\\n \\n x0=int(avX+x0-star_halfwidth)\\n \\n \\n # find the center in Y on the spectrum\\n yprofile=np.sum(full_image[:,spec_index_min:spec_index_max],axis=1)\\n y0=np.where(yprofile==yprofile.max())[0][0]\\n\\n # cut the image in vertical and normalise by exposition time\\n reduc_image=full_image[y0-20:y0+20,x0:spec_index_max]/all_expo[index] \\n reduc_image[:,0:100]=0 # erase central star\\n \\n X_Size_Pixels=np.arange(0,reduc_image.shape[1])\\n Y_Size_Pixels=np.arange(0,reduc_image.shape[0])\\n Transverse_Pixel_Size=Y_Size_Pixels-int(float(Y_Size_Pixels.shape[0])/2.)\\n \\n # calibration in wavelength\\n #grating_name=all_filt[index].replace('dia ','')\\n grating_name=get_disperser_filtname(all_filt[index])\\n \\n lambdas=Pixel_To_Lambdas(grating_name,X_Size_Pixels,all_pointing[index],True)\\n \\n #if grating_name=='Ron200':\\n # holo = Hologram('Ron400',verbose=True)\\n #else: \\n # holo = Hologram(grating_name,verbose=True)\\n #lambdas=holo.grating_pixel_to_lambda(X_Size_Pixels,all_pointing[index])\\n #if grating_name=='Ron200':\\n # lambdas=lambdas*2.\\n \\n\\n X,Y=np.meshgrid(lambdas,Transverse_Pixel_Size) \\n T=np.transpose(reduc_image)\\n \\n \\n plt.contourf(X, Y, reduc_image, 100, alpha=1., cmap='jet',origin='lower')\\n C = plt.contour(X, Y, reduc_image , 20, colors='black', linewidth=.5,origin='lower')\\n \\n \\n for line in LINES:\\n if line == O2 or line == HALPHA or line == HBETA or line == HGAMMA:\\n plt.plot([line['lambda'],line['lambda']],[YMIN,YMAX],'-',color='lime',lw=0.5)\\n plt.text(line['lambda'],YMAX-3,line['label'],verticalalignment='bottom', horizontalalignment='center',color='lime', fontweight='bold',fontsize=16)\\n \\n \\n \\n plt.axis([X.min(), X.max(), Y.min(), Y.max()]); plt.grid(True)\\n plt.title(all_titles[index])\\n plt.grid(color='white', ls='solid')\\n plt.text(200,-5.,all_filt[index],verticalalignment='bottom', horizontalalignment='center',color='yellow', fontweight='bold',fontsize=16)\\n plt.xlabel('$\\\\lambda$ (nm)')\\n plt.ylabel('pixels')\\n plt.ylim(YMIN,YMAX)\\n plt.xlim(0.,1200.)\\n plt.savefig(figfilename)\",\n \"def create_scatterplots(PrimaryParticleName, LongVectorSignals, LongVectorSignalsCher,\\n\\tShortVectorSignals, ShortVectorSignalsCher, ScinTreshold,\\n\\tCherTreshold):\\n\\n\\t#Set ROOT plots\\n\\tTH2LongScinSignals = TH2F(\\\"LongScatterplotSignals\\\", PrimaryParticleName, 46, 0., 93., 93, 0., 93.)\\n\\tTH2ShortScinSignals = TH2F(\\\"ShortScatterplotSignals\\\", PrimaryParticleName, 46, 0., 93., 93, 0., 93.)\\n\\tTH2LongCherSignals = TH2F(\\\"LongCherScatterplotSignals\\\", PrimaryParticleName, 46, 0., 93., 92, 0., 93.)\\n\\tTH2ShortCherSignals = TH2F(\\\"ShortCherScatterplotSignals\\\", PrimaryParticleName, 46, 0., 93., 92, 0., 93.)\\n\\tTH2Uniformity = TH2F(\\\"Uniformity\\\", \\\"Uniformity\\\", 46, 0., 93., 92, 0., 93.)\\n\\n\\t#Fill plots in for loop\\n\\tfor index in range(len(LongVectorSignals)):\\n\\t\\tif LongVectorSignals[index] > ScinTreshold:\\n\\t\\t\\tX,Y = Staggeredmap.mapXYLongScin(index)\\n\\t\\t\\tTH2LongScinSignals.Fill(X,Y,LongVectorSignals[index])\\n\\t\\tif LongVectorSignalsCher[index] > CherTreshold:\\n\\t\\t\\tX,Y = Staggeredmap.mapXYLongCherenkov(index)\\n\\t\\t\\tTH2LongCherSignals.Fill(X,Y,LongVectorSignalsCher[index])\\n\\t\\tif ShortVectorSignals[index] > ScinTreshold:\\n\\t\\t\\tX,Y = Staggeredmap.mapXYShortScin(index)\\n\\t\\t\\tTH2ShortScinSignals.Fill(X,Y, ShortVectorSignals[index])\\n\\t\\tif ShortVectorSignalsCher[index] > CherTreshold:\\n\\t\\t\\tX,Y = Staggeredmap.mapXYShortCherenkov(index)\\n\\t\\t\\tTH2ShortCherSignals.Fill(X,Y, ShortVectorSignalsCher[index])\\n\\n\\t#Fill uniformity plot\\n\\tfor index in range(len(LongVectorSignals)):\\n\\t\\tX,Y = Staggeredmap.mapXYLongScin(index)\\n\\t\\tTH2Uniformity.Fill(X,Y,1.)\\t\\n\\t\\tX,Y = Staggeredmap.mapXYShortScin(index)\\n\\t\\tTH2Uniformity.Fill(X,Y,1.)\\n\\t\\tX,Y = Staggeredmap.mapXYLongCherenkov(index)\\n\\t\\tTH2Uniformity.Fill(X,Y,1.)\\n\\t\\tX,Y = Staggeredmap.mapXYShortCherenkov(index)\\n\\t\\tTH2Uniformity.Fill(X,Y,1.)\\n\\n\\t#Draw + DrawOptions plots\\n\\tStyle = gStyle\\n\\tStyle.SetPalette(1) #Root palette style\\n\\tStyle.SetOptStat(0) #Do not show statistics\\n\\tTH2LongScinSignals.SetLineWidth(0) #TH2LongScinSignals #No line width\\n\\tTH2LongScinSignals.SetLineColor(2)\\n\\t#TH2Signals.SetFillColorAlpha(2, 0.)\\n\\tXAxis = TH2LongScinSignals.GetXaxis()\\n\\tXAxis.SetTitle(\\\"x (mm)\\\")\\n\\tXAxis.CenterTitle()\\n\\tXAxis.SetTitleOffset(1.8)\\n\\tYAxis = TH2LongScinSignals.GetYaxis()\\n\\tYAxis.SetTitle(\\\"y (mm)\\\")\\n\\tYAxis.CenterTitle()\\n\\tYAxis.SetTitleOffset(1.8)\\n\\tZAxis = TH2LongScinSignals.GetZaxis()\\n\\tZAxis.SetTitle(\\\"Energy (MeV)\\\")\\n\\tZAxis.SetTitleOffset(1.4)\\n\\tTH2LongScinSignals.Draw(\\\"LEGO2Z 0 FB\\\")\\n\\tgPad.SaveAs(\\\"ImageLongScintillation.eps\\\")\\n\\tTH2ShortScinSignals.SetLineWidth(0) #TH2ShortScinSignals #No line width\\n\\tTH2ShortScinSignals.SetLineColor(2)\\n\\t#TH2Signals.SetFillColorAlpha(2, 0.)\\n\\tXAxis = TH2ShortScinSignals.GetXaxis()\\n\\tXAxis.SetTitle(\\\"x (mm)\\\")\\n\\tXAxis.CenterTitle()\\n\\tXAxis.SetTitleOffset(1.8)\\n\\tYAxis = TH2ShortScinSignals.GetYaxis()\\n\\tYAxis.SetTitle(\\\"y (mm)\\\")\\n\\tYAxis.CenterTitle()\\n\\tYAxis.SetTitleOffset(1.8)\\n\\tZAxis = TH2ShortScinSignals.GetZaxis()\\n\\tZAxis.SetTitle(\\\"Energy (MeV)\\\")\\n\\tZAxis.SetTitleOffset(1.4)\\n\\tTH2ShortScinSignals.Draw(\\\"LEGO2Z 0 FB\\\")\\n\\tgPad.SaveAs(\\\"ImageShortScintillation.eps\\\")\\n\\tTH2ShortCherSignals.SetLineWidth(0) #TH2ShortCherSignals #No line width\\n\\tTH2ShortCherSignals.SetLineColor(4)\\n\\t#TH2Signals.SetFillColorAlpha(2, 0.)\\n\\tXAxis = TH2ShortCherSignals.GetXaxis()\\n\\tXAxis.SetTitle(\\\"x (mm)\\\")\\n\\tXAxis.CenterTitle()\\n\\tXAxis.SetTitleOffset(1.8)\\n\\tYAxis = TH2ShortCherSignals.GetYaxis()\\n\\tYAxis.SetTitle(\\\"y (mm)\\\")\\n\\tYAxis.CenterTitle()\\n\\tYAxis.SetTitleOffset(1.8)\\n\\tZAxis = TH2ShortCherSignals.GetZaxis()\\n\\tZAxis.SetTitle(\\\"Cher p.e.\\\")\\n\\tZAxis.SetTitleOffset(1.4)\\n\\tTH2ShortCherSignals.Draw(\\\"LEGO2Z 0 FB\\\")\\n\\tgPad.SaveAs(\\\"ImageShortCherenkov.eps\\\")\\n\\tTH2LongCherSignals.SetLineWidth(0) #TH2LongCherSignals #No line width\\n\\tTH2LongCherSignals.SetLineColor(4)\\n\\tXAxis = TH2LongCherSignals.GetXaxis()\\n\\tXAxis.SetTitle(\\\"x (mm)\\\")\\n\\tXAxis.CenterTitle()\\n\\tXAxis.SetTitleOffset(1.8)\\n\\tYAxis = TH2LongCherSignals.GetYaxis()\\n\\tYAxis.SetTitle(\\\"y (mm)\\\")\\n\\tYAxis.CenterTitle()\\n\\tYAxis.SetTitleOffset(1.8)\\n\\tZAxis = TH2LongCherSignals.GetZaxis()\\n\\tZAxis.SetTitle(\\\"Cher p.e.\\\")\\n\\tZAxis.SetTitleOffset(1.4)\\n\\tTH2LongCherSignals.Draw(\\\"LEGO2Z FB 0\\\")\\n\\tgPad.SaveAs(\\\"ImageLongCherenkov.eps\\\")\\n\\tTH2Uniformity.SetLineWidth(0) #TH2Uniformity #No line width\\n\\tTH2Uniformity.SetLineColor(2)\\n\\t#TH2Signals.SetFillColorAlpha(2, 0.)\\n\\tXAxis = TH2Uniformity.GetXaxis()\\n\\tXAxis.SetTitle(\\\"x (mm)\\\")\\n\\tXAxis.CenterTitle()\\n\\tXAxis.SetTitleOffset(1.8)\\n\\tYAxis = TH2Uniformity.GetYaxis()\\n\\tYAxis.SetTitle(\\\"y (mm)\\\")\\n\\tYAxis.CenterTitle()\\n\\tYAxis.SetTitleOffset(1.8)\\n\\tZAxis = TH2Uniformity.GetZaxis()\\n\\tZAxis.SetTitle(\\\"Set to 1\\\")\\n\\tZAxis.SetTitleOffset(1.4)\\n\\tTH2Uniformity.Draw(\\\"LEGO2Z 0 FB\\\")\\n\\t#gPad.SaveAs(\\\"ImageUniformity.eps\\\")\",\n \"def plot_pca(basis, weights):\\n file_name = '../Data/lhc_512_5.txt'\\n params = np.loadtxt(file_name)\\n\\n ncomp, imsize = basis.shape\\n npix = int(np.sqrt(imsize))\\n ncol = int(ncomp//2)\\n nsamp, _ = weights.shape\\n\\n # Shows the basis images\\n for i in range(ncomp):\\n plt.subplot(2, ncol, i+1)\\n plt.imshow(basis[i].reshape((npix, npix)))\\n plt.show()\\n\\n # Scatter plots\\n for i in range(ncomp):\\n # parameter number (0: flux, 1: radius, 2: g1 shear, 3: g2 shear, 4: psf fwhm)\\n par = 3\\n # weight number (x-axis) 0 -> ncomp-1\\n w = 11\\n plt.subplot(2, ncol, i+1)\\n plt.scatter(weights[:, w], weights[:, i], s=1, c=params[:, par])\\n plt.ylabel('Weight '+str(i+1), size=15)\\n plt.xlabel('Weight '+str(w+1), size=15)\\n plt.colorbar()\\n plt.show()\",\n \"def contour_manual(ctx, a):\\n \\n plotid = a.plotid()\\n z = a.dbl_2d('z')\\n x = a.dbl_1d('x')\\n y = a.dbl_1d('y')\\n levels = a.dbl_1d('levels')\\n\\n label = a.bool('label')\\n line = a.line()\\n display = a.display()\\n\\n ctx.set(plotid)\\n ctx.fail_if_polar()\\n ctx.fail_if_log_symlog()\\n\\n # Apply defaults for optional args\\n if len(x) == 0:\\n x = np.arange(z.shape[1])\\n if len(y) == 0:\\n y = np.arange(z.shape[0])\\n \\n # Make all the same size, and filter out rows/cols corresponding to NaN\\n x, y, z = filters.filter_2d(x, y, z)\\n \\n # Can't contour datasets which end up smaller than 2x2\\n if z.shape[0] < 2 or z.shape[1] < 2:\\n return\\n \\n # MPL pitches a fit if any elements are NaN, so use a masked array\\n if np.any(~np.isfinite(z)):\\n z = np.ma.masked_invalid(z)\\n\\n # MPL doesn't like this\\n if len(levels) == 0:\\n return\\n\\n # Limit to 500 to avoid \\\"MAXTICKS\\\" issue\\n levels = levels[0:500]\\n\\n # Note we handle line props manually, as contour uses non-standard\\n # naming for the keyword arguments.\\n k = { 'colors': 'k' if line.color is None else line.color,\\n 'linestyles': line.style,\\n 'linewidths': line.width, }\\n k.update(display._k())\\n remove_none(k)\\n\\n contourset = plt.contour(x, y, z, levels, **k)\\n \\n if label:\\n plt.clabel(contourset)\",\n \"def plot(self, *names):\\r\\n for name in names:\\r\\n if name in self.__obs.keys():\\r\\n list_obs = self.__obs[name]\\r\\n if not isinstance(list_obs[0], matrix):\\r\\n fig = plt.figure()\\r\\n plt.plot(self.__obs[name])\\r\\n else:\\r\\n fig = plt.figure()\\r\\n for i in range(list_obs[0].size):\\r\\n plt.plot([float(obs[i]) for obs in list_obs], label=\\\"Dimension {0}\\\".format(i))\\r\\n plt.legend()\\r\\n plt.ylabel(name)\\r\\n plt.show()\\r\\n else:\\r\\n for sous_objet in self.__sous_objets:\\r\\n if re.match((sous_objet+\\\"?\\\").encode('string-escape'), name.lower()):\\r\\n self.__dict__[sous_objet].plot(name)\",\n \"def plot_bivariate_contour(X, Y, Z, bounds, title, **kwargs):\\n plt.figure()\\n CS = plt.contour(X, Y, Z, colors='k', linewidths=1., linestyles=None, **kwargs)\\n plt.clabel(CS, fontsize=8, inline=1)\\n plt.xlim(bounds)\\n plt.ylim(bounds)\\n plt.title(title)\\n plt.show()\",\n \"def visualize(s):\\n n = 3\\n fig, ax = plt.subplots(1, n, sharex=True, sharey=True)\\n for x, y in zip(range(n), [s.x, s.y, s.z]):\\n plot(ax, x, y)\\n\\n plt.tight_layout()\\n plt.show()\",\n \"def plotGrid(parameters, plot_name):\\n\\n allNames = sorted(parameters)\\n combinations = it.product(*(parameters[Name] for Name in allNames))\\n\\n combs = [list(c) for c in list(combinations) ]\\n combs_df = pd.DataFrame(combs, columns=[\\\"criterion\\\", \\\"max_features\\\", \\\"n_estimators\\\"])\\n\\n trace = go.Scatter3d(\\n x=combs_df['criterion'],\\n y=combs_df['max_features'],\\n z=combs_df['n_estimators'],\\n mode='markers',\\n marker=dict(\\n size= 5,\\n color='green',\\n opacity=0.99,\\n colorscale='Viridis',\\n showscale=False\\n )\\n )\\n data = [trace]\\n layout = go.Layout(\\n margin=dict(\\n l=30,\\n r=30,\\n b=30,\\n t=30\\n ),\\n height=700,\\n width=960,\\n showlegend=True,\\n scene=dict(\\n xaxis=dict(\\n title='criterion',\\n nticks=10,\\n ),\\n yaxis=dict(\\n title='max_features',\\n nticks=10,\\n ),\\n zaxis=dict(\\n title='n_estimators',\\n nticks=10,\\n ),\\n camera=dict(\\n eye=dict(\\n y=2.089757339892154,\\n x=-0.5464711077183096,\\n z=0.14759264478960377,\\n )\\n ),\\n ),\\n )\\n\\n fig = go.Figure(data=data, layout=layout)\\n fig.show()\\n fig.write_image(plot_name)\\n fig.write_html(script_path + \\\"/results/GridSearchPlot.html\\\")\",\n \"def vplots(vname:str, *args):\\n for arg in args:\\n plt.figure()\\n N = len(arg.vm)\\n for i in range(N):\\n plt.plot(arg.t/b2.ms, arg.getattr(vname)[i]/b2.mV)\",\n \"def plot_multiLyapunov(systems, mode=2, savefig=True, figname=None):\\n if mode == 2:\\n print(systems)\\n# divnorm = colors.DivergingNorm(vmin=max([np.nanmin(np.nanmax(np.nanmax(system.lyapunov_2, axis=0), axis=0)) for system in systems]), vcenter=0, vmax=max[np.nanmax(system.lyapunov_2) for system in systems])\\n if figname == None:\\n figname = 'sum_of_first_2_lyapunov'\\n \\n fig, ax = plt.subplots()\\n for system in systems:\\n\\n lyapunov_2 = system.lyapunov_2\\n x = system.x\\n y = system.y\\n l = system.l\\n a = system.a\\n\\n\\n\\n plt.contourf(a[0,0,:,:],l[0,0,:,:],np.nanmax(np.nanmax(lyapunov_2, axis=0), axis=0), levels = 100, cmap = 'RdBu_r')\\n# , norm=divnorm)\\n for i in range(lyapunov_2.shape[0]):\\n for j in range(lyapunov_2.shape[1]):\\n plt.contour(a[0,0,:,:],l[0,0,:,:],lyapunov_2[i,j], levels = [0,], colors=('k',),alpha=0.1)\\n lyap_sum = plt.contour(a[0,0,:,:],l[0,0,:,:],lyapunov_2.max(axis=0).max(axis=0), levels = [0,], colors=('blue',),alpha=1)\\n\\n# cbar = plt.colorbar()\\n plt.plot(wild_chaos[:,0],wild_chaos[:,1],'--r',lw=3)\\n plt.title('Sum of the first 2 Lyapunov exponents ')\\n plt.ylabel('$\\\\lambda$')\\n plt.xlabel('a')\\n# cbar.ax.set_ylabel('Sum of the first 2 Lyapunov exponents')\\n\\n ax.set_ylim([l.min(),l.max()])\\n ax.set_xlim([a.min(),a.max()])\\n if savefig:\\n plt.savefig(f'images/{figname}.pdf')\\n plt.show()\",\n \"def syst_contour():\\n\\n fig, axarr = plt.subplots(1, 3, figsize=(16, 5))\\n\\n # first plot in light gray the stat contours\\n instruments = [\\\"fermi\\\", \\\"magic\\\", \\\"veritas\\\", \\\"fact\\\", \\\"hess\\\", \\\"joint\\\"]\\n\\n # with one loop we realize all the contour plots\\n color = \\\"lightgray\\\"\\n for instrument in instruments:\\n path = f\\\"{config.repo_path}/results/fit/gammapy/{instrument}\\\"\\n\\n contours_path = f\\\"{path}/fit_1.0_sigma_contours_logparabola.npy\\\"\\n results_path = f\\\"{path}/fit_results_logparabola.yaml\\\"\\n\\n # load the contours and the results of the fit\\n contours = np.load(contours_path).tolist()\\n results = load_yaml(results_path)\\n # true values to be plotted\\n amplitude = float(results[\\\"parameters\\\"][0][\\\"value\\\"])\\n alpha = float(results[\\\"parameters\\\"][2][\\\"value\\\"])\\n beta = float(results[\\\"parameters\\\"][3][\\\"value\\\"])\\n\\n # amplitude vs alpha\\n amplitude_alpha = contours[\\\"contour_amplitude_alpha\\\"]\\n axarr[0].plot(\\n amplitude_alpha[\\\"amplitude\\\"] * 10,\\n amplitude_alpha[\\\"alpha\\\"],\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n color=color,\\n )\\n # plot actual value\\n axarr[0].plot(\\n amplitude * 1e11, alpha, marker=\\\"X\\\", markersize=7, color=color, lw=2.5\\n )\\n\\n # amplitude vs beta\\n amplitude_beta = contours[\\\"contour_amplitude_beta\\\"]\\n axarr[1].plot(\\n amplitude_beta[\\\"amplitude\\\"] * 10,\\n # contour have a scale factor of 1e-10, parameters are in units of 1e-11\\n amplitude_beta[\\\"beta\\\"],\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n color=color,\\n )\\n # plot actual value\\n axarr[1].plot(amplitude * 1e11, beta, marker=\\\"X\\\", markersize=7, color=color)\\n\\n # alpha vs beta\\n alpha_beta = contours[\\\"contour_alpha_beta\\\"]\\n axarr[2].plot(\\n alpha_beta[\\\"alpha\\\"],\\n alpha_beta[\\\"beta\\\"],\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n color=color,\\n )\\n # plot actual value\\n axarr[2].plot(alpha, beta, marker=\\\"X\\\", markersize=7, color=color)\\n\\n # plotting stat vs syst result on top of instrument-wise contours in gray\\n path = config.repo_path / f\\\"results/fit/gammapy/joint\\\"\\n\\n stat_contours_path = f\\\"{path}/fit_1.0_sigma_contours_logparabola.npy\\\"\\n stat_results_path = f\\\"{path}/fit_results_logparabola.yaml\\\"\\n\\n syst_contours_path = f\\\"{path}/fit_1.0_sigma_contours_logparabola_energy_scale.npy\\\"\\n syst_results_path = f\\\"{path}/fit_results_logparabola_energy_scale.yaml\\\"\\n\\n for (contours_path, results_path, color, label) in zip(\\n [stat_contours_path, syst_contours_path],\\n [stat_results_path, syst_results_path],\\n [\\\"crimson\\\", \\\"#002E63\\\"],\\n [\\\"stat. only\\\", \\\"stat. + syst.\\\"],\\n ):\\n # load the contours and the results of the fit\\n contours = np.load(contours_path).tolist()\\n results = load_yaml(results_path)\\n # true values to be plotted\\n amplitude = float(results[\\\"parameters\\\"][0][\\\"value\\\"])\\n alpha = float(results[\\\"parameters\\\"][2][\\\"value\\\"])\\n beta = float(results[\\\"parameters\\\"][3][\\\"value\\\"])\\n\\n # amplitude vs alpha\\n amplitude_alpha = contours[\\\"contour_amplitude_alpha\\\"]\\n if label == \\\"stat. only\\\":\\n axarr[0].plot(\\n amplitude_alpha[\\\"amplitude\\\"] * 10,\\n # gammapy contour have a scale factor of 1e-10, parameters are in units of 1e-11\\n amplitude_alpha[\\\"alpha\\\"],\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n color=color,\\n )\\n else:\\n axarr[0].plot(\\n amplitude_alpha[\\\"amplitude\\\"] * 1e11,\\n amplitude_alpha[\\\"alpha\\\"],\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n color=color,\\n )\\n\\n # plot actual value\\n axarr[0].plot(\\n amplitude * 1e11, alpha, marker=\\\"X\\\", markersize=7, color=color, lw=2.5\\n )\\n\\n # amplitude vs beta\\n amplitude_beta = contours[\\\"contour_amplitude_beta\\\"]\\n if label == \\\"stat. only\\\":\\n axarr[1].plot(\\n amplitude_beta[\\\"amplitude\\\"] * 10,\\n # gammapy contour have a scale factor of 1e-10, parameters are in units of 1e-11\\n amplitude_beta[\\\"beta\\\"],\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n color=color,\\n )\\n else:\\n axarr[1].plot(\\n amplitude_beta[\\\"amplitude\\\"] * 1e11,\\n amplitude_beta[\\\"beta\\\"],\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n color=color,\\n )\\n\\n # plot actual value\\n axarr[1].plot(amplitude * 1e11, beta, marker=\\\"X\\\", markersize=7, color=color)\\n\\n # alpha vs beta\\n alpha_beta = contours[\\\"contour_alpha_beta\\\"]\\n axarr[2].plot(\\n alpha_beta[\\\"alpha\\\"],\\n alpha_beta[\\\"beta\\\"],\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n color=color,\\n )\\n # plot actual value\\n axarr[2].plot(alpha, beta, marker=\\\"X\\\", markersize=7, color=color)\\n\\n # legend\\n import matplotlib.lines as mlines\\n\\n stat_label = mlines.Line2D(\\n [],\\n [],\\n color=\\\"crimson\\\",\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n label=\\\"joint fit \\\\n stat. only\\\",\\n )\\n syst_label = mlines.Line2D(\\n [],\\n [],\\n color=\\\"#002E63\\\",\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n label=\\\"joint fit \\\\n stat. + syst.\\\",\\n )\\n single_label = mlines.Line2D(\\n [],\\n [],\\n color=\\\"lightgray\\\",\\n marker=\\\"\\\",\\n ls=\\\"-\\\",\\n lw=2.5,\\n label=\\\"instr. fit \\\\n stat. only\\\",\\n )\\n\\n axarr[0].set_xlabel(\\n r\\\"$\\\\phi_0 \\\\,/\\\\,(10^{-11}\\\\,{\\\\rm TeV} \\\\, {\\\\rm cm}^{-2} {\\\\rm s}^{-1})$\\\",\\n size=FONTSIZE_CONTOURS,\\n )\\n axarr[0].set_ylabel(r\\\"$\\\\Gamma$\\\", size=FONTSIZE_CONTOURS)\\n # make axis thicker\\n axarr[0].set_yticks([2.2, 2.4, 2.6, 2.8])\\n axarr[0].set_ylim([2.1, 2.9])\\n axarr[0].set_xticks([3, 4, 5])\\n axarr[0].set_xlim([2.8, 5.2])\\n for axis in [\\\"top\\\", \\\"bottom\\\", \\\"left\\\", \\\"right\\\"]:\\n axarr[0].spines[axis].set_linewidth(2.5)\\n axarr[0].tick_params(\\n \\\"both\\\", length=7, width=1.6, which=\\\"major\\\", labelsize=FONTSIZE_CONTOURS\\n )\\n axarr[0].tick_params(\\n \\\"both\\\", length=4, width=1.6, which=\\\"minor\\\", labelsize=FONTSIZE_CONTOURS\\n )\\n\\n axarr[1].set_xlabel(\\n r\\\"$\\\\phi_0 \\\\,/\\\\,(10^{-11}\\\\,{\\\\rm TeV} \\\\, {\\\\rm cm}^{-2} {\\\\rm s}^{-1})$\\\",\\n size=FONTSIZE_CONTOURS,\\n )\\n axarr[1].set_ylabel(r\\\"$\\\\beta$\\\", size=FONTSIZE_CONTOURS)\\n # make axis thicker\\n axarr[1].set_xticks([3, 4, 5])\\n axarr[1].set_xlim([2.8, 5.2])\\n axarr[1].set_yticks([0.2, 0.4, 0.6])\\n axarr[1].set_ylim([0.0, 0.8])\\n for axis in [\\\"top\\\", \\\"bottom\\\", \\\"left\\\", \\\"right\\\"]:\\n axarr[1].spines[axis].set_linewidth(2.5)\\n axarr[1].tick_params(\\n \\\"both\\\", length=7, width=1.6, which=\\\"major\\\", labelsize=FONTSIZE_CONTOURS\\n )\\n axarr[1].tick_params(\\n \\\"both\\\", length=4, width=1.6, which=\\\"minor\\\", labelsize=FONTSIZE_CONTOURS\\n )\\n\\n axarr[2].set_xlabel(r\\\"$\\\\Gamma$\\\", size=FONTSIZE_CONTOURS)\\n axarr[2].set_ylabel(r\\\"$\\\\beta$\\\", size=FONTSIZE_CONTOURS)\\n axarr[2].set_xticks([2.2, 2.4, 2.6, 2.8])\\n axarr[2].set_xlim([2.1, 2.9])\\n axarr[2].set_yticks([0.2, 0.4, 0.6])\\n axarr[2].set_ylim([0.0, 0.8])\\n # make axis thicker\\n for axis in [\\\"top\\\", \\\"bottom\\\", \\\"left\\\", \\\"right\\\"]:\\n axarr[2].spines[axis].set_linewidth(2.5)\\n axarr[2].tick_params(\\n \\\"both\\\", length=7, width=1.6, which=\\\"major\\\", labelsize=FONTSIZE_CONTOURS\\n )\\n axarr[2].tick_params(\\n \\\"both\\\", length=4, width=1.6, which=\\\"minor\\\", labelsize=FONTSIZE_CONTOURS\\n )\\n\\n box = axarr[2].get_position()\\n axarr[2].set_position([box.x0, box.y0, box.width * 0.97, box.height])\\n # plot the legend on top of the central plot\\n axarr[2].legend(\\n handles=[stat_label, syst_label, single_label],\\n loc=\\\"center left\\\",\\n fontsize=FONTSIZE_CONTOURS,\\n bbox_to_anchor=(1., 0.5),\\n )\\n\\n plt.tight_layout()\\n filename = \\\"results/figures/iminuit_logparabola_energy_scale_contour.png\\\"\\n filename_pdf = \\\"results/figures/iminuit_logparabola_energy_scale_contour.pdf\\\"\\n fig.savefig(filename)\\n log.info(f\\\"Writing {filename}\\\")\\n fig.savefig(filename)\\n fig.savefig(filename_pdf)\",\n \"def add_contours(self, levels=5, **kwargs):\\n\\n rs, ts = NP.meshgrid(NP.linspace(self.smin, self.smax),\\n NP.linspace(0, NP.pi/2))\\n # Compute centered RMS difference\\n rms = NP.sqrt(self.refstd**2 + rs**2 - 2*self.refstd*rs*NP.cos(ts))\\n\\n contours = self.ax.contour(ts, rs, rms, levels, **kwargs)\\n\\n return contours\",\n \"def add_contours(self, levels=5, **kwargs):\\n\\n rs, ts = NP.meshgrid(NP.linspace(self.smin, self.smax),\\n NP.linspace(0, NP.pi/2))\\n # Compute centered RMS difference\\n rms = NP.sqrt(self.refstd**2 + rs**2 - 2*self.refstd*rs*NP.cos(ts))\\n\\n contours = self.ax.contour(ts, rs, rms, levels, **kwargs)\\n\\n return contours\",\n \"def check_plots(tracklist_subset, tracklist):\\n\\n for trial in tracklist_subset: # Iterates over all available trials\\n\\n # Scale time for colormap\\n scaled_time = (tracklist[trial]['data'].index.values -\\n tracklist[trial]['data'].index.values.min()) / \\\\\\n tracklist[trial]['data'].index.values.ptp()\\n timemax = max(tracklist[trial]['data'].index.values)\\n data = tracklist[trial]['data']\\n colors = plt.cm.cubehelix(scaled_time)\\n m = cm.ScalarMappable(cmap=cm.cubehelix)\\n m.set_array(tracklist[trial]['data'].index.values)\\n\\n # filename = str(trial) + '.pdf'\\n\\n fig = plt.figure(figsize=(20, 20))\\n fig.suptitle(tracklist[trial]['sequence'] + ' ' +\\n tracklist[trial]['behavior'])\\n\\n ax1 = fig.add_subplot(4, 2, 1, projection='3d')\\n ax1.set_title('Pt 1 Position')\\n ax1.scatter3D(xs=tracklist[trial]['data']['pt1x_smth'],\\n ys=tracklist[trial]['data']['pt1y_smth'],\\n zs=tracklist[trial]['data']['pt1z_smth'],\\n zdir='z', s=3, c=colors, marker='o',\\n edgecolor='none') # 3D Scatter plot\\n ax1.autoscale(enable=True, tight=True)\\n ax1.set_xlabel('X position')\\n ax1.set_ylabel('Y position')\\n ax1.set_zlabel('Z position')\\n plt.colorbar(m, shrink=0.5, aspect=10)\\n\\n ax2 = fig.add_subplot(4, 2, 3)\\n ax2.plot(data.index.values, data['pt1y_v_smth'], 'bo')\\n ax2.set_ylabel('Y velocity (cm/s)', color='b')\\n ax2.tick_params('y', colors='b')\\n\\n ax3 = fig.add_subplot(4, 2, 5)\\n ax3.plot(data.index.values, data['pt1_net_v'], 'bo')\\n ax3.set_ylabel('Net Velocity (cm/s)', color='b')\\n ax3.tick_params('y', colors='b')\\n\\n ax4 = fig.add_subplot(4, 2, 7)\\n ax4.plot(data.index.values, data['pt1_net_a'], 'bo')\\n ax4.set_ylabel('Net accel (cm/s2)', color='b')\\n ax4.tick_params('y', colors='b')\\n\\n ax5 = fig.add_subplot(4, 2, 2, projection='3d')\\n ax5.set_title('Pt 2 Position')\\n ax5.scatter3D(xs=tracklist[trial]['data']['pt2x_smth'],\\n ys=tracklist[trial]['data']['pt2y_smth'],\\n zs=tracklist[trial]['data']['pt2z_smth'],\\n zdir='z', s=3, c=colors, marker='o',\\n edgecolor='none') # 3D Scatter plot\\n ax5.autoscale(enable=True, tight=True)\\n ax5.set_xlabel('X position')\\n ax5.set_ylabel('Y position')\\n ax5.set_zlabel('Z position')\\n plt.colorbar(m, shrink=0.5, aspect=10)\\n\\n ax6 = fig.add_subplot(4, 2, 4)\\n ax6.plot(data.index.values, data['pt2y_v_smth'], 'bo')\\n ax6.set_ylabel('Y velocity (cm/s)', color='b')\\n ax6.tick_params('y', colors='b')\\n\\n ax7 = fig.add_subplot(4, 2, 6)\\n ax7.plot(data.index.values, data['pt2_net_v'], 'bo')\\n ax7.set_ylabel('Net Velocity (cm/s)', color='b')\\n ax7.tick_params('y', colors='b')\\n\\n ax8 = fig.add_subplot(4, 2, 8)\\n ax8.plot(data.index.values, data['pt2_net_a'], 'bo')\\n ax8.set_ylabel('Net accel (cm/s2)', color='b')\\n ax8.tick_params('y', colors='b')\\n\\n plt.show()\",\n \"def convexHull(stimulus_class, subject_list, data_dir, save_dir, save_fig=False):\\n\\t\\n\\tfig = plt.figure(figsize=(15,9))\\n\\tax = fig.subplots()\\n\\tif stimulus_class == 'TIMIT':\\n\\t\\tprint('TIMIT')\\n\\t\\tplt.plot([-0.2, 1.0], [-0.2, 1.0], 'black', label='unity')\\n\\n\\t\\t\\n\\telif stimulus_class == 'MT':\\n\\t\\tprint('trailers')\\n\\t\\tplt.plot([-0.1, 0.5], [-0.1, 0.5], 'black', label='unity')\\n\\n\\telse:\\n\\t\\tprint('Undefined stimulus class')\\n\\t# plt.axis('tight')\\n\\t# plt.axis('square')\\n\\n\\tcorrs = []\\n\\tcorrs_sig = []\\n\\t#corrs_nonsig = []\\n\\tfor idx, s in enumerate(subject_list):\\n\\t\\twith h5py.File('%s/%s/%s_STRF_by_binned_pitches_%s.hf5'%(data_dir, s, s, stimulus_class), 'r') as fh: #full model\\n\\t\\t\\tfullModel = fh['corrs_%s_norm' %(stimulus_class.lower())][:] \\n\\t\\t\\tfullModel[np.isinf(fullModel)]=0\\n\\t\\t\\tp_vals_full = fh['pvals_%s' %(stimulus_class.lower())][:]\\n\\t\\t\\t#full_nonsig = (fullModel[np.where(p_vals_full[0] > 0.05)])\\n\\t\\t\\tfull_sig = (fullModel[np.where(p_vals_full[0] < 0.05)])\\n\\n\\t\\t\\t#binned pitch full model \\n\\t\\twith h5py.File('%s/%s/%s_STRF_by_binned_pitch_only_%s.hf5'%(data_dir, s, s, stimulus_class), 'r') as fh: #full model\\n\\t\\t\\tpitch = fh['corrs_%s_norm' %(stimulus_class.lower())][:] \\n\\t\\t\\tpitch[np.isinf(pitch)]=0\\n\\t\\t\\tp_val_pitch = fh['pvals_%s' %(stimulus_class.lower())][:]\\n\\t\\t\\t#binned_full_nonsig = (pitch[np.where(p_vals_full[0] > 0.05)])\\n\\t\\t\\tbinned_full_sig = (pitch[np.where(p_vals_full[0] < 0.05)])\\n\\t\\t\\tprint(pitch)\\n\\n\\t\\twith h5py.File('%s/%s/%s_STRF_by_envs_%s.hf5'%(data_dir, s, s, stimulus_class), 'r') as h: #envs model only \\n\\t\\t\\tenvs = h['corrs_%s_norm' %(stimulus_class.lower())][:]\\n\\t\\t\\tenvs[np.isinf(envs)]=0\\n\\t\\t\\tp_vals_envs = h['pvals_%s' %(stimulus_class.lower())][:]\\n\\t\\t\\t#envs_nonsig = (envs[np.where(p_vals_full[0] > 0.05)])\\n\\t\\t\\tenvs_sig = (envs[np.where(p_vals_full[0] < 0.05)])\\n\\n\\t\\twith h5py.File('%s/%s/%s_STRF_by_phnfeat_%s.hf5'%(data_dir, s, s, stimulus_class), 'r') as h: #phnfeat model only \\n\\t\\t\\tphnfeat = h['corrs_%s_norm' %(stimulus_class.lower())][:]\\n\\t\\t\\tphnfeat[np.isinf(phnfeat)]=0\\n\\t\\t\\tp_vals_phnfeat = h['pvals_%s' %(stimulus_class.lower())][:]\\n\\t\\t\\t#phnfeat_nonsig = (phnfeat[np.where(p_vals_full[0] > 0.05)])\\n\\t\\t\\tphnfeat_sig = (phnfeat[np.where(p_vals_full[0] < 0.05)])\\n\\n\\t\\tcorrs.append([fullModel, envs, phnfeat, pitch])\\n\\t\\t#corrs_nonsig.append([full_nonsig, envs_nonsig, phnfeat_nonsig, binned_full_nonsig])\\n\\t\\tcorrs_sig.append([full_sig, envs_sig, phnfeat_sig, binned_full_sig])\\n\\n\\tpoints = np.hstack(corrs).T\\n\\tpoints_sig = np.hstack(corrs_sig).T\\n\\t#points_nonsig = np.hstack(corrs_nonsig).T\\n\\n\\thull1=ConvexHull(np.vstack((points_sig[:,0] , points_sig[:,1])).T) #full model, envs\\n\\thullv1 = hull1.vertices.copy()\\n\\thullv1 = np.append(hullv1, hullv1[0])\\n\\tprint(hullv1)\\n\\n\\thull2=ConvexHull(np.vstack((points_sig[:,0] , points_sig[:,2])).T) #full model, phnfeat\\n\\thullv2 = hull2.vertices.copy()\\n\\thullv2 = np.append(hullv2, hullv2[0])\\n\\tprint(hullv2)\\n\\n\\thull3=ConvexHull(np.vstack((points_sig[:,0] , points_sig[:,3])).T) #full model, pitch\\n\\thullv3 = hull3.vertices.copy()\\n\\thullv3 = np.append(hullv3, hullv3[0])\\n\\tprint(hullv3)\\n\\n\\n\\t#fill between: \\n\\tplt.fill(points_sig[hullv2,0], points_sig[hullv2,2], facecolor='#cd1a1e', alpha=0.4, zorder=3, label='full vs. phnfeat') #phnfeat\\n\\tplt.fill(points_sig[hullv3,0], points_sig[hullv3,3], facecolor='#64a7bc', alpha=0.4, zorder=1, label='full vs. pitch') #pitch \\n\\tplt.fill(points_sig[hullv1,0], points_sig[hullv1,1], facecolor='#808080', alpha=0.4, zorder=2, label='full vs. envs') #envs\\n\\n\\t# plt.fill(points[hullv4,0], points[hullv4,4], facecolor='#4B8B3B', alpha=0.4, zorder=4, label='full vs. binned_pitch')\\n\\n\\n\\tplt.plot(points_sig[:,0], points_sig[:,1], '.', color='#808080', alpha=0.8) #envs\\n\\tplt.plot(points_sig[:,0], points_sig[:,2], '.', color='#cd1a1e', alpha=0.8) #phnfeat\\n\\tplt.plot(points_sig[:,0], points_sig[:,3], '.', color='#64a7bc',alpha=0.8) #pitch\\n\\t# plt.plot(points_sig[:,0], points_sig[:,4], '.', color='#4B8B3B', alpha=0.8) #binned pitch \\n\\n\\t# plt.plot(points_nonsig[:,0], points_nonsig[:,1], '.', color='#CDC1C5', alpha=0.7) #envs\\n\\t# plt.plot(points_nonsig[:,0], points_nonsig[:,2], '.', color='#CDC1C5', alpha=0.7) #phnfeat\\n\\t# plt.plot(points_nonsig[:,0], points_nonsig[:,3], '.', color='#CDC1C5',alpha=0.7) #pitch\\n\\t# # plt.plot(points_nonsig[:,0], points_nonsig[:,4], '.', color='#CDC1C5', alpha=0.7) #binned pitch non sig\\n\\t\\n\\tplt.legend(bbox_to_anchor=(1, 1), loc='upper left')\\n\\tplt.xlabel('Full model correlation values (r)')\\n\\tplt.ylabel('Individual model correlation values (r)')\\n\\tplt.title('Convex-Hull for %s (Feature distribution across subjects)' %(stimulus_class))\\n\\tplt.axis('tight')\\n\\tplt.axis('square')\\n\\t\\n\\t#save figure\\n\\tif save_fig:\\n\\t\\tplt.savefig('%s/%s_ConvexHull.pdf' %(save_dir, stimulus_class))\",\n \"def add_contours(self, levels=5, **kwargs):\\n\\n rs, ts = NP.meshgrid(NP.linspace(self.smin, self.smax),\\n NP.linspace(0, NP.pi / 2))\\n # Compute centered RMS difference\\n rms = NP.sqrt(self.refstd ** 2 + rs ** 2 - 2 * self.refstd * rs * NP.cos(ts))\\n\\n contours = self.ax.contour(ts, rs, rms, levels, **kwargs)\\n\\n return contours\",\n \"def PlotCollectionAddOneTwoThree(title, frit, ulist, addvalues):\\n\\tfig = pp.figure( figsize=printsize )\\n\\tnxpoints = 907\\n\\tfig.suptitle = \\\"TEST SUP TITLE\\\" #title\\n\\tplotspots = [231,232,233,234,235,236]\\n\\tfor i,(uvalue,ulabel,xrange,yrange) in enumerate(ulist):\\n\\t print('making sub-plot for u=%g' % (uvalue,)\\t\\n\\t\\tPlotOneSubplot( fig.add_subplot(plotspots[i]), frit, xrange, nxpoints, yrange, uvalue,ulabel, addvalues)\\n\\t#pp.show() \\n\\tfig.savefig(title+\\\".eps\\\", format='eps', pad_inches=0.02)\\n\\n\\naddvalues = [\\n # value to \\\"add\\\", color\\n\\t[ -2, \\\"#6688ee\\\" ],\\n\\t[ -1, \\\"#66aacc\\\" ],\\n\\t[ 0, \\\"#000000\\\" ],\\n\\t[ 1, \\\"#ee6688\\\" ],\\n\\t[ 2, \\\"#dd9966\\\" ],\\n\\t[ 3, \\\"#bbaa66\\\" ],\",\n \"def plot_contours(ax, clf, xx, yy, **params):\\r\\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\r\\n Z = Z.reshape(xx.shape)\\r\\n out = ax.contourf(xx, yy, Z, **params)\\r\\n return out\",\n \"def plot_contours(ax, clf, xx, yy, **params):\\r\\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\r\\n Z = Z.reshape(xx.shape)\\r\\n out = ax.contourf(xx, yy, Z, **params)\\r\\n return out\",\n \"def plot_contours(ax, clf, xx, yy, **params):\\r\\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\r\\n Z = Z.reshape(xx.shape)\\r\\n out = ax.contourf(xx, yy, Z, **params)\\r\\n return out\",\n \"def coffee_plot(times, Temps, axes, legends=[None, None], split=2):\\n plot_model(times[split:], Temps[split:], axes[0], legends[0])\\n plot_samples(times[:split], Temps[:split], axes[1], legends[1])\",\n \"def generate_plot(self, xlimg = None , ylimg =None , exname = '' , prefix = True , save = True):\\n print ('start with the generation of plots')\\n #plot of condensation energy\\n self.plotwrap(0,2, 'energy (a.u.)' , name = 'ge'+ exname, titel = 'the energy (a.u.)', xlim = xlimg , ylim = ylimg , prefix = prefix ,save = save )\\n self.plotwrap(0,1, 'condensation energy (a.u.)' , name = 'ce' + exname ,titel = 'the condensation energy (a.u.)',xlim = xlimg , ylim = ylimg , prefix = prefix,save = save )\",\n \"def plot_contours(ax, clf, xx, yy, **params):\\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\n Z = Z.reshape(xx.shape)\\n out = ax.contourf(xx, yy, Z, **params)\\n return out\",\n \"def plot_contours(ax, clf, xx, yy, **params):\\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\n Z = Z.reshape(xx.shape)\\n out = ax.contourf(xx, yy, Z, **params)\\n return out\",\n \"def plot_contours(ax, clf, xx, yy, **params):\\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\n Z = Z.reshape(xx.shape)\\n out = ax.contourf(xx, yy, Z, **params)\\n return out\",\n \"def plot_contours(ax, clf, xx, yy, **params):\\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\\n Z = Z.reshape(xx.shape)\\n out = ax.contourf(xx, yy, Z, **params)\\n return out\",\n \"def plt_sclr(**kwargs):\\n import argparse\\n import os.path\\n import numpy as np\\n import matplotlib\\n from read_xdr import readXDRsclr, readXDRstruct\\n import gc\\n \\n dirname=os.getcwd() + '/'\\n if not os.path.exists(dirname+\\\"plots\\\"):\\n os.makedirs(dirname+\\\"plots\\\")\\n \\n class C(object):\\n pass\\n arg=C()\\n parser = argparse.ArgumentParser(description=__doc__)\\n parser.add_argument('-i', type=int, help='time index')\\n parser.add_argument('-p', type=int, help='probe')\\n parser.add_argument('-minx', type=float, help='minimum x')\\n parser.add_argument('-maxx', type=float, help='maximum x')\\n parser.add_argument('-minz', type=float, help='minimum z')\\n parser.add_argument('-maxz', type=float, help='maximum z')\\n parser.add_argument('-theta', type=float, action='append', help='azimuthal angle (range)')\\n parser.add_argument('-polarz', type=float, action='append', help='polar projection at z (range)')\\n \\n parser.add_argument('-cmap', type=str, help='colormap')\\n parser.add_argument('-cmin', type=float, help='min value')\\n parser.add_argument('-cmax', type=float, help='max value')\\n parser.add_argument('-slicex', type=float, action='append', help='z-slice along given x')\\n parser.add_argument('-slicez', type=float, action='append', help='x-slice along given z')\\n \\n parser.add_argument('-f', action='store_true', help=\\\"add horizontal mirror image\\\")\\n parser.add_argument('-a', action='store_true', help=\\\"keep aspect ratio\\\")\\n parser.add_argument('-log', action='store_true', help=\\\"log scale\\\")\\n parser.add_argument('-smooth', action='store_true', help=\\\"smooth contour\\\")\\n parser.add_argument('-save', action='store_true', help=\\\"save only\\\")\\n parser.add_argument('-noaxis', action='store_true', help=\\\"show axis\\\")\\n parser.add_argument('-crit', type=float, help=\\\"use density criterion\\\")\\n \\n \\n if \\\"arglist\\\" in kwargs:\\n print(kwargs[\\\"arglist\\\"])\\n parser.parse_args(kwargs[\\\"arglist\\\"],namespace=arg)\\n else:\\n parser.parse_args(namespace=arg) \\n \\n num = int(arg.i) if arg.i != None else 1\\n ivar = int(arg.p) if arg.p != None else 0\\n \\n x0 = float(arg.minx) if arg.minx != None else None \\n x1 = float(arg.maxx) if arg.maxx != None else None \\n z0 = float(arg.minz) if arg.minz != None else None \\n z1 = float(arg.maxz) if arg.maxz != None else None\\n \\n if arg.theta != None:\\n theta = arg.theta \\n if type(theta)!=list: \\n theta=[theta]\\n theta = np.sort(theta)\\n else:\\n theta = None\\n \\n if arg.polarz != None:\\n polarZ = arg.polarz \\n if type(polarZ)!=list: \\n polarZ=[polarZ]\\n polarZ = np.sort(polarZ)\\n else:\\n polarZ = None\\n \\n cmap = arg.cmap if arg.cmap != None else None \\n cmin = float(arg.cmin) if arg.cmin != None else None \\n cmax = float(arg.cmax) if arg.cmax != None else None \\n \\n sliceX = arg.slicex if arg.slicex != None else None\\n sliceZ = arg.slicez if arg.slicez != None else None \\n \\n flip = arg.f\\n aspect= arg.a\\n logScale = arg.log\\n smooth = arg.smooth\\n noShow = arg.save\\n noAxis = arg.noaxis\\n \\n crit = float(arg.crit) if arg.crit != None else None \\n \\n if noShow: matplotlib.use('Agg') # to run in pdebug (not using $DISPLAY)\\n import matplotlib.pyplot as plt\\n if 'classic' in plt.style.available: plt.style.use('classic')\\n import matplotlib.collections as mc\\n from matplotlib.colors import LogNorm\\n from matplotlib import ticker\\n from mpl_toolkits.axes_grid1 import make_axes_locatable\\n import pylab as pl\\n \\n def line2d(x0,x1,z0,z1,X,Z,dat,val):\\n from scipy import interpolate\\n \\n if (z1==z0): return()\\n \\n indx=np.argwhere((X>=np.min([x0,x1]))&(X<=np.max([x0,x1])))\\n indx=indx.reshape(indx.shape[0],1)\\n indz=np.argwhere((Z>=z0)&(Z<=z1))\\n indz=indz.reshape(1,indz.shape[0])\\n \\n tanA=(x1-x0)/(z1-z0)\\n # cosA = np.cos(np.arctan(tanA))\\n npt=100\\n z = np.linspace(z0,z1,npt)\\n x = x0 + (z-z0)*tanA\\n r = np.sqrt((x-x0)**2 + (z-z0)**2).reshape((npt,1))\\n f=interpolate.interp2d(X[indx].flatten(), Z[indz].flatten(), dat[indx,indz].transpose(), kind='cubic')\\n lnout = np.zeros((npt,1))\\n for i in range(npt):\\n lnout[i]=f(x[i], z[i])\\n \\n f, ax = plt.subplots()\\n # ax.plot(z[1:],np.diff(lnout[:,0])/np.diff(r[:,0]),linewidth=2)\\n ax.plot(z,lnout,linewidth=2)\\n i = np.nonzero(lnout[::-1]>val)[0][0] \\n print(x[::-1][i])\\n print(z[::-1][i])\\n plt.show()\\n return(lnout)\\n \\n \\n def plotSliceZ():\\n data=np.zeros((len(X),1));\\n if len(sliceZ)>1:\\n zrange = np.linspace(sliceZ[0],sliceZ[1],100)\\n else:\\n zrange = sliceZ\\n \\n for i in range(len(X)):\\n data[i,0]=np.mean(np.interp(zrange,Z[:],dat[i,:]));\\n f, ax = plt.subplots()\\n ax.plot(X[:],data[:,0],linewidth=2)\\n ax.plot(X[:],np.ones(len(data[:,0]))*np.mean(data[:,0]),linewidth=2)\\n print(\\\"SliceZ mean %.2e\\\" %(np.mean(data[:,0])))\\n ax.set(title=VarNames[ivar]+\\\" (\\\" + \\\\\\n VarUnits[ivar] + \\\")\\\" +\\\" X-slice at Z = %.2f\\\" %(sliceZ[0]) + \\\" at \\\"+tstamp+\\\"ns\\\")\\n ax.set_xlim([xmin,xmax]);\\n ax.set_ylim([cmin,cmax]);\\n ax.set(xlabel='X (cm)', ylabel=VarUnits[ivar])\\n if logScale: ax.set(yscale='log')\\n f.savefig(dirname +\\\"plots/\\\" + VarNames[ivar] + \\\"_%06.1f\\\" %(time) + '_sliceZ.png',dpi=200, bbox_inches=\\\"tight\\\")\\n # fout = open(dirname +\\\"plots/slicez.out\\\", \\\"a+\\\")\\n # fout.write(\\\"%.8e %.8e\\\\n\\\" %(time, np.mean(data[0,:])));\\n # fout.close() \\n fout = open(dirname +\\\"plots/\\\" + VarNames[ivar] + \\\"_%06.1f\\\" %(time) + '_sliceZ.out', \\\"w\\\")\\n for i in range(len(X)):\\n fout.write(\\\"%.8e %.8e\\\\n\\\" %(X[i],data[i,0]))\\n fout.close()\\n if not noShow: \\n plt.show()\\n # # sheath front locator\\n # zdum=Z[0,:]\\n # ydum=np.diff(data[0,:]);\\n # zfront=zdum[np.argwhere(ydum==np.min(ydum))][0]\\n # print(\\\"sheath front z-location %.2f\\\" %(zfront))\\n # fout = open(dirname+'sheath_front.out', \\\"a\\\")\\n # fout.write(\\\"%.8e %.8e\\\" %(time, zfront));\\n # fout.write(\\\"\\\\n\\\");\\n # fout.close() \\n return()\\n \\n def plotSliceX():\\n data=np.zeros((1,len(Z)));\\n if len(sliceX)>1:\\n xrange = np.linspace(sliceX[0],sliceX[1],100)\\n else:\\n xrange = sliceX\\n \\n for j in range(len(Z)):\\n data[0,j]=np.mean(np.interp(xrange,X[:],dat[:,j]));\\n f, ax = plt.subplots()\\n ax.plot(Z[:],data[0,:],linewidth=2)\\n ax.plot(Z[:],np.ones(len(data[0,:]))*np.mean(data[0,:]),linewidth=2)\\n print(\\\"SliceX integral %.2e\\\" %(np.trapz(data[0,:],Z)))\\n ax.set(title=VarNames[ivar]+\\\" (\\\" + \\\\\\n VarUnits[ivar] + \\\")\\\" +\\\" Z-slice at X = %.4f\\\" %(sliceX[0]) + \\\" at \\\"+tstamp+\\\"ns\\\")\\n ax.set_xlim([zmin,zmax]);\\n ax.set_ylim([cmin,cmax]);\\n ax.set(xlabel='Z (cm)', ylabel=VarUnits[ivar])\\n if logScale: ax.set(yscale='log')\\n f.savefig(dirname +\\\"plots/\\\" + VarNames[ivar] + \\\"_%06.1f\\\" %(time) + '_sliceX.png',dpi=200, bbox_inches=\\\"tight\\\")\\n fout = open(dirname +\\\"plots/slicex.out\\\", \\\"a+\\\")\\n fout.write(\\\"%.8e %.8e\\\\n\\\" %(time, np.mean(data[0,:])));\\n fout.close()\\n fout = open(dirname +\\\"plots/\\\" + VarNames[ivar] + \\\"_%06.1f\\\" %(time) + '_sliceX.out', \\\"w\\\")\\n for i in range(len(Z)):\\n fout.write(\\\"%.8e %.8e\\\\n\\\" %(Z[i],data[0,i]))\\n fout.close()\\n\\n ind = np.nonzero(data[0,::-1]>2e18)[0][0]\\n fout = open(dirname +\\\"plots/front.out\\\", \\\"a+\\\")\\n fout.write(\\\"%.8e %.8e\\\\n\\\" %(time, Z[::-1][ind]));\\n fout.close() \\n \\n if not noShow: \\n plt.show()\\n return()\\n \\n def addStructure(ax):\\n (xa,ya,za,xb,yb,zb)=readXDRstruct(dirname+'struct.p4')\\n lines=[[(za[i],xa[i]),(zb[i],xb[i])] for i in range(len(xa))]\\n lc = mc.LineCollection(lines, color=(0.9,0.9,0.9),linewidths=1)\\n ax.add_collection(lc)\\n if flip:\\n lines=[[(za[i],-xa[i]),(zb[i],-xb[i])] for i in range(len(xa))]\\n lc = mc.LineCollection(lines, color=(0.9,0.9,0.9),linewidths=1)\\n ax.add_collection(lc)\\n return()\\n \\n def plotPolar():\\n r, th = np.meshgrid(X, Y)\\n f, ax = plt.subplots(subplot_kw=dict(projection='polar'),figsize=(8,8))\\n cm = matplotlib.cm.get_cmap(cmap)\\n val = dat.transpose(1,0)\\n # contour_levels = arange(cmin, 3, 0.05)\\n if logScale:\\n # im = ax.pcolor(th, r, dat.transpose(1,0),norm=LogNorm(vmin=cmin,vmax=cmax),cmap=cm)\\n im = ax.contourf(th, r, val,norm=LogNorm(vmin=cmin,vmax=cmax),cmap=cm)\\n else:\\n # im = ax.pcolor(th, r, dat.transpose(1,0),vmin=cmin,vmax=cmax,cmap=cm)\\n im = ax.contourf(th, r, val,100,vmin=cmin,vmax=cmax,cmap=cm)\\n ax.xaxis.grid(True, zorder=0)\\n ax.yaxis.grid(True, zorder=0)\\n \\n \\n if noAxis:\\n ax.set(title=VarNames[ivar]) \\n ax.get_xaxis().set_visible(True)\\n ax.get_yaxis().set_visible(False)\\n else:\\n title = VarNames[ivar] + \\\" (\\\" + VarUnits[ivar] + \\\")\\\" +\\\"/Time=%.1fns/Z=%.1fcm\\\" %(time,polarZ[0])\\n ax.set(title=title, xlabel=\\\"R (cm)\\\")\\n plt.colorbar(im)\\n \\n \\n f.savefig(dirname + \\\"plots/\\\" + VarNames[ivar] + \\\"_T%06.1f_Z%04.1f.png\\\" %(time,polarZ[0]),dpi=200, bbox_inches=\\\"tight\\\")\\n if not noShow: \\n plt.show()\\n return()\\n \\n \\n \\n #######################################################\\n \\n fname=dirname+'sclr'+str(num)+'.p4'\\n print(fname)\\n \\n if \\\"sdata\\\" in kwargs:\\n (X,Y,Z,Var,VarNames,VarUnits,time)=kwargs[\\\"sdata\\\"]\\n else:\\n (X,Y,Z,Var,VarNames,VarUnits,time)=readXDRsclr(fname,silent=noShow)\\n \\n # if not noShow:\\n # for i in range(len(Var)):\\n # print(str(i) + \\\" \\\" + VarNames[i] + \\\" \\\" + VarUnits[i])\\n tstamp = \\\"%.2f\\\" % time\\n \\n dat=Var[ivar]\\n if crit!=None:\\n cvar = VarNames.index(\\\"RhoT\\\" + VarNames[ivar][4:])\\n dat[np.nonzero(Var[cvar]<crit)]=0.0\\n \\n if flip: x0=min(X)\\n xmin=x0 if (x0!=None) and (x0>min(X)) and (x0<=max(X)) else min(X)\\n xmax=x1 if (x1!=None) and (x1>xmin) and (x1<=max(X)) else max(X)\\n zmin=z0 if (z0!=None) and (z0>min(Z)) and (z0<max(Z)) else min(Z)\\n zmax=z1 if (z1!=None) and (z1>zmin) and (z1<=max(Z)) else max(Z)\\n \\n indx=np.argwhere((X>=xmin)&(X<=xmax))\\n indx=indx.reshape(indx.shape[0],1,1)\\n indz=np.argwhere((Z>=zmin)&(Z<=zmax))\\n indz=indz.reshape(1,1,indz.shape[0])\\n indy=np.arange(len(Y)).reshape(1,len(Y),1)\\n \\n #indz=np.transpose(indz)\\n X=X[indx].flatten()\\n Y=Y[indy].flatten()\\n Z=Z[indz].flatten()\\n \\n dat=dat[indx,indy,indz]\\n \\n if type(polarZ)!=type(None):\\n # calculate weights for nonuniform grid spacing\\n dz = np.diff(Z)\\n wZ = np.ones(len(Z))\\n wZ[0] = 0.5*dz[0]\\n wZ[1:-1]=0.5*(dz + np.roll(dz,-1))[0:-1]\\n wZ[-1]=0.5*dz[-1]\\n \\n zdum = polarZ[0]\\n k10 = np.max([1,int(np.argwhere(Z>=zdum)[0][0])])\\n k00 = int(k10-1)\\n w00 = (Z[k10] - zdum)/(Z[k10] - Z[k00])\\n wZ[k00] *= w00\\n \\n zdum = polarZ[-1]\\n k11 = np.max([1,int(np.argwhere(Z>=zdum)[0][0])])\\n k01 = int(k11-1)\\n w01 = (Z[k11] - zdum)/(Z[k11] - Z[k01])\\n wZ[k11] *= (1-w01)\\n dat = np.average(dat[:,:,k00:k11+1], axis=2, weights=wZ[k00:k11+1])\\n \\n plotPolar()\\n exit() \\n \\n if type(theta)!=type(None):\\n wY = np.ones(len(Y))\\n # calculate weights for nonuniform grid spacing\\n if len(Y)>1:\\n dy = np.diff(Y)\\n wY[0] = 0.5*dy[0]\\n wY[1:-1]=0.5*(dy + np.roll(dy,-1))[0:-1]\\n wY[-1]=0.5*dy[-1] \\n \\n thdum = np.radians(theta[0])\\n thdum = divmod(thdum,np.pi*2)[1] if thdum>np.pi*2 else thdum\\n j10 = int(np.argwhere(Y>=thdum)[0][0])\\n if j10==0: \\n j10+=1\\n j00 = int(j10-1)\\n w00 = (Y[j10] - thdum)/(Y[j10] - Y[j00])\\n wY[j00] = w00*wY[j00] # weight of outermost left grid point\\n \\n thdum = np.radians(theta[-1])\\n thdum = divmod(thdum,np.pi*2)[1] if thdum>np.pi*2 else thdum\\n j11 = int(np.argwhere(Y>=thdum)[0][0])\\n if j11==0: \\n j11+=1 \\n j01 = int(j11-1)\\n w01 = (Y[j11] - thdum)/(Y[j11] - Y[j01])\\n wY[j11] = (1 - w01)*wY[j11] # # weight of outermost right grid point\\n \\n dat = np.average(dat[:,j00:j11+1,:], axis=1, weights=wY[j00:j11+1])\\n else:\\n dat=np.squeeze(dat[:,0,:])\\n \\n if cmin is None:\\n cmin = dat.min()\\n if cmax is None:\\n cmax = dat.max()\\n \\n fig, ax = plt.subplots(figsize=(8,8))\\n if flip:\\n dat=np.concatenate((np.flipud(dat),dat),axis=0);\\n X = np.concatenate([-X[::-1],X])\\n \\n cm = matplotlib.cm.get_cmap(cmap)\\n \\n if logScale:\\n locator = ticker.LogLocator(base=10)\\n if smooth:\\n dat[np.nonzero(dat>cmax)] = cmax\\n dat[np.nonzero(dat<cmin)] = cmin\\n levels = np.power(10,np.linspace(np.log10(cmin),np.log10(cmax),num=100))\\n im = ax.contourf(Z,X,dat,norm=LogNorm(vmin=cmin,vmax=cmax),cmap=cm,locator=locator,levels=levels)\\n else:\\n im = ax.pcolor(Z,X,dat,norm=LogNorm(vmin=cmin,vmax=cmax),cmap=cm)\\n else:\\n if smooth:\\n dat[np.nonzero(dat>cmax)] = cmax\\n dat[np.nonzero(dat<cmin)] = cmin \\n levels = ticker.MaxNLocator(nbins=100).tick_values(cmin, cmax)\\n im = ax.contourf(Z,X,dat,vmin=cmin,vmax=cmax,cmap=cm,levels=levels)\\n else:\\n im = ax.pcolor(Z,X,dat,vmin=cmin,vmax=cmax,cmap=cm)\\n \\n ax.set_xlim([min(Z[:]),max(Z[:])])\\n ax.set_ylim([min(X[:]),max(X[:])])\\n \\n title = VarNames[ivar] + \\\" (\\\" + VarUnits[ivar] + \\\")\\\" + \\\"/Time %.1fns\\\" %(time)\\n if type(theta)!=type(None):\\n title = title + \\\"/Angle %.1f-%.1fdeg\\\" %(theta[0],theta[-1])\\n ax.set(title=title,xlabel=\\\"Z (cm)\\\",ylabel=\\\"R (cm)\\\")\\n \\n # add structure\\n if os.path.isfile(dirname+'struct.p4'):\\n addStructure(ax)\\n \\n if (aspect or flip):\\n ax.set_aspect('equal');\\n else:\\n ax.set_aspect('auto') \\n \\n divider = make_axes_locatable(ax)\\n cax = divider.append_axes(\\\"right\\\", size=\\\"5%\\\", pad=0.05)\\n \\n if not logScale: #set exponent base for colorbar formatter\\n CB = plt.colorbar(im, cax=cax)\\n CB.formatter.set_powerlimits((0, 2))\\n CB.update_ticks()\\n else:\\n CB = plt.colorbar(im, cax=cax, ticks=locator)\\n \\n if noAxis:\\n ax.set(title=VarNames[ivar],\\n xlabel=\\\"Z (cm)\\\", ylabel=\\\"R (cm)\\\") \\n plt.axis('off')\\n ax.get_xaxis().set_visible(False)\\n ax.get_yaxis().set_visible(False)\\n \\n ftitle = VarNames[ivar] + \\\"_T%07.1f\\\" %(time)\\n if type(theta)!=type(None):\\n ftitle = ftitle + \\\"_A%5.1f-%5.1f\\\" %(theta[0],theta[-1])\\n fig.savefig(dirname + \\\"plots/\\\" + ftitle + '.png',dpi=200, bbox_inches=\\\"tight\\\")\\n #cax.yaxis.major.locator.set_params(nbins=8) \\n \\n if sliceX!= None and sliceX[-1]<=xmax and sliceX[0]>=xmin: # plot slice along Z\\n plotSliceX()\\n \\n if sliceZ!= None and sliceZ[-1]<=zmax and sliceZ[0]>=zmin: # plot slice along X\\n plotSliceZ()\\n \\n # line2d(0,2,0,8,X,Z,dat,1.5e18)\\n \\n if not noShow: \\n plt.show()\\n \\n return()\",\n \"def _nd_plot_samples(self, **kwargs):\\n\\n from pesummary.core.plots.plot import _make_comparison_corner_plot as plotfunc\\n\\n plotkwargs = kwargs.copy()\\n\\n args = [self._samples]\\n plotkwargs[\\\"corner_parameters\\\"] = self.parameters\\n if \\\"latex_labels\\\" not in kwargs:\\n plotkwargs[\\\"latex_labels\\\"] = self.latex_labels\\n\\n if \\\"plot_percentile\\\" not in kwargs:\\n plotkwargs[\\\"plot_percentile\\\"] = False\\n\\n # get ranges for each parameter to set figure axes extents\\n if \\\"range\\\" not in kwargs:\\n range = []\\n for param in self.parameters:\\n range.append(\\n [\\n np.min(\\n [samps[param].min() for samps in self._samples.values()]\\n ),\\n np.max(\\n [samps[param].max() for samps in self._samples.values()]\\n ),\\n ]\\n )\\n plotkwargs[\\\"range\\\"] = range\\n\\n # default to not show quantile lines\\n plotkwargs.setdefault(\\\"quantiles\\\", None)\\n\\n # set default injection line color\\n plotkwargs.setdefault(\\\"truth_color\\\", \\\"k\\\")\\n\\n # set injection parameter values\\n if self.injection_parameters is not None:\\n injpars = [\\n self.injection_parameters[p] - self.parameter_offsets[p]\\n for p in self.parameters\\n if self.injection_parameters[p] is not None\\n ]\\n if len(injpars) == self._num_parameters:\\n plotkwargs[\\\"truths\\\"] = injpars\\n\\n # create plot\\n with DisableLogger():\\n fig = plotfunc(*args, **plotkwargs)\\n\\n # turn frame off on legend\\n fig.legends[0].set_frame_on(False)\\n\\n return fig\",\n \"def plot(self,nwins,show=True,fname=None):\\n #setup figure and axes ...\\n maxcolumns = 5\\n ncolumns = min(maxcolumns,nwins)\\n nrows = np.ceil(nwins/ncolumns).astype(int)\\n figsize = ncolumns * 1.2, nrows * 1.2 + 0.5\\n fig, axes = plt.subplots(nrows, ncolumns, figsize=figsize)\\n for ax in axes[:-1,:].flatten():\\n for xlabel_i in ax.get_xticklabels():\\n xlabel_i.set_visible(False)\\n for ax in axes[:,1:].flatten():\\n for ylabel_i in ax.get_yticklabels():\\n ylabel_i.set_visible(False)\\n\\n #loop through tapers and plot them\\n for itaper in range( min(self.nwins, nwins) ):\\n evalue = self.eigenvalues[itaper]\\n coeffs = self._coeffs(itaper)\\n ax = axes.flatten()[itaper]\\n grid = MakeGridDH(coeffs)\\n ax.imshow(grid)\\n ax.set_title('concentration: {:2.2f}'.format(evalue))\\n fig.tight_layout(pad=0.5)\\n\\n if show: plt.show()\\n if fname is not None:\\n fig.savefig(fname)\",\n \"def plotit(X,Y=None,clf=None, markers = ('s','o'), hold = False, transform = None):\\n eps=1e-6\\n minx, maxx = np.min(X[:,0]), np.max(X[:,0])\\n miny, maxy = np.min(X[:,1]), np.max(X[:,1])\\n \\n if clf is not None:\\n npts = 150\\n x = np.linspace(minx,maxx,npts)\\n y = np.linspace(miny,maxy,npts)\\n t = np.array(list(itertools.product(x,y)))\\n if transform is not None:\\n t = transform(t)\\n z = clf(t)\\n z = np.reshape(z,(npts,npts)).T \\n extent = [minx,maxx,miny,maxy]\\n plt.contour(x,y,z,[-1+eps,0,1-eps],linewidths = [2],colors=('b','k','r'),extent=extent, label='f(x)=0')\\n #plt.imshow(np.flipud(z), extent = extent, cmap=plt.cm.Purples, vmin = -2, vmax = +2); plt.colorbar()\\n plt.pcolormesh(x, y, z,cmap=plt.cm.Purples,vmin=-2,vmax=+2);plt.colorbar()\\n plt.axis([minx,maxx,miny,maxy])\\n \\n if Y is not None:\\n \\n plt.scatter(X[Y==1,0],X[Y==1,1],marker = markers[0], c = 'y', s = 30)\\n plt.scatter(X[Y==-1,0],X[Y==-1,1],marker = markers[1],c = 'c', s = 30)\\n plt.xlabel('$x_1$')\\n plt.ylabel('$x_2$') \\n \\n else:\\n plt.scatter(X[:,0],X[:,1],marker = '.', c = 'k', s = 5)\\n if not hold:\\n plt.grid()\\n plt.show()\",\n \"def individual_plot(z, cf_lvls, cl_lvls, cline_label_format=cline_label_format_default, log_scale=False ,\\n extend=\\\"neither\\\"):\\n map_plot.drawcoastlines(linewidth=.4)\\n\\n if log_scale:\\n norm = colors.LogNorm(vmin=cf_lvls[0], vmax=cf_lvls[-1])\\n else:\\n norm = None\\n\\n if extend == 'neither':\\n contour_fills = map_plot.contourf(x_grid, y_grid, z, cf_lvls, cmap=color_map, norm=norm)\\n else:\\n contour_fills = map_plot.contourf(x_grid, y_grid, z, cf_lvls, cmap=color_map, norm=norm, extend=extend)\\n contour_lines = plt.contour(x_grid, y_grid, z, cl_lvls, colors='0.1', linewidths=1)\\n\\n # Label levels with specially formatted floats\\n plt.rcParams['font.weight'] = 'bold'\\n plt.clabel(contour_lines, fmt=cline_label_format, inline=1, fontsize=9, colors='k')\\n plt.rcParams['font.weight'] = 'normal'\\n\\n return contour_fills\",\n \"def make_plots(river_name, values):\\n\\n fig, ax = plt.subplots()\\n\\n ax.set_title(river_name)\\n ax.set_ylabel('Cubic Feet per Second (cfs)')\\n ax.axhline(y=sum(values)/len(values), color='red', linestyle=\\\"--\\\", label='Average Water Level')\\n ax.legend()\\n\\n ax.set_ylim(min(values) * .95, max(values) * 1.05)\\n\\n return ax\",\n \"def plot_multidimensional_function_slices(\\n func: Callable[[np.ndarray], NDAorTuple],\\n slice_loc: np.ndarray,\\n bounds: Union[np.ndarray, List[Tuple[float, float]]],\\n input_names: Optional[List[str]] = None,\\n obs_points: Optional[Union[np.ndarray, List[np.ndarray]]] = None,\\n input_scales: Optional[List[PLOT_SCALE]] = None,\\n output_scale: PLOT_SCALE = \\\"linear\\\",\\n output_label: str = \\\"Objective Value\\\",\\n size: float = 3,\\n slice_2d_resolution: int = 50,\\n # slide_1d_resolution: int = 100,\\n func_returns_confidence_intervals: bool = False,\\n) -> Tuple[plt.Figure, np.ndarray]:\\n # Input validation checks\\n assert output_scale in [\\\"linear\\\", \\\"log\\\", \\\"symlog\\\"]\\n\\n def func_return_just_mean(x):\\n \\\"\\\"\\\"\\n If the supplied function is a predictor returning lower and upper confidence bounds as well as mean,\\n return just the mean prediction. If not, return the function value evaluated at x.\\n \\\"\\\"\\\"\\n return func(x)[0] if func_returns_confidence_intervals else func(x)\\n\\n n_dims: int = len(bounds)\\n # If multiple batches of points supplied as a list in obs_points, make a colour palette\\n n_batches = len(obs_points) if isinstance(obs_points, (list, tuple)) else 1\\n scatter_colours = sns.color_palette(\\\"viridis\\\", n_colors=n_batches)\\n # If input_scales not specified, default all to 'linear'\\n input_scales = input_scales if input_scales else [\\\"linear\\\"] * n_dims # type: ignore # auto\\n # Keep track of contour sets returned for each axis\\n contour_sets = []\\n\\n # Construct axes\\n fig = plt.figure(figsize=(size * n_dims, size * n_dims))\\n axes, cbar_axes = make_lower_triangular_axis_grid_with_colorbar_axes(\\n fig=fig, num_cols=n_dims, num_colorbars=2, share_y_on_diagonal=True\\n )\\n\\n # Keep a running minimum and maximum of function values in 2D slices\\n func_values_min: float = np.inf\\n func_values_max: float = -np.inf\\n\\n with sns.axes_style(\\\"darkgrid\\\"):\\n for i in range(n_dims): # i iterates over the rows of the plots\\n for j in range(n_dims): # j iterates over the columns of the plots\\n ax = axes[i, j]\\n # 1D-slice plots along the diagonal\\n if i == j:\\n if func_returns_confidence_intervals:\\n plot_1d_slice_through_function_with_confidence_intervals(\\n func, # type: ignore\\n dim=i,\\n slice_loc=slice_loc,\\n slice_bounds=bounds[i],\\n ax=ax,\\n x_scale=input_scales[i],\\n )\\n else:\\n plot_1d_slice_through_function(\\n func, # type: ignore\\n dim=i,\\n slice_loc=slice_loc,\\n slice_bounds=bounds[i],\\n ax=ax,\\n x_scale=input_scales[i],\\n )\\n ax.set_yscale(output_scale)\\n\\n # lower triangle\\n elif i > j:\\n dim_x, dim_y = j, i\\n # Compute the data for the 2D slice plots\\n xx, yy, func_values_slice = calc_2d_slice(\\n func=func_return_just_mean, # type: ignore # auto\\n dim_x=dim_x,\\n dim_y=dim_y,\\n slice_loc=slice_loc,\\n slice_bounds_x=bounds[dim_x],\\n slice_bounds_y=bounds[dim_y],\\n x_scale=input_scales[dim_x],\\n y_scale=input_scales[dim_y],\\n resolution=slice_2d_resolution,\\n )\\n # Plot the 2D slice\\n _, im = plot_2d_slice_from_arrays(\\n xx,\\n yy,\\n func_values_slice,\\n ax=ax,\\n x_scale=input_scales[dim_x],\\n y_scale=input_scales[dim_y],\\n output_scale=output_scale,\\n )\\n contour_sets.append(im)\\n # Keep a running minimum and maximum of function values in slices\\n func_values_min = min(func_values_min, func_values_slice.min()) # type: ignore\\n func_values_max = max(func_values_max, func_values_slice.max()) # type: ignore\\n # Scatter points on the slices if given\\n if obs_points is not None: # pragma: no cover\\n if isinstance(obs_points, np.ndarray):\\n # If just one array given, scatter with the colour reflecting objective value\\n ax.scatter(\\n obs_points[:, dim_x], obs_points[:, dim_y], color=scatter_colours[0], s=20, zorder=15\\n )\\n else:\\n assert isinstance(obs_points, (list, tuple))\\n # If multiple arrays given, colour the points according to the batch number\\n for batch_num, batch_arr in enumerate(obs_points):\\n ax.scatter(\\n batch_arr[:, dim_x],\\n batch_arr[:, dim_y],\\n color=scatter_colours[batch_num],\\n s=25,\\n lw=0.0,\\n alpha=0.8,\\n zorder=15,\\n )\\n # Add axis labels\\n if input_names is not None: # pragma: no cover\\n # If plot in the first column (but not first row), add a y_label\\n if i != 0 and j == 0:\\n axes[i, j].set_ylabel(input_names[i])\\n # If plot is at the bottom, add an x_label\\n if i == n_dims - 1:\\n axes[i, j].set_xlabel(input_names[j])\\n if i >= j:\\n # Remove redundant ticks on inner plots\\n if i != n_dims - 1:\\n axes[i, j].xaxis.set_visible(False)\\n if j != 0:\\n axes[i, j].yaxis.set_visible(False)\\n # # Prune the upper-most tick from plot, so that the ticks don't overlap each other between plots\\n # ax.yaxis.set_major_locator(ticker.MaxNLocator(prune='upper'))\\n ax.tick_params(axis=\\\"both\\\", which=\\\"major\\\", labelsize=9)\\n ax.tick_params(axis=\\\"both\\\", which=\\\"minor\\\", labelsize=6)\\n # Update the colour limits of the slice plots\\n for contour_set in contour_sets:\\n contour_set.set_clim(vmin=func_values_min, vmax=func_values_max)\\n # Add the colourbars\\n if n_dims > 1:\\n # make a colourbar for the contour plots\\n cb1 = fig.colorbar(contour_sets[-1], cax=cbar_axes[0], aspect=50)\\n cb1.set_label(output_label)\\n cbar_axes[0].yaxis.set_ticks_position(\\\"left\\\")\\n # make a colourbar for different batches\\n if n_batches > 1: # pragma: no cover\\n cb2 = matplotlib.colorbar.ColorbarBase( # type: ignore # auto\\n cbar_axes[1],\\n cmap=matplotlib.colors.ListedColormap(scatter_colours),\\n boundaries=[x - 0.5 for x in range(n_batches + 1)],\\n ticks=list(range(n_batches)),\\n spacing=\\\"proportional\\\",\\n )\\n cb2.set_label(\\\"Batch Number\\\")\\n else:\\n cbar_axes[1].set_visible(False)\\n return fig, axes\",\n \"def visualizeObs():\\n fcontourf(fObs, [-2, 2], [-1, 1], [0, 10])\",\n \"def build_scatter_plots(dep_vars, cont_corr_data, continous, combined_data, drop_outliers=True, tps=False,\\n replacement=0.01):\\n\\n for i, column in enumerate(dep_vars.columns):\\n # Calculating number of graphs\\n cont_cols = continous.columns.to_list()\\n if i != len(dep_vars.columns) - 1 and not tps:\\n cont_cols = cont_cols + dep_vars.columns.to_list()[i + 1:]\\n if not tps:\\n num_rows = len(cont_cols) + 2 * len(continous.columns) + 2 * len(dep_vars.columns.to_list()[i + 1:])\\n else:\\n num_rows = 3 * len(cont_cols)\\n fig = make_subplots(rows=num_rows, subplot_titles=range(num_rows))\\n row_num = 1\\n for value in cont_cols:\\n # Getting data\\n scatter_data = pd.DataFrame({\\\"x\\\": combined_data[value],\\n \\\"y\\\": combined_data[column]})\\n if drop_outliers:\\n # Apply 3 sigma rule\\n scatter_data = scatter_data.loc[rem_outliers(scatter_data[\\\"x\\\"])]\\n scatter_data = scatter_data.loc[rem_outliers(scatter_data[\\\"y\\\"])]\\n # Replace zeros with little values and create logarithmic variation\\n scatter_data[\\\"y\\\"] = scatter_data[\\\"y\\\"].apply(lambda x: replacement if x == 0 else x)\\n log_y = scatter_data[\\\"y\\\"].apply(lambda x: log(x))\\n log_x = scatter_data[\\\"x\\\"].apply(lambda x: replacement if x == 0 else x).apply(lambda x: log(x))\\n\\n # Creating graph on initial data, stating first pearson p-value, kendall p-value and sample size\\n p_value_pearson = cont_corr_data.loc[f\\\"pearson_{column}\\\", value]\\n p_value_kendall = cont_corr_data.loc[f\\\"kendall_{column}\\\", value]\\n sample_size = int(cont_corr_data.loc[f\\\"sample_size_{column}\\\", value])\\n text = f\\\"pearson: {p_value_pearson:.4f}, kendall: {p_value_kendall:.4f}, sample: {sample_size}\\\"\\n build_scatter_subplot(fig, scatter_data[\\\"x\\\"], scatter_data[\\\"y\\\"], row_num, text, value, column)\\n row_num = row_num + 1\\n\\n # Creating graph on log data, stating second pearson p-value, kendall p-value and sample size\\n p_value_pearson = cont_corr_data.loc[f\\\"log_pearson_{column}\\\", value]\\n p_value_kendall = cont_corr_data.loc[f\\\"kendall_{column}\\\", value]\\n sample_size = int(cont_corr_data.loc[f\\\"sample_size_{column}\\\", value])\\n text = f\\\"log({column}) pearson: {p_value_pearson:.4f}, kendall: {p_value_kendall:.4f}, \\\" \\\\\\n f\\\"sample: {sample_size}\\\"\\n build_scatter_subplot(fig, scatter_data[\\\"x\\\"], log_y, row_num, text, value, column)\\n row_num = row_num + 1\\n\\n # Creating graph on log indep variable, stating first pearson p-value, kendall p-value and sample size\\n p_value_pearson = cont_corr_data.loc[f\\\"log({value})_{column}_pearson\\\", value]\\n sample_size = int(cont_corr_data.loc[f\\\"sample_size_{column}\\\", value])\\n text = f\\\"log({value}) pearson: {p_value_pearson:.4f}, sample: {sample_size}\\\"\\n build_scatter_subplot(fig, log_x, scatter_data[\\\"y\\\"], row_num, text, value, column)\\n row_num = row_num + 1\\n if not tps:\\n fig.update_layout(height=4800, width=600)\\n else:\\n fig.update_layout(height=600, width=600)\\n fig.update_layout(showlegend=False, plot_bgcolor='rgba(0,0,0,0)')\\n fig.write_html(f'figures/scatter_{column}_{time.time()}.html', auto_open=True)\",\n \"def plot_violins(times, func_names, output_file):\\n fig, axes = plt.subplots(1, len(func_names))\\n for name, func_time, ax in zip(func_names, times, axes):\\n sns.violinplot(y=func_time, ax=ax)\\n ax.set_ylabel('Time (ms)')\\n ax.set_title(name)\\n fig.tight_layout()\\n fig.savefig(output_file)\",\n \"def contours_and_data(epoch, model, features, filters, figname, fgal=0.5,\\n idx=-1, data='s82', N=60000):\\n if data == 's82':\\n # fetch Stripe 82 data\\n X, Xcov = fetch_prepped_s82data(epoch, fgal, features, filters)\\n Xcoadd, Xcoaddcov = fetch_prepped_s82data(epoch, fgal, features,\\n filters, use_single=False)\\n sind = np.abs(Xcoadd[:, idx]) < 0.03\\n gind = np.abs(Xcoadd[:, idx]) > 0.03\\n\\n else:\\n # fetch DR10 data\\n X, Xcov = fetch_prepped_dr10data(N, fgal, features, filters)\\n sind = np.abs(X[:, idx]) < 0.145\\n gind = np.abs(X[:, idx]) > 0.145\\n\\n # unpickle the XD model\\n if type(model) == str: \\n f = open(model, 'rb')\\n model = cPickle.load(f)\\n f.close()\\n\\n fs = 5\\n ms = 1\\n lsize = 20\\n idx = [[0, -1], [2, 3], [3, 4]]\\n xlim = [(18., 22), (-0.5, 2.5), (-0.5, 2)]\\n ylim = [(-0.1, 0.5), (-0.5, 2.5), (-0.5, 1.5)]\\n xlab = ['psfmag $r$', 'modelmag $g-r$', 'modelmag $r-i$']\\n ylab = ['psfmag - modelmag $r$', 'modelmag $r-i$', 'modelmag $i-z$']\\n\\n f = pl.figure(figsize=(3 * fs, 3 * fs))\\n Nstar = len(np.where(model.fixed_means[:, idx] != np.inf)[0])\\n pl.subplots_adjust(wspace=0.3)\\n for i in range(1, 10):\\n k = (i - 1) % 3\\n if i < 4:\\n ind = np.arange(X.shape[0], dtype=np.int)\\n rng = range(model.n_components)\\n elif 3 < i < 7:\\n ind = sind\\n rng = range(Nstar)\\n else:\\n ind = gind\\n rng = range(Nstar, model.n_components)\\n ax = pl.subplot(3, 3, i)\\n for j in rng:\\n if model.alpha[j] > 1.e-3:\\n draw_ellipse(model.mu[j, idx[k]],\\n model.V[j, idx[k]][:, idx[k]],\\n scales=[2], ec='k', fc='gray', alpha=0.2)\\n pl.plot(X[ind][::10, idx[k][0]],\\n X[ind][::10, idx[k][1]], '.k',ms=ms)\\n pl.xlim(xlim[k])\\n pl.ylim(ylim[k])\\n pl.xlabel(xlab[k], fontsize=lsize)\\n pl.ylabel(ylab[k], fontsize=lsize)\\n if ('psf' in ylab[k]) & ('model' in ylab[k]):\\n ytick = ['%0.1f' % v for v in np.linspace(-.1, 0.4, 6)]\\n ytick[0] = ''\\n ax.set_yticklabels(ytick)\\n if i == 1:\\n s = 'All'\\n elif i == 3:\\n s = '\\\"Stars\\\"'\\n else:\\n s = '\\\"Galaxies\\\"'\\n ax.text(-.3, 0.5, s, ha='center', va='center', fontsize=25,\\n rotation='vertical', transform=ax.transAxes)\\n f.savefig(figname, bbox_inches='tight')\",\n \"def scatterPlot(data_dict, features_list, name=\\\"image.png\\\", f1_name=\\\"feature 1\\\", f2_name=\\\"feature 2\\\"):\\n data = featureFormat(data_dict, features_list)\\n poi, finance_features = targetFeatureSplit(data)\\n for i, p in enumerate(poi):\\n non_poi = plt.scatter(finance_features[i][0], finance_features[i][1])\\n if p:\\n poi = plt.scatter(finance_features[i][0], finance_features[i][1], color=\\\"r\\\", marker=\\\"*\\\") \\n plt.xlabel(f1_name)\\n plt.ylabel(f2_name)\\n plt.legend((non_poi, poi), ('Non POI', 'POI'),loc='upper left')\\n plt.title('scatter plot of 2 features')\\n plt.savefig(name)\\n plt.show()\",\n \"def plot_spatial_correlations_with_significance(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/timeseries/INDICIES/', indicies = ['SAM', 'IPO', 'DMI'], seaice_source = 'nsidc'):\\n for temp_res, temp_decomp, dt in itertools.product(temporal_resolution, temporal_decomposition, detrend):\\n plot_spatial_correlation_with_significance(anomlous = temp_decomp, temporal_resolution = temp_res, detrend = dt, temp_decomp = temp_decomp, imagefolder = 'images/correlations/spatial/', n = 1, seaice_source = seaice_source)\",\n \"def set_subplots(self, names: [str]) -> None:\\n self._plots = list()\\n self._logger.debug(\\\"running\\\")\\n if len(names) < 1:\\n return\\n r = len(names)\\n c = 1\\n for i in range(0, r):\\n self._plots.append((names[i], (r, c, i + 1), True))\\n self._logger.debug(\\\"done\\\")\",\n \"def CII_vs_CO(**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\\n fig,ax1 = plt.subplots()\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n L_CO = getattr(GR,'L_CO(1-0)_sun')\\n Zsfr = getattr(GR,'Zsfr')\\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \\n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\\n sc = ax1.scatter(np.log10(L_CO)-10, np.log10(L_CII)-10, marker='o', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\\\\n vmin=np.log10(0.05), vmax=np.log10(3.1), \\\\\\n s=10, alpha=0.8)#, label='SIGAME 100Mpc_arepoPDF')\\n # print('Min Zsfr in Simba sample: ',np.min(Zsfr))\\n # print('indices with L_CO < 1e0:')\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\\\\n 4:lL_CII.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=8,zorder=10)\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\\n\\n GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0],grid_ext=p.grid_exts[1])\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n L_CO = getattr(GR,'L_CO(1-0)_sun')\\n Zsfr = getattr(GR,'Zsfr')\\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \\n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\\n lL_CO = np.append(lL_CO,np.array([6.1,5]))\\n lL_CII = np.append(lL_CII,np.array([8.9,9.7]))\\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\\\\n # s=10, alpha=0.8, label='SIGAME 25Mpc_arepoPDF')\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\\\\n 4:lL_CII.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='deepskyblue',linestyles='dotted',levels=6)\\n CS.collections[0].set_label('SIGAME 25Mpc_arepoPDF')\\n\\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\\n L_CII = getattr(GR,'L_[CII]158_sun')\\n L_CO = getattr(GR,'L_CO(1-0)_sun')\\n Zsfr = getattr(GR,'Zsfr')\\n lL_CO, lL_CII = np.log10(L_CO), np.log10(L_CII) \\n lL_CO, lL_CII = lL_CO[(L_CO > 0) & (L_CII > 0)], lL_CII[(L_CO > 0) & (L_CII > 0)]\\n lL_CO = np.append(lL_CO,np.array([-2.2,4.7]))\\n lL_CII = np.append(lL_CII,np.array([8,9.3]))\\n # ax1.scatter(np.log10(L_CO), np.log10(L_CII), marker='^', c=np.log10(Zsfr), cmap='viridis', zorder=10,\\\\\\n # vmin=np.log10(0.05), vmax=np.log10(3.1), \\\\\\n # s=10, alpha=0.8, label='SIGAME v3 Simba-%s' % (p.sim_runs[0].replace('_','').replace('Mpc','')))\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lL_CO,lL_CII]).T)\\n x, y = np.mgrid[lL_CO.min():lL_CO.max():nbins*1j, \\\\\\n 4:lL_CII.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax1.contour(x, y, z.reshape(x.shape),colors='brown',levels=8,zorder=5,linestyles='dashed')\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\\n\\n # Observations\\n K16 = pd.read_pickle('data/observations/AHIMSA_sample_lit')\\n K16_LCII = K16['[CII]158_Lsun']\\n K16_LCO = K16['CO(1-0)_Lsun']\\n ax1.plot(np.log10(K16_LCO), np.log10(K16_LCII), '>', color='grey', ms=6, fillstyle='none',alpha=0.8, mew=1,zorder=0,\\\\\\n label='Mixed type galaxies [Kamenetzky+16]')\\n\\n C15 = pd.read_pickle('data/observations/DGS_Cormier_2015')\\n C15_LCII = C15['L_[CII]158']\\n C15_LCO = C15['L_CO(1-0)']\\n C15_Z = C15['Z']\\n # L_ul = C15['L_[CII]158'][(C15['L_[CII]158'] < 0) & (C15['L_CO(1-0)'] > 0)]\\n # if len(L_ul) > 0:\\n # ax1.plot(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul),'s',ms=5,mew=0,color='grey',alpha=0.8)\\n # ax1.errorbar(np.log10(C15['L_CO(1-0)'][C15['L_[CII]158'] < 0]),np.log10(-1.*L_ul), elinewidth=1,\\\\\\n # uplims=np.ones(len(L_ul)),yerr=np.ones(len(L_ul))*1,color='grey',alpha=0.8,lw=0)\\n ax1.scatter(np.log10(C15_LCO), np.log10(C15_LCII), marker='+', c=np.log10(C15_Z), cmap='viridis', zorder=0,\\\\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\\\\n s=100, lw=3, alpha=0.8, label='Dwarf galaxies [Cormier+15]')\\n\\n A17 = pd.read_pickle('data/observations/xCOLD_GASS_Accurso_2017')\\n A17 = A17.loc[np.argwhere(A17['L_CO(1-0)'].values > 0).flatten()]\\n ax1.scatter(A17['L_CO(1-0)'],A17['L_[CII]158'], marker='d', c=np.log10(A17['Z']), cmap='viridis', zorder=0,\\\\\\n vmin=np.log10(0.05), vmax=np.log10(3.1),\\\\\\n s=50, lw=0, alpha=0.8, label='COLD GASS [Accurso+17]') #c=np.log10(A17['Z']), \\n\\n CII_obs = np.log10(np.append(K16_LCII.values,C15_LCII.values))\\n CO_obs = np.log10(np.append(K16_LCO.values,C15_LCO.values))\\n CII_obs = np.append(CII_obs,A17['L_[CII]158'].values)\\n CO_obs = np.append(CO_obs,A17['L_CO(1-0)'].values)\\n index = np.argwhere((CII_obs > 0) & (CO_obs > 0)).flatten()\\n CII_obs = CII_obs[index]\\n CO_obs = CO_obs[index]\\n\\n x = np.linspace(0, 7, 100)\\n fit = LinearRegression().fit(CO_obs.reshape(-1, 1),\\\\\\n CII_obs.reshape(-1, 1))\\n L_fit = fit.predict(x.reshape(-1, 1))\\n ax1.plot(x, L_fit, color='black', linestyle='--', label='Log-linear fit to observations')\\n\\n ax1.set_ylabel('log ' + getlabel('[CII]158'))\\n ax1.set_xlabel('log ' + getlabel('CO(1-0)'))\\n plt.colorbar(sc,label=r'log $\\\\langle$Z$\\\\rangle_{\\\\rm SFR}$ [Z$_{\\\\rm \\\\odot}$]')\\n\\n handles, labels = ax1.get_legend_handles_labels()\\n print(labels) # labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))\\n # handles = [handles[_] for _ in [2,4,3,5,0,6,7,1]]\\n # labels = [labels[_] for _ in [2,4,3,5,0,6,7,1]]\\n handles = [handles[_] for _ in [2,4,3,5,6,0,1]]\\n labels = [labels[_] for _ in [2,4,3,5,6,0,1]]\\n plt.legend(handles,labels,loc='lower left',fontsize=10.,frameon=True)\\n\\n ax1.set_xlim([-3,6.2])\\n ax1.set_ylim([4,10])\\n\\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \\n plt.savefig(p.d_plot + 'luminosity/CO_vs_CII%s%s.png' % (p.grid_ext,p.table_ext), dpi=300)\",\n \"def CombinedPlotHelper(self,minc=70,maxc=120,num=25):\\n levels = np.linspace(minc,maxc,num+1)\\n title = textwrap.dedent(\\\"\\\"\\\"\\\\\\n Orography difference between LGM and Modern ICE-5G data\\n using {0} meter contour interval\\\"\\\"\\\").format((maxc-minc)/num)\\n plt.figure()\\n ax = plt.subplot(111)\\n contourset = plt.contourf(self.difference_in_ice_5g_orography,\\n levels=levels,hold=True)\\n cbar = plt.colorbar()\\n cbar.ax.set_ylabel('Orography difference in meters')\\n plt.contour(self.difference_in_ice_5g_orography,levels=contourset.levels,\\n colors='black',hold=True)\\n ufcntr = plt.contourf(self.difference_in_ice_5g_orography,\\n levels=[np.min(self.difference_in_ice_5g_orography),minc],\\n colors='white',\\n hatches=['/'],hold=True)\\n ofcntr = plt.contourf(self.difference_in_ice_5g_orography,\\n levels=[maxc,np.max(self.difference_in_ice_5g_orography)],\\n colors='white',\\n hatches=['\\\\\\\\'],hold=True)\\n ufartists,uflabels = ufcntr.legend_elements() #@UnusedVariable\\n ofartists,oflabels = ofcntr.legend_elements() #@UnusedVariable\\n uflabels=['Difference $\\\\\\\\leq {0}$'.format(minc)]\\n oflabels=['${0} <$ Difference'.format(maxc)]\\n artists = ufartists + ofartists\\n labels = uflabels + oflabels\\n plt.title(title)\\n pts.set_ticks_to_zero()\\n axbounds = ax.get_position()\\n #Shrink box by 5%\\n ax.set_position([axbounds.x0,axbounds.y0 + axbounds.height*0.05,\\n axbounds.width,axbounds.height*0.95])\\n ax.legend(artists,labels,loc='upper center',\\n bbox_to_anchor=(0.5,-0.025),fancybox=True,ncol=2)\\n #if self.save:\\n #plt.savefig('something')\\n print(\\\"Combined plot created\\\")\",\n \"def display_contour(cnts, img):\\n for c in cnts:\\n cv2.drawContours(img, [c], -1, (0, 255, 0), 2)\\n\\n img = imutils.resize(img, width=1200)\\n cv2.imshow(\\\"Image\\\", img)\\n cv2.moveWindow('Image', 30, 30)\\n cv2.waitKey(0)\",\n \"def cov_plot(self, matrix, station=\\\"\\\", hour = \\\"\\\", date=\\\"\\\" , averaged = \\\"\\\" ):\\n var = self.var_dics[self.var]['name'] \\n fig,ax = plt.subplots()\\n date = self.date_prettyfier(date)\\n hour = str(hour).replace('0','00:00').replace('1','12:00')\\n if not averaged:\\n title = \\\"Stat: \\\" + station + ', H: ' + hour + ', Date: ' + date + ', ' + var\\n filename = 'Cov_' + station + '_hour_' + hour.replace(':','') + '_date_' + str(date).replace('/','') + '_' +var\\n \\n elif averaged :\\n title = var.replace('temp','Temp.') + \\\" , Stat: \\\" + station + ', H: ' + str(hour) + ', Date: ' + str(date)\\n filename ='Cov_' + station + '_hour_' + str(hour).replace(':','') + '_averaged_' + str(date).replace('/','') + '_' + var \\n\\n plt.title(title.replace('_', ' ' ), y=1.03, fontsize = self.font-2)\\n\\n num = len(matrix[0,:])\\n Num = range(num)\\n\\n vmin, vmax = -3, 3\\n if self.var == 'direction': \\n vmin, vmax = -10, 10\\n color_map= plt.imshow(matrix, interpolation= 'nearest', cmap = 'RdYlBu', vmin = vmin, vmax = vmax ) # nearest serves for discreete grid # cmaps blue, seismic \\n plt.ylim(-0.5, 15.5)\\n plt.xlim(-0.5, 15.5)\\n plt.xticks(Num, Num)\\n plt.xlabel('Pressure level an_dep [hPa]', fontsize = self.font-2)\\n plt.yticks(Num, Num)\\n plt.ylabel('Pressure level fg_dep [hPa]', fontsize = self.font-2)\\n ax.set_xticklabels(labels = self.pretty_pressure, fontsize = self.font-4, rotation=45)\\n ax.set_yticklabels(labels = self.pretty_pressure, fontsize = self.font-4)\\n\\n bar = plt.colorbar()\\n bar.ax.set_ylabel(\\\"Covariance\\\", fontsize = self.font)\\n \\n for i in Num: # creating text labels\\n for j in Num:\\n value = '{0:.2f}'.format(matrix[i,j])\\n text = ax.text( j,i, value , ha = 'center' , va = 'center', color = 'black', fontsize = 5)\\n\\n if not os.path.isdir('plots/covariances/'+station): os.mkdir('plots/covariances/'+station)\\n plt.savefig('plots/covariances/' + station + '/' + filename + '.png', bbox_inches='tight', dpi = 200)\\n plt.close()\",\n \"def visualize(self, features, targets, my_title=\\\"untitled\\\"):\\n try:\\n import matplotlib.pyplot as plt\\n except:\\n import matplotlib\\n matplotlib.use('Agg')\\n import matplotlib.pyplot as plt\\n\\n plt.figure(figsize=(6,4))\\n #plt.contourf(self.out, cmap=plt.cm.Paired)\\n plt.axis('off')\\n plt.scatter(features[:, 0], features[:, 1], c=self.out)\\n plt.title(my_title)\\n plt.savefig(f'{my_title}.png')\",\n \"def plot_xy(nc,params,tms,lev=None):\\n \\n import matplotlib.pyplot as plt\\n import ggWRFutils as gW\\n from datetime import datetime\\n import numpy as np\\n wvar={}\\n for p in params:\\n if p != 'Times':\\n if p=='WS10':\\n wvar[p]=np.sqrt(nc.variables['U10'][:]**2+nc.variables['U10'][:]**2)\\n elif p=='UV10': \\n wvar['U10']=nc.variables['U10'][:,:,:] \\n wvar['V10']=nc.variables['V10'][:,:,:] \\n elif p=='UV':\\n wvar['U']=nc.variables['U'][:,lev,:,:] \\n wvar['V']=nc.variables['V'][:,lev,:,:] \\n elif len(nc.variables[p].shape) > 3:\\n wvar[p]=nc.variables[p][:,lev,:,:] \\n else: \\n wvar[p]=nc.variables[p][:] \\n Nx,Ny,Nz,lon,lat,dx,dy=gW.getDimensions(nc)\\n for p in params:\\n if params[p]=='pcolor':\\n plt.pcolor(lon,lat,wvar[p][tms,:,:],shading='flat')\\n plt.colorbar()\\n if params[p]=='contourf':\\n plt.contourf(lon,lat,wvar[p][tms,:,:],50)\\n plt.colorbar()\\n if params[p]=='contour':\\n plt.contourf(lon,lat,wvar[p][tms,:,:])\\n plt.colorbar()\\n if params[p]=='quiver':\\n if p=='UV10':\\n plt.quiver(lon[::10,::10],lat[::10,::10],wvar['U10'][tms,::10,::10],wvar['V10'][tms,::10,::10],units='width')\\n elif p=='UV':\\n plt.quiver(lon,lat,wvar['U'][tms,:,:],wvar['V'][tms,:,:])\\n plt.hold(True)\\n plt.xlim(lon.min(),lon.max())\\n plt.ylim(lat.min(),lat.max())\\n fig=plt.gcf()\\n return fig\",\n \"def visualize(**images):\\r\\n n_images = len(images)\\r\\n plt.figure(figsize=(20, 8))\\r\\n for idx, (name, image) in enumerate(images.items()):\\r\\n plt.subplot(1, n_images, idx + 1)\\r\\n plt.xticks([])\\r\\n plt.yticks([])\\r\\n # get title from the parameter names\\r\\n plt.title(name.replace('_', ' ').title(), fontsize=20)\\r\\n plt.imshow(image)\\r\\n plt.show()\",\n \"def plot_HDres_histos_vs_z(\\n df,\\n nameout,\\n threshold_var=\\\"class0\\\",\\n threshold_list=[0.5, 0.7, 0.9],\\n threshold_sign=\\\">\\\",\\n):\\n\\n P = df[df[\\\"class0\\\"] > 0.5]\\n Ias = df[df[\\\"target\\\"] == 0]\\n\\n TP = P[P[\\\"target\\\"] == 0]\\n FP = P[P[\\\"target\\\"] != 0]\\n\\n sel_TP_dic = {}\\n sel_FP_dic = {}\\n for t in threshold_list:\\n if threshold_sign == \\\">\\\":\\n sel_TP_dic[t] = TP[TP[threshold_var] > t]\\n sel_FP_dic[t] = FP[FP[threshold_var] > t]\\n else:\\n sel_TP_dic[t] = TP[TP[threshold_var] < t]\\n sel_FP_dic[t] = FP[FP[threshold_var] < t]\\n\\n plt.clf()\\n cm = CMAP\\n fig = plt.figure(figsize=(14, 14))\\n # gs = gridspec.GridSpec(4, 2, width_ratios=[3, 1], height_ratios=[2, 2, 1, 1])\\n # gs.update(wspace=0.1, hspace=0.3)\\n\\n # # gridspec init\\n # ax00 = plt.subplot(gs[0, 0]) # Hres Ia\\n # ax10 = plt.subplot(gs[1, 0], sharex=ax00) # Hres CC\\n # ax20 = plt.subplot(gs[2:, 0], sharex=ax00) # efficiency\\n # ax01 = plt.subplot(gs[0, 1], sharey=ax00) # histo Ia\\n # ax11 = plt.subplot(gs[1, 1], sharey=ax10) # histo CC\\n # ax21 = plt.subplot(gs[2, 1]) # histo x1\\n # ax31 = plt.subplot(gs[3, 1]) # histo c\\n gs = gridspec.GridSpec(3, 3, height_ratios=[2, 2, 1])\\n # gs.update(wspace=0.2, hspace=0.1)\\n\\n # gridspec init\\n ax00 = plt.subplot(gs[0, 0:2]) # Hres Ia\\n ax10 = plt.subplot(gs[1, 0:2], sharex=ax00) # Hres CC\\n ax20 = plt.subplot(gs[2, 0]) # redshift dist\\n ax01 = plt.subplot(gs[0, 2], sharey=ax00) # histo Ia\\n ax11 = plt.subplot(gs[1, 2], sharey=ax10) # histo CC\\n ax21 = plt.subplot(gs[2, 1]) # histo x1\\n ax31 = plt.subplot(gs[2, 2]) # histo c\\n\\n # lines\\n ax00.plot([0, 1.2], np.zeros(len([0, 1.2])), \\\"k:\\\")\\n ax10.plot([0, 1.2], np.zeros(len([0, 1.2])), \\\"k:\\\")\\n\\n mubins = np.arange(-2, 2 + 0.1, 0.1)\\n\\n # Hres w. histogram\\n def HRwhisto(\\n df, sel_dic, ax_left, ax_right, threshold_sign, ylabel=\\\"TP\\\", visible=False\\n ):\\n if ylabel == \\\"TP\\\":\\n sntyp = \\\"Ia\\\"\\n else:\\n sntyp = \\\"CC\\\"\\n ax_left.scatter(\\n df[\\\"SIM_REDSHIFT_CMB\\\"],\\n df[\\\"delmu\\\"],\\n c=df[\\\"class0\\\"],\\n cmap=CMAP,\\n vmin=0.5,\\n vmax=1,\\n s=8,\\n )\\n ax_left.errorbar(\\n df[\\\"SIM_REDSHIFT_CMB\\\"],\\n df[\\\"delmu\\\"],\\n yerr=df[\\\"delmu_err\\\"],\\n color=\\\"gray\\\",\\n zorder=0,\\n fmt=\\\"none\\\",\\n marker=\\\"none\\\",\\n )\\n\\n ax_left.set_ylim(-2, 2)\\n ax_left.set_xlim(0, 1.2)\\n ax_left.set_ylabel(f\\\"{ylabel} residual\\\", fontsize=18)\\n ax_left.tick_params(labelsize=14)\\n plt.setp(ax_left.get_xticklabels(), visible=visible)\\n if visible is True:\\n ax_left.set_xlabel(\\\"simulated redshift\\\", fontsize=18)\\n for t in threshold_list:\\n sel = sel_dic[t]\\n n_SNe = len(sel)\\n ax_right.hist(\\n sel[\\\"delmu\\\"],\\n orientation=\\\"horizontal\\\",\\n histtype=\\\"step\\\",\\n color=cm(t),\\n bins=mubins,\\n density=True,\\n label=f\\\"{n_SNe} {sntyp} {threshold_sign} {t}\\\",\\n lw=2,\\n )\\n ax_right.legend(loc=\\\"lower center\\\", prop={\\\"size\\\": 13})\\n plt.setp(ax_right.get_yticklabels(), visible=False)\\n plt.setp(ax_right.get_xticklabels(), visible=False)\\n ax_right.plot(\\n [ax_right.get_xlim()[0], ax_right.get_xlim()[1]],\\n np.zeros(len([ax_right.get_xlim()[0], ax_right.get_xlim()[1]])),\\n \\\"k:\\\",\\n )\\n\\n HRwhisto(TP, sel_TP_dic, ax00, ax01, threshold_sign, ylabel=\\\"TP\\\", visible=False)\\n HRwhisto(FP, sel_FP_dic, ax10, ax11, threshold_sign, ylabel=\\\"FP\\\", visible=True)\\n\\n # z histos\\n n, bins_to_use, tmp = ax20.hist(\\n Ias[\\\"SIM_REDSHIFT_CMB\\\"], histtype=\\\"step\\\", color=\\\"black\\\", bins=15, lw=3\\n )\\n\\n for t in threshold_list:\\n sel_TP = sel_TP_dic[t]\\n sel_FP = sel_FP_dic[t]\\n ax20.hist(\\n sel_TP[\\\"SIM_REDSHIFT_CMB\\\"], histtype=\\\"step\\\", color=cm(t), bins=bins_to_use\\n )\\n ax20.hist(\\n sel_FP[\\\"SIM_REDSHIFT_CMB\\\"],\\n histtype=\\\"step\\\",\\n color=cm(t),\\n linestyle=\\\"--\\\",\\n bins=bins_to_use,\\n )\\n ax20.set_xlim(0, 1.2)\\n ax20.tick_params(labelsize=14)\\n ax20.set_xlabel(\\\"simulated redshift\\\", fontsize=18)\\n\\n # hist stretch\\n n, bins_to_use, tmp = ax21.hist(Ias[\\\"x1\\\"], color=\\\"black\\\", histtype=\\\"step\\\", lw=3)\\n for t in threshold_list:\\n sel_TP = sel_TP_dic[t]\\n ax21.hist(\\n sel_TP[\\\"x1\\\"],\\n orientation=\\\"vertical\\\",\\n histtype=\\\"step\\\",\\n color=cm(t),\\n bins=bins_to_use,\\n lw=2,\\n )\\n ax21.set_xlabel(\\\"x1\\\", fontsize=18)\\n ax21.yaxis.set_label_position(\\\"right\\\")\\n ax21.set_xlim(-3, 3)\\n ax21.tick_params(labelsize=14)\\n # color histo\\n n, bins_to_use, tmp = ax31.hist(Ias[\\\"c\\\"], color=\\\"black\\\", histtype=\\\"step\\\", lw=3)\\n for t in threshold_list:\\n sel_TP = sel_TP_dic[t]\\n ax31.hist(\\n sel_TP[\\\"c\\\"],\\n orientation=\\\"vertical\\\",\\n histtype=\\\"step\\\",\\n color=cm(t),\\n bins=bins_to_use,\\n lw=2,\\n )\\n ax31.set_xlabel(\\\"c\\\", fontsize=18)\\n ax31.set_xlim(-1, 1)\\n ax31.tick_params(labelsize=14)\\n ax31.yaxis.set_label_position(\\\"right\\\")\\n\\n gs.tight_layout(fig)\\n plt.savefig(nameout)\\n plt.close()\\n del fig\",\n \"def plotContour(verts, data, comp=2):\\n z = data[:,:,comp].flatten()\\n x = verts[:,0]\\n y = verts[:,1]\\n\\n xi = np.linspace(x.min(), x.max(), x.size)\\n yi = np.linspace(y.min(), y.max(), y.size)\\n zi = griddata(x,y,z, xi,yi, interp='nn') #'nn' #NOTE: for irregularly spaced data\\n\\n plt.figure()\\n #plt.gca().set_aspect('equal')\\n compDict = {0:'X',1:'Y',2:'Z'}\\n C = compDict[comp]\\n\\n CS = plt.contour(xi,yi,zi,15,linewidths=0.5,colors='k')\\n CS = plt.contourf(xi,yi,zi,15,cmap=plt.cm.jet)\\n plt.title( C + ' Displacement')\\n plt.xlabel('Distance [m]')\\n plt.ylabel('Distance [m]')\\n cb = plt.colorbar()\\n cb.set_label('[m]')\",\n \"def subject(s):\\n ir(s)\\n contours(s, colour='green')\\n matplotlib.pyplot.xlim(0, config['surveys']['atlas']['fits_width'])\\n matplotlib.pyplot.ylim(0, config['surveys']['atlas']['fits_height'])\",\n \"def plot_case(casepath, casename, framelist, max_level, subtitle):\\n\\n # paths\\n repo_path = os.path.dirname(os.path.abspath(__file__))\\n casepath = os.path.abspath(casepath)\\n figspath = os.path.join(repo_path, \\\"figs\\\")\\n casefigpath = os.path.join(figspath, casename, \\\"level{:02}\\\".format(max_level))\\n\\n # check figs folder\\n if not os.path.isdir(casefigpath):\\n os.makedirs(casefigpath)\\n\\n for fno in framelist:\\n outputfile = os.path.join(casefigpath, \\\"depth{:04}.png\\\".format(fno))\\n plot_single_frame(casepath, fno, max_level, subtitle, outputfile)\",\n \"def test_arguments_7(self):\\n fig = plt.figure()\\n ax = fig.add_subplot(projection='ternary')\\n tn0, tn1, tn2 = get_spiral()\\n ax.plot(tn0, tn1, tn2, 'C3:', tn1, tn2, tn0)\",\n \"def create_plots(self):\\n shutil.rmtree(self.param.path, ignore_errors=True)\\n os.makedirs(self.param.path)\\n\\n ids = list(range(len(self.signs)))\\n\\n \\\"\\\"\\\"True positives\\\"\\\"\\\"\\n values, kinds = self.get_evaluations(ids)\\n plots.create_plot(\\n kinds,\\n [e[0] for e in values], # True positives\\n save_dir=self.param.path,\\n y_label=\\\"number_tp\\\",\\n file_name=\\\"number_tp\\\",\\n title=\\\"Amount of true positives\\\",\\n )\\n\\n # Only signs with at least one detection!\\n ids = [i for i, _ in enumerate(self.signs) if self.signs[i].evaluate()[2] > 0]\\n values, kinds = self.get_evaluations(ids)\\n\\n \\\"\\\"\\\"Distance\\\"\\\"\\\"\\n plots.create_plot(\\n kinds,\\n values=[e[2] for e in values], # Distances\\n save_dir=self.param.path,\\n y_label=\\\"distance\\\",\\n file_name=\\\"distance\\\",\\n title=\\\"Distance\\\",\\n )\\n \\\"\\\"\\\"Precision\\\"\\\"\\\"\\n plots.create_plot(\\n kinds,\\n # Precision signs with at least one detection are used, e[0]+e[1] > 0)\\n values=[e[0] / (e[0] + e[1]) for e in values],\\n save_dir=self.param.path,\\n y_label=\\\"precision\\\",\\n file_name=\\\"precision\\\",\\n title=\\\"Precision\\\",\\n )\",\n \"def surface_contacts(struc,SASA):\\n\\tplot_single = False\\n\\n\\tcmap_cutoff = 5.0 # nm\\n\\tsasa_cutoff = 0.5\\n\\t# identify surface residues\\n\\tS = []\\n\\tN = np.array(range(struc.n_residues))\\n\\tfor r in N:\\n\\t\\tind = struc.topology.select('resid '+str(r))\\n\\t\\tSASA_per_res = np.sum(SASA[0][ind[0]:ind[-1]],axis=0)\\n\\t\\tif SASA_per_res > sasa_cutoff:\\n\\t\\t\\tS.append(SASA_per_res)\\n\\t\\telse:\\n\\t\\t\\tS.append(0.0)\\n\\t#import ipdb; ipdb.set_trace()\\n\\tsurf_res = N[np.where(np.array(S) > 0)]\\n\\tcmap = contact_maps(struc.atom_slice(struc.topology.select('name CA'))[0].xyz[0])\\n\\tfor n in N:\\n\\t\\tfor m in N:\\n\\t\\t\\tif (n not in surf_res or m not in surf_res) or n < m:\\n\\t\\t\\t\\tcmap[n][m] = 0\\n\\t\\t\\telif cmap[n][m] < cmap_cutoff:\\n\\t\\t\\t\\tcmap[n][m] = 1\\n\\tif plot_single == True:\\n\\t\\tfor it,rn in enumerate(resnames):\\n\\t\\t\\tif rn in hydrophobic:\\n\\t\\t\\t\\tplt.axhline(y=it,c='grey',linewidth=2)\\n\\t\\t\\t\\tplt.axvline(x=it,c='grey',linewidth=2)\\n\\t\\t\\telif rn in hydrophilic:\\n\\t\\t\\t\\tplt.axhline(y=it,c='g',linewidth= 2)\\n\\t\\t\\t\\tplt.axvline(x=it,c='g',linewidth= 2)\\n\\t\\t\\telif rn in poscharge:\\n\\t\\t\\t\\tplt.axhline(y=it,c='b',linewidth= 2)\\n\\t\\t\\t\\tplt.axvline(x=it,c='b',linewidth= 2)\\n\\t\\t\\telif rn in negcharge:\\n\\t\\t\\t\\tplt.axhline(y=it,c='r',linewidth= 2)\\n\\t\\t\\t\\tplt.axvline(x=it,c='r',linewidth= 2)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint \\\"unknown restype:\\\", rn\\n\\t\\tscat = np.array(zip(np.where(np.array(cmap) > 0)[0], np.where(np.array(cmap) > 0)[1])).T\\n\\t\\tplt.scatter(scat[0], scat[1],marker='s',c='k',s=90)\\n\\t\\t#plt.imshow(cmap,cmap='Greys')\\n\\t\\tplt.show()\\n\\treturn cmap\",\n \"def plot_data(samples, snps, indels, filename):\\n categories = ['SNP Precision', 'SNP Sensitivity', 'Indel Precision',\\n 'Indel Sensitivity']\\n\\n plot_data = defaultdict(list)\\n for sample in samples:\\n # Here we add the data, in the SAME ORDER as categories\\n for data in snps[sample]:\\n # We want to use the data without a threshold\\n if data['Threshold'] == 'None':\\n plot_data[sample].append(data['Precision'])\\n plot_data[sample].append(data['Sensitivity'])\\n for data in indels[sample]:\\n # We want to use the data without a threshold\\n if data['Threshold'] == 'None':\\n plot_data[sample].append(data['Precision'])\\n plot_data[sample].append(data['Sensitivity'])\\n\\n fig = go.Figure(data=[\\n go.Bar(\\n name=sample,\\n x=categories,\\n y=plot_data[sample]) for sample in samples])\\n\\n fig.update_layout(barmode='group')\\n fig.write_html(filename)\",\n \"def display4(*args):\\n #-------------------- unpack\\n twiss_func = args[0]\\n cos_like = args[1]\\n sin_like = args[2]\\n lat_plot = args[3]\\n #-------------------- beta x,y & dispersion x\\n s = [twiss_func(i,'s') for i in range(twiss_func.nbpoints)] # Abszisse\\n bx = [twiss_func(i,'bx') for i in range(twiss_func.nbpoints)] # beta x\\n by = [twiss_func(i,'by') for i in range(twiss_func.nbpoints)] # beta y\\n dx = [twiss_func(i,'dx') for i in range(twiss_func.nbpoints)] # dispersion x\\n#-------------------- longitudinal trajectories\\n z1= [cos_like(i,'s') for i in range(cos_like.nbpoints)]\\n cz= [cos_like(i,'cz') for i in range(cos_like.nbpoints)]\\n cdp= [cos_like(i,'cdp') for i in range(cos_like.nbpoints)]\\n\\n z2= [sin_like(i,'s') for i in range(sin_like.nbpoints)]\\n sz= [sin_like(i,'sz') for i in range(sin_like.nbpoints)]\\n sdp= [sin_like(i,'sdp') for i in range(sin_like.nbpoints)]\\n #-------------------- lattice viseo\\n vzero = [0. for i in range(lat_plot.nbpoints)] # zero line\\n vis_abszisse = [lat_plot(i,'s') for i in range(lat_plot.nbpoints)]\\n vis_ordinate = [lat_plot(i,'viseo') for i in range(lat_plot.nbpoints)]\\n #-------------------- figure frame\\n width=14; height=7.6\\n # fighdr = 'lattice version = {}, input file = {}'.format(PARAMS['lattice_version'],PARAMS['input_file'])\\n fig = plt.figure(num=1,figsize=(width,height),facecolor='#eaecef',tight_layout=False)\\n\\n #-------------------- beta functions\\n splot211=plt.subplot(211)\\n splot211.set_title('beta x,y')\\n # mapping box\\n splot211.text(0.01, 1.1, UTIL.FLAGS.get('mapping'),transform=splot211.transAxes,fontsize=8,bbox=dict(boxstyle='round',facecolor='wheat',alpha=0.5),verticalalignment='top')\\n # function plots\\n plt.plot(s,bx, label=r\\\"$\\\\beta$x [m]\\\", color='black', linestyle='-')\\n plt.plot(s,by, label=r\\\"$\\\\beta$y [m]\\\", color='red', linestyle='-')\\n plt.plot(s,dx, label=r'$\\\\eta_x$ [m]' , color='green', linestyle='-') # dispersion x\\n vscale=splot211.axis()[3]*0.25\\n viseoz = [x*vscale for x in vis_ordinate]\\n plt.plot(vis_abszisse,viseoz,label='',color='black')\\n plt.plot(vis_abszisse,vzero,color='green',linestyle='--')\\n # zero line\\n splot211.plot(vis_abszisse,vzero,color='green',linestyle='--')\\n plt.legend(loc='lower right',fontsize='x-small')\\n\\n #-------------------- longitudinal tracks z, dP/P\\n # ax_l = left abszisse\\n ax_l=plt.subplot(212)\\n # ax_l=plt.subplot(10,1,(7,9))\\n ax_l.set_title('synchrotron oscillation')\\n ax_l.set_ylabel(r\\\"z [mm]\\\")\\n ax_l.tick_params(axis='y', colors='green')\\n ax_l.yaxis.label.set_color('green')\\n ax_l.plot(z1,cz,label='C',color='green')\\n ax_l.plot(z2,sz,label='S',color='green',linestyle=':')\\n plt.legend(loc='lower left',fontsize='x-small')\\n # ax_r = right abszisse\\n ax_r = ax_l.twinx()\\n ax_r.set_ylabel(r'$\\\\Delta$p/p [%]')\\n ax_r.tick_params(axis='y', colors='red')\\n ax_r.yaxis.label.set_color('red')\\n ax_r.plot(z2,cdp,label='C',color='red')\\n ax_r.plot(z2,sdp,label='S',color='red',linestyle=':')\\n ax_r.plot(vis_abszisse,vzero,color='red', linestyle='--')\\n plt.legend(loc='lower right',fontsize='x-small')\\n # lattice elements\\n vscale=ax_l.axis()[3]*0.25\\n viseoz = [x*vscale for x in vis_ordinate]\\n ax_l.plot(vis_abszisse,viseoz,label='',color='black')\\n ax_l.plot(vis_abszisse,vzero,color='green',linestyle='--')\",\n \"def plot_all_regression_multiple_scatter(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/regressions/single_multiple/', indicies = ['SAM', 'IPO', 'DMI', 'ENSO']):\\n for temp_res, temp_decomp, dt in itertools.product(temporal_resolution, temporal_decomposition, detrend):\\n plot_regression_multiple_scatter(anomlous = temp_decomp, temporal_resolution = temp_res, detrend = dt, temp_decomp = temp_decomp, imagefolder = 'images/regressions/single_multiple/', n = 1)\",\n \"def plot_sbc(theta_samples, theta_test, param_names, bins=25, figsize=(8, 4), interval=0.99, show=True, filename=None, font_size=12):\\n\\n # Plot settings\\n plt.rcParams['font.size'] = font_size\\n N = int(theta_test.shape[0])\\n\\n # Determine n_subplots dynamically\\n n_row = int(np.ceil(len(param_names) / 6))\\n n_col = int(np.ceil(len(param_names) / n_row))\\n\\n # Initialize figure\\n f, axarr = plt.subplots(n_row, n_col, figsize=figsize)\\n if n_row > 1:\\n axarr = axarr.flat\\n\\n # Compute ranks (using broadcasting) \\n ranks = np.sum(theta_samples < theta_test[:, np.newaxis, :], axis=1)\\n \\n # Compute interval\\n endpoints = binom.interval(interval, N, 1 / (bins))\\n\\n # Plot histograms\\n for j in range(len(param_names)):\\n \\n # Add interval\\n axarr[j].axhspan(endpoints[0], endpoints[1], facecolor='gray', alpha=0.3)\\n axarr[j].axhline(np.mean(endpoints), color='gray', zorder=0, alpha=0.5)\\n \\n sns.histplot(ranks[:, j], kde=False, ax=axarr[j], color='#a34f4f', bins=bins, alpha=0.95)\\n \\n axarr[j].set_title(param_names[j])\\n axarr[j].spines['right'].set_visible(False)\\n axarr[j].spines['top'].set_visible(False)\\n if j == 0:\\n axarr[j].set_xlabel('Rank statistic')\\n axarr[j].get_yaxis().set_ticks([])\\n axarr[j].set_ylabel('')\\n \\n f.tight_layout()\\n # Show, if specified\\n if show:\\n plt.show()\\n # Save if specified\\n if filename is not None:\\n f.savefig(\\\"figures/{}_SBC.png\\\".format(filename), dpi=600, bbox_inches='tight')\\n return f\",\n \"def crossplot(self, analytes=None, lognorm=True,\\n bins=25, filt=False, samples=None, subset=None, **kwargs):\\n if analytes is None:\\n analytes = [a for a in self.analytes if 'Ca' not in a]\\n\\n if subset is None:\\n subset = 'All_Samples'\\n\\n self.get_focus(filt, samples, subset)\\n\\n numvars = len(analytes)\\n fig, axes = plt.subplots(nrows=numvars, ncols=numvars,\\n figsize=(12, 12))\\n fig.subplots_adjust(hspace=0.05, wspace=0.05)\\n\\n for ax in axes.flat:\\n ax.xaxis.set_visible(False)\\n ax.yaxis.set_visible(False)\\n\\n if ax.is_first_col():\\n ax.yaxis.set_ticks_position('left')\\n if ax.is_last_col():\\n ax.yaxis.set_ticks_position('right')\\n if ax.is_first_row():\\n ax.xaxis.set_ticks_position('top')\\n if ax.is_last_row():\\n ax.xaxis.set_ticks_position('bottom')\\n\\n cmlist = ['Blues', 'BuGn', 'BuPu', 'GnBu',\\n 'Greens', 'Greys', 'Oranges', 'OrRd',\\n 'PuBu', 'PuBuGn', 'PuRd', 'Purples',\\n 'RdPu', 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']\\n\\n # isolate nominal_values for all analytes\\n focus = {k: nominal_values(v) for k, v in self.focus.items()}\\n # determine units for all analytes\\n udict = {a: unitpicker(np.nanmean(focus[a])) for a in analytes}\\n # determine ranges for all analytes\\n rdict = {a: (np.nanmin(focus[a] * udict[a][0]),\\n np.nanmax(focus[a] * udict[a][0])) for a in analytes}\\n\\n for i, j in zip(*np.triu_indices_from(axes, k=1)):\\n # get analytes\\n ai = analytes[i]\\n aj = analytes[j]\\n\\n # remove nan, apply multipliers\\n pi = focus[ai][~np.isnan(focus[ai])] * udict[ai][0]\\n pj = focus[aj][~np.isnan(focus[aj])] * udict[aj][0]\\n\\n # make plot\\n if lognorm:\\n axes[i, j].hist2d(pj, pi, bins,\\n norm=mpl.colors.LogNorm(),\\n cmap=plt.get_cmap(cmlist[i]))\\n axes[j, i].hist2d(pi, pj, bins,\\n norm=mpl.colors.LogNorm(),\\n cmap=plt.get_cmap(cmlist[j]))\\n else:\\n axes[i, j].hist2d(pj, pi, bins,\\n cmap=plt.get_cmap(cmlist[i]))\\n axes[j, i].hist2d(pi, pj, bins,\\n cmap=plt.get_cmap(cmlist[j]))\\n\\n axes[i, j].set_ylim(*rdict[ai])\\n axes[i, j].set_xlim(*rdict[aj])\\n\\n axes[j, i].set_ylim(*rdict[aj])\\n axes[j, i].set_xlim(*rdict[ai])\\n\\n # diagonal labels\\n for a, n in zip(analytes, np.arange(len(analytes))):\\n axes[n, n].annotate(a + '\\\\n' + udict[a][1], (0.5, 0.5),\\n xycoords='axes fraction',\\n ha='center', va='center')\\n axes[n, n].set_xlim(*rdict[a])\\n axes[n, n].set_ylim(*rdict[a])\\n\\n # switch on alternating axes\\n for i, j in zip(range(numvars), itertools.cycle((-1, 0))):\\n axes[j, i].xaxis.set_visible(True)\\n for label in axes[j, i].get_xticklabels():\\n label.set_rotation(90)\\n axes[i, j].yaxis.set_visible(True)\\n\\n return fig, axes\",\n \"def scatter_plot(dictionary, variable1, variable2): \\r\\n plt.clf() # Deletes the previous plot \\r\\n plt.scatter(dictionary[variable1], dictionary[variable2], color='purple')\\r\\n plt.title('Scatter plot between ' + variable1 + \\\" and \\\" + variable2)\\r\\n plt.xlabel(variable1)\\r\\n plt.ylabel(variable2)\\r\\n plt.savefig(variable1 + \\\" and \\\" + variable2)\",\n \"def plot_implicit(ax, fn, bbox=(-2.5, 2.5)):\\n xmin, xmax, ymin, ymax, zmin, zmax = bbox*3\\n A = np.linspace(xmin, xmax, 100) # resolution of the contour\\n B = np.linspace(xmin, xmax, 15) # number of slices\\n A1, A2 = np.meshgrid(A, A) # grid on which the contour is plotted\\n for z in B: # plot contours in the XY plane\\n X, Y = A1, A2\\n Z = fn(X, Y, z)\\n ax.contour(X, Y, Z+z, [z], zdir='z')\\n # [z] defines the only level to plot for this contour for\\n # this value of z\\n\\n for y in B: # plot contours in the XZ plane\\n X, Z = A1, A2\\n Y = fn(X, y, Z)\\n ax.contour(X, Y+y, Z, [y], zdir='y')\\n\\n for x in B: # plot contours in the YZ plane\\n Y, Z = A1, A2\\n X = fn(x, Y, Z)\\n ax.contour(X+x, Y, Z, [x], zdir='x')\\n\\n ax.tick_params(labelsize=6) # smaller label size\\n\\n # must set plot limits because the contour will likely extend\\n # way beyond the displayed level. Otherwise matplotlib extends\\n # the plot limits to encompass all values in the contour.\\n ax.set_zlim3d(zmin, zmax)\\n ax.set_xlim3d(xmin, xmax)\\n ax.set_ylim3d(ymin, ymax)\",\n \"def _plot_imgs_and_ctrs_np(self, config) -> None:\\n imgs = config['imgs']\\n ctrs = config['ctrs']\\n orig_slices = config['slices']\\n plane = config['plane']\\n title = config['title']\\n draw_only_ctrs = config['draw_only_ctrs']\\n file_name_prefix = config['file_name_prefix']\\n labels = config['labels']\\n\\n create_folder(self._output_folder)\\n if plane != PlaneTypes.ALL: # Draw single plane\\n # This should plot a single image with all the contours overlayed\\n first_img_shape = imgs[0].shape\\n # Validate the size of the images\\n for c_img in imgs:\\n if c_img.shape != first_img_shape:\\n raise Exception('The shape of the images must be the same')\\n\\n slices = get_slices(orig_slices, imgs[0], plane)\\n for c_slice in slices:\\n draw_slice = should_display_slice(ctrs, c_slice, plane, draw_only_ctrs)\\n if draw_slice:\\n fig, ax = plt.subplots(1, len(imgs), squeeze=True, figsize=(8, 8))\\n for img_idx, c_img in enumerate(imgs):\\n img_slice = get_proper_plane(c_img, plane, c_slice)\\n ctrs_slice = [get_proper_plane(np_ctr, plane, c_slice) for np_ctr in ctrs]\\n if len(imgs) > 1:\\n plot_slice(img_slice, ctrs_slice, ax[img_idx], labels)\\n else:\\n plot_slice(img_slice, ctrs_slice, ax, labels)\\n c_title = F'{title} {plane.value} {c_slice:04d}'\\n file_name = F'{file_name_prefix}_{plane.value}_{c_slice:04d}'\\n plt.title(c_title, fontsize=20)\\n pylab.savefig(join(self._output_folder, F'{file_name}.jpg'), bbox_inches='tight')\\n else:\\n if len(imgs) != 1:\\n raise Exception('The number of image allowed for Plane type ALL must be 1')\\n # In this case it should plot 3 images, one for each plane. Here we force it to plot\\n # only the middle slice\\n c_img = imgs[0]\\n plt.subplots(1, 3, squeeze=True, figsize=(8 * 3, 8))\\n for id_plane, plane in enumerate([PlaneTypes.AXIAL, PlaneTypes.SAGITTAL, PlaneTypes.CORONAL]):\\n ax = plt.subplot(1, 3, id_plane + 1)\\n c_slice = get_slices(SliceMode.MIDDLE, c_img, plane)[0]\\n img_slice = get_proper_plane(c_img, plane, c_slice)\\n ctrs_slice = [get_proper_plane(np_ctr, plane, c_slice) for np_ctr in ctrs]\\n plot_slice(img_slice, ctrs_slice, ax, labels)\\n c_title = F'{title} ALL {c_slice:04d}'\\n plt.title(c_title, fontsize=20)\\n\\n file_name = F'{file_name_prefix}_{plane.value}_{c_slice:04d}'\\n pylab.savefig(join(self._output_folder, F'{file_name}.jpg'), bbox_inches='tight')\\n\\n self._close_figure()\",\n \"def plot_all_regression_scatter(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/regressions/single/', indicies = ['SAM', 'IPO', 'DMI', 'ENSO'],seaice_source='nsidc'):\\n for temp_res, temp_decomp, dt in itertools.product(temporal_resolution, temporal_decomposition, detrend):\\n plot_regression_scatter(anomlous = temp_decomp, temporal_resolution = temp_res, detrend = dt, temp_decomp = temp_decomp, imagefolder = 'images/regressions/single/', n = 1,seaice_source=seaice_source)\",\n \"def plot_all_regression_multiple_spatial(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/regressions/spatial_multiple/', indicies = ['SAM', 'IPO', 'DMI', 'ENSO']):\\n for temp_res, temp_decomp, dt in itertools.product(temporal_resolution, temporal_decomposition, detrend):\\n plot_regression_multiple_spatial(anomlous = temp_decomp, temporal_resolution = temp_res, detrend = dt, temp_decomp = temp_decomp, imagefolder = 'images/regressions/spatial_multiple/', n = 1)\",\n \"def plot_figure4():\\n column_titles = [\\\"5th percentile\\\", \\\"32nd percentile\\\", \\\"50th percentile\\\"]\\n\\n fixed_height_ref = 100.\\n fixed_height_id = list(fixed_heights).index(fixed_height_ref)\\n\\n plot_item0 = {\\n 'data': nc.variables[\\\"p_fixed_perc5\\\"][fixed_height_id, :, :]*1e-3,\\n 'contour_fill_levels': np.linspace(0, .03, 21),\\n 'contour_line_levels': sorted([.003]+list(np.linspace(0, .03, 21)[::5])),\\n 'contour_line_label_fmt': '%.3f',\\n 'colorbar_ticks': np.linspace(0, .03, 21)[::5],\\n 'colorbar_tick_fmt': '{:.3f}',\\n 'colorbar_label': 'Power density [$kW/m^2$]',\\n }\\n plot_item1 = {\\n 'data': nc.variables[\\\"p_fixed_perc32\\\"][fixed_height_id, :, :]*1e-3,\\n 'contour_fill_levels': np.linspace(0, .45, 21),\\n 'contour_line_levels': sorted([.04]+list(np.linspace(0, .45, 21)[::4])),\\n 'contour_line_label_fmt': '%.2f',\\n 'colorbar_ticks': np.linspace(0, .45, 21)[::4],\\n 'colorbar_tick_fmt': '{:.2f}',\\n 'colorbar_label': 'Power density [$kW/m^2$]',\\n }\\n plot_item2 = {\\n 'data': nc.variables[\\\"p_fixed_perc50\\\"][fixed_height_id, :, :]*1e-3,\\n 'contour_fill_levels': np.linspace(0, 1, 21),\\n 'contour_line_levels': sorted([.1]+list(np.linspace(0, 1, 21)[::4])),\\n 'contour_line_label_fmt': '%.2f',\\n 'colorbar_ticks': np.linspace(0, 1, 21)[::4],\\n 'colorbar_tick_fmt': '{:.2f}',\\n 'colorbar_label': 'Power density [$kW/m^2$]',\\n }\\n\\n plot_items = [plot_item0, plot_item1, plot_item2]\\n\\n eval_contour_fill_levels(plot_items)\\n plot_panel_1x3_seperate_colorbar(plot_items, column_titles)\",\n \"def _add_contours(\\n *coords,\\n zi=None,\\n ax=None,\\n contours=[],\\n cmap=DEFAULT_CONT_COLORMAP,\\n vmin=0.0,\\n extent=None,\\n **kwargs\\n):\\n # get the contour levels\\n percentiles = kwargs.pop(\\\"percentiles\\\", True)\\n levels = contours or kwargs.get(\\\"levels\\\", None)\\n pcolor, contour, contourf = get_axis_density_methods(ax)\\n if percentiles and not isinstance(levels, int):\\n # plot individual percentile contours\\n _cs = plot_Z_percentiles(\\n *coords,\\n zi=zi,\\n ax=ax,\\n percentiles=levels,\\n extent=extent,\\n cmap=cmap,\\n **kwargs\\n )\\n mappable = _cs\\n else:\\n # plot interval contours\\n if levels is None:\\n levels = MaxNLocator(nbins=10).tick_values(zi.min(), zi.max())\\n elif isinstance(levels, int):\\n levels = MaxNLocator(nbins=levels).tick_values(zi.min(), zi.max())\\n else:\\n raise NotImplementedError\\n # filled contours\\n mappable = contourf(\\n *coords, zi, extent=extent, levels=levels, cmap=cmap, vmin=vmin, **kwargs\\n )\\n # contours\\n contour(\\n *coords, zi, extent=extent, levels=levels, cmap=cmap, vmin=vmin, **kwargs\\n )\\n return mappable\",\n \"def plot_data(array_list, params):\\n\\tkey = array_list[0]\\n\\tvals = array_list[1]\\n\\tprint key\\n\\tprint len(vals)\\n\\tfigure_id = 1\\n\\tfor item in params:\\n\\t\\tx_axis = get_by_keys(item['x_keys'], key, vals)\\n\\t\\ty_axis = get_by_keys(item['y_keys'], key, vals)\\n\\t\\tplt.figure(figure_id)\\n\\t\\tplt.plot(x_axis, y_axis)\\n\\t\\tfigure_id = figure_id + 1\\n\\tplt.show()\",\n \"def plot_solutions(self, solutions_list, plot_w=False, savefig_filename=None, display=True):\\n plt.figure(figsize=(13, 8))\\n for s in solutions_list:\\n U = s[0]\\n method, T = s[1]\\n plt.plot(self.x, U[:self.N], label=rf\\\"$U$ : {method}, $T = {T}$\\\")\\n if plot_w:\\n plt.plot(self.x, U[self.N:], label=rf\\\"$U_t$ : {method}, $T = {T}$\\\")\\n plt.xlabel(r\\\"$x$\\\")\\n plt.ylabel(r\\\"$U, U_t$\\\")\\n plt.title(\\\"Plot of Various Models at Different Times\\\")\\n plt.legend()\\n plt.grid()\\n if savefig_filename:\\n plt.savefig(savefig_filename) if savefig_filename.endswith(\\\".png\\\") else plt.savefig(savefig_filename+\\\".jpg\\\")\\n if display:\\n plt.show()\",\n \"def render(static, tour_indices, save_path):\\n\\n plt.close('all')\\n print('static_shape', static.shape)\\n print('tour_indices_shape', tour_indices.shape)\\n\\n num_plots = 3 if int(np.sqrt(len(tour_indices))) >= 3 else 1\\n\\n _, axes = plt.subplots(nrows=num_plots, ncols=num_plots,\\n sharex='col', sharey='row')\\n\\n if num_plots == 1:\\n axes = [[axes]]\\n axes = [a for ax in axes for a in ax]\\n\\n for i, ax in enumerate(axes):\\n\\n # Convert the indices back into a tour\\n idx = tour_indices[i]\\n print('idx0', idx)\\n if len(idx.size()) == 1:\\n idx = idx.unsqueeze(0)\\n print('idx1', idx)\\n\\n idx = idx.expand(static.size(1), -1)\\n print('idx2', idx)\\n data = torch.gather(static[i].data, 1, idx).cpu().numpy()\\n print('data', data)\\n\\n start = static[i, :, 0].cpu().data.numpy()\\n x = np.hstack((start[0], data[0], start[0]))\\n y = np.hstack((start[1], data[1], start[1]))\\n print('x', x)\\n print('y', y)\\n\\n # Assign each subtour a different colour & label in order traveled\\n idx = np.hstack((0, tour_indices[i].cpu().numpy().flatten(), 0))\\n print('idx3', idx)\\n where = np.where(idx == 0)[0]\\n print('where', where)\\n\\n for j in range(len(where) - 1):\\n\\n low = where[j]\\n high = where[j + 1]\\n\\n if low + 1 == high:\\n continue\\n\\n ax.plot(x[low: high + 1], y[low: high + 1], zorder=1, label=j)\\n\\n ax.legend(loc=\\\"upper right\\\", fontsize=3, framealpha=0.5)\\n ax.scatter(x, y, s=4, c='r', zorder=2)\\n ax.scatter(x[0], y[0], s=20, c='k', marker='*', zorder=3)\\n\\n ax.set_xlim(0, 1)\\n ax.set_ylim(0, 1)\\n\\n plt.tight_layout()\\n plt.savefig(save_path, bbox_inches='tight', dpi=400)\",\n \"def step_plots(X, labs, steps_vec, perplexity = 30, ncols = 3, \\\\\\n verbose = False, cdict = {1: 'red', 2: 'mediumspringgreen', 3: 'royalblue'}):\\n \\n # Set dimensions of subplots\\n nrows = math.ceil(len(steps_vec) / ncols)\\n \\n # Configure axes\\n axes = []\\n fig = plt.figure(figsize = (16, 3 * nrows))\\n \\n # Run t-SNE\\n low_d = tsne(X = X, perplexity = perplexity, niter = np.max(steps_vec), verbose = verbose, optim = \\\"fastest\\\")\\n \\n # Iteratively generate plots\\n for step in range(len(steps_vec)):\\n axes.append(fig.add_subplot(nrows, ncols, step + 1))\\n axes[-1].set_title(\\\"Perplexity = \\\" + str(perplexity) + \\\", Step = \\\" + str(steps_vec[step]))\\n plt.scatter(x = low_d[steps_vec[step], :, 0], y = low_d[steps_vec[step], :, 1], \\\\\\n edgecolor = None, alpha = 0.8, c = np.array(list(map(lambda x: cdict[x], labs))))\\n axes[-1].set_xticklabels([])\\n axes[-1].set_yticklabels([])\\n axes[-1].xaxis.set_ticks_position('none')\\n axes[-1].yaxis.set_ticks_position('none')\",\n \"def plot_all_seaice(resolutions, temporal_resolution, temporal_decomposition, detrend, imagefolder = 'images/timeseries/SIC/',seaice_source='nsidc'):\\n for n, temp_res, temp_decomp, dt in itertools.product(resolutions, temporal_resolution, temporal_decomposition, detrend):\\n plot_seaice_timeseries(anomlous = 'anomalous' == temp_decomp, temporal_resolution = temp_res, spatial_resolution = n, detrend = dt == 'detrended',seaice_source=seaice_source)\",\n \"def gen_plots(uf_dict, f_dict, min_x, max_x, min_y, max_y, axes, name, histogram, total):\\n with PdfPages(name) as pdf:\\n total_xuf = []\\n total_yuf = []\\n total_xf = []\\n total_yf = []\\n for entry in uf_dict:\\n print 'Making plot for ' + entry\\n xuf, yuf = zip(*uf_dict[entry])\\n fig = plt.figure()\\n ax1 = fig.add_subplot(111)\\n ax1.scatter(xuf, yuf, c='#ad4851', marker='o', label='initial structures')\\n try:\\n xf, yf = zip(*f_dict[entry])\\n ax1.scatter(xf, yf, c='orange', marker='x', label='selected structures')\\n except ValueError:\\n xf = []\\n yf = []\\n plt.legend(loc='upper right')\\n plt.title(entry, fontsize=30)\\n plt.xlim(min_x, max_x)\\n plt.ylim(min_y, max_y)\\n plt.xlabel(axes[0], fontsize=20)\\n plt.ylabel(axes[1], fontsize=20)\\n pdf.savefig(fig)\\n plt.close()\\n\\n if total:\\n total_xuf.extend(xuf)\\n total_yuf.extend(yuf)\\n total_xf.extend(xf)\\n total_yf.extend(yf)\\n\\n if histogram:\\n bins = np.linspace(min_y, max_y, num=10)\\n plt.hist(yuf, bins, alpha=0.5, color='b', label='initial structures')\\n try:\\n plt.hist(yf, bins, alpha=0.5, color='orange', label='selected structures')\\n except ValueError:\\n pass\\n plt.legend(loc='upper right')\\n plt.title(entry, fontsize=30)\\n plt.xlabel(axes[1], fontsize=20)\\n plt.ylabel('Frequency', fontsize=20)\\n pdf.savefig()\\n plt.close()\\n\\n if total:\\n print 'Making composite plot'\\n fig = plt.figure()\\n ax1 = fig.add_subplot(111)\\n ax1.scatter(total_xuf, total_yuf, c='#ad4851', marker='o', label='initial structures')\\n ax1.scatter(total_xf, total_yf, c='orange', marker='x', label='selected structures')\\n plt.legend(loc='upper right')\\n plt.title('Composite Plot', fontsize=30)\\n plt.xlim(min_x, max_x)\\n plt.ylim(min_y, max_y)\\n plt.xlabel(axes[0], fontsize=20)\\n plt.ylabel(axes[1], fontsize=20)\\n pdf.savefig(fig)\\n plt.close()\",\n \"def _create_initial_plots(\\n criterion_history,\\n params_history,\\n group_to_param_ids,\\n group_to_param_names,\\n):\\n param_plots = []\\n for group, param_ids in group_to_param_ids.items():\\n param_names = group_to_param_names[group]\\n param_group_plot = plot_time_series(\\n data=params_history,\\n y_keys=param_ids,\\n y_names=param_names,\\n x_name=\\\"iteration\\\",\\n title=str(group),\\n )\\n param_plots.append(param_group_plot)\\n\\n arranged_param_plots = [Row(plot) for plot in param_plots]\\n\\n criterion_plot = plot_time_series(\\n data=criterion_history,\\n x_name=\\\"iteration\\\",\\n y_keys=[\\\"criterion\\\"],\\n y_names=[\\\"criterion\\\"],\\n title=\\\"Criterion\\\",\\n name=\\\"criterion_plot\\\",\\n )\\n\\n plots = [Row(criterion_plot), *arranged_param_plots]\\n return plots\",\n \"def plot3DOS(infileA, nameA, infileB, nameB, infileC, nameC):\\n\\t\\n\\tspin1occsA, spin2occsA, energiesA = readfile(infileA)\\n\\tspin1occsB, spin2occsB, energiesB = readfile(infileB)\\n\\tspin1occsC, spin2occsC, energiesC = readfile(infileC)\\n\\t\\n\\t\\n\\tspin1names = [spin1occsA, spin1occsB, spin1occsC]\\n\\tspin2names = [spin2occsA, spin2occsB, spin2occsC]\\n\\tenergynames = [energiesA, energiesB, energiesC]\\n\\tnames = [nameA, nameB, nameC]\\n\\t\\n\\t# Create figure\\n\\tfig, axs = plt.subplots(1,3,figsize=(18,12), facecolor='w', edgecolor='k')\\n\\n\\taxs = axs.ravel()\\n\\tfor i in range(0,3):\\n\\t\\taxs[i].plot(spin1names[i], energynames[i], \\\"b\\\", label='Spin 1')\\n\\t\\taxs[i].plot(spin2names[i], energynames[i], \\\"g\\\", label='Spin 2')\\n\\t\\t\\n\\t\\taxs[i].set_title(str(names[i]))\\n\\t\\taxs[i].get_xaxis().set_visible(False)\\n\\taxs[0].set_ylabel('Energy')\\n\\thandles, labels = axs[0].get_legend_handles_labels()\\n\\tfig.legend(handles, labels)\\n\\thandles, labels = axs[0].get_legend_handles_labels()\\n\\tplt.savefig(\\\"DOSPLOTS\\\")\\n\\tplt.show()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62037873","0.58769345","0.5820305","0.57832277","0.57221377","0.56909025","0.5687232","0.562841","0.5538102","0.5485024","0.54831433","0.54714435","0.5457713","0.5433277","0.54000473","0.53964806","0.5381867","0.53771883","0.53722656","0.5367312","0.53610206","0.5355005","0.5354732","0.53511924","0.5295496","0.52924967","0.52870363","0.52703613","0.5259759","0.52577716","0.52492744","0.523358","0.52261585","0.5211306","0.5186821","0.5171873","0.5168606","0.51665545","0.51665545","0.5159375","0.5151086","0.5145324","0.51401556","0.5139977","0.5139977","0.5139977","0.5138478","0.5134413","0.51343787","0.51343787","0.51343787","0.51343787","0.5130518","0.51180494","0.51141447","0.5109587","0.51054335","0.51000375","0.5094054","0.5087784","0.50819874","0.50796396","0.50793105","0.5074819","0.5070205","0.5059368","0.5054035","0.50536656","0.50447214","0.50432295","0.50414306","0.50357807","0.50323844","0.50316983","0.5023109","0.49912483","0.49876204","0.4986089","0.49847803","0.4981468","0.49686897","0.4968072","0.4964972","0.49589607","0.4954832","0.49540243","0.49522242","0.49497434","0.4946199","0.49428934","0.49418086","0.4940152","0.49312824","0.49285564","0.49280828","0.49278247","0.49252954","0.4924558","0.4917716","0.49097815"],"string":"[\n \"0.62037873\",\n \"0.58769345\",\n \"0.5820305\",\n \"0.57832277\",\n \"0.57221377\",\n \"0.56909025\",\n \"0.5687232\",\n \"0.562841\",\n \"0.5538102\",\n \"0.5485024\",\n \"0.54831433\",\n \"0.54714435\",\n \"0.5457713\",\n \"0.5433277\",\n \"0.54000473\",\n \"0.53964806\",\n \"0.5381867\",\n \"0.53771883\",\n \"0.53722656\",\n \"0.5367312\",\n \"0.53610206\",\n \"0.5355005\",\n \"0.5354732\",\n \"0.53511924\",\n \"0.5295496\",\n \"0.52924967\",\n \"0.52870363\",\n \"0.52703613\",\n \"0.5259759\",\n \"0.52577716\",\n \"0.52492744\",\n \"0.523358\",\n \"0.52261585\",\n \"0.5211306\",\n \"0.5186821\",\n \"0.5171873\",\n \"0.5168606\",\n \"0.51665545\",\n \"0.51665545\",\n \"0.5159375\",\n \"0.5151086\",\n \"0.5145324\",\n \"0.51401556\",\n \"0.5139977\",\n \"0.5139977\",\n \"0.5139977\",\n \"0.5138478\",\n \"0.5134413\",\n \"0.51343787\",\n \"0.51343787\",\n \"0.51343787\",\n \"0.51343787\",\n \"0.5130518\",\n \"0.51180494\",\n \"0.51141447\",\n \"0.5109587\",\n \"0.51054335\",\n \"0.51000375\",\n \"0.5094054\",\n \"0.5087784\",\n \"0.50819874\",\n \"0.50796396\",\n \"0.50793105\",\n \"0.5074819\",\n \"0.5070205\",\n \"0.5059368\",\n \"0.5054035\",\n \"0.50536656\",\n \"0.50447214\",\n \"0.50432295\",\n \"0.50414306\",\n \"0.50357807\",\n \"0.50323844\",\n \"0.50316983\",\n \"0.5023109\",\n \"0.49912483\",\n \"0.49876204\",\n \"0.4986089\",\n \"0.49847803\",\n \"0.4981468\",\n \"0.49686897\",\n \"0.4968072\",\n \"0.4964972\",\n \"0.49589607\",\n \"0.4954832\",\n \"0.49540243\",\n \"0.49522242\",\n \"0.49497434\",\n \"0.4946199\",\n \"0.49428934\",\n \"0.49418086\",\n \"0.4940152\",\n \"0.49312824\",\n \"0.49285564\",\n \"0.49280828\",\n \"0.49278247\",\n \"0.49252954\",\n \"0.4924558\",\n \"0.4917716\",\n \"0.49097815\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7024441"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":83098,"cells":{"query":{"kind":"string","value":"Keyword arguments specify which objects should be read from ROOT files. types sets the list of possible objects types. keys specifies a list of allowed objects' names. Only simple keys are currently allowed (no regular expressions). If both types and keys are provided, then objects that satisfy any of types or keys are read. selector is a general function that accepts an object from a ROOT file and returns a boolean. If selector is given, both types and keys must"},"document":{"kind":"string","value":"def __init__(self, types=None, keys=None, selector=None):\n try:\n import ROOT\n except ImportError:\n raise ImportError(\"ROOT not installed\")\n\n if selector is not None:\n if keys or types:\n raise lena.core.LenaTypeError(\n \"if selector is provided, keys and types \"\n \"must not be passed\"\n )\n if not callable(selector):\n raise lena.core.LenaTypeError(\n \"selector must be callable\"\n )\n self._selector = selector\n return\n\n if keys is not None:\n if not isinstance(keys, list):\n raise lena.core.LenaTypeError(\n \"keys must be a list of strings\"\n )\n # ROOT keys can have unicode names\n if (sys.version_info.major == 2 and\n any((not isinstance(key, basestring) for key in keys))) or \\\n (sys.version_info.major > 2 and\n any((not isinstance(key, str) for key in keys))):\n raise lena.core.LenaTypeError(\n \"keys must contain only strings\"\n )\n # todo: allow regular expressions\n # todo: allow ROOT object versions\n keys_selector = [lambda obj: obj.GetName() == key\n for key in keys]\n\n if types is not None:\n if not isinstance(types, list):\n raise lena.core.LenaTypeError(\n \"types must be a list of types\"\n )\n # maybe inspect is needed only for Python 2 types\n # not derived from object. Otherwise use isinstance(_, type)\n if any((not inspect.isclass(tp) for tp in types)):\n raise lena.core.LenaTypeError(\n \"types must must contain only types\"\n )\n # in Lena \"and\" means a list, while \"or\" means a tuple.\n # In Python isinstance requires a tuple.\n types = tuple(types)\n types_selector = lambda obj: isinstance(obj, types)\n\n if types is None and keys is None:\n self._selector = None\n elif keys:\n if types:\n self._selector = lena.flow.Selector(\n [types_selector, keys_selector]\n )\n else:\n self._selector = lena.flow.Selector(keys_selector)"},"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 load_select(self, selected):\n\t\tfor filename in selected:\n\t\t\tself.load(filename)\n\n\t\tif \"worddict\" in selected and \"classdict\" in selected:\n\t\t\tself.reverse_dicts()","def selector(**kwargs):\n return kwargs","def select(self, mode, types=None, types_excluded=None, layers=None, layers_excluded=None,\n objects=None, objects_excluded=None):\n scene = self.set_as_active()\n layer_numbers = set(constants.layer_numbers)\n obj_types = set(constants.obj_types)\n\n # setting up types and types excluded\n if types is None or 'ALL' in types:\n types = obj_types\n\n if types_excluded is None:\n types_excluded = set()\n\n elif 'ELSE' in types_excluded:\n types_excluded = obj_types - types\n\n types -= types_excluded\n\n # setting up layers and layers excluded\n if layers is None or 'ALL' in layers:\n layers = layer_numbers\n\n if layers_excluded is None:\n layers_excluded = set()\n\n elif 'ELSE' in layers_excluded:\n layers_excluded = layer_numbers - layers\n\n layers -= layers_excluded\n\n # setting up objects and objects excluded\n if objects is None:\n objects = w_var.objects_affected\n\n if objects_excluded is None:\n objects_excluded = set()\n\n objects -= objects_excluded\n\n previous_area = bpy.context.area.type\n\n # can't change object select property while in the 'PROPERTIES' area\n bpy.context.area.type = 'VIEW_3D'\n\n # much quicker than looping through objects\n if 'ALL' in objects and types == obj_types and layers == layer_numbers:\n bpy.ops.object.select_all(action=mode)\n\n elif mode == 'SELECT':\n if objects is not None:\n for obj in scene.objects:\n if ((obj in objects or 'ALL' in objects) and\n obj.type in types and self.object_on_layer(obj, layers)):\n obj.select = True\n\n elif (obj in objects_excluded or 'ELSE' in objects_excluded or\n obj.type in types_excluded or self.object_on_layer(obj, layers_excluded)):\n obj.select = False\n\n else:\n for obj in scene.objects:\n if obj.type in types and self.object_on_layer(obj, layers):\n obj.select = True\n\n elif obj.type in types_excluded or self.object_on_layer(obj, layers_excluded):\n obj.select = False\n\n elif mode == 'DESELECT':\n if objects is not None:\n for obj in scene.objects:\n if ((obj in objects or 'ALL' in objects) and\n obj.type in types and self.object_on_layer(obj, layers)):\n obj.select = False\n\n else:\n for obj in scene.objects:\n if obj.type in types and self.object_on_layer(obj, layers):\n obj.select = False\n\n else:\n raise ValueError(\"No such mode as '{}'.\".format(mode))\n\n bpy.context.area.type = previous_area\n self.set_active_object(types)","def Select(*keys):\n return \"Select\" >> beam.Map(lambda r: {k: r[k] for k in keys})","def get_objs_with_key_and_typeclass(self, oname, otypeclass_path):\n return self.filter(db_key__iexact=oname).filter(db_typeclass_path__exact=otypeclass_path)","def selectType(*args, allComponents: bool=True, allObjects: bool=True, animBreakdown: bool=True,\n animCurve: bool=True, animInTangent: bool=True, animKeyframe: bool=True,\n animOutTangent: bool=True, byName: Union[List[AnyStr, bool], List[List[AnyStr,\n bool]]]=None, camera: bool=True, cluster: bool=True, collisionModel: bool=True,\n controlVertex: bool=True, curve: bool=True, curveKnot: bool=True,\n curveOnSurface: bool=True, curveParameterPoint: bool=True, dimension: bool=True,\n dynamicConstraint: bool=True, edge: bool=True, editPoint: bool=True, emitter:\n bool=True, facet: bool=True, field: bool=True, fluid: bool=True, follicle:\n bool=True, hairSystem: bool=True, handle: bool=True, hull: bool=True,\n ikEndEffector: bool=True, ikHandle: bool=True, imagePlane: bool=True,\n implicitGeometry: bool=True, isoparm: bool=True, joint: bool=True, jointPivot:\n bool=True, lattice: bool=True, latticePoint: bool=True, light: bool=True,\n localRotationAxis: bool=True, locator: bool=True, locatorUV: bool=True,\n locatorXYZ: bool=True, meshUVShell: bool=True, motionTrailPoint: bool=True,\n motionTrailTangent: bool=True, nCloth: bool=True, nParticle: bool=True,\n nParticleShape: bool=True, nRigid: bool=True, nonlinear: bool=True, nurbsCurve:\n bool=True, nurbsSurface: bool=True, objectComponent: bool=True,\n orientationLocator: bool=True, particle: bool=True, particleShape: bool=True,\n plane: bool=True, polymesh: bool=True, polymeshEdge: bool=True, polymeshFace:\n bool=True, polymeshFreeEdge: bool=True, polymeshUV: bool=True, polymeshVertex:\n bool=True, polymeshVtxFace: bool=True, queryByName: Union[AnyStr, bool]=\"\",\n rigidBody: bool=True, rigidConstraint: bool=True, rotatePivot: bool=True,\n scalePivot: bool=True, sculpt: bool=True, selectHandle: bool=True, spring:\n bool=True, springComponent: bool=True, stroke: bool=True, subdiv: bool=True,\n subdivMeshEdge: bool=True, subdivMeshFace: bool=True, subdivMeshPoint: bool=True,\n subdivMeshUV: bool=True, surfaceEdge: bool=True, surfaceFace: bool=True,\n surfaceKnot: bool=True, surfaceParameterPoint: bool=True, surfaceRange:\n bool=True, surfaceUV: bool=True, texture: bool=True, vertex: bool=True, q=True,\n query=True, **kwargs)->Union[bool, Any]:\n pass","def get(self, objtype, **kwargs):\n h = \"Issue a GetObject to find an object\"\n kwargs['pytan_help'] = kwargs.get('pytan_help', h)\n\n clean_keys = ['obj', 'objtype', 'obj_map']\n clean_kwargs = pytan.utils.clean_kwargs(kwargs=kwargs, keys=clean_keys)\n\n err_keys = ['pytan_help']\n err_args = pytan.utils.clean_kwargs(kwargs=kwargs, keys=err_keys)\n\n obj_map = pytan.utils.get_obj_map(objtype=objtype)\n\n manual_search = obj_map['manual']\n api_attrs = obj_map['search']\n\n api_kwattrs = [kwargs.get(x, '') for x in api_attrs]\n\n # if the api doesn't support filtering for this object,\n # or if the user didn't supply any api_kwattrs and manual_search\n # is true, get all objects of this type and manually filter\n if not api_attrs or (not any(api_kwattrs) and manual_search):\n all_objs = self.get_all(objtype=objtype, **clean_kwargs)\n\n return_objs = getattr(taniumpy, all_objs.__class__.__name__)()\n\n for k, v in kwargs.items():\n if not v:\n continue\n if not hasattr(all_objs[0], k):\n continue\n if not pytan.utils.is_list(v):\n v = [v]\n for aobj in all_objs:\n aobj_val = getattr(aobj, k)\n aobj_val_str = str(aobj_val)\n if aobj_val not in v and aobj_val_str not in v:\n continue\n return_objs.append(aobj)\n\n if not return_objs:\n err = \"No results found searching for {} with {}!!\".format\n raise pytan.exceptions.HandlerError(err(objtype, err_args))\n\n return return_objs\n\n # if api supports filtering for this object,\n # but no filters supplied in kwargs, raise\n if not any(api_kwattrs):\n err = \"Getting a {} requires at least one filter: {}\".format\n raise pytan.exceptions.HandlerError(err(objtype, api_attrs))\n\n # if there is a multi in obj_map, that means we can pass a list\n # type to the taniumpy. the list will have an entry for each api_kw\n if obj_map['multi']:\n return self._get_multi(obj_map=obj_map, **clean_kwargs)\n\n # if there is a single in obj_map but not multi, that means\n # we have to find each object individually\n elif obj_map['single']:\n return self._get_single(obj_map=obj_map, **clean_kwargs)\n\n err = \"No single or multi search defined for {}\".format\n raise pytan.exceptions.HandlerError(err(objtype))","def select(self, *selector_objs):\n # Make sure they're all Selector objects\n selectors = [Selector.from_unknown(obj) for obj in selector_objs]\n return Document(s(self) for s in selectors)","def createSelector(self,type='select',speed=2.0):\n self.selector = self.loadObject(type, scale=2, parent=render, transparency=True, pos=Point2(0,0), glow=1)\n self.selector.hide()\n ival = self.selector.hprInterval((speed), Vec3(0, 0, 360))\n ival.loop()","def search(cls, **kwargs):\n key = [key for key in kwargs][0]\n objects = cls.get_all()\n if isinstance(objects, dict):\n return objects\n results = []\n for i in objects:\n if is_substring(kwargs[key], getattr(i, key)):\n results.append(i)\n if not results:\n return {\n \"message\": \"No objects match the searched value.\",\n \"help\": \"Ensure arguments are of existent objects.\"\n }\n return results","def get(**args):\n\tglobal _objstore\n\t_objstore = _objstore or ObjStore()\n\n\treturn _objstore.get(args['type'], args['name'])","def get_all(self, objtype, **kwargs):\n h = \"Issue a GetObject to find an object\"\n kwargs['pytan_help'] = kwargs.get('pytan_help', h)\n\n clean_keys = ['obj', 'objtype', 'obj_map']\n clean_kwargs = pytan.utils.clean_kwargs(kwargs=kwargs, keys=clean_keys)\n\n obj_map = pytan.utils.get_obj_map(objtype=objtype)\n\n all_type = obj_map['all']\n api_obj_all = pytan.utils.get_taniumpy_obj(obj_map=all_type)()\n\n found = self._find(obj=api_obj_all, **clean_kwargs)\n return found","def get_objects_by_type(self, *types) -> List[TgnObject]:\n if not types:\n return list(self.objects.values())\n types_l = [o.lower() for o in types]\n return [o for o in self.objects.values() if o.type.lower() in types_l]","def load_select_if_necessary(self, selected):\n\t\tfor which in selected:\n\t\t\tself.load_if_necessary(which)\n\n\t\tif \"worddict\" in selected and \"classdict\" in selected:\n\t\t\tself.reverse_dicts()","def load_object_lists(category=None):\n\n # type checking\n\n # not empty\n assert category\n\n # not string\n if isinstance(category, str):\n category = [category]\n\n # iterable\n try:\n iter(category)\n except TypeError:\n print(\"category should be an iterable object\")\n\n # a subset of the full category set\n assert set(category).issubset(g_shapenet_categlory_pair.values())\n\n # load obj file path\n for cat in category:\n search_path = os.path.join(g_shapenet_path, cat, '**', '*.obj')\n yield glob.iglob(search_path, recursive=True)","def retrieve(self, selected_keys):\n if not isinstance(selected_keys, list):\n selected_keys = [selected_keys]\n\n # Select the keys we want to download\n key_dict = {key: self._results[key][\"keys\"] for key in selected_keys}\n\n results = defaultdict(dict)\n for key, dateranges in key_dict.items():\n for daterange in dateranges:\n # Create a key for this range\n data_keys = key_dict[key][daterange]\n # Retrieve the data from the object store and combine into a NetCDF\n recombined_data = recombine_sections(data_keys)\n\n results[key][daterange] = recombined_data\n\n return results","def select(files, file_type):\n k = 0\n print('== ' + file_type + ' List ==')\n for file in files:\n print(\"[\" + str(k) + \"]. \" + file)\n k += 1\n print('Select a ' + file_type + ' to continue')\n idx = int(input())\n return files[idx]","def get_objects_or_children_by_type(self, *types):\n\n objects = self.get_objects_by_type(*types)\n return objects if objects else self.get_children(*types)","def load_from_files(*filenames,**kwargs):\n if 'keys' in kwargs.keys() and 'dtype' not in kwargs.keys():\n raise ValueError('Please set dtype as well.')\n elif 'keys' in kwargs.keys() and 'dtype' in kwargs.keys():\n if len(kwargs['keys']) != len(kwargs['dtype']):\n raise ValueError('Length of keys and dtype must match.')\n\n z_range = kwargs.pop('z_range',None)\n z_key = kwargs.pop('z_key',None)\n keys = kwargs.pop('keys',['Name','RA','Dec','z'])\n dtypes = kwargs.pop('dtype',[object,float,float,float])\n case_sensitive = kwargs.pop('case_sensitive',False)\n comments = kwargs.pop('comments','#')\n delimiter = kwargs.pop('delimeter',None)\n return_fileindex = kwargs.pop('return_fileindex',False)\n\n if kwargs != {}:\n unknown_kw = ' '.join(kwargs.keys())\n raise TypeError('load_from_files got unknown keyword arguments: {}'.format(unknown_kw))\n\n if not case_sensitive:\n keys = [a.upper() for a in keys]\n\n if z_range is not None and z_key is None:\n z_keys = [key for key in keys \n if key[0].upper() == 'Z' or key.upper() == \"REDSHIFT\"] \n if len(z_keys) == 0:\n raise ValueError('Failed to determine z_key, please set kwarg z_key')\n elif len(z_keys) > 1:\n raise ValueError('Ambiguous z_key, please set kwargs z_key manually')\n else:\n z_key = z_keys[0]\n\n out = None\n fileindex = []\n\n for k,filename in enumerate(filenames):\n tmp = np.genfromtxt(filename,names=True,comments=comments,dtype=None,\n case_sensitive=case_sensitive,delimiter=delimiter)\n \n if z_range is None:\n tmp2 = np.zeros((len(tmp),),dtype=zip(keys,dtypes))\n fileindex.extend([k for a in range(len(tmp))])\n for key in keys:\n tmp2[key] = tmp[key]\n else:\n z_filter = (tmp[z_key] >= z_range[0]) & (tmp[z_key] < z_range[1]) \n tmp2 = np.zeros((np.sum(z_filter),),dtype=zip(keys,dtypes))\n fileindex.extend([k for a in range(np.sum(z_filter))])\n for key in keys:\n tmp2[key] = tmp[key][z_filter]\n \n if out is None:\n out = tmp2\n else:\n out = np.concatenate((out,tmp2))\n \n if return_fileindex:\n return [out[key] for key in keys] + [np.array(fileindex)]\n else:\n return [out[key] for key in keys]","def get_object(self, object_type, object_name, user_key = None):\n\t\tobject_key = self._get_key(object_type,user_key)\n\n\t\ttarget_object = None\n\n\t\t#print object_type\n\t\tfor item in self.data['all_%s' % object_type]:\n\t\t\t## Skip items without the specified key\n\t\t\tif not item.has_key(object_key):\n\t\t\t\tcontinue\n\t\t\tif item[object_key] == object_name:\n\t\t\t\ttarget_object = item\n\t\t\t## This is for multi-key items\n\t\treturn target_object","def DataLoad(filename,path,keys_to_search=['dstr','ORT'],text_file = False):\n\n OUTPUT_DICT = {}\n\n if not text_file:\n\n rdr = EafReader(filename, path, text_file=False)\n\n for key in keys_to_search:\n\n annot, annot_df = rdr.parser(key)\n DF = rdr.dataframe_creator(annot, annot_df, annot_type=key)\n\n OUTPUT_DICT[key.lower()] = DF\n else:\n\n\n\n for key in keys_to_search:\n\n rdr = EafReader(filename+'_'+key.lower()+'.txt', path, text_file=True)\n DF = rdr.csv_reader()\n\n OUTPUT_DICT[key.lower()] = DF\n\n return OUTPUT_DICT","def _get_objects(self, object_type, **kwargs):\r\n params = dict()\r\n if kwargs:\r\n for key, val in kwargs.items():\r\n if '_' in key:\r\n new_key = key.replace(\"_\",\"-\") \r\n params[new_key] = val\r\n else:\r\n params[key] = val\r\n try: \r\n response = requests.get(self.api_endpoint + object_type, \r\n auth=(self.user,self.pwd), \r\n params=params, verify=False)\r\n\r\n devices = json.loads(response.text)\r\n\r\n except requests.exceptions.RequestException as e:\r\n print \"Error:\",e\r\n return 1\r\n\r\n return_objects = []\r\n for i in devices.keys():\r\n if i == u\"links\":\r\n continue \r\n for j in devices[i]:\r\n return_objects.append(XtremObjFactory(object_type,j,self))\r\n\r\n return return_objects","def select(*args):","def select(*args):","def load_data(self, **kwargs):\n type = kwargs['type']\n file = kwargs['file']\n with open(file, 'r') as data_file:\n for line in data_file:\n line = line[:-1]\n items_dict = ast.literal_eval(line)\n\n item = type.from_dict(items_dict)\n\n self.add_item(item, lambda i: i.uid)","def find_objects_by_type():\n try:\n keyword = request.form[\"keyword\"]\n object_type = request.form[\"object_type\"]\n\n # Get entities based on the selection\n entities = g.user.get_api().get_by_object_types(keyword, object_type)\n\n # Parse response object into table data\n data = raw_entities_to_table_data(entities)\n\n # If no entities were found reutrn with failure state and message\n result = get_result_template()\n if len(data[\"data\"]) == 0:\n result[\"status\"] = \"FAIL\"\n result[\"message\"] = 'No entities of type \"{TYPE}\" were found.'.format(\n TYPE=object_type\n )\n else:\n result[\"status\"] = \"SUCCESS\"\n result[\"data\"] = {\"table_field\": data}\n return jsonify(result_decorator(result))\n\n except Exception as e:\n result = get_result_template()\n result[\"status\"] = \"FAIL\"\n result[\"message\"] = str(e)\n return jsonify(result_decorator(result))","def common_reference_file_keywords(reftype=None,\n title=None,\n description=None,\n exp_type=None,\n author=\"STScI\",\n useafter=\"2014-01-01T00:00:00\",\n module=None,\n fname=None,\n pupil=None, **kwargs):\n if exp_type is None:\n raise ValueError(\"exp_type not set\")\n if reftype is None:\n raise ValueError(\"Expected reftype value\")\n\n ref_file_common_keywords = {\n \"author\": author,\n \"description\": description,\n \"exposure\": {\"type\": exp_type},\n \"instrument\": {\"name\": \"NIRCAM\"},\n \"pedigree\": \"ground\",\n \"reftype\": reftype,\n \"telescope\": \"JWST\",\n \"title\": title,\n \"useafter\": useafter,\n }\n\n if fname is not None:\n ref_file_common_keywords[\"instrument\"][\"filter\"] = fname\n if pupil is not None:\n ref_file_common_keywords[\"instrument\"][\"pupil\"] = pupil\n if module is not None:\n ref_file_common_keywords[\"instrument\"][\"module\"] = module\n\n ref_file_common_keywords.update(kwargs)\n return ref_file_common_keywords","def get_paths(self, *paths, **kwargs):\n filter_fn = kwargs.get(\"filter_fn\", None)\n\n def default_filter_fn(obj):\n return True\n\n def default_transport(x):\n return x\n\n if filter_fn is None:\n filter_fn = default_filter_fn\n key = kwargs.get(\"key\", None)\n if key is None:\n objs = self._analyzer_objects\n else:\n objs = sorted(self._analyzer_objects, key=key)\n\n res = []\n for obj_name in objs:\n obj = objs[obj_name]\n if not filter_fn(obj):\n continue\n obj_res = []\n for i in range(len(paths)):\n path = paths[i]\n if isinstance(path, dict):\n transport = path[\"transport\"]\n path = path[\"path\"]\n else:\n transport = default_transport\n traverse = obj[path[0]]\n for p in path[1:]:\n if traverse is not None:\n if p in traverse:\n traverse = traverse[p]\n elif p.upper() in traverse:\n traverse = traverse[p.upper()]\n else:\n traverse = None\n obj_res.append(transport(traverse))\n if len(obj_res) == len(paths):\n res.append(obj_res)\n return res","def select_filenames(filenames, select_keywords, drop_keywords):\n from os.path import relpath\n\n if select_keywords is None and drop_keywords is None:\n return filenames\n\n filenames['_relative_path'] = filenames['file_name'].apply(\n lambda x: relpath(x, root_dir))\n\n if select_keywords is not None:\n if isinstance(select_keywords, str):\n select_keywords = [select_keywords]\n\n keep = filenames['_relative_path'].apply(\n lambda x:\n True if np.any([kwrd in x for kwrd in select_keywords])\n else False)\n\n filenames = filenames.loc[keep, :]\n\n if drop_keywords is not None:\n if isinstance(select_keywords, str):\n drop_keywords = [drop_keywords]\n\n drop = filenames['_relative_path'].apply(\n lambda x:\n False if np.any([kwrd in x for kwrd in drop_keywords])\n else True)\n\n filenames = filenames.loc[drop, :]\n\n filenames.drop(columns=['_relative_path'])\n filenames.reset_index(drop=True)\n\n return filenames","def browse(self, keys=None, timestamp=None, **kwargs):\n keys = keys or kwargs.get('key')\n timestamp = timestamp or find_in_kwargs_by_alias('timestamp', kwargs)\n timestamp_is_string = isinstance(timestamp, str)\n if isinstance(keys, list) and timestamp and not timestamp_is_string:\n data = self.client.browseKeysTime(keys, timestamp, self.creds, self.transaction, self.environment)\n elif isinstance(keys, list) and timestamp and timestamp_is_string:\n data = self.client.browseKeysTimestr(keys, timestamp, self.creds, self.transaction, self.environment)\n elif isinstance(keys, list):\n data = self.client.browseKeys(keys, self.creds, self.transaction, self.environment)\n elif timestamp and not timestamp_is_string:\n data = self.client.browseKeyTime(keys, timestamp, self.creds, self.transaction, self.environment)\n elif timestamp and timestamp_is_string:\n data = self.client.browseKeyTimestr(keys, timestamp, self.creds, self.transaction, self.environment)\n elif keys:\n data = self.client.browseKey(keys, self.creds, self.transaction, self.environment)\n else:\n require_kwarg('key or keys')\n return pythonify(data)","def load_objects(self):\n \n # Load classifier\n with open('../twitterClass/classifier/classifier.p','r') as f:\n self.classifier = cPickle.load(f)\n \n #Load blocked keywords\n regex_str2 = []\n with open('../twitterClass/twitterMiningClass/private/blocked_keywords.txt','r') as f:\n keywords = f.read().split('\\n')\n for key in keywords:\n key = key.split(',')\n #key[0] = keyword name , key[1] = pattern\n print key\n regex_str2.append(key[1])\n # create regex compiler for blocked keyword search\n regex_str2 = map(lambda x: x.replace(\"\\\\\\\\\",\"\\\\\"),regex_str2)\n self.blocked_keywords_re = re.compile(r'('+'|'.join(regex_str2)+')',re.IGNORECASE)\n \n # Load keywords\n with open('../twitterClass/twitterMiningClass/private/keywords.txt','r') as f:\n keywords = f.read().split('\\n')\n for key in keywords:\n key = key.split(',')\n #key[0] = keyword name , key[1] = pattern\n self.keywords[key[0]] = key[1]\n # create regex compiler for keyword search\n regex_str = []\n for keys,pattern in self.keywords.iteritems():\n regex_str.append(pattern)\n regex_str = map(lambda x: x.replace(\"\\\\\\\\\",\"\\\\\"),regex_str)\n self.keywords_re = re.compile(r'('+'|'.join(regex_str)+')', re.VERBOSE | re.IGNORECASE)","def _selectFrom(self, selector, selectorType, response):\n sel = Selector(response)\n if selectorType == FieldSelector.CSS:\n elems = sel.css(selector)\n elif selectorType == FieldSelector.XPATH:\n elems = sel.xpath(selector)\n return elems","def get_tree(self, list_of_keys):\n cur_obj = self\n for key in list_of_keys:\n cur_obj = cur_obj.get(key)\n if not cur_obj: break\n return cur_obj","def createSelector2(self,type='select',speed=2.0):\n self.selector2 = self.loadObject(type, scale=2, parent=render, transparency=True, pos=Point2(0,0), glow=1)\n self.selector2.hide()\n ival = self.selector2.hprInterval((speed), Vec3(0, 0, 360))\n ival.loop()","def selectMode(*args, component: bool=True, hierarchical: bool=True, leaf: bool=True, object:\n bool=True, preset: bool=True, root: bool=True, template: bool=True, q=True,\n query=True, **kwargs)->Union[bool, Any]:\n pass","def get ( path , selector = lambda s : s ) :\n _g = appMgr()\n _es = _g.evtSvc()\n data = _es[path]\n #\n try : ## try to use the selector \n return selector ( data )\n except:\n pass\n \n try : ## try to use selector \n return filter ( selector , data )\n except :\n pass \n \n return data","def get_objects(vim, type, properties_to_collect=None, all=False):\n if not properties_to_collect:\n properties_to_collect = [\"name\"]\n\n client_factory = vim.client.factory\n trav_spec = vim_util.build_recursive_traversal_spec(client_factory)\n object_spec = vim_util.build_object_spec(client_factory,\n vim.service_content.rootFolder,\n [trav_spec])\n property_spec = vim_util.build_property_spec(\n client_factory, type_=type,\n properties_to_collect=properties_to_collect,\n all_properties=all)\n property_filter_spec = vim_util.build_property_filter_spec(client_factory,\n [property_spec],\n [object_spec])\n property_collector = vim.service_content.propertyCollector\n return retrieve_properties_ex(vim,\n property_collector,\n [property_filter_spec])","def selectBoxType(self,**kwargs):\n elements = self.baxter.scene.boxes.keys()\n\n entries={}\n for element in elements:\n entries[element] = self.addSceneBox\n self.mm.addGenericMenu(\"selectBox\",\"addBox\",\"Select a box type you want to add\", entries)\n self.mm.loadMenu(\"selectBox\")","def selectItem(*args):","def _load_mapping(mapping, **keys):\n if mapping.endswith(\".pmap\"):\n cls = PipelineContext\n elif mapping.endswith(\".imap\"):\n cls = InstrumentContext\n elif mapping.endswith(\".rmap\"):\n cls = ReferenceMapping\n else:\n if config.S3_ENABLED:\n m = Mapping.from_s3(mapping, **keys)\n else:\n m = Mapping.from_file(mapping, **keys)\n mapping_type = m.header[\"mapping\"].lower()\n if mapping_type == \"pipeline\":\n cls = PipelineContext\n elif mapping_type == \"instrument\":\n cls = InstrumentContext\n elif mapping_type == \"reference\":\n cls = ReferenceMapping\n else:\n raise ValueError(\"Unknown mapping type for \" + repr(mapping))\n\n if config.S3_ENABLED:\n return cls.from_s3(mapping, **keys)\n else:\n return cls.from_file(mapping, **keys)","def load_path(file_obj, file_type=None, **kwargs):\n # avoid a circular import\n from ...exchange.load import load_kwargs\n # record how long we took\n tic = util.now()\n\n if isinstance(file_obj, Path):\n # we have been passed a Path file_object so\n # do nothing and return the passed file_object\n return file_obj\n elif util.is_file(file_obj):\n # for open file file_objects use loaders\n kwargs.update(path_loaders[file_type](\n file_obj, file_type=file_type))\n elif util.is_string(file_obj):\n # strings passed are evaluated as file file_objects\n with open(file_obj, 'rb') as f:\n # get the file type from the extension\n file_type = os.path.splitext(file_obj)[-1][1:].lower()\n # call the loader\n kwargs.update(path_loaders[file_type](\n f, file_type=file_type))\n elif util.is_instance_named(file_obj, ['Polygon', 'MultiPolygon']):\n # convert from shapely polygons to Path2D\n kwargs.update(misc.polygon_to_path(file_obj))\n elif util.is_instance_named(file_obj, 'MultiLineString'):\n # convert from shapely LineStrings to Path2D\n kwargs.update(misc.linestrings_to_path(file_obj))\n elif isinstance(file_obj, dict):\n # load as kwargs\n return load_kwargs(file_obj)\n elif util.is_sequence(file_obj):\n # load as lines in space\n kwargs.update(misc.lines_to_path(file_obj))\n else:\n raise ValueError('Not a supported object type!')\n\n result = load_kwargs(kwargs)\n util.log.debug('loaded {} in {:0.4f}s'.format(\n str(result), util.now() - tic))\n\n return result","def select_object(obj, objects=[]):\n return __SelectObjects(obj, objects)","def load(self,keepTypes='ALL',factory={}):\n keepAll = (keepTypes == 'ALL')\n keepTypes = keepTypes or set() #--Turns None or 0 into an empty set.\n #--Header\n inPath = os.path.join(self.fileInfo.dir,self.fileInfo.name)\n ins = Tes3Reader(self.fileInfo.name,file(inPath,'rb'))\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\n self.tes3 = Tes3(name,size,delFlag,recFlag,ins,True)\n #--Raw data read\n while not ins.atEnd():\n #--Get record info and handle it\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\n if name in factory:\n record = factory[name](name,size,delFlag,recFlag,ins)\n self.records.append(record)\n elif keepAll or name in keepTypes:\n record = Record(name,size,delFlag,recFlag,ins)\n self.records.append(record)\n else:\n ins.seek(size,1,name)\n #--Done Reading\n ins.close()","def _type_keys(keys):\n FlaUIKeyboard.TypeSimultaneously(keys)","def load_results(filename, file_type=None):\n if file_type is None:\n if isinstance(filename, str):\n if filename[-4:] == '.pkl':\n file_type = 'pkl'\n elif filename[-3:] == '.h5' or filename[-4:] == 'hdf5':\n file_type = 'hdf5'\n if file_type == 'hdf5':\n data_dict = read_dict_hdf5(filename)\n elif file_type == 'pkl':\n data_dict = read_dict_pkl(filename)\n else:\n raise ValueError('filetype not understood')\n return result_from_dict(data_dict)","def loadPrefObjects(*args, **kwargs)->bool:\n pass","def get_object_by_type(self, *types: str) -> Optional[TgnObject]:\n children = self.get_objects_by_type(*types)\n return children[0] if any(children) else None","def fetch_dataloader(types, params, CViters):\n\tdataloaders = {}\n\tassert CViters[0] != CViters[1], 'ERROR! Test set and validation set cannot be the same!'\n\t\n\tif len(types)>0:\n\t\tfor split in types:\n\t\t\tif split in ['train', 'val', 'test']:\n\t\t\t\tdl = DataLoader(imageDataset(split, params, CViters), batch_size=params.batch_size, shuffle=True,\n\t\t\t\t\tnum_workers=params.num_workers,\n\t\t\t\t\tpin_memory=params.cuda)\n\n\t\t\t\tdataloaders[split] = dl\n\telse:\n\t\tdl = DataLoader(imageDataset('', params, CViters), batch_size=params.batch_size, shuffle=True,\n\t\t\tnum_workers=params.num_workers,\n\t\t\tpin_memory=params.cuda)\n\n\t\treturn dl\n\n\treturn dataloaders","def select(self, name, *selectors) :\n\n ct = [(s.get_name(), s.get_type()) for s in selectors]\n new_table = self.factory.new_table(name, ct)\n for row in self :\n d = row.as_dict()\n r = [s(d) for s in selectors]\n new_table._add_from_list(r)\n return new_table","def selLoadSettings(*args, activeProxy: Union[AnyStr, bool]=\"\", deferReference: bool=True,\n fileName: Union[AnyStr, bool]=\"\", numSettings: Union[int, bool]=0,\n proxyManager: Union[AnyStr, bool]=\"\", proxySetFiles: Union[AnyStr, bool]=\"\",\n proxySetTags: Union[AnyStr, bool]=\"\", proxyTag: Union[AnyStr, bool]=\"\",\n referenceNode: Union[AnyStr, bool]=\"\", shortName: bool=True,\n unresolvedName: bool=True, q=True, query=True, e=True, edit=True,\n **kwargs)->Union[AnyStr, Any]:\n pass","def loadSingleObject(*args):\n\n # Query selected assets\n objectIconsList = cmds.layout(objectScroll, query=True, childArray=True)\n # Query selection between Arnold StandIN / Assembly reference\n selectedRadio = cmds.radioCollection(loadMethodRadio, query=True, select=True)\n\n for obj in objectIconsList:\n isSelected = cmds.iconTextCheckBox(obj, query=True, value=True)\n\n if isSelected:\n # If this icon is selected, instance an icon object\n asset = AssetIcon(cmds.iconTextCheckBox(obj, query=True, label=True))\n\n # Choose how to load it\n if \"standin\" in selectedRadio:\n asset.loadArnoldAsset()\n else: \n asset.loadAsset()","def get_all_by_type(self, obj_type):\n objects = []\n node = None\n data = []\n with lzma.open(os.path.join('resources', self.game, 'dumps',\n '{}.dump.xz'.format(obj_type)), 'rt', encoding='latin1') as df:\n for line in df.readlines():\n match = re.match('^\\*\\*\\* Property dump for object \\'\\S+ (\\S+)\\'.*$', line)\n if match:\n objects.append(match.group(1))\n if node:\n node.load_from_string_list(data)\n data = [line]\n node = self.get_node_by_full_object(match.group(1))\n else:\n data.append(line)\n\n if node:\n node.load_from_string_list(data)\n\n return objects","def select(input, output, fields, delimiter, encoding, verbose, format_in, format_out, zipfile, filter):\n if verbose:\n enableVerbose()\n options = {}\n options['delimiter'] = delimiter\n options['fields'] = fields\n options['output'] = output\n options['encoding'] = encoding\n options['format_in'] = format_in\n options['format_out'] = format_out\n options['zipfile'] = zipfile\n options['filter'] = filter\n acmd = Selector()\n acmd.select(input, options)\n pass","def selectKey(*args, addTo: bool=True, animation: AnyStr=\"keysOrObjects.\", attribute:\n Union[AnyStr, List[AnyStr]]=\"\", clear: bool=True, controlPoints: bool=False,\n float: Union[floatrange, List[floatrange]]=None, hierarchy: AnyStr=\"\", inTangent:\n bool=True, includeUpperBound: bool=True, index: Union[int, List[int]]=0,\n keyframe: bool=True, outTangent: bool=True, remove: bool=True, replace: bool=True,\n shape: bool=True, time: Union[timerange, List[timerange]]=None, toggle: bool=True,\n unsnappedKeys: float=0.0, **kwargs)->int:\n pass","def test_Selector(self) -> None:\n\n class MyNode:\n def __init__(self, name) -> None:\n self.name = name\n\n def __str__(self) -> str:\n return self.name\n\n def get_suffix(self):\n return os.path.splitext(self.name)[1]\n\n s = Selector({'a': 'AAA', 'b': 'BBB'})\n assert s['a'] == 'AAA', s['a']\n assert s['b'] == 'BBB', s['b']\n exc_caught = None\n try:\n x = s['c']\n except KeyError:\n exc_caught = 1\n assert exc_caught, \"should have caught a KeyError\"\n s['c'] = 'CCC'\n assert s['c'] == 'CCC', s['c']\n\n class DummyEnv(UserDict):\n def subst(self, key):\n if key[0] == '$':\n return self[key[1:]]\n return key\n\n env = DummyEnv()\n\n s = Selector({'.d': 'DDD', '.e': 'EEE'})\n ret = s(env, [])\n assert ret is None, ret\n ret = s(env, [MyNode('foo.d')])\n assert ret == 'DDD', ret\n ret = s(env, [MyNode('bar.e')])\n assert ret == 'EEE', ret\n ret = s(env, [MyNode('bar.x')])\n assert ret is None, ret\n s[None] = 'XXX'\n ret = s(env, [MyNode('bar.x')])\n assert ret == 'XXX', ret\n\n env = DummyEnv({'FSUFF': '.f', 'GSUFF': '.g'})\n\n s = Selector({'$FSUFF': 'FFF', '$GSUFF': 'GGG'})\n ret = s(env, [MyNode('foo.f')])\n assert ret == 'FFF', ret\n ret = s(env, [MyNode('bar.g')])\n assert ret == 'GGG', ret","def fetch_dataloader(types, data_dir, hyper_params, train_idx=None, val_idx=None):\n dataloaders = {}\n \n # TODO: write this to hyper_params, make hyper_params an out variable? then save? yes, AND: when ONLY test is requested, load from hyperparams!\n # TODO: also, add 3rd variation of types: for testing, only read it from hyper_params (DO I NEED TO READ HYPER_PARAMS FOR JUST TESTING?)\n if train_idx is not None:\n mean, std = mean_std_calc(DataLoader(Subset(Heart2DSegmentationDataset(str(Path(data_dir) / \"train_heart_scans\"), hyper_params.endo_or_epi), train_idx)))\n hyper_params.mean = mean.item()\n hyper_params.std = std.item()\n else:\n if 'train' in types:\n mean, std = mean_std_calc(DataLoader(Heart2DSegmentationDataset(str(Path(data_dir) / \"train_heart_scans\"), hyper_params.endo_or_epi)))\n hyper_params.mean = mean.item()\n hyper_params.std = std.item()\n else:\n mean, std = torch.tensor(hyper_params.mean), torch.tensor(hyper_params.std)\n \n print(\"Mean: {}, Std: {}\".format(mean.item(), std.item()))\n # borrowed from http://pytorch.org/tutorials/advanced/neural_style_tutorial.html\n # and http://pytorch.org/tutorials/beginner/data_loading_tutorial.html\n train_transformer = transforms.Compose([\n transforms.Normalize(mean=[mean.item()], std=[std.item()])\n ])\n \n eval_transformer = transforms.Compose([\n transforms.Normalize(mean=[mean.item()], std=[std.item()])\n ])\n\n for split in ['train', 'val', 'test']:\n if split in types:\n path = str(Path(data_dir) / \"{}_heart_scans\".format(split if split != 'val' else 'train'))\n\n if split == 'train':\n if train_idx is not None:\n dl = DataLoader(Subset(Heart2DSegmentationDataset(path, hyper_params.endo_or_epi, train_transformer), train_idx), \n batch_size=hyper_params.batch_size, \n shuffle=True,\n num_workers=hyper_params.num_workers,\n pin_memory=hyper_params.cuda)\n else:\n dl = DataLoader(Heart2DSegmentationDataset(path, hyper_params.endo_or_epi, train_transformer), \n batch_size=hyper_params.batch_size, \n shuffle=True,\n num_workers=hyper_params.num_workers,\n pin_memory=hyper_params.cuda)\n else:\n if (split == 'val') and (val_idx is not None): \n dl = DataLoader(Subset(Heart2DSegmentationDataset(path, hyper_params.endo_or_epi, eval_transformer), val_idx), \n batch_size=hyper_params.batch_size, \n shuffle=False,\n num_workers=hyper_params.num_workers,\n pin_memory=hyper_params.cuda) \n else:\n dl = DataLoader(Heart2DSegmentationDataset(path, hyper_params.endo_or_epi, eval_transformer), \n batch_size=hyper_params.batch_size, \n shuffle=False,\n num_workers=hyper_params.num_workers,\n pin_memory=hyper_params.cuda)\n\n dataloaders[split] = dl\n\n return dataloaders","def test_two_keys():\n test = [{'key1': {'key2': 'val1'}}, ['key1', 'key2']]\n assert fetch_data_by_keys(*test).unwrap() == 'val1'","def objectTypeUI(*args, isType: AnyStr=\"\", listAll: bool=True, superClasses: bool=True,\n **kwargs)->AnyStr:\n pass","def find_files(path='', ext='', level=None, typ=list, dirs=False, files=True, verbosity=0):\n path = expand_path(path)\n gen = generate_files(path, ext=ext, level=level, dirs=dirs, files=files, verbosity=verbosity)\n if isinstance(typ(), Mapping):\n return typ((ff['path'], ff) for ff in gen)\n elif typ is not None:\n return typ(gen)\n else:\n return gen","def find_objects(self, terms=None, type=None, chunksize=None, **kwargs):\n type = type or self.default_object_type\n\n find_opts = {'chunksize' : chunksize}\n\n search_operators = {\n 'exact': '=',\n 'gt': '>',\n 'gte': '>=',\n 'lt': '<',\n\n 'lte': '<=',\n 'contains': '~'\n }\n\n if terms is not None:\n find_opts['terms'] = terms\n else:\n conditions = []\n for field, value in six.iteritems(kwargs):\n if '__' in field:\n field, filtr = field.split('__')\n if filtr not in search_operators:\n raise Exception(\"Unsupported search filter '%s'\" % filtr)\n op = search_operators[filtr]\n else:\n op = search_operators['contains'] # default search mode\n\n if field in self.search_fields_aliases:\n field = self.search_fields_aliases[field]\n if field not in self.search_fields:\n raise Exception(\"Error generating Fedora findObjects query: unknown search field '%s'\" \\\n % field)\n if ' ' in value:\n # if value contains whitespace, it must be delimited with single quotes\n value = \"'%s'\" % value\n conditions.append(\"%s%s%s\" % (field, op, value))\n\n query = ' '.join(conditions)\n find_opts['query'] = query\n\n r = self.api.findObjects(**find_opts)\n chunk = parse_xml_object(SearchResults, r.content, r.url)\n while True:\n for result in chunk.results:\n yield type(self.api, result.pid)\n\n if chunk.session_token:\n r = self.api.findObjects(session_token=chunk.session_token, **find_opts)\n chunk = parse_xml_object(SearchResults, r.content, r.url)\n else:\n break","def fetch_dataloaders(types,n_cpu,folder_name):\n shot_size=2\n #how many images in a shot\n\n test_suffix='_test'\n val_suffix='_val'\n\n dataloaders = {}\n for split in ['train', 'test', 'val']:\n if split in types:\n # use the train_transformer if training data,\n # else use eval_transformer without random flip\n if split == 'train':\n dl = DataLoader(\n RandomShotDataset(work_folder+'/'+folder_name+'/LR',\n work_folder+'/'+folder_name+'/HR',shot_size),\n batch_size=shot_size, # full-batch in episode\n shuffle=True,num_workers=n_cpu,pin_memory=True)\n elif split == 'test':\n dl = DataLoader(\n RandomShotDataset(work_folder+'/'+folder_name+test_suffix+'/LR',\n work_folder+'/'+folder_name+test_suffix+'/HR',shot_size),\n batch_size=shot_size, # full-batch in episode\n shuffle=False,num_workers=n_cpu,pin_memory=True)\n elif split == 'val':\n dl = DataLoader(\n RandomShotDataset(work_folder+'/'+folder_name+val_suffix+'/LR',\n work_folder+'/'+folder_name+val_suffix+'/HR',shot_size),\n batch_size=shot_size, # full-batch in episode\n shuffle=False,num_workers=n_cpu,pin_memory=True)\n else:\n raise NotImplementedError()\n dataloaders[split] = dl\n\n return dataloaders","def pullSerialized(targets, *keys):","def _interpret(cls, code):\n namespace = {}\n namespace.update(selectors.SELECTORS)\n exec(code, namespace)\n header = LowerCaseDict(namespace[\"header\"])\n selector = namespace[\"selector\"]\n comment = namespace.get(\"comment\", None)\n if isinstance(selector, selectors.Parameters):\n return header, selector.instantiate(header), comment\n elif isinstance(selector, dict):\n return header, selector, comment\n else:\n raise crexc.MappingFormatError(\"selector must be a dict or a Selector.\")","def pick(self, *keys):\n return _({k: self[k] for k in keys})","def select(*args, add: bool=True, addFirst: bool=True, all: bool=True, allDagObjects: bool=True,\n allDependencyNodes: bool=True, clear: bool=True, containerCentric: bool=True,\n deselect: bool=True, hierarchy: bool=True, noExpand: bool=True, replace: bool=True,\n symmetry: bool=True, symmetrySide: int=0, toggle: bool=True, visible: bool=True,\n **kwargs)->None:\n pass","def search(self, types=(), start=0, limit=None, orderby='name',\n reverse=False, paths=(), depth=None, query=None,\n hashcheck=None, filterPermissions=True, globFilters=None):\n if not self.is_model_catalog_enabled():\n return None\n \n available_indexes = self.model_catalog.searcher.get_indexes()\n # if orderby is not an index then query results will be unbrained and sorted\n areBrains = orderby in available_indexes or orderby is None\n queryOrderby = orderby if areBrains else None\n \n query, not_indexed_user_filters = self._build_query(types, paths, depth, query, filterPermissions, globFilters)\n\n #areBrains = len(not_indexed_user_filters) == 0\n\n # if we have not indexed fields, we need to get all the results and manually filter and sort\n # I guess that with solr we should be able to avoid searching by unindexed fields\n #\n # @TODO get all results if areBrains == False\n #\n \n catalog_results = self.search_model_catalog(query, start=start, limit=limit, order_by=orderby, reverse=reverse)\n\n # @TODO take care of unindexed filters\n return catalog_results","def select(self, **kwargs):\n\n res = {}\n for f, c in zip(self._key_fields, self._unzip_group_keys()):\n res[f.name] = c\n for n, c in kwargs.items():\n res[n] = eval_expression(c, {\"me\": self})\n return self._parent._fromdata(res)","def object_readers(name, *, specify_reader=False):\n\treturn object_access('read', name, specify_reader)","def select_keyframe_hierarchy(*args):\n if not check_robot_selection():\n pm.warning('No robot\\'s selected; ' \\\n 'select at least one robot')\n return\n\n object_list = [__TARGET_CTRL_PATH,\n __FK_CTRLS_PATH,\n __A1_FK_CTRL_PATH,\n __A2_FK_CTRL_PATH,\n __A3_FK_CTRL_PATH,\n __A4_FK_CTRL_PATH,\n __A5_FK_CTRL_PATH,\n __A6_FK_CTRL_PATH]\n\n keyable_robot_objects = []\n robots = get_robot_roots()\n\n for robot in robots:\n for obj in object_list:\n obj_path = format_path(obj, robot)\n keyable_robot_objects.append(obj_path)\n\n tool_ctrl_path = get_tool_ctrl_path(robot)\n if pm.objExists(tool_ctrl_path):\n keyable_robot_objects.append(tool_ctrl_path)\n\n pm.select(keyable_robot_objects)","def query(self, **kwargs):\r\n fields = set(kwargs.keys())\r\n valid_fields = set(['filename'] + self.fields)\r\n if not fields.issubset(valid_fields):\r\n raise ValueError(\"Invalid fields: {}. Must be subset of: {}\"\r\n .format(fields, valid_fields))\r\n data = self.data\r\n for field, value in kwargs.items():\r\n value = value if isinstance(value, list) else [value]\r\n data = [meta for meta in data\r\n if self._match_filter(meta, field) in value]\r\n return data","def find_by_view(self, design_name, view_name, key=None, keys=None, start_key=None, end_key=None,\n id_only=True, convert_doc=True, **kwargs):\n log.debug(\"find_by_view(%s/%s)\" % (design_name, view_name))\n if type(id_only) is not bool:\n raise BadRequest('id_only must be type bool, not %s' % type(id_only))\n ds, datastore_name = self._get_datastore()\n\n view_args = self._get_view_args(kwargs)\n view_args['include_docs'] = (not id_only)\n view_doc = design_name if design_name == \"_all_docs\" else self._get_viewname(design_name, view_name)\n if keys:\n view_args['keys'] = keys\n view = ds.view(view_doc, **view_args)\n if key is not None:\n rows = view[key]\n log.info(\"find_by_view(): key=%s\" % key)\n elif keys:\n rows = view\n log.info(\"find_by_view(): keys=%s\" % keys)\n elif start_key and end_key:\n startkey = start_key or []\n endkey = list(end_key) or []\n endkey.append(END_MARKER)\n log.info(\"find_by_view(): start_key=%s to end_key=%s\" % (startkey, endkey))\n if view_args.get('descending', False):\n rows = view[endkey:startkey]\n else:\n rows = view[startkey:endkey]\n else:\n rows = view\n\n if id_only:\n res_rows = [(row['id'],row['key'], None) for row in rows]\n else:\n if convert_doc:\n res_rows = [(row['id'],row['key'],self._persistence_dict_to_ion_object(row['doc'])) for row in rows]\n else:\n res_rows = [(row['id'],row['key'],row['doc']) for row in rows]\n\n log.info(\"find_by_view() found %s objects\" % (len(res_rows)))\n return res_rows","def Scanner(function, *args, **kwargs):\n if SCons.Util.is_Dict(function):\n return Selector(function, *args, **kwargs)\n\n return ScannerBase(function, *args, **kwargs)","def keys_of_type_exist(self, *keys):\n # k[0] = key name, k[1] = exists, k2 = expected type\n keys_exist = [(key, key in self.keys(), expected_type)\n for key, expected_type in keys]\n\n return tuple(ContextItemInfo(\n key=k[0],\n key_in_context=k[1],\n expected_type=k[2],\n is_expected_type=isinstance(self[k[0]], k[2])\n if k[1] else None,\n has_value=k[1] and not self[k[0]] is None\n ) for k in keys_exist)","def pullSerializedAll(*keys):","def test_select():\n keys = list(range(100))\n d = OrderedTreeDict((key, None) for key in keys)\n assert all(d.select(k)[0] == k for k in keys)","def fetch_dataloader(types, data_dir, params):\n dataloaders = {}\n\n for split in ['train', 'val', 'test']:\n if split in types:\n path = os.path.join(data_dir, \"{}_signs\".format(split))\n\n # use the train_transformer if training data, else use eval_transformer without random flip\n if split == 'train':\n dl = DataLoader(SIGNSDataset(path, train_transformer), batch_size=params.batch_size, shuffle=True,\n num_workers=params.num_workers,\n pin_memory=params.cuda)\n else:\n dl = DataLoader(SIGNSDataset(path, eval_transformer), batch_size=params.batch_size, shuffle=False,\n num_workers=params.num_workers,\n pin_memory=params.cuda)\n\n dataloaders[split] = dl\n\n return dataloaders","def do_all(self, args):\n args = shlex.split(args)\n my_list = []\n if len(args) == 0:\n for item in models.storage.all().values():\n my_list.append(str(item))\n print(\"\", end=\"\")\n print(\", \".join(my_list), end=\"\")\n print(\"\")\n\n elif args[0] in classes:\n for key in models.storage.all():\n if args[0] in key:\n my_list.append(str(models.storage.all()[key]))\n print(\"\", end=\"\")\n print(\", \".join(my_list), end=\"\")\n print(\"\")\n else:\n print(\"** class doesn't exist **\")","def getObjs(infile, verbose=False):\n\n # Try to Open File\n fin = ROOT.TFile(infile, \"READ\")\n file_open = fin.IsOpen()\n err_mess = \"\\n***Can not open ROOT file %s***\\n\" %(infile)\n assert (file_open),err_mess\n\n # List object for directories\n obj_list = []\n\n # Loop through keys and get directories\n for key in fin.GetListOfKeys():\n kname = key.GetName()\n if key.IsFolder():\n continue\n if ( kname != ''):\n obj_list.append(kname)\n\n fin.Close()\n\n err_mess = \"\\n*** There are no objects in this %s ***\\n\" %(infile)\n assert (obj_list),err_mess\n\n return obj_list","def get_object_or_child_by_type(self, *types):\n\n objects = self.get_objects_or_children_by_type(*types)\n return objects[0] if any(objects) else None","def __getitem__(self, keys):\n if isinstance(keys, int):\n return super().__getitem__(keys)\n elif isinstance(keys, list):\n return self.vectors(keys)\n elif isinstance(keys, str):\n return self.find(keys)\n elif isinstance(keys, tuple):\n return self.item(keys[0], keys[1])\n elif isinstance(keys, slice):\n return super().__getitem__(keys)","def read(cls, key, mode = FetchMode.All):\n assert isinstance(key, (basestring, Key))\n namespace, kind, member = Schema.Get(cls)\n if isinstance(key, Key):\n assert kind == key.kind, \"Mismatched Model, reading a %s with %s\" % (kind, key.kind)\n return Lisa.read(key, mode)\n else: \n key = Key(namespace, kind, key)\n return Lisa.read(key, mode)","def test_valid_option_types(self):\n\n opts = {\n \"descending\": True,\n \"endkey\": ['string'],\n \"endkey_docid\": 'string',\n \"group\": True,\n \"group_level\": 12,\n \"include_docs\": True,\n \"inclusive_end\": True,\n \"key\": 12,\n \"limit\": 12,\n \"reduce\": True,\n \"skip\": 12,\n \"stale\": \"ok\",\n \"startkey\": [\"string\"],\n \"startkey_docid\": \"string\"\n }\n result = python_to_couch(opts)\n self.assertEqual(result['include_docs'], 'true')\n self.assertEqual(result['endkey'], '[\"string\"]')\n self.assertEqual(result['skip'], 12)\n self.assertEqual(result['endkey_docid'], '\"string\"')\n self.assertEqual(result['stale'], '\"ok\"')","def cmd_type(args):","def get_objects(slice, plugin_type, klass, **kwargs):\n try:\n# plugins_modules = settings.PLUGIN_LOADER.plugin_settings.get(plugin_type).get(\"general\").get(\"aggregate_plugins\")[0]\n plugins_modules = PLUGIN_LOADER.plugin_settings.get(plugin_type).get(\"general\").get(\"aggregate_plugins\")[0]\n p_agg = plugins_modules.split('.')[-1]\n p_models_path = '.'.join(plugins_modules.split('.')[:-1])\n try:\n model = getattr(__import__(p_models_path,fromlist=[klass]), klass)\n except: \n try: \n model = getattr(__import__(p_models_path+'.'+klass,fromlist=[klass]), klass)\n except:\n pass \n # Filters resources by slice (will not return any aggregate's resource from another slice)\n objects = model.objects.filter(**kwargs)\n #print \"objects: %s\" % str(objects)\n for obj in objects:\n if not (obj != None and obj.aggregate in slice._get_aggregates()):\n raise Exception\n return objects\n except Exception,e:\n print \"[ERROR] PluginCommunicator could not obtain object. Details: %s \" % str(e)\n return None","def _get_by_path(tree, keys):\n return reduce(getitem, keys, tree)","def _get_by_path(tree, keys):\n return reduce(getitem, keys, tree)","def set ( self , ** keywords ) :\n for k in keywords.keys ():\n if k == \"type\" :\n self.line_type = keywords [\"type\"]\n else :\n setattr (self, k, keywords [k])","def read_documents(*paths):\n for path in paths:\n suffix = path.suffix.lower()\n\n # If compressed, open as gzip stream.\n opener = open\n if suffix == '.gz':\n suffix = path.suffixes[-2].lower()\n opener = gzip.open\n\n if suffix in ('.yaml', '.yml'):\n try:\n for parsed_doc in yaml.load_all(opener(str(path), 'r'), Loader=NoDatesSafeLoader):\n yield path, parsed_doc\n except yaml.YAMLError as e:\n raise InvalidDocException('Failed to load %s: %s' % (path, e))\n elif suffix == '.json':\n try:\n yield path, json.load(opener(str(path), 'r'))\n except ValueError as e:\n raise InvalidDocException('Failed to load %s: %s' % (path, e))\n else:\n raise ValueError('Unknown document type for {}; expected one of {!r}.'\n .format(path.name, _ALL_SUPPORTED_EXTENSIONS))","def pullAll(*keys):","def materials(cls, value: Union[dict, MaterialSelector]):\n if isinstance(value, dict):\n cls._materials.from_dict(value)\n elif isinstance(value, MaterialSelector):\n cls._materials = value\n else:\n msg = \"materials must be MaterialSelector instance or dict\"\n run_log.error(msg)\n raise TypeError(msg)","def set_selector(*args):\n return _ida_segment.set_selector(*args)","def _load(mapping, **keys):\n return keys[\"loader\"](mapping, **keys)","def __init__(__self__, *,\n name: Optional[pulumi.Input[str]] = None,\n selectors: Optional[pulumi.Input[Sequence[pulumi.Input['SelectorArgs']]]] = None):\n if name is not None:\n pulumi.set(__self__, \"name\", name)\n if selectors is not None:\n pulumi.set(__self__, \"selectors\", selectors)","def select_objects(remote, objects_list):\n select_objects = mmapi.vectori();\n for object in objects_list:\n select_objects.push_back(object);\n cmd2 = mmapi.StoredCommands()\n cmd2.AppendSceneCommand_SelectObjects(select_objects)\n remote.runCommand(cmd2)","def select(self, field_paths):\n raise NotImplementedError(\"This should have been implemented.\")","def filterObjects(obj, keys=[], accepts=[], rejects=[], strict=True):\n total_keys = []\n a_keys = []\n r_keys = []\n queries = []\n results = []\n\n for key in keys:\n temp_obj = Lucidic({\"stuff\": obj}) if isinstance(obj, list) else Lucidic(obj)\n query = temp_obj.search(key, strict=strict)\n queries.append(query)\n total_keys += [x[\"keypath\"][0] for x in query]\n if accepts:\n a_keys += [x[\"keypath\"][0] for x in query if ((x[\"match\"].get(key) in accepts) or (x[\"match\"].get(key+\"[0]\") in accepts))]\n if rejects:\n r_keys += [x[\"keypath\"][0] for x in query if x[\"match\"][key] in rejects]\n if a_keys:\n filtered_keys = sorted((set(total_keys) & set(a_keys)) - set(r_keys))\n else:\n filtered_keys = sorted(set(total_keys) - set(r_keys))\n for f_key in filtered_keys:\n m = re.search(r\"(?P<key>.+)\\[(?P<index>\\d+)\\]\", f_key).groupdict()\n results.append(temp_obj.dict[m[\"key\"]][int(m[\"index\"])])\n return results","def find_selector(*args):\n return _ida_segment.find_selector(*args)","def default(self, args):\n lists = args.split('.')\n\n if len(lists) >= 2:\n\n if lists[1] == \"all()\":\n self.do_all(lists[0])\n return\n\n if lists[1] == \"count()\":\n self.count(lists[0])\n return\n\n if lists[1][:4] == \"show\":\n self.do_show(self.format_input(lists))\n return\n\n if lists[1][:7] == \"destroy\":\n self.do_destroy(self.format_input(lists))\n return\n\n elif lists[1][:6] == \"update\":\n args = self.format_input(lists)\n\n if type(args) is list:\n\n obj = storage.all()\n key = \"{} {}\".format(args[0], args[1])\n for k in args[2]:\n self.do_update(key + ' \"{}\" \"{}\"'.format(k, args[2][k]))\n else:\n self.do_update(args)\n else:\n cmd.Cmd.default(self, args)","def __init__(self, base=None, sub_files=None):\n self.categories = dict()\n self.base = base\n self.files = list()\n if not sub_files:\n sub_files = self.sub_node_paths\n for sub in sub_files:\n loaded = import_property(sub)(self)\n self.files.append(loaded)\n if loaded.category not in self.categories:\n self.categories[loaded.category] = list()\n self.categories[loaded.category].append(loaded)\n for contents in self.categories.values():\n contents.sort(key=lambda h: h.key)","def _verify_multiple_key_types(self, data_keys=None, filters=None, x=None,\r\n y=None, variables=None, views=None):\r\n if data_keys is not None:\r\n self._verify_key_types(name='data', keys=data_keys)\r\n\r\n if filters is not None:\r\n self._verify_key_types(name='filter', keys=filters)\r\n\r\n if x is not None:\r\n self._verify_key_types(name='x', keys=x)\r\n\r\n if y is not None:\r\n self._verify_key_types(name='y', keys=y)\r\n\r\n if variables is not None:\r\n self._verify_key_types(name='variables', keys=variables)\r\n\r\n if views is not None:\r\n self._verify_key_types(name='view', keys=views)"],"string":"[\n \"def load_select(self, selected):\\n\\t\\tfor filename in selected:\\n\\t\\t\\tself.load(filename)\\n\\n\\t\\tif \\\"worddict\\\" in selected and \\\"classdict\\\" in selected:\\n\\t\\t\\tself.reverse_dicts()\",\n \"def selector(**kwargs):\\n return kwargs\",\n \"def select(self, mode, types=None, types_excluded=None, layers=None, layers_excluded=None,\\n objects=None, objects_excluded=None):\\n scene = self.set_as_active()\\n layer_numbers = set(constants.layer_numbers)\\n obj_types = set(constants.obj_types)\\n\\n # setting up types and types excluded\\n if types is None or 'ALL' in types:\\n types = obj_types\\n\\n if types_excluded is None:\\n types_excluded = set()\\n\\n elif 'ELSE' in types_excluded:\\n types_excluded = obj_types - types\\n\\n types -= types_excluded\\n\\n # setting up layers and layers excluded\\n if layers is None or 'ALL' in layers:\\n layers = layer_numbers\\n\\n if layers_excluded is None:\\n layers_excluded = set()\\n\\n elif 'ELSE' in layers_excluded:\\n layers_excluded = layer_numbers - layers\\n\\n layers -= layers_excluded\\n\\n # setting up objects and objects excluded\\n if objects is None:\\n objects = w_var.objects_affected\\n\\n if objects_excluded is None:\\n objects_excluded = set()\\n\\n objects -= objects_excluded\\n\\n previous_area = bpy.context.area.type\\n\\n # can't change object select property while in the 'PROPERTIES' area\\n bpy.context.area.type = 'VIEW_3D'\\n\\n # much quicker than looping through objects\\n if 'ALL' in objects and types == obj_types and layers == layer_numbers:\\n bpy.ops.object.select_all(action=mode)\\n\\n elif mode == 'SELECT':\\n if objects is not None:\\n for obj in scene.objects:\\n if ((obj in objects or 'ALL' in objects) and\\n obj.type in types and self.object_on_layer(obj, layers)):\\n obj.select = True\\n\\n elif (obj in objects_excluded or 'ELSE' in objects_excluded or\\n obj.type in types_excluded or self.object_on_layer(obj, layers_excluded)):\\n obj.select = False\\n\\n else:\\n for obj in scene.objects:\\n if obj.type in types and self.object_on_layer(obj, layers):\\n obj.select = True\\n\\n elif obj.type in types_excluded or self.object_on_layer(obj, layers_excluded):\\n obj.select = False\\n\\n elif mode == 'DESELECT':\\n if objects is not None:\\n for obj in scene.objects:\\n if ((obj in objects or 'ALL' in objects) and\\n obj.type in types and self.object_on_layer(obj, layers)):\\n obj.select = False\\n\\n else:\\n for obj in scene.objects:\\n if obj.type in types and self.object_on_layer(obj, layers):\\n obj.select = False\\n\\n else:\\n raise ValueError(\\\"No such mode as '{}'.\\\".format(mode))\\n\\n bpy.context.area.type = previous_area\\n self.set_active_object(types)\",\n \"def Select(*keys):\\n return \\\"Select\\\" >> beam.Map(lambda r: {k: r[k] for k in keys})\",\n \"def get_objs_with_key_and_typeclass(self, oname, otypeclass_path):\\n return self.filter(db_key__iexact=oname).filter(db_typeclass_path__exact=otypeclass_path)\",\n \"def selectType(*args, allComponents: bool=True, allObjects: bool=True, animBreakdown: bool=True,\\n animCurve: bool=True, animInTangent: bool=True, animKeyframe: bool=True,\\n animOutTangent: bool=True, byName: Union[List[AnyStr, bool], List[List[AnyStr,\\n bool]]]=None, camera: bool=True, cluster: bool=True, collisionModel: bool=True,\\n controlVertex: bool=True, curve: bool=True, curveKnot: bool=True,\\n curveOnSurface: bool=True, curveParameterPoint: bool=True, dimension: bool=True,\\n dynamicConstraint: bool=True, edge: bool=True, editPoint: bool=True, emitter:\\n bool=True, facet: bool=True, field: bool=True, fluid: bool=True, follicle:\\n bool=True, hairSystem: bool=True, handle: bool=True, hull: bool=True,\\n ikEndEffector: bool=True, ikHandle: bool=True, imagePlane: bool=True,\\n implicitGeometry: bool=True, isoparm: bool=True, joint: bool=True, jointPivot:\\n bool=True, lattice: bool=True, latticePoint: bool=True, light: bool=True,\\n localRotationAxis: bool=True, locator: bool=True, locatorUV: bool=True,\\n locatorXYZ: bool=True, meshUVShell: bool=True, motionTrailPoint: bool=True,\\n motionTrailTangent: bool=True, nCloth: bool=True, nParticle: bool=True,\\n nParticleShape: bool=True, nRigid: bool=True, nonlinear: bool=True, nurbsCurve:\\n bool=True, nurbsSurface: bool=True, objectComponent: bool=True,\\n orientationLocator: bool=True, particle: bool=True, particleShape: bool=True,\\n plane: bool=True, polymesh: bool=True, polymeshEdge: bool=True, polymeshFace:\\n bool=True, polymeshFreeEdge: bool=True, polymeshUV: bool=True, polymeshVertex:\\n bool=True, polymeshVtxFace: bool=True, queryByName: Union[AnyStr, bool]=\\\"\\\",\\n rigidBody: bool=True, rigidConstraint: bool=True, rotatePivot: bool=True,\\n scalePivot: bool=True, sculpt: bool=True, selectHandle: bool=True, spring:\\n bool=True, springComponent: bool=True, stroke: bool=True, subdiv: bool=True,\\n subdivMeshEdge: bool=True, subdivMeshFace: bool=True, subdivMeshPoint: bool=True,\\n subdivMeshUV: bool=True, surfaceEdge: bool=True, surfaceFace: bool=True,\\n surfaceKnot: bool=True, surfaceParameterPoint: bool=True, surfaceRange:\\n bool=True, surfaceUV: bool=True, texture: bool=True, vertex: bool=True, q=True,\\n query=True, **kwargs)->Union[bool, Any]:\\n pass\",\n \"def get(self, objtype, **kwargs):\\n h = \\\"Issue a GetObject to find an object\\\"\\n kwargs['pytan_help'] = kwargs.get('pytan_help', h)\\n\\n clean_keys = ['obj', 'objtype', 'obj_map']\\n clean_kwargs = pytan.utils.clean_kwargs(kwargs=kwargs, keys=clean_keys)\\n\\n err_keys = ['pytan_help']\\n err_args = pytan.utils.clean_kwargs(kwargs=kwargs, keys=err_keys)\\n\\n obj_map = pytan.utils.get_obj_map(objtype=objtype)\\n\\n manual_search = obj_map['manual']\\n api_attrs = obj_map['search']\\n\\n api_kwattrs = [kwargs.get(x, '') for x in api_attrs]\\n\\n # if the api doesn't support filtering for this object,\\n # or if the user didn't supply any api_kwattrs and manual_search\\n # is true, get all objects of this type and manually filter\\n if not api_attrs or (not any(api_kwattrs) and manual_search):\\n all_objs = self.get_all(objtype=objtype, **clean_kwargs)\\n\\n return_objs = getattr(taniumpy, all_objs.__class__.__name__)()\\n\\n for k, v in kwargs.items():\\n if not v:\\n continue\\n if not hasattr(all_objs[0], k):\\n continue\\n if not pytan.utils.is_list(v):\\n v = [v]\\n for aobj in all_objs:\\n aobj_val = getattr(aobj, k)\\n aobj_val_str = str(aobj_val)\\n if aobj_val not in v and aobj_val_str not in v:\\n continue\\n return_objs.append(aobj)\\n\\n if not return_objs:\\n err = \\\"No results found searching for {} with {}!!\\\".format\\n raise pytan.exceptions.HandlerError(err(objtype, err_args))\\n\\n return return_objs\\n\\n # if api supports filtering for this object,\\n # but no filters supplied in kwargs, raise\\n if not any(api_kwattrs):\\n err = \\\"Getting a {} requires at least one filter: {}\\\".format\\n raise pytan.exceptions.HandlerError(err(objtype, api_attrs))\\n\\n # if there is a multi in obj_map, that means we can pass a list\\n # type to the taniumpy. the list will have an entry for each api_kw\\n if obj_map['multi']:\\n return self._get_multi(obj_map=obj_map, **clean_kwargs)\\n\\n # if there is a single in obj_map but not multi, that means\\n # we have to find each object individually\\n elif obj_map['single']:\\n return self._get_single(obj_map=obj_map, **clean_kwargs)\\n\\n err = \\\"No single or multi search defined for {}\\\".format\\n raise pytan.exceptions.HandlerError(err(objtype))\",\n \"def select(self, *selector_objs):\\n # Make sure they're all Selector objects\\n selectors = [Selector.from_unknown(obj) for obj in selector_objs]\\n return Document(s(self) for s in selectors)\",\n \"def createSelector(self,type='select',speed=2.0):\\n self.selector = self.loadObject(type, scale=2, parent=render, transparency=True, pos=Point2(0,0), glow=1)\\n self.selector.hide()\\n ival = self.selector.hprInterval((speed), Vec3(0, 0, 360))\\n ival.loop()\",\n \"def search(cls, **kwargs):\\n key = [key for key in kwargs][0]\\n objects = cls.get_all()\\n if isinstance(objects, dict):\\n return objects\\n results = []\\n for i in objects:\\n if is_substring(kwargs[key], getattr(i, key)):\\n results.append(i)\\n if not results:\\n return {\\n \\\"message\\\": \\\"No objects match the searched value.\\\",\\n \\\"help\\\": \\\"Ensure arguments are of existent objects.\\\"\\n }\\n return results\",\n \"def get(**args):\\n\\tglobal _objstore\\n\\t_objstore = _objstore or ObjStore()\\n\\n\\treturn _objstore.get(args['type'], args['name'])\",\n \"def get_all(self, objtype, **kwargs):\\n h = \\\"Issue a GetObject to find an object\\\"\\n kwargs['pytan_help'] = kwargs.get('pytan_help', h)\\n\\n clean_keys = ['obj', 'objtype', 'obj_map']\\n clean_kwargs = pytan.utils.clean_kwargs(kwargs=kwargs, keys=clean_keys)\\n\\n obj_map = pytan.utils.get_obj_map(objtype=objtype)\\n\\n all_type = obj_map['all']\\n api_obj_all = pytan.utils.get_taniumpy_obj(obj_map=all_type)()\\n\\n found = self._find(obj=api_obj_all, **clean_kwargs)\\n return found\",\n \"def get_objects_by_type(self, *types) -> List[TgnObject]:\\n if not types:\\n return list(self.objects.values())\\n types_l = [o.lower() for o in types]\\n return [o for o in self.objects.values() if o.type.lower() in types_l]\",\n \"def load_select_if_necessary(self, selected):\\n\\t\\tfor which in selected:\\n\\t\\t\\tself.load_if_necessary(which)\\n\\n\\t\\tif \\\"worddict\\\" in selected and \\\"classdict\\\" in selected:\\n\\t\\t\\tself.reverse_dicts()\",\n \"def load_object_lists(category=None):\\n\\n # type checking\\n\\n # not empty\\n assert category\\n\\n # not string\\n if isinstance(category, str):\\n category = [category]\\n\\n # iterable\\n try:\\n iter(category)\\n except TypeError:\\n print(\\\"category should be an iterable object\\\")\\n\\n # a subset of the full category set\\n assert set(category).issubset(g_shapenet_categlory_pair.values())\\n\\n # load obj file path\\n for cat in category:\\n search_path = os.path.join(g_shapenet_path, cat, '**', '*.obj')\\n yield glob.iglob(search_path, recursive=True)\",\n \"def retrieve(self, selected_keys):\\n if not isinstance(selected_keys, list):\\n selected_keys = [selected_keys]\\n\\n # Select the keys we want to download\\n key_dict = {key: self._results[key][\\\"keys\\\"] for key in selected_keys}\\n\\n results = defaultdict(dict)\\n for key, dateranges in key_dict.items():\\n for daterange in dateranges:\\n # Create a key for this range\\n data_keys = key_dict[key][daterange]\\n # Retrieve the data from the object store and combine into a NetCDF\\n recombined_data = recombine_sections(data_keys)\\n\\n results[key][daterange] = recombined_data\\n\\n return results\",\n \"def select(files, file_type):\\n k = 0\\n print('== ' + file_type + ' List ==')\\n for file in files:\\n print(\\\"[\\\" + str(k) + \\\"]. \\\" + file)\\n k += 1\\n print('Select a ' + file_type + ' to continue')\\n idx = int(input())\\n return files[idx]\",\n \"def get_objects_or_children_by_type(self, *types):\\n\\n objects = self.get_objects_by_type(*types)\\n return objects if objects else self.get_children(*types)\",\n \"def load_from_files(*filenames,**kwargs):\\n if 'keys' in kwargs.keys() and 'dtype' not in kwargs.keys():\\n raise ValueError('Please set dtype as well.')\\n elif 'keys' in kwargs.keys() and 'dtype' in kwargs.keys():\\n if len(kwargs['keys']) != len(kwargs['dtype']):\\n raise ValueError('Length of keys and dtype must match.')\\n\\n z_range = kwargs.pop('z_range',None)\\n z_key = kwargs.pop('z_key',None)\\n keys = kwargs.pop('keys',['Name','RA','Dec','z'])\\n dtypes = kwargs.pop('dtype',[object,float,float,float])\\n case_sensitive = kwargs.pop('case_sensitive',False)\\n comments = kwargs.pop('comments','#')\\n delimiter = kwargs.pop('delimeter',None)\\n return_fileindex = kwargs.pop('return_fileindex',False)\\n\\n if kwargs != {}:\\n unknown_kw = ' '.join(kwargs.keys())\\n raise TypeError('load_from_files got unknown keyword arguments: {}'.format(unknown_kw))\\n\\n if not case_sensitive:\\n keys = [a.upper() for a in keys]\\n\\n if z_range is not None and z_key is None:\\n z_keys = [key for key in keys \\n if key[0].upper() == 'Z' or key.upper() == \\\"REDSHIFT\\\"] \\n if len(z_keys) == 0:\\n raise ValueError('Failed to determine z_key, please set kwarg z_key')\\n elif len(z_keys) > 1:\\n raise ValueError('Ambiguous z_key, please set kwargs z_key manually')\\n else:\\n z_key = z_keys[0]\\n\\n out = None\\n fileindex = []\\n\\n for k,filename in enumerate(filenames):\\n tmp = np.genfromtxt(filename,names=True,comments=comments,dtype=None,\\n case_sensitive=case_sensitive,delimiter=delimiter)\\n \\n if z_range is None:\\n tmp2 = np.zeros((len(tmp),),dtype=zip(keys,dtypes))\\n fileindex.extend([k for a in range(len(tmp))])\\n for key in keys:\\n tmp2[key] = tmp[key]\\n else:\\n z_filter = (tmp[z_key] >= z_range[0]) & (tmp[z_key] < z_range[1]) \\n tmp2 = np.zeros((np.sum(z_filter),),dtype=zip(keys,dtypes))\\n fileindex.extend([k for a in range(np.sum(z_filter))])\\n for key in keys:\\n tmp2[key] = tmp[key][z_filter]\\n \\n if out is None:\\n out = tmp2\\n else:\\n out = np.concatenate((out,tmp2))\\n \\n if return_fileindex:\\n return [out[key] for key in keys] + [np.array(fileindex)]\\n else:\\n return [out[key] for key in keys]\",\n \"def get_object(self, object_type, object_name, user_key = None):\\n\\t\\tobject_key = self._get_key(object_type,user_key)\\n\\n\\t\\ttarget_object = None\\n\\n\\t\\t#print object_type\\n\\t\\tfor item in self.data['all_%s' % object_type]:\\n\\t\\t\\t## Skip items without the specified key\\n\\t\\t\\tif not item.has_key(object_key):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tif item[object_key] == object_name:\\n\\t\\t\\t\\ttarget_object = item\\n\\t\\t\\t## This is for multi-key items\\n\\t\\treturn target_object\",\n \"def DataLoad(filename,path,keys_to_search=['dstr','ORT'],text_file = False):\\n\\n OUTPUT_DICT = {}\\n\\n if not text_file:\\n\\n rdr = EafReader(filename, path, text_file=False)\\n\\n for key in keys_to_search:\\n\\n annot, annot_df = rdr.parser(key)\\n DF = rdr.dataframe_creator(annot, annot_df, annot_type=key)\\n\\n OUTPUT_DICT[key.lower()] = DF\\n else:\\n\\n\\n\\n for key in keys_to_search:\\n\\n rdr = EafReader(filename+'_'+key.lower()+'.txt', path, text_file=True)\\n DF = rdr.csv_reader()\\n\\n OUTPUT_DICT[key.lower()] = DF\\n\\n return OUTPUT_DICT\",\n \"def _get_objects(self, object_type, **kwargs):\\r\\n params = dict()\\r\\n if kwargs:\\r\\n for key, val in kwargs.items():\\r\\n if '_' in key:\\r\\n new_key = key.replace(\\\"_\\\",\\\"-\\\") \\r\\n params[new_key] = val\\r\\n else:\\r\\n params[key] = val\\r\\n try: \\r\\n response = requests.get(self.api_endpoint + object_type, \\r\\n auth=(self.user,self.pwd), \\r\\n params=params, verify=False)\\r\\n\\r\\n devices = json.loads(response.text)\\r\\n\\r\\n except requests.exceptions.RequestException as e:\\r\\n print \\\"Error:\\\",e\\r\\n return 1\\r\\n\\r\\n return_objects = []\\r\\n for i in devices.keys():\\r\\n if i == u\\\"links\\\":\\r\\n continue \\r\\n for j in devices[i]:\\r\\n return_objects.append(XtremObjFactory(object_type,j,self))\\r\\n\\r\\n return return_objects\",\n \"def select(*args):\",\n \"def select(*args):\",\n \"def load_data(self, **kwargs):\\n type = kwargs['type']\\n file = kwargs['file']\\n with open(file, 'r') as data_file:\\n for line in data_file:\\n line = line[:-1]\\n items_dict = ast.literal_eval(line)\\n\\n item = type.from_dict(items_dict)\\n\\n self.add_item(item, lambda i: i.uid)\",\n \"def find_objects_by_type():\\n try:\\n keyword = request.form[\\\"keyword\\\"]\\n object_type = request.form[\\\"object_type\\\"]\\n\\n # Get entities based on the selection\\n entities = g.user.get_api().get_by_object_types(keyword, object_type)\\n\\n # Parse response object into table data\\n data = raw_entities_to_table_data(entities)\\n\\n # If no entities were found reutrn with failure state and message\\n result = get_result_template()\\n if len(data[\\\"data\\\"]) == 0:\\n result[\\\"status\\\"] = \\\"FAIL\\\"\\n result[\\\"message\\\"] = 'No entities of type \\\"{TYPE}\\\" were found.'.format(\\n TYPE=object_type\\n )\\n else:\\n result[\\\"status\\\"] = \\\"SUCCESS\\\"\\n result[\\\"data\\\"] = {\\\"table_field\\\": data}\\n return jsonify(result_decorator(result))\\n\\n except Exception as e:\\n result = get_result_template()\\n result[\\\"status\\\"] = \\\"FAIL\\\"\\n result[\\\"message\\\"] = str(e)\\n return jsonify(result_decorator(result))\",\n \"def common_reference_file_keywords(reftype=None,\\n title=None,\\n description=None,\\n exp_type=None,\\n author=\\\"STScI\\\",\\n useafter=\\\"2014-01-01T00:00:00\\\",\\n module=None,\\n fname=None,\\n pupil=None, **kwargs):\\n if exp_type is None:\\n raise ValueError(\\\"exp_type not set\\\")\\n if reftype is None:\\n raise ValueError(\\\"Expected reftype value\\\")\\n\\n ref_file_common_keywords = {\\n \\\"author\\\": author,\\n \\\"description\\\": description,\\n \\\"exposure\\\": {\\\"type\\\": exp_type},\\n \\\"instrument\\\": {\\\"name\\\": \\\"NIRCAM\\\"},\\n \\\"pedigree\\\": \\\"ground\\\",\\n \\\"reftype\\\": reftype,\\n \\\"telescope\\\": \\\"JWST\\\",\\n \\\"title\\\": title,\\n \\\"useafter\\\": useafter,\\n }\\n\\n if fname is not None:\\n ref_file_common_keywords[\\\"instrument\\\"][\\\"filter\\\"] = fname\\n if pupil is not None:\\n ref_file_common_keywords[\\\"instrument\\\"][\\\"pupil\\\"] = pupil\\n if module is not None:\\n ref_file_common_keywords[\\\"instrument\\\"][\\\"module\\\"] = module\\n\\n ref_file_common_keywords.update(kwargs)\\n return ref_file_common_keywords\",\n \"def get_paths(self, *paths, **kwargs):\\n filter_fn = kwargs.get(\\\"filter_fn\\\", None)\\n\\n def default_filter_fn(obj):\\n return True\\n\\n def default_transport(x):\\n return x\\n\\n if filter_fn is None:\\n filter_fn = default_filter_fn\\n key = kwargs.get(\\\"key\\\", None)\\n if key is None:\\n objs = self._analyzer_objects\\n else:\\n objs = sorted(self._analyzer_objects, key=key)\\n\\n res = []\\n for obj_name in objs:\\n obj = objs[obj_name]\\n if not filter_fn(obj):\\n continue\\n obj_res = []\\n for i in range(len(paths)):\\n path = paths[i]\\n if isinstance(path, dict):\\n transport = path[\\\"transport\\\"]\\n path = path[\\\"path\\\"]\\n else:\\n transport = default_transport\\n traverse = obj[path[0]]\\n for p in path[1:]:\\n if traverse is not None:\\n if p in traverse:\\n traverse = traverse[p]\\n elif p.upper() in traverse:\\n traverse = traverse[p.upper()]\\n else:\\n traverse = None\\n obj_res.append(transport(traverse))\\n if len(obj_res) == len(paths):\\n res.append(obj_res)\\n return res\",\n \"def select_filenames(filenames, select_keywords, drop_keywords):\\n from os.path import relpath\\n\\n if select_keywords is None and drop_keywords is None:\\n return filenames\\n\\n filenames['_relative_path'] = filenames['file_name'].apply(\\n lambda x: relpath(x, root_dir))\\n\\n if select_keywords is not None:\\n if isinstance(select_keywords, str):\\n select_keywords = [select_keywords]\\n\\n keep = filenames['_relative_path'].apply(\\n lambda x:\\n True if np.any([kwrd in x for kwrd in select_keywords])\\n else False)\\n\\n filenames = filenames.loc[keep, :]\\n\\n if drop_keywords is not None:\\n if isinstance(select_keywords, str):\\n drop_keywords = [drop_keywords]\\n\\n drop = filenames['_relative_path'].apply(\\n lambda x:\\n False if np.any([kwrd in x for kwrd in drop_keywords])\\n else True)\\n\\n filenames = filenames.loc[drop, :]\\n\\n filenames.drop(columns=['_relative_path'])\\n filenames.reset_index(drop=True)\\n\\n return filenames\",\n \"def browse(self, keys=None, timestamp=None, **kwargs):\\n keys = keys or kwargs.get('key')\\n timestamp = timestamp or find_in_kwargs_by_alias('timestamp', kwargs)\\n timestamp_is_string = isinstance(timestamp, str)\\n if isinstance(keys, list) and timestamp and not timestamp_is_string:\\n data = self.client.browseKeysTime(keys, timestamp, self.creds, self.transaction, self.environment)\\n elif isinstance(keys, list) and timestamp and timestamp_is_string:\\n data = self.client.browseKeysTimestr(keys, timestamp, self.creds, self.transaction, self.environment)\\n elif isinstance(keys, list):\\n data = self.client.browseKeys(keys, self.creds, self.transaction, self.environment)\\n elif timestamp and not timestamp_is_string:\\n data = self.client.browseKeyTime(keys, timestamp, self.creds, self.transaction, self.environment)\\n elif timestamp and timestamp_is_string:\\n data = self.client.browseKeyTimestr(keys, timestamp, self.creds, self.transaction, self.environment)\\n elif keys:\\n data = self.client.browseKey(keys, self.creds, self.transaction, self.environment)\\n else:\\n require_kwarg('key or keys')\\n return pythonify(data)\",\n \"def load_objects(self):\\n \\n # Load classifier\\n with open('../twitterClass/classifier/classifier.p','r') as f:\\n self.classifier = cPickle.load(f)\\n \\n #Load blocked keywords\\n regex_str2 = []\\n with open('../twitterClass/twitterMiningClass/private/blocked_keywords.txt','r') as f:\\n keywords = f.read().split('\\\\n')\\n for key in keywords:\\n key = key.split(',')\\n #key[0] = keyword name , key[1] = pattern\\n print key\\n regex_str2.append(key[1])\\n # create regex compiler for blocked keyword search\\n regex_str2 = map(lambda x: x.replace(\\\"\\\\\\\\\\\\\\\\\\\",\\\"\\\\\\\\\\\"),regex_str2)\\n self.blocked_keywords_re = re.compile(r'('+'|'.join(regex_str2)+')',re.IGNORECASE)\\n \\n # Load keywords\\n with open('../twitterClass/twitterMiningClass/private/keywords.txt','r') as f:\\n keywords = f.read().split('\\\\n')\\n for key in keywords:\\n key = key.split(',')\\n #key[0] = keyword name , key[1] = pattern\\n self.keywords[key[0]] = key[1]\\n # create regex compiler for keyword search\\n regex_str = []\\n for keys,pattern in self.keywords.iteritems():\\n regex_str.append(pattern)\\n regex_str = map(lambda x: x.replace(\\\"\\\\\\\\\\\\\\\\\\\",\\\"\\\\\\\\\\\"),regex_str)\\n self.keywords_re = re.compile(r'('+'|'.join(regex_str)+')', re.VERBOSE | re.IGNORECASE)\",\n \"def _selectFrom(self, selector, selectorType, response):\\n sel = Selector(response)\\n if selectorType == FieldSelector.CSS:\\n elems = sel.css(selector)\\n elif selectorType == FieldSelector.XPATH:\\n elems = sel.xpath(selector)\\n return elems\",\n \"def get_tree(self, list_of_keys):\\n cur_obj = self\\n for key in list_of_keys:\\n cur_obj = cur_obj.get(key)\\n if not cur_obj: break\\n return cur_obj\",\n \"def createSelector2(self,type='select',speed=2.0):\\n self.selector2 = self.loadObject(type, scale=2, parent=render, transparency=True, pos=Point2(0,0), glow=1)\\n self.selector2.hide()\\n ival = self.selector2.hprInterval((speed), Vec3(0, 0, 360))\\n ival.loop()\",\n \"def selectMode(*args, component: bool=True, hierarchical: bool=True, leaf: bool=True, object:\\n bool=True, preset: bool=True, root: bool=True, template: bool=True, q=True,\\n query=True, **kwargs)->Union[bool, Any]:\\n pass\",\n \"def get ( path , selector = lambda s : s ) :\\n _g = appMgr()\\n _es = _g.evtSvc()\\n data = _es[path]\\n #\\n try : ## try to use the selector \\n return selector ( data )\\n except:\\n pass\\n \\n try : ## try to use selector \\n return filter ( selector , data )\\n except :\\n pass \\n \\n return data\",\n \"def get_objects(vim, type, properties_to_collect=None, all=False):\\n if not properties_to_collect:\\n properties_to_collect = [\\\"name\\\"]\\n\\n client_factory = vim.client.factory\\n trav_spec = vim_util.build_recursive_traversal_spec(client_factory)\\n object_spec = vim_util.build_object_spec(client_factory,\\n vim.service_content.rootFolder,\\n [trav_spec])\\n property_spec = vim_util.build_property_spec(\\n client_factory, type_=type,\\n properties_to_collect=properties_to_collect,\\n all_properties=all)\\n property_filter_spec = vim_util.build_property_filter_spec(client_factory,\\n [property_spec],\\n [object_spec])\\n property_collector = vim.service_content.propertyCollector\\n return retrieve_properties_ex(vim,\\n property_collector,\\n [property_filter_spec])\",\n \"def selectBoxType(self,**kwargs):\\n elements = self.baxter.scene.boxes.keys()\\n\\n entries={}\\n for element in elements:\\n entries[element] = self.addSceneBox\\n self.mm.addGenericMenu(\\\"selectBox\\\",\\\"addBox\\\",\\\"Select a box type you want to add\\\", entries)\\n self.mm.loadMenu(\\\"selectBox\\\")\",\n \"def selectItem(*args):\",\n \"def _load_mapping(mapping, **keys):\\n if mapping.endswith(\\\".pmap\\\"):\\n cls = PipelineContext\\n elif mapping.endswith(\\\".imap\\\"):\\n cls = InstrumentContext\\n elif mapping.endswith(\\\".rmap\\\"):\\n cls = ReferenceMapping\\n else:\\n if config.S3_ENABLED:\\n m = Mapping.from_s3(mapping, **keys)\\n else:\\n m = Mapping.from_file(mapping, **keys)\\n mapping_type = m.header[\\\"mapping\\\"].lower()\\n if mapping_type == \\\"pipeline\\\":\\n cls = PipelineContext\\n elif mapping_type == \\\"instrument\\\":\\n cls = InstrumentContext\\n elif mapping_type == \\\"reference\\\":\\n cls = ReferenceMapping\\n else:\\n raise ValueError(\\\"Unknown mapping type for \\\" + repr(mapping))\\n\\n if config.S3_ENABLED:\\n return cls.from_s3(mapping, **keys)\\n else:\\n return cls.from_file(mapping, **keys)\",\n \"def load_path(file_obj, file_type=None, **kwargs):\\n # avoid a circular import\\n from ...exchange.load import load_kwargs\\n # record how long we took\\n tic = util.now()\\n\\n if isinstance(file_obj, Path):\\n # we have been passed a Path file_object so\\n # do nothing and return the passed file_object\\n return file_obj\\n elif util.is_file(file_obj):\\n # for open file file_objects use loaders\\n kwargs.update(path_loaders[file_type](\\n file_obj, file_type=file_type))\\n elif util.is_string(file_obj):\\n # strings passed are evaluated as file file_objects\\n with open(file_obj, 'rb') as f:\\n # get the file type from the extension\\n file_type = os.path.splitext(file_obj)[-1][1:].lower()\\n # call the loader\\n kwargs.update(path_loaders[file_type](\\n f, file_type=file_type))\\n elif util.is_instance_named(file_obj, ['Polygon', 'MultiPolygon']):\\n # convert from shapely polygons to Path2D\\n kwargs.update(misc.polygon_to_path(file_obj))\\n elif util.is_instance_named(file_obj, 'MultiLineString'):\\n # convert from shapely LineStrings to Path2D\\n kwargs.update(misc.linestrings_to_path(file_obj))\\n elif isinstance(file_obj, dict):\\n # load as kwargs\\n return load_kwargs(file_obj)\\n elif util.is_sequence(file_obj):\\n # load as lines in space\\n kwargs.update(misc.lines_to_path(file_obj))\\n else:\\n raise ValueError('Not a supported object type!')\\n\\n result = load_kwargs(kwargs)\\n util.log.debug('loaded {} in {:0.4f}s'.format(\\n str(result), util.now() - tic))\\n\\n return result\",\n \"def select_object(obj, objects=[]):\\n return __SelectObjects(obj, objects)\",\n \"def load(self,keepTypes='ALL',factory={}):\\n keepAll = (keepTypes == 'ALL')\\n keepTypes = keepTypes or set() #--Turns None or 0 into an empty set.\\n #--Header\\n inPath = os.path.join(self.fileInfo.dir,self.fileInfo.name)\\n ins = Tes3Reader(self.fileInfo.name,file(inPath,'rb'))\\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\\n self.tes3 = Tes3(name,size,delFlag,recFlag,ins,True)\\n #--Raw data read\\n while not ins.atEnd():\\n #--Get record info and handle it\\n (name,size,delFlag,recFlag) = ins.unpackRecHeader()\\n if name in factory:\\n record = factory[name](name,size,delFlag,recFlag,ins)\\n self.records.append(record)\\n elif keepAll or name in keepTypes:\\n record = Record(name,size,delFlag,recFlag,ins)\\n self.records.append(record)\\n else:\\n ins.seek(size,1,name)\\n #--Done Reading\\n ins.close()\",\n \"def _type_keys(keys):\\n FlaUIKeyboard.TypeSimultaneously(keys)\",\n \"def load_results(filename, file_type=None):\\n if file_type is None:\\n if isinstance(filename, str):\\n if filename[-4:] == '.pkl':\\n file_type = 'pkl'\\n elif filename[-3:] == '.h5' or filename[-4:] == 'hdf5':\\n file_type = 'hdf5'\\n if file_type == 'hdf5':\\n data_dict = read_dict_hdf5(filename)\\n elif file_type == 'pkl':\\n data_dict = read_dict_pkl(filename)\\n else:\\n raise ValueError('filetype not understood')\\n return result_from_dict(data_dict)\",\n \"def loadPrefObjects(*args, **kwargs)->bool:\\n pass\",\n \"def get_object_by_type(self, *types: str) -> Optional[TgnObject]:\\n children = self.get_objects_by_type(*types)\\n return children[0] if any(children) else None\",\n \"def fetch_dataloader(types, params, CViters):\\n\\tdataloaders = {}\\n\\tassert CViters[0] != CViters[1], 'ERROR! Test set and validation set cannot be the same!'\\n\\t\\n\\tif len(types)>0:\\n\\t\\tfor split in types:\\n\\t\\t\\tif split in ['train', 'val', 'test']:\\n\\t\\t\\t\\tdl = DataLoader(imageDataset(split, params, CViters), batch_size=params.batch_size, shuffle=True,\\n\\t\\t\\t\\t\\tnum_workers=params.num_workers,\\n\\t\\t\\t\\t\\tpin_memory=params.cuda)\\n\\n\\t\\t\\t\\tdataloaders[split] = dl\\n\\telse:\\n\\t\\tdl = DataLoader(imageDataset('', params, CViters), batch_size=params.batch_size, shuffle=True,\\n\\t\\t\\tnum_workers=params.num_workers,\\n\\t\\t\\tpin_memory=params.cuda)\\n\\n\\t\\treturn dl\\n\\n\\treturn dataloaders\",\n \"def select(self, name, *selectors) :\\n\\n ct = [(s.get_name(), s.get_type()) for s in selectors]\\n new_table = self.factory.new_table(name, ct)\\n for row in self :\\n d = row.as_dict()\\n r = [s(d) for s in selectors]\\n new_table._add_from_list(r)\\n return new_table\",\n \"def selLoadSettings(*args, activeProxy: Union[AnyStr, bool]=\\\"\\\", deferReference: bool=True,\\n fileName: Union[AnyStr, bool]=\\\"\\\", numSettings: Union[int, bool]=0,\\n proxyManager: Union[AnyStr, bool]=\\\"\\\", proxySetFiles: Union[AnyStr, bool]=\\\"\\\",\\n proxySetTags: Union[AnyStr, bool]=\\\"\\\", proxyTag: Union[AnyStr, bool]=\\\"\\\",\\n referenceNode: Union[AnyStr, bool]=\\\"\\\", shortName: bool=True,\\n unresolvedName: bool=True, q=True, query=True, e=True, edit=True,\\n **kwargs)->Union[AnyStr, Any]:\\n pass\",\n \"def loadSingleObject(*args):\\n\\n # Query selected assets\\n objectIconsList = cmds.layout(objectScroll, query=True, childArray=True)\\n # Query selection between Arnold StandIN / Assembly reference\\n selectedRadio = cmds.radioCollection(loadMethodRadio, query=True, select=True)\\n\\n for obj in objectIconsList:\\n isSelected = cmds.iconTextCheckBox(obj, query=True, value=True)\\n\\n if isSelected:\\n # If this icon is selected, instance an icon object\\n asset = AssetIcon(cmds.iconTextCheckBox(obj, query=True, label=True))\\n\\n # Choose how to load it\\n if \\\"standin\\\" in selectedRadio:\\n asset.loadArnoldAsset()\\n else: \\n asset.loadAsset()\",\n \"def get_all_by_type(self, obj_type):\\n objects = []\\n node = None\\n data = []\\n with lzma.open(os.path.join('resources', self.game, 'dumps',\\n '{}.dump.xz'.format(obj_type)), 'rt', encoding='latin1') as df:\\n for line in df.readlines():\\n match = re.match('^\\\\*\\\\*\\\\* Property dump for object \\\\'\\\\S+ (\\\\S+)\\\\'.*$', line)\\n if match:\\n objects.append(match.group(1))\\n if node:\\n node.load_from_string_list(data)\\n data = [line]\\n node = self.get_node_by_full_object(match.group(1))\\n else:\\n data.append(line)\\n\\n if node:\\n node.load_from_string_list(data)\\n\\n return objects\",\n \"def select(input, output, fields, delimiter, encoding, verbose, format_in, format_out, zipfile, filter):\\n if verbose:\\n enableVerbose()\\n options = {}\\n options['delimiter'] = delimiter\\n options['fields'] = fields\\n options['output'] = output\\n options['encoding'] = encoding\\n options['format_in'] = format_in\\n options['format_out'] = format_out\\n options['zipfile'] = zipfile\\n options['filter'] = filter\\n acmd = Selector()\\n acmd.select(input, options)\\n pass\",\n \"def selectKey(*args, addTo: bool=True, animation: AnyStr=\\\"keysOrObjects.\\\", attribute:\\n Union[AnyStr, List[AnyStr]]=\\\"\\\", clear: bool=True, controlPoints: bool=False,\\n float: Union[floatrange, List[floatrange]]=None, hierarchy: AnyStr=\\\"\\\", inTangent:\\n bool=True, includeUpperBound: bool=True, index: Union[int, List[int]]=0,\\n keyframe: bool=True, outTangent: bool=True, remove: bool=True, replace: bool=True,\\n shape: bool=True, time: Union[timerange, List[timerange]]=None, toggle: bool=True,\\n unsnappedKeys: float=0.0, **kwargs)->int:\\n pass\",\n \"def test_Selector(self) -> None:\\n\\n class MyNode:\\n def __init__(self, name) -> None:\\n self.name = name\\n\\n def __str__(self) -> str:\\n return self.name\\n\\n def get_suffix(self):\\n return os.path.splitext(self.name)[1]\\n\\n s = Selector({'a': 'AAA', 'b': 'BBB'})\\n assert s['a'] == 'AAA', s['a']\\n assert s['b'] == 'BBB', s['b']\\n exc_caught = None\\n try:\\n x = s['c']\\n except KeyError:\\n exc_caught = 1\\n assert exc_caught, \\\"should have caught a KeyError\\\"\\n s['c'] = 'CCC'\\n assert s['c'] == 'CCC', s['c']\\n\\n class DummyEnv(UserDict):\\n def subst(self, key):\\n if key[0] == '$':\\n return self[key[1:]]\\n return key\\n\\n env = DummyEnv()\\n\\n s = Selector({'.d': 'DDD', '.e': 'EEE'})\\n ret = s(env, [])\\n assert ret is None, ret\\n ret = s(env, [MyNode('foo.d')])\\n assert ret == 'DDD', ret\\n ret = s(env, [MyNode('bar.e')])\\n assert ret == 'EEE', ret\\n ret = s(env, [MyNode('bar.x')])\\n assert ret is None, ret\\n s[None] = 'XXX'\\n ret = s(env, [MyNode('bar.x')])\\n assert ret == 'XXX', ret\\n\\n env = DummyEnv({'FSUFF': '.f', 'GSUFF': '.g'})\\n\\n s = Selector({'$FSUFF': 'FFF', '$GSUFF': 'GGG'})\\n ret = s(env, [MyNode('foo.f')])\\n assert ret == 'FFF', ret\\n ret = s(env, [MyNode('bar.g')])\\n assert ret == 'GGG', ret\",\n \"def fetch_dataloader(types, data_dir, hyper_params, train_idx=None, val_idx=None):\\n dataloaders = {}\\n \\n # TODO: write this to hyper_params, make hyper_params an out variable? then save? yes, AND: when ONLY test is requested, load from hyperparams!\\n # TODO: also, add 3rd variation of types: for testing, only read it from hyper_params (DO I NEED TO READ HYPER_PARAMS FOR JUST TESTING?)\\n if train_idx is not None:\\n mean, std = mean_std_calc(DataLoader(Subset(Heart2DSegmentationDataset(str(Path(data_dir) / \\\"train_heart_scans\\\"), hyper_params.endo_or_epi), train_idx)))\\n hyper_params.mean = mean.item()\\n hyper_params.std = std.item()\\n else:\\n if 'train' in types:\\n mean, std = mean_std_calc(DataLoader(Heart2DSegmentationDataset(str(Path(data_dir) / \\\"train_heart_scans\\\"), hyper_params.endo_or_epi)))\\n hyper_params.mean = mean.item()\\n hyper_params.std = std.item()\\n else:\\n mean, std = torch.tensor(hyper_params.mean), torch.tensor(hyper_params.std)\\n \\n print(\\\"Mean: {}, Std: {}\\\".format(mean.item(), std.item()))\\n # borrowed from http://pytorch.org/tutorials/advanced/neural_style_tutorial.html\\n # and http://pytorch.org/tutorials/beginner/data_loading_tutorial.html\\n train_transformer = transforms.Compose([\\n transforms.Normalize(mean=[mean.item()], std=[std.item()])\\n ])\\n \\n eval_transformer = transforms.Compose([\\n transforms.Normalize(mean=[mean.item()], std=[std.item()])\\n ])\\n\\n for split in ['train', 'val', 'test']:\\n if split in types:\\n path = str(Path(data_dir) / \\\"{}_heart_scans\\\".format(split if split != 'val' else 'train'))\\n\\n if split == 'train':\\n if train_idx is not None:\\n dl = DataLoader(Subset(Heart2DSegmentationDataset(path, hyper_params.endo_or_epi, train_transformer), train_idx), \\n batch_size=hyper_params.batch_size, \\n shuffle=True,\\n num_workers=hyper_params.num_workers,\\n pin_memory=hyper_params.cuda)\\n else:\\n dl = DataLoader(Heart2DSegmentationDataset(path, hyper_params.endo_or_epi, train_transformer), \\n batch_size=hyper_params.batch_size, \\n shuffle=True,\\n num_workers=hyper_params.num_workers,\\n pin_memory=hyper_params.cuda)\\n else:\\n if (split == 'val') and (val_idx is not None): \\n dl = DataLoader(Subset(Heart2DSegmentationDataset(path, hyper_params.endo_or_epi, eval_transformer), val_idx), \\n batch_size=hyper_params.batch_size, \\n shuffle=False,\\n num_workers=hyper_params.num_workers,\\n pin_memory=hyper_params.cuda) \\n else:\\n dl = DataLoader(Heart2DSegmentationDataset(path, hyper_params.endo_or_epi, eval_transformer), \\n batch_size=hyper_params.batch_size, \\n shuffle=False,\\n num_workers=hyper_params.num_workers,\\n pin_memory=hyper_params.cuda)\\n\\n dataloaders[split] = dl\\n\\n return dataloaders\",\n \"def test_two_keys():\\n test = [{'key1': {'key2': 'val1'}}, ['key1', 'key2']]\\n assert fetch_data_by_keys(*test).unwrap() == 'val1'\",\n \"def objectTypeUI(*args, isType: AnyStr=\\\"\\\", listAll: bool=True, superClasses: bool=True,\\n **kwargs)->AnyStr:\\n pass\",\n \"def find_files(path='', ext='', level=None, typ=list, dirs=False, files=True, verbosity=0):\\n path = expand_path(path)\\n gen = generate_files(path, ext=ext, level=level, dirs=dirs, files=files, verbosity=verbosity)\\n if isinstance(typ(), Mapping):\\n return typ((ff['path'], ff) for ff in gen)\\n elif typ is not None:\\n return typ(gen)\\n else:\\n return gen\",\n \"def find_objects(self, terms=None, type=None, chunksize=None, **kwargs):\\n type = type or self.default_object_type\\n\\n find_opts = {'chunksize' : chunksize}\\n\\n search_operators = {\\n 'exact': '=',\\n 'gt': '>',\\n 'gte': '>=',\\n 'lt': '<',\\n\\n 'lte': '<=',\\n 'contains': '~'\\n }\\n\\n if terms is not None:\\n find_opts['terms'] = terms\\n else:\\n conditions = []\\n for field, value in six.iteritems(kwargs):\\n if '__' in field:\\n field, filtr = field.split('__')\\n if filtr not in search_operators:\\n raise Exception(\\\"Unsupported search filter '%s'\\\" % filtr)\\n op = search_operators[filtr]\\n else:\\n op = search_operators['contains'] # default search mode\\n\\n if field in self.search_fields_aliases:\\n field = self.search_fields_aliases[field]\\n if field not in self.search_fields:\\n raise Exception(\\\"Error generating Fedora findObjects query: unknown search field '%s'\\\" \\\\\\n % field)\\n if ' ' in value:\\n # if value contains whitespace, it must be delimited with single quotes\\n value = \\\"'%s'\\\" % value\\n conditions.append(\\\"%s%s%s\\\" % (field, op, value))\\n\\n query = ' '.join(conditions)\\n find_opts['query'] = query\\n\\n r = self.api.findObjects(**find_opts)\\n chunk = parse_xml_object(SearchResults, r.content, r.url)\\n while True:\\n for result in chunk.results:\\n yield type(self.api, result.pid)\\n\\n if chunk.session_token:\\n r = self.api.findObjects(session_token=chunk.session_token, **find_opts)\\n chunk = parse_xml_object(SearchResults, r.content, r.url)\\n else:\\n break\",\n \"def fetch_dataloaders(types,n_cpu,folder_name):\\n shot_size=2\\n #how many images in a shot\\n\\n test_suffix='_test'\\n val_suffix='_val'\\n\\n dataloaders = {}\\n for split in ['train', 'test', 'val']:\\n if split in types:\\n # use the train_transformer if training data,\\n # else use eval_transformer without random flip\\n if split == 'train':\\n dl = DataLoader(\\n RandomShotDataset(work_folder+'/'+folder_name+'/LR',\\n work_folder+'/'+folder_name+'/HR',shot_size),\\n batch_size=shot_size, # full-batch in episode\\n shuffle=True,num_workers=n_cpu,pin_memory=True)\\n elif split == 'test':\\n dl = DataLoader(\\n RandomShotDataset(work_folder+'/'+folder_name+test_suffix+'/LR',\\n work_folder+'/'+folder_name+test_suffix+'/HR',shot_size),\\n batch_size=shot_size, # full-batch in episode\\n shuffle=False,num_workers=n_cpu,pin_memory=True)\\n elif split == 'val':\\n dl = DataLoader(\\n RandomShotDataset(work_folder+'/'+folder_name+val_suffix+'/LR',\\n work_folder+'/'+folder_name+val_suffix+'/HR',shot_size),\\n batch_size=shot_size, # full-batch in episode\\n shuffle=False,num_workers=n_cpu,pin_memory=True)\\n else:\\n raise NotImplementedError()\\n dataloaders[split] = dl\\n\\n return dataloaders\",\n \"def pullSerialized(targets, *keys):\",\n \"def _interpret(cls, code):\\n namespace = {}\\n namespace.update(selectors.SELECTORS)\\n exec(code, namespace)\\n header = LowerCaseDict(namespace[\\\"header\\\"])\\n selector = namespace[\\\"selector\\\"]\\n comment = namespace.get(\\\"comment\\\", None)\\n if isinstance(selector, selectors.Parameters):\\n return header, selector.instantiate(header), comment\\n elif isinstance(selector, dict):\\n return header, selector, comment\\n else:\\n raise crexc.MappingFormatError(\\\"selector must be a dict or a Selector.\\\")\",\n \"def pick(self, *keys):\\n return _({k: self[k] for k in keys})\",\n \"def select(*args, add: bool=True, addFirst: bool=True, all: bool=True, allDagObjects: bool=True,\\n allDependencyNodes: bool=True, clear: bool=True, containerCentric: bool=True,\\n deselect: bool=True, hierarchy: bool=True, noExpand: bool=True, replace: bool=True,\\n symmetry: bool=True, symmetrySide: int=0, toggle: bool=True, visible: bool=True,\\n **kwargs)->None:\\n pass\",\n \"def search(self, types=(), start=0, limit=None, orderby='name',\\n reverse=False, paths=(), depth=None, query=None,\\n hashcheck=None, filterPermissions=True, globFilters=None):\\n if not self.is_model_catalog_enabled():\\n return None\\n \\n available_indexes = self.model_catalog.searcher.get_indexes()\\n # if orderby is not an index then query results will be unbrained and sorted\\n areBrains = orderby in available_indexes or orderby is None\\n queryOrderby = orderby if areBrains else None\\n \\n query, not_indexed_user_filters = self._build_query(types, paths, depth, query, filterPermissions, globFilters)\\n\\n #areBrains = len(not_indexed_user_filters) == 0\\n\\n # if we have not indexed fields, we need to get all the results and manually filter and sort\\n # I guess that with solr we should be able to avoid searching by unindexed fields\\n #\\n # @TODO get all results if areBrains == False\\n #\\n \\n catalog_results = self.search_model_catalog(query, start=start, limit=limit, order_by=orderby, reverse=reverse)\\n\\n # @TODO take care of unindexed filters\\n return catalog_results\",\n \"def select(self, **kwargs):\\n\\n res = {}\\n for f, c in zip(self._key_fields, self._unzip_group_keys()):\\n res[f.name] = c\\n for n, c in kwargs.items():\\n res[n] = eval_expression(c, {\\\"me\\\": self})\\n return self._parent._fromdata(res)\",\n \"def object_readers(name, *, specify_reader=False):\\n\\treturn object_access('read', name, specify_reader)\",\n \"def select_keyframe_hierarchy(*args):\\n if not check_robot_selection():\\n pm.warning('No robot\\\\'s selected; ' \\\\\\n 'select at least one robot')\\n return\\n\\n object_list = [__TARGET_CTRL_PATH,\\n __FK_CTRLS_PATH,\\n __A1_FK_CTRL_PATH,\\n __A2_FK_CTRL_PATH,\\n __A3_FK_CTRL_PATH,\\n __A4_FK_CTRL_PATH,\\n __A5_FK_CTRL_PATH,\\n __A6_FK_CTRL_PATH]\\n\\n keyable_robot_objects = []\\n robots = get_robot_roots()\\n\\n for robot in robots:\\n for obj in object_list:\\n obj_path = format_path(obj, robot)\\n keyable_robot_objects.append(obj_path)\\n\\n tool_ctrl_path = get_tool_ctrl_path(robot)\\n if pm.objExists(tool_ctrl_path):\\n keyable_robot_objects.append(tool_ctrl_path)\\n\\n pm.select(keyable_robot_objects)\",\n \"def query(self, **kwargs):\\r\\n fields = set(kwargs.keys())\\r\\n valid_fields = set(['filename'] + self.fields)\\r\\n if not fields.issubset(valid_fields):\\r\\n raise ValueError(\\\"Invalid fields: {}. Must be subset of: {}\\\"\\r\\n .format(fields, valid_fields))\\r\\n data = self.data\\r\\n for field, value in kwargs.items():\\r\\n value = value if isinstance(value, list) else [value]\\r\\n data = [meta for meta in data\\r\\n if self._match_filter(meta, field) in value]\\r\\n return data\",\n \"def find_by_view(self, design_name, view_name, key=None, keys=None, start_key=None, end_key=None,\\n id_only=True, convert_doc=True, **kwargs):\\n log.debug(\\\"find_by_view(%s/%s)\\\" % (design_name, view_name))\\n if type(id_only) is not bool:\\n raise BadRequest('id_only must be type bool, not %s' % type(id_only))\\n ds, datastore_name = self._get_datastore()\\n\\n view_args = self._get_view_args(kwargs)\\n view_args['include_docs'] = (not id_only)\\n view_doc = design_name if design_name == \\\"_all_docs\\\" else self._get_viewname(design_name, view_name)\\n if keys:\\n view_args['keys'] = keys\\n view = ds.view(view_doc, **view_args)\\n if key is not None:\\n rows = view[key]\\n log.info(\\\"find_by_view(): key=%s\\\" % key)\\n elif keys:\\n rows = view\\n log.info(\\\"find_by_view(): keys=%s\\\" % keys)\\n elif start_key and end_key:\\n startkey = start_key or []\\n endkey = list(end_key) or []\\n endkey.append(END_MARKER)\\n log.info(\\\"find_by_view(): start_key=%s to end_key=%s\\\" % (startkey, endkey))\\n if view_args.get('descending', False):\\n rows = view[endkey:startkey]\\n else:\\n rows = view[startkey:endkey]\\n else:\\n rows = view\\n\\n if id_only:\\n res_rows = [(row['id'],row['key'], None) for row in rows]\\n else:\\n if convert_doc:\\n res_rows = [(row['id'],row['key'],self._persistence_dict_to_ion_object(row['doc'])) for row in rows]\\n else:\\n res_rows = [(row['id'],row['key'],row['doc']) for row in rows]\\n\\n log.info(\\\"find_by_view() found %s objects\\\" % (len(res_rows)))\\n return res_rows\",\n \"def Scanner(function, *args, **kwargs):\\n if SCons.Util.is_Dict(function):\\n return Selector(function, *args, **kwargs)\\n\\n return ScannerBase(function, *args, **kwargs)\",\n \"def keys_of_type_exist(self, *keys):\\n # k[0] = key name, k[1] = exists, k2 = expected type\\n keys_exist = [(key, key in self.keys(), expected_type)\\n for key, expected_type in keys]\\n\\n return tuple(ContextItemInfo(\\n key=k[0],\\n key_in_context=k[1],\\n expected_type=k[2],\\n is_expected_type=isinstance(self[k[0]], k[2])\\n if k[1] else None,\\n has_value=k[1] and not self[k[0]] is None\\n ) for k in keys_exist)\",\n \"def pullSerializedAll(*keys):\",\n \"def test_select():\\n keys = list(range(100))\\n d = OrderedTreeDict((key, None) for key in keys)\\n assert all(d.select(k)[0] == k for k in keys)\",\n \"def fetch_dataloader(types, data_dir, params):\\n dataloaders = {}\\n\\n for split in ['train', 'val', 'test']:\\n if split in types:\\n path = os.path.join(data_dir, \\\"{}_signs\\\".format(split))\\n\\n # use the train_transformer if training data, else use eval_transformer without random flip\\n if split == 'train':\\n dl = DataLoader(SIGNSDataset(path, train_transformer), batch_size=params.batch_size, shuffle=True,\\n num_workers=params.num_workers,\\n pin_memory=params.cuda)\\n else:\\n dl = DataLoader(SIGNSDataset(path, eval_transformer), batch_size=params.batch_size, shuffle=False,\\n num_workers=params.num_workers,\\n pin_memory=params.cuda)\\n\\n dataloaders[split] = dl\\n\\n return dataloaders\",\n \"def do_all(self, args):\\n args = shlex.split(args)\\n my_list = []\\n if len(args) == 0:\\n for item in models.storage.all().values():\\n my_list.append(str(item))\\n print(\\\"\\\", end=\\\"\\\")\\n print(\\\", \\\".join(my_list), end=\\\"\\\")\\n print(\\\"\\\")\\n\\n elif args[0] in classes:\\n for key in models.storage.all():\\n if args[0] in key:\\n my_list.append(str(models.storage.all()[key]))\\n print(\\\"\\\", end=\\\"\\\")\\n print(\\\", \\\".join(my_list), end=\\\"\\\")\\n print(\\\"\\\")\\n else:\\n print(\\\"** class doesn't exist **\\\")\",\n \"def getObjs(infile, verbose=False):\\n\\n # Try to Open File\\n fin = ROOT.TFile(infile, \\\"READ\\\")\\n file_open = fin.IsOpen()\\n err_mess = \\\"\\\\n***Can not open ROOT file %s***\\\\n\\\" %(infile)\\n assert (file_open),err_mess\\n\\n # List object for directories\\n obj_list = []\\n\\n # Loop through keys and get directories\\n for key in fin.GetListOfKeys():\\n kname = key.GetName()\\n if key.IsFolder():\\n continue\\n if ( kname != ''):\\n obj_list.append(kname)\\n\\n fin.Close()\\n\\n err_mess = \\\"\\\\n*** There are no objects in this %s ***\\\\n\\\" %(infile)\\n assert (obj_list),err_mess\\n\\n return obj_list\",\n \"def get_object_or_child_by_type(self, *types):\\n\\n objects = self.get_objects_or_children_by_type(*types)\\n return objects[0] if any(objects) else None\",\n \"def __getitem__(self, keys):\\n if isinstance(keys, int):\\n return super().__getitem__(keys)\\n elif isinstance(keys, list):\\n return self.vectors(keys)\\n elif isinstance(keys, str):\\n return self.find(keys)\\n elif isinstance(keys, tuple):\\n return self.item(keys[0], keys[1])\\n elif isinstance(keys, slice):\\n return super().__getitem__(keys)\",\n \"def read(cls, key, mode = FetchMode.All):\\n assert isinstance(key, (basestring, Key))\\n namespace, kind, member = Schema.Get(cls)\\n if isinstance(key, Key):\\n assert kind == key.kind, \\\"Mismatched Model, reading a %s with %s\\\" % (kind, key.kind)\\n return Lisa.read(key, mode)\\n else: \\n key = Key(namespace, kind, key)\\n return Lisa.read(key, mode)\",\n \"def test_valid_option_types(self):\\n\\n opts = {\\n \\\"descending\\\": True,\\n \\\"endkey\\\": ['string'],\\n \\\"endkey_docid\\\": 'string',\\n \\\"group\\\": True,\\n \\\"group_level\\\": 12,\\n \\\"include_docs\\\": True,\\n \\\"inclusive_end\\\": True,\\n \\\"key\\\": 12,\\n \\\"limit\\\": 12,\\n \\\"reduce\\\": True,\\n \\\"skip\\\": 12,\\n \\\"stale\\\": \\\"ok\\\",\\n \\\"startkey\\\": [\\\"string\\\"],\\n \\\"startkey_docid\\\": \\\"string\\\"\\n }\\n result = python_to_couch(opts)\\n self.assertEqual(result['include_docs'], 'true')\\n self.assertEqual(result['endkey'], '[\\\"string\\\"]')\\n self.assertEqual(result['skip'], 12)\\n self.assertEqual(result['endkey_docid'], '\\\"string\\\"')\\n self.assertEqual(result['stale'], '\\\"ok\\\"')\",\n \"def cmd_type(args):\",\n \"def get_objects(slice, plugin_type, klass, **kwargs):\\n try:\\n# plugins_modules = settings.PLUGIN_LOADER.plugin_settings.get(plugin_type).get(\\\"general\\\").get(\\\"aggregate_plugins\\\")[0]\\n plugins_modules = PLUGIN_LOADER.plugin_settings.get(plugin_type).get(\\\"general\\\").get(\\\"aggregate_plugins\\\")[0]\\n p_agg = plugins_modules.split('.')[-1]\\n p_models_path = '.'.join(plugins_modules.split('.')[:-1])\\n try:\\n model = getattr(__import__(p_models_path,fromlist=[klass]), klass)\\n except: \\n try: \\n model = getattr(__import__(p_models_path+'.'+klass,fromlist=[klass]), klass)\\n except:\\n pass \\n # Filters resources by slice (will not return any aggregate's resource from another slice)\\n objects = model.objects.filter(**kwargs)\\n #print \\\"objects: %s\\\" % str(objects)\\n for obj in objects:\\n if not (obj != None and obj.aggregate in slice._get_aggregates()):\\n raise Exception\\n return objects\\n except Exception,e:\\n print \\\"[ERROR] PluginCommunicator could not obtain object. Details: %s \\\" % str(e)\\n return None\",\n \"def _get_by_path(tree, keys):\\n return reduce(getitem, keys, tree)\",\n \"def _get_by_path(tree, keys):\\n return reduce(getitem, keys, tree)\",\n \"def set ( self , ** keywords ) :\\n for k in keywords.keys ():\\n if k == \\\"type\\\" :\\n self.line_type = keywords [\\\"type\\\"]\\n else :\\n setattr (self, k, keywords [k])\",\n \"def read_documents(*paths):\\n for path in paths:\\n suffix = path.suffix.lower()\\n\\n # If compressed, open as gzip stream.\\n opener = open\\n if suffix == '.gz':\\n suffix = path.suffixes[-2].lower()\\n opener = gzip.open\\n\\n if suffix in ('.yaml', '.yml'):\\n try:\\n for parsed_doc in yaml.load_all(opener(str(path), 'r'), Loader=NoDatesSafeLoader):\\n yield path, parsed_doc\\n except yaml.YAMLError as e:\\n raise InvalidDocException('Failed to load %s: %s' % (path, e))\\n elif suffix == '.json':\\n try:\\n yield path, json.load(opener(str(path), 'r'))\\n except ValueError as e:\\n raise InvalidDocException('Failed to load %s: %s' % (path, e))\\n else:\\n raise ValueError('Unknown document type for {}; expected one of {!r}.'\\n .format(path.name, _ALL_SUPPORTED_EXTENSIONS))\",\n \"def pullAll(*keys):\",\n \"def materials(cls, value: Union[dict, MaterialSelector]):\\n if isinstance(value, dict):\\n cls._materials.from_dict(value)\\n elif isinstance(value, MaterialSelector):\\n cls._materials = value\\n else:\\n msg = \\\"materials must be MaterialSelector instance or dict\\\"\\n run_log.error(msg)\\n raise TypeError(msg)\",\n \"def set_selector(*args):\\n return _ida_segment.set_selector(*args)\",\n \"def _load(mapping, **keys):\\n return keys[\\\"loader\\\"](mapping, **keys)\",\n \"def __init__(__self__, *,\\n name: Optional[pulumi.Input[str]] = None,\\n selectors: Optional[pulumi.Input[Sequence[pulumi.Input['SelectorArgs']]]] = None):\\n if name is not None:\\n pulumi.set(__self__, \\\"name\\\", name)\\n if selectors is not None:\\n pulumi.set(__self__, \\\"selectors\\\", selectors)\",\n \"def select_objects(remote, objects_list):\\n select_objects = mmapi.vectori();\\n for object in objects_list:\\n select_objects.push_back(object);\\n cmd2 = mmapi.StoredCommands()\\n cmd2.AppendSceneCommand_SelectObjects(select_objects)\\n remote.runCommand(cmd2)\",\n \"def select(self, field_paths):\\n raise NotImplementedError(\\\"This should have been implemented.\\\")\",\n \"def filterObjects(obj, keys=[], accepts=[], rejects=[], strict=True):\\n total_keys = []\\n a_keys = []\\n r_keys = []\\n queries = []\\n results = []\\n\\n for key in keys:\\n temp_obj = Lucidic({\\\"stuff\\\": obj}) if isinstance(obj, list) else Lucidic(obj)\\n query = temp_obj.search(key, strict=strict)\\n queries.append(query)\\n total_keys += [x[\\\"keypath\\\"][0] for x in query]\\n if accepts:\\n a_keys += [x[\\\"keypath\\\"][0] for x in query if ((x[\\\"match\\\"].get(key) in accepts) or (x[\\\"match\\\"].get(key+\\\"[0]\\\") in accepts))]\\n if rejects:\\n r_keys += [x[\\\"keypath\\\"][0] for x in query if x[\\\"match\\\"][key] in rejects]\\n if a_keys:\\n filtered_keys = sorted((set(total_keys) & set(a_keys)) - set(r_keys))\\n else:\\n filtered_keys = sorted(set(total_keys) - set(r_keys))\\n for f_key in filtered_keys:\\n m = re.search(r\\\"(?P<key>.+)\\\\[(?P<index>\\\\d+)\\\\]\\\", f_key).groupdict()\\n results.append(temp_obj.dict[m[\\\"key\\\"]][int(m[\\\"index\\\"])])\\n return results\",\n \"def find_selector(*args):\\n return _ida_segment.find_selector(*args)\",\n \"def default(self, args):\\n lists = args.split('.')\\n\\n if len(lists) >= 2:\\n\\n if lists[1] == \\\"all()\\\":\\n self.do_all(lists[0])\\n return\\n\\n if lists[1] == \\\"count()\\\":\\n self.count(lists[0])\\n return\\n\\n if lists[1][:4] == \\\"show\\\":\\n self.do_show(self.format_input(lists))\\n return\\n\\n if lists[1][:7] == \\\"destroy\\\":\\n self.do_destroy(self.format_input(lists))\\n return\\n\\n elif lists[1][:6] == \\\"update\\\":\\n args = self.format_input(lists)\\n\\n if type(args) is list:\\n\\n obj = storage.all()\\n key = \\\"{} {}\\\".format(args[0], args[1])\\n for k in args[2]:\\n self.do_update(key + ' \\\"{}\\\" \\\"{}\\\"'.format(k, args[2][k]))\\n else:\\n self.do_update(args)\\n else:\\n cmd.Cmd.default(self, args)\",\n \"def __init__(self, base=None, sub_files=None):\\n self.categories = dict()\\n self.base = base\\n self.files = list()\\n if not sub_files:\\n sub_files = self.sub_node_paths\\n for sub in sub_files:\\n loaded = import_property(sub)(self)\\n self.files.append(loaded)\\n if loaded.category not in self.categories:\\n self.categories[loaded.category] = list()\\n self.categories[loaded.category].append(loaded)\\n for contents in self.categories.values():\\n contents.sort(key=lambda h: h.key)\",\n \"def _verify_multiple_key_types(self, data_keys=None, filters=None, x=None,\\r\\n y=None, variables=None, views=None):\\r\\n if data_keys is not None:\\r\\n self._verify_key_types(name='data', keys=data_keys)\\r\\n\\r\\n if filters is not None:\\r\\n self._verify_key_types(name='filter', keys=filters)\\r\\n\\r\\n if x is not None:\\r\\n self._verify_key_types(name='x', keys=x)\\r\\n\\r\\n if y is not None:\\r\\n self._verify_key_types(name='y', keys=y)\\r\\n\\r\\n if variables is not None:\\r\\n self._verify_key_types(name='variables', keys=variables)\\r\\n\\r\\n if views is not None:\\r\\n self._verify_key_types(name='view', keys=views)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5373972","0.5329947","0.51422113","0.5035492","0.47958642","0.4764183","0.4713935","0.4688114","0.46815497","0.46460122","0.46450743","0.46351984","0.4616212","0.46100155","0.46050787","0.45995423","0.457178","0.4546644","0.4534454","0.45308408","0.45245117","0.45121506","0.45003358","0.45003358","0.4455513","0.44487754","0.44264105","0.44177586","0.4390218","0.43710536","0.43391","0.4333049","0.42881152","0.4263782","0.42524257","0.4249578","0.4242845","0.4240152","0.4234329","0.42337716","0.42307162","0.42272305","0.42248878","0.42141673","0.4204282","0.42031276","0.41964906","0.4195436","0.4178715","0.4177055","0.41749626","0.41743746","0.41662872","0.41637743","0.41601276","0.4160107","0.4149355","0.4147175","0.41469443","0.41443783","0.41437396","0.41410914","0.41370016","0.4135093","0.4131151","0.41293675","0.41286808","0.41233817","0.41231173","0.41213274","0.41180685","0.41127372","0.41000158","0.40999556","0.4098297","0.4097508","0.4090191","0.40883586","0.40837258","0.4080045","0.40779144","0.40681866","0.40631723","0.4060711","0.40601793","0.40601793","0.40599117","0.40480292","0.40478876","0.4047008","0.40431017","0.4041469","0.40394223","0.4032154","0.40307546","0.40297714","0.40289134","0.40236852","0.40235087","0.40210903"],"string":"[\n \"0.5373972\",\n \"0.5329947\",\n \"0.51422113\",\n \"0.5035492\",\n \"0.47958642\",\n \"0.4764183\",\n \"0.4713935\",\n \"0.4688114\",\n \"0.46815497\",\n \"0.46460122\",\n \"0.46450743\",\n \"0.46351984\",\n \"0.4616212\",\n \"0.46100155\",\n \"0.46050787\",\n \"0.45995423\",\n \"0.457178\",\n \"0.4546644\",\n \"0.4534454\",\n \"0.45308408\",\n \"0.45245117\",\n \"0.45121506\",\n \"0.45003358\",\n \"0.45003358\",\n \"0.4455513\",\n \"0.44487754\",\n \"0.44264105\",\n \"0.44177586\",\n \"0.4390218\",\n \"0.43710536\",\n \"0.43391\",\n \"0.4333049\",\n \"0.42881152\",\n \"0.4263782\",\n \"0.42524257\",\n \"0.4249578\",\n \"0.4242845\",\n \"0.4240152\",\n \"0.4234329\",\n \"0.42337716\",\n \"0.42307162\",\n \"0.42272305\",\n \"0.42248878\",\n \"0.42141673\",\n \"0.4204282\",\n \"0.42031276\",\n \"0.41964906\",\n \"0.4195436\",\n \"0.4178715\",\n \"0.4177055\",\n \"0.41749626\",\n \"0.41743746\",\n \"0.41662872\",\n \"0.41637743\",\n \"0.41601276\",\n \"0.4160107\",\n \"0.4149355\",\n \"0.4147175\",\n \"0.41469443\",\n \"0.41443783\",\n \"0.41437396\",\n \"0.41410914\",\n \"0.41370016\",\n \"0.4135093\",\n \"0.4131151\",\n \"0.41293675\",\n \"0.41286808\",\n \"0.41233817\",\n \"0.41231173\",\n \"0.41213274\",\n \"0.41180685\",\n \"0.41127372\",\n \"0.41000158\",\n \"0.40999556\",\n \"0.4098297\",\n \"0.4097508\",\n \"0.4090191\",\n \"0.40883586\",\n \"0.40837258\",\n \"0.4080045\",\n \"0.40779144\",\n \"0.40681866\",\n \"0.40631723\",\n \"0.4060711\",\n \"0.40601793\",\n \"0.40601793\",\n \"0.40599117\",\n \"0.40480292\",\n \"0.40478876\",\n \"0.4047008\",\n \"0.40431017\",\n \"0.4041469\",\n \"0.40394223\",\n \"0.4032154\",\n \"0.40307546\",\n \"0.40297714\",\n \"0.40289134\",\n \"0.40236852\",\n \"0.40235087\",\n \"0.40210903\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71854883"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":83099,"cells":{"query":{"kind":"string","value":"Read ROOT files from flow and yield objects they contain. For file to be read, data part of the value must be a string (file's path) and context.input.read_root_file must not be `False`. Other values pass unchanged. After all entries from the file are yielded, it is closed. context.input.root_file_path is updated with the path to the ROOT file. Warning ======= After a ROOT file is closed, all its contained objects are destroyed."},"document":{"kind":"string","value":"def run(self, flow):\n from ROOT import TFile\n from lena.context import get_recursively, update_recursively\n from lena.flow import get_data_context\n from copy import deepcopy\n for val in flow:\n data, context = get_data_context(val)\n\n # skip not ROOT files\n if sys.version_info.major == 2:\n str_type = basestring\n else:\n str_type = str\n if not isinstance(data, str_type) or not \\\n get_recursively(context, \"input.read_root_file\",\n True):\n yield val\n continue\n\n root_file = TFile(data, \"read\")\n # context of separate keys shall be updated\n # when they are transformed to other types\n # in other elements\n update_recursively(\n context, {\"input\": {\"root_file_path\": data}}\n )\n\n def get_key_names(fil):\n return [key.GetName() for key in fil.GetListOfKeys()]\n key_names = get_key_names(root_file)\n\n for key_name in key_names:\n # result of TFile.Get is not a TKey, but a proper type\n obj = root_file.Get(key_name)\n if self._selector:\n if not self._selector(obj):\n continue\n yield (obj, deepcopy(context))\n\n # will be closed after\n # following elements used its data\n root_file.Close()"},"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 files_and_folders(self):\n yield from self._root.files_and_folders(0)","def files_from_roots(roots, accept):\n for root in roots:\n if root is not None:\n yield from files_from_root(root, accept)","def open_test_root_fs() -> FS:\n cwd = os.path.dirname(__file__)\n return read_only(open_fs('data', cwd=cwd))","def reading(self):\n if (\n self.reading_handle is None\n and self.writing_handle is None\n and self.filename is not None\n ):\n with self._open() as fp:\n self.reading_handle = fp\n try:\n yield\n finally:\n self.reading_handle = None\n else:\n yield","def iter_tree(root):\n\tfor file_rel in _iter_tree_next(os.path.abspath(root), '', {}):\n\t\tyield file_rel","def open_file(self) -> Iterator[NamedIO]:\n with open(self.filename) as f:\n yield cast(NamedIO, f)","def read_data(self, path, **kwargs):\n\n from glob import glob\n import os\n sc = self.sc\n pdt_lc = np.dtype([('pos', 'f4', 3),('vel', 'f4', 3)])\n\n blockids = kwargs['blockids']\n\n def set_particle_IDs_partition(index, iterator): \n \"\"\"\n Use the aggregate partition counts to set monotonically increasing \n particle indices\n \"\"\"\n p_counts = partition_counts.value\n local_index = 0\n start_index = sum([p_counts[i] for i in range(index)])\n for arr in iterator:\n arr['iOrder'] = range(start_index + local_index, start_index + local_index + len(arr))\n arr['iGroup'] = loc_to_glob_map_b.value[index]\n local_index += len(arr)\n yield arr\n \n def read_file(index, i, chunksize=102400): \n for part,filename in i:\n timein = time.time()\n with open(filename,'rb') as f: \n header = f.read(62500)\n while True:\n chunk = f.read(chunksize*24)\n if len(chunk): \n p_arr = np.frombuffer(chunk, pdt_lc)\n new_arr = np.zeros(len(p_arr), dtype=pdt)\n new_arr['pos'] = p_arr['pos']\n yield new_arr\n else: \n t_elapsed = time.time()-timein\n rate = os.path.getsize(filename)/1e6/t_elapsed\n print 'spark_fof: reading %s took %d seconds in partition %d, %f MB/sec'%(filename, t_elapsed, index, rate)\n break\n \n # determine which files to read\n get_block_ids = re.compile('blk\\.(\\d+)\\.(\\d+)\\.(\\d+)?')\n\n if blockids is None: \n files = glob(os.path.join(self.path,'*/*'))\n else: \n files = []\n for dirname, subdirlist, filelist in os.walk(path):\n try: \n dirnum = int(os.path.basename(dirname))\n if dirnum in blockids: \n for f in filelist:\n ids = get_block_ids.findall(f)\n if len(ids) > 0:\n if all(int(x) in blockids for x in ids[0]):\n files.append(os.path.join(dirname,f))\n except ValueError: \n pass\n\n files.sort()\n nfiles = len(files) \n self.nPartitions = nfiles\n\n print 'spark_fof: Number of input files: ', nfiles\n\n # get particle counts per partition\n nparts = {i:_get_nparts(filename,62500,pdt_lc.itemsize) for i,filename in enumerate(files)}\n\n print 'spark_fof: Total number of particles: ', np.array(nparts.values()).sum()\n \n # set up the map from x,y,z to partition id \n ids = map(lambda x: tuple(map(int, get_block_ids.findall(x)[0])), files)\n ids_map = {x:i for i,x in enumerate(ids)}\n self.ids_map = ids_map\n loc_to_glob_map_b = self.local_to_global_map\n \n ids_map_b = sc.broadcast(ids_map)\n loc_to_glob_map_b = sc.broadcast(loc_to_glob_map_b)\n\n partition_counts = sc.broadcast(nparts)\n\n rec_rdd = (sc.parallelize(zip(ids,files), numSlices=self.nPartitions)\n .map(lambda (id,filename): (ids_map_b.value[id],filename))\n .partitionBy(self.nPartitions).cache()\n .mapPartitionsWithIndex(read_file, preservesPartitioning=True)\n .mapPartitionsWithIndex(set_particle_IDs_partition, \n preservesPartitioning=True))\n \n return rec_rdd","def ReadFilesGenerator(self):\n\n for file in self._file_names:\n file_list = []\n\n # TODO see further into yielding one line at a time\n with open(file, 'r', encoding='mbcs') as sped:\n file_list = sped.read().splitlines()\n\n if not self.isSigned(file_list):\n file_list = self.stripSignature(file_list)\n\n yield file, file_list","def input_data(self):\n return read_file(self.file_path)","def walk(self):\n if os.path.exists(self.folder):\n for root_path, _, f_files in os.walk(self.folder):\n yield root_path, f_files\n if not self.recursive:\n break\n else:\n print(f\"[!e] Passed folder doesn't exist. Path: {self.folder}\",\n file=sys.stdout)\n exit(0)","def walk(self):\n for _root, _dirs, files in os.walk(self.root):\n for filename in files:\n if self.is_key(filename):\n yield filename","def read_inputs(self):\n curdir = os.getcwd()\n os.chdir(self.fst_dir)\n rstat = self.readFST()\n if rstat == 0:\n os.chdir(curdir)\n return 0\n # the names of the next files are either set by caller or come from the reading the FAST file\n rstat = self.readNoise()\n rstat = self.readAD()\n rstat = self.readBlade()\n rstat = self.readPtfm()\n os.chdir(curdir)","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 _read_input_file(self):\n pass","def open(self, path: str, **kwargs) -> Iterator[OpenFileType]:\n full_path = self._full_path(path)\n logger.debug(f\"entering fs.open context manager for {full_path}\")\n of = self.fs.open(full_path, **kwargs)\n logger.debug(f\"FSSpecTarget.open yielding {of}\")\n yield of\n logger.debug(\"FSSpecTarget.open yielded\")\n of.close()","def open(self, relative_path, flags):\n file_obj = open(self._get_full_path(relative_path), flags)\n yield file_obj\n file_obj.close()","def walk(self): # FileObj.walk\n yield self","def edit_root(self) -> Generator[Root, None, None]:\n with self.edit(Root.type) as root:\n if not isinstance(root, Root):\n raise RuntimeError(\"Unexpected root type\")\n yield root","def Init(self):\n # First iteration over all the files in root searching for symlinks and\n # non-regular files.\n seen_inodes = {}\n for basepath, _, filenames in sorted(os.walk(self._root)):\n for filename in sorted(filenames):\n full_path = os.path.join(basepath, filename)\n rel_path = full_path[len(self._root):]\n st = os.lstat(full_path)\n\n file_data = {\n 'size': st.st_size,\n }\n self._files[rel_path] = file_data\n\n # Track symlinks.\n if stat.S_ISLNK(st.st_mode):\n link_path = os.readlink(full_path)\n # lddtree's normpath handles a little more cases than the os.path\n # version. In particular, it handles the '//' case.\n self._symlinks[rel_path] = (\n link_path.lstrip('/') if link_path and link_path[0] == '/' else\n lddtree.normpath(os.path.join(os.path.dirname(rel_path),\n link_path)))\n file_data['deps'] = {\n 'symlink': [self._symlinks[rel_path]]\n }\n\n # Track hardlinks.\n if st.st_ino in seen_inodes:\n self._hardlinks[rel_path] = seen_inodes[st.st_ino]\n continue\n seen_inodes[st.st_ino] = rel_path","def _GetSubFileEntries(self):\n if self._directory is None:\n self._directory = self._GetDirectory()\n\n if self._directory:\n for path_spec in self._directory.entries:\n yield HFSFileEntry(self._resolver_context, self._file_system, path_spec)","def lazy_read_file(self):\n store = zarr.DirectoryStore(self.fpath)\n z_array = zarr.open(store=store, mode='r')\n self.da_input = da.from_array(z_array)\n self.data = self.da_input\n self.data_dim = self.data.shape\n self.chunk_size = z_array.chunks","def __iter__(self):\n # Wait to open file until needed. Automatically close file when done.\n with open(self.obo_file) as fstream:\n rec_curr = None # Stores current GO Term\n typedef_curr = None # Stores current typedef\n for line in fstream:\n # obo lines start with any of: [Term], [Typedef], /^\\S+:/, or /^\\s*/\n if self.data_version is None:\n self._init_obo_version(line)\n if rec_curr is None and line[0:6].lower() == \"[term]\":\n rec_curr = GOTerm()\n if self.optobj:\n self.optobj.init_datamembers(rec_curr)\n elif typedef_curr is None and line[0:9].lower() == \"[typedef]\":\n typedef_curr = TypeDef()\n elif rec_curr is not None or typedef_curr is not None:\n line = line.rstrip() # chomp\n if line:\n self._add_to_obj(rec_curr, typedef_curr, line)\n else:\n if rec_curr is not None:\n yield rec_curr\n rec_curr = None\n elif typedef_curr is not None:\n # Save typedef.\n self.typedefs[typedef_curr.id] = typedef_curr\n typedef_curr = None\n # Return last record, if necessary\n if rec_curr is not None:\n yield rec_curr","def path_tuples(self):\n # We want the following contents in the tarball\n # Metaflow package itself\n for path_tuple in self._walk(self.metaflow_root, exclude_hidden=False):\n yield path_tuple\n # the package folders for environment\n for path_tuple in self.environment.add_to_package():\n yield path_tuple\n # the user's working directory\n flowdir = os.path.dirname(os.path.abspath(sys.argv[0])) + '/'\n for path_tuple in self._walk(flowdir):\n yield path_tuple","def _GetSubFileEntries(self):\n if self._directory is None:\n self._directory = self._GetDirectory()\n\n if self._directory:\n for path_spec in self._directory.entries:\n yield NTFSFileEntry(\n self._resolver_context, self._file_system, path_spec)","async def stream_result_files(self) -> AsyncGenerator[StreamInfoUrl, None]:\n for dataset in self.datasets:\n async for file in dataset.get_data_rootfiles_stream(self.query.value()):\n yield file","def read_input(file_input):\n login = file_input.readline().strip()\n n, max_batch_size = map(int, file_input.readline().split())\n\n tree = Tree(n)\n for _ in range(n - 1):\n path = file_input.readline().strip()\n\n child_type = RecordType.Record\n if path[-1] == '/':\n child_type = RecordType.Folder\n path = path[:-1]\n\n path_tokens = path.split('/')\n parent_path, child_name = '/'.join(path_tokens[:-1]), path_tokens[-1]\n if parent_path == '':\n parent_path = '/'\n\n tree.add_edge(parent_path, child_name, child_type)\n tree.calculate_sizes()\n\n return login, max_batch_size, tree","def __iter__(self):\n # pylint: disable=no-member\n with builtins.open(self.path,\n mode=self.mode,\n buffering=self.buffering,\n encoding=self.encoding,\n errors=self.errors,\n newline=self.newline) as file_content:\n for line in file_content:\n yield line","def iter_documents(top_directory):\n for root, dirs, files in os.walk(top_directory):\n for file in filter(lambda file: file.endswith('.txt'), files):\n document = open(os.path.join(root, file)).read() # read the entire document, as one big string\n yield utils.tokenize(document, lower=True) # or whatever tokenization suits you","def __iter__(self):\n for f in self.path.glob('**/*'):\n if f.is_file() and not os.stat(str(f.resolve())).st_size == 0:\n yield Resource(str(f.resolve()), DiskCrawler.compute_digest)","def assets(self):\n # N.B.:\n # we don't cache the results of the {root} exploration so we can always\n # return a result that reflects the current contents of the filesystem\n yield from self._explore()\n # all done\n return","def __init__(self, root_dir, file_dir, fig_name, n_files=None, **kwargs):\n self.reader = FileReader(root_dir, sub_dirs=[file_dir,], num_files=[n_files,], **kwargs)\n self.fig_name = fig_name\n self.out_dir = '{:s}/{:s}/'.format(root_dir, fig_name)\n if self.reader.comm.rank == 0 and not os.path.exists('{:s}'.format(self.out_dir)):\n os.mkdir('{:s}'.format(self.out_dir))\n self.my_sync = Sync(self.reader.comm)\n\n self.files = self.reader.local_file_lists[file_dir]\n self.idle = self.reader.idle[file_dir]\n self.dist_comm = self.reader.distribution_comms[file_dir]","def _GetSubFileEntries(self):\n if self._directory is None:\n self._directory = self._GetDirectory()\n\n if self._directory:\n for path_spec in self._directory.entries:\n yield APMFileEntry(self._resolver_context, self._file_system, path_spec)","def collect(self):\n while self.proc is not None:\n self.read()\n if not len(self.datalines):\n return\n while len(self.datalines):\n # pop the first node of list\n yield self.datalines.pop(0)","def reader(self):\n @contextmanager\n def generator(data):\n \"\"\"\n Args:\n data (str): could be a filename or the text to tokenize.\n Returns:\n a context manager that can be used in a `with` contruct,\n yielding each line of the tokenized `data`.\n \"\"\"\n if not os.path.exists(data):\n yield self.format(self.predict(data))\n else:\n with open(data) as f:\n yield self.format(self.predict(f.read()))\n return generator","def __iter__(self):\n with open(self.inputfile) as fin:\n for line in fin:\n arr = line.strip().split()\n self.path = arr[0]\n self.y = int(arr[1])\n if len(arr) > 2:\n self.feats = map(float, arr[2:])\n yield self.path, self.y, self.feats\n else:\n yield self.path, self.y","def get_files(self, block):\n \n raise NotImplementedError('get_files')","def file_package_iter(self):\n files = list()\n futures = list()\n\n amount = 0\n for file in self.file_iterator:\n if amount + self._estimate_file_size(file) > self.max_size:\n if len(files) == 0: # This file is too large for one archive, special handling\n self.pool.wait(futures)\n self._calculate_hash(file)\n yield self._finish_info_package([file])\n continue\n\n self.pool.wait(futures)\n yield self._finish_info_package(files)\n\n files = list()\n amount = 0\n\n amount += file.size\n files.append(file)\n futures.append(self.pool.add_task(self._calculate_hash, file)) # todo calc small files in-thread?\n\n if len(files) > 0:\n yield self._finish_info_package(files)","def read( self, filename ):\n conf_file = cfg.ConfigFile(filename)\n self.root_node = conf_file.root_node","def inputSet_read(path, data):\n nonlocal filesRead\n nonlocal index\n nonlocal d_tree\n nonlocal fn_inputReadCallback\n\n d_read = fn_inputReadCallback((path, data), **kwargs)\n\n if 'status' in d_read.keys():\n d_tree[path] = d_read\n if 'filesRead' in d_read.keys():\n filesRead += d_read['filesRead']\n else:\n self.dp.qprint(\n \"The inputReadCallback callback did not return a 'status' value!\",\n comms = 'error',\n level = 0\n )\n error.fatal(self, 'inputReadCallback', drawBox = True)\n return d_read","def Iterator (self, block = None):\n if self.Disposed:\n raise RuntimeError ('Core is disposed')\n\n block = True if block is None else block\n top_level = False\n try:\n # Thread identity is used by Notify to make sure call to notifier is\n # really needed. And it also used to make sure core is iterating only\n # on one thread.\n if self.thread_ident is None:\n self.thread_ident = get_ident ()\n top_level = True # top level iterator (no other iterators)\n elif self.thread_ident != get_ident ():\n raise ValueError ('Core is already being run on a different thread')\n\n timer = self.timer\n files = self.files\n context = self.context\n\n events = tuple ()\n while True:\n # resolve await objects\n for fd, event in events:\n files [fd].Resolve (event)\n context.Resolve ()\n timer.Resolve ()\n\n # Yield control to check conditions before blocking (Core has been\n # stopped or desired future resolved). If there is no file\n # descriptors registered and timeout is negative poller will raise\n # StopIteration and break this loop.\n yield\n\n events = self.poller.Poll (0) if not block else \\\n self.poller.Poll (min (timer.Timeout (), context.Timeout ()))\n\n finally:\n if top_level:\n self.thread_ident = None","def filectxs(self):\n mf = self.manifest()\n m = mf.keys()\n m.sort()\n for f in m:\n yield self.filectx(f, fileid=mf[f])","def testing_context(require_enzoe_inputdir = True):\n\n path = 'input'\n\n cleanup = False\n if os.path.isfile(path): # path is allowed to be a symlink to a dir\n raise RuntimeError('./' + path + ' is a path to a file.')\n elif os.path.isdir(path): # path is allowed to be a symlink to a dir\n realpath = os.path.abspath(os.path.realpath(path))\n expected = os.path.abspath(os.path.join(_ENZOE_ROOT_DIR, 'input'))\n if require_enzoe_inputdir and (realpath != expected):\n raise RuntimeError('./' + path + \" doesn't refer to \" + expected)\n elif os.path.islink(path):\n raise RuntimeError('./' + path + ' is a broken link.')\n else: # make a symlink to {_ENZOE_ROOT_DIR}/input\n cleanup = True\n os.symlink(src = os.path.join(_ENZOE_ROOT_DIR, path),\n dst = path, target_is_directory = True)\n\n try:\n yield None\n finally:\n if cleanup:\n os.unlink(path)","def __init__(self, file_root):\n self.root = file_root","def _iter_from_disk(self):\n self.f.seek(0, 0) # relative to start\n for line in self.f:\n yield line\n self.f.seek(0, 2) # relative to end","def root_dir():\n assert root is not None\n\n old = _os.getcwd()\n try:\n _os.chdir(root)\n yield root\n finally:\n _os.chdir(old)","def flow_from_files(self, filenames=None, batch_size=32):\n\n if filenames:\n self.filenames = filenames\n\n for i in range(0, len(self.filenames), batch_size):\n yield np.concatenate([np.load(self.path / f) \\\n for f in self.filenames.iloc[i:i+batch_size]])","def run(filenames, root_dir):\n for filename in filenames:\n with open(filename, encoding='utf-8') as file:\n filename = filename.replace('\\\\', '/')[len(root_dir)::]\n yield from check(filename, file.readlines())","def _read_blocks(input_data, size=2**20):\n\n if isinstance(input_data, (BufferedReader, BytesIO)):\n f = input_data\n opened = False\n elif input_data == '-':\n f = sys.stdin.buffer # read binary instead of unicode\n opened = False\n else:\n f = open(input_data, 'rb')\n opened = True\n\n try:\n\n data = f.read(size)\n while len(data) > 0:\n yield data\n data = f.read(size)\n finally:\n if opened:\n f.close()","def _read(self, path: str):\n num_samples, length = [int(x) for x in path.split(\":\")]\n random.seed(self.seed)\n for _ in range(num_samples):\n tokens, tags = self._sample(length)\n yield self.text_to_instance(tokens, tags)","def dep_sample_generator(path_to_file):\n assert os.path.isfile(path_to_file), \"File does not exist\"\n root = DepSample(0, ROOT, ROOT, 0)\n with open(path_to_file) as fp:\n sample = [root]\n for line in fp:\n if not line.rstrip():\n yield sample\n sample = [root]\n else:\n ls = line.rstrip().split('\\t')\n # print(ls)\n sample.append(DepSample(int(ls[0]), ls[1], ls[3], int(ls[6])))\n if len(sample) > 1:\n yield sample","def parseLowLevel(self):\n file_iter = iter(self.file_object)\n while True:\n line = next(file_iter)\n self.line_number += 1\n\n ## skip comments\n if line.startswith(\"#\"):\n continue\n\n ## semi-colon multi-line strings\n if line.startswith(\";\"):\n lmerge = [line[1:]]\n while True:\n line = next(file_iter)\n self.line_number += 1\n if line.startswith(\";\"):\n break\n lmerge.append(line)\n\n lmerge[-1] = lmerge[-1].rstrip()\n yield (DATA_VALUE, \"\".join(lmerge))\n continue\n\n ## split line into tokens\n for match in _token_regexp.finditer(line):\n label1, label2, string, token = match.groups()\n if label1 is not None and label2 is not None:\n yield DATA_LABEL, (label1, label2)\n elif string is not None:\n yield DATA_VALUE, string\n elif token is not None:\n token_parts = token.split('_')\n if len(token_parts) == 1 \\\n or token_parts[0].lower() not in (\"data\", \"loop\",\n \"global\", \"save\",\n \"stop\"):\n yield DATA_VALUE, token\n else:\n yield KEYWORD, (token_parts[0].lower(),\n '_'.join(token_parts[1:]))","def read_all_trees(self, **kwargs):\n return self.read_single_tree(-1, halonum=None, **kwargs)","def __iter__(self):\r\n example = []\r\n for line in open(self.fullpath):\r\n if line != '\\n':\r\n example.append(line.rstrip()) # remove newline\r\n else:\r\n yield example\r\n example = []","def __iter__(self):\n with open(self.fn + \".fai\") as fai:\n for toks in (l.rstrip(\"\\r\\n\").split(\"\\t\") for l in fai):\n yield toks[0], int(toks[1])","def read(self, input): \n \n # 1. Assume we have the name of a file in the execution dir or a \n # file-like object. \n # Note we need to add parsing of the path to dir/filename \n try: \n with File.as_handle(input, 'rU') as fp: \n file_contents = fp.read() \n self.filename = getattr(fp, 'name', 'Unknown_nexus_file') \n except (TypeError, IOError, AttributeError): \n #2 Assume we have a string from a fh.read() \n if isinstance(input, basestring): \n file_contents = input \n self.filename = 'input_string' \n else: \n print(input.strip()[:50]) \n raise NexusError('Unrecognized input: %s ...' % input[:100]) \n file_contents = file_contents.strip() \n if file_contents.startswith('#NEXUS'): \n file_contents = file_contents[6:] \n commandlines = _get_command_lines(file_contents) \n # get rid of stupid 'NEXUS token - in merged treefiles, this might appear multiple times' \n for i, cl in enumerate(commandlines): \n try: \n if cl[:6].upper() == '#NEXUS': \n commandlines[i] = cl[6:].strip() \n except: \n pass \n # now loop through blocks (we parse only data in known blocks, thus ignoring non-block commands \n nexus_block_gen = self._get_nexus_block(commandlines) \n while True: \n try: \n title, contents = next(nexus_block_gen) \n except StopIteration: \n break \n if title in KNOWN_NEXUS_BLOCKS: \n self._parse_nexus_block(title, contents) \n else: \n self._unknown_nexus_block(title, contents)","def read_project(root_path, verbose=False, ignore=None, encoding=None):\n nodes = {}\n root_node = None\n errors = False\n ignore_files = set([\".hg\", \".svn\", \".git\", \".tox\", \"__pycache__\", \"env\", \"venv\"]) # python 2.6 comp\n\n if ignore:\n for ignored_file in ignore:\n ignore_files.add(os.path.basename(os.path.realpath(ignored_file)))\n\n # traverse root directory, and list directories as dirs and files as files\n for root, dirs, files in os.walk(root_path):\n\n dirs[:] = [d for d in dirs if d not in ignore_files]\n\n files = [fn for fn in files if os.path.splitext(fn)[1] == \".py\" and fn not in ignore_files]\n\n for file_name in files:\n full_path = os.path.join(root, file_name)\n with open_func(full_path, \"r\", encoding=encoding) as f:\n try:\n # fails on empty files\n file_data = f.read()\n lines = file_data.splitlines()\n tree = ast.parse(file_data)\n if verbose:\n click.echo(crayons.yellow('Trying to parse file: {}'.format(full_path)))\n\n if full_path in nodes:\n node = nodes[full_path]\n else:\n node = Node(file_name[:-3], full_path=full_path)\n nodes[full_path] = node\n\n if not root_node:\n root_node = node\n\n for ast_node in ast.walk(tree):\n if isinstance(ast_node, ast.Import) and ast_node.names:\n for subnode in ast_node.names:\n if not subnode.name:\n continue\n\n path_to_module = get_path_from_package_name(\n root_path, subnode.name)\n\n if path_to_module in nodes:\n new_node = nodes[path_to_module]\n else:\n new_node = Node(\n subnode.name, full_path=path_to_module)\n nodes[path_to_module] = new_node\n\n new_node.is_imported_from[full_path].append(ast_node.lineno)\n node.add(new_node)\n\n elif isinstance(ast_node, ast.ImportFrom) and ast_node.module:\n current_path = root_path\n if 0 <= ast_node.lineno - 1 < len(lines) and\\\n REGEX_RELATIVE_PATTERN.findall(lines[ast_node.lineno - 1]):\n current_path = root\n\n path_to_module = get_path_from_package_name(\n current_path, ast_node.module)\n\n if path_to_module in nodes:\n new_node = nodes[path_to_module]\n else:\n new_node = Node(\n ast_node.module, full_path=path_to_module)\n nodes[path_to_module] = new_node\n\n for obj_import in ast_node.names:\n if ast_node.lineno not in node.func_imports:\n node.func_imports[ast_node.lineno] = [obj_import.name]\n else:\n node.func_imports[ast_node.lineno].append(obj_import.name)\n\n new_node.is_imported_from[full_path].append(ast_node.lineno)\n node.add(new_node)\n elif isinstance(ast_node, (ast.ClassDef, ast.FunctionDef)):\n node.func_defs[ast_node.name] = ast_node.lineno\n\n except Exception as e:\n errors = True\n click.echo(crayons.yellow('Parsing of file failed: {}'.format(full_path)))\n if verbose:\n click.echo(crayons.red(traceback.format_exc(e)))\n\n if errors:\n click.echo(crayons.red('There were errors during the operation, perhaps you are trying to parse python 3 project, '\n 'with python 2 version of the script? (or vice versa)'))\n return root_node","def __iter__(self):\n for p in self.paths:\n yield Document.load(os.path.join(self.dirpath, p), fmt=self.fmt)","def fileStreamer(self, filePath):\n\t\tfor line in open(filePath):\n\t\t\tyield line","def readfast(self, name=\"\", *args, **kwargs):\n\n assert _os.path.isfile(self.__str__()) == True\n\n with open(self.__str__(), *args, **kwargs) as file_handler:\n for line in file_handler:\n yield line","def read(self, outstream: typ.BinaryIO):\r\n file_metadata = self.metadata.get_file(\"0\")['metadata']\r\n if self.fs_type == 'FAT':\r\n allocator_metadata = FATAllocatorMeta(file_metadata)\r\n self.fs.read(outstream, allocator_metadata)\r\n elif self.fs_type == 'NTFS':\r\n allocator_metadata = NTFSAllocatorMeta(file_metadata)\r\n self.fs.read(outstream, allocator_metadata)\r\n else:\r\n raise NotImplementedError()","def files(self) -> Iterator[List[Path]]:\n\n files: List[Path] = []\n fragments: List[str] = []\n data: Dict[str, Any] = {}\n\n inst: Optional[\"CLIInstance\"] = self\n while inst is not None:\n for k, v in inst._data.items():\n if isinstance(v, UnknownExpression) or k in data:\n fragments.append(f\"{k} = {v};\\n\")\n elif isinstance(v, EnumMeta):\n fragments.append(\n f\"{k} = {{{', '.join([i for i in v.__members__])}}};\\n\"\n )\n else:\n data[k] = v\n fragments.extend(inst._code_fragments)\n files.extend(inst._includes)\n\n inst = inst._parent\n\n gen_files = []\n try:\n if len(data) > 0:\n file = tempfile.NamedTemporaryFile(\n prefix=\"mzn_data\", suffix=\".json\", delete=False\n )\n gen_files.append(file)\n file.write(json.dumps(data, cls=MZNJSONEncoder).encode())\n file.close()\n files.append(Path(file.name))\n if len(fragments) > 0 or len(files) == 0:\n file = tempfile.NamedTemporaryFile(\n prefix=\"mzn_fragment\", suffix=\".mzn\", delete=False\n )\n gen_files.append(file)\n for code in fragments:\n file.write(code.encode())\n file.close()\n files.append(Path(file.name))\n yield files\n finally:\n for file in gen_files:\n os.remove(file.name)","def iter(self):\n for _file in self._files:\n with asHandle(_file) as fp:\n # Use FastqGeneralIterator because it provides access to\n # the unconverted quality string (i.e., it doesn't try to\n # figure out the numeric quality values, which we don't\n # care about at this point).\n for sequenceId, sequence, quality in FastqGeneralIterator(fp):\n yield self.readClass(sequenceId, sequence, quality)","def fileobjects_iter(imagefile=None,xmlfile=None,fiwalk=\"fiwalk\",flags=0):\n def local_iter(fi):\n yield fi\n fiwalk_using_sax(imagefile=imagefile,xmlfile=xmlfile,fiwalk=fiwalk,flags=flags,\n callback=local_iter)","def job_reader(path) -> Generator[ParsedActionType, None, None]:\n try:\n with open(path, \"r\") as f:\n parser = Parser()\n for line in f:\n result = parser.process_line(line)\n if result is not None:\n yield result\n except IOError as err:\n print(\"Error opening/reading from file '{0}': {1}\"\n .format(err.filename, err.strerror))","def walk_tree(self, path, topdown=True):\n if isinstance(path, File):\n # Called with File object as an argument\n root = path\n path = root.path\n else:\n root = File(path)\n\n files, dirs = [], []\n\n try:\n for item in os.listdir(path):\n file_path = os.path.join(path, item)\n\n if self.path_ignore and self.path_ignore.match(file_path):\n # Skip excluded paths\n lg.debug(\"Ignoring path %s\" % file_path)\n continue\n\n try:\n f_object = File(file_path, seen=root.already_seen)\n except UnsupportedFileType as e:\n lg.warn('%s ..skipping' % e)\n continue\n except OSError as e:\n if e.errno == errno.ENOENT:\n # File already removed, go on\n lg.debug('File already removed: %s' % e)\n continue\n elif e.errno in [errno.EPERM, errno.EACCES]:\n # Permission denied or operation not permitted, log error and go on\n lg.error(e)\n continue\n else:\n # Other errors should be fatal, but we don't want them to be\n # eg. corrupted file on GlusterFS may raise IOError, but we want to continue\n lg.exception(e)\n continue\n\n if f_object.directory is True:\n dirs.append(f_object)\n else:\n files.append(f_object)\n except OSError as e:\n # Exceptions that may come from os.listdir()\n if e.errno == errno.ENOENT:\n # Directory doesn't exist, go on\n pass\n elif e.errno in [errno.EPERM, errno.EACCES]:\n # Permission denied or operation not permitted, log error and go on\n lg.error(e)\n pass\n else:\n # Other errors should be fatal, but we don't want them to be\n # eg. corrupted file on GlusterFS may raise IOError, but we want to go on\n lg.exception(e)\n pass\n\n if topdown:\n yield root, dirs, files\n\n for item in dirs:\n for x in self.walk_tree(item):\n yield x\n\n if not topdown:\n yield root, dirs, files","def run(self):\n node_runs = [\n (node, node.run())\n for node in self.nodes\n if node.loaded\n ]\n\n while True:\n for node, node_run in node_runs:\n if node in self.buffered_input and node.mode == Node.READ:\n for d, values in self.buffered_input[node].iteritems():\n if values and not node.has_inputs(d):\n node.write_input(d, values.pop(0))\n self.num_buffered_inputs -= 1\n break\n\n yield next(node_run)","def iterRegularFileContents(self):\n unpack = {}\n for (oldFileId, newFileId), stream in self.files.iteritems():\n if not files.frozenFileHasContents(stream):\n continue\n if files.frozenFileFlags(stream).isEncapsulatedContent():\n continue\n cont = files.frozenFileContentInfo(stream)\n unpack[newFileId] = cont.sha1()\n\n want_tag = '0 ' + ChangedFileTypes.file[4:]\n while True:\n f = self._nextFile()\n if not f:\n break\n name, tag, fobj, csf = f\n if len(name) != 36 or tag != want_tag:\n continue\n fileId = name[16:]\n sha1 = unpack.get(fileId)\n if not sha1:\n continue\n yield sha1, fobj","def _inner_file_reader(\r\n file_name: str,\r\n chunk_size: int = 32*1024*1024,\r\n delimiter: str = \"\\n\"):\r\n with open(file_name, 'r', encoding=\"utf8\") as f:\r\n carry_forward = \"\"\r\n chunk = \"INITIALIZED\"\r\n while len(chunk) > 0:\r\n chunk = f.read(chunk_size)\r\n augmented_chunk = carry_forward + chunk\r\n lines = augmented_chunk.split(delimiter)\r\n carry_forward = lines.pop()\r\n yield from lines\r\n if carry_forward:\r\n yield carry_forward","def do_root(self, line):\n self.root_directory = line\n if self.source_file:\n self.source_file = self.root_directory + \"/\" + self.source_file\n print(f\"Root directory to read & write files is: {line}\")","def open_files(self, listed_files):\n for file_ in listed_files:\n try: \n with codecs.open(file_, \"r\", encoding=\"utf-8\", errors=\"ignore\") as f:\n yield f\n except Exception as error:\n print(error, file=sys.stderr)\n exit(0)","def file_iterator(file_path):\n with open(file_path, 'r') as f:\n\n for line in f:\n\n #each line is an example\n\n #each example is made of the function name and then a sequence of triplets\n #the triplets are (node, path, node)\n\n example_name, example_body = parse_line(line)\n\n #max length set while preprocessing, make sure none longer\n\n example_length = len(example_body)\n\n assert example_length <= MAX_LENGTH\n \n #need to pad all to maximum length\n\n example_body += [['<pad>', '<pad>', '<pad>']]*(MAX_LENGTH - example_length)\n \n assert len(example_body) == MAX_LENGTH\n\n yield example_name, example_body, example_length","def get_input_contents(self):\n try:\n ret_files = []\n coll = self.collections[self._primary_input_collection]\n ret_file = {'coll_id': coll['coll_id'],\n 'scope': coll['scope'],\n 'name': coll['name'],\n 'bytes': coll.coll_metadata['bytes'],\n 'adler32': None,\n 'min_id': 0,\n 'max_id': coll.coll_metadata['total_files'],\n 'content_type': ContentType.File,\n 'content_metadata': {'total_files': coll['coll_metadata']['total_files']}\n }\n ret_files.append(ret_file)\n return ret_files\n except Exception as ex:\n self.logger.error(ex)\n self.logger.error(traceback.format_exc())\n raise exceptions.IDDSException('%s: %s' % (str(ex), traceback.format_exc()))","def _read_in_chunks(self, file_object, blocksize=4096, chunks=-1,\n shard_index=None):\n i = 0\n while chunks:\n data = file_object.read(blocksize)\n if not data:\n break\n yield data\n i += 1\n\n chunks -= 1","def process(self):\n\n return self._load_next_file()","def pytest_collect_file(\n parent: pytest.Collector, file_path: Optional[\"Path\"]\n) -> Optional[\"Node\"]:\n if file_path and file_path.is_symlink():\n return None\n if file_path and file_path.name == \"molecule.yml\":\n return MoleculeFile.from_parent(path=file_path, parent=parent)\n return None","def read(cls, file, size=BLOCK_SIZE):\n while True:\n data = file.read(size)\n\n if data:\n yield data\n else:\n raise StopIteration","def read_root(self, fid):\r\n lin = self.read_line(fid) \r\n while lin[0] != ':':\r\n parts = lin.split()\r\n if parts[0]=='order':\r\n order = []\r\n for i in range(1, len(parts)):\r\n if parts[i].lower()=='rx':\r\n chan = 'Xrotation'\r\n order.append('x')\r\n elif parts[i].lower()=='ry':\r\n chan = 'Yrotation'\r\n order.append('y')\r\n elif parts[i].lower()=='rz':\r\n chan = 'Zrotation'\r\n order.append('z')\r\n elif parts[i].lower()=='tx':\r\n chan = 'Xposition'\r\n elif parts[i].lower()=='ty':\r\n chan = 'Yposition'\r\n elif parts[i].lower()=='tz':\r\n chan = 'Zposition'\r\n elif parts[i].lower()=='l':\r\n chan = 'length'\r\n self.vertices[0].meta['channels'].append(chan)\r\n # order is reversed compared to bvh\r\n self.vertices[0].meta['order'] = order[::-1]\r\n\r\n elif parts[0]=='axis':\r\n # order is reversed compared to bvh\r\n self.vertices[0].meta['axis_order'] = parts[1][::-1].lower()\r\n elif parts[0]=='position':\r\n self.vertices[0].meta['offset'] = [float(parts[1]),\r\n float(parts[2]),\r\n float(parts[3])]\r\n elif parts[0]=='orientation':\r\n self.vertices[0].meta['orientation'] = [float(parts[1]),\r\n float(parts[2]),\r\n float(parts[3])]\r\n lin = self.read_line(fid)\r\n return lin","def read(self):\n return (yield self.storage)","def files_from_root(root, accept):\n for (dir_path, _dir_names, file_names) in os.walk(root, followlinks=True):\n for file_name in file_names:\n if accept(file_name):\n path = os.path.join(dir_path, file_name)\n yield path","def GetRootFileEntry(self):\n path_spec = compressed_stream_path_spec.CompressedStreamPathSpec(\n compression_method=self._compression_method,\n parent=self._path_spec.parent)\n return self.GetFileEntryByPathSpec(path_spec)","def _read(self, file_path: str) -> Iterator[Instance]:\n with open(cached_path(file_path), \"r\", encoding=\"utf-8\") as data_file:\n for lines in read_conll_file(data_file):\n fields = [line.rstrip().split(self._delimiter) for line in lines]\n # unzipping trick returns tuples, but our Fields need lists\n fields = [list(field) for field in zip(*fields)]\n tokens, tags = fields\n yield self.text_to_instance(tokens, tags)","def _walk_to_root(path):\n if not os.path.exists(path):\n raise IOError('Starting path not found')\n\n if os.path.isfile(path):\n path = os.path.dirname(path)\n\n last_dir = None\n current_dir = os.path.abspath(path)\n while last_dir != current_dir:\n yield current_dir\n parent_dir = os.path.abspath(os.path.join(current_dir, os.path.pardir))\n last_dir, current_dir = current_dir, parent_dir","def read_large_file(file_object):\n\n # Loop until the end of file\n while True:\n\n # Read line from the file\n data = file_object.readline()\n\n # Break if this is the end of the file\n if not data:\n break\n\n # Yield the line of data\n yield data","def open_fast5_files(path, mode=\"r\"):\n for filename in find_fast5_files(path):\n try:\n hdf = Fast5File(filename, mode=mode)\n if sanity_check(hdf):\n yield hdf\n except OSError:\n try:\n hdf.close()\n except:\n pass","def iterate_corpus(corpus_file):\n logging.info(\"Opening corpus file...\")\n with zipfile.ZipFile(corpus_file) as corpus:\n for name in corpus.namelist():\n logging.info(\"Reading contents of file '{}'.\".format(name))\n with corpus.open(name) as f:\n try:\n tree = etree.parse(f)\n root = tree.getroot()\n yield root\n except etree.ParseError:\n message = \"File '{}' could not be read due to ParseError.\"\n logging.warning(message.format(name))\n continue","def __iter__(self):\n\n # Open the data reader\n self.data.open()\n\n starts = np.arange(self.start, self.stop, self.chunksize)\n for a, b in zip_longest(starts, starts[1:], fillvalue=self.stop):\n yield self.data.read(a, b, **self.kwargs)","def read_large_file(file_object):\n\n # Loop indefinitely until the end of the file\n while True:\n\n # Read a line from the file: data\n data = file_object.readline()\n\n # Break if this is the end of the file\n if not data:\n break\n\n # Yield the line of data\n yield data","def raw_feature_iterator(file_paths):\n for path in file_paths:\n with open(path, \"r\") as fin:\n for line in fin:\n yield line","def raw_feature_iterator(file_paths):\n for path in file_paths:\n with open(path, \"r\") as fin:\n for line in fin:\n yield line","def Read(self, file_object):\n try:\n self.root_key = plistlib.load(file_object)\n\n except plistlib.InvalidFileException as exception:\n raise IOError(exception)","def generate():\n with open(remote_path, \"rb\") as f:\n for chunk in iter(lambda: f.read(buffer_size), b''):\n yield chunk","def _walk_dir(self, rootpath):\n assert os.path.isabs(rootpath)\n assert rootpath not in self._dirs\n relpath = self._get_rel_path(rootpath)\n self._dirs[relpath] = Directory(rootpath, relpath, None)\n for dirpath, dirnames, filenames in os.walk(rootpath):\n if 'refdata' in dirnames:\n dirnames.remove('refdata')\n currentdir = self._dirs[self._get_rel_path(dirpath)]\n # Loop through a copy so that we can modify dirnames.\n for dirname in list(dirnames):\n fullpath = os.path.join(dirpath, dirname)\n if fullpath == self._build_root:\n dirnames.remove(dirname)\n continue\n relpath = self._get_rel_path(fullpath)\n self._dirs[relpath] = Directory(fullpath, relpath, currentdir)\n extensions = ('.h', '.cuh', '.hpp', '.c', '.cc', '.cpp', '.cu', '.bm')\n for filename in filenames:\n basename, extension = os.path.splitext(filename)\n if extension in extensions:\n fullpath = os.path.join(dirpath, filename)\n relpath = self._get_rel_path(fullpath)\n self._files[relpath] = File(fullpath, relpath, currentdir)\n elif extension == '.cmakein':\n extension = os.path.splitext(basename)[1]\n if extension in extensions:\n fullpath = os.path.join(dirpath, filename)\n relpath = self._get_rel_path(fullpath)\n sourcefile = GeneratorSourceFile(fullpath, relpath, currentdir)\n self._files[relpath] = sourcefile\n fullpath = os.path.join(dirpath, basename)\n relpath = self._get_rel_path(fullpath)\n fullpath = os.path.join(self._build_root, relpath)\n generatedfile = GeneratedFile(fullpath, relpath, currentdir)\n self._files[relpath] = generatedfile\n generatedfile.set_generator_source(sourcefile)\n elif extension in ('.l', '.y', '.pre'):\n fullpath = os.path.join(dirpath, filename)\n relpath = self._get_rel_path(fullpath)\n self._files[relpath] = GeneratorSourceFile(fullpath, relpath, currentdir)","def _yield_terraform_source(material, source_path=None):\n module_root = get_storage_path()\n source_path = source_path or get_source_path()\n if material:\n extract_binary_tf_data(module_root, material, source_path)\n ctx.logger.debug(\n 'The storage root tree:\\n{}'.format(tree(module_root)))\n path_to_init_dir = get_node_instance_dir(source_path=source_path)\n try:\n ctx.logger.debug('_yield_terraform_source {}'.format(path_to_init_dir))\n yield path_to_init_dir\n except Exception as e:\n _, _, tb = sys.exc_info()\n cause = exception_to_error_cause(e, tb)\n ctx.logger.error(str(cause))\n raise e\n finally:\n if v1_gteq_v2(get_cloudify_version(), \"6.0.0\"):\n ctx.logger.debug('Not storing zip in runtime properties in '\n 'Cloudify 6.0.0 and greater')\n else:\n store_binary_material(module_root)\n ctx.instance.runtime_properties['previous_tf_state_file'] = \\\n get_terraform_state_file(path_to_init_dir)","def files_and_folders(self, depth):\n for directory in self.rarc._directories[self.directory_index:][:self.directory_count]:\n yield depth, directory\n if isinstance(directory, Folder):\n if directory.data_offset < len(self.rarc._nodes):\n node = self.rarc._nodes[directory.data_offset]\n if directory.name == \".\" or directory.name == \"..\":\n continue\n yield from node.files_and_folders(depth + 1)","def walk(self): # DirObj.walk\n for name, subdir in self.subdirs.iteritems():\n for e in subdir.walk():\n yield e\n for name, fileEntry in self.files.iteritems():\n yield fileEntry\n yield self","def _collect_entries(rootdir: str, basedir: str):\n\n files = []\n dirs = []\n\n for entry in os.listdir(os.path.join(rootdir, basedir)):\n rel_path = os.path.join(basedir, entry)\n full_path = os.path.join(rootdir, rel_path)\n isdir = os.path.isdir(full_path)\n if isdir and (rel_path in ('./.git', './.pytest_cache') or entry == '__pycache__'):\n continue\n\n st = os.stat(full_path, follow_symlinks=False)\n\n (dirs if isdir else files).append((rel_path, dict(isdir=isdir, path=rel_path, size=(0 if isdir else st.st_size),\n mode=st.st_mode, omode=f'{st.st_mode:04o}',\n mtime=int(st.st_mtime))))\n\n for rel_path, entry in sorted(dirs):\n yield entry\n yield from _collect_entries(rootdir, rel_path)\n\n for _, entry in sorted(files):\n yield entry","def retrieve(self, path):\n if len(path) == 0:\n for leaf in self.leaves:\n yield leaf\n else:\n next_index = path[0]\n if self._is_variable(next_index):\n for leaf in self._retrieve_variable(path[1:]):\n yield leaf\n else:\n next_disc_tree = self._child_at_index(next_index)\n if next_disc_tree is None:\n return\n else:\n for leaf in next_disc_tree.retrieve(path[1:]):\n yield leaf","def temporary(self, path):\r\n if path is None:\r\n raise ValueError('Can only temporarily establish a build root given a path.')\r\n prior = self._root_dir\r\n self._root_dir = path\r\n try:\r\n yield\r\n finally:\r\n self._root_dir = prior","def is_input_file(self):\r\n return self.depth == 0","def _Open(self, mode='rb'):\n if not self._path_spec.HasParent():\n raise errors.PathSpecError(\n 'Unsupported path specification without parent.')\n\n file_object = resolver.Resolver.OpenFileObject(\n self._path_spec.parent, resolver_context=self._resolver_context)\n\n fsfat_volume = pyfsfat.volume()\n fsfat_volume.open_file_object(file_object)\n fsfat_root_directory = fsfat_volume.get_root_directory()\n\n self._file_object = file_object\n self._fsfat_volume = fsfat_volume\n self._root_directory_identifier = fsfat_root_directory.identifier"],"string":"[\n \"def files_and_folders(self):\\n yield from self._root.files_and_folders(0)\",\n \"def files_from_roots(roots, accept):\\n for root in roots:\\n if root is not None:\\n yield from files_from_root(root, accept)\",\n \"def open_test_root_fs() -> FS:\\n cwd = os.path.dirname(__file__)\\n return read_only(open_fs('data', cwd=cwd))\",\n \"def reading(self):\\n if (\\n self.reading_handle is None\\n and self.writing_handle is None\\n and self.filename is not None\\n ):\\n with self._open() as fp:\\n self.reading_handle = fp\\n try:\\n yield\\n finally:\\n self.reading_handle = None\\n else:\\n yield\",\n \"def iter_tree(root):\\n\\tfor file_rel in _iter_tree_next(os.path.abspath(root), '', {}):\\n\\t\\tyield file_rel\",\n \"def open_file(self) -> Iterator[NamedIO]:\\n with open(self.filename) as f:\\n yield cast(NamedIO, f)\",\n \"def read_data(self, path, **kwargs):\\n\\n from glob import glob\\n import os\\n sc = self.sc\\n pdt_lc = np.dtype([('pos', 'f4', 3),('vel', 'f4', 3)])\\n\\n blockids = kwargs['blockids']\\n\\n def set_particle_IDs_partition(index, iterator): \\n \\\"\\\"\\\"\\n Use the aggregate partition counts to set monotonically increasing \\n particle indices\\n \\\"\\\"\\\"\\n p_counts = partition_counts.value\\n local_index = 0\\n start_index = sum([p_counts[i] for i in range(index)])\\n for arr in iterator:\\n arr['iOrder'] = range(start_index + local_index, start_index + local_index + len(arr))\\n arr['iGroup'] = loc_to_glob_map_b.value[index]\\n local_index += len(arr)\\n yield arr\\n \\n def read_file(index, i, chunksize=102400): \\n for part,filename in i:\\n timein = time.time()\\n with open(filename,'rb') as f: \\n header = f.read(62500)\\n while True:\\n chunk = f.read(chunksize*24)\\n if len(chunk): \\n p_arr = np.frombuffer(chunk, pdt_lc)\\n new_arr = np.zeros(len(p_arr), dtype=pdt)\\n new_arr['pos'] = p_arr['pos']\\n yield new_arr\\n else: \\n t_elapsed = time.time()-timein\\n rate = os.path.getsize(filename)/1e6/t_elapsed\\n print 'spark_fof: reading %s took %d seconds in partition %d, %f MB/sec'%(filename, t_elapsed, index, rate)\\n break\\n \\n # determine which files to read\\n get_block_ids = re.compile('blk\\\\.(\\\\d+)\\\\.(\\\\d+)\\\\.(\\\\d+)?')\\n\\n if blockids is None: \\n files = glob(os.path.join(self.path,'*/*'))\\n else: \\n files = []\\n for dirname, subdirlist, filelist in os.walk(path):\\n try: \\n dirnum = int(os.path.basename(dirname))\\n if dirnum in blockids: \\n for f in filelist:\\n ids = get_block_ids.findall(f)\\n if len(ids) > 0:\\n if all(int(x) in blockids for x in ids[0]):\\n files.append(os.path.join(dirname,f))\\n except ValueError: \\n pass\\n\\n files.sort()\\n nfiles = len(files) \\n self.nPartitions = nfiles\\n\\n print 'spark_fof: Number of input files: ', nfiles\\n\\n # get particle counts per partition\\n nparts = {i:_get_nparts(filename,62500,pdt_lc.itemsize) for i,filename in enumerate(files)}\\n\\n print 'spark_fof: Total number of particles: ', np.array(nparts.values()).sum()\\n \\n # set up the map from x,y,z to partition id \\n ids = map(lambda x: tuple(map(int, get_block_ids.findall(x)[0])), files)\\n ids_map = {x:i for i,x in enumerate(ids)}\\n self.ids_map = ids_map\\n loc_to_glob_map_b = self.local_to_global_map\\n \\n ids_map_b = sc.broadcast(ids_map)\\n loc_to_glob_map_b = sc.broadcast(loc_to_glob_map_b)\\n\\n partition_counts = sc.broadcast(nparts)\\n\\n rec_rdd = (sc.parallelize(zip(ids,files), numSlices=self.nPartitions)\\n .map(lambda (id,filename): (ids_map_b.value[id],filename))\\n .partitionBy(self.nPartitions).cache()\\n .mapPartitionsWithIndex(read_file, preservesPartitioning=True)\\n .mapPartitionsWithIndex(set_particle_IDs_partition, \\n preservesPartitioning=True))\\n \\n return rec_rdd\",\n \"def ReadFilesGenerator(self):\\n\\n for file in self._file_names:\\n file_list = []\\n\\n # TODO see further into yielding one line at a time\\n with open(file, 'r', encoding='mbcs') as sped:\\n file_list = sped.read().splitlines()\\n\\n if not self.isSigned(file_list):\\n file_list = self.stripSignature(file_list)\\n\\n yield file, file_list\",\n \"def input_data(self):\\n return read_file(self.file_path)\",\n \"def walk(self):\\n if os.path.exists(self.folder):\\n for root_path, _, f_files in os.walk(self.folder):\\n yield root_path, f_files\\n if not self.recursive:\\n break\\n else:\\n print(f\\\"[!e] Passed folder doesn't exist. Path: {self.folder}\\\",\\n file=sys.stdout)\\n exit(0)\",\n \"def walk(self):\\n for _root, _dirs, files in os.walk(self.root):\\n for filename in files:\\n if self.is_key(filename):\\n yield filename\",\n \"def read_inputs(self):\\n curdir = os.getcwd()\\n os.chdir(self.fst_dir)\\n rstat = self.readFST()\\n if rstat == 0:\\n os.chdir(curdir)\\n return 0\\n # the names of the next files are either set by caller or come from the reading the FAST file\\n rstat = self.readNoise()\\n rstat = self.readAD()\\n rstat = self.readBlade()\\n rstat = self.readPtfm()\\n os.chdir(curdir)\",\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 _read_input_file(self):\\n pass\",\n \"def open(self, path: str, **kwargs) -> Iterator[OpenFileType]:\\n full_path = self._full_path(path)\\n logger.debug(f\\\"entering fs.open context manager for {full_path}\\\")\\n of = self.fs.open(full_path, **kwargs)\\n logger.debug(f\\\"FSSpecTarget.open yielding {of}\\\")\\n yield of\\n logger.debug(\\\"FSSpecTarget.open yielded\\\")\\n of.close()\",\n \"def open(self, relative_path, flags):\\n file_obj = open(self._get_full_path(relative_path), flags)\\n yield file_obj\\n file_obj.close()\",\n \"def walk(self): # FileObj.walk\\n yield self\",\n \"def edit_root(self) -> Generator[Root, None, None]:\\n with self.edit(Root.type) as root:\\n if not isinstance(root, Root):\\n raise RuntimeError(\\\"Unexpected root type\\\")\\n yield root\",\n \"def Init(self):\\n # First iteration over all the files in root searching for symlinks and\\n # non-regular files.\\n seen_inodes = {}\\n for basepath, _, filenames in sorted(os.walk(self._root)):\\n for filename in sorted(filenames):\\n full_path = os.path.join(basepath, filename)\\n rel_path = full_path[len(self._root):]\\n st = os.lstat(full_path)\\n\\n file_data = {\\n 'size': st.st_size,\\n }\\n self._files[rel_path] = file_data\\n\\n # Track symlinks.\\n if stat.S_ISLNK(st.st_mode):\\n link_path = os.readlink(full_path)\\n # lddtree's normpath handles a little more cases than the os.path\\n # version. In particular, it handles the '//' case.\\n self._symlinks[rel_path] = (\\n link_path.lstrip('/') if link_path and link_path[0] == '/' else\\n lddtree.normpath(os.path.join(os.path.dirname(rel_path),\\n link_path)))\\n file_data['deps'] = {\\n 'symlink': [self._symlinks[rel_path]]\\n }\\n\\n # Track hardlinks.\\n if st.st_ino in seen_inodes:\\n self._hardlinks[rel_path] = seen_inodes[st.st_ino]\\n continue\\n seen_inodes[st.st_ino] = rel_path\",\n \"def _GetSubFileEntries(self):\\n if self._directory is None:\\n self._directory = self._GetDirectory()\\n\\n if self._directory:\\n for path_spec in self._directory.entries:\\n yield HFSFileEntry(self._resolver_context, self._file_system, path_spec)\",\n \"def lazy_read_file(self):\\n store = zarr.DirectoryStore(self.fpath)\\n z_array = zarr.open(store=store, mode='r')\\n self.da_input = da.from_array(z_array)\\n self.data = self.da_input\\n self.data_dim = self.data.shape\\n self.chunk_size = z_array.chunks\",\n \"def __iter__(self):\\n # Wait to open file until needed. Automatically close file when done.\\n with open(self.obo_file) as fstream:\\n rec_curr = None # Stores current GO Term\\n typedef_curr = None # Stores current typedef\\n for line in fstream:\\n # obo lines start with any of: [Term], [Typedef], /^\\\\S+:/, or /^\\\\s*/\\n if self.data_version is None:\\n self._init_obo_version(line)\\n if rec_curr is None and line[0:6].lower() == \\\"[term]\\\":\\n rec_curr = GOTerm()\\n if self.optobj:\\n self.optobj.init_datamembers(rec_curr)\\n elif typedef_curr is None and line[0:9].lower() == \\\"[typedef]\\\":\\n typedef_curr = TypeDef()\\n elif rec_curr is not None or typedef_curr is not None:\\n line = line.rstrip() # chomp\\n if line:\\n self._add_to_obj(rec_curr, typedef_curr, line)\\n else:\\n if rec_curr is not None:\\n yield rec_curr\\n rec_curr = None\\n elif typedef_curr is not None:\\n # Save typedef.\\n self.typedefs[typedef_curr.id] = typedef_curr\\n typedef_curr = None\\n # Return last record, if necessary\\n if rec_curr is not None:\\n yield rec_curr\",\n \"def path_tuples(self):\\n # We want the following contents in the tarball\\n # Metaflow package itself\\n for path_tuple in self._walk(self.metaflow_root, exclude_hidden=False):\\n yield path_tuple\\n # the package folders for environment\\n for path_tuple in self.environment.add_to_package():\\n yield path_tuple\\n # the user's working directory\\n flowdir = os.path.dirname(os.path.abspath(sys.argv[0])) + '/'\\n for path_tuple in self._walk(flowdir):\\n yield path_tuple\",\n \"def _GetSubFileEntries(self):\\n if self._directory is None:\\n self._directory = self._GetDirectory()\\n\\n if self._directory:\\n for path_spec in self._directory.entries:\\n yield NTFSFileEntry(\\n self._resolver_context, self._file_system, path_spec)\",\n \"async def stream_result_files(self) -> AsyncGenerator[StreamInfoUrl, None]:\\n for dataset in self.datasets:\\n async for file in dataset.get_data_rootfiles_stream(self.query.value()):\\n yield file\",\n \"def read_input(file_input):\\n login = file_input.readline().strip()\\n n, max_batch_size = map(int, file_input.readline().split())\\n\\n tree = Tree(n)\\n for _ in range(n - 1):\\n path = file_input.readline().strip()\\n\\n child_type = RecordType.Record\\n if path[-1] == '/':\\n child_type = RecordType.Folder\\n path = path[:-1]\\n\\n path_tokens = path.split('/')\\n parent_path, child_name = '/'.join(path_tokens[:-1]), path_tokens[-1]\\n if parent_path == '':\\n parent_path = '/'\\n\\n tree.add_edge(parent_path, child_name, child_type)\\n tree.calculate_sizes()\\n\\n return login, max_batch_size, tree\",\n \"def __iter__(self):\\n # pylint: disable=no-member\\n with builtins.open(self.path,\\n mode=self.mode,\\n buffering=self.buffering,\\n encoding=self.encoding,\\n errors=self.errors,\\n newline=self.newline) as file_content:\\n for line in file_content:\\n yield line\",\n \"def iter_documents(top_directory):\\n for root, dirs, files in os.walk(top_directory):\\n for file in filter(lambda file: file.endswith('.txt'), files):\\n document = open(os.path.join(root, file)).read() # read the entire document, as one big string\\n yield utils.tokenize(document, lower=True) # or whatever tokenization suits you\",\n \"def __iter__(self):\\n for f in self.path.glob('**/*'):\\n if f.is_file() and not os.stat(str(f.resolve())).st_size == 0:\\n yield Resource(str(f.resolve()), DiskCrawler.compute_digest)\",\n \"def assets(self):\\n # N.B.:\\n # we don't cache the results of the {root} exploration so we can always\\n # return a result that reflects the current contents of the filesystem\\n yield from self._explore()\\n # all done\\n return\",\n \"def __init__(self, root_dir, file_dir, fig_name, n_files=None, **kwargs):\\n self.reader = FileReader(root_dir, sub_dirs=[file_dir,], num_files=[n_files,], **kwargs)\\n self.fig_name = fig_name\\n self.out_dir = '{:s}/{:s}/'.format(root_dir, fig_name)\\n if self.reader.comm.rank == 0 and not os.path.exists('{:s}'.format(self.out_dir)):\\n os.mkdir('{:s}'.format(self.out_dir))\\n self.my_sync = Sync(self.reader.comm)\\n\\n self.files = self.reader.local_file_lists[file_dir]\\n self.idle = self.reader.idle[file_dir]\\n self.dist_comm = self.reader.distribution_comms[file_dir]\",\n \"def _GetSubFileEntries(self):\\n if self._directory is None:\\n self._directory = self._GetDirectory()\\n\\n if self._directory:\\n for path_spec in self._directory.entries:\\n yield APMFileEntry(self._resolver_context, self._file_system, path_spec)\",\n \"def collect(self):\\n while self.proc is not None:\\n self.read()\\n if not len(self.datalines):\\n return\\n while len(self.datalines):\\n # pop the first node of list\\n yield self.datalines.pop(0)\",\n \"def reader(self):\\n @contextmanager\\n def generator(data):\\n \\\"\\\"\\\"\\n Args:\\n data (str): could be a filename or the text to tokenize.\\n Returns:\\n a context manager that can be used in a `with` contruct,\\n yielding each line of the tokenized `data`.\\n \\\"\\\"\\\"\\n if not os.path.exists(data):\\n yield self.format(self.predict(data))\\n else:\\n with open(data) as f:\\n yield self.format(self.predict(f.read()))\\n return generator\",\n \"def __iter__(self):\\n with open(self.inputfile) as fin:\\n for line in fin:\\n arr = line.strip().split()\\n self.path = arr[0]\\n self.y = int(arr[1])\\n if len(arr) > 2:\\n self.feats = map(float, arr[2:])\\n yield self.path, self.y, self.feats\\n else:\\n yield self.path, self.y\",\n \"def get_files(self, block):\\n \\n raise NotImplementedError('get_files')\",\n \"def file_package_iter(self):\\n files = list()\\n futures = list()\\n\\n amount = 0\\n for file in self.file_iterator:\\n if amount + self._estimate_file_size(file) > self.max_size:\\n if len(files) == 0: # This file is too large for one archive, special handling\\n self.pool.wait(futures)\\n self._calculate_hash(file)\\n yield self._finish_info_package([file])\\n continue\\n\\n self.pool.wait(futures)\\n yield self._finish_info_package(files)\\n\\n files = list()\\n amount = 0\\n\\n amount += file.size\\n files.append(file)\\n futures.append(self.pool.add_task(self._calculate_hash, file)) # todo calc small files in-thread?\\n\\n if len(files) > 0:\\n yield self._finish_info_package(files)\",\n \"def read( self, filename ):\\n conf_file = cfg.ConfigFile(filename)\\n self.root_node = conf_file.root_node\",\n \"def inputSet_read(path, data):\\n nonlocal filesRead\\n nonlocal index\\n nonlocal d_tree\\n nonlocal fn_inputReadCallback\\n\\n d_read = fn_inputReadCallback((path, data), **kwargs)\\n\\n if 'status' in d_read.keys():\\n d_tree[path] = d_read\\n if 'filesRead' in d_read.keys():\\n filesRead += d_read['filesRead']\\n else:\\n self.dp.qprint(\\n \\\"The inputReadCallback callback did not return a 'status' value!\\\",\\n comms = 'error',\\n level = 0\\n )\\n error.fatal(self, 'inputReadCallback', drawBox = True)\\n return d_read\",\n \"def Iterator (self, block = None):\\n if self.Disposed:\\n raise RuntimeError ('Core is disposed')\\n\\n block = True if block is None else block\\n top_level = False\\n try:\\n # Thread identity is used by Notify to make sure call to notifier is\\n # really needed. And it also used to make sure core is iterating only\\n # on one thread.\\n if self.thread_ident is None:\\n self.thread_ident = get_ident ()\\n top_level = True # top level iterator (no other iterators)\\n elif self.thread_ident != get_ident ():\\n raise ValueError ('Core is already being run on a different thread')\\n\\n timer = self.timer\\n files = self.files\\n context = self.context\\n\\n events = tuple ()\\n while True:\\n # resolve await objects\\n for fd, event in events:\\n files [fd].Resolve (event)\\n context.Resolve ()\\n timer.Resolve ()\\n\\n # Yield control to check conditions before blocking (Core has been\\n # stopped or desired future resolved). If there is no file\\n # descriptors registered and timeout is negative poller will raise\\n # StopIteration and break this loop.\\n yield\\n\\n events = self.poller.Poll (0) if not block else \\\\\\n self.poller.Poll (min (timer.Timeout (), context.Timeout ()))\\n\\n finally:\\n if top_level:\\n self.thread_ident = None\",\n \"def filectxs(self):\\n mf = self.manifest()\\n m = mf.keys()\\n m.sort()\\n for f in m:\\n yield self.filectx(f, fileid=mf[f])\",\n \"def testing_context(require_enzoe_inputdir = True):\\n\\n path = 'input'\\n\\n cleanup = False\\n if os.path.isfile(path): # path is allowed to be a symlink to a dir\\n raise RuntimeError('./' + path + ' is a path to a file.')\\n elif os.path.isdir(path): # path is allowed to be a symlink to a dir\\n realpath = os.path.abspath(os.path.realpath(path))\\n expected = os.path.abspath(os.path.join(_ENZOE_ROOT_DIR, 'input'))\\n if require_enzoe_inputdir and (realpath != expected):\\n raise RuntimeError('./' + path + \\\" doesn't refer to \\\" + expected)\\n elif os.path.islink(path):\\n raise RuntimeError('./' + path + ' is a broken link.')\\n else: # make a symlink to {_ENZOE_ROOT_DIR}/input\\n cleanup = True\\n os.symlink(src = os.path.join(_ENZOE_ROOT_DIR, path),\\n dst = path, target_is_directory = True)\\n\\n try:\\n yield None\\n finally:\\n if cleanup:\\n os.unlink(path)\",\n \"def __init__(self, file_root):\\n self.root = file_root\",\n \"def _iter_from_disk(self):\\n self.f.seek(0, 0) # relative to start\\n for line in self.f:\\n yield line\\n self.f.seek(0, 2) # relative to end\",\n \"def root_dir():\\n assert root is not None\\n\\n old = _os.getcwd()\\n try:\\n _os.chdir(root)\\n yield root\\n finally:\\n _os.chdir(old)\",\n \"def flow_from_files(self, filenames=None, batch_size=32):\\n\\n if filenames:\\n self.filenames = filenames\\n\\n for i in range(0, len(self.filenames), batch_size):\\n yield np.concatenate([np.load(self.path / f) \\\\\\n for f in self.filenames.iloc[i:i+batch_size]])\",\n \"def run(filenames, root_dir):\\n for filename in filenames:\\n with open(filename, encoding='utf-8') as file:\\n filename = filename.replace('\\\\\\\\', '/')[len(root_dir)::]\\n yield from check(filename, file.readlines())\",\n \"def _read_blocks(input_data, size=2**20):\\n\\n if isinstance(input_data, (BufferedReader, BytesIO)):\\n f = input_data\\n opened = False\\n elif input_data == '-':\\n f = sys.stdin.buffer # read binary instead of unicode\\n opened = False\\n else:\\n f = open(input_data, 'rb')\\n opened = True\\n\\n try:\\n\\n data = f.read(size)\\n while len(data) > 0:\\n yield data\\n data = f.read(size)\\n finally:\\n if opened:\\n f.close()\",\n \"def _read(self, path: str):\\n num_samples, length = [int(x) for x in path.split(\\\":\\\")]\\n random.seed(self.seed)\\n for _ in range(num_samples):\\n tokens, tags = self._sample(length)\\n yield self.text_to_instance(tokens, tags)\",\n \"def dep_sample_generator(path_to_file):\\n assert os.path.isfile(path_to_file), \\\"File does not exist\\\"\\n root = DepSample(0, ROOT, ROOT, 0)\\n with open(path_to_file) as fp:\\n sample = [root]\\n for line in fp:\\n if not line.rstrip():\\n yield sample\\n sample = [root]\\n else:\\n ls = line.rstrip().split('\\\\t')\\n # print(ls)\\n sample.append(DepSample(int(ls[0]), ls[1], ls[3], int(ls[6])))\\n if len(sample) > 1:\\n yield sample\",\n \"def parseLowLevel(self):\\n file_iter = iter(self.file_object)\\n while True:\\n line = next(file_iter)\\n self.line_number += 1\\n\\n ## skip comments\\n if line.startswith(\\\"#\\\"):\\n continue\\n\\n ## semi-colon multi-line strings\\n if line.startswith(\\\";\\\"):\\n lmerge = [line[1:]]\\n while True:\\n line = next(file_iter)\\n self.line_number += 1\\n if line.startswith(\\\";\\\"):\\n break\\n lmerge.append(line)\\n\\n lmerge[-1] = lmerge[-1].rstrip()\\n yield (DATA_VALUE, \\\"\\\".join(lmerge))\\n continue\\n\\n ## split line into tokens\\n for match in _token_regexp.finditer(line):\\n label1, label2, string, token = match.groups()\\n if label1 is not None and label2 is not None:\\n yield DATA_LABEL, (label1, label2)\\n elif string is not None:\\n yield DATA_VALUE, string\\n elif token is not None:\\n token_parts = token.split('_')\\n if len(token_parts) == 1 \\\\\\n or token_parts[0].lower() not in (\\\"data\\\", \\\"loop\\\",\\n \\\"global\\\", \\\"save\\\",\\n \\\"stop\\\"):\\n yield DATA_VALUE, token\\n else:\\n yield KEYWORD, (token_parts[0].lower(),\\n '_'.join(token_parts[1:]))\",\n \"def read_all_trees(self, **kwargs):\\n return self.read_single_tree(-1, halonum=None, **kwargs)\",\n \"def __iter__(self):\\r\\n example = []\\r\\n for line in open(self.fullpath):\\r\\n if line != '\\\\n':\\r\\n example.append(line.rstrip()) # remove newline\\r\\n else:\\r\\n yield example\\r\\n example = []\",\n \"def __iter__(self):\\n with open(self.fn + \\\".fai\\\") as fai:\\n for toks in (l.rstrip(\\\"\\\\r\\\\n\\\").split(\\\"\\\\t\\\") for l in fai):\\n yield toks[0], int(toks[1])\",\n \"def read(self, input): \\n \\n # 1. Assume we have the name of a file in the execution dir or a \\n # file-like object. \\n # Note we need to add parsing of the path to dir/filename \\n try: \\n with File.as_handle(input, 'rU') as fp: \\n file_contents = fp.read() \\n self.filename = getattr(fp, 'name', 'Unknown_nexus_file') \\n except (TypeError, IOError, AttributeError): \\n #2 Assume we have a string from a fh.read() \\n if isinstance(input, basestring): \\n file_contents = input \\n self.filename = 'input_string' \\n else: \\n print(input.strip()[:50]) \\n raise NexusError('Unrecognized input: %s ...' % input[:100]) \\n file_contents = file_contents.strip() \\n if file_contents.startswith('#NEXUS'): \\n file_contents = file_contents[6:] \\n commandlines = _get_command_lines(file_contents) \\n # get rid of stupid 'NEXUS token - in merged treefiles, this might appear multiple times' \\n for i, cl in enumerate(commandlines): \\n try: \\n if cl[:6].upper() == '#NEXUS': \\n commandlines[i] = cl[6:].strip() \\n except: \\n pass \\n # now loop through blocks (we parse only data in known blocks, thus ignoring non-block commands \\n nexus_block_gen = self._get_nexus_block(commandlines) \\n while True: \\n try: \\n title, contents = next(nexus_block_gen) \\n except StopIteration: \\n break \\n if title in KNOWN_NEXUS_BLOCKS: \\n self._parse_nexus_block(title, contents) \\n else: \\n self._unknown_nexus_block(title, contents)\",\n \"def read_project(root_path, verbose=False, ignore=None, encoding=None):\\n nodes = {}\\n root_node = None\\n errors = False\\n ignore_files = set([\\\".hg\\\", \\\".svn\\\", \\\".git\\\", \\\".tox\\\", \\\"__pycache__\\\", \\\"env\\\", \\\"venv\\\"]) # python 2.6 comp\\n\\n if ignore:\\n for ignored_file in ignore:\\n ignore_files.add(os.path.basename(os.path.realpath(ignored_file)))\\n\\n # traverse root directory, and list directories as dirs and files as files\\n for root, dirs, files in os.walk(root_path):\\n\\n dirs[:] = [d for d in dirs if d not in ignore_files]\\n\\n files = [fn for fn in files if os.path.splitext(fn)[1] == \\\".py\\\" and fn not in ignore_files]\\n\\n for file_name in files:\\n full_path = os.path.join(root, file_name)\\n with open_func(full_path, \\\"r\\\", encoding=encoding) as f:\\n try:\\n # fails on empty files\\n file_data = f.read()\\n lines = file_data.splitlines()\\n tree = ast.parse(file_data)\\n if verbose:\\n click.echo(crayons.yellow('Trying to parse file: {}'.format(full_path)))\\n\\n if full_path in nodes:\\n node = nodes[full_path]\\n else:\\n node = Node(file_name[:-3], full_path=full_path)\\n nodes[full_path] = node\\n\\n if not root_node:\\n root_node = node\\n\\n for ast_node in ast.walk(tree):\\n if isinstance(ast_node, ast.Import) and ast_node.names:\\n for subnode in ast_node.names:\\n if not subnode.name:\\n continue\\n\\n path_to_module = get_path_from_package_name(\\n root_path, subnode.name)\\n\\n if path_to_module in nodes:\\n new_node = nodes[path_to_module]\\n else:\\n new_node = Node(\\n subnode.name, full_path=path_to_module)\\n nodes[path_to_module] = new_node\\n\\n new_node.is_imported_from[full_path].append(ast_node.lineno)\\n node.add(new_node)\\n\\n elif isinstance(ast_node, ast.ImportFrom) and ast_node.module:\\n current_path = root_path\\n if 0 <= ast_node.lineno - 1 < len(lines) and\\\\\\n REGEX_RELATIVE_PATTERN.findall(lines[ast_node.lineno - 1]):\\n current_path = root\\n\\n path_to_module = get_path_from_package_name(\\n current_path, ast_node.module)\\n\\n if path_to_module in nodes:\\n new_node = nodes[path_to_module]\\n else:\\n new_node = Node(\\n ast_node.module, full_path=path_to_module)\\n nodes[path_to_module] = new_node\\n\\n for obj_import in ast_node.names:\\n if ast_node.lineno not in node.func_imports:\\n node.func_imports[ast_node.lineno] = [obj_import.name]\\n else:\\n node.func_imports[ast_node.lineno].append(obj_import.name)\\n\\n new_node.is_imported_from[full_path].append(ast_node.lineno)\\n node.add(new_node)\\n elif isinstance(ast_node, (ast.ClassDef, ast.FunctionDef)):\\n node.func_defs[ast_node.name] = ast_node.lineno\\n\\n except Exception as e:\\n errors = True\\n click.echo(crayons.yellow('Parsing of file failed: {}'.format(full_path)))\\n if verbose:\\n click.echo(crayons.red(traceback.format_exc(e)))\\n\\n if errors:\\n click.echo(crayons.red('There were errors during the operation, perhaps you are trying to parse python 3 project, '\\n 'with python 2 version of the script? (or vice versa)'))\\n return root_node\",\n \"def __iter__(self):\\n for p in self.paths:\\n yield Document.load(os.path.join(self.dirpath, p), fmt=self.fmt)\",\n \"def fileStreamer(self, filePath):\\n\\t\\tfor line in open(filePath):\\n\\t\\t\\tyield line\",\n \"def readfast(self, name=\\\"\\\", *args, **kwargs):\\n\\n assert _os.path.isfile(self.__str__()) == True\\n\\n with open(self.__str__(), *args, **kwargs) as file_handler:\\n for line in file_handler:\\n yield line\",\n \"def read(self, outstream: typ.BinaryIO):\\r\\n file_metadata = self.metadata.get_file(\\\"0\\\")['metadata']\\r\\n if self.fs_type == 'FAT':\\r\\n allocator_metadata = FATAllocatorMeta(file_metadata)\\r\\n self.fs.read(outstream, allocator_metadata)\\r\\n elif self.fs_type == 'NTFS':\\r\\n allocator_metadata = NTFSAllocatorMeta(file_metadata)\\r\\n self.fs.read(outstream, allocator_metadata)\\r\\n else:\\r\\n raise NotImplementedError()\",\n \"def files(self) -> Iterator[List[Path]]:\\n\\n files: List[Path] = []\\n fragments: List[str] = []\\n data: Dict[str, Any] = {}\\n\\n inst: Optional[\\\"CLIInstance\\\"] = self\\n while inst is not None:\\n for k, v in inst._data.items():\\n if isinstance(v, UnknownExpression) or k in data:\\n fragments.append(f\\\"{k} = {v};\\\\n\\\")\\n elif isinstance(v, EnumMeta):\\n fragments.append(\\n f\\\"{k} = {{{', '.join([i for i in v.__members__])}}};\\\\n\\\"\\n )\\n else:\\n data[k] = v\\n fragments.extend(inst._code_fragments)\\n files.extend(inst._includes)\\n\\n inst = inst._parent\\n\\n gen_files = []\\n try:\\n if len(data) > 0:\\n file = tempfile.NamedTemporaryFile(\\n prefix=\\\"mzn_data\\\", suffix=\\\".json\\\", delete=False\\n )\\n gen_files.append(file)\\n file.write(json.dumps(data, cls=MZNJSONEncoder).encode())\\n file.close()\\n files.append(Path(file.name))\\n if len(fragments) > 0 or len(files) == 0:\\n file = tempfile.NamedTemporaryFile(\\n prefix=\\\"mzn_fragment\\\", suffix=\\\".mzn\\\", delete=False\\n )\\n gen_files.append(file)\\n for code in fragments:\\n file.write(code.encode())\\n file.close()\\n files.append(Path(file.name))\\n yield files\\n finally:\\n for file in gen_files:\\n os.remove(file.name)\",\n \"def iter(self):\\n for _file in self._files:\\n with asHandle(_file) as fp:\\n # Use FastqGeneralIterator because it provides access to\\n # the unconverted quality string (i.e., it doesn't try to\\n # figure out the numeric quality values, which we don't\\n # care about at this point).\\n for sequenceId, sequence, quality in FastqGeneralIterator(fp):\\n yield self.readClass(sequenceId, sequence, quality)\",\n \"def fileobjects_iter(imagefile=None,xmlfile=None,fiwalk=\\\"fiwalk\\\",flags=0):\\n def local_iter(fi):\\n yield fi\\n fiwalk_using_sax(imagefile=imagefile,xmlfile=xmlfile,fiwalk=fiwalk,flags=flags,\\n callback=local_iter)\",\n \"def job_reader(path) -> Generator[ParsedActionType, None, None]:\\n try:\\n with open(path, \\\"r\\\") as f:\\n parser = Parser()\\n for line in f:\\n result = parser.process_line(line)\\n if result is not None:\\n yield result\\n except IOError as err:\\n print(\\\"Error opening/reading from file '{0}': {1}\\\"\\n .format(err.filename, err.strerror))\",\n \"def walk_tree(self, path, topdown=True):\\n if isinstance(path, File):\\n # Called with File object as an argument\\n root = path\\n path = root.path\\n else:\\n root = File(path)\\n\\n files, dirs = [], []\\n\\n try:\\n for item in os.listdir(path):\\n file_path = os.path.join(path, item)\\n\\n if self.path_ignore and self.path_ignore.match(file_path):\\n # Skip excluded paths\\n lg.debug(\\\"Ignoring path %s\\\" % file_path)\\n continue\\n\\n try:\\n f_object = File(file_path, seen=root.already_seen)\\n except UnsupportedFileType as e:\\n lg.warn('%s ..skipping' % e)\\n continue\\n except OSError as e:\\n if e.errno == errno.ENOENT:\\n # File already removed, go on\\n lg.debug('File already removed: %s' % e)\\n continue\\n elif e.errno in [errno.EPERM, errno.EACCES]:\\n # Permission denied or operation not permitted, log error and go on\\n lg.error(e)\\n continue\\n else:\\n # Other errors should be fatal, but we don't want them to be\\n # eg. corrupted file on GlusterFS may raise IOError, but we want to continue\\n lg.exception(e)\\n continue\\n\\n if f_object.directory is True:\\n dirs.append(f_object)\\n else:\\n files.append(f_object)\\n except OSError as e:\\n # Exceptions that may come from os.listdir()\\n if e.errno == errno.ENOENT:\\n # Directory doesn't exist, go on\\n pass\\n elif e.errno in [errno.EPERM, errno.EACCES]:\\n # Permission denied or operation not permitted, log error and go on\\n lg.error(e)\\n pass\\n else:\\n # Other errors should be fatal, but we don't want them to be\\n # eg. corrupted file on GlusterFS may raise IOError, but we want to go on\\n lg.exception(e)\\n pass\\n\\n if topdown:\\n yield root, dirs, files\\n\\n for item in dirs:\\n for x in self.walk_tree(item):\\n yield x\\n\\n if not topdown:\\n yield root, dirs, files\",\n \"def run(self):\\n node_runs = [\\n (node, node.run())\\n for node in self.nodes\\n if node.loaded\\n ]\\n\\n while True:\\n for node, node_run in node_runs:\\n if node in self.buffered_input and node.mode == Node.READ:\\n for d, values in self.buffered_input[node].iteritems():\\n if values and not node.has_inputs(d):\\n node.write_input(d, values.pop(0))\\n self.num_buffered_inputs -= 1\\n break\\n\\n yield next(node_run)\",\n \"def iterRegularFileContents(self):\\n unpack = {}\\n for (oldFileId, newFileId), stream in self.files.iteritems():\\n if not files.frozenFileHasContents(stream):\\n continue\\n if files.frozenFileFlags(stream).isEncapsulatedContent():\\n continue\\n cont = files.frozenFileContentInfo(stream)\\n unpack[newFileId] = cont.sha1()\\n\\n want_tag = '0 ' + ChangedFileTypes.file[4:]\\n while True:\\n f = self._nextFile()\\n if not f:\\n break\\n name, tag, fobj, csf = f\\n if len(name) != 36 or tag != want_tag:\\n continue\\n fileId = name[16:]\\n sha1 = unpack.get(fileId)\\n if not sha1:\\n continue\\n yield sha1, fobj\",\n \"def _inner_file_reader(\\r\\n file_name: str,\\r\\n chunk_size: int = 32*1024*1024,\\r\\n delimiter: str = \\\"\\\\n\\\"):\\r\\n with open(file_name, 'r', encoding=\\\"utf8\\\") as f:\\r\\n carry_forward = \\\"\\\"\\r\\n chunk = \\\"INITIALIZED\\\"\\r\\n while len(chunk) > 0:\\r\\n chunk = f.read(chunk_size)\\r\\n augmented_chunk = carry_forward + chunk\\r\\n lines = augmented_chunk.split(delimiter)\\r\\n carry_forward = lines.pop()\\r\\n yield from lines\\r\\n if carry_forward:\\r\\n yield carry_forward\",\n \"def do_root(self, line):\\n self.root_directory = line\\n if self.source_file:\\n self.source_file = self.root_directory + \\\"/\\\" + self.source_file\\n print(f\\\"Root directory to read & write files is: {line}\\\")\",\n \"def open_files(self, listed_files):\\n for file_ in listed_files:\\n try: \\n with codecs.open(file_, \\\"r\\\", encoding=\\\"utf-8\\\", errors=\\\"ignore\\\") as f:\\n yield f\\n except Exception as error:\\n print(error, file=sys.stderr)\\n exit(0)\",\n \"def file_iterator(file_path):\\n with open(file_path, 'r') as f:\\n\\n for line in f:\\n\\n #each line is an example\\n\\n #each example is made of the function name and then a sequence of triplets\\n #the triplets are (node, path, node)\\n\\n example_name, example_body = parse_line(line)\\n\\n #max length set while preprocessing, make sure none longer\\n\\n example_length = len(example_body)\\n\\n assert example_length <= MAX_LENGTH\\n \\n #need to pad all to maximum length\\n\\n example_body += [['<pad>', '<pad>', '<pad>']]*(MAX_LENGTH - example_length)\\n \\n assert len(example_body) == MAX_LENGTH\\n\\n yield example_name, example_body, example_length\",\n \"def get_input_contents(self):\\n try:\\n ret_files = []\\n coll = self.collections[self._primary_input_collection]\\n ret_file = {'coll_id': coll['coll_id'],\\n 'scope': coll['scope'],\\n 'name': coll['name'],\\n 'bytes': coll.coll_metadata['bytes'],\\n 'adler32': None,\\n 'min_id': 0,\\n 'max_id': coll.coll_metadata['total_files'],\\n 'content_type': ContentType.File,\\n 'content_metadata': {'total_files': coll['coll_metadata']['total_files']}\\n }\\n ret_files.append(ret_file)\\n return ret_files\\n except Exception as ex:\\n self.logger.error(ex)\\n self.logger.error(traceback.format_exc())\\n raise exceptions.IDDSException('%s: %s' % (str(ex), traceback.format_exc()))\",\n \"def _read_in_chunks(self, file_object, blocksize=4096, chunks=-1,\\n shard_index=None):\\n i = 0\\n while chunks:\\n data = file_object.read(blocksize)\\n if not data:\\n break\\n yield data\\n i += 1\\n\\n chunks -= 1\",\n \"def process(self):\\n\\n return self._load_next_file()\",\n \"def pytest_collect_file(\\n parent: pytest.Collector, file_path: Optional[\\\"Path\\\"]\\n) -> Optional[\\\"Node\\\"]:\\n if file_path and file_path.is_symlink():\\n return None\\n if file_path and file_path.name == \\\"molecule.yml\\\":\\n return MoleculeFile.from_parent(path=file_path, parent=parent)\\n return None\",\n \"def read(cls, file, size=BLOCK_SIZE):\\n while True:\\n data = file.read(size)\\n\\n if data:\\n yield data\\n else:\\n raise StopIteration\",\n \"def read_root(self, fid):\\r\\n lin = self.read_line(fid) \\r\\n while lin[0] != ':':\\r\\n parts = lin.split()\\r\\n if parts[0]=='order':\\r\\n order = []\\r\\n for i in range(1, len(parts)):\\r\\n if parts[i].lower()=='rx':\\r\\n chan = 'Xrotation'\\r\\n order.append('x')\\r\\n elif parts[i].lower()=='ry':\\r\\n chan = 'Yrotation'\\r\\n order.append('y')\\r\\n elif parts[i].lower()=='rz':\\r\\n chan = 'Zrotation'\\r\\n order.append('z')\\r\\n elif parts[i].lower()=='tx':\\r\\n chan = 'Xposition'\\r\\n elif parts[i].lower()=='ty':\\r\\n chan = 'Yposition'\\r\\n elif parts[i].lower()=='tz':\\r\\n chan = 'Zposition'\\r\\n elif parts[i].lower()=='l':\\r\\n chan = 'length'\\r\\n self.vertices[0].meta['channels'].append(chan)\\r\\n # order is reversed compared to bvh\\r\\n self.vertices[0].meta['order'] = order[::-1]\\r\\n\\r\\n elif parts[0]=='axis':\\r\\n # order is reversed compared to bvh\\r\\n self.vertices[0].meta['axis_order'] = parts[1][::-1].lower()\\r\\n elif parts[0]=='position':\\r\\n self.vertices[0].meta['offset'] = [float(parts[1]),\\r\\n float(parts[2]),\\r\\n float(parts[3])]\\r\\n elif parts[0]=='orientation':\\r\\n self.vertices[0].meta['orientation'] = [float(parts[1]),\\r\\n float(parts[2]),\\r\\n float(parts[3])]\\r\\n lin = self.read_line(fid)\\r\\n return lin\",\n \"def read(self):\\n return (yield self.storage)\",\n \"def files_from_root(root, accept):\\n for (dir_path, _dir_names, file_names) in os.walk(root, followlinks=True):\\n for file_name in file_names:\\n if accept(file_name):\\n path = os.path.join(dir_path, file_name)\\n yield path\",\n \"def GetRootFileEntry(self):\\n path_spec = compressed_stream_path_spec.CompressedStreamPathSpec(\\n compression_method=self._compression_method,\\n parent=self._path_spec.parent)\\n return self.GetFileEntryByPathSpec(path_spec)\",\n \"def _read(self, file_path: str) -> Iterator[Instance]:\\n with open(cached_path(file_path), \\\"r\\\", encoding=\\\"utf-8\\\") as data_file:\\n for lines in read_conll_file(data_file):\\n fields = [line.rstrip().split(self._delimiter) for line in lines]\\n # unzipping trick returns tuples, but our Fields need lists\\n fields = [list(field) for field in zip(*fields)]\\n tokens, tags = fields\\n yield self.text_to_instance(tokens, tags)\",\n \"def _walk_to_root(path):\\n if not os.path.exists(path):\\n raise IOError('Starting path not found')\\n\\n if os.path.isfile(path):\\n path = os.path.dirname(path)\\n\\n last_dir = None\\n current_dir = os.path.abspath(path)\\n while last_dir != current_dir:\\n yield current_dir\\n parent_dir = os.path.abspath(os.path.join(current_dir, os.path.pardir))\\n last_dir, current_dir = current_dir, parent_dir\",\n \"def read_large_file(file_object):\\n\\n # Loop until the end of file\\n while True:\\n\\n # Read line from the file\\n data = file_object.readline()\\n\\n # Break if this is the end of the file\\n if not data:\\n break\\n\\n # Yield the line of data\\n yield data\",\n \"def open_fast5_files(path, mode=\\\"r\\\"):\\n for filename in find_fast5_files(path):\\n try:\\n hdf = Fast5File(filename, mode=mode)\\n if sanity_check(hdf):\\n yield hdf\\n except OSError:\\n try:\\n hdf.close()\\n except:\\n pass\",\n \"def iterate_corpus(corpus_file):\\n logging.info(\\\"Opening corpus file...\\\")\\n with zipfile.ZipFile(corpus_file) as corpus:\\n for name in corpus.namelist():\\n logging.info(\\\"Reading contents of file '{}'.\\\".format(name))\\n with corpus.open(name) as f:\\n try:\\n tree = etree.parse(f)\\n root = tree.getroot()\\n yield root\\n except etree.ParseError:\\n message = \\\"File '{}' could not be read due to ParseError.\\\"\\n logging.warning(message.format(name))\\n continue\",\n \"def __iter__(self):\\n\\n # Open the data reader\\n self.data.open()\\n\\n starts = np.arange(self.start, self.stop, self.chunksize)\\n for a, b in zip_longest(starts, starts[1:], fillvalue=self.stop):\\n yield self.data.read(a, b, **self.kwargs)\",\n \"def read_large_file(file_object):\\n\\n # Loop indefinitely until the end of the file\\n while True:\\n\\n # Read a line from the file: data\\n data = file_object.readline()\\n\\n # Break if this is the end of the file\\n if not data:\\n break\\n\\n # Yield the line of data\\n yield data\",\n \"def raw_feature_iterator(file_paths):\\n for path in file_paths:\\n with open(path, \\\"r\\\") as fin:\\n for line in fin:\\n yield line\",\n \"def raw_feature_iterator(file_paths):\\n for path in file_paths:\\n with open(path, \\\"r\\\") as fin:\\n for line in fin:\\n yield line\",\n \"def Read(self, file_object):\\n try:\\n self.root_key = plistlib.load(file_object)\\n\\n except plistlib.InvalidFileException as exception:\\n raise IOError(exception)\",\n \"def generate():\\n with open(remote_path, \\\"rb\\\") as f:\\n for chunk in iter(lambda: f.read(buffer_size), b''):\\n yield chunk\",\n \"def _walk_dir(self, rootpath):\\n assert os.path.isabs(rootpath)\\n assert rootpath not in self._dirs\\n relpath = self._get_rel_path(rootpath)\\n self._dirs[relpath] = Directory(rootpath, relpath, None)\\n for dirpath, dirnames, filenames in os.walk(rootpath):\\n if 'refdata' in dirnames:\\n dirnames.remove('refdata')\\n currentdir = self._dirs[self._get_rel_path(dirpath)]\\n # Loop through a copy so that we can modify dirnames.\\n for dirname in list(dirnames):\\n fullpath = os.path.join(dirpath, dirname)\\n if fullpath == self._build_root:\\n dirnames.remove(dirname)\\n continue\\n relpath = self._get_rel_path(fullpath)\\n self._dirs[relpath] = Directory(fullpath, relpath, currentdir)\\n extensions = ('.h', '.cuh', '.hpp', '.c', '.cc', '.cpp', '.cu', '.bm')\\n for filename in filenames:\\n basename, extension = os.path.splitext(filename)\\n if extension in extensions:\\n fullpath = os.path.join(dirpath, filename)\\n relpath = self._get_rel_path(fullpath)\\n self._files[relpath] = File(fullpath, relpath, currentdir)\\n elif extension == '.cmakein':\\n extension = os.path.splitext(basename)[1]\\n if extension in extensions:\\n fullpath = os.path.join(dirpath, filename)\\n relpath = self._get_rel_path(fullpath)\\n sourcefile = GeneratorSourceFile(fullpath, relpath, currentdir)\\n self._files[relpath] = sourcefile\\n fullpath = os.path.join(dirpath, basename)\\n relpath = self._get_rel_path(fullpath)\\n fullpath = os.path.join(self._build_root, relpath)\\n generatedfile = GeneratedFile(fullpath, relpath, currentdir)\\n self._files[relpath] = generatedfile\\n generatedfile.set_generator_source(sourcefile)\\n elif extension in ('.l', '.y', '.pre'):\\n fullpath = os.path.join(dirpath, filename)\\n relpath = self._get_rel_path(fullpath)\\n self._files[relpath] = GeneratorSourceFile(fullpath, relpath, currentdir)\",\n \"def _yield_terraform_source(material, source_path=None):\\n module_root = get_storage_path()\\n source_path = source_path or get_source_path()\\n if material:\\n extract_binary_tf_data(module_root, material, source_path)\\n ctx.logger.debug(\\n 'The storage root tree:\\\\n{}'.format(tree(module_root)))\\n path_to_init_dir = get_node_instance_dir(source_path=source_path)\\n try:\\n ctx.logger.debug('_yield_terraform_source {}'.format(path_to_init_dir))\\n yield path_to_init_dir\\n except Exception as e:\\n _, _, tb = sys.exc_info()\\n cause = exception_to_error_cause(e, tb)\\n ctx.logger.error(str(cause))\\n raise e\\n finally:\\n if v1_gteq_v2(get_cloudify_version(), \\\"6.0.0\\\"):\\n ctx.logger.debug('Not storing zip in runtime properties in '\\n 'Cloudify 6.0.0 and greater')\\n else:\\n store_binary_material(module_root)\\n ctx.instance.runtime_properties['previous_tf_state_file'] = \\\\\\n get_terraform_state_file(path_to_init_dir)\",\n \"def files_and_folders(self, depth):\\n for directory in self.rarc._directories[self.directory_index:][:self.directory_count]:\\n yield depth, directory\\n if isinstance(directory, Folder):\\n if directory.data_offset < len(self.rarc._nodes):\\n node = self.rarc._nodes[directory.data_offset]\\n if directory.name == \\\".\\\" or directory.name == \\\"..\\\":\\n continue\\n yield from node.files_and_folders(depth + 1)\",\n \"def walk(self): # DirObj.walk\\n for name, subdir in self.subdirs.iteritems():\\n for e in subdir.walk():\\n yield e\\n for name, fileEntry in self.files.iteritems():\\n yield fileEntry\\n yield self\",\n \"def _collect_entries(rootdir: str, basedir: str):\\n\\n files = []\\n dirs = []\\n\\n for entry in os.listdir(os.path.join(rootdir, basedir)):\\n rel_path = os.path.join(basedir, entry)\\n full_path = os.path.join(rootdir, rel_path)\\n isdir = os.path.isdir(full_path)\\n if isdir and (rel_path in ('./.git', './.pytest_cache') or entry == '__pycache__'):\\n continue\\n\\n st = os.stat(full_path, follow_symlinks=False)\\n\\n (dirs if isdir else files).append((rel_path, dict(isdir=isdir, path=rel_path, size=(0 if isdir else st.st_size),\\n mode=st.st_mode, omode=f'{st.st_mode:04o}',\\n mtime=int(st.st_mtime))))\\n\\n for rel_path, entry in sorted(dirs):\\n yield entry\\n yield from _collect_entries(rootdir, rel_path)\\n\\n for _, entry in sorted(files):\\n yield entry\",\n \"def retrieve(self, path):\\n if len(path) == 0:\\n for leaf in self.leaves:\\n yield leaf\\n else:\\n next_index = path[0]\\n if self._is_variable(next_index):\\n for leaf in self._retrieve_variable(path[1:]):\\n yield leaf\\n else:\\n next_disc_tree = self._child_at_index(next_index)\\n if next_disc_tree is None:\\n return\\n else:\\n for leaf in next_disc_tree.retrieve(path[1:]):\\n yield leaf\",\n \"def temporary(self, path):\\r\\n if path is None:\\r\\n raise ValueError('Can only temporarily establish a build root given a path.')\\r\\n prior = self._root_dir\\r\\n self._root_dir = path\\r\\n try:\\r\\n yield\\r\\n finally:\\r\\n self._root_dir = prior\",\n \"def is_input_file(self):\\r\\n return self.depth == 0\",\n \"def _Open(self, mode='rb'):\\n if not self._path_spec.HasParent():\\n raise errors.PathSpecError(\\n 'Unsupported path specification without parent.')\\n\\n file_object = resolver.Resolver.OpenFileObject(\\n self._path_spec.parent, resolver_context=self._resolver_context)\\n\\n fsfat_volume = pyfsfat.volume()\\n fsfat_volume.open_file_object(file_object)\\n fsfat_root_directory = fsfat_volume.get_root_directory()\\n\\n self._file_object = file_object\\n self._fsfat_volume = fsfat_volume\\n self._root_directory_identifier = fsfat_root_directory.identifier\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.55671173","0.5524454","0.5513734","0.5512159","0.54909235","0.5358831","0.5296867","0.5270758","0.5249741","0.51901007","0.5162517","0.514831","0.5129456","0.5121177","0.5115669","0.5112054","0.51021385","0.50759375","0.5071909","0.5056866","0.5011314","0.49878433","0.49857336","0.49794272","0.49770516","0.4977022","0.4975868","0.4973958","0.49557555","0.49444452","0.4941103","0.4939294","0.493286","0.4927656","0.49147874","0.49046776","0.48981947","0.48943162","0.48879284","0.4879557","0.48424053","0.48307258","0.48272425","0.48171014","0.48148865","0.48146814","0.48139483","0.48126093","0.48108897","0.4801511","0.48004228","0.4798387","0.47860512","0.47778982","0.47761786","0.47722036","0.4765979","0.47622144","0.4757364","0.4756005","0.47454315","0.47446808","0.4718519","0.47125897","0.47122455","0.47105962","0.47076303","0.47052047","0.4700649","0.46973875","0.46964934","0.46889466","0.4688497","0.4679208","0.46755046","0.46707368","0.46694064","0.4669035","0.46653333","0.46639985","0.4661806","0.46562946","0.46540162","0.46495163","0.46447188","0.4642607","0.46424586","0.4629556","0.4629556","0.46285915","0.46248177","0.4623565","0.46194825","0.46190694","0.46089858","0.46083105","0.46056536","0.46004838","0.45990202","0.45977685"],"string":"[\n \"0.55671173\",\n \"0.5524454\",\n \"0.5513734\",\n \"0.5512159\",\n \"0.54909235\",\n \"0.5358831\",\n \"0.5296867\",\n \"0.5270758\",\n \"0.5249741\",\n \"0.51901007\",\n \"0.5162517\",\n \"0.514831\",\n \"0.5129456\",\n \"0.5121177\",\n \"0.5115669\",\n \"0.5112054\",\n \"0.51021385\",\n \"0.50759375\",\n \"0.5071909\",\n \"0.5056866\",\n \"0.5011314\",\n \"0.49878433\",\n \"0.49857336\",\n \"0.49794272\",\n \"0.49770516\",\n \"0.4977022\",\n \"0.4975868\",\n \"0.4973958\",\n \"0.49557555\",\n \"0.49444452\",\n \"0.4941103\",\n \"0.4939294\",\n \"0.493286\",\n \"0.4927656\",\n \"0.49147874\",\n \"0.49046776\",\n \"0.48981947\",\n \"0.48943162\",\n \"0.48879284\",\n \"0.4879557\",\n \"0.48424053\",\n \"0.48307258\",\n \"0.48272425\",\n \"0.48171014\",\n \"0.48148865\",\n \"0.48146814\",\n \"0.48139483\",\n \"0.48126093\",\n \"0.48108897\",\n \"0.4801511\",\n \"0.48004228\",\n \"0.4798387\",\n \"0.47860512\",\n \"0.47778982\",\n \"0.47761786\",\n \"0.47722036\",\n \"0.4765979\",\n \"0.47622144\",\n \"0.4757364\",\n \"0.4756005\",\n \"0.47454315\",\n \"0.47446808\",\n \"0.4718519\",\n \"0.47125897\",\n \"0.47122455\",\n \"0.47105962\",\n \"0.47076303\",\n \"0.47052047\",\n \"0.4700649\",\n \"0.46973875\",\n \"0.46964934\",\n \"0.46889466\",\n \"0.4688497\",\n \"0.4679208\",\n \"0.46755046\",\n \"0.46707368\",\n \"0.46694064\",\n \"0.4669035\",\n \"0.46653333\",\n \"0.46639985\",\n \"0.4661806\",\n \"0.46562946\",\n \"0.46540162\",\n \"0.46495163\",\n \"0.46447188\",\n \"0.4642607\",\n \"0.46424586\",\n \"0.4629556\",\n \"0.4629556\",\n \"0.46285915\",\n \"0.46248177\",\n \"0.4623565\",\n \"0.46194825\",\n \"0.46190694\",\n \"0.46089858\",\n \"0.46083105\",\n \"0.46056536\",\n \"0.46004838\",\n \"0.45990202\",\n \"0.45977685\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7067436"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":830,"numItemsPerPage":100,"numTotalItems":95772,"offset":83000,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjA4NTM5NiwianRpIjoiZmI1OTlmMjItNjMxNy00ZGIyLTg5MDQtMTdhMDI1MzAxZTMyIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYwODg5OTYsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.yfePDnP1okg9Hvy_QlRw-Epe4Cqej8pES9jacYPZ6D7kzkDNCdNnzxL67nATm6AI-Ll7QgjFOd3OgxdNNoSQCA","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 |
|---|---|---|---|---|---|---|
Extracts intermediate and last hidden states | def extract_hidden_states(self, output):
# Intermediate hidden states
output_fw_intermediate = output[:,:-1,0:self._hidden_size]
output_bw_intermediate = output[:,1:,self._hidden_size:]
# Last hidden states
output_fw = output[:,-1,0:self._hidden_size]
output_bw ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_hidden_states(self, output):\n \n # Extracting the forward and backward hidden states from the last BiLSTM layer\n # output (batch_size, sequence length, 2 * hidden dim)\n output_fw = output[:,:,0:self._hidden_size]\n output_bw = output[:,:,self._hidden_size:]\n ... | [
"0.7124553",
"0.66910857",
"0.668173",
"0.6495266",
"0.63891685",
"0.63413036",
"0.63327545",
"0.6322163",
"0.62529796",
"0.6247621",
"0.61202276",
"0.6086323",
"0.6083734",
"0.60036206",
"0.5999816",
"0.5995558",
"0.59741884",
"0.5928138",
"0.5910407",
"0.5886927",
"0.587857... | 0.75195116 | 0 |
Carries out internal processing for each hidden state output direction. | def implement_processing(self, output_intermediate, processing_matrix, forward = True):
# Attention implementation
output_intermediate = torch.matmul(output_intermediate, processing_matrix)
output_intermediate = torch.transpose(output_intermediate,2,1)
# Linear laye... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _TransformHidden(self, _):\n raise NotImplementedError()",
"def _assign_output_states(self):\n for mech in self.terminalMechanisms.mechanisms:\n self.outputStates[mech.name] = mech.outputStates",
"def extract_hidden_states(self, output):\n # Intermediate hidden states\n o... | [
"0.5916735",
"0.56957465",
"0.5666486",
"0.5534946",
"0.546069",
"0.54430485",
"0.543081",
"0.5429324",
"0.5394006",
"0.5337721",
"0.53335506",
"0.52718914",
"0.52442145",
"0.52384883",
"0.5234001",
"0.521342",
"0.52043676",
"0.52028215",
"0.52019",
"0.51905334",
"0.5185234",... | 0.0 | -1 |
Get the link to the mp3 that has the pronunciation of the word | def pron(word):
return send_from_directory('prons', word + ".mp3", mimetype="audio/mpeg") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def replace_url(word):\n\tfor i, _ in enumerate(word['pronunciations']):\n\t\turl = word['pronunciations'][i]['url']\n\n\t\ttry:\n\t\t\togg_file = url.rsplit('/', 1)[1]\n\t\t\tmp3_file = ogg_file.split('.')[0] + '.mp3'\n\n\t\t\tword['pronunciations'][i]['filename'] = mp3_file\n\t\t\tword['pronunciations'][i].pop('... | [
"0.6719211",
"0.6286832",
"0.58968437",
"0.58269715",
"0.57700187",
"0.5694772",
"0.561889",
"0.5577821",
"0.55301756",
"0.5515074",
"0.5479446",
"0.54604495",
"0.54530525",
"0.5450867",
"0.5440568",
"0.5434193",
"0.5410042",
"0.5328304",
"0.5322993",
"0.53166324",
"0.5315228... | 0.68460476 | 0 |
Return a random word and four different pronunciation. | def word():
word = Word(random_word())
# word = Word("arroyo")
word.speak()
word.messup()
l.debug("Displaying %s", word.word)
prons = sorted([word.word, word.word + "-a", word.word + "-b", word.word + "-c"], key=lambda x: random.random())
return jsonify({"word": word.word, "pron": prons,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_horror_title():\n d666 = random.randint(1, 666)\n if d666 <= 111:\n #the adj noun\n return \"The \" + horror_adj[random.randint(0, len(horror_adj) - 1)] + \" \" + horror_noun[random.randint(0, len(horror_noun) - 1)]\n elif d666 > 111 and d666 <= 222: \n #noun of noun\n ... | [
"0.69656295",
"0.69620365",
"0.6938039",
"0.6865141",
"0.6855903",
"0.6780548",
"0.66918164",
"0.6684589",
"0.6667548",
"0.664169",
"0.65605426",
"0.65605426",
"0.65605426",
"0.64935744",
"0.6492746",
"0.6466923",
"0.6423011",
"0.6421722",
"0.64096063",
"0.6375458",
"0.636185... | 0.60104597 | 50 |
Match sizes by comparing each set field. Process size may change inbetween two measurements. | def _match_sizes(self, pi1, pi2, ignore=[]):
if pi1.available and pi2.available:
for arg in ('vsz', 'rss', 'data_segment', 'shared_segment',
'stack_segment', 'code_segment'):
if arg in ignore:
continue
size1 = getattr(pi1, a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _assign_sizes(self):",
"def test_mid_sizes(self):\n for size in settings.MISAGO_AVATARS_SIZES:\n self.assertEqual(clean_size(size - 1), size)",
"def test_valid_sizes(self):\n for size in settings.MISAGO_AVATARS_SIZES:\n self.assertEqual(clean_size(size), size)",
"def c... | [
"0.6204795",
"0.6184761",
"0.61249745",
"0.610729",
"0.59723085",
"0.5955496",
"0.5939015",
"0.5932825",
"0.58930546",
"0.58097595",
"0.58097595",
"0.58097595",
"0.58097595",
"0.58097595",
"0.58097595",
"0.58097595",
"0.5759278",
"0.5759278",
"0.57523656",
"0.57167405",
"0.56... | 0.6783647 | 0 |
Capture ValidationError and Logs. | def test_validate_and_logger(self):
data = V20CredProblemReport(
description={
"en": "Insufficient credit",
"code": "invalid_code",
},
).serialize()
self._caplog.set_level(logging.WARNING)
V20CredProblemReportSchema().validate_field... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def error_handler(self, failure):\n log.error(failure)",
"def processErrors(self):\n e = self.formatErrors()\n if not self.config.nologtostdout:\n if e:\n print e\n else:\n print \"No errors\"\n if self.config.logtomail:\n ... | [
"0.6105171",
"0.5935109",
"0.5904136",
"0.5833974",
"0.581675",
"0.57186264",
"0.5662167",
"0.5657665",
"0.5589514",
"0.5571021",
"0.5549629",
"0.55409443",
"0.5504729",
"0.55027974",
"0.55014676",
"0.54995173",
"0.5453479",
"0.5451817",
"0.5447798",
"0.54443485",
"0.54336685... | 0.52045256 | 44 |
Returns the list of container registries each of which is an ordered dictionary. | def output_format(result):
if 'value' in result and isinstance(result['value'], list):
result = result['value']
obj_list = result if isinstance(result, list) else [result]
return [_format_group(item) for item in obj_list] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_registries():\n url = \"/\".join([REGISTRY_BASE, \"_catalog\"])\n response = req(url)\n if response is not None:\n return response[\"repositories\"]\n return []",
"def containers(self):\r\n return [JSONContainer(c) for c\r\n in self.container.get(\"Containers\", [... | [
"0.69137096",
"0.6764479",
"0.6561832",
"0.6402633",
"0.6357411",
"0.62526804",
"0.61654025",
"0.612648",
"0.60717183",
"0.60332584",
"0.60280293",
"0.60130984",
"0.59799194",
"0.5978797",
"0.59747064",
"0.5971127",
"0.5962707",
"0.59357166",
"0.5931848",
"0.588443",
"0.58628... | 0.0 | -1 |
Returns an ordered dictionary of the container registry. | def _format_group(item):
table_info = {_property_map[key]: str(item[key]) for key in item if key in _property_map}
try:
table_info['SKU'] = item['sku']['name']
except (KeyError, TypeError):
pass
try:
table_info['USERNAME'] = item['username']
except (KeyError, TypeError):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_registry_keys( ):\n return _theRegistry.get_codes().keys()",
"def registry(self):\n if self._registry is None:\n print('Creating container registry...')\n registry_ops = self.registry_client.registries\n try:\n registry = registry_ops.get(\n ... | [
"0.67599016",
"0.6461334",
"0.6459659",
"0.64198136",
"0.64024276",
"0.63673127",
"0.6053186",
"0.6050705",
"0.5892358",
"0.5852548",
"0.58208233",
"0.5747135",
"0.5703876",
"0.5644097",
"0.5632085",
"0.5615898",
"0.56062925",
"0.5587612",
"0.558675",
"0.55411214",
"0.5534757... | 0.0 | -1 |
Compile input schemas from each of the data interface classes. | def get_source_schema(cls) -> dict:
source_schema = get_base_schema(
root=True,
id_="source.schema.json",
title="Source data schema",
description="Schema for the source data, files and directories",
version="0.1.0",
)
for interface_name... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _CreateSchemas(self) -> None:\n self.schema_objs = dict() # Holds OpenAPI representations of types.\n\n # Add the OpenAPI schemas of protobuf primitive types.\n primitive_type_schemas = {\n primitive_type[\"name\"]: primitive_type[\"schema\"]\n for primitive_type in primitive_types.valu... | [
"0.61216074",
"0.60818344",
"0.6044326",
"0.58399236",
"0.5777027",
"0.57683086",
"0.5712437",
"0.56210655",
"0.5593061",
"0.55118495",
"0.54837036",
"0.54643106",
"0.5436546",
"0.5413458",
"0.54083556",
"0.5375735",
"0.53655887",
"0.53034586",
"0.5283603",
"0.5244687",
"0.52... | 0.53621715 | 17 |
Compile conversion option schemas from each of the data interface classes. | def get_conversion_options_schema(self) -> dict:
conversion_options_schema = get_base_schema(
root=True,
id_="conversion_options.schema.json",
title="Conversion options schema",
description="Schema for the conversion options",
version="0.1.0",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def schema_generators():\n return {\n \"trips\": trips_schema,\n \"status_changes\": status_changes_schema,\n \"events\": events_schema,\n \"vehicles\": vehicles_schema,\n \"stops\": stops_schema\n }",
"def buildConverters(tableSpec, tableNamespace):\n converters = []\... | [
"0.56526315",
"0.5620702",
"0.5523117",
"0.55160886",
"0.536558",
"0.51273906",
"0.5100788",
"0.50955427",
"0.50951093",
"0.50712836",
"0.49624124",
"0.49403378",
"0.49315578",
"0.49236938",
"0.48799866",
"0.4868732",
"0.4861183",
"0.48473457",
"0.48407805",
"0.48375595",
"0.... | 0.6372114 | 0 |
Validate source_data against Converter source_schema. | def validate_source(cls, source_data: Dict[str, dict], verbose: bool = True):
cls._validate_source_data(source_data=source_data, verbose=verbose) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate(cls, data, errors):",
"def validate(self, *, destination: FieldModel, source: list) -> bool:\n if not isinstance(source, list):\n raise ValueError(f\"Action source script does not conform to required structure. ({source})\")\n for chunk in self.core.chunks(lst=source, n=len(... | [
"0.622033",
"0.61825204",
"0.60589474",
"0.60442704",
"0.60242766",
"0.6022463",
"0.58533144",
"0.5791768",
"0.57778436",
"0.57204026",
"0.56741023",
"0.5669912",
"0.56534284",
"0.56511724",
"0.5648261",
"0.5627419",
"0.5624629",
"0.55918056",
"0.5581766",
"0.5579891",
"0.556... | 0.73413634 | 0 |
Validate source_data against source_schema and initialize all data interfaces. | def __init__(self, source_data: Dict[str, dict], verbose: bool = True):
self.verbose = verbose
self._validate_source_data(source_data=source_data, verbose=self.verbose)
self.data_interface_objects = {
name: data_interface(**source_data[name])
for name, data_interface in s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_source(cls, source_data: Dict[str, dict], verbose: bool = True):\n cls._validate_source_data(source_data=source_data, verbose=verbose)",
"def __post_init__(self):\n # Only do this if source_data already exists (not during its own initialization)\n if \"SOURCE_DATA\" in globals()... | [
"0.696322",
"0.62050235",
"0.5793729",
"0.5759235",
"0.5751692",
"0.5744186",
"0.57387424",
"0.5735445",
"0.5684236",
"0.56799126",
"0.5645745",
"0.56375694",
"0.5569745",
"0.5554738",
"0.55513054",
"0.55060434",
"0.5498737",
"0.54937583",
"0.5421979",
"0.5412901",
"0.5385543... | 0.7059488 | 0 |
Compile metadata schemas from each of the data interface objects. | def get_metadata_schema(self) -> dict:
metadata_schema = load_dict_from_file(Path(__file__).parent / "schemas" / "base_metadata_schema.json")
for data_interface in self.data_interface_objects.values():
interface_schema = unroot_schema(data_interface.get_metadata_schema())
metadat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _generate_schema_from_datafiles(datasets):\n\n schema = {}\n for dataset in datasets:\n schema.update(dataset.native_schema)\n\n return schema",
"def _CreateSchemas(self) -> None:\n self.schema_objs = dict() # Holds OpenAPI representations of types.\n\n # Add the OpenAP... | [
"0.61461616",
"0.60478354",
"0.59317285",
"0.58521867",
"0.57625383",
"0.56529933",
"0.56043863",
"0.553093",
"0.55201304",
"0.5496202",
"0.5449299",
"0.53587925",
"0.5321705",
"0.530396",
"0.5268813",
"0.5268668",
"0.52536666",
"0.5236021",
"0.5233846",
"0.522641",
"0.522080... | 0.6485286 | 0 |
Autofill as much of the metadata as possible. Must comply with metadata schema. | def get_metadata(self) -> DeepDict:
metadata = get_default_nwbfile_metadata()
for interface in self.data_interface_objects.values():
interface_metadata = interface.get_metadata()
metadata = dict_deep_update(metadata, interface_metadata)
return metadata | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def populate_initial_valid_metadata(self):\n pass",
"def initial_metadata(self):\n raise NotImplementedError()",
"def initMetadata(self):\n\n if not 'flags' in self.metadata:\n\n self.metadata['flags'] = {}\n\n if not 'uidvalidity' in self.metadata:\n\n\n self.... | [
"0.7373019",
"0.72648966",
"0.7245894",
"0.683571",
"0.6799102",
"0.67943895",
"0.6717908",
"0.6601634",
"0.6546511",
"0.65143317",
"0.64658564",
"0.64658564",
"0.6392921",
"0.63785046",
"0.6371377",
"0.6346855",
"0.63260806",
"0.631097",
"0.6283491",
"0.6226167",
"0.6203599"... | 0.0 | -1 |
Validate metadata against Converter metadata_schema. | def validate_metadata(self, metadata: Dict[str, dict]):
encoder = NWBMetaDataEncoder()
# The encoder produces a serialiazed object so we de serialized it for comparison
serialized_metadata = encoder.encode(metadata)
decoded_metadata = json.loads(serialized_metadata)
validate(inst... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate(self):\n if not self._contents.has_key('type'):\n raise ValidationFailed(\"Metadata file %s contains no type field\" % (self._filename))\n \n if not self._contents.has_key('version'):\n raise ValidationFailed(\"Metadata file %s contains no version field\" %\... | [
"0.67834675",
"0.6774967",
"0.6396517",
"0.6372613",
"0.6314087",
"0.62640077",
"0.61724794",
"0.61656445",
"0.61574095",
"0.61177945",
"0.60506034",
"0.6029551",
"0.6025059",
"0.6017817",
"0.601185",
"0.5954007",
"0.59126073",
"0.59023726",
"0.5883788",
"0.5880836",
"0.58434... | 0.7296295 | 0 |
Validate conversion_options against Converter conversion_options_schema. | def validate_conversion_options(self, conversion_options: Dict[str, dict]):
validate(instance=conversion_options or {}, schema=self.get_conversion_options_schema())
if self.verbose:
print("conversion_options is valid!") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_conversion_schema():\n legal_filing = {'conversion': BEN_CONVERSION}\n is_valid, errors = validate(legal_filing, 'conversion')\n\n if errors:\n for err in errors:\n print(err.message)\n print(errors)\n\n assert is_valid",
"def __validate_options__(cls, options):\n ... | [
"0.6484723",
"0.6115653",
"0.6110735",
"0.6048836",
"0.6048836",
"0.5758768",
"0.5712079",
"0.57083374",
"0.55992967",
"0.5536905",
"0.55322844",
"0.5477315",
"0.54282635",
"0.5426897",
"0.5373139",
"0.5366407",
"0.53514874",
"0.53224105",
"0.53178",
"0.52855515",
"0.52787715... | 0.84981906 | 0 |
Run the NWB conversion over all the instantiated data interfaces. | def run_conversion(
self,
nwbfile_path: Optional[str] = None,
nwbfile: Optional[NWBFile] = None,
metadata: Optional[dict] = None,
overwrite: bool = False,
conversion_options: Optional[dict] = None,
) -> None:
if metadata is None:
metadata = self.ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(dataset_path, output_path):\n print(\"converting all snirf files in the input dataset to nwb...\")\n print(f\"dataset directory: {dataset_path}\")\n print(f\"output directory: {output_path}\")\n print()\n\n for subject_id in list_subject_ids(dataset_path):\n convert_subject_snirf_to_... | [
"0.6259673",
"0.57180226",
"0.5696865",
"0.55192584",
"0.55044365",
"0.5475389",
"0.5445691",
"0.5258226",
"0.5224709",
"0.519267",
"0.5145294",
"0.5142543",
"0.5130369",
"0.51275074",
"0.5124553",
"0.511106",
"0.5101885",
"0.50836927",
"0.5040718",
"0.50096214",
"0.500791",
... | 0.5302192 | 7 |
Override this method to implement custom alignment | def temporally_align_data_interfaces(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def align(self):\n ...",
"def format_alignment(self, alignment):\n raise NotImplementedError(\"This method should be implemented\")\n ###################################################\n # You MUST implement this method in the subclass. #\n ####################################... | [
"0.8660336",
"0.7733873",
"0.70901155",
"0.70901155",
"0.70901155",
"0.7020445",
"0.66770446",
"0.65857303",
"0.6532593",
"0.6530735",
"0.6482122",
"0.6458247",
"0.63402903",
"0.6284978",
"0.6186565",
"0.61809427",
"0.61300397",
"0.6102727",
"0.6090545",
"0.6069467",
"0.60386... | 0.5587484 | 70 |
Compile conversion option schemas from each of the data interface classes. | def get_conversion_options_schema(self) -> dict:
conversion_options_schema = get_base_schema(
root=True,
id_="conversion_options.schema.json",
title="Conversion options schema",
description="Schema for the conversion options",
version="0.1.0",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def schema_generators():\n return {\n \"trips\": trips_schema,\n \"status_changes\": status_changes_schema,\n \"events\": events_schema,\n \"vehicles\": vehicles_schema,\n \"stops\": stops_schema\n }",
"def buildConverters(tableSpec, tableNamespace):\n converters = []\... | [
"0.56542283",
"0.56212246",
"0.5526179",
"0.55158335",
"0.53670424",
"0.5130123",
"0.5103227",
"0.50974584",
"0.50963557",
"0.5072053",
"0.49627963",
"0.49403316",
"0.49340504",
"0.49235007",
"0.48808965",
"0.48718503",
"0.48601958",
"0.48495325",
"0.48423228",
"0.48361728",
... | 0.636941 | 1 |
Transform parsed log line into a short format. | def transform(stop):
line = None
results = {}
while True:
if line is not None:
processor = processors.get(type(line), None)
if processor:
processor[0](line, results, *processor[1:])
if stop.is_set():
line = generate(results)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform_line(line):\n for proc, pattern in INCLUDE_LINES:\n if pattern in line:\n # Log lines look like this:\n # pylint: disable=line-too-long\n # Mar 12 09:58:43 jumphost-win-02 metadata-server[5259]: I0312 09:58:43.756257 5259 server.go:87] Updated token: [redacted]\n timestamp ... | [
"0.6709503",
"0.6165054",
"0.58948964",
"0.5781676",
"0.5649118",
"0.56183153",
"0.5591828",
"0.55390006",
"0.54398614",
"0.5426594",
"0.54089904",
"0.54073066",
"0.5348272",
"0.5308872",
"0.52630347",
"0.5261002",
"0.52508545",
"0.524962",
"0.5240933",
"0.5226485",
"0.521126... | 0.0 | -1 |
@ get all episodes in selected asset type, and if project type is not series then return None. | def bsw_getAllAssetEpisodes(astType):
# get basic inputs.
basePath = ProjectNamingInputs().basePath
projectName = ProjectNamingInputs().projectName
assetType = ProjectNamingInputs().assetType
projectType = ProjectNamingInputs().projectType
if projectType == 'series':
episodeDir = '{baseP... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_episodes(self):\n return [series_episode for series_episode in SeriesEpisode.objects.filter(series=self)]",
"def episodes(self):\n if self._episode_cache_ver == self._db._version:\n return self._episode_cache\n\n # Get only rows from the preferred provider (provider id is... | [
"0.6472294",
"0.6238427",
"0.6109521",
"0.6061909",
"0.58215487",
"0.5817781",
"0.56866485",
"0.5580703",
"0.5557396",
"0.5460225",
"0.5414427",
"0.5404259",
"0.5401844",
"0.53146833",
"0.53129226",
"0.51491994",
"0.51185447",
"0.5114288",
"0.5105471",
"0.50925493",
"0.507416... | 0.7465757 | 0 |
@ get all asset names of asset types from directory. | def bsw_getAllAssetNames(astType, episode=None):
# get basic inputs.
basePath = ProjectNamingInputs().basePath
projectName = ProjectNamingInputs().projectName
assetType = ProjectNamingInputs().assetType
projectType = ProjectNamingInputs().projectType
if projectType == 'series':
assetDire... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def listAssetTypes(self):\n return self.get_json('/assetType')",
"def get_custom_asset_types() -> list[str]:\n with GlobalDBHandler().conn.read_ctx() as cursor:\n cursor.execute('SELECT DISTINCT type FROM custom_assets ORDER BY type;')\n return [entry[0] for entry in cursor]",... | [
"0.74853826",
"0.69678617",
"0.6263411",
"0.6205344",
"0.61148363",
"0.609677",
"0.60557204",
"0.6006449",
"0.5994604",
"0.5930386",
"0.59195334",
"0.58995765",
"0.58823216",
"0.5859081",
"0.5832998",
"0.58204025",
"0.5820043",
"0.581612",
"0.5813952",
"0.5796276",
"0.5788555... | 0.71672857 | 1 |
@ get template of passed asset all department directory paths. | def bsw_getAssetDeptDirs(astType, astName, episode=None):
basePath = ProjectNamingInputs().basePath
projName = ProjectNamingInputs().projectName
assetType = ProjectNamingInputs().assetType
projectType = ProjectNamingInputs().projectType
allDeptDirs = dict()
if projectType == 'series':
ba... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def template_path(self):\n return self.get_config(\"templates\")",
"def template():\n return ENVIVIRTUALIZABLEURI('DEFile')",
"def get_template_path(self):\n raise NotImplementedError()",
"def template_path(self) -> str:\n return self._values.get(\"template_path\")",
"def get_templa... | [
"0.61192125",
"0.6096916",
"0.60494095",
"0.6015967",
"0.58758515",
"0.58758515",
"0.5812925",
"0.5809724",
"0.5803672",
"0.5794745",
"0.57694685",
"0.5765914",
"0.5734071",
"0.5684027",
"0.5677342",
"0.5666776",
"0.566474",
"0.56168336",
"0.55416065",
"0.5522847",
"0.5462942... | 0.54172635 | 28 |
@ create asset directories of all department. | def bsw_createAssetDirectories(astType, astName, episode=None):
# get all asset department directory template.
projectType = ProjectNamingInputs().projectType
if projectType == 'series':
assetDeptDir = bsw_getAssetDeptDirs(astType, astName, episode=episode)
else:
assetDeptDir = bsw_getAs... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_directories(self):\n print \"[--init] creating directory structure in %s\" % (self.target_path)\n ensure_path(self.conf_path)\n for subdir in config.PROCESSING_AREAS:\n subdir_path = self.data_path + os.sep + subdir\n ensure_path(subdir_path)",
"def createAs... | [
"0.7096028",
"0.67460805",
"0.6727249",
"0.66118586",
"0.6416269",
"0.6301152",
"0.6208901",
"0.6203855",
"0.6202322",
"0.6182706",
"0.616796",
"0.6159768",
"0.61580974",
"0.61043555",
"0.6102615",
"0.60950065",
"0.60941195",
"0.60727894",
"0.60680026",
"0.6064777",
"0.605273... | 0.71996766 | 0 |
@ get main file and version files, and create directory if not exist. | def bsw_getAssetFileAndVersions(astType, astDept, astName, episode=None):
projectShortName = ProjectNamingInputs().projectShortName
projectType = ProjectNamingInputs().projectType
if projectType == 'series':
assetDeptDir = bsw_getAssetDeptDirs(astType, astName, episode=episode)
else:
ass... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self):\n for lof in self.data_files:\n if lof[0]:\n base = getattr(self, 'install_' + lof[0])\n else:\n base = getattr(self, 'install_base')\n dir = convert_path(lof[1])\n if not os.path.isabs(dir):\n dir = os.p... | [
"0.66184783",
"0.6417428",
"0.6203714",
"0.61612284",
"0.61361146",
"0.61206895",
"0.610623",
"0.6035497",
"0.60022396",
"0.59809184",
"0.59590316",
"0.59575915",
"0.5947744",
"0.5938032",
"0.59312606",
"0.5918751",
"0.5901932",
"0.5887037",
"0.58825606",
"0.5875821",
"0.5842... | 0.0 | -1 |
@ return all department wise asset return name if file is exist. | def bsw_getValidDeptAssetNames(astType, astDept, episode=None):
# get all asset names.
projectShortName = ProjectNamingInputs().projectShortName
projectType = ProjectNamingInputs().projectType
if projectType == 'series':
assetNames = bsw_getAllAssetNames(astType, episode=episode)
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_available_data_asset_names(self) -> List[str]:\n raise NotImplementedError",
"def assetName(self):\n\t\treturn self._assetName",
"def bsw_getAssetDeptDirs(astType, astName, episode=None):\n basePath = ProjectNamingInputs().basePath\n projName = ProjectNamingInputs().projectName\n assetT... | [
"0.5815256",
"0.5636447",
"0.55993414",
"0.55701506",
"0.55508494",
"0.54256535",
"0.5399064",
"0.53980976",
"0.5394662",
"0.53775007",
"0.53440297",
"0.533159",
"0.5322432",
"0.5286498",
"0.526668",
"0.5252649",
"0.52432853",
"0.5233909",
"0.5209876",
"0.5205642",
"0.5201809... | 0.6095677 | 0 |
@ get version directory the query all maya version files and return new file name. | def bsw_versionUpPath(versionDir):
projectShortName = ProjectNamingInputs().projectShortName
if not os.path.exists(versionDir):
return False
# get all files from version directory.
if os.listdir(versionDir):
filteredVersionFiles = list()
# check and add every files from version d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_version(self, directory, version_file_name='.version'):\n if self.path_exists(directory) and (version_file_name in os.listdir(directory)):\n f = open(directory + '/' + version_file_name)\n version = f.read()\n f.close()\n return version\n return Non... | [
"0.6709382",
"0.659938",
"0.6082783",
"0.60531646",
"0.6040702",
"0.5889642",
"0.5856451",
"0.5850048",
"0.5791198",
"0.5770861",
"0.57638973",
"0.5731305",
"0.5693969",
"0.5684699",
"0.56496245",
"0.56411403",
"0.56335783",
"0.5626503",
"0.56060994",
"0.56059694",
"0.5597353... | 0.62781 | 2 |
@ get asset details from root group entries which created in asset generation time. | def bsw_getAssetDetails(rootGrpName=None):
if not rootGrpName:
if pm.objExists('Texture_Group'):
rootGrpName = 'Texture_Group'
elif pm.objExists('rig_grp'):
rootGrpName = 'rig_grp'
elif pm.objExists('geo'):
rootGrpName = 'geo'
else:
roo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAssetInfo(self):\n return self._AssetInfo",
"def getInfo(self):\n doc = minidom.parse(urllib.urlopen(serverString + \"/rest/asset/\" + self.id))\n self._getInfoFromNode(doc.getElementsByTagName(\"asset\")[0])",
"def get_assets(id_group):\n data = sql.list_assets(id_group)\n na... | [
"0.5737252",
"0.57269895",
"0.56889707",
"0.5585239",
"0.55731195",
"0.55246085",
"0.55192024",
"0.546723",
"0.5462399",
"0.53377986",
"0.533493",
"0.5249755",
"0.523537",
"0.51746964",
"0.516901",
"0.5139736",
"0.5098993",
"0.5071982",
"0.50337255",
"0.501638",
"0.49910238",... | 0.6417712 | 0 |
@ get main file name of current scene. | def bsw_getCurrentAssetMainFileName():
projectShortName = ProjectNamingInputs().projectShortName
# get asset UID from the kns_getAssetDetails function (second last return is assetUID).
assetUID = bsw_getAssetDetails()[-2]
if os.environ['BSW_PROJECT_TYPE'] == 'series':
return projectShortName + '... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_current_scene_name():\n\n scene_name = cmds.file(query=True, sceneName=True, shortName=True)\n scene_name = osp.splitext(scene_name)[0]\n\n return scene_name",
"def get_scene_name(self):\n return self._document.GetDocumentName()",
"def name(self):\n return self._scene_name",
"def g... | [
"0.8191708",
"0.7378323",
"0.71594596",
"0.7123498",
"0.7103535",
"0.7034933",
"0.7017298",
"0.69605684",
"0.69590616",
"0.6924432",
"0.6839492",
"0.6836935",
"0.68076164",
"0.6782342",
"0.67598224",
"0.6741122",
"0.67305326",
"0.6696987",
"0.66557336",
"0.6637808",
"0.660324... | 0.7283132 | 2 |
@ get only final file of required department. | def bsw_getOnlyFinalFileOfDept(astType, astDept, astName, episode=None):
projectShortName = ProjectNamingInputs().projectShortName
astTypShortCode = {'Character': 'ch', 'Prop': 'pr', 'Set': 'bg', 'Vehicle': 'vh', 'SetElement': 'se'}
astDeptShortCode = {'Model': 'mod', 'Rig': 'rig', 'Texture': 'tex', 'Light'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_file(plan, shape=False):\n if (DistrictFile.get_file_status(plan, shape) == 'done'):\n file_name = DistrictFile.get_file_name(plan, shape) + '.zip'\n return file_name",
"def get_dsc(self):\n for path in self.get_all_files():\n if path.endswith('.dsc'):\n ... | [
"0.6320338",
"0.5684126",
"0.5551462",
"0.5470761",
"0.53162855",
"0.52884984",
"0.5274076",
"0.5233386",
"0.5233386",
"0.5226018",
"0.51835144",
"0.51724654",
"0.512775",
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"0.5094329",
"0.5093315",
"0.5081137",
"0.5072476",
"0.50506085",
"0.50324",
"0.5015832",
... | 0.64966166 | 0 |
@ get asset dir from file name. | def bsw_getAssetPathFromFileName(fileName):
projectType = ProjectNamingInputs().projectType
assetType = {'ch': 'Character', 'pr': 'Prop', 'bg': 'Set', 'vh': 'Vehicle', 'se': 'SetElement'}
assetDept = {'mod': 'Model', 'tex': 'Texture', 'rig': 'Rig', 'lit': 'Light'}
astType = fileName.split('_')[1]
as... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_asset_path(name):\n return os.path.join(constants.ROOT_DIR, 'assets', name)",
"def _get_asset_dir(self, database):\n if not database:\n return 'assets'\n\n path = os.path.dirname(database)\n return os.path.join(path, 'assets')",
"def _path(name: str):\n return os.p... | [
"0.7651856",
"0.75091547",
"0.71344215",
"0.67845696",
"0.65159225",
"0.6449491",
"0.6356405",
"0.6293956",
"0.61229074",
"0.61127037",
"0.60930806",
"0.6086665",
"0.60493875",
"0.6040847",
"0.60154086",
"0.6008243",
"0.6008031",
"0.6003942",
"0.5991939",
"0.59918576",
"0.597... | 0.6320406 | 7 |
Render `data` into JSON, returning a bytestring. | def render(self, data, accepted_media_type=None, renderer_context=None):
if data is None:
return bytes()
renderer_context = renderer_context or {}
indent = self.get_indent(accepted_media_type, renderer_context)
if indent is None:
separators = SHORT_SEPARATORS if ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render(self, data):\n logging.info(\"render (start)\")\n\n seria = json.dumps(data, ensure_ascii=False, indent=4)\n logging.info(\"rendered %s characters (end)\" % len(seria))\n return seria",
"def render(self, data, accepted_media_type=None, renderer_context=None):\n if data is No... | [
"0.8322761",
"0.7805844",
"0.765805",
"0.7562658",
"0.7479649",
"0.7266738",
"0.7247055",
"0.72162443",
"0.71565706",
"0.7107005",
"0.7067796",
"0.70554405",
"0.7004682",
"0.6947288",
"0.69117856",
"0.68802786",
"0.6837645",
"0.68103766",
"0.6727808",
"0.6723814",
"0.67190343... | 0.76940095 | 2 |
Softmax cost function for word2vec models | def softmaxCostAndGradient(predicted, target, outputVectors, dataset):
# Implement the cost and gradients for one predicted word vector
# and one target word vector as a building block for word2vec
# models, assuming the softmax prediction function and cross
# entropy loss.
# Inputs:
# - predi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def model(X, Y, word_to_vec_map, learning_rate = 0.01, num_iterations = 400):\n \n # Get a valid word contained in the word_to_vec_map \n any_word = list(word_to_vec_map.keys())[0]\n \n # Initialize cost. It is needed during grading\n cost = 0\n \n # Define number of training examples\n... | [
"0.72518784",
"0.7108989",
"0.6836795",
"0.6821532",
"0.6693698",
"0.6658965",
"0.66583645",
"0.65682226",
"0.65567243",
"0.648131",
"0.6436247",
"0.64335537",
"0.64082384",
"0.640416",
"0.6380756",
"0.6324167",
"0.6319246",
"0.6310624",
"0.63057303",
"0.63019854",
"0.6281135... | 0.70910984 | 2 |
Negative sampling cost function for word2vec models | def negSamplingCostAndGradient(predicted, target, outputVectors, dataset,
K=10):
# Implement the cost and gradients for one predicted word vector
# and one target word vector as a building block for word2vec
# models, using the negative sampling technique. K is the sample
# size. You might want to ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def neg_sampling_loss_and_gradient(\n center_word_vec,\n outside_word_idx,\n outside_vectors,\n dataset,\n K=10\n):\n\n # Negative sampling of words is done for you. Do not modify this if you\n # wish to match the autograder and receive points!\n neg_sample_word_indices ... | [
"0.6708602",
"0.64941853",
"0.64483947",
"0.6427767",
"0.6373894",
"0.6373894",
"0.6373894",
"0.6267261",
"0.6267261",
"0.6156621",
"0.61330694",
"0.6097435",
"0.6084569",
"0.602716",
"0.60267574",
"0.6020172",
"0.60125136",
"0.600268",
"0.59315264",
"0.59011626",
"0.5843728"... | 0.67069376 | 1 |
Skipgram model in word2vec | def skipgram(currentWord, C, contextWords, tokens, inputVectors, outputVectors,
dataset, word2vecCostAndGradient = softmaxCostAndGradient):
# Implement the skip-gram model in this function.
# Inputs:
# - currrentWord: a string of the current center word
# - C: integer, context size
# - context... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def skipgram(currentWord, C, contextWords, tokens, inputVectors, outputVectors,\n dataset, word2vecCostAndGradient=softmaxCostAndGradient):\n \n cost = 0.0\n gradIn = np.zeros(inputVectors.shape)\n gradOut = np.zeros(outputVectors.shape)\n\n ### YOUR CODE HERE\n predicted = inputVecto... | [
"0.73811865",
"0.73697907",
"0.7339572",
"0.71801305",
"0.70820427",
"0.6863275",
"0.685266",
"0.68452936",
"0.68211156",
"0.67541444",
"0.67541444",
"0.67541444",
"0.66995925",
"0.66995925",
"0.65849197",
"0.6533643",
"0.642774",
"0.63859594",
"0.6374046",
"0.62920815",
"0.6... | 0.7008295 | 5 |
CBOW model in word2vec | def cbow(currentWord, C, contextWords, tokens, inputVectors, outputVectors,
dataset, word2vecCostAndGradient = softmaxCostAndGradient):
# Implement the continuous bag-of-words model in this function.
# Input/Output specifications: same as the skip-gram model
# We will not provide starter code for this ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cbow(currentWord, C, contextWords, tokens, inputVectors, outputVectors,\n dataset, word2vecCostAndGradient=softmaxCostAndGradient):\n\n cost = 0.0\n gradIn = np.zeros(inputVectors.shape)\n gradOut = np.zeros(outputVectors.shape)\n\n ### YOUR CODE HERE\n target = tokens[currentWord]\n\n ... | [
"0.6977331",
"0.6879846",
"0.6852588",
"0.6753627",
"0.6711709",
"0.66832954",
"0.66618025",
"0.6635551",
"0.6472121",
"0.6373049",
"0.6366068",
"0.63088983",
"0.63043493",
"0.6302005",
"0.62772036",
"0.6273346",
"0.62633795",
"0.6254702",
"0.618194",
"0.6137404",
"0.6132508"... | 0.75711095 | 0 |
Used by Ajax calls Return the upload progress and total length values | def upload_progress(request, account_slug=None):
#pylint: disable=unused-argument
if 'X-Progress-ID' in request.GET:
progress_id = request.GET['X-Progress-ID']
elif 'X-Progress-ID' in request.META:
progress_id = request.META['X-Progress-ID']
if progress_id:
cache_key = "%s_%s" % ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def progress(request):\n file_id = request.GET['X-Progress-ID']\n session = DBSession()\n u = session.query(Upload).filter_by(id=file_id).one()\n data = {'state': u.state}\n if u.state == 'uploading':\n if not os.path.exists(u.tmp_path):\n # The temporary file has not been created ... | [
"0.74291635",
"0.7131904",
"0.6652564",
"0.65933836",
"0.6477215",
"0.64750636",
"0.6418828",
"0.6418828",
"0.6418828",
"0.6418828",
"0.639952",
"0.63758796",
"0.6358746",
"0.6321468",
"0.6275455",
"0.6262116",
"0.6256155",
"0.6229986",
"0.61642486",
"0.61383957",
"0.61281985... | 0.0 | -1 |
Create Parameter Keyword Arguments for the `Minuit` constructor. | def make_minuit_par_kwargs(parameters):
kwargs = {}
for par in parameters.parameters:
kwargs[par.name] = par.value
min_ = None if np.isnan(par.min) else par.min
max_ = None if np.isnan(par.max) else par.max
kwargs['limit_{}'.format(par.name)] = (min_, max_)
if parameter... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(**params):",
"def define_parameters(self):",
"def __init__(self, *args: Union[List[AtomKey], HKT], **kwargs: str) -> None:\n ...",
"def define_parameters(self):\n self.add_argument('--prefix', dest='prefix', type=str, optional=False,\n help='prefix for file... | [
"0.6288195",
"0.6145601",
"0.608256",
"0.6077384",
"0.60681677",
"0.6064592",
"0.60551435",
"0.60317856",
"0.60270023",
"0.60270023",
"0.60270023",
"0.60270023",
"0.60270023",
"0.60270023",
"0.60270023",
"0.60239285",
"0.5958885",
"0.5912476",
"0.5897381",
"0.5871605",
"0.587... | 0.67365843 | 0 |
Get full covar matrix as Numpy array. This was added as `minuit.np_covariance` in `iminuit` in v1.3, but we still want to support v1.2 | def _get_covar(minuit):
n = len(minuit.parameters)
m = np.zeros((n, n))
for i1, k1 in enumerate(minuit.parameters):
for i2, k2 in enumerate(minuit.parameters):
if set([k1, k2]).issubset(minuit.list_of_vary_param()):
m[i1, i2] = minuit.covariance[(k1, k2)]
return m | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def covar(self) -> np.ndarray:\n if self._covar is None:\n self._covar = batched_inv_spd(batched_cholesky(self._inv_covar))\n return self._covar",
"def covariance(data_matrix):\n return np.asmatrix(np.cov(data_matrix, rowvar=0))",
"def get_cov_matrix_states(self):\n cov = num... | [
"0.715",
"0.6918695",
"0.68584454",
"0.68436575",
"0.6735441",
"0.66587955",
"0.665023",
"0.6618526",
"0.65532213",
"0.65131664",
"0.648743",
"0.6473425",
"0.6427018",
"0.64248204",
"0.640013",
"0.6393602",
"0.63809496",
"0.63302636",
"0.631944",
"0.63151485",
"0.62982684",
... | 0.75416523 | 0 |
Returns true if there is more than one active project associated with this client. | def has_multiple_projects(self):
return self.projects.filter(active=True).count() > 1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def active_projects(self):\n return self.projects.filter(active=True)",
"def is_active(self) -> bool:\n return any(x is not None for x in self._constraints)",
"def is_competition(self):\n return self.db_is_competition",
"def is_project(self, project):\n return self._projects_looku... | [
"0.65099156",
"0.6169773",
"0.61593264",
"0.6121147",
"0.61132723",
"0.61132723",
"0.5950498",
"0.5926298",
"0.58955985",
"0.5866945",
"0.5798091",
"0.5769043",
"0.57587534",
"0.5707796",
"0.5696425",
"0.56953526",
"0.5688109",
"0.5679527",
"0.5667053",
"0.5654484",
"0.562980... | 0.8511699 | 0 |
Returns the active projects for the client. | def active_projects(self):
return self.projects.filter(active=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_projects(self):\n return conf.projects",
"def get_client_projects(self, client=None):\n if type(client) is Client:\n return [p for p in self.project_list if client.client_id == p.client_id]",
"def get_projects(self):\n return self.http_call(\"get\", url=f\"{self.base_url... | [
"0.79490113",
"0.7929667",
"0.77085984",
"0.7485822",
"0.7469964",
"0.74452794",
"0.74072176",
"0.73590404",
"0.7318829",
"0.724942",
"0.7204794",
"0.71989226",
"0.719029",
"0.7152463",
"0.7124276",
"0.7114484",
"0.7078646",
"0.7010834",
"0.7005374",
"0.6980954",
"0.6970034",... | 0.8063469 | 0 |
Process a document into its final form. Translate `document` (a Docutils document tree) into the Writer's native format, and write it out to its `destination` (a `docutils.io.Output` subclass object). Normally not overridden or extended in subclasses. | def write(self, document, destination):
self.document = document
self.language = languages.get_language(
document.settings.language_code,
document.reporter)
self.destination = destination
self.translate()
output = self.destination.write(self.output)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def emit_document(self, document):\n raise NotImplementedError()",
"def _transform(self, document):\n pass",
"def process_document(self, document, **kwargs):\n \n start = time.time()\n\n if self._log_file is not None:\n map_annotations_logger.addHandler(file_handle... | [
"0.60927504",
"0.5725874",
"0.54091835",
"0.52739656",
"0.5214437",
"0.51614934",
"0.5158953",
"0.5158953",
"0.5136689",
"0.50936973",
"0.5086801",
"0.50783026",
"0.50710064",
"0.49749365",
"0.49396706",
"0.4884253",
"0.4865418",
"0.48605415",
"0.48564097",
"0.48286474",
"0.4... | 0.72632694 | 0 |
Do final translation of `self.document` into `self.output`. Called from `write`. Override in subclasses. Usually done with a `docutils.nodes.NodeVisitor` subclass, in combination with a call to `docutils.nodes.Node.walk()` or `docutils.nodes.Node.walkabout()`. The ``NodeVisitor`` subclass must support all standard elem... | def translate(self):
raise NotImplementedError('subclass must override this method') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _transform(self, document):\n pass",
"def convert(self):\n self._convert()\n self._write_docx()",
"def emit_document(self, document):\n raise NotImplementedError()",
"def __call__(self):\n self.tree = etree.parse(self.src)\n\n agent = transformer_factory(self.tre... | [
"0.6509124",
"0.60530466",
"0.59633064",
"0.5783462",
"0.5775899",
"0.57631224",
"0.5687414",
"0.5612088",
"0.55821306",
"0.554167",
"0.5445731",
"0.5440628",
"0.53633213",
"0.5300753",
"0.5219228",
"0.51925856",
"0.5184472",
"0.51653117",
"0.51413035",
"0.513734",
"0.5135935... | 0.0 | -1 |
Assemble the `self.parts` dictionary. Extend in subclasses. | def assemble_parts(self):
self.parts['whole'] = self.output
self.parts['encoding'] = self.document.settings.output_encoding
self.parts['version'] = docutils.__version__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_part(self):\n parts = []\n for part in re.split(r'\\*\\*\\* ([A-Z- ]+) \\*\\*\\*', self.hand_file): # return [ 'part1', 'splitter1', 'part2',..\n parts.append(part)\n\n for i in range(0, len(parts)):\n if i == 0:\n self.part_dict['HEADER'] = part... | [
"0.62134004",
"0.62024397",
"0.5878337",
"0.5783612",
"0.57041496",
"0.56869996",
"0.5598993",
"0.54675126",
"0.54501754",
"0.54231954",
"0.5421467",
"0.5304137",
"0.52999735",
"0.52968395",
"0.5241657",
"0.52192795",
"0.52082586",
"0.5189509",
"0.51861733",
"0.5183208",
"0.5... | 0.61536735 | 2 |
Return the Writer class from the `writer_name` module. | def get_writer_class(writer_name):
writer_name = writer_name.lower()
if writer_name in _writer_aliases:
writer_name = _writer_aliases[writer_name]
try:
module = __import__(writer_name, globals(), locals(), level=1)
except ImportError:
module = __import__(writer_name, globals(), l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_writer_class(cls) -> 'Type[SpectrumWriter]':\n raise NotImplementedError(SpectrumReader.get_writer_class.__qualname__)",
"def _get_writer(self, writer_name):\r\n if writer_name not in self._writers:\r\n path = os.path.join(self.log_dir, writer_name)\r\n writer = summary_ops_v2.create_... | [
"0.69305336",
"0.6689665",
"0.6409046",
"0.62630635",
"0.6220382",
"0.58785063",
"0.5845479",
"0.5839867",
"0.58295506",
"0.56432635",
"0.5637954",
"0.5606227",
"0.5554922",
"0.5537747",
"0.5523522",
"0.5474596",
"0.5463936",
"0.54269594",
"0.5412886",
"0.54028976",
"0.538162... | 0.9022644 | 0 |
Test to validate that custom payloads work with simple queries creates a simple query and ensures that custom payloads are passed to C. A custom query provider is used with C so we can validate that same custom payloads are sent back with the results 2.6 PYTHON280 valid custom payloads should be sent and received | def test_custom_query_basic(self):
# Create a simple query statement a
query = "SELECT * FROM system.local"
statement = SimpleStatement(query)
# Validate that various types of custom payloads are sent and received okay
self.validate_various_custom_payloads(statement=statement) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_load_kql_query_defs(self):\n la_provider = self.la_provider\n la_provider.connect(connection_str=\"test\")\n self.assertEqual(getattr(la_provider, \"test\"), \"CustomAttrib\")\n\n # Did we read and process the query definitions OK\n q_sources = la_provider.query_store.da... | [
"0.6527704",
"0.64625055",
"0.6448495",
"0.64351636",
"0.63665766",
"0.60493946",
"0.60140413",
"0.5982427",
"0.59034127",
"0.58764935",
"0.5842737",
"0.578988",
"0.5781109",
"0.5768024",
"0.5743283",
"0.5735624",
"0.5709683",
"0.5625501",
"0.5560789",
"0.5519123",
"0.5516742... | 0.80934554 | 0 |
Test to validate that custom payloads work with batch queries creates a batch query and ensures that custom payloads are passed to C. A custom query provider is used with C so we can validate that same custom payloads are sent back with the results 2.6 PYTHON280 valid custom payloads should be sent and received | def test_custom_query_batching(self):
# Construct Batch Statement
batch = BatchStatement(BatchType.LOGGED)
for i in range(10):
batch.add(SimpleStatement("INSERT INTO test3rf.test (k, v) VALUES (%s, %s)"), (i, i))
# Validate that various types of custom payloads are sent and... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_custom_query_basic(self):\n\n # Create a simple query statement a\n query = \"SELECT * FROM system.local\"\n statement = SimpleStatement(query)\n # Validate that various types of custom payloads are sent and received okay\n self.validate_various_custom_payloads(statement... | [
"0.6579624",
"0.6282645",
"0.5862671",
"0.58041817",
"0.5789634",
"0.571839",
"0.5674159",
"0.5659514",
"0.5572245",
"0.55634826",
"0.552229",
"0.54960763",
"0.54557633",
"0.5425926",
"0.5420833",
"0.54062617",
"0.5386587",
"0.5372885",
"0.5361616",
"0.531875",
"0.53118783",
... | 0.7527416 | 0 |
Test to validate that custom payloads work with prepared queries creates a batch query and ensures that custom payloads are passed to C. A custom query provider is used with C so we can validate that same custom payloads are sent back with the results 2.6 PYTHON280 valid custom payloads should be sent and received | def test_custom_query_prepared(self):
# Construct prepared statement
prepared = self.session.prepare(
"""
INSERT INTO test3rf.test (k, v) VALUES (?, ?)
""")
bound = prepared.bind((1, None))
# Validate that various custom payloads are validated corr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_custom_query_batching(self):\n\n # Construct Batch Statement\n batch = BatchStatement(BatchType.LOGGED)\n for i in range(10):\n batch.add(SimpleStatement(\"INSERT INTO test3rf.test (k, v) VALUES (%s, %s)\"), (i, i))\n\n # Validate that various types of custom payload... | [
"0.7442897",
"0.6976703",
"0.6230606",
"0.58975285",
"0.56896055",
"0.5653015",
"0.5635988",
"0.553793",
"0.5536429",
"0.5535898",
"0.54537135",
"0.54533887",
"0.54533887",
"0.543706",
"0.54330355",
"0.54176164",
"0.5416644",
"0.53958035",
"0.53950524",
"0.5392065",
"0.539002... | 0.6913831 | 2 |
This is a utility method that given a statement will attempt to submit the statement with various custom payloads. It will validate that the custom payloads are sent and received correctly. statement The statement to validate the custom queries in conjunction with | def validate_various_custom_payloads(self, statement):
# Simple key value
custom_payload = {'test': b'test_return'}
self.execute_async_validate_custom_payload(statement=statement, custom_payload=custom_payload)
# no key value
custom_payload = {'': b''}
self.execute_asyn... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_custom_query_basic(self):\n\n # Create a simple query statement a\n query = \"SELECT * FROM system.local\"\n statement = SimpleStatement(query)\n # Validate that various types of custom payloads are sent and received okay\n self.validate_various_custom_payloads(statement... | [
"0.7104347",
"0.67997825",
"0.66198033",
"0.64275306",
"0.5787356",
"0.56871104",
"0.5549678",
"0.5532289",
"0.5479497",
"0.544387",
"0.5442303",
"0.5413099",
"0.5411312",
"0.5392633",
"0.5387991",
"0.53798467",
"0.5379808",
"0.53635925",
"0.5340686",
"0.532281",
"0.52682734"... | 0.6363154 | 4 |
This is just a simple method that submits a statement with a payload, and validates that the custom payload we submitted matches the one that we got back statement The statement to execute custom_payload The custom payload to submit with | def execute_async_validate_custom_payload(self, statement, custom_payload):
# Submit the statement with our custom payload. Validate the one
# we receive from the server matches
response_future = self.session.execute_async(statement, custom_payload=custom_payload)
response_future.result... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_various_custom_payloads(self, statement):\n\n # Simple key value\n custom_payload = {'test': b'test_return'}\n self.execute_async_validate_custom_payload(statement=statement, custom_payload=custom_payload)\n\n # no key value\n custom_payload = {'': b''}\n self... | [
"0.63223815",
"0.6253099",
"0.59362966",
"0.5643522",
"0.5596571",
"0.5494747",
"0.5285481",
"0.5124251",
"0.5088654",
"0.50280416",
"0.50209314",
"0.50113577",
"0.50002974",
"0.49883088",
"0.49857587",
"0.49452132",
"0.4872657",
"0.48709938",
"0.48590347",
"0.4841354",
"0.48... | 0.7807556 | 0 |
+ read file in python | def read_file(file):
f = open(file, 'r')
print(f.read()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read(path):",
"def read_file(path_to_file):\n 8",
"def read_from_file(self, filename: str) -> None:",
"def read(self, filename):\n pass",
"def read(self, filename):\n pass",
"def read_file(file):\n with open(file, \"r\") as fid:\n return fid.read()",
"def read(file):\n ... | [
"0.82432014",
"0.7910622",
"0.7896791",
"0.77604586",
"0.77604586",
"0.7409387",
"0.7344486",
"0.7290307",
"0.7289459",
"0.7198701",
"0.7196135",
"0.7195775",
"0.7146001",
"0.71020913",
"0.7101919",
"0.7084718",
"0.7066086",
"0.7065207",
"0.7042623",
"0.70318484",
"0.7018245"... | 0.69794834 | 27 |
+ working to with keyword to processing file objects => file will close when occurs exception familiar to use tryfinally | def read_file_with_keyword(file):
with open(file) as f:
data = f.read()
print(data)
with open(file) as f2:
data = f2.readlines()
print(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __exit__(self, exception_type, exception_value, traceback):\n self.fileobject.close()",
"def __exit__(self, exc_type, exc_value, traceback):\n self.file_out.close()",
"def finalize(self):\n if self._file:\n toLog(\"Closing file `{0}`\".format(self._fname), True)\n self._file.clos... | [
"0.6539302",
"0.63316876",
"0.6122932",
"0.6120481",
"0.60801846",
"0.6031979",
"0.60103905",
"0.6002176",
"0.59708273",
"0.59708273",
"0.5958924",
"0.59514046",
"0.594744",
"0.5937519",
"0.59352446",
"0.5896435",
"0.5860249",
"0.58322245",
"0.5758804",
"0.5733952",
"0.564616... | 0.0 | -1 |
Matches each point in the region to minimum SSD template from entire list of templates | def processRegionSSD( self, r ):
ixs = TemplateMatcher.matIxs( self.n ** 2 )
## 5 x 5 windows for region r
ws = map( lambda x : TemplateMatcher.slice( r, self.n, x ), ixs )
ts = map( lambda w : map( lambda t : TemplateMatcher.SSD( t, w ), self.templates ) , ws)
tMatch = map( la... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def templateMatchMulti(img, template):\n\tgray = grayscale(img)\n\ttemp = grayscale(template)\n\tw, h = temp.shape[::-1]\n\tres = cv2.matchTemplate(gray, temp, cv2.TM_CCOEFF_NORMED)\n\tthreshold = 0.8\n\tloc = np.where(res >= threshold)\n\tpts = []\n\tfor pt in zip(*loc[::-1]):\n\t\trect = [pt, (pt[0] + w, pt[1] +... | [
"0.61586505",
"0.58594805",
"0.57886225",
"0.5669279",
"0.56338966",
"0.56214774",
"0.56101286",
"0.5605014",
"0.551976",
"0.5498334",
"0.5397037",
"0.5397037",
"0.53930295",
"0.5299927",
"0.5288984",
"0.52475137",
"0.5227831",
"0.51846814",
"0.5125249",
"0.51196617",
"0.5094... | 0.6197051 | 0 |
Slices matrix centered around point pt to have size nxn If area includes the edge of imaged area, 0 padded rows/cols are added lc uc lr | | | ur | def slice( mat, n, pt ):
lc, uc = TemplateMatcher.hinge( pt.x, n ) ## column limits
lr, ur = TemplateMatcher.hinge( pt.y, n )
nr, nc = np.shape( mat )
nr = nr - 1
nc = nc - 1
alc, auc = lc, uc
alr, aur = lr, ur
rpb, rpa, cpl, cpr = [0,0,0,0... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def applyKernelToPoints(image,pts,kernel,border_type='BLACK'):\n \n \n pts=np.asarray(pts)\n image=np.asarray(image)\n image.shape\n if len(image.shape)>2:\n grayscale=False\n shaperesult=(len(pts),image.shape[2])\n elif len(image.shape)==1:\n image=image.reshape(1,image.s... | [
"0.5488434",
"0.5432423",
"0.53871787",
"0.5326633",
"0.5318443",
"0.52965856",
"0.52940756",
"0.52368885",
"0.5177451",
"0.5161178",
"0.5160577",
"0.5142208",
"0.51415056",
"0.5134802",
"0.5108706",
"0.5104009",
"0.5100073",
"0.5083504",
"0.50543976",
"0.50510263",
"0.503998... | 0.6774917 | 0 |
Given region of size n x n, returns all col, row indices for iteration x > col y > row | def matIxs( n ):
rows, cols = np.indices( (n,n) )
row = rows.flatten()
col = cols.flatten()
return map( lambda x: Vector( x[0], x[1] ), zip( col, row ) ) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def determine_region(x, y, width, height):\n xs = [0, width / 3, 2 * width / 3, width]\n ys = [0, height / 3, 2 * height / 3, height]\n for i in range(3):\n for j in range(3):\n if (x >= xs[j] and x < xs[j + 1]) and (y >= ys[i] and y < ys[i + 1]):\n return i * 3 + j",
"def index_iterator((x_min... | [
"0.71921754",
"0.6836391",
"0.67723036",
"0.6759747",
"0.6655711",
"0.6587607",
"0.6453828",
"0.6407375",
"0.6339972",
"0.6325821",
"0.6279633",
"0.6273713",
"0.6240977",
"0.61783856",
"0.6146757",
"0.61393416",
"0.6125007",
"0.611549",
"0.6105668",
"0.6094229",
"0.6092891",
... | 0.0 | -1 |
Determines whether no events are stored in the DemoInfo. | def is_empty(self):
return not (self.killstreaks or self.bookmarks) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_empty(self):\n return self.channels is None or self.timestamp is None",
"def is_empty(self) -> bool:\n return not bool(self.samplers)",
"def is_empty(self):\n return not self.Timeline",
"def isEmpty(self):\n\n return len(self.callbacks) == 0",
"def is_empty(self, trace) -... | [
"0.6238876",
"0.61898893",
"0.6095691",
"0.6049332",
"0.603219",
"0.6004664",
"0.59738624",
"0.59670824",
"0.59637004",
"0.58920264",
"0.58544755",
"0.583065",
"0.58181715",
"0.58172005",
"0.58101654",
"0.5804732",
"0.5777685",
"0.5772077",
"0.5771824",
"0.5770287",
"0.576925... | 0.6010662 | 5 |
Parses the given data which should be retrieved from a standard format JSON file and turns it into a DemoInfo instance. | def from_json(cls, json_data, demo_name):
cur_bm = []
cur_ks = []
for k in json_data["events"]:
if k["name"] == "Killstreak":
cur_ks.append(DemoEvent(int(k["value"]), int(k["tick"]), None))
elif k["name"] == "Bookmark":
cur_bm.append(DemoEvent(k["value"], int(k["tick"]), None))
return cls(demo_na... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_json_reader_data_contents(process_data):\n json_data = process_data(file_name_or_type='scooter_data.json')\n for val in json_data:\n assert(isinstance(val['id'], int))\n assert(isinstance(val['name'], str))\n assert(isinstance(val['vin_number'], str))\n assert(isinstance(... | [
"0.6023979",
"0.58691",
"0.57561564",
"0.5740799",
"0.5706439",
"0.56778896",
"0.56699413",
"0.5652918",
"0.5647447",
"0.56199074",
"0.56082773",
"0.55712605",
"0.5567739",
"0.5567385",
"0.5561605",
"0.55434746",
"0.5526353",
"0.5500797",
"0.550043",
"0.5485761",
"0.5455614",... | 0.6014086 | 1 |
Converts the demo info to a JSONcompliant dict that can be written to a file and be reconstructed to an equal DemoInfo object by using `DemoInfo.from_json`. The event dicts will be ordered ascendingly by tick, just like they are produced by Team Fortress 2. | def to_json(self):
events = [{"name": "Killstreak", "value": v, "tick": t} for v, t, _ in self.killstreaks]
events += [{"name": "Bookmark", "value": v, "tick": t} for v, t, _ in self.bookmarks]
return {"events": sorted(events, key = lambda e: int(e["tick"]))} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def from_json(cls, json_data, demo_name):\n\t\tcur_bm = []\n\t\tcur_ks = []\n\n\t\tfor k in json_data[\"events\"]:\n\t\t\tif k[\"name\"] == \"Killstreak\":\n\t\t\t\tcur_ks.append(DemoEvent(int(k[\"value\"]), int(k[\"tick\"]), None))\n\t\t\telif k[\"name\"] == \"Bookmark\":\n\t\t\t\tcur_bm.append(DemoEvent(k[\"valu... | [
"0.6228054",
"0.6160889",
"0.5326841",
"0.5186036",
"0.50814676",
"0.5010847",
"0.49962336",
"0.49832866",
"0.48962018",
"0.48715475",
"0.48692217",
"0.47869495",
"0.47717983",
"0.47635192",
"0.47498238",
"0.47180924",
"0.4708449",
"0.46955967",
"0.46888372",
"0.468771",
"0.4... | 0.48573804 | 11 |
Takes a handle_events.RawLogchunk and converts it into DemoInfo. | def from_raw_logchunk(cls, in_chk):
loglines = in_chk.content.split("\n")
if not loglines:
raise ValueError("Logchunks may not be empty.")
regres = RE_LINE.search(loglines[0])
if not regres:
raise ValueError("Regex match failed, Logchunk malformed.")
demo = regres[GROUP.DEMO] + ".dem"
killstreaks = [... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _extract_sample(self, line, publish=True):\n sample = None\n match = self._sample_regex.match(line)\n if match:\n sample = {}\n sample['t'] = [float(match.group(1))]\n sample['c'] = [float(match.group(2))]\n sample['p'] = [float(match.group(3))]\... | [
"0.5132385",
"0.5127417",
"0.51171947",
"0.5102437",
"0.5070641",
"0.49931782",
"0.48906305",
"0.488175",
"0.4847076",
"0.48453277",
"0.48372746",
"0.48219904",
"0.4818269",
"0.47931176",
"0.47858205",
"0.4750492",
"0.47170094",
"0.46886584",
"0.4665421",
"0.46565127",
"0.464... | 0.60423684 | 0 |
Returns a string that can be written to an `_events.txt` file and will be interpretable as a valid logchunk by the events reader afterwards. All info is sorted by tick. | def to_logchunk(self):
demo_name = os.path.splitext(self.demo_name)[0]
to_write = [("Killstreak", value, tick, date) for value, tick, date in self.killstreaks]
to_write.extend(("Bookmark", value, tick, date) for value, tick, date in self.bookmarks)
to_write.sort(key = lambda t: t[2])
return "\n".join(
f'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_line():\n random.seed(datetime.utcnow().microsecond)\n dt = datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S.%f')\n event = random.choice(['event1', 'event2', 'event3'])\n return '{};{}\\n'.format(dt, event).encode('utf-8')",
"def _readin_evtx(file):\n\tcontent = []\n\tunparsed_entries = 0\n\twi... | [
"0.57899225",
"0.5709491",
"0.56271654",
"0.5571231",
"0.55297595",
"0.55278057",
"0.5437416",
"0.54274184",
"0.5308161",
"0.52946806",
"0.52873594",
"0.5220328",
"0.52083784",
"0.5135776",
"0.51283467",
"0.5092768",
"0.5068793",
"0.5058062",
"0.5047352",
"0.5044775",
"0.5036... | 0.56155944 | 3 |
Controller for all spinBoxes | def spinBoxChanged(self):
sdr = self.sender()
if sdr == self.ui.spinBoxSpectraToCut:
self.parameters['cut_m'] = self.ui.spinBoxSpectraToCut.value()
elif sdr == self.ui.spinBoxEveryNSpectra:
self.parameters['every_n'] = self.ui.spinBoxEveryNSpectra.value()
elif sd... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def spinbox_control(self, action):\n # Gets the current tab, so that it can check which of the spinboxes currently shown is\n # in focus, or choose one to be in focus \n c_tab = self.tabWidget.currentIndex()\n tab_content = {0:[self.VCV_frequency_spb,\n self.VCV... | [
"0.6635967",
"0.6404173",
"0.62968975",
"0.5838627",
"0.5778049",
"0.57579046",
"0.54349446",
"0.5320462",
"0.5312006",
"0.52779394",
"0.5275268",
"0.5254141",
"0.52477854",
"0.51787406",
"0.5172973",
"0.51545256",
"0.51098",
"0.51006585",
"0.509712",
"0.50906676",
"0.5058366... | 0.5469789 | 6 |
Action comboBox box changed | def comboBoxChanged(self):
new_action = self.ui.comboBoxAction.currentText().lower()
self.parameters['action'] = new_action
self.changed.emit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_comboBox_celuebianhao_currentIndexChanged(self, p0):\n # TODO: not implemented yet\n raise NotImplementedError",
"def on_comboBox_qihuozhanghao_currentIndexChanged(self, p0):\n # TODO: not implemented yet\n raise NotImplementedError",
"def on_comboBox_jiaoyimoxing_currentInde... | [
"0.71040225",
"0.6949202",
"0.6788177",
"0.6759619",
"0.66363245",
"0.65092915",
"0.6395416",
"0.63920945",
"0.636702",
"0.63205105",
"0.6268223",
"0.620821",
"0.6175454",
"0.6147484",
"0.6136072",
"0.613324",
"0.61324733",
"0.6116489",
"0.6108808",
"0.61041707",
"0.6085081",... | 0.8299217 | 0 |
Return the plot with the appropriate cuts | def fcn(self, data_in):
assert isinstance(data_in, _np.ndarray), 'Required input is an ndarray'
assert data_in.ndim == 1, 'Required input is a 1D ndarray'
data_out = 0*data_in
cutter = CutEveryNSpectra(self.parameters['offset'], cut_m=self.parameters['cut_m'],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plotDistributionWithLimitsRefine(lXs, llYs, lKClassif,out=\"out.png\", title=\"title\", xax=\"xax\", yax=\"yax\",legend=\"\"):\n\n fig = plt.Figure(figsize=(40,20))\n fig.suptitle(title, fontsize=32)\n nbPlots = len(llYs)\n sqrt = int(math.ceil(math.sqrt(nbPlots)))\n ymax = 0... | [
"0.64149034",
"0.6369867",
"0.6122671",
"0.597284",
"0.5914648",
"0.5899135",
"0.5868837",
"0.5853187",
"0.5831248",
"0.5792071",
"0.574637",
"0.5733046",
"0.5723124",
"0.572188",
"0.56790245",
"0.5676098",
"0.5673394",
"0.56570923",
"0.56539637",
"0.56525546",
"0.56430966",
... | 0.0 | -1 |
Create and save a User with the given email and password. | def _create_user(self, email, password, **extra_fields):
if not email:
raise ValueError('The given email must be set')
email = self.normalize_email(email)
user = self.model(email=email, **extra_fields)
user.set_password(password)
user.save(using=self._db)
retu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_user(self, email, password, **extra_fields):\n if not email:\n raise ValueError('The given email must be set')\n email = self.normalize_email(email)\n user = self.model(email=email, **extra_fields)\n user.set_password(password)\n user.save(using=self._db)\n return user",
"def... | [
"0.8545239",
"0.85126233",
"0.8508934",
"0.85035056",
"0.8503207",
"0.8490302",
"0.8490302",
"0.84895617",
"0.84895617",
"0.84895617",
"0.84866524",
"0.84778446",
"0.8476501",
"0.8457027",
"0.8457027",
"0.8457027",
"0.8455693",
"0.84490657",
"0.84451187",
"0.84442055",
"0.844... | 0.84760785 | 20 |
Create and save a regular User with the given email and password. | def create_user(self, email, password=None, **extra_fields):
extra_fields.setdefault('is_staff', False)
extra_fields.setdefault('is_superuser', False)
return self._create_user(email, password, **extra_fields) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_user(self, email, password, **extra_fields):\n\n email = self.normalize_email(email)\n user = self.model(email=email, **extra_fields)\n user.set_password(password)\n user.save(using=self._db)\n return user",
"def _create_user(self, email, password, **extra_fields):\... | [
"0.86050725",
"0.8598407",
"0.85864305",
"0.85746485",
"0.8557106",
"0.8518878",
"0.8518878",
"0.8518878",
"0.85146487",
"0.8512698",
"0.8509415",
"0.8509415",
"0.8507696",
"0.84993356",
"0.8494756",
"0.8494756",
"0.8494756",
"0.8494756",
"0.8494756",
"0.8494756",
"0.8494756"... | 0.0 | -1 |
Create and save a SuperUser with the given email and password. | def create_superuser(self, email, password, **extra_fields):
extra_fields.setdefault('is_staff', True)
extra_fields.setdefault('is_superuser', True)
if extra_fields.get('is_staff') is not True:
raise ValueError('Superuser must have is_staff=True.')
if extra_fields.get('is_su... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createsuperuser():\n\n email = prompt('User E-Mail')\n email_confirm = prompt('Confirm E-Mail')\n\n if not email == email_confirm:\n sys.exit('\\nCould not create user: E-Mail did not match')\n\n if not EMAIL_REGEX.match(email):\n sys.exit('\\nCould not create user: Invalid E-Mail add... | [
"0.8237176",
"0.79841226",
"0.7982151",
"0.79641086",
"0.7962122",
"0.7950078",
"0.7942463",
"0.7942463",
"0.7942463",
"0.79412585",
"0.7934801",
"0.79277754",
"0.79170924",
"0.7912112",
"0.7909707",
"0.79029053",
"0.79006577",
"0.78965855",
"0.78560257",
"0.7851105",
"0.7847... | 0.7790897 | 30 |
Business method to extract and format data for 'measures' table. | def format_data(results):
measures = []
counter = 0
for result in results:
counter += 1
try:
# Skip "DeviceInt*" probes
if result.get("description").startswith("DeviceInt"):
continue
if ('measures' in result and len(result.get("measures"))... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getMeasures():",
"def getMeasures(unique_name=None):",
"def get_measure_columns() -> list:\n return [\n 'Number of Services',\n 'Number of Medicare Beneficiaries',\n 'Number of Distinct Medicare Beneficiary/Per Day Services',\n 'Average Medicare Allowed Am... | [
"0.7039788",
"0.63949984",
"0.6337883",
"0.62608486",
"0.6246158",
"0.6077906",
"0.6074325",
"0.5836542",
"0.58073634",
"0.58073634",
"0.57769066",
"0.5750017",
"0.5672349",
"0.5633635",
"0.56307864",
"0.56283534",
"0.5621347",
"0.55219436",
"0.5494019",
"0.54719216",
"0.5462... | 0.6265401 | 3 |
Returns a deferred that will be called when the process ends. | def done(self):
return self._done.get() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_deferred(self):\n self.deferred_executor.process()",
"def _finish(self):\n if self.consumer:\n self.consumer.unregisterProducer()\n self.consumer = None\n\n if not self.deferred.called:\n self.deferred.callback(None)",
"def killProcess(self):\n ... | [
"0.65953773",
"0.61936015",
"0.61674356",
"0.6080718",
"0.6071847",
"0.5960446",
"0.591284",
"0.5910402",
"0.5872081",
"0.587073",
"0.58628255",
"0.5856888",
"0.5834934",
"0.5808488",
"0.57885987",
"0.57885987",
"0.57885987",
"0.57885987",
"0.5782206",
"0.5757296",
"0.5727452... | 0.0 | -1 |
Returns a deferred that will be called once the process starts. | def started(self):
return self._started.get() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def defer(self, *args, **kwargs):\n return DeferredRoutineCall(self, *args, **kwargs)",
"def handle_deferred(self):\n self.deferred_executor.process()",
"def defer(closure, **kw):\r\n\r\n Deferred(closure, **kw).start()",
"def _check_started(f):\n def inner(self, *args, **kwargs):\n ... | [
"0.63942105",
"0.6386552",
"0.6205992",
"0.60701406",
"0.5952012",
"0.5812182",
"0.5796162",
"0.5794925",
"0.57789487",
"0.57636154",
"0.57523715",
"0.57388395",
"0.57263136",
"0.57140887",
"0.5707238",
"0.5673704",
"0.5665782",
"0.5620238",
"0.56168365",
"0.5616257",
"0.5605... | 0.0 | -1 |
Kills the underlying process. | def kill(self):
if self.transport.pid is not None:
self.transport.signalProcess('KILL') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def kill(self):\n\n self.proc.kill()",
"def kill(self):\n self._stop_proc(signal.SIGKILL)",
"def kill(self):\n self._process.kill()",
"def kill(self):\n if self.process is not None:\n LOGGER.info('Killing command...')\n self.process.kill()\n self.p... | [
"0.8189746",
"0.7882126",
"0.78450996",
"0.7644135",
"0.7573756",
"0.75117594",
"0.7417189",
"0.7345615",
"0.7207803",
"0.7190397",
"0.71868044",
"0.7156865",
"0.7124084",
"0.7106891",
"0.7104847",
"0.70043904",
"0.6994104",
"0.6971545",
"0.6948991",
"0.69425774",
"0.69246817... | 0.7108763 | 13 |
Writes the command to the processes' stdin. | def send(self, command):
self.transport.write(command.to_json())
self.transport.write("\n") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(self, command):\n self.stdin_stream.write(command)\n self.stdin_stream.flush()",
"def write(self, command):\n # Write command in bytes plus newline then flush.\n self.proc.stdin.write(bytes(command + \"\\n\", 'ascii'))\n self.proc.stdin.flush()\n\n self.genout(... | [
"0.7742846",
"0.758709",
"0.7408216",
"0.74081767",
"0.70342624",
"0.67083347",
"0.6690452",
"0.66279495",
"0.6563766",
"0.628263",
"0.6267979",
"0.622254",
"0.6147673",
"0.5964244",
"0.59547204",
"0.59234786",
"0.58922905",
"0.5880339",
"0.58774865",
"0.58630073",
"0.5836130... | 0.0 | -1 |
Add additional resources should only be called before | def add_resource(self, resource_name, resource):
self.resources[resource_name] = resource | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_resources(event):\n anuket_resources.need()",
"def register_resources(self):\n raise NotImplementedError",
"def resources(self):",
"def register_resources(self, resources):\n from tw.api import merge_resources\n merge_resources(self.request_local.resources, resources)",
"def... | [
"0.73939854",
"0.666462",
"0.6575659",
"0.6547136",
"0.6332321",
"0.61183745",
"0.60682935",
"0.606145",
"0.6051648",
"0.6035564",
"0.59642357",
"0.5945735",
"0.5945208",
"0.5941",
"0.59302425",
"0.58973676",
"0.5892642",
"0.5879641",
"0.5870391",
"0.5869459",
"0.5859476",
... | 0.588855 | 17 |
Set the value of a key. | def handle_set(self, api, command):
key = self._sandboxed_key(api.sandbox_id, command.get('key'))
seconds = command.get('seconds')
if not (seconds is None or isinstance(seconds, (int, long))):
returnValue(self.reply_error(
command, "seconds must be a number or null"))... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set(self, key, value):",
"def set(self, key, value):",
"def set(self, key, value):\n self._data[key] = value",
"def set(self, key, value):\n self._data[key] = value",
"def set_value(self, key, value):\n self.data[key] = value\n self.save_data()",
"def set(self, key, value)... | [
"0.8418385",
"0.8418385",
"0.8359021",
"0.8359021",
"0.8262279",
"0.82621676",
"0.82212186",
"0.80870235",
"0.80243295",
"0.78710115",
"0.783575",
"0.77795756",
"0.7776591",
"0.7752569",
"0.7752569",
"0.7752569",
"0.7751663",
"0.771907",
"0.7687312",
"0.76835847",
"0.76741266... | 0.0 | -1 |
Retrieve the value of a key. | def handle_get(self, api, command):
key = self._sandboxed_key(api.sandbox_id, command.get('key'))
raw_value = yield self.redis.get(key)
value = json.loads(raw_value) if raw_value is not None else None
returnValue(self.reply(command, success=True,
value=valu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_value(self, key):\n return self[key]",
"def get_value(self, key):\n return self.data.get(key)",
"def get( self, key ):\n if key not in self._values:\n raise ValueError( 'Invalid argument key \"{}\".'.format( key ) )\n return self._values[ key ]",
"def get_value(... | [
"0.8579645",
"0.83154035",
"0.82923156",
"0.8278068",
"0.8175047",
"0.81501853",
"0.81434643",
"0.80820304",
"0.79780483",
"0.7949287",
"0.78817004",
"0.7817538",
"0.78010017",
"0.77884114",
"0.7778239",
"0.7753097",
"0.7736823",
"0.7727199",
"0.7710911",
"0.77064186",
"0.767... | 0.0 | -1 |
Atomically increment the value of an integer key. The current value of the key must be an integer. If the key does not exist, it is set to zero. | def handle_incr(self, api, command):
key = self._sandboxed_key(api.sandbox_id, command.get('key'))
if not (yield self.check_keys(api, key)):
returnValue(self._too_many_keys(command))
amount = command.get('amount', 1)
try:
value = yield self.redis.incr(key, amount=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __incKeyCount(self,\n key):\n if (self.__keyCount.has_key(key) == 0): self.__keyCount[key] = 0\n self.__keyCount[key] = self.__keyCount[key] + 1\n return self.__keyCount[key]",
"def incr(self, key, value=1):\n try:\n self[key] += value\n exce... | [
"0.74396485",
"0.74246794",
"0.72967505",
"0.72967505",
"0.7202752",
"0.7201442",
"0.71488696",
"0.7147027",
"0.70858496",
"0.70796204",
"0.69483596",
"0.68995315",
"0.68314445",
"0.6815712",
"0.6760519",
"0.67374295",
"0.6722753",
"0.66972864",
"0.6679329",
"0.66549736",
"0.... | 0.6366343 | 39 |
Sends a reply to the individual who sent a received message. | def handle_reply_to(self, api, command):
content = command['content']
continue_session = command.get('continue_session', True)
orig_msg = api.get_inbound_message(command['in_reply_to'])
d = self.app_worker.reply_to(orig_msg, content,
continue_session=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reply(self, body):\n return self.author.send_message(body=body, reply_to=self.id)",
"def respond(self, msg, msgID, protocol, remoteID):\r\n protocol.sendMessage(self, msg, msgID, remoteID)",
"def reply(self, msg_id, response):\n return self.hub.reply(self.get_private_key(), msg_id, res... | [
"0.74281424",
"0.6995021",
"0.68227196",
"0.6796245",
"0.6712307",
"0.6704049",
"0.6690775",
"0.6673909",
"0.6650697",
"0.66407347",
"0.66115856",
"0.65886915",
"0.6503527",
"0.64697134",
"0.6464312",
"0.64447606",
"0.6442091",
"0.64249456",
"0.6409682",
"0.6409541",
"0.63462... | 0.0 | -1 |
Sends a reply to the group from which a received message was sent. | def handle_reply_to_group(self, api, command):
content = command['content']
continue_session = command.get('continue_session', True)
orig_msg = api.get_inbound_message(command['in_reply_to'])
d = self.app_worker.reply_to_group(orig_msg, content,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reply_group(self, *args, **kw):\n out_msg = self.reply(*args, **kw)\n if self['group'] is not None:\n out_msg['to_addr'] = None\n return out_msg",
"def handle_reply_to_group(self, api, command):\n try:\n content, orig_msg, continue_session, helper_metadata = ... | [
"0.7835496",
"0.72320074",
"0.6691186",
"0.66384053",
"0.6482554",
"0.64154977",
"0.63986325",
"0.6321635",
"0.6295746",
"0.62455034",
"0.61350477",
"0.61332893",
"0.6121003",
"0.6085777",
"0.60732275",
"0.6049861",
"0.6028806",
"0.6015356",
"0.5966122",
"0.5943455",
"0.59416... | 0.7205086 | 2 |
Sends a message to a specified address. | def handle_send_to(self, api, command):
content = command['content']
to_addr = command['to_addr']
endpoint = command.get('endpoint', 'default')
d = self.app_worker.send_to(to_addr, content, endpoint=endpoint)
d.addCallback(lambda r: self.reply(command, success=True))
retu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send(self, address, message):\n self.__set_i2c_address(address)\n self.__write(message)",
"def sendto(self, data: bytes, address: Tuple) -> int:\n ...",
"async def send_to_user(self, user: User, msg: Msg, address: str = None):\n if address is None:\n address = user.cu... | [
"0.72941005",
"0.7244614",
"0.71493095",
"0.71197104",
"0.7039757",
"0.6937346",
"0.6910837",
"0.68682057",
"0.6848539",
"0.683916",
"0.66872245",
"0.6665314",
"0.6661667",
"0.66567826",
"0.6631621",
"0.6600665",
"0.6598476",
"0.6563298",
"0.6536104",
"0.6483595",
"0.6462274"... | 0.0 | -1 |
Logs a message via vumi.log (i.e. Twisted logging). Subclass should override this if they wish to log messages elsewhere. The `api` parameter is provided for use by such subclasses. The `log` method should always return a deferred. | def log(self, api, msg, level):
return succeed(log.msg(msg, logLevel=level)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log(self, message: str):",
"def log(self, message):",
"def handle_log(self, api, command, level=None):\n level = command.get('level', level)\n if level is None:\n level = logging.INFO\n msg = command.get('msg')\n if msg is None:\n returnValue(self.reply(com... | [
"0.62588555",
"0.61909205",
"0.60318035",
"0.5876172",
"0.5767462",
"0.5719353",
"0.56615025",
"0.56615025",
"0.56079364",
"0.5598509",
"0.55829424",
"0.5562299",
"0.5525649",
"0.55162686",
"0.5513187",
"0.5507155",
"0.54979646",
"0.5494922",
"0.54893714",
"0.5478701",
"0.546... | 0.6609489 | 0 |
Log a message at the specified severity level. The other log commands are identical except that ``level`` need not be specified. Using the loglevel specific commands is preferred. | def handle_log(self, api, command, level=None):
level = command.get('level', level)
if level is None:
level = logging.INFO
msg = command.get('msg')
if msg is None:
returnValue(self.reply(command, success=False,
reason="Value expe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log(self, message, level=None):\n\n if level is None or level.lower() == \"all\":\n level = \"notset\"\n level = getattr(logging, level.upper())\n\n self.logger.log(level, message)",
"def __log(level, message):\n if level == 1:\n logging.info(\" \" + str(datetime.dat... | [
"0.69736856",
"0.69717073",
"0.6819493",
"0.6795034",
"0.6610909",
"0.65883386",
"0.6571893",
"0.64859456",
"0.64780974",
"0.6469924",
"0.64075714",
"0.63845026",
"0.6365989",
"0.6314267",
"0.6300642",
"0.62879443",
"0.62683153",
"0.6249968",
"0.62346053",
"0.62274724",
"0.62... | 0.5699857 | 64 |
Logs a message at the ``DEBUG`` log level. | def handle_debug(self, api, command):
return self.handle_log(api, command, level=logging.DEBUG) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def debug(self, message: str):\n self.log(Level.DEBUG, message)",
"def log_debug(self, msg):\n self.log(msg, level=LOG_DEBUG)",
"def msg_debug(message):\n if _log_lvl == logging.DEBUG:\n to_stdout(\" (*) {message}\".format(message=message), colorf=cyan)\n if _logger:\n ... | [
"0.8366112",
"0.8357671",
"0.8334863",
"0.8205168",
"0.8183164",
"0.80656815",
"0.80617577",
"0.80188334",
"0.7997606",
"0.79878056",
"0.7979886",
"0.79605484",
"0.7957748",
"0.7924502",
"0.7890965",
"0.7888706",
"0.7876559",
"0.7776957",
"0.7755921",
"0.77538514",
"0.7752709... | 0.0 | -1 |
Logs a message at the ``INFO`` log level. | def handle_info(self, api, command):
return self.handle_log(api, command, level=logging.INFO) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def info(self, message: str):\n self.log(Level.INFO, message)",
"def info(message: str, *args: Any) -> None:\n Logger.log(logging.INFO, message, *args)",
"def log_info(self, msg):\n self.log(msg, level=LOG_INFO)",
"def info(self, message: str) -> None:\n\n self.__add_log(self.INFO... | [
"0.8580891",
"0.8531367",
"0.84378135",
"0.82716554",
"0.82706",
"0.82153296",
"0.82041216",
"0.8185319",
"0.80368036",
"0.80111647",
"0.7998667",
"0.7988161",
"0.7983948",
"0.7977032",
"0.7946762",
"0.7942989",
"0.78871095",
"0.78853965",
"0.7883028",
"0.7865361",
"0.7832679... | 0.6524697 | 62 |
Logs a message at the ``WARNING`` log level. | def handle_warning(self, api, command):
return self.handle_log(api, command, level=logging.WARNING) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def warn(self, message: str):\n self.log(Level.WARNING, message)",
"def warning(warning_message: str):\n logger.warning(warning_message)",
"def warning(message: str, *args: Any) -> None:\n Logger.log(logging.WARN, message, *args)",
"def logwarning(self, msg):\n self.logger.warning(msg... | [
"0.851772",
"0.8506773",
"0.85006905",
"0.8482524",
"0.84166867",
"0.8413865",
"0.8361264",
"0.8316793",
"0.82993543",
"0.8275807",
"0.82646966",
"0.82539433",
"0.82478034",
"0.8244659",
"0.823924",
"0.8237153",
"0.82192165",
"0.8213992",
"0.82070726",
"0.8206089",
"0.8191427... | 0.0 | -1 |
Logs a message at the ``ERROR`` log level. | def handle_error(self, api, command):
return self.handle_log(api, command, level=logging.ERROR) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_error(self, msg):\n self.log(msg, level=LOG_ERROR)",
"def error(self, message: str):\n self.log(Level.ERROR, message)",
"def error(error_message: str):\n logger.error(error_message)",
"def err(message):\n\n timestamp = format_time(get_time())\n message = '{} - [ERROR] - {}'.for... | [
"0.8237669",
"0.80030644",
"0.79834133",
"0.78904074",
"0.78339803",
"0.783222",
"0.782851",
"0.77787817",
"0.7774609",
"0.7774305",
"0.7768485",
"0.7751224",
"0.77304727",
"0.77241015",
"0.77092797",
"0.7541504",
"0.75259614",
"0.75143814",
"0.74450713",
"0.74356484",
"0.741... | 0.0 | -1 |
Logs a message at the ``CRITICAL`` log level. | def handle_critical(self, api, command):
return self.handle_log(api, command, level=logging.CRITICAL) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def critical(message: str, *args: Any) -> None: # pragma: no cover\n Logger.log(logging.CRITICAL, message, *args)",
"def critical(self, message: str):\n self.log(Level.CRITICAL, message)",
"def critical(msg):\n log_msg(CRITICAL, msg)",
"def critical(self, msg, *args, **kwargs):\n sel... | [
"0.85096586",
"0.82847667",
"0.80889934",
"0.7964336",
"0.79508466",
"0.7939955",
"0.7748192",
"0.77236235",
"0.75199497",
"0.7465908",
"0.7351842",
"0.73428136",
"0.7339046",
"0.7335877",
"0.72927964",
"0.71788436",
"0.6846049",
"0.6720184",
"0.66962856",
"0.6648653",
"0.663... | 0.6943484 | 16 |
Use ClientContextFactory directly and set the method if necessary. This will perform no host verification at all. | def _get_noverify_context(self):
from twisted.internet.ssl import ClientContextFactory
context_factory = ClientContextFactory()
if self.ssl_method is not None:
context_factory.method = self.ssl_method
return context_factory.getContext() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, url, **kwargs):\n self.hostname = self.getHostnameFromURL(url)\n\n # ``verify`` here refers to server-side verification of certificates\n # presented by a client:\n self.verify = False if self.isClient else True\n super(SSLVerifyingContextFactory, self).__init_... | [
"0.58759177",
"0.57396954",
"0.5704184",
"0.5654362",
"0.5596379",
"0.5576222",
"0.5547429",
"0.5513605",
"0.551157",
"0.54686517",
"0.5450384",
"0.54451317",
"0.54451317",
"0.54451317",
"0.54451317",
"0.54451317",
"0.5389514",
"0.53803885",
"0.53754294",
"0.5344577",
"0.5342... | 0.57632256 | 1 |
Make an HTTP GET request. | def handle_get(self, api, command):
return self._make_request_from_command('GET', command) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_GET(self):\n self.http_method = 'GET'\n self.response()",
"def get(self, *path, **data):\n\t\treturn self.request('GET', *path, **data)",
"def get(self, *args, **kw):\n kw['method'] = 'GET'\n return self.open(*args, **kw)",
"def get(self, *args, **kwargs):\n self.req... | [
"0.7992183",
"0.7964515",
"0.79520166",
"0.7856277",
"0.784097",
"0.78315854",
"0.78135085",
"0.77794534",
"0.76968414",
"0.7664457",
"0.7663519",
"0.7629618",
"0.75919056",
"0.7587477",
"0.75773835",
"0.75588286",
"0.7525723",
"0.7521065",
"0.7477174",
"0.7457246",
"0.745590... | 0.0 | -1 |
Make an HTTP PUT request. | def handle_put(self, api, command):
return self._make_request_from_command('PUT', command) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_PUT(self,):\n self.http_method = 'PUT'\n # Nothing to do for now.\n pass",
"def put(self, *args, **kw):\n kw['method'] = 'PUT'\n return self.open(*args, **kw)",
"def _put(self, *args, **kwargs):\n return self._request('put', *args, **kwargs)",
"def put(self, u... | [
"0.8385138",
"0.8301353",
"0.82257414",
"0.8194025",
"0.81546164",
"0.8014394",
"0.7950364",
"0.7868214",
"0.7865386",
"0.7856735",
"0.77816135",
"0.7774543",
"0.7757993",
"0.7712685",
"0.7612626",
"0.7602484",
"0.75962627",
"0.75962627",
"0.75962627",
"0.7547503",
"0.7535183... | 0.7208611 | 38 |
Make an HTTP DELETE request. | def handle_delete(self, api, command):
return self._make_request_from_command('DELETE', command) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def httpDelete(self, url='', data='', params={}, headers={}):\n\n return self.httpRequest('DELETE', url, data, params, headers)",
"def do_DELETE(self,):\n self.http_method = 'DELETE'\n self.response()",
"def delete(self):\r\n return http.Request('DELETE', '{0}'.format(\r\n ... | [
"0.86554825",
"0.8388014",
"0.830307",
"0.81576777",
"0.8146794",
"0.81088203",
"0.8056914",
"0.8012024",
"0.8011185",
"0.79084957",
"0.7869481",
"0.7860623",
"0.78572756",
"0.77129704",
"0.7690312",
"0.7690312",
"0.76605344",
"0.7657503",
"0.7538217",
"0.7509946",
"0.7497349... | 0.707418 | 54 |
Make an HTTP HEAD request. | def handle_head(self, api, command):
return self._make_request_from_command('HEAD', command) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_HEAD(self):\n self.do_GET(True)",
"def do_HEAD(self):\n self.log.debug('do_HEAD called')\n self.HeadGet('HEAD')",
"async def head(url, session=None, **kwargs):\n\n method = 'HEAD'\n resp = await _request(method, url, session=session, **kwargs)\n return resp",
"def head(self, url,... | [
"0.8344407",
"0.818973",
"0.813251",
"0.8088553",
"0.80747944",
"0.7905858",
"0.78950536",
"0.7855229",
"0.7854368",
"0.7849326",
"0.7813137",
"0.77831864",
"0.77831864",
"0.77831864",
"0.77831864",
"0.7763726",
"0.77396655",
"0.77286154",
"0.76971126",
"0.7648268",
"0.758873... | 0.7776205 | 15 |
Make an HTTP POST request. | def handle_post(self, api, command):
return self._make_request_from_command('POST', command) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def httpPost(self, url='', data='', params={}, headers={}):\n\n return self.httpRequest('POST', url, data, params, headers)",
"def post(self, *args, **kwargs):\n return self._requests_call(util.requests_post, *args, **kwargs)",
"def do_POST(self,):\n self.http_method = 'POST'\n self.res... | [
"0.7948866",
"0.78074974",
"0.775014",
"0.77245474",
"0.7586205",
"0.7512639",
"0.7453403",
"0.73794913",
"0.7378032",
"0.73769873",
"0.73720765",
"0.7345682",
"0.72977006",
"0.726105",
"0.7250581",
"0.72415143",
"0.72306734",
"0.7205217",
"0.7157622",
"0.7133639",
"0.7116172... | 0.0 | -1 |
Make an HTTP PATCH request. | def handle_patch(self, api, command):
return self._make_request_from_command('PATCH', command) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def httpPatch(self, url='', data='', params={}, headers={}):\n\n return self.httpRequest('PATCH', url, data, params, headers)",
"def patch(url, data=None, **kwargs):\n\n return request('patch', url, data=data, **kwargs)",
"def patch(self, *args, **kwargs):\n self.request(\"patch\", *args, **kw... | [
"0.8377986",
"0.77408034",
"0.7737159",
"0.7708333",
"0.7624386",
"0.7548285",
"0.75311804",
"0.75118834",
"0.74857336",
"0.73733705",
"0.7367035",
"0.7365305",
"0.73553634",
"0.73553634",
"0.73553634",
"0.7353132",
"0.7337053",
"0.7293803",
"0.72613055",
"0.7235854",
"0.7191... | 0.72490084 | 19 |
Return a sandbox id for a message or event. Subclasses may override this to retrieve an appropriate id. | def sandbox_id_for_message(self, msg_or_event):
return msg_or_event['sandbox_id'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def id(self) -> str:\n return self._event.get('id')",
"def getId(self):\n return _libsbml.Event_getId(self)",
"def getMessageID(self):\n return self._payload[1]",
"def message_id(self) -> int:\n enforce(self.is_set(\"message_id\"), \"message_id is not set.\")\n return cast(... | [
"0.6507702",
"0.627226",
"0.6100435",
"0.6046295",
"0.60373926",
"0.5947721",
"0.59358907",
"0.5924999",
"0.5924389",
"0.5914692",
"0.58880615",
"0.58774614",
"0.5876454",
"0.5876454",
"0.5876454",
"0.5876454",
"0.5876454",
"0.5876454",
"0.5876454",
"0.5876454",
"0.5876454",
... | 0.88027954 | 0 |
Return a sandbox protocol for a message or event. Subclasses may override this to retrieve an appropriate protocol. | def sandbox_protocol_for_message(self, msg_or_event, config):
api = self.create_sandbox_api(self.resources, config)
protocol = self.create_sandbox_protocol(api)
return protocol | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def protocol(self):\n self._recv_protocol()\n return self._protocol",
"def protocol(self) -> MessageProtocol:\n return self._sender.protocol",
"def getProtocol(self, _):\r\n return self._protocol",
"def protocol(self) -> NetworkProtocol:\n if hasattr(self, \"_protocol\"):\n... | [
"0.6581834",
"0.65799445",
"0.65358585",
"0.645299",
"0.62877756",
"0.6196647",
"0.6158876",
"0.6152343",
"0.6143666",
"0.60666347",
"0.60666347",
"0.60666347",
"0.60666347",
"0.60666347",
"0.606265",
"0.606265",
"0.5934752",
"0.59274423",
"0.58809066",
"0.5875745",
"0.586304... | 0.80012 | 0 |
Sort table by given column number. | def sort(self, p_int, order=None):
try:
self.layoutAboutToBeChanged.emit()
source_array = self.get_source_array()
if len(source_array):
source_array = sorted(source_array,
key=lambda x: (self.get_item(x, p_int) is None,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sortTable(self, table, cols):\n #productive\n profprint()\n for col in reversed(cols):\n table = sorted(table, key=operator.itemgetter(col))\n return table",
"def sortTable(self, table, cols):\r\n # productive\r\n profprint()\r\n for col in reversed(cols):\r\n table = sorted(ta... | [
"0.6988205",
"0.6941003",
"0.688725",
"0.6766009",
"0.6726265",
"0.6554286",
"0.64665633",
"0.63434094",
"0.62454164",
"0.62412584",
"0.62091345",
"0.6206283",
"0.61986214",
"0.6197047",
"0.61965746",
"0.619618",
"0.61853695",
"0.6185017",
"0.61779505",
"0.61375666",
"0.61233... | 0.0 | -1 |
Objective function, necessary for optimizing with optuna. | def objective(trial,
bounds: Optional[Iterable]=None,
func: Optional[Callable]=None,
param_names: Optional[List[str]]=None):
if param_names is None:
param_names = PARAM_NAMES
if (bounds is None):
bounds = ((-10, 10) for _ in param_names)
if not is... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def objective(self):\n pass",
"def objective(self, x):\n pass",
"def objective(self, x):\n pass",
"def objective_function(x):\n return x * 1 # change this to our actual function",
"def get_Objective(self):\r\n \r\n ans = 0\r\n for i in range(len(self.X.shape)):... | [
"0.7534007",
"0.75032496",
"0.75032496",
"0.733393",
"0.70547366",
"0.6989981",
"0.67604387",
"0.6705372",
"0.65997154",
"0.65972316",
"0.6582349",
"0.6576692",
"0.6509474",
"0.64613736",
"0.63616884",
"0.6350423",
"0.6277985",
"0.62494683",
"0.6182294",
"0.6176217",
"0.61698... | 0.0 | -1 |
Returns formatted output for a dictionary with string keys and float values. | def format_dict(d: Dict[str, float], format: str=".4f") -> Dict[str, float]:
return dict((k, float(f'{v:{format}}')) for k,v in d.items()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_dict(dictionary):\n for key in sorted(dictionary.keys()):\n value = dictionary[key]\n if type(value) == float:\n value = round(value, 8)\n print key, value",
"def print_dict(dictionary):\r\n for key in sorted(dictionary.keys()):\r\n value = dictionary[key]\r... | [
"0.70268685",
"0.70015323",
"0.6506103",
"0.65015024",
"0.63505834",
"0.63133466",
"0.61922085",
"0.6134083",
"0.6098064",
"0.6060226",
"0.6022871",
"0.6014576",
"0.5958936",
"0.5937073",
"0.5937073",
"0.5933473",
"0.5883104",
"0.5847088",
"0.5822004",
"0.5818801",
"0.581338"... | 0.7233693 | 0 |
Create contour plots for a study, given a list or tuple of two parameter names. | def study_contour_plot(study: optuna.Study,
params: Optional[List[str]]=None,
width: int=560,
height: int=500):
if params is None:
params = ["dv", "vc"]
fig = optuna.visualization.plot_contour(study, params=params)
fig.update_la... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_plots(fig_title, \n t_csd_x, t_csd_y, t_csd_z, true_csd, \n ele_x, ele_y, ele_z, pots,\n k_csd_x, k_csd_y, k_csd_z, est_csd):\n fig = plt.figure(figsize=(10,16))\n #True CSD\n z_steps = 5\n height_ratios = [1 for i in range(z_steps)]\n height_ratios... | [
"0.62037873",
"0.58769345",
"0.5820305",
"0.57832277",
"0.57221377",
"0.56909025",
"0.5687232",
"0.562841",
"0.5538102",
"0.5485024",
"0.54831433",
"0.54714435",
"0.5457713",
"0.5433277",
"0.54000473",
"0.53964806",
"0.5381867",
"0.53771883",
"0.53722656",
"0.5367312",
"0.536... | 0.7024441 | 0 |
Keyword arguments specify which objects should be read from ROOT files. types sets the list of possible objects types. keys specifies a list of allowed objects' names. Only simple keys are currently allowed (no regular expressions). If both types and keys are provided, then objects that satisfy any of types or keys are... | def __init__(self, types=None, keys=None, selector=None):
try:
import ROOT
except ImportError:
raise ImportError("ROOT not installed")
if selector is not None:
if keys or types:
raise lena.core.LenaTypeError(
"if selector i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_select(self, selected):\n\t\tfor filename in selected:\n\t\t\tself.load(filename)\n\n\t\tif \"worddict\" in selected and \"classdict\" in selected:\n\t\t\tself.reverse_dicts()",
"def selector(**kwargs):\n return kwargs",
"def select(self, mode, types=None, types_excluded=None, layers=None, layers_e... | [
"0.5373972",
"0.5329947",
"0.51422113",
"0.5035492",
"0.47958642",
"0.4764183",
"0.4713935",
"0.4688114",
"0.46815497",
"0.46460122",
"0.46450743",
"0.46351984",
"0.4616212",
"0.46100155",
"0.46050787",
"0.45995423",
"0.457178",
"0.4546644",
"0.4534454",
"0.45308408",
"0.4524... | 0.71854883 | 0 |
Read ROOT files from flow and yield objects they contain. For file to be read, data part of the value must be a string (file's path) and context.input.read_root_file must not be `False`. Other values pass unchanged. After all entries from the file are yielded, it is closed. context.input.root_file_path is updated with ... | def run(self, flow):
from ROOT import TFile
from lena.context import get_recursively, update_recursively
from lena.flow import get_data_context
from copy import deepcopy
for val in flow:
data, context = get_data_context(val)
# skip not ROOT files
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def files_and_folders(self):\n yield from self._root.files_and_folders(0)",
"def files_from_roots(roots, accept):\n for root in roots:\n if root is not None:\n yield from files_from_root(root, accept)",
"def open_test_root_fs() -> FS:\n cwd = os.path.dirname(__file__)\n return... | [
"0.55671173",
"0.5524454",
"0.5513734",
"0.5512159",
"0.54909235",
"0.5358831",
"0.5296867",
"0.5270758",
"0.5249741",
"0.51901007",
"0.5162517",
"0.514831",
"0.5129456",
"0.5121177",
"0.5115669",
"0.5112054",
"0.51021385",
"0.50759375",
"0.5071909",
"0.5056866",
"0.5011314",... | 0.7067436 | 0 |
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