1:\\n user_info = [u for u in response.json() if u[\\\"username\\\"] == username][0]\\n else:\\n user_info = auth_info[0]\\n facility_info = {\\\"user\\\": user_info, \\\"users\\\": auth_info}\\n return facility_info\",\n \"def user_list():\\n if session['user_admin'] == False:\\n abort(403)\\n\\n # Retrieve all Users\\n sqa_sess = sqa_session()\\n users = sqa_sess.query(User).all()\\n\\n return render_template('admin/user_list.html', users=users)\",\n \"def display_users(cls):\\n return cls.user_list\",\n \"def display_user(cls):\\n\\n return cls.user_list\",\n \"def list_users():\\n return jsonify(user=\\\"joe\\\")\",\n \"def describe_user(self):\\n print(\\\"We have stored next information about user \\\" +\\n self.first_name.title() + \\\" \\\" + self.last_name.title() +\\n \\\":\\\")\\n print(\\\"- Username: \\\" + self.username)\\n print(\\\"- Age: \\\" + str(self.age))\\n print(\\\"- Location: \\\" + self.location.title())\",\n \"def view_users(self):\\n con = dbcon()\\n cur = con.cursor()\\n cur.execute(\\\"SELECT * FROM my_users\\\")\\n res = cur.fetchall()\\n user_list=[]\\n for user in res:\\n user_det = {\\n 'user_id':user[0],\\n 'username':user[1],\\n 'password':user[2],\\n 'confirmpass':user[3],\\n 'addres':user[4],\\n 'role':user[5]\\n }\\n user_list.append(user_det)\\n return jsonify({'Users': user_list}), 200\",\n \"def list_users(self):\\n return self.get_admin(\\\"users\\\")\",\n \"def list_users():\\n users = User.query.order_by(User.last_name, User.first_name).all()\\n return render_template('index.html', users=users)\",\n \"def main():\\n data = get_db()\\n myCursor = data.cursor()\\n myCursor.execute(\\\"SELECT * FROM users\\\")\\n description = [desc[0] for desc in myCursor.description]\\n\\n logger = get_logger()\\n\\n for user in myCursor:\\n userInfo = \\\"\\\".join(\\n f'{des}={str(usr)}; ' for usr, des in zip(user, description)\\n )\\n logger.info(userInfo)\\n\\n myCursor.close()\\n data.close()\",\n \"def users():\\n access_token = session['access_token']\\n return \\\"%s\\\" % list_users(access_token)\",\n \"def test_list_user(self):\\n pass\",\n \"def do_user_show(cs, args):\\n key = args.user\\n if cs.users.is_id(key):\\n id = key\\n else:\\n id = cs.users.get_id_by_name(key)\\n _, user = cs.users.get(id)\\n utils.print_dict(user)\",\n \"def get_user_info_list(self):\\n\\t\\treturn Job(SDK.PrlSrv_GetUserInfoList(self.handle)[0])\",\n \"def get_users(self):\\n fields = ['name', ]\\n return self.get_data(\\\"myUsers\\\", fields)\",\n \"def get_user_list(self, connection):\\n http = get_web_service(connection)\\n try:\\n req = http.request('GET', connection[\\\"url\\\"] + '/users/?_format=json')\\n data = json.loads(req.data.decode('utf-8'))\\n # print(json.dumps(data, indent=4, sort_keys=True))\\n return data\\n except urllib3.exceptions.HTTPError as e:\\n print(\\\"Connection error\\\")\\n print(e)\",\n \"def users(self):\\n from sagas.ofbiz.entities import OfEntity as e, oc\\n rs=e().allUserLogin()\\n for r in rs:\\n print(r['userLoginId'])\",\n \"def show_users():\\r\\n users = User.query.order_by(User.last_name,User.first_name).all()\\r\\n return render_template('list.html', users=users)\",\n \"async def list_users(self, ctx):\\n \\n path = \\\"Users\\\"\\n headers = {\\n 'accept': 'application/json'\\n }\\n response = send_request(method=\\\"get\\\", path=path, headers=headers)\\n users = []\\n for page in response:\\n users.append(f\\\"**{page['Name']}**: ``{page['Id']}``\\\")\\n log.debug(users)\\n\\n embed = embeds.make_embed(ctx=ctx, title=\\\"List Users\\\", image_url=\\\"https://emby.media/resources/logowhite_1881.png\\\")\\n\\n await LinePaginator.paginate([line for line in users], ctx, embed, restrict_to_user=ctx.author)\",\n \"def GetAllUserInfo(self, output_file, verbose):\\n user_feed = self.gd_client.RetrieveAllUsers()\\n report = []\\n output_field_names = (\\n 'user_name', 'given_name', 'family_name', 'ip_whitelisted', 'suspended',\\n 'quota_limit', 'change_pw', 'admin', 'agreed_to_terms')\\n\\n for _, entry in enumerate(user_feed.entry):\\n row = {\\n 'user_name': entry.login.user_name,\\n 'given_name': entry.name.given_name,\\n 'family_name': entry.name.family_name,\\n 'ip_whitelisted': entry.login.ip_whitelisted,\\n 'suspended': entry.login.suspended,\\n 'quota_limit': entry.quota.limit,\\n 'change_pw': entry.login.change_password,\\n 'admin': entry.login.admin,\\n 'agreed_to_terms': entry.login.agreed_to_terms}\\n report.append(row)\\n if verbose is True:\\n print row\\n self.OutputWriter(report, output_file, output_field_names)\",\n \"def user_list(self):\\n self.cur.execute(\\\"SELECT username FROM users\\\")\\n users = []\\n for username in self.cur.fetchall():\\n users.append(username[0])\\n return users\",\n \"def test_user_list(self):\\r\\n self._add_demo_import()\\r\\n params = {\\r\\n 'api_key': self.api_key\\r\\n }\\r\\n res = self.testapp.get('/api/v1/a/users/list',\\r\\n params=params,\\r\\n status=200)\\r\\n\\r\\n # we should get back dict of count, users.\\r\\n data = json.loads(res.body)\\r\\n\\r\\n self.assertEqual(\\r\\n 1, data.get('count'), \\\"There are none by default. \\\" + res.body)\\r\\n self.assertEqual(\\r\\n 'admin',\\r\\n data.get('users')[0]['username'],\\r\\n \\\"The first user is from admin \\\" + res.body)\\r\\n self.assertEqual(\\r\\n 'testing@dummy.com',\\r\\n data.get('users')[0]['email'],\\r\\n \\\"The first user is from testing@dummy.com \\\" + res.body)\",\n \"def get_user_info_by_name(self, username: str) -> dict:\",\n \"def describe_user(self):\\n print(self.first_name.title() + \\\" \\\" + self.last_name.title() +\\n \\\" is a \\\" + str(self.age) + \\\" year old who identifies as \\\" +\\n self.gender + \\\".\\\")\",\n \"def describe_users(AuthenticationType=None, MaxResults=None, NextToken=None):\\n pass\",\n \"def display_user():\\n return User.display_user()\",\n \"def display_user():\\n return User.display_user()\",\n \"def desc_user(self):\\n print(f\\\"\\\\tF.N: {self.first_name.title()}\\\\n\\\\tL.N:{self.last_name.title()}\\\\n\\\\tEmail: {self.email}\\\\n\\\\tage:{self.age}\\\")\",\n \"def getdat(user):\\r\\n profile = user.profile\\r\\n return [user.username, user.email] + [getattr(profile, xkey, '') for xkey in profkeys]\",\n \"def user_list(request_dict):\\n users = User.query.all()\\n users_list = list()\\n for user in users:\\n users_list.append(user)\\n\\n return JSONTools.user_list_reply(users_list)\",\n \"def user_list(request):\\n if request.method == 'GET':\\n user_info = UserData.objects.all()\\n serializer = UserProfileSerializer(user_info, many=True)\\n return JSONResponse(serializer.data)\\n else:\\n return JSONResponse('Using wrong api.', status=404)\",\n \"def list_credentials():\\n creds = load_auth()\\n max_username_len = max([len(c.username) for c in creds]) if len(creds) > 0 else 1\\n long_format = f\\\"{{:{max_username_len}}} for {{}}\\\"\\n for cred in creds:\\n if len(cred.hostname) > 0:\\n print(str.format(long_format, cred.username, cred.hostname))\\n else:\\n print(cred.username)\\n if len(creds) == 0 and os.isatty(1):\\n print(\\\"No credentials configured\\\")\",\n \"def list(config, username, hostname):\\n if (not username and not hostname) or (username and hostname):\\n print 'Usage: igor permissions list [OPTIONS]'\\n print\\n print 'Error: Exactly one of --username or --hostname is required.'\\n exit()\\n\\n if username:\\n response = make_api_request('GET', config, '/users/' + username +\\n '/machines')\\n machines = response.json()['machines']\\n for machine in machines:\\n print machine['hostname']\\n elif hostname:\\n response = make_api_request('GET', config, '/machines/' + hostname +\\n '/users')\\n users = response.json()['users']\\n for user in users:\\n print user['username']\",\n \"def show_users():\\n\\n user = User(connection=connection, cursor=cursor)\\n\\n all_users = user.get_all_users()\\n\\n context = {\\n 'all_users': all_users\\n }\\n\\n return render_template('pages/tables/users.html', **context)\",\n \"def info_handler(userdata, *args):\\n\\t\\tinfo = database.devinfo(userdata[\\\"cursor\\\"], args[0])\\n\\t\\t\\n\\t\\tif info is None:\\n\\t\\t\\tprint(\\\"can't find user \\\" + args[0])\\n\\t\\t\\treturn\\n\\t\\t\\n\\t\\tstype, connected, status = info\\n\\t\\t\\n\\t\\tprint(shlex.quote((\\\"+\\\" if connected else \\\"-\\\") + stype), end=\\\" \\\")\\n\\t\\tprint(shlex.quote(status))\",\n \"def print_user_profile(self):\\n print ('User Name: {name}').format(name=self.name)\\n print ('User Email: {email}').format(email=self.email)\\n print ('User Phone Number: {birthday}').format(birthday=self.phone_number)\\n print ('User Country: {country}').format(country=self.country)\\n print ('User Address: {address}').format(address=self.address)\",\n \"def fake_user_data():\\n\\n print '||guest|password||||||'\\n print ('Jane|Doe|janedoe|password|female|504-555-5309|123 Sesame St.|' +\\n 'San Francsico|CA|94110')\\n\\n for _ in range(0, 6):\\n print (fake.first_name() + '|' + fake.last_name() + '|' +\\n fake.user_name() + '|' + fake.password() + '|' + fake.word() +\\n '|' + fake.phone_number() + '|' + fake.street_address() + '|' +\\n fake.city() + '|' + fake.state_abbr() + '|' + fake.postalcode())\",\n \"def user_info(self):\\n return self.auth.get_user_by_session()\",\n \"def view_list_users(self, user):\\r\\n return user.realm._users.keys()\",\n \"def user_info(self):\\n resp = self._get(get_url('user'))\\n raise_on_error(resp)\\n ret = resp.json()\\n return UserInfo(ret)\",\n \"def show_users(self):\\n\\n u = User(self.settings)\\n users_list = u.find_users()\\n\\n # transform the results in a \\\"jsonifiable\\\"-form\\n json_results = []\\n for user in users_list:\\n json_results.append(user.to_json())\\n\\n # return\\n return json_results\",\n \"def userinfo(self, **kwargs):\\n metadata = self.load_server_metadata()\\n resp = self.get(metadata['userinfo_endpoint'], **kwargs)\\n resp.raise_for_status()\\n data = resp.json()\\n return UserInfo(data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.74712443","0.7329571","0.7306948","0.7285574","0.7219221","0.7200625","0.71875286","0.7088998","0.7065823","0.70622015","0.70473915","0.70473146","0.70240086","0.7016065","0.7005358","0.69844407","0.6974977","0.69546765","0.69373524","0.6934553","0.69223195","0.6899764","0.6889832","0.6881688","0.6865968","0.6863398","0.6849283","0.6849283","0.684615","0.68347806","0.6834657","0.68195325","0.68098927","0.67995906","0.67889965","0.6769566","0.67365","0.67326343","0.67326343","0.67326343","0.67326343","0.67326343","0.67326343","0.67321694","0.6715763","0.67148834","0.6712919","0.6709805","0.66961944","0.66955906","0.6690458","0.6684027","0.6683665","0.6679088","0.66754985","0.6671493","0.6667283","0.6651571","0.6648088","0.6632885","0.6630334","0.6622247","0.66222095","0.6616232","0.6613591","0.66105443","0.65936714","0.65929186","0.65849453","0.65598434","0.65291935","0.6528802","0.6518443","0.65158206","0.6500841","0.6495219","0.6485864","0.647745","0.6476403","0.6475011","0.6467842","0.6459706","0.6455045","0.6441537","0.6441537","0.6441374","0.6431035","0.64289975","0.64281577","0.6423117","0.64102304","0.6406283","0.6399313","0.6383302","0.63749063","0.6374402","0.63710344","0.63616216","0.6356068","0.6354637"],"string":"[\n \"0.74712443\",\n \"0.7329571\",\n \"0.7306948\",\n \"0.7285574\",\n \"0.7219221\",\n \"0.7200625\",\n \"0.71875286\",\n \"0.7088998\",\n \"0.7065823\",\n \"0.70622015\",\n \"0.70473915\",\n \"0.70473146\",\n \"0.70240086\",\n \"0.7016065\",\n \"0.7005358\",\n \"0.69844407\",\n \"0.6974977\",\n \"0.69546765\",\n \"0.69373524\",\n \"0.6934553\",\n \"0.69223195\",\n \"0.6899764\",\n \"0.6889832\",\n \"0.6881688\",\n \"0.6865968\",\n \"0.6863398\",\n \"0.6849283\",\n \"0.6849283\",\n \"0.684615\",\n \"0.68347806\",\n \"0.6834657\",\n \"0.68195325\",\n \"0.68098927\",\n \"0.67995906\",\n \"0.67889965\",\n \"0.6769566\",\n \"0.67365\",\n \"0.67326343\",\n \"0.67326343\",\n \"0.67326343\",\n \"0.67326343\",\n \"0.67326343\",\n \"0.67326343\",\n \"0.67321694\",\n \"0.6715763\",\n \"0.67148834\",\n \"0.6712919\",\n \"0.6709805\",\n \"0.66961944\",\n \"0.66955906\",\n \"0.6690458\",\n \"0.6684027\",\n \"0.6683665\",\n \"0.6679088\",\n \"0.66754985\",\n \"0.6671493\",\n \"0.6667283\",\n \"0.6651571\",\n \"0.6648088\",\n \"0.6632885\",\n \"0.6630334\",\n \"0.6622247\",\n \"0.66222095\",\n \"0.6616232\",\n \"0.6613591\",\n \"0.66105443\",\n \"0.65936714\",\n \"0.65929186\",\n \"0.65849453\",\n \"0.65598434\",\n \"0.65291935\",\n \"0.6528802\",\n \"0.6518443\",\n \"0.65158206\",\n \"0.6500841\",\n \"0.6495219\",\n \"0.6485864\",\n \"0.647745\",\n \"0.6476403\",\n \"0.6475011\",\n \"0.6467842\",\n \"0.6459706\",\n \"0.6455045\",\n \"0.6441537\",\n \"0.6441537\",\n \"0.6441374\",\n \"0.6431035\",\n \"0.64289975\",\n \"0.64281577\",\n \"0.6423117\",\n \"0.64102304\",\n \"0.6406283\",\n \"0.6399313\",\n \"0.6383302\",\n \"0.63749063\",\n \"0.6374402\",\n \"0.63710344\",\n \"0.63616216\",\n \"0.6356068\",\n \"0.6354637\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68284345"},"document_rank":{"kind":"string","value":"31"}}},{"rowIdx":89281,"cells":{"query":{"kind":"string","value":"get a nice string summary of a curation"},"document":{"kind":"string","value":"def getannotationstrings2(cann):\n cdesc = ''\n if cann['description']:\n cdesc += cann['description'] + ' ('\n if cann['annotationtype'] == 'diffexp':\n chigh = []\n clow = []\n call = []\n for cdet in cann['details']:\n if cdet[0] == 'all':\n call.append(cdet[1])\n continue\n if cdet[0] == 'low':\n clow.append(cdet[1])\n continue\n if cdet[0] == 'high':\n chigh.append(cdet[1])\n continue\n cdesc += ' high in '\n for cval in chigh:\n cdesc += cval + ' '\n cdesc += ' compared to '\n for cval in clow:\n cdesc += cval + ' '\n cdesc += ' in '\n for cval in call:\n cdesc += cval + ' '\n elif cann['annotationtype'] == 'isa':\n cdesc += ' is a '\n for cdet in cann['details']:\n cdesc += 'cdet,'\n elif cann['annotationtype'] == 'contamination':\n cdesc += 'contamination'\n else:\n cdesc += cann['annotationtype'] + ' '\n for cdet in cann['details']:\n cdesc = cdesc + ' ' + cdet[1] + ','\n\n if len(cdesc) >= 1 and cdesc[-1] == ',':\n cdesc = cdesc[:-1]\n return cdesc"},"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 summary_string(self) -> str:","def summary(self):\n return ''","def summary(self) -> str:\n pass","def summary(self):\r\n return '%s%s: %s%s %s%s' % (BLUE, self.title,\r\n GREEN, self.description,\r\n NORMAL, self.link)","def summary(self) -> str:\n return pulumi.get(self, \"summary\")","def summary(self):\n name = 'name : ' + self.get_name()\n damage = 'damage : ' + str(self.get_damage())\n ammos = 'ammo : ' + str(self.get_ammos())\n owner = 'owner : ' + str(self.get_owner())\n return '\\n'.join([name, damage, ammos, owner])","def describe(self) -> str:","def summary(self):\n res = \", \".join(\n elem[\"summary\"] for elem in self.status[\"health\"][\"summary\"]\n )\n if res:\n return res\n elif self.detail:\n return self.detail[0]\n return \"\"","def summary(self):\n return \"%s: %s\" % (self.attribute.name, self.value_as_text)","def describe(self):\n return ''","def __str__(self):\n return self.summary()","def get_description(self):\n return IntrospectorHelper.get_summary(self.callback)","def summary(self):\n if self.intact and self.valid:\n return 'INTACT:' + ','.join(self.summary_fields())\n else:\n return 'INVALID'","def summary(self):\n self.tiles.refreshnames()\n self.glues.refreshnames()\n # self.check_consistent()\n info = {\n \"ntiles\": len(self.tiles),\n \"nrt\": len([x for x in self.tiles if not x.is_fake]),\n \"nft\": len([x for x in self.tiles if x.is_fake]),\n \"nends\": len(self.glues),\n \"ntends\": len(self.tiles.glues_from_tiles()),\n \"tns\": \" \".join(x.name for x in self.tiles if x.name),\n \"ens\": \" \".join(x.name for x in self.glues if x.name)\n # if (\"info\" in self.keys() and \"name\" in self[\"info\"].keys())\n # else \"\",\n }\n tun = sum(1 for x in self.tiles if x.name is None)\n if tun > 0:\n info[\"tns\"] += \" ({} unnamed)\".format(tun)\n eun = sum(1 for x in self.glues if x.name is None)\n if eun > 0:\n info[\"ens\"] += \" ({} unnamed)\".format(eun)\n if info[\"nft\"] > 0:\n info[\"nft\"] = \" (+ {} fake)\".format(info[\"nft\"])\n else:\n info[\"nft\"] = \"\"\n return \"TileSet: {nrt} tiles{nft}, {nends} ends, {ntends} ends in tiles.\\nTiles: {tns}\\nEnds: {ens}\".format(\n **info\n )","def __str__(self):\n return f\"{self._desc:16s}\"","def description():","def summary_str(self):\n if not self.results:\n return self.summary.empty() or ''\n elif self.state == Ok:\n return self.summary.ok(self.results) or ''\n return self.summary.problem(self.results) or ''","def summary(self):\n if not self: return u''\n s = u'Character\\n=====\\nName: %s\\n' % \\\n self.get('name', u'')\n bio = self.get('biography')\n if bio:\n s += u'Biography: %s\\n' % bio[0]\n filmo = self.get('filmography')\n if filmo:\n a_list = [x.get('long imdb canonical title', u'')\n for x in filmo[:5]]\n s += u'Last movies with this character: %s.\\n' % u'; '.join(a_list)\n return s","def info(self):\n import tc\n ## enumerate all options\n opts = self.to_list()\n res = \"\"\n fmt = \"%20s = %5s ## %s\\n\"\n\n for k, v in opts:\n res += fmt % (k, str(self.__getattribute__(k)),\n str(v.doc()).split('\\n')[0])\n\n return res","def summary_line_and_description():","def summarise(self):\n self.summary = az.summary(self.trace, var_names=[\"~chol\"], round_to=2)\n print(self.summary)\n return self.summary","def summary(self):\n name = 'name : ' + self.get_name()\n description = 'description : ' + self.get_description()\n agility = 'agility : ' + str(self.get_agility())\n strength = 'strength : ' + str(self.get_strength())\n health_points = 'health_points : ' + str(self.get_health_points())\n summary = '\\n'.join([name, description, agility, strength, health_points])\n if self.take_weapon():\n summary += self.take_weapon().summary()\n return summary","def get_info_string(config):\n if config.train.folds is not None:\n return f\"folds_{config.train.folds}\"\n elif config.train.validation_portion is not None:\n return \"valportion_{}\".format(config.train.validation_portion)\n return \"\"","def displayStatistics(self):\n return \"\"","def verbose_str(self):\n return self.summary.verbose(self.results) or ''","def mysummary(self):\n\n if isinstance(self.underlayer, IP):\n return self.underlayer.sprintf(\"IGMPv3: %IP.src% > %IP.dst% %IGMPv3.type% %IGMPv3.gaddr%\")\n else:\n return self.sprintf(\"IGMPv3 %IGMPv3.type% %IGMPv3.gaddr%\")","def describe(self):\n return str(self)","def summary(self):\n return \"{0:}: {1:} -> {2:}\".format(self.name, self.var, self.out)","def full_description(self):\n des = describe_dut(self.dut) if self.dut else ''\n if self.build:\n des += ' with ' + self.build\n if self.result_id:\n des += ' BVT result ID ' + str(self.result_id)\n return (self.description if self.description \n else 'unknown test') + ' on ' + des","def summary_stats(tile_summary):\n return \"Original Dimensions: %dx%d\\n\" % (tile_summary.orig_w, tile_summary.orig_h) + \\\n \"Original Tile Size: %dx%d\\n\" % (tile_summary.orig_tile_w, tile_summary.orig_tile_h) + \\\n \"Scale Factor: 1/%dx\\n\" % tile_summary.scale_factor + \\\n \"Scaled Dimensions: %dx%d\\n\" % (tile_summary.scaled_w, tile_summary.scaled_h) + \\\n \"Scaled Tile Size: %dx%d\\n\" % (tile_summary.scaled_tile_w, tile_summary.scaled_tile_w) + \\\n \"Total Mask: %3.2f%%, Total Tissue: %3.2f%%\\n\" % (\n tile_summary.mask_percentage(), tile_summary.tissue_percentage) + \\\n \"Tiles: %dx%d = %d\\n\" % (tile_summary.num_col_tiles, tile_summary.num_row_tiles, tile_summary.count) + \\\n \" %5d (%5.2f%%) tiles >=%d%% tissue\\n\" % (\n tile_summary.high, tile_summary.high / tile_summary.count * 100, TISSUE_HIGH_THRESH) + \\\n \" %5d (%5.2f%%) tiles >=%d%% and <%d%% tissue\\n\" % (\n tile_summary.medium, tile_summary.medium / tile_summary.count * 100, TISSUE_LOW_THRESH,\n TISSUE_HIGH_THRESH) + \\\n \" %5d (%5.2f%%) tiles >0%% and <%d%% tissue\\n\" % (\n tile_summary.low, tile_summary.low / tile_summary.count * 100, TISSUE_LOW_THRESH) + \\\n \" %5d (%5.2f%%) tiles =0%% tissue\" % (tile_summary.none, tile_summary.none / tile_summary.count * 100)","def description(self):","def summarize(self):\n txtStr = \"%s to %s, %d flight legs.\" %\\\n (self.origin, self.destination, self.nlegs)\n txtStr += \"\\nTakeoff at %s\\nLanding at %s\\n\" %\\\n (self.takeoff, self.landing)\n txtStr += \"Flight duration of %s including %s observing time\" %\\\n (str(self.flighttime), self.obstime)\n\n return txtStr","def summary_string(self) -> str:\n return f\"dixonoid: {self.plain_rules}\"","def __str__(self):\n\n descr = (\"Your health is \" + str(self.health))\n return descr","def describe(self):\n composition = str()\n for n, comp in enumerate(self.components):\n if self.molefractions[n] > 0.0:\n composition += comp.name\n composition += \" %.2f\" % self.molefractions[n]\n composition += \"; \"\n return composition","def descString(self):\n return \"\".join ([self.Name, \" (\", str(self.RollCount), \"d\"\\\n , str(self.RollMax), \"; \", str(self.CritRollMin), \"-\"\\\n , str(self.CritRollMax), \"x\", str (self.CritRollMult)\\\n , \") - \", str(self.Value), \" gp\"])","def description(self) -> str:\r\n descrip = 'The player must aim to put the most possible units of a ' \\\r\n 'given colour c on the outer perimeter of ' \\\r\n 'the board. The ' \\\r\n 'player’s score is the total number of unit cells ' \\\r\n 'of colour ' \\\r\n 'c that are on the perimeter. There is a ' \\\r\n 'premium on corner ' \\\r\n 'cells: they count twice towards the score. '\r\n return descrip","def Description(self) -> str:","def Description(self) -> str:","def __str__(self):\n if __debug__:\n description = ('CM' in debug.active)\n else:\n description = False\n return self.asstring(short=False, header=True, summary=True,\n description=description)","def description(self):\n return self.visual_desc","def details(self) -> str:\n return f\"- **language**: [{self.language}]\\n\" \\\n f\"- **opengame**: [{self.opengame}]\\n\" \\\n f\"- **system**: [{self.system}]\\n\" \\\n f\"- **mode**: [{self.mode}]\\n\" \\\n f\"- **attributes**: [{self.attributes}]\\n \" \\\n f\"- **score_threshold**: [{self.score_threshold}]\\n \" \\\n f\"- **monsters**: [{self.monsters}]\\n\"","def __str__(self):\n status = \"height = {}\\n\".format(self.height)\n status += \"width = {}\\n\".format(self.width)\n status += \"channels = {}\\n\".format(self.channels)\n status += \"architecture = {}\\n\".format(self.architecture)\n status += \"activations = {}\\n\".format(self.activations)\n status += \"conv_activations = {}\\n\".format(self.conv_activations)\n status += \"conv_architecture = {}\\n\".format(self.conv_architecture)\n status += \"kernel_sizes = {}\\n\".format(self.kernel_sizes)\n status += \"pool_kernel = {}\\n\".format(self.pool_kernel)\n status += \"batch_size = {}\\n\".format(self.batch_size)\n status += \"epochs = {}\\n\".format(self.epochs)\n status += \"save_step = {}\\n\".format(self.save_step)\n status += \"learning_rate = {}\\n\".format(self.learning_rate)\n status += \"momentum = {}\\n\".format(self.momentum)\n return status","def summary(self):\n return self.piece_behavior.summary","def description(self) -> str:\r\n description = \"The player must aim to put the most possible units \" + \\\r\n \"of \" + colour_name(self.colour) + \" on the outer\" +\\\r\n \" perimeter.\"\r\n return description","def describe(self):\n\n print(\"Correlation length: {0}\".format(self.cl))\n print(\"icoordchange: {0}\".format(self.icoordchange))\n print(\"ispec: {0}\".format(self.ispec))\n print(\"ireg: {0}\".format(self.ireg))\n print(\"Domain: x-axis: from {0} to {1} with {2} steps of {3}\".format(self.xori, self.xend,\n self.nx, self.dx))\n print(\"Domain: y-axis: from {0} to {1} with {2} steps of {3}\".format(self.yori, self.yend,\n self.ny, self.dy))\n print(\"Exclusion value: {0}\".format(self.valex))\n print(\"Signal-to-noise ratio: {0}\".format(self.snr))\n print(\"Variance of the background field: {0}\".format(self.varbak))","def describe():","def __str__(self):\n status = \"height = {}\\n\".format(self.height)\n status += \"width = {}\\n\".format(self.width)\n status += \"channels = {}\\n\".format(self.channels)\n status += \"classes = {}\\n\".format(self.classes)\n status += \"batch_size = {}\\n\".format(self.batch_size)\n status += \"epochs = {}\\n\".format(self.epochs)\n status += \"save_step = {}\\n\".format(self.save_step)\n status += \"learning_rate = {}\\n\".format(self.learning_rate)\n status += \"momentum = {}\\n\".format(self.momentum)\n return status","def summarize(self):\n txtSumm = ''\n\n if self.legtype == 'Takeoff':\n txtSumm = \"%02d -- %s\" %\\\n (self.legno, self.legtype)\n elif self.legtype == 'Landing':\n txtSumm = \"%02d -- %s\" %\\\n (self.legno, self.legtype)\n elif self.legtype == 'Other':\n txtSumm = \"%02d -- %s\" %\\\n (self.legno, self.legtype)\n elif self.legtype == 'Observing':\n txtSumm = \"%02d -- %s, RA: %s, Dec: %s, LegDur: %s, ObsDur: %s\" %\\\n (self.legno, self.target, self.ra, self.dec,\n str(self.duration),\n str(self.obsdur))\n txtSumm += \"\\n\"\n if self.nonsid is True:\n txtSumm += \"NONSIDERIAL TARGET -- NAIFID: %d\" % (self.naifid)\n txtSumm += \"\\n\"\n txtSumm += \"(The SOFIA project sincerely hopes you enjoy \"\n txtSumm += \"your observing breaks due to XFORMS crashes)\"\n txtSumm += \"\\n\"\n txtSumm += \"ObsPlan: %s, ObsBlk: %s\" % (self.obsplan, self.obsblk)\n txtSumm += \"\\n\\n\"\n txtSumm += \"Elevation Range: %.1f, %.1f\" % (self.range_elev[0],\n self.range_elev[1])\n txtSumm += \"\\n\\n\"\n txtSumm += \"ROF Range: %.1f, %.1f\" % (self.range_rof[0],\n self.range_rof[1])\n txtSumm += \"\\n\"\n txtSumm += \"ROF Rate Range: %.1f, %.1f %s\" % (self.range_rofrt[0],\n self.range_rofrt[1],\n self.range_rofrtu)\n txtSumm += \"\\n\\n\"\n txtSumm += \"True Heading Range: %.1f, %.1f\" % (self.range_thdg[0],\n self.range_thdg[1])\n txtSumm += \"\\n\"\n txtSumm += \"True Heading Rate Range: %.1f, %.1f %s\" %\\\n (self.range_thdgrt[0],\n self.range_thdgrt[1],\n self.range_thdgrtu)\n txtSumm += \"\\n\"\n txtSumm += \"Moon Angle: %.1f, Moon Illumination: %s\" %\\\n (self.moonangle, self.moonillum)\n\n return txtSumm","def attribute_summary(self):\n attributes = self.get_attribute_values()\n pairs = [attribute.summary() for attribute in attributes]\n return \", \".join(pairs)","def description(self):\n\n\t\treturn \"%d %s %s\" % (self.vintage, self.winery.name, self.name)","def __str__(self):\n return self.summarize()","def __str__(self):\n return self.summarize()","def summary(self):\n return get_paragraphs(self.description)[0]","def __str__(self):\n status = \"height = {}\\n\".format(self.height)\n status += \"width = {}\\n\".format(self.width)\n status += \"channels = {}\\n\".format(self.channels)\n status += \"input_dim = {}\\n\".format(self.input_dim)\n status += \"architecture = {}\\n\".format(self.architecture)\n status += \"activations = {}\\n\".format(self.activations)\n status += \"batch_size = {}\\n\".format(self.batch_size)\n status += \"epochs = {}\\n\".format(self.epochs)\n status += \"save_step = {}\\n\".format(self.save_step)\n status += \"learning_rate = {}\\n\".format(self.learning_rate)\n status += \"momentum = {}\\n\".format(self.momentum)\n return status","def summary(self, summary: str):\n return self.swag({\n 'summary': summary\n })","def toString(self):\n\t\ts = \"A %s titled '%s':\\n\\n\" % (self.getSpecString(), self.getName())\n\t\ts += \"It's summary reads: %s\\n\\n\" % (self.getDescription())\n\t\ts += \"~~\\n%s\\n~~\" % (self.getAllItemsStr())\n\t\treturn s","def summary(self):\n raise NotImplementedError","def get_summary(self):\n \n text = \"word: {}, total_score: {} \\n\".format(self.clue, self.total_score)\n for card, score in self.sorted_card_score_pairs:\n card_text = \"\\t card.name:{} (team:{}), similarity: {} \\n\".format(card.name, card.color, score)\n text += card_text\n return text","def cal_desc(self):\n desc = \"\"\n desc += \"Requested by \"\n orgs = self.org.all()\n if len(orgs) > 0:\n for org in orgs:\n desc += org.name + \", \"\n desc = desc[:-2] + \".\\n\" # removes trailing comma\n ccs = self.ccinstances.all()\n if len(ccs) > 0:\n desc += \"Crew Chiefs: \"\n for cc in ccs:\n desc += cc.crew_chief.get_full_name() + \" [\" + (cc.service.shortname if cc.service else cc.category.name) + \"], \"\n desc = desc[:-2] + \".\\n\" # removes trailing comma\n if self.description:\n desc += self.description + \"\\n\"\n return desc","def str(self):\n if self.num_dice is not None and self.dice_type is not None:\n descr = \"{}D{}\".format(self.num_dice, self.dice_type)\n if self.plus > 0:\n descr += \"+{}\".format(self.plus)\n elif self.min_value is not None and self.max_value is not None:\n descr = \"{}-{}\".format(self.min_value, self.max_value)\n elif self.plus != 0:\n descr = str(self.plus)\n else:\n descr = \"\"\n\n return descr","def mysummary(self):\n return self.sprintf(\"IGMPv3 Group Record %IGMPv3gr.type% %IGMPv3gr.maddr%\")","def _summary(obj):\n return obj.summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def summary(self):\n return self._summary","def __str__(self):\n _str = indent(self.name, color = self.color)\n _str += indent(\"-\"*len(self.name))\n _str += indent(\"( \" + self.movemement + \" - \"+ self.symbole + \" )\")\n _str += indent(\"Awareness: \" + str(self.awareness))\n for _iel in self.abilities:\n _str += indent(_iel)\n if self.description is not None:\n _str += indent(\"-\"*45 + \"\\n\")\n _str += indent(self.description)\n _str += indent(\"-\"*45 + \"\\n\")\n _str += indent(BOLD_BLACK + \" \"*15 + \"Combat Stats\" + RESET)\n _str += indent(\"-\"*45 + \"\\n\")\n _str += indent(BLUE + \" Horror\" + RESET + \" Toughness\"\\\n + RED + \" Combat\" + RESET)\n _str += indent(BLUE + \"Rating Damage \"\\\n + RED + \"Rating Damage\")\n _str += indent(BLUE + \" \" + str(self.horror_rating)\\\n + \" \"*8 + str(self.horror_damage) + RESET\\\n + \" \"*8 + str(self.toughness) + RED\\\n + \" \"*8 + str(self.combat_rating)\\\n + \" \"*8 + str(self.combat_damage) + RESET)\n _str += indent(\"-\"*45 + \"\\n\")\n return _str","def __str__(self):\n description = \"Object class Critter.\"\n description += \"\\nName: \" + self.name + \"\\nMood: \" + self.mood + \"\\nHunger: \" + str(self.hunger) + \"\\nBoredom: \" + str(self.boredom) + \"\\n\"\n return description","def all_characteristics_as_string(self):\n chars1 = _all_characteristics_\n chars2 = [ch.title() for ch in chars1]\n chars2[chars2.index('Internalstructure')] = 'InternalStructure'\n\n s = ('%-18s %-24s %-2s'%('Characteristic', 'Semantic value','#'))\n s+= '\\n'\n s+= ('%-18s %-24s %-2s' % ('-', '-', '-')) + '\\n'\n\n for i in range(len(chars1)):\n attrs = (chars2[i],\\\n getattr(self,chars2[i])(),\n getattr(self,chars1[i]))\n s += '%-18s | %-24s | %-2d' % attrs\n s += '\\n'\n return s[:-1] # cut the trailing newline character","def summarize(self):\n return self.__repr__() + \" \" \\\n \"class: \" + self.__class__.__name__ + \", \" + \\\n \"elements: \" + str(self.elements)","def nice_output(self):\n return self.des","def nice_output(self):\n return self.des","def get_description(self):","def get_full_description(self, s, base=None):\n summary = self.get_summary(s)\n extended_summary = self.get_extended_summary(s)\n ret = (summary + '\\n\\n' + extended_summary).strip()\n if base is not None:\n self.params[base + '.full_desc'] = ret\n return ret","def get_summary(self) -> str:\n connected = self.is_connected()\n info = '[{} - {}]'.format(self._index,\n 'Connected' if connected else 'Disconnected')\n if connected:\n info += ' {} ({})'.format(self.get_model_name(), self.get_serial())\n return info","def __str__(self):\n return f'{self._name} has {self._calories} calories, {self._carbohydrates}' +\\\n f'g. carbohydrates, {self._fat}g. of fat and {self._proteins}g. of proteins'","def summary(self):\n\n import yaml\n \n (source, rate, numChannels, totalSamples, duration, dataType) = self.get_info()\n info = {'Source':source,\n 'SampleRate':rate,\n 'NumChannels':numChannels,\n 'TotalSamples':totalSamples,\n 'Duration':duration,\n 'DataType':dataType}\n print(yaml.dump(info, default_flow_style=False))","def getDescription(self):\n return \"\\n\".join(\n [\n \"This diagnostic determines if the Docker daemon suffers from 20GiB COPY bug\",\n \"\",\n \"See https://github.com/adamrehn/ue4-docker/issues/99#issuecomment-1079702817 for details and workarounds\",\n \"\",\n self._parser.format_help(),\n ]\n )","def short_description(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"short_description\")","def get_full_description(self, s, base=None):\n summary = self.get_summary(s)\n extended_summary = self.get_extended_summary(s)\n ret = (summary + \"\\n\\n\" + extended_summary).strip()\n if base is not None:\n self.params[base + \".full_desc\"] = ret\n return ret","def get_summary(callback, docstring=None):\n description = get_view_description(\n callback, html=False, docstring=docstring) \\\n .split(\"\\n\")[0].split(\".\")[0]\n description = IntrospectorHelper.strip_yaml_from_docstring(\n description)\n description = IntrospectorHelper.strip_params_from_docstring(\n description)\n description = strip_tags(get_view_description(\n callback, html=True, docstring=description))\n return description","def __str__(self):\n if len(self.label) > 0:\n descr = [\"'%s', target='%s' [%s]\" % (self.label, self.target.name, self.target.body_type)]\n else:\n descr = [\"target='%s' [%s]\" % (self.target.name, self.target.body_type)]\n if self.baseline:\n descr[0] += ', initial baseline offset=%f' % (self.baseline.poly[-1],)\n if self.beam:\n descr[0] += ', beam height=%f' % (self.beam.height,)\n for scan_ind, scan in enumerate(self.scans):\n descr.append('%4d: %s' % (scan_ind, str(scan)))\n return '\\n'.join(descr)","def human_readable_info(self) -> str:\n next_session = unix_str(self._stat.next_session)\n last_session = unix_str(self._stat.last_session)\n return \"\"\"\n Next Session: {}\n Last Session: {}\n Repetitions: {}\n Health: {}\n ------------------------\n Past Quality (last 20):\n ------------------------\n {}\n \"\"\".format(\n next_session,\n last_session,\n self._stat.actual_repetitions,\n self._health(),\n self._past_quality_graph(),\n )","def summary_parameters(self):\n text = re.sub(r'\\r?\\n', ' - ', self.opt.text[:200])\n return {'adding': text}","def description(self):\n active = np.nonzero([bool(p) for p in self])[0]\n last_active = active[-1] if len(active) else -1\n return ' '.join([p.value_str for p in self][:last_active + 1])","def show_summary(self, lang):\n return self.summary % self.vars","def get_description():\n desc = dict()\n desc['cache'] = 86400\n desc['data'] = True\n desc['description'] = \"\"\"This chart presents a histogram of issuance times\n for a given watch, warning, or advisory type for a given office.\"\"\"\n desc['arguments'] = [\n dict(type='networkselect', name='station', network='WFO',\n default='DMX', label='Select WFO:'),\n dict(type='phenomena', name='phenomena',\n default='WC', label='Select Watch/Warning Phenomena Type:'),\n dict(type='significance', name='significance',\n default='W', label='Select Watch/Warning Significance Level:'),\n ]\n return desc","def getSummary(self):\n return self.summary","def __str__(self) -> str:\n return inspect.cleandoc(\n f\"\"\"\n == Report created {self.report_created} ==\n Arguments are `{self.args}`\n Time passed {self.time_passed}\n Terminated by timeout: {self.is_killed}\n Cpu(%) mean: {mean(self.cpu)}\n Cpu(%) min: {min(self.cpu)}\n Cpu(%) max: {max(self.cpu)}\n Mem(kb) mean: {mean(self.mem)}\n Mem(kb) min: {min(self.mem)}\n Mem(kb) max: {max(self.mem)}\n \"\"\"\n )","def show_stat_desc(self):\n qtw.QMessageBox.information(\n self, \"Stat Descriptions\", '*This list is not comprehensive, and '\n 'will vary depending on race/(sub)class.*\\n\\nStrength\\n\\t-damage '\n 'and attack bonuses for most melee and thrown weapons\\n\\t-carry '\n 'capacity\\n\\t-Athletics checks\\nDexterity\\n\\t-damage and attack '\n 'bonuses for ranged/finesse weapons\\n\\t-Acrobatics, Stealth, '\n 'Sleight of Hand, and Initiative checks\\nConstitution\\n\\t-hit '\n 'points\\n\\t-resistance to poisons, etc.\\nIntelligence\\n\\t-spell '\n 'save DC/attack bonus for Wizards\\n\\t-Arcana, History, '\n 'Investigation, Nature, and Religion checks\\nWisdom\\n\\t-spell save'\n ' DC/attack bonus for Druid, Cleric, and Ranger\\n\\t-Animal '\n 'Handling, Insight, Medicine, Perception, and Survival checks\\n'\n 'Charisma\\n\\t-spell save DC/attack bonus for Bard, Paladin, '\n 'Warlock and Sorceror\\n\\t-Deception, Intimidation, Performance and'\n ' Persuasion checks'\n )","def get_description():\n desc = dict()\n today = datetime.date.today()\n desc['data'] = True\n desc['description'] = \"\"\"This plot presents one of two metrics indicating\n daily humidity levels as reported by a surface weather station. The\n first being mixing ratio, which is a measure of the amount of water\n vapor in the air. The second being vapor pressure deficit, which reports\n the difference between vapor pressure and saturated vapor pressure.\n The vapor pressure calculation shown here makes no accounting for\n leaf tempeature. The saturation vapor pressure is computed by the\n Tetens formula (Buck, 1981).\n\n <br />On 22 June 2016, this plot was modified to display the range of\n daily averages and not the range of instantaneous observations.\n \"\"\"\n desc['arguments'] = [\n dict(type='zstation', name='zstation', default='AMW',\n label='Select Station:', network='IA_ASOS'),\n dict(type='year', name='year', default=today.year,\n label='Select Year to Plot'),\n dict(type='select', name='var', default='mixing_ratio',\n label='Which Humidity Variable to Compute?', options=PDICT),\n ]\n return desc","def description(self):\n pass","def description(self):\n pass","def __str__(self):\n return self.piece_behavior.summary","def conclusion_summary_map(self):\n pass","def summary(self):\n if hasattr(self,\"_summary\"):\n return self._summary\n else:\n return {}","def summarize(self):\n return self.__repr__() + \" \" \\\n \"name: \" + self.name + \", \" + \\\n \"class: \" + self.__class__.__name__ + \", \" + \\\n \"scope: \" + str(self.range) + \", \" + \\\n \"elements: \" + str(self.elements)"],"string":"[\n \"def summary_string(self) -> str:\",\n \"def summary(self):\\n return ''\",\n \"def summary(self) -> str:\\n pass\",\n \"def summary(self):\\r\\n return '%s%s: %s%s %s%s' % (BLUE, self.title,\\r\\n GREEN, self.description,\\r\\n NORMAL, self.link)\",\n \"def summary(self) -> str:\\n return pulumi.get(self, \\\"summary\\\")\",\n \"def summary(self):\\n name = 'name : ' + self.get_name()\\n damage = 'damage : ' + str(self.get_damage())\\n ammos = 'ammo : ' + str(self.get_ammos())\\n owner = 'owner : ' + str(self.get_owner())\\n return '\\\\n'.join([name, damage, ammos, owner])\",\n \"def describe(self) -> str:\",\n \"def summary(self):\\n res = \\\", \\\".join(\\n elem[\\\"summary\\\"] for elem in self.status[\\\"health\\\"][\\\"summary\\\"]\\n )\\n if res:\\n return res\\n elif self.detail:\\n return self.detail[0]\\n return \\\"\\\"\",\n \"def summary(self):\\n return \\\"%s: %s\\\" % (self.attribute.name, self.value_as_text)\",\n \"def describe(self):\\n return ''\",\n \"def __str__(self):\\n return self.summary()\",\n \"def get_description(self):\\n return IntrospectorHelper.get_summary(self.callback)\",\n \"def summary(self):\\n if self.intact and self.valid:\\n return 'INTACT:' + ','.join(self.summary_fields())\\n else:\\n return 'INVALID'\",\n \"def summary(self):\\n self.tiles.refreshnames()\\n self.glues.refreshnames()\\n # self.check_consistent()\\n info = {\\n \\\"ntiles\\\": len(self.tiles),\\n \\\"nrt\\\": len([x for x in self.tiles if not x.is_fake]),\\n \\\"nft\\\": len([x for x in self.tiles if x.is_fake]),\\n \\\"nends\\\": len(self.glues),\\n \\\"ntends\\\": len(self.tiles.glues_from_tiles()),\\n \\\"tns\\\": \\\" \\\".join(x.name for x in self.tiles if x.name),\\n \\\"ens\\\": \\\" \\\".join(x.name for x in self.glues if x.name)\\n # if (\\\"info\\\" in self.keys() and \\\"name\\\" in self[\\\"info\\\"].keys())\\n # else \\\"\\\",\\n }\\n tun = sum(1 for x in self.tiles if x.name is None)\\n if tun > 0:\\n info[\\\"tns\\\"] += \\\" ({} unnamed)\\\".format(tun)\\n eun = sum(1 for x in self.glues if x.name is None)\\n if eun > 0:\\n info[\\\"ens\\\"] += \\\" ({} unnamed)\\\".format(eun)\\n if info[\\\"nft\\\"] > 0:\\n info[\\\"nft\\\"] = \\\" (+ {} fake)\\\".format(info[\\\"nft\\\"])\\n else:\\n info[\\\"nft\\\"] = \\\"\\\"\\n return \\\"TileSet: {nrt} tiles{nft}, {nends} ends, {ntends} ends in tiles.\\\\nTiles: {tns}\\\\nEnds: {ens}\\\".format(\\n **info\\n )\",\n \"def __str__(self):\\n return f\\\"{self._desc:16s}\\\"\",\n \"def description():\",\n \"def summary_str(self):\\n if not self.results:\\n return self.summary.empty() or ''\\n elif self.state == Ok:\\n return self.summary.ok(self.results) or ''\\n return self.summary.problem(self.results) or ''\",\n \"def summary(self):\\n if not self: return u''\\n s = u'Character\\\\n=====\\\\nName: %s\\\\n' % \\\\\\n self.get('name', u'')\\n bio = self.get('biography')\\n if bio:\\n s += u'Biography: %s\\\\n' % bio[0]\\n filmo = self.get('filmography')\\n if filmo:\\n a_list = [x.get('long imdb canonical title', u'')\\n for x in filmo[:5]]\\n s += u'Last movies with this character: %s.\\\\n' % u'; '.join(a_list)\\n return s\",\n \"def info(self):\\n import tc\\n ## enumerate all options\\n opts = self.to_list()\\n res = \\\"\\\"\\n fmt = \\\"%20s = %5s ## %s\\\\n\\\"\\n\\n for k, v in opts:\\n res += fmt % (k, str(self.__getattribute__(k)),\\n str(v.doc()).split('\\\\n')[0])\\n\\n return res\",\n \"def summary_line_and_description():\",\n \"def summarise(self):\\n self.summary = az.summary(self.trace, var_names=[\\\"~chol\\\"], round_to=2)\\n print(self.summary)\\n return self.summary\",\n \"def summary(self):\\n name = 'name : ' + self.get_name()\\n description = 'description : ' + self.get_description()\\n agility = 'agility : ' + str(self.get_agility())\\n strength = 'strength : ' + str(self.get_strength())\\n health_points = 'health_points : ' + str(self.get_health_points())\\n summary = '\\\\n'.join([name, description, agility, strength, health_points])\\n if self.take_weapon():\\n summary += self.take_weapon().summary()\\n return summary\",\n \"def get_info_string(config):\\n if config.train.folds is not None:\\n return f\\\"folds_{config.train.folds}\\\"\\n elif config.train.validation_portion is not None:\\n return \\\"valportion_{}\\\".format(config.train.validation_portion)\\n return \\\"\\\"\",\n \"def displayStatistics(self):\\n return \\\"\\\"\",\n \"def verbose_str(self):\\n return self.summary.verbose(self.results) or ''\",\n \"def mysummary(self):\\n\\n if isinstance(self.underlayer, IP):\\n return self.underlayer.sprintf(\\\"IGMPv3: %IP.src% > %IP.dst% %IGMPv3.type% %IGMPv3.gaddr%\\\")\\n else:\\n return self.sprintf(\\\"IGMPv3 %IGMPv3.type% %IGMPv3.gaddr%\\\")\",\n \"def describe(self):\\n return str(self)\",\n \"def summary(self):\\n return \\\"{0:}: {1:} -> {2:}\\\".format(self.name, self.var, self.out)\",\n \"def full_description(self):\\n des = describe_dut(self.dut) if self.dut else ''\\n if self.build:\\n des += ' with ' + self.build\\n if self.result_id:\\n des += ' BVT result ID ' + str(self.result_id)\\n return (self.description if self.description \\n else 'unknown test') + ' on ' + des\",\n \"def summary_stats(tile_summary):\\n return \\\"Original Dimensions: %dx%d\\\\n\\\" % (tile_summary.orig_w, tile_summary.orig_h) + \\\\\\n \\\"Original Tile Size: %dx%d\\\\n\\\" % (tile_summary.orig_tile_w, tile_summary.orig_tile_h) + \\\\\\n \\\"Scale Factor: 1/%dx\\\\n\\\" % tile_summary.scale_factor + \\\\\\n \\\"Scaled Dimensions: %dx%d\\\\n\\\" % (tile_summary.scaled_w, tile_summary.scaled_h) + \\\\\\n \\\"Scaled Tile Size: %dx%d\\\\n\\\" % (tile_summary.scaled_tile_w, tile_summary.scaled_tile_w) + \\\\\\n \\\"Total Mask: %3.2f%%, Total Tissue: %3.2f%%\\\\n\\\" % (\\n tile_summary.mask_percentage(), tile_summary.tissue_percentage) + \\\\\\n \\\"Tiles: %dx%d = %d\\\\n\\\" % (tile_summary.num_col_tiles, tile_summary.num_row_tiles, tile_summary.count) + \\\\\\n \\\" %5d (%5.2f%%) tiles >=%d%% tissue\\\\n\\\" % (\\n tile_summary.high, tile_summary.high / tile_summary.count * 100, TISSUE_HIGH_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles >=%d%% and <%d%% tissue\\\\n\\\" % (\\n tile_summary.medium, tile_summary.medium / tile_summary.count * 100, TISSUE_LOW_THRESH,\\n TISSUE_HIGH_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles >0%% and <%d%% tissue\\\\n\\\" % (\\n tile_summary.low, tile_summary.low / tile_summary.count * 100, TISSUE_LOW_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles =0%% tissue\\\" % (tile_summary.none, tile_summary.none / tile_summary.count * 100)\",\n \"def description(self):\",\n \"def summarize(self):\\n txtStr = \\\"%s to %s, %d flight legs.\\\" %\\\\\\n (self.origin, self.destination, self.nlegs)\\n txtStr += \\\"\\\\nTakeoff at %s\\\\nLanding at %s\\\\n\\\" %\\\\\\n (self.takeoff, self.landing)\\n txtStr += \\\"Flight duration of %s including %s observing time\\\" %\\\\\\n (str(self.flighttime), self.obstime)\\n\\n return txtStr\",\n \"def summary_string(self) -> str:\\n return f\\\"dixonoid: {self.plain_rules}\\\"\",\n \"def __str__(self):\\n\\n descr = (\\\"Your health is \\\" + str(self.health))\\n return descr\",\n \"def describe(self):\\n composition = str()\\n for n, comp in enumerate(self.components):\\n if self.molefractions[n] > 0.0:\\n composition += comp.name\\n composition += \\\" %.2f\\\" % self.molefractions[n]\\n composition += \\\"; \\\"\\n return composition\",\n \"def descString(self):\\n return \\\"\\\".join ([self.Name, \\\" (\\\", str(self.RollCount), \\\"d\\\"\\\\\\n , str(self.RollMax), \\\"; \\\", str(self.CritRollMin), \\\"-\\\"\\\\\\n , str(self.CritRollMax), \\\"x\\\", str (self.CritRollMult)\\\\\\n , \\\") - \\\", str(self.Value), \\\" gp\\\"])\",\n \"def description(self) -> str:\\r\\n descrip = 'The player must aim to put the most possible units of a ' \\\\\\r\\n 'given colour c on the outer perimeter of ' \\\\\\r\\n 'the board. The ' \\\\\\r\\n 'player’s score is the total number of unit cells ' \\\\\\r\\n 'of colour ' \\\\\\r\\n 'c that are on the perimeter. There is a ' \\\\\\r\\n 'premium on corner ' \\\\\\r\\n 'cells: they count twice towards the score. '\\r\\n return descrip\",\n \"def Description(self) -> str:\",\n \"def Description(self) -> str:\",\n \"def __str__(self):\\n if __debug__:\\n description = ('CM' in debug.active)\\n else:\\n description = False\\n return self.asstring(short=False, header=True, summary=True,\\n description=description)\",\n \"def description(self):\\n return self.visual_desc\",\n \"def details(self) -> str:\\n return f\\\"- **language**: [{self.language}]\\\\n\\\" \\\\\\n f\\\"- **opengame**: [{self.opengame}]\\\\n\\\" \\\\\\n f\\\"- **system**: [{self.system}]\\\\n\\\" \\\\\\n f\\\"- **mode**: [{self.mode}]\\\\n\\\" \\\\\\n f\\\"- **attributes**: [{self.attributes}]\\\\n \\\" \\\\\\n f\\\"- **score_threshold**: [{self.score_threshold}]\\\\n \\\" \\\\\\n f\\\"- **monsters**: [{self.monsters}]\\\\n\\\"\",\n \"def __str__(self):\\n status = \\\"height = {}\\\\n\\\".format(self.height)\\n status += \\\"width = {}\\\\n\\\".format(self.width)\\n status += \\\"channels = {}\\\\n\\\".format(self.channels)\\n status += \\\"architecture = {}\\\\n\\\".format(self.architecture)\\n status += \\\"activations = {}\\\\n\\\".format(self.activations)\\n status += \\\"conv_activations = {}\\\\n\\\".format(self.conv_activations)\\n status += \\\"conv_architecture = {}\\\\n\\\".format(self.conv_architecture)\\n status += \\\"kernel_sizes = {}\\\\n\\\".format(self.kernel_sizes)\\n status += \\\"pool_kernel = {}\\\\n\\\".format(self.pool_kernel)\\n status += \\\"batch_size = {}\\\\n\\\".format(self.batch_size)\\n status += \\\"epochs = {}\\\\n\\\".format(self.epochs)\\n status += \\\"save_step = {}\\\\n\\\".format(self.save_step)\\n status += \\\"learning_rate = {}\\\\n\\\".format(self.learning_rate)\\n status += \\\"momentum = {}\\\\n\\\".format(self.momentum)\\n return status\",\n \"def summary(self):\\n return self.piece_behavior.summary\",\n \"def description(self) -> str:\\r\\n description = \\\"The player must aim to put the most possible units \\\" + \\\\\\r\\n \\\"of \\\" + colour_name(self.colour) + \\\" on the outer\\\" +\\\\\\r\\n \\\" perimeter.\\\"\\r\\n return description\",\n \"def describe(self):\\n\\n print(\\\"Correlation length: {0}\\\".format(self.cl))\\n print(\\\"icoordchange: {0}\\\".format(self.icoordchange))\\n print(\\\"ispec: {0}\\\".format(self.ispec))\\n print(\\\"ireg: {0}\\\".format(self.ireg))\\n print(\\\"Domain: x-axis: from {0} to {1} with {2} steps of {3}\\\".format(self.xori, self.xend,\\n self.nx, self.dx))\\n print(\\\"Domain: y-axis: from {0} to {1} with {2} steps of {3}\\\".format(self.yori, self.yend,\\n self.ny, self.dy))\\n print(\\\"Exclusion value: {0}\\\".format(self.valex))\\n print(\\\"Signal-to-noise ratio: {0}\\\".format(self.snr))\\n print(\\\"Variance of the background field: {0}\\\".format(self.varbak))\",\n \"def describe():\",\n \"def __str__(self):\\n status = \\\"height = {}\\\\n\\\".format(self.height)\\n status += \\\"width = {}\\\\n\\\".format(self.width)\\n status += \\\"channels = {}\\\\n\\\".format(self.channels)\\n status += \\\"classes = {}\\\\n\\\".format(self.classes)\\n status += \\\"batch_size = {}\\\\n\\\".format(self.batch_size)\\n status += \\\"epochs = {}\\\\n\\\".format(self.epochs)\\n status += \\\"save_step = {}\\\\n\\\".format(self.save_step)\\n status += \\\"learning_rate = {}\\\\n\\\".format(self.learning_rate)\\n status += \\\"momentum = {}\\\\n\\\".format(self.momentum)\\n return status\",\n \"def summarize(self):\\n txtSumm = ''\\n\\n if self.legtype == 'Takeoff':\\n txtSumm = \\\"%02d -- %s\\\" %\\\\\\n (self.legno, self.legtype)\\n elif self.legtype == 'Landing':\\n txtSumm = \\\"%02d -- %s\\\" %\\\\\\n (self.legno, self.legtype)\\n elif self.legtype == 'Other':\\n txtSumm = \\\"%02d -- %s\\\" %\\\\\\n (self.legno, self.legtype)\\n elif self.legtype == 'Observing':\\n txtSumm = \\\"%02d -- %s, RA: %s, Dec: %s, LegDur: %s, ObsDur: %s\\\" %\\\\\\n (self.legno, self.target, self.ra, self.dec,\\n str(self.duration),\\n str(self.obsdur))\\n txtSumm += \\\"\\\\n\\\"\\n if self.nonsid is True:\\n txtSumm += \\\"NONSIDERIAL TARGET -- NAIFID: %d\\\" % (self.naifid)\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"(The SOFIA project sincerely hopes you enjoy \\\"\\n txtSumm += \\\"your observing breaks due to XFORMS crashes)\\\"\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"ObsPlan: %s, ObsBlk: %s\\\" % (self.obsplan, self.obsblk)\\n txtSumm += \\\"\\\\n\\\\n\\\"\\n txtSumm += \\\"Elevation Range: %.1f, %.1f\\\" % (self.range_elev[0],\\n self.range_elev[1])\\n txtSumm += \\\"\\\\n\\\\n\\\"\\n txtSumm += \\\"ROF Range: %.1f, %.1f\\\" % (self.range_rof[0],\\n self.range_rof[1])\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"ROF Rate Range: %.1f, %.1f %s\\\" % (self.range_rofrt[0],\\n self.range_rofrt[1],\\n self.range_rofrtu)\\n txtSumm += \\\"\\\\n\\\\n\\\"\\n txtSumm += \\\"True Heading Range: %.1f, %.1f\\\" % (self.range_thdg[0],\\n self.range_thdg[1])\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"True Heading Rate Range: %.1f, %.1f %s\\\" %\\\\\\n (self.range_thdgrt[0],\\n self.range_thdgrt[1],\\n self.range_thdgrtu)\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"Moon Angle: %.1f, Moon Illumination: %s\\\" %\\\\\\n (self.moonangle, self.moonillum)\\n\\n return txtSumm\",\n \"def attribute_summary(self):\\n attributes = self.get_attribute_values()\\n pairs = [attribute.summary() for attribute in attributes]\\n return \\\", \\\".join(pairs)\",\n \"def description(self):\\n\\n\\t\\treturn \\\"%d %s %s\\\" % (self.vintage, self.winery.name, self.name)\",\n \"def __str__(self):\\n return self.summarize()\",\n \"def __str__(self):\\n return self.summarize()\",\n \"def summary(self):\\n return get_paragraphs(self.description)[0]\",\n \"def __str__(self):\\n status = \\\"height = {}\\\\n\\\".format(self.height)\\n status += \\\"width = {}\\\\n\\\".format(self.width)\\n status += \\\"channels = {}\\\\n\\\".format(self.channels)\\n status += \\\"input_dim = {}\\\\n\\\".format(self.input_dim)\\n status += \\\"architecture = {}\\\\n\\\".format(self.architecture)\\n status += \\\"activations = {}\\\\n\\\".format(self.activations)\\n status += \\\"batch_size = {}\\\\n\\\".format(self.batch_size)\\n status += \\\"epochs = {}\\\\n\\\".format(self.epochs)\\n status += \\\"save_step = {}\\\\n\\\".format(self.save_step)\\n status += \\\"learning_rate = {}\\\\n\\\".format(self.learning_rate)\\n status += \\\"momentum = {}\\\\n\\\".format(self.momentum)\\n return status\",\n \"def summary(self, summary: str):\\n return self.swag({\\n 'summary': summary\\n })\",\n \"def toString(self):\\n\\t\\ts = \\\"A %s titled '%s':\\\\n\\\\n\\\" % (self.getSpecString(), self.getName())\\n\\t\\ts += \\\"It's summary reads: %s\\\\n\\\\n\\\" % (self.getDescription())\\n\\t\\ts += \\\"~~\\\\n%s\\\\n~~\\\" % (self.getAllItemsStr())\\n\\t\\treturn s\",\n \"def summary(self):\\n raise NotImplementedError\",\n \"def get_summary(self):\\n \\n text = \\\"word: {}, total_score: {} \\\\n\\\".format(self.clue, self.total_score)\\n for card, score in self.sorted_card_score_pairs:\\n card_text = \\\"\\\\t card.name:{} (team:{}), similarity: {} \\\\n\\\".format(card.name, card.color, score)\\n text += card_text\\n return text\",\n \"def cal_desc(self):\\n desc = \\\"\\\"\\n desc += \\\"Requested by \\\"\\n orgs = self.org.all()\\n if len(orgs) > 0:\\n for org in orgs:\\n desc += org.name + \\\", \\\"\\n desc = desc[:-2] + \\\".\\\\n\\\" # removes trailing comma\\n ccs = self.ccinstances.all()\\n if len(ccs) > 0:\\n desc += \\\"Crew Chiefs: \\\"\\n for cc in ccs:\\n desc += cc.crew_chief.get_full_name() + \\\" [\\\" + (cc.service.shortname if cc.service else cc.category.name) + \\\"], \\\"\\n desc = desc[:-2] + \\\".\\\\n\\\" # removes trailing comma\\n if self.description:\\n desc += self.description + \\\"\\\\n\\\"\\n return desc\",\n \"def str(self):\\n if self.num_dice is not None and self.dice_type is not None:\\n descr = \\\"{}D{}\\\".format(self.num_dice, self.dice_type)\\n if self.plus > 0:\\n descr += \\\"+{}\\\".format(self.plus)\\n elif self.min_value is not None and self.max_value is not None:\\n descr = \\\"{}-{}\\\".format(self.min_value, self.max_value)\\n elif self.plus != 0:\\n descr = str(self.plus)\\n else:\\n descr = \\\"\\\"\\n\\n return descr\",\n \"def mysummary(self):\\n return self.sprintf(\\\"IGMPv3 Group Record %IGMPv3gr.type% %IGMPv3gr.maddr%\\\")\",\n \"def _summary(obj):\\n return obj.summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def summary(self):\\n return self._summary\",\n \"def __str__(self):\\n _str = indent(self.name, color = self.color)\\n _str += indent(\\\"-\\\"*len(self.name))\\n _str += indent(\\\"( \\\" + self.movemement + \\\" - \\\"+ self.symbole + \\\" )\\\")\\n _str += indent(\\\"Awareness: \\\" + str(self.awareness))\\n for _iel in self.abilities:\\n _str += indent(_iel)\\n if self.description is not None:\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n _str += indent(self.description)\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n _str += indent(BOLD_BLACK + \\\" \\\"*15 + \\\"Combat Stats\\\" + RESET)\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n _str += indent(BLUE + \\\" Horror\\\" + RESET + \\\" Toughness\\\"\\\\\\n + RED + \\\" Combat\\\" + RESET)\\n _str += indent(BLUE + \\\"Rating Damage \\\"\\\\\\n + RED + \\\"Rating Damage\\\")\\n _str += indent(BLUE + \\\" \\\" + str(self.horror_rating)\\\\\\n + \\\" \\\"*8 + str(self.horror_damage) + RESET\\\\\\n + \\\" \\\"*8 + str(self.toughness) + RED\\\\\\n + \\\" \\\"*8 + str(self.combat_rating)\\\\\\n + \\\" \\\"*8 + str(self.combat_damage) + RESET)\\n _str += indent(\\\"-\\\"*45 + \\\"\\\\n\\\")\\n return _str\",\n \"def __str__(self):\\n description = \\\"Object class Critter.\\\"\\n description += \\\"\\\\nName: \\\" + self.name + \\\"\\\\nMood: \\\" + self.mood + \\\"\\\\nHunger: \\\" + str(self.hunger) + \\\"\\\\nBoredom: \\\" + str(self.boredom) + \\\"\\\\n\\\"\\n return description\",\n \"def all_characteristics_as_string(self):\\n chars1 = _all_characteristics_\\n chars2 = [ch.title() for ch in chars1]\\n chars2[chars2.index('Internalstructure')] = 'InternalStructure'\\n\\n s = ('%-18s %-24s %-2s'%('Characteristic', 'Semantic value','#'))\\n s+= '\\\\n'\\n s+= ('%-18s %-24s %-2s' % ('-', '-', '-')) + '\\\\n'\\n\\n for i in range(len(chars1)):\\n attrs = (chars2[i],\\\\\\n getattr(self,chars2[i])(),\\n getattr(self,chars1[i]))\\n s += '%-18s | %-24s | %-2d' % attrs\\n s += '\\\\n'\\n return s[:-1] # cut the trailing newline character\",\n \"def summarize(self):\\n return self.__repr__() + \\\" \\\" \\\\\\n \\\"class: \\\" + self.__class__.__name__ + \\\", \\\" + \\\\\\n \\\"elements: \\\" + str(self.elements)\",\n \"def nice_output(self):\\n return self.des\",\n \"def nice_output(self):\\n return self.des\",\n \"def get_description(self):\",\n \"def get_full_description(self, s, base=None):\\n summary = self.get_summary(s)\\n extended_summary = self.get_extended_summary(s)\\n ret = (summary + '\\\\n\\\\n' + extended_summary).strip()\\n if base is not None:\\n self.params[base + '.full_desc'] = ret\\n return ret\",\n \"def get_summary(self) -> str:\\n connected = self.is_connected()\\n info = '[{} - {}]'.format(self._index,\\n 'Connected' if connected else 'Disconnected')\\n if connected:\\n info += ' {} ({})'.format(self.get_model_name(), self.get_serial())\\n return info\",\n \"def __str__(self):\\n return f'{self._name} has {self._calories} calories, {self._carbohydrates}' +\\\\\\n f'g. carbohydrates, {self._fat}g. of fat and {self._proteins}g. of proteins'\",\n \"def summary(self):\\n\\n import yaml\\n \\n (source, rate, numChannels, totalSamples, duration, dataType) = self.get_info()\\n info = {'Source':source,\\n 'SampleRate':rate,\\n 'NumChannels':numChannels,\\n 'TotalSamples':totalSamples,\\n 'Duration':duration,\\n 'DataType':dataType}\\n print(yaml.dump(info, default_flow_style=False))\",\n \"def getDescription(self):\\n return \\\"\\\\n\\\".join(\\n [\\n \\\"This diagnostic determines if the Docker daemon suffers from 20GiB COPY bug\\\",\\n \\\"\\\",\\n \\\"See https://github.com/adamrehn/ue4-docker/issues/99#issuecomment-1079702817 for details and workarounds\\\",\\n \\\"\\\",\\n self._parser.format_help(),\\n ]\\n )\",\n \"def short_description(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"short_description\\\")\",\n \"def get_full_description(self, s, base=None):\\n summary = self.get_summary(s)\\n extended_summary = self.get_extended_summary(s)\\n ret = (summary + \\\"\\\\n\\\\n\\\" + extended_summary).strip()\\n if base is not None:\\n self.params[base + \\\".full_desc\\\"] = ret\\n return ret\",\n \"def get_summary(callback, docstring=None):\\n description = get_view_description(\\n callback, html=False, docstring=docstring) \\\\\\n .split(\\\"\\\\n\\\")[0].split(\\\".\\\")[0]\\n description = IntrospectorHelper.strip_yaml_from_docstring(\\n description)\\n description = IntrospectorHelper.strip_params_from_docstring(\\n description)\\n description = strip_tags(get_view_description(\\n callback, html=True, docstring=description))\\n return description\",\n \"def __str__(self):\\n if len(self.label) > 0:\\n descr = [\\\"'%s', target='%s' [%s]\\\" % (self.label, self.target.name, self.target.body_type)]\\n else:\\n descr = [\\\"target='%s' [%s]\\\" % (self.target.name, self.target.body_type)]\\n if self.baseline:\\n descr[0] += ', initial baseline offset=%f' % (self.baseline.poly[-1],)\\n if self.beam:\\n descr[0] += ', beam height=%f' % (self.beam.height,)\\n for scan_ind, scan in enumerate(self.scans):\\n descr.append('%4d: %s' % (scan_ind, str(scan)))\\n return '\\\\n'.join(descr)\",\n \"def human_readable_info(self) -> str:\\n next_session = unix_str(self._stat.next_session)\\n last_session = unix_str(self._stat.last_session)\\n return \\\"\\\"\\\"\\n Next Session: {}\\n Last Session: {}\\n Repetitions: {}\\n Health: {}\\n ------------------------\\n Past Quality (last 20):\\n ------------------------\\n {}\\n \\\"\\\"\\\".format(\\n next_session,\\n last_session,\\n self._stat.actual_repetitions,\\n self._health(),\\n self._past_quality_graph(),\\n )\",\n \"def summary_parameters(self):\\n text = re.sub(r'\\\\r?\\\\n', ' - ', self.opt.text[:200])\\n return {'adding': text}\",\n \"def description(self):\\n active = np.nonzero([bool(p) for p in self])[0]\\n last_active = active[-1] if len(active) else -1\\n return ' '.join([p.value_str for p in self][:last_active + 1])\",\n \"def show_summary(self, lang):\\n return self.summary % self.vars\",\n \"def get_description():\\n desc = dict()\\n desc['cache'] = 86400\\n desc['data'] = True\\n desc['description'] = \\\"\\\"\\\"This chart presents a histogram of issuance times\\n for a given watch, warning, or advisory type for a given office.\\\"\\\"\\\"\\n desc['arguments'] = [\\n dict(type='networkselect', name='station', network='WFO',\\n default='DMX', label='Select WFO:'),\\n dict(type='phenomena', name='phenomena',\\n default='WC', label='Select Watch/Warning Phenomena Type:'),\\n dict(type='significance', name='significance',\\n default='W', label='Select Watch/Warning Significance Level:'),\\n ]\\n return desc\",\n \"def getSummary(self):\\n return self.summary\",\n \"def __str__(self) -> str:\\n return inspect.cleandoc(\\n f\\\"\\\"\\\"\\n == Report created {self.report_created} ==\\n Arguments are `{self.args}`\\n Time passed {self.time_passed}\\n Terminated by timeout: {self.is_killed}\\n Cpu(%) mean: {mean(self.cpu)}\\n Cpu(%) min: {min(self.cpu)}\\n Cpu(%) max: {max(self.cpu)}\\n Mem(kb) mean: {mean(self.mem)}\\n Mem(kb) min: {min(self.mem)}\\n Mem(kb) max: {max(self.mem)}\\n \\\"\\\"\\\"\\n )\",\n \"def show_stat_desc(self):\\n qtw.QMessageBox.information(\\n self, \\\"Stat Descriptions\\\", '*This list is not comprehensive, and '\\n 'will vary depending on race/(sub)class.*\\\\n\\\\nStrength\\\\n\\\\t-damage '\\n 'and attack bonuses for most melee and thrown weapons\\\\n\\\\t-carry '\\n 'capacity\\\\n\\\\t-Athletics checks\\\\nDexterity\\\\n\\\\t-damage and attack '\\n 'bonuses for ranged/finesse weapons\\\\n\\\\t-Acrobatics, Stealth, '\\n 'Sleight of Hand, and Initiative checks\\\\nConstitution\\\\n\\\\t-hit '\\n 'points\\\\n\\\\t-resistance to poisons, etc.\\\\nIntelligence\\\\n\\\\t-spell '\\n 'save DC/attack bonus for Wizards\\\\n\\\\t-Arcana, History, '\\n 'Investigation, Nature, and Religion checks\\\\nWisdom\\\\n\\\\t-spell save'\\n ' DC/attack bonus for Druid, Cleric, and Ranger\\\\n\\\\t-Animal '\\n 'Handling, Insight, Medicine, Perception, and Survival checks\\\\n'\\n 'Charisma\\\\n\\\\t-spell save DC/attack bonus for Bard, Paladin, '\\n 'Warlock and Sorceror\\\\n\\\\t-Deception, Intimidation, Performance and'\\n ' Persuasion checks'\\n )\",\n \"def get_description():\\n desc = dict()\\n today = datetime.date.today()\\n desc['data'] = True\\n desc['description'] = \\\"\\\"\\\"This plot presents one of two metrics indicating\\n daily humidity levels as reported by a surface weather station. The\\n first being mixing ratio, which is a measure of the amount of water\\n vapor in the air. The second being vapor pressure deficit, which reports\\n the difference between vapor pressure and saturated vapor pressure.\\n The vapor pressure calculation shown here makes no accounting for\\n leaf tempeature. The saturation vapor pressure is computed by the\\n Tetens formula (Buck, 1981).\\n\\n <br />On 22 June 2016, this plot was modified to display the range of\\n daily averages and not the range of instantaneous observations.\\n \\\"\\\"\\\"\\n desc['arguments'] = [\\n dict(type='zstation', name='zstation', default='AMW',\\n label='Select Station:', network='IA_ASOS'),\\n dict(type='year', name='year', default=today.year,\\n label='Select Year to Plot'),\\n dict(type='select', name='var', default='mixing_ratio',\\n label='Which Humidity Variable to Compute?', options=PDICT),\\n ]\\n return desc\",\n \"def description(self):\\n pass\",\n \"def description(self):\\n pass\",\n \"def __str__(self):\\n return self.piece_behavior.summary\",\n \"def conclusion_summary_map(self):\\n pass\",\n \"def summary(self):\\n if hasattr(self,\\\"_summary\\\"):\\n return self._summary\\n else:\\n return {}\",\n \"def summarize(self):\\n return self.__repr__() + \\\" \\\" \\\\\\n \\\"name: \\\" + self.name + \\\", \\\" + \\\\\\n \\\"class: \\\" + self.__class__.__name__ + \\\", \\\" + \\\\\\n \\\"scope: \\\" + str(self.range) + \\\", \\\" + \\\\\\n \\\"elements: \\\" + str(self.elements)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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method"},"document":{"kind":"string","value":"def setup(self):\n pass"},"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 _start(self):\n pass","def setupStarted(self, *args, **kwargs): # real signature unknown\n pass","def start(self):\n ...","def _start(self):","def start (self):\n pass","def start (self):\n pass","def start(self):\r\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def start(self):\n pass","def on_start(self):\n pass","def on_start(self):\n pass","def on_start(self):\n pass","def on_start(self):\n pass","def on_start(self):\n pass","def on_start(self):\n pass","def on_start(self):\n pass","def on_start(self):\n pass","def start(self) -> None:\n ...","def 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allow an exception is thrown."},"document":{"kind":"string","value":"def destroy(self):\n pass"},"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 on_exception(self):\n pass","def handle(self) -> None:","def handle(self):","def exception_handler(self, exception):\n pass","def handle(self):\n raise NotImplementedError","def handle_err(self):\n pass","def error(self, handler):\n pass","def _on_exception(self, exception):\n pass","def post_process(self):\n pass","def post_process(self):\n pass","def post_process(self):\n pass","def post_process(self):\n pass","def post_process(self):\n pass","def post_execute(self):","def handle_context_missing(self):","def __call__(self):\r\n raise self","def __call__(self):\r\n raise self","def handle_expt(self):\r\n self._perform_on_error_handling()","def _handle(self, *args, **options):\n return super()._handle(*args, **options)","def postLoad(self):\n pass","def event_handler(self, response):\n pass","def post_init(self):\n\t\tpass","def __init__(self, handler):\n self.__handler = handler","def handler(self, *args, **kwargs):\n return True","def _post_load(self):\n pass","def process_exception(self, request, exc):\n return None","def _post_init(self) -> None:\n return","def _post_init(self):\n pass","def handle(self, data):\n pass","def cleanup(self) -> None:\n self.handler.cleanup()\n super().cleanup()","def _on_finalize(self):\n pass","def callback(self):\n pass # pragma: no cover","def _after_serve_actions(self):\n pass","def exception(self, e):\n pass","def exc_handler(self, exc_type, exc, *args) -> None:\n self.exception = exc\n self.exit_code = 1","def _postprocess(self):","def handle(self, *args, **kwargs):\n raise NotImplementedError()","def on_response(self, response):\n pass","def after_get_hook(self):\n pass","def _handle_attempt(self):\n pass","def _handle_first_request(self):\n pass","def middleware_after(self):\n pass","def justhandle(self, rawdata):\r\n\r\n return self.__handler(rawdata)","def __post_init__(self):\n pass","def _handler_direct_access_exit(self, *args, **kwargs):","def handle_read(self):\n pass","def _afterInit(self):\n pass","def handle_event(self, event):\n pass","def _handler_direct_access_exit(self, *args, **kwargs):\n pass","def _handler_direct_access_exit(self, *args, **kwargs):\n pass","def post_process(cls, *args, **kwargs):\n pass","def post_process(cls, *args, **kwargs):\n pass","def post_process(cls, *args, **kwargs):\n pass","def post_process(cls, *args, **kwargs):\n pass","def handle(self, context: Context):\n raise NotImplementedError()","def unhandled(self):\n return True","def post_start(self):","def add_handler(self, handler):\n pass","def on_failure(self):\n pass","async def _response_handler(self):","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def handler_block(obj, handler_id): # reliably restored by inspect\n pass","def post_exec(self):\n raise NotImplementedError(\"Must implement in frontend subclass.\")","def on_processing_error(self, event, context, exc):\n pass","def __call__(self):\n\t\treturn","def handler(self):\n\t\tself.exitClient()","def rescue(self, instance):\n pass","def __init__(self):\n from django.core.handlers.wsgi import WSGIHandler as djhandler\n self._handler = djhandler()","def _hook(self):","def _handle_read(self):\n pass","def _during_execute(self, db):\n pass","def handle_other(self):\n pass","def handle_write(self):\n pass","def user_exception(self, frame, exc_info):\n pass","def on_load(self):\n pass","def on_load(self):\n pass","def make_new_handler(self, *args, **kwargs):","def _post_dispatch(self, request, *args, **kwargs):\n pass","def _request_finished_handler(self):\n handler = HandlerWrapper('.handlers:request_finished_handler', None)\n exc_handler = HandlerWrapper(self.get_setting('handler.exc'), handler)\n return exc_handler","def handle_caught(self):\n self.destroy()","def handle(self, rsm_ctx):\n pass","def handler(self):\n\t\treturn self.handle_request","def __init__(self, handler):\n self._handler = handler\n self._storage = []","def exception_handler(self, handler):\n self.java_obj.exceptionHandler(ExceptionHandler(handler))\n return self","def on_before_execution(self):\n pass","def __exit__(self, exc_type, exc_value, traceback):\r\n pass"],"string":"[\n \"def on_exception(self):\\n pass\",\n \"def handle(self) -> None:\",\n \"def handle(self):\",\n \"def exception_handler(self, exception):\\n pass\",\n \"def handle(self):\\n raise NotImplementedError\",\n \"def handle_err(self):\\n pass\",\n \"def error(self, handler):\\n pass\",\n \"def _on_exception(self, exception):\\n pass\",\n \"def post_process(self):\\n pass\",\n \"def post_process(self):\\n pass\",\n \"def post_process(self):\\n pass\",\n \"def post_process(self):\\n pass\",\n \"def post_process(self):\\n pass\",\n \"def post_execute(self):\",\n \"def handle_context_missing(self):\",\n \"def __call__(self):\\r\\n raise self\",\n \"def __call__(self):\\r\\n raise self\",\n \"def handle_expt(self):\\r\\n self._perform_on_error_handling()\",\n \"def _handle(self, *args, **options):\\n return super()._handle(*args, **options)\",\n \"def postLoad(self):\\n pass\",\n \"def event_handler(self, response):\\n pass\",\n \"def post_init(self):\\n\\t\\tpass\",\n \"def __init__(self, handler):\\n self.__handler = handler\",\n \"def handler(self, *args, **kwargs):\\n return True\",\n \"def _post_load(self):\\n pass\",\n \"def process_exception(self, request, exc):\\n return None\",\n \"def _post_init(self) -> None:\\n return\",\n \"def _post_init(self):\\n pass\",\n \"def handle(self, data):\\n pass\",\n \"def cleanup(self) -> None:\\n self.handler.cleanup()\\n super().cleanup()\",\n \"def _on_finalize(self):\\n pass\",\n \"def callback(self):\\n pass # pragma: no cover\",\n \"def _after_serve_actions(self):\\n pass\",\n \"def exception(self, e):\\n pass\",\n \"def exc_handler(self, exc_type, exc, *args) -> None:\\n self.exception = exc\\n self.exit_code = 1\",\n \"def _postprocess(self):\",\n \"def handle(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"def on_response(self, response):\\n pass\",\n \"def after_get_hook(self):\\n pass\",\n \"def _handle_attempt(self):\\n pass\",\n \"def _handle_first_request(self):\\n pass\",\n \"def middleware_after(self):\\n pass\",\n \"def justhandle(self, rawdata):\\r\\n\\r\\n return self.__handler(rawdata)\",\n \"def __post_init__(self):\\n pass\",\n \"def _handler_direct_access_exit(self, *args, **kwargs):\",\n \"def handle_read(self):\\n pass\",\n \"def _afterInit(self):\\n pass\",\n \"def handle_event(self, event):\\n pass\",\n \"def _handler_direct_access_exit(self, *args, **kwargs):\\n pass\",\n \"def _handler_direct_access_exit(self, *args, **kwargs):\\n pass\",\n \"def post_process(cls, *args, **kwargs):\\n pass\",\n \"def post_process(cls, *args, **kwargs):\\n pass\",\n \"def post_process(cls, *args, **kwargs):\\n pass\",\n \"def post_process(cls, *args, **kwargs):\\n pass\",\n \"def handle(self, context: Context):\\n raise NotImplementedError()\",\n \"def unhandled(self):\\n return True\",\n \"def post_start(self):\",\n \"def add_handler(self, handler):\\n pass\",\n \"def on_failure(self):\\n pass\",\n \"async def _response_handler(self):\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def handler_block(obj, handler_id): # reliably restored by inspect\\n pass\",\n \"def post_exec(self):\\n raise NotImplementedError(\\\"Must implement in frontend subclass.\\\")\",\n \"def on_processing_error(self, event, context, exc):\\n pass\",\n \"def __call__(self):\\n\\t\\treturn\",\n \"def handler(self):\\n\\t\\tself.exitClient()\",\n \"def rescue(self, instance):\\n pass\",\n \"def __init__(self):\\n from django.core.handlers.wsgi import WSGIHandler as djhandler\\n self._handler = djhandler()\",\n \"def _hook(self):\",\n \"def _handle_read(self):\\n pass\",\n \"def _during_execute(self, db):\\n pass\",\n \"def handle_other(self):\\n pass\",\n \"def handle_write(self):\\n pass\",\n \"def user_exception(self, frame, exc_info):\\n pass\",\n \"def on_load(self):\\n pass\",\n \"def on_load(self):\\n pass\",\n \"def make_new_handler(self, *args, **kwargs):\",\n \"def _post_dispatch(self, request, *args, **kwargs):\\n pass\",\n \"def _request_finished_handler(self):\\n handler = HandlerWrapper('.handlers:request_finished_handler', None)\\n exc_handler = HandlerWrapper(self.get_setting('handler.exc'), handler)\\n return exc_handler\",\n \"def handle_caught(self):\\n self.destroy()\",\n \"def handle(self, rsm_ctx):\\n pass\",\n \"def handler(self):\\n\\t\\treturn self.handle_request\",\n \"def __init__(self, handler):\\n self._handler = handler\\n self._storage = []\",\n \"def exception_handler(self, handler):\\n self.java_obj.exceptionHandler(ExceptionHandler(handler))\\n return self\",\n \"def on_before_execution(self):\\n pass\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\r\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7447773","0.7214745","0.7060776","0.6870695","0.6805715","0.67974174","0.671484","0.6667653","0.6628348","0.6628348","0.6628348","0.6628348","0.6628348","0.65371287","0.6492414","0.6466798","0.6466798","0.64180535","0.64134246","0.63910395","0.6383068","0.6363912","0.63625693","0.6354484","0.6353299","0.6351175","0.633631","0.63166875","0.62876326","0.6280688","0.62782335","0.6277981","0.6269402","0.6201512","0.6190993","0.616889","0.6163416","0.61590296","0.6140253","0.61365825","0.6129502","0.61228913","0.610802","0.6086918","0.60705507","0.60607177","0.6057704","0.60568887","0.605107","0.605107","0.6039267","0.6039267","0.6039267","0.6039267","0.6030047","0.6029735","0.6022489","0.6019011","0.60158515","0.60145116","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.60116035","0.6009364","0.60081893","0.6003052","0.59967613","0.59932125","0.5992161","0.59851325","0.5982687","0.5970614","0.59629476","0.59622526","0.5946925","0.5930688","0.5930688","0.59204966","0.5911926","0.59033567","0.5903084","0.59026206","0.5900126","0.58982486","0.5887712","0.5885192","0.5884408"],"string":"[\n \"0.7447773\",\n \"0.7214745\",\n \"0.7060776\",\n \"0.6870695\",\n \"0.6805715\",\n \"0.67974174\",\n \"0.671484\",\n \"0.6667653\",\n \"0.6628348\",\n \"0.6628348\",\n \"0.6628348\",\n \"0.6628348\",\n \"0.6628348\",\n \"0.65371287\",\n \"0.6492414\",\n \"0.6466798\",\n \"0.6466798\",\n \"0.64180535\",\n \"0.64134246\",\n \"0.63910395\",\n \"0.6383068\",\n \"0.6363912\",\n \"0.63625693\",\n \"0.6354484\",\n \"0.6353299\",\n \"0.6351175\",\n \"0.633631\",\n \"0.63166875\",\n \"0.62876326\",\n \"0.6280688\",\n \"0.62782335\",\n \"0.6277981\",\n \"0.6269402\",\n \"0.6201512\",\n \"0.6190993\",\n \"0.616889\",\n \"0.6163416\",\n \"0.61590296\",\n \"0.6140253\",\n \"0.61365825\",\n \"0.6129502\",\n \"0.61228913\",\n \"0.610802\",\n \"0.6086918\",\n \"0.60705507\",\n \"0.60607177\",\n \"0.6057704\",\n \"0.60568887\",\n \"0.605107\",\n \"0.605107\",\n \"0.6039267\",\n \"0.6039267\",\n \"0.6039267\",\n \"0.6039267\",\n \"0.6030047\",\n \"0.6029735\",\n \"0.6022489\",\n \"0.6019011\",\n \"0.60158515\",\n \"0.60145116\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.60116035\",\n \"0.6009364\",\n \"0.60081893\",\n \"0.6003052\",\n \"0.59967613\",\n \"0.59932125\",\n \"0.5992161\",\n \"0.59851325\",\n \"0.5982687\",\n \"0.5970614\",\n \"0.59629476\",\n \"0.59622526\",\n \"0.5946925\",\n \"0.5930688\",\n \"0.5930688\",\n \"0.59204966\",\n \"0.5911926\",\n \"0.59033567\",\n \"0.5903084\",\n \"0.59026206\",\n \"0.5900126\",\n \"0.58982486\",\n \"0.5887712\",\n \"0.5885192\",\n \"0.5884408\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":89285,"cells":{"query":{"kind":"string","value":"Start a request,this method does not allow an exception is thrown."},"document":{"kind":"string","value":"def start_request(self,request_handler,client_address):\n logger.debug('start_request(%s:%s)' % client_address)"},"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 start_request(self):\n self.session_manager.start_request()","def _StartRequest(self, request_id, manager):\n pending_path = self._GetRequestPathname(request_id, self._PENDING)\n with open(pending_path, 'r') as f:\n request_object = pickle.load(f)\n manager.StartTask(request_id, request_object)\n self._TransitionRequest(request_id, self._PENDING, self._RUNNING)","def start(self):\n self.get(self.url)","def Start(self, request, global_params=None):\n config = self.GetMethodConfig('Start')\n return self._RunMethod(\n config, request, global_params=global_params)","def _make_request(self):\n try:\n self.response = requests.request(\n method=self.method,\n url=self.url,\n params=self.params,\n data=self.data,\n )\n\n logger.debug(f\"Request URL: {self.response.url}\")\n\n self.response.raise_for_status()\n\n # wrap all `requests` library error and serve as custom application error\n except RequestException as e:\n logger.error(e.__str__(), exc_info=True)\n raise ExternalAPIError(\n \"Error while communication with External API\"\n )","def run(self):\n while True:\n req = self._requests.get()[1]\n req.start()\n logging.info('Running request %s', req)","def request(self, url, *args, **kwargs):\n raise NotImplementedError","def start():\n request.start = time.time()","def __call__(self):\n if grinder.runNumber == 0: self.initialSleep()\n (param1, param2) = self.getParam()\n self.request1(param1, param2)","def request() -> None:\n\t_flag.set()","def _request(self, *args):\n self._silent_request(*args)\n return self._get_response()","def run(self):\n self.initialize()\n\n # run the start callback\n tools.run_callback(\"start\", {'request': self._request})\n\n data = self._request.getData()\n pyhttp = self._request.getHttp()\n config = self._request.getConfiguration()\n\n # allow anyone else to handle the request at this point\n handled = tools.run_callback(\"handle\", \n {'request': self._request},\n mappingfunc=lambda x,y:x,\n donefunc=lambda x:x)\n\n if not handled == 1:\n blosxom_handler(self._request)\n\n # do end callback\n tools.run_callback(\"end\", {'request': self._request})","def start ( self ) :\n raise AbstractMethodException( self , \"start\" )","def _request(self, *args):\n raise NotImplementedError","def start_request(self):\n now = _now()\n self._last_request_no += 1\n # Instantiate a request_info object and fill its start_time\n new_request = self._request_info_class(\n request_no=self._last_request_no, start_time=now,\n _request_finalizer=self._finalize_request)\n # Add currently running request\n self._current_requests[self._last_request_no] = new_request\n\n if now - self._last_autoclean_time > self.AUTOCLEAN_INTERVAL:\n # Avoid memory leak even if client sometimes don't finish requests\n self._clean_outdated()\n\n return new_request","def request(self, *args, **kwargs):\n try:\n return self._http.request(*args, timeout=TIMEOUT, **kwargs)\n except Exception as exc:\n raise RequestException(exc, args, kwargs)","def do_request(\n self,\n version: str,\n action: str,\n protocol: str,\n method: str,\n pathname: str,\n request: dict,\n headers: Dict[str, str],\n runtime: util_models.RuntimeOptions,\n ) -> dict:\n runtime.validate()\n _runtime = {\n 'timeouted': 'retry',\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\n 'maxIdleTimeMillis': self._max_idle_time_millis,\n 'keepAliveDuration': self._keep_alive_duration_millis,\n 'maxRequests': self._max_requests,\n 'maxRequestsPerHost': self._max_requests_per_host,\n 'retry': {\n 'retryable': runtime.autoretry,\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\n },\n 'backoff': {\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\n },\n 'ignoreSSL': runtime.ignore_ssl,\n # 创建凭证One\n }\n _last_request = None\n _last_exception = None\n _now = time.time()\n _retry_times = 0\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\n if _retry_times > 0:\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\n if _backoff_time > 0:\n TeaCore.sleep(_backoff_time)\n _retry_times = _retry_times + 1\n try:\n _request = TeaRequest()\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\n _request.method = method\n _request.pathname = pathname\n _request.query = {\n 'method': action,\n 'version': version,\n 'sign_type': 'HmacSHA1',\n 'req_time': AntchainUtils.get_timestamp(),\n 'req_msg_id': AntchainUtils.get_nonce(),\n 'access_key': self._access_key_id,\n 'base_sdk_version': 'TeaSDK-2.0',\n 'sdk_version': '1.6.10',\n '_prod_code': 'SHUZIWULIU',\n '_prod_channel': 'undefined'\n }\n if not UtilClient.empty(self._security_token):\n _request.query['security_token'] = self._security_token\n _request.headers = TeaCore.merge({\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\n }, headers)\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\n _request.body = UtilClient.to_form_string(tmp)\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\n signed_param = TeaCore.merge(_request.query,\n RPCUtilClient.query(request))\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\n _last_request = _request\n _response = TeaCore.do_action(_request, _runtime)\n raw = UtilClient.read_as_string(_response.body)\n obj = UtilClient.parse_json(raw)\n res = UtilClient.assert_as_map(obj)\n resp = UtilClient.assert_as_map(res.get('response'))\n if AntchainUtils.has_error(raw, self._access_key_secret):\n raise TeaException({\n 'message': resp.get('result_msg'),\n 'data': resp,\n 'code': resp.get('result_code')\n })\n return resp\n except Exception as e:\n if TeaCore.is_retryable(e):\n _last_exception = e\n continue\n raise e\n raise UnretryableException(_last_request, _last_exception)","def make_request_thread(self, service, request):\n requestResponse = self.callbacks.makeHttpRequest(service, request)\n print self.helpers.analyzeRequest(requestResponse).getUrl().toString()","async def request(self) -> Any:\n raise NotImplementedError()","def __call__(self, requestStr):\n return self.connection.Request(requestStr)","def run(self):\n while self.running:\n self.handle_request()","def handle_one_request(self):\n \n try:\n \n self.raw_requestline = self.rfile.readline(65537)\n \n if len(self.raw_requestline) > 65536:\n \n self.requestline = ''\n \n self.request_version = ''\n \n self.command = ''\n \n self.send_error(414)\n \n return\n \n if not self.raw_requestline:\n \n self.close_connection = 1\n \n return\n \n if not self.parse_request():\n \n # An error code has been sent, just exit\n \n return\n \n mname = 'do_' + self.command\n \n if not hasattr(self, mname):\n \n self.send_error(501, \"Unsupported method (%r)\" % self.command)\n \n return\n \n method = getattr(self, mname)\n \n print \"before call do_Get\"\n \n method()\n \n #增加 debug info 及 wfile 判断是否已经 close\n \n print \"after call do_Get\"\n \n if not self.wfile.closed:\n self.wfile.flush() #actually send the response if not already done.\n \n print \"after wfile.flush()\"\n \n except socket.timeout, e:\n \n #a read or a write timed out. Discard this connection\n self.log_error(\"Request timed out: %r\", e)\n self.close_connection = 1\n return","def _request(self, *args, **kwargs):\n raise NotImplementedError()","def _request(self, method, url, payload=None, **params):\n kwargs = dict(params=params)\n kwargs[\"timeout\"] = self._timeout\n if not url.startswith('http'):\n url = self.prefix + url\n headers = self._auth_headers()\n headers['Content-Type'] = 'application/json'\n\n if payload:\n kwargs[\"data\"] = json.dumps(payload)\n gs = self._gpool.spawn if self._gpool else gevent.spawn\n r = gs(self.session.request, method, url, headers=headers, **kwargs)\n r.fetch = partial(self.join, r)\n update_wrapper(r.fetch, self.join)\n #gevent.sleep(0.05)\n return r","def request(self, flow: mitmproxy.http.HTTPFlow):","def request(self, flow: mitmproxy.http.HTTPFlow):","def _send_request(self):\n route_chosen = self.comboBox_route_list.currentText()\n route_id = route_chosen.split(',')[0] #to get the id of the route\n trip_headsign_chosen = self.comboBox_trip_headsign_list.currentText()\n stop_chosen = self.comboBox_stop_list.currentText()\n self.request(route_id, trip_headsign_chosen, stop_chosen)","def _request(self, url: str) -> http.client.HTTPResponse:\n self.request = urllib.request.Request(\n url,\n headers={'User-Agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 6_0 like Mac OS X)'})\n try:\n return urllib.request.urlopen(self.request, timeout=10)\n except Exception as e:\n # print(e)\n # exit(-1)\n print(e, url)\n raise e","def _send_request(self):\n url = self.config['url']\n agent = Agent(reactor)\n response = (yield agent.request(\n 'GET',\n url.encode(\"ASCII\"),\n ))\n\n d = defer.Deferred()\n response.deliverBody(ReceiveBody(d))\n defer.returnValue((yield d))","def request(self, flow: mitmproxy.http.HTTPFlow):\n pass","async def _request_started(self, request: Request):\n # When we consume the body, we replace the streaming mechanism with\n # a mocked version -- this workaround came from\n # https://github.com/encode/starlette/issues/495#issuecomment-513138055\n # and we call the workaround here to make sure that regardless of\n # `capture_body` settings, we will have access to the body if we need it.\n if self.client.config.capture_body != \"off\":\n await get_body(request)\n\n trace_parent = TraceParent.from_headers(dict(request.headers))\n self.client.begin_transaction(\"request\", trace_parent=trace_parent)\n\n await set_context(lambda: get_data_from_request(request, self.client.config, constants.TRANSACTION), \"request\")\n transaction_name = self.get_route_name(request) or request.url.path\n elasticapm.set_transaction_name(\"{} {}\".format(request.method, transaction_name), override=False)","def send(self, request : str):\n pass","def request(self, path, **kwargs):\n self.http_client.fetch(self.get_url(path), self.stop, **kwargs)\n return self.wait()","def start(self, **kwargs):\n return self.client.api.start(self.id, **kwargs)","def init_request(self):\n\t\tprint(\"Testes\")\n\t\treturn Request(url=self.login_page[0], callback=self.login)","def _request(self, *arg, **kwarg):\n return self.request_session.request(*arg, **kwarg)","def start_requests(self):\r\n try:\r\n\r\n for url in self.start_urls:\r\n yield scrapy.Request(url,\r\n callback=self.navigate_to)\r\n except Exception as err:\r\n logger.error(f'TekDefenceScraper : start_requests : {err}')\r\n raise err","def start(self):\n if self._params.arq:\n s = udpsocket.UDPSocket()\n else:\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n try:\n s.bind(('', self._params.port))\n s.listen(5)\n self.check_and_print_debug_message(\n 'HTTP Server is running on port: ' + str(self._params.port))\n while True:\n c, addr = s.accept()\n Thread(\n target=self.process_http_request,\n args=(c, addr)\n ).start()\n except Exception as e:\n self.check_and_print_debug_message(str(e))\n s.close()\n finally:\n self.check_and_print_debug_message(\n \"HTTP Server connection is closed.\")\n s.close()","def handle_one_request(self):\n import socket\n try:\n self.raw_requestline = self.rfile.readline(65537)\n if len(self.raw_requestline) > 65536:\n self.requestline = ''\n self.request_version = ''\n self.command = ''\n self.send_error(414)\n return\n if not self.raw_requestline:\n self.close_connection = 1\n return\n if not self.parse_request():\n # An error code has been sent, just exit\n return\n\n ##### Customization\n # origin\n \"\"\"\n mname = 'do_' + self.command\n if not hasattr(self, mname):\n self.send_error(501, \"Unsupported method (%r)\" % self.command)\n return\n method = getattr(self, mname)\n method()\n \"\"\"\n # now\n #import pdb; pdb.set_trace()\n self.delegate(self.get_environ(), self.gen_response, self.send_error)\n\n self.wfile.flush() #actually send the response if not already done.\n except socket.timeout, e:\n #a read or a write timed out. Discard this connection\n self.log_error(\"Request timed out: %r\", e)\n self.close_connection = 1\n return","def start():","def start():","def start():","def start():","def _request(self, method, url, params=None, data=None, request_type=PRIVATE, headers={}):\n self._is_valid_request_option(request_type=request_type)\n\n request_headers = copy.deepcopy(self.BASE_HEADERS)\n request_headers.update(headers)\n\n response = getattr(requests, method.lower())(\n url,\n headers=request_headers,\n params=params,\n data=data\n )\n\n return self._handle_response(response)","def do_request(\n self,\n version: str,\n action: str,\n protocol: str,\n method: str,\n pathname: str,\n request: dict,\n headers: Dict[str, str],\n runtime: util_models.RuntimeOptions,\n ) -> dict:\n runtime.validate()\n _runtime = {\n 'timeouted': 'retry',\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\n 'maxIdleTimeMillis': self._max_idle_time_millis,\n 'keepAliveDurationMillis': self._keep_alive_duration_millis,\n 'maxRequests': self._max_requests,\n 'maxRequestsPerHost': self._max_requests_per_host,\n 'retry': {\n 'retryable': runtime.autoretry,\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\n },\n 'backoff': {\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\n },\n 'ignoreSSL': runtime.ignore_ssl,\n # 链上交易中的事件\n }\n _last_request = None\n _last_exception = None\n _now = time.time()\n _retry_times = 0\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\n if _retry_times > 0:\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\n if _backoff_time > 0:\n TeaCore.sleep(_backoff_time)\n _retry_times = _retry_times + 1\n try:\n _request = TeaRequest()\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\n _request.method = method\n _request.pathname = pathname\n _request.query = {\n 'method': action,\n 'version': version,\n 'sign_type': 'HmacSHA1',\n 'req_time': AntchainUtils.get_timestamp(),\n 'req_msg_id': AntchainUtils.get_nonce(),\n 'access_key': self._access_key_id,\n 'base_sdk_version': 'TeaSDK-2.0',\n 'sdk_version': '1.3.1',\n '_prod_code': 'BAASDATAGW',\n '_prod_channel': 'undefined'\n }\n if not UtilClient.empty(self._security_token):\n _request.query['security_token'] = self._security_token\n _request.headers = TeaCore.merge({\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\n }, headers)\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\n _request.body = UtilClient.to_form_string(tmp)\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\n signed_param = TeaCore.merge(_request.query,\n RPCUtilClient.query(request))\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\n _last_request = _request\n _response = TeaCore.do_action(_request, _runtime)\n raw = UtilClient.read_as_string(_response.body)\n obj = UtilClient.parse_json(raw)\n res = UtilClient.assert_as_map(obj)\n resp = UtilClient.assert_as_map(res.get('response'))\n if AntchainUtils.has_error(raw, self._access_key_secret):\n raise TeaException({\n 'message': resp.get('result_msg'),\n 'data': resp,\n 'code': resp.get('result_code')\n })\n return resp\n except Exception as e:\n if TeaCore.is_retryable(e):\n _last_exception = e\n continue\n raise e\n raise UnretryableException(_last_request, _last_exception)","def start_request(self, request_id, user_request_id, ip, app_id, version_id,\n nickname, user_agent, host, method, resource, http_version,\n start_time=None):\n if start_time is None:\n start_time = self._get_time_usec()\n\n rl = self._pending_requests[request_id]\n rl.appId = app_id\n rl.versionId = version_id\n rl.requestId = request_id\n rl.ip = ip\n rl.nickname = nickname\n rl.startTime = start_time\n rl.method = method\n rl.resource = resource\n rl.httpVersion = http_version\n rl.userAgent = user_agent\n rl.host = host\n self._pending_requests_applogs[request_id] = rl.init_resizable_list('appLogs')","def perform_request(request):\n info = evmapy.util.get_app_info()\n try:\n return send_request(request)\n except (ConnectionRefusedError, FileNotFoundError):\n exit(\"No %s instance is running as %s\" % (\n info['name'], info['user'].pw_name\n ))\n except TimeoutError:\n exit(\"Timeout waiting for a response from %s\" % info['name'])","def do_request(\n self,\n version: str,\n action: str,\n protocol: str,\n method: str,\n pathname: str,\n request: dict,\n headers: Dict[str, str],\n runtime: util_models.RuntimeOptions,\n ) -> dict:\n runtime.validate()\n _runtime = {\n 'timeouted': 'retry',\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\n 'maxIdleTimeMillis': self._max_idle_time_millis,\n 'keepAliveDuration': self._keep_alive_duration_millis,\n 'maxRequests': self._max_requests,\n 'maxRequestsPerHost': self._max_requests_per_host,\n 'retry': {\n 'retryable': runtime.autoretry,\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\n },\n 'backoff': {\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\n },\n 'ignoreSSL': runtime.ignore_ssl,\n # 身份\n }\n _last_request = None\n _last_exception = None\n _now = time.time()\n _retry_times = 0\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\n if _retry_times > 0:\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\n if _backoff_time > 0:\n TeaCore.sleep(_backoff_time)\n _retry_times = _retry_times + 1\n try:\n _request = TeaRequest()\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\n _request.method = method\n _request.pathname = pathname\n _request.query = {\n 'method': action,\n 'version': version,\n 'sign_type': 'HmacSHA1',\n 'req_time': AntchainUtils.get_timestamp(),\n 'req_msg_id': AntchainUtils.get_nonce(),\n 'access_key': self._access_key_id,\n 'base_sdk_version': 'TeaSDK-2.0',\n 'sdk_version': '1.0.212',\n '_prod_code': 'DEMO',\n '_prod_channel': 'undefined'\n }\n if not UtilClient.empty(self._security_token):\n _request.query['security_token'] = self._security_token\n _request.headers = TeaCore.merge({\n 'host': UtilClient.default_string(self._endpoint, 'centre-openapi.antchain.antgroup.com'),\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\n }, headers)\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\n _request.body = UtilClient.to_form_string(tmp)\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\n signed_param = TeaCore.merge(_request.query,\n RPCUtilClient.query(request))\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\n _last_request = _request\n _response = TeaCore.do_action(_request, _runtime)\n raw = UtilClient.read_as_string(_response.body)\n obj = UtilClient.parse_json(raw)\n res = UtilClient.assert_as_map(obj)\n resp = UtilClient.assert_as_map(res.get('response'))\n if AntchainUtils.has_error(raw, self._access_key_secret):\n raise TeaException({\n 'message': resp.get('result_msg'),\n 'data': resp,\n 'code': resp.get('result_code')\n })\n return resp\n except Exception as e:\n if TeaCore.is_retryable(e):\n _last_exception = e\n continue\n raise e\n raise UnretryableException(_last_request, _last_exception)","def _request(self, *args, **kwargs):\n request = self._make_request(*args, **kwargs)\n\n return self._collect_request(request)","async def do_request_async(\n self,\n version: str,\n action: str,\n protocol: str,\n method: str,\n pathname: str,\n request: dict,\n headers: Dict[str, str],\n runtime: util_models.RuntimeOptions,\n ) -> dict:\n runtime.validate()\n _runtime = {\n 'timeouted': 'retry',\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\n 'maxIdleTimeMillis': self._max_idle_time_millis,\n 'keepAliveDuration': self._keep_alive_duration_millis,\n 'maxRequests': self._max_requests,\n 'maxRequestsPerHost': self._max_requests_per_host,\n 'retry': {\n 'retryable': runtime.autoretry,\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\n },\n 'backoff': {\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\n },\n 'ignoreSSL': runtime.ignore_ssl,\n # 创建凭证One\n }\n _last_request = None\n _last_exception = None\n _now = time.time()\n _retry_times = 0\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\n if _retry_times > 0:\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\n if _backoff_time > 0:\n TeaCore.sleep(_backoff_time)\n _retry_times = _retry_times + 1\n try:\n _request = TeaRequest()\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\n _request.method = method\n _request.pathname = pathname\n _request.query = {\n 'method': action,\n 'version': version,\n 'sign_type': 'HmacSHA1',\n 'req_time': AntchainUtils.get_timestamp(),\n 'req_msg_id': AntchainUtils.get_nonce(),\n 'access_key': self._access_key_id,\n 'base_sdk_version': 'TeaSDK-2.0',\n 'sdk_version': '1.6.10',\n '_prod_code': 'SHUZIWULIU',\n '_prod_channel': 'undefined'\n }\n if not UtilClient.empty(self._security_token):\n _request.query['security_token'] = self._security_token\n _request.headers = TeaCore.merge({\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\n }, headers)\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\n _request.body = UtilClient.to_form_string(tmp)\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\n signed_param = TeaCore.merge(_request.query,\n RPCUtilClient.query(request))\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\n _last_request = _request\n _response = await TeaCore.async_do_action(_request, _runtime)\n raw = await UtilClient.read_as_string_async(_response.body)\n obj = UtilClient.parse_json(raw)\n res = UtilClient.assert_as_map(obj)\n resp = UtilClient.assert_as_map(res.get('response'))\n if AntchainUtils.has_error(raw, self._access_key_secret):\n raise TeaException({\n 'message': resp.get('result_msg'),\n 'data': resp,\n 'code': resp.get('result_code')\n })\n return resp\n except Exception as e:\n if TeaCore.is_retryable(e):\n _last_exception = e\n continue\n raise e\n raise UnretryableException(_last_request, _last_exception)","def start(self):\n def f():\n if (self.started): return\n self.started = True\n with client.ServerProxy(self.host) as proxy:\n while (not self.req_shutdown):\n self.update_speed(proxy)\n time.sleep(self.com_freq)\n self.started = False\n self.req_shutdwon = False\n\n Thread(target=f).start()","def onRequestStart(self, api, request):\n logging.info('Request start ({})'.format(request))","def request(self, method, url, params=None, data=None):\n raise NotImplementedError(\n u\"%s: Method not implemented\", self.__class__.__name__)","def start(self, **kwargs):\n pass","def start(self, **kwargs):\n pass","def req():\n return Request()","def start_test(self, request):\n request.worker.start_test(request.message.test_id)\n\n return SuccessReply()","def send(self) -> None:\n\n payload = self.get_payload()\n try:\n self.response = requests.get(url=FAST_API, params=payload)\n except requests.exceptions.ConnectionError:\n print(f\"requests.exceptions.ConnectionError! Trying again in 5 seconds...\")\n sleep(5)\n self.send()","def __call__(self, request):\n response = self.get_request(request)\n return response","def get_request(self: 'WebScraper', \n url: str, \n params: Union[dict, None] = None\n ) -> req.Response:\n res = None\n while True:\n try: \n res = self.session.get(url, params=params)\n break\n except req.RequestException: \n # If a requests exception is raised, then some kind of HTTP \n # error occured, so wait for one second before re-attempting \n # the request\n time.sleep(1)\n continue\n return res","def request(self, **request):\r\n environ = {\r\n 'HTTP_COOKIE': self.cookies,\r\n 'PATH_INFO': '/',\r\n 'QUERY_STRING': '',\r\n 'REQUEST_METHOD': 'GET',\r\n 'SCRIPT_NAME': '',\r\n 'SERVER_NAME': 'testserver',\r\n 'SERVER_PORT': 80,\r\n 'SERVER_PROTOCOL': 'HTTP/1.1',\r\n }\r\n environ.update(self.defaults)\r\n environ.update(request)\r\n request = WSGIRequest(environ)\r\n\r\n handler = BaseHandler()\r\n handler.load_middleware()\r\n for middleware_method in handler._request_middleware:\r\n if middleware_method(request):\r\n raise Exception(\"Couldn't create request object - \"\r\n \"request middleware returned a response\")\r\n\r\n return request","def start(self):\n self._client.predict(\n endpoint=self._endpoint, instances=self._request)\n\n if self._completion_callback:\n if self._query_handle:\n callback_args = [self._query_handle]\n else:\n callback_args = []\n self._completion_callback(*callback_args)","def start_requests(self):\n yield scrapy.Request(url=self.start_urls[0])","def call(self):\n\n self.url = self._prepare_url()\n status_code, response = self._do_request(self.url)\n return self._process_response(status_code, response)","async def handle_request(self, request: aioweb.request.Request):","def do_request(\n self,\n version: str,\n action: str,\n protocol: str,\n method: str,\n pathname: str,\n request: dict,\n headers: Dict[str, str],\n runtime: util_models.RuntimeOptions,\n ) -> dict:\n runtime.validate()\n _runtime = {\n 'timeouted': 'retry',\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\n 'maxIdleTimeMillis': self._max_idle_time_millis,\n 'keepAliveDuration': self._keep_alive_duration_millis,\n 'maxRequests': self._max_requests,\n 'maxRequestsPerHost': self._max_requests_per_host,\n 'retry': {\n 'retryable': runtime.autoretry,\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\n },\n 'backoff': {\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\n },\n 'ignoreSSL': runtime.ignore_ssl,\n # 资源定位信息\n }\n _last_request = None\n _last_exception = None\n _now = time.time()\n _retry_times = 0\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\n if _retry_times > 0:\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\n if _backoff_time > 0:\n TeaCore.sleep(_backoff_time)\n _retry_times = _retry_times + 1\n try:\n _request = TeaRequest()\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\n _request.method = method\n _request.pathname = pathname\n _request.query = {\n 'method': action,\n 'version': version,\n 'sign_type': 'HmacSHA1',\n 'req_time': AntchainUtils.get_timestamp(),\n 'req_msg_id': AntchainUtils.get_nonce(),\n 'access_key': self._access_key_id,\n 'base_sdk_version': 'TeaSDK-2.0',\n 'sdk_version': '1.8.95',\n '_prod_code': 'BOT',\n '_prod_channel': 'undefined'\n }\n if not UtilClient.empty(self._security_token):\n _request.query['security_token'] = self._security_token\n _request.headers = TeaCore.merge({\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\n }, headers)\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\n _request.body = UtilClient.to_form_string(tmp)\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\n signed_param = TeaCore.merge(_request.query,\n RPCUtilClient.query(request))\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\n _last_request = _request\n _response = TeaCore.do_action(_request, _runtime)\n raw = UtilClient.read_as_string(_response.body)\n obj = UtilClient.parse_json(raw)\n res = UtilClient.assert_as_map(obj)\n resp = UtilClient.assert_as_map(res.get('response'))\n if AntchainUtils.has_error(raw, self._access_key_secret):\n raise TeaException({\n 'message': resp.get('result_msg'),\n 'data': resp,\n 'code': resp.get('result_code')\n })\n return resp\n except Exception as e:\n if TeaCore.is_retryable(e):\n _last_exception = e\n continue\n raise e\n raise UnretryableException(_last_request, _last_exception)","def send(self, request: Request, **requests_kwargs) -> Response:","def process_request_starts(self, request):\n pass","def _start(self):","def _process_request(self):\n if not self._requests:\n if self._stream:\n self._stream.close()\n self._stream = None\n if self._processing:\n self._processing = False\n Engine.instance().stop()\n return\n\n request = self._requests[0]\n\n request.append(\n Engine.instance().defer(request[5], self._request_timeout, request))\n\n port = request[2].port\n if not port:\n if request[2].scheme.lower() == 'https':\n port = 443\n else:\n port = 80\n\n host = \"%s:%d\" % (request[2].hostname, port)\n\n if self._stream:\n if not self._server == host.lower() or not \\\n self._is_secure == (request[2].scheme.lower() == 'https'):\n self._stream.end()\n return\n\n if not self._stream:\n # Store the current server.\n self._server = host.lower()\n\n # Create a Stream, hook into it, and connect.\n self._stream = Stream()\n\n self._stream.on_close = self._on_close\n self._stream.on_connect = self._on_connect\n\n self._is_secure = request[2].scheme.lower() == 'https'\n if self._is_secure:\n raise Exception(\"SSL has not yet been implemented in this version of Pants.\")\n self._stream.startTLS()\n\n self._stream.connect((request[2].hostname, port))\n return\n\n # If we got here, we're connected, and to the right server. Do stuff.\n self._stream.write('%s %s HTTP/1.1%s' % (request[0], request[8], CRLF))\n for k, v in request[3].iteritems():\n self._stream.write('%s: %s%s' % (k, v, CRLF))\n\n if request[4]:\n self._stream.write('%s%s' % (CRLF, request[4]))\n else:\n self._stream.write(CRLF)\n\n # Now, wait for a response.\n self._stream.on_read = self._read_headers\n self._stream.read_delimiter = DOUBLE_CRLF","def do_request(\n self,\n version: str,\n action: str,\n protocol: str,\n method: str,\n pathname: str,\n request: dict,\n headers: Dict[str, str],\n runtime: util_models.RuntimeOptions,\n ) -> dict:\n runtime.validate()\n _runtime = {\n 'timeouted': 'retry',\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\n 'maxIdleTimeMillis': self._max_idle_time_millis,\n 'keepAliveDurationMillis': self._keep_alive_duration_millis,\n 'maxRequests': self._max_requests,\n 'maxRequestsPerHost': self._max_requests_per_host,\n 'retry': {\n 'retryable': runtime.autoretry,\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\n },\n 'backoff': {\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\n },\n 'ignoreSSL': runtime.ignore_ssl,\n # 无分组设备\n }\n _last_request = None\n _last_exception = None\n _now = time.time()\n _retry_times = 0\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\n if _retry_times > 0:\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\n if _backoff_time > 0:\n TeaCore.sleep(_backoff_time)\n _retry_times = _retry_times + 1\n try:\n _request = TeaRequest()\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\n _request.method = method\n _request.pathname = pathname\n _request.query = {\n 'method': action,\n 'version': version,\n 'sign_type': 'HmacSHA1',\n 'req_time': AntchainUtils.get_timestamp(),\n 'req_msg_id': AntchainUtils.get_nonce(),\n 'access_key': self._access_key_id,\n 'base_sdk_version': 'TeaSDK-2.0',\n 'sdk_version': '1.0.45'\n }\n if not UtilClient.empty(self._security_token):\n _request.query['security_token'] = self._security_token\n _request.headers = TeaCore.merge({\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\n }, headers)\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\n _request.body = UtilClient.to_form_string(tmp)\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\n signed_param = TeaCore.merge(_request.query,\n RPCUtilClient.query(request))\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\n _last_request = _request\n _response = TeaCore.do_action(_request, _runtime)\n raw = UtilClient.read_as_string(_response.body)\n obj = UtilClient.parse_json(raw)\n res = UtilClient.assert_as_map(obj)\n resp = UtilClient.assert_as_map(res.get('response'))\n if AntchainUtils.has_error(raw, self._access_key_secret):\n raise TeaException({\n 'message': resp.get('result_msg'),\n 'data': resp,\n 'code': resp.get('result_code')\n })\n return resp\n except Exception as e:\n if TeaCore.is_retryable(e):\n _last_exception = e\n continue\n raise e\n raise UnretryableException(_last_request, _last_exception)","def do_GET(self):\n global st_point, cur_request\n if time.time() - st_point < 1 and cur_request > args.MAX_REQ:\n self.send_response(429)\n self.send_header(\"Content-type\",\"text/html\")\n self.end_headers()\n time.sleep(0.2)\n return\n elif time.time() - st_point > 1:\n st_point = time.time()\n cur_request = 1\n self.func_PARSE()\n if self.parsed_url[2] in [\"/ping\", \"/cats\"]:\n self.func_DO()\n else:\n self.send_response(400)\n text=\"<h1 align=center>Bad request</h1>\"\n self.func_PRINT(text)","def Start(self) :\n\t\t...","def _send_request(self, method, url, params=None, data=None, headers=None, json=None,\n http_status=2, parse_json=False, error_processors=[],\n allow_redirects=None, cookies=None, stream=False):\n call_time = now()\n if self.last_call_time:\n if self.request_wait_seconds:\n delta = (call_time - self.last_call_time).total_seconds()\n if delta < self.request_wait_seconds:\n self.sleep(self.request_wait_seconds - delta,\n log_reason='request wait')\n self.last_call_time = now()\n else:\n self.last_call_time = call_time\n else:\n self.last_call_time = call_time\n else:\n self.first_call_time = self.last_call_time = call_time\n\n if not urlparse(url).scheme and self.base_url:\n url = urljoin(self.base_url, url)\n\n if allow_redirects is None:\n allow_redirects = self.allow_redirects\n\n if self.debug_level >= 5:\n self.logger.debug(\n _color_em('REQUEST %s' % method) + ' ' + url + (' params=%s' % params)\n + '\\n' + _color_em('REQUEST HEADERS:', back=colorama.Back.BLUE) + '\\n'\n + pprint(headers, print_=False)\n + (('\\n' + _color_em('REQUEST BODY:', back=colorama.Back.BLUE) + '\\n'\n + pprint(data or json, print_=False)) if (data or json) else '')\n )\n\n try:\n kwargs = dict(params=params, data=data, json=json, headers=headers,\n allow_redirects=allow_redirects, proxies=self.proxy,\n verify=self.ssl_verify, cookies=cookies, stream=stream)\n if self.timeout is not None:\n # Allow session (ConfigurableSession for example) to handle timeout\n kwargs['timeout'] = self.timeout\n response = self.session.request(method, url, **kwargs)\n except Exception as exc:\n self.error_processor(exc, error_processors)\n raise\n finally:\n if self.request_wait_since_response:\n self.last_call_time = now()\n\n if self.debug_level >= 5:\n self.logger.debug(\n _color_em('RESPONSE %s' % response.request.method, back=colorama.Back.GREEN)\n + colorama.Style.RESET_ALL + colorama.Style.BRIGHT + (' %s ' % response.status_code)\n + colorama.Style.RESET_ALL + response.url\n + '\\n' + _color_em('RESPONSE HEADERS:', back=colorama.Back.GREEN) + '\\n'\n + pprint(response.headers, print_=False)\n + '\\n' + _color_em('RESPONSE BODY:', back=colorama.Back.GREEN) + '\\n'\n + (stream and '<stream>' or pprint(response.text, print_=False))\n )\n\n elapsed_seconds = response.elapsed.total_seconds()\n if elapsed_seconds > self.request_warn_elapsed_seconds:\n self.logger.warning('Request %s %s took %s seconds after calls(%s/%s) since(%s)',\n response.request.method, response.request.url,\n elapsed_seconds, self.calls_count, self.calls_elapsed_seconds,\n self.first_call_time)\n self.calls_elapsed_seconds += elapsed_seconds\n self.calls_count += 1\n self.last_response = response # NOTE: only for debug purposes!\n\n if (http_status and not check_http_status(response.status_code, http_status)):\n self.set_response_json_data(response, parse_json, raise_=False)\n exc = self.HTTPError(response, expected_status=http_status)\n self.error_processor(exc, error_processors)\n raise exc\n\n try:\n self.set_response_json_data(response, parse_json, raise_=True)\n except _JSONDecodeError as exc:\n exc = self.JSONDecodeError(response, exc)\n self.error_processor(exc, error_processors)\n raise exc\n\n return response","def start(self):\n if self._callable:\n self._is_running = True\n self._run_client()","def _start(self):\n pass","def start(self) -> None:","def start(self) -> None:","def start(self):\n try:\n pass\n except:\n pass","def start_requests(self):\n url = self.start_urls[0]\n yield scrapy.Request(url=url, callback=self.parse)","def request(*args):\r\n URL = BASE_URL + args[0]\r\n proxies = {'https': 'http://137.123.%s.135:8134' % randint(2, 233)}\r\n # print(args[0])\r\n if len(args) == 1: # GET method\r\n response = session.get(URL, headers=header, proxies=proxies)\r\n updateHeader(URL)\r\n else: # POST with data parameter addition\r\n response = session.post(URL, headers=header, data=args[1], proxies=proxies)\r\n try:\r\n response.raise_for_status()\r\n except UnboundLocalError as exc:\r\n print(exc)\r\n if len(args) == 2:\r\n print('POST FAIL! continue to file question')\r\n else:\r\n import traceback\r\n print(traceback.format_exc())\r\n exit(1)\r\n else:\r\n return response","def do_request(\n self,\n version: str,\n action: str,\n protocol: str,\n method: str,\n pathname: str,\n request: dict,\n headers: Dict[str, str],\n runtime: util_models.RuntimeOptions,\n ) -> dict:\n runtime.validate()\n _runtime = {\n 'timeouted': 'retry',\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\n 'maxIdleTimeMillis': self._max_idle_time_millis,\n 'keepAliveDuration': self._keep_alive_duration_millis,\n 'maxRequests': self._max_requests,\n 'maxRequestsPerHost': self._max_requests_per_host,\n 'retry': {\n 'retryable': runtime.autoretry,\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\n },\n 'backoff': {\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\n },\n 'ignoreSSL': runtime.ignore_ssl,\n # 金额\n }\n _last_request = None\n _last_exception = None\n _now = time.time()\n _retry_times = 0\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\n if _retry_times > 0:\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\n if _backoff_time > 0:\n TeaCore.sleep(_backoff_time)\n _retry_times = _retry_times + 1\n try:\n _request = TeaRequest()\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\n _request.method = method\n _request.pathname = pathname\n _request.query = {\n 'method': action,\n 'version': version,\n 'sign_type': 'HmacSHA1',\n 'req_time': AntchainUtils.get_timestamp(),\n 'req_msg_id': AntchainUtils.get_nonce(),\n 'access_key': self._access_key_id,\n 'base_sdk_version': 'TeaSDK-2.0',\n 'sdk_version': '1.1.2',\n '_prod_code': 'DEFINCASHIER',\n '_prod_channel': 'undefined'\n }\n if not UtilClient.empty(self._security_token):\n _request.query['security_token'] = self._security_token\n _request.headers = TeaCore.merge({\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\n }, headers)\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\n _request.body = UtilClient.to_form_string(tmp)\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\n signed_param = TeaCore.merge(_request.query,\n RPCUtilClient.query(request))\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\n _last_request = _request\n _response = TeaCore.do_action(_request, _runtime)\n raw = UtilClient.read_as_string(_response.body)\n obj = UtilClient.parse_json(raw)\n res = UtilClient.assert_as_map(obj)\n resp = UtilClient.assert_as_map(res.get('response'))\n if AntchainUtils.has_error(raw, self._access_key_secret):\n raise TeaException({\n 'message': resp.get('result_msg'),\n 'data': resp,\n 'code': resp.get('result_code')\n })\n return resp\n except Exception as e:\n if TeaCore.is_retryable(e):\n _last_exception = e\n continue\n raise e\n raise UnretryableException(_last_request, _last_exception)","def start(self) -> None:\n ...","def start(self) -> None:\n ...","def start(self) -> None:\n app = web.Application()\n app.add_routes([web.post(\"/\", self._handle_request)])\n self._runner = web.AppRunner(app)\n\n self._startup_event = threading.Event()\n self._server_loop = asyncio.new_event_loop()\n t = threading.Thread(target=self._run)\n t.start()\n\n # Wait for server to startup\n self._startup_event.wait()","def _request(self, method, uri, **kwargs):\n url = self.url + uri\n self.logger.debug(\"Requesting {} on {}\".format(method, url))\n response = requests.request(method, url, verify=self.verify, **kwargs)\n try:\n response.raise_for_status()\n except requests.exceptions.HTTPError:\n self.logger.error(\"Bad http code {} requesting Clair\".format(response.status_code))\n if response.reason == \"Not Found\":\n raise ResourceNotFoundException(\"Resource not found\")\n raise ClairConnectionError(response)\n return response","def request(self, url, data=None, params={}, files=None):\n params['token'] = self.token\n request = self.make_request(url, data=data, params=params, files=files)\n return request","def call(self):\n # if this is a POST request, process data\n if self.data:\n post_json = json.dumps(self.data)\n values = {'json': post_json, 'apikey': API_KEY}\n post = urllib.parse.urlencode(values)\n\n else:\n post = None\n\n req = urllib.request.Request(self.url, post)\n\n try:\n self.response = urllib.request.urlopen(req, timeout=self.timeout)\n\n except (URLError, HTTPError, timeout) as error:\n self.response = error","def start(self):\n self.conn.start()","def _req(self, host, path, meth, timeout, params, data, is_search):\n if is_search and self.http_search:\n url = 'http://%s%s' % (host, path)\n else:\n url = 'https://%s%s' % (host, path)\n req = self.session.request(meth, url, params=params, data=data,\n headers=self.headers)\n res = yield from req\n with async_timeout.timeout(timeout):\n if res.status // 100 == 2:\n return (yield from res.json())\n elif res.status // 100 == 4:\n message = 'HTTP Code: %d' % res.status\n try:\n message = (yield from res.json())['message']\n finally:\n raise AlgoliaException(message)\n # TODO: Check this for replacement.\n res.raise_for_status()","def perform_request(self,\n request: RequestBase,\n method: str='POST'\n ):\n headers = {\n 'Accept': 'application/json',\n 'User-Agent': self.user_agent()\n }\n if APIAuthentication.use_http_auth:\n headers['Authorization'] = 'Basic {auth}'.format(auth=self.get_auth())\n\n # Lazy loader for api credentials.\n if request.requires_api_token() and ParamValidator.is_empty(request.api_token)\\\n and ParamValidator.not_empty(APIAuthentication.api_token):\n request.api_token = APIAuthentication.api_token\n if request.requires_service_id() and ParamValidator.is_empty(request.service_id)\\\n and ParamValidator.not_empty(APIAuthentication.service_id):\n request.service_id = APIAuthentication.service_id\n\n # Build url\n url = \"{0}/{1}\".format(PAYNL_END_POINT, request.get_url())\n parameters = request.get_parameters()\n if APIAuthentication.use_http_auth and 'token' in parameters:\n del parameters['token']\n\n if self.print_debug:\n print(\"Calling {} using {}\".format(url, method))\n print(\"HTTP Headers: {}\".format(json.dumps(headers)))\n print(\"Params: {}\".format(json.dumps(parameters)))\n\n if method.upper() == 'GET':\n response = requests.get(url, verify=True, headers=headers, params=parameters)\n else:\n response = requests.post(url, verify=True, headers=headers, data=parameters)\n\n if response.status_code not in self.__supported_status_codes:\n response.raise_for_status()\n\n if self.print_debug:\n print(\"Response object: {}\".format(response))\n print(\"Raw response: {}\".format(response.text))\n\n # Now the we have a response, let the request class handle the response.\n request.raw_response = response.text\n\n if self.print_debug:\n print(type(request.response))\n\n if request.response.is_error():\n raise ErrorException(request.response.request)","def request(self, *args, **kwargs):\n req = GoodreadsRequest(self, *args, **kwargs)\n return req.request()","def start_requests(self):\n yield Request(self.base_url, \n callback=self.parse_urls)","def _request(self, method, *args, **kwargs):\n if not \"headers\" in kwargs:\n kwargs[\"headers\"] = self._headers\n return self._session.request(method, self._url(*args), **kwargs)","def Run(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\n raise NotImplementedError()","def start(self):\r\n pass","def start(self):\r\n self.debug(\"### starting gox streaming API, trading %s%s\" %\r\n (self.curr_base, self.curr_quote))\r\n self.client.start()","async def start(self) -> None:","def do_GET(self):\r\n self.logger.debug(\"SseHTTPRequestHandler(Thread-%s): do_GET, path=%s\",\r\n threading.current_thread().ident, self.path)\r\n threading.current_thread().name = (self.path + \"_thread_\" +\r\n str(threading.current_thread().ident))\r\n if self.path == SseHTTPRequestHandler.eventsource_path:\r\n self._start_event_stream()\r\n else:\r\n http.server.SimpleHTTPRequestHandler.do_GET(self)","def process(self):\n if not self._requests:\n return\n\n self._processing = True\n Engine.instance().start()"],"string":"[\n \"def start_request(self):\\n self.session_manager.start_request()\",\n \"def _StartRequest(self, request_id, manager):\\n pending_path = self._GetRequestPathname(request_id, self._PENDING)\\n with open(pending_path, 'r') as f:\\n request_object = pickle.load(f)\\n manager.StartTask(request_id, request_object)\\n self._TransitionRequest(request_id, self._PENDING, self._RUNNING)\",\n \"def start(self):\\n self.get(self.url)\",\n \"def Start(self, request, global_params=None):\\n config = self.GetMethodConfig('Start')\\n return self._RunMethod(\\n config, request, global_params=global_params)\",\n \"def _make_request(self):\\n try:\\n self.response = requests.request(\\n method=self.method,\\n url=self.url,\\n params=self.params,\\n data=self.data,\\n )\\n\\n logger.debug(f\\\"Request URL: {self.response.url}\\\")\\n\\n self.response.raise_for_status()\\n\\n # wrap all `requests` library error and serve as custom application error\\n except RequestException as e:\\n logger.error(e.__str__(), exc_info=True)\\n raise ExternalAPIError(\\n \\\"Error while communication with External API\\\"\\n )\",\n \"def run(self):\\n while True:\\n req = self._requests.get()[1]\\n req.start()\\n logging.info('Running request %s', req)\",\n \"def request(self, url, *args, **kwargs):\\n raise NotImplementedError\",\n \"def start():\\n request.start = time.time()\",\n \"def __call__(self):\\n if grinder.runNumber == 0: self.initialSleep()\\n (param1, param2) = self.getParam()\\n self.request1(param1, param2)\",\n \"def request() -> None:\\n\\t_flag.set()\",\n \"def _request(self, *args):\\n self._silent_request(*args)\\n return self._get_response()\",\n \"def run(self):\\n self.initialize()\\n\\n # run the start callback\\n tools.run_callback(\\\"start\\\", {'request': self._request})\\n\\n data = self._request.getData()\\n pyhttp = self._request.getHttp()\\n config = self._request.getConfiguration()\\n\\n # allow anyone else to handle the request at this point\\n handled = tools.run_callback(\\\"handle\\\", \\n {'request': self._request},\\n mappingfunc=lambda x,y:x,\\n donefunc=lambda x:x)\\n\\n if not handled == 1:\\n blosxom_handler(self._request)\\n\\n # do end callback\\n tools.run_callback(\\\"end\\\", {'request': self._request})\",\n \"def start ( self ) :\\n raise AbstractMethodException( self , \\\"start\\\" )\",\n \"def _request(self, *args):\\n raise NotImplementedError\",\n \"def start_request(self):\\n now = _now()\\n self._last_request_no += 1\\n # Instantiate a request_info object and fill its start_time\\n new_request = self._request_info_class(\\n request_no=self._last_request_no, start_time=now,\\n _request_finalizer=self._finalize_request)\\n # Add currently running request\\n self._current_requests[self._last_request_no] = new_request\\n\\n if now - self._last_autoclean_time > self.AUTOCLEAN_INTERVAL:\\n # Avoid memory leak even if client sometimes don't finish requests\\n self._clean_outdated()\\n\\n return new_request\",\n \"def request(self, *args, **kwargs):\\n try:\\n return self._http.request(*args, timeout=TIMEOUT, **kwargs)\\n except Exception as exc:\\n raise RequestException(exc, args, kwargs)\",\n \"def do_request(\\n self,\\n version: str,\\n action: str,\\n protocol: str,\\n method: str,\\n pathname: str,\\n request: dict,\\n headers: Dict[str, str],\\n runtime: util_models.RuntimeOptions,\\n ) -> dict:\\n runtime.validate()\\n _runtime = {\\n 'timeouted': 'retry',\\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\\n 'maxIdleTimeMillis': self._max_idle_time_millis,\\n 'keepAliveDuration': self._keep_alive_duration_millis,\\n 'maxRequests': self._max_requests,\\n 'maxRequestsPerHost': self._max_requests_per_host,\\n 'retry': {\\n 'retryable': runtime.autoretry,\\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\\n },\\n 'backoff': {\\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\\n },\\n 'ignoreSSL': runtime.ignore_ssl,\\n # 创建凭证One\\n }\\n _last_request = None\\n _last_exception = None\\n _now = time.time()\\n _retry_times = 0\\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\\n if _retry_times > 0:\\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\\n if _backoff_time > 0:\\n TeaCore.sleep(_backoff_time)\\n _retry_times = _retry_times + 1\\n try:\\n _request = TeaRequest()\\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\\n _request.method = method\\n _request.pathname = pathname\\n _request.query = {\\n 'method': action,\\n 'version': version,\\n 'sign_type': 'HmacSHA1',\\n 'req_time': AntchainUtils.get_timestamp(),\\n 'req_msg_id': AntchainUtils.get_nonce(),\\n 'access_key': self._access_key_id,\\n 'base_sdk_version': 'TeaSDK-2.0',\\n 'sdk_version': '1.6.10',\\n '_prod_code': 'SHUZIWULIU',\\n '_prod_channel': 'undefined'\\n }\\n if not UtilClient.empty(self._security_token):\\n _request.query['security_token'] = self._security_token\\n _request.headers = TeaCore.merge({\\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\\n }, headers)\\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\\n _request.body = UtilClient.to_form_string(tmp)\\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\\n signed_param = TeaCore.merge(_request.query,\\n RPCUtilClient.query(request))\\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\\n _last_request = _request\\n _response = TeaCore.do_action(_request, _runtime)\\n raw = UtilClient.read_as_string(_response.body)\\n obj = UtilClient.parse_json(raw)\\n res = UtilClient.assert_as_map(obj)\\n resp = UtilClient.assert_as_map(res.get('response'))\\n if AntchainUtils.has_error(raw, self._access_key_secret):\\n raise TeaException({\\n 'message': resp.get('result_msg'),\\n 'data': resp,\\n 'code': resp.get('result_code')\\n })\\n return resp\\n except Exception as e:\\n if TeaCore.is_retryable(e):\\n _last_exception = e\\n continue\\n raise e\\n raise UnretryableException(_last_request, _last_exception)\",\n \"def make_request_thread(self, service, request):\\n requestResponse = self.callbacks.makeHttpRequest(service, request)\\n print self.helpers.analyzeRequest(requestResponse).getUrl().toString()\",\n \"async def request(self) -> Any:\\n raise NotImplementedError()\",\n \"def __call__(self, requestStr):\\n return self.connection.Request(requestStr)\",\n \"def run(self):\\n while self.running:\\n self.handle_request()\",\n \"def handle_one_request(self):\\n \\n try:\\n \\n self.raw_requestline = self.rfile.readline(65537)\\n \\n if len(self.raw_requestline) > 65536:\\n \\n self.requestline = ''\\n \\n self.request_version = ''\\n \\n self.command = ''\\n \\n self.send_error(414)\\n \\n return\\n \\n if not self.raw_requestline:\\n \\n self.close_connection = 1\\n \\n return\\n \\n if not self.parse_request():\\n \\n # An error code has been sent, just exit\\n \\n return\\n \\n mname = 'do_' + self.command\\n \\n if not hasattr(self, mname):\\n \\n self.send_error(501, \\\"Unsupported method (%r)\\\" % self.command)\\n \\n return\\n \\n method = getattr(self, mname)\\n \\n print \\\"before call do_Get\\\"\\n \\n method()\\n \\n #增加 debug info 及 wfile 判断是否已经 close\\n \\n print \\\"after call do_Get\\\"\\n \\n if not self.wfile.closed:\\n self.wfile.flush() #actually send the response if not already done.\\n \\n print \\\"after wfile.flush()\\\"\\n \\n except socket.timeout, e:\\n \\n #a read or a write timed out. Discard this connection\\n self.log_error(\\\"Request timed out: %r\\\", e)\\n self.close_connection = 1\\n return\",\n \"def _request(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"def _request(self, method, url, payload=None, **params):\\n kwargs = dict(params=params)\\n kwargs[\\\"timeout\\\"] = self._timeout\\n if not url.startswith('http'):\\n url = self.prefix + url\\n headers = self._auth_headers()\\n headers['Content-Type'] = 'application/json'\\n\\n if payload:\\n kwargs[\\\"data\\\"] = json.dumps(payload)\\n gs = self._gpool.spawn if self._gpool else gevent.spawn\\n r = gs(self.session.request, method, url, headers=headers, **kwargs)\\n r.fetch = partial(self.join, r)\\n update_wrapper(r.fetch, self.join)\\n #gevent.sleep(0.05)\\n return r\",\n \"def request(self, flow: mitmproxy.http.HTTPFlow):\",\n \"def request(self, flow: mitmproxy.http.HTTPFlow):\",\n \"def _send_request(self):\\n route_chosen = self.comboBox_route_list.currentText()\\n route_id = route_chosen.split(',')[0] #to get the id of the route\\n trip_headsign_chosen = self.comboBox_trip_headsign_list.currentText()\\n stop_chosen = self.comboBox_stop_list.currentText()\\n self.request(route_id, trip_headsign_chosen, stop_chosen)\",\n \"def _request(self, url: str) -> http.client.HTTPResponse:\\n self.request = urllib.request.Request(\\n url,\\n headers={'User-Agent': 'Mozilla/5.0 (iPhone; CPU iPhone OS 6_0 like Mac OS X)'})\\n try:\\n return urllib.request.urlopen(self.request, timeout=10)\\n except Exception as e:\\n # print(e)\\n # exit(-1)\\n print(e, url)\\n raise e\",\n \"def _send_request(self):\\n url = self.config['url']\\n agent = Agent(reactor)\\n response = (yield agent.request(\\n 'GET',\\n url.encode(\\\"ASCII\\\"),\\n ))\\n\\n d = defer.Deferred()\\n response.deliverBody(ReceiveBody(d))\\n defer.returnValue((yield d))\",\n \"def request(self, flow: mitmproxy.http.HTTPFlow):\\n pass\",\n \"async def _request_started(self, request: Request):\\n # When we consume the body, we replace the streaming mechanism with\\n # a mocked version -- this workaround came from\\n # https://github.com/encode/starlette/issues/495#issuecomment-513138055\\n # and we call the workaround here to make sure that regardless of\\n # `capture_body` settings, we will have access to the body if we need it.\\n if self.client.config.capture_body != \\\"off\\\":\\n await get_body(request)\\n\\n trace_parent = TraceParent.from_headers(dict(request.headers))\\n self.client.begin_transaction(\\\"request\\\", trace_parent=trace_parent)\\n\\n await set_context(lambda: get_data_from_request(request, self.client.config, constants.TRANSACTION), \\\"request\\\")\\n transaction_name = self.get_route_name(request) or request.url.path\\n elasticapm.set_transaction_name(\\\"{} {}\\\".format(request.method, transaction_name), override=False)\",\n \"def send(self, request : str):\\n pass\",\n \"def request(self, path, **kwargs):\\n self.http_client.fetch(self.get_url(path), self.stop, **kwargs)\\n return self.wait()\",\n \"def start(self, **kwargs):\\n return self.client.api.start(self.id, **kwargs)\",\n \"def init_request(self):\\n\\t\\tprint(\\\"Testes\\\")\\n\\t\\treturn Request(url=self.login_page[0], callback=self.login)\",\n \"def _request(self, *arg, **kwarg):\\n return self.request_session.request(*arg, **kwarg)\",\n \"def start_requests(self):\\r\\n try:\\r\\n\\r\\n for url in self.start_urls:\\r\\n yield scrapy.Request(url,\\r\\n callback=self.navigate_to)\\r\\n except Exception as err:\\r\\n logger.error(f'TekDefenceScraper : start_requests : {err}')\\r\\n raise err\",\n \"def start(self):\\n if self._params.arq:\\n s = udpsocket.UDPSocket()\\n else:\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\\n try:\\n s.bind(('', self._params.port))\\n s.listen(5)\\n self.check_and_print_debug_message(\\n 'HTTP Server is running on port: ' + str(self._params.port))\\n while True:\\n c, addr = s.accept()\\n Thread(\\n target=self.process_http_request,\\n args=(c, addr)\\n ).start()\\n except Exception as e:\\n self.check_and_print_debug_message(str(e))\\n s.close()\\n finally:\\n self.check_and_print_debug_message(\\n \\\"HTTP Server connection is closed.\\\")\\n s.close()\",\n \"def handle_one_request(self):\\n import socket\\n try:\\n self.raw_requestline = self.rfile.readline(65537)\\n if len(self.raw_requestline) > 65536:\\n self.requestline = ''\\n self.request_version = ''\\n self.command = ''\\n self.send_error(414)\\n return\\n if not self.raw_requestline:\\n self.close_connection = 1\\n return\\n if not self.parse_request():\\n # An error code has been sent, just exit\\n return\\n\\n ##### Customization\\n # origin\\n \\\"\\\"\\\"\\n mname = 'do_' + self.command\\n if not hasattr(self, mname):\\n self.send_error(501, \\\"Unsupported method (%r)\\\" % self.command)\\n return\\n method = getattr(self, mname)\\n method()\\n \\\"\\\"\\\"\\n # now\\n #import pdb; pdb.set_trace()\\n self.delegate(self.get_environ(), self.gen_response, self.send_error)\\n\\n self.wfile.flush() #actually send the response if not already done.\\n except socket.timeout, e:\\n #a read or a write timed out. Discard this connection\\n self.log_error(\\\"Request timed out: %r\\\", e)\\n self.close_connection = 1\\n return\",\n \"def start():\",\n \"def start():\",\n \"def start():\",\n \"def start():\",\n \"def _request(self, method, url, params=None, data=None, request_type=PRIVATE, headers={}):\\n self._is_valid_request_option(request_type=request_type)\\n\\n request_headers = copy.deepcopy(self.BASE_HEADERS)\\n request_headers.update(headers)\\n\\n response = getattr(requests, method.lower())(\\n url,\\n headers=request_headers,\\n params=params,\\n data=data\\n )\\n\\n return self._handle_response(response)\",\n \"def do_request(\\n self,\\n version: str,\\n action: str,\\n protocol: str,\\n method: str,\\n pathname: str,\\n request: dict,\\n headers: Dict[str, str],\\n runtime: util_models.RuntimeOptions,\\n ) -> dict:\\n runtime.validate()\\n _runtime = {\\n 'timeouted': 'retry',\\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\\n 'maxIdleTimeMillis': self._max_idle_time_millis,\\n 'keepAliveDurationMillis': self._keep_alive_duration_millis,\\n 'maxRequests': self._max_requests,\\n 'maxRequestsPerHost': self._max_requests_per_host,\\n 'retry': {\\n 'retryable': runtime.autoretry,\\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\\n },\\n 'backoff': {\\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\\n },\\n 'ignoreSSL': runtime.ignore_ssl,\\n # 链上交易中的事件\\n }\\n _last_request = None\\n _last_exception = None\\n _now = time.time()\\n _retry_times = 0\\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\\n if _retry_times > 0:\\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\\n if _backoff_time > 0:\\n TeaCore.sleep(_backoff_time)\\n _retry_times = _retry_times + 1\\n try:\\n _request = TeaRequest()\\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\\n _request.method = method\\n _request.pathname = pathname\\n _request.query = {\\n 'method': action,\\n 'version': version,\\n 'sign_type': 'HmacSHA1',\\n 'req_time': AntchainUtils.get_timestamp(),\\n 'req_msg_id': AntchainUtils.get_nonce(),\\n 'access_key': self._access_key_id,\\n 'base_sdk_version': 'TeaSDK-2.0',\\n 'sdk_version': '1.3.1',\\n '_prod_code': 'BAASDATAGW',\\n '_prod_channel': 'undefined'\\n }\\n if not UtilClient.empty(self._security_token):\\n _request.query['security_token'] = self._security_token\\n _request.headers = TeaCore.merge({\\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\\n }, headers)\\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\\n _request.body = UtilClient.to_form_string(tmp)\\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\\n signed_param = TeaCore.merge(_request.query,\\n RPCUtilClient.query(request))\\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\\n _last_request = _request\\n _response = TeaCore.do_action(_request, _runtime)\\n raw = UtilClient.read_as_string(_response.body)\\n obj = UtilClient.parse_json(raw)\\n res = UtilClient.assert_as_map(obj)\\n resp = UtilClient.assert_as_map(res.get('response'))\\n if AntchainUtils.has_error(raw, self._access_key_secret):\\n raise TeaException({\\n 'message': resp.get('result_msg'),\\n 'data': resp,\\n 'code': resp.get('result_code')\\n })\\n return resp\\n except Exception as e:\\n if TeaCore.is_retryable(e):\\n _last_exception = e\\n continue\\n raise e\\n raise UnretryableException(_last_request, _last_exception)\",\n \"def start_request(self, request_id, user_request_id, ip, app_id, version_id,\\n nickname, user_agent, host, method, resource, http_version,\\n start_time=None):\\n if start_time is None:\\n start_time = self._get_time_usec()\\n\\n rl = self._pending_requests[request_id]\\n rl.appId = app_id\\n rl.versionId = version_id\\n rl.requestId = request_id\\n rl.ip = ip\\n rl.nickname = nickname\\n rl.startTime = start_time\\n rl.method = method\\n rl.resource = resource\\n rl.httpVersion = http_version\\n rl.userAgent = user_agent\\n rl.host = host\\n self._pending_requests_applogs[request_id] = rl.init_resizable_list('appLogs')\",\n \"def perform_request(request):\\n info = evmapy.util.get_app_info()\\n try:\\n return send_request(request)\\n except (ConnectionRefusedError, FileNotFoundError):\\n exit(\\\"No %s instance is running as %s\\\" % (\\n info['name'], info['user'].pw_name\\n ))\\n except TimeoutError:\\n exit(\\\"Timeout waiting for a response from %s\\\" % info['name'])\",\n \"def do_request(\\n self,\\n version: str,\\n action: str,\\n protocol: str,\\n method: str,\\n pathname: str,\\n request: dict,\\n headers: Dict[str, str],\\n runtime: util_models.RuntimeOptions,\\n ) -> dict:\\n runtime.validate()\\n _runtime = {\\n 'timeouted': 'retry',\\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\\n 'maxIdleTimeMillis': self._max_idle_time_millis,\\n 'keepAliveDuration': self._keep_alive_duration_millis,\\n 'maxRequests': self._max_requests,\\n 'maxRequestsPerHost': self._max_requests_per_host,\\n 'retry': {\\n 'retryable': runtime.autoretry,\\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\\n },\\n 'backoff': {\\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\\n },\\n 'ignoreSSL': runtime.ignore_ssl,\\n # 身份\\n }\\n _last_request = None\\n _last_exception = None\\n _now = time.time()\\n _retry_times = 0\\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\\n if _retry_times > 0:\\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\\n if _backoff_time > 0:\\n TeaCore.sleep(_backoff_time)\\n _retry_times = _retry_times + 1\\n try:\\n _request = TeaRequest()\\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\\n _request.method = method\\n _request.pathname = pathname\\n _request.query = {\\n 'method': action,\\n 'version': version,\\n 'sign_type': 'HmacSHA1',\\n 'req_time': AntchainUtils.get_timestamp(),\\n 'req_msg_id': AntchainUtils.get_nonce(),\\n 'access_key': self._access_key_id,\\n 'base_sdk_version': 'TeaSDK-2.0',\\n 'sdk_version': '1.0.212',\\n '_prod_code': 'DEMO',\\n '_prod_channel': 'undefined'\\n }\\n if not UtilClient.empty(self._security_token):\\n _request.query['security_token'] = self._security_token\\n _request.headers = TeaCore.merge({\\n 'host': UtilClient.default_string(self._endpoint, 'centre-openapi.antchain.antgroup.com'),\\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\\n }, headers)\\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\\n _request.body = UtilClient.to_form_string(tmp)\\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\\n signed_param = TeaCore.merge(_request.query,\\n RPCUtilClient.query(request))\\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\\n _last_request = _request\\n _response = TeaCore.do_action(_request, _runtime)\\n raw = UtilClient.read_as_string(_response.body)\\n obj = UtilClient.parse_json(raw)\\n res = UtilClient.assert_as_map(obj)\\n resp = UtilClient.assert_as_map(res.get('response'))\\n if AntchainUtils.has_error(raw, self._access_key_secret):\\n raise TeaException({\\n 'message': resp.get('result_msg'),\\n 'data': resp,\\n 'code': resp.get('result_code')\\n })\\n return resp\\n except Exception as e:\\n if TeaCore.is_retryable(e):\\n _last_exception = e\\n continue\\n raise e\\n raise UnretryableException(_last_request, _last_exception)\",\n \"def _request(self, *args, **kwargs):\\n request = self._make_request(*args, **kwargs)\\n\\n return self._collect_request(request)\",\n \"async def do_request_async(\\n self,\\n version: str,\\n action: str,\\n protocol: str,\\n method: str,\\n pathname: str,\\n request: dict,\\n headers: Dict[str, str],\\n runtime: util_models.RuntimeOptions,\\n ) -> dict:\\n runtime.validate()\\n _runtime = {\\n 'timeouted': 'retry',\\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\\n 'maxIdleTimeMillis': self._max_idle_time_millis,\\n 'keepAliveDuration': self._keep_alive_duration_millis,\\n 'maxRequests': self._max_requests,\\n 'maxRequestsPerHost': self._max_requests_per_host,\\n 'retry': {\\n 'retryable': runtime.autoretry,\\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\\n },\\n 'backoff': {\\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\\n },\\n 'ignoreSSL': runtime.ignore_ssl,\\n # 创建凭证One\\n }\\n _last_request = None\\n _last_exception = None\\n _now = time.time()\\n _retry_times = 0\\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\\n if _retry_times > 0:\\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\\n if _backoff_time > 0:\\n TeaCore.sleep(_backoff_time)\\n _retry_times = _retry_times + 1\\n try:\\n _request = TeaRequest()\\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\\n _request.method = method\\n _request.pathname = pathname\\n _request.query = {\\n 'method': action,\\n 'version': version,\\n 'sign_type': 'HmacSHA1',\\n 'req_time': AntchainUtils.get_timestamp(),\\n 'req_msg_id': AntchainUtils.get_nonce(),\\n 'access_key': self._access_key_id,\\n 'base_sdk_version': 'TeaSDK-2.0',\\n 'sdk_version': '1.6.10',\\n '_prod_code': 'SHUZIWULIU',\\n '_prod_channel': 'undefined'\\n }\\n if not UtilClient.empty(self._security_token):\\n _request.query['security_token'] = self._security_token\\n _request.headers = TeaCore.merge({\\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\\n }, headers)\\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\\n _request.body = UtilClient.to_form_string(tmp)\\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\\n signed_param = TeaCore.merge(_request.query,\\n RPCUtilClient.query(request))\\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\\n _last_request = _request\\n _response = await TeaCore.async_do_action(_request, _runtime)\\n raw = await UtilClient.read_as_string_async(_response.body)\\n obj = UtilClient.parse_json(raw)\\n res = UtilClient.assert_as_map(obj)\\n resp = UtilClient.assert_as_map(res.get('response'))\\n if AntchainUtils.has_error(raw, self._access_key_secret):\\n raise TeaException({\\n 'message': resp.get('result_msg'),\\n 'data': resp,\\n 'code': resp.get('result_code')\\n })\\n return resp\\n except Exception as e:\\n if TeaCore.is_retryable(e):\\n _last_exception = e\\n continue\\n raise e\\n raise UnretryableException(_last_request, _last_exception)\",\n \"def start(self):\\n def f():\\n if (self.started): return\\n self.started = True\\n with client.ServerProxy(self.host) as proxy:\\n while (not self.req_shutdown):\\n self.update_speed(proxy)\\n time.sleep(self.com_freq)\\n self.started = False\\n self.req_shutdwon = False\\n\\n Thread(target=f).start()\",\n \"def onRequestStart(self, api, request):\\n logging.info('Request start ({})'.format(request))\",\n \"def request(self, method, url, params=None, data=None):\\n raise NotImplementedError(\\n u\\\"%s: Method not implemented\\\", self.__class__.__name__)\",\n \"def start(self, **kwargs):\\n pass\",\n \"def start(self, **kwargs):\\n pass\",\n \"def req():\\n return Request()\",\n \"def start_test(self, request):\\n request.worker.start_test(request.message.test_id)\\n\\n return SuccessReply()\",\n \"def send(self) -> None:\\n\\n payload = self.get_payload()\\n try:\\n self.response = requests.get(url=FAST_API, params=payload)\\n except requests.exceptions.ConnectionError:\\n print(f\\\"requests.exceptions.ConnectionError! Trying again in 5 seconds...\\\")\\n sleep(5)\\n self.send()\",\n \"def __call__(self, request):\\n response = self.get_request(request)\\n return response\",\n \"def get_request(self: 'WebScraper', \\n url: str, \\n params: Union[dict, None] = None\\n ) -> req.Response:\\n res = None\\n while True:\\n try: \\n res = self.session.get(url, params=params)\\n break\\n except req.RequestException: \\n # If a requests exception is raised, then some kind of HTTP \\n # error occured, so wait for one second before re-attempting \\n # the request\\n time.sleep(1)\\n continue\\n return res\",\n \"def request(self, **request):\\r\\n environ = {\\r\\n 'HTTP_COOKIE': self.cookies,\\r\\n 'PATH_INFO': '/',\\r\\n 'QUERY_STRING': '',\\r\\n 'REQUEST_METHOD': 'GET',\\r\\n 'SCRIPT_NAME': '',\\r\\n 'SERVER_NAME': 'testserver',\\r\\n 'SERVER_PORT': 80,\\r\\n 'SERVER_PROTOCOL': 'HTTP/1.1',\\r\\n }\\r\\n environ.update(self.defaults)\\r\\n environ.update(request)\\r\\n request = WSGIRequest(environ)\\r\\n\\r\\n handler = BaseHandler()\\r\\n handler.load_middleware()\\r\\n for middleware_method in handler._request_middleware:\\r\\n if middleware_method(request):\\r\\n raise Exception(\\\"Couldn't create request object - \\\"\\r\\n \\\"request middleware returned a response\\\")\\r\\n\\r\\n return request\",\n \"def start(self):\\n self._client.predict(\\n endpoint=self._endpoint, instances=self._request)\\n\\n if self._completion_callback:\\n if self._query_handle:\\n callback_args = [self._query_handle]\\n else:\\n callback_args = []\\n self._completion_callback(*callback_args)\",\n \"def start_requests(self):\\n yield scrapy.Request(url=self.start_urls[0])\",\n \"def call(self):\\n\\n self.url = self._prepare_url()\\n status_code, response = self._do_request(self.url)\\n return self._process_response(status_code, response)\",\n \"async def handle_request(self, request: aioweb.request.Request):\",\n \"def do_request(\\n self,\\n version: str,\\n action: str,\\n protocol: str,\\n method: str,\\n pathname: str,\\n request: dict,\\n headers: Dict[str, str],\\n runtime: util_models.RuntimeOptions,\\n ) -> dict:\\n runtime.validate()\\n _runtime = {\\n 'timeouted': 'retry',\\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\\n 'maxIdleTimeMillis': self._max_idle_time_millis,\\n 'keepAliveDuration': self._keep_alive_duration_millis,\\n 'maxRequests': self._max_requests,\\n 'maxRequestsPerHost': self._max_requests_per_host,\\n 'retry': {\\n 'retryable': runtime.autoretry,\\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\\n },\\n 'backoff': {\\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\\n },\\n 'ignoreSSL': runtime.ignore_ssl,\\n # 资源定位信息\\n }\\n _last_request = None\\n _last_exception = None\\n _now = time.time()\\n _retry_times = 0\\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\\n if _retry_times > 0:\\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\\n if _backoff_time > 0:\\n TeaCore.sleep(_backoff_time)\\n _retry_times = _retry_times + 1\\n try:\\n _request = TeaRequest()\\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\\n _request.method = method\\n _request.pathname = pathname\\n _request.query = {\\n 'method': action,\\n 'version': version,\\n 'sign_type': 'HmacSHA1',\\n 'req_time': AntchainUtils.get_timestamp(),\\n 'req_msg_id': AntchainUtils.get_nonce(),\\n 'access_key': self._access_key_id,\\n 'base_sdk_version': 'TeaSDK-2.0',\\n 'sdk_version': '1.8.95',\\n '_prod_code': 'BOT',\\n '_prod_channel': 'undefined'\\n }\\n if not UtilClient.empty(self._security_token):\\n _request.query['security_token'] = self._security_token\\n _request.headers = TeaCore.merge({\\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\\n }, headers)\\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\\n _request.body = UtilClient.to_form_string(tmp)\\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\\n signed_param = TeaCore.merge(_request.query,\\n RPCUtilClient.query(request))\\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\\n _last_request = _request\\n _response = TeaCore.do_action(_request, _runtime)\\n raw = UtilClient.read_as_string(_response.body)\\n obj = UtilClient.parse_json(raw)\\n res = UtilClient.assert_as_map(obj)\\n resp = UtilClient.assert_as_map(res.get('response'))\\n if AntchainUtils.has_error(raw, self._access_key_secret):\\n raise TeaException({\\n 'message': resp.get('result_msg'),\\n 'data': resp,\\n 'code': resp.get('result_code')\\n })\\n return resp\\n except Exception as e:\\n if TeaCore.is_retryable(e):\\n _last_exception = e\\n continue\\n raise e\\n raise UnretryableException(_last_request, _last_exception)\",\n \"def send(self, request: Request, **requests_kwargs) -> Response:\",\n \"def process_request_starts(self, request):\\n pass\",\n \"def _start(self):\",\n \"def _process_request(self):\\n if not self._requests:\\n if self._stream:\\n self._stream.close()\\n self._stream = None\\n if self._processing:\\n self._processing = False\\n Engine.instance().stop()\\n return\\n\\n request = self._requests[0]\\n\\n request.append(\\n Engine.instance().defer(request[5], self._request_timeout, request))\\n\\n port = request[2].port\\n if not port:\\n if request[2].scheme.lower() == 'https':\\n port = 443\\n else:\\n port = 80\\n\\n host = \\\"%s:%d\\\" % (request[2].hostname, port)\\n\\n if self._stream:\\n if not self._server == host.lower() or not \\\\\\n self._is_secure == (request[2].scheme.lower() == 'https'):\\n self._stream.end()\\n return\\n\\n if not self._stream:\\n # Store the current server.\\n self._server = host.lower()\\n\\n # Create a Stream, hook into it, and connect.\\n self._stream = Stream()\\n\\n self._stream.on_close = self._on_close\\n self._stream.on_connect = self._on_connect\\n\\n self._is_secure = request[2].scheme.lower() == 'https'\\n if self._is_secure:\\n raise Exception(\\\"SSL has not yet been implemented in this version of Pants.\\\")\\n self._stream.startTLS()\\n\\n self._stream.connect((request[2].hostname, port))\\n return\\n\\n # If we got here, we're connected, and to the right server. Do stuff.\\n self._stream.write('%s %s HTTP/1.1%s' % (request[0], request[8], CRLF))\\n for k, v in request[3].iteritems():\\n self._stream.write('%s: %s%s' % (k, v, CRLF))\\n\\n if request[4]:\\n self._stream.write('%s%s' % (CRLF, request[4]))\\n else:\\n self._stream.write(CRLF)\\n\\n # Now, wait for a response.\\n self._stream.on_read = self._read_headers\\n self._stream.read_delimiter = DOUBLE_CRLF\",\n \"def do_request(\\n self,\\n version: str,\\n action: str,\\n protocol: str,\\n method: str,\\n pathname: str,\\n request: dict,\\n headers: Dict[str, str],\\n runtime: util_models.RuntimeOptions,\\n ) -> dict:\\n runtime.validate()\\n _runtime = {\\n 'timeouted': 'retry',\\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\\n 'maxIdleTimeMillis': self._max_idle_time_millis,\\n 'keepAliveDurationMillis': self._keep_alive_duration_millis,\\n 'maxRequests': self._max_requests,\\n 'maxRequestsPerHost': self._max_requests_per_host,\\n 'retry': {\\n 'retryable': runtime.autoretry,\\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\\n },\\n 'backoff': {\\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\\n },\\n 'ignoreSSL': runtime.ignore_ssl,\\n # 无分组设备\\n }\\n _last_request = None\\n _last_exception = None\\n _now = time.time()\\n _retry_times = 0\\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\\n if _retry_times > 0:\\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\\n if _backoff_time > 0:\\n TeaCore.sleep(_backoff_time)\\n _retry_times = _retry_times + 1\\n try:\\n _request = TeaRequest()\\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\\n _request.method = method\\n _request.pathname = pathname\\n _request.query = {\\n 'method': action,\\n 'version': version,\\n 'sign_type': 'HmacSHA1',\\n 'req_time': AntchainUtils.get_timestamp(),\\n 'req_msg_id': AntchainUtils.get_nonce(),\\n 'access_key': self._access_key_id,\\n 'base_sdk_version': 'TeaSDK-2.0',\\n 'sdk_version': '1.0.45'\\n }\\n if not UtilClient.empty(self._security_token):\\n _request.query['security_token'] = self._security_token\\n _request.headers = TeaCore.merge({\\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\\n }, headers)\\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\\n _request.body = UtilClient.to_form_string(tmp)\\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\\n signed_param = TeaCore.merge(_request.query,\\n RPCUtilClient.query(request))\\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\\n _last_request = _request\\n _response = TeaCore.do_action(_request, _runtime)\\n raw = UtilClient.read_as_string(_response.body)\\n obj = UtilClient.parse_json(raw)\\n res = UtilClient.assert_as_map(obj)\\n resp = UtilClient.assert_as_map(res.get('response'))\\n if AntchainUtils.has_error(raw, self._access_key_secret):\\n raise TeaException({\\n 'message': resp.get('result_msg'),\\n 'data': resp,\\n 'code': resp.get('result_code')\\n })\\n return resp\\n except Exception as e:\\n if TeaCore.is_retryable(e):\\n _last_exception = e\\n continue\\n raise e\\n raise UnretryableException(_last_request, _last_exception)\",\n \"def do_GET(self):\\n global st_point, cur_request\\n if time.time() - st_point < 1 and cur_request > args.MAX_REQ:\\n self.send_response(429)\\n self.send_header(\\\"Content-type\\\",\\\"text/html\\\")\\n self.end_headers()\\n time.sleep(0.2)\\n return\\n elif time.time() - st_point > 1:\\n st_point = time.time()\\n cur_request = 1\\n self.func_PARSE()\\n if self.parsed_url[2] in [\\\"/ping\\\", \\\"/cats\\\"]:\\n self.func_DO()\\n else:\\n self.send_response(400)\\n text=\\\"<h1 align=center>Bad request</h1>\\\"\\n self.func_PRINT(text)\",\n \"def Start(self) :\\n\\t\\t...\",\n \"def _send_request(self, method, url, params=None, data=None, headers=None, json=None,\\n http_status=2, parse_json=False, error_processors=[],\\n allow_redirects=None, cookies=None, stream=False):\\n call_time = now()\\n if self.last_call_time:\\n if self.request_wait_seconds:\\n delta = (call_time - self.last_call_time).total_seconds()\\n if delta < self.request_wait_seconds:\\n self.sleep(self.request_wait_seconds - delta,\\n log_reason='request wait')\\n self.last_call_time = now()\\n else:\\n self.last_call_time = call_time\\n else:\\n self.last_call_time = call_time\\n else:\\n self.first_call_time = self.last_call_time = call_time\\n\\n if not urlparse(url).scheme and self.base_url:\\n url = urljoin(self.base_url, url)\\n\\n if allow_redirects is None:\\n allow_redirects = self.allow_redirects\\n\\n if self.debug_level >= 5:\\n self.logger.debug(\\n _color_em('REQUEST %s' % method) + ' ' + url + (' params=%s' % params)\\n + '\\\\n' + _color_em('REQUEST HEADERS:', back=colorama.Back.BLUE) + '\\\\n'\\n + pprint(headers, print_=False)\\n + (('\\\\n' + _color_em('REQUEST BODY:', back=colorama.Back.BLUE) + '\\\\n'\\n + pprint(data or json, print_=False)) if (data or json) else '')\\n )\\n\\n try:\\n kwargs = dict(params=params, data=data, json=json, headers=headers,\\n allow_redirects=allow_redirects, proxies=self.proxy,\\n verify=self.ssl_verify, cookies=cookies, stream=stream)\\n if self.timeout is not None:\\n # Allow session (ConfigurableSession for example) to handle timeout\\n kwargs['timeout'] = self.timeout\\n response = self.session.request(method, url, **kwargs)\\n except Exception as exc:\\n self.error_processor(exc, error_processors)\\n raise\\n finally:\\n if self.request_wait_since_response:\\n self.last_call_time = now()\\n\\n if self.debug_level >= 5:\\n self.logger.debug(\\n _color_em('RESPONSE %s' % response.request.method, back=colorama.Back.GREEN)\\n + colorama.Style.RESET_ALL + colorama.Style.BRIGHT + (' %s ' % response.status_code)\\n + colorama.Style.RESET_ALL + response.url\\n + '\\\\n' + _color_em('RESPONSE HEADERS:', back=colorama.Back.GREEN) + '\\\\n'\\n + pprint(response.headers, print_=False)\\n + '\\\\n' + _color_em('RESPONSE BODY:', back=colorama.Back.GREEN) + '\\\\n'\\n + (stream and '<stream>' or pprint(response.text, print_=False))\\n )\\n\\n elapsed_seconds = response.elapsed.total_seconds()\\n if elapsed_seconds > self.request_warn_elapsed_seconds:\\n self.logger.warning('Request %s %s took %s seconds after calls(%s/%s) since(%s)',\\n response.request.method, response.request.url,\\n elapsed_seconds, self.calls_count, self.calls_elapsed_seconds,\\n self.first_call_time)\\n self.calls_elapsed_seconds += elapsed_seconds\\n self.calls_count += 1\\n self.last_response = response # NOTE: only for debug purposes!\\n\\n if (http_status and not check_http_status(response.status_code, http_status)):\\n self.set_response_json_data(response, parse_json, raise_=False)\\n exc = self.HTTPError(response, expected_status=http_status)\\n self.error_processor(exc, error_processors)\\n raise exc\\n\\n try:\\n self.set_response_json_data(response, parse_json, raise_=True)\\n except _JSONDecodeError as exc:\\n exc = self.JSONDecodeError(response, exc)\\n self.error_processor(exc, error_processors)\\n raise exc\\n\\n return response\",\n \"def start(self):\\n if self._callable:\\n self._is_running = True\\n self._run_client()\",\n \"def _start(self):\\n pass\",\n \"def start(self) -> None:\",\n \"def start(self) -> None:\",\n \"def start(self):\\n try:\\n pass\\n except:\\n pass\",\n \"def start_requests(self):\\n url = self.start_urls[0]\\n yield scrapy.Request(url=url, callback=self.parse)\",\n \"def request(*args):\\r\\n URL = BASE_URL + args[0]\\r\\n proxies = {'https': 'http://137.123.%s.135:8134' % randint(2, 233)}\\r\\n # print(args[0])\\r\\n if len(args) == 1: # GET method\\r\\n response = session.get(URL, headers=header, proxies=proxies)\\r\\n updateHeader(URL)\\r\\n else: # POST with data parameter addition\\r\\n response = session.post(URL, headers=header, data=args[1], proxies=proxies)\\r\\n try:\\r\\n response.raise_for_status()\\r\\n except UnboundLocalError as exc:\\r\\n print(exc)\\r\\n if len(args) == 2:\\r\\n print('POST FAIL! continue to file question')\\r\\n else:\\r\\n import traceback\\r\\n print(traceback.format_exc())\\r\\n exit(1)\\r\\n else:\\r\\n return response\",\n \"def do_request(\\n self,\\n version: str,\\n action: str,\\n protocol: str,\\n method: str,\\n pathname: str,\\n request: dict,\\n headers: Dict[str, str],\\n runtime: util_models.RuntimeOptions,\\n ) -> dict:\\n runtime.validate()\\n _runtime = {\\n 'timeouted': 'retry',\\n 'readTimeout': UtilClient.default_number(runtime.read_timeout, self._read_timeout),\\n 'connectTimeout': UtilClient.default_number(runtime.connect_timeout, self._connect_timeout),\\n 'httpProxy': UtilClient.default_string(runtime.http_proxy, self._http_proxy),\\n 'httpsProxy': UtilClient.default_string(runtime.https_proxy, self._https_proxy),\\n 'noProxy': UtilClient.default_string(runtime.no_proxy, self._no_proxy),\\n 'maxIdleConns': UtilClient.default_number(runtime.max_idle_conns, self._max_idle_conns),\\n 'maxIdleTimeMillis': self._max_idle_time_millis,\\n 'keepAliveDuration': self._keep_alive_duration_millis,\\n 'maxRequests': self._max_requests,\\n 'maxRequestsPerHost': self._max_requests_per_host,\\n 'retry': {\\n 'retryable': runtime.autoretry,\\n 'maxAttempts': UtilClient.default_number(runtime.max_attempts, 3)\\n },\\n 'backoff': {\\n 'policy': UtilClient.default_string(runtime.backoff_policy, 'no'),\\n 'period': UtilClient.default_number(runtime.backoff_period, 1)\\n },\\n 'ignoreSSL': runtime.ignore_ssl,\\n # 金额\\n }\\n _last_request = None\\n _last_exception = None\\n _now = time.time()\\n _retry_times = 0\\n while TeaCore.allow_retry(_runtime.get('retry'), _retry_times, _now):\\n if _retry_times > 0:\\n _backoff_time = TeaCore.get_backoff_time(_runtime.get('backoff'), _retry_times)\\n if _backoff_time > 0:\\n TeaCore.sleep(_backoff_time)\\n _retry_times = _retry_times + 1\\n try:\\n _request = TeaRequest()\\n _request.protocol = UtilClient.default_string(self._protocol, protocol)\\n _request.method = method\\n _request.pathname = pathname\\n _request.query = {\\n 'method': action,\\n 'version': version,\\n 'sign_type': 'HmacSHA1',\\n 'req_time': AntchainUtils.get_timestamp(),\\n 'req_msg_id': AntchainUtils.get_nonce(),\\n 'access_key': self._access_key_id,\\n 'base_sdk_version': 'TeaSDK-2.0',\\n 'sdk_version': '1.1.2',\\n '_prod_code': 'DEFINCASHIER',\\n '_prod_channel': 'undefined'\\n }\\n if not UtilClient.empty(self._security_token):\\n _request.query['security_token'] = self._security_token\\n _request.headers = TeaCore.merge({\\n 'host': UtilClient.default_string(self._endpoint, 'openapi.antchain.antgroup.com'),\\n 'user-agent': UtilClient.get_user_agent(self._user_agent)\\n }, headers)\\n tmp = UtilClient.anyify_map_value(RPCUtilClient.query(request))\\n _request.body = UtilClient.to_form_string(tmp)\\n _request.headers['content-type'] = 'application/x-www-form-urlencoded'\\n signed_param = TeaCore.merge(_request.query,\\n RPCUtilClient.query(request))\\n _request.query['sign'] = AntchainUtils.get_signature(signed_param, self._access_key_secret)\\n _last_request = _request\\n _response = TeaCore.do_action(_request, _runtime)\\n raw = UtilClient.read_as_string(_response.body)\\n obj = UtilClient.parse_json(raw)\\n res = UtilClient.assert_as_map(obj)\\n resp = UtilClient.assert_as_map(res.get('response'))\\n if AntchainUtils.has_error(raw, self._access_key_secret):\\n raise TeaException({\\n 'message': resp.get('result_msg'),\\n 'data': resp,\\n 'code': resp.get('result_code')\\n })\\n return resp\\n except Exception as e:\\n if TeaCore.is_retryable(e):\\n _last_exception = e\\n continue\\n raise e\\n raise UnretryableException(_last_request, _last_exception)\",\n \"def start(self) -> None:\\n ...\",\n \"def start(self) -> None:\\n ...\",\n \"def start(self) -> None:\\n app = web.Application()\\n app.add_routes([web.post(\\\"/\\\", self._handle_request)])\\n self._runner = web.AppRunner(app)\\n\\n self._startup_event = threading.Event()\\n self._server_loop = asyncio.new_event_loop()\\n t = threading.Thread(target=self._run)\\n t.start()\\n\\n # Wait for server to startup\\n self._startup_event.wait()\",\n \"def _request(self, method, uri, **kwargs):\\n url = self.url + uri\\n self.logger.debug(\\\"Requesting {} on {}\\\".format(method, url))\\n response = requests.request(method, url, verify=self.verify, **kwargs)\\n try:\\n response.raise_for_status()\\n except requests.exceptions.HTTPError:\\n self.logger.error(\\\"Bad http code {} requesting Clair\\\".format(response.status_code))\\n if response.reason == \\\"Not Found\\\":\\n raise ResourceNotFoundException(\\\"Resource not found\\\")\\n raise ClairConnectionError(response)\\n return response\",\n \"def request(self, url, data=None, params={}, files=None):\\n params['token'] = self.token\\n request = self.make_request(url, data=data, params=params, files=files)\\n return request\",\n \"def call(self):\\n # if this is a POST request, process data\\n if self.data:\\n post_json = json.dumps(self.data)\\n values = {'json': post_json, 'apikey': API_KEY}\\n post = urllib.parse.urlencode(values)\\n\\n else:\\n post = None\\n\\n req = urllib.request.Request(self.url, post)\\n\\n try:\\n self.response = urllib.request.urlopen(req, timeout=self.timeout)\\n\\n except (URLError, HTTPError, timeout) as error:\\n self.response = error\",\n \"def start(self):\\n self.conn.start()\",\n \"def _req(self, host, path, meth, timeout, params, data, is_search):\\n if is_search and self.http_search:\\n url = 'http://%s%s' % (host, path)\\n else:\\n url = 'https://%s%s' % (host, path)\\n req = self.session.request(meth, url, params=params, data=data,\\n headers=self.headers)\\n res = yield from req\\n with async_timeout.timeout(timeout):\\n if res.status // 100 == 2:\\n return (yield from res.json())\\n elif res.status // 100 == 4:\\n message = 'HTTP Code: %d' % res.status\\n try:\\n message = (yield from res.json())['message']\\n finally:\\n raise AlgoliaException(message)\\n # TODO: Check this for replacement.\\n res.raise_for_status()\",\n \"def perform_request(self,\\n request: RequestBase,\\n method: str='POST'\\n ):\\n headers = {\\n 'Accept': 'application/json',\\n 'User-Agent': self.user_agent()\\n }\\n if APIAuthentication.use_http_auth:\\n headers['Authorization'] = 'Basic {auth}'.format(auth=self.get_auth())\\n\\n # Lazy loader for api credentials.\\n if request.requires_api_token() and ParamValidator.is_empty(request.api_token)\\\\\\n and ParamValidator.not_empty(APIAuthentication.api_token):\\n request.api_token = APIAuthentication.api_token\\n if request.requires_service_id() and ParamValidator.is_empty(request.service_id)\\\\\\n and ParamValidator.not_empty(APIAuthentication.service_id):\\n request.service_id = APIAuthentication.service_id\\n\\n # Build url\\n url = \\\"{0}/{1}\\\".format(PAYNL_END_POINT, request.get_url())\\n parameters = request.get_parameters()\\n if APIAuthentication.use_http_auth and 'token' in parameters:\\n del parameters['token']\\n\\n if self.print_debug:\\n print(\\\"Calling {} using {}\\\".format(url, method))\\n print(\\\"HTTP Headers: {}\\\".format(json.dumps(headers)))\\n print(\\\"Params: {}\\\".format(json.dumps(parameters)))\\n\\n if method.upper() == 'GET':\\n response = requests.get(url, verify=True, headers=headers, params=parameters)\\n else:\\n response = requests.post(url, verify=True, headers=headers, data=parameters)\\n\\n if response.status_code not in self.__supported_status_codes:\\n response.raise_for_status()\\n\\n if self.print_debug:\\n print(\\\"Response object: {}\\\".format(response))\\n print(\\\"Raw response: {}\\\".format(response.text))\\n\\n # Now the we have a response, let the request class handle the response.\\n request.raw_response = response.text\\n\\n if self.print_debug:\\n print(type(request.response))\\n\\n if request.response.is_error():\\n raise ErrorException(request.response.request)\",\n \"def request(self, *args, **kwargs):\\n req = GoodreadsRequest(self, *args, **kwargs)\\n return req.request()\",\n \"def start_requests(self):\\n yield Request(self.base_url, \\n callback=self.parse_urls)\",\n \"def _request(self, method, *args, **kwargs):\\n if not \\\"headers\\\" in kwargs:\\n kwargs[\\\"headers\\\"] = self._headers\\n return self._session.request(method, self._url(*args), **kwargs)\",\n \"def Run(self, request, timeout, metadata=None, with_call=False, protocol_options=None):\\n raise NotImplementedError()\",\n \"def start(self):\\r\\n pass\",\n \"def start(self):\\r\\n self.debug(\\\"### starting gox streaming API, trading %s%s\\\" %\\r\\n (self.curr_base, self.curr_quote))\\r\\n self.client.start()\",\n \"async def start(self) -> None:\",\n \"def do_GET(self):\\r\\n self.logger.debug(\\\"SseHTTPRequestHandler(Thread-%s): do_GET, path=%s\\\",\\r\\n threading.current_thread().ident, self.path)\\r\\n threading.current_thread().name = (self.path + \\\"_thread_\\\" +\\r\\n str(threading.current_thread().ident))\\r\\n if self.path == SseHTTPRequestHandler.eventsource_path:\\r\\n self._start_event_stream()\\r\\n else:\\r\\n http.server.SimpleHTTPRequestHandler.do_GET(self)\",\n \"def process(self):\\n if not self._requests:\\n return\\n\\n self._processing = True\\n Engine.instance().start()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7657946","0.69753915","0.69528455","0.69419944","0.67416626","0.6675796","0.6675425","0.6604838","0.66033286","0.6578297","0.65579545","0.655595","0.65280735","0.63916385","0.6389097","0.6380641","0.6327037","0.6309479","0.62897944","0.62629443","0.62608266","0.6257753","0.62325054","0.6201665","0.61897796","0.61897796","0.6184833","0.61575156","0.61398023","0.6116704","0.61093944","0.610531","0.6101367","0.6093363","0.6058888","0.604427","0.6043935","0.60295355","0.6022866","0.59845436","0.59845436","0.59845436","0.59845436","0.59816957","0.5976025","0.59635675","0.5951349","0.594297","0.59371907","0.5936016","0.5932021","0.592609","0.5922463","0.5902177","0.5902177","0.5901896","0.58982843","0.58947194","0.5892755","0.58891535","0.5885327","0.5869167","0.58424765","0.5838747","0.5827918","0.58193034","0.5816807","0.581522","0.58118004","0.5808893","0.58084893","0.5803703","0.5796767","0.5789327","0.578822","0.57787615","0.5773312","0.5773312","0.57678086","0.5765021","0.57524437","0.5750367","0.5743778","0.5743778","0.5740457","0.5740338","0.57402","0.5729725","0.5725681","0.57218975","0.571752","0.57155836","0.57118136","0.5702632","0.5702613","0.57001644","0.5696167","0.569335","0.56913567","0.56898427"],"string":"[\n \"0.7657946\",\n \"0.69753915\",\n \"0.69528455\",\n \"0.69419944\",\n \"0.67416626\",\n \"0.6675796\",\n \"0.6675425\",\n \"0.6604838\",\n \"0.66033286\",\n \"0.6578297\",\n \"0.65579545\",\n \"0.655595\",\n \"0.65280735\",\n \"0.63916385\",\n \"0.6389097\",\n \"0.6380641\",\n \"0.6327037\",\n \"0.6309479\",\n \"0.62897944\",\n \"0.62629443\",\n \"0.62608266\",\n \"0.6257753\",\n \"0.62325054\",\n \"0.6201665\",\n \"0.61897796\",\n \"0.61897796\",\n \"0.6184833\",\n \"0.61575156\",\n \"0.61398023\",\n \"0.6116704\",\n \"0.61093944\",\n \"0.610531\",\n \"0.6101367\",\n \"0.6093363\",\n \"0.6058888\",\n \"0.604427\",\n \"0.6043935\",\n \"0.60295355\",\n \"0.6022866\",\n \"0.59845436\",\n \"0.59845436\",\n \"0.59845436\",\n \"0.59845436\",\n \"0.59816957\",\n \"0.5976025\",\n \"0.59635675\",\n \"0.5951349\",\n \"0.594297\",\n \"0.59371907\",\n \"0.5936016\",\n \"0.5932021\",\n \"0.592609\",\n \"0.5922463\",\n \"0.5902177\",\n \"0.5902177\",\n \"0.5901896\",\n \"0.58982843\",\n \"0.58947194\",\n \"0.5892755\",\n \"0.58891535\",\n \"0.5885327\",\n \"0.5869167\",\n \"0.58424765\",\n \"0.5838747\",\n \"0.5827918\",\n \"0.58193034\",\n \"0.5816807\",\n \"0.581522\",\n \"0.58118004\",\n \"0.5808893\",\n \"0.58084893\",\n \"0.5803703\",\n \"0.5796767\",\n \"0.5789327\",\n \"0.578822\",\n \"0.57787615\",\n \"0.5773312\",\n \"0.5773312\",\n \"0.57678086\",\n \"0.5765021\",\n \"0.57524437\",\n \"0.5750367\",\n \"0.5743778\",\n \"0.5743778\",\n \"0.5740457\",\n \"0.5740338\",\n \"0.57402\",\n \"0.5729725\",\n \"0.5725681\",\n \"0.57218975\",\n \"0.571752\",\n \"0.57155836\",\n \"0.57118136\",\n \"0.5702632\",\n \"0.5702613\",\n \"0.57001644\",\n \"0.5696167\",\n \"0.569335\",\n \"0.56913567\",\n \"0.56898427\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6528585"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":89286,"cells":{"query":{"kind":"string","value":"Verify request,this method does not allow an exception is thrown."},"document":{"kind":"string","value":"def verify_request(self, request_handler, client_address):\n logger.debug('verify_request(%s:%s)' % client_address)"},"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 verify_request(self, request, client_address):\n\t\treturn True","def verify(self, response):","def verify(self, request):\n return Response(None, status=status.HTTP_204_NO_CONTENT)","def verify(self):\r\n pass","def verify(self):\n pass","def verify(self):\n pass","def verify(self):","def _verify(self):\n pass","def verify():","def _verify_page(self):","def _validate_http_request(self):\n if self.path != '/':\n print('Invalid request path:', self.path)\n self.send_error(HTTPStatus.NOT_FOUND, 'Request Must Have Path Of /')\n raise ValueError\n\n content_type = self.headers.get('Content-Type', None)\n if content_type != 'application/json':\n print('Invalid request Content-Type:', self.path)\n self.send_error(HTTPStatus.BAD_REQUEST, 'Content-Type Must Be application/json')\n raise ValueError","def validate_response(self, response):\n pass","def process_request(self):\r\n # Middleware should pass request through\r\n self.assertEquals(self.middleware.process_request(self.request), None)","def verify(self, request, times=None):\n data = {\n 'httpRequest': request.dict()\n }\n if times:\n data['times'] = vars(times)\n else:\n data['times'] = {\n 'count': 1,\n 'exact': True\n }\n req = requests.put('{}/verify'.format(self._get_url()),\n headers=self.headers,\n data=json.dumps(data))\n resp = {\n 'status': 'OK',\n 'reason': req.content.decode('utf-8'),\n 'found': None\n }\n if req.status_code == 202:\n resp['reason'] = None\n resp['found'] = True\n elif req.status_code == 406:\n resp['found'] = False\n else:\n resp['status'] = 'ERROR'\n\n return resp","def execute_request(self, request: Request):\r\n print(\"Handler is validating data\")\r\n if request.data_input is not None or request.input_file is not None:\r\n if not self.next_handler:\r\n return True\r\n return self.next_handler.execute_request(request)\r\n else:\r\n print(\"Data is not validated\")\r\n return False","def check(self):\n self.__check_request_limit()","def verify_request_signature(req_info: StatusResponse) -> None:\n if not req_info.signature_check(req_info.xmlstr):\n raise ValueError(_(\"Message signature verification failure\"))","def __init__(self, request):\n self.request = request\n self._is_verified = False","def validation(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def test_authentication_required(self):\n self.auth.validate_token_request(self.request)\n self.mock_validator.client_authentication_required.assert_called_once_with(self.request)","def Check(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def test_verify_of_already_verified_request(self, cred, request_id):\n resp = requests.get(check_url.format('json', cred[0], cred[1],\n request_id, '0000'))\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'application/json'\n assert resp.json()['status'] == '6'\n assert resp.json()['error_text'] == request_not_found_or_verified_msg.format(request_id)","def execute_request(self, request: Request) -> bool:\r\n print(\"Handler is validating key\")\r\n if request.key is not None:\r\n if not self.next_handler:\r\n return True\r\n return self.next_handler.execute_request(request)\r\n else:\r\n print(\"Key is not valid\")\r\n return False","def request_is_valid(request):\n return 'method' in request","def check(self):\n invalid = []\n\n if not self.route:\n invalid.append(('route', 'missing'))\n elif not self.route[1] in ['GET', 'POST', 'PUT']:\n invalid.append(('route', 'invalid method: %s' % self.route[1]))\n\n has_2xx = False\n for rcode in self.return_codes:\n code = rcode[0]\n if code >= 200 and code < 300:\n has_2xx = True\n break\n if not has_2xx:\n invalid.append(('return_codes', 'Missing succes return code doc'))\n\n if self.client_auth is None:\n invalid.append(\n ('client_auth', 'Please provide client auth requirement'))\n\n if self.user_auth is None:\n invalid.append(\n ('user_auth', 'Please provide user auth requirement'))\n\n if invalid:\n msgs = []\n for error in invalid:\n msgs.append(\"%s: %s\" % error)\n raise ValueError(\n \"APIFunc for %s is invalid: %s\"\n % (self.viewfunc.__name__,\n ', '.join(msgs)))","def verify_request(self, scopes):\n uri, http_method, body, headers = extract_params(False)\n return self.server.verify_request(uri, http_method, body, headers, scopes)","def validate_http_request(request):\r\n if request != b'':\r\n # Divide the request line: [method, sp, url, version, cr lf]\r\n request = request.decode().split('\\r')[0]\r\n method = request.split()[0]\r\n url = request.split()[1]\r\n version = request.split()[2]\r\n if method == METHOD and version == VERSION:\r\n return True, url\r\n else:\r\n return False, None\r\n else:\r\n return True, None","def _ValidRequest(request):\n if not request.json:\n abort(400)\n sessId = request.json['sessionId']\n sessKey = request.json['sessionKey']\n # Check if it is active and correct key\n return database.SessionActive(sessId) and database.CorrectSessionKey(sessId, sessKey)","def debug_verify(self, request):\n token = request.auth[0]\n return Response(\n response_payload_handler(token, request.user, request),\n status=status.HTTP_200_OK)","def verify(self):\n if self.geturl():\n return True\n return False","def verify(self):\n raise NotImplementedError","def reCheck(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def test_default_unsuccessful_verify_request(self, cred):\n # make the initial request\n resp = requests.get(verify_url.format('xml', cred[0], cred[1],\n 'TestApp', test_number))\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'text/plain'\n assert resp.text.startswith('<?xml version=\"1.0\" encoding=\"UTF-8\"?>')\n tree = ElementTree.fromstring(resp.text)\n assert tree[0].tag == 'request_id' and len(tree[0].text) <= 32\n assert tree[1].tag == 'status' and tree[1].text == '0'\n # now enter invalid verify code 3 times to terminate verification process\n # first invalid code check\n request_id = tree[0].text\n resp = requests.get(check_url.format('xml', cred[0], cred[1],\n request_id, '00000'))\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'text/plain'\n assert resp.text.startswith('<?xml version=\"1.0\" encoding=\"UTF-8\"?>')\n tree = ElementTree.fromstring(resp.text)\n assert tree[0].tag == 'request_id' and tree[0].text == request_id\n assert tree[1].tag == 'status' and tree[1].text == '16'\n assert tree[2].tag == 'error_text' and tree[2].text == code_does_not_match_msg\n # second invalid check\n resp = requests.get(check_url.format('xml', cred[0], cred[1],\n request_id, '00000'))\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'text/plain'\n assert resp.text.startswith('<?xml version=\"1.0\" encoding=\"UTF-8\"?>')\n tree = ElementTree.fromstring(resp.text)\n assert tree[0].tag == 'request_id' and tree[0].text == request_id\n assert tree[1].tag == 'status' and tree[1].text == '16'\n assert tree[2].tag == 'error_text' and tree[2].text == code_does_not_match_msg\n # third invalid check\n resp = requests.get(check_url.format('xml', cred[0], cred[1],\n request_id, '00000'))\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'text/plain'\n assert resp.text.startswith('<?xml version=\"1.0\" encoding=\"UTF-8\"?>')\n tree = ElementTree.fromstring(resp.text)\n # assert 'request_id' not in [child.tag for child in tree]\n assert tree[1].tag == 'status' and tree[1].text == '17'\n assert tree[2].tag == 'error_text' and tree[2].text == workflow_terminated_msg","def verify(request):\n return HttpResponse(status=200)","def check_validity(self):","def do_validate(self, request, _object):\n\n pass","def _validate_request(route_mapper, request, schema_data, resolver):\n try:\n validate_incoming_request(\n route_mapper,\n request,\n schema_data,\n resolver\n )\n except jsonschema.exceptions.ValidationError as exc:\n # This will alter our stack trace slightly, but Pyramid knows how\n # to render it. And the real value is in the message anyway.\n raise HTTPClientError(str(exc))","def test_default_unsuccessful_verify_request(self, cred):\n # make the initial request\n resp = requests.get(verify_url.format('json', cred[0], cred[1],\n 'TestApp', test_number))\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'application/json'\n assert resp.json()['status'] == '0'\n assert len(resp.json()['request_id']) <= 32\n # now enter invalid verify code 3 times to terminate verification process\n # first invalid code check\n request_id = resp.json()['request_id']\n resp = requests.get(check_url.format('json', cred[0], cred[1],\n request_id, '00000'))\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'application/json'\n assert resp.json()['status'] == '16'\n assert resp.json()['request_id'] == request_id\n assert resp.json()['error_text'] == code_does_not_match_msg\n # second invalid check\n resp = requests.get(check_url.format('json', cred[0], cred[1],\n request_id, '00000'))\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'application/json'\n assert resp.json()['status'] == '16'\n assert resp.json()['request_id'] == request_id\n assert resp.json()['error_text'] == code_does_not_match_msg\n # third invalid check\n resp = requests.get(check_url.format('json', cred[0], cred[1],\n request_id, '00000'))\n assert resp.status_code == 200\n assert resp.headers['Content-Type'] == 'application/json'\n assert resp.json()['status'] == '17'\n assert 'request_id' not in resp.json().keys()\n assert resp.json()['error_text'] == workflow_terminated_msg","def __init_request(self, req):\n return defines.ReturnCode.SUCC","def check_redemption_request(request: IRequest) -> Optional[bytes]:\n if request.requestHeaders.getRawHeaders(b\"content-type\") != [b\"application/json\"]:\n return bad_content_type(request)\n\n p = request.content.tell()\n content = request.content.read()\n request.content.seek(p)\n\n try:\n request_body = loads(content)\n except ValueError:\n return bad_request(request, None)\n\n expected_keys = {\"redeemVoucher\", \"redeemCounter\", \"redeemTokens\"}\n actual_keys = set(request_body.keys())\n if expected_keys != actual_keys:\n return bad_request(\n request,\n {\n \"success\": False,\n \"reason\": \"{} != {}\".format(\n expected_keys,\n actual_keys,\n ),\n },\n )\n return None","def user_should_get_an_ok_response():\n assert web_app.validate_reponse()","def check_request(request_schema):\n def decorator(f):\n @functools.wraps(f)\n def wrapper(self, addr, request):\n data, err = request_schema.load(request)\n if err:\n return Header.ERROR, Error.WRONG_REQUEST\n else:\n return f(self, addr, data)\n\n return wrapper\n return decorator","def is_request_valid(request: request) -> bool:\n \n key = os.environ.get(\"SLACK_SIGNING_SECRET\")\n basestring = 'v0:' + request.headers['X-Slack-Request-Timestamp'] + ':' + str(request.get_data(), 'utf-8')\n\n # Hash the basestring using the signing secret as the key in order to get the signature\n signature = 'v0=' + hmac.new(\n bytes(key, 'utf-8'),\n bytes(basestring, 'utf-8'),\n hashlib.sha256\n ).hexdigest()\n slacksig = request.headers['X-Slack-Signature']\n\n # If the signature is equal to the signature sent by slack, then it is indeed from slack.\n return hmac.compare_digest(slacksig, signature)","def _check_response(self, res: requests.Response, token: str) -> None:\n return","def _check_response(self, res: requests.Response, token: str) -> None:\n raise RuntimeError('Cannot use _check_response of this abstract class.')","def test_client_verification_retrieve(self):\n pass","def validate_http_request(request):\n request_str = request.decode('utf-8')\n print(request_str)\n split_request = request_str.split(' ')\n if (split_request[0] == 'GET') and split_request[2].startswith('HTTP/1.1'):\n request_url = split_request[1].replace(\"/\", \"\\\\\")\n x = (True, request_url)\n return x\n y = (False, None)\n return y","def testInvalidRequest(self):\n self.mgr.sendGoProRequest(141)\n self.assertFalse(self.v.message_factory.gopro_set_request_encode.called)","def check(self, req: CheckRequest) -> CheckResult:\n raise NotImplementedError()","def validate(self, response):\n return response[\"status_code\"] == 1","def handle_request(self):\n\t\ttry:\n\t\t\trequest, client_address = self.get_request()\n\t\texcept socket.error:\n\t\t\treturn\n\t\tif self.verify_request(request, client_address):\n\t\t\ttry:\n\t\t\t\tself.process_request(request, client_address)\n\t\t\texcept:\n\t\t\t\tself.handle_error(request, client_address)\n\t\t\t\tself.close_request(request)","def check_request(self, request):\n state = self.current['state']\n return (request == self.KM_PROTOCOL_VIOLATION or\n request == self.KM_ERROR or\n request == self.KM_CANCEL or\n (state == None and request == self.KM_REQUEST_KEY) or\n (state == self.KM_REQUEST_KEY and request == self.KM_REQUEST_OK) or\n (state == self.KM_REQUEST_KEY and request == self.KM_REQUEST_DENIED) or\n (state == self.KM_REQUEST_OK and request == self.KM_TEMP_KEY_ACK) or\n (state == self.KM_TEMP_KEY_ACK and request == self.KM_TEMP_KEY2) or\n (state == self.KM_TEMP_KEY2 and request == self.KM_PERM_KEY2) or\n (state == self.KM_PERM_KEY2 and request == self.KM_FINISHED))","def _process_vep_assertion(self, request):\n # Make sure they're using a GET request.\n if request.method != \"GET\":\n resp = Response()\n resp.status = 405\n resp.content_type = \"text/plain\"\n resp.body = \"token requests must get GET\"\n request.environ[\"repoze.who.application\"] = resp\n return None\n # Make sure they're sending an Authorization header.\n if not request.authorization:\n msg = \"you must provide an authorization header\"\n return self._respond_unauthorized(request, msg)\n # Grab the assertion from the Authorization header.\n scheme, assertion = request.authorization\n if scheme.lower() != \"browser-id\":\n msg = \"The auth scheme \\\"%s\\\" is not supported\" % (scheme,)\n return self._respond_bad_request(request, msg.encode(\"utf8\"))\n # Extract the audience, so we can check against wildcards.\n try:\n audience = get_assertion_info(assertion)[\"audience\"]\n except (ValueError, KeyError):\n return self._respond_bad_request(request, \"invalid assertion\")\n if not self._check_audience(request, audience):\n msg = \"The audience \\\"%s\\\" is not acceptable\" % (audience,)\n return self._respond_bad_request(request, msg.encode(\"utf8\"))\n # Verify the assertion and find out who they are.\n try:\n data = self.verifier.verify(assertion)\n except Exception, e:\n msg = \"Invalid BrowserID assertion: \" + str(e)\n return self._respond_bad_request(request, msg)\n # OK, we can go ahead and issue a token.\n token, secret, extra = self.token_manager.make_token(request, data)\n\n if token is None:\n msg = \"that email address is not recognised\"\n return self._respond_unauthorized(request, msg)\n resp = Response()\n resp.status = 200\n resp.content_type = \"application/json\"\n\n body = {\n \"id\": token,\n \"key\": secret,\n \"algorithm\": \"hmac-sha-1\",\n }\n\n if extra is not None:\n body.update(extra)\n\n resp.body = json.dumps(body)\n request.environ[\"repoze.who.application\"] = resp","def _validate_rpc_request(self, request):\n if request.get('jsonrpc', None) != '2.0':\n raise ValueError('Invalid jsonrpc: must be \"2.0\"')\n\n id = request.get('id', None)\n if not (id is None or isinstance(id, (str, int, float))):\n raise ValueError('Invalid id: must be null, string or number')\n\n method = request.get('method', None)\n if not isinstance(method, str):\n raise ValueError('Invalid method: must be string')\n\n params = request.get('params', [])\n if not isinstance(params, (dict, list)):\n raise ValueError('Invalid params: must be array or object')","def mora_assert(response):\n assert response.status_code in (200, 201, 400, 404), response.status_code\n if response.status_code == 400:\n # Check actual response\n assert (\n response.text.find(\"not give raise to a new registration\") > 0\n ), response.text\n logger.debug(\"Request had no effect\")\n return None","def check_global_request(self, kind, msg):\n return False","def is_valid_request(self, request):\r\n auth_params = request.META.get(\"HTTP_AUTHORIZATION\", [])\r\n return self.is_in(auth_params) or self.is_in(request.REQUEST)","def verify (self, path):\n pass","async def _interaction(self, request: Request) -> web.Response:\n if (timestamp := request.headers.get(\"X-Signature-Timestamp\")) is None \\\n or (ed25519 := request.headers.get(\"X-Signature-Ed25519\")) is None:\n return web.Response(status=401, reason=\"Unauthorised\")\n\n try:\n self._verify_key.verify((timestamp + await request.text()).encode(), bytes.fromhex(ed25519))\n except BadSignatureError:\n return web.Response(status=401, reason=\"Unauthorised\")\n\n payload = await request.json()\n if payload.get('type') == 1:\n return web.Response(status=200, text=dumps({\"type\": 1}), content_type=\"application/json\")\n else:\n response = await self._handle(payload)\n return web.Response(status=200, text=response.json(), content_type=\"application/json\")","def verify():\n if flask.request.method == 'POST':\n req = flask.request.get_json(force=True)\n phone = req.get('phone')\n code = req['code']\n return check_verification(phone, code)","def _raise_performing_request_error(self, *args, **kwargs):","def _check_validity(self):\n pass","def _api_call(self, url, response_checker):\n self.request_compare(url)","async def handle_request(self, request: aioweb.request.Request):","def validateAgentJWTToken(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def check_auth():","def check_response_errors(self, resp):\n return True","def process_request(self, req):\n return None","def check_vulnerability(self):\n\t\tpass","def __verify(self):\r\n code = self.request.get('code')\r\n email = None\r\n error = False\r\n # resend if code is not given or in case of some error\r\n if code is not None and code != '':\r\n email = User.verify(code, self.request.remote_addr)\r\n if email is None:\r\n error = True\r\n\r\n if email is None:\r\n template_values = {\r\n 'user_email': self.user_email,\r\n 'error': error\r\n }\r\n template = self.jinja2_env.get_template('verification.html')\r\n self.response.out.write(template.render(template_values))\r\n\r\n # message\r\n template_values = {\r\n 'user_email': self.user_email,\r\n 'message': self.gettext('THANK_YOU')\r\n }\r\n template = self.jinja2_env.get_template('staticmessage.html')\r\n self.response.out.write(template.render(template_values))","def checkRequest(self, uID):\n\n if not RequestsDAO().getRequestByuID(uID):\n return jsonify(Error=\"No request found\"), 404\n else:\n if uID:\n approval = RequestsDAO().getRequestStatusByuID(uID)\n mapped_result = self.buildCheckRequestToDict(uID, approval[0], approval[1], approval[2])\n return jsonify(TURN=mapped_result), 200\n\n else:\n return jsonify(Error=\"Unexpected attributes in post request\"), 400","def valid_request(environ):\n if environ.get('REQUEST_METHOD') != 'GET':\n return False\n if environ.get('QUERY_STRING'):\n return False\n requested = environ.get('PATH_INFO', '/').lstrip('/')\n if not requested:\n return True\n if len(requested) > 40: # Larger than git SHA reference.\n return False\n if VALID_REQUEST.match(requested) is None:\n return False\n return True","def on_request(self):\n # extra and remove the signature from the query\n signature = request.query.get(\"_signature\")\n if signature is None:\n abort(403)\n del request.query[\"_signature\"]\n # Verifies the query keys.\n if signature != self.url_signer._sign(request.fullpath, request.query):\n abort(403)","def authenticate(self, request):\n return None","def test_validate_authorization_request_required_parameters(self):\n\n request = self.make_request()\n scopes, credentials = self.auth.validate_authorization_request(request)\n\n self.assertListEqual(scopes, request.scope.split())\n assert credentials['client_id'] == request.client_id\n assert credentials['redirect_uri'] == request.redirect_uri\n assert credentials['response_type'] == request.response_type\n assert credentials['state'] == request.state\n\n self.validator.validate_client_id\\\n .assert_called_once_with(request.client_id, request)\n self.validator.validate_redirect_uri\\\n .assert_called_once_with(request.client_id, request.redirect_uri, request)","def validate(self, apiobj, method, api, param, safe):","def check(self) -> None:","def test_verification_failed(self):\n pass","def test_bad_requests_give_400(self):\n self.assertEqual(self._request({}), 400)","def Verify(self):\n return True","def test_verification_with_valid_token(self) -> None:\n\n secret_key = str(self.author.secret_key)\n verification_url = reverse('author:verify', kwargs={'secret_key': str(secret_key)})\n\n # Make sure URL's don't change.\n self.assertEqual(verification_url, f'/api/authors/verify/{secret_key}/')\n\n # Make valid request and get response\n response: Response = self.client.get(verification_url)\n\n self.assertEqual(response.status_code, 302)\n\n # Now test if the method \"verify\" was called\n self.assertEqual(Author.objects.get().verified, True)\n # We don't wanna give him too many privileges\n self.assertEqual(self.author.is_staff, False)","def test_make_request_error(self):\n response = Helper.make_request(self.url_404)\n self.assertEqual(response.status_code, 404)","def bad_callback(_request, _uri, headers):\n return (404, headers, 'NOT AN ASSERTION')","def verify_payload():\n return True","def test_valid_request(self):\n print(\"Testing valid request...\")\n # create a file with random data in the server folder\n self.__create_test_file()\n valid_path = os.path.join(os.path.dirname(self.client_path), \\\n os.path.basename(self.test_file))\n os.chdir(os.path.dirname(self.client_path))\n subprocess.call([self.client_path, \\\n \"{}:{}\".format(self.args.ip, self.args.port), \\\n os.path.basename(valid_path)])\n self.assertTrue(os.path.isfile(valid_path))\n self.__compare_files(valid_path, self.test_file)\n os.remove(valid_path)","def _validate_get_request(self):\n\t\tdatos = self._get_param_from_request(\"lng\") and self._get_param_from_request(\"lat\")\n\n\t\tif(not datos):\n\t\t\treturn False\n\t\telse:\n\t\t\treturn True","def common_http_validator(method=None,url=None,data=None,header=None):\r\n status_code = 500\r\n error_msg = None\r\n response_data = None\r\n\r\n try:\r\n req = requests.request(method=method,url=url,data=data,headers=header)\r\n # print req.request.method #Getting the method\r\n\r\n except (requests.RequestException,requests.HTTPError,requests.ConnectionError,requests.Timeout) as e:\r\n error_msg = 'Connection/Timeout/General Exception: {}'.format(e)\r\n\r\n except Exception as e:\r\n error_msg = 'Connection/Timeout/General Exception: {}'.format(e)\r\n\r\n else:\r\n status_code = req.status_code\r\n response_data = req.content\r\n\r\n return status_code, response_data, error_msg","def process_request(self, request):\n return None","def validate_request(response):\n openapi_spec = get_openapi_spec()\n\n request = TornadoOpenAPIRequest(response.request, openapi_spec)\n if V30RequestValidator:\n result = V30RequestValidator(openapi_spec).validate(request)\n else:\n result = openapi_request_validator.validate(openapi_spec, request)\n result.raise_for_errors()\n\n response = TornadoOpenAPIResponse(response)\n if V30ResponseValidator:\n result2 = V30ResponseValidator(openapi_spec).validate(request, response)\n else:\n result2 = openapi_response_validator.validate(openapi_spec, request, response)\n result2.raise_for_errors()","def test_GET3(self):\n r = requests.get(self.address)\n self.assertEqual(r.status_code, 400)","def bad_request():\n return HttpError(400)","def verify_request(self, request, client_address):\n\n\t\tglobal configurer\n\n\t\treturn NetworkRender.allowedAddress(self.ip, client_address[0],\n\t\t\t\t\t\t\t\t\t\tconfigurer.get('ServerSecureNets'))","def validate(self, request):\n\n value = request._get_parameter_value(self)\n\n if value.object is None:\n if self.required:\n self.add_error(request, \"This input is required\")\n\n return\n\n self.do_validate(request, value.object)","def _process_request(self, request, response):\n ...","def test_13(self):\n assert 'False' == Api.requestBlock('test-13')","def test_validate_get(client):\n response = client.get('/user/')\n assert response.status_code == 400\n assert response.json['message'] == INVALID_ACTION_MESSAGE","def test_allow(self) -> None:\n response = self.request(\"/\", method=\"HEAD\")\n self.assert_allowed(response, (\"GET\", \"POST\"))","def test_invalid_verify_patch_request(self, cred):\n resp = requests.patch(verify_url.format('json', cred[0], cred[1],\n 'TestApp', test_number))\n assert resp.status_code == 403","def check_http_request_validity(http_raw_data) -> HttpRequestState:\n\n global version\n r1 = http_raw_data.split('\\n')[0]\n r2 = http_raw_data.split('\\n')[1]\n\n if (re.search(\"GET\", r1) != None) and (re.search(\"/\", r1) != None) and (re.search(\"HTTP/1.0\", r1) != None) and (re.search(\":\", r2)):\n return HttpRequestState.GOOD\n\n if (re.search(\"GET\", r1) != None) and (re.search(\"http://\", r1) != None) and (re.search(\"HTTP/1.0\", r1) != None):\n return HttpRequestState.GOOD\n\n if (re.search(\"GET\", r1)!=None) and (re.search(\"/\", r1)!=None) and (re.search(\"HTTP/1.0\",r1)!=None) :\n if (re.search(\":\", r2) == None) :\n return HttpRequestState.INVALID_INPUT\n\n if(re.search(\"GOAT\", r1)!=None):\n return HttpRequestState.INVALID_INPUT\n\n if (re.search(\"HEAD\"or\"POST\" or \"PUT\" , r1)!=None) and (re.search(\"/\",r1)!=None) and (re.search(\"HTTP/1.0\", r1) != None) and (re.search(\":\", r2)):\n\n return HttpRequestState.NOT_SUPPORTED\n\n if (re.search(\"HEAD\"or\"POST\" or \"PUT\" ,r1)!=None) and (re.search(\"/\",r1)!=None) and (re.search(\"HTTP/1.0\",r1)!=None):\n return HttpRequestState.INVALID_INPUT\n\n if (re.search(\"HEAD\"or\"POST\" or \"PUT\", r1) != None) and (re.search(\"HTTP/1.0\", r1) == None) and (re.search(\":\", r2) != None):\n return HttpRequestState.INVALID_INPUT\n print(\"*\" * 50)\n print(\"[check_http_request_validity] Implement me!\")\n print(\"*\" * 50)\n\n return HttpRequestState.PLACEHOLDER","def post(self, request, *args, **kwargs):\n verify_secure(request)\n return super().post(request, args, kwargs)"],"string":"[\n \"def verify_request(self, request, client_address):\\n\\t\\treturn True\",\n \"def verify(self, response):\",\n \"def verify(self, request):\\n return Response(None, status=status.HTTP_204_NO_CONTENT)\",\n \"def verify(self):\\r\\n pass\",\n \"def verify(self):\\n pass\",\n \"def verify(self):\\n pass\",\n \"def verify(self):\",\n \"def _verify(self):\\n pass\",\n \"def verify():\",\n \"def _verify_page(self):\",\n \"def _validate_http_request(self):\\n if self.path != '/':\\n print('Invalid request path:', self.path)\\n self.send_error(HTTPStatus.NOT_FOUND, 'Request Must Have Path Of /')\\n raise ValueError\\n\\n content_type = self.headers.get('Content-Type', None)\\n if content_type != 'application/json':\\n print('Invalid request Content-Type:', self.path)\\n self.send_error(HTTPStatus.BAD_REQUEST, 'Content-Type Must Be application/json')\\n raise ValueError\",\n \"def validate_response(self, response):\\n pass\",\n \"def process_request(self):\\r\\n # Middleware should pass request through\\r\\n self.assertEquals(self.middleware.process_request(self.request), None)\",\n \"def verify(self, request, times=None):\\n data = {\\n 'httpRequest': request.dict()\\n }\\n if times:\\n data['times'] = vars(times)\\n else:\\n data['times'] = {\\n 'count': 1,\\n 'exact': True\\n }\\n req = requests.put('{}/verify'.format(self._get_url()),\\n headers=self.headers,\\n data=json.dumps(data))\\n resp = {\\n 'status': 'OK',\\n 'reason': req.content.decode('utf-8'),\\n 'found': None\\n }\\n if req.status_code == 202:\\n resp['reason'] = None\\n resp['found'] = True\\n elif req.status_code == 406:\\n resp['found'] = False\\n else:\\n resp['status'] = 'ERROR'\\n\\n return resp\",\n \"def execute_request(self, request: Request):\\r\\n print(\\\"Handler is validating data\\\")\\r\\n if request.data_input is not None or request.input_file is not None:\\r\\n if not self.next_handler:\\r\\n return True\\r\\n return self.next_handler.execute_request(request)\\r\\n else:\\r\\n print(\\\"Data is not validated\\\")\\r\\n return False\",\n \"def check(self):\\n self.__check_request_limit()\",\n \"def verify_request_signature(req_info: StatusResponse) -> None:\\n if not req_info.signature_check(req_info.xmlstr):\\n raise ValueError(_(\\\"Message signature verification failure\\\"))\",\n \"def __init__(self, request):\\n self.request = request\\n self._is_verified = False\",\n \"def validation(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def test_authentication_required(self):\\n self.auth.validate_token_request(self.request)\\n self.mock_validator.client_authentication_required.assert_called_once_with(self.request)\",\n \"def Check(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def test_verify_of_already_verified_request(self, cred, request_id):\\n resp = requests.get(check_url.format('json', cred[0], cred[1],\\n request_id, '0000'))\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'application/json'\\n assert resp.json()['status'] == '6'\\n assert resp.json()['error_text'] == request_not_found_or_verified_msg.format(request_id)\",\n \"def execute_request(self, request: Request) -> bool:\\r\\n print(\\\"Handler is validating key\\\")\\r\\n if request.key is not None:\\r\\n if not self.next_handler:\\r\\n return True\\r\\n return self.next_handler.execute_request(request)\\r\\n else:\\r\\n print(\\\"Key is not valid\\\")\\r\\n return False\",\n \"def request_is_valid(request):\\n return 'method' in request\",\n \"def check(self):\\n invalid = []\\n\\n if not self.route:\\n invalid.append(('route', 'missing'))\\n elif not self.route[1] in ['GET', 'POST', 'PUT']:\\n invalid.append(('route', 'invalid method: %s' % self.route[1]))\\n\\n has_2xx = False\\n for rcode in self.return_codes:\\n code = rcode[0]\\n if code >= 200 and code < 300:\\n has_2xx = True\\n break\\n if not has_2xx:\\n invalid.append(('return_codes', 'Missing succes return code doc'))\\n\\n if self.client_auth is None:\\n invalid.append(\\n ('client_auth', 'Please provide client auth requirement'))\\n\\n if self.user_auth is None:\\n invalid.append(\\n ('user_auth', 'Please provide user auth requirement'))\\n\\n if invalid:\\n msgs = []\\n for error in invalid:\\n msgs.append(\\\"%s: %s\\\" % error)\\n raise ValueError(\\n \\\"APIFunc for %s is invalid: %s\\\"\\n % (self.viewfunc.__name__,\\n ', '.join(msgs)))\",\n \"def verify_request(self, scopes):\\n uri, http_method, body, headers = extract_params(False)\\n return self.server.verify_request(uri, http_method, body, headers, scopes)\",\n \"def validate_http_request(request):\\r\\n if request != b'':\\r\\n # Divide the request line: [method, sp, url, version, cr lf]\\r\\n request = request.decode().split('\\\\r')[0]\\r\\n method = request.split()[0]\\r\\n url = request.split()[1]\\r\\n version = request.split()[2]\\r\\n if method == METHOD and version == VERSION:\\r\\n return True, url\\r\\n else:\\r\\n return False, None\\r\\n else:\\r\\n return True, None\",\n \"def _ValidRequest(request):\\n if not request.json:\\n abort(400)\\n sessId = request.json['sessionId']\\n sessKey = request.json['sessionKey']\\n # Check if it is active and correct key\\n return database.SessionActive(sessId) and database.CorrectSessionKey(sessId, sessKey)\",\n \"def debug_verify(self, request):\\n token = request.auth[0]\\n return Response(\\n response_payload_handler(token, request.user, request),\\n status=status.HTTP_200_OK)\",\n \"def verify(self):\\n if self.geturl():\\n return True\\n return False\",\n \"def verify(self):\\n raise NotImplementedError\",\n \"def reCheck(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def test_default_unsuccessful_verify_request(self, cred):\\n # make the initial request\\n resp = requests.get(verify_url.format('xml', cred[0], cred[1],\\n 'TestApp', test_number))\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'text/plain'\\n assert resp.text.startswith('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>')\\n tree = ElementTree.fromstring(resp.text)\\n assert tree[0].tag == 'request_id' and len(tree[0].text) <= 32\\n assert tree[1].tag == 'status' and tree[1].text == '0'\\n # now enter invalid verify code 3 times to terminate verification process\\n # first invalid code check\\n request_id = tree[0].text\\n resp = requests.get(check_url.format('xml', cred[0], cred[1],\\n request_id, '00000'))\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'text/plain'\\n assert resp.text.startswith('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>')\\n tree = ElementTree.fromstring(resp.text)\\n assert tree[0].tag == 'request_id' and tree[0].text == request_id\\n assert tree[1].tag == 'status' and tree[1].text == '16'\\n assert tree[2].tag == 'error_text' and tree[2].text == code_does_not_match_msg\\n # second invalid check\\n resp = requests.get(check_url.format('xml', cred[0], cred[1],\\n request_id, '00000'))\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'text/plain'\\n assert resp.text.startswith('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>')\\n tree = ElementTree.fromstring(resp.text)\\n assert tree[0].tag == 'request_id' and tree[0].text == request_id\\n assert tree[1].tag == 'status' and tree[1].text == '16'\\n assert tree[2].tag == 'error_text' and tree[2].text == code_does_not_match_msg\\n # third invalid check\\n resp = requests.get(check_url.format('xml', cred[0], cred[1],\\n request_id, '00000'))\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'text/plain'\\n assert resp.text.startswith('<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>')\\n tree = ElementTree.fromstring(resp.text)\\n # assert 'request_id' not in [child.tag for child in tree]\\n assert tree[1].tag == 'status' and tree[1].text == '17'\\n assert tree[2].tag == 'error_text' and tree[2].text == workflow_terminated_msg\",\n \"def verify(request):\\n return HttpResponse(status=200)\",\n \"def check_validity(self):\",\n \"def do_validate(self, request, _object):\\n\\n pass\",\n \"def _validate_request(route_mapper, request, schema_data, resolver):\\n try:\\n validate_incoming_request(\\n route_mapper,\\n request,\\n schema_data,\\n resolver\\n )\\n except jsonschema.exceptions.ValidationError as exc:\\n # This will alter our stack trace slightly, but Pyramid knows how\\n # to render it. And the real value is in the message anyway.\\n raise HTTPClientError(str(exc))\",\n \"def test_default_unsuccessful_verify_request(self, cred):\\n # make the initial request\\n resp = requests.get(verify_url.format('json', cred[0], cred[1],\\n 'TestApp', test_number))\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'application/json'\\n assert resp.json()['status'] == '0'\\n assert len(resp.json()['request_id']) <= 32\\n # now enter invalid verify code 3 times to terminate verification process\\n # first invalid code check\\n request_id = resp.json()['request_id']\\n resp = requests.get(check_url.format('json', cred[0], cred[1],\\n request_id, '00000'))\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'application/json'\\n assert resp.json()['status'] == '16'\\n assert resp.json()['request_id'] == request_id\\n assert resp.json()['error_text'] == code_does_not_match_msg\\n # second invalid check\\n resp = requests.get(check_url.format('json', cred[0], cred[1],\\n request_id, '00000'))\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'application/json'\\n assert resp.json()['status'] == '16'\\n assert resp.json()['request_id'] == request_id\\n assert resp.json()['error_text'] == code_does_not_match_msg\\n # third invalid check\\n resp = requests.get(check_url.format('json', cred[0], cred[1],\\n request_id, '00000'))\\n assert resp.status_code == 200\\n assert resp.headers['Content-Type'] == 'application/json'\\n assert resp.json()['status'] == '17'\\n assert 'request_id' not in resp.json().keys()\\n assert resp.json()['error_text'] == workflow_terminated_msg\",\n \"def __init_request(self, req):\\n return defines.ReturnCode.SUCC\",\n \"def check_redemption_request(request: IRequest) -> Optional[bytes]:\\n if request.requestHeaders.getRawHeaders(b\\\"content-type\\\") != [b\\\"application/json\\\"]:\\n return bad_content_type(request)\\n\\n p = request.content.tell()\\n content = request.content.read()\\n request.content.seek(p)\\n\\n try:\\n request_body = loads(content)\\n except ValueError:\\n return bad_request(request, None)\\n\\n expected_keys = {\\\"redeemVoucher\\\", \\\"redeemCounter\\\", \\\"redeemTokens\\\"}\\n actual_keys = set(request_body.keys())\\n if expected_keys != actual_keys:\\n return bad_request(\\n request,\\n {\\n \\\"success\\\": False,\\n \\\"reason\\\": \\\"{} != {}\\\".format(\\n expected_keys,\\n actual_keys,\\n ),\\n },\\n )\\n return None\",\n \"def user_should_get_an_ok_response():\\n assert web_app.validate_reponse()\",\n \"def check_request(request_schema):\\n def decorator(f):\\n @functools.wraps(f)\\n def wrapper(self, addr, request):\\n data, err = request_schema.load(request)\\n if err:\\n return Header.ERROR, Error.WRONG_REQUEST\\n else:\\n return f(self, addr, data)\\n\\n return wrapper\\n return decorator\",\n \"def is_request_valid(request: request) -> bool:\\n \\n key = os.environ.get(\\\"SLACK_SIGNING_SECRET\\\")\\n basestring = 'v0:' + request.headers['X-Slack-Request-Timestamp'] + ':' + str(request.get_data(), 'utf-8')\\n\\n # Hash the basestring using the signing secret as the key in order to get the signature\\n signature = 'v0=' + hmac.new(\\n bytes(key, 'utf-8'),\\n bytes(basestring, 'utf-8'),\\n hashlib.sha256\\n ).hexdigest()\\n slacksig = request.headers['X-Slack-Signature']\\n\\n # If the signature is equal to the signature sent by slack, then it is indeed from slack.\\n return hmac.compare_digest(slacksig, signature)\",\n \"def _check_response(self, res: requests.Response, token: str) -> None:\\n return\",\n \"def _check_response(self, res: requests.Response, token: str) -> None:\\n raise RuntimeError('Cannot use _check_response of this abstract class.')\",\n \"def test_client_verification_retrieve(self):\\n pass\",\n \"def validate_http_request(request):\\n request_str = request.decode('utf-8')\\n print(request_str)\\n split_request = request_str.split(' ')\\n if (split_request[0] == 'GET') and split_request[2].startswith('HTTP/1.1'):\\n request_url = split_request[1].replace(\\\"/\\\", \\\"\\\\\\\\\\\")\\n x = (True, request_url)\\n return x\\n y = (False, None)\\n return y\",\n \"def testInvalidRequest(self):\\n self.mgr.sendGoProRequest(141)\\n self.assertFalse(self.v.message_factory.gopro_set_request_encode.called)\",\n \"def check(self, req: CheckRequest) -> CheckResult:\\n raise NotImplementedError()\",\n \"def validate(self, response):\\n return response[\\\"status_code\\\"] == 1\",\n \"def handle_request(self):\\n\\t\\ttry:\\n\\t\\t\\trequest, client_address = self.get_request()\\n\\t\\texcept socket.error:\\n\\t\\t\\treturn\\n\\t\\tif self.verify_request(request, client_address):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tself.process_request(request, client_address)\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tself.handle_error(request, client_address)\\n\\t\\t\\t\\tself.close_request(request)\",\n \"def check_request(self, request):\\n state = self.current['state']\\n return (request == self.KM_PROTOCOL_VIOLATION or\\n request == self.KM_ERROR or\\n request == self.KM_CANCEL or\\n (state == None and request == self.KM_REQUEST_KEY) or\\n (state == self.KM_REQUEST_KEY and request == self.KM_REQUEST_OK) or\\n (state == self.KM_REQUEST_KEY and request == self.KM_REQUEST_DENIED) or\\n (state == self.KM_REQUEST_OK and request == self.KM_TEMP_KEY_ACK) or\\n (state == self.KM_TEMP_KEY_ACK and request == self.KM_TEMP_KEY2) or\\n (state == self.KM_TEMP_KEY2 and request == self.KM_PERM_KEY2) or\\n (state == self.KM_PERM_KEY2 and request == self.KM_FINISHED))\",\n \"def _process_vep_assertion(self, request):\\n # Make sure they're using a GET request.\\n if request.method != \\\"GET\\\":\\n resp = Response()\\n resp.status = 405\\n resp.content_type = \\\"text/plain\\\"\\n resp.body = \\\"token requests must get GET\\\"\\n request.environ[\\\"repoze.who.application\\\"] = resp\\n return None\\n # Make sure they're sending an Authorization header.\\n if not request.authorization:\\n msg = \\\"you must provide an authorization header\\\"\\n return self._respond_unauthorized(request, msg)\\n # Grab the assertion from the Authorization header.\\n scheme, assertion = request.authorization\\n if scheme.lower() != \\\"browser-id\\\":\\n msg = \\\"The auth scheme \\\\\\\"%s\\\\\\\" is not supported\\\" % (scheme,)\\n return self._respond_bad_request(request, msg.encode(\\\"utf8\\\"))\\n # Extract the audience, so we can check against wildcards.\\n try:\\n audience = get_assertion_info(assertion)[\\\"audience\\\"]\\n except (ValueError, KeyError):\\n return self._respond_bad_request(request, \\\"invalid assertion\\\")\\n if not self._check_audience(request, audience):\\n msg = \\\"The audience \\\\\\\"%s\\\\\\\" is not acceptable\\\" % (audience,)\\n return self._respond_bad_request(request, msg.encode(\\\"utf8\\\"))\\n # Verify the assertion and find out who they are.\\n try:\\n data = self.verifier.verify(assertion)\\n except Exception, e:\\n msg = \\\"Invalid BrowserID assertion: \\\" + str(e)\\n return self._respond_bad_request(request, msg)\\n # OK, we can go ahead and issue a token.\\n token, secret, extra = self.token_manager.make_token(request, data)\\n\\n if token is None:\\n msg = \\\"that email address is not recognised\\\"\\n return self._respond_unauthorized(request, msg)\\n resp = Response()\\n resp.status = 200\\n resp.content_type = \\\"application/json\\\"\\n\\n body = {\\n \\\"id\\\": token,\\n \\\"key\\\": secret,\\n \\\"algorithm\\\": \\\"hmac-sha-1\\\",\\n }\\n\\n if extra is not None:\\n body.update(extra)\\n\\n resp.body = json.dumps(body)\\n request.environ[\\\"repoze.who.application\\\"] = resp\",\n \"def _validate_rpc_request(self, request):\\n if request.get('jsonrpc', None) != '2.0':\\n raise ValueError('Invalid jsonrpc: must be \\\"2.0\\\"')\\n\\n id = request.get('id', None)\\n if not (id is None or isinstance(id, (str, int, float))):\\n raise ValueError('Invalid id: must be null, string or number')\\n\\n method = request.get('method', None)\\n if not isinstance(method, str):\\n raise ValueError('Invalid method: must be string')\\n\\n params = request.get('params', [])\\n if not isinstance(params, (dict, list)):\\n raise ValueError('Invalid params: must be array or object')\",\n \"def mora_assert(response):\\n assert response.status_code in (200, 201, 400, 404), response.status_code\\n if response.status_code == 400:\\n # Check actual response\\n assert (\\n response.text.find(\\\"not give raise to a new registration\\\") > 0\\n ), response.text\\n logger.debug(\\\"Request had no effect\\\")\\n return None\",\n \"def check_global_request(self, kind, msg):\\n return False\",\n \"def is_valid_request(self, request):\\r\\n auth_params = request.META.get(\\\"HTTP_AUTHORIZATION\\\", [])\\r\\n return self.is_in(auth_params) or self.is_in(request.REQUEST)\",\n \"def verify (self, path):\\n pass\",\n \"async def _interaction(self, request: Request) -> web.Response:\\n if (timestamp := request.headers.get(\\\"X-Signature-Timestamp\\\")) is None \\\\\\n or (ed25519 := request.headers.get(\\\"X-Signature-Ed25519\\\")) is None:\\n return web.Response(status=401, reason=\\\"Unauthorised\\\")\\n\\n try:\\n self._verify_key.verify((timestamp + await request.text()).encode(), bytes.fromhex(ed25519))\\n except BadSignatureError:\\n return web.Response(status=401, reason=\\\"Unauthorised\\\")\\n\\n payload = await request.json()\\n if payload.get('type') == 1:\\n return web.Response(status=200, text=dumps({\\\"type\\\": 1}), content_type=\\\"application/json\\\")\\n else:\\n response = await self._handle(payload)\\n return web.Response(status=200, text=response.json(), content_type=\\\"application/json\\\")\",\n \"def verify():\\n if flask.request.method == 'POST':\\n req = flask.request.get_json(force=True)\\n phone = req.get('phone')\\n code = req['code']\\n return check_verification(phone, code)\",\n \"def _raise_performing_request_error(self, *args, **kwargs):\",\n \"def _check_validity(self):\\n pass\",\n \"def _api_call(self, url, response_checker):\\n self.request_compare(url)\",\n \"async def handle_request(self, request: aioweb.request.Request):\",\n \"def validateAgentJWTToken(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def check_auth():\",\n \"def check_response_errors(self, resp):\\n return True\",\n \"def process_request(self, req):\\n return None\",\n \"def check_vulnerability(self):\\n\\t\\tpass\",\n \"def __verify(self):\\r\\n code = self.request.get('code')\\r\\n email = None\\r\\n error = False\\r\\n # resend if code is not given or in case of some error\\r\\n if code is not None and code != '':\\r\\n email = User.verify(code, self.request.remote_addr)\\r\\n if email is None:\\r\\n error = True\\r\\n\\r\\n if email is None:\\r\\n template_values = {\\r\\n 'user_email': self.user_email,\\r\\n 'error': error\\r\\n }\\r\\n template = self.jinja2_env.get_template('verification.html')\\r\\n self.response.out.write(template.render(template_values))\\r\\n\\r\\n # message\\r\\n template_values = {\\r\\n 'user_email': self.user_email,\\r\\n 'message': self.gettext('THANK_YOU')\\r\\n }\\r\\n template = self.jinja2_env.get_template('staticmessage.html')\\r\\n self.response.out.write(template.render(template_values))\",\n \"def checkRequest(self, uID):\\n\\n if not RequestsDAO().getRequestByuID(uID):\\n return jsonify(Error=\\\"No request found\\\"), 404\\n else:\\n if uID:\\n approval = RequestsDAO().getRequestStatusByuID(uID)\\n mapped_result = self.buildCheckRequestToDict(uID, approval[0], approval[1], approval[2])\\n return jsonify(TURN=mapped_result), 200\\n\\n else:\\n return jsonify(Error=\\\"Unexpected attributes in post request\\\"), 400\",\n \"def valid_request(environ):\\n if environ.get('REQUEST_METHOD') != 'GET':\\n return False\\n if environ.get('QUERY_STRING'):\\n return False\\n requested = environ.get('PATH_INFO', '/').lstrip('/')\\n if not requested:\\n return True\\n if len(requested) > 40: # Larger than git SHA reference.\\n return False\\n if VALID_REQUEST.match(requested) is None:\\n return False\\n return True\",\n \"def on_request(self):\\n # extra and remove the signature from the query\\n signature = request.query.get(\\\"_signature\\\")\\n if signature is None:\\n abort(403)\\n del request.query[\\\"_signature\\\"]\\n # Verifies the query keys.\\n if signature != self.url_signer._sign(request.fullpath, request.query):\\n abort(403)\",\n \"def authenticate(self, request):\\n return None\",\n \"def test_validate_authorization_request_required_parameters(self):\\n\\n request = self.make_request()\\n scopes, credentials = self.auth.validate_authorization_request(request)\\n\\n self.assertListEqual(scopes, request.scope.split())\\n assert credentials['client_id'] == request.client_id\\n assert credentials['redirect_uri'] == request.redirect_uri\\n assert credentials['response_type'] == request.response_type\\n assert credentials['state'] == request.state\\n\\n self.validator.validate_client_id\\\\\\n .assert_called_once_with(request.client_id, request)\\n self.validator.validate_redirect_uri\\\\\\n .assert_called_once_with(request.client_id, request.redirect_uri, request)\",\n \"def validate(self, apiobj, method, api, param, safe):\",\n \"def check(self) -> None:\",\n \"def test_verification_failed(self):\\n pass\",\n \"def test_bad_requests_give_400(self):\\n self.assertEqual(self._request({}), 400)\",\n \"def Verify(self):\\n return True\",\n \"def test_verification_with_valid_token(self) -> None:\\n\\n secret_key = str(self.author.secret_key)\\n verification_url = reverse('author:verify', kwargs={'secret_key': str(secret_key)})\\n\\n # Make sure URL's don't change.\\n self.assertEqual(verification_url, f'/api/authors/verify/{secret_key}/')\\n\\n # Make valid request and get response\\n response: Response = self.client.get(verification_url)\\n\\n self.assertEqual(response.status_code, 302)\\n\\n # Now test if the method \\\"verify\\\" was called\\n self.assertEqual(Author.objects.get().verified, True)\\n # We don't wanna give him too many privileges\\n self.assertEqual(self.author.is_staff, False)\",\n \"def test_make_request_error(self):\\n response = Helper.make_request(self.url_404)\\n self.assertEqual(response.status_code, 404)\",\n \"def bad_callback(_request, _uri, headers):\\n return (404, headers, 'NOT AN ASSERTION')\",\n \"def verify_payload():\\n return True\",\n \"def test_valid_request(self):\\n print(\\\"Testing valid request...\\\")\\n # create a file with random data in the server folder\\n self.__create_test_file()\\n valid_path = os.path.join(os.path.dirname(self.client_path), \\\\\\n os.path.basename(self.test_file))\\n os.chdir(os.path.dirname(self.client_path))\\n subprocess.call([self.client_path, \\\\\\n \\\"{}:{}\\\".format(self.args.ip, self.args.port), \\\\\\n os.path.basename(valid_path)])\\n self.assertTrue(os.path.isfile(valid_path))\\n self.__compare_files(valid_path, self.test_file)\\n os.remove(valid_path)\",\n \"def _validate_get_request(self):\\n\\t\\tdatos = self._get_param_from_request(\\\"lng\\\") and self._get_param_from_request(\\\"lat\\\")\\n\\n\\t\\tif(not datos):\\n\\t\\t\\treturn False\\n\\t\\telse:\\n\\t\\t\\treturn True\",\n \"def common_http_validator(method=None,url=None,data=None,header=None):\\r\\n status_code = 500\\r\\n error_msg = None\\r\\n response_data = None\\r\\n\\r\\n try:\\r\\n req = requests.request(method=method,url=url,data=data,headers=header)\\r\\n # print req.request.method #Getting the method\\r\\n\\r\\n except (requests.RequestException,requests.HTTPError,requests.ConnectionError,requests.Timeout) as e:\\r\\n error_msg = 'Connection/Timeout/General Exception: {}'.format(e)\\r\\n\\r\\n except Exception as e:\\r\\n error_msg = 'Connection/Timeout/General Exception: {}'.format(e)\\r\\n\\r\\n else:\\r\\n status_code = req.status_code\\r\\n response_data = req.content\\r\\n\\r\\n return status_code, response_data, error_msg\",\n \"def process_request(self, request):\\n return None\",\n \"def validate_request(response):\\n openapi_spec = get_openapi_spec()\\n\\n request = TornadoOpenAPIRequest(response.request, openapi_spec)\\n if V30RequestValidator:\\n result = V30RequestValidator(openapi_spec).validate(request)\\n else:\\n result = openapi_request_validator.validate(openapi_spec, request)\\n result.raise_for_errors()\\n\\n response = TornadoOpenAPIResponse(response)\\n if V30ResponseValidator:\\n result2 = V30ResponseValidator(openapi_spec).validate(request, response)\\n else:\\n result2 = openapi_response_validator.validate(openapi_spec, request, response)\\n result2.raise_for_errors()\",\n \"def test_GET3(self):\\n r = requests.get(self.address)\\n self.assertEqual(r.status_code, 400)\",\n \"def bad_request():\\n return HttpError(400)\",\n \"def verify_request(self, request, client_address):\\n\\n\\t\\tglobal configurer\\n\\n\\t\\treturn NetworkRender.allowedAddress(self.ip, client_address[0],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tconfigurer.get('ServerSecureNets'))\",\n \"def validate(self, request):\\n\\n value = request._get_parameter_value(self)\\n\\n if value.object is None:\\n if self.required:\\n self.add_error(request, \\\"This input is required\\\")\\n\\n return\\n\\n self.do_validate(request, value.object)\",\n \"def _process_request(self, request, response):\\n ...\",\n \"def test_13(self):\\n assert 'False' == Api.requestBlock('test-13')\",\n \"def test_validate_get(client):\\n response = client.get('/user/')\\n assert response.status_code == 400\\n assert response.json['message'] == INVALID_ACTION_MESSAGE\",\n \"def test_allow(self) -> None:\\n response = self.request(\\\"/\\\", method=\\\"HEAD\\\")\\n self.assert_allowed(response, (\\\"GET\\\", \\\"POST\\\"))\",\n \"def test_invalid_verify_patch_request(self, cred):\\n resp = requests.patch(verify_url.format('json', cred[0], cred[1],\\n 'TestApp', test_number))\\n assert resp.status_code == 403\",\n \"def check_http_request_validity(http_raw_data) -> HttpRequestState:\\n\\n global version\\n r1 = http_raw_data.split('\\\\n')[0]\\n r2 = http_raw_data.split('\\\\n')[1]\\n\\n if (re.search(\\\"GET\\\", r1) != None) and (re.search(\\\"/\\\", r1) != None) and (re.search(\\\"HTTP/1.0\\\", r1) != None) and (re.search(\\\":\\\", r2)):\\n return HttpRequestState.GOOD\\n\\n if (re.search(\\\"GET\\\", r1) != None) and (re.search(\\\"http://\\\", r1) != None) and (re.search(\\\"HTTP/1.0\\\", r1) != None):\\n return HttpRequestState.GOOD\\n\\n if (re.search(\\\"GET\\\", r1)!=None) and (re.search(\\\"/\\\", r1)!=None) and (re.search(\\\"HTTP/1.0\\\",r1)!=None) :\\n if (re.search(\\\":\\\", r2) == None) :\\n return HttpRequestState.INVALID_INPUT\\n\\n if(re.search(\\\"GOAT\\\", r1)!=None):\\n return HttpRequestState.INVALID_INPUT\\n\\n if (re.search(\\\"HEAD\\\"or\\\"POST\\\" or \\\"PUT\\\" , r1)!=None) and (re.search(\\\"/\\\",r1)!=None) and (re.search(\\\"HTTP/1.0\\\", r1) != None) and (re.search(\\\":\\\", r2)):\\n\\n return HttpRequestState.NOT_SUPPORTED\\n\\n if (re.search(\\\"HEAD\\\"or\\\"POST\\\" or \\\"PUT\\\" ,r1)!=None) and (re.search(\\\"/\\\",r1)!=None) and (re.search(\\\"HTTP/1.0\\\",r1)!=None):\\n return HttpRequestState.INVALID_INPUT\\n\\n if (re.search(\\\"HEAD\\\"or\\\"POST\\\" or \\\"PUT\\\", r1) != None) and (re.search(\\\"HTTP/1.0\\\", r1) == None) and (re.search(\\\":\\\", r2) != None):\\n return HttpRequestState.INVALID_INPUT\\n print(\\\"*\\\" * 50)\\n print(\\\"[check_http_request_validity] Implement me!\\\")\\n print(\\\"*\\\" * 50)\\n\\n return HttpRequestState.PLACEHOLDER\",\n \"def post(self, request, *args, **kwargs):\\n verify_secure(request)\\n return super().post(request, args, kwargs)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7655331","0.7453941","0.69403994","0.6904828","0.6823398","0.6823398","0.67689514","0.6765635","0.6706381","0.66113174","0.6601375","0.6520301","0.65013844","0.641781","0.6360182","0.63253134","0.63203263","0.6315326","0.62872684","0.6265284","0.62648606","0.6240358","0.6238013","0.6222057","0.6216291","0.6211306","0.61976075","0.6193285","0.61875033","0.6180769","0.6165307","0.61549926","0.61541754","0.6139793","0.61393046","0.6129457","0.61159104","0.60976994","0.6092846","0.60873884","0.60811675","0.6077695","0.606591","0.6064559","0.6057045","0.60568035","0.6054805","0.6050183","0.60449463","0.60401386","0.6035239","0.60289216","0.6020677","0.60193324","0.60085946","0.59933025","0.5991798","0.5978562","0.5977547","0.5977432","0.59678876","0.59558517","0.5949837","0.59476846","0.5920161","0.59117925","0.5897667","0.58969146","0.5885632","0.58839875","0.5882166","0.58787566","0.5877064","0.5854811","0.58440745","0.5832399","0.5816485","0.5814135","0.580535","0.5804388","0.58033925","0.57972753","0.5796643","0.57865417","0.5776762","0.5768174","0.576526","0.5762069","0.5760902","0.5755461","0.57479566","0.5745204","0.57190305","0.57164365","0.57160985","0.57153726","0.57096326","0.5708684","0.57068974","0.57059795"],"string":"[\n \"0.7655331\",\n \"0.7453941\",\n \"0.69403994\",\n \"0.6904828\",\n \"0.6823398\",\n \"0.6823398\",\n \"0.67689514\",\n \"0.6765635\",\n \"0.6706381\",\n \"0.66113174\",\n \"0.6601375\",\n \"0.6520301\",\n \"0.65013844\",\n \"0.641781\",\n \"0.6360182\",\n \"0.63253134\",\n \"0.63203263\",\n \"0.6315326\",\n \"0.62872684\",\n \"0.6265284\",\n \"0.62648606\",\n \"0.6240358\",\n \"0.6238013\",\n \"0.6222057\",\n \"0.6216291\",\n \"0.6211306\",\n \"0.61976075\",\n \"0.6193285\",\n \"0.61875033\",\n \"0.6180769\",\n \"0.6165307\",\n \"0.61549926\",\n \"0.61541754\",\n \"0.6139793\",\n \"0.61393046\",\n \"0.6129457\",\n \"0.61159104\",\n \"0.60976994\",\n \"0.6092846\",\n \"0.60873884\",\n \"0.60811675\",\n \"0.6077695\",\n \"0.606591\",\n \"0.6064559\",\n \"0.6057045\",\n \"0.60568035\",\n \"0.6054805\",\n \"0.6050183\",\n \"0.60449463\",\n \"0.60401386\",\n \"0.6035239\",\n \"0.60289216\",\n \"0.6020677\",\n \"0.60193324\",\n \"0.60085946\",\n \"0.59933025\",\n \"0.5991798\",\n \"0.5978562\",\n \"0.5977547\",\n \"0.5977432\",\n \"0.59678876\",\n \"0.59558517\",\n \"0.5949837\",\n \"0.59476846\",\n \"0.5920161\",\n \"0.59117925\",\n \"0.5897667\",\n \"0.58969146\",\n \"0.5885632\",\n \"0.58839875\",\n \"0.5882166\",\n \"0.58787566\",\n \"0.5877064\",\n \"0.5854811\",\n \"0.58440745\",\n \"0.5832399\",\n \"0.5816485\",\n \"0.5814135\",\n \"0.580535\",\n \"0.5804388\",\n \"0.58033925\",\n \"0.57972753\",\n \"0.5796643\",\n \"0.57865417\",\n \"0.5776762\",\n \"0.5768174\",\n \"0.576526\",\n \"0.5762069\",\n \"0.5760902\",\n \"0.5755461\",\n \"0.57479566\",\n \"0.5745204\",\n \"0.57190305\",\n \"0.57164365\",\n \"0.57160985\",\n \"0.57153726\",\n \"0.57096326\",\n \"0.5708684\",\n \"0.57068974\",\n \"0.57059795\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6829749"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":89287,"cells":{"query":{"kind":"string","value":"Handler is called when an exception occurs,this method does not allow an exception is thrown."},"document":{"kind":"string","value":"def handle_error(self, request_handler, client_address):\n logger.debug('handle_error(%s:%s)' % client_address)"},"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 exception_handler(self, exception):\n pass","def on_exception(self):\n pass","def _on_exception(self, exception):\n pass","def handle_expt(self):\r\n self._perform_on_error_handling()","def error(self, handler):\n pass","def handle_err(self):\n pass","def exception(self, e):\n pass","def _error_handling(self,e,func):\n print(self.type, \" sufferred exception in \" , func , \":\" , e)","def exception(self, *args, **kwargs):","def handle_execution_exception(self, ex):\n raise(ex)","def process_exception(self, request, exc):\n return None","def _exception_dispatcher(self, e):\n # TODO Currently not doing anything\n raise e","def error_received(self, exc):\n print('Error received:', exc)","def exception(self):\n raise Exception(\"Exception test\")","def handle_exception(e):\n print(e)\n return error()","def user_exception(self, frame, exc_info):\n pass","def exc_handler(self, exc_type, exc, *args) -> None:\n self.exception = exc\n self.exit_code = 1","def on_processing_error(self, event, context, exc):\n pass","def exception_handler(self, handler):\n self.java_obj.exceptionHandler(ExceptionHandler(handler))\n return self","def unexpectedException(self):","def exception(self, *args, **kwargs):\n return super(Blueprint, self).exception(*args, **kwargs)","def on_failure(self, exc: BaseException) -> None:","def rescue(self, instance):\n pass","def handle_execution_exception(self, ex):\n if self.config.raise_errors:\n raise(ex)\n warning(str(ex))","def on_exception(self, exception):\n log.exception(\"Stream encountered an exception\")","def __call__(self):\r\n raise self","def __call__(self):\r\n raise self","def handler(self, exctype, value, traceback):\n\n message = 'error occurred({}) in {}:{}'.format(value, traceback.tb_frame.f_code.co_filename, traceback.tb_lineno)\n self.errorSignal.emit()\n self.logger.critical('Unhandled exception: {}'.format(message))","def _raise_performing_request_error(self, *args, **kwargs):","def throw(self):\n pass","def unexpected_error(self, exception):","def __empty_error_event_handler(self, govee, device, message, exception):\n\n pass","def _default_error_handler(self, exception):\n\n self.log.error(exception)\n return '', 500","def on_request_error(locust_instance, exception, tb, **kwargs):","def _check_exc(self):\n if self._exc is not None:\n raise self._exc","def on_error(self, exception):\n traceback.print_exc()","def _exception_handler(self, loop: asyncio.AbstractEventLoop, context: dict):\n try:\n if 'exception' in context:\n exc = context['exception']\n print('Uncaught exception %s' % exc, file=sys.stderr)\n print(traceback.format_tb(exc.__traceback__), file=sys.stderr)\n if not self._ignore_exceptions:\n self.shutdown(1)\n else:\n print('Uncaught exception with unknown type ignored', file=sys.stderr)\n except:\n print('Something went seriously wrong. Error in error handler.', file=sys.stderr)\n print(context, file=sys.stderr)\n print(traceback.format_exc(), file=sys.stderr)","def _exception_handler(self, exception_class, message):\n self.messages.append((exception_class, message))\n raise exception_class(message)","def handle_exception(self, e):\n\t\t# if isinstance(exc, (exc.NotAuthenticated, exc.AuthenticationFailed)):\n\t\t# \t# WWW-Authenticate header for 401 responses, else coerce to 403\n\t\t# \tauth_header = self.get_authenticate_header(self.request)\n\n\t\t# \tif auth_header:\n\t\t# \t\texc.auth_header = auth_header\n\t\t# \telse:\n\t\t#\t\texc.status_code = status.HTTP_403_FORBIDDEN\n\t\tself.raise_uncaught_exception(e)","def all_exception_handler(handler_input, exception):\n # type: (HandlerInput, Exception) -> Response\n logger.error(exception, exc_info=True)\n speech = \"Sorry, an exception occurred. Please say again!!\"\n handler_input.response_builder.speak(speech).ask(speech)\n return handler_input.response_builder.response","def on_failure(self):\n pass","def all_exception_handler(handler_input, exception):\n # type: (HandlerInput, Exception) -> None\n print(\"Encountered following exception: {}\".format(exception))\n\n speech = \"Sorry, there was some problem. Please try again!!\"\n handler_input.response_builder.speak(speech).ask(speech)\n\n return handler_input.response_builder.response","def handle_exception(self, exception, debug_mode): # pylint: disable-msg=C0103\n self.error(500)\n logger = logging\n if self.fsm:\n logger = self.fsm.logger\n logger.exception(\"FSMHandler caught Exception\")\n if debug_mode:\n import traceback, sys, cgi\n\n lines = ''.join(traceback.format_exception(*sys.exc_info()))\n self.response.clear()\n self.response.out.write('<pre>%s</pre>' % (cgi.escape(lines, quote=True)))","def _propagate_exc(self):\n catch_op = self._find_handler()\n if catch_op:\n # Exception caught! Transfer control to block\n catch_block = catch_op.parent\n self.pc = self.blockstarts[catch_block.name]\n else:\n # No exception handler!\n raise UncaughtException(self.exception)","def on_error(self, status_code, data):\n\t\tprint(\"error_code: \",status_code)","def error(self):\n ...","def all_exception_handler(handler_input, exception):\n # type: (HandlerInput, Exception) -> Response\n logger.error(exception, exc_info=True)\n\n speech = \"Sorry, there was some problem. Please try again!!\"\n handler_input.response_builder.speak(speech).ask(speech)\n\n return handler_input.response_builder.response","def error_handler(self, e, name, handler):\n msg = '' if handler else ' (no handler)'\n LOGGER.debug('Monitoring error handling %s %s: %s', name, msg, e)\n if handler:\n try:\n handler(e)\n except Exception as handler_exception:\n LOGGER.error('Monitoring exception %s fail: %s', name, handler_exception)\n LOGGER.exception(handler_exception)\n else:\n LOGGER.exception(e)","def error(self):\n pass","def handle_ioexception(self, exception):\n return True","def unhandled(self):\n return True","def on_exception(self, path, exc_info):\n\t\tlog.exception(\"Watching file %s failed.\"%path)","def _handle_exception(self, exc):\n if isinstance(exc, APIError):\n self.return_api_error(exc.error)\n else:\n return False","def handle_error(self):\n debug(\"FTPServer.handle_error()\")\n logerror(traceback.format_exc())\n self.close()","def exception_handler(engine):\n try:\n yield\n except KeyboardInterrupt:\n spooky_season = [\":skull:\", \":vampire:\", \":zombie:\", \":jack-o-lantern:\"]\n console.print(random.choice(spooky_season))\n except botocore.exceptions.BotoCoreError as e:\n console.log(\"BotoCoreError: \", e)\n except ParseException as e:\n console.log(\"Engine: ParseException\")\n syntax = Syntax(\n engine.pformat_exc(e),\n \"sql\",\n theme=\"monokai\",\n line_numbers=True,\n word_wrap=True,\n )\n console.print(Panel(syntax, title=\"Engine Details\", expand=False))\n except EngineRuntimeError as e:\n console.log(e)\n except SyntaxError as e:\n console.log(e)\n except Exception:\n console.print_exception()","def visit_ExceptHandler(self, node):\r\n current_context = self.context_stack[-1]\r\n if self.use_exceptions:\r\n current_context.increment_decision_points()\r\n \r\n ast.NodeVisitor.generic_visit(self, node)","def _raise_http_error(self, *args, **kwargs):","def _handle_exec_exception(self, err):\r\n\r\n # Log the error if we are debugging\r\n msg = 'Error occurred while evaluating CustomResponse'\r\n log.warning(msg, exc_info=True)\r\n\r\n # Notify student with a student input error\r\n _, _, traceback_obj = sys.exc_info()\r\n raise ResponseError(err.message, traceback_obj)","async def _noop_error_handler(ctx: \"RequestContext\") -> None:","def signal_handler(self, signum):\n raise Exception(\"Caught signal {0}\".format(signum))","def flask_force_error():\n raise Exception('forced 500 error')","def serious_error(self, e):\n pass","def error(self, tag, message, exc_info=False):\n \n self.log(logging.error,tag, message, exc_info)","def handle_error(self):\n self.cmd_channel.debug(\"ActiveDTP.handle_error()\")\n logerror(traceback.format_exc())\n self.close()","def ERR(self):","def whenException(self, channel, call):","def handle_exception(self,exc):\n logger.error(f\"Exception in request: {traceback.format_exc()}\")\n status_obj = status.HTTP_400_BAD_REQUEST\n if type(exc) is response.Http404:\n status_obj = status.HTTP_404_NOT_FOUND\n return Response(\n MediaUtil.generate_error_image(\n status_obj,\n str(exc),\n self.request.accepted_renderer.format),\n status=status_obj)","def report_unexpected_exception(self, *args, **kwargs):\n pass","def exception_handler(exctype, val, trace):\n logger.info(\n ''.join(traceback.format_exception(exctype, val, trace)))","def _handle_attempt(self):\n pass","async def on_handle_message_error(self, message: andesite.ReceiveOperation, exc: Exception) -> None:\n log.error(f\"uncaught error {exc} in {self} when handling message {message}\")","def all_exception_handler(handler_input, exception):\n # type: (HandlerInput, Exception) -> None\n print(\"Encountered following exception: {}\".format(exception))\n\n speech = \"Mi dispiace, c'è stato un problema!!\"\n handler_input.response_builder.set_should_end_session(True)\n handler_input.response_builder.speak(speech).ask(speech)\n\n return handler_input.response_builder.response","def _raise(self, exc):\n cb = _format_callback_source(self._callback, self._args)\n msg = 'Exception in callback {}'.format(cb)\n context = {\n 'message': msg,\n 'exception': exc,\n 'handle': self,\n }\n if self._source_traceback:\n context['source_traceback'] = self._source_traceback\n self._loop.call_exception_handler(context)","def handler500(request):\n \n #Setting the variable and template page for the 500 error\n response = render_to_response('500.html', {}, context_instance=RequestContext(request))\n response.status_code = 500\n return response","def exceptionhandler(e):\n response = e.get_response()\n response.data = json.dumps({\n \"code\" : e.code,\n \"name\": e.name,\n \"description\": e.description\n })\n response.content_type = \"application/json\"\n\n return response","def error(self, *args, **kwargs):","def callback_exception(*args, **kwargs):\n raise DemoCallbackException()","def raise_exc(self, exctype):\n\t\t_async_raise(self._get_my_tid(), exctype)","def handle_exception(exc_type, exception, traceback):\n report(UNKNOWN, \"unhandled exception: %s\" % (exception,))","def handle_exception(self, exc):\n if isinstance(exc, exceptions.Throttled):\n exc.status_code = status.HTTP_429_TOO_MANY_REQUESTS\n elif isinstance(exc, (exceptions.NotAuthenticated,\n exceptions.AuthenticationFailed)):\n # WWW-Authenticate header for 401 responses, else coerce to 403\n auth_header = self.get_authenticate_header(self.request)\n\n if auth_header:\n exc.auth_header = auth_header\n else:\n exc.status_code = status.HTTP_403_FORBIDDEN\n\n exception_handler = self.get_exception_handler()\n\n context = self.get_exception_handler_context()\n response = exception_handler(exc, context)\n\n if response is None:\n self.raise_uncaught_exception(exc)\n\n response.exception = True\n return response","def raise_exception(request):\n raise Exception(\"Let's test error handling\")","def handle_error(self, data, **kwargs):\n logger.log_err(str(data))","async def instana_exception_handler(request, exc):\n try:\n span = async_tracer.active_span\n\n if span is not None:\n if hasattr(exc, 'detail') and (500 <= exc.status_code <= 599):\n span.set_tag('http.error', exc.detail)\n span.set_tag('http.status_code', exc.status_code)\n except Exception:\n logger.debug(\"FastAPI instana_exception_handler: \", exc_info=True)\n\n return await http_exception_handler(request, exc)","def connection_lost(self, exc):\n pass","def raise_(err):\n raise err","def handle(self) -> None:","def handle_error(self,e):\n\n if self.mode=='debug':\n raise e\n else:\n if e is KeyboardInterrupt or e is SystemExit:\n if raw_input('Really quit? (y quits)').upper()=='Y':\n self.failsafe()\n sys.exit(0)\n else:\n e_string = '[*] Game Error {0}:{1}'.format(type(e),e)\n self.logger.error(e_string)\n self.failsafe()\n sys.exit(1)","def all_exception_handler(handler_input, exception):\n # type: (HandlerInput, Exception) -> None\n print(\"Encountered following exception: {}\".format(exception))\n\n speech = \"That word is not in the dictionary. Please choose another word.\"\n handler_input.response_builder.speak(speech).ask(speech)\n\n return handler_input.response_builder.response","def context_errored(self, cls, example, exception):","def _handle_error(self, failure, item, spider):\n # do nothing, just log\n log.err(failure)","def exception_callback(self, exception):\n self.exception_callback_value = exception","def exception_handler(res):\n try:\n res_data = res.json()\n error_code = res_data['status']\n error_msg = build_error_msg(res_data['errors'])\n exception = DemistoException(ERROR_TITLES.get(error_code, '') + error_msg)\n\n except Exception:\n exception = DemistoException(f'Error in API call [{res.status_code}] - {res.reason}')\n\n raise exception","def handle_error(self, request, error):\n self.log.error(\"An error occurred at request \" + repr(request) + \": \" + repr(error))","def catch_error(self, eid):\n\n if eid not in (5, 301):\n logger.error('Error %d: %s (%s)' % (eid, NETWORK_ERROR.get(eid, 'unknown error'),\n self.sender().url().toString()))","def handle_error(self, params):\n\n # Run the error handler if needed.\n if (self.must_handle_error()):\n log.warning(\"Running On Error error handler...\")\n self.got_error = False\n self.error_handler.eval(context=self, params=params)\n\n # The error has now been cleared.\n self.got_error = False","def indicate_error(self):\n pass","async def _handle_exception(self, e, sock):\n if isinstance(e, (RemoteProtocolError, AssertionError)):\n await sock.aclose()\n raise BadHttpResponse(\"Invalid HTTP response from server.\") from e\n\n if isinstance(e, Exception):\n await sock.aclose()\n raise e","def failed(self):\n\t\tpass","def handler500(request):\n response = render_to_response('500.html', {}, RequestContext(request))\n response.status_code = 500\n return response","def _call_error_handler(self, event, err, **kwargs):\n if self._on_error_handler:\n event.error = str(err)\n event.origin_state = self.fullname\n return self._on_error_handler(event)"],"string":"[\n \"def exception_handler(self, exception):\\n pass\",\n \"def on_exception(self):\\n pass\",\n \"def _on_exception(self, exception):\\n pass\",\n \"def handle_expt(self):\\r\\n self._perform_on_error_handling()\",\n \"def error(self, handler):\\n pass\",\n \"def handle_err(self):\\n pass\",\n \"def exception(self, e):\\n pass\",\n \"def _error_handling(self,e,func):\\n print(self.type, \\\" sufferred exception in \\\" , func , \\\":\\\" , e)\",\n \"def exception(self, *args, **kwargs):\",\n \"def handle_execution_exception(self, ex):\\n raise(ex)\",\n \"def process_exception(self, request, exc):\\n return None\",\n \"def _exception_dispatcher(self, e):\\n # TODO Currently not doing anything\\n raise e\",\n \"def error_received(self, exc):\\n print('Error received:', exc)\",\n \"def exception(self):\\n raise Exception(\\\"Exception test\\\")\",\n \"def handle_exception(e):\\n print(e)\\n return error()\",\n \"def user_exception(self, frame, exc_info):\\n pass\",\n \"def exc_handler(self, exc_type, exc, *args) -> None:\\n self.exception = exc\\n self.exit_code = 1\",\n \"def on_processing_error(self, event, context, exc):\\n pass\",\n \"def exception_handler(self, handler):\\n self.java_obj.exceptionHandler(ExceptionHandler(handler))\\n return self\",\n \"def unexpectedException(self):\",\n \"def exception(self, *args, **kwargs):\\n return super(Blueprint, self).exception(*args, **kwargs)\",\n \"def on_failure(self, exc: BaseException) -> None:\",\n \"def rescue(self, instance):\\n pass\",\n \"def handle_execution_exception(self, ex):\\n if self.config.raise_errors:\\n raise(ex)\\n warning(str(ex))\",\n \"def on_exception(self, exception):\\n log.exception(\\\"Stream encountered an exception\\\")\",\n \"def __call__(self):\\r\\n raise self\",\n \"def __call__(self):\\r\\n raise self\",\n \"def handler(self, exctype, value, traceback):\\n\\n message = 'error occurred({}) in {}:{}'.format(value, traceback.tb_frame.f_code.co_filename, traceback.tb_lineno)\\n self.errorSignal.emit()\\n self.logger.critical('Unhandled exception: {}'.format(message))\",\n \"def _raise_performing_request_error(self, *args, **kwargs):\",\n \"def throw(self):\\n pass\",\n \"def unexpected_error(self, exception):\",\n \"def __empty_error_event_handler(self, govee, device, message, exception):\\n\\n pass\",\n \"def _default_error_handler(self, exception):\\n\\n self.log.error(exception)\\n return '', 500\",\n \"def on_request_error(locust_instance, exception, tb, **kwargs):\",\n \"def _check_exc(self):\\n if self._exc is not None:\\n raise self._exc\",\n \"def on_error(self, exception):\\n traceback.print_exc()\",\n \"def _exception_handler(self, loop: asyncio.AbstractEventLoop, context: dict):\\n try:\\n if 'exception' in context:\\n exc = context['exception']\\n print('Uncaught exception %s' % exc, file=sys.stderr)\\n print(traceback.format_tb(exc.__traceback__), file=sys.stderr)\\n if not self._ignore_exceptions:\\n self.shutdown(1)\\n else:\\n print('Uncaught exception with unknown type ignored', file=sys.stderr)\\n except:\\n print('Something went seriously wrong. Error in error handler.', file=sys.stderr)\\n print(context, file=sys.stderr)\\n print(traceback.format_exc(), file=sys.stderr)\",\n \"def _exception_handler(self, exception_class, message):\\n self.messages.append((exception_class, message))\\n raise exception_class(message)\",\n \"def handle_exception(self, e):\\n\\t\\t# if isinstance(exc, (exc.NotAuthenticated, exc.AuthenticationFailed)):\\n\\t\\t# \\t# WWW-Authenticate header for 401 responses, else coerce to 403\\n\\t\\t# \\tauth_header = self.get_authenticate_header(self.request)\\n\\n\\t\\t# \\tif auth_header:\\n\\t\\t# \\t\\texc.auth_header = auth_header\\n\\t\\t# \\telse:\\n\\t\\t#\\t\\texc.status_code = status.HTTP_403_FORBIDDEN\\n\\t\\tself.raise_uncaught_exception(e)\",\n \"def all_exception_handler(handler_input, exception):\\n # type: (HandlerInput, Exception) -> Response\\n logger.error(exception, exc_info=True)\\n speech = \\\"Sorry, an exception occurred. Please say again!!\\\"\\n handler_input.response_builder.speak(speech).ask(speech)\\n return handler_input.response_builder.response\",\n \"def on_failure(self):\\n pass\",\n \"def all_exception_handler(handler_input, exception):\\n # type: (HandlerInput, Exception) -> None\\n print(\\\"Encountered following exception: {}\\\".format(exception))\\n\\n speech = \\\"Sorry, there was some problem. Please try again!!\\\"\\n handler_input.response_builder.speak(speech).ask(speech)\\n\\n return handler_input.response_builder.response\",\n \"def handle_exception(self, exception, debug_mode): # pylint: disable-msg=C0103\\n self.error(500)\\n logger = logging\\n if self.fsm:\\n logger = self.fsm.logger\\n logger.exception(\\\"FSMHandler caught Exception\\\")\\n if debug_mode:\\n import traceback, sys, cgi\\n\\n lines = ''.join(traceback.format_exception(*sys.exc_info()))\\n self.response.clear()\\n self.response.out.write('<pre>%s</pre>' % (cgi.escape(lines, quote=True)))\",\n \"def _propagate_exc(self):\\n catch_op = self._find_handler()\\n if catch_op:\\n # Exception caught! Transfer control to block\\n catch_block = catch_op.parent\\n self.pc = self.blockstarts[catch_block.name]\\n else:\\n # No exception handler!\\n raise UncaughtException(self.exception)\",\n \"def on_error(self, status_code, data):\\n\\t\\tprint(\\\"error_code: \\\",status_code)\",\n \"def error(self):\\n ...\",\n \"def all_exception_handler(handler_input, exception):\\n # type: (HandlerInput, Exception) -> Response\\n logger.error(exception, exc_info=True)\\n\\n speech = \\\"Sorry, there was some problem. Please try again!!\\\"\\n handler_input.response_builder.speak(speech).ask(speech)\\n\\n return handler_input.response_builder.response\",\n \"def error_handler(self, e, name, handler):\\n msg = '' if handler else ' (no handler)'\\n LOGGER.debug('Monitoring error handling %s %s: %s', name, msg, e)\\n if handler:\\n try:\\n handler(e)\\n except Exception as handler_exception:\\n LOGGER.error('Monitoring exception %s fail: %s', name, handler_exception)\\n LOGGER.exception(handler_exception)\\n else:\\n LOGGER.exception(e)\",\n \"def error(self):\\n pass\",\n \"def handle_ioexception(self, exception):\\n return True\",\n \"def unhandled(self):\\n return True\",\n \"def on_exception(self, path, exc_info):\\n\\t\\tlog.exception(\\\"Watching file %s failed.\\\"%path)\",\n \"def _handle_exception(self, exc):\\n if isinstance(exc, APIError):\\n self.return_api_error(exc.error)\\n else:\\n return False\",\n \"def handle_error(self):\\n debug(\\\"FTPServer.handle_error()\\\")\\n logerror(traceback.format_exc())\\n self.close()\",\n \"def exception_handler(engine):\\n try:\\n yield\\n except KeyboardInterrupt:\\n spooky_season = [\\\":skull:\\\", \\\":vampire:\\\", \\\":zombie:\\\", \\\":jack-o-lantern:\\\"]\\n console.print(random.choice(spooky_season))\\n except botocore.exceptions.BotoCoreError as e:\\n console.log(\\\"BotoCoreError: \\\", e)\\n except ParseException as e:\\n console.log(\\\"Engine: ParseException\\\")\\n syntax = Syntax(\\n engine.pformat_exc(e),\\n \\\"sql\\\",\\n theme=\\\"monokai\\\",\\n line_numbers=True,\\n word_wrap=True,\\n )\\n console.print(Panel(syntax, title=\\\"Engine Details\\\", expand=False))\\n except EngineRuntimeError as e:\\n console.log(e)\\n except SyntaxError as e:\\n console.log(e)\\n except Exception:\\n console.print_exception()\",\n \"def visit_ExceptHandler(self, node):\\r\\n current_context = self.context_stack[-1]\\r\\n if self.use_exceptions:\\r\\n current_context.increment_decision_points()\\r\\n \\r\\n ast.NodeVisitor.generic_visit(self, node)\",\n \"def _raise_http_error(self, *args, **kwargs):\",\n \"def _handle_exec_exception(self, err):\\r\\n\\r\\n # Log the error if we are debugging\\r\\n msg = 'Error occurred while evaluating CustomResponse'\\r\\n log.warning(msg, exc_info=True)\\r\\n\\r\\n # Notify student with a student input error\\r\\n _, _, traceback_obj = sys.exc_info()\\r\\n raise ResponseError(err.message, traceback_obj)\",\n \"async def _noop_error_handler(ctx: \\\"RequestContext\\\") -> None:\",\n \"def signal_handler(self, signum):\\n raise Exception(\\\"Caught signal {0}\\\".format(signum))\",\n \"def flask_force_error():\\n raise Exception('forced 500 error')\",\n \"def serious_error(self, e):\\n pass\",\n \"def error(self, tag, message, exc_info=False):\\n \\n self.log(logging.error,tag, message, exc_info)\",\n \"def handle_error(self):\\n self.cmd_channel.debug(\\\"ActiveDTP.handle_error()\\\")\\n logerror(traceback.format_exc())\\n self.close()\",\n \"def ERR(self):\",\n \"def whenException(self, channel, call):\",\n \"def handle_exception(self,exc):\\n logger.error(f\\\"Exception in request: {traceback.format_exc()}\\\")\\n status_obj = status.HTTP_400_BAD_REQUEST\\n if type(exc) is response.Http404:\\n status_obj = status.HTTP_404_NOT_FOUND\\n return Response(\\n MediaUtil.generate_error_image(\\n status_obj,\\n str(exc),\\n self.request.accepted_renderer.format),\\n status=status_obj)\",\n \"def report_unexpected_exception(self, *args, **kwargs):\\n pass\",\n \"def exception_handler(exctype, val, trace):\\n logger.info(\\n ''.join(traceback.format_exception(exctype, val, trace)))\",\n \"def _handle_attempt(self):\\n pass\",\n \"async def on_handle_message_error(self, message: andesite.ReceiveOperation, exc: Exception) -> None:\\n log.error(f\\\"uncaught error {exc} in {self} when handling message {message}\\\")\",\n \"def all_exception_handler(handler_input, exception):\\n # type: (HandlerInput, Exception) -> None\\n print(\\\"Encountered following exception: {}\\\".format(exception))\\n\\n speech = \\\"Mi dispiace, c'è stato un problema!!\\\"\\n handler_input.response_builder.set_should_end_session(True)\\n handler_input.response_builder.speak(speech).ask(speech)\\n\\n return handler_input.response_builder.response\",\n \"def _raise(self, exc):\\n cb = _format_callback_source(self._callback, self._args)\\n msg = 'Exception in callback {}'.format(cb)\\n context = {\\n 'message': msg,\\n 'exception': exc,\\n 'handle': self,\\n }\\n if self._source_traceback:\\n context['source_traceback'] = self._source_traceback\\n self._loop.call_exception_handler(context)\",\n \"def handler500(request):\\n \\n #Setting the variable and template page for the 500 error\\n response = render_to_response('500.html', {}, context_instance=RequestContext(request))\\n response.status_code = 500\\n return response\",\n \"def exceptionhandler(e):\\n response = e.get_response()\\n response.data = json.dumps({\\n \\\"code\\\" : e.code,\\n \\\"name\\\": e.name,\\n \\\"description\\\": e.description\\n })\\n response.content_type = \\\"application/json\\\"\\n\\n return response\",\n \"def error(self, *args, **kwargs):\",\n \"def callback_exception(*args, **kwargs):\\n raise DemoCallbackException()\",\n \"def raise_exc(self, exctype):\\n\\t\\t_async_raise(self._get_my_tid(), exctype)\",\n \"def handle_exception(exc_type, exception, traceback):\\n report(UNKNOWN, \\\"unhandled exception: %s\\\" % (exception,))\",\n \"def handle_exception(self, exc):\\n if isinstance(exc, exceptions.Throttled):\\n exc.status_code = status.HTTP_429_TOO_MANY_REQUESTS\\n elif isinstance(exc, (exceptions.NotAuthenticated,\\n exceptions.AuthenticationFailed)):\\n # WWW-Authenticate header for 401 responses, else coerce to 403\\n auth_header = self.get_authenticate_header(self.request)\\n\\n if auth_header:\\n exc.auth_header = auth_header\\n else:\\n exc.status_code = status.HTTP_403_FORBIDDEN\\n\\n exception_handler = self.get_exception_handler()\\n\\n context = self.get_exception_handler_context()\\n response = exception_handler(exc, context)\\n\\n if response is None:\\n self.raise_uncaught_exception(exc)\\n\\n response.exception = True\\n return response\",\n \"def raise_exception(request):\\n raise Exception(\\\"Let's test error handling\\\")\",\n \"def handle_error(self, data, **kwargs):\\n logger.log_err(str(data))\",\n \"async def instana_exception_handler(request, exc):\\n try:\\n span = async_tracer.active_span\\n\\n if span is not None:\\n if hasattr(exc, 'detail') and (500 <= exc.status_code <= 599):\\n span.set_tag('http.error', exc.detail)\\n span.set_tag('http.status_code', exc.status_code)\\n except Exception:\\n logger.debug(\\\"FastAPI instana_exception_handler: \\\", exc_info=True)\\n\\n return await http_exception_handler(request, exc)\",\n \"def connection_lost(self, exc):\\n pass\",\n \"def raise_(err):\\n raise err\",\n \"def handle(self) -> None:\",\n \"def handle_error(self,e):\\n\\n if self.mode=='debug':\\n raise e\\n else:\\n if e is KeyboardInterrupt or e is SystemExit:\\n if raw_input('Really quit? (y quits)').upper()=='Y':\\n self.failsafe()\\n sys.exit(0)\\n else:\\n e_string = '[*] Game Error {0}:{1}'.format(type(e),e)\\n self.logger.error(e_string)\\n self.failsafe()\\n sys.exit(1)\",\n \"def all_exception_handler(handler_input, exception):\\n # type: (HandlerInput, Exception) -> None\\n print(\\\"Encountered following exception: {}\\\".format(exception))\\n\\n speech = \\\"That word is not in the dictionary. Please choose another word.\\\"\\n handler_input.response_builder.speak(speech).ask(speech)\\n\\n return handler_input.response_builder.response\",\n \"def context_errored(self, cls, example, exception):\",\n \"def _handle_error(self, failure, item, spider):\\n # do nothing, just log\\n log.err(failure)\",\n \"def exception_callback(self, exception):\\n self.exception_callback_value = exception\",\n \"def exception_handler(res):\\n try:\\n res_data = res.json()\\n error_code = res_data['status']\\n error_msg = build_error_msg(res_data['errors'])\\n exception = DemistoException(ERROR_TITLES.get(error_code, '') + error_msg)\\n\\n except Exception:\\n exception = DemistoException(f'Error in API call [{res.status_code}] - {res.reason}')\\n\\n raise exception\",\n \"def handle_error(self, request, error):\\n self.log.error(\\\"An error occurred at request \\\" + repr(request) + \\\": \\\" + repr(error))\",\n \"def catch_error(self, eid):\\n\\n if eid not in (5, 301):\\n logger.error('Error %d: %s (%s)' % (eid, NETWORK_ERROR.get(eid, 'unknown error'),\\n self.sender().url().toString()))\",\n \"def handle_error(self, params):\\n\\n # Run the error handler if needed.\\n if (self.must_handle_error()):\\n log.warning(\\\"Running On Error error handler...\\\")\\n self.got_error = False\\n self.error_handler.eval(context=self, params=params)\\n\\n # The error has now been cleared.\\n self.got_error = False\",\n \"def indicate_error(self):\\n pass\",\n \"async def _handle_exception(self, e, sock):\\n if isinstance(e, (RemoteProtocolError, AssertionError)):\\n await sock.aclose()\\n raise BadHttpResponse(\\\"Invalid HTTP response from server.\\\") from e\\n\\n if isinstance(e, Exception):\\n await sock.aclose()\\n raise e\",\n \"def failed(self):\\n\\t\\tpass\",\n \"def handler500(request):\\n response = render_to_response('500.html', {}, RequestContext(request))\\n response.status_code = 500\\n return response\",\n \"def _call_error_handler(self, event, err, **kwargs):\\n if self._on_error_handler:\\n event.error = str(err)\\n event.origin_state = self.fullname\\n return self._on_error_handler(event)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.84199923","0.82593095","0.7844722","0.77625847","0.77586204","0.7583584","0.7447186","0.73315513","0.72311974","0.72022784","0.71068513","0.7021926","0.696029","0.69080865","0.6901035","0.6872464","0.6855411","0.6828169","0.676794","0.6766311","0.6729591","0.6729215","0.67061","0.6700362","0.66974914","0.6675271","0.6675271","0.66649735","0.66450304","0.65889114","0.6583673","0.6558562","0.6492783","0.6466733","0.6439119","0.64345354","0.64211917","0.6399033","0.63865095","0.6378377","0.63716114","0.6367065","0.63346505","0.6332311","0.63257885","0.63205737","0.63149583","0.63093436","0.6296525","0.62940586","0.62847084","0.6273458","0.62598646","0.62442374","0.6241515","0.62378657","0.62215114","0.622013","0.6201866","0.61933726","0.61914134","0.6187704","0.6180011","0.6178886","0.61723614","0.6171606","0.61687243","0.6160981","0.61551434","0.6145989","0.61345553","0.6131234","0.6122141","0.6120501","0.6116659","0.6107455","0.6102663","0.6101504","0.6096817","0.6094336","0.6089113","0.6074458","0.6056847","0.6054074","0.603522","0.6033196","0.60173565","0.6015703","0.6013849","0.60097295","0.6002864","0.6000881","0.5997114","0.59945077","0.59842104","0.59764266","0.5975163","0.5963919","0.59484255","0.59452075"],"string":"[\n \"0.84199923\",\n \"0.82593095\",\n \"0.7844722\",\n \"0.77625847\",\n \"0.77586204\",\n \"0.7583584\",\n \"0.7447186\",\n \"0.73315513\",\n \"0.72311974\",\n \"0.72022784\",\n \"0.71068513\",\n \"0.7021926\",\n \"0.696029\",\n \"0.69080865\",\n \"0.6901035\",\n \"0.6872464\",\n \"0.6855411\",\n \"0.6828169\",\n \"0.676794\",\n \"0.6766311\",\n \"0.6729591\",\n \"0.6729215\",\n \"0.67061\",\n \"0.6700362\",\n \"0.66974914\",\n \"0.6675271\",\n \"0.6675271\",\n \"0.66649735\",\n \"0.66450304\",\n \"0.65889114\",\n \"0.6583673\",\n \"0.6558562\",\n \"0.6492783\",\n \"0.6466733\",\n \"0.6439119\",\n \"0.64345354\",\n \"0.64211917\",\n \"0.6399033\",\n \"0.63865095\",\n \"0.6378377\",\n \"0.63716114\",\n \"0.6367065\",\n \"0.63346505\",\n \"0.6332311\",\n \"0.63257885\",\n \"0.63205737\",\n \"0.63149583\",\n \"0.63093436\",\n \"0.6296525\",\n \"0.62940586\",\n \"0.62847084\",\n \"0.6273458\",\n \"0.62598646\",\n \"0.62442374\",\n \"0.6241515\",\n \"0.62378657\",\n \"0.62215114\",\n \"0.622013\",\n \"0.6201866\",\n \"0.61933726\",\n \"0.61914134\",\n \"0.6187704\",\n \"0.6180011\",\n \"0.6178886\",\n \"0.61723614\",\n \"0.6171606\",\n \"0.61687243\",\n \"0.6160981\",\n \"0.61551434\",\n \"0.6145989\",\n \"0.61345553\",\n \"0.6131234\",\n \"0.6122141\",\n \"0.6120501\",\n \"0.6116659\",\n \"0.6107455\",\n \"0.6102663\",\n \"0.6101504\",\n \"0.6096817\",\n \"0.6094336\",\n \"0.6089113\",\n \"0.6074458\",\n \"0.6056847\",\n \"0.6054074\",\n \"0.603522\",\n \"0.6033196\",\n \"0.60173565\",\n \"0.6015703\",\n \"0.6013849\",\n \"0.60097295\",\n \"0.6002864\",\n \"0.6000881\",\n \"0.5997114\",\n \"0.59945077\",\n \"0.59842104\",\n \"0.59764266\",\n \"0.5975163\",\n \"0.5963919\",\n \"0.59484255\",\n \"0.59452075\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64839387"},"document_rank":{"kind":"string","value":"33"}}},{"rowIdx":89288,"cells":{"query":{"kind":"string","value":"Closing request called,this method does not allow an exception is thrown."},"document":{"kind":"string","value":"def close_request(self,request_handler, client_address):\n logger.debug('close_request(%s:%s)' % client_address)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def close_request(self, request):\n\t\tpass","def close_request(self, request):\n\t\trequest.close()","def closeRequest(self):\n self.get_bmc_website()\n self.__closeMyRequest = Close(self.browser)\n self.__closeMyRequest.closeRequest()","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):","def close(self):\r\n pass","def close(self):\r\n pass","def close(self):\r\n pass","def close(self):\n pass","def close(self):\n pass","def close(self): \n\t\tself.connection = None","def close(self):\r\n pass","def close(self):\n ...","def close(self):\n ...","def Close(self):","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self) -> None:","def close(self):\n # This is a NOOP by default","def close(self):\n # This is a NOOP by default","def close(self): \n self.connection.close()","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close(self):\n pass","def close (self):\n pass","def stop(self):\n self.close_conn()\n self.finished = True\n self.request_handler.stop()\n print \"Finished closing\"","def close(self) -> None:\n ...","def close(self) -> None:\n ...","def close(self):\n # By default, this is a NOOP","def close():","def close(self):\n #############################################################################\n # TODO: YOUR CODE HERE #\n #############################################################################\n \n #############################################################################\n # END OF YOUR CODE #\n #############################################################################\n super().close()","def _on_close(self):\n self._stream = None\n self._is_secure = False\n self._process_request()","def teardown_request(exception):\r\n try:\r\n g.conn.close()\r\n except Exception as e:\r\n pass","def teardown_request(exception):\r\n try:\r\n g.conn.close()\r\n except Exception as e:\r\n pass","def teardown_request(exception):\r\n try:\r\n g.conn.close()\r\n except Exception as e:\r\n pass","def close(self):\n return","def close(self):\n return","def close(self):\n return","def close(self) -> None:\r\n pass","def teardown_request(exception):\n try:\n g.conn.close()\n except Exception:\n print exception","def server_close(self):\n\t\tpass","def send_close_request(self):\n\tdata = struct.pack(\"!4I\", 0b1101, 0b1001, self.epoch_no, self.handle_no)\n\tself.client_socket.sendto(data, self.address)\n\tself.client_socket.close()\t\n return","def close (self):\n pass\n #TODO: implement more realistic closing semantics","def __exit__(self, exc_type, exc_value, traceback):\n return self.close()","def __exit__(self, exc_type, exc_value, traceback):\n self.close()","def __exit__(self, exc_type, exc_value, traceback):\n self.close()","def __exit__(self, exc_type, exc_value, traceback):\n self.close()","def __exit__(self, exc_type, exc_value, traceback):\n self.close()","def __exit__(self, exc_type, exc_val, exc_tb):\n self.close()","def closed(self, *args, **kwargs) -> Any:\n pass","def close(self):\n print 'close'","def teardown_request(exception):\n try:\n g.conn.close()\n except Exception as e:\n pass","def teardown_request(exception):\n try:\n g.conn.close()\n except Exception as e:\n pass","def teardown_request(exception):\n try:\n g.conn.close()\n except Exception as e:\n pass","def teardown_request(exception):\n try:\n g.conn.close()\n except Exception as e:\n pass","def teardown_request(exception):\n try:\n g.conn.close()\n except Exception as e:\n pass","def teardown_request(exception):\n try:\n g.conn.close()\n except Exception as e:\n pass","def close(self):\n self.call('close')","def Close(self):\n return","def Close(self):\n return","def __exit__(self, type, value, traceback):\n self.transport.close()","def done(self):\n ## All done with the request object\n self.closed = True\n self.d.callback('')"],"string":"[\n \"def close_request(self, request):\\n\\t\\tpass\",\n \"def close_request(self, request):\\n\\t\\trequest.close()\",\n \"def closeRequest(self):\\n self.get_bmc_website()\\n self.__closeMyRequest = Close(self.browser)\\n self.__closeMyRequest.closeRequest()\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self): \\n\\t\\tself.connection = None\",\n \"def close(self):\\r\\n pass\",\n \"def close(self):\\n ...\",\n \"def close(self):\\n ...\",\n \"def Close(self):\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self) -> None:\",\n \"def close(self):\\n # This is a NOOP by default\",\n \"def close(self):\\n # This is a NOOP by default\",\n \"def close(self): \\n self.connection.close()\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close(self):\\n pass\",\n \"def close (self):\\n pass\",\n \"def stop(self):\\n self.close_conn()\\n self.finished = True\\n self.request_handler.stop()\\n print \\\"Finished closing\\\"\",\n \"def close(self) -> None:\\n ...\",\n \"def close(self) -> None:\\n ...\",\n \"def close(self):\\n # By default, this is a NOOP\",\n \"def close():\",\n \"def close(self):\\n #############################################################################\\n # TODO: YOUR CODE HERE #\\n #############################################################################\\n \\n #############################################################################\\n # END OF YOUR CODE #\\n #############################################################################\\n super().close()\",\n \"def _on_close(self):\\n self._stream = None\\n self._is_secure = False\\n self._process_request()\",\n \"def teardown_request(exception):\\r\\n try:\\r\\n g.conn.close()\\r\\n except Exception as e:\\r\\n pass\",\n \"def teardown_request(exception):\\r\\n try:\\r\\n g.conn.close()\\r\\n except Exception as e:\\r\\n pass\",\n \"def teardown_request(exception):\\r\\n try:\\r\\n g.conn.close()\\r\\n except Exception as e:\\r\\n pass\",\n \"def close(self):\\n return\",\n \"def close(self):\\n return\",\n \"def close(self):\\n return\",\n \"def close(self) -> None:\\r\\n pass\",\n \"def teardown_request(exception):\\n try:\\n g.conn.close()\\n except Exception:\\n print exception\",\n \"def server_close(self):\\n\\t\\tpass\",\n \"def send_close_request(self):\\n\\tdata = struct.pack(\\\"!4I\\\", 0b1101, 0b1001, self.epoch_no, self.handle_no)\\n\\tself.client_socket.sendto(data, self.address)\\n\\tself.client_socket.close()\\t\\n return\",\n \"def close (self):\\n pass\\n #TODO: implement more realistic closing semantics\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n return self.close()\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n self.close()\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n self.close()\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n self.close()\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n self.close()\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n self.close()\",\n \"def closed(self, *args, **kwargs) -> Any:\\n pass\",\n \"def close(self):\\n print 'close'\",\n \"def teardown_request(exception):\\n try:\\n g.conn.close()\\n except Exception as e:\\n pass\",\n \"def teardown_request(exception):\\n try:\\n g.conn.close()\\n except Exception as e:\\n pass\",\n \"def teardown_request(exception):\\n try:\\n g.conn.close()\\n except Exception as e:\\n pass\",\n \"def teardown_request(exception):\\n try:\\n g.conn.close()\\n except Exception as e:\\n pass\",\n \"def teardown_request(exception):\\n try:\\n g.conn.close()\\n except Exception as e:\\n pass\",\n \"def teardown_request(exception):\\n try:\\n g.conn.close()\\n except Exception as e:\\n pass\",\n \"def close(self):\\n self.call('close')\",\n \"def Close(self):\\n return\",\n \"def Close(self):\\n return\",\n \"def __exit__(self, type, value, traceback):\\n self.transport.close()\",\n \"def done(self):\\n ## All done with the request object\\n self.closed = True\\n self.d.callback('')\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.87857294\",\n \"0.8312502\",\n \"0.75678617\",\n \"0.706756\",\n \"0.706756\",\n \"0.706756\",\n \"0.706756\",\n \"0.706756\",\n \"0.706756\",\n \"0.706756\",\n \"0.706756\",\n \"0.706756\",\n \"0.706756\",\n \"0.6996744\",\n \"0.6996744\",\n \"0.6996744\",\n \"0.69872475\",\n \"0.69872475\",\n \"0.6945814\",\n \"0.693658\",\n \"0.69229454\",\n \"0.69229454\",\n \"0.6898759\",\n \"0.6875146\",\n \"0.6875146\",\n \"0.6875146\",\n \"0.6875146\",\n \"0.6875146\",\n \"0.6875146\",\n \"0.6875146\",\n \"0.6875146\",\n \"0.68426186\",\n \"0.68426186\",\n \"0.6833194\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.68243027\",\n \"0.681809\",\n \"0.68143696\",\n \"0.6812568\",\n \"0.6812568\",\n \"0.6811929\",\n \"0.6761785\",\n \"0.67593473\",\n \"0.6755092\",\n \"0.6734893\",\n \"0.6734893\",\n \"0.6734893\",\n \"0.6700021\",\n \"0.6700021\",\n \"0.6700021\",\n \"0.66755456\",\n \"0.6667885\",\n \"0.6652853\",\n \"0.6647439\",\n \"0.6639427\",\n \"0.6629971\",\n \"0.66286874\",\n \"0.66286874\",\n \"0.66286874\",\n \"0.66286874\",\n \"0.66282374\",\n \"0.6627259\",\n \"0.6617231\",\n \"0.6584426\",\n \"0.6584426\",\n \"0.6584426\",\n \"0.6584426\",\n \"0.6584426\",\n \"0.6584426\",\n \"0.65781945\",\n \"0.6576922\",\n \"0.6576922\",\n \"0.65742946\",\n \"0.6564321\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71879524"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":89289,"cells":{"query":{"kind":"string","value":"Function that performs a convolution on images with channels"},"document":{"kind":"string","value":"def convolve_channels(images, kernel, padding='same', stride=(1, 1)):\n m = images.shape[0]\n image_h = images.shape[1]\n image_w = images.shape[2]\n filter_h = kernel.shape[0]\n filter_w = kernel.shape[1]\n s1 = stride[0]\n s2 = stride[1]\n\n if padding == 'valid':\n pad_h = 0\n pad_w = 0\n\n if padding == 'same':\n pad_h = int(((image_h - 1) * s1 + filter_h - image_h) / 2) + 1\n pad_w = int(((image_w - 1) * s2 + filter_w - image_w) / 2) + 1\n\n if type(padding) == tuple:\n pad_h = padding[0]\n pad_w = padding[1]\n\n n_dim1 = int((image_h + 2 * pad_h - filter_h) / stride[0]) + 1\n n_dim2 = int((image_w + 2 * pad_w - filter_w) / stride[1]) + 1\n convolve = np.zeros((m, n_dim1, n_dim2))\n new_images = np.pad(images, ((0, 0), (pad_h, pad_h), (pad_w, pad_w),\n (0, 0)), mode='constant')\n for x in range(n_dim1):\n for y in range(n_dim2):\n mini_matrix = new_images[:, x * s1: x * s1 + filter_h,\n y * s2: y * s2 + filter_w, :]\n values = np.sum(mini_matrix * kernel,\n axis=1).sum(axis=1).sum(axis=1)\n convolve[:, x, y] = values\n return (convolve)"},"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 clConvolution(self, size, mask):","def convolve_channels(images, kernel, padding='same', stride=(1, 1)):\n m, h, w, c = images.shape\n KernelHeight, kernelWidth, c = kernel.shape\n StrideHeight, StrideWidth = stride\n\n if padding == 'valid':\n PaddingHeight = 0\n PaddingWidth = 0\n elif padding == 'same':\n PaddingHeight = int(\n (((h - 1) * StrideHeight + KernelHeight - h) / 2) + 1)\n PaddingWidth = int((((w - 1) * StrideWidth + kernelWidth - w) / 2) + 1)\n else:\n PaddingHeight, PaddingWidth = padding\n\n OutputH = int(((h + 2 * PaddingHeight - KernelHeight) / StrideHeight) + 1)\n OutputW = int(((w + 2 * PaddingWidth - kernelWidth) / StrideWidth) + 1)\n\n ImagePadded = np.pad(\n images,\n ((0, 0), (PaddingHeight, PaddingHeight),\n (PaddingWidth, PaddingWidth), (0, 0)),\n 'constant'\n )\n\n output = np.zeros((m, OutputH, OutputW))\n ImageRange = np.arange(m)\n\n for i_OutputH in range(OutputH):\n for i_OutputW in range(OutputW):\n s_i_OutputH = i_OutputH * StrideHeight\n s_i_OutputW = i_OutputW * StrideWidth\n flt = ImagePadded[ImageRange,\n s_i_OutputH:KernelHeight + s_i_OutputH,\n s_i_OutputW:kernelWidth + s_i_OutputW,\n :]\n output[ImageRange, i_OutputH, i_OutputW] = np.sum(\n flt * kernel, axis=(1, 2, 3))\n return output","def convolve(image, pixel_filter, channels=3, name=None):\n with tf.name_scope(name, 'convolve'):\n tf.compat.v1.assert_type(image, tf.float32)\n channel_filter = tf.eye(channels)\n filter_ = (tf.expand_dims(tf.expand_dims(pixel_filter, -1), -1) *\n tf.expand_dims(tf.expand_dims(channel_filter, 0), 0))\n result_batch = tf.nn.conv2d(tf.stack([image]), # batch\n filter=filter_,\n strides=[1, 1, 1, 1],\n padding='SAME')\n return result_batch[0] # unbatch","def convolve_channels(images, kernel, padding='same', stride=(1, 1)):\n m, image_h, image_w, image_c = images.shape\n kernel_h, kernel_w, kernel_c = kernel.shape\n stride_h, stride_w = stride\n\n if isinstance(padding, tuple):\n padding_h, padding_w = padding\n if padding is 'same':\n padding_h = int(((stride_h * image_h)\n - stride_h + kernel_h - image_h) / 2) + 1\n padding_w = int(((stride_w * image_w)\n - stride_w + kernel_w - image_w) / 2) + 1\n if padding is 'valid':\n padding_h, padding_w = 0, 0\n\n output_h = int(((image_h + (2 * padding_h) - kernel_h) / stride_h) + 1)\n output_w = int(((image_w + (2 * padding_w) - kernel_w) / stride_w) + 1)\n conv_output = np.zeros((m, output_h, output_w))\n\n img_m = np.arange(0, m)\n\n images = np.pad(\n images,\n [(0, 0), (padding_h, padding_h), (padding_w, padding_w), (0, 0)],\n mode='constant',\n constant_values=0)\n\n for i in range(output_h):\n for j in range(output_w):\n s_h = (stride_h)\n s_w = (stride_w)\n multiply = images[\n img_m,\n i*s_h:kernel_h+i*s_h,\n j*s_w:kernel_w+j*s_w]\n conv_output[img_m, i, j] = np.sum(\n np.multiply(multiply, kernel), axis=(1, 2, 3))\n return conv_output","def convolve_grayscale_same(images, kernel):\n\n # num images\n n_images = images.shape[0]\n\n # input_width and input_height\n i_h = images.shape[1]\n i_w = images.shape[2]\n\n # kernel_width and kernel_height\n\n k_h = kernel.shape[0]\n k_w = kernel.shape[1]\n\n # pad_h ⊛ = int (k_h - 1)/2\n # pad_w ⊛ = int (k_w - 1)/2\n p_h = int((k_h - 1) / 2)\n p_w = int((k_w - 1) / 2)\n\n if k_h % 2 == 0:\n p_h = int(k_h / 2)\n\n if k_w % 2 == 0:\n p_w = int(k_w / 2)\n\n # output_height and output_width\n # H = i_h + 2pad - k_h + 1, W = i_w + 2pad - k_w + 1\n o_h = i_h + 2 * p_h - k_h + 1\n o_w = i_w + 2 * p_w - k_w + 1\n\n if k_h % 2 == 0:\n o_h = i_h + 2 * p_h - k_h\n\n if k_w % 2 == 0:\n o_w = i_w + 2 * p_w - k_w\n\n # creating outputs of size: n_images, o_h x o_w\n outputs = np.zeros((n_images, o_h, o_w))\n\n # creating pad of zeros around the output images\n padded_imgs = np.pad(images,\n pad_width=((0, 0), (p_h, p_h), (p_w, p_w)),\n mode=\"constant\",\n constant_values=0)\n\n # vectorizing the n_images into an array\n imgs_arr = np.arange(0, n_images)\n\n # iterating over the output array and generating the convolution\n for x in range(o_h):\n for y in range(o_w):\n x1 = x + k_h\n y1 = y + k_w\n outputs[imgs_arr, x, y] = np.sum(np.multiply(\n padded_imgs[imgs_arr, x: x1, y: y1], kernel), axis=(1, 2))\n\n return outputs","def convolve_grayscale_same(images, kernel):\n m = images.shape[0]\n h = images.shape[1]\n w = images.shape[2]\n kh = kernel.shape[0]\n kw = kernel.shape[1]\n padh = int(kh / 2)\n padw = int(kw / 2)\n pad = ((0, 0), (padh, padh), (padw, padw))\n conv = np.zeros([m, h, w])\n imagePad = np.pad(images, pad_width=pad, mode='constant')\n for i in range(h):\n for j in range(w):\n image = imagePad[:, i:i+kh, j:j+kw]\n conv[:, i, j] = np.multiply(image, kernel).sum(axis=(1, 2))\n return conv","def convolve_grayscale_same(images, kernel):\n imgshape = images.shape\n h = images.shape[1]\n w = images.shape[2]\n kh = kernel.shape[0]\n kw = kernel.shape[1]\n # conved = np.zeros((imgshape[0], h - kh + 1, w - kw + 1))\n conved = np.zeros(imgshape)\n ph = int((kh) / 2)\n pw = int((kw) / 2)\n # print(conved.shape)\n # print(kernel.shape, images.shape)\n # print(kernel[None, :, :].shape)\n padimg = np.pad(images, ((0, 0), (ph, ph), (pw, pw)), 'constant',\n constant_values=0)\n for i in range(0, h):\n for j in range(0, w):\n subs = padimg[:, i:i + kh, j:j + kw]\n # ip = i + ph\n # jp = j + pw\n conved[:, i, j] = np.sum((kernel[None, :, :] * subs),\n axis=(1, 2))\n\n return conved","def convolve_grayscale_valid(images, kernel):\n imgshape = images.shape\n h = images.shape[1]\n w = images.shape[2]\n kh = kernel.shape[0]\n kw = kernel.shape[1]\n conved = np.zeros((imgshape[0], h - kh + 1, w - kw + 1))\n\n for i in range(0, h - kh + 1):\n for j in range(0, w - kw + 1):\n subs = images[:, i:i + kh, j:j + kw]\n conved[:, i, j] = np.sum((kernel[None, :, :] * subs),\n axis=(1, 2))\n\n return conved","def MyConvolve(img, ff):\n result = np.zeros(img.shape)\n x_len = img.shape[0]\n y_len = img.shape[1]\n\n ff = np.flipud(np.fliplr(ff)) # Flip filters\n\n # Apply filter to pixels\n for x in range(1, x_len - 1):\n for y in range(1, y_len - 1):\n # Left column\n top_left = img[x - 1, y - 1] * ff[0, 0]\n left = img[x, y - 1] * ff[1, 0]\n btm_left = img[x + 1, y - 1] * ff[2, 0]\n # Middle column\n top = img[x - 1, y] * ff[0, 1]\n middle = img[x, y] * ff[1, 1]\n btm = img[x + 1, y] * ff[2, 1]\n # Right column\n top_right = img[x - 1, y + 1] * ff[0, 2]\n right = img[x, y + 1] * ff[1, 2]\n btm_right = img[x + 1, y + 1] * ff[2, 2]\n\n result[x, y] = top_left + left + btm_left + top + middle + btm + top_right + right + btm_right\n\n return result","def convolve_im(im: np.array,\n kernel: np.array,\n verbose=True):\n\t\n ### START YOUR CODE HERE ### (You can change anything inside this block) \n\t\n H,W = np.shape(im)\n h,w = np.shape(kernel)\n t_b = (H-h)//2\n l_r = (W-w)//2\n kernel_padded = np.pad(kernel, ((t_b, t_b+1),(l_r, l_r+1)), 'constant')\n kernel_padded = np.pad(kernel, ((0, 2*t_b),(0, 2*l_r)), 'constant')\n fft_kernel = np.fft.fft2(kernel_padded, s=None, axes=(-2, -1), norm=None)\n \n \n im_fft = np.fft.fft2(im, s=None, axes=(-2, -1), norm=None) \n im_filt = im_fft*fft_kernel \n conv_result = np.fft.ifft2(im_filt, s=None, axes=(-2, -1), norm=None).real \n\n if verbose:\n # Use plt.subplot to place two or more images beside eachother\n plt.figure(figsize=(12, 4))\n # plt.subplot(num_rows, num_cols, position (1-indexed))\n plt.subplot(1, 2, 1)\n plt.imshow(im, cmap=\"gray\")\n plt.subplot(1, 2, 2) \n plt.imshow(conv_result, cmap=\"gray\")\n\n ### END YOUR CODE HERE ###\n return conv_result","def image_conv(image, kernel):\n \n # Filter2D used for performance\n return cv2.filter2D(image, -1, kernel)","def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\n weight = weight_variable()\n return nn.Conv2d(in_channels, out_channels,\n kernel_size=kernel_size, stride=stride, padding=padding,\n weight_init=weight, has_bias=False, pad_mode=\"valid\")","def convolve(self, *args, **kwargs):\n return _image.image_convolve(self, *args, **kwargs)","def convolution(image, kernel):\n\n #Se encuentra la dimencion de la imagen\n if len(image.shape) == 3: #De 3 dimenciones\n print(\"Dimenciones de imagen: {}\".format(image.shape))\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #Se cambia a dos dimenciones\n print(\"Nuevas dimenciones: {}\".format(image.shape))\n else:\n print(\"Dimenciones de imagen: {}\".format(image.shape))\n\n image_row, image_col = image.shape #asigna alto y ancho de la imagen \n kernel_row, kernel_col = kernel.shape #asigna alto y ancho del filtro\n\n output_x = (image_col - (kernel_col / 2) * 2) + 1 #asigna el ancho del output\n output_y = (image_row - (kernel_row / 2) * 2) + 1 #asigna el alto del output\n \n output = np.zeros([int(output_y), int(output_x)]) #matriz donde se guarda el resultado\n\n padded_size = int((kernel_row - 1) / 2) #Tamaño de padding\n\n #Obtenemos la imagen con padding\n padded_image = padding(image,padded_size)\n \n for row in range(int(output_y)):\n for col in range(int(output_x)):\n output[row, col] = conv_helper(\n padded_image[row:row + kernel_row, \n col:col + kernel_col], kernel)\n \n # Se muestra la imagen en pantalla\n plt.imshow(output, cmap='gray')\n plt.title(\"Edge detection\")\n plt.show()\n\n return output","def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\n weight = weight_variable()\n return nn.Conv2d(\n in_channels,\n out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n weight_init=weight,\n has_bias=False,\n pad_mode=\"valid\",\n )","def convolve_image(image, kernel, mode='valid', boundary='symm'):\n chs = []\n for d in range(image.shape[2]):\n channel = sig.convolve2d(image[:, :, d], kernel, mode=mode, boundary=boundary)\n chs.append(channel)\n return np.stack(chs, axis=2)","def convolve(images, kernels, padding='same', stride=(1, 1)):\n m = images.shape[0]\n h = images.shape[1]\n w = images.shape[2]\n c = images.shape[3]\n kh = kernels.shape[0]\n kw = kernels.shape[1]\n nc = kernels.shape[3]\n sh = stride[0]\n sw = stride[1]\n\n if padding == 'same':\n ph = max((h - 1) * sh + kh - h, 0)\n pt = int(np.ceil(ph / 2))\n pb = pt\n pw = max((w - 1) * sw + kw - w, 0)\n pl = int(np.ceil(pw / 2))\n pr = pl\n elif padding == 'valid':\n pt, pb, pl, pr = 0, 0, 0, 0\n else:\n pt, pb = padding[0], padding[0]\n pl, pr = padding[1], padding[1]\n\n oh = ((h - kh + pt + pb) // sh) + 1\n ow = ((w - kw + pl + pr) // sw) + 1\n\n images = np.pad(images, pad_width=((0, 0), (pt, pb), (pl, pr), (0, 0)),\n mode='constant', constant_values=0)\n\n conv = np.zeros((m, oh, ow, nc))\n for k in range(nc):\n for i in range(oh):\n for j in range(ow):\n aux = images[:, i * sh:i * sh + kh, j * sw:j * sw + kw] \\\n * kernels[:, :, :, k]\n conv[:, i, j, k] = np.sum(aux, axis=(1, 2, 3))\n return conv","def conv(n_inputs, n_filters, kernel_size=3, stride=1, bias=False) -> torch.nn.Conv2d:\n return nn.Conv2d(n_inputs, n_filters,\n kernel_size=kernel_size, stride=stride,\n padding=kernel_size//2, bias=bias)","def convolve_one_image(self,input4D, one_image, image_shape, \n Pstruct, filter_shape,\n image_index,\n channel_index): \n \n \n ## We look at the composition for the first channel in the beginning \n rank = Pstruct[0]['U1'].shape[1]\n fwidth = filter_shape[2]\n fheight = filter_shape[3]\n \n \n # Construct horizontal filters\n #TODO save the filters in the correct shape\n horizontal_filter_shape = (rank, 1, fwidth)\n horizontal_filters = np.ndarray(horizontal_filter_shape)\n horizontal_filters[:, 0, :] = np.transpose(Pstruct[channel_index]['U1']);\n \n # Output is 1 x rank x W x H\n horizontal_conv_out = conv.conv2d(input=one_image, \n filters = horizontal_filters,\n filter_shape = horizontal_filter_shape, \n image_shape = image_shape)\n \n # Construct vertical filters\n vertical_filter_shape = (rank, fheight, 1)\n vertical_filters = np.ndarray(vertical_filter_shape) \n vertical_filters[:,:, 0] = np.transpose(Pstruct[channel_index]['U2']);\n\n initial_n_rows = image_shape[1]\n final_n_rows = initial_n_rows- fwidth + 1\n final_n_cols = image_shape[2] - fheight + 1 \n conv_out = theano.shared(np.zeros((rank, final_n_rows, final_n_cols)))\n for r in range(rank):\n # temp is 1x1x imageW x imageH\n A = conv.conv2d(input = horizontal_conv_out[:,r,:,:], \n filters = vertical_filters[r,:,:],\n filter_shape = (1, fheight, 1), \n image_shape = (1, initial_n_rows, final_n_cols))\n conv_out = T.set_subtensor(conv_out[r,:,:], A[0,:,:])\n \n nbr_filters = Pstruct[0]['U3'].shape[0]\n # Final number of rows and columns \n ## numberof images, number of filters, image width, image height\n alphas = Pstruct[channel_index]['U3'] \n for f in range(nbr_filters): \n temp = theano.shared(np.zeros((final_n_rows, final_n_cols)))\n for r in range(rank):\n temp = temp + conv_out[r, :,:]* alphas[f, r] * Pstruct[channel_index]['lmbda'][r]; \n input4D =T.set_subtensor(input4D[image_index,f,:,:], temp)\n return input4D","def img_conv(X, filter):\n assert filter.shape[0] % 2 == 1\n assert filter.shape[1] % 2 == 1\n x_size = filter.shape[0] // 2\n y_size = filter.shape[1] // 2\n w = X.shape[0]\n h = X.shape[1]\n out = numpy.zeros(X.shape)\n for r in range(w):\n for c in range(h):\n for x in range(filter.shape[0]):\n pixel_x = r + x - x_size\n if pixel_x < 0:\n pixel_x = -pixel_x\n if pixel_x >= w:\n pixel_x = w - pixel_x - 2\n for y in range(filter.shape[1]):\n pixel_y = c + y - y_size\n if pixel_y < 0:\n pixel_y = -pixel_y\n if pixel_y >= h:\n pixel_y = h - pixel_y - 2\n #if pixel_x >= 0 and pixel_x < w and pixel_y >= 0 and pixel_y < h:\n out[r, c] += filter[x, y] * X[pixel_x, pixel_y]\n return out","def my1_conv2d(image, kernels, strides=(1, 1)):\n raise NotImplementedError('Write me!')","def conv(dims, inplanes, outplanes, kernel_size, stride, dilation, bias):\n padding = math.floor((dilation * (kernel_size - 1) + 2 - stride) / 2)\n if dims == 2:\n return nn.Conv2d(inplanes, outplanes, kernel_size, stride,\n padding, dilation, bias=bias)\n elif dims == 3:\n return nn.Conv3d(inplanes, outplanes, kernel_size, stride,\n padding, dilation, bias=bias)\n else:\n raise ValueError('dimension of conv must be 2 or 3')","def convolution(img, kernel, padding='fill'):\n kernel = np.rot90(kernel, 2)\n h,w = kernel.shape[:2]\n t,b,l,r = (h-1)//2, h//2, (w-1)//2, w//2 # Use numpy padding because it works for >2d\n padshape = [(t,b),(l,r)]+[(0,0)]*(len(img.shape[2:]))\n padded_img = np.pad(img, padshape, mode={'fill':'constant','replicate':'edge'}[padding])\n conved_img = np.zeros_like(img)\n for i in 1+np.arange(-h//2,h//2):\n for j in 1+np.arange(-w//2,w//2):\n if kernel[t+i,l+j]==0: continue\n conved_img += kernel[t+i,l+j]*padded_img[t+i:-b+i or None,l+j:-r+j or None]\n return conved_img","def convolve(img, fourier_kernel):\n return np.fft.ifftshift(np.fft.irfft2(np.fft.rfft2(img) * fourier_kernel))","def conv2d(args):\n inp_ = args[0]\n kernel = args[1]\n stride = args[2]\n padding = args[3]\n (batch_size, in_channels, H, W) = inp_.shape\n (out_channels, in_channels_t, Hk, Wk) = kernel.shape\n Hc = int((H - Hk)/stride)+1\n Wc = int((W - Wk)/stride)+1\n conv_layer = np.zeros((batch_size, out_channels, Hc, Wc))\n for batch_i in range(batch_size):\n for o_chann_i in range(out_channels):\n for in_chann_i in range(in_channels):\n curr_ker = kernel[o_chann_i, in_chann_i, :, :]\n curr_inp = inp_[batch_i, in_chann_i, :, :]\n h_ind = 0\n while h_ind + Hk <= H:\n w_ind = 0\n while w_ind + Wk <= W:\n inp_patch = curr_inp[h_ind:h_ind+Hk, w_ind:w_ind+Wk]\n # Sum the conv_value of all the inp_channels\n conv_layer[batch_i, o_chann_i, h_ind//stride, w_ind//stride] += np.sum(inp_patch*curr_ker)\n w_ind+=stride\n h_ind+=stride\n return conv_layer","def convolve(images, kernels, padding='same', stride=(1, 1)):\n m, h, w = images.shape[:3]\n kh, kw, c, nc = kernels.shape\n sh, sw = stride\n if type(padding) is tuple:\n ph, pw = padding\n elif padding == 'valid':\n ph, pw = 0, 0\n else:\n ph = (((h - 1) * sh + kh - h) // 2) + 1\n pw = (((w - 1) * sw + kw - w) // 2) + 1\n out_images = np.zeros((m, (h - kh + (2 * ph))//sh + 1,\n (w - kw + (2 * pw))//sw + 1, nc))\n images = np.pad(images, ((0, 0), (ph, ph), (pw, pw), (0, 0)), 'constant')\n for i in range((h - kh + (2 * ph))//sh + 1):\n for j in range((w - kw + (2 * pw))//sw + 1):\n for n in range(nc):\n out_images[:, i, j, n] = np.sum(kernels[:, :, :, n] * images[\n :, i*sh: i*sh + kh, j*sw: j*sw + kw, :], axis=(1, 2, 3))\n return out_images","def conv1x1(in_channels, out_channels, groups=1):\n return nn.Conv2d(\n in_channels, \n out_channels, \n kernel_size=1, \n groups=groups,\n stride=1)","def convolution(image, kernel, scale=None, offset=0):\n kernel = np.array(kernel).flatten().tolist()\n if len(kernel)==9:\n size = (3,3)\n elif len(kernel)==25:\n size = (5,5)\n else:\n raise ValueError('Kernel size must be (3,3) or (5,5).')\n return image.filter(ImageFilter.Kernel(size, kernel, scale, offset))","def py_conv(img, kern, mode, subsample):\r\n if imported_scipy_convolve2d:\r\n return py_conv_scipy(img, kern, mode, subsample)\r\n elif mode == 'valid':\r\n return py_conv_valid_numpy(img, kern)[:, :, ::subsample[0],\r\n ::subsample[1]]\r\n elif mode == 'full':\r\n return py_conv_full_numpy(img, kern)[:, :, ::subsample[0],\r\n ::subsample[1]]\r\n else:\r\n raise Exception(\"Can't execute this kernel.\")","def py_conv(img, kern, mode, subsample):\r\n if imported_scipy_convolve2d:\r\n return py_conv_scipy(img, kern, mode, subsample)\r\n elif mode == 'valid':\r\n return py_conv_valid_numpy(img, kern)[:, :, ::subsample[0],\r\n ::subsample[1]]\r\n elif mode == 'full':\r\n return py_conv_full_numpy(img, kern)[:, :, ::subsample[0],\r\n ::subsample[1]]\r\n else:\r\n raise Exception(\"Can't execute this kernel.\")","def convolution(image: np.array, kernel: np.array) -> np.array:\n\n # default condition: apply SAME padding, and keep stride at 1\n stride_x = 1\n stride_y = 1\n padding_y = int(len(kernel - 1) / 2)\n padding_x = int(len((kernel[0]) - 1) / 2)\n # create the return array with with the same dimensions as <image>,\n # and then create a padded image\n convolved_image = np.zeros((len(image), len(image[0])))\n padded_image = np.zeros((len(image) + 2 * padding_y,\n len(image[0]) + 2 * padding_x))\n padded_image[padding_x: -padding_x, padding_y: -padding_y] = image\n\n for py in range(0, len(padded_image) - len(kernel), stride_y):\n for px in range(0, len(padded_image[0]) - len(kernel[0]), stride_x):\n # scan the matrix over columns in image array, then shift the matrix\n # down, and repeat\n padded_image_section = padded_image[py: py + len(kernel[0]),\n px: px + len(kernel)]\n # print(padded_image_section)\n convolved_image[py, px] = int(np.tensordot(padded_image_section,\n kernel))\n\n return convolved_image","def test_conv2d():\n img = np.array([\n [0.3, 0.5, 0.7, 0.9],\n [0.1, 0.3, 0.5, 0.7],\n [0.9, 0.7, 0.5, 0.3],\n ])\n template = np.array([\n [1, 0],\n [1, 0],\n ])\n template = np.flipud(np.fliplr(template))\n return fftconvolve(img, template, mode='valid')","def _conv_block( inputs, filters, kernel, strides, nl):\n channel_axis = 1 if K.image_data_format() == 'channels_first' else -1\n x = Conv2D(filters, kernel, padding='same', strides=strides)(inputs)\n x = BatchNormalization(axis=channel_axis)(x)\n return _return_activation(x, nl)","def convolve(img, fltr, same=False, stri=1, pad=0, repfilter=False):\n # focus = np.array{eltype(img),2} # scope outside of if block\n if np.ndim(img) == 3:\n imgd, imgx, imgy = np.shape(img)\n elif np.ndim(img) == 2:\n imgx, imgy = np.shape(img)\n imgd = 1\n else:\n print(\"Wrong dimensions of image file. Quitting.\")\n return\n\n if np.ndim(fltr) == 3:\n fd, fx, fy = np.shape(fltr)\n elif np.ndim(fltr) == 2:\n fx, fy = np.shape(fltr)\n fd = 1\n else:\n print(\"Wrong dimensions of filter. Quitting.\")\n return\n\n if fd != imgd: # as a convenience we could just replicate the 2d filter...\n print(\"Depths of image and filter not equal. Quitting.\")\n return\n\n if same:\n pad = math.ceil((fx - 1) / 2)\n\n if pad > 0:\n img = dopad(img, pad)\n\n # dimensions of the result of convolution\n x_out = (imgx + 2 * pad - fx) // stri + 1\n y_out = (imgy + 2 * pad - fy) // stri + 1\n\n # print(imgx, imgy)\n\n ret = np.zeros((x_out, y_out))\n if imgd > 1: # slice through the depth, the zeroth (first) dimension\n for i in zip(range(x_out), range(0, imgx, stri)):\n for j in zip(range(y_out), range(0, imgy, stri)):\n ret[i[0], j[0]] = np.sum(img[:, i[1]:i[1] + fx, j[1]:j[1] +\n fy] * fltr)\n else:\n for i in zip(range(x_out), range(0, imgx, stri)):\n for j in zip(range(y_out), range(0, imgy, stri)):\n ret[i[0], j[0]] = np.sum(img[i[1]:i[1] + fx, j[1]:j[1] +\n fy] * fltr)\n return ret","def _convk(\n in_channels, out_channels, kernel_size=3, stride=1, groups=1, dilation=1, bias=False\n):\n padding = dilation * (kernel_size - 1) // 2\n return Conv1d(\n in_channels,\n out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n groups=groups,\n bias=bias,\n dilation=dilation,\n )","def conv_layer(n_in_filters, n_filters, ker_size, stride=1, \n depthwise=False, zero_bn=False, act=True) :\n bn = nn.BatchNorm2d(n_filters)\n nn.init.constant_(bn.weight, 0. if zero_bn else 1.)\n conv = nn.Conv2d(n_in_filters, n_filters, ker_size, stride=stride,padding=ker_size//2, \n bias=False,groups = n_in_filters if depthwise else 1)\n layer = [conv, bn]\n if act: layer += [Swish()]\n return nn.Sequential(*layer)","def cnn(channels=(4, 16, 32), kernel_sizes=(8, 4), strides=(4, 2), in_size=84):\n cnn_layers = []\n output_size = in_size\n\n for i in range(len(channels)-1):\n in_channels, out_channels = channels[i], channels[i+1]\n kernel_size, stride = kernel_sizes[i], strides[i]\n conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride)\n activation = nn.ReLU()\n cnn_layers += [conv, activation]\n output_size = (output_size - kernel_size) / stride + 1\n\n cnn_layers = nn.Sequential(*cnn_layers)\n output_size = int(out_channels * (output_size**2))\n return cnn_layers, output_size","def convolve_im(im: np.array,\n kernel: np.array,\n verbose=True):\n ### START YOUR CODE HERE ### (You can change anything inside this block)\n \"\"\"\n\tcompared to the 4a solution this just adds padding to the filter if its smaller than the image\n\tthis is done by using the second parameter in fft.fft2 \n\t\n\tfirst it applies fourier transforms on the kernel and the image\n\tthen it sets the image to be the pointwise multiplication of the transforms\n\n the image is inverse fourier transformed and filtered for real values\n the domain image is shifted and taken the absolute value of\n the fourier transform of the image and kernel are also shifted and set to be the absolute value\n\tlastly everything is displayed in the subplots\n \"\"\"\n conv_result = im \n \n if verbose:\n fftKernel=np.fft.fft2(kernel,im.shape)\n fftImage=np.fft.fft2(conv_result)\n\t\t\n\t\t\n\t\t\n conv_result=np.multiply(fftImage,fftKernel)\n fftImageTransformed=conv_result\n\t\t\n \n conv_result=np.fft.ifft2(conv_result)\n \n conv_result=np.real(conv_result)\n\n fftImageTransformed=np.fft.fftshift(fftImageTransformed)\n fftImage=np.fft.fftshift(fftImage)\n fftKernel=np.fft.fftshift(fftKernel)\n\n fftImageTransformed=np.absolute(fftImageTransformed)\n fftImage=np.absolute(fftImage)\n fftKernel=np.absolute(fftKernel)\n\t\t\n\t\t\n # Use plt.subplot to place two or more images beside eachother\n plt.figure(figsize=(20, 4))\n # plt.subplot(num_rows, num_cols, position (1-indexed))\n plt.subplot(1, 5, 1)\n plt.imshow(im, cmap=\"gray\")\n plt.subplot(1, 5, 2)\n plt.imshow(fftImage, cmap=\"gray\")\n plt.subplot(1, 5, 3)\n plt.imshow(fftKernel, cmap=\"gray\")\n plt.subplot(1, 5, 4)\n plt.imshow(fftImageTransformed, cmap=\"gray\")\n plt.subplot(1, 5, 5)\n plt.imshow(conv_result, cmap=\"gray\")\n ### END YOUR CODE HERE ###\n return conv_result","def _conv_block(inputs, filters, kernel=(3, 3), strides=(1, 1), alpha=1.0, nl='RE'):\n channel_axis = 1 if K.image_data_format() == 'channels_first' else -1\n filters = int(filters * alpha)\n x = KL.Conv2D(filters, kernel, padding='same', use_bias=False, strides=strides)(inputs)\n x = KL.BatchNormalization(axis=channel_axis)(x)\n return _return_activation(x, nl=nl)","def loop_conv(X, W):\n # Go over all five dimensions \n # (#batches x #channels x #height x #width x #dur/length )\n # with filter that has\n # #filters x #channels x #height x #width x #dur/length \n num_filters = W.shape[0]\n filt_channels = W.shape[1]\n filt_height = W.shape[2]\n filt_width = W.shape[3]\n filt_duration = W.shape[4]\n num_batches = X.shape[0]\n input_channels = X.shape[1]\n assert(filt_channels == input_channels)\n out_shape = compute_out_shape(X.shape, W.shape)\n out_height = out_shape[2]\n out_width = out_shape[3]\n out_duration = out_shape[4]\n \n # The output is H :)\n H = np.zeros((out_shape))\n for batch_i in xrange(0, num_batches):\n for filt_i in xrange(0, num_filters):\n for out_x in xrange(0, out_height):\n for out_y in xrange(0, out_width):\n for out_z in xrange(0, out_duration):\n for chan_i in xrange(0, filt_channels):\n for filt_x in xrange(0, filt_height):\n for filt_y in xrange(0, filt_width):\n for filt_z in xrange(0, filt_duration):\n weight = W[filt_i, chan_i, filt_x, filt_y, filt_z]\n input_val = X[batch_i, chan_i, \\\n out_x + filt_x, out_y + filt_y, out_z + filt_z]\n H[batch_i, filt_i, out_x, out_y, out_z] += \\\n weight * input_val\n return H","def conv3x3(in_channels, out_channels, stride=1, \n padding=1, bias=True, groups=1):\n return nn.Conv2d(\n in_channels, \n out_channels, \n kernel_size=3, \n stride=stride,\n padding=padding,\n bias=bias,\n groups=groups)","def convolve2d(img, kernel):\n #Flip the kernel\n kernel = utils.flip2d(kernel) \n #print(len(kernel))\n \n c = copy.deepcopy(img)\n \n #print(len(c))\n #Padd the image\n pad = int((len(kernel)-1)/2)\n\n\n padded_img = utils.zero_pad(img,pad,pad)\n #print(len(padded_img), len(padded_img[0]))\n #print(len(kernel))\n #print(len(img)**2)\n og_img=[]\n#c = copy.deepcopy(img)\n j=0\n offset = 0\n for m in range(len(img) * len(img[0])): # size of kernel x kernel\n x = []\n \n for i in range(len(kernel)): #3 is kernel size\n #print(i,j)\n x.append(padded_img[i+offset][j:j+len(kernel)])\n #print((x))\n sum = 0\n for k in range(len(kernel)):\n for l in range(len(kernel[0])):\n sum+= x[k][l] * kernel[k][l]\n #print(i,j)\n #print(sum)\n og_img.append(sum) \n j+=1\n if (j == len(img[0])):\n j = 0\n offset+= 1\n \n #print(len(img), len(img[0]))\n final_img = []\n for i in range(0,(len(img)*len(img[0])),len(img[0])):\n final_img.append(og_img[i:i+len(img[0])])\n #print(len(final_img)), len(final_img[0])\n return final_img\n\n # TODO: implement this function.","def conv(in_planes, out_planes, stride=1):\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, padding=1, stride=stride, bias=False)","def conv(self, inputs, filters, kernel_size, strides, padding='SAME', name='conv_layer'):\n input_channels = inputs[-1]\n kernel = tf.Variable(tf.random.truncated_normal(shape=[kernel_size, kernel_size, input_channels, filters]),\n dtype=tf.float32, name='kernel')\n bias = tf.Variable(tf.zeros(shape=[filters]), name='bias')\n conv = tf.nn.conv2d(inputs, filter=kernel,\n strides=[1, strides, strides, 1],\n padding=padding, name='conv')\n out = tf.nn.relu(conv + bias, name='relu')\n return out","def _valid_convolve(images: th.Tensor, kernels: th.Tensor) -> th.Tensor:\n ret = F.conv2d(images.view((images.shape[0], *images.shape[-3:])).transpose(1, 0),\n th.flip(kernels.view((kernels.shape[0], *kernels.shape[-3:])), dims=(-1, -2)),\n groups=kernels.shape[0]).transpose(1, 0)\n return ret","def convolution(img, kernel, padding=True):\n result = np.zeros_like(img)\n p_size_i = kernel.shape[0] // 2\n p_size_j = kernel.shape[1] // 2\n\n if padding:\n padded_img = np.zeros((img.shape[0] + 2 * p_size_i, img.shape[1] + 2 * p_size_j))\n i_first = p_size_i\n i_last = padded_img.shape[0] - p_size_i - 1\n j_first = p_size_j\n j_last = padded_img.shape[1] - p_size_j - 1\n padded_img[i_first: i_last + 1, j_first: j_last + 1] = img\n else:\n padded_img = img.copy()\n i_first = p_size_i\n i_last = padded_img.shape[0] - p_size_i - 1\n j_first = p_size_j\n j_last = padded_img.shape[1] - p_size_j - 1\n \n for i in range(i_first, i_last):\n for j in range(j_first, j_last):\n window = padded_img[i - p_size_i: i + p_size_i + 1, j - p_size_j: j + p_size_j + 1]\n res_pix = np.sum(window * kernel)\n result[i - p_size_i, j - p_size_j] = res_pix\n return result","def _convolve_2d(kernel, image):\n\n nx = image.shape[0]\n ny = image.shape[1]\n nkx = kernel.shape[0]\n nky = kernel.shape[1]\n wkx = nkx // 2\n wky = nky // 2\n\n result = np.zeros(image.shape, dtype=float32)\n\n for i in prange(0, nx, 1):\n iimin = max(i - wkx, 0)\n iimax = min(i + wkx + 1, nx)\n for j in prange(0, ny, 1):\n jjmin = max(j - wky, 0)\n jjmax = min(j + wky + 1, ny)\n num = 0.0\n for ii in range(iimin, iimax, 1):\n iii = wkx + ii - i\n for jj in range(jjmin, jjmax, 1):\n jjj = wky + jj - j\n num += kernel[iii, jjj] * image[ii, jj]\n result[i, j] = num\n\n return result","def conv2D(null,channels,X,stride,kernel_shape,padding = False,initialize_weights = True,*args):\n # filters = dimensionality of output space\n # If padding is enabled, we pad the input with zeros such that the input size\n # remains the same if weights with stride 1 are applied to the input\n if initialize_weights:\n kernel = np.random.normal(size = (kernel_shape[0],kernel_shape[1],kernel_shape[2]))*math.sqrt(1/(kernel_shape[0]*kernel_shape[1]*kernel_shape[2])) # Our input\n kernel = torch.FloatTensor(kernel)\n kernel.requires_grad = False\n else:\n kernel = args[0] # weights and bias must be given if initialise weights is disabled\n bias = args[1]\n kernel_shape = kernel.shape\n \n X = X.detach().numpy()\n if padding: # Can only pad during initialization -> weights and input shapes cannot change during feedforward and backpropagation\n if kernel_shape[1] % 2 == 0 and kernel_shape[2] % 2 == 0:\n X = np.pad(X,((0,0),(math.floor(kernel_shape[1]/2)-1,math.floor(kernel_shape[1]/2)),(math.floor(kernel_shape[2]/2),math.floor(kernel_shape[2]/2)-1)), 'symmetric')\n elif kernel_shape[1] % 2 != 0 and kernel_shape[2] % 2 == 0:\n X = np.pad(X,((0,0),(math.floor(kernel_shape[1]/2),math.floor(kernel_shape[1]/2)),(math.floor(kernel_shape[2]/2),math.floor(kernel_shape[2]/2)-1)), 'symmetric')\n elif kernel_shape[1] % 2 == 0 and kernel_shape[2] % 2 != 0:\n X = np.pad(X,((0,0),(math.floor(kernel_shape[1]/2)-1,math.floor(kernel_shape[1]/2)),(math.floor(kernel_shape[2]/2),math.floor(kernel_shape[2]/2))), 'symmetric')\n else:\n X = np.pad(X,((0,0),(math.floor(kernel_shape[1]/2),math.floor(kernel_shape[1]/2)),(math.floor(kernel_shape[2]/2),math.floor(kernel_shape[2]/2))), 'symmetric')\n \n X = torch.FloatTensor(X)\n \n img_shape = X.shape\n \n output_size1 = math.floor((img_shape[1] - kernel_shape[1])/(stride)) + 1\n output_size2 = math.floor((img_shape[2] - kernel_shape[2])/(stride)) + 1\n output_shape = [channels,output_size1,output_size2]\n \n X_im2col,im = im2col(X,kernel,stride)\n \n \n if initialize_weights:\n weight = torch.reshape(kernel,(kernel_shape[0]*kernel_shape[1]*kernel_shape[2],1))\n # weight consists of only one weight vector. But the dimensionality of output space has to be\n # num_filters. So we need to stack weight vectors horizontally and create num_filters number of\n # feature maps\n for i in range(channels-1):\n weight2 = np.random.normal(size = (kernel_shape[0]*kernel_shape[1]*kernel_shape[2],1))*math.sqrt(1/(kernel_shape[0]*kernel_shape[1]*kernel_shape[2])) # Our input\n weight2 = torch.FloatTensor(weight2)\n weight2.requires_grad = False\n weight = torch.cat((weight2, weight),1) # do this num_filters - 1 number of times\n conv_output = torch.t(X_im2col).mm(weight)\n bias = torch.Tensor(np.random.normal(size = conv_output.shape))\n conv_output += bias\n conv_output = torch.reshape(conv_output,(output_shape))\n return torch.nn.Parameter(conv_output), torch.nn.Parameter(weight),X_im2col,im, output_shape,bias\n else:\n # Since weights are already initialised, the relevant channels are already dictated in the architecture.\n # Therefore, conv output is just a matmul\n conv_output = torch.t(X_im2col).mm(kernel) + bias\n return torch.nn.Parameter(conv_output),X_im2col","def _conv(self, indim, outdim, ksize, stride, padding):\n\n return nn.Sequential(\n nn.BatchNorm2d(indim),\n nn.Conv2d(indim, outdim, ksize, stride, padding),\n self.activ(),\n )","def conv(image, kernel):\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n out = np.zeros((Hi, Wi))\n\n # For this assignment, we will use edge values to pad the images.\n # Zero padding as used in the previous assignment can make\n # derivatives at the image boundary very big.\n \n pad_width0 = Hk // 2\n pad_width1 = Wk // 2\n pad_width = ((pad_width0,pad_width0),(pad_width1,pad_width1))\n padded = np.pad(image, pad_width, mode='edge') \n\n #####################################\n # START YOUR CODE HERE #\n #####################################\n kernel = np.flipud(np.fliplr(kernel)) # flip h/v\n for h in range(Hi):\n for w in range(Wi):\n out[h, w] = np.sum(np.multiply(kernel, padded[h : h + Hk, w : w + Wk]))\n ######################################\n # END OF YOUR CODE #\n ######################################\n\n return out","def dw_conv(\n in_channels: int, out_channels: int, stride: int = 1\n) -> List[torch.nn.Module]:\n return [\n torch.nn.Conv2d(\n in_channels,\n in_channels,\n kernel_size=3,\n padding=1,\n stride=stride,\n groups=in_channels,\n bias=False,\n ),\n torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=True),\n torch.nn.BatchNorm2d(out_channels, eps=_BN_EPS, momentum=_BN_MOMENTUM),\n torch.nn.ReLU(inplace=True),\n ]","def conv(depth, nfilter, ksize=3, stride=1, \r\n padding=0, dilation=1, groups=1, \r\n bias=True, lrelu=None):\r\n assert (depth>0 and nfilter>0 and ksize>0 and ksize%2==1 and \r\n stride>0 and padding>=0 and dilation>=1 and groups>=1)\r\n conv_ = nn.Conv2d(depth, nfilter, ksize, stride, \r\n padding, dilation, groups, bias)\r\n if lrelu is not None:\r\n conv_ = nn.Sequential(conv_, \r\n nn.LeakyReLU(lrelu, inplace=True))\r\n return conv_","def fftconvolve(array, kernel):\n x = numpy.fft.fftshift(numpy.fft.fftn(image))\n y = numpy.fft.fftshift(numpy.fft.fftn(kernel))\n\n return numpy.real(numpynp.fft.fftshift(\n numpy.fft.ifftn(numpy.fft.ifftshift(x * y))))","def conv_nd(dims, *args, **kwargs):\n if dims == 1:\n return nn.Conv1D(*args, **kwargs)\n elif dims == 2:\n return nn.Conv2D(*args, **kwargs)\n elif dims == 3:\n return nn.Conv3D(*args, **kwargs)\n raise ValueError(f\"unsupported dimensions: {dims}\")","def conv_block(\r\n in_channels,\r\n out_channels,\r\n kernel_size,\r\n stride,\r\n dilation=1,\r\n):\r\n pad_mode = 'same'\r\n padding = 0\r\n\r\n dbl = nn.SequentialCell(\r\n [\r\n nn.Conv2d(\r\n in_channels,\r\n out_channels,\r\n kernel_size=kernel_size,\r\n stride=stride,\r\n padding=padding,\r\n dilation=dilation,\r\n pad_mode=pad_mode,\r\n ),\r\n nn.BatchNorm2d(out_channels, momentum=0.1),\r\n nn.ReLU(),\r\n ]\r\n )\r\n init_cov(dbl[0])\r\n init_bn(dbl[1])\r\n return dbl","def conv2d(input, filters, image_shape=None, filter_shape=None,\r\n border_mode='valid', subsample=(1, 1), **kargs):\r\n\r\n #accept Constant value for image_shape and filter_shape.\r\n if image_shape is not None:\r\n image_shape = list(image_shape)\r\n for i in xrange(len(image_shape)):\r\n if image_shape[i] is not None:\r\n try:\r\n image_shape[i] = get_scalar_constant_value(\r\n as_tensor_variable(image_shape[i]))\r\n except NotScalarConstantError, e:\r\n raise NotScalarConstantError(\r\n \"The convolution need that the shape\"\r\n \" information are constant values. We got\"\r\n \" %s for the image_shape parameter\" %\r\n image_shape[i])\r\n assert str(image_shape[i].dtype).startswith('int')\r\n image_shape[i] = int(image_shape[i])\r\n if filter_shape is not None:\r\n filter_shape = list(filter_shape)\r\n for i in xrange(len(filter_shape)):\r\n if filter_shape[i] is not None:\r\n try:\r\n filter_shape[i] = get_scalar_constant_value(\r\n as_tensor_variable(filter_shape[i]))\r\n except NotScalarConstantError, e:\r\n raise NotScalarConstantError(\r\n \"The convolution need that the shape\"\r\n \" information are constant values. We got\"\r\n \" %s for the filter_shape \"\r\n \"parameter\" % filter_shape[i])\r\n assert str(filter_shape[i].dtype).startswith('int')\r\n filter_shape[i] = int(filter_shape[i])\r\n\r\n if image_shape and filter_shape:\r\n try:\r\n assert image_shape[1] == filter_shape[1]\r\n except Exception:\r\n print 'image ', image_shape, ' filters ', filter_shape\r\n raise\r\n\r\n if filter_shape is not None:\r\n nkern = filter_shape[0]\r\n kshp = filter_shape[2:]\r\n else:\r\n nkern, kshp = None, None\r\n\r\n if image_shape is not None:\r\n bsize = image_shape[0]\r\n imshp = image_shape[1:]\r\n else:\r\n bsize, imshp = None, None\r\n\r\n op = ConvOp(output_mode=border_mode, dx=subsample[0], dy=subsample[1],\r\n imshp=imshp, kshp=kshp, nkern=nkern, bsize=bsize, **kargs)\r\n\r\n return op(input, filters)","def conv(image, kernel):\n Hi, Wi = image.shape\n Hk, Wk = kernel.shape\n out = np.zeros((Hi, Wi))\n\n # For this assignment, we will use edge values to pad the images.\n # Zero padding will make derivatives at the image boundary very big,\n # whereas we want to ignore the edges at the boundary.\n pad_width0 = Hk // 2\n pad_width1 = Wk // 2\n pad_width = ((pad_width0,pad_width0),(pad_width1,pad_width1))\n padded = np.pad(image, pad_width, mode='edge')\n\n ### YOUR CODE HERE\n for i in range(Hi):\n for j in range(Wi):\n out[i,j] = np.sum(padded[i : i + Hk, j : j + Wk] * np.flip(kernel))\n ### END YOUR CODE\n\n return out","def convolution(self, x, w, name=\"\"):\n if self.gpu:\n return tf.nn.conv2d(x, w, strides=self.conv_stride, padding=self.conv_padding, name=name)\n\n return tf.nn.conv2d(x, w, strides=self.conv_stride, padding=self.conv_padding,\n use_cudnn_on_gpu=False, name=name)","def convolution(matrix, kernel):\n assert assert_odd(kernel.shape[0])\n \n # Padded matrix (0s on the outsides)\n N = kernel.shape[0] # Get the dim for the kernel\n I = np.pad(matrix, int(N/2), \"constant\")\n \n # Now do the convolution\n C = np.zeros(matrix.shape) # This is the convolved image\n h, w = C.shape # Get width and height\n s = int(N/2) # Spacing of the matrix\n positions = [(i,j) for i in range(h) for j in range(w)]\n for (i,j) in positions:\n y, x = i+s,j+s # Shift the center to the right position\n \n # Calc the convolution at each pixel\n C[i,j] = np.sum(np.multiply(kernel, I[y-s:y+s+1,x-s:x+s+1]))\n \n # Return the clipped array as uint8\n return C","def SNConv2d(*args, **kwargs):\n return spectral_norm(nn.Conv2d(*args, **kwargs))","def convolve(self, kernel):\n kernel_rows, kernel_cols = kernel.shape\n img_rows, img_cols = self.img_array.shape\n\n print(\"imgae shape: \", self.img_array.shape)\n print(self.img_array[:10,:10])\n\n # flip the kernel\n flipped_kernel = np.zeros(kernel.shape)\n \n ## column flips\n for i in range(flipped_kernel.shape[1]):\n flipped_kernel[:,i] = kernel[:,kernel_cols-i-1]\n kernel = flipped_kernel.copy()\n\n ## row flips\n for i in range(flipped_kernel.shape[0]):\n flipped_kernel[i,:] = kernel[kernel_rows-i-1,:]\n kernel = flipped_kernel.copy()\n print(\"Flipped kernel:\\n\", kernel)\n\n # Handle broders by padding the image with white pixels.\n ## padwidth = kernel_rows // 2 \n padwidth = kernel_rows // 2\n self.img_array_padded = np.pad(self.img_array, padwidth, \n mode='constant', constant_values=255)\n \n # cross correlation\n self.img_array_out = np.zeros(self.img_array.shape)\n\n for y in range(img_cols):\n for x in range(img_rows):\n self.img_array_out[x, y] = \\\n (kernel * self.img_array_padded[x:x+kernel_cols, y:y+kernel_rows]).sum()\n \n # print(self.img_array_out.shape)\n # print(self.img_array_out[:10,:10])\n return self.img_array_out","def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n layers = []\n conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\n kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\n\n layers.append(conv_layer)\n\n if batch_norm:\n layers.append(nn.BatchNorm2d(out_channels))\n return nn.Sequential(*layers)","def discreteConvolution2D( iImage, iKernel ): \n # pretvori vhodne spremenljivke v np polje in\n # inicializiraj izhodno np polje\n iImage = np.asarray( iImage )\n iKernel = np.asarray( iKernel )\n #------------------------------- za hitrost delovanja\n oImage = ni.convolve( iImage, iKernel, mode='nearest' ) \n return oImage","def conv(\n in_channels,\n out_channels,\n kernel_size,\n stride=2,\n padding=1,\n batch_norm=True,\n init_zero_weights=False,\n):\n layers = []\n conv_layer = nn.Conv2d(\n in_channels=in_channels,\n out_channels=out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n bias=False,\n )\n if init_zero_weights:\n conv_layer.weight.data = (\n torch.randn(out_channels, in_channels, kernel_size, kernel_size) * 0.001\n )\n layers.append(conv_layer)\n\n if batch_norm:\n layers.append(nn.BatchNorm2d(out_channels))\n return nn.Sequential(*layers)","def convolveH(image=None,kernel=None):\n res = sg.fftconvolve(image,kernel,mode='full')\n res = res/res.sum()\n rowM,colM = nd.center_of_mass(res)\n size = image.shape[0]/2\n resnew = res[int(rowM)-size:int(rowM)+size,int(colM)-size:int(colM)+size]\n return resnew","def conv(in_planes,\n out_planes,\n kernel_size=3,\n stride=1,\n padding=1,\n dilation=1,\n groups=1):\n return nn.Conv2d(in_planes,\n out_planes,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n dilation=dilation,\n groups=groups,\n bias=False)","def moffat_convolution(im_array,n_fwhm,beta,fwhm) :\n\n r_s = fwhm/(2. *math.sqrt(2.**(1./beta)-1.))\n\t\n im_kernel_array = gauss_kernel(n_fwhm,beta,r_s)\n conv_image = signal.convolve(im_array,im_kernel_array,mode = 'same')\n\n return (conv_image)","def moffat_convolution_fft(im_array,n_fwhm,beta,fwhm) :\n\n r_s = fwhm/(2. *math.sqrt(2.**(1./beta)-1.))\n\n im_kernel_array = moffat_kernel(n_fwhm,beta,r_s)\n fftconv_image = signal.fftconvolve(im_array,im_kernel_array,mode = 'same')\n\n return (fftconv_image)","def conv_2D(img,kernel,stride=1):\n\n m,n = img.shape\n r,c = kernel.shape\n\n kernel = np.flip(kernel,axis=1)\n kernel = np.flip(kernel,axis=0)\n\n c_m, c_n = int(np.ceil((m-r+1)/stride)), int(np.ceil((n-c+1)/stride))\n img_conv = np.zeros((c_m,c_n),dtype=float)\n\n for i,j in it.product(range(c_m),range(c_n)):\n img_conv[i,j] = (img[i*stride:i*stride+r,j*stride:j*stride+c] * kernel).sum()\n\n return img_conv","def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\n layers = []\n # TODO: ? shouldn't bias be set to NOT batch_norm instead of always being False ? \n conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, \n kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\n \n layers.append(conv_layer)\n\n if batch_norm:\n layers.append(nn.BatchNorm2d(out_channels))\n return nn.Sequential(*layers)","def conv3x3(in_ch, out_ch, stride=1):\n return nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1)","def convolutional(X, X_test, input_shape, n_filters, filter_size):\n\n\tfilters_shape = (n_filters, input_shape[1], filter_size[0], filter_size[1])\n\tfilters = theano.shared(\n\t\tnumpy.random.uniform(low=-0.1, high=0.1, size=filters_shape).astype(numpy.float32),\n\t\t'conv_filters'\n\t)\n\n\toutput_shape = (input_shape[0], n_filters, input_shape[2], input_shape[3])\n\n\toutput = conv2d(input=X, filters=filters, filter_shape=filters_shape, image_shape=input_shape, border_mode='full')\n\toutput_test = conv2d(input=X_test, filters=filters, filter_shape=filters_shape, image_shape=input_shape, border_mode='full')\n\n\tshift_x = (filter_size[0] - 1) // 2\n\tshift_y = (filter_size[1] - 1) // 2\n\n\toutput = output[:,:,shift_x:input_shape[2]+shift_x,shift_y:input_shape[3]+shift_y]\n\toutput_test = output_test[:,:,shift_x:input_shape[2]+shift_x,shift_y:input_shape[3]+shift_y]\n\n\treturn output, output_test, [filters], output_shape","def test_on_conv_transpose_2d_stride(self):\n\n # Channels/Colors, #filters, filter_size (square)\n conv_filter = objax.nn.ConvTranspose2D(1, 1, 2, strides=2, padding=objax.ConvPadding.VALID)\n weights = objax.TrainVar(jn.array([[[[1., 2.], [3., 4.]]]]).transpose((2, 3, 0, 1)))\n conv_filter.w = weights\n image = jn.array([[[[2., 1., 3., 4.],\n [5., 6., 7., 8.], [9., 10., 11., 12.], [13., 14., 15., 16.]]]])\n # NCHW: Batch, Channels/Colors, Height, Width\n features = conv_filter(image)\n expected_features = jn.array([[[[2., 4., 1., 2., 3., 6., 4., 8.],\n [6., 8., 3., 4., 9., 12., 12., 16.],\n [5., 10., 6., 12., 7., 14., 8., 16.],\n [15., 20., 18., 24., 21., 28., 24., 32.],\n [9., 18., 10., 20., 11., 22., 12., 24.],\n [27., 36., 30., 40., 33., 44., 36., 48.],\n [13., 26., 14., 28., 15., 30., 16., 32.],\n [39., 52., 42., 56., 45., 60., 48., 64.]]]])\n self.assertEqual(features.shape, (1, 1, 8, 8))\n self.assertTrue(jn.array_equal(features, expected_features))","def forward(self, image):\n height, width = image.shape\n H_out, W_out = output_shape(height, width, self.filter_size, self.padding, self.stride)\n output = np.zeros((H_out, W_out, self.num_filters))\n padded_image = pad_2d(image, self.padding)\n for patch, i, j in self.image_patch(padded_image):\n output[i,j] = np.sum(patch*self.conv_filter, axis=(1,2))\n return output","def conv(h,x):\n\n final_conv_dim=(512,512) ## dimension of the convolution result before cropping\n x_dim=(x.size(2),x.size(3)) ## dimension of x\n h_dim=(h.size(2),h.size(3)) ## dimension of h\n crop_dim=x_dim ## image obtained after cropping is the same dimension as the image x\n\n padding=(final_conv_dim[0]-(x_dim[0]-h_dim[0]+1),final_conv_dim[1]-(x_dim[1]-h_dim[1]+1)) ## calculate the amount of padding required given final_conv_dim, x_dim and h_dim\n\n x_pad=F.pad(x,(padding[0]//2,padding[0]//2+1,padding[1]//2,padding[1]//2+1)) ## pad x\n y=F.conv2d(x_pad,h.flip(2,3),padding=0) ## convolve x_pad with h\n \n ## starting and ending values along the column and the rows for cropping\n starti=(final_conv_dim[0]-crop_dim[0])//2 \n endi=crop_dim[0]+starti\n startj=(final_conv_dim[1]-crop_dim[1])//2\n endj=crop_dim[1]+startj\n\n ## Cropping\n y=y[:,:,starti:endi,startj:endj]\n \n return y","def conv2d(input, filters, image_shape=None, filter_shape=None,\r\n border_mode='valid', subsample=(1,1), **kargs):\r\n assert input.ndim in (2,3)\r\n assert filters.ndim in (2,3)\r\n\r\n ### use shape information if it is given to us ###\r\n if filter_shape and image_shape:\r\n if input.ndim==3:\r\n bsize = image_shape[0]\r\n else:\r\n bsize = 1\r\n imshp = (1,) + tuple(image_shape[-2:])\r\n\r\n if filters.ndim==3:\r\n nkern = filter_shape[0]\r\n else:\r\n nkern = 1\r\n kshp = filter_shape[-2:]\r\n else:\r\n nkern, kshp = None, None\r\n bsize, imshp = None, None\r\n\r\n ### reshape tensors to 4D, for compatibility with ConvOp ###\r\n if input.ndim==3:\r\n sym_bsize = input.shape[0]\r\n else:\r\n sym_bsize = 1\r\n\r\n if filters.ndim==3:\r\n sym_nkern = filters.shape[0]\r\n else:\r\n sym_nkern = 1\r\n\r\n new_input_shape = tensor.join(0, tensor.stack(sym_bsize,1), input.shape[-2:])\r\n input4D = tensor.reshape(input, new_input_shape, ndim=4)\r\n\r\n new_filter_shape = tensor.join(0, tensor.stack(sym_nkern,1), filters.shape[-2:])\r\n filters4D = tensor.reshape(filters, new_filter_shape, ndim=4)\r\n\r\n ### perform actual convolution ###\r\n op = conv.ConvOp(output_mode=border_mode,\r\n dx=subsample[0], dy=subsample[1],\r\n imshp=imshp, kshp=kshp, nkern=nkern, bsize=bsize,**kargs)\r\n\r\n output = op(input4D, filters4D)\r\n\r\n # flatten to 3D tensor if convolving with single filter or single image\r\n if input.ndim==2 or filters.ndim==2:\r\n output = tensor.flatten(output.T, outdim=3).T\r\n\r\n return output","def conv(f, kerneltype, kernelwidth, boundary='extend'):\n if kerneltype == 'box': kernel = Box1DKernel(kernelwidth)\n elif kerneltype == 'gaussian': kernel = Gaussian1DKernel(kernelwidth)\n fconv = convolve(f, kernel, boundary=boundary)\n return fconv","def cnn(scaled_images, **kwargs):\n activ = tf.nn.relu\n # layer_1 = activ(conv(scaled_images, 'c1', n_filters=32,\n # filter_size=8, stride=4, init_scale=np.sqrt(2), **kwargs))\n layer_1 = activ(conv(scaled_images, 'c1', n_filters=64,\n filter_size=3, stride=2, init_scale=np.sqrt(2), **kwargs))\n layer_2 = activ(conv(layer_1, 'c2', n_filters=64,\n filter_size=3, stride=1, init_scale=np.sqrt(2), **kwargs))\n layer_3 = activ(conv(layer_2, 'c3', n_filters=64,\n filter_size=3, stride=1, init_scale=np.sqrt(2), **kwargs))\n layer_3 = conv_to_fc(layer_3)\n fc1 = activ(linear(layer_3, 'fc1', n_hidden=128, init_scale=np.sqrt(2)))\n fc2 = activ(linear(fc1, 'fc2', n_hidden=128, init_scale=np.sqrt(2)))\n return fc2","def preprocess_conv(x_train, x_test, percentage, stride, grayscale=True,\n verbose=0):\n if grayscale:\n x_train = dct_set_gray(x_train, stride, verbose=verbose)\n x_test = dct_set_gray(x_test, stride, verbose=verbose)\n\n variances = np.var(x_train, axis=0)\n stdevs = np.sqrt(variances)\n idx = get_clip_index_conv(stdevs, percentage)\n for image in x_train:\n image[idx] = 0\n for image in x_test:\n image[idx] = 0\n else:\n x_train = dct_set_color(x_train, stride, verbose=verbose)\n x_test = dct_set_color(x_test, stride, verbose=verbose)\n if K.image_data_format() == 'channels_first':\n for i in range(3):\n variances = np.var(x_train[:, i, :, :], axis=0)\n stdevs = np.sqrt(variances)\n idx = get_clip_index_conv(stdevs, percentage)\n for image in x_train:\n channel = image[i]\n channel[idx] = 0\n for image in x_test:\n channel = image[i]\n channel[idx] = 0\n else:\n for i in range(3):\n variances = np.var(x_train[:, :, :, i], axis=0)\n stdevs = np.sqrt(variances)\n idx = get_clip_index_conv(stdevs, percentage)\n for image in x_train:\n channel = image[:, :, i]\n channel[idx] = 0\n for image in x_test:\n channel = image[:, :, i]\n channel[idx] = 0\n\n return x_train, x_test","def conv_block(index,\n in_channels,\n out_channels=N_FILTERS,\n padding=0,\n pooling=True):\n if pooling:\n conv = nn.Sequential(\n OrderedDict([\n ('conv'+str(index), nn.Conv2d(in_channels, out_channels, \\\n K_SIZE, padding=padding)),\n ('bn'+str(index), nn.BatchNorm2d(out_channels, momentum=1, \\\n affine=True)),\n ('relu'+str(index), nn.ReLU(inplace=True)),\n ('pool'+str(index), nn.MaxPool2d(MP_SIZE))\n ]))\n else:\n conv = nn.Sequential(\n OrderedDict([\n ('conv'+str(index), nn.Conv2d(in_channels, out_channels, \\\n K_SIZE, padding=padding)),\n ('bn'+str(index), nn.BatchNorm2d(out_channels, momentum=1, \\\n affine=True)),\n ('relu'+str(index), nn.ReLU(inplace=True))\n ]))\n return conv","def dense_conv_forward_2d_fast(\n inp_image: np.ndarray,\n filter: np.ndarray,\n output: np.ndarray,\n strides,\n padding):\n INPUT_DIMENSIONS = 4\n HEIGHT_IDX = 2 # H\n WIDTH_IDX = 3 # W\n INPUT_CHANNELS_IDX = 1 # C\n OUTPUT_CHANNELS_IDX = 0 # K\n NUM_IMAGES_IDX = 0 # N\n\n x_shape = inp_image.shape\n f_shape = filter.shape\n o_shape = output.shape\n\n if len(x_shape) != INPUT_DIMENSIONS or len(f_shape) != INPUT_DIMENSIONS or len(o_shape) != INPUT_DIMENSIONS:\n raise RuntimeError(\"conv2d: input, filter, and output must all have four dimensions.\")\n\n assert (x_shape[HEIGHT_IDX] % strides[1] == 0, \"Input height is not evenly divisible by stride size.\")\n assert (x_shape[WIDTH_IDX] % strides[0] == 0, \"Input width is not evenly divisible by stride size.\")\n assert (x_shape[INPUT_CHANNELS_IDX] == f_shape[INPUT_CHANNELS_IDX],\n \"Number of channels in input does not match number channels expected by convolution.\")\n assert (o_shape[OUTPUT_CHANNELS_IDX] == f_shape[OUTPUT_CHANNELS_IDX],\n \"Number of channels in output does not match number channels expected by convolution.\")\n\n N = x_shape[NUM_IMAGES_IDX]\n H = x_shape[HEIGHT_IDX]\n W = x_shape[WIDTH_IDX]\n K = f_shape[OUTPUT_CHANNELS_IDX]\n C = f_shape[INPUT_CHANNELS_IDX]\n R = f_shape[HEIGHT_IDX]\n S = f_shape[WIDTH_IDX]\n P = (x_shape[HEIGHT_IDX] - f_shape[HEIGHT_IDX]) / strides[1] + 1 # output height\n Q = (x_shape[WIDTH_IDX] - f_shape[WIDTH_IDX]) / strides[0] + 1 # output width\n\n assert (o_shape[HEIGHT_IDX] == P, f\"Output height should be {P}.\")\n assert (o_shape[WIDTH_IDX] == Q, f\"Output width should be {Q}.\")\n\n # output[...] = 0\n\n for i in range(0, (H - R) + 1, strides[1]):\n y = int(i / strides[1])\n for j in range(0, (W - S) + 1, strides[0]):\n x = int(j / strides[1])\n inp_view = inp_image[:, :, i:i + R, j:j + S]\n for k in range(0, K):\n f_slice = filter[k, :, :, :]\n prod = np.sum(inp_view * f_slice, (INPUT_CHANNELS_IDX, HEIGHT_IDX, WIDTH_IDX))\n output[:, k, y, x] = prod","def conv2d(x, *args, **kwargs):\n i = 0\n out = []\n batch_size = 10000\n while i < x.size(0): \n out.append(F.conv2d(x[i:min(i+batch_size, x.size(0))], *args, **kwargs))\n i += batch_size\n return torch.cat(out, 0)","def convolution(\n input, # pylint: disable=redefined-builtin\n filter, # pylint: disable=redefined-builtin\n padding,\n strides=None,\n dilation_rate=None,\n name=None,\n data_format=None,\n filters=None,\n dilations=None): # pylint: disable=g-doc-args\n filter = deprecated_argument_lookup(\"filters\", filters, \"filter\", filter)\n dilation_rate = deprecated_argument_lookup(\n \"dilations\", dilations, \"dilation_rate\", dilation_rate)\n return convolution_internal(\n input,\n filter,\n strides=strides,\n padding=padding,\n data_format=data_format,\n dilations=dilation_rate,\n name=name)","def conv(input, inch, outch, filter_h, filter_w, stride_h, stride_w, padding='SAME', name='conv_layer'):\n with tf.name_scope(name) as scope:\n layer = tf.layers.conv2d(input, outch, filter_h, strides=(stride_h, stride_w), padding=\"same\",\n activation=tf.nn.relu)\n return layer","def dense_conv_forward_2d(inp_image: np.ndarray, kernel: np.ndarray, stride, padding):\n assert len(inp_image.shape) == 3, 'single 2D images only. No batches.'\n assert len(kernel.shape) == 4\n\n height, width, colors = inp_image.shape\n kernel_height, kernel_width, colors_in, colors_out = kernel.shape\n kernel_stride_x, kernel_stride_y = stride\n kernel_padding_x, kernel_padding_y = padding\n i_f = int(np.floor(kernel_width / 2.0))\n j_f = int(np.floor(kernel_height / 2.0))\n\n out_pixels = np.zeros((height, width, colors_out))\n for y in range(kernel_padding_y, height - kernel_padding_y,\n kernel_stride_y): # todo: add kernel_padding_y and kernel_stride_y fix to glsl\n for x in range(kernel_padding_x, width - kernel_padding_x,\n kernel_stride_x): # todo: add kernel_padding_x and kernel_stride_x fix to glsl\n output_select = [y, x, 0]\n input_select = np.asarray(\n [y * kernel_stride_y, x * kernel_stride_x, 0]\n )\n for i in range(-np.int(np.floor(kernel_width / 2.0)), np.int(np.ceil(kernel_width / 2.0))):\n for j in range(-np.int(np.floor(kernel_height / 2.0)), np.int(np.ceil(kernel_height / 2.0))):\n in_pixel_select = np.copy(input_select)\n in_pixel_select += [j, i, 0]\n for co in range(colors_out):\n output_select[2] = co\n for ci in range(colors_in):\n in_pixel_select[2] = ci\n kernel_select = np.asarray([j_f + j, i_f + i, ci, co])\n\n out_pixels[tuple(output_select)] += kernel[tuple(kernel_select)] * inp_image[\n tuple(in_pixel_select)]\n return out_pixels","def convolution_2d(img, kernel):\n # TODO write convolution of arbritrary sized convolution here\n # Hint: you need the kernelsize\n\n offset = int(kernel.shape[0] / 2)\n irows, icols = img.shape\n newimg = np.zeros((irows - offset, icols - offset, offset * 2 + 1, offset * 2 + 1))\n nrows, ncols, _, _ = newimg.shape\n for x in range(nrows - 1):\n for y in range(ncols - 1):\n newimg[x, y, :, :] = img[x:x + offset * 2 + 1, y:y + offset * 2 + 1]\n newimg *= kernel\n\n newimg = np.sum(newimg, axis=3)\n newimg = np.sum(newimg, axis=2)\n\n return newimg","def conv(\n in_channels: int,\n out_channels: int,\n stride: int = 1,\n groups: int = 1,\n kernel_size: int = 3,\n padding: int = 1,\n) -> List[torch.nn.Module]:\n return [\n torch.nn.Conv2d(\n in_channels,\n out_channels,\n kernel_size=kernel_size,\n stride=stride,\n padding=padding,\n groups=groups,\n bias=False,\n ),\n torch.nn.BatchNorm2d(out_channels, eps=_BN_EPS, momentum=_BN_MOMENTUM),\n torch.nn.ReLU(inplace=True),\n ]","def convolve(self, psf):\n if self.image is None or psf.image is None:\n raise ValueError('Both images to convolve have to be initialized!')\n self.image = fftconvolve(self.image, psf.image, mode='full')\n self.image = rescale_intensity(self.image, out_range=(0, 255))\n self.metadata['Convolved'] = True\n return self.image","def conv2(x,y,mode='same'):\n\n if not(mode == 'same'):\n raise Exception(\"Mode not supported\")\n\n # Add singleton dimensions\n if (len(x.shape) < len(y.shape)):\n dim = x.shape\n for i in range(len(x.shape),len(y.shape)):\n dim = (1,) + dim\n x = x.reshape(dim)\n elif (len(y.shape) < len(x.shape)):\n dim = y.shape\n for i in range(len(y.shape),len(x.shape)):\n dim = (1,) + dim\n y = y.reshape(dim)\n\n origin = ()\n\n # Apparently, the origin must be set in a special way to reproduce\n # the results of scipy.signal.convolve and Matlab\n for i in range(len(x.shape)):\n if ( (x.shape[i] - y.shape[i]) % 2 == 0 and\n x.shape[i] > 1 and\n y.shape[i] > 1):\n origin = origin + (-1,)\n else:\n origin = origin + (0,)\n\n z = convolve(x,y, mode='constant', origin=origin)\n\n return z","def convolveAndDownsample(img):\n # Select every other pixel from G\n G = sp.signal.convolve2d(img, guassianFilter, 'same')\n return G[::2, ::2]","def _cconvolve(x, H, nfft, wlen, axis):\n \n # pad with wlen-1 zeros for overlap & FFT\n x = pad_along_axis(x, [0, wlen - 1], axis=axis)\n xf = np.fft.rfft(x, nfft, axis=axis)\n \n # take product with window in freq. domain\n product = multiply_along_axis(xf, H, axis=axis)\n\n # back transform to sample domain and return\n return np.fft.irfft(product, axis=axis).real","def _conv2d_block(in_f, out_f, *args, **kwargs):\n return nn.Sequential(\n nn.Conv2d(in_f, out_f, *args, **kwargs),\n nn.BatchNorm2d(out_f),\n nn.ReLU(),\n nn.Dropout2d(p=0.2)\n )","def conv(c_in, c_out, k_size, stride=2, pad=1, bn=True):\n layers = []\n layers.append(nn.Conv2d(c_in, c_out, k_size, stride, pad, bias=True)) # bias=False\n if bn:\n layers.append(nn.BatchNorm2d(c_out))\n return nn.Sequential(*layers)","def _conv2d_bn(x,\n filters,\n kernel_size=(3, 3),\n padding='same',\n strides=(1, 1),\n name=None):\n if name is not None:\n bn_name = name + '_bn'\n conv_name = name + '_conv'\n else:\n bn_name = None\n conv_name = None\n bn_axis = 1 if backend.image_data_format() == 'channels_first' else 3\n x = layers.Conv2D(\n filters=filters,\n kernel_size=kernel_size,\n strides=strides,\n padding=padding,\n use_bias=False,\n name=conv_name)(x)\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name)(x)\n x = layers.Activation('relu', name=name)(x)\n return x","def _conv_bn_layer(cnn_input, filters, kernel_size, strides, layer_id):\n output = tf.keras.layers.Conv2D(\n filters=filters, kernel_size=kernel_size, strides=strides, padding=\"same\",\n activation=\"linear\", name=\"cnn_{}\".format(layer_id))(cnn_input)\n output = tf.keras.layers.BatchNormalization(\n momentum=_MOMENTUM, epsilon=_EPSILON)(output)\n return output","def apply_conv2d_1x1(input_layer, num_classes, kernel_size=1):\n # tf.layers.conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', ...,\n # kernel_initializer=None, ... , kernel_regularizer=None)\n return tf.layers.conv2d(input_layer, num_classes, kernel_size, padding='same',\n kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),\n kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))","def _conv_bn(**conv_params):\n filters = conv_params[\"filters\"]\n kernel_size = conv_params[\"kernel_size\"]\n strides = conv_params.setdefault(\"strides\", (1, 1))\n kernel_initializer = conv_params.setdefault(\"kernel_initializer\", \"he_normal\")\n padding = conv_params.setdefault(\"padding\", \"same\")\n kernel_regularizer = conv_params.setdefault(\"kernel_regularizer\", l2(1.e-4))\n name = conv_params[\"name\"]\n\n def f(input):\n x = Conv2D(filters=filters, kernel_size=kernel_size,\n strides=strides, padding=padding,\n kernel_initializer=kernel_initializer,\n kernel_regularizer=kernel_regularizer,\n name=name + '_conv')(input)\n x = BatchNormalization(axis=CHANNEL_AXIS, name=name + '_bn')(x)\n return x \n return f","def compute_conv(in_size, kernel, stride, padding):\n return (in_size + 2 * padding - kernel) // stride + 1","def __init__(self, in_channels, out_channels):\n super(CNN, self).__init__()\n self.conv1 = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=5, padding=1)","def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, normalization=True, norm_type='instance_norm'):\n layers = []\n conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, \n kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\n \n layers.append(conv_layer)\n\n # optional normalization layer\n if normalization == True and norm_type == 'instance_norm':\n layers.append(nn.InstanceNorm2d(out_channels))\n elif normalization == True and norm_type == 'batch_norm':\n layers.append(nn.BatchNorm2d(out_channels))\n return nn.Sequential(*layers)"],"string":"[\n \"def clConvolution(self, size, mask):\",\n \"def convolve_channels(images, kernel, padding='same', stride=(1, 1)):\\n m, h, w, c = images.shape\\n KernelHeight, kernelWidth, c = kernel.shape\\n StrideHeight, StrideWidth = stride\\n\\n if padding == 'valid':\\n PaddingHeight = 0\\n PaddingWidth = 0\\n elif padding == 'same':\\n PaddingHeight = int(\\n (((h - 1) * StrideHeight + KernelHeight - h) / 2) + 1)\\n PaddingWidth = int((((w - 1) * StrideWidth + kernelWidth - w) / 2) + 1)\\n else:\\n PaddingHeight, PaddingWidth = padding\\n\\n OutputH = int(((h + 2 * PaddingHeight - KernelHeight) / StrideHeight) + 1)\\n OutputW = int(((w + 2 * PaddingWidth - kernelWidth) / StrideWidth) + 1)\\n\\n ImagePadded = np.pad(\\n images,\\n ((0, 0), (PaddingHeight, PaddingHeight),\\n (PaddingWidth, PaddingWidth), (0, 0)),\\n 'constant'\\n )\\n\\n output = np.zeros((m, OutputH, OutputW))\\n ImageRange = np.arange(m)\\n\\n for i_OutputH in range(OutputH):\\n for i_OutputW in range(OutputW):\\n s_i_OutputH = i_OutputH * StrideHeight\\n s_i_OutputW = i_OutputW * StrideWidth\\n flt = ImagePadded[ImageRange,\\n s_i_OutputH:KernelHeight + s_i_OutputH,\\n s_i_OutputW:kernelWidth + s_i_OutputW,\\n :]\\n output[ImageRange, i_OutputH, i_OutputW] = np.sum(\\n flt * kernel, axis=(1, 2, 3))\\n return output\",\n \"def convolve(image, pixel_filter, channels=3, name=None):\\n with tf.name_scope(name, 'convolve'):\\n tf.compat.v1.assert_type(image, tf.float32)\\n channel_filter = tf.eye(channels)\\n filter_ = (tf.expand_dims(tf.expand_dims(pixel_filter, -1), -1) *\\n tf.expand_dims(tf.expand_dims(channel_filter, 0), 0))\\n result_batch = tf.nn.conv2d(tf.stack([image]), # batch\\n filter=filter_,\\n strides=[1, 1, 1, 1],\\n padding='SAME')\\n return result_batch[0] # unbatch\",\n \"def convolve_channels(images, kernel, padding='same', stride=(1, 1)):\\n m, image_h, image_w, image_c = images.shape\\n kernel_h, kernel_w, kernel_c = kernel.shape\\n stride_h, stride_w = stride\\n\\n if isinstance(padding, tuple):\\n padding_h, padding_w = padding\\n if padding is 'same':\\n padding_h = int(((stride_h * image_h)\\n - stride_h + kernel_h - image_h) / 2) + 1\\n padding_w = int(((stride_w * image_w)\\n - stride_w + kernel_w - image_w) / 2) + 1\\n if padding is 'valid':\\n padding_h, padding_w = 0, 0\\n\\n output_h = int(((image_h + (2 * padding_h) - kernel_h) / stride_h) + 1)\\n output_w = int(((image_w + (2 * padding_w) - kernel_w) / stride_w) + 1)\\n conv_output = np.zeros((m, output_h, output_w))\\n\\n img_m = np.arange(0, m)\\n\\n images = np.pad(\\n images,\\n [(0, 0), (padding_h, padding_h), (padding_w, padding_w), (0, 0)],\\n mode='constant',\\n constant_values=0)\\n\\n for i in range(output_h):\\n for j in range(output_w):\\n s_h = (stride_h)\\n s_w = (stride_w)\\n multiply = images[\\n img_m,\\n i*s_h:kernel_h+i*s_h,\\n j*s_w:kernel_w+j*s_w]\\n conv_output[img_m, i, j] = np.sum(\\n np.multiply(multiply, kernel), axis=(1, 2, 3))\\n return conv_output\",\n \"def convolve_grayscale_same(images, kernel):\\n\\n # num images\\n n_images = images.shape[0]\\n\\n # input_width and input_height\\n i_h = images.shape[1]\\n i_w = images.shape[2]\\n\\n # kernel_width and kernel_height\\n\\n k_h = kernel.shape[0]\\n k_w = kernel.shape[1]\\n\\n # pad_h ⊛ = int (k_h - 1)/2\\n # pad_w ⊛ = int (k_w - 1)/2\\n p_h = int((k_h - 1) / 2)\\n p_w = int((k_w - 1) / 2)\\n\\n if k_h % 2 == 0:\\n p_h = int(k_h / 2)\\n\\n if k_w % 2 == 0:\\n p_w = int(k_w / 2)\\n\\n # output_height and output_width\\n # H = i_h + 2pad - k_h + 1, W = i_w + 2pad - k_w + 1\\n o_h = i_h + 2 * p_h - k_h + 1\\n o_w = i_w + 2 * p_w - k_w + 1\\n\\n if k_h % 2 == 0:\\n o_h = i_h + 2 * p_h - k_h\\n\\n if k_w % 2 == 0:\\n o_w = i_w + 2 * p_w - k_w\\n\\n # creating outputs of size: n_images, o_h x o_w\\n outputs = np.zeros((n_images, o_h, o_w))\\n\\n # creating pad of zeros around the output images\\n padded_imgs = np.pad(images,\\n pad_width=((0, 0), (p_h, p_h), (p_w, p_w)),\\n mode=\\\"constant\\\",\\n constant_values=0)\\n\\n # vectorizing the n_images into an array\\n imgs_arr = np.arange(0, n_images)\\n\\n # iterating over the output array and generating the convolution\\n for x in range(o_h):\\n for y in range(o_w):\\n x1 = x + k_h\\n y1 = y + k_w\\n outputs[imgs_arr, x, y] = np.sum(np.multiply(\\n padded_imgs[imgs_arr, x: x1, y: y1], kernel), axis=(1, 2))\\n\\n return outputs\",\n \"def convolve_grayscale_same(images, kernel):\\n m = images.shape[0]\\n h = images.shape[1]\\n w = images.shape[2]\\n kh = kernel.shape[0]\\n kw = kernel.shape[1]\\n padh = int(kh / 2)\\n padw = int(kw / 2)\\n pad = ((0, 0), (padh, padh), (padw, padw))\\n conv = np.zeros([m, h, w])\\n imagePad = np.pad(images, pad_width=pad, mode='constant')\\n for i in range(h):\\n for j in range(w):\\n image = imagePad[:, i:i+kh, j:j+kw]\\n conv[:, i, j] = np.multiply(image, kernel).sum(axis=(1, 2))\\n return conv\",\n \"def convolve_grayscale_same(images, kernel):\\n imgshape = images.shape\\n h = images.shape[1]\\n w = images.shape[2]\\n kh = kernel.shape[0]\\n kw = kernel.shape[1]\\n # conved = np.zeros((imgshape[0], h - kh + 1, w - kw + 1))\\n conved = np.zeros(imgshape)\\n ph = int((kh) / 2)\\n pw = int((kw) / 2)\\n # print(conved.shape)\\n # print(kernel.shape, images.shape)\\n # print(kernel[None, :, :].shape)\\n padimg = np.pad(images, ((0, 0), (ph, ph), (pw, pw)), 'constant',\\n constant_values=0)\\n for i in range(0, h):\\n for j in range(0, w):\\n subs = padimg[:, i:i + kh, j:j + kw]\\n # ip = i + ph\\n # jp = j + pw\\n conved[:, i, j] = np.sum((kernel[None, :, :] * subs),\\n axis=(1, 2))\\n\\n return conved\",\n \"def convolve_grayscale_valid(images, kernel):\\n imgshape = images.shape\\n h = images.shape[1]\\n w = images.shape[2]\\n kh = kernel.shape[0]\\n kw = kernel.shape[1]\\n conved = np.zeros((imgshape[0], h - kh + 1, w - kw + 1))\\n\\n for i in range(0, h - kh + 1):\\n for j in range(0, w - kw + 1):\\n subs = images[:, i:i + kh, j:j + kw]\\n conved[:, i, j] = np.sum((kernel[None, :, :] * subs),\\n axis=(1, 2))\\n\\n return conved\",\n \"def MyConvolve(img, ff):\\n result = np.zeros(img.shape)\\n x_len = img.shape[0]\\n y_len = img.shape[1]\\n\\n ff = np.flipud(np.fliplr(ff)) # Flip filters\\n\\n # Apply filter to pixels\\n for x in range(1, x_len - 1):\\n for y in range(1, y_len - 1):\\n # Left column\\n top_left = img[x - 1, y - 1] * ff[0, 0]\\n left = img[x, y - 1] * ff[1, 0]\\n btm_left = img[x + 1, y - 1] * ff[2, 0]\\n # Middle column\\n top = img[x - 1, y] * ff[0, 1]\\n middle = img[x, y] * ff[1, 1]\\n btm = img[x + 1, y] * ff[2, 1]\\n # Right column\\n top_right = img[x - 1, y + 1] * ff[0, 2]\\n right = img[x, y + 1] * ff[1, 2]\\n btm_right = img[x + 1, y + 1] * ff[2, 2]\\n\\n result[x, y] = top_left + left + btm_left + top + middle + btm + top_right + right + btm_right\\n\\n return result\",\n \"def convolve_im(im: np.array,\\n kernel: np.array,\\n verbose=True):\\n\\t\\n ### START YOUR CODE HERE ### (You can change anything inside this block) \\n\\t\\n H,W = np.shape(im)\\n h,w = np.shape(kernel)\\n t_b = (H-h)//2\\n l_r = (W-w)//2\\n kernel_padded = np.pad(kernel, ((t_b, t_b+1),(l_r, l_r+1)), 'constant')\\n kernel_padded = np.pad(kernel, ((0, 2*t_b),(0, 2*l_r)), 'constant')\\n fft_kernel = np.fft.fft2(kernel_padded, s=None, axes=(-2, -1), norm=None)\\n \\n \\n im_fft = np.fft.fft2(im, s=None, axes=(-2, -1), norm=None) \\n im_filt = im_fft*fft_kernel \\n conv_result = np.fft.ifft2(im_filt, s=None, axes=(-2, -1), norm=None).real \\n\\n if verbose:\\n # Use plt.subplot to place two or more images beside eachother\\n plt.figure(figsize=(12, 4))\\n # plt.subplot(num_rows, num_cols, position (1-indexed))\\n plt.subplot(1, 2, 1)\\n plt.imshow(im, cmap=\\\"gray\\\")\\n plt.subplot(1, 2, 2) \\n plt.imshow(conv_result, cmap=\\\"gray\\\")\\n\\n ### END YOUR CODE HERE ###\\n return conv_result\",\n \"def image_conv(image, kernel):\\n \\n # Filter2D used for performance\\n return cv2.filter2D(image, -1, kernel)\",\n \"def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\\n weight = weight_variable()\\n return nn.Conv2d(in_channels, out_channels,\\n kernel_size=kernel_size, stride=stride, padding=padding,\\n weight_init=weight, has_bias=False, pad_mode=\\\"valid\\\")\",\n \"def convolve(self, *args, **kwargs):\\n return _image.image_convolve(self, *args, **kwargs)\",\n \"def convolution(image, kernel):\\n\\n #Se encuentra la dimencion de la imagen\\n if len(image.shape) == 3: #De 3 dimenciones\\n print(\\\"Dimenciones de imagen: {}\\\".format(image.shape))\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) #Se cambia a dos dimenciones\\n print(\\\"Nuevas dimenciones: {}\\\".format(image.shape))\\n else:\\n print(\\\"Dimenciones de imagen: {}\\\".format(image.shape))\\n\\n image_row, image_col = image.shape #asigna alto y ancho de la imagen \\n kernel_row, kernel_col = kernel.shape #asigna alto y ancho del filtro\\n\\n output_x = (image_col - (kernel_col / 2) * 2) + 1 #asigna el ancho del output\\n output_y = (image_row - (kernel_row / 2) * 2) + 1 #asigna el alto del output\\n \\n output = np.zeros([int(output_y), int(output_x)]) #matriz donde se guarda el resultado\\n\\n padded_size = int((kernel_row - 1) / 2) #Tamaño de padding\\n\\n #Obtenemos la imagen con padding\\n padded_image = padding(image,padded_size)\\n \\n for row in range(int(output_y)):\\n for col in range(int(output_x)):\\n output[row, col] = conv_helper(\\n padded_image[row:row + kernel_row, \\n col:col + kernel_col], kernel)\\n \\n # Se muestra la imagen en pantalla\\n plt.imshow(output, cmap='gray')\\n plt.title(\\\"Edge detection\\\")\\n plt.show()\\n\\n return output\",\n \"def conv(in_channels, out_channels, kernel_size, stride=1, padding=0):\\n weight = weight_variable()\\n return nn.Conv2d(\\n in_channels,\\n out_channels,\\n kernel_size=kernel_size,\\n stride=stride,\\n padding=padding,\\n weight_init=weight,\\n has_bias=False,\\n pad_mode=\\\"valid\\\",\\n )\",\n \"def convolve_image(image, kernel, mode='valid', boundary='symm'):\\n chs = []\\n for d in range(image.shape[2]):\\n channel = sig.convolve2d(image[:, :, d], kernel, mode=mode, boundary=boundary)\\n chs.append(channel)\\n return np.stack(chs, axis=2)\",\n \"def convolve(images, kernels, padding='same', stride=(1, 1)):\\n m = images.shape[0]\\n h = images.shape[1]\\n w = images.shape[2]\\n c = images.shape[3]\\n kh = kernels.shape[0]\\n kw = kernels.shape[1]\\n nc = kernels.shape[3]\\n sh = stride[0]\\n sw = stride[1]\\n\\n if padding == 'same':\\n ph = max((h - 1) * sh + kh - h, 0)\\n pt = int(np.ceil(ph / 2))\\n pb = pt\\n pw = max((w - 1) * sw + kw - w, 0)\\n pl = int(np.ceil(pw / 2))\\n pr = pl\\n elif padding == 'valid':\\n pt, pb, pl, pr = 0, 0, 0, 0\\n else:\\n pt, pb = padding[0], padding[0]\\n pl, pr = padding[1], padding[1]\\n\\n oh = ((h - kh + pt + pb) // sh) + 1\\n ow = ((w - kw + pl + pr) // sw) + 1\\n\\n images = np.pad(images, pad_width=((0, 0), (pt, pb), (pl, pr), (0, 0)),\\n mode='constant', constant_values=0)\\n\\n conv = np.zeros((m, oh, ow, nc))\\n for k in range(nc):\\n for i in range(oh):\\n for j in range(ow):\\n aux = images[:, i * sh:i * sh + kh, j * sw:j * sw + kw] \\\\\\n * kernels[:, :, :, k]\\n conv[:, i, j, k] = np.sum(aux, axis=(1, 2, 3))\\n return conv\",\n \"def conv(n_inputs, n_filters, kernel_size=3, stride=1, bias=False) -> torch.nn.Conv2d:\\n return nn.Conv2d(n_inputs, n_filters,\\n kernel_size=kernel_size, stride=stride,\\n padding=kernel_size//2, bias=bias)\",\n \"def convolve_one_image(self,input4D, one_image, image_shape, \\n Pstruct, filter_shape,\\n image_index,\\n channel_index): \\n \\n \\n ## We look at the composition for the first channel in the beginning \\n rank = Pstruct[0]['U1'].shape[1]\\n fwidth = filter_shape[2]\\n fheight = filter_shape[3]\\n \\n \\n # Construct horizontal filters\\n #TODO save the filters in the correct shape\\n horizontal_filter_shape = (rank, 1, fwidth)\\n horizontal_filters = np.ndarray(horizontal_filter_shape)\\n horizontal_filters[:, 0, :] = np.transpose(Pstruct[channel_index]['U1']);\\n \\n # Output is 1 x rank x W x H\\n horizontal_conv_out = conv.conv2d(input=one_image, \\n filters = horizontal_filters,\\n filter_shape = horizontal_filter_shape, \\n image_shape = image_shape)\\n \\n # Construct vertical filters\\n vertical_filter_shape = (rank, fheight, 1)\\n vertical_filters = np.ndarray(vertical_filter_shape) \\n vertical_filters[:,:, 0] = np.transpose(Pstruct[channel_index]['U2']);\\n\\n initial_n_rows = image_shape[1]\\n final_n_rows = initial_n_rows- fwidth + 1\\n final_n_cols = image_shape[2] - fheight + 1 \\n conv_out = theano.shared(np.zeros((rank, final_n_rows, final_n_cols)))\\n for r in range(rank):\\n # temp is 1x1x imageW x imageH\\n A = conv.conv2d(input = horizontal_conv_out[:,r,:,:], \\n filters = vertical_filters[r,:,:],\\n filter_shape = (1, fheight, 1), \\n image_shape = (1, initial_n_rows, final_n_cols))\\n conv_out = T.set_subtensor(conv_out[r,:,:], A[0,:,:])\\n \\n nbr_filters = Pstruct[0]['U3'].shape[0]\\n # Final number of rows and columns \\n ## numberof images, number of filters, image width, image height\\n alphas = Pstruct[channel_index]['U3'] \\n for f in range(nbr_filters): \\n temp = theano.shared(np.zeros((final_n_rows, final_n_cols)))\\n for r in range(rank):\\n temp = temp + conv_out[r, :,:]* alphas[f, r] * Pstruct[channel_index]['lmbda'][r]; \\n input4D =T.set_subtensor(input4D[image_index,f,:,:], temp)\\n return input4D\",\n \"def img_conv(X, filter):\\n assert filter.shape[0] % 2 == 1\\n assert filter.shape[1] % 2 == 1\\n x_size = filter.shape[0] // 2\\n y_size = filter.shape[1] // 2\\n w = X.shape[0]\\n h = X.shape[1]\\n out = numpy.zeros(X.shape)\\n for r in range(w):\\n for c in range(h):\\n for x in range(filter.shape[0]):\\n pixel_x = r + x - x_size\\n if pixel_x < 0:\\n pixel_x = -pixel_x\\n if pixel_x >= w:\\n pixel_x = w - pixel_x - 2\\n for y in range(filter.shape[1]):\\n pixel_y = c + y - y_size\\n if pixel_y < 0:\\n pixel_y = -pixel_y\\n if pixel_y >= h:\\n pixel_y = h - pixel_y - 2\\n #if pixel_x >= 0 and pixel_x < w and pixel_y >= 0 and pixel_y < h:\\n out[r, c] += filter[x, y] * X[pixel_x, pixel_y]\\n return out\",\n \"def my1_conv2d(image, kernels, strides=(1, 1)):\\n raise NotImplementedError('Write me!')\",\n \"def conv(dims, inplanes, outplanes, kernel_size, stride, dilation, bias):\\n padding = math.floor((dilation * (kernel_size - 1) + 2 - stride) / 2)\\n if dims == 2:\\n return nn.Conv2d(inplanes, outplanes, kernel_size, stride,\\n padding, dilation, bias=bias)\\n elif dims == 3:\\n return nn.Conv3d(inplanes, outplanes, kernel_size, stride,\\n padding, dilation, bias=bias)\\n else:\\n raise ValueError('dimension of conv must be 2 or 3')\",\n \"def convolution(img, kernel, padding='fill'):\\n kernel = np.rot90(kernel, 2)\\n h,w = kernel.shape[:2]\\n t,b,l,r = (h-1)//2, h//2, (w-1)//2, w//2 # Use numpy padding because it works for >2d\\n padshape = [(t,b),(l,r)]+[(0,0)]*(len(img.shape[2:]))\\n padded_img = np.pad(img, padshape, mode={'fill':'constant','replicate':'edge'}[padding])\\n conved_img = np.zeros_like(img)\\n for i in 1+np.arange(-h//2,h//2):\\n for j in 1+np.arange(-w//2,w//2):\\n if kernel[t+i,l+j]==0: continue\\n conved_img += kernel[t+i,l+j]*padded_img[t+i:-b+i or None,l+j:-r+j or None]\\n return conved_img\",\n \"def convolve(img, fourier_kernel):\\n return np.fft.ifftshift(np.fft.irfft2(np.fft.rfft2(img) * fourier_kernel))\",\n \"def conv2d(args):\\n inp_ = args[0]\\n kernel = args[1]\\n stride = args[2]\\n padding = args[3]\\n (batch_size, in_channels, H, W) = inp_.shape\\n (out_channels, in_channels_t, Hk, Wk) = kernel.shape\\n Hc = int((H - Hk)/stride)+1\\n Wc = int((W - Wk)/stride)+1\\n conv_layer = np.zeros((batch_size, out_channels, Hc, Wc))\\n for batch_i in range(batch_size):\\n for o_chann_i in range(out_channels):\\n for in_chann_i in range(in_channels):\\n curr_ker = kernel[o_chann_i, in_chann_i, :, :]\\n curr_inp = inp_[batch_i, in_chann_i, :, :]\\n h_ind = 0\\n while h_ind + Hk <= H:\\n w_ind = 0\\n while w_ind + Wk <= W:\\n inp_patch = curr_inp[h_ind:h_ind+Hk, w_ind:w_ind+Wk]\\n # Sum the conv_value of all the inp_channels\\n conv_layer[batch_i, o_chann_i, h_ind//stride, w_ind//stride] += np.sum(inp_patch*curr_ker)\\n w_ind+=stride\\n h_ind+=stride\\n return conv_layer\",\n \"def convolve(images, kernels, padding='same', stride=(1, 1)):\\n m, h, w = images.shape[:3]\\n kh, kw, c, nc = kernels.shape\\n sh, sw = stride\\n if type(padding) is tuple:\\n ph, pw = padding\\n elif padding == 'valid':\\n ph, pw = 0, 0\\n else:\\n ph = (((h - 1) * sh + kh - h) // 2) + 1\\n pw = (((w - 1) * sw + kw - w) // 2) + 1\\n out_images = np.zeros((m, (h - kh + (2 * ph))//sh + 1,\\n (w - kw + (2 * pw))//sw + 1, nc))\\n images = np.pad(images, ((0, 0), (ph, ph), (pw, pw), (0, 0)), 'constant')\\n for i in range((h - kh + (2 * ph))//sh + 1):\\n for j in range((w - kw + (2 * pw))//sw + 1):\\n for n in range(nc):\\n out_images[:, i, j, n] = np.sum(kernels[:, :, :, n] * images[\\n :, i*sh: i*sh + kh, j*sw: j*sw + kw, :], axis=(1, 2, 3))\\n return out_images\",\n \"def conv1x1(in_channels, out_channels, groups=1):\\n return nn.Conv2d(\\n in_channels, \\n out_channels, \\n kernel_size=1, \\n groups=groups,\\n stride=1)\",\n \"def convolution(image, kernel, scale=None, offset=0):\\n kernel = np.array(kernel).flatten().tolist()\\n if len(kernel)==9:\\n size = (3,3)\\n elif len(kernel)==25:\\n size = (5,5)\\n else:\\n raise ValueError('Kernel size must be (3,3) or (5,5).')\\n return image.filter(ImageFilter.Kernel(size, kernel, scale, offset))\",\n \"def py_conv(img, kern, mode, subsample):\\r\\n if imported_scipy_convolve2d:\\r\\n return py_conv_scipy(img, kern, mode, subsample)\\r\\n elif mode == 'valid':\\r\\n return py_conv_valid_numpy(img, kern)[:, :, ::subsample[0],\\r\\n ::subsample[1]]\\r\\n elif mode == 'full':\\r\\n return py_conv_full_numpy(img, kern)[:, :, ::subsample[0],\\r\\n ::subsample[1]]\\r\\n else:\\r\\n raise Exception(\\\"Can't execute this kernel.\\\")\",\n \"def py_conv(img, kern, mode, subsample):\\r\\n if imported_scipy_convolve2d:\\r\\n return py_conv_scipy(img, kern, mode, subsample)\\r\\n elif mode == 'valid':\\r\\n return py_conv_valid_numpy(img, kern)[:, :, ::subsample[0],\\r\\n ::subsample[1]]\\r\\n elif mode == 'full':\\r\\n return py_conv_full_numpy(img, kern)[:, :, ::subsample[0],\\r\\n ::subsample[1]]\\r\\n else:\\r\\n raise Exception(\\\"Can't execute this kernel.\\\")\",\n \"def convolution(image: np.array, kernel: np.array) -> np.array:\\n\\n # default condition: apply SAME padding, and keep stride at 1\\n stride_x = 1\\n stride_y = 1\\n padding_y = int(len(kernel - 1) / 2)\\n padding_x = int(len((kernel[0]) - 1) / 2)\\n # create the return array with with the same dimensions as <image>,\\n # and then create a padded image\\n convolved_image = np.zeros((len(image), len(image[0])))\\n padded_image = np.zeros((len(image) + 2 * padding_y,\\n len(image[0]) + 2 * padding_x))\\n padded_image[padding_x: -padding_x, padding_y: -padding_y] = image\\n\\n for py in range(0, len(padded_image) - len(kernel), stride_y):\\n for px in range(0, len(padded_image[0]) - len(kernel[0]), stride_x):\\n # scan the matrix over columns in image array, then shift the matrix\\n # down, and repeat\\n padded_image_section = padded_image[py: py + len(kernel[0]),\\n px: px + len(kernel)]\\n # print(padded_image_section)\\n convolved_image[py, px] = int(np.tensordot(padded_image_section,\\n kernel))\\n\\n return convolved_image\",\n \"def test_conv2d():\\n img = np.array([\\n [0.3, 0.5, 0.7, 0.9],\\n [0.1, 0.3, 0.5, 0.7],\\n [0.9, 0.7, 0.5, 0.3],\\n ])\\n template = np.array([\\n [1, 0],\\n [1, 0],\\n ])\\n template = np.flipud(np.fliplr(template))\\n return fftconvolve(img, template, mode='valid')\",\n \"def _conv_block( inputs, filters, kernel, strides, nl):\\n channel_axis = 1 if K.image_data_format() == 'channels_first' else -1\\n x = Conv2D(filters, kernel, padding='same', strides=strides)(inputs)\\n x = BatchNormalization(axis=channel_axis)(x)\\n return _return_activation(x, nl)\",\n \"def convolve(img, fltr, same=False, stri=1, pad=0, repfilter=False):\\n # focus = np.array{eltype(img),2} # scope outside of if block\\n if np.ndim(img) == 3:\\n imgd, imgx, imgy = np.shape(img)\\n elif np.ndim(img) == 2:\\n imgx, imgy = np.shape(img)\\n imgd = 1\\n else:\\n print(\\\"Wrong dimensions of image file. Quitting.\\\")\\n return\\n\\n if np.ndim(fltr) == 3:\\n fd, fx, fy = np.shape(fltr)\\n elif np.ndim(fltr) == 2:\\n fx, fy = np.shape(fltr)\\n fd = 1\\n else:\\n print(\\\"Wrong dimensions of filter. Quitting.\\\")\\n return\\n\\n if fd != imgd: # as a convenience we could just replicate the 2d filter...\\n print(\\\"Depths of image and filter not equal. Quitting.\\\")\\n return\\n\\n if same:\\n pad = math.ceil((fx - 1) / 2)\\n\\n if pad > 0:\\n img = dopad(img, pad)\\n\\n # dimensions of the result of convolution\\n x_out = (imgx + 2 * pad - fx) // stri + 1\\n y_out = (imgy + 2 * pad - fy) // stri + 1\\n\\n # print(imgx, imgy)\\n\\n ret = np.zeros((x_out, y_out))\\n if imgd > 1: # slice through the depth, the zeroth (first) dimension\\n for i in zip(range(x_out), range(0, imgx, stri)):\\n for j in zip(range(y_out), range(0, imgy, stri)):\\n ret[i[0], j[0]] = np.sum(img[:, i[1]:i[1] + fx, j[1]:j[1] +\\n fy] * fltr)\\n else:\\n for i in zip(range(x_out), range(0, imgx, stri)):\\n for j in zip(range(y_out), range(0, imgy, stri)):\\n ret[i[0], j[0]] = np.sum(img[i[1]:i[1] + fx, j[1]:j[1] +\\n fy] * fltr)\\n return ret\",\n \"def _convk(\\n in_channels, out_channels, kernel_size=3, stride=1, groups=1, dilation=1, bias=False\\n):\\n padding = dilation * (kernel_size - 1) // 2\\n return Conv1d(\\n in_channels,\\n out_channels,\\n kernel_size=kernel_size,\\n stride=stride,\\n padding=padding,\\n groups=groups,\\n bias=bias,\\n dilation=dilation,\\n )\",\n \"def conv_layer(n_in_filters, n_filters, ker_size, stride=1, \\n depthwise=False, zero_bn=False, act=True) :\\n bn = nn.BatchNorm2d(n_filters)\\n nn.init.constant_(bn.weight, 0. if zero_bn else 1.)\\n conv = nn.Conv2d(n_in_filters, n_filters, ker_size, stride=stride,padding=ker_size//2, \\n bias=False,groups = n_in_filters if depthwise else 1)\\n layer = [conv, bn]\\n if act: layer += [Swish()]\\n return nn.Sequential(*layer)\",\n \"def cnn(channels=(4, 16, 32), kernel_sizes=(8, 4), strides=(4, 2), in_size=84):\\n cnn_layers = []\\n output_size = in_size\\n\\n for i in range(len(channels)-1):\\n in_channels, out_channels = channels[i], channels[i+1]\\n kernel_size, stride = kernel_sizes[i], strides[i]\\n conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride)\\n activation = nn.ReLU()\\n cnn_layers += [conv, activation]\\n output_size = (output_size - kernel_size) / stride + 1\\n\\n cnn_layers = nn.Sequential(*cnn_layers)\\n output_size = int(out_channels * (output_size**2))\\n return cnn_layers, output_size\",\n \"def convolve_im(im: np.array,\\n kernel: np.array,\\n verbose=True):\\n ### START YOUR CODE HERE ### (You can change anything inside this block)\\n \\\"\\\"\\\"\\n\\tcompared to the 4a solution this just adds padding to the filter if its smaller than the image\\n\\tthis is done by using the second parameter in fft.fft2 \\n\\t\\n\\tfirst it applies fourier transforms on the kernel and the image\\n\\tthen it sets the image to be the pointwise multiplication of the transforms\\n\\n the image is inverse fourier transformed and filtered for real values\\n the domain image is shifted and taken the absolute value of\\n the fourier transform of the image and kernel are also shifted and set to be the absolute value\\n\\tlastly everything is displayed in the subplots\\n \\\"\\\"\\\"\\n conv_result = im \\n \\n if verbose:\\n fftKernel=np.fft.fft2(kernel,im.shape)\\n fftImage=np.fft.fft2(conv_result)\\n\\t\\t\\n\\t\\t\\n\\t\\t\\n conv_result=np.multiply(fftImage,fftKernel)\\n fftImageTransformed=conv_result\\n\\t\\t\\n \\n conv_result=np.fft.ifft2(conv_result)\\n \\n conv_result=np.real(conv_result)\\n\\n fftImageTransformed=np.fft.fftshift(fftImageTransformed)\\n fftImage=np.fft.fftshift(fftImage)\\n fftKernel=np.fft.fftshift(fftKernel)\\n\\n fftImageTransformed=np.absolute(fftImageTransformed)\\n fftImage=np.absolute(fftImage)\\n fftKernel=np.absolute(fftKernel)\\n\\t\\t\\n\\t\\t\\n # Use plt.subplot to place two or more images beside eachother\\n plt.figure(figsize=(20, 4))\\n # plt.subplot(num_rows, num_cols, position (1-indexed))\\n plt.subplot(1, 5, 1)\\n plt.imshow(im, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 2)\\n plt.imshow(fftImage, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 3)\\n plt.imshow(fftKernel, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 4)\\n plt.imshow(fftImageTransformed, cmap=\\\"gray\\\")\\n plt.subplot(1, 5, 5)\\n plt.imshow(conv_result, cmap=\\\"gray\\\")\\n ### END YOUR CODE HERE ###\\n return conv_result\",\n \"def _conv_block(inputs, filters, kernel=(3, 3), strides=(1, 1), alpha=1.0, nl='RE'):\\n channel_axis = 1 if K.image_data_format() == 'channels_first' else -1\\n filters = int(filters * alpha)\\n x = KL.Conv2D(filters, kernel, padding='same', use_bias=False, strides=strides)(inputs)\\n x = KL.BatchNormalization(axis=channel_axis)(x)\\n return _return_activation(x, nl=nl)\",\n \"def loop_conv(X, W):\\n # Go over all five dimensions \\n # (#batches x #channels x #height x #width x #dur/length )\\n # with filter that has\\n # #filters x #channels x #height x #width x #dur/length \\n num_filters = W.shape[0]\\n filt_channels = W.shape[1]\\n filt_height = W.shape[2]\\n filt_width = W.shape[3]\\n filt_duration = W.shape[4]\\n num_batches = X.shape[0]\\n input_channels = X.shape[1]\\n assert(filt_channels == input_channels)\\n out_shape = compute_out_shape(X.shape, W.shape)\\n out_height = out_shape[2]\\n out_width = out_shape[3]\\n out_duration = out_shape[4]\\n \\n # The output is H :)\\n H = np.zeros((out_shape))\\n for batch_i in xrange(0, num_batches):\\n for filt_i in xrange(0, num_filters):\\n for out_x in xrange(0, out_height):\\n for out_y in xrange(0, out_width):\\n for out_z in xrange(0, out_duration):\\n for chan_i in xrange(0, filt_channels):\\n for filt_x in xrange(0, filt_height):\\n for filt_y in xrange(0, filt_width):\\n for filt_z in xrange(0, filt_duration):\\n weight = W[filt_i, chan_i, filt_x, filt_y, filt_z]\\n input_val = X[batch_i, chan_i, \\\\\\n out_x + filt_x, out_y + filt_y, out_z + filt_z]\\n H[batch_i, filt_i, out_x, out_y, out_z] += \\\\\\n weight * input_val\\n return H\",\n \"def conv3x3(in_channels, out_channels, stride=1, \\n padding=1, bias=True, groups=1):\\n return nn.Conv2d(\\n in_channels, \\n out_channels, \\n kernel_size=3, \\n stride=stride,\\n padding=padding,\\n bias=bias,\\n groups=groups)\",\n \"def convolve2d(img, kernel):\\n #Flip the kernel\\n kernel = utils.flip2d(kernel) \\n #print(len(kernel))\\n \\n c = copy.deepcopy(img)\\n \\n #print(len(c))\\n #Padd the image\\n pad = int((len(kernel)-1)/2)\\n\\n\\n padded_img = utils.zero_pad(img,pad,pad)\\n #print(len(padded_img), len(padded_img[0]))\\n #print(len(kernel))\\n #print(len(img)**2)\\n og_img=[]\\n#c = copy.deepcopy(img)\\n j=0\\n offset = 0\\n for m in range(len(img) * len(img[0])): # size of kernel x kernel\\n x = []\\n \\n for i in range(len(kernel)): #3 is kernel size\\n #print(i,j)\\n x.append(padded_img[i+offset][j:j+len(kernel)])\\n #print((x))\\n sum = 0\\n for k in range(len(kernel)):\\n for l in range(len(kernel[0])):\\n sum+= x[k][l] * kernel[k][l]\\n #print(i,j)\\n #print(sum)\\n og_img.append(sum) \\n j+=1\\n if (j == len(img[0])):\\n j = 0\\n offset+= 1\\n \\n #print(len(img), len(img[0]))\\n final_img = []\\n for i in range(0,(len(img)*len(img[0])),len(img[0])):\\n final_img.append(og_img[i:i+len(img[0])])\\n #print(len(final_img)), len(final_img[0])\\n return final_img\\n\\n # TODO: implement this function.\",\n \"def conv(in_planes, out_planes, stride=1):\\n return nn.Conv2d(in_planes, out_planes, kernel_size=3, padding=1, stride=stride, bias=False)\",\n \"def conv(self, inputs, filters, kernel_size, strides, padding='SAME', name='conv_layer'):\\n input_channels = inputs[-1]\\n kernel = tf.Variable(tf.random.truncated_normal(shape=[kernel_size, kernel_size, input_channels, filters]),\\n dtype=tf.float32, name='kernel')\\n bias = tf.Variable(tf.zeros(shape=[filters]), name='bias')\\n conv = tf.nn.conv2d(inputs, filter=kernel,\\n strides=[1, strides, strides, 1],\\n padding=padding, name='conv')\\n out = tf.nn.relu(conv + bias, name='relu')\\n return out\",\n \"def _valid_convolve(images: th.Tensor, kernels: th.Tensor) -> th.Tensor:\\n ret = F.conv2d(images.view((images.shape[0], *images.shape[-3:])).transpose(1, 0),\\n th.flip(kernels.view((kernels.shape[0], *kernels.shape[-3:])), dims=(-1, -2)),\\n groups=kernels.shape[0]).transpose(1, 0)\\n return ret\",\n \"def convolution(img, kernel, padding=True):\\n result = np.zeros_like(img)\\n p_size_i = kernel.shape[0] // 2\\n p_size_j = kernel.shape[1] // 2\\n\\n if padding:\\n padded_img = np.zeros((img.shape[0] + 2 * p_size_i, img.shape[1] + 2 * p_size_j))\\n i_first = p_size_i\\n i_last = padded_img.shape[0] - p_size_i - 1\\n j_first = p_size_j\\n j_last = padded_img.shape[1] - p_size_j - 1\\n padded_img[i_first: i_last + 1, j_first: j_last + 1] = img\\n else:\\n padded_img = img.copy()\\n i_first = p_size_i\\n i_last = padded_img.shape[0] - p_size_i - 1\\n j_first = p_size_j\\n j_last = padded_img.shape[1] - p_size_j - 1\\n \\n for i in range(i_first, i_last):\\n for j in range(j_first, j_last):\\n window = padded_img[i - p_size_i: i + p_size_i + 1, j - p_size_j: j + p_size_j + 1]\\n res_pix = np.sum(window * kernel)\\n result[i - p_size_i, j - p_size_j] = res_pix\\n return result\",\n \"def _convolve_2d(kernel, image):\\n\\n nx = image.shape[0]\\n ny = image.shape[1]\\n nkx = kernel.shape[0]\\n nky = kernel.shape[1]\\n wkx = nkx // 2\\n wky = nky // 2\\n\\n result = np.zeros(image.shape, dtype=float32)\\n\\n for i in prange(0, nx, 1):\\n iimin = max(i - wkx, 0)\\n iimax = min(i + wkx + 1, nx)\\n for j in prange(0, ny, 1):\\n jjmin = max(j - wky, 0)\\n jjmax = min(j + wky + 1, ny)\\n num = 0.0\\n for ii in range(iimin, iimax, 1):\\n iii = wkx + ii - i\\n for jj in range(jjmin, jjmax, 1):\\n jjj = wky + jj - j\\n num += kernel[iii, jjj] * image[ii, jj]\\n result[i, j] = num\\n\\n return result\",\n \"def conv2D(null,channels,X,stride,kernel_shape,padding = False,initialize_weights = True,*args):\\n # filters = dimensionality of output space\\n # If padding is enabled, we pad the input with zeros such that the input size\\n # remains the same if weights with stride 1 are applied to the input\\n if initialize_weights:\\n kernel = np.random.normal(size = (kernel_shape[0],kernel_shape[1],kernel_shape[2]))*math.sqrt(1/(kernel_shape[0]*kernel_shape[1]*kernel_shape[2])) # Our input\\n kernel = torch.FloatTensor(kernel)\\n kernel.requires_grad = False\\n else:\\n kernel = args[0] # weights and bias must be given if initialise weights is disabled\\n bias = args[1]\\n kernel_shape = kernel.shape\\n \\n X = X.detach().numpy()\\n if padding: # Can only pad during initialization -> weights and input shapes cannot change during feedforward and backpropagation\\n if kernel_shape[1] % 2 == 0 and kernel_shape[2] % 2 == 0:\\n X = np.pad(X,((0,0),(math.floor(kernel_shape[1]/2)-1,math.floor(kernel_shape[1]/2)),(math.floor(kernel_shape[2]/2),math.floor(kernel_shape[2]/2)-1)), 'symmetric')\\n elif kernel_shape[1] % 2 != 0 and kernel_shape[2] % 2 == 0:\\n X = np.pad(X,((0,0),(math.floor(kernel_shape[1]/2),math.floor(kernel_shape[1]/2)),(math.floor(kernel_shape[2]/2),math.floor(kernel_shape[2]/2)-1)), 'symmetric')\\n elif kernel_shape[1] % 2 == 0 and kernel_shape[2] % 2 != 0:\\n X = np.pad(X,((0,0),(math.floor(kernel_shape[1]/2)-1,math.floor(kernel_shape[1]/2)),(math.floor(kernel_shape[2]/2),math.floor(kernel_shape[2]/2))), 'symmetric')\\n else:\\n X = np.pad(X,((0,0),(math.floor(kernel_shape[1]/2),math.floor(kernel_shape[1]/2)),(math.floor(kernel_shape[2]/2),math.floor(kernel_shape[2]/2))), 'symmetric')\\n \\n X = torch.FloatTensor(X)\\n \\n img_shape = X.shape\\n \\n output_size1 = math.floor((img_shape[1] - kernel_shape[1])/(stride)) + 1\\n output_size2 = math.floor((img_shape[2] - kernel_shape[2])/(stride)) + 1\\n output_shape = [channels,output_size1,output_size2]\\n \\n X_im2col,im = im2col(X,kernel,stride)\\n \\n \\n if initialize_weights:\\n weight = torch.reshape(kernel,(kernel_shape[0]*kernel_shape[1]*kernel_shape[2],1))\\n # weight consists of only one weight vector. But the dimensionality of output space has to be\\n # num_filters. So we need to stack weight vectors horizontally and create num_filters number of\\n # feature maps\\n for i in range(channels-1):\\n weight2 = np.random.normal(size = (kernel_shape[0]*kernel_shape[1]*kernel_shape[2],1))*math.sqrt(1/(kernel_shape[0]*kernel_shape[1]*kernel_shape[2])) # Our input\\n weight2 = torch.FloatTensor(weight2)\\n weight2.requires_grad = False\\n weight = torch.cat((weight2, weight),1) # do this num_filters - 1 number of times\\n conv_output = torch.t(X_im2col).mm(weight)\\n bias = torch.Tensor(np.random.normal(size = conv_output.shape))\\n conv_output += bias\\n conv_output = torch.reshape(conv_output,(output_shape))\\n return torch.nn.Parameter(conv_output), torch.nn.Parameter(weight),X_im2col,im, output_shape,bias\\n else:\\n # Since weights are already initialised, the relevant channels are already dictated in the architecture.\\n # Therefore, conv output is just a matmul\\n conv_output = torch.t(X_im2col).mm(kernel) + bias\\n return torch.nn.Parameter(conv_output),X_im2col\",\n \"def _conv(self, indim, outdim, ksize, stride, padding):\\n\\n return nn.Sequential(\\n nn.BatchNorm2d(indim),\\n nn.Conv2d(indim, outdim, ksize, stride, padding),\\n self.activ(),\\n )\",\n \"def conv(image, kernel):\\n Hi, Wi = image.shape\\n Hk, Wk = kernel.shape\\n out = np.zeros((Hi, Wi))\\n\\n # For this assignment, we will use edge values to pad the images.\\n # Zero padding as used in the previous assignment can make\\n # derivatives at the image boundary very big.\\n \\n pad_width0 = Hk // 2\\n pad_width1 = Wk // 2\\n pad_width = ((pad_width0,pad_width0),(pad_width1,pad_width1))\\n padded = np.pad(image, pad_width, mode='edge') \\n\\n #####################################\\n # START YOUR CODE HERE #\\n #####################################\\n kernel = np.flipud(np.fliplr(kernel)) # flip h/v\\n for h in range(Hi):\\n for w in range(Wi):\\n out[h, w] = np.sum(np.multiply(kernel, padded[h : h + Hk, w : w + Wk]))\\n ######################################\\n # END OF YOUR CODE #\\n ######################################\\n\\n return out\",\n \"def dw_conv(\\n in_channels: int, out_channels: int, stride: int = 1\\n) -> List[torch.nn.Module]:\\n return [\\n torch.nn.Conv2d(\\n in_channels,\\n in_channels,\\n kernel_size=3,\\n padding=1,\\n stride=stride,\\n groups=in_channels,\\n bias=False,\\n ),\\n torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=True),\\n torch.nn.BatchNorm2d(out_channels, eps=_BN_EPS, momentum=_BN_MOMENTUM),\\n torch.nn.ReLU(inplace=True),\\n ]\",\n \"def conv(depth, nfilter, ksize=3, stride=1, \\r\\n padding=0, dilation=1, groups=1, \\r\\n bias=True, lrelu=None):\\r\\n assert (depth>0 and nfilter>0 and ksize>0 and ksize%2==1 and \\r\\n stride>0 and padding>=0 and dilation>=1 and groups>=1)\\r\\n conv_ = nn.Conv2d(depth, nfilter, ksize, stride, \\r\\n padding, dilation, groups, bias)\\r\\n if lrelu is not None:\\r\\n conv_ = nn.Sequential(conv_, \\r\\n nn.LeakyReLU(lrelu, inplace=True))\\r\\n return conv_\",\n \"def fftconvolve(array, kernel):\\n x = numpy.fft.fftshift(numpy.fft.fftn(image))\\n y = numpy.fft.fftshift(numpy.fft.fftn(kernel))\\n\\n return numpy.real(numpynp.fft.fftshift(\\n numpy.fft.ifftn(numpy.fft.ifftshift(x * y))))\",\n \"def conv_nd(dims, *args, **kwargs):\\n if dims == 1:\\n return nn.Conv1D(*args, **kwargs)\\n elif dims == 2:\\n return nn.Conv2D(*args, **kwargs)\\n elif dims == 3:\\n return nn.Conv3D(*args, **kwargs)\\n raise ValueError(f\\\"unsupported dimensions: {dims}\\\")\",\n \"def conv_block(\\r\\n in_channels,\\r\\n out_channels,\\r\\n kernel_size,\\r\\n stride,\\r\\n dilation=1,\\r\\n):\\r\\n pad_mode = 'same'\\r\\n padding = 0\\r\\n\\r\\n dbl = nn.SequentialCell(\\r\\n [\\r\\n nn.Conv2d(\\r\\n in_channels,\\r\\n out_channels,\\r\\n kernel_size=kernel_size,\\r\\n stride=stride,\\r\\n padding=padding,\\r\\n dilation=dilation,\\r\\n pad_mode=pad_mode,\\r\\n ),\\r\\n nn.BatchNorm2d(out_channels, momentum=0.1),\\r\\n nn.ReLU(),\\r\\n ]\\r\\n )\\r\\n init_cov(dbl[0])\\r\\n init_bn(dbl[1])\\r\\n return dbl\",\n \"def conv2d(input, filters, image_shape=None, filter_shape=None,\\r\\n border_mode='valid', subsample=(1, 1), **kargs):\\r\\n\\r\\n #accept Constant value for image_shape and filter_shape.\\r\\n if image_shape is not None:\\r\\n image_shape = list(image_shape)\\r\\n for i in xrange(len(image_shape)):\\r\\n if image_shape[i] is not None:\\r\\n try:\\r\\n image_shape[i] = get_scalar_constant_value(\\r\\n as_tensor_variable(image_shape[i]))\\r\\n except NotScalarConstantError, e:\\r\\n raise NotScalarConstantError(\\r\\n \\\"The convolution need that the shape\\\"\\r\\n \\\" information are constant values. We got\\\"\\r\\n \\\" %s for the image_shape parameter\\\" %\\r\\n image_shape[i])\\r\\n assert str(image_shape[i].dtype).startswith('int')\\r\\n image_shape[i] = int(image_shape[i])\\r\\n if filter_shape is not None:\\r\\n filter_shape = list(filter_shape)\\r\\n for i in xrange(len(filter_shape)):\\r\\n if filter_shape[i] is not None:\\r\\n try:\\r\\n filter_shape[i] = get_scalar_constant_value(\\r\\n as_tensor_variable(filter_shape[i]))\\r\\n except NotScalarConstantError, e:\\r\\n raise NotScalarConstantError(\\r\\n \\\"The convolution need that the shape\\\"\\r\\n \\\" information are constant values. We got\\\"\\r\\n \\\" %s for the filter_shape \\\"\\r\\n \\\"parameter\\\" % filter_shape[i])\\r\\n assert str(filter_shape[i].dtype).startswith('int')\\r\\n filter_shape[i] = int(filter_shape[i])\\r\\n\\r\\n if image_shape and filter_shape:\\r\\n try:\\r\\n assert image_shape[1] == filter_shape[1]\\r\\n except Exception:\\r\\n print 'image ', image_shape, ' filters ', filter_shape\\r\\n raise\\r\\n\\r\\n if filter_shape is not None:\\r\\n nkern = filter_shape[0]\\r\\n kshp = filter_shape[2:]\\r\\n else:\\r\\n nkern, kshp = None, None\\r\\n\\r\\n if image_shape is not None:\\r\\n bsize = image_shape[0]\\r\\n imshp = image_shape[1:]\\r\\n else:\\r\\n bsize, imshp = None, None\\r\\n\\r\\n op = ConvOp(output_mode=border_mode, dx=subsample[0], dy=subsample[1],\\r\\n imshp=imshp, kshp=kshp, nkern=nkern, bsize=bsize, **kargs)\\r\\n\\r\\n return op(input, filters)\",\n \"def conv(image, kernel):\\n Hi, Wi = image.shape\\n Hk, Wk = kernel.shape\\n out = np.zeros((Hi, Wi))\\n\\n # For this assignment, we will use edge values to pad the images.\\n # Zero padding will make derivatives at the image boundary very big,\\n # whereas we want to ignore the edges at the boundary.\\n pad_width0 = Hk // 2\\n pad_width1 = Wk // 2\\n pad_width = ((pad_width0,pad_width0),(pad_width1,pad_width1))\\n padded = np.pad(image, pad_width, mode='edge')\\n\\n ### YOUR CODE HERE\\n for i in range(Hi):\\n for j in range(Wi):\\n out[i,j] = np.sum(padded[i : i + Hk, j : j + Wk] * np.flip(kernel))\\n ### END YOUR CODE\\n\\n return out\",\n \"def convolution(self, x, w, name=\\\"\\\"):\\n if self.gpu:\\n return tf.nn.conv2d(x, w, strides=self.conv_stride, padding=self.conv_padding, name=name)\\n\\n return tf.nn.conv2d(x, w, strides=self.conv_stride, padding=self.conv_padding,\\n use_cudnn_on_gpu=False, name=name)\",\n \"def convolution(matrix, kernel):\\n assert assert_odd(kernel.shape[0])\\n \\n # Padded matrix (0s on the outsides)\\n N = kernel.shape[0] # Get the dim for the kernel\\n I = np.pad(matrix, int(N/2), \\\"constant\\\")\\n \\n # Now do the convolution\\n C = np.zeros(matrix.shape) # This is the convolved image\\n h, w = C.shape # Get width and height\\n s = int(N/2) # Spacing of the matrix\\n positions = [(i,j) for i in range(h) for j in range(w)]\\n for (i,j) in positions:\\n y, x = i+s,j+s # Shift the center to the right position\\n \\n # Calc the convolution at each pixel\\n C[i,j] = np.sum(np.multiply(kernel, I[y-s:y+s+1,x-s:x+s+1]))\\n \\n # Return the clipped array as uint8\\n return C\",\n \"def SNConv2d(*args, **kwargs):\\n return spectral_norm(nn.Conv2d(*args, **kwargs))\",\n \"def convolve(self, kernel):\\n kernel_rows, kernel_cols = kernel.shape\\n img_rows, img_cols = self.img_array.shape\\n\\n print(\\\"imgae shape: \\\", self.img_array.shape)\\n print(self.img_array[:10,:10])\\n\\n # flip the kernel\\n flipped_kernel = np.zeros(kernel.shape)\\n \\n ## column flips\\n for i in range(flipped_kernel.shape[1]):\\n flipped_kernel[:,i] = kernel[:,kernel_cols-i-1]\\n kernel = flipped_kernel.copy()\\n\\n ## row flips\\n for i in range(flipped_kernel.shape[0]):\\n flipped_kernel[i,:] = kernel[kernel_rows-i-1,:]\\n kernel = flipped_kernel.copy()\\n print(\\\"Flipped kernel:\\\\n\\\", kernel)\\n\\n # Handle broders by padding the image with white pixels.\\n ## padwidth = kernel_rows // 2 \\n padwidth = kernel_rows // 2\\n self.img_array_padded = np.pad(self.img_array, padwidth, \\n mode='constant', constant_values=255)\\n \\n # cross correlation\\n self.img_array_out = np.zeros(self.img_array.shape)\\n\\n for y in range(img_cols):\\n for x in range(img_rows):\\n self.img_array_out[x, y] = \\\\\\n (kernel * self.img_array_padded[x:x+kernel_cols, y:y+kernel_rows]).sum()\\n \\n # print(self.img_array_out.shape)\\n # print(self.img_array_out[:10,:10])\\n return self.img_array_out\",\n \"def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\\n layers = []\\n conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\\n kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\\n\\n layers.append(conv_layer)\\n\\n if batch_norm:\\n layers.append(nn.BatchNorm2d(out_channels))\\n return nn.Sequential(*layers)\",\n \"def discreteConvolution2D( iImage, iKernel ): \\n # pretvori vhodne spremenljivke v np polje in\\n # inicializiraj izhodno np polje\\n iImage = np.asarray( iImage )\\n iKernel = np.asarray( iKernel )\\n #------------------------------- za hitrost delovanja\\n oImage = ni.convolve( iImage, iKernel, mode='nearest' ) \\n return oImage\",\n \"def conv(\\n in_channels,\\n out_channels,\\n kernel_size,\\n stride=2,\\n padding=1,\\n batch_norm=True,\\n init_zero_weights=False,\\n):\\n layers = []\\n conv_layer = nn.Conv2d(\\n in_channels=in_channels,\\n out_channels=out_channels,\\n kernel_size=kernel_size,\\n stride=stride,\\n padding=padding,\\n bias=False,\\n )\\n if init_zero_weights:\\n conv_layer.weight.data = (\\n torch.randn(out_channels, in_channels, kernel_size, kernel_size) * 0.001\\n )\\n layers.append(conv_layer)\\n\\n if batch_norm:\\n layers.append(nn.BatchNorm2d(out_channels))\\n return nn.Sequential(*layers)\",\n \"def convolveH(image=None,kernel=None):\\n res = sg.fftconvolve(image,kernel,mode='full')\\n res = res/res.sum()\\n rowM,colM = nd.center_of_mass(res)\\n size = image.shape[0]/2\\n resnew = res[int(rowM)-size:int(rowM)+size,int(colM)-size:int(colM)+size]\\n return resnew\",\n \"def conv(in_planes,\\n out_planes,\\n kernel_size=3,\\n stride=1,\\n padding=1,\\n dilation=1,\\n groups=1):\\n return nn.Conv2d(in_planes,\\n out_planes,\\n kernel_size=kernel_size,\\n stride=stride,\\n padding=padding,\\n dilation=dilation,\\n groups=groups,\\n bias=False)\",\n \"def moffat_convolution(im_array,n_fwhm,beta,fwhm) :\\n\\n r_s = fwhm/(2. *math.sqrt(2.**(1./beta)-1.))\\n\\t\\n im_kernel_array = gauss_kernel(n_fwhm,beta,r_s)\\n conv_image = signal.convolve(im_array,im_kernel_array,mode = 'same')\\n\\n return (conv_image)\",\n \"def moffat_convolution_fft(im_array,n_fwhm,beta,fwhm) :\\n\\n r_s = fwhm/(2. *math.sqrt(2.**(1./beta)-1.))\\n\\n im_kernel_array = moffat_kernel(n_fwhm,beta,r_s)\\n fftconv_image = signal.fftconvolve(im_array,im_kernel_array,mode = 'same')\\n\\n return (fftconv_image)\",\n \"def conv_2D(img,kernel,stride=1):\\n\\n m,n = img.shape\\n r,c = kernel.shape\\n\\n kernel = np.flip(kernel,axis=1)\\n kernel = np.flip(kernel,axis=0)\\n\\n c_m, c_n = int(np.ceil((m-r+1)/stride)), int(np.ceil((n-c+1)/stride))\\n img_conv = np.zeros((c_m,c_n),dtype=float)\\n\\n for i,j in it.product(range(c_m),range(c_n)):\\n img_conv[i,j] = (img[i*stride:i*stride+r,j*stride:j*stride+c] * kernel).sum()\\n\\n return img_conv\",\n \"def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, batch_norm=True):\\n layers = []\\n # TODO: ? shouldn't bias be set to NOT batch_norm instead of always being False ? \\n conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, \\n kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\\n \\n layers.append(conv_layer)\\n\\n if batch_norm:\\n layers.append(nn.BatchNorm2d(out_channels))\\n return nn.Sequential(*layers)\",\n \"def conv3x3(in_ch, out_ch, stride=1):\\n return nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=stride, padding=1)\",\n \"def convolutional(X, X_test, input_shape, n_filters, filter_size):\\n\\n\\tfilters_shape = (n_filters, input_shape[1], filter_size[0], filter_size[1])\\n\\tfilters = theano.shared(\\n\\t\\tnumpy.random.uniform(low=-0.1, high=0.1, size=filters_shape).astype(numpy.float32),\\n\\t\\t'conv_filters'\\n\\t)\\n\\n\\toutput_shape = (input_shape[0], n_filters, input_shape[2], input_shape[3])\\n\\n\\toutput = conv2d(input=X, filters=filters, filter_shape=filters_shape, image_shape=input_shape, border_mode='full')\\n\\toutput_test = conv2d(input=X_test, filters=filters, filter_shape=filters_shape, image_shape=input_shape, border_mode='full')\\n\\n\\tshift_x = (filter_size[0] - 1) // 2\\n\\tshift_y = (filter_size[1] - 1) // 2\\n\\n\\toutput = output[:,:,shift_x:input_shape[2]+shift_x,shift_y:input_shape[3]+shift_y]\\n\\toutput_test = output_test[:,:,shift_x:input_shape[2]+shift_x,shift_y:input_shape[3]+shift_y]\\n\\n\\treturn output, output_test, [filters], output_shape\",\n \"def test_on_conv_transpose_2d_stride(self):\\n\\n # Channels/Colors, #filters, filter_size (square)\\n conv_filter = objax.nn.ConvTranspose2D(1, 1, 2, strides=2, padding=objax.ConvPadding.VALID)\\n weights = objax.TrainVar(jn.array([[[[1., 2.], [3., 4.]]]]).transpose((2, 3, 0, 1)))\\n conv_filter.w = weights\\n image = jn.array([[[[2., 1., 3., 4.],\\n [5., 6., 7., 8.], [9., 10., 11., 12.], [13., 14., 15., 16.]]]])\\n # NCHW: Batch, Channels/Colors, Height, Width\\n features = conv_filter(image)\\n expected_features = jn.array([[[[2., 4., 1., 2., 3., 6., 4., 8.],\\n [6., 8., 3., 4., 9., 12., 12., 16.],\\n [5., 10., 6., 12., 7., 14., 8., 16.],\\n [15., 20., 18., 24., 21., 28., 24., 32.],\\n [9., 18., 10., 20., 11., 22., 12., 24.],\\n [27., 36., 30., 40., 33., 44., 36., 48.],\\n [13., 26., 14., 28., 15., 30., 16., 32.],\\n [39., 52., 42., 56., 45., 60., 48., 64.]]]])\\n self.assertEqual(features.shape, (1, 1, 8, 8))\\n self.assertTrue(jn.array_equal(features, expected_features))\",\n \"def forward(self, image):\\n height, width = image.shape\\n H_out, W_out = output_shape(height, width, self.filter_size, self.padding, self.stride)\\n output = np.zeros((H_out, W_out, self.num_filters))\\n padded_image = pad_2d(image, self.padding)\\n for patch, i, j in self.image_patch(padded_image):\\n output[i,j] = np.sum(patch*self.conv_filter, axis=(1,2))\\n return output\",\n \"def conv(h,x):\\n\\n final_conv_dim=(512,512) ## dimension of the convolution result before cropping\\n x_dim=(x.size(2),x.size(3)) ## dimension of x\\n h_dim=(h.size(2),h.size(3)) ## dimension of h\\n crop_dim=x_dim ## image obtained after cropping is the same dimension as the image x\\n\\n padding=(final_conv_dim[0]-(x_dim[0]-h_dim[0]+1),final_conv_dim[1]-(x_dim[1]-h_dim[1]+1)) ## calculate the amount of padding required given final_conv_dim, x_dim and h_dim\\n\\n x_pad=F.pad(x,(padding[0]//2,padding[0]//2+1,padding[1]//2,padding[1]//2+1)) ## pad x\\n y=F.conv2d(x_pad,h.flip(2,3),padding=0) ## convolve x_pad with h\\n \\n ## starting and ending values along the column and the rows for cropping\\n starti=(final_conv_dim[0]-crop_dim[0])//2 \\n endi=crop_dim[0]+starti\\n startj=(final_conv_dim[1]-crop_dim[1])//2\\n endj=crop_dim[1]+startj\\n\\n ## Cropping\\n y=y[:,:,starti:endi,startj:endj]\\n \\n return y\",\n \"def conv2d(input, filters, image_shape=None, filter_shape=None,\\r\\n border_mode='valid', subsample=(1,1), **kargs):\\r\\n assert input.ndim in (2,3)\\r\\n assert filters.ndim in (2,3)\\r\\n\\r\\n ### use shape information if it is given to us ###\\r\\n if filter_shape and image_shape:\\r\\n if input.ndim==3:\\r\\n bsize = image_shape[0]\\r\\n else:\\r\\n bsize = 1\\r\\n imshp = (1,) + tuple(image_shape[-2:])\\r\\n\\r\\n if filters.ndim==3:\\r\\n nkern = filter_shape[0]\\r\\n else:\\r\\n nkern = 1\\r\\n kshp = filter_shape[-2:]\\r\\n else:\\r\\n nkern, kshp = None, None\\r\\n bsize, imshp = None, None\\r\\n\\r\\n ### reshape tensors to 4D, for compatibility with ConvOp ###\\r\\n if input.ndim==3:\\r\\n sym_bsize = input.shape[0]\\r\\n else:\\r\\n sym_bsize = 1\\r\\n\\r\\n if filters.ndim==3:\\r\\n sym_nkern = filters.shape[0]\\r\\n else:\\r\\n sym_nkern = 1\\r\\n\\r\\n new_input_shape = tensor.join(0, tensor.stack(sym_bsize,1), input.shape[-2:])\\r\\n input4D = tensor.reshape(input, new_input_shape, ndim=4)\\r\\n\\r\\n new_filter_shape = tensor.join(0, tensor.stack(sym_nkern,1), filters.shape[-2:])\\r\\n filters4D = tensor.reshape(filters, new_filter_shape, ndim=4)\\r\\n\\r\\n ### perform actual convolution ###\\r\\n op = conv.ConvOp(output_mode=border_mode,\\r\\n dx=subsample[0], dy=subsample[1],\\r\\n imshp=imshp, kshp=kshp, nkern=nkern, bsize=bsize,**kargs)\\r\\n\\r\\n output = op(input4D, filters4D)\\r\\n\\r\\n # flatten to 3D tensor if convolving with single filter or single image\\r\\n if input.ndim==2 or filters.ndim==2:\\r\\n output = tensor.flatten(output.T, outdim=3).T\\r\\n\\r\\n return output\",\n \"def conv(f, kerneltype, kernelwidth, boundary='extend'):\\n if kerneltype == 'box': kernel = Box1DKernel(kernelwidth)\\n elif kerneltype == 'gaussian': kernel = Gaussian1DKernel(kernelwidth)\\n fconv = convolve(f, kernel, boundary=boundary)\\n return fconv\",\n \"def cnn(scaled_images, **kwargs):\\n activ = tf.nn.relu\\n # layer_1 = activ(conv(scaled_images, 'c1', n_filters=32,\\n # filter_size=8, stride=4, init_scale=np.sqrt(2), **kwargs))\\n layer_1 = activ(conv(scaled_images, 'c1', n_filters=64,\\n filter_size=3, stride=2, init_scale=np.sqrt(2), **kwargs))\\n layer_2 = activ(conv(layer_1, 'c2', n_filters=64,\\n filter_size=3, stride=1, init_scale=np.sqrt(2), **kwargs))\\n layer_3 = activ(conv(layer_2, 'c3', n_filters=64,\\n filter_size=3, stride=1, init_scale=np.sqrt(2), **kwargs))\\n layer_3 = conv_to_fc(layer_3)\\n fc1 = activ(linear(layer_3, 'fc1', n_hidden=128, init_scale=np.sqrt(2)))\\n fc2 = activ(linear(fc1, 'fc2', n_hidden=128, init_scale=np.sqrt(2)))\\n return fc2\",\n \"def preprocess_conv(x_train, x_test, percentage, stride, grayscale=True,\\n verbose=0):\\n if grayscale:\\n x_train = dct_set_gray(x_train, stride, verbose=verbose)\\n x_test = dct_set_gray(x_test, stride, verbose=verbose)\\n\\n variances = np.var(x_train, axis=0)\\n stdevs = np.sqrt(variances)\\n idx = get_clip_index_conv(stdevs, percentage)\\n for image in x_train:\\n image[idx] = 0\\n for image in x_test:\\n image[idx] = 0\\n else:\\n x_train = dct_set_color(x_train, stride, verbose=verbose)\\n x_test = dct_set_color(x_test, stride, verbose=verbose)\\n if K.image_data_format() == 'channels_first':\\n for i in range(3):\\n variances = np.var(x_train[:, i, :, :], axis=0)\\n stdevs = np.sqrt(variances)\\n idx = get_clip_index_conv(stdevs, percentage)\\n for image in x_train:\\n channel = image[i]\\n channel[idx] = 0\\n for image in x_test:\\n channel = image[i]\\n channel[idx] = 0\\n else:\\n for i in range(3):\\n variances = np.var(x_train[:, :, :, i], axis=0)\\n stdevs = np.sqrt(variances)\\n idx = get_clip_index_conv(stdevs, percentage)\\n for image in x_train:\\n channel = image[:, :, i]\\n channel[idx] = 0\\n for image in x_test:\\n channel = image[:, :, i]\\n channel[idx] = 0\\n\\n return x_train, x_test\",\n \"def conv_block(index,\\n in_channels,\\n out_channels=N_FILTERS,\\n padding=0,\\n pooling=True):\\n if pooling:\\n conv = nn.Sequential(\\n OrderedDict([\\n ('conv'+str(index), nn.Conv2d(in_channels, out_channels, \\\\\\n K_SIZE, padding=padding)),\\n ('bn'+str(index), nn.BatchNorm2d(out_channels, momentum=1, \\\\\\n affine=True)),\\n ('relu'+str(index), nn.ReLU(inplace=True)),\\n ('pool'+str(index), nn.MaxPool2d(MP_SIZE))\\n ]))\\n else:\\n conv = nn.Sequential(\\n OrderedDict([\\n ('conv'+str(index), nn.Conv2d(in_channels, out_channels, \\\\\\n K_SIZE, padding=padding)),\\n ('bn'+str(index), nn.BatchNorm2d(out_channels, momentum=1, \\\\\\n affine=True)),\\n ('relu'+str(index), nn.ReLU(inplace=True))\\n ]))\\n return conv\",\n \"def dense_conv_forward_2d_fast(\\n inp_image: np.ndarray,\\n filter: np.ndarray,\\n output: np.ndarray,\\n strides,\\n padding):\\n INPUT_DIMENSIONS = 4\\n HEIGHT_IDX = 2 # H\\n WIDTH_IDX = 3 # W\\n INPUT_CHANNELS_IDX = 1 # C\\n OUTPUT_CHANNELS_IDX = 0 # K\\n NUM_IMAGES_IDX = 0 # N\\n\\n x_shape = inp_image.shape\\n f_shape = filter.shape\\n o_shape = output.shape\\n\\n if len(x_shape) != INPUT_DIMENSIONS or len(f_shape) != INPUT_DIMENSIONS or len(o_shape) != INPUT_DIMENSIONS:\\n raise RuntimeError(\\\"conv2d: input, filter, and output must all have four dimensions.\\\")\\n\\n assert (x_shape[HEIGHT_IDX] % strides[1] == 0, \\\"Input height is not evenly divisible by stride size.\\\")\\n assert (x_shape[WIDTH_IDX] % strides[0] == 0, \\\"Input width is not evenly divisible by stride size.\\\")\\n assert (x_shape[INPUT_CHANNELS_IDX] == f_shape[INPUT_CHANNELS_IDX],\\n \\\"Number of channels in input does not match number channels expected by convolution.\\\")\\n assert (o_shape[OUTPUT_CHANNELS_IDX] == f_shape[OUTPUT_CHANNELS_IDX],\\n \\\"Number of channels in output does not match number channels expected by convolution.\\\")\\n\\n N = x_shape[NUM_IMAGES_IDX]\\n H = x_shape[HEIGHT_IDX]\\n W = x_shape[WIDTH_IDX]\\n K = f_shape[OUTPUT_CHANNELS_IDX]\\n C = f_shape[INPUT_CHANNELS_IDX]\\n R = f_shape[HEIGHT_IDX]\\n S = f_shape[WIDTH_IDX]\\n P = (x_shape[HEIGHT_IDX] - f_shape[HEIGHT_IDX]) / strides[1] + 1 # output height\\n Q = (x_shape[WIDTH_IDX] - f_shape[WIDTH_IDX]) / strides[0] + 1 # output width\\n\\n assert (o_shape[HEIGHT_IDX] == P, f\\\"Output height should be {P}.\\\")\\n assert (o_shape[WIDTH_IDX] == Q, f\\\"Output width should be {Q}.\\\")\\n\\n # output[...] = 0\\n\\n for i in range(0, (H - R) + 1, strides[1]):\\n y = int(i / strides[1])\\n for j in range(0, (W - S) + 1, strides[0]):\\n x = int(j / strides[1])\\n inp_view = inp_image[:, :, i:i + R, j:j + S]\\n for k in range(0, K):\\n f_slice = filter[k, :, :, :]\\n prod = np.sum(inp_view * f_slice, (INPUT_CHANNELS_IDX, HEIGHT_IDX, WIDTH_IDX))\\n output[:, k, y, x] = prod\",\n \"def conv2d(x, *args, **kwargs):\\n i = 0\\n out = []\\n batch_size = 10000\\n while i < x.size(0): \\n out.append(F.conv2d(x[i:min(i+batch_size, x.size(0))], *args, **kwargs))\\n i += batch_size\\n return torch.cat(out, 0)\",\n \"def convolution(\\n input, # pylint: disable=redefined-builtin\\n filter, # pylint: disable=redefined-builtin\\n padding,\\n strides=None,\\n dilation_rate=None,\\n name=None,\\n data_format=None,\\n filters=None,\\n dilations=None): # pylint: disable=g-doc-args\\n filter = deprecated_argument_lookup(\\\"filters\\\", filters, \\\"filter\\\", filter)\\n dilation_rate = deprecated_argument_lookup(\\n \\\"dilations\\\", dilations, \\\"dilation_rate\\\", dilation_rate)\\n return convolution_internal(\\n input,\\n filter,\\n strides=strides,\\n padding=padding,\\n data_format=data_format,\\n dilations=dilation_rate,\\n name=name)\",\n \"def conv(input, inch, outch, filter_h, filter_w, stride_h, stride_w, padding='SAME', name='conv_layer'):\\n with tf.name_scope(name) as scope:\\n layer = tf.layers.conv2d(input, outch, filter_h, strides=(stride_h, stride_w), padding=\\\"same\\\",\\n activation=tf.nn.relu)\\n return layer\",\n \"def dense_conv_forward_2d(inp_image: np.ndarray, kernel: np.ndarray, stride, padding):\\n assert len(inp_image.shape) == 3, 'single 2D images only. No batches.'\\n assert len(kernel.shape) == 4\\n\\n height, width, colors = inp_image.shape\\n kernel_height, kernel_width, colors_in, colors_out = kernel.shape\\n kernel_stride_x, kernel_stride_y = stride\\n kernel_padding_x, kernel_padding_y = padding\\n i_f = int(np.floor(kernel_width / 2.0))\\n j_f = int(np.floor(kernel_height / 2.0))\\n\\n out_pixels = np.zeros((height, width, colors_out))\\n for y in range(kernel_padding_y, height - kernel_padding_y,\\n kernel_stride_y): # todo: add kernel_padding_y and kernel_stride_y fix to glsl\\n for x in range(kernel_padding_x, width - kernel_padding_x,\\n kernel_stride_x): # todo: add kernel_padding_x and kernel_stride_x fix to glsl\\n output_select = [y, x, 0]\\n input_select = np.asarray(\\n [y * kernel_stride_y, x * kernel_stride_x, 0]\\n )\\n for i in range(-np.int(np.floor(kernel_width / 2.0)), np.int(np.ceil(kernel_width / 2.0))):\\n for j in range(-np.int(np.floor(kernel_height / 2.0)), np.int(np.ceil(kernel_height / 2.0))):\\n in_pixel_select = np.copy(input_select)\\n in_pixel_select += [j, i, 0]\\n for co in range(colors_out):\\n output_select[2] = co\\n for ci in range(colors_in):\\n in_pixel_select[2] = ci\\n kernel_select = np.asarray([j_f + j, i_f + i, ci, co])\\n\\n out_pixels[tuple(output_select)] += kernel[tuple(kernel_select)] * inp_image[\\n tuple(in_pixel_select)]\\n return out_pixels\",\n \"def convolution_2d(img, kernel):\\n # TODO write convolution of arbritrary sized convolution here\\n # Hint: you need the kernelsize\\n\\n offset = int(kernel.shape[0] / 2)\\n irows, icols = img.shape\\n newimg = np.zeros((irows - offset, icols - offset, offset * 2 + 1, offset * 2 + 1))\\n nrows, ncols, _, _ = newimg.shape\\n for x in range(nrows - 1):\\n for y in range(ncols - 1):\\n newimg[x, y, :, :] = img[x:x + offset * 2 + 1, y:y + offset * 2 + 1]\\n newimg *= kernel\\n\\n newimg = np.sum(newimg, axis=3)\\n newimg = np.sum(newimg, axis=2)\\n\\n return newimg\",\n \"def conv(\\n in_channels: int,\\n out_channels: int,\\n stride: int = 1,\\n groups: int = 1,\\n kernel_size: int = 3,\\n padding: int = 1,\\n) -> List[torch.nn.Module]:\\n return [\\n torch.nn.Conv2d(\\n in_channels,\\n out_channels,\\n kernel_size=kernel_size,\\n stride=stride,\\n padding=padding,\\n groups=groups,\\n bias=False,\\n ),\\n torch.nn.BatchNorm2d(out_channels, eps=_BN_EPS, momentum=_BN_MOMENTUM),\\n torch.nn.ReLU(inplace=True),\\n ]\",\n \"def convolve(self, psf):\\n if self.image is None or psf.image is None:\\n raise ValueError('Both images to convolve have to be initialized!')\\n self.image = fftconvolve(self.image, psf.image, mode='full')\\n self.image = rescale_intensity(self.image, out_range=(0, 255))\\n self.metadata['Convolved'] = True\\n return self.image\",\n \"def conv2(x,y,mode='same'):\\n\\n if not(mode == 'same'):\\n raise Exception(\\\"Mode not supported\\\")\\n\\n # Add singleton dimensions\\n if (len(x.shape) < len(y.shape)):\\n dim = x.shape\\n for i in range(len(x.shape),len(y.shape)):\\n dim = (1,) + dim\\n x = x.reshape(dim)\\n elif (len(y.shape) < len(x.shape)):\\n dim = y.shape\\n for i in range(len(y.shape),len(x.shape)):\\n dim = (1,) + dim\\n y = y.reshape(dim)\\n\\n origin = ()\\n\\n # Apparently, the origin must be set in a special way to reproduce\\n # the results of scipy.signal.convolve and Matlab\\n for i in range(len(x.shape)):\\n if ( (x.shape[i] - y.shape[i]) % 2 == 0 and\\n x.shape[i] > 1 and\\n y.shape[i] > 1):\\n origin = origin + (-1,)\\n else:\\n origin = origin + (0,)\\n\\n z = convolve(x,y, mode='constant', origin=origin)\\n\\n return z\",\n \"def convolveAndDownsample(img):\\n # Select every other pixel from G\\n G = sp.signal.convolve2d(img, guassianFilter, 'same')\\n return G[::2, ::2]\",\n \"def _cconvolve(x, H, nfft, wlen, axis):\\n \\n # pad with wlen-1 zeros for overlap & FFT\\n x = pad_along_axis(x, [0, wlen - 1], axis=axis)\\n xf = np.fft.rfft(x, nfft, axis=axis)\\n \\n # take product with window in freq. domain\\n product = multiply_along_axis(xf, H, axis=axis)\\n\\n # back transform to sample domain and return\\n return np.fft.irfft(product, axis=axis).real\",\n \"def _conv2d_block(in_f, out_f, *args, **kwargs):\\n return nn.Sequential(\\n nn.Conv2d(in_f, out_f, *args, **kwargs),\\n nn.BatchNorm2d(out_f),\\n nn.ReLU(),\\n nn.Dropout2d(p=0.2)\\n )\",\n \"def conv(c_in, c_out, k_size, stride=2, pad=1, bn=True):\\n layers = []\\n layers.append(nn.Conv2d(c_in, c_out, k_size, stride, pad, bias=True)) # bias=False\\n if bn:\\n layers.append(nn.BatchNorm2d(c_out))\\n return nn.Sequential(*layers)\",\n \"def _conv2d_bn(x,\\n filters,\\n kernel_size=(3, 3),\\n padding='same',\\n strides=(1, 1),\\n name=None):\\n if name is not None:\\n bn_name = name + '_bn'\\n conv_name = name + '_conv'\\n else:\\n bn_name = None\\n conv_name = None\\n bn_axis = 1 if backend.image_data_format() == 'channels_first' else 3\\n x = layers.Conv2D(\\n filters=filters,\\n kernel_size=kernel_size,\\n strides=strides,\\n padding=padding,\\n use_bias=False,\\n name=conv_name)(x)\\n x = layers.BatchNormalization(axis=bn_axis, name=bn_name)(x)\\n x = layers.Activation('relu', name=name)(x)\\n return x\",\n \"def _conv_bn_layer(cnn_input, filters, kernel_size, strides, layer_id):\\n output = tf.keras.layers.Conv2D(\\n filters=filters, kernel_size=kernel_size, strides=strides, padding=\\\"same\\\",\\n activation=\\\"linear\\\", name=\\\"cnn_{}\\\".format(layer_id))(cnn_input)\\n output = tf.keras.layers.BatchNormalization(\\n momentum=_MOMENTUM, epsilon=_EPSILON)(output)\\n return output\",\n \"def apply_conv2d_1x1(input_layer, num_classes, kernel_size=1):\\n # tf.layers.conv2d(inputs, filters, kernel_size, strides=(1, 1), padding='valid', ...,\\n # kernel_initializer=None, ... , kernel_regularizer=None)\\n return tf.layers.conv2d(input_layer, num_classes, kernel_size, padding='same',\\n kernel_initializer=tf.truncated_normal_initializer(stddev=0.01),\\n kernel_regularizer=tf.contrib.layers.l2_regularizer(1e-3))\",\n \"def _conv_bn(**conv_params):\\n filters = conv_params[\\\"filters\\\"]\\n kernel_size = conv_params[\\\"kernel_size\\\"]\\n strides = conv_params.setdefault(\\\"strides\\\", (1, 1))\\n kernel_initializer = conv_params.setdefault(\\\"kernel_initializer\\\", \\\"he_normal\\\")\\n padding = conv_params.setdefault(\\\"padding\\\", \\\"same\\\")\\n kernel_regularizer = conv_params.setdefault(\\\"kernel_regularizer\\\", l2(1.e-4))\\n name = conv_params[\\\"name\\\"]\\n\\n def f(input):\\n x = Conv2D(filters=filters, kernel_size=kernel_size,\\n strides=strides, padding=padding,\\n kernel_initializer=kernel_initializer,\\n kernel_regularizer=kernel_regularizer,\\n name=name + '_conv')(input)\\n x = BatchNormalization(axis=CHANNEL_AXIS, name=name + '_bn')(x)\\n return x \\n return f\",\n \"def compute_conv(in_size, kernel, stride, padding):\\n return (in_size + 2 * padding - kernel) // stride + 1\",\n \"def __init__(self, in_channels, out_channels):\\n super(CNN, self).__init__()\\n self.conv1 = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=5, padding=1)\",\n \"def conv(in_channels, out_channels, kernel_size, stride=2, padding=1, normalization=True, norm_type='instance_norm'):\\n layers = []\\n conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, \\n kernel_size=kernel_size, stride=stride, padding=padding, bias=False)\\n \\n layers.append(conv_layer)\\n\\n # optional normalization layer\\n if normalization == True and norm_type == 'instance_norm':\\n layers.append(nn.InstanceNorm2d(out_channels))\\n elif normalization == True and norm_type == 'batch_norm':\\n layers.append(nn.BatchNorm2d(out_channels))\\n return nn.Sequential(*layers)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78048617","0.76200247","0.75978273","0.7558364","0.74831325","0.7368806","0.73137546","0.72811294","0.722849","0.7214952","0.71894956","0.71754706","0.71480143","0.71439964","0.7123431","0.7122098","0.7074655","0.70513123","0.70480937","0.7038724","0.7003021","0.68999255","0.68885905","0.68722945","0.68561953","0.6828491","0.6817216","0.678921","0.6781577","0.6781577","0.67642766","0.6755218","0.6730645","0.67098176","0.6705397","0.6697157","0.6682868","0.66633815","0.66460377","0.6641345","0.66097087","0.66028905","0.6601479","0.65965456","0.65952843","0.65849227","0.65642655","0.6550059","0.6547998","0.654624","0.6544052","0.65429014","0.6528294","0.6524825","0.65215164","0.65206695","0.6516848","0.65107864","0.64740133","0.647185","0.6463239","0.6447461","0.64381075","0.6433087","0.64319086","0.64018786","0.63977575","0.6397136","0.6396971","0.6384196","0.6378032","0.6324135","0.6319403","0.6318087","0.6317333","0.63109463","0.6309077","0.62980855","0.6280912","0.62721217","0.62713933","0.62691355","0.6247966","0.62429863","0.6242752","0.6225","0.6222459","0.621369","0.6209425","0.62062585","0.62034327","0.61974823","0.6193633","0.61915815","0.617975","0.61785424","0.61667633","0.6162613","0.61537963","0.6148135"],"string":"[\n \"0.78048617\",\n \"0.76200247\",\n \"0.75978273\",\n \"0.7558364\",\n \"0.74831325\",\n \"0.7368806\",\n \"0.73137546\",\n \"0.72811294\",\n \"0.722849\",\n \"0.7214952\",\n \"0.71894956\",\n \"0.71754706\",\n \"0.71480143\",\n \"0.71439964\",\n \"0.7123431\",\n \"0.7122098\",\n \"0.7074655\",\n \"0.70513123\",\n \"0.70480937\",\n \"0.7038724\",\n \"0.7003021\",\n \"0.68999255\",\n \"0.68885905\",\n \"0.68722945\",\n \"0.68561953\",\n \"0.6828491\",\n \"0.6817216\",\n \"0.678921\",\n \"0.6781577\",\n \"0.6781577\",\n \"0.67642766\",\n \"0.6755218\",\n \"0.6730645\",\n \"0.67098176\",\n \"0.6705397\",\n \"0.6697157\",\n \"0.6682868\",\n \"0.66633815\",\n \"0.66460377\",\n \"0.6641345\",\n \"0.66097087\",\n \"0.66028905\",\n \"0.6601479\",\n \"0.65965456\",\n \"0.65952843\",\n \"0.65849227\",\n \"0.65642655\",\n \"0.6550059\",\n \"0.6547998\",\n \"0.654624\",\n \"0.6544052\",\n \"0.65429014\",\n \"0.6528294\",\n \"0.6524825\",\n \"0.65215164\",\n \"0.65206695\",\n \"0.6516848\",\n \"0.65107864\",\n \"0.64740133\",\n \"0.647185\",\n \"0.6463239\",\n \"0.6447461\",\n \"0.64381075\",\n \"0.6433087\",\n \"0.64319086\",\n \"0.64018786\",\n \"0.63977575\",\n \"0.6397136\",\n \"0.6396971\",\n \"0.6384196\",\n \"0.6378032\",\n \"0.6324135\",\n \"0.6319403\",\n \"0.6318087\",\n \"0.6317333\",\n \"0.63109463\",\n \"0.6309077\",\n \"0.62980855\",\n \"0.6280912\",\n \"0.62721217\",\n \"0.62713933\",\n \"0.62691355\",\n \"0.6247966\",\n \"0.62429863\",\n \"0.6242752\",\n \"0.6225\",\n \"0.6222459\",\n \"0.621369\",\n \"0.6209425\",\n \"0.62062585\",\n \"0.62034327\",\n \"0.61974823\",\n \"0.6193633\",\n \"0.61915815\",\n \"0.617975\",\n \"0.61785424\",\n \"0.61667633\",\n \"0.6162613\",\n \"0.61537963\",\n \"0.6148135\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77049017"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":89290,"cells":{"query":{"kind":"string","value":"Copy test data to working directory so ``parse_spec`` works properly for both ``stsynphot`` and ASTROLIB PYSYNPHOT."},"document":{"kind":"string","value":"def setup_module(module):\n for datafile in datafiles:\n src = get_pkg_data_filename(os.path.join('data', datafile))\n shutil.copyfile(src, datafile)"},"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 processSpecs(self):\n specSubDirName=\"_spec\"\n codestructure = CodeStructure()\n for dir in self._dirs:\n if q.system.fs.exists(q.system.fs.joinPaths(dir,specSubDirName)):\n files=q.system.fs.listPyScriptsInDir(q.system.fs.joinPaths(dir,specSubDirName))\n for fileName in files:\n codestructure.addCodeFile(self.processSpecFile(q.system.fs.joinPaths(dir,specSubDirName),\"%s.py\" % fileName))\n return codestructure","def parse_test_files():\n a_copy = PY_FILES[::]\n for f in a_copy:\n if 'test' in f:\n TEST_FILES.append(f)\n PY_FILES.remove(f)","def createSpecWithMetadataInJsonAndUserdata(self):\n metadataYamlFilePath = os.path.join(os.path.dirname(\n os.path.realpath(__file__)),\n 'sample_metadata.json')\n userdataFilePath = os.path.join(os.path.dirname(\n os.path.realpath(__file__)),\n 'sample_userdata')\n with open(metadataYamlFilePath, \"r\") as fp:\n self.metadata = fp.read().rstrip('\\n')\n with open(userdataFilePath, \"r\") as fp:\n self.userdata = fp.read().rstrip('\\n')\n self.createCloudinitDataSpec('specWithMetadataInJsonAndUserdata',\n 'linux cloud-init data customization spec'\n 'with metadata in json format and '\n 'userdata')","def test_main(data, tmp_path):\n\n main(data, tmp_path)\n\n FILES = (\n \"gd32f888x(0-1)xx-pinctrl.h\",\n \"gd32f888x(2-3)xx-pinctrl.h\",\n \"gd32f888y(0-1)xx-pinctrl.h\",\n \"gd32f999x(0-1)xx-pinctrl.h\",\n \"gd32f999x(2-3)xx-pinctrl.h\",\n \"gd32f999y(0-1)xx-pinctrl.h\",\n )\n\n for file in FILES:\n ref_file = data / file\n gen_file = tmp_path / file\n\n assert gen_file.exists()\n\n with open(ref_file) as ref, open(gen_file) as gen:\n assert ref.read() == gen.read()","def setUp(self):\n\n # Create a data pipe.\n self.interpreter.pipe.create('test', 'mf')\n\n # Create a temporary file name.\n ds.tmpfile = mktemp()","def createSpecWithMetadataInYamlAndUserdata(self):\n metadataYamlFilePath = os.path.join(os.path.dirname(\n os.path.realpath(__file__)),\n 'sample_metadata.yaml')\n userdataFilePath = os.path.join(os.path.dirname(\n os.path.realpath(__file__)),\n 'sample_userdata')\n with open(metadataYamlFilePath, \"r\") as fp:\n self.metadata = fp.read().rstrip('\\n')\n with open(userdataFilePath, \"r\") as fp:\n self.userdata = fp.read().rstrip('\\n')\n self.createCloudinitDataSpec('specWithMetadataInYamlAndUserdata',\n 'linux cloud-init data customization spec'\n 'with metadata in yaml format and '\n 'userdata')","def setUp(self):\n\n try:\n os.mkdir(self.pipeline_folder)\n except FileExistsError:\n pass\n\n with open(self.pipeline_spec_file, 'w+') as stream:\n json.dump(self.pipeline_spec, stream)\n\n with open(self.pipeline_source_file, 'w+') as stream:\n json.dump(self.source_description, stream)\n\n self.source = Source(folder=self.pipeline_folder)","def setup(self):\n self.cwd = os.getcwd()\n self.t = tempfile.mkdtemp()\n dir_path = Path(\"packages\")\n tmp_dir = self.t / dir_path\n src_dir = self.cwd / Path(ROOT_DIR, dir_path)\n shutil.copytree(str(src_dir), str(tmp_dir))\n shutil.copytree(Path(CUR_PATH, \"data\", \"dummy_aea\"), Path(self.t, \"dummy_aea\"))\n os.chdir(Path(self.t, \"dummy_aea\"))\n self.runner = CliRunner()","def test_diff_src_root_metadata_file_registry(self):\n # Setup test\n filename = os.path.join(_SAMPLE_FILES_DIR, \"reg_good_mf.xml\")\n out_name = \"physics_types_ddt\"\n in_source = os.path.join(_SAMPLE_FILES_DIR, out_name + '_se.F90')\n in_meta = os.path.join(_SAMPLE_FILES_DIR, out_name + '_se.meta')\n out_source = os.path.join(_TMP_DIR, out_name + '.F90')\n out_meta = os.path.join(_TMP_DIR, out_name + '.meta')\n remove_files([out_source, out_meta])\n\n # Create new directory:\n tmp_src_dir = os.path.join(_TMP_DIR, \"test\", \"unit\", \\\n \"sample_files\")\n if not os.path.exists(tmp_src_dir):\n os.makedirs(tmp_src_dir)\n\n # Copy ref_pres.meta file to new location:\n meta_file = os.path.join(_SAMPLE_FILES_DIR, \"ref_pres.meta\")\n shutil.copy(meta_file, tmp_src_dir)\n\n # Generate registry\n retcode, files = gen_registry(filename, 'se', {}, _TMP_DIR, 2,\n _SRC_MOD_DIR, _TMP_DIR,\n loglevel=logging.ERROR,\n error_on_no_validate=True)\n # Check return code\n amsg = \"Test failure for SE dycore, retcode={}\".format(retcode)\n self.assertEqual(retcode, 0, msg=amsg)\n flen = len(files)\n amsg = \"Test failure for SE dycore: Found {} files, expected 2\"\n self.assertEqual(flen, 2, msg=amsg.format(flen))\n amsg = \"{} does not exist\".format(out_meta)\n self.assertTrue(os.path.exists(out_meta), msg=amsg)\n amsg = \"{} does not exist\".format(out_source)\n self.assertTrue(os.path.exists(out_source), msg=amsg)\n # For each output file, make sure it matches input file\n amsg = \"{} does not match {}\".format(in_meta, out_meta)\n self.assertTrue(filecmp.cmp(in_meta, out_meta,\n shallow=False), msg=amsg)\n amsg = \"{} does not match {}\".format(in_source, out_source)\n self.assertTrue(filecmp.cmp(in_source, out_source,\n shallow=False), msg=amsg)\n # Check that the metadata file has the correct number of variables\n mfile = files[1]\n mvars = mfile.variable_list()\n num_vars = len(mvars)\n amsg = \"Expected 14 metadata variables, found {}\".format(num_vars)\n self.assertEqual(num_vars, 14, msg=amsg)","def setUpClass(cls):\n cls.testDir = tempfile.mkdtemp()\n cls.readonlyDir = tempfile.mkdtemp()\n cls.testfile = os.path.join(cls.testDir, 'desispec_test_io.fits')\n cls.testyfile = os.path.join(cls.testDir, 'desispec_test_io.yaml')\n cls.testlog = os.path.join(cls.testDir, 'desispec_test_io.log')\n # cls.testbrfile appears to be unused by this class.\n cls.testbrfile = os.path.join(cls.testDir, 'desispec_test_io-br.fits')\n cls.origEnv = {'SPECPROD': None,\n \"DESI_ROOT\": None,\n \"DESI_ROOT_READONLY\": None,\n \"DESI_SPECTRO_DATA\": None,\n \"DESI_SPECTRO_REDUX\": None,\n \"DESI_SPECTRO_CALIB\": None,\n }\n cls.testEnv = {'SPECPROD':'dailytest',\n \"DESI_ROOT\": cls.testDir,\n \"DESI_ROOT_READONLY\": cls.readonlyDir,\n \"DESI_SPECTRO_DATA\": os.path.join(cls.testDir, 'spectro', 'data'),\n \"DESI_SPECTRO_REDUX\": os.path.join(cls.testDir, 'spectro', 'redux'),\n \"DESI_SPECTRO_CALIB\": os.path.join(cls.testDir, 'spectro', 'calib'),\n }\n cls.datadir = cls.testEnv['DESI_SPECTRO_DATA']\n cls.reduxdir = os.path.join(cls.testEnv['DESI_SPECTRO_REDUX'],\n cls.testEnv['SPECPROD'])\n for e in cls.origEnv:\n if e in os.environ:\n cls.origEnv[e] = os.environ[e]\n os.environ[e] = cls.testEnv[e]","def setup_function(test_function):\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'symbol-list.dat',\n TEST_LOCATION + 'symbol-list.dat')\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'nn-model.dat',\n TEST_LOCATION + 'nn-model.dat')\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'nn-model_test.dat',\n TEST_LOCATION + 'nn-model_test.dat')\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'nn-model_test2.dat',\n TEST_LOCATION + 'nn-model_test2.dat')\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER +\n 'distance-tolerance_test.dat',\n TEST_LOCATION + 'distance-tolerance_test.dat')\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER +\n 'distance-tolerance_test2.dat',\n TEST_LOCATION + 'distance-tolerance_test2.dat')\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'training-set_test.dat',\n TEST_LOCATION + 'training-set_test.dat')\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'training-set_test2.dat',\n TEST_LOCATION + 'training-set_test2.dat')","def test_runs(self):\n\n with TemporaryDirectory() as tmp_dir:\n\n mock_raw_data(tmp_dir, raw_dim=256, num_channels=3, num_images=40)","def setUp(self):\n self.test_root = tempfile.mkdtemp(dir=tmpdir)\n self.test_input = os.path.join(self.test_root, 'input')\n self.test_output = os.path.join(self.test_root, 'output')\n self.test_output_tree = os.path.join(self.test_output, 'tree')\n self.test_output_meta = os.path.join(self.test_output_tree, 'meta.js')\n self.test_output_toc = os.path.join(self.test_output_tree, 'toc.js')\n\n os.makedirs(self.test_input, exist_ok=True)\n os.makedirs(self.test_output, exist_ok=True)","def parseTestspec(self, testspec):\n if 'name' not in testspec or 'commands' not in testspec:\n sys.exit(1)\n\n # Merge user-specified arguments with default test specification\n defaultTestspec = corstone700DefaultTestspec\n defaultTestspec.update(testspec)\n return defaultTestspec","def test_study_cli(self):\n expected = etree.fromstring(self.WTML)\n\n args = [\n \"tile-study\",\n \"--placeholder-thumbnail\",\n \"--outdir\",\n self.work_path(\"study_cli\"),\n test_path(\"wcs512.fits.gz\"),\n ]\n cli.entrypoint(args)\n\n with open(\n self.work_path(\"study_cli\", \"index_rel.wtml\"), \"rt\", encoding=\"utf8\"\n ) as f:\n observed = etree.fromstring(f.read())\n\n assert_xml_elements_equal(observed, expected)\n\n args = [\n \"cascade\",\n \"--start\",\n \"1\",\n self.work_path(\"study_cli\"),\n ]\n cli.entrypoint(args)\n\n self.maybe_test_barycenter(\n self.work_path(\"study_cli\", \"0\", \"0\", \"0_0.fits\"), self.WCS512_BARYDATA\n )","def make_studyforrest_mockup(path):\n public = create(opj(path, 'public'), description=\"umbrella dataset\")\n # the following tries to capture the evolution of the project\n phase1 = public.create('phase1',\n description='old-style, no connection to RAW')\n structural = public.create('structural', description='anatomy')\n tnt = public.create('tnt', description='image templates')\n tnt.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\n tnt.clone(source=structural.path, path=opj('src', 'structural'), reckless='auto')\n aligned = public.create('aligned', description='aligned image data')\n aligned.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\n aligned.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\n # new acquisition\n labet = create(opj(path, 'private', 'labet'), description=\"raw data ET\")\n phase2_dicoms = create(opj(path, 'private', 'p2dicoms'), description=\"raw data P2MRI\")\n phase2 = public.create('phase2',\n description='new-style, RAW connection')\n phase2.clone(source=labet.path, path=opj('src', 'labet'), reckless='auto')\n phase2.clone(source=phase2_dicoms.path, path=opj('src', 'dicoms'), reckless='auto')\n # add to derivatives\n tnt.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\n aligned.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\n # never to be published media files\n media = create(opj(path, 'private', 'media'), description=\"raw data ET\")\n # assuming all annotations are in one dataset (in reality this is also\n # a superdatasets with about 10 subdatasets\n annot = public.create('annotations', description='stimulus annotation')\n annot.clone(source=media.path, path=opj('src', 'media'), reckless='auto')\n # a few typical analysis datasets\n # (just doing 3, actual status quo is just shy of 10)\n # and also the real goal -> meta analysis\n metaanalysis = public.create('metaanalysis', description=\"analysis of analyses\")\n for i in range(1, 3):\n ana = public.create('analysis{}'.format(i),\n description='analysis{}'.format(i))\n ana.clone(source=annot.path, path=opj('src', 'annot'), reckless='auto')\n ana.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\n ana.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\n # link to metaanalysis\n metaanalysis.clone(source=ana.path, path=opj('src', 'ana{}'.format(i)),\n reckless='auto')\n # simulate change in an input (but not raw) dataset\n create_tree(\n aligned.path,\n {'modification{}.txt'.format(i): 'unique{}'.format(i)})\n aligned.save()\n # finally aggregate data\n aggregate = public.create('aggregate', description='aggregate data')\n aggregate.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\n # the toplevel dataset is intentionally left dirty, to reflect the\n # most likely condition for the joint dataset to be in at any given\n # point in time","def create_workspace(topdir, sdist, spec, relnotes, changelog, csdb, patchdir):\n for name in ('SOURCES', 'SPECS'):\n directory = os.path.join(topdir, name)\n if not os.path.exists(directory):\n os.makedirs(directory)\n\n with tmpdir():\n version = unpack_source(sdist)\n if spec:\n if spec.endswith('.in'):\n prepare_spec(topdir, spec, version)\n else:\n copy(spec, os.path.join(topdir, 'SPECS', 'openafs.spec'))\n else:\n prepare_spec(topdir,\n 'src/packaging/RedHat/openafs.spec.in',\n version)\n for source in list_sources(topdir, version):\n dest = os.path.join(topdir, 'SOURCES', source)\n if '-src.tar.' in source:\n copy(os.path.join(sdist, source), dest)\n elif '-doc.tar.' in source:\n copy(os.path.join(sdist, source), dest)\n elif 'RELNOTES' in source:\n if not relnotes:\n relnotes = os.path.join(sdist, source)\n if os.path.exists(relnotes):\n copy(relnotes, dest)\n else:\n with open(dest, 'w') as f:\n f.write('No release notes provided.\\n')\n elif 'ChangeLog' in source:\n if not changelog:\n changelog = os.path.join(sdist, 'ChangeLog')\n if os.path.exists(changelog):\n copy(changelog, dest)\n else:\n with open(dest, 'w') as f:\n f.write('No change log provided.\\n')\n elif 'CellServDB' in source:\n if not csdb:\n csdb = os.path.join(sdist, 'CellServDB')\n if not os.path.exists(csdb):\n csdb = 'src/afsd/CellServDB'\n copy(csdb, dest)\n else:\n copy(os.path.join('src/packaging/RedHat', source), dest)\n\n for patch in list_patches(topdir, version):\n dest = os.path.join(topdir, 'SOURCES', patch)\n if patchdir:\n copy(os.path.join(patchdir, patch), dest)\n else:\n copy(os.path.join(sdist, patch), dest)","def convert_testing_data(mfccPath):\n inputlist, inputnamelist = ark_parser(mfccPath, 'test.ark')\n\n print(\"%d sample in testing set\" % len(inputlist))\n with open('./test_data.pkl', 'wb') as test_data:\n pickle.dump(inputlist, test_data)\n \n with open('./test_name.pkl', 'wb') as test_name:\n pickle.dump(inputnamelist, test_name)","def test_run_sphinx_stt():\n profile_data = default_profile_data\n profile_data['stt'] = 'sphinx'\n mock_flag_modeldir = mock_flag_hmm = mock.Mock()\n mock_flag_lm = mock_flag_dic = mock.Mock()\n profile_data['pocketsphinx'] = {\n 'modeldir': mock_flag_modeldir,\n 'hmm': mock_flag_hmm,\n 'lm': mock_flag_lm,\n 'dic': mock_flag_dic,\n }\n mock_profile.data = profile_data\n\n mock_open = mock.mock_open()\n with mock.patch('__builtin__.__import__', side_effect=import_mock),\\\n mock.patch('melissa.stt.sr') as mock_sr, \\\n mock.patch('melissa.stt.Decoder') as mock_decoder, \\\n mock.patch('melissa.stt.open', mock_open, create=True), \\\n mock.patch('melissa.stt.tts'):\n from melissa.stt import stt\n raised_error = ValueError\n mock_audio = mock.Mock()\n mock_sr.Recognizer.return_value.listen.side_effect = [\n mock_audio, raised_error()]\n stt()\n mock_audio.get_wav_data.assert_called_once_with()\n assert len(mock_sr.mock_calls) == 5\n assert len(mock_open.mock_calls) == 7\n mock_open_data = [\n mock.call('recording.wav', 'wb'),\n mock.call().__enter__(),\n mock.call().write(mock_audio.get_wav_data()),\n mock.call().__exit__(None, None, None),\n mock.call('recording.wav', 'rb'),\n mock.call().seek(44),\n mock.call().read()\n ]\n for item in mock_open_data:\n assert item in mock_open.mock_calls\n\n mock_decoder_config = mock_decoder.default_profile_data()\n mock_decoder_data = [\n mock.call.default_config(),\n mock.call.default_config().set_string('-hmm', mock_flag_hmm),\n mock.call.default_config().set_string('-lm', mock_flag_lm),\n mock.call.default_config().set_string('-dict', mock_flag_dic),\n mock.call.default_config().set_string('-logfn', '/dev/null'),\n mock.call(mock_decoder_config()),\n mock.call().start_utt(),\n mock.call().process_raw('', False, True),\n mock.call().end_utt(),\n mock.call().hyp(),\n # mock.call().hyp().hypstr.__radd__().__add__(\"'\"),\n # mock.call().hyp().hypstr.__radd__().__add__().__str__(),\n mock.call().hyp().hypstr.lower(),\n mock.call().hyp().hypstr.lower().replace(\"'\", ''),\n ]\n for item in mock_decoder_data:\n assert item in mock_decoder.mock_calls","def setUp(self):\r\n\r\n # Cannot use get_qiime_project_dir() due to test errors in virtual box\r\n test_dir = os.path.dirname(os.path.abspath(__file__))\r\n sff_original_fp = os.path.join(test_dir, 'test_support_files',\r\n 'test.sff')\r\n sff_original_gz_fp = os.path.join(test_dir, 'test_support_files',\r\n 'test_gz.sff.gz')\r\n\r\n # copy sff file to working directory\r\n self.sff_dir = mkdtemp()\r\n self.gz_sff_dir = mkdtemp()\r\n self.sff_fp = os.path.join(self.sff_dir, 'test.sff')\r\n self.sff_gz_fp = os.path.join(self.gz_sff_dir, 'test_gz.sff.gz')\r\n shutil.copy(sff_original_fp, self.sff_fp)\r\n shutil.copy(sff_original_gz_fp, self.sff_gz_fp)","def setup_class(cls):\n cls.cwd = os.getcwd()\n cls.t = tempfile.mkdtemp()\n dir_path = Path(\"packages\")\n tmp_dir = cls.t / dir_path\n src_dir = cls.cwd / Path(ROOT_DIR, dir_path)\n shutil.copytree(str(src_dir), str(tmp_dir))\n shutil.copytree(Path(CUR_PATH, \"data\", \"dummy_aea\"), Path(cls.t, \"dummy_aea\"))\n os.chdir(Path(cls.t, \"dummy_aea\"))\n cls.runner = CliRunner()","def setup_class(cls):\n cls.cwd = os.getcwd()\n cls.t = tempfile.mkdtemp()\n dir_path = Path(\"packages\")\n tmp_dir = cls.t / dir_path\n src_dir = cls.cwd / Path(ROOT_DIR, dir_path)\n shutil.copytree(str(src_dir), str(tmp_dir))\n shutil.copytree(Path(CUR_PATH, \"data\", \"dummy_aea\"), Path(cls.t, \"dummy_aea\"))\n os.chdir(Path(cls.t, \"dummy_aea\"))\n cls.runner = CliRunner()","def parseTestspec(self, testspec):\n if 'name' not in testspec or 'commands' not in testspec:\n sys.exit(1)\n\n # Merge user-specified arguments with default test specification\n defaultTestspec = a5dsDefaultTestspec\n defaultTestspec.update(testspec)\n return defaultTestspec","def setUp(self):\n super(PlayTests, self).setUp(\n \"tests/data/shakespeare/\", \"structure.json\", \"brief_example.xml\")","def setUp(self):\n #clear configuration object\n SCF.clearConfig()\n \n #create directories\n test_name = self.id().split(\".\")[-1]\n self.test_working_directory = os.path.join(self.iota2_tests_directory, test_name)\n self.test_working_directory_tmp = os.path.join(self.iota2_tests_directory, test_name + \"_TMP\")\n if os.path.exists(self.test_working_directory):\n shutil.rmtree(self.test_working_directory)\n if not os.path.exists(self.test_working_directory_tmp):\n os.mkdir(self.test_working_directory_tmp)\n self.config_path_test = os.path.join(self.test_working_directory_tmp, test_name + \".cfg\")\n shutil.copy(self.config_path, self.config_path_test)","def setUp(self):\n\n # try:\n os.mkdir(self.pipeline_folder)\n # except FileExistsError:\n # pass\n\n with open(self.pipeline_spec_file, 'w+') as stream:\n json.dump(self.pipeline_spec, stream)\n\n with open(self.pipeline_source_file, 'w+') as stream:\n json.dump(self.source_description, stream)\n\n self.source = Source(self.pipeline_id)","def setUp(self):\n print(\"setUp()\", os.path.exists('./testdatas'))\n if not os.path.exists('./testdatas'):\n print('Creating server test data directory')\n os.mkdir('./testdatas')\n if not os.path.exists('./testdatac'):\n print('Creating client test data directory')\n os.mkdir('./testdatac')\n if os.path.exists('testdata.tar'):\n print('Extracting test data')\n tar = tarfile.open('testdata.tar')\n tar.extractall()\n tar.close()\n else:\n print('No testdata.tar file found!', file=sys.stderr)","def setUp(self):\n super(LegacyResultsProcessorUnittest, self).setUp()\n if six.PY2:\n self.data_directory = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), 'testdata')\n else:\n self.data_directory = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), 'testdata', 'python3')","def setUp(self):\n # make directory test\n self.temp_dir_string = '/tmp/test_for_seqprep/'\n create_dir(self.temp_dir_string)\n\n # make directory with spaces test\n self.temp_dir_string_space = '/tmp/test for seqprep/'\n create_dir(self.temp_dir_string_space)\n \n # create temp file path strings\n self.test_fn1 = os.path.join(self.temp_dir_string,'reads1.fastq')\n self.test_fn1_space = os.path.join(self.temp_dir_string_space, \n 'reads1.fastq')\n self.test_fn2 = os.path.join(self.temp_dir_string,'reads2.fastq')\n self.test_fn2_space = os.path.join(self.temp_dir_string_space,\n 'reads2.fastq')","def copy_test_configuration(self, source_dir, dest_dir):\n for root, dirs, files in os.walk(source_dir):\n if '.svn' in dirs:\n dirs.remove('.svn')\n dirs = [ d for d in dirs if not d.startswith('gyptest') ]\n files = [ f for f in files if not f.startswith('gyptest') ]\n for dirname in dirs:\n source = os.path.join(root, dirname)\n destination = source.replace(source_dir, dest_dir)\n os.mkdir(destination)\n if sys.platform != 'win32':\n shutil.copystat(source, destination)\n for filename in files:\n source = os.path.join(root, filename)\n destination = source.replace(source_dir, dest_dir)\n shutil.copy2(source, destination)","def test_sects(self):\n\n test_files = glob.glob(INPUT_HYPM_PATH + '/unit_*.mdd')\n test_files.extend(glob.glob(INPUT_FLMB_PATH + '/unit_*.mdd'))\n\n sects = mdd.procall(test_files)\n\n TestSioUnpack.latest(sects)","def setUp(self):\n\n self.directory = tempfile.mkdtemp(dir=os.getcwd())\n spirv_args = self.test.spirv_args\n # Instantiate placeholders in spirv_args\n self.test.spirv_args = [\n arg.instantiate_for_spirv_args(self)\n if isinstance(arg, PlaceHolder) else arg for arg in self.test.spirv_args\n ]\n # Get all shader files' names\n self.inputs = [arg for arg in spirv_args if isinstance(arg, PlaceHolder)]\n self.file_shaders = [arg.filename for arg in self.inputs]\n\n if 'environment' in get_all_variables(self.test):\n self.test.environment.write(self.directory)\n\n expectations = [\n v for v in get_all_variables(self.test)\n if v.startswith(EXPECTED_BEHAVIOR_PREFIX)\n ]\n # Instantiate placeholders in expectations\n for expectation_name in expectations:\n expectation = getattr(self.test, expectation_name)\n if isinstance(expectation, list):\n expanded_expections = [\n element.instantiate_for_expectation(self)\n if isinstance(element, PlaceHolder) else element\n for element in expectation\n ]\n setattr(self.test, expectation_name, expanded_expections)\n elif isinstance(expectation, PlaceHolder):\n setattr(self.test, expectation_name,\n expectation.instantiate_for_expectation(self))","def setup(self):\n\n self.testInst = pysat.Instrument('pysat', 'testing',\n clean_level='clean',\n update_files=True)\n self.testInst.bounds = (dt.datetime(2008, 1, 1),\n dt.datetime(2008, 1, 31))\n self.test_bins = [0, 24, 24]\n self.test_label = 'slt'\n self.test_data = ['dummy1', 'dummy2']\n self.out_keys = ['count', 'avg_abs_dev', 'median', 'bin_x']\n self.out_data = {'dummy1':\n {'count': [111780., 111320., 111780., 111320.,\n 111780., 111320., 111780., 111320.,\n 111780., 111320., 111780., 111320.,\n 111780., 111320., 111918., 111562.,\n 112023., 111562., 112023., 111412.,\n 111780., 111320., 111780., 111320.],\n 'avg_abs_dev': np.zeros(shape=24),\n 'median': np.linspace(0.0, 23.0, 24)},\n 'dummy2':\n {'count': [111780., 111320., 111780., 111320.,\n 111780., 111320., 111780., 111320.,\n 111780., 111320., 111780., 111320.,\n 111780., 111320., 111918., 111562.,\n 112023., 111562., 112023., 111412.,\n 111780., 111320., 111780., 111320.],\n 'avg_abs_dev': np.zeros(shape=24) + 6.0,\n 'median': [11., 12., 11., 11., 12., 11., 12., 11.,\n 12., 12., 11., 12., 11., 12., 11., 11.,\n 12., 11., 12., 11., 11., 11., 11., 12.]}}\n return","def _load_test_data(self):\n self._save_test_data()","def setUp(self):\n self.vmodel_name = \"LABasin\"\n self.sim_id = int(seqnum.get_seq_num())\n self.install = InstallCfg()\n self.vmodel_obj = vmodels.get_velocity_model_by_name(self.vmodel_name)\n\n indir = os.path.join(self.install.A_IN_DATA_DIR, str(self.sim_id))\n tmpdir = os.path.join(self.install.A_TMP_DATA_DIR, str(self.sim_id))\n outdir = os.path.join(self.install.A_OUT_DATA_DIR, str(self.sim_id))\n logdir = os.path.join(self.install.A_OUT_LOG_DIR, str(self.sim_id))\n # Create all directories\n bband_utils.mkdirs([indir, tmpdir, outdir, logdir], print_cmd=False)\n\n # Copy needed files\n\n # src file\n r_src_file = \"nr_v12_11_0_fs.src\"\n src_file = os.path.join(self.install.A_TEST_REF_DIR, \"uwo\", r_src_file)\n self.src_file = os.path.join(indir, r_src_file)\n cmd = \"cp %s %s\" % (src_file, self.src_file)\n bband_utils.runprog(cmd)\n\n # exsim param template file\n vmodel_params = self.vmodel_obj.get_codebase_params('exsim')\n self.failIf('GENERIC_PARAM' not in vmodel_params)\n r_param_template = vmodel_params['GENERIC_PARAM']\n\n self.failIf(r_param_template == \"\" or r_param_template is None)\n param_template = os.path.join(self.vmodel_obj.base_dir,\n r_param_template)\n # r_param_template is relative to the velocity model basedir,\n # get only basename\n r_param_template = os.path.basename(r_param_template)\n self.param_template = os.path.join(indir, r_param_template)\n cmd = \"cp %s %s\" % (param_template, self.param_template)\n bband_utils.runprog(cmd)\n\n # station file\n r_stations = \"nr_v12_11_2.stl\"\n stations = os.path.join(self.install.A_TEST_REF_DIR, \"uwo\", r_stations)\n self.stations = os.path.join(indir, r_stations)\n cmd = \"cp %s %s\" % (stations, self.stations)\n bband_utils.runprog(cmd)","def test_SourceMods_metadata_file_registry(self):\n # Setup test\n filename = os.path.join(_SAMPLE_FILES_DIR, \"reg_good_mf.xml\")\n out_name = \"physics_types_ddt\"\n in_source = os.path.join(_SAMPLE_FILES_DIR, out_name + '_se.F90')\n in_meta = os.path.join(_SAMPLE_FILES_DIR, out_name + '_se.meta')\n out_source = os.path.join(_TMP_DIR, out_name + '.F90')\n out_meta = os.path.join(_TMP_DIR, out_name + '.meta')\n remove_files([out_source, out_meta])\n\n # Create new directory:\n tmp_src_dir = os.path.join(_TMP_DIR, \"test\", \"unit\", \\\n \"sample_files\")\n if not os.path.exists(tmp_src_dir):\n os.makedirs(tmp_src_dir)\n\n # Copy ref_pres_sm.meta file to new location:\n meta_file = os.path.join(_SAMPLE_FILES_DIR, \"ref_pres_SourceMods.meta\")\n source_mod_file = os.path.join(tmp_src_dir, \"ref_pres.meta\")\n if os.path.exists(source_mod_file):\n os.remove(source_mod_file)\n shutil.copy(meta_file, source_mod_file)\n\n # Generate registry\n retcode, files = gen_registry(filename, 'se', {}, _TMP_DIR, 2,\n tmp_src_dir, _CAM_ROOT,\n loglevel=logging.ERROR,\n error_on_no_validate=True)\n\n # Check return code\n amsg = \"Test failure for SE dycore, retcode={}\".format(retcode)\n self.assertEqual(retcode, 0, msg=amsg)\n flen = len(files)\n amsg = \"Test failure for SE dycore: Found {} files, expected 2\"\n self.assertEqual(flen, 2, msg=amsg.format(flen))\n amsg = \"{} does not exist\".format(out_meta)\n self.assertTrue(os.path.exists(out_meta), msg=amsg)\n amsg = \"{} does not exist\".format(out_source)\n self.assertTrue(os.path.exists(out_source), msg=amsg)\n # For each output file, make sure it matches input file\n amsg = \"{} does not match {}\".format(in_meta, out_meta)\n self.assertTrue(filecmp.cmp(in_meta, out_meta,\n shallow=False), msg=amsg)\n amsg = \"{} does not match {}\".format(in_source, out_source)\n self.assertTrue(filecmp.cmp(in_source, out_source,\n shallow=False), msg=amsg)\n # Check that the metadata file has the correct number of variables\n mfile = files[1]\n mvars = mfile.variable_list()\n num_vars = len(mvars)\n amsg = \"Expected 14 metadata variables, found {}\".format(num_vars)\n self.assertEqual(num_vars, 16, msg=amsg)","def test_script(self):\r\n qiime_config = load_qiime_config()\r\n tempdir = qiime_config['temp_dir'] or gettempdir()\r\n maindir = os.path.join(tempdir,\r\n ''.join(random.choice(string.ascii_letters + string.digits)\r\n for x in range(10)))\r\n\r\n os.makedirs(maindir)\r\n self.dirs_to_remove.append(maindir)\r\n otuf = os.path.join(maindir, 'otuf')\r\n treef = os.path.join(maindir, 'treef')\r\n\r\n otufh = open(otuf, 'w')\r\n otufh.write(tutorial_otu_table)\r\n otufh.close()\r\n\r\n treefh = open(treef, 'w')\r\n treefh.write(tutorial_tree)\r\n treefh.close()\r\n\r\n out_dir = os.path.join(maindir, 'simsam_out')\r\n cmd = 'simsam.py -i %s -t %s -o %s -d .003 -n 3 ' % (otuf, treef,\r\n out_dir)\r\n proc = subprocess.Popen(cmd, shell=True,\r\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\r\n scriptout, scripterr = proc.communicate()\r\n\r\n if scriptout:\r\n raise RuntimeError('script returned stdout: ' + scriptout)\r\n if scripterr:\r\n raise RuntimeError('script returned stderr: ' + scripterr)\r\n\r\n num_replicates = 3 # ensure this matches cmd above\r\n\r\n res = open(\r\n os.path.join(\r\n out_dir,\r\n 'otuf_n%d_d0.003.biom' %\r\n num_replicates),\r\n 'U')\r\n orig_table = parse_biom_table(open(otuf, 'U'))\r\n res_table = parse_biom_table(res)\r\n\r\n # 3 samples per input sample\r\n self.assertEqual(\r\n len(res_table.SampleIds),\r\n num_replicates * len(orig_table.SampleIds))\r\n\r\n # sample_ids have correct naming and order\r\n for i in range(len(orig_table.SampleIds)):\r\n for j in range(num_replicates):\r\n exp = orig_table.SampleIds[i] + '.' + str(j)\r\n self.assertEqual(\r\n res_table.SampleIds[i * num_replicates + j],\r\n exp)\r\n\r\n # same total sequences in each replicate sample\r\n num_orig_samples = len(orig_table.SampleIds)\r\n orig_sams = orig_table.iterSampleData()\r\n res_sams = res_table.iterSampleData()\r\n for i in range(num_orig_samples):\r\n orig_sam = orig_sams.next()\r\n for j in range(num_replicates):\r\n res_sam = res_sams.next()\r\n self.assertEqual(res_sam.sum(), orig_sam.sum())\r\n # would be nice to test that result otu table doesn't match input,\r\n # but not sure how probable that is, and don't want stochastic failures\r","def setUp(self):\n super().setUp()\n self.cur_source = tempfile.mkdtemp()","def setUpClass(cls):\n import os\n for root in cls.prod_s2_ssc:\n os.makedirs(root)\n metadata = root.split(\".\")[0] + \".HDR\"\n TestFunctions.touch(metadata)\n for root in cls.prod_s2_mus:\n os.makedirs(root)\n metadata = os.path.join(root, root + \"_MTD_ALL.xml\")\n TestFunctions.touch(metadata)\n for root in cls.prod_s2_nat:\n os.makedirs(root)\n metadata = os.path.join(root, \"MTD_MSIL1C.xml\")\n TestFunctions.touch(metadata)","def makeMetadataTest(metadata):\n metadata = metadata.strip()\n # convert to tabs\n metadata = str(metadata)\n metadata = metadata.replace(\" \", \"\\t\")\n # store\n originalMetadata = metadata\n # pack\n header, directory, tableData, metadata = defaultTestData(metadata=metadata)\n data = padData(packTestHeader(header) + packTestDirectory(directory) + tableData) + packTestMetadata(metadata)\n # done\n return data, originalMetadata","def setUp(self):\n\n self.hw = HMMERWrapper\n\n modpath = os.path.abspath(os.path.dirname(__file__))\n self.seqfile = os.path.join(modpath, 'data', 'P00929.fasta')\n self.badfile = os.path.join(modpath, 'data', 'bad.fasta')","def setUp(self):\n\n # Create the data pipe.\n self.interpreter.pipe.create('dasha', 'mf')\n\n # Create a temporary directory for Dasha outputs.\n ds.tmpdir = mkdtemp()","def setUp(self):\n # parse the file\n self.plateMeas = PlateMeas(pathToPlate8131MeasFile)","def setUp_tearDown():\n\n\t# setup\n\tif os.path.isdir(dir_parent):\n\t\tshutil.rmtree(dir_parent)\n\n\tos.makedirs(dir_obj)\n\t# shutil.copytree(dir_verif, dir_obj)\n\tshutil.copyfile(dir_verif+'spec.fits', dir_obj+'spec.fits')\n\n\t# yield\n\t# # tear down\n\t# if os.path.isdir(dir_parent):\n\t# \tshutil.rmtree(dir_parent)","def setUp(self):\n # create temporary directory\n if not usedir:\n self.test_dir = tempfile.mkdtemp()\n os.chdir(self.test_dir)\n else:\n os.chdir(usedir) \n\n super(SimpleTest, self).setUp()\n\n import SFramework\n self.manager = SFramework.TSStatisticsManager()\n self.manager.getWorkspaces().addObject(self.makeWS())","def setUp(self):\n\n # Add a data pipe to the data store.\n ds.add(pipe_name='orig', pipe_type='mf')\n\n # Name the first molecule.\n cdp.mol[0].name = 'Ap4Aase'\n\n # Add a second molecule to the system.\n cdp.mol.add_item(mol_name='RNA')\n\n # Add two more residues to the first molecule (and set the residue number of the first).\n cdp.mol[0].res[0].num = 1\n cdp.mol[0].res.add_item(res_num=2, res_name='Glu')\n cdp.mol[0].res.add_item(res_num=4, res_name='Pro')\n\n # Add some spin info to this molecule.\n cdp.mol[0].res[0].spin[0].name = 'NH'\n cdp.mol[0].res[0].spin[0].num = 60\n cdp.mol[0].res[1].spin[0].name = 'NH'\n cdp.mol[0].res[1].spin[0].num = 63\n\n # Add one more residue to the second molecule (and set the residue number of the first).\n cdp.mol[1].res[0].num = -5\n cdp.mol[1].res.add_item(res_num=-4)\n\n # Add a second set of spins to the second molecule (naming the first set first).\n cdp.mol[1].res[0].spin[0].name = 'C8'\n cdp.mol[1].res[1].spin[0].name = 'C8'\n cdp.mol[1].res[0].spin.add_item(spin_name='N5')\n cdp.mol[1].res[1].spin.add_item(spin_name='N5')\n cdp.mol[1].res[1].spin.add_item(spin_name='2H', spin_num=132)\n\n # Deselect a number of spins.\n cdp.mol[0].res[0].spin[0].select = 0\n cdp.mol[0].res[2].spin[0].select = 0\n cdp.mol[1].res[0].spin[0].select = 0\n cdp.mol[1].res[1].spin[1].select = 0\n\n # Update the metadata.\n mol_res_spin.metadata_update()","def prep_synthetic_test(self, outdir='synthetic_test',\n loc=(300,350,50,100), #t89\n signal='linear', #sinusoidal, exponential?\n rate=0.01, #(m)\n noise=None, #white, colored (correlated)?\n ramp=None): #linear, quadratic?\n if os.path.isdir(outdir):\n shutil.rmtree(outdir)\n os.mkdir(outdir)\n\n shape = self.Set.Shape\n ind_signal = np.zeros(shape, dtype='bool')\n ind_signal[loc[0]:loc[1],loc[2]:loc[3]] = 1\n bd = 20\n ind_bound = np.zeros_like(ind_signal)\n ind_bound[loc[0]-bd:loc[1]+bd,loc[2]-bd:loc[3]+bd] = 1\n ind_bound[ind_signal] = 0\n\n for ig in self.Set:\n amp, phs = roipy.tools.load_bil(ig)\n\n # Create synthetic signal\n phs_syn = np.zeros_like(phs)\n phs_syn[ind_signal] = (rate * ig.Timespan)\n\n # Create transition zone\n phs_syn[ind_bound] = (0.01 * ig.Timespan)\n\n # Add noise\n if noise != None:\n noise = add_noise(noise)\n phs_syn += noise\n\n # Add ramps\n if ramp != None:\n surface = make_surface(ramp)\n phs_syn += surface\n\n # Save Data & Rsc\n phs_syn = phs_syn * (4*np.pi/ig.Wavelength) #convert to phase\n outpath = os.path.join(outdir,ig.Name.replace('rect_','rect_synthetic_'))\n roipy.tools.save_bil(ig, outpath, amp, phs_syn)\n #roipy.tools.save_rsc(ig.Rsc, outpath + '.rsc') #automatically called\n\n print('synthetic test set up in:\\n{}'.format(os.path.abspath(outdir)))","def test_call_sh_files(self):\n def structs():\n \"\"\"Mockups for called scripts.\"\"\"\n server = MockUCSHttpServer('server')\n struct_r = U.UCSRepoPool(major=MAJOR, minor=MINOR, part=PART, patchlevel=PATCH, arch=ARCH)\n preup_r = struct_r.path('preup.sh')\n postup_r = struct_r.path('postup.sh')\n struct_c = U.UCSRepoPool(major=MAJOR, minor=MINOR, part='%s/component' % (PART,), patch='c', arch=ARCH)\n preup_c = struct_c.path('preup.sh')\n postup_c = struct_c.path('postup.sh')\n\n yield (server, struct_r, 'preup', preup_r, 'r_pre')\n yield (server, struct_r, 'postup', postup_r, 'r_post')\n yield (server, struct_c, 'preup', preup_c, 'c_pre')\n yield (server, struct_c, 'postup', postup_c, 'c_post')\n tmp = NamedTemporaryFile()\n\n gen = self.u.call_sh_files(structs(), tmp.name, 'arg')\n\n # The Updater only yields the intent, the content is only available after the next step\n self.assertEqual(('update', 'pre'), gen.next()) # download\n self.assertEqual([], MockPopen.mock_get())\n self.assertEqual(('preup', 'pre'), gen.next()) # pre\n self.assertEqual([], MockPopen.mock_get())\n self.assertEqual(('preup', 'main'), gen.next())\n self.assertEqual(('pre', 'arg', 'c_pre'), MockPopen.mock_get()[0][1:])\n self.assertEqual(('preup', 'post'), gen.next())\n self.assertEqual(('arg', 'r_pre'), MockPopen.mock_get()[0][1:])\n self.assertEqual(('update', 'main'), gen.next()) # update\n self.assertEqual(('post', 'arg', 'c_pre'), MockPopen.mock_get()[0][1:])\n self.assertEqual(('postup', 'pre'), gen.next()) # post\n self.assertEqual([], MockPopen.mock_get())\n self.assertEqual(('postup', 'main'), gen.next())\n self.assertEqual(('pre', 'arg', 'c_post'), MockPopen.mock_get()[0][1:])\n self.assertEqual(('postup', 'post'), gen.next())\n self.assertEqual(('arg', 'r_post'), MockPopen.mock_get()[0][1:])\n self.assertEqual(('update', 'post'), gen.next())\n self.assertEqual(('post', 'arg', 'c_post'), MockPopen.mock_get()[0][1:])\n self.assertRaises(StopIteration, gen.next) # done\n self.assertEqual([], MockPopen.mock_get())","def test_SpecConfig_class_minimal():\n res = SpecConfig(path=PATH_SPECS_2_YAML)\n assert res.path_out == PATH_SPECS_2_YAML_MODIFIED","def test_process(self, tmp_path):\n export_dir = tmp_path / 'export'\n export_dir.mkdir()\n\n process(['raw_data/small_raw_data_0.csv',\n 'raw_data/small_raw_data_1.csv',\n # File 2 does not exist.\n 'raw_data/small_raw_data_2.csv',\n 'raw_data/small_raw_data_3.csv'],\n SIGNALS,\n ['median_home_dwell_time',\n 'completely_home_prop_7dav'],\n ['state'],\n export_dir)\n\n expected = {\n 'wip_median_home_dwell_time': pd.DataFrame(data={\n 'geo_id': ['al', 'ga'],\n 'val': [6, 3.5],\n 'se': [None, 0.5],\n 'sample_size': [1, 2]\n }),\n 'completely_home_prop': pd.DataFrame(data={\n 'geo_id': ['al', 'ga'],\n 'val': [0.15, 0.055],\n 'se': [None, 0.005],\n 'sample_size': [1, 2]\n }),\n 'part_time_work_prop': pd.DataFrame(data={\n 'geo_id': ['al', 'ga'],\n 'val': [0.35, 0.055],\n 'se': [None, 0.005],\n 'sample_size': [1, 2]\n }),\n 'full_time_work_prop': pd.DataFrame(data={\n 'geo_id': ['al', 'ga'],\n 'val': [0.45, 0.055],\n 'se': [None, 0.005],\n 'sample_size': [1, 2]\n }),\n 'median_home_dwell_time_7dav': pd.DataFrame(data={\n 'geo_id': ['al', 'ga', 'pa'],\n 'val': [4.5, 3.5, 7.5],\n 'se': [1.5, 0.5, 0.5],\n 'sample_size': [2, 2, 2]\n }),\n 'wip_completely_home_prop_7dav': pd.DataFrame(data={\n 'geo_id': ['al', 'ga', 'pa'],\n 'val': [0.1, 0.055, 0.15],\n 'se': [0.05, 0.005, 0.05],\n 'sample_size': [2, 2, 2]\n }),\n 'part_time_work_prop_7dav': pd.DataFrame(data={\n 'geo_id': ['al', 'ga', 'pa'],\n 'val': [0.25, 0.055, 0.25],\n 'se': [0.1, 0.005, 0.05],\n 'sample_size': [2, 2, 2]\n }),\n 'full_time_work_prop_7dav': pd.DataFrame(data={\n 'geo_id': ['al', 'ga', 'pa'],\n 'val': [0.35, 0.055, 0.35],\n 'se': [0.1, 0.005, 0.05],\n 'sample_size': [2, 2, 2]\n })\n }\n actual = {signal: pd.read_csv(\n export_dir / f'20200612_state_{signal}.csv')\n for signal in expected}\n for signal in expected:\n pd.testing.assert_frame_equal(expected[signal], actual[signal])","def test_2_star(install_test_files, data_dir):\n with make_workdir() as workdir:\n cl = [\"bcbio_nextgen.py\",\n get_post_process_yaml(data_dir, workdir),\n os.path.join(data_dir, os.pardir, \"110907_ERP000591\"),\n os.path.join(data_dir, \"run_info-star.yaml\")]\n subprocess.check_call(cl)","def run_test(self):\n\n # populate *_ps sets\n self.enter_project_file()\n\n # populate *_dir sets\n self.enter_directories()\n\n # The files in the directories makes up the largest possible set of files\n self.result_files = self.result_files_dir\n self.design_files = self.design_files_dir\n self.design_space_files = self.design_space_files_dir\n self.test_bench_files = self.test_bench_files_dir\n\n # populate *_ms sets\n self.enter_meta_results_file()\n\n # populate *_OK sets\n self.check_analysis_status()\n\n df = {'design_files_dir' : list(self.design_files_dir),'design_files_pr' : list(self.design_files_pr),\n 'design_files_ms' : list(self.design_files_ms), 'design_files_OK' : list(self.design_files_OK)}\n\n ds = {'design_space_files_dir' : list(self.design_space_files_dir),\n 'design_space_files_pr' : list(self.design_space_files_pr)}\n\n rs = {'result_files_dir' : list(self.result_files_dir), 'result_files_ms' : list(self.result_files_ms),\n 'result_files_OK' : list(self.result_files_OK)}\n\n tb = {'test_bench_files_dir' : list(self.test_bench_files_dir),\n 'test_bench_files_ms' : list(self.test_bench_files_ms)}\n\n srl = SummaryReportsLinks(self.result_files_dir)\n\n lf = {'files_linked_from_sum_reps' : srl.get_files(),\n 'folders_linked_from_sum_reps' : srl.get_folders()}\n\n # 'test_bench_files_pr' : list(self.test_bench_files_pr),\n \n json_test = {'design_files' : df, 'design_space_files' : ds, 'result_files' : rs,\n 'test_bench_files' : tb, 'stat_files' : self.stat_files,\n 'files_linked_from_sum_reps' : lf}\n\n with open('test_run.json','wb') as f_out:\n json.dump(json_test, f_out, indent=4)","def test_tarballs_pre_extracted_tech_specific(self) -> None:\n import hammer_config\n\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\"dummy28\")\n tech_json_filename = os.path.join(tech_dir, \"dummy28.tech.json\")\n\n # Add defaults to specify tarball_dir.\n with open(os.path.join(tech_dir, \"defaults.json\"), \"w\") as f:\n f.write(json.dumps({\n \"technology.dummy28.tarball_dir\": tech_dir,\n \"vlsi.technology.extracted_tarballs_dir\": \"/should/not/be/used\",\n \"technology.dummy28.extracted_tarballs_dir\": tech_dir_base\n }, cls=HammerJSONEncoder))\n\n HammerToolTestHelpers.write_tech_json(tech_json_filename, self.add_tarballs)\n sys.path.append(tech_dir_base)\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\"dummy28\", tech_dir))\n tech.cache_dir = tech_dir\n\n database = hammer_config.HammerDatabase()\n database.update_technology(tech.get_config())\n HammerVLSISettings.load_builtins_and_core(database)\n tech.set_database(database)\n outputs = tech.process_library_filter(pre_filts=[], filt=hammer_tech.filters.gds_filter,\n must_exist=False,\n output_func=lambda str, _: [str])\n\n self.assertEqual(outputs, [\"{0}/foobar.tar.gz/test.gds\".format(tech_dir_base)])\n\n # Cleanup\n shutil.rmtree(tech_dir_base)","def read_raw(self, name, source, test_data=''):\n self.m.path.assert_absolute(source)\n step_test_data = lambda: self.test_api.read_raw(test_data)\n result = self._run(name, ['copy', source, self.m.raw_io.output()],\n step_test_data=step_test_data)\n return result.raw_io.output","def setUp(self):\n\n # Add a data pipe to the data store.\n ds.add(pipe_name='orig', pipe_type='mf')\n\n # Add a single object to the 'orig' data pipe.\n ds['orig'].x = 1\n\n # Add a single object to the single spin system of the 'orig' data pipe.\n ds['orig'].mol[0].res[0].spin[0].num = 1\n\n # Add an empty data pipe (for the 'eliminate_unused_pipes' test).\n ds.add(pipe_name='empty', pipe_type='mf')\n\n # Set the current pipe to the 'orig' data pipe.\n pipes.switch('orig')","def test_load_vspec():\n global _vspec\n _vspec = SourceSpectrum.from_vega()","def setUp(self):\n with open('tests/data/code.txt') as f:\n self.sf = StreamerFlowchart('Test', f.read())\n with open('tests/data/code_result.txt') as f:\n self.expected = f.read()[:-1] # don't want the empty line","def get_parser_test():\n copy.get_parser()","def clean_up(self):\n try:\n data_dir = os.environ[\"DATA\"]\n plots_dir = os.environ[\"PLOTS\"]\n logs_dir = os.environ[\"LOGS\"]\n except KeyError as detail:\n print \"GenerateSpectrum.clean_up: error\", detail, \"not set\"\n print \" --> source analysis environment scripts before running!\"\n sys.exit(1)\n for root, dirs, files in os.walk(os.getcwd()):\n for file in files:\n is_data = re.search(r\".*\\.root$\", file)\n is_plot = re.search(r\".*\\.png$\", file)\n hostname = socket.gethostname()\n is_log = re.search(r\"^rat\\.\"+hostname+r\"\\.[0-9]+\\.log$\", file)\n if is_data:\n try:\n root_file = TFile(file)\n tree = root_file.Get(\"T\")\n tree.ls()\n except ReferenceError as detail:\n \"generate_spectrum.clean_up: error in TFile,\", detail\n sys.exit(1)\n file_manips.copy_file(os.path.join(root, file), data_dir)\n elif is_plot:\n file_manips.copy_file(os.path.join(root, file), plots_dir)\n elif is_log:\n file_manips.copy_file(os.path.join(root, file), logs_dir)","def test_parse_various_files():\r\n TEST_DATA_FILES = [\r\n \"AnnotationTest.thrift\", \"ConstantsDemo.thrift\", \"DenseLinkingTest.thrift\",\r\n \"OptionalRequiredTest.thrift\", \"StressTest.thrift\", \"DebugProtoTest.thrift\",\r\n \"DocTest.thrift\", \"ManyTypedefs.thrift\", \"SmallTest.thrift\", \"ThriftTest.thrift\"\r\n ]\r\n TEST_DATA_DIR = 'test_data'\r\n TEST_DATA_PATH = 'twitter.thrift.descriptors'\r\n\r\n parser = ThriftParser()\r\n for test_data_file in TEST_DATA_FILES:\r\n test_data = pkgutil.get_data(TEST_DATA_PATH, os.path.join(TEST_DATA_DIR, test_data_file))\r\n print('Parsing file %s...' % test_data_file, end='')\r\n # TODO: The parser may fail silently and return a partial program. Fix this.\r\n program = parser.parse_string(test_data)\r\n print('OK.')","def setUp(self):\n self._resolver_context = context.Context()\n test_path = self._GetTestFilePath(['hfsplus.raw'])\n self._SkipIfPathNotExists(test_path)\n\n test_os_path_spec = path_spec_factory.Factory.NewPathSpec(\n definitions.TYPE_INDICATOR_OS, location=test_path)\n self._raw_path_spec = path_spec_factory.Factory.NewPathSpec(\n definitions.TYPE_INDICATOR_RAW, parent=test_os_path_spec)\n self._hfs_path_spec = path_spec_factory.Factory.NewPathSpec(\n definitions.TYPE_INDICATOR_HFS, location='/',\n parent=self._raw_path_spec)\n\n self._file_system = hfs_file_system.HFSFileSystem(\n self._resolver_context, self._hfs_path_spec)\n self._file_system.Open()","def setUp(self):\r\n\r\n fd, self.sample_fasta_fp = mkstemp(prefix=\"sample_fasta_\",\r\n suffix=\".fna\")\r\n close(fd)\r\n seq_file = open(self.sample_fasta_fp, 'w')\r\n seq_file.write(sample_fasta_file)\r\n seq_file.close()\r\n\r\n fd, self.sample_fasta_invalid_fp = mkstemp(prefix=\"sample_fasta_\",\r\n suffix=\".fna\")\r\n close(fd)\r\n seq_file = open(self.sample_fasta_invalid_fp, 'w')\r\n seq_file.write(sample_fasta_file_invalid)\r\n seq_file.close()\r\n\r\n fd, self.sample_mapping_fp = mkstemp(prefix=\"sample_mapping_\",\r\n suffix=\".txt\")\r\n close(fd)\r\n map_file = open(self.sample_mapping_fp, \"w\")\r\n map_file.write(sample_mapping_file)\r\n map_file.close()\r\n\r\n fd, self.sample_tree_3tips_fp = mkstemp(\r\n prefix=\"sample_tree3tips_\",\r\n suffix=\".tre\")\r\n close(fd)\r\n tree_file = open(self.sample_tree_3tips_fp, \"w\")\r\n tree_file.write(sample_tree_file_3tips)\r\n tree_file.close()\r\n\r\n fd, self.sample_tree_5tips_fp = mkstemp(\r\n prefix=\"sample_tree3tips_\",\r\n suffix=\".tre\")\r\n close(fd)\r\n tree_file = open(self.sample_tree_5tips_fp, \"w\")\r\n tree_file.write(sample_tree_file_5tips)\r\n tree_file.close()\r\n\r\n fd, self.sample_mapping_file_errors_fp =\\\r\n mkstemp(prefix=\"error_mapping_\", suffix=\".txt\")\r\n close(fd)\r\n map_file = open(self.sample_mapping_file_errors_fp, \"w\")\r\n map_file.write(sample_mapping_file_errors)\r\n map_file.close()\r\n\r\n self._files_to_remove = [self.sample_fasta_fp,\r\n self.sample_fasta_invalid_fp, self.sample_mapping_fp,\r\n self.sample_tree_3tips_fp, self.sample_tree_5tips_fp,\r\n self.sample_mapping_file_errors_fp]\r\n\r\n self.output_dir =\\\r\n mkdtemp(prefix=\"validate_demultiplexed_fasta_\",\r\n suffix=\"/\")","def _read_spec_test(filename):\n with open(filename, \"rt\") as file:\n source = file.read()\n return ParsedSpecTest.parse(source)","def setup(self):\n if (((not os.path.isfile(self.tempfile))\n or (os.stat(self.tempfile).st_size == 0))):\n self.write_ply(self.tempfile)\n skip_start = False\n if self.inst_kwargs.get('src_type', 1) == 0:\n skip_start = True\n super(TestCisPlyInput, self).setup(skip_start=skip_start)","def test_sample():\n print(os.getcwd())\n run_validator(SAMPLE_FILE_PATH)","def setUp(self):\n\n self.test_data_path = 'testing/test_data/'","def setUp(self):\n self.setUpPyfakefs()\n with mock.patch.object(subprocess, 'Popen', side_effect=thor_mocks.mock_thor_start):\n self._analyzer = yara_analyzer.YaraAnalyzer()\n\n # Write target file.\n # pylint: disable=no-member\n self.fs.create_file('./target.exe', contents='This is definitely not an evil file. ^_^\\n')","def test_image(self):\r\n self.testdata = open(TESTDATA_FILENAME).read()","def _store_test_result(ptfhost):\n logger.info(\"Copying file from folder: {0} to folder: {1}\".format(\n\t\tSAI_TEST_REPORT_TMP_DIR_ON_PTF, \n\t\tSAI_TEST_REPORT_DIR_ON_PTF))\n ptfhost.shell(\"cp {0}/*.* {1}/\".format(\n\t\tSAI_TEST_REPORT_TMP_DIR_ON_PTF, \n\t\tSAI_TEST_REPORT_DIR_ON_PTF))","def load(self):\n self.data = NSPSpecIO().read(self.path)","def test_outputs(self, monkeypatch, script_runner):\n monkeypatch.setattr(\"builtins.input\", lambda _: \"n\")\n _ = script_runner.run(\n \"spectrafit\",\n \"spectrafit/test/test_data.txt\",\n \"-i\",\n \"spectrafit/test/test_input_2.json\",\n )\n assert len(list(Path(\".\").glob(\"*.json\"))) == 1\n assert len(list(Path(\".\").glob(\"*.csv\"))) == 3","def data_dir(uncover):\n return os.path.join(uncover, 'tests', 'test_data')","def setUp(self):\r\n super(TestImport, self).setUp()\r\n self.content_dir = path(tempfile.mkdtemp())\r\n self.addCleanup(shutil.rmtree, self.content_dir)\r\n\r\n # Create good course xml\r\n self.good_dir = self.create_course_xml(self.content_dir, self.BASE_COURSE_KEY)\r\n\r\n # Create run changed course xml\r\n self.dupe_dir = self.create_course_xml(self.content_dir, self.DIFF_KEY)\r\n\r\n # Create course XML where TRUNCATED_COURSE.org == BASE_COURSE_ID.org\r\n # and BASE_COURSE_ID.startswith(TRUNCATED_COURSE.course)\r\n self.course_dir = self.create_course_xml(self.content_dir, self.TRUNCATED_KEY)","def setUp(self):\n # main directory of the project\n self.project_dir = os.path.dirname(self.basedir)\n\n # change to workdir so simulation process find the source files\n os.chdir(self.workdir)","def setUp(self):\n self.dir = pathlib.Path(tempfile.mkdtemp(prefix=\"test-hhsuite-\"))\n a3m = self.dir / \"query.a3m\"\n structure = self.dir / \"query.pdb\"\n\n # Write template to PDB file\n with io.StringIO(minimal.MINIMAL_MMCIF) as mmcif_buf:\n parser = Bio.PDB.MMCIFParser()\n template_structure = parser.get_structure(\"\", mmcif_buf)\n template_obj = template.Template.build(\n \"1MIN\", \"A\", template_structure[0][\"A\"])\n with structure.open(\"w\") as template_out:\n template_obj.write(template_out)\n\n # Write a3m to file.\n with a3m.open(\"w\") as a3m_out:\n a3m_out.write(self.BEFORE_A3M)\n\n self.pipeline = {\n \"template_obj\": template_obj,\n \"a3m\": str(a3m),\n \"secondary_structure\": {\"dssp\": self.SEC_STRUC}\n }","def test_script_nochange(self):\r\n qiime_config = load_qiime_config()\r\n tempdir = qiime_config['temp_dir'] or gettempdir()\r\n maindir = os.path.join(tempdir,\r\n ''.join(random.choice(string.ascii_letters + string.digits)\r\n for x in range(10)))\r\n\r\n os.makedirs(maindir)\r\n self.dirs_to_remove.append(maindir)\r\n otuf = os.path.join(maindir, 'otuf')\r\n treef = os.path.join(maindir, 'treef')\r\n\r\n otufh = open(otuf, 'w')\r\n otufh.write(tutorial_otu_table)\r\n otufh.close()\r\n\r\n treefh = open(treef, 'w')\r\n treefh.write(tutorial_tree)\r\n treefh.close()\r\n\r\n out_dir = os.path.join(maindir, 'simsam_out')\r\n cmd = 'simsam.py -i %s -t %s -o %s -d 0 -n 3 ' % (otuf, treef,\r\n out_dir)\r\n\r\n proc = subprocess.Popen(cmd, shell=True,\r\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\r\n scriptout, scripterr = proc.communicate()\r\n\r\n if scriptout:\r\n raise RuntimeError('script returned stdout: ' + scriptout)\r\n if scripterr:\r\n raise RuntimeError('script returned stderr: ' + scripterr)\r\n\r\n num_replicates = 3 # ensure this matches cmd above\r\n res = open(\r\n os.path.join(\r\n out_dir,\r\n 'otuf_n%d_d0.0.biom' %\r\n num_replicates),\r\n 'U')\r\n orig_table = parse_biom_table(open(otuf, 'U'))\r\n res_table = parse_biom_table(res)\r\n\r\n # 3 samples per input sample\r\n self.assertEqual(\r\n len(res_table.SampleIds),\r\n num_replicates * len(orig_table.SampleIds))\r\n\r\n # sample_ids have correct naming and order\r\n for i in range(len(orig_table.SampleIds)):\r\n for j in range(num_replicates):\r\n exp = orig_table.SampleIds[i] + '.' + str(j)\r\n self.assertEqual(\r\n res_table.SampleIds[i * num_replicates + j],\r\n exp)\r\n\r\n # same otu ids\r\n self.assertEqual(res_table.ObservationIds, orig_table.ObservationIds)\r\n\r\n # same otu table, just replicated thrice\r\n # note this requires the same sorting of otus, input is correct sorting\r\n num_orig_samples = len(orig_table.SampleIds)\r\n orig_sams = orig_table.iterSampleData()\r\n res_sams = res_table.iterSampleData()\r\n for i in range(num_orig_samples):\r\n orig_sam = orig_sams.next()\r\n for j in range(num_replicates):\r\n res_sam = res_sams.next()\r\n self.assertItemsEqual(res_sam, orig_sam)","def split_spec(self):\n from django_swagger_utils.spec_client.split_spec import SplitSpec\n from django_swagger_utils.core.utils.check_path_exists import check_path_exists\n\n if check_path_exists(os.path.join(self.paths['api_spec_dir'], \"specs\")):\n from shutil import rmtree\n rmtree(os.path.join(self.paths['api_spec_dir'], \"specs\"))\n split_spec = SplitSpec(self.paths['api_spec_dir'], self.paths['base_dir'])\n split_spec.split()","def prepare_training_data(\n self, dir_snippy: Path, dir_ont: Path, caller: str = 'clair',\n break_complex: bool = True, snippy_ext: str = \".ref.vcf\"\n ):\n\n self.training_dir.mkdir(parents=True, exist_ok=True)\n\n comparisons = self.get_coverage_comparisons(dir_snippy=dir_snippy, dir_ont=dir_ont, snippy_ext=snippy_ext)\n\n ont_with_truth, snippies, _ = self.get_data_from_comparisons(\n comparisons=comparisons, caller=caller, break_complex=break_complex, outdir=self.training_dir\n )\n\n features, _ = self.parse_features(ont_calls=ont_with_truth)\n\n # Combined features for training\n self.features_combined = pd.concat(features) # combined feature frames\n self.features_combined = self.features_combined.reset_index(drop=True)\n self.features_combined.to_csv(self.training_dir / 'training_features.tsv', sep='\\t', index=False)","def setup_build_tests(self):\n # Now copy the relative files\n self.cache_extra_test_sources(self.build_relpath)\n\n # Ensure the path exists since relying on a relative path at the\n # same level as the normal stage source path.\n mkdirp(self.install_test_root)","def setUp(self):\n # main directory of the project\n self.project_dir = os.path.dirname(self.basedir)\n\n # change to workdir so simulation process finds the source files\n os.chdir(self.workdir)","def test_init(self):\n test_data = (\n (self.segment.input_file, self.EXPECTED_INPUT_FILE,\n \"input file = {v}\".format(v=self.EXPECTED_INPUT_FILE)),\n (self.segment.punch_in, self.EXPECTED_PUNCH_IN,\n \"punch in = {v}\".format(v=self.EXPECTED_PUNCH_IN)),\n (self.segment.punch_out, self.EXPECTED_PUNCH_OUT,\n \"punch out = {v}\".format(v=self.EXPECTED_PUNCH_OUT)),\n (self.segment.input_stream, self.EXPECTED_INPUT_STREAM,\n \"input stream = {v}\".format(v=self.EXPECTED_INPUT_STREAM)),\n (self.segment._temp_file, self.EXPECTED_TEMP_FILE,\n \"temp file = {v}\".format(v=self.EXPECTED_TEMP_FILE)),\n (self.segment._temp_suffix, self.EXPECTED_TEMP_SUFFIX,\n \"temp suffix = {v}\".format(v=self.EXPECTED_TEMP_SUFFIX)),\n (self.segment._temp_files_list, self.EXPECTED_TEMP_LIST,\n \"temp files list = {v}\".format(v=self.EXPECTED_TEMP_LIST)),\n (self.segment._TYPE, self.EXPECTED_TYPE,\n \"type = {v}\".format(v=self.EXPECTED_TYPE)),\n (self.segment._TRIM, self.EXPECTED_TRIM,\n \"trim = {v}\".format(v=self.EXPECTED_TRIM)),\n (self.segment._SETPTS, self.EXPECTED_SETPTS,\n \"setpts = {v}\".format(v=self.EXPECTED_SETPTS)),\n )\n for actual, expected, description in test_data:\n with self.subTest(msg=description):\n self.assertEqual(actual, expected)","def _setup_run(cfg: Dict) -> Dict:\n now = datetime.now()\n day = f\"{now.day}\".zfill(2)\n month = f\"{now.month}\".zfill(2)\n hour = f\"{now.hour}\".zfill(2)\n minute = f\"{now.minute}\".zfill(2)\n run_name = f'run_{day}{month}_{hour}{minute}_seed{cfg[\"seed\"]}'\n # cfg[\"run_dir\"] = Path(__file__).absolute().parent / \"runs\" / run_name\n cfg[\"run_dir\"] = cfg[\"run_dir\"] / run_name\n if not cfg[\"run_dir\"].is_dir():\n cfg[\"train_dir\"] = cfg[\"run_dir\"] / \"data\" / \"train\"\n cfg[\"train_dir\"].mkdir(parents=True)\n cfg[\"val_dir\"] = cfg[\"run_dir\"] / \"data\" / \"val\"\n cfg[\"val_dir\"].mkdir(parents=True)\n else:\n raise RuntimeError(f\"There is already a folder at {cfg['run_dir']}\")\n\n # dump a copy of cfg to run directory\n with (cfg[\"run_dir\"] / \"cfg.json\").open(\"w\") as fp:\n temp_cfg = {}\n for key, val in cfg.items():\n if isinstance(val, PosixPath):\n temp_cfg[key] = str(val)\n elif isinstance(val, Dict):\n for k in val:\n if isinstance(val[k], PosixPath):\n val[k] = str(val[k])\n elif isinstance(val, pd.Timestamp):\n temp_cfg[key] = val.strftime(format=\"%d%m%Y\")\n else:\n temp_cfg[key] = val\n json.dump(temp_cfg, fp, sort_keys=True, indent=4)\n\n return cfg","def main(\n input_dir: Path = typer.Argument(..., exists=True),\n output_dir: Path = typer.Argument(...),\n beth_train_tar_name: str = \"i2b2_Beth_Train_Release.tar.gz\",\n partners_train_tar_name: str = \"i2b2_Partners_Train_Release.tar.gz\",\n test_zip_name: str = \"Task_1C.zip\",\n merge_docs: bool = True,\n):\n # Unpack compressed data files\n msg.info(\"Extracting raw data.\")\n beth_train_tar_path = input_dir / beth_train_tar_name\n partners_train_tar_path = input_dir / partners_train_tar_name\n test_zip_path = input_dir / test_zip_name\n\n for path in [beth_train_tar_path, partners_train_tar_path]:\n if path.name.endswith(\"tar.gz\"):\n msg.text(f\"Extracting {path}\")\n tar = tarfile.open(path, \"r:gz\")\n tar.extractall(path.parent)\n tar.close()\n\n shutil.unpack_archive(test_zip_path, input_dir / test_zip_name.replace(\".zip\", \"\"))\n\n # preprocess data\n msg.info(\"Converting to spaCy Doc objects.\")\n beth_train_docs = docs_from_many_clinical_records(\n input_dir / \"Beth_Train\", merge_docs=merge_docs\n )\n partners_train_docs = docs_from_many_clinical_records(\n input_dir / \"Partners_Train\", merge_docs=merge_docs\n )\n train_docs = beth_train_docs + partners_train_docs\n\n beth_test_docs = docs_from_many_clinical_records(\n input_dir / \"Task_1C/i2b2_Test/i2b2_Beth_Test\", merge_docs=merge_docs\n )\n partners_test_docs = docs_from_many_clinical_records(\n input_dir / \"Task_1C/i2b2_Test/i2b2_Partners_Test\", merge_docs=merge_docs\n )\n test_docs = beth_test_docs + partners_test_docs\n\n random.shuffle(train_docs)\n split_idx = int(len(train_docs) * 0.8)\n train_docs, dev_docs = train_docs[:split_idx], train_docs[split_idx:]\n\n msg.good(f\"Num Train Docs: {len(train_docs)}\")\n msg.good(f\"Num Dev Docs: {len(dev_docs)}\")\n msg.good(f\"Num Test Docs: {len(test_docs)}\")\n\n with msg.loading(f\"Saving docs to: {output_dir}...\"):\n DocBin(docs=train_docs).to_disk(output_dir / \"train.spacy\")\n DocBin(docs=dev_docs).to_disk(output_dir / \"dev.spacy\")\n DocBin(docs=test_docs).to_disk(output_dir / \"test.spacy\")\n msg.good(\"Done.\")","def test_tarballs_pre_extracted(self) -> None:\n import hammer_config\n\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\"dummy28\")\n tech_json_filename = os.path.join(tech_dir, \"dummy28.tech.json\")\n\n # Add defaults to specify tarball_dir.\n with open(os.path.join(tech_dir, \"defaults.json\"), \"w\") as f:\n f.write(json.dumps({\n \"technology.dummy28.tarball_dir\": tech_dir,\n \"vlsi.technology.extracted_tarballs_dir\": tech_dir_base\n }, cls=HammerJSONEncoder))\n\n HammerToolTestHelpers.write_tech_json(tech_json_filename, self.add_tarballs)\n sys.path.append(tech_dir_base)\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\"dummy28\", tech_dir))\n tech.cache_dir = tech_dir\n\n database = hammer_config.HammerDatabase()\n database.update_technology(tech.get_config())\n HammerVLSISettings.load_builtins_and_core(database)\n tech.set_database(database)\n outputs = tech.process_library_filter(pre_filts=[], filt=hammer_tech.filters.gds_filter,\n must_exist=False,\n output_func=lambda str, _: [str])\n\n self.assertEqual(outputs, [\"{0}/foobar.tar.gz/test.gds\".format(tech_dir_base)])\n\n # Cleanup\n shutil.rmtree(tech_dir_base)","def _parse_testtime_dir(self, data_dir, test_temporal_t=1):\n self.test_tuples = []\n categories = os.listdir(data_dir)\n for folder_name in categories:\n all_fnames_list_fname = os.path.join(data_dir, folder_name,\n folder_name + \".bmf\")\n if not os.path.isfile(all_fnames_list_fname):\n raise IOError(\"Not found file {}\".format(all_fnames_list_fname))\n all_fnames_list = np.loadtxt(all_fnames_list_fname, dtype=np.str,\n skiprows=1)\n # Correct from pgm to jpg\n all_fnames_list = [f.split('.')[0]+'.jpg' for f in all_fnames_list]\n\n all_fnames_list = [os.path.join(data_dir, folder_name, f) for f \\\n in all_fnames_list]\n # Get ground_truth\n annotation_fnames, numbers, type_weird = self.find_gt(os.path.join(data_dir,\n folder_name,\n 'GroundTruth'))\n goal_annotation_fnames = [f.split('.')[0] + '.jpg' for f in annotation_fnames]\n goal_annotation_fnames = [os.path.join(data_dir, folder_name, 'GroundTruth', f) for f \\\n in goal_annotation_fnames]\n\n # NOTE: Run the commented part only once to preprocess GT\n annotation_fnames = [os.path.join(data_dir, folder_name, 'GroundTruth', f) for f \\\n in annotation_fnames]\n for i in range(len(goal_annotation_fnames)):\n mask = cv2.imread(annotation_fnames[i])\n mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\n mask = mask / 255.0\n if type_weird:\n mask[mask>0.99] = 0.0\n if 'marple7' == folder_name:\n mask = mask>0.05\n elif 'marple2' == folder_name:\n mask = mask>0.4\n else:\n mask = mask>0.1\n mask = np.asarray(mask*255, dtype=np.uint8)\n cv2.imwrite(goal_annotation_fnames[i], mask)\n\n # Create offsets\n numbers = np.array(numbers) - np.min(numbers)\n seq_len = np.max(numbers)\n offsets = numbers + test_temporal_t\n if offsets[0] < numbers[0]:\n # test was negative, needs to increase:\n offsets[0] += 2*abs(test_temporal_t)\n if offsets[-1] > numbers[-1]:\n # test was positive, needs to decrease:\n offsets[-1] -= 2*abs(test_temporal_t)\n\n for i in range(len(offsets)):\n offsets[i] = np.maximum(offsets[i], 0)\n offsets[i] = np.minimum(offsets[i], seq_len)\n\n\n for i, k in enumerate(numbers):\n self.test_tuples.append((all_fnames_list[k], all_fnames_list[offsets[i]],\n goal_annotation_fnames[i], \"{}\".format(len(annotation_fnames))))\n\n self.samples += len(annotation_fnames)\n self.samples_per_cat[folder_name] = len(annotation_fnames)\n self.num_experiments+=1","def createSpecWithMetadataOnly(self):\n metadataYamlFilePath = os.path.join(os.path.dirname(\n os.path.realpath(__file__)),\n 'sample_metadata.yaml')\n with open(metadataYamlFilePath, \"r\") as fp:\n self.metadata = fp.read().rstrip('\\n')\n self.createCloudinitDataSpec('specWithMetadataOnly',\n 'linux cloud-init data customization spec'\n 'with metadata only')","def setUpClass(cls):\n cur_dir = os.path.dirname(os.path.realpath(__file__))\n meta_file = os.path.join(cur_dir, 'testdata/meta.json')\n graph_file = os.path.join(cur_dir, 'testdata/graph.json')\n output_file = os.path.join(cur_dir, 'testdata/graph.dat')\n builder = os.path.join(cur_dir, '../../../tools/bin/json2dat.py')\n\n command = \"python {builder} -i {input} -c {meta} -o {output}\".format(\n builder=builder, input=graph_file, meta=meta_file, output=output_file)\n\n try:\n subprocess.call(command, shell=True)\n except:\n raise RuntimeError(\"Build Graph for test failed\")\n\n base.initialize_graph({\n 'mode': 'Local',\n 'directory': os.path.join(cur_dir, 'testdata'),\n 'load_type': 'compact'\n })","def setUp(self):\n self.testfilename = 'test/testfile%d' % time.time()\n writetestfile = open(self.testfilename, 'w')\n writetestfile.write(\n '0<>81_0<>e rundensteiner:y huang<>geoinformatica<>m jones<>symbol '\n 'intersect detect method improv spatial intersect join<>\\n'\n '1<>81_1<>e rundensteiner:h kuno:p marron:v taube:y ra<>sigmodels.intern '\n 'manag data<>matthew c jones<>improv spatial intersect join symbol '\n 'intersect detect<>\\n'\n '2<>81_2<>e rundensteiner:y huang<>ssd symposium larg spatial '\n 'databas<>matthew c jones<>view materi techniqu complex hierarch '\n 'object<>\\n\\n'\n\n '0<>185_0<>l berg<>sigucc special interest group univers comput '\n 'servic<>mike w miller<>domin draw bipartit graph<>\\n'\n '1<>185_1<>undefined<>sigucc special interest group univers comput '\n 'servic<>mike w miller<>rel compromis statist databas<>\\n\\n'\n\n '0<>94_0<>d kung:j samuel:j gao:p hsia:y toyoshima<>ieee softwar<>c '\n 'chen<>formal approach scenario analysi<>\\n\\n'\n\n '0<>69_0<>undefined<>acl meet the associ comput linguist<>jane j '\n 'robinson<>discours code clue context<>\\n'\n '1<>69_1<>undefined<>cooper interfac inform system<>jane j robinson<>'\n 'diagram grammar dialogu<>\\n\\n'\n\n '0<>0_0<>a gonzalez:a hamid:c overstreet:h wahab:j wild:k maly:s ghanem'\n ':x zhu<>acm journal educ resourc comput<>a gupta<>iri h java '\n 'distanc educ<>\\n\\n'\n\n '4<>43_1<>y patt<>proceed the th ieee intern symposium high perform '\n 'comput architectur hpca intern symposium high perform comput '\n 'architectur talk slide<>mary d brown<>intern redund represent '\n 'limit bypass support pipelin adder regist file<>\\n\\n')\n writetestfile.close()","def test_json(test_data,tmp_path):\n\n for d in test_data:\n\n gpm = GenotypePhenotypeMap(wildtype=d[\"wildtype\"],\n genotype=d[\"genotype\"],\n phenotype=d[\"phenotype\"],\n uncertainty=d[\"uncertainty\"])\n\n # Write json file\n json_file = os.path.join(tmp_path,\"tmp.json\")\n gpm.to_json(filename=json_file)\n assert os.path.isfile(json_file)\n\n # Read json file\n new_gpm = gpmap.read_json(filename=json_file)\n conftest.compare_gpmap(gpm,new_gpm)","def GetTestData():\n return os.path.join(GetSrc(), 'chrome', 'test', 'data')","def extract_systtests(self):\n config_summary_fpath = os.path.join(\n self.systdir,\n 'config_summary.json'\n )\n cfg = from_file(config_summary_fpath)\n\n self.data_is_data = cfg['data_is_data']\n if self.data_is_data:\n raise ValueError('Analysis should NOT have been performed '\n 'on data since this script should only '\n 'process output from MC studies.')\n\n # Get naming scheme\n labels = Labels(\n h0_name=cfg['h0_name'],\n h1_name=cfg['h1_name'],\n data_name=cfg['data_name'],\n data_is_data=self.data_is_data,\n fluctuate_data=self.fluctuate_data,\n fluctuate_fid=self.fluctuate_fid\n )\n # Special extraction for the baseline case of all systematics\n if 'full_syst_baseline' in labels.dict['data_name']:\n trueordering = labels.dict['data_name'].split('_')[0]\n systematic = 'full_syst_baseline'\n direction = None\n else:\n if 'fixed' in labels.dict['h0_name']:\n if 'inj' in labels.dict['data_name']:\n testtype = 'fixwrong'\n trueordering = labels.dict['data_name'].split('_')[0]\n direction = labels.dict['data_name'].split('_')[-2]\n systematic = labels.dict['data_name'].split(\n '%s_inj_'%trueordering\n )[-1].split('_%s_wrong'%direction)[0]\n else:\n testtype = 'nminusone'\n trueordering = labels.dict['data_name'].split('_')[0]\n hypo = labels.dict['h0_name'].split('_')[0]\n direction = None\n systematic = labels.dict['h0_name'].split(\n '%s_fixed_'%hypo\n )[-1].split('_baseline')[0]\n else:\n testtype = 'fitwrong'\n trueordering = labels.dict['data_name'].split('_')[0]\n direction = labels.dict['data_name'].split('_')[-2]\n systematic = labels.dict['data_name'].split(\n '%s_inj_'%trueordering\n )[-1].split('_%s_wrong'%direction)[0]\n trueordering = 'toy_%s_asimov'%trueordering\n if trueordering not in self.labels.keys():\n self.labels[trueordering] = {}\n self.all_params[trueordering] = {}\n self.data_sets[trueordering] = {}\n if systematic not in self.labels[trueordering].keys():\n self.labels[trueordering][systematic] = {}\n self.all_params[trueordering][systematic] = {}\n self.data_sets[trueordering][systematic] = {}\n if direction is not None:\n if direction not in self.labels[trueordering][systematic].keys():\n self.labels[trueordering][systematic][direction] = labels\n self.all_params[trueordering][systematic][direction] = {}\n self.data_sets[trueordering][systematic][direction] = {}\n else:\n self.labels[trueordering][systematic] = labels\n # Get starting params\n self.get_starting_params(cfg=cfg, trueordering=trueordering,\n systematic=systematic, direction=direction)\n self.get_data(trueordering=trueordering, systematic=systematic,\n direction=direction)","def setUp(self):\n self.workspace_dir = tempfile.mkdtemp()","def setUp(self):\n self.workspace_dir = tempfile.mkdtemp()","def setUp(self):\n self.workspace_dir = tempfile.mkdtemp()","def setUp(self):\n self.workspace_dir = tempfile.mkdtemp()","def setUp(self):\n self.workspace_dir = tempfile.mkdtemp()","def setUp(self):\n self.workspace_dir = tempfile.mkdtemp()","def setUp(self):\n self.workspace_dir = tempfile.mkdtemp()","def test_update(self):\n\n # boilerplate\n tempdir = tempfile.mkdtemp()\n for i in range(10):\n file(os.path.join(tempdir, str(i)), 'w').write(str(i))\n\n # First, make a manifest:\n manifest = convert([tempdir])\n newtempdir = tempfile.mkdtemp()\n manifest_file = os.path.join(newtempdir, 'manifest.ini')\n file(manifest_file,'w').write(manifest)\n manifest = ManifestParser(manifests=(manifest_file,))\n self.assertEqual(manifest.get('name'),\n [str(i) for i in range(10)])\n\n # All of the tests are initially missing:\n self.assertEqual([i['name'] for i in manifest.missing()],\n [str(i) for i in range(10)])\n\n # But then we copy one over:\n self.assertEqual(manifest.get('name', name='1'), ['1'])\n manifest.update(tempdir, name='1')\n self.assertEqual(sorted(os.listdir(newtempdir)),\n ['1', 'manifest.ini'])\n\n # Update that one file and copy all the \"tests\":\n file(os.path.join(tempdir, '1'), 'w').write('secret door')\n manifest.update(tempdir)\n self.assertEqual(sorted(os.listdir(newtempdir)),\n ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'manifest.ini'])\n self.assertEqual(file(os.path.join(newtempdir, '1')).read().strip(),\n 'secret door')\n\n # clean up:\n shutil.rmtree(tempdir)\n shutil.rmtree(newtempdir)","def test_copy(self):\n\n tempdir = tempfile.mkdtemp()\n include_example = os.path.join(here, 'include-example.ini')\n manifest = ManifestParser(manifests=(include_example,))\n manifest.copy(tempdir)\n self.assertEqual(sorted(os.listdir(tempdir)),\n ['fleem', 'include', 'include-example.ini'])\n self.assertEqual(sorted(os.listdir(os.path.join(tempdir, 'include'))),\n ['bar.ini', 'crash-handling', 'flowers', 'foo.ini'])\n from_manifest = ManifestParser(manifests=(include_example,))\n to_manifest = os.path.join(tempdir, 'include-example.ini')\n to_manifest = ManifestParser(manifests=(to_manifest,))\n self.assertEqual(to_manifest.get('name'), from_manifest.get('name'))\n shutil.rmtree(tempdir)"],"string":"[\n \"def processSpecs(self):\\n specSubDirName=\\\"_spec\\\"\\n codestructure = CodeStructure()\\n for dir in self._dirs:\\n if q.system.fs.exists(q.system.fs.joinPaths(dir,specSubDirName)):\\n files=q.system.fs.listPyScriptsInDir(q.system.fs.joinPaths(dir,specSubDirName))\\n for fileName in files:\\n codestructure.addCodeFile(self.processSpecFile(q.system.fs.joinPaths(dir,specSubDirName),\\\"%s.py\\\" % fileName))\\n return codestructure\",\n \"def parse_test_files():\\n a_copy = PY_FILES[::]\\n for f in a_copy:\\n if 'test' in f:\\n TEST_FILES.append(f)\\n PY_FILES.remove(f)\",\n \"def createSpecWithMetadataInJsonAndUserdata(self):\\n metadataYamlFilePath = os.path.join(os.path.dirname(\\n os.path.realpath(__file__)),\\n 'sample_metadata.json')\\n userdataFilePath = os.path.join(os.path.dirname(\\n os.path.realpath(__file__)),\\n 'sample_userdata')\\n with open(metadataYamlFilePath, \\\"r\\\") as fp:\\n self.metadata = fp.read().rstrip('\\\\n')\\n with open(userdataFilePath, \\\"r\\\") as fp:\\n self.userdata = fp.read().rstrip('\\\\n')\\n self.createCloudinitDataSpec('specWithMetadataInJsonAndUserdata',\\n 'linux cloud-init data customization spec'\\n 'with metadata in json format and '\\n 'userdata')\",\n \"def test_main(data, tmp_path):\\n\\n main(data, tmp_path)\\n\\n FILES = (\\n \\\"gd32f888x(0-1)xx-pinctrl.h\\\",\\n \\\"gd32f888x(2-3)xx-pinctrl.h\\\",\\n \\\"gd32f888y(0-1)xx-pinctrl.h\\\",\\n \\\"gd32f999x(0-1)xx-pinctrl.h\\\",\\n \\\"gd32f999x(2-3)xx-pinctrl.h\\\",\\n \\\"gd32f999y(0-1)xx-pinctrl.h\\\",\\n )\\n\\n for file in FILES:\\n ref_file = data / file\\n gen_file = tmp_path / file\\n\\n assert gen_file.exists()\\n\\n with open(ref_file) as ref, open(gen_file) as gen:\\n assert ref.read() == gen.read()\",\n \"def setUp(self):\\n\\n # Create a data pipe.\\n self.interpreter.pipe.create('test', 'mf')\\n\\n # Create a temporary file name.\\n ds.tmpfile = mktemp()\",\n \"def createSpecWithMetadataInYamlAndUserdata(self):\\n metadataYamlFilePath = os.path.join(os.path.dirname(\\n os.path.realpath(__file__)),\\n 'sample_metadata.yaml')\\n userdataFilePath = os.path.join(os.path.dirname(\\n os.path.realpath(__file__)),\\n 'sample_userdata')\\n with open(metadataYamlFilePath, \\\"r\\\") as fp:\\n self.metadata = fp.read().rstrip('\\\\n')\\n with open(userdataFilePath, \\\"r\\\") as fp:\\n self.userdata = fp.read().rstrip('\\\\n')\\n self.createCloudinitDataSpec('specWithMetadataInYamlAndUserdata',\\n 'linux cloud-init data customization spec'\\n 'with metadata in yaml format and '\\n 'userdata')\",\n \"def setUp(self):\\n\\n try:\\n os.mkdir(self.pipeline_folder)\\n except FileExistsError:\\n pass\\n\\n with open(self.pipeline_spec_file, 'w+') as stream:\\n json.dump(self.pipeline_spec, stream)\\n\\n with open(self.pipeline_source_file, 'w+') as stream:\\n json.dump(self.source_description, stream)\\n\\n self.source = Source(folder=self.pipeline_folder)\",\n \"def setup(self):\\n self.cwd = os.getcwd()\\n self.t = tempfile.mkdtemp()\\n dir_path = Path(\\\"packages\\\")\\n tmp_dir = self.t / dir_path\\n src_dir = self.cwd / Path(ROOT_DIR, dir_path)\\n shutil.copytree(str(src_dir), str(tmp_dir))\\n shutil.copytree(Path(CUR_PATH, \\\"data\\\", \\\"dummy_aea\\\"), Path(self.t, \\\"dummy_aea\\\"))\\n os.chdir(Path(self.t, \\\"dummy_aea\\\"))\\n self.runner = CliRunner()\",\n \"def test_diff_src_root_metadata_file_registry(self):\\n # Setup test\\n filename = os.path.join(_SAMPLE_FILES_DIR, \\\"reg_good_mf.xml\\\")\\n out_name = \\\"physics_types_ddt\\\"\\n in_source = os.path.join(_SAMPLE_FILES_DIR, out_name + '_se.F90')\\n in_meta = os.path.join(_SAMPLE_FILES_DIR, out_name + '_se.meta')\\n out_source = os.path.join(_TMP_DIR, out_name + '.F90')\\n out_meta = os.path.join(_TMP_DIR, out_name + '.meta')\\n remove_files([out_source, out_meta])\\n\\n # Create new directory:\\n tmp_src_dir = os.path.join(_TMP_DIR, \\\"test\\\", \\\"unit\\\", \\\\\\n \\\"sample_files\\\")\\n if not os.path.exists(tmp_src_dir):\\n os.makedirs(tmp_src_dir)\\n\\n # Copy ref_pres.meta file to new location:\\n meta_file = os.path.join(_SAMPLE_FILES_DIR, \\\"ref_pres.meta\\\")\\n shutil.copy(meta_file, tmp_src_dir)\\n\\n # Generate registry\\n retcode, files = gen_registry(filename, 'se', {}, _TMP_DIR, 2,\\n _SRC_MOD_DIR, _TMP_DIR,\\n loglevel=logging.ERROR,\\n error_on_no_validate=True)\\n # Check return code\\n amsg = \\\"Test failure for SE dycore, retcode={}\\\".format(retcode)\\n self.assertEqual(retcode, 0, msg=amsg)\\n flen = len(files)\\n amsg = \\\"Test failure for SE dycore: Found {} files, expected 2\\\"\\n self.assertEqual(flen, 2, msg=amsg.format(flen))\\n amsg = \\\"{} does not exist\\\".format(out_meta)\\n self.assertTrue(os.path.exists(out_meta), msg=amsg)\\n amsg = \\\"{} does not exist\\\".format(out_source)\\n self.assertTrue(os.path.exists(out_source), msg=amsg)\\n # For each output file, make sure it matches input file\\n amsg = \\\"{} does not match {}\\\".format(in_meta, out_meta)\\n self.assertTrue(filecmp.cmp(in_meta, out_meta,\\n shallow=False), msg=amsg)\\n amsg = \\\"{} does not match {}\\\".format(in_source, out_source)\\n self.assertTrue(filecmp.cmp(in_source, out_source,\\n shallow=False), msg=amsg)\\n # Check that the metadata file has the correct number of variables\\n mfile = files[1]\\n mvars = mfile.variable_list()\\n num_vars = len(mvars)\\n amsg = \\\"Expected 14 metadata variables, found {}\\\".format(num_vars)\\n self.assertEqual(num_vars, 14, msg=amsg)\",\n \"def setUpClass(cls):\\n cls.testDir = tempfile.mkdtemp()\\n cls.readonlyDir = tempfile.mkdtemp()\\n cls.testfile = os.path.join(cls.testDir, 'desispec_test_io.fits')\\n cls.testyfile = os.path.join(cls.testDir, 'desispec_test_io.yaml')\\n cls.testlog = os.path.join(cls.testDir, 'desispec_test_io.log')\\n # cls.testbrfile appears to be unused by this class.\\n cls.testbrfile = os.path.join(cls.testDir, 'desispec_test_io-br.fits')\\n cls.origEnv = {'SPECPROD': None,\\n \\\"DESI_ROOT\\\": None,\\n \\\"DESI_ROOT_READONLY\\\": None,\\n \\\"DESI_SPECTRO_DATA\\\": None,\\n \\\"DESI_SPECTRO_REDUX\\\": None,\\n \\\"DESI_SPECTRO_CALIB\\\": None,\\n }\\n cls.testEnv = {'SPECPROD':'dailytest',\\n \\\"DESI_ROOT\\\": cls.testDir,\\n \\\"DESI_ROOT_READONLY\\\": cls.readonlyDir,\\n \\\"DESI_SPECTRO_DATA\\\": os.path.join(cls.testDir, 'spectro', 'data'),\\n \\\"DESI_SPECTRO_REDUX\\\": os.path.join(cls.testDir, 'spectro', 'redux'),\\n \\\"DESI_SPECTRO_CALIB\\\": os.path.join(cls.testDir, 'spectro', 'calib'),\\n }\\n cls.datadir = cls.testEnv['DESI_SPECTRO_DATA']\\n cls.reduxdir = os.path.join(cls.testEnv['DESI_SPECTRO_REDUX'],\\n cls.testEnv['SPECPROD'])\\n for e in cls.origEnv:\\n if e in os.environ:\\n cls.origEnv[e] = os.environ[e]\\n os.environ[e] = cls.testEnv[e]\",\n \"def setup_function(test_function):\\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'symbol-list.dat',\\n TEST_LOCATION + 'symbol-list.dat')\\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'nn-model.dat',\\n TEST_LOCATION + 'nn-model.dat')\\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'nn-model_test.dat',\\n TEST_LOCATION + 'nn-model_test.dat')\\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'nn-model_test2.dat',\\n TEST_LOCATION + 'nn-model_test2.dat')\\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER +\\n 'distance-tolerance_test.dat',\\n TEST_LOCATION + 'distance-tolerance_test.dat')\\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER +\\n 'distance-tolerance_test2.dat',\\n TEST_LOCATION + 'distance-tolerance_test2.dat')\\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'training-set_test.dat',\\n TEST_LOCATION + 'training-set_test.dat')\\n shutil.copy2(TEST_LOCATION + PRE_TEST_FOLDER + 'training-set_test2.dat',\\n TEST_LOCATION + 'training-set_test2.dat')\",\n \"def test_runs(self):\\n\\n with TemporaryDirectory() as tmp_dir:\\n\\n mock_raw_data(tmp_dir, raw_dim=256, num_channels=3, num_images=40)\",\n \"def setUp(self):\\n self.test_root = tempfile.mkdtemp(dir=tmpdir)\\n self.test_input = os.path.join(self.test_root, 'input')\\n self.test_output = os.path.join(self.test_root, 'output')\\n self.test_output_tree = os.path.join(self.test_output, 'tree')\\n self.test_output_meta = os.path.join(self.test_output_tree, 'meta.js')\\n self.test_output_toc = os.path.join(self.test_output_tree, 'toc.js')\\n\\n os.makedirs(self.test_input, exist_ok=True)\\n os.makedirs(self.test_output, exist_ok=True)\",\n \"def parseTestspec(self, testspec):\\n if 'name' not in testspec or 'commands' not in testspec:\\n sys.exit(1)\\n\\n # Merge user-specified arguments with default test specification\\n defaultTestspec = corstone700DefaultTestspec\\n defaultTestspec.update(testspec)\\n return defaultTestspec\",\n \"def test_study_cli(self):\\n expected = etree.fromstring(self.WTML)\\n\\n args = [\\n \\\"tile-study\\\",\\n \\\"--placeholder-thumbnail\\\",\\n \\\"--outdir\\\",\\n self.work_path(\\\"study_cli\\\"),\\n test_path(\\\"wcs512.fits.gz\\\"),\\n ]\\n cli.entrypoint(args)\\n\\n with open(\\n self.work_path(\\\"study_cli\\\", \\\"index_rel.wtml\\\"), \\\"rt\\\", encoding=\\\"utf8\\\"\\n ) as f:\\n observed = etree.fromstring(f.read())\\n\\n assert_xml_elements_equal(observed, expected)\\n\\n args = [\\n \\\"cascade\\\",\\n \\\"--start\\\",\\n \\\"1\\\",\\n self.work_path(\\\"study_cli\\\"),\\n ]\\n cli.entrypoint(args)\\n\\n self.maybe_test_barycenter(\\n self.work_path(\\\"study_cli\\\", \\\"0\\\", \\\"0\\\", \\\"0_0.fits\\\"), self.WCS512_BARYDATA\\n )\",\n \"def make_studyforrest_mockup(path):\\n public = create(opj(path, 'public'), description=\\\"umbrella dataset\\\")\\n # the following tries to capture the evolution of the project\\n phase1 = public.create('phase1',\\n description='old-style, no connection to RAW')\\n structural = public.create('structural', description='anatomy')\\n tnt = public.create('tnt', description='image templates')\\n tnt.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\\n tnt.clone(source=structural.path, path=opj('src', 'structural'), reckless='auto')\\n aligned = public.create('aligned', description='aligned image data')\\n aligned.clone(source=phase1.path, path=opj('src', 'phase1'), reckless='auto')\\n aligned.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\\n # new acquisition\\n labet = create(opj(path, 'private', 'labet'), description=\\\"raw data ET\\\")\\n phase2_dicoms = create(opj(path, 'private', 'p2dicoms'), description=\\\"raw data P2MRI\\\")\\n phase2 = public.create('phase2',\\n description='new-style, RAW connection')\\n phase2.clone(source=labet.path, path=opj('src', 'labet'), reckless='auto')\\n phase2.clone(source=phase2_dicoms.path, path=opj('src', 'dicoms'), reckless='auto')\\n # add to derivatives\\n tnt.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\\n aligned.clone(source=phase2.path, path=opj('src', 'phase2'), reckless='auto')\\n # never to be published media files\\n media = create(opj(path, 'private', 'media'), description=\\\"raw data ET\\\")\\n # assuming all annotations are in one dataset (in reality this is also\\n # a superdatasets with about 10 subdatasets\\n annot = public.create('annotations', description='stimulus annotation')\\n annot.clone(source=media.path, path=opj('src', 'media'), reckless='auto')\\n # a few typical analysis datasets\\n # (just doing 3, actual status quo is just shy of 10)\\n # and also the real goal -> meta analysis\\n metaanalysis = public.create('metaanalysis', description=\\\"analysis of analyses\\\")\\n for i in range(1, 3):\\n ana = public.create('analysis{}'.format(i),\\n description='analysis{}'.format(i))\\n ana.clone(source=annot.path, path=opj('src', 'annot'), reckless='auto')\\n ana.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\\n ana.clone(source=tnt.path, path=opj('src', 'tnt'), reckless='auto')\\n # link to metaanalysis\\n metaanalysis.clone(source=ana.path, path=opj('src', 'ana{}'.format(i)),\\n reckless='auto')\\n # simulate change in an input (but not raw) dataset\\n create_tree(\\n aligned.path,\\n {'modification{}.txt'.format(i): 'unique{}'.format(i)})\\n aligned.save()\\n # finally aggregate data\\n aggregate = public.create('aggregate', description='aggregate data')\\n aggregate.clone(source=aligned.path, path=opj('src', 'aligned'), reckless='auto')\\n # the toplevel dataset is intentionally left dirty, to reflect the\\n # most likely condition for the joint dataset to be in at any given\\n # point in time\",\n \"def create_workspace(topdir, sdist, spec, relnotes, changelog, csdb, patchdir):\\n for name in ('SOURCES', 'SPECS'):\\n directory = os.path.join(topdir, name)\\n if not os.path.exists(directory):\\n os.makedirs(directory)\\n\\n with tmpdir():\\n version = unpack_source(sdist)\\n if spec:\\n if spec.endswith('.in'):\\n prepare_spec(topdir, spec, version)\\n else:\\n copy(spec, os.path.join(topdir, 'SPECS', 'openafs.spec'))\\n else:\\n prepare_spec(topdir,\\n 'src/packaging/RedHat/openafs.spec.in',\\n version)\\n for source in list_sources(topdir, version):\\n dest = os.path.join(topdir, 'SOURCES', source)\\n if '-src.tar.' in source:\\n copy(os.path.join(sdist, source), dest)\\n elif '-doc.tar.' in source:\\n copy(os.path.join(sdist, source), dest)\\n elif 'RELNOTES' in source:\\n if not relnotes:\\n relnotes = os.path.join(sdist, source)\\n if os.path.exists(relnotes):\\n copy(relnotes, dest)\\n else:\\n with open(dest, 'w') as f:\\n f.write('No release notes provided.\\\\n')\\n elif 'ChangeLog' in source:\\n if not changelog:\\n changelog = os.path.join(sdist, 'ChangeLog')\\n if os.path.exists(changelog):\\n copy(changelog, dest)\\n else:\\n with open(dest, 'w') as f:\\n f.write('No change log provided.\\\\n')\\n elif 'CellServDB' in source:\\n if not csdb:\\n csdb = os.path.join(sdist, 'CellServDB')\\n if not os.path.exists(csdb):\\n csdb = 'src/afsd/CellServDB'\\n copy(csdb, dest)\\n else:\\n copy(os.path.join('src/packaging/RedHat', source), dest)\\n\\n for patch in list_patches(topdir, version):\\n dest = os.path.join(topdir, 'SOURCES', patch)\\n if patchdir:\\n copy(os.path.join(patchdir, patch), dest)\\n else:\\n copy(os.path.join(sdist, patch), dest)\",\n \"def convert_testing_data(mfccPath):\\n inputlist, inputnamelist = ark_parser(mfccPath, 'test.ark')\\n\\n print(\\\"%d sample in testing set\\\" % len(inputlist))\\n with open('./test_data.pkl', 'wb') as test_data:\\n pickle.dump(inputlist, test_data)\\n \\n with open('./test_name.pkl', 'wb') as test_name:\\n pickle.dump(inputnamelist, test_name)\",\n \"def test_run_sphinx_stt():\\n profile_data = default_profile_data\\n profile_data['stt'] = 'sphinx'\\n mock_flag_modeldir = mock_flag_hmm = mock.Mock()\\n mock_flag_lm = mock_flag_dic = mock.Mock()\\n profile_data['pocketsphinx'] = {\\n 'modeldir': mock_flag_modeldir,\\n 'hmm': mock_flag_hmm,\\n 'lm': mock_flag_lm,\\n 'dic': mock_flag_dic,\\n }\\n mock_profile.data = profile_data\\n\\n mock_open = mock.mock_open()\\n with mock.patch('__builtin__.__import__', side_effect=import_mock),\\\\\\n mock.patch('melissa.stt.sr') as mock_sr, \\\\\\n mock.patch('melissa.stt.Decoder') as mock_decoder, \\\\\\n mock.patch('melissa.stt.open', mock_open, create=True), \\\\\\n mock.patch('melissa.stt.tts'):\\n from melissa.stt import stt\\n raised_error = ValueError\\n mock_audio = mock.Mock()\\n mock_sr.Recognizer.return_value.listen.side_effect = [\\n mock_audio, raised_error()]\\n stt()\\n mock_audio.get_wav_data.assert_called_once_with()\\n assert len(mock_sr.mock_calls) == 5\\n assert len(mock_open.mock_calls) == 7\\n mock_open_data = [\\n mock.call('recording.wav', 'wb'),\\n mock.call().__enter__(),\\n mock.call().write(mock_audio.get_wav_data()),\\n mock.call().__exit__(None, None, None),\\n mock.call('recording.wav', 'rb'),\\n mock.call().seek(44),\\n mock.call().read()\\n ]\\n for item in mock_open_data:\\n assert item in mock_open.mock_calls\\n\\n mock_decoder_config = mock_decoder.default_profile_data()\\n mock_decoder_data = [\\n mock.call.default_config(),\\n mock.call.default_config().set_string('-hmm', mock_flag_hmm),\\n mock.call.default_config().set_string('-lm', mock_flag_lm),\\n mock.call.default_config().set_string('-dict', mock_flag_dic),\\n mock.call.default_config().set_string('-logfn', '/dev/null'),\\n mock.call(mock_decoder_config()),\\n mock.call().start_utt(),\\n mock.call().process_raw('', False, True),\\n mock.call().end_utt(),\\n mock.call().hyp(),\\n # mock.call().hyp().hypstr.__radd__().__add__(\\\"'\\\"),\\n # mock.call().hyp().hypstr.__radd__().__add__().__str__(),\\n mock.call().hyp().hypstr.lower(),\\n mock.call().hyp().hypstr.lower().replace(\\\"'\\\", ''),\\n ]\\n for item in mock_decoder_data:\\n assert item in mock_decoder.mock_calls\",\n \"def setUp(self):\\r\\n\\r\\n # Cannot use get_qiime_project_dir() due to test errors in virtual box\\r\\n test_dir = os.path.dirname(os.path.abspath(__file__))\\r\\n sff_original_fp = os.path.join(test_dir, 'test_support_files',\\r\\n 'test.sff')\\r\\n sff_original_gz_fp = os.path.join(test_dir, 'test_support_files',\\r\\n 'test_gz.sff.gz')\\r\\n\\r\\n # copy sff file to working directory\\r\\n self.sff_dir = mkdtemp()\\r\\n self.gz_sff_dir = mkdtemp()\\r\\n self.sff_fp = os.path.join(self.sff_dir, 'test.sff')\\r\\n self.sff_gz_fp = os.path.join(self.gz_sff_dir, 'test_gz.sff.gz')\\r\\n shutil.copy(sff_original_fp, self.sff_fp)\\r\\n shutil.copy(sff_original_gz_fp, self.sff_gz_fp)\",\n \"def setup_class(cls):\\n cls.cwd = os.getcwd()\\n cls.t = tempfile.mkdtemp()\\n dir_path = Path(\\\"packages\\\")\\n tmp_dir = cls.t / dir_path\\n src_dir = cls.cwd / Path(ROOT_DIR, dir_path)\\n shutil.copytree(str(src_dir), str(tmp_dir))\\n shutil.copytree(Path(CUR_PATH, \\\"data\\\", \\\"dummy_aea\\\"), Path(cls.t, \\\"dummy_aea\\\"))\\n os.chdir(Path(cls.t, \\\"dummy_aea\\\"))\\n cls.runner = CliRunner()\",\n \"def setup_class(cls):\\n cls.cwd = os.getcwd()\\n cls.t = tempfile.mkdtemp()\\n dir_path = Path(\\\"packages\\\")\\n tmp_dir = cls.t / dir_path\\n src_dir = cls.cwd / Path(ROOT_DIR, dir_path)\\n shutil.copytree(str(src_dir), str(tmp_dir))\\n shutil.copytree(Path(CUR_PATH, \\\"data\\\", \\\"dummy_aea\\\"), Path(cls.t, \\\"dummy_aea\\\"))\\n os.chdir(Path(cls.t, \\\"dummy_aea\\\"))\\n cls.runner = CliRunner()\",\n \"def parseTestspec(self, testspec):\\n if 'name' not in testspec or 'commands' not in testspec:\\n sys.exit(1)\\n\\n # Merge user-specified arguments with default test specification\\n defaultTestspec = a5dsDefaultTestspec\\n defaultTestspec.update(testspec)\\n return defaultTestspec\",\n \"def setUp(self):\\n super(PlayTests, self).setUp(\\n \\\"tests/data/shakespeare/\\\", \\\"structure.json\\\", \\\"brief_example.xml\\\")\",\n \"def setUp(self):\\n #clear configuration object\\n SCF.clearConfig()\\n \\n #create directories\\n test_name = self.id().split(\\\".\\\")[-1]\\n self.test_working_directory = os.path.join(self.iota2_tests_directory, test_name)\\n self.test_working_directory_tmp = os.path.join(self.iota2_tests_directory, test_name + \\\"_TMP\\\")\\n if os.path.exists(self.test_working_directory):\\n shutil.rmtree(self.test_working_directory)\\n if not os.path.exists(self.test_working_directory_tmp):\\n os.mkdir(self.test_working_directory_tmp)\\n self.config_path_test = os.path.join(self.test_working_directory_tmp, test_name + \\\".cfg\\\")\\n shutil.copy(self.config_path, self.config_path_test)\",\n \"def setUp(self):\\n\\n # try:\\n os.mkdir(self.pipeline_folder)\\n # except FileExistsError:\\n # pass\\n\\n with open(self.pipeline_spec_file, 'w+') as stream:\\n json.dump(self.pipeline_spec, stream)\\n\\n with open(self.pipeline_source_file, 'w+') as stream:\\n json.dump(self.source_description, stream)\\n\\n self.source = Source(self.pipeline_id)\",\n \"def setUp(self):\\n print(\\\"setUp()\\\", os.path.exists('./testdatas'))\\n if not os.path.exists('./testdatas'):\\n print('Creating server test data directory')\\n os.mkdir('./testdatas')\\n if not os.path.exists('./testdatac'):\\n print('Creating client test data directory')\\n os.mkdir('./testdatac')\\n if os.path.exists('testdata.tar'):\\n print('Extracting test data')\\n tar = tarfile.open('testdata.tar')\\n tar.extractall()\\n tar.close()\\n else:\\n print('No testdata.tar file found!', file=sys.stderr)\",\n \"def setUp(self):\\n super(LegacyResultsProcessorUnittest, self).setUp()\\n if six.PY2:\\n self.data_directory = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), 'testdata')\\n else:\\n self.data_directory = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), 'testdata', 'python3')\",\n \"def setUp(self):\\n # make directory test\\n self.temp_dir_string = '/tmp/test_for_seqprep/'\\n create_dir(self.temp_dir_string)\\n\\n # make directory with spaces test\\n self.temp_dir_string_space = '/tmp/test for seqprep/'\\n create_dir(self.temp_dir_string_space)\\n \\n # create temp file path strings\\n self.test_fn1 = os.path.join(self.temp_dir_string,'reads1.fastq')\\n self.test_fn1_space = os.path.join(self.temp_dir_string_space, \\n 'reads1.fastq')\\n self.test_fn2 = os.path.join(self.temp_dir_string,'reads2.fastq')\\n self.test_fn2_space = os.path.join(self.temp_dir_string_space,\\n 'reads2.fastq')\",\n \"def copy_test_configuration(self, source_dir, dest_dir):\\n for root, dirs, files in os.walk(source_dir):\\n if '.svn' in dirs:\\n dirs.remove('.svn')\\n dirs = [ d for d in dirs if not d.startswith('gyptest') ]\\n files = [ f for f in files if not f.startswith('gyptest') ]\\n for dirname in dirs:\\n source = os.path.join(root, dirname)\\n destination = source.replace(source_dir, dest_dir)\\n os.mkdir(destination)\\n if sys.platform != 'win32':\\n shutil.copystat(source, destination)\\n for filename in files:\\n source = os.path.join(root, filename)\\n destination = source.replace(source_dir, dest_dir)\\n shutil.copy2(source, destination)\",\n \"def test_sects(self):\\n\\n test_files = glob.glob(INPUT_HYPM_PATH + '/unit_*.mdd')\\n test_files.extend(glob.glob(INPUT_FLMB_PATH + '/unit_*.mdd'))\\n\\n sects = mdd.procall(test_files)\\n\\n TestSioUnpack.latest(sects)\",\n \"def setUp(self):\\n\\n self.directory = tempfile.mkdtemp(dir=os.getcwd())\\n spirv_args = self.test.spirv_args\\n # Instantiate placeholders in spirv_args\\n self.test.spirv_args = [\\n arg.instantiate_for_spirv_args(self)\\n if isinstance(arg, PlaceHolder) else arg for arg in self.test.spirv_args\\n ]\\n # Get all shader files' names\\n self.inputs = [arg for arg in spirv_args if isinstance(arg, PlaceHolder)]\\n self.file_shaders = [arg.filename for arg in self.inputs]\\n\\n if 'environment' in get_all_variables(self.test):\\n self.test.environment.write(self.directory)\\n\\n expectations = [\\n v for v in get_all_variables(self.test)\\n if v.startswith(EXPECTED_BEHAVIOR_PREFIX)\\n ]\\n # Instantiate placeholders in expectations\\n for expectation_name in expectations:\\n expectation = getattr(self.test, expectation_name)\\n if isinstance(expectation, list):\\n expanded_expections = [\\n element.instantiate_for_expectation(self)\\n if isinstance(element, PlaceHolder) else element\\n for element in expectation\\n ]\\n setattr(self.test, expectation_name, expanded_expections)\\n elif isinstance(expectation, PlaceHolder):\\n setattr(self.test, expectation_name,\\n expectation.instantiate_for_expectation(self))\",\n \"def setup(self):\\n\\n self.testInst = pysat.Instrument('pysat', 'testing',\\n clean_level='clean',\\n update_files=True)\\n self.testInst.bounds = (dt.datetime(2008, 1, 1),\\n dt.datetime(2008, 1, 31))\\n self.test_bins = [0, 24, 24]\\n self.test_label = 'slt'\\n self.test_data = ['dummy1', 'dummy2']\\n self.out_keys = ['count', 'avg_abs_dev', 'median', 'bin_x']\\n self.out_data = {'dummy1':\\n {'count': [111780., 111320., 111780., 111320.,\\n 111780., 111320., 111780., 111320.,\\n 111780., 111320., 111780., 111320.,\\n 111780., 111320., 111918., 111562.,\\n 112023., 111562., 112023., 111412.,\\n 111780., 111320., 111780., 111320.],\\n 'avg_abs_dev': np.zeros(shape=24),\\n 'median': np.linspace(0.0, 23.0, 24)},\\n 'dummy2':\\n {'count': [111780., 111320., 111780., 111320.,\\n 111780., 111320., 111780., 111320.,\\n 111780., 111320., 111780., 111320.,\\n 111780., 111320., 111918., 111562.,\\n 112023., 111562., 112023., 111412.,\\n 111780., 111320., 111780., 111320.],\\n 'avg_abs_dev': np.zeros(shape=24) + 6.0,\\n 'median': [11., 12., 11., 11., 12., 11., 12., 11.,\\n 12., 12., 11., 12., 11., 12., 11., 11.,\\n 12., 11., 12., 11., 11., 11., 11., 12.]}}\\n return\",\n \"def _load_test_data(self):\\n self._save_test_data()\",\n \"def setUp(self):\\n self.vmodel_name = \\\"LABasin\\\"\\n self.sim_id = int(seqnum.get_seq_num())\\n self.install = InstallCfg()\\n self.vmodel_obj = vmodels.get_velocity_model_by_name(self.vmodel_name)\\n\\n indir = os.path.join(self.install.A_IN_DATA_DIR, str(self.sim_id))\\n tmpdir = os.path.join(self.install.A_TMP_DATA_DIR, str(self.sim_id))\\n outdir = os.path.join(self.install.A_OUT_DATA_DIR, str(self.sim_id))\\n logdir = os.path.join(self.install.A_OUT_LOG_DIR, str(self.sim_id))\\n # Create all directories\\n bband_utils.mkdirs([indir, tmpdir, outdir, logdir], print_cmd=False)\\n\\n # Copy needed files\\n\\n # src file\\n r_src_file = \\\"nr_v12_11_0_fs.src\\\"\\n src_file = os.path.join(self.install.A_TEST_REF_DIR, \\\"uwo\\\", r_src_file)\\n self.src_file = os.path.join(indir, r_src_file)\\n cmd = \\\"cp %s %s\\\" % (src_file, self.src_file)\\n bband_utils.runprog(cmd)\\n\\n # exsim param template file\\n vmodel_params = self.vmodel_obj.get_codebase_params('exsim')\\n self.failIf('GENERIC_PARAM' not in vmodel_params)\\n r_param_template = vmodel_params['GENERIC_PARAM']\\n\\n self.failIf(r_param_template == \\\"\\\" or r_param_template is None)\\n param_template = os.path.join(self.vmodel_obj.base_dir,\\n r_param_template)\\n # r_param_template is relative to the velocity model basedir,\\n # get only basename\\n r_param_template = os.path.basename(r_param_template)\\n self.param_template = os.path.join(indir, r_param_template)\\n cmd = \\\"cp %s %s\\\" % (param_template, self.param_template)\\n bband_utils.runprog(cmd)\\n\\n # station file\\n r_stations = \\\"nr_v12_11_2.stl\\\"\\n stations = os.path.join(self.install.A_TEST_REF_DIR, \\\"uwo\\\", r_stations)\\n self.stations = os.path.join(indir, r_stations)\\n cmd = \\\"cp %s %s\\\" % (stations, self.stations)\\n bband_utils.runprog(cmd)\",\n \"def test_SourceMods_metadata_file_registry(self):\\n # Setup test\\n filename = os.path.join(_SAMPLE_FILES_DIR, \\\"reg_good_mf.xml\\\")\\n out_name = \\\"physics_types_ddt\\\"\\n in_source = os.path.join(_SAMPLE_FILES_DIR, out_name + '_se.F90')\\n in_meta = os.path.join(_SAMPLE_FILES_DIR, out_name + '_se.meta')\\n out_source = os.path.join(_TMP_DIR, out_name + '.F90')\\n out_meta = os.path.join(_TMP_DIR, out_name + '.meta')\\n remove_files([out_source, out_meta])\\n\\n # Create new directory:\\n tmp_src_dir = os.path.join(_TMP_DIR, \\\"test\\\", \\\"unit\\\", \\\\\\n \\\"sample_files\\\")\\n if not os.path.exists(tmp_src_dir):\\n os.makedirs(tmp_src_dir)\\n\\n # Copy ref_pres_sm.meta file to new location:\\n meta_file = os.path.join(_SAMPLE_FILES_DIR, \\\"ref_pres_SourceMods.meta\\\")\\n source_mod_file = os.path.join(tmp_src_dir, \\\"ref_pres.meta\\\")\\n if os.path.exists(source_mod_file):\\n os.remove(source_mod_file)\\n shutil.copy(meta_file, source_mod_file)\\n\\n # Generate registry\\n retcode, files = gen_registry(filename, 'se', {}, _TMP_DIR, 2,\\n tmp_src_dir, _CAM_ROOT,\\n loglevel=logging.ERROR,\\n error_on_no_validate=True)\\n\\n # Check return code\\n amsg = \\\"Test failure for SE dycore, retcode={}\\\".format(retcode)\\n self.assertEqual(retcode, 0, msg=amsg)\\n flen = len(files)\\n amsg = \\\"Test failure for SE dycore: Found {} files, expected 2\\\"\\n self.assertEqual(flen, 2, msg=amsg.format(flen))\\n amsg = \\\"{} does not exist\\\".format(out_meta)\\n self.assertTrue(os.path.exists(out_meta), msg=amsg)\\n amsg = \\\"{} does not exist\\\".format(out_source)\\n self.assertTrue(os.path.exists(out_source), msg=amsg)\\n # For each output file, make sure it matches input file\\n amsg = \\\"{} does not match {}\\\".format(in_meta, out_meta)\\n self.assertTrue(filecmp.cmp(in_meta, out_meta,\\n shallow=False), msg=amsg)\\n amsg = \\\"{} does not match {}\\\".format(in_source, out_source)\\n self.assertTrue(filecmp.cmp(in_source, out_source,\\n shallow=False), msg=amsg)\\n # Check that the metadata file has the correct number of variables\\n mfile = files[1]\\n mvars = mfile.variable_list()\\n num_vars = len(mvars)\\n amsg = \\\"Expected 14 metadata variables, found {}\\\".format(num_vars)\\n self.assertEqual(num_vars, 16, msg=amsg)\",\n \"def test_script(self):\\r\\n qiime_config = load_qiime_config()\\r\\n tempdir = qiime_config['temp_dir'] or gettempdir()\\r\\n maindir = os.path.join(tempdir,\\r\\n ''.join(random.choice(string.ascii_letters + string.digits)\\r\\n for x in range(10)))\\r\\n\\r\\n os.makedirs(maindir)\\r\\n self.dirs_to_remove.append(maindir)\\r\\n otuf = os.path.join(maindir, 'otuf')\\r\\n treef = os.path.join(maindir, 'treef')\\r\\n\\r\\n otufh = open(otuf, 'w')\\r\\n otufh.write(tutorial_otu_table)\\r\\n otufh.close()\\r\\n\\r\\n treefh = open(treef, 'w')\\r\\n treefh.write(tutorial_tree)\\r\\n treefh.close()\\r\\n\\r\\n out_dir = os.path.join(maindir, 'simsam_out')\\r\\n cmd = 'simsam.py -i %s -t %s -o %s -d .003 -n 3 ' % (otuf, treef,\\r\\n out_dir)\\r\\n proc = subprocess.Popen(cmd, shell=True,\\r\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\r\\n scriptout, scripterr = proc.communicate()\\r\\n\\r\\n if scriptout:\\r\\n raise RuntimeError('script returned stdout: ' + scriptout)\\r\\n if scripterr:\\r\\n raise RuntimeError('script returned stderr: ' + scripterr)\\r\\n\\r\\n num_replicates = 3 # ensure this matches cmd above\\r\\n\\r\\n res = open(\\r\\n os.path.join(\\r\\n out_dir,\\r\\n 'otuf_n%d_d0.003.biom' %\\r\\n num_replicates),\\r\\n 'U')\\r\\n orig_table = parse_biom_table(open(otuf, 'U'))\\r\\n res_table = parse_biom_table(res)\\r\\n\\r\\n # 3 samples per input sample\\r\\n self.assertEqual(\\r\\n len(res_table.SampleIds),\\r\\n num_replicates * len(orig_table.SampleIds))\\r\\n\\r\\n # sample_ids have correct naming and order\\r\\n for i in range(len(orig_table.SampleIds)):\\r\\n for j in range(num_replicates):\\r\\n exp = orig_table.SampleIds[i] + '.' + str(j)\\r\\n self.assertEqual(\\r\\n res_table.SampleIds[i * num_replicates + j],\\r\\n exp)\\r\\n\\r\\n # same total sequences in each replicate sample\\r\\n num_orig_samples = len(orig_table.SampleIds)\\r\\n orig_sams = orig_table.iterSampleData()\\r\\n res_sams = res_table.iterSampleData()\\r\\n for i in range(num_orig_samples):\\r\\n orig_sam = orig_sams.next()\\r\\n for j in range(num_replicates):\\r\\n res_sam = res_sams.next()\\r\\n self.assertEqual(res_sam.sum(), orig_sam.sum())\\r\\n # would be nice to test that result otu table doesn't match input,\\r\\n # but not sure how probable that is, and don't want stochastic failures\\r\",\n \"def setUp(self):\\n super().setUp()\\n self.cur_source = tempfile.mkdtemp()\",\n \"def setUpClass(cls):\\n import os\\n for root in cls.prod_s2_ssc:\\n os.makedirs(root)\\n metadata = root.split(\\\".\\\")[0] + \\\".HDR\\\"\\n TestFunctions.touch(metadata)\\n for root in cls.prod_s2_mus:\\n os.makedirs(root)\\n metadata = os.path.join(root, root + \\\"_MTD_ALL.xml\\\")\\n TestFunctions.touch(metadata)\\n for root in cls.prod_s2_nat:\\n os.makedirs(root)\\n metadata = os.path.join(root, \\\"MTD_MSIL1C.xml\\\")\\n TestFunctions.touch(metadata)\",\n \"def makeMetadataTest(metadata):\\n metadata = metadata.strip()\\n # convert to tabs\\n metadata = str(metadata)\\n metadata = metadata.replace(\\\" \\\", \\\"\\\\t\\\")\\n # store\\n originalMetadata = metadata\\n # pack\\n header, directory, tableData, metadata = defaultTestData(metadata=metadata)\\n data = padData(packTestHeader(header) + packTestDirectory(directory) + tableData) + packTestMetadata(metadata)\\n # done\\n return data, originalMetadata\",\n \"def setUp(self):\\n\\n self.hw = HMMERWrapper\\n\\n modpath = os.path.abspath(os.path.dirname(__file__))\\n self.seqfile = os.path.join(modpath, 'data', 'P00929.fasta')\\n self.badfile = os.path.join(modpath, 'data', 'bad.fasta')\",\n \"def setUp(self):\\n\\n # Create the data pipe.\\n self.interpreter.pipe.create('dasha', 'mf')\\n\\n # Create a temporary directory for Dasha outputs.\\n ds.tmpdir = mkdtemp()\",\n \"def setUp(self):\\n # parse the file\\n self.plateMeas = PlateMeas(pathToPlate8131MeasFile)\",\n \"def setUp_tearDown():\\n\\n\\t# setup\\n\\tif os.path.isdir(dir_parent):\\n\\t\\tshutil.rmtree(dir_parent)\\n\\n\\tos.makedirs(dir_obj)\\n\\t# shutil.copytree(dir_verif, dir_obj)\\n\\tshutil.copyfile(dir_verif+'spec.fits', dir_obj+'spec.fits')\\n\\n\\t# yield\\n\\t# # tear down\\n\\t# if os.path.isdir(dir_parent):\\n\\t# \\tshutil.rmtree(dir_parent)\",\n \"def setUp(self):\\n # create temporary directory\\n if not usedir:\\n self.test_dir = tempfile.mkdtemp()\\n os.chdir(self.test_dir)\\n else:\\n os.chdir(usedir) \\n\\n super(SimpleTest, self).setUp()\\n\\n import SFramework\\n self.manager = SFramework.TSStatisticsManager()\\n self.manager.getWorkspaces().addObject(self.makeWS())\",\n \"def setUp(self):\\n\\n # Add a data pipe to the data store.\\n ds.add(pipe_name='orig', pipe_type='mf')\\n\\n # Name the first molecule.\\n cdp.mol[0].name = 'Ap4Aase'\\n\\n # Add a second molecule to the system.\\n cdp.mol.add_item(mol_name='RNA')\\n\\n # Add two more residues to the first molecule (and set the residue number of the first).\\n cdp.mol[0].res[0].num = 1\\n cdp.mol[0].res.add_item(res_num=2, res_name='Glu')\\n cdp.mol[0].res.add_item(res_num=4, res_name='Pro')\\n\\n # Add some spin info to this molecule.\\n cdp.mol[0].res[0].spin[0].name = 'NH'\\n cdp.mol[0].res[0].spin[0].num = 60\\n cdp.mol[0].res[1].spin[0].name = 'NH'\\n cdp.mol[0].res[1].spin[0].num = 63\\n\\n # Add one more residue to the second molecule (and set the residue number of the first).\\n cdp.mol[1].res[0].num = -5\\n cdp.mol[1].res.add_item(res_num=-4)\\n\\n # Add a second set of spins to the second molecule (naming the first set first).\\n cdp.mol[1].res[0].spin[0].name = 'C8'\\n cdp.mol[1].res[1].spin[0].name = 'C8'\\n cdp.mol[1].res[0].spin.add_item(spin_name='N5')\\n cdp.mol[1].res[1].spin.add_item(spin_name='N5')\\n cdp.mol[1].res[1].spin.add_item(spin_name='2H', spin_num=132)\\n\\n # Deselect a number of spins.\\n cdp.mol[0].res[0].spin[0].select = 0\\n cdp.mol[0].res[2].spin[0].select = 0\\n cdp.mol[1].res[0].spin[0].select = 0\\n cdp.mol[1].res[1].spin[1].select = 0\\n\\n # Update the metadata.\\n mol_res_spin.metadata_update()\",\n \"def prep_synthetic_test(self, outdir='synthetic_test',\\n loc=(300,350,50,100), #t89\\n signal='linear', #sinusoidal, exponential?\\n rate=0.01, #(m)\\n noise=None, #white, colored (correlated)?\\n ramp=None): #linear, quadratic?\\n if os.path.isdir(outdir):\\n shutil.rmtree(outdir)\\n os.mkdir(outdir)\\n\\n shape = self.Set.Shape\\n ind_signal = np.zeros(shape, dtype='bool')\\n ind_signal[loc[0]:loc[1],loc[2]:loc[3]] = 1\\n bd = 20\\n ind_bound = np.zeros_like(ind_signal)\\n ind_bound[loc[0]-bd:loc[1]+bd,loc[2]-bd:loc[3]+bd] = 1\\n ind_bound[ind_signal] = 0\\n\\n for ig in self.Set:\\n amp, phs = roipy.tools.load_bil(ig)\\n\\n # Create synthetic signal\\n phs_syn = np.zeros_like(phs)\\n phs_syn[ind_signal] = (rate * ig.Timespan)\\n\\n # Create transition zone\\n phs_syn[ind_bound] = (0.01 * ig.Timespan)\\n\\n # Add noise\\n if noise != None:\\n noise = add_noise(noise)\\n phs_syn += noise\\n\\n # Add ramps\\n if ramp != None:\\n surface = make_surface(ramp)\\n phs_syn += surface\\n\\n # Save Data & Rsc\\n phs_syn = phs_syn * (4*np.pi/ig.Wavelength) #convert to phase\\n outpath = os.path.join(outdir,ig.Name.replace('rect_','rect_synthetic_'))\\n roipy.tools.save_bil(ig, outpath, amp, phs_syn)\\n #roipy.tools.save_rsc(ig.Rsc, outpath + '.rsc') #automatically called\\n\\n print('synthetic test set up in:\\\\n{}'.format(os.path.abspath(outdir)))\",\n \"def test_call_sh_files(self):\\n def structs():\\n \\\"\\\"\\\"Mockups for called scripts.\\\"\\\"\\\"\\n server = MockUCSHttpServer('server')\\n struct_r = U.UCSRepoPool(major=MAJOR, minor=MINOR, part=PART, patchlevel=PATCH, arch=ARCH)\\n preup_r = struct_r.path('preup.sh')\\n postup_r = struct_r.path('postup.sh')\\n struct_c = U.UCSRepoPool(major=MAJOR, minor=MINOR, part='%s/component' % (PART,), patch='c', arch=ARCH)\\n preup_c = struct_c.path('preup.sh')\\n postup_c = struct_c.path('postup.sh')\\n\\n yield (server, struct_r, 'preup', preup_r, 'r_pre')\\n yield (server, struct_r, 'postup', postup_r, 'r_post')\\n yield (server, struct_c, 'preup', preup_c, 'c_pre')\\n yield (server, struct_c, 'postup', postup_c, 'c_post')\\n tmp = NamedTemporaryFile()\\n\\n gen = self.u.call_sh_files(structs(), tmp.name, 'arg')\\n\\n # The Updater only yields the intent, the content is only available after the next step\\n self.assertEqual(('update', 'pre'), gen.next()) # download\\n self.assertEqual([], MockPopen.mock_get())\\n self.assertEqual(('preup', 'pre'), gen.next()) # pre\\n self.assertEqual([], MockPopen.mock_get())\\n self.assertEqual(('preup', 'main'), gen.next())\\n self.assertEqual(('pre', 'arg', 'c_pre'), MockPopen.mock_get()[0][1:])\\n self.assertEqual(('preup', 'post'), gen.next())\\n self.assertEqual(('arg', 'r_pre'), MockPopen.mock_get()[0][1:])\\n self.assertEqual(('update', 'main'), gen.next()) # update\\n self.assertEqual(('post', 'arg', 'c_pre'), MockPopen.mock_get()[0][1:])\\n self.assertEqual(('postup', 'pre'), gen.next()) # post\\n self.assertEqual([], MockPopen.mock_get())\\n self.assertEqual(('postup', 'main'), gen.next())\\n self.assertEqual(('pre', 'arg', 'c_post'), MockPopen.mock_get()[0][1:])\\n self.assertEqual(('postup', 'post'), gen.next())\\n self.assertEqual(('arg', 'r_post'), MockPopen.mock_get()[0][1:])\\n self.assertEqual(('update', 'post'), gen.next())\\n self.assertEqual(('post', 'arg', 'c_post'), MockPopen.mock_get()[0][1:])\\n self.assertRaises(StopIteration, gen.next) # done\\n self.assertEqual([], MockPopen.mock_get())\",\n \"def test_SpecConfig_class_minimal():\\n res = SpecConfig(path=PATH_SPECS_2_YAML)\\n assert res.path_out == PATH_SPECS_2_YAML_MODIFIED\",\n \"def test_process(self, tmp_path):\\n export_dir = tmp_path / 'export'\\n export_dir.mkdir()\\n\\n process(['raw_data/small_raw_data_0.csv',\\n 'raw_data/small_raw_data_1.csv',\\n # File 2 does not exist.\\n 'raw_data/small_raw_data_2.csv',\\n 'raw_data/small_raw_data_3.csv'],\\n SIGNALS,\\n ['median_home_dwell_time',\\n 'completely_home_prop_7dav'],\\n ['state'],\\n export_dir)\\n\\n expected = {\\n 'wip_median_home_dwell_time': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga'],\\n 'val': [6, 3.5],\\n 'se': [None, 0.5],\\n 'sample_size': [1, 2]\\n }),\\n 'completely_home_prop': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga'],\\n 'val': [0.15, 0.055],\\n 'se': [None, 0.005],\\n 'sample_size': [1, 2]\\n }),\\n 'part_time_work_prop': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga'],\\n 'val': [0.35, 0.055],\\n 'se': [None, 0.005],\\n 'sample_size': [1, 2]\\n }),\\n 'full_time_work_prop': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga'],\\n 'val': [0.45, 0.055],\\n 'se': [None, 0.005],\\n 'sample_size': [1, 2]\\n }),\\n 'median_home_dwell_time_7dav': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga', 'pa'],\\n 'val': [4.5, 3.5, 7.5],\\n 'se': [1.5, 0.5, 0.5],\\n 'sample_size': [2, 2, 2]\\n }),\\n 'wip_completely_home_prop_7dav': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga', 'pa'],\\n 'val': [0.1, 0.055, 0.15],\\n 'se': [0.05, 0.005, 0.05],\\n 'sample_size': [2, 2, 2]\\n }),\\n 'part_time_work_prop_7dav': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga', 'pa'],\\n 'val': [0.25, 0.055, 0.25],\\n 'se': [0.1, 0.005, 0.05],\\n 'sample_size': [2, 2, 2]\\n }),\\n 'full_time_work_prop_7dav': pd.DataFrame(data={\\n 'geo_id': ['al', 'ga', 'pa'],\\n 'val': [0.35, 0.055, 0.35],\\n 'se': [0.1, 0.005, 0.05],\\n 'sample_size': [2, 2, 2]\\n })\\n }\\n actual = {signal: pd.read_csv(\\n export_dir / f'20200612_state_{signal}.csv')\\n for signal in expected}\\n for signal in expected:\\n pd.testing.assert_frame_equal(expected[signal], actual[signal])\",\n \"def test_2_star(install_test_files, data_dir):\\n with make_workdir() as workdir:\\n cl = [\\\"bcbio_nextgen.py\\\",\\n get_post_process_yaml(data_dir, workdir),\\n os.path.join(data_dir, os.pardir, \\\"110907_ERP000591\\\"),\\n os.path.join(data_dir, \\\"run_info-star.yaml\\\")]\\n subprocess.check_call(cl)\",\n \"def run_test(self):\\n\\n # populate *_ps sets\\n self.enter_project_file()\\n\\n # populate *_dir sets\\n self.enter_directories()\\n\\n # The files in the directories makes up the largest possible set of files\\n self.result_files = self.result_files_dir\\n self.design_files = self.design_files_dir\\n self.design_space_files = self.design_space_files_dir\\n self.test_bench_files = self.test_bench_files_dir\\n\\n # populate *_ms sets\\n self.enter_meta_results_file()\\n\\n # populate *_OK sets\\n self.check_analysis_status()\\n\\n df = {'design_files_dir' : list(self.design_files_dir),'design_files_pr' : list(self.design_files_pr),\\n 'design_files_ms' : list(self.design_files_ms), 'design_files_OK' : list(self.design_files_OK)}\\n\\n ds = {'design_space_files_dir' : list(self.design_space_files_dir),\\n 'design_space_files_pr' : list(self.design_space_files_pr)}\\n\\n rs = {'result_files_dir' : list(self.result_files_dir), 'result_files_ms' : list(self.result_files_ms),\\n 'result_files_OK' : list(self.result_files_OK)}\\n\\n tb = {'test_bench_files_dir' : list(self.test_bench_files_dir),\\n 'test_bench_files_ms' : list(self.test_bench_files_ms)}\\n\\n srl = SummaryReportsLinks(self.result_files_dir)\\n\\n lf = {'files_linked_from_sum_reps' : srl.get_files(),\\n 'folders_linked_from_sum_reps' : srl.get_folders()}\\n\\n # 'test_bench_files_pr' : list(self.test_bench_files_pr),\\n \\n json_test = {'design_files' : df, 'design_space_files' : ds, 'result_files' : rs,\\n 'test_bench_files' : tb, 'stat_files' : self.stat_files,\\n 'files_linked_from_sum_reps' : lf}\\n\\n with open('test_run.json','wb') as f_out:\\n json.dump(json_test, f_out, indent=4)\",\n \"def test_tarballs_pre_extracted_tech_specific(self) -> None:\\n import hammer_config\\n\\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\\\"dummy28\\\")\\n tech_json_filename = os.path.join(tech_dir, \\\"dummy28.tech.json\\\")\\n\\n # Add defaults to specify tarball_dir.\\n with open(os.path.join(tech_dir, \\\"defaults.json\\\"), \\\"w\\\") as f:\\n f.write(json.dumps({\\n \\\"technology.dummy28.tarball_dir\\\": tech_dir,\\n \\\"vlsi.technology.extracted_tarballs_dir\\\": \\\"/should/not/be/used\\\",\\n \\\"technology.dummy28.extracted_tarballs_dir\\\": tech_dir_base\\n }, cls=HammerJSONEncoder))\\n\\n HammerToolTestHelpers.write_tech_json(tech_json_filename, self.add_tarballs)\\n sys.path.append(tech_dir_base)\\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\\\"dummy28\\\", tech_dir))\\n tech.cache_dir = tech_dir\\n\\n database = hammer_config.HammerDatabase()\\n database.update_technology(tech.get_config())\\n HammerVLSISettings.load_builtins_and_core(database)\\n tech.set_database(database)\\n outputs = tech.process_library_filter(pre_filts=[], filt=hammer_tech.filters.gds_filter,\\n must_exist=False,\\n output_func=lambda str, _: [str])\\n\\n self.assertEqual(outputs, [\\\"{0}/foobar.tar.gz/test.gds\\\".format(tech_dir_base)])\\n\\n # Cleanup\\n shutil.rmtree(tech_dir_base)\",\n \"def read_raw(self, name, source, test_data=''):\\n self.m.path.assert_absolute(source)\\n step_test_data = lambda: self.test_api.read_raw(test_data)\\n result = self._run(name, ['copy', source, self.m.raw_io.output()],\\n step_test_data=step_test_data)\\n return result.raw_io.output\",\n \"def setUp(self):\\n\\n # Add a data pipe to the data store.\\n ds.add(pipe_name='orig', pipe_type='mf')\\n\\n # Add a single object to the 'orig' data pipe.\\n ds['orig'].x = 1\\n\\n # Add a single object to the single spin system of the 'orig' data pipe.\\n ds['orig'].mol[0].res[0].spin[0].num = 1\\n\\n # Add an empty data pipe (for the 'eliminate_unused_pipes' test).\\n ds.add(pipe_name='empty', pipe_type='mf')\\n\\n # Set the current pipe to the 'orig' data pipe.\\n pipes.switch('orig')\",\n \"def test_load_vspec():\\n global _vspec\\n _vspec = SourceSpectrum.from_vega()\",\n \"def setUp(self):\\n with open('tests/data/code.txt') as f:\\n self.sf = StreamerFlowchart('Test', f.read())\\n with open('tests/data/code_result.txt') as f:\\n self.expected = f.read()[:-1] # don't want the empty line\",\n \"def get_parser_test():\\n copy.get_parser()\",\n \"def clean_up(self):\\n try:\\n data_dir = os.environ[\\\"DATA\\\"]\\n plots_dir = os.environ[\\\"PLOTS\\\"]\\n logs_dir = os.environ[\\\"LOGS\\\"]\\n except KeyError as detail:\\n print \\\"GenerateSpectrum.clean_up: error\\\", detail, \\\"not set\\\"\\n print \\\" --> source analysis environment scripts before running!\\\"\\n sys.exit(1)\\n for root, dirs, files in os.walk(os.getcwd()):\\n for file in files:\\n is_data = re.search(r\\\".*\\\\.root$\\\", file)\\n is_plot = re.search(r\\\".*\\\\.png$\\\", file)\\n hostname = socket.gethostname()\\n is_log = re.search(r\\\"^rat\\\\.\\\"+hostname+r\\\"\\\\.[0-9]+\\\\.log$\\\", file)\\n if is_data:\\n try:\\n root_file = TFile(file)\\n tree = root_file.Get(\\\"T\\\")\\n tree.ls()\\n except ReferenceError as detail:\\n \\\"generate_spectrum.clean_up: error in TFile,\\\", detail\\n sys.exit(1)\\n file_manips.copy_file(os.path.join(root, file), data_dir)\\n elif is_plot:\\n file_manips.copy_file(os.path.join(root, file), plots_dir)\\n elif is_log:\\n file_manips.copy_file(os.path.join(root, file), logs_dir)\",\n \"def test_parse_various_files():\\r\\n TEST_DATA_FILES = [\\r\\n \\\"AnnotationTest.thrift\\\", \\\"ConstantsDemo.thrift\\\", \\\"DenseLinkingTest.thrift\\\",\\r\\n \\\"OptionalRequiredTest.thrift\\\", \\\"StressTest.thrift\\\", \\\"DebugProtoTest.thrift\\\",\\r\\n \\\"DocTest.thrift\\\", \\\"ManyTypedefs.thrift\\\", \\\"SmallTest.thrift\\\", \\\"ThriftTest.thrift\\\"\\r\\n ]\\r\\n TEST_DATA_DIR = 'test_data'\\r\\n TEST_DATA_PATH = 'twitter.thrift.descriptors'\\r\\n\\r\\n parser = ThriftParser()\\r\\n for test_data_file in TEST_DATA_FILES:\\r\\n test_data = pkgutil.get_data(TEST_DATA_PATH, os.path.join(TEST_DATA_DIR, test_data_file))\\r\\n print('Parsing file %s...' % test_data_file, end='')\\r\\n # TODO: The parser may fail silently and return a partial program. Fix this.\\r\\n program = parser.parse_string(test_data)\\r\\n print('OK.')\",\n \"def setUp(self):\\n self._resolver_context = context.Context()\\n test_path = self._GetTestFilePath(['hfsplus.raw'])\\n self._SkipIfPathNotExists(test_path)\\n\\n test_os_path_spec = path_spec_factory.Factory.NewPathSpec(\\n definitions.TYPE_INDICATOR_OS, location=test_path)\\n self._raw_path_spec = path_spec_factory.Factory.NewPathSpec(\\n definitions.TYPE_INDICATOR_RAW, parent=test_os_path_spec)\\n self._hfs_path_spec = path_spec_factory.Factory.NewPathSpec(\\n definitions.TYPE_INDICATOR_HFS, location='/',\\n parent=self._raw_path_spec)\\n\\n self._file_system = hfs_file_system.HFSFileSystem(\\n self._resolver_context, self._hfs_path_spec)\\n self._file_system.Open()\",\n \"def setUp(self):\\r\\n\\r\\n fd, self.sample_fasta_fp = mkstemp(prefix=\\\"sample_fasta_\\\",\\r\\n suffix=\\\".fna\\\")\\r\\n close(fd)\\r\\n seq_file = open(self.sample_fasta_fp, 'w')\\r\\n seq_file.write(sample_fasta_file)\\r\\n seq_file.close()\\r\\n\\r\\n fd, self.sample_fasta_invalid_fp = mkstemp(prefix=\\\"sample_fasta_\\\",\\r\\n suffix=\\\".fna\\\")\\r\\n close(fd)\\r\\n seq_file = open(self.sample_fasta_invalid_fp, 'w')\\r\\n seq_file.write(sample_fasta_file_invalid)\\r\\n seq_file.close()\\r\\n\\r\\n fd, self.sample_mapping_fp = mkstemp(prefix=\\\"sample_mapping_\\\",\\r\\n suffix=\\\".txt\\\")\\r\\n close(fd)\\r\\n map_file = open(self.sample_mapping_fp, \\\"w\\\")\\r\\n map_file.write(sample_mapping_file)\\r\\n map_file.close()\\r\\n\\r\\n fd, self.sample_tree_3tips_fp = mkstemp(\\r\\n prefix=\\\"sample_tree3tips_\\\",\\r\\n suffix=\\\".tre\\\")\\r\\n close(fd)\\r\\n tree_file = open(self.sample_tree_3tips_fp, \\\"w\\\")\\r\\n tree_file.write(sample_tree_file_3tips)\\r\\n tree_file.close()\\r\\n\\r\\n fd, self.sample_tree_5tips_fp = mkstemp(\\r\\n prefix=\\\"sample_tree3tips_\\\",\\r\\n suffix=\\\".tre\\\")\\r\\n close(fd)\\r\\n tree_file = open(self.sample_tree_5tips_fp, \\\"w\\\")\\r\\n tree_file.write(sample_tree_file_5tips)\\r\\n tree_file.close()\\r\\n\\r\\n fd, self.sample_mapping_file_errors_fp =\\\\\\r\\n mkstemp(prefix=\\\"error_mapping_\\\", suffix=\\\".txt\\\")\\r\\n close(fd)\\r\\n map_file = open(self.sample_mapping_file_errors_fp, \\\"w\\\")\\r\\n map_file.write(sample_mapping_file_errors)\\r\\n map_file.close()\\r\\n\\r\\n self._files_to_remove = [self.sample_fasta_fp,\\r\\n self.sample_fasta_invalid_fp, self.sample_mapping_fp,\\r\\n self.sample_tree_3tips_fp, self.sample_tree_5tips_fp,\\r\\n self.sample_mapping_file_errors_fp]\\r\\n\\r\\n self.output_dir =\\\\\\r\\n mkdtemp(prefix=\\\"validate_demultiplexed_fasta_\\\",\\r\\n suffix=\\\"/\\\")\",\n \"def _read_spec_test(filename):\\n with open(filename, \\\"rt\\\") as file:\\n source = file.read()\\n return ParsedSpecTest.parse(source)\",\n \"def setup(self):\\n if (((not os.path.isfile(self.tempfile))\\n or (os.stat(self.tempfile).st_size == 0))):\\n self.write_ply(self.tempfile)\\n skip_start = False\\n if self.inst_kwargs.get('src_type', 1) == 0:\\n skip_start = True\\n super(TestCisPlyInput, self).setup(skip_start=skip_start)\",\n \"def test_sample():\\n print(os.getcwd())\\n run_validator(SAMPLE_FILE_PATH)\",\n \"def setUp(self):\\n\\n self.test_data_path = 'testing/test_data/'\",\n \"def setUp(self):\\n self.setUpPyfakefs()\\n with mock.patch.object(subprocess, 'Popen', side_effect=thor_mocks.mock_thor_start):\\n self._analyzer = yara_analyzer.YaraAnalyzer()\\n\\n # Write target file.\\n # pylint: disable=no-member\\n self.fs.create_file('./target.exe', contents='This is definitely not an evil file. ^_^\\\\n')\",\n \"def test_image(self):\\r\\n self.testdata = open(TESTDATA_FILENAME).read()\",\n \"def _store_test_result(ptfhost):\\n logger.info(\\\"Copying file from folder: {0} to folder: {1}\\\".format(\\n\\t\\tSAI_TEST_REPORT_TMP_DIR_ON_PTF, \\n\\t\\tSAI_TEST_REPORT_DIR_ON_PTF))\\n ptfhost.shell(\\\"cp {0}/*.* {1}/\\\".format(\\n\\t\\tSAI_TEST_REPORT_TMP_DIR_ON_PTF, \\n\\t\\tSAI_TEST_REPORT_DIR_ON_PTF))\",\n \"def load(self):\\n self.data = NSPSpecIO().read(self.path)\",\n \"def test_outputs(self, monkeypatch, script_runner):\\n monkeypatch.setattr(\\\"builtins.input\\\", lambda _: \\\"n\\\")\\n _ = script_runner.run(\\n \\\"spectrafit\\\",\\n \\\"spectrafit/test/test_data.txt\\\",\\n \\\"-i\\\",\\n \\\"spectrafit/test/test_input_2.json\\\",\\n )\\n assert len(list(Path(\\\".\\\").glob(\\\"*.json\\\"))) == 1\\n assert len(list(Path(\\\".\\\").glob(\\\"*.csv\\\"))) == 3\",\n \"def data_dir(uncover):\\n return os.path.join(uncover, 'tests', 'test_data')\",\n \"def setUp(self):\\r\\n super(TestImport, self).setUp()\\r\\n self.content_dir = path(tempfile.mkdtemp())\\r\\n self.addCleanup(shutil.rmtree, self.content_dir)\\r\\n\\r\\n # Create good course xml\\r\\n self.good_dir = self.create_course_xml(self.content_dir, self.BASE_COURSE_KEY)\\r\\n\\r\\n # Create run changed course xml\\r\\n self.dupe_dir = self.create_course_xml(self.content_dir, self.DIFF_KEY)\\r\\n\\r\\n # Create course XML where TRUNCATED_COURSE.org == BASE_COURSE_ID.org\\r\\n # and BASE_COURSE_ID.startswith(TRUNCATED_COURSE.course)\\r\\n self.course_dir = self.create_course_xml(self.content_dir, self.TRUNCATED_KEY)\",\n \"def setUp(self):\\n # main directory of the project\\n self.project_dir = os.path.dirname(self.basedir)\\n\\n # change to workdir so simulation process find the source files\\n os.chdir(self.workdir)\",\n \"def setUp(self):\\n self.dir = pathlib.Path(tempfile.mkdtemp(prefix=\\\"test-hhsuite-\\\"))\\n a3m = self.dir / \\\"query.a3m\\\"\\n structure = self.dir / \\\"query.pdb\\\"\\n\\n # Write template to PDB file\\n with io.StringIO(minimal.MINIMAL_MMCIF) as mmcif_buf:\\n parser = Bio.PDB.MMCIFParser()\\n template_structure = parser.get_structure(\\\"\\\", mmcif_buf)\\n template_obj = template.Template.build(\\n \\\"1MIN\\\", \\\"A\\\", template_structure[0][\\\"A\\\"])\\n with structure.open(\\\"w\\\") as template_out:\\n template_obj.write(template_out)\\n\\n # Write a3m to file.\\n with a3m.open(\\\"w\\\") as a3m_out:\\n a3m_out.write(self.BEFORE_A3M)\\n\\n self.pipeline = {\\n \\\"template_obj\\\": template_obj,\\n \\\"a3m\\\": str(a3m),\\n \\\"secondary_structure\\\": {\\\"dssp\\\": self.SEC_STRUC}\\n }\",\n \"def test_script_nochange(self):\\r\\n qiime_config = load_qiime_config()\\r\\n tempdir = qiime_config['temp_dir'] or gettempdir()\\r\\n maindir = os.path.join(tempdir,\\r\\n ''.join(random.choice(string.ascii_letters + string.digits)\\r\\n for x in range(10)))\\r\\n\\r\\n os.makedirs(maindir)\\r\\n self.dirs_to_remove.append(maindir)\\r\\n otuf = os.path.join(maindir, 'otuf')\\r\\n treef = os.path.join(maindir, 'treef')\\r\\n\\r\\n otufh = open(otuf, 'w')\\r\\n otufh.write(tutorial_otu_table)\\r\\n otufh.close()\\r\\n\\r\\n treefh = open(treef, 'w')\\r\\n treefh.write(tutorial_tree)\\r\\n treefh.close()\\r\\n\\r\\n out_dir = os.path.join(maindir, 'simsam_out')\\r\\n cmd = 'simsam.py -i %s -t %s -o %s -d 0 -n 3 ' % (otuf, treef,\\r\\n out_dir)\\r\\n\\r\\n proc = subprocess.Popen(cmd, shell=True,\\r\\n stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\r\\n scriptout, scripterr = proc.communicate()\\r\\n\\r\\n if scriptout:\\r\\n raise RuntimeError('script returned stdout: ' + scriptout)\\r\\n if scripterr:\\r\\n raise RuntimeError('script returned stderr: ' + scripterr)\\r\\n\\r\\n num_replicates = 3 # ensure this matches cmd above\\r\\n res = open(\\r\\n os.path.join(\\r\\n out_dir,\\r\\n 'otuf_n%d_d0.0.biom' %\\r\\n num_replicates),\\r\\n 'U')\\r\\n orig_table = parse_biom_table(open(otuf, 'U'))\\r\\n res_table = parse_biom_table(res)\\r\\n\\r\\n # 3 samples per input sample\\r\\n self.assertEqual(\\r\\n len(res_table.SampleIds),\\r\\n num_replicates * len(orig_table.SampleIds))\\r\\n\\r\\n # sample_ids have correct naming and order\\r\\n for i in range(len(orig_table.SampleIds)):\\r\\n for j in range(num_replicates):\\r\\n exp = orig_table.SampleIds[i] + '.' + str(j)\\r\\n self.assertEqual(\\r\\n res_table.SampleIds[i * num_replicates + j],\\r\\n exp)\\r\\n\\r\\n # same otu ids\\r\\n self.assertEqual(res_table.ObservationIds, orig_table.ObservationIds)\\r\\n\\r\\n # same otu table, just replicated thrice\\r\\n # note this requires the same sorting of otus, input is correct sorting\\r\\n num_orig_samples = len(orig_table.SampleIds)\\r\\n orig_sams = orig_table.iterSampleData()\\r\\n res_sams = res_table.iterSampleData()\\r\\n for i in range(num_orig_samples):\\r\\n orig_sam = orig_sams.next()\\r\\n for j in range(num_replicates):\\r\\n res_sam = res_sams.next()\\r\\n self.assertItemsEqual(res_sam, orig_sam)\",\n \"def split_spec(self):\\n from django_swagger_utils.spec_client.split_spec import SplitSpec\\n from django_swagger_utils.core.utils.check_path_exists import check_path_exists\\n\\n if check_path_exists(os.path.join(self.paths['api_spec_dir'], \\\"specs\\\")):\\n from shutil import rmtree\\n rmtree(os.path.join(self.paths['api_spec_dir'], \\\"specs\\\"))\\n split_spec = SplitSpec(self.paths['api_spec_dir'], self.paths['base_dir'])\\n split_spec.split()\",\n \"def prepare_training_data(\\n self, dir_snippy: Path, dir_ont: Path, caller: str = 'clair',\\n break_complex: bool = True, snippy_ext: str = \\\".ref.vcf\\\"\\n ):\\n\\n self.training_dir.mkdir(parents=True, exist_ok=True)\\n\\n comparisons = self.get_coverage_comparisons(dir_snippy=dir_snippy, dir_ont=dir_ont, snippy_ext=snippy_ext)\\n\\n ont_with_truth, snippies, _ = self.get_data_from_comparisons(\\n comparisons=comparisons, caller=caller, break_complex=break_complex, outdir=self.training_dir\\n )\\n\\n features, _ = self.parse_features(ont_calls=ont_with_truth)\\n\\n # Combined features for training\\n self.features_combined = pd.concat(features) # combined feature frames\\n self.features_combined = self.features_combined.reset_index(drop=True)\\n self.features_combined.to_csv(self.training_dir / 'training_features.tsv', sep='\\\\t', index=False)\",\n \"def setup_build_tests(self):\\n # Now copy the relative files\\n self.cache_extra_test_sources(self.build_relpath)\\n\\n # Ensure the path exists since relying on a relative path at the\\n # same level as the normal stage source path.\\n mkdirp(self.install_test_root)\",\n \"def setUp(self):\\n # main directory of the project\\n self.project_dir = os.path.dirname(self.basedir)\\n\\n # change to workdir so simulation process finds the source files\\n os.chdir(self.workdir)\",\n \"def test_init(self):\\n test_data = (\\n (self.segment.input_file, self.EXPECTED_INPUT_FILE,\\n \\\"input file = {v}\\\".format(v=self.EXPECTED_INPUT_FILE)),\\n (self.segment.punch_in, self.EXPECTED_PUNCH_IN,\\n \\\"punch in = {v}\\\".format(v=self.EXPECTED_PUNCH_IN)),\\n (self.segment.punch_out, self.EXPECTED_PUNCH_OUT,\\n \\\"punch out = {v}\\\".format(v=self.EXPECTED_PUNCH_OUT)),\\n (self.segment.input_stream, self.EXPECTED_INPUT_STREAM,\\n \\\"input stream = {v}\\\".format(v=self.EXPECTED_INPUT_STREAM)),\\n (self.segment._temp_file, self.EXPECTED_TEMP_FILE,\\n \\\"temp file = {v}\\\".format(v=self.EXPECTED_TEMP_FILE)),\\n (self.segment._temp_suffix, self.EXPECTED_TEMP_SUFFIX,\\n \\\"temp suffix = {v}\\\".format(v=self.EXPECTED_TEMP_SUFFIX)),\\n (self.segment._temp_files_list, self.EXPECTED_TEMP_LIST,\\n \\\"temp files list = {v}\\\".format(v=self.EXPECTED_TEMP_LIST)),\\n (self.segment._TYPE, self.EXPECTED_TYPE,\\n \\\"type = {v}\\\".format(v=self.EXPECTED_TYPE)),\\n (self.segment._TRIM, self.EXPECTED_TRIM,\\n \\\"trim = {v}\\\".format(v=self.EXPECTED_TRIM)),\\n (self.segment._SETPTS, self.EXPECTED_SETPTS,\\n \\\"setpts = {v}\\\".format(v=self.EXPECTED_SETPTS)),\\n )\\n for actual, expected, description in test_data:\\n with self.subTest(msg=description):\\n self.assertEqual(actual, expected)\",\n \"def _setup_run(cfg: Dict) -> Dict:\\n now = datetime.now()\\n day = f\\\"{now.day}\\\".zfill(2)\\n month = f\\\"{now.month}\\\".zfill(2)\\n hour = f\\\"{now.hour}\\\".zfill(2)\\n minute = f\\\"{now.minute}\\\".zfill(2)\\n run_name = f'run_{day}{month}_{hour}{minute}_seed{cfg[\\\"seed\\\"]}'\\n # cfg[\\\"run_dir\\\"] = Path(__file__).absolute().parent / \\\"runs\\\" / run_name\\n cfg[\\\"run_dir\\\"] = cfg[\\\"run_dir\\\"] / run_name\\n if not cfg[\\\"run_dir\\\"].is_dir():\\n cfg[\\\"train_dir\\\"] = cfg[\\\"run_dir\\\"] / \\\"data\\\" / \\\"train\\\"\\n cfg[\\\"train_dir\\\"].mkdir(parents=True)\\n cfg[\\\"val_dir\\\"] = cfg[\\\"run_dir\\\"] / \\\"data\\\" / \\\"val\\\"\\n cfg[\\\"val_dir\\\"].mkdir(parents=True)\\n else:\\n raise RuntimeError(f\\\"There is already a folder at {cfg['run_dir']}\\\")\\n\\n # dump a copy of cfg to run directory\\n with (cfg[\\\"run_dir\\\"] / \\\"cfg.json\\\").open(\\\"w\\\") as fp:\\n temp_cfg = {}\\n for key, val in cfg.items():\\n if isinstance(val, PosixPath):\\n temp_cfg[key] = str(val)\\n elif isinstance(val, Dict):\\n for k in val:\\n if isinstance(val[k], PosixPath):\\n val[k] = str(val[k])\\n elif isinstance(val, pd.Timestamp):\\n temp_cfg[key] = val.strftime(format=\\\"%d%m%Y\\\")\\n else:\\n temp_cfg[key] = val\\n json.dump(temp_cfg, fp, sort_keys=True, indent=4)\\n\\n return cfg\",\n \"def main(\\n input_dir: Path = typer.Argument(..., exists=True),\\n output_dir: Path = typer.Argument(...),\\n beth_train_tar_name: str = \\\"i2b2_Beth_Train_Release.tar.gz\\\",\\n partners_train_tar_name: str = \\\"i2b2_Partners_Train_Release.tar.gz\\\",\\n test_zip_name: str = \\\"Task_1C.zip\\\",\\n merge_docs: bool = True,\\n):\\n # Unpack compressed data files\\n msg.info(\\\"Extracting raw data.\\\")\\n beth_train_tar_path = input_dir / beth_train_tar_name\\n partners_train_tar_path = input_dir / partners_train_tar_name\\n test_zip_path = input_dir / test_zip_name\\n\\n for path in [beth_train_tar_path, partners_train_tar_path]:\\n if path.name.endswith(\\\"tar.gz\\\"):\\n msg.text(f\\\"Extracting {path}\\\")\\n tar = tarfile.open(path, \\\"r:gz\\\")\\n tar.extractall(path.parent)\\n tar.close()\\n\\n shutil.unpack_archive(test_zip_path, input_dir / test_zip_name.replace(\\\".zip\\\", \\\"\\\"))\\n\\n # preprocess data\\n msg.info(\\\"Converting to spaCy Doc objects.\\\")\\n beth_train_docs = docs_from_many_clinical_records(\\n input_dir / \\\"Beth_Train\\\", merge_docs=merge_docs\\n )\\n partners_train_docs = docs_from_many_clinical_records(\\n input_dir / \\\"Partners_Train\\\", merge_docs=merge_docs\\n )\\n train_docs = beth_train_docs + partners_train_docs\\n\\n beth_test_docs = docs_from_many_clinical_records(\\n input_dir / \\\"Task_1C/i2b2_Test/i2b2_Beth_Test\\\", merge_docs=merge_docs\\n )\\n partners_test_docs = docs_from_many_clinical_records(\\n input_dir / \\\"Task_1C/i2b2_Test/i2b2_Partners_Test\\\", merge_docs=merge_docs\\n )\\n test_docs = beth_test_docs + partners_test_docs\\n\\n random.shuffle(train_docs)\\n split_idx = int(len(train_docs) * 0.8)\\n train_docs, dev_docs = train_docs[:split_idx], train_docs[split_idx:]\\n\\n msg.good(f\\\"Num Train Docs: {len(train_docs)}\\\")\\n msg.good(f\\\"Num Dev Docs: {len(dev_docs)}\\\")\\n msg.good(f\\\"Num Test Docs: {len(test_docs)}\\\")\\n\\n with msg.loading(f\\\"Saving docs to: {output_dir}...\\\"):\\n DocBin(docs=train_docs).to_disk(output_dir / \\\"train.spacy\\\")\\n DocBin(docs=dev_docs).to_disk(output_dir / \\\"dev.spacy\\\")\\n DocBin(docs=test_docs).to_disk(output_dir / \\\"test.spacy\\\")\\n msg.good(\\\"Done.\\\")\",\n \"def test_tarballs_pre_extracted(self) -> None:\\n import hammer_config\\n\\n tech_dir, tech_dir_base = HammerToolTestHelpers.create_tech_dir(\\\"dummy28\\\")\\n tech_json_filename = os.path.join(tech_dir, \\\"dummy28.tech.json\\\")\\n\\n # Add defaults to specify tarball_dir.\\n with open(os.path.join(tech_dir, \\\"defaults.json\\\"), \\\"w\\\") as f:\\n f.write(json.dumps({\\n \\\"technology.dummy28.tarball_dir\\\": tech_dir,\\n \\\"vlsi.technology.extracted_tarballs_dir\\\": tech_dir_base\\n }, cls=HammerJSONEncoder))\\n\\n HammerToolTestHelpers.write_tech_json(tech_json_filename, self.add_tarballs)\\n sys.path.append(tech_dir_base)\\n tech = self.get_tech(hammer_tech.HammerTechnology.load_from_dir(\\\"dummy28\\\", tech_dir))\\n tech.cache_dir = tech_dir\\n\\n database = hammer_config.HammerDatabase()\\n database.update_technology(tech.get_config())\\n HammerVLSISettings.load_builtins_and_core(database)\\n tech.set_database(database)\\n outputs = tech.process_library_filter(pre_filts=[], filt=hammer_tech.filters.gds_filter,\\n must_exist=False,\\n output_func=lambda str, _: [str])\\n\\n self.assertEqual(outputs, [\\\"{0}/foobar.tar.gz/test.gds\\\".format(tech_dir_base)])\\n\\n # Cleanup\\n shutil.rmtree(tech_dir_base)\",\n \"def _parse_testtime_dir(self, data_dir, test_temporal_t=1):\\n self.test_tuples = []\\n categories = os.listdir(data_dir)\\n for folder_name in categories:\\n all_fnames_list_fname = os.path.join(data_dir, folder_name,\\n folder_name + \\\".bmf\\\")\\n if not os.path.isfile(all_fnames_list_fname):\\n raise IOError(\\\"Not found file {}\\\".format(all_fnames_list_fname))\\n all_fnames_list = np.loadtxt(all_fnames_list_fname, dtype=np.str,\\n skiprows=1)\\n # Correct from pgm to jpg\\n all_fnames_list = [f.split('.')[0]+'.jpg' for f in all_fnames_list]\\n\\n all_fnames_list = [os.path.join(data_dir, folder_name, f) for f \\\\\\n in all_fnames_list]\\n # Get ground_truth\\n annotation_fnames, numbers, type_weird = self.find_gt(os.path.join(data_dir,\\n folder_name,\\n 'GroundTruth'))\\n goal_annotation_fnames = [f.split('.')[0] + '.jpg' for f in annotation_fnames]\\n goal_annotation_fnames = [os.path.join(data_dir, folder_name, 'GroundTruth', f) for f \\\\\\n in goal_annotation_fnames]\\n\\n # NOTE: Run the commented part only once to preprocess GT\\n annotation_fnames = [os.path.join(data_dir, folder_name, 'GroundTruth', f) for f \\\\\\n in annotation_fnames]\\n for i in range(len(goal_annotation_fnames)):\\n mask = cv2.imread(annotation_fnames[i])\\n mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\\n mask = mask / 255.0\\n if type_weird:\\n mask[mask>0.99] = 0.0\\n if 'marple7' == folder_name:\\n mask = mask>0.05\\n elif 'marple2' == folder_name:\\n mask = mask>0.4\\n else:\\n mask = mask>0.1\\n mask = np.asarray(mask*255, dtype=np.uint8)\\n cv2.imwrite(goal_annotation_fnames[i], mask)\\n\\n # Create offsets\\n numbers = np.array(numbers) - np.min(numbers)\\n seq_len = np.max(numbers)\\n offsets = numbers + test_temporal_t\\n if offsets[0] < numbers[0]:\\n # test was negative, needs to increase:\\n offsets[0] += 2*abs(test_temporal_t)\\n if offsets[-1] > numbers[-1]:\\n # test was positive, needs to decrease:\\n offsets[-1] -= 2*abs(test_temporal_t)\\n\\n for i in range(len(offsets)):\\n offsets[i] = np.maximum(offsets[i], 0)\\n offsets[i] = np.minimum(offsets[i], seq_len)\\n\\n\\n for i, k in enumerate(numbers):\\n self.test_tuples.append((all_fnames_list[k], all_fnames_list[offsets[i]],\\n goal_annotation_fnames[i], \\\"{}\\\".format(len(annotation_fnames))))\\n\\n self.samples += len(annotation_fnames)\\n self.samples_per_cat[folder_name] = len(annotation_fnames)\\n self.num_experiments+=1\",\n \"def createSpecWithMetadataOnly(self):\\n metadataYamlFilePath = os.path.join(os.path.dirname(\\n os.path.realpath(__file__)),\\n 'sample_metadata.yaml')\\n with open(metadataYamlFilePath, \\\"r\\\") as fp:\\n self.metadata = fp.read().rstrip('\\\\n')\\n self.createCloudinitDataSpec('specWithMetadataOnly',\\n 'linux cloud-init data customization spec'\\n 'with metadata only')\",\n \"def setUpClass(cls):\\n cur_dir = os.path.dirname(os.path.realpath(__file__))\\n meta_file = os.path.join(cur_dir, 'testdata/meta.json')\\n graph_file = os.path.join(cur_dir, 'testdata/graph.json')\\n output_file = os.path.join(cur_dir, 'testdata/graph.dat')\\n builder = os.path.join(cur_dir, '../../../tools/bin/json2dat.py')\\n\\n command = \\\"python {builder} -i {input} -c {meta} -o {output}\\\".format(\\n builder=builder, input=graph_file, meta=meta_file, output=output_file)\\n\\n try:\\n subprocess.call(command, shell=True)\\n except:\\n raise RuntimeError(\\\"Build Graph for test failed\\\")\\n\\n base.initialize_graph({\\n 'mode': 'Local',\\n 'directory': os.path.join(cur_dir, 'testdata'),\\n 'load_type': 'compact'\\n })\",\n \"def setUp(self):\\n self.testfilename = 'test/testfile%d' % time.time()\\n writetestfile = open(self.testfilename, 'w')\\n writetestfile.write(\\n '0<>81_0<>e rundensteiner:y huang<>geoinformatica<>m jones<>symbol '\\n 'intersect detect method improv spatial intersect join<>\\\\n'\\n '1<>81_1<>e rundensteiner:h kuno:p marron:v taube:y ra<>sigmodels.intern '\\n 'manag data<>matthew c jones<>improv spatial intersect join symbol '\\n 'intersect detect<>\\\\n'\\n '2<>81_2<>e rundensteiner:y huang<>ssd symposium larg spatial '\\n 'databas<>matthew c jones<>view materi techniqu complex hierarch '\\n 'object<>\\\\n\\\\n'\\n\\n '0<>185_0<>l berg<>sigucc special interest group univers comput '\\n 'servic<>mike w miller<>domin draw bipartit graph<>\\\\n'\\n '1<>185_1<>undefined<>sigucc special interest group univers comput '\\n 'servic<>mike w miller<>rel compromis statist databas<>\\\\n\\\\n'\\n\\n '0<>94_0<>d kung:j samuel:j gao:p hsia:y toyoshima<>ieee softwar<>c '\\n 'chen<>formal approach scenario analysi<>\\\\n\\\\n'\\n\\n '0<>69_0<>undefined<>acl meet the associ comput linguist<>jane j '\\n 'robinson<>discours code clue context<>\\\\n'\\n '1<>69_1<>undefined<>cooper interfac inform system<>jane j robinson<>'\\n 'diagram grammar dialogu<>\\\\n\\\\n'\\n\\n '0<>0_0<>a gonzalez:a hamid:c overstreet:h wahab:j wild:k maly:s ghanem'\\n ':x zhu<>acm journal educ resourc comput<>a gupta<>iri h java '\\n 'distanc educ<>\\\\n\\\\n'\\n\\n '4<>43_1<>y patt<>proceed the th ieee intern symposium high perform '\\n 'comput architectur hpca intern symposium high perform comput '\\n 'architectur talk slide<>mary d brown<>intern redund represent '\\n 'limit bypass support pipelin adder regist file<>\\\\n\\\\n')\\n writetestfile.close()\",\n \"def test_json(test_data,tmp_path):\\n\\n for d in test_data:\\n\\n gpm = GenotypePhenotypeMap(wildtype=d[\\\"wildtype\\\"],\\n genotype=d[\\\"genotype\\\"],\\n phenotype=d[\\\"phenotype\\\"],\\n uncertainty=d[\\\"uncertainty\\\"])\\n\\n # Write json file\\n json_file = os.path.join(tmp_path,\\\"tmp.json\\\")\\n gpm.to_json(filename=json_file)\\n assert os.path.isfile(json_file)\\n\\n # Read json file\\n new_gpm = gpmap.read_json(filename=json_file)\\n conftest.compare_gpmap(gpm,new_gpm)\",\n \"def GetTestData():\\n return os.path.join(GetSrc(), 'chrome', 'test', 'data')\",\n \"def extract_systtests(self):\\n config_summary_fpath = os.path.join(\\n self.systdir,\\n 'config_summary.json'\\n )\\n cfg = from_file(config_summary_fpath)\\n\\n self.data_is_data = cfg['data_is_data']\\n if self.data_is_data:\\n raise ValueError('Analysis should NOT have been performed '\\n 'on data since this script should only '\\n 'process output from MC studies.')\\n\\n # Get naming scheme\\n labels = Labels(\\n h0_name=cfg['h0_name'],\\n h1_name=cfg['h1_name'],\\n data_name=cfg['data_name'],\\n data_is_data=self.data_is_data,\\n fluctuate_data=self.fluctuate_data,\\n fluctuate_fid=self.fluctuate_fid\\n )\\n # Special extraction for the baseline case of all systematics\\n if 'full_syst_baseline' in labels.dict['data_name']:\\n trueordering = labels.dict['data_name'].split('_')[0]\\n systematic = 'full_syst_baseline'\\n direction = None\\n else:\\n if 'fixed' in labels.dict['h0_name']:\\n if 'inj' in labels.dict['data_name']:\\n testtype = 'fixwrong'\\n trueordering = labels.dict['data_name'].split('_')[0]\\n direction = labels.dict['data_name'].split('_')[-2]\\n systematic = labels.dict['data_name'].split(\\n '%s_inj_'%trueordering\\n )[-1].split('_%s_wrong'%direction)[0]\\n else:\\n testtype = 'nminusone'\\n trueordering = labels.dict['data_name'].split('_')[0]\\n hypo = labels.dict['h0_name'].split('_')[0]\\n direction = None\\n systematic = labels.dict['h0_name'].split(\\n '%s_fixed_'%hypo\\n )[-1].split('_baseline')[0]\\n else:\\n testtype = 'fitwrong'\\n trueordering = labels.dict['data_name'].split('_')[0]\\n direction = labels.dict['data_name'].split('_')[-2]\\n systematic = labels.dict['data_name'].split(\\n '%s_inj_'%trueordering\\n )[-1].split('_%s_wrong'%direction)[0]\\n trueordering = 'toy_%s_asimov'%trueordering\\n if trueordering not in self.labels.keys():\\n self.labels[trueordering] = {}\\n self.all_params[trueordering] = {}\\n self.data_sets[trueordering] = {}\\n if systematic not in self.labels[trueordering].keys():\\n self.labels[trueordering][systematic] = {}\\n self.all_params[trueordering][systematic] = {}\\n self.data_sets[trueordering][systematic] = {}\\n if direction is not None:\\n if direction not in self.labels[trueordering][systematic].keys():\\n self.labels[trueordering][systematic][direction] = labels\\n self.all_params[trueordering][systematic][direction] = {}\\n self.data_sets[trueordering][systematic][direction] = {}\\n else:\\n self.labels[trueordering][systematic] = labels\\n # Get starting params\\n self.get_starting_params(cfg=cfg, trueordering=trueordering,\\n systematic=systematic, direction=direction)\\n self.get_data(trueordering=trueordering, systematic=systematic,\\n direction=direction)\",\n \"def setUp(self):\\n self.workspace_dir = tempfile.mkdtemp()\",\n \"def setUp(self):\\n self.workspace_dir = tempfile.mkdtemp()\",\n \"def setUp(self):\\n self.workspace_dir = tempfile.mkdtemp()\",\n \"def setUp(self):\\n self.workspace_dir = tempfile.mkdtemp()\",\n \"def setUp(self):\\n self.workspace_dir = tempfile.mkdtemp()\",\n \"def setUp(self):\\n self.workspace_dir = tempfile.mkdtemp()\",\n \"def setUp(self):\\n self.workspace_dir = tempfile.mkdtemp()\",\n \"def test_update(self):\\n\\n # boilerplate\\n tempdir = tempfile.mkdtemp()\\n for i in range(10):\\n file(os.path.join(tempdir, str(i)), 'w').write(str(i))\\n\\n # First, make a manifest:\\n manifest = convert([tempdir])\\n newtempdir = tempfile.mkdtemp()\\n manifest_file = os.path.join(newtempdir, 'manifest.ini')\\n file(manifest_file,'w').write(manifest)\\n manifest = ManifestParser(manifests=(manifest_file,))\\n self.assertEqual(manifest.get('name'),\\n [str(i) for i in range(10)])\\n\\n # All of the tests are initially missing:\\n self.assertEqual([i['name'] for i in manifest.missing()],\\n [str(i) for i in range(10)])\\n\\n # But then we copy one over:\\n self.assertEqual(manifest.get('name', name='1'), ['1'])\\n manifest.update(tempdir, name='1')\\n self.assertEqual(sorted(os.listdir(newtempdir)),\\n ['1', 'manifest.ini'])\\n\\n # Update that one file and copy all the \\\"tests\\\":\\n file(os.path.join(tempdir, '1'), 'w').write('secret door')\\n manifest.update(tempdir)\\n self.assertEqual(sorted(os.listdir(newtempdir)),\\n ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'manifest.ini'])\\n self.assertEqual(file(os.path.join(newtempdir, '1')).read().strip(),\\n 'secret door')\\n\\n # clean up:\\n shutil.rmtree(tempdir)\\n shutil.rmtree(newtempdir)\",\n \"def test_copy(self):\\n\\n tempdir = tempfile.mkdtemp()\\n include_example = os.path.join(here, 'include-example.ini')\\n manifest = ManifestParser(manifests=(include_example,))\\n manifest.copy(tempdir)\\n self.assertEqual(sorted(os.listdir(tempdir)),\\n ['fleem', 'include', 'include-example.ini'])\\n self.assertEqual(sorted(os.listdir(os.path.join(tempdir, 'include'))),\\n ['bar.ini', 'crash-handling', 'flowers', 'foo.ini'])\\n from_manifest = ManifestParser(manifests=(include_example,))\\n to_manifest = os.path.join(tempdir, 'include-example.ini')\\n to_manifest = ManifestParser(manifests=(to_manifest,))\\n self.assertEqual(to_manifest.get('name'), from_manifest.get('name'))\\n shutil.rmtree(tempdir)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.54588157","0.5456994","0.54161704","0.54131126","0.5380832","0.535379","0.53135467","0.52902395","0.52726877","0.5257501","0.5230544","0.52054006","0.5198462","0.51969486","0.51810604","0.5168432","0.513813","0.5131549","0.51290935","0.5128934","0.51254076","0.51254076","0.5111139","0.5075971","0.50669205","0.50652075","0.50203264","0.50171447","0.500944","0.5005196","0.50024563","0.49779257","0.49712732","0.4967269","0.4966693","0.49617627","0.49610633","0.4960822","0.4960483","0.49463502","0.49355","0.4915822","0.4908905","0.49082","0.49080223","0.49034533","0.48983538","0.4892526","0.48844612","0.48837525","0.48799595","0.48776335","0.48756889","0.4875474","0.4870746","0.48655877","0.48638874","0.48612282","0.48548323","0.48429018","0.4836782","0.4832368","0.48294348","0.48254228","0.48246625","0.482438","0.48209015","0.48181573","0.48178324","0.48139575","0.4809902","0.48092076","0.48052952","0.47998285","0.4793398","0.47916263","0.47894454","0.47854212","0.47852835","0.47824392","0.4771443","0.47692466","0.47670302","0.4766052","0.47602218","0.47582284","0.4753903","0.47468328","0.4743466","0.47398207","0.47326306","0.4732318","0.4732318","0.4732318","0.4732318","0.4732318","0.4732318","0.4732318","0.47314617","0.47298452"],"string":"[\n \"0.54588157\",\n \"0.5456994\",\n \"0.54161704\",\n \"0.54131126\",\n \"0.5380832\",\n \"0.535379\",\n \"0.53135467\",\n \"0.52902395\",\n \"0.52726877\",\n \"0.5257501\",\n \"0.5230544\",\n \"0.52054006\",\n \"0.5198462\",\n \"0.51969486\",\n \"0.51810604\",\n \"0.5168432\",\n \"0.513813\",\n \"0.5131549\",\n \"0.51290935\",\n \"0.5128934\",\n \"0.51254076\",\n \"0.51254076\",\n \"0.5111139\",\n \"0.5075971\",\n \"0.50669205\",\n \"0.50652075\",\n \"0.50203264\",\n \"0.50171447\",\n \"0.500944\",\n \"0.5005196\",\n \"0.50024563\",\n \"0.49779257\",\n \"0.49712732\",\n \"0.4967269\",\n \"0.4966693\",\n \"0.49617627\",\n \"0.49610633\",\n \"0.4960822\",\n \"0.4960483\",\n \"0.49463502\",\n \"0.49355\",\n \"0.4915822\",\n \"0.4908905\",\n \"0.49082\",\n \"0.49080223\",\n \"0.49034533\",\n \"0.48983538\",\n \"0.4892526\",\n \"0.48844612\",\n \"0.48837525\",\n \"0.48799595\",\n \"0.48776335\",\n \"0.48756889\",\n \"0.4875474\",\n \"0.4870746\",\n \"0.48655877\",\n \"0.48638874\",\n \"0.48612282\",\n \"0.48548323\",\n \"0.48429018\",\n \"0.4836782\",\n \"0.4832368\",\n \"0.48294348\",\n \"0.48254228\",\n \"0.48246625\",\n \"0.482438\",\n \"0.48209015\",\n \"0.48181573\",\n \"0.48178324\",\n \"0.48139575\",\n \"0.4809902\",\n \"0.48092076\",\n \"0.48052952\",\n \"0.47998285\",\n \"0.4793398\",\n \"0.47916263\",\n \"0.47894454\",\n \"0.47854212\",\n \"0.47852835\",\n \"0.47824392\",\n \"0.4771443\",\n \"0.47692466\",\n \"0.47670302\",\n \"0.4766052\",\n \"0.47602218\",\n \"0.47582284\",\n \"0.4753903\",\n \"0.47468328\",\n \"0.4743466\",\n \"0.47398207\",\n \"0.47326306\",\n \"0.4732318\",\n \"0.4732318\",\n \"0.4732318\",\n \"0.4732318\",\n \"0.4732318\",\n \"0.4732318\",\n \"0.4732318\",\n \"0.47314617\",\n \"0.47298452\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5101221"},"document_rank":{"kind":"string","value":"23"}}},{"rowIdx":89291,"cells":{"query":{"kind":"string","value":"Clean up test data in working directory."},"document":{"kind":"string","value":"def teardown_module(module):\n for datafile in datafiles:\n os.remove(datafile)"},"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 tearDown(self):\n shutil.rmtree(self._data_dir_path)","def tearDown(cls):\n\n # cls.test_mmp_series_object.clean_out_data_seriesobj()\n # reusable data struct\n cls.test_mmp_series_object.clean_out_data_seriesobj()\n cls.test_dataset_testresults.clear()\n # reusable results file\n # os.remove(cls.temp_file_output_series.name)","def vtest_ut_cleanup(self):\n shutil.rmtree(self.get_test_file_path(), ignore_errors=True)","def tearDown(self):\n rmtree(getcwd(), ignore_errors=True)","def tearDown(self):\n rmtree(getcwd(), ignore_errors=True)","def cleanup_test(self):\n test_variables = _get_test_variables()\n test_status = test_variables['${TEST STATUS}']\n if test_status == 'FAIL':\n # Test failed: keep directory for later inspection\n return\n\n # Test passed: remove the execution directory but preserve all\n # important log files, if any (valgrind, gcov, ...)\n\n if len(self._preserve_files) == 0:\n shutil.rmtree(self._execdir, True)\n return\n\n # Move all the files to preserve to a temporary directory\n\n backup_dir = self._execdir + '.preserve'\n os.makedirs(backup_dir)\n for file in self._preserve_files:\n shutil.move(file, backup_dir)\n\n # Delete the execution directory and rename the temporary directory\n\n shutil.rmtree(self._execdir, True)\n os.rename(backup_dir, self._execdir)","def tearDown(self):\n testing_dir = os.path.split(os.path.realpath(__file__))[0]\n for f in glob.glob(os.path.join(testing_dir, \"*\")):\n if f.split(\".\")[-1] in [\"o\", \"out\", \"pyc\", \"log\"]:\n subprocess.call(['rm', f])","def teardown(self):\n os.chdir(self.cwd)\n try:\n shutil.rmtree(self.t)\n except (OSError, IOError):\n pass","def tearDown(self):\r\n shutil.rmtree(self.working_directory)","def teardown(cls):\n os.chdir(cls.cwd)\n try:\n shutil.rmtree(cls.t)\n except (OSError, IOError):\n pass","def tearDown(self):\n shutil.rmtree(self.working_directory)","def tearDown(self):\n self.remove_test_files()","def tearDown(self):\n super().tearDown()\n shutil.rmtree(DATA_DIR, ignore_errors=True)","def tearDown(self):\n print(\n \"\\nDeleting temporary files...\\n\")\n try:\n shutil.rmtree(TEST_DIR)\n except OSError:\n pass","def tearDown(self) -> None:\n self.directory.cleanup()","def tearDown(self):\n for root, dirs, files in os.walk(TEMPDIR, topdown=False):\n for name in files:\n os.remove(os.path.join(root, name))\n for name in dirs:\n os.rmdir(os.path.join(root, name))\n os.rmdir(root)","def tearDown(self):\n if self.workdir and os.path.exists(self.workdir):\n shutil.rmtree(self.workdir)","def tearDown(self):\n\n rmtree(self.test_output_dir)\n\n return","def cleanup(self):\n if self.cleanup_allowed:\n shutil.rmtree(self.out_dir)\n self.train_df, self.valid_df, self.test_df = None, None, None","def tearDown(self) -> None:\n filtered = [f for f in glob.glob('steps/tests/test_output/*') if not re.match(r'\\.keep', f)]\n for file in filtered:\n try:\n if Path(file).is_dir():\n shutil.rmtree(file)\n else:\n os.remove(file)\n except PermissionError as pe:\n # We don't necessarily care that much\n continue","def tearDown(self):\n utils.rm_rf(TMP_DIR_PATH)","def tearDown(self):\r\n remove_files(self.files_to_remove, False)\r\n if self.tmpdir:\r\n rmtree(self.tmpdir)\r\n\r\n # clean up the file from init_flowgram_file\r\n if (hasattr(self, \"tmp_filename\") and exists(self.tmp_filename)):\r\n remove(self.tmp_filename)","def clean_tests(_context):\n _delete_file(COVERAGE_FILE)\n shutil.rmtree(COVERAGE_DIR, ignore_errors=True)","def tearDown(self):\r\n shutil.rmtree(self.temp_dir_path)","def tearDown(self):\n self.tmp.cleanup()","def clear_data():\n for i in range(_MAX_NUM_TESTS):\n rand, ref = filename(i)\n if os.path.exists(rand):\n os.remove(rand)\n if os.path.exists(ref):\n os.remove(ref)","def clean_cwd():\n\n # Generator of the files generated for each runs\n del_files = (file for file in os.listdir() if file.endswith('.vtk')\n or file.endswith('.dat')\n or file.startswith('eeldata')\n or file.endswith('.log'))\n\n for file in del_files:\n try:\n os.remove(file)\n print(\"\\rRemoved {:s} succesfully!\".format(file), end=' '*15)\n except:\n print(\"\\rFailed to remove {:s}\".format(file))\n raise\n\n print('')","def test_z_cleanup(self):\n\n TestController.tearDown(\n self,\n clear_all_tables=True,\n del_global_app_set=True,\n dirs_to_destroy=['user', 'morphology', 'corpus', 'morphological_parser'])","def teardown(self):\n super(TestCisPandasInput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def tearDown():\n for output_file_path in Path(output_dir).glob(\"test_voting_learner_cross_validate*\"):\n output_file_path.unlink()\n\n for output_file_path in Path(\".\").glob(\"test_voting_learner_cross_validate*\"):\n output_file_path.unlink()\n\n config_file_path = Path(config_dir) / \"test_voting_learner_cross_validate.cfg\"\n config_file_path.unlink()\n\n remove_jsonlines_feature_files(train_dir)","def tearDown(self) -> None:\n if os.path.isdir(self.options.results_dir):\n rmtree(self.options.results_dir)","def tearDown(self):\n if os.path.exists(self.temp):\n shutil.rmtree(self.temp)","def tearDown(self):\n for d in os.listdir(tmp_dir_path):\n d_path = os.path.join(tmp_dir_path,d)\n try:\n os.remove(d_path)\n except:\n for f in os.listdir(d_path):\n f_path = os.path.join(d_path,f)\n os.remove(f_path)\n os.rmdir(d_path)\n assert os.listdir(tmp_dir_path) == []","def _clean_up_temporary_files(dataset_dir):\n return","def tearDown(self):\n self.tempdir.cleanup()","def clean_up(self):\n directory = os.path.join(os.getcwd(), self.TMP_FOLDER)\n if os.path.exists(directory) and os.path.isdir(directory):\n shutil.rmtree(directory)","def tearDown(self):\n rmtree(self.output_path)\n rmtree(self.content_path)","def tearDown(self):\n rmtree(self.out_dir, ignore_errors=True)","def teardown_class(self):\n\n # TODO: If environment variable is set keep the workspace\n # and print out the path.\n global TEST_WORKSPACE\n\n check_env = env.import_test_cfg(TEST_WORKSPACE)[\n 'codechecker_cfg']['check_env']\n codechecker.remove_test_package_product(TEST_WORKSPACE, check_env)\n\n print(\"Removing: \" + TEST_WORKSPACE)\n shutil.rmtree(TEST_WORKSPACE, ignore_errors=True)","def teardown_class(self):\n\n # TODO: If environment variable is set keep the workspace\n # and print out the path.\n global TEST_WORKSPACE\n\n check_env = env.import_test_cfg(TEST_WORKSPACE)[\n 'codechecker_cfg']['check_env']\n codechecker.remove_test_package_product(TEST_WORKSPACE, check_env)\n\n print(\"Removing: \" + TEST_WORKSPACE)\n shutil.rmtree(TEST_WORKSPACE, ignore_errors=True)","def tearDown(self):\r\n # Change back to the start dir.\r\n chdir(self.start_dir)\r\n remove_files(self.files_to_remove)\r\n\r\n # Remove directories last, so we don't get errors trying to remove\r\n # files which may be in the directories.\r\n for d in self.dirs_to_remove:\r\n if exists(d):\r\n rmtree(d)","def tearDown(self):\n # Fail if any WARNING/ERROR/CRITICAL logs were generated\n self.logging_handler.assertNoLogsOver(logging.INFO)\n\n logging.getLogger('COT').removeHandler(self.logging_handler)\n\n self.validate_with_ovftool(self.temp_file)\n\n # Delete the temporary directory\n if os.path.exists(self.temp_dir):\n logger.debug(\"Deleting temp dir %s\", self.temp_dir)\n shutil.rmtree(self.temp_dir)\n self.temp_dir = None\n self.temp_file = None\n\n tmps2 = set(glob.glob(os.path.join(tempfile.gettempdir(), 'cot*')))\n delta = tmps2 - self.tmps\n if delta:\n self.fail(\"Temp directory(s) {0} left over after test!\"\n .format(delta))\n\n # Clear output caches for helper commands:\n for helper in helpers.values():\n helper.cached_output.clear()\n\n # Let's try to keep things lean...\n delta_t = time.time() - self.start_time\n if delta_t > 5.0:\n print(\"\\nWARNING: Test {0} took {1:.3f} seconds to execute. \"\n \"Consider refactoring it to be more efficient.\"\n .format(self.id(), delta_t))","def tearDown(self):\n if not self.test_manager.leave_output:\n shutil.rmtree(self.directory)","def tearDown(self):\n \tshutil.rmtree(self.tempdir)","def tearDown(self):\n for base_path in self.feature_paths:\n # Remove the feature files\n for feature in self.features:\n feature = os.path.join(base_path, feature)\n os.remove(feature)\n\n # Attempt to remove all the directories we created\n os.removedirs(os.path.join(base_path, 'subdir'))","def teardown(self):\n super(TestCisPandasOutput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def tearDownClass(cls):\n for testfile in [cls.testfile, cls.testyfile, cls.testbrfile, cls.testlog]:\n if os.path.exists(testfile):\n os.remove(testfile)\n\n for e in cls.origEnv:\n if cls.origEnv[e] is None:\n del os.environ[e]\n else:\n os.environ[e] = cls.origEnv[e]\n\n if os.path.exists(cls.testDir):\n rmtree(cls.testDir)","def tearDown(self):\n for f in os.listdir('/tmp'):\n if not f.startswith(self.FILE_PREFIX):\n continue\n\n os.remove(os.path.join('/tmp', f))","def teardown_test_env():\n if not keep_tmp_dirs:\n print('\\nCleaning up temporary directories...')\n shutil.rmtree(tmp_elm_dpath, ignore_errors=True)\n shutil.rmtree(tmp_elm_examples_dpath, ignore_errors=True)\n\n print('Removing conda environment used for testing...')\n sp.call('conda env remove -y -q -n {}'.format(test_env_name), shell=True, executable='/bin/bash', stdout=sp.DEVNULL)","def tearDownClass(cls):\n\n to_delete = [\"test.mtx.zip\", \"test.csv.zip\", \"test.loom\",\n \"test.mtx\", \"test.csv\", \".loom_parts\"]\n\n for path in to_delete:\n if os.path.isdir(path):\n shutil.rmtree(path)\n elif os.path.isfile(path):\n os.remove(path)","def tearDownClass(self):\n os.chdir(self.working_dir)\n shutil.rmtree(self.test_dir)","def clean_up_temp_files():\n global __tmp_model_dir\n\n if __tmp_model_dir is not None:\n FileUtils.deleteDirectory(__tmp_model_dir)\n __tmp_model_dir = None","def tearDown(self):\r\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n os.remove(self.testfilename)","def tearDown(self) -> None:\n\n self.temp_env_file.close()\n os.remove(self.temp_env_file.name)\n\n del self.temp_env_file\n del self.test_name\n del self.helper","def clean(session):\n clean_dirs = (\n get_path(\".cache\"),\n get_path(\".coverage\"),\n get_path(\".pytest_cache\"),\n get_path(\"__pycache__\"),\n get_path(\"build\"),\n get_path(\"dist\"),\n get_path(\"docs\", \"__pycache__\"),\n get_path(\"docs\", \"build\"),\n get_path(\"scripts\", \"macos\", \"__pycache__\"),\n get_path(\"src\", \"python\", \"bezier.egg-info\"),\n get_path(\"src\", \"python\", \"bezier\", \"__pycache__\"),\n get_path(\"tests\", \"__pycache__\"),\n get_path(\"tests\", \"functional\", \"__pycache__\"),\n get_path(\"tests\", \"unit\", \"__pycache__\"),\n get_path(\"tests\", \"unit\", \"hazmat\", \"__pycache__\"),\n get_path(\"wheelhouse\"),\n )\n clean_globs = (\n get_path(\".coverage\"),\n get_path(\"*.mod\"),\n get_path(\"*.pyc\"),\n get_path(\"docs\", \"abi\", \"example\"),\n get_path(\"src\", \"python\", \"bezier\", \"*.pyc\"),\n get_path(\"src\", \"python\", \"bezier\", \"*.pyd\"),\n get_path(\"src\", \"python\", \"bezier\", \"*.so\"),\n get_path(\"src\", \"fortran\", \"*.o\"),\n get_path(\"tests\", \"*.pyc\"),\n get_path(\"tests\", \"functional\", \"*.pyc\"),\n get_path(\"tests\", \"unit\", \"*.pyc\"),\n )\n for dir_path in clean_dirs:\n session.run(shutil.rmtree, dir_path, ignore_errors=True)\n for glob_path in clean_globs:\n for filename in glob.glob(glob_path):\n session.run(os.remove, filename)","def cleanup(self):\n\n print \"Cleaning up...\",\n sys.stdout.flush()\n\n builddir = os.path.join(self.build)\n\n comm = 'rm -rf '+builddir\n #+' '+libdir+' '+logdir\n (output, error, retz) = runShellCommand(comm)\n\n print \"done.\"","def cleanup(self):\n\n # uninstall sourcedata\n if self.conversion.install_dataset_path.exists():\n # without the ChangeWorkingDir the command does not operate inside\n # of dataset_path\n with utils.ChangeWorkingDir(self.dataset_path):\n datalad.uninstall(\n path=self.conversion.install_dataset_name,\n dataset=self.dataset_path,\n recursive=True\n )\n\n # remove bids conversion\n bids_dir = self._get_bids_dir()\n if bids_dir.exists():\n self.log.info(\"Remove %s\", bids_dir)\n shutil.rmtree(bids_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\n shutil.rmtree(self.workspace_dir)","def tearDown(self):\r\n remove_files(self.files_to_remove)\r\n\r\n # Remove directories last, so we don't get errors trying to remove\r\n # files which may be in the directories.\r\n for d in self.dirs_to_remove:\r\n if exists(d):\r\n rmtree(d)","def tearDown(self):\n try:\n os.remove(self.fixtureFile)\n except OSError:\n pass","def tearDown(self):\n if 'turbinia-test-tmp' in self.tmp_dir:\n shutil.rmtree(self.tmp_dir)","def tearDown(self):\n if os.path.isdir('/tmp/remote_pacha'):\n shutil.rmtree('/tmp/remote_pacha')\n if os.path.isdir('/tmp/localhost'):\n shutil.rmtree('/tmp/localhost')\n if os.path.isdir('/tmp/test_pacha'):\n shutil.rmtree('/tmp/test_pacha')\n if os.path.isdir('/tmp/pacha_test'):\n shutil.rmtree('/tmp/pacha_test')\n if os.path.isdir('/tmp/pacha_bucket'):\n shutil.rmtree('/tmp/pacha_bucket')\n\n try:\n shutil.rmtree('/tmp/test_pacha')\n shutil.rmtree('/tmp/remote_pacha')\n shutil.rmtree('/tmp/localhost')\n shutil.rmtree('/tmp/single_dir')\n except OSError:\n pass # nevermind if you could not delete this guy","def tearDown(self):\n if self.workspace is not None:\n rmtree(self.workspace.workspace, ignore_errors=True)\n Path.cwd().joinpath(\"workspace.tar.gz\").unlink(missing_ok=True)\n for item in self.items:\n if item.is_dir():\n rmtree(item)\n elif item.is_file():\n item.unlink()\n self.workspace = None","def module_cleanup():\n\n os.chdir(os.path.dirname(__file__))\n shutil.rmtree(TestModule.full_path())","def clean_up(self):\n self.fname = None\n self.failed_files = []\n self.custom_failed = []\n self.results = None","def clean(self):\n os.remove(\"temp.py\") # Delete the file \"temp.py\", to free up disk space","def clean():\n clean_files()","def tearDown(self):\n\n # turn off the alarm\n signal.alarm(0)\n\n if hasattr(self, \"result_dir\") and self.result_dir:\n try:\n rmtree(self.result_dir)\n except OSError:\n #directory probably not empty (e.g. stale nfs files)\n pass\n try:\n rmdir(self.test_dir)\n except OSError:\n #directory probably not empty, better not remove\n pass","def teardown(self):\n del self.testInst, self.dname, self.test_vals, self.test_fracs\n\n return","def tearDown(self):\n try:\n os.remove(self.fixture_file)\n except OSError:\n pass","def tearDown(self):\n shutil.rmtree(self.workspace_dir, ignore_errors=True)","def clean(ctx):\n ctx.run(\n \"find . -type f -name '*.pyc' -delete && \"\n \"find . -type f -name '*.pyo' -delete && \"\n \"rm -rf .pytest_cache && \"\n \"rm -rf .mypy_cache\"\n )","def clean_directory():\n if os.path.exists('data'):\n shutil.rmtree('data')\n os.makedirs('data')\n\n if os.path.exists('returns'):\n shutil.rmtree('returns')\n os.makedirs('returns')","def tearDown(self):\n tests.utils.cleanup_environment()","def tearDown(self):\n tests.utils.cleanup_environment()","def tearDown(self):\n os.rmdir(self.cur_source)\n super().tearDown()","def tearDownClass(cls):\n os.removedirs(cls.test_dir)\n del cls.checkpoint\n del cls.dataset\n del cls.experiment\n del cls.test_dir\n del cls.tokenizer_parameters\n gc.collect()","def cleanup(self):\r\n if self.tempDirectory != None:\r\n shutil.rmtree(self.tempDirectory, True)\r\n self.tempDirectory = None","def delete_all():\n if os.path.exists(DATA_DIR):\n shutil.rmtree(DATA_DIR)","def _clean(base_dir):\n # remove the snakemake cache\n shutil.rmtree(os.path.join(base_dir, \".snakemake\"), ignore_errors=True)\n\n # remove seq2science caches\n shutil.rmtree(os.path.expanduser(os.path.join(xdg.XDG_CACHE_HOME, \"seq2science\")), ignore_errors=True)\n\n # remove historic seq2science cache location\n shutil.rmtree(os.path.expanduser(f\"~/.config/seq2science/\"), ignore_errors=True)\n\n print(\"All cleaned up!\")","def teardown(self):\n\n del self.testInst, self.test_bins, self.test_label, self.test_data\n del self.out_keys, self.out_data\n\n return","def tearDownClass(cls):\n\n os.remove(cls.temp_file_input_csv.name)\n os.remove(cls.temp_file_input_csv_larger.name)\n os.remove(cls.temp_file_input_csv_confusion.name)\n os.remove(cls.temp_file_output_series.name)\n os.remove(cls.temp_file_output_seriessuggest.name)\n os.remove(cls.temp_file_output_seriessuggest2.name)\n os.remove(cls.temp_file_output_autosearch.name)","def tearDown(self):\n with contextlib.suppress(FileNotFoundError):\n Path(\"test.xlsx\").absolute().unlink()","def _clean_workdir(self):\n\t\ttoremove = [self._get_config_filepath(), self._get_params_filepath(), self._get_conv_filepath(), self._get_psf_filepath()]\n\t\tfor filepath in toremove:\n\t\t\tif os.path.exists(filepath):\t\n\t\t\t\tlogger.debug(\"Removing existing file %s...\" % (filepath))\n\t\t\t\tos.remove(filepath)","def tearDown(self):\n # unittest.TestCase.tearDown(self)\n\n root = os.path.join(\".\", \"files\")\n endingList = os.listdir(root)\n rmList = [fn for fn in endingList if fn not in self.startingList]\n\n if self.oldRoot == root:\n for fn in rmList:\n fnFullPath = os.path.join(root, fn)\n if os.path.isdir(fnFullPath):\n os.rmdir(fnFullPath)\n else:\n os.remove(fnFullPath)\n\n os.chdir(self.oldRoot)","def teardown(self):\n\n del self.testC, self.insts, self.testInst, self.dname, self.test_vals\n\n return","def tearDown(self):\r\n\r\n # turn off the alarm\r\n signal.alarm(0)\r\n\r\n if hasattr(self, \"result_dir\") and self.result_dir:\r\n try:\r\n rmtree(self.result_dir)\r\n except OSError:\r\n # directory probably not empty (e.g. stale nfs files)\r\n pass\r\n try:\r\n rmdir(self.test_dir)\r\n except OSError:\r\n # directory probably not empty, better not remove\r\n pass","def tearDown(self):\n logging.info(\"Delete %s\", self._temp_dir)\n shutil.rmtree(self._temp_dir)","def tearDown(self):\n self.clearTempDir()","def teardown_class(self):\n\n file_list = \\\n glob.glob(os.path.join(self.datadir,\n '*monol_test*') + HEN_FILE_EXTENSION) + \\\n glob.glob(os.path.join(self.datadir,\n '*lcurve*') + HEN_FILE_EXTENSION) + \\\n glob.glob(os.path.join(self.datadir,\n '*lcurve*.txt')) + \\\n glob.glob(os.path.join(self.datadir,\n '*.log')) + \\\n glob.glob(os.path.join(self.datadir,\n '*monol_test*.dat')) + \\\n glob.glob(os.path.join(self.datadir,\n '*monol_test*.png')) + \\\n glob.glob(os.path.join(self.datadir,\n '*monol_test*.txt')) + \\\n glob.glob(os.path.join(self.datadir,\n 'monol_test_fake*.evt')) + \\\n glob.glob(os.path.join(self.datadir,\n 'bubu*'))\n for f in file_list:\n print(\"Removing \" + f)\n os.remove(f)","def teardown(self):\n del self.testInst, self.dname, self.test_val_length\n\n return","def teardown(self):\n del self.testInst, self.dname, self.test_val_length\n\n return","def cleanDataDir(self):\n for strFile in os.listdir(self.strDataDir):\n os.remove(os.path.join(self.strDataDir, strFile))"],"string":"[\n \"def tearDown(self):\\n shutil.rmtree(self._data_dir_path)\",\n \"def tearDown(cls):\\n\\n # cls.test_mmp_series_object.clean_out_data_seriesobj()\\n # reusable data struct\\n cls.test_mmp_series_object.clean_out_data_seriesobj()\\n cls.test_dataset_testresults.clear()\\n # reusable results file\\n # os.remove(cls.temp_file_output_series.name)\",\n \"def vtest_ut_cleanup(self):\\n shutil.rmtree(self.get_test_file_path(), ignore_errors=True)\",\n \"def tearDown(self):\\n rmtree(getcwd(), ignore_errors=True)\",\n \"def tearDown(self):\\n rmtree(getcwd(), ignore_errors=True)\",\n \"def cleanup_test(self):\\n test_variables = _get_test_variables()\\n test_status = test_variables['${TEST STATUS}']\\n if test_status == 'FAIL':\\n # Test failed: keep directory for later inspection\\n return\\n\\n # Test passed: remove the execution directory but preserve all\\n # important log files, if any (valgrind, gcov, ...)\\n\\n if len(self._preserve_files) == 0:\\n shutil.rmtree(self._execdir, True)\\n return\\n\\n # Move all the files to preserve to a temporary directory\\n\\n backup_dir = self._execdir + '.preserve'\\n os.makedirs(backup_dir)\\n for file in self._preserve_files:\\n shutil.move(file, backup_dir)\\n\\n # Delete the execution directory and rename the temporary directory\\n\\n shutil.rmtree(self._execdir, True)\\n os.rename(backup_dir, self._execdir)\",\n \"def tearDown(self):\\n testing_dir = os.path.split(os.path.realpath(__file__))[0]\\n for f in glob.glob(os.path.join(testing_dir, \\\"*\\\")):\\n if f.split(\\\".\\\")[-1] in [\\\"o\\\", \\\"out\\\", \\\"pyc\\\", \\\"log\\\"]:\\n subprocess.call(['rm', f])\",\n \"def teardown(self):\\n os.chdir(self.cwd)\\n try:\\n shutil.rmtree(self.t)\\n except (OSError, IOError):\\n pass\",\n \"def tearDown(self):\\r\\n shutil.rmtree(self.working_directory)\",\n \"def teardown(cls):\\n os.chdir(cls.cwd)\\n try:\\n shutil.rmtree(cls.t)\\n except (OSError, IOError):\\n pass\",\n \"def tearDown(self):\\n shutil.rmtree(self.working_directory)\",\n \"def tearDown(self):\\n self.remove_test_files()\",\n \"def tearDown(self):\\n super().tearDown()\\n shutil.rmtree(DATA_DIR, ignore_errors=True)\",\n \"def tearDown(self):\\n print(\\n \\\"\\\\nDeleting temporary files...\\\\n\\\")\\n try:\\n shutil.rmtree(TEST_DIR)\\n except OSError:\\n pass\",\n \"def tearDown(self) -> None:\\n self.directory.cleanup()\",\n \"def tearDown(self):\\n for root, dirs, files in os.walk(TEMPDIR, topdown=False):\\n for name in files:\\n os.remove(os.path.join(root, name))\\n for name in dirs:\\n os.rmdir(os.path.join(root, name))\\n os.rmdir(root)\",\n \"def tearDown(self):\\n if self.workdir and os.path.exists(self.workdir):\\n shutil.rmtree(self.workdir)\",\n \"def tearDown(self):\\n\\n rmtree(self.test_output_dir)\\n\\n return\",\n \"def cleanup(self):\\n if self.cleanup_allowed:\\n shutil.rmtree(self.out_dir)\\n self.train_df, self.valid_df, self.test_df = None, None, None\",\n \"def tearDown(self) -> None:\\n filtered = [f for f in glob.glob('steps/tests/test_output/*') if not re.match(r'\\\\.keep', f)]\\n for file in filtered:\\n try:\\n if Path(file).is_dir():\\n shutil.rmtree(file)\\n else:\\n os.remove(file)\\n except PermissionError as pe:\\n # We don't necessarily care that much\\n continue\",\n \"def tearDown(self):\\n utils.rm_rf(TMP_DIR_PATH)\",\n \"def tearDown(self):\\r\\n remove_files(self.files_to_remove, False)\\r\\n if self.tmpdir:\\r\\n rmtree(self.tmpdir)\\r\\n\\r\\n # clean up the file from init_flowgram_file\\r\\n if (hasattr(self, \\\"tmp_filename\\\") and exists(self.tmp_filename)):\\r\\n remove(self.tmp_filename)\",\n \"def clean_tests(_context):\\n _delete_file(COVERAGE_FILE)\\n shutil.rmtree(COVERAGE_DIR, ignore_errors=True)\",\n \"def tearDown(self):\\r\\n shutil.rmtree(self.temp_dir_path)\",\n \"def tearDown(self):\\n self.tmp.cleanup()\",\n \"def clear_data():\\n for i in range(_MAX_NUM_TESTS):\\n rand, ref = filename(i)\\n if os.path.exists(rand):\\n os.remove(rand)\\n if os.path.exists(ref):\\n os.remove(ref)\",\n \"def clean_cwd():\\n\\n # Generator of the files generated for each runs\\n del_files = (file for file in os.listdir() if file.endswith('.vtk')\\n or file.endswith('.dat')\\n or file.startswith('eeldata')\\n or file.endswith('.log'))\\n\\n for file in del_files:\\n try:\\n os.remove(file)\\n print(\\\"\\\\rRemoved {:s} succesfully!\\\".format(file), end=' '*15)\\n except:\\n print(\\\"\\\\rFailed to remove {:s}\\\".format(file))\\n raise\\n\\n print('')\",\n \"def test_z_cleanup(self):\\n\\n TestController.tearDown(\\n self,\\n clear_all_tables=True,\\n del_global_app_set=True,\\n dirs_to_destroy=['user', 'morphology', 'corpus', 'morphological_parser'])\",\n \"def teardown(self):\\n super(TestCisPandasInput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def tearDown():\\n for output_file_path in Path(output_dir).glob(\\\"test_voting_learner_cross_validate*\\\"):\\n output_file_path.unlink()\\n\\n for output_file_path in Path(\\\".\\\").glob(\\\"test_voting_learner_cross_validate*\\\"):\\n output_file_path.unlink()\\n\\n config_file_path = Path(config_dir) / \\\"test_voting_learner_cross_validate.cfg\\\"\\n config_file_path.unlink()\\n\\n remove_jsonlines_feature_files(train_dir)\",\n \"def tearDown(self) -> None:\\n if os.path.isdir(self.options.results_dir):\\n rmtree(self.options.results_dir)\",\n \"def tearDown(self):\\n if os.path.exists(self.temp):\\n shutil.rmtree(self.temp)\",\n \"def tearDown(self):\\n for d in os.listdir(tmp_dir_path):\\n d_path = os.path.join(tmp_dir_path,d)\\n try:\\n os.remove(d_path)\\n except:\\n for f in os.listdir(d_path):\\n f_path = os.path.join(d_path,f)\\n os.remove(f_path)\\n os.rmdir(d_path)\\n assert os.listdir(tmp_dir_path) == []\",\n \"def _clean_up_temporary_files(dataset_dir):\\n return\",\n \"def tearDown(self):\\n self.tempdir.cleanup()\",\n \"def clean_up(self):\\n directory = os.path.join(os.getcwd(), self.TMP_FOLDER)\\n if os.path.exists(directory) and os.path.isdir(directory):\\n shutil.rmtree(directory)\",\n \"def tearDown(self):\\n rmtree(self.output_path)\\n rmtree(self.content_path)\",\n \"def tearDown(self):\\n rmtree(self.out_dir, ignore_errors=True)\",\n \"def teardown_class(self):\\n\\n # TODO: If environment variable is set keep the workspace\\n # and print out the path.\\n global TEST_WORKSPACE\\n\\n check_env = env.import_test_cfg(TEST_WORKSPACE)[\\n 'codechecker_cfg']['check_env']\\n codechecker.remove_test_package_product(TEST_WORKSPACE, check_env)\\n\\n print(\\\"Removing: \\\" + TEST_WORKSPACE)\\n shutil.rmtree(TEST_WORKSPACE, ignore_errors=True)\",\n \"def teardown_class(self):\\n\\n # TODO: If environment variable is set keep the workspace\\n # and print out the path.\\n global TEST_WORKSPACE\\n\\n check_env = env.import_test_cfg(TEST_WORKSPACE)[\\n 'codechecker_cfg']['check_env']\\n codechecker.remove_test_package_product(TEST_WORKSPACE, check_env)\\n\\n print(\\\"Removing: \\\" + TEST_WORKSPACE)\\n shutil.rmtree(TEST_WORKSPACE, ignore_errors=True)\",\n \"def tearDown(self):\\r\\n # Change back to the start dir.\\r\\n chdir(self.start_dir)\\r\\n remove_files(self.files_to_remove)\\r\\n\\r\\n # Remove directories last, so we don't get errors trying to remove\\r\\n # files which may be in the directories.\\r\\n for d in self.dirs_to_remove:\\r\\n if exists(d):\\r\\n rmtree(d)\",\n \"def tearDown(self):\\n # Fail if any WARNING/ERROR/CRITICAL logs were generated\\n self.logging_handler.assertNoLogsOver(logging.INFO)\\n\\n logging.getLogger('COT').removeHandler(self.logging_handler)\\n\\n self.validate_with_ovftool(self.temp_file)\\n\\n # Delete the temporary directory\\n if os.path.exists(self.temp_dir):\\n logger.debug(\\\"Deleting temp dir %s\\\", self.temp_dir)\\n shutil.rmtree(self.temp_dir)\\n self.temp_dir = None\\n self.temp_file = None\\n\\n tmps2 = set(glob.glob(os.path.join(tempfile.gettempdir(), 'cot*')))\\n delta = tmps2 - self.tmps\\n if delta:\\n self.fail(\\\"Temp directory(s) {0} left over after test!\\\"\\n .format(delta))\\n\\n # Clear output caches for helper commands:\\n for helper in helpers.values():\\n helper.cached_output.clear()\\n\\n # Let's try to keep things lean...\\n delta_t = time.time() - self.start_time\\n if delta_t > 5.0:\\n print(\\\"\\\\nWARNING: Test {0} took {1:.3f} seconds to execute. \\\"\\n \\\"Consider refactoring it to be more efficient.\\\"\\n .format(self.id(), delta_t))\",\n \"def tearDown(self):\\n if not self.test_manager.leave_output:\\n shutil.rmtree(self.directory)\",\n \"def tearDown(self):\\n \\tshutil.rmtree(self.tempdir)\",\n \"def tearDown(self):\\n for base_path in self.feature_paths:\\n # Remove the feature files\\n for feature in self.features:\\n feature = os.path.join(base_path, feature)\\n os.remove(feature)\\n\\n # Attempt to remove all the directories we created\\n os.removedirs(os.path.join(base_path, 'subdir'))\",\n \"def teardown(self):\\n super(TestCisPandasOutput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def tearDownClass(cls):\\n for testfile in [cls.testfile, cls.testyfile, cls.testbrfile, cls.testlog]:\\n if os.path.exists(testfile):\\n os.remove(testfile)\\n\\n for e in cls.origEnv:\\n if cls.origEnv[e] is None:\\n del os.environ[e]\\n else:\\n os.environ[e] = cls.origEnv[e]\\n\\n if os.path.exists(cls.testDir):\\n rmtree(cls.testDir)\",\n \"def tearDown(self):\\n for f in os.listdir('/tmp'):\\n if not f.startswith(self.FILE_PREFIX):\\n continue\\n\\n os.remove(os.path.join('/tmp', f))\",\n \"def teardown_test_env():\\n if not keep_tmp_dirs:\\n print('\\\\nCleaning up temporary directories...')\\n shutil.rmtree(tmp_elm_dpath, ignore_errors=True)\\n shutil.rmtree(tmp_elm_examples_dpath, ignore_errors=True)\\n\\n print('Removing conda environment used for testing...')\\n sp.call('conda env remove -y -q -n {}'.format(test_env_name), shell=True, executable='/bin/bash', stdout=sp.DEVNULL)\",\n \"def tearDownClass(cls):\\n\\n to_delete = [\\\"test.mtx.zip\\\", \\\"test.csv.zip\\\", \\\"test.loom\\\",\\n \\\"test.mtx\\\", \\\"test.csv\\\", \\\".loom_parts\\\"]\\n\\n for path in to_delete:\\n if os.path.isdir(path):\\n shutil.rmtree(path)\\n elif os.path.isfile(path):\\n os.remove(path)\",\n \"def tearDownClass(self):\\n os.chdir(self.working_dir)\\n shutil.rmtree(self.test_dir)\",\n \"def clean_up_temp_files():\\n global __tmp_model_dir\\n\\n if __tmp_model_dir is not None:\\n FileUtils.deleteDirectory(__tmp_model_dir)\\n __tmp_model_dir = None\",\n \"def tearDown(self):\\r\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n os.remove(self.testfilename)\",\n \"def tearDown(self) -> None:\\n\\n self.temp_env_file.close()\\n os.remove(self.temp_env_file.name)\\n\\n del self.temp_env_file\\n del self.test_name\\n del self.helper\",\n \"def clean(session):\\n clean_dirs = (\\n get_path(\\\".cache\\\"),\\n get_path(\\\".coverage\\\"),\\n get_path(\\\".pytest_cache\\\"),\\n get_path(\\\"__pycache__\\\"),\\n get_path(\\\"build\\\"),\\n get_path(\\\"dist\\\"),\\n get_path(\\\"docs\\\", \\\"__pycache__\\\"),\\n get_path(\\\"docs\\\", \\\"build\\\"),\\n get_path(\\\"scripts\\\", \\\"macos\\\", \\\"__pycache__\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier.egg-info\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"functional\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"unit\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"unit\\\", \\\"hazmat\\\", \\\"__pycache__\\\"),\\n get_path(\\\"wheelhouse\\\"),\\n )\\n clean_globs = (\\n get_path(\\\".coverage\\\"),\\n get_path(\\\"*.mod\\\"),\\n get_path(\\\"*.pyc\\\"),\\n get_path(\\\"docs\\\", \\\"abi\\\", \\\"example\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"*.pyc\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"*.pyd\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"*.so\\\"),\\n get_path(\\\"src\\\", \\\"fortran\\\", \\\"*.o\\\"),\\n get_path(\\\"tests\\\", \\\"*.pyc\\\"),\\n get_path(\\\"tests\\\", \\\"functional\\\", \\\"*.pyc\\\"),\\n get_path(\\\"tests\\\", \\\"unit\\\", \\\"*.pyc\\\"),\\n )\\n for dir_path in clean_dirs:\\n session.run(shutil.rmtree, dir_path, ignore_errors=True)\\n for glob_path in clean_globs:\\n for filename in glob.glob(glob_path):\\n session.run(os.remove, filename)\",\n \"def cleanup(self):\\n\\n print \\\"Cleaning up...\\\",\\n sys.stdout.flush()\\n\\n builddir = os.path.join(self.build)\\n\\n comm = 'rm -rf '+builddir\\n #+' '+libdir+' '+logdir\\n (output, error, retz) = runShellCommand(comm)\\n\\n print \\\"done.\\\"\",\n \"def cleanup(self):\\n\\n # uninstall sourcedata\\n if self.conversion.install_dataset_path.exists():\\n # without the ChangeWorkingDir the command does not operate inside\\n # of dataset_path\\n with utils.ChangeWorkingDir(self.dataset_path):\\n datalad.uninstall(\\n path=self.conversion.install_dataset_name,\\n dataset=self.dataset_path,\\n recursive=True\\n )\\n\\n # remove bids conversion\\n bids_dir = self._get_bids_dir()\\n if bids_dir.exists():\\n self.log.info(\\\"Remove %s\\\", bids_dir)\\n shutil.rmtree(bids_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir)\",\n \"def tearDown(self):\\r\\n remove_files(self.files_to_remove)\\r\\n\\r\\n # Remove directories last, so we don't get errors trying to remove\\r\\n # files which may be in the directories.\\r\\n for d in self.dirs_to_remove:\\r\\n if exists(d):\\r\\n rmtree(d)\",\n \"def tearDown(self):\\n try:\\n os.remove(self.fixtureFile)\\n except OSError:\\n pass\",\n \"def tearDown(self):\\n if 'turbinia-test-tmp' in self.tmp_dir:\\n shutil.rmtree(self.tmp_dir)\",\n \"def tearDown(self):\\n if os.path.isdir('/tmp/remote_pacha'):\\n shutil.rmtree('/tmp/remote_pacha')\\n if os.path.isdir('/tmp/localhost'):\\n shutil.rmtree('/tmp/localhost')\\n if os.path.isdir('/tmp/test_pacha'):\\n shutil.rmtree('/tmp/test_pacha')\\n if os.path.isdir('/tmp/pacha_test'):\\n shutil.rmtree('/tmp/pacha_test')\\n if os.path.isdir('/tmp/pacha_bucket'):\\n shutil.rmtree('/tmp/pacha_bucket')\\n\\n try:\\n shutil.rmtree('/tmp/test_pacha')\\n shutil.rmtree('/tmp/remote_pacha')\\n shutil.rmtree('/tmp/localhost')\\n shutil.rmtree('/tmp/single_dir')\\n except OSError:\\n pass # nevermind if you could not delete this guy\",\n \"def tearDown(self):\\n if self.workspace is not None:\\n rmtree(self.workspace.workspace, ignore_errors=True)\\n Path.cwd().joinpath(\\\"workspace.tar.gz\\\").unlink(missing_ok=True)\\n for item in self.items:\\n if item.is_dir():\\n rmtree(item)\\n elif item.is_file():\\n item.unlink()\\n self.workspace = None\",\n \"def module_cleanup():\\n\\n os.chdir(os.path.dirname(__file__))\\n shutil.rmtree(TestModule.full_path())\",\n \"def clean_up(self):\\n self.fname = None\\n self.failed_files = []\\n self.custom_failed = []\\n self.results = None\",\n \"def clean(self):\\n os.remove(\\\"temp.py\\\") # Delete the file \\\"temp.py\\\", to free up disk space\",\n \"def clean():\\n clean_files()\",\n \"def tearDown(self):\\n\\n # turn off the alarm\\n signal.alarm(0)\\n\\n if hasattr(self, \\\"result_dir\\\") and self.result_dir:\\n try:\\n rmtree(self.result_dir)\\n except OSError:\\n #directory probably not empty (e.g. stale nfs files)\\n pass\\n try:\\n rmdir(self.test_dir)\\n except OSError:\\n #directory probably not empty, better not remove\\n pass\",\n \"def teardown(self):\\n del self.testInst, self.dname, self.test_vals, self.test_fracs\\n\\n return\",\n \"def tearDown(self):\\n try:\\n os.remove(self.fixture_file)\\n except OSError:\\n pass\",\n \"def tearDown(self):\\n shutil.rmtree(self.workspace_dir, ignore_errors=True)\",\n \"def clean(ctx):\\n ctx.run(\\n \\\"find . -type f -name '*.pyc' -delete && \\\"\\n \\\"find . -type f -name '*.pyo' -delete && \\\"\\n \\\"rm -rf .pytest_cache && \\\"\\n \\\"rm -rf .mypy_cache\\\"\\n )\",\n \"def clean_directory():\\n if os.path.exists('data'):\\n shutil.rmtree('data')\\n os.makedirs('data')\\n\\n if os.path.exists('returns'):\\n shutil.rmtree('returns')\\n os.makedirs('returns')\",\n \"def tearDown(self):\\n tests.utils.cleanup_environment()\",\n \"def tearDown(self):\\n tests.utils.cleanup_environment()\",\n \"def tearDown(self):\\n os.rmdir(self.cur_source)\\n super().tearDown()\",\n \"def tearDownClass(cls):\\n os.removedirs(cls.test_dir)\\n del cls.checkpoint\\n del cls.dataset\\n del cls.experiment\\n del cls.test_dir\\n del cls.tokenizer_parameters\\n gc.collect()\",\n \"def cleanup(self):\\r\\n if self.tempDirectory != None:\\r\\n shutil.rmtree(self.tempDirectory, True)\\r\\n self.tempDirectory = None\",\n \"def delete_all():\\n if os.path.exists(DATA_DIR):\\n shutil.rmtree(DATA_DIR)\",\n \"def _clean(base_dir):\\n # remove the snakemake cache\\n shutil.rmtree(os.path.join(base_dir, \\\".snakemake\\\"), ignore_errors=True)\\n\\n # remove seq2science caches\\n shutil.rmtree(os.path.expanduser(os.path.join(xdg.XDG_CACHE_HOME, \\\"seq2science\\\")), ignore_errors=True)\\n\\n # remove historic seq2science cache location\\n shutil.rmtree(os.path.expanduser(f\\\"~/.config/seq2science/\\\"), ignore_errors=True)\\n\\n print(\\\"All cleaned up!\\\")\",\n \"def teardown(self):\\n\\n del self.testInst, self.test_bins, self.test_label, self.test_data\\n del self.out_keys, self.out_data\\n\\n return\",\n \"def tearDownClass(cls):\\n\\n os.remove(cls.temp_file_input_csv.name)\\n os.remove(cls.temp_file_input_csv_larger.name)\\n os.remove(cls.temp_file_input_csv_confusion.name)\\n os.remove(cls.temp_file_output_series.name)\\n os.remove(cls.temp_file_output_seriessuggest.name)\\n os.remove(cls.temp_file_output_seriessuggest2.name)\\n os.remove(cls.temp_file_output_autosearch.name)\",\n \"def tearDown(self):\\n with contextlib.suppress(FileNotFoundError):\\n Path(\\\"test.xlsx\\\").absolute().unlink()\",\n \"def _clean_workdir(self):\\n\\t\\ttoremove = [self._get_config_filepath(), self._get_params_filepath(), self._get_conv_filepath(), self._get_psf_filepath()]\\n\\t\\tfor filepath in toremove:\\n\\t\\t\\tif os.path.exists(filepath):\\t\\n\\t\\t\\t\\tlogger.debug(\\\"Removing existing file %s...\\\" % (filepath))\\n\\t\\t\\t\\tos.remove(filepath)\",\n \"def tearDown(self):\\n # unittest.TestCase.tearDown(self)\\n\\n root = os.path.join(\\\".\\\", \\\"files\\\")\\n endingList = os.listdir(root)\\n rmList = [fn for fn in endingList if fn not in self.startingList]\\n\\n if self.oldRoot == root:\\n for fn in rmList:\\n fnFullPath = os.path.join(root, fn)\\n if os.path.isdir(fnFullPath):\\n os.rmdir(fnFullPath)\\n else:\\n os.remove(fnFullPath)\\n\\n os.chdir(self.oldRoot)\",\n \"def teardown(self):\\n\\n del self.testC, self.insts, self.testInst, self.dname, self.test_vals\\n\\n return\",\n \"def tearDown(self):\\r\\n\\r\\n # turn off the alarm\\r\\n signal.alarm(0)\\r\\n\\r\\n if hasattr(self, \\\"result_dir\\\") and self.result_dir:\\r\\n try:\\r\\n rmtree(self.result_dir)\\r\\n except OSError:\\r\\n # directory probably not empty (e.g. stale nfs files)\\r\\n pass\\r\\n try:\\r\\n rmdir(self.test_dir)\\r\\n except OSError:\\r\\n # directory probably not empty, better not remove\\r\\n pass\",\n \"def tearDown(self):\\n logging.info(\\\"Delete %s\\\", self._temp_dir)\\n shutil.rmtree(self._temp_dir)\",\n \"def tearDown(self):\\n self.clearTempDir()\",\n \"def teardown_class(self):\\n\\n file_list = \\\\\\n glob.glob(os.path.join(self.datadir,\\n '*monol_test*') + HEN_FILE_EXTENSION) + \\\\\\n glob.glob(os.path.join(self.datadir,\\n '*lcurve*') + HEN_FILE_EXTENSION) + \\\\\\n glob.glob(os.path.join(self.datadir,\\n '*lcurve*.txt')) + \\\\\\n glob.glob(os.path.join(self.datadir,\\n '*.log')) + \\\\\\n glob.glob(os.path.join(self.datadir,\\n '*monol_test*.dat')) + \\\\\\n glob.glob(os.path.join(self.datadir,\\n '*monol_test*.png')) + \\\\\\n glob.glob(os.path.join(self.datadir,\\n '*monol_test*.txt')) + \\\\\\n glob.glob(os.path.join(self.datadir,\\n 'monol_test_fake*.evt')) + \\\\\\n glob.glob(os.path.join(self.datadir,\\n 'bubu*'))\\n for f in file_list:\\n print(\\\"Removing \\\" + f)\\n os.remove(f)\",\n \"def teardown(self):\\n del self.testInst, self.dname, self.test_val_length\\n\\n return\",\n \"def teardown(self):\\n del self.testInst, self.dname, self.test_val_length\\n\\n return\",\n \"def cleanDataDir(self):\\n for strFile in os.listdir(self.strDataDir):\\n os.remove(os.path.join(self.strDataDir, strFile))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7803513","0.76463795","0.7637779","0.7631515","0.7631515","0.7625458","0.7618858","0.7587505","0.75759536","0.75549775","0.7548522","0.75451297","0.7540642","0.75375634","0.75086534","0.7506745","0.750453","0.7503099","0.74924654","0.74106455","0.73989403","0.73811275","0.7337632","0.7321802","0.7314435","0.7310526","0.73036176","0.72964334","0.72941524","0.7293239","0.7289033","0.728739","0.72728187","0.7271517","0.72627044","0.72620916","0.72615093","0.7255736","0.7246274","0.7246274","0.72452676","0.7219304","0.7217317","0.7205611","0.72016966","0.7176339","0.71741045","0.7173411","0.71663237","0.7165198","0.71387357","0.713086","0.7125033","0.71177405","0.71117175","0.7108121","0.70976686","0.70955956","0.7094644","0.7094644","0.7094644","0.7094644","0.7094644","0.7094644","0.7094644","0.70940083","0.7089481","0.7089075","0.7086907","0.7084849","0.70839506","0.7081231","0.70769423","0.70752907","0.70749086","0.70704114","0.7068607","0.7068588","0.7064387","0.70615333","0.7058176","0.7058176","0.7055714","0.7052421","0.70496863","0.7033901","0.7033249","0.70322055","0.7031052","0.70206386","0.70201874","0.7019175","0.70166004","0.7012146","0.7005655","0.700039","0.69930464","0.69834065","0.69834065","0.6977458"],"string":"[\n \"0.7803513\",\n \"0.76463795\",\n \"0.7637779\",\n \"0.7631515\",\n \"0.7631515\",\n \"0.7625458\",\n \"0.7618858\",\n \"0.7587505\",\n \"0.75759536\",\n \"0.75549775\",\n \"0.7548522\",\n \"0.75451297\",\n \"0.7540642\",\n \"0.75375634\",\n \"0.75086534\",\n \"0.7506745\",\n \"0.750453\",\n \"0.7503099\",\n \"0.74924654\",\n \"0.74106455\",\n \"0.73989403\",\n \"0.73811275\",\n \"0.7337632\",\n \"0.7321802\",\n \"0.7314435\",\n \"0.7310526\",\n \"0.73036176\",\n \"0.72964334\",\n \"0.72941524\",\n \"0.7293239\",\n \"0.7289033\",\n \"0.728739\",\n \"0.72728187\",\n \"0.7271517\",\n \"0.72627044\",\n \"0.72620916\",\n \"0.72615093\",\n \"0.7255736\",\n \"0.7246274\",\n \"0.7246274\",\n \"0.72452676\",\n \"0.7219304\",\n \"0.7217317\",\n \"0.7205611\",\n \"0.72016966\",\n \"0.7176339\",\n \"0.71741045\",\n \"0.7173411\",\n \"0.71663237\",\n \"0.7165198\",\n \"0.71387357\",\n \"0.713086\",\n \"0.7125033\",\n \"0.71177405\",\n \"0.71117175\",\n \"0.7108121\",\n \"0.70976686\",\n \"0.70955956\",\n \"0.7094644\",\n \"0.7094644\",\n \"0.7094644\",\n \"0.7094644\",\n \"0.7094644\",\n \"0.7094644\",\n \"0.7094644\",\n \"0.70940083\",\n \"0.7089481\",\n \"0.7089075\",\n \"0.7086907\",\n \"0.7084849\",\n \"0.70839506\",\n \"0.7081231\",\n \"0.70769423\",\n \"0.70752907\",\n \"0.70749086\",\n \"0.70704114\",\n \"0.7068607\",\n \"0.7068588\",\n \"0.7064387\",\n \"0.70615333\",\n \"0.7058176\",\n \"0.7058176\",\n \"0.7055714\",\n \"0.7052421\",\n \"0.70496863\",\n \"0.7033901\",\n \"0.7033249\",\n \"0.70322055\",\n \"0.7031052\",\n \"0.70206386\",\n \"0.70201874\",\n \"0.7019175\",\n \"0.70166004\",\n \"0.7012146\",\n \"0.7005655\",\n \"0.700039\",\n \"0.69930464\",\n \"0.69834065\",\n \"0.69834065\",\n \"0.6977458\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69839936"},"document_rank":{"kind":"string","value":"97"}}},{"rowIdx":89292,"cells":{"query":{"kind":"string","value":"An instrumented Template render method, providing a signal that can be intercepted by the test system Client"},"document":{"kind":"string","value":"def instrumented_test_render(self, context):\n signals.template_rendered.send(sender=self, template=self, context=context)\n return self.nodelist.render(context)"},"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 render_to_template_mock(*_args):","def _render(self) -> str:\n html = self._template.render(self._transient_context)\n self._transient_context = None\n return html","def patch_render_template(self):\n mock_render = Mock(spec=render_template)\n mock_render.return_value = ''\n with patch('app.main.render_template', mock_render):\n yield mock_render","def _render(self) -> None:\n pass","def render(self, template_name, **kwargs):\n raise NotImplementedError()","def render(self):\n pass","def render(self):\n pass","def render(self):\n pass","def render(self):\n pass","def render(self):\n pass","def render(self):\n pass","def render(self, *args, **kwargs):\r\n raise NotImplementedError","def dspyRender(self):\n pass","def render(self, template: str, **vars) -> str:","def render( *args, **kwargs ):","def render(self, template, **kw):\n self.write(self.render_string(template, **kw))","def render(self, template, *args, **kwargs):\n self._render(template, sys.stdout, *args, **kwargs)","def render(self, template, **kw):\n self.write(self.render_str(template, **kw))","def render(self, template, **kw):\n self.write(self.render_str(template, **kw))","def render(self, template, **kw):\n self.write(self.render_str(template, **kw))","def render(self):\n raise NotImplementedError","def render_string(self, template_name, **kwargs):\n raise NotImplementedError()","def _render(self, template, out, *args, **kwargs):\n text = self.render_only(template, *args, **kwargs)\n try:\n out.write(text)\n except UnicodeEncodeError:\n text = text.encode('utf-8')\n out.write(text)","def test_render_call_without_request(self, mock_render):\n context = MagicMock()\n render('template_name.html', context)\n mock_render.assert_called_with('template_name.html', context)","def render(self):\n raise NotImplementedError()","def render(self):","def render(self, r):\n raise NotImplementedError","def render_response(self, *args, **kwargs):\n if self.template_name is not None:\n template = get_template(loader, self.template_name)\n self.response.write(template.render(**self.get_context(*args, **kwargs)))\n else:\n raise ValueError('No template provided.')","def render(request, *args, **kw):","def render( context, *args, **kwargs ):","def _render_callback(self, _sim, _viewer):\n pass","def render(self):\n raise RenderNotImplemented('Render function is not implemented.')","def render(self, template, **kw):\n self.write(self._render_str(template, **kw))","def render(self, tmpl_name, request_env):\n return super().render(tmpl_name, request_env)","def render():\n html = request.get_data().decode('utf-8')\n sio.emit('render', html)\n return 'OK'","def render(self):\r\n super().render()","def render_reponse(self, template, **context):\n content = self.jinja2.render_template(template, **context)\n self.response.write(content)","def render(self, data, *args, **kwargs):\n pass # pragma: nocover","def _render(self, f_target, data):\n \n evt = self.plugin.dispatch(\n jink.plugin.Event('onBeforeRender', self, data=data, target=f_target),\n permit_cancel=True)\n if evt.isCancelled(): return\n data = evt.extra.data\n f_target = evt.extra.target\n \n # check if inheritance is specified\n refs = jinja2.meta.find_referenced_templates(self.engine.parse(data))\n try:\n refs.next()\n except StopIteration, e:\n # no, so insert default template\n t = self._filter(f_target, self.templates)\n if t: data = ( '{%% extends \"%s\" %%}\\n' % t ) + data\n \n # render\n data = self.engine.from_string(data).render({ 'config' : self.config.clone({ 'jink.path' : f_target}).get })\n self.log(2, '------------------------------')\n self.log(2, data)\n self.log(2, '------------------------------')\n return data","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def render(self, _template, **context):\n context['_request'] = self.request\n self.response.write(self.jinja2.render_template(_template, **context))","def render_string(self, template: str, **vars) -> str:","def render(self, screen):\n pass","def render(self, screen):\n pass","def render_response(template, *args, **kwargs):\n\treturn render_template(template, *args, user=current_user(), **kwargs)","def render(self):\n raise Exception(\"Abstract method\")","def render(self, _template, context=None):\n variables = {}\n if context:\n variables.update(context)\n rv = self.jinja2.render_template(_template, **variables)\n self.response.write(rv)","def index(self, **kw):\n\n template = self.context\n request = self.request\n\n request.response.setHeader('content-type',\n template.content_type)\n\n return template.render(request, **kw)","def render_view(self, h, *args):\n return self.view(h)","def _render_template(self,**kwargs):\n\t\tkwargs[\"module_name\"] = self._modulename\n\t\tfolder = path.dirname( stack()[1][1] )\n\t\tenv = Environment(loader=FileSystemLoader(folder))\n\t\ttemplate = env.get_template(\"jitced_template.c\")\n\t\twith open(self.sourcefile, \"w\") as codefile:\n\t\t\tcodefile.write(template.render(kwargs))","def testing():\n return render_template(\"testing.html\")","def render(self, request, context, template_name):\n status_code = context.pop('status_code', httplib.OK)\n additional_headers = context.pop('additional_headers', {})\n\n self.set_renderers(request)\n\n for renderer in request.renderers:\n response = renderer(self, request, context, template_name)\n if response is NotImplemented:\n continue\n response.status_code = status_code\n response.renderer = renderer\n break\n else:\n tried_mimetypes = list(itertools.chain(*[r.mimetypes for r in request.renderers]))\n response = self.http_not_acceptable(request, tried_mimetypes)\n response.renderer = None\n for key, value in additional_headers.iteritems():\n response[key] = value\n\n # We're doing content-negotiation, so tell the user-agent that the\n # response will vary depending on the accept header.\n patch_vary_headers(response, ('Accept',))\n return response","def test_template_feedback(self):\r\n pass","def render_log(self, template):\n\n # only write to the log file if it exists\n if self._selenium_log_file:\n\n id = self.id()\n description = self.shortDescription()\n\n # grab the stack frame info from test_* method\n (obj, filename, lineno, function, code_context, index) \\\n = self.get_test_frame()\n\n # render the test case debug\n html = render_to_string(\n template, {\n 'id': id,\n 'description': description,\n 'filename': filename,\n 'lineno': lineno,\n 'function': function,\n 'code_context': code_context,\n 'index': index,\n 'png': self.get_image_uri(),\n 'text': self.get_visible_text()})\n\n # write it to the file\n self._selenium_log_file.write(html.encode('utf8'))","def render(self, rstate):\n pass","def RenderResponse(self, template, **context):\n jinja2_renderer = jinja2.get_jinja2(app=self.app)\n rendered_value = jinja2_renderer.render_template(template, **context)\n self.response.write(rendered_value)","def render( request, etype, value, tb ):","def _render(cls, request, code, ctype, msg):\r\n request.setResponseCode(code)\r\n request.setHeader('content-type', ctype)\r\n request.write(msg)\r\n request.finish()","def render(self):\n self.increase_view_count()\n return render_to_string(self.template.template_file, {'advert':self})","def mock_render_to_string(template_name, context):\r\n return str((template_name, context))","def render(self):\n raise NotImplementedError(\"Renderer is an abstract class\")","def rendered(template):\r\n def was_rendered(client, response, testcase):\r\n testcase.assertTemplateUsed(response, template)\r\n return was_rendered","def render(self, template: str, **vars) -> str:\n vars.setdefault('ctx', self._ctx)\n return self._renderer.render(template, **vars)","def render(self, mode='human'):","def render_to(template):\n def renderer(func):\n def wrapper(request, *args, **kw):\n output = func(request, *args, **kw)\n if isinstance(output, (list, tuple)):\n output, tpl = output\n else:\n tpl = template\n ct = 'text/html'\n if tpl.endswith('xml'):\n ct = 'text/xml' if dsettings.DEBUG else 'application/xml'\n if isinstance(output, dict):\n if request.is_ajax() and settings.TEMPLATE_FOR_AJAX_REQUEST:\n tpl = ('%s_body%s' % os.path.splitext(tpl), tpl)\n return render_to_response(tpl, output, RequestContext(request), mimetype=ct)\n else:\n return output\n return wrapper\n return renderer","def post(self, request, *args, **kwargs):\n return render(request, self.template_name, self.get_context_data(**kwargs))","def render_hit_html(**kwargs):\n from .log import logger\n interface_params = kwargs['configuration']['interface_params']\n logger.debug('rendering %s template', interface_params['template_file'])\n missing_args = recall_template_args(**kwargs).difference(set(kwargs.keys()))\n if missing_args:\n print(f'{missing_args} are referenced in template but not supplied by template generator')\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(\n interface_params['template_dir']))\n template = env.get_template(interface_params['template_file'])\n return template.render(**kwargs)","def _render_as(self, extra_context={}, RESPONSE=None):\n import urlparse, re\n \n request = getattr(self, 'REQUEST', None)\n \n method = None\n if (extra_context.has_key('options') and\n extra_context['options'].has_key('method')):\n method = extra_context['options']['method']\n elif request.has_key('method'):\n method = request['method']\n \n if method not in self.render_methods:\n method = self.default_render_method\n \n transform_id = getattr(self, 'transform_%s' % method, '')\n transform_id = re.sub('\\s\\([^\\(]*?\\)$', '', transform_id)\n \n content_type = self.content_type_map.get(method, 'text/plain')\n\n # note we make sure we don't have a unicode object at the later steps,\n # because that causes all sorts of headaches with the XML parser later\n xml_rendered = self.pt_render(extra_context=extra_context).encode(self.char_encoding)\n \n if not transform_id or transform_id == self.unselected_transform:\n rendered = xml_rendered\n else:\n transform = getattr(self, transform_id, None)\n if not transform or transform.meta_type != 'XSLT Template':\n for obj in self._get_path_objs(self.transform_paths):\n transform = getattr(obj, transform_id, None)\n if transform and transform.meta_type == 'XSLT Template':\n break\n if not transform:\n raise TransformError, ('Transform %s did not exist' % \n transform_id)\n self.prune_cache()\n cached = self.retrieve_cache(transform, xml_rendered)\n if cached:\n rendered = cached\n else:\n rendered = transform.render_xml(xml_rendered, content_type)\n self.update_cache(transform, xml_rendered, rendered, 0)\n \n \n # set the base properly\n pathparts = list(urlparse.urlparse(self.absolute_url()))\n base = os.path.split(pathparts[2])[0]\n pathparts[2] = base\n base = urlparse.urlunparse(pathparts)\n \n RESPONSE.setBase(base)\n RESPONSE.setHeader('Content-Type', '%s; charset=%s' % (content_type,\n self.char_encoding))\n \n return rendered","def render(self, session: Session) -> str:\n raise NotImplementedError()","def render(self):\n return render_to_string(\n self.template_name, self.get_context_data(), request=self.request\n )","def render_template(self, _template, _template_values={}):\n\t\ttemplate = JINJA_ENVIRONMENT.get_template(_template)\n\t\trv = template.render(_template_values)\n\t\tself.response.write(rv)","def render(*args, **kwargs):\n if args:\n assert len(args) == 1, \\\n 'Expected exactly one argument, but got %r' % (args,)\n template = loader.load(args[0])\n else:\n template = cherrypy.thread_data.template\n ctxt = Context(url=cherrypy.url)\n ctxt.push(kwargs)\n return template.generate(ctxt)","def render(self, renderer, right=False):\n pass # pragma: no cover","def addBeforeRender(call, args=(), kwargs={}, nodeClass='Write'):","def render(trin, config, args):\n\n from forge.embyr.twistedserver import Application\n sword = trin.sword.remote(trin, config, args, idx=0)\n env = sword.getEnv.remote()\n Application(env, sword.tick.remote)","def test_render_all_templates():\n assert templates.xhook__initialize({})\n assert templates.xhook__handlers({})\n assert templates.xhook__enable()\n assert templates.xhook__release()\n assert templates.asserts__call_count({})\n assert templates.asserts__calls({})","def get_render_fn(self):\n def render(environment, ctxt_data, file_path):\n \"Renders a jinja2 template\"\n logging.debug(\"Rendering with context data %s\", ctxt_data)\n\n template = environment.get_template(file_path)\n return template.render(**ctxt_data)\n return render","def render(strategy, io, conf, options, template):\n\n renderer = Render(strategy, conf, options, template)\n renderer.render(io)","def render(self):\n self.run()\n return [{'dest' : self.dest,\n 'text' : self.tmpl.render(**self.data)}]","def test_render_call_with_request(self, mock_render):\n mock_rc = MagicMock()\n with patch('helio.heliodjango.renderers.get_request_context', return_value=mock_rc) as mock_rc_init:\n context = MagicMock()\n request = MagicMock()\n render('template_name.html', context, request)\n mock_rc_init.assert_called_with(request)\n mock_render.assert_called_with('template_name.html', context, context_instance=mock_rc)","def render(self, *html, **opt):\n context = self.context\n # add request, response to context implicitly.\n context['request'] = self.request\n context['response'] = self.response\n if 'context' in opt:\n context.update(opt['context'])\n opt['context'] = context.dicts[0]\n cnt = self.get_controller()\n cnt.render(*html, **opt)","def test_render():\n from coffin.shortcuts import render\n response = render(None, 'render-x.html', {'x': 'foo'})\n assert response.content == 'foo'","def webstreaming_func():\n return render_template('webstreaming.html')","def get(self, request):\n return render(request, self.template, self.context)","def renderHTTP(ctx):","def render(self, mode='human'):\n pass # no use in this situation","def _render(self, template, locals_dict, tsession):\n t0 = time.time()\n dom = etree.fromstring(template)\n qcontext = self._qcontext(locals_dict, tsession)\n html = QWeb().render(dom, qcontext)\n html = html and html.strip()\n render_time = time.time() - t0\n _logger.debug('Render in %.2fs\\n qcontext:\\n%s \\nTemplate:\\n%s\\n', render_time, qcontext, template)\n options = locals_dict['options']\n handle_reply = options.get('handle_reply') or None\n if handle_reply:\n handle_reply = simplejson.dumps(handle_reply)\n\n res = {'photos': [],\n 'editMessageText': options.get('editMessageText'),\n 'handle_reply_dump': handle_reply,\n 'reply_keyboard': False,\n 'context_dump': simplejson.dumps(locals_dict.get('context', {})),\n 'html': html.decode('utf-8')}\n reply_markup = options.get('reply_markup')\n if reply_markup and not len(reply_markup.keyboard):\n # remove reply_markup if it doesn't have buttons\n reply_markup = None\n if reply_markup:\n res['markup'] = _convert_markup(reply_markup)\n if isinstance(reply_markup, types.ReplyKeyboardMarkup):\n res['reply_keyboard'] = True\n elif isinstance(reply_markup, types.InlineKeyboardMarkup):\n res['inline_keyboard'] = True\n res['keep_reply_keyboard'] = options.get('keep_reply_keyboard')\n\n for photo in options.get('photos', []):\n if photo.get('type') == 'file':\n f = photo['data']\n else:\n # type is 'base64' by default\n f = io.StringIO(base64.b64decode(photo['data']))\n f.name = photo.get('filename', 'item.png')\n res['photos'].append({'file': f})\n\n return res","def render(self, context):\n engine = Renderer()\n engine.environment.filters['format_date'] = format_date\n engine.environment.filters['format_datetime'] = format_datetime\n result = engine.render(self.template, **context)\n response = HttpResponse(\n content_type='application/vnd.oasis.opendocument.text; charset=UTF-8'\n )\n response['Content-Disposition'] = 'inline; filename=' + self.filename\n with tempfile.NamedTemporaryFile() as output:\n output.write(result)\n output.flush()\n output = open(output.name, 'rb')\n response.write(output.read())\n return response","def render_to(template_name):\n def renderer(func):\n def wrapper(request, *args, **kw):\n output = func(request, *args, **kw)\n if not isinstance(output, dict):\n return output\n return render_to_response(template_name, output,\n context_instance=RequestContext(request))\n return wrapper\n return renderer","async def render(\n self, filename: str, *args: dict, **kwargs: typing.Any\n ) -> str:\n with self._enable_async():\n return await self._get_template(filename).render_async(\n *args, **kwargs\n )","def render(self, value, renderer, workload, incognito=False):\n # get the name to use\n name = None if incognito else self.name\n # delegate to my protocol\n yield from self.protocol.pyre_render(renderer=renderer,\n name=name, component=value, workload=workload)\n # all done\n return","def render_to_response(template, context, request, *args, **kwargs):\n from django.shortcuts import render_to_response as rtr\n from django.template import RequestContext\n return rtr(template, context, context_instance=RequestContext(request), *args, **kwargs)","def render(self):\n self.rendering = True\n self.env.render()","def render_to_response(filename, context=None, request=None, mimetype=settings.DEFAULT_CONTENT_TYPE):\n rendered = render_to_string(filename, context, request)\n settings.timer.clear()\n return HttpResponse(rendered,mimetype=mimetype)","def addAfterRender(call, args=(), kwargs={}, nodeClass='Write'):","def render(self, *a, **kw):\r\n try:\r\n res = Wrapped.render(self, *a, **kw)\r\n if is_api():\r\n res = json_respond(res)\r\n elif self.space_compress:\r\n res = spaceCompress(res)\r\n c.response.content = res\r\n except NoTemplateFound, e:\r\n # re-raise the error -- development environment\r\n if g.debug:\r\n s = sys.exc_info()\r\n raise s[1], None, s[2]\r\n # die gracefully -- production environment\r\n else:\r\n abort(404, \"not found\")\r\n return c.response"],"string":"[\n \"def render_to_template_mock(*_args):\",\n \"def _render(self) -> str:\\n html = self._template.render(self._transient_context)\\n self._transient_context = None\\n return html\",\n \"def patch_render_template(self):\\n mock_render = Mock(spec=render_template)\\n mock_render.return_value = ''\\n with patch('app.main.render_template', mock_render):\\n yield mock_render\",\n \"def _render(self) -> None:\\n pass\",\n \"def render(self, template_name, **kwargs):\\n raise NotImplementedError()\",\n \"def render(self):\\n pass\",\n \"def render(self):\\n pass\",\n \"def render(self):\\n pass\",\n \"def render(self):\\n pass\",\n \"def render(self):\\n pass\",\n \"def render(self):\\n pass\",\n \"def render(self, *args, **kwargs):\\r\\n raise NotImplementedError\",\n \"def dspyRender(self):\\n pass\",\n \"def render(self, template: str, **vars) -> str:\",\n \"def render( *args, **kwargs ):\",\n \"def render(self, template, **kw):\\n self.write(self.render_string(template, **kw))\",\n \"def render(self, template, *args, **kwargs):\\n self._render(template, sys.stdout, *args, **kwargs)\",\n \"def render(self, template, **kw):\\n self.write(self.render_str(template, **kw))\",\n \"def render(self, template, **kw):\\n self.write(self.render_str(template, **kw))\",\n \"def render(self, template, **kw):\\n self.write(self.render_str(template, **kw))\",\n \"def render(self):\\n raise NotImplementedError\",\n \"def render_string(self, template_name, **kwargs):\\n raise NotImplementedError()\",\n \"def _render(self, template, out, *args, **kwargs):\\n text = self.render_only(template, *args, **kwargs)\\n try:\\n out.write(text)\\n except UnicodeEncodeError:\\n text = text.encode('utf-8')\\n out.write(text)\",\n \"def test_render_call_without_request(self, mock_render):\\n context = MagicMock()\\n render('template_name.html', context)\\n mock_render.assert_called_with('template_name.html', context)\",\n \"def render(self):\\n raise NotImplementedError()\",\n \"def render(self):\",\n \"def render(self, r):\\n raise NotImplementedError\",\n \"def render_response(self, *args, **kwargs):\\n if self.template_name is not None:\\n template = get_template(loader, self.template_name)\\n self.response.write(template.render(**self.get_context(*args, **kwargs)))\\n else:\\n raise ValueError('No template provided.')\",\n \"def render(request, *args, **kw):\",\n \"def render( context, *args, **kwargs ):\",\n \"def _render_callback(self, _sim, _viewer):\\n pass\",\n \"def render(self):\\n raise RenderNotImplemented('Render function is not implemented.')\",\n \"def render(self, template, **kw):\\n self.write(self._render_str(template, **kw))\",\n \"def render(self, tmpl_name, request_env):\\n return super().render(tmpl_name, request_env)\",\n \"def render():\\n html = request.get_data().decode('utf-8')\\n sio.emit('render', html)\\n return 'OK'\",\n \"def render(self):\\r\\n super().render()\",\n \"def render_reponse(self, template, **context):\\n content = self.jinja2.render_template(template, **context)\\n self.response.write(content)\",\n \"def render(self, data, *args, **kwargs):\\n pass # pragma: nocover\",\n \"def _render(self, f_target, data):\\n \\n evt = self.plugin.dispatch(\\n jink.plugin.Event('onBeforeRender', self, data=data, target=f_target),\\n permit_cancel=True)\\n if evt.isCancelled(): return\\n data = evt.extra.data\\n f_target = evt.extra.target\\n \\n # check if inheritance is specified\\n refs = jinja2.meta.find_referenced_templates(self.engine.parse(data))\\n try:\\n refs.next()\\n except StopIteration, e:\\n # no, so insert default template\\n t = self._filter(f_target, self.templates)\\n if t: data = ( '{%% extends \\\"%s\\\" %%}\\\\n' % t ) + data\\n \\n # render\\n data = self.engine.from_string(data).render({ 'config' : self.config.clone({ 'jink.path' : f_target}).get })\\n self.log(2, '------------------------------')\\n self.log(2, data)\\n self.log(2, '------------------------------')\\n return data\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def render(self, _template, **context):\\n context['_request'] = self.request\\n self.response.write(self.jinja2.render_template(_template, **context))\",\n \"def render_string(self, template: str, **vars) -> str:\",\n \"def render(self, screen):\\n pass\",\n \"def render(self, screen):\\n pass\",\n \"def render_response(template, *args, **kwargs):\\n\\treturn render_template(template, *args, user=current_user(), **kwargs)\",\n \"def render(self):\\n raise Exception(\\\"Abstract method\\\")\",\n \"def render(self, _template, context=None):\\n variables = {}\\n if context:\\n variables.update(context)\\n rv = self.jinja2.render_template(_template, **variables)\\n self.response.write(rv)\",\n \"def index(self, **kw):\\n\\n template = self.context\\n request = self.request\\n\\n request.response.setHeader('content-type',\\n template.content_type)\\n\\n return template.render(request, **kw)\",\n \"def render_view(self, h, *args):\\n return self.view(h)\",\n \"def _render_template(self,**kwargs):\\n\\t\\tkwargs[\\\"module_name\\\"] = self._modulename\\n\\t\\tfolder = path.dirname( stack()[1][1] )\\n\\t\\tenv = Environment(loader=FileSystemLoader(folder))\\n\\t\\ttemplate = env.get_template(\\\"jitced_template.c\\\")\\n\\t\\twith open(self.sourcefile, \\\"w\\\") as codefile:\\n\\t\\t\\tcodefile.write(template.render(kwargs))\",\n \"def testing():\\n return render_template(\\\"testing.html\\\")\",\n \"def render(self, request, context, template_name):\\n status_code = context.pop('status_code', httplib.OK)\\n additional_headers = context.pop('additional_headers', {})\\n\\n self.set_renderers(request)\\n\\n for renderer in request.renderers:\\n response = renderer(self, request, context, template_name)\\n if response is NotImplemented:\\n continue\\n response.status_code = status_code\\n response.renderer = renderer\\n break\\n else:\\n tried_mimetypes = list(itertools.chain(*[r.mimetypes for r in request.renderers]))\\n response = self.http_not_acceptable(request, tried_mimetypes)\\n response.renderer = None\\n for key, value in additional_headers.iteritems():\\n response[key] = value\\n\\n # We're doing content-negotiation, so tell the user-agent that the\\n # response will vary depending on the accept header.\\n patch_vary_headers(response, ('Accept',))\\n return response\",\n \"def test_template_feedback(self):\\r\\n pass\",\n \"def render_log(self, template):\\n\\n # only write to the log file if it exists\\n if self._selenium_log_file:\\n\\n id = self.id()\\n description = self.shortDescription()\\n\\n # grab the stack frame info from test_* method\\n (obj, filename, lineno, function, code_context, index) \\\\\\n = self.get_test_frame()\\n\\n # render the test case debug\\n html = render_to_string(\\n template, {\\n 'id': id,\\n 'description': description,\\n 'filename': filename,\\n 'lineno': lineno,\\n 'function': function,\\n 'code_context': code_context,\\n 'index': index,\\n 'png': self.get_image_uri(),\\n 'text': self.get_visible_text()})\\n\\n # write it to the file\\n self._selenium_log_file.write(html.encode('utf8'))\",\n \"def render(self, rstate):\\n pass\",\n \"def RenderResponse(self, template, **context):\\n jinja2_renderer = jinja2.get_jinja2(app=self.app)\\n rendered_value = jinja2_renderer.render_template(template, **context)\\n self.response.write(rendered_value)\",\n \"def render( request, etype, value, tb ):\",\n \"def _render(cls, request, code, ctype, msg):\\r\\n request.setResponseCode(code)\\r\\n request.setHeader('content-type', ctype)\\r\\n request.write(msg)\\r\\n request.finish()\",\n \"def render(self):\\n self.increase_view_count()\\n return render_to_string(self.template.template_file, {'advert':self})\",\n \"def mock_render_to_string(template_name, context):\\r\\n return str((template_name, context))\",\n \"def render(self):\\n raise NotImplementedError(\\\"Renderer is an abstract class\\\")\",\n \"def rendered(template):\\r\\n def was_rendered(client, response, testcase):\\r\\n testcase.assertTemplateUsed(response, template)\\r\\n return was_rendered\",\n \"def render(self, template: str, **vars) -> str:\\n vars.setdefault('ctx', self._ctx)\\n return self._renderer.render(template, **vars)\",\n \"def render(self, mode='human'):\",\n \"def render_to(template):\\n def renderer(func):\\n def wrapper(request, *args, **kw):\\n output = func(request, *args, **kw)\\n if isinstance(output, (list, tuple)):\\n output, tpl = output\\n else:\\n tpl = template\\n ct = 'text/html'\\n if tpl.endswith('xml'):\\n ct = 'text/xml' if dsettings.DEBUG else 'application/xml'\\n if isinstance(output, dict):\\n if request.is_ajax() and settings.TEMPLATE_FOR_AJAX_REQUEST:\\n tpl = ('%s_body%s' % os.path.splitext(tpl), tpl)\\n return render_to_response(tpl, output, RequestContext(request), mimetype=ct)\\n else:\\n return output\\n return wrapper\\n return renderer\",\n \"def post(self, request, *args, **kwargs):\\n return render(request, self.template_name, self.get_context_data(**kwargs))\",\n \"def render_hit_html(**kwargs):\\n from .log import logger\\n interface_params = kwargs['configuration']['interface_params']\\n logger.debug('rendering %s template', interface_params['template_file'])\\n missing_args = recall_template_args(**kwargs).difference(set(kwargs.keys()))\\n if missing_args:\\n print(f'{missing_args} are referenced in template but not supplied by template generator')\\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(\\n interface_params['template_dir']))\\n template = env.get_template(interface_params['template_file'])\\n return template.render(**kwargs)\",\n \"def _render_as(self, extra_context={}, RESPONSE=None):\\n import urlparse, re\\n \\n request = getattr(self, 'REQUEST', None)\\n \\n method = None\\n if (extra_context.has_key('options') and\\n extra_context['options'].has_key('method')):\\n method = extra_context['options']['method']\\n elif request.has_key('method'):\\n method = request['method']\\n \\n if method not in self.render_methods:\\n method = self.default_render_method\\n \\n transform_id = getattr(self, 'transform_%s' % method, '')\\n transform_id = re.sub('\\\\s\\\\([^\\\\(]*?\\\\)$', '', transform_id)\\n \\n content_type = self.content_type_map.get(method, 'text/plain')\\n\\n # note we make sure we don't have a unicode object at the later steps,\\n # because that causes all sorts of headaches with the XML parser later\\n xml_rendered = self.pt_render(extra_context=extra_context).encode(self.char_encoding)\\n \\n if not transform_id or transform_id == self.unselected_transform:\\n rendered = xml_rendered\\n else:\\n transform = getattr(self, transform_id, None)\\n if not transform or transform.meta_type != 'XSLT Template':\\n for obj in self._get_path_objs(self.transform_paths):\\n transform = getattr(obj, transform_id, None)\\n if transform and transform.meta_type == 'XSLT Template':\\n break\\n if not transform:\\n raise TransformError, ('Transform %s did not exist' % \\n transform_id)\\n self.prune_cache()\\n cached = self.retrieve_cache(transform, xml_rendered)\\n if cached:\\n rendered = cached\\n else:\\n rendered = transform.render_xml(xml_rendered, content_type)\\n self.update_cache(transform, xml_rendered, rendered, 0)\\n \\n \\n # set the base properly\\n pathparts = list(urlparse.urlparse(self.absolute_url()))\\n base = os.path.split(pathparts[2])[0]\\n pathparts[2] = base\\n base = urlparse.urlunparse(pathparts)\\n \\n RESPONSE.setBase(base)\\n RESPONSE.setHeader('Content-Type', '%s; charset=%s' % (content_type,\\n self.char_encoding))\\n \\n return rendered\",\n \"def render(self, session: Session) -> str:\\n raise NotImplementedError()\",\n \"def render(self):\\n return render_to_string(\\n self.template_name, self.get_context_data(), request=self.request\\n )\",\n \"def render_template(self, _template, _template_values={}):\\n\\t\\ttemplate = JINJA_ENVIRONMENT.get_template(_template)\\n\\t\\trv = template.render(_template_values)\\n\\t\\tself.response.write(rv)\",\n \"def render(*args, **kwargs):\\n if args:\\n assert len(args) == 1, \\\\\\n 'Expected exactly one argument, but got %r' % (args,)\\n template = loader.load(args[0])\\n else:\\n template = cherrypy.thread_data.template\\n ctxt = Context(url=cherrypy.url)\\n ctxt.push(kwargs)\\n return template.generate(ctxt)\",\n \"def render(self, renderer, right=False):\\n pass # pragma: no cover\",\n \"def addBeforeRender(call, args=(), kwargs={}, nodeClass='Write'):\",\n \"def render(trin, config, args):\\n\\n from forge.embyr.twistedserver import Application\\n sword = trin.sword.remote(trin, config, args, idx=0)\\n env = sword.getEnv.remote()\\n Application(env, sword.tick.remote)\",\n \"def test_render_all_templates():\\n assert templates.xhook__initialize({})\\n assert templates.xhook__handlers({})\\n assert templates.xhook__enable()\\n assert templates.xhook__release()\\n assert templates.asserts__call_count({})\\n assert templates.asserts__calls({})\",\n \"def get_render_fn(self):\\n def render(environment, ctxt_data, file_path):\\n \\\"Renders a jinja2 template\\\"\\n logging.debug(\\\"Rendering with context data %s\\\", ctxt_data)\\n\\n template = environment.get_template(file_path)\\n return template.render(**ctxt_data)\\n return render\",\n \"def render(strategy, io, conf, options, template):\\n\\n renderer = Render(strategy, conf, options, template)\\n renderer.render(io)\",\n \"def render(self):\\n self.run()\\n return [{'dest' : self.dest,\\n 'text' : self.tmpl.render(**self.data)}]\",\n \"def test_render_call_with_request(self, mock_render):\\n mock_rc = MagicMock()\\n with patch('helio.heliodjango.renderers.get_request_context', return_value=mock_rc) as mock_rc_init:\\n context = MagicMock()\\n request = MagicMock()\\n render('template_name.html', context, request)\\n mock_rc_init.assert_called_with(request)\\n mock_render.assert_called_with('template_name.html', context, context_instance=mock_rc)\",\n \"def render(self, *html, **opt):\\n context = self.context\\n # add request, response to context implicitly.\\n context['request'] = self.request\\n context['response'] = self.response\\n if 'context' in opt:\\n context.update(opt['context'])\\n opt['context'] = context.dicts[0]\\n cnt = self.get_controller()\\n cnt.render(*html, **opt)\",\n \"def test_render():\\n from coffin.shortcuts import render\\n response = render(None, 'render-x.html', {'x': 'foo'})\\n assert response.content == 'foo'\",\n \"def webstreaming_func():\\n return render_template('webstreaming.html')\",\n \"def get(self, request):\\n return render(request, self.template, self.context)\",\n \"def renderHTTP(ctx):\",\n \"def render(self, mode='human'):\\n pass # no use in this situation\",\n \"def _render(self, template, locals_dict, tsession):\\n t0 = time.time()\\n dom = etree.fromstring(template)\\n qcontext = self._qcontext(locals_dict, tsession)\\n html = QWeb().render(dom, qcontext)\\n html = html and html.strip()\\n render_time = time.time() - t0\\n _logger.debug('Render in %.2fs\\\\n qcontext:\\\\n%s \\\\nTemplate:\\\\n%s\\\\n', render_time, qcontext, template)\\n options = locals_dict['options']\\n handle_reply = options.get('handle_reply') or None\\n if handle_reply:\\n handle_reply = simplejson.dumps(handle_reply)\\n\\n res = {'photos': [],\\n 'editMessageText': options.get('editMessageText'),\\n 'handle_reply_dump': handle_reply,\\n 'reply_keyboard': False,\\n 'context_dump': simplejson.dumps(locals_dict.get('context', {})),\\n 'html': html.decode('utf-8')}\\n reply_markup = options.get('reply_markup')\\n if reply_markup and not len(reply_markup.keyboard):\\n # remove reply_markup if it doesn't have buttons\\n reply_markup = None\\n if reply_markup:\\n res['markup'] = _convert_markup(reply_markup)\\n if isinstance(reply_markup, types.ReplyKeyboardMarkup):\\n res['reply_keyboard'] = True\\n elif isinstance(reply_markup, types.InlineKeyboardMarkup):\\n res['inline_keyboard'] = True\\n res['keep_reply_keyboard'] = options.get('keep_reply_keyboard')\\n\\n for photo in options.get('photos', []):\\n if photo.get('type') == 'file':\\n f = photo['data']\\n else:\\n # type is 'base64' by default\\n f = io.StringIO(base64.b64decode(photo['data']))\\n f.name = photo.get('filename', 'item.png')\\n res['photos'].append({'file': f})\\n\\n return res\",\n \"def render(self, context):\\n engine = Renderer()\\n engine.environment.filters['format_date'] = format_date\\n engine.environment.filters['format_datetime'] = format_datetime\\n result = engine.render(self.template, **context)\\n response = HttpResponse(\\n content_type='application/vnd.oasis.opendocument.text; charset=UTF-8'\\n )\\n response['Content-Disposition'] = 'inline; filename=' + self.filename\\n with tempfile.NamedTemporaryFile() as output:\\n output.write(result)\\n output.flush()\\n output = open(output.name, 'rb')\\n response.write(output.read())\\n return response\",\n \"def render_to(template_name):\\n def renderer(func):\\n def wrapper(request, *args, **kw):\\n output = func(request, *args, **kw)\\n if not isinstance(output, dict):\\n return output\\n return render_to_response(template_name, output,\\n context_instance=RequestContext(request))\\n return wrapper\\n return renderer\",\n \"async def render(\\n self, filename: str, *args: dict, **kwargs: typing.Any\\n ) -> str:\\n with self._enable_async():\\n return await self._get_template(filename).render_async(\\n *args, **kwargs\\n )\",\n \"def render(self, value, renderer, workload, incognito=False):\\n # get the name to use\\n name = None if incognito else self.name\\n # delegate to my protocol\\n yield from self.protocol.pyre_render(renderer=renderer,\\n name=name, component=value, workload=workload)\\n # all done\\n return\",\n \"def render_to_response(template, context, request, *args, **kwargs):\\n from django.shortcuts import render_to_response as rtr\\n from django.template import RequestContext\\n return rtr(template, context, context_instance=RequestContext(request), *args, **kwargs)\",\n \"def render(self):\\n self.rendering = True\\n self.env.render()\",\n \"def render_to_response(filename, context=None, request=None, mimetype=settings.DEFAULT_CONTENT_TYPE):\\n rendered = render_to_string(filename, context, request)\\n settings.timer.clear()\\n return HttpResponse(rendered,mimetype=mimetype)\",\n \"def addAfterRender(call, args=(), kwargs={}, nodeClass='Write'):\",\n \"def render(self, *a, **kw):\\r\\n try:\\r\\n res = Wrapped.render(self, *a, **kw)\\r\\n if is_api():\\r\\n res = json_respond(res)\\r\\n elif self.space_compress:\\r\\n res = spaceCompress(res)\\r\\n c.response.content = res\\r\\n except NoTemplateFound, e:\\r\\n # re-raise the error -- development environment\\r\\n if g.debug:\\r\\n s = sys.exc_info()\\r\\n raise s[1], None, s[2]\\r\\n # die gracefully -- production environment\\r\\n else:\\r\\n abort(404, \\\"not found\\\")\\r\\n return c.response\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.685287","0.6700654","0.66776145","0.6645942","0.66436213","0.6585426","0.6585426","0.6585426","0.6585426","0.6585426","0.6585426","0.6547666","0.65277296","0.65042114","0.6500498","0.64901316","0.6445812","0.64102834","0.64102834","0.64102834","0.6381034","0.6378624","0.6365559","0.63544106","0.63426733","0.6342077","0.63257045","0.627541","0.62472165","0.6237956","0.6229956","0.6186744","0.6168568","0.6146617","0.61424243","0.6131168","0.61200124","0.6118168","0.60924155","0.60554117","0.60554117","0.60554117","0.60554117","0.60554117","0.60528964","0.602531","0.60103273","0.60103273","0.6004237","0.5997705","0.59850067","0.59839284","0.59797275","0.5971004","0.5955331","0.59241176","0.591924","0.5914814","0.5914319","0.58963525","0.58829486","0.5880554","0.5863","0.5848709","0.584449","0.5838951","0.5832425","0.5829382","0.5828227","0.58068544","0.5806818","0.5782846","0.5778736","0.5775887","0.57459515","0.57422304","0.5736103","0.573311","0.5717882","0.5717695","0.57131696","0.57102627","0.57066226","0.5692235","0.568785","0.56823575","0.5676485","0.5673592","0.5671828","0.5668454","0.5662231","0.565692","0.56561744","0.56503123","0.5649184","0.5646317","0.5642127","0.56400114","0.56327856","0.56307656"],"string":"[\n \"0.685287\",\n \"0.6700654\",\n \"0.66776145\",\n \"0.6645942\",\n \"0.66436213\",\n \"0.6585426\",\n \"0.6585426\",\n \"0.6585426\",\n \"0.6585426\",\n \"0.6585426\",\n \"0.6585426\",\n \"0.6547666\",\n \"0.65277296\",\n \"0.65042114\",\n \"0.6500498\",\n \"0.64901316\",\n \"0.6445812\",\n \"0.64102834\",\n \"0.64102834\",\n \"0.64102834\",\n \"0.6381034\",\n \"0.6378624\",\n \"0.6365559\",\n \"0.63544106\",\n \"0.63426733\",\n \"0.6342077\",\n \"0.63257045\",\n \"0.627541\",\n \"0.62472165\",\n \"0.6237956\",\n \"0.6229956\",\n \"0.6186744\",\n \"0.6168568\",\n \"0.6146617\",\n \"0.61424243\",\n \"0.6131168\",\n \"0.61200124\",\n \"0.6118168\",\n \"0.60924155\",\n \"0.60554117\",\n \"0.60554117\",\n \"0.60554117\",\n \"0.60554117\",\n \"0.60554117\",\n \"0.60528964\",\n \"0.602531\",\n \"0.60103273\",\n \"0.60103273\",\n \"0.6004237\",\n \"0.5997705\",\n \"0.59850067\",\n \"0.59839284\",\n \"0.59797275\",\n \"0.5971004\",\n \"0.5955331\",\n \"0.59241176\",\n \"0.591924\",\n \"0.5914814\",\n \"0.5914319\",\n \"0.58963525\",\n \"0.58829486\",\n \"0.5880554\",\n \"0.5863\",\n \"0.5848709\",\n \"0.584449\",\n \"0.5838951\",\n \"0.5832425\",\n \"0.5829382\",\n \"0.5828227\",\n \"0.58068544\",\n \"0.5806818\",\n \"0.5782846\",\n \"0.5778736\",\n \"0.5775887\",\n \"0.57459515\",\n \"0.57422304\",\n \"0.5736103\",\n \"0.573311\",\n \"0.5717882\",\n \"0.5717695\",\n \"0.57131696\",\n \"0.57102627\",\n \"0.57066226\",\n \"0.5692235\",\n \"0.568785\",\n \"0.56823575\",\n \"0.5676485\",\n \"0.5673592\",\n \"0.5671828\",\n \"0.5668454\",\n \"0.5662231\",\n \"0.565692\",\n \"0.56561744\",\n \"0.56503123\",\n \"0.5649184\",\n \"0.5646317\",\n \"0.5642127\",\n \"0.56400114\",\n \"0.56327856\",\n \"0.56307656\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75868696"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":89293,"cells":{"query":{"kind":"string","value":"Register dataset related changes. For local datasets, look them up, record the dataset event, queue dagruns, and broadcast the dataset event"},"document":{"kind":"string","value":"def register_dataset_change(\n self, *, task_instance: TaskInstance, dataset: Dataset, extra=None, session: Session, **kwargs\n ) -> None:\n dataset_model = session.scalar(select(DatasetModel).where(DatasetModel.uri == dataset.uri))\n if not dataset_model:\n self.log.warning(\"DatasetModel %s not found\", dataset)\n return\n session.add(\n DatasetEvent(\n dataset_id=dataset_model.id,\n source_task_id=task_instance.task_id,\n source_dag_id=task_instance.dag_id,\n source_run_id=task_instance.run_id,\n source_map_index=task_instance.map_index,\n extra=extra,\n )\n )\n session.flush()\n Stats.incr(\"dataset.updates\")\n if dataset_model.consuming_dags:\n self._queue_dagruns(dataset_model, session)\n session.flush()"},"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 datasets(self, datasets):\n\n self._datasets = datasets","def add_to_dataset(self, dataset: Dataset):\n pass","def datasets(self, datasets):\n self.__datasetsAll = datasets\n self.__datasets = list(datasets)\n self.__axisDomains = None\n for ds in self.__datasetsAll:\n self.__datasetsPerClass[ds[-1]] = self.__datasetsPerClass.get(ds[-1], 0) + 1\n self.dataChanged.emit()","def setDataset(self,dataset):\n self.__dataSet = dataset","def refresh_train_dataset(self):\n inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label = self.build_data(self.reader, self.train_items, self.option.max_path_length)\n self.train_dataset = CodeDataset(inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label)","def assign_dataset(self, dataset_uuid):\n assert os.path.exists(os.path.join(DATASETS_PATH, dataset_uuid))\n self.dataset_uuid = dataset_uuid\n if self.name is None:\n self.set_name(dataset_uuid)","def run_dataset(data: DataSetBase):\n\n tracks_manager = data.load_tracks_manager()\n report, reconstructions = reconstruction.incremental_reconstruction(\n data, tracks_manager\n )\n data.save_reconstruction(reconstructions)\n data.save_report(io.json_dumps(report), \"reconstruction.json\")","def set_dataset(self, role, dset):\n\n\t\tself.datasets[role] = dset","def refresh_test_dataset(self):\n inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label = self.build_data(self.reader, self.test_items, self.option.max_path_length)\n self.test_dataset = CodeDataset(inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label)","def add_dataset(self, task_name, dataset=None, *, aliases=None):\n self._datasets.append(dataset if dataset is not None else TaskData())\n last_index = len(self._datasets) - 1\n self._aliases[task_name] = last_index\n\n if aliases is not None:\n for alias in aliases:\n self._aliases[alias] = last_index\n\n if len(self._datasets) == 1:\n self._default_index = 0","def _push_to_server(self) -> None:\n timestamp = int(arrow.get().float_timestamp * 1000)\n\n datapoints: List[Dict[str, Union[str, List[Tuple[float, float]]]]] = []\n\n for metric in REGISTRY.collect():\n if type(metric) == Metric and metric.type in [\"gauge\", \"counter\"]:\n if len(metric.samples) == 0:\n continue\n\n external_id = self.external_id_prefix + metric.name\n datapoints.append({\"externalId\": external_id, \"datapoints\": [(timestamp, metric.samples[0].value)]})\n\n self.cdf_client.datapoints.insert_multiple(datapoints)\n self.logger.debug(\"Pushed metrics to CDF tenant '%s'\", self._cdf_project)","def send_chart_data(_, changes):\n for model, change in changes:\n if isinstance(model, Temperature) and change == 'insert':\n temp_data = model\n socketio.emit('chart_data', json.dumps(temp_data.serialize))","def update_data(self, name, cache_dir=None, data_dir=None, tasks=None):\n assert name, \"Must input a valid dataset name.\"\n assert name in self.data[\"dataset\"], \"The dataset \\'{}\\' does not exist in the cache.\" \\\n .format(name)\n if cache_dir:\n self.data[\"dataset\"][name][\"cache_dir\"] = cache_dir\n if data_dir:\n self.data[\"dataset\"][name][\"data_dir\"] = data_dir\n if tasks:\n self.data[\"dataset\"][name][\"tasks\"] = tasks\n self.data[\"dataset\"][name][\"keywords\"] = self._get_keywords_from_tasks(tasks)\n if cache_dir or data_dir or tasks:\n self.update_categories()\n self.write_data_cache(self.data)","def data_changed(self):\n self.data_changed_signal.emit(self)","def refresh(dataset, client):\n pass","def registerSampleData():\n # It is always recommended to provide sample data for users to make it easy to try the module,\n # but if no sample data is available then this method (and associated startupCompeted signal connection) can be removed.\n\n import SampleData\n iconsPath = os.path.join(os.path.dirname(__file__), 'Resources/Icons')\n\n # To ensure that the source code repository remains small (can be downloaded and installed quickly)\n # it is recommended to store data sets that are larger than a few MB in a Github release.\n\n # RegularizedFastMarching1\n SampleData.SampleDataLogic.registerCustomSampleDataSource(\n # Category and sample name displayed in Sample Data module\n category='RegularizedFastMarching',\n sampleName='RegularizedFastMarching1',\n # Thumbnail should have size of approximately 260x280 pixels and stored in Resources/Icons folder.\n # It can be created by Screen Capture module, \"Capture all views\" option enabled, \"Number of images\" set to \"Single\".\n thumbnailFileName=os.path.join(iconsPath, 'RegularizedFastMarching1.png'),\n # Download URL and target file name\n uris=\"https://github.com/Slicer/SlicerTestingData/releases/download/SHA256/998cb522173839c78657f4bc0ea907cea09fd04e44601f17c82ea27927937b95\",\n fileNames='RegularizedFastMarching1.nrrd',\n # Checksum to ensure file integrity. Can be computed by this command:\n # import hashlib; print(hashlib.sha256(open(filename, \"rb\").read()).hexdigest())\n checksums = 'SHA256:998cb522173839c78657f4bc0ea907cea09fd04e44601f17c82ea27927937b95',\n # This node name will be used when the data set is loaded\n nodeNames='RegularizedFastMarching1'\n )\n\n # RegularizedFastMarching2\n SampleData.SampleDataLogic.registerCustomSampleDataSource(\n # Category and sample name displayed in Sample Data module\n category='RegularizedFastMarching',\n sampleName='RegularizedFastMarching2',\n thumbnailFileName=os.path.join(iconsPath, 'RegularizedFastMarching2.png'),\n # Download URL and target file name\n uris=\"https://github.com/Slicer/SlicerTestingData/releases/download/SHA256/1a64f3f422eb3d1c9b093d1a18da354b13bcf307907c66317e2463ee530b7a97\",\n fileNames='RegularizedFastMarching2.nrrd',\n checksums = 'SHA256:1a64f3f422eb3d1c9b093d1a18da354b13bcf307907c66317e2463ee530b7a97',\n # This node name will be used when the data set is loaded\n nodeNames='RegularizedFastMarching2'\n )","def switch_to_dataset(self, dataset_name: Optional[str] = None):\n self._current_dataset_name = self._validate_dataset_name(dataset_name)","def from_dataset(self, dataset=None):\n if not dataset:\n raise aspecd.exceptions.MissingDatasetError\n self.type = aspecd.utils.full_class_name(dataset)\n self.id = dataset.id\n self.history = copy.deepcopy(dataset.history)","def _UpdateDataSetValues( self ):\n pass","def data_flow(self, data_flow):\n\n self._data_flow = data_flow","def register_dataset_split(dataset_name, split_dict):\n\n _DATASET_TYPE_LOAD_FUNC_MAP = {\n \"COCODataset\": _register_extended_coco,\n \"COCOText\": _register_coco_text,\n \"COCOTextDataset\": _register_coco_text,\n \"LVISDataset\": _register_extended_lvis,\n }\n\n factory = split_dict.get(\"DS_TYPE\", \"COCODataset\")\n _DATASET_TYPE_LOAD_FUNC_MAP[factory](\n dataset_name=dataset_name, split_dict=split_dict\n )","def rDataChanged(self):\n\n self._queues.uResolutionTab.refreshData()\n self._layerManager.updateReviewLayer()","def __init__(self, name_dataset, reduce=False):\n dataset = TUDataset(root='data/TUDataset', name=name_dataset)\n if reduce:\n new_dataset = []\n for i in tqdm(range(len(dataset))):\n aux_graph = copy.deepcopy(dataset[i])\n aux_graph.edge_index = TUDData.reduce_edges(aux_graph.edge_index)\n new_dataset.append(copy.deepcopy(aux_graph))\n dataset = WrapperSynthetic(new_dataset, dataset.num_node_features,\n dataset.num_classes, None)\n super(TUDData, self).__init__(dataset)","def add_tag(self, dataset: \"Dataset\", tag: \"DatasetTag\"):\n raise NotImplementedError","def update_data(client, dataset_id, dataset_name, updated_dataset):\n view = client.views.lookup(dataset_id)\n revision = view.revisions.create_replace_revision(permission='private')\n upload = revision.create_upload(dataset_name)\n\n # The path of the updated dataset should be a string to the csv, geojson, shapefile zip, etc.\n if type(updated_dataset) == str:\n with open(updated_dataset, 'rb') as f:\n extension = os.path.splitext(updated_dataset)[1]\n if extension == '.csv':\n source = upload.csv(f)\n elif extension == '.xls':\n source = upload.xls(f)\n elif extension == 'xlsx':\n source = upload.xlsx(f)\n elif extension == '.tsv':\n source = upload.tsv(f)\n elif extension == '.zip':\n source = upload.shapefile(f)\n elif extension == '.kml':\n source = upload.kml(f)\n elif extension == '.geojson':\n source = upload.geojson(f)\n else:\n raise Exception('File format not supported')\n elif type(updated_dataset) == pd.DataFrame or type(updated_dataset) == gpd.GeoDataFrame:\n source = upload.df(updated_dataset)\n\n output_schema = source.get_latest_input_schema().get_latest_output_schema()\n\n output_schema = output_schema.wait_for_finish()\n\n # check for errors\n assert output_schema.attributes['error_count'] == 0\n print(output_schema.attributes['error_count'])\n\n # If you want, you can get a csv stream of all the errors\n errors = output_schema.schema_errors_csv()\n for line in errors.iter_lines():\n print(line)\n\n #############################################################################\n # The next few lines of code will update the draft/revision into the asset. #\n # Do not run if you plan on keeping your draft! #\n #############################################################################\n job = revision.apply(output_schema=output_schema)\n\n # This code outputs the status from the Job object\n # Track the async process\n def job_progress(job):\n clear_output(wait=True)\n print(job.attributes['log'][0]['stage'])\n print('Job progress:', job.attributes['status'])\n\n job = job.wait_for_finish(progress = job_progress)\n sys.exit(0 if job.attributes['status'] == 'successful' else 1)","def push_dataset_realtime(platform_id, # type: str\n dataset_id, # type: str\n dataset, # type: Union[str, pandas.DataFrame]\n push_key, # type: str\n format='csv', # type: str\n csv_separator=';', # type: str\n **other_opts\n ):\n\n client = ODSClient(platform_id=platform_id)\n return client.push_dataset_realtime(dataset_id=dataset_id,\n dataset=dataset,\n push_key=push_key,\n format=format,\n csv_separator=csv_separator,\n **other_opts)","def save(self, dataset_id=None):\n if dataset_id is None:\n dataset_id = uuid.uuid4().hex\n\n record = {\n DATASET_ID: dataset_id,\n DATASET_OBSERVATION_ID: uuid.uuid4().hex,\n self.AGGREGATED_DATASETS: {},\n self.CREATED_AT: strftime(\"%Y-%m-%d %H:%M:%S\", gmtime()),\n self.STATE: self.STATE_PENDING,\n self.PENDING_UPDATES: [],\n }\n\n return super(self.__class__, self).save(record)","def _update_modified_data_sources(self):\n new_last_imported = datetime.utcnow()\n self._update_modified_since(self.last_imported)\n self.last_imported = new_last_imported","def DataSet(self, dataset):\n # Do we have dataset to store\n if dataset and dataset.VTKObject:\n # Do we need to create a vtkWeakReference\n if not hasattr(self, '_dataset') or self._dataset is None:\n self._dataset = vtkWeakReference()\n\n self._dataset.Set(dataset.VTKObject)\n else:\n self._dataset = None","def datachange_notification(self, node: Node, val, data):\n _logger.info('datachange_notification %r %s', node, val)","def test_dataset_details():\n with new_test_dataset(2) as test_ds:\n args = build_register_args(test_ds.copy_to_s3())\n ds_name = args['name']\n URLs.run(url_info=URLs.register_url(), json_body=args)\n\n ds_parts = URLs.run(url_info=URLs.dataset_parts_url(ds_name)).json\n assert ds_parts['filenames'] == test_ds.expected_parts.filenames\n expected_columns = json.loads(datafile_schema().to_json())['columns']\n\n ds_short_schema = URLs.run(url_info=URLs.dataset_schema_url(ds_name, full=False)).json\n assert ds_short_schema['columns'] == expected_columns\n\n ds_full_schema = URLs.run(url_info=URLs.dataset_schema_url(ds_name, full=True)).json\n assert ds_full_schema['columns'][DEFAULT_TIMESTAMP_COLUMN]['colattrs']['numericMin'] == BASE_TIME\n\n URLs.run(url_info=URLs.unregister_url(ds_name))","def add_to_dataset(self, begin_ts):\n\n if self.revisions_timestamps:\n last_revision_timestamp = self.revisions_timestamps[-1]\n revisions_twr = Metrics.list_twr(self.revisions_timestamps, begin_ts, last_revision_timestamp)\n fixes_twr = Metrics.list_twr(self.fixes_timestamps, begin_ts, last_revision_timestamp)\n authors_twr = Metrics.list_twr(self.authors_timestamps, begin_ts, last_revision_timestamp)\n self.last_twr = (revisions_twr, fixes_twr, authors_twr)\n Metrics.fixes_dataset.add((revisions_twr, fixes_twr, authors_twr))","def setup(self, ds):\n pass","def update_dataset(self, dataset, name=None, description=None):\n uri = URITemplate(self.baseuri + '/{owner}/{id}').expand(\n owner=self.username, id=dataset)\n return self.session.patch(uri, json=self._attribs(name, description))","def on_new_data(self):\n\n if self.connected:\n tab_open = self.tab_open()\n\n # Update plot data\n for i, series in enumerate(self.measurements_list):\n if i == tab_open:\n self.plotted_data[i].setData(self.data_indices, self.measurements_list[i])","def update_data(self, **kwargs):\n self.source_data = self.get_dict()\n for c in self.callbacks[\"update_data\"]:\n c()","def update(self, name, cache_dir=None, data_dir=None, tasks=None):\n assert name, \"Must input a valid dataset name.\"\n self.manager.update_data(name, cache_dir, data_dir, tasks)","def setup(self):\n in_dataset, out_dataset = self.get_datasets()","def setup(self):\n in_dataset, out_dataset = self.get_datasets()","def apply_changes(session: Session, unified_dataset: UnifiedDataset) -> Operation:\n op = _apply_changes_async(session, unified_dataset)\n return operation.wait(session, op)","def add_reference(self, dataset=None):\n if not dataset:\n raise aspecd.exceptions.MissingDatasetError\n dataset_reference = aspecd.dataset.DatasetReference()\n dataset_reference.from_dataset(dataset=dataset)\n self.references.append(dataset_reference)","def _update_dataset(lc, geno, dataset, delete_resources=False):\n package_update_required = False\n if not _dataset_match(geno, dataset):\n dataset.update(_dataset_fields(geno))\n package_update_required = True\n\n chromos = dict(\n (chromo['resource_name'], chromo) for chromo in geno['resources'])\n\n # migrate recombinant1 datasets which had no resource\n # name to identify resource\n if (len(chromos) == 1 and len(dataset['resources']) == 1\n and dataset['resources'][0]['name'] == 'data'):\n dataset['resources'][0]['name'] = geno['resources'][0]['resource_name']\n package_update_required = True\n\n # collect updated resources\n out_resources = []\n for resource in dataset['resources']:\n if resource['name'] not in chromos:\n if not delete_resources:\n out_resources.append(resource)\n continue\n\n r = chromos.pop(resource['name'])\n\n if not _resource_match(r, resource):\n resource.update(_resource_fields(r))\n package_update_required = True\n\n out_resources.append(resource)\n\n # missing resources\n if chromos:\n out_resources.extend(\n # dummy url for old ckan compatibility reasons\n dict(_resource_fields(chromo), url='http://')\n for chromo in chromos.values())\n package_update_required = True\n\n if (package_update_required or\n len(out_resources) != len(dataset['resources'])):\n dataset['resources'] = out_resources\n dataset = lc.call_action('package_update', dataset)\n\n return dataset","def register_signals(self):\n logging.debug(\"BigQuery class created and registering signals\")","def sync():\n\n DFS.update(get_data_without_transactions())\n DFS[c.dfs.TRANS] = get_df_transactions()\n\n YML.update(get_config())","def register_external_data_set(self, external_dataset_id=''):\n # retrieve the data_source object\n data_set_obj = self.clients.resource_registry.read(external_dataset_id)\n if data_set_obj is None:\n raise NotFound(\"External Data Set %s does not exist\" % external_dataset_id)\n\n #create data producer resource and associate to this external_dataset_id\n data_producer_obj = IonObject(RT.DataProducer,name=data_set_obj.name, description=\"primary producer resource for this data set\", is_primary=True)\n data_producer_id, rev = self.clients.resource_registry.create(data_producer_obj)\n\n # Create association\n self.clients.resource_registry.create_association(external_dataset_id, PRED.hasDataProducer, data_producer_id)\n\n return data_producer_id","def dataFeedCell(self, hostname, projects, timestamp, sensorMac, mac, deviceclass, move, cache):\n for project in [i.id for i in projects if i != None]:\n ss = self.getScanSetup(hostname, project, sensorMac)\n ss.logCell(timestamp, mac, deviceclass, move)","def init_dataset(self, queue_name, dataset_size, batch_size, dataset_types, dataset_shapes,\n input_indexs, phase='dataset', need_run=True):\n if not init_exec_dataset(queue_name=queue_name,\n size=dataset_size,\n batch_size=batch_size,\n types=dataset_types,\n shapes=dataset_shapes,\n input_indexs=input_indexs,\n phase=phase,\n need_run=need_run):\n raise RuntimeError(\"Failure to init and dataset subgraph!\")\n self._graph_executor.set_queue_name(queue_name)\n return True","def _apply_changes_async(\n session: Session, unified_dataset: UnifiedDataset\n) -> Operation:\n r = session.post(str(unified_dataset.url) + \":refresh\")\n return operation._from_response(unified_dataset.url.instance, r)","def add_new_dataset_releases(self, release: ThothRelease, **kwargs) -> None:\n dataset_release = DatasetRelease(\n dag_id=self.dag_id,\n dataset_id=self.api_dataset_id,\n dag_run_id=release.run_id,\n snapshot_date=release.snapshot_date,\n data_interval_start=kwargs[\"data_interval_start\"],\n data_interval_end=kwargs[\"data_interval_end\"],\n )\n api = make_observatory_api(observatory_api_conn_id=self.observatory_api_conn_id)\n api.post_dataset_release(dataset_release)","def merge(self, dataset):\n def merge_data(source, dest):\n for key, value in source.items():\n if isinstance(value, dict):\n merge_data(value, dest.setdefault(key, {}))\n else:\n dest[key] = value\n return dest\n\n merge_data(dataset.data, self._data)\n\n for h in dataset.task_history:\n if h not in self._task_history:\n self._task_history.append(h)","def setup(self):\n in_dataset, out_dataset = self.get_datasets()\n \n out_dataset[0].create_dataset(in_dataset[0])\n\n in_pData, out_pData = self.get_plugin_datasets()\n\n in_pData[0].plugin_data_setup( '',)\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')","def _update_data(self, selected):\n if selected.row() != self.datasets.index:\n self.datasets.index = selected.row()\n self.datasets.update_current()\n self._update_main()","def add_new_dataset_releases(self, release: CrossrefMetadataRelease, **kwargs) -> None:\n\n dataset_release = DatasetRelease(\n dag_id=self.dag_id,\n dataset_id=self.api_dataset_id,\n dag_run_id=release.run_id,\n snapshot_date=release.snapshot_date,\n data_interval_start=kwargs[\"data_interval_start\"],\n data_interval_end=kwargs[\"data_interval_end\"],\n )\n api = make_observatory_api(observatory_api_conn_id=self.observatory_api_conn_id)\n api.post_dataset_release(dataset_release)","async def create_active_dataset(\n self, dataset: python_otbr_api.OperationalDataSet\n ) -> None:\n return await self.api.create_active_dataset(dataset)","def save_changes(self):\n change_events = self._transient_event_queue.separate_out(self.is_from_tracked_aggregate)\n for event in change_events:\n self._persistent_event_store.append(event)","def add_data(self, name, data_dir, tasks):\n assert isinstance(name, str), \"Must input a valid dataset name.\"\n assert isinstance(data_dir, str), \"Must input a valid data directory.\"\n assert isinstance(tasks, dict), \"Must input a valid tasks.\"\n\n new_data = {\n \"data_dir\": data_dir,\n \"keywords\": self._get_keywords_from_tasks(tasks),\n \"tasks\": tasks\n }\n self.data[\"dataset\"][name] = new_data\n self.update_categories()\n self.write_data_cache(self.data)","def init_datasets(self, dataset_names, columns):\n for dataset_name in dataset_names:\n hdf5_dataset_name = self.schema.get(dataset_name)\n if hdf5_dataset_name is None:\n warnings.warn(\"Skipping %s (not in schema)\" % dataset_name)\n else:\n self[dataset_name] = tokio.timeseries.TimeSeries(dataset_name=hdf5_dataset_name,\n start=self.query_start,\n end=self.query_end_plusplus,\n timestep=self.timestep,\n num_columns=len(columns),\n column_names=columns,\n sort_hex=self.sort_hex)","def run(self):\r\n self.collect_data()","def run_dataset(data: DataSetBase) -> None:\n\n tracks_manager = data.load_tracks_manager()\n reconstructions = data.load_reconstruction()\n\n all_shot_ids = set(tracks_manager.get_shot_ids())\n for r in reconstructions:\n for shot in r.shots.values():\n if shot.id in all_shot_ids:\n vertices, faces = mesh.triangle_mesh(shot.id, r, tracks_manager)\n shot.mesh.vertices = vertices\n shot.mesh.faces = faces\n\n data.save_reconstruction(\n reconstructions, filename=\"reconstruction.meshed.json\", minify=True\n )","def set_data(self, dataset):\n if dataset is not None:\n self.infoa.setText('%d instances in input data set' % len(dataset))\n self.infob.setText('%d attributes in input data set' % len(dataset.domain.attributes))\n # Limited the batch size between 0.005 to 0.025, in\n # order tk=o make training fats and also accurate\n if(len(dataset) >= 200):\n self.batchsize = int(0.005 * len(dataset))\n self.batch_spin.setMinimum(int(0.005 * len(dataset)))\n self.batch_spin.setMaximum(int(0.025 * len(dataset)))\n else:\n # here the dataset is to small, hence fixed the\n # batch size programmatically\n self.batchsize = 1\n self.batch_spin.setMinimum(1)\n self.batch_spin.setMaximum(10)\n self.optionsBox.setDisabled(False)\n self.layerBox.setDisabled(False)\n self.updateLayer()\n self.dataset = dataset\n self.save_button.setDisabled(True)\n\n else:\n self.infoa.setText('No data on input yet, waiting to get something.')\n self.infob.setText('')\n self.optionsBox.setDisabled(True)\n self.layerBox.setDisabled(True)\n self.dataset = None","def activate_dataset(self, dataset_id: str) -> Dict:\n from pma_api.models import Task\n\n active_task_ids: List[str] = Task.get_present_tasks() # TO-DO 1\n if active_task_ids:\n raise PmaApiTaskDenialError\n\n app.config.SQLALCHEMY_ECHO = False # TO-DO 2\n callback: Generator = \\\n progress_update_callback(task_obj=self, verbose=True)\n task_id: str = self.request.id\n\n try:\n Task.register_active(task_id)\n next(callback) # readies callback to start receiving task updates\n\n file_path: str = download_dataset(int(dataset_id))\n\n this_task = InitDbFromWb(\n callback=callback,\n api_file_path=file_path,\n _app=app)\n this_task.run()\n return {'current': 100, 'total': 100, 'status': 'Completed'}\n finally:\n callback.close()\n Task.register_inactive(task_id)","def setup(self):\n in_dataset, out_dataset = self.get_datasets()\n \n out_dataset[0].create_dataset(in_dataset[0])\n\n in_pData, out_pData = self.get_plugin_datasets()\n\n in_pData[0].plugin_data_setup( 'PROJECTION',multiple)\n out_pData[0].plugin_data_setup( 'PROJECTION',multiple)","def run_dataset(dataset, trackers, debug=False, threads=0, num_gpus=8):\n multiprocessing.set_start_method('spawn', force=True)\n\n print('Evaluating {:4d} trackers on {:5d} sequences'.format(len(trackers), len(dataset)))\n\n multiprocessing.set_start_method('spawn', force=True)\n\n if threads == 0:\n mode = 'sequential'\n else:\n mode = 'parallel'\n\n if mode == 'sequential':\n for seq in dataset:\n for tracker_info in trackers:\n run_sequence(seq, tracker_info, debug=debug)\n elif mode == 'parallel':\n param_list = [(seq, tracker_info, debug, num_gpus) for seq, tracker_info in product(dataset, trackers)]\n with multiprocessing.Pool(processes=threads) as pool:\n pool.starmap(run_sequence, param_list)\n print('Done')","def set_all_data_internal(self, check_data=True):\n for key, dataset in self.datasets.items():\n if (\n isinstance(dataset, mfdataarray.MFArray)\n or (\n isinstance(dataset, mfdatalist.MFList)\n and dataset.structure.type == DatumType.recarray\n )\n and dataset.enabled\n ):\n dataset.store_internal(check_data=check_data)","def _push_data_to_learner(self, data_queue):\n raise NotImplementedError","def setup(self):\n in_dataset, out_dataset = self.get_datasets()\n \n out_dataset[0].create_dataset(in_dataset[0])\n out_dataset[1].create_dataset(in_dataset[1])\n\n in_pData, out_pData = self.get_plugin_datasets()\n\n in_pData[0].plugin_data_setup( 'SINOGRAM',multiple)\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')\n\n in_pData[1].plugin_data_setup( 'PROJECTION',)\n out_pData[1].plugin_data_setup( 'PROJECTION','multiple')","def datachange_notification(self, node, val, data):\n \n logger.debug(\"New data change event. node:{}, value:{}\".format(node, val))\n \n # Sorry about these lines of code, but I don't see any nicer way of determining the port number than from \n # the identifier string. Then splitting it up to isolate the port number.\n # Example \"Status.Port_2.Selected\" is split into ['Status', 'Port_2', 'Selected'] then 'Port_2' is split into \n # ['Port', '2'] and then the '2' is turned into an intiger.\n path_list = str(node.nodeid.Identifier).split(\".\")\n\n # We can safely assume that the last term is the tag that updated.\n tag = path_list[-1] \n \n # Figure out the port number\n port_number = None\n if 'Port' in path_list[1]:\n port_number = int(path_list[1].split(\"_\")[-1]) \n \n \"\"\" Switch for each possible tag\"\"\"\n # If the command tag \"Select\" changes go select that port with the instructions saved in the command tag. \n if tag == 'Select' and port_number:\n if val == True:\n node = self.ua_server.get_node(\"ns=2;s=Command.Port_{}.Instructions\".format(port_number))\n instructions = node.get_value()\n self._pbl.select_port(port_number, instructions=instructions)\n # Reset the select flag\n node = self.ua_server.get_node(\"ns=2;s=Command.Port_{}.Select\".format(port_number))\n node.set_value(False)\n \n elif tag == 'Deselect' and port_number:\n if val == True:\n self._pbl.deselect_port(port_number, work_finished=True)\n # Reset the select flag\n node = self.ua_server.get_node(\"ns=2;s=Command.Port_{}.Deselect\".format(port_number))\n node.set_value(False)\n\n elif tag == 'ContentDisplayName' and port_number:\n self._pbl.set_content_key(port_number,'display_name', str(val))\n elif tag == 'ContentName' and port_number:\n self._pbl.set_content_key(port_number,'name', str(val))\n elif tag == 'ContentDescription' and port_number:\n self._pbl.set_content_key(port_number,'description', str(val))\n elif tag == 'ContentImagePath' and port_number:\n self._pbl.set_content_key(port_number,'image_path', str(val))\n \n elif tag == 'Select' and 'ByContent' in path_list[1]:\n if val == True:\n instructions = self.ua_server.get_node(\"ns=2;s=Command.ByContent.Instructions\").get_value()\n name = self.ua_server.get_node(\"ns=2;s=Command.ByContent.Name\").get_value()\n _, selected_port = self._pbl.select_content(name = name, instructions=instructions)\n # Reset the select flag\n node = self.ua_server.get_node(\"ns=2;s=Command.ByContent.Select\")\n node.set_value(False)\n node = self.ua_server.get_node(\"ns=2;s=Command.ByContent.Result\")\n node.set_value(selected_port)\n\n elif tag == 'Deselect' and 'ByContent' in path_list[1]:\n if val == True:\n name = self.ua_server.get_node(\"ns=2;s=Command.ByContent.Name\").get_value()\n self._pbl.deselect_content(name = name, work_finished=True)\n # Reset the select flag\n node = self.ua_server.get_node(\"ns=2;s=Command.ByContent.Deselect\")\n node.set_value(False)","def set_task(task_id, train_dataset, val_dataset, test_dataset):\n assert isinstance(task_id, int)\n train_dataset.set_task(task_id)\n val_dataset.set_task(task_id)\n test_dataset.set_task(task_id)","def upload_all(self):\n adapter = self.get_adapter()\n hostname = socket.gethostname()\n for node in self.__nodes:\n if node['hostname'] != hostname:\n adapter(node,self.__local_dir).upload(self.__dataset_name)","def _notify_new_log(self, data):\n # Notify listeners\n for lstnr in self.listeners:\n lstnr.new_log_data(self, data)","def update_dataset(\n self,\n dataset: DatasetDB,\n ) -> DatasetDB:\n dataset_id = dataset.id\n\n self._es.update_document(\n index=DATASETS_INDEX_NAME,\n doc_id=dataset_id,\n document=self._dataset_to_es_doc(dataset),\n )\n return dataset","def match_data(self, datasets):\n raise NotImplementedError","def datasets(self):\n pass","def test_acquire_dataset(self):\n\n # make sure the data does not yet exist\n with self.subTest(name='no data yet'):\n response = Epidata.covid_hosp('MA', Epidata.range(20200101, 20210101))\n self.assertEqual(response['result'], -2)\n\n # acquire sample data into local database\n # mock out network calls to external hosts\n with self.subTest(name='first acquisition'), \\\n patch.object(Network, 'fetch_metadata', return_value=self.test_utils.load_sample_metadata()) as mock_fetch_meta, \\\n patch.object(Network, 'fetch_dataset', side_effect=[self.test_utils.load_sample_dataset(\"dataset0.csv\"), # dataset for 3/13\n self.test_utils.load_sample_dataset(\"dataset0.csv\"), # first dataset for 3/15\n self.test_utils.load_sample_dataset()] # second dataset for 3/15\n ) as mock_fetch:\n acquired = Update.run()\n self.assertTrue(acquired)\n self.assertEqual(mock_fetch_meta.call_count, 1)\n\n # make sure the data now exists\n with self.subTest(name='initial data checks'):\n response = Epidata.covid_hosp('WY', Epidata.range(20200101, 20210101))\n self.assertEqual(response['result'], 1)\n self.assertEqual(len(response['epidata']), 1)\n row = response['epidata'][0]\n self.assertEqual(row['state'], 'WY')\n self.assertEqual(row['date'], 20201209)\n self.assertEqual(row['issue'], 20210315)\n self.assertEqual(row['critical_staffing_shortage_today_yes'], 8)\n actual = row['inpatient_bed_covid_utilization']\n expected = 0.11729857819905214\n self.assertAlmostEqual(actual, expected)\n self.assertIsNone(row['critical_staffing_shortage_today_no'])\n\n # expect 61 fields per row (63 database columns, except `id` and `record_type`)\n self.assertEqual(len(row), 61)\n\n with self.subTest(name='all date batches acquired'):\n response = Epidata.covid_hosp('WY', Epidata.range(20200101, 20210101), issues=20210313)\n self.assertEqual(response['result'], 1)\n\n # re-acquisition of the same dataset should be a no-op\n with self.subTest(name='second acquisition'), \\\n patch.object(Network, 'fetch_metadata', return_value=self.test_utils.load_sample_metadata()) as mock_fetch_meta, \\\n patch.object(Network, 'fetch_dataset', return_value=self.test_utils.load_sample_dataset()) as mock_fetch:\n acquired = Update.run()\n self.assertFalse(acquired)\n\n # make sure the data still exists\n with self.subTest(name='final data checks'):\n response = Epidata.covid_hosp('WY', Epidata.range(20200101, 20210101))\n self.assertEqual(response['result'], 1)\n self.assertEqual(len(response['epidata']), 1)","def test_BaseDataContext_add_datasource_updates_cache(\n in_memory_runtime_context: EphemeralDataContext,\n pandas_enabled_datasource_config: dict,\n) -> None:\n context = in_memory_runtime_context\n\n name = pandas_enabled_datasource_config[\"name\"]\n\n assert name not in context.datasources\n\n context.add_datasource(**pandas_enabled_datasource_config)\n\n assert name in context.datasources","def submit_dataset():\n\n if request.method == \"POST\":\n \"\"\"\n request could look like:\n {\n \"dataset_id\": \"HRI0\", # name of relevant workflow\n \"source_bucket\": \"HRI0\", \n \"source_key\": \"HRIO/workflow/std_dev_HRI0.py\", \n \"pid\": 2, \n\n }\n \"\"\"\n\n req = request.get_json(force=True)\n if dataset_exists(req[\"dataset_id\"], req['PID']):\n\n app.logger.error(\n f\"Error saving dataset, dataset already exists. (Try updating the dataset) \"\n f\"for party {req['PID']}\"\n )\n\n response = {\n \"MSG\": f\"ERR: Dataset {req['dataset_id']} already exists.\"\n }\n else:\n\n app.logger.info(\n f\"Saving dataset {req['dataset_id']}\"\n )\n\n save_dataset(req['dataset_id'], req['source_bucket'], req['source_key'], req['PID'])\n response = {\n \"MSG\": \"OK\"\n }\n\n return jsonify(response)","def _update_datastore(lc, geno, dataset, force_update=False):\n resource_ids = dict((r['name'], r['id']) for r in dataset['resources'])\n\n for chromo in geno['resources']:\n assert chromo['resource_name'] in resource_ids, (\n \"dataset missing resource for resource name\",\n chromo['resource_name'], dataset['id'])\n resource_id = resource_ids[chromo['resource_name']]\n fields = _datastore_fields(chromo['fields'])\n try:\n ds = lc.action.datastore_search(resource_id=resource_id, limit=0)\n except NotFound:\n pass\n else:\n if not force_update and _datastore_match(\n chromo['fields'], ds['fields']):\n continue\n # extra work here to maintain existing fields+ordering\n # datastore_create rejects our list otherwise\n fields = ds['fields'][1:] # trim _id field\n seen = set(f['id'] for f in fields)\n for f in _datastore_fields(chromo['fields']):\n if f['id'] not in seen:\n fields.append(f)\n\n lc.action.datastore_create(\n resource_id=resource_id,\n fields=fields,\n primary_key=chromo.get('datastore_primary_key', []),\n indexes=chromo.get('datastore_indexes', []),\n force=True)","def test_import_datasets_sync_argument_metrics(\n import_datasets_command, app_context, fs\n):\n # pylint: disable=reimported, redefined-outer-name\n import superset.cli.importexport # noqa: F811\n\n # reload to define export_datasets correctly based on the\n # feature flags\n importlib.reload(superset.cli.importexport)\n\n # write YAML file\n with open(\"dataset.yaml\", \"w\") as fp:\n fp.write(\"hello: world\")\n\n runner = app.test_cli_runner()\n response = runner.invoke(\n superset.cli.importexport.import_datasources,\n [\"-p\", \"dataset.yaml\", \"-s\", \"metrics\"],\n )\n\n assert response.exit_code == 0\n expected_contents = {\"dataset.yaml\": \"hello: world\"}\n import_datasets_command.assert_called_with(\n expected_contents,\n sync_columns=False,\n sync_metrics=True,\n )","def add_joined_dataset(self, new_data):\n self._add_linked_data(self.JOINED_DATASETS, self.joined_dataset_ids,\n new_data)","def update_data(self):\n # Just set data_changed, the component should do the rest.\n self.data_changed = True","def update_dataset(self, data_name: str, append: pd.DataFrame):\n df = getattr(self, data_name)\n setattr(self, data_name, df.join(append, how='left'))","def _update_dataset_param(self, dataset):\n if dataset is None and self.dataset is None:\n return []\n if dataset is 'all':\n dataset = ''\n if dataset is None and self.dataset is not None:\n dataset = self.dataset\n return dataset","def _posttrain(self, dataset):\n Classifier._posttrain(self, dataset)\n if self.params.retrainable:\n self.__changedData_isset = False","def dataFeedWifiIO(self, hostname, projects, timestamp, sensorMac, hwid, type, move, cache):\n for project in [i.id for i in projects if i != None]:\n ss = self.getScanSetup(hostname, project, sensorMac)\n ss.logCell(timestamp, hwid, type, move)","def _collect_data(self) -> None:\n self.set_websocket_data()\n self.set_stratum_data()\n self.set_cache_data()\n self.collect_peer_connection_metrics()\n self.set_tx_storage_data()","def data_upgrades():\n pass","def data_upgrades():\n pass","def setup(self):\n # TODO check if need both dataset together\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \n tokenizer=self.tokenizer, vocab=\"bert\")\n self.vocabulary = self.train_dataset.vocabulary\n\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\n tokenizer=self.tokenizer, vocab=self.vocabulary)\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)","def on_data(self, data):\n # store the data\n self._storage.append(data)","def upload():\n \n if request.method == 'GET':\n return jsonify({'status': 'Error!', 'message': 'Please use POST request'})\n else:\n data = session['data']\n arg1 = data['arg1'].lower()\n rel = data['rel'].lower()\n arg2 = data['arg2'].lower()\n arg2 = arg2.replace(' ','')\n\n hash_name = 'dataset_map'\n hash_key = '{0}_{1}_{2}'.format(arg1, rel, arg2)\n\n modler = DatasetModels()\n current_ids = modler.fetch_ids('claims')\n\n url = RedisCache.fetchValue(hash_name, hash_key, 'dataset_url')\n\n if not url:\n return jsonify({'status': 'Error!', 'message': 'Url not found'})\n else:\n file_name = \"{0}.csv\".format(hash_key)\n status_code = modler.upload_dataset(url, 'claims', file_name)\n if status_code != 200:\n return jsonify({'status': 'Error!', 'message': 'Dataset Uploaded'})\n else:\n new_ids = modler.fetch_ids('claims')\n latest_id = list(set(new_ids) - set(current_ids))\n RedisCache.createHash(hash_name, hash_key, {'dataset_id': latest_id[0]})\n RedisCache.createHash('runset_map', hash_key, {'dataset_id': latest_id[0]})\n\n return redirect(url_for('runset.create_runset'), code=307)","def flush(self):\n request = self._request\n has_data = False\n for (\n run_name,\n tag_to_time_series_data,\n ) in self._run_to_tag_to_time_series_data.items():\n r = tensorboard_service.WriteTensorboardRunDataRequest(\n tensorboard_run=self._one_platform_resource_manager.get_run_resource_name(\n run_name\n )\n )\n r.time_series_data = list(tag_to_time_series_data.values())\n _prune_empty_time_series(r)\n if not r.time_series_data:\n continue\n request.write_run_data_requests.extend([r])\n has_data = True\n\n if not has_data:\n return\n\n self._rpc_rate_limiter.tick()\n\n with uploader_utils.request_logger(request):\n with self._get_tracker():\n try:\n self._api.write_tensorboard_experiment_data(\n tensorboard_experiment=request.tensorboard_experiment,\n write_run_data_requests=request.write_run_data_requests,\n )\n except grpc.RpcError as e:\n if (\n hasattr(e, \"code\")\n and getattr(e, \"code\")() == grpc.StatusCode.NOT_FOUND\n ):\n raise ExperimentNotFoundError() from e\n logger.error(\"Upload call failed with error %s\", e)\n\n self._new_request()","def update_all_data(self):\n self.dataChanged.emit(qtc.QModelIndex(), qtc.QModelIndex())","def new_data(title, data_y, mpi_reduce=False, **kwds):\n _check_type(title, data_y)\n event_id = evt.event_id()\n if(ipc.mpi.is_slave()):\n if(mpi_reduce):\n ipc.mpi.send_reduce(title, 'new_data', data_y, event_id, **kwds)\n else:\n m = hashlib.md5()\n m.update(bytes(title))\n if m.digest() in ipc.mpi.subscribed:\n ipc.mpi.send(title, [ipc.uuid, 'new_data', title, data_y,\n event_id, kwds])\n else:\n logging.debug('%s not subscribed, not sending' % (title))\n else:\n ipc.zmq().send(title, [ipc.uuid, 'new_data', title, data_y,\n event_id, kwds])\n logging.debug(\"Sending data on source '%s'\" % title)","def reload_data(self):\n self._avro_payload.reload_data()","def reloadData(self):\n self.dto.readFromData()\n print(\"Record reloaded.\")","def run_dataset(dataset, trackers, debug=False, threads=0, visdom_info=None):\n print('Evaluating {:4d} trackers on {:5d} sequences'.format(len(trackers), len(dataset)))\n\n visdom_info = {} if visdom_info is None else visdom_info\n\n if threads == 0:\n mode = 'sequential'\n else:\n mode = 'parallel'\n\n if mode == 'sequential':\n for seq in dataset:\n for tracker_info in trackers:\n run_sequence(seq, tracker_info, debug=debug, visdom_info=visdom_info)\n elif mode == 'parallel':\n param_list = [(seq, tracker_info, debug, visdom_info) for seq, tracker_info in product(dataset, trackers)]\n with multiprocessing.Pool(processes=threads) as pool:\n pool.starmap(run_sequence, param_list)\n print('Done')","def testStringDatasetId(self):\n cluster = self.make_test_cluster(num_workers=1)\n dataset = dataset_ops.Dataset.range(10)\n dataset_id = data_service_ops.register_dataset(\n cluster.dispatcher.target, dataset\n )\n dataset_id_str = (\n dataset_id\n if dataset_id.dtype == dtypes.string\n else string_ops.as_string(dataset_id)\n )\n dataset = data_service_ops.from_dataset_id(\n dataset_id=dataset_id_str,\n element_spec=dataset.element_spec,\n processing_mode=data_service_ops.ShardingPolicy.OFF,\n service=cluster.dispatcher.target,\n job_name=\"job_name\",\n )\n self.assertDatasetProduces(dataset, list(range(10)))","def to_dataset(self):\n if not self.type:\n raise aspecd.exceptions.MissingDatasetError\n dataset = aspecd.utils.object_from_class_name(self.type)\n dataset.id = self.id\n for history_record in self.history:\n history_record.replay(dataset)\n return dataset","def onGroupDataset(self, event):\n\t\tselections = self.tree.GetSelections()\n\t\tif not selections and self.selectedItem:\n\t\t\tselections = [self.selectedItem]\n\t\tif not self.groupedDataUnit:\n\t\t\tpluginLoader = Modules.DynamicLoader.getPluginLoader()\n\t\t\ttaskMod = pluginLoader.getPluginModule(\"Task\", \"Process\")\n\t\t\tunitType = taskMod.getDataUnit()\n\t\t\tmoduleType = pluginLoader.getPluginClass(\"Task\",\"Process\")\n\t\t\tself.groupedDataUnit = unitType()\n\t\t\tmodule = moduleType()\n\t\t\tself.groupedDataUnit.setModule(module)\n\t\t\n\t\tfor item in selections:\n\t\t\tif item in self.groupedItems: continue\n\t\t\tself.groupedItems.append(item)\n\t\t\tself.markBlue([item], \"}\")\n\t\t\tobj = self.tree.GetPyData(item)\n\t\t\tprint \"Adding source dataunit\",obj\n\t\t\tself.groupedDataUnit.addSourceDataUnit(obj)\n\t\tsetting = self.groupedDataUnit.getSettings()\n\t\tsetting.set(\"FilterList\",[])\n\t\t\n\t\tprint \"Now=\",self.groupedDataUnit.getSourceDataUnits()","def push_tensorboard_eval(self, epoch, dataset_name):\n pass"],"string":"[\n \"def datasets(self, datasets):\\n\\n self._datasets = datasets\",\n \"def add_to_dataset(self, dataset: Dataset):\\n pass\",\n \"def datasets(self, datasets):\\n self.__datasetsAll = datasets\\n self.__datasets = list(datasets)\\n self.__axisDomains = None\\n for ds in self.__datasetsAll:\\n self.__datasetsPerClass[ds[-1]] = self.__datasetsPerClass.get(ds[-1], 0) + 1\\n self.dataChanged.emit()\",\n \"def setDataset(self,dataset):\\n self.__dataSet = dataset\",\n \"def refresh_train_dataset(self):\\n inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label = self.build_data(self.reader, self.train_items, self.option.max_path_length)\\n self.train_dataset = CodeDataset(inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label)\",\n \"def assign_dataset(self, dataset_uuid):\\n assert os.path.exists(os.path.join(DATASETS_PATH, dataset_uuid))\\n self.dataset_uuid = dataset_uuid\\n if self.name is None:\\n self.set_name(dataset_uuid)\",\n \"def run_dataset(data: DataSetBase):\\n\\n tracks_manager = data.load_tracks_manager()\\n report, reconstructions = reconstruction.incremental_reconstruction(\\n data, tracks_manager\\n )\\n data.save_reconstruction(reconstructions)\\n data.save_report(io.json_dumps(report), \\\"reconstruction.json\\\")\",\n \"def set_dataset(self, role, dset):\\n\\n\\t\\tself.datasets[role] = dset\",\n \"def refresh_test_dataset(self):\\n inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label = self.build_data(self.reader, self.test_items, self.option.max_path_length)\\n self.test_dataset = CodeDataset(inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label)\",\n \"def add_dataset(self, task_name, dataset=None, *, aliases=None):\\n self._datasets.append(dataset if dataset is not None else TaskData())\\n last_index = len(self._datasets) - 1\\n self._aliases[task_name] = last_index\\n\\n if aliases is not None:\\n for alias in aliases:\\n self._aliases[alias] = last_index\\n\\n if len(self._datasets) == 1:\\n self._default_index = 0\",\n \"def _push_to_server(self) -> None:\\n timestamp = int(arrow.get().float_timestamp * 1000)\\n\\n datapoints: List[Dict[str, Union[str, List[Tuple[float, float]]]]] = []\\n\\n for metric in REGISTRY.collect():\\n if type(metric) == Metric and metric.type in [\\\"gauge\\\", \\\"counter\\\"]:\\n if len(metric.samples) == 0:\\n continue\\n\\n external_id = self.external_id_prefix + metric.name\\n datapoints.append({\\\"externalId\\\": external_id, \\\"datapoints\\\": [(timestamp, metric.samples[0].value)]})\\n\\n self.cdf_client.datapoints.insert_multiple(datapoints)\\n self.logger.debug(\\\"Pushed metrics to CDF tenant '%s'\\\", self._cdf_project)\",\n \"def send_chart_data(_, changes):\\n for model, change in changes:\\n if isinstance(model, Temperature) and change == 'insert':\\n temp_data = model\\n socketio.emit('chart_data', json.dumps(temp_data.serialize))\",\n \"def update_data(self, name, cache_dir=None, data_dir=None, tasks=None):\\n assert name, \\\"Must input a valid dataset name.\\\"\\n assert name in self.data[\\\"dataset\\\"], \\\"The dataset \\\\'{}\\\\' does not exist in the cache.\\\" \\\\\\n .format(name)\\n if cache_dir:\\n self.data[\\\"dataset\\\"][name][\\\"cache_dir\\\"] = cache_dir\\n if data_dir:\\n self.data[\\\"dataset\\\"][name][\\\"data_dir\\\"] = data_dir\\n if tasks:\\n self.data[\\\"dataset\\\"][name][\\\"tasks\\\"] = tasks\\n self.data[\\\"dataset\\\"][name][\\\"keywords\\\"] = self._get_keywords_from_tasks(tasks)\\n if cache_dir or data_dir or tasks:\\n self.update_categories()\\n self.write_data_cache(self.data)\",\n \"def data_changed(self):\\n self.data_changed_signal.emit(self)\",\n \"def refresh(dataset, client):\\n pass\",\n \"def registerSampleData():\\n # It is always recommended to provide sample data for users to make it easy to try the module,\\n # but if no sample data is available then this method (and associated startupCompeted signal connection) can be removed.\\n\\n import SampleData\\n iconsPath = os.path.join(os.path.dirname(__file__), 'Resources/Icons')\\n\\n # To ensure that the source code repository remains small (can be downloaded and installed quickly)\\n # it is recommended to store data sets that are larger than a few MB in a Github release.\\n\\n # RegularizedFastMarching1\\n SampleData.SampleDataLogic.registerCustomSampleDataSource(\\n # Category and sample name displayed in Sample Data module\\n category='RegularizedFastMarching',\\n sampleName='RegularizedFastMarching1',\\n # Thumbnail should have size of approximately 260x280 pixels and stored in Resources/Icons folder.\\n # It can be created by Screen Capture module, \\\"Capture all views\\\" option enabled, \\\"Number of images\\\" set to \\\"Single\\\".\\n thumbnailFileName=os.path.join(iconsPath, 'RegularizedFastMarching1.png'),\\n # Download URL and target file name\\n uris=\\\"https://github.com/Slicer/SlicerTestingData/releases/download/SHA256/998cb522173839c78657f4bc0ea907cea09fd04e44601f17c82ea27927937b95\\\",\\n fileNames='RegularizedFastMarching1.nrrd',\\n # Checksum to ensure file integrity. Can be computed by this command:\\n # import hashlib; print(hashlib.sha256(open(filename, \\\"rb\\\").read()).hexdigest())\\n checksums = 'SHA256:998cb522173839c78657f4bc0ea907cea09fd04e44601f17c82ea27927937b95',\\n # This node name will be used when the data set is loaded\\n nodeNames='RegularizedFastMarching1'\\n )\\n\\n # RegularizedFastMarching2\\n SampleData.SampleDataLogic.registerCustomSampleDataSource(\\n # Category and sample name displayed in Sample Data module\\n category='RegularizedFastMarching',\\n sampleName='RegularizedFastMarching2',\\n thumbnailFileName=os.path.join(iconsPath, 'RegularizedFastMarching2.png'),\\n # Download URL and target file name\\n uris=\\\"https://github.com/Slicer/SlicerTestingData/releases/download/SHA256/1a64f3f422eb3d1c9b093d1a18da354b13bcf307907c66317e2463ee530b7a97\\\",\\n fileNames='RegularizedFastMarching2.nrrd',\\n checksums = 'SHA256:1a64f3f422eb3d1c9b093d1a18da354b13bcf307907c66317e2463ee530b7a97',\\n # This node name will be used when the data set is loaded\\n nodeNames='RegularizedFastMarching2'\\n )\",\n \"def switch_to_dataset(self, dataset_name: Optional[str] = None):\\n self._current_dataset_name = self._validate_dataset_name(dataset_name)\",\n \"def from_dataset(self, dataset=None):\\n if not dataset:\\n raise aspecd.exceptions.MissingDatasetError\\n self.type = aspecd.utils.full_class_name(dataset)\\n self.id = dataset.id\\n self.history = copy.deepcopy(dataset.history)\",\n \"def _UpdateDataSetValues( self ):\\n pass\",\n \"def data_flow(self, data_flow):\\n\\n self._data_flow = data_flow\",\n \"def register_dataset_split(dataset_name, split_dict):\\n\\n _DATASET_TYPE_LOAD_FUNC_MAP = {\\n \\\"COCODataset\\\": _register_extended_coco,\\n \\\"COCOText\\\": _register_coco_text,\\n \\\"COCOTextDataset\\\": _register_coco_text,\\n \\\"LVISDataset\\\": _register_extended_lvis,\\n }\\n\\n factory = split_dict.get(\\\"DS_TYPE\\\", \\\"COCODataset\\\")\\n _DATASET_TYPE_LOAD_FUNC_MAP[factory](\\n dataset_name=dataset_name, split_dict=split_dict\\n )\",\n \"def rDataChanged(self):\\n\\n self._queues.uResolutionTab.refreshData()\\n self._layerManager.updateReviewLayer()\",\n \"def __init__(self, name_dataset, reduce=False):\\n dataset = TUDataset(root='data/TUDataset', name=name_dataset)\\n if reduce:\\n new_dataset = []\\n for i in tqdm(range(len(dataset))):\\n aux_graph = copy.deepcopy(dataset[i])\\n aux_graph.edge_index = TUDData.reduce_edges(aux_graph.edge_index)\\n new_dataset.append(copy.deepcopy(aux_graph))\\n dataset = WrapperSynthetic(new_dataset, dataset.num_node_features,\\n dataset.num_classes, None)\\n super(TUDData, self).__init__(dataset)\",\n \"def add_tag(self, dataset: \\\"Dataset\\\", tag: \\\"DatasetTag\\\"):\\n raise NotImplementedError\",\n \"def update_data(client, dataset_id, dataset_name, updated_dataset):\\n view = client.views.lookup(dataset_id)\\n revision = view.revisions.create_replace_revision(permission='private')\\n upload = revision.create_upload(dataset_name)\\n\\n # The path of the updated dataset should be a string to the csv, geojson, shapefile zip, etc.\\n if type(updated_dataset) == str:\\n with open(updated_dataset, 'rb') as f:\\n extension = os.path.splitext(updated_dataset)[1]\\n if extension == '.csv':\\n source = upload.csv(f)\\n elif extension == '.xls':\\n source = upload.xls(f)\\n elif extension == 'xlsx':\\n source = upload.xlsx(f)\\n elif extension == '.tsv':\\n source = upload.tsv(f)\\n elif extension == '.zip':\\n source = upload.shapefile(f)\\n elif extension == '.kml':\\n source = upload.kml(f)\\n elif extension == '.geojson':\\n source = upload.geojson(f)\\n else:\\n raise Exception('File format not supported')\\n elif type(updated_dataset) == pd.DataFrame or type(updated_dataset) == gpd.GeoDataFrame:\\n source = upload.df(updated_dataset)\\n\\n output_schema = source.get_latest_input_schema().get_latest_output_schema()\\n\\n output_schema = output_schema.wait_for_finish()\\n\\n # check for errors\\n assert output_schema.attributes['error_count'] == 0\\n print(output_schema.attributes['error_count'])\\n\\n # If you want, you can get a csv stream of all the errors\\n errors = output_schema.schema_errors_csv()\\n for line in errors.iter_lines():\\n print(line)\\n\\n #############################################################################\\n # The next few lines of code will update the draft/revision into the asset. #\\n # Do not run if you plan on keeping your draft! #\\n #############################################################################\\n job = revision.apply(output_schema=output_schema)\\n\\n # This code outputs the status from the Job object\\n # Track the async process\\n def job_progress(job):\\n clear_output(wait=True)\\n print(job.attributes['log'][0]['stage'])\\n print('Job progress:', job.attributes['status'])\\n\\n job = job.wait_for_finish(progress = job_progress)\\n sys.exit(0 if job.attributes['status'] == 'successful' else 1)\",\n \"def push_dataset_realtime(platform_id, # type: str\\n dataset_id, # type: str\\n dataset, # type: Union[str, pandas.DataFrame]\\n push_key, # type: str\\n format='csv', # type: str\\n csv_separator=';', # type: str\\n **other_opts\\n ):\\n\\n client = ODSClient(platform_id=platform_id)\\n return client.push_dataset_realtime(dataset_id=dataset_id,\\n dataset=dataset,\\n push_key=push_key,\\n format=format,\\n csv_separator=csv_separator,\\n **other_opts)\",\n \"def save(self, dataset_id=None):\\n if dataset_id is None:\\n dataset_id = uuid.uuid4().hex\\n\\n record = {\\n DATASET_ID: dataset_id,\\n DATASET_OBSERVATION_ID: uuid.uuid4().hex,\\n self.AGGREGATED_DATASETS: {},\\n self.CREATED_AT: strftime(\\\"%Y-%m-%d %H:%M:%S\\\", gmtime()),\\n self.STATE: self.STATE_PENDING,\\n self.PENDING_UPDATES: [],\\n }\\n\\n return super(self.__class__, self).save(record)\",\n \"def _update_modified_data_sources(self):\\n new_last_imported = datetime.utcnow()\\n self._update_modified_since(self.last_imported)\\n self.last_imported = new_last_imported\",\n \"def DataSet(self, dataset):\\n # Do we have dataset to store\\n if dataset and dataset.VTKObject:\\n # Do we need to create a vtkWeakReference\\n if not hasattr(self, '_dataset') or self._dataset is None:\\n self._dataset = vtkWeakReference()\\n\\n self._dataset.Set(dataset.VTKObject)\\n else:\\n self._dataset = None\",\n \"def datachange_notification(self, node: Node, val, data):\\n _logger.info('datachange_notification %r %s', node, val)\",\n \"def test_dataset_details():\\n with new_test_dataset(2) as test_ds:\\n args = build_register_args(test_ds.copy_to_s3())\\n ds_name = args['name']\\n URLs.run(url_info=URLs.register_url(), json_body=args)\\n\\n ds_parts = URLs.run(url_info=URLs.dataset_parts_url(ds_name)).json\\n assert ds_parts['filenames'] == test_ds.expected_parts.filenames\\n expected_columns = json.loads(datafile_schema().to_json())['columns']\\n\\n ds_short_schema = URLs.run(url_info=URLs.dataset_schema_url(ds_name, full=False)).json\\n assert ds_short_schema['columns'] == expected_columns\\n\\n ds_full_schema = URLs.run(url_info=URLs.dataset_schema_url(ds_name, full=True)).json\\n assert ds_full_schema['columns'][DEFAULT_TIMESTAMP_COLUMN]['colattrs']['numericMin'] == BASE_TIME\\n\\n URLs.run(url_info=URLs.unregister_url(ds_name))\",\n \"def add_to_dataset(self, begin_ts):\\n\\n if self.revisions_timestamps:\\n last_revision_timestamp = self.revisions_timestamps[-1]\\n revisions_twr = Metrics.list_twr(self.revisions_timestamps, begin_ts, last_revision_timestamp)\\n fixes_twr = Metrics.list_twr(self.fixes_timestamps, begin_ts, last_revision_timestamp)\\n authors_twr = Metrics.list_twr(self.authors_timestamps, begin_ts, last_revision_timestamp)\\n self.last_twr = (revisions_twr, fixes_twr, authors_twr)\\n Metrics.fixes_dataset.add((revisions_twr, fixes_twr, authors_twr))\",\n \"def setup(self, ds):\\n pass\",\n \"def update_dataset(self, dataset, name=None, description=None):\\n uri = URITemplate(self.baseuri + '/{owner}/{id}').expand(\\n owner=self.username, id=dataset)\\n return self.session.patch(uri, json=self._attribs(name, description))\",\n \"def on_new_data(self):\\n\\n if self.connected:\\n tab_open = self.tab_open()\\n\\n # Update plot data\\n for i, series in enumerate(self.measurements_list):\\n if i == tab_open:\\n self.plotted_data[i].setData(self.data_indices, self.measurements_list[i])\",\n \"def update_data(self, **kwargs):\\n self.source_data = self.get_dict()\\n for c in self.callbacks[\\\"update_data\\\"]:\\n c()\",\n \"def update(self, name, cache_dir=None, data_dir=None, tasks=None):\\n assert name, \\\"Must input a valid dataset name.\\\"\\n self.manager.update_data(name, cache_dir, data_dir, tasks)\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\",\n \"def apply_changes(session: Session, unified_dataset: UnifiedDataset) -> Operation:\\n op = _apply_changes_async(session, unified_dataset)\\n return operation.wait(session, op)\",\n \"def add_reference(self, dataset=None):\\n if not dataset:\\n raise aspecd.exceptions.MissingDatasetError\\n dataset_reference = aspecd.dataset.DatasetReference()\\n dataset_reference.from_dataset(dataset=dataset)\\n self.references.append(dataset_reference)\",\n \"def _update_dataset(lc, geno, dataset, delete_resources=False):\\n package_update_required = False\\n if not _dataset_match(geno, dataset):\\n dataset.update(_dataset_fields(geno))\\n package_update_required = True\\n\\n chromos = dict(\\n (chromo['resource_name'], chromo) for chromo in geno['resources'])\\n\\n # migrate recombinant1 datasets which had no resource\\n # name to identify resource\\n if (len(chromos) == 1 and len(dataset['resources']) == 1\\n and dataset['resources'][0]['name'] == 'data'):\\n dataset['resources'][0]['name'] = geno['resources'][0]['resource_name']\\n package_update_required = True\\n\\n # collect updated resources\\n out_resources = []\\n for resource in dataset['resources']:\\n if resource['name'] not in chromos:\\n if not delete_resources:\\n out_resources.append(resource)\\n continue\\n\\n r = chromos.pop(resource['name'])\\n\\n if not _resource_match(r, resource):\\n resource.update(_resource_fields(r))\\n package_update_required = True\\n\\n out_resources.append(resource)\\n\\n # missing resources\\n if chromos:\\n out_resources.extend(\\n # dummy url for old ckan compatibility reasons\\n dict(_resource_fields(chromo), url='http://')\\n for chromo in chromos.values())\\n package_update_required = True\\n\\n if (package_update_required or\\n len(out_resources) != len(dataset['resources'])):\\n dataset['resources'] = out_resources\\n dataset = lc.call_action('package_update', dataset)\\n\\n return dataset\",\n \"def register_signals(self):\\n logging.debug(\\\"BigQuery class created and registering signals\\\")\",\n \"def sync():\\n\\n DFS.update(get_data_without_transactions())\\n DFS[c.dfs.TRANS] = get_df_transactions()\\n\\n YML.update(get_config())\",\n \"def register_external_data_set(self, external_dataset_id=''):\\n # retrieve the data_source object\\n data_set_obj = self.clients.resource_registry.read(external_dataset_id)\\n if data_set_obj is None:\\n raise NotFound(\\\"External Data Set %s does not exist\\\" % external_dataset_id)\\n\\n #create data producer resource and associate to this external_dataset_id\\n data_producer_obj = IonObject(RT.DataProducer,name=data_set_obj.name, description=\\\"primary producer resource for this data set\\\", is_primary=True)\\n data_producer_id, rev = self.clients.resource_registry.create(data_producer_obj)\\n\\n # Create association\\n self.clients.resource_registry.create_association(external_dataset_id, PRED.hasDataProducer, data_producer_id)\\n\\n return data_producer_id\",\n \"def dataFeedCell(self, hostname, projects, timestamp, sensorMac, mac, deviceclass, move, cache):\\n for project in [i.id for i in projects if i != None]:\\n ss = self.getScanSetup(hostname, project, sensorMac)\\n ss.logCell(timestamp, mac, deviceclass, move)\",\n \"def init_dataset(self, queue_name, dataset_size, batch_size, dataset_types, dataset_shapes,\\n input_indexs, phase='dataset', need_run=True):\\n if not init_exec_dataset(queue_name=queue_name,\\n size=dataset_size,\\n batch_size=batch_size,\\n types=dataset_types,\\n shapes=dataset_shapes,\\n input_indexs=input_indexs,\\n phase=phase,\\n need_run=need_run):\\n raise RuntimeError(\\\"Failure to init and dataset subgraph!\\\")\\n self._graph_executor.set_queue_name(queue_name)\\n return True\",\n \"def _apply_changes_async(\\n session: Session, unified_dataset: UnifiedDataset\\n) -> Operation:\\n r = session.post(str(unified_dataset.url) + \\\":refresh\\\")\\n return operation._from_response(unified_dataset.url.instance, r)\",\n \"def add_new_dataset_releases(self, release: ThothRelease, **kwargs) -> None:\\n dataset_release = DatasetRelease(\\n dag_id=self.dag_id,\\n dataset_id=self.api_dataset_id,\\n dag_run_id=release.run_id,\\n snapshot_date=release.snapshot_date,\\n data_interval_start=kwargs[\\\"data_interval_start\\\"],\\n data_interval_end=kwargs[\\\"data_interval_end\\\"],\\n )\\n api = make_observatory_api(observatory_api_conn_id=self.observatory_api_conn_id)\\n api.post_dataset_release(dataset_release)\",\n \"def merge(self, dataset):\\n def merge_data(source, dest):\\n for key, value in source.items():\\n if isinstance(value, dict):\\n merge_data(value, dest.setdefault(key, {}))\\n else:\\n dest[key] = value\\n return dest\\n\\n merge_data(dataset.data, self._data)\\n\\n for h in dataset.task_history:\\n if h not in self._task_history:\\n self._task_history.append(h)\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\\n \\n out_dataset[0].create_dataset(in_dataset[0])\\n\\n in_pData, out_pData = self.get_plugin_datasets()\\n\\n in_pData[0].plugin_data_setup( '',)\\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')\",\n \"def _update_data(self, selected):\\n if selected.row() != self.datasets.index:\\n self.datasets.index = selected.row()\\n self.datasets.update_current()\\n self._update_main()\",\n \"def add_new_dataset_releases(self, release: CrossrefMetadataRelease, **kwargs) -> None:\\n\\n dataset_release = DatasetRelease(\\n dag_id=self.dag_id,\\n dataset_id=self.api_dataset_id,\\n dag_run_id=release.run_id,\\n snapshot_date=release.snapshot_date,\\n data_interval_start=kwargs[\\\"data_interval_start\\\"],\\n data_interval_end=kwargs[\\\"data_interval_end\\\"],\\n )\\n api = make_observatory_api(observatory_api_conn_id=self.observatory_api_conn_id)\\n api.post_dataset_release(dataset_release)\",\n \"async def create_active_dataset(\\n self, dataset: python_otbr_api.OperationalDataSet\\n ) -> None:\\n return await self.api.create_active_dataset(dataset)\",\n \"def save_changes(self):\\n change_events = self._transient_event_queue.separate_out(self.is_from_tracked_aggregate)\\n for event in change_events:\\n self._persistent_event_store.append(event)\",\n \"def add_data(self, name, data_dir, tasks):\\n assert isinstance(name, str), \\\"Must input a valid dataset name.\\\"\\n assert isinstance(data_dir, str), \\\"Must input a valid data directory.\\\"\\n assert isinstance(tasks, dict), \\\"Must input a valid tasks.\\\"\\n\\n new_data = {\\n \\\"data_dir\\\": data_dir,\\n \\\"keywords\\\": self._get_keywords_from_tasks(tasks),\\n \\\"tasks\\\": tasks\\n }\\n self.data[\\\"dataset\\\"][name] = new_data\\n self.update_categories()\\n self.write_data_cache(self.data)\",\n \"def init_datasets(self, dataset_names, columns):\\n for dataset_name in dataset_names:\\n hdf5_dataset_name = self.schema.get(dataset_name)\\n if hdf5_dataset_name is None:\\n warnings.warn(\\\"Skipping %s (not in schema)\\\" % dataset_name)\\n else:\\n self[dataset_name] = tokio.timeseries.TimeSeries(dataset_name=hdf5_dataset_name,\\n start=self.query_start,\\n end=self.query_end_plusplus,\\n timestep=self.timestep,\\n num_columns=len(columns),\\n column_names=columns,\\n sort_hex=self.sort_hex)\",\n \"def run(self):\\r\\n self.collect_data()\",\n \"def run_dataset(data: DataSetBase) -> None:\\n\\n tracks_manager = data.load_tracks_manager()\\n reconstructions = data.load_reconstruction()\\n\\n all_shot_ids = set(tracks_manager.get_shot_ids())\\n for r in reconstructions:\\n for shot in r.shots.values():\\n if shot.id in all_shot_ids:\\n vertices, faces = mesh.triangle_mesh(shot.id, r, tracks_manager)\\n shot.mesh.vertices = vertices\\n shot.mesh.faces = faces\\n\\n data.save_reconstruction(\\n reconstructions, filename=\\\"reconstruction.meshed.json\\\", minify=True\\n )\",\n \"def set_data(self, dataset):\\n if dataset is not None:\\n self.infoa.setText('%d instances in input data set' % len(dataset))\\n self.infob.setText('%d attributes in input data set' % len(dataset.domain.attributes))\\n # Limited the batch size between 0.005 to 0.025, in\\n # order tk=o make training fats and also accurate\\n if(len(dataset) >= 200):\\n self.batchsize = int(0.005 * len(dataset))\\n self.batch_spin.setMinimum(int(0.005 * len(dataset)))\\n self.batch_spin.setMaximum(int(0.025 * len(dataset)))\\n else:\\n # here the dataset is to small, hence fixed the\\n # batch size programmatically\\n self.batchsize = 1\\n self.batch_spin.setMinimum(1)\\n self.batch_spin.setMaximum(10)\\n self.optionsBox.setDisabled(False)\\n self.layerBox.setDisabled(False)\\n self.updateLayer()\\n self.dataset = dataset\\n self.save_button.setDisabled(True)\\n\\n else:\\n self.infoa.setText('No data on input yet, waiting to get something.')\\n self.infob.setText('')\\n self.optionsBox.setDisabled(True)\\n self.layerBox.setDisabled(True)\\n self.dataset = None\",\n \"def activate_dataset(self, dataset_id: str) -> Dict:\\n from pma_api.models import Task\\n\\n active_task_ids: List[str] = Task.get_present_tasks() # TO-DO 1\\n if active_task_ids:\\n raise PmaApiTaskDenialError\\n\\n app.config.SQLALCHEMY_ECHO = False # TO-DO 2\\n callback: Generator = \\\\\\n progress_update_callback(task_obj=self, verbose=True)\\n task_id: str = self.request.id\\n\\n try:\\n Task.register_active(task_id)\\n next(callback) # readies callback to start receiving task updates\\n\\n file_path: str = download_dataset(int(dataset_id))\\n\\n this_task = InitDbFromWb(\\n callback=callback,\\n api_file_path=file_path,\\n _app=app)\\n this_task.run()\\n return {'current': 100, 'total': 100, 'status': 'Completed'}\\n finally:\\n callback.close()\\n Task.register_inactive(task_id)\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\\n \\n out_dataset[0].create_dataset(in_dataset[0])\\n\\n in_pData, out_pData = self.get_plugin_datasets()\\n\\n in_pData[0].plugin_data_setup( 'PROJECTION',multiple)\\n out_pData[0].plugin_data_setup( 'PROJECTION',multiple)\",\n \"def run_dataset(dataset, trackers, debug=False, threads=0, num_gpus=8):\\n multiprocessing.set_start_method('spawn', force=True)\\n\\n print('Evaluating {:4d} trackers on {:5d} sequences'.format(len(trackers), len(dataset)))\\n\\n multiprocessing.set_start_method('spawn', force=True)\\n\\n if threads == 0:\\n mode = 'sequential'\\n else:\\n mode = 'parallel'\\n\\n if mode == 'sequential':\\n for seq in dataset:\\n for tracker_info in trackers:\\n run_sequence(seq, tracker_info, debug=debug)\\n elif mode == 'parallel':\\n param_list = [(seq, tracker_info, debug, num_gpus) for seq, tracker_info in product(dataset, trackers)]\\n with multiprocessing.Pool(processes=threads) as pool:\\n pool.starmap(run_sequence, param_list)\\n print('Done')\",\n \"def set_all_data_internal(self, check_data=True):\\n for key, dataset in self.datasets.items():\\n if (\\n isinstance(dataset, mfdataarray.MFArray)\\n or (\\n isinstance(dataset, mfdatalist.MFList)\\n and dataset.structure.type == DatumType.recarray\\n )\\n and dataset.enabled\\n ):\\n dataset.store_internal(check_data=check_data)\",\n \"def _push_data_to_learner(self, data_queue):\\n raise NotImplementedError\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\\n \\n out_dataset[0].create_dataset(in_dataset[0])\\n out_dataset[1].create_dataset(in_dataset[1])\\n\\n in_pData, out_pData = self.get_plugin_datasets()\\n\\n in_pData[0].plugin_data_setup( 'SINOGRAM',multiple)\\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')\\n\\n in_pData[1].plugin_data_setup( 'PROJECTION',)\\n out_pData[1].plugin_data_setup( 'PROJECTION','multiple')\",\n \"def datachange_notification(self, node, val, data):\\n \\n logger.debug(\\\"New data change event. node:{}, value:{}\\\".format(node, val))\\n \\n # Sorry about these lines of code, but I don't see any nicer way of determining the port number than from \\n # the identifier string. Then splitting it up to isolate the port number.\\n # Example \\\"Status.Port_2.Selected\\\" is split into ['Status', 'Port_2', 'Selected'] then 'Port_2' is split into \\n # ['Port', '2'] and then the '2' is turned into an intiger.\\n path_list = str(node.nodeid.Identifier).split(\\\".\\\")\\n\\n # We can safely assume that the last term is the tag that updated.\\n tag = path_list[-1] \\n \\n # Figure out the port number\\n port_number = None\\n if 'Port' in path_list[1]:\\n port_number = int(path_list[1].split(\\\"_\\\")[-1]) \\n \\n \\\"\\\"\\\" Switch for each possible tag\\\"\\\"\\\"\\n # If the command tag \\\"Select\\\" changes go select that port with the instructions saved in the command tag. \\n if tag == 'Select' and port_number:\\n if val == True:\\n node = self.ua_server.get_node(\\\"ns=2;s=Command.Port_{}.Instructions\\\".format(port_number))\\n instructions = node.get_value()\\n self._pbl.select_port(port_number, instructions=instructions)\\n # Reset the select flag\\n node = self.ua_server.get_node(\\\"ns=2;s=Command.Port_{}.Select\\\".format(port_number))\\n node.set_value(False)\\n \\n elif tag == 'Deselect' and port_number:\\n if val == True:\\n self._pbl.deselect_port(port_number, work_finished=True)\\n # Reset the select flag\\n node = self.ua_server.get_node(\\\"ns=2;s=Command.Port_{}.Deselect\\\".format(port_number))\\n node.set_value(False)\\n\\n elif tag == 'ContentDisplayName' and port_number:\\n self._pbl.set_content_key(port_number,'display_name', str(val))\\n elif tag == 'ContentName' and port_number:\\n self._pbl.set_content_key(port_number,'name', str(val))\\n elif tag == 'ContentDescription' and port_number:\\n self._pbl.set_content_key(port_number,'description', str(val))\\n elif tag == 'ContentImagePath' and port_number:\\n self._pbl.set_content_key(port_number,'image_path', str(val))\\n \\n elif tag == 'Select' and 'ByContent' in path_list[1]:\\n if val == True:\\n instructions = self.ua_server.get_node(\\\"ns=2;s=Command.ByContent.Instructions\\\").get_value()\\n name = self.ua_server.get_node(\\\"ns=2;s=Command.ByContent.Name\\\").get_value()\\n _, selected_port = self._pbl.select_content(name = name, instructions=instructions)\\n # Reset the select flag\\n node = self.ua_server.get_node(\\\"ns=2;s=Command.ByContent.Select\\\")\\n node.set_value(False)\\n node = self.ua_server.get_node(\\\"ns=2;s=Command.ByContent.Result\\\")\\n node.set_value(selected_port)\\n\\n elif tag == 'Deselect' and 'ByContent' in path_list[1]:\\n if val == True:\\n name = self.ua_server.get_node(\\\"ns=2;s=Command.ByContent.Name\\\").get_value()\\n self._pbl.deselect_content(name = name, work_finished=True)\\n # Reset the select flag\\n node = self.ua_server.get_node(\\\"ns=2;s=Command.ByContent.Deselect\\\")\\n node.set_value(False)\",\n \"def set_task(task_id, train_dataset, val_dataset, test_dataset):\\n assert isinstance(task_id, int)\\n train_dataset.set_task(task_id)\\n val_dataset.set_task(task_id)\\n test_dataset.set_task(task_id)\",\n \"def upload_all(self):\\n adapter = self.get_adapter()\\n hostname = socket.gethostname()\\n for node in self.__nodes:\\n if node['hostname'] != hostname:\\n adapter(node,self.__local_dir).upload(self.__dataset_name)\",\n \"def _notify_new_log(self, data):\\n # Notify listeners\\n for lstnr in self.listeners:\\n lstnr.new_log_data(self, data)\",\n \"def update_dataset(\\n self,\\n dataset: DatasetDB,\\n ) -> DatasetDB:\\n dataset_id = dataset.id\\n\\n self._es.update_document(\\n index=DATASETS_INDEX_NAME,\\n doc_id=dataset_id,\\n document=self._dataset_to_es_doc(dataset),\\n )\\n return dataset\",\n \"def match_data(self, datasets):\\n raise NotImplementedError\",\n \"def datasets(self):\\n pass\",\n \"def test_acquire_dataset(self):\\n\\n # make sure the data does not yet exist\\n with self.subTest(name='no data yet'):\\n response = Epidata.covid_hosp('MA', Epidata.range(20200101, 20210101))\\n self.assertEqual(response['result'], -2)\\n\\n # acquire sample data into local database\\n # mock out network calls to external hosts\\n with self.subTest(name='first acquisition'), \\\\\\n patch.object(Network, 'fetch_metadata', return_value=self.test_utils.load_sample_metadata()) as mock_fetch_meta, \\\\\\n patch.object(Network, 'fetch_dataset', side_effect=[self.test_utils.load_sample_dataset(\\\"dataset0.csv\\\"), # dataset for 3/13\\n self.test_utils.load_sample_dataset(\\\"dataset0.csv\\\"), # first dataset for 3/15\\n self.test_utils.load_sample_dataset()] # second dataset for 3/15\\n ) as mock_fetch:\\n acquired = Update.run()\\n self.assertTrue(acquired)\\n self.assertEqual(mock_fetch_meta.call_count, 1)\\n\\n # make sure the data now exists\\n with self.subTest(name='initial data checks'):\\n response = Epidata.covid_hosp('WY', Epidata.range(20200101, 20210101))\\n self.assertEqual(response['result'], 1)\\n self.assertEqual(len(response['epidata']), 1)\\n row = response['epidata'][0]\\n self.assertEqual(row['state'], 'WY')\\n self.assertEqual(row['date'], 20201209)\\n self.assertEqual(row['issue'], 20210315)\\n self.assertEqual(row['critical_staffing_shortage_today_yes'], 8)\\n actual = row['inpatient_bed_covid_utilization']\\n expected = 0.11729857819905214\\n self.assertAlmostEqual(actual, expected)\\n self.assertIsNone(row['critical_staffing_shortage_today_no'])\\n\\n # expect 61 fields per row (63 database columns, except `id` and `record_type`)\\n self.assertEqual(len(row), 61)\\n\\n with self.subTest(name='all date batches acquired'):\\n response = Epidata.covid_hosp('WY', Epidata.range(20200101, 20210101), issues=20210313)\\n self.assertEqual(response['result'], 1)\\n\\n # re-acquisition of the same dataset should be a no-op\\n with self.subTest(name='second acquisition'), \\\\\\n patch.object(Network, 'fetch_metadata', return_value=self.test_utils.load_sample_metadata()) as mock_fetch_meta, \\\\\\n patch.object(Network, 'fetch_dataset', return_value=self.test_utils.load_sample_dataset()) as mock_fetch:\\n acquired = Update.run()\\n self.assertFalse(acquired)\\n\\n # make sure the data still exists\\n with self.subTest(name='final data checks'):\\n response = Epidata.covid_hosp('WY', Epidata.range(20200101, 20210101))\\n self.assertEqual(response['result'], 1)\\n self.assertEqual(len(response['epidata']), 1)\",\n \"def test_BaseDataContext_add_datasource_updates_cache(\\n in_memory_runtime_context: EphemeralDataContext,\\n pandas_enabled_datasource_config: dict,\\n) -> None:\\n context = in_memory_runtime_context\\n\\n name = pandas_enabled_datasource_config[\\\"name\\\"]\\n\\n assert name not in context.datasources\\n\\n context.add_datasource(**pandas_enabled_datasource_config)\\n\\n assert name in context.datasources\",\n \"def submit_dataset():\\n\\n if request.method == \\\"POST\\\":\\n \\\"\\\"\\\"\\n request could look like:\\n {\\n \\\"dataset_id\\\": \\\"HRI0\\\", # name of relevant workflow\\n \\\"source_bucket\\\": \\\"HRI0\\\", \\n \\\"source_key\\\": \\\"HRIO/workflow/std_dev_HRI0.py\\\", \\n \\\"pid\\\": 2, \\n\\n }\\n \\\"\\\"\\\"\\n\\n req = request.get_json(force=True)\\n if dataset_exists(req[\\\"dataset_id\\\"], req['PID']):\\n\\n app.logger.error(\\n f\\\"Error saving dataset, dataset already exists. (Try updating the dataset) \\\"\\n f\\\"for party {req['PID']}\\\"\\n )\\n\\n response = {\\n \\\"MSG\\\": f\\\"ERR: Dataset {req['dataset_id']} already exists.\\\"\\n }\\n else:\\n\\n app.logger.info(\\n f\\\"Saving dataset {req['dataset_id']}\\\"\\n )\\n\\n save_dataset(req['dataset_id'], req['source_bucket'], req['source_key'], req['PID'])\\n response = {\\n \\\"MSG\\\": \\\"OK\\\"\\n }\\n\\n return jsonify(response)\",\n \"def _update_datastore(lc, geno, dataset, force_update=False):\\n resource_ids = dict((r['name'], r['id']) for r in dataset['resources'])\\n\\n for chromo in geno['resources']:\\n assert chromo['resource_name'] in resource_ids, (\\n \\\"dataset missing resource for resource name\\\",\\n chromo['resource_name'], dataset['id'])\\n resource_id = resource_ids[chromo['resource_name']]\\n fields = _datastore_fields(chromo['fields'])\\n try:\\n ds = lc.action.datastore_search(resource_id=resource_id, limit=0)\\n except NotFound:\\n pass\\n else:\\n if not force_update and _datastore_match(\\n chromo['fields'], ds['fields']):\\n continue\\n # extra work here to maintain existing fields+ordering\\n # datastore_create rejects our list otherwise\\n fields = ds['fields'][1:] # trim _id field\\n seen = set(f['id'] for f in fields)\\n for f in _datastore_fields(chromo['fields']):\\n if f['id'] not in seen:\\n fields.append(f)\\n\\n lc.action.datastore_create(\\n resource_id=resource_id,\\n fields=fields,\\n primary_key=chromo.get('datastore_primary_key', []),\\n indexes=chromo.get('datastore_indexes', []),\\n force=True)\",\n \"def test_import_datasets_sync_argument_metrics(\\n import_datasets_command, app_context, fs\\n):\\n # pylint: disable=reimported, redefined-outer-name\\n import superset.cli.importexport # noqa: F811\\n\\n # reload to define export_datasets correctly based on the\\n # feature flags\\n importlib.reload(superset.cli.importexport)\\n\\n # write YAML file\\n with open(\\\"dataset.yaml\\\", \\\"w\\\") as fp:\\n fp.write(\\\"hello: world\\\")\\n\\n runner = app.test_cli_runner()\\n response = runner.invoke(\\n superset.cli.importexport.import_datasources,\\n [\\\"-p\\\", \\\"dataset.yaml\\\", \\\"-s\\\", \\\"metrics\\\"],\\n )\\n\\n assert response.exit_code == 0\\n expected_contents = {\\\"dataset.yaml\\\": \\\"hello: world\\\"}\\n import_datasets_command.assert_called_with(\\n expected_contents,\\n sync_columns=False,\\n sync_metrics=True,\\n )\",\n \"def add_joined_dataset(self, new_data):\\n self._add_linked_data(self.JOINED_DATASETS, self.joined_dataset_ids,\\n new_data)\",\n \"def update_data(self):\\n # Just set data_changed, the component should do the rest.\\n self.data_changed = True\",\n \"def update_dataset(self, data_name: str, append: pd.DataFrame):\\n df = getattr(self, data_name)\\n setattr(self, data_name, df.join(append, how='left'))\",\n \"def _update_dataset_param(self, dataset):\\n if dataset is None and self.dataset is None:\\n return []\\n if dataset is 'all':\\n dataset = ''\\n if dataset is None and self.dataset is not None:\\n dataset = self.dataset\\n return dataset\",\n \"def _posttrain(self, dataset):\\n Classifier._posttrain(self, dataset)\\n if self.params.retrainable:\\n self.__changedData_isset = False\",\n \"def dataFeedWifiIO(self, hostname, projects, timestamp, sensorMac, hwid, type, move, cache):\\n for project in [i.id for i in projects if i != None]:\\n ss = self.getScanSetup(hostname, project, sensorMac)\\n ss.logCell(timestamp, hwid, type, move)\",\n \"def _collect_data(self) -> None:\\n self.set_websocket_data()\\n self.set_stratum_data()\\n self.set_cache_data()\\n self.collect_peer_connection_metrics()\\n self.set_tx_storage_data()\",\n \"def data_upgrades():\\n pass\",\n \"def data_upgrades():\\n pass\",\n \"def setup(self):\\n # TODO check if need both dataset together\\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \\n tokenizer=self.tokenizer, vocab=\\\"bert\\\")\\n self.vocabulary = self.train_dataset.vocabulary\\n\\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\\n tokenizer=self.tokenizer, vocab=self.vocabulary)\\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)\",\n \"def on_data(self, data):\\n # store the data\\n self._storage.append(data)\",\n \"def upload():\\n \\n if request.method == 'GET':\\n return jsonify({'status': 'Error!', 'message': 'Please use POST request'})\\n else:\\n data = session['data']\\n arg1 = data['arg1'].lower()\\n rel = data['rel'].lower()\\n arg2 = data['arg2'].lower()\\n arg2 = arg2.replace(' ','')\\n\\n hash_name = 'dataset_map'\\n hash_key = '{0}_{1}_{2}'.format(arg1, rel, arg2)\\n\\n modler = DatasetModels()\\n current_ids = modler.fetch_ids('claims')\\n\\n url = RedisCache.fetchValue(hash_name, hash_key, 'dataset_url')\\n\\n if not url:\\n return jsonify({'status': 'Error!', 'message': 'Url not found'})\\n else:\\n file_name = \\\"{0}.csv\\\".format(hash_key)\\n status_code = modler.upload_dataset(url, 'claims', file_name)\\n if status_code != 200:\\n return jsonify({'status': 'Error!', 'message': 'Dataset Uploaded'})\\n else:\\n new_ids = modler.fetch_ids('claims')\\n latest_id = list(set(new_ids) - set(current_ids))\\n RedisCache.createHash(hash_name, hash_key, {'dataset_id': latest_id[0]})\\n RedisCache.createHash('runset_map', hash_key, {'dataset_id': latest_id[0]})\\n\\n return redirect(url_for('runset.create_runset'), code=307)\",\n \"def flush(self):\\n request = self._request\\n has_data = False\\n for (\\n run_name,\\n tag_to_time_series_data,\\n ) in self._run_to_tag_to_time_series_data.items():\\n r = tensorboard_service.WriteTensorboardRunDataRequest(\\n tensorboard_run=self._one_platform_resource_manager.get_run_resource_name(\\n run_name\\n )\\n )\\n r.time_series_data = list(tag_to_time_series_data.values())\\n _prune_empty_time_series(r)\\n if not r.time_series_data:\\n continue\\n request.write_run_data_requests.extend([r])\\n has_data = True\\n\\n if not has_data:\\n return\\n\\n self._rpc_rate_limiter.tick()\\n\\n with uploader_utils.request_logger(request):\\n with self._get_tracker():\\n try:\\n self._api.write_tensorboard_experiment_data(\\n tensorboard_experiment=request.tensorboard_experiment,\\n write_run_data_requests=request.write_run_data_requests,\\n )\\n except grpc.RpcError as e:\\n if (\\n hasattr(e, \\\"code\\\")\\n and getattr(e, \\\"code\\\")() == grpc.StatusCode.NOT_FOUND\\n ):\\n raise ExperimentNotFoundError() from e\\n logger.error(\\\"Upload call failed with error %s\\\", e)\\n\\n self._new_request()\",\n \"def update_all_data(self):\\n self.dataChanged.emit(qtc.QModelIndex(), qtc.QModelIndex())\",\n \"def new_data(title, data_y, mpi_reduce=False, **kwds):\\n _check_type(title, data_y)\\n event_id = evt.event_id()\\n if(ipc.mpi.is_slave()):\\n if(mpi_reduce):\\n ipc.mpi.send_reduce(title, 'new_data', data_y, event_id, **kwds)\\n else:\\n m = hashlib.md5()\\n m.update(bytes(title))\\n if m.digest() in ipc.mpi.subscribed:\\n ipc.mpi.send(title, [ipc.uuid, 'new_data', title, data_y,\\n event_id, kwds])\\n else:\\n logging.debug('%s not subscribed, not sending' % (title))\\n else:\\n ipc.zmq().send(title, [ipc.uuid, 'new_data', title, data_y,\\n event_id, kwds])\\n logging.debug(\\\"Sending data on source '%s'\\\" % title)\",\n \"def reload_data(self):\\n self._avro_payload.reload_data()\",\n \"def reloadData(self):\\n self.dto.readFromData()\\n print(\\\"Record reloaded.\\\")\",\n \"def run_dataset(dataset, trackers, debug=False, threads=0, visdom_info=None):\\n print('Evaluating {:4d} trackers on {:5d} sequences'.format(len(trackers), len(dataset)))\\n\\n visdom_info = {} if visdom_info is None else visdom_info\\n\\n if threads == 0:\\n mode = 'sequential'\\n else:\\n mode = 'parallel'\\n\\n if mode == 'sequential':\\n for seq in dataset:\\n for tracker_info in trackers:\\n run_sequence(seq, tracker_info, debug=debug, visdom_info=visdom_info)\\n elif mode == 'parallel':\\n param_list = [(seq, tracker_info, debug, visdom_info) for seq, tracker_info in product(dataset, trackers)]\\n with multiprocessing.Pool(processes=threads) as pool:\\n pool.starmap(run_sequence, param_list)\\n print('Done')\",\n \"def testStringDatasetId(self):\\n cluster = self.make_test_cluster(num_workers=1)\\n dataset = dataset_ops.Dataset.range(10)\\n dataset_id = data_service_ops.register_dataset(\\n cluster.dispatcher.target, dataset\\n )\\n dataset_id_str = (\\n dataset_id\\n if dataset_id.dtype == dtypes.string\\n else string_ops.as_string(dataset_id)\\n )\\n dataset = data_service_ops.from_dataset_id(\\n dataset_id=dataset_id_str,\\n element_spec=dataset.element_spec,\\n processing_mode=data_service_ops.ShardingPolicy.OFF,\\n service=cluster.dispatcher.target,\\n job_name=\\\"job_name\\\",\\n )\\n self.assertDatasetProduces(dataset, list(range(10)))\",\n \"def to_dataset(self):\\n if not self.type:\\n raise aspecd.exceptions.MissingDatasetError\\n dataset = aspecd.utils.object_from_class_name(self.type)\\n dataset.id = self.id\\n for history_record in self.history:\\n history_record.replay(dataset)\\n return dataset\",\n \"def onGroupDataset(self, event):\\n\\t\\tselections = self.tree.GetSelections()\\n\\t\\tif not selections and self.selectedItem:\\n\\t\\t\\tselections = [self.selectedItem]\\n\\t\\tif not self.groupedDataUnit:\\n\\t\\t\\tpluginLoader = Modules.DynamicLoader.getPluginLoader()\\n\\t\\t\\ttaskMod = pluginLoader.getPluginModule(\\\"Task\\\", \\\"Process\\\")\\n\\t\\t\\tunitType = taskMod.getDataUnit()\\n\\t\\t\\tmoduleType = pluginLoader.getPluginClass(\\\"Task\\\",\\\"Process\\\")\\n\\t\\t\\tself.groupedDataUnit = unitType()\\n\\t\\t\\tmodule = moduleType()\\n\\t\\t\\tself.groupedDataUnit.setModule(module)\\n\\t\\t\\n\\t\\tfor item in selections:\\n\\t\\t\\tif item in self.groupedItems: continue\\n\\t\\t\\tself.groupedItems.append(item)\\n\\t\\t\\tself.markBlue([item], \\\"}\\\")\\n\\t\\t\\tobj = self.tree.GetPyData(item)\\n\\t\\t\\tprint \\\"Adding source dataunit\\\",obj\\n\\t\\t\\tself.groupedDataUnit.addSourceDataUnit(obj)\\n\\t\\tsetting = self.groupedDataUnit.getSettings()\\n\\t\\tsetting.set(\\\"FilterList\\\",[])\\n\\t\\t\\n\\t\\tprint \\\"Now=\\\",self.groupedDataUnit.getSourceDataUnits()\",\n \"def push_tensorboard_eval(self, epoch, dataset_name):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56935036","0.56498915","0.5641686","0.54096603","0.5342046","0.5339496","0.5286913","0.52014285","0.5195956","0.5182093","0.5152727","0.5134192","0.50968635","0.5025282","0.5021174","0.5016711","0.49956772","0.4995023","0.49723122","0.49516952","0.4941703","0.4939155","0.4933242","0.49237338","0.49217877","0.4905172","0.49027684","0.48845223","0.4880336","0.48787394","0.48706418","0.486885","0.4866075","0.48646894","0.48601526","0.48565134","0.48552153","0.48417708","0.48417708","0.48401317","0.48362023","0.4832254","0.48174554","0.4816978","0.48091227","0.4799784","0.4781737","0.47810394","0.47764394","0.4761276","0.47583213","0.47517136","0.47466528","0.47443596","0.47367436","0.4733433","0.47303843","0.47259992","0.47225314","0.47136703","0.47136176","0.47132298","0.47052675","0.47046676","0.4696718","0.46960956","0.46937925","0.46876696","0.46753448","0.467429","0.46697932","0.46668145","0.46653357","0.4650903","0.46445528","0.46431524","0.46304637","0.46265233","0.46220723","0.46149293","0.46122772","0.46114907","0.46090227","0.46059158","0.45896077","0.4589402","0.4589402","0.45773777","0.45722434","0.45687446","0.4567923","0.45547372","0.45487657","0.45431757","0.45423904","0.45386177","0.4537234","0.45366555","0.45348892","0.4516044"],"string":"[\n \"0.56935036\",\n \"0.56498915\",\n \"0.5641686\",\n \"0.54096603\",\n \"0.5342046\",\n \"0.5339496\",\n \"0.5286913\",\n \"0.52014285\",\n \"0.5195956\",\n \"0.5182093\",\n \"0.5152727\",\n \"0.5134192\",\n \"0.50968635\",\n \"0.5025282\",\n \"0.5021174\",\n \"0.5016711\",\n \"0.49956772\",\n \"0.4995023\",\n \"0.49723122\",\n \"0.49516952\",\n \"0.4941703\",\n \"0.4939155\",\n \"0.4933242\",\n \"0.49237338\",\n \"0.49217877\",\n \"0.4905172\",\n \"0.49027684\",\n \"0.48845223\",\n \"0.4880336\",\n \"0.48787394\",\n \"0.48706418\",\n \"0.486885\",\n \"0.4866075\",\n \"0.48646894\",\n \"0.48601526\",\n \"0.48565134\",\n \"0.48552153\",\n \"0.48417708\",\n \"0.48417708\",\n \"0.48401317\",\n \"0.48362023\",\n \"0.4832254\",\n \"0.48174554\",\n \"0.4816978\",\n \"0.48091227\",\n \"0.4799784\",\n \"0.4781737\",\n \"0.47810394\",\n \"0.47764394\",\n \"0.4761276\",\n \"0.47583213\",\n \"0.47517136\",\n \"0.47466528\",\n \"0.47443596\",\n \"0.47367436\",\n \"0.4733433\",\n \"0.47303843\",\n \"0.47259992\",\n \"0.47225314\",\n \"0.47136703\",\n \"0.47136176\",\n \"0.47132298\",\n \"0.47052675\",\n \"0.47046676\",\n \"0.4696718\",\n \"0.46960956\",\n \"0.46937925\",\n \"0.46876696\",\n \"0.46753448\",\n \"0.467429\",\n \"0.46697932\",\n \"0.46668145\",\n \"0.46653357\",\n \"0.4650903\",\n \"0.46445528\",\n \"0.46431524\",\n \"0.46304637\",\n \"0.46265233\",\n \"0.46220723\",\n \"0.46149293\",\n \"0.46122772\",\n \"0.46114907\",\n \"0.46090227\",\n \"0.46059158\",\n \"0.45896077\",\n \"0.4589402\",\n \"0.4589402\",\n \"0.45773777\",\n \"0.45722434\",\n \"0.45687446\",\n \"0.4567923\",\n \"0.45547372\",\n \"0.45487657\",\n \"0.45431757\",\n \"0.45423904\",\n \"0.45386177\",\n \"0.4537234\",\n \"0.45366555\",\n \"0.45348892\",\n \"0.4516044\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7455701"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":89294,"cells":{"query":{"kind":"string","value":"Retrieve the dataset manager."},"document":{"kind":"string","value":"def resolve_dataset_manager() -> DatasetManager:\n _dataset_manager_class = conf.getimport(\n section=\"core\",\n key=\"dataset_manager_class\",\n fallback=\"airflow.datasets.manager.DatasetManager\",\n )\n _dataset_manager_kwargs = conf.getjson(\n section=\"core\",\n key=\"dataset_manager_kwargs\",\n fallback={},\n )\n return _dataset_manager_class(**_dataset_manager_kwargs)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_data_manager(self):\n\n return self._data_manager","def _get_data_manager(self):\n\n ftype = self.conf['General']['save_as']\n if ftype == 'npz':\n return NPZDataManager(self.conf, self.log)\n elif ftype == 'hdf5':\n return HDF5DataManager(self.conf, self.log)\n else:\n raise ValueError('Invalid file type in config')","def GetManager(self):\r\n\r\n return self.manager","def get_dataset(self) -> datasets.OpenMLDataset:\n return datasets.get_dataset(self.dataset_id)","def getManager(self):\n return self._manager","def get_data_manager():\n return IncomingEdge.items","def dataloader(self):\n return DataLoader","def dbm(cls):\n return cls.dbmanager","def get_dataset(self):\n return datasets.get_dataset(self.dataset_id)","def getDataTypeManager(self) -> ghidra.program.model.pcode.PcodeDataTypeManager:\n ...","def get_dataset(self):\n return","def get_dataloader(self):\n shuffle = True if self.mode == \"train\" else False\n return DataLoader(self.get_dataset(), batch_size=self.batch_size, shuffle = shuffle, \n collate_fn=create_mini_batch)","def get_manager():\n return __manager__","def get_data_manager(\n hass: HomeAssistantType, entry: ConfigEntry\n) -> WithingsDataManager:\n profile = entry.data.get(const.PROFILE)\n\n if not hass.data.get(const.DOMAIN):\n hass.data[const.DOMAIN] = {}\n\n if not hass.data[const.DOMAIN].get(const.DATA_MANAGER):\n hass.data[const.DOMAIN][const.DATA_MANAGER] = {}\n\n if not hass.data[const.DOMAIN][const.DATA_MANAGER].get(profile):\n hass.data[const.DOMAIN][const.DATA_MANAGER][\n profile\n ] = create_withings_data_manager(hass, entry)\n\n return hass.data[const.DOMAIN][const.DATA_MANAGER][profile]","def get_datastore(self, datastore_cls: Type[BaseDatasetLoader], dataset_conf: Dict) -> BaseDatasetLoader:\n pass","def get_dataset():\n\n return db.store.all()","def get_dataset(self):\n if self.mode == \"test\":\n return OnlineQueryDataset(self.mode, self.df, self.tokenizer)\n else:\n return OnlineQueryDataset(self.mode, self.df_reindex, self.tokenizer)","def get_data_set(self, data_set_name):\n return self._data_sets[data_set_name]","def Get(self):\n\n if not hasattr(self, \"_instance\"):\n self._instance = PersistenceManager()\n\n return self._instance","def getDatastore(cls, d_type, log_level=logging.INFO):\n database_env_dir = '{}/{}_env'.format(DATASTORE_DIR, d_type)\n sys.path.append(database_env_dir)\n\n module_name = '{}_interface'.format(d_type)\n handle, path, description = imp.find_module(module_name)\n\n try:\n db_module = imp.load_module(module_name, handle, path, description)\n datastore = db_module.DatastoreProxy(log_level=log_level)\n finally:\n if handle:\n handle.close()\n\n return datastore","def db(self):\n return DbManager(self)","def get_dataset(self, identifier):\n # Test if a subfolder for the given dataset identifier exists. If not\n # return None.\n dataset_dir = self.get_dataset_dir(identifier)\n if not os.path.isdir(dataset_dir):\n return None\n # Load the dataset handle\n return FileSystemDatasetHandle.from_file(\n descriptor_file=os.path.join(dataset_dir, DESCRIPTOR_FILE),\n data_file=os.path.join(dataset_dir, DATA_FILE),\n annotations=DatasetMetadata.from_file(\n self.get_metadata_filename(identifier)\n )\n )","def get_dataset(self, name, enforce=None, download=True):\n if self._meta is None:\n self._get_meta()\n return self._get_dataset(name, enforce=enforce, download=download)","def get_manager():\n\n return multiprocessing.Manager()","def get_instance():\n if PersistenceManager._instance is None:\n PersistenceManager._instance = PersistenceManager()\n return PersistenceManager._instance","def GetDocManager(self):\r\n return self._docManager","def data_store(self) -> 'outputs.DataStoreInfoBaseResponse':\n return pulumi.get(self, \"data_store\")","def get(self, context, id_):\n try:\n db_resource_mgr_data = self.db_api.get_resource_manager(\n context, id_)\n _resource_mgr_data = _make_response(db_resource_mgr_data)\n\n except exception.NotFound as e:\n raise e\n\n except Exception as e:\n LOG.exception(e)\n msg = (\"Error retrieving the 'resource manager': %s.\"\n \" Reason: %s\") % (id_, e.message)\n LOG.error(msg)\n raise exception.RetrieveException(e.message)\n\n LOG.info(\"eon resource_manager data is %s \" %\n logging.mask_password(_resource_mgr_data))\n return _resource_mgr_data","def dataloader(self):\n\n # load / split data\n train_data = self.data.get_train_data()\n if self.args.use_dev:\n train_data, dev_data = self.data.split_data(train_data)\n test_data = self.data.get_test_data()\n\n #print(train_data[0])\n #print(dev_data[0])\n #print(test_data[0])\n\n # build dataset\n train_dataset = self.loader.build_dataset(\n train_data, \n self.args.train_max_seq_len)\n train_loader = self.loader.build_dataloader(\n train_dataset, 'train')\n\n test_dataset = self.loader.build_dataset(\n test_data,\n self.args.eval_max_seq_len)\n test_loader = self.loader.build_dataloader(\n test_dataset, 'test')\n\n if self.args.use_dev:\n dev_dataset = self.loader.build_dataset(\n dev_data,\n self.args.eval_max_seq_len)\n dev_loader = self.loader.build_dataloader(\n dev_dataset, 'dev')\n return train_loader, dev_loader, test_loader\n else:\n return train_loader, test_loader","def get_metadata_manager(config):\n\n context = config.contextualization_type\n metadata_manager_class = '%sMetadataManager' % context\n if not (metadata_manager_class in globals()):\n raise NotImplementedError('Implementation for %s not available' % context)\n return (globals()[metadata_manager_class])(config)","def get_extension_manager(self):\n return get_extension_manager()","def get_entity_manager(self):\n return self.game.entity_manager","def DataSet(self):\n if hasattr(self, '_dataset') and self._dataset and self._dataset.Get():\n return WrapDataObject(self._dataset.Get())\n\n return None","def get_test_dataset_DataLoader(self):\n test_info = self.get_test_DataLoader_info()\n name = test_info[\"name\"]\n task = test_info[\"task\"]\n data_dir = test_info[\"data_dir\"]\n hdf5_file = test_info[\"hdf5_file\"]\n\n data_loader = DataLoader(name, task, data_dir, hdf5_file)\n\n return data_loader, self.dataset, self.data_fields","def getAPIsManager(self):\n return self.apisManager","def get_datasource(self):\n return None","def get_offline_dataloaders(params):\n # Train\n trainDS = EnvironmentLoader(params[\"dataset\"][\"train_data\"])\n train_data_loader = torch.utils.data.DataLoader(\n trainDS, shuffle=True, batch_size=params[\"optimization\"][\"batch_size\"])\n\n # Test\n test_DS = EnvironmentLoader(params[\"dataset\"][\"test_data\"])\n test_data_loader = torch.utils.data.DataLoader(\n test_DS, shuffle=True, batch_size=params[\"optimization\"][\"batch_size\"])\n\n return train_data_loader, test_data_loader","def Datastores(self):\n if not self._datastores:\n ds = self._get_objects(vim.Datastore)\n for d in ds:\n self._datastores[d.name] = Datastore(d)\n return self._datastores","def _get_dataset(self):\n if self.mode == 'train':\n return (\n tf.data.Dataset.from_tensor_slices(\n tensors=(tf.constant(value=self.file_paths),\n tf.reshape(tensor=tf.constant(self.labels), shape=[-1]))\n )\n .shuffle(buffer_size=self.num_samples, reshuffle_each_iteration=True)\n .map(map_func=self.import_waveforms_fn_train, num_parallel_calls=self.num_parallel_calls)\n .repeat()\n .batch(batch_size=self.batch_size)\n .prefetch(buffer_size=self.prefetch_buffer)\n )\n else:\n return (\n tf.data.Dataset.from_tensor_slices(\n tensors=(tf.constant(value=self.file_paths),\n tf.reshape(tensor=tf.constant(self.labels), shape=[-1]))\n )\n .map(map_func=self.import_waveforms_fn_val, num_parallel_calls=self.num_parallel_calls)\n .repeat()\n .batch(batch_size=self.batch_size)\n .prefetch(buffer_size=self.prefetch_buffer)\n )","def get_data_source_registry():\n global _data_source_registry\n if not _data_source_registry.list():\n _init_data_source_registry(_data_source_registry)\n return _data_source_registry","def get_datasets(self):\n return self.ds_primary, self.ds_secondary","def get_dataset(opts):\n dataset_type = opts.dataset_params.dataset_type\n if dataset_type in 'synth':\n return synthgraph.SynthGraphDataset(opts, opts.dataset_params)\n elif dataset_type in 'synthnoise':\n return synthgraph.SynthNoiseGraphDataset(opts, opts.dataset_params)\n elif dataset_type in 'synthoutlier':\n return synthgraph.SynthOutlierGraphDataset(opts, opts.dataset_params)\n elif dataset_type in 'rome16kgeom':\n return spreal.GeomKNNRome16KDataset(opts, opts.dataset_params)\n elif dataset_type in 'graffiti':\n return graffiti.GraffitiDataset(opts, opts.dataset_params)\n else:\n print(\"ERROR: Dataset type {} not implemented yet\".format(dataset_type))\n sys.exit(1)","def _get_client():\n\n return datastore.Client()","def _get_client():\n\n return datastore.Client()","def get_pydicom(self) -> pydicom.FileDataset:\n return util.get_pydicom(self.client, self.id_)","def get_dataset_loader(self, batch_size, workers, is_gpu):\n\n train_loader = torch.utils.data.DataLoader(\n self.trainset,\n batch_size=batch_size, shuffle=True,\n num_workers=workers, pin_memory=is_gpu, sampler=None)\n\n val_loader = torch.utils.data.DataLoader(\n self.valset,\n batch_size=batch_size, shuffle=False,\n num_workers=workers, pin_memory=is_gpu)\n\n return train_loader, val_loader","def get_dataset_loader(self, batch_size, workers, is_gpu):\n\n train_loader = torch.utils.data.DataLoader(\n self.trainset,\n batch_size=batch_size, shuffle=True,\n num_workers=workers, pin_memory=is_gpu, sampler=None)\n\n val_loader = torch.utils.data.DataLoader(\n self.valset,\n batch_size=batch_size, shuffle=False,\n num_workers=workers, pin_memory=is_gpu)\n\n return train_loader, val_loader","def get_dataset_loader(self, batch_size, workers, is_gpu):\n\n train_loader = torch.utils.data.DataLoader(\n self.trainset,\n batch_size=batch_size, shuffle=True,\n num_workers=workers, pin_memory=is_gpu, sampler=None)\n\n val_loader = torch.utils.data.DataLoader(\n self.valset,\n batch_size=batch_size, shuffle=False,\n num_workers=workers, pin_memory=is_gpu)\n\n return train_loader, val_loader","def get_dataset_loader(self, batch_size, workers, is_gpu):\n\n train_loader = torch.utils.data.DataLoader(\n self.trainset,\n batch_size=batch_size, shuffle=True,\n num_workers=workers, pin_memory=is_gpu, sampler=None)\n\n val_loader = torch.utils.data.DataLoader(\n self.valset,\n batch_size=batch_size, shuffle=False,\n num_workers=workers, pin_memory=is_gpu)\n\n return train_loader, val_loader","def get_dataset_loader(self, batch_size, workers, is_gpu):\n\n train_loader = torch.utils.data.DataLoader(\n self.trainset,\n batch_size=batch_size, shuffle=True,\n num_workers=workers, pin_memory=is_gpu, sampler=None)\n\n val_loader = torch.utils.data.DataLoader(\n self.valset,\n batch_size=batch_size, shuffle=False,\n num_workers=workers, pin_memory=is_gpu)\n\n return train_loader, val_loader","def get_dataset_loader(self, batch_size, workers, is_gpu):\n\n train_loader = torch.utils.data.DataLoader(\n self.trainset,\n batch_size=batch_size, shuffle=True,\n num_workers=workers, pin_memory=is_gpu, sampler=None)\n\n val_loader = torch.utils.data.DataLoader(\n self.valset,\n batch_size=batch_size, shuffle=False,\n num_workers=workers, pin_memory=is_gpu)\n\n return train_loader, val_loader","def get_dataset_loader(self, batch_size, workers, is_gpu):\n\n train_loader = torch.utils.data.DataLoader(\n self.trainset,\n batch_size=batch_size, shuffle=True,\n num_workers=workers, pin_memory=is_gpu, sampler=None)\n\n val_loader = torch.utils.data.DataLoader(\n self.valset,\n batch_size=batch_size, shuffle=False,\n num_workers=workers, pin_memory=is_gpu)\n\n return train_loader, val_loader","def get_dataset_loader(self, batch_size, workers, is_gpu):\n\n train_loader = torch.utils.data.DataLoader(\n self.trainset,\n batch_size=batch_size, shuffle=True,\n num_workers=workers, pin_memory=is_gpu, sampler=None)\n\n val_loader = torch.utils.data.DataLoader(\n self.valset,\n batch_size=batch_size, shuffle=False,\n num_workers=workers, pin_memory=is_gpu)\n\n return train_loader, val_loader","def get_dataset_loader(self, batch_size, workers, is_gpu):\n\n train_loader = torch.utils.data.DataLoader(\n self.trainset,\n batch_size=batch_size, shuffle=True,\n num_workers=workers, pin_memory=is_gpu, sampler=None)\n\n val_loader = torch.utils.data.DataLoader(\n self.valset,\n batch_size=batch_size, shuffle=False,\n num_workers=workers, pin_memory=is_gpu)\n\n return train_loader, val_loader","def get(cls, _id):\n return DataStore.get_instance(cls, _id)","def _getDatasetPath(self):\n return self.__dataset_path","def get_dataloader(self, cid, batch_size=None, type=\"train\"):\n dataset = self.get_dataset(cid, type)\n batch_size = len(dataset) if batch_size is None else batch_size\n data_loader = DataLoader(dataset, batch_size=batch_size)\n return data_loader","def get_instance(cls):\n global DNS_MANAGER_API\n if not DNS_MANAGER_API:\n DNS_MANAGER_API = cls()\n return DNS_MANAGER_API","def get_data_set(self):\n raise exceptions.NotImplemented","def data_store(self) -> pulumi.Input['DataStoreInfoBaseArgs']:\n return pulumi.get(self, \"data_store\")","def _get_store(self):\n return self._store","def get_manager(self, name):\n\n if name == \"control\":\n manager = self._control_manager\n elif name == \"alarm\":\n manager = self._alarm_manager\n elif name == \"state\":\n manager = self._machine_manager\n else:\n manager = self._function_manager\n\n return manager","def data_model(self) -> DataModel:\n return self._data_model","def getDb(self):\n return self.db","def __init__(self, dataset_name, teacher_model, students_model):\n self.data_manager = DataManager(dataset_name)\n self.dataset_name = dataset_name\n self.teacher_model = teacher_model\n self.student_model = students_model","def get_dataset(self, dataset_path=None, normalize=True, return_original=False):\n if dataset_path is None:\n dataset_path = self.dir\n \n if \"mocap\" in dataset_path.lower():\n print(\"Loading Mocap dataset.\")\n df = get_mocap()\n df_orig = df\n elif \"profi\" in dataset_path.lower():\n print(\"Loading Profiset dataset.\")\n df = get_profiset()\n df_orig = df\n else:\n print(\"Loading CoPhIR dataset.\")\n df_orig, attr_lengths = get_objects_with_indexes(self.labels, f'{dataset_path}/level-{str(self.n_levels)}.txt', f'{dataset_path}/objects.txt')\n if normalize:\n df = scale_per_descriptor(df_orig, self.labels, attr_lengths)\n else:\n df = df_orig\n \n assert df.shape[1] == self.descriptor_values + self.n_levels + len([\"object_id\"])\n logging.info(f\"Loaded dataset of shape: {df.shape}\")\n if return_original:\n return df, df_orig\n else:\n return df","def get_db(self):\n return self._db","def get_database(self):\n return self.database","def get_dataset_reference(self, dataset_name):\n\n print_debug(\"Geting dataset :\" + dataset_name)\n dataset = DatasetFactory.get(dataset_file_name=dataset_name)\n return dataset","def get_dataset(params):\r\n module_name, class_name = params.dataset.name.rsplit('.', 1)\r\n i = importlib.import_module(module_name)\r\n return getattr(i, class_name)","def get_persistence(self):\n return self.persistence","def persist(cls, dataset):\n return dataset","def manager(self):\n if \"manager\" in self._prop_dict:\n if isinstance(self._prop_dict[\"manager\"], OneDriveObjectBase):\n return self._prop_dict[\"manager\"]\n else :\n self._prop_dict[\"manager\"] = DirectoryObject(self._prop_dict[\"manager\"])\n return self._prop_dict[\"manager\"]\n\n return None","def get_cls(dataset_name):\n return find_dataset_using_name(dataset_name)","def open(self):\n return xr.open_dataset(self)","def get_global_adaptation_manager():\n global adaptation_manager\n return adaptation_manager","def as_manager(cls):\n manager = DefaultManager.from_queryset(cls)()\n manager._built_with_as_manager = True\n return manager","def get(self, name):\n assert name, \"Must input a valid dataset name.\"\n return self.manager.get_data(name)","def get_db():\n if \"db\" not in context:\n context[\"db\"] = dataset.connect(os.getenv(\"DATABASE_URL\", \"sqlite:///jobs.db\"))\n return context[\"db\"]","def get_collection_manager(self, *args, **kwargs):\n return CollectionManager(self, *args, **kwargs)","def _get_db(self):\n return DB(\n ClientStorage.ClientStorage((self.server, self.port))\n )","def _get_db(self):\n gt_db = ...\n return gt_db","def get_manager(api_version=None):\n from manager import get_keystone_manager\n return get_keystone_manager(get_local_endpoint(), get_admin_token(),\n api_version)","def get_default_datastore(self):\n try:\n return self.client.list_datastores()[0]['datastore']\n except VMwareError as e:\n raise VMwareBackendError(e)","def get_dbs_obj(self):\n dbs_xml = self.get_DatabaseAndServer_XML()\n return self.get_DatabaseAndServer_obj(dbs_xml)","def _configure_manager(self):\n self._manager = CloudDatabaseManager(self,\n resource_class=CloudDatabaseInstance, response_key=\"instance\",\n uri_base=\"instances\")\n self._flavor_manager = BaseManager(self,\n resource_class=CloudDatabaseFlavor, response_key=\"flavor\",\n uri_base=\"flavors\")\n self._backup_manager = CloudDatabaseBackupManager(self,\n resource_class=CloudDatabaseBackup, response_key=\"backup\",\n uri_base=\"backups\")","def cluster_manager(self):\n # Lazily instantiate the cluster manager the first time it is asked for.\n if not hasattr(self, '_cluster_manager'):\n if self._cluster_engine:\n self._cluster_manager = self._cluster_engine.create_manager(\n self._username,\n self._tenancy\n )\n else:\n self._cluster_manager = None\n # If there is still no cluster manager, clusters are not supported\n if not self._cluster_manager:\n raise errors.UnsupportedOperationError(\n 'Clusters are not supported for this tenancy.'\n )\n return self._cluster_manager","def GetAuiManager(self):\r\n\r\n return self._mgr","def get_mgr(cls, id):\n assert id in cls.s_memory_mgrs, 'invalid id[%s] for memory managers' % (\n id)\n return cls.s_memory_mgrs[id]","def get_datasource_of():\n global datasource_of\n\n if not datasource_of:\n datasource_of = stixhelpers.datasource_of()\n \n return datasource_of","def mgmt_tool(self) -> MgmtClient:\n return self._mgmt_tool","def configStore(self):\n return getattr(self._data.configStore, self._name)","def dataset_dir(self):\n return self._dataset_dir","def get_dataset(dataset_name, use_cached=True, dataset_folder=DATASET_FOLDER, cache_path=CACHE_PATH, cache_file=None):\n\n dataset_npy = cache_file if cache_file else os.path.join(cache_path, 'dataset_{}.npy'.format(dataset_name))\n\n if use_cached and os.path.exists(dataset_npy):\n X, Y = get_dataset_cached(dataset_npy)\n else:\n X, Y = get_dataset_module(dataset_folder, dataset_name).fetch()\n\n # Test dataset validity before saving it\n test_dataset_validity(X, Y)\n\n with open(dataset_npy, 'wb') as f:\n pickle.dump((X, Y), f)\n\n test_dataset_validity(X, Y)\n return X, Y","def dataset(self):\n return self.predictor_data_manager.dataset(\n self.data_name, self.trait_name, data=self._data, trait=self.trait,\n categorical_trait=self.categorical_trait)","def get_backend(self):\n return self.analyze_db_task(constants.TRAIN_DB).backend","def get_dataset(self, dataset_id):\n return self.session.query(self.Dataset).get(dataset_id)","def event_manager(self):\n return ScriptDB.objects.get(db_key=\"Event Manager\")","def manager(self):\n if not self._manager:\n self._manager = TwistedEventLoopManager()\n\n return self._manager","def manager(self):\n if not self._manager:\n self._manager = TwistedEventLoopManager()\n\n return self._manager"],"string":"[\n \"def get_data_manager(self):\\n\\n return self._data_manager\",\n \"def _get_data_manager(self):\\n\\n ftype = self.conf['General']['save_as']\\n if ftype == 'npz':\\n return NPZDataManager(self.conf, self.log)\\n elif ftype == 'hdf5':\\n return HDF5DataManager(self.conf, self.log)\\n else:\\n raise ValueError('Invalid file type in config')\",\n \"def GetManager(self):\\r\\n\\r\\n return self.manager\",\n \"def get_dataset(self) -> datasets.OpenMLDataset:\\n return datasets.get_dataset(self.dataset_id)\",\n \"def getManager(self):\\n return self._manager\",\n \"def get_data_manager():\\n return IncomingEdge.items\",\n \"def dataloader(self):\\n return DataLoader\",\n \"def dbm(cls):\\n return cls.dbmanager\",\n \"def get_dataset(self):\\n return datasets.get_dataset(self.dataset_id)\",\n \"def getDataTypeManager(self) -> ghidra.program.model.pcode.PcodeDataTypeManager:\\n ...\",\n \"def get_dataset(self):\\n return\",\n \"def get_dataloader(self):\\n shuffle = True if self.mode == \\\"train\\\" else False\\n return DataLoader(self.get_dataset(), batch_size=self.batch_size, shuffle = shuffle, \\n collate_fn=create_mini_batch)\",\n \"def get_manager():\\n return __manager__\",\n \"def get_data_manager(\\n hass: HomeAssistantType, entry: ConfigEntry\\n) -> WithingsDataManager:\\n profile = entry.data.get(const.PROFILE)\\n\\n if not hass.data.get(const.DOMAIN):\\n hass.data[const.DOMAIN] = {}\\n\\n if not hass.data[const.DOMAIN].get(const.DATA_MANAGER):\\n hass.data[const.DOMAIN][const.DATA_MANAGER] = {}\\n\\n if not hass.data[const.DOMAIN][const.DATA_MANAGER].get(profile):\\n hass.data[const.DOMAIN][const.DATA_MANAGER][\\n profile\\n ] = create_withings_data_manager(hass, entry)\\n\\n return hass.data[const.DOMAIN][const.DATA_MANAGER][profile]\",\n \"def get_datastore(self, datastore_cls: Type[BaseDatasetLoader], dataset_conf: Dict) -> BaseDatasetLoader:\\n pass\",\n \"def get_dataset():\\n\\n return db.store.all()\",\n \"def get_dataset(self):\\n if self.mode == \\\"test\\\":\\n return OnlineQueryDataset(self.mode, self.df, self.tokenizer)\\n else:\\n return OnlineQueryDataset(self.mode, self.df_reindex, self.tokenizer)\",\n \"def get_data_set(self, data_set_name):\\n return self._data_sets[data_set_name]\",\n \"def Get(self):\\n\\n if not hasattr(self, \\\"_instance\\\"):\\n self._instance = PersistenceManager()\\n\\n return self._instance\",\n \"def getDatastore(cls, d_type, log_level=logging.INFO):\\n database_env_dir = '{}/{}_env'.format(DATASTORE_DIR, d_type)\\n sys.path.append(database_env_dir)\\n\\n module_name = '{}_interface'.format(d_type)\\n handle, path, description = imp.find_module(module_name)\\n\\n try:\\n db_module = imp.load_module(module_name, handle, path, description)\\n datastore = db_module.DatastoreProxy(log_level=log_level)\\n finally:\\n if handle:\\n handle.close()\\n\\n return datastore\",\n \"def db(self):\\n return DbManager(self)\",\n \"def get_dataset(self, identifier):\\n # Test if a subfolder for the given dataset identifier exists. If not\\n # return None.\\n dataset_dir = self.get_dataset_dir(identifier)\\n if not os.path.isdir(dataset_dir):\\n return None\\n # Load the dataset handle\\n return FileSystemDatasetHandle.from_file(\\n descriptor_file=os.path.join(dataset_dir, DESCRIPTOR_FILE),\\n data_file=os.path.join(dataset_dir, DATA_FILE),\\n annotations=DatasetMetadata.from_file(\\n self.get_metadata_filename(identifier)\\n )\\n )\",\n \"def get_dataset(self, name, enforce=None, download=True):\\n if self._meta is None:\\n self._get_meta()\\n return self._get_dataset(name, enforce=enforce, download=download)\",\n \"def get_manager():\\n\\n return multiprocessing.Manager()\",\n \"def get_instance():\\n if PersistenceManager._instance is None:\\n PersistenceManager._instance = PersistenceManager()\\n return PersistenceManager._instance\",\n \"def GetDocManager(self):\\r\\n return self._docManager\",\n \"def data_store(self) -> 'outputs.DataStoreInfoBaseResponse':\\n return pulumi.get(self, \\\"data_store\\\")\",\n \"def get(self, context, id_):\\n try:\\n db_resource_mgr_data = self.db_api.get_resource_manager(\\n context, id_)\\n _resource_mgr_data = _make_response(db_resource_mgr_data)\\n\\n except exception.NotFound as e:\\n raise e\\n\\n except Exception as e:\\n LOG.exception(e)\\n msg = (\\\"Error retrieving the 'resource manager': %s.\\\"\\n \\\" Reason: %s\\\") % (id_, e.message)\\n LOG.error(msg)\\n raise exception.RetrieveException(e.message)\\n\\n LOG.info(\\\"eon resource_manager data is %s \\\" %\\n logging.mask_password(_resource_mgr_data))\\n return _resource_mgr_data\",\n \"def dataloader(self):\\n\\n # load / split data\\n train_data = self.data.get_train_data()\\n if self.args.use_dev:\\n train_data, dev_data = self.data.split_data(train_data)\\n test_data = self.data.get_test_data()\\n\\n #print(train_data[0])\\n #print(dev_data[0])\\n #print(test_data[0])\\n\\n # build dataset\\n train_dataset = self.loader.build_dataset(\\n train_data, \\n self.args.train_max_seq_len)\\n train_loader = self.loader.build_dataloader(\\n train_dataset, 'train')\\n\\n test_dataset = self.loader.build_dataset(\\n test_data,\\n self.args.eval_max_seq_len)\\n test_loader = self.loader.build_dataloader(\\n test_dataset, 'test')\\n\\n if self.args.use_dev:\\n dev_dataset = self.loader.build_dataset(\\n dev_data,\\n self.args.eval_max_seq_len)\\n dev_loader = self.loader.build_dataloader(\\n dev_dataset, 'dev')\\n return train_loader, dev_loader, test_loader\\n else:\\n return train_loader, test_loader\",\n \"def get_metadata_manager(config):\\n\\n context = config.contextualization_type\\n metadata_manager_class = '%sMetadataManager' % context\\n if not (metadata_manager_class in globals()):\\n raise NotImplementedError('Implementation for %s not available' % context)\\n return (globals()[metadata_manager_class])(config)\",\n \"def get_extension_manager(self):\\n return get_extension_manager()\",\n \"def get_entity_manager(self):\\n return self.game.entity_manager\",\n \"def DataSet(self):\\n if hasattr(self, '_dataset') and self._dataset and self._dataset.Get():\\n return WrapDataObject(self._dataset.Get())\\n\\n return None\",\n \"def get_test_dataset_DataLoader(self):\\n test_info = self.get_test_DataLoader_info()\\n name = test_info[\\\"name\\\"]\\n task = test_info[\\\"task\\\"]\\n data_dir = test_info[\\\"data_dir\\\"]\\n hdf5_file = test_info[\\\"hdf5_file\\\"]\\n\\n data_loader = DataLoader(name, task, data_dir, hdf5_file)\\n\\n return data_loader, self.dataset, self.data_fields\",\n \"def getAPIsManager(self):\\n return self.apisManager\",\n \"def get_datasource(self):\\n return None\",\n \"def get_offline_dataloaders(params):\\n # Train\\n trainDS = EnvironmentLoader(params[\\\"dataset\\\"][\\\"train_data\\\"])\\n train_data_loader = torch.utils.data.DataLoader(\\n trainDS, shuffle=True, batch_size=params[\\\"optimization\\\"][\\\"batch_size\\\"])\\n\\n # Test\\n test_DS = EnvironmentLoader(params[\\\"dataset\\\"][\\\"test_data\\\"])\\n test_data_loader = torch.utils.data.DataLoader(\\n test_DS, shuffle=True, batch_size=params[\\\"optimization\\\"][\\\"batch_size\\\"])\\n\\n return train_data_loader, test_data_loader\",\n \"def Datastores(self):\\n if not self._datastores:\\n ds = self._get_objects(vim.Datastore)\\n for d in ds:\\n self._datastores[d.name] = Datastore(d)\\n return self._datastores\",\n \"def _get_dataset(self):\\n if self.mode == 'train':\\n return (\\n tf.data.Dataset.from_tensor_slices(\\n tensors=(tf.constant(value=self.file_paths),\\n tf.reshape(tensor=tf.constant(self.labels), shape=[-1]))\\n )\\n .shuffle(buffer_size=self.num_samples, reshuffle_each_iteration=True)\\n .map(map_func=self.import_waveforms_fn_train, num_parallel_calls=self.num_parallel_calls)\\n .repeat()\\n .batch(batch_size=self.batch_size)\\n .prefetch(buffer_size=self.prefetch_buffer)\\n )\\n else:\\n return (\\n tf.data.Dataset.from_tensor_slices(\\n tensors=(tf.constant(value=self.file_paths),\\n tf.reshape(tensor=tf.constant(self.labels), shape=[-1]))\\n )\\n .map(map_func=self.import_waveforms_fn_val, num_parallel_calls=self.num_parallel_calls)\\n .repeat()\\n .batch(batch_size=self.batch_size)\\n .prefetch(buffer_size=self.prefetch_buffer)\\n )\",\n \"def get_data_source_registry():\\n global _data_source_registry\\n if not _data_source_registry.list():\\n _init_data_source_registry(_data_source_registry)\\n return _data_source_registry\",\n \"def get_datasets(self):\\n return self.ds_primary, self.ds_secondary\",\n \"def get_dataset(opts):\\n dataset_type = opts.dataset_params.dataset_type\\n if dataset_type in 'synth':\\n return synthgraph.SynthGraphDataset(opts, opts.dataset_params)\\n elif dataset_type in 'synthnoise':\\n return synthgraph.SynthNoiseGraphDataset(opts, opts.dataset_params)\\n elif dataset_type in 'synthoutlier':\\n return synthgraph.SynthOutlierGraphDataset(opts, opts.dataset_params)\\n elif dataset_type in 'rome16kgeom':\\n return spreal.GeomKNNRome16KDataset(opts, opts.dataset_params)\\n elif dataset_type in 'graffiti':\\n return graffiti.GraffitiDataset(opts, opts.dataset_params)\\n else:\\n print(\\\"ERROR: Dataset type {} not implemented yet\\\".format(dataset_type))\\n sys.exit(1)\",\n \"def _get_client():\\n\\n return datastore.Client()\",\n \"def _get_client():\\n\\n return datastore.Client()\",\n \"def get_pydicom(self) -> pydicom.FileDataset:\\n return util.get_pydicom(self.client, self.id_)\",\n \"def get_dataset_loader(self, batch_size, workers, is_gpu):\\n\\n train_loader = torch.utils.data.DataLoader(\\n self.trainset,\\n batch_size=batch_size, shuffle=True,\\n num_workers=workers, pin_memory=is_gpu, sampler=None)\\n\\n val_loader = torch.utils.data.DataLoader(\\n self.valset,\\n batch_size=batch_size, shuffle=False,\\n num_workers=workers, pin_memory=is_gpu)\\n\\n return train_loader, val_loader\",\n \"def get_dataset_loader(self, batch_size, workers, is_gpu):\\n\\n train_loader = torch.utils.data.DataLoader(\\n self.trainset,\\n batch_size=batch_size, shuffle=True,\\n num_workers=workers, pin_memory=is_gpu, sampler=None)\\n\\n val_loader = torch.utils.data.DataLoader(\\n self.valset,\\n batch_size=batch_size, shuffle=False,\\n num_workers=workers, pin_memory=is_gpu)\\n\\n return train_loader, val_loader\",\n \"def get_dataset_loader(self, batch_size, workers, is_gpu):\\n\\n train_loader = torch.utils.data.DataLoader(\\n self.trainset,\\n batch_size=batch_size, shuffle=True,\\n num_workers=workers, pin_memory=is_gpu, sampler=None)\\n\\n val_loader = torch.utils.data.DataLoader(\\n self.valset,\\n batch_size=batch_size, shuffle=False,\\n num_workers=workers, pin_memory=is_gpu)\\n\\n return train_loader, val_loader\",\n \"def get_dataset_loader(self, batch_size, workers, is_gpu):\\n\\n train_loader = torch.utils.data.DataLoader(\\n self.trainset,\\n batch_size=batch_size, shuffle=True,\\n num_workers=workers, pin_memory=is_gpu, sampler=None)\\n\\n val_loader = torch.utils.data.DataLoader(\\n self.valset,\\n batch_size=batch_size, shuffle=False,\\n num_workers=workers, pin_memory=is_gpu)\\n\\n return train_loader, val_loader\",\n \"def get_dataset_loader(self, batch_size, workers, is_gpu):\\n\\n train_loader = torch.utils.data.DataLoader(\\n self.trainset,\\n batch_size=batch_size, shuffle=True,\\n num_workers=workers, pin_memory=is_gpu, sampler=None)\\n\\n val_loader = torch.utils.data.DataLoader(\\n self.valset,\\n batch_size=batch_size, shuffle=False,\\n num_workers=workers, pin_memory=is_gpu)\\n\\n return train_loader, val_loader\",\n \"def get_dataset_loader(self, batch_size, workers, is_gpu):\\n\\n train_loader = torch.utils.data.DataLoader(\\n self.trainset,\\n batch_size=batch_size, shuffle=True,\\n num_workers=workers, pin_memory=is_gpu, sampler=None)\\n\\n val_loader = torch.utils.data.DataLoader(\\n self.valset,\\n batch_size=batch_size, shuffle=False,\\n num_workers=workers, pin_memory=is_gpu)\\n\\n return train_loader, val_loader\",\n \"def get_dataset_loader(self, batch_size, workers, is_gpu):\\n\\n train_loader = torch.utils.data.DataLoader(\\n self.trainset,\\n batch_size=batch_size, shuffle=True,\\n num_workers=workers, pin_memory=is_gpu, sampler=None)\\n\\n val_loader = torch.utils.data.DataLoader(\\n self.valset,\\n batch_size=batch_size, shuffle=False,\\n num_workers=workers, pin_memory=is_gpu)\\n\\n return train_loader, val_loader\",\n \"def get_dataset_loader(self, batch_size, workers, is_gpu):\\n\\n train_loader = torch.utils.data.DataLoader(\\n self.trainset,\\n batch_size=batch_size, shuffle=True,\\n num_workers=workers, pin_memory=is_gpu, sampler=None)\\n\\n val_loader = torch.utils.data.DataLoader(\\n self.valset,\\n batch_size=batch_size, shuffle=False,\\n num_workers=workers, pin_memory=is_gpu)\\n\\n return train_loader, val_loader\",\n \"def get_dataset_loader(self, batch_size, workers, is_gpu):\\n\\n train_loader = torch.utils.data.DataLoader(\\n self.trainset,\\n batch_size=batch_size, shuffle=True,\\n num_workers=workers, pin_memory=is_gpu, sampler=None)\\n\\n val_loader = torch.utils.data.DataLoader(\\n self.valset,\\n batch_size=batch_size, shuffle=False,\\n num_workers=workers, pin_memory=is_gpu)\\n\\n return train_loader, val_loader\",\n \"def get(cls, _id):\\n return DataStore.get_instance(cls, _id)\",\n \"def _getDatasetPath(self):\\n return self.__dataset_path\",\n \"def get_dataloader(self, cid, batch_size=None, type=\\\"train\\\"):\\n dataset = self.get_dataset(cid, type)\\n batch_size = len(dataset) if batch_size is None else batch_size\\n data_loader = DataLoader(dataset, batch_size=batch_size)\\n return data_loader\",\n \"def get_instance(cls):\\n global DNS_MANAGER_API\\n if not DNS_MANAGER_API:\\n DNS_MANAGER_API = cls()\\n return DNS_MANAGER_API\",\n \"def get_data_set(self):\\n raise exceptions.NotImplemented\",\n \"def data_store(self) -> pulumi.Input['DataStoreInfoBaseArgs']:\\n return pulumi.get(self, \\\"data_store\\\")\",\n \"def _get_store(self):\\n return self._store\",\n \"def get_manager(self, name):\\n\\n if name == \\\"control\\\":\\n manager = self._control_manager\\n elif name == \\\"alarm\\\":\\n manager = self._alarm_manager\\n elif name == \\\"state\\\":\\n manager = self._machine_manager\\n else:\\n manager = self._function_manager\\n\\n return manager\",\n \"def data_model(self) -> DataModel:\\n return self._data_model\",\n \"def getDb(self):\\n return self.db\",\n \"def __init__(self, dataset_name, teacher_model, students_model):\\n self.data_manager = DataManager(dataset_name)\\n self.dataset_name = dataset_name\\n self.teacher_model = teacher_model\\n self.student_model = students_model\",\n \"def get_dataset(self, dataset_path=None, normalize=True, return_original=False):\\n if dataset_path is None:\\n dataset_path = self.dir\\n \\n if \\\"mocap\\\" in dataset_path.lower():\\n print(\\\"Loading Mocap dataset.\\\")\\n df = get_mocap()\\n df_orig = df\\n elif \\\"profi\\\" in dataset_path.lower():\\n print(\\\"Loading Profiset dataset.\\\")\\n df = get_profiset()\\n df_orig = df\\n else:\\n print(\\\"Loading CoPhIR dataset.\\\")\\n df_orig, attr_lengths = get_objects_with_indexes(self.labels, f'{dataset_path}/level-{str(self.n_levels)}.txt', f'{dataset_path}/objects.txt')\\n if normalize:\\n df = scale_per_descriptor(df_orig, self.labels, attr_lengths)\\n else:\\n df = df_orig\\n \\n assert df.shape[1] == self.descriptor_values + self.n_levels + len([\\\"object_id\\\"])\\n logging.info(f\\\"Loaded dataset of shape: {df.shape}\\\")\\n if return_original:\\n return df, df_orig\\n else:\\n return df\",\n \"def get_db(self):\\n return self._db\",\n \"def get_database(self):\\n return self.database\",\n \"def get_dataset_reference(self, dataset_name):\\n\\n print_debug(\\\"Geting dataset :\\\" + dataset_name)\\n dataset = DatasetFactory.get(dataset_file_name=dataset_name)\\n return dataset\",\n \"def get_dataset(params):\\r\\n module_name, class_name = params.dataset.name.rsplit('.', 1)\\r\\n i = importlib.import_module(module_name)\\r\\n return getattr(i, class_name)\",\n \"def get_persistence(self):\\n return self.persistence\",\n \"def persist(cls, dataset):\\n return dataset\",\n \"def manager(self):\\n if \\\"manager\\\" in self._prop_dict:\\n if isinstance(self._prop_dict[\\\"manager\\\"], OneDriveObjectBase):\\n return self._prop_dict[\\\"manager\\\"]\\n else :\\n self._prop_dict[\\\"manager\\\"] = DirectoryObject(self._prop_dict[\\\"manager\\\"])\\n return self._prop_dict[\\\"manager\\\"]\\n\\n return None\",\n \"def get_cls(dataset_name):\\n return find_dataset_using_name(dataset_name)\",\n \"def open(self):\\n return xr.open_dataset(self)\",\n \"def get_global_adaptation_manager():\\n global adaptation_manager\\n return adaptation_manager\",\n \"def as_manager(cls):\\n manager = DefaultManager.from_queryset(cls)()\\n manager._built_with_as_manager = True\\n return manager\",\n \"def get(self, name):\\n assert name, \\\"Must input a valid dataset name.\\\"\\n return self.manager.get_data(name)\",\n \"def get_db():\\n if \\\"db\\\" not in context:\\n context[\\\"db\\\"] = dataset.connect(os.getenv(\\\"DATABASE_URL\\\", \\\"sqlite:///jobs.db\\\"))\\n return context[\\\"db\\\"]\",\n \"def get_collection_manager(self, *args, **kwargs):\\n return CollectionManager(self, *args, **kwargs)\",\n \"def _get_db(self):\\n return DB(\\n ClientStorage.ClientStorage((self.server, self.port))\\n )\",\n \"def _get_db(self):\\n gt_db = ...\\n return gt_db\",\n \"def get_manager(api_version=None):\\n from manager import get_keystone_manager\\n return get_keystone_manager(get_local_endpoint(), get_admin_token(),\\n api_version)\",\n \"def get_default_datastore(self):\\n try:\\n return self.client.list_datastores()[0]['datastore']\\n except VMwareError as e:\\n raise VMwareBackendError(e)\",\n \"def get_dbs_obj(self):\\n dbs_xml = self.get_DatabaseAndServer_XML()\\n return self.get_DatabaseAndServer_obj(dbs_xml)\",\n \"def _configure_manager(self):\\n self._manager = CloudDatabaseManager(self,\\n resource_class=CloudDatabaseInstance, response_key=\\\"instance\\\",\\n uri_base=\\\"instances\\\")\\n self._flavor_manager = BaseManager(self,\\n resource_class=CloudDatabaseFlavor, response_key=\\\"flavor\\\",\\n uri_base=\\\"flavors\\\")\\n self._backup_manager = CloudDatabaseBackupManager(self,\\n resource_class=CloudDatabaseBackup, response_key=\\\"backup\\\",\\n uri_base=\\\"backups\\\")\",\n \"def cluster_manager(self):\\n # Lazily instantiate the cluster manager the first time it is asked for.\\n if not hasattr(self, '_cluster_manager'):\\n if self._cluster_engine:\\n self._cluster_manager = self._cluster_engine.create_manager(\\n self._username,\\n self._tenancy\\n )\\n else:\\n self._cluster_manager = None\\n # If there is still no cluster manager, clusters are not supported\\n if not self._cluster_manager:\\n raise errors.UnsupportedOperationError(\\n 'Clusters are not supported for this tenancy.'\\n )\\n return self._cluster_manager\",\n \"def GetAuiManager(self):\\r\\n\\r\\n return self._mgr\",\n \"def get_mgr(cls, id):\\n assert id in cls.s_memory_mgrs, 'invalid id[%s] for memory managers' % (\\n id)\\n return cls.s_memory_mgrs[id]\",\n \"def get_datasource_of():\\n global datasource_of\\n\\n if not datasource_of:\\n datasource_of = stixhelpers.datasource_of()\\n \\n return datasource_of\",\n \"def mgmt_tool(self) -> MgmtClient:\\n return self._mgmt_tool\",\n \"def configStore(self):\\n return getattr(self._data.configStore, self._name)\",\n \"def dataset_dir(self):\\n return self._dataset_dir\",\n \"def get_dataset(dataset_name, use_cached=True, dataset_folder=DATASET_FOLDER, cache_path=CACHE_PATH, cache_file=None):\\n\\n dataset_npy = cache_file if cache_file else os.path.join(cache_path, 'dataset_{}.npy'.format(dataset_name))\\n\\n if use_cached and os.path.exists(dataset_npy):\\n X, Y = get_dataset_cached(dataset_npy)\\n else:\\n X, Y = get_dataset_module(dataset_folder, dataset_name).fetch()\\n\\n # Test dataset validity before saving it\\n test_dataset_validity(X, Y)\\n\\n with open(dataset_npy, 'wb') as f:\\n pickle.dump((X, Y), f)\\n\\n test_dataset_validity(X, Y)\\n return X, Y\",\n \"def dataset(self):\\n return self.predictor_data_manager.dataset(\\n self.data_name, self.trait_name, data=self._data, trait=self.trait,\\n categorical_trait=self.categorical_trait)\",\n \"def get_backend(self):\\n return self.analyze_db_task(constants.TRAIN_DB).backend\",\n \"def get_dataset(self, dataset_id):\\n return self.session.query(self.Dataset).get(dataset_id)\",\n \"def event_manager(self):\\n return ScriptDB.objects.get(db_key=\\\"Event Manager\\\")\",\n \"def manager(self):\\n if not self._manager:\\n self._manager = TwistedEventLoopManager()\\n\\n return self._manager\",\n \"def manager(self):\\n if not self._manager:\\n self._manager = TwistedEventLoopManager()\\n\\n return self._manager\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.84404564","0.7547824","0.69010204","0.6821794","0.6821317","0.6761993","0.67312783","0.6668024","0.6635121","0.6446262","0.64157367","0.6388273","0.6379517","0.63311225","0.62744164","0.6216641","0.618155","0.61262697","0.6106576","0.6034368","0.6029758","0.60243684","0.6005251","0.5947128","0.59277856","0.59039575","0.588222","0.5860066","0.5805874","0.5795176","0.5776333","0.5772625","0.5749945","0.5688606","0.5677532","0.5673529","0.567192","0.5667651","0.56626487","0.5593736","0.55825","0.5574652","0.5567044","0.5567044","0.5559717","0.55576754","0.55576754","0.55576754","0.55576754","0.55576754","0.55576754","0.55576754","0.55576754","0.55576754","0.5545639","0.5541807","0.5537441","0.5498612","0.54901963","0.54893625","0.54830873","0.54775846","0.54747075","0.54709435","0.5470589","0.54605865","0.54547775","0.5438651","0.5432234","0.5429262","0.5426118","0.541466","0.54144883","0.54021776","0.5397048","0.5371797","0.53642046","0.536272","0.5349496","0.53446984","0.53399855","0.5325128","0.5321763","0.5320012","0.5306357","0.53036064","0.5298477","0.52951664","0.52934694","0.5290708","0.52755946","0.5274411","0.52706766","0.52694947","0.52653044","0.52583486","0.52539486","0.52433497","0.5242407","0.5242407"],"string":"[\n \"0.84404564\",\n \"0.7547824\",\n \"0.69010204\",\n \"0.6821794\",\n \"0.6821317\",\n \"0.6761993\",\n \"0.67312783\",\n \"0.6668024\",\n \"0.6635121\",\n \"0.6446262\",\n \"0.64157367\",\n \"0.6388273\",\n \"0.6379517\",\n \"0.63311225\",\n \"0.62744164\",\n \"0.6216641\",\n \"0.618155\",\n \"0.61262697\",\n \"0.6106576\",\n \"0.6034368\",\n \"0.6029758\",\n \"0.60243684\",\n \"0.6005251\",\n \"0.5947128\",\n \"0.59277856\",\n \"0.59039575\",\n \"0.588222\",\n \"0.5860066\",\n \"0.5805874\",\n \"0.5795176\",\n \"0.5776333\",\n \"0.5772625\",\n \"0.5749945\",\n \"0.5688606\",\n \"0.5677532\",\n \"0.5673529\",\n \"0.567192\",\n \"0.5667651\",\n \"0.56626487\",\n \"0.5593736\",\n \"0.55825\",\n \"0.5574652\",\n \"0.5567044\",\n \"0.5567044\",\n \"0.5559717\",\n \"0.55576754\",\n \"0.55576754\",\n \"0.55576754\",\n \"0.55576754\",\n \"0.55576754\",\n \"0.55576754\",\n \"0.55576754\",\n \"0.55576754\",\n \"0.55576754\",\n \"0.5545639\",\n \"0.5541807\",\n \"0.5537441\",\n \"0.5498612\",\n \"0.54901963\",\n \"0.54893625\",\n \"0.54830873\",\n \"0.54775846\",\n \"0.54747075\",\n \"0.54709435\",\n \"0.5470589\",\n \"0.54605865\",\n \"0.54547775\",\n \"0.5438651\",\n \"0.5432234\",\n \"0.5429262\",\n \"0.5426118\",\n \"0.541466\",\n \"0.54144883\",\n \"0.54021776\",\n \"0.5397048\",\n \"0.5371797\",\n \"0.53642046\",\n \"0.536272\",\n \"0.5349496\",\n \"0.53446984\",\n \"0.53399855\",\n \"0.5325128\",\n \"0.5321763\",\n \"0.5320012\",\n \"0.5306357\",\n \"0.53036064\",\n \"0.5298477\",\n \"0.52951664\",\n \"0.52934694\",\n \"0.5290708\",\n \"0.52755946\",\n \"0.5274411\",\n \"0.52706766\",\n \"0.52694947\",\n \"0.52653044\",\n \"0.52583486\",\n \"0.52539486\",\n \"0.52433497\",\n \"0.5242407\",\n \"0.5242407\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7545222"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":89295,"cells":{"query":{"kind":"string","value":"Create the cifar10 dataset."},"document":{"kind":"string","value":"def create_cifar10_dataset(cifar_dir):\n ds = de.Cifar10Dataset(cifar_dir)\n\n training = True\n resize_height = 224\n resize_width = 224\n rescale = 1.0 / 255.0\n shift = 0.0\n repeat_num = 10\n batch_size = 32\n\n # define map operations\n random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) # padding_mode default CONSTANT\n random_horizontal_op = vision.RandomHorizontalFlip()\n resize_op = vision.Resize((resize_height, resize_width)) # interpolation default BILINEAR\n rescale_op = vision.Rescale(rescale, shift)\n normalize_op = vision.Normalize((0.4465, 0.4822, 0.4914), (0.2010, 0.1994, 0.2023))\n changeswap_op = vision.HWC2CHW()\n type_cast_op = C.TypeCast(mstype.int32)\n\n if training:\n c_trans = [random_crop_op, random_horizontal_op]\n c_trans += [resize_op, rescale_op, normalize_op,\n changeswap_op]\n\n # apply map operations on images\n ds = ds.map(operations=type_cast_op, input_columns=\"label\")\n ds = ds.map(operations=c_trans, input_columns=\"image\")\n\n # apply repeat operations\n ds = ds.repeat(repeat_num)\n\n # apply shuffle operations\n ds = ds.shuffle(buffer_size=10)\n\n # apply batch operations\n ds = ds.batch(batch_size=batch_size, drop_remainder=True)\n\n return ds"},"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 cifar10(transform=transforms.ToTensor()):\n\n\t# Directories\n\tscript_dir = os.path.dirname(os.path.realpath(__file__))\n\tdata_dir = os.path.join(script_dir, 'data', 'cifar10')\n\n\t# Load training set, downloading if necessary\n\tdataset = datasets.CIFAR10(data_dir, train=True, transform=transform, download=True)\n\n\t# Split into train, val, and test sets\n\n\t# Add 'classes' list and 'class_to_idx' lookup dictionary to both sets\n\tclasses = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', \n\t\t'horse', 'ship', 'truck']\n\tclass_to_idx = {classes[i]: i for i in range(len(classes))}\n\n\tdataset.classes = classes\n\tdataset.class_to_idx = class_to_idx\n\n\treturn dataset","def create_dataset_cifar10(data_path, batch_size=32, num_parallel_workers=8, do_train=True):\r\n # define dataset\r\n data_path = os.path.join(data_path, \"cifar-10-batches-bin\" if do_train else \"cifar-10-verify-bin\")\r\n\r\n cifar_ds = ds.Cifar10Dataset(data_path, num_parallel_workers=num_parallel_workers, shuffle=do_train)\r\n\r\n # define map operations\r\n resize_height, resize_width = 32, 32\r\n rescale = 1.0 / 255.0\r\n shift = 0.0\r\n random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])\r\n random_horizontal_op = CV.RandomHorizontalFlip(prob=0.5)\r\n resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)\r\n rescale_op = CV.Rescale(rescale, shift)\r\n normalize_op = CV.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])\r\n hwc2chw_op = CV.HWC2CHW()\r\n type_cast_op = C.TypeCast(mstype.int32)\r\n\r\n if do_train:\r\n compose_op = [random_crop_op, random_horizontal_op, resize_op, rescale_op, normalize_op, hwc2chw_op]\r\n else:\r\n compose_op = [resize_op, rescale_op, normalize_op, hwc2chw_op]\r\n cifar_ds = cifar_ds.map(input_columns=\"image\", operations=compose_op, num_parallel_workers=num_parallel_workers)\r\n cifar_ds = cifar_ds.map(input_columns=\"label\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\r\n cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)\r\n\r\n return cifar_ds","def get_CIFAR10_data():\n # Load the raw CIFAR-10 data\n cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n X_train, _, X_test, _ = load_CIFAR10(cifar10_dir)\n X = np.concatenate((X_train, X_test), axis =0)\n return X","def load_dataset_cifar10():\n dirname = 'cifar-10-batches-py'\n origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n path = get_file(dirname, origin=origin, untar=True)\n\n num_train_samples = 50000\n\n x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')\n y_train = np.empty((num_train_samples,), dtype='uint8')\n\n for i in range(1, 6):\n fpath = os.path.join(path, 'data_batch_' + str(i))\n (x_train[(i - 1) * 10000: i * 10000, :, :, :],\n y_train[(i - 1) * 10000: i * 10000]) = load_batch(fpath)\n\n fpath = os.path.join(path, 'test_batch')\n x_test, y_test = load_batch(fpath)\n\n y_train = np.reshape(y_train, (len(y_train), 1))\n y_test = np.reshape(y_test, (len(y_test), 1))\n\n return (x_train, y_train), (x_test, y_test)","def get_cifar10(self):\n\t\t# Get the data.\n\t\tself.x_train = self.x_train.reshape(self.nb_train, self.input_dim)\n\t\tself.x_test = self.x_test.reshape(self.nb_test, self.input_dim)\n\t\tself.x_train = self.x_train.astype('float32')\n\t\tself.x_test = self.x_test.astype('float32')\n\t\tself.x_train /= 255\n\t\tself.x_test /= 255\n\n\t\t# convert class vectors to binary class matrices\n\t\tself.y_train = np_utils.to_categorical(self.y_train, self.nb_classes)\n\t\tself.y_test = np_utils.to_categorical(self.y_test, self.nb_classes)","def get_cifar_data(num_classes=10):\n\n (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n x_train = x_train.astype(np.float32) / 255.\n x_test = x_test.astype(np.float32) / 255.\n\n y_train_cat = to_categorical(y_train, num_classes=num_classes).astype(np.float32)\n y_test_cat = to_categorical(y_test, num_classes=num_classes).astype(np.float32)\n\n return x_train, y_train, x_test, y_test, y_train_cat, y_test_cat","def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n # Load the raw CIFAR-10 data\n cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n \n # Subsample the data\n mask = range(num_training, num_training + num_validation)\n X_val = X_train[mask]\n y_val = y_train[mask]\n mask = range(num_training)\n X_train = X_train[mask]\n y_train = y_train[mask]\n mask = range(num_test)\n X_test = X_test[mask]\n y_test = y_test[mask]\n\n # Normalize the data: subtract the mean image\n mean_image = np.mean(X_train, axis=0)\n X_train -= mean_image\n X_val -= mean_image\n X_test -= mean_image\n\n # Reshape data to rows\n X_train = X_train.reshape(num_training, -1)\n X_val = X_val.reshape(num_validation, -1)\n X_test = X_test.reshape(num_test, -1)\n\n pca = decomposition.PCA(n_components=None, whiten=False)\n X_train = pca.fit_transform(X_train)\n X_val = pca.transform(X_val)\n X_test = pca.transform(X_test)\n\n return X_train, y_train, X_val, y_val, X_test, y_test","def setUp(self):\n super().setUp()\n self._batch_size = 16\n self._batch_size_test = 10\n self._shuffle_buffer_size = 8\n\n self._dataset = cifar10.CIFAR10Dataset(\n self._batch_size,\n batch_size_test=self._batch_size_test,\n shuffle_buffer_size=self._shuffle_buffer_size)","def get_cifar10_cnn():\n # Set defaults.\n nb_classes = 10 #dataset dependent \n batch_size = 128\n epochs = 4\n \n # the data, shuffled and split between train and test sets\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n \n # convert class vectors to binary class matrices\n y_train = to_categorical(y_train, nb_classes)\n y_test = to_categorical(y_test, nb_classes)\n\n #x._train shape: (50000, 32, 32, 3)\n #input shape (32, 32, 3)\n input_shape = x_train.shape[1:]\n\n #print('x_train shape:', x_train.shape)\n #print(x_train.shape[0], 'train samples')\n #print(x_test.shape[0], 'test samples')\n #print('input shape', input_shape)\n \n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n x_train /= 255\n x_test /= 255\n\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test, epochs)","def train_cifar10():\r\n # type: () -> None\r\n\r\n # Build dataset and model\r\n dataset = CIFAR10_TRAIN(path=Config.video_folder)\r\n model = LSACIFAR10(input_shape=dataset.shape, code_length=64,\r\n cpd_channels=100).to(device).train()\r\n\r\n # Set up result helper and perform test\r\n helper = OneClassResultHelper(dataset, model,\r\n checkpoints_dir=Config.model_ckpt,\r\n output_file='cifar10.txt')\r\n helper.train_one_class_classification()","def get_cifar10_c(dataset_root, batch_size, train, noise_type):\n \n # image pre-processing\n pre_process = transforms.Compose([transforms.Resize(28),\n transforms.ToTensor(),\n transforms.Normalize(\n mean=(0.491, 0.482, 0.446), \n std=(0.202, 0.199, 0.201)\n )\n ])\n\n # datasets and data loader\n cifar10_c_dataset = GetLoader(\n data_root=os.path.join(dataset_root),\n noise_type=noise_type,\n transform=pre_process)\n \n print(\"dataset len: \",len(cifar10_c_dataset))\n\n train_size = int(0.8 * len(cifar10_c_dataset))\n test_size = len(cifar10_c_dataset) - train_size\n\n train_dataset, test_dataset = torch.utils.data.random_split(cifar10_c_dataset, [train_size, test_size])\n print(\"train len: \",len(train_dataset))\n print(\"test len: \",len(test_dataset))\n if train:\n cifar10_c_dataset=train_dataset\n else:\n cifar10_c_dataset=test_dataset\n\n cifar10_c_data_loader = torch.utils.data.DataLoader(\n dataset=cifar10_c_dataset,\n batch_size=batch_size,\n shuffle=True,\n drop_last=True,\n num_workers=8)\n\n return cifar10_c_data_loader","def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n # Load the raw CIFAR-10 data\n cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n\n # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n try:\n del X_train, y_train\n del X_test, y_test\n print('Clear previously loaded data.')\n except:\n pass\n\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n\n # Subsample the data\n mask = list(range(num_training, num_training + num_validation))\n X_val = X_train[mask]\n y_val = y_train[mask]\n mask = list(range(num_training))\n X_train = X_train[mask]\n y_train = y_train[mask]\n mask = list(range(num_test))\n X_test = X_test[mask]\n y_test = y_test[mask]\n\n # Normalize the data: subtract the mean image\n mean_image = np.mean(X_train, axis=0)\n X_train -= mean_image\n X_val -= mean_image\n X_test -= mean_image\n\n # Reshape data to rows\n X_train = X_train.reshape(num_training, -1)\n X_val = X_val.reshape(num_validation, -1)\n X_test = X_test.reshape(num_test, -1)\n\n return X_train, y_train, X_val, y_val, X_test, y_test","def load_CIFAR10(ROOT):\r\n xs = []\r\n ys = []\r\n for b in range(1,6):\r\n f = os.path.join(ROOT, 'data_batch_%d' % (b, ))\r\n X, Y = load_CIFAR_batch(f)\r\n xs.append(X)\r\n ys.append(Y)\r\n Xtr = np.concatenate(xs)\r\n Ytr = np.concatenate(ys)\r\n del X, Y\r\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\r\n return Xtr, Ytr, Xte, Yte\r\n\r\n\tdef get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):\r\n # Load the raw CIFAR-10 data\r\n \r\n cifar10_dir = 'Downloads/cifar-10-batches-py'\r\n \r\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\r\n\r\n # Subsample the data\r\n mask = range(num_training, num_training + num_validation)\r\n X_val = X_train[mask]\r\n y_val = y_train[mask]\r\n mask = range(num_training)\r\n X_train = X_train[mask]\r\n y_train = y_train[mask]\r\n mask = range(num_test)\r\n X_test = X_test[mask]\r\n y_test = y_test[mask]\r\n\r\n x_train = X_train.astype('float32') \r\n x_test = X_test.astype('float32')\r\n \r\n x_train = x_train.reshape(-1, 32, 32, 3)\r\n x_test = x_test.reshape(-1, 32, 32, 3)\r\n x_train /= 255\r\n x_test /= 255\r\n\r\n return x_train, y_train, X_val, y_val, x_test, y_test","def get_cifar10():\n\n from keras.datasets import cifar10\n\n # input image dimensions\n img_rows, img_cols = 32, 32\n n_channels = 3\n num_classes = 10\n # the data, shuffled and split between train and test sets\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n\n if K.image_data_format() == 'channels_first':\n print (\"Using Channels first\")\n x_train = x_train.reshape(x_train.shape[0], n_channels, img_rows, img_cols)\n x_test = x_test.reshape(x_test.shape[0], n_channels, img_rows, img_cols)\n input_shape = (n_channels, img_rows, img_cols)\n else:\n print(\"Channels last\")\n x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, n_channels)\n x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, n_channels)\n input_shape = (img_rows, img_cols, n_channels)\n\n return (x_train, y_train), (x_test, y_test)","def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\n # Load the raw CIFAR-10 data\n cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n\n # subsample the data\n mask = range(num_training, num_training + num_validation)\n X_val = X_train[mask]\n y_val = y_train[mask]\n mask = range(num_training)\n X_train = X_train[mask]\n y_train = y_train[mask]\n mask = range(num_test)\n X_test = X_test[mask]\n y_test = y_test[mask]\n mask = np.random.choice(num_training, num_dev, replace=False)\n X_dev = X_train[mask]\n y_dev = y_train[mask]\n\n # Preprocessing: reshape the image data into rows\n X_train = np.reshape(X_train, (X_train.shape[0], -1))\n X_val = np.reshape(X_val, (X_val.shape[0], -1))\n X_test = np.reshape(X_test, (X_test.shape[0], -1))\n X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n\n # Normalize the data: subtract the mean image\n mean_image = np.mean(X_train, axis=0)\n X_train -= mean_image\n X_val -= mean_image\n X_test -= mean_image\n X_dev -= mean_image\n\n # add bias dimension and transform into columns\n X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\n X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n\n return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev","def load_cifar10_data(self, data_path='data/cifar-10-batches-py',\n n_train_samples=50000, n_test_samples=10000):\n train_data = None\n train_labels = []\n\n for i in range(1, 6):\n data_dic = unpickle(data_path + '/data_batch_{}'.format(i))\n if i == 1:\n train_data = data_dic['data']\n else:\n train_data = np.vstack((train_data, data_dic['data']))\n\n train_labels += data_dic['labels']\n\n test_data_dic = unpickle(data_path + '/test_batch')\n test_data = test_data_dic['data']\n test_labels = test_data_dic['labels']\n\n train_data = train_data.reshape((len(train_data),\n self.LOADED_IMG_DEPTH,\n self.LOADED_IMG_HEIGHT,\n self.LOADED_IMG_HEIGHT))\n\n train_data = np.rollaxis(train_data, 1, 4)\n train_labels = np.array(train_labels)\n\n test_data = test_data.reshape((len(test_data),\n self.LOADED_IMG_DEPTH,\n self.LOADED_IMG_HEIGHT,\n self.LOADED_IMG_HEIGHT))\n\n test_data = np.rollaxis(test_data, 1, 4)\n test_labels = np.array(test_labels)\n\n self.train_dataset = {'data': train_data[0:n_train_samples],\n 'labels': train_labels[0:n_train_samples],\n 'cls': [np.zeros(10)\n for i in range(n_train_samples)]}\n\n for i in range(0, n_train_samples):\n self.train_dataset['cls'][i][self.train_dataset['labels'][i]] = 1.\n\n self.test_dataset = {'data': test_data[0:n_test_samples],\n 'labels': test_labels[0:n_test_samples],\n 'cls': [np.zeros(10)\n for i in range(n_train_samples)]}\n\n for i in range(0, n_test_samples):\n self.test_dataset['cls'][i][self.test_dataset['labels'][i]] = 1.\n\n self.train_dataset['data_array'] = np.array(\n [item.flatten() for item in self.train_dataset['data']])\n\n self.train_dataset['labels_array'] = np.array(\n [item.flatten() for item in self.train_dataset['labels']])\n\n self.train_dataset['cls_array'] = np.array(\n [item.flatten() for item in self.train_dataset['cls']])\n\n self.test_dataset['data_array'] = np.array(\n [item.flatten() for item in self.test_dataset['data']])\n\n self.test_dataset['labels_array'] = np.array(\n [item.flatten() for item in self.test_dataset['labels']])\n\n self.test_dataset['cls_array'] = np.array(\n [item.flatten() for item in self.test_dataset['cls']])\n\n return None","def loadData(self):\n # Load the raw CIFAR-10 data\n num_training = 49000\n num_validation = 1000\n num_test = 1000\n subtract_mean = True\n\n cifar10_dir = '/home/parallels/PycharmProjects/Courses/232A/project2/stats232a/datasets/cifar-10-batches-py'\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n\n # Subsample the data\n mask = list(range(num_training, num_training + num_validation))\n X_val = X_train[mask]\n y_val = y_train[mask]\n mask = list(range(num_training))\n X_train = X_train[mask]\n y_train = y_train[mask]\n mask = list(range(num_test))\n X_test = X_test[mask]\n y_test = y_test[mask]\n\n # Normalize the data: subtract the mean image\n if subtract_mean:\n mean_image = np.mean(X_train, axis=0)\n X_train -= mean_image\n X_val -= mean_image\n X_test -= mean_image\n\n # Transpose so that channels come first\n X_train = X_train.transpose(0, 3, 1, 2)\n X_val = X_val.transpose(0, 3, 1, 2)\n X_test = X_test.transpose(0, 3, 1, 2)\n\n # Package data into a dictionary\n self.data = {\n 'X_train': X_train, 'y_train': y_train,\n 'X_val': X_val, 'y_val': y_val,\n 'X_test': X_test, 'y_test': y_test,\n }","def load_cifar() -> Tuple[torchvision.datasets.CIFAR10, torchvision.datasets.CIFAR10]:\n \n # Define the transform for the data.\n transform = transforms.Compose(\n [transforms.ToTensor(), \n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n )\n \n # Initialize Datasets. CIFAR-10 will automatically download if not present\n trainset = torchvision.datasets.CIFAR10(\n root=DATA_ROOT, train=True, download=True, transform=transform\n )\n testset = torchvision.datasets.CIFAR10(\n root=DATA_ROOT, train=False, download=True, transform=transform\n )\n \n # Return the datasets\n return trainset, testset","def load_cifar10(data_path=\".\", test_size=0.2, random_state=1337):\n test_path = os.path.join(data_path, \"cifar-10-batches-py/test_batch\")\n train_paths = [os.path.join(data_path, \"cifar-10-batches-py/data_batch_%i\" % i) for i in range(1, 6)]\n\n if not os.path.exists(test_path) or not all(list(map(os.path.exists, train_paths))):\n print (\"Dataset not found. Downloading...\")\n download_cifar(data_path,\n url='https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\n tarname='cifar-10-python.tar.gz')\n\n train_batches = list(map(unpickle, train_paths))\n test_batch = unpickle(test_path)\n\n X = np.concatenate([batch[\"data\"] for batch in train_batches]).reshape([-1, 3, 32, 32]).astype('float32') / 255\n y = np.concatenate([batch[\"labels\"] for batch in train_batches]).astype('int32')\n X_train, X_val, y_train, y_val = train_test_split(X, y,\n test_size=test_size,\n random_state=random_state)\n\n X_test = test_batch[\"data\"].reshape([-1, 3, 32, 32]).astype('float32') / 255\n y_test = np.array(test_batch[\"labels\"]).astype('int32')\n\n return X_train, y_train, X_val, y_val, X_test, y_test","def load_data5():\n# dirname = 'cifar-10-batches-py'\n# origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n# path = get_file(dirname, origin=origin, untar=True)\n# path= './cifar-10-batches-py'\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n# Below shows a test class has 999 examples instead of the claimed 1000\n# tclasscount=np.zeros((10,), dtype=int)\n# for i in range(0, len(y_test)-1):\n# tclasscount[y_test[i][0]]= tclasscount[y_test[i][0]] + 1\n# print('Test class count',tclasscount)\n num_train_samples = 50000\n num_5_class = 25000\n num_5_test = 4999 # should be 5000 if all the categories had 1000 in them but they do not. One is missing.\n print('x_train shape orig:', x_train.shape)\n print('More:', x_train.shape[1:])\n print('y_test shape',y_test.shape)\n\n x5_train = np.empty((num_5_class, 32, 32, 3), dtype='uint8')\n y5_train = np.empty((num_5_class,), dtype='uint8')\n\n count=0\n\n for i in range(0, len(y_train)-1):\n if (y_train[i][0] == 2) or (y_train[i][0] == 3) or (y_train[i][0] == 4) or (y_train[i][0] == 5) or (y_train[i][0] == 7):\n x5_train[count]=x_train[i]\n y5_train[count]=y_train[i]\n count=count+1\n \n # find test data of interest\n count=0\n x5_test=np.empty((num_5_test, 32, 32, 3), dtype='uint8')\n y5_test= np.empty((num_5_test,), dtype='uint8')\n\n for i in range(0, len(y_test)-1):\n if (y_test[i][0] == 2) or (y_test[i][0] == 3) or (y_test[i][0] == 4) or (y_test[i][0] == 5) or (y_test[i][0] == 7):\n x5_test[count]=x_test[i]\n y5_test[count]=y_test[i]\n count=count+1\n# Below shows class 7 is only 999 and not 1000 examples!!! One horse got away it seems.\n# if(y_test[i][0] == 2):\n# c2=c2+1\n# if(y_test[i][0] == 3):\n# c3=c3+1\n# if(y_test[i][0] == 4):\n# c4=c4+1\n# if(y_test[i][0] == 5):\n# c5=c5+1\n# if(y_test[i][0] == 7):\n# c7=c7+1\n# print('c2count, c3count, c4count, c5count, c7count',c2,c3,c3,c5,c7)\n# print('y5tstshape',y5_test.shape, count)\n# print('y5tst',y5_test)\n# return (x_train, y_train), (x_test, y_test)\n return (x5_train, y5_train), (x5_test, y5_test)","def get_cifar10_mlp():\n # Set defaults.\n nb_classes = 10 #dataset dependent \n batch_size = 64\n epochs = 4\n input_shape = (3072,) #because it's RGB\n\n # Get the data.\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n x_train = x_train.reshape(50000, 3072)\n x_test = x_test.reshape(10000, 3072)\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n x_train /= 255\n x_test /= 255\n\n # convert class vectors to binary class matrices\n y_train = to_categorical(y_train, nb_classes)\n y_test = to_categorical(y_test, nb_classes)\n\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test, epochs)","def load_cifar10(directory):\n train_data = []\n train_labels = []\n for b in range(1, 6):\n f = os.path.join(directory, 'data_batch_%d' % (b,))\n X, Y = load_cifar10_batch(f)\n train_data.append(X)\n train_labels.append(Y)\n train_data = np.concatenate(train_data)\n train_labels = np.concatenate(train_labels)\n del X, Y\n test_data, test_labels = load_cifar10_batch(os.path.join(directory, 'test_batch'))\n return train_data, train_labels, test_data, test_labels","def load_cifar10s(data_dir, use_augmentation=False, aux_take_amount=None, \n aux_data_filename='/cluster/scratch/rarade/cifar10s/ti_500K_pseudo_labeled.pickle', \n aux_take_ids_path=None,\n validation=False):\n data_dir = re.sub('cifar10s', 'cifar10', data_dir)\n test_transform = transforms.Compose([transforms.ToTensor()])\n if use_augmentation:\n train_transform = transforms.Compose([transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(0.5), \n transforms.ToTensor()])\n else: \n train_transform = test_transform\n \n train_dataset = SemiSupervisedCIFAR10(base_dataset='cifar10', root=data_dir, train=True, download=True, \n transform=train_transform, aux_data_filename=aux_data_filename, \n add_aux_labels=True, aux_take_amount=aux_take_amount, aux_take_ids_path=aux_take_ids_path,\n validation=validation)\n test_dataset = SemiSupervisedCIFAR10(base_dataset='cifar10', root=data_dir, train=False, download=True, \n transform=test_transform)\n if validation:\n val_dataset = torchvision.datasets.CIFAR10(root=data_dir, train=True, download=True, transform=test_transform)\n val_dataset = torch.utils.data.Subset(val_dataset, np.arange(0, 1024)) \n return train_dataset, test_dataset, val_dataset\n return train_dataset, test_dataset","def load_CIFAR10(path):\r\n sampleList = []\r\n labelList = []\r\n # load all the data, as there only five training samples name as data_batch_id\r\n for i in range(1, 6):\r\n # get full filename\r\n filename = os.path.join(path, 'data_batch_%d' % (i, ))\r\n x, y = load_CIFAR_batch(filename)\r\n\r\n sampleList.append(x)\r\n labelList.append(y)\r\n\r\n # combine elements as one array\r\n Xtr = np.concatenate(sampleList)\r\n Ytr = np.concatenate(labelList)\r\n del x, y\r\n print(\"Training data loaded, total size : %d\", len(Xtr))\r\n # load test data\r\n Xte, Yte = load_CIFAR_batch(os.path.join(path, 'test_batch'))\r\n return Xtr, Ytr, Xte, Yte","def load_CIFAR10(ROOT):\n xs = []\n ys = []\n for b in range(1,6):\n f = os.path.join(ROOT, 'data_batch_%d' % (b, ))\n X, Y = load_CIFAR_batch(f)\n xs.append(X)\n ys.append(Y)\n Xtr = np.concatenate(xs)\n Ytr = np.concatenate(ys)\n del X, Y\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\n return Xtr, Ytr, Xte, Yte","def load_CIFAR10(ROOT):\n xs = []\n ys = []\n for b in range(1, 6):\n f = os.path.join(ROOT, 'data_batch_%d' % (b,))\n X, Y = load_CIFAR_batch(f)\n xs.append(X)\n ys.append(Y)\n Xtr = np.concatenate(xs)\n Ytr = np.concatenate(ys)\n del X, Y\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\n return Xtr, Ytr, Xte, Yte","def load_cifar():\n print('==> Preparing data..')\n transform_train = transforms.Compose([\n transforms.RandomCrop(32, padding=4),\n transforms.RandomHorizontalFlip(),\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ])\n\n transform_test = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n ])\n\n trainset = torchvision.datasets.CIFAR10(\n root='./data', train=True, download=True, transform=transform_train)\n trainloader = torch.utils.data.DataLoader(\n trainset, batch_size=1024, shuffle=True, num_workers=8)\n\n testset = torchvision.datasets.CIFAR10(\n root='./data', train=False, download=True, transform=transform_test)\n testloader = torch.utils.data.DataLoader(\n testset, batch_size=128, shuffle=False, num_workers=8)\n return trainloader, testloader","def get_dataset(self):\n\n trainset = datasets.CIFAR10('datasets/CIFAR10/train/', train=True, transform=self.train_transforms,\n target_transform=None, download=True)\n valset = datasets.CIFAR10('datasets/CIFAR10/test/', train=False, transform=self.val_transforms,\n target_transform=None, download=True)\n\n return trainset, valset","def load_cifar(dataset_name='cifar10'):\n dataset_name = dataset_name.strip().lower().replace(' ', '')\n\n if dataset_name.lower() not in ['cifar10', 'cifar100']:\n raise ValueError('Only cifar10 or cifar100 are valid dataset_name.')\n baseURL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n if dataset_name == 'cifar100':\n baseURL = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'\n\n dirname = os.path.join(_trident_dir, dataset_name.strip())\n if not os.path.exists(dirname):\n try:\n os.makedirs(dirname)\n except OSError:\n # Except permission denied and potential race conditions\n # in multi-threaded environments.\n pass\n\n \"\"\"Load CIFAR data from `path`\"\"\"\n _,filename,ext=split_path(baseURL)\n download_file(baseURL, dirname, filename+ext, dataset_name)\n file_path = os.path.join(dirname, filename+ext)\n\n\n if '.tar' in ext:\n extract_archive(file_path, dirname, archive_format='auto')\n filelist = glob.glob(dirname + '/*/*.*')\n extract_path ,_,_= split_path(filelist[0])\n filelist = [f for f in os.listdir(extract_path) if os.path.isfile(os.path.join(extract_path, f))]\n data=[]\n label=[]\n test_data=[]\n test_label=[]\n for file_path in filelist:\n if 'data_batch' in file_path:\n with open(os.path.join(extract_path,file_path), 'rb') as f:\n entry = pickle.load(f, encoding='latin1')\n data.append(entry['data'])\n label.append(entry['labels'])\n elif 'test_batch' in file_path:\n with open(os.path.join(extract_path,file_path), 'rb') as f:\n entry = pickle.load(f, encoding='latin1')\n test_data.append(entry['data'])\n test_label.append(entry['labels'])\n data = np.concatenate(data)\n data = data.reshape((data.shape[0], 3, 32, 32))\n data = data.transpose(0, 2, 3, 1).astype(np.float32)\n\n test_data = np.concatenate(test_data)\n test_data = test_data.reshape((test_data.shape[0], 3, 32, 32))\n test_data = test_data.transpose(0, 2, 3, 1).astype(np.float32)\n\n # Prepare labels\n label = np.concatenate(label)\n test_label = np.concatenate(test_label)\n\n trainData = Iterator(data=ImageDataset(data,object_type=ObjectType.rgb), label=LabelDataset(label,object_type=ObjectType.classification_label))\n testData = Iterator(data=ImageDataset(test_data,object_type=ObjectType.rgb), label=LabelDataset(test_label,object_type=ObjectType.classification_label))\n dataset = DataProvider(dataset_name, traindata=trainData, testdata=testData)\n dataset.binding_class_names(['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship',\n 'truck'] if dataset_name == 'cifar10' else [], 'en-US')\n return dataset","def load_CIFAR10(ROOT):\n xs = []\n ys = []\n for b in range(1,2):\n f = os.path.join(ROOT, 'data_batch_%d' % b)\n X, Y = load_CIFAR_batch(f)\n xs.append(X)\n ys.append(Y) \n #利用np.concatenate将xs、ys弄成一行\n Xtr = np.concatenate(xs)\n Ytr = np.concatenate(ys)\n del X, Y\n #获取测试集\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\n return Xtr, Ytr, Xte, Yte","def load_cifar10_data(img_rows=48,\n\timg_cols=48):\n\n\t(X_train, Y_train), (X_valid, Y_valid) = cifar10.load_data()\n\n\tX_train = np.array([cv2.resize(img, (img_rows,img_cols)) for img in X_train[:,:,:,:]])\n\tX_valid = np.array([cv2.resize(img, (img_rows,img_cols)) for img in X_valid[:,:,:,:]])\n\n\tY_train = to_categorical(Y_train[:], num_classes)\n\tY_valid = to_categorical(Y_valid[:], num_classes)\n\n\treturn X_train, Y_train, X_valid, Y_valid","def load_cifar10(split, with_info=False, data_augmentation=True,\n subsample_n=0):\n dataset, ds_info = tfds.load('cifar10',\n split=split,\n with_info=True,\n batch_size=-1)\n image_shape = ds_info.features['image'].shape\n\n numpy_ds = tfds.as_numpy(dataset)\n numpy_images = numpy_ds['image']\n numpy_labels = numpy_ds['label']\n\n # Perform subsampling if requested\n original_train_size = numpy_images.shape[0]\n if subsample_n > 0:\n subsample_n = min(numpy_images.shape[0], subsample_n)\n numpy_images = numpy_images[0:subsample_n, :, :, :]\n numpy_labels = numpy_labels[0:subsample_n]\n\n dataset = tf.data.Dataset.from_tensor_slices((numpy_images, numpy_labels))\n\n def preprocess(image, label):\n \"\"\"Image preprocessing function.\"\"\"\n if data_augmentation and split == tfds.Split.TRAIN:\n image = tf.image.random_flip_left_right(image)\n image = tf.pad(image, [[4, 4], [4, 4], [0, 0]])\n image = tf.image.random_crop(image, image_shape)\n\n image = tf.image.convert_image_dtype(image, tf.float32)\n return image, label\n\n dataset = dataset.map(preprocess)\n\n if with_info:\n info = {\n 'train_num_examples': ds_info.splits['train'].num_examples,\n 'train_num_examples_orig': original_train_size,\n 'test_num_examples': ds_info.splits['test'].num_examples,\n 'input_shape': ds_info.features['image'].shape,\n 'num_classes': ds_info.features['label'].num_classes,\n }\n return dataset, info\n return dataset","def load_data():\r\n global labelNames\r\n print(\"Loading Data...\")\r\n\r\n fnpath = \"rawdata\\\\cifar-10-batches-py\"\r\n fnprefix = 'data_batch_'\r\n fnlblnames = 'batches.meta'\r\n fntstbatch = 'test_batch'\r\n\r\n labelNames = unpickle(path.join(fnpath, fnlblnames))\r\n label_names = []\r\n for label in labelNames['label_names']:\r\n label_names.append(\"\".join(map(chr, label)))\r\n labelNames['label_names'] = label_names\r\n\r\n CIFAR_Data.append(unpickle(path.join(fnpath, fntstbatch)))\r\n for n in range(1, 6):\r\n CIFAR_Data.append(unpickle(path.join(fnpath, fnprefix + str(n))))","def _init_img_dataset(self, dataset_path):\n\n # ==\n # Define the classes used in the various states\n # form: (state class : cifar label class)\n class_dict = {\n 'initial': 'automobile',\n 'choice_1': 'dog',\n 'choice_2': 'cat',\n 'corridor': 'bird',\n }\n\n # ==\n # Download / initialize dataset\n ds = CIFAR10(dataset_path, train=self.training,\n download=True)\n\n # Get the CIFAR class index for each of the state classes\n cifar_class_dict = {\n k: ds.class_to_idx[class_dict[k]] for k in class_dict\n }\n\n # Iterate over the CIFAR dataset and get the idxs to each class\n cifar_indexes = {k: [] for k in class_dict}\n for i in range(len(ds)):\n cur_cifar_class = ds[i][1]\n for k in class_dict:\n if cur_cifar_class == cifar_class_dict[k]:\n cifar_indexes[k].append(i)\n\n # Manually sub-sample choice classes\n for k in ['choice_1', 'choice_2']:\n n_imgs = min(self.num_ds_imgs, len(cifar_indexes[k]))\n rng = np.random.default_rng()\n choice_imgs = rng.choice(cifar_indexes[k], size=n_imgs,\n replace=False)\n cifar_indexes[k] = choice_imgs\n\n # Manually shuffle the corridor class\n rng = np.random.default_rng()\n corri_img_shufIdxs = rng.choice(cifar_indexes['corridor'],\n size=len(cifar_indexes['corridor']),\n replace=False)\n cifar_indexes['corridor'] = corri_img_shufIdxs\n\n # ==\n # Construct the data subset dictionary\n ds_dict = {}\n for k in class_dict:\n ds_dict[k] = Subset(ds, cifar_indexes[k])\n\n return ds_dict","def test_cifar(args):\n # type: () -> None\n\n # Build dataset and model\n dataset = CIFAR10(path=args.path)\n model = MEMCIFAR10(input_shape=dataset.shape, code_length=64, cpd_channels=100, mem_dim=500, shrink_thres=1.0 / 500).cuda().eval()\n\n # Set up result helper and perform test\n helper = MEMResultHelper(dataset, model, checkpoints_dir=args.checkpoints, output_file='mem_cifar.txt')\n helper.test_one_class_classification()","def load_cifar10(data_path=None, data_home=None, subsets=None):\n if data_path is None:\n data_path = _utils.validate_data_home(data_home)\n data_path /= 'cifar-10-python.tar.gz'\n url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n _ds_utils.get_file(data_path, url)\n \n if subsets is None:\n subsets = ['training', 'test']\n subsets = _ds_utils.validate_tvt(subsets, return_list=True)\n X, Y = [], []\n with arlib.open(data_path) as ar:\n for subset in subsets:\n if subset == 'training':\n for i in range(1, 6):\n mname = [x for x in ar.member_names\n if x.endswith('data_batch_'+str(i))]\n assert len(mname) == 1\n mname = mname[0]\n tmp = _load_cifar_batch(ar.open_member(mname,'rb'))\n X.append(tmp[0])\n Y.append(tmp[1])\n elif subset == 'test':\n mname = [x for x in ar.member_names if x.endswith('test_batch')]\n assert len(mname) == 1\n mname = mname[0]\n tmp = _load_cifar_batch(ar.open_member(mname, 'rb'))\n X.append(tmp[0])\n Y.append(tmp[1])\n else:\n raise ValueError('Subset:', subset, ' not supported.')\n return np.concatenate(X), np.concatenate(Y)","def CreateDataset(all_arrays):\n dataset = Dataset()\n\n dataset._addData(all_arrays[0])\n dataset._addData(all_arrays[1])\n dataset._addData(all_arrays[3])\n dataset._addData(all_arrays[5])\n dataset._addData(all_arrays[6])\n dataset._addData(all_arrays[9])\n dataset._addData(all_arrays[8])\n dataset._addData(all_arrays[4])\n\n return dataset","def load_cifar10_img_form(directory):\n train_data, train_labels, test_data, test_labels = load_cifar10(directory)\n R, testR = train_data[:, :1024].reshape(-1, 32, 32, 1), test_data[:, :1024].reshape(-1, 32, 32, 1)\n G, testG = train_data[:, 1024:2048].reshape(-1, 32, 32, 1), test_data[:, 1024:2048].reshape(-1, 32, 32, 1)\n B, testB = train_data[:, 2048:].reshape(-1, 32, 32, 1), test_data[:, 2048:].reshape(-1, 32, 32, 1)\n train_data, test_data = np.concatenate((R, G, B), axis=3), np.concatenate((testR, testG, testB), axis=3)\n return train_data, train_labels, test_data, test_labels","def load_cifar_data():\n train_loader = torch.utils.data.DataLoader(\n torchvision.datasets.CIFAR10('cifarfiles/', train=True, download=True,\n transform=torchvision.transforms.Compose([\n torchvision.transforms.ToTensor(),\n torchvision.transforms.Normalize(\n (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ])),\n batch_size=batch_size_train, shuffle=True, pin_memory=True)\n\n test_loader = torch.utils.data.DataLoader(\n torchvision.datasets.CIFAR10('cifarfiles/', train=False, download=True,\n transform=torchvision.transforms.Compose([\n torchvision.transforms.ToTensor(),\n torchvision.transforms.Normalize(\n (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n ])),\n batch_size=batch_size_test, shuffle=True, pin_memory=True)\n return train_loader, test_loader","def load_data(self, dataset='cifar10', label_mode='fine'):\n if dataset == 'cifar10':\n if self.root:\n x_train, y_train = self.load_from_path(\n [os.path.join(self.root, f'data_batch_{i}') for i in range(1, 6)])\n x_test, y_test = self.load_from_path(\n [os.path.join(self.root, 'test_batch')])\n x_test = x_test.astype(x_train.dtype)\n y_test = y_test.astype(y_train.dtype)\n return (x_train, y_train), (x_test, y_test)\n else:\n return tf.keras.datasets.cifar10.load_data()\n elif dataset in ['cifar20', 'cifar100']:\n if self.root:\n x_train, y_train = self.load_from_path(\n [os.path.join(self.root, 'train')], label_key=label_mode)\n x_test, y_test = self.load_from_path([os.path.join(self.root, 'test')])\n x_test = x_test.astype(x_train.dtype)\n y_test = y_test.astype(y_train.dtype)\n return (x_train, y_train), (x_test, y_test)\n else:\n return tf.keras.datasets.cifar100.load_data(label_mode=label_mode)","def create_ds(self, data, is_train=True):\n ds = tf.data.Dataset.from_tensor_slices(data)\n map_fn = lambda x, y: (cifar_process(x, is_train), y)\n ds = ds.map(map_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n return ds","def generate_coco_dataset(args):\n\targs.data_root = Path(args.data_root)\n\targs.save_root = Path(args.save_root)\n\targs.save_root.mkdir()\n\n\tgenerate_coco_dataset_sub(args, 'train', 'A', args.cat1)\n\tgenerate_coco_dataset_sub(args, 'train', 'B', args.cat2)\n\tgenerate_coco_dataset_sub(args, 'val', 'A', args.cat1)\n\tgenerate_coco_dataset_sub(args, 'val', 'B', args.cat2)","def test_create_dataset(self):\n self.assertIsInstance(self._dataset, cifar10.CIFAR10Dataset)","def download():\n\n trainset = torchvision.datasets.CIFAR10(root=paths.raw_cifar10_dir(), train=True, download=True)\n testset = torchvision.datasets.CIFAR10(root=paths.raw_cifar10_dir(), train=False, download=True)\n train_images = numpy.array(trainset.data)\n train_labels = numpy.array(trainset.targets)\n test_images = numpy.array(testset.data)\n test_labels = numpy.array(testset.targets)\n\n assert numpy.max(train_images) == 255\n\n train_images = train_images/255.\n test_images = test_images/255.\n\n utils.write_hdf5(paths.cifar10_train_images_file(), train_images.astype(numpy.float32))\n log('wrote %s' % paths.cifar10_train_images_file())\n utils.write_hdf5(paths.cifar10_test_images_file(), test_images.astype(numpy.float32))\n log('wrote %s' % paths.cifar10_test_images_file())\n utils.write_hdf5(paths.cifar10_train_labels_file(), train_labels.reshape(-1, 1).astype(numpy.int))\n log('wrote %s' % paths.cifar10_train_labels_file())\n utils.write_hdf5(paths.cifar10_test_labels_file(), test_labels.reshape(-1, 1).astype(numpy.int))\n log('wrote %s' % paths.cifar10_test_labels_file())","def load_cifar10_data(img_rows, img_cols):\n\n # Load cifar10 training and validation sets\n (X_train, Y_train), (X_valid, Y_valid) = cifar10.load_data()\n\n # Resize training images\n X_train = np.array([cv2.resize(img, (img_rows, \\\n img_cols)) for img in X_train[:, :, :, :]])\n\n X_valid = np.array([cv2.resize(img, (img_rows, \\\n img_cols)) for img in X_valid[:, :, :, :]])\n\n # Check the data type of X_train or X_valid\n for each in X_train:\n print(type(each))\n\n # Transform targets to keras compatible format\n Y_train = np_utils.to_categorical(Y_train, num_classes)\n Y_valid = np_utils.to_categorical(Y_valid, num_classes)\n\n X_train = X_train.astype('float32')\n X_valid = X_valid.astype('float32')\n\n # Data normalization\n X_train = X_train / 255.0\n X_valid = X_valid / 255.0\n\n return X_train, Y_train, X_valid, Y_valid","def load_cifar(hparams):\n all_labels = []\n\n total_batches_to_load = 5\n assert hparams.train_size + hparams.validation_size <= 50000\n if hparams.eval_test:\n total_batches_to_load += 1\n # Determine how many images we have loaded\n total_dataset_size = 50000\n train_dataset_size = total_dataset_size\n if hparams.eval_test:\n total_dataset_size += 10000\n\n if hparams.dataset == 'cifar10':\n all_images = []\n elif hparams.dataset == 'cifar100':\n all_images = np.empty((1, 50000, 3072), dtype=np.uint8)\n if hparams.eval_test:\n test_data = np.empty((1, 10000, 3072), dtype=np.uint8)\n if hparams.dataset == 'cifar10':\n datafiles = [\n 'data_batch_1', 'data_batch_2', 'data_batch_3', 'data_batch_4',\n 'data_batch_5']\n\n if hparams.eval_test:\n datafiles.append('test_batch')\n num_classes = 10\n elif hparams.dataset == 'cifar100':\n datafiles = ['train']\n if hparams.eval_test:\n datafiles.append('test')\n num_classes = 100\n else:\n raise NotImplementedError('Unimplemented dataset: ', hparams.dataset)\n if hparams.dataset != 'test':\n for file_num, f in enumerate(datafiles):\n d = unpickle(os.path.join(hparams.data_path, f))\n if hparams.dataset == 'cifar10':\n labels = np.array(d['labels'])\n else:\n labels = np.array(d['fine_labels'])\n if f == 'test':\n test_data[0] = copy.deepcopy(d['data'])\n if hparams.dataset == 'cifar10':\n all_images.append(test_data)\n else:\n all_images = np.concatenate([all_images, test_data], axis=1)\n else:\n if hparams.dataset == 'cifar10':\n all_images.append(copy.deepcopy(d['data']))\n else:\n all_images[file_num] = copy.deepcopy(d['data'])\n nsamples = len(labels)\n for idx in range(nsamples):\n all_labels.append(labels[idx])\n if hparams.dataset == 'cifar10':\n all_images = np.concatenate(all_images, axis=0)\n all_images = all_images.reshape(-1, 3072)\n all_images = all_images.reshape(-1, 3, 32, 32) # pylint: disable=too-many-function-args\n all_images = all_images.transpose(0, 2, 3, 1).copy()\n all_images = all_images / 255.0\n mean = augmentation_transforms.MEANS\n std = augmentation_transforms.STDS\n tf.logging.info('mean:{} std: {}'.format(mean, std))\n all_images = (all_images - mean) / std\n all_labels = np.eye(num_classes)[np.array(all_labels, dtype=np.int32)]\n\n assert len(all_images) == len(all_labels)\n tf.logging.info(\n 'In CIFAR10 loader, number of images: {}'.format(len(all_images)))\n\n extra_test_images = None\n extra_test_labels = None\n if hparams.extra_dataset == 'cifar10_1':\n extra_test_ds = tfds.as_numpy(\n tfds.load('cifar10_1', split='test', batch_size=-1))\n extra_test_images = ((extra_test_ds['image'] / 255.0) - mean) / std\n extra_test_labels = np.eye(num_classes)[np.array(\n extra_test_ds['label'], dtype=np.int32)]\n\n # Break off test data\n if hparams.eval_test:\n test_images = all_images[train_dataset_size:]\n test_labels = all_labels[train_dataset_size:]\n else:\n test_images = []\n test_labels = []\n all_images = all_images[:train_dataset_size]\n all_labels = all_labels[:train_dataset_size]\n return all_images, all_labels, test_images, test_labels, extra_test_images, extra_test_labels","def _build_datasets(self):\n self._build_datasets_sis3302()\n self._build_datasets_sis3305()","def make_dataset():\n digits = load_digits(n_class=2)\n df = pd.DataFrame(np.column_stack((digits.data, digits.target)))\n df = df.rename(columns={64: 'Class'})\n df.loc[df.loc[:, 'Class'] == 0, 'Class'] = -1\n df.to_csv('digits_binary.csv', index=False)","def create_new_dataset(self,\n dataset_dir, \n split,\n description=\"\",\n url=\"\",\n version=\"\",\n year=0,\n contributor=\"\",\n date_created=\"\",\n license_url=\"\",\n license_id=0,\n license_name=\"\"\n ):\n\n self.dataset_dir = os.path.abspath(dataset_dir)\n self.logger.info('Creating an empty COCO dataset at {}'.format(self.dataset_dir))\n assert self.annotation_file is None, \\\n \"COCO dataset is already initialized with the annotation file: {}\".format(self.annotation_file)\n \n ## Create the dataset directory\n self.imgs_dir = os.path.join(dataset_dir, split)\n os.makedirs(self.imgs_dir, exist_ok=True)\n anns_dir = os.path.join(dataset_dir, 'annotations')\n os.makedirs(anns_dir, exist_ok=True)\n\n self.annotation_file = os.path.join(anns_dir, \n \"instances_{}.json\".format(split)) \n ## Create class members\n self.catNameToId = {}\n self.pointclouds = {}\n self.imgToPc = {}\n self.dataset = {'annotations':[], 'images':[], 'categories':[], 'pointclouds':[]}\n self.dataset['info'] = {\n \"description\": description,\n \"url\": url,\n \"version\": version,\n \"year\": year,\n \"contributor\": contributor,\n \"date_created\": date_created}\n self.dataset['licenses'] = [{\n \"url\": license_url,\n \"id\": license_id,\n \"name\": license_name}]","def load_cifar10_batch(directory):\n with open(directory, 'rb') as fo:\n datadict = pickle.load(fo, encoding='bytes')\n X = np.array(datadict[b'data'])\n Y = np.array(datadict[b'labels'])\n return X, Y","def _download_and_extract_cifar10(self):\n # Possibly download the dataset\n if not self.tgz_filename.is_file():\n print(f'Downloading CIFAR-10 dataset from {self.cifar_url}...', flush=True)\n Downloader().download_file(url=self.cifar_url, filename=self.tgz_filename)\n print('\\nDone.')\n\n # Extract downloaded archive\n print(f'Extracting {self.tgz_filename} to {self.dataset_root}...', flush=True)\n Extractor().extract(self.tgz_filename, extract_path=self.dataset_root)\n print('Done.')\n\n # Move all files into the chosen dataset root (up one directory)\n [f.rename(f.absolute().parents[1] / f.name) for f in self.dataset_root.glob('*/*')]\n Path(self.dataset_root / 'cifar-10-batches-py').rmdir() # remove inner directory\n\n # Finally remove the archive\n self.tgz_filename.unlink()","def makecldf(args):\n with_dataset(args, Dataset._install)","def load_cifar10(batch_paths):\n batches = [load_cifar10_batch(path) for path in batch_paths]\n data = torch.cat([batch[0] for batch in batches])\n labels = torch.cat([batch[1] for batch in batches])\n return data, labels","def make_dataset():\n\n\tnumberOfTrials = dataset_params.num_of_samples\n\tnumberOfTrials_train = int(numberOfTrials*0.8)\n\tnumberOfTrials_test = int(numberOfTrials*0.2)\n\n\tprint(\"==================================================\")\n\tprint(\"1. Generating Train images ......\")\n\tprint(\"\\nTrain image per variation\", numberOfTrials_train)\n\tmakeDataset(numberOfTrials_train, \"train\")\n\n\tprint(\"==================================================\")\n\tprint(\"2. Generating Test images ......\")\n\tprint(\"\\nTest image per variation\", numberOfTrials_test)\n\tmakeDataset(numberOfTrials_test, \"test\")\n\n\tprint(\"==================================================\")\n\tprint(\"Done!!!\")","def _make_data(self):\n pdf_datasets_all = make_pdf_datasets(self.pdf_list, self.xlims, self.ylims, self.tlims, self.dims, 9)\n self.pdf_dataset = np.concatenate(pdf_datasets_all, axis = 0)\n self.PDE_dataset = make_PDE_dataset(self.num_collocation, self.xlims, self.ylims, self.tlims, self.dims)\n self.BC_dataset = make_BC_dataset(self.num_BC, self.xlims, self.ylims, self.tlims, self.dims)","def load_ccs10():\n ccs10 = pd.read_csv(pkg_resources.resource_filename(__name__,'ccs_dx_icd10cm_2019_1.csv'))\n ccs10.columns=[i.strip('\\'') for i in ccs10.columns]\n for col in ccs10.columns:\n ccs10.loc[:,col]=ccs10[col].str.strip('\\'')\n ccs10 = ccs10.replace(r'^\\s*$', np.nan, regex=True)\n ccs10.loc[:,'ICD-10-CM CODE'] = ccs10['ICD-10-CM CODE'].str.replace(' ','')\n ccs10=ccs10.iloc[:,0:4]\n return ccs10","def __init__(self,\n base_dataset='cifar10',\n take_amount=None,\n take_amount_seed=13,\n add_svhn_extra=False,\n aux_data_filename=None,\n add_aux_labels=True,\n aux_take_amount=None,\n train=False,\n **kwargs):\n\n if base_dataset == 'cifar10':\n self.dataset = datasets.CIFAR10(train=train, **kwargs)\n else:\n raise ValueError('Dataset %s not supported' % base_dataset)\n self.base_dataset = base_dataset\n self.train = train\n\n if self.train:\n if take_amount is not None:\n rng_state = np.random.get_state()\n np.random.seed(take_amount_seed)\n take_inds = np.random.choice(len(self.sup_indices),\n take_amount, replace=False)\n np.random.set_state(rng_state)\n\n logger = logging.getLogger()\n logger.info('Randomly taking only %d/%d examples from training'\n ' set, seed=%d, indices=%s',\n take_amount, len(self.sup_indices),\n take_amount_seed, take_inds)\n self.targets = self.targets[take_inds]\n self.data = self.data[take_inds]\n\n self.sup_indices = list(range(len(self.targets)))\n self.unsup_indices = []\n\n if aux_data_filename is not None:\n aux_path = aux_data_filename\n print(\"Loading data from %s\" % aux_path)\n with open(aux_path, 'rb') as f:\n aux = pickle.load(f)\n aux_data = aux['data']\n aux_targets = aux['extrapolated_targets']\n orig_len = len(self.data)\n\n if aux_take_amount is not None:\n rng_state = np.random.get_state()\n np.random.seed(take_amount_seed)\n take_inds = np.random.choice(len(aux_data),\n aux_take_amount, replace=False)\n np.random.set_state(rng_state)\n\n logger = logging.getLogger()\n logger.info(\n 'Randomly taking only %d/%d examples from aux data'\n ' set, seed=%d, indices=%s',\n aux_take_amount, len(aux_data),\n take_amount_seed, take_inds)\n aux_data = aux_data[take_inds]\n aux_targets = aux_targets[take_inds]\n\n self.data = np.concatenate((self.data, aux_data), axis=0)\n\n if not add_aux_labels:\n self.targets.extend([-1] * len(aux_data))\n else:\n self.targets.extend(aux_targets)\n # note that we use unsup indices to track the labeled datapoints\n # whose labels are \"fake\"\n self.unsup_indices.extend(\n range(orig_len, orig_len+len(aux_data)))\n\n logger = logging.getLogger()\n logger.info(\"Training set\")\n logger.info(\"Number of training samples: %d\", len(self.targets))\n logger.info(\"Number of supervised samples: %d\",\n len(self.sup_indices))\n logger.info(\"Number of unsup samples: %d\", len(self.unsup_indices))\n logger.info(\"Label (and pseudo-label) histogram: %s\",\n tuple(\n zip(*np.unique(self.targets, return_counts=True))))\n logger.info(\"Shape of training data: %s\", np.shape(self.data))\n\n # Test set\n else:\n self.sup_indices = list(range(len(self.targets)))\n self.unsup_indices = []\n\n logger = logging.getLogger()\n logger.info(\"Test set\")\n logger.info(\"Number of samples: %d\", len(self.targets))\n logger.info(\"Label histogram: %s\",\n tuple(\n zip(*np.unique(self.targets, return_counts=True))))\n logger.info(\"Shape of data: %s\", np.shape(self.data))","def prepare_train_coco_data(args):\n image_dir, annotation_file, data_dir = args.train_coco_image_dir, args.train_coco_annotation_file, args.train_coco_data_dir\n batch_size = args.batch_size\n basic_model = args.basic_model\n num_roi = args.num_roi\n\n coco = COCO(annotation_file)\n\n img_ids = list(coco.imgToAnns.keys())\n img_files = []\n img_heights = []\n img_widths = []\n anchor_files = []\n gt_classes = []\n gt_bboxes = []\n\n for img_id in img_ids:\n img_files.append(os.path.join(image_dir, coco.imgs[img_id]['file_name'])) \n img_heights.append(coco.imgs[img_id]['height']) \n img_widths.append(coco.imgs[img_id]['width']) \n anchor_files.append(os.path.join(data_dir, os.path.splitext(coco.imgs[img_id]['file_name'])[0]+'_'+basic_model+'_anchor.npz')) \n\n classes = [] \n bboxes = [] \n for ann in coco.imgToAnns[img_id]: \n classes.append(coco_category_to_class[ann['category_id']]) \n bboxes.append([ann['bbox'][1], ann['bbox'][0], ann['bbox'][3]+1, ann['bbox'][2]+1]) \n\n gt_classes.append(classes) \n gt_bboxes.append(bboxes) \n \n print(\"Building the training dataset...\")\n dataset = DataSet(img_ids, img_files, img_heights, img_widths, batch_size, anchor_files, gt_classes, gt_bboxes, True, True)\n print(\"Dataset built.\")\n return coco, dataset","def test_basic_cdata(self):\n data = array([[1, 0, 0], [0, 1, 0]])\n cdata = CData(data)\n self.assertEqual(cdata.num_features, 3)\n self.assertEqual(cdata.num_samples, 2)","def get_data():\n transform = Compose([paddle.vision.Resize(32),\n Normalize(mean=[127.5], std=[127.5], data_format='CHW'),\n paddle.vision.transforms.Transpose()])\n train_data = paddle.vision.datasets.Cifar10(mode='train', transform=transform)\n l = len(train_data)\n return paddle.io.random_split(train_data, [l // 2, l - l // 2])","def dataset(request):\n X, y = make_classification(\n n_samples=700, n_features=10, n_informative=8, n_redundant=2,\n n_classes=2, n_clusters_per_class=2, random_state=6483\n )\n\n request.cls.dataset = Dataset(X, y)","def build_dataset(self):\n print(\"reading data of images currently , please wait......\")\n x_train, y_train, _ = get_images(self.train_directory)\n x_test, y_test, _ = get_images(self.test_directory)\n x_train, y_train = image_subset(self.num_classes, x_train, y_train)\n x_test, y_test = image_subset(self.num_classes, x_test, y_test)\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n self.x_train = x_train / 255\n self.x_test = x_test / 255\n self.y_train = utils.to_categorical(y_train, self.num_classes)\n self.y_test = utils.to_categorical(y_test, self.num_classes)","def creation_data_sets(quality, dataset, test_case=False):\n current_path = Path.cwd()\n if dataset == 0:\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n del y_train, y_test\n train_path = current_path.joinpath(\"Mnist_{}\".format(quality))\n test_path = current_path.joinpath(\"Mnist_{}_test\".format(quality))\n else:\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n del y_train, y_test\n train_path = current_path.joinpath(\"Cifar-10_{}\".format(quality))\n test_path = current_path.joinpath(\"Cifar-10_{}_test\".format(quality))\n\n create_directories(train_path, test_path)\n convert(train_path, x_train, dataset, quality, test_case)\n convert(test_path, x_test, dataset, quality, test_case)","def generate_dataset():\n if not os.path.exists(\"../data/COVID-19/COVID-19.npy\"):\n print(\"Processing Training Data.\")\n training_data = get_training_data('../data/COVID-19/train')\n print(\"Processing Test Data.\")\n test_data = get_training_data('../data/COVID-19/test')\n\n x_train, y_train, x_test, y_test = [], [], [], []\n\n for feature, label in training_data:\n x_train.append(feature)\n y_train.append(label)\n\n for feature, label in test_data:\n x_test.append(feature)\n y_test.append(label)\n\n # Normalize the data\n x_train = np.array(x_train) / 255\n x_test = np.array(x_test) / 255\n\n # resize data for deep learning\n x_train = x_train.reshape(-1, 3, img_size, img_size)\n y_train = np.array(y_train)\n x_test = x_test.reshape(-1, 3, img_size, img_size)\n y_test = np.array(y_test)\n\n # With data augmentation to prevent overfitting and handling the imbalance in dataset\n dataset = {\"x_train\": x_train, \"y_train\": y_train, \"x_test\": x_test, \"y_test\": y_test}\n np.save(\"../data/COVID-19/COVID-19.npy\", dataset)\n else:\n dataset = np.load(\"../data/COVID-19/COVID-19.npy\", allow_pickle=True).item()\n x_train, y_train, x_test, y_test = dataset[\"x_train\"], dataset[\"y_train\"], dataset[\"x_test\"], dataset[\"y_test\"]\n\n x_train_tensor = torch.from_numpy(x_train)\n x_train_tensor = x_train_tensor.type(torch.FloatTensor)\n y_train_tensor = torch.from_numpy(y_train)\n y_train_tensor = y_train_tensor.type(torch.LongTensor)\n x_test_tensor = torch.from_numpy(x_test)\n x_test_tensor = x_test_tensor.type(torch.FloatTensor)\n y_test_tensor = torch.from_numpy(y_test)\n y_test_tensor = y_test_tensor.type(torch.LongTensor)\n\n train_dataset = TensorDataset(x_train_tensor, y_train_tensor)\n test_dataset = TensorDataset(x_test_tensor, y_test_tensor)\n\n return train_dataset, test_dataset","def load_CIFAR_batch(filename):\r\n with open(filename, 'rb')as f:\r\n datadict = p.load(f)\r\n \r\n X = datadict['data']\r\n Y = datadict['labels']\r\n \r\n print X.shape\r\n X = X.reshape(X.shape[0], SHAPE[0], SHAPE[1], SHAPE[2])\r\n Y = np.array(Y)\r\n return X, Y","def load_cifar10(directory, normalize=True):\n training_data = []\n training_labels = []\n for i in range(1, 6):\n try:\n d = unpickle(directory + f\"/data_batch_{i}\")\n except FileNotFoundError:\n raise Exception(f\"File 'data_batch_{i}' is not found in the specified directory '{directory}'.\")\n training_data.append(d[b\"data\"])\n training_labels.append(d[b\"labels\"])\n training_data = np.vstack(training_data)\n training_data = np.reshape(training_data, newshape=(-1, 3, 32, 32))\n training_labels = np.concatenate(training_labels)\n training_labels = np.array(list(map(lambda hot: one_hot(10, hot), training_labels)))\n\n try:\n test = unpickle(directory + \"/test_batch\")\n except FileNotFoundError:\n raise Exception(f\"File 'test_batch' is not found in the specified directory '{directory}'.\")\n test_data = np.reshape(test[b\"data\"], newshape=(-1, 3, 32, 32))\n test_labels = np.array(list(map(lambda hot: one_hot(10, hot), test[b\"labels\"])))\n\n try:\n meta = unpickle(directory + \"/batches.meta\")\n except FileNotFoundError:\n raise Exception(f\"File 'batches.meta' is not found in the specified directory '{directory}'.\")\n label_names = meta[b\"label_names\"]\n label_names = list(map(lambda x: x.decode(\"utf-8\"), label_names))\n\n if normalize:\n training_data = training_data / 255\n test_data = test_data / 255\n\n return training_data, training_labels, test_data, test_labels, label_names","def create_weka_mfcc_13():\n global ARGS\n\n ## ten thu muc can trich chon vector dac trung (RLS, LMS, NLMS, Kalman, Non)\n name = '';\n fout = open('weka/MFCC78_TUNNING_{}_dataset.arff'.format(name), 'w')\n fout.write('@RELATION {}_dataset\\n\\n'.format(name))\n\n fout.write('@ATTRIBUTE MEAN_MFCC1\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC2\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC3\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC4\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC5\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC6\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC7\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC8\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC9\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC10\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC11\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC12\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCC13\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD1\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD2\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD3\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD4\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD5\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD6\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD7\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD8\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD9\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD10\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD11\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD12\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCD13\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD1\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD2\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD3\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD4\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD5\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD6\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD7\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD8\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD9\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD10\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD11\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD12\tREAL\\n')\n fout.write('@ATTRIBUTE MEAN_MFCCDD13\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC1\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC2\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC3\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC4\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC5\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC6\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC7\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC8\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC9\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC10\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC11\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC12\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCC13\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD1\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD2\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD3\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD4\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD5\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD6\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD7\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD8\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD9\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD10\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD11\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD12\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCD13\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD1\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD2\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD3\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD4\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD5\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD6\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD7\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD8\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD9\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD10\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD11\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD12\tREAL\\n')\n fout.write('@ATTRIBUTE STD_MFCCDD13\tREAL\\n')\n fout.write('@ATTRIBUTE class \t{'+ARGS.labels+'}\\n\\n')\n \n fout.write('@DATA\\n')\n\n ## cua so\n windowing = Windowing(type='hamming',\n size=1104,\n zeroPhase=False)\n \n ## quang pho\n spectrum = Spectrum(size=1104)\n\n ##khoi tao MFCC\n mfcc = MFCC(highFrequencyBound=4000, ## gioi han tren cua tan so\n inputSize=201, \t\t\t ## kich thuoc pho dau vao\n lowFrequencyBound=0,\t ## gioi han duoi cua tan so\n numberBands=40,\t\t\t ## so luong cac dai Mels trong bo loc\n numberCoefficients=13, ## so luong dau ra cac he so Mel\n sampleRate=16000)\t\t ## tan so lay mau\n\n for label in ARGS.labels.split(','): ## duyet cac thu muc giong voi ten nhan\n\n ## dia chi thu muc\n dir = os.path.join(ARGS.dir, label)\n\n logging.info('Access folder <{}>'.format(dir))\n\n for file in sorted(os.listdir(dir)):\n\n \t## duyet cac file .wav\n if file.endswith('.wav'):\n logging.info('Process <{}>'.format(file))\n path = os.path.join(dir, file)\n \n ## doc file am thanh\n loader = MonoLoader(filename=path, sampleRate=ARGS.sampleRate)\n audio = loader()\n cnt = 0\n\n for window in FrameGenerator(audio, \n frameSize=ARGS.window_length*ARGS.sampleRate/1000, \n hopSize=ARGS.window_stride*ARGS.sampleRate/1000, \n startFromZero=True):\n mfccs = []\n for frame in FrameGenerator(window, \n frameSize=ARGS.frame_length*ARGS.sampleRate/1000, \n hopSize=ARGS.frame_stride*ARGS.sampleRate/1000, \n startFromZero=True):\n s = spectrum(windowing(frame))\n\n _, m = mfcc(s)\n\n m_delta = librosa.feature.delta(m, order=1) ## dao ham bac 1\n m_delta_delta = librosa.feature.delta(m, order=2) ## dao ham bac 2\n\n m_all = np.concatenate((m, m_delta, m_delta_delta), axis=0) ## them vao chuoi\n mfccs.append(m_all)\n mfccs = np.array(mfccs)\n mfccs_mean = np.mean(mfccs, axis=0)\n mfccs_std = np.std(mfccs, axis=0)\n feat = np.concatenate((mfccs_mean, mfccs_std), axis=0).tolist()\n str_feat = [str(x) for x in feat]\n line = ','.join(str_feat)+','+label\n fout.write(line+'\\n')\n cnt = cnt+1\n logging.info('{} samples'.format(cnt))","def get_coco_dataset():\n ds = AttrDict()\n # classes = [\n # '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',\n # 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',\n # 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',\n # 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',\n # 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',\n # 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',\n # 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass',\n # 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',\n # 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',\n # 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',\n # 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',\n # 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',\n # 'scissors', 'teddy bear', 'hair drier', 'toothbrush'\n # ]\n # classes = ['__background__', 'lane']\n #\n base_classes = [\n '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',\n 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',\n 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',\n 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',\n 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',\n 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',\n 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass',\n 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',\n 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',\n 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',\n 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',\n 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',\n 'scissors', 'teddy bear', 'hair drier', 'toothbrush'\n ]\n classes = ['__background__',\n 'guard rail',\n # 'car',\n 'dashed',\n 'solid',\n 'solid solid',\n 'dashed dashed',\n 'dashed-solid',\n 'solid-dashed',\n 'yellow dashed',\n 'yellow solid',\n 'yellow solid solid',\n 'yellow dashed dashed',\n 'yellow dashed-solid',\n 'yellow solid-dashed',\n 'boundary',\n 'fork_line',\n 'fork_edge',\n 'arrow_s',\n 'arrow_r',\n 'arrow_l',\n 'arrow_lr',\n 'arrow_inclined_r',\n 'arrow_r_s',\n 'arrow_l_s',\n 'sidewalk',\n 'handrail'\n ]\n base_classes.extend(classes[1:])\n classes = base_classes\n ds.classes = {i: name for i, name in enumerate(classes)}\n return ds","def __init__(self, data_path, batch_size, **kwargs):\n super().__init__(batch_size, **kwargs)\n\n _, num_classes, X_train, y_train, X_val, y_val = load_cifar10_shard(shard_num=data_path, **kwargs)\n\n self.training_data_size = len(X_train)\n self.validation_data_size = len(X_val)\n self.num_classes = num_classes\n self.train_loader = self.create_loader(X=X_train, y=y_train, shuffle=True)\n self.val_loader = self.create_loader(X=X_val, y=y_val, shuffle=False)","def create_forecast_dataset(self):\n pass","def create_random_dataset(self, num_of_data, cluster_number, dimension):\n x, y = make_blobs(n_samples=num_of_data, centers=cluster_number, n_features=dimension)\n self._dimension = dimension\n self._num_classes = cluster_number\n self._data = x\n self._y_star = y","def get_dataset(self):\n\n trainset = datasets.CIFAR100('datasets/CIFAR100/train/', train=True, transform=self.train_transforms,\n target_transform=None, download=True)\n valset = datasets.CIFAR100('datasets/CIFAR100/test/', train=False, transform=self.val_transforms,\n target_transform=None, download=True)\n\n return trainset, valset","def load_cifa_10():\n train_set_x = np.ndarray([ 50000, 3072 ])\n train_set_y = np.ndarray( [50000] )\n\n batch_size = 10000\n for i in xrange(5):\n batch = open( datapath + \"data_batch_\"+str(i+1), 'rb')\n map = cPickle.load( batch )\n batch.close()\n train_set_x[ i*batch_size : (i+1)*batch_size , : ] = np.asarray( map[ 'data' ], dtype = 'float32' )\n train_set_y[ i*batch_size : (i+1)*batch_size ] = np.asarray( map[ 'labels' ], dtype = 'float32' )\n\n test_file = open( datapath + 'test_batch', 'rb')\n map = cPickle.load( test_file )\n test_file.close()\n \n test_set_x = np.asarray( map['data'], dtype = 'float32' )\n test_set_y = np.asarray( map['labels'], dtype = 'float32' )\n \n\n return train_set_x, train_set_y, test_set_x, test_set_y","def create_data(data_size,heme, nucleotide, control, steroid,data_total,path_to_data):\n\n os.chdir(path_to_data)\n\n x_array = np.zeros(shape = (data_size,14,32,32,32))\n\n y_array = np.zeros(shape = data_size)\n\n print(\"data size = \", data_size)\n\n #training set :\n\n file_count = 0\n\n for file in data_total:\n\n y_array[file_count]= find_class(str(file), heme, nucleotide, control, steroid)\n\n x_array[file_count] = np.load(str(file+\".npy\"))\n\n file_count+=1\n\n\n return (x_array, y_array)","def create_random_dataset(self, num_of_data, cluster_number, dimension):\n x, y = make_blobs(n_samples=num_of_data, centers=cluster_number, n_features=dimension)\n self._dimension = dimension\n self._data = x\n self._y_star = y","def load_CIFAR_batch(filename):\n with open(filename, 'rb')as f:\n datadict = p.load(f, encoding='iso-8859-1')\n X = datadict['data']\n Y = datadict['labels']\n X = X.reshape(10000, 3, 32, 32)\n Y = np.array(Y)\n return X, Y","def load_CIFAR_batch(filename):\r\n with open(filename, 'rb') as f:\r\n datadict = pickle.load(f, encoding='latin1')\r\n X = datadict['data']\r\n Y = datadict['labels']\r\n X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype(\"float\")\r\n Y = np.array(Y)\r\n return X, Y","def load_cifar10_shard(shard_num, nb_collaborators, categorical=True, channels_last=False, **kwargs): \n img_rows, img_cols, img_channel = 32, 32, 3\n num_classes = 10 \n \n (X_train, y_train), (X_test, y_test) = _load_raw_datashards(shard_num, nb_collaborators)\n \n if channels_last: \n X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, img_channel) \n X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, img_channel) \n input_shape = (img_rows, img_cols, img_channel) \n else: \n X_train = X_train.reshape(X_train.shape[0], img_channel, img_rows, img_cols) \n X_test = X_test.reshape(X_test.shape[0], img_channel, img_rows, img_cols) \n input_shape = (img_channel, img_rows, img_cols) \n \n X_train = X_train.astype('float32') \n X_test = X_test.astype('float32') \n X_train /= 255 \n X_test /= 255\n \n if categorical: \n def to_categorical(x, num_classes):\n \"\"\"one-hot encodes an array\"\"\"\n return np.eye(num_classes, dtype='uint8')[x]\n # convert class vectors to binary class matrices \n y_train = to_categorical(y_train, num_classes) \n y_test = to_categorical(y_test, num_classes) \n \n return input_shape, num_classes, X_train, y_train, X_test, y_test","def random_cls_dataset(request):\n set_seed()\n shape = request.param.get('shape', 10)\n size = request.param.get('size', 100)\n X, Y = make_classification(n_samples=2*size, n_features=shape, n_classes=10, n_informative=10, n_redundant=0)\n X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5)\n Y_train, Y_test = Y_train.astype(np.int64), Y_test.astype(np.int64)\n return (X_train, Y_train), (X_test, Y_test)","def __init__(self, **kwargs):\n super(CIFAR10Classifier, self).__init__() #pylint: disable=super-with-arguments\n self.model_conv = models.resnet50(pretrained=True)\n for param in self.model_conv.parameters():\n param.requires_grad = False\n num_ftrs = self.model_conv.fc.in_features\n num_classes = 10\n self.model_conv.fc = nn.Linear(num_ftrs, num_classes)\n\n self.scheduler = None\n self.optimizer = None\n self.args = kwargs\n\n self.train_acc = Accuracy()\n self.val_acc = Accuracy()\n self.test_acc = Accuracy()\n\n self.preds = []\n self.target = []\n self.example_input_array = torch.rand((1, 3, 64, 64))","def run(dataset_dir):\n if not tf.gfile.Exists(dataset_dir):\n tf.gfile.MakeDirs(dataset_dir)\n\n training_filename = _get_output_filename(dataset_dir, 'train')\n testing_filename = _get_output_filename(dataset_dir, 'test')\n\n if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):\n print('Dataset files already exist. Exiting without re-creating them.')\n return\n\n dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)\n\n # First, process the training data:\n with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:\n offset = 0\n for i in range(_NUM_TRAIN_FILES):\n filename = os.path.join(dataset_dir,\n 'cifar-100-python',\n 'train')\n offset = _add_to_tfrecord(filename, tfrecord_writer, offset)\n\n # Next, process the testing data:\n with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:\n filename = os.path.join(dataset_dir,\n 'cifar-100-python',\n 'test')\n _add_to_tfrecord(filename, tfrecord_writer)\n\n # Finally, write the labels file:\n labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))\n dataset_utils.write_label_file(labels_to_class_names, dataset_dir)\n\n _clean_up_temporary_files(dataset_dir)\n print('\\nFinished converting the Cifar100 dataset!')","def __init__(self, image_set, root_path, data_path, category='all', task='detection'):\n super(coco, self).__init__('COCO', image_set, root_path, data_path)\n self.root_path = root_path\n self.data_path = data_path\n self.category = category\n self.task = task\n self.name = self.name + '_' + category\n # deal with data name\n view_map = {'minival2014': 'val2014',\n 'valminusminival2014': 'val2014'}\n self.data_name = view_map[image_set] if image_set in view_map else image_set","def cifar100(path, label_mode='fine'):\n def _load_batch(filepath, label_key):\n with open(filepath, 'rb') as f:\n if sys.version_info < (3,):\n d = cPickle.load(f)\n else:\n d = cPickle.load(f, encoding='bytes')\n d_decoded = {} # decode utf8\n for k, v in six.iteritems(d):\n d_decoded[k.decode('utf8')] = v\n d = d_decoded\n images = d['data']\n labels = d[label_key]\n images = images.reshape(images.shape[0], 3, 32, 32)\n labels = np.reshape(labels, len(labels,))\n return images, labels\n path = os.path.expanduser(path)\n directory = 'cifar-100-python'\n if not os.path.exists(os.path.join(path, directory)):\n url = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'\n maybe_download_and_extract(path, url)\n\n filepath = os.path.join(path, directory, 'train')\n x_train, y_train = _load_batch(filepath, label_mode + '_labels')\n\n filepath = os.path.join(path, directory, 'test')\n x_test, y_test = _load_batch(filepath, label_mode + '_labels')\n return (x_train, y_train), (x_test, y_test)","def make_ctd_file():\n # Get the correct netCDF4 dataset\n __location__ = os.path.realpath(os.path.join(os.getcwd(),\n os.path.dirname(__file__),\n 'output'))\n nc_file = os.path.join(__location__,'test_BM54.nc')\n\n # Be sure to start with the original, unedited CTD data\n test_ambient.test_from_ctd()\n\n # Load the data into a netCDF file\n nc = Dataset(nc_file, 'a')\n\n return nc","def load_CIFAR_batch(filename):\n with open(filename, 'rb') as f:\n datadict = pickle.load(f, encoding='latin1')\n X = datadict['data']\n Y = datadict['labels']\n X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype(\"float64\")\n Y = np.array(Y)\n return X, Y","def load_CIFAR100(batch_dir):\r\n ims, coarse_labels, fine_labels = load_CIFAR_batch(batch_dir + '/train')\r\n ims_t, c_labels, f_labels = load_CIFAR_batch(batch_dir + '/test')\r\n ims = np.concatenate((ims, ims_t))\r\n coarse_labels = np.concatenate((coarse_labels, c_labels))\r\n fine_labels = np.concatenate((fine_labels, f_labels))\r\n return ims, coarse_labels, fine_labels","def build():\n\tconsole = Console()\n\tconsole.clear()\n\tconsole.print(BANNER)\n\tif not os.path.exists(\"dataset.yaml\"):\n\t\tclick.clear()\n\t\tconsole.print(\"Dataset config file not found\\nRun - idt init\\n\")\n\t\texit(0)\n\n\twith open('dataset.yaml') as f:\n\t\tdata = yaml.load(f, Loader=yaml.FullLoader)\n\t\n\tclick.clear()\n\tconsole.print(\"Building [bold blue]{dataset_name}[/bold blue] dataset...\\n\".format(dataset_name=data['DATASET_NAME']))\n\tfor classes in data['CLASSES']:\n\t\tclick.clear()\n\t\tconsole.print('Creating [bold blue]{name} class[/bold blue] \\n'.format(name=classes['CLASS_NAME']))\n\t\tsearch_list = classes['SEARCH_KEYWORDS'].split(\",\")\n\t\tfor keywords in search_list:\n\t\t\tfactory = SearchEngineFactory(keywords,data['SAMPLES_PER_SEARCH'],classes['CLASS_NAME'],data['RESIZE_METHOD'], data['DATASET_NAME'],data['IMAGE_SIZE'], data['ENGINE'],data['API_KEY'])\n\t# Remove corrupt files\n\tremove_corrupt(data['DATASET_NAME'])\n\n\t# Create a CSV with dataset info\n\tcreate_dataset_csv(data['DATASET_NAME'])\n\tclick.clear()\n\tconsole.print(\"Dataset READY!\")","def prepare_val_coco_data(args):\n image_dir, annotation_file = args.val_coco_image_dir, args.val_coco_annotation_file\n\n coco = COCO(annotation_file)\n\n img_ids = list(coco.imgToAnns.keys())\n img_files = []\n img_heights = []\n img_widths = []\n\n for img_id in img_ids:\n img_files.append(os.path.join(image_dir, coco.imgs[img_id]['file_name']))\n img_heights.append(coco.imgs[img_id]['height']) \n img_widths.append(coco.imgs[img_id]['width']) \n\n print(\"Building the validation dataset...\")\n dataset = DataSet(img_ids, img_files, img_heights, img_widths)\n print(\"Dataset built.\")\n return coco, dataset","def produce_init(filename):\n training_dataset = pd.read_csv(f'../Modified Data/{filename}')\n test_dataset = pd.read_csv(f'../Raw Data/test.csv')\n features = list(training_dataset.columns)\n features.remove('SalePrice')\n predict_feature = ['SalePrice']\n\n # Produce Test Data\n test_X = test_dataset.loc[:, features]\n ids_test = test_dataset.loc[:, 'Id']\n\n for column in features:\n if str(training_dataset.loc[:, column].dtype) == 'object':\n # Initialize encoder\n labelencoder = LabelEncoder()\n # Encode Train Data\n training_dataset.loc[:, column] = training_dataset.loc[:, column].fillna('Missing')\n training_dataset.loc[:, column] = pd.Series(labelencoder.fit_transform(training_dataset.loc[:, column]))\n # Encode Test Data\n test_X.loc[:, column] = test_X.loc[:, column].fillna('Missing')\n test_X.loc[:, column] = pd.Series(labelencoder.fit_transform(test_X.loc[:, column]))\n else:\n # Fix missing values for train data\n training_dataset.loc[:, column] = training_dataset.loc[:, column].fillna(int(training_dataset.loc[:, column].mean()))\n # Fix missing values for test data\n test_X.loc[:, column] = test_X.loc[:, column].fillna(int(test_X.loc[:, column].mean()))\n\n return training_dataset, test_X, ids_test","def load_CIFAR_batch(filename):\n with open(filename, 'rb') as f:\n datadict = load_pickle(f)\n X = datadict['data']\n Y = datadict['labels']\n X = X.reshape(10000,3072)\n Y = np.array(Y)\n return X, Y","def make_dataset(condition, root, base_path, files_json_path):\n normalize = transforms.Normalize(\n mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n dataset = ZapposDataset(root, base_path, files_json_path, condition,\n transform=transforms.Compose([\n transforms.Scale(112),\n transforms.CenterCrop(112),\n transforms.ToTensor(),\n normalize,\n ]))\n return dataset","def test_get_random_data_basic(self):\n cdata = random_data(num_features=2,\n num_samples=4,\n labels=None)\n self.assertEqual(cdata.num_features, 2)\n self.assertEqual(cdata.num_samples, 4)","def Cifar10_preload_and_split(path=None, splits=[0.4, 0.1, 0.25, 0.25], transform=None):\n\n if path is None:\n path = DATASETS_DIR\n index_file = os.path.join(path, 'cifar10.index.csv')\n\n indices = None\n if os.path.exists(index_file):\n index_csv = np.loadtxt(index_file)\n indices = torch.tensor(index_csv)\n print('Found predefined indexing file {}'.format(index_file))\n \n trainset = torchvision.datasets.CIFAR10(path, train=True, transform=transform[0], download=False)\n testset = torchvision.datasets.CIFAR10(path, train=False, transform=transform[0], download=False)\n fullset = ConcatDataset([trainset, testset])\n print('Initializing CIFAR10Dataset splits')\n \n # Currently five equal splits\n dset_size = fullset.cumulative_sizes[-1]\n int_splits = []\n for i in range(len(splits)):\n int_splits.append(int(dset_size * splits[i]))\n if sum(int_splits) < dset_size:\n rem = dset_size - sum(int_splits)\n int_splits[-1] += rem\n\n indices, splitsets = dataset_split(fullset, int_splits, indices=indices)\n\n if not os.path.exists(index_file):\n print('No predefined indexing file found, so index permutations saving to {}'.format(index_file))\n np.savetxt(index_file, indices.numpy(), fmt='%i', delimiter=',')\n\n print('Finished splitting data.')\n\n return splitsets","def get_coco_dataset():\n ds = AttrDict()\n classes = [\n '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',\n 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',\n 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',\n 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',\n 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',\n 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',\n 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass',\n 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',\n 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',\n 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',\n 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',\n 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',\n 'scissors', 'teddy bear', 'hair drier', 'toothbrush'\n ]\n ds.classes = {i: name for i, name in enumerate(classes)}\n return ds","def __init__(self, data_array, cui2name):\n\t\tassert isinstance(data_array, xr.DataArray), 'Constructor requires xr.DataArray.'\n\n\t\tself.data_array = data_array\n\t\tself.data = self.data_array.values\n\t\tself.sources = self.data_array.source.values\n\t\tself.targets = self.data_array.target.values\n\t\tself.metrics = self.data_array.metric.values\n\t\tself.metapaths = self.data_array.metapath.values\n\n\t\t# Fetch the verbose names\n\t\t#int_rf_df = pd.read_csv('data/impd_cogn_sources.tsv', delimiter='\\t')\n\t\t#cui2name = {cui:name for cui, name in zip(int_rf_df.identifier, int_rf_df.name)} \n\t\tself.source_names = np.array([cui2name[source] for source in self.sources])","def load_CIFAR_batch(filename):\r\n with open(filename, 'rb') as f:\r\n datadict = load_pickle(f)\r\n X = datadict['data']\r\n Y = datadict['labels']\r\n X = X.reshape(10000,3072)\r\n Y = np.array(Y)\r\n return X, Y","def __call__(self):\n\n dataset = TextOnlyCocoAnnotation()\n\n with open(self.path) as read_file:\n\n json_loaded = json.load(read_file)\n\n for i, value in tqdm(json_loaded['imgs'].items()):\n image_path = os.path.join(os.path.dirname(self.path), 'train2014',\n value['file_name'])\n dataset_type = value['set']\n\n if dataset_type not in self.sets:\n print(dataset_type)\n continue\n\n for annotation_id in json_loaded['imgToAnns'][i]:\n annotation_value = json_loaded['anns'][str(annotation_id)]\n word_annotation = self.parse_annotation_instance(annotation_value)\n dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation)\n\n return dataset","def create_dataset(df, genre_count, image_count, output_fname):\n\n counts = df['Class'].value_counts()\n filter = list(counts[counts > image_count].index)\n\n # filter for genres that appear image_count times or more\n more_than_img_count = (df[df['Class'].isin(filter)])\n topn = more_than_img_count.groupby('Class').head(image_count)\n topn.sort_values(by='Class', inplace=True, ascending=True)\n topn = topn.head(genre_count*image_count)\n\n topn['Path'] = os.getenv('dataset_location') + \"/\" + topn['Painting']\n\n topn.to_csv(os.path.join(os.getenv('dataset_location'), output_fname), sep=';', index=False)","def load_cci10():\n \n cci10 = pd.read_csv(pkg_resources.resource_filename(__name__,'cci_icd10cm_2019_1.csv'))\n \n cci10.columns = [i.strip('\\'') for i in cci10.columns]\n \n for col in cci10.columns:\n cci10.loc[:,col] = cci10[col].str.strip('\\'')\n cci10 = cci10.replace(r'^\\s*$', np.nan, regex=True)\n cci10.columns = [i.replace('CHRONIC INDICATOR','CHRONIC') for i in cci10.columns]\n \n dict_bodysystem = [\n ('1','Infectious and parasitic disease'),\n ('2','Neoplasms'),\n ('3','Endocrine, nutritional, and metabolic diseases and immunity disorders'),\n ('4','Diseases of blood and blood-forming organs'),\n ('5','Mental disorders'),\n ('6','Diseases of the nervous system and sense organs'),\n ('7','Diseases of the circulatory system'),\n ('8','Diseases of the respiratory system'),\n ('9','Diseases of the digestive system'),\n ('10','Diseases of the genitourinary system'),\n ('11','Complications of pregnancy, childbirth, and the puerperium'),\n ('12','Diseases of the skin and subcutaneous tissue'),\n ('13','Diseases of the musculoskeletal system'),\n ('14','Congenital anomalies'),\n ('15','Certain conditions originating in the perinatal period'),\n ('16','Symptoms, signs, and ill-defined conditions'),\n ('17','Injury and poisoning'),\n ('18','Factors influencing health status and contact with health services'),\n ]\n \n cci10 = cci10.merge(pd.DataFrame(dict_bodysystem,columns=['BODY SYSTEM','BODY SYSTEM DESCRIPTION']),how='left',on='BODY SYSTEM')\n \n cci10.loc[:,'ICD-10-CM CODE'] = cci10['ICD-10-CM CODE'].str.replace(' ','')\n \n return cci10","def createDataset(n, d=100, d_inf=5, is_classification=True, no_signal=False):\n \n # making random inputs, outputs\n X = np.random.normal(0, 1, (3*n, d))\n y = np.random.normal(0, 1, (3*n, 1))\n \n # thresholding y values for classification\n if is_classification:\n y = 2.0*((y>0) - 0.5)\n \n # making the first d_inf dimensions informative\n\n if is_classification:\n X[:,:d_inf] += y*np.random.normal(1.0, 1.5, X[:,:d_inf].shape)\n else:\n snr = 0.05\n X[:,:d_inf] += snr*y\n X = zscore(X, axis=0)\n \n # if you dont want useful signal, randomize the labels\n if no_signal:\n np.random.shuffle(y)\n\n # Divide into train/test/holdout pairs\n outputs = [[X[i::3, :], y[i::3, 0]] for i in range(3)] \n \n return outputs"],"string":"[\n \"def cifar10(transform=transforms.ToTensor()):\\n\\n\\t# Directories\\n\\tscript_dir = os.path.dirname(os.path.realpath(__file__))\\n\\tdata_dir = os.path.join(script_dir, 'data', 'cifar10')\\n\\n\\t# Load training set, downloading if necessary\\n\\tdataset = datasets.CIFAR10(data_dir, train=True, transform=transform, download=True)\\n\\n\\t# Split into train, val, and test sets\\n\\n\\t# Add 'classes' list and 'class_to_idx' lookup dictionary to both sets\\n\\tclasses = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', \\n\\t\\t'horse', 'ship', 'truck']\\n\\tclass_to_idx = {classes[i]: i for i in range(len(classes))}\\n\\n\\tdataset.classes = classes\\n\\tdataset.class_to_idx = class_to_idx\\n\\n\\treturn dataset\",\n \"def create_dataset_cifar10(data_path, batch_size=32, num_parallel_workers=8, do_train=True):\\r\\n # define dataset\\r\\n data_path = os.path.join(data_path, \\\"cifar-10-batches-bin\\\" if do_train else \\\"cifar-10-verify-bin\\\")\\r\\n\\r\\n cifar_ds = ds.Cifar10Dataset(data_path, num_parallel_workers=num_parallel_workers, shuffle=do_train)\\r\\n\\r\\n # define map operations\\r\\n resize_height, resize_width = 32, 32\\r\\n rescale = 1.0 / 255.0\\r\\n shift = 0.0\\r\\n random_crop_op = CV.RandomCrop([32, 32], [4, 4, 4, 4])\\r\\n random_horizontal_op = CV.RandomHorizontalFlip(prob=0.5)\\r\\n resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)\\r\\n rescale_op = CV.Rescale(rescale, shift)\\r\\n normalize_op = CV.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010])\\r\\n hwc2chw_op = CV.HWC2CHW()\\r\\n type_cast_op = C.TypeCast(mstype.int32)\\r\\n\\r\\n if do_train:\\r\\n compose_op = [random_crop_op, random_horizontal_op, resize_op, rescale_op, normalize_op, hwc2chw_op]\\r\\n else:\\r\\n compose_op = [resize_op, rescale_op, normalize_op, hwc2chw_op]\\r\\n cifar_ds = cifar_ds.map(input_columns=\\\"image\\\", operations=compose_op, num_parallel_workers=num_parallel_workers)\\r\\n cifar_ds = cifar_ds.map(input_columns=\\\"label\\\", operations=type_cast_op, num_parallel_workers=num_parallel_workers)\\r\\n cifar_ds = cifar_ds.batch(batch_size, drop_remainder=True)\\r\\n\\r\\n return cifar_ds\",\n \"def get_CIFAR10_data():\\n # Load the raw CIFAR-10 data\\n cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\\n X_train, _, X_test, _ = load_CIFAR10(cifar10_dir)\\n X = np.concatenate((X_train, X_test), axis =0)\\n return X\",\n \"def load_dataset_cifar10():\\n dirname = 'cifar-10-batches-py'\\n origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\\n path = get_file(dirname, origin=origin, untar=True)\\n\\n num_train_samples = 50000\\n\\n x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')\\n y_train = np.empty((num_train_samples,), dtype='uint8')\\n\\n for i in range(1, 6):\\n fpath = os.path.join(path, 'data_batch_' + str(i))\\n (x_train[(i - 1) * 10000: i * 10000, :, :, :],\\n y_train[(i - 1) * 10000: i * 10000]) = load_batch(fpath)\\n\\n fpath = os.path.join(path, 'test_batch')\\n x_test, y_test = load_batch(fpath)\\n\\n y_train = np.reshape(y_train, (len(y_train), 1))\\n y_test = np.reshape(y_test, (len(y_test), 1))\\n\\n return (x_train, y_train), (x_test, y_test)\",\n \"def get_cifar10(self):\\n\\t\\t# Get the data.\\n\\t\\tself.x_train = self.x_train.reshape(self.nb_train, self.input_dim)\\n\\t\\tself.x_test = self.x_test.reshape(self.nb_test, self.input_dim)\\n\\t\\tself.x_train = self.x_train.astype('float32')\\n\\t\\tself.x_test = self.x_test.astype('float32')\\n\\t\\tself.x_train /= 255\\n\\t\\tself.x_test /= 255\\n\\n\\t\\t# convert class vectors to binary class matrices\\n\\t\\tself.y_train = np_utils.to_categorical(self.y_train, self.nb_classes)\\n\\t\\tself.y_test = np_utils.to_categorical(self.y_test, self.nb_classes)\",\n \"def get_cifar_data(num_classes=10):\\n\\n (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\\n x_train = x_train.astype(np.float32) / 255.\\n x_test = x_test.astype(np.float32) / 255.\\n\\n y_train_cat = to_categorical(y_train, num_classes=num_classes).astype(np.float32)\\n y_test_cat = to_categorical(y_test, num_classes=num_classes).astype(np.float32)\\n\\n return x_train, y_train, x_test, y_test, y_train_cat, y_test_cat\",\n \"def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\\n # Load the raw CIFAR-10 data\\n cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\\n \\n # Subsample the data\\n mask = range(num_training, num_training + num_validation)\\n X_val = X_train[mask]\\n y_val = y_train[mask]\\n mask = range(num_training)\\n X_train = X_train[mask]\\n y_train = y_train[mask]\\n mask = range(num_test)\\n X_test = X_test[mask]\\n y_test = y_test[mask]\\n\\n # Normalize the data: subtract the mean image\\n mean_image = np.mean(X_train, axis=0)\\n X_train -= mean_image\\n X_val -= mean_image\\n X_test -= mean_image\\n\\n # Reshape data to rows\\n X_train = X_train.reshape(num_training, -1)\\n X_val = X_val.reshape(num_validation, -1)\\n X_test = X_test.reshape(num_test, -1)\\n\\n pca = decomposition.PCA(n_components=None, whiten=False)\\n X_train = pca.fit_transform(X_train)\\n X_val = pca.transform(X_val)\\n X_test = pca.transform(X_test)\\n\\n return X_train, y_train, X_val, y_val, X_test, y_test\",\n \"def setUp(self):\\n super().setUp()\\n self._batch_size = 16\\n self._batch_size_test = 10\\n self._shuffle_buffer_size = 8\\n\\n self._dataset = cifar10.CIFAR10Dataset(\\n self._batch_size,\\n batch_size_test=self._batch_size_test,\\n shuffle_buffer_size=self._shuffle_buffer_size)\",\n \"def get_cifar10_cnn():\\n # Set defaults.\\n nb_classes = 10 #dataset dependent \\n batch_size = 128\\n epochs = 4\\n \\n # the data, shuffled and split between train and test sets\\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\\n \\n # convert class vectors to binary class matrices\\n y_train = to_categorical(y_train, nb_classes)\\n y_test = to_categorical(y_test, nb_classes)\\n\\n #x._train shape: (50000, 32, 32, 3)\\n #input shape (32, 32, 3)\\n input_shape = x_train.shape[1:]\\n\\n #print('x_train shape:', x_train.shape)\\n #print(x_train.shape[0], 'train samples')\\n #print(x_test.shape[0], 'test samples')\\n #print('input shape', input_shape)\\n \\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n x_train /= 255\\n x_test /= 255\\n\\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test, epochs)\",\n \"def train_cifar10():\\r\\n # type: () -> None\\r\\n\\r\\n # Build dataset and model\\r\\n dataset = CIFAR10_TRAIN(path=Config.video_folder)\\r\\n model = LSACIFAR10(input_shape=dataset.shape, code_length=64,\\r\\n cpd_channels=100).to(device).train()\\r\\n\\r\\n # Set up result helper and perform test\\r\\n helper = OneClassResultHelper(dataset, model,\\r\\n checkpoints_dir=Config.model_ckpt,\\r\\n output_file='cifar10.txt')\\r\\n helper.train_one_class_classification()\",\n \"def get_cifar10_c(dataset_root, batch_size, train, noise_type):\\n \\n # image pre-processing\\n pre_process = transforms.Compose([transforms.Resize(28),\\n transforms.ToTensor(),\\n transforms.Normalize(\\n mean=(0.491, 0.482, 0.446), \\n std=(0.202, 0.199, 0.201)\\n )\\n ])\\n\\n # datasets and data loader\\n cifar10_c_dataset = GetLoader(\\n data_root=os.path.join(dataset_root),\\n noise_type=noise_type,\\n transform=pre_process)\\n \\n print(\\\"dataset len: \\\",len(cifar10_c_dataset))\\n\\n train_size = int(0.8 * len(cifar10_c_dataset))\\n test_size = len(cifar10_c_dataset) - train_size\\n\\n train_dataset, test_dataset = torch.utils.data.random_split(cifar10_c_dataset, [train_size, test_size])\\n print(\\\"train len: \\\",len(train_dataset))\\n print(\\\"test len: \\\",len(test_dataset))\\n if train:\\n cifar10_c_dataset=train_dataset\\n else:\\n cifar10_c_dataset=test_dataset\\n\\n cifar10_c_data_loader = torch.utils.data.DataLoader(\\n dataset=cifar10_c_dataset,\\n batch_size=batch_size,\\n shuffle=True,\\n drop_last=True,\\n num_workers=8)\\n\\n return cifar10_c_data_loader\",\n \"def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\\n # Load the raw CIFAR-10 data\\n cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\\n\\n # Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\\n try:\\n del X_train, y_train\\n del X_test, y_test\\n print('Clear previously loaded data.')\\n except:\\n pass\\n\\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\\n\\n # Subsample the data\\n mask = list(range(num_training, num_training + num_validation))\\n X_val = X_train[mask]\\n y_val = y_train[mask]\\n mask = list(range(num_training))\\n X_train = X_train[mask]\\n y_train = y_train[mask]\\n mask = list(range(num_test))\\n X_test = X_test[mask]\\n y_test = y_test[mask]\\n\\n # Normalize the data: subtract the mean image\\n mean_image = np.mean(X_train, axis=0)\\n X_train -= mean_image\\n X_val -= mean_image\\n X_test -= mean_image\\n\\n # Reshape data to rows\\n X_train = X_train.reshape(num_training, -1)\\n X_val = X_val.reshape(num_validation, -1)\\n X_test = X_test.reshape(num_test, -1)\\n\\n return X_train, y_train, X_val, y_val, X_test, y_test\",\n \"def load_CIFAR10(ROOT):\\r\\n xs = []\\r\\n ys = []\\r\\n for b in range(1,6):\\r\\n f = os.path.join(ROOT, 'data_batch_%d' % (b, ))\\r\\n X, Y = load_CIFAR_batch(f)\\r\\n xs.append(X)\\r\\n ys.append(Y)\\r\\n Xtr = np.concatenate(xs)\\r\\n Ytr = np.concatenate(ys)\\r\\n del X, Y\\r\\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\\r\\n return Xtr, Ytr, Xte, Yte\\r\\n\\r\\n\\tdef get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):\\r\\n # Load the raw CIFAR-10 data\\r\\n \\r\\n cifar10_dir = 'Downloads/cifar-10-batches-py'\\r\\n \\r\\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\\r\\n\\r\\n # Subsample the data\\r\\n mask = range(num_training, num_training + num_validation)\\r\\n X_val = X_train[mask]\\r\\n y_val = y_train[mask]\\r\\n mask = range(num_training)\\r\\n X_train = X_train[mask]\\r\\n y_train = y_train[mask]\\r\\n mask = range(num_test)\\r\\n X_test = X_test[mask]\\r\\n y_test = y_test[mask]\\r\\n\\r\\n x_train = X_train.astype('float32') \\r\\n x_test = X_test.astype('float32')\\r\\n \\r\\n x_train = x_train.reshape(-1, 32, 32, 3)\\r\\n x_test = x_test.reshape(-1, 32, 32, 3)\\r\\n x_train /= 255\\r\\n x_test /= 255\\r\\n\\r\\n return x_train, y_train, X_val, y_val, x_test, y_test\",\n \"def get_cifar10():\\n\\n from keras.datasets import cifar10\\n\\n # input image dimensions\\n img_rows, img_cols = 32, 32\\n n_channels = 3\\n num_classes = 10\\n # the data, shuffled and split between train and test sets\\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\\n\\n if K.image_data_format() == 'channels_first':\\n print (\\\"Using Channels first\\\")\\n x_train = x_train.reshape(x_train.shape[0], n_channels, img_rows, img_cols)\\n x_test = x_test.reshape(x_test.shape[0], n_channels, img_rows, img_cols)\\n input_shape = (n_channels, img_rows, img_cols)\\n else:\\n print(\\\"Channels last\\\")\\n x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, n_channels)\\n x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, n_channels)\\n input_shape = (img_rows, img_cols, n_channels)\\n\\n return (x_train, y_train), (x_test, y_test)\",\n \"def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000, num_dev=500):\\n # Load the raw CIFAR-10 data\\n cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\\n\\n # subsample the data\\n mask = range(num_training, num_training + num_validation)\\n X_val = X_train[mask]\\n y_val = y_train[mask]\\n mask = range(num_training)\\n X_train = X_train[mask]\\n y_train = y_train[mask]\\n mask = range(num_test)\\n X_test = X_test[mask]\\n y_test = y_test[mask]\\n mask = np.random.choice(num_training, num_dev, replace=False)\\n X_dev = X_train[mask]\\n y_dev = y_train[mask]\\n\\n # Preprocessing: reshape the image data into rows\\n X_train = np.reshape(X_train, (X_train.shape[0], -1))\\n X_val = np.reshape(X_val, (X_val.shape[0], -1))\\n X_test = np.reshape(X_test, (X_test.shape[0], -1))\\n X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\\n\\n # Normalize the data: subtract the mean image\\n mean_image = np.mean(X_train, axis=0)\\n X_train -= mean_image\\n X_val -= mean_image\\n X_test -= mean_image\\n X_dev -= mean_image\\n\\n # add bias dimension and transform into columns\\n X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])\\n X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\\n X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\\n X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\\n\\n return X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev\",\n \"def load_cifar10_data(self, data_path='data/cifar-10-batches-py',\\n n_train_samples=50000, n_test_samples=10000):\\n train_data = None\\n train_labels = []\\n\\n for i in range(1, 6):\\n data_dic = unpickle(data_path + '/data_batch_{}'.format(i))\\n if i == 1:\\n train_data = data_dic['data']\\n else:\\n train_data = np.vstack((train_data, data_dic['data']))\\n\\n train_labels += data_dic['labels']\\n\\n test_data_dic = unpickle(data_path + '/test_batch')\\n test_data = test_data_dic['data']\\n test_labels = test_data_dic['labels']\\n\\n train_data = train_data.reshape((len(train_data),\\n self.LOADED_IMG_DEPTH,\\n self.LOADED_IMG_HEIGHT,\\n self.LOADED_IMG_HEIGHT))\\n\\n train_data = np.rollaxis(train_data, 1, 4)\\n train_labels = np.array(train_labels)\\n\\n test_data = test_data.reshape((len(test_data),\\n self.LOADED_IMG_DEPTH,\\n self.LOADED_IMG_HEIGHT,\\n self.LOADED_IMG_HEIGHT))\\n\\n test_data = np.rollaxis(test_data, 1, 4)\\n test_labels = np.array(test_labels)\\n\\n self.train_dataset = {'data': train_data[0:n_train_samples],\\n 'labels': train_labels[0:n_train_samples],\\n 'cls': [np.zeros(10)\\n for i in range(n_train_samples)]}\\n\\n for i in range(0, n_train_samples):\\n self.train_dataset['cls'][i][self.train_dataset['labels'][i]] = 1.\\n\\n self.test_dataset = {'data': test_data[0:n_test_samples],\\n 'labels': test_labels[0:n_test_samples],\\n 'cls': [np.zeros(10)\\n for i in range(n_train_samples)]}\\n\\n for i in range(0, n_test_samples):\\n self.test_dataset['cls'][i][self.test_dataset['labels'][i]] = 1.\\n\\n self.train_dataset['data_array'] = np.array(\\n [item.flatten() for item in self.train_dataset['data']])\\n\\n self.train_dataset['labels_array'] = np.array(\\n [item.flatten() for item in self.train_dataset['labels']])\\n\\n self.train_dataset['cls_array'] = np.array(\\n [item.flatten() for item in self.train_dataset['cls']])\\n\\n self.test_dataset['data_array'] = np.array(\\n [item.flatten() for item in self.test_dataset['data']])\\n\\n self.test_dataset['labels_array'] = np.array(\\n [item.flatten() for item in self.test_dataset['labels']])\\n\\n self.test_dataset['cls_array'] = np.array(\\n [item.flatten() for item in self.test_dataset['cls']])\\n\\n return None\",\n \"def loadData(self):\\n # Load the raw CIFAR-10 data\\n num_training = 49000\\n num_validation = 1000\\n num_test = 1000\\n subtract_mean = True\\n\\n cifar10_dir = '/home/parallels/PycharmProjects/Courses/232A/project2/stats232a/datasets/cifar-10-batches-py'\\n X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\\n\\n # Subsample the data\\n mask = list(range(num_training, num_training + num_validation))\\n X_val = X_train[mask]\\n y_val = y_train[mask]\\n mask = list(range(num_training))\\n X_train = X_train[mask]\\n y_train = y_train[mask]\\n mask = list(range(num_test))\\n X_test = X_test[mask]\\n y_test = y_test[mask]\\n\\n # Normalize the data: subtract the mean image\\n if subtract_mean:\\n mean_image = np.mean(X_train, axis=0)\\n X_train -= mean_image\\n X_val -= mean_image\\n X_test -= mean_image\\n\\n # Transpose so that channels come first\\n X_train = X_train.transpose(0, 3, 1, 2)\\n X_val = X_val.transpose(0, 3, 1, 2)\\n X_test = X_test.transpose(0, 3, 1, 2)\\n\\n # Package data into a dictionary\\n self.data = {\\n 'X_train': X_train, 'y_train': y_train,\\n 'X_val': X_val, 'y_val': y_val,\\n 'X_test': X_test, 'y_test': y_test,\\n }\",\n \"def load_cifar() -> Tuple[torchvision.datasets.CIFAR10, torchvision.datasets.CIFAR10]:\\n \\n # Define the transform for the data.\\n transform = transforms.Compose(\\n [transforms.ToTensor(), \\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\\n )\\n \\n # Initialize Datasets. CIFAR-10 will automatically download if not present\\n trainset = torchvision.datasets.CIFAR10(\\n root=DATA_ROOT, train=True, download=True, transform=transform\\n )\\n testset = torchvision.datasets.CIFAR10(\\n root=DATA_ROOT, train=False, download=True, transform=transform\\n )\\n \\n # Return the datasets\\n return trainset, testset\",\n \"def load_cifar10(data_path=\\\".\\\", test_size=0.2, random_state=1337):\\n test_path = os.path.join(data_path, \\\"cifar-10-batches-py/test_batch\\\")\\n train_paths = [os.path.join(data_path, \\\"cifar-10-batches-py/data_batch_%i\\\" % i) for i in range(1, 6)]\\n\\n if not os.path.exists(test_path) or not all(list(map(os.path.exists, train_paths))):\\n print (\\\"Dataset not found. Downloading...\\\")\\n download_cifar(data_path,\\n url='https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz',\\n tarname='cifar-10-python.tar.gz')\\n\\n train_batches = list(map(unpickle, train_paths))\\n test_batch = unpickle(test_path)\\n\\n X = np.concatenate([batch[\\\"data\\\"] for batch in train_batches]).reshape([-1, 3, 32, 32]).astype('float32') / 255\\n y = np.concatenate([batch[\\\"labels\\\"] for batch in train_batches]).astype('int32')\\n X_train, X_val, y_train, y_val = train_test_split(X, y,\\n test_size=test_size,\\n random_state=random_state)\\n\\n X_test = test_batch[\\\"data\\\"].reshape([-1, 3, 32, 32]).astype('float32') / 255\\n y_test = np.array(test_batch[\\\"labels\\\"]).astype('int32')\\n\\n return X_train, y_train, X_val, y_val, X_test, y_test\",\n \"def load_data5():\\n# dirname = 'cifar-10-batches-py'\\n# origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\\n# path = get_file(dirname, origin=origin, untar=True)\\n# path= './cifar-10-batches-py'\\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\\n# Below shows a test class has 999 examples instead of the claimed 1000\\n# tclasscount=np.zeros((10,), dtype=int)\\n# for i in range(0, len(y_test)-1):\\n# tclasscount[y_test[i][0]]= tclasscount[y_test[i][0]] + 1\\n# print('Test class count',tclasscount)\\n num_train_samples = 50000\\n num_5_class = 25000\\n num_5_test = 4999 # should be 5000 if all the categories had 1000 in them but they do not. One is missing.\\n print('x_train shape orig:', x_train.shape)\\n print('More:', x_train.shape[1:])\\n print('y_test shape',y_test.shape)\\n\\n x5_train = np.empty((num_5_class, 32, 32, 3), dtype='uint8')\\n y5_train = np.empty((num_5_class,), dtype='uint8')\\n\\n count=0\\n\\n for i in range(0, len(y_train)-1):\\n if (y_train[i][0] == 2) or (y_train[i][0] == 3) or (y_train[i][0] == 4) or (y_train[i][0] == 5) or (y_train[i][0] == 7):\\n x5_train[count]=x_train[i]\\n y5_train[count]=y_train[i]\\n count=count+1\\n \\n # find test data of interest\\n count=0\\n x5_test=np.empty((num_5_test, 32, 32, 3), dtype='uint8')\\n y5_test= np.empty((num_5_test,), dtype='uint8')\\n\\n for i in range(0, len(y_test)-1):\\n if (y_test[i][0] == 2) or (y_test[i][0] == 3) or (y_test[i][0] == 4) or (y_test[i][0] == 5) or (y_test[i][0] == 7):\\n x5_test[count]=x_test[i]\\n y5_test[count]=y_test[i]\\n count=count+1\\n# Below shows class 7 is only 999 and not 1000 examples!!! One horse got away it seems.\\n# if(y_test[i][0] == 2):\\n# c2=c2+1\\n# if(y_test[i][0] == 3):\\n# c3=c3+1\\n# if(y_test[i][0] == 4):\\n# c4=c4+1\\n# if(y_test[i][0] == 5):\\n# c5=c5+1\\n# if(y_test[i][0] == 7):\\n# c7=c7+1\\n# print('c2count, c3count, c4count, c5count, c7count',c2,c3,c3,c5,c7)\\n# print('y5tstshape',y5_test.shape, count)\\n# print('y5tst',y5_test)\\n# return (x_train, y_train), (x_test, y_test)\\n return (x5_train, y5_train), (x5_test, y5_test)\",\n \"def get_cifar10_mlp():\\n # Set defaults.\\n nb_classes = 10 #dataset dependent \\n batch_size = 64\\n epochs = 4\\n input_shape = (3072,) #because it's RGB\\n\\n # Get the data.\\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\\n x_train = x_train.reshape(50000, 3072)\\n x_test = x_test.reshape(10000, 3072)\\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n x_train /= 255\\n x_test /= 255\\n\\n # convert class vectors to binary class matrices\\n y_train = to_categorical(y_train, nb_classes)\\n y_test = to_categorical(y_test, nb_classes)\\n\\n return (nb_classes, batch_size, input_shape, x_train, x_test, y_train, y_test, epochs)\",\n \"def load_cifar10(directory):\\n train_data = []\\n train_labels = []\\n for b in range(1, 6):\\n f = os.path.join(directory, 'data_batch_%d' % (b,))\\n X, Y = load_cifar10_batch(f)\\n train_data.append(X)\\n train_labels.append(Y)\\n train_data = np.concatenate(train_data)\\n train_labels = np.concatenate(train_labels)\\n del X, Y\\n test_data, test_labels = load_cifar10_batch(os.path.join(directory, 'test_batch'))\\n return train_data, train_labels, test_data, test_labels\",\n \"def load_cifar10s(data_dir, use_augmentation=False, aux_take_amount=None, \\n aux_data_filename='/cluster/scratch/rarade/cifar10s/ti_500K_pseudo_labeled.pickle', \\n aux_take_ids_path=None,\\n validation=False):\\n data_dir = re.sub('cifar10s', 'cifar10', data_dir)\\n test_transform = transforms.Compose([transforms.ToTensor()])\\n if use_augmentation:\\n train_transform = transforms.Compose([transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(0.5), \\n transforms.ToTensor()])\\n else: \\n train_transform = test_transform\\n \\n train_dataset = SemiSupervisedCIFAR10(base_dataset='cifar10', root=data_dir, train=True, download=True, \\n transform=train_transform, aux_data_filename=aux_data_filename, \\n add_aux_labels=True, aux_take_amount=aux_take_amount, aux_take_ids_path=aux_take_ids_path,\\n validation=validation)\\n test_dataset = SemiSupervisedCIFAR10(base_dataset='cifar10', root=data_dir, train=False, download=True, \\n transform=test_transform)\\n if validation:\\n val_dataset = torchvision.datasets.CIFAR10(root=data_dir, train=True, download=True, transform=test_transform)\\n val_dataset = torch.utils.data.Subset(val_dataset, np.arange(0, 1024)) \\n return train_dataset, test_dataset, val_dataset\\n return train_dataset, test_dataset\",\n \"def load_CIFAR10(path):\\r\\n sampleList = []\\r\\n labelList = []\\r\\n # load all the data, as there only five training samples name as data_batch_id\\r\\n for i in range(1, 6):\\r\\n # get full filename\\r\\n filename = os.path.join(path, 'data_batch_%d' % (i, ))\\r\\n x, y = load_CIFAR_batch(filename)\\r\\n\\r\\n sampleList.append(x)\\r\\n labelList.append(y)\\r\\n\\r\\n # combine elements as one array\\r\\n Xtr = np.concatenate(sampleList)\\r\\n Ytr = np.concatenate(labelList)\\r\\n del x, y\\r\\n print(\\\"Training data loaded, total size : %d\\\", len(Xtr))\\r\\n # load test data\\r\\n Xte, Yte = load_CIFAR_batch(os.path.join(path, 'test_batch'))\\r\\n return Xtr, Ytr, Xte, Yte\",\n \"def load_CIFAR10(ROOT):\\n xs = []\\n ys = []\\n for b in range(1,6):\\n f = os.path.join(ROOT, 'data_batch_%d' % (b, ))\\n X, Y = load_CIFAR_batch(f)\\n xs.append(X)\\n ys.append(Y)\\n Xtr = np.concatenate(xs)\\n Ytr = np.concatenate(ys)\\n del X, Y\\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\\n return Xtr, Ytr, Xte, Yte\",\n \"def load_CIFAR10(ROOT):\\n xs = []\\n ys = []\\n for b in range(1, 6):\\n f = os.path.join(ROOT, 'data_batch_%d' % (b,))\\n X, Y = load_CIFAR_batch(f)\\n xs.append(X)\\n ys.append(Y)\\n Xtr = np.concatenate(xs)\\n Ytr = np.concatenate(ys)\\n del X, Y\\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\\n return Xtr, Ytr, Xte, Yte\",\n \"def load_cifar():\\n print('==> Preparing data..')\\n transform_train = transforms.Compose([\\n transforms.RandomCrop(32, padding=4),\\n transforms.RandomHorizontalFlip(),\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\\n ])\\n\\n transform_test = transforms.Compose([\\n transforms.ToTensor(),\\n transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\\n ])\\n\\n trainset = torchvision.datasets.CIFAR10(\\n root='./data', train=True, download=True, transform=transform_train)\\n trainloader = torch.utils.data.DataLoader(\\n trainset, batch_size=1024, shuffle=True, num_workers=8)\\n\\n testset = torchvision.datasets.CIFAR10(\\n root='./data', train=False, download=True, transform=transform_test)\\n testloader = torch.utils.data.DataLoader(\\n testset, batch_size=128, shuffle=False, num_workers=8)\\n return trainloader, testloader\",\n \"def get_dataset(self):\\n\\n trainset = datasets.CIFAR10('datasets/CIFAR10/train/', train=True, transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.CIFAR10('datasets/CIFAR10/test/', train=False, transform=self.val_transforms,\\n target_transform=None, download=True)\\n\\n return trainset, valset\",\n \"def load_cifar(dataset_name='cifar10'):\\n dataset_name = dataset_name.strip().lower().replace(' ', '')\\n\\n if dataset_name.lower() not in ['cifar10', 'cifar100']:\\n raise ValueError('Only cifar10 or cifar100 are valid dataset_name.')\\n baseURL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\\n if dataset_name == 'cifar100':\\n baseURL = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'\\n\\n dirname = os.path.join(_trident_dir, dataset_name.strip())\\n if not os.path.exists(dirname):\\n try:\\n os.makedirs(dirname)\\n except OSError:\\n # Except permission denied and potential race conditions\\n # in multi-threaded environments.\\n pass\\n\\n \\\"\\\"\\\"Load CIFAR data from `path`\\\"\\\"\\\"\\n _,filename,ext=split_path(baseURL)\\n download_file(baseURL, dirname, filename+ext, dataset_name)\\n file_path = os.path.join(dirname, filename+ext)\\n\\n\\n if '.tar' in ext:\\n extract_archive(file_path, dirname, archive_format='auto')\\n filelist = glob.glob(dirname + '/*/*.*')\\n extract_path ,_,_= split_path(filelist[0])\\n filelist = [f for f in os.listdir(extract_path) if os.path.isfile(os.path.join(extract_path, f))]\\n data=[]\\n label=[]\\n test_data=[]\\n test_label=[]\\n for file_path in filelist:\\n if 'data_batch' in file_path:\\n with open(os.path.join(extract_path,file_path), 'rb') as f:\\n entry = pickle.load(f, encoding='latin1')\\n data.append(entry['data'])\\n label.append(entry['labels'])\\n elif 'test_batch' in file_path:\\n with open(os.path.join(extract_path,file_path), 'rb') as f:\\n entry = pickle.load(f, encoding='latin1')\\n test_data.append(entry['data'])\\n test_label.append(entry['labels'])\\n data = np.concatenate(data)\\n data = data.reshape((data.shape[0], 3, 32, 32))\\n data = data.transpose(0, 2, 3, 1).astype(np.float32)\\n\\n test_data = np.concatenate(test_data)\\n test_data = test_data.reshape((test_data.shape[0], 3, 32, 32))\\n test_data = test_data.transpose(0, 2, 3, 1).astype(np.float32)\\n\\n # Prepare labels\\n label = np.concatenate(label)\\n test_label = np.concatenate(test_label)\\n\\n trainData = Iterator(data=ImageDataset(data,object_type=ObjectType.rgb), label=LabelDataset(label,object_type=ObjectType.classification_label))\\n testData = Iterator(data=ImageDataset(test_data,object_type=ObjectType.rgb), label=LabelDataset(test_label,object_type=ObjectType.classification_label))\\n dataset = DataProvider(dataset_name, traindata=trainData, testdata=testData)\\n dataset.binding_class_names(['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship',\\n 'truck'] if dataset_name == 'cifar10' else [], 'en-US')\\n return dataset\",\n \"def load_CIFAR10(ROOT):\\n xs = []\\n ys = []\\n for b in range(1,2):\\n f = os.path.join(ROOT, 'data_batch_%d' % b)\\n X, Y = load_CIFAR_batch(f)\\n xs.append(X)\\n ys.append(Y) \\n #利用np.concatenate将xs、ys弄成一行\\n Xtr = np.concatenate(xs)\\n Ytr = np.concatenate(ys)\\n del X, Y\\n #获取测试集\\n Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch'))\\n return Xtr, Ytr, Xte, Yte\",\n \"def load_cifar10_data(img_rows=48,\\n\\timg_cols=48):\\n\\n\\t(X_train, Y_train), (X_valid, Y_valid) = cifar10.load_data()\\n\\n\\tX_train = np.array([cv2.resize(img, (img_rows,img_cols)) for img in X_train[:,:,:,:]])\\n\\tX_valid = np.array([cv2.resize(img, (img_rows,img_cols)) for img in X_valid[:,:,:,:]])\\n\\n\\tY_train = to_categorical(Y_train[:], num_classes)\\n\\tY_valid = to_categorical(Y_valid[:], num_classes)\\n\\n\\treturn X_train, Y_train, X_valid, Y_valid\",\n \"def load_cifar10(split, with_info=False, data_augmentation=True,\\n subsample_n=0):\\n dataset, ds_info = tfds.load('cifar10',\\n split=split,\\n with_info=True,\\n batch_size=-1)\\n image_shape = ds_info.features['image'].shape\\n\\n numpy_ds = tfds.as_numpy(dataset)\\n numpy_images = numpy_ds['image']\\n numpy_labels = numpy_ds['label']\\n\\n # Perform subsampling if requested\\n original_train_size = numpy_images.shape[0]\\n if subsample_n > 0:\\n subsample_n = min(numpy_images.shape[0], subsample_n)\\n numpy_images = numpy_images[0:subsample_n, :, :, :]\\n numpy_labels = numpy_labels[0:subsample_n]\\n\\n dataset = tf.data.Dataset.from_tensor_slices((numpy_images, numpy_labels))\\n\\n def preprocess(image, label):\\n \\\"\\\"\\\"Image preprocessing function.\\\"\\\"\\\"\\n if data_augmentation and split == tfds.Split.TRAIN:\\n image = tf.image.random_flip_left_right(image)\\n image = tf.pad(image, [[4, 4], [4, 4], [0, 0]])\\n image = tf.image.random_crop(image, image_shape)\\n\\n image = tf.image.convert_image_dtype(image, tf.float32)\\n return image, label\\n\\n dataset = dataset.map(preprocess)\\n\\n if with_info:\\n info = {\\n 'train_num_examples': ds_info.splits['train'].num_examples,\\n 'train_num_examples_orig': original_train_size,\\n 'test_num_examples': ds_info.splits['test'].num_examples,\\n 'input_shape': ds_info.features['image'].shape,\\n 'num_classes': ds_info.features['label'].num_classes,\\n }\\n return dataset, info\\n return dataset\",\n \"def load_data():\\r\\n global labelNames\\r\\n print(\\\"Loading Data...\\\")\\r\\n\\r\\n fnpath = \\\"rawdata\\\\\\\\cifar-10-batches-py\\\"\\r\\n fnprefix = 'data_batch_'\\r\\n fnlblnames = 'batches.meta'\\r\\n fntstbatch = 'test_batch'\\r\\n\\r\\n labelNames = unpickle(path.join(fnpath, fnlblnames))\\r\\n label_names = []\\r\\n for label in labelNames['label_names']:\\r\\n label_names.append(\\\"\\\".join(map(chr, label)))\\r\\n labelNames['label_names'] = label_names\\r\\n\\r\\n CIFAR_Data.append(unpickle(path.join(fnpath, fntstbatch)))\\r\\n for n in range(1, 6):\\r\\n CIFAR_Data.append(unpickle(path.join(fnpath, fnprefix + str(n))))\",\n \"def _init_img_dataset(self, dataset_path):\\n\\n # ==\\n # Define the classes used in the various states\\n # form: (state class : cifar label class)\\n class_dict = {\\n 'initial': 'automobile',\\n 'choice_1': 'dog',\\n 'choice_2': 'cat',\\n 'corridor': 'bird',\\n }\\n\\n # ==\\n # Download / initialize dataset\\n ds = CIFAR10(dataset_path, train=self.training,\\n download=True)\\n\\n # Get the CIFAR class index for each of the state classes\\n cifar_class_dict = {\\n k: ds.class_to_idx[class_dict[k]] for k in class_dict\\n }\\n\\n # Iterate over the CIFAR dataset and get the idxs to each class\\n cifar_indexes = {k: [] for k in class_dict}\\n for i in range(len(ds)):\\n cur_cifar_class = ds[i][1]\\n for k in class_dict:\\n if cur_cifar_class == cifar_class_dict[k]:\\n cifar_indexes[k].append(i)\\n\\n # Manually sub-sample choice classes\\n for k in ['choice_1', 'choice_2']:\\n n_imgs = min(self.num_ds_imgs, len(cifar_indexes[k]))\\n rng = np.random.default_rng()\\n choice_imgs = rng.choice(cifar_indexes[k], size=n_imgs,\\n replace=False)\\n cifar_indexes[k] = choice_imgs\\n\\n # Manually shuffle the corridor class\\n rng = np.random.default_rng()\\n corri_img_shufIdxs = rng.choice(cifar_indexes['corridor'],\\n size=len(cifar_indexes['corridor']),\\n replace=False)\\n cifar_indexes['corridor'] = corri_img_shufIdxs\\n\\n # ==\\n # Construct the data subset dictionary\\n ds_dict = {}\\n for k in class_dict:\\n ds_dict[k] = Subset(ds, cifar_indexes[k])\\n\\n return ds_dict\",\n \"def test_cifar(args):\\n # type: () -> None\\n\\n # Build dataset and model\\n dataset = CIFAR10(path=args.path)\\n model = MEMCIFAR10(input_shape=dataset.shape, code_length=64, cpd_channels=100, mem_dim=500, shrink_thres=1.0 / 500).cuda().eval()\\n\\n # Set up result helper and perform test\\n helper = MEMResultHelper(dataset, model, checkpoints_dir=args.checkpoints, output_file='mem_cifar.txt')\\n helper.test_one_class_classification()\",\n \"def load_cifar10(data_path=None, data_home=None, subsets=None):\\n if data_path is None:\\n data_path = _utils.validate_data_home(data_home)\\n data_path /= 'cifar-10-python.tar.gz'\\n url = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\\n _ds_utils.get_file(data_path, url)\\n \\n if subsets is None:\\n subsets = ['training', 'test']\\n subsets = _ds_utils.validate_tvt(subsets, return_list=True)\\n X, Y = [], []\\n with arlib.open(data_path) as ar:\\n for subset in subsets:\\n if subset == 'training':\\n for i in range(1, 6):\\n mname = [x for x in ar.member_names\\n if x.endswith('data_batch_'+str(i))]\\n assert len(mname) == 1\\n mname = mname[0]\\n tmp = _load_cifar_batch(ar.open_member(mname,'rb'))\\n X.append(tmp[0])\\n Y.append(tmp[1])\\n elif subset == 'test':\\n mname = [x for x in ar.member_names if x.endswith('test_batch')]\\n assert len(mname) == 1\\n mname = mname[0]\\n tmp = _load_cifar_batch(ar.open_member(mname, 'rb'))\\n X.append(tmp[0])\\n Y.append(tmp[1])\\n else:\\n raise ValueError('Subset:', subset, ' not supported.')\\n return np.concatenate(X), np.concatenate(Y)\",\n \"def CreateDataset(all_arrays):\\n dataset = Dataset()\\n\\n dataset._addData(all_arrays[0])\\n dataset._addData(all_arrays[1])\\n dataset._addData(all_arrays[3])\\n dataset._addData(all_arrays[5])\\n dataset._addData(all_arrays[6])\\n dataset._addData(all_arrays[9])\\n dataset._addData(all_arrays[8])\\n dataset._addData(all_arrays[4])\\n\\n return dataset\",\n \"def load_cifar10_img_form(directory):\\n train_data, train_labels, test_data, test_labels = load_cifar10(directory)\\n R, testR = train_data[:, :1024].reshape(-1, 32, 32, 1), test_data[:, :1024].reshape(-1, 32, 32, 1)\\n G, testG = train_data[:, 1024:2048].reshape(-1, 32, 32, 1), test_data[:, 1024:2048].reshape(-1, 32, 32, 1)\\n B, testB = train_data[:, 2048:].reshape(-1, 32, 32, 1), test_data[:, 2048:].reshape(-1, 32, 32, 1)\\n train_data, test_data = np.concatenate((R, G, B), axis=3), np.concatenate((testR, testG, testB), axis=3)\\n return train_data, train_labels, test_data, test_labels\",\n \"def load_cifar_data():\\n train_loader = torch.utils.data.DataLoader(\\n torchvision.datasets.CIFAR10('cifarfiles/', train=True, download=True,\\n transform=torchvision.transforms.Compose([\\n torchvision.transforms.ToTensor(),\\n torchvision.transforms.Normalize(\\n (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n ])),\\n batch_size=batch_size_train, shuffle=True, pin_memory=True)\\n\\n test_loader = torch.utils.data.DataLoader(\\n torchvision.datasets.CIFAR10('cifarfiles/', train=False, download=True,\\n transform=torchvision.transforms.Compose([\\n torchvision.transforms.ToTensor(),\\n torchvision.transforms.Normalize(\\n (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n ])),\\n batch_size=batch_size_test, shuffle=True, pin_memory=True)\\n return train_loader, test_loader\",\n \"def load_data(self, dataset='cifar10', label_mode='fine'):\\n if dataset == 'cifar10':\\n if self.root:\\n x_train, y_train = self.load_from_path(\\n [os.path.join(self.root, f'data_batch_{i}') for i in range(1, 6)])\\n x_test, y_test = self.load_from_path(\\n [os.path.join(self.root, 'test_batch')])\\n x_test = x_test.astype(x_train.dtype)\\n y_test = y_test.astype(y_train.dtype)\\n return (x_train, y_train), (x_test, y_test)\\n else:\\n return tf.keras.datasets.cifar10.load_data()\\n elif dataset in ['cifar20', 'cifar100']:\\n if self.root:\\n x_train, y_train = self.load_from_path(\\n [os.path.join(self.root, 'train')], label_key=label_mode)\\n x_test, y_test = self.load_from_path([os.path.join(self.root, 'test')])\\n x_test = x_test.astype(x_train.dtype)\\n y_test = y_test.astype(y_train.dtype)\\n return (x_train, y_train), (x_test, y_test)\\n else:\\n return tf.keras.datasets.cifar100.load_data(label_mode=label_mode)\",\n \"def create_ds(self, data, is_train=True):\\n ds = tf.data.Dataset.from_tensor_slices(data)\\n map_fn = lambda x, y: (cifar_process(x, is_train), y)\\n ds = ds.map(map_fn, num_parallel_calls=tf.data.experimental.AUTOTUNE)\\n return ds\",\n \"def generate_coco_dataset(args):\\n\\targs.data_root = Path(args.data_root)\\n\\targs.save_root = Path(args.save_root)\\n\\targs.save_root.mkdir()\\n\\n\\tgenerate_coco_dataset_sub(args, 'train', 'A', args.cat1)\\n\\tgenerate_coco_dataset_sub(args, 'train', 'B', args.cat2)\\n\\tgenerate_coco_dataset_sub(args, 'val', 'A', args.cat1)\\n\\tgenerate_coco_dataset_sub(args, 'val', 'B', args.cat2)\",\n \"def test_create_dataset(self):\\n self.assertIsInstance(self._dataset, cifar10.CIFAR10Dataset)\",\n \"def download():\\n\\n trainset = torchvision.datasets.CIFAR10(root=paths.raw_cifar10_dir(), train=True, download=True)\\n testset = torchvision.datasets.CIFAR10(root=paths.raw_cifar10_dir(), train=False, download=True)\\n train_images = numpy.array(trainset.data)\\n train_labels = numpy.array(trainset.targets)\\n test_images = numpy.array(testset.data)\\n test_labels = numpy.array(testset.targets)\\n\\n assert numpy.max(train_images) == 255\\n\\n train_images = train_images/255.\\n test_images = test_images/255.\\n\\n utils.write_hdf5(paths.cifar10_train_images_file(), train_images.astype(numpy.float32))\\n log('wrote %s' % paths.cifar10_train_images_file())\\n utils.write_hdf5(paths.cifar10_test_images_file(), test_images.astype(numpy.float32))\\n log('wrote %s' % paths.cifar10_test_images_file())\\n utils.write_hdf5(paths.cifar10_train_labels_file(), train_labels.reshape(-1, 1).astype(numpy.int))\\n log('wrote %s' % paths.cifar10_train_labels_file())\\n utils.write_hdf5(paths.cifar10_test_labels_file(), test_labels.reshape(-1, 1).astype(numpy.int))\\n log('wrote %s' % paths.cifar10_test_labels_file())\",\n \"def load_cifar10_data(img_rows, img_cols):\\n\\n # Load cifar10 training and validation sets\\n (X_train, Y_train), (X_valid, Y_valid) = cifar10.load_data()\\n\\n # Resize training images\\n X_train = np.array([cv2.resize(img, (img_rows, \\\\\\n img_cols)) for img in X_train[:, :, :, :]])\\n\\n X_valid = np.array([cv2.resize(img, (img_rows, \\\\\\n img_cols)) for img in X_valid[:, :, :, :]])\\n\\n # Check the data type of X_train or X_valid\\n for each in X_train:\\n print(type(each))\\n\\n # Transform targets to keras compatible format\\n Y_train = np_utils.to_categorical(Y_train, num_classes)\\n Y_valid = np_utils.to_categorical(Y_valid, num_classes)\\n\\n X_train = X_train.astype('float32')\\n X_valid = X_valid.astype('float32')\\n\\n # Data normalization\\n X_train = X_train / 255.0\\n X_valid = X_valid / 255.0\\n\\n return X_train, Y_train, X_valid, Y_valid\",\n \"def load_cifar(hparams):\\n all_labels = []\\n\\n total_batches_to_load = 5\\n assert hparams.train_size + hparams.validation_size <= 50000\\n if hparams.eval_test:\\n total_batches_to_load += 1\\n # Determine how many images we have loaded\\n total_dataset_size = 50000\\n train_dataset_size = total_dataset_size\\n if hparams.eval_test:\\n total_dataset_size += 10000\\n\\n if hparams.dataset == 'cifar10':\\n all_images = []\\n elif hparams.dataset == 'cifar100':\\n all_images = np.empty((1, 50000, 3072), dtype=np.uint8)\\n if hparams.eval_test:\\n test_data = np.empty((1, 10000, 3072), dtype=np.uint8)\\n if hparams.dataset == 'cifar10':\\n datafiles = [\\n 'data_batch_1', 'data_batch_2', 'data_batch_3', 'data_batch_4',\\n 'data_batch_5']\\n\\n if hparams.eval_test:\\n datafiles.append('test_batch')\\n num_classes = 10\\n elif hparams.dataset == 'cifar100':\\n datafiles = ['train']\\n if hparams.eval_test:\\n datafiles.append('test')\\n num_classes = 100\\n else:\\n raise NotImplementedError('Unimplemented dataset: ', hparams.dataset)\\n if hparams.dataset != 'test':\\n for file_num, f in enumerate(datafiles):\\n d = unpickle(os.path.join(hparams.data_path, f))\\n if hparams.dataset == 'cifar10':\\n labels = np.array(d['labels'])\\n else:\\n labels = np.array(d['fine_labels'])\\n if f == 'test':\\n test_data[0] = copy.deepcopy(d['data'])\\n if hparams.dataset == 'cifar10':\\n all_images.append(test_data)\\n else:\\n all_images = np.concatenate([all_images, test_data], axis=1)\\n else:\\n if hparams.dataset == 'cifar10':\\n all_images.append(copy.deepcopy(d['data']))\\n else:\\n all_images[file_num] = copy.deepcopy(d['data'])\\n nsamples = len(labels)\\n for idx in range(nsamples):\\n all_labels.append(labels[idx])\\n if hparams.dataset == 'cifar10':\\n all_images = np.concatenate(all_images, axis=0)\\n all_images = all_images.reshape(-1, 3072)\\n all_images = all_images.reshape(-1, 3, 32, 32) # pylint: disable=too-many-function-args\\n all_images = all_images.transpose(0, 2, 3, 1).copy()\\n all_images = all_images / 255.0\\n mean = augmentation_transforms.MEANS\\n std = augmentation_transforms.STDS\\n tf.logging.info('mean:{} std: {}'.format(mean, std))\\n all_images = (all_images - mean) / std\\n all_labels = np.eye(num_classes)[np.array(all_labels, dtype=np.int32)]\\n\\n assert len(all_images) == len(all_labels)\\n tf.logging.info(\\n 'In CIFAR10 loader, number of images: {}'.format(len(all_images)))\\n\\n extra_test_images = None\\n extra_test_labels = None\\n if hparams.extra_dataset == 'cifar10_1':\\n extra_test_ds = tfds.as_numpy(\\n tfds.load('cifar10_1', split='test', batch_size=-1))\\n extra_test_images = ((extra_test_ds['image'] / 255.0) - mean) / std\\n extra_test_labels = np.eye(num_classes)[np.array(\\n extra_test_ds['label'], dtype=np.int32)]\\n\\n # Break off test data\\n if hparams.eval_test:\\n test_images = all_images[train_dataset_size:]\\n test_labels = all_labels[train_dataset_size:]\\n else:\\n test_images = []\\n test_labels = []\\n all_images = all_images[:train_dataset_size]\\n all_labels = all_labels[:train_dataset_size]\\n return all_images, all_labels, test_images, test_labels, extra_test_images, extra_test_labels\",\n \"def _build_datasets(self):\\n self._build_datasets_sis3302()\\n self._build_datasets_sis3305()\",\n \"def make_dataset():\\n digits = load_digits(n_class=2)\\n df = pd.DataFrame(np.column_stack((digits.data, digits.target)))\\n df = df.rename(columns={64: 'Class'})\\n df.loc[df.loc[:, 'Class'] == 0, 'Class'] = -1\\n df.to_csv('digits_binary.csv', index=False)\",\n \"def create_new_dataset(self,\\n dataset_dir, \\n split,\\n description=\\\"\\\",\\n url=\\\"\\\",\\n version=\\\"\\\",\\n year=0,\\n contributor=\\\"\\\",\\n date_created=\\\"\\\",\\n license_url=\\\"\\\",\\n license_id=0,\\n license_name=\\\"\\\"\\n ):\\n\\n self.dataset_dir = os.path.abspath(dataset_dir)\\n self.logger.info('Creating an empty COCO dataset at {}'.format(self.dataset_dir))\\n assert self.annotation_file is None, \\\\\\n \\\"COCO dataset is already initialized with the annotation file: {}\\\".format(self.annotation_file)\\n \\n ## Create the dataset directory\\n self.imgs_dir = os.path.join(dataset_dir, split)\\n os.makedirs(self.imgs_dir, exist_ok=True)\\n anns_dir = os.path.join(dataset_dir, 'annotations')\\n os.makedirs(anns_dir, exist_ok=True)\\n\\n self.annotation_file = os.path.join(anns_dir, \\n \\\"instances_{}.json\\\".format(split)) \\n ## Create class members\\n self.catNameToId = {}\\n self.pointclouds = {}\\n self.imgToPc = {}\\n self.dataset = {'annotations':[], 'images':[], 'categories':[], 'pointclouds':[]}\\n self.dataset['info'] = {\\n \\\"description\\\": description,\\n \\\"url\\\": url,\\n \\\"version\\\": version,\\n \\\"year\\\": year,\\n \\\"contributor\\\": contributor,\\n \\\"date_created\\\": date_created}\\n self.dataset['licenses'] = [{\\n \\\"url\\\": license_url,\\n \\\"id\\\": license_id,\\n \\\"name\\\": license_name}]\",\n \"def load_cifar10_batch(directory):\\n with open(directory, 'rb') as fo:\\n datadict = pickle.load(fo, encoding='bytes')\\n X = np.array(datadict[b'data'])\\n Y = np.array(datadict[b'labels'])\\n return X, Y\",\n \"def _download_and_extract_cifar10(self):\\n # Possibly download the dataset\\n if not self.tgz_filename.is_file():\\n print(f'Downloading CIFAR-10 dataset from {self.cifar_url}...', flush=True)\\n Downloader().download_file(url=self.cifar_url, filename=self.tgz_filename)\\n print('\\\\nDone.')\\n\\n # Extract downloaded archive\\n print(f'Extracting {self.tgz_filename} to {self.dataset_root}...', flush=True)\\n Extractor().extract(self.tgz_filename, extract_path=self.dataset_root)\\n print('Done.')\\n\\n # Move all files into the chosen dataset root (up one directory)\\n [f.rename(f.absolute().parents[1] / f.name) for f in self.dataset_root.glob('*/*')]\\n Path(self.dataset_root / 'cifar-10-batches-py').rmdir() # remove inner directory\\n\\n # Finally remove the archive\\n self.tgz_filename.unlink()\",\n \"def makecldf(args):\\n with_dataset(args, Dataset._install)\",\n \"def load_cifar10(batch_paths):\\n batches = [load_cifar10_batch(path) for path in batch_paths]\\n data = torch.cat([batch[0] for batch in batches])\\n labels = torch.cat([batch[1] for batch in batches])\\n return data, labels\",\n \"def make_dataset():\\n\\n\\tnumberOfTrials = dataset_params.num_of_samples\\n\\tnumberOfTrials_train = int(numberOfTrials*0.8)\\n\\tnumberOfTrials_test = int(numberOfTrials*0.2)\\n\\n\\tprint(\\\"==================================================\\\")\\n\\tprint(\\\"1. Generating Train images ......\\\")\\n\\tprint(\\\"\\\\nTrain image per variation\\\", numberOfTrials_train)\\n\\tmakeDataset(numberOfTrials_train, \\\"train\\\")\\n\\n\\tprint(\\\"==================================================\\\")\\n\\tprint(\\\"2. Generating Test images ......\\\")\\n\\tprint(\\\"\\\\nTest image per variation\\\", numberOfTrials_test)\\n\\tmakeDataset(numberOfTrials_test, \\\"test\\\")\\n\\n\\tprint(\\\"==================================================\\\")\\n\\tprint(\\\"Done!!!\\\")\",\n \"def _make_data(self):\\n pdf_datasets_all = make_pdf_datasets(self.pdf_list, self.xlims, self.ylims, self.tlims, self.dims, 9)\\n self.pdf_dataset = np.concatenate(pdf_datasets_all, axis = 0)\\n self.PDE_dataset = make_PDE_dataset(self.num_collocation, self.xlims, self.ylims, self.tlims, self.dims)\\n self.BC_dataset = make_BC_dataset(self.num_BC, self.xlims, self.ylims, self.tlims, self.dims)\",\n \"def load_ccs10():\\n ccs10 = pd.read_csv(pkg_resources.resource_filename(__name__,'ccs_dx_icd10cm_2019_1.csv'))\\n ccs10.columns=[i.strip('\\\\'') for i in ccs10.columns]\\n for col in ccs10.columns:\\n ccs10.loc[:,col]=ccs10[col].str.strip('\\\\'')\\n ccs10 = ccs10.replace(r'^\\\\s*$', np.nan, regex=True)\\n ccs10.loc[:,'ICD-10-CM CODE'] = ccs10['ICD-10-CM CODE'].str.replace(' ','')\\n ccs10=ccs10.iloc[:,0:4]\\n return ccs10\",\n \"def __init__(self,\\n base_dataset='cifar10',\\n take_amount=None,\\n take_amount_seed=13,\\n add_svhn_extra=False,\\n aux_data_filename=None,\\n add_aux_labels=True,\\n aux_take_amount=None,\\n train=False,\\n **kwargs):\\n\\n if base_dataset == 'cifar10':\\n self.dataset = datasets.CIFAR10(train=train, **kwargs)\\n else:\\n raise ValueError('Dataset %s not supported' % base_dataset)\\n self.base_dataset = base_dataset\\n self.train = train\\n\\n if self.train:\\n if take_amount is not None:\\n rng_state = np.random.get_state()\\n np.random.seed(take_amount_seed)\\n take_inds = np.random.choice(len(self.sup_indices),\\n take_amount, replace=False)\\n np.random.set_state(rng_state)\\n\\n logger = logging.getLogger()\\n logger.info('Randomly taking only %d/%d examples from training'\\n ' set, seed=%d, indices=%s',\\n take_amount, len(self.sup_indices),\\n take_amount_seed, take_inds)\\n self.targets = self.targets[take_inds]\\n self.data = self.data[take_inds]\\n\\n self.sup_indices = list(range(len(self.targets)))\\n self.unsup_indices = []\\n\\n if aux_data_filename is not None:\\n aux_path = aux_data_filename\\n print(\\\"Loading data from %s\\\" % aux_path)\\n with open(aux_path, 'rb') as f:\\n aux = pickle.load(f)\\n aux_data = aux['data']\\n aux_targets = aux['extrapolated_targets']\\n orig_len = len(self.data)\\n\\n if aux_take_amount is not None:\\n rng_state = np.random.get_state()\\n np.random.seed(take_amount_seed)\\n take_inds = np.random.choice(len(aux_data),\\n aux_take_amount, replace=False)\\n np.random.set_state(rng_state)\\n\\n logger = logging.getLogger()\\n logger.info(\\n 'Randomly taking only %d/%d examples from aux data'\\n ' set, seed=%d, indices=%s',\\n aux_take_amount, len(aux_data),\\n take_amount_seed, take_inds)\\n aux_data = aux_data[take_inds]\\n aux_targets = aux_targets[take_inds]\\n\\n self.data = np.concatenate((self.data, aux_data), axis=0)\\n\\n if not add_aux_labels:\\n self.targets.extend([-1] * len(aux_data))\\n else:\\n self.targets.extend(aux_targets)\\n # note that we use unsup indices to track the labeled datapoints\\n # whose labels are \\\"fake\\\"\\n self.unsup_indices.extend(\\n range(orig_len, orig_len+len(aux_data)))\\n\\n logger = logging.getLogger()\\n logger.info(\\\"Training set\\\")\\n logger.info(\\\"Number of training samples: %d\\\", len(self.targets))\\n logger.info(\\\"Number of supervised samples: %d\\\",\\n len(self.sup_indices))\\n logger.info(\\\"Number of unsup samples: %d\\\", len(self.unsup_indices))\\n logger.info(\\\"Label (and pseudo-label) histogram: %s\\\",\\n tuple(\\n zip(*np.unique(self.targets, return_counts=True))))\\n logger.info(\\\"Shape of training data: %s\\\", np.shape(self.data))\\n\\n # Test set\\n else:\\n self.sup_indices = list(range(len(self.targets)))\\n self.unsup_indices = []\\n\\n logger = logging.getLogger()\\n logger.info(\\\"Test set\\\")\\n logger.info(\\\"Number of samples: %d\\\", len(self.targets))\\n logger.info(\\\"Label histogram: %s\\\",\\n tuple(\\n zip(*np.unique(self.targets, return_counts=True))))\\n logger.info(\\\"Shape of data: %s\\\", np.shape(self.data))\",\n \"def prepare_train_coco_data(args):\\n image_dir, annotation_file, data_dir = args.train_coco_image_dir, args.train_coco_annotation_file, args.train_coco_data_dir\\n batch_size = args.batch_size\\n basic_model = args.basic_model\\n num_roi = args.num_roi\\n\\n coco = COCO(annotation_file)\\n\\n img_ids = list(coco.imgToAnns.keys())\\n img_files = []\\n img_heights = []\\n img_widths = []\\n anchor_files = []\\n gt_classes = []\\n gt_bboxes = []\\n\\n for img_id in img_ids:\\n img_files.append(os.path.join(image_dir, coco.imgs[img_id]['file_name'])) \\n img_heights.append(coco.imgs[img_id]['height']) \\n img_widths.append(coco.imgs[img_id]['width']) \\n anchor_files.append(os.path.join(data_dir, os.path.splitext(coco.imgs[img_id]['file_name'])[0]+'_'+basic_model+'_anchor.npz')) \\n\\n classes = [] \\n bboxes = [] \\n for ann in coco.imgToAnns[img_id]: \\n classes.append(coco_category_to_class[ann['category_id']]) \\n bboxes.append([ann['bbox'][1], ann['bbox'][0], ann['bbox'][3]+1, ann['bbox'][2]+1]) \\n\\n gt_classes.append(classes) \\n gt_bboxes.append(bboxes) \\n \\n print(\\\"Building the training dataset...\\\")\\n dataset = DataSet(img_ids, img_files, img_heights, img_widths, batch_size, anchor_files, gt_classes, gt_bboxes, True, True)\\n print(\\\"Dataset built.\\\")\\n return coco, dataset\",\n \"def test_basic_cdata(self):\\n data = array([[1, 0, 0], [0, 1, 0]])\\n cdata = CData(data)\\n self.assertEqual(cdata.num_features, 3)\\n self.assertEqual(cdata.num_samples, 2)\",\n \"def get_data():\\n transform = Compose([paddle.vision.Resize(32),\\n Normalize(mean=[127.5], std=[127.5], data_format='CHW'),\\n paddle.vision.transforms.Transpose()])\\n train_data = paddle.vision.datasets.Cifar10(mode='train', transform=transform)\\n l = len(train_data)\\n return paddle.io.random_split(train_data, [l // 2, l - l // 2])\",\n \"def dataset(request):\\n X, y = make_classification(\\n n_samples=700, n_features=10, n_informative=8, n_redundant=2,\\n n_classes=2, n_clusters_per_class=2, random_state=6483\\n )\\n\\n request.cls.dataset = Dataset(X, y)\",\n \"def build_dataset(self):\\n print(\\\"reading data of images currently , please wait......\\\")\\n x_train, y_train, _ = get_images(self.train_directory)\\n x_test, y_test, _ = get_images(self.test_directory)\\n x_train, y_train = image_subset(self.num_classes, x_train, y_train)\\n x_test, y_test = image_subset(self.num_classes, x_test, y_test)\\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n self.x_train = x_train / 255\\n self.x_test = x_test / 255\\n self.y_train = utils.to_categorical(y_train, self.num_classes)\\n self.y_test = utils.to_categorical(y_test, self.num_classes)\",\n \"def creation_data_sets(quality, dataset, test_case=False):\\n current_path = Path.cwd()\\n if dataset == 0:\\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\\n del y_train, y_test\\n train_path = current_path.joinpath(\\\"Mnist_{}\\\".format(quality))\\n test_path = current_path.joinpath(\\\"Mnist_{}_test\\\".format(quality))\\n else:\\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\\n del y_train, y_test\\n train_path = current_path.joinpath(\\\"Cifar-10_{}\\\".format(quality))\\n test_path = current_path.joinpath(\\\"Cifar-10_{}_test\\\".format(quality))\\n\\n create_directories(train_path, test_path)\\n convert(train_path, x_train, dataset, quality, test_case)\\n convert(test_path, x_test, dataset, quality, test_case)\",\n \"def generate_dataset():\\n if not os.path.exists(\\\"../data/COVID-19/COVID-19.npy\\\"):\\n print(\\\"Processing Training Data.\\\")\\n training_data = get_training_data('../data/COVID-19/train')\\n print(\\\"Processing Test Data.\\\")\\n test_data = get_training_data('../data/COVID-19/test')\\n\\n x_train, y_train, x_test, y_test = [], [], [], []\\n\\n for feature, label in training_data:\\n x_train.append(feature)\\n y_train.append(label)\\n\\n for feature, label in test_data:\\n x_test.append(feature)\\n y_test.append(label)\\n\\n # Normalize the data\\n x_train = np.array(x_train) / 255\\n x_test = np.array(x_test) / 255\\n\\n # resize data for deep learning\\n x_train = x_train.reshape(-1, 3, img_size, img_size)\\n y_train = np.array(y_train)\\n x_test = x_test.reshape(-1, 3, img_size, img_size)\\n y_test = np.array(y_test)\\n\\n # With data augmentation to prevent overfitting and handling the imbalance in dataset\\n dataset = {\\\"x_train\\\": x_train, \\\"y_train\\\": y_train, \\\"x_test\\\": x_test, \\\"y_test\\\": y_test}\\n np.save(\\\"../data/COVID-19/COVID-19.npy\\\", dataset)\\n else:\\n dataset = np.load(\\\"../data/COVID-19/COVID-19.npy\\\", allow_pickle=True).item()\\n x_train, y_train, x_test, y_test = dataset[\\\"x_train\\\"], dataset[\\\"y_train\\\"], dataset[\\\"x_test\\\"], dataset[\\\"y_test\\\"]\\n\\n x_train_tensor = torch.from_numpy(x_train)\\n x_train_tensor = x_train_tensor.type(torch.FloatTensor)\\n y_train_tensor = torch.from_numpy(y_train)\\n y_train_tensor = y_train_tensor.type(torch.LongTensor)\\n x_test_tensor = torch.from_numpy(x_test)\\n x_test_tensor = x_test_tensor.type(torch.FloatTensor)\\n y_test_tensor = torch.from_numpy(y_test)\\n y_test_tensor = y_test_tensor.type(torch.LongTensor)\\n\\n train_dataset = TensorDataset(x_train_tensor, y_train_tensor)\\n test_dataset = TensorDataset(x_test_tensor, y_test_tensor)\\n\\n return train_dataset, test_dataset\",\n \"def load_CIFAR_batch(filename):\\r\\n with open(filename, 'rb')as f:\\r\\n datadict = p.load(f)\\r\\n \\r\\n X = datadict['data']\\r\\n Y = datadict['labels']\\r\\n \\r\\n print X.shape\\r\\n X = X.reshape(X.shape[0], SHAPE[0], SHAPE[1], SHAPE[2])\\r\\n Y = np.array(Y)\\r\\n return X, Y\",\n \"def load_cifar10(directory, normalize=True):\\n training_data = []\\n training_labels = []\\n for i in range(1, 6):\\n try:\\n d = unpickle(directory + f\\\"/data_batch_{i}\\\")\\n except FileNotFoundError:\\n raise Exception(f\\\"File 'data_batch_{i}' is not found in the specified directory '{directory}'.\\\")\\n training_data.append(d[b\\\"data\\\"])\\n training_labels.append(d[b\\\"labels\\\"])\\n training_data = np.vstack(training_data)\\n training_data = np.reshape(training_data, newshape=(-1, 3, 32, 32))\\n training_labels = np.concatenate(training_labels)\\n training_labels = np.array(list(map(lambda hot: one_hot(10, hot), training_labels)))\\n\\n try:\\n test = unpickle(directory + \\\"/test_batch\\\")\\n except FileNotFoundError:\\n raise Exception(f\\\"File 'test_batch' is not found in the specified directory '{directory}'.\\\")\\n test_data = np.reshape(test[b\\\"data\\\"], newshape=(-1, 3, 32, 32))\\n test_labels = np.array(list(map(lambda hot: one_hot(10, hot), test[b\\\"labels\\\"])))\\n\\n try:\\n meta = unpickle(directory + \\\"/batches.meta\\\")\\n except FileNotFoundError:\\n raise Exception(f\\\"File 'batches.meta' is not found in the specified directory '{directory}'.\\\")\\n label_names = meta[b\\\"label_names\\\"]\\n label_names = list(map(lambda x: x.decode(\\\"utf-8\\\"), label_names))\\n\\n if normalize:\\n training_data = training_data / 255\\n test_data = test_data / 255\\n\\n return training_data, training_labels, test_data, test_labels, label_names\",\n \"def create_weka_mfcc_13():\\n global ARGS\\n\\n ## ten thu muc can trich chon vector dac trung (RLS, LMS, NLMS, Kalman, Non)\\n name = '';\\n fout = open('weka/MFCC78_TUNNING_{}_dataset.arff'.format(name), 'w')\\n fout.write('@RELATION {}_dataset\\\\n\\\\n'.format(name))\\n\\n fout.write('@ATTRIBUTE MEAN_MFCC1\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC2\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC3\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC4\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC5\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC6\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC7\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC8\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC9\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC10\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC11\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC12\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCC13\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD1\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD2\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD3\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD4\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD5\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD6\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD7\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD8\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD9\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD10\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD11\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD12\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCD13\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD1\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD2\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD3\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD4\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD5\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD6\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD7\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD8\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD9\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD10\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD11\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD12\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE MEAN_MFCCDD13\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC1\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC2\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC3\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC4\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC5\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC6\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC7\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC8\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC9\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC10\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC11\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC12\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCC13\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD1\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD2\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD3\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD4\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD5\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD6\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD7\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD8\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD9\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD10\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD11\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD12\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCD13\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD1\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD2\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD3\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD4\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD5\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD6\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD7\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD8\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD9\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD10\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD11\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD12\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE STD_MFCCDD13\\tREAL\\\\n')\\n fout.write('@ATTRIBUTE class \\t{'+ARGS.labels+'}\\\\n\\\\n')\\n \\n fout.write('@DATA\\\\n')\\n\\n ## cua so\\n windowing = Windowing(type='hamming',\\n size=1104,\\n zeroPhase=False)\\n \\n ## quang pho\\n spectrum = Spectrum(size=1104)\\n\\n ##khoi tao MFCC\\n mfcc = MFCC(highFrequencyBound=4000, ## gioi han tren cua tan so\\n inputSize=201, \\t\\t\\t ## kich thuoc pho dau vao\\n lowFrequencyBound=0,\\t ## gioi han duoi cua tan so\\n numberBands=40,\\t\\t\\t ## so luong cac dai Mels trong bo loc\\n numberCoefficients=13, ## so luong dau ra cac he so Mel\\n sampleRate=16000)\\t\\t ## tan so lay mau\\n\\n for label in ARGS.labels.split(','): ## duyet cac thu muc giong voi ten nhan\\n\\n ## dia chi thu muc\\n dir = os.path.join(ARGS.dir, label)\\n\\n logging.info('Access folder <{}>'.format(dir))\\n\\n for file in sorted(os.listdir(dir)):\\n\\n \\t## duyet cac file .wav\\n if file.endswith('.wav'):\\n logging.info('Process <{}>'.format(file))\\n path = os.path.join(dir, file)\\n \\n ## doc file am thanh\\n loader = MonoLoader(filename=path, sampleRate=ARGS.sampleRate)\\n audio = loader()\\n cnt = 0\\n\\n for window in FrameGenerator(audio, \\n frameSize=ARGS.window_length*ARGS.sampleRate/1000, \\n hopSize=ARGS.window_stride*ARGS.sampleRate/1000, \\n startFromZero=True):\\n mfccs = []\\n for frame in FrameGenerator(window, \\n frameSize=ARGS.frame_length*ARGS.sampleRate/1000, \\n hopSize=ARGS.frame_stride*ARGS.sampleRate/1000, \\n startFromZero=True):\\n s = spectrum(windowing(frame))\\n\\n _, m = mfcc(s)\\n\\n m_delta = librosa.feature.delta(m, order=1) ## dao ham bac 1\\n m_delta_delta = librosa.feature.delta(m, order=2) ## dao ham bac 2\\n\\n m_all = np.concatenate((m, m_delta, m_delta_delta), axis=0) ## them vao chuoi\\n mfccs.append(m_all)\\n mfccs = np.array(mfccs)\\n mfccs_mean = np.mean(mfccs, axis=0)\\n mfccs_std = np.std(mfccs, axis=0)\\n feat = np.concatenate((mfccs_mean, mfccs_std), axis=0).tolist()\\n str_feat = [str(x) for x in feat]\\n line = ','.join(str_feat)+','+label\\n fout.write(line+'\\\\n')\\n cnt = cnt+1\\n logging.info('{} samples'.format(cnt))\",\n \"def get_coco_dataset():\\n ds = AttrDict()\\n # classes = [\\n # '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',\\n # 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',\\n # 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',\\n # 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',\\n # 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',\\n # 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',\\n # 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass',\\n # 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',\\n # 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',\\n # 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',\\n # 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',\\n # 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',\\n # 'scissors', 'teddy bear', 'hair drier', 'toothbrush'\\n # ]\\n # classes = ['__background__', 'lane']\\n #\\n base_classes = [\\n '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',\\n 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',\\n 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',\\n 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',\\n 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',\\n 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',\\n 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass',\\n 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',\\n 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',\\n 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',\\n 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',\\n 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',\\n 'scissors', 'teddy bear', 'hair drier', 'toothbrush'\\n ]\\n classes = ['__background__',\\n 'guard rail',\\n # 'car',\\n 'dashed',\\n 'solid',\\n 'solid solid',\\n 'dashed dashed',\\n 'dashed-solid',\\n 'solid-dashed',\\n 'yellow dashed',\\n 'yellow solid',\\n 'yellow solid solid',\\n 'yellow dashed dashed',\\n 'yellow dashed-solid',\\n 'yellow solid-dashed',\\n 'boundary',\\n 'fork_line',\\n 'fork_edge',\\n 'arrow_s',\\n 'arrow_r',\\n 'arrow_l',\\n 'arrow_lr',\\n 'arrow_inclined_r',\\n 'arrow_r_s',\\n 'arrow_l_s',\\n 'sidewalk',\\n 'handrail'\\n ]\\n base_classes.extend(classes[1:])\\n classes = base_classes\\n ds.classes = {i: name for i, name in enumerate(classes)}\\n return ds\",\n \"def __init__(self, data_path, batch_size, **kwargs):\\n super().__init__(batch_size, **kwargs)\\n\\n _, num_classes, X_train, y_train, X_val, y_val = load_cifar10_shard(shard_num=data_path, **kwargs)\\n\\n self.training_data_size = len(X_train)\\n self.validation_data_size = len(X_val)\\n self.num_classes = num_classes\\n self.train_loader = self.create_loader(X=X_train, y=y_train, shuffle=True)\\n self.val_loader = self.create_loader(X=X_val, y=y_val, shuffle=False)\",\n \"def create_forecast_dataset(self):\\n pass\",\n \"def create_random_dataset(self, num_of_data, cluster_number, dimension):\\n x, y = make_blobs(n_samples=num_of_data, centers=cluster_number, n_features=dimension)\\n self._dimension = dimension\\n self._num_classes = cluster_number\\n self._data = x\\n self._y_star = y\",\n \"def get_dataset(self):\\n\\n trainset = datasets.CIFAR100('datasets/CIFAR100/train/', train=True, transform=self.train_transforms,\\n target_transform=None, download=True)\\n valset = datasets.CIFAR100('datasets/CIFAR100/test/', train=False, transform=self.val_transforms,\\n target_transform=None, download=True)\\n\\n return trainset, valset\",\n \"def load_cifa_10():\\n train_set_x = np.ndarray([ 50000, 3072 ])\\n train_set_y = np.ndarray( [50000] )\\n\\n batch_size = 10000\\n for i in xrange(5):\\n batch = open( datapath + \\\"data_batch_\\\"+str(i+1), 'rb')\\n map = cPickle.load( batch )\\n batch.close()\\n train_set_x[ i*batch_size : (i+1)*batch_size , : ] = np.asarray( map[ 'data' ], dtype = 'float32' )\\n train_set_y[ i*batch_size : (i+1)*batch_size ] = np.asarray( map[ 'labels' ], dtype = 'float32' )\\n\\n test_file = open( datapath + 'test_batch', 'rb')\\n map = cPickle.load( test_file )\\n test_file.close()\\n \\n test_set_x = np.asarray( map['data'], dtype = 'float32' )\\n test_set_y = np.asarray( map['labels'], dtype = 'float32' )\\n \\n\\n return train_set_x, train_set_y, test_set_x, test_set_y\",\n \"def create_data(data_size,heme, nucleotide, control, steroid,data_total,path_to_data):\\n\\n os.chdir(path_to_data)\\n\\n x_array = np.zeros(shape = (data_size,14,32,32,32))\\n\\n y_array = np.zeros(shape = data_size)\\n\\n print(\\\"data size = \\\", data_size)\\n\\n #training set :\\n\\n file_count = 0\\n\\n for file in data_total:\\n\\n y_array[file_count]= find_class(str(file), heme, nucleotide, control, steroid)\\n\\n x_array[file_count] = np.load(str(file+\\\".npy\\\"))\\n\\n file_count+=1\\n\\n\\n return (x_array, y_array)\",\n \"def create_random_dataset(self, num_of_data, cluster_number, dimension):\\n x, y = make_blobs(n_samples=num_of_data, centers=cluster_number, n_features=dimension)\\n self._dimension = dimension\\n self._data = x\\n self._y_star = y\",\n \"def load_CIFAR_batch(filename):\\n with open(filename, 'rb')as f:\\n datadict = p.load(f, encoding='iso-8859-1')\\n X = datadict['data']\\n Y = datadict['labels']\\n X = X.reshape(10000, 3, 32, 32)\\n Y = np.array(Y)\\n return X, Y\",\n \"def load_CIFAR_batch(filename):\\r\\n with open(filename, 'rb') as f:\\r\\n datadict = pickle.load(f, encoding='latin1')\\r\\n X = datadict['data']\\r\\n Y = datadict['labels']\\r\\n X = X.reshape(10000, 3, 32, 32).transpose(0,2,3,1).astype(\\\"float\\\")\\r\\n Y = np.array(Y)\\r\\n return X, Y\",\n \"def load_cifar10_shard(shard_num, nb_collaborators, categorical=True, channels_last=False, **kwargs): \\n img_rows, img_cols, img_channel = 32, 32, 3\\n num_classes = 10 \\n \\n (X_train, y_train), (X_test, y_test) = _load_raw_datashards(shard_num, nb_collaborators)\\n \\n if channels_last: \\n X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, img_channel) \\n X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, img_channel) \\n input_shape = (img_rows, img_cols, img_channel) \\n else: \\n X_train = X_train.reshape(X_train.shape[0], img_channel, img_rows, img_cols) \\n X_test = X_test.reshape(X_test.shape[0], img_channel, img_rows, img_cols) \\n input_shape = (img_channel, img_rows, img_cols) \\n \\n X_train = X_train.astype('float32') \\n X_test = X_test.astype('float32') \\n X_train /= 255 \\n X_test /= 255\\n \\n if categorical: \\n def to_categorical(x, num_classes):\\n \\\"\\\"\\\"one-hot encodes an array\\\"\\\"\\\"\\n return np.eye(num_classes, dtype='uint8')[x]\\n # convert class vectors to binary class matrices \\n y_train = to_categorical(y_train, num_classes) \\n y_test = to_categorical(y_test, num_classes) \\n \\n return input_shape, num_classes, X_train, y_train, X_test, y_test\",\n \"def random_cls_dataset(request):\\n set_seed()\\n shape = request.param.get('shape', 10)\\n size = request.param.get('size', 100)\\n X, Y = make_classification(n_samples=2*size, n_features=shape, n_classes=10, n_informative=10, n_redundant=0)\\n X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5)\\n Y_train, Y_test = Y_train.astype(np.int64), Y_test.astype(np.int64)\\n return (X_train, Y_train), (X_test, Y_test)\",\n \"def __init__(self, **kwargs):\\n super(CIFAR10Classifier, self).__init__() #pylint: disable=super-with-arguments\\n self.model_conv = models.resnet50(pretrained=True)\\n for param in self.model_conv.parameters():\\n param.requires_grad = False\\n num_ftrs = self.model_conv.fc.in_features\\n num_classes = 10\\n self.model_conv.fc = nn.Linear(num_ftrs, num_classes)\\n\\n self.scheduler = None\\n self.optimizer = None\\n self.args = kwargs\\n\\n self.train_acc = Accuracy()\\n self.val_acc = Accuracy()\\n self.test_acc = Accuracy()\\n\\n self.preds = []\\n self.target = []\\n self.example_input_array = torch.rand((1, 3, 64, 64))\",\n \"def run(dataset_dir):\\n if not tf.gfile.Exists(dataset_dir):\\n tf.gfile.MakeDirs(dataset_dir)\\n\\n training_filename = _get_output_filename(dataset_dir, 'train')\\n testing_filename = _get_output_filename(dataset_dir, 'test')\\n\\n if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):\\n print('Dataset files already exist. Exiting without re-creating them.')\\n return\\n\\n dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)\\n\\n # First, process the training data:\\n with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:\\n offset = 0\\n for i in range(_NUM_TRAIN_FILES):\\n filename = os.path.join(dataset_dir,\\n 'cifar-100-python',\\n 'train')\\n offset = _add_to_tfrecord(filename, tfrecord_writer, offset)\\n\\n # Next, process the testing data:\\n with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:\\n filename = os.path.join(dataset_dir,\\n 'cifar-100-python',\\n 'test')\\n _add_to_tfrecord(filename, tfrecord_writer)\\n\\n # Finally, write the labels file:\\n labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))\\n dataset_utils.write_label_file(labels_to_class_names, dataset_dir)\\n\\n _clean_up_temporary_files(dataset_dir)\\n print('\\\\nFinished converting the Cifar100 dataset!')\",\n \"def __init__(self, image_set, root_path, data_path, category='all', task='detection'):\\n super(coco, self).__init__('COCO', image_set, root_path, data_path)\\n self.root_path = root_path\\n self.data_path = data_path\\n self.category = category\\n self.task = task\\n self.name = self.name + '_' + category\\n # deal with data name\\n view_map = {'minival2014': 'val2014',\\n 'valminusminival2014': 'val2014'}\\n self.data_name = view_map[image_set] if image_set in view_map else image_set\",\n \"def cifar100(path, label_mode='fine'):\\n def _load_batch(filepath, label_key):\\n with open(filepath, 'rb') as f:\\n if sys.version_info < (3,):\\n d = cPickle.load(f)\\n else:\\n d = cPickle.load(f, encoding='bytes')\\n d_decoded = {} # decode utf8\\n for k, v in six.iteritems(d):\\n d_decoded[k.decode('utf8')] = v\\n d = d_decoded\\n images = d['data']\\n labels = d[label_key]\\n images = images.reshape(images.shape[0], 3, 32, 32)\\n labels = np.reshape(labels, len(labels,))\\n return images, labels\\n path = os.path.expanduser(path)\\n directory = 'cifar-100-python'\\n if not os.path.exists(os.path.join(path, directory)):\\n url = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'\\n maybe_download_and_extract(path, url)\\n\\n filepath = os.path.join(path, directory, 'train')\\n x_train, y_train = _load_batch(filepath, label_mode + '_labels')\\n\\n filepath = os.path.join(path, directory, 'test')\\n x_test, y_test = _load_batch(filepath, label_mode + '_labels')\\n return (x_train, y_train), (x_test, y_test)\",\n \"def make_ctd_file():\\n # Get the correct netCDF4 dataset\\n __location__ = os.path.realpath(os.path.join(os.getcwd(),\\n os.path.dirname(__file__),\\n 'output'))\\n nc_file = os.path.join(__location__,'test_BM54.nc')\\n\\n # Be sure to start with the original, unedited CTD data\\n test_ambient.test_from_ctd()\\n\\n # Load the data into a netCDF file\\n nc = Dataset(nc_file, 'a')\\n\\n return nc\",\n \"def load_CIFAR_batch(filename):\\n with open(filename, 'rb') as f:\\n datadict = pickle.load(f, encoding='latin1')\\n X = datadict['data']\\n Y = datadict['labels']\\n X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype(\\\"float64\\\")\\n Y = np.array(Y)\\n return X, Y\",\n \"def load_CIFAR100(batch_dir):\\r\\n ims, coarse_labels, fine_labels = load_CIFAR_batch(batch_dir + '/train')\\r\\n ims_t, c_labels, f_labels = load_CIFAR_batch(batch_dir + '/test')\\r\\n ims = np.concatenate((ims, ims_t))\\r\\n coarse_labels = np.concatenate((coarse_labels, c_labels))\\r\\n fine_labels = np.concatenate((fine_labels, f_labels))\\r\\n return ims, coarse_labels, fine_labels\",\n \"def build():\\n\\tconsole = Console()\\n\\tconsole.clear()\\n\\tconsole.print(BANNER)\\n\\tif not os.path.exists(\\\"dataset.yaml\\\"):\\n\\t\\tclick.clear()\\n\\t\\tconsole.print(\\\"Dataset config file not found\\\\nRun - idt init\\\\n\\\")\\n\\t\\texit(0)\\n\\n\\twith open('dataset.yaml') as f:\\n\\t\\tdata = yaml.load(f, Loader=yaml.FullLoader)\\n\\t\\n\\tclick.clear()\\n\\tconsole.print(\\\"Building [bold blue]{dataset_name}[/bold blue] dataset...\\\\n\\\".format(dataset_name=data['DATASET_NAME']))\\n\\tfor classes in data['CLASSES']:\\n\\t\\tclick.clear()\\n\\t\\tconsole.print('Creating [bold blue]{name} class[/bold blue] \\\\n'.format(name=classes['CLASS_NAME']))\\n\\t\\tsearch_list = classes['SEARCH_KEYWORDS'].split(\\\",\\\")\\n\\t\\tfor keywords in search_list:\\n\\t\\t\\tfactory = SearchEngineFactory(keywords,data['SAMPLES_PER_SEARCH'],classes['CLASS_NAME'],data['RESIZE_METHOD'], data['DATASET_NAME'],data['IMAGE_SIZE'], data['ENGINE'],data['API_KEY'])\\n\\t# Remove corrupt files\\n\\tremove_corrupt(data['DATASET_NAME'])\\n\\n\\t# Create a CSV with dataset info\\n\\tcreate_dataset_csv(data['DATASET_NAME'])\\n\\tclick.clear()\\n\\tconsole.print(\\\"Dataset READY!\\\")\",\n \"def prepare_val_coco_data(args):\\n image_dir, annotation_file = args.val_coco_image_dir, args.val_coco_annotation_file\\n\\n coco = COCO(annotation_file)\\n\\n img_ids = list(coco.imgToAnns.keys())\\n img_files = []\\n img_heights = []\\n img_widths = []\\n\\n for img_id in img_ids:\\n img_files.append(os.path.join(image_dir, coco.imgs[img_id]['file_name']))\\n img_heights.append(coco.imgs[img_id]['height']) \\n img_widths.append(coco.imgs[img_id]['width']) \\n\\n print(\\\"Building the validation dataset...\\\")\\n dataset = DataSet(img_ids, img_files, img_heights, img_widths)\\n print(\\\"Dataset built.\\\")\\n return coco, dataset\",\n \"def produce_init(filename):\\n training_dataset = pd.read_csv(f'../Modified Data/{filename}')\\n test_dataset = pd.read_csv(f'../Raw Data/test.csv')\\n features = list(training_dataset.columns)\\n features.remove('SalePrice')\\n predict_feature = ['SalePrice']\\n\\n # Produce Test Data\\n test_X = test_dataset.loc[:, features]\\n ids_test = test_dataset.loc[:, 'Id']\\n\\n for column in features:\\n if str(training_dataset.loc[:, column].dtype) == 'object':\\n # Initialize encoder\\n labelencoder = LabelEncoder()\\n # Encode Train Data\\n training_dataset.loc[:, column] = training_dataset.loc[:, column].fillna('Missing')\\n training_dataset.loc[:, column] = pd.Series(labelencoder.fit_transform(training_dataset.loc[:, column]))\\n # Encode Test Data\\n test_X.loc[:, column] = test_X.loc[:, column].fillna('Missing')\\n test_X.loc[:, column] = pd.Series(labelencoder.fit_transform(test_X.loc[:, column]))\\n else:\\n # Fix missing values for train data\\n training_dataset.loc[:, column] = training_dataset.loc[:, column].fillna(int(training_dataset.loc[:, column].mean()))\\n # Fix missing values for test data\\n test_X.loc[:, column] = test_X.loc[:, column].fillna(int(test_X.loc[:, column].mean()))\\n\\n return training_dataset, test_X, ids_test\",\n \"def load_CIFAR_batch(filename):\\n with open(filename, 'rb') as f:\\n datadict = load_pickle(f)\\n X = datadict['data']\\n Y = datadict['labels']\\n X = X.reshape(10000,3072)\\n Y = np.array(Y)\\n return X, Y\",\n \"def make_dataset(condition, root, base_path, files_json_path):\\n normalize = transforms.Normalize(\\n mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\\n dataset = ZapposDataset(root, base_path, files_json_path, condition,\\n transform=transforms.Compose([\\n transforms.Scale(112),\\n transforms.CenterCrop(112),\\n transforms.ToTensor(),\\n normalize,\\n ]))\\n return dataset\",\n \"def test_get_random_data_basic(self):\\n cdata = random_data(num_features=2,\\n num_samples=4,\\n labels=None)\\n self.assertEqual(cdata.num_features, 2)\\n self.assertEqual(cdata.num_samples, 4)\",\n \"def Cifar10_preload_and_split(path=None, splits=[0.4, 0.1, 0.25, 0.25], transform=None):\\n\\n if path is None:\\n path = DATASETS_DIR\\n index_file = os.path.join(path, 'cifar10.index.csv')\\n\\n indices = None\\n if os.path.exists(index_file):\\n index_csv = np.loadtxt(index_file)\\n indices = torch.tensor(index_csv)\\n print('Found predefined indexing file {}'.format(index_file))\\n \\n trainset = torchvision.datasets.CIFAR10(path, train=True, transform=transform[0], download=False)\\n testset = torchvision.datasets.CIFAR10(path, train=False, transform=transform[0], download=False)\\n fullset = ConcatDataset([trainset, testset])\\n print('Initializing CIFAR10Dataset splits')\\n \\n # Currently five equal splits\\n dset_size = fullset.cumulative_sizes[-1]\\n int_splits = []\\n for i in range(len(splits)):\\n int_splits.append(int(dset_size * splits[i]))\\n if sum(int_splits) < dset_size:\\n rem = dset_size - sum(int_splits)\\n int_splits[-1] += rem\\n\\n indices, splitsets = dataset_split(fullset, int_splits, indices=indices)\\n\\n if not os.path.exists(index_file):\\n print('No predefined indexing file found, so index permutations saving to {}'.format(index_file))\\n np.savetxt(index_file, indices.numpy(), fmt='%i', delimiter=',')\\n\\n print('Finished splitting data.')\\n\\n return splitsets\",\n \"def get_coco_dataset():\\n ds = AttrDict()\\n classes = [\\n '__background__', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',\\n 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant',\\n 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',\\n 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack',\\n 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',\\n 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',\\n 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass',\\n 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',\\n 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',\\n 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',\\n 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',\\n 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',\\n 'scissors', 'teddy bear', 'hair drier', 'toothbrush'\\n ]\\n ds.classes = {i: name for i, name in enumerate(classes)}\\n return ds\",\n \"def __init__(self, data_array, cui2name):\\n\\t\\tassert isinstance(data_array, xr.DataArray), 'Constructor requires xr.DataArray.'\\n\\n\\t\\tself.data_array = data_array\\n\\t\\tself.data = self.data_array.values\\n\\t\\tself.sources = self.data_array.source.values\\n\\t\\tself.targets = self.data_array.target.values\\n\\t\\tself.metrics = self.data_array.metric.values\\n\\t\\tself.metapaths = self.data_array.metapath.values\\n\\n\\t\\t# Fetch the verbose names\\n\\t\\t#int_rf_df = pd.read_csv('data/impd_cogn_sources.tsv', delimiter='\\\\t')\\n\\t\\t#cui2name = {cui:name for cui, name in zip(int_rf_df.identifier, int_rf_df.name)} \\n\\t\\tself.source_names = np.array([cui2name[source] for source in self.sources])\",\n \"def load_CIFAR_batch(filename):\\r\\n with open(filename, 'rb') as f:\\r\\n datadict = load_pickle(f)\\r\\n X = datadict['data']\\r\\n Y = datadict['labels']\\r\\n X = X.reshape(10000,3072)\\r\\n Y = np.array(Y)\\r\\n return X, Y\",\n \"def __call__(self):\\n\\n dataset = TextOnlyCocoAnnotation()\\n\\n with open(self.path) as read_file:\\n\\n json_loaded = json.load(read_file)\\n\\n for i, value in tqdm(json_loaded['imgs'].items()):\\n image_path = os.path.join(os.path.dirname(self.path), 'train2014',\\n value['file_name'])\\n dataset_type = value['set']\\n\\n if dataset_type not in self.sets:\\n print(dataset_type)\\n continue\\n\\n for annotation_id in json_loaded['imgToAnns'][i]:\\n annotation_value = json_loaded['anns'][str(annotation_id)]\\n word_annotation = self.parse_annotation_instance(annotation_value)\\n dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation)\\n\\n return dataset\",\n \"def create_dataset(df, genre_count, image_count, output_fname):\\n\\n counts = df['Class'].value_counts()\\n filter = list(counts[counts > image_count].index)\\n\\n # filter for genres that appear image_count times or more\\n more_than_img_count = (df[df['Class'].isin(filter)])\\n topn = more_than_img_count.groupby('Class').head(image_count)\\n topn.sort_values(by='Class', inplace=True, ascending=True)\\n topn = topn.head(genre_count*image_count)\\n\\n topn['Path'] = os.getenv('dataset_location') + \\\"/\\\" + topn['Painting']\\n\\n topn.to_csv(os.path.join(os.getenv('dataset_location'), output_fname), sep=';', index=False)\",\n \"def load_cci10():\\n \\n cci10 = pd.read_csv(pkg_resources.resource_filename(__name__,'cci_icd10cm_2019_1.csv'))\\n \\n cci10.columns = [i.strip('\\\\'') for i in cci10.columns]\\n \\n for col in cci10.columns:\\n cci10.loc[:,col] = cci10[col].str.strip('\\\\'')\\n cci10 = cci10.replace(r'^\\\\s*$', np.nan, regex=True)\\n cci10.columns = [i.replace('CHRONIC INDICATOR','CHRONIC') for i in cci10.columns]\\n \\n dict_bodysystem = [\\n ('1','Infectious and parasitic disease'),\\n ('2','Neoplasms'),\\n ('3','Endocrine, nutritional, and metabolic diseases and immunity disorders'),\\n ('4','Diseases of blood and blood-forming organs'),\\n ('5','Mental disorders'),\\n ('6','Diseases of the nervous system and sense organs'),\\n ('7','Diseases of the circulatory system'),\\n ('8','Diseases of the respiratory system'),\\n ('9','Diseases of the digestive system'),\\n ('10','Diseases of the genitourinary system'),\\n ('11','Complications of pregnancy, childbirth, and the puerperium'),\\n ('12','Diseases of the skin and subcutaneous tissue'),\\n ('13','Diseases of the musculoskeletal system'),\\n ('14','Congenital anomalies'),\\n ('15','Certain conditions originating in the perinatal period'),\\n ('16','Symptoms, signs, and ill-defined conditions'),\\n ('17','Injury and poisoning'),\\n ('18','Factors influencing health status and contact with health services'),\\n ]\\n \\n cci10 = cci10.merge(pd.DataFrame(dict_bodysystem,columns=['BODY SYSTEM','BODY SYSTEM DESCRIPTION']),how='left',on='BODY SYSTEM')\\n \\n cci10.loc[:,'ICD-10-CM CODE'] = cci10['ICD-10-CM CODE'].str.replace(' ','')\\n \\n return cci10\",\n \"def createDataset(n, d=100, d_inf=5, is_classification=True, no_signal=False):\\n \\n # making random inputs, outputs\\n X = np.random.normal(0, 1, (3*n, d))\\n y = np.random.normal(0, 1, (3*n, 1))\\n \\n # thresholding y values for classification\\n if is_classification:\\n y = 2.0*((y>0) - 0.5)\\n \\n # making the first d_inf dimensions informative\\n\\n if is_classification:\\n X[:,:d_inf] += y*np.random.normal(1.0, 1.5, X[:,:d_inf].shape)\\n else:\\n snr = 0.05\\n X[:,:d_inf] += snr*y\\n X = zscore(X, axis=0)\\n \\n # if you dont want useful signal, randomize the labels\\n if no_signal:\\n np.random.shuffle(y)\\n\\n # Divide into train/test/holdout pairs\\n outputs = [[X[i::3, :], y[i::3, 0]] for i in range(3)] \\n \\n return outputs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7966689","0.75073844","0.7161493","0.71176726","0.7109075","0.7096102","0.69510275","0.6845723","0.6814827","0.68126905","0.68040884","0.670599","0.66763234","0.6664927","0.65709835","0.656717","0.64492744","0.6426107","0.6410631","0.6406311","0.6405234","0.639947","0.63016933","0.63012904","0.6256072","0.6250758","0.6199727","0.6197304","0.61831105","0.61498904","0.613708","0.61308867","0.6128181","0.6121643","0.61193633","0.60752237","0.60411644","0.6034985","0.6025629","0.60084265","0.59828824","0.5976642","0.5974548","0.5969685","0.59638125","0.59504294","0.5931213","0.59269756","0.5923881","0.5918741","0.5901253","0.59005105","0.5882884","0.5852871","0.5821434","0.5817992","0.58018786","0.58014405","0.5788718","0.5729279","0.5723007","0.57187","0.57124543","0.56955224","0.5694451","0.56924736","0.56907207","0.56617033","0.56508464","0.5633181","0.5631475","0.5619261","0.5606789","0.5596306","0.5592124","0.5578788","0.55747724","0.55496854","0.55315644","0.5525464","0.5522006","0.551907","0.5511789","0.5500591","0.5491507","0.54913086","0.5472965","0.5472651","0.547046","0.54675055","0.5465372","0.54547364","0.54536915","0.54527783","0.54522544","0.54420125","0.5440932","0.54335326","0.54334104","0.5430235"],"string":"[\n \"0.7966689\",\n \"0.75073844\",\n \"0.7161493\",\n \"0.71176726\",\n \"0.7109075\",\n \"0.7096102\",\n \"0.69510275\",\n \"0.6845723\",\n \"0.6814827\",\n \"0.68126905\",\n \"0.68040884\",\n \"0.670599\",\n \"0.66763234\",\n \"0.6664927\",\n \"0.65709835\",\n \"0.656717\",\n \"0.64492744\",\n \"0.6426107\",\n \"0.6410631\",\n \"0.6406311\",\n \"0.6405234\",\n \"0.639947\",\n \"0.63016933\",\n \"0.63012904\",\n \"0.6256072\",\n \"0.6250758\",\n \"0.6199727\",\n \"0.6197304\",\n \"0.61831105\",\n \"0.61498904\",\n \"0.613708\",\n \"0.61308867\",\n \"0.6128181\",\n \"0.6121643\",\n \"0.61193633\",\n \"0.60752237\",\n \"0.60411644\",\n \"0.6034985\",\n \"0.6025629\",\n \"0.60084265\",\n \"0.59828824\",\n \"0.5976642\",\n \"0.5974548\",\n \"0.5969685\",\n \"0.59638125\",\n \"0.59504294\",\n \"0.5931213\",\n \"0.59269756\",\n \"0.5923881\",\n \"0.5918741\",\n \"0.5901253\",\n \"0.59005105\",\n \"0.5882884\",\n \"0.5852871\",\n \"0.5821434\",\n \"0.5817992\",\n \"0.58018786\",\n \"0.58014405\",\n \"0.5788718\",\n \"0.5729279\",\n \"0.5723007\",\n \"0.57187\",\n \"0.57124543\",\n \"0.56955224\",\n \"0.5694451\",\n \"0.56924736\",\n \"0.56907207\",\n \"0.56617033\",\n \"0.56508464\",\n \"0.5633181\",\n \"0.5631475\",\n \"0.5619261\",\n \"0.5606789\",\n \"0.5596306\",\n \"0.5592124\",\n \"0.5578788\",\n \"0.55747724\",\n \"0.55496854\",\n \"0.55315644\",\n \"0.5525464\",\n \"0.5522006\",\n \"0.551907\",\n \"0.5511789\",\n \"0.5500591\",\n \"0.5491507\",\n \"0.54913086\",\n \"0.5472965\",\n \"0.5472651\",\n \"0.547046\",\n \"0.54675055\",\n \"0.5465372\",\n \"0.54547364\",\n \"0.54536915\",\n \"0.54527783\",\n \"0.54522544\",\n \"0.54420125\",\n \"0.5440932\",\n \"0.54335326\",\n \"0.54334104\",\n \"0.5430235\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7870253"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":89296,"cells":{"query":{"kind":"string","value":"If an identical token already exists in dictionary, will merge tokens and cumulate their counts. Checks to see that morphology and pronunciations are identical, otherwise the tokens will not be merged."},"document":{"kind":"string","value":"def AddToken(self, token, merge=False):\n token.SetLangId(self.id_)\n if not merge:\n self.tokens_.append(token)\n else:\n exists = self.MatchToken(token)\n if exists is None:\n self.tokens_.append(token)\n else:\n exists.IncrementCount(token.Count())"},"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 merge_token_counters(\n token_counter1: Dict[str, int], token_counter2: Dict[str, int]\n ) -> Dict[str, int]:\n for token, number in token_counter2.items():\n if token in token_counter1:\n token_counter1[token] += number\n else:\n token_counter1[token] = number\n\n return token_counter1","def word_frequency( tokenized, dic ):\n print( 'computing word frequencies' )\n start = time.time()\n for i, text in enumerate( tokenized ):\n for token in text:\n if token not in dic:\n dic[ token ] = 1\n else:\n dic[ token ] += 1\n if i % 10000 == 0:\n sys.stdout.write( '\\rprocessed : {}/{} reviews in {}s'.format( i, NO_REVIEWS, time.time() - start ) )\n sys.stdout.write( '\\rprocessed : {}/{} reviews in {}s\\n'.format( i, NO_REVIEWS, time.time() - start ) )","def count_words(tokenized_sentences):\r\n \r\n word_counts = {}\r\n \r\n # Loop through each sentence\r\n for sentence in tokenized_sentences: # complete this line\r\n \r\n for token in sentence: # complete this line\r\n\r\n # If the token is not in the dictionary yet, set the count to 1\r\n if token not in word_counts.keys(): # complete this line\r\n word_counts[token] = 1\r\n \r\n # If the token is already in the dictionary, increment the count by 1\r\n else:\r\n word_counts[token] += 1\r\n \r\n return word_counts","def add_to_dict(self, tokens):\n# TODO: ?add normalization of a token?\n for token in tokens:\n if self.embedding_words and (token not in self.embedding_words):\n continue\n self.freq[token] += 1\n if token not in self.tok2ind:\n index = len(self.tok2ind)\n self.tok2ind[token] = index\n self.ind2tok[index] = token","def merge_with(self, other):\n old2new = {}\n for other_id, other_token in iteritems(other):\n if other_token in self.token2id:\n new_id = self.token2id[other_token]\n else:\n new_id = len(self.token2id)\n self.token2id[other_token] = new_id\n self.dfs[new_id] = 0\n old2new[other_id] = new_id\n try:\n self.dfs[new_id] += other.dfs[other_id]\n except Exception:\n # `other` isn't a Dictionary (probably just a dict) => ignore dfs, keep going\n pass\n try:\n self.num_docs += other.num_docs\n self.num_nnz += other.num_nnz\n self.num_pos += other.num_pos\n except Exception:\n pass\n\n import gensim.models\n return gensim.models.VocabTransform(old2new)","def compute_token_count(token_dict):\n return sum([token_dict[key] for key in token_dict.keys()])","def calculate(self, tokens: list):\n vektor_tf_unigram = {}\n\n for token in tokens:\n if token in vektor_tf_unigram:\n vektor_tf_unigram[token] = vektor_tf_unigram[token] + 1\n else:\n vektor_tf_unigram[token] = 1\n\n return vektor_tf_unigram","def merged_tokens(self, i, tokenizer):\n out = []\n counts = []\n last_id = None\n tokens = tokenizer.convert_ids_to_tokens(self.token_ids[i].tolist())\n for token, current_id in zip(tokens[1:-1], self.conll_ids[i]):\n if last_id == current_id.item():\n if token.startswith(\"##\"):\n out[-1] += (token[2:])\n else:\n out[-1] += token\n counts[-1] += 1\n else:\n out.append(token)\n counts.append(1)\n last_id = current_id\n return out, counts","def convert_word_to_count(counter={}, doc=[]):\n for sentence in doc:\n for word in sentence.split():\n if word not in counter:\n counter[word] = 1\n else:\n counter[word] += 1\n return counter","def getTTokens(wordlist, tokens = {}, count = 0, token = TOKEN):\n\n\tif len(wordlist) == 0:\n\t\treturn tokens, count\n\n\tfor i in wordlist:\n\t\tfor x in i:\n\t\t\tif tokens.has_key(x[token]):\n\t\t\t\ttokens[x[token]] += 1\n\t\t\telse:\n\t\t\t\ttokens[x[token]] = 1\n\t\t\tcount += 1\n\treturn tokens, count","def count_terms(self, tokens):\n\n terms = [self.term_match(t) for t in tokens ]\n \n terms = [t for t in terms if t != None]\n\n #print terms\n lf = dict(Counter(terms))\n for k in lf:\n lf[k] /= float(len(tokens))\n #lf[k] = 1 # binarize?\n pass\n return lf","def make_bag_words(document_tokenized):\n bag_words = dict()\n for token in document_tokenized:\n if token in bag_words.keys():\n bag_words[token] += 1\n else:\n bag_words[token] = 1\n return bag_words","def buildDict(self, dict):\n self.all_words = set(dict)\n self.wc_dict = collections.defaultdict(int)\n for w in dict:\n for wc in self.get_wildcards(w):\n self.wc_dict[wc] += 1","def _merge_conllu_subtokens(self, lines: List[str], doc: Doc)-> Doc:\n # identify and process all subtoken spans to prepare attrs for merging\n subtok_spans = []\n for line in lines:\n parts = line.split(\"\\t\")\n id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts\n if \"-\" in id_:\n subtok_start, subtok_end = id_.split(\"-\")\n subtok_span = doc[int(subtok_start) - 1 : int(subtok_end)]\n subtok_spans.append(subtok_span)\n # create merged tag, morph, and lemma values\n tags = []\n morphs = {}\n lemmas = []\n for token in subtok_span:\n tags.append(token.tag_)\n lemmas.append(token.lemma_)\n if token._.merged_morph:\n for feature in token._.merged_morph.split(\"|\"):\n field, values = feature.split(\"=\", 1)\n if field not in morphs:\n morphs[field] = set()\n for value in values.split(\",\"):\n morphs[field].add(value)\n # create merged features for each morph field\n for field, values in morphs.items():\n morphs[field] = field + \"=\" + \",\".join(sorted(values))\n # set the same attrs on all subtok tokens so that whatever head the\n # retokenizer chooses, the final attrs are available on that token\n for token in subtok_span:\n token._.merged_orth = token.orth_\n token._.merged_lemma = \" \".join(lemmas)\n token.tag_ = \"_\".join(tags)\n token._.merged_morph = \"|\".join(sorted(morphs.values()))\n token._.merged_spaceafter = (\n True if subtok_span[-1].whitespace_ else False\n )\n\n with doc.retokenize() as retokenizer:\n for span in subtok_spans:\n retokenizer.merge(span)\n\n return doc","def __add_to_word_dict__(self, word, tag):\n if self.word_dict.get(word, None) is None:\n self.word_dict[word] = defaultdict(int)\n self.word_dict[word][tag] += 1","def buildDict(self, dict):\n for word in dict:\n self.s.add(word)\n self.length_set = set([len(word) for word in dict])","def sort_by_tokens(\n self, tokens_or_dictionary, allow_mismatch=False,\n allow_missed_tokens=False, allow_missed_embeddings=False\n ):\n\n # The input can a dictionary or a list of tokens.\n if isinstance(tokens_or_dictionary, h.dictionary.Dictionary):\n dictionary = tokens_or_dictionary\n tokens = dictionary.tokens\n else:\n tokens = tokens_or_dictionary\n dictionary = h.dictionary.Dictionary(tokens)\n\n self_set = set(self.dictionary.tokens)\n other_set = set(tokens)\n\n # Check for extraneous tokens.\n self_coverage = other_set - self_set\n if len(self_coverage) > 0:\n\n if allow_mismatch or allow_missed_embeddings:\n # Drop the extraneous tokens\n tokens = [token for token in tokens if token in self.dictionary]\n\n else:\n raise ValueError(\n 'The new dictionary has {} tokens that do not have a '\n 'corresponding embedding.\\n{}\"'.format(\n len(self_coverage), '\"\\n\"'.join(self_coverage))\n )\n\n # Check for missing tokens.\n dictionary_coverage = self_set - other_set\n if len(dictionary_coverage) > 0:\n if not allow_mismatch and not allow_missed_tokens:\n raise ValueError(\n \"The new dictionary is missing {} entries for some \"\n \"embedded tokens.\\n{}\".format(\n len(dictionary_coverage),\n '\\n'.join(dictionary_coverage)\n )\n )\n else:\n if self.verbose:\n print(\n \"Warning, some embeddings were dropped because they \"\n \"have no corresponding token in the provided \"\n \"dictoinary.\"\n )\n\n sort_ids = [self.dictionary.get_id(token) for token in tokens]\n\n # This implicitly drops missing tokens\n self.V = self.V[sort_ids]\n if self.W is not None:\n self.W = self.W[sort_ids]\n if self.vb is not None:\n self.vb = self.vb[sort_ids]\n if self.wb is not None:\n self.wb = self.wb[sort_ids]\n\n self.dictionary = h.dictionary.Dictionary(tokens)","def count_words_and_dublicates(novel):","def computeWordFrequencies(self, tokens: ['token'], frequencies: {'token': int}):\n # project2: update this method to take existing dict as parameter and modify it\n # additionally, stopwords are not inserted in the dict;\n # words shorter than 3 character or contains all digits are ignored\n for token in tokens:\n # if the key is not in dict, dict.setdefault method initiates the value at 0\n # if token not in stopwords and len(token) >= 3 and not token.isdigit():\n frequencies[token] = frequencies.setdefault(token, 0) + 1","def __find_union(dicts: list):\n global other_time\n start = time.time()\n union_list = []\n while len(dicts) > 1:\n word_count = {}\n final_dict = {}\n for d in dicts:\n # print(json.dumps(d, indent=4))\n for word in d:\n if word not in word_count:\n word_count[word] = 1\n else:\n word_count[word] = word_count[word] + 1\n for item in word_count:\n if word_count[item] == len(dicts):\n union_list.append(item)\n for d in dicts:\n for word in d:\n if word in union_list:\n if word not in final_dict:\n final_dict[word] = d[word]\n else:\n final_dict[word] += d[word]\n if len(final_dict) > 0:\n # print(len(dicts))\n end = time.time()\n diff = end - start\n other_time += diff\n # print(json.dumps(final_dict, indent=4))\n return (final_dict)\n else:\n\n dicts = dicts[:len(dicts) - 1]\n\n end = time.time()\n diff = end - start\n other_time += diff\n return {}","def insight_github_unique_word_results():\n counter_container = OrderedDict()\n\n path_to_input_file = os.path.join(constants.TEST_DIRECTORY,\n \"fixtures/ft1_expected_results_v1.txt\")\n with open(path_to_input_file, 'r') as input_file:\n for line in input_file:\n word, count = line.split()\n counter_container[word] = int(count)\n return counter_container","def calculate_poses_for_word(corpus):\n total_pos_count = {}\n poses_for_word = {}\n # For each word, find all POSes.\n for sentence in corpus.get_sentences():\n for word in sentence:\n id, form, lemma, plemma, pos, ppos = word\n #form = form.lower()\n\n if pos in total_pos_count:\n total_pos_count[pos] += 1\n else:\n total_pos_count[pos] = 1\n\n if form in poses_for_word:\n if pos in poses_for_word[form]:\n poses_for_word[form][pos] += 1\n else:\n poses_for_word[form][pos] = 1\n else:\n poses_for_word[form] = {pos: 1}\n\n return poses_for_word, total_pos_count","def task1(sentence):\n split_sentence = sentence.split()\n dictionary = dict()\n for word in split_sentence:\n if word in dictionary:\n dictionary[word] += 1\n else:\n dictionary[word] = 1\n for item in dictionary:\n print(\"Word \" + item + \" used \" + str(dictionary[item]) + \" times\")\n return dictionary","def calcCountDict(TFdict):\n\n countDict = {}\n\n for doc in TFdict:\n for term in doc:\n if term in countDict:\n countDict[term] +=1\n else:\n countDict[term] = 1\n\n return countDict","def get_frequencies(tokens):\n cnt = {}\n\n for word in tokens:\n if word not in cnt:\n cnt[word] = 0\n\n cnt[word] += 1\n\n return cnt","def _disambiguate_merged_glosses(self, merged_glosses):\n\t\tprocessed_glosses = {}\n\t\ttotal_glosses = len(merged_glosses)\n\t\tcurrent_gloss = 0\n\t\tdisambiguated_glosses_count = 0\n\t\tskipped_glosses_count = 0\n\n\t\t# go over all glosses and disambiguate them if possible\n\t\tfor gloss_key in merged_glosses:\n\t\t\tcurrent_gloss += 1\n\t\t\tgloss = merged_glosses[gloss_key]\n\t\t\ttaggable_tokens = []\n\t\t\ttagged_tokens = []\n\n\t\t\tif len(gloss.tokens) == 0:\n\t\t\t\tself._log_message(\"WARNING: unmerged gloss {0}\".format(gloss_key))\n\n\t\t\t# for each token inside the gloss determine if the token is already disambiguated, needs to be disambiguated or isnt part of WordNet anyways\n\t\t\tfor token_index in gloss.tokens:\n\t\t\t\ttoken = gloss.tokens[token_index]\n\t\t\t\ttokens_wn_ss_types = list(map(int, re.findall(\"[0-9]+\", token.lemma)))\n\t\t\t\t# tokens that are tagged as \"man\" or \"auto\" are already disambiguated\n\t\t\t\tif token.tag in [\"man\", \"auto\"]:\n\t\t\t\t\ttagged_tokens.append(token)\n\t\t\t\t# if a token is tagged as \"un\" it is not yet annotated, but possibly should be; if the lemma contains entries of the type \"LEMMA%SS_TYPE\" and the POS if the word matches one of the lemma-ss_types it needs to be/can be disambiguated\n\t\t\t\telif token.tag == \"un\" and re.match(\"([a-z]+%[0-9]\\|?)+\", token.lemma) and not set(GLOSSTAG_POS_POSSIBLE_SS_TYPES[token.pos]).isdisjoint(set(tokens_wn_ss_types)) and type(token) != CollocationMember:\n\t\t\t\t\ttaggable_tokens.append(token)\n\n\t\t\t# if there are no remaining taggable tokens, then proceed with the next gloss\n\t\t\tif len(taggable_tokens) == 0:\n\t\t\t\tprocessed_glosses[gloss_key] = gloss\n\t\t\t\tskipped_glosses_count += 1\n\t\t\t\tcontinue\n\t\t\tdisambiguated_glosses_count += 1\n\n\t\t\t## DISAMBUGATION PROCEDURE ##\n\t\t\tprint(\"\\tat gloss {0} of {1}\".format(current_gloss, total_glosses))\n\t\t\tprint(\"\\t\\t...disambiguating {0} words in gloss {1}\".format(len(taggable_tokens), gloss_key))\n\t\t\t# disambiguated_gloss = self._disambiguate_gloss_with_path_similarity(gloss, taggable_tokens, tagged_tokens)\n\t\t\tdisambiguated_gloss = self._disambiguate_gloss_by_most_frequent_sense(gloss, taggable_tokens, tagged_tokens)\n\t\t\t# finished, append to output\n\t\t\tprocessed_glosses[gloss_key] = disambiguated_gloss\n\n\t\tprint(\"\\tdisambiguated {0} glosses, skipped {1}\".format(disambiguated_glosses_count, skipped_glosses_count))\n\n\t\treturn processed_glosses","def common_words(words: Dict[str, int], count: int) -> None:\n \n original_order = list(words.values())\n max_to_min = list(reversed(sorted(words.values())))\n temp_lst1 = []\n temp_lst2 = []\n \n \n if count == len(words):\n words = words\n \n \n for num in range(len(max_to_min)):\n if len(temp_lst1) < count:\n temp_lst1.append(max_to_min[num])\n \n for nums in range(len(temp_lst1)):\n max_to_min.remove(temp_lst1[nums])\n \n \n temp_lst2 = remove_repeats(max_to_min, temp_lst1)\n \n a = reformat_order(original_order, temp_lst2)\n \n words_to_counts = combine_list(format_dict(words, a), a)\n \n words.clear()\n \n for keys in words_to_counts:\n words[keys] = words_to_counts[keys]","def _count_word_frequency(self, data):\n _dict = {}\n for _docs in data:\n for _word in _docs:\n if _word in _dict:\n _dict[_word] += 1\n else:\n _dict[_word] = 1\n return _dict","def CountWords(input1):\n tokens = []\n\n d={}\n\n tokens = tokens+input1.lower().split()\n\n for token in tokens:\n # Remove Punctuation\n word=token.replace(\"!\", \"\")\n word=word.replace(\".\", \"\")\n d[word] = d.get(word,0)+1\n\n word_frq=[]\n for i,v in d.items():\n word_frq.append((v,i))\n word_frq.sort(reverse=True)\n\n for word in word_frq:\n print (word[1],word[0])","def _build_token_dict(self, corpus: List[List[str]], min_count: int = 3):\n token2idx = {\n self.token_pad: 0,\n self.token_unk: 1,\n self.token_bos: 2,\n self.token_eos: 3\n }\n\n token2count = {}\n for sentence in corpus:\n for token in sentence:\n count = token2count.get(token, 0)\n token2count[token] = count + 1\n\n # 按照词频降序排序\n sorted_token2count = sorted(token2count.items(),\n key=operator.itemgetter(1),\n reverse=True)\n token2count = collections.OrderedDict(sorted_token2count)\n\n for token, token_count in token2count.items():\n if token not in token2idx and token_count >= min_count:\n token2idx[token] = len(token2idx)\n\n self.token2idx = token2idx\n self.idx2token = dict([(value, key)\n for key, value in self.token2idx.items()])\n logging.debug(f\"build token2idx dict finished, contains {len(self.token2idx)} tokens.\")\n self.dataset_info['token_count'] = len(self.token2idx)","def count_ngrams(self, corpus):\n \n self.unigramcounts = {} # might want to use defaultdict or Counter instead\n self.bigramcounts = {} \n self.trigramcounts = {} \n\n self.total = 2\n ##Your code here\n\n for sentence in corpus:\n temp_1 = get_ngrams(sentence,1)\n temp_2 = get_ngrams(sentence,2)\n temp_3 = get_ngrams(sentence,3)\n for i in range(len(temp_1)):\n if temp_1[i] in self.unigramcounts:\n self.unigramcounts[temp_1[i]] += 1\n else:\n self.unigramcounts[temp_1[i]] = 1\n self.total += 1\n\n for i in range(len(temp_2)):\n if temp_2[i] in self.bigramcounts:\n self.bigramcounts[temp_2[i]] += 1\n else:\n self.bigramcounts[temp_2[i]] = 1\n\n for i in range(len(temp_3)):\n if temp_3[i] in self.trigramcounts:\n self.trigramcounts[temp_3[i]] += 1\n else:\n self.trigramcounts[temp_3[i]] = 1\n return","def vectorize_content(self, content):\r\n file_dict = {}\r\n for word in content:\r\n if word in file_dict:\r\n file_dict[word] += 1\r\n else:\r\n file_dict[word] = 1\r\n return file_dict","def CompactTokens(self):\n map = {}\n for token_ in self.tokens_:\n hash_string = token_.EncodeForHash()\n try: map[hash_string].append(token_)\n except KeyError: map[hash_string] = [token_]\n ntokens = []\n keys = map.keys()\n keys.sort()\n for k in keys:\n token_ = map[k][0]\n for otoken in map[k][1:]:\n token_.IncrementCount(otoken.Count())\n ntokens.append(token_)\n self.tokens_ = ntokens","def word_count(self, document):\n start_time = time.time()\n dictionary = dict()\n counter = defaultdict(int)\n for line in document.splitlines():\n for word in line.split():\n if word not in PUNCTUATION_MARK:\n counter[word] += 1\n for word, cnt in sorted(counter.items(), key=lambda x: (-x[1], x[0])):\n dictionary[word] = cnt\n self.log.info(\"Duration count dictionary: {duration}\".format(duration=float(time.time() - start_time)))\n return dictionary","def tag():\n iso_list = []\n tags = [\"spatial_entity\", \"place\", \"motion\", \"location\", \"signal\", \"qslink\", \"olink\"]\n for token in doc:\n if token.norm_ in tags:\n iso_list.append(token.norm_)\n setList = list(set(iso_list))\n my_dict = {i: iso_list.count(i) for i in setList}\n\n for i in tags:\n if i.lower() not in my_dict:\n my_dict[i] = 0\n print(my_dict)","def count_words():\n paragraph = \"a distinct section of a piece of writing,\"\n # 替换\n paragraph.replace(\",\", \" \").replace(\":\", \" \").replace(\";\", \" \").replace(\".\", \" \").replace(\"?\", \" \")\n words = paragraph.split(\" \")\n nums = {}\n\n for word in words:\n nums[word] = nums[word]+1 if word in nums else 1\n # nums[word] = nums.get(word, 0) + 1\n\n for word, num in nums.items():\n print(word, \": \", num)","def common_token_len(self, w1, d1, w2, d2):\n w1_tk = set(self.__stem_Tokens(w1))\n w2_tk = set(self.__stem_Tokens(w2))\n common_len = len(w1_tk.intersection(w2_tk))\n return common_len","def getTokens(wordlist, tokens = {}, count = 0):\n\n\tif len(wordlist) == 0:\n\t\treturn tokens, count\n\n\tfor i in wordlist:\n\t\tif tokens.has_key(i):\n\t\t\ttokens[i] += 1\n\t\telse:\n\t\t\ttokens[i] = 1\n\t\tcount += 1\n\treturn tokens, count","def clsWordCounts(wd_dict, wd_list, cl):\n\n # set up counter object for words in passed word list\n word_counts = Counter(wd_list)\n\n # for each term in counter object\n for term in word_counts:\n\n # if already in dictionary add to the count\n if term in wd_dict[cl]:\n\n wd_dict[cl][term] += word_counts[term]\n\n # else add to the dictionary\n else:\n\n wd_dict[cl][term] = word_counts[term]\n\n return wd_dict","def update(self, iterable):\n for word in iterable:\n if word in self:\n self[word] = self[word] + 1\n self.tokens += 1\n else:\n self[word] = 1\n self.types += 1\n self.tokens += 1","def count_tokens(self, words: Iterable[str]) -> Dict[str, int]:\r\n token_counts = Counter(words)\r\n return {\" \".join(token): count for token, count in token_counts.items()}","def analyzeFile(filename): \n fileData = open(filename, encoding=\"utf-8\") # open the file\n \n counts = {}\n\n for line in fileData:\t\t # iterates over every line of the file\n words = line.split() # turns each line into a list\n for word in words: #iterates over the words in each line list\n word = word.lower().strip(string.whitespace+string.punctuation)\n if len(word) > 0: #make sure word is longer than 0 before adding it to the dictionary\n counts[word] = counts.get(word, 0) + 1 #look up if the dictionary has that word and if not then it'll add that word with the value 0 associated with it and then add one to that, if it has seen it it'll add 1 to the value stored in the counts dictionary\n #when it gets here for the first line it goes back up to the top and repeats for the 2nd line\n mostCommonWord = [word]\n leastCommonWord = [word]\n shortestWord = [word]\n longestWord = [word]\n \n for item in counts:\n if counts[mostCommonWord[0]] < counts[item]:\n mostCommonWord = [item]\n elif counts[mostCommonWord[0]] == counts[item]:\n mostCommonWord.append(item)\n if counts[leastCommonWord[0]] > counts[item]:\n leastCommonWord = [item]\n elif counts[leastCommonWord[0]] == counts[item]:\n leastCommonWord.append(item)\n if len(shortestWord[0]) > len(item):\n shortestWord = [item] \n elif len((shortestWord[0])) == len(item):\n shortestWord.append(item)\n if len(longestWord[0]) < len(item):\n longestWord = [item]\n elif len(longestWord[0]) == len(item):\n longestWord.append(item)\n \n return (mostCommonWord, leastCommonWord, shortestWord, longestWord)","def tokenize(self):\n count = 0\n for entry in self._entries:\n token_pairs = []\n for relation in entry['relations']:\n assert len(relation) == 3\n token_pairs.append((relation[0][0],relation[1][0],relation[2][0]))\n\n num_rels = len(entry['relations'])\n num_random_rels = (self._max_seq_length - 2) // 3 - num_rels\n\n if num_random_rels>0:\n pass\n # gt_pairs = {(rel[0],rel[2]) for rel in entry['relations']}\n # random_pairs = self._get_random_pair(entry['objects'], gt_pairs, num_random_rels)\n # for pair in list(random_pairs):\n # token_pairs.append((pair[0][0],'background', pair[1][0]))\n else:\n for i in range(-num_random_rels):\n token_pairs.pop()\n\n random.shuffle(token_pairs)\n tokens = []\n for pair in token_pairs:\n tokens.extend(pair)\n\n tokens = ['[CLS]'] + tokens + ['[SEP]']\n tokens_char = tokens\n\n target = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3==2 else -1 for i, x in enumerate(tokens)]\n tokens = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3!=2 else self._tokenizer.vocab.get('[MASK]', self._tokenizer.vocab['[UNK]']) for i, x in enumerate(tokens)]\n \n for i in range(len(tokens)):\n if target[i] != -1:\n print(tokens_char[i],tokens[i],target[i])\n\n segment_ids = [0] * len(tokens)\n input_mask = [1] * len(tokens)\n # input_mask = [1 if i%3==2 else 0 for i in range(len(tokens))]\n # co_attention_mask = [-1 if i%3==2 else 1 for i in range(len(tokens))]\n # co_attention_mask = torch.zeros((self._max_region_num, self._max_seq_length))\n # co_attention_mask[0] = -1\n # co_attention_mask[-1] = -1\n \n if len(tokens) < self._max_seq_length:\n padding = [self._padding_index] * (self._max_seq_length - len(tokens))\n tokens = tokens + padding\n input_mask += padding\n segment_ids += padding \n target += [-1] * len(padding) \n\n assert_eq(len(tokens), self._max_seq_length)\n entry['input_ids'] = tokens \n entry[\"input_mask\"] = input_mask\n entry['segment_ids'] = segment_ids\n # entry[\"co_attention_mask\"] = co_attention_mask\n entry['target'] = target\n\n sys.stdout.write('%d/%d\\r' % (count, len(self._entries)))\n sys.stdout.flush()\n count += 1","def __parse_corpus(self, corpus):\n corpus = self.__handle_corpus_unkwon_words(corpus)\n start_token = ' '.join([NGramModel.START_SENTENCE_TOKEN]*(self.__n-1))\n word_list = corpus.replace(NGramModel.START_SENTENCE_TOKEN, start_token).split()\n \n for n in range(1, self.__n+1): \n self.__ngram_counts[n] = {}\n for ngram, count in Counter(self.__generate_n_grams(word_list, n)).items():\n self.__ngram_counts[n][' '.join(ngram)] = count","def _count_occurrences(self, corpus_tokens: list, window_size: int):\n ngram_occurrences = {}\n for i, corpus_token in enumerate(corpus_tokens):\n ngram = []\n if i + window_size > len(corpus_tokens) - 1:\n break\n else:\n for j in range(window_size):\n ngram.append(corpus_tokens[i + j])\n if self.TOKENS.END.value in ngram:\n continue\n else:\n ngram_key = tuple(ngram)\n if ngram_key in ngram_occurrences:\n ngram_occurrences[ngram_key] += 1\n else:\n ngram_occurrences[ngram_key] = 1\n return ngram_occurrences","def getCounter(self):\n word_count, noun_word_count = Counter(), Counter()\n word_rating, noun_word_rating = defaultdict(list), defaultdict(list)\n docs = self.nlp.pipe(\n self.docs, n_process=1, disable=self.disablelayers)\n \n\n for index, doc in enumerate(docs):\n for token in doc:\n if not token.is_stop and not token.is_punct and token.pos_ in self.pos:\n if token.pos_ == 'PROPN':\n word_count[token.lemma_] += 1\n word_rating[token.lemma_].append(self.ratings[index])\n else:\n noun_word_count[token.lemma_] += 1\n noun_word_rating[token.lemma_].append(self.ratings[index])\n\n # if 0<=proper nouns<=5 found, add regular nouns\n if not word_count or len(word_count) <= 5:\n word_count += noun_word_count\n word_rating = {**word_rating, **noun_word_rating}\n \n word_color = {word: self.getColor(\n ratings)[1] for word, ratings in word_rating.items()}\n word_sentiment = {word: self.getColor(\n ratings)[0] for word, ratings in word_rating.items()}\n\n return word_count, word_color, word_sentiment","def main():\n text = str(input(\"Sentence: \"))\n print(\"text: {}\".format(text))\n text_dict = {}\n text_list = text.split()\n text_list.sort()\n for text in text_list:\n if text in text_dict:\n text_dict[text] += 1\n else:\n text_dict[text] = 1\n # for text2 in text_dict:\n # if text_list[text] == text_dict[text2]:\n # text_dict[text2] += 1\n # else:\n # text_dict[text_list[text]] += 1\n for text in text_dict:\n print(text, \":\", text_dict[text])","def merge_words(word_list, merge_method = None, **kwargs):\n \n for word in word_list:\n word['minframe'] = word['frame']\n word['maxframe'] = word['frame']\n \n if merge_method is None:\n for word in word_list:\n word['merge_size'] = 1\n return word_list\n \n clusterize_words(word_list, **kwargs)\n \n #Merge words within clusters\n result = []\n word_list.sort(key = lambda word: word['cluster'])\n cluster = []\n current_cluster = - 1\n for word in word_list:\n #First word\n if len(cluster) == 0:\n current_cluster = word['cluster']\n cluster = [word]\n elif word['cluster'] == current_cluster:\n cluster.append(word)\n #New cluster\n else:\n #process this cluster\n result.append(merge_cluster(cluster, merge_method))\n \n current_cluster = word['cluster']\n cluster = [word]\n \n return result","def bow(tokens):\n return dict(collections.Counter(re.findall(r'\\w+', \" \".join(tokens))))","def merge_cluster(word_list, merge_method = None):\n \n #Calculates average of property of list of dictionaries.\n def avg(l, key):\n s = 0\n for w in l:\n s+= w[key]\n return s / len(l)\n \n result = {}\n result['longleft'] = avg(word_list, 'longleft')\n result['longright'] = avg(word_list, 'longright')\n result['top'] = avg(word_list, 'top')\n result['bottom'] = avg(word_list, 'bottom')\n result['confidence'] = avg(word_list, 'confidence')\n result['merge_size'] = len(word_list)\n result['minframe'] = min([word['minframe'] for word in word_list])\n result['maxframe'] = max([word['maxframe'] for word in word_list])\n result['cluster'] = word_list[0]['cluster']\n \n if merge_method is None:\n merge_method = 'char_frequency'\n \n #Pick most popular character of each position in word\n if merge_method == 'char_frequency':\n result['text'] = average_word(word_list)\n #Pick word with highest confidence\n elif merge_method == 'confidence':\n max_confidence = -1\n for word in word_list:\n if word['confidence'] > max_confidence:\n max_confidence = word['confidence']\n result['text'] = word['text']\n elif merge_method == 'random':\n result['text'] = word_list[random.randint(0, len(word_list) - 1)]['text']\n else:\n raise ValueError('Method ' + merge_method + ' not implemented.')\n \n return result","def number_tokens(dgraph):\n cnt = 0\n for node in dgraph.subgraphs(exclude_root=True):\n node[WORD] = (cnt, node[WORD])\n cnt += 1\n return dgraph","def build_tf_dict(self, sentences):\n tf_dict = defaultdict(int)\n for sentence in sentences:\n for word in sentence:\n tf_dict[word] += 1\n return tf_dict","def reconcile_output(self):\n final_string = \"\"\n final_string += \"TEXT: {0}\\n\".format(self._text)\n final_string += \"ID: {0}\\n\".format(self._index)\n final_string += \"Count: {0}\\n\".format(self._count)\n\n final_string += \"=Doc Count Begin=\\n\"\n for doc in list(self._doc_count.keys()):\n final_string += \"{0} $!$ {1}\\n\".format(doc, self._doc_count[doc])\n final_string += \"=Doc Count End=\\n\"\n\n final_string += \"=CEs Begin=\\n\"\n for doc in list(self.word_pairs.keys()):\n for ce in set(self.word_pairs[doc]):\n #format: doc $!$ ce \n final_string += \"{0} $!$ {1} $!$ {2}\\n\".format(doc, ce, self.word_pairs[doc].count(ce))\n final_string += \"=CEs End=\\n\"\n\n final_string += \"=Self Tags Begin=\\n\"\n for source in list(self.this_semantic_tags.keys()):\n for tag in set(self.this_semantic_tags[source]):\n final_string += \"{0} $!$ {1} $!$ {2}\\n\".format(source, tag, self.this_semantic_tags[source].count(tag))\n final_string += \"=Self Tags End=\\n\"\n\n final_string += \"=Semantic Begin=\\n\"\n for source in list(self.lexico_semantic.keys()):\n #for some reason, I had each semantic class getting assigned the\n #same overall count?\n #sem_counts = self.getLexicoSemanticCounts(source)\n for ce in self.lexico_semantic[source]:\n for sem_cls in set(self.lexico_semantic[source][ce]):\n c = self.lexico_semantic[source][ce].count(sem_cls)\n final_string += \"{0} $!$ {1} $!$ {2} $!$ {3}\\n\".format(source, ce, sem_cls, c)\n final_string += \"=Semantic End=\\n\"\n final_string += \"$!$\\n\"\n return final_string","def merge_tokens(token_dict, idx_cluster, cluster_num, token_weight=None):\n x = token_dict['x']\n idx_token = token_dict['idx_token']\n agg_weight = token_dict['agg_weight']\n B, N, C = x.shape\n if token_weight is None:\n token_weight = x.new_ones(B, N, 1)\n idx_batch = torch.arange(B, device=x.device)[:, None]\n idx = idx_cluster + idx_batch * cluster_num\n all_weight = token_weight.new_zeros(B * cluster_num, 1)\n all_weight.index_add_(dim=0, index=idx.reshape(B * N), source=token_weight.reshape(B * N, 1))\n all_weight = all_weight + 1e-06\n norm_weight = token_weight / all_weight[idx]\n x_merged = x.new_zeros(B * cluster_num, C)\n source = x * norm_weight\n x_merged.index_add_(dim=0, index=idx.reshape(B * N), source=source.reshape(B * N, C).type(x.dtype))\n x_merged = x_merged.reshape(B, cluster_num, C)\n idx_token_new = index_points(idx_cluster[..., None], idx_token).squeeze(-1)\n weight_t = index_points(norm_weight, idx_token)\n agg_weight_new = agg_weight * weight_t\n agg_weight_new / agg_weight_new.max(dim=1, keepdim=True)[0]\n out_dict = {}\n out_dict['x'] = x_merged\n out_dict['token_num'] = cluster_num\n out_dict['map_size'] = token_dict['map_size']\n out_dict['init_grid_size'] = token_dict['init_grid_size']\n out_dict['idx_token'] = idx_token_new\n out_dict['agg_weight'] = agg_weight_new\n return out_dict","def __cross_wiki_counts(self):\n\n print(\"Updating counts by merging with CrossWiki\")\n\n cnt = 0\n crosswiki_path = os.path.join(\n self.base_url, \"generic/p_e_m_data/crosswikis_p_e_m.txt\"\n )\n\n with open(crosswiki_path, \"r\", encoding=\"utf-8\") as f:\n for line in f:\n parts = line.split(\"\\t\")\n mention = unquote(parts[0])\n\n if (\"Wikipedia\" not in mention) and (\"wikipedia\" not in mention):\n if mention not in self.wiki_freq:\n self.wiki_freq[mention] = {}\n\n num_ents = len(parts)\n for i in range(2, num_ents):\n ent_str = parts[i].split(\",\")\n ent_wiki_id = int(ent_str[0])\n freq_ent = int(ent_str[1])\n\n if (\n ent_wiki_id\n not in self.wikipedia.wiki_id_name_map[\"ent_id_to_name\"]\n ):\n ent_name_re = self.wikipedia.wiki_redirect_id(ent_wiki_id)\n if (\n ent_name_re\n in self.wikipedia.wiki_id_name_map[\"ent_name_to_id\"]\n ):\n ent_wiki_id = self.wikipedia.wiki_id_name_map[\n \"ent_name_to_id\"\n ][ent_name_re]\n\n cnt += 1\n if (\n ent_wiki_id\n in self.wikipedia.wiki_id_name_map[\"ent_id_to_name\"]\n ):\n if mention not in self.mention_freq:\n self.mention_freq[mention] = 0\n self.mention_freq[mention] += freq_ent\n\n ent_name = self.wikipedia.wiki_id_name_map[\n \"ent_id_to_name\"\n ][ent_wiki_id].replace(\" \", \"_\")\n if ent_name not in self.wiki_freq[mention]:\n self.wiki_freq[mention][ent_name] = 0\n self.wiki_freq[mention][ent_name] += freq_ent","def build_morpheme_vocab(self):\n max_morph_per_word = 0\n morpheme_dict = collections.defaultdict(int)\n splitter = \"@@\"\n for token in self.train_data:\n if token == self.eos or token == self.sos:\n continue\n token = '^' + token + '$'\n morphemes = token.split(splitter)\n if len(morphemes) > max_morph_per_word:\n max_morph_per_word = len(morphemes)\n for morpheme in morphemes:\n morpheme_dict[morpheme] += 1\n\n unk_morpheme_list = set()\n item_to_id = dict()\n item_to_id[constants.PAD_ITEM] = len(item_to_id)\n item_to_id[constants.UNK_ITEM] = len(item_to_id)\n sorted_dict = sorted(morpheme_dict.items(), key=operator.itemgetter(1), reverse=True)\n for token, freq in sorted_dict:\n if freq == 1:\n unk_morpheme_list.add(token)\n if token not in item_to_id:\n item_to_id[token] = len(item_to_id)\n return item_to_id, unk_morpheme_list, max_morph_per_word","def nrc_hashtag_emotion(self, tokens):\n num_features = 10 # 'anger', 'anticipation', 'disgust', 'fear', 'joy', 'negative', 'positive', 'sadness', 'surprise', 'trust'\n sum_vec = [0.0] * num_features\n for token in tokens:\n if token in self.nrc_hashtag_emotion_map:\n sum_vec = [a + b for a, b in zip(sum_vec, self.nrc_hashtag_emotion_map[token])] # sum up the individual word feature vectors\n feature_names = ['anger', 'anticipation', 'disgust', 'fear', 'joy', 'negative', 'positive', 'sadness', 'surprise', 'trust']\n feature_names = ['nrc_hashtag_emotion_' + name for name in feature_names]\n return dict(zip(feature_names, sum_vec))","def make_word_to_freq(self):\n\t\tword_to_freq = {}\n\t\tdocuments = self.tokenized_documents[\"train\"]\n\t\tfor document in documents:\n\t\t\tfor word in document:\n\t\t\t\tif not word in self.worddict: # make sure we have not found one of the pre-defined words\n\t\t\t\t\tword_to_freq[word] = word_to_freq.get(word, 0) + 1\n\t\t\n\t\treturn word_to_freq","def _build_token_dict(self, corpus: List[List[str]]):\n self.token2idx = self.load_from_vocab_file(self.vocab_path)\n self.idx2token = dict([(value, key)\n for key, value in self.token2idx.items()])\n logging.debug(f\"build token2idx dict finished, contains {len(self.token2idx)} tokens.\")\n self.dataset_info['token_count'] = len(self.token2idx)","def tokenize_and_split_bis(sms_file):\n \n dic = {}\n list1 = []\n list2 = []\n list3 = []\n list4 = []\n i = -1\n document = 0\n terms = 0\n new_document = True\n ham = True\n for line in open(sms_file, 'r').readlines():\n w = []\n document += 1\n new_document = True\n for word in line.split():\n i = i + 1\n if word == \"ham\":\n ham = True\n i = i - 1\n elif word == \"spam\":\n ham = False\n i = i - 1\n else:\n if word not in dic:\n dic[word] = i\n w.append(dic[word])\n list3.append(1)\n list4.append(1)\n new_document = False\n terms += 1\n else : \n i = i - 1\n w.append(dic[word])\n list4[dic[word]] += 1\n terms += 1\n if new_document: \n list3[dic[word]] += 1\n new_document = False\n \n if ham and w !=[]:\n list2.append(w)\n elif ham == False and w !=[]:\n list1.append(w)\n\n moy = 0\n len_dic = len(dic.keys())\n list5 = [0 for x in range(len_dic)]\n for key in dic.keys():\n if list4[dic[key]] > 0:\n tf = list4[dic[key]] / terms\n idf = math.log(document / list3[dic[key]])\n tfIdf = tf * idf\n list5[dic[key]] = tfIdf\n # print(\"the word \" + str(key) + \" appairs \" + str(list4[dic[key]]) + \" times.\")\n # print(\"his frequency is \" + str(list4[dic[key]] / terms) )\n # print(\"the word \" + str(key) + \" appairs \" + str(list3[dic[key]]) + \" times in each document.\")\n # print(\"his frequency is \" + str(idf))\n # print(\"utility \" + str(tfIdf))\n moy += tfIdf\n \n moy = moy / len_dic \n # print(moy)\n dic_bis = {}\n i = -1\n for key in dic.keys():\n value = list5[dic[key]]\n # print(str(value))\n if (value > oracle * moy):\n i += 1\n dic_bis[key] = i\n # else:\n # print(\"not pass \" + key + \" \" + str(value))\n \n \n # print(dic_bis == dic)\n # print(dic)\n return dic_bis,list1,list2","def add_ngrams(mydict,sentence):\n ngrams = get_ngrams(sentence,2,3)\n for ngram in ngrams:\n if ngram in mydict:\n mydict[ngram]+=1\n else:\n mydict[ngram]=1\n return mydict","def MergeDictionary(self, dict1, dict2):\r\n for key_2, val_2 in dict2.items():\r\n for key_2_2, val_2_2 in val_2.items():\r\n if key_2 in dict1 and key_2_2 in dict1[key_2]:\r\n dict1[key_2][key_2_2] += val_2_2\r\n else:\r\n self.addtwodimdict(dict1, key_2, key_2_2, val_2_2)\r\n return dict1","def add_word(self, word_one, word_two):\n \n # if word_one is in the corpus\n if word_one in self.corpus.keys():\n # if the word_two is already in word_one's corpus\n if word_two in self.corpus[word_one].keys():\n # increment the count by 1\n self.corpus[word_one][word_two] += 1\n # if word_two is not in word_one's corpus\n else:\n # add word_two to the corpus with an initial value of 1\n self.corpus[word_one][word_two] = 1\n # if word_one is not already in the corpus\n else:\n # add it and initialize its dictionary with word_two\n self.corpus[word_one] = {word_two : 1}","def merged_gene_feature_counts(self, merge_multi_splice_variants=True, merge_boundary_features=True, \n merge_confusing_features=False):\n merged_feature_count_dict = defaultdict(int)\n for feature, count in self.mutant_counts_by_feature.items():\n # note that anything containing '??' AND '/' never gets merged as boundary\n if '??' in feature:\n if merge_confusing_features: merged_feature_count_dict['??'] += count\n else: merged_feature_count_dict[feature] += count\n elif '|' in feature and merge_multiple_splice_variants: merged_feature_count_dict['multiple_splice_variants'] += count\n elif '/' in feature and merge_boundary_features: merged_feature_count_dict['boundary'] += count\n else: merged_feature_count_dict[feature] += count\n return merged_feature_count_dict","def calculate(self, tokens:list):\n vektor_tf_bigram = {}\n sz = len(tokens) - 1\n\n # range(0) adalah empty list di Python\n for i in range(sz):\n bigram_token = self.__get_term_bigram(i, tokens)\n\n if bigram_token in vektor_tf_bigram:\n vektor_tf_bigram[bigram_token] = vektor_tf_bigram[bigram_token] + 1\n else:\n vektor_tf_bigram[bigram_token] = 1\n\n return vektor_tf_bigram","def count_ngrams(self, corpus):\n \n self.unigramcounts = defaultdict(int)\n self.bigramcounts = defaultdict(int)\n self.trigramcounts = defaultdict(int)\n\n self.sentence_counts = 0\n self.word_count = 0\n\n for line in corpus:\n sequence = line\n self.sentence_counts +=1\n\n unigrams = get_ngrams(sequence, n=1)\n for gram in unigrams:\n self.word_count += 1\n self.unigramcounts[gram] +=1\n\n bigrams = get_ngrams(sequence, n=2)\n for gram in bigrams:\n self.bigramcounts[gram] +=1\n\n trigrams = get_ngrams(sequence, n=3)\n for gram in trigrams:\n self.trigramcounts[gram] +=1\n\n #self.unigramcounts[('START')] = self.sentence_counts *2\n self.bigramcounts[('START', 'START')] = self.sentence_counts\n\n #return self","def count(words):\n word_count = {}\n num_words = 0\n unique_words = 0\n for word in words:\n num_words += 1\n if word_count.has_key(word):\n word_count[word] += 1\n else:\n word_count[word] = 1\n unique_words += 1\n word_count[\"total\"] = num_words\n word_count[\"unique\"] = unique_words\n return word_count","def pos():\n pos_list = []\n for token in doc:\n pos_list.append(token.pos_)\n setList = list(set(pos_list))\n my_dict = {i: pos_list.count(i) for i in setList}\n print(my_dict)","def token_key(token):\n morphotagged = analysis(token).get('raw')\n lemma_pos = (analysis(token).get('lemma'), analysis(token).get('partOfSpeech'))\n return morphotagged or lemma_pos","def test_merge_word_dicts():\n ref_dict = {\"accordeon\": [\"acordeon\"],\n \"accoster\": [\"accostage\", \"acostage\"]}\n\n new_dict = {\"accordeon\": [\"accordeons\"],\n \"accostage\": [\"accostages\"],\n \"accumulateur\": [\"acumulateur\", \"acc\"]}\n\n word_dict.merge_word_dicts(ref_dict, new_dict)\n\n expected_dict = {\"accordeon\": [\"acordeon\", \"accordeons\"],\n \"accoster\": [\"accostage\", \"acostage\", \"accostages\"],\n \"accumulateur\": [\"acumulateur\", \"acc\"]}\n\n for correct_word in ref_dict:\n assert sorted(list(set(ref_dict[correct_word]))) == sorted(list(set(expected_dict[correct_word])))","def _insert_into_dict(words_dict: Dict[str, int], key: str) \\\n -> Dict[str, int]:\n if key in words_dict.keys():\n words_dict[key] = words_dict[key] + 1\n else:\n words_dict[key] = 1\n\n return words_dict","def get_token_phonology(token, fileHasPhonology, phon_criteria, token_order):\n if fileHasPhonology:\n if phon_criteria['actual']['include']:\n actual_pho = phon_criteria['actual']['phons']\n token['pho'] = actual_pho[(token_order -1)]\n else:\n # mismatch in actual_pho and utterance length; not including actual pho at the word level\n token['pho'] = ''\n \n if phon_criteria['model']['include']:\n model_pho = phon_criteria['model']['phons']\n token['mod'] = model_pho[(token_order -1)]\n else: \n # mismatch in model_pho and utterance length; not including model pho at the word level\n token['mod'] = ''\n else:\n # whole file does not have phonology\n token['pho'] = ''\n token['mod'] = ''\n return token","def merge_utterance_lines(utt_dict):\n new_utterances = {}\n for uid, utt in utt_dict.items():\n merged = False\n if utt.reply_to is not None and utt.speaker is not None:\n u0 = utt_dict[utt.reply_to]\n if u0.root == utt.root and u0.speaker == utt.speaker:\n new_utterances[u0.id].text += \" \" + utt.text\n merged = True\n if not merged:\n new_utterances[utt.id] = utt\n return new_utterances","def add_word_to_dict(self, word):\n\n # if the word already exist in the dict\n # we increment 1 the value\n if word in self.words_to_counts:\n self.words_to_counts[word] += 1\n\n # if the word in the dict is capitalize\n # we add the new word lower case, with the value of the\n # capitalize word plus 1\n # and delete the old capitalize word\n elif word.capitalize() in self.words_to_counts:\n self.words_to_counts[word] = self.words_to_counts[\n word.capitalize()] + 1\n del self.words_to_counts[word.capitalize()]\n\n # if the word in the dict is lower case but the input\n # is capitalize, we just increment the value of the word by 1\n elif word.lower() in self.words_to_counts:\n self.words_to_counts[word] += 1\n\n # if the word doen't exist in the dict, we add the new word\n else:\n self.words_to_counts[word] = 1","def _annotate_brackets(\n self,\n tokens: List[tokenize.TokenInfo],\n ) -> Mapping[int, int]:\n brackets = {bracket: 0 for bracket in MATCHING}\n for token in tokens:\n if token.exact_type in MATCHING.keys():\n brackets[token.exact_type] += 1\n if token.exact_type in MATCHING.values():\n reverse_bracket = get_reverse_bracket(token)\n if brackets[reverse_bracket] > 0:\n brackets[reverse_bracket] -= 1\n return brackets","def add(counts):\n if counts:\n for k in grammar.keys():\n grammar[k] = grammar[k] + counts[k]","def countMutatedSentences(sentence):\n # BEGIN_YOUR_ANSWER (our solution is 17 lines of code, but don't worry if you deviate from this)\n words = sentence.split()\n distinct_words = set(words)\n\n pairs = {word: set() for word in distinct_words}\n cache = [{j: None for j in distinct_words} for i in range(len(words))]\n\n for cur_word, next_word in zip(words[:-1], words[1:]):\n pairs[cur_word].add(next_word)\n\n def count_mutate_sentences(target_word: str, current_length: int, target_length: int):\n if current_length == target_length:\n return 1\n else:\n result = 0\n for word in pairs[target_word]:\n if cache[current_length - 1][word] is None:\n cache[current_length - 1][word] = count_mutate_sentences(word, current_length + 1, target_length)\n result += cache[current_length - 1][word]\n\n return result\n\n return sum([count_mutate_sentences(word, 1, len(words)) for word in distinct_words])\n # END_YOUR_ANSWER","def getReviewsWithToken(self, token):\n\n wordid = self.find_word_in_dictionary(token)\n # word is not in the dictionary\n if wordid == -1:\n print(\"Token is not in the dictionary\")\n return 0\n\n with open(self.doc_to_words_path, 'rb') as bin:\n tup = []\n while bin.tell() != os.fstat(bin.fileno()).st_size:\n # get wordid:\n docid_in_file = int.from_bytes(bin.read(4), 'big')\n # get frequency:\n frequency = int.from_bytes(bin.read(4), 'big')\n # count words:\n count = 0\n for i in range(frequency):\n wordid_in_file = int.from_bytes(bin.read(4), 'big')\n if wordid == wordid_in_file:\n count += 1\n tup.append(docid_in_file)\n tup.append(count)\n return tuple(tup)","def init_correction_map(token, dictionary):\n if token is None:\n return None\n\n if len(token) <= 2 or token.lower() in dictionary:\n return {token: 1}\n\n return {}","def word_count(poem):\n lines = [line for line in poem.split(\"\\n\") if line]\n word_map = {}\n for line in lines:\n for word in line.split(\" \"):\n if word:\n if word in word_map:\n word_map[word] += 1\n else:\n word_map[word] = 1\n return word_map","def iterate_words(counter, li, all_dict, emotion_dict):\n\n counter += 1\n # iterate through the words in the list\n for word in li:\n # if word not in the dict of all words add it with frequency 1, else increase its frequency by 1\n if word not in all_dict:\n all_dict[word] = 1\n else:\n all_dict[word] += 1\n # if word not in the dict of words with certain emotion add it with frequency 1, else increase its frequency by 1\n if word not in emotion_dict:\n emotion_dict[word] = 1\n else:\n emotion_dict[word] += 1\n\n return counter","def countTagsInFile(fname):\n with open(fname, 'r', encoding='utf-8') as f:\n for line in f:\n words = line.split(' ')\n for w in words:\n tag = w.split('_')[1].rstrip()\n cat = tag[0].upper()\n if tag not in dictionaries[cat]:\n dictionaries[cat][tag] = 1\n else:\n dictionaries[cat][tag] += 1","def countOverlapTokens(self, annotatedMention):\n if self.tokens[0].sentence != annotatedMention.tokens[0].sentence:\n return 0 # mentions in different sentences, no overlap \n \n if self.end < annotatedMention.start or self.start > annotatedMention.end:\n return 0 # mention ends before or starts after annotated one\n \n if self.start == annotatedMention.start and self.end == annotatedMention.end:\n return len(self.tokens) # exact match for annotated mention\n \n # There is some overlap. Does it consist of anything substantial?\n importantTokens = 0\n for token in self.tokens:\n if token.index >= annotatedMention.start \\\n and token.index <= annotatedMention.end \\\n and token.isSymbol() == False and token.isStopWord() == False:\n importantTokens += 1\n \n return importantTokens","def word_count(phrase):\n words = phrase.split()\n deDupedWords = set(words)\n wordCount = {}\n\n for element in deDupedWords:\n wordCount.update({element: words.count(element)})\n\n return wordCount","def getTokenFrequency(self, token):\n\n wordid = self.find_word_in_dictionary(token)\n # word is not in the dictionary\n if wordid == -1:\n print(\"Token is not in the dictionary\")\n return 0\n\n with open(self.word_to_docs_path, 'rb') as bin:\n while bin.tell() != os.fstat(bin.fileno()).st_size:\n # get wordid:\n wordid_in_file = int.from_bytes(bin.read(4), 'big')\n # get frequency:\n frequency = int.from_bytes(bin.read(4), 'big')\n if wordid_in_file == wordid:\n break\n # skip documents:\n int.from_bytes(bin.read(4 * frequency), 'big')\n\n prevFile = int.from_bytes(bin.read(4), 'big')\n num = 1\n for i in range(frequency):\n currFile = int.from_bytes(bin.read(4), 'big')\n if currFile != prevFile:\n num += 1\n prevFile = currFile\n\n return num","def select_ocrsims(token, structures):\n delta = 2\n ocr_sims = {}\n\n word_hash = ocr_key_hash(token)\n\n sim_hash_list = {} # Using a dictionary avoid multiple entries if a key is retrieved twice\n key_index = -1\n\n # for (key, value) in word_hash:\n for key, value in word_hash:\n key_index += 1\n sim_hash = deepcopy(word_hash)\n\n for d in range(-delta, delta+1):\n if d != 0:\n card = max(int(value)+d, 1)\n\n sim_hash[key_index] = (key, card)\n\n # Rebuild OCR key string\n sim_hash_str = \"\"\n for k, v in sim_hash:\n sim_hash_str += k + str(v)\n\n if sim_hash_str in structures[\"ocrkeys\"]:\n card_diff = abs(int(value)-card)\n\n sim_hash_list[sim_hash_str] = [(sim_word, card_diff)\n for sim_word in structures[\"ocrkeys\"][sim_hash_str]\n if edit_distance(sim_word, token) <= 2]\n\n for sim_hash_str, sim_list in sim_hash_list.items():\n for sim_word, card_diff in sim_list:\n sim_score = rate_ocr_key(structures[\"occurence_map\"], token, sim_word, card_diff)\n\n if sim_score > 0:\n ocr_sims[sim_word] = sim_score\n\n return ocr_sims","def add_word(self, pair, next_pair = None):\n if pair not in self:\n self.types += 1\n if next_pair:\n self[pair] = Dictogram([next_pair])\n elif next_pair:\n self[pair].add_count(next_pair)\n self.tokens += 1","def unigram(tokens):\n model = collections.defaultdict(lambda: 0.01)\n for token in tokens:\n try:\n model[token] += 1\n except KeyError:\n model[token] = 1\n continue\n for word in model:\n model[word] = model[word]/float(sum(model.values()))\n return model","def _tokenize(self, raw_text):\n\n doc = self.nlp(raw_text.strip())\n\n # Loop through tokens and find known entities aren't already marked\n for token in doc:\n # Is this word in our known_entities, but is not recognized by the spaCy parser?\n if token.text.lower() in self.known_entities and token.ent_type not in self.entities:\n # We need to set the new entity to doc.ents directly (I believe the getter for doc.ents does\n # some important massaging. However, counter to the online docs, setting doc.ents wipes out\n # all of the previously recognized ents, so we stash the value, then we combine and reset.\n stash = doc.ents\n doc.ents = [(token.text.title(), doc.vocab.strings['PERSON'], token.i, token.i + 1)]\n doc.ents = doc.ents + stash\n\n # Find proper noun n-grams: (a) find a known entity, (b) is the next word also a known entity?,\n # (c) merge, (d) repeat\n # TODO: Joining multi-word named entities sometimes causes us trouble.\n doc_len = len(doc) # Helps us know when to exit the 'for loop' (since we change the # of items via merge)\n for token in doc:\n # if we're not at the end of the loop, and we recognize this as a proper noun and it's not a stop word\n # and the token isn't a space...\n if token.i + 1 < doc_len and token.ent_type in self.entities and \\\n token.text.lower() not in self.stop_words and token.text not in ' ':\n next_token = doc[token.i + 1]\n # keep looping while we're not at the end of the loop and this token has the same entity type as\n # the previous token and it's not a stop word or a space.\n while token.i + 1 < doc_len and next_token.ent_type == token.ent_type and \\\n next_token.text.lower() not in self.stop_words and next_token.text not in ' ':\n n_gram = doc[token.i:token.i + 2]\n n_gram.merge()\n doc_len -= 1 # the merge changes the list length, so we just shrunk the list!\n # print(x)\n if token.i + 1 >= doc_len:\n break\n\n return doc","def prep_dict(word):\n counts = {}\n for l in word.lower():\n if l!=\" \":\n counts[l] = counts.get(l,0) + 1\n return counts","def get_data(articles): # Here, the articles will be very long strings\r\n vdictionary = {} # dictionary for tokens that are found in dictionary\r\n _odata = [0] * 12 # list collecting everything except date; last number of words=index:0\r\n word_length = 0 # initializing the value of word length; will be updated via loop\r\n tokens = re.findall('\\w+', articles) # Note that \\w+ splits hyphenated words\r\n for token in tokens: # Goes through generated tokens from articles\r\n if (not token.isdigit()) and (len(token) > 1) and (token in lm_dictionary.keys()): # conditions for checking if token is in dictionary\r\n _odata[1] += 1 # updating word count \r\n word_length += len(token) # updating word length\r\n if token not in vdictionary: # initial statement regarding steps for handling tokens not in the dictionary\r\n vdictionary[token] = 1 # count of tokens in text that show up in dictionary\r\n \r\n####### Keeping Track of Categorical Token Counts (Nonzero entry=True) also checks if word is stop word\r\n if lm_dictionary[token].positive and not lm_dictionary[token].stopword: _odata[2] += 1\r\n if lm_dictionary[token].negative and not lm_dictionary[token].stopword: _odata[3] += 1\r\n if lm_dictionary[token].uncertainty and not lm_dictionary[token].stopword: _odata[4] += 1\r\n if lm_dictionary[token].litigious and not lm_dictionary[token].stopword: _odata[5] += 1\r\n if lm_dictionary[token].weak_modal and not lm_dictionary[token].stopword: _odata[6] += 1\r\n if lm_dictionary[token].moderate_modal and not lm_dictionary[token].stopword: _odata[7] += 1\r\n if lm_dictionary[token].strong_modal and not lm_dictionary[token].stopword: _odata[8] += 1\r\n if lm_dictionary[token].constraining and not lm_dictionary[token].stopword: _odata[9] += 1\r\n #total_syllables += lm_dictionary[token].syllables # interesting parameter to measure\r\n\r\n #_odata[12] = len(re.findall('[0-9]', doc))\r\n # drop punctuation within numbers for number count\r\n articles = re.sub('(?!=[0-9])(\\.|,)(?=[0-9])', '', articles)\r\n articles = articles.translate(str.maketrans(string.punctuation, \" \" * len(string.punctuation)))\r\n #_odata[13] = len(re.findall(r'\\b[-+\\(]?[$€£]?[-+(]?\\d+\\)?\\b', doc))\r\n # _odata[14] = total_syllables / _odata[2]\r\n #print(_odata[1])\r\n _odata[10] = word_length / _odata[1] # computing average word length\r\n _odata[11] = len(vdictionary) # total vocab count\r\n \r\n # Convert counts to %\r\n for i in range(2, 9 + 1): # specifying range of percentages\r\n try:\r\n _odata[i] = (_odata[i] / _odata[1]) * 100 # updating count to percent\r\n except:\r\n print(\"zero denominator\")\r\n # Vocabulary\r\n \r\n return _odata # returning the data\r","def update_word_pos_vocab(text_list, threshold, base_word_vocab, base_pos_vocab, word_vocab_path, pos_vocab_path, tokenizer_type=\"mecab\"):\n\n def do_concurrent_tagging(start, end, text_list, word_counter, pos_counter):\n jpype.attachThreadToJVM()\n for i, text in enumerate(text_list[start:end]):\n text = text.strip()\n text = text.lower()\n\n try:\n if tokenizer_type == \"mecab\":\n tokens_ko = mecab.pos(text)\n else:\n tokens_ko = twitter.pos(text, norm=True) # , stem=True)\n\n word_tokens_ko = [str(pos[0]) for pos in tokens_ko]\n pos_tokens_ko = [str(pos[1]) for pos in tokens_ko]\n word_counter.update(word_tokens_ko)\n pos_counter.update(pos_tokens_ko)\n\n if i % 1000 == 0:\n print(\"[%d/%d (total: %d)] Tokenized input text.\" % (\n start + i, start + len(text_list[start:end]), len(text_list)))\n\n except Exception as e: # for Out of memory\n print(e)\n continue\n\n word_counter = Counter()\n pos_counter = Counter()\n\n num_thread = 4\n thread_list = []\n n_x_text = len(text_list)\n for i in range(num_thread):\n thread_list.append(Thread(target=do_concurrent_tagging, args=(\n int(i * n_x_text / num_thread), int((i + 1) * n_x_text / num_thread), text_list, word_counter, pos_counter)))\n\n for thread in thread_list:\n thread.start()\n\n for thread in thread_list:\n thread.join()\n\n print(word_counter.most_common(10)) # print most common words\n\n ## Word\n words = [word for word, cnt in word_counter.items() if cnt >= threshold]\n\n for i, word in enumerate(words):\n base_word_vocab.add_word(str(word))\n updated_word_vocab = base_word_vocab\n\n with open(word_vocab_path, 'wb') as f:\n pickle.dump(updated_word_vocab, f)\n\n ## Pos\n poss = [pos for pos, cnt in pos_counter.items() if cnt >= 0] # insert all pos tag\n\n for i, pos in enumerate(poss):\n base_pos_vocab.add_word(str(pos))\n updated_pos_vocab = base_pos_vocab\n\n with open(pos_vocab_path, 'wb') as f:\n pickle.dump(updated_pos_vocab, f)\n\n return updated_word_vocab, updated_pos_vocab","def RenumberComms(dictionary):\n count = 0\n ret = dictionary.copy()\n new_values = dict([])\n for key in dictionary.keys() :\n newSet = set()\n comms = dictionary[key]\n for comm in comms:\n new_value = new_values.get(comm, -1)\n if new_value == -1 :\n new_values[comm] = count\n new_value = count\n count = count + 1\n newSet.add(new_value)\n ret[key] = newSet\n\n return ret","def frequent_nouns_counts(self, tokens):\n\n unigram_fd = nltk.FreqDist(tokens)\n pos = POS()\n common_unigrams = unigram_fd.most_common(\n int(self.top_pct * len(unigram_fd)))\n\n nouns = [pair for pair in common_unigrams\n if pair[0] not in self.stopwords()\n and pos.percent_noun(pair[0]) > 0.5]\n\n return nouns","def word_enders():\n space = ''\n for k,v in key.items():\n if v==' ':\n space = k\n\n enders = Counter()\n for word in ciphertext.split( space ):\n last_two = word[-2]+word[-1]\n enders[last_two] += 1\n\n return enders","def word_frequencies(word_list: TextIO) -> dict:\n words = word_list.read().split(' ')\n amount_of_words = len(set(words))\n frequencies = {}\n for index, word in enumerate(words):\n clean_word = remove_punctuation(word)\n if clean_word not in frequencies:\n frequencies[clean_word] = (index + 1) / amount_of_words\n del frequencies[\"\"]\n return frequencies","def get_counts(self):\n counts = {}\n for document in self.docs:\n for word in document:\n if word not in counts.keys():\n counts[word] = 1\n else:\n counts[word] += 1\n return counts","def add_unique_word_count(self):\n call_count = lambda letter_: self._count_unique_words(letter_)\n self.dataframe['unique_words'] = self.dataframe['letter'].map(call_count)","def test_dictionary_create_repeat():\n tokens = [['testing'], ['testing', 'testing', 'testing'], ['make', 'tokens']]\n dictionary = edurate_gensim.dictionary_create(tokens)\n corp = [dictionary.doc2bow(token) for token in tokens]\n assert corp == [[(0, 1)], [(0, 3)], [(1, 1), (2, 1)]]","def train():\n\tA = collections.defaultdict(dict)\n\tB = collections.defaultdict(dict)\n\tpos_list = []\n\tword_list = []\n\tstr_buf = []\n\n\n\t# read each line and count A and B\n\tfor line in sys.stdin:\n\t\tline = line.split()\n\t\t# print(line)\n\t\tif len(line) == 3:\n\t\t\tstr_buf.append((str(line[0]), str(line[1])))\n\t\t\tword_list.append(str(line[0]))\n\t\t\tpos_list.append(str(line[1]))\n\n\t\telse:\n\t\t\t# if come to the end of a sentence\n\t\t\tif len(str_buf) != 0:\n\t\t\t\tstr_buf = [('<s>','BOS')] + str_buf + [('</s>', 'EOS')]\n\t\t\t\tword_list += ['<s>', '</s>']\n\t\t\t\tpos_list += ['BOS', 'EOS']\n\n\t\t\t\tfor i, s in enumerate(str_buf):\n\t\t\t\t\tif s[0] in B[s[1]]:\n\t\t\t\t\t\tB[s[1]][s[0]] += 1\n\t\t\t\t\telse:\n\t\t\t\t\t\tB[s[1]][s[0]] = 1\n\n\t\t\t\t\tif s[0] != '</s>':\n\t\t\t\t\t\t# print('strbuf[i]:',str_buf[i], 's[1]', s[1])\n\t\t\t\t\t\tif str_buf[i+1][1] in A[s[1]]:\n\t\t\t\t\t\t\tA[s[1]][str_buf[i+1][1]] += 1\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tA[s[1]][str_buf[i+1][1]] = 1\n\n\t\t\t\tstr_buf = []\n\n\t# get unique POS list and word list\n\tpos_list_uniq = list(set(pos_list))\n\tword_list_uniq = list(set(word_list))\n\n\n\t# assume <UNK>, smoothing, normalize\n\tB_sm = collections.defaultdict(dict)\n\tA_sm = A.copy()\n\n\t# assume words apeear less than 2 times as <UNK>\n\tword_count = collections.Counter(word_list)\n\tfor pos in B:\n\t\tfor word in B[pos]:\n\t\t\tif word_count[word] > 1:\n\t\t\t\tB_sm[pos][word] = B[pos][word]\n\n\t\t\telse: # add <UNK> to B_sm\n\t\t\t\tword_list_uniq.remove(word)\n\t\t\t\tif '<UNK>' in B_sm[pos]:\n\t\t\t\t\tB_sm[pos]['<UNK>'] += 1\n\t\t\t\telse:\n\t\t\t\t\tB_sm[pos]['<UNK>'] = 1\n\n\tword_list_uniq += ['<UNK>']\n\n\t# add 1 smoothing\n\tfor pos in pos_list_uniq:\n\t\tfor word in word_list_uniq:\n\t\t\tif word in B_sm[pos]:\n\t\t\t\tB_sm[pos][word] += 1\n\t\t\telse:\n\t\t\t\tB_sm[pos][word] = 1\n\n\tfor prev in pos_list_uniq:\n\t\tfor next in pos_list_uniq:\n\t\t\tif next in A_sm[prev]:\n\t\t\t\tA_sm[prev][next] += 1\n\t\t\telse:\n\t\t\t\tA_sm[prev][next] = 1\n\n\t# delete instances like A[VB][BOS], A[EOS][VB],\n\t# B[VB]['</s>'], B[EOS]['Jack']\n\n\tfor pos in B_sm:\n\t\tfor word in B_sm[pos]:\n\t\t\tif (pos == 'BOS' and word != '<s>') or \\\n\t\t\t(pos == 'EOS' and word != '</s>') or \\\n\t\t\t(word == '<s>' and pos != 'BOS') or \\\n\t\t\t(word == '</s>' and pos != 'EOS'):\n\t\t\t\tB_sm[pos][word] = 0\n\n\tfor prev in A_sm:\n\t\tfor next in A_sm[prev]:\n\t\t\tif prev == 'EOS' or next == 'BOS':\n\t\t\t\tA_sm[prev][next] = 0\n\n\t# normalize\n\tfor pos in B_sm:\n\t\ts = sum(B_sm[pos].values())\n\t\tfor word in B_sm[pos]:\n\t\t\tif B_sm[pos][word] != 0:\n\t\t\t\tB_sm[pos][word] /= s\n\n\tfor prev in A_sm:\n\t\ts = sum(A_sm[prev].values())\n\t\tfor next in A_sm[prev]:\n\t\t\tif A_sm[prev][next] != 0:\n\t\t\t\tA_sm[prev][next] /= s\n\n\treturn A_sm, B_sm, word_list_uniq"],"string":"[\n \"def merge_token_counters(\\n token_counter1: Dict[str, int], token_counter2: Dict[str, int]\\n ) -> Dict[str, int]:\\n for token, number in token_counter2.items():\\n if token in token_counter1:\\n token_counter1[token] += number\\n else:\\n token_counter1[token] = number\\n\\n return token_counter1\",\n \"def word_frequency( tokenized, dic ):\\n print( 'computing word frequencies' )\\n start = time.time()\\n for i, text in enumerate( tokenized ):\\n for token in text:\\n if token not in dic:\\n dic[ token ] = 1\\n else:\\n dic[ token ] += 1\\n if i % 10000 == 0:\\n sys.stdout.write( '\\\\rprocessed : {}/{} reviews in {}s'.format( i, NO_REVIEWS, time.time() - start ) )\\n sys.stdout.write( '\\\\rprocessed : {}/{} reviews in {}s\\\\n'.format( i, NO_REVIEWS, time.time() - start ) )\",\n \"def count_words(tokenized_sentences):\\r\\n \\r\\n word_counts = {}\\r\\n \\r\\n # Loop through each sentence\\r\\n for sentence in tokenized_sentences: # complete this line\\r\\n \\r\\n for token in sentence: # complete this line\\r\\n\\r\\n # If the token is not in the dictionary yet, set the count to 1\\r\\n if token not in word_counts.keys(): # complete this line\\r\\n word_counts[token] = 1\\r\\n \\r\\n # If the token is already in the dictionary, increment the count by 1\\r\\n else:\\r\\n word_counts[token] += 1\\r\\n \\r\\n return word_counts\",\n \"def add_to_dict(self, tokens):\\n# TODO: ?add normalization of a token?\\n for token in tokens:\\n if self.embedding_words and (token not in self.embedding_words):\\n continue\\n self.freq[token] += 1\\n if token not in self.tok2ind:\\n index = len(self.tok2ind)\\n self.tok2ind[token] = index\\n self.ind2tok[index] = token\",\n \"def merge_with(self, other):\\n old2new = {}\\n for other_id, other_token in iteritems(other):\\n if other_token in self.token2id:\\n new_id = self.token2id[other_token]\\n else:\\n new_id = len(self.token2id)\\n self.token2id[other_token] = new_id\\n self.dfs[new_id] = 0\\n old2new[other_id] = new_id\\n try:\\n self.dfs[new_id] += other.dfs[other_id]\\n except Exception:\\n # `other` isn't a Dictionary (probably just a dict) => ignore dfs, keep going\\n pass\\n try:\\n self.num_docs += other.num_docs\\n self.num_nnz += other.num_nnz\\n self.num_pos += other.num_pos\\n except Exception:\\n pass\\n\\n import gensim.models\\n return gensim.models.VocabTransform(old2new)\",\n \"def compute_token_count(token_dict):\\n return sum([token_dict[key] for key in token_dict.keys()])\",\n \"def calculate(self, tokens: list):\\n vektor_tf_unigram = {}\\n\\n for token in tokens:\\n if token in vektor_tf_unigram:\\n vektor_tf_unigram[token] = vektor_tf_unigram[token] + 1\\n else:\\n vektor_tf_unigram[token] = 1\\n\\n return vektor_tf_unigram\",\n \"def merged_tokens(self, i, tokenizer):\\n out = []\\n counts = []\\n last_id = None\\n tokens = tokenizer.convert_ids_to_tokens(self.token_ids[i].tolist())\\n for token, current_id in zip(tokens[1:-1], self.conll_ids[i]):\\n if last_id == current_id.item():\\n if token.startswith(\\\"##\\\"):\\n out[-1] += (token[2:])\\n else:\\n out[-1] += token\\n counts[-1] += 1\\n else:\\n out.append(token)\\n counts.append(1)\\n last_id = current_id\\n return out, counts\",\n \"def convert_word_to_count(counter={}, doc=[]):\\n for sentence in doc:\\n for word in sentence.split():\\n if word not in counter:\\n counter[word] = 1\\n else:\\n counter[word] += 1\\n return counter\",\n \"def getTTokens(wordlist, tokens = {}, count = 0, token = TOKEN):\\n\\n\\tif len(wordlist) == 0:\\n\\t\\treturn tokens, count\\n\\n\\tfor i in wordlist:\\n\\t\\tfor x in i:\\n\\t\\t\\tif tokens.has_key(x[token]):\\n\\t\\t\\t\\ttokens[x[token]] += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttokens[x[token]] = 1\\n\\t\\t\\tcount += 1\\n\\treturn tokens, count\",\n \"def count_terms(self, tokens):\\n\\n terms = [self.term_match(t) for t in tokens ]\\n \\n terms = [t for t in terms if t != None]\\n\\n #print terms\\n lf = dict(Counter(terms))\\n for k in lf:\\n lf[k] /= float(len(tokens))\\n #lf[k] = 1 # binarize?\\n pass\\n return lf\",\n \"def make_bag_words(document_tokenized):\\n bag_words = dict()\\n for token in document_tokenized:\\n if token in bag_words.keys():\\n bag_words[token] += 1\\n else:\\n bag_words[token] = 1\\n return bag_words\",\n \"def buildDict(self, dict):\\n self.all_words = set(dict)\\n self.wc_dict = collections.defaultdict(int)\\n for w in dict:\\n for wc in self.get_wildcards(w):\\n self.wc_dict[wc] += 1\",\n \"def _merge_conllu_subtokens(self, lines: List[str], doc: Doc)-> Doc:\\n # identify and process all subtoken spans to prepare attrs for merging\\n subtok_spans = []\\n for line in lines:\\n parts = line.split(\\\"\\\\t\\\")\\n id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts\\n if \\\"-\\\" in id_:\\n subtok_start, subtok_end = id_.split(\\\"-\\\")\\n subtok_span = doc[int(subtok_start) - 1 : int(subtok_end)]\\n subtok_spans.append(subtok_span)\\n # create merged tag, morph, and lemma values\\n tags = []\\n morphs = {}\\n lemmas = []\\n for token in subtok_span:\\n tags.append(token.tag_)\\n lemmas.append(token.lemma_)\\n if token._.merged_morph:\\n for feature in token._.merged_morph.split(\\\"|\\\"):\\n field, values = feature.split(\\\"=\\\", 1)\\n if field not in morphs:\\n morphs[field] = set()\\n for value in values.split(\\\",\\\"):\\n morphs[field].add(value)\\n # create merged features for each morph field\\n for field, values in morphs.items():\\n morphs[field] = field + \\\"=\\\" + \\\",\\\".join(sorted(values))\\n # set the same attrs on all subtok tokens so that whatever head the\\n # retokenizer chooses, the final attrs are available on that token\\n for token in subtok_span:\\n token._.merged_orth = token.orth_\\n token._.merged_lemma = \\\" \\\".join(lemmas)\\n token.tag_ = \\\"_\\\".join(tags)\\n token._.merged_morph = \\\"|\\\".join(sorted(morphs.values()))\\n token._.merged_spaceafter = (\\n True if subtok_span[-1].whitespace_ else False\\n )\\n\\n with doc.retokenize() as retokenizer:\\n for span in subtok_spans:\\n retokenizer.merge(span)\\n\\n return doc\",\n \"def __add_to_word_dict__(self, word, tag):\\n if self.word_dict.get(word, None) is None:\\n self.word_dict[word] = defaultdict(int)\\n self.word_dict[word][tag] += 1\",\n \"def buildDict(self, dict):\\n for word in dict:\\n self.s.add(word)\\n self.length_set = set([len(word) for word in dict])\",\n \"def sort_by_tokens(\\n self, tokens_or_dictionary, allow_mismatch=False,\\n allow_missed_tokens=False, allow_missed_embeddings=False\\n ):\\n\\n # The input can a dictionary or a list of tokens.\\n if isinstance(tokens_or_dictionary, h.dictionary.Dictionary):\\n dictionary = tokens_or_dictionary\\n tokens = dictionary.tokens\\n else:\\n tokens = tokens_or_dictionary\\n dictionary = h.dictionary.Dictionary(tokens)\\n\\n self_set = set(self.dictionary.tokens)\\n other_set = set(tokens)\\n\\n # Check for extraneous tokens.\\n self_coverage = other_set - self_set\\n if len(self_coverage) > 0:\\n\\n if allow_mismatch or allow_missed_embeddings:\\n # Drop the extraneous tokens\\n tokens = [token for token in tokens if token in self.dictionary]\\n\\n else:\\n raise ValueError(\\n 'The new dictionary has {} tokens that do not have a '\\n 'corresponding embedding.\\\\n{}\\\"'.format(\\n len(self_coverage), '\\\"\\\\n\\\"'.join(self_coverage))\\n )\\n\\n # Check for missing tokens.\\n dictionary_coverage = self_set - other_set\\n if len(dictionary_coverage) > 0:\\n if not allow_mismatch and not allow_missed_tokens:\\n raise ValueError(\\n \\\"The new dictionary is missing {} entries for some \\\"\\n \\\"embedded tokens.\\\\n{}\\\".format(\\n len(dictionary_coverage),\\n '\\\\n'.join(dictionary_coverage)\\n )\\n )\\n else:\\n if self.verbose:\\n print(\\n \\\"Warning, some embeddings were dropped because they \\\"\\n \\\"have no corresponding token in the provided \\\"\\n \\\"dictoinary.\\\"\\n )\\n\\n sort_ids = [self.dictionary.get_id(token) for token in tokens]\\n\\n # This implicitly drops missing tokens\\n self.V = self.V[sort_ids]\\n if self.W is not None:\\n self.W = self.W[sort_ids]\\n if self.vb is not None:\\n self.vb = self.vb[sort_ids]\\n if self.wb is not None:\\n self.wb = self.wb[sort_ids]\\n\\n self.dictionary = h.dictionary.Dictionary(tokens)\",\n \"def count_words_and_dublicates(novel):\",\n \"def computeWordFrequencies(self, tokens: ['token'], frequencies: {'token': int}):\\n # project2: update this method to take existing dict as parameter and modify it\\n # additionally, stopwords are not inserted in the dict;\\n # words shorter than 3 character or contains all digits are ignored\\n for token in tokens:\\n # if the key is not in dict, dict.setdefault method initiates the value at 0\\n # if token not in stopwords and len(token) >= 3 and not token.isdigit():\\n frequencies[token] = frequencies.setdefault(token, 0) + 1\",\n \"def __find_union(dicts: list):\\n global other_time\\n start = time.time()\\n union_list = []\\n while len(dicts) > 1:\\n word_count = {}\\n final_dict = {}\\n for d in dicts:\\n # print(json.dumps(d, indent=4))\\n for word in d:\\n if word not in word_count:\\n word_count[word] = 1\\n else:\\n word_count[word] = word_count[word] + 1\\n for item in word_count:\\n if word_count[item] == len(dicts):\\n union_list.append(item)\\n for d in dicts:\\n for word in d:\\n if word in union_list:\\n if word not in final_dict:\\n final_dict[word] = d[word]\\n else:\\n final_dict[word] += d[word]\\n if len(final_dict) > 0:\\n # print(len(dicts))\\n end = time.time()\\n diff = end - start\\n other_time += diff\\n # print(json.dumps(final_dict, indent=4))\\n return (final_dict)\\n else:\\n\\n dicts = dicts[:len(dicts) - 1]\\n\\n end = time.time()\\n diff = end - start\\n other_time += diff\\n return {}\",\n \"def insight_github_unique_word_results():\\n counter_container = OrderedDict()\\n\\n path_to_input_file = os.path.join(constants.TEST_DIRECTORY,\\n \\\"fixtures/ft1_expected_results_v1.txt\\\")\\n with open(path_to_input_file, 'r') as input_file:\\n for line in input_file:\\n word, count = line.split()\\n counter_container[word] = int(count)\\n return counter_container\",\n \"def calculate_poses_for_word(corpus):\\n total_pos_count = {}\\n poses_for_word = {}\\n # For each word, find all POSes.\\n for sentence in corpus.get_sentences():\\n for word in sentence:\\n id, form, lemma, plemma, pos, ppos = word\\n #form = form.lower()\\n\\n if pos in total_pos_count:\\n total_pos_count[pos] += 1\\n else:\\n total_pos_count[pos] = 1\\n\\n if form in poses_for_word:\\n if pos in poses_for_word[form]:\\n poses_for_word[form][pos] += 1\\n else:\\n poses_for_word[form][pos] = 1\\n else:\\n poses_for_word[form] = {pos: 1}\\n\\n return poses_for_word, total_pos_count\",\n \"def task1(sentence):\\n split_sentence = sentence.split()\\n dictionary = dict()\\n for word in split_sentence:\\n if word in dictionary:\\n dictionary[word] += 1\\n else:\\n dictionary[word] = 1\\n for item in dictionary:\\n print(\\\"Word \\\" + item + \\\" used \\\" + str(dictionary[item]) + \\\" times\\\")\\n return dictionary\",\n \"def calcCountDict(TFdict):\\n\\n countDict = {}\\n\\n for doc in TFdict:\\n for term in doc:\\n if term in countDict:\\n countDict[term] +=1\\n else:\\n countDict[term] = 1\\n\\n return countDict\",\n \"def get_frequencies(tokens):\\n cnt = {}\\n\\n for word in tokens:\\n if word not in cnt:\\n cnt[word] = 0\\n\\n cnt[word] += 1\\n\\n return cnt\",\n \"def _disambiguate_merged_glosses(self, merged_glosses):\\n\\t\\tprocessed_glosses = {}\\n\\t\\ttotal_glosses = len(merged_glosses)\\n\\t\\tcurrent_gloss = 0\\n\\t\\tdisambiguated_glosses_count = 0\\n\\t\\tskipped_glosses_count = 0\\n\\n\\t\\t# go over all glosses and disambiguate them if possible\\n\\t\\tfor gloss_key in merged_glosses:\\n\\t\\t\\tcurrent_gloss += 1\\n\\t\\t\\tgloss = merged_glosses[gloss_key]\\n\\t\\t\\ttaggable_tokens = []\\n\\t\\t\\ttagged_tokens = []\\n\\n\\t\\t\\tif len(gloss.tokens) == 0:\\n\\t\\t\\t\\tself._log_message(\\\"WARNING: unmerged gloss {0}\\\".format(gloss_key))\\n\\n\\t\\t\\t# for each token inside the gloss determine if the token is already disambiguated, needs to be disambiguated or isnt part of WordNet anyways\\n\\t\\t\\tfor token_index in gloss.tokens:\\n\\t\\t\\t\\ttoken = gloss.tokens[token_index]\\n\\t\\t\\t\\ttokens_wn_ss_types = list(map(int, re.findall(\\\"[0-9]+\\\", token.lemma)))\\n\\t\\t\\t\\t# tokens that are tagged as \\\"man\\\" or \\\"auto\\\" are already disambiguated\\n\\t\\t\\t\\tif token.tag in [\\\"man\\\", \\\"auto\\\"]:\\n\\t\\t\\t\\t\\ttagged_tokens.append(token)\\n\\t\\t\\t\\t# if a token is tagged as \\\"un\\\" it is not yet annotated, but possibly should be; if the lemma contains entries of the type \\\"LEMMA%SS_TYPE\\\" and the POS if the word matches one of the lemma-ss_types it needs to be/can be disambiguated\\n\\t\\t\\t\\telif token.tag == \\\"un\\\" and re.match(\\\"([a-z]+%[0-9]\\\\|?)+\\\", token.lemma) and not set(GLOSSTAG_POS_POSSIBLE_SS_TYPES[token.pos]).isdisjoint(set(tokens_wn_ss_types)) and type(token) != CollocationMember:\\n\\t\\t\\t\\t\\ttaggable_tokens.append(token)\\n\\n\\t\\t\\t# if there are no remaining taggable tokens, then proceed with the next gloss\\n\\t\\t\\tif len(taggable_tokens) == 0:\\n\\t\\t\\t\\tprocessed_glosses[gloss_key] = gloss\\n\\t\\t\\t\\tskipped_glosses_count += 1\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tdisambiguated_glosses_count += 1\\n\\n\\t\\t\\t## DISAMBUGATION PROCEDURE ##\\n\\t\\t\\tprint(\\\"\\\\tat gloss {0} of {1}\\\".format(current_gloss, total_glosses))\\n\\t\\t\\tprint(\\\"\\\\t\\\\t...disambiguating {0} words in gloss {1}\\\".format(len(taggable_tokens), gloss_key))\\n\\t\\t\\t# disambiguated_gloss = self._disambiguate_gloss_with_path_similarity(gloss, taggable_tokens, tagged_tokens)\\n\\t\\t\\tdisambiguated_gloss = self._disambiguate_gloss_by_most_frequent_sense(gloss, taggable_tokens, tagged_tokens)\\n\\t\\t\\t# finished, append to output\\n\\t\\t\\tprocessed_glosses[gloss_key] = disambiguated_gloss\\n\\n\\t\\tprint(\\\"\\\\tdisambiguated {0} glosses, skipped {1}\\\".format(disambiguated_glosses_count, skipped_glosses_count))\\n\\n\\t\\treturn processed_glosses\",\n \"def common_words(words: Dict[str, int], count: int) -> None:\\n \\n original_order = list(words.values())\\n max_to_min = list(reversed(sorted(words.values())))\\n temp_lst1 = []\\n temp_lst2 = []\\n \\n \\n if count == len(words):\\n words = words\\n \\n \\n for num in range(len(max_to_min)):\\n if len(temp_lst1) < count:\\n temp_lst1.append(max_to_min[num])\\n \\n for nums in range(len(temp_lst1)):\\n max_to_min.remove(temp_lst1[nums])\\n \\n \\n temp_lst2 = remove_repeats(max_to_min, temp_lst1)\\n \\n a = reformat_order(original_order, temp_lst2)\\n \\n words_to_counts = combine_list(format_dict(words, a), a)\\n \\n words.clear()\\n \\n for keys in words_to_counts:\\n words[keys] = words_to_counts[keys]\",\n \"def _count_word_frequency(self, data):\\n _dict = {}\\n for _docs in data:\\n for _word in _docs:\\n if _word in _dict:\\n _dict[_word] += 1\\n else:\\n _dict[_word] = 1\\n return _dict\",\n \"def CountWords(input1):\\n tokens = []\\n\\n d={}\\n\\n tokens = tokens+input1.lower().split()\\n\\n for token in tokens:\\n # Remove Punctuation\\n word=token.replace(\\\"!\\\", \\\"\\\")\\n word=word.replace(\\\".\\\", \\\"\\\")\\n d[word] = d.get(word,0)+1\\n\\n word_frq=[]\\n for i,v in d.items():\\n word_frq.append((v,i))\\n word_frq.sort(reverse=True)\\n\\n for word in word_frq:\\n print (word[1],word[0])\",\n \"def _build_token_dict(self, corpus: List[List[str]], min_count: int = 3):\\n token2idx = {\\n self.token_pad: 0,\\n self.token_unk: 1,\\n self.token_bos: 2,\\n self.token_eos: 3\\n }\\n\\n token2count = {}\\n for sentence in corpus:\\n for token in sentence:\\n count = token2count.get(token, 0)\\n token2count[token] = count + 1\\n\\n # 按照词频降序排序\\n sorted_token2count = sorted(token2count.items(),\\n key=operator.itemgetter(1),\\n reverse=True)\\n token2count = collections.OrderedDict(sorted_token2count)\\n\\n for token, token_count in token2count.items():\\n if token not in token2idx and token_count >= min_count:\\n token2idx[token] = len(token2idx)\\n\\n self.token2idx = token2idx\\n self.idx2token = dict([(value, key)\\n for key, value in self.token2idx.items()])\\n logging.debug(f\\\"build token2idx dict finished, contains {len(self.token2idx)} tokens.\\\")\\n self.dataset_info['token_count'] = len(self.token2idx)\",\n \"def count_ngrams(self, corpus):\\n \\n self.unigramcounts = {} # might want to use defaultdict or Counter instead\\n self.bigramcounts = {} \\n self.trigramcounts = {} \\n\\n self.total = 2\\n ##Your code here\\n\\n for sentence in corpus:\\n temp_1 = get_ngrams(sentence,1)\\n temp_2 = get_ngrams(sentence,2)\\n temp_3 = get_ngrams(sentence,3)\\n for i in range(len(temp_1)):\\n if temp_1[i] in self.unigramcounts:\\n self.unigramcounts[temp_1[i]] += 1\\n else:\\n self.unigramcounts[temp_1[i]] = 1\\n self.total += 1\\n\\n for i in range(len(temp_2)):\\n if temp_2[i] in self.bigramcounts:\\n self.bigramcounts[temp_2[i]] += 1\\n else:\\n self.bigramcounts[temp_2[i]] = 1\\n\\n for i in range(len(temp_3)):\\n if temp_3[i] in self.trigramcounts:\\n self.trigramcounts[temp_3[i]] += 1\\n else:\\n self.trigramcounts[temp_3[i]] = 1\\n return\",\n \"def vectorize_content(self, content):\\r\\n file_dict = {}\\r\\n for word in content:\\r\\n if word in file_dict:\\r\\n file_dict[word] += 1\\r\\n else:\\r\\n file_dict[word] = 1\\r\\n return file_dict\",\n \"def CompactTokens(self):\\n map = {}\\n for token_ in self.tokens_:\\n hash_string = token_.EncodeForHash()\\n try: map[hash_string].append(token_)\\n except KeyError: map[hash_string] = [token_]\\n ntokens = []\\n keys = map.keys()\\n keys.sort()\\n for k in keys:\\n token_ = map[k][0]\\n for otoken in map[k][1:]:\\n token_.IncrementCount(otoken.Count())\\n ntokens.append(token_)\\n self.tokens_ = ntokens\",\n \"def word_count(self, document):\\n start_time = time.time()\\n dictionary = dict()\\n counter = defaultdict(int)\\n for line in document.splitlines():\\n for word in line.split():\\n if word not in PUNCTUATION_MARK:\\n counter[word] += 1\\n for word, cnt in sorted(counter.items(), key=lambda x: (-x[1], x[0])):\\n dictionary[word] = cnt\\n self.log.info(\\\"Duration count dictionary: {duration}\\\".format(duration=float(time.time() - start_time)))\\n return dictionary\",\n \"def tag():\\n iso_list = []\\n tags = [\\\"spatial_entity\\\", \\\"place\\\", \\\"motion\\\", \\\"location\\\", \\\"signal\\\", \\\"qslink\\\", \\\"olink\\\"]\\n for token in doc:\\n if token.norm_ in tags:\\n iso_list.append(token.norm_)\\n setList = list(set(iso_list))\\n my_dict = {i: iso_list.count(i) for i in setList}\\n\\n for i in tags:\\n if i.lower() not in my_dict:\\n my_dict[i] = 0\\n print(my_dict)\",\n \"def count_words():\\n paragraph = \\\"a distinct section of a piece of writing,\\\"\\n # 替换\\n paragraph.replace(\\\",\\\", \\\" \\\").replace(\\\":\\\", \\\" \\\").replace(\\\";\\\", \\\" \\\").replace(\\\".\\\", \\\" \\\").replace(\\\"?\\\", \\\" \\\")\\n words = paragraph.split(\\\" \\\")\\n nums = {}\\n\\n for word in words:\\n nums[word] = nums[word]+1 if word in nums else 1\\n # nums[word] = nums.get(word, 0) + 1\\n\\n for word, num in nums.items():\\n print(word, \\\": \\\", num)\",\n \"def common_token_len(self, w1, d1, w2, d2):\\n w1_tk = set(self.__stem_Tokens(w1))\\n w2_tk = set(self.__stem_Tokens(w2))\\n common_len = len(w1_tk.intersection(w2_tk))\\n return common_len\",\n \"def getTokens(wordlist, tokens = {}, count = 0):\\n\\n\\tif len(wordlist) == 0:\\n\\t\\treturn tokens, count\\n\\n\\tfor i in wordlist:\\n\\t\\tif tokens.has_key(i):\\n\\t\\t\\ttokens[i] += 1\\n\\t\\telse:\\n\\t\\t\\ttokens[i] = 1\\n\\t\\tcount += 1\\n\\treturn tokens, count\",\n \"def clsWordCounts(wd_dict, wd_list, cl):\\n\\n # set up counter object for words in passed word list\\n word_counts = Counter(wd_list)\\n\\n # for each term in counter object\\n for term in word_counts:\\n\\n # if already in dictionary add to the count\\n if term in wd_dict[cl]:\\n\\n wd_dict[cl][term] += word_counts[term]\\n\\n # else add to the dictionary\\n else:\\n\\n wd_dict[cl][term] = word_counts[term]\\n\\n return wd_dict\",\n \"def update(self, iterable):\\n for word in iterable:\\n if word in self:\\n self[word] = self[word] + 1\\n self.tokens += 1\\n else:\\n self[word] = 1\\n self.types += 1\\n self.tokens += 1\",\n \"def count_tokens(self, words: Iterable[str]) -> Dict[str, int]:\\r\\n token_counts = Counter(words)\\r\\n return {\\\" \\\".join(token): count for token, count in token_counts.items()}\",\n \"def analyzeFile(filename): \\n fileData = open(filename, encoding=\\\"utf-8\\\") # open the file\\n \\n counts = {}\\n\\n for line in fileData:\\t\\t # iterates over every line of the file\\n words = line.split() # turns each line into a list\\n for word in words: #iterates over the words in each line list\\n word = word.lower().strip(string.whitespace+string.punctuation)\\n if len(word) > 0: #make sure word is longer than 0 before adding it to the dictionary\\n counts[word] = counts.get(word, 0) + 1 #look up if the dictionary has that word and if not then it'll add that word with the value 0 associated with it and then add one to that, if it has seen it it'll add 1 to the value stored in the counts dictionary\\n #when it gets here for the first line it goes back up to the top and repeats for the 2nd line\\n mostCommonWord = [word]\\n leastCommonWord = [word]\\n shortestWord = [word]\\n longestWord = [word]\\n \\n for item in counts:\\n if counts[mostCommonWord[0]] < counts[item]:\\n mostCommonWord = [item]\\n elif counts[mostCommonWord[0]] == counts[item]:\\n mostCommonWord.append(item)\\n if counts[leastCommonWord[0]] > counts[item]:\\n leastCommonWord = [item]\\n elif counts[leastCommonWord[0]] == counts[item]:\\n leastCommonWord.append(item)\\n if len(shortestWord[0]) > len(item):\\n shortestWord = [item] \\n elif len((shortestWord[0])) == len(item):\\n shortestWord.append(item)\\n if len(longestWord[0]) < len(item):\\n longestWord = [item]\\n elif len(longestWord[0]) == len(item):\\n longestWord.append(item)\\n \\n return (mostCommonWord, leastCommonWord, shortestWord, longestWord)\",\n \"def tokenize(self):\\n count = 0\\n for entry in self._entries:\\n token_pairs = []\\n for relation in entry['relations']:\\n assert len(relation) == 3\\n token_pairs.append((relation[0][0],relation[1][0],relation[2][0]))\\n\\n num_rels = len(entry['relations'])\\n num_random_rels = (self._max_seq_length - 2) // 3 - num_rels\\n\\n if num_random_rels>0:\\n pass\\n # gt_pairs = {(rel[0],rel[2]) for rel in entry['relations']}\\n # random_pairs = self._get_random_pair(entry['objects'], gt_pairs, num_random_rels)\\n # for pair in list(random_pairs):\\n # token_pairs.append((pair[0][0],'background', pair[1][0]))\\n else:\\n for i in range(-num_random_rels):\\n token_pairs.pop()\\n\\n random.shuffle(token_pairs)\\n tokens = []\\n for pair in token_pairs:\\n tokens.extend(pair)\\n\\n tokens = ['[CLS]'] + tokens + ['[SEP]']\\n tokens_char = tokens\\n\\n target = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3==2 else -1 for i, x in enumerate(tokens)]\\n tokens = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3!=2 else self._tokenizer.vocab.get('[MASK]', self._tokenizer.vocab['[UNK]']) for i, x in enumerate(tokens)]\\n \\n for i in range(len(tokens)):\\n if target[i] != -1:\\n print(tokens_char[i],tokens[i],target[i])\\n\\n segment_ids = [0] * len(tokens)\\n input_mask = [1] * len(tokens)\\n # input_mask = [1 if i%3==2 else 0 for i in range(len(tokens))]\\n # co_attention_mask = [-1 if i%3==2 else 1 for i in range(len(tokens))]\\n # co_attention_mask = torch.zeros((self._max_region_num, self._max_seq_length))\\n # co_attention_mask[0] = -1\\n # co_attention_mask[-1] = -1\\n \\n if len(tokens) < self._max_seq_length:\\n padding = [self._padding_index] * (self._max_seq_length - len(tokens))\\n tokens = tokens + padding\\n input_mask += padding\\n segment_ids += padding \\n target += [-1] * len(padding) \\n\\n assert_eq(len(tokens), self._max_seq_length)\\n entry['input_ids'] = tokens \\n entry[\\\"input_mask\\\"] = input_mask\\n entry['segment_ids'] = segment_ids\\n # entry[\\\"co_attention_mask\\\"] = co_attention_mask\\n entry['target'] = target\\n\\n sys.stdout.write('%d/%d\\\\r' % (count, len(self._entries)))\\n sys.stdout.flush()\\n count += 1\",\n \"def __parse_corpus(self, corpus):\\n corpus = self.__handle_corpus_unkwon_words(corpus)\\n start_token = ' '.join([NGramModel.START_SENTENCE_TOKEN]*(self.__n-1))\\n word_list = corpus.replace(NGramModel.START_SENTENCE_TOKEN, start_token).split()\\n \\n for n in range(1, self.__n+1): \\n self.__ngram_counts[n] = {}\\n for ngram, count in Counter(self.__generate_n_grams(word_list, n)).items():\\n self.__ngram_counts[n][' '.join(ngram)] = count\",\n \"def _count_occurrences(self, corpus_tokens: list, window_size: int):\\n ngram_occurrences = {}\\n for i, corpus_token in enumerate(corpus_tokens):\\n ngram = []\\n if i + window_size > len(corpus_tokens) - 1:\\n break\\n else:\\n for j in range(window_size):\\n ngram.append(corpus_tokens[i + j])\\n if self.TOKENS.END.value in ngram:\\n continue\\n else:\\n ngram_key = tuple(ngram)\\n if ngram_key in ngram_occurrences:\\n ngram_occurrences[ngram_key] += 1\\n else:\\n ngram_occurrences[ngram_key] = 1\\n return ngram_occurrences\",\n \"def getCounter(self):\\n word_count, noun_word_count = Counter(), Counter()\\n word_rating, noun_word_rating = defaultdict(list), defaultdict(list)\\n docs = self.nlp.pipe(\\n self.docs, n_process=1, disable=self.disablelayers)\\n \\n\\n for index, doc in enumerate(docs):\\n for token in doc:\\n if not token.is_stop and not token.is_punct and token.pos_ in self.pos:\\n if token.pos_ == 'PROPN':\\n word_count[token.lemma_] += 1\\n word_rating[token.lemma_].append(self.ratings[index])\\n else:\\n noun_word_count[token.lemma_] += 1\\n noun_word_rating[token.lemma_].append(self.ratings[index])\\n\\n # if 0<=proper nouns<=5 found, add regular nouns\\n if not word_count or len(word_count) <= 5:\\n word_count += noun_word_count\\n word_rating = {**word_rating, **noun_word_rating}\\n \\n word_color = {word: self.getColor(\\n ratings)[1] for word, ratings in word_rating.items()}\\n word_sentiment = {word: self.getColor(\\n ratings)[0] for word, ratings in word_rating.items()}\\n\\n return word_count, word_color, word_sentiment\",\n \"def main():\\n text = str(input(\\\"Sentence: \\\"))\\n print(\\\"text: {}\\\".format(text))\\n text_dict = {}\\n text_list = text.split()\\n text_list.sort()\\n for text in text_list:\\n if text in text_dict:\\n text_dict[text] += 1\\n else:\\n text_dict[text] = 1\\n # for text2 in text_dict:\\n # if text_list[text] == text_dict[text2]:\\n # text_dict[text2] += 1\\n # else:\\n # text_dict[text_list[text]] += 1\\n for text in text_dict:\\n print(text, \\\":\\\", text_dict[text])\",\n \"def merge_words(word_list, merge_method = None, **kwargs):\\n \\n for word in word_list:\\n word['minframe'] = word['frame']\\n word['maxframe'] = word['frame']\\n \\n if merge_method is None:\\n for word in word_list:\\n word['merge_size'] = 1\\n return word_list\\n \\n clusterize_words(word_list, **kwargs)\\n \\n #Merge words within clusters\\n result = []\\n word_list.sort(key = lambda word: word['cluster'])\\n cluster = []\\n current_cluster = - 1\\n for word in word_list:\\n #First word\\n if len(cluster) == 0:\\n current_cluster = word['cluster']\\n cluster = [word]\\n elif word['cluster'] == current_cluster:\\n cluster.append(word)\\n #New cluster\\n else:\\n #process this cluster\\n result.append(merge_cluster(cluster, merge_method))\\n \\n current_cluster = word['cluster']\\n cluster = [word]\\n \\n return result\",\n \"def bow(tokens):\\n return dict(collections.Counter(re.findall(r'\\\\w+', \\\" \\\".join(tokens))))\",\n \"def merge_cluster(word_list, merge_method = None):\\n \\n #Calculates average of property of list of dictionaries.\\n def avg(l, key):\\n s = 0\\n for w in l:\\n s+= w[key]\\n return s / len(l)\\n \\n result = {}\\n result['longleft'] = avg(word_list, 'longleft')\\n result['longright'] = avg(word_list, 'longright')\\n result['top'] = avg(word_list, 'top')\\n result['bottom'] = avg(word_list, 'bottom')\\n result['confidence'] = avg(word_list, 'confidence')\\n result['merge_size'] = len(word_list)\\n result['minframe'] = min([word['minframe'] for word in word_list])\\n result['maxframe'] = max([word['maxframe'] for word in word_list])\\n result['cluster'] = word_list[0]['cluster']\\n \\n if merge_method is None:\\n merge_method = 'char_frequency'\\n \\n #Pick most popular character of each position in word\\n if merge_method == 'char_frequency':\\n result['text'] = average_word(word_list)\\n #Pick word with highest confidence\\n elif merge_method == 'confidence':\\n max_confidence = -1\\n for word in word_list:\\n if word['confidence'] > max_confidence:\\n max_confidence = word['confidence']\\n result['text'] = word['text']\\n elif merge_method == 'random':\\n result['text'] = word_list[random.randint(0, len(word_list) - 1)]['text']\\n else:\\n raise ValueError('Method ' + merge_method + ' not implemented.')\\n \\n return result\",\n \"def number_tokens(dgraph):\\n cnt = 0\\n for node in dgraph.subgraphs(exclude_root=True):\\n node[WORD] = (cnt, node[WORD])\\n cnt += 1\\n return dgraph\",\n \"def build_tf_dict(self, sentences):\\n tf_dict = defaultdict(int)\\n for sentence in sentences:\\n for word in sentence:\\n tf_dict[word] += 1\\n return tf_dict\",\n \"def reconcile_output(self):\\n final_string = \\\"\\\"\\n final_string += \\\"TEXT: {0}\\\\n\\\".format(self._text)\\n final_string += \\\"ID: {0}\\\\n\\\".format(self._index)\\n final_string += \\\"Count: {0}\\\\n\\\".format(self._count)\\n\\n final_string += \\\"=Doc Count Begin=\\\\n\\\"\\n for doc in list(self._doc_count.keys()):\\n final_string += \\\"{0} $!$ {1}\\\\n\\\".format(doc, self._doc_count[doc])\\n final_string += \\\"=Doc Count End=\\\\n\\\"\\n\\n final_string += \\\"=CEs Begin=\\\\n\\\"\\n for doc in list(self.word_pairs.keys()):\\n for ce in set(self.word_pairs[doc]):\\n #format: doc $!$ ce \\n final_string += \\\"{0} $!$ {1} $!$ {2}\\\\n\\\".format(doc, ce, self.word_pairs[doc].count(ce))\\n final_string += \\\"=CEs End=\\\\n\\\"\\n\\n final_string += \\\"=Self Tags Begin=\\\\n\\\"\\n for source in list(self.this_semantic_tags.keys()):\\n for tag in set(self.this_semantic_tags[source]):\\n final_string += \\\"{0} $!$ {1} $!$ {2}\\\\n\\\".format(source, tag, self.this_semantic_tags[source].count(tag))\\n final_string += \\\"=Self Tags End=\\\\n\\\"\\n\\n final_string += \\\"=Semantic Begin=\\\\n\\\"\\n for source in list(self.lexico_semantic.keys()):\\n #for some reason, I had each semantic class getting assigned the\\n #same overall count?\\n #sem_counts = self.getLexicoSemanticCounts(source)\\n for ce in self.lexico_semantic[source]:\\n for sem_cls in set(self.lexico_semantic[source][ce]):\\n c = self.lexico_semantic[source][ce].count(sem_cls)\\n final_string += \\\"{0} $!$ {1} $!$ {2} $!$ {3}\\\\n\\\".format(source, ce, sem_cls, c)\\n final_string += \\\"=Semantic End=\\\\n\\\"\\n final_string += \\\"$!$\\\\n\\\"\\n return final_string\",\n \"def merge_tokens(token_dict, idx_cluster, cluster_num, token_weight=None):\\n x = token_dict['x']\\n idx_token = token_dict['idx_token']\\n agg_weight = token_dict['agg_weight']\\n B, N, C = x.shape\\n if token_weight is None:\\n token_weight = x.new_ones(B, N, 1)\\n idx_batch = torch.arange(B, device=x.device)[:, None]\\n idx = idx_cluster + idx_batch * cluster_num\\n all_weight = token_weight.new_zeros(B * cluster_num, 1)\\n all_weight.index_add_(dim=0, index=idx.reshape(B * N), source=token_weight.reshape(B * N, 1))\\n all_weight = all_weight + 1e-06\\n norm_weight = token_weight / all_weight[idx]\\n x_merged = x.new_zeros(B * cluster_num, C)\\n source = x * norm_weight\\n x_merged.index_add_(dim=0, index=idx.reshape(B * N), source=source.reshape(B * N, C).type(x.dtype))\\n x_merged = x_merged.reshape(B, cluster_num, C)\\n idx_token_new = index_points(idx_cluster[..., None], idx_token).squeeze(-1)\\n weight_t = index_points(norm_weight, idx_token)\\n agg_weight_new = agg_weight * weight_t\\n agg_weight_new / agg_weight_new.max(dim=1, keepdim=True)[0]\\n out_dict = {}\\n out_dict['x'] = x_merged\\n out_dict['token_num'] = cluster_num\\n out_dict['map_size'] = token_dict['map_size']\\n out_dict['init_grid_size'] = token_dict['init_grid_size']\\n out_dict['idx_token'] = idx_token_new\\n out_dict['agg_weight'] = agg_weight_new\\n return out_dict\",\n \"def __cross_wiki_counts(self):\\n\\n print(\\\"Updating counts by merging with CrossWiki\\\")\\n\\n cnt = 0\\n crosswiki_path = os.path.join(\\n self.base_url, \\\"generic/p_e_m_data/crosswikis_p_e_m.txt\\\"\\n )\\n\\n with open(crosswiki_path, \\\"r\\\", encoding=\\\"utf-8\\\") as f:\\n for line in f:\\n parts = line.split(\\\"\\\\t\\\")\\n mention = unquote(parts[0])\\n\\n if (\\\"Wikipedia\\\" not in mention) and (\\\"wikipedia\\\" not in mention):\\n if mention not in self.wiki_freq:\\n self.wiki_freq[mention] = {}\\n\\n num_ents = len(parts)\\n for i in range(2, num_ents):\\n ent_str = parts[i].split(\\\",\\\")\\n ent_wiki_id = int(ent_str[0])\\n freq_ent = int(ent_str[1])\\n\\n if (\\n ent_wiki_id\\n not in self.wikipedia.wiki_id_name_map[\\\"ent_id_to_name\\\"]\\n ):\\n ent_name_re = self.wikipedia.wiki_redirect_id(ent_wiki_id)\\n if (\\n ent_name_re\\n in self.wikipedia.wiki_id_name_map[\\\"ent_name_to_id\\\"]\\n ):\\n ent_wiki_id = self.wikipedia.wiki_id_name_map[\\n \\\"ent_name_to_id\\\"\\n ][ent_name_re]\\n\\n cnt += 1\\n if (\\n ent_wiki_id\\n in self.wikipedia.wiki_id_name_map[\\\"ent_id_to_name\\\"]\\n ):\\n if mention not in self.mention_freq:\\n self.mention_freq[mention] = 0\\n self.mention_freq[mention] += freq_ent\\n\\n ent_name = self.wikipedia.wiki_id_name_map[\\n \\\"ent_id_to_name\\\"\\n ][ent_wiki_id].replace(\\\" \\\", \\\"_\\\")\\n if ent_name not in self.wiki_freq[mention]:\\n self.wiki_freq[mention][ent_name] = 0\\n self.wiki_freq[mention][ent_name] += freq_ent\",\n \"def build_morpheme_vocab(self):\\n max_morph_per_word = 0\\n morpheme_dict = collections.defaultdict(int)\\n splitter = \\\"@@\\\"\\n for token in self.train_data:\\n if token == self.eos or token == self.sos:\\n continue\\n token = '^' + token + '$'\\n morphemes = token.split(splitter)\\n if len(morphemes) > max_morph_per_word:\\n max_morph_per_word = len(morphemes)\\n for morpheme in morphemes:\\n morpheme_dict[morpheme] += 1\\n\\n unk_morpheme_list = set()\\n item_to_id = dict()\\n item_to_id[constants.PAD_ITEM] = len(item_to_id)\\n item_to_id[constants.UNK_ITEM] = len(item_to_id)\\n sorted_dict = sorted(morpheme_dict.items(), key=operator.itemgetter(1), reverse=True)\\n for token, freq in sorted_dict:\\n if freq == 1:\\n unk_morpheme_list.add(token)\\n if token not in item_to_id:\\n item_to_id[token] = len(item_to_id)\\n return item_to_id, unk_morpheme_list, max_morph_per_word\",\n \"def nrc_hashtag_emotion(self, tokens):\\n num_features = 10 # 'anger', 'anticipation', 'disgust', 'fear', 'joy', 'negative', 'positive', 'sadness', 'surprise', 'trust'\\n sum_vec = [0.0] * num_features\\n for token in tokens:\\n if token in self.nrc_hashtag_emotion_map:\\n sum_vec = [a + b for a, b in zip(sum_vec, self.nrc_hashtag_emotion_map[token])] # sum up the individual word feature vectors\\n feature_names = ['anger', 'anticipation', 'disgust', 'fear', 'joy', 'negative', 'positive', 'sadness', 'surprise', 'trust']\\n feature_names = ['nrc_hashtag_emotion_' + name for name in feature_names]\\n return dict(zip(feature_names, sum_vec))\",\n \"def make_word_to_freq(self):\\n\\t\\tword_to_freq = {}\\n\\t\\tdocuments = self.tokenized_documents[\\\"train\\\"]\\n\\t\\tfor document in documents:\\n\\t\\t\\tfor word in document:\\n\\t\\t\\t\\tif not word in self.worddict: # make sure we have not found one of the pre-defined words\\n\\t\\t\\t\\t\\tword_to_freq[word] = word_to_freq.get(word, 0) + 1\\n\\t\\t\\n\\t\\treturn word_to_freq\",\n \"def _build_token_dict(self, corpus: List[List[str]]):\\n self.token2idx = self.load_from_vocab_file(self.vocab_path)\\n self.idx2token = dict([(value, key)\\n for key, value in self.token2idx.items()])\\n logging.debug(f\\\"build token2idx dict finished, contains {len(self.token2idx)} tokens.\\\")\\n self.dataset_info['token_count'] = len(self.token2idx)\",\n \"def tokenize_and_split_bis(sms_file):\\n \\n dic = {}\\n list1 = []\\n list2 = []\\n list3 = []\\n list4 = []\\n i = -1\\n document = 0\\n terms = 0\\n new_document = True\\n ham = True\\n for line in open(sms_file, 'r').readlines():\\n w = []\\n document += 1\\n new_document = True\\n for word in line.split():\\n i = i + 1\\n if word == \\\"ham\\\":\\n ham = True\\n i = i - 1\\n elif word == \\\"spam\\\":\\n ham = False\\n i = i - 1\\n else:\\n if word not in dic:\\n dic[word] = i\\n w.append(dic[word])\\n list3.append(1)\\n list4.append(1)\\n new_document = False\\n terms += 1\\n else : \\n i = i - 1\\n w.append(dic[word])\\n list4[dic[word]] += 1\\n terms += 1\\n if new_document: \\n list3[dic[word]] += 1\\n new_document = False\\n \\n if ham and w !=[]:\\n list2.append(w)\\n elif ham == False and w !=[]:\\n list1.append(w)\\n\\n moy = 0\\n len_dic = len(dic.keys())\\n list5 = [0 for x in range(len_dic)]\\n for key in dic.keys():\\n if list4[dic[key]] > 0:\\n tf = list4[dic[key]] / terms\\n idf = math.log(document / list3[dic[key]])\\n tfIdf = tf * idf\\n list5[dic[key]] = tfIdf\\n # print(\\\"the word \\\" + str(key) + \\\" appairs \\\" + str(list4[dic[key]]) + \\\" times.\\\")\\n # print(\\\"his frequency is \\\" + str(list4[dic[key]] / terms) )\\n # print(\\\"the word \\\" + str(key) + \\\" appairs \\\" + str(list3[dic[key]]) + \\\" times in each document.\\\")\\n # print(\\\"his frequency is \\\" + str(idf))\\n # print(\\\"utility \\\" + str(tfIdf))\\n moy += tfIdf\\n \\n moy = moy / len_dic \\n # print(moy)\\n dic_bis = {}\\n i = -1\\n for key in dic.keys():\\n value = list5[dic[key]]\\n # print(str(value))\\n if (value > oracle * moy):\\n i += 1\\n dic_bis[key] = i\\n # else:\\n # print(\\\"not pass \\\" + key + \\\" \\\" + str(value))\\n \\n \\n # print(dic_bis == dic)\\n # print(dic)\\n return dic_bis,list1,list2\",\n \"def add_ngrams(mydict,sentence):\\n ngrams = get_ngrams(sentence,2,3)\\n for ngram in ngrams:\\n if ngram in mydict:\\n mydict[ngram]+=1\\n else:\\n mydict[ngram]=1\\n return mydict\",\n \"def MergeDictionary(self, dict1, dict2):\\r\\n for key_2, val_2 in dict2.items():\\r\\n for key_2_2, val_2_2 in val_2.items():\\r\\n if key_2 in dict1 and key_2_2 in dict1[key_2]:\\r\\n dict1[key_2][key_2_2] += val_2_2\\r\\n else:\\r\\n self.addtwodimdict(dict1, key_2, key_2_2, val_2_2)\\r\\n return dict1\",\n \"def add_word(self, word_one, word_two):\\n \\n # if word_one is in the corpus\\n if word_one in self.corpus.keys():\\n # if the word_two is already in word_one's corpus\\n if word_two in self.corpus[word_one].keys():\\n # increment the count by 1\\n self.corpus[word_one][word_two] += 1\\n # if word_two is not in word_one's corpus\\n else:\\n # add word_two to the corpus with an initial value of 1\\n self.corpus[word_one][word_two] = 1\\n # if word_one is not already in the corpus\\n else:\\n # add it and initialize its dictionary with word_two\\n self.corpus[word_one] = {word_two : 1}\",\n \"def merged_gene_feature_counts(self, merge_multi_splice_variants=True, merge_boundary_features=True, \\n merge_confusing_features=False):\\n merged_feature_count_dict = defaultdict(int)\\n for feature, count in self.mutant_counts_by_feature.items():\\n # note that anything containing '??' AND '/' never gets merged as boundary\\n if '??' in feature:\\n if merge_confusing_features: merged_feature_count_dict['??'] += count\\n else: merged_feature_count_dict[feature] += count\\n elif '|' in feature and merge_multiple_splice_variants: merged_feature_count_dict['multiple_splice_variants'] += count\\n elif '/' in feature and merge_boundary_features: merged_feature_count_dict['boundary'] += count\\n else: merged_feature_count_dict[feature] += count\\n return merged_feature_count_dict\",\n \"def calculate(self, tokens:list):\\n vektor_tf_bigram = {}\\n sz = len(tokens) - 1\\n\\n # range(0) adalah empty list di Python\\n for i in range(sz):\\n bigram_token = self.__get_term_bigram(i, tokens)\\n\\n if bigram_token in vektor_tf_bigram:\\n vektor_tf_bigram[bigram_token] = vektor_tf_bigram[bigram_token] + 1\\n else:\\n vektor_tf_bigram[bigram_token] = 1\\n\\n return vektor_tf_bigram\",\n \"def count_ngrams(self, corpus):\\n \\n self.unigramcounts = defaultdict(int)\\n self.bigramcounts = defaultdict(int)\\n self.trigramcounts = defaultdict(int)\\n\\n self.sentence_counts = 0\\n self.word_count = 0\\n\\n for line in corpus:\\n sequence = line\\n self.sentence_counts +=1\\n\\n unigrams = get_ngrams(sequence, n=1)\\n for gram in unigrams:\\n self.word_count += 1\\n self.unigramcounts[gram] +=1\\n\\n bigrams = get_ngrams(sequence, n=2)\\n for gram in bigrams:\\n self.bigramcounts[gram] +=1\\n\\n trigrams = get_ngrams(sequence, n=3)\\n for gram in trigrams:\\n self.trigramcounts[gram] +=1\\n\\n #self.unigramcounts[('START')] = self.sentence_counts *2\\n self.bigramcounts[('START', 'START')] = self.sentence_counts\\n\\n #return self\",\n \"def count(words):\\n word_count = {}\\n num_words = 0\\n unique_words = 0\\n for word in words:\\n num_words += 1\\n if word_count.has_key(word):\\n word_count[word] += 1\\n else:\\n word_count[word] = 1\\n unique_words += 1\\n word_count[\\\"total\\\"] = num_words\\n word_count[\\\"unique\\\"] = unique_words\\n return word_count\",\n \"def pos():\\n pos_list = []\\n for token in doc:\\n pos_list.append(token.pos_)\\n setList = list(set(pos_list))\\n my_dict = {i: pos_list.count(i) for i in setList}\\n print(my_dict)\",\n \"def token_key(token):\\n morphotagged = analysis(token).get('raw')\\n lemma_pos = (analysis(token).get('lemma'), analysis(token).get('partOfSpeech'))\\n return morphotagged or lemma_pos\",\n \"def test_merge_word_dicts():\\n ref_dict = {\\\"accordeon\\\": [\\\"acordeon\\\"],\\n \\\"accoster\\\": [\\\"accostage\\\", \\\"acostage\\\"]}\\n\\n new_dict = {\\\"accordeon\\\": [\\\"accordeons\\\"],\\n \\\"accostage\\\": [\\\"accostages\\\"],\\n \\\"accumulateur\\\": [\\\"acumulateur\\\", \\\"acc\\\"]}\\n\\n word_dict.merge_word_dicts(ref_dict, new_dict)\\n\\n expected_dict = {\\\"accordeon\\\": [\\\"acordeon\\\", \\\"accordeons\\\"],\\n \\\"accoster\\\": [\\\"accostage\\\", \\\"acostage\\\", \\\"accostages\\\"],\\n \\\"accumulateur\\\": [\\\"acumulateur\\\", \\\"acc\\\"]}\\n\\n for correct_word in ref_dict:\\n assert sorted(list(set(ref_dict[correct_word]))) == sorted(list(set(expected_dict[correct_word])))\",\n \"def _insert_into_dict(words_dict: Dict[str, int], key: str) \\\\\\n -> Dict[str, int]:\\n if key in words_dict.keys():\\n words_dict[key] = words_dict[key] + 1\\n else:\\n words_dict[key] = 1\\n\\n return words_dict\",\n \"def get_token_phonology(token, fileHasPhonology, phon_criteria, token_order):\\n if fileHasPhonology:\\n if phon_criteria['actual']['include']:\\n actual_pho = phon_criteria['actual']['phons']\\n token['pho'] = actual_pho[(token_order -1)]\\n else:\\n # mismatch in actual_pho and utterance length; not including actual pho at the word level\\n token['pho'] = ''\\n \\n if phon_criteria['model']['include']:\\n model_pho = phon_criteria['model']['phons']\\n token['mod'] = model_pho[(token_order -1)]\\n else: \\n # mismatch in model_pho and utterance length; not including model pho at the word level\\n token['mod'] = ''\\n else:\\n # whole file does not have phonology\\n token['pho'] = ''\\n token['mod'] = ''\\n return token\",\n \"def merge_utterance_lines(utt_dict):\\n new_utterances = {}\\n for uid, utt in utt_dict.items():\\n merged = False\\n if utt.reply_to is not None and utt.speaker is not None:\\n u0 = utt_dict[utt.reply_to]\\n if u0.root == utt.root and u0.speaker == utt.speaker:\\n new_utterances[u0.id].text += \\\" \\\" + utt.text\\n merged = True\\n if not merged:\\n new_utterances[utt.id] = utt\\n return new_utterances\",\n \"def add_word_to_dict(self, word):\\n\\n # if the word already exist in the dict\\n # we increment 1 the value\\n if word in self.words_to_counts:\\n self.words_to_counts[word] += 1\\n\\n # if the word in the dict is capitalize\\n # we add the new word lower case, with the value of the\\n # capitalize word plus 1\\n # and delete the old capitalize word\\n elif word.capitalize() in self.words_to_counts:\\n self.words_to_counts[word] = self.words_to_counts[\\n word.capitalize()] + 1\\n del self.words_to_counts[word.capitalize()]\\n\\n # if the word in the dict is lower case but the input\\n # is capitalize, we just increment the value of the word by 1\\n elif word.lower() in self.words_to_counts:\\n self.words_to_counts[word] += 1\\n\\n # if the word doen't exist in the dict, we add the new word\\n else:\\n self.words_to_counts[word] = 1\",\n \"def _annotate_brackets(\\n self,\\n tokens: List[tokenize.TokenInfo],\\n ) -> Mapping[int, int]:\\n brackets = {bracket: 0 for bracket in MATCHING}\\n for token in tokens:\\n if token.exact_type in MATCHING.keys():\\n brackets[token.exact_type] += 1\\n if token.exact_type in MATCHING.values():\\n reverse_bracket = get_reverse_bracket(token)\\n if brackets[reverse_bracket] > 0:\\n brackets[reverse_bracket] -= 1\\n return brackets\",\n \"def add(counts):\\n if counts:\\n for k in grammar.keys():\\n grammar[k] = grammar[k] + counts[k]\",\n \"def countMutatedSentences(sentence):\\n # BEGIN_YOUR_ANSWER (our solution is 17 lines of code, but don't worry if you deviate from this)\\n words = sentence.split()\\n distinct_words = set(words)\\n\\n pairs = {word: set() for word in distinct_words}\\n cache = [{j: None for j in distinct_words} for i in range(len(words))]\\n\\n for cur_word, next_word in zip(words[:-1], words[1:]):\\n pairs[cur_word].add(next_word)\\n\\n def count_mutate_sentences(target_word: str, current_length: int, target_length: int):\\n if current_length == target_length:\\n return 1\\n else:\\n result = 0\\n for word in pairs[target_word]:\\n if cache[current_length - 1][word] is None:\\n cache[current_length - 1][word] = count_mutate_sentences(word, current_length + 1, target_length)\\n result += cache[current_length - 1][word]\\n\\n return result\\n\\n return sum([count_mutate_sentences(word, 1, len(words)) for word in distinct_words])\\n # END_YOUR_ANSWER\",\n \"def getReviewsWithToken(self, token):\\n\\n wordid = self.find_word_in_dictionary(token)\\n # word is not in the dictionary\\n if wordid == -1:\\n print(\\\"Token is not in the dictionary\\\")\\n return 0\\n\\n with open(self.doc_to_words_path, 'rb') as bin:\\n tup = []\\n while bin.tell() != os.fstat(bin.fileno()).st_size:\\n # get wordid:\\n docid_in_file = int.from_bytes(bin.read(4), 'big')\\n # get frequency:\\n frequency = int.from_bytes(bin.read(4), 'big')\\n # count words:\\n count = 0\\n for i in range(frequency):\\n wordid_in_file = int.from_bytes(bin.read(4), 'big')\\n if wordid == wordid_in_file:\\n count += 1\\n tup.append(docid_in_file)\\n tup.append(count)\\n return tuple(tup)\",\n \"def init_correction_map(token, dictionary):\\n if token is None:\\n return None\\n\\n if len(token) <= 2 or token.lower() in dictionary:\\n return {token: 1}\\n\\n return {}\",\n \"def word_count(poem):\\n lines = [line for line in poem.split(\\\"\\\\n\\\") if line]\\n word_map = {}\\n for line in lines:\\n for word in line.split(\\\" \\\"):\\n if word:\\n if word in word_map:\\n word_map[word] += 1\\n else:\\n word_map[word] = 1\\n return word_map\",\n \"def iterate_words(counter, li, all_dict, emotion_dict):\\n\\n counter += 1\\n # iterate through the words in the list\\n for word in li:\\n # if word not in the dict of all words add it with frequency 1, else increase its frequency by 1\\n if word not in all_dict:\\n all_dict[word] = 1\\n else:\\n all_dict[word] += 1\\n # if word not in the dict of words with certain emotion add it with frequency 1, else increase its frequency by 1\\n if word not in emotion_dict:\\n emotion_dict[word] = 1\\n else:\\n emotion_dict[word] += 1\\n\\n return counter\",\n \"def countTagsInFile(fname):\\n with open(fname, 'r', encoding='utf-8') as f:\\n for line in f:\\n words = line.split(' ')\\n for w in words:\\n tag = w.split('_')[1].rstrip()\\n cat = tag[0].upper()\\n if tag not in dictionaries[cat]:\\n dictionaries[cat][tag] = 1\\n else:\\n dictionaries[cat][tag] += 1\",\n \"def countOverlapTokens(self, annotatedMention):\\n if self.tokens[0].sentence != annotatedMention.tokens[0].sentence:\\n return 0 # mentions in different sentences, no overlap \\n \\n if self.end < annotatedMention.start or self.start > annotatedMention.end:\\n return 0 # mention ends before or starts after annotated one\\n \\n if self.start == annotatedMention.start and self.end == annotatedMention.end:\\n return len(self.tokens) # exact match for annotated mention\\n \\n # There is some overlap. Does it consist of anything substantial?\\n importantTokens = 0\\n for token in self.tokens:\\n if token.index >= annotatedMention.start \\\\\\n and token.index <= annotatedMention.end \\\\\\n and token.isSymbol() == False and token.isStopWord() == False:\\n importantTokens += 1\\n \\n return importantTokens\",\n \"def word_count(phrase):\\n words = phrase.split()\\n deDupedWords = set(words)\\n wordCount = {}\\n\\n for element in deDupedWords:\\n wordCount.update({element: words.count(element)})\\n\\n return wordCount\",\n \"def getTokenFrequency(self, token):\\n\\n wordid = self.find_word_in_dictionary(token)\\n # word is not in the dictionary\\n if wordid == -1:\\n print(\\\"Token is not in the dictionary\\\")\\n return 0\\n\\n with open(self.word_to_docs_path, 'rb') as bin:\\n while bin.tell() != os.fstat(bin.fileno()).st_size:\\n # get wordid:\\n wordid_in_file = int.from_bytes(bin.read(4), 'big')\\n # get frequency:\\n frequency = int.from_bytes(bin.read(4), 'big')\\n if wordid_in_file == wordid:\\n break\\n # skip documents:\\n int.from_bytes(bin.read(4 * frequency), 'big')\\n\\n prevFile = int.from_bytes(bin.read(4), 'big')\\n num = 1\\n for i in range(frequency):\\n currFile = int.from_bytes(bin.read(4), 'big')\\n if currFile != prevFile:\\n num += 1\\n prevFile = currFile\\n\\n return num\",\n \"def select_ocrsims(token, structures):\\n delta = 2\\n ocr_sims = {}\\n\\n word_hash = ocr_key_hash(token)\\n\\n sim_hash_list = {} # Using a dictionary avoid multiple entries if a key is retrieved twice\\n key_index = -1\\n\\n # for (key, value) in word_hash:\\n for key, value in word_hash:\\n key_index += 1\\n sim_hash = deepcopy(word_hash)\\n\\n for d in range(-delta, delta+1):\\n if d != 0:\\n card = max(int(value)+d, 1)\\n\\n sim_hash[key_index] = (key, card)\\n\\n # Rebuild OCR key string\\n sim_hash_str = \\\"\\\"\\n for k, v in sim_hash:\\n sim_hash_str += k + str(v)\\n\\n if sim_hash_str in structures[\\\"ocrkeys\\\"]:\\n card_diff = abs(int(value)-card)\\n\\n sim_hash_list[sim_hash_str] = [(sim_word, card_diff)\\n for sim_word in structures[\\\"ocrkeys\\\"][sim_hash_str]\\n if edit_distance(sim_word, token) <= 2]\\n\\n for sim_hash_str, sim_list in sim_hash_list.items():\\n for sim_word, card_diff in sim_list:\\n sim_score = rate_ocr_key(structures[\\\"occurence_map\\\"], token, sim_word, card_diff)\\n\\n if sim_score > 0:\\n ocr_sims[sim_word] = sim_score\\n\\n return ocr_sims\",\n \"def add_word(self, pair, next_pair = None):\\n if pair not in self:\\n self.types += 1\\n if next_pair:\\n self[pair] = Dictogram([next_pair])\\n elif next_pair:\\n self[pair].add_count(next_pair)\\n self.tokens += 1\",\n \"def unigram(tokens):\\n model = collections.defaultdict(lambda: 0.01)\\n for token in tokens:\\n try:\\n model[token] += 1\\n except KeyError:\\n model[token] = 1\\n continue\\n for word in model:\\n model[word] = model[word]/float(sum(model.values()))\\n return model\",\n \"def _tokenize(self, raw_text):\\n\\n doc = self.nlp(raw_text.strip())\\n\\n # Loop through tokens and find known entities aren't already marked\\n for token in doc:\\n # Is this word in our known_entities, but is not recognized by the spaCy parser?\\n if token.text.lower() in self.known_entities and token.ent_type not in self.entities:\\n # We need to set the new entity to doc.ents directly (I believe the getter for doc.ents does\\n # some important massaging. However, counter to the online docs, setting doc.ents wipes out\\n # all of the previously recognized ents, so we stash the value, then we combine and reset.\\n stash = doc.ents\\n doc.ents = [(token.text.title(), doc.vocab.strings['PERSON'], token.i, token.i + 1)]\\n doc.ents = doc.ents + stash\\n\\n # Find proper noun n-grams: (a) find a known entity, (b) is the next word also a known entity?,\\n # (c) merge, (d) repeat\\n # TODO: Joining multi-word named entities sometimes causes us trouble.\\n doc_len = len(doc) # Helps us know when to exit the 'for loop' (since we change the # of items via merge)\\n for token in doc:\\n # if we're not at the end of the loop, and we recognize this as a proper noun and it's not a stop word\\n # and the token isn't a space...\\n if token.i + 1 < doc_len and token.ent_type in self.entities and \\\\\\n token.text.lower() not in self.stop_words and token.text not in ' ':\\n next_token = doc[token.i + 1]\\n # keep looping while we're not at the end of the loop and this token has the same entity type as\\n # the previous token and it's not a stop word or a space.\\n while token.i + 1 < doc_len and next_token.ent_type == token.ent_type and \\\\\\n next_token.text.lower() not in self.stop_words and next_token.text not in ' ':\\n n_gram = doc[token.i:token.i + 2]\\n n_gram.merge()\\n doc_len -= 1 # the merge changes the list length, so we just shrunk the list!\\n # print(x)\\n if token.i + 1 >= doc_len:\\n break\\n\\n return doc\",\n \"def prep_dict(word):\\n counts = {}\\n for l in word.lower():\\n if l!=\\\" \\\":\\n counts[l] = counts.get(l,0) + 1\\n return counts\",\n \"def get_data(articles): # Here, the articles will be very long strings\\r\\n vdictionary = {} # dictionary for tokens that are found in dictionary\\r\\n _odata = [0] * 12 # list collecting everything except date; last number of words=index:0\\r\\n word_length = 0 # initializing the value of word length; will be updated via loop\\r\\n tokens = re.findall('\\\\w+', articles) # Note that \\\\w+ splits hyphenated words\\r\\n for token in tokens: # Goes through generated tokens from articles\\r\\n if (not token.isdigit()) and (len(token) > 1) and (token in lm_dictionary.keys()): # conditions for checking if token is in dictionary\\r\\n _odata[1] += 1 # updating word count \\r\\n word_length += len(token) # updating word length\\r\\n if token not in vdictionary: # initial statement regarding steps for handling tokens not in the dictionary\\r\\n vdictionary[token] = 1 # count of tokens in text that show up in dictionary\\r\\n \\r\\n####### Keeping Track of Categorical Token Counts (Nonzero entry=True) also checks if word is stop word\\r\\n if lm_dictionary[token].positive and not lm_dictionary[token].stopword: _odata[2] += 1\\r\\n if lm_dictionary[token].negative and not lm_dictionary[token].stopword: _odata[3] += 1\\r\\n if lm_dictionary[token].uncertainty and not lm_dictionary[token].stopword: _odata[4] += 1\\r\\n if lm_dictionary[token].litigious and not lm_dictionary[token].stopword: _odata[5] += 1\\r\\n if lm_dictionary[token].weak_modal and not lm_dictionary[token].stopword: _odata[6] += 1\\r\\n if lm_dictionary[token].moderate_modal and not lm_dictionary[token].stopword: _odata[7] += 1\\r\\n if lm_dictionary[token].strong_modal and not lm_dictionary[token].stopword: _odata[8] += 1\\r\\n if lm_dictionary[token].constraining and not lm_dictionary[token].stopword: _odata[9] += 1\\r\\n #total_syllables += lm_dictionary[token].syllables # interesting parameter to measure\\r\\n\\r\\n #_odata[12] = len(re.findall('[0-9]', doc))\\r\\n # drop punctuation within numbers for number count\\r\\n articles = re.sub('(?!=[0-9])(\\\\.|,)(?=[0-9])', '', articles)\\r\\n articles = articles.translate(str.maketrans(string.punctuation, \\\" \\\" * len(string.punctuation)))\\r\\n #_odata[13] = len(re.findall(r'\\\\b[-+\\\\(]?[$€£]?[-+(]?\\\\d+\\\\)?\\\\b', doc))\\r\\n # _odata[14] = total_syllables / _odata[2]\\r\\n #print(_odata[1])\\r\\n _odata[10] = word_length / _odata[1] # computing average word length\\r\\n _odata[11] = len(vdictionary) # total vocab count\\r\\n \\r\\n # Convert counts to %\\r\\n for i in range(2, 9 + 1): # specifying range of percentages\\r\\n try:\\r\\n _odata[i] = (_odata[i] / _odata[1]) * 100 # updating count to percent\\r\\n except:\\r\\n print(\\\"zero denominator\\\")\\r\\n # Vocabulary\\r\\n \\r\\n return _odata # returning the data\\r\",\n \"def update_word_pos_vocab(text_list, threshold, base_word_vocab, base_pos_vocab, word_vocab_path, pos_vocab_path, tokenizer_type=\\\"mecab\\\"):\\n\\n def do_concurrent_tagging(start, end, text_list, word_counter, pos_counter):\\n jpype.attachThreadToJVM()\\n for i, text in enumerate(text_list[start:end]):\\n text = text.strip()\\n text = text.lower()\\n\\n try:\\n if tokenizer_type == \\\"mecab\\\":\\n tokens_ko = mecab.pos(text)\\n else:\\n tokens_ko = twitter.pos(text, norm=True) # , stem=True)\\n\\n word_tokens_ko = [str(pos[0]) for pos in tokens_ko]\\n pos_tokens_ko = [str(pos[1]) for pos in tokens_ko]\\n word_counter.update(word_tokens_ko)\\n pos_counter.update(pos_tokens_ko)\\n\\n if i % 1000 == 0:\\n print(\\\"[%d/%d (total: %d)] Tokenized input text.\\\" % (\\n start + i, start + len(text_list[start:end]), len(text_list)))\\n\\n except Exception as e: # for Out of memory\\n print(e)\\n continue\\n\\n word_counter = Counter()\\n pos_counter = Counter()\\n\\n num_thread = 4\\n thread_list = []\\n n_x_text = len(text_list)\\n for i in range(num_thread):\\n thread_list.append(Thread(target=do_concurrent_tagging, args=(\\n int(i * n_x_text / num_thread), int((i + 1) * n_x_text / num_thread), text_list, word_counter, pos_counter)))\\n\\n for thread in thread_list:\\n thread.start()\\n\\n for thread in thread_list:\\n thread.join()\\n\\n print(word_counter.most_common(10)) # print most common words\\n\\n ## Word\\n words = [word for word, cnt in word_counter.items() if cnt >= threshold]\\n\\n for i, word in enumerate(words):\\n base_word_vocab.add_word(str(word))\\n updated_word_vocab = base_word_vocab\\n\\n with open(word_vocab_path, 'wb') as f:\\n pickle.dump(updated_word_vocab, f)\\n\\n ## Pos\\n poss = [pos for pos, cnt in pos_counter.items() if cnt >= 0] # insert all pos tag\\n\\n for i, pos in enumerate(poss):\\n base_pos_vocab.add_word(str(pos))\\n updated_pos_vocab = base_pos_vocab\\n\\n with open(pos_vocab_path, 'wb') as f:\\n pickle.dump(updated_pos_vocab, f)\\n\\n return updated_word_vocab, updated_pos_vocab\",\n \"def RenumberComms(dictionary):\\n count = 0\\n ret = dictionary.copy()\\n new_values = dict([])\\n for key in dictionary.keys() :\\n newSet = set()\\n comms = dictionary[key]\\n for comm in comms:\\n new_value = new_values.get(comm, -1)\\n if new_value == -1 :\\n new_values[comm] = count\\n new_value = count\\n count = count + 1\\n newSet.add(new_value)\\n ret[key] = newSet\\n\\n return ret\",\n \"def frequent_nouns_counts(self, tokens):\\n\\n unigram_fd = nltk.FreqDist(tokens)\\n pos = POS()\\n common_unigrams = unigram_fd.most_common(\\n int(self.top_pct * len(unigram_fd)))\\n\\n nouns = [pair for pair in common_unigrams\\n if pair[0] not in self.stopwords()\\n and pos.percent_noun(pair[0]) > 0.5]\\n\\n return nouns\",\n \"def word_enders():\\n space = ''\\n for k,v in key.items():\\n if v==' ':\\n space = k\\n\\n enders = Counter()\\n for word in ciphertext.split( space ):\\n last_two = word[-2]+word[-1]\\n enders[last_two] += 1\\n\\n return enders\",\n \"def word_frequencies(word_list: TextIO) -> dict:\\n words = word_list.read().split(' ')\\n amount_of_words = len(set(words))\\n frequencies = {}\\n for index, word in enumerate(words):\\n clean_word = remove_punctuation(word)\\n if clean_word not in frequencies:\\n frequencies[clean_word] = (index + 1) / amount_of_words\\n del frequencies[\\\"\\\"]\\n return frequencies\",\n \"def get_counts(self):\\n counts = {}\\n for document in self.docs:\\n for word in document:\\n if word not in counts.keys():\\n counts[word] = 1\\n else:\\n counts[word] += 1\\n return counts\",\n \"def add_unique_word_count(self):\\n call_count = lambda letter_: self._count_unique_words(letter_)\\n self.dataframe['unique_words'] = self.dataframe['letter'].map(call_count)\",\n \"def test_dictionary_create_repeat():\\n tokens = [['testing'], ['testing', 'testing', 'testing'], ['make', 'tokens']]\\n dictionary = edurate_gensim.dictionary_create(tokens)\\n corp = [dictionary.doc2bow(token) for token in tokens]\\n assert corp == [[(0, 1)], [(0, 3)], [(1, 1), (2, 1)]]\",\n \"def train():\\n\\tA = collections.defaultdict(dict)\\n\\tB = collections.defaultdict(dict)\\n\\tpos_list = []\\n\\tword_list = []\\n\\tstr_buf = []\\n\\n\\n\\t# read each line and count A and B\\n\\tfor line in sys.stdin:\\n\\t\\tline = line.split()\\n\\t\\t# print(line)\\n\\t\\tif len(line) == 3:\\n\\t\\t\\tstr_buf.append((str(line[0]), str(line[1])))\\n\\t\\t\\tword_list.append(str(line[0]))\\n\\t\\t\\tpos_list.append(str(line[1]))\\n\\n\\t\\telse:\\n\\t\\t\\t# if come to the end of a sentence\\n\\t\\t\\tif len(str_buf) != 0:\\n\\t\\t\\t\\tstr_buf = [('<s>','BOS')] + str_buf + [('</s>', 'EOS')]\\n\\t\\t\\t\\tword_list += ['<s>', '</s>']\\n\\t\\t\\t\\tpos_list += ['BOS', 'EOS']\\n\\n\\t\\t\\t\\tfor i, s in enumerate(str_buf):\\n\\t\\t\\t\\t\\tif s[0] in B[s[1]]:\\n\\t\\t\\t\\t\\t\\tB[s[1]][s[0]] += 1\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tB[s[1]][s[0]] = 1\\n\\n\\t\\t\\t\\t\\tif s[0] != '</s>':\\n\\t\\t\\t\\t\\t\\t# print('strbuf[i]:',str_buf[i], 's[1]', s[1])\\n\\t\\t\\t\\t\\t\\tif str_buf[i+1][1] in A[s[1]]:\\n\\t\\t\\t\\t\\t\\t\\tA[s[1]][str_buf[i+1][1]] += 1\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tA[s[1]][str_buf[i+1][1]] = 1\\n\\n\\t\\t\\t\\tstr_buf = []\\n\\n\\t# get unique POS list and word list\\n\\tpos_list_uniq = list(set(pos_list))\\n\\tword_list_uniq = list(set(word_list))\\n\\n\\n\\t# assume <UNK>, smoothing, normalize\\n\\tB_sm = collections.defaultdict(dict)\\n\\tA_sm = A.copy()\\n\\n\\t# assume words apeear less than 2 times as <UNK>\\n\\tword_count = collections.Counter(word_list)\\n\\tfor pos in B:\\n\\t\\tfor word in B[pos]:\\n\\t\\t\\tif word_count[word] > 1:\\n\\t\\t\\t\\tB_sm[pos][word] = B[pos][word]\\n\\n\\t\\t\\telse: # add <UNK> to B_sm\\n\\t\\t\\t\\tword_list_uniq.remove(word)\\n\\t\\t\\t\\tif '<UNK>' in B_sm[pos]:\\n\\t\\t\\t\\t\\tB_sm[pos]['<UNK>'] += 1\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tB_sm[pos]['<UNK>'] = 1\\n\\n\\tword_list_uniq += ['<UNK>']\\n\\n\\t# add 1 smoothing\\n\\tfor pos in pos_list_uniq:\\n\\t\\tfor word in word_list_uniq:\\n\\t\\t\\tif word in B_sm[pos]:\\n\\t\\t\\t\\tB_sm[pos][word] += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tB_sm[pos][word] = 1\\n\\n\\tfor prev in pos_list_uniq:\\n\\t\\tfor next in pos_list_uniq:\\n\\t\\t\\tif next in A_sm[prev]:\\n\\t\\t\\t\\tA_sm[prev][next] += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tA_sm[prev][next] = 1\\n\\n\\t# delete instances like A[VB][BOS], A[EOS][VB],\\n\\t# B[VB]['</s>'], B[EOS]['Jack']\\n\\n\\tfor pos in B_sm:\\n\\t\\tfor word in B_sm[pos]:\\n\\t\\t\\tif (pos == 'BOS' and word != '<s>') or \\\\\\n\\t\\t\\t(pos == 'EOS' and word != '</s>') or \\\\\\n\\t\\t\\t(word == '<s>' and pos != 'BOS') or \\\\\\n\\t\\t\\t(word == '</s>' and pos != 'EOS'):\\n\\t\\t\\t\\tB_sm[pos][word] = 0\\n\\n\\tfor prev in A_sm:\\n\\t\\tfor next in A_sm[prev]:\\n\\t\\t\\tif prev == 'EOS' or next == 'BOS':\\n\\t\\t\\t\\tA_sm[prev][next] = 0\\n\\n\\t# normalize\\n\\tfor pos in B_sm:\\n\\t\\ts = sum(B_sm[pos].values())\\n\\t\\tfor word in B_sm[pos]:\\n\\t\\t\\tif B_sm[pos][word] != 0:\\n\\t\\t\\t\\tB_sm[pos][word] /= s\\n\\n\\tfor prev in A_sm:\\n\\t\\ts = sum(A_sm[prev].values())\\n\\t\\tfor next in A_sm[prev]:\\n\\t\\t\\tif A_sm[prev][next] != 0:\\n\\t\\t\\t\\tA_sm[prev][next] /= s\\n\\n\\treturn A_sm, B_sm, word_list_uniq\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66677827","0.595904","0.5881344","0.5862591","0.58134353","0.5744879","0.5677451","0.5661367","0.56101036","0.553344","0.53926057","0.5391481","0.5351704","0.53189","0.5299593","0.5298756","0.5296687","0.5291537","0.5290174","0.52845526","0.5283439","0.5281952","0.5274648","0.52737886","0.52721256","0.5261697","0.52487993","0.5243122","0.52332777","0.52322245","0.521473","0.521351","0.5207929","0.52037716","0.520125","0.517871","0.51653624","0.51609254","0.5153961","0.5151152","0.5141428","0.51328224","0.5132596","0.51264685","0.51185375","0.5107017","0.5086728","0.50808704","0.5073573","0.5071616","0.506174","0.5061389","0.5051848","0.5047461","0.50244343","0.5022381","0.5015531","0.50140727","0.5007126","0.49759677","0.497167","0.4970743","0.4964832","0.49640024","0.49623355","0.4960598","0.49552494","0.49495357","0.4947721","0.49378106","0.4932747","0.4930306","0.49292144","0.49229375","0.4910067","0.48946196","0.48822892","0.48814222","0.4876171","0.4874449","0.48711053","0.48644313","0.4864414","0.48547778","0.48492587","0.48429498","0.4840107","0.48391318","0.48361897","0.483594","0.48261663","0.48163125","0.48148787","0.48141688","0.48115847","0.4797446","0.47933894","0.47914195","0.47880155","0.47829244"],"string":"[\n \"0.66677827\",\n \"0.595904\",\n \"0.5881344\",\n \"0.5862591\",\n \"0.58134353\",\n \"0.5744879\",\n \"0.5677451\",\n \"0.5661367\",\n \"0.56101036\",\n \"0.553344\",\n \"0.53926057\",\n \"0.5391481\",\n \"0.5351704\",\n \"0.53189\",\n \"0.5299593\",\n \"0.5298756\",\n \"0.5296687\",\n \"0.5291537\",\n \"0.5290174\",\n \"0.52845526\",\n \"0.5283439\",\n \"0.5281952\",\n \"0.5274648\",\n \"0.52737886\",\n \"0.52721256\",\n \"0.5261697\",\n \"0.52487993\",\n \"0.5243122\",\n \"0.52332777\",\n \"0.52322245\",\n \"0.521473\",\n \"0.521351\",\n \"0.5207929\",\n \"0.52037716\",\n \"0.520125\",\n \"0.517871\",\n \"0.51653624\",\n \"0.51609254\",\n \"0.5153961\",\n \"0.5151152\",\n \"0.5141428\",\n \"0.51328224\",\n \"0.5132596\",\n \"0.51264685\",\n \"0.51185375\",\n \"0.5107017\",\n \"0.5086728\",\n \"0.50808704\",\n \"0.5073573\",\n \"0.5071616\",\n \"0.506174\",\n \"0.5061389\",\n \"0.5051848\",\n \"0.5047461\",\n \"0.50244343\",\n \"0.5022381\",\n \"0.5015531\",\n \"0.50140727\",\n \"0.5007126\",\n \"0.49759677\",\n \"0.497167\",\n \"0.4970743\",\n \"0.4964832\",\n \"0.49640024\",\n \"0.49623355\",\n \"0.4960598\",\n \"0.49552494\",\n \"0.49495357\",\n \"0.4947721\",\n \"0.49378106\",\n \"0.4932747\",\n \"0.4930306\",\n \"0.49292144\",\n \"0.49229375\",\n \"0.4910067\",\n \"0.48946196\",\n \"0.48822892\",\n \"0.48814222\",\n \"0.4876171\",\n \"0.4874449\",\n \"0.48711053\",\n \"0.48644313\",\n \"0.4864414\",\n \"0.48547778\",\n \"0.48492587\",\n \"0.48429498\",\n \"0.4840107\",\n \"0.48391318\",\n \"0.48361897\",\n \"0.483594\",\n \"0.48261663\",\n \"0.48163125\",\n \"0.48148787\",\n \"0.48141688\",\n \"0.48115847\",\n \"0.4797446\",\n \"0.47933894\",\n \"0.47914195\",\n \"0.47880155\",\n \"0.47829244\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.51305556"},"document_rank":{"kind":"string","value":"43"}}},{"rowIdx":89297,"cells":{"query":{"kind":"string","value":"Merge identical tokens and cumulate their counts. Checks to see that morphology and pronunciations are identical, otherwise the tokens will not be merged."},"document":{"kind":"string","value":"def CompactTokens(self):\n map = {}\n for token_ in self.tokens_:\n hash_string = token_.EncodeForHash()\n try: map[hash_string].append(token_)\n except KeyError: map[hash_string] = [token_]\n ntokens = []\n keys = map.keys()\n keys.sort()\n for k in keys:\n token_ = map[k][0]\n for otoken in map[k][1:]:\n token_.IncrementCount(otoken.Count())\n ntokens.append(token_)\n self.tokens_ = ntokens"},"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 merge_token_counters(\n token_counter1: Dict[str, int], token_counter2: Dict[str, int]\n ) -> Dict[str, int]:\n for token, number in token_counter2.items():\n if token in token_counter1:\n token_counter1[token] += number\n else:\n token_counter1[token] = number\n\n return token_counter1","def merged_tokens(self, i, tokenizer):\n out = []\n counts = []\n last_id = None\n tokens = tokenizer.convert_ids_to_tokens(self.token_ids[i].tolist())\n for token, current_id in zip(tokens[1:-1], self.conll_ids[i]):\n if last_id == current_id.item():\n if token.startswith(\"##\"):\n out[-1] += (token[2:])\n else:\n out[-1] += token\n counts[-1] += 1\n else:\n out.append(token)\n counts.append(1)\n last_id = current_id\n return out, counts","def _merge_conllu_subtokens(self, lines: List[str], doc: Doc)-> Doc:\n # identify and process all subtoken spans to prepare attrs for merging\n subtok_spans = []\n for line in lines:\n parts = line.split(\"\\t\")\n id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts\n if \"-\" in id_:\n subtok_start, subtok_end = id_.split(\"-\")\n subtok_span = doc[int(subtok_start) - 1 : int(subtok_end)]\n subtok_spans.append(subtok_span)\n # create merged tag, morph, and lemma values\n tags = []\n morphs = {}\n lemmas = []\n for token in subtok_span:\n tags.append(token.tag_)\n lemmas.append(token.lemma_)\n if token._.merged_morph:\n for feature in token._.merged_morph.split(\"|\"):\n field, values = feature.split(\"=\", 1)\n if field not in morphs:\n morphs[field] = set()\n for value in values.split(\",\"):\n morphs[field].add(value)\n # create merged features for each morph field\n for field, values in morphs.items():\n morphs[field] = field + \"=\" + \",\".join(sorted(values))\n # set the same attrs on all subtok tokens so that whatever head the\n # retokenizer chooses, the final attrs are available on that token\n for token in subtok_span:\n token._.merged_orth = token.orth_\n token._.merged_lemma = \" \".join(lemmas)\n token.tag_ = \"_\".join(tags)\n token._.merged_morph = \"|\".join(sorted(morphs.values()))\n token._.merged_spaceafter = (\n True if subtok_span[-1].whitespace_ else False\n )\n\n with doc.retokenize() as retokenizer:\n for span in subtok_spans:\n retokenizer.merge(span)\n\n return doc","def reconcile_output(self):\n final_string = \"\"\n final_string += \"TEXT: {0}\\n\".format(self._text)\n final_string += \"ID: {0}\\n\".format(self._index)\n final_string += \"Count: {0}\\n\".format(self._count)\n\n final_string += \"=Doc Count Begin=\\n\"\n for doc in list(self._doc_count.keys()):\n final_string += \"{0} $!$ {1}\\n\".format(doc, self._doc_count[doc])\n final_string += \"=Doc Count End=\\n\"\n\n final_string += \"=CEs Begin=\\n\"\n for doc in list(self.word_pairs.keys()):\n for ce in set(self.word_pairs[doc]):\n #format: doc $!$ ce \n final_string += \"{0} $!$ {1} $!$ {2}\\n\".format(doc, ce, self.word_pairs[doc].count(ce))\n final_string += \"=CEs End=\\n\"\n\n final_string += \"=Self Tags Begin=\\n\"\n for source in list(self.this_semantic_tags.keys()):\n for tag in set(self.this_semantic_tags[source]):\n final_string += \"{0} $!$ {1} $!$ {2}\\n\".format(source, tag, self.this_semantic_tags[source].count(tag))\n final_string += \"=Self Tags End=\\n\"\n\n final_string += \"=Semantic Begin=\\n\"\n for source in list(self.lexico_semantic.keys()):\n #for some reason, I had each semantic class getting assigned the\n #same overall count?\n #sem_counts = self.getLexicoSemanticCounts(source)\n for ce in self.lexico_semantic[source]:\n for sem_cls in set(self.lexico_semantic[source][ce]):\n c = self.lexico_semantic[source][ce].count(sem_cls)\n final_string += \"{0} $!$ {1} $!$ {2} $!$ {3}\\n\".format(source, ce, sem_cls, c)\n final_string += \"=Semantic End=\\n\"\n final_string += \"$!$\\n\"\n return final_string","def merge_with(self, other):\n old2new = {}\n for other_id, other_token in iteritems(other):\n if other_token in self.token2id:\n new_id = self.token2id[other_token]\n else:\n new_id = len(self.token2id)\n self.token2id[other_token] = new_id\n self.dfs[new_id] = 0\n old2new[other_id] = new_id\n try:\n self.dfs[new_id] += other.dfs[other_id]\n except Exception:\n # `other` isn't a Dictionary (probably just a dict) => ignore dfs, keep going\n pass\n try:\n self.num_docs += other.num_docs\n self.num_nnz += other.num_nnz\n self.num_pos += other.num_pos\n except Exception:\n pass\n\n import gensim.models\n return gensim.models.VocabTransform(old2new)","def count_words_and_dublicates(novel):","def merge(self, tokens):\n tokens = iter(tokens)\n (lasttype, lastval) = tokens.next()\n for ttype, value in tokens:\n if ttype is lasttype:\n lastval += value\n else:\n yield(lasttype, lastval)\n (lasttype, lastval) = (ttype, value)\n if lastval.endswith('\\n'):\n lastval = lastval[:-1]\n if lastval:\n yield(lasttype, lastval)","def common_token_len(self, w1, d1, w2, d2):\n w1_tk = set(self.__stem_Tokens(w1))\n w2_tk = set(self.__stem_Tokens(w2))\n common_len = len(w1_tk.intersection(w2_tk))\n return common_len","def calculate(self, tokens: list):\n vektor_tf_unigram = {}\n\n for token in tokens:\n if token in vektor_tf_unigram:\n vektor_tf_unigram[token] = vektor_tf_unigram[token] + 1\n else:\n vektor_tf_unigram[token] = 1\n\n return vektor_tf_unigram","def tokenize(self):\n count = 0\n for entry in self._entries:\n token_pairs = []\n for relation in entry['relations']:\n assert len(relation) == 3\n token_pairs.append((relation[0][0],relation[1][0],relation[2][0]))\n\n num_rels = len(entry['relations'])\n num_random_rels = (self._max_seq_length - 2) // 3 - num_rels\n\n if num_random_rels>0:\n pass\n # gt_pairs = {(rel[0],rel[2]) for rel in entry['relations']}\n # random_pairs = self._get_random_pair(entry['objects'], gt_pairs, num_random_rels)\n # for pair in list(random_pairs):\n # token_pairs.append((pair[0][0],'background', pair[1][0]))\n else:\n for i in range(-num_random_rels):\n token_pairs.pop()\n\n random.shuffle(token_pairs)\n tokens = []\n for pair in token_pairs:\n tokens.extend(pair)\n\n tokens = ['[CLS]'] + tokens + ['[SEP]']\n tokens_char = tokens\n\n target = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3==2 else -1 for i, x in enumerate(tokens)]\n tokens = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3!=2 else self._tokenizer.vocab.get('[MASK]', self._tokenizer.vocab['[UNK]']) for i, x in enumerate(tokens)]\n \n for i in range(len(tokens)):\n if target[i] != -1:\n print(tokens_char[i],tokens[i],target[i])\n\n segment_ids = [0] * len(tokens)\n input_mask = [1] * len(tokens)\n # input_mask = [1 if i%3==2 else 0 for i in range(len(tokens))]\n # co_attention_mask = [-1 if i%3==2 else 1 for i in range(len(tokens))]\n # co_attention_mask = torch.zeros((self._max_region_num, self._max_seq_length))\n # co_attention_mask[0] = -1\n # co_attention_mask[-1] = -1\n \n if len(tokens) < self._max_seq_length:\n padding = [self._padding_index] * (self._max_seq_length - len(tokens))\n tokens = tokens + padding\n input_mask += padding\n segment_ids += padding \n target += [-1] * len(padding) \n\n assert_eq(len(tokens), self._max_seq_length)\n entry['input_ids'] = tokens \n entry[\"input_mask\"] = input_mask\n entry['segment_ids'] = segment_ids\n # entry[\"co_attention_mask\"] = co_attention_mask\n entry['target'] = target\n\n sys.stdout.write('%d/%d\\r' % (count, len(self._entries)))\n sys.stdout.flush()\n count += 1","def number_tokens(dgraph):\n cnt = 0\n for node in dgraph.subgraphs(exclude_root=True):\n node[WORD] = (cnt, node[WORD])\n cnt += 1\n return dgraph","def gather_counts(directory):\n counts_un = defaultdict(int)\n counts_bi = defaultdict(int)\n counts_tri = defaultdict(int)\n prev_prev = \"<s>\"\n prev = \"<s>\"\n for filename in os.listdir(f\"./{directory}\"):\n if \".DS_Store\" in filename:\n continue\n with open(f\"./{directory}/{filename}\", \"r\") as f:\n for line in f:\n line = line.strip()\n if len(line) == 0:\n continue\n counts_un[line+\"\\n\"] += 1\n counts_bi[prev+\"\\n\"+line+\"\\n\"] += 1\n counts_tri[prev_prev+\"\\n\"+prev+\"\\n\"+line+\"\\n\"] += 1\n prev_prev = prev\n prev = line\n counts_un[\"</s>\\n\"] += 2\n counts_bi[\"</s>\\n</s>\\n\"] += 1\n counts_bi[prev+\"\\n\"+\"</s>\\n\"] += 1\n counts_tri[prev_prev+\"\\n\"+prev+\"\\n\" + \"</s>\\n\"] += 1\n counts_tri[prev+\"\\n</s>\\n</s>\\n\"] += 1\n return counts_un, counts_bi, counts_tri","def compute_token_count(token_dict):\n return sum([token_dict[key] for key in token_dict.keys()])","def getCounter(self):\n word_count, noun_word_count = Counter(), Counter()\n word_rating, noun_word_rating = defaultdict(list), defaultdict(list)\n docs = self.nlp.pipe(\n self.docs, n_process=1, disable=self.disablelayers)\n \n\n for index, doc in enumerate(docs):\n for token in doc:\n if not token.is_stop and not token.is_punct and token.pos_ in self.pos:\n if token.pos_ == 'PROPN':\n word_count[token.lemma_] += 1\n word_rating[token.lemma_].append(self.ratings[index])\n else:\n noun_word_count[token.lemma_] += 1\n noun_word_rating[token.lemma_].append(self.ratings[index])\n\n # if 0<=proper nouns<=5 found, add regular nouns\n if not word_count or len(word_count) <= 5:\n word_count += noun_word_count\n word_rating = {**word_rating, **noun_word_rating}\n \n word_color = {word: self.getColor(\n ratings)[1] for word, ratings in word_rating.items()}\n word_sentiment = {word: self.getColor(\n ratings)[0] for word, ratings in word_rating.items()}\n\n return word_count, word_color, word_sentiment","def count_terms(self, tokens):\n\n terms = [self.term_match(t) for t in tokens ]\n \n terms = [t for t in terms if t != None]\n\n #print terms\n lf = dict(Counter(terms))\n for k in lf:\n lf[k] /= float(len(tokens))\n #lf[k] = 1 # binarize?\n pass\n return lf","def word_frequency( tokenized, dic ):\n print( 'computing word frequencies' )\n start = time.time()\n for i, text in enumerate( tokenized ):\n for token in text:\n if token not in dic:\n dic[ token ] = 1\n else:\n dic[ token ] += 1\n if i % 10000 == 0:\n sys.stdout.write( '\\rprocessed : {}/{} reviews in {}s'.format( i, NO_REVIEWS, time.time() - start ) )\n sys.stdout.write( '\\rprocessed : {}/{} reviews in {}s\\n'.format( i, NO_REVIEWS, time.time() - start ) )","def count_words(tokenized_sentences):\r\n \r\n word_counts = {}\r\n \r\n # Loop through each sentence\r\n for sentence in tokenized_sentences: # complete this line\r\n \r\n for token in sentence: # complete this line\r\n\r\n # If the token is not in the dictionary yet, set the count to 1\r\n if token not in word_counts.keys(): # complete this line\r\n word_counts[token] = 1\r\n \r\n # If the token is already in the dictionary, increment the count by 1\r\n else:\r\n word_counts[token] += 1\r\n \r\n return word_counts","def merge_accumulators(self, accumulators):\n accumulator = WordOccurrenceAccumulator(self.relevant_ids, self.dictionary)\n for other_accumulator in accumulators:\n accumulator.merge(other_accumulator)\n # Workers do partial accumulation, so none of the co-occurrence matrices are symmetrized.\n # This is by design, to avoid unnecessary matrix additions/conversions during accumulation.\n accumulator._symmetrize()\n logger.info(\"accumulated word occurrence stats for %d virtual documents\", accumulator.num_docs)\n return accumulator","def countOverlapTokens(self, annotatedMention):\n if self.tokens[0].sentence != annotatedMention.tokens[0].sentence:\n return 0 # mentions in different sentences, no overlap \n \n if self.end < annotatedMention.start or self.start > annotatedMention.end:\n return 0 # mention ends before or starts after annotated one\n \n if self.start == annotatedMention.start and self.end == annotatedMention.end:\n return len(self.tokens) # exact match for annotated mention\n \n # There is some overlap. Does it consist of anything substantial?\n importantTokens = 0\n for token in self.tokens:\n if token.index >= annotatedMention.start \\\n and token.index <= annotatedMention.end \\\n and token.isSymbol() == False and token.isStopWord() == False:\n importantTokens += 1\n \n return importantTokens","def common_words(words: Dict[str, int], count: int) -> None:\n \n original_order = list(words.values())\n max_to_min = list(reversed(sorted(words.values())))\n temp_lst1 = []\n temp_lst2 = []\n \n \n if count == len(words):\n words = words\n \n \n for num in range(len(max_to_min)):\n if len(temp_lst1) < count:\n temp_lst1.append(max_to_min[num])\n \n for nums in range(len(temp_lst1)):\n max_to_min.remove(temp_lst1[nums])\n \n \n temp_lst2 = remove_repeats(max_to_min, temp_lst1)\n \n a = reformat_order(original_order, temp_lst2)\n \n words_to_counts = combine_list(format_dict(words, a), a)\n \n words.clear()\n \n for keys in words_to_counts:\n words[keys] = words_to_counts[keys]","def partial_accumulate(self, texts, window_size):\n self._current_doc_num = -1\n self._token_at_edge = None\n self._counter.clear()\n\n super(WordOccurrenceAccumulator, self).accumulate(texts, window_size)\n for combo, count in iteritems(self._counter):\n self._co_occurrences[combo] += count\n\n return self","def common_token_len_with_slash(self, w1, d1, w2, d2):\n w1_tk = set(re.split(\"[\\s-]\", w1))\n w2_tk = set(re.split(\"[\\s-]\", w2))\n common_len = len(w1_tk.intersection(w2_tk))\n return common_len","def merge_same_tones(self, tone_list):\n merged_tone_list = {'m': [], 'v': []}\n\n prev_mala = tone_list[0][0]\n prev_vela = tone_list[0][1]\n\n # same length on the beggining\n prev_mala_tone_length = tone_list[0][2]\n prev_vela_tone_length = tone_list[0][2]\n\n for mala_tone, vela_tone, tone_length in tone_list[1:]:\n\n if prev_mala == mala_tone:\n prev_mala_tone_length += tone_length\n else:\n merged_tone_list['m'].append(\n (prev_mala, prev_mala_tone_length))\n prev_mala_tone_length = tone_length\n prev_mala = mala_tone\n\n if prev_vela == vela_tone:\n prev_vela_tone_length += tone_length\n else:\n merged_tone_list['v'].append(\n (prev_vela, prev_vela_tone_length))\n prev_vela_tone_length = tone_length\n prev_vela = vela_tone\n\n # append last tone\n if prev_mala_tone_length > 0:\n merged_tone_list['m'].append((prev_mala, prev_mala_tone_length))\n\n if prev_vela_tone_length > 0:\n merged_tone_list['v'].append((prev_vela, prev_vela_tone_length))\n\n return merged_tone_list","def getTTokens(wordlist, tokens = {}, count = 0, token = TOKEN):\n\n\tif len(wordlist) == 0:\n\t\treturn tokens, count\n\n\tfor i in wordlist:\n\t\tfor x in i:\n\t\t\tif tokens.has_key(x[token]):\n\t\t\t\ttokens[x[token]] += 1\n\t\t\telse:\n\t\t\t\ttokens[x[token]] = 1\n\t\t\tcount += 1\n\treturn tokens, count","def build_morpheme_vocab(self):\n max_morph_per_word = 0\n morpheme_dict = collections.defaultdict(int)\n splitter = \"@@\"\n for token in self.train_data:\n if token == self.eos or token == self.sos:\n continue\n token = '^' + token + '$'\n morphemes = token.split(splitter)\n if len(morphemes) > max_morph_per_word:\n max_morph_per_word = len(morphemes)\n for morpheme in morphemes:\n morpheme_dict[morpheme] += 1\n\n unk_morpheme_list = set()\n item_to_id = dict()\n item_to_id[constants.PAD_ITEM] = len(item_to_id)\n item_to_id[constants.UNK_ITEM] = len(item_to_id)\n sorted_dict = sorted(morpheme_dict.items(), key=operator.itemgetter(1), reverse=True)\n for token, freq in sorted_dict:\n if freq == 1:\n unk_morpheme_list.add(token)\n if token not in item_to_id:\n item_to_id[token] = len(item_to_id)\n return item_to_id, unk_morpheme_list, max_morph_per_word","def count_ngrams(self, corpus):\n \n self.unigramcounts = {} # might want to use defaultdict or Counter instead\n self.bigramcounts = {} \n self.trigramcounts = {} \n\n self.total = 2\n ##Your code here\n\n for sentence in corpus:\n temp_1 = get_ngrams(sentence,1)\n temp_2 = get_ngrams(sentence,2)\n temp_3 = get_ngrams(sentence,3)\n for i in range(len(temp_1)):\n if temp_1[i] in self.unigramcounts:\n self.unigramcounts[temp_1[i]] += 1\n else:\n self.unigramcounts[temp_1[i]] = 1\n self.total += 1\n\n for i in range(len(temp_2)):\n if temp_2[i] in self.bigramcounts:\n self.bigramcounts[temp_2[i]] += 1\n else:\n self.bigramcounts[temp_2[i]] = 1\n\n for i in range(len(temp_3)):\n if temp_3[i] in self.trigramcounts:\n self.trigramcounts[temp_3[i]] += 1\n else:\n self.trigramcounts[temp_3[i]] = 1\n return","def reducer_count_words(self, word, counts):\n aux3 = word.split(\"+\")\n yield aux3[0], (sum(counts), aux3[1])","def __parse_corpus(self, corpus):\n corpus = self.__handle_corpus_unkwon_words(corpus)\n start_token = ' '.join([NGramModel.START_SENTENCE_TOKEN]*(self.__n-1))\n word_list = corpus.replace(NGramModel.START_SENTENCE_TOKEN, start_token).split()\n \n for n in range(1, self.__n+1): \n self.__ngram_counts[n] = {}\n for ngram, count in Counter(self.__generate_n_grams(word_list, n)).items():\n self.__ngram_counts[n][' '.join(ngram)] = count","def insight_github_unique_word_results():\n counter_container = OrderedDict()\n\n path_to_input_file = os.path.join(constants.TEST_DIRECTORY,\n \"fixtures/ft1_expected_results_v1.txt\")\n with open(path_to_input_file, 'r') as input_file:\n for line in input_file:\n word, count = line.split()\n counter_container[word] = int(count)\n return counter_container","def _tokenize(self, raw_text):\n\n doc = self.nlp(raw_text.strip())\n\n # Loop through tokens and find known entities aren't already marked\n for token in doc:\n # Is this word in our known_entities, but is not recognized by the spaCy parser?\n if token.text.lower() in self.known_entities and token.ent_type not in self.entities:\n # We need to set the new entity to doc.ents directly (I believe the getter for doc.ents does\n # some important massaging. However, counter to the online docs, setting doc.ents wipes out\n # all of the previously recognized ents, so we stash the value, then we combine and reset.\n stash = doc.ents\n doc.ents = [(token.text.title(), doc.vocab.strings['PERSON'], token.i, token.i + 1)]\n doc.ents = doc.ents + stash\n\n # Find proper noun n-grams: (a) find a known entity, (b) is the next word also a known entity?,\n # (c) merge, (d) repeat\n # TODO: Joining multi-word named entities sometimes causes us trouble.\n doc_len = len(doc) # Helps us know when to exit the 'for loop' (since we change the # of items via merge)\n for token in doc:\n # if we're not at the end of the loop, and we recognize this as a proper noun and it's not a stop word\n # and the token isn't a space...\n if token.i + 1 < doc_len and token.ent_type in self.entities and \\\n token.text.lower() not in self.stop_words and token.text not in ' ':\n next_token = doc[token.i + 1]\n # keep looping while we're not at the end of the loop and this token has the same entity type as\n # the previous token and it's not a stop word or a space.\n while token.i + 1 < doc_len and next_token.ent_type == token.ent_type and \\\n next_token.text.lower() not in self.stop_words and next_token.text not in ' ':\n n_gram = doc[token.i:token.i + 2]\n n_gram.merge()\n doc_len -= 1 # the merge changes the list length, so we just shrunk the list!\n # print(x)\n if token.i + 1 >= doc_len:\n break\n\n return doc","def add_to_dict(self, tokens):\n# TODO: ?add normalization of a token?\n for token in tokens:\n if self.embedding_words and (token not in self.embedding_words):\n continue\n self.freq[token] += 1\n if token not in self.tok2ind:\n index = len(self.tok2ind)\n self.tok2ind[token] = index\n self.ind2tok[index] = token","def countMutatedSentences(sentence):\n # BEGIN_YOUR_ANSWER (our solution is 17 lines of code, but don't worry if you deviate from this)\n words = sentence.split()\n distinct_words = set(words)\n\n pairs = {word: set() for word in distinct_words}\n cache = [{j: None for j in distinct_words} for i in range(len(words))]\n\n for cur_word, next_word in zip(words[:-1], words[1:]):\n pairs[cur_word].add(next_word)\n\n def count_mutate_sentences(target_word: str, current_length: int, target_length: int):\n if current_length == target_length:\n return 1\n else:\n result = 0\n for word in pairs[target_word]:\n if cache[current_length - 1][word] is None:\n cache[current_length - 1][word] = count_mutate_sentences(word, current_length + 1, target_length)\n result += cache[current_length - 1][word]\n\n return result\n\n return sum([count_mutate_sentences(word, 1, len(words)) for word in distinct_words])\n # END_YOUR_ANSWER","def merge_words(word_list, merge_method = None, **kwargs):\n \n for word in word_list:\n word['minframe'] = word['frame']\n word['maxframe'] = word['frame']\n \n if merge_method is None:\n for word in word_list:\n word['merge_size'] = 1\n return word_list\n \n clusterize_words(word_list, **kwargs)\n \n #Merge words within clusters\n result = []\n word_list.sort(key = lambda word: word['cluster'])\n cluster = []\n current_cluster = - 1\n for word in word_list:\n #First word\n if len(cluster) == 0:\n current_cluster = word['cluster']\n cluster = [word]\n elif word['cluster'] == current_cluster:\n cluster.append(word)\n #New cluster\n else:\n #process this cluster\n result.append(merge_cluster(cluster, merge_method))\n \n current_cluster = word['cluster']\n cluster = [word]\n \n return result","def convert_word_to_count(counter={}, doc=[]):\n for sentence in doc:\n for word in sentence.split():\n if word not in counter:\n counter[word] = 1\n else:\n counter[word] += 1\n return counter","def count_tokens(self, words: Iterable[str]) -> Dict[str, int]:\r\n token_counts = Counter(words)\r\n return {\" \".join(token): count for token, count in token_counts.items()}","def merged_gene_feature_counts(self, merge_multi_splice_variants=True, merge_boundary_features=True, \n merge_confusing_features=False):\n merged_feature_count_dict = defaultdict(int)\n for feature, count in self.mutant_counts_by_feature.items():\n # note that anything containing '??' AND '/' never gets merged as boundary\n if '??' in feature:\n if merge_confusing_features: merged_feature_count_dict['??'] += count\n else: merged_feature_count_dict[feature] += count\n elif '|' in feature and merge_multiple_splice_variants: merged_feature_count_dict['multiple_splice_variants'] += count\n elif '/' in feature and merge_boundary_features: merged_feature_count_dict['boundary'] += count\n else: merged_feature_count_dict[feature] += count\n return merged_feature_count_dict","def reducer_count_words(self, word, counts):\n aux3 = word.split(\"+\") # Split the string\n yield aux3[0], (sum(counts), aux3[1])","def count_ngrams(self, corpus):\n \n self.unigramcounts = defaultdict(int)\n self.bigramcounts = defaultdict(int)\n self.trigramcounts = defaultdict(int)\n\n self.sentence_counts = 0\n self.word_count = 0\n\n for line in corpus:\n sequence = line\n self.sentence_counts +=1\n\n unigrams = get_ngrams(sequence, n=1)\n for gram in unigrams:\n self.word_count += 1\n self.unigramcounts[gram] +=1\n\n bigrams = get_ngrams(sequence, n=2)\n for gram in bigrams:\n self.bigramcounts[gram] +=1\n\n trigrams = get_ngrams(sequence, n=3)\n for gram in trigrams:\n self.trigramcounts[gram] +=1\n\n #self.unigramcounts[('START')] = self.sentence_counts *2\n self.bigramcounts[('START', 'START')] = self.sentence_counts\n\n #return self","def __init__(self, n, sents):\n assert n > 0\n self._n = n\n print(\"Counting...\")\n count = defaultdict(int)\n while n >= 0:\n for sent in sents:\n s = sent[:] # En una oracion auxiliar agrego el item de start y end para contarlos\n s.insert(0, \"<s>\")\n s.append(\"</s>\")\n for i in range(len(s) - n + 1):\n count[tuple(s[i:i + n])] += 1\n n -= 1\n count[()] = count[()] - count[('<s>',)] - count[\n ('</s>',)] # Pero no quiero que <s> y </s> sean considerados por ()\n self._count = count\n print(\"Computing vocabulary...\")\n self._voc = voc = set()\n for sent in sents:\n voc = voc.union(set(sent))\n voc.add('</s>')\n self._voc = voc\n self._V = len(voc) # vocabulary size\n print(\"Done\")","def __cross_wiki_counts(self):\n\n print(\"Updating counts by merging with CrossWiki\")\n\n cnt = 0\n crosswiki_path = os.path.join(\n self.base_url, \"generic/p_e_m_data/crosswikis_p_e_m.txt\"\n )\n\n with open(crosswiki_path, \"r\", encoding=\"utf-8\") as f:\n for line in f:\n parts = line.split(\"\\t\")\n mention = unquote(parts[0])\n\n if (\"Wikipedia\" not in mention) and (\"wikipedia\" not in mention):\n if mention not in self.wiki_freq:\n self.wiki_freq[mention] = {}\n\n num_ents = len(parts)\n for i in range(2, num_ents):\n ent_str = parts[i].split(\",\")\n ent_wiki_id = int(ent_str[0])\n freq_ent = int(ent_str[1])\n\n if (\n ent_wiki_id\n not in self.wikipedia.wiki_id_name_map[\"ent_id_to_name\"]\n ):\n ent_name_re = self.wikipedia.wiki_redirect_id(ent_wiki_id)\n if (\n ent_name_re\n in self.wikipedia.wiki_id_name_map[\"ent_name_to_id\"]\n ):\n ent_wiki_id = self.wikipedia.wiki_id_name_map[\n \"ent_name_to_id\"\n ][ent_name_re]\n\n cnt += 1\n if (\n ent_wiki_id\n in self.wikipedia.wiki_id_name_map[\"ent_id_to_name\"]\n ):\n if mention not in self.mention_freq:\n self.mention_freq[mention] = 0\n self.mention_freq[mention] += freq_ent\n\n ent_name = self.wikipedia.wiki_id_name_map[\n \"ent_id_to_name\"\n ][ent_wiki_id].replace(\" \", \"_\")\n if ent_name not in self.wiki_freq[mention]:\n self.wiki_freq[mention][ent_name] = 0\n self.wiki_freq[mention][ent_name] += freq_ent","def merge_df_counts(pin, pin2):\n df = pd.read_csv(pin)\n df = add_mutkey(df)\n df = add_codonkey(df)\n # df_syn = df.loc[df[\"mutkey\"].str[0] == df[\"mutkey\"].str[-1]]\n\n df2 = pd.read_csv(pin2)\n df2 = add_codonkey(df2)\n # df2_syn = df.loc[df[\"mutkey\"].str[0] == df[\"mutkey\"].str[-1]]\n df_reps = df.merge(\n df2, left_on=\"codonkey\", right_on=\"codonkey\", suffixes=(\"_r1\", \"_r2\")\n )\n return df_reps","def vectorize_pos_tags(self, tokens):\n\n pos_counts = defaultdict(float)\n for token in tokens:\n pos_counts[token.pos] += 1.0\n return pos_counts","def cherche_anagramme(mot):\n global DICO\n res = []\n lettres = dict((l, mot.count(l)) for l in set(mot))\n if len(mot) == 1:\n res.append(mot)\n else:\n for ana in DICO:\n if len(ana) == len(mot):\n anag = True\n for lettre in mot:\n if lettre not in ana:\n anag = False\n if anag:\n lettres2 = dict((l, ana.count(l)) for l in set(ana))\n if lettres == lettres2:\n res.append(ana)\n return res","def __find_union(dicts: list):\n global other_time\n start = time.time()\n union_list = []\n while len(dicts) > 1:\n word_count = {}\n final_dict = {}\n for d in dicts:\n # print(json.dumps(d, indent=4))\n for word in d:\n if word not in word_count:\n word_count[word] = 1\n else:\n word_count[word] = word_count[word] + 1\n for item in word_count:\n if word_count[item] == len(dicts):\n union_list.append(item)\n for d in dicts:\n for word in d:\n if word in union_list:\n if word not in final_dict:\n final_dict[word] = d[word]\n else:\n final_dict[word] += d[word]\n if len(final_dict) > 0:\n # print(len(dicts))\n end = time.time()\n diff = end - start\n other_time += diff\n # print(json.dumps(final_dict, indent=4))\n return (final_dict)\n else:\n\n dicts = dicts[:len(dicts) - 1]\n\n end = time.time()\n diff = end - start\n other_time += diff\n return {}","def remove_duplicated_words(tokens):\n\n return sorted(set(tokens))","def count_tokens(txt_to_tokenise_and_count):\n\n txt_tokens = nltk.word_tokenize(txt_to_tokenise_and_count) \n counts = Counter(txt_tokens)\n\n print (counts.most_common(10))\n return counts","def analyzeFile(filename): \n fileData = open(filename, encoding=\"utf-8\") # open the file\n \n counts = {}\n\n for line in fileData:\t\t # iterates over every line of the file\n words = line.split() # turns each line into a list\n for word in words: #iterates over the words in each line list\n word = word.lower().strip(string.whitespace+string.punctuation)\n if len(word) > 0: #make sure word is longer than 0 before adding it to the dictionary\n counts[word] = counts.get(word, 0) + 1 #look up if the dictionary has that word and if not then it'll add that word with the value 0 associated with it and then add one to that, if it has seen it it'll add 1 to the value stored in the counts dictionary\n #when it gets here for the first line it goes back up to the top and repeats for the 2nd line\n mostCommonWord = [word]\n leastCommonWord = [word]\n shortestWord = [word]\n longestWord = [word]\n \n for item in counts:\n if counts[mostCommonWord[0]] < counts[item]:\n mostCommonWord = [item]\n elif counts[mostCommonWord[0]] == counts[item]:\n mostCommonWord.append(item)\n if counts[leastCommonWord[0]] > counts[item]:\n leastCommonWord = [item]\n elif counts[leastCommonWord[0]] == counts[item]:\n leastCommonWord.append(item)\n if len(shortestWord[0]) > len(item):\n shortestWord = [item] \n elif len((shortestWord[0])) == len(item):\n shortestWord.append(item)\n if len(longestWord[0]) < len(item):\n longestWord = [item]\n elif len(longestWord[0]) == len(item):\n longestWord.append(item)\n \n return (mostCommonWord, leastCommonWord, shortestWord, longestWord)","def merge_cooccur(args):\n if not args.quiet:\n logger.setLevel(logging.INFO)\n\n merged = dict.fromkeys(['mat', 'tokenizer', 'window_size', 'uniform_count'])\n with tqdm(total=len(args.cooccurfiles), ncols=80, disable=args.quiet) as prog:\n for file in args.cooccurfiles:\n # load the data\n corpus = load_corpus(file)\n\n if merged['tokenizer'] is None:\n merged['tokenizer'] = corpus._tokenizer.to_str()\n\n if merged['window_size'] is None:\n merged['window_size'] = corpus.window_size\n\n if merged['uniform_count'] is None:\n merged['uniform_count'] = corpus.uniform_count\n\n mat = corpus.mat.astype('float32')\n if args.symmetrization:\n mat = (mat + mat.T.tocoo()).tocoo()\n\n if merged['mat'] is None:\n merged['mat'] = mat\n else:\n merged['mat'] += mat\n\n prog.update()\n merged['mat'] = merged['mat'].tocoo()\n\n # save output\n logger.info('Saving to disk...')\n out_fn = join(args.path, args.out)\n with open(out_fn, 'wb') as fp:\n pkl.dump(\n {\n 'mat': {\n 'row': merged['mat'].row,\n 'col': merged['mat'].col,\n 'counts': merged['mat'].data\n },\n 'tokenizer': merged['tokenizer'],\n 'uniform_count': merged['uniform_count'],\n 'window_size': merged['window_size']\n },\n fp\n )","def train():\n\tA = collections.defaultdict(dict)\n\tB = collections.defaultdict(dict)\n\tpos_list = []\n\tword_list = []\n\tstr_buf = []\n\n\n\t# read each line and count A and B\n\tfor line in sys.stdin:\n\t\tline = line.split()\n\t\t# print(line)\n\t\tif len(line) == 3:\n\t\t\tstr_buf.append((str(line[0]), str(line[1])))\n\t\t\tword_list.append(str(line[0]))\n\t\t\tpos_list.append(str(line[1]))\n\n\t\telse:\n\t\t\t# if come to the end of a sentence\n\t\t\tif len(str_buf) != 0:\n\t\t\t\tstr_buf = [('<s>','BOS')] + str_buf + [('</s>', 'EOS')]\n\t\t\t\tword_list += ['<s>', '</s>']\n\t\t\t\tpos_list += ['BOS', 'EOS']\n\n\t\t\t\tfor i, s in enumerate(str_buf):\n\t\t\t\t\tif s[0] in B[s[1]]:\n\t\t\t\t\t\tB[s[1]][s[0]] += 1\n\t\t\t\t\telse:\n\t\t\t\t\t\tB[s[1]][s[0]] = 1\n\n\t\t\t\t\tif s[0] != '</s>':\n\t\t\t\t\t\t# print('strbuf[i]:',str_buf[i], 's[1]', s[1])\n\t\t\t\t\t\tif str_buf[i+1][1] in A[s[1]]:\n\t\t\t\t\t\t\tA[s[1]][str_buf[i+1][1]] += 1\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tA[s[1]][str_buf[i+1][1]] = 1\n\n\t\t\t\tstr_buf = []\n\n\t# get unique POS list and word list\n\tpos_list_uniq = list(set(pos_list))\n\tword_list_uniq = list(set(word_list))\n\n\n\t# assume <UNK>, smoothing, normalize\n\tB_sm = collections.defaultdict(dict)\n\tA_sm = A.copy()\n\n\t# assume words apeear less than 2 times as <UNK>\n\tword_count = collections.Counter(word_list)\n\tfor pos in B:\n\t\tfor word in B[pos]:\n\t\t\tif word_count[word] > 1:\n\t\t\t\tB_sm[pos][word] = B[pos][word]\n\n\t\t\telse: # add <UNK> to B_sm\n\t\t\t\tword_list_uniq.remove(word)\n\t\t\t\tif '<UNK>' in B_sm[pos]:\n\t\t\t\t\tB_sm[pos]['<UNK>'] += 1\n\t\t\t\telse:\n\t\t\t\t\tB_sm[pos]['<UNK>'] = 1\n\n\tword_list_uniq += ['<UNK>']\n\n\t# add 1 smoothing\n\tfor pos in pos_list_uniq:\n\t\tfor word in word_list_uniq:\n\t\t\tif word in B_sm[pos]:\n\t\t\t\tB_sm[pos][word] += 1\n\t\t\telse:\n\t\t\t\tB_sm[pos][word] = 1\n\n\tfor prev in pos_list_uniq:\n\t\tfor next in pos_list_uniq:\n\t\t\tif next in A_sm[prev]:\n\t\t\t\tA_sm[prev][next] += 1\n\t\t\telse:\n\t\t\t\tA_sm[prev][next] = 1\n\n\t# delete instances like A[VB][BOS], A[EOS][VB],\n\t# B[VB]['</s>'], B[EOS]['Jack']\n\n\tfor pos in B_sm:\n\t\tfor word in B_sm[pos]:\n\t\t\tif (pos == 'BOS' and word != '<s>') or \\\n\t\t\t(pos == 'EOS' and word != '</s>') or \\\n\t\t\t(word == '<s>' and pos != 'BOS') or \\\n\t\t\t(word == '</s>' and pos != 'EOS'):\n\t\t\t\tB_sm[pos][word] = 0\n\n\tfor prev in A_sm:\n\t\tfor next in A_sm[prev]:\n\t\t\tif prev == 'EOS' or next == 'BOS':\n\t\t\t\tA_sm[prev][next] = 0\n\n\t# normalize\n\tfor pos in B_sm:\n\t\ts = sum(B_sm[pos].values())\n\t\tfor word in B_sm[pos]:\n\t\t\tif B_sm[pos][word] != 0:\n\t\t\t\tB_sm[pos][word] /= s\n\n\tfor prev in A_sm:\n\t\ts = sum(A_sm[prev].values())\n\t\tfor next in A_sm[prev]:\n\t\t\tif A_sm[prev][next] != 0:\n\t\t\t\tA_sm[prev][next] /= s\n\n\treturn A_sm, B_sm, word_list_uniq","def word_overlap_phi(t1, t2):\n overlap = set([w1 for w1 in t1.leaves() if w1 in t2.leaves()])\n return Counter(overlap)","def __init__(self, n, sents, gamma=None, addone=True):\n assert n > 0\n self._n = n\n\n if gamma is not None:\n # everything is training data\n train_sents = sents\n else:\n # 90% training, 10% held-out\n m = int(0.45 * len(sents))\n l = int(0.65 * len(sents))\n train_sents = sents[:m] + sents[l:]\n held_out_sents = sents[m:l]\n\n print('Computing counts...')\n count = defaultdict(int)\n while (n >= 0):\n for sent in train_sents:\n s = sent[:] ## En una oracion auxiliar agrego el item de start y end para contarlos\n s.insert(0, \"<s>\")\n s.append(\"</s>\")\n for i in range(len(s) - n + 1):\n count[tuple(s[i:i + n])] += 1\n n -= 1\n count[()] = count[()] - count[('<s>',)] - count[\n ('</s>',)] # Pero no quiero que <s> y </s> sean considerados por ()\n self._count = count\n # WORKed HERE!!\n # COMPUTE COUNTS FOR ALL K-GRAMS WITH K <= N\n\n # compute vocabulary size for add-one in the last step\n self._addone = addone\n if addone:\n print('Computing vocabulary...')\n self._voc = voc = set()\n for sent in sents:\n voc = voc.union(set(sent))\n voc.add('</s>')\n self._voc = voc\n self._V = len(voc)\n\n # compute gamma if not given\n if gamma is not None:\n self._gamma = gamma\n else:\n print('Computing gamma...')\n self._gamma = gamma = 1\n p = self.log_prob(held_out_sents)\n new_gamma = 2\n streak = 1\n growing = True\n turns = 0\n while (turns < 15):\n self._gamma = new_gamma\n np = self.log_prob(held_out_sents)\n gamma = new_gamma\n if (np > p):\n if growing:\n streak += 1\n else:\n turns += 1\n streak = 0\n growing = True\n new_gamma = new_gamma + 2 ** streak\n else:\n if growing:\n turns += 1\n streak = 0\n growing = False\n else:\n streak += 1\n new_gamma = new_gamma - 2 ** streak\n p = np\n self._gamma = new_gamma\n print(self._gamma)","def _build_token_dict(self, corpus: List[List[str]], min_count: int = 3):\n token2idx = {\n self.token_pad: 0,\n self.token_unk: 1,\n self.token_bos: 2,\n self.token_eos: 3\n }\n\n token2count = {}\n for sentence in corpus:\n for token in sentence:\n count = token2count.get(token, 0)\n token2count[token] = count + 1\n\n # 按照词频降序排序\n sorted_token2count = sorted(token2count.items(),\n key=operator.itemgetter(1),\n reverse=True)\n token2count = collections.OrderedDict(sorted_token2count)\n\n for token, token_count in token2count.items():\n if token not in token2idx and token_count >= min_count:\n token2idx[token] = len(token2idx)\n\n self.token2idx = token2idx\n self.idx2token = dict([(value, key)\n for key, value in self.token2idx.items()])\n logging.debug(f\"build token2idx dict finished, contains {len(self.token2idx)} tokens.\")\n self.dataset_info['token_count'] = len(self.token2idx)","def _annotate_brackets(\n self,\n tokens: List[tokenize.TokenInfo],\n ) -> Mapping[int, int]:\n brackets = {bracket: 0 for bracket in MATCHING}\n for token in tokens:\n if token.exact_type in MATCHING.keys():\n brackets[token.exact_type] += 1\n if token.exact_type in MATCHING.values():\n reverse_bracket = get_reverse_bracket(token)\n if brackets[reverse_bracket] > 0:\n brackets[reverse_bracket] -= 1\n return brackets","def merge_ners(tokens):\n ners = list()\n merged_tokens = list()\n\n candid_entity = list()\n keep = False\n prev_tag = 'O'\n\n for i, (token, tag) in enumerate(tokens):\n if keep:\n if tag not in IGNORE_NER_TAG:\n candid_entity.append(token)\n keep = True\n else:\n # ner ends in prev step\n merged_tokens.append(candid_entity)\n merged_tokens.append(token)\n ners.append((candid_entity, prev_tag))\n keep = False\n else:\n if tag not in IGNORE_NER_TAG:\n # new ner\n candid_entity = list()\n candid_entity.append(token)\n keep = True\n else:\n # not ner token\n merged_tokens.append(token)\n prev_tag = tag\n\n return ners, merged_tokens","def _disambiguate_merged_glosses(self, merged_glosses):\n\t\tprocessed_glosses = {}\n\t\ttotal_glosses = len(merged_glosses)\n\t\tcurrent_gloss = 0\n\t\tdisambiguated_glosses_count = 0\n\t\tskipped_glosses_count = 0\n\n\t\t# go over all glosses and disambiguate them if possible\n\t\tfor gloss_key in merged_glosses:\n\t\t\tcurrent_gloss += 1\n\t\t\tgloss = merged_glosses[gloss_key]\n\t\t\ttaggable_tokens = []\n\t\t\ttagged_tokens = []\n\n\t\t\tif len(gloss.tokens) == 0:\n\t\t\t\tself._log_message(\"WARNING: unmerged gloss {0}\".format(gloss_key))\n\n\t\t\t# for each token inside the gloss determine if the token is already disambiguated, needs to be disambiguated or isnt part of WordNet anyways\n\t\t\tfor token_index in gloss.tokens:\n\t\t\t\ttoken = gloss.tokens[token_index]\n\t\t\t\ttokens_wn_ss_types = list(map(int, re.findall(\"[0-9]+\", token.lemma)))\n\t\t\t\t# tokens that are tagged as \"man\" or \"auto\" are already disambiguated\n\t\t\t\tif token.tag in [\"man\", \"auto\"]:\n\t\t\t\t\ttagged_tokens.append(token)\n\t\t\t\t# if a token is tagged as \"un\" it is not yet annotated, but possibly should be; if the lemma contains entries of the type \"LEMMA%SS_TYPE\" and the POS if the word matches one of the lemma-ss_types it needs to be/can be disambiguated\n\t\t\t\telif token.tag == \"un\" and re.match(\"([a-z]+%[0-9]\\|?)+\", token.lemma) and not set(GLOSSTAG_POS_POSSIBLE_SS_TYPES[token.pos]).isdisjoint(set(tokens_wn_ss_types)) and type(token) != CollocationMember:\n\t\t\t\t\ttaggable_tokens.append(token)\n\n\t\t\t# if there are no remaining taggable tokens, then proceed with the next gloss\n\t\t\tif len(taggable_tokens) == 0:\n\t\t\t\tprocessed_glosses[gloss_key] = gloss\n\t\t\t\tskipped_glosses_count += 1\n\t\t\t\tcontinue\n\t\t\tdisambiguated_glosses_count += 1\n\n\t\t\t## DISAMBUGATION PROCEDURE ##\n\t\t\tprint(\"\\tat gloss {0} of {1}\".format(current_gloss, total_glosses))\n\t\t\tprint(\"\\t\\t...disambiguating {0} words in gloss {1}\".format(len(taggable_tokens), gloss_key))\n\t\t\t# disambiguated_gloss = self._disambiguate_gloss_with_path_similarity(gloss, taggable_tokens, tagged_tokens)\n\t\t\tdisambiguated_gloss = self._disambiguate_gloss_by_most_frequent_sense(gloss, taggable_tokens, tagged_tokens)\n\t\t\t# finished, append to output\n\t\t\tprocessed_glosses[gloss_key] = disambiguated_gloss\n\n\t\tprint(\"\\tdisambiguated {0} glosses, skipped {1}\".format(disambiguated_glosses_count, skipped_glosses_count))\n\n\t\treturn processed_glosses","def bow(tokens):\n return dict(collections.Counter(re.findall(r'\\w+', \" \".join(tokens))))","def can_be_merged(prev, cur):\n\n WHITESPACE = (' ', '\\t')\n if not cur.mergeable or not prev.mergeable:\n return False\n # offset is char offset, not byte, so length is the char length!\n elif cur.offset != (prev.offset + prev.length):\n return False\n elif cur.text in WHITESPACE and not prev.text in WHITESPACE:\n return False\n elif prev.text in WHITESPACE and not cur.text in WHITESPACE:\n return False\n return True","def train(self, corpus):\n lastToken = \"#\"\n for sentence in corpus.corpus:\n for datum in sentence.data:\n token = datum.word\n self.reverseBigramCount[token][lastToken] += 1\n self.bigramCount[lastToken][token] += 1\n self.unigramCount[token] += 1\n self.total += 1\n lastToken = token","def __iadd__(self, other):\n\n if not isinstance(other, Unigram):\n return NotImplemented\n\n self.sort_by_tokens(other.dictionary.tokens)\n\n for i in range(len(other.dictionary)):\n other_count = other.Nx[i]\n self.Nx[i] += other_count\n\n self.N = sum(self.Nx)\n\n return self","def _count_occurrences(self, corpus_tokens: list, window_size: int):\n ngram_occurrences = {}\n for i, corpus_token in enumerate(corpus_tokens):\n ngram = []\n if i + window_size > len(corpus_tokens) - 1:\n break\n else:\n for j in range(window_size):\n ngram.append(corpus_tokens[i + j])\n if self.TOKENS.END.value in ngram:\n continue\n else:\n ngram_key = tuple(ngram)\n if ngram_key in ngram_occurrences:\n ngram_occurrences[ngram_key] += 1\n else:\n ngram_occurrences[ngram_key] = 1\n return ngram_occurrences","def can_be_merged(prev, cur):\n\n WHITESPACE = (' ', '\\t')\n if not cur.mergeable or not prev.mergeable:\n return False\n elif prev.delete_key_used != cur.delete_key_used:\n return False\n elif prev.start != cur.start and prev.start != cur.end:\n return False\n elif cur.text not in WHITESPACE and \\\n prev.text in WHITESPACE:\n return False\n elif cur.text in WHITESPACE and \\\n prev.text not in WHITESPACE:\n return False\n return True","def merge_accumulators(self, accumulators):\n raise NotImplementedError","def naive_with_counts(p, t):\n occurences = []\n num_alignments = 0\n num_character_comparisons = 0\n for i in range(len(t) - len(p) + 1):\n match = True\n for j in range(len(p)):\n if t[i + j] != p[j]:\n match = False\n break\n if match:\n occurences.append(i)\n num_alignments += 1\n num_character_comparisons += (j + 1)\n return occurences, num_alignments, num_character_comparisons","def merge(self, nums1: [int], m: int, nums2: [int], n: int) -> None:\n for i in range(m, len(nums1)):\n del nums1[m]\n for i in range(n, len(nums2)):\n del nums2[n]\n\n nums1 += nums2\n nums1.sort()","def num_special_tokens_to_add(self, pair=False):\n token_ids_0 = []\n token_ids_1 = []\n return len(\n self.build_inputs_with_special_tokens(token_ids_0, token_ids_1\n if pair else None))","def frequent_nouns_counts(self, tokens):\n\n unigram_fd = nltk.FreqDist(tokens)\n pos = POS()\n common_unigrams = unigram_fd.most_common(\n int(self.top_pct * len(unigram_fd)))\n\n nouns = [pair for pair in common_unigrams\n if pair[0] not in self.stopwords()\n and pos.percent_noun(pair[0]) > 0.5]\n\n return nouns","def getTokens(wordlist, tokens = {}, count = 0):\n\n\tif len(wordlist) == 0:\n\t\treturn tokens, count\n\n\tfor i in wordlist:\n\t\tif tokens.has_key(i):\n\t\t\ttokens[i] += 1\n\t\telse:\n\t\t\ttokens[i] = 1\n\t\tcount += 1\n\treturn tokens, count","def CountWords(input1):\n tokens = []\n\n d={}\n\n tokens = tokens+input1.lower().split()\n\n for token in tokens:\n # Remove Punctuation\n word=token.replace(\"!\", \"\")\n word=word.replace(\".\", \"\")\n d[word] = d.get(word,0)+1\n\n word_frq=[]\n for i,v in d.items():\n word_frq.append((v,i))\n word_frq.sort(reverse=True)\n\n for word in word_frq:\n print (word[1],word[0])","def get_number_of_different_tokens(token_list):\n \n return len(set(token_list))","def merge_like_ops(self):\n\n cigs = []\n for op, grps in groupby(self.items(), itemgetter(1)):\n cigs.append((sum(g[0] for g in grps), op))\n\n return Cigar(self.string_from_elements(cigs))","def _aggregate_text_embedding(self, token_ids, embeddings):\n if self._hparams['obj_text_aggregation'] == 'max':\n # Find valid tokens (not PADDING/EOS/UNK/START).\n valid_token_mask = tf.greater_equal(token_ids, 4)\n # Use large negative bias for invalid tokens.\n invalid_token_bias = tf.cast(\n tf.logical_not(valid_token_mask), tf.float32) * -1e9\n # [batch, node_num, word_num, hidden_size]\n embeddings = embeddings + tf.expand_dims(invalid_token_bias, axis=-1)\n # Max value for each dimension, [batch, node_num, hidden_size].\n embeddings = tf.reduce_max(embeddings, axis=-2)\n # For objects with no text, use 0.\n valid_object_mask = tf.cast(\n tf.reduce_any(valid_token_mask, axis=-1), tf.float32)\n embeddings = embeddings * tf.expand_dims(valid_object_mask, axis=-1)\n\n elif self._hparams['obj_text_aggregation'] == 'sum':\n # [batch, step, #max_obj, #max_token] 0 for padded tokens\n real_objects = tf.cast(tf.greater_equal(token_ids, 4), tf.float32)\n # [batch, step, #max_obj, hidden] 0s for padded objects\n embeddings = tf.reduce_sum(\n input_tensor=embeddings * tf.expand_dims(real_objects, 3), axis=-2)\n\n else:\n raise ValueError('Unrecognized token aggregation %s' %\n (self._hparams['obj_text_aggregation']))\n return embeddings","def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:\n if m == 0:\n for i in range(len(nums2)):\n nums1[i] = nums2[i]\n return\n if n == 0:\n return\n\n for i in range(m - 1, -1, -1):\n nums1[i], nums1[i - m] = nums1[i - m], nums1[i]\n count = 0\n i = len(nums1)-m\n while i < len(nums1) or nums2:\n print(count)\n if i < len(nums1) and nums2:\n if nums1[i] <= nums2[0]:\n nums1[count], nums1[i] = nums1[i], nums1[count]\n i += 1\n else:\n nums1[count] = nums2.pop(0)\n print(nums1)\n elif i >= len(nums1) and nums2:\n nums1[count] = nums2.pop(0)\n elif i < len(nums1) and not nums2:\n nums1[count], nums1[i] = nums1[i], nums1[count]\n i += 1\n count += 1","def get_frequencies(tokens):\n cnt = {}\n\n for word in tokens:\n if word not in cnt:\n cnt[word] = 0\n\n cnt[word] += 1\n\n return cnt","def semcor2token(args):\r\n input_files = list_files(*args.input_files)\r\n output_dir = Path(args.output_dir)\r\n if not output_dir.is_dir():\r\n try:\r\n output_dir.mkdir()\r\n except:\r\n print('Invalid output directory name. Files will be stored in default directory.', file = stderr)\r\n output_dir = output_default / 'typetoken'\r\n if not output_dir.is_dir():\r\n output_dir.mkdir()\r\n multiword = args.multiword\r\n for input_file in input_files:\r\n corpus_file = CorpusFile(input_file)\r\n filename = corpus_file.shortname + '.txt'\r\n dirname = output_dir / corpus_file.concordance\r\n if not dirname.exists():\r\n dirname.mkdir()\r\n output_file_name = dirname / filename\r\n with output_file_name.open('w') as output_file:\r\n for word in corpus_file.text.find_all(['wf', 'punc']):\r\n if word.name == 'punc':\r\n output_file.write('\\t'.join([word.string, word.string, 'punc\\n']))\r\n elif not multiword:\r\n for token in Token.from_tag(word).get_components():\r\n if args.verbose and type(token.status)==tuple:\r\n token_id = '/'.join([corpus_file.shortname, token.wordform])\r\n report_token_status(token, token_id)\r\n output_file.write('\\t'.join([token.wordform, token.lemma, token.pos]) + '\\n')\r\n else:\r\n token = Token.from_tag(word)\r\n if args.verbose and type(token.status)==tuple:\r\n token_id = '/'.join([corpus_file.shortname, token.wordform])\r\n report_token_status(token, token_id)\r\n output_file.write('\\t'.join([token.wordform, token.lemma, token.pos]) + '\\n')","def _counter(title_list):\n t = Tokenizer()\n words_count = defaultdict(int)\n words = []\n for title in title_list:\n tokens = t.tokenize(title)\n for token in tokens:\n pos = token.part_of_speech.split(',')[0]\n if pos == '名詞':\n words_count[token.base_form] += 1\n words.append(token.base_form)\n return words_count, words","def merge_cluster(word_list, merge_method = None):\n \n #Calculates average of property of list of dictionaries.\n def avg(l, key):\n s = 0\n for w in l:\n s+= w[key]\n return s / len(l)\n \n result = {}\n result['longleft'] = avg(word_list, 'longleft')\n result['longright'] = avg(word_list, 'longright')\n result['top'] = avg(word_list, 'top')\n result['bottom'] = avg(word_list, 'bottom')\n result['confidence'] = avg(word_list, 'confidence')\n result['merge_size'] = len(word_list)\n result['minframe'] = min([word['minframe'] for word in word_list])\n result['maxframe'] = max([word['maxframe'] for word in word_list])\n result['cluster'] = word_list[0]['cluster']\n \n if merge_method is None:\n merge_method = 'char_frequency'\n \n #Pick most popular character of each position in word\n if merge_method == 'char_frequency':\n result['text'] = average_word(word_list)\n #Pick word with highest confidence\n elif merge_method == 'confidence':\n max_confidence = -1\n for word in word_list:\n if word['confidence'] > max_confidence:\n max_confidence = word['confidence']\n result['text'] = word['text']\n elif merge_method == 'random':\n result['text'] = word_list[random.randint(0, len(word_list) - 1)]['text']\n else:\n raise ValueError('Method ' + merge_method + ' not implemented.')\n \n return result","def _concatenates(self, chain_with_counts, char_counts, level=0, in_one_liner=False):\n # evaluate if it's feasible in one-line\n one_liner = in_one_liner or char_counts < self.max_len - (self.indent * level)\n new_level = level if one_liner else level + 1\n elements = [\n self._concatenates(c, n, level=new_level, in_one_liner=one_liner)\n if isinstance(c, list)\n else c\n for c, n in chain_with_counts]\n elements = self._apply_stick(elements)\n prefix = self.prefix if level and not in_one_liner else \"\"\n join_char = \" \" if one_liner else (\"\\n\" + prefix)\n return prefix + join_char.join(l for c in elements for l in c.split(\"\\n\"))","def merge_files(filename_list, merged_file, encode):\n lines_counter = list()\n for file_name in filename_list:\n lines_counter.append(count_lines(file_name))\n lines_counter.sort(key=lambda item: item[-1])\n with open(merged_file, 'w', encoding=encode) as file:\n for doc in lines_counter:\n file.write(f'{doc[0]}\\n')\n file.write(f'{doc[1]}\\n')\n text = get_text(doc[0])\n file.write(f'{text}\\n\\n')","def compute_pair_frequencies(text, alphabet_size=256):\n\n curr = text[1:]\n prev = text[:-1]\n pairs = np.stack([prev, curr])\n\n print(\"Counting pairs . . .\")\n (char1, char2), counts = np.unique(pairs, return_counts=True, axis=1)\n print(\"Done.\\n\")\n\n joints = np.zeros((alphabet_size, alphabet_size))\n joints[char1, char2] = counts / np.sum(counts)\n assert np.isclose(np.sum(joints), 1.0)\n\n return joints","def computeWordsFrequencies(self):\n token_stream = self._tokenize(self.readable)\n token_map = self._countTokens(token_stream)\n # print token_map.items()\n return sorted(token_map.items(), key = lambda x : x[1], reverse = True)","def count_synonymous(self):\n if not self.is_coding():\n log_message(\n logging_callback=logging.warning,\n msg=\"Cannot count synonymous mutations in noncoding data\",\n extra={\"oname\": self.name},\n )\n return\n\n if self.check_store(\"/main/synonymous/counts\"):\n return\n\n log_message(\n logging_callback=logging.info,\n msg=\"Counting synonymous variants\",\n extra={\"oname\": self.name},\n )\n df_dict = dict()\n\n for variant, count in self.store[\"/main/variants/counts\"].iterrows():\n if variant == WILD_TYPE_VARIANT:\n df_dict[variant] = count[\"count\"]\n else:\n variant = protein_variant(variant)\n if len(variant) == 0:\n variant = SYNONYMOUS_VARIANT\n try:\n df_dict[variant] += count[\"count\"]\n except KeyError:\n df_dict[variant] = count[\"count\"]\n\n self.save_counts(\"synonymous\", df_dict, raw=False)\n del df_dict","def total_char_similarity(a,b):\n\ta_words, b_words = map(norm.set_clean_tokens, [a,b])\n\n\ttotal_score = 0\n\tfor ai in a_words:\n\t\tfor bi in b_words:\n\t\t\ttotal_score += similar(ai, bi)\n\treturn total_score","def merge_texts(self, dataset):\n for ds in dataset:\n empty_data = ds._data.copy()\n ds._data = ds._data[ds._data.index < 0]\n self.vmerge(dataset, verbose=False, overwrite_text=True)\n return None","def calculate_poses_for_word(corpus):\n total_pos_count = {}\n poses_for_word = {}\n # For each word, find all POSes.\n for sentence in corpus.get_sentences():\n for word in sentence:\n id, form, lemma, plemma, pos, ppos = word\n #form = form.lower()\n\n if pos in total_pos_count:\n total_pos_count[pos] += 1\n else:\n total_pos_count[pos] = 1\n\n if form in poses_for_word:\n if pos in poses_for_word[form]:\n poses_for_word[form][pos] += 1\n else:\n poses_for_word[form][pos] = 1\n else:\n poses_for_word[form] = {pos: 1}\n\n return poses_for_word, total_pos_count","def _count_tokens(self,\n token_lists):\n ## Filter out empty lists\n fn_tokens = list(filter(lambda i: len(i) > 0, token_lists))\n ## Get N-grams from Tokens\n fn_ngrams = list(map(lambda t: self.get_ngrams(t, self._ngrams[0], self._ngrams[1]),\n fn_tokens))\n ## Count Tokens (binary if class is unfit and binarization turned on)\n if hasattr(self, \"_class_state\") and \\\n self._class_state == \"untrained\" and \\\n hasattr(self, \"_binarize_counter\") and \\\n self._binarize_counter:\n fn_counts = Counter(list(set(flatten(fn_ngrams))))\n else:\n fn_counts = Counter(flatten(fn_ngrams))\n return fn_counts","def update(self, iterable):\n for word in iterable:\n if word in self:\n self[word] = self[word] + 1\n self.tokens += 1\n else:\n self[word] = 1\n self.types += 1\n self.tokens += 1","def merge(nums1, m, nums2, n):\r\n while len(nums1) != m and nums1[len(nums1)-1] == 0:\r\n nums1.pop()\r\n for each in nums2:\r\n nums1.append(each)\r\n nums1.sort()","def wordNet_similarity(sentence1, sentence2):\r\n # Tokenize and tag\r\n \r\n # sentence1 = pos_tag(word_tokenize(sentence1))\r\n sentence1=st_tagger.tag(word_tokenize(sentence1))\r\n \r\n # sentence2 = pos_tag(word_tokenize(sentence2))\r\n sentence2=st_tagger.tag(word_tokenize(sentence2))\r\n\r\n \r\n # Get the synsets for the tagged words\r\n #################################################\r\n\r\n # synsets1=[]\r\n # synsets2=[]\r\n # for tagged_word in sentence1:\r\n # print(tagged_word)\r\n # tagged_word = list(tagged_word)\r\n # synsets1.append(tagged_to_synset(tagged_word[0],tagged_word[1]))\r\n # for tagged_word in sentence2:\r\n # print(tagged_word)\r\n # tagged_word = list(tagged_word)\r\n # print(tagged_word)\r\n # synsets2.append(tagged_to_synset(tagged_word[0],tagged_word[1]))\r\n\r\n # The code above is the elaboration of code below\r\n synsets1 = [tagged_to_synset(*tagged_word) for tagged_word in sentence1]\r\n synsets2 = [tagged_to_synset(*tagged_word) for tagged_word in sentence2]\r\n \r\n # Filter out the Nones in the synonym set\r\n synsets1 = [ss for ss in synsets1 if ss]\r\n synsets2 = [ss for ss in synsets2 if ss]\r\n \r\n score, count = 0.0, 0\r\n \r\n###########################################################################\r\n # for syn1 in synsets1:\r\n # arr_simi_score = []\r\n # print('=========================================')\r\n # print(syn1)\r\n # print('----------------')\r\n # for syn2 in synsets2:\r\n # print(syn2)\r\n # simi_score = syn1.path_similarity(syn2)\r\n # print(simi_score)\r\n # if simi_score is not None:\r\n # arr_simi_score.append(simi_score)\r\n # print('----------------')\r\n # print(arr_simi_score)\r\n # if(len(arr_simi_score) > 0):\r\n # best = max(arr_simi_score)\r\n # print(best)\r\n # score += best\r\n # count += 1\r\n # # Average the values\r\n # print('score: ', score)\r\n # print('count: ', count)\r\n # score /= count\r\n\r\n###########################################################################\r\n\r\n for syn1 in synsets1:\r\n arr_simi_score = []\r\n # print('=========================================')\r\n print(\"Each word from Synonym se1\",syn1)\r\n # print('----------------')\r\n for syn2 in synsets2:\r\n print(\"Each word from Synonym se2\",syn2)\r\n # simi_score = syn1.path_similarity(syn2)\r\n simi_score = syn1.wup_similarity(syn2)\r\n print(\"word to word path_similarity score\",simi_score)\r\n if simi_score is not None:\r\n arr_simi_score.append(simi_score)\r\n print('----------------')\r\n print(arr_simi_score)\r\n if(len(arr_simi_score) > 0):\r\n best = max(arr_simi_score)\r\n print(\"best score so far\", best)\r\n score += best\r\n count += 1\r\n # Average the values\r\n print('score: ', score)\r\n print('count: ', count)\r\n if count!=0:\r\n score /= count\r\n else:\r\n score=0.0\r\n return score","def merge(self, other):\n result = IntSet(0)\n len_first = len(self)\n len_second = len(other)\n if len_first >= len_second:\n for i in range(len_second):\n num = self._nums[i] + other[i]\n for digit in self._digits(num):\n result.append(digit)\n for i in range(len_second, len_first):\n for digit in self._digits(self._nums[i]):\n result.append(digit)\n else:\n for i in range(len_first):\n num = self._nums[i] + other[i]\n for digit in self._digits(num):\n result.append(digit)\n for i in range(len_first, len_second):\n for digit in self._digits(other[i]):\n result.append(digit)\n return result","def computeWordFrequencies(self, tokens: ['token'], frequencies: {'token': int}):\n # project2: update this method to take existing dict as parameter and modify it\n # additionally, stopwords are not inserted in the dict;\n # words shorter than 3 character or contains all digits are ignored\n for token in tokens:\n # if the key is not in dict, dict.setdefault method initiates the value at 0\n # if token not in stopwords and len(token) >= 3 and not token.isdigit():\n frequencies[token] = frequencies.setdefault(token, 0) + 1","def __call__(self, tree):\n chains = list(self._get_chains(tree))\n chain_with_counts, total = self._count_chars(chains)\n return self._concatenates(chain_with_counts, total)","def make_bag_words(document_tokenized):\n bag_words = dict()\n for token in document_tokenized:\n if token in bag_words.keys():\n bag_words[token] += 1\n else:\n bag_words[token] = 1\n return bag_words","def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:\n nums1[:]=sorted((nums1[:m]+nums2[:n]))","def _aggregate_token_confidence(self, hypothesis: Hypothesis) -> List[float]:\n return self._aggregate_token_confidence_subwords_sentencepiece(\n self.decode_tokens_to_str(hypothesis.text[0]).split(), hypothesis.token_confidence, hypothesis.text[0]\n )","def MergeLogic(self) -> str:","def __add__(self, other):\n if isinstance(other, Token):\n return Token(self.text + other.text, self.position, self.category)\n else:\n return Token(self.text + other, self.position, self.category)","def _merge_majorana_terms(left_term, right_term):\n merged_term = []\n parity = 0\n i, j = 0, 0\n while i < len(left_term) and j < len(right_term):\n if left_term[i] < right_term[j]:\n merged_term.append(left_term[i])\n i += 1\n elif left_term[i] > right_term[j]:\n merged_term.append(right_term[j])\n j += 1\n parity += len(left_term) - i\n else:\n parity += len(left_term) - i - 1\n i += 1\n j += 1\n if i == len(left_term):\n merged_term.extend(right_term[j:])\n else:\n merged_term.extend(left_term[i:])\n return tuple(merged_term), parity % 2","def tokenize_and_split_bis(sms_file):\n \n dic = {}\n list1 = []\n list2 = []\n list3 = []\n list4 = []\n i = -1\n document = 0\n terms = 0\n new_document = True\n ham = True\n for line in open(sms_file, 'r').readlines():\n w = []\n document += 1\n new_document = True\n for word in line.split():\n i = i + 1\n if word == \"ham\":\n ham = True\n i = i - 1\n elif word == \"spam\":\n ham = False\n i = i - 1\n else:\n if word not in dic:\n dic[word] = i\n w.append(dic[word])\n list3.append(1)\n list4.append(1)\n new_document = False\n terms += 1\n else : \n i = i - 1\n w.append(dic[word])\n list4[dic[word]] += 1\n terms += 1\n if new_document: \n list3[dic[word]] += 1\n new_document = False\n \n if ham and w !=[]:\n list2.append(w)\n elif ham == False and w !=[]:\n list1.append(w)\n\n moy = 0\n len_dic = len(dic.keys())\n list5 = [0 for x in range(len_dic)]\n for key in dic.keys():\n if list4[dic[key]] > 0:\n tf = list4[dic[key]] / terms\n idf = math.log(document / list3[dic[key]])\n tfIdf = tf * idf\n list5[dic[key]] = tfIdf\n # print(\"the word \" + str(key) + \" appairs \" + str(list4[dic[key]]) + \" times.\")\n # print(\"his frequency is \" + str(list4[dic[key]] / terms) )\n # print(\"the word \" + str(key) + \" appairs \" + str(list3[dic[key]]) + \" times in each document.\")\n # print(\"his frequency is \" + str(idf))\n # print(\"utility \" + str(tfIdf))\n moy += tfIdf\n \n moy = moy / len_dic \n # print(moy)\n dic_bis = {}\n i = -1\n for key in dic.keys():\n value = list5[dic[key]]\n # print(str(value))\n if (value > oracle * moy):\n i += 1\n dic_bis[key] = i\n # else:\n # print(\"not pass \" + key + \" \" + str(value))\n \n \n # print(dic_bis == dic)\n # print(dic)\n return dic_bis,list1,list2","def count_token(text):\n count=0\n if isinstance(text, list):\n for ayah in text:\n count=count+ayah.count(' ')+1\n else:\n count=text.count(' ')+1\n\n return count","def _count_word_frequency(self, data):\n _dict = {}\n for _docs in data:\n for _word in _docs:\n if _word in _dict:\n _dict[_word] += 1\n else:\n _dict[_word] = 1\n return _dict"],"string":"[\n \"def merge_token_counters(\\n token_counter1: Dict[str, int], token_counter2: Dict[str, int]\\n ) -> Dict[str, int]:\\n for token, number in token_counter2.items():\\n if token in token_counter1:\\n token_counter1[token] += number\\n else:\\n token_counter1[token] = number\\n\\n return token_counter1\",\n \"def merged_tokens(self, i, tokenizer):\\n out = []\\n counts = []\\n last_id = None\\n tokens = tokenizer.convert_ids_to_tokens(self.token_ids[i].tolist())\\n for token, current_id in zip(tokens[1:-1], self.conll_ids[i]):\\n if last_id == current_id.item():\\n if token.startswith(\\\"##\\\"):\\n out[-1] += (token[2:])\\n else:\\n out[-1] += token\\n counts[-1] += 1\\n else:\\n out.append(token)\\n counts.append(1)\\n last_id = current_id\\n return out, counts\",\n \"def _merge_conllu_subtokens(self, lines: List[str], doc: Doc)-> Doc:\\n # identify and process all subtoken spans to prepare attrs for merging\\n subtok_spans = []\\n for line in lines:\\n parts = line.split(\\\"\\\\t\\\")\\n id_, word, lemma, pos, tag, morph, head, dep, _1, misc = parts\\n if \\\"-\\\" in id_:\\n subtok_start, subtok_end = id_.split(\\\"-\\\")\\n subtok_span = doc[int(subtok_start) - 1 : int(subtok_end)]\\n subtok_spans.append(subtok_span)\\n # create merged tag, morph, and lemma values\\n tags = []\\n morphs = {}\\n lemmas = []\\n for token in subtok_span:\\n tags.append(token.tag_)\\n lemmas.append(token.lemma_)\\n if token._.merged_morph:\\n for feature in token._.merged_morph.split(\\\"|\\\"):\\n field, values = feature.split(\\\"=\\\", 1)\\n if field not in morphs:\\n morphs[field] = set()\\n for value in values.split(\\\",\\\"):\\n morphs[field].add(value)\\n # create merged features for each morph field\\n for field, values in morphs.items():\\n morphs[field] = field + \\\"=\\\" + \\\",\\\".join(sorted(values))\\n # set the same attrs on all subtok tokens so that whatever head the\\n # retokenizer chooses, the final attrs are available on that token\\n for token in subtok_span:\\n token._.merged_orth = token.orth_\\n token._.merged_lemma = \\\" \\\".join(lemmas)\\n token.tag_ = \\\"_\\\".join(tags)\\n token._.merged_morph = \\\"|\\\".join(sorted(morphs.values()))\\n token._.merged_spaceafter = (\\n True if subtok_span[-1].whitespace_ else False\\n )\\n\\n with doc.retokenize() as retokenizer:\\n for span in subtok_spans:\\n retokenizer.merge(span)\\n\\n return doc\",\n \"def reconcile_output(self):\\n final_string = \\\"\\\"\\n final_string += \\\"TEXT: {0}\\\\n\\\".format(self._text)\\n final_string += \\\"ID: {0}\\\\n\\\".format(self._index)\\n final_string += \\\"Count: {0}\\\\n\\\".format(self._count)\\n\\n final_string += \\\"=Doc Count Begin=\\\\n\\\"\\n for doc in list(self._doc_count.keys()):\\n final_string += \\\"{0} $!$ {1}\\\\n\\\".format(doc, self._doc_count[doc])\\n final_string += \\\"=Doc Count End=\\\\n\\\"\\n\\n final_string += \\\"=CEs Begin=\\\\n\\\"\\n for doc in list(self.word_pairs.keys()):\\n for ce in set(self.word_pairs[doc]):\\n #format: doc $!$ ce \\n final_string += \\\"{0} $!$ {1} $!$ {2}\\\\n\\\".format(doc, ce, self.word_pairs[doc].count(ce))\\n final_string += \\\"=CEs End=\\\\n\\\"\\n\\n final_string += \\\"=Self Tags Begin=\\\\n\\\"\\n for source in list(self.this_semantic_tags.keys()):\\n for tag in set(self.this_semantic_tags[source]):\\n final_string += \\\"{0} $!$ {1} $!$ {2}\\\\n\\\".format(source, tag, self.this_semantic_tags[source].count(tag))\\n final_string += \\\"=Self Tags End=\\\\n\\\"\\n\\n final_string += \\\"=Semantic Begin=\\\\n\\\"\\n for source in list(self.lexico_semantic.keys()):\\n #for some reason, I had each semantic class getting assigned the\\n #same overall count?\\n #sem_counts = self.getLexicoSemanticCounts(source)\\n for ce in self.lexico_semantic[source]:\\n for sem_cls in set(self.lexico_semantic[source][ce]):\\n c = self.lexico_semantic[source][ce].count(sem_cls)\\n final_string += \\\"{0} $!$ {1} $!$ {2} $!$ {3}\\\\n\\\".format(source, ce, sem_cls, c)\\n final_string += \\\"=Semantic End=\\\\n\\\"\\n final_string += \\\"$!$\\\\n\\\"\\n return final_string\",\n \"def merge_with(self, other):\\n old2new = {}\\n for other_id, other_token in iteritems(other):\\n if other_token in self.token2id:\\n new_id = self.token2id[other_token]\\n else:\\n new_id = len(self.token2id)\\n self.token2id[other_token] = new_id\\n self.dfs[new_id] = 0\\n old2new[other_id] = new_id\\n try:\\n self.dfs[new_id] += other.dfs[other_id]\\n except Exception:\\n # `other` isn't a Dictionary (probably just a dict) => ignore dfs, keep going\\n pass\\n try:\\n self.num_docs += other.num_docs\\n self.num_nnz += other.num_nnz\\n self.num_pos += other.num_pos\\n except Exception:\\n pass\\n\\n import gensim.models\\n return gensim.models.VocabTransform(old2new)\",\n \"def count_words_and_dublicates(novel):\",\n \"def merge(self, tokens):\\n tokens = iter(tokens)\\n (lasttype, lastval) = tokens.next()\\n for ttype, value in tokens:\\n if ttype is lasttype:\\n lastval += value\\n else:\\n yield(lasttype, lastval)\\n (lasttype, lastval) = (ttype, value)\\n if lastval.endswith('\\\\n'):\\n lastval = lastval[:-1]\\n if lastval:\\n yield(lasttype, lastval)\",\n \"def common_token_len(self, w1, d1, w2, d2):\\n w1_tk = set(self.__stem_Tokens(w1))\\n w2_tk = set(self.__stem_Tokens(w2))\\n common_len = len(w1_tk.intersection(w2_tk))\\n return common_len\",\n \"def calculate(self, tokens: list):\\n vektor_tf_unigram = {}\\n\\n for token in tokens:\\n if token in vektor_tf_unigram:\\n vektor_tf_unigram[token] = vektor_tf_unigram[token] + 1\\n else:\\n vektor_tf_unigram[token] = 1\\n\\n return vektor_tf_unigram\",\n \"def tokenize(self):\\n count = 0\\n for entry in self._entries:\\n token_pairs = []\\n for relation in entry['relations']:\\n assert len(relation) == 3\\n token_pairs.append((relation[0][0],relation[1][0],relation[2][0]))\\n\\n num_rels = len(entry['relations'])\\n num_random_rels = (self._max_seq_length - 2) // 3 - num_rels\\n\\n if num_random_rels>0:\\n pass\\n # gt_pairs = {(rel[0],rel[2]) for rel in entry['relations']}\\n # random_pairs = self._get_random_pair(entry['objects'], gt_pairs, num_random_rels)\\n # for pair in list(random_pairs):\\n # token_pairs.append((pair[0][0],'background', pair[1][0]))\\n else:\\n for i in range(-num_random_rels):\\n token_pairs.pop()\\n\\n random.shuffle(token_pairs)\\n tokens = []\\n for pair in token_pairs:\\n tokens.extend(pair)\\n\\n tokens = ['[CLS]'] + tokens + ['[SEP]']\\n tokens_char = tokens\\n\\n target = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3==2 else -1 for i, x in enumerate(tokens)]\\n tokens = [self._tokenizer.vocab.get(self._tokenizer.tokenize(x)[0], self._tokenizer.vocab['[UNK]']) if i%3!=2 else self._tokenizer.vocab.get('[MASK]', self._tokenizer.vocab['[UNK]']) for i, x in enumerate(tokens)]\\n \\n for i in range(len(tokens)):\\n if target[i] != -1:\\n print(tokens_char[i],tokens[i],target[i])\\n\\n segment_ids = [0] * len(tokens)\\n input_mask = [1] * len(tokens)\\n # input_mask = [1 if i%3==2 else 0 for i in range(len(tokens))]\\n # co_attention_mask = [-1 if i%3==2 else 1 for i in range(len(tokens))]\\n # co_attention_mask = torch.zeros((self._max_region_num, self._max_seq_length))\\n # co_attention_mask[0] = -1\\n # co_attention_mask[-1] = -1\\n \\n if len(tokens) < self._max_seq_length:\\n padding = [self._padding_index] * (self._max_seq_length - len(tokens))\\n tokens = tokens + padding\\n input_mask += padding\\n segment_ids += padding \\n target += [-1] * len(padding) \\n\\n assert_eq(len(tokens), self._max_seq_length)\\n entry['input_ids'] = tokens \\n entry[\\\"input_mask\\\"] = input_mask\\n entry['segment_ids'] = segment_ids\\n # entry[\\\"co_attention_mask\\\"] = co_attention_mask\\n entry['target'] = target\\n\\n sys.stdout.write('%d/%d\\\\r' % (count, len(self._entries)))\\n sys.stdout.flush()\\n count += 1\",\n \"def number_tokens(dgraph):\\n cnt = 0\\n for node in dgraph.subgraphs(exclude_root=True):\\n node[WORD] = (cnt, node[WORD])\\n cnt += 1\\n return dgraph\",\n \"def gather_counts(directory):\\n counts_un = defaultdict(int)\\n counts_bi = defaultdict(int)\\n counts_tri = defaultdict(int)\\n prev_prev = \\\"<s>\\\"\\n prev = \\\"<s>\\\"\\n for filename in os.listdir(f\\\"./{directory}\\\"):\\n if \\\".DS_Store\\\" in filename:\\n continue\\n with open(f\\\"./{directory}/{filename}\\\", \\\"r\\\") as f:\\n for line in f:\\n line = line.strip()\\n if len(line) == 0:\\n continue\\n counts_un[line+\\\"\\\\n\\\"] += 1\\n counts_bi[prev+\\\"\\\\n\\\"+line+\\\"\\\\n\\\"] += 1\\n counts_tri[prev_prev+\\\"\\\\n\\\"+prev+\\\"\\\\n\\\"+line+\\\"\\\\n\\\"] += 1\\n prev_prev = prev\\n prev = line\\n counts_un[\\\"</s>\\\\n\\\"] += 2\\n counts_bi[\\\"</s>\\\\n</s>\\\\n\\\"] += 1\\n counts_bi[prev+\\\"\\\\n\\\"+\\\"</s>\\\\n\\\"] += 1\\n counts_tri[prev_prev+\\\"\\\\n\\\"+prev+\\\"\\\\n\\\" + \\\"</s>\\\\n\\\"] += 1\\n counts_tri[prev+\\\"\\\\n</s>\\\\n</s>\\\\n\\\"] += 1\\n return counts_un, counts_bi, counts_tri\",\n \"def compute_token_count(token_dict):\\n return sum([token_dict[key] for key in token_dict.keys()])\",\n \"def getCounter(self):\\n word_count, noun_word_count = Counter(), Counter()\\n word_rating, noun_word_rating = defaultdict(list), defaultdict(list)\\n docs = self.nlp.pipe(\\n self.docs, n_process=1, disable=self.disablelayers)\\n \\n\\n for index, doc in enumerate(docs):\\n for token in doc:\\n if not token.is_stop and not token.is_punct and token.pos_ in self.pos:\\n if token.pos_ == 'PROPN':\\n word_count[token.lemma_] += 1\\n word_rating[token.lemma_].append(self.ratings[index])\\n else:\\n noun_word_count[token.lemma_] += 1\\n noun_word_rating[token.lemma_].append(self.ratings[index])\\n\\n # if 0<=proper nouns<=5 found, add regular nouns\\n if not word_count or len(word_count) <= 5:\\n word_count += noun_word_count\\n word_rating = {**word_rating, **noun_word_rating}\\n \\n word_color = {word: self.getColor(\\n ratings)[1] for word, ratings in word_rating.items()}\\n word_sentiment = {word: self.getColor(\\n ratings)[0] for word, ratings in word_rating.items()}\\n\\n return word_count, word_color, word_sentiment\",\n \"def count_terms(self, tokens):\\n\\n terms = [self.term_match(t) for t in tokens ]\\n \\n terms = [t for t in terms if t != None]\\n\\n #print terms\\n lf = dict(Counter(terms))\\n for k in lf:\\n lf[k] /= float(len(tokens))\\n #lf[k] = 1 # binarize?\\n pass\\n return lf\",\n \"def word_frequency( tokenized, dic ):\\n print( 'computing word frequencies' )\\n start = time.time()\\n for i, text in enumerate( tokenized ):\\n for token in text:\\n if token not in dic:\\n dic[ token ] = 1\\n else:\\n dic[ token ] += 1\\n if i % 10000 == 0:\\n sys.stdout.write( '\\\\rprocessed : {}/{} reviews in {}s'.format( i, NO_REVIEWS, time.time() - start ) )\\n sys.stdout.write( '\\\\rprocessed : {}/{} reviews in {}s\\\\n'.format( i, NO_REVIEWS, time.time() - start ) )\",\n \"def count_words(tokenized_sentences):\\r\\n \\r\\n word_counts = {}\\r\\n \\r\\n # Loop through each sentence\\r\\n for sentence in tokenized_sentences: # complete this line\\r\\n \\r\\n for token in sentence: # complete this line\\r\\n\\r\\n # If the token is not in the dictionary yet, set the count to 1\\r\\n if token not in word_counts.keys(): # complete this line\\r\\n word_counts[token] = 1\\r\\n \\r\\n # If the token is already in the dictionary, increment the count by 1\\r\\n else:\\r\\n word_counts[token] += 1\\r\\n \\r\\n return word_counts\",\n \"def merge_accumulators(self, accumulators):\\n accumulator = WordOccurrenceAccumulator(self.relevant_ids, self.dictionary)\\n for other_accumulator in accumulators:\\n accumulator.merge(other_accumulator)\\n # Workers do partial accumulation, so none of the co-occurrence matrices are symmetrized.\\n # This is by design, to avoid unnecessary matrix additions/conversions during accumulation.\\n accumulator._symmetrize()\\n logger.info(\\\"accumulated word occurrence stats for %d virtual documents\\\", accumulator.num_docs)\\n return accumulator\",\n \"def countOverlapTokens(self, annotatedMention):\\n if self.tokens[0].sentence != annotatedMention.tokens[0].sentence:\\n return 0 # mentions in different sentences, no overlap \\n \\n if self.end < annotatedMention.start or self.start > annotatedMention.end:\\n return 0 # mention ends before or starts after annotated one\\n \\n if self.start == annotatedMention.start and self.end == annotatedMention.end:\\n return len(self.tokens) # exact match for annotated mention\\n \\n # There is some overlap. Does it consist of anything substantial?\\n importantTokens = 0\\n for token in self.tokens:\\n if token.index >= annotatedMention.start \\\\\\n and token.index <= annotatedMention.end \\\\\\n and token.isSymbol() == False and token.isStopWord() == False:\\n importantTokens += 1\\n \\n return importantTokens\",\n \"def common_words(words: Dict[str, int], count: int) -> None:\\n \\n original_order = list(words.values())\\n max_to_min = list(reversed(sorted(words.values())))\\n temp_lst1 = []\\n temp_lst2 = []\\n \\n \\n if count == len(words):\\n words = words\\n \\n \\n for num in range(len(max_to_min)):\\n if len(temp_lst1) < count:\\n temp_lst1.append(max_to_min[num])\\n \\n for nums in range(len(temp_lst1)):\\n max_to_min.remove(temp_lst1[nums])\\n \\n \\n temp_lst2 = remove_repeats(max_to_min, temp_lst1)\\n \\n a = reformat_order(original_order, temp_lst2)\\n \\n words_to_counts = combine_list(format_dict(words, a), a)\\n \\n words.clear()\\n \\n for keys in words_to_counts:\\n words[keys] = words_to_counts[keys]\",\n \"def partial_accumulate(self, texts, window_size):\\n self._current_doc_num = -1\\n self._token_at_edge = None\\n self._counter.clear()\\n\\n super(WordOccurrenceAccumulator, self).accumulate(texts, window_size)\\n for combo, count in iteritems(self._counter):\\n self._co_occurrences[combo] += count\\n\\n return self\",\n \"def common_token_len_with_slash(self, w1, d1, w2, d2):\\n w1_tk = set(re.split(\\\"[\\\\s-]\\\", w1))\\n w2_tk = set(re.split(\\\"[\\\\s-]\\\", w2))\\n common_len = len(w1_tk.intersection(w2_tk))\\n return common_len\",\n \"def merge_same_tones(self, tone_list):\\n merged_tone_list = {'m': [], 'v': []}\\n\\n prev_mala = tone_list[0][0]\\n prev_vela = tone_list[0][1]\\n\\n # same length on the beggining\\n prev_mala_tone_length = tone_list[0][2]\\n prev_vela_tone_length = tone_list[0][2]\\n\\n for mala_tone, vela_tone, tone_length in tone_list[1:]:\\n\\n if prev_mala == mala_tone:\\n prev_mala_tone_length += tone_length\\n else:\\n merged_tone_list['m'].append(\\n (prev_mala, prev_mala_tone_length))\\n prev_mala_tone_length = tone_length\\n prev_mala = mala_tone\\n\\n if prev_vela == vela_tone:\\n prev_vela_tone_length += tone_length\\n else:\\n merged_tone_list['v'].append(\\n (prev_vela, prev_vela_tone_length))\\n prev_vela_tone_length = tone_length\\n prev_vela = vela_tone\\n\\n # append last tone\\n if prev_mala_tone_length > 0:\\n merged_tone_list['m'].append((prev_mala, prev_mala_tone_length))\\n\\n if prev_vela_tone_length > 0:\\n merged_tone_list['v'].append((prev_vela, prev_vela_tone_length))\\n\\n return merged_tone_list\",\n \"def getTTokens(wordlist, tokens = {}, count = 0, token = TOKEN):\\n\\n\\tif len(wordlist) == 0:\\n\\t\\treturn tokens, count\\n\\n\\tfor i in wordlist:\\n\\t\\tfor x in i:\\n\\t\\t\\tif tokens.has_key(x[token]):\\n\\t\\t\\t\\ttokens[x[token]] += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttokens[x[token]] = 1\\n\\t\\t\\tcount += 1\\n\\treturn tokens, count\",\n \"def build_morpheme_vocab(self):\\n max_morph_per_word = 0\\n morpheme_dict = collections.defaultdict(int)\\n splitter = \\\"@@\\\"\\n for token in self.train_data:\\n if token == self.eos or token == self.sos:\\n continue\\n token = '^' + token + '$'\\n morphemes = token.split(splitter)\\n if len(morphemes) > max_morph_per_word:\\n max_morph_per_word = len(morphemes)\\n for morpheme in morphemes:\\n morpheme_dict[morpheme] += 1\\n\\n unk_morpheme_list = set()\\n item_to_id = dict()\\n item_to_id[constants.PAD_ITEM] = len(item_to_id)\\n item_to_id[constants.UNK_ITEM] = len(item_to_id)\\n sorted_dict = sorted(morpheme_dict.items(), key=operator.itemgetter(1), reverse=True)\\n for token, freq in sorted_dict:\\n if freq == 1:\\n unk_morpheme_list.add(token)\\n if token not in item_to_id:\\n item_to_id[token] = len(item_to_id)\\n return item_to_id, unk_morpheme_list, max_morph_per_word\",\n \"def count_ngrams(self, corpus):\\n \\n self.unigramcounts = {} # might want to use defaultdict or Counter instead\\n self.bigramcounts = {} \\n self.trigramcounts = {} \\n\\n self.total = 2\\n ##Your code here\\n\\n for sentence in corpus:\\n temp_1 = get_ngrams(sentence,1)\\n temp_2 = get_ngrams(sentence,2)\\n temp_3 = get_ngrams(sentence,3)\\n for i in range(len(temp_1)):\\n if temp_1[i] in self.unigramcounts:\\n self.unigramcounts[temp_1[i]] += 1\\n else:\\n self.unigramcounts[temp_1[i]] = 1\\n self.total += 1\\n\\n for i in range(len(temp_2)):\\n if temp_2[i] in self.bigramcounts:\\n self.bigramcounts[temp_2[i]] += 1\\n else:\\n self.bigramcounts[temp_2[i]] = 1\\n\\n for i in range(len(temp_3)):\\n if temp_3[i] in self.trigramcounts:\\n self.trigramcounts[temp_3[i]] += 1\\n else:\\n self.trigramcounts[temp_3[i]] = 1\\n return\",\n \"def reducer_count_words(self, word, counts):\\n aux3 = word.split(\\\"+\\\")\\n yield aux3[0], (sum(counts), aux3[1])\",\n \"def __parse_corpus(self, corpus):\\n corpus = self.__handle_corpus_unkwon_words(corpus)\\n start_token = ' '.join([NGramModel.START_SENTENCE_TOKEN]*(self.__n-1))\\n word_list = corpus.replace(NGramModel.START_SENTENCE_TOKEN, start_token).split()\\n \\n for n in range(1, self.__n+1): \\n self.__ngram_counts[n] = {}\\n for ngram, count in Counter(self.__generate_n_grams(word_list, n)).items():\\n self.__ngram_counts[n][' '.join(ngram)] = count\",\n \"def insight_github_unique_word_results():\\n counter_container = OrderedDict()\\n\\n path_to_input_file = os.path.join(constants.TEST_DIRECTORY,\\n \\\"fixtures/ft1_expected_results_v1.txt\\\")\\n with open(path_to_input_file, 'r') as input_file:\\n for line in input_file:\\n word, count = line.split()\\n counter_container[word] = int(count)\\n return counter_container\",\n \"def _tokenize(self, raw_text):\\n\\n doc = self.nlp(raw_text.strip())\\n\\n # Loop through tokens and find known entities aren't already marked\\n for token in doc:\\n # Is this word in our known_entities, but is not recognized by the spaCy parser?\\n if token.text.lower() in self.known_entities and token.ent_type not in self.entities:\\n # We need to set the new entity to doc.ents directly (I believe the getter for doc.ents does\\n # some important massaging. However, counter to the online docs, setting doc.ents wipes out\\n # all of the previously recognized ents, so we stash the value, then we combine and reset.\\n stash = doc.ents\\n doc.ents = [(token.text.title(), doc.vocab.strings['PERSON'], token.i, token.i + 1)]\\n doc.ents = doc.ents + stash\\n\\n # Find proper noun n-grams: (a) find a known entity, (b) is the next word also a known entity?,\\n # (c) merge, (d) repeat\\n # TODO: Joining multi-word named entities sometimes causes us trouble.\\n doc_len = len(doc) # Helps us know when to exit the 'for loop' (since we change the # of items via merge)\\n for token in doc:\\n # if we're not at the end of the loop, and we recognize this as a proper noun and it's not a stop word\\n # and the token isn't a space...\\n if token.i + 1 < doc_len and token.ent_type in self.entities and \\\\\\n token.text.lower() not in self.stop_words and token.text not in ' ':\\n next_token = doc[token.i + 1]\\n # keep looping while we're not at the end of the loop and this token has the same entity type as\\n # the previous token and it's not a stop word or a space.\\n while token.i + 1 < doc_len and next_token.ent_type == token.ent_type and \\\\\\n next_token.text.lower() not in self.stop_words and next_token.text not in ' ':\\n n_gram = doc[token.i:token.i + 2]\\n n_gram.merge()\\n doc_len -= 1 # the merge changes the list length, so we just shrunk the list!\\n # print(x)\\n if token.i + 1 >= doc_len:\\n break\\n\\n return doc\",\n \"def add_to_dict(self, tokens):\\n# TODO: ?add normalization of a token?\\n for token in tokens:\\n if self.embedding_words and (token not in self.embedding_words):\\n continue\\n self.freq[token] += 1\\n if token not in self.tok2ind:\\n index = len(self.tok2ind)\\n self.tok2ind[token] = index\\n self.ind2tok[index] = token\",\n \"def countMutatedSentences(sentence):\\n # BEGIN_YOUR_ANSWER (our solution is 17 lines of code, but don't worry if you deviate from this)\\n words = sentence.split()\\n distinct_words = set(words)\\n\\n pairs = {word: set() for word in distinct_words}\\n cache = [{j: None for j in distinct_words} for i in range(len(words))]\\n\\n for cur_word, next_word in zip(words[:-1], words[1:]):\\n pairs[cur_word].add(next_word)\\n\\n def count_mutate_sentences(target_word: str, current_length: int, target_length: int):\\n if current_length == target_length:\\n return 1\\n else:\\n result = 0\\n for word in pairs[target_word]:\\n if cache[current_length - 1][word] is None:\\n cache[current_length - 1][word] = count_mutate_sentences(word, current_length + 1, target_length)\\n result += cache[current_length - 1][word]\\n\\n return result\\n\\n return sum([count_mutate_sentences(word, 1, len(words)) for word in distinct_words])\\n # END_YOUR_ANSWER\",\n \"def merge_words(word_list, merge_method = None, **kwargs):\\n \\n for word in word_list:\\n word['minframe'] = word['frame']\\n word['maxframe'] = word['frame']\\n \\n if merge_method is None:\\n for word in word_list:\\n word['merge_size'] = 1\\n return word_list\\n \\n clusterize_words(word_list, **kwargs)\\n \\n #Merge words within clusters\\n result = []\\n word_list.sort(key = lambda word: word['cluster'])\\n cluster = []\\n current_cluster = - 1\\n for word in word_list:\\n #First word\\n if len(cluster) == 0:\\n current_cluster = word['cluster']\\n cluster = [word]\\n elif word['cluster'] == current_cluster:\\n cluster.append(word)\\n #New cluster\\n else:\\n #process this cluster\\n result.append(merge_cluster(cluster, merge_method))\\n \\n current_cluster = word['cluster']\\n cluster = [word]\\n \\n return result\",\n \"def convert_word_to_count(counter={}, doc=[]):\\n for sentence in doc:\\n for word in sentence.split():\\n if word not in counter:\\n counter[word] = 1\\n else:\\n counter[word] += 1\\n return counter\",\n \"def count_tokens(self, words: Iterable[str]) -> Dict[str, int]:\\r\\n token_counts = Counter(words)\\r\\n return {\\\" \\\".join(token): count for token, count in token_counts.items()}\",\n \"def merged_gene_feature_counts(self, merge_multi_splice_variants=True, merge_boundary_features=True, \\n merge_confusing_features=False):\\n merged_feature_count_dict = defaultdict(int)\\n for feature, count in self.mutant_counts_by_feature.items():\\n # note that anything containing '??' AND '/' never gets merged as boundary\\n if '??' in feature:\\n if merge_confusing_features: merged_feature_count_dict['??'] += count\\n else: merged_feature_count_dict[feature] += count\\n elif '|' in feature and merge_multiple_splice_variants: merged_feature_count_dict['multiple_splice_variants'] += count\\n elif '/' in feature and merge_boundary_features: merged_feature_count_dict['boundary'] += count\\n else: merged_feature_count_dict[feature] += count\\n return merged_feature_count_dict\",\n \"def reducer_count_words(self, word, counts):\\n aux3 = word.split(\\\"+\\\") # Split the string\\n yield aux3[0], (sum(counts), aux3[1])\",\n \"def count_ngrams(self, corpus):\\n \\n self.unigramcounts = defaultdict(int)\\n self.bigramcounts = defaultdict(int)\\n self.trigramcounts = defaultdict(int)\\n\\n self.sentence_counts = 0\\n self.word_count = 0\\n\\n for line in corpus:\\n sequence = line\\n self.sentence_counts +=1\\n\\n unigrams = get_ngrams(sequence, n=1)\\n for gram in unigrams:\\n self.word_count += 1\\n self.unigramcounts[gram] +=1\\n\\n bigrams = get_ngrams(sequence, n=2)\\n for gram in bigrams:\\n self.bigramcounts[gram] +=1\\n\\n trigrams = get_ngrams(sequence, n=3)\\n for gram in trigrams:\\n self.trigramcounts[gram] +=1\\n\\n #self.unigramcounts[('START')] = self.sentence_counts *2\\n self.bigramcounts[('START', 'START')] = self.sentence_counts\\n\\n #return self\",\n \"def __init__(self, n, sents):\\n assert n > 0\\n self._n = n\\n print(\\\"Counting...\\\")\\n count = defaultdict(int)\\n while n >= 0:\\n for sent in sents:\\n s = sent[:] # En una oracion auxiliar agrego el item de start y end para contarlos\\n s.insert(0, \\\"<s>\\\")\\n s.append(\\\"</s>\\\")\\n for i in range(len(s) - n + 1):\\n count[tuple(s[i:i + n])] += 1\\n n -= 1\\n count[()] = count[()] - count[('<s>',)] - count[\\n ('</s>',)] # Pero no quiero que <s> y </s> sean considerados por ()\\n self._count = count\\n print(\\\"Computing vocabulary...\\\")\\n self._voc = voc = set()\\n for sent in sents:\\n voc = voc.union(set(sent))\\n voc.add('</s>')\\n self._voc = voc\\n self._V = len(voc) # vocabulary size\\n print(\\\"Done\\\")\",\n \"def __cross_wiki_counts(self):\\n\\n print(\\\"Updating counts by merging with CrossWiki\\\")\\n\\n cnt = 0\\n crosswiki_path = os.path.join(\\n self.base_url, \\\"generic/p_e_m_data/crosswikis_p_e_m.txt\\\"\\n )\\n\\n with open(crosswiki_path, \\\"r\\\", encoding=\\\"utf-8\\\") as f:\\n for line in f:\\n parts = line.split(\\\"\\\\t\\\")\\n mention = unquote(parts[0])\\n\\n if (\\\"Wikipedia\\\" not in mention) and (\\\"wikipedia\\\" not in mention):\\n if mention not in self.wiki_freq:\\n self.wiki_freq[mention] = {}\\n\\n num_ents = len(parts)\\n for i in range(2, num_ents):\\n ent_str = parts[i].split(\\\",\\\")\\n ent_wiki_id = int(ent_str[0])\\n freq_ent = int(ent_str[1])\\n\\n if (\\n ent_wiki_id\\n not in self.wikipedia.wiki_id_name_map[\\\"ent_id_to_name\\\"]\\n ):\\n ent_name_re = self.wikipedia.wiki_redirect_id(ent_wiki_id)\\n if (\\n ent_name_re\\n in self.wikipedia.wiki_id_name_map[\\\"ent_name_to_id\\\"]\\n ):\\n ent_wiki_id = self.wikipedia.wiki_id_name_map[\\n \\\"ent_name_to_id\\\"\\n ][ent_name_re]\\n\\n cnt += 1\\n if (\\n ent_wiki_id\\n in self.wikipedia.wiki_id_name_map[\\\"ent_id_to_name\\\"]\\n ):\\n if mention not in self.mention_freq:\\n self.mention_freq[mention] = 0\\n self.mention_freq[mention] += freq_ent\\n\\n ent_name = self.wikipedia.wiki_id_name_map[\\n \\\"ent_id_to_name\\\"\\n ][ent_wiki_id].replace(\\\" \\\", \\\"_\\\")\\n if ent_name not in self.wiki_freq[mention]:\\n self.wiki_freq[mention][ent_name] = 0\\n self.wiki_freq[mention][ent_name] += freq_ent\",\n \"def merge_df_counts(pin, pin2):\\n df = pd.read_csv(pin)\\n df = add_mutkey(df)\\n df = add_codonkey(df)\\n # df_syn = df.loc[df[\\\"mutkey\\\"].str[0] == df[\\\"mutkey\\\"].str[-1]]\\n\\n df2 = pd.read_csv(pin2)\\n df2 = add_codonkey(df2)\\n # df2_syn = df.loc[df[\\\"mutkey\\\"].str[0] == df[\\\"mutkey\\\"].str[-1]]\\n df_reps = df.merge(\\n df2, left_on=\\\"codonkey\\\", right_on=\\\"codonkey\\\", suffixes=(\\\"_r1\\\", \\\"_r2\\\")\\n )\\n return df_reps\",\n \"def vectorize_pos_tags(self, tokens):\\n\\n pos_counts = defaultdict(float)\\n for token in tokens:\\n pos_counts[token.pos] += 1.0\\n return pos_counts\",\n \"def cherche_anagramme(mot):\\n global DICO\\n res = []\\n lettres = dict((l, mot.count(l)) for l in set(mot))\\n if len(mot) == 1:\\n res.append(mot)\\n else:\\n for ana in DICO:\\n if len(ana) == len(mot):\\n anag = True\\n for lettre in mot:\\n if lettre not in ana:\\n anag = False\\n if anag:\\n lettres2 = dict((l, ana.count(l)) for l in set(ana))\\n if lettres == lettres2:\\n res.append(ana)\\n return res\",\n \"def __find_union(dicts: list):\\n global other_time\\n start = time.time()\\n union_list = []\\n while len(dicts) > 1:\\n word_count = {}\\n final_dict = {}\\n for d in dicts:\\n # print(json.dumps(d, indent=4))\\n for word in d:\\n if word not in word_count:\\n word_count[word] = 1\\n else:\\n word_count[word] = word_count[word] + 1\\n for item in word_count:\\n if word_count[item] == len(dicts):\\n union_list.append(item)\\n for d in dicts:\\n for word in d:\\n if word in union_list:\\n if word not in final_dict:\\n final_dict[word] = d[word]\\n else:\\n final_dict[word] += d[word]\\n if len(final_dict) > 0:\\n # print(len(dicts))\\n end = time.time()\\n diff = end - start\\n other_time += diff\\n # print(json.dumps(final_dict, indent=4))\\n return (final_dict)\\n else:\\n\\n dicts = dicts[:len(dicts) - 1]\\n\\n end = time.time()\\n diff = end - start\\n other_time += diff\\n return {}\",\n \"def remove_duplicated_words(tokens):\\n\\n return sorted(set(tokens))\",\n \"def count_tokens(txt_to_tokenise_and_count):\\n\\n txt_tokens = nltk.word_tokenize(txt_to_tokenise_and_count) \\n counts = Counter(txt_tokens)\\n\\n print (counts.most_common(10))\\n return counts\",\n \"def analyzeFile(filename): \\n fileData = open(filename, encoding=\\\"utf-8\\\") # open the file\\n \\n counts = {}\\n\\n for line in fileData:\\t\\t # iterates over every line of the file\\n words = line.split() # turns each line into a list\\n for word in words: #iterates over the words in each line list\\n word = word.lower().strip(string.whitespace+string.punctuation)\\n if len(word) > 0: #make sure word is longer than 0 before adding it to the dictionary\\n counts[word] = counts.get(word, 0) + 1 #look up if the dictionary has that word and if not then it'll add that word with the value 0 associated with it and then add one to that, if it has seen it it'll add 1 to the value stored in the counts dictionary\\n #when it gets here for the first line it goes back up to the top and repeats for the 2nd line\\n mostCommonWord = [word]\\n leastCommonWord = [word]\\n shortestWord = [word]\\n longestWord = [word]\\n \\n for item in counts:\\n if counts[mostCommonWord[0]] < counts[item]:\\n mostCommonWord = [item]\\n elif counts[mostCommonWord[0]] == counts[item]:\\n mostCommonWord.append(item)\\n if counts[leastCommonWord[0]] > counts[item]:\\n leastCommonWord = [item]\\n elif counts[leastCommonWord[0]] == counts[item]:\\n leastCommonWord.append(item)\\n if len(shortestWord[0]) > len(item):\\n shortestWord = [item] \\n elif len((shortestWord[0])) == len(item):\\n shortestWord.append(item)\\n if len(longestWord[0]) < len(item):\\n longestWord = [item]\\n elif len(longestWord[0]) == len(item):\\n longestWord.append(item)\\n \\n return (mostCommonWord, leastCommonWord, shortestWord, longestWord)\",\n \"def merge_cooccur(args):\\n if not args.quiet:\\n logger.setLevel(logging.INFO)\\n\\n merged = dict.fromkeys(['mat', 'tokenizer', 'window_size', 'uniform_count'])\\n with tqdm(total=len(args.cooccurfiles), ncols=80, disable=args.quiet) as prog:\\n for file in args.cooccurfiles:\\n # load the data\\n corpus = load_corpus(file)\\n\\n if merged['tokenizer'] is None:\\n merged['tokenizer'] = corpus._tokenizer.to_str()\\n\\n if merged['window_size'] is None:\\n merged['window_size'] = corpus.window_size\\n\\n if merged['uniform_count'] is None:\\n merged['uniform_count'] = corpus.uniform_count\\n\\n mat = corpus.mat.astype('float32')\\n if args.symmetrization:\\n mat = (mat + mat.T.tocoo()).tocoo()\\n\\n if merged['mat'] is None:\\n merged['mat'] = mat\\n else:\\n merged['mat'] += mat\\n\\n prog.update()\\n merged['mat'] = merged['mat'].tocoo()\\n\\n # save output\\n logger.info('Saving to disk...')\\n out_fn = join(args.path, args.out)\\n with open(out_fn, 'wb') as fp:\\n pkl.dump(\\n {\\n 'mat': {\\n 'row': merged['mat'].row,\\n 'col': merged['mat'].col,\\n 'counts': merged['mat'].data\\n },\\n 'tokenizer': merged['tokenizer'],\\n 'uniform_count': merged['uniform_count'],\\n 'window_size': merged['window_size']\\n },\\n fp\\n )\",\n \"def train():\\n\\tA = collections.defaultdict(dict)\\n\\tB = collections.defaultdict(dict)\\n\\tpos_list = []\\n\\tword_list = []\\n\\tstr_buf = []\\n\\n\\n\\t# read each line and count A and B\\n\\tfor line in sys.stdin:\\n\\t\\tline = line.split()\\n\\t\\t# print(line)\\n\\t\\tif len(line) == 3:\\n\\t\\t\\tstr_buf.append((str(line[0]), str(line[1])))\\n\\t\\t\\tword_list.append(str(line[0]))\\n\\t\\t\\tpos_list.append(str(line[1]))\\n\\n\\t\\telse:\\n\\t\\t\\t# if come to the end of a sentence\\n\\t\\t\\tif len(str_buf) != 0:\\n\\t\\t\\t\\tstr_buf = [('<s>','BOS')] + str_buf + [('</s>', 'EOS')]\\n\\t\\t\\t\\tword_list += ['<s>', '</s>']\\n\\t\\t\\t\\tpos_list += ['BOS', 'EOS']\\n\\n\\t\\t\\t\\tfor i, s in enumerate(str_buf):\\n\\t\\t\\t\\t\\tif s[0] in B[s[1]]:\\n\\t\\t\\t\\t\\t\\tB[s[1]][s[0]] += 1\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tB[s[1]][s[0]] = 1\\n\\n\\t\\t\\t\\t\\tif s[0] != '</s>':\\n\\t\\t\\t\\t\\t\\t# print('strbuf[i]:',str_buf[i], 's[1]', s[1])\\n\\t\\t\\t\\t\\t\\tif str_buf[i+1][1] in A[s[1]]:\\n\\t\\t\\t\\t\\t\\t\\tA[s[1]][str_buf[i+1][1]] += 1\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\tA[s[1]][str_buf[i+1][1]] = 1\\n\\n\\t\\t\\t\\tstr_buf = []\\n\\n\\t# get unique POS list and word list\\n\\tpos_list_uniq = list(set(pos_list))\\n\\tword_list_uniq = list(set(word_list))\\n\\n\\n\\t# assume <UNK>, smoothing, normalize\\n\\tB_sm = collections.defaultdict(dict)\\n\\tA_sm = A.copy()\\n\\n\\t# assume words apeear less than 2 times as <UNK>\\n\\tword_count = collections.Counter(word_list)\\n\\tfor pos in B:\\n\\t\\tfor word in B[pos]:\\n\\t\\t\\tif word_count[word] > 1:\\n\\t\\t\\t\\tB_sm[pos][word] = B[pos][word]\\n\\n\\t\\t\\telse: # add <UNK> to B_sm\\n\\t\\t\\t\\tword_list_uniq.remove(word)\\n\\t\\t\\t\\tif '<UNK>' in B_sm[pos]:\\n\\t\\t\\t\\t\\tB_sm[pos]['<UNK>'] += 1\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tB_sm[pos]['<UNK>'] = 1\\n\\n\\tword_list_uniq += ['<UNK>']\\n\\n\\t# add 1 smoothing\\n\\tfor pos in pos_list_uniq:\\n\\t\\tfor word in word_list_uniq:\\n\\t\\t\\tif word in B_sm[pos]:\\n\\t\\t\\t\\tB_sm[pos][word] += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tB_sm[pos][word] = 1\\n\\n\\tfor prev in pos_list_uniq:\\n\\t\\tfor next in pos_list_uniq:\\n\\t\\t\\tif next in A_sm[prev]:\\n\\t\\t\\t\\tA_sm[prev][next] += 1\\n\\t\\t\\telse:\\n\\t\\t\\t\\tA_sm[prev][next] = 1\\n\\n\\t# delete instances like A[VB][BOS], A[EOS][VB],\\n\\t# B[VB]['</s>'], B[EOS]['Jack']\\n\\n\\tfor pos in B_sm:\\n\\t\\tfor word in B_sm[pos]:\\n\\t\\t\\tif (pos == 'BOS' and word != '<s>') or \\\\\\n\\t\\t\\t(pos == 'EOS' and word != '</s>') or \\\\\\n\\t\\t\\t(word == '<s>' and pos != 'BOS') or \\\\\\n\\t\\t\\t(word == '</s>' and pos != 'EOS'):\\n\\t\\t\\t\\tB_sm[pos][word] = 0\\n\\n\\tfor prev in A_sm:\\n\\t\\tfor next in A_sm[prev]:\\n\\t\\t\\tif prev == 'EOS' or next == 'BOS':\\n\\t\\t\\t\\tA_sm[prev][next] = 0\\n\\n\\t# normalize\\n\\tfor pos in B_sm:\\n\\t\\ts = sum(B_sm[pos].values())\\n\\t\\tfor word in B_sm[pos]:\\n\\t\\t\\tif B_sm[pos][word] != 0:\\n\\t\\t\\t\\tB_sm[pos][word] /= s\\n\\n\\tfor prev in A_sm:\\n\\t\\ts = sum(A_sm[prev].values())\\n\\t\\tfor next in A_sm[prev]:\\n\\t\\t\\tif A_sm[prev][next] != 0:\\n\\t\\t\\t\\tA_sm[prev][next] /= s\\n\\n\\treturn A_sm, B_sm, word_list_uniq\",\n \"def word_overlap_phi(t1, t2):\\n overlap = set([w1 for w1 in t1.leaves() if w1 in t2.leaves()])\\n return Counter(overlap)\",\n \"def __init__(self, n, sents, gamma=None, addone=True):\\n assert n > 0\\n self._n = n\\n\\n if gamma is not None:\\n # everything is training data\\n train_sents = sents\\n else:\\n # 90% training, 10% held-out\\n m = int(0.45 * len(sents))\\n l = int(0.65 * len(sents))\\n train_sents = sents[:m] + sents[l:]\\n held_out_sents = sents[m:l]\\n\\n print('Computing counts...')\\n count = defaultdict(int)\\n while (n >= 0):\\n for sent in train_sents:\\n s = sent[:] ## En una oracion auxiliar agrego el item de start y end para contarlos\\n s.insert(0, \\\"<s>\\\")\\n s.append(\\\"</s>\\\")\\n for i in range(len(s) - n + 1):\\n count[tuple(s[i:i + n])] += 1\\n n -= 1\\n count[()] = count[()] - count[('<s>',)] - count[\\n ('</s>',)] # Pero no quiero que <s> y </s> sean considerados por ()\\n self._count = count\\n # WORKed HERE!!\\n # COMPUTE COUNTS FOR ALL K-GRAMS WITH K <= N\\n\\n # compute vocabulary size for add-one in the last step\\n self._addone = addone\\n if addone:\\n print('Computing vocabulary...')\\n self._voc = voc = set()\\n for sent in sents:\\n voc = voc.union(set(sent))\\n voc.add('</s>')\\n self._voc = voc\\n self._V = len(voc)\\n\\n # compute gamma if not given\\n if gamma is not None:\\n self._gamma = gamma\\n else:\\n print('Computing gamma...')\\n self._gamma = gamma = 1\\n p = self.log_prob(held_out_sents)\\n new_gamma = 2\\n streak = 1\\n growing = True\\n turns = 0\\n while (turns < 15):\\n self._gamma = new_gamma\\n np = self.log_prob(held_out_sents)\\n gamma = new_gamma\\n if (np > p):\\n if growing:\\n streak += 1\\n else:\\n turns += 1\\n streak = 0\\n growing = True\\n new_gamma = new_gamma + 2 ** streak\\n else:\\n if growing:\\n turns += 1\\n streak = 0\\n growing = False\\n else:\\n streak += 1\\n new_gamma = new_gamma - 2 ** streak\\n p = np\\n self._gamma = new_gamma\\n print(self._gamma)\",\n \"def _build_token_dict(self, corpus: List[List[str]], min_count: int = 3):\\n token2idx = {\\n self.token_pad: 0,\\n self.token_unk: 1,\\n self.token_bos: 2,\\n self.token_eos: 3\\n }\\n\\n token2count = {}\\n for sentence in corpus:\\n for token in sentence:\\n count = token2count.get(token, 0)\\n token2count[token] = count + 1\\n\\n # 按照词频降序排序\\n sorted_token2count = sorted(token2count.items(),\\n key=operator.itemgetter(1),\\n reverse=True)\\n token2count = collections.OrderedDict(sorted_token2count)\\n\\n for token, token_count in token2count.items():\\n if token not in token2idx and token_count >= min_count:\\n token2idx[token] = len(token2idx)\\n\\n self.token2idx = token2idx\\n self.idx2token = dict([(value, key)\\n for key, value in self.token2idx.items()])\\n logging.debug(f\\\"build token2idx dict finished, contains {len(self.token2idx)} tokens.\\\")\\n self.dataset_info['token_count'] = len(self.token2idx)\",\n \"def _annotate_brackets(\\n self,\\n tokens: List[tokenize.TokenInfo],\\n ) -> Mapping[int, int]:\\n brackets = {bracket: 0 for bracket in MATCHING}\\n for token in tokens:\\n if token.exact_type in MATCHING.keys():\\n brackets[token.exact_type] += 1\\n if token.exact_type in MATCHING.values():\\n reverse_bracket = get_reverse_bracket(token)\\n if brackets[reverse_bracket] > 0:\\n brackets[reverse_bracket] -= 1\\n return brackets\",\n \"def merge_ners(tokens):\\n ners = list()\\n merged_tokens = list()\\n\\n candid_entity = list()\\n keep = False\\n prev_tag = 'O'\\n\\n for i, (token, tag) in enumerate(tokens):\\n if keep:\\n if tag not in IGNORE_NER_TAG:\\n candid_entity.append(token)\\n keep = True\\n else:\\n # ner ends in prev step\\n merged_tokens.append(candid_entity)\\n merged_tokens.append(token)\\n ners.append((candid_entity, prev_tag))\\n keep = False\\n else:\\n if tag not in IGNORE_NER_TAG:\\n # new ner\\n candid_entity = list()\\n candid_entity.append(token)\\n keep = True\\n else:\\n # not ner token\\n merged_tokens.append(token)\\n prev_tag = tag\\n\\n return ners, merged_tokens\",\n \"def _disambiguate_merged_glosses(self, merged_glosses):\\n\\t\\tprocessed_glosses = {}\\n\\t\\ttotal_glosses = len(merged_glosses)\\n\\t\\tcurrent_gloss = 0\\n\\t\\tdisambiguated_glosses_count = 0\\n\\t\\tskipped_glosses_count = 0\\n\\n\\t\\t# go over all glosses and disambiguate them if possible\\n\\t\\tfor gloss_key in merged_glosses:\\n\\t\\t\\tcurrent_gloss += 1\\n\\t\\t\\tgloss = merged_glosses[gloss_key]\\n\\t\\t\\ttaggable_tokens = []\\n\\t\\t\\ttagged_tokens = []\\n\\n\\t\\t\\tif len(gloss.tokens) == 0:\\n\\t\\t\\t\\tself._log_message(\\\"WARNING: unmerged gloss {0}\\\".format(gloss_key))\\n\\n\\t\\t\\t# for each token inside the gloss determine if the token is already disambiguated, needs to be disambiguated or isnt part of WordNet anyways\\n\\t\\t\\tfor token_index in gloss.tokens:\\n\\t\\t\\t\\ttoken = gloss.tokens[token_index]\\n\\t\\t\\t\\ttokens_wn_ss_types = list(map(int, re.findall(\\\"[0-9]+\\\", token.lemma)))\\n\\t\\t\\t\\t# tokens that are tagged as \\\"man\\\" or \\\"auto\\\" are already disambiguated\\n\\t\\t\\t\\tif token.tag in [\\\"man\\\", \\\"auto\\\"]:\\n\\t\\t\\t\\t\\ttagged_tokens.append(token)\\n\\t\\t\\t\\t# if a token is tagged as \\\"un\\\" it is not yet annotated, but possibly should be; if the lemma contains entries of the type \\\"LEMMA%SS_TYPE\\\" and the POS if the word matches one of the lemma-ss_types it needs to be/can be disambiguated\\n\\t\\t\\t\\telif token.tag == \\\"un\\\" and re.match(\\\"([a-z]+%[0-9]\\\\|?)+\\\", token.lemma) and not set(GLOSSTAG_POS_POSSIBLE_SS_TYPES[token.pos]).isdisjoint(set(tokens_wn_ss_types)) and type(token) != CollocationMember:\\n\\t\\t\\t\\t\\ttaggable_tokens.append(token)\\n\\n\\t\\t\\t# if there are no remaining taggable tokens, then proceed with the next gloss\\n\\t\\t\\tif len(taggable_tokens) == 0:\\n\\t\\t\\t\\tprocessed_glosses[gloss_key] = gloss\\n\\t\\t\\t\\tskipped_glosses_count += 1\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tdisambiguated_glosses_count += 1\\n\\n\\t\\t\\t## DISAMBUGATION PROCEDURE ##\\n\\t\\t\\tprint(\\\"\\\\tat gloss {0} of {1}\\\".format(current_gloss, total_glosses))\\n\\t\\t\\tprint(\\\"\\\\t\\\\t...disambiguating {0} words in gloss {1}\\\".format(len(taggable_tokens), gloss_key))\\n\\t\\t\\t# disambiguated_gloss = self._disambiguate_gloss_with_path_similarity(gloss, taggable_tokens, tagged_tokens)\\n\\t\\t\\tdisambiguated_gloss = self._disambiguate_gloss_by_most_frequent_sense(gloss, taggable_tokens, tagged_tokens)\\n\\t\\t\\t# finished, append to output\\n\\t\\t\\tprocessed_glosses[gloss_key] = disambiguated_gloss\\n\\n\\t\\tprint(\\\"\\\\tdisambiguated {0} glosses, skipped {1}\\\".format(disambiguated_glosses_count, skipped_glosses_count))\\n\\n\\t\\treturn processed_glosses\",\n \"def bow(tokens):\\n return dict(collections.Counter(re.findall(r'\\\\w+', \\\" \\\".join(tokens))))\",\n \"def can_be_merged(prev, cur):\\n\\n WHITESPACE = (' ', '\\\\t')\\n if not cur.mergeable or not prev.mergeable:\\n return False\\n # offset is char offset, not byte, so length is the char length!\\n elif cur.offset != (prev.offset + prev.length):\\n return False\\n elif cur.text in WHITESPACE and not prev.text in WHITESPACE:\\n return False\\n elif prev.text in WHITESPACE and not cur.text in WHITESPACE:\\n return False\\n return True\",\n \"def train(self, corpus):\\n lastToken = \\\"#\\\"\\n for sentence in corpus.corpus:\\n for datum in sentence.data:\\n token = datum.word\\n self.reverseBigramCount[token][lastToken] += 1\\n self.bigramCount[lastToken][token] += 1\\n self.unigramCount[token] += 1\\n self.total += 1\\n lastToken = token\",\n \"def __iadd__(self, other):\\n\\n if not isinstance(other, Unigram):\\n return NotImplemented\\n\\n self.sort_by_tokens(other.dictionary.tokens)\\n\\n for i in range(len(other.dictionary)):\\n other_count = other.Nx[i]\\n self.Nx[i] += other_count\\n\\n self.N = sum(self.Nx)\\n\\n return self\",\n \"def _count_occurrences(self, corpus_tokens: list, window_size: int):\\n ngram_occurrences = {}\\n for i, corpus_token in enumerate(corpus_tokens):\\n ngram = []\\n if i + window_size > len(corpus_tokens) - 1:\\n break\\n else:\\n for j in range(window_size):\\n ngram.append(corpus_tokens[i + j])\\n if self.TOKENS.END.value in ngram:\\n continue\\n else:\\n ngram_key = tuple(ngram)\\n if ngram_key in ngram_occurrences:\\n ngram_occurrences[ngram_key] += 1\\n else:\\n ngram_occurrences[ngram_key] = 1\\n return ngram_occurrences\",\n \"def can_be_merged(prev, cur):\\n\\n WHITESPACE = (' ', '\\\\t')\\n if not cur.mergeable or not prev.mergeable:\\n return False\\n elif prev.delete_key_used != cur.delete_key_used:\\n return False\\n elif prev.start != cur.start and prev.start != cur.end:\\n return False\\n elif cur.text not in WHITESPACE and \\\\\\n prev.text in WHITESPACE:\\n return False\\n elif cur.text in WHITESPACE and \\\\\\n prev.text not in WHITESPACE:\\n return False\\n return True\",\n \"def merge_accumulators(self, accumulators):\\n raise NotImplementedError\",\n \"def naive_with_counts(p, t):\\n occurences = []\\n num_alignments = 0\\n num_character_comparisons = 0\\n for i in range(len(t) - len(p) + 1):\\n match = True\\n for j in range(len(p)):\\n if t[i + j] != p[j]:\\n match = False\\n break\\n if match:\\n occurences.append(i)\\n num_alignments += 1\\n num_character_comparisons += (j + 1)\\n return occurences, num_alignments, num_character_comparisons\",\n \"def merge(self, nums1: [int], m: int, nums2: [int], n: int) -> None:\\n for i in range(m, len(nums1)):\\n del nums1[m]\\n for i in range(n, len(nums2)):\\n del nums2[n]\\n\\n nums1 += nums2\\n nums1.sort()\",\n \"def num_special_tokens_to_add(self, pair=False):\\n token_ids_0 = []\\n token_ids_1 = []\\n return len(\\n self.build_inputs_with_special_tokens(token_ids_0, token_ids_1\\n if pair else None))\",\n \"def frequent_nouns_counts(self, tokens):\\n\\n unigram_fd = nltk.FreqDist(tokens)\\n pos = POS()\\n common_unigrams = unigram_fd.most_common(\\n int(self.top_pct * len(unigram_fd)))\\n\\n nouns = [pair for pair in common_unigrams\\n if pair[0] not in self.stopwords()\\n and pos.percent_noun(pair[0]) > 0.5]\\n\\n return nouns\",\n \"def getTokens(wordlist, tokens = {}, count = 0):\\n\\n\\tif len(wordlist) == 0:\\n\\t\\treturn tokens, count\\n\\n\\tfor i in wordlist:\\n\\t\\tif tokens.has_key(i):\\n\\t\\t\\ttokens[i] += 1\\n\\t\\telse:\\n\\t\\t\\ttokens[i] = 1\\n\\t\\tcount += 1\\n\\treturn tokens, count\",\n \"def CountWords(input1):\\n tokens = []\\n\\n d={}\\n\\n tokens = tokens+input1.lower().split()\\n\\n for token in tokens:\\n # Remove Punctuation\\n word=token.replace(\\\"!\\\", \\\"\\\")\\n word=word.replace(\\\".\\\", \\\"\\\")\\n d[word] = d.get(word,0)+1\\n\\n word_frq=[]\\n for i,v in d.items():\\n word_frq.append((v,i))\\n word_frq.sort(reverse=True)\\n\\n for word in word_frq:\\n print (word[1],word[0])\",\n \"def get_number_of_different_tokens(token_list):\\n \\n return len(set(token_list))\",\n \"def merge_like_ops(self):\\n\\n cigs = []\\n for op, grps in groupby(self.items(), itemgetter(1)):\\n cigs.append((sum(g[0] for g in grps), op))\\n\\n return Cigar(self.string_from_elements(cigs))\",\n \"def _aggregate_text_embedding(self, token_ids, embeddings):\\n if self._hparams['obj_text_aggregation'] == 'max':\\n # Find valid tokens (not PADDING/EOS/UNK/START).\\n valid_token_mask = tf.greater_equal(token_ids, 4)\\n # Use large negative bias for invalid tokens.\\n invalid_token_bias = tf.cast(\\n tf.logical_not(valid_token_mask), tf.float32) * -1e9\\n # [batch, node_num, word_num, hidden_size]\\n embeddings = embeddings + tf.expand_dims(invalid_token_bias, axis=-1)\\n # Max value for each dimension, [batch, node_num, hidden_size].\\n embeddings = tf.reduce_max(embeddings, axis=-2)\\n # For objects with no text, use 0.\\n valid_object_mask = tf.cast(\\n tf.reduce_any(valid_token_mask, axis=-1), tf.float32)\\n embeddings = embeddings * tf.expand_dims(valid_object_mask, axis=-1)\\n\\n elif self._hparams['obj_text_aggregation'] == 'sum':\\n # [batch, step, #max_obj, #max_token] 0 for padded tokens\\n real_objects = tf.cast(tf.greater_equal(token_ids, 4), tf.float32)\\n # [batch, step, #max_obj, hidden] 0s for padded objects\\n embeddings = tf.reduce_sum(\\n input_tensor=embeddings * tf.expand_dims(real_objects, 3), axis=-2)\\n\\n else:\\n raise ValueError('Unrecognized token aggregation %s' %\\n (self._hparams['obj_text_aggregation']))\\n return embeddings\",\n \"def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:\\n if m == 0:\\n for i in range(len(nums2)):\\n nums1[i] = nums2[i]\\n return\\n if n == 0:\\n return\\n\\n for i in range(m - 1, -1, -1):\\n nums1[i], nums1[i - m] = nums1[i - m], nums1[i]\\n count = 0\\n i = len(nums1)-m\\n while i < len(nums1) or nums2:\\n print(count)\\n if i < len(nums1) and nums2:\\n if nums1[i] <= nums2[0]:\\n nums1[count], nums1[i] = nums1[i], nums1[count]\\n i += 1\\n else:\\n nums1[count] = nums2.pop(0)\\n print(nums1)\\n elif i >= len(nums1) and nums2:\\n nums1[count] = nums2.pop(0)\\n elif i < len(nums1) and not nums2:\\n nums1[count], nums1[i] = nums1[i], nums1[count]\\n i += 1\\n count += 1\",\n \"def get_frequencies(tokens):\\n cnt = {}\\n\\n for word in tokens:\\n if word not in cnt:\\n cnt[word] = 0\\n\\n cnt[word] += 1\\n\\n return cnt\",\n \"def semcor2token(args):\\r\\n input_files = list_files(*args.input_files)\\r\\n output_dir = Path(args.output_dir)\\r\\n if not output_dir.is_dir():\\r\\n try:\\r\\n output_dir.mkdir()\\r\\n except:\\r\\n print('Invalid output directory name. Files will be stored in default directory.', file = stderr)\\r\\n output_dir = output_default / 'typetoken'\\r\\n if not output_dir.is_dir():\\r\\n output_dir.mkdir()\\r\\n multiword = args.multiword\\r\\n for input_file in input_files:\\r\\n corpus_file = CorpusFile(input_file)\\r\\n filename = corpus_file.shortname + '.txt'\\r\\n dirname = output_dir / corpus_file.concordance\\r\\n if not dirname.exists():\\r\\n dirname.mkdir()\\r\\n output_file_name = dirname / filename\\r\\n with output_file_name.open('w') as output_file:\\r\\n for word in corpus_file.text.find_all(['wf', 'punc']):\\r\\n if word.name == 'punc':\\r\\n output_file.write('\\\\t'.join([word.string, word.string, 'punc\\\\n']))\\r\\n elif not multiword:\\r\\n for token in Token.from_tag(word).get_components():\\r\\n if args.verbose and type(token.status)==tuple:\\r\\n token_id = '/'.join([corpus_file.shortname, token.wordform])\\r\\n report_token_status(token, token_id)\\r\\n output_file.write('\\\\t'.join([token.wordform, token.lemma, token.pos]) + '\\\\n')\\r\\n else:\\r\\n token = Token.from_tag(word)\\r\\n if args.verbose and type(token.status)==tuple:\\r\\n token_id = '/'.join([corpus_file.shortname, token.wordform])\\r\\n report_token_status(token, token_id)\\r\\n output_file.write('\\\\t'.join([token.wordform, token.lemma, token.pos]) + '\\\\n')\",\n \"def _counter(title_list):\\n t = Tokenizer()\\n words_count = defaultdict(int)\\n words = []\\n for title in title_list:\\n tokens = t.tokenize(title)\\n for token in tokens:\\n pos = token.part_of_speech.split(',')[0]\\n if pos == '名詞':\\n words_count[token.base_form] += 1\\n words.append(token.base_form)\\n return words_count, words\",\n \"def merge_cluster(word_list, merge_method = None):\\n \\n #Calculates average of property of list of dictionaries.\\n def avg(l, key):\\n s = 0\\n for w in l:\\n s+= w[key]\\n return s / len(l)\\n \\n result = {}\\n result['longleft'] = avg(word_list, 'longleft')\\n result['longright'] = avg(word_list, 'longright')\\n result['top'] = avg(word_list, 'top')\\n result['bottom'] = avg(word_list, 'bottom')\\n result['confidence'] = avg(word_list, 'confidence')\\n result['merge_size'] = len(word_list)\\n result['minframe'] = min([word['minframe'] for word in word_list])\\n result['maxframe'] = max([word['maxframe'] for word in word_list])\\n result['cluster'] = word_list[0]['cluster']\\n \\n if merge_method is None:\\n merge_method = 'char_frequency'\\n \\n #Pick most popular character of each position in word\\n if merge_method == 'char_frequency':\\n result['text'] = average_word(word_list)\\n #Pick word with highest confidence\\n elif merge_method == 'confidence':\\n max_confidence = -1\\n for word in word_list:\\n if word['confidence'] > max_confidence:\\n max_confidence = word['confidence']\\n result['text'] = word['text']\\n elif merge_method == 'random':\\n result['text'] = word_list[random.randint(0, len(word_list) - 1)]['text']\\n else:\\n raise ValueError('Method ' + merge_method + ' not implemented.')\\n \\n return result\",\n \"def _concatenates(self, chain_with_counts, char_counts, level=0, in_one_liner=False):\\n # evaluate if it's feasible in one-line\\n one_liner = in_one_liner or char_counts < self.max_len - (self.indent * level)\\n new_level = level if one_liner else level + 1\\n elements = [\\n self._concatenates(c, n, level=new_level, in_one_liner=one_liner)\\n if isinstance(c, list)\\n else c\\n for c, n in chain_with_counts]\\n elements = self._apply_stick(elements)\\n prefix = self.prefix if level and not in_one_liner else \\\"\\\"\\n join_char = \\\" \\\" if one_liner else (\\\"\\\\n\\\" + prefix)\\n return prefix + join_char.join(l for c in elements for l in c.split(\\\"\\\\n\\\"))\",\n \"def merge_files(filename_list, merged_file, encode):\\n lines_counter = list()\\n for file_name in filename_list:\\n lines_counter.append(count_lines(file_name))\\n lines_counter.sort(key=lambda item: item[-1])\\n with open(merged_file, 'w', encoding=encode) as file:\\n for doc in lines_counter:\\n file.write(f'{doc[0]}\\\\n')\\n file.write(f'{doc[1]}\\\\n')\\n text = get_text(doc[0])\\n file.write(f'{text}\\\\n\\\\n')\",\n \"def compute_pair_frequencies(text, alphabet_size=256):\\n\\n curr = text[1:]\\n prev = text[:-1]\\n pairs = np.stack([prev, curr])\\n\\n print(\\\"Counting pairs . . .\\\")\\n (char1, char2), counts = np.unique(pairs, return_counts=True, axis=1)\\n print(\\\"Done.\\\\n\\\")\\n\\n joints = np.zeros((alphabet_size, alphabet_size))\\n joints[char1, char2] = counts / np.sum(counts)\\n assert np.isclose(np.sum(joints), 1.0)\\n\\n return joints\",\n \"def computeWordsFrequencies(self):\\n token_stream = self._tokenize(self.readable)\\n token_map = self._countTokens(token_stream)\\n # print token_map.items()\\n return sorted(token_map.items(), key = lambda x : x[1], reverse = True)\",\n \"def count_synonymous(self):\\n if not self.is_coding():\\n log_message(\\n logging_callback=logging.warning,\\n msg=\\\"Cannot count synonymous mutations in noncoding data\\\",\\n extra={\\\"oname\\\": self.name},\\n )\\n return\\n\\n if self.check_store(\\\"/main/synonymous/counts\\\"):\\n return\\n\\n log_message(\\n logging_callback=logging.info,\\n msg=\\\"Counting synonymous variants\\\",\\n extra={\\\"oname\\\": self.name},\\n )\\n df_dict = dict()\\n\\n for variant, count in self.store[\\\"/main/variants/counts\\\"].iterrows():\\n if variant == WILD_TYPE_VARIANT:\\n df_dict[variant] = count[\\\"count\\\"]\\n else:\\n variant = protein_variant(variant)\\n if len(variant) == 0:\\n variant = SYNONYMOUS_VARIANT\\n try:\\n df_dict[variant] += count[\\\"count\\\"]\\n except KeyError:\\n df_dict[variant] = count[\\\"count\\\"]\\n\\n self.save_counts(\\\"synonymous\\\", df_dict, raw=False)\\n del df_dict\",\n \"def total_char_similarity(a,b):\\n\\ta_words, b_words = map(norm.set_clean_tokens, [a,b])\\n\\n\\ttotal_score = 0\\n\\tfor ai in a_words:\\n\\t\\tfor bi in b_words:\\n\\t\\t\\ttotal_score += similar(ai, bi)\\n\\treturn total_score\",\n \"def merge_texts(self, dataset):\\n for ds in dataset:\\n empty_data = ds._data.copy()\\n ds._data = ds._data[ds._data.index < 0]\\n self.vmerge(dataset, verbose=False, overwrite_text=True)\\n return None\",\n \"def calculate_poses_for_word(corpus):\\n total_pos_count = {}\\n poses_for_word = {}\\n # For each word, find all POSes.\\n for sentence in corpus.get_sentences():\\n for word in sentence:\\n id, form, lemma, plemma, pos, ppos = word\\n #form = form.lower()\\n\\n if pos in total_pos_count:\\n total_pos_count[pos] += 1\\n else:\\n total_pos_count[pos] = 1\\n\\n if form in poses_for_word:\\n if pos in poses_for_word[form]:\\n poses_for_word[form][pos] += 1\\n else:\\n poses_for_word[form][pos] = 1\\n else:\\n poses_for_word[form] = {pos: 1}\\n\\n return poses_for_word, total_pos_count\",\n \"def _count_tokens(self,\\n token_lists):\\n ## Filter out empty lists\\n fn_tokens = list(filter(lambda i: len(i) > 0, token_lists))\\n ## Get N-grams from Tokens\\n fn_ngrams = list(map(lambda t: self.get_ngrams(t, self._ngrams[0], self._ngrams[1]),\\n fn_tokens))\\n ## Count Tokens (binary if class is unfit and binarization turned on)\\n if hasattr(self, \\\"_class_state\\\") and \\\\\\n self._class_state == \\\"untrained\\\" and \\\\\\n hasattr(self, \\\"_binarize_counter\\\") and \\\\\\n self._binarize_counter:\\n fn_counts = Counter(list(set(flatten(fn_ngrams))))\\n else:\\n fn_counts = Counter(flatten(fn_ngrams))\\n return fn_counts\",\n \"def update(self, iterable):\\n for word in iterable:\\n if word in self:\\n self[word] = self[word] + 1\\n self.tokens += 1\\n else:\\n self[word] = 1\\n self.types += 1\\n self.tokens += 1\",\n \"def merge(nums1, m, nums2, n):\\r\\n while len(nums1) != m and nums1[len(nums1)-1] == 0:\\r\\n nums1.pop()\\r\\n for each in nums2:\\r\\n nums1.append(each)\\r\\n nums1.sort()\",\n \"def wordNet_similarity(sentence1, sentence2):\\r\\n # Tokenize and tag\\r\\n \\r\\n # sentence1 = pos_tag(word_tokenize(sentence1))\\r\\n sentence1=st_tagger.tag(word_tokenize(sentence1))\\r\\n \\r\\n # sentence2 = pos_tag(word_tokenize(sentence2))\\r\\n sentence2=st_tagger.tag(word_tokenize(sentence2))\\r\\n\\r\\n \\r\\n # Get the synsets for the tagged words\\r\\n #################################################\\r\\n\\r\\n # synsets1=[]\\r\\n # synsets2=[]\\r\\n # for tagged_word in sentence1:\\r\\n # print(tagged_word)\\r\\n # tagged_word = list(tagged_word)\\r\\n # synsets1.append(tagged_to_synset(tagged_word[0],tagged_word[1]))\\r\\n # for tagged_word in sentence2:\\r\\n # print(tagged_word)\\r\\n # tagged_word = list(tagged_word)\\r\\n # print(tagged_word)\\r\\n # synsets2.append(tagged_to_synset(tagged_word[0],tagged_word[1]))\\r\\n\\r\\n # The code above is the elaboration of code below\\r\\n synsets1 = [tagged_to_synset(*tagged_word) for tagged_word in sentence1]\\r\\n synsets2 = [tagged_to_synset(*tagged_word) for tagged_word in sentence2]\\r\\n \\r\\n # Filter out the Nones in the synonym set\\r\\n synsets1 = [ss for ss in synsets1 if ss]\\r\\n synsets2 = [ss for ss in synsets2 if ss]\\r\\n \\r\\n score, count = 0.0, 0\\r\\n \\r\\n###########################################################################\\r\\n # for syn1 in synsets1:\\r\\n # arr_simi_score = []\\r\\n # print('=========================================')\\r\\n # print(syn1)\\r\\n # print('----------------')\\r\\n # for syn2 in synsets2:\\r\\n # print(syn2)\\r\\n # simi_score = syn1.path_similarity(syn2)\\r\\n # print(simi_score)\\r\\n # if simi_score is not None:\\r\\n # arr_simi_score.append(simi_score)\\r\\n # print('----------------')\\r\\n # print(arr_simi_score)\\r\\n # if(len(arr_simi_score) > 0):\\r\\n # best = max(arr_simi_score)\\r\\n # print(best)\\r\\n # score += best\\r\\n # count += 1\\r\\n # # Average the values\\r\\n # print('score: ', score)\\r\\n # print('count: ', count)\\r\\n # score /= count\\r\\n\\r\\n###########################################################################\\r\\n\\r\\n for syn1 in synsets1:\\r\\n arr_simi_score = []\\r\\n # print('=========================================')\\r\\n print(\\\"Each word from Synonym se1\\\",syn1)\\r\\n # print('----------------')\\r\\n for syn2 in synsets2:\\r\\n print(\\\"Each word from Synonym se2\\\",syn2)\\r\\n # simi_score = syn1.path_similarity(syn2)\\r\\n simi_score = syn1.wup_similarity(syn2)\\r\\n print(\\\"word to word path_similarity score\\\",simi_score)\\r\\n if simi_score is not None:\\r\\n arr_simi_score.append(simi_score)\\r\\n print('----------------')\\r\\n print(arr_simi_score)\\r\\n if(len(arr_simi_score) > 0):\\r\\n best = max(arr_simi_score)\\r\\n print(\\\"best score so far\\\", best)\\r\\n score += best\\r\\n count += 1\\r\\n # Average the values\\r\\n print('score: ', score)\\r\\n print('count: ', count)\\r\\n if count!=0:\\r\\n score /= count\\r\\n else:\\r\\n score=0.0\\r\\n return score\",\n \"def merge(self, other):\\n result = IntSet(0)\\n len_first = len(self)\\n len_second = len(other)\\n if len_first >= len_second:\\n for i in range(len_second):\\n num = self._nums[i] + other[i]\\n for digit in self._digits(num):\\n result.append(digit)\\n for i in range(len_second, len_first):\\n for digit in self._digits(self._nums[i]):\\n result.append(digit)\\n else:\\n for i in range(len_first):\\n num = self._nums[i] + other[i]\\n for digit in self._digits(num):\\n result.append(digit)\\n for i in range(len_first, len_second):\\n for digit in self._digits(other[i]):\\n result.append(digit)\\n return result\",\n \"def computeWordFrequencies(self, tokens: ['token'], frequencies: {'token': int}):\\n # project2: update this method to take existing dict as parameter and modify it\\n # additionally, stopwords are not inserted in the dict;\\n # words shorter than 3 character or contains all digits are ignored\\n for token in tokens:\\n # if the key is not in dict, dict.setdefault method initiates the value at 0\\n # if token not in stopwords and len(token) >= 3 and not token.isdigit():\\n frequencies[token] = frequencies.setdefault(token, 0) + 1\",\n \"def __call__(self, tree):\\n chains = list(self._get_chains(tree))\\n chain_with_counts, total = self._count_chars(chains)\\n return self._concatenates(chain_with_counts, total)\",\n \"def make_bag_words(document_tokenized):\\n bag_words = dict()\\n for token in document_tokenized:\\n if token in bag_words.keys():\\n bag_words[token] += 1\\n else:\\n bag_words[token] = 1\\n return bag_words\",\n \"def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:\\n nums1[:]=sorted((nums1[:m]+nums2[:n]))\",\n \"def _aggregate_token_confidence(self, hypothesis: Hypothesis) -> List[float]:\\n return self._aggregate_token_confidence_subwords_sentencepiece(\\n self.decode_tokens_to_str(hypothesis.text[0]).split(), hypothesis.token_confidence, hypothesis.text[0]\\n )\",\n \"def MergeLogic(self) -> str:\",\n \"def __add__(self, other):\\n if isinstance(other, Token):\\n return Token(self.text + other.text, self.position, self.category)\\n else:\\n return Token(self.text + other, self.position, self.category)\",\n \"def _merge_majorana_terms(left_term, right_term):\\n merged_term = []\\n parity = 0\\n i, j = 0, 0\\n while i < len(left_term) and j < len(right_term):\\n if left_term[i] < right_term[j]:\\n merged_term.append(left_term[i])\\n i += 1\\n elif left_term[i] > right_term[j]:\\n merged_term.append(right_term[j])\\n j += 1\\n parity += len(left_term) - i\\n else:\\n parity += len(left_term) - i - 1\\n i += 1\\n j += 1\\n if i == len(left_term):\\n merged_term.extend(right_term[j:])\\n else:\\n merged_term.extend(left_term[i:])\\n return tuple(merged_term), parity % 2\",\n \"def tokenize_and_split_bis(sms_file):\\n \\n dic = {}\\n list1 = []\\n list2 = []\\n list3 = []\\n list4 = []\\n i = -1\\n document = 0\\n terms = 0\\n new_document = True\\n ham = True\\n for line in open(sms_file, 'r').readlines():\\n w = []\\n document += 1\\n new_document = True\\n for word in line.split():\\n i = i + 1\\n if word == \\\"ham\\\":\\n ham = True\\n i = i - 1\\n elif word == \\\"spam\\\":\\n ham = False\\n i = i - 1\\n else:\\n if word not in dic:\\n dic[word] = i\\n w.append(dic[word])\\n list3.append(1)\\n list4.append(1)\\n new_document = False\\n terms += 1\\n else : \\n i = i - 1\\n w.append(dic[word])\\n list4[dic[word]] += 1\\n terms += 1\\n if new_document: \\n list3[dic[word]] += 1\\n new_document = False\\n \\n if ham and w !=[]:\\n list2.append(w)\\n elif ham == False and w !=[]:\\n list1.append(w)\\n\\n moy = 0\\n len_dic = len(dic.keys())\\n list5 = [0 for x in range(len_dic)]\\n for key in dic.keys():\\n if list4[dic[key]] > 0:\\n tf = list4[dic[key]] / terms\\n idf = math.log(document / list3[dic[key]])\\n tfIdf = tf * idf\\n list5[dic[key]] = tfIdf\\n # print(\\\"the word \\\" + str(key) + \\\" appairs \\\" + str(list4[dic[key]]) + \\\" times.\\\")\\n # print(\\\"his frequency is \\\" + str(list4[dic[key]] / terms) )\\n # print(\\\"the word \\\" + str(key) + \\\" appairs \\\" + str(list3[dic[key]]) + \\\" times in each document.\\\")\\n # print(\\\"his frequency is \\\" + str(idf))\\n # print(\\\"utility \\\" + str(tfIdf))\\n moy += tfIdf\\n \\n moy = moy / len_dic \\n # print(moy)\\n dic_bis = {}\\n i = -1\\n for key in dic.keys():\\n value = list5[dic[key]]\\n # print(str(value))\\n if (value > oracle * moy):\\n i += 1\\n dic_bis[key] = i\\n # else:\\n # print(\\\"not pass \\\" + key + \\\" \\\" + str(value))\\n \\n \\n # print(dic_bis == dic)\\n # print(dic)\\n return dic_bis,list1,list2\",\n \"def count_token(text):\\n count=0\\n if isinstance(text, list):\\n for ayah in text:\\n count=count+ayah.count(' ')+1\\n else:\\n count=text.count(' ')+1\\n\\n return count\",\n \"def _count_word_frequency(self, data):\\n _dict = {}\\n for _docs in data:\\n for _word in _docs:\\n if _word in _dict:\\n _dict[_word] += 1\\n else:\\n _dict[_word] = 1\\n return _dict\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6380701","0.6303569","0.5749085","0.56798166","0.5565582","0.5413877","0.536061","0.53069425","0.52431405","0.52182555","0.51754475","0.5064471","0.50563955","0.50545037","0.50540966","0.5040915","0.5038194","0.5008785","0.4992635","0.49735785","0.49611026","0.49429673","0.493614","0.49267116","0.49266514","0.4926096","0.49206343","0.4916547","0.4914982","0.48957682","0.4886758","0.4882647","0.48792425","0.48681363","0.48643368","0.4856626","0.48541075","0.48491043","0.4842647","0.4832953","0.48289278","0.48150694","0.48149315","0.4809518","0.48074782","0.48039347","0.4796184","0.47961247","0.47943905","0.4783521","0.4783209","0.478138","0.47813413","0.47344458","0.47335422","0.4727909","0.47278768","0.47199097","0.47092235","0.4699797","0.4696337","0.46901244","0.4689737","0.46797112","0.46791112","0.46759784","0.4674791","0.46744743","0.46728224","0.4671541","0.46659526","0.46616957","0.46612135","0.46499792","0.4645801","0.46457154","0.4636198","0.46340084","0.46321028","0.46230415","0.4621967","0.46205276","0.46189678","0.46161854","0.46130726","0.46120495","0.46118364","0.4611151","0.46080077","0.46060258","0.4605371","0.46027038","0.4597989","0.45950627","0.4590806","0.45875224","0.45807093","0.45779058","0.45765445","0.45761585"],"string":"[\n \"0.6380701\",\n \"0.6303569\",\n \"0.5749085\",\n \"0.56798166\",\n \"0.5565582\",\n \"0.5413877\",\n \"0.536061\",\n \"0.53069425\",\n \"0.52431405\",\n \"0.52182555\",\n \"0.51754475\",\n \"0.5064471\",\n \"0.50563955\",\n \"0.50545037\",\n \"0.50540966\",\n \"0.5040915\",\n \"0.5038194\",\n \"0.5008785\",\n \"0.4992635\",\n \"0.49735785\",\n \"0.49611026\",\n \"0.49429673\",\n \"0.493614\",\n \"0.49267116\",\n \"0.49266514\",\n \"0.4926096\",\n \"0.49206343\",\n \"0.4916547\",\n \"0.4914982\",\n \"0.48957682\",\n \"0.4886758\",\n \"0.4882647\",\n \"0.48792425\",\n \"0.48681363\",\n \"0.48643368\",\n \"0.4856626\",\n \"0.48541075\",\n \"0.48491043\",\n \"0.4842647\",\n \"0.4832953\",\n \"0.48289278\",\n \"0.48150694\",\n \"0.48149315\",\n \"0.4809518\",\n \"0.48074782\",\n \"0.48039347\",\n \"0.4796184\",\n \"0.47961247\",\n \"0.47943905\",\n \"0.4783521\",\n \"0.4783209\",\n \"0.478138\",\n \"0.47813413\",\n \"0.47344458\",\n \"0.47335422\",\n \"0.4727909\",\n \"0.47278768\",\n \"0.47199097\",\n \"0.47092235\",\n \"0.4699797\",\n \"0.4696337\",\n \"0.46901244\",\n \"0.4689737\",\n \"0.46797112\",\n \"0.46791112\",\n \"0.46759784\",\n \"0.4674791\",\n \"0.46744743\",\n \"0.46728224\",\n \"0.4671541\",\n \"0.46659526\",\n \"0.46616957\",\n \"0.46612135\",\n \"0.46499792\",\n \"0.4645801\",\n \"0.46457154\",\n \"0.4636198\",\n \"0.46340084\",\n \"0.46321028\",\n \"0.46230415\",\n \"0.4621967\",\n \"0.46205276\",\n \"0.46189678\",\n \"0.46161854\",\n \"0.46130726\",\n \"0.46120495\",\n \"0.46118364\",\n \"0.4611151\",\n \"0.46080077\",\n \"0.46060258\",\n \"0.4605371\",\n \"0.46027038\",\n \"0.4597989\",\n \"0.45950627\",\n \"0.4590806\",\n \"0.45875224\",\n \"0.45807093\",\n \"0.45779058\",\n \"0.45765445\",\n \"0.45761585\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5457384"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":89298,"cells":{"query":{"kind":"string","value":"Renders photorealistic images of a droplet interface in data from Michael or Pablo's simulations, given a case config file and render config file."},"document":{"kind":"string","value":"def photorealistic(case_config_filepath, render_config_filepath):\n\n # Load config file with all common directory names\n dirname_config = configparser.ConfigParser()\n dirname_config.read(\"dirname.cfg\")\n\n # Get information from config files\n cconfd = load_config.get_config_params(case_config_filepath) # Case file\n rconfd = load_config.get_config_params(render_config_filepath) # Render file\n\n # Main output directory\n case_output = dirname_config[\"DIRECTORIES\"][\"RenderOutput\"] + cconfd[\"case_name\"] + \"/\"\n \n # Determine interface geometry output dir\n geometry_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"ply\"]\n\n # Determine individual frame output dir\n image_output_dir_spec = dircheck.count_png_dirs(case_output + dirname_config[\"DIRECTORIES\"][\"tstep_sequence_photorealistic\"])\n dircheck.check_make(image_output_dir_spec) # Make it if nonexistent\n \n # Write Blender config file\n blender_config_filedir = case_output + rconfd[\"render_name\"] + \"_blender.cfg\"\n load_config.write_config_file(config_filedir=blender_config_filedir,\n config_dict={\"image_output_dir_spec\": image_output_dir_spec,\n \"ply_input_dir\": geometry_output_dir,\n \"interface_material_name\": \"WaterMaterial5\",\n \"bg_image_filepath\": rconfd[\"bg_image_filepath\"],\n \"view_fraction\": cconfd[\"dropd\"]/rconfd[\"droplet_scale\"],\n \"render_scale\": 10,\n \"resolution_percentage\": rconfd[\"resolution_percentage\"],\n \"xres\": cconfd[\"xres\"], \"yres\": cconfd[\"yres\"], \"zres\": cconfd[\"zres\"],\n \"tres\": cconfd[\"tres\"],\n \"interface_half_enabled\": rconfd[\"interface_half_enabled\"],\n \"fog_enabled\": rconfd[\"fog_enabled\"],\n \"camera_azimuth_angle\": rconfd[\"camera_azimuth_angle\"],\n \"camera_elevation_angle\": rconfd[\"camera_elevation_angle\"],\n \"bg_color_1\": rconfd[\"bg_color_1\"], \"bg_color_2\": rconfd[\"bg_color_2\"]})\n\n # Extract droplet interface geometry\n convert_data.conv_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=geometry_output_dir, tres=int(cconfd[\"tres\"]))\n\n # Extract vapor fog (YV) if enabled\n if rconfd[\"fog_enabled\"]:\n # Determine individual fog dir and make it if necessary\n fog_halved = rconfd[\"fog_half_enabled\"]\n fog_dir_specifier = str(rconfd[\"fog_vapor_min\"]) + \"halved\" + str(fog_halved) + \"/\"\n bvox_output_dir_spec = case_output + dirname_config[\"DIRECTORIES\"][\"bvox\"] + fog_dir_specifier\n dircheck.check_make(bvox_output_dir_spec)\n # Convert fog data\n convert_data.conv_bvox(h5dns_path=cconfd[\"h5dns_path\"], output_dir=bvox_output_dir_spec, tres=int(cconfd[\"tres\"]), vapor_min=float(rconfd[\"fog_vapor_min\"]), fog_halved=fog_halved)\n # Add fog dir to Blender config file\n load_config.write_config_file(config_filedir=blender_config_filedir, config_dict={\"bvox_input_dir\": bvox_output_dir_spec}, append_config=True)\n \n # Launch Blender to perform rendering\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\"droplet_render.py\", blend_name=\"droplet_render.blend\")"},"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 surf_tempmap(case_config_filepath, render_config_filepath):\n\n # Load config file with all common directory names\n dirname_config = configparser.ConfigParser()\n dirname_config.read(\"dirname.cfg\")\n\n # Get information from config files\n cconfd = load_config.get_config_params(case_config_filepath)\n rconfd = load_config.get_config_params(render_config_filepath)\n\n # Main output directory\n case_output = dirname_config[\"DIRECTORIES\"][\"RenderOutput\"] + cconfd[\"case_name\"] + \"/\"\n \n # Determine surface temperature geometry output dir\n ply_temp_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"ply_temp\"]\n\n # Get temperature bounds\n if rconfd[\"temp_bounds_auto\"]:\n temp_min, temp_max = convert_data.temp_bounds(h5dns_path=cconfd[\"h5dns_path\"],\n ply_temp_output_dir=ply_temp_output_dir,\n prc_min=rconfd[\"temp_min_percentile\"],\n prc_max=rconfd[\"temp_max_percentile\"])\n else:\n temp_min = rconfd[\"temp_min\"]\n temp_max = rconfd[\"temp_max\"]\n\n # Determine specific output dirs and make them if necessary\n image_output_dir_spec = dircheck.count_png_dirs(case_output + dirname_config[\"DIRECTORIES\"][\"tstep_sequence_surftempmap\"])\n ply_temp_output_dir_spec = ply_temp_output_dir + str(temp_min) + \"to\" + str(temp_max)\n dircheck.check_make([ply_temp_output_dir_spec, image_output_dir_spec])\n\n # Write Blender config file\n blender_config_filedir = case_output + rconfd[\"render_name\"] + \"_blender.cfg\"\n load_config.write_config_file(config_filedir=blender_config_filedir,\n config_dict={\"image_output_dir_spec\": image_output_dir_spec,\n \"ply_input_dir\": ply_temp_output_dir_spec,\n \"interface_material_name\": \"heatmapMaterial\",\n \"bg_image_filepath\": rconfd[\"bg_image_filepath\"],\n \"view_fraction\": cconfd[\"dropd\"]/rconfd[\"droplet_scale\"],\n \"render_scale\": 10,\n \"resolution_percentage\": rconfd[\"resolution_percentage\"],\n \"xres\": cconfd[\"xres\"], \"yres\": cconfd[\"yres\"], \"zres\": cconfd[\"zres\"],\n \"tres\": cconfd[\"tres\"],\n \"interface_half_enabled\": False,\n \"fog_enabled\": False,\n \"camera_azimuth_angle\": rconfd[\"camera_azimuth_angle\"],\n \"camera_elevation_angle\": rconfd[\"camera_elevation_angle\"],\n \"bg_color_1\": rconfd[\"bg_color_1\"], \"bg_color_2\": rconfd[\"bg_color_2\"]})\n\n # Extract droplet interface geometry\n ply_output_dir_uncolored = case_output + dirname_config[\"DIRECTORIES\"][\"ply\"]\n convert_data.conv_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=ply_output_dir_uncolored, tres=cconfd[\"tres\"])\n\n # Add surface temperature color to droplet interface\n convert_data.conv_color_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=ply_temp_output_dir_spec, uncolored_ply_dir=ply_output_dir_uncolored, tres=cconfd[\"tres\"], temp_min=temp_min, temp_max=temp_max)\n\n # Launch Blender to perform rendering\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\"droplet_render.py\", blend_name=\"droplet_render.blend\")\n\n # Add temperature legend colorbar to images\n if rconfd[\"add_temp_bar\"]:\n imedit.add_tempmap(bound_min=temp_min*cconfd[\"tgas\"], bound_max=temp_max*cconfd[\"tgas\"], image_dir=image_output_dir_spec, tres=cconfd[\"tres\"])","def main(config):\n file_paths_info = [('GLOFRIS','WATCH','ARG_inunriver_historical_000000000WATCH_1980_rp01000.tif'),\n \t\t\t\t('GLOFRIS','RCP45','ARG_inunriver_rcp4p5_0000GFDL-ESM2M_2030_rp01000.tif'),\n \t\t\t\t('GLOFRIS','RCP85','ARG_inunriver_rcp8p5_0000GFDL-ESM2M_2030_rp01000.tif'),\n \t\t\t\t('FATHOM','AR_fluvial_undefended_merged','AR-FU-1000.tif'),\n \t\t\t\t('FATHOM','AR_pluvial_undefended_merged','AR-PU-1000.tif')\n \t\t\t\t]\n figure_names = ['GLOFRIS-WATCH-fluvial','GLOFRIS-RCP45-fluvial','GLOFRIS-RCP85-fluvial','FATHOM-fluvial','FATHOM-pluvial']\n figure_titles = ['current fluvial flooding','RCP4.5 fluvial flooding','RCP8.5 fluvial flooding','current fluvial flooding','current pluvial flooding']\n for f_i in range(len(file_paths_info)):\n\t hazard_file = os.path.join(config['paths']['data'],'flood_data', file_paths_info[f_i][0],file_paths_info[f_i][1],file_paths_info[f_i][2])\n\t output_file = os.path.join(config['paths']['figures'], 'flood-map-{}.png'.format(figure_names[f_i]))\n\t ax = get_axes()\n\t plot_basemap(ax, config['paths']['data'])\n\t scale_bar(ax, location=(0.8, 0.05))\n\t plot_basemap_labels(ax, config['paths']['data'], include_regions=True,include_zorder=3)\n\n\t proj_lat_lon = ccrs.PlateCarree()\n\n\n\t # Create color map\n\t colors = plt.get_cmap('Blues')\n\n\t # Read in raster data\n\t data, lat_lon_extent = get_data(hazard_file)\n\t data[(data <= 0) | (data > 5)] = np.nan\n\t max_val = np.nanmax(data)\n\t norm=mpl.colors.Normalize(vmin=0, vmax=max_val)\n\n\t # Plot population data\n\t im = ax.imshow(data, extent=lat_lon_extent,transform=proj_lat_lon, cmap=colors,norm =norm, zorder=2)\n\n\t # Add colorbar\n\t cbar = plt.colorbar(im, ax=ax,fraction=0.1, shrink=0.87,pad=0.01, drawedges=False, orientation='horizontal',\n\t norm=mpl.colors.Normalize(vmin=0, vmax=max_val), ticks=list(np.linspace(0,max_val,3)))\n\t cbar.set_clim(vmin=0,vmax=max_val)\n\n\n\t cbar.outline.set_color(\"none\")\n\t cbar.ax.yaxis.set_tick_params(color='black')\n\t cbar.ax.set_xlabel('Flood depths (m)',fontsize=12,color='black')\n\n\t plt.title('1 in 1000 year {}'.format(figure_titles[f_i]), fontsize = 14)\n\t save_fig(output_file)\n\t plt.close()","def generate_cfg():\n \n if not os.path.exists(cfg_path):\n os.mkdir(cfg_path)\n \n for img_path in get_template_paths():\n extractor = BlockExtractor(img_path)\n extractor.get_cfg()\n for block in extractor.get_blocks():\n img = BlockParser(img_path, block).block_image()\n #cv.imshow(\"Block\", img)\n #cv.waitKey() & 0xFF","def imdisplay(filename, representation):\n img = read_image(filename, representation)\n if representation == GS_REP:\n plt.imshow(img, cmap=plt.cm.gray)\n else:\n plt.imshow(img)","def lambda2(case_config_filepath, render_config_filepath):\n\n # Load config file with all common directory names\n dirname_config = configparser.ConfigParser()\n dirname_config.read(\"dirname.cfg\")\n\n # Get information from config files\n cconfd = load_config.get_config_params(case_config_filepath)\n rconfd = load_config.get_config_params(render_config_filepath)\n \n # Main output directory\n case_output = dirname_config[\"DIRECTORIES\"][\"RenderOutput\"] + cconfd[\"case_name\"] + \"/\"\n\n # Determine lambda2 output directories and make them if necessary\n lambda2_level = rconfd[\"lambda2_level\"]\n lambda2_specifier = \"l2\" + str(lambda2_level) + \"/\"\n geometry_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"ply\"]\n ply_lambda2_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"ply_lambda2\"] + lambda2_specifier\n image_lambda2_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"tstep_lambda2\"] + lambda2_specifier\n image_lambda2_output_dir_spec = dircheck.count_png_dirs(image_lambda2_output_dir)\n dircheck.check_make([ply_lambda2_output_dir, image_lambda2_output_dir_spec])\n\n # Write Blender config file\n blender_config_filedir = case_output + rconfd[\"render_name\"] + \"_blender.cfg\"\n load_config.write_config_file(config_filedir=blender_config_filedir,\n config_dict={\"image_output_dir_spec\": image_lambda2_output_dir_spec,\n \"ply_input_dir\": ply_lambda2_output_dir,\n \"interface_material_name\": \"Lambda2Contour\",\n \"bg_image_filepath\": rconfd[\"bg_image_filepath\"],\n \"view_fraction\": rconfd[\"view_fraction\"], \"render_scale\": 10,\n \"resolution_percentage\": rconfd[\"resolution_percentage\"],\n \"xres\": cconfd[\"xres\"], \"yres\": cconfd[\"yres\"], \"zres\": cconfd[\"zres\"],\n \"tres\": cconfd[\"tres\"], \"interface_half_enabled\": False,\n \"fog_enabled\": False,\n \"camera_azimuth_angle\": rconfd[\"camera_azimuth_angle\"],\n \"camera_elevation_angle\": rconfd[\"camera_elevation_angle\"],\n \"bg_color_1\": rconfd[\"bg_color_1\"], \"bg_color_2\": rconfd[\"bg_color_2\"]})\n\n # Extract droplet geometry\n # convert_data.conv_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=geometry_output_dir, tres=int(cconfd[\"tres\"]))\n\n # Extract lambda2 contour geometry\n convert_data.conv_lambda2_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=ply_lambda2_output_dir, tres=cconfd[\"tres\"], contour_level=lambda2_level)\n\n # Launch Blender to perform rendering\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\"droplet_render.py\", blend_name=\"droplet_render.blend\")","def imdisplay(filename, representation):\n image = read_image(filename, representation)\n\n if representation == GRAY_OUT:\n plt.imshow(image, cmap='gray')\n else:\n plt.imshow(image)\n\n plt.show()","def imdisplay(filename, representation):\n im = read_image(filename, representation)\n if representation == 1:\n plt.imshow(im, cmap='gray')\n plt.show()\n if representation == 2:\n plt.imshow(im)\n plt.show()","def main(argv):\n\n epd = EPD()\n\n epd.clear()\n\n print('panel = {p:s} {w:d} x {h:d} version={v:s}'.format(p=epd.panel, w=epd.width, h=epd.height, v=epd.version))\n\n for file_name in argv:\n if not os.path.exists(file_name):\n sys.exit('error: image file{f:s} does not exist'.format(f=file_name))\n print('display: {f:s}'.format(f=file_name))\n display_file(epd, file_name)","def main():\n dpi = 1\n dpi = 2\n width = int(360)\n height = int(130)\n mywidth = int(width*dpi)\n myheight = int(height*dpi)\n FWHM = 7.5 # degrees\n FWHM = 10.0 # degrees\n FWHM = 5.0 # degrees\n FWHM = 3.0 # degrees\n FWHM = 1.0 # degrees\n weight = 1.\n\n nargs = len(sys.argv)\n if nargs < 2:\n print('GR: GRid Observations of integrated intensity produced by the T Command')\n print('GR produces fits images for each of the horns used for the observations.')\n print('For observations at the same coordinates, the ratios of intensities are also produced.')\n print('The FITS format files require header information, which is copied from the')\n print('Cold Load File provided by the user')\n print('GR RA|GAL <cold file name> <savefile1> [<savefile2> ... <savefileN>]')\n print(\"\")\n print('Glen Langston, National Science Foundation -- 20 May 12')\n exit()\n\n gridtype = sys.argv[1]\n gridtype = gridtype.upper()\n print('Grid Type: ', gridtype)\n\n # enable having ra going from 24 to 0 hours == 360 to 0 degrees\n xsign = 1.\n xoffset = 0.\n if gridtype == 'RA':\n xmin = 0.\n xmax = 360.\n ymin = -40.\n ymax = 90.\n maptype = 'RA'\n elif gridtype == '-RA':\n xmin = 0.\n xmax = 360.\n ymin = -40.\n ymax = 90.\n xsign = -1.\n xoffset = 360. # when x = 360. should be at zero.\n maptype = 'RA'\n elif gridtype == '-EL':\n xmin = 0.\n xmax = 360.\n ymin = 0.\n ymax = 90.\n xsign = -1.\n xoffset = 360. # when x = 360. should be at zero.\n maptype = 'AZEL'\n elif gridtype == 'RA0':\n xmin = 0.\n xmax = 360.\n ymin = -41.\n ymax = 89.\n xsign = -1.\n xoffset = 180. # when x = 360. should be at zero.\n gridtype = 'RA'\n elif gridtype == 'GAL':\n xmin = -180.\n xmax = 180.\n ymin = -90.\n ymax = 90.\n maptype = 'GAL'\n\n if gridtype != 'RA' and gridtype != 'GAL' and gridtype != '-RA' and gridtype != \"RA0\":\n print('Error parsing grid type: ', gridtype)\n print('1st argument should be either RA, -RA or GAL')\n exit()\n\n rs = radioastronomy.Spectrum()\n\n if doRatio: \n #create the grid with map parameters\n grid1 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n grid2 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n grid3 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n grid4 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n # put each telescope in a different grid\n grids = [grid1, grid2, grid3, grid4]\n\n gridall = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n \n\n projection = \"-AIT\"\n# coldfile \n coldfile = sys.argv[2]\n# get telescope geographic location etc\n print(\"Reading Observing parameters from: %s\" % (coldfile))\n rs.read_spec_ast(coldfile)\n print(\"Observer: %s \" % (rs.observer))\n\n# first read through all data and find hot load\n names = sys.argv[3:]\n names = sorted(names)\n\n firsttime = \"\"\n lasttime = \"\"\n count = 0\n # setup grid indicies so that cpuIndex goes to the correct grid\n # This assumes telescopes 2,3,4,5 are being used] \n gridIndex = [0,0,0,1,2,3]\n # for all save Files to Grid\n for filename in names:\n print(\"File: %s\" % (filename))\n f = open(filename)\n\n date = \"Unknown\"\n while date != \"\":\n date, time, cpuIndex, telaz, telel, tSys, tRx, tRms, tint, KperC, tSourcemax, velSource, dV, tVSum, tVSumRms, tSumKmSec, dTSumKmSec, gainFactor = gainfactor.readSaveValues( f)\n dlen = len(date)\n if dlen < 1:\n break\n if date[0] == \"#\":\n continue\n # else not a comment process the line\n count = count + 1\n isodate = \"20\"+date+\"T\"+time\n# print(\"DateTime: %s\" % (isodate))\n rs.utc = datetime.datetime.strptime(isodate,\"%Y-%m-%dT%H:%M:%S\")\n# print(\"Utc: %s\" % (rs.utc))\n rs.telaz = telaz\n rs.telel = telel\n rs.azel2radec()\n\n ra = rs.ra\n dec = rs.dec\n lon = rs.gallon\n lat = rs.gallat\n tsum = tSumKmSec\n tsdv = dTSumKmSec\n tmax = tSourcemax\n vave = tVSum\n vsdv = tVSumRms\n if firsttime == \"\":\n firsttime = date\n else:\n lasttime = date\n\n# if vave > -100. and vave < 100:\n# mygrid.convolve( lon, lat, vave, 1.)\n iGrid = gridIndex[cpuIndex]\n gainCorr = telescopefactors[iGrid]\n tsum = tsum * gainCorr\n if gridtype == 'RA':\n if doRatio:\n grids[iGrid].convolve(ra, dec, tsum, weight)\n gridall.convolve( ra, dec, tsum, weight)\n elif gridtype == '-RA':\n x = (ra*xsign) + xoffset\n if doRatio:\n grids[iGrid].convolve(x, dec, tsum, weight)\n gridall.convolve( x, dec, tsum, weight)\n elif gridtype == 'RA0':\n x = (ra*xsign) + xoffset\n if x < 0:\n x = x + xmax\n elif x > xmax:\n x = x - xmax\n if doRatio:\n grids[iGrid].convolve(x, dec, tsum, weight)\n gridall.convolve( x, dec, tsum, weight)\n else:\n if doRatio:\n grids[iGrid].convolve(lon, lat, tsum, weight)\n gridall.convolve( lon, lat, tsum, weight)\n\n if count == 0:\n print('Convolving Coordinates: ', ra, dec, lon, lat)\n print('Convolving Intensities: ', tsum, tsdv, vave, vsdv)\n print('Convolvign Parameters : ', n, time)\n count = count + 1\n # end reading all lines in save file\n f.close()\n\n # normalize each of the gridded images\n if doRatio:\n grids[0].normalize()\n grids[1].normalize()\n grids[2].normalize()\n grids[3].normalize()\n gridall.normalize()\n# mygrid.check()\n# zmin = -1000.\n# zmax = 3000.\n# limit grid intensities for plotting\n# mygrid.set_ij( 0, 0, zmax, 1.)\n# mygrid.set_ij( 1, 1, zmin, 1.)\n# mygrid.limit(zmin, zmax)\n\n subplots = False\n\n if subplots:\n fig, ax = plt.subplots(figsize=(myheight, mywidth), dpi=dpi)\n\n if gridtype == 'RA':\n cax = fig.add_axes([-180, 180], [-90, 90])\n else:\n cax = fig.add_axes([0, 24], [-90, 90])\n\n cbar = fig.colorbar(cax, ticks=[zmin, zmax], orientation='horizontal')\n cbar.ax.set_yticklabels([str(zmin), str(zmax)])\n\n ax.set_title(\"Citizen Science: Horn observations of our Galaxy\")\n else:\n#y_ticks = ymin + (ymax-ymin)*ticks/myheight\n\n ticks = np.arange(0, mywidth, 30*dpi)\n x_ticks = xmin + ((xmax-xmin)*ticks/mywidth)\n\n plt.imshow(gridall.image, interpolation='nearest', cmap=plt.get_cmap('jet'))\n\n if firsttime != lasttime:\n plt.title(\"Citizen Science: Observing our Galaxy: %s to %s\" % (firsttime, lasttime))\n else:\n plt.title(\"Citizen Science: Observing our Galaxy: %s\" % (firsttime))\n if gridtype == 'RA':\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Declination (degrees)\")\n labels = ticks/(mywidth/24)\n yticks = np.arange(0, myheight, 15*dpi)\n elif gridtype == '-RA':\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Declination (degrees)\")\n labels = 24 - (ticks/(mywidth/24))\n labels[0] = 0\n labels[0] = 24\n yticks = np.arange(0, myheight, 15*dpi)\n elif gridtype == '-EL':\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Elevation (degrees)\")\n labels = 24 - (ticks/(mywidth/24))\n labels[0] = 0\n labels[0] = 24\n yticks = np.arange(0, myheight, 15*dpi)\n elif gridtype == 'RA0': # put 0 hours in middle of plot\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Declination (degrees)\")\n labels = 12 - (ticks/(mywidth/24))\n nlabels = len(labels)\n for iii in range(nlabels):\n if labels[iii] < 0:\n labels[iii] = 24 + labels[iii]\n if labels[iii] == 24:\n labels[iii] = 0\n yticks = np.arange(0, myheight, 15*dpi)\n else:\n yticks = np.arange(0, myheight, 30*dpi)\n ticks = np.arange(0, mywidth, 30*dpi)\n x_ticks = xmin + (xmax-xmin)*ticks/mywidth\n labels = x_ticks\n plt.xlabel(\"Galactic Longitude (degrees)\")\n plt.ylabel(\"Galactic Latitude (degrees)\")\n # wnat an integer list of labels\n# slabels = str(labels)\n print(ticks, labels)\n y_ticks = ymax - (ymax-ymin)*yticks/myheight\n plt.yticks(yticks, y_ticks)\n plt.xticks(ticks, labels, rotation='horizontal')\n plt.colorbar()\n\n crval2 = (xmin + xmax)/2.\n crval1 = (ymin + ymax)/2.\n cdelt1 = (-1./float(dpi)) - .001\n cdelt2 = (1./float(dpi)) + .001\n if doRatio:\n# now show eacsh of the images\n for iGrid in range(4):\n imagetemp = copy.deepcopy(grids[iGrid].image)\n imagetemp2 = copy.deepcopy(grids[iGrid].image)\n kkk = myheight - 1\n for jjj in range(myheight):\n imagetemp[:][kkk] = imagetemp2[:][jjj]\n kkk = kkk - 1\n grids[iGrid].image = imagetemp\n writeFitsImage( rs, iGrid+2, grids[iGrid], projection)\n\n # put each telescope in a different grid\n ratio1 = copy.deepcopy(grid1)\n ratio2 = copy.deepcopy(grid1)\n ratio3 = copy.deepcopy(grid1)\n gratios = [ratio1, ratio2, ratio3]\n ratios = np.zeros(3)\n rmss = np.zeros(3)\n\n jGrid = 3\n for iGrid in range(3):\n print(\"Gain Ratios for Telescopes T%d and T%d\" % (iGrid+2, jGrid+2))\n ratio, rms, aratio = gridratio(grids[iGrid], grids[jGrid])\n ratios[iGrid] = ratio\n rmss[iGrid] = rms\n writeFitsImage( rs, iGrid+2, aratio, projection)\n \n writeFitsImage( rs, 0, gridall, projection)\n plt.show()","def imdisplay(filename, representation):\n\n image = read_image(filename, representation)\n plt.imshow(image, cmap=\"gray\")\n plt.show()","def draw(src_data: dict, config: dict):\n puml = '@startuml'\n\n puml_inits = ''\n puml_connections = ''\n for diagram_config in config['diagrams']:\n puml = pumlify.generate_puml(src_data, diagram_config['mapping_scheme'])\n # todo: ensure this works in windows\n write_file(puml, os.path.join('./docs', 'src-map.puml'))","def step_run(cls, config):\n\n nir_paw_image_fname = config.get(cls.step_name, 'nir_paw_image')\n nir_paw_conf_fname = config.get(cls.step_name, 'nir_paw_conf')\n output_template = config.get(cls.step_name, 'output_template')\n conf_limit = config.getfloat(cls.step_name, 'conf_limit')\n\n# logger.info('reading Comparison image from %s', comp_fname)\n# comp_im = DESImage.load(comp_fname)\n\n ret_code = cls.__call__(nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit)\n return ret_code","def run(self):\n # read (x,y) - Set SIFT parameters\n action = self.cfg['param']['action']\n x = float(self.cfg['param']['x']) \n # Expressed en PIL coordinates system y| x-\n y = float(self.cfg['param']['y'])\n \n # read image size and store in 'param' dict to control html rendering\n work_dir = self.work_dir\n [w1, h1] = Image.open(work_dir+'input_0.orig.png').size\n [w2, h2] = Image.open(work_dir+'input_1.orig.png').size\n wdth = max(w1, w2)\n hght = max(h1, h2)\n wpair = int(w1+w2+max(w1, w2)/10)\n self.cfg['param']['hght'] = hght\n self.cfg['param']['wdth'] = wdth\n self.cfg.save()\n # Convert x y provided by the form <input type=image ..; > \n # We assume that the width of the html body is assumed width is 920px\n x = x*wpair/920\n y = y*wpair/920\n self.cfg['param']['x'] = x\n self.cfg['param']['y'] = y\n self.cfg.save()\n if (action == 'std_sift_matching'):\n try:\n self.load_standard_parameters()\n self.run_std_sift()\n self.run_matching()\n self.illustrate_std_sift()\n self.illustrate_matching()\n except TimeoutError:\n return self.error(errcode='timeout',\n errmsg=\"Try again with simpler images.\")\n except RuntimeError:\n return self.error(errcode='runtime',\n errmsg=\"Runtime error in std_sift_matching.\")\n elif (action == \"cust_sift_matching\"):\n try:\n self.run_sift()\n print \"after run_sift()\"\n self.run_matching()\n print \"after run_matching()\"\n self.illustrate_sift()\n print \"after illustrate_sift()\"\n self.illustrate_matching()\n self.detail_matching()\n except TimeoutError:\n return self.error(errcode='timeout',\n errmsg=\"Try with simpler images.\")\n except RuntimeError:\n return self.error(errcode='runtime',\n errmsg=\"Runtime error in cust_sift_matching.\")\n elif (action == \"cust_matching\"): \n try:\n self.run_matching()\n self.illustrate_matching()\n except TimeoutError:\n return self.error(errcode='timeout',\n errmsg=\"Try with simpler images.\")\n except RuntimeError:\n return self.error(errcode='runtime',\n errmsg=\"Runtime error in cust_matching.\")\n else:\n try:\n self.detail_matching()\n except TimeoutError:\n return self.error(errcode='timeout',\n errmsg=\"Try with simpler images.\")\n except RuntimeError:\n return self.error(errcode='runtime',\n errmsg=\"Runtime error in else (you know).\")\n ## archive\n if self.cfg['meta']['original']:\n ar = self.make_archive()\n ar.add_file(\"input_0.png\", info=\"first input image\")\n ar.add_file(\"input_1.png\", info=\"second input image\")\n ar.add_file(\"input_0.orig.png\", info=\"first uploaded image\")\n ar.add_file(\"input_1.orig.png\", info=\"second uploaded image\")\n ar.add_file(\"OUTmatches.png\", info=\"matches\")\n ar.add_file(\"keys_im0.txt\", compress=True)\n ar.add_file(\"keys_im1.txt\", compress=True)\n ar.add_file(\"OUTmatches.txt\", compress=True)\n ar.save()\n\n self.cfg.save()\n http.redir_303(self.base_url + 'result?key=%s' % self.key)\n return self.tmpl_out(\"run.html\")","def render(filename,i):\n print('running render')\n A = np.genfromtxt(filename,skip_header=1,dtype=float,delimiter=',')\n img = np.array(A[i,:],copy=True)\n print(img.shape)\n img = img.reshape(28,28)\n img = 255 - img\n print(img.shape)\n plt.imshow(img, cmap=\"gray\", vmin=0, vmax=255)\n plt.savefig(\"img\" + str(i)+\"render\"+ \".png\")","def render_viewer(configfile, datafile):\n try:\n page = 'analogviewer.html'\n with open(page, 'w') as fp:\n fp.write(templating.render_template(\n page,\n configfile=configfile,\n datafile=datafile,\n # static_url=config.output_url() + '/static'))\n static_url='../static'))\n prepare_static_folder()\n except Exception:\n msg = \"Failed to render analogviewer.\"\n LOGGER.exception(msg)\n raise Exception(msg)\n else:\n return page","def image_capture_demo():\n return render_template('image_capture_demo.html')","def plot_bit_for_bit(case, var_name, model_data, bench_data, diff_data):\n plot_title = \"\"\n plot_name = case + \"_\" + var_name + \".png\"\n plot_path = os.path.join(os.path.join(livvkit.output_dir, \"verification\", \"imgs\"))\n functions.mkdir_p(plot_path)\n m_ndim = np.ndim(model_data)\n b_ndim = np.ndim(bench_data)\n if m_ndim != b_ndim:\n return \"Dataset dimensions didn't match!\"\n if m_ndim == 3:\n model_data = model_data[-1]\n bench_data = bench_data[-1]\n diff_data = diff_data[-1]\n plot_title = \"Showing \"+var_name+\"[-1,:,:]\"\n elif m_ndim == 4:\n model_data = model_data[-1][0]\n bench_data = bench_data[-1][0]\n diff_data = diff_data[-1][0]\n plot_title = \"Showing \"+var_name+\"[-1,0,:,:]\"\n plt.figure(figsize=(12, 3), dpi=80)\n plt.clf()\n\n # Calculate min and max to scale the colorbars\n _max = np.amax([np.amax(model_data), np.amax(bench_data)])\n _min = np.amin([np.amin(model_data), np.amin(bench_data)])\n\n # Plot the model output\n plt.subplot(1, 3, 1)\n plt.xlabel(\"Model Data\")\n plt.ylabel(var_name)\n plt.xticks([])\n plt.yticks([])\n plt.imshow(model_data, vmin=_min, vmax=_max, interpolation='nearest', cmap=colormaps.viridis)\n plt.colorbar()\n\n # Plot the benchmark data\n plt.subplot(1, 3, 2)\n plt.xlabel(\"Benchmark Data\")\n plt.xticks([])\n plt.yticks([])\n plt.imshow(bench_data, vmin=_min, vmax=_max, interpolation='nearest', cmap=colormaps.viridis)\n plt.colorbar()\n\n # Plot the difference\n plt.subplot(1, 3, 3)\n plt.xlabel(\"Difference\")\n plt.xticks([])\n plt.yticks([])\n plt.imshow(diff_data, interpolation='nearest', cmap=colormaps.viridis)\n plt.colorbar()\n\n plt.tight_layout(rect=(0, 0, 0.95, 0.9))\n plt.suptitle(plot_title)\n\n plot_file = os.path.sep.join([plot_path, plot_name])\n if livvkit.publish:\n plt.savefig(os.path.splitext(plot_file)[0]+'.eps', dpi=600)\n plt.savefig(plot_file)\n plt.close()\n return os.path.join(os.path.relpath(plot_path,\n os.path.join(livvkit.output_dir, \"verification\")),\n plot_name)","def plot(path, subjects):\n transformToXYZmm = np.array([[-3.125, 0, 0, 81.250], [0, 3.125, 0, -115.625], [0, 0, 6, -54.000], [0, 0, 0, 1.000]])\n data = data_load.load_data(path, subjects)\n dimx = int(data[0][\"meta\"][\"dimx\"][0])\n dimy = int(data[0][\"meta\"][\"dimy\"][0])\n dimz = int(data[0][\"meta\"][\"dimz\"][0])\n coordToCol = data[0][\"meta\"][\"coordToCol\"][0][0]\n images = {}\n max_val = 0\n voxels = np.load(\"data/general_selected_500_1.npy\")\n directory = os.listdir(\"data/input/\")\n bar = pyprind.ProgBar(len(directory), title='Info extraction and Image Building')\n bar2 = pyprind.ProgBar(len(images.keys()), title='Saving Pictures')\n for file in directory:\n file_name = \"data/input/{}\".format(file)\n fh = open(file_name)\n activation_values = np.asarray(list(map(lambda x: float(x), filter(lambda x: x != '', fh.read().split(\",\")))))\n fh.close()\n plot_matrix = np.zeros((dimx, dimy, dimz))\n for x in range(dimx):\n for y in range(dimy):\n for z in range(dimz):\n indice = coordToCol[x][y][z]\n if indice != 0:\n if indice in list(voxels):\n voxel_indice = list(voxels).index(indice)\n value = activation_values[voxel_indice]\n if abs(value) > max_val:\n max_val = abs(value)\n plot_matrix[x][y][z] = value\n image = nib.Nifti1Image(plot_matrix, transformToXYZmm)\n images[file_name] = image\n bar.update(force_flush=True)\n print(bar)\n for image in images:\n plotting.plot_glass_brain(images[image], display_mode='ortho', vmax=max_val, plot_abs=False, threshold=None, colorbar=True, output_file=\"{}-wom1.png\".format(image))\n bar2.update(force_flush=True)\n print(bar2)","def display_file(epd, file_name):\n\n image = Image.open(file_name)\n image = ImageOps.grayscale(image)\n\n # crop to the middle\n w,h = image.size\n x = w / 2 - epd.width / 2\n y = h / 2 - epd.height / 2\n\n cropped = image.crop((x, y, x + epd.width, y + epd.height))\n bw = cropped.convert(\"1\", dither=Image.FLOYDSTEINBERG)\n\n epd.display(bw)\n epd.update()\n\n\n time.sleep(3) # delay in seconds\n\n rs = image.resize((epd.width, epd.height))\n bw = rs.convert(\"1\", dither=Image.FLOYDSTEINBERG)\n\n epd.display(bw)\n epd.update()\n\n time.sleep(3) # delay in seconds","def render(sim_file: str, only_stuck: bool) -> None:\n import DLA\n DLA.GREEN = (0, 0, 0) # type: ignore\n DLA.WHITE = (151, 151, 151, 150) # type: ignore\n from DLA import config\n config.USE_PYGAME = True # type: ignore\n from DLA import renderer\n renderer.render(Path(sim_file), only_stuck)","def main(argv):\n logging.basicConfig(format=\"%(message)s\", level=logging.INFO, stream=sys.stdout)\n logger = logging.getLogger(\"demo4\")\n\n # Define some parameters we'll use below and make directories if needed.\n cat_file_name = os.path.join('input','galsim_default_input.asc')\n if not os.path.isdir('output'):\n os.mkdir('output')\n multi_file_name = os.path.join('output','multi.fits')\n\n random_seed = galsim.BaseDeviate(8241573).raw()\n sky_level = 1.e6 # ADU / arcsec^2\n pixel_scale = 1.0 # arcsec / pixel (size units in input catalog are pixels)\n gal_flux = 1.e6 # arbitrary choice, makes nice (not too) noisy images\n gal_g1 = -0.009 #\n gal_g2 = 0.011 #\n\n # the fraction of flux in each component\n # 40% is in the bulge, 60% in a disk. 70% of that disk light is placed\n # into point sources distributed as a random walk\n\n bulge_frac = 0.4\n disk_frac = 0.6\n knot_frac = 0.42\n smooth_disk_frac = 0.18\n\n # number of knots of star formation. To simulate a nice irregular (all the\n # flux is in knots) we find ~100 is a minimum number needed, but we will\n # just use 10 here to make the demo run fast.\n\n n_knots = 10\n\n xsize = 64 # pixels\n ysize = 64 # pixels\n\n logger.info('Starting demo script 4 using:')\n logger.info(' - parameters taken from catalog %r',cat_file_name)\n logger.info(' - Moffat PSF (parameters from catalog)')\n logger.info(' - pixel scale = %.2f',pixel_scale)\n logger.info(' - Bulge + Disc galaxies (parameters from catalog)')\n logger.info(' - 100 Point sources, distributed as random walk')\n logger.info(' - Applied gravitational shear = (%.3f,%.3f)',gal_g1,gal_g2)\n logger.info(' - Poisson noise (sky level = %.1e).', sky_level)\n\n # Read in the input catalog\n cat = galsim.Catalog(cat_file_name)\n\n\n # save a list of the galaxy images in the \"images\" list variable:\n images = []\n for k in range(cat.nobjects):\n # Initialize the (pseudo-)random number generator that we will be using below.\n # Use a different random seed for each object to get different noise realizations.\n # Using sequential random seeds here is safer than it sounds. We use Mersenne Twister\n # random number generators that are designed to be used with this kind of seeding.\n # However, to be extra safe, we actually initialize one random number generator with this\n # seed, generate and throw away two random values with that, and then use the next value\n # to seed a completely different Mersenne Twister RNG. The result is that successive\n # RNGs created this way produce very independent random number streams.\n rng = galsim.BaseDeviate(random_seed+k+1)\n\n # Take the Moffat beta from the first column (called 0) of the input catalog:\n # Note: cat.get(k,col) returns a string. To get the value as a float, use either\n # cat.getFloat(k,col) or float(cat.get(k,col))\n beta = cat.getFloat(k,0)\n # A Moffat's size may be either scale_radius, fwhm, or half_light_radius.\n # Here we use fwhm, taking from the catalog as well.\n fwhm = cat.getFloat(k,1)\n # A Moffat profile may be truncated if desired\n # The units for this are expected to be arcsec (or specifically -- whatever units\n # you are using for all the size values as defined by the pixel_scale).\n trunc = cat.getFloat(k,4)\n # Note: You may omit the flux, since the default is flux=1.\n psf = galsim.Moffat(beta=beta, fwhm=fwhm, trunc=trunc)\n\n # Take the (e1, e2) shape parameters from the catalog as well.\n psf = psf.shear(e1=cat.getFloat(k,2), e2=cat.getFloat(k,3))\n\n # Galaxy is a bulge + disk(+knots) with parameters taken from the catalog:\n\n # put some fraction of the disk light into knots of star formation\n\n disk_hlr = cat.getFloat(k,5)\n disk_e1 = cat.getFloat(k,6)\n disk_e2 = cat.getFloat(k,7)\n bulge_hlr = cat.getFloat(k,8)\n bulge_e1 = cat.getFloat(k,9)\n bulge_e2 = cat.getFloat(k,10)\n\n smooth_disk = galsim.Exponential(flux=smooth_disk_frac, half_light_radius=disk_hlr)\n\n knots = galsim.RandomKnots(n_knots, half_light_radius=disk_hlr, flux=knot_frac, rng=rng)\n\n disk = galsim.Add([smooth_disk, knots])\n disk = disk.shear(e1=disk_e1, e2=disk_e2)\n\n # the rest of the light goes into the bulge\n bulge = galsim.DeVaucouleurs(flux=bulge_frac, half_light_radius=bulge_hlr)\n bulge = bulge.shear(e1=bulge_e1, e2=bulge_e2)\n\n # The flux of an Add object is the sum of the component fluxes.\n # Note that in demo3.py, a similar addition was performed by the binary operator \"+\".\n gal = galsim.Add([disk, bulge])\n\n # This flux may be overridden by withFlux. The relative fluxes of the components\n # remains the same, but the total flux is set to gal_flux.\n gal = gal.withFlux(gal_flux)\n gal = gal.shear(g1=gal_g1, g2=gal_g2)\n\n # The center of the object is normally placed at the center of the postage stamp image.\n # You can change that with shift:\n gal = gal.shift(dx=cat.getFloat(k,11), dy=cat.getFloat(k,12))\n\n final = galsim.Convolve([psf, gal])\n\n # Draw the profile\n image = galsim.ImageF(xsize, ysize)\n final.drawImage(image, scale=pixel_scale)\n\n # Add Poisson noise to the image:\n image.addNoise(galsim.PoissonNoise(rng, sky_level * pixel_scale**2))\n\n logger.info('Drew image for object at row %d in the input catalog'%k)\n \n # Add the image to our list of images\n images.append(image)\n \n # Now write the images to a multi-extension fits file. Each image will be in its own HDU.\n galsim.fits.writeMulti(images, multi_file_name)\n logger.info('Images written to multi-extension fits file %r',multi_file_name)","def main():\n \n # for inserting other images, add tem to /input folder and list them here\n images = (\n 'image-0',\n 'image-1',\n 'image-2'\n )\n\n for image_name in images:\n print(image_name, \"image:\")\n\n image = open_image(image_name)\n display_image(image, \"Original input \" + image_name)\n\n grayscale_v = transform_colors(image)\n display_image(grayscale_v[:,:,0], \"Grayscale \" + image_name)\n save_image(image_name + \"-grayscale\", grayscale_v[:,:,0])\n\n contours_v, contours = get_contours(grayscale_v)\n display_image(contours_v, \"Contours \" + image_name)\n save_image(image_name + \"-contours\", contours_v)\n\n labeled_img, areas = get_measures(image, contours[1:])\n display_image(labeled_img, \"Labeled \" + image_name)\n save_image(image_name + \"-labeled\", labeled_img)\n\n areas_histogram(areas, image_name)","def render_pov(pov_name, image_name, height=800, width=800,\n display=False, transparent=True, antialias=True,\n num_threads=0, open_image=True, render=True, \n render_quality=9):\n from os import system\n import re\n\n # Determine the file type\n file_type_dict = {\n \".png$\":\"N\", \n \".bmp$\":\"B\", \n \".rle$\":\"C\",\n \".exr$\":\"E\",\n \".hdr$\":\"H\",\n \".jp[e]*g$\":\"J\", \n \".ppm$\":\"P\",\n \".t[ar]*ga$\":\"T\"\n }\n file_type_list = list(file_type_dict)\n found = False\n i = 0\n while found == False:\n pattern = re.compile(file_type_list[i])\n if pattern.search(image_name):\n use_type = file_type_dict[file_type_list[i]]\n found = True\n else:\n i += 1\n if i == len(file_type_dict):\n print(\"Warning: Image file format not supported! Using .png!\")\n image_name += \".png\"\n use_type = \"N\"\n found = True\n\n # Create .ini file with render settings\n ini_string = (\"; POV-Ray version 3.7 INI file\\n\"\n + f\"; MANTIS auto-generated INI for {pov_name}\\n\\n\"\n + f\"+I{pov_name}\\n+O{image_name}\\n\"\n + f\"+H{height}\\n+W{width}\\n\")\n\n if display:\n ini_string += \"+D\\n\"\n else:\n ini_string += \"-D\\n\"\n\n if transparent:\n ini_string += \"+UA\\n\"\n\n if antialias:\n ini_string += \"+A\\n\"\n\n if num_threads != 0:\n ini_string += f\"+WT{num_threads}\\n\"\n\n if use_type != \"N\":\n ini_string += f\"+F{use_type}\\n\"\n\n if render_quality != 9:\n if render_quality not in range(0,12):\n render_quality = 9\n ini_string += f\"+Q{render_quality}\\n\"\n\n ini_name = pov_name.replace(\".pov\",\".ini\")\n fileID = open(ini_name, \"w\")\n fileID.write(ini_string)\n fileID.close()\n\n # Create render command\n# command = f\"povray {ini_name.replace('.ini','')}\"\n command = f\"povray {ini_name}\"\n if open_image:\n command += \" && eog {0}\".format(image_name)\n if render == True:\n system(command)\n\n div = '----------------------------------------------------'\n print(\"For additional rendering options, see POV-Ray's documentation,\")\n print(\"particularly the file output and tracing options:\")\n print(\"http://wiki.povray.org/content/Reference:File_Output_Options\")\n print(\"http://wiki.povray.org/content/Reference:Tracing_Options\")\n print(f\"Render with \\n{div}\\n{command}\\n{div}\")\n\n return","def draw(input_file_name, height,\n absorption_image,\n differential_phase_image,\n dark_field_image,\n language=\"it\"):\n if language == \"it\":\n absorption_image_title = \"assorbimento\"\n differential_phase_image_title = \"fase differenziale\"\n dark_field_image_title = \"riduzione di visibilit\\\\`a\"\n else:\n absorption_image_title = \"absorption\"\n differential_phase_image_title = \"differential phase\"\n dark_field_image_title = \"dark field\"\n _, ((abs1_plot, phase1_plot, df1_plot),\n (abs2_plot, phase2_plot, df2_plot)) = plt.subplots(\n 2, 3, figsize=(6, height), dpi=300)\n plt.subplots_adjust(\n wspace=0.02,\n hspace=0.02)\n min_x = 300\n max_x = 550\n min_y = 60\n max_y = 130\n abs1 = abs1_plot.imshow(absorption_image,\n cmap=plt.cm.Greys,\n aspect='auto')\n abs1_plot.add_patch(mpl.patches.Rectangle(\n (min_x, min_y),\n max_x - min_x,\n max_y - min_y,\n fill=False,\n edgecolor=\"r\"))\n diff_image = absorption_image[min_y:max_y, min_x:max_x]\n abs2 = abs2_plot.imshow(\n diff_image,\n cmap=plt.cm.Greys, aspect='auto')\n abs1_plot.set_title(absorption_image_title,\n size=\"large\")\n abs1_plot.set_frame_on(False)\n abs1_plot.axes.yaxis.set_ticks([])\n abs1_plot.axes.xaxis.set_ticks([])\n abs2_plot.axes.yaxis.set_ticks([])\n abs2_plot.axes.xaxis.set_ticks([])\n for pos in [\"top\", \"bottom\", \"left\", \"right\"]:\n abs2_plot.spines[pos].set_color(\"r\")\n abs2_plot.axes.xaxis.set_ticks([])\n diff_limits = stats.mstats.mquantiles(diff_image,\n prob=[0.02, 0.98])\n abs2.set_clim(*diff_limits)\n plt.colorbar(abs2,\n ax=abs2_plot,\n orientation=\"horizontal\",\n format=\"% .2f\",\n ticks=np.arange(0, 1, 0.25).tolist())\n limits = stats.mstats.mquantiles(absorption_image,\n prob=[0.02, 0.98])\n abs1.set_clim(*limits)\n phase1 = phase1_plot.imshow(differential_phase_image)\n phase1_plot.add_patch(mpl.patches.Rectangle(\n (min_x, min_y),\n max_x - min_x,\n max_y - min_y,\n fill=False,\n edgecolor=\"r\"))\n phase2 = phase2_plot.imshow(\n differential_phase_image[min_y:max_y, min_x:max_x])\n limits = stats.mstats.mquantiles(differential_phase_image,\n prob=[0.02, 0.98])\n #limits = (-3, 3)\n phase1_plot.set_title(differential_phase_image_title,\n size=\"large\")\n phase1_plot.set_frame_on(False)\n phase1_plot.axes.yaxis.set_ticks([])\n phase1_plot.axes.xaxis.set_ticks([])\n phase2_plot.axes.yaxis.set_ticks([])\n phase2_plot.axes.xaxis.set_ticks([])\n for pos in [\"top\", \"bottom\", \"left\", \"right\"]:\n phase2_plot.spines[pos].set_color(\"r\")\n phase2_plot.axes.xaxis.set_ticks([])\n plt.colorbar(phase2,\n ax=phase2_plot,\n orientation=\"horizontal\",\n format=\"% .1f\",\n ticks=np.arange(-0.4, 0.4, 0.2).tolist())\n phase1.set_clim(*limits)\n phase2.set_clim(*limits)\n df1 = df1_plot.imshow(dark_field_image)\n df1_plot.add_patch(mpl.patches.Rectangle(\n (min_x, min_y),\n max_x - min_x,\n max_y - min_y,\n fill=False,\n edgecolor=\"r\"))\n df2 = df2_plot.imshow(\n dark_field_image[min_y:max_y, min_x:max_x])\n df1_plot.set_title(dark_field_image_title,\n size=\"large\")\n df1_plot.set_frame_on(False)\n df1_plot.axes.yaxis.set_ticks([])\n df1_plot.axes.xaxis.set_ticks([])\n df2_plot.axes.yaxis.set_ticks([])\n df2_plot.axes.xaxis.set_ticks([])\n for pos in [\"top\", \"bottom\", \"left\", \"right\"]:\n df2_plot.spines[pos].set_color(\"r\")\n df2_plot.axes.xaxis.set_ticks([])\n plt.colorbar(df2,\n ax=df2_plot,\n orientation=\"horizontal\",\n format=\"% .1f\",\n ticks=np.arange(0, 1, 0.25).tolist())\n limits = stats.mstats.mquantiles(dark_field_image,\n prob=[0.02, 0.98])\n df1.set_clim(*limits)\n df2.set_clim(*limits)\n plt.savefig('images_{0}.png'.format(\n os.path.splitext(os.path.basename(input_file_name))[0]),\n bbox_inches=\"tight\", dpi=300)","def render_configuration_file(self, conf_template_string):\n environment = self.params['environment']\n code_root = self.params['code_root']\n\n reject = self.get_rejected_pillow_types(code_root, environment)\n\n configs = []\n all_pillows = self.get_pillows_from_settings(settings.PILLOWTOPS, reject)\n for full_name in all_pillows:\n pillow_name = full_name.split('.')[-1]\n pillow_params = {\n 'pillow_name': pillow_name,\n 'pillow_option': ' --pillow-name %s' % pillow_name\n }\n pillow_params.update(self.params)\n pillow_rendering = conf_template_string % pillow_params\n configs.append(pillow_rendering)\n return '\\n\\n'.join(configs)","def make_visual(self,\r\n ruler = None,\r\n options: list = ['rows',\r\n 'centers',\r\n 'distances']\r\n ) -> 'Image':\r\n \r\n original = self.get_picture().get_photo()\r\n\r\n # Copy the original image for drawing\r\n img = original.copy()\r\n draw = ImageDraw.Draw(img)\r\n\r\n # check all the choices provided by the user\r\n for i in options:\r\n \r\n if i == 'clusters':\r\n # Color all cluster pixels red\r\n \r\n for j in self.get_clusters():\r\n for k in j:\r\n img.putpixel(k, (25,275,25))\r\n\r\n elif i == 'row_ids':\r\n # Make row id numbers\r\n\r\n # Font specifications\r\n size = 75\r\n font = ImageFont.truetype('ariblk.ttf', size)\r\n color = (88, 214, 216)\r\n num = 1\r\n\r\n # Draw the ids\r\n for j in self.rows:\r\n draw.text((j[0].get_center()[0],\r\n j[0].get_center()[1] - 0.25 * size),\r\n str(num),\r\n fill = color,\r\n font = font)\r\n num += 1\r\n\r\n elif i == 'boxes':\r\n # Show all bounding boxes\r\n \r\n for i in self.get_boxes():\r\n draw.rectangle(i, outline=(255, 0, 255))\r\n\r\n elif i == 'dirt':\r\n # Remove Background\r\n \r\n img = Image.new('RGB', img.size, (130, 90, 50))\r\n draw = ImageDraw.Draw(img)\r\n\r\n elif i == 'centers':\r\n # Show all centers\r\n \r\n rad = 9\r\n for i in self.get_centers():\r\n draw.arc([(i[0] - rad, i[1] - rad),\r\n (i[0] + rad, i[1] + rad)],\r\n 0, 360, (0, 0, 255))\r\n\r\n elif i == 'ditches':\r\n # Show ditches between plants\r\n\r\n # Line attribute settings\r\n width = 10\r\n color = (55,65,65)\r\n\r\n # Iterate over all ditches\r\n for line in self.ditches:\r\n line = [line[0], line[1]]\r\n\r\n # Point in ditch on left border of picture\r\n start_point = (0, line[1])\r\n\r\n # Point in ditch on right border of picture\r\n end_point = (self.picture.get_size()[0] - 1,\r\n line[0]\r\n * (self.picture.get_size()[0] - 1)\r\n + line[1])\r\n\r\n ## Check if the end point is within the picture\r\n if end_point[1] < 0:\r\n if start_point[1] < 0:\r\n continue\r\n \r\n # Point in ditch on top border of picture\r\n end_point = (-1 * line[1] / line[0], 0)\r\n\r\n elif end_point[1] > self.picture.get_size()[1] - 1:\r\n if start_point[1] > self.picture.get_size()[1] - 1:\r\n continue\r\n \r\n # Point in ditch on bottom border of picture\r\n end_point = (-1\r\n * (self.picture.get_size()[1] - 1)\r\n / line[0],\r\n self.picture.get_size()[1] - 1)\r\n\r\n # Draw the ditches\r\n for i in self.get_rows():\r\n draw.line((start_point, end_point), color, width)\r\n\r\n elif i == 'lines':\r\n # Show row line approximations\r\n\r\n # Line attribute settings\r\n width = 1\r\n color = (255, 255, 75)\r\n\r\n # Iterate over all the lines\r\n for line in self.lines:\r\n line = [line[0], line[1]]\r\n\r\n # Point on line on left border of picture\r\n start_point = (0, line[1])\r\n\r\n # Point on line on right border of picture\r\n end_point = (self.picture.get_size()[0] - 1,\r\n line[0]\r\n * (self.picture.get_size()[0] - 1)\r\n + line[1])\r\n\r\n\r\n ## Check if the end point is within the picture\r\n if end_point[1] < 0:\r\n if start_point[1] < 0:\r\n continue\r\n\r\n # Point on line on top border of picture\r\n end_point = (-1 * line[1] / line[0], 0)\r\n \r\n\r\n elif end_point[1] > self.picture.get_size()[1] - 1:\r\n if start_point[1] > self.picture.get_size()[1] - 1:\r\n continue\r\n\r\n # Point on line on bottom border of picture\r\n end_point = (-1\r\n * (self.picture.get_size()[1] - 1)\r\n / line[0],\r\n self.picture.get_size()[1] - 1)\r\n\r\n # Draw the lines\r\n for i in self.get_rows():\r\n draw.line((start_point, end_point), color, width)\r\n\r\n elif i == 'rows':\r\n if self.get_rows():\r\n # Show lines between rows\r\n \r\n width = 3\r\n color = (255,0,0)\r\n\r\n for i in self.get_rows():\r\n draw.line([j.get_center() for j in i], color, width)\r\n else:\r\n print('Rows have not been made for this field')\r\n\r\n elif i == 'numbers':\r\n # Display numbers between plants\r\n\r\n # Find where to put the numbers\r\n midpoints = [(int(round((row[c].get_center()[0]\r\n + row[c + 1].get_center()[0]) / 2)),\r\n int(round((row[c].get_center()[1]\r\n + row[c + 1].get_center()[1]) / 2)))\r\n \r\n for row in self.get_rows()\r\n for c in range(len(row) - 1)]\r\n\r\n # Font specifications\r\n size = 10\r\n font = ImageFont.truetype('ariblk.ttf', size)\r\n num = 1\r\n\r\n # Write numbers\r\n for i in midpoints:\r\n draw.text((i[0] - 3 * len(str(round(num, 1))),\r\n i[1]),\r\n str(round(num,1)), font = font)\r\n \r\n num += 1\r\n\r\n elif i == 'tight':\r\n # Display tight boxes\r\n\r\n for i in self.get_tight_boxes():\r\n draw.rectangle(i, outline=(100, 255, 255))\r\n\r\n elif i == 'distances':\r\n # display distances between plants\r\n\r\n # find where to put the distances\r\n midpoints = [(int(round((row[c].get_center()[0]\r\n + row[c + 1].get_center()[0]) / 2)),\r\n int(round((row[c].get_center()[1]\r\n + row[c + 1].get_center()[1]) / 2)))\r\n for row in self.get_rows()\r\n for c in range(len(row) - 1)]\r\n\r\n # Font specifications\r\n size = 10\r\n font = ImageFont.truetype('arial.ttf', size)\r\n num = 1\r\n\r\n # Write numbers\r\n for i in midpoints:\r\n draw.text((i[0] - 3 * len(str(ruler.get_distances()[num])),\r\n i[1]),\r\n str(ruler.get_distances()[num]) + '\"',\r\n font = font)\r\n \r\n num += 1\r\n\r\n # If the user inputs something that isn't an option \r\n else:\r\n raise Exception(i + ' is not a valid option.\\n')\r\n\r\n return img","def imDisplay(filename: str, representation: int):\r\n img=imReadAndConvert(filename, representation)\r\n if representation==LOAD_GRAY_SCALE:\r\n plt.imshow(img,cmap='gray')\r\n else:\r\n plt.imshow(img)\r\n plt.show()","def Interpret(self, **kwargs):\n # visit file\n self.options[\"VISIT_FILE\"] = self.visit_file\n # particle file\n particle_file = os.path.join(self.output_dir, 'particles.visit')\n if os.access(particle_file, os.R_OK):\n self.options[\"VISIT_PARTICLE_FILE\"] = particle_file\n # directory to output data\n self.options[\"DATA_OUTPUT_DIR\"] = self.output_dir\n # directory to output images\n if not os.path.isdir(self.img_dir):\n os.mkdir(self.img_dir)\n self.options[\"IMG_OUTPUT_DIR\"] = self.img_dir\n # own implementations\n # initial adaptive refinement\n self.options['INITIAL_ADAPTIVE_REFINEMENT'] = self.idict['Mesh refinement'].get('Initial adaptive refinement', '6')\n # get snaps for plots\n graphical_snaps, _, _ = GetSnapsSteps(self.case_dir, 'graphical')\n self.options['ALL_AVAILABLE_GRAPHICAL_SNAPSHOTS'] = str(graphical_snaps)\n # plot slab \n self.options['IF_PLOT_SLAB'] = 'True'\n last_step = graphical_snaps[-1] - int(self.options['INITIAL_ADAPTIVE_REFINEMENT'])\n last_step = kwargs.get('last_step', None)\n if type(last_step) == int:\n self.options['PLOT_SLAB_STEPS'] = [i for i in range(last_step - last_step + 1, last_step + 1)]\n else:\n self.options['PLOT_SLAB_STEPS'] = [0, 1, 2, 3, 4, 5, 6, 7]\n # self.options['IF_DEFORM_MECHANISM'] = value.get('deform_mechanism', 0)\n self.options['IF_DEFORM_MECHANISM'] = 1","def main():\n\n in_file = ('/home/desi2/candidatesp9/asteroids_decals_dr2.fits')\n out_dir = os.path.join(os.environ.get('HOME'), 'asteroid_cutouts/')\n\n cand_info = fits_table(in_file)\n # Pre-select asteroids in the ra, dec box you know they exist.\n ramin = 107\n ramax = 130\n decmin = 16\n decmax = 30\n these = np.where((cand_info.ra0>ramin)*(cand_info.ra0<ramax)*\n (cand_info.dec0>decmin)*(cand_info.dec0<decmax))[0]\n #pdb.set_trace() # Runs Python Debugger on code up to this line. \n cand_info = cand_info[these]\n\n urls = []\n jpgfiles = []\n for ii in range(100):\n print('Working on candidate {}'.format(ii))\n ra = cand_info.ra0[ii]\n dec = cand_info.dec0[ii]\n \n jpgurl = 'http://legacysurvey.org/viewer/jpeg-cutout-decals-dr2?ra={:.6f}&dec={:.6f}&pixscale=0.262&size=200'.format(ra, dec)\n \n jpgfile = 'obj-{:03d}.jpg'.format(ii)\n jpgfile = os.path.join(out_dir, jpgfile)\n grab = 'wget --continue -O {:s} \"{:s}\"' .format(jpgfile, jpgurl)\n print(grab)\n os.system(grab)\n #pdb.set_trace() # Runs Python Debugger on code up to this line. \n if os.stat(jpgfile).st_size < 18000: # Remove partial or empty images\n # The cut on filesize takes care of most of the bad images but\n # leaves some behind. If the restriction is any larger,\n # it can remove some valid files.\n os.remove(jpgfile)\n else:\n print(jpgurl)\n jpgfiles.append(jpgfile)\n urls.append(jpgurl)\n # for HTML file. What should the URL be?\n #print('<html>')\n #print('<head> Planet Nine Candidates </head>')\n #print('<body>')\n #for thisurl, thisjpg in zip(urls, jpgfiles):\n # print('<div class=\"image\">')\n # print('<a href=\"{}\"><img src=\"{:s}\"></a>'.format(thisurl, thisjpg))\n # print('<div class=\"caption\"> Image of {:s} </div>' .format(thisjpg))\n # print('</div>')\n #print('</body></html>')","def heatmap():\n\n team = int(request.form.get('teamname'))\n player = int(request.form.get('player'))\n attacks = request.form.get('attacks')\n data = request.form.get('datafiles')\n\n print 'Making heat map for player #%d, team #%d, attacks %s' % (player, team, attacks)\n\n if attacks.lower() == 'all':\n attacks = ['ALL']\n else:\n attacks = attacks.split(',')\n\n only_kills = request.form.get('kills')\n\n files = []\n if data == 'uploads':\n data = 'uploads/%d' % session['user_id']\n folder = 'data/' + data\n\n data_path = os.path.join(os.getcwd(), folder)\n if not os.path.isdir(data_path):\n os.makedirs(data_path)\n\n for f in os.listdir(data_path):\n if f.endswith('.dvw'):\n files.append(folder + '/' + f)\n\n locations = []\n for file_name in files:\n parser = Parser(file_name)\n new_locations = parser.get_attack_info(team, player, attacks, only_kills=only_kills)\n locations.extend(new_locations)\n\n top_caption, bottom_caption = generate_caption(team, player, attacks, only_kills)\n output_url = generate_output_filename(team, player, attacks, only_kills, session['user_id'])\n output_dict = heat_map.draw_arcs_pillow(locations, output_url, top_caption=top_caption, bottom_caption=bottom_caption)\n\n result_dict = {\n 'output_url': output_url,\n 'width': output_dict['width'],\n 'height': output_dict['height'],\n }\n print locations\n print 'Rendering finished image', output_url\n return render_template('heatmap.html', result_dict=result_dict)","def main(config):\n data_path = config['paths']['data']\n\n # data\n output_file = os.path.join(config['paths']['figures'], 'network-bridge-map.png')\n road_edge_file = os.path.join(data_path, 'network', 'road_edges.shp')\n bridge_file = os.path.join(data_path, 'network', 'bridges.shp')\n\n # basemap\n proj_lat_lon = ccrs.PlateCarree()\n ax = get_axes()\n plot_basemap(ax, data_path)\n scale_bar(ax, location=(0.8, 0.05))\n plot_basemap_labels(ax, data_path, include_regions=False)\n\n styles = OrderedDict([\n ('national', Style(color='#ba0f03', zindex=5, label='National roads')),\n ('MAYOR SOBRE AGUA Y RUTA', Style(color='#9467bd', zindex=7, label='AGUA Y RUTA')),\n ('MAYOR SOBRE FERROCARRIL', Style(color='#2ca02c', zindex=8, label='FERROCARRIL')),\n ('MAYOR SOBRE RUTA', Style(color= '#ff7f0e', zindex=9, label='RUTA')),\n ('MAYOR SOBRE RUTA Y FERROCARRIL', Style(color='#e377c2', zindex=10, label='RUTA Y FERROCARRIL')),\n ('MAYOR SOBRE RUTA, AGUA Y FERROCARRIL', Style(color='#8c564b', zindex=11, label='RUTA, AGUA Y FERROCARRIL')),\n ('MAYOR SOBRE VIA DE AGUA', Style(color='#1f77b4', zindex=6, label='VIA DE AGUA')),\n ])\n\n # edges\n geoms_by_category = {\n 'national': [],\n 'MAYOR SOBRE AGUA Y RUTA': [],\n 'MAYOR SOBRE FERROCARRIL': [],\n 'MAYOR SOBRE RUTA':[],\n 'MAYOR SOBRE RUTA Y FERROCARRIL':[],\n 'MAYOR SOBRE RUTA, AGUA Y FERROCARRIL':[],\n 'MAYOR SOBRE VIA DE AGUA':[]\n }\n\n edges_national = geopandas.read_file(road_edge_file)\n edges_national = edges_national[edges_national['road_type'] == 'national']\n geoms_by_category['national'] = list(edges_national.geometry)\n\n\n bridges = geopandas.read_file(bridge_file,encoding='utf-8')\n for iter_, val in bridges.iterrows():\n cat = val['structur_1'].strip()\n geoms_by_category[cat].append(val.geometry)\n\n\n for cat, geoms in geoms_by_category.items():\n cat_style = styles[cat]\n if cat == 'national':\n ax.add_geometries(\n geoms,\n crs=proj_lat_lon,\n linewidth=1.25,\n facecolor='none',\n edgecolor=cat_style.color,\n zorder=cat_style.zindex\n )\n else:\n ax.scatter(\n [g.x for g in geoms],\n [g.y for g in geoms],\n transform=proj_lat_lon,\n facecolor=cat_style.color,\n s=6,\n zorder=cat_style.zindex\n )\n\n # legend\n legend_from_style_spec(ax, styles, loc=(0.48,0.2))\n\n # save\n save_fig(output_file)","def pibooth_setup_picture_factory(cfg, opt_index, factory):","def process(self):\n # Opening and preprocessing of the input file\n if self.options.mbtiles_fromdisk or self.options.mbtiles_todisk:\n if self.options.mbtiles_fromdisk:\n i_parm=10\n if self.options.mbtiles_todisk:\n i_parm=11\n if self.options.verbose:\n print \"GDAL2MbTiles :mbtiles from/to disk [\",i_parm,\"] mbtiles_fromdisk[\",self.options.mbtiles_fromdisk,\"] mbtiles_todisk[\",self.options.mbtiles_todisk,\"]\"\n self.mbtiles_setup(i_parm)\n return\n else:\n if self.options.verbose:\n print \"GDAL2MbTiles :tile creation mbtiles[\",self.options.mbtiles,\"]\"\n self.open_input()\n # Generation of main metadata files and HTML viewers\n self.generate_metadata()\n # Generation of the lowest tiles\n self.generate_base_tiles()\n # Generation of the overview tiles (higher in the pyramid)\n self.generate_overview_tiles()\n # Generating of KML\n self.generate_kml()","def display_sample(display_list):\n plt.figure(figsize=(18, 18))\n\n title = ['Input Image', 'True Mask', 'Predicted Mask']\n\n for i in range(len(display_list)):\n plt.subplot(1, len(display_list), i+1)\n plt.title(title[i])\n plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))\n plt.axis('off')\n plt.show()","def run_visualization(filepath, MODEL, outputFilePath):\n try:\n # print(\"Trying to open : \" + sys.argv[1])\n \t# f = open(sys.argv[1])\n \tjpeg_str = open(filepath, \"rb\").read()\n \torignal_im = Image.open(BytesIO(jpeg_str))\n except IOError:\n print('Cannot retrieve image. Please check file: ' + filepath)\n return\n\n print('running deeplab on image %s...' % filepath)\n resized_im, seg_map = MODEL.run(orignal_im)\n\n # vis_segmentation(resized_im, seg_map)\n drawSegment(resized_im, seg_map, outputFilePath)","def main():\n print(\"For each image, type the new name of the file.\" +\n \" No extension necessary!\", end=\"\\n\\n\")\n file_list = input_path.glob(f\"*.{args.ext}\")\n plt.ion()\n\n for pic in file_list:\n img = io.imread(str(pic))\n img = rescale(img, 0.25)\n img = rotate(img, 90, resize = True)\n plt.draw()\n plt.pause(0.001)\n if args.vinyl:\n new_name = get_vinyl_name()\n else:\n print(\"\\n\")\n new_name = input(\n \"Please enter a new filename. Press [enter] to skip: \")\n if new_name:\n if not new_name.endswith(args.ext):\n new_name += \".\" + args.ext\n # io.imsave(output_path / new_name, img)\n shutil.copyfile(pic, output_path / new_name)\n if args.replace:\n os.remove(pic)","def fileCmd(self):\n filename = askopenfilename() \n self.cnvImgOrig.displayImage(filename)\n self.cnvImgTest.displayImage(filename)","def test(file_path: str):\n # Build the generator network\n generator = build_generator()\n\n # Load model weights\n generator.load_weights(\"models/generator.h5\")\n\n # Make a low-res counterpart to predict upon\n image = imread(file_path, mode=\"RGB\")\n image = image.astype(np.float32)\n low_resolution_image = [imresize(image, low_resolution_shape)]\n\n low_resolution_image = np.array(low_resolution_image) / 127.5 - 1.0\n\n # Generate high-resolution images from low-resolution images\n generated_image = generator.predict_on_batch(low_resolution_image)\n\n # Make a common figure\n fig = plt.figure(figsize=(16, 9))\n\n # Add the low resolution image on the left side\n ax = fig.add_subplot(1, 2, 1)\n ax.imshow(low_resolution_image[0], interpolation=\"nearest\")\n ax.axis(\"off\")\n ax.set_title(\"Low-resolution\")\n\n # Add generated image to figure on the right side\n ax = fig.add_subplot(1, 2, 2)\n ax.imshow(generated_image[0], interpolation=\"nearest\")\n ax.axis(\"off\")\n ax.set_title(\"Generated\")\n\n # Save the figure\n plt.savefig(\"{0}{1}\".format(TEST_IMAGES_RESULT, file_path.split(\"/\")[-1]))","def make_image(self):\n\n if self.type == 'passthrough':\n return\n render_template(\n os.path.dirname(self.main_module),\n os.path.basename(self.main_module_path),\n self.language,\n self.requirements,\n self.whitelist,\n self.type,\n into=self.code_dir)\n self.build()","def render_configs(task):\n\n # run function to render global configs\n global_cfg = ibns_global(task)\n # write global config file for each host\n with open(f\"configs/{task.host}_dot1x_global.txt\", \"w+\") as file:\n file.write(global_cfg)\n # print completed hosts\n c_print(f\"*** {task.host}: dot1x global configuration rendered ***\")\n\n # run function to render snmp configs\n snmp_cfg = ibns_snmp(task)\n # function to run interface configs\n with open(f\"configs/{task.host}_snmp.txt\", \"w+\") as file:\n file.write(snmp_cfg)\n # print completed hosts\n c_print(f\"*** {task.host}: SNMP configuration rendered ***\")\n\n # run function to run interface configs\n intf_cfg = ibns_intf(task)\n # write interface config file for each host\n with open(f\"configs/{task.host}_dot1x_intf.txt\", \"w+\") as file:\n file.write(intf_cfg)\n # print completed hosts\n c_print(f\"*** {task.host}: dot1x intf configurations rendered ***\")","def ImageOutput(name, out_ds, tile_size, resampling, init_dest, output_dir, verbose,mbtiles):\n\n resampler = Resampler(resampling)\n\n if name == \"hybrid\":\n return HybridImageOutput(out_ds, tile_size, resampler, init_dest, output_dir, verbose)\n\n if name == \"png\":\n image_format = \"PNG\"\n elif name == \"jpeg\":\n image_format = \"JPEG\"\n\n return SimpleImageOutput(out_ds, tile_size, resampler, init_dest, output_dir, verbose, [image_format],mbtiles)","def _state_main(self, gui):\n gui.entry.wait_variable(gui.entry_sv)\n\n '''Clean string'''\n files = literal_eval(gui.entry_sv.get())\n\n '''Remove previous images'''\n if hasattr(gui, \"panel\"):\n gui.panel.destroy()\n\n '''Load each image'''\n for file_name in files:\n file_name = file_name.replace(\"{\", \"\").replace(\"}\", \"\")\n # image = tk.PhotoImage(file=file_name)\n if \".CR2\" in file_name:\n '''Rawpy implementation'''\n file_image = rawpy.imread(file_name)\n file_image = file_image.postprocess()\n '''Rawkit implementation'''\n '''file_image = Raw(file_name)\n file_image = np.array(file_image.to_buffer())'''\n '''OpenCV implementation'''\n '''file_image = cv2.imread(file_name)'''\n else:\n file_image = Image.open(file_name)\n '''image = file_image.resize((500, 500), Image.ANTIALIAS)\n image = ImageTk.PhotoImage(image)\n gui.panel = tk.Label(gui.root, image=image)\n gui.panel.image = image\n gui.panel.pack()'''\n # panel.grid(row=2)\n\n image_data = np.array(file_image)\n image_data = cv2.cvtColor(image_data, cv2.COLOR_RGB2GRAY)\n '''print(image_data.shape)\n print(image_data)\n print(len(image_data))\n print(len(image_data[0]))'''\n returned_image = Image.fromarray(image_data)\n '''cv2.imshow(\"Gray\", image_data)\n cv2.waitKey()\n cv2.destroyWindow(\"Gray\")'''\n\n '''enhanced_contrast = ImageEnhance.Contrast(Image.fromarray(file_image))\n enhanced_image = enhanced_contrast.enhance(255)\n enhanced_data = np.array(enhanced_image)\n plot_functions.imshow(enhanced_image)\n plot_functions.show()'''\n\n # color_space = cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV)\n # print(color_space)\n \n '''Create mask for white-ish pixels'''\n '''lower_background = np.array([150, 150, 150])\n upper_background = np.array([255, 255, 255])\n print(image_data)\n white_mask = cv2.inRange(image_data, lower_background, upper_background)\n white_mask = cv2.morphologyEx(white_mask, cv2.MORPH_OPEN, np.ones((3,3),np.uint8))\n white_mask = cv2.morphologyEx(white_mask, cv2.MORPH_DILATE, np.ones((3, 3), np.uint8))\n white_mask = white_mask / 255'''\n\n '''Create mask for black-ish pixels'''\n '''lower_background = np.array([0, 0, 0])\n upper_background = np.array([25, 25, 25])\n black_mask = cv2.inRange(image_data, lower_background, upper_background)\n black_mask = cv2.morphologyEx(black_mask, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8))\n black_mask = cv2.morphologyEx(black_mask, cv2.MORPH_DILATE, np.ones((3, 3), np.uint8))\n black_mask = black_mask / 255'''\n\n '''Add masks together'''\n '''background_mask = white_mask\n # Ensure no value is above 1\n background_mask = np.clip(background_mask, 0, 1)'''\n \n copied_image_data = np.asarray(returned_image).copy()\n # background_mask = np.logical_not(background_mask)\n '''for row_index, [mask_row, image_row] in enumerate(zip(background_mask, copied_image_data)):\n # place black pixel on corresponding masked pixels\n # copied_image_data[row_index] = np.array([image_row[pixel] * int(mask_row[pixel]) for pixel in range(len(mask_row))])\n # make pixel fully white on corresponding masked pixels\n copied_image_data[row_index] = np.array([np.array([255, 255, 255]) if int(mask_row[pixel]) else image_row[pixel] for pixel in range(len(mask_row))])'''\n\n '''Turn removed pixels red'''\n '''mask_image = Image.fromarray(copied_image_data)\n plot_functions.imshow(mask_image)\n plot_functions.show()'''\n trapezoid_data = copied_image_data.copy()\n\n enhanced_contrast = ImageEnhance.Contrast(Image.fromarray(trapezoid_data))\n enhanced_image = enhanced_contrast.enhance(255)\n trapezoid_data = np.array(enhanced_image)\n\n '''Detect lines'''\n edges = cv2.Canny(trapezoid_data, 75, 150)\n lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, maxLineGap=1000)\n # print(lines)\n for line in lines:\n x1, y1, x2, y2 = line[0]\n if y1 == y2:\n cv2.line(copied_image_data, (x1, y1), (x2, y2), (255, 255, 255), 1)\n\n '''Trapezoid attempt'''\n\n # filters image bilaterally and displays it\n bilatImg = cv2.bilateralFilter(trapezoid_data, 5, 175, 175)\n\n # finds edges of bilaterally filtered image and displays it\n edgeImg = cv2.Canny(bilatImg, 75, 200)\n\n # gets contours (outlines) for shapes and sorts from largest area to smallest area\n contours, hierarchy = cv2.findContours(edgeImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n contours = sorted(contours, key=cv2.contourArea, reverse=True)\n\n # drawing red contours on the image\n for con in contours:\n cv2.drawContours(trapezoid_data, con, -1, (255, 255, 255), 3)\n\n '''Detect corners'''\n dst = cv2.cornerHarris(edges, 30, 31, 0.001)\n dst = cv2.dilate(dst, None)\n ret, dst = cv2.threshold(dst, 0.01 * dst.max(), 255, 0)\n dst = np.uint8(dst)\n\n # find centroids\n ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)\n # define the criteria to stop and refine the corners\n criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100,\n 0.001)\n corners = cv2.cornerSubPix(edges, np.float32(centroids), (5, 5),\n (-1, -1), criteria)\n\n good_corners = []\n for corner in corners:\n if (corner[1] < 1000) & (corner[1] > 650) & (corner[0] > 250) & (corner[0] < 2250):\n good_corners.append(corner)\n cv2.circle(edges, (corner[0], corner[1]), 10, (255, 255, 255))\n\n print(good_corners)\n if len(good_corners) >= 3:\n corner_combos = itertools.combinations(good_corners, 3)\n elif len(good_corners) > 1:\n corner_combos = itertools.combinations(good_corners, 2)\n\n best_corner_combo = None\n best_coef = np.inf\n for corner_combo in corner_combos:\n regression = LinearRegression().fit(np.array([corner[0] for corner in corner_combo]).reshape(-1, 1),\n np.array([corner[1] for corner in corner_combo]))\n if np.abs(regression.coef_) < best_coef:\n best_coef = np.abs(regression.coef_)\n best_corner_combo = np.array([corner[1] for corner in corner_combo])\n\n y_edge = int(round(np.mean(best_corner_combo)))\n edges = edges[y_edge:3000, 200:2200]\n copied_image_data = copied_image_data[y_edge:2500, 200:2200]\n trapezoid_data = trapezoid_data[y_edge:2500, 200:2200]\n\n # and double-checking the outcome\n cv2.imshow(\"linesEdges\", edges)\n cv2.imshow(\"linesDetected\", copied_image_data)\n cv2.imshow(\"Contours check\", trapezoid_data)\n cv2.waitKey()\n cv2.destroyWindow(\"Contours check\")\n\n # find the perimeter of the first closed contour\n perim = cv2.arcLength(contours[0], True)\n # setting the precision\n epsilon = 0.02 * perim\n # approximating the contour with a polygon\n approxCorners = cv2.approxPolyDP(contours[0], epsilon, True)\n # check how many vertices has the approximate polygon\n approxCornersNumber = len(approxCorners)\n\n for corners in approxCorners:\n cv2.circle(trapezoid_data, (corners[0], corners[1]), radius=10, color=(255, 255, 255), thickness=-1)\n cv2.imshow(\"Vertex position\", trapezoid_data)\n cv2.waitKey()\n cv2.destroyWindow(\"Vertex position\")\n cv2.imshow(\"linesEdges\", edges)\n cv2.imshow(\"linesDetected\", copied_image_data)\n cv2.waitKey(0)\n cv2.destroyAllWindows()","def run(self):\n self.simulate_test_data()\n self.pipeline_test_data()\n self.plot_jump_flags_image()\n self.plot_groupdq_flags(pixel=[884, 550])\n self.plot_ramps_pre_post_correction(pixel=[884, 550])","def main():\n me = SimpleImage(\"images/me.JPG\")\n dinosaur = SimpleImage(\"images/dinosaur.jpg\")\n\n dinosaur.make_as_big_as(me)\n combine = magic(me, dinosaur)\n combine.show()","def run(self):\n\n # need to think about outpath\n\n # Make sure all files are here and okay...\n\n if not self.config.galfile_pixelized:\n raise ValueError(\"Code only runs with pixelized galfile.\")\n\n self.config.check_files(check_zredfile=True, check_bkgfile=True, check_bkgfile_components=True, check_parfile=True, check_zlambdafile=True)\n\n # Compute the border size\n\n self.config.border = self.config.compute_border()\n\n self.config.d.hpix = [self.pixel]\n self.config.d.nside = self.nside\n self.config.d.outbase = '%s_%d_%05d' % (self.config.outbase, self.nside, self.pixel)\n\n # Do the run\n self.config.start_file_logging()\n self.config.logger.info(\"Running redMaPPer on pixel %d\" % (self.pixel))\n\n firstpass = RunFirstPass(self.config)\n\n if not os.path.isfile(firstpass.filename):\n firstpass.run()\n firstpass.output(savemembers=False, withversion=False)\n else:\n self.config.logger.info(\"Firstpass file %s already present. Skipping...\" % (firstpass.filename))\n\n self.config.catfile = firstpass.filename\n\n # Clear out the firstpass memory\n del firstpass\n\n like = RunLikelihoods(self.config)\n\n if not os.path.isfile(like.filename):\n like.run()\n like.output(savemembers=False, withversion=False)\n else:\n self.config.logger.info(\"Likelihood file %s already present. Skipping...\" % (like.filename))\n\n self.config.catfile = like.filename\n\n # Clear out the likelihood memory\n del like\n\n perc = RunPercolation(self.config)\n\n if not os.path.isfile(perc.filename):\n perc.run()\n perc.output(savemembers=True, withversion=False)\n else:\n self.config.logger.info(\"Percolation file %s already present. Skipping...\" % (perc.filename))\n\n self.config.stop_file_logging()","def generate(self):\n\n # create temporary folder for copying image files\n try:\n os.mkdir(ReportGenerator.TEMP_FOLDER)\n except FileExistsError:\n shutil.rmtree(ReportGenerator.TEMP_FOLDER)\n os.mkdir(ReportGenerator.TEMP_FOLDER)\n\n # find all simulations to be included in report\n group_directory = self.config['group_directory']\n sim_directory = self.config['sim_directory']\n assert (group_directory or sim_directory) and not(group_directory and sim_directory), \\\n \"A group directory or a simulation directory must be specified, but not both\"\n\n if group_directory:\n assert os.path.exists(f'results/{group_directory}'), \"ERROR: Group directory does not exist\"\n\n sim_list = os.listdir(f'results/{group_directory}')\n sim_label = group_directory.split('-')[0]\n sim_list = [f'{group_directory}/{x}' for x in sim_list if sim_label in x]\n\n # filters\n if self.config['filters']:\n \n seed_filters = self.config['filters']['seed']\n dilution_filters = self.config['filters']['dilution']\n order_filters = self.config['filters']['order']\n\n for symbol, filters in zip(['S', 'D', 'O'], [seed_filters, dilution_filters, order_filters]):\n if type(filters) == int:\n sim_list = [x for x in sim_list if f'{symbol}{filters}' in x.split('-')[2]]\n elif type(filters) == list:\n sim_list = [x for x in sim_list if any([f\"{symbol}{f}\" in x.split('-')[-2] for f in filters])]\n\n if len(sim_list) == 0:\n raise Exception('Simulation filters resulted in no satisfactory simulations')\n\n else:\n assert os.path.exists(f'results/{sim_directory}'), \"ERROR: Simulation directory does not exist\"\n sim_list = [sim_directory]\n\n # REPORT HEADER\n self.add_heading(level=1, text=self.config['title'])\n now = datetime.now()\n self.add_text(text=f'Report generated on {now.strftime(\"%B %d, %Y\")} at {now.strftime(\"%H:%M:%S\")}')\n\n # REPORT CONTENTS\n for i, sim in enumerate(sim_list):\n for content in self.config['content']:\n content_type = list(content.keys())[0]\n params = content[content_type]\n\n self.add_content(content_type, params, sim)\n\n if i+1 < len(sim_list):\n self.add_content(content_type='break')\n\n # CREATE HTML TEMPLATE\n base_template = self.templateEnv.get_template(f'{ReportGenerator.COMPONENTS_FOLDER}/base.html')\n base_output = base_template.render(content='\\n\\n'.join(self.contents))\n with open(f'{ReportGenerator.TEMP_FOLDER}/template.html', 'w') as html_file:\n html_file.write(base_output)\n\n # CREATE PDF FROM TEMPLATE\n if group_directory:\n HTML(f'{ReportGenerator.TEMP_FOLDER}/template.html').write_pdf(\n f'results/{group_directory}/{self.config[\"title\"]}.pdf', stylesheets=['reports/style.css'])\n else:\n HTML(f'{ReportGenerator.TEMP_FOLDER}/template.html').write_pdf(\n f'results/{sim_directory}/{self.config[\"title\"]}.pdf', stylesheets=['reports/style.css'])\n\n # delete temp folder for storing image files\n shutil.rmtree(f'{ReportGenerator.TEMP_FOLDER}')\n return None","def render(self, mode=\"human\", width=500, height=500):\n if mode == \"human\":\n return self.mujoco_simulation.mujoco_viewer.render()\n elif mode == \"rgb_array\":\n return self.mujoco_simulation.render(width=width, height=height)\n else:\n raise ValueError(\"Unsupported mode %s\" % mode)","def run_visualization(filepath):\n try:\n \t#print(\"Trying to open : \" + sys.argv[1])\n \t# f = open(sys.argv[1])\n \tjpeg_str = open(filepath, \"rb\").read()\n \torignal_im = Image.open(BytesIO(jpeg_str))\n except IOError:\n print('Cannot retrieve image. Please check file: ' + filepath)\n return\n\n #print('running deeplab on image %s...' % filepath)\n resized_im, seg_map = MODEL.run(orignal_im)\n\n # vis_segmentation(resized_im, seg_map)\n drawSegment(resized_im, seg_map)","def run_plots(self):\n # load the files\n self.pre_dark_file = os.path.join(self.input_dir, 'step_lastframe.fits')\n self.post_dark_file = os.path.join(self.input_dir, 'step_dark_current.fits')\n self.jump_file = os.path.join(self.input_dir, 'step_jump.fits')\n self.rate_file = os.path.join(self.input_dir, 'step_rate.fits')\n self.ramp_file = glob.glob(os.path.join(self.input_dir, '*.fits'))[0]\n\n # plots\n self.plot_jump_flags_image()\n self.plot_groupdq_flags(pixel=[884, 550])\n self.plot_ramps_pre_post_correction(pixel=[884, 550])","def to_html(self, file_name, kohonen_map):\n if file_name[-5:] != '.html':\n print('Not html file... appending HTML extension!')\n file_name += '.html'\n with open(file_name, 'w') as html_file:\n html_content = \"\"\"<html>\n <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.2/jquery.min.js\"></script>\n <style type=\"text/css\">\n #overlay {\n position: absolute;\n top: 40;\n left: 40;\n width: 400px;\n height: 400px;\n background-color: #000;\n }\n </style>\n <script type=\"text/javascript\">\n $(document).ready(function () {\n $('#overlay').hide();\n });\n\n function close() {\n $('#overlay').hide();\n }\n\n function show_images(imgs) {\n if (imgs.length == 0) {\n alert(\"No other images in that neuron!\");\n } else {\n var div = $('#overlay');\n div.empty();\n div.css('text-align', 'center')\n\n var btn = $('<button>Close</button><br/><br/>');\n btn.on('click', close);\n div.append(btn);\n\n for (var i = 0; i < imgs.length; ++i) {\n var img = imgs[i];\n div.append($('<img src=\"' + img + '\" style=\"width:100px;height:100px;\"/>'));\n div.show();\n }\n }\n }\n </script>\n\n <div id=\"container\">\n <table>\n \"\"\" # noqa\n html_file.write(html_content)\n for i in range(kohonen_map._map.shape[0]):\n html_file.write('<tr>')\n for j in range(kohonen_map._map.shape[1]):\n html_file.write('<td>')\n key = str(i) + ',' + str(j)\n sample_ids = kohonen_map.samples_dict[key]\n if len(sample_ids) > 0:\n style = 'style=\\\"width:100px;height:100px;\\\"'\n images_list = []\n for index, sample_id in enumerate(sample_ids):\n image_index = self.image_indices[sample_id]\n image_name = str(image_index) + '.jpg'\n image_file = os.path.join(self.directory,\n image_name)\n if index == 0:\n src = 'src=\"' + image_file + '\"'\n alt = 'alt=\"' + image_file + '\"'\n else:\n images_list.append(\"'\" + image_file + \"'\")\n images_list = ','.join(images_list)\n onclick = 'onclick=\"show_images([' + images_list + \\\n '])\"'\n img = '<img ' + src + ' ' + alt + ' ' + style + ' ' + \\\n onclick + '>'\n html_file.write(img)\n else:\n alt = 'alt=\\\"No image\\\"'\n style = 'style=\\\"width:100px;height:100px;\\\"'\n #html_file.write(img)\n html_file.write('</td>')\n html_file.write('</tr>')\n html_content = \"\"\"</table>\n </div>\n\n <div id=\"overlay\">\n </div>\n </body>\n </html>\"\"\"\n html_file.write(html_content)","def main():\n data_visualisation()\n write_hyper_params()\n write_result_tables()\n write_box_plots()","def config_case(\n context: CGConfig,\n case_id: str,\n gender: str,\n genome_version: str,\n panel_bed: str,\n pon_cnn: click.Path,\n observations: List[click.Path],\n dry_run: bool,\n):\n\n analysis_api: AnalysisAPI = context.meta_apis[\"analysis_api\"]\n try:\n LOG.info(f\"Creating config file for {case_id}.\")\n analysis_api.status_db.verify_case_exists(case_internal_id=case_id)\n analysis_api.config_case(\n case_id=case_id,\n gender=gender,\n genome_version=genome_version,\n panel_bed=panel_bed,\n pon_cnn=pon_cnn,\n observations=observations,\n dry_run=dry_run,\n )\n except CgError as error:\n LOG.error(f\"Could not create config: {error}\")\n raise click.Abort()\n except Exception as error:\n LOG.error(f\"Could not create config: {error}\")\n raise click.Abort()","def plot_ratios(path='/Volumes/OptiHDD/data/pylith/3d/agu2014/output',\n\t\t\t\tsteps=['step01','step02'],\n\t\t\t\t#labels='',\n\t\t\t\tshow=True,\n\t\t\t\txscale=1e3,\n\t\t\t\tyscale=1e-2):\n\tplt.figure()\n\t#path = '/Users/scott/Desktop/elastic'\n\n\t# Deep source\n\t#labels = ['no APMB', 'APMB']\n\t#if labels == '':\n\tlabels = steps\n\tdeep = {}\n\t#uzmax = 0.824873455364\n\t# NOT sure why hardcoded...\n\tuzmax = 1\n\tfor i,outdir in enumerate(steps):\n\t\tpointsFile = os.path.join(path, outdir, 'points.h5')\n\t\tprint(pointsFile)\n\t\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\n\n\t\tX = x / xscale\n\t\tY1 = ux / yscale\n\n\t\tx_fem = X #/ xscale #double scaling!\n\t\tur_fem = Y1 #/ yscale\n\t\tuz_fem = uz / yscale\n\n\t\t#print(pointsFile)\n\t\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\n\n\t\t#normalize\n\t\tuz_fem = uz_fem / uzmax\n\t\tur_fem = ur_fem / uzmax\n\t\tx_fem = x_fem / 30.0\n\n\t\tl, = plt.plot(x_fem,uz_fem,'o-',ms=4,lw=4,label=labels[i])\n\t\tplt.plot(x_fem,ur_fem,'o--',ms=4,lw=4,color=l.get_color()) #mfc='none' transparent\n\t\tdeep[outdir] = uz_fem/uz_fem\n\n\t'''\n\t# Shallow Source\n\tshallow = {}\n\tuzmax = 0.949652827795\n\tfor i,outdir in enumerate(['step11','step12']):\n\t\tpointsFile = os.path.join(path, outdir, 'points.h5')\n\n\t\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\n\n\t\tX = x / xscale\n\t\tY1 = ux / yscale\n\n\t\tx_fem = X #/ xscale #double scaling!\n\t\tur_fem = Y1 #/ yscale\n\t\tuz_fem = uz / yscale\n\n\t\t#print(pointsFile)\n\t\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\n\n\t#normalize\n\tuz_fem = uz_fem / uzmax\n\tur_fem = ur_fem / uzmax\n\tx_fem = x_fem / 20.0\n\n\t\tl, = plt.plot(x_fem,uz_fem,'.-', mfc='w', lw=4,label=labels[i])\n\t\tplt.plot(x_fem,ur_fem,'.--',lw=4, mfc='w',color=l.get_color()) #mfc='none' transparent\n\n\t\tshallow[outdir] = uz_fem/ur_fem\n\t'''\n\n\t# Annotate\n\tplt.axhline(color='k',lw=0.5)\n\t#plt.xlabel('Distance [{}]'.format(get_unit(xscale)))\n\t#plt.ylabel('Displacement [{}]'.format(get_unit(yscale)))\n\tplt.legend()\n\tplt.grid()\n\t#plt.ylim(-0.5, 3.5)\n\t#plt.savefig('deep.png',bbox_inches='tight')\n\t#plt.savefig('shallow.png',bbox_inches='tight')\n\n\t# normalized\n\tplt.ylim(-0.5, 4)\n\tplt.xlim(0,10)\n\tplt.xlabel('Normalized Radial Distance [R / D]')\n\tplt.ylabel('Normalized Displacement [U / Uz_max]')\n\t#plt.savefig('normalized_deep.png',bbox_inches='tight')\n\tplt.savefig('normalized_shallow.png',bbox_inches='tight')\n\n\n\t# Plot ratios of uz versus NOTE: this plot is confusing,,, just keep ratio of uz_max to ur_max\n\t'''\n\tplt.figure()\n\tplt.plot(x_fem, deep['step01'], label='Deep no APMB')\n\tplt.plot(x_fem, deep['step02'], label='Deep w/ APMB')\n\tplt.plot(x_fem, shallow['step11'], label='Shallow no APMB')\n\tplt.plot(x_fem, shallow['step12'], label='Shallow w/ APMB')\n\tplt.xlabel('Distance [km]') #NOTE: maybe plot normailzed X-axis (R-d)\n\t#plt.xlabel('Normalized Distance [R/d]')\n\tplt.ylabel('Ratio [Uz/Ur]')\n\tplt.title('Ratio of vertical to radial displacement')\n\tplt.legend()\n\tplt.show()\n\t'''","def run_variations(filename, N=50, N_scales=7, shape=(512, 512),\n steps=[(10,4.0), (20,1.0)], display_inline=False, min_radius=1.5,\n max_radius=90.0):\n\n colors = [[1, 0, 0], [0, 1, 0], [0, 0, 0.9], [1, 1, 0],\n [1, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0.6, 0]]\n perm = np.random.permutation\n for n in range(N):\n # generate palette\n pal = perm(colors)[:4]\n pal = perm(r_[pal, pal])\n pal = (pal * .6 + roll(pal, 4, axis=0) * 0.4)[:4]\n pal = shade(N_scales, *pal)\n # generate parameters and MSTP object\n ra = exp(rseq(log(min_radius), log(max_radius), N=N_scales, randomness=0.9)).round(2)\n ri = ((1.333 + rand(N_scales)) * ra).round(2)\n dt = (.01 * frange(1, N_scales) ** 0.8).round(3)\n wt = (1.33 + arctan(5 * (rand(N_scales) - .5))).round(2)\n m = MSTP(shape, ra=ra, ri=ri, dt=dt, wt=wt, pal=pal)\n\n print '\\n-------- rendering image', filename % n\n print m\n # display(HTML(' '.join('<span style=\"background:rgb(%d,%d,%d)\">(o_O)</span> ' % tuple(255*k) for k in array(pal))))\n for i, (N_steps, speed) in enumerate(steps):\n print 'rendering %s steps at %1.1fx speed' % (N_steps, dt_multiplier)\n sys.stdout.flush()\n m.run(n=N_steps, speed=speed)\n if display_inline:\n display(IM(m.rgb_image()))\n if i < 0:\n first = False\n print 'renoising after iter 1.',\n m.z += 0.25 * rand(*m.z.shape) * m.z.ptp()\n\n # display(IM(m.rgb_image()))\n imsave(filename % n, m.rgb_image())","def visualize(stuff, **options):\n separate = r\"\\newpage\" #by default, a new tupel is put on a new page\n name = \"some_text_file\" #by default this file is used\n for key in options:\n if key == \"separate\":\n separate = options[key]\n if key == \"name\":\n name = options[key]\n works = True\n totallines = [r\"\\documentclass{article}\", r\"\\usepackage{xcolor}\", r\"\\usepackage{tikz,pgf}\", r\"\\usepackage[left = 0 cm, top = 0cm, bottom = 0cm, right = 2cm]{geometry}\", r\"\\begin{document}\", r\"\\pagestyle{empty}\"]\n for description in stuff:\n data = stuff[description]\n if checkdataformat(description, data):\n if description == \"config\":\n lines = gentikz(data)\n elif description == \"movelist\":\n lines = showmoveslist(data[0], data[1], data[2])\n elif description == \"movelists\":\n lines = compareshowmoveslists(data[0], data[1], data[2])\n elif description == \"list\":\n lines = showlist(data)\n elif description == \"configurations\":\n lines = showconfigurations(data)\n elif description == \"movetable\":\n lines = nktable(data[0], data[1], sort = 'value')\n elif description == \"incrementtable\":\n lines = nktable(data[0], data[1], sort = 'increment')\n elif description == \"totalptable\":\n lines = nktable(data[0], data[1], sort = 'totalpossibilities')\n elif description == \"ptable\":\n lines = nktable(data[0], data[1], sort = 'adjustedpossibilities')\n elif description == \"bfptable\":\n lines = nktable(data[0], data[1], sort = 'bfadjustedpossibilities')\n else:\n print(\"unknown description\")\n lines = []\n for line in lines:\n totallines.append(line)\n totallines.append(separate)\n else:\n print(description, \":\", data, \"don't match, please read help(visualization)\")\n works = False\n totallines.append(r\"\\end{document}\")\n if works:\n compile(totallines, name)","def visualize(self, paths, instance, during_analysis):\r\n xvalues = np.arange(self.data.shape[0])\r\n\r\n model_data = self.model_data_from_instance(instance=instance)\r\n\r\n residual_map = self.data - model_data\r\n chi_squared_map = (residual_map / self.noise_map) ** 2.0\r\n\r\n \"\"\"The visualizer now outputs images of the best-fit results to hard-disk (checkout `visualizer.py`).\"\"\"\r\n plot_profile_1d(\r\n xvalues=xvalues,\r\n profile_1d=self.data,\r\n title=\"Data\",\r\n ylabel=\"Data Values\",\r\n color=\"k\",\r\n output_path=paths.image_path,\r\n output_filename=\"data\",\r\n )\r\n\r\n plot_profile_1d(\r\n xvalues=xvalues,\r\n profile_1d=model_data,\r\n title=\"Model Data\",\r\n ylabel=\"Model Data Values\",\r\n color=\"k\",\r\n output_path=paths.image_path,\r\n output_filename=\"model_data\",\r\n )\r\n\r\n plot_profile_1d(\r\n xvalues=xvalues,\r\n profile_1d=residual_map,\r\n title=\"Residual Map\",\r\n ylabel=\"Residuals\",\r\n color=\"k\",\r\n output_path=paths.image_path,\r\n output_filename=\"residual_map\",\r\n )\r\n\r\n plot_profile_1d(\r\n xvalues=xvalues,\r\n profile_1d=chi_squared_map,\r\n title=\"Chi-Squared Map\",\r\n ylabel=\"Chi-Squareds\",\r\n color=\"k\",\r\n output_path=paths.image_path,\r\n output_filename=\"chi_squared_map\",\r\n )","def setUp(self):\n print \"Setting Up: %s\" % self.id()\n # render the template\n g.render_template(self.template_file,\n self.template_vars,\n self.output_file,\n self.search_path)\n\n # read the resulting config file built from template\n self.output_config = g.load_config(self.output_file)\n g.show_config(self.output_config)","def plot_vis_test(plotfile,pdf_file):\n\t# First some parameters looked up from configfile---------------------------------\n\t\n\tgrbdir = runconf['l2file'][0:10]\n\tpre_tstart = runconf['bkg1start']\n\tpre_tend = runconf['bkg1end']\n\ttrigtime = runconf['trigtime']\n\tgrb_tstart = runconf['transtart']\n\tgrb_tend = runconf['tranend']\n\tpost_tstart = runconf['bkg2start']\n\tpost_tend = runconf['bkg2end']\n\tt_src = grb_tend - grb_tstart \n\tt_tot = (pre_tend-pre_tstart)+(post_tend-post_tstart)\n\tra_tran = runconf['ra']\n\tdec_tran = runconf['dec']\n\tlc_bin = runconf['lc_bin']\n\talpha = runconf['alpha']\n\tbeta = runconf['beta']\n\tE0 = runconf['E0']\n\tA = runconf['A']\n\tsim_scale = t_src\n\tpixbin = int(runconf['pixsize'])\n\tcomp_bin = int(runconf['comp_bin'])\n\ttyp = runconf['typ']\n\n\t# Calling txy to calculate thetax thetay and the coordinates----------------------\n\t\n\tthetax,thetay,x,y,z,t = txy(runconf['mkffile'], trigtime, ra_tran, dec_tran)\n\t\n\t# Plot the 3d visualisation for the position of the transient---------------------\n\tplt.figure()\n\tfig = visualize_3d(grbdir,x,y,z, t, thetax, thetay, grbdir)\t\n\tpdf_file.savefig(fig)\n\t\n\t# Plotting the lightcurves for the four quadrants---------------------------------\n\tfig = plt.figure()\n\tclean_file = fits.open(runconf['infile'])\n\tplt.title('Light curves for '+grbdir + \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n\t\n\tquad0 = clean_file[1].data\n\tdata0,bin_edge = np.histogram(quad0['time'], bins=np.arange(quad0['time'][0],quad0['time'][-1],lc_bin))\n\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data0,label='Quad 0',lw=0.7)\n quad1 = clean_file[2].data\n\tdata1,bin_edge = np.histogram(quad1['time'], bins=np.arange(quad1['time'][0],quad1['time'][-1],lc_bin))\n\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data1,label='Quad 1',lw=0.7) \n\tquad2 = clean_file[3].data\n\tdata2,bin_edge = np.histogram(quad2['time'], bins=np.arange(quad2['time'][0],quad2['time'][-1],lc_bin))\n\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data2,label='Quad 2',lw=0.7)\n quad3 = clean_file[4].data\n data3,bin_edge = np.histogram(quad3['time'], bins=np.arange(quad3['time'][0],quad3['time'][-1],lc_bin))\n plt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data3,label='Quad 3',lw=0.7)\n\tplt.axvspan(grb_tstart,grb_tend,color='blue',alpha=0.1,label='GRB')\n\tplt.axvspan(pre_tstart,pre_tend,color='orange',alpha=0.2)\n\tplt.axvspan(post_tstart,post_tend,color='orange',alpha=0.2,label='Background')\n\tplt.legend(prop={'size':6})\n\tplt.xlim(pre_tstart-100,post_tend+100)\n\tpdf_file.savefig(fig)\n\t\n\t# Calling the sim_dph--------------------------------------------------------------\n\t\n\tgrb_flat,bkgd_flat,grb_dph,bkgd_dph,t_src,t_total = data_bkgd_image(grbdir,pre_tstart,pre_tend,grb_tstart,grb_tend,post_tstart,post_tend)\n\n\tsim_flat,sim_dph,badpix_mask,sim_err_dph = simulated_dph(grbdir,typ,t_src,alpha,beta,E0,A)\n\n\tsrc_dph = grb_dph-bkgd_dph*t_src/t_tot\n\n print \"Total counts in simulated dph: \",(sim_dph).sum()\n print \"Total counts after badpix mask is applied: \",(sim_dph*badpix_mask).sum()\n\tprint \"Excess counts in badpix masked src dph: \",(src_dph*badpix_mask).sum()\n \n\t# Plotting the DPHs before badpix correction---------------------------------------\n\t\n\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n\tplt.suptitle('DPHs before badpix correction for '+grbdir + \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n \t# Sim\n\tim = ax3.imshow(sim_dph,interpolation='none')\n\tax3.set_title('Sim DPH',fontsize=8)\n\tax3.set_xlim(-1,128 - 0.5)\n\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax3.spines['left'].set_position(('data',-0.5))\n\tax3.set_yticklabels([])\n\tax3.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\n\t\n\t # Source \n\tim = ax4.imshow(src_dph,interpolation='none',vmin=0)\n\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\n\tax4.set_xlim(-1,128 -0.5)\n\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax4.spines['left'].set_position(('data',-0.5))\n\tax4.set_yticklabels([])\n\tax4.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\n\n \t# Source + Background\n\tim = ax1.imshow(grb_dph,interpolation='none')\n\tax1.set_title('Src + Bkg DPH',fontsize=8)\n\tax1.set_xlim(-1,128 -0.5)\n\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax1.spines['left'].set_position(('data',-0.5))\n\tax1.set_yticklabels([])\n\tax1.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\n\n \t# Background\n\tim = ax2.imshow(bkgd_dph*t_src/t_total,interpolation='none')\n\tax2.set_title('Bkg DPH',fontsize=8)\n\tax2.set_xlim(-1,128 -0.5)\n\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax2.spines['left'].set_position(('data',-0.5))\n\tax2.set_yticklabels([])\n\tax2.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\n\tf.set_size_inches([6.5,6.5])\n\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\n\t\n\t# Plotting the Badpix mask---------------------------------------------\n\n\tfig = plt.figure()\n\tax = plt.subplot(111)\n\tplt.title('Badpix Mask for '+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n\tim = ax.imshow(badpix_mask,interpolation='none')\n\tax.set_xlim(-9,128 -0.5)\n\tax.axvline(x=-5.,ymin=0,ymax=64,linewidth=5,color='k')\n\tax.spines['left'].set_position(('data',-0.5))\n\tax.xaxis.set_ticks(np.arange(0,128,16))\n\tax.yaxis.set_ticks(np.arange(0,128,16))\n\tfig.colorbar(im,ax=ax,fraction=0.046, pad=0.04)\n\t\n\tpdf_file.savefig(fig) # saves the current figure into a pdf_file page\n\n\t# Plotting badpix masked graphs--------------------------------------------\n\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n \t# Sim\n\tim = ax3.imshow(sim_dph*badpix_mask,interpolation='none')\n\tax3.set_title('Sim DPH',fontsize=8)\n\tax3.set_xlim(-1,128 -0.5)\n\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax3.spines['left'].set_position(('data',-0.5))\n\tax3.set_yticklabels([])\n\tax3.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\n\n\t # Source \n\tim = ax4.imshow(src_dph*badpix_mask,interpolation='none',vmin=0)\n\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\n\tax4.set_xlim(-1,128 -0.5)\n\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax4.spines['left'].set_position(('data',-0.5))\n\tax4.set_yticklabels([])\n\tax4.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\n\n\t # Source + Background\n\tim = ax1.imshow(grb_dph*badpix_mask,interpolation='none')\n\tax1.set_title('Src + Bkg DPH',fontsize=8)\n\tax1.set_xlim(-1,128 -0.5)\n\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax1.spines['left'].set_position(('data',-0.5))\n\tax1.set_yticklabels([])\n\tax1.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\n\t\n\t # Background\n\tim = ax2.imshow(bkgd_dph*badpix_mask*t_src/t_total,interpolation='none')\n\tax2.set_title('Bkg DPH',fontsize=8)\n\tax2.set_xlim(-1,128 -0.5)\n\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax2.spines['left'].set_position(('data',-0.5))\n\tax2.set_yticklabels([])\n\tax2.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\n\tf.set_size_inches([6.5,6.5])\n\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\n\n\t# Plotting badpix masked graphs (Binned) ----------------------------------------------------\n\tfor p in [4,8,16]:\n\t\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n\t\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay)+ \"pixsize=\"+str(p))\n\t\t # Sim\n\t\tim = ax3.imshow(resample(sim_dph*badpix_mask,p),interpolation='none')\n\t\tax3.set_title('Sim DPH',fontsize=8)\n\t\tax3.set_xlim(-1,128/p -0.5)\n\t\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax3.spines['left'].set_position(('data',-0.5))\n\t\tax3.set_yticklabels([])\n\t\tax3.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n\t\tax3.set_xticklabels(np.arange(0,128,16))\n\t\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\n\t\t\n\t\t # Source \n\t\tim = ax4.imshow(resample(src_dph*badpix_mask,p),interpolation='none',vmin=0)\n\t\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\n\t\tax4.set_xlim(-1,128/p -0.5)\n\t\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax4.spines['left'].set_position(('data',-0.5))\n\t\tax4.set_yticklabels([])\n ax4.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n ax4.set_xticklabels(np.arange(0,128,16))\t\t\n\t\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\n\t\t\n\t\t # Source + Background\n\t\tim = ax1.imshow(resample(grb_dph*badpix_mask,p),interpolation='none')\n\t\tax1.set_title('Src + Bkg DPH',fontsize=10)\n\t\tax1.set_xlim(-1,128/p -0.5)\n\t\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax1.spines['left'].set_position(('data',-0.5))\n\t\tax1.set_yticklabels([])\n ax1.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n ax1.set_xticklabels(np.arange(0,128,16))\t\t\n\t\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\n\t\t\n\t\t # Background\n\t\tim = ax2.imshow(resample(bkgd_dph*badpix_mask*t_src/t_total,p),interpolation='none')\n\t\tax2.set_title('Bkg DPH',fontsize=8)\n\t\tax2.set_xlim(-1,128/p -0.5)\n\t\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax2.spines['left'].set_position(('data',-0.5))\n\t\tax2.set_yticklabels([])\n ax2.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n ax2.set_xticklabels(np.arange(0,128,16))\t\t\n\t\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\n\t\tf.set_size_inches([6.5,6.5])\n\t\t\n\t\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\n\n\n\t# Plotting the comparison graphs with equal bins ---------------------------------------\n\tprint \"No. of pixels with zero counts in sim_dph: \",sim_dph[sim_dph==0].size\n\tprint \"No. of pixels with zero counts in grb_dph(no bkg subtration): \",grb_dph[grb_dph==0].size\n\t\n\t# Generating the array for module number ------------------------------------------------\n\tA = ['A'+str(i) for i in range(16)]\n\tB = np.flip(['B'+str(i) for i in range(16)],0)\n\tC = np.flip(['C'+str(i) for i in range(16)],0)\n\tD = ['D'+str(i) for i in range(16)]\n\tquad_a = np.reshape(A,(4,4))\n\tquad_b = np.reshape(B,(4,4))\n\tquad_c = np.reshape(C,(4,4))\n\tquad_d = np.reshape(D,(4,4))\n\tMod_arr = np.ndarray((8,8),dtype='|S3')\n\tMod_arr[:4,:4] = quad_a\n\tMod_arr[:4,4:] = quad_b\n\tMod_arr[4:,4:] = quad_c\n\tMod_arr[4:,:4] = quad_d\n\tMod_names = Mod_arr.flatten()\n\t#print \"Module name array : \",Mod_names\n\t#-----------------------------------------------------------------------------------------\n\t\t\n\tsim_dph = sim_dph*badpix_mask\n\tsim_err_dph = sim_err_dph*badpix_mask\n grb_dph = grb_dph*badpix_mask\n bkgd_dph = bkgd_dph*badpix_mask\n\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\n\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\n\n\tsim_bin = resample(sim_dph,pixbin)\n\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\t\n\tgrb_bin = resample(grb_dph,pixbin)\n\tbkgd_bin = resample(bkgd_dph,pixbin)\n\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\t\n\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\t\n\n\tsim_flat_bin = sim_bin.flatten()\n\tsim_err_flat_bin = sim_err_bin.flatten()\n\tgrb_flat_bin = grb_bin.flatten()\n\tbkgd_flat_bin = bkgd_bin.flatten()\n\tgrb_err_flat_bin = grb_err_bin.flatten()\n\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\n\t\n\n\t # Defining model background and data\n\tmodel = sim_flat_bin\n\tmodel_copy = np.copy(model)\n\tbkgd = bkgd_flat_bin*t_src/t_tot\n\tsrc = grb_flat_bin\n\t\n\tdata = src - bkgd\n\tdata_copy = np.copy(data)\n\t\n\terr_src = grb_err_flat_bin\n\terr_bkgd = bkgd_err_flat_bin\n\terr_model = sim_err_flat_bin\n\terr_model_copy = np.copy(err_model)\n\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\terr_data_copy = np.copy(err_data)\n\t\n\tratio = data/model\n\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\n\t\n\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n\t\n\t # PLotting the comparison plots\n\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\n\t\n\tax1.set_title(\"Comparison between simulated and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f}\".format(tx=thetax,ty=thetay,c=chi_sq))\n\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation\",elinewidth=0.5)\n\tax1.legend()\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\n\tax1.set_ylabel('Counts')\n\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n\n\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\"Ratio = Data/Model\",elinewidth=0.5)\n\tax2.xaxis.set_ticks(np.arange(0,len(data)))\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\n\tax2.tick_params(labelsize=5)\n\tax2.axhline(y=1,linewidth=0.5,color='k')\n\tax2.legend()\n\tax2.set_xlabel('CZT Modules')\n\tax2.set_ylabel('Ratio of counts')\n\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\n\tplt.tight_layout(h_pad=0.0)\n\tf.set_size_inches([6.5,10])\n\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\n\n\t# Plotting comparison graphs with random binning------------------------------\n\t\n sim_flat = sim_dph.flatten()\n\tsim_err_flat = sim_err_dph.flatten()\n grb_flat = grb_dph.flatten()\n bkgd_flat = bkgd_dph.flatten()\n\tsrc_flat = src_dph.flatten()\n\t\n\torder = np.random.permutation(np.arange(0,len(sim_flat)))\n\t\n sim_flat = sim_flat[order]\n\tsim_err_flat = sim_err_flat[order]\n\tgrb_flat = grb_flat[order]\n\tbkgd_flat = bkgd_flat[order]\n\tsrc_flat = src_flat[order]\n\t\n\tprint \"No. of pixels with zero counts in sim_flat: \",sim_flat[sim_flat==0].size\n\tprint \"No. of pixels with zero counts in src_flat: \",src_flat[src_flat==0].size\n\t\n\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\n\tx = np.zeros(len(bins)+2,dtype=np.int64)\n\tx[0] = 0\n\tx[-1] = len(sim_flat)\n\tx[1:-1] = bins\n\t\n\t#print \"The bin edges: \",x # ---------------------------------------------------------------\n\t\n\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\n\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\t\n\tprint \"Total sim_flat_bin : \",sim_flat_bin.sum() #-----------------------------------------\n\t#print \" Max(cumsum) : \",max(np.cumsum(sim_flat)) #-----------------------------------------\n\n # Defining model background and data\n model = sim_flat_bin #avg_flat_bin\n bkgd = bkgd_flat_bin*t_src/t_tot\n src = grb_flat_bin\n\t\n data = src - bkgd\n\n err_src = np.sqrt(src)\n err_bkgd = np.sqrt(bkgd_flat_bin)\n err_model = sim_err_flat_bin\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\t\n\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n # PLotting the comparison plots\n fig = plt.figure()\n plt.title(\"Comparison between simulated and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f} \".format(tx=thetax,ty=thetay,c=chi_sq_new))\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation\",elinewidth=0.5)\n plt.ylabel('Counts')\n\tplt.xlabel('Random Bins')\n\tplt.xticks(np.arange(0,(len(data)),1))\n\tplt.legend()\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\n\n\t# Plotting observed vs predicted counts------------------------------------------------------\n\n\tfig = plt.figure()\n\tplt.title(grbdir + r\" : Observed vs Predicted counts with $\\chi^2$={cs:0.1f}\".format(cs=chi_sq))\n\tplt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\n\tfor i in range(len(model_copy)):\n\t\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\n\tplt.plot(np.arange(-1000,1000),np.arange(-1000,1000),'k',linewidth=0.5)\n\tplt.xlim(min(model_copy)-5,max(model_copy)+5)\n\tplt.ylim(min(data_copy)-5,max(data_copy)+5)\n\tplt.xlabel('Predicted Counts')\n\tplt.ylabel('Observed Counts')\n\tplt.legend()\n\tplt.grid()\n\tpdf_file.savefig(fig)\n\n\t# Scaling the model using curve fit =============================================================== \n\t\n\tparam,pcov = curve_fit(fit_line_int,model_copy,data_copy)\n\tscaling = param[0]\n\tintercept = param[1]\n\t\n\t# Plotting the scaled plots ===================================================================\n\t# Plotting the comparison graphs with equal bins ---------------------------------------\n\n\tsim_dph = sim_dph*badpix_mask\n\tsim_err_dph = sim_err_dph*badpix_mask\n grb_dph = grb_dph*badpix_mask\n bkgd_dph = bkgd_dph*badpix_mask\n\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\n\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\n\n\tsim_bin = resample(sim_dph,pixbin)\n\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\t\n\tgrb_bin = resample(grb_dph,pixbin)\n\tbkgd_bin = resample(bkgd_dph,pixbin)\n\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\t\n\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\t\n\n\tsim_flat_bin = sim_bin.flatten()\n\tsim_err_flat_bin = sim_err_bin.flatten()\n\tgrb_flat_bin = grb_bin.flatten()\n\tbkgd_flat_bin = bkgd_bin.flatten()\n\tgrb_err_flat_bin = grb_err_bin.flatten()\n\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\n\t\n\n\t # Defining model background and data\n\t#model = sim_flat_bin*scaling\n\tmodel = sim_flat_bin*scaling + intercept\n\tbkgd = bkgd_flat_bin*t_src/t_tot\n\tsrc = grb_flat_bin\n\t\n\tdata = src - bkgd\n\t\n\terr_src = grb_err_flat_bin\n\terr_bkgd = bkgd_err_flat_bin\n\t#err_model = sim_err_flat_bin*scaling\n\terr_model = sim_err_flat_bin*scaling\n\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\t\n\tratio = data/model\n\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\n\t\n\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n\t\n\t # PLotting the comparison plots\n\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\n\t\n\tax1.set_title(\"Comparison between simulated (scaled) and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f} \".format(tx=thetax,ty=thetay,c=chi_sq))\n\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation (scaling = {s:0.2f},offset = {o:0.2f})\".format(s=scaling,o=intercept),elinewidth=0.5)\n\tax1.legend()\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\n\tax1.set_ylabel('Counts')\n\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n\t\n\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\"Ratio = Data/Model(scaling = {s:0.2f}, offset={o:0.2f})\".format(s=scaling,o=intercept),elinewidth=0.5)\n\tax2.xaxis.set_ticks(np.arange(0,len(data)))\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\n\tax2.tick_params(labelsize=5)\n\tax2.axhline(y=1,linewidth=0.5,color='k')\n\tax2.legend()\n\tax2.set_xlabel('CZT Modules')\n\tax2.set_ylabel('Ratio of counts')\n\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\n\tplt.tight_layout(h_pad=0.0)\n\tf.set_size_inches([6.5,10])\n\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\n\n\t# Plotting comparison graphs with random binning------------------------------\n\t\n sim_flat = sim_dph.flatten()\n\tsim_err_flat = sim_err_dph.flatten()\n grb_flat = grb_dph.flatten()\n bkgd_flat = bkgd_dph.flatten()\n\tsrc_flat = src_dph.flatten()\n\t\n\torder = np.random.permutation(np.arange(0,len(sim_flat)))\n\t\n sim_flat = sim_flat[order]\n\tsim_err_flat = sim_err_flat[order]\n\tgrb_flat = grb_flat[order]\n\tbkgd_flat = bkgd_flat[order]\n\tsrc_flat = src_flat[order]\n\t\n\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\n\tx = np.zeros(len(bins)+2,dtype=np.int64)\n\tx[0] = 0\n\tx[-1] = len(sim_flat)\n\tx[1:-1] = bins\n\t\n\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\n\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\t\n # Defining model background and data\n #model = sim_flat_bin*scaling\n\tmodel = sim_flat_bin*scaling + intercept\n bkgd = bkgd_flat_bin*t_src/t_tot\n src = grb_flat_bin\n\t\n data = src - bkgd\n\n err_src = np.sqrt(src)\n err_bkgd = np.sqrt(bkgd_flat_bin)\n #err_model = sim_err_flat_bin*scaling\n\terr_model = sim_err_flat_bin*scaling\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\t\n\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n # PLotting the comparison plots\n fig = plt.figure()\n plt.title(\"Comparison between simulated(scaled) and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f} \".format(tx=thetax,ty=thetay,c=chi_sq_new))\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation (scaling = {s:0.2f}, offset = {o:0.2f})\".format(s=scaling,o=intercept),elinewidth=0.5)\n plt.ylabel('Counts')\n\tplt.xlabel('Random Bins')\n\tplt.xticks(np.arange(0,(len(data)),1))\n\tplt.legend()\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\n\n\n\t# Plotting observed vs predicted counts--------------------------------------------------------\n\n\tfig = plt.figure()\n plt.title(grbdir + r\" : Observed vs Predicted counts with $\\chi^2$ = {cs:0.1f}\".format(cs=chi_sq))\n plt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\n\tfor i in range(len(model_copy)):\t\n\t\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\n #plt.plot(np.arange(-1000,1000),fit_line(np.arange(-1000,1000),scaling),'k',linewidth=0.5,label='m = {s:0.2f}'.format(s=scaling))\n\tplt.plot(np.arange(-1000,1000),fit_line_int(np.arange(-1000,1000),scaling,intercept),'k',linewidth=0.5,label='scaling = {s:0.2f}, offset = {i:0.2f}'.format(s=scaling,i=intercept))\n\tplt.plot(np.arange(min(model_copy)-5,max(model_copy)+5),np.ones(len(np.arange(min(model_copy)-5,max(model_copy)+5)))*intercept,'r-',label='intercept',linewidth=0.5)\n plt.xlim(min(model_copy)-5,max(model_copy)+5)\n plt.ylim(min(data_copy)-5,max(data_copy)+5)\n plt.xlabel('Predicted Counts')\n plt.ylabel('Observed Counts')\n\tplt.legend()\n\tplt.grid()\n pdf_file.savefig(fig)\n\t\t\n\tprint \"===============================================================================================\"\n\t\n\treturn","def main(config_file, pre_nfiles=None, post_nfiles=None,\n cutoffs=[12.0, 60.0]):\n imu_complement_fig = (config_file[:-4] +\n \"_IMU_complementary_spectra.pdf\")\n imu_spectra_fig = (config_file[:-4] + \"_IMU_spectra.pdf\")\n nperseg1 = 60 * 5 * 10\n nperseg2 = 60 * 10\n fig, axs, leg = check.plot_IMU_euler_angles_spectra(config_file,\n nperseg=nperseg2,\n nfiles=pre_nfiles)\n fig.savefig(imu_complement_fig, bbox_extra_artists=(leg,),\n bbox_inches=\"tight\")\n plt.close(fig)\n fig_rate, _, fig_acc, _ = check.plot_IMU_spectra(config_file,\n nperseg=nperseg2,\n nfiles=pre_nfiles)\n with PdfPages(imu_spectra_fig) as pdf:\n pdf.savefig(fig_rate, bbox_inches=\"tight\")\n plt.close(fig_rate)\n pdf.savefig(fig_acc, bbox_inches=\"tight\")\n plt.close(fig_acc)\n\n # Look at individual periods\n config = db_flux.parse_config(config_file)\n input_files = config[\"EC Inputs\"][\"input_files\"]\n if post_nfiles is not None and post_nfiles < len(input_files):\n input_files = np.random.choice(input_files, post_nfiles,\n replace=False)\n imu2anem_pos = config[\"EC Motion Correction\"][\"imu2anemometer_pos\"]\n sample_freq_hz = config[\"EC Inputs\"][\"sample_frequency\"]\n Tcf, Ta = cutoffs\n for ifile in input_files:\n file_idx = ifile.index(ifile)\n ec_prep, _ = db_flux.prepare_period(ifile, config)\n wind = ec_prep[[\"wind_speed_u\", \"wind_speed_v\", \"wind_speed_w\"]]\n acceleration = ec_prep[[\"acceleration_x\",\n \"acceleration_y\",\n \"acceleration_z\"]]\n rate = ec_prep[[\"rate_x\", \"rate_y\", \"rate_z\"]]\n ec_corr = db_flux.wind3D_correct(wind.values, acceleration.values,\n rate.values,\n ec_prep.heading.values,\n ec_prep.speed_over_ground.values,\n imu2anem_pos, sample_freq_hz,\n Tcf, Ta)\n fig, _, leg = check.plot1_IMU_complementary_spectra(config_file,\n file_idx,\n Tcf=Tcf,\n Ta=Ta,\n nperseg=nperseg1)\n fig.savefig((ifile[:-4] + \"_euler_angle_spectra.pdf\"),\n bbox_extra_artists=(leg,), bbox_inches=\"tight\")\n plt.close()\n fig, axs, leg = check.plot_wind3D_spectra(wind.values,\n ec_corr.uvw_ship,\n nperseg=nperseg2)\n fig.savefig((ifile[:-4] + \"_uvw_spectra.pdf\"),\n bbox_extra_artists=(leg,), bbox_inches=\"tight\")\n plt.close()\n # Plot corrected wind cospectra\n fig, _, leg = check.plot_wind3D_cospectra(wind.values,\n ec_corr.uvw_ship,\n nperseg=nperseg2)\n fig.savefig((ifile[:-4] + \"_uvw_cospectra.pdf\"),\n bbox_extra_artists=(leg,), bbox_inches=\"tight\")\n plt.close()","def make_badge(VERBOSE=1, FILENAME=\"GENERATED_BADGE.png\", DeviceDescriptionsDict={}, CreatureDescriptionsDict={},\r\n OrdinaryDescriptionsDict={}, textfile=None, USESTAINS=1, SAVETOFILE=1, SILENT=0, GRAPHICSMODE=\"PIL\"):\r\n\r\n TEST = 1\r\n\r\n if USESTAINS == 1:\r\n var1 = random.choice((\"Colour\", \"Colour\", \"Colour\", \"Metal\", \"Metal\", \"Stain\"))\r\n else:\r\n var1 = random.choice((\"Colour\", \"Colour\", \"Colour\", \"Metal\", \"Metal\"))\r\n if var1 == \"Metal\":\r\n color1Dict = getMetal()\r\n color1 = color1Dict[\"Name\"]\r\n color1name = color1Dict[\"Colour\"]\r\n color2Dict = getColour()\r\n color2 = color2Dict[\"Name\"]\r\n color2name = color2Dict[\"Colour\"]\r\n elif var1 == \"Colour\":\r\n color1Dict = getColour()\r\n color1 = color1Dict[\"Name\"]\r\n color1name = color1Dict[\"Colour\"]\r\n color2Dict = getMetal()\r\n color2 = color2Dict[\"Name\"]\r\n color2name = color2Dict[\"Colour\"]\r\n else:\r\n color1Dict = getStain()\r\n color1 = color1Dict[\"Name\"]\r\n color1name = color1Dict[\"Colour\"]\r\n color2Dict = getMetal()\r\n color2 = color2Dict[\"Name\"]\r\n color2name = color2Dict[\"Colour\"]\r\n\r\n if VERBOSE > 0:\r\n print\r\n print \"color1:\\t%s\\t(%s)\" % (color1, color1name)\r\n print \"color2:\\t%s\\t(%s)\" % (color2, color2name)\r\n\r\n# if os.path.isdir(\"game\"):\r\n# os.chdir(\"game\")\r\n\r\n## if string.split(os.getcwd(), os.path.sep)[-1] != \"game\":\r\n## if VERBOSE > 0:\r\n## print \"changing directory FROM '%s'\" % os.getcwd()\r\n## while string.split(os.getcwd(), os.path.sep)[-1] != \"game\":\r\n## os.chdir(\"..\")\r\n## if VERBOSE > 0:\r\n## print \"changed directory TO '%s'\" % os.getcwd()\r\n\r\n rootdir = os.getcwd()\r\n\r\n # special fiddle for when being run from the Renpy launcher\r\n if string.find(rootdir, \"renpy-6.99.14.3-sdk\") > -1:\r\n #os.chdir(\"E:\\Projects-DISTROS\\Random_Heraldry_Generator-%s-win\\Random_Heraldry_Generator-%s-win\" % (__VERSION__, __VERSION__))\r\n os.chdir(\"E:\\Projects\\NEW RENPY GAME\\game\")\r\n rootdir = os.getcwd()\r\n\r\n badgesdir = os.path.join(rootdir, \"images\", \"badges\")\r\n #TEMPDIR = os.path.join(rootdir, \"..\", \"TEMP\")\r\n TEMPDIR = os.path.join(rootdir, \"TEMP\")\r\n\r\n creaturesdir = os.path.join(badgesdir, \"Creatures\")\r\n devicesdir = os.path.join(badgesdir, \"Devices\")\r\n #print devicesdir\r\n ordinariesdir = os.path.join(badgesdir, \"Ordinaries\")\r\n shieldsdir = os.path.join(badgesdir, \"Shields\")\r\n\r\n badgeType = random.choice((\"Creature\", \"Device\", \"Ordinary\", \"Quartered\"))\r\n\r\n if badgeType == \"Creature\":\r\n if VERBOSE > 0:\r\n print \"badgeType: Creature\"\r\n os.chdir(creaturesdir)\r\n blazon, badge = make_Creature_Badge(color1Dict, color2Dict, VERBOSE, CreatureDescriptionsDict, SILENT, GRAPHICSMODE)\r\n #will make more use of the blazon later...\r\n if VERBOSE > 0:\r\n print \" BLAZON:\"\r\n print \" '%s'\" % blazon\r\n elif badgeType == \"Device\":\r\n if VERBOSE > 0:\r\n print \"badgeType: Device\"\r\n os.chdir(devicesdir)\r\n blazon, badge = make_Device_Badge(color1Dict, color2Dict, VERBOSE, DeviceDescriptionsDict, SILENT, GRAPHICSMODE)\r\n #will make more use of the blazon later...\r\n if VERBOSE > 0:\r\n print \" BLAZON:\"\r\n print \" '%s'\" % blazon\r\n elif badgeType == \"Ordinary\":\r\n if VERBOSE > 0:\r\n print \"badgeType: Ordinary\"\r\n os.chdir(ordinariesdir)\r\n blazon, badge = make_Ordinary_Badge(color1Dict, color2Dict, VERBOSE, OrdinaryDescriptionsDict, SILENT, GRAPHICSMODE)\r\n #will make more use of the blazon later...\r\n if VERBOSE > 0:\r\n print \" BLAZON:\"\r\n print \" '%s'\" % blazon\r\n elif badgeType == \"Quartered\":\r\n if VERBOSE > 0:\r\n print \"badgeType: Quartered\"\r\n os.chdir(ordinariesdir)\r\n blazon, badge = make_Quartered_Badge(color1Dict, color2Dict, VERBOSE, OrdinaryDescriptionsDict,\r\n DeviceDescriptionsDict, SILENT, GRAPHICSMODE)\r\n if VERBOSE > 0:\r\n print \" BLAZON:\"\r\n print \" '%s'\" % blazon\r\n if textfile != None:\r\n if SAVETOFILE == 1:\r\n textfile.write(\"%s\\n\" % FILENAME)\r\n textfile.write(\"\\t%s\\n\\n\" % blazon)\r\n\r\n if SAVETOFILE == 1:\r\n if GRAPHICSMODE == \"PIL\":\r\n badge.save(os.path.join(TEMPDIR, FILENAME))\r\n #badge.save(os.path.join(rootdir, \"images\", \"temp\", FILENAME))\r\n elif GRAPHICSMODE == \"PyGame\":\r\n pygame.image.save(badge, os.path.join(TEMPDIR, FILENAME))\r\n #pygame.image.save(badge, os.path.join(rootdir, \"images\", \"temp\", FILENAME))\r\n if VERBOSE > 0:\r\n print \"\\twrote file '%s'\" % os.path.join(TEMPDIR, FILENAME)\r\n #print \"\\twrote file '%s'\" % os.path.join(rootdir, \"images\", \"temp\", FILENAME)\r\n print\r\n\r\n os.chdir(rootdir)\r\n\r\n return blazon, badge","def run_image_viewer( self ):\n\n # XXX: hardcoded program name and image size.\n subprocess.Popen( [\"feh\", \"-dZ\", \"-g\", \"800x600\", self.record[\"filename\"]] )","def singleHierarchy(config_path, model_path, image_path, cuda, crf, sizeThresh=1/9, nIterations=10, doPlot=True):\r\n\r\n # Setup\r\n CONFIG = OmegaConf.load(config_path)\r\n device = get_device(cuda)\r\n torch.set_grad_enabled(False)\r\n\r\n classes = get_classtable(CONFIG)\r\n postprocessor = setup_postprocessor(CONFIG) if crf else None\r\n\r\n model = eval(CONFIG.MODEL.NAME)(n_classes=CONFIG.DATASET.N_CLASSES)\r\n state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)\r\n model.load_state_dict(state_dict)\r\n model.eval()\r\n model.to(device)\r\n print(\"Model:\", CONFIG.MODEL.NAME)\r\n\r\n # Inference\r\n image = cv2.imread(image_path, cv2.IMREAD_COLOR)\r\n image, raw_image = preprocessing(image, device, CONFIG)\r\n # labelmap = inference(model, image, raw_image, postprocessor)\r\n labelmapList = inferenceHierarchy(model, image, raw_image, postprocessor, sizeThresh, nIterations)\r\n if doPlot:\r\n for labelmap in labelmapList:\r\n labels = np.unique(labelmap)\r\n \r\n # Show result for each class\r\n rows = np.floor(np.sqrt(len(labels) + 1))\r\n cols = np.ceil((len(labels) + 1) / rows)\r\n \r\n plt.figure(figsize=(10, 10))\r\n ax = plt.subplot(rows, cols, 1)\r\n ax.set_title(\"Input image\")\r\n ax.imshow(raw_image[:, :, ::-1])\r\n ax.axis(\"off\")\r\n \r\n for i, label in enumerate(labels):\r\n mask = labelmap == label\r\n ax = plt.subplot(rows, cols, i + 2)\r\n ax.set_title(classes[label])\r\n ax.imshow(raw_image[..., ::-1])\r\n ax.imshow(mask.astype(np.float32), alpha=0.5)\r\n ax.axis(\"off\")\r\n \r\n plt.tight_layout()\r\n plt.show()","def main():\n os.makedirs(\"./img/event_generated/\", exist_ok=True)\n for category in categories():\n render_icon(category + \".png\")","def image_processor(file_name, variations, storage):\n process_photo_image.delay(file_name, variations)\n return False # prevent default rendering","def test_config():\n import yaml\n import astropy.units as u\n from tqdm import tqdm\n # Need these for `eval` below\n from numpy import array\n\n # Same test suite as used in test_imsim above.\n # This time, we just use this for the det names.\n with open(DATA_DIR / \"wcs_466749.yaml\", 'r') as f:\n wcss = yaml.safe_load(f)\n\n cmds = {}\n with open(DATA_DIR / \"phosim_cat_466749.txt\", 'r') as f:\n for line in f:\n k, v = line.split()\n try:\n v = int(v)\n except ValueError:\n try:\n v = float(v)\n except ValueError:\n pass\n cmds[k] = v\n\n # Values below (and others) from phosim_cat_466749.txt\n rc = cmds['rightascension']\n dc = cmds['declination']\n boresight = galsim.CelestialCoord(\n rc*galsim.degrees,\n dc*galsim.degrees\n )\n obstime = Time(cmds['mjd'], format='mjd', scale='utc')\n obstime -= 15*u.s\n band = \"ugrizy\"[cmds['filter']]\n wavelength_dict = dict(\n u=365.49,\n g=480.03,\n r=622.20,\n i=754.06,\n z=868.21,\n y=991.66\n )\n wavelength = wavelength_dict[band]\n camera = imsim.get_camera()\n\n rotTelPos = cmds['rottelpos'] * galsim.degrees\n telescope = imsim.load_telescope(f\"LSST_{band}.yaml\", rotTelPos=rotTelPos)\n # Non-default values.\n temperature = 293.\n pressure = 69.0\n H2O_pressure = 2.0\n\n factory = imsim.BatoidWCSFactory(\n boresight, obstime, telescope, wavelength,\n camera,\n temperature=temperature,\n pressure=pressure,\n H2O_pressure=H2O_pressure\n )\n\n config = {\n 'input': {\n 'telescope': {\n 'file_name':f\"LSST_{band}.yaml\",\n 'rotTelPos': rotTelPos\n }\n },\n 'image': {\n 'wcs': {\n 'type': 'Batoid',\n 'boresight': boresight,\n 'camera': 'LsstCam',\n 'obstime': obstime,\n 'wavelength': wavelength,\n 'temperature': temperature,\n 'pressure': pressure,\n 'H2O_pressure': H2O_pressure,\n 'order': 2,\n }\n }\n }\n\n rng = np.random.default_rng(1234)\n for k in tqdm(wcss.keys()):\n name = k[18:25].replace('-', '_')\n det = camera[name]\n\n wcs1 = factory.getWCS(det, order=2)\n config['image']['wcs']['det_name'] = name\n galsim.config.RemoveCurrent(config['image']['wcs'])\n galsim.config.ProcessInput(config)\n wcs2 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n\n # Test points\n xs = rng.uniform(0, 4000, 100)\n ys = rng.uniform(0, 4000, 100)\n ra1, dec1 = wcs1.xyToradec(xs, ys, units='radians')\n ra2, dec2 = wcs2.xyToradec(xs, ys, units='radians')\n np.testing.assert_allclose(ra1, ra2)\n np.testing.assert_allclose(dec1, dec2)\n\n # Test == when identical\n galsim.config.RemoveCurrent(config['image']['wcs'])\n wcs3 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs3 == wcs2\n\n # Test that pressure and temperature matter.\n config['image']['wcs']['temperature'] = 250\n galsim.config.RemoveCurrent(config['image']['wcs'])\n wcs4 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs4 != wcs2\n\n config['image']['wcs']['temperature'] = temperature\n config['image']['wcs']['pressure'] = 55\n galsim.config.RemoveCurrent(config['image']['wcs'])\n wcs5 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs5 != wcs2\n\n config['image']['wcs']['pressure'] = pressure\n config['image']['wcs']['H2O_pressure'] = 10\n galsim.config.RemoveCurrent(config['image']['wcs'])\n wcs6 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs6 != wcs2\n\n # Test defaults\n del config['image']['wcs']['temperature']\n del config['image']['wcs']['pressure']\n del config['image']['wcs']['H2O_pressure']\n galsim.config.RemoveCurrent(config['image']['wcs'])\n config = galsim.config.CleanConfig(config)\n galsim.config.ProcessInput(config)\n wcs7 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n default_pressure = 101.325 * (1-2.25577e-5*2715)**5.25588\n wcs7a = imsim.BatoidWCSFactory(\n boresight, obstime, telescope, wavelength, camera,\n temperature=280,\n pressure=default_pressure,\n H2O_pressure=1.0,\n ).getWCS(det, order=2)\n assert wcs7 == wcs7a\n\n # Default wavelength from bandpass\n del config['image']['wcs']['wavelength']\n config['bandpass'] = imsim.RubinBandpass('r')\n galsim.config.RemoveCurrent(config['image']['wcs'])\n config = galsim.config.CleanConfig(config)\n galsim.config.ProcessInput(config)\n wcs8 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n wcs8a = imsim.BatoidWCSFactory(\n boresight, obstime, telescope,\n wavelength=config['bandpass'].effective_wavelength,\n camera=camera,\n temperature=280,\n pressure=default_pressure,\n H2O_pressure=1.0,\n ).getWCS(det, order=2)\n assert wcs8 == wcs8a\n\n del config['bandpass']\n config['image']['bandpass'] = {\n 'type': 'RubinBandpass',\n 'band' : 'r',\n }\n galsim.config.RemoveCurrent(config['image']['wcs'])\n config = galsim.config.CleanConfig(config)\n galsim.config.ProcessInput(config)\n wcs8b = galsim.config.BuildWCS(config['image'], 'wcs', config)\n assert wcs8b == wcs8a\n\n # Obstime can be a string\n print('obstime = ',obstime.to_value('iso'), type(obstime.to_value('iso')))\n config['image']['wcs']['obstime'] = obstime.to_value('iso')\n # Doesn't quite roundtrip perfectly. But within a millisecond.\n obstime = Time(obstime.to_value('iso'), scale='tai')\n print('obstime => ',obstime)\n galsim.config.RemoveCurrent(config['image']['wcs'])\n config = galsim.config.CleanConfig(config)\n galsim.config.ProcessInput(config)\n wcs9 = galsim.config.BuildWCS(config['image'], 'wcs', config)\n wcs9a = imsim.BatoidWCSFactory(\n boresight, obstime, telescope,\n wavelength=config['bandpass'].effective_wavelength,\n camera=camera,\n temperature=280,\n pressure=default_pressure,\n H2O_pressure=1.0,\n ).getWCS(det, order=2)\n assert wcs9 == wcs9a","def display_sample(display_list):\n fig, axs = plt.subplots(1, len(display_list), figsize=(18, 18))\n title = ['Input Image', 'True Mask', 'Predicted Mask']\n for i in range(len(display_list)):\n axs[i].set_title(title[i])\n axs[i].imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))\n axs[i].axis('off')","def instructions():\n txt_naming = ConfigReader.texture_naming_dict()\n\n text = \"<b>Texture naming rules:</b><br>(put an underscore _ at the end of file name; \" \\\n \"you can enumerate textures using two digits after texture type without any other character\" \\\n \"<br> e.g. _normal01 or in general _normalXX)\" \\\n \"<br>\"\n\n for key, value in txt_naming.iteritems():\n text += \"<br>- {0}: {1}\".format(key, ', '.join(a for a in value['text']))\n\n text += \"<br>\"\n text += \"<br><b>File formats:</b>\"\n text += \"<br>Meshes:\"\n text += ConfigReader.generate_file_filter()\n text += \"<br>Textures:\"\n text += ConfigReader.generate_texture_filter()\n\n return text","def print_image(indiv,name):\n routine = gp.compile(indiv,pset)\n output = gen_beat_output(routine)\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\n plt.style.use('classic')\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\n plt.savefig(name+\".png\",dpi=150)","def plot_generated_images(decoder):\n\n # display a nxn 2D manifold of digits\n n = 15\n digit_size = 28\n\n figure = np.zeros((digit_size * n, digit_size * n))\n # linearly spaced coordinates corresponding to the 2D plot\n # of digit classes in the latent space\n grid_x = np.linspace(-4, 4, n)\n grid_y = np.linspace(-4, 4, n)[::-1]\n\n # start sampling z1 and z2 in the ranges grid_x and grid_y\n for i, yi in enumerate(grid_y):\n for j, xi in enumerate(grid_x):\n z_sample = np.array([[xi, yi]])\n x_decoded = decoder.predict(z_sample)\n digit = x_decoded[0].reshape(digit_size, digit_size)\n slice_i = slice(i * digit_size, (i + 1) * digit_size)\n slice_j = slice(j * digit_size, (j + 1) * digit_size)\n figure[slice_i, slice_j] = digit\n\n # plot the results\n plt.figure(figsize=(6, 5))\n start_range = digit_size // 2\n end_range = n * digit_size + start_range + 1\n pixel_range = np.arange(start_range, end_range, digit_size)\n sample_range_x = np.round(grid_x, 1)\n sample_range_y = np.round(grid_y, 1)\n plt.xticks(pixel_range, sample_range_x)\n plt.yticks(pixel_range, sample_range_y)\n plt.xlabel(\"z[0]\")\n plt.ylabel(\"z[1]\")\n plt.imshow(figure, cmap='Greys_r')\n plt.show()","def main():\n\n # first figure: betas for each predictor\n fig, axes = plt.subplots(figsize=(8, 18), nrows=3)\n\n image_paths = {\n \"3T\": (\n f\"{PATHS['figures_misc']}/figure_3_images/\"\n f\"C1051_20161006_childVSall_depth_1.png\"\n ),\n \"7T\": (\n f\"{PATHS['figures_misc']}/figure_3_images/\"\n f\"C1051_20160212_childVSall_depth_1_sim2pt4.png\"\n ),\n \"7T_noise\": (\n f\"{PATHS['figures_misc']}/figure_3_images/\"\n f\"C1051_20160212_childVSall_depth_1_sim2pt4_noise.png\"\n ),\n }\n\n for ax, (_, image_path) in zip(axes, image_paths.items()):\n assert os.path.isfile(image_path)\n img = imread(image_path)\n img[np.where(np.sum(img, axis=2) == 0.0)] = 1.0\n\n ax.imshow(img[200:-100, 50:-50, :])\n ax.axis(\"off\")\n\n savefig(f\"{PATHS['figures']}/figure_3ac.png\")\n plt.close(fig)","def __call__(cls, nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit):\n\n# on with the show\n logger.info('Opening science and confidence frames')\n ifits=fitsio.FITS(nir_paw_image_fname,'r')\n cfits=fitsio.FITS(nir_paw_conf_fname,'r')\n\n#\n# Check that the number of HDUs match\n#\n\n if (len(ifits) != len(cfits)):\n print(\"Number of HDUs/extensions in IMAGE and CONFidence files do not match.\")\n print(\"Aborting\")\n exit(1)\n\n p_ih=ifits[0].read_header()\n p_ch=cfits[0].read_header()\n# Remove reserve keywords\n p_ih.clean()\n\n#\n# Extract some keywords from PRIMARY header to propagate into the individual images.\n#\n base_dict={}\n base_header=[]\n for hkeep in nci.nir_paw_primary_keep:\n if (hkeep in p_ih):\n base_header.append({'name':hkeep,'value':p_ih[hkeep],'comment':p_ih.get_comment(hkeep)})\n base_dict[hkeep]={'value':p_ih[hkeep],'comment':p_ih.get_comment(hkeep)}\n else:\n print(\"Keyword {:s} missing in HDU[{:d}]\".format(hkeep,0))\n#\n# If possible, need too keep track of REQTIME (requested frametime) because sometimes \n# EXPTIME seems to be mispopulated in the CCD image HDUs with TEXPTIME\n#\n if ('TEXPTIME' in p_ih):\n texptime=p_ih['TEXPTIME']\n else:\n texptime=None\n if ('REQTIME' in p_ih):\n reqtime=p_ih['REQTIME']\n else:\n reqtime=None\n#\n# print(base_header)\n \n\n#\n# Step through HDUs... and form \"CCD\" images for each HDU\n#\n ExtList=[]\n for hnum in range(1,len(ifits)):\n print(\"############ Begin work on extnum={:d} ###############\".format(hnum))\n\n# Check that extensions match (after that concentrate on image).\n print(hnum,ifits[hnum].get_extname(),cfits[hnum].get_extname())\n if (ifits[hnum].get_extname() != cfits[hnum].get_extname()):\n print(\"Working on extension {:d}. Extension names (image,conf) of ([{:s}],[{:s}]) do not match!\".format(\n hnum,ifits[hnum].get_extname(),cfits[hnum].get_extname()))\n print(\"Aborting!\")\n exit(1)\n\n f_ih=ifits[hnum].read_header()\n f_ih.clean()\n#\n# Fix occurences where the CCD-level keyword EXPTIME has inherited the value of TEXPTIME\n#\n exptime=f_ih['EXPTIME']\n if (reqtime is not None):\n if (exptime > reqtime):\n print(\"Warning: possible corrupt EXPTIME (total exptime rather than frame time present).\")\n print(\"Attempting to update EXTIME to REQTIME (requested frame time).\")\n print(\" Primary HDU: TEXPTIME: {:}\".format(texptime))\n print(\" Primary HDU: REQTIME: {:}\".format(reqtime))\n print(\" Current HDU: EXPTIME: {:} --> {:}\".format(exptime,reqtime))\n exptime=reqtime\n f_ih['EXPTIME']=reqtime\n#\n# Augment keywords pulled from primary header with keywords from current HDU\n#\n c_header=base_header[:]\n c_dict=dict(base_dict)\n for hkeep in nci.nir_paw_hdu_keep:\n if (hkeep in f_ih):\n# print(hkeep,f_ih[hkeep],f_ih.get_comment(hkeep))\n c_header.append({'name':hkeep,'value':f_ih[hkeep],'comment':f_ih.get_comment(hkeep)})\n if (hkeep in c_dict):\n print(\"Warning: Replacing keyword {:s} with value from hdu={:d}\".format(hkeep,hnum))\n c_dict[hkeep]={'value':f_ih[hkeep],'comment':f_ih.get_comment(hkeep)}\n else:\n print(\"Keyword {:s} missing in HDU[{:d}]\".format(hkeep,hnum))\n\n#\n# Get the CCDNUM from special keyword and propagate\n# Get SKYLEVEL, SKYNOISE, ZEROPOINT and form basis value for the weight plane\n#\n ccdnum=f_ih['HIERARCH ESO DET CHIP NO']\n c_header.append({'name':'CCDNUM','value':ccdnum,'comment':'Unique Detector Number'})\n\n# exptime=f_ih['EXPTIME']\n## Fix occurences where the CCD-level keyword EXPTIME has inherited the value of TEXPTIME\n# if (exptime > reqtime):\n# print(\"Warning: possible corrupt EXPTIME (total exptime rather than frame time present).\")\n# print(\"Attempting to update EXTIME to REQTIME (requested frame time).\")\n# print(\" Primary HDU: TEXPTIME: {:.2f}\".format(texptime))\n# print(\" Primary HDU: REQTIME: {:.2f}\".format(reqtime))\n# print(\" Current HDU: EXPTIME: {:.2f} --> {:.2f}\".format(exptime,reqtime))\n# exptime=reqtime\n# f_ih['EXPTIME']=reqtime\n\n mtime=2.5*np.log10(exptime)\n skylev=f_ih['SKYLEVEL']\n skyrms=f_ih['SKYNOISE']\n seeing=f_ih['SEEING']\n magzpt=f_ih['MAGZPT']\n\n# zeropoint include a correction from VEGA->AB\n# zeropoint in headers was found to have a factor for EXPTIME removed (have to add back in for DES-like processing)\n\n if (p_ih['BAND'] in nci.nir_vega_to_ab):\n magzpt=magzpt+nci.nir_vega_to_ab[p_ih['BAND']]+mtime\n else:\n print(\"Warning! Unknown BAND ({:s}) for conversion of zeropoint from VEGA to AB system\".format(p_ih['BAND']))\n\n c_header.append({'name':'SKYBRITE', 'value':skylev, 'comment':'Sky level estimate from IMCORE'})\n c_header.append({'name':'SKYSIGMA', 'value':skyrms, 'comment':'Sky noise estimate from IMCORE'})\n c_header.append({'name':'SKYVARA', 'value':skyrms*skyrms, 'comment':'Sky noise estimate from IMCORE'})\n c_header.append({'name':'SKYVARB', 'value':skyrms*skyrms, 'comment':'Sky noise estimate from IMCORE'})\n c_header.append({'name':'FWHM', 'value':seeing, 'comment':'Average FWHM (pixels)'})\n c_header.append({'name':'MAG_ZERO', 'value':magzpt, 'comment':'Converted MAGZPT(Vega) to AB system'})\n nite_val=convert_utc_str_to_nite(f_ih['DATE-OBS'])\n c_header.append({'name':'NITE', 'value':nite_val, 'comment':'Observation Nite'})\n c_header.append({'name':'SATURATE', 'value':nci.nircam_satval[ccdnum], 'comment': 'Saturation Level (ADU)'})\n c_header.append({'name':'PIXSCAL1', 'value':0.341, 'comment': 'Fiducial pixel scale (arcsec/pix)'})\n c_header.append({'name':'PIXSCAL2', 'value':0.341, 'comment': 'Fiducial pixel scale (arcsec/pix)'})\n\n# bval=f_ih['BSCALE']\n# print(\"BSCALE was: \",bval)\n print(\"SKYLEVEL was: \",skylev)\n print(\"SKYRMS was: \",skyrms)\n#\n# Searching for a proper WGT prescription\n#\n# This was what I took to be equivalent to DES (but perhaps it does not properly factor in N-image stack\n# wgtval=skylev+(skyrms*skyrms)\n print(\"SKYLEV + (SKYRMS*SKYRMS): \",skylev+(skyrms*skyrms))\n#\n# This was assuming SKYLEVEL does not properly inform stats\n# wgtval=(skyrms*skyrms)\n print(\"(SKYRMS*SKYRMS): \",skyrms*skyrms)\n\n#\n# Read the image data from the science and confidence files.\n#\n sci_data=ifits[hnum].read()\n print(\"Median of data {:.3f} \".format(np.median(sci_data)))\n conf_data=cfits[hnum].read()\n\n#\n# Better seemed to be a re-measurement of STD\n#\n print(\"Attempting an improved SKYRMS with 3-sigma clip to remove objects\")\n avgval, medval, stdval = medclip(sci_data,verbose=3)\n# print(avgval,medval,stdval)\n print(\"stdval^2: \",stdval*stdval)\n wgtval=(stdval*stdval)\n# print(wgtval)\n#\n# Use the new (i.e. chip-based header) to feed a WCS \n# Use image size to feed calculations for center and corners (similar to despyastro.CCD_corners\n#\n print(\"Calculating center/corners assuuming native ZPN projection\")\n w=WCS(fitsio.FITSHDR(c_header))\n\n fnax2=float(sci_data.shape[0])\n fnax1=float(sci_data.shape[1])\n corn_x=np.array([fnax1/2.0,1.,fnax1,fnax1,1.])\n corn_y=np.array([fnax2/2.0,1.,1.,fnax2,fnax2])\n sky = w.pixel_to_world(corn_x,corn_y)\n corn_ra=sky.ra.degree\n corn_dec=sky.dec.degree\n\n c_header.append({'name':'RA_CENT', 'value':corn_ra[0], 'comment':'RA center'})\n c_header.append({'name':'DEC_CENT','value':corn_dec[0],'comment':'DEC center'})\n for i in range(1,5):\n c_header.append({'name':'RAC{:d}'.format(i), 'value':corn_ra[i], 'comment':'RA corner {:d}'.format(i)})\n c_header.append({'name':'DECC{:d}'.format(i),'value':corn_dec[i],'comment':'DEC corner {:d}'.format(i)})\n RACMIN, RACMAX, DECCMIN, DECCMAX, CROSSRA0 = get_DESDM_corners_extent(corn_ra, corn_dec)\n c_header.append({'name':'RACMIN', 'value':RACMIN, 'comment':'Minimum extent of image in RA'})\n c_header.append({'name':'RACMAX', 'value':RACMAX, 'comment':'Maximum extent of image in RA'})\n c_header.append({'name':'DECCMIN', 'value':DECCMIN, 'comment':'Minimum extent of image in Declination'})\n c_header.append({'name':'DECCMAX', 'value':DECCMAX, 'comment':'Maximum extent of image in Declination'})\n c_header.append({'name':'CROSSRA0','value':CROSSRA0,'comment':'Does Image Span RA 0h (Y/N)'})\n c_header.append({'name':'DESEPOCH','value':'NIREPOCH','comment':'Default DES epoch definition for including NIR data'})\n#\n#\n#\n print(\"Stripping ZPN projection from WCS and creating a shift to get a rough TAN\")\n recs_to_delete=[] \n for i, hrec in enumerate(c_header):\n if (hrec['name'] == 'CTYPE1'):\n c_header[i]['value']='RA---TAN'\n if (hrec['name'] == 'CTYPE2'):\n c_header[i]['value']='DEC--TAN'\n\n if (hrec['name'] == 'CRVAL1'):\n c_header[i]['value']=corn_ra[0]\n if (hrec['name'] == 'CRVAL2'):\n c_header[i]['value']=corn_dec[0]\n if (hrec['name'] == 'CRPIX1'):\n c_header[i]['value']=fnax1/2.0\n if (hrec['name'] == 'CRPIX2'):\n c_header[i]['value']=fnax2/2.0\n\n if (hrec['name'] in ['PV2_1','PV2_2','PV2_3','PV2_4','PV2_5']):\n recs_to_delete.append(i)\n if (len(recs_to_delete) > 0):\n for i in sorted(recs_to_delete,reverse=True):\n x=c_header.pop(i)\n print(\"Removing: {:}\".format(x))\n\n whack=WCS(fitsio.FITSHDR(c_header))\n skyhack = whack.pixel_to_world(corn_x,corn_y)\n whack_corn_ra=skyhack.ra.degree\n whack_corn_dec=skyhack.dec.degree\n for i in range(5):\n cosdec=np.cos(corn_dec[i]*np.pi/180.)\n dra=3600.*(corn_ra[i]-whack_corn_ra[i])*cosdec\n ddec=3600.*(corn_dec[i]-whack_corn_dec[i])\n print(\" WCS shift {:d} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} \".format(ccdnum,corn_ra[i],corn_dec[i],whack_corn_ra[i],whack_corn_dec[i],dra,ddec))\n\n# for i, hrec in enumerate(c_header):\n# print(i,hrec)\n\n#\n# Form the SCI, MSK, and WGT HDUs\n#\n im=DESImage(init_data=True,init_mask=True,init_weight=True,shape=sci_data.shape)\n\n im.data=np.float32(sci_data)\n msk_wsm=np.where(conf_data<conf_limit)\n im.mask[msk_wsm] |= BADPIX_BPM\n im.weight=np.float32(conf_data/100./wgtval)\n#\n# Check for extra conditions where further masking is needed\n# Here is where CCD=6 check was started (now removed and placed \n# in nir_starmask to take advantage of bright object masking\n#\n\n\n#\n# Deal with individual header-isms and write out SCI, MSK, WGT\n# Note this is using fitsio (duplicating some of the DESIMAGE.save \n# but customization was needed to deal with foibles of the current\n#\n fname=re.sub('%02d','{:02d}'.format(ccdnum),output_template,1)\n ofits = fitsio.FITS(fname, 'rw', clobber=True)\n\n im.header=fitsio.FITSHDR(c_header) \n im.header['DES_EXT']='IMAGE'\n im.header = update_hdr_compression(im.header, 'SCI')\n ofits.write(im.data,extname='SCI',header=im.header)\n\n\n im.mask_hdr=fitsio.FITSHDR(c_header) \n im.mask_hdr['DES_EXT']='MASK'\n im.mask_hdr = update_hdr_compression(im.mask_hdr, 'MSK')\n im.mask_hdr['DES_EXT']='MASK'\n ofits.write(im.mask,extname='MSK',header=im.mask_hdr)\n\n# im.weight_hdr=fitsio.FITSHDR(c_header) \n# print(im.weight_hdr)\n im.weight_hdr = update_hdr_compression(im.weight_hdr, 'WGT')\n# print(im.weight_hdr)\n im.weight_hdr['DES_EXT']='WEIGHT'\n ofits.write(im.weight,extname='WGT',header=im.weight_hdr)\n\n ofits.close()\n print(\"Wrote {:s}\".format(fname))\n print(\" \")\n \n\n ifits.close()\n cfits.close()\n\n ret_code = 0\n return ret_code","def labelled_config(infile, index):\n with gsd.hoomd.open(infile) as trj:\n snap = HoomdFrame(trj[index])\n\n fig = plot_labelled_config(snap)\n\n fig.output_backend = \"svg\"\n export_svgs(fig, \"figures/labelled_config.png\", height=1600, width=3200)","def generate_image_grid(sess, df, filenames,op, op2):\n #x_points = np.arange(0, 1, 1.5).astype(np.float32)\n #y_points = np.arange(0, 1, 1.5).astype(np.float32)\n\n nx, ny = 12, 1\n #plt.subplot()\n gs = gridspec.GridSpec(nx, ny, hspace=1, wspace=0.05)\n # input_x = sess.run(op2, feed_dict={x_input: df[0:24]})\n #\n # plt.imshow(np.array(df[0].tolist()).reshape(28, 28), cmap='gray')\n # plt.show()\n # x = sess.run(op, feed_dict={decoder_input: input_x[0].reshape(1,2)})\n # img = np.array(x.tolist()).reshape(28, 28)\n #\n # plt.imshow(img, cmap='gray')\n # plt.show()\n\n \"\"\" grid \"\"\"\n input_x = sess.run(op2, feed_dict={x_input: df[0:24]})\n for i, g in enumerate(gs):\n\n x = sess.run(op, feed_dict={decoder_input: input_x[i].reshape(1,2)})\n ax = plt.subplot(g)\n img = np.array(x.tolist()).reshape(28, 28)\n ax.imshow(img, cmap='gray')\n ax.set_xticks([])\n ax.set_yticks([])\n #ax.set_aspect('auto')\n ax.set_title(filenames[i])\n plt.show()\n\n for i, g in enumerate(gs):\n\n ax = plt.subplot(g)\n img = np.array(df[i].tolist()).reshape(28, 28)\n ax.imshow(img, cmap='gray')\n ax.set_xticks([])\n ax.set_yticks([])\n #ax.set_aspect('auto')\n ax.set_title(filenames[i])\n plt.show()","def test_rhostats_config():\n if __name__ == '__main__':\n logger = piff.config.setup_logger(verbose=2)\n else:\n logger = piff.config.setup_logger(log_file='output/test_rhostats_config.log')\n\n image_file = os.path.join('output','test_stats_image.fits')\n cat_file = os.path.join('output','test_stats_cat.fits')\n psf_file = os.path.join('output','test_rhostats.fits')\n rho_file = os.path.join('output','test_rhostats.pdf')\n config = {\n 'input' : {\n 'image_file_name' : image_file,\n 'cat_file_name' : cat_file,\n 'stamp_size' : 48\n },\n 'psf' : {\n 'model' : { 'type' : 'Gaussian',\n 'fastfit': True,\n 'include_pixel': False },\n 'interp' : { 'type' : 'Mean' },\n },\n 'output' : {\n 'file_name' : psf_file,\n 'stats' : { # Note: stats doesn't have to be a list.\n 'type': 'Rho',\n 'file_name': rho_file,\n 'min_sep': 30,\n 'max_sep': 600,\n 'sep_units': 'arcsec',\n 'bin_type': 'Linear',\n 'bin_size': 30,\n }\n },\n }\n piff.piffify(config, logger)\n assert os.path.isfile(rho_file)\n\n # repeat with plotify function\n os.remove(rho_file)\n piff.plotify(config, logger)\n assert os.path.isfile(rho_file)\n\n # Test rho statistics directly.\n min_sep = 1\n max_sep = 100\n bin_size = 0.1\n psf = piff.read(psf_file)\n orig_stars, wcs, pointing = piff.Input.process(config['input'], logger)\n stats = piff.RhoStats(min_sep=min_sep, max_sep=max_sep, bin_size=bin_size)\n with np.testing.assert_raises(RuntimeError):\n stats.write('dummy') # Cannot write before compute\n stats.compute(psf, orig_stars)\n\n rhos = [stats.rho1, stats.rho2, stats.rho3, stats.rho4, stats.rho5]\n for rho in rhos:\n # Test the range of separations\n radius = np.exp(rho.logr)\n np.testing.assert_array_less(radius, max_sep)\n np.testing.assert_array_less(min_sep, radius)\n # bin_size is reduced slightly to get integer number of bins\n assert rho.bin_size < bin_size\n assert np.isclose(rho.bin_size, bin_size, rtol=0.1)\n np.testing.assert_array_almost_equal(np.diff(rho.logr), rho.bin_size, decimal=5)\n\n # Test that the max absolute value of each rho isn't crazy\n np.testing.assert_array_less(np.abs(rho.xip), 1)\n\n # # Check that each rho isn't precisely zero. This means the sum of abs > 0\n np.testing.assert_array_less(0, np.sum(np.abs(rho.xip)))\n\n # Test using the piffify executable\n os.remove(rho_file)\n config['verbose'] = 0\n with open('rho.yaml','w') as f:\n f.write(yaml.dump(config, default_flow_style=False))\n piffify_exe = get_script_name('piffify')\n p = subprocess.Popen( [piffify_exe, 'rho.yaml'] )\n p.communicate()\n assert os.path.isfile(rho_file)\n\n # Test using the plotify executable\n os.remove(rho_file)\n plotify_exe = get_script_name('plotify')\n p = subprocess.Popen( [plotify_exe, 'rho.yaml'] )\n p.communicate()\n assert os.path.isfile(rho_file)\n\n # test running plotify with dir in config, with no logger, and with a modules specification.\n # (all to improve test coverage)\n config['output']['dir'] = '.'\n config['modules'] = [ 'custom_wcs' ]\n os.remove(rho_file)\n piff.plotify(config)\n assert os.path.isfile(rho_file)","def index():\n\n # list with url for pictures\n pic_url_list = []\n\n # default resolution\n x_resolution = 2048\n y_resolution = 2048\n\n # converting flask type od dict to default python dict\n request_arguments = request.args.to_dict(flat=False)\n\n # check if optional argument (resolution) exists\n if 'rozdzielczosc' in request_arguments:\n # splitting x and y resolution values by 'x' and mapping it on int\n x_resolution, y_resolution = map(int, request_arguments['rozdzielczosc'][0].split('x'))\n\n if 'zdjecia' in request_arguments:\n pic_url_list = request_arguments['zdjecia'][0].split(',')\n\n # check if optional argument (random) exists, if so shuffle it\n if 'losowo' in request_arguments and request_arguments['losowo'][0] == '1':\n shuffle(pic_url_list)\n\n # call function which glues images together\n try:\n mosaic = make_mosaic(pic_url_list, x_resolution, y_resolution)\n mosaic.save(basepath + '/static/img/mosaic.jpeg', 'JPEG')\n return render_template('mosaic.html')\n except ValueError as e:\n return render_template('mosaic.html', message=str(e))\n except requests.exceptions.ConnectionError:\n return render_template('mosaic.html', message='Wrong image url passed or no internet access')","def run(self):\n _, paint = self.config_image()\n if paint:\n self.load_image()\n else:\n self.config_select(True)\n\n while True:\n self.paint()\n self.config_select()","def main():\n ##############################\n # Options\n ##############################\n\n bev_generator = 'slices'\n slices_config = \\\n \"\"\"\n slices {\n height_lo: -0.2\n height_hi: 2.3\n num_slices: 5\n }\n \"\"\"\n # Use None for a random image\n #img_idx = None\n img_idx = 6\n\n show_ground_truth = True # Whether to overlay ground_truth boxes\n show_height_maps = False # Whether to show the five height maps\n show_images = False # Whether to show the images\n\n point_cloud_source = 'lidar'\n pre_label_dir = '/home/cecilia/leo_projects/bishe2019/3D-Detection/avod/data/outputs/pyramid_cars_with_aug_rep_loss/predictions/kitti_native_eval/0.1/112000/data/'\n ##############################\n # End of Options\n ##############################\n\n dataset_config = DatasetBuilder.copy_config(DatasetBuilder.KITTI_VAL)\n dataset_config = DatasetBuilder.merge_defaults(dataset_config)\n\n # Overwrite bev_generator\n if bev_generator == 'slices':\n text_format.Merge(slices_config,\n dataset_config.kitti_utils_config.bev_generator)\n else:\n raise ValueError('Invalid bev_generator')\n\n dataset = DatasetBuilder.build_kitti_dataset(dataset_config,\n use_defaults=False)\n\n if img_idx is None:\n img_idx = int(random.random() * dataset.num_samples)\n\n sample_name = \"{:06}\".format(img_idx)\n print('=== Showing BEV maps for image: {}.png ==='.format(sample_name))\n\n # Load image\n image = cv2.imread(dataset.get_rgb_image_path(sample_name))\n image_shape = image.shape[0:2]\n\n kitti_utils = dataset.kitti_utils\n point_cloud = kitti_utils.get_point_cloud(\n point_cloud_source, img_idx, image_shape)\n ground_plane = kitti_utils.get_ground_plane(sample_name)\n bev_images = kitti_utils.create_bev_maps(point_cloud, ground_plane)\n\n height_maps = np.array(bev_images.get(\"height_maps\"))\n density_map = np.array(bev_images.get(\"density_map\"))\n\n # Get groundtruth bev-info\n gt_box_points, gt_box_points_norm = [None, None]\n if show_ground_truth:\n gt_obj_labels = obj_utils.read_labels(dataset.label_dir, img_idx)\n gt_filtered_objs = gt_obj_labels\n\n gt_label_boxes = []\n for gt_label in gt_filtered_objs:\n gt_box = box_3d_encoder.object_label_to_box_3d(gt_label)\n gt_label_boxes.append(gt_box)\n\n gt_label_boxes = np.array(gt_label_boxes)\n gt_box_points, gt_box_points_norm = box_3d_projector.project_to_bev(\n gt_label_boxes, [[-40, 40], [0, 70]])\n\n # Get prediction bev-info\n pre_box_points, pre_box_points_norm = [None, None]\n pre_obj_labels = obj_utils.read_labels(pre_label_dir, img_idx)\n\n pre_filtered_objs = pre_obj_labels\n\n pre_label_boxes = []\n for pre_label in pre_filtered_objs:\n pre_box = box_3d_encoder.object_label_to_box_3d(pre_label)\n pre_label_boxes.append(pre_box)\n\n pre_label_boxes = np.array(pre_label_boxes)\n pre_box_points, pre_box_points_norm = box_3d_projector.project_to_bev(\n pre_label_boxes, [[-40, 40], [0, 70]])\n\n \n rgb_img_size = (np.array((1242, 375)) * 0.75).astype(np.int16)\n img_x_start = 60\n img_y_start = 330\n\n img_x = img_x_start\n img_y = img_y_start\n img_w = 400\n img_h = 350\n img_titlebar_h = 20\n\n # Show images if show_images = True\n if show_images:\n vis_utils.cv2_show_image(\"Image\", image,\n size_wh=rgb_img_size, location_xy=(img_x, 0))\n\n # Height maps if show_height_maps = True\n if show_height_maps:\n\n for map_idx in range(len(height_maps)):\n height_map = height_maps[map_idx]\n\n height_map = draw_boxes(height_map, gt_box_points_norm, pre_box_points_norm)\n vis_utils.cv2_show_image(\n \"Height Map {}\".format(map_idx), height_map, size_wh=(\n img_w, img_h), location_xy=(\n img_x, img_y))\n\n img_x += img_w\n # Wrap around\n if (img_x + img_w) > 1920:\n img_x = img_x_start\n img_y += img_h + img_titlebar_h\n\n # Density map (Normal BEV)\n density_map = draw_boxes(density_map, gt_box_points_norm, pre_box_points_norm)\n vis_utils.cv2_show_image(\n \"Density Map\", density_map, size_wh=(\n img_w, img_h), location_xy=(\n img_x, img_y))\n\n cv2.waitKey()","def show(data, types=(\"inflated\", ), recache=False, cmap='RdBu_r', layout=None,\n autoclose=None, open_browser=None, port=None, pickerfun=None,\n template=\"mixer.html\", overlays_available=None,\n overlays_visible=('rois', 'sulci'), labels_visible=('rois', ),\n overlay_file=None, title='Brain', **kwargs):\n\n # populate default webshow args\n if autoclose is None:\n autoclose = options.config.get('webshow', 'autoclose', fallback='true') == 'true'\n if open_browser is None:\n open_browser = options.config.get('webshow', 'open_browser', fallback='true') == 'true'\n\n data = dataset.normalize(data)\n if not isinstance(data, dataset.Dataset):\n data = dataset.Dataset(data=data)\n\n html = FallbackLoader([os.path.split(os.path.abspath(template))[0], serve.cwd]).load(template)\n db.auxfile = data\n\n #Extract the list of stimuli, for special-casing\n stims = dict()\n for name, view in data:\n if 'stim' in view.attrs and os.path.exists(view.attrs['stim']):\n sname = os.path.split(view.attrs['stim'])[1]\n stims[sname] = view.attrs['stim']\n\n package = Package(data)\n metadata = json.dumps(package.metadata())\n images = package.images\n subjects = list(package.subjects)\n\n ctmargs = dict(method='mg2', level=9, recache=recache,\n external_svg=overlay_file, overlays_available=overlays_available)\n ctms = dict((subj, utils.get_ctmpack(subj, types, **ctmargs))\n for subj in subjects)\n package.reorder(ctms)\n\n subjectjs = json.dumps(dict((subj, \"ctm/%s/\"%subj) for subj in subjects))\n db.auxfile = None\n\n if layout is None:\n layout = [None, (1, 1), (2, 1), (3, 1), (2, 2), (3, 2), (3, 2), (3, 3), (3, 3), (3, 3)][len(subjects)]\n\n linear = lambda x, y, m: (1.-m)*x + m*y\n mixes = dict(\n linear=linear,\n smoothstep=(lambda x, y, m: linear(x, y, 3*m**2 - 2*m**3)),\n smootherstep=(lambda x, y, m: linear(x, y, 6*m**5 - 15*m**4 + 10*m**3))\n )\n\n post_name = Queue()\n\n # Put together all view options\n my_viewopts = dict(options.config.items('webgl_viewopts'))\n my_viewopts['overlays_visible'] = overlays_visible\n my_viewopts['labels_visible'] = labels_visible\n my_viewopts['brightness'] = options.config.get('curvature', 'brightness')\n my_viewopts['smoothness'] = options.config.get('curvature', 'webgl_smooth')\n my_viewopts['contrast'] = options.config.get('curvature', 'contrast')\n\n for sec in options.config.sections():\n if 'paths' in sec or 'labels' in sec:\n my_viewopts[sec] = dict(options.config.items(sec))\n\n if pickerfun is None:\n pickerfun = lambda a, b: None\n\n class CTMHandler(web.RequestHandler):\n def get(self, path):\n subj, path = path.split('/')\n if path == '':\n self.set_header(\"Content-Type\", \"application/json\")\n self.write(open(ctms[subj]).read())\n else:\n fpath = os.path.split(ctms[subj])[0]\n mtype = mimetypes.guess_type(os.path.join(fpath, path))[0]\n if mtype is None:\n mtype = \"application/octet-stream\"\n self.set_header(\"Content-Type\", mtype)\n self.write(open(os.path.join(fpath, path), 'rb').read())\n\n class DataHandler(web.RequestHandler):\n def get(self, path):\n path = path.strip(\"/\")\n try:\n dataname, frame = path.split('/')\n except ValueError:\n dataname = path\n frame = 0\n\n if dataname in images:\n dataimg = images[dataname][int(frame)]\n if dataimg[1:6] == \"NUMPY\":\n self.set_header(\"Content-Type\", \"application/octet-stream\")\n else:\n self.set_header(\"Content-Type\", \"image/png\")\n\n if 'Range' in self.request.headers:\n self.set_status(206)\n rangestr = self.request.headers['Range'].split('=')[1]\n start, end = [ int(i) if len(i) > 0 else None for i in rangestr.split('-') ]\n\n clenheader = 'bytes %s-%s/%s' % (start, end or len(dataimg), len(dataimg) )\n self.set_header('Content-Range', clenheader)\n self.set_header('Content-Length', end-start+1)\n self.write(dataimg[start:end+1])\n else:\n self.write(dataimg)\n else:\n self.set_status(404)\n self.write_error(404)\n\n class StimHandler(web.StaticFileHandler):\n def initialize(self):\n pass\n\n def get(self, path):\n if path not in stims:\n self.set_status(404)\n self.write_error(404)\n else:\n self.root, fname = os.path.split(stims[path])\n super(StimHandler, self).get(fname)\n\n class StaticHandler(web.StaticFileHandler):\n def initialize(self):\n self.root = ''\n\n class MixerHandler(web.RequestHandler):\n def get(self):\n self.set_header(\"Content-Type\", \"text/html\")\n generated = html.generate(data=metadata,\n colormaps=colormaps,\n default_cmap=cmap,\n python_interface=True,\n leapmotion=True,\n layout=layout,\n subjects=subjectjs,\n viewopts=json.dumps(my_viewopts),\n title=title,\n **kwargs)\n #overlays_visible=json.dumps(overlays_visible),\n #labels_visible=json.dumps(labels_visible),\n #**viewopts)\n self.write(generated)\n\n def post(self):\n data = self.get_argument(\"svg\", default=None)\n png = self.get_argument(\"png\", default=None)\n with open(post_name.get(), \"wb\") as svgfile:\n if png is not None:\n data = png[22:].strip()\n try:\n data = binascii.a2b_base64(data)\n except:\n print(\"Error writing image!\")\n data = png\n svgfile.write(data)\n\n class JSMixer(serve.JSProxy):\n @property\n def view_props(self):\n \"\"\"An enumerated list of settable properties for views. \n There may be a way to get this from the javascript object, \n but I (ML) don't know how.\n\n There may be additional properties we want to set in views\n and animations; those must be added here.\n\n Old property list that used to be settable before webgl refactor:\n view_props = ['altitude', 'azimuth', 'target', 'mix', 'radius', 'pivot',\n 'visL', 'visR', 'alpha', 'rotationR', 'rotationL', 'projection',\n 'volume_vis', 'frame', 'slices']\n \"\"\"\n camera = getattr(self.ui, \"camera\")\n _camera_props = ['camera.%s' % k for k in camera._controls.attrs.keys()]\n surface = getattr(self.ui, \"surface\")\n _subject = list(surface._folders.attrs.keys())[0]\n _surface = getattr(surface, _subject)\n _surface_props = ['surface.{subject}.%s'%k for k in _surface._controls.attrs.keys()]\n _curvature_props = ['surface.{subject}.curvature.brightness',\n 'surface.{subject}.curvature.contrast',\n 'surface.{subject}.curvature.smoothness']\n return _camera_props + _surface_props + _curvature_props\n\n def _set_view(self, **kwargs):\n \"\"\"Low-level command: sets view parameters in the current viewer\n\n Sets each the state of each keyword argument provided. View parameters\n that can be set include all parameters in the data.gui in the html view.\n\n \"\"\"\n # Set unfolding level first, as it interacts with other arguments\n surface = getattr(self.ui, \"surface\")\n subject_list = surface._folders.attrs.keys()\n # Better to only self.view_props once; it interacts with javascript, \n # don't want to do that too often, it leads to glitches.\n vw_props = copy.copy(self.view_props)\n for subject in subject_list:\n if 'surface.{subject}.unfold' in kwargs:\n unfold = kwargs.pop('surface.{subject}.unfold')\n self.ui.set('surface.{subject}.unfold'.format(subject=subject), unfold)\n for k, v in kwargs.items():\n if not k in vw_props:\n print('Unknown parameter %s!'%k)\n continue\n else:\n self.ui.set(k.format(subject=subject) if '{subject}' in k else k, v)\n # Wait for webgl. Wait for it. .... WAAAAAIIIT.\n time.sleep(0.03)\n\n def _capture_view(self, frame_time=None):\n \"\"\"Low-level command: returns a dict of current view parameters\n\n Retrieves the following view parameters from current viewer:\n\n altitude, azimuth, target, mix, radius, visL, visR, alpha,\n rotationR, rotationL, projection, pivot\n\n Parameters\n ----------\n frame_time : scalar\n time (in seconds) to specify for this frame.\n \n Notes\n -----\n If multiple subjects are present, only retrieves view for first subject.\n \"\"\"\n view = {}\n subject = list(self.ui.surface._folders.attrs.keys())[0]\n for p in self.view_props:\n try:\n view[p] = self.ui.get(p.format(subject=subject) if '{subject}' in p else p)[0]\n # Wait for webgl.\n time.sleep(0.03)\n except Exception as err:\n # TO DO: Fix this hack with an error class in serve.py & catch it here\n print(err) #msg = \"Cannot read property 'undefined'\"\n #if err.message[:len(msg)] != msg:\n # raise err\n if frame_time is not None:\n view['time'] = frame_time\n return view\n\n def save_view(self, subject, name, is_overwrite=False):\n \"\"\"Saves current view parameters to pycortex database\n\n Parameters\n ----------\n subject : string\n pycortex subject id\n name : string\n name for view to store\n is_overwrite: bool\n whether to overwrite an extant view (default : False)\n\n Notes\n -----\n Equivalent to call to cortex.db.save_view(subject, vw, name)\n For a list of the view parameters saved, see viewer._capture_view\n \"\"\"\n db.save_view(self, subject, name, is_overwrite)\n\n def get_view(self, subject, name):\n \"\"\"Get saved view from pycortex database.\n\n Retrieves named view from pycortex database and sets current\n viewer parameters to retrieved values.\n\n Parameters\n ----------\n subject : string\n pycortex subject ID\n name : string\n name of saved view to re-load\n\n Notes\n -----\n Equivalent to call to cortex.db.get_view(subject, vw, name)\n For a list of the view parameters set, see viewer._capture_view\n \"\"\"\n view = db.get_view(self, subject, name)\n\n def addData(self, **kwargs):\n Proxy = serve.JSProxy(self.send, \"window.viewers.addData\")\n new_meta, new_ims = _convert_dataset(Dataset(**kwargs), path='/data/', fmt='%s_%d.png')\n metadata.update(new_meta)\n images.update(new_ims)\n return Proxy(metadata)\n\n def getImage(self, filename, size=(1920, 1080)):\n \"\"\"Saves currently displayed view to a .png image file\n\n Parameters\n ----------\n filename : string\n duh.\n size : tuple (x, y)\n size (in pixels) of image to save.\n \"\"\"\n post_name.put(filename)\n Proxy = serve.JSProxy(self.send, \"window.viewer.getImage\")\n return Proxy(size[0], size[1], \"mixer.html\")\n\n def makeMovie(self, animation, filename=\"brainmovie%07d.png\", offset=0,\n fps=30, size=(1920, 1080), interpolation=\"linear\"):\n \"\"\"Renders movie frames for animation of mesh movement\n\n Makes an animation (for example, a transition between inflated and\n flattened brain or a rotating brain) of a cortical surface. Takes a\n list of dictionaries (`animation`) as input, and uses the values in\n the dictionaries as keyframes for the animation.\n\n Mesh display parameters that can be animated include 'elevation',\n 'azimuth', 'mix', 'radius', 'target' (more?)\n\n\n Parameters\n ----------\n animation : list of dicts\n Each dict should have keys `idx`, `state`, and `value`.\n `idx` is the time (in seconds) at which you want to set `state` to `value`\n `state` is the parameter to animate (e.g. 'altitude', 'azimuth')\n `value` is the value to set for `state`\n filename : string path name\n Must contain '%d' (or some variant thereof) to account for frame\n number, e.g. '/some/directory/brainmovie%07d.png'\n offset : int\n Frame number for first frame rendered. Useful for concatenating\n animations.\n fps : int\n Frame rate of resultant movie\n size : tuple (x, y)\n Size (in pixels) of resulting movie\n interpolation : {\"linear\", \"smoothstep\", \"smootherstep\"}\n Interpolation method for values between keyframes.\n\n Example\n -------\n # Called after a call of the form: js_handle = cortex.webgl.show(DataViewObject)\n # Start with left hemisphere view\n js_handle._setView(azimuth=[90], altitude=[90.5], mix=[0])\n # Initialize list\n animation = []\n # Append 5 key frames for a simple rotation\n for az, idx in zip([90, 180, 270, 360, 450], [0, .5, 1.0, 1.5, 2.0]):\n animation.append({'state':'azimuth', 'idx':idx, 'value':[az]})\n # Animate! (use default settings)\n js_handle.makeMovie(animation)\n \"\"\"\n # build up two variables: State and Anim.\n # state is a dict of all values being modified at any time\n state = dict()\n # anim is a list of transitions between keyframes\n anim = []\n setfunc = self.ui.set\n for f in sorted(animation, key=lambda x:x['idx']):\n if f['idx'] == 0:\n setfunc(f['state'], f['value'])\n state[f['state']] = dict(idx=f['idx'], val=f['value'])\n else:\n if f['state'] not in state:\n state[f['state']] = dict(idx=0, val=self.getState(f['state'])[0])\n start = dict(idx=state[f['state']]['idx'],\n state=f['state'],\n value=state[f['state']]['val'])\n end = dict(idx=f['idx'], state=f['state'], value=f['value'])\n state[f['state']]['idx'] = f['idx']\n state[f['state']]['val'] = f['value']\n if start['value'] != end['value']:\n anim.append((start, end))\n\n for i, sec in enumerate(np.arange(0, anim[-1][1]['idx']+1./fps, 1./fps)):\n for start, end in anim:\n if start['idx'] < sec <= end['idx']:\n idx = (sec - start['idx']) / float(end['idx'] - start['idx'])\n if start['state'] == 'frame':\n func = mixes['linear']\n else:\n func = mixes[interpolation]\n\n val = func(np.array(start['value']), np.array(end['value']), idx)\n if isinstance(val, np.ndarray):\n setfunc(start['state'], val.ravel().tolist())\n else:\n setfunc(start['state'], val)\n self.getImage(filename%(i+offset), size=size)\n\n def _get_anim_seq(self, keyframes, fps=30, interpolation='linear'):\n \"\"\"Convert a list of keyframes to a list of EVERY frame in an animation.\n\n Utility function called by make_movie; separated out so that individual\n frames of an animation can be re-rendered, or for more control over the\n animation process in general.\n\n \"\"\"\n # Misc. setup\n fr = 0\n a = np.array\n func = mixes[interpolation]\n #skip_props = ['surface.{subject}.right', 'surface.{subject}.left', ] #'projection',\n # Get keyframes\n keyframes = sorted(keyframes, key=lambda x:x['time'])\n # Normalize all time to frame rate\n fs = 1./fps\n for k in range(len(keyframes)):\n t = keyframes[k]['time']\n t = np.round(t/fs)*fs\n keyframes[k]['time'] = t\n allframes = []\n for start, end in zip(keyframes[:-1], keyframes[1:]):\n t0 = start['time']\n t1 = end['time']\n tdif = float(t1-t0)\n # Check whether to continue frame sequence to endpoint\n use_endpoint = keyframes[-1]==end\n nvalues = np.round(tdif/fs).astype(int)\n if use_endpoint:\n nvalues += 1\n fr_time = np.linspace(0, 1, nvalues, endpoint=use_endpoint)\n # Interpolate between values\n for t in fr_time:\n frame = {}\n for prop in start.keys():\n if prop=='time':\n continue\n if (start[prop] is None) or (start[prop] == end[prop]) or isinstance(start[prop], (bool, str)):\n frame[prop] = start[prop]\n continue\n val = func(a(start[prop]), a(end[prop]), t)\n if isinstance(val, np.ndarray):\n frame[prop] = val.tolist()\n else:\n frame[prop] = val\n allframes.append(frame)\n return allframes\n\n def make_movie_views(self, animation, filename=\"brainmovie%07d.png\", \n offset=0, fps=30, size=(1920, 1080), alpha=1, frame_sleep=0.05,\n frame_start=0, interpolation=\"linear\"):\n \"\"\"Renders movie frames for animation of mesh movement\n\n Makes an animation (for example, a transition between inflated and\n flattened brain or a rotating brain) of a cortical surface. Takes a\n list of dictionaries (`animation`) as input, and uses the values in\n the dictionaries as keyframes for the animation.\n\n Mesh display parameters that can be animated include 'elevation',\n 'azimuth', 'mix', 'radius', 'target' (more?)\n\n\n Parameters\n ----------\n animation : list of dicts\n This is a list of keyframes for the animation. Each keyframe should be\n a dict in the form captured by the ._capture_view method. NOTE: every\n view must include all view parameters. Additionally, there should be\n one extra key/value pair for \"time\". The value for time should be\n in seconds. The list of keyframes is sorted by time before applying,\n so they need not be in order in the input.\n filename : string path name\n Must contain '%d' (or some variant thereof) to account for frame\n number, e.g. '/some/directory/brainmovie%07d.png'\n offset : int\n Frame number for first frame rendered. Useful for concatenating\n animations.\n fps : int\n Frame rate of resultant movie\n size : tuple (x, y)\n Size (in pixels) of resulting movie\n interpolation : {\"linear\", \"smoothstep\", \"smootherstep\"}\n Interpolation method for values between keyframes.\n\n Notes\n -----\n Make sure that all values that will be modified over the course\n of the animation are initialized (have some starting value) in the first\n frame.\n\n Example\n -------\n # Called after a call of the form: js_handle = cortex.webgl.show(DataViewObject)\n # Start with left hemisphere view\n js_handle._setView(azimuth=[90], altitude=[90.5], mix=[0])\n # Initialize list\n animation = []\n # Append 5 key frames for a simple rotation\n for az, t in zip([90, 180, 270, 360, 450], [0, .5, 1.0, 1.5, 2.0]):\n animation.append({'time':t, 'azimuth':[az]})\n # Animate! (use default settings)\n js_handle.make_movie(animation)\n \"\"\"\n allframes = self._get_anim_seq(animation, fps, interpolation)\n for fr, frame in enumerate(allframes[frame_start:], frame_start):\n self._set_view(**frame)\n time.sleep(frame_sleep)\n self.getImage(filename%(fr+offset+1), size=size)\n time.sleep(frame_sleep)\n\n class PickerHandler(web.RequestHandler):\n def get(self):\n pickerfun(int(self.get_argument(\"voxel\")), int(self.get_argument(\"vertex\")))\n\n class WebApp(serve.WebApp):\n disconnect_on_close = autoclose\n def get_client(self):\n self.connect.wait()\n self.connect.clear()\n return JSMixer(self.send, \"window.viewer\")\n\n def get_local_client(self):\n return JSMixer(self.srvsend, \"window.viewer\")\n\n if port is None:\n port = random.randint(1024, 65536)\n\n server = WebApp([(r'/ctm/(.*)', CTMHandler),\n (r'/data/(.*)', DataHandler),\n (r'/stim/(.*)', StimHandler),\n (r'/mixer.html', MixerHandler),\n (r'/picker', PickerHandler),\n (r'/', MixerHandler),\n (r'/static/(.*)', StaticHandler)],\n port)\n\n server.start()\n print(\"Started server on port %d\"%server.port)\n url = \"http://%s%s:%d/mixer.html\"%(serve.hostname, domain_name, server.port)\n if open_browser:\n webbrowser.open(url)\n client = server.get_client()\n client.server = server\n return client\n else:\n try:\n from IPython.display import display, HTML\n display(HTML('Open viewer: <a href=\"{0}\" target=\"_blank\">{0}</a>'.format(url)))\n except:\n pass\n return server","def main():\n space_ship = SimpleImage(\"images/MillenniumFalcon.png\")\n figure = SimpleImage(\"images/ReyGreenScreen.png\")\n result = combine(space_ship, figure)\n result.show()","def displayImages(self):\n\n plt.figure(figsize=(8,6))\n plt.subplot(1,2,1)\n plt.imshow( self.original_image, cmap=\"gray\")\n plt.title(\"Original Image\")\n plt.subplot(1,2,2)\n plt.imshow( self.blurred_image, cmap=\"gray\")\n plt.title(\"Blurred Image\")","def plot_final_grid(generated_images):\n\n fig = plt.figure(figsize=(8, 6))\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.001, hspace=0.001)\n\n for ind in range(GRID_IMGS):\n ax = fig.add_subplot(GRID_SIZE, GRID_SIZE, ind + 1, xticks=[], yticks=[])\n ax.imshow(np.uint8(((generated_images[ind] + 1) / 2) * 255), cmap='gray')\n\n plt.savefig('/content/drive/My Drive/WGAN/generated_image_grid.png')\n plt.savefig('/content/drive/My Drive/WGAN/results/DCGAN.png')\n plt.show()","def main(config):\n save_main_dir = config[\"save_main_dir\"]\n\n if not os.path.isdir(save_main_dir):\n print(\"{} does not exist, creating it now...\", save_main_dir)\n pathlib.Path(save_main_dir).mkdir(parents=True, exist_ok=True)\n\n app = inviwopy.app\n network = app.network\n \n # Resize the canvas to improve rendering speed, only affects visual output\n if config[\"should_resize\"]:\n ivw_helpers.set_canvas_sizes(128, 128)\n\n hdf5_path = os.path.join(save_main_dir, config[\"hdf5_name\"])\n with h5py.File(hdf5_path, mode='w', libver='latest') as hdf5_file:\n hdf5_file.swmr_mode = True\n create_hdf_storage(hdf5_file, config)\n count = {\"train\": 0, \"val\": 0}\n for set_type in 'train', 'val':\n capture_lf_samples(hdf5_file, set_type, config, network, count) \n print(\"Finished writing to HDF5 in {}\".format(hdf5_path))","def main(argv):\n scanSpeed = 1.\n iList = -1\n opt1 = \"\"\n intMode = False\n if (len(argv) >= 1): opt1 = argv[0]\n if \"-i\" in (opt1): intMode = True\n\n # Set input data and cuts\n inputFile = TFile(\"~/project/match-skim/waveletSkimDS5_run23920.root\") # calibration\n # inputFile = TFile(\"~/project/v2-processwfs/waveletSkimDS5_90.root\") # noisy BG\n waveTree = inputFile.Get(\"skimTree\")\n theCut = inputFile.Get(\"cutUsedHere\").GetTitle()\n theCut += \" && waveS5/trapENFCal < 1200 && trapENFCal > 1.5 && trapENFCal < 3\"\n\n # Print cut and events passing cut\n waveTree.Draw(\">>elist\", theCut, \"entrylist\")\n elist = gDirectory.Get(\"elist\")\n waveTree.SetEntryList(elist)\n nList = elist.GetN()\n print \"Using cut:\\n\",theCut,\"\\n\"\n print \"Found\",waveTree.GetEntries(),\"input entries.\"\n print \"Found\",nList,\"entries passing cuts.\"\n\n # Make a figure\n fig = plt.figure(figsize=(15,10), facecolor='w')\n p1 = plt.subplot2grid((6,7), (0,0), colspan=4, rowspan=2) # original\n p2 = plt.subplot2grid((6,7), (2,0), colspan=4, rowspan=3) # rising edge\n p3 = plt.subplot2grid((6,7), (5,0), colspan=4, rowspan=1) # residual\n p4 = plt.subplot2grid((6,7), (0,4), colspan=3, rowspan=2) # trace 1\n p5 = plt.subplot2grid((6,7), (2,4), colspan=3, rowspan=2) # trace 2\n p6 = plt.subplot2grid((6,7), (4,4), colspan=3, rowspan=2) # trace 3\n plt.show(block=False)\n\n # Make template(s)\n # npzfile = np.load(\"./data/genTemplateWF.npz\") # gen-template.py\n # temp, tempTS, tempE, tempST = npzfile['arr_0']+1, npzfile['arr_1'], npzfile['arr_2'], npzfile['arr_3']*10\n\n samp, r, z, tempE, tempST, smooth = 2016, 0, 15, 3938, 1000, 100 # gen-template.py\n # samp, r, z, tempE, tempST, smooth = 5000, 0, 15, 10, 2500, 100 # huge template\n # samp, r, z, tempE, tempST, smooth = 2016, 0, 15, 10, 1000, 100 # regular size template\n # samp, r, z, tempE, tempST, smooth = 2016, 30, 30, 10, 1000, 100 # regular size temp, slower rise\n # samp, r, z, tempE, tempST, smooth = 500, 0, 15, 10, 100, 100 # small template\n temp, tempTS = wl.MakeSiggenWaveform(samp,r,z,tempE,tempST,smooth)\n tempST = tempST * 10 # convert to ns\n\n\n # Loop over events\n while True:\n saveMe = False\n iList += 1\n if intMode==True and iList != 0:\n value = raw_input()\n if value=='q': break\n if value=='s': saveMe=True\n if value=='p': iList -= 2 # previous\n if (value.isdigit()): iList = int(value) # go to entry\n if iList >= elist.GetN(): break\n\n entry = waveTree.GetEntryNumber(iList);\n waveTree.LoadTree(entry)\n waveTree.GetEntry(entry)\n nChans = waveTree.channel.size()\n numPass = waveTree.Draw(\"channel\",theCut,\"GOFF\",1,iList)\n chans = waveTree.GetV1()\n chanList = list(set(int(chans[n]) for n in xrange(numPass)))\n\n # Loop over hits passing cuts\n hitList = (iH for iH in xrange(nChans) if waveTree.channel.at(iH) in chanList) # a 'generator expression'\n for iH in hitList:\n\n # ------------------------------------------------------------------------\n\n # Load waveform for this hit\n run = waveTree.run\n chan = waveTree.channel.at(iH)\n dataE = waveTree.trapENFCal.at(iH)\n dataST = waveTree.butterTime.at(iH) # replace with blrwfFMR50?\n toe = waveTree.kvorrT.at(iH)/dataE\n print \"%d / %d Run %d nCh %d chan %d trapENF %.1f t/e %.1f\" % (iList,nList,run,nChans,chan,dataE,toe)\n signal = wl.processWaveform(waveTree.MGTWaveforms.at(iH),opt='full')\n waveBLSub = signal.GetWaveBLSub()\n waveFilt = signal.GetWaveFilt()\n waveTS = signal.GetTS()\n baseAvg, dataNoise = signal.GetBaseNoise()\n\n # Denoise the data waveform (take only lowest-frequency components)\n wp = pywt.WaveletPacket(data=waveBLSub, wavelet='haar', mode='symmetric',maxlevel=3)\n new_wp = pywt.WaveletPacket(data=None, wavelet='haar', mode='symmetric')\n new_wp['aaa'] = wp['aaa'].data\n waveDenoised = new_wp.reconstruct(update=False)\n\n # Window the fit around rising edge - start time calculator method\n loWin, hiWin = dataST - 1000, dataST + 4000 # ns\n if loWin < waveTS[0] or hiWin > waveTS[-1]:\n print \"Window out of range! dataST: %.1f loWin %.1f hiWin %.1f\" % (dataST,loWin,hiWin)\n idx = np.where((waveTS >= loWin) & (waveTS <= hiWin))\n data = waveBLSub[idx]\n # data = waveDenoised[idx]\n dataTS = waveTS[idx]\n\n # Pack into lists\n rawList = [waveBLSub, waveTS, dataE, dataST]\n dataList = [data, dataTS, dataE, dataST, loWin, hiWin, dataNoise]\n tempList = [temp, tempTS, tempE, tempST]\n\n # Optionally save something to a file\n if saveMe: np.savez(\"./data/tailSlopeInputs.npz\",rawList,tempList)\n\n # Recreate the guess and the guess's rising edge\n guessFull, guessFullTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\"full\")\n guess, guessTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\"!fancy\")\n\n # Make an \"almost complete\" guess - no fitting\n # st, en, slo = dataST-100, dataE, 5\n InterpFn = interpolate.interp1d(tempTS, temp, kind=\"linear\", copy=\"False\", assume_sorted=\"True\")\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\n\n # Fit with MCMC and get best-fit parameters\n # numSteps, burnIn = 10000, 5000 # default - 10000, 5000. try 3000, 2000. long test: 20000,10000\n # myModel = TemplateModel( dataList, dataNoise, tempList )\n # waveModel = pymc.Model( myModel )\n # M = pymc.MCMC( waveModel )\n # M.use_step_method(pymc.Metropolis, M.startTime, proposal_sd=100., proposal_distribution='Normal')\n # M.use_step_method(pymc.Metropolis, M.energy, proposal_sd=1., proposal_distribution='Normal')\n # M.use_step_method(pymc.Metropolis, M.slowness, proposal_sd=100., proposal_distribution='Normal')\n # M.sample(iter=numSteps, verbose=0) # do the fit\n # st = np.median(M.trace('startTime')[burnIn:])\n # en = np.median(M.trace('energy')[burnIn:])\n # slo = np.median(M.trace('slowness')[burnIn:])\n # InterpFn = interpolate.interp1d(tempTS, temp, kind=\"linear\", copy=\"False\", assume_sorted=\"True\")\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\n\n\n # Fit with SciPy minimizer(s) and get best-fit parameters\n\n # how long does it take?\n start = time.time()\n\n MakeTracesGlobal() # creates 3 global arrays: startTrace, enTrace, sloTrace\n datas = [dataList, tempList, InterpFn]\n\n # floats = [1.]\n # result = op.basinhopping(findLnLike, floats, T=1000, stepsize=15, niter_success=2, minimizer_kwargs={\"args\":datas})\n # print \"slowness:\",result[\"x\"],\"dataE\",dataE,\"dataST\",dataST\n # floats = [dataST, dataE, result[\"x\"]]\n # MakeTracesGlobal() # resets traces\n\n floats = [dataST, dataE, 20.]\n result = op.minimize(findLnLike, floats, args=datas, method=\"Nelder-Mead\")\n if not result[\"success\"]: print result[\"message\"]\n st, en, slo = result[\"x\"]\n\n model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\n\n stop = time.time()\n print \"fitting took\",stop-start\n\n\n\n # Calculate residual, Chi2/NDF, likelihood, etc.\n residual = model - data\n frac = (np.power(data - model, 2)) / np.abs(model)\n chi2NDF = np.sum(frac) / len(model)\n inv_sigma2 = 1.0/(dataNoise**2)\n lnLike = -0.5*(np.sum((data - model)**2*inv_sigma2 - np.log(inv_sigma2)))\n\n # Fill the figure\n p1.cla()\n p1.set_ylabel(\"ADC\")\n p1.set_title(\"Run %d Channel %d Entry %d\\ntrapENFCal %.1f T/E %.1f ST %.1f\" % (run,chan,iList,dataE,toe,dataST))\n p1.plot(waveTS,waveBLSub,color='blue')\n p1.plot(waveTS,waveDenoised,color='red',alpha=0.8)\n p1.plot(guessFullTS,guessFull,color='orange',linewidth=2)\n p1.axvline(x = dataST, color='green',linewidth=2)\n p1.axvline(x = loWin, color='black')\n p1.axvline(x = hiWin, color='black')\n\n p2.cla()\n p2.plot(dataTS,data,color='blue',label='Data')\n p2.plot(guessTS,guess,color='orange',label='Guess')\n p2.plot(modelTS,model,color='red',linewidth=3,label='Best Fit')\n p2.legend(loc=4)\n\n p3.cla()\n p3.set_xlabel(\"Time [ns]\", x=0.95, ha='right')\n p3.set_ylabel(\"Residual [ADC]\")\n p3.plot(guessTS,residual, color='red')\n p3.axhline(y = 0, color='blue', alpha=0.3)\n p3.axhline(y = dataNoise, color='blue', alpha=0.3)\n p3.axhline(y = -1.0*dataNoise, color='blue', alpha=0.3)\n\n p4.cla()\n p4.set_title(\"startTime %.1f Energy %.2f Slow %.1f\" % (st,en,slo))\n\n p4.plot(stTrace[1:])\n p4.set_ylabel('startTime')\n\n p5.cla()\n p5.plot(enTrace[1:])\n p5.set_ylabel('energy')\n\n p6.cla()\n p6.plot(sloTrace[1:])\n p6.set_ylabel('slowness')\n\n plt.tight_layout()\n plt.subplots_adjust(hspace=0.35)\n plt.pause(scanSpeed)\n\n # ------------------------------------------------------------------------","def img_render(kve, lopt_str, sopt_str, gopt_str, popt_str, glopt_str, img_path):\n i_before = ''\n i_layer = ''\n i_after = ''\n i_label_str_html = ''\n if 'img' in kve:\n img_paths = [x.strip() for x in kve['img'].split(':')]\n for opt_str in glopt_str, popt_str, gopt_str, sopt_str, lopt_str:\n if 'autoilabel' in opt_str:\n i_label_str = os.path.splitext(os.path.basename(img_paths[0]))[0]\n i_label_str_html = ' <div class=\"label bottom\">' \\\n + i_label_str + '</div>'\n if 'ilabel' in kve:\n i_label_str = kve['ilabel']\n i_label_str_html = ' <div class=\"label bottom\">' \\\n + i_label_str + '</div>'\n img_tag_str = ''\n for idx, path in enumerate(img_paths):\n img_tag_str = img_tag_str + '<img src=\"' + img_path + img_paths[idx] + '\"/>'\n for opt_str in [glopt_str, popt_str, gopt_str, sopt_str, lopt_str]:\n if 'ibefore' in opt_str:\n i_before = ' <div class=\"layout ' + lopt_str \\\n + '\"><div class=\"img\">' + img_tag_str + '</div>' \\\n + i_label_str_html + '</div>'\n if 'iafter' in opt_str:\n i_after = ' <div class=\"layout ' + lopt_str \\\n + '\"><div class=\"img\">' + img_tag_str + '</div>' \\\n + i_label_str_html + '</div>'\n if not (i_before or i_after):\n i_layer = ' <div class=\"img\">' + img_tag_str + '</div>'\n return i_before, i_layer, i_after\n return '', '', ''","def showAssetImage(*args):\n\n selTab = cmds.tabLayout(widgets[\"shotAssRigListTLO\"], q=True, st=True)\n\n fType = \"\"\n asset = \"\"\n assetPath = \"\"\n path = \"\"\n imagePath = \"\"\n\n if selTab == \"Chars\":\n asset = cmds.textScrollList(widgets[\"shotAssRigCharListTSL\"], q=True, si=True)\n if asset:\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \"characters\", asset[0], \"icon\",\"{0}Icon.png\".format(asset[0])))\n if os.path.isfile(imagePath):\n cFuncs.assetImageUI(imagePath)\n else:\n cmds.warning(\"Can't find an image for {0}\".format(asset[0]))\n\n if selTab == \"Props\":\n asset = cmds.textScrollList(widgets[\"shotAssRigPropListTSL\"], q=True, si=True)\n if asset:\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \"props\", asset[0], \"icon\",\"{0}Icon.png\".format(asset[0])))\n if os.path.isfile(imagePath):\n cFuncs.assetImageUI(imagePath)\n else:\n cmds.warning(\"Can't find an image for {0}\".format(asset[0]))\n \n if selTab == \"Sets\":\n asset = cmds.textScrollList(widgets[\"shotAssRigSetListTSL\"], q=True, si=True)\n if asset:\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \"sets\", asset[0], \"icon\",\"{0}Icon.png\".format(asset[0])))\n if os.path.isfile(imagePath):\n cFuncs.assetImageUI(imagePath)\n else:\n cmds.warning(\"Can't find an image for {0}\".format(asset[0]))\n \n # if selTab == \"Anm\":\n # #need to split this up\n # var_shot = cmds.textScrollList(widgets[\"shotAnmMstListTSL\"], q=True, si=True)\n # if var_shot:\n # var, buf, shot = var_shot[0].partition(\".\")\n # path = cFuncs.getVarMaster(cFuncs.fixPath(os.path.join(pi.shotsFolder, shot, \"anm\", var)))","def test004(testDir, dirDict, pflag):\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_system.cfg\"), testDir)\n shutil.copy(osp.join(dirDict[\"baseConfig\"], \"params_c.cfg\"), testDir)\n psys = osp.join(testDir, \"params_system.cfg\")\n ptrode = osp.join(testDir, \"params_c.cfg\")\n P_s = IO.get_config(psys)\n P_s.set(\"Sim Params\", \"Crate\", \"1e-2\")\n IO.write_config_file(P_s, psys)\n P = IO.get_config(ptrode)\n P.set(\"Particles\", \"type\", \"CHR\")\n P.set(\"Particles\", \"discretization\", \"2e-9\")\n P.set(\"Particles\", \"shape\", \"sphere\")\n P.set(\"Material\", \"muRfunc\", \"LiFePO4\")\n P.set(\"Material\", \"noise\", \"true\")\n P.set(\"Material\", \"numnoise\", \"100\")\n P.set(\"Material\", \"Dfunc\", \"lattice\")\n P.set(\"Material\", \"dgammadc\", \"-2e-30\")\n IO.write_config_file(P, ptrode)\n main.main(psys, keepArchive=False)\n shutil.move(dirDict[\"simOut\"], testDir)\n if pflag:\n corePlots(testDir, dirDict)\n electrodePlots(testDir, dirDict, \"c\")","def call(self, **kwargs):\n # Format template\n template = self._cw.vreg.template_env.get_template(\"startup.logged.jinja2\")\n html = template.render(\n header_url=self._cw.data_url(\"creative/img/neurospin.jpg\"),\n moderator=True)\n self.w(html)","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_histogram_and_image(hist_pred, hist_true, img, tile_name, out_dir=None, volume_weighted=False):\n img = img.astype(np.uint8)\n\n index = np.arange(len(hist_pred)) + 0.5\n\n KL_div = KL(hist_true, hist_pred)\n\n iou = calculate_iou(hist_true, hist_pred)\n\n dm_true = get_dm(hist_true, volume_weighted=volume_weighted)\n dm_pred = get_dm(hist_pred, volume_weighted=volume_weighted)\n\n # Create Figure and Axes instances\n fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(14, 6.5))\n\n # add title of the whole figure\n fig.suptitle('Comparison of Distribution\\n%s' % (tile_name), fontsize=18)\n\n ax1.bar(index, hist_true, width=1.0, label='true histogram')\n ax1.bar(index, hist_pred, width=1.0, alpha=0.5, label='predicted histogram')\n ax1.legend(fontsize=14)\n\n # axis labels\n ax1.set_xlabel('Grain diameter [cm]', fontsize=16)\n\n if volume_weighted:\n ax1.set_ylabel('Relative volume', fontsize=16)\n else:\n ax1.set_ylabel('Relative frequency', fontsize=16)\n\n # x ticks labels\n group_labels = np.array([0.00, 0.01, 0.02, 0.03, 0.04, 0.06, 0.08, 0.10, 0.12, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.50, 0.60, 0.80, 1.0, 1.2, 1.5, 2.0]) * 100\n group_labels = np.array(group_labels, dtype=np.int)\n ax1.set_xticks(np.arange(len(group_labels)))\n ax1.set_xticklabels(group_labels, rotation='vertical')\n\n ax1.text( # position text relative to Axes\n 0.98, 0.82, 'KL: %.2f' % (KL_div),\n ha='right', va='top',\n transform=ax1.transAxes,\n fontsize=16\n )\n\n ax1.text( # position text relative to Axes\n 0.98, 0.76, 'IoU: %.2f' % (iou),\n ha='right', va='top',\n transform=ax1.transAxes,\n fontsize=16\n )\n\n ax1.text( # position text relative to Axes\n 0.98, 0.70, 'dm true: %.2f cm' % (dm_true),\n ha='right', va='top',\n transform=ax1.transAxes,\n fontsize=16\n )\n ax1.text( # position text relative to Axes\n 0.98, 0.64, 'dm pred: %.2f cm' % (dm_pred),\n ha='right', va='top',\n transform=ax1.transAxes,\n fontsize=16\n )\n\n ax2.set_xticks(())\n ax2.set_yticks(())\n\n ax2.imshow(img)\n\n fig.tight_layout()\n fig.subplots_adjust(top=0.88)\n\n if out_dir is not None:\n if not os.path.isdir(out_dir):\n os.makedirs(out_dir)\n\n plt.savefig(os.path.join(out_dir, '{}.png'.format(tile_name)), bbox_inches='tight')\n plt.close(fig) # close the figure","def build_static_files(cfg_prep_used, area_threshold=0.25, beef_cuttings=4, dairy_cuttings=5, overwrite_flag=False):\n logging.info('\\nBuilding ET-Demands Static Files')\n\n # Default values\n permeability = -999\n soil_depth = 60 # inches\n aridity = 50\n irrigation = 1\n\n # Input paths\n # DEADBEEF - For now, get cropET folder from INI file\n # This function may eventually be moved into the main cropET code\n config = copy.deepcopy(cfg_prep_used)\n\n try:\n project_ws = config.CROP_ET.project_folder\n except:\n logging.error('project_folder parameter must be set in the INI file, exiting')\n return False\n try:\n gis_ws = config.CROP_ET.gis_folder\n except:\n logging.error('gis_folder parameter must be set in the INI file, exiting')\n return False\n try:\n et_cells_path = config.CROP_ET.cells_path\n except:\n logging.error('cells_path parameter must be set in the INI file, exiting')\n return False\n try:\n crop_et_ws = config.CROP_ET.crop_et_folder\n except:\n logging.error('crop_et_folder parameter must be set in the INI file, exiting')\n return False\n try:\n template_ws = config.CROP_ET.template_folder\n except:\n logging.error('CROP_ET template_folder parameter must be set in the INI file, exiting')\n return False\n\n # Read data from geodatabase or shapefile\n # if '.gdb' in et_cells_path and not et_cells_path.endswith('.shp'):\n # _flag = False\n # _path = os.path.dirname(et_cells_path)\n # gdb_path = r'D:\\Projects\\CAT_Basins\\AltusOK\\et-demands_py\\et_demands.gdb'\n # _cells_path = os.path.join(gdb_path, 'et_cells')\n\n # Output sub-folder names\n static_ws = os.path.join(project_ws, 'static')\n\n # basin attributes fields\n basin_id_filed = 'STAID'\n basin_elev_field = 'ELEV_MEAN_M_BASIN'\n\n # ET Cell field names\n cell_lat_field = 'LAT'\n cell_lon_field = 'LON'\n cell_id_field = 'GAGE_ID'\n # cell_station_id_field = 'STATION_ID'\n # awc_field = 'AWC'\n clay_field = 'CLAY'\n sand_field = 'SAND'\n awc_in_ft_field = 'AWC_IN_FT'\n hydgrp_num_field = 'HYDGRP_NUM'\n hydgrp_field = 'HYDGRP'\n\n # huc_field = 'HUC{}'.format(huc)\n # permeability_field = 'PERMEABILITY'\n # soil_depth_field = 'SOIL_DEPTH'\n # aridity_field = 'ARIDITY'\n # dairy_cutting_field = 'DAIRY_CUTTINGS'\n # beef_cutting_field = 'BEEF_CUTTINGS'\n\n # Static file names\n cell_props_name = 'ETCellsProperties.txt'\n cell_crops_name = 'ETCellsCrops.txt'\n cell_cuttings_name = 'MeanCuttings.txt'\n crop_params_name = 'CropParams.txt'\n crop_coefs_name = 'CropCoefs.txt'\n crop_coefs_eto = 'CropCoefs_eto.txt'\n crop_coefs_etr = 'CropCoefs_etr.txt'\n eto_ratio_name = 'EToRatiosMon.txt'\n etr_ratio_name = 'ETrRatiosMon.txt'\n static_list = [crop_params_name, crop_coefs_name, crop_coefs_eto, crop_coefs_etr, cell_props_name, cell_crops_name,\n cell_cuttings_name, eto_ratio_name, etr_ratio_name]\n\n # Check input folders\n if not os.path.isdir(crop_et_ws):\n logging.critical('\\nERROR: The INI cropET folder does not exist\\n {}'.format(crop_et_ws))\n sys.exit()\n elif not os.path.isdir(project_ws):\n logging.critical('\\nERROR: The project folder does not exist\\n {}'.format(project_ws))\n sys.exit()\n elif not os.path.isdir(gis_ws):\n logging.critical('\\nERROR: The GIS folder does not exist\\n {}'.format(gis_ws))\n sys.exit()\n logging.info('\\nGIS Workspace: {}'.format(gis_ws))\n logging.info('Project Workspace: {}'.format(project_ws))\n logging.info('CropET Workspace: {}'.format(crop_et_ws))\n logging.info('Template Workspace: {}'.format(template_ws))\n\n # Check input files\n if not _arcpy.exists(et_cells_path):\n logging.critical('\\nERROR: The ET Cell shapefile does not exist\\n {}'.format(et_cells_path))\n sys.exit()\n for static_name in static_list:\n if not os.path.isfile(os.path.join(template_ws, static_name)):\n logging.error(\n '\\nERROR: The static template does not exist\\n {}'.format(os.path.join(template_ws, static_name)))\n sys.exit()\n logging.debug('ET Cells Path: {}'.format(et_cells_path))\n\n # Build output table folder if necessary\n if not os.path.isdir(static_ws):\n os.makedirs(static_ws)\n\n # Read ET Cell zonal stats\n logging.info('\\nReading ET Cell Zonal Stats')\n logging.debug(' {}'.format(et_cells_path))\n crop_field_list = sorted([f for f in _arcpy.list_fields(et_cells_path) if re.match('CROP_\\d{3}', f)])\n fields = [cell_id_field, cell_lat_field, cell_lon_field, awc_in_ft_field, clay_field, sand_field, hydgrp_num_field,\n hydgrp_field]\n fields = fields + crop_field_list\n logging.debug(' Fields: {}'.format(fields))\n cell_data_dict = defaultdict(dict)\n\n for fid, row in _arcpy.search_cursor(et_cells_path, fields).items():\n # Switch to cell_id_field as index (instead of FID)\n for f in fields[1:]:\n cell_data_dict[str(row[cell_id_field])][f] = row[f]\n\n # Read basin data\n logging.info('\\nReading basin attributes')\n fields = [basin_elev_field]\n logging.debug(' Fields: {}'.format(fields))\n basin_data_dict = defaultdict(dict)\n basin_topo = pd.read_csv(config.GAGES.basin_topo_file, sep=',', dtype={0: str})\n for cell_id_tmp in list(cell_data_dict.keys()):\n basin_data_dict[cell_id_tmp][basin_elev_field] = \\\n basin_topo.loc[basin_topo[basin_id_filed] == cell_id_tmp, basin_elev_field].values[0]\n # Convert elevation units if necessary\n logging.debug(' Convert station elevation from meters to feet')\n for k in basin_data_dict.keys():\n basin_data_dict[k][basin_elev_field] /= 0.3048\n\n # static files\n logging.info('\\nCopying template static files')\n for static_name in static_list:\n logging.debug(' {}'.format(static_name))\n shutil.copy(os.path.join(template_ws, static_name), static_ws)\n\n logging.info('\\nWriting static text files')\n cell_props_path = os.path.join(static_ws, cell_props_name)\n cell_crops_path = os.path.join(static_ws, cell_crops_name)\n cell_cuttings_path = os.path.join(static_ws, cell_cuttings_name)\n eto_ratio_path = os.path.join(static_ws, eto_ratio_name)\n etr_ratio_path = os.path.join(static_ws, etr_ratio_name)\n\n # Write cell properties\n logging.debug(' {}'.format(cell_props_path))\n with open(cell_props_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n basin_id = cell_id\n basin_lat = '{:>9.4f}'.format(cell_data[cell_lat_field])\n basin_lon = '{:>9.4f}'.format(cell_data[cell_lon_field])\n basin_data = basin_data_dict[basin_id]\n basin_elev = '{:.2f}'.format(basin_data[basin_elev_field])\n # There is an extra/unused column in the template and excel files\n output_list = [cell_id, cell_id, basin_id, basin_lat, basin_lon, basin_elev, permeability,\n '{:.4f}'.format(cell_data[awc_in_ft_field]), soil_depth, cell_data[hydgrp_field],\n cell_data[hydgrp_num_field], aridity, '']\n output_f.write('\\t'.join(map(str, output_list)) + '\\n')\n\n del output_list\n del basin_id, basin_lat, basin_lon, basin_elev\n\n # Write cell crops\n crops = util.parse_int_set(config.USDA.cdl_crops)\n logging.debug(' {}'.format(cell_crops_path))\n with open(cell_crops_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n basin_id = cell_id\n output_list = [cell_id, cell_id, basin_id, irrigation]\n crop_list = ['CROP_{:03d}'.format(i) for i in crops]\n crop_area_list = []\n for crop in crop_list:\n if crop in cell_data.keys() and cell_data[crop] is not None:\n crop_area_list.append(cell_data[crop])\n else:\n crop_area_list.append(0)\n crop_flag_list = [1 if area > area_threshold else 0 for area in crop_area_list]\n output_list = output_list + crop_flag_list\n output_f.write('\\t'.join(map(str, output_list)) + '\\n')\n\n del crop_list, crop_area_list, crop_flag_list, output_list\n\n # Write cell cuttings\n logging.debug(' {}'.format(cell_cuttings_path))\n with open(cell_cuttings_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n output_list = [cell_id, cell_id, '{:>9.4f}'.format(cell_data[cell_lat_field]), dairy_cuttings,\n beef_cuttings]\n output_f.write('\\t'.join(map(str, output_list)) + '\\n')\n\n # Write monthly ETo ratios\n logging.debug(' {}'.format(eto_ratio_path))\n with open(eto_ratio_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n basin_id = cell_id\n output_f.write('\\t'.join(map(str, [basin_id, ''] + [1.0] * 12)) + '\\n')\n\n # Write monthly ETr ratios\n logging.debug(' {}'.format(etr_ratio_path))\n with open(etr_ratio_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n basin_id = cell_id\n output_f.write('\\t'.join(map(str, [basin_id, ''] + [1.0] * 12)) + '\\n')","def plot_final_grid(generated_images):\n\n fig = plt.figure(figsize=(GRID_SIZE, GRID_SIZE))\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.001, hspace=0.001)\n\n for ind in range(GRID_IMGS):\n ax = fig.add_subplot(GRID_SIZE, GRID_SIZE, ind + 1, xticks=[], yticks=[])\n ax.imshow(np.uint8(((generated_images[ind] + 1) / 2) * 255), cmap='gray')\n\n plt.savefig('/content/drive/My Drive/WGAN/generated_image_grid.png')\n plt.savefig('/content/drive/My Drive/WGAN/results/WGAN.png')\n plt.show()","def make_image(config, slot_names, mean_frame_pattern='_mean_bias_image.fits'):\n file_list = sorted(find_files(config))\n fits_files_dict = {slot_names[i] : file_list[i] for i in range(len(file_list))}\n ccd_dict = {}\n ccd_dict_wbias = {}\n gains_dict = {}\n for slot in slot_names:\n\n mean_bias_file = slot + mean_frame_pattern\n\n ccd_dict[slot] = sensorTest.MaskedCCD(fits_files_dict[slot])\n outfile = os.path.join(config['tmp_dir'],'ccd' + slot + '.fits')\n image={}\n \n ccd_dict_wbias[slot]=sensorTest.MaskedCCD(fits_files_dict[slot],\\\n bias_frame=os.path.join(config['tmp_dir'],mean_bias_file))\n outfile_wbias = os.path.join(config['tmp_dir'],'ccd' + slot + '_wbias.fits')\n image_wbias={}\n \n eotest_results_file = os.path.join(config['eo_data_path'],'{}_eotest_results.fits'.format(ccd_dict[slot].md('LSST_NUM')))\n gains_dict[slot] = gains(eotest_results_file)\n \n for amp in ccd_dict[slot]:\n image[amp] = ccd_dict[slot].bias_subtracted_image(amp)\n image[amp] *= gains_dict[slot][amp]\n image_wbias[amp] = ccd_dict_wbias[slot].bias_subtracted_image(amp)\n image_wbias[amp] *= gains_dict[slot][amp]\n \n imutils.writeFits({amp: image_wbias[amp].getImage() for amp in ccd_dict_wbias[slot]}, \n outfile_wbias, fits_files_dict[slot])\n imutils.writeFits({amp: image[amp].getImage() for amp in ccd_dict[slot]}, \n outfile, fits_files_dict[slot])\n\n fits_files_dict_corr={slot : os.path.join(config['tmp_dir'],'ccd'+slot+'.fits') for slot in slot_names}\n fits_files_dict_corr_wbias={slot : os.path.join(config['tmp_dir'],'ccd'+slot+'_wbias.fits') for slot in slot_names}\n \n im_corr = raft.RaftMosaic(fits_files_dict_corr, bias_subtract=False)\n im_corr_wbias = raft.RaftMosaic(fits_files_dict_corr_wbias, bias_subtract=False)\n im_raw = raft.RaftMosaic(fits_files_dict, bias_subtract=False)\n \n return im_raw, im_corr, im_corr_wbias","def processImages(self):\n for file in os.listdir(self.config[\"tempPath\"]):\n self.logger.debug(\"Calling generateImages for the file: {0}\".format(file))\n self.generateText(file)","def camera():\n return render_template('home/cam.html')","def _generate_single_image(self, char, config, char_idx=None):\n '''\n #there are 13 box each line\n #width: 760 #height:80\n #2 lines: 1500 #height: 80\n #==>> calculate width each box: 58\n #each line will be choose a:\n - height\n - padding bottom\n - padding top\n #each character will be choose:\n - height => calculate width\n - padding left right base on width of box and width of character after resizing\n '''\n\n if char == '・':\n charChoice = '.'\n else:\n charChoice = char\n\n if charChoice not in self.char_2_imgs.keys():\n return None\n\n isFinishChoose = False\n while not isFinishChoose:\n choice = self._get_random_choice_index(char)\n\n image = self.char_2_imgs[charChoice][choice]\n # numberRandom = random.randint(0, 10000)\n # cv2.imwrite('C:\\\\Users\\\\ABC\\\\Desktop\\\\deletetemp\\\\GenDataHWBB\\\\synthesizedKana\\\\test_bf'+str(numberRandom)+'_'+str(choice)+'.png', image)\n\n # # rotate image\n # image = rotate(image, config['skewness'], order=1,\n # cval=self.background_value)\n\n # resize image\n if charChoice not in constants.NOT_RESIZE:\n image = self._resize_character_kata(image, config['height'])\n else:\n random.seed()\n height, _ = image.shape\n if char == '.':\n config['bottom'] = config['bottom_base']\n config['top'] = max(0,self.configKata['FIX_ALL_LINE_HEIGHT'] - config['bottom'] - height)\n else:\n\n config['bottom'] = config['bottom_base'] + int(config['height_line']/2)\n config['top'] = max(0,self.configKata['FIX_ALL_LINE_HEIGHT'] - config['bottom'] - height)\n\n\n\n # add horizontal space and bottom space\n _, width = image.shape\n if width < self.configKata['FIX_BOX_WIDTH']-8:\n remain = int((self.configKata['FIX_BOX_WIDTH']-width)/2) + 3\n random.seed()\n left = max(0, random.randint(5, remain))\n if char == '゙' or char == '゚':\n random.seed()\n left = random.randint(3, 7)\n elif char == ')':\n left = 0\n right = max(0, self.configKata['FIX_BOX_WIDTH']-left-width)\n if right == 0:\n left = max(0, self.configKata['FIX_BOX_WIDTH']-right-width)\n\n image = np.pad(image, ((config['top'], config['bottom']), (left, right)),'constant', constant_values=self.background_value)\n\n # cv2.imwrite('C:\\\\Users\\\\ABC\\\\Desktop\\\\deletetemp\\\\GenDataHWBB\\\\synthesizedKana\\\\test_af'+str(numberRandom)+'.png', image)\n isFinishChoose = True\n else:\n continue\n return image","def display(self):\n display(self.image)","def show_flow(filename):\n flow = read_flow(filename)\n img = flow_to_image(flow)\n plt.imshow(img)\n plt.show()","def render_sample(cls,\n nusc: 'NuScenes',\n radar_data_gp: 'RadarDataGroup',\n front_cam_data: 'CameraData',\n host_data:'HostData',\n fig_info:dict,\n box_vis_level: BoxVisibility = BoxVisibility.ANY,\n out_path: str = None) -> None:\n cls.colors=radar_data_gp.colors\n fig, axes = plt.subplots(1, 2, figsize=(16, 24))\n #left diagram to show front view\n ax1 = axes[0]\n cls.render_front_view(cls, nusc=nusc, radar_data_gp=radar_data_gp, front_cam_data=front_cam_data,\n ax =ax1, use_flat_vehicle_coordinates=False, with_anns=False, with_radar=True)\n #right diagram to show bird view\n ax2 = axes[1] \n \n cls.render_bird_view(cls, nusc=nusc, radar_data_gp=radar_data_gp, front_cam_data=front_cam_data, \n host_data=host_data, ax =ax2, use_flat_vehicle_coordinates=True)\n #figure information\n fig.text(0.01, 0.97, 'Figure information: ', weight='bold', color='green',fontsize = 15) \n fig.text(0.01, 0.93, 'package: '+nusc.version, fontsize = 12) \n fig.text(0.01, 0.9, 'scene name: '+fig_info['scene_name'], fontsize = 12) \n fig.text(0.01, 0.87, 'timestamp: '+str(front_cam_data.time_stamp*1e-6)+'(s)', fontsize = 12)\n fig.text(0.01, 0.84, 'elapse time: : '+str((fig_info['elapse_time'])*1e-3)+'(ms)', fontsize = 12)\n \n plt.tight_layout()\n fig.subplots_adjust(wspace=0, hspace=0)\n if out_path is not None:\n plt.savefig(out_path,dpi=200)\n if fig_info['verbose']: \n plt.show()","def init():\n \n # General parameters\n exp_path = '/home/laura/Documents/stacks tif/1705_regMovie.tif' # experimental tif stack (grayscale)\n bin_path = '/home/laura/Documents/stacks tif/1705/1705_binarizedMovie.tif' # binarized tif stack\n vect_path = '/home/laura/Documents/STAGE3/1705_NET/' # gpickle directory\n dest_path = '/home/laura/Documents/STAGE3/1705_NET/superposition' # output directory\n verbose = True\n debug = True\n invert = True \n main_params = [exp_path, bin_path, vect_path, dest_path, verbose, debug, invert]\n \n # Output options\n doImg = -1 # image index\n doStack = False \n doVideo = False \n compress = 3 # advice: no more than 5\n output_params = [doImg, doStack, doVideo, compress]\n \n # Drawing options (colors as BGR)\n line = True # edges drawing\n line_color = (0, 255, 0) # green \n line_size = 1 \n apex_color = (0, 0, 255) # red\n apex_size = 5\n node_color = (255, 0, 0) # blue\n node_size = 5\n body_color = (0, 255, 0) # green\n body_size = 3\n drawing_params = [line, line_color, line_size, apex_color, apex_size,\n node_color, node_size, body_color, body_size]\n \n return main_params, output_params, drawing_params","def main():\n usage = \"usage: %prog [options] input: BioC File (args[0]); Output Directory for the (picture) .svg file.\"\n parser = OptionParser(version='%prog 0.99', usage=usage)\n\n parser.add_option('-l', '--logfile', dest='logfilename',\n help='write log to FILE', metavar='FILE')\n parser.add_option('-q', '--quiet',\n action='store_true', dest='quiet', default=False,\n help='do not print status messages to stderr')\n parser.add_option('-d', '--debug',\n action='store_true', dest='debug', default=False,\n help='print debug information')\n\n\n\n (options, args) = parser.parse_args()\n\n if options.debug: print >> sys.stderr, '# Starting processing'\n\n process(options=options,args=args)\n\n\n\n\n sys.exit(0) # Everything went ok!"],"string":"[\n \"def surf_tempmap(case_config_filepath, render_config_filepath):\\n\\n # Load config file with all common directory names\\n dirname_config = configparser.ConfigParser()\\n dirname_config.read(\\\"dirname.cfg\\\")\\n\\n # Get information from config files\\n cconfd = load_config.get_config_params(case_config_filepath)\\n rconfd = load_config.get_config_params(render_config_filepath)\\n\\n # Main output directory\\n case_output = dirname_config[\\\"DIRECTORIES\\\"][\\\"RenderOutput\\\"] + cconfd[\\\"case_name\\\"] + \\\"/\\\"\\n \\n # Determine surface temperature geometry output dir\\n ply_temp_output_dir = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"ply_temp\\\"]\\n\\n # Get temperature bounds\\n if rconfd[\\\"temp_bounds_auto\\\"]:\\n temp_min, temp_max = convert_data.temp_bounds(h5dns_path=cconfd[\\\"h5dns_path\\\"],\\n ply_temp_output_dir=ply_temp_output_dir,\\n prc_min=rconfd[\\\"temp_min_percentile\\\"],\\n prc_max=rconfd[\\\"temp_max_percentile\\\"])\\n else:\\n temp_min = rconfd[\\\"temp_min\\\"]\\n temp_max = rconfd[\\\"temp_max\\\"]\\n\\n # Determine specific output dirs and make them if necessary\\n image_output_dir_spec = dircheck.count_png_dirs(case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"tstep_sequence_surftempmap\\\"])\\n ply_temp_output_dir_spec = ply_temp_output_dir + str(temp_min) + \\\"to\\\" + str(temp_max)\\n dircheck.check_make([ply_temp_output_dir_spec, image_output_dir_spec])\\n\\n # Write Blender config file\\n blender_config_filedir = case_output + rconfd[\\\"render_name\\\"] + \\\"_blender.cfg\\\"\\n load_config.write_config_file(config_filedir=blender_config_filedir,\\n config_dict={\\\"image_output_dir_spec\\\": image_output_dir_spec,\\n \\\"ply_input_dir\\\": ply_temp_output_dir_spec,\\n \\\"interface_material_name\\\": \\\"heatmapMaterial\\\",\\n \\\"bg_image_filepath\\\": rconfd[\\\"bg_image_filepath\\\"],\\n \\\"view_fraction\\\": cconfd[\\\"dropd\\\"]/rconfd[\\\"droplet_scale\\\"],\\n \\\"render_scale\\\": 10,\\n \\\"resolution_percentage\\\": rconfd[\\\"resolution_percentage\\\"],\\n \\\"xres\\\": cconfd[\\\"xres\\\"], \\\"yres\\\": cconfd[\\\"yres\\\"], \\\"zres\\\": cconfd[\\\"zres\\\"],\\n \\\"tres\\\": cconfd[\\\"tres\\\"],\\n \\\"interface_half_enabled\\\": False,\\n \\\"fog_enabled\\\": False,\\n \\\"camera_azimuth_angle\\\": rconfd[\\\"camera_azimuth_angle\\\"],\\n \\\"camera_elevation_angle\\\": rconfd[\\\"camera_elevation_angle\\\"],\\n \\\"bg_color_1\\\": rconfd[\\\"bg_color_1\\\"], \\\"bg_color_2\\\": rconfd[\\\"bg_color_2\\\"]})\\n\\n # Extract droplet interface geometry\\n ply_output_dir_uncolored = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"ply\\\"]\\n convert_data.conv_ply(h5dns_path=cconfd[\\\"h5dns_path\\\"], output_dir=ply_output_dir_uncolored, tres=cconfd[\\\"tres\\\"])\\n\\n # Add surface temperature color to droplet interface\\n convert_data.conv_color_ply(h5dns_path=cconfd[\\\"h5dns_path\\\"], output_dir=ply_temp_output_dir_spec, uncolored_ply_dir=ply_output_dir_uncolored, tres=cconfd[\\\"tres\\\"], temp_min=temp_min, temp_max=temp_max)\\n\\n # Launch Blender to perform rendering\\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\\\"droplet_render.py\\\", blend_name=\\\"droplet_render.blend\\\")\\n\\n # Add temperature legend colorbar to images\\n if rconfd[\\\"add_temp_bar\\\"]:\\n imedit.add_tempmap(bound_min=temp_min*cconfd[\\\"tgas\\\"], bound_max=temp_max*cconfd[\\\"tgas\\\"], image_dir=image_output_dir_spec, tres=cconfd[\\\"tres\\\"])\",\n \"def main(config):\\n file_paths_info = [('GLOFRIS','WATCH','ARG_inunriver_historical_000000000WATCH_1980_rp01000.tif'),\\n \\t\\t\\t\\t('GLOFRIS','RCP45','ARG_inunriver_rcp4p5_0000GFDL-ESM2M_2030_rp01000.tif'),\\n \\t\\t\\t\\t('GLOFRIS','RCP85','ARG_inunriver_rcp8p5_0000GFDL-ESM2M_2030_rp01000.tif'),\\n \\t\\t\\t\\t('FATHOM','AR_fluvial_undefended_merged','AR-FU-1000.tif'),\\n \\t\\t\\t\\t('FATHOM','AR_pluvial_undefended_merged','AR-PU-1000.tif')\\n \\t\\t\\t\\t]\\n figure_names = ['GLOFRIS-WATCH-fluvial','GLOFRIS-RCP45-fluvial','GLOFRIS-RCP85-fluvial','FATHOM-fluvial','FATHOM-pluvial']\\n figure_titles = ['current fluvial flooding','RCP4.5 fluvial flooding','RCP8.5 fluvial flooding','current fluvial flooding','current pluvial flooding']\\n for f_i in range(len(file_paths_info)):\\n\\t hazard_file = os.path.join(config['paths']['data'],'flood_data', file_paths_info[f_i][0],file_paths_info[f_i][1],file_paths_info[f_i][2])\\n\\t output_file = os.path.join(config['paths']['figures'], 'flood-map-{}.png'.format(figure_names[f_i]))\\n\\t ax = get_axes()\\n\\t plot_basemap(ax, config['paths']['data'])\\n\\t scale_bar(ax, location=(0.8, 0.05))\\n\\t plot_basemap_labels(ax, config['paths']['data'], include_regions=True,include_zorder=3)\\n\\n\\t proj_lat_lon = ccrs.PlateCarree()\\n\\n\\n\\t # Create color map\\n\\t colors = plt.get_cmap('Blues')\\n\\n\\t # Read in raster data\\n\\t data, lat_lon_extent = get_data(hazard_file)\\n\\t data[(data <= 0) | (data > 5)] = np.nan\\n\\t max_val = np.nanmax(data)\\n\\t norm=mpl.colors.Normalize(vmin=0, vmax=max_val)\\n\\n\\t # Plot population data\\n\\t im = ax.imshow(data, extent=lat_lon_extent,transform=proj_lat_lon, cmap=colors,norm =norm, zorder=2)\\n\\n\\t # Add colorbar\\n\\t cbar = plt.colorbar(im, ax=ax,fraction=0.1, shrink=0.87,pad=0.01, drawedges=False, orientation='horizontal',\\n\\t norm=mpl.colors.Normalize(vmin=0, vmax=max_val), ticks=list(np.linspace(0,max_val,3)))\\n\\t cbar.set_clim(vmin=0,vmax=max_val)\\n\\n\\n\\t cbar.outline.set_color(\\\"none\\\")\\n\\t cbar.ax.yaxis.set_tick_params(color='black')\\n\\t cbar.ax.set_xlabel('Flood depths (m)',fontsize=12,color='black')\\n\\n\\t plt.title('1 in 1000 year {}'.format(figure_titles[f_i]), fontsize = 14)\\n\\t save_fig(output_file)\\n\\t plt.close()\",\n \"def generate_cfg():\\n \\n if not os.path.exists(cfg_path):\\n os.mkdir(cfg_path)\\n \\n for img_path in get_template_paths():\\n extractor = BlockExtractor(img_path)\\n extractor.get_cfg()\\n for block in extractor.get_blocks():\\n img = BlockParser(img_path, block).block_image()\\n #cv.imshow(\\\"Block\\\", img)\\n #cv.waitKey() & 0xFF\",\n \"def imdisplay(filename, representation):\\n img = read_image(filename, representation)\\n if representation == GS_REP:\\n plt.imshow(img, cmap=plt.cm.gray)\\n else:\\n plt.imshow(img)\",\n \"def lambda2(case_config_filepath, render_config_filepath):\\n\\n # Load config file with all common directory names\\n dirname_config = configparser.ConfigParser()\\n dirname_config.read(\\\"dirname.cfg\\\")\\n\\n # Get information from config files\\n cconfd = load_config.get_config_params(case_config_filepath)\\n rconfd = load_config.get_config_params(render_config_filepath)\\n \\n # Main output directory\\n case_output = dirname_config[\\\"DIRECTORIES\\\"][\\\"RenderOutput\\\"] + cconfd[\\\"case_name\\\"] + \\\"/\\\"\\n\\n # Determine lambda2 output directories and make them if necessary\\n lambda2_level = rconfd[\\\"lambda2_level\\\"]\\n lambda2_specifier = \\\"l2\\\" + str(lambda2_level) + \\\"/\\\"\\n geometry_output_dir = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"ply\\\"]\\n ply_lambda2_output_dir = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"ply_lambda2\\\"] + lambda2_specifier\\n image_lambda2_output_dir = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"tstep_lambda2\\\"] + lambda2_specifier\\n image_lambda2_output_dir_spec = dircheck.count_png_dirs(image_lambda2_output_dir)\\n dircheck.check_make([ply_lambda2_output_dir, image_lambda2_output_dir_spec])\\n\\n # Write Blender config file\\n blender_config_filedir = case_output + rconfd[\\\"render_name\\\"] + \\\"_blender.cfg\\\"\\n load_config.write_config_file(config_filedir=blender_config_filedir,\\n config_dict={\\\"image_output_dir_spec\\\": image_lambda2_output_dir_spec,\\n \\\"ply_input_dir\\\": ply_lambda2_output_dir,\\n \\\"interface_material_name\\\": \\\"Lambda2Contour\\\",\\n \\\"bg_image_filepath\\\": rconfd[\\\"bg_image_filepath\\\"],\\n \\\"view_fraction\\\": rconfd[\\\"view_fraction\\\"], \\\"render_scale\\\": 10,\\n \\\"resolution_percentage\\\": rconfd[\\\"resolution_percentage\\\"],\\n \\\"xres\\\": cconfd[\\\"xres\\\"], \\\"yres\\\": cconfd[\\\"yres\\\"], \\\"zres\\\": cconfd[\\\"zres\\\"],\\n \\\"tres\\\": cconfd[\\\"tres\\\"], \\\"interface_half_enabled\\\": False,\\n \\\"fog_enabled\\\": False,\\n \\\"camera_azimuth_angle\\\": rconfd[\\\"camera_azimuth_angle\\\"],\\n \\\"camera_elevation_angle\\\": rconfd[\\\"camera_elevation_angle\\\"],\\n \\\"bg_color_1\\\": rconfd[\\\"bg_color_1\\\"], \\\"bg_color_2\\\": rconfd[\\\"bg_color_2\\\"]})\\n\\n # Extract droplet geometry\\n # convert_data.conv_ply(h5dns_path=cconfd[\\\"h5dns_path\\\"], output_dir=geometry_output_dir, tres=int(cconfd[\\\"tres\\\"]))\\n\\n # Extract lambda2 contour geometry\\n convert_data.conv_lambda2_ply(h5dns_path=cconfd[\\\"h5dns_path\\\"], output_dir=ply_lambda2_output_dir, tres=cconfd[\\\"tres\\\"], contour_level=lambda2_level)\\n\\n # Launch Blender to perform rendering\\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\\\"droplet_render.py\\\", blend_name=\\\"droplet_render.blend\\\")\",\n \"def imdisplay(filename, representation):\\n image = read_image(filename, representation)\\n\\n if representation == GRAY_OUT:\\n plt.imshow(image, cmap='gray')\\n else:\\n plt.imshow(image)\\n\\n plt.show()\",\n \"def imdisplay(filename, representation):\\n im = read_image(filename, representation)\\n if representation == 1:\\n plt.imshow(im, cmap='gray')\\n plt.show()\\n if representation == 2:\\n plt.imshow(im)\\n plt.show()\",\n \"def main(argv):\\n\\n epd = EPD()\\n\\n epd.clear()\\n\\n print('panel = {p:s} {w:d} x {h:d} version={v:s}'.format(p=epd.panel, w=epd.width, h=epd.height, v=epd.version))\\n\\n for file_name in argv:\\n if not os.path.exists(file_name):\\n sys.exit('error: image file{f:s} does not exist'.format(f=file_name))\\n print('display: {f:s}'.format(f=file_name))\\n display_file(epd, file_name)\",\n \"def main():\\n dpi = 1\\n dpi = 2\\n width = int(360)\\n height = int(130)\\n mywidth = int(width*dpi)\\n myheight = int(height*dpi)\\n FWHM = 7.5 # degrees\\n FWHM = 10.0 # degrees\\n FWHM = 5.0 # degrees\\n FWHM = 3.0 # degrees\\n FWHM = 1.0 # degrees\\n weight = 1.\\n\\n nargs = len(sys.argv)\\n if nargs < 2:\\n print('GR: GRid Observations of integrated intensity produced by the T Command')\\n print('GR produces fits images for each of the horns used for the observations.')\\n print('For observations at the same coordinates, the ratios of intensities are also produced.')\\n print('The FITS format files require header information, which is copied from the')\\n print('Cold Load File provided by the user')\\n print('GR RA|GAL <cold file name> <savefile1> [<savefile2> ... <savefileN>]')\\n print(\\\"\\\")\\n print('Glen Langston, National Science Foundation -- 20 May 12')\\n exit()\\n\\n gridtype = sys.argv[1]\\n gridtype = gridtype.upper()\\n print('Grid Type: ', gridtype)\\n\\n # enable having ra going from 24 to 0 hours == 360 to 0 degrees\\n xsign = 1.\\n xoffset = 0.\\n if gridtype == 'RA':\\n xmin = 0.\\n xmax = 360.\\n ymin = -40.\\n ymax = 90.\\n maptype = 'RA'\\n elif gridtype == '-RA':\\n xmin = 0.\\n xmax = 360.\\n ymin = -40.\\n ymax = 90.\\n xsign = -1.\\n xoffset = 360. # when x = 360. should be at zero.\\n maptype = 'RA'\\n elif gridtype == '-EL':\\n xmin = 0.\\n xmax = 360.\\n ymin = 0.\\n ymax = 90.\\n xsign = -1.\\n xoffset = 360. # when x = 360. should be at zero.\\n maptype = 'AZEL'\\n elif gridtype == 'RA0':\\n xmin = 0.\\n xmax = 360.\\n ymin = -41.\\n ymax = 89.\\n xsign = -1.\\n xoffset = 180. # when x = 360. should be at zero.\\n gridtype = 'RA'\\n elif gridtype == 'GAL':\\n xmin = -180.\\n xmax = 180.\\n ymin = -90.\\n ymax = 90.\\n maptype = 'GAL'\\n\\n if gridtype != 'RA' and gridtype != 'GAL' and gridtype != '-RA' and gridtype != \\\"RA0\\\":\\n print('Error parsing grid type: ', gridtype)\\n print('1st argument should be either RA, -RA or GAL')\\n exit()\\n\\n rs = radioastronomy.Spectrum()\\n\\n if doRatio: \\n #create the grid with map parameters\\n grid1 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n grid2 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n grid3 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n grid4 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n # put each telescope in a different grid\\n grids = [grid1, grid2, grid3, grid4]\\n\\n gridall = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n \\n\\n projection = \\\"-AIT\\\"\\n# coldfile \\n coldfile = sys.argv[2]\\n# get telescope geographic location etc\\n print(\\\"Reading Observing parameters from: %s\\\" % (coldfile))\\n rs.read_spec_ast(coldfile)\\n print(\\\"Observer: %s \\\" % (rs.observer))\\n\\n# first read through all data and find hot load\\n names = sys.argv[3:]\\n names = sorted(names)\\n\\n firsttime = \\\"\\\"\\n lasttime = \\\"\\\"\\n count = 0\\n # setup grid indicies so that cpuIndex goes to the correct grid\\n # This assumes telescopes 2,3,4,5 are being used] \\n gridIndex = [0,0,0,1,2,3]\\n # for all save Files to Grid\\n for filename in names:\\n print(\\\"File: %s\\\" % (filename))\\n f = open(filename)\\n\\n date = \\\"Unknown\\\"\\n while date != \\\"\\\":\\n date, time, cpuIndex, telaz, telel, tSys, tRx, tRms, tint, KperC, tSourcemax, velSource, dV, tVSum, tVSumRms, tSumKmSec, dTSumKmSec, gainFactor = gainfactor.readSaveValues( f)\\n dlen = len(date)\\n if dlen < 1:\\n break\\n if date[0] == \\\"#\\\":\\n continue\\n # else not a comment process the line\\n count = count + 1\\n isodate = \\\"20\\\"+date+\\\"T\\\"+time\\n# print(\\\"DateTime: %s\\\" % (isodate))\\n rs.utc = datetime.datetime.strptime(isodate,\\\"%Y-%m-%dT%H:%M:%S\\\")\\n# print(\\\"Utc: %s\\\" % (rs.utc))\\n rs.telaz = telaz\\n rs.telel = telel\\n rs.azel2radec()\\n\\n ra = rs.ra\\n dec = rs.dec\\n lon = rs.gallon\\n lat = rs.gallat\\n tsum = tSumKmSec\\n tsdv = dTSumKmSec\\n tmax = tSourcemax\\n vave = tVSum\\n vsdv = tVSumRms\\n if firsttime == \\\"\\\":\\n firsttime = date\\n else:\\n lasttime = date\\n\\n# if vave > -100. and vave < 100:\\n# mygrid.convolve( lon, lat, vave, 1.)\\n iGrid = gridIndex[cpuIndex]\\n gainCorr = telescopefactors[iGrid]\\n tsum = tsum * gainCorr\\n if gridtype == 'RA':\\n if doRatio:\\n grids[iGrid].convolve(ra, dec, tsum, weight)\\n gridall.convolve( ra, dec, tsum, weight)\\n elif gridtype == '-RA':\\n x = (ra*xsign) + xoffset\\n if doRatio:\\n grids[iGrid].convolve(x, dec, tsum, weight)\\n gridall.convolve( x, dec, tsum, weight)\\n elif gridtype == 'RA0':\\n x = (ra*xsign) + xoffset\\n if x < 0:\\n x = x + xmax\\n elif x > xmax:\\n x = x - xmax\\n if doRatio:\\n grids[iGrid].convolve(x, dec, tsum, weight)\\n gridall.convolve( x, dec, tsum, weight)\\n else:\\n if doRatio:\\n grids[iGrid].convolve(lon, lat, tsum, weight)\\n gridall.convolve( lon, lat, tsum, weight)\\n\\n if count == 0:\\n print('Convolving Coordinates: ', ra, dec, lon, lat)\\n print('Convolving Intensities: ', tsum, tsdv, vave, vsdv)\\n print('Convolvign Parameters : ', n, time)\\n count = count + 1\\n # end reading all lines in save file\\n f.close()\\n\\n # normalize each of the gridded images\\n if doRatio:\\n grids[0].normalize()\\n grids[1].normalize()\\n grids[2].normalize()\\n grids[3].normalize()\\n gridall.normalize()\\n# mygrid.check()\\n# zmin = -1000.\\n# zmax = 3000.\\n# limit grid intensities for plotting\\n# mygrid.set_ij( 0, 0, zmax, 1.)\\n# mygrid.set_ij( 1, 1, zmin, 1.)\\n# mygrid.limit(zmin, zmax)\\n\\n subplots = False\\n\\n if subplots:\\n fig, ax = plt.subplots(figsize=(myheight, mywidth), dpi=dpi)\\n\\n if gridtype == 'RA':\\n cax = fig.add_axes([-180, 180], [-90, 90])\\n else:\\n cax = fig.add_axes([0, 24], [-90, 90])\\n\\n cbar = fig.colorbar(cax, ticks=[zmin, zmax], orientation='horizontal')\\n cbar.ax.set_yticklabels([str(zmin), str(zmax)])\\n\\n ax.set_title(\\\"Citizen Science: Horn observations of our Galaxy\\\")\\n else:\\n#y_ticks = ymin + (ymax-ymin)*ticks/myheight\\n\\n ticks = np.arange(0, mywidth, 30*dpi)\\n x_ticks = xmin + ((xmax-xmin)*ticks/mywidth)\\n\\n plt.imshow(gridall.image, interpolation='nearest', cmap=plt.get_cmap('jet'))\\n\\n if firsttime != lasttime:\\n plt.title(\\\"Citizen Science: Observing our Galaxy: %s to %s\\\" % (firsttime, lasttime))\\n else:\\n plt.title(\\\"Citizen Science: Observing our Galaxy: %s\\\" % (firsttime))\\n if gridtype == 'RA':\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Declination (degrees)\\\")\\n labels = ticks/(mywidth/24)\\n yticks = np.arange(0, myheight, 15*dpi)\\n elif gridtype == '-RA':\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Declination (degrees)\\\")\\n labels = 24 - (ticks/(mywidth/24))\\n labels[0] = 0\\n labels[0] = 24\\n yticks = np.arange(0, myheight, 15*dpi)\\n elif gridtype == '-EL':\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Elevation (degrees)\\\")\\n labels = 24 - (ticks/(mywidth/24))\\n labels[0] = 0\\n labels[0] = 24\\n yticks = np.arange(0, myheight, 15*dpi)\\n elif gridtype == 'RA0': # put 0 hours in middle of plot\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Declination (degrees)\\\")\\n labels = 12 - (ticks/(mywidth/24))\\n nlabels = len(labels)\\n for iii in range(nlabels):\\n if labels[iii] < 0:\\n labels[iii] = 24 + labels[iii]\\n if labels[iii] == 24:\\n labels[iii] = 0\\n yticks = np.arange(0, myheight, 15*dpi)\\n else:\\n yticks = np.arange(0, myheight, 30*dpi)\\n ticks = np.arange(0, mywidth, 30*dpi)\\n x_ticks = xmin + (xmax-xmin)*ticks/mywidth\\n labels = x_ticks\\n plt.xlabel(\\\"Galactic Longitude (degrees)\\\")\\n plt.ylabel(\\\"Galactic Latitude (degrees)\\\")\\n # wnat an integer list of labels\\n# slabels = str(labels)\\n print(ticks, labels)\\n y_ticks = ymax - (ymax-ymin)*yticks/myheight\\n plt.yticks(yticks, y_ticks)\\n plt.xticks(ticks, labels, rotation='horizontal')\\n plt.colorbar()\\n\\n crval2 = (xmin + xmax)/2.\\n crval1 = (ymin + ymax)/2.\\n cdelt1 = (-1./float(dpi)) - .001\\n cdelt2 = (1./float(dpi)) + .001\\n if doRatio:\\n# now show eacsh of the images\\n for iGrid in range(4):\\n imagetemp = copy.deepcopy(grids[iGrid].image)\\n imagetemp2 = copy.deepcopy(grids[iGrid].image)\\n kkk = myheight - 1\\n for jjj in range(myheight):\\n imagetemp[:][kkk] = imagetemp2[:][jjj]\\n kkk = kkk - 1\\n grids[iGrid].image = imagetemp\\n writeFitsImage( rs, iGrid+2, grids[iGrid], projection)\\n\\n # put each telescope in a different grid\\n ratio1 = copy.deepcopy(grid1)\\n ratio2 = copy.deepcopy(grid1)\\n ratio3 = copy.deepcopy(grid1)\\n gratios = [ratio1, ratio2, ratio3]\\n ratios = np.zeros(3)\\n rmss = np.zeros(3)\\n\\n jGrid = 3\\n for iGrid in range(3):\\n print(\\\"Gain Ratios for Telescopes T%d and T%d\\\" % (iGrid+2, jGrid+2))\\n ratio, rms, aratio = gridratio(grids[iGrid], grids[jGrid])\\n ratios[iGrid] = ratio\\n rmss[iGrid] = rms\\n writeFitsImage( rs, iGrid+2, aratio, projection)\\n \\n writeFitsImage( rs, 0, gridall, projection)\\n plt.show()\",\n \"def imdisplay(filename, representation):\\n\\n image = read_image(filename, representation)\\n plt.imshow(image, cmap=\\\"gray\\\")\\n plt.show()\",\n \"def draw(src_data: dict, config: dict):\\n puml = '@startuml'\\n\\n puml_inits = ''\\n puml_connections = ''\\n for diagram_config in config['diagrams']:\\n puml = pumlify.generate_puml(src_data, diagram_config['mapping_scheme'])\\n # todo: ensure this works in windows\\n write_file(puml, os.path.join('./docs', 'src-map.puml'))\",\n \"def step_run(cls, config):\\n\\n nir_paw_image_fname = config.get(cls.step_name, 'nir_paw_image')\\n nir_paw_conf_fname = config.get(cls.step_name, 'nir_paw_conf')\\n output_template = config.get(cls.step_name, 'output_template')\\n conf_limit = config.getfloat(cls.step_name, 'conf_limit')\\n\\n# logger.info('reading Comparison image from %s', comp_fname)\\n# comp_im = DESImage.load(comp_fname)\\n\\n ret_code = cls.__call__(nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit)\\n return ret_code\",\n \"def run(self):\\n # read (x,y) - Set SIFT parameters\\n action = self.cfg['param']['action']\\n x = float(self.cfg['param']['x']) \\n # Expressed en PIL coordinates system y| x-\\n y = float(self.cfg['param']['y'])\\n \\n # read image size and store in 'param' dict to control html rendering\\n work_dir = self.work_dir\\n [w1, h1] = Image.open(work_dir+'input_0.orig.png').size\\n [w2, h2] = Image.open(work_dir+'input_1.orig.png').size\\n wdth = max(w1, w2)\\n hght = max(h1, h2)\\n wpair = int(w1+w2+max(w1, w2)/10)\\n self.cfg['param']['hght'] = hght\\n self.cfg['param']['wdth'] = wdth\\n self.cfg.save()\\n # Convert x y provided by the form <input type=image ..; > \\n # We assume that the width of the html body is assumed width is 920px\\n x = x*wpair/920\\n y = y*wpair/920\\n self.cfg['param']['x'] = x\\n self.cfg['param']['y'] = y\\n self.cfg.save()\\n if (action == 'std_sift_matching'):\\n try:\\n self.load_standard_parameters()\\n self.run_std_sift()\\n self.run_matching()\\n self.illustrate_std_sift()\\n self.illustrate_matching()\\n except TimeoutError:\\n return self.error(errcode='timeout',\\n errmsg=\\\"Try again with simpler images.\\\")\\n except RuntimeError:\\n return self.error(errcode='runtime',\\n errmsg=\\\"Runtime error in std_sift_matching.\\\")\\n elif (action == \\\"cust_sift_matching\\\"):\\n try:\\n self.run_sift()\\n print \\\"after run_sift()\\\"\\n self.run_matching()\\n print \\\"after run_matching()\\\"\\n self.illustrate_sift()\\n print \\\"after illustrate_sift()\\\"\\n self.illustrate_matching()\\n self.detail_matching()\\n except TimeoutError:\\n return self.error(errcode='timeout',\\n errmsg=\\\"Try with simpler images.\\\")\\n except RuntimeError:\\n return self.error(errcode='runtime',\\n errmsg=\\\"Runtime error in cust_sift_matching.\\\")\\n elif (action == \\\"cust_matching\\\"): \\n try:\\n self.run_matching()\\n self.illustrate_matching()\\n except TimeoutError:\\n return self.error(errcode='timeout',\\n errmsg=\\\"Try with simpler images.\\\")\\n except RuntimeError:\\n return self.error(errcode='runtime',\\n errmsg=\\\"Runtime error in cust_matching.\\\")\\n else:\\n try:\\n self.detail_matching()\\n except TimeoutError:\\n return self.error(errcode='timeout',\\n errmsg=\\\"Try with simpler images.\\\")\\n except RuntimeError:\\n return self.error(errcode='runtime',\\n errmsg=\\\"Runtime error in else (you know).\\\")\\n ## archive\\n if self.cfg['meta']['original']:\\n ar = self.make_archive()\\n ar.add_file(\\\"input_0.png\\\", info=\\\"first input image\\\")\\n ar.add_file(\\\"input_1.png\\\", info=\\\"second input image\\\")\\n ar.add_file(\\\"input_0.orig.png\\\", info=\\\"first uploaded image\\\")\\n ar.add_file(\\\"input_1.orig.png\\\", info=\\\"second uploaded image\\\")\\n ar.add_file(\\\"OUTmatches.png\\\", info=\\\"matches\\\")\\n ar.add_file(\\\"keys_im0.txt\\\", compress=True)\\n ar.add_file(\\\"keys_im1.txt\\\", compress=True)\\n ar.add_file(\\\"OUTmatches.txt\\\", compress=True)\\n ar.save()\\n\\n self.cfg.save()\\n http.redir_303(self.base_url + 'result?key=%s' % self.key)\\n return self.tmpl_out(\\\"run.html\\\")\",\n \"def render(filename,i):\\n print('running render')\\n A = np.genfromtxt(filename,skip_header=1,dtype=float,delimiter=',')\\n img = np.array(A[i,:],copy=True)\\n print(img.shape)\\n img = img.reshape(28,28)\\n img = 255 - img\\n print(img.shape)\\n plt.imshow(img, cmap=\\\"gray\\\", vmin=0, vmax=255)\\n plt.savefig(\\\"img\\\" + str(i)+\\\"render\\\"+ \\\".png\\\")\",\n \"def render_viewer(configfile, datafile):\\n try:\\n page = 'analogviewer.html'\\n with open(page, 'w') as fp:\\n fp.write(templating.render_template(\\n page,\\n configfile=configfile,\\n datafile=datafile,\\n # static_url=config.output_url() + '/static'))\\n static_url='../static'))\\n prepare_static_folder()\\n except Exception:\\n msg = \\\"Failed to render analogviewer.\\\"\\n LOGGER.exception(msg)\\n raise Exception(msg)\\n else:\\n return page\",\n \"def image_capture_demo():\\n return render_template('image_capture_demo.html')\",\n \"def plot_bit_for_bit(case, var_name, model_data, bench_data, diff_data):\\n plot_title = \\\"\\\"\\n plot_name = case + \\\"_\\\" + var_name + \\\".png\\\"\\n plot_path = os.path.join(os.path.join(livvkit.output_dir, \\\"verification\\\", \\\"imgs\\\"))\\n functions.mkdir_p(plot_path)\\n m_ndim = np.ndim(model_data)\\n b_ndim = np.ndim(bench_data)\\n if m_ndim != b_ndim:\\n return \\\"Dataset dimensions didn't match!\\\"\\n if m_ndim == 3:\\n model_data = model_data[-1]\\n bench_data = bench_data[-1]\\n diff_data = diff_data[-1]\\n plot_title = \\\"Showing \\\"+var_name+\\\"[-1,:,:]\\\"\\n elif m_ndim == 4:\\n model_data = model_data[-1][0]\\n bench_data = bench_data[-1][0]\\n diff_data = diff_data[-1][0]\\n plot_title = \\\"Showing \\\"+var_name+\\\"[-1,0,:,:]\\\"\\n plt.figure(figsize=(12, 3), dpi=80)\\n plt.clf()\\n\\n # Calculate min and max to scale the colorbars\\n _max = np.amax([np.amax(model_data), np.amax(bench_data)])\\n _min = np.amin([np.amin(model_data), np.amin(bench_data)])\\n\\n # Plot the model output\\n plt.subplot(1, 3, 1)\\n plt.xlabel(\\\"Model Data\\\")\\n plt.ylabel(var_name)\\n plt.xticks([])\\n plt.yticks([])\\n plt.imshow(model_data, vmin=_min, vmax=_max, interpolation='nearest', cmap=colormaps.viridis)\\n plt.colorbar()\\n\\n # Plot the benchmark data\\n plt.subplot(1, 3, 2)\\n plt.xlabel(\\\"Benchmark Data\\\")\\n plt.xticks([])\\n plt.yticks([])\\n plt.imshow(bench_data, vmin=_min, vmax=_max, interpolation='nearest', cmap=colormaps.viridis)\\n plt.colorbar()\\n\\n # Plot the difference\\n plt.subplot(1, 3, 3)\\n plt.xlabel(\\\"Difference\\\")\\n plt.xticks([])\\n plt.yticks([])\\n plt.imshow(diff_data, interpolation='nearest', cmap=colormaps.viridis)\\n plt.colorbar()\\n\\n plt.tight_layout(rect=(0, 0, 0.95, 0.9))\\n plt.suptitle(plot_title)\\n\\n plot_file = os.path.sep.join([plot_path, plot_name])\\n if livvkit.publish:\\n plt.savefig(os.path.splitext(plot_file)[0]+'.eps', dpi=600)\\n plt.savefig(plot_file)\\n plt.close()\\n return os.path.join(os.path.relpath(plot_path,\\n os.path.join(livvkit.output_dir, \\\"verification\\\")),\\n plot_name)\",\n \"def plot(path, subjects):\\n transformToXYZmm = np.array([[-3.125, 0, 0, 81.250], [0, 3.125, 0, -115.625], [0, 0, 6, -54.000], [0, 0, 0, 1.000]])\\n data = data_load.load_data(path, subjects)\\n dimx = int(data[0][\\\"meta\\\"][\\\"dimx\\\"][0])\\n dimy = int(data[0][\\\"meta\\\"][\\\"dimy\\\"][0])\\n dimz = int(data[0][\\\"meta\\\"][\\\"dimz\\\"][0])\\n coordToCol = data[0][\\\"meta\\\"][\\\"coordToCol\\\"][0][0]\\n images = {}\\n max_val = 0\\n voxels = np.load(\\\"data/general_selected_500_1.npy\\\")\\n directory = os.listdir(\\\"data/input/\\\")\\n bar = pyprind.ProgBar(len(directory), title='Info extraction and Image Building')\\n bar2 = pyprind.ProgBar(len(images.keys()), title='Saving Pictures')\\n for file in directory:\\n file_name = \\\"data/input/{}\\\".format(file)\\n fh = open(file_name)\\n activation_values = np.asarray(list(map(lambda x: float(x), filter(lambda x: x != '', fh.read().split(\\\",\\\")))))\\n fh.close()\\n plot_matrix = np.zeros((dimx, dimy, dimz))\\n for x in range(dimx):\\n for y in range(dimy):\\n for z in range(dimz):\\n indice = coordToCol[x][y][z]\\n if indice != 0:\\n if indice in list(voxels):\\n voxel_indice = list(voxels).index(indice)\\n value = activation_values[voxel_indice]\\n if abs(value) > max_val:\\n max_val = abs(value)\\n plot_matrix[x][y][z] = value\\n image = nib.Nifti1Image(plot_matrix, transformToXYZmm)\\n images[file_name] = image\\n bar.update(force_flush=True)\\n print(bar)\\n for image in images:\\n plotting.plot_glass_brain(images[image], display_mode='ortho', vmax=max_val, plot_abs=False, threshold=None, colorbar=True, output_file=\\\"{}-wom1.png\\\".format(image))\\n bar2.update(force_flush=True)\\n print(bar2)\",\n \"def display_file(epd, file_name):\\n\\n image = Image.open(file_name)\\n image = ImageOps.grayscale(image)\\n\\n # crop to the middle\\n w,h = image.size\\n x = w / 2 - epd.width / 2\\n y = h / 2 - epd.height / 2\\n\\n cropped = image.crop((x, y, x + epd.width, y + epd.height))\\n bw = cropped.convert(\\\"1\\\", dither=Image.FLOYDSTEINBERG)\\n\\n epd.display(bw)\\n epd.update()\\n\\n\\n time.sleep(3) # delay in seconds\\n\\n rs = image.resize((epd.width, epd.height))\\n bw = rs.convert(\\\"1\\\", dither=Image.FLOYDSTEINBERG)\\n\\n epd.display(bw)\\n epd.update()\\n\\n time.sleep(3) # delay in seconds\",\n \"def render(sim_file: str, only_stuck: bool) -> None:\\n import DLA\\n DLA.GREEN = (0, 0, 0) # type: ignore\\n DLA.WHITE = (151, 151, 151, 150) # type: ignore\\n from DLA import config\\n config.USE_PYGAME = True # type: ignore\\n from DLA import renderer\\n renderer.render(Path(sim_file), only_stuck)\",\n \"def main(argv):\\n logging.basicConfig(format=\\\"%(message)s\\\", level=logging.INFO, stream=sys.stdout)\\n logger = logging.getLogger(\\\"demo4\\\")\\n\\n # Define some parameters we'll use below and make directories if needed.\\n cat_file_name = os.path.join('input','galsim_default_input.asc')\\n if not os.path.isdir('output'):\\n os.mkdir('output')\\n multi_file_name = os.path.join('output','multi.fits')\\n\\n random_seed = galsim.BaseDeviate(8241573).raw()\\n sky_level = 1.e6 # ADU / arcsec^2\\n pixel_scale = 1.0 # arcsec / pixel (size units in input catalog are pixels)\\n gal_flux = 1.e6 # arbitrary choice, makes nice (not too) noisy images\\n gal_g1 = -0.009 #\\n gal_g2 = 0.011 #\\n\\n # the fraction of flux in each component\\n # 40% is in the bulge, 60% in a disk. 70% of that disk light is placed\\n # into point sources distributed as a random walk\\n\\n bulge_frac = 0.4\\n disk_frac = 0.6\\n knot_frac = 0.42\\n smooth_disk_frac = 0.18\\n\\n # number of knots of star formation. To simulate a nice irregular (all the\\n # flux is in knots) we find ~100 is a minimum number needed, but we will\\n # just use 10 here to make the demo run fast.\\n\\n n_knots = 10\\n\\n xsize = 64 # pixels\\n ysize = 64 # pixels\\n\\n logger.info('Starting demo script 4 using:')\\n logger.info(' - parameters taken from catalog %r',cat_file_name)\\n logger.info(' - Moffat PSF (parameters from catalog)')\\n logger.info(' - pixel scale = %.2f',pixel_scale)\\n logger.info(' - Bulge + Disc galaxies (parameters from catalog)')\\n logger.info(' - 100 Point sources, distributed as random walk')\\n logger.info(' - Applied gravitational shear = (%.3f,%.3f)',gal_g1,gal_g2)\\n logger.info(' - Poisson noise (sky level = %.1e).', sky_level)\\n\\n # Read in the input catalog\\n cat = galsim.Catalog(cat_file_name)\\n\\n\\n # save a list of the galaxy images in the \\\"images\\\" list variable:\\n images = []\\n for k in range(cat.nobjects):\\n # Initialize the (pseudo-)random number generator that we will be using below.\\n # Use a different random seed for each object to get different noise realizations.\\n # Using sequential random seeds here is safer than it sounds. We use Mersenne Twister\\n # random number generators that are designed to be used with this kind of seeding.\\n # However, to be extra safe, we actually initialize one random number generator with this\\n # seed, generate and throw away two random values with that, and then use the next value\\n # to seed a completely different Mersenne Twister RNG. The result is that successive\\n # RNGs created this way produce very independent random number streams.\\n rng = galsim.BaseDeviate(random_seed+k+1)\\n\\n # Take the Moffat beta from the first column (called 0) of the input catalog:\\n # Note: cat.get(k,col) returns a string. To get the value as a float, use either\\n # cat.getFloat(k,col) or float(cat.get(k,col))\\n beta = cat.getFloat(k,0)\\n # A Moffat's size may be either scale_radius, fwhm, or half_light_radius.\\n # Here we use fwhm, taking from the catalog as well.\\n fwhm = cat.getFloat(k,1)\\n # A Moffat profile may be truncated if desired\\n # The units for this are expected to be arcsec (or specifically -- whatever units\\n # you are using for all the size values as defined by the pixel_scale).\\n trunc = cat.getFloat(k,4)\\n # Note: You may omit the flux, since the default is flux=1.\\n psf = galsim.Moffat(beta=beta, fwhm=fwhm, trunc=trunc)\\n\\n # Take the (e1, e2) shape parameters from the catalog as well.\\n psf = psf.shear(e1=cat.getFloat(k,2), e2=cat.getFloat(k,3))\\n\\n # Galaxy is a bulge + disk(+knots) with parameters taken from the catalog:\\n\\n # put some fraction of the disk light into knots of star formation\\n\\n disk_hlr = cat.getFloat(k,5)\\n disk_e1 = cat.getFloat(k,6)\\n disk_e2 = cat.getFloat(k,7)\\n bulge_hlr = cat.getFloat(k,8)\\n bulge_e1 = cat.getFloat(k,9)\\n bulge_e2 = cat.getFloat(k,10)\\n\\n smooth_disk = galsim.Exponential(flux=smooth_disk_frac, half_light_radius=disk_hlr)\\n\\n knots = galsim.RandomKnots(n_knots, half_light_radius=disk_hlr, flux=knot_frac, rng=rng)\\n\\n disk = galsim.Add([smooth_disk, knots])\\n disk = disk.shear(e1=disk_e1, e2=disk_e2)\\n\\n # the rest of the light goes into the bulge\\n bulge = galsim.DeVaucouleurs(flux=bulge_frac, half_light_radius=bulge_hlr)\\n bulge = bulge.shear(e1=bulge_e1, e2=bulge_e2)\\n\\n # The flux of an Add object is the sum of the component fluxes.\\n # Note that in demo3.py, a similar addition was performed by the binary operator \\\"+\\\".\\n gal = galsim.Add([disk, bulge])\\n\\n # This flux may be overridden by withFlux. The relative fluxes of the components\\n # remains the same, but the total flux is set to gal_flux.\\n gal = gal.withFlux(gal_flux)\\n gal = gal.shear(g1=gal_g1, g2=gal_g2)\\n\\n # The center of the object is normally placed at the center of the postage stamp image.\\n # You can change that with shift:\\n gal = gal.shift(dx=cat.getFloat(k,11), dy=cat.getFloat(k,12))\\n\\n final = galsim.Convolve([psf, gal])\\n\\n # Draw the profile\\n image = galsim.ImageF(xsize, ysize)\\n final.drawImage(image, scale=pixel_scale)\\n\\n # Add Poisson noise to the image:\\n image.addNoise(galsim.PoissonNoise(rng, sky_level * pixel_scale**2))\\n\\n logger.info('Drew image for object at row %d in the input catalog'%k)\\n \\n # Add the image to our list of images\\n images.append(image)\\n \\n # Now write the images to a multi-extension fits file. Each image will be in its own HDU.\\n galsim.fits.writeMulti(images, multi_file_name)\\n logger.info('Images written to multi-extension fits file %r',multi_file_name)\",\n \"def main():\\n \\n # for inserting other images, add tem to /input folder and list them here\\n images = (\\n 'image-0',\\n 'image-1',\\n 'image-2'\\n )\\n\\n for image_name in images:\\n print(image_name, \\\"image:\\\")\\n\\n image = open_image(image_name)\\n display_image(image, \\\"Original input \\\" + image_name)\\n\\n grayscale_v = transform_colors(image)\\n display_image(grayscale_v[:,:,0], \\\"Grayscale \\\" + image_name)\\n save_image(image_name + \\\"-grayscale\\\", grayscale_v[:,:,0])\\n\\n contours_v, contours = get_contours(grayscale_v)\\n display_image(contours_v, \\\"Contours \\\" + image_name)\\n save_image(image_name + \\\"-contours\\\", contours_v)\\n\\n labeled_img, areas = get_measures(image, contours[1:])\\n display_image(labeled_img, \\\"Labeled \\\" + image_name)\\n save_image(image_name + \\\"-labeled\\\", labeled_img)\\n\\n areas_histogram(areas, image_name)\",\n \"def render_pov(pov_name, image_name, height=800, width=800,\\n display=False, transparent=True, antialias=True,\\n num_threads=0, open_image=True, render=True, \\n render_quality=9):\\n from os import system\\n import re\\n\\n # Determine the file type\\n file_type_dict = {\\n \\\".png$\\\":\\\"N\\\", \\n \\\".bmp$\\\":\\\"B\\\", \\n \\\".rle$\\\":\\\"C\\\",\\n \\\".exr$\\\":\\\"E\\\",\\n \\\".hdr$\\\":\\\"H\\\",\\n \\\".jp[e]*g$\\\":\\\"J\\\", \\n \\\".ppm$\\\":\\\"P\\\",\\n \\\".t[ar]*ga$\\\":\\\"T\\\"\\n }\\n file_type_list = list(file_type_dict)\\n found = False\\n i = 0\\n while found == False:\\n pattern = re.compile(file_type_list[i])\\n if pattern.search(image_name):\\n use_type = file_type_dict[file_type_list[i]]\\n found = True\\n else:\\n i += 1\\n if i == len(file_type_dict):\\n print(\\\"Warning: Image file format not supported! Using .png!\\\")\\n image_name += \\\".png\\\"\\n use_type = \\\"N\\\"\\n found = True\\n\\n # Create .ini file with render settings\\n ini_string = (\\\"; POV-Ray version 3.7 INI file\\\\n\\\"\\n + f\\\"; MANTIS auto-generated INI for {pov_name}\\\\n\\\\n\\\"\\n + f\\\"+I{pov_name}\\\\n+O{image_name}\\\\n\\\"\\n + f\\\"+H{height}\\\\n+W{width}\\\\n\\\")\\n\\n if display:\\n ini_string += \\\"+D\\\\n\\\"\\n else:\\n ini_string += \\\"-D\\\\n\\\"\\n\\n if transparent:\\n ini_string += \\\"+UA\\\\n\\\"\\n\\n if antialias:\\n ini_string += \\\"+A\\\\n\\\"\\n\\n if num_threads != 0:\\n ini_string += f\\\"+WT{num_threads}\\\\n\\\"\\n\\n if use_type != \\\"N\\\":\\n ini_string += f\\\"+F{use_type}\\\\n\\\"\\n\\n if render_quality != 9:\\n if render_quality not in range(0,12):\\n render_quality = 9\\n ini_string += f\\\"+Q{render_quality}\\\\n\\\"\\n\\n ini_name = pov_name.replace(\\\".pov\\\",\\\".ini\\\")\\n fileID = open(ini_name, \\\"w\\\")\\n fileID.write(ini_string)\\n fileID.close()\\n\\n # Create render command\\n# command = f\\\"povray {ini_name.replace('.ini','')}\\\"\\n command = f\\\"povray {ini_name}\\\"\\n if open_image:\\n command += \\\" && eog {0}\\\".format(image_name)\\n if render == True:\\n system(command)\\n\\n div = '----------------------------------------------------'\\n print(\\\"For additional rendering options, see POV-Ray's documentation,\\\")\\n print(\\\"particularly the file output and tracing options:\\\")\\n print(\\\"http://wiki.povray.org/content/Reference:File_Output_Options\\\")\\n print(\\\"http://wiki.povray.org/content/Reference:Tracing_Options\\\")\\n print(f\\\"Render with \\\\n{div}\\\\n{command}\\\\n{div}\\\")\\n\\n return\",\n \"def draw(input_file_name, height,\\n absorption_image,\\n differential_phase_image,\\n dark_field_image,\\n language=\\\"it\\\"):\\n if language == \\\"it\\\":\\n absorption_image_title = \\\"assorbimento\\\"\\n differential_phase_image_title = \\\"fase differenziale\\\"\\n dark_field_image_title = \\\"riduzione di visibilit\\\\\\\\`a\\\"\\n else:\\n absorption_image_title = \\\"absorption\\\"\\n differential_phase_image_title = \\\"differential phase\\\"\\n dark_field_image_title = \\\"dark field\\\"\\n _, ((abs1_plot, phase1_plot, df1_plot),\\n (abs2_plot, phase2_plot, df2_plot)) = plt.subplots(\\n 2, 3, figsize=(6, height), dpi=300)\\n plt.subplots_adjust(\\n wspace=0.02,\\n hspace=0.02)\\n min_x = 300\\n max_x = 550\\n min_y = 60\\n max_y = 130\\n abs1 = abs1_plot.imshow(absorption_image,\\n cmap=plt.cm.Greys,\\n aspect='auto')\\n abs1_plot.add_patch(mpl.patches.Rectangle(\\n (min_x, min_y),\\n max_x - min_x,\\n max_y - min_y,\\n fill=False,\\n edgecolor=\\\"r\\\"))\\n diff_image = absorption_image[min_y:max_y, min_x:max_x]\\n abs2 = abs2_plot.imshow(\\n diff_image,\\n cmap=plt.cm.Greys, aspect='auto')\\n abs1_plot.set_title(absorption_image_title,\\n size=\\\"large\\\")\\n abs1_plot.set_frame_on(False)\\n abs1_plot.axes.yaxis.set_ticks([])\\n abs1_plot.axes.xaxis.set_ticks([])\\n abs2_plot.axes.yaxis.set_ticks([])\\n abs2_plot.axes.xaxis.set_ticks([])\\n for pos in [\\\"top\\\", \\\"bottom\\\", \\\"left\\\", \\\"right\\\"]:\\n abs2_plot.spines[pos].set_color(\\\"r\\\")\\n abs2_plot.axes.xaxis.set_ticks([])\\n diff_limits = stats.mstats.mquantiles(diff_image,\\n prob=[0.02, 0.98])\\n abs2.set_clim(*diff_limits)\\n plt.colorbar(abs2,\\n ax=abs2_plot,\\n orientation=\\\"horizontal\\\",\\n format=\\\"% .2f\\\",\\n ticks=np.arange(0, 1, 0.25).tolist())\\n limits = stats.mstats.mquantiles(absorption_image,\\n prob=[0.02, 0.98])\\n abs1.set_clim(*limits)\\n phase1 = phase1_plot.imshow(differential_phase_image)\\n phase1_plot.add_patch(mpl.patches.Rectangle(\\n (min_x, min_y),\\n max_x - min_x,\\n max_y - min_y,\\n fill=False,\\n edgecolor=\\\"r\\\"))\\n phase2 = phase2_plot.imshow(\\n differential_phase_image[min_y:max_y, min_x:max_x])\\n limits = stats.mstats.mquantiles(differential_phase_image,\\n prob=[0.02, 0.98])\\n #limits = (-3, 3)\\n phase1_plot.set_title(differential_phase_image_title,\\n size=\\\"large\\\")\\n phase1_plot.set_frame_on(False)\\n phase1_plot.axes.yaxis.set_ticks([])\\n phase1_plot.axes.xaxis.set_ticks([])\\n phase2_plot.axes.yaxis.set_ticks([])\\n phase2_plot.axes.xaxis.set_ticks([])\\n for pos in [\\\"top\\\", \\\"bottom\\\", \\\"left\\\", \\\"right\\\"]:\\n phase2_plot.spines[pos].set_color(\\\"r\\\")\\n phase2_plot.axes.xaxis.set_ticks([])\\n plt.colorbar(phase2,\\n ax=phase2_plot,\\n orientation=\\\"horizontal\\\",\\n format=\\\"% .1f\\\",\\n ticks=np.arange(-0.4, 0.4, 0.2).tolist())\\n phase1.set_clim(*limits)\\n phase2.set_clim(*limits)\\n df1 = df1_plot.imshow(dark_field_image)\\n df1_plot.add_patch(mpl.patches.Rectangle(\\n (min_x, min_y),\\n max_x - min_x,\\n max_y - min_y,\\n fill=False,\\n edgecolor=\\\"r\\\"))\\n df2 = df2_plot.imshow(\\n dark_field_image[min_y:max_y, min_x:max_x])\\n df1_plot.set_title(dark_field_image_title,\\n size=\\\"large\\\")\\n df1_plot.set_frame_on(False)\\n df1_plot.axes.yaxis.set_ticks([])\\n df1_plot.axes.xaxis.set_ticks([])\\n df2_plot.axes.yaxis.set_ticks([])\\n df2_plot.axes.xaxis.set_ticks([])\\n for pos in [\\\"top\\\", \\\"bottom\\\", \\\"left\\\", \\\"right\\\"]:\\n df2_plot.spines[pos].set_color(\\\"r\\\")\\n df2_plot.axes.xaxis.set_ticks([])\\n plt.colorbar(df2,\\n ax=df2_plot,\\n orientation=\\\"horizontal\\\",\\n format=\\\"% .1f\\\",\\n ticks=np.arange(0, 1, 0.25).tolist())\\n limits = stats.mstats.mquantiles(dark_field_image,\\n prob=[0.02, 0.98])\\n df1.set_clim(*limits)\\n df2.set_clim(*limits)\\n plt.savefig('images_{0}.png'.format(\\n os.path.splitext(os.path.basename(input_file_name))[0]),\\n bbox_inches=\\\"tight\\\", dpi=300)\",\n \"def render_configuration_file(self, conf_template_string):\\n environment = self.params['environment']\\n code_root = self.params['code_root']\\n\\n reject = self.get_rejected_pillow_types(code_root, environment)\\n\\n configs = []\\n all_pillows = self.get_pillows_from_settings(settings.PILLOWTOPS, reject)\\n for full_name in all_pillows:\\n pillow_name = full_name.split('.')[-1]\\n pillow_params = {\\n 'pillow_name': pillow_name,\\n 'pillow_option': ' --pillow-name %s' % pillow_name\\n }\\n pillow_params.update(self.params)\\n pillow_rendering = conf_template_string % pillow_params\\n configs.append(pillow_rendering)\\n return '\\\\n\\\\n'.join(configs)\",\n \"def make_visual(self,\\r\\n ruler = None,\\r\\n options: list = ['rows',\\r\\n 'centers',\\r\\n 'distances']\\r\\n ) -> 'Image':\\r\\n \\r\\n original = self.get_picture().get_photo()\\r\\n\\r\\n # Copy the original image for drawing\\r\\n img = original.copy()\\r\\n draw = ImageDraw.Draw(img)\\r\\n\\r\\n # check all the choices provided by the user\\r\\n for i in options:\\r\\n \\r\\n if i == 'clusters':\\r\\n # Color all cluster pixels red\\r\\n \\r\\n for j in self.get_clusters():\\r\\n for k in j:\\r\\n img.putpixel(k, (25,275,25))\\r\\n\\r\\n elif i == 'row_ids':\\r\\n # Make row id numbers\\r\\n\\r\\n # Font specifications\\r\\n size = 75\\r\\n font = ImageFont.truetype('ariblk.ttf', size)\\r\\n color = (88, 214, 216)\\r\\n num = 1\\r\\n\\r\\n # Draw the ids\\r\\n for j in self.rows:\\r\\n draw.text((j[0].get_center()[0],\\r\\n j[0].get_center()[1] - 0.25 * size),\\r\\n str(num),\\r\\n fill = color,\\r\\n font = font)\\r\\n num += 1\\r\\n\\r\\n elif i == 'boxes':\\r\\n # Show all bounding boxes\\r\\n \\r\\n for i in self.get_boxes():\\r\\n draw.rectangle(i, outline=(255, 0, 255))\\r\\n\\r\\n elif i == 'dirt':\\r\\n # Remove Background\\r\\n \\r\\n img = Image.new('RGB', img.size, (130, 90, 50))\\r\\n draw = ImageDraw.Draw(img)\\r\\n\\r\\n elif i == 'centers':\\r\\n # Show all centers\\r\\n \\r\\n rad = 9\\r\\n for i in self.get_centers():\\r\\n draw.arc([(i[0] - rad, i[1] - rad),\\r\\n (i[0] + rad, i[1] + rad)],\\r\\n 0, 360, (0, 0, 255))\\r\\n\\r\\n elif i == 'ditches':\\r\\n # Show ditches between plants\\r\\n\\r\\n # Line attribute settings\\r\\n width = 10\\r\\n color = (55,65,65)\\r\\n\\r\\n # Iterate over all ditches\\r\\n for line in self.ditches:\\r\\n line = [line[0], line[1]]\\r\\n\\r\\n # Point in ditch on left border of picture\\r\\n start_point = (0, line[1])\\r\\n\\r\\n # Point in ditch on right border of picture\\r\\n end_point = (self.picture.get_size()[0] - 1,\\r\\n line[0]\\r\\n * (self.picture.get_size()[0] - 1)\\r\\n + line[1])\\r\\n\\r\\n ## Check if the end point is within the picture\\r\\n if end_point[1] < 0:\\r\\n if start_point[1] < 0:\\r\\n continue\\r\\n \\r\\n # Point in ditch on top border of picture\\r\\n end_point = (-1 * line[1] / line[0], 0)\\r\\n\\r\\n elif end_point[1] > self.picture.get_size()[1] - 1:\\r\\n if start_point[1] > self.picture.get_size()[1] - 1:\\r\\n continue\\r\\n \\r\\n # Point in ditch on bottom border of picture\\r\\n end_point = (-1\\r\\n * (self.picture.get_size()[1] - 1)\\r\\n / line[0],\\r\\n self.picture.get_size()[1] - 1)\\r\\n\\r\\n # Draw the ditches\\r\\n for i in self.get_rows():\\r\\n draw.line((start_point, end_point), color, width)\\r\\n\\r\\n elif i == 'lines':\\r\\n # Show row line approximations\\r\\n\\r\\n # Line attribute settings\\r\\n width = 1\\r\\n color = (255, 255, 75)\\r\\n\\r\\n # Iterate over all the lines\\r\\n for line in self.lines:\\r\\n line = [line[0], line[1]]\\r\\n\\r\\n # Point on line on left border of picture\\r\\n start_point = (0, line[1])\\r\\n\\r\\n # Point on line on right border of picture\\r\\n end_point = (self.picture.get_size()[0] - 1,\\r\\n line[0]\\r\\n * (self.picture.get_size()[0] - 1)\\r\\n + line[1])\\r\\n\\r\\n\\r\\n ## Check if the end point is within the picture\\r\\n if end_point[1] < 0:\\r\\n if start_point[1] < 0:\\r\\n continue\\r\\n\\r\\n # Point on line on top border of picture\\r\\n end_point = (-1 * line[1] / line[0], 0)\\r\\n \\r\\n\\r\\n elif end_point[1] > self.picture.get_size()[1] - 1:\\r\\n if start_point[1] > self.picture.get_size()[1] - 1:\\r\\n continue\\r\\n\\r\\n # Point on line on bottom border of picture\\r\\n end_point = (-1\\r\\n * (self.picture.get_size()[1] - 1)\\r\\n / line[0],\\r\\n self.picture.get_size()[1] - 1)\\r\\n\\r\\n # Draw the lines\\r\\n for i in self.get_rows():\\r\\n draw.line((start_point, end_point), color, width)\\r\\n\\r\\n elif i == 'rows':\\r\\n if self.get_rows():\\r\\n # Show lines between rows\\r\\n \\r\\n width = 3\\r\\n color = (255,0,0)\\r\\n\\r\\n for i in self.get_rows():\\r\\n draw.line([j.get_center() for j in i], color, width)\\r\\n else:\\r\\n print('Rows have not been made for this field')\\r\\n\\r\\n elif i == 'numbers':\\r\\n # Display numbers between plants\\r\\n\\r\\n # Find where to put the numbers\\r\\n midpoints = [(int(round((row[c].get_center()[0]\\r\\n + row[c + 1].get_center()[0]) / 2)),\\r\\n int(round((row[c].get_center()[1]\\r\\n + row[c + 1].get_center()[1]) / 2)))\\r\\n \\r\\n for row in self.get_rows()\\r\\n for c in range(len(row) - 1)]\\r\\n\\r\\n # Font specifications\\r\\n size = 10\\r\\n font = ImageFont.truetype('ariblk.ttf', size)\\r\\n num = 1\\r\\n\\r\\n # Write numbers\\r\\n for i in midpoints:\\r\\n draw.text((i[0] - 3 * len(str(round(num, 1))),\\r\\n i[1]),\\r\\n str(round(num,1)), font = font)\\r\\n \\r\\n num += 1\\r\\n\\r\\n elif i == 'tight':\\r\\n # Display tight boxes\\r\\n\\r\\n for i in self.get_tight_boxes():\\r\\n draw.rectangle(i, outline=(100, 255, 255))\\r\\n\\r\\n elif i == 'distances':\\r\\n # display distances between plants\\r\\n\\r\\n # find where to put the distances\\r\\n midpoints = [(int(round((row[c].get_center()[0]\\r\\n + row[c + 1].get_center()[0]) / 2)),\\r\\n int(round((row[c].get_center()[1]\\r\\n + row[c + 1].get_center()[1]) / 2)))\\r\\n for row in self.get_rows()\\r\\n for c in range(len(row) - 1)]\\r\\n\\r\\n # Font specifications\\r\\n size = 10\\r\\n font = ImageFont.truetype('arial.ttf', size)\\r\\n num = 1\\r\\n\\r\\n # Write numbers\\r\\n for i in midpoints:\\r\\n draw.text((i[0] - 3 * len(str(ruler.get_distances()[num])),\\r\\n i[1]),\\r\\n str(ruler.get_distances()[num]) + '\\\"',\\r\\n font = font)\\r\\n \\r\\n num += 1\\r\\n\\r\\n # If the user inputs something that isn't an option \\r\\n else:\\r\\n raise Exception(i + ' is not a valid option.\\\\n')\\r\\n\\r\\n return img\",\n \"def imDisplay(filename: str, representation: int):\\r\\n img=imReadAndConvert(filename, representation)\\r\\n if representation==LOAD_GRAY_SCALE:\\r\\n plt.imshow(img,cmap='gray')\\r\\n else:\\r\\n plt.imshow(img)\\r\\n plt.show()\",\n \"def Interpret(self, **kwargs):\\n # visit file\\n self.options[\\\"VISIT_FILE\\\"] = self.visit_file\\n # particle file\\n particle_file = os.path.join(self.output_dir, 'particles.visit')\\n if os.access(particle_file, os.R_OK):\\n self.options[\\\"VISIT_PARTICLE_FILE\\\"] = particle_file\\n # directory to output data\\n self.options[\\\"DATA_OUTPUT_DIR\\\"] = self.output_dir\\n # directory to output images\\n if not os.path.isdir(self.img_dir):\\n os.mkdir(self.img_dir)\\n self.options[\\\"IMG_OUTPUT_DIR\\\"] = self.img_dir\\n # own implementations\\n # initial adaptive refinement\\n self.options['INITIAL_ADAPTIVE_REFINEMENT'] = self.idict['Mesh refinement'].get('Initial adaptive refinement', '6')\\n # get snaps for plots\\n graphical_snaps, _, _ = GetSnapsSteps(self.case_dir, 'graphical')\\n self.options['ALL_AVAILABLE_GRAPHICAL_SNAPSHOTS'] = str(graphical_snaps)\\n # plot slab \\n self.options['IF_PLOT_SLAB'] = 'True'\\n last_step = graphical_snaps[-1] - int(self.options['INITIAL_ADAPTIVE_REFINEMENT'])\\n last_step = kwargs.get('last_step', None)\\n if type(last_step) == int:\\n self.options['PLOT_SLAB_STEPS'] = [i for i in range(last_step - last_step + 1, last_step + 1)]\\n else:\\n self.options['PLOT_SLAB_STEPS'] = [0, 1, 2, 3, 4, 5, 6, 7]\\n # self.options['IF_DEFORM_MECHANISM'] = value.get('deform_mechanism', 0)\\n self.options['IF_DEFORM_MECHANISM'] = 1\",\n \"def main():\\n\\n in_file = ('/home/desi2/candidatesp9/asteroids_decals_dr2.fits')\\n out_dir = os.path.join(os.environ.get('HOME'), 'asteroid_cutouts/')\\n\\n cand_info = fits_table(in_file)\\n # Pre-select asteroids in the ra, dec box you know they exist.\\n ramin = 107\\n ramax = 130\\n decmin = 16\\n decmax = 30\\n these = np.where((cand_info.ra0>ramin)*(cand_info.ra0<ramax)*\\n (cand_info.dec0>decmin)*(cand_info.dec0<decmax))[0]\\n #pdb.set_trace() # Runs Python Debugger on code up to this line. \\n cand_info = cand_info[these]\\n\\n urls = []\\n jpgfiles = []\\n for ii in range(100):\\n print('Working on candidate {}'.format(ii))\\n ra = cand_info.ra0[ii]\\n dec = cand_info.dec0[ii]\\n \\n jpgurl = 'http://legacysurvey.org/viewer/jpeg-cutout-decals-dr2?ra={:.6f}&dec={:.6f}&pixscale=0.262&size=200'.format(ra, dec)\\n \\n jpgfile = 'obj-{:03d}.jpg'.format(ii)\\n jpgfile = os.path.join(out_dir, jpgfile)\\n grab = 'wget --continue -O {:s} \\\"{:s}\\\"' .format(jpgfile, jpgurl)\\n print(grab)\\n os.system(grab)\\n #pdb.set_trace() # Runs Python Debugger on code up to this line. \\n if os.stat(jpgfile).st_size < 18000: # Remove partial or empty images\\n # The cut on filesize takes care of most of the bad images but\\n # leaves some behind. If the restriction is any larger,\\n # it can remove some valid files.\\n os.remove(jpgfile)\\n else:\\n print(jpgurl)\\n jpgfiles.append(jpgfile)\\n urls.append(jpgurl)\\n # for HTML file. What should the URL be?\\n #print('<html>')\\n #print('<head> Planet Nine Candidates </head>')\\n #print('<body>')\\n #for thisurl, thisjpg in zip(urls, jpgfiles):\\n # print('<div class=\\\"image\\\">')\\n # print('<a href=\\\"{}\\\"><img src=\\\"{:s}\\\"></a>'.format(thisurl, thisjpg))\\n # print('<div class=\\\"caption\\\"> Image of {:s} </div>' .format(thisjpg))\\n # print('</div>')\\n #print('</body></html>')\",\n \"def heatmap():\\n\\n team = int(request.form.get('teamname'))\\n player = int(request.form.get('player'))\\n attacks = request.form.get('attacks')\\n data = request.form.get('datafiles')\\n\\n print 'Making heat map for player #%d, team #%d, attacks %s' % (player, team, attacks)\\n\\n if attacks.lower() == 'all':\\n attacks = ['ALL']\\n else:\\n attacks = attacks.split(',')\\n\\n only_kills = request.form.get('kills')\\n\\n files = []\\n if data == 'uploads':\\n data = 'uploads/%d' % session['user_id']\\n folder = 'data/' + data\\n\\n data_path = os.path.join(os.getcwd(), folder)\\n if not os.path.isdir(data_path):\\n os.makedirs(data_path)\\n\\n for f in os.listdir(data_path):\\n if f.endswith('.dvw'):\\n files.append(folder + '/' + f)\\n\\n locations = []\\n for file_name in files:\\n parser = Parser(file_name)\\n new_locations = parser.get_attack_info(team, player, attacks, only_kills=only_kills)\\n locations.extend(new_locations)\\n\\n top_caption, bottom_caption = generate_caption(team, player, attacks, only_kills)\\n output_url = generate_output_filename(team, player, attacks, only_kills, session['user_id'])\\n output_dict = heat_map.draw_arcs_pillow(locations, output_url, top_caption=top_caption, bottom_caption=bottom_caption)\\n\\n result_dict = {\\n 'output_url': output_url,\\n 'width': output_dict['width'],\\n 'height': output_dict['height'],\\n }\\n print locations\\n print 'Rendering finished image', output_url\\n return render_template('heatmap.html', result_dict=result_dict)\",\n \"def main(config):\\n data_path = config['paths']['data']\\n\\n # data\\n output_file = os.path.join(config['paths']['figures'], 'network-bridge-map.png')\\n road_edge_file = os.path.join(data_path, 'network', 'road_edges.shp')\\n bridge_file = os.path.join(data_path, 'network', 'bridges.shp')\\n\\n # basemap\\n proj_lat_lon = ccrs.PlateCarree()\\n ax = get_axes()\\n plot_basemap(ax, data_path)\\n scale_bar(ax, location=(0.8, 0.05))\\n plot_basemap_labels(ax, data_path, include_regions=False)\\n\\n styles = OrderedDict([\\n ('national', Style(color='#ba0f03', zindex=5, label='National roads')),\\n ('MAYOR SOBRE AGUA Y RUTA', Style(color='#9467bd', zindex=7, label='AGUA Y RUTA')),\\n ('MAYOR SOBRE FERROCARRIL', Style(color='#2ca02c', zindex=8, label='FERROCARRIL')),\\n ('MAYOR SOBRE RUTA', Style(color= '#ff7f0e', zindex=9, label='RUTA')),\\n ('MAYOR SOBRE RUTA Y FERROCARRIL', Style(color='#e377c2', zindex=10, label='RUTA Y FERROCARRIL')),\\n ('MAYOR SOBRE RUTA, AGUA Y FERROCARRIL', Style(color='#8c564b', zindex=11, label='RUTA, AGUA Y FERROCARRIL')),\\n ('MAYOR SOBRE VIA DE AGUA', Style(color='#1f77b4', zindex=6, label='VIA DE AGUA')),\\n ])\\n\\n # edges\\n geoms_by_category = {\\n 'national': [],\\n 'MAYOR SOBRE AGUA Y RUTA': [],\\n 'MAYOR SOBRE FERROCARRIL': [],\\n 'MAYOR SOBRE RUTA':[],\\n 'MAYOR SOBRE RUTA Y FERROCARRIL':[],\\n 'MAYOR SOBRE RUTA, AGUA Y FERROCARRIL':[],\\n 'MAYOR SOBRE VIA DE AGUA':[]\\n }\\n\\n edges_national = geopandas.read_file(road_edge_file)\\n edges_national = edges_national[edges_national['road_type'] == 'national']\\n geoms_by_category['national'] = list(edges_national.geometry)\\n\\n\\n bridges = geopandas.read_file(bridge_file,encoding='utf-8')\\n for iter_, val in bridges.iterrows():\\n cat = val['structur_1'].strip()\\n geoms_by_category[cat].append(val.geometry)\\n\\n\\n for cat, geoms in geoms_by_category.items():\\n cat_style = styles[cat]\\n if cat == 'national':\\n ax.add_geometries(\\n geoms,\\n crs=proj_lat_lon,\\n linewidth=1.25,\\n facecolor='none',\\n edgecolor=cat_style.color,\\n zorder=cat_style.zindex\\n )\\n else:\\n ax.scatter(\\n [g.x for g in geoms],\\n [g.y for g in geoms],\\n transform=proj_lat_lon,\\n facecolor=cat_style.color,\\n s=6,\\n zorder=cat_style.zindex\\n )\\n\\n # legend\\n legend_from_style_spec(ax, styles, loc=(0.48,0.2))\\n\\n # save\\n save_fig(output_file)\",\n \"def pibooth_setup_picture_factory(cfg, opt_index, factory):\",\n \"def process(self):\\n # Opening and preprocessing of the input file\\n if self.options.mbtiles_fromdisk or self.options.mbtiles_todisk:\\n if self.options.mbtiles_fromdisk:\\n i_parm=10\\n if self.options.mbtiles_todisk:\\n i_parm=11\\n if self.options.verbose:\\n print \\\"GDAL2MbTiles :mbtiles from/to disk [\\\",i_parm,\\\"] mbtiles_fromdisk[\\\",self.options.mbtiles_fromdisk,\\\"] mbtiles_todisk[\\\",self.options.mbtiles_todisk,\\\"]\\\"\\n self.mbtiles_setup(i_parm)\\n return\\n else:\\n if self.options.verbose:\\n print \\\"GDAL2MbTiles :tile creation mbtiles[\\\",self.options.mbtiles,\\\"]\\\"\\n self.open_input()\\n # Generation of main metadata files and HTML viewers\\n self.generate_metadata()\\n # Generation of the lowest tiles\\n self.generate_base_tiles()\\n # Generation of the overview tiles (higher in the pyramid)\\n self.generate_overview_tiles()\\n # Generating of KML\\n self.generate_kml()\",\n \"def display_sample(display_list):\\n plt.figure(figsize=(18, 18))\\n\\n title = ['Input Image', 'True Mask', 'Predicted Mask']\\n\\n for i in range(len(display_list)):\\n plt.subplot(1, len(display_list), i+1)\\n plt.title(title[i])\\n plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))\\n plt.axis('off')\\n plt.show()\",\n \"def run_visualization(filepath, MODEL, outputFilePath):\\n try:\\n # print(\\\"Trying to open : \\\" + sys.argv[1])\\n \\t# f = open(sys.argv[1])\\n \\tjpeg_str = open(filepath, \\\"rb\\\").read()\\n \\torignal_im = Image.open(BytesIO(jpeg_str))\\n except IOError:\\n print('Cannot retrieve image. Please check file: ' + filepath)\\n return\\n\\n print('running deeplab on image %s...' % filepath)\\n resized_im, seg_map = MODEL.run(orignal_im)\\n\\n # vis_segmentation(resized_im, seg_map)\\n drawSegment(resized_im, seg_map, outputFilePath)\",\n \"def main():\\n print(\\\"For each image, type the new name of the file.\\\" +\\n \\\" No extension necessary!\\\", end=\\\"\\\\n\\\\n\\\")\\n file_list = input_path.glob(f\\\"*.{args.ext}\\\")\\n plt.ion()\\n\\n for pic in file_list:\\n img = io.imread(str(pic))\\n img = rescale(img, 0.25)\\n img = rotate(img, 90, resize = True)\\n plt.draw()\\n plt.pause(0.001)\\n if args.vinyl:\\n new_name = get_vinyl_name()\\n else:\\n print(\\\"\\\\n\\\")\\n new_name = input(\\n \\\"Please enter a new filename. Press [enter] to skip: \\\")\\n if new_name:\\n if not new_name.endswith(args.ext):\\n new_name += \\\".\\\" + args.ext\\n # io.imsave(output_path / new_name, img)\\n shutil.copyfile(pic, output_path / new_name)\\n if args.replace:\\n os.remove(pic)\",\n \"def fileCmd(self):\\n filename = askopenfilename() \\n self.cnvImgOrig.displayImage(filename)\\n self.cnvImgTest.displayImage(filename)\",\n \"def test(file_path: str):\\n # Build the generator network\\n generator = build_generator()\\n\\n # Load model weights\\n generator.load_weights(\\\"models/generator.h5\\\")\\n\\n # Make a low-res counterpart to predict upon\\n image = imread(file_path, mode=\\\"RGB\\\")\\n image = image.astype(np.float32)\\n low_resolution_image = [imresize(image, low_resolution_shape)]\\n\\n low_resolution_image = np.array(low_resolution_image) / 127.5 - 1.0\\n\\n # Generate high-resolution images from low-resolution images\\n generated_image = generator.predict_on_batch(low_resolution_image)\\n\\n # Make a common figure\\n fig = plt.figure(figsize=(16, 9))\\n\\n # Add the low resolution image on the left side\\n ax = fig.add_subplot(1, 2, 1)\\n ax.imshow(low_resolution_image[0], interpolation=\\\"nearest\\\")\\n ax.axis(\\\"off\\\")\\n ax.set_title(\\\"Low-resolution\\\")\\n\\n # Add generated image to figure on the right side\\n ax = fig.add_subplot(1, 2, 2)\\n ax.imshow(generated_image[0], interpolation=\\\"nearest\\\")\\n ax.axis(\\\"off\\\")\\n ax.set_title(\\\"Generated\\\")\\n\\n # Save the figure\\n plt.savefig(\\\"{0}{1}\\\".format(TEST_IMAGES_RESULT, file_path.split(\\\"/\\\")[-1]))\",\n \"def make_image(self):\\n\\n if self.type == 'passthrough':\\n return\\n render_template(\\n os.path.dirname(self.main_module),\\n os.path.basename(self.main_module_path),\\n self.language,\\n self.requirements,\\n self.whitelist,\\n self.type,\\n into=self.code_dir)\\n self.build()\",\n \"def render_configs(task):\\n\\n # run function to render global configs\\n global_cfg = ibns_global(task)\\n # write global config file for each host\\n with open(f\\\"configs/{task.host}_dot1x_global.txt\\\", \\\"w+\\\") as file:\\n file.write(global_cfg)\\n # print completed hosts\\n c_print(f\\\"*** {task.host}: dot1x global configuration rendered ***\\\")\\n\\n # run function to render snmp configs\\n snmp_cfg = ibns_snmp(task)\\n # function to run interface configs\\n with open(f\\\"configs/{task.host}_snmp.txt\\\", \\\"w+\\\") as file:\\n file.write(snmp_cfg)\\n # print completed hosts\\n c_print(f\\\"*** {task.host}: SNMP configuration rendered ***\\\")\\n\\n # run function to run interface configs\\n intf_cfg = ibns_intf(task)\\n # write interface config file for each host\\n with open(f\\\"configs/{task.host}_dot1x_intf.txt\\\", \\\"w+\\\") as file:\\n file.write(intf_cfg)\\n # print completed hosts\\n c_print(f\\\"*** {task.host}: dot1x intf configurations rendered ***\\\")\",\n \"def ImageOutput(name, out_ds, tile_size, resampling, init_dest, output_dir, verbose,mbtiles):\\n\\n resampler = Resampler(resampling)\\n\\n if name == \\\"hybrid\\\":\\n return HybridImageOutput(out_ds, tile_size, resampler, init_dest, output_dir, verbose)\\n\\n if name == \\\"png\\\":\\n image_format = \\\"PNG\\\"\\n elif name == \\\"jpeg\\\":\\n image_format = \\\"JPEG\\\"\\n\\n return SimpleImageOutput(out_ds, tile_size, resampler, init_dest, output_dir, verbose, [image_format],mbtiles)\",\n \"def _state_main(self, gui):\\n gui.entry.wait_variable(gui.entry_sv)\\n\\n '''Clean string'''\\n files = literal_eval(gui.entry_sv.get())\\n\\n '''Remove previous images'''\\n if hasattr(gui, \\\"panel\\\"):\\n gui.panel.destroy()\\n\\n '''Load each image'''\\n for file_name in files:\\n file_name = file_name.replace(\\\"{\\\", \\\"\\\").replace(\\\"}\\\", \\\"\\\")\\n # image = tk.PhotoImage(file=file_name)\\n if \\\".CR2\\\" in file_name:\\n '''Rawpy implementation'''\\n file_image = rawpy.imread(file_name)\\n file_image = file_image.postprocess()\\n '''Rawkit implementation'''\\n '''file_image = Raw(file_name)\\n file_image = np.array(file_image.to_buffer())'''\\n '''OpenCV implementation'''\\n '''file_image = cv2.imread(file_name)'''\\n else:\\n file_image = Image.open(file_name)\\n '''image = file_image.resize((500, 500), Image.ANTIALIAS)\\n image = ImageTk.PhotoImage(image)\\n gui.panel = tk.Label(gui.root, image=image)\\n gui.panel.image = image\\n gui.panel.pack()'''\\n # panel.grid(row=2)\\n\\n image_data = np.array(file_image)\\n image_data = cv2.cvtColor(image_data, cv2.COLOR_RGB2GRAY)\\n '''print(image_data.shape)\\n print(image_data)\\n print(len(image_data))\\n print(len(image_data[0]))'''\\n returned_image = Image.fromarray(image_data)\\n '''cv2.imshow(\\\"Gray\\\", image_data)\\n cv2.waitKey()\\n cv2.destroyWindow(\\\"Gray\\\")'''\\n\\n '''enhanced_contrast = ImageEnhance.Contrast(Image.fromarray(file_image))\\n enhanced_image = enhanced_contrast.enhance(255)\\n enhanced_data = np.array(enhanced_image)\\n plot_functions.imshow(enhanced_image)\\n plot_functions.show()'''\\n\\n # color_space = cv2.cvtColor(image_data, cv2.COLOR_RGB2HSV)\\n # print(color_space)\\n \\n '''Create mask for white-ish pixels'''\\n '''lower_background = np.array([150, 150, 150])\\n upper_background = np.array([255, 255, 255])\\n print(image_data)\\n white_mask = cv2.inRange(image_data, lower_background, upper_background)\\n white_mask = cv2.morphologyEx(white_mask, cv2.MORPH_OPEN, np.ones((3,3),np.uint8))\\n white_mask = cv2.morphologyEx(white_mask, cv2.MORPH_DILATE, np.ones((3, 3), np.uint8))\\n white_mask = white_mask / 255'''\\n\\n '''Create mask for black-ish pixels'''\\n '''lower_background = np.array([0, 0, 0])\\n upper_background = np.array([25, 25, 25])\\n black_mask = cv2.inRange(image_data, lower_background, upper_background)\\n black_mask = cv2.morphologyEx(black_mask, cv2.MORPH_OPEN, np.ones((3, 3), np.uint8))\\n black_mask = cv2.morphologyEx(black_mask, cv2.MORPH_DILATE, np.ones((3, 3), np.uint8))\\n black_mask = black_mask / 255'''\\n\\n '''Add masks together'''\\n '''background_mask = white_mask\\n # Ensure no value is above 1\\n background_mask = np.clip(background_mask, 0, 1)'''\\n \\n copied_image_data = np.asarray(returned_image).copy()\\n # background_mask = np.logical_not(background_mask)\\n '''for row_index, [mask_row, image_row] in enumerate(zip(background_mask, copied_image_data)):\\n # place black pixel on corresponding masked pixels\\n # copied_image_data[row_index] = np.array([image_row[pixel] * int(mask_row[pixel]) for pixel in range(len(mask_row))])\\n # make pixel fully white on corresponding masked pixels\\n copied_image_data[row_index] = np.array([np.array([255, 255, 255]) if int(mask_row[pixel]) else image_row[pixel] for pixel in range(len(mask_row))])'''\\n\\n '''Turn removed pixels red'''\\n '''mask_image = Image.fromarray(copied_image_data)\\n plot_functions.imshow(mask_image)\\n plot_functions.show()'''\\n trapezoid_data = copied_image_data.copy()\\n\\n enhanced_contrast = ImageEnhance.Contrast(Image.fromarray(trapezoid_data))\\n enhanced_image = enhanced_contrast.enhance(255)\\n trapezoid_data = np.array(enhanced_image)\\n\\n '''Detect lines'''\\n edges = cv2.Canny(trapezoid_data, 75, 150)\\n lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 100, maxLineGap=1000)\\n # print(lines)\\n for line in lines:\\n x1, y1, x2, y2 = line[0]\\n if y1 == y2:\\n cv2.line(copied_image_data, (x1, y1), (x2, y2), (255, 255, 255), 1)\\n\\n '''Trapezoid attempt'''\\n\\n # filters image bilaterally and displays it\\n bilatImg = cv2.bilateralFilter(trapezoid_data, 5, 175, 175)\\n\\n # finds edges of bilaterally filtered image and displays it\\n edgeImg = cv2.Canny(bilatImg, 75, 200)\\n\\n # gets contours (outlines) for shapes and sorts from largest area to smallest area\\n contours, hierarchy = cv2.findContours(edgeImg, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\\n contours = sorted(contours, key=cv2.contourArea, reverse=True)\\n\\n # drawing red contours on the image\\n for con in contours:\\n cv2.drawContours(trapezoid_data, con, -1, (255, 255, 255), 3)\\n\\n '''Detect corners'''\\n dst = cv2.cornerHarris(edges, 30, 31, 0.001)\\n dst = cv2.dilate(dst, None)\\n ret, dst = cv2.threshold(dst, 0.01 * dst.max(), 255, 0)\\n dst = np.uint8(dst)\\n\\n # find centroids\\n ret, labels, stats, centroids = cv2.connectedComponentsWithStats(dst)\\n # define the criteria to stop and refine the corners\\n criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100,\\n 0.001)\\n corners = cv2.cornerSubPix(edges, np.float32(centroids), (5, 5),\\n (-1, -1), criteria)\\n\\n good_corners = []\\n for corner in corners:\\n if (corner[1] < 1000) & (corner[1] > 650) & (corner[0] > 250) & (corner[0] < 2250):\\n good_corners.append(corner)\\n cv2.circle(edges, (corner[0], corner[1]), 10, (255, 255, 255))\\n\\n print(good_corners)\\n if len(good_corners) >= 3:\\n corner_combos = itertools.combinations(good_corners, 3)\\n elif len(good_corners) > 1:\\n corner_combos = itertools.combinations(good_corners, 2)\\n\\n best_corner_combo = None\\n best_coef = np.inf\\n for corner_combo in corner_combos:\\n regression = LinearRegression().fit(np.array([corner[0] for corner in corner_combo]).reshape(-1, 1),\\n np.array([corner[1] for corner in corner_combo]))\\n if np.abs(regression.coef_) < best_coef:\\n best_coef = np.abs(regression.coef_)\\n best_corner_combo = np.array([corner[1] for corner in corner_combo])\\n\\n y_edge = int(round(np.mean(best_corner_combo)))\\n edges = edges[y_edge:3000, 200:2200]\\n copied_image_data = copied_image_data[y_edge:2500, 200:2200]\\n trapezoid_data = trapezoid_data[y_edge:2500, 200:2200]\\n\\n # and double-checking the outcome\\n cv2.imshow(\\\"linesEdges\\\", edges)\\n cv2.imshow(\\\"linesDetected\\\", copied_image_data)\\n cv2.imshow(\\\"Contours check\\\", trapezoid_data)\\n cv2.waitKey()\\n cv2.destroyWindow(\\\"Contours check\\\")\\n\\n # find the perimeter of the first closed contour\\n perim = cv2.arcLength(contours[0], True)\\n # setting the precision\\n epsilon = 0.02 * perim\\n # approximating the contour with a polygon\\n approxCorners = cv2.approxPolyDP(contours[0], epsilon, True)\\n # check how many vertices has the approximate polygon\\n approxCornersNumber = len(approxCorners)\\n\\n for corners in approxCorners:\\n cv2.circle(trapezoid_data, (corners[0], corners[1]), radius=10, color=(255, 255, 255), thickness=-1)\\n cv2.imshow(\\\"Vertex position\\\", trapezoid_data)\\n cv2.waitKey()\\n cv2.destroyWindow(\\\"Vertex position\\\")\\n cv2.imshow(\\\"linesEdges\\\", edges)\\n cv2.imshow(\\\"linesDetected\\\", copied_image_data)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\",\n \"def run(self):\\n self.simulate_test_data()\\n self.pipeline_test_data()\\n self.plot_jump_flags_image()\\n self.plot_groupdq_flags(pixel=[884, 550])\\n self.plot_ramps_pre_post_correction(pixel=[884, 550])\",\n \"def main():\\n me = SimpleImage(\\\"images/me.JPG\\\")\\n dinosaur = SimpleImage(\\\"images/dinosaur.jpg\\\")\\n\\n dinosaur.make_as_big_as(me)\\n combine = magic(me, dinosaur)\\n combine.show()\",\n \"def run(self):\\n\\n # need to think about outpath\\n\\n # Make sure all files are here and okay...\\n\\n if not self.config.galfile_pixelized:\\n raise ValueError(\\\"Code only runs with pixelized galfile.\\\")\\n\\n self.config.check_files(check_zredfile=True, check_bkgfile=True, check_bkgfile_components=True, check_parfile=True, check_zlambdafile=True)\\n\\n # Compute the border size\\n\\n self.config.border = self.config.compute_border()\\n\\n self.config.d.hpix = [self.pixel]\\n self.config.d.nside = self.nside\\n self.config.d.outbase = '%s_%d_%05d' % (self.config.outbase, self.nside, self.pixel)\\n\\n # Do the run\\n self.config.start_file_logging()\\n self.config.logger.info(\\\"Running redMaPPer on pixel %d\\\" % (self.pixel))\\n\\n firstpass = RunFirstPass(self.config)\\n\\n if not os.path.isfile(firstpass.filename):\\n firstpass.run()\\n firstpass.output(savemembers=False, withversion=False)\\n else:\\n self.config.logger.info(\\\"Firstpass file %s already present. Skipping...\\\" % (firstpass.filename))\\n\\n self.config.catfile = firstpass.filename\\n\\n # Clear out the firstpass memory\\n del firstpass\\n\\n like = RunLikelihoods(self.config)\\n\\n if not os.path.isfile(like.filename):\\n like.run()\\n like.output(savemembers=False, withversion=False)\\n else:\\n self.config.logger.info(\\\"Likelihood file %s already present. Skipping...\\\" % (like.filename))\\n\\n self.config.catfile = like.filename\\n\\n # Clear out the likelihood memory\\n del like\\n\\n perc = RunPercolation(self.config)\\n\\n if not os.path.isfile(perc.filename):\\n perc.run()\\n perc.output(savemembers=True, withversion=False)\\n else:\\n self.config.logger.info(\\\"Percolation file %s already present. Skipping...\\\" % (perc.filename))\\n\\n self.config.stop_file_logging()\",\n \"def generate(self):\\n\\n # create temporary folder for copying image files\\n try:\\n os.mkdir(ReportGenerator.TEMP_FOLDER)\\n except FileExistsError:\\n shutil.rmtree(ReportGenerator.TEMP_FOLDER)\\n os.mkdir(ReportGenerator.TEMP_FOLDER)\\n\\n # find all simulations to be included in report\\n group_directory = self.config['group_directory']\\n sim_directory = self.config['sim_directory']\\n assert (group_directory or sim_directory) and not(group_directory and sim_directory), \\\\\\n \\\"A group directory or a simulation directory must be specified, but not both\\\"\\n\\n if group_directory:\\n assert os.path.exists(f'results/{group_directory}'), \\\"ERROR: Group directory does not exist\\\"\\n\\n sim_list = os.listdir(f'results/{group_directory}')\\n sim_label = group_directory.split('-')[0]\\n sim_list = [f'{group_directory}/{x}' for x in sim_list if sim_label in x]\\n\\n # filters\\n if self.config['filters']:\\n \\n seed_filters = self.config['filters']['seed']\\n dilution_filters = self.config['filters']['dilution']\\n order_filters = self.config['filters']['order']\\n\\n for symbol, filters in zip(['S', 'D', 'O'], [seed_filters, dilution_filters, order_filters]):\\n if type(filters) == int:\\n sim_list = [x for x in sim_list if f'{symbol}{filters}' in x.split('-')[2]]\\n elif type(filters) == list:\\n sim_list = [x for x in sim_list if any([f\\\"{symbol}{f}\\\" in x.split('-')[-2] for f in filters])]\\n\\n if len(sim_list) == 0:\\n raise Exception('Simulation filters resulted in no satisfactory simulations')\\n\\n else:\\n assert os.path.exists(f'results/{sim_directory}'), \\\"ERROR: Simulation directory does not exist\\\"\\n sim_list = [sim_directory]\\n\\n # REPORT HEADER\\n self.add_heading(level=1, text=self.config['title'])\\n now = datetime.now()\\n self.add_text(text=f'Report generated on {now.strftime(\\\"%B %d, %Y\\\")} at {now.strftime(\\\"%H:%M:%S\\\")}')\\n\\n # REPORT CONTENTS\\n for i, sim in enumerate(sim_list):\\n for content in self.config['content']:\\n content_type = list(content.keys())[0]\\n params = content[content_type]\\n\\n self.add_content(content_type, params, sim)\\n\\n if i+1 < len(sim_list):\\n self.add_content(content_type='break')\\n\\n # CREATE HTML TEMPLATE\\n base_template = self.templateEnv.get_template(f'{ReportGenerator.COMPONENTS_FOLDER}/base.html')\\n base_output = base_template.render(content='\\\\n\\\\n'.join(self.contents))\\n with open(f'{ReportGenerator.TEMP_FOLDER}/template.html', 'w') as html_file:\\n html_file.write(base_output)\\n\\n # CREATE PDF FROM TEMPLATE\\n if group_directory:\\n HTML(f'{ReportGenerator.TEMP_FOLDER}/template.html').write_pdf(\\n f'results/{group_directory}/{self.config[\\\"title\\\"]}.pdf', stylesheets=['reports/style.css'])\\n else:\\n HTML(f'{ReportGenerator.TEMP_FOLDER}/template.html').write_pdf(\\n f'results/{sim_directory}/{self.config[\\\"title\\\"]}.pdf', stylesheets=['reports/style.css'])\\n\\n # delete temp folder for storing image files\\n shutil.rmtree(f'{ReportGenerator.TEMP_FOLDER}')\\n return None\",\n \"def render(self, mode=\\\"human\\\", width=500, height=500):\\n if mode == \\\"human\\\":\\n return self.mujoco_simulation.mujoco_viewer.render()\\n elif mode == \\\"rgb_array\\\":\\n return self.mujoco_simulation.render(width=width, height=height)\\n else:\\n raise ValueError(\\\"Unsupported mode %s\\\" % mode)\",\n \"def run_visualization(filepath):\\n try:\\n \\t#print(\\\"Trying to open : \\\" + sys.argv[1])\\n \\t# f = open(sys.argv[1])\\n \\tjpeg_str = open(filepath, \\\"rb\\\").read()\\n \\torignal_im = Image.open(BytesIO(jpeg_str))\\n except IOError:\\n print('Cannot retrieve image. Please check file: ' + filepath)\\n return\\n\\n #print('running deeplab on image %s...' % filepath)\\n resized_im, seg_map = MODEL.run(orignal_im)\\n\\n # vis_segmentation(resized_im, seg_map)\\n drawSegment(resized_im, seg_map)\",\n \"def run_plots(self):\\n # load the files\\n self.pre_dark_file = os.path.join(self.input_dir, 'step_lastframe.fits')\\n self.post_dark_file = os.path.join(self.input_dir, 'step_dark_current.fits')\\n self.jump_file = os.path.join(self.input_dir, 'step_jump.fits')\\n self.rate_file = os.path.join(self.input_dir, 'step_rate.fits')\\n self.ramp_file = glob.glob(os.path.join(self.input_dir, '*.fits'))[0]\\n\\n # plots\\n self.plot_jump_flags_image()\\n self.plot_groupdq_flags(pixel=[884, 550])\\n self.plot_ramps_pre_post_correction(pixel=[884, 550])\",\n \"def to_html(self, file_name, kohonen_map):\\n if file_name[-5:] != '.html':\\n print('Not html file... appending HTML extension!')\\n file_name += '.html'\\n with open(file_name, 'w') as html_file:\\n html_content = \\\"\\\"\\\"<html>\\n <script src=\\\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.2/jquery.min.js\\\"></script>\\n <style type=\\\"text/css\\\">\\n #overlay {\\n position: absolute;\\n top: 40;\\n left: 40;\\n width: 400px;\\n height: 400px;\\n background-color: #000;\\n }\\n </style>\\n <script type=\\\"text/javascript\\\">\\n $(document).ready(function () {\\n $('#overlay').hide();\\n });\\n\\n function close() {\\n $('#overlay').hide();\\n }\\n\\n function show_images(imgs) {\\n if (imgs.length == 0) {\\n alert(\\\"No other images in that neuron!\\\");\\n } else {\\n var div = $('#overlay');\\n div.empty();\\n div.css('text-align', 'center')\\n\\n var btn = $('<button>Close</button><br/><br/>');\\n btn.on('click', close);\\n div.append(btn);\\n\\n for (var i = 0; i < imgs.length; ++i) {\\n var img = imgs[i];\\n div.append($('<img src=\\\"' + img + '\\\" style=\\\"width:100px;height:100px;\\\"/>'));\\n div.show();\\n }\\n }\\n }\\n </script>\\n\\n <div id=\\\"container\\\">\\n <table>\\n \\\"\\\"\\\" # noqa\\n html_file.write(html_content)\\n for i in range(kohonen_map._map.shape[0]):\\n html_file.write('<tr>')\\n for j in range(kohonen_map._map.shape[1]):\\n html_file.write('<td>')\\n key = str(i) + ',' + str(j)\\n sample_ids = kohonen_map.samples_dict[key]\\n if len(sample_ids) > 0:\\n style = 'style=\\\\\\\"width:100px;height:100px;\\\\\\\"'\\n images_list = []\\n for index, sample_id in enumerate(sample_ids):\\n image_index = self.image_indices[sample_id]\\n image_name = str(image_index) + '.jpg'\\n image_file = os.path.join(self.directory,\\n image_name)\\n if index == 0:\\n src = 'src=\\\"' + image_file + '\\\"'\\n alt = 'alt=\\\"' + image_file + '\\\"'\\n else:\\n images_list.append(\\\"'\\\" + image_file + \\\"'\\\")\\n images_list = ','.join(images_list)\\n onclick = 'onclick=\\\"show_images([' + images_list + \\\\\\n '])\\\"'\\n img = '<img ' + src + ' ' + alt + ' ' + style + ' ' + \\\\\\n onclick + '>'\\n html_file.write(img)\\n else:\\n alt = 'alt=\\\\\\\"No image\\\\\\\"'\\n style = 'style=\\\\\\\"width:100px;height:100px;\\\\\\\"'\\n #html_file.write(img)\\n html_file.write('</td>')\\n html_file.write('</tr>')\\n html_content = \\\"\\\"\\\"</table>\\n </div>\\n\\n <div id=\\\"overlay\\\">\\n </div>\\n </body>\\n </html>\\\"\\\"\\\"\\n html_file.write(html_content)\",\n \"def main():\\n data_visualisation()\\n write_hyper_params()\\n write_result_tables()\\n write_box_plots()\",\n \"def config_case(\\n context: CGConfig,\\n case_id: str,\\n gender: str,\\n genome_version: str,\\n panel_bed: str,\\n pon_cnn: click.Path,\\n observations: List[click.Path],\\n dry_run: bool,\\n):\\n\\n analysis_api: AnalysisAPI = context.meta_apis[\\\"analysis_api\\\"]\\n try:\\n LOG.info(f\\\"Creating config file for {case_id}.\\\")\\n analysis_api.status_db.verify_case_exists(case_internal_id=case_id)\\n analysis_api.config_case(\\n case_id=case_id,\\n gender=gender,\\n genome_version=genome_version,\\n panel_bed=panel_bed,\\n pon_cnn=pon_cnn,\\n observations=observations,\\n dry_run=dry_run,\\n )\\n except CgError as error:\\n LOG.error(f\\\"Could not create config: {error}\\\")\\n raise click.Abort()\\n except Exception as error:\\n LOG.error(f\\\"Could not create config: {error}\\\")\\n raise click.Abort()\",\n \"def plot_ratios(path='/Volumes/OptiHDD/data/pylith/3d/agu2014/output',\\n\\t\\t\\t\\tsteps=['step01','step02'],\\n\\t\\t\\t\\t#labels='',\\n\\t\\t\\t\\tshow=True,\\n\\t\\t\\t\\txscale=1e3,\\n\\t\\t\\t\\tyscale=1e-2):\\n\\tplt.figure()\\n\\t#path = '/Users/scott/Desktop/elastic'\\n\\n\\t# Deep source\\n\\t#labels = ['no APMB', 'APMB']\\n\\t#if labels == '':\\n\\tlabels = steps\\n\\tdeep = {}\\n\\t#uzmax = 0.824873455364\\n\\t# NOT sure why hardcoded...\\n\\tuzmax = 1\\n\\tfor i,outdir in enumerate(steps):\\n\\t\\tpointsFile = os.path.join(path, outdir, 'points.h5')\\n\\t\\tprint(pointsFile)\\n\\t\\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\\n\\n\\t\\tX = x / xscale\\n\\t\\tY1 = ux / yscale\\n\\n\\t\\tx_fem = X #/ xscale #double scaling!\\n\\t\\tur_fem = Y1 #/ yscale\\n\\t\\tuz_fem = uz / yscale\\n\\n\\t\\t#print(pointsFile)\\n\\t\\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\\n\\n\\t\\t#normalize\\n\\t\\tuz_fem = uz_fem / uzmax\\n\\t\\tur_fem = ur_fem / uzmax\\n\\t\\tx_fem = x_fem / 30.0\\n\\n\\t\\tl, = plt.plot(x_fem,uz_fem,'o-',ms=4,lw=4,label=labels[i])\\n\\t\\tplt.plot(x_fem,ur_fem,'o--',ms=4,lw=4,color=l.get_color()) #mfc='none' transparent\\n\\t\\tdeep[outdir] = uz_fem/uz_fem\\n\\n\\t'''\\n\\t# Shallow Source\\n\\tshallow = {}\\n\\tuzmax = 0.949652827795\\n\\tfor i,outdir in enumerate(['step11','step12']):\\n\\t\\tpointsFile = os.path.join(path, outdir, 'points.h5')\\n\\n\\t\\tx,y,z,ux,uy,uz = pu.extract_points(pointsFile)\\n\\n\\t\\tX = x / xscale\\n\\t\\tY1 = ux / yscale\\n\\n\\t\\tx_fem = X #/ xscale #double scaling!\\n\\t\\tur_fem = Y1 #/ yscale\\n\\t\\tuz_fem = uz / yscale\\n\\n\\t\\t#print(pointsFile)\\n\\t\\tprint(ur_fem.min(), ur_fem.max(), uz_fem.min(), uz_fem.max(), uz_fem.max() / ur_fem.max())\\n\\n\\t#normalize\\n\\tuz_fem = uz_fem / uzmax\\n\\tur_fem = ur_fem / uzmax\\n\\tx_fem = x_fem / 20.0\\n\\n\\t\\tl, = plt.plot(x_fem,uz_fem,'.-', mfc='w', lw=4,label=labels[i])\\n\\t\\tplt.plot(x_fem,ur_fem,'.--',lw=4, mfc='w',color=l.get_color()) #mfc='none' transparent\\n\\n\\t\\tshallow[outdir] = uz_fem/ur_fem\\n\\t'''\\n\\n\\t# Annotate\\n\\tplt.axhline(color='k',lw=0.5)\\n\\t#plt.xlabel('Distance [{}]'.format(get_unit(xscale)))\\n\\t#plt.ylabel('Displacement [{}]'.format(get_unit(yscale)))\\n\\tplt.legend()\\n\\tplt.grid()\\n\\t#plt.ylim(-0.5, 3.5)\\n\\t#plt.savefig('deep.png',bbox_inches='tight')\\n\\t#plt.savefig('shallow.png',bbox_inches='tight')\\n\\n\\t# normalized\\n\\tplt.ylim(-0.5, 4)\\n\\tplt.xlim(0,10)\\n\\tplt.xlabel('Normalized Radial Distance [R / D]')\\n\\tplt.ylabel('Normalized Displacement [U / Uz_max]')\\n\\t#plt.savefig('normalized_deep.png',bbox_inches='tight')\\n\\tplt.savefig('normalized_shallow.png',bbox_inches='tight')\\n\\n\\n\\t# Plot ratios of uz versus NOTE: this plot is confusing,,, just keep ratio of uz_max to ur_max\\n\\t'''\\n\\tplt.figure()\\n\\tplt.plot(x_fem, deep['step01'], label='Deep no APMB')\\n\\tplt.plot(x_fem, deep['step02'], label='Deep w/ APMB')\\n\\tplt.plot(x_fem, shallow['step11'], label='Shallow no APMB')\\n\\tplt.plot(x_fem, shallow['step12'], label='Shallow w/ APMB')\\n\\tplt.xlabel('Distance [km]') #NOTE: maybe plot normailzed X-axis (R-d)\\n\\t#plt.xlabel('Normalized Distance [R/d]')\\n\\tplt.ylabel('Ratio [Uz/Ur]')\\n\\tplt.title('Ratio of vertical to radial displacement')\\n\\tplt.legend()\\n\\tplt.show()\\n\\t'''\",\n \"def run_variations(filename, N=50, N_scales=7, shape=(512, 512),\\n steps=[(10,4.0), (20,1.0)], display_inline=False, min_radius=1.5,\\n max_radius=90.0):\\n\\n colors = [[1, 0, 0], [0, 1, 0], [0, 0, 0.9], [1, 1, 0],\\n [1, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0.6, 0]]\\n perm = np.random.permutation\\n for n in range(N):\\n # generate palette\\n pal = perm(colors)[:4]\\n pal = perm(r_[pal, pal])\\n pal = (pal * .6 + roll(pal, 4, axis=0) * 0.4)[:4]\\n pal = shade(N_scales, *pal)\\n # generate parameters and MSTP object\\n ra = exp(rseq(log(min_radius), log(max_radius), N=N_scales, randomness=0.9)).round(2)\\n ri = ((1.333 + rand(N_scales)) * ra).round(2)\\n dt = (.01 * frange(1, N_scales) ** 0.8).round(3)\\n wt = (1.33 + arctan(5 * (rand(N_scales) - .5))).round(2)\\n m = MSTP(shape, ra=ra, ri=ri, dt=dt, wt=wt, pal=pal)\\n\\n print '\\\\n-------- rendering image', filename % n\\n print m\\n # display(HTML(' '.join('<span style=\\\"background:rgb(%d,%d,%d)\\\">(o_O)</span> ' % tuple(255*k) for k in array(pal))))\\n for i, (N_steps, speed) in enumerate(steps):\\n print 'rendering %s steps at %1.1fx speed' % (N_steps, dt_multiplier)\\n sys.stdout.flush()\\n m.run(n=N_steps, speed=speed)\\n if display_inline:\\n display(IM(m.rgb_image()))\\n if i < 0:\\n first = False\\n print 'renoising after iter 1.',\\n m.z += 0.25 * rand(*m.z.shape) * m.z.ptp()\\n\\n # display(IM(m.rgb_image()))\\n imsave(filename % n, m.rgb_image())\",\n \"def visualize(stuff, **options):\\n separate = r\\\"\\\\newpage\\\" #by default, a new tupel is put on a new page\\n name = \\\"some_text_file\\\" #by default this file is used\\n for key in options:\\n if key == \\\"separate\\\":\\n separate = options[key]\\n if key == \\\"name\\\":\\n name = options[key]\\n works = True\\n totallines = [r\\\"\\\\documentclass{article}\\\", r\\\"\\\\usepackage{xcolor}\\\", r\\\"\\\\usepackage{tikz,pgf}\\\", r\\\"\\\\usepackage[left = 0 cm, top = 0cm, bottom = 0cm, right = 2cm]{geometry}\\\", r\\\"\\\\begin{document}\\\", r\\\"\\\\pagestyle{empty}\\\"]\\n for description in stuff:\\n data = stuff[description]\\n if checkdataformat(description, data):\\n if description == \\\"config\\\":\\n lines = gentikz(data)\\n elif description == \\\"movelist\\\":\\n lines = showmoveslist(data[0], data[1], data[2])\\n elif description == \\\"movelists\\\":\\n lines = compareshowmoveslists(data[0], data[1], data[2])\\n elif description == \\\"list\\\":\\n lines = showlist(data)\\n elif description == \\\"configurations\\\":\\n lines = showconfigurations(data)\\n elif description == \\\"movetable\\\":\\n lines = nktable(data[0], data[1], sort = 'value')\\n elif description == \\\"incrementtable\\\":\\n lines = nktable(data[0], data[1], sort = 'increment')\\n elif description == \\\"totalptable\\\":\\n lines = nktable(data[0], data[1], sort = 'totalpossibilities')\\n elif description == \\\"ptable\\\":\\n lines = nktable(data[0], data[1], sort = 'adjustedpossibilities')\\n elif description == \\\"bfptable\\\":\\n lines = nktable(data[0], data[1], sort = 'bfadjustedpossibilities')\\n else:\\n print(\\\"unknown description\\\")\\n lines = []\\n for line in lines:\\n totallines.append(line)\\n totallines.append(separate)\\n else:\\n print(description, \\\":\\\", data, \\\"don't match, please read help(visualization)\\\")\\n works = False\\n totallines.append(r\\\"\\\\end{document}\\\")\\n if works:\\n compile(totallines, name)\",\n \"def visualize(self, paths, instance, during_analysis):\\r\\n xvalues = np.arange(self.data.shape[0])\\r\\n\\r\\n model_data = self.model_data_from_instance(instance=instance)\\r\\n\\r\\n residual_map = self.data - model_data\\r\\n chi_squared_map = (residual_map / self.noise_map) ** 2.0\\r\\n\\r\\n \\\"\\\"\\\"The visualizer now outputs images of the best-fit results to hard-disk (checkout `visualizer.py`).\\\"\\\"\\\"\\r\\n plot_profile_1d(\\r\\n xvalues=xvalues,\\r\\n profile_1d=self.data,\\r\\n title=\\\"Data\\\",\\r\\n ylabel=\\\"Data Values\\\",\\r\\n color=\\\"k\\\",\\r\\n output_path=paths.image_path,\\r\\n output_filename=\\\"data\\\",\\r\\n )\\r\\n\\r\\n plot_profile_1d(\\r\\n xvalues=xvalues,\\r\\n profile_1d=model_data,\\r\\n title=\\\"Model Data\\\",\\r\\n ylabel=\\\"Model Data Values\\\",\\r\\n color=\\\"k\\\",\\r\\n output_path=paths.image_path,\\r\\n output_filename=\\\"model_data\\\",\\r\\n )\\r\\n\\r\\n plot_profile_1d(\\r\\n xvalues=xvalues,\\r\\n profile_1d=residual_map,\\r\\n title=\\\"Residual Map\\\",\\r\\n ylabel=\\\"Residuals\\\",\\r\\n color=\\\"k\\\",\\r\\n output_path=paths.image_path,\\r\\n output_filename=\\\"residual_map\\\",\\r\\n )\\r\\n\\r\\n plot_profile_1d(\\r\\n xvalues=xvalues,\\r\\n profile_1d=chi_squared_map,\\r\\n title=\\\"Chi-Squared Map\\\",\\r\\n ylabel=\\\"Chi-Squareds\\\",\\r\\n color=\\\"k\\\",\\r\\n output_path=paths.image_path,\\r\\n output_filename=\\\"chi_squared_map\\\",\\r\\n )\",\n \"def setUp(self):\\n print \\\"Setting Up: %s\\\" % self.id()\\n # render the template\\n g.render_template(self.template_file,\\n self.template_vars,\\n self.output_file,\\n self.search_path)\\n\\n # read the resulting config file built from template\\n self.output_config = g.load_config(self.output_file)\\n g.show_config(self.output_config)\",\n \"def plot_vis_test(plotfile,pdf_file):\\n\\t# First some parameters looked up from configfile---------------------------------\\n\\t\\n\\tgrbdir = runconf['l2file'][0:10]\\n\\tpre_tstart = runconf['bkg1start']\\n\\tpre_tend = runconf['bkg1end']\\n\\ttrigtime = runconf['trigtime']\\n\\tgrb_tstart = runconf['transtart']\\n\\tgrb_tend = runconf['tranend']\\n\\tpost_tstart = runconf['bkg2start']\\n\\tpost_tend = runconf['bkg2end']\\n\\tt_src = grb_tend - grb_tstart \\n\\tt_tot = (pre_tend-pre_tstart)+(post_tend-post_tstart)\\n\\tra_tran = runconf['ra']\\n\\tdec_tran = runconf['dec']\\n\\tlc_bin = runconf['lc_bin']\\n\\talpha = runconf['alpha']\\n\\tbeta = runconf['beta']\\n\\tE0 = runconf['E0']\\n\\tA = runconf['A']\\n\\tsim_scale = t_src\\n\\tpixbin = int(runconf['pixsize'])\\n\\tcomp_bin = int(runconf['comp_bin'])\\n\\ttyp = runconf['typ']\\n\\n\\t# Calling txy to calculate thetax thetay and the coordinates----------------------\\n\\t\\n\\tthetax,thetay,x,y,z,t = txy(runconf['mkffile'], trigtime, ra_tran, dec_tran)\\n\\t\\n\\t# Plot the 3d visualisation for the position of the transient---------------------\\n\\tplt.figure()\\n\\tfig = visualize_3d(grbdir,x,y,z, t, thetax, thetay, grbdir)\\t\\n\\tpdf_file.savefig(fig)\\n\\t\\n\\t# Plotting the lightcurves for the four quadrants---------------------------------\\n\\tfig = plt.figure()\\n\\tclean_file = fits.open(runconf['infile'])\\n\\tplt.title('Light curves for '+grbdir + \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n\\t\\n\\tquad0 = clean_file[1].data\\n\\tdata0,bin_edge = np.histogram(quad0['time'], bins=np.arange(quad0['time'][0],quad0['time'][-1],lc_bin))\\n\\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data0,label='Quad 0',lw=0.7)\\n quad1 = clean_file[2].data\\n\\tdata1,bin_edge = np.histogram(quad1['time'], bins=np.arange(quad1['time'][0],quad1['time'][-1],lc_bin))\\n\\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data1,label='Quad 1',lw=0.7) \\n\\tquad2 = clean_file[3].data\\n\\tdata2,bin_edge = np.histogram(quad2['time'], bins=np.arange(quad2['time'][0],quad2['time'][-1],lc_bin))\\n\\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data2,label='Quad 2',lw=0.7)\\n quad3 = clean_file[4].data\\n data3,bin_edge = np.histogram(quad3['time'], bins=np.arange(quad3['time'][0],quad3['time'][-1],lc_bin))\\n plt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data3,label='Quad 3',lw=0.7)\\n\\tplt.axvspan(grb_tstart,grb_tend,color='blue',alpha=0.1,label='GRB')\\n\\tplt.axvspan(pre_tstart,pre_tend,color='orange',alpha=0.2)\\n\\tplt.axvspan(post_tstart,post_tend,color='orange',alpha=0.2,label='Background')\\n\\tplt.legend(prop={'size':6})\\n\\tplt.xlim(pre_tstart-100,post_tend+100)\\n\\tpdf_file.savefig(fig)\\n\\t\\n\\t# Calling the sim_dph--------------------------------------------------------------\\n\\t\\n\\tgrb_flat,bkgd_flat,grb_dph,bkgd_dph,t_src,t_total = data_bkgd_image(grbdir,pre_tstart,pre_tend,grb_tstart,grb_tend,post_tstart,post_tend)\\n\\n\\tsim_flat,sim_dph,badpix_mask,sim_err_dph = simulated_dph(grbdir,typ,t_src,alpha,beta,E0,A)\\n\\n\\tsrc_dph = grb_dph-bkgd_dph*t_src/t_tot\\n\\n print \\\"Total counts in simulated dph: \\\",(sim_dph).sum()\\n print \\\"Total counts after badpix mask is applied: \\\",(sim_dph*badpix_mask).sum()\\n\\tprint \\\"Excess counts in badpix masked src dph: \\\",(src_dph*badpix_mask).sum()\\n \\n\\t# Plotting the DPHs before badpix correction---------------------------------------\\n\\t\\n\\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\\n\\tplt.suptitle('DPHs before badpix correction for '+grbdir + \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n \\t# Sim\\n\\tim = ax3.imshow(sim_dph,interpolation='none')\\n\\tax3.set_title('Sim DPH',fontsize=8)\\n\\tax3.set_xlim(-1,128 - 0.5)\\n\\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax3.spines['left'].set_position(('data',-0.5))\\n\\tax3.set_yticklabels([])\\n\\tax3.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\\n\\t\\n\\t # Source \\n\\tim = ax4.imshow(src_dph,interpolation='none',vmin=0)\\n\\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\\n\\tax4.set_xlim(-1,128 -0.5)\\n\\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax4.spines['left'].set_position(('data',-0.5))\\n\\tax4.set_yticklabels([])\\n\\tax4.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\\n\\n \\t# Source + Background\\n\\tim = ax1.imshow(grb_dph,interpolation='none')\\n\\tax1.set_title('Src + Bkg DPH',fontsize=8)\\n\\tax1.set_xlim(-1,128 -0.5)\\n\\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax1.spines['left'].set_position(('data',-0.5))\\n\\tax1.set_yticklabels([])\\n\\tax1.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\\n\\n \\t# Background\\n\\tim = ax2.imshow(bkgd_dph*t_src/t_total,interpolation='none')\\n\\tax2.set_title('Bkg DPH',fontsize=8)\\n\\tax2.set_xlim(-1,128 -0.5)\\n\\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax2.spines['left'].set_position(('data',-0.5))\\n\\tax2.set_yticklabels([])\\n\\tax2.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\\n\\tf.set_size_inches([6.5,6.5])\\n\\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\\n\\t\\n\\t# Plotting the Badpix mask---------------------------------------------\\n\\n\\tfig = plt.figure()\\n\\tax = plt.subplot(111)\\n\\tplt.title('Badpix Mask for '+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n\\tim = ax.imshow(badpix_mask,interpolation='none')\\n\\tax.set_xlim(-9,128 -0.5)\\n\\tax.axvline(x=-5.,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax.spines['left'].set_position(('data',-0.5))\\n\\tax.xaxis.set_ticks(np.arange(0,128,16))\\n\\tax.yaxis.set_ticks(np.arange(0,128,16))\\n\\tfig.colorbar(im,ax=ax,fraction=0.046, pad=0.04)\\n\\t\\n\\tpdf_file.savefig(fig) # saves the current figure into a pdf_file page\\n\\n\\t# Plotting badpix masked graphs--------------------------------------------\\n\\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\\n\\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n \\t# Sim\\n\\tim = ax3.imshow(sim_dph*badpix_mask,interpolation='none')\\n\\tax3.set_title('Sim DPH',fontsize=8)\\n\\tax3.set_xlim(-1,128 -0.5)\\n\\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax3.spines['left'].set_position(('data',-0.5))\\n\\tax3.set_yticklabels([])\\n\\tax3.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\\n\\n\\t # Source \\n\\tim = ax4.imshow(src_dph*badpix_mask,interpolation='none',vmin=0)\\n\\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\\n\\tax4.set_xlim(-1,128 -0.5)\\n\\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax4.spines['left'].set_position(('data',-0.5))\\n\\tax4.set_yticklabels([])\\n\\tax4.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\\n\\n\\t # Source + Background\\n\\tim = ax1.imshow(grb_dph*badpix_mask,interpolation='none')\\n\\tax1.set_title('Src + Bkg DPH',fontsize=8)\\n\\tax1.set_xlim(-1,128 -0.5)\\n\\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax1.spines['left'].set_position(('data',-0.5))\\n\\tax1.set_yticklabels([])\\n\\tax1.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\\n\\t\\n\\t # Background\\n\\tim = ax2.imshow(bkgd_dph*badpix_mask*t_src/t_total,interpolation='none')\\n\\tax2.set_title('Bkg DPH',fontsize=8)\\n\\tax2.set_xlim(-1,128 -0.5)\\n\\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax2.spines['left'].set_position(('data',-0.5))\\n\\tax2.set_yticklabels([])\\n\\tax2.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\\n\\tf.set_size_inches([6.5,6.5])\\n\\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\\n\\n\\t# Plotting badpix masked graphs (Binned) ----------------------------------------------------\\n\\tfor p in [4,8,16]:\\n\\t\\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\\n\\t\\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay)+ \\\"pixsize=\\\"+str(p))\\n\\t\\t # Sim\\n\\t\\tim = ax3.imshow(resample(sim_dph*badpix_mask,p),interpolation='none')\\n\\t\\tax3.set_title('Sim DPH',fontsize=8)\\n\\t\\tax3.set_xlim(-1,128/p -0.5)\\n\\t\\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax3.spines['left'].set_position(('data',-0.5))\\n\\t\\tax3.set_yticklabels([])\\n\\t\\tax3.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n\\t\\tax3.set_xticklabels(np.arange(0,128,16))\\n\\t\\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\\n\\t\\t\\n\\t\\t # Source \\n\\t\\tim = ax4.imshow(resample(src_dph*badpix_mask,p),interpolation='none',vmin=0)\\n\\t\\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\\n\\t\\tax4.set_xlim(-1,128/p -0.5)\\n\\t\\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax4.spines['left'].set_position(('data',-0.5))\\n\\t\\tax4.set_yticklabels([])\\n ax4.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n ax4.set_xticklabels(np.arange(0,128,16))\\t\\t\\n\\t\\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\\n\\t\\t\\n\\t\\t # Source + Background\\n\\t\\tim = ax1.imshow(resample(grb_dph*badpix_mask,p),interpolation='none')\\n\\t\\tax1.set_title('Src + Bkg DPH',fontsize=10)\\n\\t\\tax1.set_xlim(-1,128/p -0.5)\\n\\t\\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax1.spines['left'].set_position(('data',-0.5))\\n\\t\\tax1.set_yticklabels([])\\n ax1.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n ax1.set_xticklabels(np.arange(0,128,16))\\t\\t\\n\\t\\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\\n\\t\\t\\n\\t\\t # Background\\n\\t\\tim = ax2.imshow(resample(bkgd_dph*badpix_mask*t_src/t_total,p),interpolation='none')\\n\\t\\tax2.set_title('Bkg DPH',fontsize=8)\\n\\t\\tax2.set_xlim(-1,128/p -0.5)\\n\\t\\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax2.spines['left'].set_position(('data',-0.5))\\n\\t\\tax2.set_yticklabels([])\\n ax2.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n ax2.set_xticklabels(np.arange(0,128,16))\\t\\t\\n\\t\\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\\n\\t\\tf.set_size_inches([6.5,6.5])\\n\\t\\t\\n\\t\\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\\n\\n\\n\\t# Plotting the comparison graphs with equal bins ---------------------------------------\\n\\tprint \\\"No. of pixels with zero counts in sim_dph: \\\",sim_dph[sim_dph==0].size\\n\\tprint \\\"No. of pixels with zero counts in grb_dph(no bkg subtration): \\\",grb_dph[grb_dph==0].size\\n\\t\\n\\t# Generating the array for module number ------------------------------------------------\\n\\tA = ['A'+str(i) for i in range(16)]\\n\\tB = np.flip(['B'+str(i) for i in range(16)],0)\\n\\tC = np.flip(['C'+str(i) for i in range(16)],0)\\n\\tD = ['D'+str(i) for i in range(16)]\\n\\tquad_a = np.reshape(A,(4,4))\\n\\tquad_b = np.reshape(B,(4,4))\\n\\tquad_c = np.reshape(C,(4,4))\\n\\tquad_d = np.reshape(D,(4,4))\\n\\tMod_arr = np.ndarray((8,8),dtype='|S3')\\n\\tMod_arr[:4,:4] = quad_a\\n\\tMod_arr[:4,4:] = quad_b\\n\\tMod_arr[4:,4:] = quad_c\\n\\tMod_arr[4:,:4] = quad_d\\n\\tMod_names = Mod_arr.flatten()\\n\\t#print \\\"Module name array : \\\",Mod_names\\n\\t#-----------------------------------------------------------------------------------------\\n\\t\\t\\n\\tsim_dph = sim_dph*badpix_mask\\n\\tsim_err_dph = sim_err_dph*badpix_mask\\n grb_dph = grb_dph*badpix_mask\\n bkgd_dph = bkgd_dph*badpix_mask\\n\\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\\n\\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\\n\\n\\tsim_bin = resample(sim_dph,pixbin)\\n\\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\\t\\n\\tgrb_bin = resample(grb_dph,pixbin)\\n\\tbkgd_bin = resample(bkgd_dph,pixbin)\\n\\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\\t\\n\\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\\t\\n\\n\\tsim_flat_bin = sim_bin.flatten()\\n\\tsim_err_flat_bin = sim_err_bin.flatten()\\n\\tgrb_flat_bin = grb_bin.flatten()\\n\\tbkgd_flat_bin = bkgd_bin.flatten()\\n\\tgrb_err_flat_bin = grb_err_bin.flatten()\\n\\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\\n\\t\\n\\n\\t # Defining model background and data\\n\\tmodel = sim_flat_bin\\n\\tmodel_copy = np.copy(model)\\n\\tbkgd = bkgd_flat_bin*t_src/t_tot\\n\\tsrc = grb_flat_bin\\n\\t\\n\\tdata = src - bkgd\\n\\tdata_copy = np.copy(data)\\n\\t\\n\\terr_src = grb_err_flat_bin\\n\\terr_bkgd = bkgd_err_flat_bin\\n\\terr_model = sim_err_flat_bin\\n\\terr_model_copy = np.copy(err_model)\\n\\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\terr_data_copy = np.copy(err_data)\\n\\t\\n\\tratio = data/model\\n\\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\\n\\t\\n\\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n\\t\\n\\t # PLotting the comparison plots\\n\\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\\n\\t\\n\\tax1.set_title(\\\"Comparison between simulated and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f}\\\".format(tx=thetax,ty=thetay,c=chi_sq))\\n\\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n\\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation\\\",elinewidth=0.5)\\n\\tax1.legend()\\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\\n\\tax1.set_ylabel('Counts')\\n\\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n\\n\\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\\\"Ratio = Data/Model\\\",elinewidth=0.5)\\n\\tax2.xaxis.set_ticks(np.arange(0,len(data)))\\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\\n\\tax2.tick_params(labelsize=5)\\n\\tax2.axhline(y=1,linewidth=0.5,color='k')\\n\\tax2.legend()\\n\\tax2.set_xlabel('CZT Modules')\\n\\tax2.set_ylabel('Ratio of counts')\\n\\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\\n\\tplt.tight_layout(h_pad=0.0)\\n\\tf.set_size_inches([6.5,10])\\n\\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\\n\\n\\t# Plotting comparison graphs with random binning------------------------------\\n\\t\\n sim_flat = sim_dph.flatten()\\n\\tsim_err_flat = sim_err_dph.flatten()\\n grb_flat = grb_dph.flatten()\\n bkgd_flat = bkgd_dph.flatten()\\n\\tsrc_flat = src_dph.flatten()\\n\\t\\n\\torder = np.random.permutation(np.arange(0,len(sim_flat)))\\n\\t\\n sim_flat = sim_flat[order]\\n\\tsim_err_flat = sim_err_flat[order]\\n\\tgrb_flat = grb_flat[order]\\n\\tbkgd_flat = bkgd_flat[order]\\n\\tsrc_flat = src_flat[order]\\n\\t\\n\\tprint \\\"No. of pixels with zero counts in sim_flat: \\\",sim_flat[sim_flat==0].size\\n\\tprint \\\"No. of pixels with zero counts in src_flat: \\\",src_flat[src_flat==0].size\\n\\t\\n\\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\\n\\tx = np.zeros(len(bins)+2,dtype=np.int64)\\n\\tx[0] = 0\\n\\tx[-1] = len(sim_flat)\\n\\tx[1:-1] = bins\\n\\t\\n\\t#print \\\"The bin edges: \\\",x # ---------------------------------------------------------------\\n\\t\\n\\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\\n\\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\t\\n\\tprint \\\"Total sim_flat_bin : \\\",sim_flat_bin.sum() #-----------------------------------------\\n\\t#print \\\" Max(cumsum) : \\\",max(np.cumsum(sim_flat)) #-----------------------------------------\\n\\n # Defining model background and data\\n model = sim_flat_bin #avg_flat_bin\\n bkgd = bkgd_flat_bin*t_src/t_tot\\n src = grb_flat_bin\\n\\t\\n data = src - bkgd\\n\\n err_src = np.sqrt(src)\\n err_bkgd = np.sqrt(bkgd_flat_bin)\\n err_model = sim_err_flat_bin\\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\t\\n\\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n # PLotting the comparison plots\\n fig = plt.figure()\\n plt.title(\\\"Comparison between simulated and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f} \\\".format(tx=thetax,ty=thetay,c=chi_sq_new))\\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation\\\",elinewidth=0.5)\\n plt.ylabel('Counts')\\n\\tplt.xlabel('Random Bins')\\n\\tplt.xticks(np.arange(0,(len(data)),1))\\n\\tplt.legend()\\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\\n\\n\\t# Plotting observed vs predicted counts------------------------------------------------------\\n\\n\\tfig = plt.figure()\\n\\tplt.title(grbdir + r\\\" : Observed vs Predicted counts with $\\\\chi^2$={cs:0.1f}\\\".format(cs=chi_sq))\\n\\tplt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\\n\\tfor i in range(len(model_copy)):\\n\\t\\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\\n\\tplt.plot(np.arange(-1000,1000),np.arange(-1000,1000),'k',linewidth=0.5)\\n\\tplt.xlim(min(model_copy)-5,max(model_copy)+5)\\n\\tplt.ylim(min(data_copy)-5,max(data_copy)+5)\\n\\tplt.xlabel('Predicted Counts')\\n\\tplt.ylabel('Observed Counts')\\n\\tplt.legend()\\n\\tplt.grid()\\n\\tpdf_file.savefig(fig)\\n\\n\\t# Scaling the model using curve fit =============================================================== \\n\\t\\n\\tparam,pcov = curve_fit(fit_line_int,model_copy,data_copy)\\n\\tscaling = param[0]\\n\\tintercept = param[1]\\n\\t\\n\\t# Plotting the scaled plots ===================================================================\\n\\t# Plotting the comparison graphs with equal bins ---------------------------------------\\n\\n\\tsim_dph = sim_dph*badpix_mask\\n\\tsim_err_dph = sim_err_dph*badpix_mask\\n grb_dph = grb_dph*badpix_mask\\n bkgd_dph = bkgd_dph*badpix_mask\\n\\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\\n\\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\\n\\n\\tsim_bin = resample(sim_dph,pixbin)\\n\\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\\t\\n\\tgrb_bin = resample(grb_dph,pixbin)\\n\\tbkgd_bin = resample(bkgd_dph,pixbin)\\n\\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\\t\\n\\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\\t\\n\\n\\tsim_flat_bin = sim_bin.flatten()\\n\\tsim_err_flat_bin = sim_err_bin.flatten()\\n\\tgrb_flat_bin = grb_bin.flatten()\\n\\tbkgd_flat_bin = bkgd_bin.flatten()\\n\\tgrb_err_flat_bin = grb_err_bin.flatten()\\n\\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\\n\\t\\n\\n\\t # Defining model background and data\\n\\t#model = sim_flat_bin*scaling\\n\\tmodel = sim_flat_bin*scaling + intercept\\n\\tbkgd = bkgd_flat_bin*t_src/t_tot\\n\\tsrc = grb_flat_bin\\n\\t\\n\\tdata = src - bkgd\\n\\t\\n\\terr_src = grb_err_flat_bin\\n\\terr_bkgd = bkgd_err_flat_bin\\n\\t#err_model = sim_err_flat_bin*scaling\\n\\terr_model = sim_err_flat_bin*scaling\\n\\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\t\\n\\tratio = data/model\\n\\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\\n\\t\\n\\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n\\t\\n\\t # PLotting the comparison plots\\n\\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\\n\\t\\n\\tax1.set_title(\\\"Comparison between simulated (scaled) and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f} \\\".format(tx=thetax,ty=thetay,c=chi_sq))\\n\\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n\\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation (scaling = {s:0.2f},offset = {o:0.2f})\\\".format(s=scaling,o=intercept),elinewidth=0.5)\\n\\tax1.legend()\\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\\n\\tax1.set_ylabel('Counts')\\n\\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n\\t\\n\\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\\\"Ratio = Data/Model(scaling = {s:0.2f}, offset={o:0.2f})\\\".format(s=scaling,o=intercept),elinewidth=0.5)\\n\\tax2.xaxis.set_ticks(np.arange(0,len(data)))\\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\\n\\tax2.tick_params(labelsize=5)\\n\\tax2.axhline(y=1,linewidth=0.5,color='k')\\n\\tax2.legend()\\n\\tax2.set_xlabel('CZT Modules')\\n\\tax2.set_ylabel('Ratio of counts')\\n\\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\\n\\tplt.tight_layout(h_pad=0.0)\\n\\tf.set_size_inches([6.5,10])\\n\\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\\n\\n\\t# Plotting comparison graphs with random binning------------------------------\\n\\t\\n sim_flat = sim_dph.flatten()\\n\\tsim_err_flat = sim_err_dph.flatten()\\n grb_flat = grb_dph.flatten()\\n bkgd_flat = bkgd_dph.flatten()\\n\\tsrc_flat = src_dph.flatten()\\n\\t\\n\\torder = np.random.permutation(np.arange(0,len(sim_flat)))\\n\\t\\n sim_flat = sim_flat[order]\\n\\tsim_err_flat = sim_err_flat[order]\\n\\tgrb_flat = grb_flat[order]\\n\\tbkgd_flat = bkgd_flat[order]\\n\\tsrc_flat = src_flat[order]\\n\\t\\n\\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\\n\\tx = np.zeros(len(bins)+2,dtype=np.int64)\\n\\tx[0] = 0\\n\\tx[-1] = len(sim_flat)\\n\\tx[1:-1] = bins\\n\\t\\n\\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\\n\\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\t\\n # Defining model background and data\\n #model = sim_flat_bin*scaling\\n\\tmodel = sim_flat_bin*scaling + intercept\\n bkgd = bkgd_flat_bin*t_src/t_tot\\n src = grb_flat_bin\\n\\t\\n data = src - bkgd\\n\\n err_src = np.sqrt(src)\\n err_bkgd = np.sqrt(bkgd_flat_bin)\\n #err_model = sim_err_flat_bin*scaling\\n\\terr_model = sim_err_flat_bin*scaling\\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\t\\n\\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n # PLotting the comparison plots\\n fig = plt.figure()\\n plt.title(\\\"Comparison between simulated(scaled) and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f} \\\".format(tx=thetax,ty=thetay,c=chi_sq_new))\\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation (scaling = {s:0.2f}, offset = {o:0.2f})\\\".format(s=scaling,o=intercept),elinewidth=0.5)\\n plt.ylabel('Counts')\\n\\tplt.xlabel('Random Bins')\\n\\tplt.xticks(np.arange(0,(len(data)),1))\\n\\tplt.legend()\\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\\n\\n\\n\\t# Plotting observed vs predicted counts--------------------------------------------------------\\n\\n\\tfig = plt.figure()\\n plt.title(grbdir + r\\\" : Observed vs Predicted counts with $\\\\chi^2$ = {cs:0.1f}\\\".format(cs=chi_sq))\\n plt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\\n\\tfor i in range(len(model_copy)):\\t\\n\\t\\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\\n #plt.plot(np.arange(-1000,1000),fit_line(np.arange(-1000,1000),scaling),'k',linewidth=0.5,label='m = {s:0.2f}'.format(s=scaling))\\n\\tplt.plot(np.arange(-1000,1000),fit_line_int(np.arange(-1000,1000),scaling,intercept),'k',linewidth=0.5,label='scaling = {s:0.2f}, offset = {i:0.2f}'.format(s=scaling,i=intercept))\\n\\tplt.plot(np.arange(min(model_copy)-5,max(model_copy)+5),np.ones(len(np.arange(min(model_copy)-5,max(model_copy)+5)))*intercept,'r-',label='intercept',linewidth=0.5)\\n plt.xlim(min(model_copy)-5,max(model_copy)+5)\\n plt.ylim(min(data_copy)-5,max(data_copy)+5)\\n plt.xlabel('Predicted Counts')\\n plt.ylabel('Observed Counts')\\n\\tplt.legend()\\n\\tplt.grid()\\n pdf_file.savefig(fig)\\n\\t\\t\\n\\tprint \\\"===============================================================================================\\\"\\n\\t\\n\\treturn\",\n \"def main(config_file, pre_nfiles=None, post_nfiles=None,\\n cutoffs=[12.0, 60.0]):\\n imu_complement_fig = (config_file[:-4] +\\n \\\"_IMU_complementary_spectra.pdf\\\")\\n imu_spectra_fig = (config_file[:-4] + \\\"_IMU_spectra.pdf\\\")\\n nperseg1 = 60 * 5 * 10\\n nperseg2 = 60 * 10\\n fig, axs, leg = check.plot_IMU_euler_angles_spectra(config_file,\\n nperseg=nperseg2,\\n nfiles=pre_nfiles)\\n fig.savefig(imu_complement_fig, bbox_extra_artists=(leg,),\\n bbox_inches=\\\"tight\\\")\\n plt.close(fig)\\n fig_rate, _, fig_acc, _ = check.plot_IMU_spectra(config_file,\\n nperseg=nperseg2,\\n nfiles=pre_nfiles)\\n with PdfPages(imu_spectra_fig) as pdf:\\n pdf.savefig(fig_rate, bbox_inches=\\\"tight\\\")\\n plt.close(fig_rate)\\n pdf.savefig(fig_acc, bbox_inches=\\\"tight\\\")\\n plt.close(fig_acc)\\n\\n # Look at individual periods\\n config = db_flux.parse_config(config_file)\\n input_files = config[\\\"EC Inputs\\\"][\\\"input_files\\\"]\\n if post_nfiles is not None and post_nfiles < len(input_files):\\n input_files = np.random.choice(input_files, post_nfiles,\\n replace=False)\\n imu2anem_pos = config[\\\"EC Motion Correction\\\"][\\\"imu2anemometer_pos\\\"]\\n sample_freq_hz = config[\\\"EC Inputs\\\"][\\\"sample_frequency\\\"]\\n Tcf, Ta = cutoffs\\n for ifile in input_files:\\n file_idx = ifile.index(ifile)\\n ec_prep, _ = db_flux.prepare_period(ifile, config)\\n wind = ec_prep[[\\\"wind_speed_u\\\", \\\"wind_speed_v\\\", \\\"wind_speed_w\\\"]]\\n acceleration = ec_prep[[\\\"acceleration_x\\\",\\n \\\"acceleration_y\\\",\\n \\\"acceleration_z\\\"]]\\n rate = ec_prep[[\\\"rate_x\\\", \\\"rate_y\\\", \\\"rate_z\\\"]]\\n ec_corr = db_flux.wind3D_correct(wind.values, acceleration.values,\\n rate.values,\\n ec_prep.heading.values,\\n ec_prep.speed_over_ground.values,\\n imu2anem_pos, sample_freq_hz,\\n Tcf, Ta)\\n fig, _, leg = check.plot1_IMU_complementary_spectra(config_file,\\n file_idx,\\n Tcf=Tcf,\\n Ta=Ta,\\n nperseg=nperseg1)\\n fig.savefig((ifile[:-4] + \\\"_euler_angle_spectra.pdf\\\"),\\n bbox_extra_artists=(leg,), bbox_inches=\\\"tight\\\")\\n plt.close()\\n fig, axs, leg = check.plot_wind3D_spectra(wind.values,\\n ec_corr.uvw_ship,\\n nperseg=nperseg2)\\n fig.savefig((ifile[:-4] + \\\"_uvw_spectra.pdf\\\"),\\n bbox_extra_artists=(leg,), bbox_inches=\\\"tight\\\")\\n plt.close()\\n # Plot corrected wind cospectra\\n fig, _, leg = check.plot_wind3D_cospectra(wind.values,\\n ec_corr.uvw_ship,\\n nperseg=nperseg2)\\n fig.savefig((ifile[:-4] + \\\"_uvw_cospectra.pdf\\\"),\\n bbox_extra_artists=(leg,), bbox_inches=\\\"tight\\\")\\n plt.close()\",\n \"def make_badge(VERBOSE=1, FILENAME=\\\"GENERATED_BADGE.png\\\", DeviceDescriptionsDict={}, CreatureDescriptionsDict={},\\r\\n OrdinaryDescriptionsDict={}, textfile=None, USESTAINS=1, SAVETOFILE=1, SILENT=0, GRAPHICSMODE=\\\"PIL\\\"):\\r\\n\\r\\n TEST = 1\\r\\n\\r\\n if USESTAINS == 1:\\r\\n var1 = random.choice((\\\"Colour\\\", \\\"Colour\\\", \\\"Colour\\\", \\\"Metal\\\", \\\"Metal\\\", \\\"Stain\\\"))\\r\\n else:\\r\\n var1 = random.choice((\\\"Colour\\\", \\\"Colour\\\", \\\"Colour\\\", \\\"Metal\\\", \\\"Metal\\\"))\\r\\n if var1 == \\\"Metal\\\":\\r\\n color1Dict = getMetal()\\r\\n color1 = color1Dict[\\\"Name\\\"]\\r\\n color1name = color1Dict[\\\"Colour\\\"]\\r\\n color2Dict = getColour()\\r\\n color2 = color2Dict[\\\"Name\\\"]\\r\\n color2name = color2Dict[\\\"Colour\\\"]\\r\\n elif var1 == \\\"Colour\\\":\\r\\n color1Dict = getColour()\\r\\n color1 = color1Dict[\\\"Name\\\"]\\r\\n color1name = color1Dict[\\\"Colour\\\"]\\r\\n color2Dict = getMetal()\\r\\n color2 = color2Dict[\\\"Name\\\"]\\r\\n color2name = color2Dict[\\\"Colour\\\"]\\r\\n else:\\r\\n color1Dict = getStain()\\r\\n color1 = color1Dict[\\\"Name\\\"]\\r\\n color1name = color1Dict[\\\"Colour\\\"]\\r\\n color2Dict = getMetal()\\r\\n color2 = color2Dict[\\\"Name\\\"]\\r\\n color2name = color2Dict[\\\"Colour\\\"]\\r\\n\\r\\n if VERBOSE > 0:\\r\\n print\\r\\n print \\\"color1:\\\\t%s\\\\t(%s)\\\" % (color1, color1name)\\r\\n print \\\"color2:\\\\t%s\\\\t(%s)\\\" % (color2, color2name)\\r\\n\\r\\n# if os.path.isdir(\\\"game\\\"):\\r\\n# os.chdir(\\\"game\\\")\\r\\n\\r\\n## if string.split(os.getcwd(), os.path.sep)[-1] != \\\"game\\\":\\r\\n## if VERBOSE > 0:\\r\\n## print \\\"changing directory FROM '%s'\\\" % os.getcwd()\\r\\n## while string.split(os.getcwd(), os.path.sep)[-1] != \\\"game\\\":\\r\\n## os.chdir(\\\"..\\\")\\r\\n## if VERBOSE > 0:\\r\\n## print \\\"changed directory TO '%s'\\\" % os.getcwd()\\r\\n\\r\\n rootdir = os.getcwd()\\r\\n\\r\\n # special fiddle for when being run from the Renpy launcher\\r\\n if string.find(rootdir, \\\"renpy-6.99.14.3-sdk\\\") > -1:\\r\\n #os.chdir(\\\"E:\\\\Projects-DISTROS\\\\Random_Heraldry_Generator-%s-win\\\\Random_Heraldry_Generator-%s-win\\\" % (__VERSION__, __VERSION__))\\r\\n os.chdir(\\\"E:\\\\Projects\\\\NEW RENPY GAME\\\\game\\\")\\r\\n rootdir = os.getcwd()\\r\\n\\r\\n badgesdir = os.path.join(rootdir, \\\"images\\\", \\\"badges\\\")\\r\\n #TEMPDIR = os.path.join(rootdir, \\\"..\\\", \\\"TEMP\\\")\\r\\n TEMPDIR = os.path.join(rootdir, \\\"TEMP\\\")\\r\\n\\r\\n creaturesdir = os.path.join(badgesdir, \\\"Creatures\\\")\\r\\n devicesdir = os.path.join(badgesdir, \\\"Devices\\\")\\r\\n #print devicesdir\\r\\n ordinariesdir = os.path.join(badgesdir, \\\"Ordinaries\\\")\\r\\n shieldsdir = os.path.join(badgesdir, \\\"Shields\\\")\\r\\n\\r\\n badgeType = random.choice((\\\"Creature\\\", \\\"Device\\\", \\\"Ordinary\\\", \\\"Quartered\\\"))\\r\\n\\r\\n if badgeType == \\\"Creature\\\":\\r\\n if VERBOSE > 0:\\r\\n print \\\"badgeType: Creature\\\"\\r\\n os.chdir(creaturesdir)\\r\\n blazon, badge = make_Creature_Badge(color1Dict, color2Dict, VERBOSE, CreatureDescriptionsDict, SILENT, GRAPHICSMODE)\\r\\n #will make more use of the blazon later...\\r\\n if VERBOSE > 0:\\r\\n print \\\" BLAZON:\\\"\\r\\n print \\\" '%s'\\\" % blazon\\r\\n elif badgeType == \\\"Device\\\":\\r\\n if VERBOSE > 0:\\r\\n print \\\"badgeType: Device\\\"\\r\\n os.chdir(devicesdir)\\r\\n blazon, badge = make_Device_Badge(color1Dict, color2Dict, VERBOSE, DeviceDescriptionsDict, SILENT, GRAPHICSMODE)\\r\\n #will make more use of the blazon later...\\r\\n if VERBOSE > 0:\\r\\n print \\\" BLAZON:\\\"\\r\\n print \\\" '%s'\\\" % blazon\\r\\n elif badgeType == \\\"Ordinary\\\":\\r\\n if VERBOSE > 0:\\r\\n print \\\"badgeType: Ordinary\\\"\\r\\n os.chdir(ordinariesdir)\\r\\n blazon, badge = make_Ordinary_Badge(color1Dict, color2Dict, VERBOSE, OrdinaryDescriptionsDict, SILENT, GRAPHICSMODE)\\r\\n #will make more use of the blazon later...\\r\\n if VERBOSE > 0:\\r\\n print \\\" BLAZON:\\\"\\r\\n print \\\" '%s'\\\" % blazon\\r\\n elif badgeType == \\\"Quartered\\\":\\r\\n if VERBOSE > 0:\\r\\n print \\\"badgeType: Quartered\\\"\\r\\n os.chdir(ordinariesdir)\\r\\n blazon, badge = make_Quartered_Badge(color1Dict, color2Dict, VERBOSE, OrdinaryDescriptionsDict,\\r\\n DeviceDescriptionsDict, SILENT, GRAPHICSMODE)\\r\\n if VERBOSE > 0:\\r\\n print \\\" BLAZON:\\\"\\r\\n print \\\" '%s'\\\" % blazon\\r\\n if textfile != None:\\r\\n if SAVETOFILE == 1:\\r\\n textfile.write(\\\"%s\\\\n\\\" % FILENAME)\\r\\n textfile.write(\\\"\\\\t%s\\\\n\\\\n\\\" % blazon)\\r\\n\\r\\n if SAVETOFILE == 1:\\r\\n if GRAPHICSMODE == \\\"PIL\\\":\\r\\n badge.save(os.path.join(TEMPDIR, FILENAME))\\r\\n #badge.save(os.path.join(rootdir, \\\"images\\\", \\\"temp\\\", FILENAME))\\r\\n elif GRAPHICSMODE == \\\"PyGame\\\":\\r\\n pygame.image.save(badge, os.path.join(TEMPDIR, FILENAME))\\r\\n #pygame.image.save(badge, os.path.join(rootdir, \\\"images\\\", \\\"temp\\\", FILENAME))\\r\\n if VERBOSE > 0:\\r\\n print \\\"\\\\twrote file '%s'\\\" % os.path.join(TEMPDIR, FILENAME)\\r\\n #print \\\"\\\\twrote file '%s'\\\" % os.path.join(rootdir, \\\"images\\\", \\\"temp\\\", FILENAME)\\r\\n print\\r\\n\\r\\n os.chdir(rootdir)\\r\\n\\r\\n return blazon, badge\",\n \"def run_image_viewer( self ):\\n\\n # XXX: hardcoded program name and image size.\\n subprocess.Popen( [\\\"feh\\\", \\\"-dZ\\\", \\\"-g\\\", \\\"800x600\\\", self.record[\\\"filename\\\"]] )\",\n \"def singleHierarchy(config_path, model_path, image_path, cuda, crf, sizeThresh=1/9, nIterations=10, doPlot=True):\\r\\n\\r\\n # Setup\\r\\n CONFIG = OmegaConf.load(config_path)\\r\\n device = get_device(cuda)\\r\\n torch.set_grad_enabled(False)\\r\\n\\r\\n classes = get_classtable(CONFIG)\\r\\n postprocessor = setup_postprocessor(CONFIG) if crf else None\\r\\n\\r\\n model = eval(CONFIG.MODEL.NAME)(n_classes=CONFIG.DATASET.N_CLASSES)\\r\\n state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)\\r\\n model.load_state_dict(state_dict)\\r\\n model.eval()\\r\\n model.to(device)\\r\\n print(\\\"Model:\\\", CONFIG.MODEL.NAME)\\r\\n\\r\\n # Inference\\r\\n image = cv2.imread(image_path, cv2.IMREAD_COLOR)\\r\\n image, raw_image = preprocessing(image, device, CONFIG)\\r\\n # labelmap = inference(model, image, raw_image, postprocessor)\\r\\n labelmapList = inferenceHierarchy(model, image, raw_image, postprocessor, sizeThresh, nIterations)\\r\\n if doPlot:\\r\\n for labelmap in labelmapList:\\r\\n labels = np.unique(labelmap)\\r\\n \\r\\n # Show result for each class\\r\\n rows = np.floor(np.sqrt(len(labels) + 1))\\r\\n cols = np.ceil((len(labels) + 1) / rows)\\r\\n \\r\\n plt.figure(figsize=(10, 10))\\r\\n ax = plt.subplot(rows, cols, 1)\\r\\n ax.set_title(\\\"Input image\\\")\\r\\n ax.imshow(raw_image[:, :, ::-1])\\r\\n ax.axis(\\\"off\\\")\\r\\n \\r\\n for i, label in enumerate(labels):\\r\\n mask = labelmap == label\\r\\n ax = plt.subplot(rows, cols, i + 2)\\r\\n ax.set_title(classes[label])\\r\\n ax.imshow(raw_image[..., ::-1])\\r\\n ax.imshow(mask.astype(np.float32), alpha=0.5)\\r\\n ax.axis(\\\"off\\\")\\r\\n \\r\\n plt.tight_layout()\\r\\n plt.show()\",\n \"def main():\\n os.makedirs(\\\"./img/event_generated/\\\", exist_ok=True)\\n for category in categories():\\n render_icon(category + \\\".png\\\")\",\n \"def image_processor(file_name, variations, storage):\\n process_photo_image.delay(file_name, variations)\\n return False # prevent default rendering\",\n \"def test_config():\\n import yaml\\n import astropy.units as u\\n from tqdm import tqdm\\n # Need these for `eval` below\\n from numpy import array\\n\\n # Same test suite as used in test_imsim above.\\n # This time, we just use this for the det names.\\n with open(DATA_DIR / \\\"wcs_466749.yaml\\\", 'r') as f:\\n wcss = yaml.safe_load(f)\\n\\n cmds = {}\\n with open(DATA_DIR / \\\"phosim_cat_466749.txt\\\", 'r') as f:\\n for line in f:\\n k, v = line.split()\\n try:\\n v = int(v)\\n except ValueError:\\n try:\\n v = float(v)\\n except ValueError:\\n pass\\n cmds[k] = v\\n\\n # Values below (and others) from phosim_cat_466749.txt\\n rc = cmds['rightascension']\\n dc = cmds['declination']\\n boresight = galsim.CelestialCoord(\\n rc*galsim.degrees,\\n dc*galsim.degrees\\n )\\n obstime = Time(cmds['mjd'], format='mjd', scale='utc')\\n obstime -= 15*u.s\\n band = \\\"ugrizy\\\"[cmds['filter']]\\n wavelength_dict = dict(\\n u=365.49,\\n g=480.03,\\n r=622.20,\\n i=754.06,\\n z=868.21,\\n y=991.66\\n )\\n wavelength = wavelength_dict[band]\\n camera = imsim.get_camera()\\n\\n rotTelPos = cmds['rottelpos'] * galsim.degrees\\n telescope = imsim.load_telescope(f\\\"LSST_{band}.yaml\\\", rotTelPos=rotTelPos)\\n # Non-default values.\\n temperature = 293.\\n pressure = 69.0\\n H2O_pressure = 2.0\\n\\n factory = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope, wavelength,\\n camera,\\n temperature=temperature,\\n pressure=pressure,\\n H2O_pressure=H2O_pressure\\n )\\n\\n config = {\\n 'input': {\\n 'telescope': {\\n 'file_name':f\\\"LSST_{band}.yaml\\\",\\n 'rotTelPos': rotTelPos\\n }\\n },\\n 'image': {\\n 'wcs': {\\n 'type': 'Batoid',\\n 'boresight': boresight,\\n 'camera': 'LsstCam',\\n 'obstime': obstime,\\n 'wavelength': wavelength,\\n 'temperature': temperature,\\n 'pressure': pressure,\\n 'H2O_pressure': H2O_pressure,\\n 'order': 2,\\n }\\n }\\n }\\n\\n rng = np.random.default_rng(1234)\\n for k in tqdm(wcss.keys()):\\n name = k[18:25].replace('-', '_')\\n det = camera[name]\\n\\n wcs1 = factory.getWCS(det, order=2)\\n config['image']['wcs']['det_name'] = name\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n galsim.config.ProcessInput(config)\\n wcs2 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n\\n # Test points\\n xs = rng.uniform(0, 4000, 100)\\n ys = rng.uniform(0, 4000, 100)\\n ra1, dec1 = wcs1.xyToradec(xs, ys, units='radians')\\n ra2, dec2 = wcs2.xyToradec(xs, ys, units='radians')\\n np.testing.assert_allclose(ra1, ra2)\\n np.testing.assert_allclose(dec1, dec2)\\n\\n # Test == when identical\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n wcs3 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs3 == wcs2\\n\\n # Test that pressure and temperature matter.\\n config['image']['wcs']['temperature'] = 250\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n wcs4 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs4 != wcs2\\n\\n config['image']['wcs']['temperature'] = temperature\\n config['image']['wcs']['pressure'] = 55\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n wcs5 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs5 != wcs2\\n\\n config['image']['wcs']['pressure'] = pressure\\n config['image']['wcs']['H2O_pressure'] = 10\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n wcs6 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs6 != wcs2\\n\\n # Test defaults\\n del config['image']['wcs']['temperature']\\n del config['image']['wcs']['pressure']\\n del config['image']['wcs']['H2O_pressure']\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n config = galsim.config.CleanConfig(config)\\n galsim.config.ProcessInput(config)\\n wcs7 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n default_pressure = 101.325 * (1-2.25577e-5*2715)**5.25588\\n wcs7a = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope, wavelength, camera,\\n temperature=280,\\n pressure=default_pressure,\\n H2O_pressure=1.0,\\n ).getWCS(det, order=2)\\n assert wcs7 == wcs7a\\n\\n # Default wavelength from bandpass\\n del config['image']['wcs']['wavelength']\\n config['bandpass'] = imsim.RubinBandpass('r')\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n config = galsim.config.CleanConfig(config)\\n galsim.config.ProcessInput(config)\\n wcs8 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n wcs8a = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope,\\n wavelength=config['bandpass'].effective_wavelength,\\n camera=camera,\\n temperature=280,\\n pressure=default_pressure,\\n H2O_pressure=1.0,\\n ).getWCS(det, order=2)\\n assert wcs8 == wcs8a\\n\\n del config['bandpass']\\n config['image']['bandpass'] = {\\n 'type': 'RubinBandpass',\\n 'band' : 'r',\\n }\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n config = galsim.config.CleanConfig(config)\\n galsim.config.ProcessInput(config)\\n wcs8b = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n assert wcs8b == wcs8a\\n\\n # Obstime can be a string\\n print('obstime = ',obstime.to_value('iso'), type(obstime.to_value('iso')))\\n config['image']['wcs']['obstime'] = obstime.to_value('iso')\\n # Doesn't quite roundtrip perfectly. But within a millisecond.\\n obstime = Time(obstime.to_value('iso'), scale='tai')\\n print('obstime => ',obstime)\\n galsim.config.RemoveCurrent(config['image']['wcs'])\\n config = galsim.config.CleanConfig(config)\\n galsim.config.ProcessInput(config)\\n wcs9 = galsim.config.BuildWCS(config['image'], 'wcs', config)\\n wcs9a = imsim.BatoidWCSFactory(\\n boresight, obstime, telescope,\\n wavelength=config['bandpass'].effective_wavelength,\\n camera=camera,\\n temperature=280,\\n pressure=default_pressure,\\n H2O_pressure=1.0,\\n ).getWCS(det, order=2)\\n assert wcs9 == wcs9a\",\n \"def display_sample(display_list):\\n fig, axs = plt.subplots(1, len(display_list), figsize=(18, 18))\\n title = ['Input Image', 'True Mask', 'Predicted Mask']\\n for i in range(len(display_list)):\\n axs[i].set_title(title[i])\\n axs[i].imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))\\n axs[i].axis('off')\",\n \"def instructions():\\n txt_naming = ConfigReader.texture_naming_dict()\\n\\n text = \\\"<b>Texture naming rules:</b><br>(put an underscore _ at the end of file name; \\\" \\\\\\n \\\"you can enumerate textures using two digits after texture type without any other character\\\" \\\\\\n \\\"<br> e.g. _normal01 or in general _normalXX)\\\" \\\\\\n \\\"<br>\\\"\\n\\n for key, value in txt_naming.iteritems():\\n text += \\\"<br>- {0}: {1}\\\".format(key, ', '.join(a for a in value['text']))\\n\\n text += \\\"<br>\\\"\\n text += \\\"<br><b>File formats:</b>\\\"\\n text += \\\"<br>Meshes:\\\"\\n text += ConfigReader.generate_file_filter()\\n text += \\\"<br>Textures:\\\"\\n text += ConfigReader.generate_texture_filter()\\n\\n return text\",\n \"def print_image(indiv,name):\\n routine = gp.compile(indiv,pset)\\n output = gen_beat_output(routine)\\n bits = np.array(map(bitlist,output)[0:24000]).transpose()\\n plt.style.use('classic')\\n plt.imshow(bits,interpolation='nearest',aspect='auto',cmap=plt.get_cmap('Greys'))\\n plt.savefig(name+\\\".png\\\",dpi=150)\",\n \"def plot_generated_images(decoder):\\n\\n # display a nxn 2D manifold of digits\\n n = 15\\n digit_size = 28\\n\\n figure = np.zeros((digit_size * n, digit_size * n))\\n # linearly spaced coordinates corresponding to the 2D plot\\n # of digit classes in the latent space\\n grid_x = np.linspace(-4, 4, n)\\n grid_y = np.linspace(-4, 4, n)[::-1]\\n\\n # start sampling z1 and z2 in the ranges grid_x and grid_y\\n for i, yi in enumerate(grid_y):\\n for j, xi in enumerate(grid_x):\\n z_sample = np.array([[xi, yi]])\\n x_decoded = decoder.predict(z_sample)\\n digit = x_decoded[0].reshape(digit_size, digit_size)\\n slice_i = slice(i * digit_size, (i + 1) * digit_size)\\n slice_j = slice(j * digit_size, (j + 1) * digit_size)\\n figure[slice_i, slice_j] = digit\\n\\n # plot the results\\n plt.figure(figsize=(6, 5))\\n start_range = digit_size // 2\\n end_range = n * digit_size + start_range + 1\\n pixel_range = np.arange(start_range, end_range, digit_size)\\n sample_range_x = np.round(grid_x, 1)\\n sample_range_y = np.round(grid_y, 1)\\n plt.xticks(pixel_range, sample_range_x)\\n plt.yticks(pixel_range, sample_range_y)\\n plt.xlabel(\\\"z[0]\\\")\\n plt.ylabel(\\\"z[1]\\\")\\n plt.imshow(figure, cmap='Greys_r')\\n plt.show()\",\n \"def main():\\n\\n # first figure: betas for each predictor\\n fig, axes = plt.subplots(figsize=(8, 18), nrows=3)\\n\\n image_paths = {\\n \\\"3T\\\": (\\n f\\\"{PATHS['figures_misc']}/figure_3_images/\\\"\\n f\\\"C1051_20161006_childVSall_depth_1.png\\\"\\n ),\\n \\\"7T\\\": (\\n f\\\"{PATHS['figures_misc']}/figure_3_images/\\\"\\n f\\\"C1051_20160212_childVSall_depth_1_sim2pt4.png\\\"\\n ),\\n \\\"7T_noise\\\": (\\n f\\\"{PATHS['figures_misc']}/figure_3_images/\\\"\\n f\\\"C1051_20160212_childVSall_depth_1_sim2pt4_noise.png\\\"\\n ),\\n }\\n\\n for ax, (_, image_path) in zip(axes, image_paths.items()):\\n assert os.path.isfile(image_path)\\n img = imread(image_path)\\n img[np.where(np.sum(img, axis=2) == 0.0)] = 1.0\\n\\n ax.imshow(img[200:-100, 50:-50, :])\\n ax.axis(\\\"off\\\")\\n\\n savefig(f\\\"{PATHS['figures']}/figure_3ac.png\\\")\\n plt.close(fig)\",\n \"def __call__(cls, nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit):\\n\\n# on with the show\\n logger.info('Opening science and confidence frames')\\n ifits=fitsio.FITS(nir_paw_image_fname,'r')\\n cfits=fitsio.FITS(nir_paw_conf_fname,'r')\\n\\n#\\n# Check that the number of HDUs match\\n#\\n\\n if (len(ifits) != len(cfits)):\\n print(\\\"Number of HDUs/extensions in IMAGE and CONFidence files do not match.\\\")\\n print(\\\"Aborting\\\")\\n exit(1)\\n\\n p_ih=ifits[0].read_header()\\n p_ch=cfits[0].read_header()\\n# Remove reserve keywords\\n p_ih.clean()\\n\\n#\\n# Extract some keywords from PRIMARY header to propagate into the individual images.\\n#\\n base_dict={}\\n base_header=[]\\n for hkeep in nci.nir_paw_primary_keep:\\n if (hkeep in p_ih):\\n base_header.append({'name':hkeep,'value':p_ih[hkeep],'comment':p_ih.get_comment(hkeep)})\\n base_dict[hkeep]={'value':p_ih[hkeep],'comment':p_ih.get_comment(hkeep)}\\n else:\\n print(\\\"Keyword {:s} missing in HDU[{:d}]\\\".format(hkeep,0))\\n#\\n# If possible, need too keep track of REQTIME (requested frametime) because sometimes \\n# EXPTIME seems to be mispopulated in the CCD image HDUs with TEXPTIME\\n#\\n if ('TEXPTIME' in p_ih):\\n texptime=p_ih['TEXPTIME']\\n else:\\n texptime=None\\n if ('REQTIME' in p_ih):\\n reqtime=p_ih['REQTIME']\\n else:\\n reqtime=None\\n#\\n# print(base_header)\\n \\n\\n#\\n# Step through HDUs... and form \\\"CCD\\\" images for each HDU\\n#\\n ExtList=[]\\n for hnum in range(1,len(ifits)):\\n print(\\\"############ Begin work on extnum={:d} ###############\\\".format(hnum))\\n\\n# Check that extensions match (after that concentrate on image).\\n print(hnum,ifits[hnum].get_extname(),cfits[hnum].get_extname())\\n if (ifits[hnum].get_extname() != cfits[hnum].get_extname()):\\n print(\\\"Working on extension {:d}. Extension names (image,conf) of ([{:s}],[{:s}]) do not match!\\\".format(\\n hnum,ifits[hnum].get_extname(),cfits[hnum].get_extname()))\\n print(\\\"Aborting!\\\")\\n exit(1)\\n\\n f_ih=ifits[hnum].read_header()\\n f_ih.clean()\\n#\\n# Fix occurences where the CCD-level keyword EXPTIME has inherited the value of TEXPTIME\\n#\\n exptime=f_ih['EXPTIME']\\n if (reqtime is not None):\\n if (exptime > reqtime):\\n print(\\\"Warning: possible corrupt EXPTIME (total exptime rather than frame time present).\\\")\\n print(\\\"Attempting to update EXTIME to REQTIME (requested frame time).\\\")\\n print(\\\" Primary HDU: TEXPTIME: {:}\\\".format(texptime))\\n print(\\\" Primary HDU: REQTIME: {:}\\\".format(reqtime))\\n print(\\\" Current HDU: EXPTIME: {:} --> {:}\\\".format(exptime,reqtime))\\n exptime=reqtime\\n f_ih['EXPTIME']=reqtime\\n#\\n# Augment keywords pulled from primary header with keywords from current HDU\\n#\\n c_header=base_header[:]\\n c_dict=dict(base_dict)\\n for hkeep in nci.nir_paw_hdu_keep:\\n if (hkeep in f_ih):\\n# print(hkeep,f_ih[hkeep],f_ih.get_comment(hkeep))\\n c_header.append({'name':hkeep,'value':f_ih[hkeep],'comment':f_ih.get_comment(hkeep)})\\n if (hkeep in c_dict):\\n print(\\\"Warning: Replacing keyword {:s} with value from hdu={:d}\\\".format(hkeep,hnum))\\n c_dict[hkeep]={'value':f_ih[hkeep],'comment':f_ih.get_comment(hkeep)}\\n else:\\n print(\\\"Keyword {:s} missing in HDU[{:d}]\\\".format(hkeep,hnum))\\n\\n#\\n# Get the CCDNUM from special keyword and propagate\\n# Get SKYLEVEL, SKYNOISE, ZEROPOINT and form basis value for the weight plane\\n#\\n ccdnum=f_ih['HIERARCH ESO DET CHIP NO']\\n c_header.append({'name':'CCDNUM','value':ccdnum,'comment':'Unique Detector Number'})\\n\\n# exptime=f_ih['EXPTIME']\\n## Fix occurences where the CCD-level keyword EXPTIME has inherited the value of TEXPTIME\\n# if (exptime > reqtime):\\n# print(\\\"Warning: possible corrupt EXPTIME (total exptime rather than frame time present).\\\")\\n# print(\\\"Attempting to update EXTIME to REQTIME (requested frame time).\\\")\\n# print(\\\" Primary HDU: TEXPTIME: {:.2f}\\\".format(texptime))\\n# print(\\\" Primary HDU: REQTIME: {:.2f}\\\".format(reqtime))\\n# print(\\\" Current HDU: EXPTIME: {:.2f} --> {:.2f}\\\".format(exptime,reqtime))\\n# exptime=reqtime\\n# f_ih['EXPTIME']=reqtime\\n\\n mtime=2.5*np.log10(exptime)\\n skylev=f_ih['SKYLEVEL']\\n skyrms=f_ih['SKYNOISE']\\n seeing=f_ih['SEEING']\\n magzpt=f_ih['MAGZPT']\\n\\n# zeropoint include a correction from VEGA->AB\\n# zeropoint in headers was found to have a factor for EXPTIME removed (have to add back in for DES-like processing)\\n\\n if (p_ih['BAND'] in nci.nir_vega_to_ab):\\n magzpt=magzpt+nci.nir_vega_to_ab[p_ih['BAND']]+mtime\\n else:\\n print(\\\"Warning! Unknown BAND ({:s}) for conversion of zeropoint from VEGA to AB system\\\".format(p_ih['BAND']))\\n\\n c_header.append({'name':'SKYBRITE', 'value':skylev, 'comment':'Sky level estimate from IMCORE'})\\n c_header.append({'name':'SKYSIGMA', 'value':skyrms, 'comment':'Sky noise estimate from IMCORE'})\\n c_header.append({'name':'SKYVARA', 'value':skyrms*skyrms, 'comment':'Sky noise estimate from IMCORE'})\\n c_header.append({'name':'SKYVARB', 'value':skyrms*skyrms, 'comment':'Sky noise estimate from IMCORE'})\\n c_header.append({'name':'FWHM', 'value':seeing, 'comment':'Average FWHM (pixels)'})\\n c_header.append({'name':'MAG_ZERO', 'value':magzpt, 'comment':'Converted MAGZPT(Vega) to AB system'})\\n nite_val=convert_utc_str_to_nite(f_ih['DATE-OBS'])\\n c_header.append({'name':'NITE', 'value':nite_val, 'comment':'Observation Nite'})\\n c_header.append({'name':'SATURATE', 'value':nci.nircam_satval[ccdnum], 'comment': 'Saturation Level (ADU)'})\\n c_header.append({'name':'PIXSCAL1', 'value':0.341, 'comment': 'Fiducial pixel scale (arcsec/pix)'})\\n c_header.append({'name':'PIXSCAL2', 'value':0.341, 'comment': 'Fiducial pixel scale (arcsec/pix)'})\\n\\n# bval=f_ih['BSCALE']\\n# print(\\\"BSCALE was: \\\",bval)\\n print(\\\"SKYLEVEL was: \\\",skylev)\\n print(\\\"SKYRMS was: \\\",skyrms)\\n#\\n# Searching for a proper WGT prescription\\n#\\n# This was what I took to be equivalent to DES (but perhaps it does not properly factor in N-image stack\\n# wgtval=skylev+(skyrms*skyrms)\\n print(\\\"SKYLEV + (SKYRMS*SKYRMS): \\\",skylev+(skyrms*skyrms))\\n#\\n# This was assuming SKYLEVEL does not properly inform stats\\n# wgtval=(skyrms*skyrms)\\n print(\\\"(SKYRMS*SKYRMS): \\\",skyrms*skyrms)\\n\\n#\\n# Read the image data from the science and confidence files.\\n#\\n sci_data=ifits[hnum].read()\\n print(\\\"Median of data {:.3f} \\\".format(np.median(sci_data)))\\n conf_data=cfits[hnum].read()\\n\\n#\\n# Better seemed to be a re-measurement of STD\\n#\\n print(\\\"Attempting an improved SKYRMS with 3-sigma clip to remove objects\\\")\\n avgval, medval, stdval = medclip(sci_data,verbose=3)\\n# print(avgval,medval,stdval)\\n print(\\\"stdval^2: \\\",stdval*stdval)\\n wgtval=(stdval*stdval)\\n# print(wgtval)\\n#\\n# Use the new (i.e. chip-based header) to feed a WCS \\n# Use image size to feed calculations for center and corners (similar to despyastro.CCD_corners\\n#\\n print(\\\"Calculating center/corners assuuming native ZPN projection\\\")\\n w=WCS(fitsio.FITSHDR(c_header))\\n\\n fnax2=float(sci_data.shape[0])\\n fnax1=float(sci_data.shape[1])\\n corn_x=np.array([fnax1/2.0,1.,fnax1,fnax1,1.])\\n corn_y=np.array([fnax2/2.0,1.,1.,fnax2,fnax2])\\n sky = w.pixel_to_world(corn_x,corn_y)\\n corn_ra=sky.ra.degree\\n corn_dec=sky.dec.degree\\n\\n c_header.append({'name':'RA_CENT', 'value':corn_ra[0], 'comment':'RA center'})\\n c_header.append({'name':'DEC_CENT','value':corn_dec[0],'comment':'DEC center'})\\n for i in range(1,5):\\n c_header.append({'name':'RAC{:d}'.format(i), 'value':corn_ra[i], 'comment':'RA corner {:d}'.format(i)})\\n c_header.append({'name':'DECC{:d}'.format(i),'value':corn_dec[i],'comment':'DEC corner {:d}'.format(i)})\\n RACMIN, RACMAX, DECCMIN, DECCMAX, CROSSRA0 = get_DESDM_corners_extent(corn_ra, corn_dec)\\n c_header.append({'name':'RACMIN', 'value':RACMIN, 'comment':'Minimum extent of image in RA'})\\n c_header.append({'name':'RACMAX', 'value':RACMAX, 'comment':'Maximum extent of image in RA'})\\n c_header.append({'name':'DECCMIN', 'value':DECCMIN, 'comment':'Minimum extent of image in Declination'})\\n c_header.append({'name':'DECCMAX', 'value':DECCMAX, 'comment':'Maximum extent of image in Declination'})\\n c_header.append({'name':'CROSSRA0','value':CROSSRA0,'comment':'Does Image Span RA 0h (Y/N)'})\\n c_header.append({'name':'DESEPOCH','value':'NIREPOCH','comment':'Default DES epoch definition for including NIR data'})\\n#\\n#\\n#\\n print(\\\"Stripping ZPN projection from WCS and creating a shift to get a rough TAN\\\")\\n recs_to_delete=[] \\n for i, hrec in enumerate(c_header):\\n if (hrec['name'] == 'CTYPE1'):\\n c_header[i]['value']='RA---TAN'\\n if (hrec['name'] == 'CTYPE2'):\\n c_header[i]['value']='DEC--TAN'\\n\\n if (hrec['name'] == 'CRVAL1'):\\n c_header[i]['value']=corn_ra[0]\\n if (hrec['name'] == 'CRVAL2'):\\n c_header[i]['value']=corn_dec[0]\\n if (hrec['name'] == 'CRPIX1'):\\n c_header[i]['value']=fnax1/2.0\\n if (hrec['name'] == 'CRPIX2'):\\n c_header[i]['value']=fnax2/2.0\\n\\n if (hrec['name'] in ['PV2_1','PV2_2','PV2_3','PV2_4','PV2_5']):\\n recs_to_delete.append(i)\\n if (len(recs_to_delete) > 0):\\n for i in sorted(recs_to_delete,reverse=True):\\n x=c_header.pop(i)\\n print(\\\"Removing: {:}\\\".format(x))\\n\\n whack=WCS(fitsio.FITSHDR(c_header))\\n skyhack = whack.pixel_to_world(corn_x,corn_y)\\n whack_corn_ra=skyhack.ra.degree\\n whack_corn_dec=skyhack.dec.degree\\n for i in range(5):\\n cosdec=np.cos(corn_dec[i]*np.pi/180.)\\n dra=3600.*(corn_ra[i]-whack_corn_ra[i])*cosdec\\n ddec=3600.*(corn_dec[i]-whack_corn_dec[i])\\n print(\\\" WCS shift {:d} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} \\\".format(ccdnum,corn_ra[i],corn_dec[i],whack_corn_ra[i],whack_corn_dec[i],dra,ddec))\\n\\n# for i, hrec in enumerate(c_header):\\n# print(i,hrec)\\n\\n#\\n# Form the SCI, MSK, and WGT HDUs\\n#\\n im=DESImage(init_data=True,init_mask=True,init_weight=True,shape=sci_data.shape)\\n\\n im.data=np.float32(sci_data)\\n msk_wsm=np.where(conf_data<conf_limit)\\n im.mask[msk_wsm] |= BADPIX_BPM\\n im.weight=np.float32(conf_data/100./wgtval)\\n#\\n# Check for extra conditions where further masking is needed\\n# Here is where CCD=6 check was started (now removed and placed \\n# in nir_starmask to take advantage of bright object masking\\n#\\n\\n\\n#\\n# Deal with individual header-isms and write out SCI, MSK, WGT\\n# Note this is using fitsio (duplicating some of the DESIMAGE.save \\n# but customization was needed to deal with foibles of the current\\n#\\n fname=re.sub('%02d','{:02d}'.format(ccdnum),output_template,1)\\n ofits = fitsio.FITS(fname, 'rw', clobber=True)\\n\\n im.header=fitsio.FITSHDR(c_header) \\n im.header['DES_EXT']='IMAGE'\\n im.header = update_hdr_compression(im.header, 'SCI')\\n ofits.write(im.data,extname='SCI',header=im.header)\\n\\n\\n im.mask_hdr=fitsio.FITSHDR(c_header) \\n im.mask_hdr['DES_EXT']='MASK'\\n im.mask_hdr = update_hdr_compression(im.mask_hdr, 'MSK')\\n im.mask_hdr['DES_EXT']='MASK'\\n ofits.write(im.mask,extname='MSK',header=im.mask_hdr)\\n\\n# im.weight_hdr=fitsio.FITSHDR(c_header) \\n# print(im.weight_hdr)\\n im.weight_hdr = update_hdr_compression(im.weight_hdr, 'WGT')\\n# print(im.weight_hdr)\\n im.weight_hdr['DES_EXT']='WEIGHT'\\n ofits.write(im.weight,extname='WGT',header=im.weight_hdr)\\n\\n ofits.close()\\n print(\\\"Wrote {:s}\\\".format(fname))\\n print(\\\" \\\")\\n \\n\\n ifits.close()\\n cfits.close()\\n\\n ret_code = 0\\n return ret_code\",\n \"def labelled_config(infile, index):\\n with gsd.hoomd.open(infile) as trj:\\n snap = HoomdFrame(trj[index])\\n\\n fig = plot_labelled_config(snap)\\n\\n fig.output_backend = \\\"svg\\\"\\n export_svgs(fig, \\\"figures/labelled_config.png\\\", height=1600, width=3200)\",\n \"def generate_image_grid(sess, df, filenames,op, op2):\\n #x_points = np.arange(0, 1, 1.5).astype(np.float32)\\n #y_points = np.arange(0, 1, 1.5).astype(np.float32)\\n\\n nx, ny = 12, 1\\n #plt.subplot()\\n gs = gridspec.GridSpec(nx, ny, hspace=1, wspace=0.05)\\n # input_x = sess.run(op2, feed_dict={x_input: df[0:24]})\\n #\\n # plt.imshow(np.array(df[0].tolist()).reshape(28, 28), cmap='gray')\\n # plt.show()\\n # x = sess.run(op, feed_dict={decoder_input: input_x[0].reshape(1,2)})\\n # img = np.array(x.tolist()).reshape(28, 28)\\n #\\n # plt.imshow(img, cmap='gray')\\n # plt.show()\\n\\n \\\"\\\"\\\" grid \\\"\\\"\\\"\\n input_x = sess.run(op2, feed_dict={x_input: df[0:24]})\\n for i, g in enumerate(gs):\\n\\n x = sess.run(op, feed_dict={decoder_input: input_x[i].reshape(1,2)})\\n ax = plt.subplot(g)\\n img = np.array(x.tolist()).reshape(28, 28)\\n ax.imshow(img, cmap='gray')\\n ax.set_xticks([])\\n ax.set_yticks([])\\n #ax.set_aspect('auto')\\n ax.set_title(filenames[i])\\n plt.show()\\n\\n for i, g in enumerate(gs):\\n\\n ax = plt.subplot(g)\\n img = np.array(df[i].tolist()).reshape(28, 28)\\n ax.imshow(img, cmap='gray')\\n ax.set_xticks([])\\n ax.set_yticks([])\\n #ax.set_aspect('auto')\\n ax.set_title(filenames[i])\\n plt.show()\",\n \"def test_rhostats_config():\\n if __name__ == '__main__':\\n logger = piff.config.setup_logger(verbose=2)\\n else:\\n logger = piff.config.setup_logger(log_file='output/test_rhostats_config.log')\\n\\n image_file = os.path.join('output','test_stats_image.fits')\\n cat_file = os.path.join('output','test_stats_cat.fits')\\n psf_file = os.path.join('output','test_rhostats.fits')\\n rho_file = os.path.join('output','test_rhostats.pdf')\\n config = {\\n 'input' : {\\n 'image_file_name' : image_file,\\n 'cat_file_name' : cat_file,\\n 'stamp_size' : 48\\n },\\n 'psf' : {\\n 'model' : { 'type' : 'Gaussian',\\n 'fastfit': True,\\n 'include_pixel': False },\\n 'interp' : { 'type' : 'Mean' },\\n },\\n 'output' : {\\n 'file_name' : psf_file,\\n 'stats' : { # Note: stats doesn't have to be a list.\\n 'type': 'Rho',\\n 'file_name': rho_file,\\n 'min_sep': 30,\\n 'max_sep': 600,\\n 'sep_units': 'arcsec',\\n 'bin_type': 'Linear',\\n 'bin_size': 30,\\n }\\n },\\n }\\n piff.piffify(config, logger)\\n assert os.path.isfile(rho_file)\\n\\n # repeat with plotify function\\n os.remove(rho_file)\\n piff.plotify(config, logger)\\n assert os.path.isfile(rho_file)\\n\\n # Test rho statistics directly.\\n min_sep = 1\\n max_sep = 100\\n bin_size = 0.1\\n psf = piff.read(psf_file)\\n orig_stars, wcs, pointing = piff.Input.process(config['input'], logger)\\n stats = piff.RhoStats(min_sep=min_sep, max_sep=max_sep, bin_size=bin_size)\\n with np.testing.assert_raises(RuntimeError):\\n stats.write('dummy') # Cannot write before compute\\n stats.compute(psf, orig_stars)\\n\\n rhos = [stats.rho1, stats.rho2, stats.rho3, stats.rho4, stats.rho5]\\n for rho in rhos:\\n # Test the range of separations\\n radius = np.exp(rho.logr)\\n np.testing.assert_array_less(radius, max_sep)\\n np.testing.assert_array_less(min_sep, radius)\\n # bin_size is reduced slightly to get integer number of bins\\n assert rho.bin_size < bin_size\\n assert np.isclose(rho.bin_size, bin_size, rtol=0.1)\\n np.testing.assert_array_almost_equal(np.diff(rho.logr), rho.bin_size, decimal=5)\\n\\n # Test that the max absolute value of each rho isn't crazy\\n np.testing.assert_array_less(np.abs(rho.xip), 1)\\n\\n # # Check that each rho isn't precisely zero. This means the sum of abs > 0\\n np.testing.assert_array_less(0, np.sum(np.abs(rho.xip)))\\n\\n # Test using the piffify executable\\n os.remove(rho_file)\\n config['verbose'] = 0\\n with open('rho.yaml','w') as f:\\n f.write(yaml.dump(config, default_flow_style=False))\\n piffify_exe = get_script_name('piffify')\\n p = subprocess.Popen( [piffify_exe, 'rho.yaml'] )\\n p.communicate()\\n assert os.path.isfile(rho_file)\\n\\n # Test using the plotify executable\\n os.remove(rho_file)\\n plotify_exe = get_script_name('plotify')\\n p = subprocess.Popen( [plotify_exe, 'rho.yaml'] )\\n p.communicate()\\n assert os.path.isfile(rho_file)\\n\\n # test running plotify with dir in config, with no logger, and with a modules specification.\\n # (all to improve test coverage)\\n config['output']['dir'] = '.'\\n config['modules'] = [ 'custom_wcs' ]\\n os.remove(rho_file)\\n piff.plotify(config)\\n assert os.path.isfile(rho_file)\",\n \"def index():\\n\\n # list with url for pictures\\n pic_url_list = []\\n\\n # default resolution\\n x_resolution = 2048\\n y_resolution = 2048\\n\\n # converting flask type od dict to default python dict\\n request_arguments = request.args.to_dict(flat=False)\\n\\n # check if optional argument (resolution) exists\\n if 'rozdzielczosc' in request_arguments:\\n # splitting x and y resolution values by 'x' and mapping it on int\\n x_resolution, y_resolution = map(int, request_arguments['rozdzielczosc'][0].split('x'))\\n\\n if 'zdjecia' in request_arguments:\\n pic_url_list = request_arguments['zdjecia'][0].split(',')\\n\\n # check if optional argument (random) exists, if so shuffle it\\n if 'losowo' in request_arguments and request_arguments['losowo'][0] == '1':\\n shuffle(pic_url_list)\\n\\n # call function which glues images together\\n try:\\n mosaic = make_mosaic(pic_url_list, x_resolution, y_resolution)\\n mosaic.save(basepath + '/static/img/mosaic.jpeg', 'JPEG')\\n return render_template('mosaic.html')\\n except ValueError as e:\\n return render_template('mosaic.html', message=str(e))\\n except requests.exceptions.ConnectionError:\\n return render_template('mosaic.html', message='Wrong image url passed or no internet access')\",\n \"def run(self):\\n _, paint = self.config_image()\\n if paint:\\n self.load_image()\\n else:\\n self.config_select(True)\\n\\n while True:\\n self.paint()\\n self.config_select()\",\n \"def main():\\n ##############################\\n # Options\\n ##############################\\n\\n bev_generator = 'slices'\\n slices_config = \\\\\\n \\\"\\\"\\\"\\n slices {\\n height_lo: -0.2\\n height_hi: 2.3\\n num_slices: 5\\n }\\n \\\"\\\"\\\"\\n # Use None for a random image\\n #img_idx = None\\n img_idx = 6\\n\\n show_ground_truth = True # Whether to overlay ground_truth boxes\\n show_height_maps = False # Whether to show the five height maps\\n show_images = False # Whether to show the images\\n\\n point_cloud_source = 'lidar'\\n pre_label_dir = '/home/cecilia/leo_projects/bishe2019/3D-Detection/avod/data/outputs/pyramid_cars_with_aug_rep_loss/predictions/kitti_native_eval/0.1/112000/data/'\\n ##############################\\n # End of Options\\n ##############################\\n\\n dataset_config = DatasetBuilder.copy_config(DatasetBuilder.KITTI_VAL)\\n dataset_config = DatasetBuilder.merge_defaults(dataset_config)\\n\\n # Overwrite bev_generator\\n if bev_generator == 'slices':\\n text_format.Merge(slices_config,\\n dataset_config.kitti_utils_config.bev_generator)\\n else:\\n raise ValueError('Invalid bev_generator')\\n\\n dataset = DatasetBuilder.build_kitti_dataset(dataset_config,\\n use_defaults=False)\\n\\n if img_idx is None:\\n img_idx = int(random.random() * dataset.num_samples)\\n\\n sample_name = \\\"{:06}\\\".format(img_idx)\\n print('=== Showing BEV maps for image: {}.png ==='.format(sample_name))\\n\\n # Load image\\n image = cv2.imread(dataset.get_rgb_image_path(sample_name))\\n image_shape = image.shape[0:2]\\n\\n kitti_utils = dataset.kitti_utils\\n point_cloud = kitti_utils.get_point_cloud(\\n point_cloud_source, img_idx, image_shape)\\n ground_plane = kitti_utils.get_ground_plane(sample_name)\\n bev_images = kitti_utils.create_bev_maps(point_cloud, ground_plane)\\n\\n height_maps = np.array(bev_images.get(\\\"height_maps\\\"))\\n density_map = np.array(bev_images.get(\\\"density_map\\\"))\\n\\n # Get groundtruth bev-info\\n gt_box_points, gt_box_points_norm = [None, None]\\n if show_ground_truth:\\n gt_obj_labels = obj_utils.read_labels(dataset.label_dir, img_idx)\\n gt_filtered_objs = gt_obj_labels\\n\\n gt_label_boxes = []\\n for gt_label in gt_filtered_objs:\\n gt_box = box_3d_encoder.object_label_to_box_3d(gt_label)\\n gt_label_boxes.append(gt_box)\\n\\n gt_label_boxes = np.array(gt_label_boxes)\\n gt_box_points, gt_box_points_norm = box_3d_projector.project_to_bev(\\n gt_label_boxes, [[-40, 40], [0, 70]])\\n\\n # Get prediction bev-info\\n pre_box_points, pre_box_points_norm = [None, None]\\n pre_obj_labels = obj_utils.read_labels(pre_label_dir, img_idx)\\n\\n pre_filtered_objs = pre_obj_labels\\n\\n pre_label_boxes = []\\n for pre_label in pre_filtered_objs:\\n pre_box = box_3d_encoder.object_label_to_box_3d(pre_label)\\n pre_label_boxes.append(pre_box)\\n\\n pre_label_boxes = np.array(pre_label_boxes)\\n pre_box_points, pre_box_points_norm = box_3d_projector.project_to_bev(\\n pre_label_boxes, [[-40, 40], [0, 70]])\\n\\n \\n rgb_img_size = (np.array((1242, 375)) * 0.75).astype(np.int16)\\n img_x_start = 60\\n img_y_start = 330\\n\\n img_x = img_x_start\\n img_y = img_y_start\\n img_w = 400\\n img_h = 350\\n img_titlebar_h = 20\\n\\n # Show images if show_images = True\\n if show_images:\\n vis_utils.cv2_show_image(\\\"Image\\\", image,\\n size_wh=rgb_img_size, location_xy=(img_x, 0))\\n\\n # Height maps if show_height_maps = True\\n if show_height_maps:\\n\\n for map_idx in range(len(height_maps)):\\n height_map = height_maps[map_idx]\\n\\n height_map = draw_boxes(height_map, gt_box_points_norm, pre_box_points_norm)\\n vis_utils.cv2_show_image(\\n \\\"Height Map {}\\\".format(map_idx), height_map, size_wh=(\\n img_w, img_h), location_xy=(\\n img_x, img_y))\\n\\n img_x += img_w\\n # Wrap around\\n if (img_x + img_w) > 1920:\\n img_x = img_x_start\\n img_y += img_h + img_titlebar_h\\n\\n # Density map (Normal BEV)\\n density_map = draw_boxes(density_map, gt_box_points_norm, pre_box_points_norm)\\n vis_utils.cv2_show_image(\\n \\\"Density Map\\\", density_map, size_wh=(\\n img_w, img_h), location_xy=(\\n img_x, img_y))\\n\\n cv2.waitKey()\",\n \"def show(data, types=(\\\"inflated\\\", ), recache=False, cmap='RdBu_r', layout=None,\\n autoclose=None, open_browser=None, port=None, pickerfun=None,\\n template=\\\"mixer.html\\\", overlays_available=None,\\n overlays_visible=('rois', 'sulci'), labels_visible=('rois', ),\\n overlay_file=None, title='Brain', **kwargs):\\n\\n # populate default webshow args\\n if autoclose is None:\\n autoclose = options.config.get('webshow', 'autoclose', fallback='true') == 'true'\\n if open_browser is None:\\n open_browser = options.config.get('webshow', 'open_browser', fallback='true') == 'true'\\n\\n data = dataset.normalize(data)\\n if not isinstance(data, dataset.Dataset):\\n data = dataset.Dataset(data=data)\\n\\n html = FallbackLoader([os.path.split(os.path.abspath(template))[0], serve.cwd]).load(template)\\n db.auxfile = data\\n\\n #Extract the list of stimuli, for special-casing\\n stims = dict()\\n for name, view in data:\\n if 'stim' in view.attrs and os.path.exists(view.attrs['stim']):\\n sname = os.path.split(view.attrs['stim'])[1]\\n stims[sname] = view.attrs['stim']\\n\\n package = Package(data)\\n metadata = json.dumps(package.metadata())\\n images = package.images\\n subjects = list(package.subjects)\\n\\n ctmargs = dict(method='mg2', level=9, recache=recache,\\n external_svg=overlay_file, overlays_available=overlays_available)\\n ctms = dict((subj, utils.get_ctmpack(subj, types, **ctmargs))\\n for subj in subjects)\\n package.reorder(ctms)\\n\\n subjectjs = json.dumps(dict((subj, \\\"ctm/%s/\\\"%subj) for subj in subjects))\\n db.auxfile = None\\n\\n if layout is None:\\n layout = [None, (1, 1), (2, 1), (3, 1), (2, 2), (3, 2), (3, 2), (3, 3), (3, 3), (3, 3)][len(subjects)]\\n\\n linear = lambda x, y, m: (1.-m)*x + m*y\\n mixes = dict(\\n linear=linear,\\n smoothstep=(lambda x, y, m: linear(x, y, 3*m**2 - 2*m**3)),\\n smootherstep=(lambda x, y, m: linear(x, y, 6*m**5 - 15*m**4 + 10*m**3))\\n )\\n\\n post_name = Queue()\\n\\n # Put together all view options\\n my_viewopts = dict(options.config.items('webgl_viewopts'))\\n my_viewopts['overlays_visible'] = overlays_visible\\n my_viewopts['labels_visible'] = labels_visible\\n my_viewopts['brightness'] = options.config.get('curvature', 'brightness')\\n my_viewopts['smoothness'] = options.config.get('curvature', 'webgl_smooth')\\n my_viewopts['contrast'] = options.config.get('curvature', 'contrast')\\n\\n for sec in options.config.sections():\\n if 'paths' in sec or 'labels' in sec:\\n my_viewopts[sec] = dict(options.config.items(sec))\\n\\n if pickerfun is None:\\n pickerfun = lambda a, b: None\\n\\n class CTMHandler(web.RequestHandler):\\n def get(self, path):\\n subj, path = path.split('/')\\n if path == '':\\n self.set_header(\\\"Content-Type\\\", \\\"application/json\\\")\\n self.write(open(ctms[subj]).read())\\n else:\\n fpath = os.path.split(ctms[subj])[0]\\n mtype = mimetypes.guess_type(os.path.join(fpath, path))[0]\\n if mtype is None:\\n mtype = \\\"application/octet-stream\\\"\\n self.set_header(\\\"Content-Type\\\", mtype)\\n self.write(open(os.path.join(fpath, path), 'rb').read())\\n\\n class DataHandler(web.RequestHandler):\\n def get(self, path):\\n path = path.strip(\\\"/\\\")\\n try:\\n dataname, frame = path.split('/')\\n except ValueError:\\n dataname = path\\n frame = 0\\n\\n if dataname in images:\\n dataimg = images[dataname][int(frame)]\\n if dataimg[1:6] == \\\"NUMPY\\\":\\n self.set_header(\\\"Content-Type\\\", \\\"application/octet-stream\\\")\\n else:\\n self.set_header(\\\"Content-Type\\\", \\\"image/png\\\")\\n\\n if 'Range' in self.request.headers:\\n self.set_status(206)\\n rangestr = self.request.headers['Range'].split('=')[1]\\n start, end = [ int(i) if len(i) > 0 else None for i in rangestr.split('-') ]\\n\\n clenheader = 'bytes %s-%s/%s' % (start, end or len(dataimg), len(dataimg) )\\n self.set_header('Content-Range', clenheader)\\n self.set_header('Content-Length', end-start+1)\\n self.write(dataimg[start:end+1])\\n else:\\n self.write(dataimg)\\n else:\\n self.set_status(404)\\n self.write_error(404)\\n\\n class StimHandler(web.StaticFileHandler):\\n def initialize(self):\\n pass\\n\\n def get(self, path):\\n if path not in stims:\\n self.set_status(404)\\n self.write_error(404)\\n else:\\n self.root, fname = os.path.split(stims[path])\\n super(StimHandler, self).get(fname)\\n\\n class StaticHandler(web.StaticFileHandler):\\n def initialize(self):\\n self.root = ''\\n\\n class MixerHandler(web.RequestHandler):\\n def get(self):\\n self.set_header(\\\"Content-Type\\\", \\\"text/html\\\")\\n generated = html.generate(data=metadata,\\n colormaps=colormaps,\\n default_cmap=cmap,\\n python_interface=True,\\n leapmotion=True,\\n layout=layout,\\n subjects=subjectjs,\\n viewopts=json.dumps(my_viewopts),\\n title=title,\\n **kwargs)\\n #overlays_visible=json.dumps(overlays_visible),\\n #labels_visible=json.dumps(labels_visible),\\n #**viewopts)\\n self.write(generated)\\n\\n def post(self):\\n data = self.get_argument(\\\"svg\\\", default=None)\\n png = self.get_argument(\\\"png\\\", default=None)\\n with open(post_name.get(), \\\"wb\\\") as svgfile:\\n if png is not None:\\n data = png[22:].strip()\\n try:\\n data = binascii.a2b_base64(data)\\n except:\\n print(\\\"Error writing image!\\\")\\n data = png\\n svgfile.write(data)\\n\\n class JSMixer(serve.JSProxy):\\n @property\\n def view_props(self):\\n \\\"\\\"\\\"An enumerated list of settable properties for views. \\n There may be a way to get this from the javascript object, \\n but I (ML) don't know how.\\n\\n There may be additional properties we want to set in views\\n and animations; those must be added here.\\n\\n Old property list that used to be settable before webgl refactor:\\n view_props = ['altitude', 'azimuth', 'target', 'mix', 'radius', 'pivot',\\n 'visL', 'visR', 'alpha', 'rotationR', 'rotationL', 'projection',\\n 'volume_vis', 'frame', 'slices']\\n \\\"\\\"\\\"\\n camera = getattr(self.ui, \\\"camera\\\")\\n _camera_props = ['camera.%s' % k for k in camera._controls.attrs.keys()]\\n surface = getattr(self.ui, \\\"surface\\\")\\n _subject = list(surface._folders.attrs.keys())[0]\\n _surface = getattr(surface, _subject)\\n _surface_props = ['surface.{subject}.%s'%k for k in _surface._controls.attrs.keys()]\\n _curvature_props = ['surface.{subject}.curvature.brightness',\\n 'surface.{subject}.curvature.contrast',\\n 'surface.{subject}.curvature.smoothness']\\n return _camera_props + _surface_props + _curvature_props\\n\\n def _set_view(self, **kwargs):\\n \\\"\\\"\\\"Low-level command: sets view parameters in the current viewer\\n\\n Sets each the state of each keyword argument provided. View parameters\\n that can be set include all parameters in the data.gui in the html view.\\n\\n \\\"\\\"\\\"\\n # Set unfolding level first, as it interacts with other arguments\\n surface = getattr(self.ui, \\\"surface\\\")\\n subject_list = surface._folders.attrs.keys()\\n # Better to only self.view_props once; it interacts with javascript, \\n # don't want to do that too often, it leads to glitches.\\n vw_props = copy.copy(self.view_props)\\n for subject in subject_list:\\n if 'surface.{subject}.unfold' in kwargs:\\n unfold = kwargs.pop('surface.{subject}.unfold')\\n self.ui.set('surface.{subject}.unfold'.format(subject=subject), unfold)\\n for k, v in kwargs.items():\\n if not k in vw_props:\\n print('Unknown parameter %s!'%k)\\n continue\\n else:\\n self.ui.set(k.format(subject=subject) if '{subject}' in k else k, v)\\n # Wait for webgl. Wait for it. .... WAAAAAIIIT.\\n time.sleep(0.03)\\n\\n def _capture_view(self, frame_time=None):\\n \\\"\\\"\\\"Low-level command: returns a dict of current view parameters\\n\\n Retrieves the following view parameters from current viewer:\\n\\n altitude, azimuth, target, mix, radius, visL, visR, alpha,\\n rotationR, rotationL, projection, pivot\\n\\n Parameters\\n ----------\\n frame_time : scalar\\n time (in seconds) to specify for this frame.\\n \\n Notes\\n -----\\n If multiple subjects are present, only retrieves view for first subject.\\n \\\"\\\"\\\"\\n view = {}\\n subject = list(self.ui.surface._folders.attrs.keys())[0]\\n for p in self.view_props:\\n try:\\n view[p] = self.ui.get(p.format(subject=subject) if '{subject}' in p else p)[0]\\n # Wait for webgl.\\n time.sleep(0.03)\\n except Exception as err:\\n # TO DO: Fix this hack with an error class in serve.py & catch it here\\n print(err) #msg = \\\"Cannot read property 'undefined'\\\"\\n #if err.message[:len(msg)] != msg:\\n # raise err\\n if frame_time is not None:\\n view['time'] = frame_time\\n return view\\n\\n def save_view(self, subject, name, is_overwrite=False):\\n \\\"\\\"\\\"Saves current view parameters to pycortex database\\n\\n Parameters\\n ----------\\n subject : string\\n pycortex subject id\\n name : string\\n name for view to store\\n is_overwrite: bool\\n whether to overwrite an extant view (default : False)\\n\\n Notes\\n -----\\n Equivalent to call to cortex.db.save_view(subject, vw, name)\\n For a list of the view parameters saved, see viewer._capture_view\\n \\\"\\\"\\\"\\n db.save_view(self, subject, name, is_overwrite)\\n\\n def get_view(self, subject, name):\\n \\\"\\\"\\\"Get saved view from pycortex database.\\n\\n Retrieves named view from pycortex database and sets current\\n viewer parameters to retrieved values.\\n\\n Parameters\\n ----------\\n subject : string\\n pycortex subject ID\\n name : string\\n name of saved view to re-load\\n\\n Notes\\n -----\\n Equivalent to call to cortex.db.get_view(subject, vw, name)\\n For a list of the view parameters set, see viewer._capture_view\\n \\\"\\\"\\\"\\n view = db.get_view(self, subject, name)\\n\\n def addData(self, **kwargs):\\n Proxy = serve.JSProxy(self.send, \\\"window.viewers.addData\\\")\\n new_meta, new_ims = _convert_dataset(Dataset(**kwargs), path='/data/', fmt='%s_%d.png')\\n metadata.update(new_meta)\\n images.update(new_ims)\\n return Proxy(metadata)\\n\\n def getImage(self, filename, size=(1920, 1080)):\\n \\\"\\\"\\\"Saves currently displayed view to a .png image file\\n\\n Parameters\\n ----------\\n filename : string\\n duh.\\n size : tuple (x, y)\\n size (in pixels) of image to save.\\n \\\"\\\"\\\"\\n post_name.put(filename)\\n Proxy = serve.JSProxy(self.send, \\\"window.viewer.getImage\\\")\\n return Proxy(size[0], size[1], \\\"mixer.html\\\")\\n\\n def makeMovie(self, animation, filename=\\\"brainmovie%07d.png\\\", offset=0,\\n fps=30, size=(1920, 1080), interpolation=\\\"linear\\\"):\\n \\\"\\\"\\\"Renders movie frames for animation of mesh movement\\n\\n Makes an animation (for example, a transition between inflated and\\n flattened brain or a rotating brain) of a cortical surface. Takes a\\n list of dictionaries (`animation`) as input, and uses the values in\\n the dictionaries as keyframes for the animation.\\n\\n Mesh display parameters that can be animated include 'elevation',\\n 'azimuth', 'mix', 'radius', 'target' (more?)\\n\\n\\n Parameters\\n ----------\\n animation : list of dicts\\n Each dict should have keys `idx`, `state`, and `value`.\\n `idx` is the time (in seconds) at which you want to set `state` to `value`\\n `state` is the parameter to animate (e.g. 'altitude', 'azimuth')\\n `value` is the value to set for `state`\\n filename : string path name\\n Must contain '%d' (or some variant thereof) to account for frame\\n number, e.g. '/some/directory/brainmovie%07d.png'\\n offset : int\\n Frame number for first frame rendered. Useful for concatenating\\n animations.\\n fps : int\\n Frame rate of resultant movie\\n size : tuple (x, y)\\n Size (in pixels) of resulting movie\\n interpolation : {\\\"linear\\\", \\\"smoothstep\\\", \\\"smootherstep\\\"}\\n Interpolation method for values between keyframes.\\n\\n Example\\n -------\\n # Called after a call of the form: js_handle = cortex.webgl.show(DataViewObject)\\n # Start with left hemisphere view\\n js_handle._setView(azimuth=[90], altitude=[90.5], mix=[0])\\n # Initialize list\\n animation = []\\n # Append 5 key frames for a simple rotation\\n for az, idx in zip([90, 180, 270, 360, 450], [0, .5, 1.0, 1.5, 2.0]):\\n animation.append({'state':'azimuth', 'idx':idx, 'value':[az]})\\n # Animate! (use default settings)\\n js_handle.makeMovie(animation)\\n \\\"\\\"\\\"\\n # build up two variables: State and Anim.\\n # state is a dict of all values being modified at any time\\n state = dict()\\n # anim is a list of transitions between keyframes\\n anim = []\\n setfunc = self.ui.set\\n for f in sorted(animation, key=lambda x:x['idx']):\\n if f['idx'] == 0:\\n setfunc(f['state'], f['value'])\\n state[f['state']] = dict(idx=f['idx'], val=f['value'])\\n else:\\n if f['state'] not in state:\\n state[f['state']] = dict(idx=0, val=self.getState(f['state'])[0])\\n start = dict(idx=state[f['state']]['idx'],\\n state=f['state'],\\n value=state[f['state']]['val'])\\n end = dict(idx=f['idx'], state=f['state'], value=f['value'])\\n state[f['state']]['idx'] = f['idx']\\n state[f['state']]['val'] = f['value']\\n if start['value'] != end['value']:\\n anim.append((start, end))\\n\\n for i, sec in enumerate(np.arange(0, anim[-1][1]['idx']+1./fps, 1./fps)):\\n for start, end in anim:\\n if start['idx'] < sec <= end['idx']:\\n idx = (sec - start['idx']) / float(end['idx'] - start['idx'])\\n if start['state'] == 'frame':\\n func = mixes['linear']\\n else:\\n func = mixes[interpolation]\\n\\n val = func(np.array(start['value']), np.array(end['value']), idx)\\n if isinstance(val, np.ndarray):\\n setfunc(start['state'], val.ravel().tolist())\\n else:\\n setfunc(start['state'], val)\\n self.getImage(filename%(i+offset), size=size)\\n\\n def _get_anim_seq(self, keyframes, fps=30, interpolation='linear'):\\n \\\"\\\"\\\"Convert a list of keyframes to a list of EVERY frame in an animation.\\n\\n Utility function called by make_movie; separated out so that individual\\n frames of an animation can be re-rendered, or for more control over the\\n animation process in general.\\n\\n \\\"\\\"\\\"\\n # Misc. setup\\n fr = 0\\n a = np.array\\n func = mixes[interpolation]\\n #skip_props = ['surface.{subject}.right', 'surface.{subject}.left', ] #'projection',\\n # Get keyframes\\n keyframes = sorted(keyframes, key=lambda x:x['time'])\\n # Normalize all time to frame rate\\n fs = 1./fps\\n for k in range(len(keyframes)):\\n t = keyframes[k]['time']\\n t = np.round(t/fs)*fs\\n keyframes[k]['time'] = t\\n allframes = []\\n for start, end in zip(keyframes[:-1], keyframes[1:]):\\n t0 = start['time']\\n t1 = end['time']\\n tdif = float(t1-t0)\\n # Check whether to continue frame sequence to endpoint\\n use_endpoint = keyframes[-1]==end\\n nvalues = np.round(tdif/fs).astype(int)\\n if use_endpoint:\\n nvalues += 1\\n fr_time = np.linspace(0, 1, nvalues, endpoint=use_endpoint)\\n # Interpolate between values\\n for t in fr_time:\\n frame = {}\\n for prop in start.keys():\\n if prop=='time':\\n continue\\n if (start[prop] is None) or (start[prop] == end[prop]) or isinstance(start[prop], (bool, str)):\\n frame[prop] = start[prop]\\n continue\\n val = func(a(start[prop]), a(end[prop]), t)\\n if isinstance(val, np.ndarray):\\n frame[prop] = val.tolist()\\n else:\\n frame[prop] = val\\n allframes.append(frame)\\n return allframes\\n\\n def make_movie_views(self, animation, filename=\\\"brainmovie%07d.png\\\", \\n offset=0, fps=30, size=(1920, 1080), alpha=1, frame_sleep=0.05,\\n frame_start=0, interpolation=\\\"linear\\\"):\\n \\\"\\\"\\\"Renders movie frames for animation of mesh movement\\n\\n Makes an animation (for example, a transition between inflated and\\n flattened brain or a rotating brain) of a cortical surface. Takes a\\n list of dictionaries (`animation`) as input, and uses the values in\\n the dictionaries as keyframes for the animation.\\n\\n Mesh display parameters that can be animated include 'elevation',\\n 'azimuth', 'mix', 'radius', 'target' (more?)\\n\\n\\n Parameters\\n ----------\\n animation : list of dicts\\n This is a list of keyframes for the animation. Each keyframe should be\\n a dict in the form captured by the ._capture_view method. NOTE: every\\n view must include all view parameters. Additionally, there should be\\n one extra key/value pair for \\\"time\\\". The value for time should be\\n in seconds. The list of keyframes is sorted by time before applying,\\n so they need not be in order in the input.\\n filename : string path name\\n Must contain '%d' (or some variant thereof) to account for frame\\n number, e.g. '/some/directory/brainmovie%07d.png'\\n offset : int\\n Frame number for first frame rendered. Useful for concatenating\\n animations.\\n fps : int\\n Frame rate of resultant movie\\n size : tuple (x, y)\\n Size (in pixels) of resulting movie\\n interpolation : {\\\"linear\\\", \\\"smoothstep\\\", \\\"smootherstep\\\"}\\n Interpolation method for values between keyframes.\\n\\n Notes\\n -----\\n Make sure that all values that will be modified over the course\\n of the animation are initialized (have some starting value) in the first\\n frame.\\n\\n Example\\n -------\\n # Called after a call of the form: js_handle = cortex.webgl.show(DataViewObject)\\n # Start with left hemisphere view\\n js_handle._setView(azimuth=[90], altitude=[90.5], mix=[0])\\n # Initialize list\\n animation = []\\n # Append 5 key frames for a simple rotation\\n for az, t in zip([90, 180, 270, 360, 450], [0, .5, 1.0, 1.5, 2.0]):\\n animation.append({'time':t, 'azimuth':[az]})\\n # Animate! (use default settings)\\n js_handle.make_movie(animation)\\n \\\"\\\"\\\"\\n allframes = self._get_anim_seq(animation, fps, interpolation)\\n for fr, frame in enumerate(allframes[frame_start:], frame_start):\\n self._set_view(**frame)\\n time.sleep(frame_sleep)\\n self.getImage(filename%(fr+offset+1), size=size)\\n time.sleep(frame_sleep)\\n\\n class PickerHandler(web.RequestHandler):\\n def get(self):\\n pickerfun(int(self.get_argument(\\\"voxel\\\")), int(self.get_argument(\\\"vertex\\\")))\\n\\n class WebApp(serve.WebApp):\\n disconnect_on_close = autoclose\\n def get_client(self):\\n self.connect.wait()\\n self.connect.clear()\\n return JSMixer(self.send, \\\"window.viewer\\\")\\n\\n def get_local_client(self):\\n return JSMixer(self.srvsend, \\\"window.viewer\\\")\\n\\n if port is None:\\n port = random.randint(1024, 65536)\\n\\n server = WebApp([(r'/ctm/(.*)', CTMHandler),\\n (r'/data/(.*)', DataHandler),\\n (r'/stim/(.*)', StimHandler),\\n (r'/mixer.html', MixerHandler),\\n (r'/picker', PickerHandler),\\n (r'/', MixerHandler),\\n (r'/static/(.*)', StaticHandler)],\\n port)\\n\\n server.start()\\n print(\\\"Started server on port %d\\\"%server.port)\\n url = \\\"http://%s%s:%d/mixer.html\\\"%(serve.hostname, domain_name, server.port)\\n if open_browser:\\n webbrowser.open(url)\\n client = server.get_client()\\n client.server = server\\n return client\\n else:\\n try:\\n from IPython.display import display, HTML\\n display(HTML('Open viewer: <a href=\\\"{0}\\\" target=\\\"_blank\\\">{0}</a>'.format(url)))\\n except:\\n pass\\n return server\",\n \"def main():\\n space_ship = SimpleImage(\\\"images/MillenniumFalcon.png\\\")\\n figure = SimpleImage(\\\"images/ReyGreenScreen.png\\\")\\n result = combine(space_ship, figure)\\n result.show()\",\n \"def displayImages(self):\\n\\n plt.figure(figsize=(8,6))\\n plt.subplot(1,2,1)\\n plt.imshow( self.original_image, cmap=\\\"gray\\\")\\n plt.title(\\\"Original Image\\\")\\n plt.subplot(1,2,2)\\n plt.imshow( self.blurred_image, cmap=\\\"gray\\\")\\n plt.title(\\\"Blurred Image\\\")\",\n \"def plot_final_grid(generated_images):\\n\\n fig = plt.figure(figsize=(8, 6))\\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.001, hspace=0.001)\\n\\n for ind in range(GRID_IMGS):\\n ax = fig.add_subplot(GRID_SIZE, GRID_SIZE, ind + 1, xticks=[], yticks=[])\\n ax.imshow(np.uint8(((generated_images[ind] + 1) / 2) * 255), cmap='gray')\\n\\n plt.savefig('/content/drive/My Drive/WGAN/generated_image_grid.png')\\n plt.savefig('/content/drive/My Drive/WGAN/results/DCGAN.png')\\n plt.show()\",\n \"def main(config):\\n save_main_dir = config[\\\"save_main_dir\\\"]\\n\\n if not os.path.isdir(save_main_dir):\\n print(\\\"{} does not exist, creating it now...\\\", save_main_dir)\\n pathlib.Path(save_main_dir).mkdir(parents=True, exist_ok=True)\\n\\n app = inviwopy.app\\n network = app.network\\n \\n # Resize the canvas to improve rendering speed, only affects visual output\\n if config[\\\"should_resize\\\"]:\\n ivw_helpers.set_canvas_sizes(128, 128)\\n\\n hdf5_path = os.path.join(save_main_dir, config[\\\"hdf5_name\\\"])\\n with h5py.File(hdf5_path, mode='w', libver='latest') as hdf5_file:\\n hdf5_file.swmr_mode = True\\n create_hdf_storage(hdf5_file, config)\\n count = {\\\"train\\\": 0, \\\"val\\\": 0}\\n for set_type in 'train', 'val':\\n capture_lf_samples(hdf5_file, set_type, config, network, count) \\n print(\\\"Finished writing to HDF5 in {}\\\".format(hdf5_path))\",\n \"def main(argv):\\n scanSpeed = 1.\\n iList = -1\\n opt1 = \\\"\\\"\\n intMode = False\\n if (len(argv) >= 1): opt1 = argv[0]\\n if \\\"-i\\\" in (opt1): intMode = True\\n\\n # Set input data and cuts\\n inputFile = TFile(\\\"~/project/match-skim/waveletSkimDS5_run23920.root\\\") # calibration\\n # inputFile = TFile(\\\"~/project/v2-processwfs/waveletSkimDS5_90.root\\\") # noisy BG\\n waveTree = inputFile.Get(\\\"skimTree\\\")\\n theCut = inputFile.Get(\\\"cutUsedHere\\\").GetTitle()\\n theCut += \\\" && waveS5/trapENFCal < 1200 && trapENFCal > 1.5 && trapENFCal < 3\\\"\\n\\n # Print cut and events passing cut\\n waveTree.Draw(\\\">>elist\\\", theCut, \\\"entrylist\\\")\\n elist = gDirectory.Get(\\\"elist\\\")\\n waveTree.SetEntryList(elist)\\n nList = elist.GetN()\\n print \\\"Using cut:\\\\n\\\",theCut,\\\"\\\\n\\\"\\n print \\\"Found\\\",waveTree.GetEntries(),\\\"input entries.\\\"\\n print \\\"Found\\\",nList,\\\"entries passing cuts.\\\"\\n\\n # Make a figure\\n fig = plt.figure(figsize=(15,10), facecolor='w')\\n p1 = plt.subplot2grid((6,7), (0,0), colspan=4, rowspan=2) # original\\n p2 = plt.subplot2grid((6,7), (2,0), colspan=4, rowspan=3) # rising edge\\n p3 = plt.subplot2grid((6,7), (5,0), colspan=4, rowspan=1) # residual\\n p4 = plt.subplot2grid((6,7), (0,4), colspan=3, rowspan=2) # trace 1\\n p5 = plt.subplot2grid((6,7), (2,4), colspan=3, rowspan=2) # trace 2\\n p6 = plt.subplot2grid((6,7), (4,4), colspan=3, rowspan=2) # trace 3\\n plt.show(block=False)\\n\\n # Make template(s)\\n # npzfile = np.load(\\\"./data/genTemplateWF.npz\\\") # gen-template.py\\n # temp, tempTS, tempE, tempST = npzfile['arr_0']+1, npzfile['arr_1'], npzfile['arr_2'], npzfile['arr_3']*10\\n\\n samp, r, z, tempE, tempST, smooth = 2016, 0, 15, 3938, 1000, 100 # gen-template.py\\n # samp, r, z, tempE, tempST, smooth = 5000, 0, 15, 10, 2500, 100 # huge template\\n # samp, r, z, tempE, tempST, smooth = 2016, 0, 15, 10, 1000, 100 # regular size template\\n # samp, r, z, tempE, tempST, smooth = 2016, 30, 30, 10, 1000, 100 # regular size temp, slower rise\\n # samp, r, z, tempE, tempST, smooth = 500, 0, 15, 10, 100, 100 # small template\\n temp, tempTS = wl.MakeSiggenWaveform(samp,r,z,tempE,tempST,smooth)\\n tempST = tempST * 10 # convert to ns\\n\\n\\n # Loop over events\\n while True:\\n saveMe = False\\n iList += 1\\n if intMode==True and iList != 0:\\n value = raw_input()\\n if value=='q': break\\n if value=='s': saveMe=True\\n if value=='p': iList -= 2 # previous\\n if (value.isdigit()): iList = int(value) # go to entry\\n if iList >= elist.GetN(): break\\n\\n entry = waveTree.GetEntryNumber(iList);\\n waveTree.LoadTree(entry)\\n waveTree.GetEntry(entry)\\n nChans = waveTree.channel.size()\\n numPass = waveTree.Draw(\\\"channel\\\",theCut,\\\"GOFF\\\",1,iList)\\n chans = waveTree.GetV1()\\n chanList = list(set(int(chans[n]) for n in xrange(numPass)))\\n\\n # Loop over hits passing cuts\\n hitList = (iH for iH in xrange(nChans) if waveTree.channel.at(iH) in chanList) # a 'generator expression'\\n for iH in hitList:\\n\\n # ------------------------------------------------------------------------\\n\\n # Load waveform for this hit\\n run = waveTree.run\\n chan = waveTree.channel.at(iH)\\n dataE = waveTree.trapENFCal.at(iH)\\n dataST = waveTree.butterTime.at(iH) # replace with blrwfFMR50?\\n toe = waveTree.kvorrT.at(iH)/dataE\\n print \\\"%d / %d Run %d nCh %d chan %d trapENF %.1f t/e %.1f\\\" % (iList,nList,run,nChans,chan,dataE,toe)\\n signal = wl.processWaveform(waveTree.MGTWaveforms.at(iH),opt='full')\\n waveBLSub = signal.GetWaveBLSub()\\n waveFilt = signal.GetWaveFilt()\\n waveTS = signal.GetTS()\\n baseAvg, dataNoise = signal.GetBaseNoise()\\n\\n # Denoise the data waveform (take only lowest-frequency components)\\n wp = pywt.WaveletPacket(data=waveBLSub, wavelet='haar', mode='symmetric',maxlevel=3)\\n new_wp = pywt.WaveletPacket(data=None, wavelet='haar', mode='symmetric')\\n new_wp['aaa'] = wp['aaa'].data\\n waveDenoised = new_wp.reconstruct(update=False)\\n\\n # Window the fit around rising edge - start time calculator method\\n loWin, hiWin = dataST - 1000, dataST + 4000 # ns\\n if loWin < waveTS[0] or hiWin > waveTS[-1]:\\n print \\\"Window out of range! dataST: %.1f loWin %.1f hiWin %.1f\\\" % (dataST,loWin,hiWin)\\n idx = np.where((waveTS >= loWin) & (waveTS <= hiWin))\\n data = waveBLSub[idx]\\n # data = waveDenoised[idx]\\n dataTS = waveTS[idx]\\n\\n # Pack into lists\\n rawList = [waveBLSub, waveTS, dataE, dataST]\\n dataList = [data, dataTS, dataE, dataST, loWin, hiWin, dataNoise]\\n tempList = [temp, tempTS, tempE, tempST]\\n\\n # Optionally save something to a file\\n if saveMe: np.savez(\\\"./data/tailSlopeInputs.npz\\\",rawList,tempList)\\n\\n # Recreate the guess and the guess's rising edge\\n guessFull, guessFullTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\\\"full\\\")\\n guess, guessTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\\\"!fancy\\\")\\n\\n # Make an \\\"almost complete\\\" guess - no fitting\\n # st, en, slo = dataST-100, dataE, 5\\n InterpFn = interpolate.interp1d(tempTS, temp, kind=\\\"linear\\\", copy=\\\"False\\\", assume_sorted=\\\"True\\\")\\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\\n\\n # Fit with MCMC and get best-fit parameters\\n # numSteps, burnIn = 10000, 5000 # default - 10000, 5000. try 3000, 2000. long test: 20000,10000\\n # myModel = TemplateModel( dataList, dataNoise, tempList )\\n # waveModel = pymc.Model( myModel )\\n # M = pymc.MCMC( waveModel )\\n # M.use_step_method(pymc.Metropolis, M.startTime, proposal_sd=100., proposal_distribution='Normal')\\n # M.use_step_method(pymc.Metropolis, M.energy, proposal_sd=1., proposal_distribution='Normal')\\n # M.use_step_method(pymc.Metropolis, M.slowness, proposal_sd=100., proposal_distribution='Normal')\\n # M.sample(iter=numSteps, verbose=0) # do the fit\\n # st = np.median(M.trace('startTime')[burnIn:])\\n # en = np.median(M.trace('energy')[burnIn:])\\n # slo = np.median(M.trace('slowness')[burnIn:])\\n # InterpFn = interpolate.interp1d(tempTS, temp, kind=\\\"linear\\\", copy=\\\"False\\\", assume_sorted=\\\"True\\\")\\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\\n\\n\\n # Fit with SciPy minimizer(s) and get best-fit parameters\\n\\n # how long does it take?\\n start = time.time()\\n\\n MakeTracesGlobal() # creates 3 global arrays: startTrace, enTrace, sloTrace\\n datas = [dataList, tempList, InterpFn]\\n\\n # floats = [1.]\\n # result = op.basinhopping(findLnLike, floats, T=1000, stepsize=15, niter_success=2, minimizer_kwargs={\\\"args\\\":datas})\\n # print \\\"slowness:\\\",result[\\\"x\\\"],\\\"dataE\\\",dataE,\\\"dataST\\\",dataST\\n # floats = [dataST, dataE, result[\\\"x\\\"]]\\n # MakeTracesGlobal() # resets traces\\n\\n floats = [dataST, dataE, 20.]\\n result = op.minimize(findLnLike, floats, args=datas, method=\\\"Nelder-Mead\\\")\\n if not result[\\\"success\\\"]: print result[\\\"message\\\"]\\n st, en, slo = result[\\\"x\\\"]\\n\\n model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\\n\\n stop = time.time()\\n print \\\"fitting took\\\",stop-start\\n\\n\\n\\n # Calculate residual, Chi2/NDF, likelihood, etc.\\n residual = model - data\\n frac = (np.power(data - model, 2)) / np.abs(model)\\n chi2NDF = np.sum(frac) / len(model)\\n inv_sigma2 = 1.0/(dataNoise**2)\\n lnLike = -0.5*(np.sum((data - model)**2*inv_sigma2 - np.log(inv_sigma2)))\\n\\n # Fill the figure\\n p1.cla()\\n p1.set_ylabel(\\\"ADC\\\")\\n p1.set_title(\\\"Run %d Channel %d Entry %d\\\\ntrapENFCal %.1f T/E %.1f ST %.1f\\\" % (run,chan,iList,dataE,toe,dataST))\\n p1.plot(waveTS,waveBLSub,color='blue')\\n p1.plot(waveTS,waveDenoised,color='red',alpha=0.8)\\n p1.plot(guessFullTS,guessFull,color='orange',linewidth=2)\\n p1.axvline(x = dataST, color='green',linewidth=2)\\n p1.axvline(x = loWin, color='black')\\n p1.axvline(x = hiWin, color='black')\\n\\n p2.cla()\\n p2.plot(dataTS,data,color='blue',label='Data')\\n p2.plot(guessTS,guess,color='orange',label='Guess')\\n p2.plot(modelTS,model,color='red',linewidth=3,label='Best Fit')\\n p2.legend(loc=4)\\n\\n p3.cla()\\n p3.set_xlabel(\\\"Time [ns]\\\", x=0.95, ha='right')\\n p3.set_ylabel(\\\"Residual [ADC]\\\")\\n p3.plot(guessTS,residual, color='red')\\n p3.axhline(y = 0, color='blue', alpha=0.3)\\n p3.axhline(y = dataNoise, color='blue', alpha=0.3)\\n p3.axhline(y = -1.0*dataNoise, color='blue', alpha=0.3)\\n\\n p4.cla()\\n p4.set_title(\\\"startTime %.1f Energy %.2f Slow %.1f\\\" % (st,en,slo))\\n\\n p4.plot(stTrace[1:])\\n p4.set_ylabel('startTime')\\n\\n p5.cla()\\n p5.plot(enTrace[1:])\\n p5.set_ylabel('energy')\\n\\n p6.cla()\\n p6.plot(sloTrace[1:])\\n p6.set_ylabel('slowness')\\n\\n plt.tight_layout()\\n plt.subplots_adjust(hspace=0.35)\\n plt.pause(scanSpeed)\\n\\n # ------------------------------------------------------------------------\",\n \"def img_render(kve, lopt_str, sopt_str, gopt_str, popt_str, glopt_str, img_path):\\n i_before = ''\\n i_layer = ''\\n i_after = ''\\n i_label_str_html = ''\\n if 'img' in kve:\\n img_paths = [x.strip() for x in kve['img'].split(':')]\\n for opt_str in glopt_str, popt_str, gopt_str, sopt_str, lopt_str:\\n if 'autoilabel' in opt_str:\\n i_label_str = os.path.splitext(os.path.basename(img_paths[0]))[0]\\n i_label_str_html = ' <div class=\\\"label bottom\\\">' \\\\\\n + i_label_str + '</div>'\\n if 'ilabel' in kve:\\n i_label_str = kve['ilabel']\\n i_label_str_html = ' <div class=\\\"label bottom\\\">' \\\\\\n + i_label_str + '</div>'\\n img_tag_str = ''\\n for idx, path in enumerate(img_paths):\\n img_tag_str = img_tag_str + '<img src=\\\"' + img_path + img_paths[idx] + '\\\"/>'\\n for opt_str in [glopt_str, popt_str, gopt_str, sopt_str, lopt_str]:\\n if 'ibefore' in opt_str:\\n i_before = ' <div class=\\\"layout ' + lopt_str \\\\\\n + '\\\"><div class=\\\"img\\\">' + img_tag_str + '</div>' \\\\\\n + i_label_str_html + '</div>'\\n if 'iafter' in opt_str:\\n i_after = ' <div class=\\\"layout ' + lopt_str \\\\\\n + '\\\"><div class=\\\"img\\\">' + img_tag_str + '</div>' \\\\\\n + i_label_str_html + '</div>'\\n if not (i_before or i_after):\\n i_layer = ' <div class=\\\"img\\\">' + img_tag_str + '</div>'\\n return i_before, i_layer, i_after\\n return '', '', ''\",\n \"def showAssetImage(*args):\\n\\n selTab = cmds.tabLayout(widgets[\\\"shotAssRigListTLO\\\"], q=True, st=True)\\n\\n fType = \\\"\\\"\\n asset = \\\"\\\"\\n assetPath = \\\"\\\"\\n path = \\\"\\\"\\n imagePath = \\\"\\\"\\n\\n if selTab == \\\"Chars\\\":\\n asset = cmds.textScrollList(widgets[\\\"shotAssRigCharListTSL\\\"], q=True, si=True)\\n if asset:\\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \\\"characters\\\", asset[0], \\\"icon\\\",\\\"{0}Icon.png\\\".format(asset[0])))\\n if os.path.isfile(imagePath):\\n cFuncs.assetImageUI(imagePath)\\n else:\\n cmds.warning(\\\"Can't find an image for {0}\\\".format(asset[0]))\\n\\n if selTab == \\\"Props\\\":\\n asset = cmds.textScrollList(widgets[\\\"shotAssRigPropListTSL\\\"], q=True, si=True)\\n if asset:\\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \\\"props\\\", asset[0], \\\"icon\\\",\\\"{0}Icon.png\\\".format(asset[0])))\\n if os.path.isfile(imagePath):\\n cFuncs.assetImageUI(imagePath)\\n else:\\n cmds.warning(\\\"Can't find an image for {0}\\\".format(asset[0]))\\n \\n if selTab == \\\"Sets\\\":\\n asset = cmds.textScrollList(widgets[\\\"shotAssRigSetListTSL\\\"], q=True, si=True)\\n if asset:\\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \\\"sets\\\", asset[0], \\\"icon\\\",\\\"{0}Icon.png\\\".format(asset[0])))\\n if os.path.isfile(imagePath):\\n cFuncs.assetImageUI(imagePath)\\n else:\\n cmds.warning(\\\"Can't find an image for {0}\\\".format(asset[0]))\\n \\n # if selTab == \\\"Anm\\\":\\n # #need to split this up\\n # var_shot = cmds.textScrollList(widgets[\\\"shotAnmMstListTSL\\\"], q=True, si=True)\\n # if var_shot:\\n # var, buf, shot = var_shot[0].partition(\\\".\\\")\\n # path = cFuncs.getVarMaster(cFuncs.fixPath(os.path.join(pi.shotsFolder, shot, \\\"anm\\\", var)))\",\n \"def test004(testDir, dirDict, pflag):\\n shutil.copy(osp.join(dirDict[\\\"baseConfig\\\"], \\\"params_system.cfg\\\"), testDir)\\n shutil.copy(osp.join(dirDict[\\\"baseConfig\\\"], \\\"params_c.cfg\\\"), testDir)\\n psys = osp.join(testDir, \\\"params_system.cfg\\\")\\n ptrode = osp.join(testDir, \\\"params_c.cfg\\\")\\n P_s = IO.get_config(psys)\\n P_s.set(\\\"Sim Params\\\", \\\"Crate\\\", \\\"1e-2\\\")\\n IO.write_config_file(P_s, psys)\\n P = IO.get_config(ptrode)\\n P.set(\\\"Particles\\\", \\\"type\\\", \\\"CHR\\\")\\n P.set(\\\"Particles\\\", \\\"discretization\\\", \\\"2e-9\\\")\\n P.set(\\\"Particles\\\", \\\"shape\\\", \\\"sphere\\\")\\n P.set(\\\"Material\\\", \\\"muRfunc\\\", \\\"LiFePO4\\\")\\n P.set(\\\"Material\\\", \\\"noise\\\", \\\"true\\\")\\n P.set(\\\"Material\\\", \\\"numnoise\\\", \\\"100\\\")\\n P.set(\\\"Material\\\", \\\"Dfunc\\\", \\\"lattice\\\")\\n P.set(\\\"Material\\\", \\\"dgammadc\\\", \\\"-2e-30\\\")\\n IO.write_config_file(P, ptrode)\\n main.main(psys, keepArchive=False)\\n shutil.move(dirDict[\\\"simOut\\\"], testDir)\\n if pflag:\\n corePlots(testDir, dirDict)\\n electrodePlots(testDir, dirDict, \\\"c\\\")\",\n \"def call(self, **kwargs):\\n # Format template\\n template = self._cw.vreg.template_env.get_template(\\\"startup.logged.jinja2\\\")\\n html = template.render(\\n header_url=self._cw.data_url(\\\"creative/img/neurospin.jpg\\\"),\\n moderator=True)\\n self.w(html)\",\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_histogram_and_image(hist_pred, hist_true, img, tile_name, out_dir=None, volume_weighted=False):\\n img = img.astype(np.uint8)\\n\\n index = np.arange(len(hist_pred)) + 0.5\\n\\n KL_div = KL(hist_true, hist_pred)\\n\\n iou = calculate_iou(hist_true, hist_pred)\\n\\n dm_true = get_dm(hist_true, volume_weighted=volume_weighted)\\n dm_pred = get_dm(hist_pred, volume_weighted=volume_weighted)\\n\\n # Create Figure and Axes instances\\n fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(14, 6.5))\\n\\n # add title of the whole figure\\n fig.suptitle('Comparison of Distribution\\\\n%s' % (tile_name), fontsize=18)\\n\\n ax1.bar(index, hist_true, width=1.0, label='true histogram')\\n ax1.bar(index, hist_pred, width=1.0, alpha=0.5, label='predicted histogram')\\n ax1.legend(fontsize=14)\\n\\n # axis labels\\n ax1.set_xlabel('Grain diameter [cm]', fontsize=16)\\n\\n if volume_weighted:\\n ax1.set_ylabel('Relative volume', fontsize=16)\\n else:\\n ax1.set_ylabel('Relative frequency', fontsize=16)\\n\\n # x ticks labels\\n group_labels = np.array([0.00, 0.01, 0.02, 0.03, 0.04, 0.06, 0.08, 0.10, 0.12, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.50, 0.60, 0.80, 1.0, 1.2, 1.5, 2.0]) * 100\\n group_labels = np.array(group_labels, dtype=np.int)\\n ax1.set_xticks(np.arange(len(group_labels)))\\n ax1.set_xticklabels(group_labels, rotation='vertical')\\n\\n ax1.text( # position text relative to Axes\\n 0.98, 0.82, 'KL: %.2f' % (KL_div),\\n ha='right', va='top',\\n transform=ax1.transAxes,\\n fontsize=16\\n )\\n\\n ax1.text( # position text relative to Axes\\n 0.98, 0.76, 'IoU: %.2f' % (iou),\\n ha='right', va='top',\\n transform=ax1.transAxes,\\n fontsize=16\\n )\\n\\n ax1.text( # position text relative to Axes\\n 0.98, 0.70, 'dm true: %.2f cm' % (dm_true),\\n ha='right', va='top',\\n transform=ax1.transAxes,\\n fontsize=16\\n )\\n ax1.text( # position text relative to Axes\\n 0.98, 0.64, 'dm pred: %.2f cm' % (dm_pred),\\n ha='right', va='top',\\n transform=ax1.transAxes,\\n fontsize=16\\n )\\n\\n ax2.set_xticks(())\\n ax2.set_yticks(())\\n\\n ax2.imshow(img)\\n\\n fig.tight_layout()\\n fig.subplots_adjust(top=0.88)\\n\\n if out_dir is not None:\\n if not os.path.isdir(out_dir):\\n os.makedirs(out_dir)\\n\\n plt.savefig(os.path.join(out_dir, '{}.png'.format(tile_name)), bbox_inches='tight')\\n plt.close(fig) # close the figure\",\n \"def build_static_files(cfg_prep_used, area_threshold=0.25, beef_cuttings=4, dairy_cuttings=5, overwrite_flag=False):\\n logging.info('\\\\nBuilding ET-Demands Static Files')\\n\\n # Default values\\n permeability = -999\\n soil_depth = 60 # inches\\n aridity = 50\\n irrigation = 1\\n\\n # Input paths\\n # DEADBEEF - For now, get cropET folder from INI file\\n # This function may eventually be moved into the main cropET code\\n config = copy.deepcopy(cfg_prep_used)\\n\\n try:\\n project_ws = config.CROP_ET.project_folder\\n except:\\n logging.error('project_folder parameter must be set in the INI file, exiting')\\n return False\\n try:\\n gis_ws = config.CROP_ET.gis_folder\\n except:\\n logging.error('gis_folder parameter must be set in the INI file, exiting')\\n return False\\n try:\\n et_cells_path = config.CROP_ET.cells_path\\n except:\\n logging.error('cells_path parameter must be set in the INI file, exiting')\\n return False\\n try:\\n crop_et_ws = config.CROP_ET.crop_et_folder\\n except:\\n logging.error('crop_et_folder parameter must be set in the INI file, exiting')\\n return False\\n try:\\n template_ws = config.CROP_ET.template_folder\\n except:\\n logging.error('CROP_ET template_folder parameter must be set in the INI file, exiting')\\n return False\\n\\n # Read data from geodatabase or shapefile\\n # if '.gdb' in et_cells_path and not et_cells_path.endswith('.shp'):\\n # _flag = False\\n # _path = os.path.dirname(et_cells_path)\\n # gdb_path = r'D:\\\\Projects\\\\CAT_Basins\\\\AltusOK\\\\et-demands_py\\\\et_demands.gdb'\\n # _cells_path = os.path.join(gdb_path, 'et_cells')\\n\\n # Output sub-folder names\\n static_ws = os.path.join(project_ws, 'static')\\n\\n # basin attributes fields\\n basin_id_filed = 'STAID'\\n basin_elev_field = 'ELEV_MEAN_M_BASIN'\\n\\n # ET Cell field names\\n cell_lat_field = 'LAT'\\n cell_lon_field = 'LON'\\n cell_id_field = 'GAGE_ID'\\n # cell_station_id_field = 'STATION_ID'\\n # awc_field = 'AWC'\\n clay_field = 'CLAY'\\n sand_field = 'SAND'\\n awc_in_ft_field = 'AWC_IN_FT'\\n hydgrp_num_field = 'HYDGRP_NUM'\\n hydgrp_field = 'HYDGRP'\\n\\n # huc_field = 'HUC{}'.format(huc)\\n # permeability_field = 'PERMEABILITY'\\n # soil_depth_field = 'SOIL_DEPTH'\\n # aridity_field = 'ARIDITY'\\n # dairy_cutting_field = 'DAIRY_CUTTINGS'\\n # beef_cutting_field = 'BEEF_CUTTINGS'\\n\\n # Static file names\\n cell_props_name = 'ETCellsProperties.txt'\\n cell_crops_name = 'ETCellsCrops.txt'\\n cell_cuttings_name = 'MeanCuttings.txt'\\n crop_params_name = 'CropParams.txt'\\n crop_coefs_name = 'CropCoefs.txt'\\n crop_coefs_eto = 'CropCoefs_eto.txt'\\n crop_coefs_etr = 'CropCoefs_etr.txt'\\n eto_ratio_name = 'EToRatiosMon.txt'\\n etr_ratio_name = 'ETrRatiosMon.txt'\\n static_list = [crop_params_name, crop_coefs_name, crop_coefs_eto, crop_coefs_etr, cell_props_name, cell_crops_name,\\n cell_cuttings_name, eto_ratio_name, etr_ratio_name]\\n\\n # Check input folders\\n if not os.path.isdir(crop_et_ws):\\n logging.critical('\\\\nERROR: The INI cropET folder does not exist\\\\n {}'.format(crop_et_ws))\\n sys.exit()\\n elif not os.path.isdir(project_ws):\\n logging.critical('\\\\nERROR: The project folder does not exist\\\\n {}'.format(project_ws))\\n sys.exit()\\n elif not os.path.isdir(gis_ws):\\n logging.critical('\\\\nERROR: The GIS folder does not exist\\\\n {}'.format(gis_ws))\\n sys.exit()\\n logging.info('\\\\nGIS Workspace: {}'.format(gis_ws))\\n logging.info('Project Workspace: {}'.format(project_ws))\\n logging.info('CropET Workspace: {}'.format(crop_et_ws))\\n logging.info('Template Workspace: {}'.format(template_ws))\\n\\n # Check input files\\n if not _arcpy.exists(et_cells_path):\\n logging.critical('\\\\nERROR: The ET Cell shapefile does not exist\\\\n {}'.format(et_cells_path))\\n sys.exit()\\n for static_name in static_list:\\n if not os.path.isfile(os.path.join(template_ws, static_name)):\\n logging.error(\\n '\\\\nERROR: The static template does not exist\\\\n {}'.format(os.path.join(template_ws, static_name)))\\n sys.exit()\\n logging.debug('ET Cells Path: {}'.format(et_cells_path))\\n\\n # Build output table folder if necessary\\n if not os.path.isdir(static_ws):\\n os.makedirs(static_ws)\\n\\n # Read ET Cell zonal stats\\n logging.info('\\\\nReading ET Cell Zonal Stats')\\n logging.debug(' {}'.format(et_cells_path))\\n crop_field_list = sorted([f for f in _arcpy.list_fields(et_cells_path) if re.match('CROP_\\\\d{3}', f)])\\n fields = [cell_id_field, cell_lat_field, cell_lon_field, awc_in_ft_field, clay_field, sand_field, hydgrp_num_field,\\n hydgrp_field]\\n fields = fields + crop_field_list\\n logging.debug(' Fields: {}'.format(fields))\\n cell_data_dict = defaultdict(dict)\\n\\n for fid, row in _arcpy.search_cursor(et_cells_path, fields).items():\\n # Switch to cell_id_field as index (instead of FID)\\n for f in fields[1:]:\\n cell_data_dict[str(row[cell_id_field])][f] = row[f]\\n\\n # Read basin data\\n logging.info('\\\\nReading basin attributes')\\n fields = [basin_elev_field]\\n logging.debug(' Fields: {}'.format(fields))\\n basin_data_dict = defaultdict(dict)\\n basin_topo = pd.read_csv(config.GAGES.basin_topo_file, sep=',', dtype={0: str})\\n for cell_id_tmp in list(cell_data_dict.keys()):\\n basin_data_dict[cell_id_tmp][basin_elev_field] = \\\\\\n basin_topo.loc[basin_topo[basin_id_filed] == cell_id_tmp, basin_elev_field].values[0]\\n # Convert elevation units if necessary\\n logging.debug(' Convert station elevation from meters to feet')\\n for k in basin_data_dict.keys():\\n basin_data_dict[k][basin_elev_field] /= 0.3048\\n\\n # static files\\n logging.info('\\\\nCopying template static files')\\n for static_name in static_list:\\n logging.debug(' {}'.format(static_name))\\n shutil.copy(os.path.join(template_ws, static_name), static_ws)\\n\\n logging.info('\\\\nWriting static text files')\\n cell_props_path = os.path.join(static_ws, cell_props_name)\\n cell_crops_path = os.path.join(static_ws, cell_crops_name)\\n cell_cuttings_path = os.path.join(static_ws, cell_cuttings_name)\\n eto_ratio_path = os.path.join(static_ws, eto_ratio_name)\\n etr_ratio_path = os.path.join(static_ws, etr_ratio_name)\\n\\n # Write cell properties\\n logging.debug(' {}'.format(cell_props_path))\\n with open(cell_props_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n basin_id = cell_id\\n basin_lat = '{:>9.4f}'.format(cell_data[cell_lat_field])\\n basin_lon = '{:>9.4f}'.format(cell_data[cell_lon_field])\\n basin_data = basin_data_dict[basin_id]\\n basin_elev = '{:.2f}'.format(basin_data[basin_elev_field])\\n # There is an extra/unused column in the template and excel files\\n output_list = [cell_id, cell_id, basin_id, basin_lat, basin_lon, basin_elev, permeability,\\n '{:.4f}'.format(cell_data[awc_in_ft_field]), soil_depth, cell_data[hydgrp_field],\\n cell_data[hydgrp_num_field], aridity, '']\\n output_f.write('\\\\t'.join(map(str, output_list)) + '\\\\n')\\n\\n del output_list\\n del basin_id, basin_lat, basin_lon, basin_elev\\n\\n # Write cell crops\\n crops = util.parse_int_set(config.USDA.cdl_crops)\\n logging.debug(' {}'.format(cell_crops_path))\\n with open(cell_crops_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n basin_id = cell_id\\n output_list = [cell_id, cell_id, basin_id, irrigation]\\n crop_list = ['CROP_{:03d}'.format(i) for i in crops]\\n crop_area_list = []\\n for crop in crop_list:\\n if crop in cell_data.keys() and cell_data[crop] is not None:\\n crop_area_list.append(cell_data[crop])\\n else:\\n crop_area_list.append(0)\\n crop_flag_list = [1 if area > area_threshold else 0 for area in crop_area_list]\\n output_list = output_list + crop_flag_list\\n output_f.write('\\\\t'.join(map(str, output_list)) + '\\\\n')\\n\\n del crop_list, crop_area_list, crop_flag_list, output_list\\n\\n # Write cell cuttings\\n logging.debug(' {}'.format(cell_cuttings_path))\\n with open(cell_cuttings_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n output_list = [cell_id, cell_id, '{:>9.4f}'.format(cell_data[cell_lat_field]), dairy_cuttings,\\n beef_cuttings]\\n output_f.write('\\\\t'.join(map(str, output_list)) + '\\\\n')\\n\\n # Write monthly ETo ratios\\n logging.debug(' {}'.format(eto_ratio_path))\\n with open(eto_ratio_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n basin_id = cell_id\\n output_f.write('\\\\t'.join(map(str, [basin_id, ''] + [1.0] * 12)) + '\\\\n')\\n\\n # Write monthly ETr ratios\\n logging.debug(' {}'.format(etr_ratio_path))\\n with open(etr_ratio_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n basin_id = cell_id\\n output_f.write('\\\\t'.join(map(str, [basin_id, ''] + [1.0] * 12)) + '\\\\n')\",\n \"def plot_final_grid(generated_images):\\n\\n fig = plt.figure(figsize=(GRID_SIZE, GRID_SIZE))\\n fig.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=0.001, hspace=0.001)\\n\\n for ind in range(GRID_IMGS):\\n ax = fig.add_subplot(GRID_SIZE, GRID_SIZE, ind + 1, xticks=[], yticks=[])\\n ax.imshow(np.uint8(((generated_images[ind] + 1) / 2) * 255), cmap='gray')\\n\\n plt.savefig('/content/drive/My Drive/WGAN/generated_image_grid.png')\\n plt.savefig('/content/drive/My Drive/WGAN/results/WGAN.png')\\n plt.show()\",\n \"def make_image(config, slot_names, mean_frame_pattern='_mean_bias_image.fits'):\\n file_list = sorted(find_files(config))\\n fits_files_dict = {slot_names[i] : file_list[i] for i in range(len(file_list))}\\n ccd_dict = {}\\n ccd_dict_wbias = {}\\n gains_dict = {}\\n for slot in slot_names:\\n\\n mean_bias_file = slot + mean_frame_pattern\\n\\n ccd_dict[slot] = sensorTest.MaskedCCD(fits_files_dict[slot])\\n outfile = os.path.join(config['tmp_dir'],'ccd' + slot + '.fits')\\n image={}\\n \\n ccd_dict_wbias[slot]=sensorTest.MaskedCCD(fits_files_dict[slot],\\\\\\n bias_frame=os.path.join(config['tmp_dir'],mean_bias_file))\\n outfile_wbias = os.path.join(config['tmp_dir'],'ccd' + slot + '_wbias.fits')\\n image_wbias={}\\n \\n eotest_results_file = os.path.join(config['eo_data_path'],'{}_eotest_results.fits'.format(ccd_dict[slot].md('LSST_NUM')))\\n gains_dict[slot] = gains(eotest_results_file)\\n \\n for amp in ccd_dict[slot]:\\n image[amp] = ccd_dict[slot].bias_subtracted_image(amp)\\n image[amp] *= gains_dict[slot][amp]\\n image_wbias[amp] = ccd_dict_wbias[slot].bias_subtracted_image(amp)\\n image_wbias[amp] *= gains_dict[slot][amp]\\n \\n imutils.writeFits({amp: image_wbias[amp].getImage() for amp in ccd_dict_wbias[slot]}, \\n outfile_wbias, fits_files_dict[slot])\\n imutils.writeFits({amp: image[amp].getImage() for amp in ccd_dict[slot]}, \\n outfile, fits_files_dict[slot])\\n\\n fits_files_dict_corr={slot : os.path.join(config['tmp_dir'],'ccd'+slot+'.fits') for slot in slot_names}\\n fits_files_dict_corr_wbias={slot : os.path.join(config['tmp_dir'],'ccd'+slot+'_wbias.fits') for slot in slot_names}\\n \\n im_corr = raft.RaftMosaic(fits_files_dict_corr, bias_subtract=False)\\n im_corr_wbias = raft.RaftMosaic(fits_files_dict_corr_wbias, bias_subtract=False)\\n im_raw = raft.RaftMosaic(fits_files_dict, bias_subtract=False)\\n \\n return im_raw, im_corr, im_corr_wbias\",\n \"def processImages(self):\\n for file in os.listdir(self.config[\\\"tempPath\\\"]):\\n self.logger.debug(\\\"Calling generateImages for the file: {0}\\\".format(file))\\n self.generateText(file)\",\n \"def camera():\\n return render_template('home/cam.html')\",\n \"def _generate_single_image(self, char, config, char_idx=None):\\n '''\\n #there are 13 box each line\\n #width: 760 #height:80\\n #2 lines: 1500 #height: 80\\n #==>> calculate width each box: 58\\n #each line will be choose a:\\n - height\\n - padding bottom\\n - padding top\\n #each character will be choose:\\n - height => calculate width\\n - padding left right base on width of box and width of character after resizing\\n '''\\n\\n if char == '・':\\n charChoice = '.'\\n else:\\n charChoice = char\\n\\n if charChoice not in self.char_2_imgs.keys():\\n return None\\n\\n isFinishChoose = False\\n while not isFinishChoose:\\n choice = self._get_random_choice_index(char)\\n\\n image = self.char_2_imgs[charChoice][choice]\\n # numberRandom = random.randint(0, 10000)\\n # cv2.imwrite('C:\\\\\\\\Users\\\\\\\\ABC\\\\\\\\Desktop\\\\\\\\deletetemp\\\\\\\\GenDataHWBB\\\\\\\\synthesizedKana\\\\\\\\test_bf'+str(numberRandom)+'_'+str(choice)+'.png', image)\\n\\n # # rotate image\\n # image = rotate(image, config['skewness'], order=1,\\n # cval=self.background_value)\\n\\n # resize image\\n if charChoice not in constants.NOT_RESIZE:\\n image = self._resize_character_kata(image, config['height'])\\n else:\\n random.seed()\\n height, _ = image.shape\\n if char == '.':\\n config['bottom'] = config['bottom_base']\\n config['top'] = max(0,self.configKata['FIX_ALL_LINE_HEIGHT'] - config['bottom'] - height)\\n else:\\n\\n config['bottom'] = config['bottom_base'] + int(config['height_line']/2)\\n config['top'] = max(0,self.configKata['FIX_ALL_LINE_HEIGHT'] - config['bottom'] - height)\\n\\n\\n\\n # add horizontal space and bottom space\\n _, width = image.shape\\n if width < self.configKata['FIX_BOX_WIDTH']-8:\\n remain = int((self.configKata['FIX_BOX_WIDTH']-width)/2) + 3\\n random.seed()\\n left = max(0, random.randint(5, remain))\\n if char == '゙' or char == '゚':\\n random.seed()\\n left = random.randint(3, 7)\\n elif char == ')':\\n left = 0\\n right = max(0, self.configKata['FIX_BOX_WIDTH']-left-width)\\n if right == 0:\\n left = max(0, self.configKata['FIX_BOX_WIDTH']-right-width)\\n\\n image = np.pad(image, ((config['top'], config['bottom']), (left, right)),'constant', constant_values=self.background_value)\\n\\n # cv2.imwrite('C:\\\\\\\\Users\\\\\\\\ABC\\\\\\\\Desktop\\\\\\\\deletetemp\\\\\\\\GenDataHWBB\\\\\\\\synthesizedKana\\\\\\\\test_af'+str(numberRandom)+'.png', image)\\n isFinishChoose = True\\n else:\\n continue\\n return image\",\n \"def display(self):\\n display(self.image)\",\n \"def show_flow(filename):\\n flow = read_flow(filename)\\n img = flow_to_image(flow)\\n plt.imshow(img)\\n plt.show()\",\n \"def render_sample(cls,\\n nusc: 'NuScenes',\\n radar_data_gp: 'RadarDataGroup',\\n front_cam_data: 'CameraData',\\n host_data:'HostData',\\n fig_info:dict,\\n box_vis_level: BoxVisibility = BoxVisibility.ANY,\\n out_path: str = None) -> None:\\n cls.colors=radar_data_gp.colors\\n fig, axes = plt.subplots(1, 2, figsize=(16, 24))\\n #left diagram to show front view\\n ax1 = axes[0]\\n cls.render_front_view(cls, nusc=nusc, radar_data_gp=radar_data_gp, front_cam_data=front_cam_data,\\n ax =ax1, use_flat_vehicle_coordinates=False, with_anns=False, with_radar=True)\\n #right diagram to show bird view\\n ax2 = axes[1] \\n \\n cls.render_bird_view(cls, nusc=nusc, radar_data_gp=radar_data_gp, front_cam_data=front_cam_data, \\n host_data=host_data, ax =ax2, use_flat_vehicle_coordinates=True)\\n #figure information\\n fig.text(0.01, 0.97, 'Figure information: ', weight='bold', color='green',fontsize = 15) \\n fig.text(0.01, 0.93, 'package: '+nusc.version, fontsize = 12) \\n fig.text(0.01, 0.9, 'scene name: '+fig_info['scene_name'], fontsize = 12) \\n fig.text(0.01, 0.87, 'timestamp: '+str(front_cam_data.time_stamp*1e-6)+'(s)', fontsize = 12)\\n fig.text(0.01, 0.84, 'elapse time: : '+str((fig_info['elapse_time'])*1e-3)+'(ms)', fontsize = 12)\\n \\n plt.tight_layout()\\n fig.subplots_adjust(wspace=0, hspace=0)\\n if out_path is not None:\\n plt.savefig(out_path,dpi=200)\\n if fig_info['verbose']: \\n plt.show()\",\n \"def init():\\n \\n # General parameters\\n exp_path = '/home/laura/Documents/stacks tif/1705_regMovie.tif' # experimental tif stack (grayscale)\\n bin_path = '/home/laura/Documents/stacks tif/1705/1705_binarizedMovie.tif' # binarized tif stack\\n vect_path = '/home/laura/Documents/STAGE3/1705_NET/' # gpickle directory\\n dest_path = '/home/laura/Documents/STAGE3/1705_NET/superposition' # output directory\\n verbose = True\\n debug = True\\n invert = True \\n main_params = [exp_path, bin_path, vect_path, dest_path, verbose, debug, invert]\\n \\n # Output options\\n doImg = -1 # image index\\n doStack = False \\n doVideo = False \\n compress = 3 # advice: no more than 5\\n output_params = [doImg, doStack, doVideo, compress]\\n \\n # Drawing options (colors as BGR)\\n line = True # edges drawing\\n line_color = (0, 255, 0) # green \\n line_size = 1 \\n apex_color = (0, 0, 255) # red\\n apex_size = 5\\n node_color = (255, 0, 0) # blue\\n node_size = 5\\n body_color = (0, 255, 0) # green\\n body_size = 3\\n drawing_params = [line, line_color, line_size, apex_color, apex_size,\\n node_color, node_size, body_color, body_size]\\n \\n return main_params, output_params, drawing_params\",\n \"def main():\\n usage = \\\"usage: %prog [options] input: BioC File (args[0]); Output Directory for the (picture) .svg file.\\\"\\n parser = OptionParser(version='%prog 0.99', usage=usage)\\n\\n parser.add_option('-l', '--logfile', dest='logfilename',\\n help='write log to FILE', metavar='FILE')\\n parser.add_option('-q', '--quiet',\\n action='store_true', dest='quiet', default=False,\\n help='do not print status messages to stderr')\\n parser.add_option('-d', '--debug',\\n action='store_true', dest='debug', default=False,\\n help='print debug information')\\n\\n\\n\\n (options, args) = parser.parse_args()\\n\\n if options.debug: print >> sys.stderr, '# Starting processing'\\n\\n process(options=options,args=args)\\n\\n\\n\\n\\n sys.exit(0) # Everything went ok!\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6152582","0.5941791","0.58843994","0.5855938","0.57763773","0.5693176","0.5657049","0.55853724","0.55809975","0.5573157","0.55586225","0.552534","0.54541004","0.5444121","0.540116","0.5400519","0.53548855","0.5344291","0.5332354","0.53094125","0.5267152","0.52532035","0.524693","0.52370363","0.52098894","0.5207756","0.5199763","0.51908123","0.5190494","0.5184676","0.51836276","0.51719576","0.5170688","0.5162561","0.5160298","0.51546264","0.5154507","0.51508963","0.51463485","0.5141295","0.5135086","0.5125916","0.5125391","0.5121314","0.5115416","0.51069504","0.5102398","0.5095038","0.50906324","0.508914","0.5084382","0.5074911","0.50550926","0.5054231","0.50535345","0.505037","0.5048152","0.5040638","0.5038981","0.503727","0.5033963","0.5028889","0.50212306","0.50211185","0.50210583","0.50157726","0.5012178","0.50085074","0.50042105","0.50035954","0.5001791","0.4998822","0.49973777","0.4995547","0.49932232","0.49913806","0.49865007","0.49821708","0.49686727","0.49677718","0.49676397","0.49657696","0.49606577","0.49602675","0.49565485","0.4956045","0.49547008","0.49494934","0.49469274","0.49388582","0.49387985","0.49373206","0.4935399","0.49286425","0.49281275","0.49268806","0.4926818","0.492493","0.49244416","0.4915033"],"string":"[\n \"0.6152582\",\n \"0.5941791\",\n \"0.58843994\",\n \"0.5855938\",\n \"0.57763773\",\n \"0.5693176\",\n \"0.5657049\",\n \"0.55853724\",\n \"0.55809975\",\n \"0.5573157\",\n \"0.55586225\",\n \"0.552534\",\n \"0.54541004\",\n \"0.5444121\",\n \"0.540116\",\n \"0.5400519\",\n \"0.53548855\",\n \"0.5344291\",\n \"0.5332354\",\n \"0.53094125\",\n \"0.5267152\",\n \"0.52532035\",\n \"0.524693\",\n \"0.52370363\",\n \"0.52098894\",\n \"0.5207756\",\n \"0.5199763\",\n \"0.51908123\",\n \"0.5190494\",\n \"0.5184676\",\n \"0.51836276\",\n \"0.51719576\",\n \"0.5170688\",\n \"0.5162561\",\n \"0.5160298\",\n \"0.51546264\",\n \"0.5154507\",\n \"0.51508963\",\n \"0.51463485\",\n \"0.5141295\",\n \"0.5135086\",\n \"0.5125916\",\n \"0.5125391\",\n \"0.5121314\",\n \"0.5115416\",\n \"0.51069504\",\n \"0.5102398\",\n \"0.5095038\",\n \"0.50906324\",\n \"0.508914\",\n \"0.5084382\",\n \"0.5074911\",\n \"0.50550926\",\n \"0.5054231\",\n \"0.50535345\",\n \"0.505037\",\n \"0.5048152\",\n \"0.5040638\",\n \"0.5038981\",\n \"0.503727\",\n \"0.5033963\",\n \"0.5028889\",\n \"0.50212306\",\n \"0.50211185\",\n \"0.50210583\",\n \"0.50157726\",\n \"0.5012178\",\n \"0.50085074\",\n \"0.50042105\",\n \"0.50035954\",\n \"0.5001791\",\n \"0.4998822\",\n \"0.49973777\",\n \"0.4995547\",\n \"0.49932232\",\n \"0.49913806\",\n \"0.49865007\",\n \"0.49821708\",\n \"0.49686727\",\n \"0.49677718\",\n \"0.49676397\",\n \"0.49657696\",\n \"0.49606577\",\n \"0.49602675\",\n \"0.49565485\",\n \"0.4956045\",\n \"0.49547008\",\n \"0.49494934\",\n \"0.49469274\",\n \"0.49388582\",\n \"0.49387985\",\n \"0.49373206\",\n \"0.4935399\",\n \"0.49286425\",\n \"0.49281275\",\n \"0.49268806\",\n \"0.4926818\",\n \"0.492493\",\n \"0.49244416\",\n \"0.4915033\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7577917"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":89299,"cells":{"query":{"kind":"string","value":"Renders surface temperature images of a droplet interface in data from Michael or Pablo's simulations, given a case config file and render config file."},"document":{"kind":"string","value":"def surf_tempmap(case_config_filepath, render_config_filepath):\n\n # Load config file with all common directory names\n dirname_config = configparser.ConfigParser()\n dirname_config.read(\"dirname.cfg\")\n\n # Get information from config files\n cconfd = load_config.get_config_params(case_config_filepath)\n rconfd = load_config.get_config_params(render_config_filepath)\n\n # Main output directory\n case_output = dirname_config[\"DIRECTORIES\"][\"RenderOutput\"] + cconfd[\"case_name\"] + \"/\"\n \n # Determine surface temperature geometry output dir\n ply_temp_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"ply_temp\"]\n\n # Get temperature bounds\n if rconfd[\"temp_bounds_auto\"]:\n temp_min, temp_max = convert_data.temp_bounds(h5dns_path=cconfd[\"h5dns_path\"],\n ply_temp_output_dir=ply_temp_output_dir,\n prc_min=rconfd[\"temp_min_percentile\"],\n prc_max=rconfd[\"temp_max_percentile\"])\n else:\n temp_min = rconfd[\"temp_min\"]\n temp_max = rconfd[\"temp_max\"]\n\n # Determine specific output dirs and make them if necessary\n image_output_dir_spec = dircheck.count_png_dirs(case_output + dirname_config[\"DIRECTORIES\"][\"tstep_sequence_surftempmap\"])\n ply_temp_output_dir_spec = ply_temp_output_dir + str(temp_min) + \"to\" + str(temp_max)\n dircheck.check_make([ply_temp_output_dir_spec, image_output_dir_spec])\n\n # Write Blender config file\n blender_config_filedir = case_output + rconfd[\"render_name\"] + \"_blender.cfg\"\n load_config.write_config_file(config_filedir=blender_config_filedir,\n config_dict={\"image_output_dir_spec\": image_output_dir_spec,\n \"ply_input_dir\": ply_temp_output_dir_spec,\n \"interface_material_name\": \"heatmapMaterial\",\n \"bg_image_filepath\": rconfd[\"bg_image_filepath\"],\n \"view_fraction\": cconfd[\"dropd\"]/rconfd[\"droplet_scale\"],\n \"render_scale\": 10,\n \"resolution_percentage\": rconfd[\"resolution_percentage\"],\n \"xres\": cconfd[\"xres\"], \"yres\": cconfd[\"yres\"], \"zres\": cconfd[\"zres\"],\n \"tres\": cconfd[\"tres\"],\n \"interface_half_enabled\": False,\n \"fog_enabled\": False,\n \"camera_azimuth_angle\": rconfd[\"camera_azimuth_angle\"],\n \"camera_elevation_angle\": rconfd[\"camera_elevation_angle\"],\n \"bg_color_1\": rconfd[\"bg_color_1\"], \"bg_color_2\": rconfd[\"bg_color_2\"]})\n\n # Extract droplet interface geometry\n ply_output_dir_uncolored = case_output + dirname_config[\"DIRECTORIES\"][\"ply\"]\n convert_data.conv_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=ply_output_dir_uncolored, tres=cconfd[\"tres\"])\n\n # Add surface temperature color to droplet interface\n convert_data.conv_color_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=ply_temp_output_dir_spec, uncolored_ply_dir=ply_output_dir_uncolored, tres=cconfd[\"tres\"], temp_min=temp_min, temp_max=temp_max)\n\n # Launch Blender to perform rendering\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\"droplet_render.py\", blend_name=\"droplet_render.blend\")\n\n # Add temperature legend colorbar to images\n if rconfd[\"add_temp_bar\"]:\n imedit.add_tempmap(bound_min=temp_min*cconfd[\"tgas\"], bound_max=temp_max*cconfd[\"tgas\"], image_dir=image_output_dir_spec, tres=cconfd[\"tres\"])"},"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 photorealistic(case_config_filepath, render_config_filepath):\n\n # Load config file with all common directory names\n dirname_config = configparser.ConfigParser()\n dirname_config.read(\"dirname.cfg\")\n\n # Get information from config files\n cconfd = load_config.get_config_params(case_config_filepath) # Case file\n rconfd = load_config.get_config_params(render_config_filepath) # Render file\n\n # Main output directory\n case_output = dirname_config[\"DIRECTORIES\"][\"RenderOutput\"] + cconfd[\"case_name\"] + \"/\"\n \n # Determine interface geometry output dir\n geometry_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"ply\"]\n\n # Determine individual frame output dir\n image_output_dir_spec = dircheck.count_png_dirs(case_output + dirname_config[\"DIRECTORIES\"][\"tstep_sequence_photorealistic\"])\n dircheck.check_make(image_output_dir_spec) # Make it if nonexistent\n \n # Write Blender config file\n blender_config_filedir = case_output + rconfd[\"render_name\"] + \"_blender.cfg\"\n load_config.write_config_file(config_filedir=blender_config_filedir,\n config_dict={\"image_output_dir_spec\": image_output_dir_spec,\n \"ply_input_dir\": geometry_output_dir,\n \"interface_material_name\": \"WaterMaterial5\",\n \"bg_image_filepath\": rconfd[\"bg_image_filepath\"],\n \"view_fraction\": cconfd[\"dropd\"]/rconfd[\"droplet_scale\"],\n \"render_scale\": 10,\n \"resolution_percentage\": rconfd[\"resolution_percentage\"],\n \"xres\": cconfd[\"xres\"], \"yres\": cconfd[\"yres\"], \"zres\": cconfd[\"zres\"],\n \"tres\": cconfd[\"tres\"],\n \"interface_half_enabled\": rconfd[\"interface_half_enabled\"],\n \"fog_enabled\": rconfd[\"fog_enabled\"],\n \"camera_azimuth_angle\": rconfd[\"camera_azimuth_angle\"],\n \"camera_elevation_angle\": rconfd[\"camera_elevation_angle\"],\n \"bg_color_1\": rconfd[\"bg_color_1\"], \"bg_color_2\": rconfd[\"bg_color_2\"]})\n\n # Extract droplet interface geometry\n convert_data.conv_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=geometry_output_dir, tres=int(cconfd[\"tres\"]))\n\n # Extract vapor fog (YV) if enabled\n if rconfd[\"fog_enabled\"]:\n # Determine individual fog dir and make it if necessary\n fog_halved = rconfd[\"fog_half_enabled\"]\n fog_dir_specifier = str(rconfd[\"fog_vapor_min\"]) + \"halved\" + str(fog_halved) + \"/\"\n bvox_output_dir_spec = case_output + dirname_config[\"DIRECTORIES\"][\"bvox\"] + fog_dir_specifier\n dircheck.check_make(bvox_output_dir_spec)\n # Convert fog data\n convert_data.conv_bvox(h5dns_path=cconfd[\"h5dns_path\"], output_dir=bvox_output_dir_spec, tres=int(cconfd[\"tres\"]), vapor_min=float(rconfd[\"fog_vapor_min\"]), fog_halved=fog_halved)\n # Add fog dir to Blender config file\n load_config.write_config_file(config_filedir=blender_config_filedir, config_dict={\"bvox_input_dir\": bvox_output_dir_spec}, append_config=True)\n \n # Launch Blender to perform rendering\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\"droplet_render.py\", blend_name=\"droplet_render.blend\")","def main(config):\n file_paths_info = [('GLOFRIS','WATCH','ARG_inunriver_historical_000000000WATCH_1980_rp01000.tif'),\n \t\t\t\t('GLOFRIS','RCP45','ARG_inunriver_rcp4p5_0000GFDL-ESM2M_2030_rp01000.tif'),\n \t\t\t\t('GLOFRIS','RCP85','ARG_inunriver_rcp8p5_0000GFDL-ESM2M_2030_rp01000.tif'),\n \t\t\t\t('FATHOM','AR_fluvial_undefended_merged','AR-FU-1000.tif'),\n \t\t\t\t('FATHOM','AR_pluvial_undefended_merged','AR-PU-1000.tif')\n \t\t\t\t]\n figure_names = ['GLOFRIS-WATCH-fluvial','GLOFRIS-RCP45-fluvial','GLOFRIS-RCP85-fluvial','FATHOM-fluvial','FATHOM-pluvial']\n figure_titles = ['current fluvial flooding','RCP4.5 fluvial flooding','RCP8.5 fluvial flooding','current fluvial flooding','current pluvial flooding']\n for f_i in range(len(file_paths_info)):\n\t hazard_file = os.path.join(config['paths']['data'],'flood_data', file_paths_info[f_i][0],file_paths_info[f_i][1],file_paths_info[f_i][2])\n\t output_file = os.path.join(config['paths']['figures'], 'flood-map-{}.png'.format(figure_names[f_i]))\n\t ax = get_axes()\n\t plot_basemap(ax, config['paths']['data'])\n\t scale_bar(ax, location=(0.8, 0.05))\n\t plot_basemap_labels(ax, config['paths']['data'], include_regions=True,include_zorder=3)\n\n\t proj_lat_lon = ccrs.PlateCarree()\n\n\n\t # Create color map\n\t colors = plt.get_cmap('Blues')\n\n\t # Read in raster data\n\t data, lat_lon_extent = get_data(hazard_file)\n\t data[(data <= 0) | (data > 5)] = np.nan\n\t max_val = np.nanmax(data)\n\t norm=mpl.colors.Normalize(vmin=0, vmax=max_val)\n\n\t # Plot population data\n\t im = ax.imshow(data, extent=lat_lon_extent,transform=proj_lat_lon, cmap=colors,norm =norm, zorder=2)\n\n\t # Add colorbar\n\t cbar = plt.colorbar(im, ax=ax,fraction=0.1, shrink=0.87,pad=0.01, drawedges=False, orientation='horizontal',\n\t norm=mpl.colors.Normalize(vmin=0, vmax=max_val), ticks=list(np.linspace(0,max_val,3)))\n\t cbar.set_clim(vmin=0,vmax=max_val)\n\n\n\t cbar.outline.set_color(\"none\")\n\t cbar.ax.yaxis.set_tick_params(color='black')\n\t cbar.ax.set_xlabel('Flood depths (m)',fontsize=12,color='black')\n\n\t plt.title('1 in 1000 year {}'.format(figure_titles[f_i]), fontsize = 14)\n\t save_fig(output_file)\n\t plt.close()","def main(argv):\n\n epd = EPD()\n\n epd.clear()\n\n print('panel = {p:s} {w:d} x {h:d} version={v:s}'.format(p=epd.panel, w=epd.width, h=epd.height, v=epd.version))\n\n for file_name in argv:\n if not os.path.exists(file_name):\n sys.exit('error: image file{f:s} does not exist'.format(f=file_name))\n print('display: {f:s}'.format(f=file_name))\n display_file(epd, file_name)","def plot_ebtel(data_directory,data_file,**kwargs):\n \n #Load the data\n data = np.loadtxt(data_directory+data_file)\n #Slice the array to get the vectors we want\n time = data[:,0]\n temp = data[:,1]\n dens = data[:,2]\n temp_apex = data[:,5]\n dens_apex = data[:,6]\n heat = data[:,10]\n \n #Set up the figure\n fig,axes = plt.subplots(3,1,figsize=(12,8))\n #Set the fontsize\n fs = 16\n\n #Plot the heating\n axes[0].plot(time,heat)\n axes[0].set_ylabel(r'$h$ (erg cm$^{-3}$ s$^{-1}$)',fontsize=fs)\n axes[0].set_title(r'EBTEL-C Parameters',fontsize=fs)\n axes[0].set_xlim([time[0],time[-1]])\n axes[0].locator_params(nbins=5)\n axes[0].ticklabel_format(axis='y', style='sci', scilimits=(-2,2) )\n #Plot the temperatures\n axes[1].plot(time,temp/10**6,label=r'$T$')\n axes[1].plot(time,temp_apex/10**6,'r--',label=r'$T_a$')\n axes[1].set_ylabel(r'$T$ (MK)',fontsize=fs)\n axes[1].legend(loc=1)\n axes[1].set_xlim([time[0],time[-1]])\n axes[1].locator_params(nbins=5)\n #Plot the densities\n axes[2].plot(time,dens/10**8,label=r'$n$')\n axes[2].plot(time,dens_apex/10**8,'r--',label=r'$n_a$')\n axes[2].set_xlabel(r'$t$ (s)',fontsize=fs)\n axes[2].set_ylabel(r'$n$ ($10^{8}$ cm$^{-3}$)',fontsize=fs)\n axes[2].set_xlim([time[0],time[-1]])\n axes[2].locator_params(nbins=5)\n axes[2].legend(loc=1)\n\n #Check if output filename is specified\n if 'print_fig_filename' in kwargs:\n plt.savefig(kwargs['print_fig_filename'],format='eps',dpi=1000)\n else:\n plt.show()","def render_viewer(configfile, datafile):\n try:\n page = 'analogviewer.html'\n with open(page, 'w') as fp:\n fp.write(templating.render_template(\n page,\n configfile=configfile,\n datafile=datafile,\n # static_url=config.output_url() + '/static'))\n static_url='../static'))\n prepare_static_folder()\n except Exception:\n msg = \"Failed to render analogviewer.\"\n LOGGER.exception(msg)\n raise Exception(msg)\n else:\n return page","def main():\n dpi = 1\n dpi = 2\n width = int(360)\n height = int(130)\n mywidth = int(width*dpi)\n myheight = int(height*dpi)\n FWHM = 7.5 # degrees\n FWHM = 10.0 # degrees\n FWHM = 5.0 # degrees\n FWHM = 3.0 # degrees\n FWHM = 1.0 # degrees\n weight = 1.\n\n nargs = len(sys.argv)\n if nargs < 2:\n print('GR: GRid Observations of integrated intensity produced by the T Command')\n print('GR produces fits images for each of the horns used for the observations.')\n print('For observations at the same coordinates, the ratios of intensities are also produced.')\n print('The FITS format files require header information, which is copied from the')\n print('Cold Load File provided by the user')\n print('GR RA|GAL <cold file name> <savefile1> [<savefile2> ... <savefileN>]')\n print(\"\")\n print('Glen Langston, National Science Foundation -- 20 May 12')\n exit()\n\n gridtype = sys.argv[1]\n gridtype = gridtype.upper()\n print('Grid Type: ', gridtype)\n\n # enable having ra going from 24 to 0 hours == 360 to 0 degrees\n xsign = 1.\n xoffset = 0.\n if gridtype == 'RA':\n xmin = 0.\n xmax = 360.\n ymin = -40.\n ymax = 90.\n maptype = 'RA'\n elif gridtype == '-RA':\n xmin = 0.\n xmax = 360.\n ymin = -40.\n ymax = 90.\n xsign = -1.\n xoffset = 360. # when x = 360. should be at zero.\n maptype = 'RA'\n elif gridtype == '-EL':\n xmin = 0.\n xmax = 360.\n ymin = 0.\n ymax = 90.\n xsign = -1.\n xoffset = 360. # when x = 360. should be at zero.\n maptype = 'AZEL'\n elif gridtype == 'RA0':\n xmin = 0.\n xmax = 360.\n ymin = -41.\n ymax = 89.\n xsign = -1.\n xoffset = 180. # when x = 360. should be at zero.\n gridtype = 'RA'\n elif gridtype == 'GAL':\n xmin = -180.\n xmax = 180.\n ymin = -90.\n ymax = 90.\n maptype = 'GAL'\n\n if gridtype != 'RA' and gridtype != 'GAL' and gridtype != '-RA' and gridtype != \"RA0\":\n print('Error parsing grid type: ', gridtype)\n print('1st argument should be either RA, -RA or GAL')\n exit()\n\n rs = radioastronomy.Spectrum()\n\n if doRatio: \n #create the grid with map parameters\n grid1 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n grid2 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n grid3 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n grid4 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n # put each telescope in a different grid\n grids = [grid1, grid2, grid3, grid4]\n\n gridall = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\n height=height, dpi=dpi, FWHM=FWHM, \\\n projection=\"-CAR\", gridtype=maptype)\n \n\n projection = \"-AIT\"\n# coldfile \n coldfile = sys.argv[2]\n# get telescope geographic location etc\n print(\"Reading Observing parameters from: %s\" % (coldfile))\n rs.read_spec_ast(coldfile)\n print(\"Observer: %s \" % (rs.observer))\n\n# first read through all data and find hot load\n names = sys.argv[3:]\n names = sorted(names)\n\n firsttime = \"\"\n lasttime = \"\"\n count = 0\n # setup grid indicies so that cpuIndex goes to the correct grid\n # This assumes telescopes 2,3,4,5 are being used] \n gridIndex = [0,0,0,1,2,3]\n # for all save Files to Grid\n for filename in names:\n print(\"File: %s\" % (filename))\n f = open(filename)\n\n date = \"Unknown\"\n while date != \"\":\n date, time, cpuIndex, telaz, telel, tSys, tRx, tRms, tint, KperC, tSourcemax, velSource, dV, tVSum, tVSumRms, tSumKmSec, dTSumKmSec, gainFactor = gainfactor.readSaveValues( f)\n dlen = len(date)\n if dlen < 1:\n break\n if date[0] == \"#\":\n continue\n # else not a comment process the line\n count = count + 1\n isodate = \"20\"+date+\"T\"+time\n# print(\"DateTime: %s\" % (isodate))\n rs.utc = datetime.datetime.strptime(isodate,\"%Y-%m-%dT%H:%M:%S\")\n# print(\"Utc: %s\" % (rs.utc))\n rs.telaz = telaz\n rs.telel = telel\n rs.azel2radec()\n\n ra = rs.ra\n dec = rs.dec\n lon = rs.gallon\n lat = rs.gallat\n tsum = tSumKmSec\n tsdv = dTSumKmSec\n tmax = tSourcemax\n vave = tVSum\n vsdv = tVSumRms\n if firsttime == \"\":\n firsttime = date\n else:\n lasttime = date\n\n# if vave > -100. and vave < 100:\n# mygrid.convolve( lon, lat, vave, 1.)\n iGrid = gridIndex[cpuIndex]\n gainCorr = telescopefactors[iGrid]\n tsum = tsum * gainCorr\n if gridtype == 'RA':\n if doRatio:\n grids[iGrid].convolve(ra, dec, tsum, weight)\n gridall.convolve( ra, dec, tsum, weight)\n elif gridtype == '-RA':\n x = (ra*xsign) + xoffset\n if doRatio:\n grids[iGrid].convolve(x, dec, tsum, weight)\n gridall.convolve( x, dec, tsum, weight)\n elif gridtype == 'RA0':\n x = (ra*xsign) + xoffset\n if x < 0:\n x = x + xmax\n elif x > xmax:\n x = x - xmax\n if doRatio:\n grids[iGrid].convolve(x, dec, tsum, weight)\n gridall.convolve( x, dec, tsum, weight)\n else:\n if doRatio:\n grids[iGrid].convolve(lon, lat, tsum, weight)\n gridall.convolve( lon, lat, tsum, weight)\n\n if count == 0:\n print('Convolving Coordinates: ', ra, dec, lon, lat)\n print('Convolving Intensities: ', tsum, tsdv, vave, vsdv)\n print('Convolvign Parameters : ', n, time)\n count = count + 1\n # end reading all lines in save file\n f.close()\n\n # normalize each of the gridded images\n if doRatio:\n grids[0].normalize()\n grids[1].normalize()\n grids[2].normalize()\n grids[3].normalize()\n gridall.normalize()\n# mygrid.check()\n# zmin = -1000.\n# zmax = 3000.\n# limit grid intensities for plotting\n# mygrid.set_ij( 0, 0, zmax, 1.)\n# mygrid.set_ij( 1, 1, zmin, 1.)\n# mygrid.limit(zmin, zmax)\n\n subplots = False\n\n if subplots:\n fig, ax = plt.subplots(figsize=(myheight, mywidth), dpi=dpi)\n\n if gridtype == 'RA':\n cax = fig.add_axes([-180, 180], [-90, 90])\n else:\n cax = fig.add_axes([0, 24], [-90, 90])\n\n cbar = fig.colorbar(cax, ticks=[zmin, zmax], orientation='horizontal')\n cbar.ax.set_yticklabels([str(zmin), str(zmax)])\n\n ax.set_title(\"Citizen Science: Horn observations of our Galaxy\")\n else:\n#y_ticks = ymin + (ymax-ymin)*ticks/myheight\n\n ticks = np.arange(0, mywidth, 30*dpi)\n x_ticks = xmin + ((xmax-xmin)*ticks/mywidth)\n\n plt.imshow(gridall.image, interpolation='nearest', cmap=plt.get_cmap('jet'))\n\n if firsttime != lasttime:\n plt.title(\"Citizen Science: Observing our Galaxy: %s to %s\" % (firsttime, lasttime))\n else:\n plt.title(\"Citizen Science: Observing our Galaxy: %s\" % (firsttime))\n if gridtype == 'RA':\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Declination (degrees)\")\n labels = ticks/(mywidth/24)\n yticks = np.arange(0, myheight, 15*dpi)\n elif gridtype == '-RA':\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Declination (degrees)\")\n labels = 24 - (ticks/(mywidth/24))\n labels[0] = 0\n labels[0] = 24\n yticks = np.arange(0, myheight, 15*dpi)\n elif gridtype == '-EL':\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Elevation (degrees)\")\n labels = 24 - (ticks/(mywidth/24))\n labels[0] = 0\n labels[0] = 24\n yticks = np.arange(0, myheight, 15*dpi)\n elif gridtype == 'RA0': # put 0 hours in middle of plot\n plt.xlabel(\"Right Ascension (hours)\")\n plt.ylabel(\"Declination (degrees)\")\n labels = 12 - (ticks/(mywidth/24))\n nlabels = len(labels)\n for iii in range(nlabels):\n if labels[iii] < 0:\n labels[iii] = 24 + labels[iii]\n if labels[iii] == 24:\n labels[iii] = 0\n yticks = np.arange(0, myheight, 15*dpi)\n else:\n yticks = np.arange(0, myheight, 30*dpi)\n ticks = np.arange(0, mywidth, 30*dpi)\n x_ticks = xmin + (xmax-xmin)*ticks/mywidth\n labels = x_ticks\n plt.xlabel(\"Galactic Longitude (degrees)\")\n plt.ylabel(\"Galactic Latitude (degrees)\")\n # wnat an integer list of labels\n# slabels = str(labels)\n print(ticks, labels)\n y_ticks = ymax - (ymax-ymin)*yticks/myheight\n plt.yticks(yticks, y_ticks)\n plt.xticks(ticks, labels, rotation='horizontal')\n plt.colorbar()\n\n crval2 = (xmin + xmax)/2.\n crval1 = (ymin + ymax)/2.\n cdelt1 = (-1./float(dpi)) - .001\n cdelt2 = (1./float(dpi)) + .001\n if doRatio:\n# now show eacsh of the images\n for iGrid in range(4):\n imagetemp = copy.deepcopy(grids[iGrid].image)\n imagetemp2 = copy.deepcopy(grids[iGrid].image)\n kkk = myheight - 1\n for jjj in range(myheight):\n imagetemp[:][kkk] = imagetemp2[:][jjj]\n kkk = kkk - 1\n grids[iGrid].image = imagetemp\n writeFitsImage( rs, iGrid+2, grids[iGrid], projection)\n\n # put each telescope in a different grid\n ratio1 = copy.deepcopy(grid1)\n ratio2 = copy.deepcopy(grid1)\n ratio3 = copy.deepcopy(grid1)\n gratios = [ratio1, ratio2, ratio3]\n ratios = np.zeros(3)\n rmss = np.zeros(3)\n\n jGrid = 3\n for iGrid in range(3):\n print(\"Gain Ratios for Telescopes T%d and T%d\" % (iGrid+2, jGrid+2))\n ratio, rms, aratio = gridratio(grids[iGrid], grids[jGrid])\n ratios[iGrid] = ratio\n rmss[iGrid] = rms\n writeFitsImage( rs, iGrid+2, aratio, projection)\n \n writeFitsImage( rs, 0, gridall, projection)\n plt.show()","def heatmap():\n\n team = int(request.form.get('teamname'))\n player = int(request.form.get('player'))\n attacks = request.form.get('attacks')\n data = request.form.get('datafiles')\n\n print 'Making heat map for player #%d, team #%d, attacks %s' % (player, team, attacks)\n\n if attacks.lower() == 'all':\n attacks = ['ALL']\n else:\n attacks = attacks.split(',')\n\n only_kills = request.form.get('kills')\n\n files = []\n if data == 'uploads':\n data = 'uploads/%d' % session['user_id']\n folder = 'data/' + data\n\n data_path = os.path.join(os.getcwd(), folder)\n if not os.path.isdir(data_path):\n os.makedirs(data_path)\n\n for f in os.listdir(data_path):\n if f.endswith('.dvw'):\n files.append(folder + '/' + f)\n\n locations = []\n for file_name in files:\n parser = Parser(file_name)\n new_locations = parser.get_attack_info(team, player, attacks, only_kills=only_kills)\n locations.extend(new_locations)\n\n top_caption, bottom_caption = generate_caption(team, player, attacks, only_kills)\n output_url = generate_output_filename(team, player, attacks, only_kills, session['user_id'])\n output_dict = heat_map.draw_arcs_pillow(locations, output_url, top_caption=top_caption, bottom_caption=bottom_caption)\n\n result_dict = {\n 'output_url': output_url,\n 'width': output_dict['width'],\n 'height': output_dict['height'],\n }\n print locations\n print 'Rendering finished image', output_url\n return render_template('heatmap.html', result_dict=result_dict)","def plot_ebtel_dem(data_directory,data_file,**kwargs):\n \n #Load DEM data\n data = np.loadtxt(data_directory+data_file)\n #Slice array to get necessary vectors\n temp = data[:,0]\n dem_tr = data[:,1]\n dem_cor = data[:,2]\n dem_tot = data[:,3]\n em_cor = data[:,4]\n \n #Set up the figure\n fig = plt.figure(figsize=(10,10))\n ax = fig.gca()\n fs = 16\n ax.plot(temp,dem_tr,label=r'TR')\n ax.plot(temp,dem_cor,'r',label=r'Corona')\n ax.plot(temp,dem_tot,'g',label=r'Total')\n ax.plot(temp,em_cor,'--r',label=r'Corona EM')\n ax.legend()\n ax.set_title(r'EBTEL-C DEM',fontsize=fs)\n ax.set_xlabel(r'$\\log(T_{DEM})$ (K)',fontsize=fs)\n ax.set_ylabel(r'$\\log($DEM$)$ (cm$^{-5}$ K$^{-1}$)',fontsize=fs)\n ax.set_xlim([5.5,7.5])\n ax.set_ylim([18.0,30.0])\n \n #Check if output filename is specified\n if 'print_fig_filename' in kwargs:\n plt.savefig(kwargs['print_fig_filename'],format='eps',dpi=1000)\n else:\n plt.show()","def setUp(self):\n print \"Setting Up: %s\" % self.id()\n # render the template\n g.render_template(self.template_file,\n self.template_vars,\n self.output_file,\n self.search_path)\n\n # read the resulting config file built from template\n self.output_config = g.load_config(self.output_file)\n g.show_config(self.output_config)","def main():\n args = argument_parser()\n args.mbtiles_file.close()\n mbtiles_file = args.mbtiles_file.name\n zoom = args.zoom\n tile_x = args.tilex\n tile_y = args.tiley\n mvt_content = run(tile_x, tile_y, zoom, mbtiles_file)\n print(mvt_content, end='')","def lambda2(case_config_filepath, render_config_filepath):\n\n # Load config file with all common directory names\n dirname_config = configparser.ConfigParser()\n dirname_config.read(\"dirname.cfg\")\n\n # Get information from config files\n cconfd = load_config.get_config_params(case_config_filepath)\n rconfd = load_config.get_config_params(render_config_filepath)\n \n # Main output directory\n case_output = dirname_config[\"DIRECTORIES\"][\"RenderOutput\"] + cconfd[\"case_name\"] + \"/\"\n\n # Determine lambda2 output directories and make them if necessary\n lambda2_level = rconfd[\"lambda2_level\"]\n lambda2_specifier = \"l2\" + str(lambda2_level) + \"/\"\n geometry_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"ply\"]\n ply_lambda2_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"ply_lambda2\"] + lambda2_specifier\n image_lambda2_output_dir = case_output + dirname_config[\"DIRECTORIES\"][\"tstep_lambda2\"] + lambda2_specifier\n image_lambda2_output_dir_spec = dircheck.count_png_dirs(image_lambda2_output_dir)\n dircheck.check_make([ply_lambda2_output_dir, image_lambda2_output_dir_spec])\n\n # Write Blender config file\n blender_config_filedir = case_output + rconfd[\"render_name\"] + \"_blender.cfg\"\n load_config.write_config_file(config_filedir=blender_config_filedir,\n config_dict={\"image_output_dir_spec\": image_lambda2_output_dir_spec,\n \"ply_input_dir\": ply_lambda2_output_dir,\n \"interface_material_name\": \"Lambda2Contour\",\n \"bg_image_filepath\": rconfd[\"bg_image_filepath\"],\n \"view_fraction\": rconfd[\"view_fraction\"], \"render_scale\": 10,\n \"resolution_percentage\": rconfd[\"resolution_percentage\"],\n \"xres\": cconfd[\"xres\"], \"yres\": cconfd[\"yres\"], \"zres\": cconfd[\"zres\"],\n \"tres\": cconfd[\"tres\"], \"interface_half_enabled\": False,\n \"fog_enabled\": False,\n \"camera_azimuth_angle\": rconfd[\"camera_azimuth_angle\"],\n \"camera_elevation_angle\": rconfd[\"camera_elevation_angle\"],\n \"bg_color_1\": rconfd[\"bg_color_1\"], \"bg_color_2\": rconfd[\"bg_color_2\"]})\n\n # Extract droplet geometry\n # convert_data.conv_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=geometry_output_dir, tres=int(cconfd[\"tres\"]))\n\n # Extract lambda2 contour geometry\n convert_data.conv_lambda2_ply(h5dns_path=cconfd[\"h5dns_path\"], output_dir=ply_lambda2_output_dir, tres=cconfd[\"tres\"], contour_level=lambda2_level)\n\n # Launch Blender to perform rendering\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\"droplet_render.py\", blend_name=\"droplet_render.blend\")","def render(sim_file: str, only_stuck: bool) -> None:\n import DLA\n DLA.GREEN = (0, 0, 0) # type: ignore\n DLA.WHITE = (151, 151, 151, 150) # type: ignore\n from DLA import config\n config.USE_PYGAME = True # type: ignore\n from DLA import renderer\n renderer.render(Path(sim_file), only_stuck)","def plotMEC(dimensions = 2, J = 1, filename = None,N = [20], \\\r\n anneal = True, Tlim = [1,4], prelims = 50, sweeps = 200, \\\r\n plots = True, plainlines = False, steps = 150):\r\n \r\n #temperature linespace\r\n T = np.linspace(Tlim[0],Tlim[1], steps)\r\n \r\n #tabulated magnetisation arry list\r\n Ms = []\r\n \r\n #tabulated energy array list\r\n Es = []\r\n \r\n #tabulated heat capacities\r\n Cs = []\r\n \r\n #labels used for datasets in the plots\r\n labels = []\r\n \r\n #critical exponent function used to fit data.\r\n def f (x, p1, p2, p3) : return p1*(((p2-x)/p2) ** p3)\r\n \r\n \r\n \r\n #itterate over wanted values of N\r\n for k in range(len(N)):\r\n \r\n #magnetisations and energies for N(i)\r\n M = np.zeros(T.shape)\r\n E = np.zeros(T.shape)\r\n C = np.zeros(T.shape)\r\n \r\n #lattice for N(i)\r\n lattice = initialiser(N[k],dimensions = dimensions)\r\n\r\n\r\n \r\n #itterate over all temperatures, highest first\r\n for i in range(len(T)):\r\n #highest first\r\n index = len(T) - i - 1\r\n \r\n #run simulation\r\n (Mi,Ei,l) = simulation(N[k],T[index],sweeps, lattice,\\\r\n dimensions = dimensions, J = J, prelims = prelims)\r\n \r\n #tabulate obtained data\r\n M[index] = np.abs(np.mean(Mi))\r\n E[index] = np.mean(Ei)\r\n Ci = (np.std(Ei)/T[index] * N[k] /2)**2\r\n C[index] = np.mean(Ci)\r\n \r\n #change lattice that will be fed to the next simulation\r\n if anneal:\r\n lattice = l \r\n \r\n #tabulate data for N(i)\r\n Ms.append(M)\r\n Es.append(E)\r\n Cs.append(C)\r\n \r\n labels.append(\"N = \"+str(N[k]))\r\n \r\n if plots:\r\n orderpar = \"Magnetisation\" if J>0 else \"Staggered Magnetisation\"\r\n \r\n #plot data\r\n magfigure = makeplot(T,Ms, labels, \"Temperature / $[J/k_B]$\", orderpar,\\\r\n plainlines=plainlines)\r\n \r\n magfigure.show()\r\n \r\n enfigure = makeplot(T,Es, labels, \"Temperature / $[J/k_B]$\", \"Energy per spin / $[J]$\",\\\r\n plainlines=plainlines)\r\n enfigure.show()\r\n \r\n cfigure = makeplot(T, Cs, labels, \"Temperature / $[J/k_B]$\", \"Heat Capacity / $[k_B]$\",\\\r\n plainlines=plainlines)\r\n cfigure.show()\r\n \r\n #save plots\r\n if filename is not None:\r\n magfigure.savefig(filename+\".svg\")\r\n enfigure.savefig(filename+\"E.svg\")\r\n cfigure.savefig(filename+\"C.svg\")\r\n \r\n return(T, Ms, Cs)","def imdisplay(filename, representation):\n img = read_image(filename, representation)\n if representation == GS_REP:\n plt.imshow(img, cmap=plt.cm.gray)\n else:\n plt.imshow(img)","def render_configs(task):\n\n # run function to render global configs\n global_cfg = ibns_global(task)\n # write global config file for each host\n with open(f\"configs/{task.host}_dot1x_global.txt\", \"w+\") as file:\n file.write(global_cfg)\n # print completed hosts\n c_print(f\"*** {task.host}: dot1x global configuration rendered ***\")\n\n # run function to render snmp configs\n snmp_cfg = ibns_snmp(task)\n # function to run interface configs\n with open(f\"configs/{task.host}_snmp.txt\", \"w+\") as file:\n file.write(snmp_cfg)\n # print completed hosts\n c_print(f\"*** {task.host}: SNMP configuration rendered ***\")\n\n # run function to run interface configs\n intf_cfg = ibns_intf(task)\n # write interface config file for each host\n with open(f\"configs/{task.host}_dot1x_intf.txt\", \"w+\") as file:\n file.write(intf_cfg)\n # print completed hosts\n c_print(f\"*** {task.host}: dot1x intf configurations rendered ***\")","def generate_cfg():\n \n if not os.path.exists(cfg_path):\n os.mkdir(cfg_path)\n \n for img_path in get_template_paths():\n extractor = BlockExtractor(img_path)\n extractor.get_cfg()\n for block in extractor.get_blocks():\n img = BlockParser(img_path, block).block_image()\n #cv.imshow(\"Block\", img)\n #cv.waitKey() & 0xFF","def render(filename,i):\n print('running render')\n A = np.genfromtxt(filename,skip_header=1,dtype=float,delimiter=',')\n img = np.array(A[i,:],copy=True)\n print(img.shape)\n img = img.reshape(28,28)\n img = 255 - img\n print(img.shape)\n plt.imshow(img, cmap=\"gray\", vmin=0, vmax=255)\n plt.savefig(\"img\" + str(i)+\"render\"+ \".png\")","def _run():\n\n temperatures_kelvins = _create_temperature_grid()\n first_derivs_kelvins_pt01 = numpy.gradient(temperatures_kelvins)\n second_derivs_kelvins_pt01 = numpy.gradient(\n numpy.absolute(first_derivs_kelvins_pt01)\n )\n\n this_ratio = (\n numpy.max(temperatures_kelvins) /\n numpy.max(first_derivs_kelvins_pt01)\n )\n\n first_derivs_unitless = first_derivs_kelvins_pt01 * this_ratio\n\n this_ratio = (\n numpy.max(temperatures_kelvins) /\n numpy.max(second_derivs_kelvins_pt01)\n )\n\n second_derivs_unitless = second_derivs_kelvins_pt01 * this_ratio\n\n _, axes_object = pyplot.subplots(\n 1, 1, figsize=(FIGURE_WIDTH_INCHES, FIGURE_HEIGHT_INCHES)\n )\n\n temperature_handle = axes_object.plot(\n temperatures_kelvins, color=TEMPERATURE_COLOUR, linestyle='solid',\n linewidth=SOLID_LINE_WIDTH\n )[0]\n\n second_deriv_handle = axes_object.plot(\n second_derivs_unitless, color=SECOND_DERIV_COLOUR, linestyle='solid',\n linewidth=SOLID_LINE_WIDTH\n )[0]\n\n first_deriv_handle = axes_object.plot(\n first_derivs_unitless, color=FIRST_DERIV_COLOUR, linestyle='dashed',\n linewidth=DASHED_LINE_WIDTH\n )[0]\n\n this_min_index = numpy.argmin(second_derivs_unitless)\n second_derivs_unitless[\n (this_min_index - 10):(this_min_index + 10)\n ] = second_derivs_unitless[this_min_index]\n\n tfp_handle = axes_object.plot(\n -1 * second_derivs_unitless, color=TFP_COLOUR, linestyle='dashed',\n linewidth=DASHED_LINE_WIDTH\n )[0]\n\n axes_object.set_yticks([0])\n axes_object.set_xticks([], [])\n\n x_label_string = r'$x$-coordinate (increasing to the right)'\n axes_object.set_xlabel(x_label_string)\n\n legend_handles = [\n temperature_handle, first_deriv_handle, second_deriv_handle,\n tfp_handle\n ]\n\n legend_strings = [\n TEMPERATURE_LEGEND_STRING, FIRST_DERIV_LEGEND_STRING,\n SECOND_DERIV_LEGEND_STRING, TFP_LEGEND_STRING\n ]\n\n axes_object.legend(legend_handles, legend_strings, loc='lower right')\n\n print 'Saving figure to file: \"{0:s}\"...'.format(OUTPUT_FILE_NAME)\n pyplot.savefig(OUTPUT_FILE_NAME, dpi=FIGURE_RESOLUTION_DPI)\n pyplot.close()","def main(config_file, pre_nfiles=None, post_nfiles=None,\n cutoffs=[12.0, 60.0]):\n imu_complement_fig = (config_file[:-4] +\n \"_IMU_complementary_spectra.pdf\")\n imu_spectra_fig = (config_file[:-4] + \"_IMU_spectra.pdf\")\n nperseg1 = 60 * 5 * 10\n nperseg2 = 60 * 10\n fig, axs, leg = check.plot_IMU_euler_angles_spectra(config_file,\n nperseg=nperseg2,\n nfiles=pre_nfiles)\n fig.savefig(imu_complement_fig, bbox_extra_artists=(leg,),\n bbox_inches=\"tight\")\n plt.close(fig)\n fig_rate, _, fig_acc, _ = check.plot_IMU_spectra(config_file,\n nperseg=nperseg2,\n nfiles=pre_nfiles)\n with PdfPages(imu_spectra_fig) as pdf:\n pdf.savefig(fig_rate, bbox_inches=\"tight\")\n plt.close(fig_rate)\n pdf.savefig(fig_acc, bbox_inches=\"tight\")\n plt.close(fig_acc)\n\n # Look at individual periods\n config = db_flux.parse_config(config_file)\n input_files = config[\"EC Inputs\"][\"input_files\"]\n if post_nfiles is not None and post_nfiles < len(input_files):\n input_files = np.random.choice(input_files, post_nfiles,\n replace=False)\n imu2anem_pos = config[\"EC Motion Correction\"][\"imu2anemometer_pos\"]\n sample_freq_hz = config[\"EC Inputs\"][\"sample_frequency\"]\n Tcf, Ta = cutoffs\n for ifile in input_files:\n file_idx = ifile.index(ifile)\n ec_prep, _ = db_flux.prepare_period(ifile, config)\n wind = ec_prep[[\"wind_speed_u\", \"wind_speed_v\", \"wind_speed_w\"]]\n acceleration = ec_prep[[\"acceleration_x\",\n \"acceleration_y\",\n \"acceleration_z\"]]\n rate = ec_prep[[\"rate_x\", \"rate_y\", \"rate_z\"]]\n ec_corr = db_flux.wind3D_correct(wind.values, acceleration.values,\n rate.values,\n ec_prep.heading.values,\n ec_prep.speed_over_ground.values,\n imu2anem_pos, sample_freq_hz,\n Tcf, Ta)\n fig, _, leg = check.plot1_IMU_complementary_spectra(config_file,\n file_idx,\n Tcf=Tcf,\n Ta=Ta,\n nperseg=nperseg1)\n fig.savefig((ifile[:-4] + \"_euler_angle_spectra.pdf\"),\n bbox_extra_artists=(leg,), bbox_inches=\"tight\")\n plt.close()\n fig, axs, leg = check.plot_wind3D_spectra(wind.values,\n ec_corr.uvw_ship,\n nperseg=nperseg2)\n fig.savefig((ifile[:-4] + \"_uvw_spectra.pdf\"),\n bbox_extra_artists=(leg,), bbox_inches=\"tight\")\n plt.close()\n # Plot corrected wind cospectra\n fig, _, leg = check.plot_wind3D_cospectra(wind.values,\n ec_corr.uvw_ship,\n nperseg=nperseg2)\n fig.savefig((ifile[:-4] + \"_uvw_cospectra.pdf\"),\n bbox_extra_artists=(leg,), bbox_inches=\"tight\")\n plt.close()","def plot_vis_test(plotfile,pdf_file):\n\t# First some parameters looked up from configfile---------------------------------\n\t\n\tgrbdir = runconf['l2file'][0:10]\n\tpre_tstart = runconf['bkg1start']\n\tpre_tend = runconf['bkg1end']\n\ttrigtime = runconf['trigtime']\n\tgrb_tstart = runconf['transtart']\n\tgrb_tend = runconf['tranend']\n\tpost_tstart = runconf['bkg2start']\n\tpost_tend = runconf['bkg2end']\n\tt_src = grb_tend - grb_tstart \n\tt_tot = (pre_tend-pre_tstart)+(post_tend-post_tstart)\n\tra_tran = runconf['ra']\n\tdec_tran = runconf['dec']\n\tlc_bin = runconf['lc_bin']\n\talpha = runconf['alpha']\n\tbeta = runconf['beta']\n\tE0 = runconf['E0']\n\tA = runconf['A']\n\tsim_scale = t_src\n\tpixbin = int(runconf['pixsize'])\n\tcomp_bin = int(runconf['comp_bin'])\n\ttyp = runconf['typ']\n\n\t# Calling txy to calculate thetax thetay and the coordinates----------------------\n\t\n\tthetax,thetay,x,y,z,t = txy(runconf['mkffile'], trigtime, ra_tran, dec_tran)\n\t\n\t# Plot the 3d visualisation for the position of the transient---------------------\n\tplt.figure()\n\tfig = visualize_3d(grbdir,x,y,z, t, thetax, thetay, grbdir)\t\n\tpdf_file.savefig(fig)\n\t\n\t# Plotting the lightcurves for the four quadrants---------------------------------\n\tfig = plt.figure()\n\tclean_file = fits.open(runconf['infile'])\n\tplt.title('Light curves for '+grbdir + \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n\t\n\tquad0 = clean_file[1].data\n\tdata0,bin_edge = np.histogram(quad0['time'], bins=np.arange(quad0['time'][0],quad0['time'][-1],lc_bin))\n\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data0,label='Quad 0',lw=0.7)\n quad1 = clean_file[2].data\n\tdata1,bin_edge = np.histogram(quad1['time'], bins=np.arange(quad1['time'][0],quad1['time'][-1],lc_bin))\n\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data1,label='Quad 1',lw=0.7) \n\tquad2 = clean_file[3].data\n\tdata2,bin_edge = np.histogram(quad2['time'], bins=np.arange(quad2['time'][0],quad2['time'][-1],lc_bin))\n\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data2,label='Quad 2',lw=0.7)\n quad3 = clean_file[4].data\n data3,bin_edge = np.histogram(quad3['time'], bins=np.arange(quad3['time'][0],quad3['time'][-1],lc_bin))\n plt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data3,label='Quad 3',lw=0.7)\n\tplt.axvspan(grb_tstart,grb_tend,color='blue',alpha=0.1,label='GRB')\n\tplt.axvspan(pre_tstart,pre_tend,color='orange',alpha=0.2)\n\tplt.axvspan(post_tstart,post_tend,color='orange',alpha=0.2,label='Background')\n\tplt.legend(prop={'size':6})\n\tplt.xlim(pre_tstart-100,post_tend+100)\n\tpdf_file.savefig(fig)\n\t\n\t# Calling the sim_dph--------------------------------------------------------------\n\t\n\tgrb_flat,bkgd_flat,grb_dph,bkgd_dph,t_src,t_total = data_bkgd_image(grbdir,pre_tstart,pre_tend,grb_tstart,grb_tend,post_tstart,post_tend)\n\n\tsim_flat,sim_dph,badpix_mask,sim_err_dph = simulated_dph(grbdir,typ,t_src,alpha,beta,E0,A)\n\n\tsrc_dph = grb_dph-bkgd_dph*t_src/t_tot\n\n print \"Total counts in simulated dph: \",(sim_dph).sum()\n print \"Total counts after badpix mask is applied: \",(sim_dph*badpix_mask).sum()\n\tprint \"Excess counts in badpix masked src dph: \",(src_dph*badpix_mask).sum()\n \n\t# Plotting the DPHs before badpix correction---------------------------------------\n\t\n\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n\tplt.suptitle('DPHs before badpix correction for '+grbdir + \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n \t# Sim\n\tim = ax3.imshow(sim_dph,interpolation='none')\n\tax3.set_title('Sim DPH',fontsize=8)\n\tax3.set_xlim(-1,128 - 0.5)\n\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax3.spines['left'].set_position(('data',-0.5))\n\tax3.set_yticklabels([])\n\tax3.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\n\t\n\t # Source \n\tim = ax4.imshow(src_dph,interpolation='none',vmin=0)\n\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\n\tax4.set_xlim(-1,128 -0.5)\n\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax4.spines['left'].set_position(('data',-0.5))\n\tax4.set_yticklabels([])\n\tax4.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\n\n \t# Source + Background\n\tim = ax1.imshow(grb_dph,interpolation='none')\n\tax1.set_title('Src + Bkg DPH',fontsize=8)\n\tax1.set_xlim(-1,128 -0.5)\n\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax1.spines['left'].set_position(('data',-0.5))\n\tax1.set_yticklabels([])\n\tax1.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\n\n \t# Background\n\tim = ax2.imshow(bkgd_dph*t_src/t_total,interpolation='none')\n\tax2.set_title('Bkg DPH',fontsize=8)\n\tax2.set_xlim(-1,128 -0.5)\n\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax2.spines['left'].set_position(('data',-0.5))\n\tax2.set_yticklabels([])\n\tax2.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\n\tf.set_size_inches([6.5,6.5])\n\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\n\t\n\t# Plotting the Badpix mask---------------------------------------------\n\n\tfig = plt.figure()\n\tax = plt.subplot(111)\n\tplt.title('Badpix Mask for '+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n\tim = ax.imshow(badpix_mask,interpolation='none')\n\tax.set_xlim(-9,128 -0.5)\n\tax.axvline(x=-5.,ymin=0,ymax=64,linewidth=5,color='k')\n\tax.spines['left'].set_position(('data',-0.5))\n\tax.xaxis.set_ticks(np.arange(0,128,16))\n\tax.yaxis.set_ticks(np.arange(0,128,16))\n\tfig.colorbar(im,ax=ax,fraction=0.046, pad=0.04)\n\t\n\tpdf_file.savefig(fig) # saves the current figure into a pdf_file page\n\n\t# Plotting badpix masked graphs--------------------------------------------\n\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay))\n \t# Sim\n\tim = ax3.imshow(sim_dph*badpix_mask,interpolation='none')\n\tax3.set_title('Sim DPH',fontsize=8)\n\tax3.set_xlim(-1,128 -0.5)\n\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax3.spines['left'].set_position(('data',-0.5))\n\tax3.set_yticklabels([])\n\tax3.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\n\n\t # Source \n\tim = ax4.imshow(src_dph*badpix_mask,interpolation='none',vmin=0)\n\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\n\tax4.set_xlim(-1,128 -0.5)\n\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax4.spines['left'].set_position(('data',-0.5))\n\tax4.set_yticklabels([])\n\tax4.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\n\n\t # Source + Background\n\tim = ax1.imshow(grb_dph*badpix_mask,interpolation='none')\n\tax1.set_title('Src + Bkg DPH',fontsize=8)\n\tax1.set_xlim(-1,128 -0.5)\n\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax1.spines['left'].set_position(('data',-0.5))\n\tax1.set_yticklabels([])\n\tax1.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\n\t\n\t # Background\n\tim = ax2.imshow(bkgd_dph*badpix_mask*t_src/t_total,interpolation='none')\n\tax2.set_title('Bkg DPH',fontsize=8)\n\tax2.set_xlim(-1,128 -0.5)\n\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\tax2.spines['left'].set_position(('data',-0.5))\n\tax2.set_yticklabels([])\n\tax2.xaxis.set_ticks(np.arange(0,128,16))\n\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\n\tf.set_size_inches([6.5,6.5])\n\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\n\n\t# Plotting badpix masked graphs (Binned) ----------------------------------------------------\n\tfor p in [4,8,16]:\n\t\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\n\t\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} \".format(tx=thetax,ty=thetay)+ \"pixsize=\"+str(p))\n\t\t # Sim\n\t\tim = ax3.imshow(resample(sim_dph*badpix_mask,p),interpolation='none')\n\t\tax3.set_title('Sim DPH',fontsize=8)\n\t\tax3.set_xlim(-1,128/p -0.5)\n\t\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax3.spines['left'].set_position(('data',-0.5))\n\t\tax3.set_yticklabels([])\n\t\tax3.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n\t\tax3.set_xticklabels(np.arange(0,128,16))\n\t\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\n\t\t\n\t\t # Source \n\t\tim = ax4.imshow(resample(src_dph*badpix_mask,p),interpolation='none',vmin=0)\n\t\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\n\t\tax4.set_xlim(-1,128/p -0.5)\n\t\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax4.spines['left'].set_position(('data',-0.5))\n\t\tax4.set_yticklabels([])\n ax4.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n ax4.set_xticklabels(np.arange(0,128,16))\t\t\n\t\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\n\t\t\n\t\t # Source + Background\n\t\tim = ax1.imshow(resample(grb_dph*badpix_mask,p),interpolation='none')\n\t\tax1.set_title('Src + Bkg DPH',fontsize=10)\n\t\tax1.set_xlim(-1,128/p -0.5)\n\t\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax1.spines['left'].set_position(('data',-0.5))\n\t\tax1.set_yticklabels([])\n ax1.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n ax1.set_xticklabels(np.arange(0,128,16))\t\t\n\t\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\n\t\t\n\t\t # Background\n\t\tim = ax2.imshow(resample(bkgd_dph*badpix_mask*t_src/t_total,p),interpolation='none')\n\t\tax2.set_title('Bkg DPH',fontsize=8)\n\t\tax2.set_xlim(-1,128/p -0.5)\n\t\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\n\t\tax2.spines['left'].set_position(('data',-0.5))\n\t\tax2.set_yticklabels([])\n ax2.xaxis.set_ticks(np.arange(0,(128/p),16/p))\n ax2.set_xticklabels(np.arange(0,128,16))\t\t\n\t\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\n\t\tf.set_size_inches([6.5,6.5])\n\t\t\n\t\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\n\n\n\t# Plotting the comparison graphs with equal bins ---------------------------------------\n\tprint \"No. of pixels with zero counts in sim_dph: \",sim_dph[sim_dph==0].size\n\tprint \"No. of pixels with zero counts in grb_dph(no bkg subtration): \",grb_dph[grb_dph==0].size\n\t\n\t# Generating the array for module number ------------------------------------------------\n\tA = ['A'+str(i) for i in range(16)]\n\tB = np.flip(['B'+str(i) for i in range(16)],0)\n\tC = np.flip(['C'+str(i) for i in range(16)],0)\n\tD = ['D'+str(i) for i in range(16)]\n\tquad_a = np.reshape(A,(4,4))\n\tquad_b = np.reshape(B,(4,4))\n\tquad_c = np.reshape(C,(4,4))\n\tquad_d = np.reshape(D,(4,4))\n\tMod_arr = np.ndarray((8,8),dtype='|S3')\n\tMod_arr[:4,:4] = quad_a\n\tMod_arr[:4,4:] = quad_b\n\tMod_arr[4:,4:] = quad_c\n\tMod_arr[4:,:4] = quad_d\n\tMod_names = Mod_arr.flatten()\n\t#print \"Module name array : \",Mod_names\n\t#-----------------------------------------------------------------------------------------\n\t\t\n\tsim_dph = sim_dph*badpix_mask\n\tsim_err_dph = sim_err_dph*badpix_mask\n grb_dph = grb_dph*badpix_mask\n bkgd_dph = bkgd_dph*badpix_mask\n\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\n\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\n\n\tsim_bin = resample(sim_dph,pixbin)\n\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\t\n\tgrb_bin = resample(grb_dph,pixbin)\n\tbkgd_bin = resample(bkgd_dph,pixbin)\n\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\t\n\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\t\n\n\tsim_flat_bin = sim_bin.flatten()\n\tsim_err_flat_bin = sim_err_bin.flatten()\n\tgrb_flat_bin = grb_bin.flatten()\n\tbkgd_flat_bin = bkgd_bin.flatten()\n\tgrb_err_flat_bin = grb_err_bin.flatten()\n\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\n\t\n\n\t # Defining model background and data\n\tmodel = sim_flat_bin\n\tmodel_copy = np.copy(model)\n\tbkgd = bkgd_flat_bin*t_src/t_tot\n\tsrc = grb_flat_bin\n\t\n\tdata = src - bkgd\n\tdata_copy = np.copy(data)\n\t\n\terr_src = grb_err_flat_bin\n\terr_bkgd = bkgd_err_flat_bin\n\terr_model = sim_err_flat_bin\n\terr_model_copy = np.copy(err_model)\n\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\terr_data_copy = np.copy(err_data)\n\t\n\tratio = data/model\n\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\n\t\n\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n\t\n\t # PLotting the comparison plots\n\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\n\t\n\tax1.set_title(\"Comparison between simulated and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f}\".format(tx=thetax,ty=thetay,c=chi_sq))\n\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation\",elinewidth=0.5)\n\tax1.legend()\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\n\tax1.set_ylabel('Counts')\n\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n\n\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\"Ratio = Data/Model\",elinewidth=0.5)\n\tax2.xaxis.set_ticks(np.arange(0,len(data)))\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\n\tax2.tick_params(labelsize=5)\n\tax2.axhline(y=1,linewidth=0.5,color='k')\n\tax2.legend()\n\tax2.set_xlabel('CZT Modules')\n\tax2.set_ylabel('Ratio of counts')\n\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\n\tplt.tight_layout(h_pad=0.0)\n\tf.set_size_inches([6.5,10])\n\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\n\n\t# Plotting comparison graphs with random binning------------------------------\n\t\n sim_flat = sim_dph.flatten()\n\tsim_err_flat = sim_err_dph.flatten()\n grb_flat = grb_dph.flatten()\n bkgd_flat = bkgd_dph.flatten()\n\tsrc_flat = src_dph.flatten()\n\t\n\torder = np.random.permutation(np.arange(0,len(sim_flat)))\n\t\n sim_flat = sim_flat[order]\n\tsim_err_flat = sim_err_flat[order]\n\tgrb_flat = grb_flat[order]\n\tbkgd_flat = bkgd_flat[order]\n\tsrc_flat = src_flat[order]\n\t\n\tprint \"No. of pixels with zero counts in sim_flat: \",sim_flat[sim_flat==0].size\n\tprint \"No. of pixels with zero counts in src_flat: \",src_flat[src_flat==0].size\n\t\n\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\n\tx = np.zeros(len(bins)+2,dtype=np.int64)\n\tx[0] = 0\n\tx[-1] = len(sim_flat)\n\tx[1:-1] = bins\n\t\n\t#print \"The bin edges: \",x # ---------------------------------------------------------------\n\t\n\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\n\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\t\n\tprint \"Total sim_flat_bin : \",sim_flat_bin.sum() #-----------------------------------------\n\t#print \" Max(cumsum) : \",max(np.cumsum(sim_flat)) #-----------------------------------------\n\n # Defining model background and data\n model = sim_flat_bin #avg_flat_bin\n bkgd = bkgd_flat_bin*t_src/t_tot\n src = grb_flat_bin\n\t\n data = src - bkgd\n\n err_src = np.sqrt(src)\n err_bkgd = np.sqrt(bkgd_flat_bin)\n err_model = sim_err_flat_bin\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\t\n\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n # PLotting the comparison plots\n fig = plt.figure()\n plt.title(\"Comparison between simulated and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f} \".format(tx=thetax,ty=thetay,c=chi_sq_new))\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation\",elinewidth=0.5)\n plt.ylabel('Counts')\n\tplt.xlabel('Random Bins')\n\tplt.xticks(np.arange(0,(len(data)),1))\n\tplt.legend()\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\n\n\t# Plotting observed vs predicted counts------------------------------------------------------\n\n\tfig = plt.figure()\n\tplt.title(grbdir + r\" : Observed vs Predicted counts with $\\chi^2$={cs:0.1f}\".format(cs=chi_sq))\n\tplt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\n\tfor i in range(len(model_copy)):\n\t\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\n\tplt.plot(np.arange(-1000,1000),np.arange(-1000,1000),'k',linewidth=0.5)\n\tplt.xlim(min(model_copy)-5,max(model_copy)+5)\n\tplt.ylim(min(data_copy)-5,max(data_copy)+5)\n\tplt.xlabel('Predicted Counts')\n\tplt.ylabel('Observed Counts')\n\tplt.legend()\n\tplt.grid()\n\tpdf_file.savefig(fig)\n\n\t# Scaling the model using curve fit =============================================================== \n\t\n\tparam,pcov = curve_fit(fit_line_int,model_copy,data_copy)\n\tscaling = param[0]\n\tintercept = param[1]\n\t\n\t# Plotting the scaled plots ===================================================================\n\t# Plotting the comparison graphs with equal bins ---------------------------------------\n\n\tsim_dph = sim_dph*badpix_mask\n\tsim_err_dph = sim_err_dph*badpix_mask\n grb_dph = grb_dph*badpix_mask\n bkgd_dph = bkgd_dph*badpix_mask\n\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\n\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\n\n\tsim_bin = resample(sim_dph,pixbin)\n\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\t\n\tgrb_bin = resample(grb_dph,pixbin)\n\tbkgd_bin = resample(bkgd_dph,pixbin)\n\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\t\n\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\t\n\n\tsim_flat_bin = sim_bin.flatten()\n\tsim_err_flat_bin = sim_err_bin.flatten()\n\tgrb_flat_bin = grb_bin.flatten()\n\tbkgd_flat_bin = bkgd_bin.flatten()\n\tgrb_err_flat_bin = grb_err_bin.flatten()\n\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\n\t\n\n\t # Defining model background and data\n\t#model = sim_flat_bin*scaling\n\tmodel = sim_flat_bin*scaling + intercept\n\tbkgd = bkgd_flat_bin*t_src/t_tot\n\tsrc = grb_flat_bin\n\t\n\tdata = src - bkgd\n\t\n\terr_src = grb_err_flat_bin\n\terr_bkgd = bkgd_err_flat_bin\n\t#err_model = sim_err_flat_bin*scaling\n\terr_model = sim_err_flat_bin*scaling\n\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\t\n\tratio = data/model\n\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\n\t\n\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n\t\n\t # PLotting the comparison plots\n\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\n\t\n\tax1.set_title(\"Comparison between simulated (scaled) and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f} \".format(tx=thetax,ty=thetay,c=chi_sq))\n\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation (scaling = {s:0.2f},offset = {o:0.2f})\".format(s=scaling,o=intercept),elinewidth=0.5)\n\tax1.legend()\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\n\tax1.set_ylabel('Counts')\n\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n\t\n\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\"Ratio = Data/Model(scaling = {s:0.2f}, offset={o:0.2f})\".format(s=scaling,o=intercept),elinewidth=0.5)\n\tax2.xaxis.set_ticks(np.arange(0,len(data)))\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\n\tax2.tick_params(labelsize=5)\n\tax2.axhline(y=1,linewidth=0.5,color='k')\n\tax2.legend()\n\tax2.set_xlabel('CZT Modules')\n\tax2.set_ylabel('Ratio of counts')\n\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\n\tplt.tight_layout(h_pad=0.0)\n\tf.set_size_inches([6.5,10])\n\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\n\n\t# Plotting comparison graphs with random binning------------------------------\n\t\n sim_flat = sim_dph.flatten()\n\tsim_err_flat = sim_err_dph.flatten()\n grb_flat = grb_dph.flatten()\n bkgd_flat = bkgd_dph.flatten()\n\tsrc_flat = src_dph.flatten()\n\t\n\torder = np.random.permutation(np.arange(0,len(sim_flat)))\n\t\n sim_flat = sim_flat[order]\n\tsim_err_flat = sim_err_flat[order]\n\tgrb_flat = grb_flat[order]\n\tbkgd_flat = bkgd_flat[order]\n\tsrc_flat = src_flat[order]\n\t\n\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\n\tx = np.zeros(len(bins)+2,dtype=np.int64)\n\tx[0] = 0\n\tx[-1] = len(sim_flat)\n\tx[1:-1] = bins\n\t\n\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\n\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\n\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\n\t\n # Defining model background and data\n #model = sim_flat_bin*scaling\n\tmodel = sim_flat_bin*scaling + intercept\n bkgd = bkgd_flat_bin*t_src/t_tot\n src = grb_flat_bin\n\t\n data = src - bkgd\n\n err_src = np.sqrt(src)\n err_bkgd = np.sqrt(bkgd_flat_bin)\n #err_model = sim_err_flat_bin*scaling\n\terr_model = sim_err_flat_bin*scaling\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\n\t\n\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\n # PLotting the comparison plots\n fig = plt.figure()\n plt.title(\"Comparison between simulated(scaled) and real data for \"+grbdir+ \"\\n\" + r\"$\\theta_x$={tx:0.1f} and $\\theta_y$={ty:0.1f} $\\chi^2$={c:0.1f} \".format(tx=thetax,ty=thetay,c=chi_sq_new))\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\"Data\",elinewidth=0.5)\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\"Simulation (scaling = {s:0.2f}, offset = {o:0.2f})\".format(s=scaling,o=intercept),elinewidth=0.5)\n plt.ylabel('Counts')\n\tplt.xlabel('Random Bins')\n\tplt.xticks(np.arange(0,(len(data)),1))\n\tplt.legend()\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\n\n\n\t# Plotting observed vs predicted counts--------------------------------------------------------\n\n\tfig = plt.figure()\n plt.title(grbdir + r\" : Observed vs Predicted counts with $\\chi^2$ = {cs:0.1f}\".format(cs=chi_sq))\n plt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\n\tfor i in range(len(model_copy)):\t\n\t\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\n #plt.plot(np.arange(-1000,1000),fit_line(np.arange(-1000,1000),scaling),'k',linewidth=0.5,label='m = {s:0.2f}'.format(s=scaling))\n\tplt.plot(np.arange(-1000,1000),fit_line_int(np.arange(-1000,1000),scaling,intercept),'k',linewidth=0.5,label='scaling = {s:0.2f}, offset = {i:0.2f}'.format(s=scaling,i=intercept))\n\tplt.plot(np.arange(min(model_copy)-5,max(model_copy)+5),np.ones(len(np.arange(min(model_copy)-5,max(model_copy)+5)))*intercept,'r-',label='intercept',linewidth=0.5)\n plt.xlim(min(model_copy)-5,max(model_copy)+5)\n plt.ylim(min(data_copy)-5,max(data_copy)+5)\n plt.xlabel('Predicted Counts')\n plt.ylabel('Observed Counts')\n\tplt.legend()\n\tplt.grid()\n pdf_file.savefig(fig)\n\t\t\n\tprint \"===============================================================================================\"\n\t\n\treturn","def show_template_bundles(final_streamlines, template_path, fname):\n import nibabel as nib\n from fury import actor, window\n\n renderer = window.Renderer()\n template_img_data = nib.load(template_path).get_data().astype(\"bool\")\n template_actor = actor.contour_from_roi(\n template_img_data, color=(50, 50, 50), opacity=0.05\n )\n renderer.add(template_actor)\n lines_actor = actor.streamtube(\n final_streamlines, window.colors.orange, linewidth=0.3\n )\n renderer.add(lines_actor)\n window.record(renderer, n_frames=1, out_path=fname, size=(900, 900))\n return","def main(argv=None):\n parser = argparse.ArgumentParser(\"Test TFL attention mechanism\")\n parser.add_argument('-i', '--image', type=str, help='Path to an image')\n parser.add_argument(\"-j\", \"--json\", type=str, help=\"Path to json GT for comparison\")\n parser.add_argument('-d', '--dir', type=str, help='Directory to scan images in')\n args = parser.parse_args(argv)\n default_base = '../data'\n if args.dir is None:\n args.dir = default_base\n flist = glob.glob(os.path.join(args.dir, '*_leftImg8bit.png'))\n for image in flist:\n json_fn = image.replace('_leftImg8bit.png', '_gtFine_polygons.json')\n if not os.path.exists(json_fn):\n json_fn = None\n test_find_tfl_lights(image, json_fn)\n if len(flist):\n print(\"You should now see some images, with the ground truth marked on them. Close all to quit.\")\n else:\n print(\"Bad configuration?? Didn't find any picture to show\")\n plt.show(block=True)","def main(argv=None):\n parser = argparse.ArgumentParser(\"Test TFL attention mechanism\")\n parser.add_argument('-i', '--image', type=str, help='Path to an image')\n parser.add_argument(\"-j\", \"--json\", type=str, help=\"Path to json GT for comparison\")\n parser.add_argument('-d', '--dir', type=str, help='Directory to scan images in')\n args = parser.parse_args(argv)\n default_base = '../data'\n if args.dir is None:\n args.dir = default_base\n flist = glob.glob(os.path.join(args.dir, '*_leftImg8bit.png'))\n for image in flist:\n json_fn = image.replace('_leftImg8bit.png', '_gtFine_polygons.json')\n if not os.path.exists(json_fn):\n json_fn = None\n test_find_tfl_lights(image, json_fn)\n if len(flist):\n print(\"You should now see some images, with the ground truth marked on them. Close all to quit.\")\n else:\n print(\"Bad configuration?? Didn't find any picture to show\")\n plt.show(block=True)","def configureScenario(out, templatepath, geometrypath, nx, ny, nz, sx, sy, sz,\n vtkpath, enable_timing, timingpath, enable_vtk, initial,\n bakspath):\n tree = ET.parse(templatepath)\n root = tree.getroot()\n\n for child in root.findall(\"parallel\"):\n child.attrib[\"numProcessorsX\"] = str(nx)\n child.attrib[\"numProcessorsY\"] = str(ny)\n child.attrib[\"numProcessorsZ\"] = str(nz)\n\n for child in root.findall(\"vtk\"):\n child.attrib[\"enabled\"] = str(enable_vtk).lower()\n child.text = os.path.join(vtkpath, \"vtk\")\n\n for child in root.findall(\"timing\"):\n child.attrib[\"enabled\"] = str(enable_timing).lower()\n child.text = timingpath\n else:\n node = ET.Element(\"timing\", {\"enabled\": str(enable_timing).lower()})\n node.text = timingpath\n root.append(node)\n\n if sx != None and sy != None:\n for child in root.findall(\"geometry\"):\n child.attrib[\"sizeX\"] = str(sx)\n child.attrib[\"sizeY\"] = str(sy)\n child.attrib[\"sizeZ\"] = str(sz)\n\n for child in root.findall(\"geometry\"):\n attrs = child.attrib\n\n if \"obstacle\" in attrs and os.path.isfile(attrs[\"obstacle\"]):\n shutil.copyfile(attrs[\"obstacle\"], geometrypath)\n child.attrib[\"obstacle\"] = geometrypath\n\n for child in root.findall(\"restart\"):\n attrs = child.attrib\n\n if \"in\" in attrs and os.path.isfile(attrs[\"in\"] + \".bak\"):\n shutil.copyfile(attrs[\"in\"] + \".bak\", initial + \".bak\")\n child.attrib[\"in\"] = initial\n\n if \"out\" in attrs:\n child.attrib[\"out\"] = bakspath + \"//\" + child.attrib[\"out\"]\n\n tree.write(out)","def prepare_templates(params, outfile, redo=False):\n if os.path.exists(outfile) and not redo:\n return\n emiles = EMILES()\n wmin = params[\"wmin\"] * u.micrometer\n wmax = params[\"wmax\"] * u.micrometer\n # Modify wmin to compensate for the recession velocity of the system\n zmax = (params[\"vsyst\"] + 3000) / const.c.to(\"km/s\").value\n wrest = wmin / (1 + zmax)\n grid = np.array(np.meshgrid(params[\"ages\"], params[\"metals\"],\n params[\"bis\"])).T.reshape(-1, 3)\n ssppars = Table(grid, names=[\"T\", \"Z\", \"imf\"])\n filenames = []\n for args in grid:\n filenames.append(os.path.join(emiles.data_dir,\n emiles.filename(*args)))\n wave, spec = misc.read_spec(filenames[0])\n wave = wave * u.angstrom\n idx = np.where((wave > wrest) & (wave <= wmax))\n wave = wave[idx]\n spec = spec[idx]\n wrange = [wave[0].to(\"angstrom\").value, wave[-1].to(\"angstrom\").value]\n newflux, logLam, velscale = util.log_rebin(wrange, spec,\n velscale=params[\"velscale\"])\n ssps = np.zeros((len(filenames), len(newflux)))\n print(\"Processing SSP files\")\n for i, fname in tqdm(enumerate(filenames)):\n spec = fits.getdata(fname)[idx]\n newflux, logLam, velscale = util.log_rebin(wrange, spec,\n velscale=params[\"velscale\"])\n ssps[i] = newflux\n norm = np.median(ssps)\n ssps /= norm\n hdu1 = fits.PrimaryHDU(ssps)\n hdu1.header[\"EXTNAME\"] = \"SSPS\"\n hdu1.header[\"BSCALE\"] = (norm, \"Scale to convert from ADU to flux.\")\n hdu2 = fits.BinTableHDU(ssppars)\n hdu2.header[\"EXTNAME\"] = \"PARAMS\"\n hdu1.header[\"CRVAL1\"] = logLam[0]\n hdu1.header[\"CD1_1\"] = logLam[1] - logLam[0]\n hdu1.header[\"CRPIX1\"] = 1.\n # Making wavelength array\n hdu3 = fits.BinTableHDU(Table([logLam], names=[\"loglam\"]))\n hdu3.header[\"EXTNAME\"] = \"LOGLAM\"\n hdulist = fits.HDUList([hdu1, hdu2, hdu3])\n hdulist.writeto(outfile, overwrite=True)\n return","def display_file(epd, file_name):\n\n image = Image.open(file_name)\n image = ImageOps.grayscale(image)\n\n # crop to the middle\n w,h = image.size\n x = w / 2 - epd.width / 2\n y = h / 2 - epd.height / 2\n\n cropped = image.crop((x, y, x + epd.width, y + epd.height))\n bw = cropped.convert(\"1\", dither=Image.FLOYDSTEINBERG)\n\n epd.display(bw)\n epd.update()\n\n\n time.sleep(3) # delay in seconds\n\n rs = image.resize((epd.width, epd.height))\n bw = rs.convert(\"1\", dither=Image.FLOYDSTEINBERG)\n\n epd.display(bw)\n epd.update()\n\n time.sleep(3) # delay in seconds","def display_character(window, name, path_template):\n # Could be improved a lot.\n border_size = 20\n path = \".\".join((path_template, \"200\", \"png\"))\n pic = pygame.image.load(path)\n pic_w, pic_h = pic.get_size()\n text = ft_title.render(\" \".join((\"<-\", name, \"->\")), 1, WHITE)\n text_w, text_h = text.get_size()\n pygame.draw.rect(window, GREY, (SCREEN_W/2 - pic_w/2 - border_size,\n SCREEN_H/2 - pic_h/2 - text_h - border_size,\n pic_w + border_size*2, pic_h + border_size*2),\n border_size)\n window.blit(pic, (SCREEN_W/2 - pic_w/2, SCREEN_H/2 - pic_h/2 - text_h))\n window.blit(text, (SCREEN_W/2 - text_w/2, SCREEN_H/2 + pic_h/2 - text_h/2))","def load_templates(fwhm=400, line_complexes=True, stars=False,\n full_line_list=None, continuum_list=None,\n fsps_templates=False, alf_template=False):\n \n if stars:\n # templates = glob.glob('%s/templates/Pickles_stars/ext/*dat' %(os.getenv('GRIZLI')))\n # templates = []\n # for t in 'obafgkmrw':\n # templates.extend( glob.glob('%s/templates/Pickles_stars/ext/uk%s*dat' %(os.getenv('THREEDHST'), t)))\n # templates.extend(glob.glob('%s/templates/SPEX/spex-prism-M*txt' %(os.getenv('THREEDHST'))))\n # templates.extend(glob.glob('%s/templates/SPEX/spex-prism-[LT]*txt' %(os.getenv('THREEDHST'))))\n # \n # #templates = glob.glob('/Users/brammer/Downloads/templates/spex*txt')\n # templates = glob.glob('bpgs/*ascii')\n # info = catIO.Table('bpgs/bpgs.info')\n # type = np.array([t[:2] for t in info['type']])\n # templates = []\n # for t in 'OBAFGKM':\n # test = type == '-%s' %(t)\n # so = np.argsort(info['type'][test])\n # templates.extend(info['file'][test][so])\n # \n # temp_list = OrderedDict()\n # for temp in templates:\n # #data = np.loadtxt('bpgs/'+temp, unpack=True)\n # data = np.loadtxt(temp, unpack=True)\n # #data[0] *= 1.e4 # spex\n # scl = np.interp(5500., data[0], data[1])\n # name = os.path.basename(temp)\n # #ix = info['file'] == temp\n # #name='%5s %s' %(info['type'][ix][0][1:], temp.split('.as')[0])\n # print(name)\n # temp_list[name] = utils.SpectrumTemplate(wave=data[0],\n # flux=data[1]/scl)\n \n # np.save('stars_bpgs.npy', [temp_list])\n \n \n # tall = np.load(os.path.join(os.getenv('GRIZLI'), \n # 'templates/stars.npy'))[0]\n # \n # return tall\n # \n # temp_list = OrderedDict()\n # for k in tall:\n # if k.startswith('uk'):\n # temp_list[k] = tall[k]\n # \n # return temp_list\n # \n # for t in 'MLT':\n # for k in tall:\n # if k.startswith('spex-prism-'+t):\n # temp_list[k] = tall[k]\n # \n # return temp_list\n \n #return temp_list\n templates = ['M6.5.txt', 'M8.0.txt', 'L1.0.txt', 'L3.5.txt', 'L6.0.txt', 'T2.0.txt', 'T6.0.txt', 'T7.5.txt']\n templates = ['stars/'+t for t in templates]\n else:\n ## Intermediate and very old\n # templates = ['templates/EAZY_v1.0_lines/eazy_v1.0_sed3_nolines.dat', \n # 'templates/cvd12_t11_solar_Chabrier.extend.skip10.dat'] \n templates = ['eazy_intermediate.dat', \n 'cvd12_t11_solar_Chabrier.dat']\n \n ## Post starburst\n #templates.append('templates/UltraVISTA/eazy_v1.1_sed9.dat')\n templates.append('post_starburst.dat')\n \n ## Very blue continuum\n #templates.append('templates/YoungSB/erb2010_continuum.dat')\n templates.append('erb2010_continuum.dat')\n \n ### Test new templates\n # templates = ['templates/erb2010_continuum.dat',\n # 'templates/fsps/tweak_fsps_temp_kc13_12_006.dat',\n # 'templates/fsps/tweak_fsps_temp_kc13_12_008.dat']\n \n if fsps_templates:\n #templates = ['templates/fsps/tweak_fsps_temp_kc13_12_0{0:02d}.dat'.format(i+1) for i in range(12)]\n templates = ['fsps/fsps_QSF_12_v3_nolines_0{0:02d}.dat'.format(i+1) for i in range(12)]\n #templates = ['fsps/fsps_QSF_7_v3_nolines_0{0:02d}.dat'.format(i+1) for i in range(7)]\n \n \n if alf_template:\n templates.append('alf_SSP.dat')\n \n if continuum_list is not None:\n templates = continuum_list\n \n temp_list = OrderedDict()\n for temp in templates:\n data = np.loadtxt(os.path.join(os.getenv('GRIZLI'), 'templates', temp), unpack=True)\n #scl = np.interp(5500., data[0], data[1])\n scl = 1.\n name = temp #os.path.basename(temp)\n temp_list[name] = SpectrumTemplate(wave=data[0], flux=data[1]/scl,\n name=name)\n \n temp_list[name].name = name\n \n if stars:\n return temp_list\n \n ### Emission lines:\n line_wavelengths, line_ratios = get_line_wavelengths()\n \n if line_complexes:\n #line_list = ['Ha+SII', 'OIII+Hb+Ha', 'OII']\n #line_list = ['Ha+SII', 'OIII+Hb', 'OII']\n line_list = ['Ha+NII+SII+SIII+He', 'OIII+Hb', 'OII+Ne', 'Lya+CIV']\n else:\n if full_line_list is None:\n line_list = DEFAULT_LINE_LIST\n else:\n line_list = full_line_list\n \n #line_list = ['Ha', 'SII']\n \n # Use FSPS grid for lines\n wave_grid = None\n # if fsps_templates:\n # wave_grid = data[0]\n # else:\n # wave_grid = None \n \n for li in line_list:\n scl = line_ratios[li]/np.sum(line_ratios[li])\n for i in range(len(scl)):\n line_i = SpectrumTemplate(wave=wave_grid, \n central_wave=line_wavelengths[li][i], \n flux=None, fwhm=fwhm, velocity=True)\n \n if i == 0:\n line_temp = line_i*scl[i]\n else:\n line_temp = line_temp + line_i*scl[i]\n \n name = 'line {0}'.format(li)\n line_temp.name = name\n temp_list[name] = line_temp\n \n return temp_list","def main(argv):\n scanSpeed = 1.\n iList = -1\n opt1 = \"\"\n intMode = False\n if (len(argv) >= 1): opt1 = argv[0]\n if \"-i\" in (opt1): intMode = True\n\n # Set input data and cuts\n inputFile = TFile(\"~/project/match-skim/waveletSkimDS5_run23920.root\") # calibration\n # inputFile = TFile(\"~/project/v2-processwfs/waveletSkimDS5_90.root\") # noisy BG\n waveTree = inputFile.Get(\"skimTree\")\n theCut = inputFile.Get(\"cutUsedHere\").GetTitle()\n theCut += \" && waveS5/trapENFCal < 1200 && trapENFCal > 1.5 && trapENFCal < 3\"\n\n # Print cut and events passing cut\n waveTree.Draw(\">>elist\", theCut, \"entrylist\")\n elist = gDirectory.Get(\"elist\")\n waveTree.SetEntryList(elist)\n nList = elist.GetN()\n print \"Using cut:\\n\",theCut,\"\\n\"\n print \"Found\",waveTree.GetEntries(),\"input entries.\"\n print \"Found\",nList,\"entries passing cuts.\"\n\n # Make a figure\n fig = plt.figure(figsize=(15,10), facecolor='w')\n p1 = plt.subplot2grid((6,7), (0,0), colspan=4, rowspan=2) # original\n p2 = plt.subplot2grid((6,7), (2,0), colspan=4, rowspan=3) # rising edge\n p3 = plt.subplot2grid((6,7), (5,0), colspan=4, rowspan=1) # residual\n p4 = plt.subplot2grid((6,7), (0,4), colspan=3, rowspan=2) # trace 1\n p5 = plt.subplot2grid((6,7), (2,4), colspan=3, rowspan=2) # trace 2\n p6 = plt.subplot2grid((6,7), (4,4), colspan=3, rowspan=2) # trace 3\n plt.show(block=False)\n\n # Make template(s)\n # npzfile = np.load(\"./data/genTemplateWF.npz\") # gen-template.py\n # temp, tempTS, tempE, tempST = npzfile['arr_0']+1, npzfile['arr_1'], npzfile['arr_2'], npzfile['arr_3']*10\n\n samp, r, z, tempE, tempST, smooth = 2016, 0, 15, 3938, 1000, 100 # gen-template.py\n # samp, r, z, tempE, tempST, smooth = 5000, 0, 15, 10, 2500, 100 # huge template\n # samp, r, z, tempE, tempST, smooth = 2016, 0, 15, 10, 1000, 100 # regular size template\n # samp, r, z, tempE, tempST, smooth = 2016, 30, 30, 10, 1000, 100 # regular size temp, slower rise\n # samp, r, z, tempE, tempST, smooth = 500, 0, 15, 10, 100, 100 # small template\n temp, tempTS = wl.MakeSiggenWaveform(samp,r,z,tempE,tempST,smooth)\n tempST = tempST * 10 # convert to ns\n\n\n # Loop over events\n while True:\n saveMe = False\n iList += 1\n if intMode==True and iList != 0:\n value = raw_input()\n if value=='q': break\n if value=='s': saveMe=True\n if value=='p': iList -= 2 # previous\n if (value.isdigit()): iList = int(value) # go to entry\n if iList >= elist.GetN(): break\n\n entry = waveTree.GetEntryNumber(iList);\n waveTree.LoadTree(entry)\n waveTree.GetEntry(entry)\n nChans = waveTree.channel.size()\n numPass = waveTree.Draw(\"channel\",theCut,\"GOFF\",1,iList)\n chans = waveTree.GetV1()\n chanList = list(set(int(chans[n]) for n in xrange(numPass)))\n\n # Loop over hits passing cuts\n hitList = (iH for iH in xrange(nChans) if waveTree.channel.at(iH) in chanList) # a 'generator expression'\n for iH in hitList:\n\n # ------------------------------------------------------------------------\n\n # Load waveform for this hit\n run = waveTree.run\n chan = waveTree.channel.at(iH)\n dataE = waveTree.trapENFCal.at(iH)\n dataST = waveTree.butterTime.at(iH) # replace with blrwfFMR50?\n toe = waveTree.kvorrT.at(iH)/dataE\n print \"%d / %d Run %d nCh %d chan %d trapENF %.1f t/e %.1f\" % (iList,nList,run,nChans,chan,dataE,toe)\n signal = wl.processWaveform(waveTree.MGTWaveforms.at(iH),opt='full')\n waveBLSub = signal.GetWaveBLSub()\n waveFilt = signal.GetWaveFilt()\n waveTS = signal.GetTS()\n baseAvg, dataNoise = signal.GetBaseNoise()\n\n # Denoise the data waveform (take only lowest-frequency components)\n wp = pywt.WaveletPacket(data=waveBLSub, wavelet='haar', mode='symmetric',maxlevel=3)\n new_wp = pywt.WaveletPacket(data=None, wavelet='haar', mode='symmetric')\n new_wp['aaa'] = wp['aaa'].data\n waveDenoised = new_wp.reconstruct(update=False)\n\n # Window the fit around rising edge - start time calculator method\n loWin, hiWin = dataST - 1000, dataST + 4000 # ns\n if loWin < waveTS[0] or hiWin > waveTS[-1]:\n print \"Window out of range! dataST: %.1f loWin %.1f hiWin %.1f\" % (dataST,loWin,hiWin)\n idx = np.where((waveTS >= loWin) & (waveTS <= hiWin))\n data = waveBLSub[idx]\n # data = waveDenoised[idx]\n dataTS = waveTS[idx]\n\n # Pack into lists\n rawList = [waveBLSub, waveTS, dataE, dataST]\n dataList = [data, dataTS, dataE, dataST, loWin, hiWin, dataNoise]\n tempList = [temp, tempTS, tempE, tempST]\n\n # Optionally save something to a file\n if saveMe: np.savez(\"./data/tailSlopeInputs.npz\",rawList,tempList)\n\n # Recreate the guess and the guess's rising edge\n guessFull, guessFullTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\"full\")\n guess, guessTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\"!fancy\")\n\n # Make an \"almost complete\" guess - no fitting\n # st, en, slo = dataST-100, dataE, 5\n InterpFn = interpolate.interp1d(tempTS, temp, kind=\"linear\", copy=\"False\", assume_sorted=\"True\")\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\n\n # Fit with MCMC and get best-fit parameters\n # numSteps, burnIn = 10000, 5000 # default - 10000, 5000. try 3000, 2000. long test: 20000,10000\n # myModel = TemplateModel( dataList, dataNoise, tempList )\n # waveModel = pymc.Model( myModel )\n # M = pymc.MCMC( waveModel )\n # M.use_step_method(pymc.Metropolis, M.startTime, proposal_sd=100., proposal_distribution='Normal')\n # M.use_step_method(pymc.Metropolis, M.energy, proposal_sd=1., proposal_distribution='Normal')\n # M.use_step_method(pymc.Metropolis, M.slowness, proposal_sd=100., proposal_distribution='Normal')\n # M.sample(iter=numSteps, verbose=0) # do the fit\n # st = np.median(M.trace('startTime')[burnIn:])\n # en = np.median(M.trace('energy')[burnIn:])\n # slo = np.median(M.trace('slowness')[burnIn:])\n # InterpFn = interpolate.interp1d(tempTS, temp, kind=\"linear\", copy=\"False\", assume_sorted=\"True\")\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\n\n\n # Fit with SciPy minimizer(s) and get best-fit parameters\n\n # how long does it take?\n start = time.time()\n\n MakeTracesGlobal() # creates 3 global arrays: startTrace, enTrace, sloTrace\n datas = [dataList, tempList, InterpFn]\n\n # floats = [1.]\n # result = op.basinhopping(findLnLike, floats, T=1000, stepsize=15, niter_success=2, minimizer_kwargs={\"args\":datas})\n # print \"slowness:\",result[\"x\"],\"dataE\",dataE,\"dataST\",dataST\n # floats = [dataST, dataE, result[\"x\"]]\n # MakeTracesGlobal() # resets traces\n\n floats = [dataST, dataE, 20.]\n result = op.minimize(findLnLike, floats, args=datas, method=\"Nelder-Mead\")\n if not result[\"success\"]: print result[\"message\"]\n st, en, slo = result[\"x\"]\n\n model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\n\n stop = time.time()\n print \"fitting took\",stop-start\n\n\n\n # Calculate residual, Chi2/NDF, likelihood, etc.\n residual = model - data\n frac = (np.power(data - model, 2)) / np.abs(model)\n chi2NDF = np.sum(frac) / len(model)\n inv_sigma2 = 1.0/(dataNoise**2)\n lnLike = -0.5*(np.sum((data - model)**2*inv_sigma2 - np.log(inv_sigma2)))\n\n # Fill the figure\n p1.cla()\n p1.set_ylabel(\"ADC\")\n p1.set_title(\"Run %d Channel %d Entry %d\\ntrapENFCal %.1f T/E %.1f ST %.1f\" % (run,chan,iList,dataE,toe,dataST))\n p1.plot(waveTS,waveBLSub,color='blue')\n p1.plot(waveTS,waveDenoised,color='red',alpha=0.8)\n p1.plot(guessFullTS,guessFull,color='orange',linewidth=2)\n p1.axvline(x = dataST, color='green',linewidth=2)\n p1.axvline(x = loWin, color='black')\n p1.axvline(x = hiWin, color='black')\n\n p2.cla()\n p2.plot(dataTS,data,color='blue',label='Data')\n p2.plot(guessTS,guess,color='orange',label='Guess')\n p2.plot(modelTS,model,color='red',linewidth=3,label='Best Fit')\n p2.legend(loc=4)\n\n p3.cla()\n p3.set_xlabel(\"Time [ns]\", x=0.95, ha='right')\n p3.set_ylabel(\"Residual [ADC]\")\n p3.plot(guessTS,residual, color='red')\n p3.axhline(y = 0, color='blue', alpha=0.3)\n p3.axhline(y = dataNoise, color='blue', alpha=0.3)\n p3.axhline(y = -1.0*dataNoise, color='blue', alpha=0.3)\n\n p4.cla()\n p4.set_title(\"startTime %.1f Energy %.2f Slow %.1f\" % (st,en,slo))\n\n p4.plot(stTrace[1:])\n p4.set_ylabel('startTime')\n\n p5.cla()\n p5.plot(enTrace[1:])\n p5.set_ylabel('energy')\n\n p6.cla()\n p6.plot(sloTrace[1:])\n p6.set_ylabel('slowness')\n\n plt.tight_layout()\n plt.subplots_adjust(hspace=0.35)\n plt.pause(scanSpeed)\n\n # ------------------------------------------------------------------------","def imdisplay(filename, representation):\n image = read_image(filename, representation)\n\n if representation == GRAY_OUT:\n plt.imshow(image, cmap='gray')\n else:\n plt.imshow(image)\n\n plt.show()","def exercise():\n\n #\n # Convert Lena Tiff image to raw format\n #\n for f in glob.glob('*.jpg'):\n os.remove(f)\n \n for f in glob.glob('*.dat'):\n os.remove(f)\n \n input_raw_file = convert_to_raw('Lena.tiff')\n\n for device in ['cpu', 'gpu']:\n for interp in ['nn', 'bl']:\n for (w,h) in ((256, 300), (486, 486),(2000, 1000),(1000, 2000),(8000, 4000)):\n (t, f) = interpolate(input_raw_file, device + '_' + interp + '_lena.dat', device, 0, interp, w, h)\n convert_to_jpg(f)\n\n \n for f in glob.glob('*.dat'):\n convert_to_jpg(f)\n os.remove(f)\n \n quit()","def plot_bit_for_bit(case, var_name, model_data, bench_data, diff_data):\n plot_title = \"\"\n plot_name = case + \"_\" + var_name + \".png\"\n plot_path = os.path.join(os.path.join(livvkit.output_dir, \"verification\", \"imgs\"))\n functions.mkdir_p(plot_path)\n m_ndim = np.ndim(model_data)\n b_ndim = np.ndim(bench_data)\n if m_ndim != b_ndim:\n return \"Dataset dimensions didn't match!\"\n if m_ndim == 3:\n model_data = model_data[-1]\n bench_data = bench_data[-1]\n diff_data = diff_data[-1]\n plot_title = \"Showing \"+var_name+\"[-1,:,:]\"\n elif m_ndim == 4:\n model_data = model_data[-1][0]\n bench_data = bench_data[-1][0]\n diff_data = diff_data[-1][0]\n plot_title = \"Showing \"+var_name+\"[-1,0,:,:]\"\n plt.figure(figsize=(12, 3), dpi=80)\n plt.clf()\n\n # Calculate min and max to scale the colorbars\n _max = np.amax([np.amax(model_data), np.amax(bench_data)])\n _min = np.amin([np.amin(model_data), np.amin(bench_data)])\n\n # Plot the model output\n plt.subplot(1, 3, 1)\n plt.xlabel(\"Model Data\")\n plt.ylabel(var_name)\n plt.xticks([])\n plt.yticks([])\n plt.imshow(model_data, vmin=_min, vmax=_max, interpolation='nearest', cmap=colormaps.viridis)\n plt.colorbar()\n\n # Plot the benchmark data\n plt.subplot(1, 3, 2)\n plt.xlabel(\"Benchmark Data\")\n plt.xticks([])\n plt.yticks([])\n plt.imshow(bench_data, vmin=_min, vmax=_max, interpolation='nearest', cmap=colormaps.viridis)\n plt.colorbar()\n\n # Plot the difference\n plt.subplot(1, 3, 3)\n plt.xlabel(\"Difference\")\n plt.xticks([])\n plt.yticks([])\n plt.imshow(diff_data, interpolation='nearest', cmap=colormaps.viridis)\n plt.colorbar()\n\n plt.tight_layout(rect=(0, 0, 0.95, 0.9))\n plt.suptitle(plot_title)\n\n plot_file = os.path.sep.join([plot_path, plot_name])\n if livvkit.publish:\n plt.savefig(os.path.splitext(plot_file)[0]+'.eps', dpi=600)\n plt.savefig(plot_file)\n plt.close()\n return os.path.join(os.path.relpath(plot_path,\n os.path.join(livvkit.output_dir, \"verification\")),\n plot_name)","def Interpret(self, **kwargs):\n # visit file\n self.options[\"VISIT_FILE\"] = self.visit_file\n # particle file\n particle_file = os.path.join(self.output_dir, 'particles.visit')\n if os.access(particle_file, os.R_OK):\n self.options[\"VISIT_PARTICLE_FILE\"] = particle_file\n # directory to output data\n self.options[\"DATA_OUTPUT_DIR\"] = self.output_dir\n # directory to output images\n if not os.path.isdir(self.img_dir):\n os.mkdir(self.img_dir)\n self.options[\"IMG_OUTPUT_DIR\"] = self.img_dir\n # own implementations\n # initial adaptive refinement\n self.options['INITIAL_ADAPTIVE_REFINEMENT'] = self.idict['Mesh refinement'].get('Initial adaptive refinement', '6')\n # get snaps for plots\n graphical_snaps, _, _ = GetSnapsSteps(self.case_dir, 'graphical')\n self.options['ALL_AVAILABLE_GRAPHICAL_SNAPSHOTS'] = str(graphical_snaps)\n # plot slab \n self.options['IF_PLOT_SLAB'] = 'True'\n last_step = graphical_snaps[-1] - int(self.options['INITIAL_ADAPTIVE_REFINEMENT'])\n last_step = kwargs.get('last_step', None)\n if type(last_step) == int:\n self.options['PLOT_SLAB_STEPS'] = [i for i in range(last_step - last_step + 1, last_step + 1)]\n else:\n self.options['PLOT_SLAB_STEPS'] = [0, 1, 2, 3, 4, 5, 6, 7]\n # self.options['IF_DEFORM_MECHANISM'] = value.get('deform_mechanism', 0)\n self.options['IF_DEFORM_MECHANISM'] = 1","def simulate_UVSPEC(file, config):\n\n wavelength = config['wavelength']\n\n # Coordenates from position of the station Hannover\n latitude = 52.39 # positive in the northern hemisphere\n longitud = 9.7 # negative reckoning west from prime meridian in Greenwich,\n\n # Read name of the file (correct time)\n name = os.path.split(file)\n time_n = name[1][0:15]\n print(\"Time name\", time_n)\n # convert time to datetime format\n time = datetime.datetime.strptime(time_n,\n '%Y%m%d_%H%M%S')\n # Calculate the azimuth and zenith angles in function of the date\n elev = ps.GetAltitude(latitude, longitud, time)\n azi = ps.GetAzimuth(latitude, longitud, time)\n zenith = 90 - elev\n\n # Correction between the sign and real azimuth for plot of radiance\n if -180 <= azi < 0:\n azi = 180 - azi\n elif -360 <= azi < -180:\n azi = -azi - 180\n else:\n pass\n\n print(\"Azimuth: {:5.1f}\".format(azi),\n \"\\nZenith: {:5.1f}\".format(zenith))\n\n # Change the value of zenith and azimuth angles in function of the time and\n # position in the UVSPEC file\n\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'r') as file:\n data = file.readlines()\n\n data[14] = \"day_of_year \" + str(time.timetuple().tm_yday) + \" \" + \"\\n\"\n data[15] = \"wavelength \" + str(\"{}\".format(wavelength)) + \" \" + \\\n str(\"{}\".format(wavelength)) + \\\n \" # wavelength to calcule [nm] \\n\"\n data[17] = \"sza \" + str(\"{:2.3f}\".format(zenith)) + \\\n \" # Solar zenith angle \\n\"\n data[18] = \"phi0 \" + str(\"{:2.3f}\".format(azi)) + \\\n \" #Azimuth angle with zenith position \\n\"\n\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'w') as file:\n file.writelines(data)\n\n # Create the directory to save the results\n os.makedirs(os.path.dirname(config['str_dir'] + '/simulation/' + '{}/{}nm/txt_files/'.format(time_n[0:8],\n wavelength)),\n exist_ok=True)\n\n # Run the program UVSPEC in the terminal\n os.system(config['UVSPEC_path'] + 'uvspec < ' + config['personal_libraries'] +\n 'MUDIS_HDF5/MUDIS_radiance_Input.txt> ' + config['str_dir'] + '/simulation/' +\n '{}/{}nm/txt_files/'.format(time_n[0:8], wavelength) + time_n +\n '.txt')","def run(self):\n # read (x,y) - Set SIFT parameters\n action = self.cfg['param']['action']\n x = float(self.cfg['param']['x']) \n # Expressed en PIL coordinates system y| x-\n y = float(self.cfg['param']['y'])\n \n # read image size and store in 'param' dict to control html rendering\n work_dir = self.work_dir\n [w1, h1] = Image.open(work_dir+'input_0.orig.png').size\n [w2, h2] = Image.open(work_dir+'input_1.orig.png').size\n wdth = max(w1, w2)\n hght = max(h1, h2)\n wpair = int(w1+w2+max(w1, w2)/10)\n self.cfg['param']['hght'] = hght\n self.cfg['param']['wdth'] = wdth\n self.cfg.save()\n # Convert x y provided by the form <input type=image ..; > \n # We assume that the width of the html body is assumed width is 920px\n x = x*wpair/920\n y = y*wpair/920\n self.cfg['param']['x'] = x\n self.cfg['param']['y'] = y\n self.cfg.save()\n if (action == 'std_sift_matching'):\n try:\n self.load_standard_parameters()\n self.run_std_sift()\n self.run_matching()\n self.illustrate_std_sift()\n self.illustrate_matching()\n except TimeoutError:\n return self.error(errcode='timeout',\n errmsg=\"Try again with simpler images.\")\n except RuntimeError:\n return self.error(errcode='runtime',\n errmsg=\"Runtime error in std_sift_matching.\")\n elif (action == \"cust_sift_matching\"):\n try:\n self.run_sift()\n print \"after run_sift()\"\n self.run_matching()\n print \"after run_matching()\"\n self.illustrate_sift()\n print \"after illustrate_sift()\"\n self.illustrate_matching()\n self.detail_matching()\n except TimeoutError:\n return self.error(errcode='timeout',\n errmsg=\"Try with simpler images.\")\n except RuntimeError:\n return self.error(errcode='runtime',\n errmsg=\"Runtime error in cust_sift_matching.\")\n elif (action == \"cust_matching\"): \n try:\n self.run_matching()\n self.illustrate_matching()\n except TimeoutError:\n return self.error(errcode='timeout',\n errmsg=\"Try with simpler images.\")\n except RuntimeError:\n return self.error(errcode='runtime',\n errmsg=\"Runtime error in cust_matching.\")\n else:\n try:\n self.detail_matching()\n except TimeoutError:\n return self.error(errcode='timeout',\n errmsg=\"Try with simpler images.\")\n except RuntimeError:\n return self.error(errcode='runtime',\n errmsg=\"Runtime error in else (you know).\")\n ## archive\n if self.cfg['meta']['original']:\n ar = self.make_archive()\n ar.add_file(\"input_0.png\", info=\"first input image\")\n ar.add_file(\"input_1.png\", info=\"second input image\")\n ar.add_file(\"input_0.orig.png\", info=\"first uploaded image\")\n ar.add_file(\"input_1.orig.png\", info=\"second uploaded image\")\n ar.add_file(\"OUTmatches.png\", info=\"matches\")\n ar.add_file(\"keys_im0.txt\", compress=True)\n ar.add_file(\"keys_im1.txt\", compress=True)\n ar.add_file(\"OUTmatches.txt\", compress=True)\n ar.save()\n\n self.cfg.save()\n http.redir_303(self.base_url + 'result?key=%s' % self.key)\n return self.tmpl_out(\"run.html\")","def render_configuration_file(self, conf_template_string):\n environment = self.params['environment']\n code_root = self.params['code_root']\n\n reject = self.get_rejected_pillow_types(code_root, environment)\n\n configs = []\n all_pillows = self.get_pillows_from_settings(settings.PILLOWTOPS, reject)\n for full_name in all_pillows:\n pillow_name = full_name.split('.')[-1]\n pillow_params = {\n 'pillow_name': pillow_name,\n 'pillow_option': ' --pillow-name %s' % pillow_name\n }\n pillow_params.update(self.params)\n pillow_rendering = conf_template_string % pillow_params\n configs.append(pillow_rendering)\n return '\\n\\n'.join(configs)","def render(self):\r\n super().render()\r\n layers, titles, latVect, lonVect = self.make_layers()\r\n LON, LAT = np.meshgrid(lonVect, latVect)\r\n lon = LON.flatten()\r\n lat = LAT.flatten()\r\n for i in range(len(layers)):\r\n vals = layers[i].flatten()\r\n hovertext = []\r\n for k in range(len(vals)):\r\n hovertext.append('lon: {:.2f}<br>lat: {:.2f}<br>{}: {:.1e}'.format(lon[k], lat[k], self.variable + self.unit,vals[k]))\r\n if self.levels == 0:\r\n data = [\r\n go.Heatmap(\r\n x=lon,\r\n y=lat,\r\n z=vals,\r\n colorscale=self.cmap,\r\n zmin=self.vmin,\r\n zmax=self.vmax,\r\n hoverinfo='text',\r\n text=hovertext \r\n )\r\n ]\r\n elif self.levels > 0:\r\n data = [\r\n go.Contour(\r\n x=lon,\r\n y=lat,\r\n z=vals,\r\n colorscale=self.cmap,\r\n hoverinfo='text',\r\n text=hovertext, \r\n connectgaps=False,\r\n contours=dict(\r\n coloring='heatmap',\r\n showlabels=True,\r\n start=self.vmin,\r\n end=self.vmax,\r\n size=(self.vmax-self.vmin) / float(self.levels)\r\n )\r\n # line=dict(smoothing=0.85) \r\n )\r\n ] \r\n\r\n\r\n layout = go.Layout(\r\n autosize=False,\r\n title=titles[i],\r\n width=self.width,\r\n height=self.height,\r\n xaxis={'title': self.xlabel},\r\n yaxis={'title': self.ylabel}\r\n ) \r\n\r\n\r\n\r\n if self.surface3D:\r\n data = [\r\n go.Surface(\r\n x=lonVect,\r\n y=latVect,\r\n z=layers[i],\r\n colorscale=self.cmap,\r\n # hoverinfo='text',\r\n # text=hovertext \r\n )\r\n ]\r\n\r\n layout = go.Layout(\r\n autosize=False,\r\n title=titles[i],\r\n width=self.width,\r\n height=self.height,\r\n scene = dict(\r\n xaxis={'title': self.xlabel},\r\n yaxis={'title': self.ylabel},\r\n zaxis={'title': self.variable + self.unit}\r\n )\r\n ) \r\n\r\n\r\n self._save_plotly_(go, data, layout)","def processTemplates(self, tk, templateFile = '', id = '', shotNum = '', inprogressBar = ''):\r\n ## Now fetch all the template paths from shotgun\r\n getTemplatePaths = tk.paths_from_template(templateFile, {'Step' : 'Light', 'id' : id, 'Shot' : shotNum})\r\n debug(app = self, method = 'processTemplates', message = 'getTemplatePaths: %s' % getTemplatePaths, verbose = False)\r\n \r\n ## Now look for each assets template path: \r\n xmlFile = max(getTemplatePaths) \r\n debug(app = self, method = 'processTemplates', message = 'Max Version xmlFile.... %s' % xmlFile, verbose = False)\r\n \r\n ## Now if versions has stuff in it..\r\n if not xmlFile:\r\n debug(app = self, method = 'processTemplates', message = 'Can not find any xml files for %s' % shotNum, verbose = False)\r\n pass\r\n else:\r\n \r\n debug(app = self, method = 'processTemplates', message = 'PathTo: %s' % os.path.isfile(xmlFile.replace(os.path.sep, \"/\")), verbose = False)\r\n if os.path.isfile(xmlFile.replace(os.path.sep, \"/\")):## is this a valid xml file!?\r\n inprogressBar.updateProgress(percent = 10, doingWhat = 'createAll shaders...')\r\n self._createAllShaders(XMLPath = xmlFile.replace(os.path.sep, \"/\"), Namespace = '', Root = 'MaterialNodes')\r\n \r\n inprogressBar.updateProgress(percent = 30, doingWhat = 'connectAll shaders...')\r\n self._connectAllShaders(XMLPath = xmlFile.replace(os.path.sep, \"/\"), Namespace = '', Root = 'MaterialNodes')\r\n else:\r\n debug(app = self, method = 'processTemplates', message = 'FAILED Can not find a valid published xml file for %s ...' % os.path.isfile(xmlFile.replace(os.path.sep, \"/\")), verbose = False)\r\n pass","def test_rhostats_config():\n if __name__ == '__main__':\n logger = piff.config.setup_logger(verbose=2)\n else:\n logger = piff.config.setup_logger(log_file='output/test_rhostats_config.log')\n\n image_file = os.path.join('output','test_stats_image.fits')\n cat_file = os.path.join('output','test_stats_cat.fits')\n psf_file = os.path.join('output','test_rhostats.fits')\n rho_file = os.path.join('output','test_rhostats.pdf')\n config = {\n 'input' : {\n 'image_file_name' : image_file,\n 'cat_file_name' : cat_file,\n 'stamp_size' : 48\n },\n 'psf' : {\n 'model' : { 'type' : 'Gaussian',\n 'fastfit': True,\n 'include_pixel': False },\n 'interp' : { 'type' : 'Mean' },\n },\n 'output' : {\n 'file_name' : psf_file,\n 'stats' : { # Note: stats doesn't have to be a list.\n 'type': 'Rho',\n 'file_name': rho_file,\n 'min_sep': 30,\n 'max_sep': 600,\n 'sep_units': 'arcsec',\n 'bin_type': 'Linear',\n 'bin_size': 30,\n }\n },\n }\n piff.piffify(config, logger)\n assert os.path.isfile(rho_file)\n\n # repeat with plotify function\n os.remove(rho_file)\n piff.plotify(config, logger)\n assert os.path.isfile(rho_file)\n\n # Test rho statistics directly.\n min_sep = 1\n max_sep = 100\n bin_size = 0.1\n psf = piff.read(psf_file)\n orig_stars, wcs, pointing = piff.Input.process(config['input'], logger)\n stats = piff.RhoStats(min_sep=min_sep, max_sep=max_sep, bin_size=bin_size)\n with np.testing.assert_raises(RuntimeError):\n stats.write('dummy') # Cannot write before compute\n stats.compute(psf, orig_stars)\n\n rhos = [stats.rho1, stats.rho2, stats.rho3, stats.rho4, stats.rho5]\n for rho in rhos:\n # Test the range of separations\n radius = np.exp(rho.logr)\n np.testing.assert_array_less(radius, max_sep)\n np.testing.assert_array_less(min_sep, radius)\n # bin_size is reduced slightly to get integer number of bins\n assert rho.bin_size < bin_size\n assert np.isclose(rho.bin_size, bin_size, rtol=0.1)\n np.testing.assert_array_almost_equal(np.diff(rho.logr), rho.bin_size, decimal=5)\n\n # Test that the max absolute value of each rho isn't crazy\n np.testing.assert_array_less(np.abs(rho.xip), 1)\n\n # # Check that each rho isn't precisely zero. This means the sum of abs > 0\n np.testing.assert_array_less(0, np.sum(np.abs(rho.xip)))\n\n # Test using the piffify executable\n os.remove(rho_file)\n config['verbose'] = 0\n with open('rho.yaml','w') as f:\n f.write(yaml.dump(config, default_flow_style=False))\n piffify_exe = get_script_name('piffify')\n p = subprocess.Popen( [piffify_exe, 'rho.yaml'] )\n p.communicate()\n assert os.path.isfile(rho_file)\n\n # Test using the plotify executable\n os.remove(rho_file)\n plotify_exe = get_script_name('plotify')\n p = subprocess.Popen( [plotify_exe, 'rho.yaml'] )\n p.communicate()\n assert os.path.isfile(rho_file)\n\n # test running plotify with dir in config, with no logger, and with a modules specification.\n # (all to improve test coverage)\n config['output']['dir'] = '.'\n config['modules'] = [ 'custom_wcs' ]\n os.remove(rho_file)\n piff.plotify(config)\n assert os.path.isfile(rho_file)","def call(self, **kwargs):\n # Format template\n template = self._cw.vreg.template_env.get_template(\"startup.logged.jinja2\")\n html = template.render(\n header_url=self._cw.data_url(\"creative/img/neurospin.jpg\"),\n moderator=False)\n self.w(html)","def call(self, **kwargs):\n # Format template\n template = self._cw.vreg.template_env.get_template(\"startup.logged.jinja2\")\n html = template.render(\n header_url=self._cw.data_url(\"creative/img/neurospin.jpg\"),\n moderator=True)\n self.w(html)","def imdisplay(filename, representation):\n im = read_image(filename, representation)\n if representation == 1:\n plt.imshow(im, cmap='gray')\n plt.show()\n if representation == 2:\n plt.imshow(im)\n plt.show()","def plot(\r\n self,\r\n file_name: str = \"index\",\r\n path: str = \"./\",\r\n template: str = \"default\",\r\n notebook_height: int = 500,\r\n ):\r\n self.notebook_height = notebook_height\r\n\r\n script_path = os.path.dirname(os.path.abspath(__file__))\r\n if template in [\"default\", \"reaction_smiles\", \"smiles\", \"url_image\"]:\r\n template = \"template_\" + template + \".j2\"\r\n else:\r\n script_path = os.path.dirname(template)\r\n\r\n html_path = os.path.join(path, file_name + \".html\")\r\n js_path = os.path.join(path, file_name + \".js\")\r\n jenv = jinja2.Environment(loader=jinja2.FileSystemLoader(script_path))\r\n\r\n has_legend = False\r\n\r\n for _, value in self.scatters.items():\r\n if value[\"has_legend\"]:\r\n has_legend = True\r\n break\r\n\r\n if not self.show_legend:\r\n has_legend = False\r\n\r\n # Drop colormaps before passing them to the document, as they are\r\n # not JSON serializable.\r\n trees_copy = copy.deepcopy(self.trees)\r\n scatters_copy = copy.deepcopy(self.scatters)\r\n\r\n for key, _ in trees_copy.items():\r\n del trees_copy[key][\"colormap\"]\r\n\r\n for key, _ in scatters_copy.items():\r\n del scatters_copy[key][\"colormap\"]\r\n\r\n model = {\r\n \"title\": self.title,\r\n \"file_name\": file_name + \".js\",\r\n \"clear_color\": self.clear_color,\r\n \"view\": self.view,\r\n \"coords\": str(self.coords).lower(),\r\n \"coords_color\": self.coords_color,\r\n \"coords_box\": str(self.coords_box).lower(),\r\n \"coords_ticks\": str(self.coords_ticks).lower(),\r\n \"coords_grid\": str(self.coords_grid).lower(),\r\n \"coords_tick_count\": self.coords_tick_count,\r\n \"coords_tick_length\": self.coords_tick_length,\r\n \"coords_offset\": self.coords_offset,\r\n \"x_title\": self.x_title,\r\n \"y_title\": self.y_title,\r\n \"tree_helpers\": list(trees_copy.values()),\r\n \"point_helpers\": list(scatters_copy.values()),\r\n \"has_legend\": str(has_legend).lower(),\r\n \"legend_title\": self.legend_title,\r\n \"legend_orientation\": self.legend_orientation,\r\n \"alpha_blending\": str(self.alpha_blending).lower(),\r\n \"anti_aliasing\": str(self.anti_aliasing).lower(),\r\n \"style\": self.style,\r\n \"impress\": self.impress,\r\n \"in_notebook\": Faerun.in_notebook(),\r\n \"thumbnail_width\": self.thumbnail_width,\r\n \"thumbnail_fixed\": str(self.thumbnail_fixed).lower(),\r\n }\r\n\r\n if Faerun.in_notebook():\r\n model[\"data\"] = self.create_data()\r\n else:\r\n with open(js_path, \"w\") as f:\r\n f.write(self.create_data())\r\n\r\n output_text = jenv.get_template(template).render(model)\r\n\r\n with open(html_path, \"w\") as result_file:\r\n result_file.write(output_text)\r\n\r\n if Faerun.in_notebook():\r\n display(IFrame(html_path, width=\"100%\", height=self.notebook_height))\r\n display(FileLink(html_path))","def appelTemp():\n try:\n if os.path.getsize('aspifile/temp.json'):\n subprocess.call('aspifile/aspitemp.py', shell=True)\n label['text'] = (\"Date: \" + textDate.get() +\" -- \"+ \"Nom: \" + textName.get() +\n \"\\nTempérature: \" + textTemp.get())\n except FileNotFoundError as errorgraph5:\n print('+ Sorry the Temp plot doesn\\'t work ! Data missing !', errorgraph5)\n label['text'] = \"Sorry the Temp plot doesn\\'t work ! Data missing !\"","def dynamic_figure(r=None,z=None,t=None):\n global yt, t0, nt, zcut, theta, cnt, main_title\n\n if r is None:\n RunTimeError(\"r is None\")\n if z is None:\n RunTimeError(\"z is None\")\n if t is None:\n RunTimeError(\"t is None\")\n \n theta = linspace( 0.0, 2.0*scipy.pi, num=100)\n \n R, Z = meshgrid(r,z)\n \n fu = FuModel(nt=100)\n \n ycube = fu.FuCon(R, Z, t) # ycube[:,:,:] DATA CUBE!!!\n\n #-----------------------------FIGURES------------------------------\n fig = figure(figsize=(6,8.5))\n \n t0 = 0\n yt = ycube[:,:,t0]\n \n titulo = 'Fu drug diffusion model: t = %07.2f' % t[t0]\n main_title=fig.text(0.25, 0.95, titulo,\n horizontalalignment='left',\n fontproperties=FontProperties(size=16))\n \n cmap = cm.cool\n subplots_adjust(hspace=0.0)\n \n subplot(211)\n ax = gca()\n im1 = imshow(yt,\n interpolation='bilinear',\n cmap=cmap,\n origin='lower',\n extent=(0,1.0,z[0],z[-1]),\n aspect = 0.23\n )\n \n xlabel(r'R = r/a', fontsize=14)\n ylabel(r'Z = z/l', fontsize=14)\n\n subplot(212,aspect='equal')\n ax = gca()\n\n #\n #--- cylindrical cross-section\n \n RAD, THETA = meshgrid(r,theta)\n Xpos = RAD*cos(THETA)\n Ypos = RAD*sin(THETA)\n \n #\n # top cap of cylinder\n #\n #zcut = len(z)-1\n zcut = 0\n if z[0] != 0.0:\n zcut = int(len(z)/2+0.5)\n title('Z = %f'%z[zcut],fontsize=16)\n #\n # Take a cross-section of the cylinder at some point Z ==> radius array of length len(r) \n # -----\n # Trick\n # -----\n # Now create a matrix[len(theta),len(r)] by replicating the above array len(theta) times\n # along dimension 0\n #\n \n zx = outer( ones(len(theta)), yt[zcut,:] )\n \n im2 = pcolormesh(Xpos, Ypos,\n zx,\n shading='flat',\n cmap=cmap)\n \n xlabel(r'X/a', fontsize=14)\n ylabel(r'Y/a', fontsize=14)\n \n #\n #--- new axis for colorbar\n #\n norm = colors.Normalize(vmin=0.0, vmax=1.0)\n im1.set_norm(norm)\n im2.set_norm(norm)\n im1.add_observer(im2)\n \n pos = ax.get_position()\n l, b, w, h = getattr(pos, 'bounds', pos)\n cax = axes([l+w+0.015, b, 0.025, h]) # setup colorbar axes\n colorbar(im1, cax, orientation='vertical') \n \n manager = get_current_fig_manager()\n \n cnt = 0\n files = []\n nt = len(t)\n #\n #------------------------------------------------------------------\n def updatefig(*args):\n global yt, t0, nt, zcut, theta, cnt, main_title\n \n t0 += 1\n if t0 > nt-1:\n return False\n \n yt = ycube[:,:,t0]\n zx = outer( ones(len(theta)), yt[zcut,:] )\n Nx, Ny = yt.shape\n im1.set_array(yt)\n im2.set_array(ravel(zx[0:Nx-1,0:Ny-1]))\n titulo = 'Fu drug diffusion model: t = %07.2f' % t[t0]\n main_title.set_text(titulo)\n manager.canvas.draw()\n \n #fname = '_tmp%03d.jpg' % t0\n fname = '_tmp%03d.png' % t0\n savefig(fname)\n files.append(fname)\n \n cnt += 1\n return True\n #\n #------------------------------------------------------------------\n\n cnt = 0\n gobject.idle_add(updatefig)\n\n ioff()\n show()\n\n command = \"ffmpeg -r 10 -sameq -i _tmp%03d.png test.mp4\"\n #command = \"mencoder -ovc xvid -xvidencopts \" + \\\n # \"pass=2:bitrate=15999:max_bframes=0 \" + \\\n # \"-oac copy -mf fps=10:type=jpeg 'mf://_tmp*.jpg' -vf harddup -ofps 10 \" + \\\n # \"-noskip -of avi -o outputfile.avi\"\n os.system(command)\n for fname in files: os.remove(fname) #clean up\n\n return True","def generic_case_figure(wind_speed=5.,\n wind_dir=np.pi / 2) -> \"fire_rs.geodata.display.GeoDataDisplay\":\n\n # Geographic environment (elevation, landcover, wind...)\n wind = (wind_speed, wind_dir)\n area = ((480000.0, 485000.0), (6210000.0, 6215000.0))\n env = PlanningEnvironment(area, wind_speed=wind[0], wind_dir=wind[1],\n planning_elevation_mode='flat', flat_altitude=0)\n\n # Fire applied to the previous environment\n ignition_points = [\n TimedPoint(area[0][0] + 1000.0, area[1][0] + 1000.0, 0.),\n TimedPoint(area[0][0] + 4000.0, area[1][0] + 3000.0, 0.)\n ]\n logging.info(\"Start of propagation\")\n fire = propagation.propagate_from_points(env, ignition_points, 120 * 60)\n logging.info(\"End of propagation\")\n\n # Configure some flight\n start_t = 90 * 60\n uav = UAVConf.x8()\n uav.max_flight_time/=5\n f_confs = [\n FlightConf(uav, start_t, Waypoint(area[0][0] + 100., area[1][0] + 100., 0., 0.), None,\n wind),\n FlightConf(uav, start_t, Waypoint(area[0][1] - 100., area[1][0] + 1000., 0., 0.), None,\n wind),\n ]\n\n conf = SAOP_conf(start_t, start_t + uav.max_flight_time)\n\n fire1 = fire.ignitions()\n pl = Planner(env, fire1, f_confs, conf)\n pl.compute_plan()\n sr_1 = pl.search_result\n fmapper = FireMapper(env, fire1)\n\n gdd = fire_rs.geodata.display.GeoDataDisplay.pyplot_figure(env.raster.combine(fire1),\n frame=(0, 0))\n gdd.add_extension(TrajectoryDisplayExtension, (None,), {})\n\n # Draw expected fire contour\n t_range = (sr_1.final_plan().trajectories()[0].start_time(0) - 120,\n sr_1.final_plan().trajectories()[0].end_time(len(\n sr_1.final_plan().trajectories()[0]) - 1) + 120)\n t_range_fire = (0, np.inf)\n gdd.draw_ignition_contour(geodata=fire1, time_range=t_range_fire, cmap=matplotlib.cm.Reds)\n\n # Draw observed fire\n for i in range(len(f_confs)):\n executed_path = sr_1.final_plan().trajectories()[i].sampled_with_time(step_size=10)\n fmapper.observe(executed_path, pl.flights[i].uav)\n gdd.draw_ignition_shade(geodata=fmapper.firemap, cmap=matplotlib.cm.summer,\n vmin=t_range[0], vmax=t_range[1], with_colorbar=False)\n\n # Draw trajectory\n colors = ['blue', 'green', 'magenta']\n labels = [\"UAV \" + str(i) for i in range(len(f_confs))]\n\n for i in range(len(f_confs)):\n plot_plan_trajectories(sr_1.final_plan(), gdd, trajectories=i, draw_path=True,\n draw_flighttime_path=False, colors=[colors[i]], labels=[labels[i]],\n linestyles=['-'])\n gdd.legend()\n return gdd","def imdisplay(filename, representation):\n\n image = read_image(filename, representation)\n plt.imshow(image, cmap=\"gray\")\n plt.show()","def dftb_filling_fermi(unit, elec_temp):\n dftb_filling_fermi = \"\"\"\n Filling = Fermi {\n Temperature [{{ unit }}] = {{ elec_temp }}\n }\n \"\"\"\n return Environment().from_string(dftb_filling_fermi).render(unit=unit, elec_temp=elec_temp)","def show(data, types=(\"inflated\", ), recache=False, cmap='RdBu_r', layout=None,\n autoclose=None, open_browser=None, port=None, pickerfun=None,\n template=\"mixer.html\", overlays_available=None,\n overlays_visible=('rois', 'sulci'), labels_visible=('rois', ),\n overlay_file=None, title='Brain', **kwargs):\n\n # populate default webshow args\n if autoclose is None:\n autoclose = options.config.get('webshow', 'autoclose', fallback='true') == 'true'\n if open_browser is None:\n open_browser = options.config.get('webshow', 'open_browser', fallback='true') == 'true'\n\n data = dataset.normalize(data)\n if not isinstance(data, dataset.Dataset):\n data = dataset.Dataset(data=data)\n\n html = FallbackLoader([os.path.split(os.path.abspath(template))[0], serve.cwd]).load(template)\n db.auxfile = data\n\n #Extract the list of stimuli, for special-casing\n stims = dict()\n for name, view in data:\n if 'stim' in view.attrs and os.path.exists(view.attrs['stim']):\n sname = os.path.split(view.attrs['stim'])[1]\n stims[sname] = view.attrs['stim']\n\n package = Package(data)\n metadata = json.dumps(package.metadata())\n images = package.images\n subjects = list(package.subjects)\n\n ctmargs = dict(method='mg2', level=9, recache=recache,\n external_svg=overlay_file, overlays_available=overlays_available)\n ctms = dict((subj, utils.get_ctmpack(subj, types, **ctmargs))\n for subj in subjects)\n package.reorder(ctms)\n\n subjectjs = json.dumps(dict((subj, \"ctm/%s/\"%subj) for subj in subjects))\n db.auxfile = None\n\n if layout is None:\n layout = [None, (1, 1), (2, 1), (3, 1), (2, 2), (3, 2), (3, 2), (3, 3), (3, 3), (3, 3)][len(subjects)]\n\n linear = lambda x, y, m: (1.-m)*x + m*y\n mixes = dict(\n linear=linear,\n smoothstep=(lambda x, y, m: linear(x, y, 3*m**2 - 2*m**3)),\n smootherstep=(lambda x, y, m: linear(x, y, 6*m**5 - 15*m**4 + 10*m**3))\n )\n\n post_name = Queue()\n\n # Put together all view options\n my_viewopts = dict(options.config.items('webgl_viewopts'))\n my_viewopts['overlays_visible'] = overlays_visible\n my_viewopts['labels_visible'] = labels_visible\n my_viewopts['brightness'] = options.config.get('curvature', 'brightness')\n my_viewopts['smoothness'] = options.config.get('curvature', 'webgl_smooth')\n my_viewopts['contrast'] = options.config.get('curvature', 'contrast')\n\n for sec in options.config.sections():\n if 'paths' in sec or 'labels' in sec:\n my_viewopts[sec] = dict(options.config.items(sec))\n\n if pickerfun is None:\n pickerfun = lambda a, b: None\n\n class CTMHandler(web.RequestHandler):\n def get(self, path):\n subj, path = path.split('/')\n if path == '':\n self.set_header(\"Content-Type\", \"application/json\")\n self.write(open(ctms[subj]).read())\n else:\n fpath = os.path.split(ctms[subj])[0]\n mtype = mimetypes.guess_type(os.path.join(fpath, path))[0]\n if mtype is None:\n mtype = \"application/octet-stream\"\n self.set_header(\"Content-Type\", mtype)\n self.write(open(os.path.join(fpath, path), 'rb').read())\n\n class DataHandler(web.RequestHandler):\n def get(self, path):\n path = path.strip(\"/\")\n try:\n dataname, frame = path.split('/')\n except ValueError:\n dataname = path\n frame = 0\n\n if dataname in images:\n dataimg = images[dataname][int(frame)]\n if dataimg[1:6] == \"NUMPY\":\n self.set_header(\"Content-Type\", \"application/octet-stream\")\n else:\n self.set_header(\"Content-Type\", \"image/png\")\n\n if 'Range' in self.request.headers:\n self.set_status(206)\n rangestr = self.request.headers['Range'].split('=')[1]\n start, end = [ int(i) if len(i) > 0 else None for i in rangestr.split('-') ]\n\n clenheader = 'bytes %s-%s/%s' % (start, end or len(dataimg), len(dataimg) )\n self.set_header('Content-Range', clenheader)\n self.set_header('Content-Length', end-start+1)\n self.write(dataimg[start:end+1])\n else:\n self.write(dataimg)\n else:\n self.set_status(404)\n self.write_error(404)\n\n class StimHandler(web.StaticFileHandler):\n def initialize(self):\n pass\n\n def get(self, path):\n if path not in stims:\n self.set_status(404)\n self.write_error(404)\n else:\n self.root, fname = os.path.split(stims[path])\n super(StimHandler, self).get(fname)\n\n class StaticHandler(web.StaticFileHandler):\n def initialize(self):\n self.root = ''\n\n class MixerHandler(web.RequestHandler):\n def get(self):\n self.set_header(\"Content-Type\", \"text/html\")\n generated = html.generate(data=metadata,\n colormaps=colormaps,\n default_cmap=cmap,\n python_interface=True,\n leapmotion=True,\n layout=layout,\n subjects=subjectjs,\n viewopts=json.dumps(my_viewopts),\n title=title,\n **kwargs)\n #overlays_visible=json.dumps(overlays_visible),\n #labels_visible=json.dumps(labels_visible),\n #**viewopts)\n self.write(generated)\n\n def post(self):\n data = self.get_argument(\"svg\", default=None)\n png = self.get_argument(\"png\", default=None)\n with open(post_name.get(), \"wb\") as svgfile:\n if png is not None:\n data = png[22:].strip()\n try:\n data = binascii.a2b_base64(data)\n except:\n print(\"Error writing image!\")\n data = png\n svgfile.write(data)\n\n class JSMixer(serve.JSProxy):\n @property\n def view_props(self):\n \"\"\"An enumerated list of settable properties for views. \n There may be a way to get this from the javascript object, \n but I (ML) don't know how.\n\n There may be additional properties we want to set in views\n and animations; those must be added here.\n\n Old property list that used to be settable before webgl refactor:\n view_props = ['altitude', 'azimuth', 'target', 'mix', 'radius', 'pivot',\n 'visL', 'visR', 'alpha', 'rotationR', 'rotationL', 'projection',\n 'volume_vis', 'frame', 'slices']\n \"\"\"\n camera = getattr(self.ui, \"camera\")\n _camera_props = ['camera.%s' % k for k in camera._controls.attrs.keys()]\n surface = getattr(self.ui, \"surface\")\n _subject = list(surface._folders.attrs.keys())[0]\n _surface = getattr(surface, _subject)\n _surface_props = ['surface.{subject}.%s'%k for k in _surface._controls.attrs.keys()]\n _curvature_props = ['surface.{subject}.curvature.brightness',\n 'surface.{subject}.curvature.contrast',\n 'surface.{subject}.curvature.smoothness']\n return _camera_props + _surface_props + _curvature_props\n\n def _set_view(self, **kwargs):\n \"\"\"Low-level command: sets view parameters in the current viewer\n\n Sets each the state of each keyword argument provided. View parameters\n that can be set include all parameters in the data.gui in the html view.\n\n \"\"\"\n # Set unfolding level first, as it interacts with other arguments\n surface = getattr(self.ui, \"surface\")\n subject_list = surface._folders.attrs.keys()\n # Better to only self.view_props once; it interacts with javascript, \n # don't want to do that too often, it leads to glitches.\n vw_props = copy.copy(self.view_props)\n for subject in subject_list:\n if 'surface.{subject}.unfold' in kwargs:\n unfold = kwargs.pop('surface.{subject}.unfold')\n self.ui.set('surface.{subject}.unfold'.format(subject=subject), unfold)\n for k, v in kwargs.items():\n if not k in vw_props:\n print('Unknown parameter %s!'%k)\n continue\n else:\n self.ui.set(k.format(subject=subject) if '{subject}' in k else k, v)\n # Wait for webgl. Wait for it. .... WAAAAAIIIT.\n time.sleep(0.03)\n\n def _capture_view(self, frame_time=None):\n \"\"\"Low-level command: returns a dict of current view parameters\n\n Retrieves the following view parameters from current viewer:\n\n altitude, azimuth, target, mix, radius, visL, visR, alpha,\n rotationR, rotationL, projection, pivot\n\n Parameters\n ----------\n frame_time : scalar\n time (in seconds) to specify for this frame.\n \n Notes\n -----\n If multiple subjects are present, only retrieves view for first subject.\n \"\"\"\n view = {}\n subject = list(self.ui.surface._folders.attrs.keys())[0]\n for p in self.view_props:\n try:\n view[p] = self.ui.get(p.format(subject=subject) if '{subject}' in p else p)[0]\n # Wait for webgl.\n time.sleep(0.03)\n except Exception as err:\n # TO DO: Fix this hack with an error class in serve.py & catch it here\n print(err) #msg = \"Cannot read property 'undefined'\"\n #if err.message[:len(msg)] != msg:\n # raise err\n if frame_time is not None:\n view['time'] = frame_time\n return view\n\n def save_view(self, subject, name, is_overwrite=False):\n \"\"\"Saves current view parameters to pycortex database\n\n Parameters\n ----------\n subject : string\n pycortex subject id\n name : string\n name for view to store\n is_overwrite: bool\n whether to overwrite an extant view (default : False)\n\n Notes\n -----\n Equivalent to call to cortex.db.save_view(subject, vw, name)\n For a list of the view parameters saved, see viewer._capture_view\n \"\"\"\n db.save_view(self, subject, name, is_overwrite)\n\n def get_view(self, subject, name):\n \"\"\"Get saved view from pycortex database.\n\n Retrieves named view from pycortex database and sets current\n viewer parameters to retrieved values.\n\n Parameters\n ----------\n subject : string\n pycortex subject ID\n name : string\n name of saved view to re-load\n\n Notes\n -----\n Equivalent to call to cortex.db.get_view(subject, vw, name)\n For a list of the view parameters set, see viewer._capture_view\n \"\"\"\n view = db.get_view(self, subject, name)\n\n def addData(self, **kwargs):\n Proxy = serve.JSProxy(self.send, \"window.viewers.addData\")\n new_meta, new_ims = _convert_dataset(Dataset(**kwargs), path='/data/', fmt='%s_%d.png')\n metadata.update(new_meta)\n images.update(new_ims)\n return Proxy(metadata)\n\n def getImage(self, filename, size=(1920, 1080)):\n \"\"\"Saves currently displayed view to a .png image file\n\n Parameters\n ----------\n filename : string\n duh.\n size : tuple (x, y)\n size (in pixels) of image to save.\n \"\"\"\n post_name.put(filename)\n Proxy = serve.JSProxy(self.send, \"window.viewer.getImage\")\n return Proxy(size[0], size[1], \"mixer.html\")\n\n def makeMovie(self, animation, filename=\"brainmovie%07d.png\", offset=0,\n fps=30, size=(1920, 1080), interpolation=\"linear\"):\n \"\"\"Renders movie frames for animation of mesh movement\n\n Makes an animation (for example, a transition between inflated and\n flattened brain or a rotating brain) of a cortical surface. Takes a\n list of dictionaries (`animation`) as input, and uses the values in\n the dictionaries as keyframes for the animation.\n\n Mesh display parameters that can be animated include 'elevation',\n 'azimuth', 'mix', 'radius', 'target' (more?)\n\n\n Parameters\n ----------\n animation : list of dicts\n Each dict should have keys `idx`, `state`, and `value`.\n `idx` is the time (in seconds) at which you want to set `state` to `value`\n `state` is the parameter to animate (e.g. 'altitude', 'azimuth')\n `value` is the value to set for `state`\n filename : string path name\n Must contain '%d' (or some variant thereof) to account for frame\n number, e.g. '/some/directory/brainmovie%07d.png'\n offset : int\n Frame number for first frame rendered. Useful for concatenating\n animations.\n fps : int\n Frame rate of resultant movie\n size : tuple (x, y)\n Size (in pixels) of resulting movie\n interpolation : {\"linear\", \"smoothstep\", \"smootherstep\"}\n Interpolation method for values between keyframes.\n\n Example\n -------\n # Called after a call of the form: js_handle = cortex.webgl.show(DataViewObject)\n # Start with left hemisphere view\n js_handle._setView(azimuth=[90], altitude=[90.5], mix=[0])\n # Initialize list\n animation = []\n # Append 5 key frames for a simple rotation\n for az, idx in zip([90, 180, 270, 360, 450], [0, .5, 1.0, 1.5, 2.0]):\n animation.append({'state':'azimuth', 'idx':idx, 'value':[az]})\n # Animate! (use default settings)\n js_handle.makeMovie(animation)\n \"\"\"\n # build up two variables: State and Anim.\n # state is a dict of all values being modified at any time\n state = dict()\n # anim is a list of transitions between keyframes\n anim = []\n setfunc = self.ui.set\n for f in sorted(animation, key=lambda x:x['idx']):\n if f['idx'] == 0:\n setfunc(f['state'], f['value'])\n state[f['state']] = dict(idx=f['idx'], val=f['value'])\n else:\n if f['state'] not in state:\n state[f['state']] = dict(idx=0, val=self.getState(f['state'])[0])\n start = dict(idx=state[f['state']]['idx'],\n state=f['state'],\n value=state[f['state']]['val'])\n end = dict(idx=f['idx'], state=f['state'], value=f['value'])\n state[f['state']]['idx'] = f['idx']\n state[f['state']]['val'] = f['value']\n if start['value'] != end['value']:\n anim.append((start, end))\n\n for i, sec in enumerate(np.arange(0, anim[-1][1]['idx']+1./fps, 1./fps)):\n for start, end in anim:\n if start['idx'] < sec <= end['idx']:\n idx = (sec - start['idx']) / float(end['idx'] - start['idx'])\n if start['state'] == 'frame':\n func = mixes['linear']\n else:\n func = mixes[interpolation]\n\n val = func(np.array(start['value']), np.array(end['value']), idx)\n if isinstance(val, np.ndarray):\n setfunc(start['state'], val.ravel().tolist())\n else:\n setfunc(start['state'], val)\n self.getImage(filename%(i+offset), size=size)\n\n def _get_anim_seq(self, keyframes, fps=30, interpolation='linear'):\n \"\"\"Convert a list of keyframes to a list of EVERY frame in an animation.\n\n Utility function called by make_movie; separated out so that individual\n frames of an animation can be re-rendered, or for more control over the\n animation process in general.\n\n \"\"\"\n # Misc. setup\n fr = 0\n a = np.array\n func = mixes[interpolation]\n #skip_props = ['surface.{subject}.right', 'surface.{subject}.left', ] #'projection',\n # Get keyframes\n keyframes = sorted(keyframes, key=lambda x:x['time'])\n # Normalize all time to frame rate\n fs = 1./fps\n for k in range(len(keyframes)):\n t = keyframes[k]['time']\n t = np.round(t/fs)*fs\n keyframes[k]['time'] = t\n allframes = []\n for start, end in zip(keyframes[:-1], keyframes[1:]):\n t0 = start['time']\n t1 = end['time']\n tdif = float(t1-t0)\n # Check whether to continue frame sequence to endpoint\n use_endpoint = keyframes[-1]==end\n nvalues = np.round(tdif/fs).astype(int)\n if use_endpoint:\n nvalues += 1\n fr_time = np.linspace(0, 1, nvalues, endpoint=use_endpoint)\n # Interpolate between values\n for t in fr_time:\n frame = {}\n for prop in start.keys():\n if prop=='time':\n continue\n if (start[prop] is None) or (start[prop] == end[prop]) or isinstance(start[prop], (bool, str)):\n frame[prop] = start[prop]\n continue\n val = func(a(start[prop]), a(end[prop]), t)\n if isinstance(val, np.ndarray):\n frame[prop] = val.tolist()\n else:\n frame[prop] = val\n allframes.append(frame)\n return allframes\n\n def make_movie_views(self, animation, filename=\"brainmovie%07d.png\", \n offset=0, fps=30, size=(1920, 1080), alpha=1, frame_sleep=0.05,\n frame_start=0, interpolation=\"linear\"):\n \"\"\"Renders movie frames for animation of mesh movement\n\n Makes an animation (for example, a transition between inflated and\n flattened brain or a rotating brain) of a cortical surface. Takes a\n list of dictionaries (`animation`) as input, and uses the values in\n the dictionaries as keyframes for the animation.\n\n Mesh display parameters that can be animated include 'elevation',\n 'azimuth', 'mix', 'radius', 'target' (more?)\n\n\n Parameters\n ----------\n animation : list of dicts\n This is a list of keyframes for the animation. Each keyframe should be\n a dict in the form captured by the ._capture_view method. NOTE: every\n view must include all view parameters. Additionally, there should be\n one extra key/value pair for \"time\". The value for time should be\n in seconds. The list of keyframes is sorted by time before applying,\n so they need not be in order in the input.\n filename : string path name\n Must contain '%d' (or some variant thereof) to account for frame\n number, e.g. '/some/directory/brainmovie%07d.png'\n offset : int\n Frame number for first frame rendered. Useful for concatenating\n animations.\n fps : int\n Frame rate of resultant movie\n size : tuple (x, y)\n Size (in pixels) of resulting movie\n interpolation : {\"linear\", \"smoothstep\", \"smootherstep\"}\n Interpolation method for values between keyframes.\n\n Notes\n -----\n Make sure that all values that will be modified over the course\n of the animation are initialized (have some starting value) in the first\n frame.\n\n Example\n -------\n # Called after a call of the form: js_handle = cortex.webgl.show(DataViewObject)\n # Start with left hemisphere view\n js_handle._setView(azimuth=[90], altitude=[90.5], mix=[0])\n # Initialize list\n animation = []\n # Append 5 key frames for a simple rotation\n for az, t in zip([90, 180, 270, 360, 450], [0, .5, 1.0, 1.5, 2.0]):\n animation.append({'time':t, 'azimuth':[az]})\n # Animate! (use default settings)\n js_handle.make_movie(animation)\n \"\"\"\n allframes = self._get_anim_seq(animation, fps, interpolation)\n for fr, frame in enumerate(allframes[frame_start:], frame_start):\n self._set_view(**frame)\n time.sleep(frame_sleep)\n self.getImage(filename%(fr+offset+1), size=size)\n time.sleep(frame_sleep)\n\n class PickerHandler(web.RequestHandler):\n def get(self):\n pickerfun(int(self.get_argument(\"voxel\")), int(self.get_argument(\"vertex\")))\n\n class WebApp(serve.WebApp):\n disconnect_on_close = autoclose\n def get_client(self):\n self.connect.wait()\n self.connect.clear()\n return JSMixer(self.send, \"window.viewer\")\n\n def get_local_client(self):\n return JSMixer(self.srvsend, \"window.viewer\")\n\n if port is None:\n port = random.randint(1024, 65536)\n\n server = WebApp([(r'/ctm/(.*)', CTMHandler),\n (r'/data/(.*)', DataHandler),\n (r'/stim/(.*)', StimHandler),\n (r'/mixer.html', MixerHandler),\n (r'/picker', PickerHandler),\n (r'/', MixerHandler),\n (r'/static/(.*)', StaticHandler)],\n port)\n\n server.start()\n print(\"Started server on port %d\"%server.port)\n url = \"http://%s%s:%d/mixer.html\"%(serve.hostname, domain_name, server.port)\n if open_browser:\n webbrowser.open(url)\n client = server.get_client()\n client.server = server\n return client\n else:\n try:\n from IPython.display import display, HTML\n display(HTML('Open viewer: <a href=\"{0}\" target=\"_blank\">{0}</a>'.format(url)))\n except:\n pass\n return server","def render(self, mode='human'):\n screen_width = 800\n screen_height = 550\n\n # Width is one column for each variable\n n_sect = 7\n world_width = n_sect*2 # X axis is just pixels\n \n buff_axis = cfg['buff_axis']\n #bottom of the screen scales to the input/output range of values\n world_height_bottom = np.max(self.maxes)+buff_axis\n \n # Top is for counting steps\n world_height_top = 100\n\n #Split the screen:\n world_top = .3\n world_bottom = 1-world_top\n screen_height_bottom = world_bottom*screen_height\n\n #Set where to draw the steps axis\n axes_line1 = screen_height*(world_bottom + .2)\n\n # Scale the pixels in the screen:\n scalex = screen_width/world_width\n scaley_bottom= screen_height_bottom/world_height_bottom\n\n # Some adjustments to move some objects up/ right\n move_oval = -scalex*.2\n move_up= scaley_bottom * buff_axis*.5\n\n #set sizes of shapes:\n self.oval_length = 25.0\n self.oval_width = 50.0\n self.rect_width = 70.0\n self.rect_height = 5.0 \n\n #Step plot:\n scalestep = screen_width/cfg['scalestep']\n\n #color shades:\n light_col = .7\n dark_col = 1\n c11 = .6\n c22 = .8\n c33 = 1\n\n if self.viewer is None:\n #TO DO: find an alternative to copy-paste to generate multiple similar shapes\n self.viewer = rendering.Viewer(screen_width, screen_height)\n \n #Input states:\n\n #the temp action\n self.temptrans1 = self.make_oval(0,0,light_col)\n self.temptrans2 = self.make_oval(0,0,dark_col)\n #flow action:\n self.flowtrans1 = self.make_oval(light_col,0,light_col)\n self.flowtrans2 = self.make_oval(dark_col,0,dark_col)\n\n #output states:\n #out1:\n #the gauge is a rectangle \n self.outgauge1 = self.make_rect(0,c33,0)\n #goal is red rectangle\n self.outgoal1= self.make_rect(c33,0,0)\n \n #out2:\n #the gauge is a rectangle \n self.outgauge2 = self.make_rect(0,c22,0)\n #goal is red rectangle\n self.outgoal2= self.make_rect(c22,0,0)\n\n #out3:\n #the gauge is a rectangle \n self.outgauge3 = self.make_rect(0,c11,0)\n #goal is red rectangle\n self.outgoal3 = self.make_rect(c11,0,0)\n\n #lines on which \"controls\" sit\n for l in range(n_sect): \n self.make_line(scalex*((l*2)+1),0, scalex*((l*2)+1),screen_height*world_bottom)\n\n # Line separating the top and bottom of the screen. \n self.make_line(0,world_bottom*screen_height,screen_width,world_bottom*screen_height)\n # Step # axis.\n self.make_line(scalex*1.5,axes_line1,screen_width-scalex*1,axes_line1)\n\n # The dot tracking the step #\n dot = rendering.make_circle(self.oval_length)\n self.dottrans = rendering.Transform()\n dot.add_attr(self.dottrans)\n dot.set_color(0,0,0)\n self.viewer.add_geom(dot)\n\n #labels: \n num = 0\n label_buff_y = 1.07\n label_buff_x = .2\n img_scale = .5\n img_wid = 179 *img_scale\n img_height = 124 * img_scale\n\n for label in self.labels:\n pth = (self.label_dir+label+'.png')\n self.txt = rendering.Image(pth,img_wid,img_height)\n locx = (num*2)+1\n self.txtis = rendering.Transform(translation=(scalex*locx +locx* label_buff_x,world_bottom*screen_height*label_buff_y))\n self.txt.add_attr(self.txtis)\n self.viewer.add_geom(self.txt)\n num = num+1\n\n #step label\n pth = (self.label_dir+'Step.png')\n self.txt = rendering.Image(pth,img_wid,img_height)\n self.txtis = rendering.Transform(translation=(scalex*.5,axes_line1))\n self.txt.add_attr(self.txtis)\n self.viewer.add_geom(self.txt)\n\n if self.state is None: return None\n\n x = self.state\n\n # 4 ins:\n self.flowtrans1.set_translation(move_oval+scalex*1,move_up+scaley_bottom*x[0])\n self.temptrans1.set_translation(move_oval+scalex*3,move_up+scaley_bottom*x[1])\n self.flowtrans2.set_translation(move_oval+scalex*5,move_up+scaley_bottom*x[2])\n self.temptrans2.set_translation(move_oval+scalex*7,move_up+scaley_bottom*x[3])\n\n # 3 outs: current & goal:\n self.outgauge1.set_translation(scalex*9,move_up+scaley_bottom*x[4])\n self.outgoal1.set_translation(scalex*9,move_up+scaley_bottom*x[7])\n self.outgauge2.set_translation(scalex*11,move_up+scaley_bottom*x[5])\n self.outgoal2.set_translation(scalex*11,move_up+scaley_bottom*x[8])\n self.outgauge3.set_translation(scalex*13,move_up+scaley_bottom*x[6])\n self.outgoal3.set_translation(scalex*13,move_up+scaley_bottom*x[9])\n\n #step info:\n self.dottrans.set_translation(scalex*1.5 + self.steps*scalestep, axes_line1)\n done_grow = .5*self.done\n self.dottrans.set_scale(1+done_grow,1+done_grow) #expand size when done\n\n return self.viewer.render(return_rgb_array = mode=='rgb_array')","def _render_file(\n self,\n abs_fpath: Text,\n template_name: Text,\n context: Dict,\n style_config: Dict = None\n ) -> None:\n try:\n say(f'rendering {abs_fpath[1+len(self.embryo.destination):]}')\n rendered_text = self.render_template(\n template_name=template_name, context=context\n )\n except Exception:\n shout('problem rendering {p}', p=abs_fpath)\n raise\n\n formatted_text = rendered_text\n # get the full filepath with root prefix\n fpath = self.get_abs_path(abs_fpath)\n # load up the adapter for the file\n adapter = self.get_adapter(fpath)\n loaded_text = adapter.load(formatted_text)\n # write the loaded data\n adapter.write(fpath, loaded_text)","def main(argv):\n logging.basicConfig(format=\"%(message)s\", level=logging.INFO, stream=sys.stdout)\n logger = logging.getLogger(\"demo4\")\n\n # Define some parameters we'll use below and make directories if needed.\n cat_file_name = os.path.join('input','galsim_default_input.asc')\n if not os.path.isdir('output'):\n os.mkdir('output')\n multi_file_name = os.path.join('output','multi.fits')\n\n random_seed = galsim.BaseDeviate(8241573).raw()\n sky_level = 1.e6 # ADU / arcsec^2\n pixel_scale = 1.0 # arcsec / pixel (size units in input catalog are pixels)\n gal_flux = 1.e6 # arbitrary choice, makes nice (not too) noisy images\n gal_g1 = -0.009 #\n gal_g2 = 0.011 #\n\n # the fraction of flux in each component\n # 40% is in the bulge, 60% in a disk. 70% of that disk light is placed\n # into point sources distributed as a random walk\n\n bulge_frac = 0.4\n disk_frac = 0.6\n knot_frac = 0.42\n smooth_disk_frac = 0.18\n\n # number of knots of star formation. To simulate a nice irregular (all the\n # flux is in knots) we find ~100 is a minimum number needed, but we will\n # just use 10 here to make the demo run fast.\n\n n_knots = 10\n\n xsize = 64 # pixels\n ysize = 64 # pixels\n\n logger.info('Starting demo script 4 using:')\n logger.info(' - parameters taken from catalog %r',cat_file_name)\n logger.info(' - Moffat PSF (parameters from catalog)')\n logger.info(' - pixel scale = %.2f',pixel_scale)\n logger.info(' - Bulge + Disc galaxies (parameters from catalog)')\n logger.info(' - 100 Point sources, distributed as random walk')\n logger.info(' - Applied gravitational shear = (%.3f,%.3f)',gal_g1,gal_g2)\n logger.info(' - Poisson noise (sky level = %.1e).', sky_level)\n\n # Read in the input catalog\n cat = galsim.Catalog(cat_file_name)\n\n\n # save a list of the galaxy images in the \"images\" list variable:\n images = []\n for k in range(cat.nobjects):\n # Initialize the (pseudo-)random number generator that we will be using below.\n # Use a different random seed for each object to get different noise realizations.\n # Using sequential random seeds here is safer than it sounds. We use Mersenne Twister\n # random number generators that are designed to be used with this kind of seeding.\n # However, to be extra safe, we actually initialize one random number generator with this\n # seed, generate and throw away two random values with that, and then use the next value\n # to seed a completely different Mersenne Twister RNG. The result is that successive\n # RNGs created this way produce very independent random number streams.\n rng = galsim.BaseDeviate(random_seed+k+1)\n\n # Take the Moffat beta from the first column (called 0) of the input catalog:\n # Note: cat.get(k,col) returns a string. To get the value as a float, use either\n # cat.getFloat(k,col) or float(cat.get(k,col))\n beta = cat.getFloat(k,0)\n # A Moffat's size may be either scale_radius, fwhm, or half_light_radius.\n # Here we use fwhm, taking from the catalog as well.\n fwhm = cat.getFloat(k,1)\n # A Moffat profile may be truncated if desired\n # The units for this are expected to be arcsec (or specifically -- whatever units\n # you are using for all the size values as defined by the pixel_scale).\n trunc = cat.getFloat(k,4)\n # Note: You may omit the flux, since the default is flux=1.\n psf = galsim.Moffat(beta=beta, fwhm=fwhm, trunc=trunc)\n\n # Take the (e1, e2) shape parameters from the catalog as well.\n psf = psf.shear(e1=cat.getFloat(k,2), e2=cat.getFloat(k,3))\n\n # Galaxy is a bulge + disk(+knots) with parameters taken from the catalog:\n\n # put some fraction of the disk light into knots of star formation\n\n disk_hlr = cat.getFloat(k,5)\n disk_e1 = cat.getFloat(k,6)\n disk_e2 = cat.getFloat(k,7)\n bulge_hlr = cat.getFloat(k,8)\n bulge_e1 = cat.getFloat(k,9)\n bulge_e2 = cat.getFloat(k,10)\n\n smooth_disk = galsim.Exponential(flux=smooth_disk_frac, half_light_radius=disk_hlr)\n\n knots = galsim.RandomKnots(n_knots, half_light_radius=disk_hlr, flux=knot_frac, rng=rng)\n\n disk = galsim.Add([smooth_disk, knots])\n disk = disk.shear(e1=disk_e1, e2=disk_e2)\n\n # the rest of the light goes into the bulge\n bulge = galsim.DeVaucouleurs(flux=bulge_frac, half_light_radius=bulge_hlr)\n bulge = bulge.shear(e1=bulge_e1, e2=bulge_e2)\n\n # The flux of an Add object is the sum of the component fluxes.\n # Note that in demo3.py, a similar addition was performed by the binary operator \"+\".\n gal = galsim.Add([disk, bulge])\n\n # This flux may be overridden by withFlux. The relative fluxes of the components\n # remains the same, but the total flux is set to gal_flux.\n gal = gal.withFlux(gal_flux)\n gal = gal.shear(g1=gal_g1, g2=gal_g2)\n\n # The center of the object is normally placed at the center of the postage stamp image.\n # You can change that with shift:\n gal = gal.shift(dx=cat.getFloat(k,11), dy=cat.getFloat(k,12))\n\n final = galsim.Convolve([psf, gal])\n\n # Draw the profile\n image = galsim.ImageF(xsize, ysize)\n final.drawImage(image, scale=pixel_scale)\n\n # Add Poisson noise to the image:\n image.addNoise(galsim.PoissonNoise(rng, sky_level * pixel_scale**2))\n\n logger.info('Drew image for object at row %d in the input catalog'%k)\n \n # Add the image to our list of images\n images.append(image)\n \n # Now write the images to a multi-extension fits file. Each image will be in its own HDU.\n galsim.fits.writeMulti(images, multi_file_name)\n logger.info('Images written to multi-extension fits file %r',multi_file_name)","def run(self):\n\n print \"\\n\\n\\tPlease Note: Templates are generated based off\"\n print \"\\t of the OS environment variables that are set.\"\n print \"\\t* Running ReHeat.\"\n\n self.set_creds()\n self.gen_ip() # used in template description\n self.gen_tenant_id()\n if self.reheat_error:\n return self.reheat_errmsg\n\n print \"\\t* You have opted to generate %s file[s]\" % self.template_type\n if 'all' in self.template_type:\n self.gen_heat_data()\n self.gen_heat_template()\n self.gen_compute_data()\n return self.gen_compute_template()\n elif 'heat' in self.template_type:\n self.gen_heat_data()\n return self.gen_heat_template()\n elif 'compute' in self.template_type:\n self.gen_compute_data()\n return self.gen_compute_template()\n else:\n raise Exception(\"User provided an improper template type.\")","def test_render_templates():\n water_mol = Atoms(cclib.bridge.cclib2openbabel.readfile(\"data/water.xyz\", \"xyz\"))\n if not water_mol.name:\n water_mol.name = \"data/water.xyz\"\n\n main([\"-g\", \"/tmp/foo.ADF.in\"])\n main([\"/tmp/foo.ADF.in\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.in\").read().strip(),\n \"\"\"TITLE data/water.xyz\n\nCHARGE 0 0\n\nNumber of atoms\n 3\n\nATOMS Cartesian\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\nEnd\n\nBasis\nEnd\n\nGeometry\nEnd\"\"\",\n )\n\n main([\"-g\", \"/tmp/test.GAMESS.inp\"])\n main([\"/tmp/test.GAMESS.inp\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.inp\").read(),\n \"\"\" $CONTRL COORD=CART UNITS=ANGS $END\n\n $DATA\ndata/water.xyz\nC1\nO 8.0 0.0584027061 0.0584027059 0.0000000000\nH 1.0 1.0096135406 -0.0680162466 0.0000000000\nH 1.0 -0.0680162466 1.0096135407 0.0000000000\n $END\n\n\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/hello.GAMESSUK.inp\"])\n main([\"/tmp/hello.GAMESSUK.inp\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.inp\").read(), water_mol.to_string(\"gukin\"))\n\n main([\"-g\", \"/tmp/hello.world.Gaussian.gjf\"])\n main([\"/tmp/hello.world.Gaussian.gjf\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.gjf\").read(),\n \"\"\"#Put Keywords Here, check Charge and Multiplicity.\n\n data/water.xyz\n\n0 1\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\n\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/bar.Jaguar.in\"])\n main([\"/tmp/bar.Jaguar.in\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.in\").read(), water_mol.to_string(\"jin\"))\n\n main([\"-g\", \"/tmp/foo.Molpro.inp\"])\n main([\"/tmp/foo.Molpro.inp\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.inp\").read(), water_mol.to_string(\"mp\"))\n\n main([\"-g\", \"/tmp/example.MOPAC.mop\"])\n main([\"/tmp/example.MOPAC.mop\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.mop\").read(),\n \"\"\"CHARGE=0 MS=0.0\ndata/water.xyz\n\nO 0.05840 1 0.05840 1 0.00000 1\nH 1.00961 1 -0.06802 1 0.00000 1\nH -0.06802 1 1.00961 1 0.00000 1\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/bar.MPQC.in\"])\n main([\"/tmp/bar.MPQC.in\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.in\").read(), water_mol.to_string(\"mpqcin\"))\n\n main([\"-g\", \"/tmp/foo.NWChem.nw\"])\n main([\"/tmp/foo.NWChem.nw\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.nw\").read(),\n \"\"\"start molecule\n\ntitle data/water.xyz\n\ngeometry units angstroms print xyz autosym\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\nend\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/example.ORCA.inp\"])\n main([\"/tmp/example.ORCA.inp\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.inp\").read(),\n \"\"\"# data/water.xyz\n! Opt\n\n* xyz 0 1\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\n*\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/bar.Psi.dat\"])\n main([\"/tmp/bar.Psi.dat\", \"data/water.xyz\"])\n assert_equals(\n open(\"data/water.dat\").read(),\n \"\"\"# data/water.xyz\n\nmolecule {\n0 1\nO 0.0584027061 0.0584027059 0.0000000000\nH 1.0096135406 -0.0680162466 0.0000000000\nH -0.0680162466 1.0096135407 0.0000000000\nunits angstrom\n}\n\noptimize('scf')\n\"\"\",\n )\n\n main([\"-g\", \"/tmp/example.QChem.in\"])\n main([\"/tmp/example.QChem.in\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.in\").read(), water_mol.to_string(\"qcin\"))\n\n main([\"-g\", \"/tmp/foo.ZINDO.input\"])\n main([\"/tmp/foo.ZINDO.input\", \"data/water.xyz\"])\n assert_equals(open(\"data/water.input\").read(), water_mol.to_string(\"zin\"))","def show_me():\n # Scumbag thumbnail code\n try:\n from PIL import Image\n except ImportError:\n pass\n else:\n filename = os.path.join(app.static_folder, 'img', 'badumtss.png')\n image = Image.open(filename)\n\n return render_template('show_me.html')","def render(self, mode='human', type_of='color'):\n if type_of == 'color':\n plot_color(self.observation, self.servers_mem ,self.services_mem)\n elif type_of=='black':\n plot_black(self.observation, self.servers_mem ,self.services_mem)\n print('\\n----services mem----\\n')\n print('services resource usage: {}\\n'.format(self.services_mem))\n print('servers capacity: {}\\n'.format(self.servers_mem))\n print('\\n----observation----\\n')\n print('service placements: {}\\n'.format(self.observation))","def main():\n \n ## read parameters from command line\n parser = argparse.ArgumentParser() \n parser.add_argument(\"-d\", \"--density\", help=\"Density\")\n parser.add_argument(\"-k\", \"--kappa\", help=\"Bending rigidity\")\n parser.add_argument(\"-t\", \"--time\", help=\"Timestep\") \n parser.add_argument(\"-s\", \"--save\", help=\"Save options\", action=\"store_true\") \n args = parser.parse_args()\n\n ## load data \n database = \"/local/duman/SIMULATIONS/long_filaments\"\n datafolder = database + \"/density_\" + args.density + \"/kappa_\" + args.kappa\n infile = datafolder + \"/VORTEX/data.hdf5\"\n lx, ly, time, xedges, yedges, rho_hist, vx_hist, vy_hist, vorticity \\\n = load_data(infile, int(args.time))\n \n ## plot data\n savebase = \"~/RolfData/many_filaments_5\" \n if args.save:\n sfolder = savebase + \"/plots/VORTEX\" \n os.system('mkdir -p ' + sfolder)\n else:\n sfolder = \"~/Desktop\"\n plot_data(sfolder, lx, ly, time, xedges, yedges, rho_hist, vx_hist, vy_hist, vorticity, args.save)","def step_run(cls, config):\n\n nir_paw_image_fname = config.get(cls.step_name, 'nir_paw_image')\n nir_paw_conf_fname = config.get(cls.step_name, 'nir_paw_conf')\n output_template = config.get(cls.step_name, 'output_template')\n conf_limit = config.getfloat(cls.step_name, 'conf_limit')\n\n# logger.info('reading Comparison image from %s', comp_fname)\n# comp_im = DESImage.load(comp_fname)\n\n ret_code = cls.__call__(nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit)\n return ret_code","def fillBackgroundTemplates(opt):\n\n totalBkg={}\n templates=[]\n\n #import signal events\n data=ROOT.TChain('data')\n for f in [os.path.join(opt.input,x) for x in os.listdir(opt.input) if 'Data13TeV' in x]:\n if 'MuonEG' in f : continue\n data.AddFile(f)\n\n #define final preselection cuts\n cuts='xangle==%d'%opt.xangle\n if len(opt.presel) : cuts += ' && ' + opt.presel \n if opt.csiacc:\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\n\n #loop over categories build templates\n for icat in range(len(opt.categs)):\n\n #apply category cuts\n categCut=opt.categs[icat]\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\n\n print '\\t',catName,categCut\n\n #background modelling histos\n histos=[]\n data_obs=None\n for name,pfix in [('bkg_'+catName,'mix'),('bkg_%s_bkgShape'%catName,'mixem')]:\n\n templCuts=categCut.replace('csi1',pfix+'csi1')\n templCuts=templCuts.replace('csi2',pfix+'csi2')\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),templCuts,'goff')\n h=data.GetHistogram()\n histos.append(h.Clone(name))\n histos[-1].SetDirectory(0)\n\n if len(histos)==1:\n totalBkg[icat]=h.Integral()\n if not opt.unblind :\n data_obs=h.Clone('data_obs_'+catName)\n data_obs.SetDirectory(0)\n\n h.Reset('ICE')\n templates += defineProcessTemplates(histos)\n\n #observed data in this category if unblinding\n if opt.unblind:\n data.Draw('mmiss >> h({1},{2},{3})'.format(opt.nbins,opt.mMin,opt.mMax),categCut,'goff')\n data_obs=data.GetHistogram().Clone('data_obs_'+catName)\n data_obs.SetDirectory(0)\n\n templates.append(data_obs)\n\n print '\\t total background:',totalBkg\n return totalBkg,templates","def plot_tiltres(setup, mtilt, ytilt, yfit, slit=None, outfile=None, show_QA=False, out_dir=None):\n\n plt.rcdefaults()\n plt.rcParams['font.family']= 'times new roman'\n\n # Outfil\n method = inspect.stack()[0][3]\n if (outfile is None) and (not show_QA):\n outfile = qa.set_qa_filename(setup, method, slit=slit, out_dir=out_dir)\n\n # Setup\n plt.figure(figsize=(8, 4.0))\n plt.clf()\n ax = plt.gca()\n\n # Scatter plot\n res = (mtilt-ytilt) - yfit\n ax.scatter(mtilt, res)\n\n rms = np.std(res)\n ax.text(0.90, 0.90, 'Slit {:d}: RMS (pix) = {:0.5f}'.format(slit, rms),\n transform=ax.transAxes, size='large', ha='right', color='black')\n # Label\n ax.set_xlabel('Row')\n ax.set_ylabel('Residual (pix)')\n\n # Finish\n plt.tight_layout(pad=0.2, h_pad=0.0, w_pad=0.0)\n if show_QA:\n plt.show()\n else:\n plt.savefig(outfile, dpi=400)\n plt.close()\n\n plt.rcdefaults()\n\n return","def create_preview(name):\n file_type = os.path.splitext(name)[1]\n\n fig = Figure()\n axis = fig.add_subplot(1, 1, 1)\n\n dir = os.path.dirname(os.path.realpath(__file__))\n file = open(dir+'/instances/'+name)\n if file_type == '.csv':\n\n for nodeNo,line in enumerate(file): #enumerate used to obtain line numbers and thus node numbers\n coords = line.rsplit()[0].split(\",\")\n\n x = int(coords[0])\n y = int(coords[1])\n axis.scatter(x, y, c = 'b', label = nodeNo)\n axis.set_title(name)\n axis.text(x+5,y+5, str(nodeNo))\n else:\n file.readline()\n file.readline()\n file.readline()\n no_nodes = int(file.readline().strip().split()[1])\n file.readline()\n file.readline()\n file.readline()\n\n for i in range(0, no_nodes):\n\n coords = file.readline().strip().split()[1:]\n x = float(coords[0])\n y = float(coords[1])\n axis.scatter(x, y, c = 'b', label = i)\n axis.set_title(name)\n axis.text(x,y, str(i))\n\n return fig","def main(args):\n args = parse_args(args)\n setup_logging(args.loglevel)\n _logger.debug(\"Generating NUFEB simulation files\")\n\n # create nutrients\n light = Nutrient(1e-1, None, None, \"g\", \"nn\")\n co2 = Nutrient(float(args.co2), 1.9e-09, 44.01, \"l\", \"nn\")\n o2 = Nutrient(0.28125, 2.30e-9, 32, \"l\", \"nn\")\n sucrose = Nutrient(float(args.sucrose), 5.2e-10, 342.3, \"l\", \"nn\")\n gco2 = Nutrient(0, None, 44.01, \"g\", \"nn\")\n TEMPLATES_DIR = (Path(__file__).parent) / \"templates\"\n\n captureRate = round(1000 / args.timestep)\n # define dump parameters\n dump_list = {\n \"vtk_dump\": f\"dump atom_vtk all vtk {captureRate} dump*.vtu id type diameter vx vy vz fx fy fz \\n dump grid_vtk all grid/vtk {captureRate} dump_%_*.vti con\",\n \"image_dump\": f\"dump du_image all image {captureRate} image.*.jpg type diameter zoom 2 bacillus type size 1280 720 view 45 60 \\n dump_modify du_image acolor 1 green acolor 2 red\",\n \"movie_dump\": f\"dump du_mov all movie {captureRate} movie.avi type diameter zoom 1.5 bacillus type size 1280 720 view 0 0 \\n dump_modify du_mov acolor 1 green acolor 2 red\",\n \"hdf_dump\": f\"dump du_h5 all nufeb/hdf5 {captureRate} dump.h5 id type x y z vx vy vz fx fy fz radius conc reac\",\n }\n\n dumps = defaultdict(list)\n for i in range(4):\n tmp = [\"vtk_dump\", \"image_dump\", \"movie_dump\", \"hdf_dump\"]\n dumps[tmp[i]]\n\n for dump, dump_var in zip(\n [args.vtk, args.img, args.movie, args.hdf],\n [\"vtk_dump\", \"image_dump\", \"movie_dump\", \"hdf_dump\"],\n ):\n if dump is True or dump == \"True\":\n dumps[dump_var] = dump_list[dump_var]\n else:\n dumps[dump_var] = \"\"\n\n ## Species-specific parameters\n\n # check for runs folder\n if not os.path.isdir(\"runs\"):\n os.mkdir(\"runs\")\n x = float(args.dims.split(\",\")[0])\n y = float(args.dims.split(\",\")[1])\n z = float(args.dims.split(\",\")[2])\n for n in range(1, int(args.num) + 1):\n culture = Culture(args)\n atoms_list = []\n bacilli_list = []\n # Create list of atoms and bacilli for atom definition file\n for cell in culture.cells:\n atoms_list.append(cell.Atom())\n bacilli_list.append(cell.Bacillus())\n # make atom definition file\n for r in range(1, int(args.reps) + 1):\n L = [\n \" NUFEB Simulation\\r\\n\\n\",\n f\" {args.cells_init} atoms \\n\",\n f\" {len(culture.cell_types)} atom types \\n\",\n f\" {args.cells_init} bacilli \\n\\n\",\n f\" 0.0e-4 {x :.2e} xlo xhi \\n\",\n f\" 0.0e-4 {y :.2e} ylo yhi \\n\",\n f\" 0.0e-4 {z :.2e} zlo zhi \\n\\n\",\n \" Atoms \\n\\n\",\n ]\n atoms = L + atoms_list\n atoms.append(\"\\n\")\n atoms.append(\" Bacilli \\n\\n\")\n atoms = atoms + bacilli_list\n # write atom definition file\n f = open(\n f\"runs/atom_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}_{r}.in\",\n \"w+\",\n )\n f.writelines(atoms)\n RUN_DIR = (\n Path(\"runs\")\n / f\"Run_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}_{args.reps}\"\n )\n if not os.path.isdir(RUN_DIR):\n os.mkdir(RUN_DIR)\n # os.mkdir(f'runs/Run_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}_{args.reps}')\n # write initial conditions json file\n dumpfile = open(RUN_DIR / \"metadata.json\", \"w\")\n # dumpfile = open(f\"/runs/Run_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}_{args.reps}/metadata.json\",'w')\n json.dump(CellInfo, dumpfile, indent=6)\n dumpfile.close()\n ###\n\n # write Inputscript\n # open the file\n filein = open(TEMPLATES_DIR / \"bacillus.txt\")\n # filein = resources.read_text(\"nufeb_tools.templates\", \"Bacillus.txt\")\n # read it\n src = Template(filein.read())\n # do the substitution\n result = src.safe_substitute(\n {\n \"n\": args.cells_init,\n \"SucRatio\": culture.SucRatio,\n \"SucPct\": culture.SucPct,\n \"n_cyanos\": culture.n_cyanos,\n \"n_ecw\": culture.n_ecw,\n \"Replicates\": args.reps,\n \"Timesteps\": args.ntimesteps,\n \"ts\": args.timestep,\n \"CYANOGroup\": culture.cyGroup,\n \"ECWGroup\": culture.ecwGroup,\n \"Zheight\": float(args.dims.split(\",\")[2]),\n \"CYANODiv\": culture.cyDiv,\n \"ECWDiv\": culture.ecwDiv,\n \"light\": light.concentration,\n \"co2\": co2.concentration,\n \"o2\": o2.concentration,\n \"sucrose\": sucrose.concentration,\n \"gco2\": gco2.concentration,\n \"CYANOMonod\": culture.cyMonod,\n \"ECWMonod\": culture.ecwMonod,\n \"CYANOcount\": culture.cyanoCount,\n \"ECWcount\": culture.ecwCount,\n \"v_ncyano\": culture.vcyano,\n \"v_necw\": culture.vecw,\n \"vtk_dump\": dumps[\"vtk_dump\"],\n \"image_dump\": dumps[\"image_dump\"],\n \"movie_dump\": dumps[\"movie_dump\"],\n \"hdf_dump\": dumps[\"hdf_dump\"],\n }\n )\n f = open(\n f\"./runs/Inputscript_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}.lmp\",\n \"w+\",\n )\n f.writelines(result)\n\n # write local run script\n # open the file\n filein = open(TEMPLATES_DIR / \"local.txt\")\n # filein = resources.read_text(\"nufeb_tools.templates\", \"local.txt\")\n # read it\n src = Template(filein.read())\n # do the substitution\n result = src.safe_substitute(\n {\n \"n\": n,\n \"SucRatio\": culture.SucRatio,\n \"SucPct\": culture.SucPct,\n \"n_cyanos\": culture.n_cyanos,\n \"n_ecw\": culture.n_ecw,\n \"Reps\": args.reps,\n }\n )\n f = open(\n f\"./runs/local_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}.sh\", \"w+\"\n )\n f.writelines(result)\n # write slurm script\n # open the file\n filein = open(TEMPLATES_DIR / \"slurm_dev.txt\")\n # filein = resources.read_text(\"nufeb_tools.templates\", \"Slurm.txt\")\n # read it\n src = Template(filein.read())\n # do the substitution\n result = src.safe_substitute(\n {\n \"n\": args.cells_init,\n \"job\": f\"NUFEB_cyano{n}\",\n \"USER\": args.user,\n \"Replicates\": args.reps,\n \"SucPct\": culture.SucPct,\n \"n_cyanos\": culture.n_cyanos,\n \"n_ecw\": culture.n_ecw,\n }\n )\n _logger.info(\"Script ends here\")","def generate_surface_temp(self):\n tcl_name = output_folder + \"/surface_output_\" + str(self.input_pdb_path).split(\"/\")[-1][0:-4] + \"_\" + str(self.current_chain) + \".tcl\"\n opened_file = open(tcl_name, \"w\")\n writeable_string = surface(self.input_pdb_path).surface_template(chain = str(self.current_chain))\n opened_file.write(writeable_string)","def get_mapdata():\n return render_template(\"l_heatmap.html\")","def run_task(self, config, simParams):\n super().run_task(config, simParams)\n\n fovSize = simParams.get_microscope().get_image_dimensions()\n umPerPix = simParams.get_microscope().get_microns_per_pixel()\n \n sx = self._parameters[\"margin\"]/umPerPix\n sy = self._parameters[\"margin\"]/umPerPix\n ex = fovSize[0] - self._parameters[\"margin\"]/umPerPix\n ey = fovSize[1] - self._parameters[\"margin\"]/umPerPix\n\n # Create save polygons.\n allPoly = []\n for zi in range(simParams.get_number_z()):\n\n tmp = []\n for fov in range(simParams.get_number_positions()):\n [px, py] = simParams.get_fov_xy(fov)\n\n exRect = shapely.geometry.Polygon([[px + sx, py + sy],\n [px + sx, py + ey],\n [px + ex, py + ey],\n [px + ex, py + sy]])\n tmp.append(exRect)\n\n allPoly.append(tmp)\n\n self.save_data({\"extra-cellular\" : allPoly})\n\n # Reference images.\n allFOV = []\n for fov in range(simParams.get_number_positions()):\n allFOV.append(simParams.get_fov_rect(fov))\n\n for zi in range(simParams.get_number_z()):\n fig = plt.figure(figsize = (8,8))\n\n # Draw FOV.\n for elt in allFOV:\n coords = elt.exterior.coords.xy\n x = list(coords[0])\n y = list(coords[1])\n plt.plot(x, y, color = 'gray')\n\n # Draw extra-cellular space.\n for elt in allPoly[zi]:\n coords = elt.exterior.coords.xy\n x = list(coords[0])\n y = list(coords[1])\n plt.plot(x, y, color = 'black')\n\n ax = plt.gca()\n ax.set_aspect('equal', 'datalim')\n \n plt.title(\"z plane {0:d}\".format(zi))\n plt.xlabel(\"pixels\")\n plt.ylabel(\"pixels\")\n\n fname = \"z_{0:d}.pdf\".format(zi)\n fig.savefig(os.path.join(self.get_path(), fname),\n format='pdf',\n dpi=100)\n\n plt.close()","def writeVelocityPlot(self):\n name = \"velocity.vtk\"\n chargeFile = open(name,'w')\n chargeFile.write(\"%s\\n\"%(\"# vtk DataFile Version 2.0\"))\n chargeFile.write(\"%s\\n\"%(\"obtained via hydraulicmodule\"))\n chargeFile.write(\"%s\\n\"%(\"ASCII\"))\n chargeFile.write(\"%s\\n\"%(\"DATASET UNSTRUCTURED_GRID\"))\n chargeFile.write(\"%s %i %s\\n\"%(\"POINTS\",len(self.points),\"double\"))\n dim = self.mesh.getSpaceDimensions()\n if (dim==2): \n for ind in range(0,len(self.points)):\n chargeFile.write(\"%15.8e %15.8e %15.8e\\n\"%(self.points[ind][0],\\\n self.points[ind][1],\\\n 0.))\n pass\n pass\n elif (dim==3): \n for ind in range(0,len(self.points)):\n chargeFile.write(\"%15.8e %15.8e %15.8e\\n\"%(self.points[ind][0],\\\n self.points[ind][1],\\\n self.points[ind][2]))\n pass\n pass\n else:\n raise Exception(\" error in mesh dimension \") \n numberOfCells = self.mesh.getNumberOfCells()\n connectivity = self.mesh.getConnectivity()\n\n cellListSize = 0\n for i in range(0,numberOfCells): # gmsh meshes: type of elements\n gmshType = connectivity[i][1]\n if gmshType == 1: # 2-node line\n cellListSize += 3\n pass\n elif gmshType == 2: # 3-node triangles\n cellListSize += 4\n pass\n elif gmshType == 3: # 4-node quadrangles\n cellListSize += 5\n pass\n elif gmshType == 4: # 4-node tetrahedron\n cellListSize += 5\n pass\n elif gmshType == 5: # 8-node hexahedrons\n cellListSize += 9\n pass\n pass\n chargeFile.write(\"CELLS %i %i\\n\"%(numberOfCells,cellListSize))\n ind = 0\n for cell in connectivity:\n ind = cell[2]+3\n# print \" ctm dbg cell \",vtkTyp,ind,cell,\" perm \",permutation[ind],permutation[ind+1],permutation[ind+2],permutation[ind+3]\n # \n vtkTyp = _vtkGmsh(cell[1])\n if (vtkTyp==3): # 2-node line\n ind = cell[2]+3\n chargeFile.write(\"%i %i %i\\n\"%(\n 2,\\\n cell[ind]-1,\\\n cell[ind+1]-1)\n )\n pass\n \n elif (vtkTyp==5): # triangles\n chargeFile.write(\"%i %i %i %i\\n\"%(\n 3, \n cell[ind]-1,\\\n cell[ind+1]-1,\\\n cell[ind+2]-1)\n )\n pass\n elif (vtkTyp==9): # quadr\n chargeFile.write(\"%i %i %i %i %i\\n\"%(\n 4,\\\n cell[ind]-1,\\\n cell[ind+1]-1,\\\n cell[ind+2]-1,\\\n cell[ind+3]-1)\n )\n pass\n elif (vtkTyp==10): # tetra\n chargeFile.write(\"%i %i %i %i %i\\n\"%(\n 4,\\\n cell[ind]-1,\\\n cell[ind+1]-1,\\\n cell[ind+2]-1,\\\n cell[ind+3]-1)\n )\n pass\n elif (vtkTyp==12): # hexahedron\n chargeFile.write(\"%i %i %i %i %i %i %i %i %i\\n\"%(\n 8,\\\n cell[ind]-1,\\\n cell[ind+1]-1,\\\n cell[ind+2]-1,\\\n cell[ind+3]-1,\\\n cell[ind+4]-1,\\\n cell[ind+5]-1,\\\n cell[ind+6]-1,\\\n cell[ind+7]-1)\n )\n pass\n pass\n chargeFile.write(\"%s %i\\n\"%(\"CELL_TYPES\",numberOfCells))\n#\n for i in range(0,numberOfCells):\n gmshType = connectivity[i][1]\n\n if (gmshType)==1:\n cellTyp = 3\n pass\n elif (gmshType)==2:\n cellTyp = 5\n pass\n elif (gmshType)==3:\n cellTyp = 9\n pass\n elif (gmshType)==4:\n cellTyp = 10\n pass\n elif (gmshType)==5:\n cellTyp = 12\n pass\n elif (gmshType)==6:\n cellTyp = 13\n pass\n elif gmshType == 7:\n cellTyp = 14\n pass\n else:\n raise Exception(\" check gmshtype \")\n chargeFile.write(\"%i\\n\"%(cellTyp))\n chargeFile.write(\"%s %d\\n\"%(\"POINT_DATA\",len(self.points)))\n chargeFile.write(\"%s\\n\"%(\"VECTORS vectors float\"))\n for velocityComponent in self.velocity:\n chargeFile.write(\" %e %e %e\\n \"%(velocityComponent[0], velocityComponent[1], velocityComponent[2]))\n chargeFile.write(\"%s\\n\"%(\"SCALARS charge double\"))\n chargeFile.write(\"%s\\n\"%(\"LOOKUP_TABLE default\"))\n#\n \n chargeDataFile=open(\"./\" + self.flowComponent.meshDirectoryName + \"/\" + \"HeVel.dat\",'r')\n line = chargeDataFile.readline()\n while \"Number Of Nodes\" not in line:\n line = chargeDataFile.readline()\n#line.split()\n nodesNumber = line.split()[-1]\n while \"Perm\" not in line:\n line = chargeDataFile.readline()\n#\n# We read the permutation\n#\n for i in range(int(nodesNumber)): chargeDataFile.readline()\n#\n# We read the charge\n#\n for i in range(int(nodesNumber)): chargeFile.write(\" %15.10e\\n \"%(float(chargeDataFile.readline())))","def render(self, mode = 'human'):\n if mode == 'human':\n\n if self.is_2d:\n fig = plt.figure()\n for index, value in np.ndenumerate(self.board):\n if value == 1:\n plt.scatter(*index, c='red', s=1000, alpha=0.2)\n elif value == -1:\n plt.scatter(*index, c='blue', s=1000, alpha=0.2)\n plt.xlim(-1, self.dim[0])\n plt.ylim(-1, self.dim[1])\n plt.xticks([])\n plt.yticks([])\n plt.grid(True)\n\n if self.is_3d:\n fig = plt.figure()\n ax = Axes3D(fig)\n for index, value in np.ndenumerate(self.board):\n if value == 1:\n ax.scatter(*index, c='red', s=1000, alpha=0.2)\n elif value == -1:\n ax.scatter(*index, c='blue', s=1000, alpha=0.2)\n ax.set_xlim(0, self.dim[0] - 1)\n ax.set_ylim(0, self.dim[1] - 1)\n ax.set_zlim(0, self.dim[2] - 1)\n\n else:\n fig = plt.figure()\n ax = Axes3D(fig)\n for index, value in np.ndenumerate(self.board):\n if value == 1:\n ax.scatter(*index, c='red', s=1000, alpha=0.2)\n elif value == -1:\n ax.scatter(*index, c='blue', s=1000, alpha=0.2)\n\n ax.set_xlim(0, self.dim[0] - 1)\n ax.set_ylim(0, self.dim[1] - 1)\n ax.set_zlim(0, self.dim[2] - 1)\n ax.set_title('Nr of steps: ' + str(self.steps))\n\n plt.show()\n return fig","def tkGui(settings):\n tkApp=Tk()\n tkApp.doit=False\n tkApp.title('Render File with Blender')\n fileField=None\n def onBrowse():\n settings.filename=tkFileDialog.askopenfilename()\n # for some annoying reason this returns \"/\" separators even in windoze\n if os.sep!=\"/\":\n settings.filename=os.sep.join(settings.filename.split(\"/\"))\n fileField.delete(0, END)\n fileField.insert(0, settings.filename) \n def onOK():\n tkApp.doit=True\n tkApp.quit()\n def onCancel():\n tkApp.doit=False\n tkApp.quit()\n row=0\n settings.filename=\"\"\n filenameEntry=Frame(tkApp)\n filenameEntry.grid(row=row,columnspan=4)\n Message(filenameEntry,text=\"File:\").grid(row=0,column=0,sticky=E)\n fileField=Entry(filenameEntry,textvariable=settings.filename,width=75)\n fileField.grid(row=0,column=1,sticky=W)\n Button(filenameEntry,text=\"...\",command=onBrowse).grid(row=0,column=2,sticky=W)\n row=row+1\n Button(tkApp,text=\"OK\",command=onOK,width=10).grid(row=row,column=1,sticky=E)\n Button(tkApp,text=\"Cancel\",command=onCancel,width=10).grid(row=row,column=2,sticky=W)\n tkApp.mainloop()\n if hasattr(settings,\"filename\")==False:\n print \"Missing filename!\"\n return False\n return tkApp.doit","def print_TRT_cell_map(samples_df,cfg_set_tds):\r\n\r\n fig, axes, extent = ccs4_map(cfg_set_tds)\r\n axes.scatter(samples_df[\"LV03_x\"].loc[samples_df[\"category\"] == \"DEVELOPING\"],\r\n samples_df[\"LV03_y\"].loc[samples_df[\"category\"] == \"DEVELOPING\"],c='w',edgecolor=(.7,.7,.7),s=18)\r\n axes.scatter(samples_df[\"LV03_x\"].loc[samples_df[\"category\"] == \"MODERATE\"],\r\n samples_df[\"LV03_y\"].loc[samples_df[\"category\"] == \"MODERATE\"],c='g',edgecolor=(.7,.7,.7),s=22)\r\n axes.scatter(samples_df[\"LV03_x\"].loc[samples_df[\"category\"] == \"SEVERE\"],\r\n samples_df[\"LV03_y\"].loc[samples_df[\"category\"] == \"SEVERE\"],c='y',edgecolor=(.7,.7,.7),s=26)\r\n axes.scatter(samples_df[\"LV03_x\"].loc[samples_df[\"category\"] == \"VERY SEVERE\"],\r\n samples_df[\"LV03_y\"].loc[samples_df[\"category\"] == \"VERY SEVERE\"],c='r',edgecolor=(.7,.7,.7),s=30)\r\n \r\n fig.savefig(os.path.join(cfg_set_tds[\"fig_output_path\"],u\"TRT_Map.pdf\"))","def do_data_plots(cat, subdir):\n dla_data.noterdaeme_12_data()\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(zmax=5,color=\"blue\")\n np.savetxt(path.join(subdir,\"cddf_all.txt\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\n plt.xlim(1e20, 1e23)\n plt.ylim(1e-28, 5e-21)\n plt.legend(loc=0)\n save_figure(path.join(subdir, \"cddf_gp\"))\n plt.clf()\n\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(zmax=5,color=\"blue\", moment=True)\n plt.xlim(1e20, 1e23)\n plt.legend(loc=0)\n save_figure(path.join(subdir, \"cddf_moment_gp\"))\n plt.clf()\n\n #Evolution with redshift\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(4,5, label=\"4-5\", color=\"brown\")\n np.savetxt(path.join(subdir,\"cddf_z45.txt\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(3,4, label=\"3-4\", color=\"black\")\n np.savetxt(path.join(subdir,\"cddf_z34.txt\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(2.5,3, label=\"2.5-3\", color=\"green\")\n np.savetxt(path.join(subdir,\"cddf_z253.txt\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(2,2.5, label=\"2-2.5\", color=\"blue\")\n np.savetxt(path.join(subdir,\"cddf_z225.txt\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\n plt.xlim(1e20, 1e23)\n plt.ylim(1e-28, 5e-21)\n plt.legend(loc=0)\n save_figure(path.join(subdir,\"cddf_zz_gp\"))\n plt.clf()\n\n #dNdX\n dla_data.dndx_not()\n dla_data.dndx_pro()\n (z_cent, dNdX, dndx68, dndx95) = cat.plot_line_density(zmax=5)\n np.savetxt(path.join(subdir,\"dndx_all.txt\"), (z_cent, dNdX, dndx68[:,0],dndx68[:,1], dndx95[:,0],dndx95[:,1]) )\n plt.legend(loc=0)\n plt.ylim(0,0.16)\n save_figure(path.join(subdir,\"dndx_gp\"))\n plt.clf()\n\n #Omega_DLA\n dla_data.omegahi_not()\n dla_data.omegahi_pro()\n dla_data.crighton_omega()\n (z_cent, omega_dla, omega_dla_68, omega_dla_95) = cat.plot_omega_dla(zmax=5)\n# cat.tophat_prior = True\n# cat.plot_omega_dla(zmax=5, label=\"Tophat Prior\", twosigma=False)\n# cat.tophat_prior = False\n np.savetxt(path.join(subdir,\"omega_dla_all.txt\"), (z_cent, omega_dla, omega_dla_68[:,0],omega_dla_68[:,1], omega_dla_95[:,0], omega_dla_95[:,1]))\n plt.legend(loc=0)\n plt.xlim(2,5)\n plt.ylim(0,2.5)\n save_figure(path.join(subdir,\"omega_gp\"))\n plt.clf()","def run_variations(filename, N=50, N_scales=7, shape=(512, 512),\n steps=[(10,4.0), (20,1.0)], display_inline=False, min_radius=1.5,\n max_radius=90.0):\n\n colors = [[1, 0, 0], [0, 1, 0], [0, 0, 0.9], [1, 1, 0],\n [1, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0.6, 0]]\n perm = np.random.permutation\n for n in range(N):\n # generate palette\n pal = perm(colors)[:4]\n pal = perm(r_[pal, pal])\n pal = (pal * .6 + roll(pal, 4, axis=0) * 0.4)[:4]\n pal = shade(N_scales, *pal)\n # generate parameters and MSTP object\n ra = exp(rseq(log(min_radius), log(max_radius), N=N_scales, randomness=0.9)).round(2)\n ri = ((1.333 + rand(N_scales)) * ra).round(2)\n dt = (.01 * frange(1, N_scales) ** 0.8).round(3)\n wt = (1.33 + arctan(5 * (rand(N_scales) - .5))).round(2)\n m = MSTP(shape, ra=ra, ri=ri, dt=dt, wt=wt, pal=pal)\n\n print '\\n-------- rendering image', filename % n\n print m\n # display(HTML(' '.join('<span style=\"background:rgb(%d,%d,%d)\">(o_O)</span> ' % tuple(255*k) for k in array(pal))))\n for i, (N_steps, speed) in enumerate(steps):\n print 'rendering %s steps at %1.1fx speed' % (N_steps, dt_multiplier)\n sys.stdout.flush()\n m.run(n=N_steps, speed=speed)\n if display_inline:\n display(IM(m.rgb_image()))\n if i < 0:\n first = False\n print 'renoising after iter 1.',\n m.z += 0.25 * rand(*m.z.shape) * m.z.ptp()\n\n # display(IM(m.rgb_image()))\n imsave(filename % n, m.rgb_image())","def main(temp_dir, extensions, template):\n env = load_env(template_dir=temp_dir)\n if not template:\n # Get all the templates and return a dict with enumerated \n # templates names\n ext = extensions if extensions else []\n template_dict = get_templates(env, extensions=ext)\n # Echo the content of the template directory by enumerating \n # the templates and a simple list join\n temp_list = list()\n for x in template_dict.items():\n num = str(x[0])\n # Remove whitespace, underscores and capitalize words\n temp_name = x[1].strip().replace(\"_\", \" \").title()\n temp_string = \"{}. {}\".format(num, temp_name)\n temp_list.append(temp_string)\n click.echo(\"\\n\".join(temp_list))\n # Prompt the user to give the number of the template\n temp_num = click.prompt(\n \"Choose a templeta by entering the number of the template.\",\n type=int\n )\n # Get the template from the template dictionary\n template = template_dict.get(temp_num)\n # Get the variables\n temp_vars = get_vars(template, env)\n # Crate a dict with variables and let the user input the variables\n vars_to_render = dict()\n for var in temp_vars:\n user_var = click.prompt(\"{}?\".format(var.capitalize()))\n vars_to_render[var] = user_var\n # Get the template\n temp = env.get_template(template)\n # Render the template\n click.echo(temp.render(vars_to_render))","def process(self):\n # Opening and preprocessing of the input file\n if self.options.mbtiles_fromdisk or self.options.mbtiles_todisk:\n if self.options.mbtiles_fromdisk:\n i_parm=10\n if self.options.mbtiles_todisk:\n i_parm=11\n if self.options.verbose:\n print \"GDAL2MbTiles :mbtiles from/to disk [\",i_parm,\"] mbtiles_fromdisk[\",self.options.mbtiles_fromdisk,\"] mbtiles_todisk[\",self.options.mbtiles_todisk,\"]\"\n self.mbtiles_setup(i_parm)\n return\n else:\n if self.options.verbose:\n print \"GDAL2MbTiles :tile creation mbtiles[\",self.options.mbtiles,\"]\"\n self.open_input()\n # Generation of main metadata files and HTML viewers\n self.generate_metadata()\n # Generation of the lowest tiles\n self.generate_base_tiles()\n # Generation of the overview tiles (higher in the pyramid)\n self.generate_overview_tiles()\n # Generating of KML\n self.generate_kml()","def render_path_visualisation(projectRoot, configName, prepared_paths):\n with open(os.path.join(os.path.dirname(__file__), 'assets/visualizer_template.html')) as file_:\n template = Template(file_.read())\n \n visuFileName = \"pathvisualizer_%s.html\" % configName.replace(\" \",\"\")\n visuFilePath = os.path.join(projectRoot, visuFileName)\n renderedTemplate = template.render(configName=configName, currentDate=datetime.datetime.now(), paths=prepared_paths)\n with open(visuFilePath,'w') as f:\n f.write(renderedTemplate)\n\n logger.info(\"Path visualized - find the file at %s\" % visuFilePath)","def do_stuff(self):\n self.create_tourism_raster()","def build_static_files(cfg_prep_used, area_threshold=0.25, beef_cuttings=4, dairy_cuttings=5, overwrite_flag=False):\n logging.info('\\nBuilding ET-Demands Static Files')\n\n # Default values\n permeability = -999\n soil_depth = 60 # inches\n aridity = 50\n irrigation = 1\n\n # Input paths\n # DEADBEEF - For now, get cropET folder from INI file\n # This function may eventually be moved into the main cropET code\n config = copy.deepcopy(cfg_prep_used)\n\n try:\n project_ws = config.CROP_ET.project_folder\n except:\n logging.error('project_folder parameter must be set in the INI file, exiting')\n return False\n try:\n gis_ws = config.CROP_ET.gis_folder\n except:\n logging.error('gis_folder parameter must be set in the INI file, exiting')\n return False\n try:\n et_cells_path = config.CROP_ET.cells_path\n except:\n logging.error('cells_path parameter must be set in the INI file, exiting')\n return False\n try:\n crop_et_ws = config.CROP_ET.crop_et_folder\n except:\n logging.error('crop_et_folder parameter must be set in the INI file, exiting')\n return False\n try:\n template_ws = config.CROP_ET.template_folder\n except:\n logging.error('CROP_ET template_folder parameter must be set in the INI file, exiting')\n return False\n\n # Read data from geodatabase or shapefile\n # if '.gdb' in et_cells_path and not et_cells_path.endswith('.shp'):\n # _flag = False\n # _path = os.path.dirname(et_cells_path)\n # gdb_path = r'D:\\Projects\\CAT_Basins\\AltusOK\\et-demands_py\\et_demands.gdb'\n # _cells_path = os.path.join(gdb_path, 'et_cells')\n\n # Output sub-folder names\n static_ws = os.path.join(project_ws, 'static')\n\n # basin attributes fields\n basin_id_filed = 'STAID'\n basin_elev_field = 'ELEV_MEAN_M_BASIN'\n\n # ET Cell field names\n cell_lat_field = 'LAT'\n cell_lon_field = 'LON'\n cell_id_field = 'GAGE_ID'\n # cell_station_id_field = 'STATION_ID'\n # awc_field = 'AWC'\n clay_field = 'CLAY'\n sand_field = 'SAND'\n awc_in_ft_field = 'AWC_IN_FT'\n hydgrp_num_field = 'HYDGRP_NUM'\n hydgrp_field = 'HYDGRP'\n\n # huc_field = 'HUC{}'.format(huc)\n # permeability_field = 'PERMEABILITY'\n # soil_depth_field = 'SOIL_DEPTH'\n # aridity_field = 'ARIDITY'\n # dairy_cutting_field = 'DAIRY_CUTTINGS'\n # beef_cutting_field = 'BEEF_CUTTINGS'\n\n # Static file names\n cell_props_name = 'ETCellsProperties.txt'\n cell_crops_name = 'ETCellsCrops.txt'\n cell_cuttings_name = 'MeanCuttings.txt'\n crop_params_name = 'CropParams.txt'\n crop_coefs_name = 'CropCoefs.txt'\n crop_coefs_eto = 'CropCoefs_eto.txt'\n crop_coefs_etr = 'CropCoefs_etr.txt'\n eto_ratio_name = 'EToRatiosMon.txt'\n etr_ratio_name = 'ETrRatiosMon.txt'\n static_list = [crop_params_name, crop_coefs_name, crop_coefs_eto, crop_coefs_etr, cell_props_name, cell_crops_name,\n cell_cuttings_name, eto_ratio_name, etr_ratio_name]\n\n # Check input folders\n if not os.path.isdir(crop_et_ws):\n logging.critical('\\nERROR: The INI cropET folder does not exist\\n {}'.format(crop_et_ws))\n sys.exit()\n elif not os.path.isdir(project_ws):\n logging.critical('\\nERROR: The project folder does not exist\\n {}'.format(project_ws))\n sys.exit()\n elif not os.path.isdir(gis_ws):\n logging.critical('\\nERROR: The GIS folder does not exist\\n {}'.format(gis_ws))\n sys.exit()\n logging.info('\\nGIS Workspace: {}'.format(gis_ws))\n logging.info('Project Workspace: {}'.format(project_ws))\n logging.info('CropET Workspace: {}'.format(crop_et_ws))\n logging.info('Template Workspace: {}'.format(template_ws))\n\n # Check input files\n if not _arcpy.exists(et_cells_path):\n logging.critical('\\nERROR: The ET Cell shapefile does not exist\\n {}'.format(et_cells_path))\n sys.exit()\n for static_name in static_list:\n if not os.path.isfile(os.path.join(template_ws, static_name)):\n logging.error(\n '\\nERROR: The static template does not exist\\n {}'.format(os.path.join(template_ws, static_name)))\n sys.exit()\n logging.debug('ET Cells Path: {}'.format(et_cells_path))\n\n # Build output table folder if necessary\n if not os.path.isdir(static_ws):\n os.makedirs(static_ws)\n\n # Read ET Cell zonal stats\n logging.info('\\nReading ET Cell Zonal Stats')\n logging.debug(' {}'.format(et_cells_path))\n crop_field_list = sorted([f for f in _arcpy.list_fields(et_cells_path) if re.match('CROP_\\d{3}', f)])\n fields = [cell_id_field, cell_lat_field, cell_lon_field, awc_in_ft_field, clay_field, sand_field, hydgrp_num_field,\n hydgrp_field]\n fields = fields + crop_field_list\n logging.debug(' Fields: {}'.format(fields))\n cell_data_dict = defaultdict(dict)\n\n for fid, row in _arcpy.search_cursor(et_cells_path, fields).items():\n # Switch to cell_id_field as index (instead of FID)\n for f in fields[1:]:\n cell_data_dict[str(row[cell_id_field])][f] = row[f]\n\n # Read basin data\n logging.info('\\nReading basin attributes')\n fields = [basin_elev_field]\n logging.debug(' Fields: {}'.format(fields))\n basin_data_dict = defaultdict(dict)\n basin_topo = pd.read_csv(config.GAGES.basin_topo_file, sep=',', dtype={0: str})\n for cell_id_tmp in list(cell_data_dict.keys()):\n basin_data_dict[cell_id_tmp][basin_elev_field] = \\\n basin_topo.loc[basin_topo[basin_id_filed] == cell_id_tmp, basin_elev_field].values[0]\n # Convert elevation units if necessary\n logging.debug(' Convert station elevation from meters to feet')\n for k in basin_data_dict.keys():\n basin_data_dict[k][basin_elev_field] /= 0.3048\n\n # static files\n logging.info('\\nCopying template static files')\n for static_name in static_list:\n logging.debug(' {}'.format(static_name))\n shutil.copy(os.path.join(template_ws, static_name), static_ws)\n\n logging.info('\\nWriting static text files')\n cell_props_path = os.path.join(static_ws, cell_props_name)\n cell_crops_path = os.path.join(static_ws, cell_crops_name)\n cell_cuttings_path = os.path.join(static_ws, cell_cuttings_name)\n eto_ratio_path = os.path.join(static_ws, eto_ratio_name)\n etr_ratio_path = os.path.join(static_ws, etr_ratio_name)\n\n # Write cell properties\n logging.debug(' {}'.format(cell_props_path))\n with open(cell_props_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n basin_id = cell_id\n basin_lat = '{:>9.4f}'.format(cell_data[cell_lat_field])\n basin_lon = '{:>9.4f}'.format(cell_data[cell_lon_field])\n basin_data = basin_data_dict[basin_id]\n basin_elev = '{:.2f}'.format(basin_data[basin_elev_field])\n # There is an extra/unused column in the template and excel files\n output_list = [cell_id, cell_id, basin_id, basin_lat, basin_lon, basin_elev, permeability,\n '{:.4f}'.format(cell_data[awc_in_ft_field]), soil_depth, cell_data[hydgrp_field],\n cell_data[hydgrp_num_field], aridity, '']\n output_f.write('\\t'.join(map(str, output_list)) + '\\n')\n\n del output_list\n del basin_id, basin_lat, basin_lon, basin_elev\n\n # Write cell crops\n crops = util.parse_int_set(config.USDA.cdl_crops)\n logging.debug(' {}'.format(cell_crops_path))\n with open(cell_crops_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n basin_id = cell_id\n output_list = [cell_id, cell_id, basin_id, irrigation]\n crop_list = ['CROP_{:03d}'.format(i) for i in crops]\n crop_area_list = []\n for crop in crop_list:\n if crop in cell_data.keys() and cell_data[crop] is not None:\n crop_area_list.append(cell_data[crop])\n else:\n crop_area_list.append(0)\n crop_flag_list = [1 if area > area_threshold else 0 for area in crop_area_list]\n output_list = output_list + crop_flag_list\n output_f.write('\\t'.join(map(str, output_list)) + '\\n')\n\n del crop_list, crop_area_list, crop_flag_list, output_list\n\n # Write cell cuttings\n logging.debug(' {}'.format(cell_cuttings_path))\n with open(cell_cuttings_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n output_list = [cell_id, cell_id, '{:>9.4f}'.format(cell_data[cell_lat_field]), dairy_cuttings,\n beef_cuttings]\n output_f.write('\\t'.join(map(str, output_list)) + '\\n')\n\n # Write monthly ETo ratios\n logging.debug(' {}'.format(eto_ratio_path))\n with open(eto_ratio_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n basin_id = cell_id\n output_f.write('\\t'.join(map(str, [basin_id, ''] + [1.0] * 12)) + '\\n')\n\n # Write monthly ETr ratios\n logging.debug(' {}'.format(etr_ratio_path))\n with open(etr_ratio_path, 'a') as output_f:\n for cell_id, cell_data in sorted(cell_data_dict.items()):\n basin_id = cell_id\n output_f.write('\\t'.join(map(str, [basin_id, ''] + [1.0] * 12)) + '\\n')","def main():\n\t#print(scipy.__version__)\n\t#image()\n\t#heat_capacity2()\n\t#hist()\n\t#single_plot()\n\n\t#heat_capacity2()\n\t#single_plot()\n\t#plt.show()\n\t#u0_tc()\n\t#multi_heat_capacity(\"HL_DM_flux5\",True)\n\t#multi_heat_capacity2()\n\t#plot_spin()\n\t#plt.show()\n\theat_capacity2(1,2)\n\t#hist()\n\tplt.show()\n\t#potential()\n\t#plt.show()\n\t#heat_capacity(3,4)\n\t#heat_capacity(5,6)\n\t#heat_capacity(7,8)\n\t#final_spins()\n\t#plot_spin()\n\t#plot_from_csv()\n\t#difference_plot()","def read_pfile(self, filename:str, filemode:str='vcnmodel.v0', vscale:float=1e-3, iscale:float=1e-9, plot=False):\n \n\n # r = df['Results'][0]\n #\n # if plot:\n # P = PH.Plotter((1, 1), figsize=(6, 4))\n # cell_ax = list(P.axdict.keys())[0]\n # for trial in range(len(df['Results'])):\n # ds = df['Results'][trial]\n # k0 = list(df['Results'][trial].keys())[0]\n # dx = ds[k0]['monitor']\n # P.axdict[cell_ax].plot(dx['time'], dx['postsynapticV'], linewidth=1.0)\n # P.axdict[cell_ax].set_xlim(0., 150.)\n # P.axdict[cell_ax].set_ylim(-200., 50.)\n # PH.calbar(P.axdict[cell_ax], calbar=[120., -95., 25., 20.], axesoff=True, orient='left',\n # unitNames={'x': 'ms', 'y': 'mV'}, font='Arial', fontsize=8)\n #\n # # mpl.savefig(outfile)\n # mpl.show()\n # print(list(df.keys()))\n # print('\\nbasename: ', df['basename'])\n # print('\\nruninfo: ', df['runInfo'])\n \"\"\"\n The runInfo dictionary holds somethign like this:\n runinfo: {'folder': PosixPath('VCN_Cells/VCN_c08/Simulations/IV'), 'fileName': 'Normal', 'runName': 'Run', \n 'manipulation': 'Canonical', 'preMode': 'cc', 'postMode': 'cc', 'TargetCellType': 'Bushy', \n 'electrodeSection': 'soma', 'dendriticElectrodeSection': 'dendrite', \n 'dendriticSectionDistance': 100.0, 'celsius': 37, 'nStim': 1, \n 'stimFreq': 200.0, 'stimInj': {'pulse': [-1.0, 2.01, 0.2]}, \n 'stimDur': 100.0, 'stimDelay': 5.0, 'stimPost': 3.0, \n 'vnStim': 1, 'vstimFreq': 200.0, 'vstimInj': 50, \n 'vstimDur': 50.0, 'vstimDelay': 2.0, 'vstimPost': 3.0, 'vstimHolding': -60, \n 'gif_i0': 0.0, 'gif_sigma': 0.5, 'gif_fmod': 0.2, 'gif_tau': 3.0, \n 'gif_dur': 10.0, 'gif_skew': 0.0, \n 'runTime': 'Wed Oct 9 13:05:54 2019', \n 'inFile': None, 'inFileRep': 1, 'spikeTimeList': {}, \n 'v_init': -61.0, 'useSaveState': True, 'tstop': 8.0, 'filename': 'VCN_c08_pulse_'}\n \"\"\"\n # print('\\nmodelPars: ', df['modelPars'])\n \"\"\"\n The modelPars dict holds the following:\n modelPars: {'species': 'mouse', 'cellClass': 'bushy', 'modelType': 'II', \n 'modelName': 'mGBC', 'soma': True, 'axon': False, \n 'dendrites': False, 'pumps': False, 'hillock': False, \n 'initialsegment': False, 'myelinatedaxon': False, \n 'unmyelinatedaxon': False, 'na': 'nav11', 'ttx': False, \n 'name': 'bushy', 'morphology': 'VCN_Cells/VCN_c08/Morphology/VCN_c08.hoc', \n 'temperature': 34.0}\n \n Note 10/28/2019 changed structure so that runInfo and modelPars are both \n subdictionaries of Params (filemode is 'vcnmodel.v0')\n ... and undone later, so that all are top-level (filemode is 'vcnmodel.v1')\n \"\"\"\n with open(filename, 'rb') as fh:\n df = FPM.pickle_load(fh)\n if filemode in ['vcnmodel.v0']:\n print(f\"Reading model file in version v0:, with {len(df['Results']):4d} trials\")\n elif filemode in ['vcnmodel.v1']:\n print(f\"Reading model file in version v1:, with {len(df['Results']):4d} trials\")\n else:\n raise ValueError(f'Unknown file mode: {filemode:s}')\n # print('\\nrpfile v0: File keys: ', df.keys())\n #\n # print('\\nrpfile v0: basename: ', df['basename'])\n mtime = Path(filename).stat().st_mtime\n timestamp_str = datetime.datetime.fromtimestamp(mtime).strftime('%Y-%m-%d-%H:%M')\n if filemode == 'vcnmodel.v0':\n # print(df['Params'].keys())\n try:\n dinfo = df['Params']['runInfo']\n except:\n try:\n dinfo = df['runInfo']\n except:\n raise ValueError (\"Cannot read the file in v0 mode?\")\n run_protocol = df['Params']['runProtocol']\n if isinstance(dinfo, Params):\n dinfo = dinfo.todict()\n dur = dinfo['stimDur']\n delay = dinfo['stimDelay']\n mode = dinfo['postMode'].upper()\n ntr = len(df['Results'])\n # print(df.keys())\n # print('runinfo: ', df['runInfo'])\n # print('Params: ', df['Params'].keys())\n # print('dinfo: ', dinfo)\n if 'dt' in df['Params'].keys():\n self.rate = df['Params']['dt']\n else:\n self.rate = df['Params'].dt\n V = [[]]*ntr\n I = [[]]*ntr\n for i in range(len(df['Results'])):\n fk = list(df['Results'][i].keys())[0]\n dfx = df['Results'][i][fk]['monitor']\n timebase = dfx['time']\n V[i] = dfx['postsynapticV']*vscale\n I[i] = dfx['i_stim0']*iscale\n else:\n dinfo = df['runInfo']\n x = dir(dinfo)\n if 'stimVC' not in x: # create fields for missing values from older versions of files.\n dinfo.stimVC = None\n # print('rpfile v0: dinfo: ', dinfo)\n mode = dinfo.postMode.upper()\n dur = dinfo.stimDur\n delay = dinfo.stimDelay\n mode = dinfo.postMode\n print(\"Keys found in file: \", df.keys())\n print('Mode: ', mode)\n try:\n self.rate = df['Params'].dt # old version, now separated IC and VC\n except:\n if mode == 'VC':\n self.rate = df['Params'].dtVC\n elif mode == \"CC\":\n self.rate = df['Params'].dtIC\n else:\n raise ValueError(\"Cannot find rate for data mode: \", mode)\n\n run_protocol = dinfo.runProtocol\n if dinfo.runProtocol in ['runIV', 'initIV', 'testIV']:\n ntr = len(df['Results'])\n V = [[]]*ntr\n I = [[]]*ntr\n for ii, i in enumerate(df['Results'].keys()):\n dfx = df['Results'][i]['monitor']\n timebase = dfx['time']\n V[ii] = np.array(dfx['postsynapticV'])*vscale\n I[ii] = np.array(dfx['i_stim0'])*iscale\n elif dinfo.runProtocol in ['runVC', 'initVC', 'testVC']:\n dur = dinfo.vstimDur # msec\n delay = dinfo.vstimDelay # msec\n ntr = len(df['Results'])\n V = [[]]*ntr\n I = [[]]*ntr\n for ii, i in enumerate(df['Results'].keys()):\n dfx = df['Results'][i]['monitor']\n timebase = dfx['time']\n V[ii] = np.array(dfx['postsynapticV'])*vscale\n I[ii] = np.array(dfx['postsynapticI'])*iscale\n \n elif dinfo.runProtocol in ['initAN', 'runANPSTH', 'runANIO', 'runANSingles']:\n\n # two ways data can be organized, so try both\n try: # cnmodel_models simulations\n ntr = len(df['Results'])\n V = [[]]*ntr\n I = [[]]*ntr\n for j in list(df['Results'].keys()):\n dfx = df['Results'][j]\n timebase = dfx['time']\n V[j] = np.array(dfx['somaVoltage'])*vscale\n I[j] = np.zeros_like(V[j])\n except: # vcnmodel simulatipns\n ntr = len(df[\"Results\"]['somaVoltage'])\n V = [[]]*ntr\n I = [[]]*ntr\n for j in range(ntr):\n timebase = df['Results']['time']\n V[j] = np.array(df['Results']['somaVoltage'][j])*vscale\n I[j] = np.zeros_like(V[j])\n\n V = np.array(V)\n I = np.array(I)\n # print('V shape: ', V.shape, 'I shape: ', I.shape, ' timebase: ', timebase.shape, V.shape[1]*self.rate, np.max(timebase))\n # exit()\n\n if run_protocol in ['runVC', 'initVC', 'testVC']:\n self.MC.set_clamps(dmode=mode, time=timebase, data=I, cmddata=V, tstart_tdur=[delay, dur])\n else:\n self.MC.set_clamps(dmode=mode, time=timebase, data=V, cmddata=I, tstart_tdur=[delay, dur])\n self.MC.getClampData()","def radiance_map(file, config, vmax=4200, levels=20, typ=''):\n \n # Select data from configuration \n azimuths = config['skymap'][:, 0] # +180 # azimuths\n zeniths = config['skymap'][:, 1] # zeniths\n\n if typ == 'sim':\n # look for wavelength index in array\n waves_sim = dataset.attrs['simulated_Columns'].split('nm')[0].split('[')[1].split(\n ']')[0].split(',')\n waves = np.asarray(list(map(int, waves_sim)))\n wave_indx = np.where(waves == wave)\n try:\n wave_indx = np.int(wave_indx[0][0])\n except:\n print(\"Wavelength is not in dataset\")\n z = dataset.simulated[:, wave_indx, time_indx]\n\n elif typ == 'meas':\n wave_indx = int((config['wavelength'] - 250 - config['wave_correction']) / 0.446)\n with h5py.File(file, 'r') as data:\n z = data['data'][:, wave_indx]\n else:\n print('Select a input data type(sim or meas)')\n\n # Add values in the origin to close the surface interpolation\n azimuths = np.append(azimuths, [270, 0, 0, 0, 0, 0, 0, 0])\n zeniths = np.append(zeniths, [0, 12, 24, 36, 48, 60, 72, 84])\n z = np.append(z, [z[0], z[3], z[9], z[19], z[33], z[51], z[73], z[99]])\n\n # Convert x to radians\n azimuths = np.radians(azimuths)\n zeniths = np.radians(zeniths)\n\n # Remove dead channels of the dataset\n azimuths = np.delete(azimuths, config['dead_fibre'])\n zeniths = np.delete(zeniths, config['dead_fibre'])\n z = np.delete(z, config['dead_fibre'])\n\n # Set up a regular grid of interpolation point\n thetai, ri = np.linspace(azimuths.min(), azimuths.max(),\n num=len(azimuths)), \\\n np.linspace(zeniths.min(), zeniths.max(), num=len(zeniths))\n\n ri, thetai = np.meshgrid(ri, thetai, indexing='ij')\n\n # zi = scipy.interpolate.griddata((azimuths, zeniths), z, (thetai, ri),\n # method='linear')\n\n rbf = scipy.interpolate.Rbf(azimuths, zeniths, z, fucntion='gaussian',\n epsilon=0.05)\n\n ZI = rbf(thetai, ri)\n\n if typ == 'sim':\n name = str(dataset.time[time_indx].values) # ''\n else:\n name = 'testing' #str(dataset.time[time_indx].values)\n\n # Create the directory to save the results\n # os.makedirs(os.path.dirname(config['path_note'] + '/figures/'),\n # exist_ok=True)\n if vmax == 'default':\n vmax = 4200\n else:\n vmax = vmax\n\n # Plot the dataset\n fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))\n cmap = 'Spectral_r' # 'rainbow'\n a = plt.contourf(thetai, ri, ZI, levels, cmap=cmap, vmin=0,\n vmax=vmax) # , vmax=4932)\n plt.title('{} UTC {}nm'.format(name, config['wavelength']))\n plt.axis([0, 2*np.pi, 0, 1.48])\n\n plt.scatter(azimuths, zeniths, cmap=cmap, s=1)\n ax.grid(False)\n ax.set_theta_zero_location(\"N\") # Set the direction of polar plot\n ax.set_theta_direction(1) # Set the increase direction on azimuth angles\n # (-1 to clockwise, 1 counterclockwise)\n cbar = plt.colorbar(a)\n cbar.set_label(\"counts\", rotation=90)\n\n # if typ == 'sim':\n # plt.savefig(\n # 'figures/skymap/simulated/skymap{}nm_{}UTC_sim.jpeg'.format(wave,\n # name),\n # dpi=300)\n # plt.show();\n # else:\n # plt.savefig(\n # 'figures/skymap/measured/skymap{}nm_{}UTC_meas.jpeg'.format(wave,\n # name),\n # dpi=300)","def main():\n ##############################\n # Options\n ##############################\n\n bev_generator = 'slices'\n slices_config = \\\n \"\"\"\n slices {\n height_lo: -0.2\n height_hi: 2.3\n num_slices: 5\n }\n \"\"\"\n # Use None for a random image\n #img_idx = None\n img_idx = 6\n\n show_ground_truth = True # Whether to overlay ground_truth boxes\n show_height_maps = False # Whether to show the five height maps\n show_images = False # Whether to show the images\n\n point_cloud_source = 'lidar'\n pre_label_dir = '/home/cecilia/leo_projects/bishe2019/3D-Detection/avod/data/outputs/pyramid_cars_with_aug_rep_loss/predictions/kitti_native_eval/0.1/112000/data/'\n ##############################\n # End of Options\n ##############################\n\n dataset_config = DatasetBuilder.copy_config(DatasetBuilder.KITTI_VAL)\n dataset_config = DatasetBuilder.merge_defaults(dataset_config)\n\n # Overwrite bev_generator\n if bev_generator == 'slices':\n text_format.Merge(slices_config,\n dataset_config.kitti_utils_config.bev_generator)\n else:\n raise ValueError('Invalid bev_generator')\n\n dataset = DatasetBuilder.build_kitti_dataset(dataset_config,\n use_defaults=False)\n\n if img_idx is None:\n img_idx = int(random.random() * dataset.num_samples)\n\n sample_name = \"{:06}\".format(img_idx)\n print('=== Showing BEV maps for image: {}.png ==='.format(sample_name))\n\n # Load image\n image = cv2.imread(dataset.get_rgb_image_path(sample_name))\n image_shape = image.shape[0:2]\n\n kitti_utils = dataset.kitti_utils\n point_cloud = kitti_utils.get_point_cloud(\n point_cloud_source, img_idx, image_shape)\n ground_plane = kitti_utils.get_ground_plane(sample_name)\n bev_images = kitti_utils.create_bev_maps(point_cloud, ground_plane)\n\n height_maps = np.array(bev_images.get(\"height_maps\"))\n density_map = np.array(bev_images.get(\"density_map\"))\n\n # Get groundtruth bev-info\n gt_box_points, gt_box_points_norm = [None, None]\n if show_ground_truth:\n gt_obj_labels = obj_utils.read_labels(dataset.label_dir, img_idx)\n gt_filtered_objs = gt_obj_labels\n\n gt_label_boxes = []\n for gt_label in gt_filtered_objs:\n gt_box = box_3d_encoder.object_label_to_box_3d(gt_label)\n gt_label_boxes.append(gt_box)\n\n gt_label_boxes = np.array(gt_label_boxes)\n gt_box_points, gt_box_points_norm = box_3d_projector.project_to_bev(\n gt_label_boxes, [[-40, 40], [0, 70]])\n\n # Get prediction bev-info\n pre_box_points, pre_box_points_norm = [None, None]\n pre_obj_labels = obj_utils.read_labels(pre_label_dir, img_idx)\n\n pre_filtered_objs = pre_obj_labels\n\n pre_label_boxes = []\n for pre_label in pre_filtered_objs:\n pre_box = box_3d_encoder.object_label_to_box_3d(pre_label)\n pre_label_boxes.append(pre_box)\n\n pre_label_boxes = np.array(pre_label_boxes)\n pre_box_points, pre_box_points_norm = box_3d_projector.project_to_bev(\n pre_label_boxes, [[-40, 40], [0, 70]])\n\n \n rgb_img_size = (np.array((1242, 375)) * 0.75).astype(np.int16)\n img_x_start = 60\n img_y_start = 330\n\n img_x = img_x_start\n img_y = img_y_start\n img_w = 400\n img_h = 350\n img_titlebar_h = 20\n\n # Show images if show_images = True\n if show_images:\n vis_utils.cv2_show_image(\"Image\", image,\n size_wh=rgb_img_size, location_xy=(img_x, 0))\n\n # Height maps if show_height_maps = True\n if show_height_maps:\n\n for map_idx in range(len(height_maps)):\n height_map = height_maps[map_idx]\n\n height_map = draw_boxes(height_map, gt_box_points_norm, pre_box_points_norm)\n vis_utils.cv2_show_image(\n \"Height Map {}\".format(map_idx), height_map, size_wh=(\n img_w, img_h), location_xy=(\n img_x, img_y))\n\n img_x += img_w\n # Wrap around\n if (img_x + img_w) > 1920:\n img_x = img_x_start\n img_y += img_h + img_titlebar_h\n\n # Density map (Normal BEV)\n density_map = draw_boxes(density_map, gt_box_points_norm, pre_box_points_norm)\n vis_utils.cv2_show_image(\n \"Density Map\", density_map, size_wh=(\n img_w, img_h), location_xy=(\n img_x, img_y))\n\n cv2.waitKey()","def visualize(z_in, azimuth=25., elevation=30.,\n thresholds=[0.95, .9, .75, .5, .25, .125], opacities=[1, .9, .7, .5, .2, .1],\n# thresholds=[0.94, .89, .75, .5, .25, .1], opacities=[.9, .8, .7, .5, .2, .1],\n# thresholds=[0.94, .89, .75], opacities=[.99, .7, .2],\n# thresholds=[0.7, .5, .2], opacities=[.95, .5, .2],\n fourier_label = {'f_x':'f_x', 'f_y':'f_y', 'f_t':'f_t'},\n filename=None, do_axis=True, do_grids=False, draw_projections=True,\n colorbar=False, f_N=2., f_tN=2., figsize=figsize, dpi=300, figpath=figpath, **kwargs):\n z = z_in.copy()\n N_X, N_Y, N_frame = z.shape\n fx, fy, ft = get_grids(N_X, N_Y, N_frame)\n\n # Normalize the amplitude.\n z /= z.max()\n\n from vispy import app, scene, use\n try:\n AffineTransform = scene.transforms.AffineTransform\n except:\n AffineTransform = scene.transforms.MatrixTransform\n\n use(app='pyglet', gl='pyopengl2')\n #from vispy.util.transforms import perspective, translate, rotate\n from vispy.color import Color\n transparent = Color(color='black', alpha=0.)\n import colorsys\n canvas = scene.SceneCanvas(size=figsize, bgcolor='white', dpi=dpi)\n view = canvas.central_widget.add_view()\n\n vol_data = np.rollaxis(np.rollaxis(z, 1), 2)\n# volume = scene.visuals.Volume(vol_data, parent=view.scene)#frame)\n center = scene.transforms.STTransform(translate=( -N_X/2, -N_Y/2, -N_frame/2))\n# volume.transform = center\n# volume.cmap = 'blues'\n\n if draw_projections:\n from vispy.color import Colormap\n cm = Colormap([(1.0, 1.0, 1.0, 1.0), 'k'])\n opts = {'parent':view.scene, 'cmap':cm, 'clim':(0., 1.)}\n\n energy_xy = np.rot90(np.max(z, axis=2)[:, ::-1], 3)#[:, ::-1]\n fourier_xy = scene.visuals.Image(np.rot90(energy_xy), **opts)\n tr_xy = AffineTransform()\n tr_xy.rotate(90, (0, 0, 1))\n tr_xy.translate((N_X/2, -N_Y/2, -N_frame/2))\n fourier_xy.transform = tr_xy\n\n energy_xt = np.rot90(np.max(z, axis=1)[:, ::-1], 3)[::-1, ::-1]\n fourier_xt = scene.visuals.Image(energy_xt, **opts)\n tr_xt = AffineTransform()\n tr_xt.rotate(90, (1, 0, 0))\n tr_xt.translate((-N_X/2, N_Y/2, -N_frame/2))\n fourier_xt.transform = tr_xt\n\n energy_yt = np.max(z, axis=0)[:, ::-1]\n fourier_yt = scene.visuals.Image(energy_yt, **opts)\n tr_yt = AffineTransform()\n tr_yt.rotate(90, (0, 1, 0))\n tr_yt.translate((-N_X/2, -N_Y/2, N_frame/2))\n fourier_yt.transform = tr_yt\n\n # Generate iso-surfaces at different energy levels\n surfaces = []\n for i_, (threshold, opacity) in enumerate(zip(thresholds, opacities)):\n surfaces.append(scene.visuals.Isosurface(z, level=threshold,\n# color=Color(np.array(colorsys.hsv_to_rgb(1.*i_/len(thresholds), 1., 1.)), alpha=opacity),\n color=Color(np.array(colorsys.hsv_to_rgb(.66, 1., 1.)), alpha=opacity),\n shading='smooth', parent=view.scene)\n )\n surfaces[-1].transform = center\n\n # Draw a sphere at the origin\n axis = scene.visuals.XYZAxis(parent=view.scene)\n for p in ([1, 1, 1, -1, 1, 1], [1, 1, -1, -1, 1, -1], [1, -1, 1, -1, -1, 1],[1, -1, -1, -1, -1, -1],\n [1, 1, 1, 1, -1, 1], [-1, 1, 1, -1, -1, 1], [1, 1, -1, 1, -1, -1], [-1, 1, -1, -1, -1, -1],\n [1, 1, 1, 1, 1, -1], [-1, 1, 1, -1, 1, -1], [1, -1, 1, 1, -1, -1], [-1, -1, 1, -1, -1, -1]):\n line = scene.visuals.Line(pos=np.array([[p[0]*N_X/2, p[1]*N_Y/2, p[2]*N_frame/2], [p[3]*N_X/2, p[4]*N_Y/2, p[5]*N_frame/2]]), color='black', parent=view.scene)\n\n axisX = scene.visuals.Line(pos=np.array([[0, -N_Y/2, 0], [0, N_Y/2, 0]]), color='red', parent=view.scene)\n axisY = scene.visuals.Line(pos=np.array([[-N_X/2, 0, 0], [N_X/2, 0, 0]]), color='green', parent=view.scene)\n axisZ = scene.visuals.Line(pos=np.array([[0, 0, -N_frame/2], [0, 0, N_frame/2]]), color='blue', parent=view.scene)\n\n if do_axis:\n t = {}\n for text in ['f_x', 'f_y', 'f_t']:\n t[text] = scene.visuals.Text(fourier_label[text], parent=canvas.scene, face='Helvetica', color='black')\n t[text].font_size = 8\n t['f_x'].pos = canvas.size[0] // 3, canvas.size[1] - canvas.size[1] // 8\n t['f_y'].pos = canvas.size[0] - canvas.size[0] // 8, canvas.size[1] - canvas.size[1] // 6\n t['f_t'].pos = canvas.size[0] // 8, canvas.size[1] // 2\n\n cam = scene.TurntableCamera(elevation=elevation, azimuth=azimuth, up='z')\n cam.fov = 48\n cam.scale_factor = N_X * 1.8\n if do_axis: margin = 1.35\n else: margin = 1\n cam.set_range((-N_X/2*margin, N_X/2/margin), (-N_Y/2*margin, N_Y/2/margin), (-N_frame/2*margin, N_frame/2/margin))\n view.camera = cam\n\n render_im = canvas.render()\n app.quit()\n if not(filename is None):\n import vispy.io as io\n io.write_png(filename, render_im)\n else:\n return render_im","def createPlots(ncfile, files, loc, savepath, sim_name, bfric, tight=False, \\\n ratio=1.0, debug=False, plot=False):\n\n sns.set(font=\"serif\")\n\n # find the location centre for flood/tide split calculation\n # not yet working...\n if loc == 'GP':\n centre = [-66.33906, 44.26898]\n if tight:\n bounds = [-66.355, -66.31, 44.245, 44.2925]\n else:\n bounds = []\n elif loc == 'DG':\n centre = [-65.76000, 44.67751]\n if tight:\n bounds = [-65.775, -65.77, 44.665, 44.69]\n else:\n bounds = []\n elif loc == 'PP':\n centre = [-66.206924, 44.389368]\n # find out the tightness required for PP\n if tight:\n bounds = [-66.225, -66.195, -44.37, -44.41]\n else:\n bounds = []\n\n if debug:\n print 'calculating ebb/flood split at centre of location...'\n print 'calculating model velocity norm...'\n\n fI, eI, _, _ = ncfile.Util2D.ebb_flood_split_at_point(centre[0], centre[1])\n ncfile.Util3D.velo_norm()\n\n if debug:\n print '{} plot(s) will be created...'.format(len(files))\n\n if not plot:\n savepath = savepath + 'bfric_' + bfric + '/' + loc + '_' + sim_name\n if ratio != 1.0:\n savepath = savepath + '/with_ratio_{}'.format(str(ratio))\n\n # creates a subdirectory, so as not to overwrite existing files\n if debug:\n print 'creating new subdirectory...'\n now = datetime.now()\n now = now.strftime(\"%Y%m%d\")\n if not osp.exists(savepath):\n os.makedirs(savepath)\n else:\n savepath = savepath + '/_' + now\n os.makedirs(savepath)\n savepath = savepath + '/'\n\n for i, fname in enumerate(files, start=1):\n if debug:\n print 'creating drifter object...'\n drift = Drifter(fname, debug=False)\n fname = fname[48:-4]\n\n # creates drifter object window for flow map\n if debug:\n print 'creating drifter object window...'\n tModel = ncfile.Variables.matlabTime\n tDrift = drift.Variables.matlabTime\n win1 = (np.abs(tModel-tDrift.min())).argmin()\n win2 = (np.abs(tModel-tDrift.max())).argmin()\n\n tide = str(drift.Data['water_level'].tide)\n # averages velocity norm over flood or ebb cycle within drifter window\n if tide == 'flood':\n tideNorm = np.mean(ncfile.Variables.velo_norm[win1:win2,:,:], 0)\n elif tide == 'ebb':\n tideNorm = np.mean(ncfile.Variables.velo_norm[win1:win2,:,:], 0)\n\n # create spatially varying color map of mean velocity norm\n if debug:\n print 'preparing to create colormap...'\n fig = createColorMap(ncfile, tideNorm[0,:], mesh=False, bounds=bounds, \\\n title='Trajectory for ' + fname[:-4], debug=debug, \\\n label='Mean Velocity Norm during '+tide.capitalize()+' Tide (m/s)')\n # create title\n # fig.suptitle('Data from ' + fname[:-4], fontsize=14)\n\n x = drift.Variables.lon\n y = drift.Variables.lat\n u = drift.Variables.u\n v = drift.Variables.v\n\n if debug:\n print 'creating scatter plot...'\n plt.scatter(x,y)\n\n if debug:\n print 'preparing to plot time series...'\n\n # create validation structure\n if debug:\n print 'creating validation object...'\n\n try:\n valid = Validation(drift, ncfile, flow='sf', debug=False)\n except IndexError:\n print 'cannot create validation object for drifter %i.' % i\n continue\n\n # calculate speed from interpolated and observed date\n mTimes = valid.Variables.struct['mod_time']\n oU = valid.Variables.struct['obs_timeseries']['u']\n oV = valid.Variables.struct['obs_timeseries']['v']\n mU = valid.Variables.struct['mod_timeseries']['u']\n mV = valid.Variables.struct['mod_timeseries']['v']\n\n if debug:\n print '\\tcalculating speeds...'\n speedS = np.asarray(np.sqrt(mU**2 + mV**2))\n speedO = np.asarray(np.sqrt(oU**2 + oV**2))\n\n # ratio addition\n if debug:\n print '\\tadding ratio adjustments...'\n speedS = speedS * ratio\n datetimes = np.asarray([dn2dt(time) for time in mTimes])\n\n # For now, separate the two plots.\n # Set BFRIC for now\n # WHAT IS WRONG WITH THIS\n # fig2=plt.figure()\n # result, axis = plotTimeSeries(fig2, np.reshape(np.tile(datetimes,2),\\\n # (2, len(datetimes))), np.vstack((speedS, speedO)), \\\n # loc, label=['Simulated','Observed'], where=111, \\\n # title='Path Speeds for ' + fname[:-4] + ' | BFRIC=0.015', \\\n # axis_label='Speed (m/s)')\n\n # if not result:\n # if debug:\n # print '...error encountered with drifter {}.'.format(i)\n # print 'continuing...'\n # plt.close()\n # continue\n\n if plot:\n if debug:\n print 'displaying plot...'\n plt.show()\n else:\n if debug:\n print 'saving plot...'\n fig.savefig(savepath + fname + '_traj.png')\n # result.savefig(savepath + fname + '_speed.png')\n if debug:\n print '...plot saved to: ', savepath+fname\n\n # clear the figure window\n plt.close()","def run_plots(self):\n # load the files\n self.pre_dark_file = os.path.join(self.input_dir, 'step_lastframe.fits')\n self.post_dark_file = os.path.join(self.input_dir, 'step_dark_current.fits')\n self.jump_file = os.path.join(self.input_dir, 'step_jump.fits')\n self.rate_file = os.path.join(self.input_dir, 'step_rate.fits')\n self.ramp_file = glob.glob(os.path.join(self.input_dir, '*.fits'))[0]\n\n # plots\n self.plot_jump_flags_image()\n self.plot_groupdq_flags(pixel=[884, 550])\n self.plot_ramps_pre_post_correction(pixel=[884, 550])","def RvsTPlot(self, datafile, boardindex, chindex, description='',\n Tcline=True):\n try:\n self._checkfigure()\n except HKEPlotterNotInitializedError(self.figure,\n self.axes) as e:\n print e\n\n temperature = datafile['temperature']\n Ts = temperature['Ts']\n\n board = datafile['boards'][boardindex]\n register = board['registers'][chindex]\n\n dataR = board['data']\n Rs = dataR[chindex]\n\n chdesc = register['name']\n print \"description = \", repr(description) #DELME\n if description:\n description = ' - ' + description\n print \"chdesc = \", repr(chdesc) #DELME\n if chdesc:\n description = description + ' - ' + chdesc\n\n label = 'Ch {i}{d}'.format(i=chindex, d=description)\n line = self.plot(Ts, Rs, label=label)\n\n linedict = self._get_linedict(line)\n linedict['label'] = label\n\n Tc = board['Tcs'][chindex]\n linedict['Tc'] = Tc\n\n if Tcline:\n self.addTcline(line, Tc)\n\n return linedict\n\n # Ts = datafile['Temperatures']\n # dataR = datafile[dataRname]\n # Rs = dataR[index]\n\n # sidenum = dataRname.split()[1]\n # desclist = datafile['Side {s} Descriptions'.format(s=sidenum)]\n # chdesc = desclist[index]\n\n # if description:\n # description = ' - ' + description\n # if chdesc:\n # description = description + ' - ' + chdesc\n\n # label = 'Ch {i}{d}'.format(i=index, d=description)\n # line = self.plot(Ts, Rs, label=label)\n\n # linedict = self._get_linedict(line)\n # linedict['label'] = label\n\n # Tcs = datafile['Side {i} Transition Temperatures'.format(i=sidenum)]\n # Tc = Tcs[index]\n # linedict['Tc'] = Tc\n\n # if Tcline:\n # self.addTcline(line, Tc)\n\n # return linedict","def render_sample(cls,\n nusc: 'NuScenes',\n radar_data_gp: 'RadarDataGroup',\n front_cam_data: 'CameraData',\n host_data:'HostData',\n fig_info:dict,\n box_vis_level: BoxVisibility = BoxVisibility.ANY,\n out_path: str = None) -> None:\n cls.colors=radar_data_gp.colors\n fig, axes = plt.subplots(1, 2, figsize=(16, 24))\n #left diagram to show front view\n ax1 = axes[0]\n cls.render_front_view(cls, nusc=nusc, radar_data_gp=radar_data_gp, front_cam_data=front_cam_data,\n ax =ax1, use_flat_vehicle_coordinates=False, with_anns=False, with_radar=True)\n #right diagram to show bird view\n ax2 = axes[1] \n \n cls.render_bird_view(cls, nusc=nusc, radar_data_gp=radar_data_gp, front_cam_data=front_cam_data, \n host_data=host_data, ax =ax2, use_flat_vehicle_coordinates=True)\n #figure information\n fig.text(0.01, 0.97, 'Figure information: ', weight='bold', color='green',fontsize = 15) \n fig.text(0.01, 0.93, 'package: '+nusc.version, fontsize = 12) \n fig.text(0.01, 0.9, 'scene name: '+fig_info['scene_name'], fontsize = 12) \n fig.text(0.01, 0.87, 'timestamp: '+str(front_cam_data.time_stamp*1e-6)+'(s)', fontsize = 12)\n fig.text(0.01, 0.84, 'elapse time: : '+str((fig_info['elapse_time'])*1e-3)+'(ms)', fontsize = 12)\n \n plt.tight_layout()\n fig.subplots_adjust(wspace=0, hspace=0)\n if out_path is not None:\n plt.savefig(out_path,dpi=200)\n if fig_info['verbose']: \n plt.show()","def graphic_manuel():\r\n root = Tk()\r\n grid = FileManager.read_sudoku(args.file)[0]\r\n inte = GraphicInterface.GraphicInterface(root, grid)\r\n inte.open()","def tftDisplay(libtft):\n tempMsg = getTemperature()\n print(\"%s\" % tempMsg)\n\n timeMsg = getTime()\n print(\"%s\" % timeMsg)\n\n ipMsg = GetIPAddr()\n print(\"%s\" % ipMsg)\n\n lightMsg = \"UKN\" #getLight()\n print(\"%s\" % lightMsg)\n \n libtft.ClearScreen()\n \n libtft.PutString(0, 0, \"IP \", libtft.YELLOW)\n libtft.PutString(0, 20, ipMsg, libtft.WHITE)\n\n libtft.PutString(0, 40, \"Time\", libtft.YELLOW)\n libtft.PutString(0, 60, timeMsg, libtft.WHITE)\n\n libtft.PutString(0, 80, \"Temp\", libtft.YELLOW)\n libtft.PutString(0, 100, tempMsg, libtft.WHITE)","def detect_report(day, json_conf):\n rpt = mk_css()\n rpt += \"<h1>Detection Report for \" + STATION_ID + \" on \" + day + \"</h1>\"; \n data_dir = \"/mnt/ams2/SD/proc2/\" + day + \"/data/\"\n image_dir = \"/mnt/ams2/SD/proc2/\" + day + \"/images/\"\n video_dir = \"/mnt/ams2/SD/proc2/\" + day + \"/hd_save/\"\n meteor_files = glob.glob(data_dir + \"*-meteor.json\")\n non_meteor_files = glob.glob(data_dir + \"*-nometeor.json\")\n detect_files = glob.glob(data_dir + \"*-detect.json\")\n rpt += \"<h2>\" + str(len(meteor_files)) + \" Meteors detected </h2>\" \n for mf in sorted(meteor_files):\n print(mf)\n fn = mf.split(\"/\")[-1]\n img = mf.replace(\"data/\", \"images/\")\n vid = mf.replace(\"data/\", \"\")\n img = img.replace(\"-meteor.json\", \"-stacked-tn.png\")\n vid = vid.replace(\"-meteor.json\", \".mp4\")\n if cfe(img) == 0:\n if cfe(vid) == 1:\n print(\"Image missing stack vid\")\n stack = quick_video_stack(vid)\n thumb = thumb = cv2.resize(stack, (PREVIEW_W, PREVIEW_H)) \n cv2.imwrite(img, thumb)\n print(\"THUMB:\", thumb)\n \n rpt += \"<div class='float_div'>\"\n rpt += \"<img src=\" + img + \">\"\n rpt += \"<br><label style='text-align: center'>\" + fn + \" <br>\" \n rpt += \"</label></div>\"\n\n table = det_table(non_meteor_files, \"nometeor\")\n rpt += \"<div style='clear: both'></div>\"\n rpt += \"<h2>\" + str(len(non_meteor_files)) + \" Auto Rejected Meteor Detections</h2>\"\n rpt += table\n\n if False:\n table = det_table(detect_files, \"detect\")\n rpt += \"<div style='clear: both'></div>\"\n rpt += \"<h2>\" + str(len(detect_files)) + \" Non Meteor Detections</h2>\"\n rpt += table\n\n\n out = open(data_dir + \"report.html\", \"w\")\n out.write(rpt)\n out.close()\n print(data_dir + \"report.html\")","def image_capture_demo():\n return render_template('image_capture_demo.html')","def index():\n vegform = Upload() \n imageform = ImageUpdate() \n stormform = Storm() \n form = Landscape() \n \n # Set image defaults to zero so template knows what's going on\n result_im = None\n result_bin = None \n result_RF = None\n result_vmax = None\n\n feature_dir = '/'.join([os.getcwd(), 'feature' + request.remote_addr])\n feature_path = '/'.join([feature_dir, 'features.json'])\n os.mkdir(feature_dir) if os.path.isdir(feature_dir) == False else True\n if 'features.json' in os.listdir(feature_dir):\n with open(feature_path, 'r') as fp:\n features = json.load(fp)\n \n else:\n features = reset_features()\n\n p_choices = {'0.5' : ['2.5', '5.0', '7.5', '10'],\n '1' : ['1.7','3.4','5.1','6.8'],\n '3' : ['0.8','1.6','2.4','3.2'],\n '6' :['0.5','1.0','1.5','2.0']\n }\n\n\n if request.method == 'POST' and form.reset.data == True :\n flash('Reset!')\n features = reset_features() \n with open(feature_path, 'w') as fp:\n json.dump(features, fp, indent = 2) \n \n if 'tr' in features.keys():\n form.p.choices = [(str(p),str(p)) for p in p_choices[features['tr']]] \n \n if vegform.validate_on_submit():\n \n if vegform.delete.data == True:\n flash('File deleted ')\n \n #os.remove(os.path.join( UPLOAD_FOLDER, features['filename'] ))\n if 'filename' in features:\n features.pop('filename')\n if 'filepath' in features:\n features.pop('filepath') \n result_bin = None\n result_im = None \n \n features = reset_features() \n \n if vegform.submit.data == True:\n \n flash('File upload ')\n f = vegform.veg.data\n filename = secure_filename(f.filename)\n filepath = os.path.join( UPLOAD_FOLDER, filename)\n f.save(filepath)\n \n features['filename'] = filename \n features['filepath'] = filepath \n\n #### image handling \n if imageform.submit.data and imageform.validate_on_submit():\n \n flash('Image update')\n keys = [key for key in request.form.keys() if key not in ['csrf_token', 'ascii']]\n #for key in keys: \n if 'grid' in keys:\n features['grid'] = request.form['grid']\n if 'threshold' in keys:\n features['threshold'] = request.form['threshold']\n \n if request.form['submit'] == 'rotate-right':\n features['rotate'] = np.mod(features['rotate'] - 90., 360)\n print \"rotate right\"\n\n if request.form['submit'] == 'rotate-left':\n features['rotate'] = np.mod(features['rotate'] + 90., 360)\n print \"rotate left\"\n \n #### precip handling \n if stormform.submit.data and stormform.validate_on_submit():\n \n flash('Rain duration added ') \n \n features['tr'] = (request.form['tr'])\n if 'p' in features.keys():\n features['rainD'] = float(features['tr'])*float(features['p'])\n \n \n form.p.choices = [(str(p),str(p)) for p in p_choices[features['tr']]] \n keys = [key for key in request.form.keys() if key not in ['csrf_token', 'ascii']]\n \n\n #### update landscape \n if form.update.data and form.validate_on_submit():\n \n flash('Landscape featuress added')\n \n keys = [key for key in request.form.keys() if key not in ['csrf_token', 'ascii']]\n \n if 'tr' not in features.keys():\n features['tr'] = '0.5'\n \n features['slope'] = request.form['slope']\n features['p'] = (request.form['p'])\n features['KsV'] = (request.form['KsV']) \n features['rainD'] = float(features['tr'])*float(features['p'])\n \n if 'filepath' in features.keys():\n \n result_im = png_plot(features['filepath'])\n \n threshold = features['threshold'] if 'threshold' in features.keys() else 0.5 \n result_bin, bw = binarize(features['filepath'], features = features)\n features['Lx'] = bw.shape[0]\n features['Ly'] = bw.shape[1] \n features['fV'] = np.round(np.mean(bw), 2)\n \n if form.submit.data and form.validate_on_submit():\n flash('Running the random forest model!')\n \n keys = [key for key in request.form.keys() if key not in ['csrf_token', 'ascii']]\n \n if 'tr' not in features.keys():\n features['tr'] = '0.5'\n \n features['slope'] = request.form['slope']\n features['p'] = (request.form['p'])\n features['KsV'] = (request.form['KsV']) \n features['rainD'] = float(features['tr'])*float(features['p'])\n \n result_RF, zinflc = run_RF(features['filepath'], features = features, target_col = 'zinflc')\n result_vmax, vmax = run_RF(features['filepath'], features = features, target_col = 'vmax') \n \n features['inflDveg'] = np.round(np.mean(zinflc[bw == 1]),2)\n features['inflD'] = np.round(np.mean(zinflc),2)\n features['vmax'] = np.round(np.mean(vmax),2)\n features['vmaxmax'] = np.round(np.percentile(vmax, 95),2) \n\n with open(feature_dir + '/features.json', 'w') as fp:\n json.dump(features, fp, indent = 2) \n\n keys = [key for key in features.keys() if key not in ['filepath', 'filename', 'tr']]\n\n for key in ['grid', 'threshold']:\n if key in features.keys():\n imageform[key].default = float(features[key] ) \n \n imageform.process()\n\n for key in ['tr']:\n if key in features.keys():\n stormform[key].default = features[key]\n \n stormform.process() \n \n for key in [ 'p', 'slope', 'KsV']:\n if key in features.keys():\n form[key].default = features[key]\n\n form.process()\n \n return render_template('home.html', form = form, \\\n vegform = vegform, \n imageform = imageform, \n stormform = stormform, \n features = features, \n result_bin = result_bin,\n result_im = result_im,\n result_RF = result_RF,\n result_vmax = result_vmax\n )","def simulate(self):\n self.fig = plt.figure(figsize=(15,15), dpi=80)\n self.cplot(self.fig, 0 , 1)\n \n #Save the configuration at the defined times\n self.metro_monte_carlo(save_conf=True)\n #cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n #fig.colorbar(self.ax, cax=cbar_ax)\n \n plt.savefig('config.pdf')","def render(self, mode='human', type_of='color'):\n if type_of == 'color':\n plot_color(self.observation[0:self.num_of_services],\n self.servers_mem ,self.services_mem)\n elif type_of=='black':\n plot_black(self.observation[0:self.num_of_services],\n self.servers_mem ,self.services_mem)\n print('\\n----services mem----\\n')\n print('services resource usage: {}'.format(self.services_mem))\n print('servers capacity: {}'.format(self.servers_mem))\n print('users services: {}'.format(self.users_services))\n print('\\n----observation----\\n')\n print('services servers: {}'.format(self.observation[0:self.num_of_services]))\n print('users stations: {}'.format(self.observation[self.num_of_services:]))","def showAssetImage(*args):\n\n selTab = cmds.tabLayout(widgets[\"shotAssRigListTLO\"], q=True, st=True)\n\n fType = \"\"\n asset = \"\"\n assetPath = \"\"\n path = \"\"\n imagePath = \"\"\n\n if selTab == \"Chars\":\n asset = cmds.textScrollList(widgets[\"shotAssRigCharListTSL\"], q=True, si=True)\n if asset:\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \"characters\", asset[0], \"icon\",\"{0}Icon.png\".format(asset[0])))\n if os.path.isfile(imagePath):\n cFuncs.assetImageUI(imagePath)\n else:\n cmds.warning(\"Can't find an image for {0}\".format(asset[0]))\n\n if selTab == \"Props\":\n asset = cmds.textScrollList(widgets[\"shotAssRigPropListTSL\"], q=True, si=True)\n if asset:\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \"props\", asset[0], \"icon\",\"{0}Icon.png\".format(asset[0])))\n if os.path.isfile(imagePath):\n cFuncs.assetImageUI(imagePath)\n else:\n cmds.warning(\"Can't find an image for {0}\".format(asset[0]))\n \n if selTab == \"Sets\":\n asset = cmds.textScrollList(widgets[\"shotAssRigSetListTSL\"], q=True, si=True)\n if asset:\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \"sets\", asset[0], \"icon\",\"{0}Icon.png\".format(asset[0])))\n if os.path.isfile(imagePath):\n cFuncs.assetImageUI(imagePath)\n else:\n cmds.warning(\"Can't find an image for {0}\".format(asset[0]))\n \n # if selTab == \"Anm\":\n # #need to split this up\n # var_shot = cmds.textScrollList(widgets[\"shotAnmMstListTSL\"], q=True, si=True)\n # if var_shot:\n # var, buf, shot = var_shot[0].partition(\".\")\n # path = cFuncs.getVarMaster(cFuncs.fixPath(os.path.join(pi.shotsFolder, shot, \"anm\", var)))","def main(argv):\n # pylint: disable=too-many-locals\n\n parsed_args = parse_arguments(argv)\n\n template = get_template(parsed_args)\n\n dts = pydevicetree.Devicetree.parseFile(\n parsed_args.dts, followIncludes=True)\n\n memories = get_memories(dts)\n print_memories(memories)\n sorted_ram_memories = get_sorted_ram_memories(dts)\n\n ram, rom, itim, lim = get_load_map(memories, scratchpad=parsed_args.scratchpad)\n\n text_in_itim = False\n if parsed_args.ramrodata and get_itim_length(memories) >= MAGIC_RAMRODATA_TEXT_THRESHOLD:\n text_in_itim = True\n print(\".text section included in ITIM\", file=sys.stderr)\n elif parsed_args.ramrodata:\n print(\".text section included in ROM\", file=sys.stderr)\n\n harts = dts.get_by_path(\"/cpus\").children\n chosenboothart = dts.chosen(\"metal,boothart\")\n is_worlguard = dts.match(\"sifive,worldguard1\")\n if is_worlguard:\n boot_hart = 0\n else:\n if chosenboothart:\n boot_hart = dts.get_by_reference(chosenboothart[0]).get_reg()[0][0]\n elif len(harts) > 1:\n boot_hart = 1\n else:\n boot_hart = 0\n\n if len(sorted_ram_memories) == 0:\n # If there are no rams to scrub, don't bother scrubbing them\n ecc_scrub = 0\n elif dts.chosen(\"metal,eccscrub\"):\n # Otherwise default to scrubbing if metal,eccscrub = <1>;\n ecc_scrub = dts.chosen(\"metal,eccscrub\")[0]\n else:\n ecc_scrub = 0\n\n # Pass sorted memories to the template generator so that the generated linker\n # script is reproducible.\n sorted_memories = list(memories.values())\n sorted_memories.sort(key=lambda m: m[\"name\"])\n\n values = {\n \"memories\": sorted_memories,\n \"ram_memories\": sorted_ram_memories,\n \"default_stack_size\": \"0x400\",\n \"default_heap_size\": \"0x800\",\n \"num_harts\": len(harts),\n \"boot_hart\": boot_hart,\n \"chicken_bit\": 1,\n \"eccscrub_bit\": ecc_scrub,\n \"text_in_itim\": text_in_itim,\n \"rom\": rom,\n \"itim\": itim,\n \"lim\": lim,\n \"ram\": ram,\n }\n\n if parsed_args.output:\n parsed_args.output.write(template.render(values))\n parsed_args.output.close()\n else:\n print(template.render(values))","def main():\n\n # first figure: betas for each predictor\n fig, axes = plt.subplots(figsize=(8, 18), nrows=3)\n\n image_paths = {\n \"3T\": (\n f\"{PATHS['figures_misc']}/figure_3_images/\"\n f\"C1051_20161006_childVSall_depth_1.png\"\n ),\n \"7T\": (\n f\"{PATHS['figures_misc']}/figure_3_images/\"\n f\"C1051_20160212_childVSall_depth_1_sim2pt4.png\"\n ),\n \"7T_noise\": (\n f\"{PATHS['figures_misc']}/figure_3_images/\"\n f\"C1051_20160212_childVSall_depth_1_sim2pt4_noise.png\"\n ),\n }\n\n for ax, (_, image_path) in zip(axes, image_paths.items()):\n assert os.path.isfile(image_path)\n img = imread(image_path)\n img[np.where(np.sum(img, axis=2) == 0.0)] = 1.0\n\n ax.imshow(img[200:-100, 50:-50, :])\n ax.axis(\"off\")\n\n savefig(f\"{PATHS['figures']}/figure_3ac.png\")\n plt.close(fig)","def produce_13TeV_template(tag_name=\"HKHI\"):\n num_rebin = 1\n file_name = \"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\"+tag_name+\"_Lin.root\"\n print \"Input: \", file_name\n fin = ROOT.TFile.Open(file_name, \"read\")\n h_nom = fin.Get(\"bkg_total_gg_full\").Clone(\"bkg_nominal_old\")\n h_nom.Rebin(num_rebin)\n fout = ROOT.TFile.Open(\"hists_input_\"+tag_name+\".root\", \"recreate\")\n\n h_purity_sys = fin.Get(\"bkg_purity_syst_gg_full\").Clone(\"bkg_purity_syst_gg\")\n h_reducible_sys = fin.Get(\"bkg_reducible_syst_gg_full\").Clone(\"bkg_reducible_syst_gg\")\n h_irreducible_sys = fin.Get(\"bkg_irreducible_syst_gg_full\").Clone(\"bkg_irreducible_syst_gg\")\n h_iso_sys = fin.Get(\"bkg_iso_syst_gg_full\").Clone(\"bkg_iso_syst_gg\")\n\n #file_iso = \"isolation_sys/hist.root\"\n #fin2 = ROOT.TFile.Open(file_iso, \"read\")\n #h_iso_sys = fin2.Get(\"bkg_isolation_syst_gg\")\n ## inflat irreducible uncertainty by factor of 10\n # so that it closes to stats uncertainty in data\n sf = 1\n if INFLATE_SYS:\n sf = 10\n\n # after rebinning systematic uncertainties, need to scale down,\n # otherwise the uncertainties are inflated.\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n\n ## truncate the histograms to [200, 2000] GeV\n h_nom_new = truncate_hist(h_nom, \"bkg_nominal\")\n h_purity_sys_new = truncate_hist(h_purity_sys, \"h_purity_sys_new\")\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \"h_irreducible_sys_new\")\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \"h_reducible_sys_new\")\n h_iso_sys_new = truncate_hist(h_iso_sys, \"h_iso_sys_new\")\n\n #write down sys and nominal\n fout.cd()\n h_nom_new.Write()\n h_purity_sys_new.Write()\n h_reducible_sys_new.Write()\n h_irreducible_sys_new.Write()\n h_iso_sys_new.Write()\n\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \"purity_sys\")\n h_purity_up.Write()\n h_purity_down.Write()\n\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \"reducible_sys\")\n h_red_up.Write()\n h_red_down.Write()\n\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \"irreducible_sys\")\n h_irred_up.Write()\n h_irred_down.Write()\n\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \"isolation_sys\")\n h_iso_up.Write()\n h_iso_down.Write()\n\n fin.Close()\n fout.Close()","def render(name, conf):\n\n template_filename = '/etc/pgpool-II/templates/{}'.format(name)\n output_filename = '/etc/pgpool-II/{}'.format(name)\n\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(os.path.dirname(template_filename)),\n extensions=['jinja2.ext.with_'])\n\n try:\n template = env.get_template(os.path.basename(template_filename))\n except exceptions.TemplateNotFound as e:\n logger.info('Error reading template file {}!'.format(template_filename))\n\n logger.debug('Conf list passed to Jinja template file {}.'.format(conf))\n with open(output_filename, \"w\") as f:\n f.write(template.render(conf))\n\n # set permission of rendered files to postgres:postgres\n os.chown(output_filename, UID, GID)","def useThibautsData(log, output, bcgr=72.2, sigma=0.75, iterations=4, loc=1900, galaxies=1000,\n datadir='/Users/smn2/EUCLID/CTItesting/uniform/',\n thibautCDM03=False, beta=False, serial=1, parallel=1):\n files = g.glob(datadir + '*.fits')\n #pick randomly\n files = np.random.choice(files, galaxies, replace=False)\n\n #trap parameters: parallel\n if thibautCDM03:\n f1 = '/Users/smn2/EUCLID/vissim-python/data/cdm_thibaut_parallel.dat'\n f2 = '/Users/smn2/EUCLID/vissim-python/data/cdm_thibaut_serial.dat'\n params = ThibautsCDM03params()\n params.update(dict(parallelTrapfile=f1, serialTrapfile=f2, rdose=8.0e9, serial=serial, parallel=parallel))\n else:\n f1 = '/Users/smn2/EUCLID/vissim-python/data/cdm_euclid_parallel.dat'\n f2 = '/Users/smn2/EUCLID/vissim-python/data/cdm_euclid_serial.dat'\n params = MSSLCDM03params()\n params.update(dict(parallelTrapfile=f1, serialTrapfile=f2, rdose=8.0e9, serial=serial, parallel=parallel))\n if beta:\n params.update(dict(beta_p=0.6, beta_s=0.6))\n\n print f1, f2\n\n #store shapes\n eclean = []\n e1clean = []\n e2clean = []\n R2clean = []\n xclean = []\n yclean = []\n eCTI = []\n e1CTI = []\n e2CTI = []\n R2CTI = []\n xCTI = []\n yCTI = []\n eCTIfixed = []\n e1CTIfixed = []\n e2CTIfixed = []\n R2CTIfixed = []\n xCTIfixed = []\n yCTIfixed = []\n\n fh = open(output.replace('.pk', '.csv'), 'w')\n fh.write('#files: %s and %s\\n' % (f1, f2))\n for key in params:\n print key, params[key]\n fh.write('# %s = %s\\n' % (key, str(params[key])))\n fh.write('#file, delta_e, delta_e1, delta_e2, delta_R2, delta_x, delta_y\\n')\n for f in files:\n print 'Processing: ', f\n\n #load data\n nocti = pf.getdata(f)\n\n #scale to SNR about 10 (average galaxy, a single exposure)\n nocti /= np.sum(nocti)\n nocti *= 1500.\n\n #place it on canvas\n tmp = np.zeros((2066, 2048))\n ysize, xsize = nocti.shape\n ysize /= 2\n xsize /= 2\n tmp[loc-ysize:loc+ysize, loc-xsize:loc+xsize] = nocti.copy()\n\n #add background\n tmp += bcgr\n\n #run CDM03\n c = CTI.CDM03bidir(params, [])\n tmp = c.applyRadiationDamage(tmp.copy().transpose()).transpose()\n\n #remove background and make a cutout\n CTIdata = tmp[loc-ysize:loc+ysize, loc-xsize:loc+xsize]\n CTIdata -= bcgr\n CTIdata[CTIdata < 0.] = 0.\n\n #write files\n #fileIO.writeFITS(nocti, f.replace('.fits', 'noCTI.fits'), int=False)\n #fileIO.writeFITS(CTI, f.replace('.fits', 'CTI.fits'), int=False)\n\n #reset settings\n settings = dict(sigma=sigma, iterations=iterations)\n\n #calculate shapes\n sh = shape.shapeMeasurement(nocti.copy(), log, **settings)\n results = sh.measureRefinedEllipticity()\n\n eclean.append(results['ellipticity'])\n e1clean.append(results['e1'])\n e2clean.append(results['e2'])\n R2clean.append(results['R2'])\n xclean.append(results['centreX'])\n yclean.append(results['centreY'])\n\n #CTI, fitted centroid\n sh = shape.shapeMeasurement(CTIdata.copy(), log, **settings)\n results2 = sh.measureRefinedEllipticity()\n\n eCTI.append(results2['ellipticity'])\n e1CTI.append(results2['e1'])\n e2CTI.append(results2['e2'])\n R2CTI.append(results2['R2'])\n xCTI.append(results2['centreX'])\n yCTI.append(results2['centreY'])\n\n #fixed centroid\n settings['fixedPosition'] = True\n settings['fixedX'] = results['centreX']\n settings['fixedY'] = results['centreY']\n settings['iterations'] = 1\n sh = shape.shapeMeasurement(CTIdata.copy(), log, **settings)\n results3 = sh.measureRefinedEllipticity()\n\n eCTIfixed.append(results3['ellipticity'])\n e1CTIfixed.append(results3['e1'])\n e2CTIfixed.append(results3['e2'])\n R2CTIfixed.append(results3['R2'])\n xCTIfixed.append(results3['centreX'])\n yCTIfixed.append(results3['centreY'])\n\n text = '%s,%e,%e,%e,%e,%e,%e\\n' % (f, results['ellipticity'] - results2['ellipticity'],\n results['e1'] - results2['e1'], results['e2'] - results2['e2'],\n results['R2'] - results2['R2'],\n results['centreX'] - results2['centreX'],\n results['centreY'] - results2['centreY'])\n fh.write(text)\n print text\n\n fh.close()\n\n results = {'eclean': np.asarray(eclean),\n 'e1clean': np.asarray(e1clean),\n 'e2clean': np.asarray(e2clean),\n 'R2clean': np.asarray(R2clean),\n 'xclean': np.asarray(xclean),\n 'yclean': np.asarray(yclean),\n 'eCTI': np.asarray(eCTI),\n 'e1CTI': np.asarray(e1CTI),\n 'e2CTI': np.asarray(e2CTI),\n 'R2CTI': np.asarray(R2CTI),\n 'xCTI': np.asarray(xCTI),\n 'yCTI': np.asarray(yCTI),\n 'eCTIfixed': np.asarray(eCTIfixed),\n 'e1CTIfixed': np.asarray(e1CTIfixed),\n 'e2CTIfixed': np.asarray(e2CTIfixed),\n 'R2CTIfixed': np.asarray(R2CTIfixed),\n 'xCTIfixed': np.asarray(xCTIfixed),\n 'yCTIfixed': np.asarray(yCTIfixed)}\n\n #save to a file\n fileIO.cPickleDumpDictionary(results, output)\n\n return results","def main():\n data_visualisation()\n write_hyper_params()\n write_result_tables()\n write_box_plots()","def plot12(self, dataset, ts_string_indices, source_jpg_folder='jpg_images', extension='jpg', rows=3, cols=4,\n outfname='Sample Frames.png', cmap=None, gui_color='green'):\n # Settings ############################################################\n font_label_box = {\n 'color': 'green',\n 'size': 16,\n }\n font_steering = {'family': 'monospace',\n # 'color': 'darkred',\n 'weight': 'normal',\n 'size': 20,\n }\n ROWS = rows\n COLS = cols\n NUM_IMAGES = ROWS * COLS\n\n # Figure ##############################################################\n # figsize = [width, height]\n fig = plt.figure(figsize=PAPER_A3_LAND, facecolor='white')\n fig.suptitle(\"Sample frames, Dataset: {}\".format(dataset.data_folder), fontsize=20)\n\n for i, ts_string_index in enumerate(ts_string_indices):\n rec = dataset.df.loc[ts_string_index]\n\n timestamp_string = rec['datetime'].strftime(\"%D %H:%M:%S.\") + \"{:.2}\".format(\n str(rec['datetime'].microsecond))\n\n if 'steering_pred_signal' in dataset.df.columns:\n this_label = \"{}\\n{:0.2f}/{:0.2f} steering \\n{:0.2f} throttle\".format(timestamp_string,\n rec['steering_signal'],\n rec['steering_pred_signal'],\n rec['throttle_signal'])\n else:\n this_label = \"{}\\n{:0.2f}/ steering \\n{:0.2f} throttle\".format(timestamp_string, rec['steering_signal'],\n rec['throttle_signal'])\n\n ax = fig.add_subplot(ROWS, COLS, i + 1)\n\n # Main Image ##########################################################\n jpg_path = os.path.join(dataset.path_dataset, source_jpg_folder, ts_string_index + '.' + extension)\n assert os.path.exists(jpg_path), \"{} does not exist\".format(jpg_path)\n img = mpl.image.imread(jpg_path)\n ax.imshow(img, cmap=cmap)\n # plt.title(str_label)\n\n # Data box ########################################################\n\n # ax.axes.get_xaxis().set_visible(False)\n # ax.axes.get_yaxis().set_visible(False)\n t = ax.text(5, 25, this_label, color=gui_color, alpha=1)\n # t = plt.text(0.5, 0.5, 'text', transform=ax.transAxes, fontsize=30)\n t.set_bbox(dict(facecolor='white', alpha=0.7, edgecolor='none'))\n\n # Steering widget HUD #################################################\n # Steering HUD: Actual steering signal\n steer_actual = ''.join(['|' if v else '-' for v in dataset.linear_bin(rec['steering_signal'])])\n text_steer = ax.text(80, 105, steer_actual, fontdict=font_steering, horizontalalignment='center',\n verticalalignment='center', color=gui_color)\n # Steering HUD: Predicted steering angle\n if 'steering_pred_signal' in dataset.df.columns:\n steer_pred = ''.join(['◈' if v else ' ' for v in dataset.linear_bin(rec['steering_pred_signal'])])\n text_steer_pred = ax.text(80, 95, steer_pred, fontdict=font_steering, horizontalalignment='center',\n verticalalignment='center', color='red')\n\n outpath = os.path.join(dataset.path_dataset, outfname)\n fig.savefig(outpath)\n logging.debug(\"Wrote Sample Frames figure to {}\".format(outpath))"],"string":"[\n \"def photorealistic(case_config_filepath, render_config_filepath):\\n\\n # Load config file with all common directory names\\n dirname_config = configparser.ConfigParser()\\n dirname_config.read(\\\"dirname.cfg\\\")\\n\\n # Get information from config files\\n cconfd = load_config.get_config_params(case_config_filepath) # Case file\\n rconfd = load_config.get_config_params(render_config_filepath) # Render file\\n\\n # Main output directory\\n case_output = dirname_config[\\\"DIRECTORIES\\\"][\\\"RenderOutput\\\"] + cconfd[\\\"case_name\\\"] + \\\"/\\\"\\n \\n # Determine interface geometry output dir\\n geometry_output_dir = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"ply\\\"]\\n\\n # Determine individual frame output dir\\n image_output_dir_spec = dircheck.count_png_dirs(case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"tstep_sequence_photorealistic\\\"])\\n dircheck.check_make(image_output_dir_spec) # Make it if nonexistent\\n \\n # Write Blender config file\\n blender_config_filedir = case_output + rconfd[\\\"render_name\\\"] + \\\"_blender.cfg\\\"\\n load_config.write_config_file(config_filedir=blender_config_filedir,\\n config_dict={\\\"image_output_dir_spec\\\": image_output_dir_spec,\\n \\\"ply_input_dir\\\": geometry_output_dir,\\n \\\"interface_material_name\\\": \\\"WaterMaterial5\\\",\\n \\\"bg_image_filepath\\\": rconfd[\\\"bg_image_filepath\\\"],\\n \\\"view_fraction\\\": cconfd[\\\"dropd\\\"]/rconfd[\\\"droplet_scale\\\"],\\n \\\"render_scale\\\": 10,\\n \\\"resolution_percentage\\\": rconfd[\\\"resolution_percentage\\\"],\\n \\\"xres\\\": cconfd[\\\"xres\\\"], \\\"yres\\\": cconfd[\\\"yres\\\"], \\\"zres\\\": cconfd[\\\"zres\\\"],\\n \\\"tres\\\": cconfd[\\\"tres\\\"],\\n \\\"interface_half_enabled\\\": rconfd[\\\"interface_half_enabled\\\"],\\n \\\"fog_enabled\\\": rconfd[\\\"fog_enabled\\\"],\\n \\\"camera_azimuth_angle\\\": rconfd[\\\"camera_azimuth_angle\\\"],\\n \\\"camera_elevation_angle\\\": rconfd[\\\"camera_elevation_angle\\\"],\\n \\\"bg_color_1\\\": rconfd[\\\"bg_color_1\\\"], \\\"bg_color_2\\\": rconfd[\\\"bg_color_2\\\"]})\\n\\n # Extract droplet interface geometry\\n convert_data.conv_ply(h5dns_path=cconfd[\\\"h5dns_path\\\"], output_dir=geometry_output_dir, tres=int(cconfd[\\\"tres\\\"]))\\n\\n # Extract vapor fog (YV) if enabled\\n if rconfd[\\\"fog_enabled\\\"]:\\n # Determine individual fog dir and make it if necessary\\n fog_halved = rconfd[\\\"fog_half_enabled\\\"]\\n fog_dir_specifier = str(rconfd[\\\"fog_vapor_min\\\"]) + \\\"halved\\\" + str(fog_halved) + \\\"/\\\"\\n bvox_output_dir_spec = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"bvox\\\"] + fog_dir_specifier\\n dircheck.check_make(bvox_output_dir_spec)\\n # Convert fog data\\n convert_data.conv_bvox(h5dns_path=cconfd[\\\"h5dns_path\\\"], output_dir=bvox_output_dir_spec, tres=int(cconfd[\\\"tres\\\"]), vapor_min=float(rconfd[\\\"fog_vapor_min\\\"]), fog_halved=fog_halved)\\n # Add fog dir to Blender config file\\n load_config.write_config_file(config_filedir=blender_config_filedir, config_dict={\\\"bvox_input_dir\\\": bvox_output_dir_spec}, append_config=True)\\n \\n # Launch Blender to perform rendering\\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\\\"droplet_render.py\\\", blend_name=\\\"droplet_render.blend\\\")\",\n \"def main(config):\\n file_paths_info = [('GLOFRIS','WATCH','ARG_inunriver_historical_000000000WATCH_1980_rp01000.tif'),\\n \\t\\t\\t\\t('GLOFRIS','RCP45','ARG_inunriver_rcp4p5_0000GFDL-ESM2M_2030_rp01000.tif'),\\n \\t\\t\\t\\t('GLOFRIS','RCP85','ARG_inunriver_rcp8p5_0000GFDL-ESM2M_2030_rp01000.tif'),\\n \\t\\t\\t\\t('FATHOM','AR_fluvial_undefended_merged','AR-FU-1000.tif'),\\n \\t\\t\\t\\t('FATHOM','AR_pluvial_undefended_merged','AR-PU-1000.tif')\\n \\t\\t\\t\\t]\\n figure_names = ['GLOFRIS-WATCH-fluvial','GLOFRIS-RCP45-fluvial','GLOFRIS-RCP85-fluvial','FATHOM-fluvial','FATHOM-pluvial']\\n figure_titles = ['current fluvial flooding','RCP4.5 fluvial flooding','RCP8.5 fluvial flooding','current fluvial flooding','current pluvial flooding']\\n for f_i in range(len(file_paths_info)):\\n\\t hazard_file = os.path.join(config['paths']['data'],'flood_data', file_paths_info[f_i][0],file_paths_info[f_i][1],file_paths_info[f_i][2])\\n\\t output_file = os.path.join(config['paths']['figures'], 'flood-map-{}.png'.format(figure_names[f_i]))\\n\\t ax = get_axes()\\n\\t plot_basemap(ax, config['paths']['data'])\\n\\t scale_bar(ax, location=(0.8, 0.05))\\n\\t plot_basemap_labels(ax, config['paths']['data'], include_regions=True,include_zorder=3)\\n\\n\\t proj_lat_lon = ccrs.PlateCarree()\\n\\n\\n\\t # Create color map\\n\\t colors = plt.get_cmap('Blues')\\n\\n\\t # Read in raster data\\n\\t data, lat_lon_extent = get_data(hazard_file)\\n\\t data[(data <= 0) | (data > 5)] = np.nan\\n\\t max_val = np.nanmax(data)\\n\\t norm=mpl.colors.Normalize(vmin=0, vmax=max_val)\\n\\n\\t # Plot population data\\n\\t im = ax.imshow(data, extent=lat_lon_extent,transform=proj_lat_lon, cmap=colors,norm =norm, zorder=2)\\n\\n\\t # Add colorbar\\n\\t cbar = plt.colorbar(im, ax=ax,fraction=0.1, shrink=0.87,pad=0.01, drawedges=False, orientation='horizontal',\\n\\t norm=mpl.colors.Normalize(vmin=0, vmax=max_val), ticks=list(np.linspace(0,max_val,3)))\\n\\t cbar.set_clim(vmin=0,vmax=max_val)\\n\\n\\n\\t cbar.outline.set_color(\\\"none\\\")\\n\\t cbar.ax.yaxis.set_tick_params(color='black')\\n\\t cbar.ax.set_xlabel('Flood depths (m)',fontsize=12,color='black')\\n\\n\\t plt.title('1 in 1000 year {}'.format(figure_titles[f_i]), fontsize = 14)\\n\\t save_fig(output_file)\\n\\t plt.close()\",\n \"def main(argv):\\n\\n epd = EPD()\\n\\n epd.clear()\\n\\n print('panel = {p:s} {w:d} x {h:d} version={v:s}'.format(p=epd.panel, w=epd.width, h=epd.height, v=epd.version))\\n\\n for file_name in argv:\\n if not os.path.exists(file_name):\\n sys.exit('error: image file{f:s} does not exist'.format(f=file_name))\\n print('display: {f:s}'.format(f=file_name))\\n display_file(epd, file_name)\",\n \"def plot_ebtel(data_directory,data_file,**kwargs):\\n \\n #Load the data\\n data = np.loadtxt(data_directory+data_file)\\n #Slice the array to get the vectors we want\\n time = data[:,0]\\n temp = data[:,1]\\n dens = data[:,2]\\n temp_apex = data[:,5]\\n dens_apex = data[:,6]\\n heat = data[:,10]\\n \\n #Set up the figure\\n fig,axes = plt.subplots(3,1,figsize=(12,8))\\n #Set the fontsize\\n fs = 16\\n\\n #Plot the heating\\n axes[0].plot(time,heat)\\n axes[0].set_ylabel(r'$h$ (erg cm$^{-3}$ s$^{-1}$)',fontsize=fs)\\n axes[0].set_title(r'EBTEL-C Parameters',fontsize=fs)\\n axes[0].set_xlim([time[0],time[-1]])\\n axes[0].locator_params(nbins=5)\\n axes[0].ticklabel_format(axis='y', style='sci', scilimits=(-2,2) )\\n #Plot the temperatures\\n axes[1].plot(time,temp/10**6,label=r'$T$')\\n axes[1].plot(time,temp_apex/10**6,'r--',label=r'$T_a$')\\n axes[1].set_ylabel(r'$T$ (MK)',fontsize=fs)\\n axes[1].legend(loc=1)\\n axes[1].set_xlim([time[0],time[-1]])\\n axes[1].locator_params(nbins=5)\\n #Plot the densities\\n axes[2].plot(time,dens/10**8,label=r'$n$')\\n axes[2].plot(time,dens_apex/10**8,'r--',label=r'$n_a$')\\n axes[2].set_xlabel(r'$t$ (s)',fontsize=fs)\\n axes[2].set_ylabel(r'$n$ ($10^{8}$ cm$^{-3}$)',fontsize=fs)\\n axes[2].set_xlim([time[0],time[-1]])\\n axes[2].locator_params(nbins=5)\\n axes[2].legend(loc=1)\\n\\n #Check if output filename is specified\\n if 'print_fig_filename' in kwargs:\\n plt.savefig(kwargs['print_fig_filename'],format='eps',dpi=1000)\\n else:\\n plt.show()\",\n \"def render_viewer(configfile, datafile):\\n try:\\n page = 'analogviewer.html'\\n with open(page, 'w') as fp:\\n fp.write(templating.render_template(\\n page,\\n configfile=configfile,\\n datafile=datafile,\\n # static_url=config.output_url() + '/static'))\\n static_url='../static'))\\n prepare_static_folder()\\n except Exception:\\n msg = \\\"Failed to render analogviewer.\\\"\\n LOGGER.exception(msg)\\n raise Exception(msg)\\n else:\\n return page\",\n \"def main():\\n dpi = 1\\n dpi = 2\\n width = int(360)\\n height = int(130)\\n mywidth = int(width*dpi)\\n myheight = int(height*dpi)\\n FWHM = 7.5 # degrees\\n FWHM = 10.0 # degrees\\n FWHM = 5.0 # degrees\\n FWHM = 3.0 # degrees\\n FWHM = 1.0 # degrees\\n weight = 1.\\n\\n nargs = len(sys.argv)\\n if nargs < 2:\\n print('GR: GRid Observations of integrated intensity produced by the T Command')\\n print('GR produces fits images for each of the horns used for the observations.')\\n print('For observations at the same coordinates, the ratios of intensities are also produced.')\\n print('The FITS format files require header information, which is copied from the')\\n print('Cold Load File provided by the user')\\n print('GR RA|GAL <cold file name> <savefile1> [<savefile2> ... <savefileN>]')\\n print(\\\"\\\")\\n print('Glen Langston, National Science Foundation -- 20 May 12')\\n exit()\\n\\n gridtype = sys.argv[1]\\n gridtype = gridtype.upper()\\n print('Grid Type: ', gridtype)\\n\\n # enable having ra going from 24 to 0 hours == 360 to 0 degrees\\n xsign = 1.\\n xoffset = 0.\\n if gridtype == 'RA':\\n xmin = 0.\\n xmax = 360.\\n ymin = -40.\\n ymax = 90.\\n maptype = 'RA'\\n elif gridtype == '-RA':\\n xmin = 0.\\n xmax = 360.\\n ymin = -40.\\n ymax = 90.\\n xsign = -1.\\n xoffset = 360. # when x = 360. should be at zero.\\n maptype = 'RA'\\n elif gridtype == '-EL':\\n xmin = 0.\\n xmax = 360.\\n ymin = 0.\\n ymax = 90.\\n xsign = -1.\\n xoffset = 360. # when x = 360. should be at zero.\\n maptype = 'AZEL'\\n elif gridtype == 'RA0':\\n xmin = 0.\\n xmax = 360.\\n ymin = -41.\\n ymax = 89.\\n xsign = -1.\\n xoffset = 180. # when x = 360. should be at zero.\\n gridtype = 'RA'\\n elif gridtype == 'GAL':\\n xmin = -180.\\n xmax = 180.\\n ymin = -90.\\n ymax = 90.\\n maptype = 'GAL'\\n\\n if gridtype != 'RA' and gridtype != 'GAL' and gridtype != '-RA' and gridtype != \\\"RA0\\\":\\n print('Error parsing grid type: ', gridtype)\\n print('1st argument should be either RA, -RA or GAL')\\n exit()\\n\\n rs = radioastronomy.Spectrum()\\n\\n if doRatio: \\n #create the grid with map parameters\\n grid1 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n grid2 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n grid3 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n grid4 = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n # put each telescope in a different grid\\n grids = [grid1, grid2, grid3, grid4]\\n\\n gridall = GridClass.Grid(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, width=width, \\\\\\n height=height, dpi=dpi, FWHM=FWHM, \\\\\\n projection=\\\"-CAR\\\", gridtype=maptype)\\n \\n\\n projection = \\\"-AIT\\\"\\n# coldfile \\n coldfile = sys.argv[2]\\n# get telescope geographic location etc\\n print(\\\"Reading Observing parameters from: %s\\\" % (coldfile))\\n rs.read_spec_ast(coldfile)\\n print(\\\"Observer: %s \\\" % (rs.observer))\\n\\n# first read through all data and find hot load\\n names = sys.argv[3:]\\n names = sorted(names)\\n\\n firsttime = \\\"\\\"\\n lasttime = \\\"\\\"\\n count = 0\\n # setup grid indicies so that cpuIndex goes to the correct grid\\n # This assumes telescopes 2,3,4,5 are being used] \\n gridIndex = [0,0,0,1,2,3]\\n # for all save Files to Grid\\n for filename in names:\\n print(\\\"File: %s\\\" % (filename))\\n f = open(filename)\\n\\n date = \\\"Unknown\\\"\\n while date != \\\"\\\":\\n date, time, cpuIndex, telaz, telel, tSys, tRx, tRms, tint, KperC, tSourcemax, velSource, dV, tVSum, tVSumRms, tSumKmSec, dTSumKmSec, gainFactor = gainfactor.readSaveValues( f)\\n dlen = len(date)\\n if dlen < 1:\\n break\\n if date[0] == \\\"#\\\":\\n continue\\n # else not a comment process the line\\n count = count + 1\\n isodate = \\\"20\\\"+date+\\\"T\\\"+time\\n# print(\\\"DateTime: %s\\\" % (isodate))\\n rs.utc = datetime.datetime.strptime(isodate,\\\"%Y-%m-%dT%H:%M:%S\\\")\\n# print(\\\"Utc: %s\\\" % (rs.utc))\\n rs.telaz = telaz\\n rs.telel = telel\\n rs.azel2radec()\\n\\n ra = rs.ra\\n dec = rs.dec\\n lon = rs.gallon\\n lat = rs.gallat\\n tsum = tSumKmSec\\n tsdv = dTSumKmSec\\n tmax = tSourcemax\\n vave = tVSum\\n vsdv = tVSumRms\\n if firsttime == \\\"\\\":\\n firsttime = date\\n else:\\n lasttime = date\\n\\n# if vave > -100. and vave < 100:\\n# mygrid.convolve( lon, lat, vave, 1.)\\n iGrid = gridIndex[cpuIndex]\\n gainCorr = telescopefactors[iGrid]\\n tsum = tsum * gainCorr\\n if gridtype == 'RA':\\n if doRatio:\\n grids[iGrid].convolve(ra, dec, tsum, weight)\\n gridall.convolve( ra, dec, tsum, weight)\\n elif gridtype == '-RA':\\n x = (ra*xsign) + xoffset\\n if doRatio:\\n grids[iGrid].convolve(x, dec, tsum, weight)\\n gridall.convolve( x, dec, tsum, weight)\\n elif gridtype == 'RA0':\\n x = (ra*xsign) + xoffset\\n if x < 0:\\n x = x + xmax\\n elif x > xmax:\\n x = x - xmax\\n if doRatio:\\n grids[iGrid].convolve(x, dec, tsum, weight)\\n gridall.convolve( x, dec, tsum, weight)\\n else:\\n if doRatio:\\n grids[iGrid].convolve(lon, lat, tsum, weight)\\n gridall.convolve( lon, lat, tsum, weight)\\n\\n if count == 0:\\n print('Convolving Coordinates: ', ra, dec, lon, lat)\\n print('Convolving Intensities: ', tsum, tsdv, vave, vsdv)\\n print('Convolvign Parameters : ', n, time)\\n count = count + 1\\n # end reading all lines in save file\\n f.close()\\n\\n # normalize each of the gridded images\\n if doRatio:\\n grids[0].normalize()\\n grids[1].normalize()\\n grids[2].normalize()\\n grids[3].normalize()\\n gridall.normalize()\\n# mygrid.check()\\n# zmin = -1000.\\n# zmax = 3000.\\n# limit grid intensities for plotting\\n# mygrid.set_ij( 0, 0, zmax, 1.)\\n# mygrid.set_ij( 1, 1, zmin, 1.)\\n# mygrid.limit(zmin, zmax)\\n\\n subplots = False\\n\\n if subplots:\\n fig, ax = plt.subplots(figsize=(myheight, mywidth), dpi=dpi)\\n\\n if gridtype == 'RA':\\n cax = fig.add_axes([-180, 180], [-90, 90])\\n else:\\n cax = fig.add_axes([0, 24], [-90, 90])\\n\\n cbar = fig.colorbar(cax, ticks=[zmin, zmax], orientation='horizontal')\\n cbar.ax.set_yticklabels([str(zmin), str(zmax)])\\n\\n ax.set_title(\\\"Citizen Science: Horn observations of our Galaxy\\\")\\n else:\\n#y_ticks = ymin + (ymax-ymin)*ticks/myheight\\n\\n ticks = np.arange(0, mywidth, 30*dpi)\\n x_ticks = xmin + ((xmax-xmin)*ticks/mywidth)\\n\\n plt.imshow(gridall.image, interpolation='nearest', cmap=plt.get_cmap('jet'))\\n\\n if firsttime != lasttime:\\n plt.title(\\\"Citizen Science: Observing our Galaxy: %s to %s\\\" % (firsttime, lasttime))\\n else:\\n plt.title(\\\"Citizen Science: Observing our Galaxy: %s\\\" % (firsttime))\\n if gridtype == 'RA':\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Declination (degrees)\\\")\\n labels = ticks/(mywidth/24)\\n yticks = np.arange(0, myheight, 15*dpi)\\n elif gridtype == '-RA':\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Declination (degrees)\\\")\\n labels = 24 - (ticks/(mywidth/24))\\n labels[0] = 0\\n labels[0] = 24\\n yticks = np.arange(0, myheight, 15*dpi)\\n elif gridtype == '-EL':\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Elevation (degrees)\\\")\\n labels = 24 - (ticks/(mywidth/24))\\n labels[0] = 0\\n labels[0] = 24\\n yticks = np.arange(0, myheight, 15*dpi)\\n elif gridtype == 'RA0': # put 0 hours in middle of plot\\n plt.xlabel(\\\"Right Ascension (hours)\\\")\\n plt.ylabel(\\\"Declination (degrees)\\\")\\n labels = 12 - (ticks/(mywidth/24))\\n nlabels = len(labels)\\n for iii in range(nlabels):\\n if labels[iii] < 0:\\n labels[iii] = 24 + labels[iii]\\n if labels[iii] == 24:\\n labels[iii] = 0\\n yticks = np.arange(0, myheight, 15*dpi)\\n else:\\n yticks = np.arange(0, myheight, 30*dpi)\\n ticks = np.arange(0, mywidth, 30*dpi)\\n x_ticks = xmin + (xmax-xmin)*ticks/mywidth\\n labels = x_ticks\\n plt.xlabel(\\\"Galactic Longitude (degrees)\\\")\\n plt.ylabel(\\\"Galactic Latitude (degrees)\\\")\\n # wnat an integer list of labels\\n# slabels = str(labels)\\n print(ticks, labels)\\n y_ticks = ymax - (ymax-ymin)*yticks/myheight\\n plt.yticks(yticks, y_ticks)\\n plt.xticks(ticks, labels, rotation='horizontal')\\n plt.colorbar()\\n\\n crval2 = (xmin + xmax)/2.\\n crval1 = (ymin + ymax)/2.\\n cdelt1 = (-1./float(dpi)) - .001\\n cdelt2 = (1./float(dpi)) + .001\\n if doRatio:\\n# now show eacsh of the images\\n for iGrid in range(4):\\n imagetemp = copy.deepcopy(grids[iGrid].image)\\n imagetemp2 = copy.deepcopy(grids[iGrid].image)\\n kkk = myheight - 1\\n for jjj in range(myheight):\\n imagetemp[:][kkk] = imagetemp2[:][jjj]\\n kkk = kkk - 1\\n grids[iGrid].image = imagetemp\\n writeFitsImage( rs, iGrid+2, grids[iGrid], projection)\\n\\n # put each telescope in a different grid\\n ratio1 = copy.deepcopy(grid1)\\n ratio2 = copy.deepcopy(grid1)\\n ratio3 = copy.deepcopy(grid1)\\n gratios = [ratio1, ratio2, ratio3]\\n ratios = np.zeros(3)\\n rmss = np.zeros(3)\\n\\n jGrid = 3\\n for iGrid in range(3):\\n print(\\\"Gain Ratios for Telescopes T%d and T%d\\\" % (iGrid+2, jGrid+2))\\n ratio, rms, aratio = gridratio(grids[iGrid], grids[jGrid])\\n ratios[iGrid] = ratio\\n rmss[iGrid] = rms\\n writeFitsImage( rs, iGrid+2, aratio, projection)\\n \\n writeFitsImage( rs, 0, gridall, projection)\\n plt.show()\",\n \"def heatmap():\\n\\n team = int(request.form.get('teamname'))\\n player = int(request.form.get('player'))\\n attacks = request.form.get('attacks')\\n data = request.form.get('datafiles')\\n\\n print 'Making heat map for player #%d, team #%d, attacks %s' % (player, team, attacks)\\n\\n if attacks.lower() == 'all':\\n attacks = ['ALL']\\n else:\\n attacks = attacks.split(',')\\n\\n only_kills = request.form.get('kills')\\n\\n files = []\\n if data == 'uploads':\\n data = 'uploads/%d' % session['user_id']\\n folder = 'data/' + data\\n\\n data_path = os.path.join(os.getcwd(), folder)\\n if not os.path.isdir(data_path):\\n os.makedirs(data_path)\\n\\n for f in os.listdir(data_path):\\n if f.endswith('.dvw'):\\n files.append(folder + '/' + f)\\n\\n locations = []\\n for file_name in files:\\n parser = Parser(file_name)\\n new_locations = parser.get_attack_info(team, player, attacks, only_kills=only_kills)\\n locations.extend(new_locations)\\n\\n top_caption, bottom_caption = generate_caption(team, player, attacks, only_kills)\\n output_url = generate_output_filename(team, player, attacks, only_kills, session['user_id'])\\n output_dict = heat_map.draw_arcs_pillow(locations, output_url, top_caption=top_caption, bottom_caption=bottom_caption)\\n\\n result_dict = {\\n 'output_url': output_url,\\n 'width': output_dict['width'],\\n 'height': output_dict['height'],\\n }\\n print locations\\n print 'Rendering finished image', output_url\\n return render_template('heatmap.html', result_dict=result_dict)\",\n \"def plot_ebtel_dem(data_directory,data_file,**kwargs):\\n \\n #Load DEM data\\n data = np.loadtxt(data_directory+data_file)\\n #Slice array to get necessary vectors\\n temp = data[:,0]\\n dem_tr = data[:,1]\\n dem_cor = data[:,2]\\n dem_tot = data[:,3]\\n em_cor = data[:,4]\\n \\n #Set up the figure\\n fig = plt.figure(figsize=(10,10))\\n ax = fig.gca()\\n fs = 16\\n ax.plot(temp,dem_tr,label=r'TR')\\n ax.plot(temp,dem_cor,'r',label=r'Corona')\\n ax.plot(temp,dem_tot,'g',label=r'Total')\\n ax.plot(temp,em_cor,'--r',label=r'Corona EM')\\n ax.legend()\\n ax.set_title(r'EBTEL-C DEM',fontsize=fs)\\n ax.set_xlabel(r'$\\\\log(T_{DEM})$ (K)',fontsize=fs)\\n ax.set_ylabel(r'$\\\\log($DEM$)$ (cm$^{-5}$ K$^{-1}$)',fontsize=fs)\\n ax.set_xlim([5.5,7.5])\\n ax.set_ylim([18.0,30.0])\\n \\n #Check if output filename is specified\\n if 'print_fig_filename' in kwargs:\\n plt.savefig(kwargs['print_fig_filename'],format='eps',dpi=1000)\\n else:\\n plt.show()\",\n \"def setUp(self):\\n print \\\"Setting Up: %s\\\" % self.id()\\n # render the template\\n g.render_template(self.template_file,\\n self.template_vars,\\n self.output_file,\\n self.search_path)\\n\\n # read the resulting config file built from template\\n self.output_config = g.load_config(self.output_file)\\n g.show_config(self.output_config)\",\n \"def main():\\n args = argument_parser()\\n args.mbtiles_file.close()\\n mbtiles_file = args.mbtiles_file.name\\n zoom = args.zoom\\n tile_x = args.tilex\\n tile_y = args.tiley\\n mvt_content = run(tile_x, tile_y, zoom, mbtiles_file)\\n print(mvt_content, end='')\",\n \"def lambda2(case_config_filepath, render_config_filepath):\\n\\n # Load config file with all common directory names\\n dirname_config = configparser.ConfigParser()\\n dirname_config.read(\\\"dirname.cfg\\\")\\n\\n # Get information from config files\\n cconfd = load_config.get_config_params(case_config_filepath)\\n rconfd = load_config.get_config_params(render_config_filepath)\\n \\n # Main output directory\\n case_output = dirname_config[\\\"DIRECTORIES\\\"][\\\"RenderOutput\\\"] + cconfd[\\\"case_name\\\"] + \\\"/\\\"\\n\\n # Determine lambda2 output directories and make them if necessary\\n lambda2_level = rconfd[\\\"lambda2_level\\\"]\\n lambda2_specifier = \\\"l2\\\" + str(lambda2_level) + \\\"/\\\"\\n geometry_output_dir = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"ply\\\"]\\n ply_lambda2_output_dir = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"ply_lambda2\\\"] + lambda2_specifier\\n image_lambda2_output_dir = case_output + dirname_config[\\\"DIRECTORIES\\\"][\\\"tstep_lambda2\\\"] + lambda2_specifier\\n image_lambda2_output_dir_spec = dircheck.count_png_dirs(image_lambda2_output_dir)\\n dircheck.check_make([ply_lambda2_output_dir, image_lambda2_output_dir_spec])\\n\\n # Write Blender config file\\n blender_config_filedir = case_output + rconfd[\\\"render_name\\\"] + \\\"_blender.cfg\\\"\\n load_config.write_config_file(config_filedir=blender_config_filedir,\\n config_dict={\\\"image_output_dir_spec\\\": image_lambda2_output_dir_spec,\\n \\\"ply_input_dir\\\": ply_lambda2_output_dir,\\n \\\"interface_material_name\\\": \\\"Lambda2Contour\\\",\\n \\\"bg_image_filepath\\\": rconfd[\\\"bg_image_filepath\\\"],\\n \\\"view_fraction\\\": rconfd[\\\"view_fraction\\\"], \\\"render_scale\\\": 10,\\n \\\"resolution_percentage\\\": rconfd[\\\"resolution_percentage\\\"],\\n \\\"xres\\\": cconfd[\\\"xres\\\"], \\\"yres\\\": cconfd[\\\"yres\\\"], \\\"zres\\\": cconfd[\\\"zres\\\"],\\n \\\"tres\\\": cconfd[\\\"tres\\\"], \\\"interface_half_enabled\\\": False,\\n \\\"fog_enabled\\\": False,\\n \\\"camera_azimuth_angle\\\": rconfd[\\\"camera_azimuth_angle\\\"],\\n \\\"camera_elevation_angle\\\": rconfd[\\\"camera_elevation_angle\\\"],\\n \\\"bg_color_1\\\": rconfd[\\\"bg_color_1\\\"], \\\"bg_color_2\\\": rconfd[\\\"bg_color_2\\\"]})\\n\\n # Extract droplet geometry\\n # convert_data.conv_ply(h5dns_path=cconfd[\\\"h5dns_path\\\"], output_dir=geometry_output_dir, tres=int(cconfd[\\\"tres\\\"]))\\n\\n # Extract lambda2 contour geometry\\n convert_data.conv_lambda2_ply(h5dns_path=cconfd[\\\"h5dns_path\\\"], output_dir=ply_lambda2_output_dir, tres=cconfd[\\\"tres\\\"], contour_level=lambda2_level)\\n\\n # Launch Blender to perform rendering\\n blender_launcher.launch_blender_new(blender_config_filedir=blender_config_filedir, python_name=\\\"droplet_render.py\\\", blend_name=\\\"droplet_render.blend\\\")\",\n \"def render(sim_file: str, only_stuck: bool) -> None:\\n import DLA\\n DLA.GREEN = (0, 0, 0) # type: ignore\\n DLA.WHITE = (151, 151, 151, 150) # type: ignore\\n from DLA import config\\n config.USE_PYGAME = True # type: ignore\\n from DLA import renderer\\n renderer.render(Path(sim_file), only_stuck)\",\n \"def plotMEC(dimensions = 2, J = 1, filename = None,N = [20], \\\\\\r\\n anneal = True, Tlim = [1,4], prelims = 50, sweeps = 200, \\\\\\r\\n plots = True, plainlines = False, steps = 150):\\r\\n \\r\\n #temperature linespace\\r\\n T = np.linspace(Tlim[0],Tlim[1], steps)\\r\\n \\r\\n #tabulated magnetisation arry list\\r\\n Ms = []\\r\\n \\r\\n #tabulated energy array list\\r\\n Es = []\\r\\n \\r\\n #tabulated heat capacities\\r\\n Cs = []\\r\\n \\r\\n #labels used for datasets in the plots\\r\\n labels = []\\r\\n \\r\\n #critical exponent function used to fit data.\\r\\n def f (x, p1, p2, p3) : return p1*(((p2-x)/p2) ** p3)\\r\\n \\r\\n \\r\\n \\r\\n #itterate over wanted values of N\\r\\n for k in range(len(N)):\\r\\n \\r\\n #magnetisations and energies for N(i)\\r\\n M = np.zeros(T.shape)\\r\\n E = np.zeros(T.shape)\\r\\n C = np.zeros(T.shape)\\r\\n \\r\\n #lattice for N(i)\\r\\n lattice = initialiser(N[k],dimensions = dimensions)\\r\\n\\r\\n\\r\\n \\r\\n #itterate over all temperatures, highest first\\r\\n for i in range(len(T)):\\r\\n #highest first\\r\\n index = len(T) - i - 1\\r\\n \\r\\n #run simulation\\r\\n (Mi,Ei,l) = simulation(N[k],T[index],sweeps, lattice,\\\\\\r\\n dimensions = dimensions, J = J, prelims = prelims)\\r\\n \\r\\n #tabulate obtained data\\r\\n M[index] = np.abs(np.mean(Mi))\\r\\n E[index] = np.mean(Ei)\\r\\n Ci = (np.std(Ei)/T[index] * N[k] /2)**2\\r\\n C[index] = np.mean(Ci)\\r\\n \\r\\n #change lattice that will be fed to the next simulation\\r\\n if anneal:\\r\\n lattice = l \\r\\n \\r\\n #tabulate data for N(i)\\r\\n Ms.append(M)\\r\\n Es.append(E)\\r\\n Cs.append(C)\\r\\n \\r\\n labels.append(\\\"N = \\\"+str(N[k]))\\r\\n \\r\\n if plots:\\r\\n orderpar = \\\"Magnetisation\\\" if J>0 else \\\"Staggered Magnetisation\\\"\\r\\n \\r\\n #plot data\\r\\n magfigure = makeplot(T,Ms, labels, \\\"Temperature / $[J/k_B]$\\\", orderpar,\\\\\\r\\n plainlines=plainlines)\\r\\n \\r\\n magfigure.show()\\r\\n \\r\\n enfigure = makeplot(T,Es, labels, \\\"Temperature / $[J/k_B]$\\\", \\\"Energy per spin / $[J]$\\\",\\\\\\r\\n plainlines=plainlines)\\r\\n enfigure.show()\\r\\n \\r\\n cfigure = makeplot(T, Cs, labels, \\\"Temperature / $[J/k_B]$\\\", \\\"Heat Capacity / $[k_B]$\\\",\\\\\\r\\n plainlines=plainlines)\\r\\n cfigure.show()\\r\\n \\r\\n #save plots\\r\\n if filename is not None:\\r\\n magfigure.savefig(filename+\\\".svg\\\")\\r\\n enfigure.savefig(filename+\\\"E.svg\\\")\\r\\n cfigure.savefig(filename+\\\"C.svg\\\")\\r\\n \\r\\n return(T, Ms, Cs)\",\n \"def imdisplay(filename, representation):\\n img = read_image(filename, representation)\\n if representation == GS_REP:\\n plt.imshow(img, cmap=plt.cm.gray)\\n else:\\n plt.imshow(img)\",\n \"def render_configs(task):\\n\\n # run function to render global configs\\n global_cfg = ibns_global(task)\\n # write global config file for each host\\n with open(f\\\"configs/{task.host}_dot1x_global.txt\\\", \\\"w+\\\") as file:\\n file.write(global_cfg)\\n # print completed hosts\\n c_print(f\\\"*** {task.host}: dot1x global configuration rendered ***\\\")\\n\\n # run function to render snmp configs\\n snmp_cfg = ibns_snmp(task)\\n # function to run interface configs\\n with open(f\\\"configs/{task.host}_snmp.txt\\\", \\\"w+\\\") as file:\\n file.write(snmp_cfg)\\n # print completed hosts\\n c_print(f\\\"*** {task.host}: SNMP configuration rendered ***\\\")\\n\\n # run function to run interface configs\\n intf_cfg = ibns_intf(task)\\n # write interface config file for each host\\n with open(f\\\"configs/{task.host}_dot1x_intf.txt\\\", \\\"w+\\\") as file:\\n file.write(intf_cfg)\\n # print completed hosts\\n c_print(f\\\"*** {task.host}: dot1x intf configurations rendered ***\\\")\",\n \"def generate_cfg():\\n \\n if not os.path.exists(cfg_path):\\n os.mkdir(cfg_path)\\n \\n for img_path in get_template_paths():\\n extractor = BlockExtractor(img_path)\\n extractor.get_cfg()\\n for block in extractor.get_blocks():\\n img = BlockParser(img_path, block).block_image()\\n #cv.imshow(\\\"Block\\\", img)\\n #cv.waitKey() & 0xFF\",\n \"def render(filename,i):\\n print('running render')\\n A = np.genfromtxt(filename,skip_header=1,dtype=float,delimiter=',')\\n img = np.array(A[i,:],copy=True)\\n print(img.shape)\\n img = img.reshape(28,28)\\n img = 255 - img\\n print(img.shape)\\n plt.imshow(img, cmap=\\\"gray\\\", vmin=0, vmax=255)\\n plt.savefig(\\\"img\\\" + str(i)+\\\"render\\\"+ \\\".png\\\")\",\n \"def _run():\\n\\n temperatures_kelvins = _create_temperature_grid()\\n first_derivs_kelvins_pt01 = numpy.gradient(temperatures_kelvins)\\n second_derivs_kelvins_pt01 = numpy.gradient(\\n numpy.absolute(first_derivs_kelvins_pt01)\\n )\\n\\n this_ratio = (\\n numpy.max(temperatures_kelvins) /\\n numpy.max(first_derivs_kelvins_pt01)\\n )\\n\\n first_derivs_unitless = first_derivs_kelvins_pt01 * this_ratio\\n\\n this_ratio = (\\n numpy.max(temperatures_kelvins) /\\n numpy.max(second_derivs_kelvins_pt01)\\n )\\n\\n second_derivs_unitless = second_derivs_kelvins_pt01 * this_ratio\\n\\n _, axes_object = pyplot.subplots(\\n 1, 1, figsize=(FIGURE_WIDTH_INCHES, FIGURE_HEIGHT_INCHES)\\n )\\n\\n temperature_handle = axes_object.plot(\\n temperatures_kelvins, color=TEMPERATURE_COLOUR, linestyle='solid',\\n linewidth=SOLID_LINE_WIDTH\\n )[0]\\n\\n second_deriv_handle = axes_object.plot(\\n second_derivs_unitless, color=SECOND_DERIV_COLOUR, linestyle='solid',\\n linewidth=SOLID_LINE_WIDTH\\n )[0]\\n\\n first_deriv_handle = axes_object.plot(\\n first_derivs_unitless, color=FIRST_DERIV_COLOUR, linestyle='dashed',\\n linewidth=DASHED_LINE_WIDTH\\n )[0]\\n\\n this_min_index = numpy.argmin(second_derivs_unitless)\\n second_derivs_unitless[\\n (this_min_index - 10):(this_min_index + 10)\\n ] = second_derivs_unitless[this_min_index]\\n\\n tfp_handle = axes_object.plot(\\n -1 * second_derivs_unitless, color=TFP_COLOUR, linestyle='dashed',\\n linewidth=DASHED_LINE_WIDTH\\n )[0]\\n\\n axes_object.set_yticks([0])\\n axes_object.set_xticks([], [])\\n\\n x_label_string = r'$x$-coordinate (increasing to the right)'\\n axes_object.set_xlabel(x_label_string)\\n\\n legend_handles = [\\n temperature_handle, first_deriv_handle, second_deriv_handle,\\n tfp_handle\\n ]\\n\\n legend_strings = [\\n TEMPERATURE_LEGEND_STRING, FIRST_DERIV_LEGEND_STRING,\\n SECOND_DERIV_LEGEND_STRING, TFP_LEGEND_STRING\\n ]\\n\\n axes_object.legend(legend_handles, legend_strings, loc='lower right')\\n\\n print 'Saving figure to file: \\\"{0:s}\\\"...'.format(OUTPUT_FILE_NAME)\\n pyplot.savefig(OUTPUT_FILE_NAME, dpi=FIGURE_RESOLUTION_DPI)\\n pyplot.close()\",\n \"def main(config_file, pre_nfiles=None, post_nfiles=None,\\n cutoffs=[12.0, 60.0]):\\n imu_complement_fig = (config_file[:-4] +\\n \\\"_IMU_complementary_spectra.pdf\\\")\\n imu_spectra_fig = (config_file[:-4] + \\\"_IMU_spectra.pdf\\\")\\n nperseg1 = 60 * 5 * 10\\n nperseg2 = 60 * 10\\n fig, axs, leg = check.plot_IMU_euler_angles_spectra(config_file,\\n nperseg=nperseg2,\\n nfiles=pre_nfiles)\\n fig.savefig(imu_complement_fig, bbox_extra_artists=(leg,),\\n bbox_inches=\\\"tight\\\")\\n plt.close(fig)\\n fig_rate, _, fig_acc, _ = check.plot_IMU_spectra(config_file,\\n nperseg=nperseg2,\\n nfiles=pre_nfiles)\\n with PdfPages(imu_spectra_fig) as pdf:\\n pdf.savefig(fig_rate, bbox_inches=\\\"tight\\\")\\n plt.close(fig_rate)\\n pdf.savefig(fig_acc, bbox_inches=\\\"tight\\\")\\n plt.close(fig_acc)\\n\\n # Look at individual periods\\n config = db_flux.parse_config(config_file)\\n input_files = config[\\\"EC Inputs\\\"][\\\"input_files\\\"]\\n if post_nfiles is not None and post_nfiles < len(input_files):\\n input_files = np.random.choice(input_files, post_nfiles,\\n replace=False)\\n imu2anem_pos = config[\\\"EC Motion Correction\\\"][\\\"imu2anemometer_pos\\\"]\\n sample_freq_hz = config[\\\"EC Inputs\\\"][\\\"sample_frequency\\\"]\\n Tcf, Ta = cutoffs\\n for ifile in input_files:\\n file_idx = ifile.index(ifile)\\n ec_prep, _ = db_flux.prepare_period(ifile, config)\\n wind = ec_prep[[\\\"wind_speed_u\\\", \\\"wind_speed_v\\\", \\\"wind_speed_w\\\"]]\\n acceleration = ec_prep[[\\\"acceleration_x\\\",\\n \\\"acceleration_y\\\",\\n \\\"acceleration_z\\\"]]\\n rate = ec_prep[[\\\"rate_x\\\", \\\"rate_y\\\", \\\"rate_z\\\"]]\\n ec_corr = db_flux.wind3D_correct(wind.values, acceleration.values,\\n rate.values,\\n ec_prep.heading.values,\\n ec_prep.speed_over_ground.values,\\n imu2anem_pos, sample_freq_hz,\\n Tcf, Ta)\\n fig, _, leg = check.plot1_IMU_complementary_spectra(config_file,\\n file_idx,\\n Tcf=Tcf,\\n Ta=Ta,\\n nperseg=nperseg1)\\n fig.savefig((ifile[:-4] + \\\"_euler_angle_spectra.pdf\\\"),\\n bbox_extra_artists=(leg,), bbox_inches=\\\"tight\\\")\\n plt.close()\\n fig, axs, leg = check.plot_wind3D_spectra(wind.values,\\n ec_corr.uvw_ship,\\n nperseg=nperseg2)\\n fig.savefig((ifile[:-4] + \\\"_uvw_spectra.pdf\\\"),\\n bbox_extra_artists=(leg,), bbox_inches=\\\"tight\\\")\\n plt.close()\\n # Plot corrected wind cospectra\\n fig, _, leg = check.plot_wind3D_cospectra(wind.values,\\n ec_corr.uvw_ship,\\n nperseg=nperseg2)\\n fig.savefig((ifile[:-4] + \\\"_uvw_cospectra.pdf\\\"),\\n bbox_extra_artists=(leg,), bbox_inches=\\\"tight\\\")\\n plt.close()\",\n \"def plot_vis_test(plotfile,pdf_file):\\n\\t# First some parameters looked up from configfile---------------------------------\\n\\t\\n\\tgrbdir = runconf['l2file'][0:10]\\n\\tpre_tstart = runconf['bkg1start']\\n\\tpre_tend = runconf['bkg1end']\\n\\ttrigtime = runconf['trigtime']\\n\\tgrb_tstart = runconf['transtart']\\n\\tgrb_tend = runconf['tranend']\\n\\tpost_tstart = runconf['bkg2start']\\n\\tpost_tend = runconf['bkg2end']\\n\\tt_src = grb_tend - grb_tstart \\n\\tt_tot = (pre_tend-pre_tstart)+(post_tend-post_tstart)\\n\\tra_tran = runconf['ra']\\n\\tdec_tran = runconf['dec']\\n\\tlc_bin = runconf['lc_bin']\\n\\talpha = runconf['alpha']\\n\\tbeta = runconf['beta']\\n\\tE0 = runconf['E0']\\n\\tA = runconf['A']\\n\\tsim_scale = t_src\\n\\tpixbin = int(runconf['pixsize'])\\n\\tcomp_bin = int(runconf['comp_bin'])\\n\\ttyp = runconf['typ']\\n\\n\\t# Calling txy to calculate thetax thetay and the coordinates----------------------\\n\\t\\n\\tthetax,thetay,x,y,z,t = txy(runconf['mkffile'], trigtime, ra_tran, dec_tran)\\n\\t\\n\\t# Plot the 3d visualisation for the position of the transient---------------------\\n\\tplt.figure()\\n\\tfig = visualize_3d(grbdir,x,y,z, t, thetax, thetay, grbdir)\\t\\n\\tpdf_file.savefig(fig)\\n\\t\\n\\t# Plotting the lightcurves for the four quadrants---------------------------------\\n\\tfig = plt.figure()\\n\\tclean_file = fits.open(runconf['infile'])\\n\\tplt.title('Light curves for '+grbdir + \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n\\t\\n\\tquad0 = clean_file[1].data\\n\\tdata0,bin_edge = np.histogram(quad0['time'], bins=np.arange(quad0['time'][0],quad0['time'][-1],lc_bin))\\n\\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data0,label='Quad 0',lw=0.7)\\n quad1 = clean_file[2].data\\n\\tdata1,bin_edge = np.histogram(quad1['time'], bins=np.arange(quad1['time'][0],quad1['time'][-1],lc_bin))\\n\\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data1,label='Quad 1',lw=0.7) \\n\\tquad2 = clean_file[3].data\\n\\tdata2,bin_edge = np.histogram(quad2['time'], bins=np.arange(quad2['time'][0],quad2['time'][-1],lc_bin))\\n\\tplt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data2,label='Quad 2',lw=0.7)\\n quad3 = clean_file[4].data\\n data3,bin_edge = np.histogram(quad3['time'], bins=np.arange(quad3['time'][0],quad3['time'][-1],lc_bin))\\n plt.plot((bin_edge[:-1]+bin_edge[1:])/2.0,data3,label='Quad 3',lw=0.7)\\n\\tplt.axvspan(grb_tstart,grb_tend,color='blue',alpha=0.1,label='GRB')\\n\\tplt.axvspan(pre_tstart,pre_tend,color='orange',alpha=0.2)\\n\\tplt.axvspan(post_tstart,post_tend,color='orange',alpha=0.2,label='Background')\\n\\tplt.legend(prop={'size':6})\\n\\tplt.xlim(pre_tstart-100,post_tend+100)\\n\\tpdf_file.savefig(fig)\\n\\t\\n\\t# Calling the sim_dph--------------------------------------------------------------\\n\\t\\n\\tgrb_flat,bkgd_flat,grb_dph,bkgd_dph,t_src,t_total = data_bkgd_image(grbdir,pre_tstart,pre_tend,grb_tstart,grb_tend,post_tstart,post_tend)\\n\\n\\tsim_flat,sim_dph,badpix_mask,sim_err_dph = simulated_dph(grbdir,typ,t_src,alpha,beta,E0,A)\\n\\n\\tsrc_dph = grb_dph-bkgd_dph*t_src/t_tot\\n\\n print \\\"Total counts in simulated dph: \\\",(sim_dph).sum()\\n print \\\"Total counts after badpix mask is applied: \\\",(sim_dph*badpix_mask).sum()\\n\\tprint \\\"Excess counts in badpix masked src dph: \\\",(src_dph*badpix_mask).sum()\\n \\n\\t# Plotting the DPHs before badpix correction---------------------------------------\\n\\t\\n\\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\\n\\tplt.suptitle('DPHs before badpix correction for '+grbdir + \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n \\t# Sim\\n\\tim = ax3.imshow(sim_dph,interpolation='none')\\n\\tax3.set_title('Sim DPH',fontsize=8)\\n\\tax3.set_xlim(-1,128 - 0.5)\\n\\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax3.spines['left'].set_position(('data',-0.5))\\n\\tax3.set_yticklabels([])\\n\\tax3.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\\n\\t\\n\\t # Source \\n\\tim = ax4.imshow(src_dph,interpolation='none',vmin=0)\\n\\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\\n\\tax4.set_xlim(-1,128 -0.5)\\n\\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax4.spines['left'].set_position(('data',-0.5))\\n\\tax4.set_yticklabels([])\\n\\tax4.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\\n\\n \\t# Source + Background\\n\\tim = ax1.imshow(grb_dph,interpolation='none')\\n\\tax1.set_title('Src + Bkg DPH',fontsize=8)\\n\\tax1.set_xlim(-1,128 -0.5)\\n\\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax1.spines['left'].set_position(('data',-0.5))\\n\\tax1.set_yticklabels([])\\n\\tax1.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\\n\\n \\t# Background\\n\\tim = ax2.imshow(bkgd_dph*t_src/t_total,interpolation='none')\\n\\tax2.set_title('Bkg DPH',fontsize=8)\\n\\tax2.set_xlim(-1,128 -0.5)\\n\\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax2.spines['left'].set_position(('data',-0.5))\\n\\tax2.set_yticklabels([])\\n\\tax2.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\\n\\tf.set_size_inches([6.5,6.5])\\n\\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\\n\\t\\n\\t# Plotting the Badpix mask---------------------------------------------\\n\\n\\tfig = plt.figure()\\n\\tax = plt.subplot(111)\\n\\tplt.title('Badpix Mask for '+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n\\tim = ax.imshow(badpix_mask,interpolation='none')\\n\\tax.set_xlim(-9,128 -0.5)\\n\\tax.axvline(x=-5.,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax.spines['left'].set_position(('data',-0.5))\\n\\tax.xaxis.set_ticks(np.arange(0,128,16))\\n\\tax.yaxis.set_ticks(np.arange(0,128,16))\\n\\tfig.colorbar(im,ax=ax,fraction=0.046, pad=0.04)\\n\\t\\n\\tpdf_file.savefig(fig) # saves the current figure into a pdf_file page\\n\\n\\t# Plotting badpix masked graphs--------------------------------------------\\n\\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\\n\\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay))\\n \\t# Sim\\n\\tim = ax3.imshow(sim_dph*badpix_mask,interpolation='none')\\n\\tax3.set_title('Sim DPH',fontsize=8)\\n\\tax3.set_xlim(-1,128 -0.5)\\n\\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax3.spines['left'].set_position(('data',-0.5))\\n\\tax3.set_yticklabels([])\\n\\tax3.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\\n\\n\\t # Source \\n\\tim = ax4.imshow(src_dph*badpix_mask,interpolation='none',vmin=0)\\n\\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\\n\\tax4.set_xlim(-1,128 -0.5)\\n\\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax4.spines['left'].set_position(('data',-0.5))\\n\\tax4.set_yticklabels([])\\n\\tax4.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\\n\\n\\t # Source + Background\\n\\tim = ax1.imshow(grb_dph*badpix_mask,interpolation='none')\\n\\tax1.set_title('Src + Bkg DPH',fontsize=8)\\n\\tax1.set_xlim(-1,128 -0.5)\\n\\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax1.spines['left'].set_position(('data',-0.5))\\n\\tax1.set_yticklabels([])\\n\\tax1.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\\n\\t\\n\\t # Background\\n\\tim = ax2.imshow(bkgd_dph*badpix_mask*t_src/t_total,interpolation='none')\\n\\tax2.set_title('Bkg DPH',fontsize=8)\\n\\tax2.set_xlim(-1,128 -0.5)\\n\\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\tax2.spines['left'].set_position(('data',-0.5))\\n\\tax2.set_yticklabels([])\\n\\tax2.xaxis.set_ticks(np.arange(0,128,16))\\n\\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\\n\\tf.set_size_inches([6.5,6.5])\\n\\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\\n\\n\\t# Plotting badpix masked graphs (Binned) ----------------------------------------------------\\n\\tfor p in [4,8,16]:\\n\\t\\tf,((ax1,ax2),(ax3,ax4)) = plt.subplots(2,2)\\n\\t\\tplt.suptitle('DPHs after badpix correction for '+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} \\\".format(tx=thetax,ty=thetay)+ \\\"pixsize=\\\"+str(p))\\n\\t\\t # Sim\\n\\t\\tim = ax3.imshow(resample(sim_dph*badpix_mask,p),interpolation='none')\\n\\t\\tax3.set_title('Sim DPH',fontsize=8)\\n\\t\\tax3.set_xlim(-1,128/p -0.5)\\n\\t\\tax3.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax3.spines['left'].set_position(('data',-0.5))\\n\\t\\tax3.set_yticklabels([])\\n\\t\\tax3.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n\\t\\tax3.set_xticklabels(np.arange(0,128,16))\\n\\t\\tf.colorbar(im,ax=ax3,fraction=0.046, pad=0.04)\\n\\t\\t\\n\\t\\t # Source \\n\\t\\tim = ax4.imshow(resample(src_dph*badpix_mask,p),interpolation='none',vmin=0)\\n\\t\\tax4.set_title('Src DPH (bkg subtracted)',fontsize=8)\\n\\t\\tax4.set_xlim(-1,128/p -0.5)\\n\\t\\tax4.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax4.spines['left'].set_position(('data',-0.5))\\n\\t\\tax4.set_yticklabels([])\\n ax4.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n ax4.set_xticklabels(np.arange(0,128,16))\\t\\t\\n\\t\\tf.colorbar(im,ax=ax4,fraction=0.046, pad=0.04)\\n\\t\\t\\n\\t\\t # Source + Background\\n\\t\\tim = ax1.imshow(resample(grb_dph*badpix_mask,p),interpolation='none')\\n\\t\\tax1.set_title('Src + Bkg DPH',fontsize=10)\\n\\t\\tax1.set_xlim(-1,128/p -0.5)\\n\\t\\tax1.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax1.spines['left'].set_position(('data',-0.5))\\n\\t\\tax1.set_yticklabels([])\\n ax1.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n ax1.set_xticklabels(np.arange(0,128,16))\\t\\t\\n\\t\\tf.colorbar(im,ax=ax1,fraction=0.046, pad=0.04)\\n\\t\\t\\n\\t\\t # Background\\n\\t\\tim = ax2.imshow(resample(bkgd_dph*badpix_mask*t_src/t_total,p),interpolation='none')\\n\\t\\tax2.set_title('Bkg DPH',fontsize=8)\\n\\t\\tax2.set_xlim(-1,128/p -0.5)\\n\\t\\tax2.axvline(x=-0.75,ymin=0,ymax=64,linewidth=5,color='k')\\n\\t\\tax2.spines['left'].set_position(('data',-0.5))\\n\\t\\tax2.set_yticklabels([])\\n ax2.xaxis.set_ticks(np.arange(0,(128/p),16/p))\\n ax2.set_xticklabels(np.arange(0,128,16))\\t\\t\\n\\t\\tf.colorbar(im,ax=ax2,fraction=0.046, pad=0.04)\\n\\t\\tf.set_size_inches([6.5,6.5])\\n\\t\\t\\n\\t\\tpdf_file.savefig(f) # saves the current figure into a pdf_file page\\n\\n\\n\\t# Plotting the comparison graphs with equal bins ---------------------------------------\\n\\tprint \\\"No. of pixels with zero counts in sim_dph: \\\",sim_dph[sim_dph==0].size\\n\\tprint \\\"No. of pixels with zero counts in grb_dph(no bkg subtration): \\\",grb_dph[grb_dph==0].size\\n\\t\\n\\t# Generating the array for module number ------------------------------------------------\\n\\tA = ['A'+str(i) for i in range(16)]\\n\\tB = np.flip(['B'+str(i) for i in range(16)],0)\\n\\tC = np.flip(['C'+str(i) for i in range(16)],0)\\n\\tD = ['D'+str(i) for i in range(16)]\\n\\tquad_a = np.reshape(A,(4,4))\\n\\tquad_b = np.reshape(B,(4,4))\\n\\tquad_c = np.reshape(C,(4,4))\\n\\tquad_d = np.reshape(D,(4,4))\\n\\tMod_arr = np.ndarray((8,8),dtype='|S3')\\n\\tMod_arr[:4,:4] = quad_a\\n\\tMod_arr[:4,4:] = quad_b\\n\\tMod_arr[4:,4:] = quad_c\\n\\tMod_arr[4:,:4] = quad_d\\n\\tMod_names = Mod_arr.flatten()\\n\\t#print \\\"Module name array : \\\",Mod_names\\n\\t#-----------------------------------------------------------------------------------------\\n\\t\\t\\n\\tsim_dph = sim_dph*badpix_mask\\n\\tsim_err_dph = sim_err_dph*badpix_mask\\n grb_dph = grb_dph*badpix_mask\\n bkgd_dph = bkgd_dph*badpix_mask\\n\\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\\n\\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\\n\\n\\tsim_bin = resample(sim_dph,pixbin)\\n\\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\\t\\n\\tgrb_bin = resample(grb_dph,pixbin)\\n\\tbkgd_bin = resample(bkgd_dph,pixbin)\\n\\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\\t\\n\\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\\t\\n\\n\\tsim_flat_bin = sim_bin.flatten()\\n\\tsim_err_flat_bin = sim_err_bin.flatten()\\n\\tgrb_flat_bin = grb_bin.flatten()\\n\\tbkgd_flat_bin = bkgd_bin.flatten()\\n\\tgrb_err_flat_bin = grb_err_bin.flatten()\\n\\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\\n\\t\\n\\n\\t # Defining model background and data\\n\\tmodel = sim_flat_bin\\n\\tmodel_copy = np.copy(model)\\n\\tbkgd = bkgd_flat_bin*t_src/t_tot\\n\\tsrc = grb_flat_bin\\n\\t\\n\\tdata = src - bkgd\\n\\tdata_copy = np.copy(data)\\n\\t\\n\\terr_src = grb_err_flat_bin\\n\\terr_bkgd = bkgd_err_flat_bin\\n\\terr_model = sim_err_flat_bin\\n\\terr_model_copy = np.copy(err_model)\\n\\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\terr_data_copy = np.copy(err_data)\\n\\t\\n\\tratio = data/model\\n\\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\\n\\t\\n\\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n\\t\\n\\t # PLotting the comparison plots\\n\\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\\n\\t\\n\\tax1.set_title(\\\"Comparison between simulated and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f}\\\".format(tx=thetax,ty=thetay,c=chi_sq))\\n\\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n\\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation\\\",elinewidth=0.5)\\n\\tax1.legend()\\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\\n\\tax1.set_ylabel('Counts')\\n\\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n\\n\\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\\\"Ratio = Data/Model\\\",elinewidth=0.5)\\n\\tax2.xaxis.set_ticks(np.arange(0,len(data)))\\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\\n\\tax2.tick_params(labelsize=5)\\n\\tax2.axhline(y=1,linewidth=0.5,color='k')\\n\\tax2.legend()\\n\\tax2.set_xlabel('CZT Modules')\\n\\tax2.set_ylabel('Ratio of counts')\\n\\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\\n\\tplt.tight_layout(h_pad=0.0)\\n\\tf.set_size_inches([6.5,10])\\n\\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\\n\\n\\t# Plotting comparison graphs with random binning------------------------------\\n\\t\\n sim_flat = sim_dph.flatten()\\n\\tsim_err_flat = sim_err_dph.flatten()\\n grb_flat = grb_dph.flatten()\\n bkgd_flat = bkgd_dph.flatten()\\n\\tsrc_flat = src_dph.flatten()\\n\\t\\n\\torder = np.random.permutation(np.arange(0,len(sim_flat)))\\n\\t\\n sim_flat = sim_flat[order]\\n\\tsim_err_flat = sim_err_flat[order]\\n\\tgrb_flat = grb_flat[order]\\n\\tbkgd_flat = bkgd_flat[order]\\n\\tsrc_flat = src_flat[order]\\n\\t\\n\\tprint \\\"No. of pixels with zero counts in sim_flat: \\\",sim_flat[sim_flat==0].size\\n\\tprint \\\"No. of pixels with zero counts in src_flat: \\\",src_flat[src_flat==0].size\\n\\t\\n\\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\\n\\tx = np.zeros(len(bins)+2,dtype=np.int64)\\n\\tx[0] = 0\\n\\tx[-1] = len(sim_flat)\\n\\tx[1:-1] = bins\\n\\t\\n\\t#print \\\"The bin edges: \\\",x # ---------------------------------------------------------------\\n\\t\\n\\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\\n\\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\t\\n\\tprint \\\"Total sim_flat_bin : \\\",sim_flat_bin.sum() #-----------------------------------------\\n\\t#print \\\" Max(cumsum) : \\\",max(np.cumsum(sim_flat)) #-----------------------------------------\\n\\n # Defining model background and data\\n model = sim_flat_bin #avg_flat_bin\\n bkgd = bkgd_flat_bin*t_src/t_tot\\n src = grb_flat_bin\\n\\t\\n data = src - bkgd\\n\\n err_src = np.sqrt(src)\\n err_bkgd = np.sqrt(bkgd_flat_bin)\\n err_model = sim_err_flat_bin\\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\t\\n\\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n # PLotting the comparison plots\\n fig = plt.figure()\\n plt.title(\\\"Comparison between simulated and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f} \\\".format(tx=thetax,ty=thetay,c=chi_sq_new))\\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation\\\",elinewidth=0.5)\\n plt.ylabel('Counts')\\n\\tplt.xlabel('Random Bins')\\n\\tplt.xticks(np.arange(0,(len(data)),1))\\n\\tplt.legend()\\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\\n\\n\\t# Plotting observed vs predicted counts------------------------------------------------------\\n\\n\\tfig = plt.figure()\\n\\tplt.title(grbdir + r\\\" : Observed vs Predicted counts with $\\\\chi^2$={cs:0.1f}\\\".format(cs=chi_sq))\\n\\tplt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\\n\\tfor i in range(len(model_copy)):\\n\\t\\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\\n\\tplt.plot(np.arange(-1000,1000),np.arange(-1000,1000),'k',linewidth=0.5)\\n\\tplt.xlim(min(model_copy)-5,max(model_copy)+5)\\n\\tplt.ylim(min(data_copy)-5,max(data_copy)+5)\\n\\tplt.xlabel('Predicted Counts')\\n\\tplt.ylabel('Observed Counts')\\n\\tplt.legend()\\n\\tplt.grid()\\n\\tpdf_file.savefig(fig)\\n\\n\\t# Scaling the model using curve fit =============================================================== \\n\\t\\n\\tparam,pcov = curve_fit(fit_line_int,model_copy,data_copy)\\n\\tscaling = param[0]\\n\\tintercept = param[1]\\n\\t\\n\\t# Plotting the scaled plots ===================================================================\\n\\t# Plotting the comparison graphs with equal bins ---------------------------------------\\n\\n\\tsim_dph = sim_dph*badpix_mask\\n\\tsim_err_dph = sim_err_dph*badpix_mask\\n grb_dph = grb_dph*badpix_mask\\n bkgd_dph = bkgd_dph*badpix_mask\\n\\tgrb_err_dph = np.sqrt(grb_dph)*badpix_mask\\n\\tbkgd_err_dph = np.sqrt(bkgd_dph)*badpix_mask\\n\\n\\tsim_bin = resample(sim_dph,pixbin)\\n\\tsim_err_bin = np.sqrt(resample(sim_err_dph**2,pixbin))\\t\\n\\tgrb_bin = resample(grb_dph,pixbin)\\n\\tbkgd_bin = resample(bkgd_dph,pixbin)\\n\\tgrb_err_bin = np.sqrt(resample(grb_err_dph,pixbin))\\t\\n\\tbkgd_err_bin = np.sqrt(resample(bkgd_err_dph,pixbin))\\t\\n\\n\\tsim_flat_bin = sim_bin.flatten()\\n\\tsim_err_flat_bin = sim_err_bin.flatten()\\n\\tgrb_flat_bin = grb_bin.flatten()\\n\\tbkgd_flat_bin = bkgd_bin.flatten()\\n\\tgrb_err_flat_bin = grb_err_bin.flatten()\\n\\tbkgd_err_flat_bin = bkgd_err_bin.flatten()\\n\\t\\n\\n\\t # Defining model background and data\\n\\t#model = sim_flat_bin*scaling\\n\\tmodel = sim_flat_bin*scaling + intercept\\n\\tbkgd = bkgd_flat_bin*t_src/t_tot\\n\\tsrc = grb_flat_bin\\n\\t\\n\\tdata = src - bkgd\\n\\t\\n\\terr_src = grb_err_flat_bin\\n\\terr_bkgd = bkgd_err_flat_bin\\n\\t#err_model = sim_err_flat_bin*scaling\\n\\terr_model = sim_err_flat_bin*scaling\\n\\terr_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\t\\n\\tratio = data/model\\n\\terr_ratio = ratio*np.sqrt(((err_data/data)**2) + ((err_model/model)**2))\\n\\t\\n\\tchi_sq = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n\\t\\n\\t # PLotting the comparison plots\\n\\tf,(ax1,ax2) = plt.subplots(2,gridspec_kw={'height_ratios':[2,1]},sharex='row')\\n\\t\\n\\tax1.set_title(\\\"Comparison between simulated (scaled) and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f} \\\".format(tx=thetax,ty=thetay,c=chi_sq))\\n\\tax1.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n\\tax1.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation (scaling = {s:0.2f},offset = {o:0.2f})\\\".format(s=scaling,o=intercept),elinewidth=0.5)\\n\\tax1.legend()\\n ax1.xaxis.set_ticks(np.arange(0,len(data)))\\n\\tax1.set_ylabel('Counts')\\n\\tax1.xaxis.grid(linewidth=0.5,alpha=0.3)\\n ax1.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n\\t\\n\\tax2.errorbar(np.arange(0,(len(ratio))),ratio,yerr=err_ratio,fmt='.',markersize=2,label=\\\"Ratio = Data/Model(scaling = {s:0.2f}, offset={o:0.2f})\\\".format(s=scaling,o=intercept),elinewidth=0.5)\\n\\tax2.xaxis.set_ticks(np.arange(0,len(data)))\\n ax2.set_xticklabels(Mod_names,rotation=90,fontsize=5)\\n ax2.yaxis.set_ticks(np.arange(int(min(ratio-err_ratio)-1),int(max(ratio+err_ratio)+2),1))\\n\\tax2.tick_params(labelsize=5)\\n\\tax2.axhline(y=1,linewidth=0.5,color='k')\\n\\tax2.legend()\\n\\tax2.set_xlabel('CZT Modules')\\n\\tax2.set_ylabel('Ratio of counts')\\n\\tax2.xaxis.grid(linewidth=0.5,alpha=0.3)\\n\\tplt.tight_layout(h_pad=0.0)\\n\\tf.set_size_inches([6.5,10])\\n\\tpdf_file.savefig(f,orientation='portrait') # saves the current figure into a pdf_file page\\n\\n\\t# Plotting comparison graphs with random binning------------------------------\\n\\t\\n sim_flat = sim_dph.flatten()\\n\\tsim_err_flat = sim_err_dph.flatten()\\n grb_flat = grb_dph.flatten()\\n bkgd_flat = bkgd_dph.flatten()\\n\\tsrc_flat = src_dph.flatten()\\n\\t\\n\\torder = np.random.permutation(np.arange(0,len(sim_flat)))\\n\\t\\n sim_flat = sim_flat[order]\\n\\tsim_err_flat = sim_err_flat[order]\\n\\tgrb_flat = grb_flat[order]\\n\\tbkgd_flat = bkgd_flat[order]\\n\\tsrc_flat = src_flat[order]\\n\\t\\n\\tbins = np.array(np.sort(np.random.uniform(0,1,comp_bin)*len(sim_flat)),dtype=np.int64)\\n\\tx = np.zeros(len(bins)+2,dtype=np.int64)\\n\\tx[0] = 0\\n\\tx[-1] = len(sim_flat)\\n\\tx[1:-1] = bins\\n\\t\\n\\tsim_flat_bin = np.array([sim_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tsim_err_flat_bin = np.sqrt(np.array([(sim_err_flat[x[i]:x[i+1]]**2).sum() for i in range(comp_bin+1)]))\\n\\tgrb_flat_bin = np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tgrb_err_flat_bin = np.sqrt(np.array([grb_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tbkgd_flat_bin = np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\tbkgd_err_flat_bin = np.sqrt(np.array([bkgd_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)]))\\n\\tsrc_flat_bin = np.array([src_flat[x[i]:x[i+1]].sum() for i in range(comp_bin+1)])\\n\\t\\n # Defining model background and data\\n #model = sim_flat_bin*scaling\\n\\tmodel = sim_flat_bin*scaling + intercept\\n bkgd = bkgd_flat_bin*t_src/t_tot\\n src = grb_flat_bin\\n\\t\\n data = src - bkgd\\n\\n err_src = np.sqrt(src)\\n err_bkgd = np.sqrt(bkgd_flat_bin)\\n #err_model = sim_err_flat_bin*scaling\\n\\terr_model = sim_err_flat_bin*scaling\\n err_data = np.sqrt(((err_src)**2) + ((err_bkgd)**2)*(t_src/t_total)**2)\\n\\t\\n\\tchi_sq_new = (((model-data)**2)/((err_model)**2 + (err_data)**2)).sum()\\n # PLotting the comparison plots\\n fig = plt.figure()\\n plt.title(\\\"Comparison between simulated(scaled) and real data for \\\"+grbdir+ \\\"\\\\n\\\" + r\\\"$\\\\theta_x$={tx:0.1f} and $\\\\theta_y$={ty:0.1f} $\\\\chi^2$={c:0.1f} \\\".format(tx=thetax,ty=thetay,c=chi_sq_new))\\n plt.errorbar(np.arange(0,(len(data))),data,yerr=err_data,fmt='.',markersize=2,label=\\\"Data\\\",elinewidth=0.5)\\n plt.errorbar(np.arange(0,(len(model))),model,yerr=err_model,fmt='.',markersize=2,label=\\\"Simulation (scaling = {s:0.2f}, offset = {o:0.2f})\\\".format(s=scaling,o=intercept),elinewidth=0.5)\\n plt.ylabel('Counts')\\n\\tplt.xlabel('Random Bins')\\n\\tplt.xticks(np.arange(0,(len(data)),1))\\n\\tplt.legend()\\n pdf_file.savefig(fig) #saves the current figure into a pdf_file page\\n\\n\\n\\t# Plotting observed vs predicted counts--------------------------------------------------------\\n\\n\\tfig = plt.figure()\\n plt.title(grbdir + r\\\" : Observed vs Predicted counts with $\\\\chi^2$ = {cs:0.1f}\\\".format(cs=chi_sq))\\n plt.errorbar(model_copy,data_copy,xerr=err_model_copy,yerr=err_data_copy,fmt='g.',markersize=2,elinewidth=0.5)\\n\\tfor i in range(len(model_copy)):\\t\\n\\t\\tplt.text(model_copy[i],data_copy[i],Mod_names[i],fontsize=5)\\n #plt.plot(np.arange(-1000,1000),fit_line(np.arange(-1000,1000),scaling),'k',linewidth=0.5,label='m = {s:0.2f}'.format(s=scaling))\\n\\tplt.plot(np.arange(-1000,1000),fit_line_int(np.arange(-1000,1000),scaling,intercept),'k',linewidth=0.5,label='scaling = {s:0.2f}, offset = {i:0.2f}'.format(s=scaling,i=intercept))\\n\\tplt.plot(np.arange(min(model_copy)-5,max(model_copy)+5),np.ones(len(np.arange(min(model_copy)-5,max(model_copy)+5)))*intercept,'r-',label='intercept',linewidth=0.5)\\n plt.xlim(min(model_copy)-5,max(model_copy)+5)\\n plt.ylim(min(data_copy)-5,max(data_copy)+5)\\n plt.xlabel('Predicted Counts')\\n plt.ylabel('Observed Counts')\\n\\tplt.legend()\\n\\tplt.grid()\\n pdf_file.savefig(fig)\\n\\t\\t\\n\\tprint \\\"===============================================================================================\\\"\\n\\t\\n\\treturn\",\n \"def show_template_bundles(final_streamlines, template_path, fname):\\n import nibabel as nib\\n from fury import actor, window\\n\\n renderer = window.Renderer()\\n template_img_data = nib.load(template_path).get_data().astype(\\\"bool\\\")\\n template_actor = actor.contour_from_roi(\\n template_img_data, color=(50, 50, 50), opacity=0.05\\n )\\n renderer.add(template_actor)\\n lines_actor = actor.streamtube(\\n final_streamlines, window.colors.orange, linewidth=0.3\\n )\\n renderer.add(lines_actor)\\n window.record(renderer, n_frames=1, out_path=fname, size=(900, 900))\\n return\",\n \"def main(argv=None):\\n parser = argparse.ArgumentParser(\\\"Test TFL attention mechanism\\\")\\n parser.add_argument('-i', '--image', type=str, help='Path to an image')\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", type=str, help=\\\"Path to json GT for comparison\\\")\\n parser.add_argument('-d', '--dir', type=str, help='Directory to scan images in')\\n args = parser.parse_args(argv)\\n default_base = '../data'\\n if args.dir is None:\\n args.dir = default_base\\n flist = glob.glob(os.path.join(args.dir, '*_leftImg8bit.png'))\\n for image in flist:\\n json_fn = image.replace('_leftImg8bit.png', '_gtFine_polygons.json')\\n if not os.path.exists(json_fn):\\n json_fn = None\\n test_find_tfl_lights(image, json_fn)\\n if len(flist):\\n print(\\\"You should now see some images, with the ground truth marked on them. Close all to quit.\\\")\\n else:\\n print(\\\"Bad configuration?? Didn't find any picture to show\\\")\\n plt.show(block=True)\",\n \"def main(argv=None):\\n parser = argparse.ArgumentParser(\\\"Test TFL attention mechanism\\\")\\n parser.add_argument('-i', '--image', type=str, help='Path to an image')\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", type=str, help=\\\"Path to json GT for comparison\\\")\\n parser.add_argument('-d', '--dir', type=str, help='Directory to scan images in')\\n args = parser.parse_args(argv)\\n default_base = '../data'\\n if args.dir is None:\\n args.dir = default_base\\n flist = glob.glob(os.path.join(args.dir, '*_leftImg8bit.png'))\\n for image in flist:\\n json_fn = image.replace('_leftImg8bit.png', '_gtFine_polygons.json')\\n if not os.path.exists(json_fn):\\n json_fn = None\\n test_find_tfl_lights(image, json_fn)\\n if len(flist):\\n print(\\\"You should now see some images, with the ground truth marked on them. Close all to quit.\\\")\\n else:\\n print(\\\"Bad configuration?? Didn't find any picture to show\\\")\\n plt.show(block=True)\",\n \"def configureScenario(out, templatepath, geometrypath, nx, ny, nz, sx, sy, sz,\\n vtkpath, enable_timing, timingpath, enable_vtk, initial,\\n bakspath):\\n tree = ET.parse(templatepath)\\n root = tree.getroot()\\n\\n for child in root.findall(\\\"parallel\\\"):\\n child.attrib[\\\"numProcessorsX\\\"] = str(nx)\\n child.attrib[\\\"numProcessorsY\\\"] = str(ny)\\n child.attrib[\\\"numProcessorsZ\\\"] = str(nz)\\n\\n for child in root.findall(\\\"vtk\\\"):\\n child.attrib[\\\"enabled\\\"] = str(enable_vtk).lower()\\n child.text = os.path.join(vtkpath, \\\"vtk\\\")\\n\\n for child in root.findall(\\\"timing\\\"):\\n child.attrib[\\\"enabled\\\"] = str(enable_timing).lower()\\n child.text = timingpath\\n else:\\n node = ET.Element(\\\"timing\\\", {\\\"enabled\\\": str(enable_timing).lower()})\\n node.text = timingpath\\n root.append(node)\\n\\n if sx != None and sy != None:\\n for child in root.findall(\\\"geometry\\\"):\\n child.attrib[\\\"sizeX\\\"] = str(sx)\\n child.attrib[\\\"sizeY\\\"] = str(sy)\\n child.attrib[\\\"sizeZ\\\"] = str(sz)\\n\\n for child in root.findall(\\\"geometry\\\"):\\n attrs = child.attrib\\n\\n if \\\"obstacle\\\" in attrs and os.path.isfile(attrs[\\\"obstacle\\\"]):\\n shutil.copyfile(attrs[\\\"obstacle\\\"], geometrypath)\\n child.attrib[\\\"obstacle\\\"] = geometrypath\\n\\n for child in root.findall(\\\"restart\\\"):\\n attrs = child.attrib\\n\\n if \\\"in\\\" in attrs and os.path.isfile(attrs[\\\"in\\\"] + \\\".bak\\\"):\\n shutil.copyfile(attrs[\\\"in\\\"] + \\\".bak\\\", initial + \\\".bak\\\")\\n child.attrib[\\\"in\\\"] = initial\\n\\n if \\\"out\\\" in attrs:\\n child.attrib[\\\"out\\\"] = bakspath + \\\"//\\\" + child.attrib[\\\"out\\\"]\\n\\n tree.write(out)\",\n \"def prepare_templates(params, outfile, redo=False):\\n if os.path.exists(outfile) and not redo:\\n return\\n emiles = EMILES()\\n wmin = params[\\\"wmin\\\"] * u.micrometer\\n wmax = params[\\\"wmax\\\"] * u.micrometer\\n # Modify wmin to compensate for the recession velocity of the system\\n zmax = (params[\\\"vsyst\\\"] + 3000) / const.c.to(\\\"km/s\\\").value\\n wrest = wmin / (1 + zmax)\\n grid = np.array(np.meshgrid(params[\\\"ages\\\"], params[\\\"metals\\\"],\\n params[\\\"bis\\\"])).T.reshape(-1, 3)\\n ssppars = Table(grid, names=[\\\"T\\\", \\\"Z\\\", \\\"imf\\\"])\\n filenames = []\\n for args in grid:\\n filenames.append(os.path.join(emiles.data_dir,\\n emiles.filename(*args)))\\n wave, spec = misc.read_spec(filenames[0])\\n wave = wave * u.angstrom\\n idx = np.where((wave > wrest) & (wave <= wmax))\\n wave = wave[idx]\\n spec = spec[idx]\\n wrange = [wave[0].to(\\\"angstrom\\\").value, wave[-1].to(\\\"angstrom\\\").value]\\n newflux, logLam, velscale = util.log_rebin(wrange, spec,\\n velscale=params[\\\"velscale\\\"])\\n ssps = np.zeros((len(filenames), len(newflux)))\\n print(\\\"Processing SSP files\\\")\\n for i, fname in tqdm(enumerate(filenames)):\\n spec = fits.getdata(fname)[idx]\\n newflux, logLam, velscale = util.log_rebin(wrange, spec,\\n velscale=params[\\\"velscale\\\"])\\n ssps[i] = newflux\\n norm = np.median(ssps)\\n ssps /= norm\\n hdu1 = fits.PrimaryHDU(ssps)\\n hdu1.header[\\\"EXTNAME\\\"] = \\\"SSPS\\\"\\n hdu1.header[\\\"BSCALE\\\"] = (norm, \\\"Scale to convert from ADU to flux.\\\")\\n hdu2 = fits.BinTableHDU(ssppars)\\n hdu2.header[\\\"EXTNAME\\\"] = \\\"PARAMS\\\"\\n hdu1.header[\\\"CRVAL1\\\"] = logLam[0]\\n hdu1.header[\\\"CD1_1\\\"] = logLam[1] - logLam[0]\\n hdu1.header[\\\"CRPIX1\\\"] = 1.\\n # Making wavelength array\\n hdu3 = fits.BinTableHDU(Table([logLam], names=[\\\"loglam\\\"]))\\n hdu3.header[\\\"EXTNAME\\\"] = \\\"LOGLAM\\\"\\n hdulist = fits.HDUList([hdu1, hdu2, hdu3])\\n hdulist.writeto(outfile, overwrite=True)\\n return\",\n \"def display_file(epd, file_name):\\n\\n image = Image.open(file_name)\\n image = ImageOps.grayscale(image)\\n\\n # crop to the middle\\n w,h = image.size\\n x = w / 2 - epd.width / 2\\n y = h / 2 - epd.height / 2\\n\\n cropped = image.crop((x, y, x + epd.width, y + epd.height))\\n bw = cropped.convert(\\\"1\\\", dither=Image.FLOYDSTEINBERG)\\n\\n epd.display(bw)\\n epd.update()\\n\\n\\n time.sleep(3) # delay in seconds\\n\\n rs = image.resize((epd.width, epd.height))\\n bw = rs.convert(\\\"1\\\", dither=Image.FLOYDSTEINBERG)\\n\\n epd.display(bw)\\n epd.update()\\n\\n time.sleep(3) # delay in seconds\",\n \"def display_character(window, name, path_template):\\n # Could be improved a lot.\\n border_size = 20\\n path = \\\".\\\".join((path_template, \\\"200\\\", \\\"png\\\"))\\n pic = pygame.image.load(path)\\n pic_w, pic_h = pic.get_size()\\n text = ft_title.render(\\\" \\\".join((\\\"<-\\\", name, \\\"->\\\")), 1, WHITE)\\n text_w, text_h = text.get_size()\\n pygame.draw.rect(window, GREY, (SCREEN_W/2 - pic_w/2 - border_size,\\n SCREEN_H/2 - pic_h/2 - text_h - border_size,\\n pic_w + border_size*2, pic_h + border_size*2),\\n border_size)\\n window.blit(pic, (SCREEN_W/2 - pic_w/2, SCREEN_H/2 - pic_h/2 - text_h))\\n window.blit(text, (SCREEN_W/2 - text_w/2, SCREEN_H/2 + pic_h/2 - text_h/2))\",\n \"def load_templates(fwhm=400, line_complexes=True, stars=False,\\n full_line_list=None, continuum_list=None,\\n fsps_templates=False, alf_template=False):\\n \\n if stars:\\n # templates = glob.glob('%s/templates/Pickles_stars/ext/*dat' %(os.getenv('GRIZLI')))\\n # templates = []\\n # for t in 'obafgkmrw':\\n # templates.extend( glob.glob('%s/templates/Pickles_stars/ext/uk%s*dat' %(os.getenv('THREEDHST'), t)))\\n # templates.extend(glob.glob('%s/templates/SPEX/spex-prism-M*txt' %(os.getenv('THREEDHST'))))\\n # templates.extend(glob.glob('%s/templates/SPEX/spex-prism-[LT]*txt' %(os.getenv('THREEDHST'))))\\n # \\n # #templates = glob.glob('/Users/brammer/Downloads/templates/spex*txt')\\n # templates = glob.glob('bpgs/*ascii')\\n # info = catIO.Table('bpgs/bpgs.info')\\n # type = np.array([t[:2] for t in info['type']])\\n # templates = []\\n # for t in 'OBAFGKM':\\n # test = type == '-%s' %(t)\\n # so = np.argsort(info['type'][test])\\n # templates.extend(info['file'][test][so])\\n # \\n # temp_list = OrderedDict()\\n # for temp in templates:\\n # #data = np.loadtxt('bpgs/'+temp, unpack=True)\\n # data = np.loadtxt(temp, unpack=True)\\n # #data[0] *= 1.e4 # spex\\n # scl = np.interp(5500., data[0], data[1])\\n # name = os.path.basename(temp)\\n # #ix = info['file'] == temp\\n # #name='%5s %s' %(info['type'][ix][0][1:], temp.split('.as')[0])\\n # print(name)\\n # temp_list[name] = utils.SpectrumTemplate(wave=data[0],\\n # flux=data[1]/scl)\\n \\n # np.save('stars_bpgs.npy', [temp_list])\\n \\n \\n # tall = np.load(os.path.join(os.getenv('GRIZLI'), \\n # 'templates/stars.npy'))[0]\\n # \\n # return tall\\n # \\n # temp_list = OrderedDict()\\n # for k in tall:\\n # if k.startswith('uk'):\\n # temp_list[k] = tall[k]\\n # \\n # return temp_list\\n # \\n # for t in 'MLT':\\n # for k in tall:\\n # if k.startswith('spex-prism-'+t):\\n # temp_list[k] = tall[k]\\n # \\n # return temp_list\\n \\n #return temp_list\\n templates = ['M6.5.txt', 'M8.0.txt', 'L1.0.txt', 'L3.5.txt', 'L6.0.txt', 'T2.0.txt', 'T6.0.txt', 'T7.5.txt']\\n templates = ['stars/'+t for t in templates]\\n else:\\n ## Intermediate and very old\\n # templates = ['templates/EAZY_v1.0_lines/eazy_v1.0_sed3_nolines.dat', \\n # 'templates/cvd12_t11_solar_Chabrier.extend.skip10.dat'] \\n templates = ['eazy_intermediate.dat', \\n 'cvd12_t11_solar_Chabrier.dat']\\n \\n ## Post starburst\\n #templates.append('templates/UltraVISTA/eazy_v1.1_sed9.dat')\\n templates.append('post_starburst.dat')\\n \\n ## Very blue continuum\\n #templates.append('templates/YoungSB/erb2010_continuum.dat')\\n templates.append('erb2010_continuum.dat')\\n \\n ### Test new templates\\n # templates = ['templates/erb2010_continuum.dat',\\n # 'templates/fsps/tweak_fsps_temp_kc13_12_006.dat',\\n # 'templates/fsps/tweak_fsps_temp_kc13_12_008.dat']\\n \\n if fsps_templates:\\n #templates = ['templates/fsps/tweak_fsps_temp_kc13_12_0{0:02d}.dat'.format(i+1) for i in range(12)]\\n templates = ['fsps/fsps_QSF_12_v3_nolines_0{0:02d}.dat'.format(i+1) for i in range(12)]\\n #templates = ['fsps/fsps_QSF_7_v3_nolines_0{0:02d}.dat'.format(i+1) for i in range(7)]\\n \\n \\n if alf_template:\\n templates.append('alf_SSP.dat')\\n \\n if continuum_list is not None:\\n templates = continuum_list\\n \\n temp_list = OrderedDict()\\n for temp in templates:\\n data = np.loadtxt(os.path.join(os.getenv('GRIZLI'), 'templates', temp), unpack=True)\\n #scl = np.interp(5500., data[0], data[1])\\n scl = 1.\\n name = temp #os.path.basename(temp)\\n temp_list[name] = SpectrumTemplate(wave=data[0], flux=data[1]/scl,\\n name=name)\\n \\n temp_list[name].name = name\\n \\n if stars:\\n return temp_list\\n \\n ### Emission lines:\\n line_wavelengths, line_ratios = get_line_wavelengths()\\n \\n if line_complexes:\\n #line_list = ['Ha+SII', 'OIII+Hb+Ha', 'OII']\\n #line_list = ['Ha+SII', 'OIII+Hb', 'OII']\\n line_list = ['Ha+NII+SII+SIII+He', 'OIII+Hb', 'OII+Ne', 'Lya+CIV']\\n else:\\n if full_line_list is None:\\n line_list = DEFAULT_LINE_LIST\\n else:\\n line_list = full_line_list\\n \\n #line_list = ['Ha', 'SII']\\n \\n # Use FSPS grid for lines\\n wave_grid = None\\n # if fsps_templates:\\n # wave_grid = data[0]\\n # else:\\n # wave_grid = None \\n \\n for li in line_list:\\n scl = line_ratios[li]/np.sum(line_ratios[li])\\n for i in range(len(scl)):\\n line_i = SpectrumTemplate(wave=wave_grid, \\n central_wave=line_wavelengths[li][i], \\n flux=None, fwhm=fwhm, velocity=True)\\n \\n if i == 0:\\n line_temp = line_i*scl[i]\\n else:\\n line_temp = line_temp + line_i*scl[i]\\n \\n name = 'line {0}'.format(li)\\n line_temp.name = name\\n temp_list[name] = line_temp\\n \\n return temp_list\",\n \"def main(argv):\\n scanSpeed = 1.\\n iList = -1\\n opt1 = \\\"\\\"\\n intMode = False\\n if (len(argv) >= 1): opt1 = argv[0]\\n if \\\"-i\\\" in (opt1): intMode = True\\n\\n # Set input data and cuts\\n inputFile = TFile(\\\"~/project/match-skim/waveletSkimDS5_run23920.root\\\") # calibration\\n # inputFile = TFile(\\\"~/project/v2-processwfs/waveletSkimDS5_90.root\\\") # noisy BG\\n waveTree = inputFile.Get(\\\"skimTree\\\")\\n theCut = inputFile.Get(\\\"cutUsedHere\\\").GetTitle()\\n theCut += \\\" && waveS5/trapENFCal < 1200 && trapENFCal > 1.5 && trapENFCal < 3\\\"\\n\\n # Print cut and events passing cut\\n waveTree.Draw(\\\">>elist\\\", theCut, \\\"entrylist\\\")\\n elist = gDirectory.Get(\\\"elist\\\")\\n waveTree.SetEntryList(elist)\\n nList = elist.GetN()\\n print \\\"Using cut:\\\\n\\\",theCut,\\\"\\\\n\\\"\\n print \\\"Found\\\",waveTree.GetEntries(),\\\"input entries.\\\"\\n print \\\"Found\\\",nList,\\\"entries passing cuts.\\\"\\n\\n # Make a figure\\n fig = plt.figure(figsize=(15,10), facecolor='w')\\n p1 = plt.subplot2grid((6,7), (0,0), colspan=4, rowspan=2) # original\\n p2 = plt.subplot2grid((6,7), (2,0), colspan=4, rowspan=3) # rising edge\\n p3 = plt.subplot2grid((6,7), (5,0), colspan=4, rowspan=1) # residual\\n p4 = plt.subplot2grid((6,7), (0,4), colspan=3, rowspan=2) # trace 1\\n p5 = plt.subplot2grid((6,7), (2,4), colspan=3, rowspan=2) # trace 2\\n p6 = plt.subplot2grid((6,7), (4,4), colspan=3, rowspan=2) # trace 3\\n plt.show(block=False)\\n\\n # Make template(s)\\n # npzfile = np.load(\\\"./data/genTemplateWF.npz\\\") # gen-template.py\\n # temp, tempTS, tempE, tempST = npzfile['arr_0']+1, npzfile['arr_1'], npzfile['arr_2'], npzfile['arr_3']*10\\n\\n samp, r, z, tempE, tempST, smooth = 2016, 0, 15, 3938, 1000, 100 # gen-template.py\\n # samp, r, z, tempE, tempST, smooth = 5000, 0, 15, 10, 2500, 100 # huge template\\n # samp, r, z, tempE, tempST, smooth = 2016, 0, 15, 10, 1000, 100 # regular size template\\n # samp, r, z, tempE, tempST, smooth = 2016, 30, 30, 10, 1000, 100 # regular size temp, slower rise\\n # samp, r, z, tempE, tempST, smooth = 500, 0, 15, 10, 100, 100 # small template\\n temp, tempTS = wl.MakeSiggenWaveform(samp,r,z,tempE,tempST,smooth)\\n tempST = tempST * 10 # convert to ns\\n\\n\\n # Loop over events\\n while True:\\n saveMe = False\\n iList += 1\\n if intMode==True and iList != 0:\\n value = raw_input()\\n if value=='q': break\\n if value=='s': saveMe=True\\n if value=='p': iList -= 2 # previous\\n if (value.isdigit()): iList = int(value) # go to entry\\n if iList >= elist.GetN(): break\\n\\n entry = waveTree.GetEntryNumber(iList);\\n waveTree.LoadTree(entry)\\n waveTree.GetEntry(entry)\\n nChans = waveTree.channel.size()\\n numPass = waveTree.Draw(\\\"channel\\\",theCut,\\\"GOFF\\\",1,iList)\\n chans = waveTree.GetV1()\\n chanList = list(set(int(chans[n]) for n in xrange(numPass)))\\n\\n # Loop over hits passing cuts\\n hitList = (iH for iH in xrange(nChans) if waveTree.channel.at(iH) in chanList) # a 'generator expression'\\n for iH in hitList:\\n\\n # ------------------------------------------------------------------------\\n\\n # Load waveform for this hit\\n run = waveTree.run\\n chan = waveTree.channel.at(iH)\\n dataE = waveTree.trapENFCal.at(iH)\\n dataST = waveTree.butterTime.at(iH) # replace with blrwfFMR50?\\n toe = waveTree.kvorrT.at(iH)/dataE\\n print \\\"%d / %d Run %d nCh %d chan %d trapENF %.1f t/e %.1f\\\" % (iList,nList,run,nChans,chan,dataE,toe)\\n signal = wl.processWaveform(waveTree.MGTWaveforms.at(iH),opt='full')\\n waveBLSub = signal.GetWaveBLSub()\\n waveFilt = signal.GetWaveFilt()\\n waveTS = signal.GetTS()\\n baseAvg, dataNoise = signal.GetBaseNoise()\\n\\n # Denoise the data waveform (take only lowest-frequency components)\\n wp = pywt.WaveletPacket(data=waveBLSub, wavelet='haar', mode='symmetric',maxlevel=3)\\n new_wp = pywt.WaveletPacket(data=None, wavelet='haar', mode='symmetric')\\n new_wp['aaa'] = wp['aaa'].data\\n waveDenoised = new_wp.reconstruct(update=False)\\n\\n # Window the fit around rising edge - start time calculator method\\n loWin, hiWin = dataST - 1000, dataST + 4000 # ns\\n if loWin < waveTS[0] or hiWin > waveTS[-1]:\\n print \\\"Window out of range! dataST: %.1f loWin %.1f hiWin %.1f\\\" % (dataST,loWin,hiWin)\\n idx = np.where((waveTS >= loWin) & (waveTS <= hiWin))\\n data = waveBLSub[idx]\\n # data = waveDenoised[idx]\\n dataTS = waveTS[idx]\\n\\n # Pack into lists\\n rawList = [waveBLSub, waveTS, dataE, dataST]\\n dataList = [data, dataTS, dataE, dataST, loWin, hiWin, dataNoise]\\n tempList = [temp, tempTS, tempE, tempST]\\n\\n # Optionally save something to a file\\n if saveMe: np.savez(\\\"./data/tailSlopeInputs.npz\\\",rawList,tempList)\\n\\n # Recreate the guess and the guess's rising edge\\n guessFull, guessFullTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\\\"full\\\")\\n guess, guessTS = wm.MakeModel(dataList, tempList, [dataST,dataE,1.], opt=\\\"!fancy\\\")\\n\\n # Make an \\\"almost complete\\\" guess - no fitting\\n # st, en, slo = dataST-100, dataE, 5\\n InterpFn = interpolate.interp1d(tempTS, temp, kind=\\\"linear\\\", copy=\\\"False\\\", assume_sorted=\\\"True\\\")\\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\\n\\n # Fit with MCMC and get best-fit parameters\\n # numSteps, burnIn = 10000, 5000 # default - 10000, 5000. try 3000, 2000. long test: 20000,10000\\n # myModel = TemplateModel( dataList, dataNoise, tempList )\\n # waveModel = pymc.Model( myModel )\\n # M = pymc.MCMC( waveModel )\\n # M.use_step_method(pymc.Metropolis, M.startTime, proposal_sd=100., proposal_distribution='Normal')\\n # M.use_step_method(pymc.Metropolis, M.energy, proposal_sd=1., proposal_distribution='Normal')\\n # M.use_step_method(pymc.Metropolis, M.slowness, proposal_sd=100., proposal_distribution='Normal')\\n # M.sample(iter=numSteps, verbose=0) # do the fit\\n # st = np.median(M.trace('startTime')[burnIn:])\\n # en = np.median(M.trace('energy')[burnIn:])\\n # slo = np.median(M.trace('slowness')[burnIn:])\\n # InterpFn = interpolate.interp1d(tempTS, temp, kind=\\\"linear\\\", copy=\\\"False\\\", assume_sorted=\\\"True\\\")\\n # model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\\n\\n\\n # Fit with SciPy minimizer(s) and get best-fit parameters\\n\\n # how long does it take?\\n start = time.time()\\n\\n MakeTracesGlobal() # creates 3 global arrays: startTrace, enTrace, sloTrace\\n datas = [dataList, tempList, InterpFn]\\n\\n # floats = [1.]\\n # result = op.basinhopping(findLnLike, floats, T=1000, stepsize=15, niter_success=2, minimizer_kwargs={\\\"args\\\":datas})\\n # print \\\"slowness:\\\",result[\\\"x\\\"],\\\"dataE\\\",dataE,\\\"dataST\\\",dataST\\n # floats = [dataST, dataE, result[\\\"x\\\"]]\\n # MakeTracesGlobal() # resets traces\\n\\n floats = [dataST, dataE, 20.]\\n result = op.minimize(findLnLike, floats, args=datas, method=\\\"Nelder-Mead\\\")\\n if not result[\\\"success\\\"]: print result[\\\"message\\\"]\\n st, en, slo = result[\\\"x\\\"]\\n\\n model, modelTS = wm.MakeModel(dataList, tempList, [st,en,slo], fn=InterpFn)\\n\\n stop = time.time()\\n print \\\"fitting took\\\",stop-start\\n\\n\\n\\n # Calculate residual, Chi2/NDF, likelihood, etc.\\n residual = model - data\\n frac = (np.power(data - model, 2)) / np.abs(model)\\n chi2NDF = np.sum(frac) / len(model)\\n inv_sigma2 = 1.0/(dataNoise**2)\\n lnLike = -0.5*(np.sum((data - model)**2*inv_sigma2 - np.log(inv_sigma2)))\\n\\n # Fill the figure\\n p1.cla()\\n p1.set_ylabel(\\\"ADC\\\")\\n p1.set_title(\\\"Run %d Channel %d Entry %d\\\\ntrapENFCal %.1f T/E %.1f ST %.1f\\\" % (run,chan,iList,dataE,toe,dataST))\\n p1.plot(waveTS,waveBLSub,color='blue')\\n p1.plot(waveTS,waveDenoised,color='red',alpha=0.8)\\n p1.plot(guessFullTS,guessFull,color='orange',linewidth=2)\\n p1.axvline(x = dataST, color='green',linewidth=2)\\n p1.axvline(x = loWin, color='black')\\n p1.axvline(x = hiWin, color='black')\\n\\n p2.cla()\\n p2.plot(dataTS,data,color='blue',label='Data')\\n p2.plot(guessTS,guess,color='orange',label='Guess')\\n p2.plot(modelTS,model,color='red',linewidth=3,label='Best Fit')\\n p2.legend(loc=4)\\n\\n p3.cla()\\n p3.set_xlabel(\\\"Time [ns]\\\", x=0.95, ha='right')\\n p3.set_ylabel(\\\"Residual [ADC]\\\")\\n p3.plot(guessTS,residual, color='red')\\n p3.axhline(y = 0, color='blue', alpha=0.3)\\n p3.axhline(y = dataNoise, color='blue', alpha=0.3)\\n p3.axhline(y = -1.0*dataNoise, color='blue', alpha=0.3)\\n\\n p4.cla()\\n p4.set_title(\\\"startTime %.1f Energy %.2f Slow %.1f\\\" % (st,en,slo))\\n\\n p4.plot(stTrace[1:])\\n p4.set_ylabel('startTime')\\n\\n p5.cla()\\n p5.plot(enTrace[1:])\\n p5.set_ylabel('energy')\\n\\n p6.cla()\\n p6.plot(sloTrace[1:])\\n p6.set_ylabel('slowness')\\n\\n plt.tight_layout()\\n plt.subplots_adjust(hspace=0.35)\\n plt.pause(scanSpeed)\\n\\n # ------------------------------------------------------------------------\",\n \"def imdisplay(filename, representation):\\n image = read_image(filename, representation)\\n\\n if representation == GRAY_OUT:\\n plt.imshow(image, cmap='gray')\\n else:\\n plt.imshow(image)\\n\\n plt.show()\",\n \"def exercise():\\n\\n #\\n # Convert Lena Tiff image to raw format\\n #\\n for f in glob.glob('*.jpg'):\\n os.remove(f)\\n \\n for f in glob.glob('*.dat'):\\n os.remove(f)\\n \\n input_raw_file = convert_to_raw('Lena.tiff')\\n\\n for device in ['cpu', 'gpu']:\\n for interp in ['nn', 'bl']:\\n for (w,h) in ((256, 300), (486, 486),(2000, 1000),(1000, 2000),(8000, 4000)):\\n (t, f) = interpolate(input_raw_file, device + '_' + interp + '_lena.dat', device, 0, interp, w, h)\\n convert_to_jpg(f)\\n\\n \\n for f in glob.glob('*.dat'):\\n convert_to_jpg(f)\\n os.remove(f)\\n \\n quit()\",\n \"def plot_bit_for_bit(case, var_name, model_data, bench_data, diff_data):\\n plot_title = \\\"\\\"\\n plot_name = case + \\\"_\\\" + var_name + \\\".png\\\"\\n plot_path = os.path.join(os.path.join(livvkit.output_dir, \\\"verification\\\", \\\"imgs\\\"))\\n functions.mkdir_p(plot_path)\\n m_ndim = np.ndim(model_data)\\n b_ndim = np.ndim(bench_data)\\n if m_ndim != b_ndim:\\n return \\\"Dataset dimensions didn't match!\\\"\\n if m_ndim == 3:\\n model_data = model_data[-1]\\n bench_data = bench_data[-1]\\n diff_data = diff_data[-1]\\n plot_title = \\\"Showing \\\"+var_name+\\\"[-1,:,:]\\\"\\n elif m_ndim == 4:\\n model_data = model_data[-1][0]\\n bench_data = bench_data[-1][0]\\n diff_data = diff_data[-1][0]\\n plot_title = \\\"Showing \\\"+var_name+\\\"[-1,0,:,:]\\\"\\n plt.figure(figsize=(12, 3), dpi=80)\\n plt.clf()\\n\\n # Calculate min and max to scale the colorbars\\n _max = np.amax([np.amax(model_data), np.amax(bench_data)])\\n _min = np.amin([np.amin(model_data), np.amin(bench_data)])\\n\\n # Plot the model output\\n plt.subplot(1, 3, 1)\\n plt.xlabel(\\\"Model Data\\\")\\n plt.ylabel(var_name)\\n plt.xticks([])\\n plt.yticks([])\\n plt.imshow(model_data, vmin=_min, vmax=_max, interpolation='nearest', cmap=colormaps.viridis)\\n plt.colorbar()\\n\\n # Plot the benchmark data\\n plt.subplot(1, 3, 2)\\n plt.xlabel(\\\"Benchmark Data\\\")\\n plt.xticks([])\\n plt.yticks([])\\n plt.imshow(bench_data, vmin=_min, vmax=_max, interpolation='nearest', cmap=colormaps.viridis)\\n plt.colorbar()\\n\\n # Plot the difference\\n plt.subplot(1, 3, 3)\\n plt.xlabel(\\\"Difference\\\")\\n plt.xticks([])\\n plt.yticks([])\\n plt.imshow(diff_data, interpolation='nearest', cmap=colormaps.viridis)\\n plt.colorbar()\\n\\n plt.tight_layout(rect=(0, 0, 0.95, 0.9))\\n plt.suptitle(plot_title)\\n\\n plot_file = os.path.sep.join([plot_path, plot_name])\\n if livvkit.publish:\\n plt.savefig(os.path.splitext(plot_file)[0]+'.eps', dpi=600)\\n plt.savefig(plot_file)\\n plt.close()\\n return os.path.join(os.path.relpath(plot_path,\\n os.path.join(livvkit.output_dir, \\\"verification\\\")),\\n plot_name)\",\n \"def Interpret(self, **kwargs):\\n # visit file\\n self.options[\\\"VISIT_FILE\\\"] = self.visit_file\\n # particle file\\n particle_file = os.path.join(self.output_dir, 'particles.visit')\\n if os.access(particle_file, os.R_OK):\\n self.options[\\\"VISIT_PARTICLE_FILE\\\"] = particle_file\\n # directory to output data\\n self.options[\\\"DATA_OUTPUT_DIR\\\"] = self.output_dir\\n # directory to output images\\n if not os.path.isdir(self.img_dir):\\n os.mkdir(self.img_dir)\\n self.options[\\\"IMG_OUTPUT_DIR\\\"] = self.img_dir\\n # own implementations\\n # initial adaptive refinement\\n self.options['INITIAL_ADAPTIVE_REFINEMENT'] = self.idict['Mesh refinement'].get('Initial adaptive refinement', '6')\\n # get snaps for plots\\n graphical_snaps, _, _ = GetSnapsSteps(self.case_dir, 'graphical')\\n self.options['ALL_AVAILABLE_GRAPHICAL_SNAPSHOTS'] = str(graphical_snaps)\\n # plot slab \\n self.options['IF_PLOT_SLAB'] = 'True'\\n last_step = graphical_snaps[-1] - int(self.options['INITIAL_ADAPTIVE_REFINEMENT'])\\n last_step = kwargs.get('last_step', None)\\n if type(last_step) == int:\\n self.options['PLOT_SLAB_STEPS'] = [i for i in range(last_step - last_step + 1, last_step + 1)]\\n else:\\n self.options['PLOT_SLAB_STEPS'] = [0, 1, 2, 3, 4, 5, 6, 7]\\n # self.options['IF_DEFORM_MECHANISM'] = value.get('deform_mechanism', 0)\\n self.options['IF_DEFORM_MECHANISM'] = 1\",\n \"def simulate_UVSPEC(file, config):\\n\\n wavelength = config['wavelength']\\n\\n # Coordenates from position of the station Hannover\\n latitude = 52.39 # positive in the northern hemisphere\\n longitud = 9.7 # negative reckoning west from prime meridian in Greenwich,\\n\\n # Read name of the file (correct time)\\n name = os.path.split(file)\\n time_n = name[1][0:15]\\n print(\\\"Time name\\\", time_n)\\n # convert time to datetime format\\n time = datetime.datetime.strptime(time_n,\\n '%Y%m%d_%H%M%S')\\n # Calculate the azimuth and zenith angles in function of the date\\n elev = ps.GetAltitude(latitude, longitud, time)\\n azi = ps.GetAzimuth(latitude, longitud, time)\\n zenith = 90 - elev\\n\\n # Correction between the sign and real azimuth for plot of radiance\\n if -180 <= azi < 0:\\n azi = 180 - azi\\n elif -360 <= azi < -180:\\n azi = -azi - 180\\n else:\\n pass\\n\\n print(\\\"Azimuth: {:5.1f}\\\".format(azi),\\n \\\"\\\\nZenith: {:5.1f}\\\".format(zenith))\\n\\n # Change the value of zenith and azimuth angles in function of the time and\\n # position in the UVSPEC file\\n\\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'r') as file:\\n data = file.readlines()\\n\\n data[14] = \\\"day_of_year \\\" + str(time.timetuple().tm_yday) + \\\" \\\" + \\\"\\\\n\\\"\\n data[15] = \\\"wavelength \\\" + str(\\\"{}\\\".format(wavelength)) + \\\" \\\" + \\\\\\n str(\\\"{}\\\".format(wavelength)) + \\\\\\n \\\" # wavelength to calcule [nm] \\\\n\\\"\\n data[17] = \\\"sza \\\" + str(\\\"{:2.3f}\\\".format(zenith)) + \\\\\\n \\\" # Solar zenith angle \\\\n\\\"\\n data[18] = \\\"phi0 \\\" + str(\\\"{:2.3f}\\\".format(azi)) + \\\\\\n \\\" #Azimuth angle with zenith position \\\\n\\\"\\n\\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'w') as file:\\n file.writelines(data)\\n\\n # Create the directory to save the results\\n os.makedirs(os.path.dirname(config['str_dir'] + '/simulation/' + '{}/{}nm/txt_files/'.format(time_n[0:8],\\n wavelength)),\\n exist_ok=True)\\n\\n # Run the program UVSPEC in the terminal\\n os.system(config['UVSPEC_path'] + 'uvspec < ' + config['personal_libraries'] +\\n 'MUDIS_HDF5/MUDIS_radiance_Input.txt> ' + config['str_dir'] + '/simulation/' +\\n '{}/{}nm/txt_files/'.format(time_n[0:8], wavelength) + time_n +\\n '.txt')\",\n \"def run(self):\\n # read (x,y) - Set SIFT parameters\\n action = self.cfg['param']['action']\\n x = float(self.cfg['param']['x']) \\n # Expressed en PIL coordinates system y| x-\\n y = float(self.cfg['param']['y'])\\n \\n # read image size and store in 'param' dict to control html rendering\\n work_dir = self.work_dir\\n [w1, h1] = Image.open(work_dir+'input_0.orig.png').size\\n [w2, h2] = Image.open(work_dir+'input_1.orig.png').size\\n wdth = max(w1, w2)\\n hght = max(h1, h2)\\n wpair = int(w1+w2+max(w1, w2)/10)\\n self.cfg['param']['hght'] = hght\\n self.cfg['param']['wdth'] = wdth\\n self.cfg.save()\\n # Convert x y provided by the form <input type=image ..; > \\n # We assume that the width of the html body is assumed width is 920px\\n x = x*wpair/920\\n y = y*wpair/920\\n self.cfg['param']['x'] = x\\n self.cfg['param']['y'] = y\\n self.cfg.save()\\n if (action == 'std_sift_matching'):\\n try:\\n self.load_standard_parameters()\\n self.run_std_sift()\\n self.run_matching()\\n self.illustrate_std_sift()\\n self.illustrate_matching()\\n except TimeoutError:\\n return self.error(errcode='timeout',\\n errmsg=\\\"Try again with simpler images.\\\")\\n except RuntimeError:\\n return self.error(errcode='runtime',\\n errmsg=\\\"Runtime error in std_sift_matching.\\\")\\n elif (action == \\\"cust_sift_matching\\\"):\\n try:\\n self.run_sift()\\n print \\\"after run_sift()\\\"\\n self.run_matching()\\n print \\\"after run_matching()\\\"\\n self.illustrate_sift()\\n print \\\"after illustrate_sift()\\\"\\n self.illustrate_matching()\\n self.detail_matching()\\n except TimeoutError:\\n return self.error(errcode='timeout',\\n errmsg=\\\"Try with simpler images.\\\")\\n except RuntimeError:\\n return self.error(errcode='runtime',\\n errmsg=\\\"Runtime error in cust_sift_matching.\\\")\\n elif (action == \\\"cust_matching\\\"): \\n try:\\n self.run_matching()\\n self.illustrate_matching()\\n except TimeoutError:\\n return self.error(errcode='timeout',\\n errmsg=\\\"Try with simpler images.\\\")\\n except RuntimeError:\\n return self.error(errcode='runtime',\\n errmsg=\\\"Runtime error in cust_matching.\\\")\\n else:\\n try:\\n self.detail_matching()\\n except TimeoutError:\\n return self.error(errcode='timeout',\\n errmsg=\\\"Try with simpler images.\\\")\\n except RuntimeError:\\n return self.error(errcode='runtime',\\n errmsg=\\\"Runtime error in else (you know).\\\")\\n ## archive\\n if self.cfg['meta']['original']:\\n ar = self.make_archive()\\n ar.add_file(\\\"input_0.png\\\", info=\\\"first input image\\\")\\n ar.add_file(\\\"input_1.png\\\", info=\\\"second input image\\\")\\n ar.add_file(\\\"input_0.orig.png\\\", info=\\\"first uploaded image\\\")\\n ar.add_file(\\\"input_1.orig.png\\\", info=\\\"second uploaded image\\\")\\n ar.add_file(\\\"OUTmatches.png\\\", info=\\\"matches\\\")\\n ar.add_file(\\\"keys_im0.txt\\\", compress=True)\\n ar.add_file(\\\"keys_im1.txt\\\", compress=True)\\n ar.add_file(\\\"OUTmatches.txt\\\", compress=True)\\n ar.save()\\n\\n self.cfg.save()\\n http.redir_303(self.base_url + 'result?key=%s' % self.key)\\n return self.tmpl_out(\\\"run.html\\\")\",\n \"def render_configuration_file(self, conf_template_string):\\n environment = self.params['environment']\\n code_root = self.params['code_root']\\n\\n reject = self.get_rejected_pillow_types(code_root, environment)\\n\\n configs = []\\n all_pillows = self.get_pillows_from_settings(settings.PILLOWTOPS, reject)\\n for full_name in all_pillows:\\n pillow_name = full_name.split('.')[-1]\\n pillow_params = {\\n 'pillow_name': pillow_name,\\n 'pillow_option': ' --pillow-name %s' % pillow_name\\n }\\n pillow_params.update(self.params)\\n pillow_rendering = conf_template_string % pillow_params\\n configs.append(pillow_rendering)\\n return '\\\\n\\\\n'.join(configs)\",\n \"def render(self):\\r\\n super().render()\\r\\n layers, titles, latVect, lonVect = self.make_layers()\\r\\n LON, LAT = np.meshgrid(lonVect, latVect)\\r\\n lon = LON.flatten()\\r\\n lat = LAT.flatten()\\r\\n for i in range(len(layers)):\\r\\n vals = layers[i].flatten()\\r\\n hovertext = []\\r\\n for k in range(len(vals)):\\r\\n hovertext.append('lon: {:.2f}<br>lat: {:.2f}<br>{}: {:.1e}'.format(lon[k], lat[k], self.variable + self.unit,vals[k]))\\r\\n if self.levels == 0:\\r\\n data = [\\r\\n go.Heatmap(\\r\\n x=lon,\\r\\n y=lat,\\r\\n z=vals,\\r\\n colorscale=self.cmap,\\r\\n zmin=self.vmin,\\r\\n zmax=self.vmax,\\r\\n hoverinfo='text',\\r\\n text=hovertext \\r\\n )\\r\\n ]\\r\\n elif self.levels > 0:\\r\\n data = [\\r\\n go.Contour(\\r\\n x=lon,\\r\\n y=lat,\\r\\n z=vals,\\r\\n colorscale=self.cmap,\\r\\n hoverinfo='text',\\r\\n text=hovertext, \\r\\n connectgaps=False,\\r\\n contours=dict(\\r\\n coloring='heatmap',\\r\\n showlabels=True,\\r\\n start=self.vmin,\\r\\n end=self.vmax,\\r\\n size=(self.vmax-self.vmin) / float(self.levels)\\r\\n )\\r\\n # line=dict(smoothing=0.85) \\r\\n )\\r\\n ] \\r\\n\\r\\n\\r\\n layout = go.Layout(\\r\\n autosize=False,\\r\\n title=titles[i],\\r\\n width=self.width,\\r\\n height=self.height,\\r\\n xaxis={'title': self.xlabel},\\r\\n yaxis={'title': self.ylabel}\\r\\n ) \\r\\n\\r\\n\\r\\n\\r\\n if self.surface3D:\\r\\n data = [\\r\\n go.Surface(\\r\\n x=lonVect,\\r\\n y=latVect,\\r\\n z=layers[i],\\r\\n colorscale=self.cmap,\\r\\n # hoverinfo='text',\\r\\n # text=hovertext \\r\\n )\\r\\n ]\\r\\n\\r\\n layout = go.Layout(\\r\\n autosize=False,\\r\\n title=titles[i],\\r\\n width=self.width,\\r\\n height=self.height,\\r\\n scene = dict(\\r\\n xaxis={'title': self.xlabel},\\r\\n yaxis={'title': self.ylabel},\\r\\n zaxis={'title': self.variable + self.unit}\\r\\n )\\r\\n ) \\r\\n\\r\\n\\r\\n self._save_plotly_(go, data, layout)\",\n \"def processTemplates(self, tk, templateFile = '', id = '', shotNum = '', inprogressBar = ''):\\r\\n ## Now fetch all the template paths from shotgun\\r\\n getTemplatePaths = tk.paths_from_template(templateFile, {'Step' : 'Light', 'id' : id, 'Shot' : shotNum})\\r\\n debug(app = self, method = 'processTemplates', message = 'getTemplatePaths: %s' % getTemplatePaths, verbose = False)\\r\\n \\r\\n ## Now look for each assets template path: \\r\\n xmlFile = max(getTemplatePaths) \\r\\n debug(app = self, method = 'processTemplates', message = 'Max Version xmlFile.... %s' % xmlFile, verbose = False)\\r\\n \\r\\n ## Now if versions has stuff in it..\\r\\n if not xmlFile:\\r\\n debug(app = self, method = 'processTemplates', message = 'Can not find any xml files for %s' % shotNum, verbose = False)\\r\\n pass\\r\\n else:\\r\\n \\r\\n debug(app = self, method = 'processTemplates', message = 'PathTo: %s' % os.path.isfile(xmlFile.replace(os.path.sep, \\\"/\\\")), verbose = False)\\r\\n if os.path.isfile(xmlFile.replace(os.path.sep, \\\"/\\\")):## is this a valid xml file!?\\r\\n inprogressBar.updateProgress(percent = 10, doingWhat = 'createAll shaders...')\\r\\n self._createAllShaders(XMLPath = xmlFile.replace(os.path.sep, \\\"/\\\"), Namespace = '', Root = 'MaterialNodes')\\r\\n \\r\\n inprogressBar.updateProgress(percent = 30, doingWhat = 'connectAll shaders...')\\r\\n self._connectAllShaders(XMLPath = xmlFile.replace(os.path.sep, \\\"/\\\"), Namespace = '', Root = 'MaterialNodes')\\r\\n else:\\r\\n debug(app = self, method = 'processTemplates', message = 'FAILED Can not find a valid published xml file for %s ...' % os.path.isfile(xmlFile.replace(os.path.sep, \\\"/\\\")), verbose = False)\\r\\n pass\",\n \"def test_rhostats_config():\\n if __name__ == '__main__':\\n logger = piff.config.setup_logger(verbose=2)\\n else:\\n logger = piff.config.setup_logger(log_file='output/test_rhostats_config.log')\\n\\n image_file = os.path.join('output','test_stats_image.fits')\\n cat_file = os.path.join('output','test_stats_cat.fits')\\n psf_file = os.path.join('output','test_rhostats.fits')\\n rho_file = os.path.join('output','test_rhostats.pdf')\\n config = {\\n 'input' : {\\n 'image_file_name' : image_file,\\n 'cat_file_name' : cat_file,\\n 'stamp_size' : 48\\n },\\n 'psf' : {\\n 'model' : { 'type' : 'Gaussian',\\n 'fastfit': True,\\n 'include_pixel': False },\\n 'interp' : { 'type' : 'Mean' },\\n },\\n 'output' : {\\n 'file_name' : psf_file,\\n 'stats' : { # Note: stats doesn't have to be a list.\\n 'type': 'Rho',\\n 'file_name': rho_file,\\n 'min_sep': 30,\\n 'max_sep': 600,\\n 'sep_units': 'arcsec',\\n 'bin_type': 'Linear',\\n 'bin_size': 30,\\n }\\n },\\n }\\n piff.piffify(config, logger)\\n assert os.path.isfile(rho_file)\\n\\n # repeat with plotify function\\n os.remove(rho_file)\\n piff.plotify(config, logger)\\n assert os.path.isfile(rho_file)\\n\\n # Test rho statistics directly.\\n min_sep = 1\\n max_sep = 100\\n bin_size = 0.1\\n psf = piff.read(psf_file)\\n orig_stars, wcs, pointing = piff.Input.process(config['input'], logger)\\n stats = piff.RhoStats(min_sep=min_sep, max_sep=max_sep, bin_size=bin_size)\\n with np.testing.assert_raises(RuntimeError):\\n stats.write('dummy') # Cannot write before compute\\n stats.compute(psf, orig_stars)\\n\\n rhos = [stats.rho1, stats.rho2, stats.rho3, stats.rho4, stats.rho5]\\n for rho in rhos:\\n # Test the range of separations\\n radius = np.exp(rho.logr)\\n np.testing.assert_array_less(radius, max_sep)\\n np.testing.assert_array_less(min_sep, radius)\\n # bin_size is reduced slightly to get integer number of bins\\n assert rho.bin_size < bin_size\\n assert np.isclose(rho.bin_size, bin_size, rtol=0.1)\\n np.testing.assert_array_almost_equal(np.diff(rho.logr), rho.bin_size, decimal=5)\\n\\n # Test that the max absolute value of each rho isn't crazy\\n np.testing.assert_array_less(np.abs(rho.xip), 1)\\n\\n # # Check that each rho isn't precisely zero. This means the sum of abs > 0\\n np.testing.assert_array_less(0, np.sum(np.abs(rho.xip)))\\n\\n # Test using the piffify executable\\n os.remove(rho_file)\\n config['verbose'] = 0\\n with open('rho.yaml','w') as f:\\n f.write(yaml.dump(config, default_flow_style=False))\\n piffify_exe = get_script_name('piffify')\\n p = subprocess.Popen( [piffify_exe, 'rho.yaml'] )\\n p.communicate()\\n assert os.path.isfile(rho_file)\\n\\n # Test using the plotify executable\\n os.remove(rho_file)\\n plotify_exe = get_script_name('plotify')\\n p = subprocess.Popen( [plotify_exe, 'rho.yaml'] )\\n p.communicate()\\n assert os.path.isfile(rho_file)\\n\\n # test running plotify with dir in config, with no logger, and with a modules specification.\\n # (all to improve test coverage)\\n config['output']['dir'] = '.'\\n config['modules'] = [ 'custom_wcs' ]\\n os.remove(rho_file)\\n piff.plotify(config)\\n assert os.path.isfile(rho_file)\",\n \"def call(self, **kwargs):\\n # Format template\\n template = self._cw.vreg.template_env.get_template(\\\"startup.logged.jinja2\\\")\\n html = template.render(\\n header_url=self._cw.data_url(\\\"creative/img/neurospin.jpg\\\"),\\n moderator=False)\\n self.w(html)\",\n \"def call(self, **kwargs):\\n # Format template\\n template = self._cw.vreg.template_env.get_template(\\\"startup.logged.jinja2\\\")\\n html = template.render(\\n header_url=self._cw.data_url(\\\"creative/img/neurospin.jpg\\\"),\\n moderator=True)\\n self.w(html)\",\n \"def imdisplay(filename, representation):\\n im = read_image(filename, representation)\\n if representation == 1:\\n plt.imshow(im, cmap='gray')\\n plt.show()\\n if representation == 2:\\n plt.imshow(im)\\n plt.show()\",\n \"def plot(\\r\\n self,\\r\\n file_name: str = \\\"index\\\",\\r\\n path: str = \\\"./\\\",\\r\\n template: str = \\\"default\\\",\\r\\n notebook_height: int = 500,\\r\\n ):\\r\\n self.notebook_height = notebook_height\\r\\n\\r\\n script_path = os.path.dirname(os.path.abspath(__file__))\\r\\n if template in [\\\"default\\\", \\\"reaction_smiles\\\", \\\"smiles\\\", \\\"url_image\\\"]:\\r\\n template = \\\"template_\\\" + template + \\\".j2\\\"\\r\\n else:\\r\\n script_path = os.path.dirname(template)\\r\\n\\r\\n html_path = os.path.join(path, file_name + \\\".html\\\")\\r\\n js_path = os.path.join(path, file_name + \\\".js\\\")\\r\\n jenv = jinja2.Environment(loader=jinja2.FileSystemLoader(script_path))\\r\\n\\r\\n has_legend = False\\r\\n\\r\\n for _, value in self.scatters.items():\\r\\n if value[\\\"has_legend\\\"]:\\r\\n has_legend = True\\r\\n break\\r\\n\\r\\n if not self.show_legend:\\r\\n has_legend = False\\r\\n\\r\\n # Drop colormaps before passing them to the document, as they are\\r\\n # not JSON serializable.\\r\\n trees_copy = copy.deepcopy(self.trees)\\r\\n scatters_copy = copy.deepcopy(self.scatters)\\r\\n\\r\\n for key, _ in trees_copy.items():\\r\\n del trees_copy[key][\\\"colormap\\\"]\\r\\n\\r\\n for key, _ in scatters_copy.items():\\r\\n del scatters_copy[key][\\\"colormap\\\"]\\r\\n\\r\\n model = {\\r\\n \\\"title\\\": self.title,\\r\\n \\\"file_name\\\": file_name + \\\".js\\\",\\r\\n \\\"clear_color\\\": self.clear_color,\\r\\n \\\"view\\\": self.view,\\r\\n \\\"coords\\\": str(self.coords).lower(),\\r\\n \\\"coords_color\\\": self.coords_color,\\r\\n \\\"coords_box\\\": str(self.coords_box).lower(),\\r\\n \\\"coords_ticks\\\": str(self.coords_ticks).lower(),\\r\\n \\\"coords_grid\\\": str(self.coords_grid).lower(),\\r\\n \\\"coords_tick_count\\\": self.coords_tick_count,\\r\\n \\\"coords_tick_length\\\": self.coords_tick_length,\\r\\n \\\"coords_offset\\\": self.coords_offset,\\r\\n \\\"x_title\\\": self.x_title,\\r\\n \\\"y_title\\\": self.y_title,\\r\\n \\\"tree_helpers\\\": list(trees_copy.values()),\\r\\n \\\"point_helpers\\\": list(scatters_copy.values()),\\r\\n \\\"has_legend\\\": str(has_legend).lower(),\\r\\n \\\"legend_title\\\": self.legend_title,\\r\\n \\\"legend_orientation\\\": self.legend_orientation,\\r\\n \\\"alpha_blending\\\": str(self.alpha_blending).lower(),\\r\\n \\\"anti_aliasing\\\": str(self.anti_aliasing).lower(),\\r\\n \\\"style\\\": self.style,\\r\\n \\\"impress\\\": self.impress,\\r\\n \\\"in_notebook\\\": Faerun.in_notebook(),\\r\\n \\\"thumbnail_width\\\": self.thumbnail_width,\\r\\n \\\"thumbnail_fixed\\\": str(self.thumbnail_fixed).lower(),\\r\\n }\\r\\n\\r\\n if Faerun.in_notebook():\\r\\n model[\\\"data\\\"] = self.create_data()\\r\\n else:\\r\\n with open(js_path, \\\"w\\\") as f:\\r\\n f.write(self.create_data())\\r\\n\\r\\n output_text = jenv.get_template(template).render(model)\\r\\n\\r\\n with open(html_path, \\\"w\\\") as result_file:\\r\\n result_file.write(output_text)\\r\\n\\r\\n if Faerun.in_notebook():\\r\\n display(IFrame(html_path, width=\\\"100%\\\", height=self.notebook_height))\\r\\n display(FileLink(html_path))\",\n \"def appelTemp():\\n try:\\n if os.path.getsize('aspifile/temp.json'):\\n subprocess.call('aspifile/aspitemp.py', shell=True)\\n label['text'] = (\\\"Date: \\\" + textDate.get() +\\\" -- \\\"+ \\\"Nom: \\\" + textName.get() +\\n \\\"\\\\nTempérature: \\\" + textTemp.get())\\n except FileNotFoundError as errorgraph5:\\n print('+ Sorry the Temp plot doesn\\\\'t work ! Data missing !', errorgraph5)\\n label['text'] = \\\"Sorry the Temp plot doesn\\\\'t work ! Data missing !\\\"\",\n \"def dynamic_figure(r=None,z=None,t=None):\\n global yt, t0, nt, zcut, theta, cnt, main_title\\n\\n if r is None:\\n RunTimeError(\\\"r is None\\\")\\n if z is None:\\n RunTimeError(\\\"z is None\\\")\\n if t is None:\\n RunTimeError(\\\"t is None\\\")\\n \\n theta = linspace( 0.0, 2.0*scipy.pi, num=100)\\n \\n R, Z = meshgrid(r,z)\\n \\n fu = FuModel(nt=100)\\n \\n ycube = fu.FuCon(R, Z, t) # ycube[:,:,:] DATA CUBE!!!\\n\\n #-----------------------------FIGURES------------------------------\\n fig = figure(figsize=(6,8.5))\\n \\n t0 = 0\\n yt = ycube[:,:,t0]\\n \\n titulo = 'Fu drug diffusion model: t = %07.2f' % t[t0]\\n main_title=fig.text(0.25, 0.95, titulo,\\n horizontalalignment='left',\\n fontproperties=FontProperties(size=16))\\n \\n cmap = cm.cool\\n subplots_adjust(hspace=0.0)\\n \\n subplot(211)\\n ax = gca()\\n im1 = imshow(yt,\\n interpolation='bilinear',\\n cmap=cmap,\\n origin='lower',\\n extent=(0,1.0,z[0],z[-1]),\\n aspect = 0.23\\n )\\n \\n xlabel(r'R = r/a', fontsize=14)\\n ylabel(r'Z = z/l', fontsize=14)\\n\\n subplot(212,aspect='equal')\\n ax = gca()\\n\\n #\\n #--- cylindrical cross-section\\n \\n RAD, THETA = meshgrid(r,theta)\\n Xpos = RAD*cos(THETA)\\n Ypos = RAD*sin(THETA)\\n \\n #\\n # top cap of cylinder\\n #\\n #zcut = len(z)-1\\n zcut = 0\\n if z[0] != 0.0:\\n zcut = int(len(z)/2+0.5)\\n title('Z = %f'%z[zcut],fontsize=16)\\n #\\n # Take a cross-section of the cylinder at some point Z ==> radius array of length len(r) \\n # -----\\n # Trick\\n # -----\\n # Now create a matrix[len(theta),len(r)] by replicating the above array len(theta) times\\n # along dimension 0\\n #\\n \\n zx = outer( ones(len(theta)), yt[zcut,:] )\\n \\n im2 = pcolormesh(Xpos, Ypos,\\n zx,\\n shading='flat',\\n cmap=cmap)\\n \\n xlabel(r'X/a', fontsize=14)\\n ylabel(r'Y/a', fontsize=14)\\n \\n #\\n #--- new axis for colorbar\\n #\\n norm = colors.Normalize(vmin=0.0, vmax=1.0)\\n im1.set_norm(norm)\\n im2.set_norm(norm)\\n im1.add_observer(im2)\\n \\n pos = ax.get_position()\\n l, b, w, h = getattr(pos, 'bounds', pos)\\n cax = axes([l+w+0.015, b, 0.025, h]) # setup colorbar axes\\n colorbar(im1, cax, orientation='vertical') \\n \\n manager = get_current_fig_manager()\\n \\n cnt = 0\\n files = []\\n nt = len(t)\\n #\\n #------------------------------------------------------------------\\n def updatefig(*args):\\n global yt, t0, nt, zcut, theta, cnt, main_title\\n \\n t0 += 1\\n if t0 > nt-1:\\n return False\\n \\n yt = ycube[:,:,t0]\\n zx = outer( ones(len(theta)), yt[zcut,:] )\\n Nx, Ny = yt.shape\\n im1.set_array(yt)\\n im2.set_array(ravel(zx[0:Nx-1,0:Ny-1]))\\n titulo = 'Fu drug diffusion model: t = %07.2f' % t[t0]\\n main_title.set_text(titulo)\\n manager.canvas.draw()\\n \\n #fname = '_tmp%03d.jpg' % t0\\n fname = '_tmp%03d.png' % t0\\n savefig(fname)\\n files.append(fname)\\n \\n cnt += 1\\n return True\\n #\\n #------------------------------------------------------------------\\n\\n cnt = 0\\n gobject.idle_add(updatefig)\\n\\n ioff()\\n show()\\n\\n command = \\\"ffmpeg -r 10 -sameq -i _tmp%03d.png test.mp4\\\"\\n #command = \\\"mencoder -ovc xvid -xvidencopts \\\" + \\\\\\n # \\\"pass=2:bitrate=15999:max_bframes=0 \\\" + \\\\\\n # \\\"-oac copy -mf fps=10:type=jpeg 'mf://_tmp*.jpg' -vf harddup -ofps 10 \\\" + \\\\\\n # \\\"-noskip -of avi -o outputfile.avi\\\"\\n os.system(command)\\n for fname in files: os.remove(fname) #clean up\\n\\n return True\",\n \"def generic_case_figure(wind_speed=5.,\\n wind_dir=np.pi / 2) -> \\\"fire_rs.geodata.display.GeoDataDisplay\\\":\\n\\n # Geographic environment (elevation, landcover, wind...)\\n wind = (wind_speed, wind_dir)\\n area = ((480000.0, 485000.0), (6210000.0, 6215000.0))\\n env = PlanningEnvironment(area, wind_speed=wind[0], wind_dir=wind[1],\\n planning_elevation_mode='flat', flat_altitude=0)\\n\\n # Fire applied to the previous environment\\n ignition_points = [\\n TimedPoint(area[0][0] + 1000.0, area[1][0] + 1000.0, 0.),\\n TimedPoint(area[0][0] + 4000.0, area[1][0] + 3000.0, 0.)\\n ]\\n logging.info(\\\"Start of propagation\\\")\\n fire = propagation.propagate_from_points(env, ignition_points, 120 * 60)\\n logging.info(\\\"End of propagation\\\")\\n\\n # Configure some flight\\n start_t = 90 * 60\\n uav = UAVConf.x8()\\n uav.max_flight_time/=5\\n f_confs = [\\n FlightConf(uav, start_t, Waypoint(area[0][0] + 100., area[1][0] + 100., 0., 0.), None,\\n wind),\\n FlightConf(uav, start_t, Waypoint(area[0][1] - 100., area[1][0] + 1000., 0., 0.), None,\\n wind),\\n ]\\n\\n conf = SAOP_conf(start_t, start_t + uav.max_flight_time)\\n\\n fire1 = fire.ignitions()\\n pl = Planner(env, fire1, f_confs, conf)\\n pl.compute_plan()\\n sr_1 = pl.search_result\\n fmapper = FireMapper(env, fire1)\\n\\n gdd = fire_rs.geodata.display.GeoDataDisplay.pyplot_figure(env.raster.combine(fire1),\\n frame=(0, 0))\\n gdd.add_extension(TrajectoryDisplayExtension, (None,), {})\\n\\n # Draw expected fire contour\\n t_range = (sr_1.final_plan().trajectories()[0].start_time(0) - 120,\\n sr_1.final_plan().trajectories()[0].end_time(len(\\n sr_1.final_plan().trajectories()[0]) - 1) + 120)\\n t_range_fire = (0, np.inf)\\n gdd.draw_ignition_contour(geodata=fire1, time_range=t_range_fire, cmap=matplotlib.cm.Reds)\\n\\n # Draw observed fire\\n for i in range(len(f_confs)):\\n executed_path = sr_1.final_plan().trajectories()[i].sampled_with_time(step_size=10)\\n fmapper.observe(executed_path, pl.flights[i].uav)\\n gdd.draw_ignition_shade(geodata=fmapper.firemap, cmap=matplotlib.cm.summer,\\n vmin=t_range[0], vmax=t_range[1], with_colorbar=False)\\n\\n # Draw trajectory\\n colors = ['blue', 'green', 'magenta']\\n labels = [\\\"UAV \\\" + str(i) for i in range(len(f_confs))]\\n\\n for i in range(len(f_confs)):\\n plot_plan_trajectories(sr_1.final_plan(), gdd, trajectories=i, draw_path=True,\\n draw_flighttime_path=False, colors=[colors[i]], labels=[labels[i]],\\n linestyles=['-'])\\n gdd.legend()\\n return gdd\",\n \"def imdisplay(filename, representation):\\n\\n image = read_image(filename, representation)\\n plt.imshow(image, cmap=\\\"gray\\\")\\n plt.show()\",\n \"def dftb_filling_fermi(unit, elec_temp):\\n dftb_filling_fermi = \\\"\\\"\\\"\\n Filling = Fermi {\\n Temperature [{{ unit }}] = {{ elec_temp }}\\n }\\n \\\"\\\"\\\"\\n return Environment().from_string(dftb_filling_fermi).render(unit=unit, elec_temp=elec_temp)\",\n \"def show(data, types=(\\\"inflated\\\", ), recache=False, cmap='RdBu_r', layout=None,\\n autoclose=None, open_browser=None, port=None, pickerfun=None,\\n template=\\\"mixer.html\\\", overlays_available=None,\\n overlays_visible=('rois', 'sulci'), labels_visible=('rois', ),\\n overlay_file=None, title='Brain', **kwargs):\\n\\n # populate default webshow args\\n if autoclose is None:\\n autoclose = options.config.get('webshow', 'autoclose', fallback='true') == 'true'\\n if open_browser is None:\\n open_browser = options.config.get('webshow', 'open_browser', fallback='true') == 'true'\\n\\n data = dataset.normalize(data)\\n if not isinstance(data, dataset.Dataset):\\n data = dataset.Dataset(data=data)\\n\\n html = FallbackLoader([os.path.split(os.path.abspath(template))[0], serve.cwd]).load(template)\\n db.auxfile = data\\n\\n #Extract the list of stimuli, for special-casing\\n stims = dict()\\n for name, view in data:\\n if 'stim' in view.attrs and os.path.exists(view.attrs['stim']):\\n sname = os.path.split(view.attrs['stim'])[1]\\n stims[sname] = view.attrs['stim']\\n\\n package = Package(data)\\n metadata = json.dumps(package.metadata())\\n images = package.images\\n subjects = list(package.subjects)\\n\\n ctmargs = dict(method='mg2', level=9, recache=recache,\\n external_svg=overlay_file, overlays_available=overlays_available)\\n ctms = dict((subj, utils.get_ctmpack(subj, types, **ctmargs))\\n for subj in subjects)\\n package.reorder(ctms)\\n\\n subjectjs = json.dumps(dict((subj, \\\"ctm/%s/\\\"%subj) for subj in subjects))\\n db.auxfile = None\\n\\n if layout is None:\\n layout = [None, (1, 1), (2, 1), (3, 1), (2, 2), (3, 2), (3, 2), (3, 3), (3, 3), (3, 3)][len(subjects)]\\n\\n linear = lambda x, y, m: (1.-m)*x + m*y\\n mixes = dict(\\n linear=linear,\\n smoothstep=(lambda x, y, m: linear(x, y, 3*m**2 - 2*m**3)),\\n smootherstep=(lambda x, y, m: linear(x, y, 6*m**5 - 15*m**4 + 10*m**3))\\n )\\n\\n post_name = Queue()\\n\\n # Put together all view options\\n my_viewopts = dict(options.config.items('webgl_viewopts'))\\n my_viewopts['overlays_visible'] = overlays_visible\\n my_viewopts['labels_visible'] = labels_visible\\n my_viewopts['brightness'] = options.config.get('curvature', 'brightness')\\n my_viewopts['smoothness'] = options.config.get('curvature', 'webgl_smooth')\\n my_viewopts['contrast'] = options.config.get('curvature', 'contrast')\\n\\n for sec in options.config.sections():\\n if 'paths' in sec or 'labels' in sec:\\n my_viewopts[sec] = dict(options.config.items(sec))\\n\\n if pickerfun is None:\\n pickerfun = lambda a, b: None\\n\\n class CTMHandler(web.RequestHandler):\\n def get(self, path):\\n subj, path = path.split('/')\\n if path == '':\\n self.set_header(\\\"Content-Type\\\", \\\"application/json\\\")\\n self.write(open(ctms[subj]).read())\\n else:\\n fpath = os.path.split(ctms[subj])[0]\\n mtype = mimetypes.guess_type(os.path.join(fpath, path))[0]\\n if mtype is None:\\n mtype = \\\"application/octet-stream\\\"\\n self.set_header(\\\"Content-Type\\\", mtype)\\n self.write(open(os.path.join(fpath, path), 'rb').read())\\n\\n class DataHandler(web.RequestHandler):\\n def get(self, path):\\n path = path.strip(\\\"/\\\")\\n try:\\n dataname, frame = path.split('/')\\n except ValueError:\\n dataname = path\\n frame = 0\\n\\n if dataname in images:\\n dataimg = images[dataname][int(frame)]\\n if dataimg[1:6] == \\\"NUMPY\\\":\\n self.set_header(\\\"Content-Type\\\", \\\"application/octet-stream\\\")\\n else:\\n self.set_header(\\\"Content-Type\\\", \\\"image/png\\\")\\n\\n if 'Range' in self.request.headers:\\n self.set_status(206)\\n rangestr = self.request.headers['Range'].split('=')[1]\\n start, end = [ int(i) if len(i) > 0 else None for i in rangestr.split('-') ]\\n\\n clenheader = 'bytes %s-%s/%s' % (start, end or len(dataimg), len(dataimg) )\\n self.set_header('Content-Range', clenheader)\\n self.set_header('Content-Length', end-start+1)\\n self.write(dataimg[start:end+1])\\n else:\\n self.write(dataimg)\\n else:\\n self.set_status(404)\\n self.write_error(404)\\n\\n class StimHandler(web.StaticFileHandler):\\n def initialize(self):\\n pass\\n\\n def get(self, path):\\n if path not in stims:\\n self.set_status(404)\\n self.write_error(404)\\n else:\\n self.root, fname = os.path.split(stims[path])\\n super(StimHandler, self).get(fname)\\n\\n class StaticHandler(web.StaticFileHandler):\\n def initialize(self):\\n self.root = ''\\n\\n class MixerHandler(web.RequestHandler):\\n def get(self):\\n self.set_header(\\\"Content-Type\\\", \\\"text/html\\\")\\n generated = html.generate(data=metadata,\\n colormaps=colormaps,\\n default_cmap=cmap,\\n python_interface=True,\\n leapmotion=True,\\n layout=layout,\\n subjects=subjectjs,\\n viewopts=json.dumps(my_viewopts),\\n title=title,\\n **kwargs)\\n #overlays_visible=json.dumps(overlays_visible),\\n #labels_visible=json.dumps(labels_visible),\\n #**viewopts)\\n self.write(generated)\\n\\n def post(self):\\n data = self.get_argument(\\\"svg\\\", default=None)\\n png = self.get_argument(\\\"png\\\", default=None)\\n with open(post_name.get(), \\\"wb\\\") as svgfile:\\n if png is not None:\\n data = png[22:].strip()\\n try:\\n data = binascii.a2b_base64(data)\\n except:\\n print(\\\"Error writing image!\\\")\\n data = png\\n svgfile.write(data)\\n\\n class JSMixer(serve.JSProxy):\\n @property\\n def view_props(self):\\n \\\"\\\"\\\"An enumerated list of settable properties for views. \\n There may be a way to get this from the javascript object, \\n but I (ML) don't know how.\\n\\n There may be additional properties we want to set in views\\n and animations; those must be added here.\\n\\n Old property list that used to be settable before webgl refactor:\\n view_props = ['altitude', 'azimuth', 'target', 'mix', 'radius', 'pivot',\\n 'visL', 'visR', 'alpha', 'rotationR', 'rotationL', 'projection',\\n 'volume_vis', 'frame', 'slices']\\n \\\"\\\"\\\"\\n camera = getattr(self.ui, \\\"camera\\\")\\n _camera_props = ['camera.%s' % k for k in camera._controls.attrs.keys()]\\n surface = getattr(self.ui, \\\"surface\\\")\\n _subject = list(surface._folders.attrs.keys())[0]\\n _surface = getattr(surface, _subject)\\n _surface_props = ['surface.{subject}.%s'%k for k in _surface._controls.attrs.keys()]\\n _curvature_props = ['surface.{subject}.curvature.brightness',\\n 'surface.{subject}.curvature.contrast',\\n 'surface.{subject}.curvature.smoothness']\\n return _camera_props + _surface_props + _curvature_props\\n\\n def _set_view(self, **kwargs):\\n \\\"\\\"\\\"Low-level command: sets view parameters in the current viewer\\n\\n Sets each the state of each keyword argument provided. View parameters\\n that can be set include all parameters in the data.gui in the html view.\\n\\n \\\"\\\"\\\"\\n # Set unfolding level first, as it interacts with other arguments\\n surface = getattr(self.ui, \\\"surface\\\")\\n subject_list = surface._folders.attrs.keys()\\n # Better to only self.view_props once; it interacts with javascript, \\n # don't want to do that too often, it leads to glitches.\\n vw_props = copy.copy(self.view_props)\\n for subject in subject_list:\\n if 'surface.{subject}.unfold' in kwargs:\\n unfold = kwargs.pop('surface.{subject}.unfold')\\n self.ui.set('surface.{subject}.unfold'.format(subject=subject), unfold)\\n for k, v in kwargs.items():\\n if not k in vw_props:\\n print('Unknown parameter %s!'%k)\\n continue\\n else:\\n self.ui.set(k.format(subject=subject) if '{subject}' in k else k, v)\\n # Wait for webgl. Wait for it. .... WAAAAAIIIT.\\n time.sleep(0.03)\\n\\n def _capture_view(self, frame_time=None):\\n \\\"\\\"\\\"Low-level command: returns a dict of current view parameters\\n\\n Retrieves the following view parameters from current viewer:\\n\\n altitude, azimuth, target, mix, radius, visL, visR, alpha,\\n rotationR, rotationL, projection, pivot\\n\\n Parameters\\n ----------\\n frame_time : scalar\\n time (in seconds) to specify for this frame.\\n \\n Notes\\n -----\\n If multiple subjects are present, only retrieves view for first subject.\\n \\\"\\\"\\\"\\n view = {}\\n subject = list(self.ui.surface._folders.attrs.keys())[0]\\n for p in self.view_props:\\n try:\\n view[p] = self.ui.get(p.format(subject=subject) if '{subject}' in p else p)[0]\\n # Wait for webgl.\\n time.sleep(0.03)\\n except Exception as err:\\n # TO DO: Fix this hack with an error class in serve.py & catch it here\\n print(err) #msg = \\\"Cannot read property 'undefined'\\\"\\n #if err.message[:len(msg)] != msg:\\n # raise err\\n if frame_time is not None:\\n view['time'] = frame_time\\n return view\\n\\n def save_view(self, subject, name, is_overwrite=False):\\n \\\"\\\"\\\"Saves current view parameters to pycortex database\\n\\n Parameters\\n ----------\\n subject : string\\n pycortex subject id\\n name : string\\n name for view to store\\n is_overwrite: bool\\n whether to overwrite an extant view (default : False)\\n\\n Notes\\n -----\\n Equivalent to call to cortex.db.save_view(subject, vw, name)\\n For a list of the view parameters saved, see viewer._capture_view\\n \\\"\\\"\\\"\\n db.save_view(self, subject, name, is_overwrite)\\n\\n def get_view(self, subject, name):\\n \\\"\\\"\\\"Get saved view from pycortex database.\\n\\n Retrieves named view from pycortex database and sets current\\n viewer parameters to retrieved values.\\n\\n Parameters\\n ----------\\n subject : string\\n pycortex subject ID\\n name : string\\n name of saved view to re-load\\n\\n Notes\\n -----\\n Equivalent to call to cortex.db.get_view(subject, vw, name)\\n For a list of the view parameters set, see viewer._capture_view\\n \\\"\\\"\\\"\\n view = db.get_view(self, subject, name)\\n\\n def addData(self, **kwargs):\\n Proxy = serve.JSProxy(self.send, \\\"window.viewers.addData\\\")\\n new_meta, new_ims = _convert_dataset(Dataset(**kwargs), path='/data/', fmt='%s_%d.png')\\n metadata.update(new_meta)\\n images.update(new_ims)\\n return Proxy(metadata)\\n\\n def getImage(self, filename, size=(1920, 1080)):\\n \\\"\\\"\\\"Saves currently displayed view to a .png image file\\n\\n Parameters\\n ----------\\n filename : string\\n duh.\\n size : tuple (x, y)\\n size (in pixels) of image to save.\\n \\\"\\\"\\\"\\n post_name.put(filename)\\n Proxy = serve.JSProxy(self.send, \\\"window.viewer.getImage\\\")\\n return Proxy(size[0], size[1], \\\"mixer.html\\\")\\n\\n def makeMovie(self, animation, filename=\\\"brainmovie%07d.png\\\", offset=0,\\n fps=30, size=(1920, 1080), interpolation=\\\"linear\\\"):\\n \\\"\\\"\\\"Renders movie frames for animation of mesh movement\\n\\n Makes an animation (for example, a transition between inflated and\\n flattened brain or a rotating brain) of a cortical surface. Takes a\\n list of dictionaries (`animation`) as input, and uses the values in\\n the dictionaries as keyframes for the animation.\\n\\n Mesh display parameters that can be animated include 'elevation',\\n 'azimuth', 'mix', 'radius', 'target' (more?)\\n\\n\\n Parameters\\n ----------\\n animation : list of dicts\\n Each dict should have keys `idx`, `state`, and `value`.\\n `idx` is the time (in seconds) at which you want to set `state` to `value`\\n `state` is the parameter to animate (e.g. 'altitude', 'azimuth')\\n `value` is the value to set for `state`\\n filename : string path name\\n Must contain '%d' (or some variant thereof) to account for frame\\n number, e.g. '/some/directory/brainmovie%07d.png'\\n offset : int\\n Frame number for first frame rendered. Useful for concatenating\\n animations.\\n fps : int\\n Frame rate of resultant movie\\n size : tuple (x, y)\\n Size (in pixels) of resulting movie\\n interpolation : {\\\"linear\\\", \\\"smoothstep\\\", \\\"smootherstep\\\"}\\n Interpolation method for values between keyframes.\\n\\n Example\\n -------\\n # Called after a call of the form: js_handle = cortex.webgl.show(DataViewObject)\\n # Start with left hemisphere view\\n js_handle._setView(azimuth=[90], altitude=[90.5], mix=[0])\\n # Initialize list\\n animation = []\\n # Append 5 key frames for a simple rotation\\n for az, idx in zip([90, 180, 270, 360, 450], [0, .5, 1.0, 1.5, 2.0]):\\n animation.append({'state':'azimuth', 'idx':idx, 'value':[az]})\\n # Animate! (use default settings)\\n js_handle.makeMovie(animation)\\n \\\"\\\"\\\"\\n # build up two variables: State and Anim.\\n # state is a dict of all values being modified at any time\\n state = dict()\\n # anim is a list of transitions between keyframes\\n anim = []\\n setfunc = self.ui.set\\n for f in sorted(animation, key=lambda x:x['idx']):\\n if f['idx'] == 0:\\n setfunc(f['state'], f['value'])\\n state[f['state']] = dict(idx=f['idx'], val=f['value'])\\n else:\\n if f['state'] not in state:\\n state[f['state']] = dict(idx=0, val=self.getState(f['state'])[0])\\n start = dict(idx=state[f['state']]['idx'],\\n state=f['state'],\\n value=state[f['state']]['val'])\\n end = dict(idx=f['idx'], state=f['state'], value=f['value'])\\n state[f['state']]['idx'] = f['idx']\\n state[f['state']]['val'] = f['value']\\n if start['value'] != end['value']:\\n anim.append((start, end))\\n\\n for i, sec in enumerate(np.arange(0, anim[-1][1]['idx']+1./fps, 1./fps)):\\n for start, end in anim:\\n if start['idx'] < sec <= end['idx']:\\n idx = (sec - start['idx']) / float(end['idx'] - start['idx'])\\n if start['state'] == 'frame':\\n func = mixes['linear']\\n else:\\n func = mixes[interpolation]\\n\\n val = func(np.array(start['value']), np.array(end['value']), idx)\\n if isinstance(val, np.ndarray):\\n setfunc(start['state'], val.ravel().tolist())\\n else:\\n setfunc(start['state'], val)\\n self.getImage(filename%(i+offset), size=size)\\n\\n def _get_anim_seq(self, keyframes, fps=30, interpolation='linear'):\\n \\\"\\\"\\\"Convert a list of keyframes to a list of EVERY frame in an animation.\\n\\n Utility function called by make_movie; separated out so that individual\\n frames of an animation can be re-rendered, or for more control over the\\n animation process in general.\\n\\n \\\"\\\"\\\"\\n # Misc. setup\\n fr = 0\\n a = np.array\\n func = mixes[interpolation]\\n #skip_props = ['surface.{subject}.right', 'surface.{subject}.left', ] #'projection',\\n # Get keyframes\\n keyframes = sorted(keyframes, key=lambda x:x['time'])\\n # Normalize all time to frame rate\\n fs = 1./fps\\n for k in range(len(keyframes)):\\n t = keyframes[k]['time']\\n t = np.round(t/fs)*fs\\n keyframes[k]['time'] = t\\n allframes = []\\n for start, end in zip(keyframes[:-1], keyframes[1:]):\\n t0 = start['time']\\n t1 = end['time']\\n tdif = float(t1-t0)\\n # Check whether to continue frame sequence to endpoint\\n use_endpoint = keyframes[-1]==end\\n nvalues = np.round(tdif/fs).astype(int)\\n if use_endpoint:\\n nvalues += 1\\n fr_time = np.linspace(0, 1, nvalues, endpoint=use_endpoint)\\n # Interpolate between values\\n for t in fr_time:\\n frame = {}\\n for prop in start.keys():\\n if prop=='time':\\n continue\\n if (start[prop] is None) or (start[prop] == end[prop]) or isinstance(start[prop], (bool, str)):\\n frame[prop] = start[prop]\\n continue\\n val = func(a(start[prop]), a(end[prop]), t)\\n if isinstance(val, np.ndarray):\\n frame[prop] = val.tolist()\\n else:\\n frame[prop] = val\\n allframes.append(frame)\\n return allframes\\n\\n def make_movie_views(self, animation, filename=\\\"brainmovie%07d.png\\\", \\n offset=0, fps=30, size=(1920, 1080), alpha=1, frame_sleep=0.05,\\n frame_start=0, interpolation=\\\"linear\\\"):\\n \\\"\\\"\\\"Renders movie frames for animation of mesh movement\\n\\n Makes an animation (for example, a transition between inflated and\\n flattened brain or a rotating brain) of a cortical surface. Takes a\\n list of dictionaries (`animation`) as input, and uses the values in\\n the dictionaries as keyframes for the animation.\\n\\n Mesh display parameters that can be animated include 'elevation',\\n 'azimuth', 'mix', 'radius', 'target' (more?)\\n\\n\\n Parameters\\n ----------\\n animation : list of dicts\\n This is a list of keyframes for the animation. Each keyframe should be\\n a dict in the form captured by the ._capture_view method. NOTE: every\\n view must include all view parameters. Additionally, there should be\\n one extra key/value pair for \\\"time\\\". The value for time should be\\n in seconds. The list of keyframes is sorted by time before applying,\\n so they need not be in order in the input.\\n filename : string path name\\n Must contain '%d' (or some variant thereof) to account for frame\\n number, e.g. '/some/directory/brainmovie%07d.png'\\n offset : int\\n Frame number for first frame rendered. Useful for concatenating\\n animations.\\n fps : int\\n Frame rate of resultant movie\\n size : tuple (x, y)\\n Size (in pixels) of resulting movie\\n interpolation : {\\\"linear\\\", \\\"smoothstep\\\", \\\"smootherstep\\\"}\\n Interpolation method for values between keyframes.\\n\\n Notes\\n -----\\n Make sure that all values that will be modified over the course\\n of the animation are initialized (have some starting value) in the first\\n frame.\\n\\n Example\\n -------\\n # Called after a call of the form: js_handle = cortex.webgl.show(DataViewObject)\\n # Start with left hemisphere view\\n js_handle._setView(azimuth=[90], altitude=[90.5], mix=[0])\\n # Initialize list\\n animation = []\\n # Append 5 key frames for a simple rotation\\n for az, t in zip([90, 180, 270, 360, 450], [0, .5, 1.0, 1.5, 2.0]):\\n animation.append({'time':t, 'azimuth':[az]})\\n # Animate! (use default settings)\\n js_handle.make_movie(animation)\\n \\\"\\\"\\\"\\n allframes = self._get_anim_seq(animation, fps, interpolation)\\n for fr, frame in enumerate(allframes[frame_start:], frame_start):\\n self._set_view(**frame)\\n time.sleep(frame_sleep)\\n self.getImage(filename%(fr+offset+1), size=size)\\n time.sleep(frame_sleep)\\n\\n class PickerHandler(web.RequestHandler):\\n def get(self):\\n pickerfun(int(self.get_argument(\\\"voxel\\\")), int(self.get_argument(\\\"vertex\\\")))\\n\\n class WebApp(serve.WebApp):\\n disconnect_on_close = autoclose\\n def get_client(self):\\n self.connect.wait()\\n self.connect.clear()\\n return JSMixer(self.send, \\\"window.viewer\\\")\\n\\n def get_local_client(self):\\n return JSMixer(self.srvsend, \\\"window.viewer\\\")\\n\\n if port is None:\\n port = random.randint(1024, 65536)\\n\\n server = WebApp([(r'/ctm/(.*)', CTMHandler),\\n (r'/data/(.*)', DataHandler),\\n (r'/stim/(.*)', StimHandler),\\n (r'/mixer.html', MixerHandler),\\n (r'/picker', PickerHandler),\\n (r'/', MixerHandler),\\n (r'/static/(.*)', StaticHandler)],\\n port)\\n\\n server.start()\\n print(\\\"Started server on port %d\\\"%server.port)\\n url = \\\"http://%s%s:%d/mixer.html\\\"%(serve.hostname, domain_name, server.port)\\n if open_browser:\\n webbrowser.open(url)\\n client = server.get_client()\\n client.server = server\\n return client\\n else:\\n try:\\n from IPython.display import display, HTML\\n display(HTML('Open viewer: <a href=\\\"{0}\\\" target=\\\"_blank\\\">{0}</a>'.format(url)))\\n except:\\n pass\\n return server\",\n \"def render(self, mode='human'):\\n screen_width = 800\\n screen_height = 550\\n\\n # Width is one column for each variable\\n n_sect = 7\\n world_width = n_sect*2 # X axis is just pixels\\n \\n buff_axis = cfg['buff_axis']\\n #bottom of the screen scales to the input/output range of values\\n world_height_bottom = np.max(self.maxes)+buff_axis\\n \\n # Top is for counting steps\\n world_height_top = 100\\n\\n #Split the screen:\\n world_top = .3\\n world_bottom = 1-world_top\\n screen_height_bottom = world_bottom*screen_height\\n\\n #Set where to draw the steps axis\\n axes_line1 = screen_height*(world_bottom + .2)\\n\\n # Scale the pixels in the screen:\\n scalex = screen_width/world_width\\n scaley_bottom= screen_height_bottom/world_height_bottom\\n\\n # Some adjustments to move some objects up/ right\\n move_oval = -scalex*.2\\n move_up= scaley_bottom * buff_axis*.5\\n\\n #set sizes of shapes:\\n self.oval_length = 25.0\\n self.oval_width = 50.0\\n self.rect_width = 70.0\\n self.rect_height = 5.0 \\n\\n #Step plot:\\n scalestep = screen_width/cfg['scalestep']\\n\\n #color shades:\\n light_col = .7\\n dark_col = 1\\n c11 = .6\\n c22 = .8\\n c33 = 1\\n\\n if self.viewer is None:\\n #TO DO: find an alternative to copy-paste to generate multiple similar shapes\\n self.viewer = rendering.Viewer(screen_width, screen_height)\\n \\n #Input states:\\n\\n #the temp action\\n self.temptrans1 = self.make_oval(0,0,light_col)\\n self.temptrans2 = self.make_oval(0,0,dark_col)\\n #flow action:\\n self.flowtrans1 = self.make_oval(light_col,0,light_col)\\n self.flowtrans2 = self.make_oval(dark_col,0,dark_col)\\n\\n #output states:\\n #out1:\\n #the gauge is a rectangle \\n self.outgauge1 = self.make_rect(0,c33,0)\\n #goal is red rectangle\\n self.outgoal1= self.make_rect(c33,0,0)\\n \\n #out2:\\n #the gauge is a rectangle \\n self.outgauge2 = self.make_rect(0,c22,0)\\n #goal is red rectangle\\n self.outgoal2= self.make_rect(c22,0,0)\\n\\n #out3:\\n #the gauge is a rectangle \\n self.outgauge3 = self.make_rect(0,c11,0)\\n #goal is red rectangle\\n self.outgoal3 = self.make_rect(c11,0,0)\\n\\n #lines on which \\\"controls\\\" sit\\n for l in range(n_sect): \\n self.make_line(scalex*((l*2)+1),0, scalex*((l*2)+1),screen_height*world_bottom)\\n\\n # Line separating the top and bottom of the screen. \\n self.make_line(0,world_bottom*screen_height,screen_width,world_bottom*screen_height)\\n # Step # axis.\\n self.make_line(scalex*1.5,axes_line1,screen_width-scalex*1,axes_line1)\\n\\n # The dot tracking the step #\\n dot = rendering.make_circle(self.oval_length)\\n self.dottrans = rendering.Transform()\\n dot.add_attr(self.dottrans)\\n dot.set_color(0,0,0)\\n self.viewer.add_geom(dot)\\n\\n #labels: \\n num = 0\\n label_buff_y = 1.07\\n label_buff_x = .2\\n img_scale = .5\\n img_wid = 179 *img_scale\\n img_height = 124 * img_scale\\n\\n for label in self.labels:\\n pth = (self.label_dir+label+'.png')\\n self.txt = rendering.Image(pth,img_wid,img_height)\\n locx = (num*2)+1\\n self.txtis = rendering.Transform(translation=(scalex*locx +locx* label_buff_x,world_bottom*screen_height*label_buff_y))\\n self.txt.add_attr(self.txtis)\\n self.viewer.add_geom(self.txt)\\n num = num+1\\n\\n #step label\\n pth = (self.label_dir+'Step.png')\\n self.txt = rendering.Image(pth,img_wid,img_height)\\n self.txtis = rendering.Transform(translation=(scalex*.5,axes_line1))\\n self.txt.add_attr(self.txtis)\\n self.viewer.add_geom(self.txt)\\n\\n if self.state is None: return None\\n\\n x = self.state\\n\\n # 4 ins:\\n self.flowtrans1.set_translation(move_oval+scalex*1,move_up+scaley_bottom*x[0])\\n self.temptrans1.set_translation(move_oval+scalex*3,move_up+scaley_bottom*x[1])\\n self.flowtrans2.set_translation(move_oval+scalex*5,move_up+scaley_bottom*x[2])\\n self.temptrans2.set_translation(move_oval+scalex*7,move_up+scaley_bottom*x[3])\\n\\n # 3 outs: current & goal:\\n self.outgauge1.set_translation(scalex*9,move_up+scaley_bottom*x[4])\\n self.outgoal1.set_translation(scalex*9,move_up+scaley_bottom*x[7])\\n self.outgauge2.set_translation(scalex*11,move_up+scaley_bottom*x[5])\\n self.outgoal2.set_translation(scalex*11,move_up+scaley_bottom*x[8])\\n self.outgauge3.set_translation(scalex*13,move_up+scaley_bottom*x[6])\\n self.outgoal3.set_translation(scalex*13,move_up+scaley_bottom*x[9])\\n\\n #step info:\\n self.dottrans.set_translation(scalex*1.5 + self.steps*scalestep, axes_line1)\\n done_grow = .5*self.done\\n self.dottrans.set_scale(1+done_grow,1+done_grow) #expand size when done\\n\\n return self.viewer.render(return_rgb_array = mode=='rgb_array')\",\n \"def _render_file(\\n self,\\n abs_fpath: Text,\\n template_name: Text,\\n context: Dict,\\n style_config: Dict = None\\n ) -> None:\\n try:\\n say(f'rendering {abs_fpath[1+len(self.embryo.destination):]}')\\n rendered_text = self.render_template(\\n template_name=template_name, context=context\\n )\\n except Exception:\\n shout('problem rendering {p}', p=abs_fpath)\\n raise\\n\\n formatted_text = rendered_text\\n # get the full filepath with root prefix\\n fpath = self.get_abs_path(abs_fpath)\\n # load up the adapter for the file\\n adapter = self.get_adapter(fpath)\\n loaded_text = adapter.load(formatted_text)\\n # write the loaded data\\n adapter.write(fpath, loaded_text)\",\n \"def main(argv):\\n logging.basicConfig(format=\\\"%(message)s\\\", level=logging.INFO, stream=sys.stdout)\\n logger = logging.getLogger(\\\"demo4\\\")\\n\\n # Define some parameters we'll use below and make directories if needed.\\n cat_file_name = os.path.join('input','galsim_default_input.asc')\\n if not os.path.isdir('output'):\\n os.mkdir('output')\\n multi_file_name = os.path.join('output','multi.fits')\\n\\n random_seed = galsim.BaseDeviate(8241573).raw()\\n sky_level = 1.e6 # ADU / arcsec^2\\n pixel_scale = 1.0 # arcsec / pixel (size units in input catalog are pixels)\\n gal_flux = 1.e6 # arbitrary choice, makes nice (not too) noisy images\\n gal_g1 = -0.009 #\\n gal_g2 = 0.011 #\\n\\n # the fraction of flux in each component\\n # 40% is in the bulge, 60% in a disk. 70% of that disk light is placed\\n # into point sources distributed as a random walk\\n\\n bulge_frac = 0.4\\n disk_frac = 0.6\\n knot_frac = 0.42\\n smooth_disk_frac = 0.18\\n\\n # number of knots of star formation. To simulate a nice irregular (all the\\n # flux is in knots) we find ~100 is a minimum number needed, but we will\\n # just use 10 here to make the demo run fast.\\n\\n n_knots = 10\\n\\n xsize = 64 # pixels\\n ysize = 64 # pixels\\n\\n logger.info('Starting demo script 4 using:')\\n logger.info(' - parameters taken from catalog %r',cat_file_name)\\n logger.info(' - Moffat PSF (parameters from catalog)')\\n logger.info(' - pixel scale = %.2f',pixel_scale)\\n logger.info(' - Bulge + Disc galaxies (parameters from catalog)')\\n logger.info(' - 100 Point sources, distributed as random walk')\\n logger.info(' - Applied gravitational shear = (%.3f,%.3f)',gal_g1,gal_g2)\\n logger.info(' - Poisson noise (sky level = %.1e).', sky_level)\\n\\n # Read in the input catalog\\n cat = galsim.Catalog(cat_file_name)\\n\\n\\n # save a list of the galaxy images in the \\\"images\\\" list variable:\\n images = []\\n for k in range(cat.nobjects):\\n # Initialize the (pseudo-)random number generator that we will be using below.\\n # Use a different random seed for each object to get different noise realizations.\\n # Using sequential random seeds here is safer than it sounds. We use Mersenne Twister\\n # random number generators that are designed to be used with this kind of seeding.\\n # However, to be extra safe, we actually initialize one random number generator with this\\n # seed, generate and throw away two random values with that, and then use the next value\\n # to seed a completely different Mersenne Twister RNG. The result is that successive\\n # RNGs created this way produce very independent random number streams.\\n rng = galsim.BaseDeviate(random_seed+k+1)\\n\\n # Take the Moffat beta from the first column (called 0) of the input catalog:\\n # Note: cat.get(k,col) returns a string. To get the value as a float, use either\\n # cat.getFloat(k,col) or float(cat.get(k,col))\\n beta = cat.getFloat(k,0)\\n # A Moffat's size may be either scale_radius, fwhm, or half_light_radius.\\n # Here we use fwhm, taking from the catalog as well.\\n fwhm = cat.getFloat(k,1)\\n # A Moffat profile may be truncated if desired\\n # The units for this are expected to be arcsec (or specifically -- whatever units\\n # you are using for all the size values as defined by the pixel_scale).\\n trunc = cat.getFloat(k,4)\\n # Note: You may omit the flux, since the default is flux=1.\\n psf = galsim.Moffat(beta=beta, fwhm=fwhm, trunc=trunc)\\n\\n # Take the (e1, e2) shape parameters from the catalog as well.\\n psf = psf.shear(e1=cat.getFloat(k,2), e2=cat.getFloat(k,3))\\n\\n # Galaxy is a bulge + disk(+knots) with parameters taken from the catalog:\\n\\n # put some fraction of the disk light into knots of star formation\\n\\n disk_hlr = cat.getFloat(k,5)\\n disk_e1 = cat.getFloat(k,6)\\n disk_e2 = cat.getFloat(k,7)\\n bulge_hlr = cat.getFloat(k,8)\\n bulge_e1 = cat.getFloat(k,9)\\n bulge_e2 = cat.getFloat(k,10)\\n\\n smooth_disk = galsim.Exponential(flux=smooth_disk_frac, half_light_radius=disk_hlr)\\n\\n knots = galsim.RandomKnots(n_knots, half_light_radius=disk_hlr, flux=knot_frac, rng=rng)\\n\\n disk = galsim.Add([smooth_disk, knots])\\n disk = disk.shear(e1=disk_e1, e2=disk_e2)\\n\\n # the rest of the light goes into the bulge\\n bulge = galsim.DeVaucouleurs(flux=bulge_frac, half_light_radius=bulge_hlr)\\n bulge = bulge.shear(e1=bulge_e1, e2=bulge_e2)\\n\\n # The flux of an Add object is the sum of the component fluxes.\\n # Note that in demo3.py, a similar addition was performed by the binary operator \\\"+\\\".\\n gal = galsim.Add([disk, bulge])\\n\\n # This flux may be overridden by withFlux. The relative fluxes of the components\\n # remains the same, but the total flux is set to gal_flux.\\n gal = gal.withFlux(gal_flux)\\n gal = gal.shear(g1=gal_g1, g2=gal_g2)\\n\\n # The center of the object is normally placed at the center of the postage stamp image.\\n # You can change that with shift:\\n gal = gal.shift(dx=cat.getFloat(k,11), dy=cat.getFloat(k,12))\\n\\n final = galsim.Convolve([psf, gal])\\n\\n # Draw the profile\\n image = galsim.ImageF(xsize, ysize)\\n final.drawImage(image, scale=pixel_scale)\\n\\n # Add Poisson noise to the image:\\n image.addNoise(galsim.PoissonNoise(rng, sky_level * pixel_scale**2))\\n\\n logger.info('Drew image for object at row %d in the input catalog'%k)\\n \\n # Add the image to our list of images\\n images.append(image)\\n \\n # Now write the images to a multi-extension fits file. Each image will be in its own HDU.\\n galsim.fits.writeMulti(images, multi_file_name)\\n logger.info('Images written to multi-extension fits file %r',multi_file_name)\",\n \"def run(self):\\n\\n print \\\"\\\\n\\\\n\\\\tPlease Note: Templates are generated based off\\\"\\n print \\\"\\\\t of the OS environment variables that are set.\\\"\\n print \\\"\\\\t* Running ReHeat.\\\"\\n\\n self.set_creds()\\n self.gen_ip() # used in template description\\n self.gen_tenant_id()\\n if self.reheat_error:\\n return self.reheat_errmsg\\n\\n print \\\"\\\\t* You have opted to generate %s file[s]\\\" % self.template_type\\n if 'all' in self.template_type:\\n self.gen_heat_data()\\n self.gen_heat_template()\\n self.gen_compute_data()\\n return self.gen_compute_template()\\n elif 'heat' in self.template_type:\\n self.gen_heat_data()\\n return self.gen_heat_template()\\n elif 'compute' in self.template_type:\\n self.gen_compute_data()\\n return self.gen_compute_template()\\n else:\\n raise Exception(\\\"User provided an improper template type.\\\")\",\n \"def test_render_templates():\\n water_mol = Atoms(cclib.bridge.cclib2openbabel.readfile(\\\"data/water.xyz\\\", \\\"xyz\\\"))\\n if not water_mol.name:\\n water_mol.name = \\\"data/water.xyz\\\"\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.ADF.in\\\"])\\n main([\\\"/tmp/foo.ADF.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.in\\\").read().strip(),\\n \\\"\\\"\\\"TITLE data/water.xyz\\n\\nCHARGE 0 0\\n\\nNumber of atoms\\n 3\\n\\nATOMS Cartesian\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\nEnd\\n\\nBasis\\nEnd\\n\\nGeometry\\nEnd\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/test.GAMESS.inp\\\"])\\n main([\\\"/tmp/test.GAMESS.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.inp\\\").read(),\\n \\\"\\\"\\\" $CONTRL COORD=CART UNITS=ANGS $END\\n\\n $DATA\\ndata/water.xyz\\nC1\\nO 8.0 0.0584027061 0.0584027059 0.0000000000\\nH 1.0 1.0096135406 -0.0680162466 0.0000000000\\nH 1.0 -0.0680162466 1.0096135407 0.0000000000\\n $END\\n\\n\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/hello.GAMESSUK.inp\\\"])\\n main([\\\"/tmp/hello.GAMESSUK.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.inp\\\").read(), water_mol.to_string(\\\"gukin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/hello.world.Gaussian.gjf\\\"])\\n main([\\\"/tmp/hello.world.Gaussian.gjf\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.gjf\\\").read(),\\n \\\"\\\"\\\"#Put Keywords Here, check Charge and Multiplicity.\\n\\n data/water.xyz\\n\\n0 1\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\n\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/bar.Jaguar.in\\\"])\\n main([\\\"/tmp/bar.Jaguar.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.in\\\").read(), water_mol.to_string(\\\"jin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.Molpro.inp\\\"])\\n main([\\\"/tmp/foo.Molpro.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.inp\\\").read(), water_mol.to_string(\\\"mp\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/example.MOPAC.mop\\\"])\\n main([\\\"/tmp/example.MOPAC.mop\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.mop\\\").read(),\\n \\\"\\\"\\\"CHARGE=0 MS=0.0\\ndata/water.xyz\\n\\nO 0.05840 1 0.05840 1 0.00000 1\\nH 1.00961 1 -0.06802 1 0.00000 1\\nH -0.06802 1 1.00961 1 0.00000 1\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/bar.MPQC.in\\\"])\\n main([\\\"/tmp/bar.MPQC.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.in\\\").read(), water_mol.to_string(\\\"mpqcin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.NWChem.nw\\\"])\\n main([\\\"/tmp/foo.NWChem.nw\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.nw\\\").read(),\\n \\\"\\\"\\\"start molecule\\n\\ntitle data/water.xyz\\n\\ngeometry units angstroms print xyz autosym\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\nend\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/example.ORCA.inp\\\"])\\n main([\\\"/tmp/example.ORCA.inp\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.inp\\\").read(),\\n \\\"\\\"\\\"# data/water.xyz\\n! Opt\\n\\n* xyz 0 1\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\n*\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/bar.Psi.dat\\\"])\\n main([\\\"/tmp/bar.Psi.dat\\\", \\\"data/water.xyz\\\"])\\n assert_equals(\\n open(\\\"data/water.dat\\\").read(),\\n \\\"\\\"\\\"# data/water.xyz\\n\\nmolecule {\\n0 1\\nO 0.0584027061 0.0584027059 0.0000000000\\nH 1.0096135406 -0.0680162466 0.0000000000\\nH -0.0680162466 1.0096135407 0.0000000000\\nunits angstrom\\n}\\n\\noptimize('scf')\\n\\\"\\\"\\\",\\n )\\n\\n main([\\\"-g\\\", \\\"/tmp/example.QChem.in\\\"])\\n main([\\\"/tmp/example.QChem.in\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.in\\\").read(), water_mol.to_string(\\\"qcin\\\"))\\n\\n main([\\\"-g\\\", \\\"/tmp/foo.ZINDO.input\\\"])\\n main([\\\"/tmp/foo.ZINDO.input\\\", \\\"data/water.xyz\\\"])\\n assert_equals(open(\\\"data/water.input\\\").read(), water_mol.to_string(\\\"zin\\\"))\",\n \"def show_me():\\n # Scumbag thumbnail code\\n try:\\n from PIL import Image\\n except ImportError:\\n pass\\n else:\\n filename = os.path.join(app.static_folder, 'img', 'badumtss.png')\\n image = Image.open(filename)\\n\\n return render_template('show_me.html')\",\n \"def render(self, mode='human', type_of='color'):\\n if type_of == 'color':\\n plot_color(self.observation, self.servers_mem ,self.services_mem)\\n elif type_of=='black':\\n plot_black(self.observation, self.servers_mem ,self.services_mem)\\n print('\\\\n----services mem----\\\\n')\\n print('services resource usage: {}\\\\n'.format(self.services_mem))\\n print('servers capacity: {}\\\\n'.format(self.servers_mem))\\n print('\\\\n----observation----\\\\n')\\n print('service placements: {}\\\\n'.format(self.observation))\",\n \"def main():\\n \\n ## read parameters from command line\\n parser = argparse.ArgumentParser() \\n parser.add_argument(\\\"-d\\\", \\\"--density\\\", help=\\\"Density\\\")\\n parser.add_argument(\\\"-k\\\", \\\"--kappa\\\", help=\\\"Bending rigidity\\\")\\n parser.add_argument(\\\"-t\\\", \\\"--time\\\", help=\\\"Timestep\\\") \\n parser.add_argument(\\\"-s\\\", \\\"--save\\\", help=\\\"Save options\\\", action=\\\"store_true\\\") \\n args = parser.parse_args()\\n\\n ## load data \\n database = \\\"/local/duman/SIMULATIONS/long_filaments\\\"\\n datafolder = database + \\\"/density_\\\" + args.density + \\\"/kappa_\\\" + args.kappa\\n infile = datafolder + \\\"/VORTEX/data.hdf5\\\"\\n lx, ly, time, xedges, yedges, rho_hist, vx_hist, vy_hist, vorticity \\\\\\n = load_data(infile, int(args.time))\\n \\n ## plot data\\n savebase = \\\"~/RolfData/many_filaments_5\\\" \\n if args.save:\\n sfolder = savebase + \\\"/plots/VORTEX\\\" \\n os.system('mkdir -p ' + sfolder)\\n else:\\n sfolder = \\\"~/Desktop\\\"\\n plot_data(sfolder, lx, ly, time, xedges, yedges, rho_hist, vx_hist, vy_hist, vorticity, args.save)\",\n \"def step_run(cls, config):\\n\\n nir_paw_image_fname = config.get(cls.step_name, 'nir_paw_image')\\n nir_paw_conf_fname = config.get(cls.step_name, 'nir_paw_conf')\\n output_template = config.get(cls.step_name, 'output_template')\\n conf_limit = config.getfloat(cls.step_name, 'conf_limit')\\n\\n# logger.info('reading Comparison image from %s', comp_fname)\\n# comp_im = DESImage.load(comp_fname)\\n\\n ret_code = cls.__call__(nir_paw_image_fname, nir_paw_conf_fname, output_template, conf_limit)\\n return ret_code\",\n \"def fillBackgroundTemplates(opt):\\n\\n totalBkg={}\\n templates=[]\\n\\n #import signal events\\n data=ROOT.TChain('data')\\n for f in [os.path.join(opt.input,x) for x in os.listdir(opt.input) if 'Data13TeV' in x]:\\n if 'MuonEG' in f : continue\\n data.AddFile(f)\\n\\n #define final preselection cuts\\n cuts='xangle==%d'%opt.xangle\\n if len(opt.presel) : cuts += ' && ' + opt.presel \\n if opt.csiacc:\\n csiCuts ='csi1>%f && csi1<%f && '%opt.csiacc[opt.xangle][0]\\n csiCuts+='csi2>%f && csi2<%f'%opt.csiacc[opt.xangle][1]\\n cuts=csiCuts if len(cuts)==0 else '{0} && {1}'.format(cuts,csiCuts)\\n\\n #loop over categories build templates\\n for icat in range(len(opt.categs)):\\n\\n #apply category cuts\\n categCut=opt.categs[icat]\\n categCut=cuts if len(categCut)==0 else '%s && %s'%(categCut,cuts)\\n catName='%s_a%d_%d'%(opt.chTag,opt.xangle,icat)\\n\\n print '\\\\t',catName,categCut\\n\\n #background modelling histos\\n histos=[]\\n data_obs=None\\n for name,pfix in [('bkg_'+catName,'mix'),('bkg_%s_bkgShape'%catName,'mixem')]:\\n\\n templCuts=categCut.replace('csi1',pfix+'csi1')\\n templCuts=templCuts.replace('csi2',pfix+'csi2')\\n data.Draw('{0}mmiss >> h({1},{2},{3})'.format(pfix,opt.nbins,opt.mMin,opt.mMax),templCuts,'goff')\\n h=data.GetHistogram()\\n histos.append(h.Clone(name))\\n histos[-1].SetDirectory(0)\\n\\n if len(histos)==1:\\n totalBkg[icat]=h.Integral()\\n if not opt.unblind :\\n data_obs=h.Clone('data_obs_'+catName)\\n data_obs.SetDirectory(0)\\n\\n h.Reset('ICE')\\n templates += defineProcessTemplates(histos)\\n\\n #observed data in this category if unblinding\\n if opt.unblind:\\n data.Draw('mmiss >> h({1},{2},{3})'.format(opt.nbins,opt.mMin,opt.mMax),categCut,'goff')\\n data_obs=data.GetHistogram().Clone('data_obs_'+catName)\\n data_obs.SetDirectory(0)\\n\\n templates.append(data_obs)\\n\\n print '\\\\t total background:',totalBkg\\n return totalBkg,templates\",\n \"def plot_tiltres(setup, mtilt, ytilt, yfit, slit=None, outfile=None, show_QA=False, out_dir=None):\\n\\n plt.rcdefaults()\\n plt.rcParams['font.family']= 'times new roman'\\n\\n # Outfil\\n method = inspect.stack()[0][3]\\n if (outfile is None) and (not show_QA):\\n outfile = qa.set_qa_filename(setup, method, slit=slit, out_dir=out_dir)\\n\\n # Setup\\n plt.figure(figsize=(8, 4.0))\\n plt.clf()\\n ax = plt.gca()\\n\\n # Scatter plot\\n res = (mtilt-ytilt) - yfit\\n ax.scatter(mtilt, res)\\n\\n rms = np.std(res)\\n ax.text(0.90, 0.90, 'Slit {:d}: RMS (pix) = {:0.5f}'.format(slit, rms),\\n transform=ax.transAxes, size='large', ha='right', color='black')\\n # Label\\n ax.set_xlabel('Row')\\n ax.set_ylabel('Residual (pix)')\\n\\n # Finish\\n plt.tight_layout(pad=0.2, h_pad=0.0, w_pad=0.0)\\n if show_QA:\\n plt.show()\\n else:\\n plt.savefig(outfile, dpi=400)\\n plt.close()\\n\\n plt.rcdefaults()\\n\\n return\",\n \"def create_preview(name):\\n file_type = os.path.splitext(name)[1]\\n\\n fig = Figure()\\n axis = fig.add_subplot(1, 1, 1)\\n\\n dir = os.path.dirname(os.path.realpath(__file__))\\n file = open(dir+'/instances/'+name)\\n if file_type == '.csv':\\n\\n for nodeNo,line in enumerate(file): #enumerate used to obtain line numbers and thus node numbers\\n coords = line.rsplit()[0].split(\\\",\\\")\\n\\n x = int(coords[0])\\n y = int(coords[1])\\n axis.scatter(x, y, c = 'b', label = nodeNo)\\n axis.set_title(name)\\n axis.text(x+5,y+5, str(nodeNo))\\n else:\\n file.readline()\\n file.readline()\\n file.readline()\\n no_nodes = int(file.readline().strip().split()[1])\\n file.readline()\\n file.readline()\\n file.readline()\\n\\n for i in range(0, no_nodes):\\n\\n coords = file.readline().strip().split()[1:]\\n x = float(coords[0])\\n y = float(coords[1])\\n axis.scatter(x, y, c = 'b', label = i)\\n axis.set_title(name)\\n axis.text(x,y, str(i))\\n\\n return fig\",\n \"def main(args):\\n args = parse_args(args)\\n setup_logging(args.loglevel)\\n _logger.debug(\\\"Generating NUFEB simulation files\\\")\\n\\n # create nutrients\\n light = Nutrient(1e-1, None, None, \\\"g\\\", \\\"nn\\\")\\n co2 = Nutrient(float(args.co2), 1.9e-09, 44.01, \\\"l\\\", \\\"nn\\\")\\n o2 = Nutrient(0.28125, 2.30e-9, 32, \\\"l\\\", \\\"nn\\\")\\n sucrose = Nutrient(float(args.sucrose), 5.2e-10, 342.3, \\\"l\\\", \\\"nn\\\")\\n gco2 = Nutrient(0, None, 44.01, \\\"g\\\", \\\"nn\\\")\\n TEMPLATES_DIR = (Path(__file__).parent) / \\\"templates\\\"\\n\\n captureRate = round(1000 / args.timestep)\\n # define dump parameters\\n dump_list = {\\n \\\"vtk_dump\\\": f\\\"dump atom_vtk all vtk {captureRate} dump*.vtu id type diameter vx vy vz fx fy fz \\\\n dump grid_vtk all grid/vtk {captureRate} dump_%_*.vti con\\\",\\n \\\"image_dump\\\": f\\\"dump du_image all image {captureRate} image.*.jpg type diameter zoom 2 bacillus type size 1280 720 view 45 60 \\\\n dump_modify du_image acolor 1 green acolor 2 red\\\",\\n \\\"movie_dump\\\": f\\\"dump du_mov all movie {captureRate} movie.avi type diameter zoom 1.5 bacillus type size 1280 720 view 0 0 \\\\n dump_modify du_mov acolor 1 green acolor 2 red\\\",\\n \\\"hdf_dump\\\": f\\\"dump du_h5 all nufeb/hdf5 {captureRate} dump.h5 id type x y z vx vy vz fx fy fz radius conc reac\\\",\\n }\\n\\n dumps = defaultdict(list)\\n for i in range(4):\\n tmp = [\\\"vtk_dump\\\", \\\"image_dump\\\", \\\"movie_dump\\\", \\\"hdf_dump\\\"]\\n dumps[tmp[i]]\\n\\n for dump, dump_var in zip(\\n [args.vtk, args.img, args.movie, args.hdf],\\n [\\\"vtk_dump\\\", \\\"image_dump\\\", \\\"movie_dump\\\", \\\"hdf_dump\\\"],\\n ):\\n if dump is True or dump == \\\"True\\\":\\n dumps[dump_var] = dump_list[dump_var]\\n else:\\n dumps[dump_var] = \\\"\\\"\\n\\n ## Species-specific parameters\\n\\n # check for runs folder\\n if not os.path.isdir(\\\"runs\\\"):\\n os.mkdir(\\\"runs\\\")\\n x = float(args.dims.split(\\\",\\\")[0])\\n y = float(args.dims.split(\\\",\\\")[1])\\n z = float(args.dims.split(\\\",\\\")[2])\\n for n in range(1, int(args.num) + 1):\\n culture = Culture(args)\\n atoms_list = []\\n bacilli_list = []\\n # Create list of atoms and bacilli for atom definition file\\n for cell in culture.cells:\\n atoms_list.append(cell.Atom())\\n bacilli_list.append(cell.Bacillus())\\n # make atom definition file\\n for r in range(1, int(args.reps) + 1):\\n L = [\\n \\\" NUFEB Simulation\\\\r\\\\n\\\\n\\\",\\n f\\\" {args.cells_init} atoms \\\\n\\\",\\n f\\\" {len(culture.cell_types)} atom types \\\\n\\\",\\n f\\\" {args.cells_init} bacilli \\\\n\\\\n\\\",\\n f\\\" 0.0e-4 {x :.2e} xlo xhi \\\\n\\\",\\n f\\\" 0.0e-4 {y :.2e} ylo yhi \\\\n\\\",\\n f\\\" 0.0e-4 {z :.2e} zlo zhi \\\\n\\\\n\\\",\\n \\\" Atoms \\\\n\\\\n\\\",\\n ]\\n atoms = L + atoms_list\\n atoms.append(\\\"\\\\n\\\")\\n atoms.append(\\\" Bacilli \\\\n\\\\n\\\")\\n atoms = atoms + bacilli_list\\n # write atom definition file\\n f = open(\\n f\\\"runs/atom_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}_{r}.in\\\",\\n \\\"w+\\\",\\n )\\n f.writelines(atoms)\\n RUN_DIR = (\\n Path(\\\"runs\\\")\\n / f\\\"Run_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}_{args.reps}\\\"\\n )\\n if not os.path.isdir(RUN_DIR):\\n os.mkdir(RUN_DIR)\\n # os.mkdir(f'runs/Run_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}_{args.reps}')\\n # write initial conditions json file\\n dumpfile = open(RUN_DIR / \\\"metadata.json\\\", \\\"w\\\")\\n # dumpfile = open(f\\\"/runs/Run_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}_{args.reps}/metadata.json\\\",'w')\\n json.dump(CellInfo, dumpfile, indent=6)\\n dumpfile.close()\\n ###\\n\\n # write Inputscript\\n # open the file\\n filein = open(TEMPLATES_DIR / \\\"bacillus.txt\\\")\\n # filein = resources.read_text(\\\"nufeb_tools.templates\\\", \\\"Bacillus.txt\\\")\\n # read it\\n src = Template(filein.read())\\n # do the substitution\\n result = src.safe_substitute(\\n {\\n \\\"n\\\": args.cells_init,\\n \\\"SucRatio\\\": culture.SucRatio,\\n \\\"SucPct\\\": culture.SucPct,\\n \\\"n_cyanos\\\": culture.n_cyanos,\\n \\\"n_ecw\\\": culture.n_ecw,\\n \\\"Replicates\\\": args.reps,\\n \\\"Timesteps\\\": args.ntimesteps,\\n \\\"ts\\\": args.timestep,\\n \\\"CYANOGroup\\\": culture.cyGroup,\\n \\\"ECWGroup\\\": culture.ecwGroup,\\n \\\"Zheight\\\": float(args.dims.split(\\\",\\\")[2]),\\n \\\"CYANODiv\\\": culture.cyDiv,\\n \\\"ECWDiv\\\": culture.ecwDiv,\\n \\\"light\\\": light.concentration,\\n \\\"co2\\\": co2.concentration,\\n \\\"o2\\\": o2.concentration,\\n \\\"sucrose\\\": sucrose.concentration,\\n \\\"gco2\\\": gco2.concentration,\\n \\\"CYANOMonod\\\": culture.cyMonod,\\n \\\"ECWMonod\\\": culture.ecwMonod,\\n \\\"CYANOcount\\\": culture.cyanoCount,\\n \\\"ECWcount\\\": culture.ecwCount,\\n \\\"v_ncyano\\\": culture.vcyano,\\n \\\"v_necw\\\": culture.vecw,\\n \\\"vtk_dump\\\": dumps[\\\"vtk_dump\\\"],\\n \\\"image_dump\\\": dumps[\\\"image_dump\\\"],\\n \\\"movie_dump\\\": dumps[\\\"movie_dump\\\"],\\n \\\"hdf_dump\\\": dumps[\\\"hdf_dump\\\"],\\n }\\n )\\n f = open(\\n f\\\"./runs/Inputscript_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}.lmp\\\",\\n \\\"w+\\\",\\n )\\n f.writelines(result)\\n\\n # write local run script\\n # open the file\\n filein = open(TEMPLATES_DIR / \\\"local.txt\\\")\\n # filein = resources.read_text(\\\"nufeb_tools.templates\\\", \\\"local.txt\\\")\\n # read it\\n src = Template(filein.read())\\n # do the substitution\\n result = src.safe_substitute(\\n {\\n \\\"n\\\": n,\\n \\\"SucRatio\\\": culture.SucRatio,\\n \\\"SucPct\\\": culture.SucPct,\\n \\\"n_cyanos\\\": culture.n_cyanos,\\n \\\"n_ecw\\\": culture.n_ecw,\\n \\\"Reps\\\": args.reps,\\n }\\n )\\n f = open(\\n f\\\"./runs/local_{culture.n_cyanos}_{culture.n_ecw}_{culture.SucPct}.sh\\\", \\\"w+\\\"\\n )\\n f.writelines(result)\\n # write slurm script\\n # open the file\\n filein = open(TEMPLATES_DIR / \\\"slurm_dev.txt\\\")\\n # filein = resources.read_text(\\\"nufeb_tools.templates\\\", \\\"Slurm.txt\\\")\\n # read it\\n src = Template(filein.read())\\n # do the substitution\\n result = src.safe_substitute(\\n {\\n \\\"n\\\": args.cells_init,\\n \\\"job\\\": f\\\"NUFEB_cyano{n}\\\",\\n \\\"USER\\\": args.user,\\n \\\"Replicates\\\": args.reps,\\n \\\"SucPct\\\": culture.SucPct,\\n \\\"n_cyanos\\\": culture.n_cyanos,\\n \\\"n_ecw\\\": culture.n_ecw,\\n }\\n )\\n _logger.info(\\\"Script ends here\\\")\",\n \"def generate_surface_temp(self):\\n tcl_name = output_folder + \\\"/surface_output_\\\" + str(self.input_pdb_path).split(\\\"/\\\")[-1][0:-4] + \\\"_\\\" + str(self.current_chain) + \\\".tcl\\\"\\n opened_file = open(tcl_name, \\\"w\\\")\\n writeable_string = surface(self.input_pdb_path).surface_template(chain = str(self.current_chain))\\n opened_file.write(writeable_string)\",\n \"def get_mapdata():\\n return render_template(\\\"l_heatmap.html\\\")\",\n \"def run_task(self, config, simParams):\\n super().run_task(config, simParams)\\n\\n fovSize = simParams.get_microscope().get_image_dimensions()\\n umPerPix = simParams.get_microscope().get_microns_per_pixel()\\n \\n sx = self._parameters[\\\"margin\\\"]/umPerPix\\n sy = self._parameters[\\\"margin\\\"]/umPerPix\\n ex = fovSize[0] - self._parameters[\\\"margin\\\"]/umPerPix\\n ey = fovSize[1] - self._parameters[\\\"margin\\\"]/umPerPix\\n\\n # Create save polygons.\\n allPoly = []\\n for zi in range(simParams.get_number_z()):\\n\\n tmp = []\\n for fov in range(simParams.get_number_positions()):\\n [px, py] = simParams.get_fov_xy(fov)\\n\\n exRect = shapely.geometry.Polygon([[px + sx, py + sy],\\n [px + sx, py + ey],\\n [px + ex, py + ey],\\n [px + ex, py + sy]])\\n tmp.append(exRect)\\n\\n allPoly.append(tmp)\\n\\n self.save_data({\\\"extra-cellular\\\" : allPoly})\\n\\n # Reference images.\\n allFOV = []\\n for fov in range(simParams.get_number_positions()):\\n allFOV.append(simParams.get_fov_rect(fov))\\n\\n for zi in range(simParams.get_number_z()):\\n fig = plt.figure(figsize = (8,8))\\n\\n # Draw FOV.\\n for elt in allFOV:\\n coords = elt.exterior.coords.xy\\n x = list(coords[0])\\n y = list(coords[1])\\n plt.plot(x, y, color = 'gray')\\n\\n # Draw extra-cellular space.\\n for elt in allPoly[zi]:\\n coords = elt.exterior.coords.xy\\n x = list(coords[0])\\n y = list(coords[1])\\n plt.plot(x, y, color = 'black')\\n\\n ax = plt.gca()\\n ax.set_aspect('equal', 'datalim')\\n \\n plt.title(\\\"z plane {0:d}\\\".format(zi))\\n plt.xlabel(\\\"pixels\\\")\\n plt.ylabel(\\\"pixels\\\")\\n\\n fname = \\\"z_{0:d}.pdf\\\".format(zi)\\n fig.savefig(os.path.join(self.get_path(), fname),\\n format='pdf',\\n dpi=100)\\n\\n plt.close()\",\n \"def writeVelocityPlot(self):\\n name = \\\"velocity.vtk\\\"\\n chargeFile = open(name,'w')\\n chargeFile.write(\\\"%s\\\\n\\\"%(\\\"# vtk DataFile Version 2.0\\\"))\\n chargeFile.write(\\\"%s\\\\n\\\"%(\\\"obtained via hydraulicmodule\\\"))\\n chargeFile.write(\\\"%s\\\\n\\\"%(\\\"ASCII\\\"))\\n chargeFile.write(\\\"%s\\\\n\\\"%(\\\"DATASET UNSTRUCTURED_GRID\\\"))\\n chargeFile.write(\\\"%s %i %s\\\\n\\\"%(\\\"POINTS\\\",len(self.points),\\\"double\\\"))\\n dim = self.mesh.getSpaceDimensions()\\n if (dim==2): \\n for ind in range(0,len(self.points)):\\n chargeFile.write(\\\"%15.8e %15.8e %15.8e\\\\n\\\"%(self.points[ind][0],\\\\\\n self.points[ind][1],\\\\\\n 0.))\\n pass\\n pass\\n elif (dim==3): \\n for ind in range(0,len(self.points)):\\n chargeFile.write(\\\"%15.8e %15.8e %15.8e\\\\n\\\"%(self.points[ind][0],\\\\\\n self.points[ind][1],\\\\\\n self.points[ind][2]))\\n pass\\n pass\\n else:\\n raise Exception(\\\" error in mesh dimension \\\") \\n numberOfCells = self.mesh.getNumberOfCells()\\n connectivity = self.mesh.getConnectivity()\\n\\n cellListSize = 0\\n for i in range(0,numberOfCells): # gmsh meshes: type of elements\\n gmshType = connectivity[i][1]\\n if gmshType == 1: # 2-node line\\n cellListSize += 3\\n pass\\n elif gmshType == 2: # 3-node triangles\\n cellListSize += 4\\n pass\\n elif gmshType == 3: # 4-node quadrangles\\n cellListSize += 5\\n pass\\n elif gmshType == 4: # 4-node tetrahedron\\n cellListSize += 5\\n pass\\n elif gmshType == 5: # 8-node hexahedrons\\n cellListSize += 9\\n pass\\n pass\\n chargeFile.write(\\\"CELLS %i %i\\\\n\\\"%(numberOfCells,cellListSize))\\n ind = 0\\n for cell in connectivity:\\n ind = cell[2]+3\\n# print \\\" ctm dbg cell \\\",vtkTyp,ind,cell,\\\" perm \\\",permutation[ind],permutation[ind+1],permutation[ind+2],permutation[ind+3]\\n # \\n vtkTyp = _vtkGmsh(cell[1])\\n if (vtkTyp==3): # 2-node line\\n ind = cell[2]+3\\n chargeFile.write(\\\"%i %i %i\\\\n\\\"%(\\n 2,\\\\\\n cell[ind]-1,\\\\\\n cell[ind+1]-1)\\n )\\n pass\\n \\n elif (vtkTyp==5): # triangles\\n chargeFile.write(\\\"%i %i %i %i\\\\n\\\"%(\\n 3, \\n cell[ind]-1,\\\\\\n cell[ind+1]-1,\\\\\\n cell[ind+2]-1)\\n )\\n pass\\n elif (vtkTyp==9): # quadr\\n chargeFile.write(\\\"%i %i %i %i %i\\\\n\\\"%(\\n 4,\\\\\\n cell[ind]-1,\\\\\\n cell[ind+1]-1,\\\\\\n cell[ind+2]-1,\\\\\\n cell[ind+3]-1)\\n )\\n pass\\n elif (vtkTyp==10): # tetra\\n chargeFile.write(\\\"%i %i %i %i %i\\\\n\\\"%(\\n 4,\\\\\\n cell[ind]-1,\\\\\\n cell[ind+1]-1,\\\\\\n cell[ind+2]-1,\\\\\\n cell[ind+3]-1)\\n )\\n pass\\n elif (vtkTyp==12): # hexahedron\\n chargeFile.write(\\\"%i %i %i %i %i %i %i %i %i\\\\n\\\"%(\\n 8,\\\\\\n cell[ind]-1,\\\\\\n cell[ind+1]-1,\\\\\\n cell[ind+2]-1,\\\\\\n cell[ind+3]-1,\\\\\\n cell[ind+4]-1,\\\\\\n cell[ind+5]-1,\\\\\\n cell[ind+6]-1,\\\\\\n cell[ind+7]-1)\\n )\\n pass\\n pass\\n chargeFile.write(\\\"%s %i\\\\n\\\"%(\\\"CELL_TYPES\\\",numberOfCells))\\n#\\n for i in range(0,numberOfCells):\\n gmshType = connectivity[i][1]\\n\\n if (gmshType)==1:\\n cellTyp = 3\\n pass\\n elif (gmshType)==2:\\n cellTyp = 5\\n pass\\n elif (gmshType)==3:\\n cellTyp = 9\\n pass\\n elif (gmshType)==4:\\n cellTyp = 10\\n pass\\n elif (gmshType)==5:\\n cellTyp = 12\\n pass\\n elif (gmshType)==6:\\n cellTyp = 13\\n pass\\n elif gmshType == 7:\\n cellTyp = 14\\n pass\\n else:\\n raise Exception(\\\" check gmshtype \\\")\\n chargeFile.write(\\\"%i\\\\n\\\"%(cellTyp))\\n chargeFile.write(\\\"%s %d\\\\n\\\"%(\\\"POINT_DATA\\\",len(self.points)))\\n chargeFile.write(\\\"%s\\\\n\\\"%(\\\"VECTORS vectors float\\\"))\\n for velocityComponent in self.velocity:\\n chargeFile.write(\\\" %e %e %e\\\\n \\\"%(velocityComponent[0], velocityComponent[1], velocityComponent[2]))\\n chargeFile.write(\\\"%s\\\\n\\\"%(\\\"SCALARS charge double\\\"))\\n chargeFile.write(\\\"%s\\\\n\\\"%(\\\"LOOKUP_TABLE default\\\"))\\n#\\n \\n chargeDataFile=open(\\\"./\\\" + self.flowComponent.meshDirectoryName + \\\"/\\\" + \\\"HeVel.dat\\\",'r')\\n line = chargeDataFile.readline()\\n while \\\"Number Of Nodes\\\" not in line:\\n line = chargeDataFile.readline()\\n#line.split()\\n nodesNumber = line.split()[-1]\\n while \\\"Perm\\\" not in line:\\n line = chargeDataFile.readline()\\n#\\n# We read the permutation\\n#\\n for i in range(int(nodesNumber)): chargeDataFile.readline()\\n#\\n# We read the charge\\n#\\n for i in range(int(nodesNumber)): chargeFile.write(\\\" %15.10e\\\\n \\\"%(float(chargeDataFile.readline())))\",\n \"def render(self, mode = 'human'):\\n if mode == 'human':\\n\\n if self.is_2d:\\n fig = plt.figure()\\n for index, value in np.ndenumerate(self.board):\\n if value == 1:\\n plt.scatter(*index, c='red', s=1000, alpha=0.2)\\n elif value == -1:\\n plt.scatter(*index, c='blue', s=1000, alpha=0.2)\\n plt.xlim(-1, self.dim[0])\\n plt.ylim(-1, self.dim[1])\\n plt.xticks([])\\n plt.yticks([])\\n plt.grid(True)\\n\\n if self.is_3d:\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n for index, value in np.ndenumerate(self.board):\\n if value == 1:\\n ax.scatter(*index, c='red', s=1000, alpha=0.2)\\n elif value == -1:\\n ax.scatter(*index, c='blue', s=1000, alpha=0.2)\\n ax.set_xlim(0, self.dim[0] - 1)\\n ax.set_ylim(0, self.dim[1] - 1)\\n ax.set_zlim(0, self.dim[2] - 1)\\n\\n else:\\n fig = plt.figure()\\n ax = Axes3D(fig)\\n for index, value in np.ndenumerate(self.board):\\n if value == 1:\\n ax.scatter(*index, c='red', s=1000, alpha=0.2)\\n elif value == -1:\\n ax.scatter(*index, c='blue', s=1000, alpha=0.2)\\n\\n ax.set_xlim(0, self.dim[0] - 1)\\n ax.set_ylim(0, self.dim[1] - 1)\\n ax.set_zlim(0, self.dim[2] - 1)\\n ax.set_title('Nr of steps: ' + str(self.steps))\\n\\n plt.show()\\n return fig\",\n \"def tkGui(settings):\\n tkApp=Tk()\\n tkApp.doit=False\\n tkApp.title('Render File with Blender')\\n fileField=None\\n def onBrowse():\\n settings.filename=tkFileDialog.askopenfilename()\\n # for some annoying reason this returns \\\"/\\\" separators even in windoze\\n if os.sep!=\\\"/\\\":\\n settings.filename=os.sep.join(settings.filename.split(\\\"/\\\"))\\n fileField.delete(0, END)\\n fileField.insert(0, settings.filename) \\n def onOK():\\n tkApp.doit=True\\n tkApp.quit()\\n def onCancel():\\n tkApp.doit=False\\n tkApp.quit()\\n row=0\\n settings.filename=\\\"\\\"\\n filenameEntry=Frame(tkApp)\\n filenameEntry.grid(row=row,columnspan=4)\\n Message(filenameEntry,text=\\\"File:\\\").grid(row=0,column=0,sticky=E)\\n fileField=Entry(filenameEntry,textvariable=settings.filename,width=75)\\n fileField.grid(row=0,column=1,sticky=W)\\n Button(filenameEntry,text=\\\"...\\\",command=onBrowse).grid(row=0,column=2,sticky=W)\\n row=row+1\\n Button(tkApp,text=\\\"OK\\\",command=onOK,width=10).grid(row=row,column=1,sticky=E)\\n Button(tkApp,text=\\\"Cancel\\\",command=onCancel,width=10).grid(row=row,column=2,sticky=W)\\n tkApp.mainloop()\\n if hasattr(settings,\\\"filename\\\")==False:\\n print \\\"Missing filename!\\\"\\n return False\\n return tkApp.doit\",\n \"def print_TRT_cell_map(samples_df,cfg_set_tds):\\r\\n\\r\\n fig, axes, extent = ccs4_map(cfg_set_tds)\\r\\n axes.scatter(samples_df[\\\"LV03_x\\\"].loc[samples_df[\\\"category\\\"] == \\\"DEVELOPING\\\"],\\r\\n samples_df[\\\"LV03_y\\\"].loc[samples_df[\\\"category\\\"] == \\\"DEVELOPING\\\"],c='w',edgecolor=(.7,.7,.7),s=18)\\r\\n axes.scatter(samples_df[\\\"LV03_x\\\"].loc[samples_df[\\\"category\\\"] == \\\"MODERATE\\\"],\\r\\n samples_df[\\\"LV03_y\\\"].loc[samples_df[\\\"category\\\"] == \\\"MODERATE\\\"],c='g',edgecolor=(.7,.7,.7),s=22)\\r\\n axes.scatter(samples_df[\\\"LV03_x\\\"].loc[samples_df[\\\"category\\\"] == \\\"SEVERE\\\"],\\r\\n samples_df[\\\"LV03_y\\\"].loc[samples_df[\\\"category\\\"] == \\\"SEVERE\\\"],c='y',edgecolor=(.7,.7,.7),s=26)\\r\\n axes.scatter(samples_df[\\\"LV03_x\\\"].loc[samples_df[\\\"category\\\"] == \\\"VERY SEVERE\\\"],\\r\\n samples_df[\\\"LV03_y\\\"].loc[samples_df[\\\"category\\\"] == \\\"VERY SEVERE\\\"],c='r',edgecolor=(.7,.7,.7),s=30)\\r\\n \\r\\n fig.savefig(os.path.join(cfg_set_tds[\\\"fig_output_path\\\"],u\\\"TRT_Map.pdf\\\"))\",\n \"def do_data_plots(cat, subdir):\\n dla_data.noterdaeme_12_data()\\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(zmax=5,color=\\\"blue\\\")\\n np.savetxt(path.join(subdir,\\\"cddf_all.txt\\\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\\n plt.xlim(1e20, 1e23)\\n plt.ylim(1e-28, 5e-21)\\n plt.legend(loc=0)\\n save_figure(path.join(subdir, \\\"cddf_gp\\\"))\\n plt.clf()\\n\\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(zmax=5,color=\\\"blue\\\", moment=True)\\n plt.xlim(1e20, 1e23)\\n plt.legend(loc=0)\\n save_figure(path.join(subdir, \\\"cddf_moment_gp\\\"))\\n plt.clf()\\n\\n #Evolution with redshift\\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(4,5, label=\\\"4-5\\\", color=\\\"brown\\\")\\n np.savetxt(path.join(subdir,\\\"cddf_z45.txt\\\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(3,4, label=\\\"3-4\\\", color=\\\"black\\\")\\n np.savetxt(path.join(subdir,\\\"cddf_z34.txt\\\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(2.5,3, label=\\\"2.5-3\\\", color=\\\"green\\\")\\n np.savetxt(path.join(subdir,\\\"cddf_z253.txt\\\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\\n (l_N, cddf, cddf68, cddf95) = cat.plot_cddf(2,2.5, label=\\\"2-2.5\\\", color=\\\"blue\\\")\\n np.savetxt(path.join(subdir,\\\"cddf_z225.txt\\\"), (l_N, cddf, cddf68[:,0], cddf68[:,1], cddf95[:,0],cddf95[:,1]))\\n plt.xlim(1e20, 1e23)\\n plt.ylim(1e-28, 5e-21)\\n plt.legend(loc=0)\\n save_figure(path.join(subdir,\\\"cddf_zz_gp\\\"))\\n plt.clf()\\n\\n #dNdX\\n dla_data.dndx_not()\\n dla_data.dndx_pro()\\n (z_cent, dNdX, dndx68, dndx95) = cat.plot_line_density(zmax=5)\\n np.savetxt(path.join(subdir,\\\"dndx_all.txt\\\"), (z_cent, dNdX, dndx68[:,0],dndx68[:,1], dndx95[:,0],dndx95[:,1]) )\\n plt.legend(loc=0)\\n plt.ylim(0,0.16)\\n save_figure(path.join(subdir,\\\"dndx_gp\\\"))\\n plt.clf()\\n\\n #Omega_DLA\\n dla_data.omegahi_not()\\n dla_data.omegahi_pro()\\n dla_data.crighton_omega()\\n (z_cent, omega_dla, omega_dla_68, omega_dla_95) = cat.plot_omega_dla(zmax=5)\\n# cat.tophat_prior = True\\n# cat.plot_omega_dla(zmax=5, label=\\\"Tophat Prior\\\", twosigma=False)\\n# cat.tophat_prior = False\\n np.savetxt(path.join(subdir,\\\"omega_dla_all.txt\\\"), (z_cent, omega_dla, omega_dla_68[:,0],omega_dla_68[:,1], omega_dla_95[:,0], omega_dla_95[:,1]))\\n plt.legend(loc=0)\\n plt.xlim(2,5)\\n plt.ylim(0,2.5)\\n save_figure(path.join(subdir,\\\"omega_gp\\\"))\\n plt.clf()\",\n \"def run_variations(filename, N=50, N_scales=7, shape=(512, 512),\\n steps=[(10,4.0), (20,1.0)], display_inline=False, min_radius=1.5,\\n max_radius=90.0):\\n\\n colors = [[1, 0, 0], [0, 1, 0], [0, 0, 0.9], [1, 1, 0],\\n [1, 0, 1], [0, 1, 1], [1, 1, 1], [1, 0.6, 0]]\\n perm = np.random.permutation\\n for n in range(N):\\n # generate palette\\n pal = perm(colors)[:4]\\n pal = perm(r_[pal, pal])\\n pal = (pal * .6 + roll(pal, 4, axis=0) * 0.4)[:4]\\n pal = shade(N_scales, *pal)\\n # generate parameters and MSTP object\\n ra = exp(rseq(log(min_radius), log(max_radius), N=N_scales, randomness=0.9)).round(2)\\n ri = ((1.333 + rand(N_scales)) * ra).round(2)\\n dt = (.01 * frange(1, N_scales) ** 0.8).round(3)\\n wt = (1.33 + arctan(5 * (rand(N_scales) - .5))).round(2)\\n m = MSTP(shape, ra=ra, ri=ri, dt=dt, wt=wt, pal=pal)\\n\\n print '\\\\n-------- rendering image', filename % n\\n print m\\n # display(HTML(' '.join('<span style=\\\"background:rgb(%d,%d,%d)\\\">(o_O)</span> ' % tuple(255*k) for k in array(pal))))\\n for i, (N_steps, speed) in enumerate(steps):\\n print 'rendering %s steps at %1.1fx speed' % (N_steps, dt_multiplier)\\n sys.stdout.flush()\\n m.run(n=N_steps, speed=speed)\\n if display_inline:\\n display(IM(m.rgb_image()))\\n if i < 0:\\n first = False\\n print 'renoising after iter 1.',\\n m.z += 0.25 * rand(*m.z.shape) * m.z.ptp()\\n\\n # display(IM(m.rgb_image()))\\n imsave(filename % n, m.rgb_image())\",\n \"def main(temp_dir, extensions, template):\\n env = load_env(template_dir=temp_dir)\\n if not template:\\n # Get all the templates and return a dict with enumerated \\n # templates names\\n ext = extensions if extensions else []\\n template_dict = get_templates(env, extensions=ext)\\n # Echo the content of the template directory by enumerating \\n # the templates and a simple list join\\n temp_list = list()\\n for x in template_dict.items():\\n num = str(x[0])\\n # Remove whitespace, underscores and capitalize words\\n temp_name = x[1].strip().replace(\\\"_\\\", \\\" \\\").title()\\n temp_string = \\\"{}. {}\\\".format(num, temp_name)\\n temp_list.append(temp_string)\\n click.echo(\\\"\\\\n\\\".join(temp_list))\\n # Prompt the user to give the number of the template\\n temp_num = click.prompt(\\n \\\"Choose a templeta by entering the number of the template.\\\",\\n type=int\\n )\\n # Get the template from the template dictionary\\n template = template_dict.get(temp_num)\\n # Get the variables\\n temp_vars = get_vars(template, env)\\n # Crate a dict with variables and let the user input the variables\\n vars_to_render = dict()\\n for var in temp_vars:\\n user_var = click.prompt(\\\"{}?\\\".format(var.capitalize()))\\n vars_to_render[var] = user_var\\n # Get the template\\n temp = env.get_template(template)\\n # Render the template\\n click.echo(temp.render(vars_to_render))\",\n \"def process(self):\\n # Opening and preprocessing of the input file\\n if self.options.mbtiles_fromdisk or self.options.mbtiles_todisk:\\n if self.options.mbtiles_fromdisk:\\n i_parm=10\\n if self.options.mbtiles_todisk:\\n i_parm=11\\n if self.options.verbose:\\n print \\\"GDAL2MbTiles :mbtiles from/to disk [\\\",i_parm,\\\"] mbtiles_fromdisk[\\\",self.options.mbtiles_fromdisk,\\\"] mbtiles_todisk[\\\",self.options.mbtiles_todisk,\\\"]\\\"\\n self.mbtiles_setup(i_parm)\\n return\\n else:\\n if self.options.verbose:\\n print \\\"GDAL2MbTiles :tile creation mbtiles[\\\",self.options.mbtiles,\\\"]\\\"\\n self.open_input()\\n # Generation of main metadata files and HTML viewers\\n self.generate_metadata()\\n # Generation of the lowest tiles\\n self.generate_base_tiles()\\n # Generation of the overview tiles (higher in the pyramid)\\n self.generate_overview_tiles()\\n # Generating of KML\\n self.generate_kml()\",\n \"def render_path_visualisation(projectRoot, configName, prepared_paths):\\n with open(os.path.join(os.path.dirname(__file__), 'assets/visualizer_template.html')) as file_:\\n template = Template(file_.read())\\n \\n visuFileName = \\\"pathvisualizer_%s.html\\\" % configName.replace(\\\" \\\",\\\"\\\")\\n visuFilePath = os.path.join(projectRoot, visuFileName)\\n renderedTemplate = template.render(configName=configName, currentDate=datetime.datetime.now(), paths=prepared_paths)\\n with open(visuFilePath,'w') as f:\\n f.write(renderedTemplate)\\n\\n logger.info(\\\"Path visualized - find the file at %s\\\" % visuFilePath)\",\n \"def do_stuff(self):\\n self.create_tourism_raster()\",\n \"def build_static_files(cfg_prep_used, area_threshold=0.25, beef_cuttings=4, dairy_cuttings=5, overwrite_flag=False):\\n logging.info('\\\\nBuilding ET-Demands Static Files')\\n\\n # Default values\\n permeability = -999\\n soil_depth = 60 # inches\\n aridity = 50\\n irrigation = 1\\n\\n # Input paths\\n # DEADBEEF - For now, get cropET folder from INI file\\n # This function may eventually be moved into the main cropET code\\n config = copy.deepcopy(cfg_prep_used)\\n\\n try:\\n project_ws = config.CROP_ET.project_folder\\n except:\\n logging.error('project_folder parameter must be set in the INI file, exiting')\\n return False\\n try:\\n gis_ws = config.CROP_ET.gis_folder\\n except:\\n logging.error('gis_folder parameter must be set in the INI file, exiting')\\n return False\\n try:\\n et_cells_path = config.CROP_ET.cells_path\\n except:\\n logging.error('cells_path parameter must be set in the INI file, exiting')\\n return False\\n try:\\n crop_et_ws = config.CROP_ET.crop_et_folder\\n except:\\n logging.error('crop_et_folder parameter must be set in the INI file, exiting')\\n return False\\n try:\\n template_ws = config.CROP_ET.template_folder\\n except:\\n logging.error('CROP_ET template_folder parameter must be set in the INI file, exiting')\\n return False\\n\\n # Read data from geodatabase or shapefile\\n # if '.gdb' in et_cells_path and not et_cells_path.endswith('.shp'):\\n # _flag = False\\n # _path = os.path.dirname(et_cells_path)\\n # gdb_path = r'D:\\\\Projects\\\\CAT_Basins\\\\AltusOK\\\\et-demands_py\\\\et_demands.gdb'\\n # _cells_path = os.path.join(gdb_path, 'et_cells')\\n\\n # Output sub-folder names\\n static_ws = os.path.join(project_ws, 'static')\\n\\n # basin attributes fields\\n basin_id_filed = 'STAID'\\n basin_elev_field = 'ELEV_MEAN_M_BASIN'\\n\\n # ET Cell field names\\n cell_lat_field = 'LAT'\\n cell_lon_field = 'LON'\\n cell_id_field = 'GAGE_ID'\\n # cell_station_id_field = 'STATION_ID'\\n # awc_field = 'AWC'\\n clay_field = 'CLAY'\\n sand_field = 'SAND'\\n awc_in_ft_field = 'AWC_IN_FT'\\n hydgrp_num_field = 'HYDGRP_NUM'\\n hydgrp_field = 'HYDGRP'\\n\\n # huc_field = 'HUC{}'.format(huc)\\n # permeability_field = 'PERMEABILITY'\\n # soil_depth_field = 'SOIL_DEPTH'\\n # aridity_field = 'ARIDITY'\\n # dairy_cutting_field = 'DAIRY_CUTTINGS'\\n # beef_cutting_field = 'BEEF_CUTTINGS'\\n\\n # Static file names\\n cell_props_name = 'ETCellsProperties.txt'\\n cell_crops_name = 'ETCellsCrops.txt'\\n cell_cuttings_name = 'MeanCuttings.txt'\\n crop_params_name = 'CropParams.txt'\\n crop_coefs_name = 'CropCoefs.txt'\\n crop_coefs_eto = 'CropCoefs_eto.txt'\\n crop_coefs_etr = 'CropCoefs_etr.txt'\\n eto_ratio_name = 'EToRatiosMon.txt'\\n etr_ratio_name = 'ETrRatiosMon.txt'\\n static_list = [crop_params_name, crop_coefs_name, crop_coefs_eto, crop_coefs_etr, cell_props_name, cell_crops_name,\\n cell_cuttings_name, eto_ratio_name, etr_ratio_name]\\n\\n # Check input folders\\n if not os.path.isdir(crop_et_ws):\\n logging.critical('\\\\nERROR: The INI cropET folder does not exist\\\\n {}'.format(crop_et_ws))\\n sys.exit()\\n elif not os.path.isdir(project_ws):\\n logging.critical('\\\\nERROR: The project folder does not exist\\\\n {}'.format(project_ws))\\n sys.exit()\\n elif not os.path.isdir(gis_ws):\\n logging.critical('\\\\nERROR: The GIS folder does not exist\\\\n {}'.format(gis_ws))\\n sys.exit()\\n logging.info('\\\\nGIS Workspace: {}'.format(gis_ws))\\n logging.info('Project Workspace: {}'.format(project_ws))\\n logging.info('CropET Workspace: {}'.format(crop_et_ws))\\n logging.info('Template Workspace: {}'.format(template_ws))\\n\\n # Check input files\\n if not _arcpy.exists(et_cells_path):\\n logging.critical('\\\\nERROR: The ET Cell shapefile does not exist\\\\n {}'.format(et_cells_path))\\n sys.exit()\\n for static_name in static_list:\\n if not os.path.isfile(os.path.join(template_ws, static_name)):\\n logging.error(\\n '\\\\nERROR: The static template does not exist\\\\n {}'.format(os.path.join(template_ws, static_name)))\\n sys.exit()\\n logging.debug('ET Cells Path: {}'.format(et_cells_path))\\n\\n # Build output table folder if necessary\\n if not os.path.isdir(static_ws):\\n os.makedirs(static_ws)\\n\\n # Read ET Cell zonal stats\\n logging.info('\\\\nReading ET Cell Zonal Stats')\\n logging.debug(' {}'.format(et_cells_path))\\n crop_field_list = sorted([f for f in _arcpy.list_fields(et_cells_path) if re.match('CROP_\\\\d{3}', f)])\\n fields = [cell_id_field, cell_lat_field, cell_lon_field, awc_in_ft_field, clay_field, sand_field, hydgrp_num_field,\\n hydgrp_field]\\n fields = fields + crop_field_list\\n logging.debug(' Fields: {}'.format(fields))\\n cell_data_dict = defaultdict(dict)\\n\\n for fid, row in _arcpy.search_cursor(et_cells_path, fields).items():\\n # Switch to cell_id_field as index (instead of FID)\\n for f in fields[1:]:\\n cell_data_dict[str(row[cell_id_field])][f] = row[f]\\n\\n # Read basin data\\n logging.info('\\\\nReading basin attributes')\\n fields = [basin_elev_field]\\n logging.debug(' Fields: {}'.format(fields))\\n basin_data_dict = defaultdict(dict)\\n basin_topo = pd.read_csv(config.GAGES.basin_topo_file, sep=',', dtype={0: str})\\n for cell_id_tmp in list(cell_data_dict.keys()):\\n basin_data_dict[cell_id_tmp][basin_elev_field] = \\\\\\n basin_topo.loc[basin_topo[basin_id_filed] == cell_id_tmp, basin_elev_field].values[0]\\n # Convert elevation units if necessary\\n logging.debug(' Convert station elevation from meters to feet')\\n for k in basin_data_dict.keys():\\n basin_data_dict[k][basin_elev_field] /= 0.3048\\n\\n # static files\\n logging.info('\\\\nCopying template static files')\\n for static_name in static_list:\\n logging.debug(' {}'.format(static_name))\\n shutil.copy(os.path.join(template_ws, static_name), static_ws)\\n\\n logging.info('\\\\nWriting static text files')\\n cell_props_path = os.path.join(static_ws, cell_props_name)\\n cell_crops_path = os.path.join(static_ws, cell_crops_name)\\n cell_cuttings_path = os.path.join(static_ws, cell_cuttings_name)\\n eto_ratio_path = os.path.join(static_ws, eto_ratio_name)\\n etr_ratio_path = os.path.join(static_ws, etr_ratio_name)\\n\\n # Write cell properties\\n logging.debug(' {}'.format(cell_props_path))\\n with open(cell_props_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n basin_id = cell_id\\n basin_lat = '{:>9.4f}'.format(cell_data[cell_lat_field])\\n basin_lon = '{:>9.4f}'.format(cell_data[cell_lon_field])\\n basin_data = basin_data_dict[basin_id]\\n basin_elev = '{:.2f}'.format(basin_data[basin_elev_field])\\n # There is an extra/unused column in the template and excel files\\n output_list = [cell_id, cell_id, basin_id, basin_lat, basin_lon, basin_elev, permeability,\\n '{:.4f}'.format(cell_data[awc_in_ft_field]), soil_depth, cell_data[hydgrp_field],\\n cell_data[hydgrp_num_field], aridity, '']\\n output_f.write('\\\\t'.join(map(str, output_list)) + '\\\\n')\\n\\n del output_list\\n del basin_id, basin_lat, basin_lon, basin_elev\\n\\n # Write cell crops\\n crops = util.parse_int_set(config.USDA.cdl_crops)\\n logging.debug(' {}'.format(cell_crops_path))\\n with open(cell_crops_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n basin_id = cell_id\\n output_list = [cell_id, cell_id, basin_id, irrigation]\\n crop_list = ['CROP_{:03d}'.format(i) for i in crops]\\n crop_area_list = []\\n for crop in crop_list:\\n if crop in cell_data.keys() and cell_data[crop] is not None:\\n crop_area_list.append(cell_data[crop])\\n else:\\n crop_area_list.append(0)\\n crop_flag_list = [1 if area > area_threshold else 0 for area in crop_area_list]\\n output_list = output_list + crop_flag_list\\n output_f.write('\\\\t'.join(map(str, output_list)) + '\\\\n')\\n\\n del crop_list, crop_area_list, crop_flag_list, output_list\\n\\n # Write cell cuttings\\n logging.debug(' {}'.format(cell_cuttings_path))\\n with open(cell_cuttings_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n output_list = [cell_id, cell_id, '{:>9.4f}'.format(cell_data[cell_lat_field]), dairy_cuttings,\\n beef_cuttings]\\n output_f.write('\\\\t'.join(map(str, output_list)) + '\\\\n')\\n\\n # Write monthly ETo ratios\\n logging.debug(' {}'.format(eto_ratio_path))\\n with open(eto_ratio_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n basin_id = cell_id\\n output_f.write('\\\\t'.join(map(str, [basin_id, ''] + [1.0] * 12)) + '\\\\n')\\n\\n # Write monthly ETr ratios\\n logging.debug(' {}'.format(etr_ratio_path))\\n with open(etr_ratio_path, 'a') as output_f:\\n for cell_id, cell_data in sorted(cell_data_dict.items()):\\n basin_id = cell_id\\n output_f.write('\\\\t'.join(map(str, [basin_id, ''] + [1.0] * 12)) + '\\\\n')\",\n \"def main():\\n\\t#print(scipy.__version__)\\n\\t#image()\\n\\t#heat_capacity2()\\n\\t#hist()\\n\\t#single_plot()\\n\\n\\t#heat_capacity2()\\n\\t#single_plot()\\n\\t#plt.show()\\n\\t#u0_tc()\\n\\t#multi_heat_capacity(\\\"HL_DM_flux5\\\",True)\\n\\t#multi_heat_capacity2()\\n\\t#plot_spin()\\n\\t#plt.show()\\n\\theat_capacity2(1,2)\\n\\t#hist()\\n\\tplt.show()\\n\\t#potential()\\n\\t#plt.show()\\n\\t#heat_capacity(3,4)\\n\\t#heat_capacity(5,6)\\n\\t#heat_capacity(7,8)\\n\\t#final_spins()\\n\\t#plot_spin()\\n\\t#plot_from_csv()\\n\\t#difference_plot()\",\n \"def read_pfile(self, filename:str, filemode:str='vcnmodel.v0', vscale:float=1e-3, iscale:float=1e-9, plot=False):\\n \\n\\n # r = df['Results'][0]\\n #\\n # if plot:\\n # P = PH.Plotter((1, 1), figsize=(6, 4))\\n # cell_ax = list(P.axdict.keys())[0]\\n # for trial in range(len(df['Results'])):\\n # ds = df['Results'][trial]\\n # k0 = list(df['Results'][trial].keys())[0]\\n # dx = ds[k0]['monitor']\\n # P.axdict[cell_ax].plot(dx['time'], dx['postsynapticV'], linewidth=1.0)\\n # P.axdict[cell_ax].set_xlim(0., 150.)\\n # P.axdict[cell_ax].set_ylim(-200., 50.)\\n # PH.calbar(P.axdict[cell_ax], calbar=[120., -95., 25., 20.], axesoff=True, orient='left',\\n # unitNames={'x': 'ms', 'y': 'mV'}, font='Arial', fontsize=8)\\n #\\n # # mpl.savefig(outfile)\\n # mpl.show()\\n # print(list(df.keys()))\\n # print('\\\\nbasename: ', df['basename'])\\n # print('\\\\nruninfo: ', df['runInfo'])\\n \\\"\\\"\\\"\\n The runInfo dictionary holds somethign like this:\\n runinfo: {'folder': PosixPath('VCN_Cells/VCN_c08/Simulations/IV'), 'fileName': 'Normal', 'runName': 'Run', \\n 'manipulation': 'Canonical', 'preMode': 'cc', 'postMode': 'cc', 'TargetCellType': 'Bushy', \\n 'electrodeSection': 'soma', 'dendriticElectrodeSection': 'dendrite', \\n 'dendriticSectionDistance': 100.0, 'celsius': 37, 'nStim': 1, \\n 'stimFreq': 200.0, 'stimInj': {'pulse': [-1.0, 2.01, 0.2]}, \\n 'stimDur': 100.0, 'stimDelay': 5.0, 'stimPost': 3.0, \\n 'vnStim': 1, 'vstimFreq': 200.0, 'vstimInj': 50, \\n 'vstimDur': 50.0, 'vstimDelay': 2.0, 'vstimPost': 3.0, 'vstimHolding': -60, \\n 'gif_i0': 0.0, 'gif_sigma': 0.5, 'gif_fmod': 0.2, 'gif_tau': 3.0, \\n 'gif_dur': 10.0, 'gif_skew': 0.0, \\n 'runTime': 'Wed Oct 9 13:05:54 2019', \\n 'inFile': None, 'inFileRep': 1, 'spikeTimeList': {}, \\n 'v_init': -61.0, 'useSaveState': True, 'tstop': 8.0, 'filename': 'VCN_c08_pulse_'}\\n \\\"\\\"\\\"\\n # print('\\\\nmodelPars: ', df['modelPars'])\\n \\\"\\\"\\\"\\n The modelPars dict holds the following:\\n modelPars: {'species': 'mouse', 'cellClass': 'bushy', 'modelType': 'II', \\n 'modelName': 'mGBC', 'soma': True, 'axon': False, \\n 'dendrites': False, 'pumps': False, 'hillock': False, \\n 'initialsegment': False, 'myelinatedaxon': False, \\n 'unmyelinatedaxon': False, 'na': 'nav11', 'ttx': False, \\n 'name': 'bushy', 'morphology': 'VCN_Cells/VCN_c08/Morphology/VCN_c08.hoc', \\n 'temperature': 34.0}\\n \\n Note 10/28/2019 changed structure so that runInfo and modelPars are both \\n subdictionaries of Params (filemode is 'vcnmodel.v0')\\n ... and undone later, so that all are top-level (filemode is 'vcnmodel.v1')\\n \\\"\\\"\\\"\\n with open(filename, 'rb') as fh:\\n df = FPM.pickle_load(fh)\\n if filemode in ['vcnmodel.v0']:\\n print(f\\\"Reading model file in version v0:, with {len(df['Results']):4d} trials\\\")\\n elif filemode in ['vcnmodel.v1']:\\n print(f\\\"Reading model file in version v1:, with {len(df['Results']):4d} trials\\\")\\n else:\\n raise ValueError(f'Unknown file mode: {filemode:s}')\\n # print('\\\\nrpfile v0: File keys: ', df.keys())\\n #\\n # print('\\\\nrpfile v0: basename: ', df['basename'])\\n mtime = Path(filename).stat().st_mtime\\n timestamp_str = datetime.datetime.fromtimestamp(mtime).strftime('%Y-%m-%d-%H:%M')\\n if filemode == 'vcnmodel.v0':\\n # print(df['Params'].keys())\\n try:\\n dinfo = df['Params']['runInfo']\\n except:\\n try:\\n dinfo = df['runInfo']\\n except:\\n raise ValueError (\\\"Cannot read the file in v0 mode?\\\")\\n run_protocol = df['Params']['runProtocol']\\n if isinstance(dinfo, Params):\\n dinfo = dinfo.todict()\\n dur = dinfo['stimDur']\\n delay = dinfo['stimDelay']\\n mode = dinfo['postMode'].upper()\\n ntr = len(df['Results'])\\n # print(df.keys())\\n # print('runinfo: ', df['runInfo'])\\n # print('Params: ', df['Params'].keys())\\n # print('dinfo: ', dinfo)\\n if 'dt' in df['Params'].keys():\\n self.rate = df['Params']['dt']\\n else:\\n self.rate = df['Params'].dt\\n V = [[]]*ntr\\n I = [[]]*ntr\\n for i in range(len(df['Results'])):\\n fk = list(df['Results'][i].keys())[0]\\n dfx = df['Results'][i][fk]['monitor']\\n timebase = dfx['time']\\n V[i] = dfx['postsynapticV']*vscale\\n I[i] = dfx['i_stim0']*iscale\\n else:\\n dinfo = df['runInfo']\\n x = dir(dinfo)\\n if 'stimVC' not in x: # create fields for missing values from older versions of files.\\n dinfo.stimVC = None\\n # print('rpfile v0: dinfo: ', dinfo)\\n mode = dinfo.postMode.upper()\\n dur = dinfo.stimDur\\n delay = dinfo.stimDelay\\n mode = dinfo.postMode\\n print(\\\"Keys found in file: \\\", df.keys())\\n print('Mode: ', mode)\\n try:\\n self.rate = df['Params'].dt # old version, now separated IC and VC\\n except:\\n if mode == 'VC':\\n self.rate = df['Params'].dtVC\\n elif mode == \\\"CC\\\":\\n self.rate = df['Params'].dtIC\\n else:\\n raise ValueError(\\\"Cannot find rate for data mode: \\\", mode)\\n\\n run_protocol = dinfo.runProtocol\\n if dinfo.runProtocol in ['runIV', 'initIV', 'testIV']:\\n ntr = len(df['Results'])\\n V = [[]]*ntr\\n I = [[]]*ntr\\n for ii, i in enumerate(df['Results'].keys()):\\n dfx = df['Results'][i]['monitor']\\n timebase = dfx['time']\\n V[ii] = np.array(dfx['postsynapticV'])*vscale\\n I[ii] = np.array(dfx['i_stim0'])*iscale\\n elif dinfo.runProtocol in ['runVC', 'initVC', 'testVC']:\\n dur = dinfo.vstimDur # msec\\n delay = dinfo.vstimDelay # msec\\n ntr = len(df['Results'])\\n V = [[]]*ntr\\n I = [[]]*ntr\\n for ii, i in enumerate(df['Results'].keys()):\\n dfx = df['Results'][i]['monitor']\\n timebase = dfx['time']\\n V[ii] = np.array(dfx['postsynapticV'])*vscale\\n I[ii] = np.array(dfx['postsynapticI'])*iscale\\n \\n elif dinfo.runProtocol in ['initAN', 'runANPSTH', 'runANIO', 'runANSingles']:\\n\\n # two ways data can be organized, so try both\\n try: # cnmodel_models simulations\\n ntr = len(df['Results'])\\n V = [[]]*ntr\\n I = [[]]*ntr\\n for j in list(df['Results'].keys()):\\n dfx = df['Results'][j]\\n timebase = dfx['time']\\n V[j] = np.array(dfx['somaVoltage'])*vscale\\n I[j] = np.zeros_like(V[j])\\n except: # vcnmodel simulatipns\\n ntr = len(df[\\\"Results\\\"]['somaVoltage'])\\n V = [[]]*ntr\\n I = [[]]*ntr\\n for j in range(ntr):\\n timebase = df['Results']['time']\\n V[j] = np.array(df['Results']['somaVoltage'][j])*vscale\\n I[j] = np.zeros_like(V[j])\\n\\n V = np.array(V)\\n I = np.array(I)\\n # print('V shape: ', V.shape, 'I shape: ', I.shape, ' timebase: ', timebase.shape, V.shape[1]*self.rate, np.max(timebase))\\n # exit()\\n\\n if run_protocol in ['runVC', 'initVC', 'testVC']:\\n self.MC.set_clamps(dmode=mode, time=timebase, data=I, cmddata=V, tstart_tdur=[delay, dur])\\n else:\\n self.MC.set_clamps(dmode=mode, time=timebase, data=V, cmddata=I, tstart_tdur=[delay, dur])\\n self.MC.getClampData()\",\n \"def radiance_map(file, config, vmax=4200, levels=20, typ=''):\\n \\n # Select data from configuration \\n azimuths = config['skymap'][:, 0] # +180 # azimuths\\n zeniths = config['skymap'][:, 1] # zeniths\\n\\n if typ == 'sim':\\n # look for wavelength index in array\\n waves_sim = dataset.attrs['simulated_Columns'].split('nm')[0].split('[')[1].split(\\n ']')[0].split(',')\\n waves = np.asarray(list(map(int, waves_sim)))\\n wave_indx = np.where(waves == wave)\\n try:\\n wave_indx = np.int(wave_indx[0][0])\\n except:\\n print(\\\"Wavelength is not in dataset\\\")\\n z = dataset.simulated[:, wave_indx, time_indx]\\n\\n elif typ == 'meas':\\n wave_indx = int((config['wavelength'] - 250 - config['wave_correction']) / 0.446)\\n with h5py.File(file, 'r') as data:\\n z = data['data'][:, wave_indx]\\n else:\\n print('Select a input data type(sim or meas)')\\n\\n # Add values in the origin to close the surface interpolation\\n azimuths = np.append(azimuths, [270, 0, 0, 0, 0, 0, 0, 0])\\n zeniths = np.append(zeniths, [0, 12, 24, 36, 48, 60, 72, 84])\\n z = np.append(z, [z[0], z[3], z[9], z[19], z[33], z[51], z[73], z[99]])\\n\\n # Convert x to radians\\n azimuths = np.radians(azimuths)\\n zeniths = np.radians(zeniths)\\n\\n # Remove dead channels of the dataset\\n azimuths = np.delete(azimuths, config['dead_fibre'])\\n zeniths = np.delete(zeniths, config['dead_fibre'])\\n z = np.delete(z, config['dead_fibre'])\\n\\n # Set up a regular grid of interpolation point\\n thetai, ri = np.linspace(azimuths.min(), azimuths.max(),\\n num=len(azimuths)), \\\\\\n np.linspace(zeniths.min(), zeniths.max(), num=len(zeniths))\\n\\n ri, thetai = np.meshgrid(ri, thetai, indexing='ij')\\n\\n # zi = scipy.interpolate.griddata((azimuths, zeniths), z, (thetai, ri),\\n # method='linear')\\n\\n rbf = scipy.interpolate.Rbf(azimuths, zeniths, z, fucntion='gaussian',\\n epsilon=0.05)\\n\\n ZI = rbf(thetai, ri)\\n\\n if typ == 'sim':\\n name = str(dataset.time[time_indx].values) # ''\\n else:\\n name = 'testing' #str(dataset.time[time_indx].values)\\n\\n # Create the directory to save the results\\n # os.makedirs(os.path.dirname(config['path_note'] + '/figures/'),\\n # exist_ok=True)\\n if vmax == 'default':\\n vmax = 4200\\n else:\\n vmax = vmax\\n\\n # Plot the dataset\\n fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))\\n cmap = 'Spectral_r' # 'rainbow'\\n a = plt.contourf(thetai, ri, ZI, levels, cmap=cmap, vmin=0,\\n vmax=vmax) # , vmax=4932)\\n plt.title('{} UTC {}nm'.format(name, config['wavelength']))\\n plt.axis([0, 2*np.pi, 0, 1.48])\\n\\n plt.scatter(azimuths, zeniths, cmap=cmap, s=1)\\n ax.grid(False)\\n ax.set_theta_zero_location(\\\"N\\\") # Set the direction of polar plot\\n ax.set_theta_direction(1) # Set the increase direction on azimuth angles\\n # (-1 to clockwise, 1 counterclockwise)\\n cbar = plt.colorbar(a)\\n cbar.set_label(\\\"counts\\\", rotation=90)\\n\\n # if typ == 'sim':\\n # plt.savefig(\\n # 'figures/skymap/simulated/skymap{}nm_{}UTC_sim.jpeg'.format(wave,\\n # name),\\n # dpi=300)\\n # plt.show();\\n # else:\\n # plt.savefig(\\n # 'figures/skymap/measured/skymap{}nm_{}UTC_meas.jpeg'.format(wave,\\n # name),\\n # dpi=300)\",\n \"def main():\\n ##############################\\n # Options\\n ##############################\\n\\n bev_generator = 'slices'\\n slices_config = \\\\\\n \\\"\\\"\\\"\\n slices {\\n height_lo: -0.2\\n height_hi: 2.3\\n num_slices: 5\\n }\\n \\\"\\\"\\\"\\n # Use None for a random image\\n #img_idx = None\\n img_idx = 6\\n\\n show_ground_truth = True # Whether to overlay ground_truth boxes\\n show_height_maps = False # Whether to show the five height maps\\n show_images = False # Whether to show the images\\n\\n point_cloud_source = 'lidar'\\n pre_label_dir = '/home/cecilia/leo_projects/bishe2019/3D-Detection/avod/data/outputs/pyramid_cars_with_aug_rep_loss/predictions/kitti_native_eval/0.1/112000/data/'\\n ##############################\\n # End of Options\\n ##############################\\n\\n dataset_config = DatasetBuilder.copy_config(DatasetBuilder.KITTI_VAL)\\n dataset_config = DatasetBuilder.merge_defaults(dataset_config)\\n\\n # Overwrite bev_generator\\n if bev_generator == 'slices':\\n text_format.Merge(slices_config,\\n dataset_config.kitti_utils_config.bev_generator)\\n else:\\n raise ValueError('Invalid bev_generator')\\n\\n dataset = DatasetBuilder.build_kitti_dataset(dataset_config,\\n use_defaults=False)\\n\\n if img_idx is None:\\n img_idx = int(random.random() * dataset.num_samples)\\n\\n sample_name = \\\"{:06}\\\".format(img_idx)\\n print('=== Showing BEV maps for image: {}.png ==='.format(sample_name))\\n\\n # Load image\\n image = cv2.imread(dataset.get_rgb_image_path(sample_name))\\n image_shape = image.shape[0:2]\\n\\n kitti_utils = dataset.kitti_utils\\n point_cloud = kitti_utils.get_point_cloud(\\n point_cloud_source, img_idx, image_shape)\\n ground_plane = kitti_utils.get_ground_plane(sample_name)\\n bev_images = kitti_utils.create_bev_maps(point_cloud, ground_plane)\\n\\n height_maps = np.array(bev_images.get(\\\"height_maps\\\"))\\n density_map = np.array(bev_images.get(\\\"density_map\\\"))\\n\\n # Get groundtruth bev-info\\n gt_box_points, gt_box_points_norm = [None, None]\\n if show_ground_truth:\\n gt_obj_labels = obj_utils.read_labels(dataset.label_dir, img_idx)\\n gt_filtered_objs = gt_obj_labels\\n\\n gt_label_boxes = []\\n for gt_label in gt_filtered_objs:\\n gt_box = box_3d_encoder.object_label_to_box_3d(gt_label)\\n gt_label_boxes.append(gt_box)\\n\\n gt_label_boxes = np.array(gt_label_boxes)\\n gt_box_points, gt_box_points_norm = box_3d_projector.project_to_bev(\\n gt_label_boxes, [[-40, 40], [0, 70]])\\n\\n # Get prediction bev-info\\n pre_box_points, pre_box_points_norm = [None, None]\\n pre_obj_labels = obj_utils.read_labels(pre_label_dir, img_idx)\\n\\n pre_filtered_objs = pre_obj_labels\\n\\n pre_label_boxes = []\\n for pre_label in pre_filtered_objs:\\n pre_box = box_3d_encoder.object_label_to_box_3d(pre_label)\\n pre_label_boxes.append(pre_box)\\n\\n pre_label_boxes = np.array(pre_label_boxes)\\n pre_box_points, pre_box_points_norm = box_3d_projector.project_to_bev(\\n pre_label_boxes, [[-40, 40], [0, 70]])\\n\\n \\n rgb_img_size = (np.array((1242, 375)) * 0.75).astype(np.int16)\\n img_x_start = 60\\n img_y_start = 330\\n\\n img_x = img_x_start\\n img_y = img_y_start\\n img_w = 400\\n img_h = 350\\n img_titlebar_h = 20\\n\\n # Show images if show_images = True\\n if show_images:\\n vis_utils.cv2_show_image(\\\"Image\\\", image,\\n size_wh=rgb_img_size, location_xy=(img_x, 0))\\n\\n # Height maps if show_height_maps = True\\n if show_height_maps:\\n\\n for map_idx in range(len(height_maps)):\\n height_map = height_maps[map_idx]\\n\\n height_map = draw_boxes(height_map, gt_box_points_norm, pre_box_points_norm)\\n vis_utils.cv2_show_image(\\n \\\"Height Map {}\\\".format(map_idx), height_map, size_wh=(\\n img_w, img_h), location_xy=(\\n img_x, img_y))\\n\\n img_x += img_w\\n # Wrap around\\n if (img_x + img_w) > 1920:\\n img_x = img_x_start\\n img_y += img_h + img_titlebar_h\\n\\n # Density map (Normal BEV)\\n density_map = draw_boxes(density_map, gt_box_points_norm, pre_box_points_norm)\\n vis_utils.cv2_show_image(\\n \\\"Density Map\\\", density_map, size_wh=(\\n img_w, img_h), location_xy=(\\n img_x, img_y))\\n\\n cv2.waitKey()\",\n \"def visualize(z_in, azimuth=25., elevation=30.,\\n thresholds=[0.95, .9, .75, .5, .25, .125], opacities=[1, .9, .7, .5, .2, .1],\\n# thresholds=[0.94, .89, .75, .5, .25, .1], opacities=[.9, .8, .7, .5, .2, .1],\\n# thresholds=[0.94, .89, .75], opacities=[.99, .7, .2],\\n# thresholds=[0.7, .5, .2], opacities=[.95, .5, .2],\\n fourier_label = {'f_x':'f_x', 'f_y':'f_y', 'f_t':'f_t'},\\n filename=None, do_axis=True, do_grids=False, draw_projections=True,\\n colorbar=False, f_N=2., f_tN=2., figsize=figsize, dpi=300, figpath=figpath, **kwargs):\\n z = z_in.copy()\\n N_X, N_Y, N_frame = z.shape\\n fx, fy, ft = get_grids(N_X, N_Y, N_frame)\\n\\n # Normalize the amplitude.\\n z /= z.max()\\n\\n from vispy import app, scene, use\\n try:\\n AffineTransform = scene.transforms.AffineTransform\\n except:\\n AffineTransform = scene.transforms.MatrixTransform\\n\\n use(app='pyglet', gl='pyopengl2')\\n #from vispy.util.transforms import perspective, translate, rotate\\n from vispy.color import Color\\n transparent = Color(color='black', alpha=0.)\\n import colorsys\\n canvas = scene.SceneCanvas(size=figsize, bgcolor='white', dpi=dpi)\\n view = canvas.central_widget.add_view()\\n\\n vol_data = np.rollaxis(np.rollaxis(z, 1), 2)\\n# volume = scene.visuals.Volume(vol_data, parent=view.scene)#frame)\\n center = scene.transforms.STTransform(translate=( -N_X/2, -N_Y/2, -N_frame/2))\\n# volume.transform = center\\n# volume.cmap = 'blues'\\n\\n if draw_projections:\\n from vispy.color import Colormap\\n cm = Colormap([(1.0, 1.0, 1.0, 1.0), 'k'])\\n opts = {'parent':view.scene, 'cmap':cm, 'clim':(0., 1.)}\\n\\n energy_xy = np.rot90(np.max(z, axis=2)[:, ::-1], 3)#[:, ::-1]\\n fourier_xy = scene.visuals.Image(np.rot90(energy_xy), **opts)\\n tr_xy = AffineTransform()\\n tr_xy.rotate(90, (0, 0, 1))\\n tr_xy.translate((N_X/2, -N_Y/2, -N_frame/2))\\n fourier_xy.transform = tr_xy\\n\\n energy_xt = np.rot90(np.max(z, axis=1)[:, ::-1], 3)[::-1, ::-1]\\n fourier_xt = scene.visuals.Image(energy_xt, **opts)\\n tr_xt = AffineTransform()\\n tr_xt.rotate(90, (1, 0, 0))\\n tr_xt.translate((-N_X/2, N_Y/2, -N_frame/2))\\n fourier_xt.transform = tr_xt\\n\\n energy_yt = np.max(z, axis=0)[:, ::-1]\\n fourier_yt = scene.visuals.Image(energy_yt, **opts)\\n tr_yt = AffineTransform()\\n tr_yt.rotate(90, (0, 1, 0))\\n tr_yt.translate((-N_X/2, -N_Y/2, N_frame/2))\\n fourier_yt.transform = tr_yt\\n\\n # Generate iso-surfaces at different energy levels\\n surfaces = []\\n for i_, (threshold, opacity) in enumerate(zip(thresholds, opacities)):\\n surfaces.append(scene.visuals.Isosurface(z, level=threshold,\\n# color=Color(np.array(colorsys.hsv_to_rgb(1.*i_/len(thresholds), 1., 1.)), alpha=opacity),\\n color=Color(np.array(colorsys.hsv_to_rgb(.66, 1., 1.)), alpha=opacity),\\n shading='smooth', parent=view.scene)\\n )\\n surfaces[-1].transform = center\\n\\n # Draw a sphere at the origin\\n axis = scene.visuals.XYZAxis(parent=view.scene)\\n for p in ([1, 1, 1, -1, 1, 1], [1, 1, -1, -1, 1, -1], [1, -1, 1, -1, -1, 1],[1, -1, -1, -1, -1, -1],\\n [1, 1, 1, 1, -1, 1], [-1, 1, 1, -1, -1, 1], [1, 1, -1, 1, -1, -1], [-1, 1, -1, -1, -1, -1],\\n [1, 1, 1, 1, 1, -1], [-1, 1, 1, -1, 1, -1], [1, -1, 1, 1, -1, -1], [-1, -1, 1, -1, -1, -1]):\\n line = scene.visuals.Line(pos=np.array([[p[0]*N_X/2, p[1]*N_Y/2, p[2]*N_frame/2], [p[3]*N_X/2, p[4]*N_Y/2, p[5]*N_frame/2]]), color='black', parent=view.scene)\\n\\n axisX = scene.visuals.Line(pos=np.array([[0, -N_Y/2, 0], [0, N_Y/2, 0]]), color='red', parent=view.scene)\\n axisY = scene.visuals.Line(pos=np.array([[-N_X/2, 0, 0], [N_X/2, 0, 0]]), color='green', parent=view.scene)\\n axisZ = scene.visuals.Line(pos=np.array([[0, 0, -N_frame/2], [0, 0, N_frame/2]]), color='blue', parent=view.scene)\\n\\n if do_axis:\\n t = {}\\n for text in ['f_x', 'f_y', 'f_t']:\\n t[text] = scene.visuals.Text(fourier_label[text], parent=canvas.scene, face='Helvetica', color='black')\\n t[text].font_size = 8\\n t['f_x'].pos = canvas.size[0] // 3, canvas.size[1] - canvas.size[1] // 8\\n t['f_y'].pos = canvas.size[0] - canvas.size[0] // 8, canvas.size[1] - canvas.size[1] // 6\\n t['f_t'].pos = canvas.size[0] // 8, canvas.size[1] // 2\\n\\n cam = scene.TurntableCamera(elevation=elevation, azimuth=azimuth, up='z')\\n cam.fov = 48\\n cam.scale_factor = N_X * 1.8\\n if do_axis: margin = 1.35\\n else: margin = 1\\n cam.set_range((-N_X/2*margin, N_X/2/margin), (-N_Y/2*margin, N_Y/2/margin), (-N_frame/2*margin, N_frame/2/margin))\\n view.camera = cam\\n\\n render_im = canvas.render()\\n app.quit()\\n if not(filename is None):\\n import vispy.io as io\\n io.write_png(filename, render_im)\\n else:\\n return render_im\",\n \"def createPlots(ncfile, files, loc, savepath, sim_name, bfric, tight=False, \\\\\\n ratio=1.0, debug=False, plot=False):\\n\\n sns.set(font=\\\"serif\\\")\\n\\n # find the location centre for flood/tide split calculation\\n # not yet working...\\n if loc == 'GP':\\n centre = [-66.33906, 44.26898]\\n if tight:\\n bounds = [-66.355, -66.31, 44.245, 44.2925]\\n else:\\n bounds = []\\n elif loc == 'DG':\\n centre = [-65.76000, 44.67751]\\n if tight:\\n bounds = [-65.775, -65.77, 44.665, 44.69]\\n else:\\n bounds = []\\n elif loc == 'PP':\\n centre = [-66.206924, 44.389368]\\n # find out the tightness required for PP\\n if tight:\\n bounds = [-66.225, -66.195, -44.37, -44.41]\\n else:\\n bounds = []\\n\\n if debug:\\n print 'calculating ebb/flood split at centre of location...'\\n print 'calculating model velocity norm...'\\n\\n fI, eI, _, _ = ncfile.Util2D.ebb_flood_split_at_point(centre[0], centre[1])\\n ncfile.Util3D.velo_norm()\\n\\n if debug:\\n print '{} plot(s) will be created...'.format(len(files))\\n\\n if not plot:\\n savepath = savepath + 'bfric_' + bfric + '/' + loc + '_' + sim_name\\n if ratio != 1.0:\\n savepath = savepath + '/with_ratio_{}'.format(str(ratio))\\n\\n # creates a subdirectory, so as not to overwrite existing files\\n if debug:\\n print 'creating new subdirectory...'\\n now = datetime.now()\\n now = now.strftime(\\\"%Y%m%d\\\")\\n if not osp.exists(savepath):\\n os.makedirs(savepath)\\n else:\\n savepath = savepath + '/_' + now\\n os.makedirs(savepath)\\n savepath = savepath + '/'\\n\\n for i, fname in enumerate(files, start=1):\\n if debug:\\n print 'creating drifter object...'\\n drift = Drifter(fname, debug=False)\\n fname = fname[48:-4]\\n\\n # creates drifter object window for flow map\\n if debug:\\n print 'creating drifter object window...'\\n tModel = ncfile.Variables.matlabTime\\n tDrift = drift.Variables.matlabTime\\n win1 = (np.abs(tModel-tDrift.min())).argmin()\\n win2 = (np.abs(tModel-tDrift.max())).argmin()\\n\\n tide = str(drift.Data['water_level'].tide)\\n # averages velocity norm over flood or ebb cycle within drifter window\\n if tide == 'flood':\\n tideNorm = np.mean(ncfile.Variables.velo_norm[win1:win2,:,:], 0)\\n elif tide == 'ebb':\\n tideNorm = np.mean(ncfile.Variables.velo_norm[win1:win2,:,:], 0)\\n\\n # create spatially varying color map of mean velocity norm\\n if debug:\\n print 'preparing to create colormap...'\\n fig = createColorMap(ncfile, tideNorm[0,:], mesh=False, bounds=bounds, \\\\\\n title='Trajectory for ' + fname[:-4], debug=debug, \\\\\\n label='Mean Velocity Norm during '+tide.capitalize()+' Tide (m/s)')\\n # create title\\n # fig.suptitle('Data from ' + fname[:-4], fontsize=14)\\n\\n x = drift.Variables.lon\\n y = drift.Variables.lat\\n u = drift.Variables.u\\n v = drift.Variables.v\\n\\n if debug:\\n print 'creating scatter plot...'\\n plt.scatter(x,y)\\n\\n if debug:\\n print 'preparing to plot time series...'\\n\\n # create validation structure\\n if debug:\\n print 'creating validation object...'\\n\\n try:\\n valid = Validation(drift, ncfile, flow='sf', debug=False)\\n except IndexError:\\n print 'cannot create validation object for drifter %i.' % i\\n continue\\n\\n # calculate speed from interpolated and observed date\\n mTimes = valid.Variables.struct['mod_time']\\n oU = valid.Variables.struct['obs_timeseries']['u']\\n oV = valid.Variables.struct['obs_timeseries']['v']\\n mU = valid.Variables.struct['mod_timeseries']['u']\\n mV = valid.Variables.struct['mod_timeseries']['v']\\n\\n if debug:\\n print '\\\\tcalculating speeds...'\\n speedS = np.asarray(np.sqrt(mU**2 + mV**2))\\n speedO = np.asarray(np.sqrt(oU**2 + oV**2))\\n\\n # ratio addition\\n if debug:\\n print '\\\\tadding ratio adjustments...'\\n speedS = speedS * ratio\\n datetimes = np.asarray([dn2dt(time) for time in mTimes])\\n\\n # For now, separate the two plots.\\n # Set BFRIC for now\\n # WHAT IS WRONG WITH THIS\\n # fig2=plt.figure()\\n # result, axis = plotTimeSeries(fig2, np.reshape(np.tile(datetimes,2),\\\\\\n # (2, len(datetimes))), np.vstack((speedS, speedO)), \\\\\\n # loc, label=['Simulated','Observed'], where=111, \\\\\\n # title='Path Speeds for ' + fname[:-4] + ' | BFRIC=0.015', \\\\\\n # axis_label='Speed (m/s)')\\n\\n # if not result:\\n # if debug:\\n # print '...error encountered with drifter {}.'.format(i)\\n # print 'continuing...'\\n # plt.close()\\n # continue\\n\\n if plot:\\n if debug:\\n print 'displaying plot...'\\n plt.show()\\n else:\\n if debug:\\n print 'saving plot...'\\n fig.savefig(savepath + fname + '_traj.png')\\n # result.savefig(savepath + fname + '_speed.png')\\n if debug:\\n print '...plot saved to: ', savepath+fname\\n\\n # clear the figure window\\n plt.close()\",\n \"def run_plots(self):\\n # load the files\\n self.pre_dark_file = os.path.join(self.input_dir, 'step_lastframe.fits')\\n self.post_dark_file = os.path.join(self.input_dir, 'step_dark_current.fits')\\n self.jump_file = os.path.join(self.input_dir, 'step_jump.fits')\\n self.rate_file = os.path.join(self.input_dir, 'step_rate.fits')\\n self.ramp_file = glob.glob(os.path.join(self.input_dir, '*.fits'))[0]\\n\\n # plots\\n self.plot_jump_flags_image()\\n self.plot_groupdq_flags(pixel=[884, 550])\\n self.plot_ramps_pre_post_correction(pixel=[884, 550])\",\n \"def RvsTPlot(self, datafile, boardindex, chindex, description='',\\n Tcline=True):\\n try:\\n self._checkfigure()\\n except HKEPlotterNotInitializedError(self.figure,\\n self.axes) as e:\\n print e\\n\\n temperature = datafile['temperature']\\n Ts = temperature['Ts']\\n\\n board = datafile['boards'][boardindex]\\n register = board['registers'][chindex]\\n\\n dataR = board['data']\\n Rs = dataR[chindex]\\n\\n chdesc = register['name']\\n print \\\"description = \\\", repr(description) #DELME\\n if description:\\n description = ' - ' + description\\n print \\\"chdesc = \\\", repr(chdesc) #DELME\\n if chdesc:\\n description = description + ' - ' + chdesc\\n\\n label = 'Ch {i}{d}'.format(i=chindex, d=description)\\n line = self.plot(Ts, Rs, label=label)\\n\\n linedict = self._get_linedict(line)\\n linedict['label'] = label\\n\\n Tc = board['Tcs'][chindex]\\n linedict['Tc'] = Tc\\n\\n if Tcline:\\n self.addTcline(line, Tc)\\n\\n return linedict\\n\\n # Ts = datafile['Temperatures']\\n # dataR = datafile[dataRname]\\n # Rs = dataR[index]\\n\\n # sidenum = dataRname.split()[1]\\n # desclist = datafile['Side {s} Descriptions'.format(s=sidenum)]\\n # chdesc = desclist[index]\\n\\n # if description:\\n # description = ' - ' + description\\n # if chdesc:\\n # description = description + ' - ' + chdesc\\n\\n # label = 'Ch {i}{d}'.format(i=index, d=description)\\n # line = self.plot(Ts, Rs, label=label)\\n\\n # linedict = self._get_linedict(line)\\n # linedict['label'] = label\\n\\n # Tcs = datafile['Side {i} Transition Temperatures'.format(i=sidenum)]\\n # Tc = Tcs[index]\\n # linedict['Tc'] = Tc\\n\\n # if Tcline:\\n # self.addTcline(line, Tc)\\n\\n # return linedict\",\n \"def render_sample(cls,\\n nusc: 'NuScenes',\\n radar_data_gp: 'RadarDataGroup',\\n front_cam_data: 'CameraData',\\n host_data:'HostData',\\n fig_info:dict,\\n box_vis_level: BoxVisibility = BoxVisibility.ANY,\\n out_path: str = None) -> None:\\n cls.colors=radar_data_gp.colors\\n fig, axes = plt.subplots(1, 2, figsize=(16, 24))\\n #left diagram to show front view\\n ax1 = axes[0]\\n cls.render_front_view(cls, nusc=nusc, radar_data_gp=radar_data_gp, front_cam_data=front_cam_data,\\n ax =ax1, use_flat_vehicle_coordinates=False, with_anns=False, with_radar=True)\\n #right diagram to show bird view\\n ax2 = axes[1] \\n \\n cls.render_bird_view(cls, nusc=nusc, radar_data_gp=radar_data_gp, front_cam_data=front_cam_data, \\n host_data=host_data, ax =ax2, use_flat_vehicle_coordinates=True)\\n #figure information\\n fig.text(0.01, 0.97, 'Figure information: ', weight='bold', color='green',fontsize = 15) \\n fig.text(0.01, 0.93, 'package: '+nusc.version, fontsize = 12) \\n fig.text(0.01, 0.9, 'scene name: '+fig_info['scene_name'], fontsize = 12) \\n fig.text(0.01, 0.87, 'timestamp: '+str(front_cam_data.time_stamp*1e-6)+'(s)', fontsize = 12)\\n fig.text(0.01, 0.84, 'elapse time: : '+str((fig_info['elapse_time'])*1e-3)+'(ms)', fontsize = 12)\\n \\n plt.tight_layout()\\n fig.subplots_adjust(wspace=0, hspace=0)\\n if out_path is not None:\\n plt.savefig(out_path,dpi=200)\\n if fig_info['verbose']: \\n plt.show()\",\n \"def graphic_manuel():\\r\\n root = Tk()\\r\\n grid = FileManager.read_sudoku(args.file)[0]\\r\\n inte = GraphicInterface.GraphicInterface(root, grid)\\r\\n inte.open()\",\n \"def tftDisplay(libtft):\\n tempMsg = getTemperature()\\n print(\\\"%s\\\" % tempMsg)\\n\\n timeMsg = getTime()\\n print(\\\"%s\\\" % timeMsg)\\n\\n ipMsg = GetIPAddr()\\n print(\\\"%s\\\" % ipMsg)\\n\\n lightMsg = \\\"UKN\\\" #getLight()\\n print(\\\"%s\\\" % lightMsg)\\n \\n libtft.ClearScreen()\\n \\n libtft.PutString(0, 0, \\\"IP \\\", libtft.YELLOW)\\n libtft.PutString(0, 20, ipMsg, libtft.WHITE)\\n\\n libtft.PutString(0, 40, \\\"Time\\\", libtft.YELLOW)\\n libtft.PutString(0, 60, timeMsg, libtft.WHITE)\\n\\n libtft.PutString(0, 80, \\\"Temp\\\", libtft.YELLOW)\\n libtft.PutString(0, 100, tempMsg, libtft.WHITE)\",\n \"def detect_report(day, json_conf):\\n rpt = mk_css()\\n rpt += \\\"<h1>Detection Report for \\\" + STATION_ID + \\\" on \\\" + day + \\\"</h1>\\\"; \\n data_dir = \\\"/mnt/ams2/SD/proc2/\\\" + day + \\\"/data/\\\"\\n image_dir = \\\"/mnt/ams2/SD/proc2/\\\" + day + \\\"/images/\\\"\\n video_dir = \\\"/mnt/ams2/SD/proc2/\\\" + day + \\\"/hd_save/\\\"\\n meteor_files = glob.glob(data_dir + \\\"*-meteor.json\\\")\\n non_meteor_files = glob.glob(data_dir + \\\"*-nometeor.json\\\")\\n detect_files = glob.glob(data_dir + \\\"*-detect.json\\\")\\n rpt += \\\"<h2>\\\" + str(len(meteor_files)) + \\\" Meteors detected </h2>\\\" \\n for mf in sorted(meteor_files):\\n print(mf)\\n fn = mf.split(\\\"/\\\")[-1]\\n img = mf.replace(\\\"data/\\\", \\\"images/\\\")\\n vid = mf.replace(\\\"data/\\\", \\\"\\\")\\n img = img.replace(\\\"-meteor.json\\\", \\\"-stacked-tn.png\\\")\\n vid = vid.replace(\\\"-meteor.json\\\", \\\".mp4\\\")\\n if cfe(img) == 0:\\n if cfe(vid) == 1:\\n print(\\\"Image missing stack vid\\\")\\n stack = quick_video_stack(vid)\\n thumb = thumb = cv2.resize(stack, (PREVIEW_W, PREVIEW_H)) \\n cv2.imwrite(img, thumb)\\n print(\\\"THUMB:\\\", thumb)\\n \\n rpt += \\\"<div class='float_div'>\\\"\\n rpt += \\\"<img src=\\\" + img + \\\">\\\"\\n rpt += \\\"<br><label style='text-align: center'>\\\" + fn + \\\" <br>\\\" \\n rpt += \\\"</label></div>\\\"\\n\\n table = det_table(non_meteor_files, \\\"nometeor\\\")\\n rpt += \\\"<div style='clear: both'></div>\\\"\\n rpt += \\\"<h2>\\\" + str(len(non_meteor_files)) + \\\" Auto Rejected Meteor Detections</h2>\\\"\\n rpt += table\\n\\n if False:\\n table = det_table(detect_files, \\\"detect\\\")\\n rpt += \\\"<div style='clear: both'></div>\\\"\\n rpt += \\\"<h2>\\\" + str(len(detect_files)) + \\\" Non Meteor Detections</h2>\\\"\\n rpt += table\\n\\n\\n out = open(data_dir + \\\"report.html\\\", \\\"w\\\")\\n out.write(rpt)\\n out.close()\\n print(data_dir + \\\"report.html\\\")\",\n \"def image_capture_demo():\\n return render_template('image_capture_demo.html')\",\n \"def index():\\n vegform = Upload() \\n imageform = ImageUpdate() \\n stormform = Storm() \\n form = Landscape() \\n \\n # Set image defaults to zero so template knows what's going on\\n result_im = None\\n result_bin = None \\n result_RF = None\\n result_vmax = None\\n\\n feature_dir = '/'.join([os.getcwd(), 'feature' + request.remote_addr])\\n feature_path = '/'.join([feature_dir, 'features.json'])\\n os.mkdir(feature_dir) if os.path.isdir(feature_dir) == False else True\\n if 'features.json' in os.listdir(feature_dir):\\n with open(feature_path, 'r') as fp:\\n features = json.load(fp)\\n \\n else:\\n features = reset_features()\\n\\n p_choices = {'0.5' : ['2.5', '5.0', '7.5', '10'],\\n '1' : ['1.7','3.4','5.1','6.8'],\\n '3' : ['0.8','1.6','2.4','3.2'],\\n '6' :['0.5','1.0','1.5','2.0']\\n }\\n\\n\\n if request.method == 'POST' and form.reset.data == True :\\n flash('Reset!')\\n features = reset_features() \\n with open(feature_path, 'w') as fp:\\n json.dump(features, fp, indent = 2) \\n \\n if 'tr' in features.keys():\\n form.p.choices = [(str(p),str(p)) for p in p_choices[features['tr']]] \\n \\n if vegform.validate_on_submit():\\n \\n if vegform.delete.data == True:\\n flash('File deleted ')\\n \\n #os.remove(os.path.join( UPLOAD_FOLDER, features['filename'] ))\\n if 'filename' in features:\\n features.pop('filename')\\n if 'filepath' in features:\\n features.pop('filepath') \\n result_bin = None\\n result_im = None \\n \\n features = reset_features() \\n \\n if vegform.submit.data == True:\\n \\n flash('File upload ')\\n f = vegform.veg.data\\n filename = secure_filename(f.filename)\\n filepath = os.path.join( UPLOAD_FOLDER, filename)\\n f.save(filepath)\\n \\n features['filename'] = filename \\n features['filepath'] = filepath \\n\\n #### image handling \\n if imageform.submit.data and imageform.validate_on_submit():\\n \\n flash('Image update')\\n keys = [key for key in request.form.keys() if key not in ['csrf_token', 'ascii']]\\n #for key in keys: \\n if 'grid' in keys:\\n features['grid'] = request.form['grid']\\n if 'threshold' in keys:\\n features['threshold'] = request.form['threshold']\\n \\n if request.form['submit'] == 'rotate-right':\\n features['rotate'] = np.mod(features['rotate'] - 90., 360)\\n print \\\"rotate right\\\"\\n\\n if request.form['submit'] == 'rotate-left':\\n features['rotate'] = np.mod(features['rotate'] + 90., 360)\\n print \\\"rotate left\\\"\\n \\n #### precip handling \\n if stormform.submit.data and stormform.validate_on_submit():\\n \\n flash('Rain duration added ') \\n \\n features['tr'] = (request.form['tr'])\\n if 'p' in features.keys():\\n features['rainD'] = float(features['tr'])*float(features['p'])\\n \\n \\n form.p.choices = [(str(p),str(p)) for p in p_choices[features['tr']]] \\n keys = [key for key in request.form.keys() if key not in ['csrf_token', 'ascii']]\\n \\n\\n #### update landscape \\n if form.update.data and form.validate_on_submit():\\n \\n flash('Landscape featuress added')\\n \\n keys = [key for key in request.form.keys() if key not in ['csrf_token', 'ascii']]\\n \\n if 'tr' not in features.keys():\\n features['tr'] = '0.5'\\n \\n features['slope'] = request.form['slope']\\n features['p'] = (request.form['p'])\\n features['KsV'] = (request.form['KsV']) \\n features['rainD'] = float(features['tr'])*float(features['p'])\\n \\n if 'filepath' in features.keys():\\n \\n result_im = png_plot(features['filepath'])\\n \\n threshold = features['threshold'] if 'threshold' in features.keys() else 0.5 \\n result_bin, bw = binarize(features['filepath'], features = features)\\n features['Lx'] = bw.shape[0]\\n features['Ly'] = bw.shape[1] \\n features['fV'] = np.round(np.mean(bw), 2)\\n \\n if form.submit.data and form.validate_on_submit():\\n flash('Running the random forest model!')\\n \\n keys = [key for key in request.form.keys() if key not in ['csrf_token', 'ascii']]\\n \\n if 'tr' not in features.keys():\\n features['tr'] = '0.5'\\n \\n features['slope'] = request.form['slope']\\n features['p'] = (request.form['p'])\\n features['KsV'] = (request.form['KsV']) \\n features['rainD'] = float(features['tr'])*float(features['p'])\\n \\n result_RF, zinflc = run_RF(features['filepath'], features = features, target_col = 'zinflc')\\n result_vmax, vmax = run_RF(features['filepath'], features = features, target_col = 'vmax') \\n \\n features['inflDveg'] = np.round(np.mean(zinflc[bw == 1]),2)\\n features['inflD'] = np.round(np.mean(zinflc),2)\\n features['vmax'] = np.round(np.mean(vmax),2)\\n features['vmaxmax'] = np.round(np.percentile(vmax, 95),2) \\n\\n with open(feature_dir + '/features.json', 'w') as fp:\\n json.dump(features, fp, indent = 2) \\n\\n keys = [key for key in features.keys() if key not in ['filepath', 'filename', 'tr']]\\n\\n for key in ['grid', 'threshold']:\\n if key in features.keys():\\n imageform[key].default = float(features[key] ) \\n \\n imageform.process()\\n\\n for key in ['tr']:\\n if key in features.keys():\\n stormform[key].default = features[key]\\n \\n stormform.process() \\n \\n for key in [ 'p', 'slope', 'KsV']:\\n if key in features.keys():\\n form[key].default = features[key]\\n\\n form.process()\\n \\n return render_template('home.html', form = form, \\\\\\n vegform = vegform, \\n imageform = imageform, \\n stormform = stormform, \\n features = features, \\n result_bin = result_bin,\\n result_im = result_im,\\n result_RF = result_RF,\\n result_vmax = result_vmax\\n )\",\n \"def simulate(self):\\n self.fig = plt.figure(figsize=(15,15), dpi=80)\\n self.cplot(self.fig, 0 , 1)\\n \\n #Save the configuration at the defined times\\n self.metro_monte_carlo(save_conf=True)\\n #cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\\n #fig.colorbar(self.ax, cax=cbar_ax)\\n \\n plt.savefig('config.pdf')\",\n \"def render(self, mode='human', type_of='color'):\\n if type_of == 'color':\\n plot_color(self.observation[0:self.num_of_services],\\n self.servers_mem ,self.services_mem)\\n elif type_of=='black':\\n plot_black(self.observation[0:self.num_of_services],\\n self.servers_mem ,self.services_mem)\\n print('\\\\n----services mem----\\\\n')\\n print('services resource usage: {}'.format(self.services_mem))\\n print('servers capacity: {}'.format(self.servers_mem))\\n print('users services: {}'.format(self.users_services))\\n print('\\\\n----observation----\\\\n')\\n print('services servers: {}'.format(self.observation[0:self.num_of_services]))\\n print('users stations: {}'.format(self.observation[self.num_of_services:]))\",\n \"def showAssetImage(*args):\\n\\n selTab = cmds.tabLayout(widgets[\\\"shotAssRigListTLO\\\"], q=True, st=True)\\n\\n fType = \\\"\\\"\\n asset = \\\"\\\"\\n assetPath = \\\"\\\"\\n path = \\\"\\\"\\n imagePath = \\\"\\\"\\n\\n if selTab == \\\"Chars\\\":\\n asset = cmds.textScrollList(widgets[\\\"shotAssRigCharListTSL\\\"], q=True, si=True)\\n if asset:\\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \\\"characters\\\", asset[0], \\\"icon\\\",\\\"{0}Icon.png\\\".format(asset[0])))\\n if os.path.isfile(imagePath):\\n cFuncs.assetImageUI(imagePath)\\n else:\\n cmds.warning(\\\"Can't find an image for {0}\\\".format(asset[0]))\\n\\n if selTab == \\\"Props\\\":\\n asset = cmds.textScrollList(widgets[\\\"shotAssRigPropListTSL\\\"], q=True, si=True)\\n if asset:\\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \\\"props\\\", asset[0], \\\"icon\\\",\\\"{0}Icon.png\\\".format(asset[0])))\\n if os.path.isfile(imagePath):\\n cFuncs.assetImageUI(imagePath)\\n else:\\n cmds.warning(\\\"Can't find an image for {0}\\\".format(asset[0]))\\n \\n if selTab == \\\"Sets\\\":\\n asset = cmds.textScrollList(widgets[\\\"shotAssRigSetListTSL\\\"], q=True, si=True)\\n if asset:\\n imagePath = cFuncs.fixPath(os.path.join(pi.assetFolder, \\\"sets\\\", asset[0], \\\"icon\\\",\\\"{0}Icon.png\\\".format(asset[0])))\\n if os.path.isfile(imagePath):\\n cFuncs.assetImageUI(imagePath)\\n else:\\n cmds.warning(\\\"Can't find an image for {0}\\\".format(asset[0]))\\n \\n # if selTab == \\\"Anm\\\":\\n # #need to split this up\\n # var_shot = cmds.textScrollList(widgets[\\\"shotAnmMstListTSL\\\"], q=True, si=True)\\n # if var_shot:\\n # var, buf, shot = var_shot[0].partition(\\\".\\\")\\n # path = cFuncs.getVarMaster(cFuncs.fixPath(os.path.join(pi.shotsFolder, shot, \\\"anm\\\", var)))\",\n \"def main(argv):\\n # pylint: disable=too-many-locals\\n\\n parsed_args = parse_arguments(argv)\\n\\n template = get_template(parsed_args)\\n\\n dts = pydevicetree.Devicetree.parseFile(\\n parsed_args.dts, followIncludes=True)\\n\\n memories = get_memories(dts)\\n print_memories(memories)\\n sorted_ram_memories = get_sorted_ram_memories(dts)\\n\\n ram, rom, itim, lim = get_load_map(memories, scratchpad=parsed_args.scratchpad)\\n\\n text_in_itim = False\\n if parsed_args.ramrodata and get_itim_length(memories) >= MAGIC_RAMRODATA_TEXT_THRESHOLD:\\n text_in_itim = True\\n print(\\\".text section included in ITIM\\\", file=sys.stderr)\\n elif parsed_args.ramrodata:\\n print(\\\".text section included in ROM\\\", file=sys.stderr)\\n\\n harts = dts.get_by_path(\\\"/cpus\\\").children\\n chosenboothart = dts.chosen(\\\"metal,boothart\\\")\\n is_worlguard = dts.match(\\\"sifive,worldguard1\\\")\\n if is_worlguard:\\n boot_hart = 0\\n else:\\n if chosenboothart:\\n boot_hart = dts.get_by_reference(chosenboothart[0]).get_reg()[0][0]\\n elif len(harts) > 1:\\n boot_hart = 1\\n else:\\n boot_hart = 0\\n\\n if len(sorted_ram_memories) == 0:\\n # If there are no rams to scrub, don't bother scrubbing them\\n ecc_scrub = 0\\n elif dts.chosen(\\\"metal,eccscrub\\\"):\\n # Otherwise default to scrubbing if metal,eccscrub = <1>;\\n ecc_scrub = dts.chosen(\\\"metal,eccscrub\\\")[0]\\n else:\\n ecc_scrub = 0\\n\\n # Pass sorted memories to the template generator so that the generated linker\\n # script is reproducible.\\n sorted_memories = list(memories.values())\\n sorted_memories.sort(key=lambda m: m[\\\"name\\\"])\\n\\n values = {\\n \\\"memories\\\": sorted_memories,\\n \\\"ram_memories\\\": sorted_ram_memories,\\n \\\"default_stack_size\\\": \\\"0x400\\\",\\n \\\"default_heap_size\\\": \\\"0x800\\\",\\n \\\"num_harts\\\": len(harts),\\n \\\"boot_hart\\\": boot_hart,\\n \\\"chicken_bit\\\": 1,\\n \\\"eccscrub_bit\\\": ecc_scrub,\\n \\\"text_in_itim\\\": text_in_itim,\\n \\\"rom\\\": rom,\\n \\\"itim\\\": itim,\\n \\\"lim\\\": lim,\\n \\\"ram\\\": ram,\\n }\\n\\n if parsed_args.output:\\n parsed_args.output.write(template.render(values))\\n parsed_args.output.close()\\n else:\\n print(template.render(values))\",\n \"def main():\\n\\n # first figure: betas for each predictor\\n fig, axes = plt.subplots(figsize=(8, 18), nrows=3)\\n\\n image_paths = {\\n \\\"3T\\\": (\\n f\\\"{PATHS['figures_misc']}/figure_3_images/\\\"\\n f\\\"C1051_20161006_childVSall_depth_1.png\\\"\\n ),\\n \\\"7T\\\": (\\n f\\\"{PATHS['figures_misc']}/figure_3_images/\\\"\\n f\\\"C1051_20160212_childVSall_depth_1_sim2pt4.png\\\"\\n ),\\n \\\"7T_noise\\\": (\\n f\\\"{PATHS['figures_misc']}/figure_3_images/\\\"\\n f\\\"C1051_20160212_childVSall_depth_1_sim2pt4_noise.png\\\"\\n ),\\n }\\n\\n for ax, (_, image_path) in zip(axes, image_paths.items()):\\n assert os.path.isfile(image_path)\\n img = imread(image_path)\\n img[np.where(np.sum(img, axis=2) == 0.0)] = 1.0\\n\\n ax.imshow(img[200:-100, 50:-50, :])\\n ax.axis(\\\"off\\\")\\n\\n savefig(f\\\"{PATHS['figures']}/figure_3ac.png\\\")\\n plt.close(fig)\",\n \"def produce_13TeV_template(tag_name=\\\"HKHI\\\"):\\n num_rebin = 1\\n file_name = \\\"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\\\"+tag_name+\\\"_Lin.root\\\"\\n print \\\"Input: \\\", file_name\\n fin = ROOT.TFile.Open(file_name, \\\"read\\\")\\n h_nom = fin.Get(\\\"bkg_total_gg_full\\\").Clone(\\\"bkg_nominal_old\\\")\\n h_nom.Rebin(num_rebin)\\n fout = ROOT.TFile.Open(\\\"hists_input_\\\"+tag_name+\\\".root\\\", \\\"recreate\\\")\\n\\n h_purity_sys = fin.Get(\\\"bkg_purity_syst_gg_full\\\").Clone(\\\"bkg_purity_syst_gg\\\")\\n h_reducible_sys = fin.Get(\\\"bkg_reducible_syst_gg_full\\\").Clone(\\\"bkg_reducible_syst_gg\\\")\\n h_irreducible_sys = fin.Get(\\\"bkg_irreducible_syst_gg_full\\\").Clone(\\\"bkg_irreducible_syst_gg\\\")\\n h_iso_sys = fin.Get(\\\"bkg_iso_syst_gg_full\\\").Clone(\\\"bkg_iso_syst_gg\\\")\\n\\n #file_iso = \\\"isolation_sys/hist.root\\\"\\n #fin2 = ROOT.TFile.Open(file_iso, \\\"read\\\")\\n #h_iso_sys = fin2.Get(\\\"bkg_isolation_syst_gg\\\")\\n ## inflat irreducible uncertainty by factor of 10\\n # so that it closes to stats uncertainty in data\\n sf = 1\\n if INFLATE_SYS:\\n sf = 10\\n\\n # after rebinning systematic uncertainties, need to scale down,\\n # otherwise the uncertainties are inflated.\\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n\\n ## truncate the histograms to [200, 2000] GeV\\n h_nom_new = truncate_hist(h_nom, \\\"bkg_nominal\\\")\\n h_purity_sys_new = truncate_hist(h_purity_sys, \\\"h_purity_sys_new\\\")\\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \\\"h_irreducible_sys_new\\\")\\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \\\"h_reducible_sys_new\\\")\\n h_iso_sys_new = truncate_hist(h_iso_sys, \\\"h_iso_sys_new\\\")\\n\\n #write down sys and nominal\\n fout.cd()\\n h_nom_new.Write()\\n h_purity_sys_new.Write()\\n h_reducible_sys_new.Write()\\n h_irreducible_sys_new.Write()\\n h_iso_sys_new.Write()\\n\\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \\\"purity_sys\\\")\\n h_purity_up.Write()\\n h_purity_down.Write()\\n\\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \\\"reducible_sys\\\")\\n h_red_up.Write()\\n h_red_down.Write()\\n\\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \\\"irreducible_sys\\\")\\n h_irred_up.Write()\\n h_irred_down.Write()\\n\\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \\\"isolation_sys\\\")\\n h_iso_up.Write()\\n h_iso_down.Write()\\n\\n fin.Close()\\n fout.Close()\",\n \"def render(name, conf):\\n\\n template_filename = '/etc/pgpool-II/templates/{}'.format(name)\\n output_filename = '/etc/pgpool-II/{}'.format(name)\\n\\n env = jinja2.Environment(loader=jinja2.FileSystemLoader(os.path.dirname(template_filename)),\\n extensions=['jinja2.ext.with_'])\\n\\n try:\\n template = env.get_template(os.path.basename(template_filename))\\n except exceptions.TemplateNotFound as e:\\n logger.info('Error reading template file {}!'.format(template_filename))\\n\\n logger.debug('Conf list passed to Jinja template file {}.'.format(conf))\\n with open(output_filename, \\\"w\\\") as f:\\n f.write(template.render(conf))\\n\\n # set permission of rendered files to postgres:postgres\\n os.chown(output_filename, UID, GID)\",\n \"def useThibautsData(log, output, bcgr=72.2, sigma=0.75, iterations=4, loc=1900, galaxies=1000,\\n datadir='/Users/smn2/EUCLID/CTItesting/uniform/',\\n thibautCDM03=False, beta=False, serial=1, parallel=1):\\n files = g.glob(datadir + '*.fits')\\n #pick randomly\\n files = np.random.choice(files, galaxies, replace=False)\\n\\n #trap parameters: parallel\\n if thibautCDM03:\\n f1 = '/Users/smn2/EUCLID/vissim-python/data/cdm_thibaut_parallel.dat'\\n f2 = '/Users/smn2/EUCLID/vissim-python/data/cdm_thibaut_serial.dat'\\n params = ThibautsCDM03params()\\n params.update(dict(parallelTrapfile=f1, serialTrapfile=f2, rdose=8.0e9, serial=serial, parallel=parallel))\\n else:\\n f1 = '/Users/smn2/EUCLID/vissim-python/data/cdm_euclid_parallel.dat'\\n f2 = '/Users/smn2/EUCLID/vissim-python/data/cdm_euclid_serial.dat'\\n params = MSSLCDM03params()\\n params.update(dict(parallelTrapfile=f1, serialTrapfile=f2, rdose=8.0e9, serial=serial, parallel=parallel))\\n if beta:\\n params.update(dict(beta_p=0.6, beta_s=0.6))\\n\\n print f1, f2\\n\\n #store shapes\\n eclean = []\\n e1clean = []\\n e2clean = []\\n R2clean = []\\n xclean = []\\n yclean = []\\n eCTI = []\\n e1CTI = []\\n e2CTI = []\\n R2CTI = []\\n xCTI = []\\n yCTI = []\\n eCTIfixed = []\\n e1CTIfixed = []\\n e2CTIfixed = []\\n R2CTIfixed = []\\n xCTIfixed = []\\n yCTIfixed = []\\n\\n fh = open(output.replace('.pk', '.csv'), 'w')\\n fh.write('#files: %s and %s\\\\n' % (f1, f2))\\n for key in params:\\n print key, params[key]\\n fh.write('# %s = %s\\\\n' % (key, str(params[key])))\\n fh.write('#file, delta_e, delta_e1, delta_e2, delta_R2, delta_x, delta_y\\\\n')\\n for f in files:\\n print 'Processing: ', f\\n\\n #load data\\n nocti = pf.getdata(f)\\n\\n #scale to SNR about 10 (average galaxy, a single exposure)\\n nocti /= np.sum(nocti)\\n nocti *= 1500.\\n\\n #place it on canvas\\n tmp = np.zeros((2066, 2048))\\n ysize, xsize = nocti.shape\\n ysize /= 2\\n xsize /= 2\\n tmp[loc-ysize:loc+ysize, loc-xsize:loc+xsize] = nocti.copy()\\n\\n #add background\\n tmp += bcgr\\n\\n #run CDM03\\n c = CTI.CDM03bidir(params, [])\\n tmp = c.applyRadiationDamage(tmp.copy().transpose()).transpose()\\n\\n #remove background and make a cutout\\n CTIdata = tmp[loc-ysize:loc+ysize, loc-xsize:loc+xsize]\\n CTIdata -= bcgr\\n CTIdata[CTIdata < 0.] = 0.\\n\\n #write files\\n #fileIO.writeFITS(nocti, f.replace('.fits', 'noCTI.fits'), int=False)\\n #fileIO.writeFITS(CTI, f.replace('.fits', 'CTI.fits'), int=False)\\n\\n #reset settings\\n settings = dict(sigma=sigma, iterations=iterations)\\n\\n #calculate shapes\\n sh = shape.shapeMeasurement(nocti.copy(), log, **settings)\\n results = sh.measureRefinedEllipticity()\\n\\n eclean.append(results['ellipticity'])\\n e1clean.append(results['e1'])\\n e2clean.append(results['e2'])\\n R2clean.append(results['R2'])\\n xclean.append(results['centreX'])\\n yclean.append(results['centreY'])\\n\\n #CTI, fitted centroid\\n sh = shape.shapeMeasurement(CTIdata.copy(), log, **settings)\\n results2 = sh.measureRefinedEllipticity()\\n\\n eCTI.append(results2['ellipticity'])\\n e1CTI.append(results2['e1'])\\n e2CTI.append(results2['e2'])\\n R2CTI.append(results2['R2'])\\n xCTI.append(results2['centreX'])\\n yCTI.append(results2['centreY'])\\n\\n #fixed centroid\\n settings['fixedPosition'] = True\\n settings['fixedX'] = results['centreX']\\n settings['fixedY'] = results['centreY']\\n settings['iterations'] = 1\\n sh = shape.shapeMeasurement(CTIdata.copy(), log, **settings)\\n results3 = sh.measureRefinedEllipticity()\\n\\n eCTIfixed.append(results3['ellipticity'])\\n e1CTIfixed.append(results3['e1'])\\n e2CTIfixed.append(results3['e2'])\\n R2CTIfixed.append(results3['R2'])\\n xCTIfixed.append(results3['centreX'])\\n yCTIfixed.append(results3['centreY'])\\n\\n text = '%s,%e,%e,%e,%e,%e,%e\\\\n' % (f, results['ellipticity'] - results2['ellipticity'],\\n results['e1'] - results2['e1'], results['e2'] - results2['e2'],\\n results['R2'] - results2['R2'],\\n results['centreX'] - results2['centreX'],\\n results['centreY'] - results2['centreY'])\\n fh.write(text)\\n print text\\n\\n fh.close()\\n\\n results = {'eclean': np.asarray(eclean),\\n 'e1clean': np.asarray(e1clean),\\n 'e2clean': np.asarray(e2clean),\\n 'R2clean': np.asarray(R2clean),\\n 'xclean': np.asarray(xclean),\\n 'yclean': np.asarray(yclean),\\n 'eCTI': np.asarray(eCTI),\\n 'e1CTI': np.asarray(e1CTI),\\n 'e2CTI': np.asarray(e2CTI),\\n 'R2CTI': np.asarray(R2CTI),\\n 'xCTI': np.asarray(xCTI),\\n 'yCTI': np.asarray(yCTI),\\n 'eCTIfixed': np.asarray(eCTIfixed),\\n 'e1CTIfixed': np.asarray(e1CTIfixed),\\n 'e2CTIfixed': np.asarray(e2CTIfixed),\\n 'R2CTIfixed': np.asarray(R2CTIfixed),\\n 'xCTIfixed': np.asarray(xCTIfixed),\\n 'yCTIfixed': np.asarray(yCTIfixed)}\\n\\n #save to a file\\n fileIO.cPickleDumpDictionary(results, output)\\n\\n return results\",\n \"def main():\\n data_visualisation()\\n write_hyper_params()\\n write_result_tables()\\n write_box_plots()\",\n \"def plot12(self, dataset, ts_string_indices, source_jpg_folder='jpg_images', extension='jpg', rows=3, cols=4,\\n outfname='Sample Frames.png', cmap=None, gui_color='green'):\\n # Settings ############################################################\\n font_label_box = {\\n 'color': 'green',\\n 'size': 16,\\n }\\n font_steering = {'family': 'monospace',\\n # 'color': 'darkred',\\n 'weight': 'normal',\\n 'size': 20,\\n }\\n ROWS = rows\\n COLS = cols\\n NUM_IMAGES = ROWS * COLS\\n\\n # Figure ##############################################################\\n # figsize = [width, height]\\n fig = plt.figure(figsize=PAPER_A3_LAND, facecolor='white')\\n fig.suptitle(\\\"Sample frames, Dataset: {}\\\".format(dataset.data_folder), fontsize=20)\\n\\n for i, ts_string_index in enumerate(ts_string_indices):\\n rec = dataset.df.loc[ts_string_index]\\n\\n timestamp_string = rec['datetime'].strftime(\\\"%D %H:%M:%S.\\\") + \\\"{:.2}\\\".format(\\n str(rec['datetime'].microsecond))\\n\\n if 'steering_pred_signal' in dataset.df.columns:\\n this_label = \\\"{}\\\\n{:0.2f}/{:0.2f} steering \\\\n{:0.2f} throttle\\\".format(timestamp_string,\\n rec['steering_signal'],\\n rec['steering_pred_signal'],\\n rec['throttle_signal'])\\n else:\\n this_label = \\\"{}\\\\n{:0.2f}/ steering \\\\n{:0.2f} throttle\\\".format(timestamp_string, rec['steering_signal'],\\n rec['throttle_signal'])\\n\\n ax = fig.add_subplot(ROWS, COLS, i + 1)\\n\\n # Main Image ##########################################################\\n jpg_path = os.path.join(dataset.path_dataset, source_jpg_folder, ts_string_index + '.' + extension)\\n assert os.path.exists(jpg_path), \\\"{} does not exist\\\".format(jpg_path)\\n img = mpl.image.imread(jpg_path)\\n ax.imshow(img, cmap=cmap)\\n # plt.title(str_label)\\n\\n # Data box ########################################################\\n\\n # ax.axes.get_xaxis().set_visible(False)\\n # ax.axes.get_yaxis().set_visible(False)\\n t = ax.text(5, 25, this_label, color=gui_color, alpha=1)\\n # t = plt.text(0.5, 0.5, 'text', transform=ax.transAxes, fontsize=30)\\n t.set_bbox(dict(facecolor='white', alpha=0.7, edgecolor='none'))\\n\\n # Steering widget HUD #################################################\\n # Steering HUD: Actual steering signal\\n steer_actual = ''.join(['|' if v else '-' for v in dataset.linear_bin(rec['steering_signal'])])\\n text_steer = ax.text(80, 105, steer_actual, fontdict=font_steering, horizontalalignment='center',\\n verticalalignment='center', color=gui_color)\\n # Steering HUD: Predicted steering angle\\n if 'steering_pred_signal' in dataset.df.columns:\\n steer_pred = ''.join(['◈' if v else ' ' for v in dataset.linear_bin(rec['steering_pred_signal'])])\\n text_steer_pred = ax.text(80, 95, steer_pred, fontdict=font_steering, horizontalalignment='center',\\n verticalalignment='center', color='red')\\n\\n outpath = os.path.join(dataset.path_dataset, outfname)\\n fig.savefig(outpath)\\n logging.debug(\\\"Wrote Sample Frames figure to {}\\\".format(outpath))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6288007","0.58538276","0.5514038","0.5480608","0.5456143","0.5447625","0.54016596","0.53474796","0.5288365","0.5260717","0.525005","0.5233247","0.5214957","0.52136034","0.52131844","0.5211817","0.52017117","0.5193529","0.51920855","0.5187744","0.5187449","0.51674294","0.51674294","0.51468796","0.514585","0.5107353","0.50866246","0.5068456","0.50637203","0.5062353","0.5053072","0.5046738","0.5044502","0.50406414","0.5039135","0.50390774","0.5038784","0.5032629","0.5028683","0.5028428","0.5017153","0.50056744","0.4997448","0.49831972","0.49796155","0.4979296","0.49731106","0.49695316","0.49655098","0.49598175","0.49564072","0.4946367","0.49429315","0.49395967","0.49375737","0.4935722","0.49296132","0.49228907","0.49187163","0.4911404","0.49075767","0.48991665","0.48987174","0.48976117","0.48959115","0.48948887","0.48912102","0.4881339","0.4879501","0.4879138","0.4870014","0.48686013","0.4867546","0.48645437","0.48621204","0.48616543","0.48577183","0.485066","0.48491096","0.48476258","0.48433614","0.48406094","0.4839225","0.48336273","0.48334494","0.48308513","0.48266086","0.48246467","0.48243332","0.4823793","0.4822343","0.4821733","0.48210856","0.48194915","0.48095512","0.48081866","0.48069015","0.48030734","0.48025185","0.47935924"],"string":"[\n \"0.6288007\",\n \"0.58538276\",\n \"0.5514038\",\n \"0.5480608\",\n \"0.5456143\",\n \"0.5447625\",\n \"0.54016596\",\n \"0.53474796\",\n \"0.5288365\",\n \"0.5260717\",\n \"0.525005\",\n \"0.5233247\",\n \"0.5214957\",\n \"0.52136034\",\n \"0.52131844\",\n \"0.5211817\",\n \"0.52017117\",\n \"0.5193529\",\n \"0.51920855\",\n \"0.5187744\",\n \"0.5187449\",\n \"0.51674294\",\n \"0.51674294\",\n \"0.51468796\",\n \"0.514585\",\n \"0.5107353\",\n \"0.50866246\",\n \"0.5068456\",\n \"0.50637203\",\n \"0.5062353\",\n \"0.5053072\",\n \"0.5046738\",\n \"0.5044502\",\n \"0.50406414\",\n \"0.5039135\",\n \"0.50390774\",\n \"0.5038784\",\n \"0.5032629\",\n \"0.5028683\",\n \"0.5028428\",\n \"0.5017153\",\n \"0.50056744\",\n \"0.4997448\",\n \"0.49831972\",\n \"0.49796155\",\n \"0.4979296\",\n \"0.49731106\",\n \"0.49695316\",\n \"0.49655098\",\n \"0.49598175\",\n \"0.49564072\",\n \"0.4946367\",\n \"0.49429315\",\n \"0.49395967\",\n \"0.49375737\",\n \"0.4935722\",\n \"0.49296132\",\n \"0.49228907\",\n \"0.49187163\",\n \"0.4911404\",\n \"0.49075767\",\n \"0.48991665\",\n \"0.48987174\",\n \"0.48976117\",\n \"0.48959115\",\n \"0.48948887\",\n \"0.48912102\",\n \"0.4881339\",\n \"0.4879501\",\n \"0.4879138\",\n \"0.4870014\",\n \"0.48686013\",\n \"0.4867546\",\n \"0.48645437\",\n \"0.48621204\",\n \"0.48616543\",\n \"0.48577183\",\n \"0.485066\",\n \"0.48491096\",\n \"0.48476258\",\n \"0.48433614\",\n \"0.48406094\",\n \"0.4839225\",\n \"0.48336273\",\n \"0.48334494\",\n \"0.48308513\",\n \"0.48266086\",\n \"0.48246467\",\n \"0.48243332\",\n \"0.4823793\",\n \"0.4822343\",\n \"0.4821733\",\n \"0.48210856\",\n \"0.48194915\",\n \"0.48095512\",\n \"0.48081866\",\n \"0.48069015\",\n \"0.48030734\",\n \"0.48025185\",\n \"0.47935924\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7388833"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":892,"numItemsPerPage":100,"numTotalItems":95772,"offset":89200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTc4MjE5NywianRpIjoiN2FmMjM1MjktNzc4OC00ZWM4LTgzOTEtZTQ2NDUyZjM2NjU1Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU3ODU3OTcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.PX3HTISzsvUoqf8ZKA-BywHr_tnTi31y5i4hPoRgPJNgT3i_g6Cp63mZiNpgfmtsr8G4xmYISPYukRg6RUxhAA","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 |
|---|---|---|---|---|---|---|
Flush data and dispose stream | def flush_and_dispose(self):
yield self.flush()
self.dispose() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flush(self, data):",
"def flush(self):\n self._stream.flush()",
"def _close_stream(self):\n if self.stream:\n try:\n if not self.stream.closed:\n self.stream.flush()\n self.stream.close()\n finally:\n self.s... | [
"0.7562036",
"0.74502873",
"0.7204735",
"0.7163517",
"0.6788774",
"0.6694124",
"0.66281307",
"0.66063815",
"0.66062015",
"0.65169686",
"0.6492155",
"0.64790374",
"0.64739585",
"0.64717114",
"0.6460515",
"0.64388275",
"0.641191",
"0.63762635",
"0.63762635",
"0.63762635",
"0.63... | 0.65826124 | 9 |
Copy content of this stream to different stream Stream can be either asynchronous stream or python binary stream. | def copy_to(self, stream, bufsize=None):
bufsize = bufsize or PRETZEL_BUFSIZE
if isinstance(stream.write(b''), int):
# destination stream is synchronous python stream
try:
while True:
stream.write((yield self.read(bufsize)))
except ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _copy_stream(self):\n return DataStream(\n data_type=self.data_type,\n name=self.name,\n labels=self.labels.copy(),\n callbacks=self.callbacks.copy(),\n uid=self.uid)",
"def copyStreamToStream(streamFrom, streamTo, input_length=sys.maxint, offset=... | [
"0.6567908",
"0.6458702",
"0.63707775",
"0.63395",
"0.6333425",
"0.6158691",
"0.6021784",
"0.5996409",
"0.59475935",
"0.58277017",
"0.57225204",
"0.5707159",
"0.5683792",
"0.56806785",
"0.56806785",
"0.56780136",
"0.56698525",
"0.56685907",
"0.56647164",
"0.56537086",
"0.5566... | 0.71742684 | 0 |
Get a program id from program.program_id. This is not an actual attribute of cirq.Circuit, but thanks to the magic of python, it can be! If your circuit does not have a program_id, this function will return a uuid4(). Program ids can only contain alphanumeric and _ so we replace | def _get_program_id(program: Any):
if not hasattr(program, 'program_id'):
return str(uuid.uuid4())
program_id: str = program.program_id
program_id = program_id.replace(':', '')
parts = program_id.split('/')
parts.append(str(uuid.uuid4()))
chars_per_part = math.floor(64 / len(parts)) - 1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def program_uuid(self):\n return self.kwargs['program_uuid']",
"def unique_id(self):\n return f\"bhyve:program:{self._program_id}\"",
"def prog_id(self):\n return self.properties.get(\"ProgID\", None)",
"def generate_fwan_process_id() -> str:\n return str(uuid.uuid4())",
"def get_re... | [
"0.79068476",
"0.7264113",
"0.65031767",
"0.6405029",
"0.6306429",
"0.6228734",
"0.6212107",
"0.62085164",
"0.6196261",
"0.6175726",
"0.6149326",
"0.61228895",
"0.6092414",
"0.604155",
"0.60018843",
"0.59885347",
"0.5971074",
"0.5971074",
"0.5971074",
"0.5952242",
"0.5940573"... | 0.8863723 | 0 |
Maintain a respectful queue of work | async def execute_in_queue(func, tasks, num_workers: int):
queue = asyncio.Queue()
async def worker():
while True:
task = await queue.get()
print(f"Processing {task.fn}. Current queue size: {queue.qsize()}")
await func(task)
print(f"{task.fn} completed")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def monitor_queue(self):\n\n while True:\n job = self.queue.next()\n if job:\n # print(\"found %s\" % (job.job_id))\n\n job_name = job.payload[\"job_name\"]\n\n if job_name in self.mul_func_map:\n\n t = self.mul_func_map[j... | [
"0.6978231",
"0.69729084",
"0.6931101",
"0.68955994",
"0.6872209",
"0.68439406",
"0.6832862",
"0.6821273",
"0.6793218",
"0.6765122",
"0.67272234",
"0.6726119",
"0.67211956",
"0.6699042",
"0.6697412",
"0.6669965",
"0.6655158",
"0.6640652",
"0.6640652",
"0.6579681",
"0.6564476"... | 0.0 | -1 |
Handles user input to remove a file or set of files from the selected DISTRESS network. This functionality can all be overriden via the configuration file. | def delete(socket, args, config, library, cmd=False):
files=args['<nameid>']
ignore=args['--ignore']
for nameid in files:
receipt = library.get_receipt( nameid )
if not receipt:
if cmd: print "Could not find receipt for:",nameid
if not ignore: return False
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handleRemoveFile(self):\n for w in self.filesList.selectedItems():\n self.filesList.removeFile(w.text(2))\n self.metadataList.clear()\n self.metadataList.setRowCount(0)\n self.metadataList.setHorizontalHeaderLabels([\"Metadata Header\", \"Value\"])\n self.personalD... | [
"0.59984297",
"0.5738072",
"0.5735693",
"0.570255",
"0.5700912",
"0.55771846",
"0.557139",
"0.5505206",
"0.54967844",
"0.54964334",
"0.5432532",
"0.54182154",
"0.5397033",
"0.5331703",
"0.53284043",
"0.52995676",
"0.52982754",
"0.5277517",
"0.5267115",
"0.521343",
"0.5203183"... | 0.54584485 | 10 |
Find needle in known_hosts | def match_hosts(needle):
matched_hosts = []
with open(known_hosts_path, "r") as known_hosts_file:
for line in known_hosts_file:
host, _, _ = line.split(" ")
if needle in host:
matched_hosts.append(host)
return matched_hosts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_host_key(self, value):\n for key in self:\n if value in key.hosts:\n return key\n return None",
"def findwhois_server(self, buf, hostname, query):\n nhost = None\n match = re.compile('Domain Name: {}\\s*.*?Whois Server: (.*?)\\s'.format(query), flags... | [
"0.67115235",
"0.5897425",
"0.58485895",
"0.5797194",
"0.5788939",
"0.57481563",
"0.56678516",
"0.56509316",
"0.5648003",
"0.5577273",
"0.55597615",
"0.5547292",
"0.55391026",
"0.5459835",
"0.5459835",
"0.538749",
"0.53682864",
"0.53653854",
"0.5352104",
"0.53495044",
"0.5329... | 0.78194994 | 0 |
Select a particular host | def select_host(hosts):
print("Select a host to delete:\n")
for i, host in enumerate(hosts):
print("[{0}] {1}".format(i, host))
print("[X] Cancel\n")
selections = str(input("Choose a host (csv allowed): "))
if "x" in selections.lower():
return []
hosts = [hosts[int(host)] ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select_host(host_list):\n if len(host_list) > 1:\n print('[+] Detected multiple hosts: (Port 62001 will be the first and default port for adb devices)')\n for host_id, host in enumerate(host_list):\n print(f\" > ({host_id+1}) {host}\")\n print('[+] Input t... | [
"0.7400062",
"0.7396214",
"0.6528219",
"0.65000296",
"0.64538556",
"0.640917",
"0.63133",
"0.6268976",
"0.62632835",
"0.6248636",
"0.6248354",
"0.6248354",
"0.6156825",
"0.61134",
"0.6086269",
"0.60218847",
"0.60055834",
"0.5921318",
"0.5917707",
"0.5866374",
"0.5859256",
"... | 0.5990763 | 17 |
Delete a particular host | def delete_host(host):
lines = []
with open(known_hosts_path, "r") as f:
lines = f.readlines()
with open(known_hosts_path, "w") as f:
for line in lines:
if host != line.split()[0]:
f.write(line) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_host(self, conf, tenant_id, network_id, host_id):\n\t\tpass",
"def delete(self, host_id):\n self._delete('/os-hosts/%s' % host_id)",
"def host_delete(self, host):\n\n endpoint = '/Domain/Host/Delete'\n\n params = {\n 'Host' : host,\n }\n \n respon... | [
"0.85617054",
"0.83163726",
"0.8190062",
"0.78757465",
"0.7744529",
"0.7730757",
"0.7690718",
"0.7684262",
"0.7683934",
"0.7637056",
"0.75802505",
"0.7510345",
"0.7424208",
"0.7371803",
"0.73513824",
"0.722818",
"0.71924126",
"0.7182905",
"0.717348",
"0.7128607",
"0.7064725",... | 0.73869604 | 13 |
Function for computing attention on several heads simultaneously Splits tensor to be multi headed. | def split_heads(self, tensor, batch_size):
# (batch_size, seq_len, output_dim) -> (batch_size, seq_len, n_heads, head_depth)
splitted_tensor = tensor.view(batch_size, -1, self.n_heads, self.head_depth)
return splitted_tensor.transpose(1, 2) # (batch_size, n_heads, seq_len, head_depth) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _MultiHeadedAtten(\n self, name, num_heads=None, enable_qkv_proj_in_onestep=False\n ):\n p = self.params\n if num_heads is None:\n num_heads = p.num_heads\n attn_memory_tpl = layers.LSHTaskWithMultiplierLayer.Params().Set(\n log_num_buckets=p.attn_log_num_buckets,\n num_hash_f... | [
"0.6686328",
"0.6551467",
"0.6496521",
"0.6359135",
"0.6349045",
"0.63056546",
"0.6289936",
"0.62841004",
"0.6159032",
"0.61402965",
"0.61257666",
"0.605103",
"0.5996085",
"0.59759814",
"0.59319884",
"0.5907628",
"0.5905594",
"0.5892423",
"0.58865345",
"0.58604753",
"0.581902... | 0.0 | -1 |
Select next node based on decoding type. | def _select_node(self, logits):
# assert tf.reduce_all(logits) == logits, "Probs should not contain any nans"
if self.decode_type == "greedy":
selected = torch.argmax(logits, dim=-1) # (batch_size, 1)
elif self.decode_type == "sampling":
# logits has a shape of (batch... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_next(node):\n return node['next']",
"def _next(self):\n node = self.head\n while node != None:\n yield node.data\n node = node.right",
"def next():",
"def next():",
"def get_next_decoder(current_decoder, decoder_dict):\n switch = { ... | [
"0.5909177",
"0.55684704",
"0.5517093",
"0.5517093",
"0.5508113",
"0.5435157",
"0.5415138",
"0.5362074",
"0.5362074",
"0.5362074",
"0.5341539",
"0.53378487",
"0.53029335",
"0.52910507",
"0.528096",
"0.52807254",
"0.52695376",
"0.5262345",
"0.52601093",
"0.5259646",
"0.5239189... | 0.0 | -1 |
Takes a state and graph embeddings, Returns a part [h_N, D] of context vector [h_c, h_N, D], that is related to RL Agent last step. | def get_step_context(self, state, embeddings):
# index of previous node
prev_node = state.prev_a.to(self.dev) # (batch_size, 1)
# from embeddings=(batch_size, n_nodes, input_dim) select embeddings of previous nodes
cur_embedded_node = embeddings.gather(1, prev_node.view(prev_node.shape... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_nn(cell, rnn_size, input_data, vocab_size, embed_dim):\n embed = get_embed(input_data, vocab_size, embed_dim) \n output, final_state = build_rnn(cell, embed)\n \n logits = tf.contrib.layers.fully_connected(output, vocab_size, activation_fn=None)\n #final_state = tf.identity(final_stat... | [
"0.5710615",
"0.5697232",
"0.56184053",
"0.5616776",
"0.5595174",
"0.5548798",
"0.5524491",
"0.5477435",
"0.54760706",
"0.54628956",
"0.54440236",
"0.54390854",
"0.53898245",
"0.5347198",
"0.5345448",
"0.5341874",
"0.5341261",
"0.53183186",
"0.53023475",
"0.53003037",
"0.5272... | 0.7374239 | 0 |
Computes MultiHead Attention part of decoder | def decoder_mha(self, Q, K, V, mask=None):
# Add dimension to mask so that it can be broadcasted across heads
# (batch_size, seq_len_q, seq_len_k) --> (batch_size, 1, seq_len_q, seq_len_k)
if mask is not None:
mask = mask.unsqueeze(1)
attention = scaled_attention(Q,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def attention_module_multi_head(roi_feat, position_embedding,\n nongt_dim, fc_dim, feat_dim,\n dim=(1024, 1024, 1024),\n group=16, index=1):\n dim_group = (dim[0] / group, dim[1] / group, dim[2] / group)\n nongt_roi_... | [
"0.65307134",
"0.6444963",
"0.6292002",
"0.60863566",
"0.6076711",
"0.6036302",
"0.6034969",
"0.60121447",
"0.5967123",
"0.59568924",
"0.5932581",
"0.59028465",
"0.5889598",
"0.58632296",
"0.58586293",
"0.5821471",
"0.58062166",
"0.58036745",
"0.57800764",
"0.5769691",
"0.576... | 0.0 | -1 |
SingleHead attention sublayer in decoder, computes logprobabilities for node selection. | def get_log_p(self, Q, K, mask=None):
compatibility = torch.matmul(Q, K.transpose(-2, -1)) / math.sqrt(Q.shape[-1])
compatibility = torch.tanh(compatibility) * self.tanh_clipping
if mask is not None: compatibility = compatibility.masked_fill(mask == 1, -1e9)
log_p = F.log_softm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_model_multihead_attention_multiscaleCNN4_covermore(self,\n dim_attention,headnum,\n embedding_vec,\n load_weights=False, weight_dir=None,\n nb_filter... | [
"0.539668",
"0.5370601",
"0.5208628",
"0.5201065",
"0.5195737",
"0.5145377",
"0.51447594",
"0.506822",
"0.5060384",
"0.5052498",
"0.5042207",
"0.5021003",
"0.50186706",
"0.49782065",
"0.49544263",
"0.4953579",
"0.49511325",
"0.49411935",
"0.4940318",
"0.4935457",
"0.49295557"... | 0.0 | -1 |
Forward and calculate loss for REINFORCE algorithm in a memory efficient way. This sacrifices a bit of performance but is way better in memory terms and works by reordering the terms in the gradient formula such that we don't store gradients for all the seguence for a long time which hence produces a lot of memory cons... | def fwd_rein_loss(self, inputs, baseline, bl_vals, num_batch, return_pi=False):
on_training = self.training
self.eval()
with torch.no_grad():
cost, log_likelihood, seq = self(inputs, True)
bl_val = bl_vals[num_batch] if bl_vals is not None else baseline.eval(inputs, cost... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def backward_linear(Data):\n\n # data\n graph = tf.Graph()\n X = Data[0]\n Y_ = Data[1]\n\n if X.shape[0] != Y_.shape[0]:\n raise Exception(\"The quantity of Input X and Compare Y_ are not same!\")\n\n Loss = []\n with graph.as_default():\n print(\"This is the process of all the ... | [
"0.67428344",
"0.67154306",
"0.6599166",
"0.658898",
"0.6557158",
"0.65417147",
"0.6540556",
"0.65325093",
"0.65008616",
"0.649814",
"0.6443805",
"0.64413273",
"0.64185274",
"0.64034337",
"0.6393763",
"0.639042",
"0.63873094",
"0.638429",
"0.6372747",
"0.6360011",
"0.6358676"... | 0.6155039 | 49 |
Forward method. Works as expected except and as described on the paper, however if pre_selects is None which hence implies that pre_cost should be none it's because fwd_rein_loss is calling it; check that method for a description of why this is useful. | def forward(self, inputs, return_pi=False, pre_selects=None, pre_cost=None):
self.batch_size = inputs[0].shape[0]
state = self.problem(inputs) # use CPU inputs for state
inputs = self.set_input_device(inputs) # sent inputs to GPU for training if it's being used
sequences = []
l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _pre_forward(\n state: _State,\n handles: List[FlatParamHandle],\n unshard_fn: Callable,\n module: nn.Module,\n input: Any,\n):\n state.training_state = TrainingState.FORWARD_BACKWARD\n state._exec_order_data.record_pre_forward(handles, module.training)\n for handle in handles:\n ... | [
"0.6193878",
"0.61060727",
"0.59223294",
"0.58666104",
"0.5782265",
"0.56982285",
"0.5692076",
"0.56716996",
"0.5626985",
"0.55718166",
"0.5547559",
"0.5527229",
"0.55199444",
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"0.5519075",
"0.5505503",
"0.5492375",
"0.54898894",
"0.54689044",
"0.546703",
"0.54422... | 0.53767645 | 32 |
Move vehicle in direction based on specified velocity vectors. | def send_ned_velocity(uvwLoc):
vel = 5. * 2. # m/s at average cdf=.5
m = norm(7.8, 15.22).cdf(np.sum(uvwLoc**2.))
uvwLocNorm = -(uvwLoc / np.abs(uvwLoc).max())
uvwVel = m * vel * uvwLocNorm
velocity_x, velocity_y, velocity_z = uvwVel[1], uvwVel[0], uvwVel[2]
rospy.logdebug("{} m={} mXv={} uvw={... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_velocity(self):\n # Set thruster (up/down) movement\n if self.thrusters:\n self.velocity_y -= self.gravity\n else:\n self.velocity_y += self.velocity_slowing\n\n # Set left movement\n if self.moving_left:\n self.velocity_x -= self.gravi... | [
"0.68131894",
"0.66878253",
"0.66767144",
"0.6664651",
"0.6620746",
"0.6420614",
"0.6397649",
"0.6369574",
"0.6361948",
"0.6354659",
"0.63036567",
"0.63017106",
"0.6244916",
"0.6221734",
"0.6219887",
"0.62121576",
"0.6205084",
"0.619217",
"0.61751765",
"0.61676943",
"0.616657... | 0.0 | -1 |
converts index from NED coordinate in array X to index of array J | def indXtoJ(indX):
return np.unravel_index(indX % xx.size, xx.shape) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def xy_to_index(x, y):\n index = y * columns + x\n return index",
"def one_dim_index(self, i, j):\n return int(i + j * self.nx)",
"def index(i, j):\n return i * N + j",
"def coor2idx(x, y):\r\n a = round(x/4000,0)*4000\r\n b = (round_down(y/4000,0)+0.5)*4000\r\n i = int((a - 24000)/... | [
"0.6976895",
"0.68221605",
"0.6785867",
"0.66706675",
"0.66188246",
"0.6592676",
"0.6390636",
"0.6366008",
"0.6351581",
"0.632472",
"0.6315053",
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"0.6266986",
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"0.621769",
"0.62147",
"0.6204049",
"0.61921877",
"0.6169835",
"0.6155819",
"0.6117954",
... | 0.79179806 | 0 |
take hold of a lock on a resource | def lock(self, nick, channel, resourcestr):
return (channel, self._lock(nick, nick, resourcestr)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __call__(self, resource: LockResource, timeout: timedelta) -> Lock:",
"def thread_lock(self, bay_uuid):\n try:\n self.acquire()\n yield\n except exception.OperationInProgress:\n raise\n except: # noqa\n with excutils.save_and_reraise_exception... | [
"0.7309355",
"0.6777241",
"0.6667399",
"0.66405743",
"0.6597714",
"0.6532337",
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"0.65129644",
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"0.6449373",
"0.64459926",
"0.638227",
"0.6381445",
"0.6355099",
"0.6349529",
"0.63402885"... | 0.0 | -1 |
add a new resource to the database | def register(self, nick, channel, resourcestr):
resources, multi = self.splitResources(resourcestr)
for r in resources:
if r in self.locks:
raise LockBotException('ERROR, resource "%s" is already registered' % r,
resourcestr, self.verb)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_resource(self, resource_name, resource):\n self.resources[resource_name] = resource",
"def save(database, resource):\n # TODO\n pass",
"def add_resource(self, *args, **kwargs):\n return self._resources_manager.add_resource(*args, **kwargs)",
"def add(self):\n\n db.session.a... | [
"0.6960172",
"0.68792987",
"0.6706377",
"0.6657957",
"0.6657957",
"0.6652387",
"0.6512611",
"0.63855875",
"0.6365135",
"0.63592577",
"0.6354482",
"0.63451076",
"0.63057184",
"0.6290238",
"0.62426555",
"0.62412417",
"0.6189581",
"0.6189581",
"0.6189581",
"0.6187583",
"0.618330... | 0.0 | -1 |
remove a resource from the database | def unregister(self, nick, channel, resourcestr):
resources, multi = self.getlocks(resourcestr)
for r in resources:
if self.locks[r]:
raise LockBotException('ERROR, resource "%s" is locked by %s' %
(r, self.locks[r]),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove():\n\n db_remove()",
"def remove_resource(self, graph_db):\n with mutex:\n neo_resource.delete_node(graph_db, self.index)",
"def remove(self):\n db.session.delete(self)\n db.session.commit()",
"def remove(self, resource, **kwargs):\n log.info(\n ... | [
"0.7847243",
"0.75787425",
"0.72596234",
"0.717408",
"0.712051",
"0.697973",
"0.6978793",
"0.6978793",
"0.6957655",
"0.6940326",
"0.6937562",
"0.69012076",
"0.68811667",
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"0.6858983",
"0.6839443",
"0.6837163",
"0.6831113",
"0.6767776",
"0.6735871",
"0.6728387",
... | 0.0 | -1 |
release the resource lock | def unlock(self, nick, channel, resourcestr):
resources, multi = self.getlocks(resourcestr)
# iterate over all resources once to check for errors
for r in resources:
l = self.locks[r]
if not l.owner:
raise LockBotException("%s is already free" % r, resourc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def release(self):\n fcntl.flock(self.lock_file, fcntl.LOCK_UN)",
"def release_lock(self):\n self._multistore._unlock()",
"def unlock(lock):\n lock.release()",
"def release(self):\r\n if self.is_locked:\r\n os.close(self.fd)\r\n os.unlink(self.lockfile)\r\n ... | [
"0.80886894",
"0.80755836",
"0.7986181",
"0.7926654",
"0.7918799",
"0.7900972",
"0.7896809",
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"0.74279904",
"0.7386935",
"0.73810226",
"0.7365187",
"0.73564476",
"0.7331302... | 0.0 | -1 |
assign a resource lock to someone else other than the caller | def assignlock(self, nick, channel, assignee, resourcestr):
return (channel, self._lock(nick, assignee, resourcestr)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _lock_resource(self, resource, user_name):\n for sub_resource in resource.get_sub_resources():\n self._lock_resource(sub_resource, user_name)\n\n resource.owner = user_name\n resource.owner_time = datetime.now()\n resource.save()",
"def __call__(self, o):\n if no... | [
"0.7998552",
"0.6857944",
"0.65522623",
"0.65328693",
"0.63625455",
"0.6329278",
"0.6238407",
"0.6220345",
"0.6207784",
"0.619553",
"0.61907506",
"0.61907345",
"0.6148036",
"0.6120895",
"0.6093301",
"0.608594",
"0.6082099",
"0.60701936",
"0.6067369",
"0.6031181",
"0.60109025"... | 0.69686687 | 1 |
release a resource lock even if the caller does not hold the lock (USE WITH CAUTION) | def freelock(self, nick, channel, resourcestr):
resources, multi = self.getlocks(resourcestr)
# iterate over all resources once to check for errors
for r in resources:
if not self.locks[r].owner:
raise LockBotException('ERROR: resource %s is already unlocked' % r,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unlock(lock):\n lock.release()",
"def release_lock(self):\n self._multistore._unlock()",
"def release_lock(self):\n if self.lock:\n self.lock.release()",
"def release_lock():\r\n get_lock.n_lock -= 1\r\n assert get_lock.n_lock >= 0\r\n # Only really release lock o... | [
"0.7918944",
"0.7745639",
"0.7699182",
"0.7658934",
"0.75480586",
"0.7525814",
"0.7482436",
"0.74612516",
"0.74337447",
"0.73481554",
"0.73064387",
"0.73064387",
"0.7155336",
"0.71434677",
"0.71340674",
"0.7126347",
"0.7125614",
"0.71157146",
"0.71079975",
"0.7076271",
"0.706... | 0.64460754 | 51 |
try to take the lock, or get on queue if it is currently locked | def waitlock(self, nick, channel, resourcestr):
return (channel, self._lock(nick, nick, resourcestr, wait=True)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lock(self):\r\n return self._lock",
"def get_lock(self, name, try_=False):\n lock = Lock(self, name, try_)\n with lock as got_lock:\n yield got_lock",
"def acquire_nowait(self) -> None:\n task = get_current_task()\n if self._owner_task == task:\n raise R... | [
"0.6813398",
"0.67853355",
"0.677115",
"0.670761",
"0.66525537",
"0.6612082",
"0.66009504",
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"0.6546076",
"0.653102",
"0.6525628",
"0.65045685",
"0.64708346",
"0.64623094",
"0.6449686",
"0.6447692",
"0.64386505",
"0.6394093",
"0.63887835",
"0.63136595",
"0.6301616... | 0.0 | -1 |
list locked resources and their owners | def status(self, nick, channel):
lockeditems = sorted([item[0] for item in self.locks.items() if item[1].owner])
if len(lockeditems) == 0:
return (channel, "There are no locked resources")
else:
messages = []
messages += [(channel, "Status of locked resources... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def locks(self):\r\n params = {'f' : 'json'}\r\n url = \"%s/lockInfos\" % self._url\r\n return self._con.post(url, params)['lockInfos']",
"def lock_resources(self, request):\n locked_resources = []\n\n client = request.worker.name\n user_name, _ = client.split(\":\") # ... | [
"0.66722405",
"0.6582977",
"0.6438537",
"0.634539",
"0.6334195",
"0.60580474",
"0.60307276",
"0.5862831",
"0.58229685",
"0.58210385",
"0.58006316",
"0.57709336",
"0.57266915",
"0.5692866",
"0.5691849",
"0.5633208",
"0.56149334",
"0.55855703",
"0.55745804",
"0.5522181",
"0.550... | 0.68914664 | 0 |
list all registered resources | def list(self, nick, channel):
if len(self.locks.keys()) == 0:
return (channel, "There are no registered resources")
else:
return (channel, "List of registered resources: %s" %
', '.join(sorted(self.locks.keys()))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_resources(self):\n return []",
"def get_resources():\n return Response(f\"{Resource.get_all_resources()}\", 200, mimetype='text/plain')",
"def resources(self):\r\n return self.page.object_list",
"def list(self):\n resources = self._os_resource_manager.list()\n resource_... | [
"0.78437096",
"0.7594706",
"0.7558492",
"0.75522166",
"0.7542724",
"0.7387004",
"0.73857987",
"0.73242646",
"0.7251398",
"0.7248098",
"0.72020996",
"0.71079016",
"0.71052307",
"0.70958483",
"0.70832217",
"0.70832217",
"0.70832217",
"0.6993058",
"0.6984397",
"0.6976492",
"0.69... | 0.0 | -1 |
display this help message | def help(self, nick, channel):
def getCmdArguments(handler):
return inspect.getargspec(handler)[0][3:]
helpTuples = []
rules = self.getRules()
processedHandlers = set()
for _, handler in rules:
if handler in processedHandlers:
continue
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def display_help(self):\n pass",
"def help():",
"def help(self) -> str:\n\t\treturn None",
"def help():\n print(UI.HELP)",
"def printhelp():",
"def help(self):\n res = \"\"",
"def show_help():\n messagebox.showinfo(title='How to Use', message=\"It's really easy.\")",
"def ... | [
"0.8669022",
"0.83941054",
"0.8359416",
"0.83579797",
"0.8353962",
"0.82706004",
"0.82511544",
"0.82244396",
"0.81900793",
"0.8179038",
"0.8179038",
"0.8173914",
"0.8168498",
"0.81642497",
"0.8164061",
"0.81596285",
"0.81596285",
"0.8117534",
"0.8091054",
"0.80642176",
"0.802... | 0.0 | -1 |
Draw a series of values as a histogram line. Each yvalue is the count of a histogram bin. If xvalues are also given, they are the bin centers. If no xvalues are given, the yvalues are bined at integers starting with 0. | def draw_point_hist(y, x=None, **kwargs):
if len(y) < 2:
raise RuntimeError("Need at least 2 points for histogram")
if x is None:
# Use integers starting at 0
x = np.arange(len(y))
else:
# Use given x, and ensure points are sorted
x = np.array(x)
isort = np.a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def makeHistogram(values, numBins, xLabel, yLabel, title=None):",
"def makeHistogram(values, numBins, xLabel, yLabel, title=None):\r\n pylab.hist(values, bins = numBins)\r\n pylab.xlabel(xLabel)\r\n pylab.ylabel(yLabel)\r\n if title != None:\r\n pylab.title(title)\r\n pylab.show()",
"def ... | [
"0.73796916",
"0.6798891",
"0.67624927",
"0.67370737",
"0.6501336",
"0.6177134",
"0.6069408",
"0.6060996",
"0.6033122",
"0.60297614",
"0.6015766",
"0.59853196",
"0.5974089",
"0.597163",
"0.5960965",
"0.59432226",
"0.5934229",
"0.59233856",
"0.592121",
"0.58921146",
"0.5887044... | 0.6794348 | 2 |
Convenience function to draw a spectrum. | def draw_spectrum(meas, x, rel=False, scale=True, **kwargs):
if rel:
s0 = meas.spec(meas.spec.central)
s = meas.spec(x)
line = draw_point_hist(100*(s/s0-1), **kwargs)
plt.ylabel('Relative offset percentage')
if scale:
rescale_plot()
else:
line = draw... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw_spectra(meas, normalize=True):\n\n # Draw the plain spectrum\n x = list(meas.spec.central)\n lfull = draw_spectrum(meas, x, scale=True, label='full')\n\n x[meas.spec.ipar('xsec_sig')] = 1\n x[meas.spec.ipar('xsec_bg1')] = 0\n x[meas.spec.ipar('xsec_bg2')] = 0\n lsig = draw_spectrum(me... | [
"0.6886257",
"0.67275256",
"0.66948736",
"0.6687127",
"0.6679135",
"0.66400295",
"0.6405625",
"0.6373359",
"0.63331544",
"0.62864983",
"0.6221333",
"0.62205774",
"0.6168466",
"0.6124746",
"0.6118125",
"0.60210186",
"0.59727067",
"0.59527946",
"0.5943664",
"0.5928661",
"0.5904... | 0.6519947 | 6 |
Build a template measurment object. | def build_template_meas(name='example'):
meas = templates.TemplateMeasurement(name)
meas.set_lumi(1, 0.02)
# Base shape for the signal, triangular distribution
sig = np.array([10000, 12500, 15000, 12500, 10000], dtype=float)
# Add a source for the signal
src_sig = meas.new_source('sig', sig)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_template(self, state: gamelib.AdvancedGameState):\n for unit in self.BASE_TEMPLATE:\n if state.get_resource(state.CORES) > self.UNIT_COSTS[unit[1]]:\n if state.can_spawn(unit[1], unit[0]):\n state.attempt_spawn(unit[1], unit[0])\n\n low_health_un... | [
"0.605412",
"0.605101",
"0.60259765",
"0.5781559",
"0.5705319",
"0.5661125",
"0.5560697",
"0.54971033",
"0.5424929",
"0.53872424",
"0.5383353",
"0.53767645",
"0.5330913",
"0.5301977",
"0.5290818",
"0.5241824",
"0.5231358",
"0.5226303",
"0.5188401",
"0.5178917",
"0.51679087",
... | 0.7202414 | 0 |
Generate a plausible data spectrum for a pseudoexperiment in which the true underlying parameters are not known. | def make_pseudo(meas, systs=True, signal=True, stats=True):
# Get the scales for the paramters controlling the spectrum
scales = meas.spec.scales
# Randomize the true underlying values for constrained parameters
truth = np.array(meas.spec.central)
if systs:
# Vary the constrained parameter... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_spectrum(self):\n matlab_method = self.matlab_mapper[self.matlab_script]\n n, dm, peak_locations, omega_res, n_shell, gamma_amp = matlab_method(float(self.n_max), float(self.n_max_s),\n float(self.num_channels), float(self.scale),\n ... | [
"0.63703376",
"0.61298424",
"0.6076039",
"0.6042859",
"0.60020065",
"0.5754672",
"0.5692847",
"0.56431437",
"0.56001097",
"0.5560816",
"0.55325127",
"0.5526705",
"0.54973495",
"0.5476813",
"0.54699695",
"0.54654425",
"0.5463588",
"0.54630315",
"0.5450797",
"0.5449483",
"0.541... | 0.6240589 | 1 |
Sample the likelihood space with a Markov Chain Monte Carlo. | def run_mcmc(meas, x, nsamples, covm=None, scales=None):
mcmc = MCMC(meas.spec.npars)
mcmc.set_values(x)
if covm is not None and scales is None:
mcmc.set_covm(covm)
elif scales is not None:
mcmc.set_scales(scales)
else:
raise ValueError("Must provide covariance OR scales")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _sample_lam(self, cur_y, cur_z):\n old_loglik = self._loglik(cur_y, cur_z)\n old_lam = self.lam\n \n # modify the feature ownership matrix\n self.lam = np.random.beta(1,1)\n new_loglik = self._loglik(cur_y, cur_z)\n move_prob = 1 / (1 + np.exp(old_loglik - new_logli... | [
"0.6364029",
"0.6157119",
"0.61371166",
"0.59664613",
"0.59470826",
"0.59432334",
"0.5931655",
"0.5923204",
"0.5896645",
"0.5854254",
"0.58510005",
"0.5840389",
"0.5831509",
"0.57536685",
"0.5750755",
"0.5727051",
"0.57206815",
"0.57000595",
"0.56945896",
"0.568531",
"0.56829... | 0.0 | -1 |
Assess the structure of the likelihood space with all parameters shifted to +1 sigma. | def asses_space(meas):
truth = list(meas.spec.central)
truth[meas.spec.ipar('syst_s1')] = 1
data = meas.spec(truth)
meas.spec.set_data(data)
lls, xs, rels, prob = minutils.find_minima(meas.spec)
print("Found %d minima with likelihoods:" % len(lls))
print(', '.join(["%.3f" % l for l in lls]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _sample_likelihood_params(self):\n if self.marginalize:\n # We integrated out `beta` a la Bayesian linear regression.\n pass\n else:\n self._sample_beta_and_sigma_y()",
"def __log_likelihood(self, params, *args):\n\t\tX, y, feature_set, lambda_reg, empirical_wei... | [
"0.59982514",
"0.59781104",
"0.59630436",
"0.5947138",
"0.5790235",
"0.57825786",
"0.57464623",
"0.5710098",
"0.5587495",
"0.556041",
"0.55523425",
"0.55367815",
"0.55273217",
"0.5508631",
"0.549958",
"0.5497607",
"0.54881877",
"0.5447547",
"0.5439081",
"0.5427865",
"0.541616... | 0.0 | -1 |
Generate a fake data spectrum using a template measurement spectrum, and randomizing its parameters. Then fit this fake data to see if its true underlying parameters can be recovered. | def measure_template(meas):
# Make a pseudo-experiment
data, truth = make_pseudo(meas)
meas.spec.set_data(data)
# First fit without randomization
fit_first, _, _ = minutils.single_fit(meas.spec, randomize=False)
# Global fit with randomization to find better minimum
minx, ll, minimizer = mi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_data(path=resource_filename('locals', 'data/fake/'), mag_range=(11.13,18)):\n # Get some random spectra\n try:\n files = glob.glob('/user/jfilippazzo/Models/ACES/default/*.fits')[::50]\n except:\n files = glob.glob('/Users/jfilippazzo/Documents/Modules/_DEPRECATED/limb_dark_jeff... | [
"0.63488543",
"0.6271454",
"0.605595",
"0.5991764",
"0.59657055",
"0.5922426",
"0.58975416",
"0.5804206",
"0.57323813",
"0.5716077",
"0.57142216",
"0.5693554",
"0.5623713",
"0.561851",
"0.56114596",
"0.5588405",
"0.54932594",
"0.54904705",
"0.548813",
"0.548179",
"0.5470604",... | 0.504452 | 75 |
Draw a spectrum with a parameter fluctuated to +/ 1 sigma, for each parameter. Unconstrained parameters are set to +/ 1. | def draw_spectra(meas, normalize=True):
# Draw the plain spectrum
x = list(meas.spec.central)
lfull = draw_spectrum(meas, x, scale=True, label='full')
x[meas.spec.ipar('xsec_sig')] = 1
x[meas.spec.ipar('xsec_bg1')] = 0
x[meas.spec.ipar('xsec_bg2')] = 0
lsig = draw_spectrum(meas, x, scale=F... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_spectrum():\n filename = \"parameters.txt\"\n #If the file exists already, we should read the parameters from memory. \n #If the file does not exist, default variables are in Variables.py\n if path.exists(filename): \n para= open(filename,\"r\")\n para_lines = para.readlines(... | [
"0.5893153",
"0.58147323",
"0.58019096",
"0.5797836",
"0.57860005",
"0.5742457",
"0.5662904",
"0.56534266",
"0.5623216",
"0.5619086",
"0.557973",
"0.5557214",
"0.5554759",
"0.54910123",
"0.54774016",
"0.5462636",
"0.54308754",
"0.5417131",
"0.54069245",
"0.54056835",
"0.53973... | 0.63044024 | 0 |
Return a random date between two dates, either as epoch seconds or human readable string. | def randdate(field):
timefmt = "%B %d, %Y %H:%M:%S"
if 'format' in field.keys() and field['format'] != 'stamp':
start = int(time.mktime(time.strptime(field['start'], timefmt)))
end = int(time.mktime(time.strptime(field['end'], timefmt)))
else:
start = field['start']
end = fie... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_date(date_from='01/01/1970', date_to='01/01/2019'):\n try:\n time_from = time.mktime(time.strptime(date_from, '%d/%m/%Y'))\n time_to = time.mktime(time.strptime(date_to, '%d/%m/%Y'))\n assert time_from <= time_to\n except (ValueError, AssertionError) as e:\n print(e)\n ... | [
"0.761147",
"0.75701857",
"0.73856366",
"0.72474754",
"0.7162615",
"0.7155106",
"0.71383643",
"0.71084374",
"0.71084374",
"0.71084374",
"0.71084374",
"0.71084374",
"0.7099026",
"0.70805055",
"0.68570757",
"0.6788878",
"0.66872346",
"0.66075",
"0.6586677",
"0.6356645",
"0.6351... | 0.63875705 | 19 |
Return the appropriate (lambda) function for the given field | def bind_function(field):
t = field['type']
try:
if t == 'int':
return lambda x: random.randint(field['min'], field['max'])
elif t == 'float':
return lambda x: field['min'] + ((field['max'] - field['min']) * random.random())
elif t == 'string':
return ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def picker(field_name):\n return lambda row: row[field_name]",
"def picker(field_name):\n return lambda row: row[field_name]",
"def use_cached_field(anki: AnkiDeck, mid: int, field: int) -> FieldFunction[str]:\n return lambda sort_field: lookup_field(anki, mid, field, sort_field)",
"def _generateLam... | [
"0.6644591",
"0.6644591",
"0.63634723",
"0.60030925",
"0.59671044",
"0.5951441",
"0.5808804",
"0.57361925",
"0.57241744",
"0.5697697",
"0.568813",
"0.5683597",
"0.56689215",
"0.56686634",
"0.56650335",
"0.5607115",
"0.5566443",
"0.5554812",
"0.5526383",
"0.55215496",
"0.55166... | 0.6951546 | 0 |
Write the contents into the xls tables. | def write_xls(contents, filepath):
if isinstance(contents, dict):
wb = xlwt.Workbook()
ws = wb.add_sheet('sheet 1')
if not isinstance(contents, OrderedDict):
print("contents should be a instance of 'OrderedDict'.")
for i, (head, content) in enumerate(contents.items()):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_xls(self):\n self.wb = xlwt.Workbook()\n ws = self.wb.add_sheet('Sheet1')\n heading_style = xlwt.easyxf('font: bold true; alignment: horizontal center, wrap true;')\n extra_row = 0\n if self.date:\n date_style = xlwt.easyxf('font: bold true; alignment: horizontal left, wrap true;')... | [
"0.68169695",
"0.6532215",
"0.6330335",
"0.6291492",
"0.62891114",
"0.6191087",
"0.61149186",
"0.60687804",
"0.60447156",
"0.6033515",
"0.60242647",
"0.601848",
"0.6017455",
"0.5963695",
"0.5960102",
"0.59508705",
"0.5922449",
"0.5917792",
"0.58684105",
"0.58632475",
"0.58558... | 0.584632 | 21 |
Given the DATA, then pickle it into the PATH. | def pickle_dump(data, path):
ba = os.path.dirname(os.path.join(os.getcwd(), path))
print(ba)
if os.path.exists(ba):
print("Exist")
else:
print("Not Exist")
pickle.dump(data, open(os.path.join(os.getcwd(), path), 'wb'), protocol=pickle.HIGHEST_PROTOCOL) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_data_pickle(PATH, data, dataset, filename):\n with open(PATH + '/' + dataset + \"_\" + filename + \".pkl\",\"wb\") as f:\n pickle.dump(data,f)\n print(filename, \"created\")",
"def _save_data(self, data):\n path = os.path.join(self._cache_path, '%s.data' % self._name)\n\n f = ... | [
"0.7468239",
"0.72949636",
"0.72261554",
"0.71485525",
"0.7036902",
"0.69876665",
"0.68345493",
"0.6828924",
"0.6784259",
"0.6766072",
"0.6744111",
"0.6680099",
"0.66214156",
"0.66212255",
"0.6545225",
"0.65206987",
"0.6472664",
"0.6466441",
"0.64605117",
"0.6456279",
"0.6450... | 0.7054465 | 4 |
From the PATH get the DATA. | def pickle_load(path):
data = pickle.load(open(os.path.join(os.getcwd(), path), 'rb'))
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_path_data(self, path):\n url = self.api_server + path\n return self.get_url_data(url)",
"def get_data(path):\n return os.path.join(_ROOT, 'data', path)",
"def get_data(path):\n root = os.path.abspath(os.path.dirname(__file__))\n return os.path.join(root, 'data', path)",
"def ge... | [
"0.79337823",
"0.76894516",
"0.7435934",
"0.73360354",
"0.715587",
"0.7088204",
"0.69893074",
"0.6930001",
"0.68865585",
"0.6830704",
"0.67435986",
"0.6663715",
"0.66344297",
"0.65767264",
"0.6565499",
"0.6555404",
"0.65155256",
"0.6461694",
"0.64513165",
"0.64495975",
"0.644... | 0.0 | -1 |
Check the duplicate path. If there is a same filepath, for example 'file/tests.txt', Then this path will be 'file/tests(1).txt' If 'file/tests(1).txt' also exists, then this path will be 'file/tests(2).txt' | def check_duplicate_path(filepath):
if not os.path.exists(os.path.join(os.getcwd(), filepath)):
return filepath
i = 1
splits = filepath.split(".")
head, tail = '.'.join(splits[:-1]), splits[-1]
while os.path.exists(os.path.join(os.getcwd(), "%s(%d).%s" % (head, i, tail))):
i += 1
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def duplicate_timestamp_path(existing_path):\n logfile = parse.parse_filename(existing_path)\n index = 0\n while index < 25:\n if index == 0:\n suffix = ''\n else:\n suffix = '-%02d' % index\n\n new_path = parse.unparse_filename(\n (\n l... | [
"0.66834664",
"0.66339064",
"0.65070987",
"0.6452547",
"0.6442084",
"0.6263671",
"0.6235103",
"0.61986953",
"0.618657",
"0.6059476",
"0.60134727",
"0.59828204",
"0.58634245",
"0.58248854",
"0.5793048",
"0.57827705",
"0.5739223",
"0.57352346",
"0.5700874",
"0.5699016",
"0.5665... | 0.7494454 | 0 |
Invert a list of numbers, add a small number to avoid division by zero. | def apply_confidence_inversion(data: pd.DataFrame, uncertainty_measure: str) -> Tuple[Any, Any]:
if uncertainty_measure not in data:
raise KeyError("The key %s not in the dictionary provided" % uncertainty_measure)
# Make sure no value is less than zero.
min_not_zero = min(i for i in data[uncertai... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def floor_inplace(a):",
"def inverse(self, x):\n x = np.asarray(x)\n def r(vec):\n return utils.recycled(vec, as_=x)\n if self.zero is not None and self.multiplier is not None:\n x = x / r(self.multiplier) + r(self.zero)\n elif self.zero is not None:\n ... | [
"0.600199",
"0.59363973",
"0.5918509",
"0.5888451",
"0.58583796",
"0.58359736",
"0.573017",
"0.5699582",
"0.5694955",
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"0.5592835",
"0.5587856",
"0.5576161",
"0.5572586",
"0.5534111",
"0.5517688",
"... | 0.0 | -1 |
Run from the command line | def main():
era = dt.datetime.now()
parser = xlslisp_compile_argdoc()
args = parser.parse_args()
space = os.path.splitext(args.file)[0]
# Import the Values of Sheets of one Xlsx File
sheet_by_name = openpyxl.load_workbook(args.file, data_only=True)
sheet_by_name_keys_list = sheet_by_nam... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli(args): # noqa; pylint: disable=unused-argument",
"def cli():\n config, auth, execute_now = read_command_line_arguments()\n main(config, auth, execute_now)",
"def cli():\n pass",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",... | [
"0.7901053",
"0.7876391",
"0.7841544",
"0.7735341",
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"0.7735341",
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"0.7735341",
"0.7735341",
"0.7735341",
"0.7735341",
"0.7735341",
"0.7735341",
"... | 0.0 | -1 |
Parse the commandline args per topoffile arg doc | def xlslisp_compile_argdoc():
doc = __main__.__doc__
prog = doc.strip().splitlines()[0].split()[1]
description = list(_ for _ in doc.strip().splitlines() if _)[1]
epilog_at = doc.index("dependencies:")
epilog = doc[epilog_at:]
parser = argparse.ArgumentParser(
prog=prog,
descr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_args():\n help_text = \"\"\"\n Analyzer of the frequency of use of nouns in the headings of posts on hubr.com\n \"\"\"\n parser = argparse.ArgumentParser(\n description=help_text\n )\n parser.add_argument(\n '-p',\n '--pages',\n type=int,\n dest='page_... | [
"0.7149355",
"0.71120995",
"0.6781854",
"0.67598855",
"0.67295283",
"0.6718709",
"0.67026305",
"0.66968477",
"0.6638329",
"0.66280264",
"0.6622432",
"0.66204315",
"0.6615597",
"0.6614482",
"0.66136837",
"0.66085476",
"0.6605811",
"0.6577573",
"0.6563334",
"0.65595543",
"0.655... | 0.0 | -1 |
Map row indices 0 1 2 ... to Excel A .. Z, AA .. ZZ, AAA .. XFD ... | def excel_az_mark(row_index):
marks = ""
az = string.ascii_uppercase
base = len(az)
width = 1 # width 1 for Excel A .. Z, 2 for AA .. ZZ, etc
floor = 0 # min index of this width
ceil = base # one beyond the max index of this width
while ceil <= row_index:
width += 1
flo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def toindex(col, row):\n a2z = 'ABCDEFGHIJLKMNOPQRSTUVWXYZ'\n\n total = 0\n mult = 0\n for char in col:\n total += (a2z.find(char) + (26 * mult))\n mult += 1\n\n return total, row - 1",
"def generate_excel_colmap(end_after=\"CS\"):\n alpha = [chr(i) for i in range(ord('A'), ord('Z... | [
"0.61751103",
"0.59768474",
"0.59152246",
"0.5844486",
"0.56204456",
"0.56035423",
"0.5514038",
"0.54950935",
"0.5492066",
"0.5488739",
"0.5472828",
"0.54693335",
"0.54417187",
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"0.53833336",
"0.53810394",
"0.53658247",
"0.5351053",
"0.5351053",
"0.53202826",
"0.5... | 0.57663226 | 4 |
Exit nonzero now, unless __main__.__doc__ equals "parser.format_help()" | def exit_unless_main_doc_eq(parser):
file_filename = os.path.split(__file__)[-1]
main_doc = __main__.__doc__.strip()
parser_doc = parser.format_help()
got = main_doc
got_filename = "./{} --help".format(file_filename)
want = parser_doc
want_filename = "argparse.ArgumentParser(..."
dif... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _usage(args, contents): # pragma: no cover\n sys.exit(0)",
"def invoke(self) -> int:\n if self.parser is not None:\n self.parser.print_help()\n return 0",
"def help(self, dummy):\r\n help = self.doc + \"\\n\"\r\n if help.find(\"%s\") > 0:\r\n help = help... | [
"0.6791217",
"0.6745974",
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"0.6601277",
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"0.63474774",
"0.63447297",
"0.63438344",
"0.632575",
"0.6322747",
"0.6318159"... | 0.7570494 | 0 |
Add cells to make every row as wide as the widest row | def rows_complete(rows, cell):
completed_rows = list()
if rows:
max_row_width = max(len(_) for _ in rows)
for row in rows:
completed_row = row + ((max_row_width - len(row)) * [cell])
completed_rows.append(completed_row)
return completed_rows | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auto_width(sheet):\r\n column_widths = []\r\n for row in sheet.iter_rows():\r\n for i, cell in enumerate(row):\r\n try:\r\n # if cell alignment is vertical, use 4, else len(str(cell.value)\r\n if cell.alignment.textRotation == 90:\r\n cel... | [
"0.63688403",
"0.6182518",
"0.6171803",
"0.60856235",
"0.60856235",
"0.5965144",
"0.5938641",
"0.58785474",
"0.58003074",
"0.57560503",
"0.56418216",
"0.5596207",
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"0.5537626",
"0.551584",
"0.54317534",
"0.5418037",
"0.5411256",
"0.5403474",
"0.5402... | 0.0 | -1 |
Like Print, but flush don't write Stdout and do write and flush Stderr | def stderr_print(*args, **kwargs):
sys.stdout.flush()
print(*args, **kwargs, file=sys.stderr)
sys.stderr.flush()
# else caller has to "{}\n".format(...) and flush | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pflush(*args, **kwargs):\n print(*args, **kwargs)\n sys.stdout.flush()",
"def print_flush(msg):\n print(msg, end='')\n sys.stdout.flush()",
"def flush(self):\n if self.stderr:\n sys.__stderr__.flush()\n else:\n sys.__stdout__.flush()",
"def nostdout():\n\n ... | [
"0.7566904",
"0.720894",
"0.71354693",
"0.6839325",
"0.67390317",
"0.6726788",
"0.6714382",
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"0.6686574",
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"0.6625729",
"0.65611565",
"0.6557721",
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"0.6498742",
"0.6490778",
"0.64754987",
"0.64341015",
"0.64341015",
"0.6431635... | 0.6017821 | 42 |
Generates a hash from a file. | def add_hash(path):
if re.search(r"^/.+", path):
path = path[1:]
# If a story, fix the path.
is_story = False
original_path = path
if re.search(r"^\d{4}\-\d{2}\-\d{2}", path):
path = "static/stories/%s.json" % path
is_story = True
blocksize = 32768
file_hash = hashl... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hash_from_file(file_path):\r\n return hash_from_code(open(file_path, 'rb').read())",
"def hashfile(file):\n\n hasher = hashlib.sha256()\n\n with open(file, 'rb') as afile:\n buf = afile.read(BLOCKSIZE)\n hasher.update(buf)\n\n return(hasher.hexdigest())",
"def hash_file(filename):... | [
"0.8413941",
"0.8247085",
"0.8025007",
"0.8014472",
"0.8008434",
"0.80050236",
"0.80050236",
"0.8000812",
"0.7981743",
"0.79328674",
"0.7855871",
"0.77818984",
"0.77593553",
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"0.7743753",
"0.76894206",
"0.7664323",
"0.76567453",
"0.7637276",
"0.7627875",
"0.762194... | 0.0 | -1 |
When bot is ready and online it prints that its online | async def on_ready():
bot.timer_manager = timers.TimerManager(bot)
logger.debug("Bot is ready!") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def event_ready():\n print(f\"{BOT_NICK} is online!\")",
"async def do_online():\n\n download = urllib.request.urlopen(server_api)\n data = json.loads(download.read())\n online = data['online']\n await bot.send_message(c, online)",
"async def on_ready(self):\n info =... | [
"0.82676",
"0.788439",
"0.7646275",
"0.7383779",
"0.73371416",
"0.73308116",
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"0.6857944",
"0.68562305",
"0.6849317",
"0.68489826",
"0.6846909",
"0.6825786",
... | 0.68757445 | 14 |
This will square the value | def square(a):
return(a**2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def square(value):\n return value ** 2",
"def square(x):\n\n\treturn x * x",
"def square_value(s):\n return s ** 2",
"def my_square(y):\n\treturn (y ** 2)",
"def sq(self, x):\n\t\treturn x * x",
"def my_square(x):\n return x ** 2",
"def square(x):\n return x * x",
"def square(x: float) ->... | [
"0.85597026",
"0.7863827",
"0.77941453",
"0.76777524",
"0.76651424",
"0.7657983",
"0.7602298",
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"0.75729704",
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"0.7403273",
"0.7390915",
"0.7238024",
"0.7178847",
"0.712606",
"0.7096003",
"0.705398... | 0.72208315 | 17 |
Extracts the resource id from the provided resource. This method validates the input resource dict and checks that the properties which names are passed in `props` argument match corresponding lists in `allowed` argument. In case of mismatch exception of type exc is raised. | def _extract_resource(resource: Optional[dict],
allowed_vals: tuple[tuple[str, ...]],
exc: Type[exception.CinderException],
resource_name: str,
props: tuple[str] = ('status',)) -> Optional[str]:
resource_id ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_resource_id(resource_instance, resource):\n if resource and \"id\" in resource:\n return resource[\"id\"] and encoding.force_str(resource[\"id\"]) or None\n if resource_instance:\n return (\n hasattr(resource_instance, \"pk\")\n and encoding.force_str(resource_inst... | [
"0.6286703",
"0.61337155",
"0.60168695",
"0.5848513",
"0.58351415",
"0.5820666",
"0.5820666",
"0.5820666",
"0.5820666",
"0.5820666",
"0.5820666",
"0.5820666",
"0.5820666",
"0.5820666",
"0.5730958",
"0.5730958",
"0.5730958",
"0.5730958",
"0.5730958",
"0.5730958",
"0.5730958",
... | 0.78969824 | 0 |
Extracts and validates the volume size. This function will validate or when not provided fill in the provided size variable from the source_volume or snapshot and then does validation on the size that is found and returns said validated size. | def _extract_size(size: int,
source_volume: Optional[objects.Volume],
snapshot: Optional[objects.Snapshot],
backup: Optional[objects.Backup]) -> int:
def validate_snap_size(size: int) -> None:
if snapshot and size < snapshot.volume_s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def manage_existing_get_size(self, volume, existing_ref):\n\n target_vol_name = existing_ref['source-name']\n\n self.client_login()\n try:\n size = self.client.get_volume_size(target_vol_name)\n return size\n except stx_exception.RequestError as ex:\n LO... | [
"0.6791952",
"0.6446001",
"0.63496536",
"0.63496536",
"0.63496536",
"0.6164712",
"0.6164712",
"0.6162713",
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"0.57876194",
"0.5696484",
"0.56486726",
"0.56468433",
"0.56148785",
"0.560581",
"0.554... | 0.8289146 | 0 |
Checks image existence and validates the image metadata. | def _get_image_metadata(self,
context: context.RequestContext,
image_id: Optional[str],
size: int) -> Optional[dict[str, Any]]:
# Check image existence
if image_id is None:
return None
# NOTE(harlow... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def images_exist(self):\n pass",
"def check_image(image):\n\n if not path.isfile(image):\n raise ImageException('Error: Singularity image \"%s\" not found.' % image)\n return True",
"def check_image(self, tag):\n image_name = self.build_image_name(tag)\n try:\n self... | [
"0.7366039",
"0.73193944",
"0.7294394",
"0.7135925",
"0.6935113",
"0.69088024",
"0.6900316",
"0.6767054",
"0.67597514",
"0.66630125",
"0.6647561",
"0.6628914",
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"0.6425224",
"0.63961387",
"0.6390532",
"0.63888633",
"0.63705474",
"0.63301325",
"0.6322... | 0.5633726 | 91 |
Extracts and returns a validated availability zone list. This function will extract the availability zone (if not provided) from the snapshot or source_volume and then performs a set of validation checks on the provided or extracted availability zone and then returns the validated availability zone. | def _extract_availability_zones(
self,
availability_zone: Optional[str],
snapshot,
source_volume,
group: Optional[dict],
volume_type: Optional[dict[str, Any]] = None) -> tuple[list[str],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_availability_zones(self, context, resource_type,\n availability_zones):",
"def validate_availability_zones(self, context, resource_type,\n availability_zones):\n if not availability_zones:\n return\n if le... | [
"0.63943464",
"0.57642007",
"0.56424534",
"0.54593474",
"0.54593474",
"0.54593474",
"0.54593474",
"0.54593474",
"0.54593474",
"0.5400208",
"0.526738",
"0.5254807",
"0.5165108",
"0.5131632",
"0.50807095",
"0.5023778",
"0.5017825",
"0.48526326",
"0.48313126",
"0.48292732",
"0.4... | 0.7768819 | 0 |
Returns a volume_type object or raises. Never returns None. | def _get_volume_type(
context: context.RequestContext,
volume_type: Optional[Any],
source_volume: Optional[objects.Volume],
snapshot: Optional[objects.Snapshot],
image_volume_type_id: Optional[str]) -> objects.VolumeType:
if volume_type:
re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def volume_type(self) -> Optional['ContainerRecipeEbsInstanceBlockDeviceSpecificationVolumeType']:\n return pulumi.get(self, \"volume_type\")",
"def volume_type(self) -> Optional['ImageRecipeEbsInstanceBlockDeviceSpecificationVolumeType']:\n return pulumi.get(self, \"volume_type\")",
"def volume_... | [
"0.7283127",
"0.7193424",
"0.7087327",
"0.68482715",
"0.68080485",
"0.6795139",
"0.6580239",
"0.6580239",
"0.6580239",
"0.6423317",
"0.62992185",
"0.6290493",
"0.6046961",
"0.6025863",
"0.5984102",
"0.5798099",
"0.5745572",
"0.573968",
"0.5659779",
"0.5654844",
"0.55516165",
... | 0.67574567 | 6 |
Creates a database entry for the given inputs and returns details. Accesses the database and creates a new entry for the to be created volume using the given volume properties which are extracted from the input kwargs (and associated requirements this task needs). These requirements should be previously satisfied and v... | def execute(self,
context: context.RequestContext,
optional_args: dict,
**kwargs) -> dict[str, Any]:
src_volid = kwargs.get('source_volid')
src_vol = None
if src_volid is not None:
src_vol = objects.Volume.get_by_id(context, src_volid)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_volume(self, context, volume_id, request_spec=None,\n filter_properties=None, allow_reschedule=True,\n snapshot_id=None, image_id=None, source_volid=None,\n source_replicaid=None, consistencygroup_id=None):\n\n ctx_dict = context.__di... | [
"0.5725086",
"0.5718247",
"0.565376",
"0.55094403",
"0.54922634",
"0.54513985",
"0.5444849",
"0.5347211",
"0.5332229",
"0.53095233",
"0.52728635",
"0.52706724",
"0.5251625",
"0.5236023",
"0.5232849",
"0.52302337",
"0.52094674",
"0.52048177",
"0.5203383",
"0.51883715",
"0.5169... | 0.6186938 | 0 |
Constructs and returns the api entrypoint flow. | def get_flow(db_api, image_service_api, availability_zones, create_what,
scheduler_rpcapi=None, volume_rpcapi=None):
flow_name = ACTION.replace(":", "_") + "_api"
api_flow = linear_flow.Flow(flow_name)
api_flow.add(ExtractVolumeRequestTask(
image_service_api,
availability_zone... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def entry_point():",
"def entry_point():",
"def entry_point():",
"def main():\n return execute_api(Freta(), [Endpoint], __version__)",
"def start_flow(api_url):\n authorization_url, state = client.authorization_url(authorization_base_url)\n webbrowser.open(authorization_url)\n redirect_response... | [
"0.6311223",
"0.6311223",
"0.6311223",
"0.6176308",
"0.59881395",
"0.5891488",
"0.5844838",
"0.5835675",
"0.5780259",
"0.5699845",
"0.5690561",
"0.5665652",
"0.5637382",
"0.56177443",
"0.5609358",
"0.5598943",
"0.5583704",
"0.5568971",
"0.5568607",
"0.5525004",
"0.5508841",
... | 0.5800678 | 8 |
Function to convert string objects to Python spatial objects | def spatializer(row):
#############################
# coordinates field
#############################
try:
# look for the coordinates column
data = row['coordinates'].strip(' \t\n\r')
except:
pass
try:
import shapel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def geos_geom_from_py(ob, create_func=...): # -> tuple[Any | Unknown, Unknown]:\n ...",
"def convertor(geometry, method=\"wgs2gcj\"):\n if geometry['type'] == 'Point':\n coords = geometry['coordinates']\n coords[0], coords[1] = methods[method](coords[0], coords[1])\n elif geometry['type']... | [
"0.6853914",
"0.64596736",
"0.6459579",
"0.62780887",
"0.62452716",
"0.61807716",
"0.60729116",
"0.59981173",
"0.59837383",
"0.58806914",
"0.58697754",
"0.5864173",
"0.5859972",
"0.5858616",
"0.5851232",
"0.5834999",
"0.5803715",
"0.57748467",
"0.5740699",
"0.5716194",
"0.567... | 0.0 | -1 |
Takes Keyhole Markup Language Zipped (KMZ) or KML file as input. The output is a pandas dataframe, geopandas geodataframe, csv, geojson, or shapefile. | def keyholemarkup2x(file,output='df'):
r = re.compile(r'(?<=\.)km+[lz]?',re.I)
try:
extension = r.search(file).group(0) #(re.findall(r'(?<=\.)[\w]+',file))[-1]
except IOError as e:
logging.error("I/O error {0}".format(e))
if (extension.lower()=='kml') is True:
buffe... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def readKML(filename):\n\n kml_file = path.join(filename)\n\n #### se leen los elementos del KML\n with open(kml_file) as f:\n folder = parser.parse(f).getroot().Document.Folder\n\n #### se separan los elementos, nombres de los puntos y las coordenadas\n plnm=[]\n cordi=[]\n for pm in f... | [
"0.62045234",
"0.59694266",
"0.5614283",
"0.5588096",
"0.55593634",
"0.55198014",
"0.54530036",
"0.5381782",
"0.53294533",
"0.532009",
"0.52355796",
"0.52240384",
"0.52035177",
"0.51956874",
"0.51648325",
"0.5145558",
"0.51388747",
"0.51381135",
"0.50973314",
"0.5092485",
"0.... | 0.7567482 | 0 |
Emits metric to datadog. Returns nothing. | def emit(source, event, event_type):
if event_type in ALLOWED_EVENTS:
statsd.increment(
"segment.event",
tags=[
"source:" + source,
"event:" + "-".join(event.split()),
"type:" + event_type,
],
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def emit(self, metric):\n metric_data = self.unmarshal(metric)\n self.logger.log(\n self.log_level, metric.DEFAULT_LOG_FORMAT.format(**metric_data)\n )",
"def sendMeasurement(self, metric, value, source, timestamp=None):\n sys.stdout.write('{0} {1} {2} {3}\\n'.format(metric... | [
"0.69526815",
"0.69020224",
"0.6604191",
"0.6255041",
"0.62070864",
"0.61427426",
"0.6100609",
"0.5948163",
"0.58825403",
"0.5874048",
"0.5786339",
"0.5769456",
"0.576064",
"0.57383424",
"0.57234097",
"0.57168025",
"0.5674542",
"0.56710356",
"0.5647139",
"0.5640953",
"0.56353... | 0.0 | -1 |
Verifies signature (ensures matched shared secrets). Returns Bool. | def check_signature(signature, data):
if SIGNATURE_DISABLED:
return True
# check signature
try:
digest = hmac.new(
SEGMENT_SHARED_SECRET.encode(), msg=data, digestmod=hashlib.sha1
).hexdigest()
if digest == signature:
return True
else:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check(self, request, consumer, token, signature):\r\n built = self.sign(request, consumer, token)\r\n return built == signature",
"def check_signature(signature, *args, **kwargs):\n return hmac.compare_digest(signature, create_signature(*args, **kwargs))",
"def verify(signature: Signature,... | [
"0.77133894",
"0.7456966",
"0.7451283",
"0.7370368",
"0.7342798",
"0.723132",
"0.723132",
"0.7131091",
"0.7098203",
"0.70439786",
"0.7042887",
"0.70322865",
"0.6982362",
"0.6980211",
"0.69460005",
"0.6936151",
"0.6911655",
"0.68586284",
"0.6817355",
"0.67785805",
"0.67738426"... | 0.76915914 | 1 |
Main function. Accepts JSON payload on POST only. | def segment2datadog(source):
print(f"Received request on /api/{source}")
signature = request.headers.get("x-signature", "")
if not check_signature(signature=signature, data=request.data):
abort(403, "Signature not valid.")
content = request.get_json()
event_type = content["type"]
if ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_post_parse_json(self, *args, **kwargs): # real signature unknown\n pass",
"def post_algorithm():\n try:\n request_json = request.get_json()\n result = json.dumps([])\n response = app.response_class(\n response=result,\n status=200,\n mimetype... | [
"0.7054873",
"0.6815509",
"0.67008907",
"0.66781956",
"0.6614997",
"0.6503812",
"0.64270383",
"0.6375233",
"0.62853044",
"0.62853044",
"0.62686986",
"0.62677866",
"0.6220274",
"0.6206321",
"0.6182537",
"0.611851",
"0.6091516",
"0.6067001",
"0.6007093",
"0.5983591",
"0.5981839... | 0.0 | -1 |
Return count number of prime numbers, starting at 2. | def primes(count):
primes = []
number_to_check = 2
while len(primes) < count:
# check if number is prime
# if prime, add to list
# if not prime, move on
# increment number to check
is_prime = True
for num in range(2,number_to_check):
if... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_primes(n):\n i, total = 1, 0\n while i <= n:\n if is_prime(i):\n total += 1\n i += 1\n return total",
"def count_prime():\n nums = []\n for i in range(2, 10000):\n if is_prime(i):\n nums.append(i)\n return nums",
"def prime_pi(n):\n if n... | [
"0.7909468",
"0.7787611",
"0.7671918",
"0.73836786",
"0.70847523",
"0.7067671",
"0.7066009",
"0.7037659",
"0.6979948",
"0.69589514",
"0.6910707",
"0.67586225",
"0.67508656",
"0.6749178",
"0.67472607",
"0.67446923",
"0.67408115",
"0.66339564",
"0.6617919",
"0.6593536",
"0.6567... | 0.6282796 | 35 |
lowercase sentence `sent` given as parameter | def lowercase(self, sent):
SPECIAL_TAG_PREFIX = ur"__"
_sent = []
for w in sent:
# make sure w is in network vocabulary
if not w.startswith(SPECIAL_TAG_PREFIX) and \
w.lower() in self.word_vectors.getVocab():
_sent.append(w.lower())
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lower_caser(self, sentence):\n return sentence.strip().lower()",
"def lowerCase(self,phrase):\n if(\"normalizeText\" in self._classes):\n return self._normalize.lowerCase(phrase)",
"def handle_sentence_simple(self, sentence, ctxinfo):\n global text_version\n global mo... | [
"0.7798232",
"0.70910996",
"0.6897251",
"0.67695826",
"0.67441666",
"0.6727888",
"0.66985834",
"0.6653774",
"0.6544608",
"0.6498189",
"0.64789736",
"0.6471879",
"0.6421757",
"0.64176786",
"0.64147437",
"0.6348549",
"0.6320604",
"0.6318996",
"0.6287475",
"0.6287458",
"0.626490... | 0.7131649 | 1 |
this function returns a list with word vectors for the given sentence | def text_to_vecs(self):
# convert word strings into word vectors
sent_vec = []
for w in self.sentence:
if w in self.word_vectors.getVocab():
sent_vec.append( self.word_vectors.getWordVectors()[w] )
else:
sent_vec.append( self.word_vectors.g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def word2vec(self, sentence: str):\n tokens = nltk.word_tokenize(sentence)\n v = [self.word_dict.get(token, 0) for token in tokens]\n return v",
"def _words_to_vec(self, sentence):\n return torch.FloatTensor([self._use_embeddings(word) for word in sentence])",
"def sentences2vec(sel... | [
"0.82366437",
"0.7883474",
"0.7258772",
"0.7223416",
"0.7195357",
"0.7181377",
"0.7163394",
"0.7127464",
"0.7067281",
"0.7060212",
"0.6987103",
"0.68918663",
"0.6876809",
"0.6845333",
"0.68435115",
"0.6823049",
"0.68151784",
"0.67574716",
"0.67419696",
"0.6704787",
"0.6700244... | 0.7729908 | 2 |
Get permissions for a given path. | def getPermission(self, session, category, action, path):
path = path.decode('utf-8')
try:
operation = getOperation(category, action)
except KeyError as error:
session.log.exception(error)
error = TBadRequest(
'Action %r not possible on catego... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_permission(path):\n return oct(stat.S_IMODE(os.stat(path).st_mode))",
"def octopus_permissions_get(self, msg, args):\r\n return self.permissions.get_permissions()",
"def get_permissions():\n return config.get_cfg_storage(ID_PERMISSION)",
"def get_permissions(self):\n\t\treturn call_sdk_f... | [
"0.68241113",
"0.6537136",
"0.64681613",
"0.64153135",
"0.62711513",
"0.60393375",
"0.60207146",
"0.5920989",
"0.5871282",
"0.5864923",
"0.58453476",
"0.5794055",
"0.5773593",
"0.5764084",
"0.5757678",
"0.57044595",
"0.57008797",
"0.56832653",
"0.567347",
"0.566702",
"0.56650... | 0.59620655 | 7 |
Update permissions for a given path. | def updatePermission(self, session, category, action, path,
policyAndExceptions):
path = path.decode('utf-8')
try:
operation = getOperation(category, action)
except KeyError as error:
session.log.exception(error)
error = TBadRequest(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_permissions(path, permission='777'):\r\n if os.path.exists(path):\r\n subprocess.call('chmod -R %s %s'%(permission,path),shell=True)\r\n else:\r\n raise NameError('invalid path %s'% path)",
"def chmod(self, path, mod):\n self._call(\"SETPERMISSION\", method=\"put\", path=pat... | [
"0.76146567",
"0.7123683",
"0.6615019",
"0.6444273",
"0.63782036",
"0.6246743",
"0.62407917",
"0.61677694",
"0.6152735",
"0.60507303",
"0.60146147",
"0.6010073",
"0.58685565",
"0.5853819",
"0.5815282",
"0.57976615",
"0.57416964",
"0.5725209",
"0.57007605",
"0.5695074",
"0.568... | 0.6568419 | 3 |
Generate a presigned URL to share an S3 object | def create_presigned_url(bucket_name, object_name, expiration=3600):
# Generate a presigned URL for the S3 object
s3_client = boto3.client('s3')
try:
response = s3_client.generate_presigned_url('get_object',
Params={'Bucket': bucket_name,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_presigned_url(bucket_name, object_name):\n\n logger = logging.getLogger(\"SimpleReplayLogger\")\n\n s3_client = boto3.client('s3')\n try:\n response = s3_client.generate_presigned_url('get_object',\n Params={'Bucket': bucket_name,\n ... | [
"0.781918",
"0.7818755",
"0.775786",
"0.7680955",
"0.7658105",
"0.7649027",
"0.76473826",
"0.7646118",
"0.76096344",
"0.75693727",
"0.7362465",
"0.73525697",
"0.73306215",
"0.7309494",
"0.7161238",
"0.7114588",
"0.70948786",
"0.7060615",
"0.70502365",
"0.7022286",
"0.70070326... | 0.76516396 | 5 |
Generate classification samples (images + target variable) and beta. | def load(n_samples=100, shape=(30, 30, 1),
snr=2., sigma_logit=5., random_seed=None,
**kwargs):
X3d, y, beta3d = dice5regression.load(n_samples=n_samples, shape=shape,
r2=1.0, random_seed=random_seed,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample_train_batch(self):\r\n batch = []\r\n labels =[]\r\n num_groups = self.batch_size // self.batch_k\r\n sampleed_classes = np.random.choice(self.train_class_ids,num_groups,replace=False)\r\n for class_id in sampleed_classes:\r\n img_fname = np.random.choice(se... | [
"0.6656381",
"0.64440495",
"0.6413988",
"0.638363",
"0.63697946",
"0.6313206",
"0.63107324",
"0.6300423",
"0.62833965",
"0.6268202",
"0.62545025",
"0.6234451",
"0.62101275",
"0.62069523",
"0.6193542",
"0.61783683",
"0.6169238",
"0.61611104",
"0.6116174",
"0.61135644",
"0.6112... | 0.0 | -1 |
Get code block based on position in codes, sized based on opcode. | def get_code_block(self, pos, opcode):
if opcode == 99:
print('99 IS THE END')
block_size = 0
return [self.codes[pos]]
elif opcode in (1, 2, 7, 8):
block_size = 4
elif opcode in (5, 6):
block_size = 3
elif opcode in (3,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_code(self, data_start, data_size, offset):\n first_block = 0x1000 - data_start % 0x1000\n full_blocks = ((data_size + (data_start % 0x1000)) / 0x1000) - 1\n left_over = (data_size + data_start) % 0x1000\n\n code = \"\"\n\n # Deal with reads that are smaller than a block\n... | [
"0.6831133",
"0.5879704",
"0.58197665",
"0.57767147",
"0.5509524",
"0.53633565",
"0.53602576",
"0.53167605",
"0.52453434",
"0.5240448",
"0.52288264",
"0.52158344",
"0.520227",
"0.51487017",
"0.51393586",
"0.5114082",
"0.5100469",
"0.50978047",
"0.50817233",
"0.5070789",
"0.50... | 0.8200708 | 0 |
Get the opcode string from a codeblock. | def get_opcode(self, code):
opcode = int(str(code)[-2:])
return opcode | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_op_code_str(self, op):\n try:\n from tflite.BuiltinOperator import BuiltinOperator\n except ImportError:\n raise ImportError(\"The tflite package must be installed\")\n\n op_code_list_idx = op.OpcodeIndex()\n op_code_id = self.model.OperatorCodes(op_code_li... | [
"0.6249712",
"0.5808073",
"0.5758956",
"0.57538587",
"0.570763",
"0.570763",
"0.5671009",
"0.56138945",
"0.55983204",
"0.55761415",
"0.557361",
"0.55591536",
"0.5555312",
"0.5523228",
"0.5506385",
"0.5496092",
"0.5473834",
"0.5467974",
"0.5443687",
"0.54090476",
"0.53862494",... | 0.6715364 | 0 |
Get the two modes for a code block's parameters. | def get_modes(self, code_block):
# FUCK YOU INDEX ERRORS, LIST COMPS, AND EVEN YOU LAMBDAS I DON'T NEED PRETTY
# 0 = pos mode
# 1 = imm mode
modes, mode_codes = [0, 0], list(reversed(str(code_block[0])))[2:]
x = 0
for mode in mode_codes:
modes[x] = int(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_modes(self):\n return self.__modes",
"def partial_modes(self, level, node=None):\n if node:\n return self.operator.modes[self._index_list(level, node)]\n\n indeces = [self._index_list(level, i) for i in range(2**level)]\n return np.hstack(tuple([self.operator.modes[idx... | [
"0.6424788",
"0.6175102",
"0.6116689",
"0.60188097",
"0.5950464",
"0.5902551",
"0.58972424",
"0.5825283",
"0.5721097",
"0.565327",
"0.5622471",
"0.558995",
"0.5571778",
"0.5556218",
"0.5544023",
"0.5539861",
"0.5522755",
"0.54928595",
"0.54884505",
"0.54445195",
"0.54361504",... | 0.712229 | 0 |
Get the value for each code based on if it's position mode (pos_mode) or immediate mode (imm_mode) and its position in the code block. | def get_values(self, code_block):
pos_mode, imm_mode = 0, 1
x, values = 1, []
modes = self.get_modes(code_block)
for mode in modes:
if mode == pos_mode:
values.append(int(self.codes[code_block[x]]))
elif mode == imm_mode:
va... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_modes(self, code_block):\r\n # FUCK YOU INDEX ERRORS, LIST COMPS, AND EVEN YOU LAMBDAS I DON'T NEED PRETTY\r\n # 0 = pos mode\r\n # 1 = imm mode\r\n modes, mode_codes = [0, 0], list(reversed(str(code_block[0])))[2:]\r\n x = 0\r\n for mode in mode_codes:\r\n ... | [
"0.73331314",
"0.5679162",
"0.5547544",
"0.5291059",
"0.52655387",
"0.52335066",
"0.52260995",
"0.5219121",
"0.520287",
"0.51944584",
"0.5185736",
"0.51602906",
"0.51379883",
"0.50631815",
"0.5047685",
"0.50382125",
"0.50302804",
"0.48911408",
"0.48609778",
"0.4847673",
"0.48... | 0.79959965 | 0 |
Remove the base line using a SavitzkyGolay method | def remove_baseline(self):
print(" \t Apply Savitzky-Golay filter \t %d" %self.nwin)
base_savgol = signal.savgol_filter(self.input, self.nwin, 1)
self.input_nobase = self.input - base_savgol | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cut_line(self):\r\n self.parachute.pop(0)",
"def remove_line(self, origin):\n current_tile = self.board[origin[0]][origin[1]]\n\n if current_tile.is_dot:\n temp = current_tile.next\n current_tile.next = None\n current_tile = temp\n\n # Remove color... | [
"0.7042391",
"0.64433926",
"0.6224675",
"0.61690134",
"0.61472166",
"0.60406417",
"0.59359074",
"0.593024",
"0.59090394",
"0.5900306",
"0.5895631",
"0.5871592",
"0.58644634",
"0.58512706",
"0.58499116",
"0.5848815",
"0.58475477",
"0.58448726",
"0.57755435",
"0.5750193",
"0.57... | 0.66475266 | 1 |
denoise the data using the 2stage kurtosis denoising | def denoise(self):
#make sure the data has a len dividible by 2^2
self.len_swt = self.len
while not (self.len_swt/4).is_integer():
self.len_swt -= 1
inp = self.input_nobase[:self.len_swt]
self.wave = pywt.Wavelet(self.wave_type)
nLevel = pywt.swt_max_level(s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ICA_Denoise(Y, ica_model, noise_std):\n\n # TODO: YOUR CODE HERE",
"def filter_denoise(self, x):\n b, a = self.c_notch\n return filtfilt(b, a, x)",
"def MVN_Denoise(Y, mvn_model, noise_std):\n return calc_weiner_filter(Y, mvn_model.mean, mvn_model.cov, noise_std)",
"def run_denoising(... | [
"0.6254114",
"0.6207546",
"0.61320424",
"0.5874847",
"0.5839554",
"0.57731473",
"0.5748484",
"0.5744758",
"0.5728441",
"0.572605",
"0.56469405",
"0.56469405",
"0.56469405",
"0.56418467",
"0.56233346",
"0.56080765",
"0.55805033",
"0.5566257",
"0.55619085",
"0.55411446",
"0.552... | 0.68143743 | 0 |
Detect bursts of activity. | def get_burst(self):
print('\t Detect bursts \t\t\t %d %1.2f' %(self.burst_s,self.burst_gamma))
# compute the cum sum of the positive values of the datan ...
_tmp = np.copy(self.input_denoised)
_tmp[_tmp<0] = 0
_tmp += 1E-12
self.input_cummulative = np.cumsum(_tmp)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_burst_dispersion(self):\n # some reproducible arbitrariness\n np.random.seed(7342642)\n\n n = 25\n t_max = 50\n dt = 0.1\n n_sim = 10\n \n G = RateHVCLayer(n)\n\n burst_starts = []\n for i in xrange(n_sim):\n M = simulation.StateMonitor(G, 'out')\n sim = simulation.... | [
"0.6244456",
"0.6123166",
"0.5961598",
"0.5944838",
"0.5854187",
"0.56908864",
"0.5619055",
"0.5532552",
"0.5509673",
"0.5503266",
"0.54505146",
"0.5449114",
"0.5444159",
"0.54377466",
"0.5432748",
"0.53841823",
"0.5360802",
"0.53578424",
"0.5348974",
"0.53150326",
"0.5264427... | 0.5991301 | 2 |
Compute the rolling kurtosis. | def _rolling_kts(y,N):
# number of points
nPTS,N2 = len(y), int(N/2)
# define the out
kts = np.zeros(nPTS)
# for all points comopute snr
for i in range(nPTS):
s,e = i-N2, i+N2
if s<0:
s = 0
if s > nPTS-1:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def kurtosis(self) -> float:\n return float(ss.kurtosis(self.tsdf.pct_change(), fisher=True, bias=True, nan_policy='omit'))",
"def calc_kurtosis(sig):\n return kurtosis(sig)",
"def kurtosis(r):\n demeaned_r = r - r.mean()\n # use the population standard deviation, so set dof=0\n sigma_r = r... | [
"0.7280021",
"0.72142196",
"0.70349604",
"0.6872883",
"0.68170637",
"0.679628",
"0.62929606",
"0.6168766",
"0.6020426",
"0.5842821",
"0.5828331",
"0.5625072",
"0.56153303",
"0.56038046",
"0.5593879",
"0.55546606",
"0.55122936",
"0.538985",
"0.53348935",
"0.5323968",
"0.530855... | 0.73427486 | 0 |
Check if the characters in string s are in ASCII, U+0U+7F. | def isascii(s):
return len(s) == len(s.encode()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __contains_nonascii_characters(string):\n for c in string:\n if not ord(c) < 128:\n return True\n return False",
"def ascii_hexchar(s: str) -> bool:\n return frozenset(s).issubset(_ascii_h)",
"def ascii_printable(s: str) -> bool:\n return frozenset(s).issubset(... | [
"0.77660894",
"0.76544166",
"0.7450944",
"0.7449345",
"0.7429723",
"0.74006027",
"0.7388017",
"0.73855674",
"0.73839515",
"0.706439",
"0.69804156",
"0.6951423",
"0.6786677",
"0.67115295",
"0.6556867",
"0.6519561",
"0.65190905",
"0.64559346",
"0.6436238",
"0.6435861",
"0.63829... | 0.75282675 | 3 |
Authenticates the user either by existing pickle token, or generate new one // TODO add instruction on how to get the credentials.json | def get_creds():
# If modifying these scopes, delete the file token.pickle.
SCOPES = ['https://www.googleapis.com/auth/gmail.readonly']
creds = None
dir_pre = '../secrets'
# The file token.pickle stores the user's access and refresh tokens, and is
# created automatically when the authorization f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def authenticate(self):\n #it's weird i have to do this here, but the code makes this not simple\n auth_json={'email':self.user, 'password':self.password}\n #send a post with no auth. prevents an infinite loop\n auth_response = self.post('/auth', data = json.dumps(auth_json), auth =\n ... | [
"0.6854116",
"0.67800575",
"0.67152345",
"0.66444993",
"0.66333526",
"0.6539042",
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"0.62366295",
"0.6230506",
"0.623001",
"0.6220421",
"0.6217435",
"0.6210547"... | 0.0 | -1 |
list basic user info | def list_user_info(service):
profile = service.users().getProfile(userId='me').execute()
return profile | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_info(self):\n response = self.query('user_info')\n return response",
"def list_users(item):\n users = User.load_all_users(item)\n for user in users:\n print(user.username)",
"def show_user_info():\n \n vprint( 'Effective User :', os.geteuid())\n vprint( 'Effective Gr... | [
"0.74712443",
"0.7329571",
"0.7306948",
"0.7285574",
"0.7219221",
"0.7200625",
"0.71875286",
"0.7088998",
"0.7065823",
"0.70622015",
"0.70473915",
"0.70473146",
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"0.7005358",
"0.69844407",
"0.6974977",
"0.69546765",
"0.69373524",
"0.6934553",
"0.6922... | 0.68284345 | 31 |
get a nice string summary of a curation | def getannotationstrings2(cann):
cdesc = ''
if cann['description']:
cdesc += cann['description'] + ' ('
if cann['annotationtype'] == 'diffexp':
chigh = []
clow = []
call = []
for cdet in cann['details']:
if cdet[0] == 'all':
call.append(cde... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def summary_string(self) -> str:",
"def summary(self):\n return ''",
"def summary(self) -> str:\n pass",
"def summary(self):\r\n return '%s%s: %s%s %s%s' % (BLUE, self.title,\r\n GREEN, self.description,\r\n NORMAL, se... | [
"0.7652006",
"0.70306504",
"0.69144917",
"0.68029356",
"0.67171127",
"0.66573215",
"0.66102165",
"0.6602745",
"0.65948147",
"0.6494361",
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"0.6391767",
"0.63757336",
"0.6369811",
"0.636381",
"0.6358612",
"0.63477284",
"0.6311934",
"0.629051... | 0.0 | -1 |
The start handler must perform setup method | def setup(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _start(self):\n pass",
"def setupStarted(self, *args, **kwargs): # real signature unknown\n pass",
"def start(self):\n ...",
"def _start(self):",
"def start (self):\n pass",
"def start (self):\n pass",
"def start(self):\r\n pass",
"def start(self):\n ... | [
"0.80769825",
"0.8064321",
"0.7994348",
"0.78918386",
"0.78906196",
"0.78906196",
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"0.7844179",
"0.78069323",
"0.78069323",
"0.78069323",
"0.78069323",
"0.78069323",
"0.7806... | 0.71826375 | 78 |
If the successful execution of the handler, will run this method | def finish(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handler(self, *args, **kwargs):\n return True",
"def on_success(self):\n pass",
"def on_success(self) -> None:",
"def _handle_task_succeeded(self):\n if self.on_success:\n return self.on_success(self)\n else:\n return HandlerResult.cont()",
"def OnSucce... | [
"0.7266319",
"0.7121877",
"0.70454407",
"0.7027097",
"0.69653887",
"0.68011975",
"0.6729216",
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"0.66771865",
"0.6661995",
"0.66406864",
"0.66227895",
"0.6568652",
"0.6522288",
"... | 0.0 | -1 |
Called after this handler lifecycle,this method does not allow an exception is thrown. | def destroy(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_exception(self):\n pass",
"def handle(self) -> None:",
"def handle(self):",
"def exception_handler(self, exception):\n pass",
"def handle(self):\n raise NotImplementedError",
"def handle_err(self):\n pass",
"def error(self, handler):\n pass",
"def _on_excepti... | [
"0.7447773",
"0.7214745",
"0.7060776",
"0.6870695",
"0.6805715",
"0.67974174",
"0.671484",
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"0.6492414",
"0.6466798",
"0.6466798",
"0.64180535",
"0.64134246",
"0.63910395",
"0.6383068",... | 0.0 | -1 |
Start a request,this method does not allow an exception is thrown. | def start_request(self,request_handler,client_address):
logger.debug('start_request(%s:%s)' % client_address) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_request(self):\n self.session_manager.start_request()",
"def _StartRequest(self, request_id, manager):\n pending_path = self._GetRequestPathname(request_id, self._PENDING)\n with open(pending_path, 'r') as f:\n request_object = pickle.load(f)\n manager.StartTask(request_id, request... | [
"0.7657946",
"0.69753915",
"0.69528455",
"0.69419944",
"0.67416626",
"0.6675796",
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"0.6389097",
"0.6380641",
"0.6327037",
"0.6309479",
"0.62897944",
"0.62629443",
"0.6260... | 0.6528585 | 12 |
Verify request,this method does not allow an exception is thrown. | def verify_request(self, request_handler, client_address):
logger.debug('verify_request(%s:%s)' % client_address) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify_request(self, request, client_address):\n\t\treturn True",
"def verify(self, response):",
"def verify(self, request):\n return Response(None, status=status.HTTP_204_NO_CONTENT)",
"def verify(self):\r\n pass",
"def verify(self):\n pass",
"def verify(self):\n pass",
... | [
"0.7655331",
"0.7453941",
"0.69403994",
"0.6904828",
"0.6823398",
"0.6823398",
"0.67689514",
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"0.6360182",
"0.63253134",
"0.63203263",
"0.6315326",
"0.62872684",
"0.6265284",
"0.6264860... | 0.6829749 | 4 |
Handler is called when an exception occurs,this method does not allow an exception is thrown. | def handle_error(self, request_handler, client_address):
logger.debug('handle_error(%s:%s)' % client_address) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exception_handler(self, exception):\n pass",
"def on_exception(self):\n pass",
"def _on_exception(self, exception):\n pass",
"def handle_expt(self):\r\n self._perform_on_error_handling()",
"def error(self, handler):\n pass",
"def handle_err(self):\n pass",
... | [
"0.84199923",
"0.82593095",
"0.7844722",
"0.77625847",
"0.77586204",
"0.7583584",
"0.7447186",
"0.73315513",
"0.72311974",
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"0.6901035",
"0.6872464",
"0.6855411",
"0.6828169",
"0.676794",
"0.6766311",
"0.672959... | 0.64839387 | 33 |
Closing request called,this method does not allow an exception is thrown. | def close_request(self,request_handler, client_address):
logger.debug('close_request(%s:%s)' % client_address) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def close_request(self, request):\n\t\tpass",
"def close_request(self, request):\n\t\trequest.close()",
"def closeRequest(self):\n self.get_bmc_website()\n self.__closeMyRequest = Close(self.browser)\n self.__closeMyRequest.closeRequest()",
"def close(self):",
"def close(self):",
"de... | [
"0.87857294",
"0.8312502",
"0.75678617",
"0.706756",
"0.706756",
"0.706756",
"0.706756",
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"0.69872475",
"0.69872475",
"0.6945814",
"0.693658",
"0.69229454",
"0.6922... | 0.71879524 | 3 |
Function that performs a convolution on images with channels | def convolve_channels(images, kernel, padding='same', stride=(1, 1)):
m = images.shape[0]
image_h = images.shape[1]
image_w = images.shape[2]
filter_h = kernel.shape[0]
filter_w = kernel.shape[1]
s1 = stride[0]
s2 = stride[1]
if padding == 'valid':
pad_h = 0
pad_w = 0
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clConvolution(self, size, mask):",
"def convolve_channels(images, kernel, padding='same', stride=(1, 1)):\n m, h, w, c = images.shape\n KernelHeight, kernelWidth, c = kernel.shape\n StrideHeight, StrideWidth = stride\n\n if padding == 'valid':\n PaddingHeight = 0\n PaddingWidth = 0\... | [
"0.78048617",
"0.76200247",
"0.75978273",
"0.7558364",
"0.74831325",
"0.7368806",
"0.73137546",
"0.72811294",
"0.722849",
"0.7214952",
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"0.7122098",
"0.7074655",
"0.70513123",
"0.70480937",
"0.7038724",
"0.70... | 0.77049017 | 1 |
Copy test data to working directory so ``parse_spec`` works properly for both ``stsynphot`` and ASTROLIB PYSYNPHOT. | def setup_module(module):
for datafile in datafiles:
src = get_pkg_data_filename(os.path.join('data', datafile))
shutil.copyfile(src, datafile) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processSpecs(self):\n specSubDirName=\"_spec\"\n codestructure = CodeStructure()\n for dir in self._dirs:\n if q.system.fs.exists(q.system.fs.joinPaths(dir,specSubDirName)):\n files=q.system.fs.listPyScriptsInDir(q.system.fs.joinPaths(dir,specSubDirName))\n ... | [
"0.54588157",
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"0.54161704",
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"0.51810604",
"0.5168432",
"0.513813",
"0.5131549",
"0.51290935",
"0.5128934",
"0.51254... | 0.5101221 | 23 |
Clean up test data in working directory. | def teardown_module(module):
for datafile in datafiles:
os.remove(datafile) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tearDown(self):\n shutil.rmtree(self._data_dir_path)",
"def tearDown(cls):\n\n # cls.test_mmp_series_object.clean_out_data_seriesobj()\n # reusable data struct\n cls.test_mmp_series_object.clean_out_data_seriesobj()\n cls.test_dataset_testresults.clear()\n # reusable... | [
"0.7803513",
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"0.75086534",
"0.7506745",
"0.750453",
"0.7503099",
"0.74924654",
"0.74106455",
"0.739894... | 0.69839936 | 97 |
An instrumented Template render method, providing a signal that can be intercepted by the test system Client | def instrumented_test_render(self, context):
signals.template_rendered.send(sender=self, template=self, context=context)
return self.nodelist.render(context) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render_to_template_mock(*_args):",
"def _render(self) -> str:\n html = self._template.render(self._transient_context)\n self._transient_context = None\n return html",
"def patch_render_template(self):\n mock_render = Mock(spec=render_template)\n mock_render.return_value =... | [
"0.685287",
"0.6700654",
"0.66776145",
"0.6645942",
"0.66436213",
"0.6585426",
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"0.6585426",
"0.6585426",
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"0.6500498",
"0.64901316",
"0.6445812",
"0.64102834",
"0.64102834",
"0.64102834",
"0.638103... | 0.75868696 | 0 |
Register dataset related changes. For local datasets, look them up, record the dataset event, queue dagruns, and broadcast the dataset event | def register_dataset_change(
self, *, task_instance: TaskInstance, dataset: Dataset, extra=None, session: Session, **kwargs
) -> None:
dataset_model = session.scalar(select(DatasetModel).where(DatasetModel.uri == dataset.uri))
if not dataset_model:
self.log.warning("DatasetModel ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def datasets(self, datasets):\n\n self._datasets = datasets",
"def add_to_dataset(self, dataset: Dataset):\n pass",
"def datasets(self, datasets):\n self.__datasetsAll = datasets\n self.__datasets = list(datasets)\n self.__axisDomains = None\n for ds in self.__datasets... | [
"0.56935036",
"0.56498915",
"0.5641686",
"0.54096603",
"0.5342046",
"0.5339496",
"0.5286913",
"0.52014285",
"0.5195956",
"0.5182093",
"0.5152727",
"0.5134192",
"0.50968635",
"0.5025282",
"0.5021174",
"0.5016711",
"0.49956772",
"0.4995023",
"0.49723122",
"0.49516952",
"0.49417... | 0.7455701 | 0 |
Retrieve the dataset manager. | def resolve_dataset_manager() -> DatasetManager:
_dataset_manager_class = conf.getimport(
section="core",
key="dataset_manager_class",
fallback="airflow.datasets.manager.DatasetManager",
)
_dataset_manager_kwargs = conf.getjson(
section="core",
key="dataset_manager_kw... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data_manager(self):\n\n return self._data_manager",
"def _get_data_manager(self):\n\n ftype = self.conf['General']['save_as']\n if ftype == 'npz':\n return NPZDataManager(self.conf, self.log)\n elif ftype == 'hdf5':\n return HDF5DataManager(self.conf, sel... | [
"0.84404564",
"0.7547824",
"0.69010204",
"0.6821794",
"0.6821317",
"0.6761993",
"0.67312783",
"0.6668024",
"0.6635121",
"0.6446262",
"0.64157367",
"0.6388273",
"0.6379517",
"0.63311225",
"0.62744164",
"0.6216641",
"0.618155",
"0.61262697",
"0.6106576",
"0.6034368",
"0.6029758... | 0.7545222 | 2 |
Create the cifar10 dataset. | def create_cifar10_dataset(cifar_dir):
ds = de.Cifar10Dataset(cifar_dir)
training = True
resize_height = 224
resize_width = 224
rescale = 1.0 / 255.0
shift = 0.0
repeat_num = 10
batch_size = 32
# define map operations
random_crop_op = vision.RandomCrop((32, 32), (4, 4, 4, 4)) #... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cifar10(transform=transforms.ToTensor()):\n\n\t# Directories\n\tscript_dir = os.path.dirname(os.path.realpath(__file__))\n\tdata_dir = os.path.join(script_dir, 'data', 'cifar10')\n\n\t# Load training set, downloading if necessary\n\tdataset = datasets.CIFAR10(data_dir, train=True, transform=transform, download... | [
"0.7966689",
"0.75073844",
"0.7161493",
"0.71176726",
"0.7109075",
"0.7096102",
"0.69510275",
"0.6845723",
"0.6814827",
"0.68126905",
"0.68040884",
"0.670599",
"0.66763234",
"0.6664927",
"0.65709835",
"0.656717",
"0.64492744",
"0.6426107",
"0.6410631",
"0.6406311",
"0.6405234... | 0.7870253 | 1 |
If an identical token already exists in dictionary, will merge tokens and cumulate their counts. Checks to see that morphology and pronunciations are identical, otherwise the tokens will not be merged. | def AddToken(self, token, merge=False):
token.SetLangId(self.id_)
if not merge:
self.tokens_.append(token)
else:
exists = self.MatchToken(token)
if exists is None:
self.tokens_.append(token)
else:
exists.IncrementCount(token.Count()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge_token_counters(\n token_counter1: Dict[str, int], token_counter2: Dict[str, int]\n ) -> Dict[str, int]:\n for token, number in token_counter2.items():\n if token in token_counter1:\n token_counter1[token] += number\n else:\n token_count... | [
"0.66677827",
"0.595904",
"0.5881344",
"0.5862591",
"0.58134353",
"0.5744879",
"0.5677451",
"0.5661367",
"0.56101036",
"0.553344",
"0.53926057",
"0.5391481",
"0.5351704",
"0.53189",
"0.5299593",
"0.5298756",
"0.5296687",
"0.5291537",
"0.5290174",
"0.52845526",
"0.5283439",
... | 0.51305556 | 43 |
Merge identical tokens and cumulate their counts. Checks to see that morphology and pronunciations are identical, otherwise the tokens will not be merged. | def CompactTokens(self):
map = {}
for token_ in self.tokens_:
hash_string = token_.EncodeForHash()
try: map[hash_string].append(token_)
except KeyError: map[hash_string] = [token_]
ntokens = []
keys = map.keys()
keys.sort()
for k in keys:
token_ = map[k][0]
for otok... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def merge_token_counters(\n token_counter1: Dict[str, int], token_counter2: Dict[str, int]\n ) -> Dict[str, int]:\n for token, number in token_counter2.items():\n if token in token_counter1:\n token_counter1[token] += number\n else:\n token_count... | [
"0.6380701",
"0.6303569",
"0.5749085",
"0.56798166",
"0.5565582",
"0.5413877",
"0.536061",
"0.53069425",
"0.52431405",
"0.52182555",
"0.51754475",
"0.5064471",
"0.50563955",
"0.50545037",
"0.50540966",
"0.5040915",
"0.5038194",
"0.5008785",
"0.4992635",
"0.49735785",
"0.49611... | 0.5457384 | 5 |
Renders photorealistic images of a droplet interface in data from Michael or Pablo's simulations, given a case config file and render config file. | def photorealistic(case_config_filepath, render_config_filepath):
# Load config file with all common directory names
dirname_config = configparser.ConfigParser()
dirname_config.read("dirname.cfg")
# Get information from config files
cconfd = load_config.get_config_params(case_config_filepath) # C... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def surf_tempmap(case_config_filepath, render_config_filepath):\n\n # Load config file with all common directory names\n dirname_config = configparser.ConfigParser()\n dirname_config.read(\"dirname.cfg\")\n\n # Get information from config files\n cconfd = load_config.get_config_params(case_config_fi... | [
"0.6152582",
"0.5941791",
"0.58843994",
"0.5855938",
"0.57763773",
"0.5693176",
"0.5657049",
"0.55853724",
"0.55809975",
"0.5573157",
"0.55586225",
"0.552534",
"0.54541004",
"0.5444121",
"0.540116",
"0.5400519",
"0.53548855",
"0.5344291",
"0.5332354",
"0.53094125",
"0.5267152... | 0.7577917 | 0 |
Renders surface temperature images of a droplet interface in data from Michael or Pablo's simulations, given a case config file and render config file. | def surf_tempmap(case_config_filepath, render_config_filepath):
# Load config file with all common directory names
dirname_config = configparser.ConfigParser()
dirname_config.read("dirname.cfg")
# Get information from config files
cconfd = load_config.get_config_params(case_config_filepath)
rc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def photorealistic(case_config_filepath, render_config_filepath):\n\n # Load config file with all common directory names\n dirname_config = configparser.ConfigParser()\n dirname_config.read(\"dirname.cfg\")\n\n # Get information from config files\n cconfd = load_config.get_config_params(case_config_... | [
"0.6288007",
"0.58538276",
"0.5514038",
"0.5480608",
"0.5456143",
"0.5447625",
"0.54016596",
"0.53474796",
"0.5288365",
"0.5260717",
"0.525005",
"0.5233247",
"0.5214957",
"0.52136034",
"0.52131844",
"0.5211817",
"0.52017117",
"0.5193529",
"0.51920855",
"0.5187744",
"0.5187449... | 0.7388833 | 0 |
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